From d8183c4a2a2776a40e52e16f67375ad18ab8f6dc Mon Sep 17 00:00:00 2001 From: Lenard Dome <23503601+lenarddome@users.noreply.github.com> Date: Wed, 7 Aug 2024 14:48:24 +0200 Subject: [PATCH 01/54] start with some structural builds for the brain explorer module --- cpm/brainexplorer/__init__.py | 1 + cpm/brainexplorer/spacebus.py | 15 +++++++++++++++ 2 files changed, 16 insertions(+) create mode 100644 cpm/brainexplorer/__init__.py create mode 100644 cpm/brainexplorer/spacebus.py diff --git a/cpm/brainexplorer/__init__.py b/cpm/brainexplorer/__init__.py new file mode 100644 index 0000000..8ce5c75 --- /dev/null +++ b/cpm/brainexplorer/__init__.py @@ -0,0 +1 @@ +import .spacebus \ No newline at end of file diff --git a/cpm/brainexplorer/spacebus.py b/cpm/brainexplorer/spacebus.py new file mode 100644 index 0000000..d6b64df --- /dev/null +++ b/cpm/brainexplorer/spacebus.py @@ -0,0 +1,15 @@ +## space bus is a stop-signal task, so we can actually include several different analyses methods +## Here is an incomplete list: integration method, correlation of go and no-go RTs, + +def preprocess(data=None, id=None, trialtypes=None, rt=None, accuracy=None, exclude_method=None): + """ + Process spacebus data + + Examples + -------- + >>> import cpm.brainexplorer as be + >>> import pandas as pd + >>> spacebus = pd.read_csv('spacebus.csv') + >>> spacebus_aggregate = be.spacebus.preprocess(data=spacebus, id='userID', trialtypes='stopSignal', rt='RT_seconds', accuracy) + """ + pass \ No newline at end of file From c5c66e9119c891948483590989df7a95c3395d20 Mon Sep 17 00:00:00 2001 From: Lenard Dome <23503601+lenarddome@users.noreply.github.com> Date: Mon, 19 Aug 2024 11:21:41 +0200 Subject: [PATCH 02/54] transitional commit --- cpm/generators/parameters.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/cpm/generators/parameters.py b/cpm/generators/parameters.py index 9063897..41e9efe 100644 --- a/cpm/generators/parameters.py +++ b/cpm/generators/parameters.py @@ -139,12 +139,15 @@ def PDF(self, log=False): for _, value in self.__dict__.items(): if value.prior is not None: prior += value.PDF(log=True) + if np.isneginf(prior): - prior = np.finfo(np.float64).min + prior = np.finfo(np.float64).tiny + if not log: prior = np.exp(prior) if prior <= 0: prior = np.finfo(np.float64).tiny + return prior def update_prior(self, **kwargs): From 29f2f36d21e9573a1e26c1065289160d8d52c24a Mon Sep 17 00:00:00 2001 From: lenarddome Date: Wed, 28 Aug 2024 09:48:50 +0200 Subject: [PATCH 03/54] start some skeletal preparations --- cpm/brainexplorer/__init__.py | 5 ++++- cpm/brainexplorer/bandits.py | 0 cpm/brainexplorer/perceptual.py | 0 cpm/brainexplorer/riskychoices.py | 0 cpm/brainexplorer/{spacebus.py => stopsignal.py} | 0 5 files changed, 4 insertions(+), 1 deletion(-) create mode 100644 cpm/brainexplorer/bandits.py create mode 100644 cpm/brainexplorer/perceptual.py create mode 100644 cpm/brainexplorer/riskychoices.py rename cpm/brainexplorer/{spacebus.py => stopsignal.py} (100%) diff --git a/cpm/brainexplorer/__init__.py b/cpm/brainexplorer/__init__.py index 8ce5c75..5b39fbf 100644 --- a/cpm/brainexplorer/__init__.py +++ b/cpm/brainexplorer/__init__.py @@ -1 +1,4 @@ -import .spacebus \ No newline at end of file +from cpm.brainexplorer import stopsignal +from cpm.brainexplorer import perceptual +from cpm.brainexplorer import riskychoices +from cpm.brainexplorer import bandits diff --git a/cpm/brainexplorer/bandits.py b/cpm/brainexplorer/bandits.py new file mode 100644 index 0000000..e69de29 diff --git a/cpm/brainexplorer/perceptual.py b/cpm/brainexplorer/perceptual.py new file mode 100644 index 0000000..e69de29 diff --git a/cpm/brainexplorer/riskychoices.py b/cpm/brainexplorer/riskychoices.py new file mode 100644 index 0000000..e69de29 diff --git a/cpm/brainexplorer/spacebus.py b/cpm/brainexplorer/stopsignal.py similarity index 100% rename from cpm/brainexplorer/spacebus.py rename to cpm/brainexplorer/stopsignal.py From 92330b8e9979a363db6fbb79d839fb7e5a4328f2 Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Sat, 18 Jan 2025 10:00:10 +0100 Subject: [PATCH 04/54] Create scavenger.py --- cpm/brainexplorer/scavenger.py | 306 +++++++++++++++++++++++++++++++++ 1 file changed, 306 insertions(+) create mode 100644 cpm/brainexplorer/scavenger.py diff --git a/cpm/brainexplorer/scavenger.py b/cpm/brainexplorer/scavenger.py new file mode 100644 index 0000000..d913a5c --- /dev/null +++ b/cpm/brainexplorer/scavenger.py @@ -0,0 +1,306 @@ +import pandas as pd +import numpy as np +import statsmodels.api as sm +from scipy.stats import zscore +import statsmodels.formula.api as smf +from sklearn.linear_model import LogisticRegression + +class Scavenger(): + """ + Process Scavenger data + + Parameters + ---------- + filepath : str + The path to the CSV file to be processed. + + data : pd.DataFrame + The data to be processed. + + Example + ---------- + + filepath = "/example/2025-01-10_Scavenger.xlsx" + scavenger = Scavenger(filepath) + metrics_result = scavenger.metrics() + codebook_result = scavenger.codebook() + + + """ + + def __init__(self, filepath=None): + """ + Load data to be analyized and turn into Pandas DataFrame + + Parameters + ---------- + filepath : str + The path to the CSV file to be loaded. + + """ + + self.data = pd.read_excel(filepath, header = 0) + self.results = pd.DataFrame() + + + def metrics(self): + """ + Compute metrics from the loaded data. + + Parameters + ---------- + data : pd.DataFrame + Data from all users and trials to be processed. + + Returns + ------- + results : pd.DataFrame + A DataFrame containing the computed metrics. + + A_EV: expected value of choosing option A + B_EV: expected value of choosing option B + A_perc: Percent visible to user for option A + B_perc: Percent visible to user for option B + A_magn: Trial Values for option A + B_magn: Trial Values for option B + EV_diff_chosen: Expected value difference between option A and B + chosen: 1 if option A chosen, 2 if option B chosen + chosen_left: 1 if option A chosen, 0 if option B chosen + t_loss_all: 1 if loss trial, 0 if win trial + t_abg_all: 1 if ambiguous trial, 0 if non-ambiguous trial + + Notes + ----- + + - Catches an error in the loss case logistic regression for one user in which a singular matrix error occurs. + + + """ + + + # Group data by userID + grouped_data = self.data.groupby('userID') + + all_users_data = [] + + # Loop through each group of user data + for user_id, user_data in grouped_data: + user_data = user_data.sort_values('trial') + + # Initialize a dictionary to store results for this user + user_metrics = { + "userID": user_id, + "rsk_all": [], + "t_loss_all": [], + "t_abg_all": [], + "EV_diff_chosen_all": [], + "chosen_left_all": [], + "A_EV": [], + "B_EV": [], + "chosen": [] + } + + # Loop through each trial in the user's data + for _, trial_data in user_data.iterrows(): + metrics_data_trial = {} + + fields_to_process = [ + 'chosenOption_outcomes_percent', + 'chosenOption_outcomes_userVisiblePercent', + 'unchosenOption_outcomes_userVisiblePercent', + 'chosenOption_outcomes_trialValue', + 'unchosenOption_outcomes_trialValue' + ] + + # Normalize the strings into arrays + normalized_data = {} + + for field in fields_to_process: + if field in trial_data: + value = trial_data[field] + if isinstance(value, str): + normalized_data[field] = [float(x.strip()) for x in value.split(",")] + else: + normalized_data[field] = [float(value)] + + + + if len(normalized_data['chosenOption_outcomes_percent']) == 1: + metrics_data_trial['chosen'] = 1 # Option A chosen + metrics_data_trial['A_perc'] = normalized_data['chosenOption_outcomes_userVisiblePercent'] + metrics_data_trial['A_magn'] = normalized_data['chosenOption_outcomes_trialValue'] + metrics_data_trial['B_perc'] = normalized_data['unchosenOption_outcomes_userVisiblePercent'] + metrics_data_trial['B_magn'] = normalized_data['unchosenOption_outcomes_trialValue'] + if sum(metrics_data_trial['B_perc']) != 1: + metrics_data_trial['ambiguous'] = 1 + else: + metrics_data_trial['ambiguous'] = 0 + else: + metrics_data_trial['chosen'] = 2 # Option B chosen + metrics_data_trial['A_perc'] = normalized_data['unchosenOption_outcomes_userVisiblePercent'] + metrics_data_trial['A_magn'] = normalized_data['unchosenOption_outcomes_trialValue'] + metrics_data_trial['B_perc'] = normalized_data['chosenOption_outcomes_userVisiblePercent'] + metrics_data_trial['B_magn'] = normalized_data['chosenOption_outcomes_trialValue'] + + if sum(metrics_data_trial['B_perc']) != 1: + metrics_data_trial['ambiguous'] = 1 + else: + metrics_data_trial['ambiguous'] = 0 + + if trial_data['decision'] == 'optionA': + metrics_data_trial['chosenLeft'] = 1 + else: + metrics_data_trial['chosenLeft'] = 0 + + + # Compute expected values + metrics_data_trial['A_EV'] = np.dot(np.array(metrics_data_trial['A_perc']), np.array(metrics_data_trial['A_magn'])) + metrics_data_trial['B_EV'] = 0.5 * np.array(metrics_data_trial['B_perc']) + 0.5 * np.array(metrics_data_trial['B_magn']) + metrics_data_trial['B_EV'] = metrics_data_trial['B_EV'][0] + metrics_data_trial['EV_diff_safe'] = metrics_data_trial['A_EV'] - metrics_data_trial['B_EV'] + if metrics_data_trial['chosen'] == 1: + metrics_data_trial['EV_diff_chosen'] = metrics_data_trial['A_EV'] - metrics_data_trial['B_EV'] + elif metrics_data_trial['chosen'] == 2: + metrics_data_trial['EV_diff_chosen'] = metrics_data_trial['B_EV'] - metrics_data_trial['A_EV'] + + + + # Store the trial data for the user in the user's metrics dictionary + user_metrics['A_EV'].append(metrics_data_trial['A_EV']) + user_metrics['B_EV'].append(metrics_data_trial['B_EV']) + user_metrics['chosen'].append(metrics_data_trial['chosen']) + user_metrics['rsk_all'].append(metrics_data_trial['chosen'] - 1) + user_metrics['t_loss_all'].append(metrics_data_trial['A_EV'] < 0) + user_metrics['t_abg_all'].append(bool(metrics_data_trial['ambiguous'])) + user_metrics['EV_diff_chosen_all'].append(metrics_data_trial['EV_diff_chosen']) + user_metrics['chosen_left_all'].append(metrics_data_trial['chosenLeft']) + + + all_users_data.append(user_metrics) + + # iterate over all users + for user_data in all_users_data: + + user_id = user_data["userID"] + rsk = np.array(user_data["rsk_all"]) + t_loss = np.array(user_data["t_loss_all"]) + t_abg = np.array(user_data["t_abg_all"]) + ev_diff_chosen = np.array(user_data["EV_diff_chosen_all"], dtype=object) + + + user_results = {"userID": user_id} + + # Number of risky choices + user_results['win_risk'] = np.sum(rsk[~t_loss]) / np.sum(~t_loss) + print(user_results['win_risk']) + user_results['loss_risk'] = np.sum(rsk[t_loss]) / np.sum(t_loss) + user_results['win_risk_abg'] = np.sum(rsk[~t_loss & t_abg]) / np.sum(~t_loss & t_abg) + user_results['win_risk_NoAbg'] = np.sum(rsk[~t_loss & ~t_abg]) / np.sum(~t_loss & ~t_abg) + user_results['loss_risk_abg'] = np.sum(rsk[t_loss & t_abg]) / np.sum(t_loss & t_abg) + user_results['loss_risk_NoAbg'] = np.sum(rsk[t_loss & ~t_abg]) / np.sum(t_loss & ~t_abg) + + # Number of optimal choices + user_results['optimal_all'] = np.sum(ev_diff_chosen > 0) / len(ev_diff_chosen) + user_results['optimal_win'] = np.sum(ev_diff_chosen[~t_loss] > 0) / np.sum(~t_loss) + user_results['optimal_loss'] = np.sum(ev_diff_chosen[t_loss] > 0) / np.sum(t_loss) + user_results['optimal_all_abg'] = np.sum(ev_diff_chosen[t_abg] > 0) / np.sum(t_abg) + user_results['optimal_win_abg'] = np.sum(ev_diff_chosen[~t_loss & t_abg] > 0) / np.sum(~t_loss & t_abg) + user_results['optimal_loss_abg'] = np.sum(ev_diff_chosen[t_loss & t_abg] > 0) / np.sum(t_loss & t_abg) + + + # logistic regression + logit_data = user_data.copy() + logit_data = pd.DataFrame(logit_data) + + logit_data['chosen'] = np.array(logit_data['chosen'])- 1 + + modelspec = 'chosen ~ A_EV + B_EV' + + # Standardize A_EV and B_EV + logit_data['A_EV'] = zscore(logit_data['A_EV']) + logit_data['B_EV'] = zscore(logit_data['B_EV']) + + # Fit logistic regression model for all data + logit_all = smf.logit(modelspec, data=logit_data).fit() + + # Create a mask for t_loss + t_loss = logit_data['t_loss_all'] + + # Standardize A_EV and B_EV for non-loss cases + logit_data.loc[~t_loss, 'A_EV'] = zscore(logit_data.loc[~t_loss, 'A_EV']) + logit_data.loc[~t_loss, 'B_EV'] = zscore(logit_data.loc[~t_loss, 'B_EV']) + + # Fit logistic regression model for non-loss cases + logit_win = smf.logit(modelspec, data=logit_data[~t_loss]).fit() + + # Standardize A_EV and B_EV for loss cases + logit_data.loc[t_loss, 'A_EV'] = zscore(logit_data.loc[t_loss, 'A_EV']) + logit_data.loc[t_loss, 'B_EV'] = zscore(logit_data.loc[t_loss, 'B_EV']) + + # Fit logistic regression model for loss cases + # Catch singular matrix error + try: + logit_loss = smf.logit(modelspec, data=logit_data[t_loss]).fit() + except np.linalg.LinAlgError: + print(f"Singular matrix error for user {user_id} in loss case logistic regression. Skipping.") + continue + + + # Add results for each logistic regression model + user_results[f'{'logit_all'}_itcp'] = logit_all.params.get('const', None) + + for coef_name, estimate in logit_all.params.items(): + if coef_name != 'const': # Skip intercept here as it's already added + user_results[f'{'logit_all'}_{coef_name}'] = estimate + + user_results[f'{'logit_win'}_itcp'] = logit_all.params.get('const', None) + + for coef_name, estimate in logit_all.params.items(): + if coef_name != 'const': # Skip intercept here as it's already added + user_results[f'{'logit_win'}_{coef_name}'] = estimate + + user_results[f'{'logit_loss'}_itcp'] = logit_all.params.get('const', None) + + for coef_name, estimate in logit_all.params.items(): + if coef_name != 'const': # Skip intercept here as it's already added + user_results[f'{'logit_loss'}_{coef_name}'] = estimate + + + + # Append user-level results + self.results = pd.concat([self.results, pd.DataFrame([user_results])], ignore_index=True) + + # Convert results to a DataFrame + self.results = pd.DataFrame(self.results) + return self.results + + + + def codebook(self): + """ + Return a codebook describing the metrics. + """ + self.codebook = { + "win_risk": "Proportion of risky wins", + "loss_risk": "Proportion of risky losses", + "win_risk_abg": "Proportion of risky wins in ambiguous trials", + "win_risk_NoAbg": "Proportion of risky wins in non-ambiguous trials", + "loss_risk_abg": "Proportion of risky losses in ambiguous trials", + "loss_risk_NoAbg": "Proportion of risky losses in non-ambiguous trials", + "optimal_all": "Proportion of optimal choices", + "optimal_win": "Proportion of optimal choices in wins", + "optimal_loss": "Proportion of optimal choices in losses", + "optimal_all_abg": "Proportion of optimal choices in ambiguous trials", + "optimal_win_abg": "Proportion of optimal choices in wins in ambiguous trials", + "optimal_loss_abg": "Proportion of optimal choices in losses in ambiguous trials" + } + + + +if __name__ == "__main__": + filepath = "2025-01-10_Scavenger_correct.xlsx" + scavenger = Scavenger(filepath) + metrics_result = scavenger.metrics() + codebook_result = scavenger.codebook() + #metrics_result.to_csv('/Users/lucyk/Downloads/test_metrics_wReg.csv', index=False) + print("Metrics:", metrics_result) \ No newline at end of file From 6a16c6a0a148a6bb7ddded46445383938cebe20b Mon Sep 17 00:00:00 2001 From: lenarddome Date: Thu, 23 Jan 2025 15:07:41 +0100 Subject: [PATCH 05/54] clean: clean and improve documentation structure and class attributes in Scavenger --- cpm/brainexplorer/scavenger.py | 366 +++++++++++++++++++-------------- 1 file changed, 206 insertions(+), 160 deletions(-) diff --git a/cpm/brainexplorer/scavenger.py b/cpm/brainexplorer/scavenger.py index d913a5c..7cab250 100644 --- a/cpm/brainexplorer/scavenger.py +++ b/cpm/brainexplorer/scavenger.py @@ -1,91 +1,115 @@ import pandas as pd import numpy as np -import statsmodels.api as sm +import warnings from scipy.stats import zscore import statsmodels.formula.api as smf -from sklearn.linear_model import LogisticRegression -class Scavenger(): + +class Scavenger: """ - Process Scavenger data + A class to analyze and compute various descriptive statistics from behavioural data obtained in the risky decision-making + task implemented in BrainExplorer, Scavenger. The task requires participants to choose between two options: + The "safe" options returns an outcome with 100% probability, whereas the "risky" option returns one of + two potential outcomes with 50% probability each. - Parameters + Attributes ---------- - filepath : str - The path to the CSV file to be processed. - data : pd.DataFrame - The data to be processed. - - Example - ---------- - - filepath = "/example/2025-01-10_Scavenger.xlsx" - scavenger = Scavenger(filepath) - metrics_result = scavenger.metrics() - codebook_result = scavenger.codebook() - - + The data loaded from the CSV or Excel file. + results : pd.DataFrame + A DataFrame to store the computed metrics. + codebook : dict + A dictionary containing the codebook for the computed metrics. """ def __init__(self, filepath=None): """ - Load data to be analyized and turn into Pandas DataFrame + Load data to be analyised and turn into Pandas DataFrame Parameters ---------- filepath : str - The path to the CSV file to be loaded. + The path to the CSV file to be processed. + data : pd.DataFrame + The data to be processed. + + Example + ---------- + + >>> scavenger = Scavenger("/example/2025-01-10_Scavenger.xlsx") + >>> scavenger.metrics() + >>> scavenger.codebook() """ - - self.data = pd.read_excel(filepath, header = 0) - self.results = pd.DataFrame() + ## check file extension + if filepath.endswith(".csv"): + self.data = pd.read_csv(filepath, header=0) + elif filepath.endswith(".xlsx"): + self.data = pd.read_excel(filepath, header=0) + + self.results = pd.DataFrame() + self.codebook = { + "win_risk": "Proportion of risky wins", + "loss_risk": "Proportion of risky losses", + "win_risk_abg": "Proportion of risky wins in ambiguous trials", + "win_risk_NoAbg": "Proportion of risky wins in non-ambiguous trials", + "loss_risk_abg": "Proportion of risky losses in ambiguous trials", + "loss_risk_NoAbg": "Proportion of risky losses in non-ambiguous trials", + "optimal_all": "Proportion of optimal choices", + "optimal_win": "Proportion of optimal choices in wins", + "optimal_loss": "Proportion of optimal choices in losses", + "optimal_all_abg": "Proportion of optimal choices in ambiguous trials", + "optimal_win_abg": "Proportion of optimal choices in wins in ambiguous trials", + "optimal_loss_abg": "Proportion of optimal choices in losses in ambiguous trials", + } def metrics(self): """ - Compute metrics from the loaded data. + Compute various descriptive statistics. See Notes for more information of the exact descriptive statistics we calculate. Parameters ---------- data : pd.DataFrame Data from all users and trials to be processed. - Returns + Returns ------- results : pd.DataFrame A DataFrame containing the computed metrics. - A_EV: expected value of choosing option A - B_EV: expected value of choosing option B - A_perc: Percent visible to user for option A - B_perc: Percent visible to user for option B - A_magn: Trial Values for option A - B_magn: Trial Values for option B - EV_diff_chosen: Expected value difference between option A and B - chosen: 1 if option A chosen, 2 if option B chosen - chosen_left: 1 if option A chosen, 0 if option B chosen - t_loss_all: 1 if loss trial, 0 if win trial - t_abg_all: 1 if ambiguous trial, 0 if non-ambiguous trial - Notes ----- - - - Catches an error in the loss case logistic regression for one user in which a singular matrix error occurs. + We provide a codebook for the calculated descriptive statistics: + + - **A_EV**: expected value of choosing option A + - **B_EV**: expected value of choosing option B + - **A_perc**: Percent visible to user for option A + - **B_perc**: Percent visible to user for option B + - **A_magn**: Trial Values for option A + - **B_magn**: Trial Values for option B + - **EV_diff_chosen**: Expected value difference between option A and B + - **chosen**: 1 if option A chosen, 2 if option B chosen + - **chosen_left**: 1 if option A chosen, 0 if option B chosen + - **t_loss_all**: 1 if loss trial, 0 if win trial + - **t_abg_all**: 1 if ambiguous trial, 0 if non-ambiguous trial + + Warns + ----- + UserWarning + Provides a warning for `np.linalg.LinAlgError` in logistic regression from `statsmodels` for a given user when a singular matrix error occurs. """ - # Group data by userID - grouped_data = self.data.groupby('userID') + grouped_data = self.data.groupby("userID") all_users_data = [] # Loop through each group of user data for user_id, user_data in grouped_data: - user_data = user_data.sort_values('trial') + user_data = user_data.sort_values("trial") # Initialize a dictionary to store results for this user user_metrics = { @@ -97,85 +121,110 @@ def metrics(self): "chosen_left_all": [], "A_EV": [], "B_EV": [], - "chosen": [] + "chosen": [], } # Loop through each trial in the user's data for _, trial_data in user_data.iterrows(): metrics_data_trial = {} - + fields_to_process = [ - 'chosenOption_outcomes_percent', - 'chosenOption_outcomes_userVisiblePercent', - 'unchosenOption_outcomes_userVisiblePercent', - 'chosenOption_outcomes_trialValue', - 'unchosenOption_outcomes_trialValue' + "chosenOption_outcomes_percent", + "chosenOption_outcomes_userVisiblePercent", + "unchosenOption_outcomes_userVisiblePercent", + "chosenOption_outcomes_trialValue", + "unchosenOption_outcomes_trialValue", ] # Normalize the strings into arrays normalized_data = {} for field in fields_to_process: - if field in trial_data: + if field in trial_data: value = trial_data[field] if isinstance(value, str): - normalized_data[field] = [float(x.strip()) for x in value.split(",")] - else: + normalized_data[field] = [ + float(x.strip()) for x in value.split(",") + ] + else: normalized_data[field] = [float(value)] - - - if len(normalized_data['chosenOption_outcomes_percent']) == 1: - metrics_data_trial['chosen'] = 1 # Option A chosen - metrics_data_trial['A_perc'] = normalized_data['chosenOption_outcomes_userVisiblePercent'] - metrics_data_trial['A_magn'] = normalized_data['chosenOption_outcomes_trialValue'] - metrics_data_trial['B_perc'] = normalized_data['unchosenOption_outcomes_userVisiblePercent'] - metrics_data_trial['B_magn'] = normalized_data['unchosenOption_outcomes_trialValue'] - if sum(metrics_data_trial['B_perc']) != 1: - metrics_data_trial['ambiguous'] = 1 + if len(normalized_data["chosenOption_outcomes_percent"]) == 1: + metrics_data_trial["chosen"] = 1 # Option A chosen + metrics_data_trial["A_perc"] = normalized_data[ + "chosenOption_outcomes_userVisiblePercent" + ] + metrics_data_trial["A_magn"] = normalized_data[ + "chosenOption_outcomes_trialValue" + ] + metrics_data_trial["B_perc"] = normalized_data[ + "unchosenOption_outcomes_userVisiblePercent" + ] + metrics_data_trial["B_magn"] = normalized_data[ + "unchosenOption_outcomes_trialValue" + ] + if sum(metrics_data_trial["B_perc"]) != 1: + metrics_data_trial["ambiguous"] = 1 else: - metrics_data_trial['ambiguous'] = 0 + metrics_data_trial["ambiguous"] = 0 else: - metrics_data_trial['chosen'] = 2 # Option B chosen - metrics_data_trial['A_perc'] = normalized_data['unchosenOption_outcomes_userVisiblePercent'] - metrics_data_trial['A_magn'] = normalized_data['unchosenOption_outcomes_trialValue'] - metrics_data_trial['B_perc'] = normalized_data['chosenOption_outcomes_userVisiblePercent'] - metrics_data_trial['B_magn'] = normalized_data['chosenOption_outcomes_trialValue'] - - if sum(metrics_data_trial['B_perc']) != 1: - metrics_data_trial['ambiguous'] = 1 + metrics_data_trial["chosen"] = 2 # Option B chosen + metrics_data_trial["A_perc"] = normalized_data[ + "unchosenOption_outcomes_userVisiblePercent" + ] + metrics_data_trial["A_magn"] = normalized_data[ + "unchosenOption_outcomes_trialValue" + ] + metrics_data_trial["B_perc"] = normalized_data[ + "chosenOption_outcomes_userVisiblePercent" + ] + metrics_data_trial["B_magn"] = normalized_data[ + "chosenOption_outcomes_trialValue" + ] + + if sum(metrics_data_trial["B_perc"]) != 1: + metrics_data_trial["ambiguous"] = 1 else: - metrics_data_trial['ambiguous'] = 0 + metrics_data_trial["ambiguous"] = 0 - if trial_data['decision'] == 'optionA': - metrics_data_trial['chosenLeft'] = 1 + if trial_data["decision"] == "optionA": + metrics_data_trial["chosenLeft"] = 1 else: - metrics_data_trial['chosenLeft'] = 0 + metrics_data_trial["chosenLeft"] = 0 - # Compute expected values - metrics_data_trial['A_EV'] = np.dot(np.array(metrics_data_trial['A_perc']), np.array(metrics_data_trial['A_magn'])) - metrics_data_trial['B_EV'] = 0.5 * np.array(metrics_data_trial['B_perc']) + 0.5 * np.array(metrics_data_trial['B_magn']) - metrics_data_trial['B_EV'] = metrics_data_trial['B_EV'][0] - metrics_data_trial['EV_diff_safe'] = metrics_data_trial['A_EV'] - metrics_data_trial['B_EV'] - if metrics_data_trial['chosen'] == 1: - metrics_data_trial['EV_diff_chosen'] = metrics_data_trial['A_EV'] - metrics_data_trial['B_EV'] - elif metrics_data_trial['chosen'] == 2: - metrics_data_trial['EV_diff_chosen'] = metrics_data_trial['B_EV'] - metrics_data_trial['A_EV'] - - + metrics_data_trial["A_EV"] = np.dot( + np.array(metrics_data_trial["A_perc"]), + np.array(metrics_data_trial["A_magn"]), + ) + metrics_data_trial["B_EV"] = 0.5 * np.array( + metrics_data_trial["B_perc"] + ) + 0.5 * np.array(metrics_data_trial["B_magn"]) + metrics_data_trial["B_EV"] = metrics_data_trial["B_EV"][0] + metrics_data_trial["EV_diff_safe"] = ( + metrics_data_trial["A_EV"] - metrics_data_trial["B_EV"] + ) + if metrics_data_trial["chosen"] == 1: + metrics_data_trial["EV_diff_chosen"] = ( + metrics_data_trial["A_EV"] - metrics_data_trial["B_EV"] + ) + elif metrics_data_trial["chosen"] == 2: + metrics_data_trial["EV_diff_chosen"] = ( + metrics_data_trial["B_EV"] - metrics_data_trial["A_EV"] + ) # Store the trial data for the user in the user's metrics dictionary - user_metrics['A_EV'].append(metrics_data_trial['A_EV']) - user_metrics['B_EV'].append(metrics_data_trial['B_EV']) - user_metrics['chosen'].append(metrics_data_trial['chosen']) - user_metrics['rsk_all'].append(metrics_data_trial['chosen'] - 1) - user_metrics['t_loss_all'].append(metrics_data_trial['A_EV'] < 0) - user_metrics['t_abg_all'].append(bool(metrics_data_trial['ambiguous'])) - user_metrics['EV_diff_chosen_all'].append(metrics_data_trial['EV_diff_chosen']) - user_metrics['chosen_left_all'].append(metrics_data_trial['chosenLeft']) - - + user_metrics["A_EV"].append(metrics_data_trial["A_EV"]) + user_metrics["B_EV"].append(metrics_data_trial["B_EV"]) + user_metrics["chosen"].append(metrics_data_trial["chosen"]) + user_metrics["rsk_all"].append(metrics_data_trial["chosen"] - 1) + user_metrics["t_loss_all"].append(metrics_data_trial["A_EV"] < 0) + user_metrics["t_abg_all"].append(bool(metrics_data_trial["ambiguous"])) + user_metrics["EV_diff_chosen_all"].append( + metrics_data_trial["EV_diff_chosen"] + ) + user_metrics["chosen_left_all"].append(metrics_data_trial["chosenLeft"]) + all_users_data.append(user_metrics) # iterate over all users @@ -187,120 +236,117 @@ def metrics(self): t_abg = np.array(user_data["t_abg_all"]) ev_diff_chosen = np.array(user_data["EV_diff_chosen_all"], dtype=object) - user_results = {"userID": user_id} # Number of risky choices - user_results['win_risk'] = np.sum(rsk[~t_loss]) / np.sum(~t_loss) - print(user_results['win_risk']) - user_results['loss_risk'] = np.sum(rsk[t_loss]) / np.sum(t_loss) - user_results['win_risk_abg'] = np.sum(rsk[~t_loss & t_abg]) / np.sum(~t_loss & t_abg) - user_results['win_risk_NoAbg'] = np.sum(rsk[~t_loss & ~t_abg]) / np.sum(~t_loss & ~t_abg) - user_results['loss_risk_abg'] = np.sum(rsk[t_loss & t_abg]) / np.sum(t_loss & t_abg) - user_results['loss_risk_NoAbg'] = np.sum(rsk[t_loss & ~t_abg]) / np.sum(t_loss & ~t_abg) + user_results["win_risk"] = np.sum(rsk[~t_loss]) / np.sum(~t_loss) + print(user_results["win_risk"]) + user_results["loss_risk"] = np.sum(rsk[t_loss]) / np.sum(t_loss) + user_results["win_risk_abg"] = np.sum(rsk[~t_loss & t_abg]) / np.sum( + ~t_loss & t_abg + ) + user_results["win_risk_NoAbg"] = np.sum(rsk[~t_loss & ~t_abg]) / np.sum( + ~t_loss & ~t_abg + ) + user_results["loss_risk_abg"] = np.sum(rsk[t_loss & t_abg]) / np.sum( + t_loss & t_abg + ) + user_results["loss_risk_NoAbg"] = np.sum(rsk[t_loss & ~t_abg]) / np.sum( + t_loss & ~t_abg + ) # Number of optimal choices - user_results['optimal_all'] = np.sum(ev_diff_chosen > 0) / len(ev_diff_chosen) - user_results['optimal_win'] = np.sum(ev_diff_chosen[~t_loss] > 0) / np.sum(~t_loss) - user_results['optimal_loss'] = np.sum(ev_diff_chosen[t_loss] > 0) / np.sum(t_loss) - user_results['optimal_all_abg'] = np.sum(ev_diff_chosen[t_abg] > 0) / np.sum(t_abg) - user_results['optimal_win_abg'] = np.sum(ev_diff_chosen[~t_loss & t_abg] > 0) / np.sum(~t_loss & t_abg) - user_results['optimal_loss_abg'] = np.sum(ev_diff_chosen[t_loss & t_abg] > 0) / np.sum(t_loss & t_abg) - - + user_results["optimal_all"] = np.sum(ev_diff_chosen > 0) / len( + ev_diff_chosen + ) + user_results["optimal_win"] = np.sum(ev_diff_chosen[~t_loss] > 0) / np.sum( + ~t_loss + ) + user_results["optimal_loss"] = np.sum(ev_diff_chosen[t_loss] > 0) / np.sum( + t_loss + ) + user_results["optimal_all_abg"] = np.sum( + ev_diff_chosen[t_abg] > 0 + ) / np.sum(t_abg) + user_results["optimal_win_abg"] = np.sum( + ev_diff_chosen[~t_loss & t_abg] > 0 + ) / np.sum(~t_loss & t_abg) + user_results["optimal_loss_abg"] = np.sum( + ev_diff_chosen[t_loss & t_abg] > 0 + ) / np.sum(t_loss & t_abg) + # logistic regression logit_data = user_data.copy() logit_data = pd.DataFrame(logit_data) - logit_data['chosen'] = np.array(logit_data['chosen'])- 1 + logit_data["chosen"] = np.array(logit_data["chosen"]) - 1 - modelspec = 'chosen ~ A_EV + B_EV' + modelspec = "chosen ~ A_EV + B_EV" # Standardize A_EV and B_EV - logit_data['A_EV'] = zscore(logit_data['A_EV']) - logit_data['B_EV'] = zscore(logit_data['B_EV']) + logit_data["A_EV"] = zscore(logit_data["A_EV"]) + logit_data["B_EV"] = zscore(logit_data["B_EV"]) # Fit logistic regression model for all data logit_all = smf.logit(modelspec, data=logit_data).fit() # Create a mask for t_loss - t_loss = logit_data['t_loss_all'] + t_loss = logit_data["t_loss_all"] # Standardize A_EV and B_EV for non-loss cases - logit_data.loc[~t_loss, 'A_EV'] = zscore(logit_data.loc[~t_loss, 'A_EV']) - logit_data.loc[~t_loss, 'B_EV'] = zscore(logit_data.loc[~t_loss, 'B_EV']) + logit_data.loc[~t_loss, "A_EV"] = zscore(logit_data.loc[~t_loss, "A_EV"]) + logit_data.loc[~t_loss, "B_EV"] = zscore(logit_data.loc[~t_loss, "B_EV"]) # Fit logistic regression model for non-loss cases logit_win = smf.logit(modelspec, data=logit_data[~t_loss]).fit() # Standardize A_EV and B_EV for loss cases - logit_data.loc[t_loss, 'A_EV'] = zscore(logit_data.loc[t_loss, 'A_EV']) - logit_data.loc[t_loss, 'B_EV'] = zscore(logit_data.loc[t_loss, 'B_EV']) + logit_data.loc[t_loss, "A_EV"] = zscore(logit_data.loc[t_loss, "A_EV"]) + logit_data.loc[t_loss, "B_EV"] = zscore(logit_data.loc[t_loss, "B_EV"]) # Fit logistic regression model for loss cases # Catch singular matrix error try: logit_loss = smf.logit(modelspec, data=logit_data[t_loss]).fit() except np.linalg.LinAlgError: - print(f"Singular matrix error for user {user_id} in loss case logistic regression. Skipping.") + warnings.warn( + f"Singular matrix error for user {user_id} in loss case logistic regression. Skipping." + ) continue - # Add results for each logistic regression model - user_results[f'{'logit_all'}_itcp'] = logit_all.params.get('const', None) + user_results[f"{'logit_all'}_itcp"] = logit_all.params.get("const", None) for coef_name, estimate in logit_all.params.items(): - if coef_name != 'const': # Skip intercept here as it's already added - user_results[f'{'logit_all'}_{coef_name}'] = estimate + if coef_name != "const": # Skip intercept here as it's already added + user_results[f"{'logit_all'}_{coef_name}"] = estimate - user_results[f'{'logit_win'}_itcp'] = logit_all.params.get('const', None) + user_results[f"{'logit_win'}_itcp"] = logit_all.params.get("const", None) for coef_name, estimate in logit_all.params.items(): - if coef_name != 'const': # Skip intercept here as it's already added - user_results[f'{'logit_win'}_{coef_name}'] = estimate + if coef_name != "const": # Skip intercept here as it's already added + user_results[f"{'logit_win'}_{coef_name}"] = estimate - user_results[f'{'logit_loss'}_itcp'] = logit_all.params.get('const', None) + user_results[f"{'logit_loss'}_itcp"] = logit_all.params.get("const", None) for coef_name, estimate in logit_all.params.items(): - if coef_name != 'const': # Skip intercept here as it's already added - user_results[f'{'logit_loss'}_{coef_name}'] = estimate - - + if coef_name != "const": # Skip intercept here as it's already added + user_results[f"{'logit_loss'}_{coef_name}"] = estimate # Append user-level results - self.results = pd.concat([self.results, pd.DataFrame([user_results])], ignore_index=True) + self.results = pd.concat( + [self.results, pd.DataFrame([user_results])], ignore_index=True + ) # Convert results to a DataFrame self.results = pd.DataFrame(self.results) return self.results - - def codebook(self): """ Return a codebook describing the metrics. """ - self.codebook = { - "win_risk": "Proportion of risky wins", - "loss_risk": "Proportion of risky losses", - "win_risk_abg": "Proportion of risky wins in ambiguous trials", - "win_risk_NoAbg": "Proportion of risky wins in non-ambiguous trials", - "loss_risk_abg": "Proportion of risky losses in ambiguous trials", - "loss_risk_NoAbg": "Proportion of risky losses in non-ambiguous trials", - "optimal_all": "Proportion of optimal choices", - "optimal_win": "Proportion of optimal choices in wins", - "optimal_loss": "Proportion of optimal choices in losses", - "optimal_all_abg": "Proportion of optimal choices in ambiguous trials", - "optimal_win_abg": "Proportion of optimal choices in wins in ambiguous trials", - "optimal_loss_abg": "Proportion of optimal choices in losses in ambiguous trials" - } - + return self.codebook -if __name__ == "__main__": - filepath = "2025-01-10_Scavenger_correct.xlsx" - scavenger = Scavenger(filepath) - metrics_result = scavenger.metrics() - codebook_result = scavenger.codebook() - #metrics_result.to_csv('/Users/lucyk/Downloads/test_metrics_wReg.csv', index=False) - print("Metrics:", metrics_result) \ No newline at end of file +x = Scavenger("./example/2025-01-10_Scavenger_correct.xlsx") From eefe548067c89f153bab72220eb529c3cae02a5a Mon Sep 17 00:00:00 2001 From: lenarddome Date: Thu, 23 Jan 2025 15:11:08 +0100 Subject: [PATCH 06/54] docs: fix incorrect reference to codebook --- cpm/brainexplorer/scavenger.py | 43 ++++++++++++++-------------------- 1 file changed, 18 insertions(+), 25 deletions(-) diff --git a/cpm/brainexplorer/scavenger.py b/cpm/brainexplorer/scavenger.py index 7cab250..109ecf4 100644 --- a/cpm/brainexplorer/scavenger.py +++ b/cpm/brainexplorer/scavenger.py @@ -29,10 +29,7 @@ def __init__(self, filepath=None): Parameters ---------- filepath : str - The path to the CSV file to be processed. - - data : pd.DataFrame - The data to be processed. + The path to the CSV file to be processed. The column names in the data must subscribe to a prespecified convention, see Notes. Example ---------- @@ -40,6 +37,22 @@ def __init__(self, filepath=None): >>> scavenger = Scavenger("/example/2025-01-10_Scavenger.xlsx") >>> scavenger.metrics() >>> scavenger.codebook() + + Notes + ----- + The columns required in data: + + - **A_EV**: expected value of choosing option A + - **B_EV**: expected value of choosing option B + - **A_perc**: Percent visible to user for option A + - **B_perc**: Percent visible to user for option B + - **A_magn**: Trial Values for option A + - **B_magn**: Trial Values for option B + - **EV_diff_chosen**: Expected value difference between option A and B + - **chosen**: 1 if option A chosen, 2 if option B chosen + - **chosen_left**: 1 if option A chosen, 0 if option B chosen + - **t_loss_all**: 1 if loss trial, 0 if win trial + - **t_abg_all**: 1 if ambiguous trial, 0 if non-ambiguous trial """ ## check file extension @@ -66,7 +79,7 @@ def __init__(self, filepath=None): def metrics(self): """ - Compute various descriptive statistics. See Notes for more information of the exact descriptive statistics we calculate. + Compute various descriptive statistics. See the `codebook` attribute for more information of the exact descriptive statistics we calculate. Parameters ---------- @@ -78,23 +91,6 @@ def metrics(self): results : pd.DataFrame A DataFrame containing the computed metrics. - Notes - ----- - - We provide a codebook for the calculated descriptive statistics: - - - **A_EV**: expected value of choosing option A - - **B_EV**: expected value of choosing option B - - **A_perc**: Percent visible to user for option A - - **B_perc**: Percent visible to user for option B - - **A_magn**: Trial Values for option A - - **B_magn**: Trial Values for option B - - **EV_diff_chosen**: Expected value difference between option A and B - - **chosen**: 1 if option A chosen, 2 if option B chosen - - **chosen_left**: 1 if option A chosen, 0 if option B chosen - - **t_loss_all**: 1 if loss trial, 0 if win trial - - **t_abg_all**: 1 if ambiguous trial, 0 if non-ambiguous trial - Warns ----- UserWarning @@ -347,6 +343,3 @@ def codebook(self): Return a codebook describing the metrics. """ return self.codebook - - -x = Scavenger("./example/2025-01-10_Scavenger_correct.xlsx") From 0337a6659c2eb9dccef96c36fb74f03cc56939f0 Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Mon, 3 Feb 2025 14:13:29 +0100 Subject: [PATCH 07/54] added documentation --- cpm/brainexplorer/scavenger.py | 51 ++++++++++++++++++++++++++++++++-- 1 file changed, 49 insertions(+), 2 deletions(-) diff --git a/cpm/brainexplorer/scavenger.py b/cpm/brainexplorer/scavenger.py index 109ecf4..485ef8f 100644 --- a/cpm/brainexplorer/scavenger.py +++ b/cpm/brainexplorer/scavenger.py @@ -78,24 +78,71 @@ def __init__(self, filepath=None): } def metrics(self): + """ - Compute various descriptive statistics. See the `codebook` attribute for more information of the exact descriptive statistics we calculate. + Compute metrics from the loaded data: expected values of rewards when choosing safe vs. risky options, percentages of + risky and safe wins/losses as well as total optimal choices. Parameters ---------- data : pd.DataFrame Data from all users and trials to be processed. - Returns + Returns ------- results : pd.DataFrame A DataFrame containing the computed metrics. + A_EV: expected value of choosing option A + B_EV: expected value of choosing option B + EV_diff_chosen: Expected value difference between option A and B + chosen: 1 if option A chosen, 2 if option B chosen + t_loss_all: 1 if loss trial, 0 if win trial + t_abg_all: 1 if ambiguous trial, 0 if non-ambiguous trial, + win_risk: Proportion of wins for risky choices, + loss_risk: Proportion of losses for risky choices, + win_risk_abg: Proportion of risky wins in ambiguous trials, + win_risk_NoAbg: Proportion of risky wins in non-ambiguous trials, + loss_risk_abg: Proportion of risky losses in ambiguous trials, + loss_risk_NoAbg: Proportion of risky losses in non-ambiguous trials, + optimal_all: Proportion of optimal choices, + optimal_win: Proportion of optimal choices in wins, + optimal_loss: Proportion of optimal choices in losses, + optimal_all_abg: Proportion of optimal choices in ambiguous trials, + optimal_win_abg: Proportion of optimal choices in win trials in ambiguous trials, + optimal_loss_abg: Proportion of optimal choices in loss trials in ambiguous trials + + + Notes + ----- + + In the game users can choose between two options with uncertain reward or loss probabilities. They reiceve information about reward probabilities for two choise options and can choose either the safe or risky option. + The data is split into two categories and options, A and B. Option A is choosing the safe option, i.e. the one which has more reward, which is labeled with 1 in "chosen" and Option B is choosing the risky option, labeled with 2 in "chosen". + Risk values are transfromed to 0 (no risk) and 1 (risky). + + We differentiate between the percentages of winning reward visible to the users in the task vs. actual percentages. + A_perc is the percent visible to the user for the safe option A, B_perc is the percent visible to the user for risky option B. + A_magn is the trial reward or loss values for option A, B_magn is the trial reward or loss values for option B. + + If visible percentages do not add up to 1 the trial is labeled as ambiguous. + + We calculate the expected value of the safe option A and the risky option B and then use these values to calculate the loss trials in which expected values are < 0. + Percentages of loss and win trials are calculated for risky choice trials (by objetive risk, option B) and number of optimal choices are calculated for all trials, win trials and loss trials. + + Warns ----- UserWarning Provides a warning for `np.linalg.LinAlgError` in logistic regression from `statsmodels` for a given user when a singular matrix error occurs. + + + References + ---------- + + Habicht J, Dubois M, Michely J and Hauser TU. Do propranolol and amisulpride modulate confidence in risk-taking? Wellcome Open Res 2022, 7:23. + + """ # Group data by userID From a6bf7048f07919229297edafc2feb674cffac67e Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Fri, 28 Feb 2025 11:08:42 +0100 Subject: [PATCH 08/54] Create spaceObserver.py --- cpm/brainexplorer/spaceObserver.py | 288 +++++++++++++++++++++++++++++ 1 file changed, 288 insertions(+) create mode 100644 cpm/brainexplorer/spaceObserver.py diff --git a/cpm/brainexplorer/spaceObserver.py b/cpm/brainexplorer/spaceObserver.py new file mode 100644 index 0000000..e96044e --- /dev/null +++ b/cpm/brainexplorer/spaceObserver.py @@ -0,0 +1,288 @@ +import pandas as pd +import numpy as np +import warnings +from scipy.stats import zscore + +class spaceObserver: + """ + A class to analyze and compute various descriptive statistics from behavioural data from a metacognition task + in BrainExplorer, Space Observer. Participants have to choose if stimulus has more of one type of alien or the other, + and rate their confidence in their choice. + + Attributes + ---------- + data : pd.DataFrame + The data loaded from the CSV or Excel file. + results : pd.DataFrame + A DataFrame to store the computed metrics. + codebook : dict + A dictionary containing the codebook for the computed metrics. + """ + + def __init__(self, filepath=None): + """ + Load data to be analyised and turn into Pandas DataFrame + + Parameters + ---------- + filepath : str + The path to the CSV file to be processed. The column names in the data must subscribe to a prespecified convention, see Notes. + + Example + ---------- + + >>> spaceObserver = SpaceObserver("/example/2025-02-20_SpaceObserver_Data_short.xlsx") + >>> spaceObserver.metrics() + >>> spaceObserver.codebook() + + if data in modelling format: + + >>> spaceObserver = SpaceObserver("/example/SpaceObserver_modelling_data.csv") + >>> spaceObserver.metrics("modelling") + >>> spaceObserver.codebook() + + Notes + ----- + The columns required in data: + + - **nA**: Number if aliens of type A. + - **nB**: Number of aliens of type B. + - **nDiff**: Difference between the number of aliens of type A and B. + - **correct**: TRUE if the participant made the correct choice, FALSE if incorrect. + - **choiceRT**: Response time for the choice. + - **confidence**: Confidence rating. + - **confidenceRT**: Response time for the confidence rating. + + + Columns in the modelling format: + + - **signal**: 0 if number of aliens of type A >= number of aliens of type B, 1 if number of aliens of type A < number of aliens of type B. + - **response**: 0 if left was chosen, 1 if right was chosen. + - **accuracy**: 1 if the participant made the correct choice, 0 if incorrect. + - **RT_choice**: Response time for the choice. + - **stimulus_intensity**: Absolute difference between the number of aliens of type A and B. + - **confidence**: Confidence rating. + - **confidenceRT**: Response time for the confidence rating. + + """ + + ## check file extension + if filepath.endswith(".csv"): + self.data = pd.read_csv(filepath, header=0) + elif filepath.endswith(".xlsx"): + self.data = pd.read_excel(filepath, header=0) + # self.data = pd.read_excel(filepath, header=0, nrows=500) + + self.results = pd.DataFrame() + self.codebook = { + } + + def metrics(self, mode=None): + """ + + Returns + ------- + results : pd.DataFrame + A DataFrame containing the results of the metrics. + + Variables + --------- + + accuracy : float + The percentage of correct choices made by the participant. + + mean_abs_evdiff : float + The mean absolute difference between the number of aliens in the two groups. + + evdiff_correct : float + The mean absolute difference between the number of aliens in the two groups for trials where the participant made the correct choice. + + evdiff_incorrect : float + The mean absolute difference between the number of aliens in the two groups for trials where the participant made the incorrect choice. + + mean_RT : float + The mean response time across all trials. + + std_RT : float + The standard deciation of the response time across all trials. + + mean_confidence : float + The mean confidence rating across all trials. + + mean_confidenceRT : float + The mean confidence response time across all trials. + + std_confidenceRT : float + The standard deciation of the confidence response time across all trials. + + conf_corr : np.array + The confidence ratings for trials where the participant made the correct choice. + + conf_inc : np.array + The confidence ratings for trials where the participant made the incorrect choice. + + conf_corr_mean : float + The mean confidence rating for trials where the participant made the correct choice. + + conf_inc_mean : float + The mean confidence rating for trials where the participant made the incorrect choice. + + + acc_per_conf : np.array + The accuracy for each of the four confidence bins. The confidence ratings are sorted and divided into four bins, and the accuracy is calculated for each bin. + + + In "modelling" mode: + ------------------- + + mean_accuracy : float + The mean accuracy across all trials. + + mean_RT : float + The mean response time across all trials. + + mean_confidence : float + The mean confidence rating across all trials. + + mean_confidenceRT : float + The mean confidence response time across all trials. + + """ + if mode == None: + + # Group data by userID + grouped_data = self.data.groupby("userID") + + # Loop through each group of user data + for user_id, user_data in grouped_data: + user_data = user_data.sort_values("run") + + user_results = {"userID": user_id} + + correct = np.array(user_data["correct"]) + # transform false to 0 and true to 1 + correct = correct.astype(int) + user_results["accuracy"] = sum(correct) / len(correct) + + abs_evdiff = np.array(np.abs(user_data["nDiff"])) + user_results["mean_abs_evdiff"] = np.mean(abs_evdiff) + + correct_indices = np.where(correct == 1)[0] # Indices where correct == 1 + incorrect_indices = np.where(correct != 1)[0] # Indices where correct != 1 + + # Absolute difference between the number of aliens in correct and incorrect trials + user_results["evdiff_correct"] = np.mean(abs_evdiff[correct_indices]) + user_results["evdiff_incorrect"] = np.mean(abs_evdiff[incorrect_indices]) + + confidence = np.array(user_data["confidence"]) + + # mean confidence + user_results["mean_confidence"] = np.mean(user_data["confidence"]) + # std + user_results["std_confidence"] = np.std(user_data["confidence"]) + + # mean reaction times + user_results["mean_RT"] = np.mean(user_data["choiceRT"]) + user_results["std_RT"] = np.std(user_data["choiceRT"]) + user_results['mean_confidenceRT'] = np.mean(user_data["confidenceRT"]) + user_results['std_confidenceRT'] = np.std(user_data["confidenceRT"]) + + # confidence by performance + conf_corr = confidence[correct == 1] + conf_incorr = confidence[~correct != 1] + user_results["conf_corr_mean"] = np.mean(conf_corr) + user_results["conf_incorr_mean"] = np.mean(conf_incorr) + + + B = np.sort(confidence) + I = np.argsort(confidence) + + # performance by confidence + + idxB = np.where(~np.isnan(B))[0] + + acc_per_conf = np.zeros(4) # Preallocate array for accuracy per confidence bin + + for b in range(1, 5): + idx = np.arange((b - 1) * np.ceil(len(idxB) / 4), b * np.ceil(len(idxB) / 4), dtype=int) + idx = idx[idx < len(idxB)] + + idx_co = I[idx] + acc_per_conf[b - 1] = np.sum(correct[idx_co]) / len(idx_co) if len(idx_co) > 0 else np.nan + + user_results["acc_per_conf"] = acc_per_conf + + + # Append user-level results + self.results = pd.concat( + [self.results, pd.DataFrame([user_results])], ignore_index=True + ) + + # Convert results to a DataFrame + self.results = pd.DataFrame(self.results) + return self.results + + + elif mode == "modelling": + + # Group data by subject + grouped_data = self.data.groupby("subject") + + all_users_data = [] + + # Loop through each group of user data + for user_id, user_data in grouped_data: + user_data = user_data.sort_values("trial") + + + user_metrics = { + "userID": user_id, + "choice": [], + "nA": [], + "nB": [], + "nDiff": [], + "correct": [], + "RT": [], + "confidence": [], + "confidenceRT": [], + "signal": [] + } + + # Loop through each trial in the user's data + for i, trial_data in user_data.iterrows(): + + user_metrics["correct"].append(trial_data["accuracy"]) + user_metrics["choice"].append(trial_data["response"]) #left = 0, right = 1 + user_metrics["signal"].append(trial_data["signal"]) #0 if nA >= nB, 1 if nA < nB + user_metrics["RT"].append(trial_data["RT_choice"]) + user_metrics["confidence"].append(trial_data["confidence"]) + user_metrics["confidenceRT"].append(trial_data["confidenceRT"]) + user_metrics["nDiff"].append(trial_data["stimulus_intensity"]) + + all_users_data.append(user_metrics) + + for user_data in all_users_data: + + user_results = {"userID": user_data["userID"]} + + user_results["mean_accuracy"] = np.mean(user_data["correct"]) + user_results["mean_RT"] = np.mean(user_data["RT"]) + user_results["mean_confidence"] = np.nanmean(user_data["confidence"]) + user_results["mean_confidenceRT"] = np.nanmean(user_data["confidenceRT"]) + + # Append user-level results + self.results = pd.concat( + [self.results, pd.DataFrame([user_results])], ignore_index=True + ) + + # Convert results to a DataFrame + self.results = pd.DataFrame(self.results) + return self.results + + + + def codebook(self): + """ + Return a codebook describing the metrics. + """ + return self.codebook \ No newline at end of file From 9d211338406d1369c11ef8bcc368808cddf48a68 Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Tue, 11 Mar 2025 12:33:29 +0100 Subject: [PATCH 09/54] add group means --- cpm/brainexplorer/spaceObserver.py | 32 ++++++++++++++++++++++++++++-- 1 file changed, 30 insertions(+), 2 deletions(-) diff --git a/cpm/brainexplorer/spaceObserver.py b/cpm/brainexplorer/spaceObserver.py index e96044e..0081a0e 100644 --- a/cpm/brainexplorer/spaceObserver.py +++ b/cpm/brainexplorer/spaceObserver.py @@ -74,6 +74,7 @@ def __init__(self, filepath=None): # self.data = pd.read_excel(filepath, header=0, nrows=500) self.results = pd.DataFrame() + self.group_results = pd.DataFrame() self.codebook = { } @@ -220,7 +221,24 @@ def metrics(self, mode=None): # Convert results to a DataFrame self.results = pd.DataFrame(self.results) - return self.results + + # calculate group means + rt_mean = np.mean(self.results["mean_RT"]) + rt_std = np.std(self.results["mean_RT"]) + conf_mean = np.mean(self.results["mean_confidence"]) + conf_std = np.std(self.results["mean_confidence"]) + rt_conf_mean = np.mean(self.results["mean_confidenceRT"]) + acc_mean = np.mean(self.results["accuracy"]) + acc_std = np.std(self.results["accuracy"]) + conf_corr_mean = np.mean(self.results["conf_corr_mean"]) + conf_incorr_mean = np.mean(self.results["conf_incorr_mean"]) + abs_evdiff_mean = np.mean(self.results["mean_abs_evdiff"]) + evdiff_correct_mean = np.mean(self.results["evdiff_correct"]) + evdiff_incorrect_mean = np.mean(self.results["evdiff_incorrect"]) + + self.group_results = pd.DataFrame({"mean_RT": rt_mean, "std_RT": rt_std, "mean_confidence": conf_mean, "std_confidence": conf_std, "mean_confidenceRT": rt_conf_mean, "mean_accuracy": acc_mean, "std_accuracy": acc_std, "mean_confidence_corr": conf_corr_mean, "mean_confidence_incorr": conf_incorr_mean, "mean_abs_evdiff": abs_evdiff_mean, "mean_evdiff_correct": evdiff_correct_mean, "mean_evdiff_incorrect": evdiff_incorrect_mean}, index=[0]) + + return self.results, self.group_results elif mode == "modelling": @@ -277,7 +295,17 @@ def metrics(self, mode=None): # Convert results to a DataFrame self.results = pd.DataFrame(self.results) - return self.results + + # calculate group means + rt_mean = np.mean(self.results["mean_RT"]) + rt_std = np.std(self.results["mean_RT"]) + conf_mean = np.mean(self.results["mean_confidence"]) + conf_std = np.std(self.results["mean_confidence"]) + acc_mean = np.mean(self.results["accuracy"]) + acc_std = np.std(self.results["accuracy"]) + self.group_results = pd.DataFrame({"mean_RT": rt_mean, "std_RT": rt_std, "mean_confidence": conf_mean, "std_confidence": conf_std, "mean_accuracy": acc_mean, "std_accuracy": acc_std}, index=[0]) + + return self.results, self.group_results From dff138266cbdcf5650a5c5c362c870c3a725f8e1 Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Tue, 11 Mar 2025 12:38:19 +0100 Subject: [PATCH 10/54] add group means --- cpm/brainexplorer/scavenger.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/cpm/brainexplorer/scavenger.py b/cpm/brainexplorer/scavenger.py index 485ef8f..517e485 100644 --- a/cpm/brainexplorer/scavenger.py +++ b/cpm/brainexplorer/scavenger.py @@ -62,6 +62,7 @@ def __init__(self, filepath=None): self.data = pd.read_excel(filepath, header=0) self.results = pd.DataFrame() + self.group_results = pd.DataFrame() self.codebook = { "win_risk": "Proportion of risky wins", "loss_risk": "Proportion of risky losses", @@ -383,10 +384,15 @@ def metrics(self): # Convert results to a DataFrame self.results = pd.DataFrame(self.results) - return self.results + + # calculate group means + group_results = self.results.mean() + self.group_results = pd.DataFrame(group_results).T + + return self.results, self.group_results def codebook(self): """ Return a codebook describing the metrics. """ - return self.codebook + return self.codebook \ No newline at end of file From 64949f674871e50ba051303b4e30e14ada204718 Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Fri, 11 Apr 2025 10:15:28 +0200 Subject: [PATCH 11/54] Update scavenger.py --- cpm/brainexplorer/scavenger.py | 16 +++++----------- 1 file changed, 5 insertions(+), 11 deletions(-) diff --git a/cpm/brainexplorer/scavenger.py b/cpm/brainexplorer/scavenger.py index 517e485..e11c480 100644 --- a/cpm/brainexplorer/scavenger.py +++ b/cpm/brainexplorer/scavenger.py @@ -42,17 +42,11 @@ def __init__(self, filepath=None): ----- The columns required in data: - - **A_EV**: expected value of choosing option A - - **B_EV**: expected value of choosing option B - - **A_perc**: Percent visible to user for option A - - **B_perc**: Percent visible to user for option B - - **A_magn**: Trial Values for option A - - **B_magn**: Trial Values for option B - - **EV_diff_chosen**: Expected value difference between option A and B - - **chosen**: 1 if option A chosen, 2 if option B chosen - - **chosen_left**: 1 if option A chosen, 0 if option B chosen - - **t_loss_all**: 1 if loss trial, 0 if win trial - - **t_abg_all**: 1 if ambiguous trial, 0 if non-ambiguous trial + - "chosenOption_outcomes_percent": if option A or B chosen, + - "chosenOption_outcomes_userVisiblePercent": Percent visible to user for chosen option, + - "unchosenOption_outcomes_userVisiblePercent": Percent visible to user for option not chosen, + - "chosenOption_outcomes_trialValue": Trial Values for option chosen, + - "unchosenOption_outcomes_trialValue": Trial Values for option not chosen """ ## check file extension From 499a654091594827ab19e714aa67c35791234e6e Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Tue, 22 Apr 2025 09:57:37 +0200 Subject: [PATCH 12/54] updated scavenger --- cpm/brainexplorer/scavenger.py | 237 ++++++++++++--------------------- 1 file changed, 82 insertions(+), 155 deletions(-) diff --git a/cpm/brainexplorer/scavenger.py b/cpm/brainexplorer/scavenger.py index e11c480..1e58d59 100644 --- a/cpm/brainexplorer/scavenger.py +++ b/cpm/brainexplorer/scavenger.py @@ -42,11 +42,11 @@ def __init__(self, filepath=None): ----- The columns required in data: - - "chosenOption_outcomes_percent": if option A or B chosen, - - "chosenOption_outcomes_userVisiblePercent": Percent visible to user for chosen option, - - "unchosenOption_outcomes_userVisiblePercent": Percent visible to user for option not chosen, - - "chosenOption_outcomes_trialValue": Trial Values for option chosen, - - "unchosenOption_outcomes_trialValue": Trial Values for option not chosen + - "choice": if option A or B chosen, + - "risky_prob": Likelihood of winning for option B, + - "safe_magn": Trial Values for Option A, + - "risky_magn1" & "risky_magn2: Trial Values for Option B, + - "ambg": if the trial is ambiguous or not """ ## check file extension @@ -64,19 +64,19 @@ def __init__(self, filepath=None): "win_risk_NoAbg": "Proportion of risky wins in non-ambiguous trials", "loss_risk_abg": "Proportion of risky losses in ambiguous trials", "loss_risk_NoAbg": "Proportion of risky losses in non-ambiguous trials", - "optimal_all": "Proportion of optimal choices", - "optimal_win": "Proportion of optimal choices in wins", - "optimal_loss": "Proportion of optimal choices in losses", - "optimal_all_abg": "Proportion of optimal choices in ambiguous trials", - "optimal_win_abg": "Proportion of optimal choices in wins in ambiguous trials", - "optimal_loss_abg": "Proportion of optimal choices in losses in ambiguous trials", + "rational_all": "Proportion of rational choices", + "rational_win": "Proportion of rational choices in wins", + "rational_loss": "Proportion of rational choices in losses", + "rational_all_abg": "Proportion of rational choices in ambiguous trials", + "rational_win_abg": "Proportion of rational choices in wins in ambiguous trials", + "rational_loss_abg": "Proportion of rational choices in losses in ambiguous trials", } def metrics(self): """ Compute metrics from the loaded data: expected values of rewards when choosing safe vs. risky options, percentages of - risky and safe wins/losses as well as total optimal choices. + risky and safe wins/losses as well as total rational choices. Parameters ---------- @@ -100,12 +100,15 @@ def metrics(self): win_risk_NoAbg: Proportion of risky wins in non-ambiguous trials, loss_risk_abg: Proportion of risky losses in ambiguous trials, loss_risk_NoAbg: Proportion of risky losses in non-ambiguous trials, - optimal_all: Proportion of optimal choices, - optimal_win: Proportion of optimal choices in wins, - optimal_loss: Proportion of optimal choices in losses, - optimal_all_abg: Proportion of optimal choices in ambiguous trials, - optimal_win_abg: Proportion of optimal choices in win trials in ambiguous trials, - optimal_loss_abg: Proportion of optimal choices in loss trials in ambiguous trials + rational_all: Proportion of rational choices, + rational_win: Proportion of rational choices in wins, + rational_loss: Proportion of rational choices in losses, + rational_all_abg: Proportion of rational choices in ambiguous trials, + rational_win_abg: Proportion of rational choices in win trials in ambiguous trials, + rational_loss_abg: Proportion of rational choices in loss trials in ambiguous trials, + rational_all_NoAbg: Proportion of rational choices in non-ambiguous trials, + rational_win_NoAbg: Proportion of rational choices in win trials in non-ambiguous trials, + rational_loss_NoAbg: Proportion of rational choices in loss trials in non-ambiguous trials Notes @@ -122,13 +125,7 @@ def metrics(self): If visible percentages do not add up to 1 the trial is labeled as ambiguous. We calculate the expected value of the safe option A and the risky option B and then use these values to calculate the loss trials in which expected values are < 0. - Percentages of loss and win trials are calculated for risky choice trials (by objetive risk, option B) and number of optimal choices are calculated for all trials, win trials and loss trials. - - - Warns - ----- - UserWarning - Provides a warning for `np.linalg.LinAlgError` in logistic regression from `statsmodels` for a given user when a singular matrix error occurs. + Percentages of loss and win trials are calculated for risky choice trials (by objetive risk, option B) and number of rational choices are calculated for all trials, win trials and loss trials. @@ -141,13 +138,12 @@ def metrics(self): """ # Group data by userID - grouped_data = self.data.groupby("userID") + grouped_data = self.data.groupby("loopID") all_users_data = [] # Loop through each group of user data for user_id, user_data in grouped_data: - user_data = user_data.sort_values("trial") # Initialize a dictionary to store results for this user user_metrics = { @@ -160,75 +156,42 @@ def metrics(self): "A_EV": [], "B_EV": [], "chosen": [], + "all_rewards": [], + "choice_stickiness": [], } # Loop through each trial in the user's data for _, trial_data in user_data.iterrows(): metrics_data_trial = {} - fields_to_process = [ - "chosenOption_outcomes_percent", - "chosenOption_outcomes_userVisiblePercent", - "unchosenOption_outcomes_userVisiblePercent", - "chosenOption_outcomes_trialValue", - "unchosenOption_outcomes_trialValue", - ] - - # Normalize the strings into arrays - normalized_data = {} - - for field in fields_to_process: - if field in trial_data: - value = trial_data[field] - if isinstance(value, str): - normalized_data[field] = [ - float(x.strip()) for x in value.split(",") - ] - else: - normalized_data[field] = [float(value)] - - if len(normalized_data["chosenOption_outcomes_percent"]) == 1: - metrics_data_trial["chosen"] = 1 # Option A chosen - metrics_data_trial["A_perc"] = normalized_data[ - "chosenOption_outcomes_userVisiblePercent" + if trial_data["choice"] == 0: + metrics_data_trial["chosen"] = 1 # Option A chosen (safe) + metrics_data_trial["A_perc"] = 1 + metrics_data_trial["A_magn"] = trial_data["safe_magn"]*100 + metrics_data_trial["B_perc"] = trial_data[ + "risky_prob" ] - metrics_data_trial["A_magn"] = normalized_data[ - "chosenOption_outcomes_trialValue" + metrics_data_trial["B_magn"] = [ + trial_data["risky_magn1"]*100, + trial_data["risky_magn2"]*100 ] - metrics_data_trial["B_perc"] = normalized_data[ - "unchosenOption_outcomes_userVisiblePercent" - ] - metrics_data_trial["B_magn"] = normalized_data[ - "unchosenOption_outcomes_trialValue" - ] - if sum(metrics_data_trial["B_perc"]) != 1: - metrics_data_trial["ambiguous"] = 1 - else: - metrics_data_trial["ambiguous"] = 0 else: metrics_data_trial["chosen"] = 2 # Option B chosen - metrics_data_trial["A_perc"] = normalized_data[ - "unchosenOption_outcomes_userVisiblePercent" + metrics_data_trial["A_perc"] = 1 + metrics_data_trial["A_magn"] = trial_data[ + "safe_magn" ] - metrics_data_trial["A_magn"] = normalized_data[ - "unchosenOption_outcomes_trialValue" + metrics_data_trial["B_perc"] = trial_data[ + "risky_prob" ] - metrics_data_trial["B_perc"] = normalized_data[ - "chosenOption_outcomes_userVisiblePercent" + metrics_data_trial["B_magn"] = [ + trial_data["risky_magn1"], + trial_data["risky_magn2"] ] - metrics_data_trial["B_magn"] = normalized_data[ - "chosenOption_outcomes_trialValue" - ] - - if sum(metrics_data_trial["B_perc"]) != 1: - metrics_data_trial["ambiguous"] = 1 - else: - metrics_data_trial["ambiguous"] = 0 - if trial_data["decision"] == "optionA": - metrics_data_trial["chosenLeft"] = 1 - else: - metrics_data_trial["chosenLeft"] = 0 + metrics_data_trial["ambiguous"] = trial_data[ + "ambg" + ] # Compute expected values metrics_data_trial["A_EV"] = np.dot( @@ -236,12 +199,13 @@ def metrics(self): np.array(metrics_data_trial["A_magn"]), ) metrics_data_trial["B_EV"] = 0.5 * np.array( - metrics_data_trial["B_perc"] - ) + 0.5 * np.array(metrics_data_trial["B_magn"]) - metrics_data_trial["B_EV"] = metrics_data_trial["B_EV"][0] + metrics_data_trial["B_magn"][0] + ) + 0.5 * np.array(metrics_data_trial["B_magn"][1]) # assuming 50% propbs + metrics_data_trial["EV_diff_safe"] = ( metrics_data_trial["A_EV"] - metrics_data_trial["B_EV"] ) + if metrics_data_trial["chosen"] == 1: metrics_data_trial["EV_diff_chosen"] = ( metrics_data_trial["A_EV"] - metrics_data_trial["B_EV"] @@ -261,7 +225,8 @@ def metrics(self): user_metrics["EV_diff_chosen_all"].append( metrics_data_trial["EV_diff_chosen"] ) - user_metrics["chosen_left_all"].append(metrics_data_trial["chosenLeft"]) + user_metrics["RT"] = trial_data["rt"] + user_metrics["all_rewards"].append(trial_data["feedback"]) all_users_data.append(user_metrics) @@ -276,6 +241,14 @@ def metrics(self): user_results = {"userID": user_id} + user_results["mean_RT"] = np.mean(user_data["RT"]) + user_results["std_RT"] = np.std(user_data["RT"]) + + user_results["total_rewards"] = np.sum(user_data["all_rewards"]) + + # calculate choice stickiness + user_results["choice_stickiness"] = np.sum(user_data["chosen"][i] == 1 and user_data["chosen"][i + 1] == 1 for i in range(len(user_data["chosen"]) - 1)) + # Number of risky choices user_results["win_risk"] = np.sum(rsk[~t_loss]) / np.sum(~t_loss) print(user_results["win_risk"]) @@ -293,83 +266,37 @@ def metrics(self): t_loss & ~t_abg ) - # Number of optimal choices - user_results["optimal_all"] = np.sum(ev_diff_chosen > 0) / len( + # Number of rational choices + user_results["rational_all"] = np.sum(ev_diff_chosen >= 0) / len( ev_diff_chosen ) - user_results["optimal_win"] = np.sum(ev_diff_chosen[~t_loss] > 0) / np.sum( + user_results["rational_win"] = np.sum(ev_diff_chosen[~t_loss] >= 0) / np.sum( ~t_loss ) - user_results["optimal_loss"] = np.sum(ev_diff_chosen[t_loss] > 0) / np.sum( + user_results["rational_loss"] = np.sum(ev_diff_chosen[t_loss] >= 0) / np.sum( t_loss ) - user_results["optimal_all_abg"] = np.sum( - ev_diff_chosen[t_abg] > 0 - ) / np.sum(t_abg) - user_results["optimal_win_abg"] = np.sum( - ev_diff_chosen[~t_loss & t_abg] > 0 - ) / np.sum(~t_loss & t_abg) - user_results["optimal_loss_abg"] = np.sum( - ev_diff_chosen[t_loss & t_abg] > 0 - ) / np.sum(t_loss & t_abg) - - # logistic regression - logit_data = user_data.copy() - logit_data = pd.DataFrame(logit_data) - - logit_data["chosen"] = np.array(logit_data["chosen"]) - 1 - - modelspec = "chosen ~ A_EV + B_EV" - - # Standardize A_EV and B_EV - logit_data["A_EV"] = zscore(logit_data["A_EV"]) - logit_data["B_EV"] = zscore(logit_data["B_EV"]) - - # Fit logistic regression model for all data - logit_all = smf.logit(modelspec, data=logit_data).fit() - - # Create a mask for t_loss - t_loss = logit_data["t_loss_all"] - - # Standardize A_EV and B_EV for non-loss cases - logit_data.loc[~t_loss, "A_EV"] = zscore(logit_data.loc[~t_loss, "A_EV"]) - logit_data.loc[~t_loss, "B_EV"] = zscore(logit_data.loc[~t_loss, "B_EV"]) - - # Fit logistic regression model for non-loss cases - logit_win = smf.logit(modelspec, data=logit_data[~t_loss]).fit() - # Standardize A_EV and B_EV for loss cases - logit_data.loc[t_loss, "A_EV"] = zscore(logit_data.loc[t_loss, "A_EV"]) - logit_data.loc[t_loss, "B_EV"] = zscore(logit_data.loc[t_loss, "B_EV"]) - - # Fit logistic regression model for loss cases - # Catch singular matrix error - try: - logit_loss = smf.logit(modelspec, data=logit_data[t_loss]).fit() - except np.linalg.LinAlgError: - warnings.warn( - f"Singular matrix error for user {user_id} in loss case logistic regression. Skipping." - ) - continue - - # Add results for each logistic regression model - user_results[f"{'logit_all'}_itcp"] = logit_all.params.get("const", None) - - for coef_name, estimate in logit_all.params.items(): - if coef_name != "const": # Skip intercept here as it's already added - user_results[f"{'logit_all'}_{coef_name}"] = estimate - - user_results[f"{'logit_win'}_itcp"] = logit_all.params.get("const", None) - - for coef_name, estimate in logit_all.params.items(): - if coef_name != "const": # Skip intercept here as it's already added - user_results[f"{'logit_win'}_{coef_name}"] = estimate + user_results["rational_all_abg"] = np.sum( + ev_diff_chosen[t_abg] >= 0 + ) / np.sum(t_abg) + user_results["rational_all_NoAbg"] = np.sum( + ev_diff_chosen[~t_abg] >= 0 + ) / np.sum(~t_abg) - user_results[f"{'logit_loss'}_itcp"] = logit_all.params.get("const", None) + user_results["rational_win_abg"] = np.sum( + ev_diff_chosen[~t_loss & t_abg] >= 0 + ) / np.sum(~t_loss & t_abg) + user_results["rational_win_NoAbg"] = np.sum( + ev_diff_chosen[~t_loss & ~t_abg] >= 0 + ) / np.sum(~t_loss & ~t_abg) - for coef_name, estimate in logit_all.params.items(): - if coef_name != "const": # Skip intercept here as it's already added - user_results[f"{'logit_loss'}_{coef_name}"] = estimate + user_results["rational_loss_abg"] = np.sum( + ev_diff_chosen[t_loss & t_abg] >= 0 + ) / np.sum(t_loss & t_abg) + user_results["rational_loss_NoAbg"] = np.sum( + ev_diff_chosen[t_loss & ~t_abg] >= 0 + ) / np.sum(t_loss & ~t_abg) # Append user-level results self.results = pd.concat( @@ -389,4 +316,4 @@ def codebook(self): """ Return a codebook describing the metrics. """ - return self.codebook \ No newline at end of file + return self.codebook From 9d562ab0a8f205609ef5e17b84d93a4a6b190ae0 Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Thu, 8 May 2025 14:22:07 +0200 Subject: [PATCH 13/54] add GH model --- cpm/brainexplorer/GoblinHeist_model.py | 146 +++++++++++++++++++++++++ 1 file changed, 146 insertions(+) create mode 100644 cpm/brainexplorer/GoblinHeist_model.py diff --git a/cpm/brainexplorer/GoblinHeist_model.py b/cpm/brainexplorer/GoblinHeist_model.py new file mode 100644 index 0000000..de247c8 --- /dev/null +++ b/cpm/brainexplorer/GoblinHeist_model.py @@ -0,0 +1,146 @@ +import pandas as pd +import numpy as np +import warnings + +from scipy.special import logsumexp + + +class GoblinHeist: + """ + Class for the Goblin Heist task + + Attributes + ---------- + data : pd.DataFrame + The individual user data to be processed. The column names in the data must subscribe to a prespecified convention, see Notes. + + parameters: list, optional + A list of parameters to be used in the model. If not specified, default parameters are used. + Order of parameters: inverse temperature, learning rate, eligibility trace decay, mixing weight for low stake trials, mixing weight for high stake trials, stickiness, response stickiness. + + Example + ---------- + + goblinHeist = GoblinHeist(data, [1, 0.8, 0.5, 0.8, 0.8, -0.1, -0.1]) + goblinHeist.MB_MF_model() + + Notes + ----- + The columns required in data: + - userID: unique identifier for each participant + - stimuli: which planets presented in stage of the trial + - s: the state of the trial + - rews: the rewards for each stimulus / planet + - choice: the choice made by the participant + - stake: the stake of the trial (1 or 5) + - points: the points received in the trial + + + References + ---------- + + Kool W, Cushman FA, Gershman SJ (2016) When Does Model-Based Control Pay Off? PLoS Comput Biol 12 + + Smid, C.R., K. Ganesan, A. Thompson, R. Cañigueral, S. Veselic, J. Royer, W. Kool, T.U. Hauser, B. Bernhardt, and N. Steinbeis. “Neurocognitive Basis of Model-Based Decision Making and Its Metacontrol in Childhood.” Developmental Cognitive Neuroscience 62 (August 1, 2023): 101269. + + """ + + def __init__(self, data, parameters=None): + + self.data = data + + if parameters is None: + self.parameters=[1, 0.8, 0.5, 0.8, 0.8, -0.1, -0.1] + warnings.warn("No parameters specified, using default parameters.") + else: + self.parameters = parameters + + + def MF_MB_model(self): + + """ + Model for model-based vs model-free decision making in a decison making task + + + Returns + ------- + LL: + Log Likelihood + Q: + Q values + Q_MF: + Q values for model-free learning + Q_MB: + Q values for model-based learning + """ + + # pull out the paramters + b = self.parameters[0] # softmax inverse tempterature + lr = self.parameters[1] # learning rate + lamb = self.parameters[2] # eligibility trace decay + w_lo = self.parameters[3] # mixing weight for low stake trials + w_hi = self.parameters[4] # mixing weight for high stake trials + st = self.parameters[5] # stickiness + respst = self.parameters[6] # response stickiness + + # initialization + Q_MF = np.ones((2,2))*4.5 + Q2 = np.ones((2,1))*4.5 + TM = [np.eye(2), np.eye(2)] #transition matrix + M = np.zeros((2,2)) # last action matrix + R = [0, 0] # last choice structure + LL = 0 # log likelihood + + for i in range(len(self.data["stimuli"])): + + if self.data["stimuli"][i] == 2 or self.data["stimuli"][i] == 4: + R = R = np.flipud(R) + + s1 = self.data["s"][i][0] # first choice stage + s2 = self.data["s"][i][1] # second choice stage + action = self.data["choice"][i] + a = action[0] - (s1 == 2) * 2 + + # choose which weight to update based on stake of trial + if self.data["stake"][i] == 1: + w = w_lo + else: + w = w_hi + + # calculating the Q values for model-based learning + Q_MB = TM[s1-1].T @ Q2 + + # calculating the Q values for mixing model + Q = w*Q_MB + (1-w)*Q_MF[s1-1, :].T + st * M[s1-1, :].T + respst * np.array(R) + + # calculating the probability choosing action a given Q + LL = LL + b*Q[a-1] - logsumexp(b * Q) + + M = np.zeros((2,2)) + M[s1-1, a-1] = 1 + + R = np.zeros((2,1)) + if action == self.data["stimuli"][i][0]: + R[0] = 1 + else: + R[1] = 1 + + # update Q values for model-free learning + # first choice stage + dtQ1 = Q2[s2-1] - Q_MF[s1-1, a-1] + Q_MF[s1-1, a-1] = Q_MF[s1-1, a-1] + lr * dtQ1 + # second choice stage + dtQ2 = self.data["points"][i] - Q2[s2-1] + Q2[s2-1] = Q2[s2-1] + lr * dtQ2 + Q_MF[s1-1, a-1] = Q_MF[s1-1, a-1] + lamb * lr * dtQ2 + + # compile results + results = pd.DataFrame({ + "userID": self.data["userID"][0], + "LL": [LL], + "Q": [Q.flatten()], + "Q_MF": [Q_MF.flatten()], + "Q_MB": [Q_MB.flatten()] + }) + + return results \ No newline at end of file From 793af256af08aa3ee0f210c1f0ef4ae96fe936ac Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Thu, 15 May 2025 13:52:20 +0200 Subject: [PATCH 14/54] update GH --- cpm/brainexplorer/GoblinHeist_model.py | 33 ++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) diff --git a/cpm/brainexplorer/GoblinHeist_model.py b/cpm/brainexplorer/GoblinHeist_model.py index de247c8..823ff4c 100644 --- a/cpm/brainexplorer/GoblinHeist_model.py +++ b/cpm/brainexplorer/GoblinHeist_model.py @@ -49,11 +49,35 @@ def __init__(self, data, parameters=None): self.data = data + """ if parameters is None: self.parameters=[1, 0.8, 0.5, 0.8, 0.8, -0.1, -0.1] warnings.warn("No parameters specified, using default parameters.") else: self.parameters = parameters + """ + + if parameters is None: + self.parameters = { + "temperature": 0.8, + "learning_rate": 0.5, + "eligibility_trace_decay": 0.8, + "mixing_weight_low_stake": 0.8, + "mixing_weight_high_stake": 0.8, + "stickiness": -0.1, + "response_stickiness": -0.1 + } + warnings.warn("No parameters specified, using default parameters.") + else: + self.parameters = { + "temperature": parameters[0], + "learning_rate": parameters[1], + "eligibility_trace_decay": parameters[2], + "mixing_weight_low_stake": parameters[3], + "mixing_weight_high_stake": parameters[4], + "stickiness": parameters[5], + "response_stickiness": parameters[6] + } def MF_MB_model(self): @@ -75,6 +99,7 @@ def MF_MB_model(self): """ # pull out the paramters + """ b = self.parameters[0] # softmax inverse tempterature lr = self.parameters[1] # learning rate lamb = self.parameters[2] # eligibility trace decay @@ -82,6 +107,14 @@ def MF_MB_model(self): w_hi = self.parameters[4] # mixing weight for high stake trials st = self.parameters[5] # stickiness respst = self.parameters[6] # response stickiness + """ + b = self.parameters["temperature"] # softmax inverse temperature + lr = self.parameters["learning_rate"] # learning rate + lamb = self.parameters["eligibility_trace_decay"] # eligibility trace decay + w_lo = self.parameters["mixing_weight_low_stake"] # mixing weight for low stake trials + w_hi = self.parameters["mixing_weight_high_stake"] # mixing weight for high stake trials + st = self.parameters["stickiness"] # stickiness + respst = self.parameters["response_stickiness"] # response stickiness # initialization Q_MF = np.ones((2,2))*4.5 From fcfa5dc3144a12559939b1acdd14dd1622d84959 Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Thu, 15 May 2025 14:00:40 +0200 Subject: [PATCH 15/54] add median RTs --- cpm/brainexplorer/scavenger.py | 12 +++++------- cpm/brainexplorer/spaceObserver.py | 17 +++++++++++++++++ 2 files changed, 22 insertions(+), 7 deletions(-) diff --git a/cpm/brainexplorer/scavenger.py b/cpm/brainexplorer/scavenger.py index 1e58d59..055f1bf 100644 --- a/cpm/brainexplorer/scavenger.py +++ b/cpm/brainexplorer/scavenger.py @@ -88,12 +88,9 @@ def metrics(self): results : pd.DataFrame A DataFrame containing the computed metrics. - A_EV: expected value of choosing option A - B_EV: expected value of choosing option B - EV_diff_chosen: Expected value difference between option A and B - chosen: 1 if option A chosen, 2 if option B chosen - t_loss_all: 1 if loss trial, 0 if win trial - t_abg_all: 1 if ambiguous trial, 0 if non-ambiguous trial, + mean_RT: Mean reaction time, + median_RT: Median reaction time, + std_RT: Standard deviation of reaction time, win_risk: Proportion of wins for risky choices, loss_risk: Proportion of losses for risky choices, win_risk_abg: Proportion of risky wins in ambiguous trials, @@ -242,6 +239,7 @@ def metrics(self): user_results = {"userID": user_id} user_results["mean_RT"] = np.mean(user_data["RT"]) + user_results["median_RT"] = np.median(user_data["RT"]) user_results["std_RT"] = np.std(user_data["RT"]) user_results["total_rewards"] = np.sum(user_data["all_rewards"]) @@ -316,4 +314,4 @@ def codebook(self): """ Return a codebook describing the metrics. """ - return self.codebook + return self.codebook \ No newline at end of file diff --git a/cpm/brainexplorer/spaceObserver.py b/cpm/brainexplorer/spaceObserver.py index 0081a0e..6ef4a9a 100644 --- a/cpm/brainexplorer/spaceObserver.py +++ b/cpm/brainexplorer/spaceObserver.py @@ -104,6 +104,9 @@ def metrics(self, mode=None): mean_RT : float The mean response time across all trials. + median_RT : float + The median response time across all trials. + std_RT : float The standard deciation of the response time across all trials. @@ -184,6 +187,7 @@ def metrics(self, mode=None): # mean reaction times user_results["mean_RT"] = np.mean(user_data["choiceRT"]) + user_results["median_RT"] = np.median(user_data["choiceRT"]) user_results["std_RT"] = np.std(user_data["choiceRT"]) user_results['mean_confidenceRT'] = np.mean(user_data["confidenceRT"]) user_results['std_confidenceRT'] = np.std(user_data["confidenceRT"]) @@ -285,9 +289,22 @@ def metrics(self, mode=None): user_results["mean_accuracy"] = np.mean(user_data["correct"]) user_results["mean_RT"] = np.mean(user_data["RT"]) + user_results["median_RT"] = np.median(user_data["RT"]) + user_results["std_RT"] = np.std(user_data["RT"]) user_results["mean_confidence"] = np.nanmean(user_data["confidence"]) user_results["mean_confidenceRT"] = np.nanmean(user_data["confidenceRT"]) + # confidence by performance + correct = np.array(user_data["correct"]) + # transform false to 0 and true to 1 + correct = correct.astype(int) + confidence = np.array(user_data["confidence"]) + + conf_corr = confidence[correct == 1] + conf_incorr = confidence[~correct != 1] + user_results["conf_corr_mean"] = np.mean(conf_corr) + user_results["conf_incorr_mean"] = np.mean(conf_incorr) + # Append user-level results self.results = pd.concat( [self.results, pd.DataFrame([user_results])], ignore_index=True From 76daa51373442b6665d742134c105ab2820f5cb7 Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Tue, 20 May 2025 11:15:28 +0200 Subject: [PATCH 16/54] Update GoblinHeist_model.py --- cpm/brainexplorer/GoblinHeist_model.py | 20 ++------------------ 1 file changed, 2 insertions(+), 18 deletions(-) diff --git a/cpm/brainexplorer/GoblinHeist_model.py b/cpm/brainexplorer/GoblinHeist_model.py index 823ff4c..1cd7513 100644 --- a/cpm/brainexplorer/GoblinHeist_model.py +++ b/cpm/brainexplorer/GoblinHeist_model.py @@ -7,7 +7,7 @@ class GoblinHeist: """ - Class for the Goblin Heist task + Model for model-based vs model-free decision making in a decision making task Attributes ---------- @@ -49,14 +49,6 @@ def __init__(self, data, parameters=None): self.data = data - """ - if parameters is None: - self.parameters=[1, 0.8, 0.5, 0.8, 0.8, -0.1, -0.1] - warnings.warn("No parameters specified, using default parameters.") - else: - self.parameters = parameters - """ - if parameters is None: self.parameters = { "temperature": 0.8, @@ -99,15 +91,6 @@ def MF_MB_model(self): """ # pull out the paramters - """ - b = self.parameters[0] # softmax inverse tempterature - lr = self.parameters[1] # learning rate - lamb = self.parameters[2] # eligibility trace decay - w_lo = self.parameters[3] # mixing weight for low stake trials - w_hi = self.parameters[4] # mixing weight for high stake trials - st = self.parameters[5] # stickiness - respst = self.parameters[6] # response stickiness - """ b = self.parameters["temperature"] # softmax inverse temperature lr = self.parameters["learning_rate"] # learning rate lamb = self.parameters["eligibility_trace_decay"] # eligibility trace decay @@ -149,6 +132,7 @@ def MF_MB_model(self): # calculating the probability choosing action a given Q LL = LL + b*Q[a-1] - logsumexp(b * Q) + # update choice structure M = np.zeros((2,2)) M[s1-1, a-1] = 1 From 9c18787fd99b6588ca89625466dde2c1a045f77e Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Fri, 30 May 2025 09:50:37 +0200 Subject: [PATCH 17/54] new SO --- cpm/brainexplorer/spaceObserver.py | 341 ++++++++++------------------- 1 file changed, 114 insertions(+), 227 deletions(-) diff --git a/cpm/brainexplorer/spaceObserver.py b/cpm/brainexplorer/spaceObserver.py index 6ef4a9a..5523256 100644 --- a/cpm/brainexplorer/spaceObserver.py +++ b/cpm/brainexplorer/spaceObserver.py @@ -5,7 +5,7 @@ class spaceObserver: """ - A class to analyze and compute various descriptive statistics from behavioural data from a metacognition task + A class to analyze and compute statistics from behavioural data from a metacognition task in BrainExplorer, Space Observer. Participants have to choose if stimulus has more of one type of alien or the other, and rate their confidence in their choice. @@ -31,51 +31,43 @@ def __init__(self, filepath=None): Example ---------- - >>> spaceObserver = SpaceObserver("/example/2025-02-20_SpaceObserver_Data_short.xlsx") - >>> spaceObserver.metrics() - >>> spaceObserver.codebook() - - if data in modelling format: - - >>> spaceObserver = SpaceObserver("/example/SpaceObserver_modelling_data.csv") - >>> spaceObserver.metrics("modelling") + >>> spaceObserver = SpaceObserver("/example/2025-02-20_SpaceObserver_Data.csv") + >>> results = spaceObserver.metrics() + >>> results.to_csv("/example/spaceObserver_results.csv", index=False) >>> spaceObserver.codebook() Notes ----- The columns required in data: - - **nA**: Number if aliens of type A. - - **nB**: Number of aliens of type B. - - **nDiff**: Difference between the number of aliens of type A and B. - - **correct**: TRUE if the participant made the correct choice, FALSE if incorrect. - - **choiceRT**: Response time for the choice. - - **confidence**: Confidence rating. - - **confidenceRT**: Response time for the confidence rating. - - - Columns in the modelling format: - - - **signal**: 0 if number of aliens of type A >= number of aliens of type B, 1 if number of aliens of type A < number of aliens of type B. - - **response**: 0 if left was chosen, 1 if right was chosen. - - **accuracy**: 1 if the participant made the correct choice, 0 if incorrect. - - **RT_choice**: Response time for the choice. - - **stimulus_intensity**: Absolute difference between the number of aliens of type A and B. - - **confidence**: Confidence rating. - - **confidenceRT**: Response time for the confidence rating. - + - **userID**: unique identifier for each participant + - **round**: the round of the trial + - **accuracy**: the accuracy of the trial (1 for correct, 0 for incorrect) + - **choiceRT**: the reaction time of the choice in the trial + - **confidence**: the confidence rating of the trial + - **confidenceRT**: the reaction time of the confidence rating in the trial + - **stimulus_intensity**: the evidence strength of the trial, which is the difference of evidence for each group of stimuli. """ - ## check file extension - if filepath.endswith(".csv"): - self.data = pd.read_csv(filepath, header=0) - elif filepath.endswith(".xlsx"): - self.data = pd.read_excel(filepath, header=0) - # self.data = pd.read_excel(filepath, header=0, nrows=500) + ## read data + self.data = pd.read_csv(filepath, header=0, na_values=["NaN", "nan"]) self.results = pd.DataFrame() - self.group_results = pd.DataFrame() self.codebook = { + "userID": "Unique identifier for each participant", + "mean_accuracy": "Mean accuracy across all trials", + "mean_RT": "Mean response time across all trials", + "median_RT": "Median response time across all trials", + "mean_confidence": "Mean confidence rating across all trials", + "mean_confidenceRT": "Mean confidence response time across all trials", + "median_confidenceRT": "Median confidence response time across all trials", + "mean_RT_correct": "Mean response time for correct choices", + "median_RT_correct": "Median response time for correct choices", + "mean_RT_incorrect": "Mean response time for incorrect choices", + "median_RT_incorrect": "Median response time for incorrect choices", + "conf_corr_mean": "Mean confidence rating for correct choices", + "conf_incorr_mean": "Mean confidence rating for incorrect choices", + "ES_bins": "Mean evidence strength for each of the bins (4 bins)" } def metrics(self, mode=None): @@ -89,240 +81,135 @@ def metrics(self, mode=None): Variables --------- - accuracy : float - The percentage of correct choices made by the participant. - - mean_abs_evdiff : float - The mean absolute difference between the number of aliens in the two groups. - - evdiff_correct : float - The mean absolute difference between the number of aliens in the two groups for trials where the participant made the correct choice. - - evdiff_incorrect : float - The mean absolute difference between the number of aliens in the two groups for trials where the participant made the incorrect choice. + mean_accuracy : float + The mean accuracy across all trials. - mean_RT : float - The mean response time across all trials. + mean_RT : float + The mean response time across all trials. median_RT : float The median response time across all trials. - std_RT : float - The standard deciation of the response time across all trials. - mean_confidence : float The mean confidence rating across all trials. - mean_confidenceRT : float + mean_confidenceRT : float The mean confidence response time across all trials. - std_confidenceRT : float - The standard deciation of the confidence response time across all trials. + median_confidenceRT : float + The median confidence response time across all trials. + + mean_RT_correct : float + The mean response time for trials where the participant made the correct choice. + + median_RT_correct : float + The median response time for trials where the participant made the correct choice. - conf_corr : np.array - The confidence ratings for trials where the participant made the correct choice. + mean_RT_incorrect : float + The mean response time for trials where the participant made the incorrect choice. - conf_inc : np.array - The confidence ratings for trials where the participant made the incorrect choice. + median_RT_incorrect : float + The median response time for trials where the participant made the incorrect choice. conf_corr_mean : float The mean confidence rating for trials where the participant made the correct choice. - conf_inc_mean : float + conf_incorr_mean : float The mean confidence rating for trials where the participant made the incorrect choice. + + ES_bins : np.array + The mean evidence strength for each of the bins. The trials here are split into 4 quarters. The evidence strength is divided into four equal bins and the mean is calculated for each bin. + """ - acc_per_conf : np.array - The accuracy for each of the four confidence bins. The confidence ratings are sorted and divided into four bins, and the accuracy is calculated for each bin. + # delete practice trials + # if > 8 subseequent trials where confidence data is missing it is practice run + """ + practice_trials = self.data.groupby(["userID", "round"]).filter( + lambda run_data: run_data["confidence"][(pd.isna(run_data["confidence"])) | (run_data["confidence"] == "NaN")].count() > 8 + ) + self.data = self.data.drop(practice_trials.index) + """ - - In "modelling" mode: - ------------------- + self.data["confidence"] = self.data["confidence"].replace( + ["NaN", "nan", "NAN", ""], pd.NA + ) - mean_accuracy : float - The mean accuracy across all trials. + self.data["confidence"] = pd.to_numeric(self.data["confidence"], errors='coerce') - mean_RT : float - The mean response time across all trials. + print(len(self.data)) - mean_confidence : float - The mean confidence rating across all trials. + self.data = self.data.groupby(["userID", "round"]).filter( + lambda run: ( + ( + (run["confidence"].isna() != run["confidence"].isna().shift()).cumsum() + .where(run["confidence"].isna()) + .value_counts() + .max() + ) if run["confidence"].isna().any() else 0 + ) < 9 + ) - mean_confidenceRT : float - The mean confidence response time across all trials. - - """ - if mode == None: + print(self.data["confidence"].unique()) - # Group data by userID - grouped_data = self.data.groupby("userID") + print(len(self.data)) - # Loop through each group of user data - for user_id, user_data in grouped_data: - user_data = user_data.sort_values("run") + print(self.data[self.data["userID"] == 121]["round"].unique()) - user_results = {"userID": user_id} + # Group data by subject + grouped_data = self.data.groupby("userID") - correct = np.array(user_data["correct"]) - # transform false to 0 and true to 1 - correct = correct.astype(int) - user_results["accuracy"] = sum(correct) / len(correct) + for user_id, user_data in grouped_data: - abs_evdiff = np.array(np.abs(user_data["nDiff"])) - user_results["mean_abs_evdiff"] = np.mean(abs_evdiff) - - correct_indices = np.where(correct == 1)[0] # Indices where correct == 1 - incorrect_indices = np.where(correct != 1)[0] # Indices where correct != 1 + user_results = {"userID": user_id} - # Absolute difference between the number of aliens in correct and incorrect trials - user_results["evdiff_correct"] = np.mean(abs_evdiff[correct_indices]) - user_results["evdiff_incorrect"] = np.mean(abs_evdiff[incorrect_indices]) - - confidence = np.array(user_data["confidence"]) + user_results["mean_accuracy"] = np.mean(user_data["accuracy"]) - # mean confidence - user_results["mean_confidence"] = np.mean(user_data["confidence"]) - # std - user_results["std_confidence"] = np.std(user_data["confidence"]) + user_results["mean_RT"] = np.mean(user_data["choiceRT"]) + user_results["median_RT"] = np.median(user_data["choiceRT"]) - # mean reaction times - user_results["mean_RT"] = np.mean(user_data["choiceRT"]) - user_results["median_RT"] = np.median(user_data["choiceRT"]) - user_results["std_RT"] = np.std(user_data["choiceRT"]) - user_results['mean_confidenceRT'] = np.mean(user_data["confidenceRT"]) - user_results['std_confidenceRT'] = np.std(user_data["confidenceRT"]) + user_results["mean_confidence"] = np.nanmean(user_data["confidence"]) - # confidence by performance - conf_corr = confidence[correct == 1] - conf_incorr = confidence[~correct != 1] - user_results["conf_corr_mean"] = np.mean(conf_corr) - user_results["conf_incorr_mean"] = np.mean(conf_incorr) + user_results["mean_confidenceRT"] = np.nanmean(user_data["confidenceRT"]) + user_results["median_confidenceRT"] = np.nanmedian(user_data["confidenceRT"]) + # confidence and RT by performance + correct = np.array(user_data["accuracy"]) + confidence = np.array(user_data["confidence"]) + + conf_corr = confidence[(correct == 1) & ~np.isnan(confidence)] + conf_incorr = confidence[(correct != 1) & ~np.isnan(confidence)] + user_results["conf_corr_mean"] = np.mean(conf_corr) if len(conf_corr) > 0 else np.nan + user_results["conf_incorr_mean"] = np.mean(conf_incorr) if len(conf_incorr) > 0 else np.nan - B = np.sort(confidence) - I = np.argsort(confidence) + user_results["mean_RT_correct"] = np.mean(np.array(user_data["choiceRT"])[correct == 1]) + user_results["median_RT_correct"] = np.median(np.array(user_data["choiceRT"])[correct == 1]) + user_results["mean_RT_incorrect"] = np.mean(np.array(user_data["choiceRT"])[correct != 1]) + user_results["median_RT_incorrect"] = np.median(np.array(user_data["choiceRT"])[correct != 1]) - # performance by confidence + evidence_strenght = user_data["stimulus_intensity"] - idxB = np.where(~np.isnan(B))[0] + # calcualte evidence strength for each bin - acc_per_conf = np.zeros(4) # Preallocate array for accuracy per confidence bin + bin_size = 4 - for b in range(1, 5): - idx = np.arange((b - 1) * np.ceil(len(idxB) / 4), b * np.ceil(len(idxB) / 4), dtype=int) - idx = idx[idx < len(idxB)] - - idx_co = I[idx] - acc_per_conf[b - 1] = np.sum(correct[idx_co]) / len(idx_co) if len(idx_co) > 0 else np.nan + # Divide evidence strength into 4 equal bins and calculate the mean for each bin + bins = np.array_split(evidence_strenght, bin_size) + ES_1 = np.mean(bins[0]) if len(bins[0]) > 0 else np.nan + ES_2 = np.mean(bins[1]) if len(bins[1]) > 0 else np.nan + ES_3 = np.mean(bins[2]) if len(bins[2]) > 0 else np.nan + ES_4 = np.mean(bins[3]) if len(bins[3]) > 0 else np.nan + user_results["ES_bins"] = np.array([ES_1, ES_2, ES_3, ES_4]) - user_results["acc_per_conf"] = acc_per_conf + # Append user-level results + self.results = pd.concat( + [self.results, pd.DataFrame([user_results])], ignore_index=True + ) - - # Append user-level results - self.results = pd.concat( - [self.results, pd.DataFrame([user_results])], ignore_index=True - ) + # Convert results to a DataFrame + self.results = pd.DataFrame(self.results) - # Convert results to a DataFrame - self.results = pd.DataFrame(self.results) - - # calculate group means - rt_mean = np.mean(self.results["mean_RT"]) - rt_std = np.std(self.results["mean_RT"]) - conf_mean = np.mean(self.results["mean_confidence"]) - conf_std = np.std(self.results["mean_confidence"]) - rt_conf_mean = np.mean(self.results["mean_confidenceRT"]) - acc_mean = np.mean(self.results["accuracy"]) - acc_std = np.std(self.results["accuracy"]) - conf_corr_mean = np.mean(self.results["conf_corr_mean"]) - conf_incorr_mean = np.mean(self.results["conf_incorr_mean"]) - abs_evdiff_mean = np.mean(self.results["mean_abs_evdiff"]) - evdiff_correct_mean = np.mean(self.results["evdiff_correct"]) - evdiff_incorrect_mean = np.mean(self.results["evdiff_incorrect"]) - - self.group_results = pd.DataFrame({"mean_RT": rt_mean, "std_RT": rt_std, "mean_confidence": conf_mean, "std_confidence": conf_std, "mean_confidenceRT": rt_conf_mean, "mean_accuracy": acc_mean, "std_accuracy": acc_std, "mean_confidence_corr": conf_corr_mean, "mean_confidence_incorr": conf_incorr_mean, "mean_abs_evdiff": abs_evdiff_mean, "mean_evdiff_correct": evdiff_correct_mean, "mean_evdiff_incorrect": evdiff_incorrect_mean}, index=[0]) - - return self.results, self.group_results - - - elif mode == "modelling": - - # Group data by subject - grouped_data = self.data.groupby("subject") - - all_users_data = [] - - # Loop through each group of user data - for user_id, user_data in grouped_data: - user_data = user_data.sort_values("trial") - - - user_metrics = { - "userID": user_id, - "choice": [], - "nA": [], - "nB": [], - "nDiff": [], - "correct": [], - "RT": [], - "confidence": [], - "confidenceRT": [], - "signal": [] - } - - # Loop through each trial in the user's data - for i, trial_data in user_data.iterrows(): - - user_metrics["correct"].append(trial_data["accuracy"]) - user_metrics["choice"].append(trial_data["response"]) #left = 0, right = 1 - user_metrics["signal"].append(trial_data["signal"]) #0 if nA >= nB, 1 if nA < nB - user_metrics["RT"].append(trial_data["RT_choice"]) - user_metrics["confidence"].append(trial_data["confidence"]) - user_metrics["confidenceRT"].append(trial_data["confidenceRT"]) - user_metrics["nDiff"].append(trial_data["stimulus_intensity"]) - - all_users_data.append(user_metrics) - - for user_data in all_users_data: - - user_results = {"userID": user_data["userID"]} - - user_results["mean_accuracy"] = np.mean(user_data["correct"]) - user_results["mean_RT"] = np.mean(user_data["RT"]) - user_results["median_RT"] = np.median(user_data["RT"]) - user_results["std_RT"] = np.std(user_data["RT"]) - user_results["mean_confidence"] = np.nanmean(user_data["confidence"]) - user_results["mean_confidenceRT"] = np.nanmean(user_data["confidenceRT"]) - - # confidence by performance - correct = np.array(user_data["correct"]) - # transform false to 0 and true to 1 - correct = correct.astype(int) - confidence = np.array(user_data["confidence"]) - - conf_corr = confidence[correct == 1] - conf_incorr = confidence[~correct != 1] - user_results["conf_corr_mean"] = np.mean(conf_corr) - user_results["conf_incorr_mean"] = np.mean(conf_incorr) - - # Append user-level results - self.results = pd.concat( - [self.results, pd.DataFrame([user_results])], ignore_index=True - ) - - # Convert results to a DataFrame - self.results = pd.DataFrame(self.results) - - # calculate group means - rt_mean = np.mean(self.results["mean_RT"]) - rt_std = np.std(self.results["mean_RT"]) - conf_mean = np.mean(self.results["mean_confidence"]) - conf_std = np.std(self.results["mean_confidence"]) - acc_mean = np.mean(self.results["accuracy"]) - acc_std = np.std(self.results["accuracy"]) - self.group_results = pd.DataFrame({"mean_RT": rt_mean, "std_RT": rt_std, "mean_confidence": conf_mean, "std_confidence": conf_std, "mean_accuracy": acc_mean, "std_accuracy": acc_std}, index=[0]) - - return self.results, self.group_results + return self.results From 463d45b49d2fa355bb119382dc079159a7fab800 Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Fri, 30 May 2025 09:53:19 +0200 Subject: [PATCH 18/54] add treasurehunt --- cpm/brainexplorer/treasurehunt.py | 198 ++++++++++++++++++++++++++++++ 1 file changed, 198 insertions(+) create mode 100644 cpm/brainexplorer/treasurehunt.py diff --git a/cpm/brainexplorer/treasurehunt.py b/cpm/brainexplorer/treasurehunt.py new file mode 100644 index 0000000..5405af2 --- /dev/null +++ b/cpm/brainexplorer/treasurehunt.py @@ -0,0 +1,198 @@ +import pandas as pd +import numpy as np +import warnings +from scipy.stats import zscore +import statsmodels.api as sm +import statsmodels.formula.api as smf + + +class treasurehunt: + """ + Class to calculate metrics for the Treasure Hunt task. Includes regresssion model to predict the influence of previous evidence on current choice. + + Attributes + ---------- + data : pd.DataFrame + The data loaded from the CSV or Excel file. + results : pd.DataFrame + A DataFrame to store the computed metrics. + codebook : dict + A dictionary containing the codebook for the computed metrics. + """ + + def __init__(self, filepath=None): + """ + Load data to be analyised and turn into Pandas DataFrame + + Parameters + ---------- + filepath : str + The path to the CSV file to be processed. The column names in the data must subscribe to a prespecified convention, see Notes. + + Example + ---------- + + >>> spaceObserver = SpaceObserver("/example/2025-02-20_SpaceObserver_Data_short.xlsx") + >>> spaceObserver.metrics() + >>> spaceObserver.codebook() + + + Notes + ----- + The columns required in data: + - userID: unique identifier for each participant + - run: + - outcome: the outcome of the trial + - confidence: the confidence of the trial + - RT: the reaction time of the trial + - confidenceRT: the confidence reaction time of the trial + - ev: the evidence of the trial + + """ + + ## check file extension + if filepath.endswith(".csv"): + self.data = pd.read_csv(filepath, header=0) + elif filepath.endswith(".xlsx"): + self.data = pd.read_excel(filepath, header=0) + # self.data = pd.read_excel(filepath, header=0, nrows=500) + + self.results = pd.DataFrame() + self.group_results = pd.DataFrame() + self.codebook = { + "userID": "Unique identifier for each participant", + "mean_accuracy": "Mean accuracy across all trials", + "mean_points": "Mean points received across all trials", + "mean_confidence": "Mean confidence rating across all trials", + "mean_RTuntildec": "Mean reaction time until decision across all trials", + "median_RTuntildec": "Median reaction time until decision across all trials", + "mean_diffRT": "Mean difference in reaction time between the last two stimulus presentations", + "median_diffRT": "Median difference in reaction time between the last two stimulus presentations", + "mean_lastRT": "Mean reaction time after the last stimulus presentation", + "median_lastRT": "Median reaction time after the last stimulus presentation", + "mean_confidenceRT": "Mean confidence reaction time across all trials", + "median_confidenceRT": "Median confidence reaction time across all trials", + "mean_n_draws": "Mean number of stimulus draws before making a decision", + "totevminus_coef": "Coefficient for the previous total evidence in the regression model", + "deltaev_coef": "Coefficient for the change in total evidence in the regression model" + } + + def metrics(self, mode=None): + """ + + Returns + ------- + results : pd.DataFrame + A DataFrame containing the results of the metrics. + + Variables + ---------- + - mean_accuracy: mean accuracy (if the choice was the majority choice) + - mean_points: mean points received + - mean_confidence: mean confidence of the choice + - mean_RTuntildec: mean reaction time until decision + - median_RTuntildec: median reaction time until decision + - mean_diffRT: mean difference in reaction time between the last two stimulus presentations + - median_diffRT: median difference in reaction time between the last two stimulus presentations + - mean_lastRT: mean reaction time after the last stimulus presentation + - median_lastRT: median reaction time after the last stimulus presentation + - mean_confidenceRT: mean confidence reaction time + - median_confidenceRT: median confidence reaction time + - n_draws: mean number of stimulus draws before making a decision + - totevminus_coef: coefficient for the previous total evidence in the regression model + - deltaev_coef: coefficient for the change in total evidence in the regression model + """ + + # Group data by userID + grouped_data = self.data.groupby("userID") + + # turn string fields into list of ints + self.data["ev"] = self.data["ev"].apply(lambda x: [int(i) for i in x.strip("[]").split(" ")]) + + # Loop through each group of user data + for user_id, user_data in grouped_data: + + user_results = {"userID": user_id} + + user_results["mean_points"] = np.mean(user_data["outcome"]) + + user_results["mean_accuracy"] = np.mean(user_data["correctChoice"]) + + user_results["mean_confidence"] = np.mean(user_data["confidence"]) + + user_results["mean_RTuntildec"] = np.mean(user_data["RTuntildec"]) + user_results["median_RTuntildec"] = np.median(user_data["RTuntildec"]) + user_results["mean_diffRT"] = np.mean(user_data["mean_diffRT"]) + user_results["median_diffRT"] = np.median(user_data["median_diffRT"]) + user_results["mean_lastRT"] = np.mean(user_data["lastRT"]) + user_results["median_lastRT"] = np.median(user_data["lastRT"]) + + user_results["mean_confidenceRT"] = np.mean(user_data["confidenceRT"]) + user_results["median_confidenceRT"] = np.median(user_data["confidenceRT"]) + + user_results["mean_n_draws"] = np.mean(user_data["draws"]) + + correct_responses = [] + for i in range(len(user_data["outcome"])): + if user_data["outcome"].iloc[i] == 100: + correct_responses.append(1) + + user_results["mean_accuracy"] = len(correct_responses) / len(user_data["outcome"]) + + # regression for influence of recent results on current choice + + ev = [] + + for idx, row in user_data.iterrows(): + ev_sequence = row["ev"] + + for i in range(len(ev_sequence)): + if i == 0: + totevminus = np.nan + deltaev = np.nan + else: + totevminus = ev_sequence[i - 1] + deltaev = ev_sequence[i] - ev_sequence[i - 1] + + cont_ch = 1 if i < len(ev_sequence) - 1 else 0 # continue vs stop + + ev.append({ + "totev": ev_sequence[i], + "totevminus": totevminus, + "deltaev": deltaev, + "cont_ch": cont_ch + }) + + ev_reg_data = pd.DataFrame(ev) + + model_data = ev_reg_data.dropna(subset=["totevminus", "deltaev", "cont_ch"]) + + if not model_data.empty: + model = smf.glm( + formula="cont_ch ~ totevminus + deltaev", + data=model_data, + family=sm.families.Binomial() + ).fit() + + user_results["totevminus_coef"] = model.params.get("totevminus", np.nan) + user_results["deltaev_coef"] = model.params.get("deltaev", np.nan) + else: + user_results["totevminus_coef"] = np.nan + user_results["deltaev_coef"] = np.nan + + + # Append user-level results + self.results = pd.concat( + [self.results, pd.DataFrame([user_results])], ignore_index=True + ) + + # Convert results to a DataFrame + self.results = pd.DataFrame(self.results) + + return self.results + + def codebook(self): + """ + Return a codebook describing the metrics. + """ + return self.codebook \ No newline at end of file From ae45a6581e34049c73c5a53327d8dae30f1dde16 Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Fri, 30 May 2025 10:35:42 +0200 Subject: [PATCH 19/54] add milkyway --- cpm/brainexplorer/milkyWay.py | 125 ++++++++++++++++++++++++++++++ cpm/brainexplorer/treasurehunt.py | 1 - 2 files changed, 125 insertions(+), 1 deletion(-) create mode 100644 cpm/brainexplorer/milkyWay.py diff --git a/cpm/brainexplorer/milkyWay.py b/cpm/brainexplorer/milkyWay.py new file mode 100644 index 0000000..9feeed1 --- /dev/null +++ b/cpm/brainexplorer/milkyWay.py @@ -0,0 +1,125 @@ +import numpy as np +import matplotlib.pyplot as plt +import pandas as pd + +class milkyWay: + """ + Class to calculate metrics for a two-arm bandit task, Milky Way & Pirate Market in BrainExplorer. + + """ + + def __init__(self, filepath): + """ + Load data to be analyised and turn into Pandas DataFrame + + Parameters + ---------- + filepath : str + The path to the CSV file to be processed. The column names in the data must subscribe to a prespecified convention, see Notes. + + Example + ---------- + + >>> milkyWay = milkyWay("/example/2025-02-20_SpaceObserver_Data_short.xlsx") + >>> milkyWay.metrics() + >>> results_MW = milkyWay.results_MW + >>> results_PM = milkyWay.results_PM + >>> milkyWay.codebook() + + + Notes + ----- + The columns required in data: + - userID: unique identifier for each participant + - trial_type: the type of trial (Milky Way or Pirate Market) + - rt_adj: the adjusted reaction time of the trial + - choice: the choice made by the participant + - correct: whether the choice was correct (1) or incorrect (0) + """ + + self.data = pd.read_csv(filepath) + + self.results_MW = pd.DataFrame() + self.results_PM = pd.DataFrame() + self.codebook = { + } + + def metrics(self): + """ + Calculate the metrics for the data. + + Returns + ------- + results : pd.DataFrame + A DataFrame containing the results of the metrics. + + Variables + ---------- + - mean_RT_MW: Mean reaction time for Milky Way trials + - median_RT_MW: Median reaction time for Milky Way trials + - mean_RT_PM: Mean reaction time for Pirate Market trials + - median_RT_PM: Median reaction time for Pirate Market trials + - accuracy_MW: Accuracy for Milky Way trials + - accuracy_PM: Accuracy for Pirate Market trials + - prop_same_choice_MW: Proportion of same choice in Milky Way trials + - prop_same_choice_PM: Proportion of same choice in Pirate Market trials + """ + # seperate MilkyWay and Pirate Market data + MW_data = self.data[self.data['trial_type'] == 'reward'] + PM_data = self.data[self.data['trial_type'] == 'punish'] + + MW_data = MW_data.groupby("userID") + PM_data = PM_data.groupby("userID") + + # Loop through each group of user data + for user_id, user_data_MW in MW_data: + + user_results_MW = {"userID": user_data_MW['userID']} + + user_results_MW["mean_RT_MW"] = np.mean(user_data_MW['rt_adj']) + user_results_MW["median_RT_MW"] = np.median(user_data_MW['rt_adj']) + + user_results_MW["accuracy_MW"] = np.mean(user_data_MW["correct"]) + + user_results_MW["prop_same_choice_MW"] = np.mean(user_data_MW["choice"] == 1) + + # Append user-level results + self.results_MW = pd.concat( + [self.results_MW, pd.DataFrame([user_results_MW])], ignore_index=True + ) + + self.results_MW = pd.DataFrame(self.results_MW) + + for user_id, user_data_PM in PM_data: + + user_results_PM = {"userID": user_data_PM['userID']} + + user_results_PM["mean_RT_PM"] = np.mean(user_data_PM['rt_adj']) + user_results_PM["median_RT_PM"] = np.median(user_data_PM['rt_adj']) + + user_results_PM["accuracy_PM"] = np.mean(user_data_PM["correct"]) + + user_results_PM["prop_same_choice_PM"] = np.mean(user_data_PM["choice"] == 1) + + # Append user-level results + self.results_PM = pd.concat( + [self.results_PM, pd.DataFrame([user_results_PM])], ignore_index=True + ) + + self.results_PM = pd.DataFrame(self.results_PM) + + def codebook(self): + """ + Create a codebook for the data. + """ + self.codebook = { + "userID": "Unique identifier for each participant", + "rt": "Reaction time of the trial", + "choice": "Choice made by the participant", + "outchosen": "Outcome reward of the chosen option", + "reward1": "Reward of option 1", + "reward2": "Reward of option 2" + } + + return self.codebook + \ No newline at end of file diff --git a/cpm/brainexplorer/treasurehunt.py b/cpm/brainexplorer/treasurehunt.py index 5405af2..a445e51 100644 --- a/cpm/brainexplorer/treasurehunt.py +++ b/cpm/brainexplorer/treasurehunt.py @@ -58,7 +58,6 @@ def __init__(self, filepath=None): # self.data = pd.read_excel(filepath, header=0, nrows=500) self.results = pd.DataFrame() - self.group_results = pd.DataFrame() self.codebook = { "userID": "Unique identifier for each participant", "mean_accuracy": "Mean accuracy across all trials", From 6f5bcf62ad4d809dfcd4670b58c79921f1705712 Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Fri, 30 May 2025 11:14:24 +0200 Subject: [PATCH 20/54] update scavenger --- cpm/brainexplorer/scavenger.py | 93 +++++++++++++++++++++++++--------- 1 file changed, 69 insertions(+), 24 deletions(-) diff --git a/cpm/brainexplorer/scavenger.py b/cpm/brainexplorer/scavenger.py index 055f1bf..e79e11b 100644 --- a/cpm/brainexplorer/scavenger.py +++ b/cpm/brainexplorer/scavenger.py @@ -36,6 +36,8 @@ def __init__(self, filepath=None): >>> scavenger = Scavenger("/example/2025-01-10_Scavenger.xlsx") >>> scavenger.metrics() + >>> results = scavenger.results + >>> results.to_csv("/example/scavenger_results.csv", index=False) >>> scavenger.codebook() Notes @@ -58,6 +60,12 @@ def __init__(self, filepath=None): self.results = pd.DataFrame() self.group_results = pd.DataFrame() self.codebook = { + "userID": "Unique identifier for each participant", + "mean_RT": "Mean reaction time", + "median_RT": "Median reaction time", + "total_rewards": "Total rewards earned", + "choice_stickiness": "Number of consecutive choices for the same option", + "risky_choices": "Proportion of risky choices overall", "win_risk": "Proportion of risky wins", "loss_risk": "Proportion of risky losses", "win_risk_abg": "Proportion of risky wins in ambiguous trials", @@ -70,6 +78,12 @@ def __init__(self, filepath=None): "rational_all_abg": "Proportion of rational choices in ambiguous trials", "rational_win_abg": "Proportion of rational choices in wins in ambiguous trials", "rational_loss_abg": "Proportion of rational choices in losses in ambiguous trials", + "rational_all_NoAbg": "Proportion of rational choices in non-ambiguous trials", + "rational_win_NoAbg": "Proportion of rational choices in wins in non-ambiguous trials", + "rational_loss_NoAbg": "Proportion of rational choices in losses in non-ambiguous trials", + "rational_catchTrials": "Proportion of rational choices in catch trials", + "above_chance": "Proportion of trials with choices above chance level", + "chosen_left_all": "Proportion of trials where option A (safe) was chosen" } def metrics(self): @@ -90,9 +104,10 @@ def metrics(self): mean_RT: Mean reaction time, median_RT: Median reaction time, - std_RT: Standard deviation of reaction time, + risky_choices: Proportion of risky choices overall, win_risk: Proportion of wins for risky choices, loss_risk: Proportion of losses for risky choices, + risk_ambg: Proportion of risky choices in ambiguous trials, win_risk_abg: Proportion of risky wins in ambiguous trials, win_risk_NoAbg: Proportion of risky wins in non-ambiguous trials, loss_risk_abg: Proportion of risky losses in ambiguous trials, @@ -106,6 +121,9 @@ def metrics(self): rational_all_NoAbg: Proportion of rational choices in non-ambiguous trials, rational_win_NoAbg: Proportion of rational choices in win trials in non-ambiguous trials, rational_loss_NoAbg: Proportion of rational choices in loss trials in non-ambiguous trials + rational_catchTrials: Proportion of rational choices in catch trials, + above_chance: Proportion of trials with choices above chance level, + chosen_left_all: Proportion of trials where option A (safe) was chosen. Notes @@ -135,7 +153,7 @@ def metrics(self): """ # Group data by userID - grouped_data = self.data.groupby("loopID") + grouped_data = self.data.groupby("userID") all_users_data = [] @@ -154,23 +172,28 @@ def metrics(self): "B_EV": [], "chosen": [], "all_rewards": [], - "choice_stickiness": [], + "choice_stickiness": [], + "RT": [], + "catchtrial": [], + "abovechance": [], + "chosenLeft": [] } # Loop through each trial in the user's data for _, trial_data in user_data.iterrows(): metrics_data_trial = {} - if trial_data["choice"] == 0: + if trial_data["chosen"] == 0: metrics_data_trial["chosen"] = 1 # Option A chosen (safe) metrics_data_trial["A_perc"] = 1 - metrics_data_trial["A_magn"] = trial_data["safe_magn"]*100 - metrics_data_trial["B_perc"] = trial_data[ - "risky_prob" + metrics_data_trial["A_magn"] = trial_data["safe_magn"] + metrics_data_trial["B_perc"] = [ + trial_data["risky_prob1"], + trial_data["risky_prob2"] ] metrics_data_trial["B_magn"] = [ - trial_data["risky_magn1"]*100, - trial_data["risky_magn2"]*100 + trial_data["risky_magn1"], + trial_data["risky_magn2"] ] else: metrics_data_trial["chosen"] = 2 # Option B chosen @@ -178,8 +201,9 @@ def metrics(self): metrics_data_trial["A_magn"] = trial_data[ "safe_magn" ] - metrics_data_trial["B_perc"] = trial_data[ - "risky_prob" + metrics_data_trial["B_perc"] = [ + trial_data["risky_prob1"], + trial_data["risky_prob2"] ] metrics_data_trial["B_magn"] = [ trial_data["risky_magn1"], @@ -187,7 +211,7 @@ def metrics(self): ] metrics_data_trial["ambiguous"] = trial_data[ - "ambg" + "ambiguousTrial" ] # Compute expected values @@ -195,9 +219,13 @@ def metrics(self): np.array(metrics_data_trial["A_perc"]), np.array(metrics_data_trial["A_magn"]), ) - metrics_data_trial["B_EV"] = 0.5 * np.array( + metrics_data_trial["B_EV"] = np.array( + metrics_data_trial["B_perc"][0] + ) * np.array( metrics_data_trial["B_magn"][0] - ) + 0.5 * np.array(metrics_data_trial["B_magn"][1]) # assuming 50% propbs + ) + np.array( + metrics_data_trial["B_perc"][1] + ) * np.array(metrics_data_trial["B_magn"][1]) metrics_data_trial["EV_diff_safe"] = ( metrics_data_trial["A_EV"] - metrics_data_trial["B_EV"] @@ -222,8 +250,11 @@ def metrics(self): user_metrics["EV_diff_chosen_all"].append( metrics_data_trial["EV_diff_chosen"] ) - user_metrics["RT"] = trial_data["rt"] - user_metrics["all_rewards"].append(trial_data["feedback"]) + user_metrics["RT"].append(trial_data["RT"]) + user_metrics["all_rewards"].append(trial_data["outcome"]) + user_metrics["catchtrial"].append(trial_data["catchtrial"]) + user_metrics["abovechance"].append(trial_data["abovechance"]) + user_metrics["chosenLeft"].append(trial_data["chosenLeft"]) all_users_data.append(user_metrics) @@ -235,12 +266,14 @@ def metrics(self): t_loss = np.array(user_data["t_loss_all"]) t_abg = np.array(user_data["t_abg_all"]) ev_diff_chosen = np.array(user_data["EV_diff_chosen_all"], dtype=object) + catch_trials = np.array(user_data["catchtrial"], dtype=bool) + abovechance = np.array(user_data["abovechance"]) + chosen_left = np.array(user_data["chosenLeft"]) user_results = {"userID": user_id} user_results["mean_RT"] = np.mean(user_data["RT"]) user_results["median_RT"] = np.median(user_data["RT"]) - user_results["std_RT"] = np.std(user_data["RT"]) user_results["total_rewards"] = np.sum(user_data["all_rewards"]) @@ -248,9 +281,10 @@ def metrics(self): user_results["choice_stickiness"] = np.sum(user_data["chosen"][i] == 1 and user_data["chosen"][i + 1] == 1 for i in range(len(user_data["chosen"]) - 1)) # Number of risky choices + user_results["risky_choices"] = np.mean(rsk == 1) user_results["win_risk"] = np.sum(rsk[~t_loss]) / np.sum(~t_loss) - print(user_results["win_risk"]) user_results["loss_risk"] = np.sum(rsk[t_loss]) / np.sum(t_loss) + user_results["risk_ambg"] = np.sum(rsk[t_abg]) / np.sum(t_abg) user_results["win_risk_abg"] = np.sum(rsk[~t_loss & t_abg]) / np.sum( ~t_loss & t_abg ) @@ -296,6 +330,21 @@ def metrics(self): ev_diff_chosen[t_loss & ~t_abg] >= 0 ) / np.sum(t_loss & ~t_abg) + # proportion rational chocies on catch trials + user_results["rational_catchTrials"] = np.mean( + ev_diff_chosen[catch_trials] >= 0 + ) + + # proportion above chance level + user_results["above_chance"] = np.sum( + abovechance == 1 + ) / len(abovechance) + + # proportion chosen left + user_results["chosen_left_all"] = np.sum( + chosen_left == 1 + ) / len(chosen_left) + # Append user-level results self.results = pd.concat( [self.results, pd.DataFrame([user_results])], ignore_index=True @@ -304,14 +353,10 @@ def metrics(self): # Convert results to a DataFrame self.results = pd.DataFrame(self.results) - # calculate group means - group_results = self.results.mean() - self.group_results = pd.DataFrame(group_results).T - - return self.results, self.group_results + return self.results def codebook(self): """ Return a codebook describing the metrics. """ - return self.codebook \ No newline at end of file + return self.codebook \ No newline at end of file From 866f5d2b87558f334dc17f6e15b1ba312e09cf57 Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Fri, 30 May 2025 11:44:51 +0200 Subject: [PATCH 21/54] update MW --- cpm/brainexplorer/milkyWay.py | 46 +++++++++++++++++++++++++++-------- 1 file changed, 36 insertions(+), 10 deletions(-) diff --git a/cpm/brainexplorer/milkyWay.py b/cpm/brainexplorer/milkyWay.py index 9feeed1..5946518 100644 --- a/cpm/brainexplorer/milkyWay.py +++ b/cpm/brainexplorer/milkyWay.py @@ -42,6 +42,21 @@ def __init__(self, filepath): self.results_MW = pd.DataFrame() self.results_PM = pd.DataFrame() self.codebook = { + "userID": "Unique identifier for each participant", + "trial_type": "Type of trial (Milky Way or Pirate Market)", + "rt_adj": "Adjusted reaction time of the trial", + "choice": "Choice made by the participant (1 or 2)", + "correct": "Whether the choice was correct (1) or incorrect (0)", + "mean_RT_MW": "Mean reaction time for Milky Way trials", + "median_RT_MW": "Median reaction time for Milky Way trials", + "mean_RT_PM": "Mean reaction time for Pirate Market trials", + "median_RT_PM": "Median reaction time for Pirate Market trials", + "accuracy_MW": "Accuracy for Milky Way trials", + "accuracy_PM": "Accuracy for Pirate Market trials", + "prop_same_choice_MW": "Proportion of same choice in Milky Way trials", + "prop_same_choice_PM": "Proportion of same choice in Pirate Market trials", + "prop_WSLS_1": "Proportion of win-stay/loose-shift choices for correct trials based on whether the choice was correct", + "prop_WSLS_2": "Proportion of win-stay/loose-shift choices for correct trials based on whether participant got at least 50 points (MW) or less than 50 points (PM)" } def metrics(self): @@ -63,6 +78,8 @@ def metrics(self): - accuracy_PM: Accuracy for Pirate Market trials - prop_same_choice_MW: Proportion of same choice in Milky Way trials - prop_same_choice_PM: Proportion of same choice in Pirate Market trials + - prop_WSLS_1: Proportion of win-stay/loose-shift choices for correct trials based on whether the choice was correct + - prop_WSLS_2: Proportion of win-stay/loose-shift choices for correct trials based on whether participant got at least 50 points (MW) or less than 50 points (PM) """ # seperate MilkyWay and Pirate Market data MW_data = self.data[self.data['trial_type'] == 'reward'] @@ -83,6 +100,15 @@ def metrics(self): user_results_MW["prop_same_choice_MW"] = np.mean(user_data_MW["choice"] == 1) + # proportion of win-stay/loose-shift choices + user_results_MW["prop_WSLS_1"] = np.mean( + user_data_MW["WSLS_v1"] + ) + + user_results_MW["prop_WSLS_2"] = np.mean( + user_data_MW["WSLS_v2"] + ) + # Append user-level results self.results_MW = pd.concat( [self.results_MW, pd.DataFrame([user_results_MW])], ignore_index=True @@ -101,6 +127,15 @@ def metrics(self): user_results_PM["prop_same_choice_PM"] = np.mean(user_data_PM["choice"] == 1) + # proportion of win-stay/loose-shift choices + user_results_PM["prop_WSLS_1"] = np.mean( + user_data_PM["WSLS_v1"] + ) + + user_results_PM["prop_WSLS_2"] = np.mean( + user_data_PM["WSLS_v2"] + ) + # Append user-level results self.results_PM = pd.concat( [self.results_PM, pd.DataFrame([user_results_PM])], ignore_index=True @@ -111,15 +146,6 @@ def metrics(self): def codebook(self): """ Create a codebook for the data. - """ - self.codebook = { - "userID": "Unique identifier for each participant", - "rt": "Reaction time of the trial", - "choice": "Choice made by the participant", - "outchosen": "Outcome reward of the chosen option", - "reward1": "Reward of option 1", - "reward2": "Reward of option 2" - } - + """ return self.codebook \ No newline at end of file From 17075864c6c62c41c60fd6e9a5bd3071d9b6b68a Mon Sep 17 00:00:00 2001 From: lenarddome Date: Mon, 2 Jun 2025 13:13:55 +0200 Subject: [PATCH 22/54] Add GoblinHeist model and initial implementation for decision-making tasks --- cpm/applications/GoblinHeist_model.py | 5 + cpm/applications/reinforcement_learning.py | 165 +++++++++++++++++++++ cpm/brainexplorer/GoblinHeist_model.py | 163 -------------------- 3 files changed, 170 insertions(+), 163 deletions(-) create mode 100644 cpm/applications/GoblinHeist_model.py delete mode 100644 cpm/brainexplorer/GoblinHeist_model.py diff --git a/cpm/applications/GoblinHeist_model.py b/cpm/applications/GoblinHeist_model.py new file mode 100644 index 0000000..82b5e75 --- /dev/null +++ b/cpm/applications/GoblinHeist_model.py @@ -0,0 +1,5 @@ +import pandas as pd +import numpy as np +import warnings + + diff --git a/cpm/applications/reinforcement_learning.py b/cpm/applications/reinforcement_learning.py index 8ef24eb..21e2843 100644 --- a/cpm/applications/reinforcement_learning.py +++ b/cpm/applications/reinforcement_learning.py @@ -7,6 +7,8 @@ import ipyparallel as ipp ## for parallel computing with ipython (specific for Jupyter Notebook) +from scipy.special import logsumexp + class RLRW(Wrapper): """ The class implements a simple reinforcement learning model for a multi-armed bandit tasks using a standard update rule calculating prediction error and a Softmax decision rule. @@ -146,3 +148,166 @@ def model(parameters, trial, generate=generate): return output super().__init__(data=data, model=model, parameters=parameters) + + +class MBMF: + """ + Model for model-based vs model-free decision making in a decision making task + + Attributes + ---------- + data : pd.DataFrame + The individual user data to be processed. The column names in the data must subscribe to a prespecified convention, see Notes. + + parameters: list, optional + A list of parameters to be used in the model. If not specified, default parameters are used. + Order of parameters: inverse temperature, learning rate, eligibility trace decay, mixing weight for low stake trials, mixing weight for high stake trials, stickiness, response stickiness. + + Example + ---------- + + goblinHeist = GoblinHeist(data, [1, 0.8, 0.5, 0.8, 0.8, -0.1, -0.1]) + goblinHeist.MB_MF_model() + + Notes + ----- + The columns required in data: + - userID: unique identifier for each participant + - stimuli: which planets presented in stage of the trial + - s: the state of the trial + - rews: the rewards for each stimulus / planet + - choice: the choice made by the participant + - stake: the stake of the trial (1 or 5) + - points: the points received in the trial + + + References + ---------- + + Kool W, Cushman FA, Gershman SJ (2016) When Does Model-Based Control Pay Off? PLoS Comput Biol 12 + + Smid, C.R., K. Ganesan, A. Thompson, R. Cañigueral, S. Veselic, J. Royer, W. Kool, T.U. Hauser, B. Bernhardt, and N. Steinbeis. “Neurocognitive Basis of Model-Based Decision Making and Its Metacontrol in Childhood.” Developmental Cognitive Neuroscience 62 (August 1, 2023): 101269. + + """ + + def __init__( + self, data=None, dimensions=2, parameters_settings=None, generate=False + ): + self.data = data + + ## TODO: cpm.generators.Value should be used here + ## TODO: cpm.generators.Parameters should be used here + if parameters is None: + self.parameters = { + "temperature": 0.8, + "learning_rate": 0.5, + "eligibility_trace_decay": 0.8, + "mixing_weight_low_stake": 0.8, + "mixing_weight_high_stake": 0.8, + "stickiness": -0.1, + "response_stickiness": -0.1 + } + warnings.warn("No parameters specified, using default parameters.") + else: + self.parameters = { + "temperature": parameters[0], + "learning_rate": parameters[1], + "eligibility_trace_decay": parameters[2], + "mixing_weight_low_stake": parameters[3], + "mixing_weight_high_stake": parameters[4], + "stickiness": parameters[5], + "response_stickiness": parameters[6] + } + + + def MF_MB_model(self): + + """ + Model for model-based vs model-free decision making in a decison making task + + + Returns + ------- + LL: + Log Likelihood + Q: + Q values + Q_MF: + Q values for model-free learning + Q_MB: + Q values for model-based learning + """ + + # pull out the paramters + b = self.parameters["temperature"] # softmax inverse temperature + lr = self.parameters["learning_rate"] # learning rate + lamb = self.parameters["eligibility_trace_decay"] # eligibility trace decay + w_lo = self.parameters["mixing_weight_low_stake"] # mixing weight for low stake trials + w_hi = self.parameters["mixing_weight_high_stake"] # mixing weight for high stake trials + st = self.parameters["stickiness"] # stickiness + respst = self.parameters["response_stickiness"] # response stickiness + + # initialization + Q_MF = np.ones((2,2))*4.5 + Q2 = np.ones((2,1))*4.5 + TM = [np.eye(2), np.eye(2)] #transition matrix + M = np.zeros((2,2)) # last action matrix + R = [0, 0] # last choice structure + LL = 0 # log likelihood + + for i in range(len(self.data["stimuli"])): + + if self.data["stimuli"][i] == 2 or self.data["stimuli"][i] == 4: + R = R = np.flipud(R) + + s1 = self.data["s"][i][0] # first choice stage + s2 = self.data["s"][i][1] # second choice stage + action = self.data["choice"][i] + a = action[0] - (s1 == 2) * 2 + + # choose which weight to update based on stake of trial + if self.data["stake"][i] == 1: + w = w_lo + else: + w = w_hi + + # calculating the Q values for model-based learning + Q_MB = TM[s1-1].T @ Q2 + + # calculating the Q values for mixing model + Q = w*Q_MB + (1-w)*Q_MF[s1-1, :].T + st * M[s1-1, :].T + respst * np.array(R) + + # calculating the probability choosing action a given Q + LL = LL + b*Q[a-1] - logsumexp(b * Q) + + # update choice structure + M = np.zeros((2,2)) + M[s1-1, a-1] = 1 + + R = np.zeros((2,1)) + if action == self.data["stimuli"][i][0]: + R[0] = 1 + else: + R[1] = 1 + + # update Q values for model-free learning + # first choice stage + dtQ1 = Q2[s2-1] - Q_MF[s1-1, a-1] + Q_MF[s1-1, a-1] = Q_MF[s1-1, a-1] + lr * dtQ1 + # second choice stage + dtQ2 = self.data["points"][i] - Q2[s2-1] + Q2[s2-1] = Q2[s2-1] + lr * dtQ2 + Q_MF[s1-1, a-1] = Q_MF[s1-1, a-1] + lamb * lr * dtQ2 + + # compile results + results = pd.DataFrame({ + "userID": self.data["userID"][0], + "LL": [LL], + "Q": [Q.flatten()], + "Q_MF": [Q_MF.flatten()], + "Q_MB": [Q_MB.flatten()] + }) + + return results + + super().__init__(data=data, model=model, parameters=parameters) diff --git a/cpm/brainexplorer/GoblinHeist_model.py b/cpm/brainexplorer/GoblinHeist_model.py deleted file mode 100644 index 1cd7513..0000000 --- a/cpm/brainexplorer/GoblinHeist_model.py +++ /dev/null @@ -1,163 +0,0 @@ -import pandas as pd -import numpy as np -import warnings - -from scipy.special import logsumexp - - -class GoblinHeist: - """ - Model for model-based vs model-free decision making in a decision making task - - Attributes - ---------- - data : pd.DataFrame - The individual user data to be processed. The column names in the data must subscribe to a prespecified convention, see Notes. - - parameters: list, optional - A list of parameters to be used in the model. If not specified, default parameters are used. - Order of parameters: inverse temperature, learning rate, eligibility trace decay, mixing weight for low stake trials, mixing weight for high stake trials, stickiness, response stickiness. - - Example - ---------- - - goblinHeist = GoblinHeist(data, [1, 0.8, 0.5, 0.8, 0.8, -0.1, -0.1]) - goblinHeist.MB_MF_model() - - Notes - ----- - The columns required in data: - - userID: unique identifier for each participant - - stimuli: which planets presented in stage of the trial - - s: the state of the trial - - rews: the rewards for each stimulus / planet - - choice: the choice made by the participant - - stake: the stake of the trial (1 or 5) - - points: the points received in the trial - - - References - ---------- - - Kool W, Cushman FA, Gershman SJ (2016) When Does Model-Based Control Pay Off? PLoS Comput Biol 12 - - Smid, C.R., K. Ganesan, A. Thompson, R. Cañigueral, S. Veselic, J. Royer, W. Kool, T.U. Hauser, B. Bernhardt, and N. Steinbeis. “Neurocognitive Basis of Model-Based Decision Making and Its Metacontrol in Childhood.” Developmental Cognitive Neuroscience 62 (August 1, 2023): 101269. - - """ - - def __init__(self, data, parameters=None): - - self.data = data - - if parameters is None: - self.parameters = { - "temperature": 0.8, - "learning_rate": 0.5, - "eligibility_trace_decay": 0.8, - "mixing_weight_low_stake": 0.8, - "mixing_weight_high_stake": 0.8, - "stickiness": -0.1, - "response_stickiness": -0.1 - } - warnings.warn("No parameters specified, using default parameters.") - else: - self.parameters = { - "temperature": parameters[0], - "learning_rate": parameters[1], - "eligibility_trace_decay": parameters[2], - "mixing_weight_low_stake": parameters[3], - "mixing_weight_high_stake": parameters[4], - "stickiness": parameters[5], - "response_stickiness": parameters[6] - } - - - def MF_MB_model(self): - - """ - Model for model-based vs model-free decision making in a decison making task - - - Returns - ------- - LL: - Log Likelihood - Q: - Q values - Q_MF: - Q values for model-free learning - Q_MB: - Q values for model-based learning - """ - - # pull out the paramters - b = self.parameters["temperature"] # softmax inverse temperature - lr = self.parameters["learning_rate"] # learning rate - lamb = self.parameters["eligibility_trace_decay"] # eligibility trace decay - w_lo = self.parameters["mixing_weight_low_stake"] # mixing weight for low stake trials - w_hi = self.parameters["mixing_weight_high_stake"] # mixing weight for high stake trials - st = self.parameters["stickiness"] # stickiness - respst = self.parameters["response_stickiness"] # response stickiness - - # initialization - Q_MF = np.ones((2,2))*4.5 - Q2 = np.ones((2,1))*4.5 - TM = [np.eye(2), np.eye(2)] #transition matrix - M = np.zeros((2,2)) # last action matrix - R = [0, 0] # last choice structure - LL = 0 # log likelihood - - for i in range(len(self.data["stimuli"])): - - if self.data["stimuli"][i] == 2 or self.data["stimuli"][i] == 4: - R = R = np.flipud(R) - - s1 = self.data["s"][i][0] # first choice stage - s2 = self.data["s"][i][1] # second choice stage - action = self.data["choice"][i] - a = action[0] - (s1 == 2) * 2 - - # choose which weight to update based on stake of trial - if self.data["stake"][i] == 1: - w = w_lo - else: - w = w_hi - - # calculating the Q values for model-based learning - Q_MB = TM[s1-1].T @ Q2 - - # calculating the Q values for mixing model - Q = w*Q_MB + (1-w)*Q_MF[s1-1, :].T + st * M[s1-1, :].T + respst * np.array(R) - - # calculating the probability choosing action a given Q - LL = LL + b*Q[a-1] - logsumexp(b * Q) - - # update choice structure - M = np.zeros((2,2)) - M[s1-1, a-1] = 1 - - R = np.zeros((2,1)) - if action == self.data["stimuli"][i][0]: - R[0] = 1 - else: - R[1] = 1 - - # update Q values for model-free learning - # first choice stage - dtQ1 = Q2[s2-1] - Q_MF[s1-1, a-1] - Q_MF[s1-1, a-1] = Q_MF[s1-1, a-1] + lr * dtQ1 - # second choice stage - dtQ2 = self.data["points"][i] - Q2[s2-1] - Q2[s2-1] = Q2[s2-1] + lr * dtQ2 - Q_MF[s1-1, a-1] = Q_MF[s1-1, a-1] + lamb * lr * dtQ2 - - # compile results - results = pd.DataFrame({ - "userID": self.data["userID"][0], - "LL": [LL], - "Q": [Q.flatten()], - "Q_MF": [Q_MF.flatten()], - "Q_MB": [Q_MB.flatten()] - }) - - return results \ No newline at end of file From d7664330ed03657463afd9e3216d2551f8ebad64 Mon Sep 17 00:00:00 2001 From: lenarddome Date: Mon, 2 Jun 2025 13:21:48 +0200 Subject: [PATCH 23/54] docs: add todo list for MBMF --- README.md | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 9d30649..5ad102f 100644 --- a/README.md +++ b/README.md @@ -35,7 +35,7 @@ mkdocs build mkdocs serve ``` -Then open a browser and go to http://127.0.0.1:8000/ +Then open a browser and go to Depending on the version you have, you might need to add Jupyter to PATH, see this [link](https://github.com/jupyter/nbconvert/issues/1773#issuecomment-1283852572) for more information. @@ -75,7 +75,7 @@ Feel free to use gitmoji for commit messages, but insert them at the end of the See this [link](https://gitmoji.dev/) for more information. ## Pull request conventions - + When creating a pull request, make sure to follow these conventions: [link](https://github.blog/2015-01-21-how-to-write-the-perfect-pull-request/) ## Compiling the package @@ -100,3 +100,15 @@ twine upload dist/* * `numpy`-style docstrings for documentation * `mkdocs` for documentation generation * `pytest` for testing + +## TODO + +* [ ] MBMF parameterisation should use toolbox functions +* [ ] MBMF should be part of application (use RWRL template) +* [ ] Create example dataset for MBMF (goblin heist) -- cpm.datasets.model_based_model_free +* [ ] Example jupyter notebook for MBMF: explaining model with references, and fitting it to data, plotting parameters +* [ ] (least important for now) Parameter recovery for MBMF + * [ ] generating data with MBMF + * [ ] save parameters used to generate data + * [ ] fitting MBMF to generated data + * [ ] plotting parameters (pearson correlation, etc.) From 40970dbaf0bf51d314f1a737e3e3b86fd581ef55 Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Tue, 3 Jun 2025 10:49:00 +0200 Subject: [PATCH 24/54] add exclusion criteria --- cpm/brainexplorer/milkyWay.py | 49 ++++++++++++- cpm/brainexplorer/scavenger.py | 43 ++++++++++-- cpm/brainexplorer/spaceObserver.py | 108 +++++++++++++++++++---------- cpm/brainexplorer/treasurehunt.py | 60 ++++++++++++---- 4 files changed, 201 insertions(+), 59 deletions(-) diff --git a/cpm/brainexplorer/milkyWay.py b/cpm/brainexplorer/milkyWay.py index 5946518..40623c0 100644 --- a/cpm/brainexplorer/milkyWay.py +++ b/cpm/brainexplorer/milkyWay.py @@ -10,7 +10,9 @@ class milkyWay: def __init__(self, filepath): """ - Load data to be analyised and turn into Pandas DataFrame + Load data to be analyised and turn into Pandas DataFrame. + Trial-level exclusion criteria are applied to the data. + The results for Milky Way and Pirate Market trials are stored in separate DataFrames. Parameters ---------- @@ -22,6 +24,7 @@ def __init__(self, filepath): >>> milkyWay = milkyWay("/example/2025-02-20_SpaceObserver_Data_short.xlsx") >>> milkyWay.metrics() + >>> milkyWay.clean_data() >>> results_MW = milkyWay.results_MW >>> results_PM = milkyWay.results_PM >>> milkyWay.codebook() @@ -35,10 +38,26 @@ def __init__(self, filepath): - rt_adj: the adjusted reaction time of the trial - choice: the choice made by the participant - correct: whether the choice was correct (1) or incorrect (0) + + The trial-level exclusion criteria are applied: + - Reaction time < 150 or > 10000 ms + - Attempts after the first attempt ("run" variable) + - Participants with more than 72 trials (due to technical error) """ self.data = pd.read_csv(filepath) + nr_part_before = len(self.data["userID"].unique()) + + #trial-level exclusion criteria + self.data = self.data[self.data["run"] == 1] # only keep first attempt + self.data = self.data[self.data["rt_adj"] > 150] # only keep trials with reaction time > 150 ms + self.data = self.data[self.data["rt_adj"] < 10000] # only keep trials with reaction time < 10000 ms + self.data = self.data[len(self.data["run"]) < 72] # only keep participants with less than 72 trials + + nr_part_after = len(self.data["userID"].unique()) + self.deleted_participants = nr_part_before - nr_part_after + self.results_MW = pd.DataFrame() self.results_PM = pd.DataFrame() self.codebook = { @@ -58,7 +77,8 @@ def __init__(self, filepath): "prop_WSLS_1": "Proportion of win-stay/loose-shift choices for correct trials based on whether the choice was correct", "prop_WSLS_2": "Proportion of win-stay/loose-shift choices for correct trials based on whether participant got at least 50 points (MW) or less than 50 points (PM)" } - + + def metrics(self): """ Calculate the metrics for the data. @@ -143,6 +163,31 @@ def metrics(self): self.results_PM = pd.DataFrame(self.results_PM) + + def clean_data(self): + """ + Clean the data by removing participants who do not meet the participant-level inclusion criteria. + + Exclusion Critera + ----------------- + + Participant-level: + - Same choice on >= 95% of trials + - Median RT > 10000 ms + """ + combined_results = pd.concat([self.results_MW, self.results_PM], ignore_index=True) + nr_part_before = len(combined_results["userID"].unique()) + + self.results_MW = self.results_MW[self.results_MW["prop_same_choice_MW"] < 0.95] + self.results_PM = self.results_PM[self.results_PM["prop_same_choice_PM"] < 0.95] + + self.results_MW = self.results_MW[self.results_MW["median_RT_MW"] < 10000] + self.results_PM = self.results_PM[self.results_PM["median_RT_PM"] < 10000] + + combined_after = pd.concat([self.results_MW, self.results_PM], ignore_index=True) + + self.deleted_participants += nr_part_before - len(combined_after["userID"].unique()) + def codebook(self): """ Create a codebook for the data. diff --git a/cpm/brainexplorer/scavenger.py b/cpm/brainexplorer/scavenger.py index e79e11b..cc8ab17 100644 --- a/cpm/brainexplorer/scavenger.py +++ b/cpm/brainexplorer/scavenger.py @@ -36,6 +36,7 @@ def __init__(self, filepath=None): >>> scavenger = Scavenger("/example/2025-01-10_Scavenger.xlsx") >>> scavenger.metrics() + >>> scavenger.clean_data() >>> results = scavenger.results >>> results.to_csv("/example/scavenger_results.csv", index=False) >>> scavenger.codebook() @@ -49,13 +50,25 @@ def __init__(self, filepath=None): - "safe_magn": Trial Values for Option A, - "risky_magn1" & "risky_magn2: Trial Values for Option B, - "ambg": if the trial is ambiguous or not + + Trial-level exclusion criteria: + - Reaction time < 150 or > 10000 ms + - Attempts after the first attempt ("run" variable) + - Participants with more than 40 trials (due to technical error) + - Participants with configID = 242 """ + self.data = pd.read_csv(filepath, header=0) + + nr_participants_before = len(self.data["userID"].unique()) + + self.data = self.data[self.data["run"] == 1] # only keep first attempt + self.data = self.data[self.data["rt"] > 150] # only keep trials with reaction time > 150 ms + self.data = self.data[self.data["rt"] < 10000] # only keep trials with reaction time < 10000 ms + self.data = self.data[len(self.data["run"]) < 40] # only keep participants with less than 40 trials + self.data = self.data[self.data["configID"] != 242] - ## check file extension - if filepath.endswith(".csv"): - self.data = pd.read_csv(filepath, header=0) - elif filepath.endswith(".xlsx"): - self.data = pd.read_excel(filepath, header=0) + nr_participants_after = len(self.data["userID"].unique()) + self.deleted_participants = nr_participants_before - nr_participants_after self.results = pd.DataFrame() self.group_results = pd.DataFrame() @@ -355,6 +368,26 @@ def metrics(self): return self.results + def clean_data(self): + """ + Clean the data by applying participant-level exlusion criteria. + + Exclusion criteria: + ---------- + - Median reaction time > 5000 ms + - Same choice safe/risky on >= 95% of trials + - Same choice left/right on >= 95% of trials + - Fail >= 50% of catch trials + """ + nr_part_before = len(self.results["userID"].unique()) + + self.data = self.data[self.data["median_RT"] < 5000] + self.data = self.data[self.data["rational_catchTrials"] >= 0.5] + self.data = self.data[self.data["risky_choices"] < 0.95] + self.data = self.data[self.data["chosen_left_all"] < 0.95] + + self.deleted_participants += nr_part_before - len(self.results["userID"].unique()) + def codebook(self): """ Return a codebook describing the metrics. diff --git a/cpm/brainexplorer/spaceObserver.py b/cpm/brainexplorer/spaceObserver.py index 5523256..f9fb972 100644 --- a/cpm/brainexplorer/spaceObserver.py +++ b/cpm/brainexplorer/spaceObserver.py @@ -21,7 +21,8 @@ class spaceObserver: def __init__(self, filepath=None): """ - Load data to be analyised and turn into Pandas DataFrame + Load data to be analyised and turn into Pandas DataFrame. + Exlude trials not meeting the criteria. Parameters ---------- @@ -33,6 +34,8 @@ def __init__(self, filepath=None): >>> spaceObserver = SpaceObserver("/example/2025-02-20_SpaceObserver_Data.csv") >>> results = spaceObserver.metrics() + >>> spaceObserver.clean_data() + >>> results = spaceObserver.results >>> results.to_csv("/example/spaceObserver_results.csv", index=False) >>> spaceObserver.codebook() @@ -47,11 +50,55 @@ def __init__(self, filepath=None): - **confidence**: the confidence rating of the trial - **confidenceRT**: the reaction time of the confidence rating in the trial - **stimulus_intensity**: the evidence strength of the trial, which is the difference of evidence for each group of stimuli. + + Trial-level exclusion critera: + - Attempts after the first attempt ("run" variable) + - Reaction time < 150 or > 10000 ms + - Reaction time of confidence < 150 or > 10000 ms + - practice trials: if more than 8 subsequent trials where confidence data is missing, it is a practice run and will be excluded. + - Trials with missing confidence data + - Participants with more than 80 trials (due to technical error) """ ## read data self.data = pd.read_csv(filepath, header=0, na_values=["NaN", "nan"]) + nr_part_before = len(self.data["userID"].unique()) + + self.data = self.data[self.data["run"] == 1] # only keep first attempt + self.data = self.data[self.data["choiceRT"] > 150] # only keep trials with reaction time > 150 ms + self.data = self.data[self.data["choiceRT"] < 10000] # only keep trials with reaction time < 10000 ms + self.data = self.data[self.data["confidenceRT"] > 150] # only keep trials with confidence reaction time > 150 ms + self.data = self.data[self.data["confidenceRT"] < 10000] # only keep trials with confidence reaction time < 10000 ms + self.data = self.data[self.data["confidence"].notna()] # only keep trials with confidence data + self.data = self.data[len(self.data["userID"]) < 80] # only keep participants with less than 80 trials + + nr_part_after = len(self.data["userID"].unique()) + + self.deleted_participants = nr_part_before - nr_part_after + + # delete practice trials + # if > 8 subseequent trials where confidence data is missing it is practice run + + self.data["confidence"] = self.data["confidence"].replace( + ["NaN", "nan", "NAN", ""], pd.NA + ) + + self.data["confidence"] = pd.to_numeric(self.data["confidence"], errors='coerce') + + print(len(self.data)) + + self.data = self.data.groupby(["userID", "round"]).filter( + lambda run: ( + ( + (run["confidence"].isna() != run["confidence"].isna().shift()).cumsum() + .where(run["confidence"].isna()) + .value_counts() + .max() + ) if run["confidence"].isna().any() else 0 + ) < 9 + ) + self.results = pd.DataFrame() self.codebook = { "userID": "Unique identifier for each participant", @@ -121,41 +168,6 @@ def metrics(self, mode=None): The mean evidence strength for each of the bins. The trials here are split into 4 quarters. The evidence strength is divided into four equal bins and the mean is calculated for each bin. """ - - # delete practice trials - # if > 8 subseequent trials where confidence data is missing it is practice run - """ - practice_trials = self.data.groupby(["userID", "round"]).filter( - lambda run_data: run_data["confidence"][(pd.isna(run_data["confidence"])) | (run_data["confidence"] == "NaN")].count() > 8 - ) - self.data = self.data.drop(practice_trials.index) - """ - - self.data["confidence"] = self.data["confidence"].replace( - ["NaN", "nan", "NAN", ""], pd.NA - ) - - self.data["confidence"] = pd.to_numeric(self.data["confidence"], errors='coerce') - - print(len(self.data)) - - self.data = self.data.groupby(["userID", "round"]).filter( - lambda run: ( - ( - (run["confidence"].isna() != run["confidence"].isna().shift()).cumsum() - .where(run["confidence"].isna()) - .value_counts() - .max() - ) if run["confidence"].isna().any() else 0 - ) < 9 - ) - - print(self.data["confidence"].unique()) - - print(len(self.data)) - - print(self.data[self.data["userID"] == 121]["round"].unique()) - # Group data by subject grouped_data = self.data.groupby("userID") @@ -187,14 +199,14 @@ def metrics(self, mode=None): user_results["mean_RT_incorrect"] = np.mean(np.array(user_data["choiceRT"])[correct != 1]) user_results["median_RT_incorrect"] = np.median(np.array(user_data["choiceRT"])[correct != 1]) - evidence_strenght = user_data["stimulus_intensity"] + evidence_strength = user_data["stimulus_intensity"] # calcualte evidence strength for each bin bin_size = 4 # Divide evidence strength into 4 equal bins and calculate the mean for each bin - bins = np.array_split(evidence_strenght, bin_size) + bins = np.array_split(evidence_strength, bin_size) ES_1 = np.mean(bins[0]) if len(bins[0]) > 0 else np.nan ES_2 = np.mean(bins[1]) if len(bins[1]) > 0 else np.nan ES_3 = np.mean(bins[2]) if len(bins[2]) > 0 else np.nan @@ -211,7 +223,27 @@ def metrics(self, mode=None): return self.results + def clean_data(self): + """ + Clean aggregated data by applyign participant-level exclusion criteria. + Exclusion criteria: + --------- + - Mean accuracy < 0.5 + - Median reaction time > 5000 ms + - Median reaction time for confidence ratings > 5000 ms + - Median evidence strength > 25 + - Mean confidence > 97 + """ + + nr_part_before = len(self.results["userID"].unique()) + + self.results = self.results[self.results["mean_accuracy"] >= 0.5] + self.results = self.results[self.results["median_RT"] < 5000] + self.results = self.results[self.results["median_confidenceRT"] < 5000] + self.results = self.results[self.results["ES_bins"].apply(lambda x: np.nanmean(x) < 25)] + self.results = self.results[self.results["mean_confidence"] < 97] + self.deleted_participants += nr_part_before - len(self.results["userID"].unique()) def codebook(self): """ diff --git a/cpm/brainexplorer/treasurehunt.py b/cpm/brainexplorer/treasurehunt.py index a445e51..d132636 100644 --- a/cpm/brainexplorer/treasurehunt.py +++ b/cpm/brainexplorer/treasurehunt.py @@ -34,6 +34,8 @@ def __init__(self, filepath=None): >>> spaceObserver = SpaceObserver("/example/2025-02-20_SpaceObserver_Data_short.xlsx") >>> spaceObserver.metrics() + >>> spaceObserver.clean_data() + >>> results = spaceObserver.results >>> spaceObserver.codebook() @@ -41,23 +43,34 @@ def __init__(self, filepath=None): ----- The columns required in data: - userID: unique identifier for each participant - - run: + - run: number of attempt by participant - outcome: the outcome of the trial - confidence: the confidence of the trial - RT: the reaction time of the trial - confidenceRT: the confidence reaction time of the trial - ev: the evidence of the trial - + + Trial-level exclusion criteria: + + - attempts after the first attempt ("run" variable) + - < 3 unique values in confidence rating + - < 3 unique values in number of samples """ - ## check file extension - if filepath.endswith(".csv"): - self.data = pd.read_csv(filepath, header=0) - elif filepath.endswith(".xlsx"): - self.data = pd.read_excel(filepath, header=0) - # self.data = pd.read_excel(filepath, header=0, nrows=500) + self.data = pd.read_csv(filepath, header=0) + + nr_part_before = len(self.data["userID"].unique()) + + self.data = self.data[self.data["run"] == 1] # only keep first attempt + self.data = self.data[self.data["confidence"].nunique() > 3] # only keep trials with more than 3 unique values in confidence rating + self.data = self.data[self.data["draws"].nunique() > 3] # only keep trials with more than 3 unique values in number of samples + + nr_part_after = len(self.data["userID"].unique()) + + self.deleted_participants = nr_part_before - nr_part_after self.results = pd.DataFrame() + self.codebook = { "userID": "Unique identifier for each participant", "mean_accuracy": "Mean accuracy across all trials", @@ -131,12 +144,8 @@ def metrics(self, mode=None): user_results["mean_n_draws"] = np.mean(user_data["draws"]) - correct_responses = [] - for i in range(len(user_data["outcome"])): - if user_data["outcome"].iloc[i] == 100: - correct_responses.append(1) - - user_results["mean_accuracy"] = len(correct_responses) / len(user_data["outcome"]) + # if chocie in line with current evidence + user_results["mean_chosEv"] = np.mean(user_results["chEv"]) # regression for influence of recent results on current choice @@ -189,6 +198,29 @@ def metrics(self, mode=None): self.results = pd.DataFrame(self.results) return self.results + + def clean_data(self): + """ + Clean the aggregated data by applying participant-level exclusion criteria. + + Exclusion criteria: + --------- + - Mean number of draws < 2 or > 23 + - Make choice not in line with current evidence in >= 20% of trials + - Mean confidence < 15 or > 98 + + """ + nr_part_before = len(self.results["userID"].unique()) + + self.results = self.results[self.results["draws"] > 2] # only keep participants with mean number of draws > 2 + self.results = self.results[self.results["draws"] < 23] # only keep participants with mean number of draws < 23 + + self.results = self.results[self.results["mean_confidence"] > 15] # only keep participants with mean confidence > 15 + self.results = self.results[self.results["mean_confidence"] < 98] # only keep participants with mean confidence < 98 + + self.results = self.results[self.results["mean_chosEv"] >= 0.8] # only keep participants with mean choice in line with current evidence >= 80% + + self.deleted_participants += nr_part_before - len(self.results["userID"].unique()) def codebook(self): """ From 30000c4ad13501ba8a2ffcaa34a120927c70431a Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Tue, 3 Jun 2025 18:04:28 +0200 Subject: [PATCH 25/54] fix data cleaning --- cpm/brainexplorer/milkyWay.py | 17 +++++++++-------- cpm/brainexplorer/scavenger.py | 16 ++++++++-------- cpm/brainexplorer/spaceObserver.py | 4 ++-- cpm/brainexplorer/treasurehunt.py | 28 ++++++++++++++++------------ 4 files changed, 35 insertions(+), 30 deletions(-) diff --git a/cpm/brainexplorer/milkyWay.py b/cpm/brainexplorer/milkyWay.py index 40623c0..637e73d 100644 --- a/cpm/brainexplorer/milkyWay.py +++ b/cpm/brainexplorer/milkyWay.py @@ -53,7 +53,7 @@ def __init__(self, filepath): self.data = self.data[self.data["run"] == 1] # only keep first attempt self.data = self.data[self.data["rt_adj"] > 150] # only keep trials with reaction time > 150 ms self.data = self.data[self.data["rt_adj"] < 10000] # only keep trials with reaction time < 10000 ms - self.data = self.data[len(self.data["run"]) < 72] # only keep participants with less than 72 trials + self.data = self.data[self.data.groupby("userID")["userID"].transform('count') < 72] # only keep participants with less than 72 trials nr_part_after = len(self.data["userID"].unique()) self.deleted_participants = nr_part_before - nr_part_after @@ -111,7 +111,7 @@ def metrics(self): # Loop through each group of user data for user_id, user_data_MW in MW_data: - user_results_MW = {"userID": user_data_MW['userID']} + user_results_MW = {"userID": user_id} user_results_MW["mean_RT_MW"] = np.mean(user_data_MW['rt_adj']) user_results_MW["median_RT_MW"] = np.median(user_data_MW['rt_adj']) @@ -138,7 +138,7 @@ def metrics(self): for user_id, user_data_PM in PM_data: - user_results_PM = {"userID": user_data_PM['userID']} + user_results_PM = {"userID": user_id} user_results_PM["mean_RT_PM"] = np.mean(user_data_PM['rt_adj']) user_results_PM["median_RT_PM"] = np.median(user_data_PM['rt_adj']) @@ -176,7 +176,8 @@ def clean_data(self): - Median RT > 10000 ms """ combined_results = pd.concat([self.results_MW, self.results_PM], ignore_index=True) - nr_part_before = len(combined_results["userID"].unique()) + combined_results["userID"] = combined_results["userID"].apply(lambda x: x.iloc[0] if isinstance(x, pd.Series) else x) + nr_part_before = len(pd.unique(combined_results["userID"])) self.results_MW = self.results_MW[self.results_MW["prop_same_choice_MW"] < 0.95] self.results_PM = self.results_PM[self.results_PM["prop_same_choice_PM"] < 0.95] @@ -185,12 +186,12 @@ def clean_data(self): self.results_PM = self.results_PM[self.results_PM["median_RT_PM"] < 10000] combined_after = pd.concat([self.results_MW, self.results_PM], ignore_index=True) - - self.deleted_participants += nr_part_before - len(combined_after["userID"].unique()) + combined_after["userID"] = combined_after["userID"].apply(lambda x: x.iloc[0] if isinstance(x, pd.Series) else x) + + self.deleted_participants += nr_part_before - len(pd.unique(combined_after["userID"])) def codebook(self): """ Create a codebook for the data. """ - return self.codebook - \ No newline at end of file + return self.codebook \ No newline at end of file diff --git a/cpm/brainexplorer/scavenger.py b/cpm/brainexplorer/scavenger.py index cc8ab17..aee0ba8 100644 --- a/cpm/brainexplorer/scavenger.py +++ b/cpm/brainexplorer/scavenger.py @@ -62,10 +62,10 @@ def __init__(self, filepath=None): nr_participants_before = len(self.data["userID"].unique()) self.data = self.data[self.data["run"] == 1] # only keep first attempt - self.data = self.data[self.data["rt"] > 150] # only keep trials with reaction time > 150 ms - self.data = self.data[self.data["rt"] < 10000] # only keep trials with reaction time < 10000 ms - self.data = self.data[len(self.data["run"]) < 40] # only keep participants with less than 40 trials - self.data = self.data[self.data["configID"] != 242] + self.data = self.data[self.data["RT"] > 150] # only keep trials with reaction time > 150 ms + self.data = self.data[self.data["RT"] < 10000] # only keep trials with reaction time < 10000 ms + self.data = self.data[self.data.groupby("userID")["userID"].transform('count') < 40] # only keep participants with less than 40 trials + self.data = self.data[self.data["config_id"] != 242] nr_participants_after = len(self.data["userID"].unique()) self.deleted_participants = nr_participants_before - nr_participants_after @@ -381,10 +381,10 @@ def clean_data(self): """ nr_part_before = len(self.results["userID"].unique()) - self.data = self.data[self.data["median_RT"] < 5000] - self.data = self.data[self.data["rational_catchTrials"] >= 0.5] - self.data = self.data[self.data["risky_choices"] < 0.95] - self.data = self.data[self.data["chosen_left_all"] < 0.95] + self.results = self.results[self.results["median_RT"] < 5000] + self.results = self.results[self.results["rational_catchTrials"] >= 0.5] + self.results = self.results[self.results["risky_choices"] < 0.95] + self.results = self.results[self.results["chosen_left_all"] < 0.95] self.deleted_participants += nr_part_before - len(self.results["userID"].unique()) diff --git a/cpm/brainexplorer/spaceObserver.py b/cpm/brainexplorer/spaceObserver.py index f9fb972..88bf563 100644 --- a/cpm/brainexplorer/spaceObserver.py +++ b/cpm/brainexplorer/spaceObserver.py @@ -71,7 +71,7 @@ def __init__(self, filepath=None): self.data = self.data[self.data["confidenceRT"] > 150] # only keep trials with confidence reaction time > 150 ms self.data = self.data[self.data["confidenceRT"] < 10000] # only keep trials with confidence reaction time < 10000 ms self.data = self.data[self.data["confidence"].notna()] # only keep trials with confidence data - self.data = self.data[len(self.data["userID"]) < 80] # only keep participants with less than 80 trials + self.data = self.data[self.data.groupby("userID")["userID"].transform('count') < 80] # only keep participants with less than 80 trials nr_part_after = len(self.data["userID"].unique()) @@ -249,4 +249,4 @@ def codebook(self): """ Return a codebook describing the metrics. """ - return self.codebook \ No newline at end of file + return self.codebook diff --git a/cpm/brainexplorer/treasurehunt.py b/cpm/brainexplorer/treasurehunt.py index d132636..3b327a3 100644 --- a/cpm/brainexplorer/treasurehunt.py +++ b/cpm/brainexplorer/treasurehunt.py @@ -32,11 +32,11 @@ def __init__(self, filepath=None): Example ---------- - >>> spaceObserver = SpaceObserver("/example/2025-02-20_SpaceObserver_Data_short.xlsx") - >>> spaceObserver.metrics() - >>> spaceObserver.clean_data() - >>> results = spaceObserver.results - >>> spaceObserver.codebook() + >>> treasurehunt = treasurehunt("/example/2025-02-20_SpaceObserver_Data_short.xlsx") + >>> treasurehunt.metrics() + >>> treasurehunt.clean_data() + >>> results = treasurehunt.results + >>> treasurehunt.codebook() Notes @@ -62,8 +62,12 @@ def __init__(self, filepath=None): nr_part_before = len(self.data["userID"].unique()) self.data = self.data[self.data["run"] == 1] # only keep first attempt - self.data = self.data[self.data["confidence"].nunique() > 3] # only keep trials with more than 3 unique values in confidence rating - self.data = self.data[self.data["draws"].nunique() > 3] # only keep trials with more than 3 unique values in number of samples + + # Filter out userIDs where all trials have less than 3 unique values for confidence or draws + valid_users = self.data.groupby("userID").filter( + lambda group: group["confidence"].nunique() > 3 and group["draws"].nunique() > 3 + )["userID"].unique() + self.data = self.data[self.data["userID"].isin(valid_users)] nr_part_after = len(self.data["userID"].unique()) @@ -89,7 +93,7 @@ def __init__(self, filepath=None): "deltaev_coef": "Coefficient for the change in total evidence in the regression model" } - def metrics(self, mode=None): + def metrics(self): """ Returns @@ -145,7 +149,7 @@ def metrics(self, mode=None): user_results["mean_n_draws"] = np.mean(user_data["draws"]) # if chocie in line with current evidence - user_results["mean_chosEv"] = np.mean(user_results["chEv"]) + user_results["mean_chosEv"] = np.mean(user_data["chEv"]) # regression for influence of recent results on current choice @@ -212,8 +216,8 @@ def clean_data(self): """ nr_part_before = len(self.results["userID"].unique()) - self.results = self.results[self.results["draws"] > 2] # only keep participants with mean number of draws > 2 - self.results = self.results[self.results["draws"] < 23] # only keep participants with mean number of draws < 23 + self.results = self.results[self.results["mean_n_draws"] > 2] # only keep participants with mean number of draws > 2 + self.results = self.results[self.results["mean_n_draws"] < 23] # only keep participants with mean number of draws < 23 self.results = self.results[self.results["mean_confidence"] > 15] # only keep participants with mean confidence > 15 self.results = self.results[self.results["mean_confidence"] < 98] # only keep participants with mean confidence < 98 @@ -226,4 +230,4 @@ def codebook(self): """ Return a codebook describing the metrics. """ - return self.codebook \ No newline at end of file + return self.codebook From 3f0fb217df9268e11afbd27c1a0bf189ab56fbb0 Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Tue, 10 Jun 2025 10:40:26 +0200 Subject: [PATCH 26/54] add MBMF model --- cpm/applications/reinforcement_learning.py | 199 ++++++++++++--------- 1 file changed, 110 insertions(+), 89 deletions(-) diff --git a/cpm/applications/reinforcement_learning.py b/cpm/applications/reinforcement_learning.py index 21e2843..5d8f34d 100644 --- a/cpm/applications/reinforcement_learning.py +++ b/cpm/applications/reinforcement_learning.py @@ -150,9 +150,11 @@ def model(parameters, trial, generate=generate): super().__init__(data=data, model=model, parameters=parameters) -class MBMF: +class MBMF(Wrapper): """ - Model for model-based vs model-free decision making in a decision making task + This class implements a model for model-based vs model-free decision making in a decision making task. + The model uses a combination of model-based and model-free learning to predict choices based on the rewards received for each stimulus. + It is based on the model described in Kool et al. (2016) and adds choice stickiness calcualtions to the model. Attributes ---------- @@ -166,8 +168,12 @@ class MBMF: Example ---------- - goblinHeist = GoblinHeist(data, [1, 0.8, 0.5, 0.8, 0.8, -0.1, -0.1]) - goblinHeist.MB_MF_model() + >>> import numpy + >>> import pandas + >>> from cpm.applications import MBMF + >>> data = pandas.read_csv("datasets/data/GH_example_data.csv") + + >>> model = MBMF(data=data, parameters_settings=[[0.8, 0, 2], [0.5, 0, 1], [0.8, 0, 1], [0.8, 0, 1], [0.8, 0, 2], [-0.1, -0.5, 0.5], [-0.1, -0.5, 0.5]]) Notes ----- @@ -180,6 +186,7 @@ class MBMF: - stake: the stake of the trial (1 or 5) - points: the points received in the trial + parameters_settings must be a 2D array, like [[0.8, 0, 2], [0.5, 0, 1], [0.8, 0, 1], [0.8, 0, 1], [0.8, 0, 2], [-0.1, -0.5, 0.5], [-0.1, -0.5, 0.5]], where the first list specifies the inverse temperature, the second list specifies the learning rate, the third list specifies the eligibility trace decay, the fourth list specifies the mixing weight for low stake trials, the fifth list specifies the mixing weight for high stake trials, the sixth list specifies the stickiness, and the seventh list specifies the response stickiness. The first element of each list is the initial value of the parameter, the second element is the lower bound, and the third element is the upper bound. References ---------- @@ -191,82 +198,96 @@ class MBMF: """ def __init__( - self, data=None, dimensions=2, parameters_settings=None, generate=False + self, data=None, parameters_settings=None ): - self.data = data - - ## TODO: cpm.generators.Value should be used here - ## TODO: cpm.generators.Parameters should be used here - if parameters is None: - self.parameters = { - "temperature": 0.8, - "learning_rate": 0.5, - "eligibility_trace_decay": 0.8, - "mixing_weight_low_stake": 0.8, - "mixing_weight_high_stake": 0.8, - "stickiness": -0.1, - "response_stickiness": -0.1 - } - warnings.warn("No parameters specified, using default parameters.") - else: - self.parameters = { - "temperature": parameters[0], - "learning_rate": parameters[1], - "eligibility_trace_decay": parameters[2], - "mixing_weight_low_stake": parameters[3], - "mixing_weight_high_stake": parameters[4], - "stickiness": parameters[5], - "response_stickiness": parameters[6] - } + if parameters_settings is None: + parameters_settings = [[0.8, 0, 2], [0.5, 0, 1], [0.8, 0, 1], [0.8, 0, 1], [0.8, 0, 2], [-0.1, -0.5, 0.5], [-0.1, -0.5, 0.5]] + parameters = Parameters( + temperature=Value( + value=parameters_settings[0][0], + lower=parameters_settings[0][1], + upper=parameters_settings[0][2] + ), + learning_rate=Value( + value=parameters_settings[1][0], + lower=parameters_settings[1][1], + upper=parameters_settings[1][2] + ), + eligibility_trace_decay=Value( + value=parameters_settings[2][0], + lower=parameters_settings[2][1], + upper=parameters_settings[2][2] + ), + mixing_weight_low_stake=Value( + value=parameters_settings[3][0], + lower=parameters_settings[3][1], + upper=parameters_settings[3][2] + ), + mixing_weight_high_stake=Value( + value=parameters_settings[4][0], + lower=parameters_settings[4][1], + upper=parameters_settings[4][2] + ), + stickiness=Value( + value=parameters_settings[5][0], + lower=parameters_settings[5][1], + upper=parameters_settings[5][2] + ), + response_stickiness=Value( + value=parameters_settings[6][0], + lower=parameters_settings[6][1], + upper=parameters_settings[6][2] + ) + ) + @ipp.require("numpy") - def MF_MB_model(self): - - """ - Model for model-based vs model-free decision making in a decison making task - - - Returns - ------- - LL: - Log Likelihood - Q: - Q values - Q_MF: - Q values for model-free learning - Q_MB: - Q values for model-based learning - """ - - # pull out the paramters - b = self.parameters["temperature"] # softmax inverse temperature - lr = self.parameters["learning_rate"] # learning rate - lamb = self.parameters["eligibility_trace_decay"] # eligibility trace decay - w_lo = self.parameters["mixing_weight_low_stake"] # mixing weight for low stake trials - w_hi = self.parameters["mixing_weight_high_stake"] # mixing weight for high stake trials - st = self.parameters["stickiness"] # stickiness - respst = self.parameters["response_stickiness"] # response stickiness - - # initialization - Q_MF = np.ones((2,2))*4.5 - Q2 = np.ones((2,1))*4.5 - TM = [np.eye(2), np.eye(2)] #transition matrix - M = np.zeros((2,2)) # last action matrix - R = [0, 0] # last choice structure - LL = 0 # log likelihood - - for i in range(len(self.data["stimuli"])): - - if self.data["stimuli"][i] == 2 or self.data["stimuli"][i] == 4: - R = R = np.flipud(R) - - s1 = self.data["s"][i][0] # first choice stage - s2 = self.data["s"][i][1] # second choice stage - action = self.data["choice"][i] + def MF_MB_model(parameters, trial): + + """ + Model for model-based vs model-free decision making in a decison making task + + + Returns + ------- + LL: + Log Likelihood + Q: + Q values for the mixing model + Q_MF: + Q values for model-free learning + Q_MB: + Q values for model-based learning + """ + + # pull out the paramters + b = parameters.temperature # softmax inverse temperature + lr = parameters.learning_rate # learning rate + lamb = parameters.eligibility_trace_decay # eligibility trace decay + w_lo = parameters.mixing_weight_low_stake # mixing weight for low stake trials + w_hi = parameters.mixing_weight_high_stake # mixing weight for high stake trials + st = parameters.stickiness # stickiness + respst = parameters.response_stickiness # response stickiness + + # initialization + Q_MF = numpy.ones((2,2)) + Q2 = numpy.ones((2,1)) + TM = [numpy.eye(2), numpy.eye(2)] #transition matrix + M = numpy.zeros((2,2)) # last action matrix + R = [0, 0] # last choice structure + LL = 0 # log likelihood + + + if trial["stimuli"][0] == 2 or trial["stimuli"][0] == 4: + R = R = numpy.flipud(R) + + s1 = trial["s"][0] # first choice stage + s2 = trial["s"][1] # second choice stage + action = self.data["choice"] a = action[0] - (s1 == 2) * 2 # choose which weight to update based on stake of trial - if self.data["stake"][i] == 1: + if trial["stake"] == 1: w = w_lo else: w = w_hi @@ -275,17 +296,17 @@ def MF_MB_model(self): Q_MB = TM[s1-1].T @ Q2 # calculating the Q values for mixing model - Q = w*Q_MB + (1-w)*Q_MF[s1-1, :].T + st * M[s1-1, :].T + respst * np.array(R) + Q = w*Q_MB + (1-w)*Q_MF[s1-1, :].T + st * M[s1-1, :].T + respst * numpy.array(R) # calculating the probability choosing action a given Q LL = LL + b*Q[a-1] - logsumexp(b * Q) # update choice structure - M = np.zeros((2,2)) + M = numpy.zeros((2,2)) M[s1-1, a-1] = 1 - R = np.zeros((2,1)) - if action == self.data["stimuli"][i][0]: + R = numpy.zeros((2,1)) + if action == trial["stimuli"][0]: R[0] = 1 else: R[1] = 1 @@ -295,19 +316,19 @@ def MF_MB_model(self): dtQ1 = Q2[s2-1] - Q_MF[s1-1, a-1] Q_MF[s1-1, a-1] = Q_MF[s1-1, a-1] + lr * dtQ1 # second choice stage - dtQ2 = self.data["points"][i] - Q2[s2-1] + dtQ2 = trial["points"] - Q2[s2-1] Q2[s2-1] = Q2[s2-1] + lr * dtQ2 Q_MF[s1-1, a-1] = Q_MF[s1-1, a-1] + lamb * lr * dtQ2 - # compile results - results = pd.DataFrame({ - "userID": self.data["userID"][0], - "LL": [LL], - "Q": [Q.flatten()], - "Q_MF": [Q_MF.flatten()], - "Q_MB": [Q_MB.flatten()] - }) + # compile results + results = pandas.DataFrame({ + "userID": trial["userID"][0], + "LL": [LL], + "Q": [Q.flatten()], + "Q_MF": [Q_MF.flatten()], + "Q_MB": [Q_MB.flatten()] + }) - return results + return results - super().__init__(data=data, model=model, parameters=parameters) + super().__init__(data=data, model=MF_MB_model, parameters=parameters) From 97e1090cc62280c5de218d1fa9cf1516726ee4e6 Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Tue, 10 Jun 2025 10:40:36 +0200 Subject: [PATCH 27/54] add GH example data --- cpm/datasets/data/GH_example_data.csv | 16491 ++++++++++++++++++++++++ 1 file changed, 16491 insertions(+) create mode 100644 cpm/datasets/data/GH_example_data.csv diff --git a/cpm/datasets/data/GH_example_data.csv b/cpm/datasets/data/GH_example_data.csv new file mode 100644 index 0000000..0cf68a2 --- /dev/null +++ b/cpm/datasets/data/GH_example_data.csv @@ -0,0 +1,16491 @@ +userID,trial,stimuli,rews,s,block,stake,choice,points_real,points,planet,theor_best_choice,RT +1,1,"[3, 4]","[70, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[8320] +1,2,"[1, 2]","[60, 0]","[1, 2]",[1],[1],[2],[0],[0],[2],[1],[4211] +1,3,"[2, 1]","[10, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2082] +1,4,"[4, 3]","[10, 90]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[2236] +1,5,"[3, 4]","[70, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2182] +1,6,"[3, 4]","[80, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2714] +1,7,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[3044] +1,8,"[1, 2]","[70, 90]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2853] +1,9,"[1, 2]","[50, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2631] +1,10,"[1, 2]","[30, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2315] +1,11,"[3, 4]","[30, 80]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[2540] +1,12,"[3, 4]","[20, 70]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1876] +1,13,"[3, 4]","[10, 40]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[1858] +1,14,"[4, 3]","[30, 10]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[1926] +1,15,"[3, 4]","[20, 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1]",[2],[1],[1],[10],[1],[1],[1],[2024] +58,72,"[3, 4]","[20, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2407] +58,73,"[4, 3]","[50, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1674] +58,74,"[1, 2]","[40, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1755] +58,75,"[1, 2]","[30, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[1892] +58,76,"[1, 2]","[50, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1707] +58,77,"[3, 4]","[30, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2973] +58,78,"[2, 1]","[40, 10]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2072] +58,79,"[1, 2]","[10, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[2],[1957] +58,80,"[2, 1]","[0, 0]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[3339] From 715bffee1756e8360e1fea22772513a61b54fe8e Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Tue, 10 Jun 2025 11:06:30 +0200 Subject: [PATCH 28/54] adjusted cleaning --- cpm/brainexplorer/milkyWay.py | 81 +++++++++++++++++++----------- cpm/brainexplorer/scavenger.py | 37 ++++++++------ cpm/brainexplorer/spaceObserver.py | 62 +++++++++-------------- cpm/brainexplorer/treasurehunt.py | 50 +++++++++--------- 4 files changed, 123 insertions(+), 107 deletions(-) diff --git a/cpm/brainexplorer/milkyWay.py b/cpm/brainexplorer/milkyWay.py index 637e73d..205d539 100644 --- a/cpm/brainexplorer/milkyWay.py +++ b/cpm/brainexplorer/milkyWay.py @@ -42,21 +42,34 @@ def __init__(self, filepath): The trial-level exclusion criteria are applied: - Reaction time < 150 or > 10000 ms - Attempts after the first attempt ("run" variable) - - Participants with more than 72 trials (due to technical error) + + The reaction times have to be corrected by the average time between the previous choice and new stimulus presentation which is 4200 ms. However, this correction can lead to negative reaction times, which may exclude some participants from the analysis. """ self.data = pd.read_csv(filepath) - nr_part_before = len(self.data["userID"].unique()) + #average time between previous choice and new stimulus presentation (including feedback on previous choice etc) is 4.2 s, so he removed that from all RTs + #BUT: as we will see later, this correct does generate some negative RTs and thereby excludes some participants. so think about this! + self.data["rt_adj_cor"] = self.data["rt_adj"] - 4200 + + #Separate into MW and PM data + self.MW_data = self.data[self.data['trial_type'] == 'reward'] + self.PM_data = self.data[self.data['trial_type'] == 'punish'] + + #Take first attempt (i.e., not neccessarily run == 1 for PM!) + min_run_per_user = self.MW_data.groupby('userID')['run'].transform('min') + self.MW_data = self.MW_data[self.MW_data['run'] == min_run_per_user].reset_index(drop=True) + + min_run_per_user = self.PM_data.groupby('userID')['run'].transform('min') + self.PM_data = self.PM_data[self.PM_data['run'] == min_run_per_user].reset_index(drop=True) - #trial-level exclusion criteria - self.data = self.data[self.data["run"] == 1] # only keep first attempt - self.data = self.data[self.data["rt_adj"] > 150] # only keep trials with reaction time > 150 ms - self.data = self.data[self.data["rt_adj"] < 10000] # only keep trials with reaction time < 10000 ms - self.data = self.data[self.data.groupby("userID")["userID"].transform('count') < 72] # only keep participants with less than 72 trials + #Apply trial-level exclusion criteria + self.MW_data = self.MW_data[self.MW_data["rt_adj_cor"] >= 150] # only keep trials with reaction time >= 150 ms + self.MW_data = self.MW_data[self.MW_data["rt_adj_cor"] <= 10000] # only keep trials with reaction time <= 10000 ms + + self.PM_data = self.PM_data[self.PM_data["rt_adj_cor"] >= 150] # only keep trials with reaction time > 150 ms + self.PM_data = self.PM_data[self.PM_data["rt_adj_cor"] <= 10000] # only keep trials with reaction time < 10000 ms - nr_part_after = len(self.data["userID"].unique()) - self.deleted_participants = nr_part_before - nr_part_after self.results_MW = pd.DataFrame() self.results_PM = pd.DataFrame() @@ -101,12 +114,9 @@ def metrics(self): - prop_WSLS_1: Proportion of win-stay/loose-shift choices for correct trials based on whether the choice was correct - prop_WSLS_2: Proportion of win-stay/loose-shift choices for correct trials based on whether participant got at least 50 points (MW) or less than 50 points (PM) """ - # seperate MilkyWay and Pirate Market data - MW_data = self.data[self.data['trial_type'] == 'reward'] - PM_data = self.data[self.data['trial_type'] == 'punish'] - - MW_data = MW_data.groupby("userID") - PM_data = PM_data.groupby("userID") + # group MilkyWay and Pirate Market data + MW_data = self.MW_data.groupby("userID") + PM_data = self.PM_data.groupby("userID") # Loop through each group of user data for user_id, user_data_MW in MW_data: @@ -118,7 +128,7 @@ def metrics(self): user_results_MW["accuracy_MW"] = np.mean(user_data_MW["correct"]) - user_results_MW["prop_same_choice_MW"] = np.mean(user_data_MW["choice"] == 1) + user_results_MW["prop_choice1_MW"] = np.mean(user_data_MW["choice"] == 1) # proportion of win-stay/loose-shift choices user_results_MW["prop_WSLS_1"] = np.mean( @@ -145,7 +155,7 @@ def metrics(self): user_results_PM["accuracy_PM"] = np.mean(user_data_PM["correct"]) - user_results_PM["prop_same_choice_PM"] = np.mean(user_data_PM["choice"] == 1) + user_results_PM["prop_choice1_PM"] = np.mean(user_data_PM["choice"] == 1) # proportion of win-stay/loose-shift choices user_results_PM["prop_WSLS_1"] = np.mean( @@ -173,25 +183,38 @@ def clean_data(self): Participant-level: - Same choice on >= 95% of trials - - Median RT > 10000 ms + - Missing accuracy data + - Participants with more than 72 trials (due to technical error) """ - combined_results = pd.concat([self.results_MW, self.results_PM], ignore_index=True) - combined_results["userID"] = combined_results["userID"].apply(lambda x: x.iloc[0] if isinstance(x, pd.Series) else x) - nr_part_before = len(pd.unique(combined_results["userID"])) + nr_part_before_MW = len(self.results_MW["userID"].unique()) + nr_part_before_PM = len(self.results_PM["userID"].unique()) - self.results_MW = self.results_MW[self.results_MW["prop_same_choice_MW"] < 0.95] - self.results_PM = self.results_PM[self.results_PM["prop_same_choice_PM"] < 0.95] + #created cleaned verison of results object but also keep old object for comparison + self.cleanedresults_MW = self.results_MW.copy() + self.cleanedresults_PM = self.results_PM.copy() - self.results_MW = self.results_MW[self.results_MW["median_RT_MW"] < 10000] - self.results_PM = self.results_PM[self.results_PM["median_RT_PM"] < 10000] + #Remove users with more than 72 trials + valid_user_ids = self.MW_data.groupby("userID")["userID"].transform("count") <= 72 + user_ids_to_keep = self.MW_data.loc[valid_user_ids, "userID"].unique() + self.cleanedresults_MW = self.cleanedresults_MW[self.cleanedresults_MW["userID"].isin(user_ids_to_keep)] + + valid_user_ids = self.PM_data.groupby("userID")["userID"].transform("count") <= 72 + user_ids_to_keep = self.PM_data.loc[valid_user_ids, "userID"].unique() + self.cleanedresults_PM = self.cleanedresults_PM[self.cleanedresults_PM["userID"].isin(user_ids_to_keep)] - combined_after = pd.concat([self.results_MW, self.results_PM], ignore_index=True) - combined_after["userID"] = combined_after["userID"].apply(lambda x: x.iloc[0] if isinstance(x, pd.Series) else x) + #Remove users with at least 95% same choice + self.cleanedresults_MW = self.cleanedresults_MW[(self.cleanedresults_MW["prop_choice1_MW"] > 0.05) & (self.cleanedresults_MW["prop_choice1_MW"] < 0.95) ] + self.cleanedresults_PM = self.cleanedresults_PM[(self.cleanedresults_PM["prop_choice1_PM"] > 0.05) & (self.cleanedresults_PM["prop_choice1_PM"] < 0.95) ] + + #remove users with missing accuracy + self.cleanedresults_MW = self.cleanedresults_MW[self.cleanedresults_MW["accuracy_MW"].notna()] + self.cleanedresults_PM = self.cleanedresults_PM[self.cleanedresults_PM["accuracy_PM"].notna()] - self.deleted_participants += nr_part_before - len(pd.unique(combined_after["userID"])) + self.deleted_participants_MW = nr_part_before_MW - len(pd.unique(self.cleanedresults_MW["userID"])) + self.deleted_participants_PM = nr_part_before_PM - len(pd.unique(self.cleanedresults_PM["userID"])) def codebook(self): """ Create a codebook for the data. """ - return self.codebook \ No newline at end of file + return self.codebook \ No newline at end of file diff --git a/cpm/brainexplorer/scavenger.py b/cpm/brainexplorer/scavenger.py index aee0ba8..03d34c9 100644 --- a/cpm/brainexplorer/scavenger.py +++ b/cpm/brainexplorer/scavenger.py @@ -54,21 +54,12 @@ def __init__(self, filepath=None): Trial-level exclusion criteria: - Reaction time < 150 or > 10000 ms - Attempts after the first attempt ("run" variable) - - Participants with more than 40 trials (due to technical error) - - Participants with configID = 242 """ self.data = pd.read_csv(filepath, header=0) - nr_participants_before = len(self.data["userID"].unique()) - self.data = self.data[self.data["run"] == 1] # only keep first attempt - self.data = self.data[self.data["RT"] > 150] # only keep trials with reaction time > 150 ms - self.data = self.data[self.data["RT"] < 10000] # only keep trials with reaction time < 10000 ms - self.data = self.data[self.data.groupby("userID")["userID"].transform('count') < 40] # only keep participants with less than 40 trials - self.data = self.data[self.data["config_id"] != 242] - - nr_participants_after = len(self.data["userID"].unique()) - self.deleted_participants = nr_participants_before - nr_participants_after + self.data = self.data[self.data["RT"] >= 150] # only keep trials with reaction time > 150 ms + self.data = self.data[self.data["RT"] <= 10000] # only keep trials with reaction time < 10000 ms self.results = pd.DataFrame() self.group_results = pd.DataFrame() @@ -378,15 +369,29 @@ def clean_data(self): - Same choice safe/risky on >= 95% of trials - Same choice left/right on >= 95% of trials - Fail >= 50% of catch trials + - Participants with more than 40 trials (due to technical error) + - Participants with configID = 242 """ nr_part_before = len(self.results["userID"].unique()) - self.results = self.results[self.results["median_RT"] < 5000] - self.results = self.results[self.results["rational_catchTrials"] >= 0.5] - self.results = self.results[self.results["risky_choices"] < 0.95] - self.results = self.results[self.results["chosen_left_all"] < 0.95] + self.cleanedresults = self.results.copy() + + user_id_counts = self.data["userID"].value_counts() + user_ids_to_keep = user_id_counts[user_id_counts <= 40].index + self.cleanedresults = self.cleanedresults[self.cleanedresults["userID"].isin(user_ids_to_keep)] + + user_ids_to_exclude = ( + self.data.loc[self.data["config_id"] == 242, "userID"].unique() + ) + user_ids_to_keep = [uid for uid in self.data["userID"].unique() if uid not in user_ids_to_exclude] + self.cleanedresults = self.cleanedresults[self.cleanedresults["userID"].isin(user_ids_to_keep)] + + self.cleanedresults = self.cleanedresults[self.cleanedresults["median_RT"] < 5000] + self.cleanedresults = self.cleanedresults[self.cleanedresults["rational_catchTrials"] > 0.5] + self.cleanedresults = self.cleanedresults[self.cleanedresults["risky_choices"] < 0.95] + self.cleanedresults = self.cleanedresults[self.cleanedresults["chosen_left_all"] < 0.95] - self.deleted_participants += nr_part_before - len(self.results["userID"].unique()) + self.deleted_participants = nr_part_before - len(self.cleanedresults["userID"].unique()) def codebook(self): """ diff --git a/cpm/brainexplorer/spaceObserver.py b/cpm/brainexplorer/spaceObserver.py index 88bf563..c4809e8 100644 --- a/cpm/brainexplorer/spaceObserver.py +++ b/cpm/brainexplorer/spaceObserver.py @@ -55,50 +55,25 @@ def __init__(self, filepath=None): - Attempts after the first attempt ("run" variable) - Reaction time < 150 or > 10000 ms - Reaction time of confidence < 150 or > 10000 ms - - practice trials: if more than 8 subsequent trials where confidence data is missing, it is a practice run and will be excluded. - Trials with missing confidence data - - Participants with more than 80 trials (due to technical error) """ ## read data self.data = pd.read_csv(filepath, header=0, na_values=["NaN", "nan"]) - nr_part_before = len(self.data["userID"].unique()) - self.data = self.data[self.data["run"] == 1] # only keep first attempt - self.data = self.data[self.data["choiceRT"] > 150] # only keep trials with reaction time > 150 ms - self.data = self.data[self.data["choiceRT"] < 10000] # only keep trials with reaction time < 10000 ms - self.data = self.data[self.data["confidenceRT"] > 150] # only keep trials with confidence reaction time > 150 ms - self.data = self.data[self.data["confidenceRT"] < 10000] # only keep trials with confidence reaction time < 10000 ms + self.data = self.data[self.data["choiceRT"] >= 150] # only keep trials with reaction time > 150 ms + self.data = self.data[self.data["choiceRT"] <= 10000] # only keep trials with reaction time < 10000 ms + self.data = self.data[self.data["confidenceRT"] >= 150] # only keep trials with confidence reaction time > 150 ms + self.data = self.data[self.data["confidenceRT"] <= 10000] # only keep trials with confidence reaction time < 10000 ms self.data = self.data[self.data["confidence"].notna()] # only keep trials with confidence data - self.data = self.data[self.data.groupby("userID")["userID"].transform('count') < 80] # only keep participants with less than 80 trials - - nr_part_after = len(self.data["userID"].unique()) - - self.deleted_participants = nr_part_before - nr_part_after - - # delete practice trials - # if > 8 subseequent trials where confidence data is missing it is practice run - + self.data["confidence"] = self.data["confidence"].replace( ["NaN", "nan", "NAN", ""], pd.NA ) self.data["confidence"] = pd.to_numeric(self.data["confidence"], errors='coerce') - print(len(self.data)) - - self.data = self.data.groupby(["userID", "round"]).filter( - lambda run: ( - ( - (run["confidence"].isna() != run["confidence"].isna().shift()).cumsum() - .where(run["confidence"].isna()) - .value_counts() - .max() - ) if run["confidence"].isna().any() else 0 - ) < 9 - ) - self.results = pd.DataFrame() self.codebook = { "userID": "Unique identifier for each participant", @@ -213,6 +188,8 @@ def metrics(self, mode=None): ES_4 = np.mean(bins[3]) if len(bins[3]) > 0 else np.nan user_results["ES_bins"] = np.array([ES_1, ES_2, ES_3, ES_4]) + self.results["ES_total_mean"] = np.mean(evidence_strength) + # Append user-level results self.results = pd.concat( [self.results, pd.DataFrame([user_results])], ignore_index=True @@ -232,18 +209,29 @@ def clean_data(self): - Median reaction time > 5000 ms - Median reaction time for confidence ratings > 5000 ms - Median evidence strength > 25 - - Mean confidence > 97 + - Mean confidence > 97 + - Participants with more than 80 trials (due to technical error) """ nr_part_before = len(self.results["userID"].unique()) - self.results = self.results[self.results["mean_accuracy"] >= 0.5] - self.results = self.results[self.results["median_RT"] < 5000] - self.results = self.results[self.results["median_confidenceRT"] < 5000] - self.results = self.results[self.results["ES_bins"].apply(lambda x: np.nanmean(x) < 25)] - self.results = self.results[self.results["mean_confidence"] < 97] + self.cleanedresults = self.results.copy() + + users_80trials = set( + self.data.loc[ + self.data.groupby("userID")["userID"].transform('count') <= 80, + "userID" + ].unique() + ) + self.cleanedresults = self.cleanedresults[self.cleanedresults["userID"].isin(users_80trials)] + + self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_accuracy"] >= 0.5] + self.cleanedresults = self.cleanedresults[self.cleanedresults["median_RT"] <= 3000] + self.cleanedresults = self.cleanedresults[self.cleanedresults["median_confidenceRT"] <= 3000] + self.cleanedresults = self.cleanedresults[self.cleanedresults["ES_total_mean"] <= 25] + self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_confidence"] <= 97] - self.deleted_participants += nr_part_before - len(self.results["userID"].unique()) + self.deleted_participants = nr_part_before - len(self.cleanedresults["userID"].unique()) def codebook(self): """ diff --git a/cpm/brainexplorer/treasurehunt.py b/cpm/brainexplorer/treasurehunt.py index 3b327a3..66a4896 100644 --- a/cpm/brainexplorer/treasurehunt.py +++ b/cpm/brainexplorer/treasurehunt.py @@ -53,26 +53,12 @@ def __init__(self, filepath=None): Trial-level exclusion criteria: - attempts after the first attempt ("run" variable) - - < 3 unique values in confidence rating - - < 3 unique values in number of samples """ self.data = pd.read_csv(filepath, header=0) - nr_part_before = len(self.data["userID"].unique()) - self.data = self.data[self.data["run"] == 1] # only keep first attempt - # Filter out userIDs where all trials have less than 3 unique values for confidence or draws - valid_users = self.data.groupby("userID").filter( - lambda group: group["confidence"].nunique() > 3 and group["draws"].nunique() > 3 - )["userID"].unique() - self.data = self.data[self.data["userID"].isin(valid_users)] - - nr_part_after = len(self.data["userID"].unique()) - - self.deleted_participants = nr_part_before - nr_part_after - self.results = pd.DataFrame() self.codebook = { @@ -118,13 +104,12 @@ def metrics(self): - totevminus_coef: coefficient for the previous total evidence in the regression model - deltaev_coef: coefficient for the change in total evidence in the regression model """ + # turn string fields into list of ints + self.data["ev"] = self.data["ev"].apply(lambda x: [int(i) for i in x.strip("[]").split(" ")]) # Group data by userID grouped_data = self.data.groupby("userID") - # turn string fields into list of ints - self.data["ev"] = self.data["ev"].apply(lambda x: [int(i) for i in x.strip("[]").split(" ")]) - # Loop through each group of user data for user_id, user_data in grouped_data: @@ -212,22 +197,37 @@ def clean_data(self): - Mean number of draws < 2 or > 23 - Make choice not in line with current evidence in >= 20% of trials - Mean confidence < 15 or > 98 - + - < 3 unique values in confidence rating + - < 3 unique values in number of samples """ nr_part_before = len(self.results["userID"].unique()) - self.results = self.results[self.results["mean_n_draws"] > 2] # only keep participants with mean number of draws > 2 - self.results = self.results[self.results["mean_n_draws"] < 23] # only keep participants with mean number of draws < 23 + self.cleanedresults = self.results.copy() + + # Filter out participants who have less than 3 unique values for confidence + valid_users = self.data.groupby("userID").filter( + lambda group: group["confidence"].nunique() >= 3 + )["userID"].unique() + self.cleanedresults = self.cleanedresults[self.cleanedresults["userID"].isin(valid_users)] + + # Filter out participants who have less than 3 unique values for draws + valid_users = self.data.groupby("userID").filter( + lambda group: group["draws"].nunique() >= 3 + )["userID"].unique() + self.cleanedresults = self.cleanedresults[self.cleanedresults["userID"].isin(valid_users)] + + self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_n_draws"] >= 2] # only keep participants with mean number of draws > 2 + self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_n_draws"] <= 23] # only keep participants with mean number of draws < 23 - self.results = self.results[self.results["mean_confidence"] > 15] # only keep participants with mean confidence > 15 - self.results = self.results[self.results["mean_confidence"] < 98] # only keep participants with mean confidence < 98 + self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_confidence"] >= 15] # only keep participants with mean confidence > 15 + self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_confidence"] <= 98] # only keep participants with mean confidence < 98 - self.results = self.results[self.results["mean_chosEv"] >= 0.8] # only keep participants with mean choice in line with current evidence >= 80% + self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_chosEv"] > 0.8] # only keep participants with mean choice in line with current evidence >= 80% - self.deleted_participants += nr_part_before - len(self.results["userID"].unique()) + self.deleted_participants = nr_part_before - len(self.cleanedresults["userID"].unique()) def codebook(self): """ Return a codebook describing the metrics. """ - return self.codebook + return self.codebook \ No newline at end of file From 10facce32200b94523e0b1250715a3d127252785 Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Tue, 10 Jun 2025 12:45:11 +0200 Subject: [PATCH 29/54] add MFMB data loading --- cpm/applications/reinforcement_learning.py | 2 +- cpm/datasets/__init__.py | 3 +- cpm/datasets/base.py | 29 + cpm/datasets/data/GH_example_data.csv | 16491 ----------------- cpm/datasets/data/model_based_model_free.csv | 16491 +++++++++++++++++ 5 files changed, 16523 insertions(+), 16493 deletions(-) delete mode 100644 cpm/datasets/data/GH_example_data.csv create mode 100644 cpm/datasets/data/model_based_model_free.csv diff --git a/cpm/applications/reinforcement_learning.py b/cpm/applications/reinforcement_learning.py index 5d8f34d..013063a 100644 --- a/cpm/applications/reinforcement_learning.py +++ b/cpm/applications/reinforcement_learning.py @@ -283,7 +283,7 @@ def MF_MB_model(parameters, trial): s1 = trial["s"][0] # first choice stage s2 = trial["s"][1] # second choice stage - action = self.data["choice"] + action = trial["choice"] a = action[0] - (s1 == 2) * 2 # choose which weight to update based on stake of trial diff --git a/cpm/datasets/__init__.py b/cpm/datasets/__init__.py index d9ea909..35d69c1 100644 --- a/cpm/datasets/__init__.py +++ b/cpm/datasets/__init__.py @@ -1,3 +1,4 @@ from .base import load_bandit_data from .base import load_risky_choices -from .base import load_csv \ No newline at end of file +from .base import load_csv +from .base import load_model_based_model_free \ No newline at end of file diff --git a/cpm/datasets/base.py b/cpm/datasets/base.py index 1dc228c..b3c50aa 100644 --- a/cpm/datasets/base.py +++ b/cpm/datasets/base.py @@ -86,3 +86,32 @@ def load_risky_choices(): """ return load_csv("risky_choices.csv") + +def load_model_based_model_free(): + """ + Load the risky choices dataset from a CSV file. + + Returns + ------- + pd.DataFrame + A pandas DataFrame containing the risky choices data. + + Examples + -------- + >>> data = load_model_based_model_free() + >>> print(data.head()) + + Notes + ----- + The dataset will include the following columns: + + - userID: unique identifier for each participant + - stimuli: which planets presented in stage of the trial + - s: the state of the trial + - rews: the rewards for each stimulus / planet + - choice: the choice made by the participant + - stake: the stake of the trial (1 or 5) + - points: the points received in the trial + + """ + return load_csv("model_based_model_free.csv") diff --git a/cpm/datasets/data/GH_example_data.csv b/cpm/datasets/data/GH_example_data.csv deleted file mode 100644 index 0cf68a2..0000000 --- a/cpm/datasets/data/GH_example_data.csv +++ /dev/null @@ -1,16491 +0,0 @@ -userID,trial,stimuli,rews,s,block,stake,choice,points_real,points,planet,theor_best_choice,RT -1,1,"[3, 4]","[70, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[8320] -1,2,"[1, 2]","[60, 0]","[1, 2]",[1],[1],[2],[0],[0],[2],[1],[4211] -1,3,"[2, 1]","[10, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2082] -1,4,"[4, 3]","[10, 90]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[2236] -1,5,"[3, 4]","[70, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2182] -1,6,"[3, 4]","[80, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2714] -1,7,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[3044] -1,8,"[1, 2]","[70, 90]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2853] -1,9,"[1, 2]","[50, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2631] -1,10,"[1, 2]","[30, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2315] -1,11,"[3, 4]","[30, 80]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[2540] -1,12,"[3, 4]","[20, 70]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1876] -1,13,"[3, 4]","[10, 40]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[1858] -1,14,"[4, 3]","[30, 10]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[1926] -1,15,"[3, 4]","[20, 10]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[2046] -1,16,"[3, 4]","[20, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2100] -1,17,"[1, 2]","[20, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[2241] -1,18,"[2, 1]","[20, 30]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[1884] -1,19,"[1, 2]","[40, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[2333] -1,20,"[3, 4]","[50, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2882] -1,21,"[1, 2]","[30, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2058] -1,22,"[3, 4]","[30, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2607] -1,23,"[3, 4]","[60, 0]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[2987] -1,24,"[3, 4]","[60, 30]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1943] -1,25,"[1, 2]","[70, 20]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[3238] -1,26,"[2, 1]","[50, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[3166] -1,27,"[4, 3]","[80, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[2232] -1,28,"[3, 4]","[80, 70]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2032] -1,29,"[2, 1]","[80, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2215] -1,30,"[1, 2]","[60, 80]","[1, 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1]",[1],[5],[3],[350],[7],[1],[4],[4095] -5,5,"[4, 3]","[50, 90]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2113] -5,6,"[3, 4]","[80, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[2246] -5,7,"[3, 4]","[60, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[3729] -5,8,"[3, 4]","[50, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[2096] -5,9,"[4, 3]","[10, 70]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[1745] -5,10,"[1, 2]","[60, 20]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[3745] -5,11,"[1, 2]","[60, 10]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2521] -5,12,"[1, 2]","[30, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[1],[1873] -5,13,"[2, 1]","[0, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[3255] -5,14,"[1, 2]","[20, 0]","[1, 2]",[1],[5],[2],[0],[0],[2],[1],[3378] -5,15,"[1, 2]","[30, 10]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[1822] -5,16,"[2, 1]","[20, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[3021] -5,17,"[4, 3]","[10, 40]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[3705] -5,18,"[4, 3]","[20, 30]","[2, 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2]",[1],[1],[4],[70],[7],[2],[4],[1940] -6,27,"[2, 1]","[40, 30]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[1956] -6,28,"[1, 2]","[50, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[1707] -6,29,"[3, 4]","[20, 30]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[2474] -6,30,"[4, 3]","[30, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[2090] -6,31,"[4, 3]","[10, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[2425] -6,32,"[4, 3]","[10, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[1874] -6,33,"[4, 3]","[0, 20]","[2, 2]",[1],[1],[4],[0],[0],[2],[4],[1939] -6,34,"[1, 2]","[10, 0]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[2790] -6,35,"[2, 1]","[10, 0]","[1, 1]",[1],[5],[1],[0],[0],[1],[1],[1805] -6,36,"[3, 4]","[40, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[2257] -6,37,"[4, 3]","[10, 50]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[2756] -6,38,"[2, 1]","[20, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1888] -6,39,"[3, 4]","[60, 20]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1839] -6,40,"[3, 4]","[60, 10]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1938] -6,41,"[2, 1]","[20, 60]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[4281] -6,42,"[1, 2]","[90, 40]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[2158] -6,43,"[2, 1]","[50, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1773] -6,44,"[1, 2]","[80, 40]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1706] -6,45,"[1, 2]","[60, 50]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1907] -6,46,"[2, 1]","[40, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[1740] -6,47,"[4, 3]","[30, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[2407] -6,48,"[1, 2]","[0, 10]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[1857] -6,49,"[3, 4]","[10, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[2107] -6,50,"[1, 2]","[30, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2040] -6,51,"[2, 1]","[10, 60]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[1723] -6,52,"[4, 3]","[0, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2157] -6,53,"[2, 1]","[30, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2057] -6,54,"[4, 3]","[30, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1741] -6,55,"[1, 2]","[10, 20]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[2339] -6,56,"[1, 2]","[20, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[1743] -6,57,"[4, 3]","[20, 0]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2241] -6,58,"[1, 2]","[0, 10]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[2090] -6,59,"[4, 3]","[20, 10]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2255] -6,60,"[1, 2]","[0, 0]","[1, 2]",[2],[5],[2],[0],[0],[2],[1],[1973] -6,61,"[3, 4]","[10, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[2057] -6,62,"[2, 1]","[10, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[1990] -6,63,"[4, 3]","[10, 0]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[3674] -6,64,"[3, 4]","[20, 30]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[3172] -6,65,"[4, 3]","[90, 30]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[2508] -6,66,"[3, 4]","[40, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2074] -6,67,"[1, 2]","[70, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[1824] -6,68,"[3, 4]","[60, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[1925] -6,69,"[3, 4]","[80, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[2557] -6,70,"[3, 4]","[80, 30]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1790] -6,71,"[1, 2]","[70, 50]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[1838] -6,72,"[3, 4]","[60, 70]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1758] -6,73,"[3, 4]","[30, 40]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[1790] -6,74,"[2, 1]","[60, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[2256] -6,75,"[1, 2]","[40, 50]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[1906] -6,76,"[2, 1]","[40, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[1906] -6,77,"[1, 2]","[20, 40]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[1756] -6,78,"[2, 1]","[60, 30]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2123] -6,79,"[4, 3]","[60, 30]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2058] -6,80,"[1, 2]","[20, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[1774] -7,1,"[1, 2]","[40, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[80243] -7,2,"[3, 4]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[2006] -7,3,"[2, 1]","[40, 50]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[2299] -7,4,"[2, 1]","[40, 80]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[1909] -7,5,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1804] -7,6,"[4, 3]","[50, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2256] -7,7,"[2, 1]","[20, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[4031] -7,8,"[2, 1]","[20, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[2210] -7,9,"[4, 3]","[50, 60]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2109] -7,10,"[3, 4]","[80, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[1842] -7,11,"[4, 3]","[60, 90]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[4710] -7,12,"[3, 4]","[80, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[4062] -7,13,"[1, 2]","[60, 80]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2326] -7,14,"[4, 3]","[60, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[3909] -7,15,"[2, 1]","[50, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[4223] -7,16,"[4, 3]","[50, 30]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[4423] -7,17,"[3, 4]","[30, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2741] -7,18,"[1, 2]","[70, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[4279] -7,19,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[3983] -7,20,"[3, 4]","[90, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[2320] -7,21,"[4, 3]","[50, 80]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2700] -7,22,"[2, 1]","[30, 90]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[3759] -7,23,"[2, 1]","[0, 80]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[1897] -7,24,"[4, 3]","[10, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[2075] -7,25,"[3, 4]","[10, 0]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[3547] -7,26,"[3, 4]","[30, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[5970] -7,27,"[1, 2]","[80, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[4993] -7,28,"[4, 3]","[60, 50]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2692] -7,29,"[3, 4]","[20, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2001] -7,30,"[1, 2]","[60, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[3969] -7,31,"[1, 2]","[80, 90]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2600] -7,32,"[3, 4]","[90, 60]","[2, 1]",[1],[1],[3],[90],[9],[1],[3],[2946] -7,33,"[2, 1]","[70, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2722] -7,34,"[1, 2]","[50, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[3775] -7,35,"[1, 2]","[70, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2106] -7,36,"[4, 3]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[4344] -7,37,"[4, 3]","[70, 60]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2114] -7,38,"[2, 1]","[70, 60]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[3012] -7,39,"[3, 4]","[70, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[3751] -7,40,"[4, 3]","[70, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[2092] -7,41,"[4, 3]","[70, 90]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[13292] -7,42,"[4, 3]","[80, 50]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2007] -7,43,"[2, 1]","[50, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[2720] -7,44,"[3, 4]","[60, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[3081] -7,45,"[1, 2]","[70, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2659] -7,46,"[1, 2]","[30, 20]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[2578] -7,47,"[2, 1]","[10, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[2334] -7,48,"[3, 4]","[40, 30]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[3061] -7,49,"[1, 2]","[60, 0]","[1, 2]",[2],[5],[2],[0],[0],[2],[1],[3422] -7,50,"[4, 3]","[20, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[3423] -7,51,"[1, 2]","[90, 10]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[4595] -7,52,"[3, 4]","[60, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[3396] -7,53,"[4, 3]","[40, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[2537] -7,54,"[1, 2]","[50, 60]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[3915] -7,55,"[3, 4]","[70, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[3127] -7,56,"[2, 1]","[60, 50]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[3053] -7,57,"[4, 3]","[50, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2798] -7,58,"[1, 2]","[50, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[3338] -7,59,"[3, 4]","[60, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[1876] -7,60,"[1, 2]","[40, 90]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[3888] -7,61,"[4, 3]","[70, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[3156] -7,62,"[3, 4]","[20, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[2028] -7,63,"[3, 4]","[50, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2164] -7,64,"[2, 1]","[50, 90]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[6061] -7,65,"[3, 4]","[80, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[4119] -7,66,"[2, 1]","[60, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[4454] -7,67,"[4, 3]","[90, 40]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[1800] -7,68,"[1, 2]","[50, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[3153] -7,69,"[4, 3]","[20, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[2492] -7,70,"[2, 1]","[20, 10]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[2852] -7,71,"[2, 1]","[40, 10]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[3092] -7,72,"[3, 4]","[20, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2094] -7,73,"[4, 3]","[50, 30]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1995] -7,74,"[1, 2]","[40, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[3189] -7,75,"[1, 2]","[30, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2206] -7,76,"[1, 2]","[50, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[3424] -7,77,"[3, 4]","[30, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2134] -7,78,"[2, 1]","[40, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[1957] -7,79,"[1, 2]","[10, 20]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[2932] -7,80,"[2, 1]","[0, 0]","[1, 2]",[2],[5],[2],[0],[0],[2],[2],[3944] -8,1,"[4, 3]","[40, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[7275] -8,2,"[2, 1]","[40, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2362] -8,3,"[4, 3]","[90, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2147] -8,4,"[3, 4]","[80, 90]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[2233] -8,5,"[4, 3]","[70, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2101] -8,6,"[2, 1]","[40, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2395] -8,7,"[4, 3]","[30, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[2453] -8,8,"[3, 4]","[40, 10]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2391] -8,9,"[2, 1]","[10, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[4577] -8,10,"[3, 4]","[40, 0]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[2217] -8,11,"[3, 4]","[40, 20]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2333] -8,12,"[4, 3]","[0, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[2201] -8,13,"[1, 2]","[60, 10]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2747] -8,14,"[1, 2]","[50, 20]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2231] -8,15,"[3, 4]","[30, 10]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[2190] -8,16,"[1, 2]","[20, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[1],[3095] -8,17,"[2, 1]","[30, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[1894] -8,18,"[1, 2]","[0, 20]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[2739] -8,19,"[3, 4]","[0, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[1985] -8,20,"[1, 2]","[10, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[2491] -8,21,"[3, 4]","[30, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2135] -8,22,"[4, 3]","[70, 50]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2052] -8,23,"[4, 3]","[70, 60]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2211] -8,24,"[1, 2]","[50, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2117] -8,25,"[2, 1]","[70, 40]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2039] -8,26,"[3, 4]","[20, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2270] -8,27,"[4, 3]","[40, 10]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2142] -8,28,"[4, 3]","[70, 0]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2276] -8,29,"[4, 3]","[30, 10]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2133] -8,30,"[3, 4]","[30, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2036] -8,31,"[1, 2]","[0, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[2265] -8,32,"[2, 1]","[40, 20]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2013] -8,33,"[1, 2]","[30, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[2134] -8,34,"[2, 1]","[20, 30]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[2033] -8,35,"[4, 3]","[30, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[2035] -8,36,"[1, 2]","[10, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[3464] -8,37,"[2, 1]","[30, 0]","[1, 1]",[1],[5],[1],[0],[0],[1],[1],[2885] -8,38,"[2, 1]","[70, 30]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1948] -8,39,"[3, 4]","[70, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[2304] -8,40,"[1, 2]","[60, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[1984] -8,41,"[2, 1]","[80, 70]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[9219] -8,42,"[1, 2]","[80, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1908] -8,43,"[2, 1]","[30, 70]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2600] -8,44,"[1, 2]","[90, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1941] -8,45,"[4, 3]","[80, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2057] -8,46,"[4, 3]","[90, 70]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[2121] -8,47,"[2, 1]","[70, 60]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2089] -8,48,"[2, 1]","[50, 70]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[2198] -8,49,"[2, 1]","[80, 60]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[2257] -8,50,"[4, 3]","[90, 40]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[2089] -8,51,"[4, 3]","[80, 20]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2106] -8,52,"[3, 4]","[0, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[2017] -8,53,"[2, 1]","[30, 0]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2040] -8,54,"[1, 2]","[30, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[2102] -8,55,"[1, 2]","[20, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[2005] -8,56,"[3, 4]","[20, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2010] -8,57,"[2, 1]","[40, 60]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2280] -8,58,"[4, 3]","[50, 40]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2219] -8,59,"[2, 1]","[20, 50]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[2679] -8,60,"[4, 3]","[50, 60]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[2396] -8,61,"[2, 1]","[40, 60]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[4552] -8,62,"[1, 2]","[40, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[3181] -8,63,"[1, 2]","[20, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[2274] -8,64,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2251] -8,65,"[3, 4]","[20, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2089] -8,66,"[2, 1]","[10, 20]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2297] -8,67,"[3, 4]","[40, 10]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1994] -8,68,"[3, 4]","[30, 10]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[2112] -8,69,"[2, 1]","[10, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2142] -8,70,"[4, 3]","[0, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[2848] -8,71,"[2, 1]","[0, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[2168] -8,72,"[2, 1]","[0, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[2045] -8,73,"[4, 3]","[10, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[3350] -8,74,"[4, 3]","[20, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[2182] -8,75,"[3, 4]","[10, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2189] -8,76,"[4, 3]","[30, 10]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[3743] -8,77,"[1, 2]","[40, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[2075] -8,78,"[3, 4]","[40, 10]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1889] -8,79,"[4, 3]","[10, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[1992] -8,80,"[1, 2]","[20, 0]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[2187] -8,1,"[4, 3]","[70, 20]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[4921] -8,2,"[1, 2]","[10, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2359] -8,3,"[2, 1]","[50, 0]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2057] -8,4,"[3, 4]","[30, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[1967] -8,5,"[1, 2]","[30, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[1748] -8,6,"[4, 3]","[80, 0]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2047] -8,7,"[1, 2]","[0, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1764] -8,8,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[1852] -8,9,"[2, 1]","[70, 0]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1882] -8,10,"[1, 2]","[50, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1785] -8,11,"[1, 2]","[50, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[1792] -8,12,"[1, 2]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2008] -8,13,"[3, 4]","[20, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[1995] -8,14,"[1, 2]","[40, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2320] -8,15,"[3, 4]","[50, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2132] -8,16,"[3, 4]","[0, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1804] -8,17,"[1, 2]","[40, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[1977] -8,18,"[3, 4]","[60, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[3048] -8,19,"[2, 1]","[60, 80]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2177] -8,20,"[1, 2]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1846] -8,21,"[1, 2]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1867] -8,22,"[1, 2]","[70, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2088] -8,23,"[1, 2]","[70, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[1759] -8,24,"[4, 3]","[30, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[4321] -8,25,"[2, 1]","[50, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2018] -8,26,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1897] -8,27,"[3, 4]","[80, 60]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2078] -8,28,"[4, 3]","[20, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1970] -8,29,"[4, 3]","[0, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1924] -8,30,"[3, 4]","[60, 30]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2040] -8,31,"[2, 1]","[40, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2072] -8,32,"[4, 3]","[50, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[1980] -8,33,"[4, 3]","[80, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1879] -8,34,"[1, 2]","[30, 70]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[1957] -8,35,"[3, 4]","[50, 70]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[1970] -8,36,"[1, 2]","[40, 60]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1922] -8,37,"[2, 1]","[80, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[1865] -8,38,"[2, 1]","[80, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1890] -8,39,"[1, 2]","[40, 60]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1974] -8,40,"[2, 1]","[70, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[1951] -8,41,"[1, 2]","[50, 70]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2826] -8,42,"[3, 4]","[50, 80]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[1888] -8,43,"[4, 3]","[70, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1832] -8,44,"[1, 2]","[0, 70]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[2032] -8,45,"[4, 3]","[80, 0]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1877] -8,46,"[3, 4]","[10, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1874] -8,47,"[2, 1]","[70, 10]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[1978] -8,48,"[4, 3]","[60, 30]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2095] -8,49,"[4, 3]","[30, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2010] -8,50,"[1, 2]","[10, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[1885] -8,51,"[1, 2]","[10, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1837] -8,52,"[2, 1]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[1979] -8,53,"[3, 4]","[30, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[2050] -8,54,"[3, 4]","[40, 20]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1853] -8,55,"[2, 1]","[0, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[2078] -8,56,"[1, 2]","[0, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1822] -8,57,"[2, 1]","[30, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[1870] -8,58,"[4, 3]","[30, 40]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1964] -8,59,"[1, 2]","[20, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[1969] -8,60,"[1, 2]","[30, 20]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[1911] -8,61,"[1, 2]","[0, 80]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[1993] -8,62,"[2, 1]","[70, 0]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1943] -8,63,"[4, 3]","[90, 50]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[2016] -8,64,"[4, 3]","[50, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[1896] -8,65,"[3, 4]","[20, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1885] -8,66,"[3, 4]","[20, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1868] -8,67,"[1, 2]","[70, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[1961] -8,68,"[3, 4]","[40, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1787] -8,69,"[4, 3]","[80, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2040] -8,70,"[2, 1]","[60, 80]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2052] -8,71,"[3, 4]","[90, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1866] -8,72,"[4, 3]","[70, 80]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[2140] -8,73,"[1, 2]","[60, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2778] -8,74,"[3, 4]","[70, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1870] -8,75,"[2, 1]","[80, 50]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[1922] -8,76,"[3, 4]","[60, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[1774] -8,77,"[2, 1]","[80, 90]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[1894] -8,78,"[4, 3]","[80, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2309] -8,79,"[4, 3]","[50, 60]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[2004] -8,80,"[2, 1]","[80, 90]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[2212] -9,1,"[1, 2]","[30, 60]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[3930] -9,2,"[3, 4]","[40, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[1921] -9,3,"[1, 2]","[50, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1933] -9,4,"[4, 3]","[60, 80]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2045] -9,5,"[4, 3]","[80, 90]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[1975] -9,6,"[1, 2]","[80, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[1928] -9,7,"[2, 1]","[80, 90]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2030] -9,8,"[1, 2]","[90, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[1893] -9,9,"[2, 1]","[70, 40]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2226] -9,10,"[4, 3]","[30, 40]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[1952] -9,11,"[1, 2]","[40, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[1815] -9,12,"[4, 3]","[60, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[1905] -9,13,"[2, 1]","[70, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[1837] -9,14,"[4, 3]","[60, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1766] -9,15,"[1, 2]","[10, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[1719] -9,16,"[1, 2]","[0, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[1955] -9,17,"[2, 1]","[10, 50]","[1, 2]",[1],[1],[2],[10],[1],[2],[2],[1876] -9,18,"[1, 2]","[50, 20]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[1913] -9,19,"[2, 1]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[1730] -9,20,"[4, 3]","[40, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2021] -9,21,"[1, 2]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[1896] -9,22,"[1, 2]","[10, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[2],[1722] -9,23,"[4, 3]","[0, 0]","[2, 2]",[1],[5],[4],[0],[0],[2],[4],[1966] -9,24,"[2, 1]","[10, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1779] -9,25,"[2, 1]","[20, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1698] -9,26,"[1, 2]","[10, 0]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[1803] -9,27,"[2, 1]","[10, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[1673] -9,28,"[4, 3]","[30, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[3],[1698] -9,29,"[1, 2]","[0, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[1776] -9,30,"[4, 3]","[20, 0]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1944] -9,31,"[3, 4]","[40, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[1682] -9,32,"[2, 1]","[20, 10]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[1878] -9,33,"[2, 1]","[20, 10]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[1963] -9,34,"[3, 4]","[20, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[1771] -9,35,"[1, 2]","[0, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[2426] -9,36,"[2, 1]","[70, 10]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1782] -9,37,"[2, 1]","[90, 30]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[1726] -9,38,"[4, 3]","[90, 30]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2249] -9,39,"[3, 4]","[20, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[1722] -9,40,"[2, 1]","[80, 10]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[2083] -9,41,"[4, 3]","[90, 0]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[7587] -9,42,"[2, 1]","[70, 10]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2244] -9,43,"[2, 1]","[70, 20]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1705] -9,44,"[3, 4]","[0, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1834] -9,45,"[1, 2]","[10, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[1825] -9,46,"[1, 2]","[30, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[1768] -9,47,"[2, 1]","[60, 20]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[1827] -9,48,"[3, 4]","[20, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[1898] -9,49,"[2, 1]","[70, 0]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[8813] -9,50,"[4, 3]","[90, 0]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[6082] -9,51,"[2, 1]","[80, 0]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2649] -9,52,"[1, 2]","[10, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2286] -9,53,"[4, 3]","[80, 40]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1975] -9,54,"[1, 2]","[30, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[6609] -9,55,"[4, 3]","[60, 40]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[1989] -9,56,"[4, 3]","[60, 10]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2030] -9,57,"[3, 4]","[50, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[1766] -9,58,"[3, 4]","[40, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1723] -9,59,"[2, 1]","[30, 0]","[1, 1]",[2],[1],[1],[0],[0],[1],[1],[2082] -9,60,"[2, 1]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1728] -9,61,"[1, 2]","[20, 30]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[1853] -9,62,"[3, 4]","[10, 40]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[2335] -9,63,"[2, 1]","[40, 20]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2250] -9,64,"[4, 3]","[10, 60]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[2070] -9,65,"[2, 1]","[10, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1776] -9,66,"[1, 2]","[60, 20]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[1800] -9,67,"[4, 3]","[20, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[2177] -9,68,"[4, 3]","[0, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1804] -9,69,"[4, 3]","[10, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1970] -9,70,"[2, 1]","[40, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1739] -9,71,"[1, 2]","[80, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1901] -9,72,"[1, 2]","[90, 70]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[1791] -9,73,"[3, 4]","[90, 50]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[2046] -9,74,"[4, 3]","[30, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1662] -9,75,"[4, 3]","[20, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1697] -9,76,"[3, 4]","[90, 10]","[2, 1]",[2],[5],[3],[450],[9],[1],[3],[1826] -9,77,"[4, 3]","[20, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1642] -9,78,"[2, 1]","[40, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1751] -9,79,"[4, 3]","[50, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1784] -9,80,"[3, 4]","[70, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[2003] -10,1,"[1, 2]","[10, 50]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[14940] -10,2,"[1, 2]","[0, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2752] -10,3,"[3, 4]","[0, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2521] -10,4,"[4, 3]","[70, 20]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2673] -10,5,"[4, 3]","[50, 0]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2806] -10,6,"[2, 1]","[90, 0]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[2438] -10,7,"[2, 1]","[50, 20]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[3569] -10,8,"[2, 1]","[30, 10]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[4435] -10,9,"[4, 3]","[60, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[2380] -10,10,"[1, 2]","[10, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[3329] -10,11,"[3, 4]","[20, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2630] -10,12,"[4, 3]","[30, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[3],[2156] -10,13,"[1, 2]","[20, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[3062] -10,14,"[2, 1]","[0, 10]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[2385] -10,15,"[4, 3]","[0, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[4],[2068] -10,16,"[2, 1]","[10, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[2],[6182] -10,17,"[1, 2]","[0, 10]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[3485] -10,18,"[3, 4]","[10, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[2334] -10,19,"[2, 1]","[20, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[2],[2996] -10,20,"[3, 4]","[20, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[2570] -10,21,"[3, 4]","[30, 0]","[2, 2]",[1],[1],[4],[0],[0],[2],[3],[3202] -10,22,"[3, 4]","[50, 20]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[2051] -10,23,"[4, 3]","[20, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1984] -10,24,"[4, 3]","[30, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2168] -10,25,"[3, 4]","[70, 40]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[2289] -10,26,"[2, 1]","[40, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2954] -10,27,"[1, 2]","[30, 20]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2952] -10,28,"[1, 2]","[50, 20]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2484] -10,29,"[3, 4]","[50, 20]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[2468] -10,30,"[4, 3]","[20, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2583] -10,31,"[1, 2]","[60, 10]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[3232] -10,32,"[3, 4]","[40, 20]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[2601] -10,33,"[3, 4]","[70, 10]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[2102] -10,34,"[2, 1]","[10, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[2310] -10,35,"[4, 3]","[20, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[2267] -10,36,"[4, 3]","[30, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1936] -10,37,"[4, 3]","[20, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1967] -10,38,"[2, 1]","[60, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2022] -10,39,"[1, 2]","[70, 40]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2271] -10,40,"[2, 1]","[40, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[2974] -10,41,"[3, 4]","[70, 30]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[7484] -10,42,"[4, 3]","[10, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2492] -10,43,"[4, 3]","[20, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[2071] -10,44,"[2, 1]","[10, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2029] -10,45,"[2, 1]","[10, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2118] -10,46,"[1, 2]","[80, 20]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2785] -10,47,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2304] -10,48,"[1, 2]","[70, 30]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2269] -10,49,"[3, 4]","[50, 10]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2512] -10,50,"[2, 1]","[10, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2153] -10,51,"[2, 1]","[20, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[2170] -10,52,"[4, 3]","[30, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[3096] -10,53,"[4, 3]","[50, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[2409] -10,54,"[3, 4]","[90, 60]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[2079] -10,55,"[3, 4]","[50, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2211] -10,56,"[3, 4]","[40, 80]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[2054] -10,57,"[1, 2]","[60, 70]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2178] -10,58,"[4, 3]","[70, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[2079] -10,59,"[3, 4]","[80, 30]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2051] -10,60,"[1, 2]","[70, 20]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[2406] -10,61,"[4, 3]","[30, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2044] -10,62,"[2, 1]","[10, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2445] -10,63,"[3, 4]","[80, 20]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2645] -10,64,"[4, 3]","[50, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[2130] -10,65,"[2, 1]","[50, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[2129] -10,66,"[2, 1]","[80, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[3429] -10,67,"[2, 1]","[60, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[2022] -10,68,"[1, 2]","[50, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[4285] -10,69,"[4, 3]","[40, 50]","[2, 2]",[2],[1],[4],[40],[4],[2],[4],[2643] -10,70,"[3, 4]","[40, 60]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[2351] -10,71,"[4, 3]","[90, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2178] -10,72,"[3, 4]","[90, 70]","[2, 1]",[2],[5],[3],[450],[9],[1],[3],[2285] -10,73,"[4, 3]","[80, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[2201] -10,74,"[4, 3]","[50, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[2270] -10,75,"[1, 2]","[70, 80]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2712] -10,76,"[2, 1]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2654] -10,77,"[2, 1]","[70, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[2200] -10,78,"[1, 2]","[10, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2751] -10,79,"[4, 3]","[70, 20]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2271] -10,80,"[4, 3]","[60, 80]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[3201] -10,1,"[2, 1]","[20, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[5505] -10,2,"[4, 3]","[20, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[2054] -10,3,"[2, 1]","[10, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2195] -10,4,"[1, 2]","[70, 20]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[3080] -10,5,"[3, 4]","[80, 0]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[2418] -10,6,"[3, 4]","[80, 10]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1945] -10,7,"[3, 4]","[70, 10]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1936] -10,8,"[4, 3]","[30, 40]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[2753] -10,9,"[2, 1]","[40, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2401] -10,10,"[2, 1]","[50, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[2318] -10,11,"[3, 4]","[60, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2285] -10,12,"[4, 3]","[60, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1901] -10,13,"[3, 4]","[90, 40]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[2155] -10,14,"[2, 1]","[60, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2235] -10,15,"[2, 1]","[20, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2074] -10,16,"[1, 2]","[80, 30]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2637] -10,17,"[2, 1]","[50, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2318] -10,18,"[3, 4]","[80, 60]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2195] -10,19,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2104] -10,20,"[1, 2]","[50, 80]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[2989] -10,21,"[2, 1]","[60, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[2547] -10,22,"[2, 1]","[70, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[3518] -10,23,"[2, 1]","[60, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[3396] -10,24,"[4, 3]","[70, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2287] -10,25,"[1, 2]","[80, 60]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2345] -10,26,"[3, 4]","[60, 70]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2304] -10,27,"[2, 1]","[80, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[3354] -10,28,"[2, 1]","[80, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[3480] -10,29,"[2, 1]","[60, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2288] -10,30,"[1, 2]","[90, 70]","[1, 1]",[1],[5],[1],[450],[9],[1],[1],[3436] -10,31,"[3, 4]","[60, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[2279] -10,32,"[4, 3]","[70, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2817] -10,33,"[3, 4]","[90, 70]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[2038] -10,34,"[1, 2]","[70, 60]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[4654] -10,35,"[1, 2]","[50, 60]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2234] -10,36,"[4, 3]","[50, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[2390] -10,37,"[3, 4]","[10, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2320] -10,38,"[4, 3]","[80, 30]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2195] -10,39,"[1, 2]","[20, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2445] -10,40,"[1, 2]","[10, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2651] -10,41,"[2, 1]","[80, 30]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[4200] -10,42,"[2, 1]","[80, 10]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2122] -10,43,"[3, 4]","[10, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2301] -10,44,"[2, 1]","[70, 40]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2555] -10,45,"[4, 3]","[40, 70]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2246] -10,46,"[4, 3]","[50, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1984] -10,47,"[3, 4]","[50, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2482] -10,48,"[4, 3]","[70, 20]","[2, 1]",[2],[1],[3],[20],[2],[1],[3],[2252] -10,49,"[2, 1]","[70, 40]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2112] -10,50,"[3, 4]","[40, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[2645] -10,51,"[3, 4]","[40, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[2085] -10,52,"[1, 2]","[40, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[2438] -10,53,"[4, 3]","[50, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[2238] -10,54,"[1, 2]","[40, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2569] -10,55,"[3, 4]","[10, 20]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[1935] -10,56,"[4, 3]","[10, 30]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[2120] -10,57,"[4, 3]","[10, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[2151] -10,58,"[2, 1]","[0, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2121] -10,59,"[3, 4]","[60, 10]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[2271] -10,60,"[1, 2]","[50, 10]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[2262] -10,61,"[2, 1]","[20, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[3244] -10,62,"[2, 1]","[60, 30]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2891] -10,63,"[2, 1]","[60, 40]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2013] -10,64,"[2, 1]","[30, 50]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2154] -10,65,"[4, 3]","[30, 70]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2261] -10,66,"[1, 2]","[50, 30]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[5836] -10,67,"[4, 3]","[30, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[2062] -10,68,"[3, 4]","[70, 30]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[2012] -10,69,"[2, 1]","[60, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2606] -10,70,"[2, 1]","[70, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[2937] -10,71,"[3, 4]","[10, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[2167] -10,72,"[3, 4]","[40, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1979] -10,73,"[1, 2]","[40, 20]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[4653] -10,74,"[1, 2]","[60, 30]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2370] -10,75,"[2, 1]","[40, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[2118] -10,76,"[3, 4]","[60, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[3205] -10,77,"[4, 3]","[50, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[2055] -10,78,"[3, 4]","[40, 70]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[2094] -10,79,"[4, 3]","[90, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[2120] -10,80,"[3, 4]","[80, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2135] -11,1,"[4, 3]","[70, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[12251] -11,2,"[3, 4]","[0, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[3817] -11,3,"[2, 1]","[90, 10]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[7718] -11,4,"[2, 1]","[80, 20]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1800] -11,5,"[3, 4]","[10, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[3532] -11,6,"[3, 4]","[10, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[1717] -11,7,"[2, 1]","[60, 0]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[4516] -11,8,"[3, 4]","[0, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[3834] -11,9,"[2, 1]","[40, 0]","[1, 1]",[1],[1],[1],[0],[0],[1],[1],[2617] -11,10,"[3, 4]","[10, 40]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[2667] -11,11,"[1, 2]","[10, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[3017] -11,12,"[3, 4]","[10, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[3283] -11,13,"[3, 4]","[20, 60]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[2484] -11,14,"[1, 2]","[10, 70]","[1, 1]",[1],[5],[1],[50],[1],[1],[2],[1750] -11,15,"[2, 1]","[80, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[2600] -11,16,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[3351] -11,17,"[4, 3]","[90, 30]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2983] -11,18,"[3, 4]","[0, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[3134] -11,19,"[4, 3]","[80, 0]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2083] -11,20,"[4, 3]","[90, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1917] -11,21,"[2, 1]","[80, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[3634] -11,22,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[3133] -11,23,"[2, 1]","[80, 20]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1824] -11,24,"[3, 4]","[30, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2217] -11,25,"[1, 2]","[40, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[3250] -11,26,"[1, 2]","[20, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1867] -11,27,"[4, 3]","[80, 30]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2217] -11,28,"[1, 2]","[30, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[3093] -11,29,"[3, 4]","[50, 70]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2417] -11,30,"[3, 4]","[70, 50]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1727] -11,31,"[2, 1]","[30, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[3084] -11,32,"[4, 3]","[20, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[1766] -11,33,"[3, 4]","[50, 20]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[1802] -11,34,"[4, 3]","[40, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[1701] -11,35,"[3, 4]","[30, 60]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[1750] -11,36,"[3, 4]","[10, 60]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[1715] -11,37,"[3, 4]","[20, 50]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[2218] -11,38,"[4, 3]","[80, 40]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[3085] -11,39,"[4, 3]","[70, 50]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[1800] -11,40,"[4, 3]","[80, 60]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[1684] -11,41,"[2, 1]","[50, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[6665] -11,42,"[3, 4]","[60, 30]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1761] -11,43,"[2, 1]","[30, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2717] -11,44,"[4, 3]","[30, 40]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2467] -11,45,"[3, 4]","[70, 50]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1701] -11,46,"[1, 2]","[80, 20]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2350] -11,47,"[1, 2]","[70, 40]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[1852] -11,48,"[2, 1]","[40, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2233] -11,49,"[4, 3]","[50, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1839] -11,50,"[3, 4]","[70, 40]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1701] -11,51,"[1, 2]","[30, 70]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[2467] -11,52,"[1, 2]","[0, 60]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[1918] -11,53,"[4, 3]","[40, 10]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[2184] -11,54,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1834] -11,55,"[3, 4]","[10, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2134] -11,56,"[2, 1]","[10, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[1700] -11,57,"[2, 1]","[10, 50]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2416] -11,58,"[4, 3]","[0, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2166] -11,59,"[2, 1]","[0, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1783] -11,60,"[3, 4]","[80, 30]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1934] -11,61,"[4, 3]","[10, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1699] -11,62,"[2, 1]","[10, 90]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2233] -11,63,"[4, 3]","[40, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1684] -11,64,"[2, 1]","[20, 70]","[1, 2]",[2],[1],[2],[20],[2],[2],[2],[2283] -11,65,"[2, 1]","[20, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1867] -11,66,"[3, 4]","[60, 0]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1651] -11,67,"[2, 1]","[0, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[1784] -11,68,"[2, 1]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1816] -11,69,"[4, 3]","[30, 20]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2075] -11,70,"[4, 3]","[30, 40]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1694] -11,71,"[3, 4]","[50, 10]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2333] -11,72,"[2, 1]","[20, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2120] -11,73,"[4, 3]","[40, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[1800] -11,74,"[3, 4]","[30, 50]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[1717] -11,75,"[3, 4]","[20, 10]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[1767] -11,76,"[4, 3]","[10, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1667] -11,77,"[1, 2]","[0, 10]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[2133] -11,78,"[3, 4]","[0, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[1967] -11,79,"[3, 4]","[20, 0]","[2, 2]",[2],[5],[4],[0],[0],[2],[3],[2517] -11,80,"[4, 3]","[20, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1733] -12,1,"[4, 3]","[40, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[6351] -12,2,"[2, 1]","[40, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2496] -12,3,"[4, 3]","[90, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[3314] -12,4,"[3, 4]","[80, 90]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[2565] -12,5,"[4, 3]","[70, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2071] -12,6,"[2, 1]","[40, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2270] -12,7,"[4, 3]","[30, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[1987] -12,8,"[3, 4]","[40, 10]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2339] -12,9,"[2, 1]","[10, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2288] -12,10,"[3, 4]","[40, 0]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[2097] -12,11,"[3, 4]","[40, 20]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2046] -12,12,"[4, 3]","[0, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[2264] -12,13,"[1, 2]","[60, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[3672] -12,14,"[1, 2]","[50, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[4898] -12,15,"[3, 4]","[30, 10]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[2347] -12,16,"[1, 2]","[20, 10]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[2638] -12,17,"[2, 1]","[30, 20]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2322] -12,18,"[1, 2]","[0, 20]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[2504] -12,19,"[3, 4]","[0, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[2647] -12,20,"[1, 2]","[10, 40]","[1, 1]",[1],[5],[1],[50],[1],[1],[2],[2587] -12,21,"[3, 4]","[30, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2079] -12,22,"[4, 3]","[70, 50]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2305] -12,23,"[4, 3]","[70, 60]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[1880] -12,24,"[1, 2]","[50, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2721] -12,25,"[2, 1]","[70, 40]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[3204] -12,26,"[3, 4]","[20, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[3521] -12,27,"[4, 3]","[40, 10]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2397] -12,28,"[4, 3]","[70, 0]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2263] -12,29,"[4, 3]","[30, 10]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[1955] -12,30,"[3, 4]","[30, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2013] -12,31,"[1, 2]","[0, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[2237] -12,32,"[2, 1]","[40, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[2405] -12,33,"[1, 2]","[30, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[2596] -12,34,"[2, 1]","[20, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[2014] -12,35,"[4, 3]","[30, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[2556] -12,36,"[1, 2]","[10, 20]","[1, 1]",[1],[5],[1],[50],[1],[1],[2],[2213] -12,37,"[2, 1]","[30, 0]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[2180] -12,38,"[2, 1]","[70, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2263] -12,39,"[3, 4]","[70, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[2003] -12,40,"[1, 2]","[60, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[2623] -12,41,"[2, 1]","[80, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[4350] -12,42,"[1, 2]","[80, 50]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1938] -12,43,"[2, 1]","[30, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1713] -12,44,"[1, 2]","[90, 70]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[1913] -12,45,"[4, 3]","[80, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[2105] -12,46,"[4, 3]","[90, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[2439] -12,47,"[2, 1]","[70, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1847] -12,48,"[2, 1]","[50, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1738] -12,49,"[2, 1]","[80, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2005] -12,50,"[4, 3]","[90, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2055] -12,51,"[4, 3]","[80, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[1771] -12,52,"[3, 4]","[0, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[3296] -12,53,"[2, 1]","[30, 0]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2662] -12,54,"[1, 2]","[30, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[2347] -12,55,"[1, 2]","[20, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[2555] -12,56,"[3, 4]","[20, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2264] -12,57,"[2, 1]","[40, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1871] -12,58,"[4, 3]","[50, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2104] -12,59,"[2, 1]","[20, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[1863] -12,60,"[4, 3]","[50, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2171] -12,61,"[2, 1]","[40, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1795] -12,62,"[1, 2]","[40, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[2271] -12,63,"[1, 2]","[20, 30]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[2028] -12,64,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2205] -12,65,"[3, 4]","[20, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2970] -12,66,"[2, 1]","[10, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[10015] -12,67,"[3, 4]","[40, 10]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2179] -12,68,"[3, 4]","[30, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[2004] -12,69,"[2, 1]","[10, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2421] -12,70,"[4, 3]","[0, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[2146] -12,71,"[2, 1]","[0, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[1880] -12,72,"[2, 1]","[0, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[1737] -12,73,"[4, 3]","[10, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1962] -12,74,"[4, 3]","[20, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[2095] -12,75,"[3, 4]","[10, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2011] -12,76,"[4, 3]","[30, 10]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1903] -12,77,"[1, 2]","[40, 10]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[1989] -12,78,"[3, 4]","[40, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[1805] -12,79,"[4, 3]","[10, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[1963] -12,80,"[1, 2]","[20, 0]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[2146] -12,1,"[2, 1]","[90, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[3377] -12,2,"[4, 3]","[70, 40]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2521] -12,3,"[2, 1]","[70, 40]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2212] -12,4,"[3, 4]","[70, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[1953] -12,5,"[4, 3]","[50, 70]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[1921] -12,6,"[3, 4]","[60, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2053] -12,7,"[4, 3]","[50, 60]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[1954] -12,8,"[4, 3]","[60, 70]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1830] -12,9,"[3, 4]","[80, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1713] -12,10,"[4, 3]","[30, 80]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[1923] -12,11,"[3, 4]","[60, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[1763] -12,12,"[4, 3]","[20, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[2079] -12,13,"[2, 1]","[40, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1946] -12,14,"[1, 2]","[30, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[2039] -12,15,"[4, 3]","[60, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2071] -12,16,"[3, 4]","[60, 30]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1897] -12,17,"[1, 2]","[20, 50]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[2104] -12,18,"[1, 2]","[20, 30]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[2003] -12,19,"[1, 2]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[1978] -12,20,"[1, 2]","[10, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[1804] -12,21,"[3, 4]","[30, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2645] -12,22,"[4, 3]","[20, 70]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[2005] -12,23,"[1, 2]","[70, 20]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1996] -12,24,"[2, 1]","[20, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2270] -12,25,"[4, 3]","[0, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[2163] -12,26,"[3, 4]","[30, 0]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[9064] -12,27,"[4, 3]","[30, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[3213] -12,28,"[2, 1]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[5497] -12,29,"[1, 2]","[50, 20]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[5446] -12,30,"[3, 4]","[30, 0]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[4579] -12,31,"[2, 1]","[20, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[2045] -12,32,"[1, 2]","[10, 20]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[1894] -12,33,"[1, 2]","[20, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[1745] -12,34,"[4, 3]","[80, 10]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[1816] -12,35,"[4, 3]","[80, 10]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[1870] -12,36,"[3, 4]","[10, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[1920] -12,37,"[3, 4]","[30, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[1922] -12,38,"[3, 4]","[10, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[1804] -12,39,"[2, 1]","[50, 40]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2229] -12,40,"[2, 1]","[50, 40]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2705] -12,41,"[1, 2]","[30, 70]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[2799] -12,42,"[4, 3]","[80, 30]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2486] -12,43,"[3, 4]","[30, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1970] -12,44,"[2, 1]","[80, 10]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2420] -12,45,"[3, 4]","[20, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[2105] -12,46,"[1, 2]","[0, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[5855] -12,47,"[1, 2]","[80, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1962] -12,48,"[1, 2]","[70, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1762] -12,49,"[4, 3]","[40, 70]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2095] -12,50,"[1, 2]","[50, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1519] -12,51,"[1, 2]","[30, 40]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[1895] -12,52,"[4, 3]","[30, 20]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1921] -12,53,"[1, 2]","[10, 50]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[1879] -12,54,"[2, 1]","[40, 20]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2779] -12,55,"[4, 3]","[30, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1945] -12,56,"[4, 3]","[20, 30]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[1913] -12,57,"[2, 1]","[10, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[2362] -12,58,"[2, 1]","[10, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[1895] -12,59,"[2, 1]","[30, 10]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2203] -12,60,"[3, 4]","[10, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[2154] -12,61,"[1, 2]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[1930] -12,62,"[3, 4]","[30, 20]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[2078] -12,63,"[1, 2]","[60, 0]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2153] -12,64,"[4, 3]","[20, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1813] -12,65,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1887] -12,66,"[3, 4]","[90, 20]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[1836] -12,67,"[3, 4]","[80, 40]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1954] -12,68,"[3, 4]","[80, 40]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[1729] -12,69,"[4, 3]","[10, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[1913] -12,70,"[1, 2]","[80, 0]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2063] -12,71,"[2, 1]","[10, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[1955] -12,72,"[1, 2]","[20, 0]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[1703] -12,73,"[4, 3]","[0, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1970] -12,74,"[1, 2]","[30, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1879] -12,75,"[2, 1]","[40, 0]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2137] -12,76,"[3, 4]","[0, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1836] -12,77,"[2, 1]","[0, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[1687] -12,78,"[2, 1]","[40, 20]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1912] -12,79,"[2, 1]","[80, 30]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[1837] -12,80,"[3, 4]","[0, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1911] -13,1,"[3, 4]","[20, 60]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[4348] -13,2,"[2, 1]","[70, 10]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1978] -13,3,"[2, 1]","[60, 20]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2195] -13,4,"[4, 3]","[40, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[2186] -13,5,"[2, 1]","[40, 30]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[2277] -13,6,"[4, 3]","[50, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1837] -13,7,"[1, 2]","[60, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[2760] -13,8,"[1, 2]","[30, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2794] -13,9,"[2, 1]","[30, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[2645] -13,10,"[4, 3]","[30, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[3],[2602] -13,11,"[1, 2]","[0, 60]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[2703] -13,12,"[1, 2]","[40, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2303] -13,13,"[4, 3]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[3927] -13,14,"[4, 3]","[40, 90]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[2928] -13,15,"[3, 4]","[60, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[2878] -13,16,"[2, 1]","[70, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[3902] -13,17,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[3202] -13,18,"[3, 4]","[30, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2586] -13,19,"[1, 2]","[40, 60]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2778] -13,20,"[1, 2]","[10, 50]","[1, 1]",[1],[5],[1],[50],[1],[1],[2],[2385] -13,21,"[4, 3]","[50, 10]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[2761] -13,22,"[3, 4]","[20, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2454] -13,23,"[1, 2]","[50, 30]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[1937] -13,24,"[4, 3]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2295] -13,25,"[1, 2]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1912] -13,26,"[3, 4]","[60, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1887] -13,27,"[2, 1]","[70, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[2128] -13,28,"[1, 2]","[30, 60]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[1971] -13,29,"[4, 3]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1886] -13,30,"[1, 2]","[70, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2229] -13,31,"[1, 2]","[50, 30]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[1920] -13,32,"[3, 4]","[20, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1827] -13,33,"[1, 2]","[10, 60]","[1, 1]",[1],[5],[1],[50],[1],[1],[2],[1812] -13,34,"[2, 1]","[30, 0]","[1, 1]",[1],[1],[1],[0],[0],[1],[1],[2002] -13,35,"[4, 3]","[50, 0]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[2062] -13,36,"[1, 2]","[10, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2395] -13,37,"[1, 2]","[0, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[3236] -13,38,"[2, 1]","[70, 20]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2353] -13,39,"[2, 1]","[50, 10]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[3012] -13,40,"[2, 1]","[30, 20]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[4594] -13,41,"[1, 2]","[10, 30]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[9382] -13,42,"[3, 4]","[0, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[2286] -13,43,"[4, 3]","[10, 0]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[1812] -13,44,"[1, 2]","[30, 20]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[1861] -13,45,"[2, 1]","[40, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[1920] -13,46,"[3, 4]","[0, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2220] -13,47,"[3, 4]","[30, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1695] -13,48,"[4, 3]","[20, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[1869] -13,49,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1978] -13,50,"[2, 1]","[30, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[2479] -13,51,"[3, 4]","[20, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[4],[2551] -13,52,"[2, 1]","[30, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[2128] -13,53,"[1, 2]","[60, 20]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2144] -13,54,"[1, 2]","[80, 40]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1754] -13,55,"[3, 4]","[80, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1962] -13,56,"[2, 1]","[0, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1770] -13,57,"[4, 3]","[0, 80]","[2, 2]",[2],[1],[4],[0],[0],[2],[4],[1944] -13,58,"[4, 3]","[20, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1829] -13,59,"[2, 1]","[30, 60]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[3062] -13,60,"[3, 4]","[70, 50]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1954] -13,61,"[4, 3]","[80, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[2070] -13,62,"[3, 4]","[50, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2103] -13,63,"[2, 1]","[50, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[2328] -13,64,"[1, 2]","[60, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1919] -13,65,"[3, 4]","[40, 20]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1828] -13,66,"[4, 3]","[10, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[2054] -13,67,"[1, 2]","[40, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[1979] -13,68,"[4, 3]","[20, 0]","[2, 1]",[2],[5],[3],[0],[0],[1],[3],[1736] -13,69,"[2, 1]","[50, 20]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2027] -13,70,"[3, 4]","[20, 50]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[1895] -13,71,"[2, 1]","[60, 0]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[1794] -13,72,"[4, 3]","[60, 0]","[2, 1]",[2],[5],[3],[0],[0],[1],[3],[1709] -13,73,"[3, 4]","[10, 70]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[1754] -13,74,"[1, 2]","[40, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[1895] -13,75,"[2, 1]","[70, 60]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[1769] -13,76,"[2, 1]","[60, 70]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1761] -13,77,"[3, 4]","[70, 50]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1771] -13,78,"[3, 4]","[90, 60]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[1795] -13,79,"[3, 4]","[60, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1920] -13,80,"[4, 3]","[70, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[1870] -14,1,"[1, 2]","[60, 40]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[14416] -14,2,"[2, 1]","[30, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[3377] -14,3,"[3, 4]","[10, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[2995] -14,4,"[3, 4]","[40, 10]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[2660] -14,5,"[2, 1]","[0, 50]","[1, 2]",[1],[1],[2],[0],[0],[2],[2],[5176] -14,6,"[4, 3]","[50, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[4155] -14,7,"[1, 2]","[80, 60]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2928] -14,8,"[4, 3]","[80, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1855] -14,9,"[2, 1]","[50, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2125] -14,10,"[2, 1]","[60, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2011] -14,11,"[1, 2]","[70, 60]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1794] -14,12,"[2, 1]","[60, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2424] -14,13,"[4, 3]","[70, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1826] -14,14,"[2, 1]","[70, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2027] -14,15,"[1, 2]","[80, 90]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2594] -14,16,"[2, 1]","[90, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2156] -14,17,"[2, 1]","[50, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1928] -14,18,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1844] -14,19,"[3, 4]","[40, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[1742] -14,20,"[4, 3]","[20, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1721] -14,21,"[1, 2]","[40, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[1804] -14,22,"[4, 3]","[30, 60]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[1843] -14,23,"[1, 2]","[80, 40]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1876] -14,24,"[4, 3]","[60, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1643] -14,25,"[4, 3]","[40, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2009] -14,26,"[4, 3]","[60, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1741] -14,27,"[4, 3]","[60, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1927] -14,28,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1759] -14,29,"[1, 2]","[80, 40]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2127] -14,30,"[4, 3]","[40, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1825] -14,31,"[3, 4]","[80, 50]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1875] -14,32,"[3, 4]","[80, 40]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1709] -14,33,"[3, 4]","[60, 30]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1876] -14,34,"[1, 2]","[50, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[3226] -14,35,"[4, 3]","[40, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1993] -14,36,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1806] -14,37,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1893] -14,38,"[3, 4]","[90, 70]","[2, 1]",[1],[1],[3],[90],[9],[1],[3],[1754] -14,39,"[2, 1]","[50, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2008] -14,40,"[2, 1]","[70, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[1873] -14,41,"[2, 1]","[80, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[5899] -14,42,"[2, 1]","[80, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1657] -14,43,"[4, 3]","[70, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1705] -14,44,"[3, 4]","[80, 40]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[4371] -14,45,"[1, 2]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[1925] -14,46,"[2, 1]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1794] -14,47,"[3, 4]","[80, 70]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1938] -14,48,"[4, 3]","[40, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[1877] -14,49,"[1, 2]","[70, 60]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2062] -14,50,"[3, 4]","[60, 40]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1845] -14,51,"[4, 3]","[30, 60]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2160] -14,52,"[2, 1]","[30, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2041] -14,53,"[1, 2]","[0, 30]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[1900] -14,54,"[4, 3]","[10, 20]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[1944] -14,55,"[3, 4]","[20, 0]","[2, 2]",[2],[1],[4],[0],[0],[2],[3],[2056] -14,56,"[2, 1]","[0, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1759] -14,57,"[1, 2]","[0, 10]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[2009] -14,58,"[1, 2]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2058] -14,59,"[4, 3]","[30, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2111] -14,60,"[4, 3]","[30, 10]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1756] -14,61,"[3, 4]","[40, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1778] -14,62,"[2, 1]","[40, 60]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2520] -14,63,"[1, 2]","[60, 40]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2376] -14,64,"[1, 2]","[50, 60]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[1876] -14,65,"[4, 3]","[30, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2009] -14,66,"[4, 3]","[30, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1704] -14,67,"[2, 1]","[40, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[1839] -14,68,"[3, 4]","[70, 20]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1910] -14,69,"[4, 3]","[10, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1796] -14,70,"[1, 2]","[80, 60]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1842] -14,71,"[3, 4]","[70, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1826] -14,72,"[4, 3]","[70, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1744] -14,73,"[1, 2]","[20, 80]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[1859] -14,74,"[3, 4]","[60, 80]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[1959] -14,75,"[3, 4]","[70, 80]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1661] -14,76,"[2, 1]","[50, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1893] -14,77,"[1, 2]","[50, 30]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[1690] -14,78,"[4, 3]","[60, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1712] -14,79,"[4, 3]","[90, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1692] -14,80,"[1, 2]","[40, 80]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[1809] -10,1,"[4, 3]","[70, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[14637] -10,2,"[3, 4]","[0, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2784] -10,3,"[2, 1]","[90, 10]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[2701] -10,4,"[2, 1]","[80, 20]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[3184] -10,5,"[3, 4]","[10, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[3502] -10,6,"[3, 4]","[10, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[3613] -10,7,"[2, 1]","[60, 0]","[1, 1]",[1],[5],[1],[0],[0],[1],[1],[2234] -10,8,"[3, 4]","[0, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2418] -10,9,"[2, 1]","[40, 0]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[2901] -10,10,"[3, 4]","[10, 40]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[2167] -10,11,"[1, 2]","[10, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[2501] -10,12,"[3, 4]","[10, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[2702] -10,13,"[3, 4]","[20, 60]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1917] -10,14,"[1, 2]","[10, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2517] -10,15,"[2, 1]","[80, 20]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2068] -10,16,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[2568] -10,17,"[4, 3]","[90, 30]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2197] -10,18,"[3, 4]","[0, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[2568] -10,19,"[4, 3]","[80, 0]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[5335] -10,20,"[4, 3]","[90, 20]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[4669] -10,21,"[2, 1]","[80, 20]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[7453] -10,22,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[2568] -10,23,"[2, 1]","[80, 20]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[3152] -10,24,"[3, 4]","[30, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1967] -10,25,"[1, 2]","[40, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[3301] -10,26,"[1, 2]","[20, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1718] -10,27,"[4, 3]","[80, 30]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2034] -10,28,"[1, 2]","[30, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2018] -10,29,"[3, 4]","[50, 70]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1917] -10,30,"[3, 4]","[70, 50]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1884] -10,31,"[2, 1]","[30, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2368] -10,32,"[4, 3]","[20, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[1969] -10,33,"[3, 4]","[50, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1834] -10,34,"[4, 3]","[40, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[2001] -10,35,"[3, 4]","[30, 60]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[2318] -10,36,"[3, 4]","[10, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2018] -10,37,"[3, 4]","[20, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[1737] -10,38,"[4, 3]","[80, 40]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2268] -10,39,"[4, 3]","[70, 50]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2188] -10,40,"[4, 3]","[80, 60]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[3301] -10,41,"[2, 1]","[50, 60]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[5802] -10,42,"[3, 4]","[60, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[2086] -10,43,"[2, 1]","[30, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2185] -10,44,"[4, 3]","[30, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1651] -10,45,"[3, 4]","[70, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2217] -10,46,"[1, 2]","[80, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[2218] -10,47,"[1, 2]","[70, 40]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2535] -10,48,"[2, 1]","[40, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1917] -10,49,"[4, 3]","[50, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1934] -10,50,"[3, 4]","[70, 40]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[2134] -10,51,"[1, 2]","[30, 70]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[1950] -10,52,"[1, 2]","[0, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2735] -10,53,"[4, 3]","[40, 10]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[2151] -10,54,"[4, 3]","[30, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[2684] -10,55,"[3, 4]","[10, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[3135] -10,56,"[2, 1]","[10, 30]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[3085] -10,57,"[2, 1]","[10, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1684] -10,58,"[4, 3]","[0, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2835] -10,59,"[2, 1]","[0, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1934] -10,60,"[3, 4]","[80, 30]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1818] -10,61,"[4, 3]","[10, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1850] -10,62,"[2, 1]","[10, 90]","[1, 1]",[2],[1],[1],[90],[9],[1],[2],[2208] -10,63,"[4, 3]","[40, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1951] -10,64,"[2, 1]","[20, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2001] -10,65,"[2, 1]","[20, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2018] -10,66,"[3, 4]","[60, 0]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2520] -10,67,"[2, 1]","[0, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[2084] -10,68,"[2, 1]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2556] -10,69,"[4, 3]","[30, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[1817] -10,70,"[4, 3]","[30, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[2101] -10,71,"[3, 4]","[50, 10]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2034] -10,72,"[2, 1]","[20, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2284] -10,73,"[4, 3]","[40, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[2101] -10,74,"[3, 4]","[30, 50]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[2006] -10,75,"[3, 4]","[20, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[2051] -10,76,"[4, 3]","[10, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1751] -10,77,"[1, 2]","[0, 10]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[2434] -10,78,"[3, 4]","[0, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[2054] -10,79,"[3, 4]","[20, 0]","[2, 2]",[2],[5],[4],[0],[0],[2],[3],[2101] -10,80,"[4, 3]","[20, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1768] -15,1,"[2, 1]","[10, 90]","[1, 2]",[1],[1],[2],[10],[1],[2],[2],[4592] -15,2,"[3, 4]","[70, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[3708] -15,3,"[1, 2]","[80, 40]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2589] -15,4,"[2, 1]","[50, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2155] -15,5,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[6698] -15,6,"[4, 3]","[20, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[3759] -15,7,"[3, 4]","[60, 40]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[4009] -15,8,"[4, 3]","[30, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[7262] -15,9,"[3, 4]","[10, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2789] -15,10,"[4, 3]","[40, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[2905] -15,11,"[2, 1]","[50, 60]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[4860] -15,12,"[2, 1]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[9870] -15,13,"[3, 4]","[80, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2639] -15,14,"[1, 2]","[60, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2857] -15,15,"[2, 1]","[80, 40]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[4677] -15,16,"[1, 2]","[30, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[6210] -15,17,"[4, 3]","[70, 50]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[5427] -15,18,"[2, 1]","[40, 80]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[5161] -15,19,"[1, 2]","[90, 40]","[1, 1]",[1],[5],[1],[450],[9],[1],[1],[3608] -15,20,"[2, 1]","[40, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2556] -15,21,"[1, 2]","[70, 20]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2923] -15,22,"[2, 1]","[0, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[3290] -15,23,"[4, 3]","[0, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[4344] -15,24,"[4, 3]","[20, 30]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[1787] -15,25,"[2, 1]","[0, 0]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[2105] -15,26,"[1, 2]","[10, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[2],[3726] -15,27,"[2, 1]","[30, 20]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[2172] -15,28,"[1, 2]","[10, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[1],[9053] -15,29,"[2, 1]","[20, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[2939] -15,30,"[1, 2]","[10, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[2473] -15,31,"[1, 2]","[40, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[2088] -15,32,"[4, 3]","[30, 10]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[3156] -15,33,"[3, 4]","[10, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[2221] -15,34,"[2, 1]","[70, 0]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2171] -15,35,"[4, 3]","[50, 0]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[5557] -15,36,"[3, 4]","[10, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2188] -15,37,"[2, 1]","[60, 10]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2438] -15,38,"[1, 2]","[10, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[4524] -15,39,"[4, 3]","[90, 0]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[6879] -15,40,"[4, 3]","[70, 10]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2220] -15,41,"[3, 4]","[20, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2868] -15,42,"[4, 3]","[80, 30]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[2037] -15,43,"[2, 1]","[80, 20]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2171] -15,44,"[4, 3]","[80, 10]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2204] -15,45,"[2, 1]","[80, 20]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2104] -15,46,"[4, 3]","[40, 30]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2907] -15,47,"[3, 4]","[50, 40]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2805] -15,48,"[1, 2]","[40, 30]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[2939] -15,49,"[1, 2]","[60, 70]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[3539] -15,50,"[4, 3]","[70, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1987] -15,51,"[2, 1]","[40, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2854] -15,52,"[3, 4]","[60, 20]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[5159] -15,53,"[3, 4]","[40, 10]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2021] -15,54,"[4, 3]","[10, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[4224] -15,55,"[2, 1]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[2055] -15,56,"[2, 1]","[0, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[4658] -15,57,"[3, 4]","[10, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[7546] -15,58,"[3, 4]","[20, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[2021] -15,59,"[2, 1]","[40, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[2555] -15,60,"[3, 4]","[60, 30]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[1954] -15,61,"[3, 4]","[70, 10]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1720] -15,62,"[1, 2]","[80, 20]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1803] -15,63,"[3, 4]","[50, 0]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2020] -15,64,"[1, 2]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[1837] -15,65,"[2, 1]","[30, 10]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1753] -15,66,"[2, 1]","[40, 20]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[3022] -15,67,"[1, 2]","[0, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2788] -15,68,"[1, 2]","[0, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[5875] -15,69,"[4, 3]","[60, 20]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[3388] -15,70,"[4, 3]","[70, 20]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2487] -15,71,"[3, 4]","[10, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[2237] -15,72,"[1, 2]","[10, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[1887] -15,73,"[1, 2]","[0, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[4957] -15,74,"[4, 3]","[40, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[4508] -15,75,"[2, 1]","[10, 40]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2204] -15,76,"[2, 1]","[20, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[2],[9779] -15,77,"[4, 3]","[10, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2137] -15,78,"[4, 3]","[10, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[3305] -15,79,"[4, 3]","[10, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[2637] -15,80,"[3, 4]","[70, 10]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[3673] -16,1,"[1, 2]","[50, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[5165] -16,2,"[4, 3]","[40, 70]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[3258] -16,3,"[2, 1]","[50, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[3693] -16,4,"[2, 1]","[50, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2123] -16,5,"[2, 1]","[40, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1881] -16,6,"[1, 2]","[60, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2716] -16,7,"[4, 3]","[40, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[3276] -16,8,"[3, 4]","[60, 40]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[2190] -16,9,"[3, 4]","[70, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2256] -16,10,"[2, 1]","[70, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[1865] -16,11,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1940] -16,12,"[4, 3]","[70, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2624] -16,13,"[1, 2]","[50, 80]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2257] -16,14,"[3, 4]","[50, 60]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[1974] -16,15,"[1, 2]","[40, 90]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[2633] -16,16,"[3, 4]","[0, 80]","[2, 1]",[1],[5],[3],[0],[0],[1],[4],[1939] -16,17,"[1, 2]","[20, 90]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[2233] -16,18,"[2, 1]","[50, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[2291] -16,19,"[1, 2]","[0, 60]","[1, 1]",[1],[1],[1],[0],[0],[1],[2],[1931] -16,20,"[4, 3]","[60, 10]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[2191] -16,21,"[4, 3]","[50, 20]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[1748] -16,22,"[3, 4]","[0, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1866] -16,23,"[4, 3]","[40, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[2067] -16,24,"[2, 1]","[40, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[5021] -16,25,"[1, 2]","[20, 30]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[1775] -16,26,"[4, 3]","[50, 40]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2566] -16,27,"[4, 3]","[40, 80]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2234] -16,28,"[4, 3]","[40, 70]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2183] -16,29,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2308] -16,30,"[3, 4]","[90, 50]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[1967] -16,31,"[4, 3]","[0, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[2117] -16,32,"[1, 2]","[60, 30]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[5346] -16,33,"[2, 1]","[20, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1866] -16,34,"[3, 4]","[60, 40]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1825] -16,35,"[1, 2]","[30, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[1974] -16,36,"[4, 3]","[30, 20]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2166] -16,37,"[3, 4]","[40, 30]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[1808] -16,38,"[1, 2]","[30, 10]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2050] -16,39,"[2, 1]","[20, 30]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[1798] -16,40,"[3, 4]","[30, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2167] -16,41,"[3, 4]","[40, 50]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[162088] -16,42,"[4, 3]","[50, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[2269] -16,43,"[1, 2]","[40, 50]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[2627] -16,44,"[2, 1]","[30, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2243] -16,45,"[2, 1]","[20, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2375] -16,46,"[2, 1]","[60, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2017] -16,47,"[4, 3]","[70, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2175] -16,48,"[4, 3]","[90, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1591] -16,49,"[4, 3]","[70, 40]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1690] -16,50,"[3, 4]","[40, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[1741] -16,51,"[3, 4]","[0, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[1790] -16,52,"[1, 2]","[0, 30]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[2216] -16,53,"[4, 3]","[20, 10]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2674] -16,54,"[2, 1]","[50, 0]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[2334] -16,55,"[3, 4]","[10, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[3185] -16,56,"[4, 3]","[40, 20]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1823] -16,57,"[4, 3]","[40, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[2158] -16,58,"[2, 1]","[60, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[2124] -16,59,"[2, 1]","[80, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[2242] -16,60,"[1, 2]","[30, 90]","[1, 2]",[2],[1],[2],[90],[9],[2],[2],[2016] -16,61,"[2, 1]","[70, 10]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1991] -16,62,"[1, 2]","[0, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[1774] -16,63,"[1, 2]","[0, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2259] -16,64,"[4, 3]","[50, 30]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2158] -16,65,"[3, 4]","[40, 50]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[1707] -16,66,"[2, 1]","[50, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2073] -16,67,"[2, 1]","[70, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[1990] -16,68,"[2, 1]","[50, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2368] -16,69,"[2, 1]","[30, 50]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2183] -16,70,"[2, 1]","[20, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[1916] -16,71,"[2, 1]","[20, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[3718] -16,72,"[3, 4]","[40, 10]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1724] -16,73,"[3, 4]","[50, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[1665] -16,74,"[1, 2]","[40, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[1816] -16,75,"[4, 3]","[30, 20]","[2, 1]",[2],[1],[3],[20],[2],[1],[3],[2625] -16,76,"[4, 3]","[20, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[1757] -16,77,"[4, 3]","[10, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[1800] -16,78,"[2, 1]","[30, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[2140] -16,79,"[3, 4]","[20, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2058] -16,80,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1880] -17,1,"[1, 2]","[40, 50]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[17827] -17,2,"[4, 3]","[40, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[3010] -17,3,"[2, 1]","[30, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2322] -17,4,"[3, 4]","[70, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1973] -17,5,"[3, 4]","[80, 40]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2355] -17,6,"[1, 2]","[80, 50]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1841] -17,7,"[2, 1]","[40, 90]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2471] -17,8,"[2, 1]","[10, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[2020] -17,9,"[4, 3]","[0, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[3034] -17,10,"[2, 1]","[20, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[2350] -17,11,"[4, 3]","[20, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[1795] -17,12,"[2, 1]","[30, 0]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[2277] -17,13,"[3, 4]","[50, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[2205] -17,14,"[1, 2]","[60, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2371] -17,15,"[3, 4]","[50, 80]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[2535] -17,16,"[1, 2]","[90, 70]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[1826] -17,17,"[1, 2]","[80, 60]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2143] -17,18,"[3, 4]","[80, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2126] -17,19,"[3, 4]","[90, 90]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[1841] -17,20,"[3, 4]","[80, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2160] -17,21,"[4, 3]","[70, 20]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2462] -17,22,"[2, 1]","[50, 10]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2992] -17,23,"[3, 4]","[10, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2151] -17,24,"[4, 3]","[40, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[3343] -17,25,"[4, 3]","[20, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[2190] -17,26,"[4, 3]","[40, 20]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[1998] -17,27,"[3, 4]","[20, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2198] -17,28,"[2, 1]","[70, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[2084] -17,29,"[2, 1]","[80, 10]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2467] -17,30,"[3, 4]","[30, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[1908] -17,31,"[1, 2]","[30, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2493] -17,32,"[4, 3]","[80, 30]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[4016] -17,33,"[1, 2]","[20, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2025] -17,34,"[3, 4]","[40, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[2572] -17,35,"[4, 3]","[40, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[1952] -17,36,"[1, 2]","[50, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2164] -17,37,"[1, 2]","[10, 80]","[1, 1]",[1],[5],[1],[50],[1],[1],[2],[8274] -17,38,"[1, 2]","[0, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[1786] -17,39,"[2, 1]","[50, 10]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[1696] -17,40,"[3, 4]","[50, 20]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[2033] -17,41,"[4, 3]","[20, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[5995] -17,42,"[3, 4]","[70, 10]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[2318] -17,43,"[1, 2]","[50, 20]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[1757] -17,44,"[2, 1]","[40, 70]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1926] -17,45,"[4, 3]","[60, 70]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2259] -17,46,"[1, 2]","[70, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[2630] -17,47,"[2, 1]","[90, 70]","[1, 2]",[2],[1],[2],[90],[9],[2],[1],[3361] -17,48,"[4, 3]","[40, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2506] -17,49,"[3, 4]","[50, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1983] -17,50,"[1, 2]","[50, 20]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[2083] -17,51,"[2, 1]","[0, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[1902] -17,52,"[4, 3]","[30, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[8650] -17,53,"[1, 2]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[1706] -17,54,"[2, 1]","[50, 20]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2195] -17,55,"[1, 2]","[20, 10]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[2538] -17,56,"[1, 2]","[0, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2504] -17,57,"[3, 4]","[30, 10]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1866] -17,58,"[3, 4]","[0, 20]","[2, 1]",[2],[5],[3],[0],[0],[1],[4],[1999] -17,59,"[3, 4]","[10, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2104] -17,60,"[3, 4]","[40, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2372] -17,61,"[2, 1]","[40, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[1987] -17,62,"[1, 2]","[60, 50]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[2022] -17,63,"[1, 2]","[70, 40]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[1840] -17,64,"[3, 4]","[50, 40]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2099] -17,65,"[2, 1]","[80, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2818] -17,66,"[2, 1]","[60, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[1758] -17,67,"[4, 3]","[40, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[2049] -17,68,"[2, 1]","[50, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2002] -17,69,"[1, 2]","[70, 60]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[3056] -17,70,"[3, 4]","[70, 50]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1613] -17,71,"[3, 4]","[80, 60]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1641] -17,72,"[3, 4]","[60, 10]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[2739] -17,73,"[1, 2]","[50, 10]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[3369] -17,74,"[1, 2]","[30, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[1691] -17,75,"[4, 3]","[30, 10]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1751] -17,76,"[3, 4]","[70, 30]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1860] -17,77,"[2, 1]","[30, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2022] -17,78,"[2, 1]","[10, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[2424] -17,79,"[2, 1]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[3496] -17,80,"[2, 1]","[10, 30]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2785] -18,1,"[4, 3]","[20, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[8945] -18,2,"[1, 2]","[80, 30]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[3103] -18,3,"[4, 3]","[20, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2346] -18,4,"[2, 1]","[20, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2267] -18,5,"[2, 1]","[50, 80]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[2251] -18,6,"[2, 1]","[70, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1869] -18,7,"[1, 2]","[80, 50]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1784] -18,8,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1871] -18,9,"[3, 4]","[80, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2007] -18,10,"[1, 2]","[90, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[1839] -18,11,"[2, 1]","[50, 70]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[1851] -18,12,"[3, 4]","[40, 70]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1753] -18,13,"[2, 1]","[70, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[1651] -18,14,"[4, 3]","[80, 70]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2319] -18,15,"[4, 3]","[80, 90]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[2189] -18,16,"[1, 2]","[70, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[1771] -18,17,"[3, 4]","[40, 90]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[1732] -18,18,"[1, 2]","[40, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1753] -18,19,"[4, 3]","[80, 30]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[1783] -18,20,"[2, 1]","[90, 40]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[2252] -18,21,"[1, 2]","[50, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2110] -18,22,"[2, 1]","[80, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2471] -18,23,"[4, 3]","[90, 70]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[1680] -18,24,"[1, 2]","[70, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1636] -18,25,"[1, 2]","[60, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[1731] -18,26,"[2, 1]","[50, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[1647] -18,27,"[1, 2]","[80, 70]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1611] -18,28,"[2, 1]","[60, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1713] -18,29,"[3, 4]","[90, 60]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[1702] -18,30,"[1, 2]","[90, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[1730] -18,31,"[1, 2]","[80, 30]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1741] -18,32,"[4, 3]","[80, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1735] -18,33,"[3, 4]","[80, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1690] -18,34,"[2, 1]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[1881] -18,35,"[4, 3]","[70, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1898] -18,36,"[4, 3]","[60, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1861] -18,37,"[1, 2]","[40, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[1595] -18,38,"[3, 4]","[70, 50]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[1869] -18,39,"[4, 3]","[30, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2637] -18,40,"[3, 4]","[80, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[1705] -18,41,"[2, 1]","[0, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[3935] -18,42,"[1, 2]","[80, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[4524] -18,43,"[2, 1]","[0, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2076] -18,44,"[3, 4]","[80, 0]","[2, 2]",[2],[5],[4],[0],[0],[2],[3],[1397] -18,45,"[3, 4]","[90, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[15823] -18,46,"[3, 4]","[70, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[4016] -18,47,"[4, 3]","[0, 80]","[2, 2]",[2],[1],[4],[0],[0],[2],[4],[9036] -18,48,"[4, 3]","[0, 70]","[2, 2]",[2],[5],[4],[0],[0],[2],[4],[3910] -18,49,"[3, 4]","[70, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1490] -18,50,"[2, 1]","[40, 70]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[1684] -18,51,"[2, 1]","[30, 50]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[1360] -18,52,"[2, 1]","[30, 80]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[1358] -18,53,"[2, 1]","[40, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1564] -18,54,"[3, 4]","[80, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[1479] -18,55,"[1, 2]","[80, 90]","[1, 2]",[2],[1],[2],[90],[9],[2],[2],[8352] -18,56,"[4, 3]","[80, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[2622] -18,57,"[4, 3]","[80, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1732] -18,58,"[3, 4]","[90, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[1596] -18,59,"[2, 1]","[80, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[1858] -18,60,"[4, 3]","[50, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[7904] -18,61,"[1, 2]","[60, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[3226] -18,62,"[4, 3]","[30, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1460] -18,63,"[1, 2]","[60, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1332] -18,64,"[2, 1]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1806] -18,65,"[1, 2]","[70, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[1417] -18,66,"[3, 4]","[70, 30]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1493] -18,67,"[3, 4]","[80, 20]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2051] -18,68,"[1, 2]","[90, 0]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[1821] -18,69,"[2, 1]","[10, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1272] -18,70,"[4, 3]","[10, 90]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[7964] -18,71,"[2, 1]","[10, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[23888] -18,72,"[2, 1]","[0, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[5860] -18,73,"[2, 1]","[10, 60]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[1384] -18,74,"[4, 3]","[30, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[1464] -18,75,"[1, 2]","[0, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[13405] -18,76,"[1, 2]","[10, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[3606] -18,77,"[4, 3]","[0, 20]","[2, 2]",[2],[1],[4],[0],[0],[2],[4],[1616] -18,78,"[4, 3]","[10, 0]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[15263] -18,79,"[1, 2]","[20, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[5649] -18,80,"[2, 1]","[20, 40]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[15111] -19,1,"[2, 1]","[40, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[4019] -19,2,"[3, 4]","[70, 10]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[1955] -19,3,"[1, 2]","[80, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[2626] -19,4,"[3, 4]","[70, 10]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[1755] -19,5,"[3, 4]","[60, 20]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1976] -19,6,"[3, 4]","[60, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1744] -19,7,"[2, 1]","[10, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2794] -19,8,"[1, 2]","[60, 20]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2292] -19,9,"[3, 4]","[90, 30]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[1892] -19,10,"[2, 1]","[10, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2094] -19,11,"[1, 2]","[60, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[1943] -19,12,"[4, 3]","[10, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2141] -19,13,"[3, 4]","[50, 40]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[1992] -19,14,"[3, 4]","[90, 40]","[2, 1]",[1],[1],[3],[90],[9],[1],[3],[1810] -19,15,"[2, 1]","[50, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2423] -19,16,"[2, 1]","[50, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[2005] -19,17,"[4, 3]","[40, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[1926] -19,18,"[3, 4]","[40, 20]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[2526] -19,19,"[2, 1]","[40, 30]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[1858] -19,20,"[2, 1]","[30, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[2288] -19,21,"[2, 1]","[10, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[2111] -19,22,"[4, 3]","[0, 10]","[2, 2]",[1],[5],[4],[0],[0],[2],[4],[1858] -19,23,"[3, 4]","[10, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[4156] -19,24,"[2, 1]","[10, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[2],[2426] -19,25,"[3, 4]","[10, 30]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[1859] -19,26,"[4, 3]","[40, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[1878] -19,27,"[1, 2]","[10, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2708] -19,28,"[2, 1]","[50, 40]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[1926] -19,29,"[3, 4]","[50, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2040] -19,30,"[1, 2]","[50, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1926] -19,31,"[3, 4]","[30, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2026] -19,32,"[4, 3]","[70, 30]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[1942] -19,33,"[2, 1]","[60, 30]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2643] -19,34,"[2, 1]","[60, 50]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2225] -19,35,"[2, 1]","[90, 30]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[1840] -19,36,"[3, 4]","[50, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2092] -19,37,"[1, 2]","[50, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2143] -19,38,"[2, 1]","[40, 70]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[2211] -19,39,"[4, 3]","[50, 40]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[1976] -19,40,"[3, 4]","[40, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2757] -19,41,"[2, 1]","[80, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[5187] -19,42,"[3, 4]","[70, 80]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1710] -19,43,"[1, 2]","[60, 80]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1952] -19,44,"[1, 2]","[50, 70]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[1900] -19,45,"[2, 1]","[70, 40]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2825] -19,46,"[2, 1]","[30, 20]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[1821] -19,47,"[2, 1]","[40, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[1692] -19,48,"[4, 3]","[60, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[1692] -19,49,"[1, 2]","[80, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2209] -19,50,"[3, 4]","[70, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[2158] -19,51,"[3, 4]","[60, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1693] -19,52,"[3, 4]","[80, 40]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1728] -19,53,"[4, 3]","[60, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1859] -19,54,"[4, 3]","[60, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[1737] -19,55,"[1, 2]","[40, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2353] -19,56,"[4, 3]","[40, 20]","[2, 1]",[2],[1],[3],[20],[2],[1],[3],[1876] -19,57,"[4, 3]","[30, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[1872] -19,58,"[1, 2]","[10, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[1992] -19,59,"[4, 3]","[10, 20]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1727] -19,60,"[3, 4]","[10, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[1805] -19,61,"[4, 3]","[20, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1708] -19,62,"[2, 1]","[10, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[2510] -19,63,"[3, 4]","[30, 20]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[2027] -19,64,"[4, 3]","[20, 20]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[2009] -19,65,"[2, 1]","[40, 0]","[1, 1]",[2],[1],[1],[0],[0],[1],[1],[1760] -19,66,"[2, 1]","[50, 20]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[3292] -19,67,"[3, 4]","[30, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[1760] -19,68,"[3, 4]","[40, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[1825] -19,69,"[1, 2]","[50, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[2027] -19,70,"[2, 1]","[70, 50]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2494] -19,71,"[1, 2]","[40, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[1977] -19,72,"[4, 3]","[90, 40]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[1975] -19,73,"[1, 2]","[50, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2143] -19,74,"[4, 3]","[90, 60]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[2643] -19,75,"[2, 1]","[90, 60]","[1, 2]",[2],[5],[2],[450],[9],[2],[1],[2608] -19,76,"[3, 4]","[50, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2395] -19,77,"[1, 2]","[70, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2343] -19,78,"[4, 3]","[80, 70]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1951] -19,79,"[1, 2]","[40, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[1977] -19,80,"[4, 3]","[90, 20]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[3425] -19,1,"[1, 2]","[90, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[3306] -19,2,"[2, 1]","[30, 70]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[2077] -19,3,"[3, 4]","[50, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[1672] -19,4,"[3, 4]","[20, 70]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[1880] -19,5,"[2, 1]","[90, 30]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[1943] -19,6,"[1, 2]","[0, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2092] -19,7,"[4, 3]","[90, 0]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[1809] -19,8,"[1, 2]","[20, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1669] -19,9,"[4, 3]","[80, 30]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[1670] -19,10,"[3, 4]","[60, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1909] -19,11,"[2, 1]","[60, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[1839] -19,12,"[1, 2]","[60, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[1961] -19,13,"[2, 1]","[70, 40]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1956] -19,14,"[4, 3]","[90, 30]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[1758] -19,15,"[3, 4]","[10, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2062] -19,16,"[4, 3]","[50, 40]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[1811] -19,17,"[2, 1]","[20, 0]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[1744] -19,18,"[4, 3]","[30, 0]","[2, 1]",[1],[5],[3],[0],[0],[1],[3],[1926] -19,19,"[4, 3]","[50, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[2175] -19,20,"[2, 1]","[50, 20]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[1771] -19,21,"[1, 2]","[40, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[3211] -19,22,"[3, 4]","[40, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[2044] -19,23,"[3, 4]","[60, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2006] -19,24,"[3, 4]","[30, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[2211] -19,25,"[2, 1]","[80, 30]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1906] -19,26,"[2, 1]","[70, 10]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1959] -19,27,"[2, 1]","[90, 0]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[2026] -19,28,"[4, 3]","[80, 0]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[1827] -19,29,"[2, 1]","[80, 10]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1759] -19,30,"[1, 2]","[30, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2058] -19,31,"[1, 2]","[40, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[1927] -19,32,"[4, 3]","[60, 40]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[1760] -19,33,"[2, 1]","[60, 70]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[1744] -19,34,"[4, 3]","[40, 70]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1693] -19,35,"[4, 3]","[10, 60]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[2124] -19,36,"[4, 3]","[20, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1792] -19,37,"[3, 4]","[50, 80]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1926] -19,38,"[1, 2]","[70, 90]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2160] -19,39,"[3, 4]","[70, 40]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[2393] -19,40,"[3, 4]","[70, 40]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[3393] -19,41,"[4, 3]","[20, 70]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[5589] -19,42,"[4, 3]","[20, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1708] -19,43,"[4, 3]","[30, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[2010] -19,44,"[4, 3]","[40, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1793] -19,45,"[1, 2]","[60, 50]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2109] -19,46,"[1, 2]","[50, 20]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[1740] -19,47,"[1, 2]","[40, 10]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[1876] -19,48,"[2, 1]","[20, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[2056] -19,49,"[1, 2]","[20, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[1977] -19,50,"[3, 4]","[10, 40]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[2042] -19,51,"[1, 2]","[30, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1993] -19,52,"[1, 2]","[30, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[1960] -19,53,"[2, 1]","[90, 70]","[1, 2]",[2],[5],[2],[450],[9],[2],[1],[1978] -19,54,"[3, 4]","[60, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2009] -19,55,"[4, 3]","[60, 80]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[2061] -19,56,"[1, 2]","[60, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[1977] -19,57,"[1, 2]","[80, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[1775] -19,58,"[3, 4]","[80, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1894] -19,59,"[3, 4]","[80, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1807] -19,60,"[3, 4]","[80, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[1793] -19,61,"[4, 3]","[50, 80]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[2089] -19,62,"[3, 4]","[70, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1725] -19,63,"[4, 3]","[40, 50]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1794] -19,64,"[2, 1]","[30, 20]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2227] -19,65,"[4, 3]","[40, 10]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1867] -19,66,"[2, 1]","[60, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2011] -19,67,"[4, 3]","[70, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1656] -19,68,"[4, 3]","[80, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1880] -19,69,"[4, 3]","[80, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[1891] -19,70,"[1, 2]","[40, 90]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[1826] -19,71,"[2, 1]","[70, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[1793] -19,72,"[1, 2]","[10, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2060] -19,73,"[2, 1]","[60, 10]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[2060] -19,74,"[1, 2]","[10, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2076] -19,75,"[2, 1]","[90, 10]","[1, 2]",[2],[1],[2],[90],[9],[2],[1],[2060] -19,76,"[2, 1]","[80, 10]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[1878] -19,77,"[3, 4]","[60, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[1989] -19,78,"[3, 4]","[40, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2042] -19,79,"[4, 3]","[80, 20]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2023] -19,80,"[4, 3]","[80, 30]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[2010] -20,1,"[4, 3]","[20, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[20371] -20,2,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2231] -20,3,"[1, 2]","[80, 30]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2322] -20,4,"[2, 1]","[60, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2389] -20,5,"[4, 3]","[40, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[2264] -20,6,"[4, 3]","[10, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[2214] -20,7,"[1, 2]","[70, 0]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2872] -20,8,"[3, 4]","[40, 10]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[2496] -20,9,"[2, 1]","[30, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2205] -20,10,"[4, 3]","[40, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[2213] -20,11,"[1, 2]","[20, 40]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[2248] -20,12,"[3, 4]","[20, 50]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[2814] -20,13,"[1, 2]","[50, 60]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[3713] -20,14,"[1, 2]","[50, 40]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2480] -20,15,"[4, 3]","[50, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2097] -20,16,"[2, 1]","[70, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2296] -20,17,"[4, 3]","[70, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[2047] -20,18,"[3, 4]","[60, 90]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[2856] -20,19,"[2, 1]","[70, 80]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2997] -20,20,"[4, 3]","[80, 70]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[3088] -20,21,"[3, 4]","[70, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2305] -20,22,"[2, 1]","[80, 60]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2281] -20,23,"[4, 3]","[70, 30]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[1905] -20,24,"[4, 3]","[50, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[3055] -20,25,"[1, 2]","[20, 50]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[2255] -20,26,"[1, 2]","[10, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2188] -20,27,"[1, 2]","[20, 40]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[3388] -20,28,"[3, 4]","[40, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1873] -20,29,"[2, 1]","[80, 60]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2163] -20,30,"[3, 4]","[50, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[2263] -20,31,"[1, 2]","[60, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2531] -20,32,"[3, 4]","[80, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2521] -20,33,"[1, 2]","[90, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2005] -20,34,"[3, 4]","[80, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2605] -20,35,"[3, 4]","[80, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2122] -20,36,"[3, 4]","[70, 20]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2096] -20,37,"[1, 2]","[60, 20]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[1956] -20,38,"[1, 2]","[60, 20]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[1905] -20,39,"[3, 4]","[90, 30]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[2580] -20,40,"[4, 3]","[20, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[4163] -20,41,"[4, 3]","[20, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[6474] -20,42,"[4, 3]","[30, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1839] -20,43,"[4, 3]","[20, 90]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[2521] -20,44,"[3, 4]","[50, 10]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2114] -20,45,"[2, 1]","[50, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2172] -20,46,"[2, 1]","[60, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[2272] -20,47,"[3, 4]","[90, 10]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[1955] -20,48,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2389] -20,49,"[3, 4]","[50, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2056] -20,50,"[2, 1]","[30, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2155] -20,51,"[1, 2]","[50, 10]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[2222] -20,52,"[3, 4]","[40, 30]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1964] -20,53,"[2, 1]","[20, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[2080] -20,54,"[1, 2]","[30, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2064] -20,55,"[1, 2]","[60, 20]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[2597] -20,56,"[1, 2]","[40, 20]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[1963] -20,57,"[2, 1]","[10, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[2089] -20,58,"[3, 4]","[10, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[2405] -20,59,"[4, 3]","[20, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[2196] -20,60,"[1, 2]","[10, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[1980] -20,61,"[1, 2]","[20, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2089] -20,62,"[4, 3]","[80, 40]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2306] -20,63,"[4, 3]","[60, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2554] -20,64,"[4, 3]","[80, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[178739] -20,65,"[4, 3]","[70, 80]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[2056] -20,66,"[1, 2]","[80, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1947] -20,67,"[1, 2]","[70, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[2606] -20,68,"[4, 3]","[10, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1881] -20,69,"[2, 1]","[10, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[2031] -20,70,"[3, 4]","[80, 20]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2049] -20,71,"[2, 1]","[50, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1988] -20,72,"[2, 1]","[40, 90]","[1, 1]",[2],[1],[1],[90],[9],[1],[2],[1854] -20,73,"[4, 3]","[50, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1833] -20,74,"[3, 4]","[70, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[2072] -20,75,"[4, 3]","[30, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2150] -20,76,"[1, 2]","[90, 30]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[2030] -20,77,"[2, 1]","[50, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2116] -20,78,"[1, 2]","[80, 40]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2122] -20,79,"[4, 3]","[50, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1856] -20,80,"[3, 4]","[80, 30]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2496] -20,1,"[4, 3]","[30, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[4087] -20,2,"[3, 4]","[60, 10]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[2139] -20,3,"[2, 1]","[0, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2056] -20,4,"[1, 2]","[40, 0]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[2056] -20,5,"[4, 3]","[30, 70]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[2306] -20,6,"[3, 4]","[60, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2056] -20,7,"[3, 4]","[80, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[1806] -20,8,"[2, 1]","[80, 90]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2030] -20,9,"[2, 1]","[70, 80]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2814] -20,10,"[3, 4]","[90, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[1954] -20,11,"[2, 1]","[50, 80]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2098] -20,12,"[2, 1]","[60, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2164] -20,13,"[1, 2]","[70, 50]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1921] -20,14,"[1, 2]","[70, 30]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1997] -20,15,"[2, 1]","[30, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[2154] -20,16,"[1, 2]","[70, 30]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1990] -20,17,"[4, 3]","[30, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[2072] -20,18,"[2, 1]","[50, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1922] -20,19,"[1, 2]","[20, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[3699] -20,20,"[4, 3]","[30, 20]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[2056] -20,21,"[3, 4]","[20, 40]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[3472] -20,22,"[4, 3]","[50, 0]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2481] -20,23,"[1, 2]","[30, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[1956] -20,24,"[4, 3]","[40, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[1855] -20,25,"[1, 2]","[40, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[2005] -20,26,"[1, 2]","[60, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[1930] -20,27,"[2, 1]","[20, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1914] -20,28,"[4, 3]","[40, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[1931] -20,29,"[2, 1]","[10, 20]","[1, 2]",[1],[1],[2],[10],[1],[2],[2],[2365] -20,30,"[1, 2]","[10, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[3363] -20,31,"[4, 3]","[20, 40]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[2206] -20,32,"[3, 4]","[50, 40]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[3030] -20,33,"[3, 4]","[50, 30]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[2116] -20,34,"[2, 1]","[60, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[2088] -20,35,"[1, 2]","[0, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[3205] -20,36,"[1, 2]","[0, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2772] -20,37,"[4, 3]","[70, 0]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2031] -20,38,"[2, 1]","[80, 10]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[1919] -20,39,"[3, 4]","[20, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1984] -20,40,"[4, 3]","[80, 60]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2172] -20,41,"[4, 3]","[60, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[5541] -20,42,"[4, 3]","[70, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1866] -20,43,"[3, 4]","[60, 90]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[2423] -20,44,"[1, 2]","[60, 80]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2105] -20,45,"[3, 4]","[70, 60]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1946] -20,46,"[4, 3]","[60, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1863] -20,47,"[4, 3]","[80, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1997] -20,48,"[4, 3]","[70, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1933] -20,49,"[2, 1]","[60, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1839] -20,50,"[3, 4]","[60, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2255] -20,51,"[2, 1]","[50, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1879] -20,52,"[4, 3]","[60, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1881] -20,53,"[1, 2]","[80, 50]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2122] -20,54,"[4, 3]","[30, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1823] -20,55,"[1, 2]","[70, 40]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2722] -20,56,"[3, 4]","[50, 0]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2148] -20,57,"[2, 1]","[0, 20]","[1, 2]",[2],[5],[2],[0],[0],[2],[2],[2639] -20,58,"[1, 2]","[30, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[2172] -20,59,"[2, 1]","[40, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[2183] -20,60,"[3, 4]","[40, 30]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[2897] -20,61,"[1, 2]","[0, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[2239] -20,62,"[4, 3]","[10, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[2233] -20,63,"[1, 2]","[50, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[3097] -20,64,"[2, 1]","[10, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2339] -20,65,"[3, 4]","[60, 10]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[2582] -20,66,"[2, 1]","[20, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1830] -20,67,"[2, 1]","[10, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1881] -20,68,"[3, 4]","[50, 30]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2156] -20,69,"[1, 2]","[40, 60]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[2322] -20,70,"[4, 3]","[60, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[1889] -20,71,"[2, 1]","[40, 10]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2180] -20,72,"[3, 4]","[30, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2122] -20,73,"[4, 3]","[80, 30]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[2021] -20,74,"[3, 4]","[40, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[2171] -20,75,"[3, 4]","[20, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1848] -20,76,"[1, 2]","[10, 30]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[1872] -20,77,"[4, 3]","[20, 10]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1946] -20,78,"[1, 2]","[20, 0]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[1948] -20,79,"[2, 1]","[40, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[3120] -20,80,"[2, 1]","[60, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[2355] -21,1,"[1, 2]","[60, 40]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[4791] -21,2,"[2, 1]","[30, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[2229] -21,3,"[3, 4]","[10, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[1748] -21,4,"[3, 4]","[40, 10]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[2661] -21,5,"[2, 1]","[0, 50]","[1, 2]",[1],[1],[2],[0],[0],[2],[2],[3103] -21,6,"[4, 3]","[50, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[1844] -21,7,"[1, 2]","[80, 60]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2137] -21,8,"[4, 3]","[80, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1853] -21,9,"[2, 1]","[50, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[1994] -21,10,"[2, 1]","[60, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[3119] -21,11,"[1, 2]","[70, 60]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2444] -21,12,"[2, 1]","[60, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2195] -21,13,"[4, 3]","[70, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1810] -21,14,"[2, 1]","[70, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1837] -21,15,"[1, 2]","[80, 90]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2220] -21,16,"[2, 1]","[90, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2120] -21,17,"[2, 1]","[50, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2152] -21,18,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1794] -21,19,"[3, 4]","[40, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2903] -21,20,"[4, 3]","[20, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[2220] -21,21,"[1, 2]","[40, 20]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[2362] -21,22,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1853] -21,23,"[1, 2]","[80, 40]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1828] -21,24,"[4, 3]","[60, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1912] -21,25,"[4, 3]","[40, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2003] -21,26,"[4, 3]","[60, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1711] -21,27,"[4, 3]","[60, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1770] -21,28,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[3411] -21,29,"[1, 2]","[80, 40]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2220] -21,30,"[4, 3]","[40, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1754] -21,31,"[3, 4]","[80, 50]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1928] -21,32,"[3, 4]","[80, 40]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[2154] -21,33,"[3, 4]","[60, 30]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2029] -21,34,"[1, 2]","[50, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[1803] -21,35,"[4, 3]","[40, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1804] -21,36,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1771] -21,37,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1811] -21,38,"[3, 4]","[90, 70]","[2, 1]",[1],[1],[3],[90],[9],[1],[3],[1779] -21,39,"[2, 1]","[50, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1736] -21,40,"[2, 1]","[70, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[1978] -21,41,"[2, 1]","[80, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[4183] -21,42,"[2, 1]","[80, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[2587] -21,43,"[4, 3]","[70, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[2045] -21,44,"[3, 4]","[80, 40]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2054] -21,45,"[1, 2]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[2203] -21,46,"[2, 1]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2004] -21,47,"[3, 4]","[80, 70]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1870] -21,48,"[4, 3]","[40, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[1903] -21,49,"[1, 2]","[70, 60]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2053] -21,50,"[3, 4]","[60, 40]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1887] -21,51,"[4, 3]","[30, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1828] -21,52,"[2, 1]","[30, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1862] -21,53,"[1, 2]","[0, 30]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[2111] -21,54,"[4, 3]","[10, 20]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[1961] -21,55,"[3, 4]","[20, 0]","[2, 2]",[2],[1],[4],[0],[0],[2],[3],[1811] -21,56,"[2, 1]","[0, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[2136] -21,57,"[1, 2]","[0, 10]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[2204] -21,58,"[1, 2]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2461] -21,59,"[4, 3]","[30, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[1763] -21,60,"[4, 3]","[30, 10]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1945] -21,61,"[3, 4]","[40, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[2095] -21,62,"[2, 1]","[40, 60]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2353] -21,63,"[1, 2]","[60, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[1895] -21,64,"[1, 2]","[50, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1903] -21,65,"[4, 3]","[30, 60]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[1996] -21,66,"[4, 3]","[30, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1929] -21,67,"[2, 1]","[40, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[1878] -21,68,"[3, 4]","[70, 20]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[3611] -21,69,"[4, 3]","[10, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1978] -21,70,"[1, 2]","[80, 60]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2027] -21,71,"[3, 4]","[70, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1954] -21,72,"[4, 3]","[70, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1837] -21,73,"[1, 2]","[20, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2537] -21,74,"[3, 4]","[60, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2012] -21,75,"[3, 4]","[70, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1862] -21,76,"[2, 1]","[50, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2737] -21,77,"[1, 2]","[50, 30]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[2270] -21,78,"[4, 3]","[60, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1962] -21,79,"[4, 3]","[90, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1986] -21,80,"[1, 2]","[40, 80]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[2020] -21,1,"[4, 3]","[70, 10]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[3779] -21,2,"[3, 4]","[0, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2270] -21,3,"[2, 1]","[90, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[1754] -21,4,"[2, 1]","[80, 20]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2078] -21,5,"[3, 4]","[10, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[2005] -21,6,"[3, 4]","[10, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2130] -21,7,"[2, 1]","[60, 0]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2887] -21,8,"[3, 4]","[0, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2330] -21,9,"[2, 1]","[40, 0]","[1, 1]",[1],[1],[1],[0],[0],[1],[1],[2272] -21,10,"[3, 4]","[10, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1962] -21,11,"[1, 2]","[10, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[2054] -21,12,"[3, 4]","[10, 20]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[1897] -21,13,"[3, 4]","[20, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2013] -21,14,"[1, 2]","[10, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2112] -21,15,"[2, 1]","[80, 20]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2222] -21,16,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[2546] -21,17,"[4, 3]","[90, 30]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2305] -21,18,"[3, 4]","[0, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1770] -21,19,"[4, 3]","[80, 0]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[1922] -21,20,"[4, 3]","[90, 20]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[1745] -21,21,"[2, 1]","[80, 20]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2604] -21,22,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[2087] -21,23,"[2, 1]","[80, 20]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2780] -21,24,"[3, 4]","[30, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1995] -21,25,"[1, 2]","[40, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2055] -21,26,"[1, 2]","[20, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2221] -21,27,"[4, 3]","[80, 30]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2104] -21,28,"[1, 2]","[30, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2238] -21,29,"[3, 4]","[50, 70]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2071] -21,30,"[3, 4]","[70, 50]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1779] -21,31,"[2, 1]","[30, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2254] -21,32,"[4, 3]","[20, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[1962] -21,33,"[3, 4]","[50, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1871] -21,34,"[4, 3]","[40, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[1755] -21,35,"[3, 4]","[30, 60]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[1821] -21,36,"[3, 4]","[10, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2012] -21,37,"[3, 4]","[20, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2054] -21,38,"[4, 3]","[80, 40]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2029] -21,39,"[4, 3]","[70, 50]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[1888] -21,40,"[4, 3]","[80, 60]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[1880] -21,41,"[2, 1]","[50, 60]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[3300] -21,42,"[3, 4]","[60, 30]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2253] -21,43,"[2, 1]","[30, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2038] -21,44,"[4, 3]","[30, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1905] -21,45,"[3, 4]","[70, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2054] -21,46,"[1, 2]","[80, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[2220] -21,47,"[1, 2]","[70, 40]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2221] -21,48,"[2, 1]","[40, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2470] -21,49,"[4, 3]","[50, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1846] -21,50,"[3, 4]","[70, 40]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1955] -21,51,"[1, 2]","[30, 70]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[2080] -21,52,"[1, 2]","[0, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[1929] -21,53,"[4, 3]","[40, 10]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[2320] -21,54,"[4, 3]","[30, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[2054] -21,55,"[3, 4]","[10, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2170] -21,56,"[2, 1]","[10, 30]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2329] -21,57,"[2, 1]","[10, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2096] -21,58,"[4, 3]","[0, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1821] -21,59,"[2, 1]","[0, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2446] -21,60,"[3, 4]","[80, 30]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2346] -21,61,"[4, 3]","[10, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1854] -21,62,"[2, 1]","[10, 90]","[1, 1]",[2],[1],[1],[90],[9],[1],[2],[2222] -21,63,"[4, 3]","[40, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1813] -21,64,"[2, 1]","[20, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2062] -21,65,"[2, 1]","[20, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1946] -21,66,"[3, 4]","[60, 0]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2029] -21,67,"[2, 1]","[0, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[3087] -21,68,"[2, 1]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2412] -21,69,"[4, 3]","[30, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[1845] -21,70,"[4, 3]","[30, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[1939] -21,71,"[3, 4]","[50, 10]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1804] -21,72,"[2, 1]","[20, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2087] -21,73,"[4, 3]","[40, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[2104] -21,74,"[3, 4]","[30, 50]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[2113] -21,75,"[3, 4]","[20, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[2047] -21,76,"[4, 3]","[10, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[2487] -21,77,"[1, 2]","[0, 10]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[2113] -21,78,"[3, 4]","[0, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[2079] -21,79,"[3, 4]","[20, 0]","[2, 2]",[2],[5],[4],[0],[0],[2],[3],[1880] -21,80,"[4, 3]","[20, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[2095] -22,1,"[1, 2]","[10, 50]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[7704] -22,2,"[1, 2]","[0, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[4504] -22,3,"[3, 4]","[0, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2307] -22,4,"[4, 3]","[70, 20]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[3257] -22,5,"[4, 3]","[50, 0]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2781] -22,6,"[2, 1]","[90, 0]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[6058] -22,7,"[2, 1]","[50, 20]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2808] -22,8,"[2, 1]","[30, 10]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[2276] -22,9,"[4, 3]","[60, 10]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[3015] -22,10,"[1, 2]","[10, 30]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[3965] -22,11,"[3, 4]","[20, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[3549] -22,12,"[4, 3]","[30, 0]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[3647] -22,13,"[1, 2]","[20, 30]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[4097] -22,14,"[2, 1]","[0, 10]","[1, 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1]",[1],[1],[1],[80],[8],[1],[1],[1734] -24,7,"[2, 1]","[40, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[1734] -24,8,"[2, 1]","[10, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1884] -24,9,"[4, 3]","[0, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[9353] -24,10,"[2, 1]","[20, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[1801] -24,11,"[4, 3]","[20, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[1851] -24,12,"[2, 1]","[30, 0]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[19969] -24,13,"[3, 4]","[50, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[4802] -24,14,"[1, 2]","[60, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[6836] -24,15,"[3, 4]","[50, 80]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[1572] -24,16,"[1, 2]","[90, 70]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[2967] -24,17,"[1, 2]","[80, 60]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[32206] -24,18,"[3, 4]","[80, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2400] -24,19,"[3, 4]","[90, 90]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[1665] -24,20,"[3, 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2]",[1],[1],[2],[50],[5],[2],[2],[1943] -24,34,"[3, 4]","[40, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2178] -24,35,"[4, 3]","[40, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[1784] -24,36,"[1, 2]","[50, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2068] -24,37,"[1, 2]","[10, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[3002] -24,38,"[1, 2]","[0, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[1634] -24,39,"[2, 1]","[50, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[9934] -24,40,"[3, 4]","[50, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1784] -24,41,"[4, 3]","[20, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[11447] -24,42,"[3, 4]","[70, 10]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1900] -24,43,"[1, 2]","[50, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[2219] -24,44,"[2, 1]","[40, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2985] -24,45,"[4, 3]","[60, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2903] -24,46,"[1, 2]","[70, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[4585] -24,47,"[2, 1]","[90, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[3300] -24,48,"[4, 3]","[40, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1750] -24,49,"[3, 4]","[50, 30]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1851] -24,50,"[1, 2]","[50, 20]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[2852] -24,51,"[2, 1]","[0, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[3901] -24,52,"[4, 3]","[30, 10]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[4235] -24,53,"[1, 2]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[4801] -24,54,"[2, 1]","[50, 20]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1864] -24,55,"[1, 2]","[20, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[2352] -24,56,"[1, 2]","[0, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[1984] -24,57,"[3, 4]","[30, 10]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[7669] -24,58,"[3, 4]","[0, 20]","[2, 1]",[2],[5],[3],[0],[0],[1],[4],[2400] -24,59,"[3, 4]","[10, 30]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[12069] -24,60,"[3, 4]","[40, 20]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1767] -24,61,"[2, 1]","[40, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[1703] -24,62,"[1, 2]","[60, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[1751] -24,63,"[1, 2]","[70, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[8235] -24,64,"[3, 4]","[50, 40]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2202] -24,65,"[2, 1]","[80, 70]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[19050] -24,66,"[2, 1]","[60, 80]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[7485] -24,67,"[4, 3]","[40, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[6570] -24,68,"[2, 1]","[50, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2251] -24,69,"[1, 2]","[70, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[8152] -24,70,"[3, 4]","[70, 50]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[3334] -24,71,"[3, 4]","[80, 60]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[9752] -24,72,"[3, 4]","[60, 10]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[1920] -24,73,"[1, 2]","[50, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[2070] -24,74,"[1, 2]","[30, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[1852] -24,75,"[4, 3]","[30, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1771] -24,76,"[3, 4]","[70, 30]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[6819] -24,77,"[2, 1]","[30, 50]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[4218] -24,78,"[2, 1]","[10, 20]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[4785] -24,79,"[2, 1]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[1951] -24,80,"[2, 1]","[10, 30]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[1766] -24,1,"[1, 2]","[40, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[7636] -24,2,"[3, 4]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[1803] -24,3,"[2, 1]","[40, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[1818] -24,4,"[2, 1]","[40, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1768] -24,5,"[2, 1]","[70, 80]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[1751] -24,6,"[4, 3]","[50, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1701] -24,7,"[2, 1]","[20, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1784] -24,8,"[2, 1]","[20, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1818] -24,9,"[4, 3]","[50, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[1854] -24,10,"[3, 4]","[80, 50]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[1837] -24,11,"[4, 3]","[60, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[1771] -24,12,"[3, 4]","[80, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1617] -24,13,"[1, 2]","[60, 80]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2468] -24,14,"[4, 3]","[60, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[12353] -24,15,"[4, 3]","[60, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[12354] -24,16,"[4, 3]","[50, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[1722] -24,17,"[3, 4]","[30, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[2638] -24,18,"[1, 2]","[70, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[5806] -24,19,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[4805] -24,20,"[3, 4]","[90, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[1671] -24,21,"[4, 3]","[50, 80]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[1683] 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-24,77,"[4, 3]","[40, 20]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[3155] -24,78,"[1, 2]","[10, 20]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1654] -24,79,"[1, 2]","[20, 0]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[2105] -24,80,"[4, 3]","[20, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1855] -24,81,"[4, 3]","[20, 40]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[1856] -24,1,"[1, 2]","[0, 70]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[6570] -24,2,"[1, 2]","[20, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[1804] -24,3,"[3, 4]","[10, 50]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[1751] -24,4,"[4, 3]","[30, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[1719] -24,5,"[2, 1]","[30, 20]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[1419] -24,6,"[1, 2]","[10, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[1256] -24,7,"[4, 3]","[60, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1255] -24,8,"[2, 1]","[70, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[1321] -24,9,"[3, 4]","[0, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1256] -24,10,"[2, 1]","[40, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[1386] -24,11,"[1, 2]","[20, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[1222] -24,12,"[2, 1]","[50, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[1339] -24,13,"[4, 3]","[40, 30]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[1322] -24,14,"[2, 1]","[40, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[1335] -24,15,"[4, 3]","[30, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1353] -24,16,"[1, 2]","[10, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[1256] -24,17,"[2, 1]","[70, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[1353] -24,18,"[2, 1]","[50, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[1354] -24,19,"[4, 3]","[20, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[1319] -24,20,"[2, 1]","[0, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2385] -24,21,"[2, 1]","[0, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1322] -24,22,"[3, 4]","[90, 0]","[2, 2]",[1],[1],[4],[0],[0],[2],[3],[1205] -24,23,"[4, 3]","[0, 70]","[2, 2]",[1],[1],[4],[0],[0],[2],[4],[1352] -24,24,"[1, 2]","[60, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[1],[1337] -24,25,"[1, 2]","[70, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[1419] -24,26,"[1, 2]","[80, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[1288] -24,27,"[2, 1]","[0, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1388] -24,28,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1372] -24,29,"[3, 4]","[80, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[1286] -24,30,"[2, 1]","[10, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1319] -24,31,"[3, 4]","[20, 0]","[2, 2]",[1],[5],[4],[0],[0],[2],[3],[1337] -24,32,"[1, 2]","[50, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[1402] -24,33,"[4, 3]","[60, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1403] -24,34,"[2, 1]","[30, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1403] -24,35,"[3, 4]","[50, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[1371] -24,36,"[1, 2]","[60, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1289] -24,37,"[3, 4]","[80, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[1518] -24,38,"[3, 4]","[80, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[1238] -24,39,"[3, 4]","[70, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[1205] -24,40,"[3, 4]","[60, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[1634] -24,41,"[4, 3]","[0, 50]","[2, 2]",[2],[5],[4],[0],[0],[2],[4],[222826] -24,42,"[1, 2]","[50, 10]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[1453] -24,43,"[1, 2]","[30, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[1985] -24,44,"[2, 1]","[10, 20]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[1835] -24,45,"[1, 2]","[30, 10]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[3486] -24,46,"[4, 3]","[10, 40]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1718] -24,47,"[4, 3]","[10, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1453] -24,48,"[1, 2]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[1903] -24,49,"[3, 4]","[10, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[1468] -24,50,"[4, 3]","[10, 0]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[1291] -24,51,"[4, 3]","[30, 40]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2652] -24,52,"[4, 3]","[10, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[3603] -24,53,"[2, 1]","[30, 0]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[1289] -24,54,"[2, 1]","[30, 20]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[1386] -24,55,"[4, 3]","[40, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1420] -24,56,"[3, 4]","[50, 0]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1402] -24,57,"[4, 3]","[20, 40]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[1273] -24,58,"[4, 3]","[0, 10]","[2, 2]",[2],[5],[4],[0],[0],[2],[4],[1204] -24,59,"[1, 2]","[0, 40]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[1668] -24,60,"[4, 3]","[10, 30]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1452] -24,61,"[4, 3]","[0, 10]","[2, 2]",[2],[1],[4],[0],[0],[2],[4],[2800] -24,62,"[4, 3]","[30, 10]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1271] -24,63,"[4, 3]","[40, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1318] -24,64,"[2, 1]","[30, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1586] -24,65,"[1, 2]","[90, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1600] -24,66,"[2, 1]","[20, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1685] -24,67,"[2, 1]","[20, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2017] -24,68,"[2, 1]","[40, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1733] -24,69,"[3, 4]","[80, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[1617] -24,70,"[1, 2]","[80, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[1337] -24,71,"[1, 2]","[70, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[1335] -24,72,"[1, 2]","[60, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[1651] -24,73,"[1, 2]","[70, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1618] -24,74,"[3, 4]","[80, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1499] -24,75,"[2, 1]","[20, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1418] -24,76,"[2, 1]","[60, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1782] -24,77,"[3, 4]","[70, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[1634] -24,78,"[4, 3]","[40, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[1618] -24,79,"[1, 2]","[60, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[1750] -24,80,"[2, 1]","[40, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1600] -24,1,"[1, 2]","[60, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[3052] -24,2,"[2, 1]","[30, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[1571] -24,3,"[3, 4]","[10, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[1321] -24,4,"[3, 4]","[40, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[2751] -24,5,"[2, 1]","[0, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1668] -24,6,"[4, 3]","[50, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[1535] -24,7,"[1, 2]","[80, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[3336] -24,8,"[4, 3]","[80, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1785] -24,9,"[2, 1]","[50, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2238] -24,10,"[2, 1]","[60, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1634] -24,11,"[1, 2]","[70, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2219] -24,12,"[2, 1]","[60, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1288] -24,13,"[4, 3]","[70, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1304] -24,14,"[2, 1]","[70, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1884] -24,15,"[1, 2]","[80, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[3005] -24,16,"[2, 1]","[90, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1288] -24,17,"[2, 1]","[50, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1569] -24,18,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1504] -24,19,"[3, 4]","[40, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[1305] -24,20,"[4, 3]","[20, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1254] -24,21,"[1, 2]","[40, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[3666] -24,22,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1154] -24,23,"[1, 2]","[80, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[1319] -24,24,"[4, 3]","[60, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1469] -24,25,"[4, 3]","[40, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1633] -24,26,"[4, 3]","[60, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1652] -24,27,"[4, 3]","[60, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1802] -24,28,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1350] -24,29,"[1, 2]","[80, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[1784] -24,30,"[4, 3]","[40, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1769] -24,31,"[3, 4]","[80, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[4265] -24,32,"[3, 4]","[80, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[1617] -24,33,"[3, 4]","[60, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[1603] -24,34,"[1, 2]","[50, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[1635] -24,35,"[4, 3]","[40, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1618] -24,36,"[1, 2]","[80, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[1702] -24,37,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1717] -24,38,"[3, 4]","[90, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[1584] -24,39,"[2, 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30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1352] -24,71,"[2, 1]","[60, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[1319] -24,72,"[4, 3]","[60, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1419] -24,73,"[2, 1]","[70, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[1336] -24,74,"[4, 3]","[90, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[1452] -24,75,"[1, 2]","[40, 70]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[1368] -24,76,"[2, 1]","[80, 50]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[1254] -24,77,"[1, 2]","[80, 70]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1520] -24,78,"[2, 1]","[90, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[2073] -24,79,"[4, 3]","[90, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2987] -24,80,"[1, 2]","[60, 90]","[1, 2]",[2],[1],[2],[90],[9],[2],[2],[1635] -25,1,"[2, 1]","[40, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[11362] -25,2,"[3, 4]","[80, 40]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[2089] -25,3,"[3, 4]","[80, 60]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2114] -25,4,"[2, 1]","[30, 90]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[2204] -25,5,"[1, 2]","[70, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2967] -25,6,"[4, 3]","[10, 80]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[2240] -25,7,"[4, 3]","[0, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2315] -25,8,"[4, 3]","[20, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[2298] -25,9,"[4, 3]","[50, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[3],[2188] -25,10,"[3, 4]","[0, 60]","[2, 1]",[1],[1],[3],[0],[0],[1],[4],[2206] -25,11,"[4, 3]","[80, 0]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2214] -25,12,"[4, 3]","[70, 30]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2207] -25,13,"[2, 1]","[90, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[2641] -25,14,"[4, 3]","[90, 60]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[2701] -25,15,"[2, 1]","[80, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[2824] -25,16,"[1, 2]","[20, 60]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[2641] -25,17,"[1, 2]","[30, 60]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[2774] -25,18,"[1, 2]","[40, 50]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2233] -25,19,"[1, 2]","[50, 60]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2565] -25,20,"[3, 4]","[60, 80]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[2483] -25,21,"[2, 1]","[50, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2475] -25,22,"[1, 2]","[60, 30]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2073] -25,23,"[2, 1]","[50, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[2474] -25,24,"[2, 1]","[10, 0]","[1, 1]",[1],[5],[1],[0],[0],[1],[1],[2322] -25,25,"[1, 2]","[0, 50]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[2189] -25,26,"[2, 1]","[60, 20]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2508] -25,27,"[2, 1]","[40, 0]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[2115] -25,28,"[2, 1]","[70, 20]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2358] -25,29,"[1, 2]","[40, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2416] -25,30,"[1, 2]","[20, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2317] -25,31,"[4, 3]","[70, 40]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2341] -25,32,"[3, 4]","[50, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[2216] -25,33,"[3, 4]","[60, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2357] -25,34,"[2, 1]","[60, 80]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2548] -25,35,"[3, 4]","[90, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[2397] -25,36,"[1, 2]","[80, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2407] -25,37,"[2, 1]","[40, 70]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2501] -25,38,"[2, 1]","[0, 80]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[2458] -25,39,"[4, 3]","[10, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[2583] -25,40,"[2, 1]","[0, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[2350] -25,41,"[3, 4]","[80, 10]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[5796] -25,42,"[4, 3]","[10, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[3392] -25,43,"[2, 1]","[20, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[2408] -25,44,"[1, 2]","[60, 20]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2281] -25,45,"[4, 3]","[0, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[2049] -25,46,"[4, 3]","[40, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[2608] -25,47,"[1, 2]","[30, 20]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[2515] -25,48,"[1, 2]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[2305] -25,49,"[3, 4]","[10, 50]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[2148] -25,50,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2349] -25,51,"[2, 1]","[10, 0]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[2548] -25,52,"[1, 2]","[30, 50]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[2583] -25,53,"[2, 1]","[40, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2491] -25,54,"[2, 1]","[40, 80]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2133] -25,55,"[3, 4]","[80, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2574] -25,56,"[2, 1]","[0, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2156] -25,57,"[4, 3]","[10, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[2248] -25,58,"[3, 4]","[70, 20]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[2147] -25,59,"[1, 2]","[80, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[2475] -25,60,"[2, 1]","[0, 90]","[1, 2]",[2],[5],[2],[0],[0],[2],[2],[2366] -25,61,"[3, 4]","[70, 40]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[2334] -25,62,"[3, 4]","[60, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2182] -25,63,"[3, 4]","[90, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2207] -25,64,"[3, 4]","[60, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[2250] -25,65,"[3, 4]","[80, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2079] -25,66,"[1, 2]","[70, 80]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2233] -25,67,"[1, 2]","[90, 70]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[2216] -25,68,"[2, 1]","[50, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2558] -25,69,"[3, 4]","[90, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[2172] -25,70,"[2, 1]","[30, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2165] -25,71,"[3, 4]","[80, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2199] -25,72,"[4, 3]","[50, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[2147] -25,73,"[1, 2]","[90, 30]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[2198] -25,74,"[2, 1]","[20, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2080] -25,75,"[1, 2]","[40, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2147] -25,76,"[2, 1]","[30, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[2099] -25,77,"[4, 3]","[70, 10]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2165] -25,78,"[1, 2]","[40, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2356] -25,79,"[4, 3]","[90, 20]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[1996] -25,80,"[1, 2]","[10, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2155] -25,1,"[1, 2]","[50, 20]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[23361] -25,2,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2083] -25,3,"[2, 1]","[50, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2317] -25,4,"[2, 1]","[50, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2025] -25,5,"[2, 1]","[40, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[2317] -25,6,"[1, 2]","[60, 30]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2073] -25,7,"[4, 3]","[40, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2124] -25,8,"[3, 4]","[60, 40]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1998] -25,9,"[3, 4]","[70, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2006] -25,10,"[2, 1]","[70, 60]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2041] -25,11,"[2, 1]","[70, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2175] -25,12,"[4, 3]","[70, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1983] -25,13,"[1, 2]","[50, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2055] -25,14,"[3, 4]","[50, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2508] -25,15,"[1, 2]","[40, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2232] -25,16,"[3, 4]","[0, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2240] -25,17,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[2139] -25,18,"[2, 1]","[50, 10]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[1999] -25,19,"[1, 2]","[0, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2405] -25,20,"[4, 3]","[60, 10]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[2064] -25,21,"[4, 3]","[50, 20]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[2064] -25,22,"[3, 4]","[0, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2031] -25,23,"[4, 3]","[40, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[2066] -25,24,"[2, 1]","[40, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[2217] -25,25,"[1, 2]","[20, 30]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[2059] -25,26,"[4, 3]","[50, 40]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2148] -25,27,"[4, 3]","[40, 80]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1928] -25,28,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2015] -25,29,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1863] -25,30,"[3, 4]","[90, 50]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[2210] -25,31,"[4, 3]","[0, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1923] -25,32,"[1, 2]","[60, 30]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2221] -25,33,"[2, 1]","[20, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2224] -25,34,"[3, 4]","[60, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2374] -25,35,"[1, 2]","[30, 40]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[2541] -25,36,"[4, 3]","[30, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[2057] -25,37,"[3, 4]","[40, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[2349] -25,38,"[1, 2]","[30, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[1],[2152] -25,39,"[2, 1]","[20, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[2054] -25,40,"[3, 4]","[30, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[2215] -25,41,"[3, 4]","[40, 50]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[4575] -25,42,"[4, 3]","[50, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[2063] -25,43,"[1, 2]","[40, 50]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[4788] -25,44,"[2, 1]","[30, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2037] -25,45,"[2, 1]","[20, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2071] -25,46,"[2, 1]","[60, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2041] -25,47,"[4, 3]","[70, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2155] -25,48,"[4, 3]","[90, 10]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[1932] -25,49,"[4, 3]","[70, 40]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[2181] -25,50,"[3, 4]","[40, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[1955] -25,51,"[3, 4]","[0, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[1940] -25,52,"[1, 2]","[0, 30]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[1946] -25,53,"[4, 3]","[20, 10]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2540] -25,54,"[2, 1]","[50, 0]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[2182] -25,55,"[3, 4]","[10, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2300] -25,56,"[4, 3]","[40, 20]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2326] -25,57,"[4, 3]","[40, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[1915] -25,58,"[2, 1]","[60, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[1966] -25,59,"[2, 1]","[80, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[2416] -25,60,"[1, 2]","[30, 90]","[1, 2]",[2],[1],[2],[90],[9],[2],[2],[2507] -25,61,"[2, 1]","[70, 10]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2281] -25,62,"[1, 2]","[0, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2225] -25,63,"[1, 2]","[0, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2141] -25,64,"[4, 3]","[50, 30]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2324] -25,65,"[3, 4]","[40, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[2107] -25,66,"[2, 1]","[50, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[2132] -25,67,"[2, 1]","[70, 40]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2383] -25,68,"[2, 1]","[50, 40]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2715] -25,69,"[2, 1]","[30, 50]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2332] -25,70,"[2, 1]","[20, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[1966] -25,71,"[2, 1]","[20, 60]","[1, 2]",[2],[1],[2],[20],[2],[2],[2],[2283] -25,72,"[3, 4]","[40, 10]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2349] -25,73,"[3, 4]","[50, 10]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2273] -25,74,"[1, 2]","[40, 30]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2224] -25,75,"[4, 3]","[30, 20]","[2, 1]",[2],[1],[3],[20],[2],[1],[3],[2065] -25,76,"[4, 3]","[20, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[2232] -25,77,"[4, 3]","[10, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[2217] -25,78,"[2, 1]","[30, 10]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2324] -25,79,"[3, 4]","[20, 40]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[2182] -25,80,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[3689] -25,1,"[4, 3]","[40, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[4921] -25,2,"[2, 1]","[40, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2182] -25,3,"[4, 3]","[90, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1989] -25,4,"[3, 4]","[80, 90]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[2154] -25,5,"[4, 3]","[70, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1997] -25,6,"[2, 1]","[40, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2224] -25,7,"[4, 3]","[30, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[2147] -25,8,"[3, 4]","[40, 10]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2022] -25,9,"[2, 1]","[10, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1922] -25,10,"[3, 4]","[40, 0]","[2, 2]",[1],[5],[4],[0],[0],[2],[3],[2082] -25,11,"[3, 4]","[40, 20]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2108] -25,12,"[4, 3]","[0, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[2005] -25,13,"[1, 2]","[60, 10]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[1856] -25,14,"[1, 2]","[50, 20]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2082] -25,15,"[3, 4]","[30, 10]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[1999] -25,16,"[1, 2]","[20, 10]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[1848] -25,17,"[2, 1]","[30, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[2399] -25,18,"[1, 2]","[0, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[2353] -25,19,"[3, 4]","[0, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[2238] -25,20,"[1, 2]","[10, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[2367] -25,21,"[3, 4]","[30, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2192] -25,22,"[4, 3]","[70, 50]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[1998] -25,23,"[4, 3]","[70, 60]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2016] -25,24,"[1, 2]","[50, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2014] -25,25,"[2, 1]","[70, 40]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1898] -25,26,"[3, 4]","[20, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2190] -25,27,"[4, 3]","[40, 10]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[1938] -25,28,"[4, 3]","[70, 0]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[1949] -25,29,"[4, 3]","[30, 10]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2093] -25,30,"[3, 4]","[30, 20]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[2182] -25,31,"[1, 2]","[0, 30]","[1, 1]",[1],[1],[1],[0],[0],[1],[2],[2140] -25,32,"[2, 1]","[40, 20]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2372] -25,33,"[1, 2]","[30, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[2490] -25,34,"[2, 1]","[20, 30]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[2382] -25,35,"[4, 3]","[30, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[2073] -25,36,"[1, 2]","[10, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[1988] -25,37,"[2, 1]","[30, 0]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[2233] -25,38,"[2, 1]","[70, 30]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2334] -25,39,"[3, 4]","[70, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[2539] -25,40,"[1, 2]","[60, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[2345] -25,41,"[2, 1]","[80, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[4433] -25,42,"[1, 2]","[80, 50]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2265] -25,43,"[2, 1]","[30, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2057] -25,44,"[1, 2]","[90, 70]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[1960] -25,45,"[4, 3]","[80, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[2125] -25,46,"[4, 3]","[90, 70]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[1930] -25,47,"[2, 1]","[70, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2005] -25,48,"[2, 1]","[50, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1932] -25,49,"[2, 1]","[80, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2212] -25,50,"[4, 3]","[90, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2023] -25,51,"[4, 3]","[80, 20]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1874] -25,52,"[3, 4]","[0, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[2122] -25,53,"[2, 1]","[30, 0]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1929] -25,54,"[1, 2]","[30, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[2059] -25,55,"[1, 2]","[20, 30]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[2307] -25,56,"[3, 4]","[20, 30]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[2166] -25,57,"[2, 1]","[40, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1980] -25,58,"[4, 3]","[50, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2058] -25,59,"[2, 1]","[20, 50]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[2048] -25,60,"[4, 3]","[50, 60]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[2099] -25,61,"[2, 1]","[40, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1933] -25,62,"[1, 2]","[40, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[1988] -25,63,"[1, 2]","[20, 30]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[2056] -25,64,"[4, 3]","[30, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[2048] -25,65,"[3, 4]","[20, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[2048] -25,66,"[2, 1]","[10, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[2357] -25,67,"[3, 4]","[40, 10]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1978] -25,68,"[3, 4]","[30, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[1990] -25,69,"[2, 1]","[10, 50]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2508] -25,70,"[4, 3]","[0, 50]","[2, 2]",[2],[1],[4],[0],[0],[2],[4],[2032] -25,71,"[2, 1]","[0, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[2099] -25,72,"[2, 1]","[0, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[2063] -25,73,"[4, 3]","[10, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[2098] -25,74,"[4, 3]","[20, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[2033] -25,75,"[3, 4]","[10, 60]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[2064] -25,76,"[4, 3]","[30, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[2017] -25,77,"[1, 2]","[40, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[2328] -25,78,"[3, 4]","[40, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[2149] -25,79,"[4, 3]","[10, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[2049] -25,80,"[1, 2]","[20, 0]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[2040] -24,1,"[2, 1]","[10, 90]","[1, 2]",[1],[5],[2],[50],[1],[2],[2],[3817] -24,2,"[1, 2]","[80, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[1651] -24,3,"[4, 3]","[80, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[1901] -24,4,"[1, 2]","[60, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2268] -24,5,"[4, 3]","[80, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[2118] -24,6,"[1, 2]","[70, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2235] -24,7,"[1, 2]","[60, 0]","[1, 2]",[1],[1],[2],[0],[0],[2],[1],[4203] -24,8,"[1, 2]","[70, 0]","[1, 2]",[1],[1],[2],[0],[0],[2],[1],[1236] -24,9,"[1, 2]","[90, 20]","[1, 1]",[1],[5],[1],[450],[9],[1],[1],[1535] -24,10,"[2, 1]","[0, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1939] -24,11,"[2, 1]","[0, 70]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[1940] -24,12,"[2, 1]","[30, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1473] -24,13,"[2, 1]","[20, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1205] -24,14,"[2, 1]","[50, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[1320] -24,15,"[1, 2]","[60, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[1154] -24,16,"[1, 2]","[60, 40]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[1355] -24,17,"[3, 4]","[30, 0]","[2, 2]",[1],[1],[4],[0],[0],[2],[3],[1388] -24,18,"[1, 2]","[0, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[2],[1819] -24,19,"[4, 3]","[10, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[1685] -24,20,"[2, 1]","[0, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[2],[1603] -24,21,"[3, 4]","[20, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1650] -24,22,"[3, 4]","[10, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[1618] -24,23,"[4, 3]","[0, 20]","[2, 2]",[1],[1],[4],[0],[0],[2],[4],[2036] -24,24,"[4, 3]","[0, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1652] -24,25,"[3, 4]","[30, 10]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1719] -24,26,"[1, 2]","[40, 10]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[3704] -24,27,"[2, 1]","[50, 40]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2602] -24,28,"[1, 2]","[40, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[1670] -24,29,"[1, 2]","[70, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[1851] -24,30,"[4, 3]","[80, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1322] -24,31,"[2, 1]","[70, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[1256] -24,32,"[3, 4]","[90, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[1189] -24,33,"[3, 4]","[70, 90]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1386] -24,34,"[3, 4]","[60, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1352] -24,35,"[2, 1]","[50, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1454] -24,36,"[1, 2]","[30, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[1470] -24,37,"[3, 4]","[0, 20]","[2, 1]",[1],[5],[3],[0],[0],[1],[4],[1504] -24,38,"[1, 2]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[1438] -24,39,"[1, 2]","[30, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[1439] -24,40,"[3, 4]","[20, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2352] -24,41,"[3, 4]","[0, 10]","[2, 1]",[2],[1],[3],[0],[0],[1],[4],[1965] -24,42,"[1, 2]","[10, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[1602] -24,43,"[2, 1]","[10, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[1651] -24,44,"[3, 4]","[20, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[1668] -24,45,"[1, 2]","[10, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[2694] -24,46,"[3, 4]","[10, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[1717] -24,47,"[3, 4]","[10, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2018] -24,48,"[4, 3]","[20, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1769] -24,49,"[3, 4]","[40, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1650] -24,50,"[4, 3]","[40, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[1535] -24,51,"[3, 4]","[40, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[1884] -24,52,"[3, 4]","[50, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1288] -24,53,"[1, 2]","[60, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1636] -24,54,"[4, 3]","[50, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1818] -24,55,"[2, 1]","[40, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[1337] -24,56,"[3, 4]","[40, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1368] -24,57,"[2, 1]","[10, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[1419] -24,58,"[2, 1]","[30, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1702] -24,59,"[4, 3]","[20, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1584] -24,60,"[3, 4]","[20, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[4],[1586] -24,61,"[1, 2]","[20, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1620] -24,62,"[3, 4]","[20, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[1336] -24,63,"[4, 3]","[30, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[1601] -24,64,"[3, 4]","[60, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1254] -24,65,"[4, 3]","[0, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1204] -24,66,"[2, 1]","[10, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1734] -24,67,"[2, 1]","[0, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1305] -24,68,"[1, 2]","[50, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[1485] -24,69,"[3, 4]","[40, 0]","[2, 2]",[2],[5],[4],[0],[0],[2],[3],[1287] -24,70,"[4, 3]","[10, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1402] -24,71,"[1, 2]","[80, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[1337] -24,72,"[2, 1]","[50, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1417] -24,73,"[2, 1]","[40, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1488] -24,74,"[2, 1]","[50, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[1785] -24,75,"[2, 1]","[50, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[1719] -24,76,"[1, 2]","[10, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[1602] -24,77,"[3, 4]","[20, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[1687] -24,78,"[1, 2]","[10, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[1685] -24,79,"[2, 1]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1802] 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-24,25,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[1387] -24,26,"[1, 2]","[30, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[1390] -24,27,"[4, 3]","[80, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[1390] -24,28,"[2, 1]","[90, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[1384] -24,29,"[4, 3]","[70, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[1382] -24,30,"[3, 4]","[40, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1288] -24,31,"[2, 1]","[50, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1386] -24,32,"[1, 2]","[80, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[1],[1386] -24,33,"[3, 4]","[70, 0]","[2, 2]",[1],[5],[4],[0],[0],[2],[3],[1384] -24,34,"[3, 4]","[90, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1384] -24,35,"[4, 3]","[40, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1385] -24,36,"[2, 1]","[60, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1387] -24,37,"[4, 3]","[60, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1389] -24,38,"[1, 2]","[70, 20]","[1, 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1]",[2],[5],[1],[300],[6],[1],[1],[3438] -24,80,"[1, 2]","[60, 90]","[1, 2]",[2],[1],[2],[90],[9],[2],[2],[5419] -24,1,"[2, 1]","[20, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[15941] -24,2,"[2, 1]","[20, 70]","[1, 2]",[1],[1],[2],[20],[2],[2],[2],[15948] -24,3,"[2, 1]","[10, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1552] -24,4,"[1, 2]","[70, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[1634] -24,5,"[3, 4]","[80, 0]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[122349] -24,6,"[3, 4]","[80, 0]","[2, 2]",[1],[5],[4],[0],[0],[2],[3],[122351] -24,7,"[3, 4]","[70, 10]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1783] -24,8,"[3, 4]","[70, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[1784] -24,9,"[2, 1]","[40, 70]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[1984] -24,10,"[2, 1]","[50, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1652] -24,11,"[3, 4]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[1368] -24,12,"[3, 4]","[60, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1369] -24,13,"[3, 4]","[90, 40]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[2472] -24,14,"[2, 1]","[60, 80]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[1690] -24,15,"[2, 1]","[20, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2272] -24,16,"[1, 2]","[80, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2154] -24,17,"[2, 1]","[50, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[1937] -24,18,"[3, 4]","[80, 60]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[13819] -24,19,"[2, 1]","[70, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[12784] -24,20,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[12785] -24,21,"[2, 1]","[60, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[25438] -24,22,"[2, 1]","[60, 50]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[25439] -24,23,"[2, 1]","[60, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[8470] -24,24,"[2, 1]","[60, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[8471] -24,25,"[1, 2]","[80, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[1385] -24,26,"[3, 4]","[60, 70]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1971] -24,27,"[2, 1]","[80, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[62371] -24,28,"[2, 1]","[80, 70]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[62372] -24,29,"[2, 1]","[60, 90]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[1888] -24,30,"[1, 2]","[90, 70]","[1, 1]",[1],[5],[1],[450],[9],[1],[1],[1771] -24,31,"[3, 4]","[60, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1769] -24,32,"[3, 4]","[60, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1770] -24,33,"[3, 4]","[90, 70]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[3187] -24,34,"[1, 2]","[70, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2119] -24,35,"[1, 2]","[70, 60]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2120] -24,36,"[4, 3]","[50, 20]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2135] -24,37,"[3, 4]","[10, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1753] -24,38,"[3, 4]","[10, 70]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[1754] -24,39,"[1, 2]","[20, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[3369] -24,40,"[1, 2]","[20, 70]","[1, 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-24,68,"[4, 3]","[30, 50]","[2, 1]",[3],[1],[3],[50],[5],[1],[4],[2519] -24,69,"[4, 3]","[30, 50]","[2, 2]",[3],[1],[4],[30],[3],[2],[4],[2519] -24,70,"[2, 1]","[60, 70]","[1, 2]",[3],[5],[2],[300],[6],[2],[2],[3168] -24,71,"[2, 1]","[70, 20]","[1, 2]",[3],[5],[2],[350],[7],[2],[1],[5835] -24,72,"[2, 1]","[70, 20]","[1, 1]",[3],[5],[1],[100],[2],[1],[1],[5836] -24,73,"[3, 4]","[40, 40]","[2, 1]",[3],[1],[3],[40],[4],[1],[3],[7751] -24,74,"[1, 2]","[40, 20]","[1, 1]",[3],[5],[1],[200],[4],[1],[1],[22219] -24,75,"[1, 2]","[60, 30]","[1, 1]",[3],[1],[1],[60],[6],[1],[1],[1733] -24,76,"[2, 1]","[40, 40]","[1, 1]",[3],[1],[1],[40],[4],[1],[2],[1705] -24,77,"[3, 4]","[60, 20]","[2, 1]",[3],[1],[3],[60],[6],[1],[3],[1402] -24,78,"[3, 4]","[60, 20]","[2, 2]",[3],[1],[4],[20],[2],[2],[3],[1403] -24,79,"[3, 4]","[40, 70]","[2, 2]",[3],[5],[4],[350],[7],[2],[4],[1436] -24,80,"[4, 3]","[90, 70]","[2, 1]",[3],[1],[3],[70],[7],[1],[3],[1303] -24,81,"[4, 3]","[90, 70]","[2, 2]",[3],[1],[4],[90],[9],[2],[3],[1304] -24,82,"[3, 4]","[80, 80]","[2, 1]",[4],[5],[3],[400],[8],[1],[3],[2151] -24,83,"[3, 4]","[80, 80]","[2, 2]",[4],[5],[4],[400],[8],[2],[3],[2152] -24,1,"[4, 3]","[20, 50]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[3352] -24,2,"[4, 3]","[20, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[3353] -24,3,"[1, 2]","[40, 0]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[1237] -24,4,"[1, 2]","[80, 10]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1271] -24,5,"[4, 3]","[40, 70]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1636] -24,6,"[4, 3]","[50, 40]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[1335] -24,7,"[3, 4]","[30, 40]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[1770] -24,8,"[3, 4]","[30, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1771] -24,9,"[3, 4]","[60, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[1750] -24,10,"[2, 1]","[30, 60]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[1519] -24,11,"[2, 1]","[40, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[1539] -24,12,"[4, 3]","[70, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1556] -24,13,"[4, 3]","[80, 20]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[1319] -24,14,"[2, 1]","[80, 60]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1418] -24,15,"[2, 1]","[80, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[1418] -24,16,"[3, 4]","[80, 90]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1453] -24,17,"[3, 4]","[80, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[1720] -24,18,"[3, 4]","[50, 80]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1323] -24,19,"[1, 2]","[70, 80]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1355] -24,20,"[2, 1]","[80, 70]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[1303] -24,21,"[1, 2]","[50, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[1686] -24,22,"[1, 2]","[50, 90]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[1687] -24,23,"[2, 1]","[80, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[84011] -24,24,"[2, 1]","[80, 10]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[84012] -24,25,"[4, 3]","[70, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[2155] -24,26,"[3, 4]","[20, 80]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[2439] -24,27,"[4, 3]","[90, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[1419] -24,28,"[2, 1]","[80, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[1318] -24,29,"[1, 2]","[30, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1502] -24,30,"[2, 1]","[60, 40]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[1552] -24,31,"[3, 4]","[60, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[1317] -24,32,"[4, 3]","[20, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1417] -24,33,"[1, 2]","[50, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[1570] -24,34,"[1, 2]","[50, 40]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[1571] -24,35,"[4, 3]","[50, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[1304] -24,36,"[3, 4]","[20, 70]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1620] -24,37,"[3, 4]","[20, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1621] -24,38,"[4, 3]","[70, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[1788] -24,39,"[1, 2]","[40, 70]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1589] -24,40,"[3, 4]","[30, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1638] -24,41,"[2, 1]","[70, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[1851] -24,42,"[1, 2]","[20, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[2605] -24,43,"[2, 1]","[30, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[1752] -24,44,"[2, 1]","[40, 10]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[1687] -24,45,"[4, 3]","[30, 0]","[2, 1]",[2],[5],[3],[0],[0],[1],[3],[1622] -24,46,"[1, 2]","[10, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[1671] -24,47,"[2, 1]","[20, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[1604] -24,48,"[4, 3]","[20, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[1571] -24,49,"[2, 1]","[30, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[1471] -24,50,"[3, 4]","[30, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1669] -24,51,"[2, 1]","[30, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[1739] -24,52,"[4, 3]","[20, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1552] -24,53,"[2, 1]","[30, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[1454] -24,54,"[3, 4]","[20, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[1753] -24,55,"[1, 2]","[0, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[1921] -24,56,"[4, 3]","[0, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[1672] -24,57,"[1, 2]","[10, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[1789] -24,58,"[1, 2]","[10, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[1621] -24,59,"[3, 4]","[10, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[14924] -24,60,"[4, 3]","[30, 20]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[3839] -24,61,"[4, 3]","[30, 20]","[2, 1]",[2],[1],[3],[20],[2],[1],[3],[3839] -24,62,"[3, 4]","[60, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1988] -24,63,"[1, 2]","[70, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[1636] -24,64,"[1, 2]","[70, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[1335] -24,65,"[4, 3]","[90, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1756] -24,66,"[4, 3]","[80, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[1476] -24,67,"[1, 2]","[60, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[1488] -24,68,"[1, 2]","[80, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1356] -24,69,"[2, 1]","[40, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1787] -24,70,"[2, 1]","[40, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1585] -24,71,"[4, 3]","[40, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1634] -24,72,"[1, 2]","[90, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[1333] -24,73,"[3, 4]","[80, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[1520] -24,74,"[3, 4]","[90, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[7067] -24,75,"[1, 2]","[70, 30]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2922] -24,76,"[1, 2]","[70, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2923] -24,77,"[4, 3]","[60, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[3569] -24,78,"[3, 4]","[40, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2452] -24,79,"[4, 3]","[80, 0]","[2, 1]",[2],[1],[3],[0],[0],[1],[3],[2205] -24,80,"[2, 1]","[80, 10]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2520] -24,81,"[2, 1]","[80, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[2521] -25,1,"[4, 3]","[20, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[22009] -25,2,"[2, 1]","[20, 80]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[3352] -25,3,"[4, 3]","[0, 40]","[2, 2]",[1],[1],[4],[0],[0],[2],[4],[2121] -25,4,"[3, 4]","[40, 30]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2357] -25,5,"[1, 2]","[0, 10]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[2919] -25,6,"[2, 1]","[10, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[3302] -25,7,"[2, 1]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[2365] -25,8,"[4, 3]","[40, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[2124] -25,9,"[4, 3]","[70, 20]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2032] -25,10,"[3, 4]","[30, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2265] -25,11,"[4, 3]","[80, 50]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2333] -25,12,"[1, 2]","[30, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2542] -25,13,"[3, 4]","[30, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2625] -25,14,"[4, 3]","[50, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[3],[2468] -25,15,"[4, 3]","[20, 0]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[2208] -25,16,"[3, 4]","[10, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[2289] -25,17,"[3, 4]","[30, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[2558] -25,18,"[4, 3]","[50, 40]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2142] -25,19,"[1, 2]","[10, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2759] -25,20,"[2, 1]","[70, 10]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2407] -25,21,"[3, 4]","[30, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2006] -25,22,"[1, 2]","[40, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2232] -25,23,"[4, 3]","[60, 30]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2201] -25,24,"[1, 2]","[10, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2349] -25,25,"[3, 4]","[10, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2090] -25,26,"[3, 4]","[0, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2014] -25,27,"[2, 1]","[40, 30]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[2708] -25,28,"[1, 2]","[50, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[2342] -25,29,"[3, 4]","[20, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[2274] -25,30,"[4, 3]","[30, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[2074] -25,31,"[4, 3]","[10, 20]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[2066] -25,32,"[4, 3]","[10, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[1999] -25,33,"[4, 3]","[0, 20]","[2, 2]",[1],[1],[4],[0],[0],[2],[4],[1988] -25,34,"[1, 2]","[10, 0]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[2106] -25,35,"[2, 1]","[10, 0]","[1, 1]",[1],[5],[1],[0],[0],[1],[1],[2181] -25,36,"[3, 4]","[40, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[2065] -25,37,"[4, 3]","[10, 50]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[1982] -25,38,"[2, 1]","[20, 80]","[1, 2]",[1],[1],[2],[20],[2],[2],[2],[2541] -25,39,"[3, 4]","[60, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[2199] -25,40,"[3, 4]","[60, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[2208] -25,41,"[2, 1]","[20, 60]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[17430] -25,42,"[1, 2]","[90, 40]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[2398] -25,43,"[2, 1]","[50, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2507] -25,44,"[1, 2]","[80, 40]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2567] -25,45,"[1, 2]","[60, 50]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2783] -25,46,"[2, 1]","[40, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2096] -25,47,"[4, 3]","[30, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[2190] -25,48,"[1, 2]","[0, 10]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[2098] -25,49,"[3, 4]","[10, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[2339] -25,50,"[1, 2]","[30, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2382] -25,51,"[2, 1]","[10, 60]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[3051] -25,52,"[4, 3]","[0, 70]","[2, 2]",[2],[5],[4],[0],[0],[2],[4],[1956] -25,53,"[2, 1]","[30, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2125] -25,54,"[4, 3]","[30, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2165] -25,55,"[1, 2]","[10, 20]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[2248] -25,56,"[1, 2]","[20, 10]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[1967] -25,57,"[4, 3]","[20, 0]","[2, 1]",[2],[1],[3],[0],[0],[1],[3],[2141] -25,58,"[1, 2]","[0, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2014] -25,59,"[4, 3]","[20, 10]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1839] -25,60,"[1, 2]","[0, 0]","[1, 2]",[2],[5],[2],[0],[0],[2],[1],[2116] -25,61,"[3, 4]","[10, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[2274] -25,62,"[2, 1]","[10, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[2040] -25,63,"[4, 3]","[10, 0]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[1990] -25,64,"[3, 4]","[20, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2066] -25,65,"[4, 3]","[90, 30]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[2015] -25,66,"[3, 4]","[40, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2123] -25,67,"[1, 2]","[70, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2365] -25,68,"[3, 4]","[60, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2249] -25,69,"[3, 4]","[80, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[2191] -25,70,"[3, 4]","[80, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2174] -25,71,"[1, 2]","[70, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2300] -25,72,"[3, 4]","[60, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[2202] -25,73,"[3, 4]","[30, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2207] -25,74,"[2, 1]","[60, 50]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[2307] -25,75,"[1, 2]","[40, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2423] -25,76,"[2, 1]","[40, 30]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2191] -25,77,"[1, 2]","[20, 40]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[2299] -25,78,"[2, 1]","[60, 30]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2457] -25,79,"[4, 3]","[60, 30]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2040] -25,80,"[1, 2]","[20, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[2075] -24,1,"[1, 2]","[40, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[3203] -24,2,"[3, 4]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[1689] -24,3,"[2, 1]","[40, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[1672] -24,4,"[2, 1]","[40, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1739] -24,5,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[4903] -24,6,"[4, 3]","[50, 80]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[1919] -24,7,"[2, 1]","[20, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1705] -24,8,"[2, 1]","[20, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1603] -24,9,"[4, 3]","[50, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[1802] -24,10,"[3, 4]","[80, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[1989] -24,11,"[4, 3]","[60, 90]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[1504] -24,12,"[3, 4]","[80, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1572] -24,13,"[1, 2]","[60, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1788] -24,14,"[4, 3]","[60, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[8488] -24,15,"[2, 1]","[50, 50]","[1, 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-24,17,"[4, 3]","[50, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[1604] -24,18,"[1, 2]","[70, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[1520] -24,19,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1635] -24,20,"[3, 4]","[90, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[1670] -24,21,"[4, 3]","[50, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1737] -24,22,"[2, 1]","[30, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[1587] -24,23,"[2, 1]","[0, 80]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[1770] -24,24,"[2, 1]","[0, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1771] -24,25,"[3, 4]","[10, 0]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[1869] -24,26,"[3, 4]","[30, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[1720] -24,27,"[3, 4]","[30, 20]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1721] -24,28,"[4, 3]","[60, 50]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1620] -24,29,"[4, 3]","[60, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[1620] -24,30,"[1, 2]","[60, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[1637] -24,31,"[1, 2]","[80, 90]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1754] -24,32,"[3, 4]","[90, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1805] -24,33,"[2, 1]","[70, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[1969] -24,34,"[1, 2]","[50, 70]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1704] -24,35,"[1, 2]","[70, 80]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1637] -24,36,"[4, 3]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1704] -24,37,"[4, 3]","[70, 60]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[1586] -24,38,"[4, 3]","[70, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1587] -24,39,"[3, 4]","[70, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1735] -24,40,"[4, 3]","[70, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1597] -24,41,"[4, 3]","[70, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[2352] -24,42,"[4, 3]","[80, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1685] -24,43,"[2, 1]","[50, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[1971] -24,44,"[3, 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1]",[2],[1],[1],[10],[1],[1],[1],[2186] -24,72,"[3, 4]","[20, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[1684] -24,73,"[4, 3]","[50, 30]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1635] -24,74,"[1, 2]","[40, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1535] -24,75,"[1, 2]","[40, 60]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[1536] -24,76,"[1, 2]","[50, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1634] -24,77,"[3, 4]","[30, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[1803] -24,78,"[3, 4]","[30, 80]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1804] -24,79,"[1, 2]","[10, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[2],[1753] -24,80,"[2, 1]","[0, 0]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[2103] -24,81,"[2, 1]","[0, 0]","[1, 2]",[2],[5],[2],[0],[0],[2],[2],[2104] -25,1,"[4, 3]","[40, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[22577] -25,2,"[2, 1]","[40, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2273] -25,3,"[4, 3]","[90, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2090] -25,4,"[3, 4]","[80, 90]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[2349] -25,5,"[4, 3]","[70, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2057] -25,6,"[2, 1]","[40, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2445] -25,7,"[4, 3]","[30, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[2062] -25,8,"[3, 4]","[40, 10]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2282] -25,9,"[2, 1]","[10, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1973] -25,10,"[3, 4]","[40, 0]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[2166] -25,11,"[3, 4]","[40, 20]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2116] -25,12,"[4, 3]","[0, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[2216] -25,13,"[1, 2]","[60, 10]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2174] -25,14,"[1, 2]","[50, 20]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2057] -25,15,"[3, 4]","[30, 10]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[2083] -25,16,"[1, 2]","[20, 10]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[2740] -25,17,"[2, 1]","[30, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[1915] -25,18,"[1, 2]","[0, 20]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[2264] -25,19,"[3, 4]","[0, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[2104] -25,20,"[1, 2]","[10, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[2182] -25,21,"[3, 4]","[30, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2090] -25,22,"[4, 3]","[70, 50]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2081] -25,23,"[4, 3]","[70, 60]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[1933] -25,24,"[1, 2]","[50, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2124] -25,25,"[2, 1]","[70, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[2057] -25,26,"[3, 4]","[20, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2332] -25,27,"[4, 3]","[40, 10]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2165] -25,28,"[4, 3]","[70, 0]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[1912] -25,29,"[4, 3]","[30, 10]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2130] -25,30,"[3, 4]","[30, 20]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[2119] -25,31,"[1, 2]","[0, 30]","[1, 1]",[1],[1],[1],[0],[0],[1],[2],[2041] -25,32,"[2, 1]","[40, 20]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2291] -25,33,"[1, 2]","[30, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[2632] -25,34,"[2, 1]","[20, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[2207] -25,35,"[4, 3]","[30, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[2115] -25,36,"[1, 2]","[10, 20]","[1, 1]",[1],[5],[1],[50],[1],[1],[2],[1990] -25,37,"[2, 1]","[30, 0]","[1, 1]",[1],[5],[1],[0],[0],[1],[1],[1964] -25,38,"[2, 1]","[70, 30]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2617] -25,39,"[3, 4]","[70, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[2089] -25,40,"[1, 2]","[60, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[2705] -25,41,"[2, 1]","[80, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[3778] -25,42,"[1, 2]","[80, 50]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1912] -25,43,"[2, 1]","[30, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1889] -25,44,"[1, 2]","[90, 70]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[1887] -25,45,"[4, 3]","[80, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[2097] -25,46,"[4, 3]","[90, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1970] -25,47,"[2, 1]","[70, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1998] -25,48,"[2, 1]","[50, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2122] -25,49,"[2, 1]","[80, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2237] -25,50,"[4, 3]","[90, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2049] -25,51,"[4, 3]","[80, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[2015] -25,52,"[3, 4]","[0, 50]","[2, 1]",[2],[1],[3],[0],[0],[1],[4],[2113] -25,53,"[2, 1]","[30, 0]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2357] -25,54,"[1, 2]","[30, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[2566] -25,55,"[1, 2]","[20, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[3376] -25,56,"[3, 4]","[20, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2146] -25,57,"[2, 1]","[40, 60]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2298] -25,58,"[4, 3]","[50, 40]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[1948] -25,59,"[2, 1]","[20, 50]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[2082] -25,60,"[4, 3]","[50, 60]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[2005] -25,61,"[2, 1]","[40, 60]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2106] -25,62,"[1, 2]","[40, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2156] -25,63,"[1, 2]","[20, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[1981] -25,64,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2065] -25,65,"[3, 4]","[20, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2499] -25,66,"[2, 1]","[10, 20]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2199] -25,67,"[3, 4]","[40, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[2224] -25,68,"[3, 4]","[30, 10]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[2123] -25,69,"[2, 1]","[10, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2149] -25,70,"[4, 3]","[0, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[2049] -25,71,"[2, 1]","[0, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[1949] -25,72,"[2, 1]","[0, 30]","[1, 2]",[2],[1],[2],[0],[0],[2],[2],[2256] -25,73,"[4, 3]","[10, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1999] -25,74,"[4, 3]","[20, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[2007] -25,75,"[3, 4]","[10, 60]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[2049] -25,76,"[4, 3]","[30, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[2059] -25,77,"[1, 2]","[40, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[2099] -25,78,"[3, 4]","[40, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[2062] -25,79,"[4, 3]","[10, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[2114] -25,80,"[1, 2]","[20, 0]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[1889] -25,1,"[2, 1]","[20, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[5891] -25,2,"[3, 4]","[70, 40]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2185] -25,3,"[1, 2]","[90, 70]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[1939] -25,4,"[4, 3]","[40, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2032] -25,5,"[3, 4]","[70, 50]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2025] -25,6,"[4, 3]","[70, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[2076] -25,7,"[2, 1]","[60, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[2588] -25,8,"[2, 1]","[40, 0]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2772] -25,9,"[4, 3]","[90, 0]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[1867] -25,10,"[4, 3]","[90, 10]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[1909] -25,11,"[4, 3]","[90, 20]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[1967] -25,12,"[1, 2]","[10, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[1996] -25,13,"[3, 4]","[20, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2027] -25,14,"[2, 1]","[70, 30]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1867] -25,15,"[3, 4]","[30, 50]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[2006] -25,16,"[3, 4]","[40, 60]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[2372] -25,17,"[1, 2]","[40, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2150] -25,18,"[4, 3]","[40, 60]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1866] -25,19,"[3, 4]","[60, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[1914] -25,20,"[1, 2]","[70, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[2115] -25,21,"[1, 2]","[70, 0]","[1, 2]",[1],[5],[2],[0],[0],[2],[1],[2020] -25,22,"[2, 1]","[20, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2177] -25,23,"[1, 2]","[80, 10]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2045] -25,24,"[4, 3]","[0, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1966] -25,25,"[1, 2]","[70, 10]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1993] -25,26,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2050] -25,27,"[2, 1]","[50, 40]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2295] -25,28,"[2, 1]","[50, 60]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2472] -25,29,"[1, 2]","[70, 60]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2510] -25,30,"[3, 4]","[50, 70]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2086] -25,31,"[4, 3]","[80, 70]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2191] -25,32,"[4, 3]","[60, 20]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[2093] -25,33,"[3, 4]","[10, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[1901] -25,34,"[3, 4]","[30, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[2094] -25,35,"[4, 3]","[40, 20]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[1855] -25,36,"[3, 4]","[10, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[1979] -25,37,"[1, 2]","[20, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2048] -25,38,"[1, 2]","[80, 60]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1968] -25,39,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2082] -25,40,"[3, 4]","[70, 40]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[2121] -25,41,"[2, 1]","[50, 80]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[3340] -25,42,"[3, 4]","[70, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[2238] -25,43,"[4, 3]","[50, 90]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[2217] -25,44,"[1, 2]","[50, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[2026] -25,45,"[4, 3]","[60, 40]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[1863] -25,46,"[1, 2]","[10, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2060] -25,47,"[4, 3]","[60, 0]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[1872] -25,48,"[2, 1]","[60, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[2108] -25,49,"[1, 2]","[10, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[2122] -25,50,"[4, 3]","[80, 30]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1853] -25,51,"[4, 3]","[70, 10]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[1872] -25,52,"[1, 2]","[10, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2039] -25,53,"[3, 4]","[20, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[1877] -25,54,"[4, 3]","[90, 10]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[1813] -25,55,"[1, 2]","[20, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1952] -25,56,"[4, 3]","[70, 20]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[1853] -25,57,"[1, 2]","[10, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[1938] -25,58,"[4, 3]","[50, 20]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1864] -25,59,"[2, 1]","[50, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[2294] -25,60,"[1, 2]","[30, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2285] -25,61,"[3, 4]","[20, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[1960] -25,62,"[3, 4]","[50, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[1927] -25,63,"[1, 2]","[60, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[1981] -25,64,"[4, 3]","[70, 60]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1934] -25,65,"[2, 1]","[90, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2057] -25,66,"[1, 2]","[40, 50]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[1972] -25,67,"[3, 4]","[40, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1875] -25,68,"[3, 4]","[20, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1888] -25,69,"[3, 4]","[50, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[1926] -25,70,"[4, 3]","[30, 80]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[1811] -25,71,"[3, 4]","[70, 0]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1921] -25,72,"[3, 4]","[70, 10]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1910] -25,73,"[4, 3]","[10, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1930] -25,74,"[4, 3]","[20, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1926] -25,75,"[4, 3]","[50, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1960] -25,76,"[3, 4]","[20, 40]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[1967] -25,77,"[4, 3]","[20, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1985] -25,78,"[3, 4]","[30, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1927] -25,79,"[2, 1]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2156] -25,80,"[3, 4]","[30, 20]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1939] -26,1,"[4, 3]","[60, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[8468] -26,2,"[1, 2]","[20, 50]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[2646] -26,3,"[3, 4]","[30, 50]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[3636] -26,4,"[2, 1]","[50, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[3411] -26,5,"[2, 1]","[30, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[2639] -26,6,"[1, 2]","[20, 0]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[2530] -26,7,"[2, 1]","[30, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[2402] -26,8,"[3, 4]","[20, 30]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[2072] -26,9,"[2, 1]","[10, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[3327] -26,10,"[4, 3]","[0, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2406] -26,11,"[3, 4]","[70, 10]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2469] -26,12,"[2, 1]","[10, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[2574] -26,13,"[4, 3]","[30, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[2555] -26,14,"[2, 1]","[20, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[2175] -26,15,"[4, 3]","[30, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2238] -26,16,"[3, 4]","[60, 20]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2219] -26,17,"[4, 3]","[40, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2855] -26,18,"[4, 3]","[60, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[2263] -26,19,"[2, 1]","[40, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2505] -26,20,"[3, 4]","[70, 60]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[2338] -26,21,"[2, 1]","[60, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2434] -26,22,"[1, 2]","[70, 90]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2215] -26,23,"[1, 2]","[80, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2279] -26,24,"[2, 1]","[70, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2581] -26,25,"[1, 2]","[70, 60]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2158] -26,26,"[1, 2]","[60, 40]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2212] -26,27,"[1, 2]","[60, 10]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2018] -26,28,"[1, 2]","[80, 10]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2027] -26,29,"[3, 4]","[70, 50]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1905] -26,30,"[4, 3]","[70, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2537] -26,31,"[3, 4]","[50, 60]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2279] -26,32,"[2, 1]","[90, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[2315] -26,33,"[4, 3]","[50, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1905] -26,34,"[4, 3]","[40, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[2654] -26,35,"[4, 3]","[60, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[2049] -26,36,"[1, 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2]",[2],[1],[2],[60],[6],[2],[2],[2296] -26,50,"[3, 4]","[50, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2417] -26,51,"[2, 1]","[80, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[1759] -26,52,"[3, 4]","[60, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2456] -26,53,"[2, 1]","[70, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2948] -26,54,"[2, 1]","[90, 60]","[1, 2]",[2],[1],[2],[90],[9],[2],[1],[2221] -26,55,"[3, 4]","[70, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2923] -26,56,"[4, 3]","[90, 80]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[2456] -26,57,"[1, 2]","[90, 90]","[1, 2]",[2],[1],[2],[90],[9],[2],[1],[2617] -26,58,"[4, 3]","[60, 70]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[2653] -26,59,"[3, 4]","[90, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[2607] -26,60,"[1, 2]","[60, 50]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2413] -26,61,"[4, 3]","[60, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[2168] -26,62,"[2, 1]","[50, 60]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[2804] -26,63,"[3, 4]","[60, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2443] -26,64,"[2, 1]","[80, 70]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[3076] -26,65,"[2, 1]","[90, 70]","[1, 2]",[2],[5],[2],[450],[9],[2],[1],[2256] -26,66,"[2, 1]","[80, 70]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2212] -26,67,"[2, 1]","[80, 60]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[2275] -26,68,"[1, 2]","[80, 50]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2128] -26,69,"[2, 1]","[30, 90]","[1, 1]",[2],[1],[1],[90],[9],[1],[2],[2291] -26,70,"[4, 3]","[30, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[2538] -26,71,"[3, 4]","[70, 30]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[2547] -26,72,"[2, 1]","[10, 90]","[1, 1]",[2],[1],[1],[90],[9],[1],[2],[2440] -26,73,"[1, 2]","[80, 0]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2375] -26,74,"[1, 2]","[80, 0]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2298] -26,75,"[3, 4]","[50, 10]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2087] -26,76,"[2, 1]","[0, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2004] -26,77,"[1, 2]","[80, 20]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1888] -26,78,"[2, 1]","[0, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[2071] -26,79,"[2, 1]","[20, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[1956] -26,80,"[1, 2]","[80, 40]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2653] -27,1,"[4, 3]","[70, 20]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[8724] -27,2,"[1, 2]","[10, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[6810] -27,3,"[2, 1]","[50, 0]","[1, 1]",[1],[5],[1],[0],[0],[1],[1],[4018] -27,4,"[3, 4]","[30, 60]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[5665] -27,5,"[1, 2]","[30, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[3327] -27,6,"[4, 3]","[80, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[3],[3921] -27,7,"[1, 2]","[0, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2516] -27,8,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[2532] -27,9,"[2, 1]","[70, 0]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[4790] -27,10,"[1, 2]","[50, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2071] -27,11,"[1, 2]","[50, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2055] -27,12,"[1, 2]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[1999] -27,13,"[3, 4]","[20, 90]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[4622] -27,14,"[1, 2]","[40, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[3184] -27,15,"[3, 4]","[50, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2897] -27,16,"[3, 4]","[0, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2419] -27,17,"[1, 2]","[40, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[3548] -27,18,"[3, 4]","[60, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2595] -27,19,"[2, 1]","[60, 80]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2762] -27,20,"[1, 2]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2457] -27,21,"[1, 2]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2072] -27,22,"[1, 2]","[70, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[1834] -27,23,"[1, 2]","[70, 20]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2006] -27,24,"[4, 3]","[30, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2425] -27,25,"[2, 1]","[50, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[1915] -27,26,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1979] -27,27,"[3, 4]","[80, 60]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[3123] -27,28,"[4, 3]","[20, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2329] -27,29,"[4, 3]","[0, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[3769] -27,30,"[3, 4]","[60, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[3624] -27,31,"[2, 1]","[40, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[4048] -27,32,"[4, 3]","[50, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[2698] -27,33,"[4, 3]","[80, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[2262] -27,34,"[1, 2]","[30, 70]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[2760] -27,35,"[3, 4]","[50, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[2932] -27,36,"[1, 2]","[40, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[3176] -27,37,"[2, 1]","[80, 60]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2951] -27,38,"[2, 1]","[80, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2097] -27,39,"[1, 2]","[40, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2149] -27,40,"[2, 1]","[70, 20]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2129] -27,41,"[1, 2]","[50, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[6578] -27,42,"[3, 4]","[50, 80]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[3530] -27,43,"[4, 3]","[70, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2448] -27,44,"[1, 2]","[0, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2359] -27,45,"[4, 3]","[80, 0]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2522] -27,46,"[3, 4]","[10, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2386] -27,47,"[2, 1]","[70, 10]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[2239] -27,48,"[4, 3]","[60, 30]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2750] -27,49,"[4, 3]","[30, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2667] -27,50,"[1, 2]","[10, 40]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[2401] -27,51,"[1, 2]","[10, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[3008] -27,52,"[2, 1]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2207] -27,53,"[3, 4]","[30, 10]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[3328] -27,54,"[3, 4]","[40, 20]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2076] -27,55,"[2, 1]","[0, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[5195] -27,56,"[1, 2]","[0, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2690] -27,57,"[2, 1]","[30, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[2085] -27,58,"[4, 3]","[30, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[2659] -27,59,"[1, 2]","[20, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[2881] -27,60,"[1, 2]","[30, 20]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[1934] -27,61,"[1, 2]","[0, 80]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[1980] -27,62,"[2, 1]","[70, 0]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2344] -27,63,"[4, 3]","[90, 50]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[2186] -27,64,"[4, 3]","[50, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[2242] -27,65,"[3, 4]","[20, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2219] -27,66,"[3, 4]","[20, 80]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[2290] -27,67,"[1, 2]","[70, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2139] -27,68,"[3, 4]","[40, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2159] -27,69,"[4, 3]","[80, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2310] -27,70,"[2, 1]","[60, 80]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2163] -27,71,"[3, 4]","[90, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2262] -27,72,"[4, 3]","[70, 80]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[2155] -27,73,"[1, 2]","[60, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2360] -27,74,"[3, 4]","[70, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[2104] -27,75,"[2, 1]","[80, 50]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[2208] -27,76,"[3, 4]","[60, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[2070] -27,77,"[2, 1]","[80, 90]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[2165] -27,78,"[4, 3]","[80, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[3582] -27,79,"[4, 3]","[50, 60]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[1981] -27,80,"[2, 1]","[80, 90]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[1895] -28,1,"[4, 3]","[20, 60]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[8362] -28,2,"[2, 1]","[30, 60]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[6068] -28,3,"[2, 1]","[40, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[4625] -28,4,"[3, 4]","[50, 30]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[1909] -28,5,"[3, 4]","[50, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2210] -28,6,"[4, 3]","[30, 50]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[2669] -28,7,"[1, 2]","[70, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[3505] -28,8,"[1, 2]","[50, 40]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[2275] -28,9,"[1, 2]","[50, 70]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2142] -28,10,"[1, 2]","[60, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[2611] -28,11,"[1, 2]","[50, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[3347] -28,12,"[2, 1]","[90, 60]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[3247] -28,13,"[2, 1]","[80, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2742] -28,14,"[1, 2]","[30, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2997] -28,15,"[1, 2]","[20, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2645] -28,16,"[3, 4]","[10, 80]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[1967] -28,17,"[3, 4]","[0, 60]","[2, 1]",[1],[5],[3],[0],[0],[1],[4],[2302] -28,18,"[4, 3]","[30, 20]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[3094] -28,19,"[3, 4]","[30, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[2152] -28,20,"[1, 2]","[10, 40]","[1, 1]",[1],[5],[1],[50],[1],[1],[2],[2293] -28,21,"[1, 2]","[20, 60]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[2369] -28,22,"[4, 3]","[50, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[2232] -28,23,"[2, 1]","[90, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[2663] -28,24,"[4, 3]","[60, 20]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[2435] -28,25,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[3414] -28,26,"[1, 2]","[30, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[1818] -28,27,"[4, 3]","[80, 10]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2502] -28,28,"[2, 1]","[90, 20]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[2635] -28,29,"[4, 3]","[70, 50]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2443] -28,30,"[3, 4]","[40, 70]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[1863] -28,31,"[2, 1]","[50, 60]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[2317] -28,32,"[1, 2]","[80, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[1],[1967] -28,33,"[3, 4]","[70, 0]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1843] -28,34,"[3, 4]","[90, 20]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[1825] -28,35,"[4, 3]","[40, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[3355] -28,36,"[2, 1]","[60, 70]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2286] -28,37,"[4, 3]","[60, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1892] -28,38,"[1, 2]","[70, 20]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2452] -28,39,"[3, 4]","[60, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[2026] -28,40,"[3, 4]","[90, 0]","[2, 2]",[1],[5],[4],[0],[0],[2],[3],[3463] -28,41,"[1, 2]","[60, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[15906] -28,42,"[3, 4]","[50, 30]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1783] -28,43,"[2, 1]","[0, 80]","[1, 2]",[2],[5],[2],[0],[0],[2],[2],[1753] -28,44,"[2, 1]","[20, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2686] -28,45,"[1, 2]","[90, 20]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[2552] -28,46,"[3, 4]","[90, 30]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[1843] -28,47,"[4, 3]","[20, 80]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[2083] -28,48,"[4, 3]","[20, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1832] -28,49,"[1, 2]","[50, 10]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[1809] -28,50,"[1, 2]","[60, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[2043] -28,51,"[4, 3]","[70, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1834] -28,52,"[3, 4]","[70, 20]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[2209] -28,53,"[1, 2]","[60, 20]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1832] -28,54,"[4, 3]","[10, 40]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[2115] -28,55,"[2, 1]","[10, 70]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2412] -28,56,"[2, 1]","[10, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2101] -28,57,"[1, 2]","[50, 10]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[2201] -28,58,"[4, 3]","[0, 50]","[2, 2]",[2],[1],[4],[0],[0],[2],[4],[1916] -28,59,"[4, 3]","[10, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2018] -28,60,"[4, 3]","[30, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1850] -28,61,"[3, 4]","[10, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2426] -28,62,"[2, 1]","[30, 20]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[5276] -28,63,"[3, 4]","[50, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2979] -28,64,"[4, 3]","[80, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1826] -28,65,"[2, 1]","[60, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2645] -28,66,"[4, 3]","[20, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1798] -28,67,"[4, 3]","[40, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[2025] -28,68,"[3, 4]","[80, 30]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2285] -28,69,"[3, 4]","[50, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1985] -28,70,"[4, 3]","[60, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1826] -28,71,"[2, 1]","[60, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[3196] -28,72,"[4, 3]","[60, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[2044] -28,73,"[2, 1]","[70, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2168] -28,74,"[4, 3]","[90, 10]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[2043] -28,75,"[1, 2]","[40, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[2644] -28,76,"[2, 1]","[80, 50]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[3455] -28,77,"[1, 2]","[80, 70]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2637] -28,78,"[2, 1]","[90, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[2893] -28,79,"[4, 3]","[90, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2445] -28,80,"[1, 2]","[60, 90]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[3130] -29,1,"[2, 1]","[90, 10]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[8384] -29,2,"[4, 3]","[70, 40]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2307] -29,3,"[2, 1]","[70, 40]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2040] -29,4,"[3, 4]","[70, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[1940] -29,5,"[4, 3]","[50, 70]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2023] -29,6,"[3, 4]","[60, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[1974] -29,7,"[4, 3]","[50, 60]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[1924] -29,8,"[4, 3]","[60, 70]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1824] -29,9,"[3, 4]","[80, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2207] -29,10,"[4, 3]","[30, 80]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[2223] -29,11,"[3, 4]","[60, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[1974] -29,12,"[4, 3]","[20, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1807] -29,13,"[2, 1]","[40, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1790] -29,14,"[1, 2]","[30, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[2041] -29,15,"[4, 3]","[60, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2090] -29,16,"[3, 4]","[60, 30]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[2424] -29,17,"[1, 2]","[20, 50]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[2140] -29,18,"[1, 2]","[20, 30]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[1907] -29,19,"[1, 2]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[1857] -29,20,"[1, 2]","[10, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2173] -29,21,"[3, 4]","[30, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[1807] -29,22,"[4, 3]","[20, 70]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[1924] -29,23,"[1, 2]","[70, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[3757] -29,24,"[2, 1]","[20, 70]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[1940] -29,25,"[4, 3]","[0, 30]","[2, 2]",[1],[1],[4],[0],[0],[2],[4],[2307] -29,26,"[3, 4]","[30, 0]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1857] -29,27,"[4, 3]","[30, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2474] -29,28,"[2, 1]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[3040] -29,29,"[1, 2]","[50, 20]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[1891] -29,30,"[3, 4]","[30, 0]","[2, 2]",[1],[1],[4],[0],[0],[2],[3],[1807] -29,31,"[2, 1]","[20, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[3507] -29,32,"[1, 2]","[10, 20]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[2273] -29,33,"[1, 2]","[20, 30]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[1857] -29,34,"[4, 3]","[80, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[2007] -29,35,"[4, 3]","[80, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[2157] -29,36,"[3, 4]","[10, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[1871] -29,37,"[3, 4]","[30, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[2023] -29,38,"[3, 4]","[10, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[1858] -29,39,"[2, 1]","[50, 40]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2556] -29,40,"[2, 1]","[50, 40]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2240] -29,41,"[1, 2]","[30, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[7819] -29,42,"[4, 3]","[80, 30]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1823] -29,43,"[3, 4]","[30, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2890] -29,44,"[2, 1]","[80, 10]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[1873] -29,45,"[3, 4]","[20, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[1841] -29,46,"[1, 2]","[0, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[2207] -29,47,"[1, 2]","[80, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1857] -29,48,"[1, 2]","[70, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1907] -29,49,"[4, 3]","[40, 70]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2240] -29,50,"[1, 2]","[50, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2074] -29,51,"[1, 2]","[30, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2090] -29,52,"[4, 3]","[30, 20]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1890] -29,53,"[1, 2]","[10, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1857] -29,54,"[2, 1]","[40, 20]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2074] -29,55,"[4, 3]","[30, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[2058] -29,56,"[4, 3]","[20, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[4857] -29,57,"[2, 1]","[10, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[2307] -29,58,"[2, 1]","[10, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[2741] -29,59,"[2, 1]","[30, 10]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[4458] -29,60,"[3, 4]","[10, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[2643] -29,61,"[1, 2]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[4754] -29,62,"[3, 4]","[30, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2670] -29,63,"[1, 2]","[60, 0]","[1, 2]",[2],[1],[2],[0],[0],[2],[1],[1785] -29,64,"[4, 3]","[20, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2040] -29,65,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2020] -29,66,"[3, 4]","[90, 20]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[2053] -29,67,"[3, 4]","[80, 40]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1707] -29,68,"[3, 4]","[80, 40]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[1774] -29,69,"[4, 3]","[10, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[2307] -29,70,"[1, 2]","[80, 0]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2374] -29,71,"[2, 1]","[10, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[1940] -29,72,"[1, 2]","[20, 0]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[2224] -29,73,"[4, 3]","[0, 10]","[2, 2]",[2],[5],[4],[0],[0],[2],[4],[1790] -29,74,"[1, 2]","[30, 40]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[1841] -29,75,"[2, 1]","[40, 0]","[1, 1]",[2],[1],[1],[0],[0],[1],[1],[1940] -29,76,"[3, 4]","[0, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1739] -29,77,"[2, 1]","[0, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[2207] -29,78,"[2, 1]","[40, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[1757] -29,79,"[2, 1]","[80, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[1723] -29,80,"[3, 4]","[0, 80]","[2, 1]",[2],[5],[3],[0],[0],[1],[4],[2141] -29,1,"[3, 4]","[30, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[8923] -29,2,"[3, 4]","[30, 90]","[2, 2]",[1],[5],[4],[450],[9],[2],[4],[2023] -29,3,"[1, 2]","[50, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1956] -29,4,"[3, 4]","[70, 90]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1804] -29,5,"[1, 2]","[50, 90]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[2056] -29,6,"[2, 1]","[80, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[2207] -29,7,"[2, 1]","[60, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[1821] -29,8,"[2, 1]","[70, 60]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1924] -29,9,"[2, 1]","[90, 60]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[2625] -29,10,"[2, 1]","[80, 40]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1774] -29,11,"[4, 3]","[80, 40]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2224] -29,12,"[1, 2]","[50, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1824] -29,13,"[1, 2]","[70, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[1874] -29,14,"[3, 4]","[80, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1790] -29,15,"[3, 4]","[70, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[1923] -29,16,"[4, 3]","[50, 80]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[1990] -29,17,"[3, 4]","[60, 30]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1840] -29,18,"[1, 2]","[60, 50]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[1790] -29,19,"[3, 4]","[80, 20]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1757] -29,20,"[1, 2]","[80, 50]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1691] -29,21,"[2, 1]","[30, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1923] -29,22,"[2, 1]","[20, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2007] -29,23,"[1, 2]","[20, 10]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[2207] -29,24,"[3, 4]","[30, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[1890] -29,25,"[4, 3]","[10, 70]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[1923] -29,26,"[1, 2]","[80, 10]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2723] -29,27,"[4, 3]","[0, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1806] -29,28,"[1, 2]","[60, 40]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2020] -29,29,"[2, 1]","[40, 60]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[1940] -29,30,"[1, 2]","[70, 0]","[1, 2]",[1],[1],[2],[0],[0],[2],[1],[2873] -29,31,"[3, 4]","[40, 30]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2473] -29,32,"[3, 4]","[40, 10]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[1957] -29,33,"[1, 2]","[30, 50]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[1890] -29,34,"[4, 3]","[40, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[1907] -29,35,"[2, 1]","[40, 50]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2388] -29,36,"[2, 1]","[50, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2156] -29,37,"[1, 2]","[40, 50]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[2040] -29,38,"[2, 1]","[50, 40]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[1957] -29,39,"[4, 3]","[60, 30]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[2657] -29,40,"[3, 4]","[40, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1958] -29,41,"[2, 1]","[50, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[3786] -29,42,"[2, 1]","[10, 40]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2038] -29,43,"[3, 4]","[60, 40]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[2640] -29,44,"[2, 1]","[30, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[1875] -29,45,"[3, 4]","[80, 30]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1807] -29,46,"[1, 2]","[80, 30]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1907] -29,47,"[3, 4]","[90, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[1890] -29,48,"[4, 3]","[50, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[1940] -29,49,"[4, 3]","[50, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1791] -29,50,"[4, 3]","[90, 20]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[1740] -29,51,"[4, 3]","[70, 30]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1788] -29,52,"[2, 1]","[80, 40]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[1808] -29,53,"[4, 3]","[60, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[1857] -29,54,"[3, 4]","[50, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1907] -29,55,"[4, 3]","[60, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1756] -29,56,"[1, 2]","[80, 90]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[1907] -29,57,"[4, 3]","[80, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[2140] -29,58,"[2, 1]","[70, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[2038] -29,59,"[1, 2]","[80, 60]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1940] -29,60,"[3, 4]","[90, 60]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[1939] -29,61,"[1, 2]","[80, 50]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1772] -29,62,"[2, 1]","[30, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[2107] -29,63,"[4, 3]","[20, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1823] -29,64,"[3, 4]","[70, 40]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1923] -29,65,"[2, 1]","[40, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2189] -29,66,"[3, 4]","[90, 10]","[2, 1]",[2],[5],[3],[450],[9],[1],[3],[1971] -29,67,"[3, 4]","[90, 20]","[2, 1]",[2],[5],[3],[450],[9],[1],[3],[1787] -29,68,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1873] -29,69,"[4, 3]","[0, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1956] -29,70,"[4, 3]","[10, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1803] -29,71,"[4, 3]","[30, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1856] -29,72,"[1, 2]","[60, 10]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[3290] -29,73,"[4, 3]","[0, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1874] -29,74,"[1, 2]","[80, 10]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1974] -29,75,"[1, 2]","[60, 0]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[1990] -29,76,"[2, 1]","[20, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[2224] -29,77,"[4, 3]","[40, 20]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1873] -29,78,"[1, 2]","[10, 20]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[2137] -29,79,"[1, 2]","[20, 0]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[2074] -29,80,"[4, 3]","[20, 40]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[2074] -29,1,"[4, 3]","[40, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[5373] -29,2,"[2, 1]","[40, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2413] -29,3,"[4, 3]","[90, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1857] -29,4,"[3, 4]","[80, 90]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[2607] -29,5,"[4, 3]","[70, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[3007] -29,6,"[2, 1]","[40, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1774] -29,7,"[4, 3]","[30, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[2806] -29,8,"[3, 4]","[40, 10]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2640] -29,9,"[2, 1]","[10, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1989] -29,10,"[3, 4]","[40, 0]","[2, 2]",[1],[5],[4],[0],[0],[2],[3],[2073] -29,11,"[3, 4]","[40, 20]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2053] -29,12,"[4, 3]","[0, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[1957] -29,13,"[1, 2]","[60, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[2973] -29,14,"[1, 2]","[50, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[4523] -29,15,"[3, 4]","[30, 10]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[1643] -29,16,"[1, 2]","[20, 10]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[1856] -29,17,"[2, 1]","[30, 20]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[3273] -29,18,"[1, 2]","[0, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[1823] -29,19,"[3, 4]","[0, 10]","[2, 1]",[1],[5],[3],[0],[0],[1],[4],[1673] -29,20,"[1, 2]","[10, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[1788] -29,21,"[3, 4]","[30, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2173] -29,22,"[4, 3]","[70, 50]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2140] -29,23,"[4, 3]","[70, 60]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[1772] -29,24,"[1, 2]","[50, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2490] -29,25,"[2, 1]","[70, 40]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2927] -29,26,"[3, 4]","[20, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[1757] -29,27,"[4, 3]","[40, 10]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2824] -29,28,"[4, 3]","[70, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[3],[2023] -29,29,"[4, 3]","[30, 10]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[1791] -29,30,"[3, 4]","[30, 20]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[3139] -29,31,"[1, 2]","[0, 30]","[1, 1]",[1],[1],[1],[0],[0],[1],[2],[2057] -29,32,"[2, 1]","[40, 20]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2606] -29,33,"[1, 2]","[30, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[2274] -29,34,"[2, 1]","[20, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[3290] -29,35,"[4, 3]","[30, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[1739] -29,36,"[1, 2]","[10, 20]","[1, 1]",[1],[5],[1],[50],[1],[1],[2],[2689] -29,37,"[2, 1]","[30, 0]","[1, 1]",[1],[5],[1],[0],[0],[1],[1],[2306] -29,38,"[2, 1]","[70, 30]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2124] -29,39,"[3, 4]","[70, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[1840] -29,40,"[1, 2]","[60, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[3857] -29,41,"[2, 1]","[80, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[7769] -29,42,"[1, 2]","[80, 50]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2189] -29,43,"[2, 1]","[30, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2004] -29,44,"[1, 2]","[90, 70]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[1851] -29,45,"[4, 3]","[80, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1739] -29,46,"[4, 3]","[90, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1906] -29,47,"[2, 1]","[70, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2323] -29,48,"[2, 1]","[50, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1857] -29,49,"[2, 1]","[80, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1739] -29,50,"[4, 3]","[90, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1787] -29,51,"[4, 3]","[80, 20]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1840] -29,52,"[3, 4]","[0, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[2306] -29,53,"[2, 1]","[30, 0]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2056] -29,54,"[1, 2]","[30, 40]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[1940] -29,55,"[1, 2]","[20, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[1789] -29,56,"[3, 4]","[20, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1857] -29,57,"[2, 1]","[40, 60]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[3555] -29,58,"[4, 3]","[50, 40]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2006] -29,59,"[2, 1]","[20, 50]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[1757] -29,60,"[4, 3]","[50, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1789] -29,61,"[2, 1]","[40, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2089] -29,62,"[1, 2]","[40, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[2225] -29,63,"[1, 2]","[20, 30]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[1989] -29,64,"[4, 3]","[30, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[2838] -29,65,"[3, 4]","[20, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[2007] -29,66,"[2, 1]","[10, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[1990] -29,67,"[3, 4]","[40, 10]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2040] -29,68,"[3, 4]","[30, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[1704] -29,69,"[2, 1]","[10, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2107] -29,70,"[4, 3]","[0, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[1807] -29,71,"[2, 1]","[0, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[3225] -29,72,"[2, 1]","[0, 30]","[1, 2]",[2],[1],[2],[0],[0],[2],[2],[2306] -29,73,"[4, 3]","[10, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[2319] -29,74,"[4, 3]","[20, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[2290] -29,75,"[3, 4]","[10, 60]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1922] -29,76,"[4, 3]","[30, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1789] -29,77,"[1, 2]","[40, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[2112] -29,78,"[3, 4]","[40, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[1954] -29,79,"[4, 3]","[10, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[2572] -29,80,"[1, 2]","[20, 0]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[2440] -29,1,"[3, 4]","[80, 0]","[2, 2]",[1],[5],[4],[0],[0],[2],[3],[3893] -29,2,"[3, 4]","[70, 40]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[1839] -29,3,"[1, 2]","[90, 10]","[1, 1]",[1],[5],[1],[450],[9],[1],[1],[1895] -29,4,"[4, 3]","[10, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1827] -29,5,"[2, 1]","[10, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[1805] -29,6,"[2, 1]","[0, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[1745] -29,7,"[2, 1]","[30, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1728] -29,8,"[4, 3]","[40, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2509] -29,9,"[3, 4]","[30, 40]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[1883] -29,10,"[1, 2]","[20, 70]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[4910] -29,11,"[1, 2]","[40, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[1738] -29,12,"[3, 4]","[50, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1826] -29,13,"[3, 4]","[70, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1808] -29,14,"[2, 1]","[50, 30]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[3040] -29,15,"[1, 2]","[20, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[1959] -29,16,"[4, 3]","[50, 30]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2228] -29,17,"[3, 4]","[20, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1943] -29,18,"[3, 4]","[10, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[2076] -29,19,"[4, 3]","[10, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[1805] -29,20,"[1, 2]","[20, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[2182] -29,21,"[1, 2]","[20, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[1928] -29,22,"[3, 4]","[40, 30]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[2134] -29,23,"[3, 4]","[30, 50]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[1960] -29,24,"[3, 4]","[0, 10]","[2, 1]",[1],[5],[3],[0],[0],[1],[4],[1845] -29,25,"[3, 4]","[20, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[1826] -29,26,"[4, 3]","[10, 30]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[1860] -29,27,"[2, 1]","[0, 60]","[1, 2]",[1],[1],[2],[0],[0],[2],[2],[2127] -29,28,"[3, 4]","[70, 20]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[2226] -29,29,"[4, 3]","[0, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1788] -29,30,"[1, 2]","[40, 10]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[2028] -29,31,"[4, 3]","[30, 60]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[2445] -29,32,"[1, 2]","[70, 40]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1961] -29,33,"[2, 1]","[30, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2060] -29,34,"[3, 4]","[60, 40]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[2061] -29,35,"[2, 1]","[70, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2045] -29,36,"[2, 1]","[90, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1789] -29,37,"[1, 2]","[90, 70]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[1826] -29,38,"[2, 1]","[50, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[1776] -29,39,"[4, 3]","[70, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[2009] -29,40,"[4, 3]","[80, 40]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[3643] -29,41,"[2, 1]","[60, 30]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[6638] -29,42,"[1, 2]","[20, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[2378] -29,43,"[3, 4]","[20, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[2159] -29,44,"[4, 3]","[20, 0]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[1795] -29,45,"[2, 1]","[0, 0]","[1, 2]",[2],[1],[2],[0],[0],[2],[2],[2895] -29,46,"[1, 2]","[10, 0]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[1771] -29,47,"[2, 1]","[40, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[1995] -29,48,"[4, 3]","[50, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[1771] -29,49,"[4, 3]","[70, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[1776] -29,50,"[2, 1]","[70, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1927] -29,51,"[1, 2]","[80, 70]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1895] -29,52,"[1, 2]","[50, 80]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1878] -29,53,"[1, 2]","[40, 70]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[1795] -29,54,"[4, 3]","[90, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1789] -29,55,"[2, 1]","[60, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[1826] -29,56,"[1, 2]","[30, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2328] -29,57,"[2, 1]","[40, 50]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[2045] -29,58,"[4, 3]","[60, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1704] -29,59,"[4, 3]","[60, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1737] -29,60,"[1, 2]","[20, 70]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[2044] -29,61,"[1, 2]","[10, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[1653] -29,62,"[4, 3]","[80, 40]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1827] -29,63,"[2, 1]","[90, 30]","[1, 2]",[2],[1],[2],[90],[9],[2],[1],[1945] -29,64,"[2, 1]","[50, 10]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1978] -29,65,"[1, 2]","[0, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2395] -29,66,"[2, 1]","[50, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[2459] -29,67,"[3, 4]","[20, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[1755] -29,68,"[2, 1]","[50, 10]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2195] -29,69,"[1, 2]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[1810] -29,70,"[4, 3]","[10, 30]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[2028] -29,71,"[1, 2]","[40, 0]","[1, 2]",[2],[1],[2],[0],[0],[2],[1],[1943] -29,72,"[3, 4]","[40, 0]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1843] -29,73,"[2, 1]","[30, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[2109] -29,74,"[1, 2]","[0, 0]","[1, 1]",[2],[5],[1],[0],[0],[1],[1],[2396] -29,75,"[4, 3]","[20, 10]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2147] -29,76,"[1, 2]","[30, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[2292] -29,77,"[3, 4]","[40, 0]","[2, 2]",[2],[5],[4],[0],[0],[2],[3],[1842] -29,78,"[4, 3]","[40, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1827] -29,79,"[2, 1]","[20, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[1945] -29,80,"[1, 2]","[0, 50]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[2045] -29,1,"[1, 2]","[40, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[4459] -29,2,"[4, 3]","[40, 50]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1809] -29,3,"[2, 1]","[30, 70]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[2095] -29,4,"[3, 4]","[70, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2084] -29,5,"[3, 4]","[80, 40]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[1921] -29,6,"[1, 2]","[80, 50]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2245] -29,7,"[2, 1]","[40, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2094] -29,8,"[2, 1]","[10, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1977] -29,9,"[4, 3]","[0, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1826] -29,10,"[2, 1]","[20, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[1960] -29,11,"[4, 3]","[20, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[1878] -29,12,"[2, 1]","[30, 0]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[1895] -29,13,"[3, 4]","[50, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[1977] -29,14,"[1, 2]","[60, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[1845] -29,15,"[3, 4]","[50, 80]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[1845] -29,16,"[1, 2]","[90, 70]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[1977] -29,17,"[1, 2]","[80, 60]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1896] -29,18,"[3, 4]","[80, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[1876] -29,19,"[3, 4]","[90, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[1893] -29,20,"[3, 4]","[80, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1809] -29,21,"[4, 3]","[70, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1876] -29,22,"[2, 1]","[50, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[2144] -29,23,"[3, 4]","[10, 40]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[2095] -29,24,"[4, 3]","[40, 10]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[1795] -29,25,"[4, 3]","[20, 10]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[2045] -29,26,"[4, 3]","[40, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1810] -29,27,"[3, 4]","[20, 60]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[2144] -29,28,"[2, 1]","[70, 20]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2560] -29,29,"[2, 1]","[80, 10]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2226] -29,30,"[3, 4]","[30, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[1827] -29,31,"[1, 2]","[30, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1855] -29,32,"[4, 3]","[80, 30]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2109] -29,33,"[1, 2]","[20, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2092] -29,34,"[3, 4]","[40, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[1760] -29,35,"[4, 3]","[40, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1828] -29,36,"[1, 2]","[50, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2144] -29,37,"[1, 2]","[10, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2226] -29,38,"[1, 2]","[0, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2060] -29,39,"[2, 1]","[50, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[1960] -29,40,"[3, 4]","[50, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[3427] -29,41,"[4, 3]","[20, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[5421] -29,42,"[3, 4]","[70, 10]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1911] -29,43,"[1, 2]","[50, 20]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[2227] -29,44,"[2, 1]","[40, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1976] -29,45,"[4, 3]","[60, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1859] -29,46,"[1, 2]","[70, 90]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2045] -29,47,"[2, 1]","[90, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[1859] -29,48,"[4, 3]","[40, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[1821] -29,49,"[3, 4]","[50, 30]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1909] -29,50,"[1, 2]","[50, 20]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[1778] -29,51,"[2, 1]","[0, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[1959] -29,52,"[4, 3]","[30, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1793] -29,53,"[1, 2]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2259] -29,54,"[2, 1]","[50, 20]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2759] -29,55,"[1, 2]","[20, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[2245] -29,56,"[1, 2]","[0, 10]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[1910] -29,57,"[3, 4]","[30, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[1745] -29,58,"[3, 4]","[0, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[1794] -29,59,"[3, 4]","[10, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1858] -29,60,"[3, 4]","[40, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1927] -29,61,"[2, 1]","[40, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[2010] -29,62,"[1, 2]","[60, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[2178] -29,63,"[1, 2]","[70, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[1860] -29,64,"[3, 4]","[50, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1778] -29,65,"[2, 1]","[80, 70]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2305] -29,66,"[2, 1]","[60, 80]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1910] -29,67,"[4, 3]","[40, 60]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2543] -29,68,"[2, 1]","[50, 70]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2843] -29,69,"[1, 2]","[70, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[2360] -29,70,"[3, 4]","[70, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2026] -29,71,"[3, 4]","[80, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[1795] -29,72,"[3, 4]","[60, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[1846] -29,73,"[1, 2]","[50, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[2008] -29,74,"[1, 2]","[30, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[2195] -29,75,"[4, 3]","[30, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1761] -29,76,"[3, 4]","[70, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1746] -29,77,"[2, 1]","[30, 50]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[1738] -29,78,"[2, 1]","[10, 20]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[1859] -29,79,"[2, 1]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1909] -29,80,"[2, 1]","[10, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[1808] -30,1,"[2, 1]","[20, 60]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[4904] -30,2,"[1, 2]","[90, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[1822] -30,3,"[4, 3]","[10, 70]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[2834] -30,4,"[4, 3]","[40, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1791] -30,5,"[1, 2]","[50, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2016] -30,6,"[1, 2]","[60, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[12707] -30,7,"[2, 1]","[30, 70]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[1916] -30,8,"[2, 1]","[30, 70]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[2281] -30,9,"[1, 2]","[50, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2067] -30,10,"[2, 1]","[50, 40]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2125] -30,11,"[1, 2]","[20, 30]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[2974] -30,12,"[1, 2]","[30, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[2127] -30,13,"[3, 4]","[20, 50]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1765] -30,14,"[3, 4]","[0, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[5617] -30,15,"[4, 3]","[30, 20]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2914] -30,16,"[4, 3]","[30, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1730] -30,17,"[2, 1]","[60, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[2175] -30,18,"[2, 1]","[60, 40]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[1972] -30,19,"[4, 3]","[70, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[1880] -30,20,"[3, 4]","[40, 90]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1804] -30,21,"[2, 1]","[80, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1707] -30,22,"[2, 1]","[80, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1740] -30,23,"[1, 2]","[40, 80]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1831] -30,24,"[3, 4]","[0, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1756] -30,25,"[3, 4]","[40, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[2151] -30,26,"[3, 4]","[10, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2927] -30,27,"[2, 1]","[60, 10]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2494] -30,28,"[3, 4]","[10, 60]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[3713] -30,29,"[4, 3]","[50, 0]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[2808] -30,30,"[1, 2]","[10, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2545] -30,31,"[1, 2]","[10, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[2076] -30,32,"[3, 4]","[30, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[3202] -30,33,"[1, 2]","[30, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2344] -30,34,"[2, 1]","[70, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[5181] -30,35,"[2, 1]","[80, 30]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[3564] -30,36,"[1, 2]","[30, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1920] -30,37,"[3, 4]","[30, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1896] -30,38,"[1, 2]","[20, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2595] -30,39,"[2, 1]","[40, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[13717] -30,40,"[1, 2]","[40, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[1],[2307] -30,41,"[3, 4]","[10, 40]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[6672] -30,42,"[3, 4]","[30, 40]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1824] -30,43,"[3, 4]","[70, 40]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1697] -30,44,"[3, 4]","[80, 50]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[1747] -30,45,"[3, 4]","[70, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[1780] -30,46,"[1, 2]","[80, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[1928] -30,47,"[3, 4]","[50, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[1690] -30,48,"[4, 3]","[10, 60]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[1945] -30,49,"[1, 2]","[40, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[1847] -30,50,"[1, 2]","[40, 0]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[1985] -30,51,"[1, 2]","[40, 10]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2521] -30,52,"[1, 2]","[10, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[2217] -30,53,"[4, 3]","[0, 10]","[2, 2]",[2],[5],[4],[0],[0],[2],[4],[2241] -30,54,"[1, 2]","[0, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[1656] -30,55,"[2, 1]","[0, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[1880] -30,56,"[1, 2]","[20, 10]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[1829] -30,57,"[2, 1]","[20, 0]","[1, 1]",[2],[1],[1],[0],[0],[1],[1],[1713] -30,58,"[1, 2]","[30, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[2693] -30,59,"[3, 4]","[40, 0]","[2, 2]",[2],[5],[4],[0],[0],[2],[3],[1773] -30,60,"[4, 3]","[10, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2955] -30,61,"[2, 1]","[40, 30]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[1871] -30,62,"[2, 1]","[60, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[2069] -30,63,"[4, 3]","[30, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2479] -30,64,"[4, 3]","[30, 80]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1690] -30,65,"[1, 2]","[70, 60]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[2486] -30,66,"[2, 1]","[70, 80]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2676] -30,67,"[2, 1]","[50, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1697] -30,68,"[4, 3]","[70, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[1813] -30,69,"[3, 4]","[70, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1657] -30,70,"[1, 2]","[50, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[1922] -30,71,"[4, 3]","[10, 80]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1820] -30,72,"[1, 2]","[80, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[1674] -30,73,"[3, 4]","[60, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1807] -30,74,"[3, 4]","[50, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[3572] -30,75,"[4, 3]","[40, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[6218] -30,76,"[1, 2]","[80, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[3309] -30,77,"[2, 1]","[60, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2177] -30,78,"[1, 2]","[50, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1879] -30,79,"[1, 2]","[80, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2075] -30,80,"[3, 4]","[70, 50]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[3619] -29,1,"[4, 3]","[20, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[7546] -29,2,"[2, 1]","[20, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2141] -29,3,"[4, 3]","[0, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[1753] -29,4,"[3, 4]","[40, 30]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[1891] -29,5,"[1, 2]","[0, 10]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[2350] -29,6,"[2, 1]","[10, 40]","[1, 2]",[1],[5],[2],[50],[1],[2],[2],[1911] -29,7,"[2, 1]","[30, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[2057] -29,8,"[4, 3]","[40, 10]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[1915] -29,9,"[4, 3]","[70, 20]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[1823] -29,10,"[3, 4]","[30, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2206] -29,11,"[4, 3]","[80, 50]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2023] -29,12,"[1, 2]","[30, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1939] -29,13,"[3, 4]","[30, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2122] -29,14,"[4, 3]","[50, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[3],[2855] -29,15,"[4, 3]","[20, 0]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[1906] -29,16,"[3, 4]","[10, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[2238] -29,17,"[3, 4]","[30, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[1841] -29,18,"[4, 3]","[50, 40]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[1815] -29,19,"[1, 2]","[10, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[1971] -29,20,"[2, 1]","[70, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[1838] -29,21,"[3, 4]","[30, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2039] -29,22,"[1, 2]","[40, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[1989] -29,23,"[4, 3]","[60, 30]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2155] -29,24,"[1, 2]","[10, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1973] -29,25,"[3, 4]","[10, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[1806] -29,26,"[3, 4]","[0, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1773] -29,27,"[2, 1]","[40, 30]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[3224] -29,28,"[1, 2]","[50, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[1890] -29,29,"[3, 4]","[20, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[1973] -29,30,"[4, 3]","[30, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[1972] -29,31,"[4, 3]","[10, 20]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[1989] -29,32,"[4, 3]","[10, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[1790] -29,33,"[4, 3]","[0, 20]","[2, 2]",[1],[1],[4],[0],[0],[2],[4],[1756] -29,34,"[1, 2]","[10, 0]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[2139] -29,35,"[2, 1]","[10, 0]","[1, 1]",[1],[5],[1],[0],[0],[1],[1],[1940] -29,36,"[3, 4]","[40, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[1790] -29,37,"[4, 3]","[10, 50]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[2105] -29,38,"[2, 1]","[20, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2622] -29,39,"[3, 4]","[60, 20]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1741] -29,40,"[3, 4]","[60, 10]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1773] -29,41,"[2, 1]","[20, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[3652] -29,42,"[1, 2]","[90, 40]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[1873] -29,43,"[2, 1]","[50, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2124] -29,44,"[1, 2]","[80, 40]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2039] -29,45,"[1, 2]","[60, 50]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2106] -29,46,"[2, 1]","[40, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[1973] -29,47,"[4, 3]","[30, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[1723] -29,48,"[1, 2]","[0, 10]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[2075] -29,49,"[3, 4]","[10, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[1972] -29,50,"[1, 2]","[30, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1790] -29,51,"[2, 1]","[10, 60]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[3507] -29,52,"[4, 3]","[0, 70]","[2, 2]",[2],[5],[4],[0],[0],[2],[4],[2340] -29,53,"[2, 1]","[30, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1836] -29,54,"[4, 3]","[30, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1940] -29,55,"[1, 2]","[10, 20]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[2973] -29,56,"[1, 2]","[20, 10]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[2072] -29,57,"[4, 3]","[20, 0]","[2, 1]",[2],[1],[3],[0],[0],[1],[3],[1806] -29,58,"[1, 2]","[0, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2123] -29,59,"[4, 3]","[20, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1890] -29,60,"[1, 2]","[0, 0]","[1, 2]",[2],[5],[2],[0],[0],[2],[1],[2073] -29,61,"[3, 4]","[10, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[2223] -29,62,"[2, 1]","[10, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[1991] -29,63,"[4, 3]","[10, 0]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[1606] -29,64,"[3, 4]","[20, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2073] -29,65,"[4, 3]","[90, 30]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[1757] -29,66,"[3, 4]","[40, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1689] -29,67,"[1, 2]","[70, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2172] -29,68,"[3, 4]","[60, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[1823] -29,69,"[3, 4]","[80, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[1742] -29,70,"[3, 4]","[80, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1839] -29,71,"[1, 2]","[70, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2056] -29,72,"[3, 4]","[60, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[1873] -29,73,"[3, 4]","[30, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1806] -29,74,"[2, 1]","[60, 50]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[2989] -29,75,"[1, 2]","[40, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2041] -29,76,"[2, 1]","[40, 30]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[1857] -29,77,"[1, 2]","[20, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[1956] -29,78,"[2, 1]","[60, 30]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2460] -29,79,"[4, 3]","[60, 30]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2057] -29,80,"[1, 2]","[20, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[2891] -31,1,"[1, 2]","[60, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[4177] -31,2,"[2, 1]","[30, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[2113] -31,3,"[3, 4]","[10, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[1880] -31,4,"[3, 4]","[40, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[1923] -31,5,"[2, 1]","[0, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1985] -31,6,"[4, 3]","[50, 60]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2314] -31,7,"[1, 2]","[80, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2048] -31,8,"[4, 3]","[80, 70]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2872] -31,9,"[2, 1]","[50, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2012] -31,10,"[2, 1]","[60, 80]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2141] -31,11,"[1, 2]","[70, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2336] -31,12,"[2, 1]","[60, 70]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2163] -31,13,"[4, 3]","[70, 60]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2131] -31,14,"[2, 1]","[70, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1962] -31,15,"[1, 2]","[80, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2153] -31,16,"[2, 1]","[90, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2876] -31,17,"[2, 1]","[50, 80]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[2519] -31,18,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1985] -31,19,"[3, 4]","[40, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2114] -31,20,"[4, 3]","[20, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[2012] -31,21,"[1, 2]","[40, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[1818] -31,22,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1991] -31,23,"[1, 2]","[80, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[1874] -31,24,"[4, 3]","[60, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[2013] -31,25,"[4, 3]","[40, 70]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[2102] -31,26,"[4, 3]","[60, 90]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2111] -31,27,"[4, 3]","[60, 70]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2114] -31,28,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1846] -31,29,"[1, 2]","[80, 40]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2297] -31,30,"[4, 3]","[40, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2092] -31,31,"[3, 4]","[80, 50]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[2241] -31,32,"[3, 4]","[80, 40]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[2630] -31,33,"[3, 4]","[60, 30]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2419] -31,34,"[1, 2]","[50, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2911] -31,35,"[4, 3]","[40, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2169] -31,36,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2150] -31,37,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2186] -31,38,"[3, 4]","[90, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2062] -31,39,"[2, 1]","[50, 60]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[2224] -31,40,"[2, 1]","[70, 90]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2191] -31,41,"[2, 1]","[80, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[2804607] -31,42,"[2, 1]","[80, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[2287] -31,43,"[4, 3]","[70, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[2007] -31,44,"[3, 4]","[80, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2024] -31,45,"[1, 2]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[2258] -31,46,"[2, 1]","[70, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[2365] -31,47,"[3, 4]","[80, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2047] -31,48,"[4, 3]","[40, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[2348] -31,49,"[1, 2]","[70, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2427] -31,50,"[3, 4]","[60, 40]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2046] -31,51,"[4, 3]","[30, 60]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2000] -31,52,"[2, 1]","[30, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1925] -31,53,"[1, 2]","[0, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[2196] -31,54,"[4, 3]","[10, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[2169] -31,55,"[3, 4]","[20, 0]","[2, 2]",[2],[1],[4],[0],[0],[2],[3],[2621] -31,56,"[2, 1]","[0, 10]","[1, 2]",[2],[5],[2],[0],[0],[2],[2],[2553] -31,57,"[1, 2]","[0, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[1975] -31,58,"[1, 2]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2136] -31,59,"[4, 3]","[30, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2539] -31,60,"[4, 3]","[30, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[2054] -31,61,"[3, 4]","[40, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[2012] -31,62,"[2, 1]","[40, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2042] -31,63,"[1, 2]","[60, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2142] -31,64,"[1, 2]","[50, 60]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2121] -31,65,"[4, 3]","[30, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2136] -31,66,"[4, 3]","[30, 90]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2328] -31,67,"[2, 1]","[40, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[1986] -31,68,"[3, 4]","[70, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2230] -31,69,"[4, 3]","[10, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2136] -31,70,"[1, 2]","[80, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[1995] -31,71,"[3, 4]","[70, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[1958] -31,72,"[4, 3]","[70, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2152] -31,73,"[1, 2]","[20, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[1946] -31,74,"[3, 4]","[60, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2041] -31,75,"[3, 4]","[70, 80]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2358] -31,76,"[2, 1]","[50, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1960] -31,77,"[1, 2]","[50, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[1834] -31,78,"[4, 3]","[60, 80]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[2138] -31,79,"[4, 3]","[90, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1879] -31,80,"[1, 2]","[40, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[1808] -29,1,"[1, 2]","[60, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[4372] -29,2,"[2, 1]","[30, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[1891] -29,3,"[3, 4]","[10, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[2423] -29,4,"[3, 4]","[40, 10]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[1852] -29,5,"[2, 1]","[0, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2239] -29,6,"[4, 3]","[50, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[1857] -29,7,"[1, 2]","[80, 60]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2140] -29,8,"[4, 3]","[80, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1790] -29,9,"[2, 1]","[50, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2189] -29,10,"[2, 1]","[60, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2204] -29,11,"[1, 2]","[70, 60]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2140] -29,12,"[2, 1]","[60, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2007] -29,13,"[4, 3]","[70, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1824] -29,14,"[2, 1]","[70, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2041] -29,15,"[1, 2]","[80, 90]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2140] -29,16,"[2, 1]","[90, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1969] -29,17,"[2, 1]","[50, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1856] -29,18,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1790] -29,19,"[3, 4]","[40, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[1840] -29,20,"[4, 3]","[20, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1740] -29,21,"[1, 2]","[40, 20]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[2056] -29,22,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1804] -29,23,"[1, 2]","[80, 40]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1891] -29,24,"[4, 3]","[60, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1758] -29,25,"[4, 3]","[40, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1774] -29,26,"[4, 3]","[60, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1754] -29,27,"[4, 3]","[60, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1720] -29,28,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2023] -29,29,"[1, 2]","[80, 40]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1990] -29,30,"[4, 3]","[40, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1740] -29,31,"[3, 4]","[80, 50]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1841] -29,32,"[3, 4]","[80, 40]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1723] -29,33,"[3, 4]","[60, 30]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1707] -29,34,"[1, 2]","[50, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[1841] -29,35,"[4, 3]","[40, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1820] -29,36,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2058] -29,37,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2223] -29,38,"[3, 4]","[90, 70]","[2, 1]",[1],[1],[3],[90],[9],[1],[3],[1857] -29,39,"[2, 1]","[50, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2274] -29,40,"[2, 1]","[70, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[2324] -29,41,"[2, 1]","[80, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[3519] -29,42,"[2, 1]","[80, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1873] -29,43,"[4, 3]","[70, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1840] -29,44,"[3, 4]","[80, 40]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2023] -29,45,"[1, 2]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[1873] -29,46,"[2, 1]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1841] -29,47,"[3, 4]","[80, 70]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1936] -29,48,"[4, 3]","[40, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[1938] -29,49,"[1, 2]","[70, 60]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2022] -29,50,"[3, 4]","[60, 40]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1805] -29,51,"[4, 3]","[30, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1939] -29,52,"[2, 1]","[30, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1841] -29,53,"[1, 2]","[0, 30]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[2006] -29,54,"[4, 3]","[10, 20]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[1889] -29,55,"[3, 4]","[20, 0]","[2, 2]",[2],[1],[4],[0],[0],[2],[3],[1920] -29,56,"[2, 1]","[0, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1973] -29,57,"[1, 2]","[0, 10]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[2090] -29,58,"[1, 2]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[1823] -29,59,"[4, 3]","[30, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2154] -29,60,"[4, 3]","[30, 10]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1752] -29,61,"[3, 4]","[40, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[2498] -29,62,"[2, 1]","[40, 60]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2274] -29,63,"[1, 2]","[60, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2325] -29,64,"[1, 2]","[50, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1805] -29,65,"[4, 3]","[30, 60]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2157] -29,66,"[4, 3]","[30, 90]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[1757] -29,67,"[2, 1]","[40, 80]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2090] -29,68,"[3, 4]","[70, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2087] -29,69,"[4, 3]","[10, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2374] -29,70,"[1, 2]","[80, 60]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2057] -29,71,"[3, 4]","[70, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1687] -29,72,"[4, 3]","[70, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[3790] -29,73,"[1, 2]","[20, 80]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[1873] -29,74,"[3, 4]","[60, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1790] -29,75,"[3, 4]","[70, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1973] -29,76,"[2, 1]","[50, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1974] -29,77,"[1, 2]","[50, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[4541] -29,78,"[4, 3]","[60, 80]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[2090] -29,79,"[4, 3]","[90, 70]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[1757] -29,80,"[1, 2]","[40, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2607] -32,1,"[3, 4]","[60, 40]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[6267] -32,2,"[3, 4]","[60, 80]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[2639] -32,3,"[1, 2]","[30, 50]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[2313] -32,4,"[4, 3]","[50, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[2518] -32,5,"[1, 2]","[10, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[2759] -32,6,"[2, 1]","[50, 30]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2216] -32,7,"[1, 2]","[40, 80]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[1904] -32,8,"[1, 2]","[60, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[1988] -32,9,"[4, 3]","[90, 80]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2284] -32,10,"[3, 4]","[80, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2467] -32,11,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2517] -32,12,"[4, 3]","[70, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2865] -32,13,"[2, 1]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2721] -32,14,"[1, 2]","[80, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1948] -32,15,"[2, 1]","[60, 70]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2958] -32,16,"[4, 3]","[50, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2200] -32,17,"[3, 4]","[40, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2109] -32,18,"[3, 4]","[40, 70]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[2807] -32,19,"[1, 2]","[30, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2296] -32,20,"[3, 4]","[20, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[2018] -32,21,"[2, 1]","[80, 60]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[2334] -32,22,"[3, 4]","[70, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2109] -32,23,"[3, 4]","[70, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[1986] -32,24,"[1, 2]","[70, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[2883] -32,25,"[1, 2]","[70, 20]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2200] -32,26,"[2, 1]","[30, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2396] -32,27,"[1, 2]","[60, 10]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2201] -32,28,"[4, 3]","[10, 30]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[2616] -32,29,"[2, 1]","[20, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[2103] -32,30,"[1, 2]","[10, 20]","[1, 1]",[1],[5],[1],[50],[1],[1],[2],[2506] -32,31,"[2, 1]","[10, 0]","[1, 1]",[1],[1],[1],[0],[0],[1],[1],[2144] -32,32,"[3, 4]","[0, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[3775] -32,33,"[4, 3]","[0, 10]","[2, 2]",[1],[1],[4],[0],[0],[2],[4],[2188] -32,34,"[4, 3]","[20, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[2220] -32,35,"[2, 1]","[20, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2311] -32,36,"[1, 2]","[30, 10]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[1945] -32,37,"[2, 1]","[10, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1976] -32,38,"[4, 3]","[0, 20]","[2, 2]",[1],[5],[4],[0],[0],[2],[4],[2613] -32,39,"[1, 2]","[10, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[3846] -32,40,"[4, 3]","[20, 40]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[3850] -32,41,"[4, 3]","[30, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[7845] -32,42,"[3, 4]","[60, 30]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2341] -32,43,"[4, 3]","[60, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2035] -32,44,"[2, 1]","[40, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[4208] -32,45,"[4, 3]","[20, 50]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[2503] -32,46,"[2, 1]","[40, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[2426] -32,47,"[1, 2]","[50, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[2410] -32,48,"[1, 2]","[50, 70]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2190] -32,49,"[3, 4]","[50, 70]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[1887] -32,50,"[4, 3]","[50, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[2002] -32,51,"[1, 2]","[30, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2826] -32,52,"[2, 1]","[50, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[2118] -32,53,"[1, 2]","[40, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1853] -32,54,"[1, 2]","[20, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1785] -32,55,"[2, 1]","[70, 10]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[2094] -32,56,"[2, 1]","[90, 0]","[1, 2]",[2],[5],[2],[450],[9],[2],[1],[1870] -32,57,"[1, 2]","[10, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[1835] -32,58,"[4, 3]","[80, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[2773] -32,59,"[1, 2]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2381] -32,60,"[2, 1]","[30, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[2184] -32,61,"[2, 1]","[60, 20]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[1770] -32,62,"[1, 2]","[30, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[1812] -32,63,"[3, 4]","[40, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2302] -32,64,"[4, 3]","[40, 30]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[2100] -32,65,"[4, 3]","[10, 30]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[2299] -32,66,"[1, 2]","[40, 20]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2490] -32,67,"[2, 1]","[30, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[2218] -32,68,"[4, 3]","[10, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[2569] -32,69,"[3, 4]","[20, 0]","[2, 2]",[2],[1],[4],[0],[0],[2],[3],[2330] -32,70,"[2, 1]","[10, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[3352] -32,71,"[3, 4]","[20, 20]","[2, 1]",[2],[1],[3],[20],[2],[1],[3],[2009] -32,72,"[2, 1]","[80, 20]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2621] -32,73,"[1, 2]","[20, 90]","[1, 2]",[2],[1],[2],[90],[9],[2],[2],[2462] -32,74,"[2, 1]","[80, 50]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[2475] -32,75,"[2, 1]","[80, 50]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[1790] -32,76,"[3, 4]","[60, 80]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[3270] -32,77,"[1, 2]","[70, 80]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2625] -32,78,"[3, 4]","[70, 80]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[2445] -32,79,"[2, 1]","[80, 60]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[3159] -32,80,"[1, 2]","[40, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[2351] -31,1,"[1, 2]","[30, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[3446] -31,2,"[1, 2]","[20, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2930] -31,3,"[3, 4]","[10, 60]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[1985] -31,4,"[4, 3]","[90, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[1931] -31,5,"[2, 1]","[70, 0]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2169] -31,6,"[1, 2]","[20, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2023] -31,7,"[1, 2]","[10, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[1846] -31,8,"[2, 1]","[60, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[1954] -31,9,"[2, 1]","[80, 10]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2024] -31,10,"[2, 1]","[70, 10]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2313] -31,11,"[1, 2]","[30, 40]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[2297] -31,12,"[4, 3]","[40, 20]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[1929] -31,13,"[4, 3]","[50, 30]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[1811] -31,14,"[4, 3]","[10, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[2438] -31,15,"[4, 3]","[0, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1815] -31,16,"[2, 1]","[20, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[1918] -31,17,"[3, 4]","[20, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[2154] -31,18,"[4, 3]","[40, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[2091] -31,19,"[1, 2]","[50, 20]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[2603] -31,20,"[3, 4]","[50, 10]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[2125] -31,21,"[2, 1]","[30, 60]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[2203] -31,22,"[3, 4]","[30, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[2262] -31,23,"[1, 2]","[30, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2073] -31,24,"[4, 3]","[0, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[2019] -31,25,"[1, 2]","[20, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[2164] -31,26,"[1, 2]","[60, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[1986] -31,27,"[2, 1]","[40, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2377] -31,28,"[2, 1]","[50, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[2164] -31,29,"[3, 4]","[30, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[2104] -31,30,"[2, 1]","[70, 40]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2752] -31,31,"[1, 2]","[40, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2839] -31,32,"[4, 3]","[90, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2153] -31,33,"[1, 2]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2276] -31,34,"[4, 3]","[80, 60]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2104] -31,35,"[2, 1]","[90, 80]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[2588] -31,36,"[4, 3]","[90, 70]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2190] -31,37,"[1, 2]","[40, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2690] -31,38,"[2, 1]","[70, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[1931] -31,39,"[2, 1]","[60, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[2603] -31,40,"[4, 3]","[80, 10]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2320] -31,41,"[1, 2]","[20, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[2654] -31,42,"[2, 1]","[70, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[3157] -31,43,"[3, 4]","[20, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2041] -31,44,"[2, 1]","[70, 0]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[2187] -31,45,"[2, 1]","[30, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[2079] -31,46,"[4, 3]","[40, 10]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2111] -31,47,"[2, 1]","[30, 10]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2383] -31,48,"[4, 3]","[60, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[2114] -31,49,"[4, 3]","[70, 10]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[2763] -31,50,"[1, 2]","[10, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2299] -31,51,"[2, 1]","[40, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[2344] -31,52,"[4, 3]","[20, 40]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[2346] -31,53,"[4, 3]","[30, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[2761] -31,54,"[2, 1]","[20, 50]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[2187] -31,55,"[1, 2]","[60, 10]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2121] -31,56,"[3, 4]","[40, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2632] -31,57,"[3, 4]","[50, 30]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2748] -31,58,"[3, 4]","[30, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2493] -31,59,"[1, 2]","[30, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2371] -31,60,"[4, 3]","[30, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[2153] -31,61,"[3, 4]","[40, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2194] -31,62,"[3, 4]","[20, 20]","[2, 1]",[2],[1],[3],[20],[2],[1],[3],[2711] -31,63,"[4, 3]","[20, 30]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[2329] -31,64,"[2, 1]","[40, 60]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[2476] -31,65,"[1, 2]","[80, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2287] -31,66,"[1, 2]","[70, 0]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2410] -31,67,"[4, 3]","[10, 80]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[2565] -31,68,"[4, 3]","[20, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2091] -31,69,"[3, 4]","[40, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[2292] -31,70,"[2, 1]","[10, 30]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2119] -31,71,"[4, 3]","[10, 40]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[2536] -31,72,"[1, 2]","[70, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2186] -31,73,"[4, 3]","[10, 60]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[2126] -31,74,"[4, 3]","[20, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2281] -31,75,"[4, 3]","[10, 80]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[2077] -31,76,"[2, 1]","[20, 60]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[2380] -31,77,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2215] -31,78,"[4, 3]","[10, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[2757] -31,79,"[1, 2]","[90, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[2116] -31,80,"[2, 1]","[10, 70]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2023] -29,1,"[2, 1]","[20, 60]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[7256] -29,2,"[1, 2]","[90, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[1987] -29,3,"[4, 3]","[10, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2656] -29,4,"[4, 3]","[40, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1906] -29,5,"[1, 2]","[50, 60]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[2224] -29,6,"[1, 2]","[60, 30]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[1889] -29,7,"[2, 1]","[30, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1857] -29,8,"[2, 1]","[30, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1870] -29,9,"[1, 2]","[50, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2056] -29,10,"[2, 1]","[50, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[1872] -29,11,"[1, 2]","[20, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[2000] -29,12,"[1, 2]","[30, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[2040] -29,13,"[3, 4]","[20, 50]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[2040] -29,14,"[3, 4]","[0, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2220] -29,15,"[4, 3]","[30, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[1773] -29,16,"[4, 3]","[30, 20]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[1761] -29,17,"[2, 1]","[60, 20]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2005] -29,18,"[2, 1]","[60, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[2021] -29,19,"[4, 3]","[70, 40]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2224] -29,20,"[3, 4]","[40, 90]","[2, 2]",[1],[5],[4],[450],[9],[2],[4],[3238] -29,21,"[2, 1]","[80, 70]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1840] -29,22,"[2, 1]","[80, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2959] -29,23,"[1, 2]","[40, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1989] -29,24,"[3, 4]","[0, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2203] -29,25,"[3, 4]","[40, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[1940] -29,26,"[3, 4]","[10, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2089] -29,27,"[2, 1]","[60, 10]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2073] -29,28,"[3, 4]","[10, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2241] -29,29,"[4, 3]","[50, 0]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[1990] -29,30,"[1, 2]","[10, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[3565] -29,31,"[1, 2]","[10, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[1991] -29,32,"[3, 4]","[30, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2021] -29,33,"[1, 2]","[30, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[1971] -29,34,"[2, 1]","[70, 40]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[3574] -29,35,"[2, 1]","[80, 30]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1823] -29,36,"[1, 2]","[30, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2207] -29,37,"[3, 4]","[30, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2023] -29,38,"[1, 2]","[20, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[1756] -29,39,"[2, 1]","[40, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2906] -29,40,"[1, 2]","[40, 10]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[2357] -29,41,"[3, 4]","[10, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[4986] -29,42,"[3, 4]","[30, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[4],[1973] -29,43,"[3, 4]","[70, 40]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1707] -29,44,"[3, 4]","[80, 50]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[1757] -29,45,"[3, 4]","[70, 20]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1757] -29,46,"[1, 2]","[80, 20]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2274] -29,47,"[3, 4]","[50, 10]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1873] -29,48,"[4, 3]","[10, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[2073] -29,49,"[1, 2]","[40, 10]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2271] -29,50,"[1, 2]","[40, 0]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[1907] -29,51,"[1, 2]","[40, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[1773] -29,52,"[1, 2]","[10, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[2073] -29,53,"[4, 3]","[0, 10]","[2, 2]",[2],[5],[4],[0],[0],[2],[4],[2290] -29,54,"[1, 2]","[0, 10]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[2907] -29,55,"[2, 1]","[0, 10]","[1, 2]",[2],[1],[2],[0],[0],[2],[2],[1990] -29,56,"[1, 2]","[20, 10]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[2138] -29,57,"[2, 1]","[20, 0]","[1, 1]",[2],[1],[1],[0],[0],[1],[1],[2257] -29,58,"[1, 2]","[30, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[2089] -29,59,"[3, 4]","[40, 0]","[2, 2]",[2],[5],[4],[0],[0],[2],[3],[1957] -29,60,"[4, 3]","[10, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1724] -29,61,"[2, 1]","[40, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[1855] -29,62,"[2, 1]","[60, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[1871] -29,63,"[4, 3]","[30, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1990] -29,64,"[4, 3]","[30, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1789] -29,65,"[1, 2]","[70, 60]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[2090] -29,66,"[2, 1]","[70, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[2024] -29,67,"[2, 1]","[50, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2890] -29,68,"[4, 3]","[70, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[2257] -29,69,"[3, 4]","[70, 30]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1823] -29,70,"[1, 2]","[50, 30]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[1773] -29,71,"[4, 3]","[10, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1773] -29,72,"[1, 2]","[80, 10]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1823] -29,73,"[3, 4]","[60, 20]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1790] -29,74,"[3, 4]","[50, 20]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1840] -29,75,"[4, 3]","[40, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1823] -29,76,"[1, 2]","[80, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1990] -29,77,"[2, 1]","[60, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1857] -29,78,"[1, 2]","[50, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[1955] -29,79,"[1, 2]","[80, 50]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1890] -29,80,"[3, 4]","[70, 50]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1837] -33,1,"[1, 2]","[60, 40]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[3877] -33,2,"[2, 1]","[30, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[1842] -33,3,"[3, 4]","[10, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[4112] -33,4,"[3, 4]","[40, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[2019] -33,5,"[2, 1]","[0, 50]","[1, 2]",[1],[1],[2],[0],[0],[2],[2],[2116] -33,6,"[4, 3]","[50, 60]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[1973] -33,7,"[1, 2]","[80, 60]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2173] -33,8,"[4, 3]","[80, 70]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[1997] -33,9,"[2, 1]","[50, 90]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[1950] -33,10,"[2, 1]","[60, 80]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2111] -33,11,"[1, 2]","[70, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[1884] -33,12,"[2, 1]","[60, 70]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[8109] -33,13,"[4, 3]","[70, 60]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2094] -33,14,"[2, 1]","[70, 80]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2903] -33,15,"[1, 2]","[80, 90]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2253] -33,16,"[2, 1]","[90, 70]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[2349] -33,17,"[2, 1]","[50, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2235] -33,18,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1960] -33,19,"[3, 4]","[40, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[1981] -33,20,"[4, 3]","[20, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[2466] -33,21,"[1, 2]","[40, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[1869] -33,22,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1728] -33,23,"[1, 2]","[80, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[1777] -33,24,"[4, 3]","[60, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1962] -33,25,"[4, 3]","[40, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1693] -33,26,"[4, 3]","[60, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1753] -33,27,"[4, 3]","[60, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1826] -33,28,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1659] -33,29,"[1, 2]","[80, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[1651] -33,30,"[4, 3]","[40, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1593] -33,31,"[3, 4]","[80, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[1678] -33,32,"[3, 4]","[80, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[1668] -33,33,"[3, 4]","[60, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[1726] -33,34,"[1, 2]","[50, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[1710] -33,35,"[4, 3]","[40, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1685] -33,36,"[1, 2]","[80, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[1685] -33,37,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1703] -33,38,"[3, 4]","[90, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[1686] -33,39,"[2, 1]","[50, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1752] -33,40,"[2, 1]","[70, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[5461] -33,41,"[2, 1]","[80, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[7881] -33,42,"[2, 1]","[80, 90]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[1717] -33,43,"[4, 3]","[70, 80]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[3152] -33,44,"[3, 4]","[80, 40]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[1668] -33,45,"[1, 2]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[1743] -33,46,"[2, 1]","[70, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[1693] -33,47,"[3, 4]","[80, 70]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1700] -33,48,"[4, 3]","[40, 60]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1650] -33,49,"[1, 2]","[70, 60]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[1705] -33,50,"[3, 4]","[60, 40]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1709] -33,51,"[4, 3]","[30, 60]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[1799] -33,52,"[2, 1]","[30, 70]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[1678] -33,53,"[1, 2]","[0, 30]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[1685] -33,54,"[4, 3]","[10, 20]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[1690] -33,55,"[3, 4]","[20, 0]","[2, 1]",[2],[1],[3],[20],[2],[1],[3],[1627] -33,56,"[2, 1]","[0, 10]","[1, 2]",[2],[5],[2],[0],[0],[2],[2],[1659] -33,57,"[1, 2]","[0, 10]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[3210] -33,58,"[1, 2]","[30, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[3827] -33,59,"[4, 3]","[30, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[1709] -33,60,"[4, 3]","[30, 10]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1801] -33,61,"[3, 4]","[40, 30]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1677] -33,62,"[2, 1]","[40, 60]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1716] -33,63,"[1, 2]","[60, 40]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1643] -33,64,"[1, 2]","[50, 60]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[1668] -33,65,"[4, 3]","[30, 60]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[1692] -33,66,"[4, 3]","[30, 90]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[1760] -33,67,"[2, 1]","[40, 80]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1686] -33,68,"[3, 4]","[70, 20]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1742] -33,69,"[4, 3]","[10, 70]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[1676] -33,70,"[1, 2]","[80, 60]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1703] -33,71,"[3, 4]","[70, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1760] -33,72,"[4, 3]","[70, 40]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1693] -33,73,"[1, 2]","[20, 80]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[1716] -33,74,"[3, 4]","[60, 80]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[1691] -33,75,"[3, 4]","[70, 80]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1640] -33,76,"[2, 1]","[50, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1736] -33,77,"[1, 2]","[50, 30]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[1689] -33,78,"[4, 3]","[60, 80]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[1602] -33,79,"[4, 3]","[90, 70]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[1661] -33,80,"[1, 2]","[40, 80]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[1794] -30,1,"[4, 3]","[60, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[3849] -30,2,"[1, 2]","[40, 50]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1763] -30,3,"[4, 3]","[50, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1705] -30,4,"[2, 1]","[20, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[2],[1803] -30,5,"[3, 4]","[20, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[1640] -30,6,"[2, 1]","[30, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[2026] -30,7,"[3, 4]","[10, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[1831] -30,8,"[1, 2]","[10, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[5452] -30,9,"[2, 1]","[20, 10]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[19239] -30,10,"[2, 1]","[30, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[1780] -30,11,"[2, 1]","[10, 0]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[1699] -30,12,"[3, 4]","[0, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[1715] -30,13,"[2, 1]","[20, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[1829] -30,14,"[4, 3]","[20, 30]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[1978] -30,15,"[1, 2]","[30, 20]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[1878] -30,16,"[4, 3]","[0, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1675] -30,17,"[2, 1]","[10, 70]","[1, 2]",[1],[5],[2],[50],[1],[2],[2],[1740] -30,18,"[1, 2]","[30, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[3391] -30,19,"[4, 3]","[10, 60]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[1830] -30,20,"[3, 4]","[50, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[1723] -30,21,"[3, 4]","[60, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[1994] -30,22,"[4, 3]","[10, 30]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[2009] -30,23,"[3, 4]","[20, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[1696] -30,24,"[4, 3]","[20, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[2010] -30,25,"[2, 1]","[0, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1715] -30,26,"[4, 3]","[10, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1782] -30,27,"[2, 1]","[10, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[1608] -30,28,"[2, 1]","[40, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[1961] -30,29,"[3, 4]","[70, 0]","[2, 2]",[1],[5],[4],[0],[0],[2],[3],[1821] -30,30,"[1, 2]","[90, 10]","[1, 1]",[1],[5],[1],[450],[9],[1],[1],[1813] -30,31,"[2, 1]","[0, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1896] -30,32,"[3, 4]","[50, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[1815] -30,33,"[1, 2]","[50, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[1763] -30,34,"[1, 2]","[40, 20]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[1722] -30,35,"[1, 2]","[70, 10]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1727] -30,36,"[4, 3]","[20, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[2034] -30,37,"[3, 4]","[70, 10]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1655] -30,38,"[2, 1]","[30, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[2504] -30,39,"[2, 1]","[50, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[1798] -30,40,"[1, 2]","[30, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[1],[9962] -30,41,"[3, 4]","[10, 30]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[3499] -30,42,"[1, 2]","[10, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[1848] -30,43,"[1, 2]","[20, 30]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[2140] -30,44,"[2, 1]","[50, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1706] -30,45,"[3, 4]","[40, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[1748] -30,46,"[3, 4]","[40, 30]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1823] -30,47,"[3, 4]","[0, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1797] -30,48,"[2, 1]","[0, 10]","[1, 2]",[2],[1],[2],[0],[0],[2],[2],[34067] -30,49,"[1, 2]","[10, 0]","[1, 2]",[2],[1],[2],[0],[0],[2],[1],[2033] -30,50,"[2, 1]","[0, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1838] -30,51,"[4, 3]","[10, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[2019] -30,52,"[4, 3]","[10, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[2142] -30,53,"[4, 3]","[10, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1724] -30,54,"[3, 4]","[60, 30]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[2143] -30,55,"[3, 4]","[80, 50]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1782] -30,56,"[2, 1]","[60, 70]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[1865] -30,57,"[3, 4]","[90, 60]","[2, 1]",[2],[5],[3],[450],[9],[1],[3],[1936] -30,58,"[4, 3]","[80, 60]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[2051] -30,59,"[3, 4]","[70, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2696] -30,60,"[2, 1]","[30, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1830] -30,61,"[2, 1]","[30, 70]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2198] -30,62,"[2, 1]","[30, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1715] -30,63,"[3, 4]","[70, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2162] -30,64,"[1, 2]","[80, 50]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2084] -30,65,"[3, 4]","[80, 40]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2109] -30,66,"[4, 3]","[30, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1722] -30,67,"[2, 1]","[50, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1878] -30,68,"[2, 1]","[70, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[1739] -30,69,"[4, 3]","[90, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2455] -30,70,"[4, 3]","[80, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1759] -30,71,"[1, 2]","[50, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[1953] -30,72,"[2, 1]","[80, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[1943] -30,73,"[4, 3]","[70, 50]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1911] -30,74,"[3, 4]","[70, 80]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1765] -30,75,"[2, 1]","[80, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2734] -30,76,"[4, 3]","[70, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[2249] -30,77,"[2, 1]","[70, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2061] -30,78,"[1, 2]","[80, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[1862] -30,79,"[2, 1]","[70, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1994] -30,80,"[1, 2]","[90, 30]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[2553] -29,1,"[3, 4]","[60, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[4419] -29,2,"[3, 4]","[60, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2088] -29,3,"[1, 2]","[30, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[3203] -29,4,"[4, 3]","[50, 10]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2157] -29,5,"[1, 2]","[10, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[2353] -29,6,"[2, 1]","[50, 30]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2623] -29,7,"[1, 2]","[40, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1909] -29,8,"[1, 2]","[60, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[1990] -29,9,"[4, 3]","[90, 80]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2109] -29,10,"[3, 4]","[80, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2141] -29,11,"[2, 1]","[70, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2190] -29,12,"[4, 3]","[70, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2090] -29,13,"[2, 1]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[1791] -29,14,"[1, 2]","[80, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1974] -29,15,"[2, 1]","[60, 70]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2307] -29,16,"[4, 3]","[50, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2107] -29,17,"[3, 4]","[40, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2173] -29,18,"[3, 4]","[40, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1990] -29,19,"[1, 2]","[30, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2174] -29,20,"[3, 4]","[20, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[1973] -29,21,"[2, 1]","[80, 60]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[2490] -29,22,"[3, 4]","[70, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2290] -29,23,"[3, 4]","[70, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2124] -29,24,"[1, 2]","[70, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[2057] -29,25,"[1, 2]","[70, 20]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2258] -29,26,"[2, 1]","[30, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2207] -29,27,"[1, 2]","[60, 10]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2074] -29,28,"[4, 3]","[10, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[2457] -29,29,"[2, 1]","[20, 30]","[1, 2]",[1],[1],[2],[20],[2],[2],[2],[2076] -29,30,"[1, 2]","[10, 20]","[1, 1]",[1],[5],[1],[50],[1],[1],[2],[2308] -29,31,"[2, 1]","[10, 0]","[1, 1]",[1],[1],[1],[0],[0],[1],[1],[3641] -29,32,"[3, 4]","[0, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[1808] -29,33,"[4, 3]","[0, 10]","[2, 2]",[1],[1],[4],[0],[0],[2],[4],[1874] -29,34,"[4, 3]","[20, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[1791] -29,35,"[2, 1]","[20, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2624] -29,36,"[1, 2]","[30, 10]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[2558] -29,37,"[2, 1]","[10, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2023] -29,38,"[4, 3]","[0, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1907] -29,39,"[1, 2]","[10, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[2456] -29,40,"[4, 3]","[20, 40]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[2307] -29,41,"[4, 3]","[30, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[6105] -29,42,"[3, 4]","[60, 30]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1791] -29,43,"[4, 3]","[60, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1823] -29,44,"[2, 1]","[40, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[1890] -29,45,"[4, 3]","[20, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[2440] -29,46,"[2, 1]","[40, 80]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[3207] -29,47,"[1, 2]","[50, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[2957] -29,48,"[1, 2]","[50, 70]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2158] -29,49,"[3, 4]","[50, 70]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[1908] -29,50,"[4, 3]","[50, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[2473] -29,51,"[1, 2]","[30, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[3107] -29,52,"[2, 1]","[50, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[2007] -29,53,"[1, 2]","[40, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2306] -29,54,"[1, 2]","[20, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1940] -29,55,"[2, 1]","[70, 10]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[2057] -29,56,"[2, 1]","[90, 0]","[1, 2]",[2],[5],[2],[450],[9],[2],[1],[2007] -29,57,"[1, 2]","[10, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[1791] -29,58,"[4, 3]","[80, 30]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[2170] -29,59,"[1, 2]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2076] -29,60,"[2, 1]","[30, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[1840] -29,61,"[2, 1]","[60, 20]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[3591] -29,62,"[1, 2]","[30, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[1907] -29,63,"[3, 4]","[40, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1924] -29,64,"[4, 3]","[40, 30]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[1859] -29,65,"[4, 3]","[10, 30]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1890] -29,66,"[1, 2]","[40, 20]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2257] -29,67,"[2, 1]","[30, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[2041] -29,68,"[4, 3]","[10, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[1721] -29,69,"[3, 4]","[20, 0]","[2, 1]",[2],[1],[3],[20],[2],[1],[3],[2674] -29,70,"[2, 1]","[10, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[3090] -29,71,"[3, 4]","[20, 20]","[2, 1]",[2],[1],[3],[20],[2],[1],[3],[2440] -29,72,"[2, 1]","[80, 20]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2491] -29,73,"[1, 2]","[20, 90]","[1, 2]",[2],[1],[2],[90],[9],[2],[2],[1909] -29,74,"[2, 1]","[80, 50]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[2059] -29,75,"[2, 1]","[80, 50]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[1856] -29,76,"[3, 4]","[60, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2107] -29,77,"[1, 2]","[70, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[1873] -29,78,"[3, 4]","[70, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[1856] -29,79,"[2, 1]","[80, 60]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2306] -29,80,"[1, 2]","[40, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[2090] -29,1,"[1, 2]","[40, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[7056] -29,2,"[4, 3]","[40, 50]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1823] -29,3,"[2, 1]","[30, 70]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[1868] -29,4,"[3, 4]","[70, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2390] -29,5,"[3, 4]","[80, 40]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[1789] -29,6,"[1, 2]","[80, 50]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2257] -29,7,"[2, 1]","[40, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2157] -29,8,"[2, 1]","[10, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1820] -29,9,"[4, 3]","[0, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[2019] -29,10,"[2, 1]","[20, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[2024] -29,11,"[4, 3]","[20, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[2140] -29,12,"[2, 1]","[30, 0]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[2107] -29,13,"[3, 4]","[50, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[2389] -29,14,"[1, 2]","[60, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[1890] -29,15,"[3, 4]","[50, 80]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[1790] -29,16,"[1, 2]","[90, 70]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[1856] -29,17,"[1, 2]","[80, 60]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1883] -29,18,"[3, 4]","[80, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[1771] -29,19,"[3, 4]","[90, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[1770] -29,20,"[3, 4]","[80, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1806] -29,21,"[4, 3]","[70, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1873] -29,22,"[2, 1]","[50, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[2039] -29,23,"[3, 4]","[10, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2489] -29,24,"[4, 3]","[40, 10]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2323] -29,25,"[4, 3]","[20, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[2140] -29,26,"[4, 3]","[40, 20]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[1740] -29,27,"[3, 4]","[20, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[1757] -29,28,"[2, 1]","[70, 20]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2239] -29,29,"[2, 1]","[80, 10]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2007] -29,30,"[3, 4]","[30, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2123] -29,31,"[1, 2]","[30, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2156] -29,32,"[4, 3]","[80, 30]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2022] -29,33,"[1, 2]","[20, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2107] -29,34,"[3, 4]","[40, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[1957] -29,35,"[4, 3]","[40, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1774] -29,36,"[1, 2]","[50, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2190] -29,37,"[1, 2]","[10, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1857] -29,38,"[1, 2]","[0, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[1839] -29,39,"[2, 1]","[50, 10]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2107] -29,40,"[3, 4]","[50, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1840] -29,41,"[4, 3]","[20, 90]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[4685] -29,42,"[3, 4]","[70, 10]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[2240] -29,43,"[1, 2]","[50, 20]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[2023] -29,44,"[2, 1]","[40, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1790] -29,45,"[4, 3]","[60, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1740] -29,46,"[1, 2]","[70, 90]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1923] -29,47,"[2, 1]","[90, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[1956] -29,48,"[4, 3]","[40, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[1873] -29,49,"[3, 4]","[50, 30]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1907] -29,50,"[1, 2]","[50, 20]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[1873] -29,51,"[2, 1]","[0, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[2090] -29,52,"[4, 3]","[30, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1773] -29,53,"[1, 2]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2479] -29,54,"[2, 1]","[50, 20]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1956] -29,55,"[1, 2]","[20, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[2056] -29,56,"[1, 2]","[0, 10]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[1754] -29,57,"[3, 4]","[30, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[2007] -29,58,"[3, 4]","[0, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[1873] -29,59,"[3, 4]","[10, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1807] -29,60,"[3, 4]","[40, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1740] -29,61,"[2, 1]","[40, 20]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2322] -29,62,"[1, 2]","[60, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[1923] -29,63,"[1, 2]","[70, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2673] -29,64,"[3, 4]","[50, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2624] -29,65,"[2, 1]","[80, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2588] -29,66,"[2, 1]","[60, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[2107] -29,67,"[4, 3]","[40, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[2040] -29,68,"[2, 1]","[50, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1973] -29,69,"[1, 2]","[70, 60]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[2057] -29,70,"[3, 4]","[70, 50]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1824] -29,71,"[3, 4]","[80, 60]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1806] -29,72,"[3, 4]","[60, 10]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[1840] -29,73,"[1, 2]","[50, 10]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[2106] -29,74,"[1, 2]","[30, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[1840] -29,75,"[4, 3]","[30, 10]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1940] -29,76,"[3, 4]","[70, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1973] -29,77,"[2, 1]","[30, 50]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[2190] -29,78,"[2, 1]","[10, 20]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2039] -29,79,"[2, 1]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1789] -29,80,"[2, 1]","[10, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[1840] -29,1,"[2, 1]","[20, 60]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[5206] -29,2,"[3, 4]","[70, 40]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2072] -29,3,"[1, 2]","[90, 70]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[1840] -29,4,"[4, 3]","[40, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1823] -29,5,"[3, 4]","[70, 50]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1888] -29,6,"[4, 3]","[70, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[1757] -29,7,"[2, 1]","[60, 10]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2223] -29,8,"[2, 1]","[40, 0]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[1906] -29,9,"[4, 3]","[90, 0]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[1831] -29,10,"[4, 3]","[90, 10]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[1807] -29,11,"[4, 3]","[90, 20]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2007] -29,12,"[1, 2]","[10, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2023] -29,13,"[3, 4]","[20, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1857] -29,14,"[2, 1]","[70, 30]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2124] -29,15,"[3, 4]","[30, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[1940] -29,16,"[3, 4]","[40, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[1824] -29,17,"[1, 2]","[40, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[1736] -29,18,"[4, 3]","[40, 60]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1974] -29,19,"[3, 4]","[60, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[1871] -29,20,"[1, 2]","[70, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[2940] -29,21,"[1, 2]","[70, 0]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1907] -29,22,"[2, 1]","[20, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2040] -29,23,"[1, 2]","[80, 10]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1891] -29,24,"[4, 3]","[0, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1740] -29,25,"[1, 2]","[70, 10]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1873] -29,26,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1724] -29,27,"[2, 1]","[50, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[1923] -29,28,"[2, 1]","[50, 60]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2123] -29,29,"[1, 2]","[70, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[1957] -29,30,"[3, 4]","[50, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2408] -29,31,"[4, 3]","[80, 70]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[1960] -29,32,"[4, 3]","[60, 20]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[1740] -29,33,"[3, 4]","[10, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[1806] -29,34,"[3, 4]","[30, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1824] -29,35,"[4, 3]","[40, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1690] -29,36,"[3, 4]","[10, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[1823] -29,37,"[1, 2]","[20, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[1740] -29,38,"[1, 2]","[80, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[1755] -29,39,"[2, 1]","[70, 80]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[1856] -29,40,"[3, 4]","[70, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2423] -29,41,"[2, 1]","[50, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[1985] -29,42,"[3, 4]","[70, 60]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1774] -29,43,"[4, 3]","[50, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1807] -29,44,"[1, 2]","[50, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[1974] -29,45,"[4, 3]","[60, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1773] -29,46,"[1, 2]","[10, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2871] -29,47,"[4, 3]","[60, 0]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[2123] -29,48,"[2, 1]","[60, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[1936] -29,49,"[1, 2]","[10, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[2090] -29,50,"[4, 3]","[80, 30]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1907] -29,51,"[4, 3]","[70, 10]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[1737] -29,52,"[1, 2]","[10, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2739] -29,53,"[3, 4]","[20, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[1790] -29,54,"[4, 3]","[90, 10]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[2040] -29,55,"[1, 2]","[20, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2755] -29,56,"[4, 3]","[70, 20]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2023] -29,57,"[1, 2]","[10, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[2324] -29,58,"[4, 3]","[50, 20]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2107] -29,59,"[2, 1]","[50, 20]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1973] -29,60,"[1, 2]","[30, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1857] -29,61,"[3, 4]","[20, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2190] -29,62,"[3, 4]","[50, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[1924] -29,63,"[1, 2]","[60, 50]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[1824] -29,64,"[4, 3]","[70, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1821] -29,65,"[2, 1]","[90, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1906] -29,66,"[1, 2]","[40, 50]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[2023] -29,67,"[3, 4]","[40, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1911] -29,68,"[3, 4]","[20, 30]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[1691] -29,69,"[3, 4]","[50, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[1673] -29,70,"[4, 3]","[30, 80]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[1757] -29,71,"[3, 4]","[70, 0]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1924] -29,72,"[3, 4]","[70, 10]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1741] -29,73,"[4, 3]","[10, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1891] -29,74,"[4, 3]","[20, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1740] -29,75,"[4, 3]","[50, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1757] -29,76,"[3, 4]","[20, 40]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[1940] -29,77,"[4, 3]","[20, 10]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2388] -29,78,"[3, 4]","[30, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1757] -29,79,"[2, 1]","[30, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[2023] -29,80,"[3, 4]","[30, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[1741] -29,1,"[1, 2]","[50, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[4024] -29,2,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1790] -29,3,"[2, 1]","[50, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1923] -29,4,"[2, 1]","[50, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1757] -29,5,"[2, 1]","[40, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1874] -29,6,"[1, 2]","[60, 30]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[1839] -29,7,"[4, 3]","[40, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1774] -29,8,"[3, 4]","[60, 40]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1723] -29,9,"[3, 4]","[70, 60]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[1724] -29,10,"[2, 1]","[70, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2006] -29,11,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1873] -29,12,"[4, 3]","[70, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1739] -29,13,"[1, 2]","[50, 80]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1854] -29,14,"[3, 4]","[50, 60]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[1706] -29,15,"[1, 2]","[40, 90]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[1889] -29,16,"[3, 4]","[0, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[1707] -29,17,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[2140] -29,18,"[2, 1]","[50, 10]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2240] -29,19,"[1, 2]","[0, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2006] -29,20,"[4, 3]","[60, 10]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[2090] -29,21,"[4, 3]","[50, 20]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[2320] -29,22,"[3, 4]","[0, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1990] -29,23,"[4, 3]","[40, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[1857] -29,24,"[2, 1]","[40, 30]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2122] -29,25,"[1, 2]","[20, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[1956] -29,26,"[4, 3]","[50, 40]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[1906] -29,27,"[4, 3]","[40, 80]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1790] -29,28,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1690] -29,29,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1990] -29,30,"[3, 4]","[90, 50]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[1806] -29,31,"[4, 3]","[0, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1823] -29,32,"[1, 2]","[60, 30]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[1990] -29,33,"[2, 1]","[20, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2290] -29,34,"[3, 4]","[60, 40]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1889] -29,35,"[1, 2]","[30, 40]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[1973] -29,36,"[4, 3]","[30, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[1773] -29,37,"[3, 4]","[40, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[2123] -29,38,"[1, 2]","[30, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[1],[1856] -29,39,"[2, 1]","[20, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[1940] -29,40,"[3, 4]","[30, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1909] -29,41,"[3, 4]","[40, 50]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[3185] -29,42,"[4, 3]","[50, 30]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1974] -29,43,"[1, 2]","[40, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[2323] -29,44,"[2, 1]","[30, 50]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[1840] -29,45,"[2, 1]","[20, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1823] -29,46,"[2, 1]","[60, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1890] -29,47,"[4, 3]","[70, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1807] -29,48,"[4, 3]","[90, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1723] -29,49,"[4, 3]","[70, 40]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1873] -29,50,"[3, 4]","[40, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2157] -29,51,"[3, 4]","[0, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[1923] -29,52,"[1, 2]","[0, 30]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[2140] -29,53,"[4, 3]","[20, 10]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[1940] -29,54,"[2, 1]","[50, 0]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[1907] -29,55,"[3, 4]","[10, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2124] -29,56,"[4, 3]","[40, 20]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1839] -29,57,"[4, 3]","[40, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[1774] -29,58,"[2, 1]","[60, 10]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2673] -29,59,"[2, 1]","[80, 10]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[1873] -29,60,"[1, 2]","[30, 90]","[1, 2]",[2],[1],[2],[90],[9],[2],[2],[2023] -29,61,"[2, 1]","[70, 10]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1856] -29,62,"[1, 2]","[0, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2039] -29,63,"[1, 2]","[0, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1803] -29,64,"[4, 3]","[50, 30]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[1987] -29,65,"[3, 4]","[40, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[1953] -29,66,"[2, 1]","[50, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[2191] -29,67,"[2, 1]","[70, 40]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1886] -29,68,"[2, 1]","[50, 40]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1937] -29,69,"[2, 1]","[30, 50]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[1839] -29,70,"[2, 1]","[20, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[1841] -29,71,"[2, 1]","[20, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1974] -29,72,"[3, 4]","[40, 10]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1873] -29,73,"[3, 4]","[50, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[1706] -29,74,"[1, 2]","[40, 30]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[1823] -29,75,"[4, 3]","[30, 20]","[2, 1]",[2],[1],[3],[20],[2],[1],[3],[1824] -29,76,"[4, 3]","[20, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[1905] -29,77,"[4, 3]","[10, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1707] -29,78,"[2, 1]","[30, 10]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[1989] -29,79,"[3, 4]","[20, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2157] -29,80,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1907] -29,1,"[3, 4]","[10, 70]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[15739] -29,2,"[2, 1]","[50, 20]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2207] -29,3,"[1, 2]","[40, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[2023] -29,4,"[2, 1]","[70, 40]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1873] -29,5,"[4, 3]","[70, 30]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[1956] -29,6,"[3, 4]","[40, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1891] -29,7,"[3, 4]","[50, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1824] -29,8,"[2, 1]","[90, 50]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[2036] -29,9,"[1, 2]","[90, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[1907] -29,10,"[2, 1]","[90, 70]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[2010] -29,11,"[4, 3]","[80, 60]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[1807] -29,12,"[3, 4]","[20, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[1756] -29,13,"[2, 1]","[40, 20]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[1973] -29,14,"[2, 1]","[0, 50]","[1, 2]",[1],[1],[2],[0],[0],[2],[2],[2057] -29,15,"[2, 1]","[10, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[1724] -29,16,"[4, 3]","[0, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[2005] -29,17,"[4, 3]","[50, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[1790] -29,18,"[4, 3]","[60, 10]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[1673] -29,19,"[3, 4]","[10, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[1724] -29,20,"[1, 2]","[30, 40]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[1874] -29,21,"[1, 2]","[30, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[2040] -29,22,"[1, 2]","[30, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2074] -29,23,"[4, 3]","[30, 50]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[2276] -29,24,"[2, 1]","[10, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1973] -29,25,"[1, 2]","[70, 10]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2023] -29,26,"[2, 1]","[20, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1873] -29,27,"[4, 3]","[20, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2656] -29,28,"[4, 3]","[20, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1707] -29,29,"[1, 2]","[70, 10]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1974] -29,30,"[3, 4]","[90, 10]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[2020] -29,31,"[4, 3]","[20, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1826] -29,32,"[4, 3]","[10, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1790] -29,33,"[2, 1]","[20, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1873] -29,34,"[3, 4]","[50, 40]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[1774] -29,35,"[3, 4]","[30, 60]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[1790] -29,36,"[1, 2]","[60, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[1905] -29,37,"[2, 1]","[40, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2023] -29,38,"[1, 2]","[80, 40]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2141] -29,39,"[4, 3]","[60, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2757] -29,40,"[2, 1]","[50, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2056] -29,41,"[1, 2]","[50, 80]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2252] -29,42,"[2, 1]","[80, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[1824] -29,43,"[1, 2]","[70, 50]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2173] -29,44,"[3, 4]","[70, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1824] -29,45,"[1, 2]","[80, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1990] -29,46,"[4, 3]","[90, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[1739] -29,47,"[1, 2]","[70, 80]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2041] -29,48,"[1, 2]","[80, 70]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2107] -29,49,"[2, 1]","[80, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[9441] -29,50,"[2, 1]","[50, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1874] -29,51,"[4, 3]","[50, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[1822] -29,52,"[4, 3]","[40, 20]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[5973] -29,53,"[2, 1]","[40, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[2123] -29,54,"[3, 4]","[0, 40]","[2, 1]",[2],[1],[3],[0],[0],[1],[4],[1823] -29,55,"[2, 1]","[20, 40]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[2524] -29,56,"[3, 4]","[30, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[2040] -29,57,"[2, 1]","[60, 40]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[1907] -29,58,"[3, 4]","[70, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1857] -29,59,"[1, 2]","[50, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2174] -29,60,"[4, 3]","[80, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2041] -29,61,"[1, 2]","[80, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[1873] -29,62,"[1, 2]","[50, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[1841] -29,63,"[1, 2]","[60, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1690] -29,64,"[4, 3]","[60, 30]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2123] -29,65,"[2, 1]","[80, 20]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[1840] -29,66,"[3, 4]","[30, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[1907] -29,67,"[1, 2]","[30, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1824] -29,68,"[2, 1]","[50, 20]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2023] -29,69,"[3, 4]","[30, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1839] -29,70,"[2, 1]","[60, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[2454] -29,71,"[2, 1]","[70, 10]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[2724] -29,72,"[2, 1]","[40, 0]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[1873] -29,73,"[3, 4]","[10, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2108] -29,74,"[4, 3]","[20, 10]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[1839] -29,75,"[1, 2]","[30, 20]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[2040] -29,76,"[2, 1]","[10, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[2022] -29,77,"[3, 4]","[60, 10]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1907] -29,78,"[1, 2]","[90, 0]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[1990] -29,79,"[4, 3]","[10, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1802] -29,80,"[2, 1]","[10, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[2290] -29,1,"[2, 1]","[10, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[4811] -29,2,"[1, 2]","[80, 20]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2070] -29,3,"[4, 3]","[80, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[1774] -29,4,"[1, 2]","[60, 70]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1940] -29,5,"[4, 3]","[80, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[1757] -29,6,"[1, 2]","[70, 30]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1873] -29,7,"[1, 2]","[60, 0]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2140] -29,8,"[1, 2]","[70, 0]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1806] -29,9,"[1, 2]","[90, 20]","[1, 1]",[1],[5],[1],[450],[9],[1],[1],[1794] -29,10,"[2, 1]","[0, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1912] -29,11,"[2, 1]","[0, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1955] -29,12,"[2, 1]","[30, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1890] -29,13,"[2, 1]","[20, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1806] -29,14,"[2, 1]","[50, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[1770] -29,15,"[1, 2]","[60, 50]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[1857] -29,16,"[1, 2]","[60, 40]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[1876] -29,17,"[3, 4]","[30, 0]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1740] -29,18,"[1, 2]","[0, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[2],[2040] -29,19,"[4, 3]","[10, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[1939] -29,20,"[2, 1]","[0, 10]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[2173] -29,21,"[3, 4]","[20, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[2123] -29,22,"[3, 4]","[10, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[1723] -29,23,"[4, 3]","[0, 20]","[2, 2]",[1],[1],[4],[0],[0],[2],[4],[2157] -29,24,"[4, 3]","[0, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1673] -29,25,"[3, 4]","[30, 10]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1686] -29,26,"[1, 2]","[40, 10]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[2257] -29,27,"[2, 1]","[50, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[1857] -29,28,"[1, 2]","[40, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[1790] -29,29,"[1, 2]","[70, 60]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1940] -29,30,"[4, 3]","[80, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1723] -29,31,"[2, 1]","[70, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[1873] -29,32,"[3, 4]","[90, 60]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[1739] -29,33,"[3, 4]","[70, 90]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1756] -29,34,"[3, 4]","[60, 70]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1840] -29,35,"[2, 1]","[50, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1806] -29,36,"[1, 2]","[30, 50]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[1774] -29,37,"[3, 4]","[0, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[2706] -29,38,"[1, 2]","[30, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[1974] -29,39,"[3, 4]","[10, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[3373] -29,40,"[3, 4]","[20, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1973] -29,41,"[3, 4]","[0, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[3352] -29,42,"[1, 2]","[10, 30]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[1906] -29,43,"[2, 1]","[10, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2974] -29,44,"[3, 4]","[20, 30]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[3140] -29,45,"[1, 2]","[10, 50]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1823] -29,46,"[3, 4]","[10, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[1758] -29,47,"[3, 4]","[10, 30]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[1889] -29,48,"[4, 3]","[20, 30]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[1941] -29,49,"[3, 4]","[40, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1874] -29,50,"[4, 3]","[40, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1856] -29,51,"[3, 4]","[40, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[1772] -29,52,"[3, 4]","[50, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1689] -29,53,"[1, 2]","[60, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2107] -29,54,"[4, 3]","[50, 40]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2223] -29,55,"[2, 1]","[40, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2057] -29,56,"[3, 4]","[40, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1873] -29,57,"[2, 1]","[10, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[2007] -29,58,"[2, 1]","[30, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2037] -29,59,"[4, 3]","[20, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1973] -29,60,"[3, 4]","[20, 40]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[1706] -29,61,"[1, 2]","[20, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2440] -29,62,"[3, 4]","[20, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[2006] -29,63,"[4, 3]","[30, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[2007] -29,64,"[3, 4]","[60, 40]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1740] -29,65,"[4, 3]","[0, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1723] -29,66,"[2, 1]","[10, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1990] -29,67,"[2, 1]","[0, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1890] -29,68,"[1, 2]","[50, 10]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[1907] -29,69,"[3, 4]","[40, 0]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1790] -29,70,"[4, 3]","[10, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1824] -29,71,"[1, 2]","[80, 10]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1922] -29,72,"[2, 1]","[50, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1839] -29,73,"[2, 1]","[40, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1790] -29,74,"[2, 1]","[50, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[1823] -29,75,"[2, 1]","[50, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[1839] -29,76,"[1, 2]","[10, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2156] -29,77,"[3, 4]","[20, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[1689] -29,78,"[1, 2]","[10, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[1907] -29,79,"[2, 1]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2323] -29,80,"[2, 1]","[10, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1756] -29,1,"[3, 4]","[20, 60]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[4205] -29,2,"[2, 1]","[70, 10]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2073] -29,3,"[2, 1]","[60, 20]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[1906] -29,4,"[4, 3]","[40, 30]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[1772] -29,5,"[2, 1]","[40, 30]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[1753] -29,6,"[4, 3]","[50, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1974] -29,7,"[1, 2]","[60, 20]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[1889] -29,8,"[1, 2]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[1857] -29,9,"[2, 1]","[30, 20]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[1957] -29,10,"[4, 3]","[30, 0]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2090] -29,11,"[1, 2]","[0, 60]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[1824] -29,12,"[1, 2]","[40, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2412] -29,13,"[4, 3]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1807] -29,14,"[4, 3]","[40, 90]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1673] -29,15,"[3, 4]","[60, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[1706] -29,16,"[2, 1]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[1955] -29,17,"[2, 1]","[70, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2072] -29,18,"[3, 4]","[30, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2057] -29,19,"[1, 2]","[40, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[1737] -29,20,"[1, 2]","[10, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[1822] -29,21,"[4, 3]","[50, 10]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[3672] -29,22,"[3, 4]","[20, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1840] -29,23,"[1, 2]","[50, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[2074] -29,24,"[4, 3]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1640] -29,25,"[1, 2]","[70, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1773] -29,26,"[3, 4]","[60, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1789] -29,27,"[2, 1]","[70, 40]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2089] -29,28,"[1, 2]","[30, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2071] -29,29,"[4, 3]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2258] -29,30,"[1, 2]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2806] -29,31,"[1, 2]","[50, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[1705] -29,32,"[3, 4]","[20, 40]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1690] -29,33,"[1, 2]","[10, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[1823] -29,34,"[2, 1]","[30, 0]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[1944] -29,35,"[4, 3]","[50, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[3],[1990] -29,36,"[1, 2]","[10, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2006] -29,37,"[1, 2]","[0, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1956] -29,38,"[2, 1]","[70, 20]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1740] -29,39,"[2, 1]","[50, 10]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[1690] -29,40,"[2, 1]","[30, 20]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[1703] -29,41,"[1, 2]","[10, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2552] -29,42,"[3, 4]","[0, 20]","[2, 1]",[2],[5],[3],[0],[0],[1],[4],[1690] -29,43,"[4, 3]","[10, 0]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[1773] -29,44,"[1, 2]","[30, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[2139] -29,45,"[2, 1]","[40, 10]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2040] -29,46,"[3, 4]","[0, 30]","[2, 1]",[2],[5],[3],[0],[0],[1],[4],[2306] -29,47,"[3, 4]","[30, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1723] -29,48,"[4, 3]","[20, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[1874] -29,49,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1674] -29,50,"[2, 1]","[30, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[1857] -29,51,"[3, 4]","[20, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[4],[2057] -29,52,"[2, 1]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[1972] -29,53,"[1, 2]","[60, 20]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2074] -29,54,"[1, 2]","[80, 40]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1890] -29,55,"[3, 4]","[80, 40]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[1653] -29,56,"[2, 1]","[0, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2340] -29,57,"[4, 3]","[0, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1673] -29,58,"[4, 3]","[20, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1689] -29,59,"[2, 1]","[30, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1873] -29,60,"[3, 4]","[70, 50]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1774] -29,61,"[4, 3]","[80, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[1740] -29,62,"[3, 4]","[50, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1740] -29,63,"[2, 1]","[50, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[1874] -29,64,"[1, 2]","[60, 50]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1906] -29,65,"[3, 4]","[40, 20]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1777] -29,66,"[4, 3]","[10, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1740] -29,67,"[1, 2]","[40, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[1757] -29,68,"[4, 3]","[20, 0]","[2, 1]",[2],[5],[3],[0],[0],[1],[3],[2740] -29,69,"[2, 1]","[50, 20]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1972] -29,70,"[3, 4]","[20, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[1973] -29,71,"[2, 1]","[60, 0]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[1874] -29,72,"[4, 3]","[60, 0]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2073] -29,73,"[3, 4]","[10, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[1974] -29,74,"[1, 2]","[40, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[1856] -29,75,"[2, 1]","[70, 60]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[3023] -29,76,"[2, 1]","[60, 70]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1723] -29,77,"[3, 4]","[70, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[1904] -29,78,"[3, 4]","[90, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[1891] -29,79,"[3, 4]","[60, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[1857] -29,80,"[4, 3]","[70, 60]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[1923] -29,1,"[1, 2]","[90, 20]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[4039] -29,2,"[2, 1]","[30, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1874] -29,3,"[3, 4]","[50, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[1723] -29,4,"[3, 4]","[20, 70]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[1702] -29,5,"[2, 1]","[90, 30]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[1923] -29,6,"[1, 2]","[0, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2090] -29,7,"[4, 3]","[90, 0]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[2039] -29,8,"[1, 2]","[20, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1739] -29,9,"[4, 3]","[80, 30]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[1990] -29,10,"[3, 4]","[60, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1824] -29,11,"[2, 1]","[60, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2190] -29,12,"[1, 2]","[60, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[1807] -29,13,"[2, 1]","[70, 40]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1707] -29,14,"[4, 3]","[90, 30]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[2174] -29,15,"[3, 4]","[10, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[1823] -29,16,"[4, 3]","[50, 40]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[1739] -29,17,"[2, 1]","[20, 0]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[1907] -29,18,"[4, 3]","[30, 0]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[1807] -29,19,"[4, 3]","[50, 10]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[1773] -29,20,"[2, 1]","[50, 20]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[1806] -29,21,"[1, 2]","[40, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2491] -29,22,"[3, 4]","[40, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[2156] -29,23,"[3, 4]","[60, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[1722] -29,24,"[3, 4]","[30, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[1773] -29,25,"[2, 1]","[80, 30]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1990] -29,26,"[2, 1]","[70, 10]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1989] -29,27,"[2, 1]","[90, 0]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[1751] -29,28,"[4, 3]","[80, 0]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[1922] -29,29,"[2, 1]","[80, 10]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1826] -29,30,"[1, 2]","[30, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2040] -29,31,"[1, 2]","[40, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[1724] -29,32,"[4, 3]","[60, 40]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[1922] -29,33,"[2, 1]","[60, 70]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[1923] -29,34,"[4, 3]","[40, 70]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1824] -29,35,"[4, 3]","[10, 60]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[1707] -29,36,"[4, 3]","[20, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1673] -29,37,"[3, 4]","[50, 80]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1721] -29,38,"[1, 2]","[70, 90]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2224] -29,39,"[3, 4]","[70, 40]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[1706] -29,40,"[3, 4]","[70, 40]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[2873] -29,41,"[4, 3]","[20, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1968] -29,42,"[4, 3]","[20, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1689] -29,43,"[4, 3]","[30, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[2072] -29,44,"[4, 3]","[40, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1738] -29,45,"[1, 2]","[60, 50]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2056] -29,46,"[1, 2]","[50, 20]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[1823] -29,47,"[1, 2]","[40, 10]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[1787] -29,48,"[2, 1]","[20, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[1857] -29,49,"[1, 2]","[20, 10]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[2357] -29,50,"[3, 4]","[10, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1822] -29,51,"[1, 2]","[30, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1805] -29,52,"[1, 2]","[30, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[1756] -29,53,"[2, 1]","[90, 70]","[1, 2]",[2],[5],[2],[450],[9],[2],[1],[1824] -29,54,"[3, 4]","[60, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[1907] -29,55,"[4, 3]","[60, 80]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[1723] -29,56,"[1, 2]","[60, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[2090] -29,57,"[1, 2]","[80, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[1740] -29,58,"[3, 4]","[80, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[2105] -29,59,"[3, 4]","[80, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1971] -29,60,"[3, 4]","[80, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[1707] -29,61,"[4, 3]","[50, 80]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[2173] -29,62,"[3, 4]","[70, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1741] -29,63,"[4, 3]","[40, 50]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1753] -29,64,"[2, 1]","[30, 20]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1907] -29,65,"[4, 3]","[40, 10]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1806] -29,66,"[2, 1]","[60, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[1807] -29,67,"[4, 3]","[70, 60]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1673] -29,68,"[4, 3]","[80, 70]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1639] -29,69,"[4, 3]","[80, 60]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1807] -29,70,"[1, 2]","[40, 90]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[1757] -29,71,"[2, 1]","[70, 20]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2340] -29,72,"[1, 2]","[10, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1872] -29,73,"[2, 1]","[60, 10]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[1740] -29,74,"[1, 2]","[10, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[1807] -29,75,"[2, 1]","[90, 10]","[1, 2]",[2],[1],[2],[90],[9],[2],[1],[1873] -29,76,"[2, 1]","[80, 10]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[1773] -29,77,"[3, 4]","[60, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[1945] -29,78,"[3, 4]","[40, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[1737] -29,79,"[4, 3]","[80, 20]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1740] -29,80,"[4, 3]","[80, 30]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[1907] -29,1,"[1, 2]","[60, 40]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[3637] -29,2,"[2, 1]","[30, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[1923] -29,3,"[3, 4]","[10, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[2040] -29,4,"[3, 4]","[40, 10]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[1706] -29,5,"[2, 1]","[0, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[3106] -29,6,"[4, 3]","[50, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[1842] -29,7,"[1, 2]","[80, 60]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1893] -29,8,"[4, 3]","[80, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1742] -29,9,"[2, 1]","[50, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[1858] -29,10,"[2, 1]","[60, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1825] -29,11,"[1, 2]","[70, 60]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1925] -29,12,"[2, 1]","[60, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1923] -29,13,"[4, 3]","[70, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[4458] -29,14,"[2, 1]","[70, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1807] -29,15,"[1, 2]","[80, 90]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1858] -29,16,"[2, 1]","[90, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1806] -29,17,"[2, 1]","[50, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1890] -29,18,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1757] -29,19,"[3, 4]","[40, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[1723] -29,20,"[4, 3]","[20, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[1736] -29,21,"[1, 2]","[40, 20]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[1990] -29,22,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1740] -29,23,"[1, 2]","[80, 40]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1922] -29,24,"[4, 3]","[60, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1740] -29,25,"[4, 3]","[40, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1706] -29,26,"[4, 3]","[60, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1739] -29,27,"[4, 3]","[60, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1723] -29,28,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1805] -29,29,"[1, 2]","[80, 40]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1823] -29,30,"[4, 3]","[40, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1724] -29,31,"[3, 4]","[80, 50]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1724] -29,32,"[3, 4]","[80, 40]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1703] -29,33,"[3, 4]","[60, 30]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1759] -29,34,"[1, 2]","[50, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[1940] -29,35,"[4, 3]","[40, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1973] -29,36,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2073] -29,37,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1939] -29,38,"[3, 4]","[90, 70]","[2, 1]",[1],[1],[3],[90],[9],[1],[3],[1773] -29,39,"[2, 1]","[50, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1755] -29,40,"[2, 1]","[70, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[1941] -29,41,"[2, 1]","[80, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[2253] -29,42,"[2, 1]","[80, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1755] -29,43,"[4, 3]","[70, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1773] -29,44,"[3, 4]","[80, 40]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2157] -29,45,"[1, 2]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[1974] -29,46,"[2, 1]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2007] -29,47,"[3, 4]","[80, 70]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1974] -29,48,"[4, 3]","[40, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[1757] -29,49,"[1, 2]","[70, 60]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[1873] -29,50,"[3, 4]","[60, 40]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[5240] -29,51,"[4, 3]","[30, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1741] -29,52,"[2, 1]","[30, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1856] -29,53,"[1, 2]","[0, 30]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[2106] -29,54,"[4, 3]","[10, 20]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[1807] -29,55,"[3, 4]","[20, 0]","[2, 2]",[2],[1],[4],[0],[0],[2],[3],[1973] -29,56,"[2, 1]","[0, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1971] -29,57,"[1, 2]","[0, 10]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[1873] -29,58,"[1, 2]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[1840] -29,59,"[4, 3]","[30, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[1990] -29,60,"[4, 3]","[30, 10]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1790] -29,61,"[3, 4]","[40, 30]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1723] -29,62,"[2, 1]","[40, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2007] -29,63,"[1, 2]","[60, 40]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1873] -29,64,"[1, 2]","[50, 60]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[1856] -29,65,"[4, 3]","[30, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1790] -29,66,"[4, 3]","[30, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1740] -29,67,"[2, 1]","[40, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[1923] -29,68,"[3, 4]","[70, 20]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[2023] -29,69,"[4, 3]","[10, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1758] -29,70,"[1, 2]","[80, 60]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2041] -29,71,"[3, 4]","[70, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1757] -29,72,"[4, 3]","[70, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1806] -29,73,"[1, 2]","[20, 80]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[2340] -29,74,"[3, 4]","[60, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1740] -29,75,"[3, 4]","[70, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1856] -29,76,"[2, 1]","[50, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2023] -29,77,"[1, 2]","[50, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2023] -29,78,"[4, 3]","[60, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1823] -29,79,"[4, 3]","[90, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1757] -29,80,"[1, 2]","[40, 80]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[2090] -34,1,"[4, 3]","[40, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[4339] -34,2,"[2, 1]","[40, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[3377] -34,3,"[4, 3]","[90, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1926] -34,4,"[3, 4]","[80, 90]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[2603] -34,5,"[4, 3]","[70, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2231] -34,6,"[2, 1]","[40, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[4112] -34,7,"[4, 3]","[30, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[2527] -34,8,"[3, 4]","[40, 10]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2200] -34,9,"[2, 1]","[10, 50]","[1, 2]",[1],[1],[2],[10],[1],[2],[2],[6159] -34,10,"[3, 4]","[40, 0]","[2, 2]",[1],[5],[4],[0],[0],[2],[3],[2660] -34,11,"[3, 4]","[40, 20]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[1928] -34,12,"[4, 3]","[0, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[2318] -34,13,"[1, 2]","[60, 10]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[4842] -34,14,"[1, 2]","[50, 20]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[5390] -34,15,"[3, 4]","[30, 10]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[15047] -34,16,"[1, 2]","[20, 10]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[3834] -34,17,"[2, 1]","[30, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[10180] -34,18,"[1, 2]","[0, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[2307] -34,19,"[3, 4]","[0, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[2438] -34,20,"[1, 2]","[10, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[2680] -34,21,"[3, 4]","[30, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2504] -34,22,"[4, 3]","[70, 50]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[6377] -34,23,"[4, 3]","[70, 60]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[15778] -34,24,"[1, 2]","[50, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[3074] -34,25,"[2, 1]","[70, 40]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[3465] -34,26,"[3, 4]","[20, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[16300] -34,27,"[4, 3]","[40, 10]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[4238] -34,28,"[4, 3]","[70, 0]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[1994] -34,29,"[4, 3]","[30, 10]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2475] -34,30,"[3, 4]","[30, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2812] -34,31,"[1, 2]","[0, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[8164] -34,32,"[2, 1]","[40, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[2675] -34,33,"[1, 2]","[30, 20]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[4685] -34,34,"[2, 1]","[20, 30]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[2867] -34,35,"[4, 3]","[30, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[2863] -34,36,"[1, 2]","[10, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[2850] -34,37,"[2, 1]","[30, 0]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[2737] -34,38,"[2, 1]","[70, 30]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2167] -34,39,"[3, 4]","[70, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[2429] -34,40,"[1, 2]","[60, 90]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2875] -34,41,"[2, 1]","[80, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[3443] -34,42,"[1, 2]","[80, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1949] -34,43,"[2, 1]","[30, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2366] -34,44,"[1, 2]","[90, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1966] -34,45,"[4, 3]","[80, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2404] -34,46,"[4, 3]","[90, 70]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[3467] -34,47,"[2, 1]","[70, 60]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[3104] -34,48,"[2, 1]","[50, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2191] -34,49,"[2, 1]","[80, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2188] -34,50,"[4, 3]","[90, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2778] -34,51,"[4, 3]","[80, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[4828] -34,52,"[3, 4]","[0, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[2604] -34,53,"[2, 1]","[30, 0]","[1, 1]",[2],[5],[1],[0],[0],[1],[1],[4698] -34,54,"[1, 2]","[30, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[2674] -34,55,"[1, 2]","[20, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[2258] -34,56,"[3, 4]","[20, 30]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[3041] -34,57,"[2, 1]","[40, 60]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[4375] -34,58,"[4, 3]","[50, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1930] -34,59,"[2, 1]","[20, 50]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[2544] -34,60,"[4, 3]","[50, 60]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[2578] -34,61,"[2, 1]","[40, 60]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2242] -34,62,"[1, 2]","[40, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2603] -34,63,"[1, 2]","[20, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[2059] -34,64,"[4, 3]","[30, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[2604] -34,65,"[3, 4]","[20, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[3909] -34,66,"[2, 1]","[10, 20]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2692] -34,67,"[3, 4]","[40, 10]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2299] -34,68,"[3, 4]","[30, 10]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[1974] -34,69,"[2, 1]","[10, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2373] -34,70,"[4, 3]","[0, 50]","[2, 2]",[2],[1],[4],[0],[0],[2],[4],[2437] -34,71,"[2, 1]","[0, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[3016] -34,72,"[2, 1]","[0, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[3667] -34,73,"[4, 3]","[10, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[2804] -34,74,"[4, 3]","[20, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[3967] -34,75,"[3, 4]","[10, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2473] -34,76,"[4, 3]","[30, 10]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[2260] -34,77,"[1, 2]","[40, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[3390] -34,78,"[3, 4]","[40, 10]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2792] -34,79,"[4, 3]","[10, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[2490] -34,80,"[1, 2]","[20, 0]","[1, 2]",[2],[1],[2],[0],[0],[2],[1],[3256] -35,1,"[1, 2]","[50, 20]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[344938] -35,2,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2721] -35,3,"[2, 1]","[50, 60]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[2646] -35,4,"[2, 1]","[50, 70]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[4993] -35,5,"[2, 1]","[40, 60]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[1712] -35,6,"[1, 2]","[60, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[1747] -35,7,"[4, 3]","[40, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2130] -35,8,"[3, 4]","[60, 40]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[2212] -35,9,"[3, 4]","[70, 60]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[2713] -35,10,"[2, 1]","[70, 60]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2928] -35,11,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[3729] -35,12,"[4, 3]","[70, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[3222] -35,13,"[1, 2]","[50, 80]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[5079] -35,14,"[3, 4]","[50, 60]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[1995] -35,15,"[1, 2]","[40, 90]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[1862] -35,16,"[3, 4]","[0, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[4071] -35,17,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[2645] -35,18,"[2, 1]","[50, 10]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2620] -35,19,"[1, 2]","[0, 60]","[1, 1]",[1],[1],[1],[0],[0],[1],[2],[5036] -35,20,"[4, 3]","[60, 10]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[2655] -35,21,"[4, 3]","[50, 20]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[1845] -35,22,"[3, 4]","[0, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1872] -35,23,"[4, 3]","[40, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[2538] -35,24,"[2, 1]","[40, 30]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[3112] -35,25,"[1, 2]","[20, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[3430] -35,26,"[4, 3]","[50, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[3271] -35,27,"[4, 3]","[40, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1971] -35,28,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1746] -35,29,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[4612] -35,30,"[3, 4]","[90, 50]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[2029] -35,31,"[4, 3]","[0, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1914] -35,32,"[1, 2]","[60, 30]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2330] -35,33,"[2, 1]","[20, 50]","[1, 2]",[1],[1],[2],[20],[2],[2],[2],[3896] -35,34,"[3, 4]","[60, 40]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[2438] -35,35,"[1, 2]","[30, 40]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[2262] -35,36,"[4, 3]","[30, 20]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2213] -35,37,"[3, 4]","[40, 30]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[1979] -35,38,"[1, 2]","[30, 10]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2797] -35,39,"[2, 1]","[20, 30]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[2355] -35,40,"[3, 4]","[30, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2505] -35,41,"[3, 4]","[40, 50]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[2116] -35,42,"[4, 3]","[50, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1872] -35,43,"[1, 2]","[40, 50]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[2283] -35,44,"[2, 1]","[30, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[3962] -35,45,"[2, 1]","[20, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2071] -35,46,"[2, 1]","[60, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2246] -35,47,"[4, 3]","[70, 60]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[2332] -35,48,"[4, 3]","[90, 10]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[1772] -35,49,"[4, 3]","[70, 40]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1779] -35,50,"[3, 4]","[40, 60]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[2489] -35,51,"[3, 4]","[0, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[3695] -35,52,"[1, 2]","[0, 30]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[2563] -35,53,"[4, 3]","[20, 10]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2764] -35,54,"[2, 1]","[50, 0]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[3437] -35,55,"[3, 4]","[10, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2096] -35,56,"[4, 3]","[40, 20]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1955] -35,57,"[4, 3]","[40, 20]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[2688] -35,58,"[2, 1]","[60, 10]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2996] -35,59,"[2, 1]","[80, 10]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[1755] -35,60,"[1, 2]","[30, 90]","[1, 2]",[2],[1],[2],[90],[9],[2],[2],[1863] -35,61,"[2, 1]","[70, 10]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2213] -35,62,"[1, 2]","[0, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2013] -35,63,"[1, 2]","[0, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1763] -35,64,"[4, 3]","[50, 30]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[4012] -35,65,"[3, 4]","[40, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[2229] -35,66,"[2, 1]","[50, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[2487] -35,67,"[2, 1]","[70, 40]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1729] -35,68,"[2, 1]","[50, 40]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1996] -35,69,"[2, 1]","[30, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1771] -35,70,"[2, 1]","[20, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[1846] -35,71,"[2, 1]","[20, 60]","[1, 2]",[2],[1],[2],[20],[2],[2],[2],[1753] -35,72,"[3, 4]","[40, 10]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2762] -35,73,"[3, 4]","[50, 10]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2021] -35,74,"[1, 2]","[40, 30]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2205] -35,75,"[4, 3]","[30, 20]","[2, 1]",[2],[1],[3],[20],[2],[1],[3],[1929] -35,76,"[4, 3]","[20, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[1880] -35,77,"[4, 3]","[10, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[2722] -35,78,"[2, 1]","[30, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[2096] -35,79,"[3, 4]","[20, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2304] -35,80,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2779] -35,1,"[4, 3]","[30, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[3476] -35,2,"[2, 1]","[0, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2763] -35,3,"[4, 3]","[20, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1880] -35,4,"[4, 3]","[0, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[1821] -35,5,"[4, 3]","[10, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[3313] -35,6,"[1, 2]","[30, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[4104] -35,7,"[4, 3]","[10, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1971] -35,8,"[4, 3]","[30, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[3829] -35,9,"[3, 4]","[60, 10]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1905] -35,10,"[1, 2]","[60, 30]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[1929] -35,11,"[3, 4]","[70, 40]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1897] -35,12,"[2, 1]","[50, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2355] -35,13,"[1, 2]","[60, 0]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2539] -35,14,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2145] -35,15,"[1, 2]","[60, 50]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2054] -35,16,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2204] -35,17,"[1, 2]","[80, 40]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2305] -35,18,"[2, 1]","[40, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[6087] -35,19,"[1, 2]","[60, 50]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2988] -35,20,"[3, 4]","[80, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2521] -35,21,"[2, 1]","[40, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[3905] -35,22,"[1, 2]","[80, 30]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[3505] -35,23,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1996] -35,24,"[4, 3]","[50, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[3514] -35,25,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2953] -35,26,"[2, 1]","[60, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[2104] -35,27,"[2, 1]","[80, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[2388] -35,28,"[2, 1]","[80, 60]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1770] -35,29,"[4, 3]","[90, 50]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[1879] -35,30,"[2, 1]","[80, 40]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2979] -35,31,"[4, 3]","[70, 40]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[1813] -35,32,"[3, 4]","[30, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2196] -35,33,"[3, 4]","[60, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[1913] -35,34,"[4, 3]","[30, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1987] -35,35,"[4, 3]","[30, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1988] -35,36,"[3, 4]","[20, 30]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[1905] -35,37,"[3, 4]","[20, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1670] -35,38,"[4, 3]","[20, 60]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[1888] -35,39,"[4, 3]","[60, 70]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2138] -35,40,"[1, 2]","[70, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[3430] -35,41,"[2, 1]","[90, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[3807] -35,42,"[3, 4]","[50, 80]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[1872] -35,43,"[3, 4]","[50, 70]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[1671] -35,44,"[1, 2]","[50, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[2321] -35,45,"[2, 1]","[40, 30]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1972] -35,46,"[1, 2]","[50, 30]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[2614] -35,47,"[2, 1]","[40, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2054] -35,48,"[3, 4]","[20, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[3289] -35,49,"[2, 1]","[40, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[3037] -35,50,"[2, 1]","[40, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[2295] -35,51,"[1, 2]","[20, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[3488] -35,52,"[3, 4]","[30, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1997] -35,53,"[1, 2]","[30, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2405] -35,54,"[2, 1]","[80, 10]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2555] -35,55,"[4, 3]","[70, 10]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[4913] -35,56,"[4, 3]","[80, 50]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[2031] -35,57,"[1, 2]","[70, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[15912] -35,58,"[2, 1]","[60, 80]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2138] -35,59,"[1, 2]","[70, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1913] -35,60,"[1, 2]","[50, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1888] -35,61,"[4, 3]","[30, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[2338] -35,62,"[4, 3]","[10, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1987] -35,63,"[3, 4]","[50, 10]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2854] -35,64,"[4, 3]","[10, 80]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1937] -35,65,"[1, 2]","[30, 0]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[2396] -35,66,"[1, 2]","[30, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[3204] -35,67,"[1, 2]","[20, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[2945] -35,68,"[3, 4]","[40, 20]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[2229] -35,69,"[4, 3]","[0, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[3947] -35,70,"[2, 1]","[30, 60]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2636] -35,71,"[2, 1]","[30, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1961] -35,72,"[3, 4]","[40, 50]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[3612] -35,73,"[2, 1]","[60, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[13487] -35,74,"[1, 2]","[80, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1896] -35,75,"[3, 4]","[80, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[2162] -35,76,"[2, 1]","[70, 90]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[3430] -35,77,"[4, 3]","[60, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[4997] -35,78,"[2, 1]","[60, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[32246] -35,79,"[4, 3]","[40, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1861] -35,80,"[1, 2]","[20, 80]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[1771] -36,1,"[4, 3]","[70, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[4074] -36,2,"[1, 2]","[10, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2525] -36,3,"[2, 1]","[50, 0]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2894] -36,4,"[3, 4]","[30, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2725] -36,5,"[1, 2]","[30, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2308] -36,6,"[4, 3]","[80, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[3],[2442] -36,7,"[1, 2]","[0, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2177] -36,8,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[1992] -36,9,"[2, 1]","[70, 0]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2458] -36,10,"[1, 2]","[50, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1876] -36,11,"[1, 2]","[50, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[1827] -36,12,"[1, 2]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[1805] -36,13,"[3, 4]","[20, 90]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[4377] -36,14,"[1, 2]","[40, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[1992] -36,15,"[3, 4]","[50, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[3591] -36,16,"[3, 4]","[0, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2275] -36,17,"[1, 2]","[40, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[1908] -36,18,"[3, 4]","[60, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2242] -36,19,"[2, 1]","[60, 80]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2443] -36,20,"[1, 2]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1856] -36,21,"[1, 2]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1760] -36,22,"[1, 2]","[70, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[1759] -36,23,"[1, 2]","[70, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[1758] -36,24,"[4, 3]","[30, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2390] -36,25,"[2, 1]","[50, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2391] -36,26,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2214] -36,27,"[3, 4]","[80, 60]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2456] -36,28,"[4, 3]","[20, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1941] -36,29,"[4, 3]","[0, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1975] -36,30,"[3, 4]","[60, 30]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2171] -36,31,"[2, 1]","[40, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2177] -36,32,"[4, 3]","[50, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2358] -36,33,"[4, 3]","[80, 60]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2074] -36,34,"[1, 2]","[30, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2465] -36,35,"[3, 4]","[50, 70]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[1960] -36,36,"[1, 2]","[40, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2258] -36,37,"[2, 1]","[80, 60]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2260] -36,38,"[2, 1]","[80, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1793] -36,39,"[1, 2]","[40, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[1943] -36,40,"[2, 1]","[70, 20]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1976] -36,41,"[1, 2]","[50, 70]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[3518] -36,42,"[3, 4]","[50, 80]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[2693] -36,43,"[4, 3]","[70, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2143] -36,44,"[1, 2]","[0, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[3008] -36,45,"[4, 3]","[80, 0]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2607] -36,46,"[3, 4]","[10, 80]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1894] -36,47,"[2, 1]","[70, 10]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[2491] -36,48,"[4, 3]","[60, 30]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2373] -36,49,"[4, 3]","[30, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2210] -36,50,"[1, 2]","[10, 40]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[2393] -36,51,"[1, 2]","[10, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2179] -36,52,"[2, 1]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2559] -36,53,"[3, 4]","[30, 10]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[2359] -36,54,"[3, 4]","[40, 20]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2141] -36,55,"[2, 1]","[0, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[2506] -36,56,"[1, 2]","[0, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2326] -36,57,"[2, 1]","[30, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[2143] -36,58,"[4, 3]","[30, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[2221] -36,59,"[1, 2]","[20, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[2321] -36,60,"[1, 2]","[30, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[2175] -36,61,"[1, 2]","[0, 80]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[2326] -36,62,"[2, 1]","[70, 0]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2443] -36,63,"[4, 3]","[90, 50]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[2624] -36,64,"[4, 3]","[50, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[2042] -36,65,"[3, 4]","[20, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2224] -36,66,"[3, 4]","[20, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2022] -36,67,"[1, 2]","[70, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2425] -36,68,"[3, 4]","[40, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1993] -36,69,"[4, 3]","[80, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2376] -36,70,"[2, 1]","[60, 80]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2240] -36,71,"[3, 4]","[90, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[3692] -36,72,"[4, 3]","[70, 80]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[1993] -36,73,"[1, 2]","[60, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2291] -36,74,"[3, 4]","[70, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[2191] -36,75,"[2, 1]","[80, 50]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[2159] -36,76,"[3, 4]","[60, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[3425] -36,77,"[2, 1]","[80, 90]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[2460] -36,78,"[4, 3]","[80, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2108] -36,79,"[4, 3]","[50, 60]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[1976] -36,80,"[2, 1]","[80, 90]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[2241] -29,1,"[1, 2]","[90, 20]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[4489] -29,2,"[2, 1]","[30, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2137] -29,3,"[3, 4]","[50, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[1958] -29,4,"[3, 4]","[20, 70]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[1861] -29,5,"[2, 1]","[90, 30]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[2054] -29,6,"[1, 2]","[0, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[1760] -29,7,"[4, 3]","[90, 0]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[2060] -29,8,"[1, 2]","[20, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1910] -29,9,"[4, 3]","[80, 30]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2077] -29,10,"[3, 4]","[60, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1909] -29,11,"[2, 1]","[60, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2594] -29,12,"[1, 2]","[60, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[1844] -29,13,"[2, 1]","[70, 40]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1878] -29,14,"[4, 3]","[90, 30]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[2163] -29,15,"[3, 4]","[10, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[1893] -29,16,"[4, 3]","[50, 40]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[1842] -29,17,"[2, 1]","[20, 0]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[2059] -29,18,"[4, 3]","[30, 0]","[2, 1]",[1],[5],[3],[0],[0],[1],[3],[1744] -29,19,"[4, 3]","[50, 10]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[1845] -29,20,"[2, 1]","[50, 20]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2410] -29,21,"[1, 2]","[40, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2209] -29,22,"[3, 4]","[40, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[1877] -29,23,"[3, 4]","[60, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[1744] -29,24,"[3, 4]","[30, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[1744] -29,25,"[2, 1]","[80, 30]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2010] -29,26,"[2, 1]","[70, 10]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1827] -29,27,"[2, 1]","[90, 0]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[1745] -29,28,"[4, 3]","[80, 0]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[1877] -29,29,"[2, 1]","[80, 10]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1958] -29,30,"[1, 2]","[30, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[1793] -29,31,"[1, 2]","[40, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[1825] -29,32,"[4, 3]","[60, 40]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[1844] -29,33,"[2, 1]","[60, 70]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[1909] -29,34,"[4, 3]","[40, 70]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1877] -29,35,"[4, 3]","[10, 60]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[1857] -29,36,"[4, 3]","[20, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1627] -29,37,"[3, 4]","[50, 80]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1710] -29,38,"[1, 2]","[70, 90]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1961] -29,39,"[3, 4]","[70, 40]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[1827] -29,40,"[3, 4]","[70, 40]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[1895] -29,41,"[4, 3]","[20, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1855] -29,42,"[4, 3]","[20, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1745] -29,43,"[4, 3]","[30, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1659] -29,44,"[4, 3]","[40, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1726] -29,45,"[1, 2]","[60, 50]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1975] -29,46,"[1, 2]","[50, 20]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[1959] -29,47,"[1, 2]","[40, 10]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[1842] -29,48,"[2, 1]","[20, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[2076] -29,49,"[1, 2]","[20, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[1859] -29,50,"[3, 4]","[10, 40]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1961] -29,51,"[1, 2]","[30, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1789] -29,52,"[1, 2]","[30, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[1894] -29,53,"[2, 1]","[90, 70]","[1, 2]",[2],[5],[2],[450],[9],[2],[1],[1859] -29,54,"[3, 4]","[60, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2408] -29,55,"[4, 3]","[60, 80]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[23427] -29,56,"[1, 2]","[60, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[4261] -29,57,"[1, 2]","[80, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[1812] -29,58,"[3, 4]","[80, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[2226] -29,59,"[3, 4]","[80, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1694] -29,60,"[3, 4]","[80, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[1827] -29,61,"[4, 3]","[50, 80]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[1895] -29,62,"[3, 4]","[70, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1946] -29,63,"[4, 3]","[40, 50]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1777] -29,64,"[2, 1]","[30, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[2077] -29,65,"[4, 3]","[40, 10]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2295] -29,66,"[2, 1]","[60, 40]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[1893] -29,67,"[4, 3]","[70, 60]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1959] -29,68,"[4, 3]","[80, 70]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1709] -29,69,"[4, 3]","[80, 60]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1710] -29,70,"[1, 2]","[40, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[2258] -29,71,"[2, 1]","[70, 20]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2661] -29,72,"[1, 2]","[10, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1828] -29,73,"[2, 1]","[60, 10]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[1777] -29,74,"[1, 2]","[10, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[1826] -29,75,"[2, 1]","[90, 10]","[1, 2]",[2],[1],[2],[90],[9],[2],[1],[1794] -29,76,"[2, 1]","[80, 10]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[1759] -29,77,"[3, 4]","[60, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2010] -29,78,"[3, 4]","[40, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[1795] -29,79,"[4, 3]","[80, 20]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1859] -29,80,"[4, 3]","[80, 30]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[1759] -29,1,"[2, 1]","[20, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[4146] -29,2,"[4, 3]","[20, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[1779] -29,3,"[2, 1]","[10, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1893] -29,4,"[1, 2]","[70, 20]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1805] -29,5,"[3, 4]","[80, 0]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1687] -29,6,"[3, 4]","[80, 10]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1777] -29,7,"[3, 4]","[70, 10]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1710] -29,8,"[4, 3]","[30, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[1791] -29,9,"[2, 1]","[40, 70]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2009] -29,10,"[2, 1]","[50, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1827] -29,11,"[3, 4]","[60, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1761] -29,12,"[4, 3]","[60, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1859] -29,13,"[3, 4]","[90, 40]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[1726] -29,14,"[2, 1]","[60, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1825] -29,15,"[2, 1]","[20, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1793] -29,16,"[1, 2]","[80, 30]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1777] -29,17,"[2, 1]","[50, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[1796] -29,18,"[3, 4]","[80, 60]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[1777] -29,19,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1894] -29,20,"[1, 2]","[50, 80]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[1795] -29,21,"[2, 1]","[60, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[1759] -29,22,"[2, 1]","[70, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[1773] -29,23,"[2, 1]","[60, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1926] -29,24,"[4, 3]","[70, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1859] -29,25,"[1, 2]","[80, 60]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2010] -29,26,"[3, 4]","[60, 70]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1772] -29,27,"[2, 1]","[80, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1844] -29,28,"[2, 1]","[80, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[1794] -29,29,"[2, 1]","[60, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[1709] -29,30,"[1, 2]","[90, 70]","[1, 1]",[1],[5],[1],[450],[9],[1],[1],[1795] -29,31,"[3, 4]","[60, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1895] -29,32,"[4, 3]","[70, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1777] -29,33,"[3, 4]","[90, 70]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[1777] -29,34,"[1, 2]","[70, 60]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1913] -29,35,"[1, 2]","[50, 60]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1845] -29,36,"[4, 3]","[50, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1711] -29,37,"[3, 4]","[10, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1776] -29,38,"[4, 3]","[80, 30]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[1727] -29,39,"[1, 2]","[20, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2078] -29,40,"[1, 2]","[10, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1811] -29,41,"[2, 1]","[80, 30]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[1798] -29,42,"[2, 1]","[80, 10]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[1777] -29,43,"[3, 4]","[10, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2027] -29,44,"[2, 1]","[70, 40]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[5976] -29,45,"[4, 3]","[40, 70]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1909] -29,46,"[4, 3]","[50, 40]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2204] -29,47,"[3, 4]","[50, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[1892] -29,48,"[4, 3]","[70, 20]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1794] -29,49,"[2, 1]","[70, 40]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1928] -29,50,"[3, 4]","[40, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[1809] -29,51,"[3, 4]","[40, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[1809] -29,52,"[1, 2]","[40, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[2009] -29,53,"[4, 3]","[50, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[1794] -29,54,"[1, 2]","[40, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2010] -29,55,"[3, 4]","[10, 20]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[1743] -29,56,"[4, 3]","[10, 30]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1710] -29,57,"[4, 3]","[10, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[1694] -29,58,"[2, 1]","[0, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1810] -29,59,"[3, 4]","[60, 10]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[1744] -29,60,"[1, 2]","[50, 10]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[1927] -29,61,"[2, 1]","[20, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[1994] -29,62,"[2, 1]","[60, 30]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[1644] -29,63,"[2, 1]","[60, 40]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[1847] -29,64,"[2, 1]","[30, 50]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[1744] -29,65,"[4, 3]","[30, 70]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[1842] -29,66,"[1, 2]","[50, 30]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[2227] -29,67,"[4, 3]","[30, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[1726] -29,68,"[3, 4]","[70, 30]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1727] -29,69,"[2, 1]","[60, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1794] -29,70,"[2, 1]","[70, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[1845] -29,71,"[3, 4]","[10, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[1845] -29,72,"[3, 4]","[40, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1760] -29,73,"[1, 2]","[40, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[2595] -29,74,"[1, 2]","[60, 30]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1845] -29,75,"[2, 1]","[40, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[1877] -29,76,"[3, 4]","[60, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1810] -29,77,"[4, 3]","[50, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[1810] -29,78,"[3, 4]","[40, 70]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[1759] -29,79,"[4, 3]","[90, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1793] -29,80,"[3, 4]","[80, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1794] -29,1,"[3, 4]","[80, 0]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[6652] -29,2,"[3, 4]","[70, 40]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[2140] -29,3,"[1, 2]","[90, 10]","[1, 1]",[1],[5],[1],[450],[9],[1],[1],[1855] -29,4,"[4, 3]","[10, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1857] -29,5,"[2, 1]","[10, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[1840] -29,6,"[2, 1]","[0, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[1807] -29,7,"[2, 1]","[30, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1740] -29,8,"[4, 3]","[40, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1707] -29,9,"[3, 4]","[30, 40]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[1723] -29,10,"[1, 2]","[20, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2056] -29,11,"[1, 2]","[40, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[1858] -29,12,"[3, 4]","[50, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1773] -29,13,"[3, 4]","[70, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1657] -29,14,"[2, 1]","[50, 30]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2308] -29,15,"[1, 2]","[20, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[1940] -29,16,"[4, 3]","[50, 30]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[1974] -29,17,"[3, 4]","[20, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1807] -29,18,"[3, 4]","[10, 30]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[1691] -29,19,"[4, 3]","[10, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[1807] -29,20,"[1, 2]","[20, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[2174] -29,21,"[1, 2]","[20, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[1757] -29,22,"[3, 4]","[40, 30]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[1725] -29,23,"[3, 4]","[30, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2607] -29,24,"[3, 4]","[0, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[1757] -29,25,"[3, 4]","[20, 10]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1590] -29,26,"[4, 3]","[10, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[3040] -29,27,"[2, 1]","[0, 60]","[1, 2]",[1],[1],[2],[0],[0],[2],[2],[2074] -29,28,"[3, 4]","[70, 20]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[3441] -29,29,"[4, 3]","[0, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1740] -29,30,"[1, 2]","[40, 10]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[1875] -29,31,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1774] -29,32,"[1, 2]","[70, 40]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1907] -29,33,"[2, 1]","[30, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1774] -29,34,"[3, 4]","[60, 40]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1774] -29,35,"[2, 1]","[70, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2307] -29,36,"[2, 1]","[90, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1742] -29,37,"[1, 2]","[90, 70]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[1991] -29,38,"[2, 1]","[50, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[1807] -29,39,"[4, 3]","[70, 10]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[3290] -29,40,"[4, 3]","[80, 40]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[1874] -29,41,"[2, 1]","[60, 30]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[2418] -29,42,"[1, 2]","[20, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[2407] -29,43,"[3, 4]","[20, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[2007] -29,44,"[4, 3]","[20, 0]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[1824] -29,45,"[2, 1]","[0, 0]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[2040] -29,46,"[1, 2]","[10, 0]","[1, 2]",[2],[5],[2],[0],[0],[2],[1],[2140] -29,47,"[2, 1]","[40, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[2323] -29,48,"[4, 3]","[50, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[1790] -29,49,"[4, 3]","[70, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[1707] -29,50,"[2, 1]","[70, 60]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1923] -29,51,"[1, 2]","[80, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[1891] -29,52,"[1, 2]","[50, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[1723] -29,53,"[1, 2]","[40, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[1791] -29,54,"[4, 3]","[90, 50]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[2141] -29,55,"[2, 1]","[60, 20]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[1988] -29,56,"[1, 2]","[30, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[1991] -29,57,"[2, 1]","[40, 50]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[1857] -29,58,"[4, 3]","[60, 50]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[1924] -29,59,"[4, 3]","[60, 40]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[1824] -29,60,"[1, 2]","[20, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[2121] -29,61,"[1, 2]","[10, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2124] -29,62,"[4, 3]","[80, 40]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1873] -29,63,"[2, 1]","[90, 30]","[1, 2]",[2],[1],[2],[90],[9],[2],[1],[1773] -29,64,"[2, 1]","[50, 10]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1857] -29,65,"[1, 2]","[0, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[1874] -29,66,"[2, 1]","[50, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[2124] -29,67,"[3, 4]","[20, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[1991] -29,68,"[2, 1]","[50, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[1973] -29,69,"[1, 2]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[1923] -29,70,"[4, 3]","[10, 30]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[2091] -29,71,"[1, 2]","[40, 0]","[1, 2]",[2],[1],[2],[0],[0],[2],[1],[1989] -29,72,"[3, 4]","[40, 0]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1690] -29,73,"[2, 1]","[30, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[1857] -29,74,"[1, 2]","[0, 0]","[1, 1]",[2],[5],[1],[0],[0],[1],[1],[1841] -29,75,"[4, 3]","[20, 10]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[1941] -29,76,"[1, 2]","[30, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[2007] -29,77,"[3, 4]","[40, 0]","[2, 2]",[2],[5],[4],[0],[0],[2],[3],[1824] -29,78,"[4, 3]","[40, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1740] -29,79,"[2, 1]","[20, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[1925] -29,80,"[1, 2]","[0, 50]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[1874] -29,1,"[1, 2]","[60, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[4537] -29,2,"[4, 3]","[40, 60]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1873] -29,3,"[3, 4]","[40, 30]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[1707] -29,4,"[4, 3]","[30, 60]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[1774] -29,5,"[1, 2]","[80, 30]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1891] -29,6,"[1, 2]","[70, 60]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1790] -29,7,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1754] -29,8,"[4, 3]","[40, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1791] -29,9,"[1, 2]","[70, 20]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1874] -29,10,"[2, 1]","[20, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1824] -29,11,"[4, 3]","[50, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1741] -29,12,"[4, 3]","[50, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1690] -29,13,"[1, 2]","[50, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[1807] -29,14,"[1, 2]","[60, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[1824] -29,15,"[1, 2]","[90, 40]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[1856] -29,16,"[1, 2]","[80, 10]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1790] -29,17,"[4, 3]","[60, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1757] -29,18,"[4, 3]","[30, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[1774] -29,19,"[2, 1]","[20, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[1807] -29,20,"[4, 3]","[0, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[1741] -29,21,"[2, 1]","[10, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[1858] -29,22,"[3, 4]","[40, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1724] -29,23,"[2, 1]","[40, 10]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[1673] -29,24,"[3, 4]","[30, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1824] -29,25,"[4, 3]","[30, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1857] -29,26,"[3, 4]","[10, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[1674] -29,27,"[4, 3]","[20, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[1673] -29,28,"[1, 2]","[10, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[1891] -29,29,"[2, 1]","[0, 10]","[1, 2]",[1],[1],[2],[0],[0],[2],[2],[1957] -29,30,"[3, 4]","[10, 30]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[3307] -29,31,"[4, 3]","[20, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[1790] -29,32,"[1, 2]","[0, 50]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[2024] -29,33,"[2, 1]","[30, 10]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2174] -29,34,"[4, 3]","[40, 0]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[1687] -29,35,"[4, 3]","[10, 0]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[1757] -29,36,"[2, 1]","[0, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1841] -29,37,"[2, 1]","[10, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1757] -29,38,"[1, 2]","[40, 20]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[1857] -29,39,"[1, 2]","[30, 0]","[1, 2]",[1],[1],[2],[0],[0],[2],[1],[1823] -29,40,"[1, 2]","[30, 10]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[1740] -29,41,"[4, 3]","[10, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[3553] -29,42,"[4, 3]","[0, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1707] -29,43,"[4, 3]","[20, 10]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1873] -29,44,"[2, 1]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1991] -29,45,"[4, 3]","[10, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[1757] -29,46,"[3, 4]","[40, 10]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1757] -29,47,"[3, 4]","[80, 50]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1707] -29,48,"[1, 2]","[80, 40]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2174] -29,49,"[4, 3]","[50, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[2375] -29,50,"[2, 1]","[80, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[1890] -29,51,"[3, 4]","[80, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2174] -29,52,"[2, 1]","[80, 90]","[1, 1]",[2],[1],[1],[90],[9],[1],[2],[1824] -29,53,"[3, 4]","[80, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1707] -29,54,"[2, 1]","[60, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[1890] -29,55,"[4, 3]","[70, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1741] -29,56,"[2, 1]","[70, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[1807] -29,57,"[2, 1]","[60, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[1791] -29,58,"[4, 3]","[50, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[4157] -29,59,"[2, 1]","[50, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1757] -29,60,"[3, 4]","[80, 50]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2140] -29,61,"[2, 1]","[50, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1774] -29,62,"[4, 3]","[70, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1741] -29,63,"[4, 3]","[80, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1724] -29,64,"[2, 1]","[90, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[1841] -29,65,"[3, 4]","[10, 90]","[2, 2]",[2],[5],[4],[450],[9],[2],[4],[2090] -29,66,"[3, 4]","[40, 90]","[2, 2]",[2],[1],[4],[90],[9],[2],[4],[1824] -29,67,"[3, 4]","[30, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[1723] -29,68,"[4, 3]","[90, 30]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[1790] -29,69,"[3, 4]","[20, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[1806] -29,70,"[3, 4]","[10, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1774] -29,71,"[4, 3]","[70, 20]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1824] -29,72,"[2, 1]","[90, 20]","[1, 2]",[2],[1],[2],[90],[9],[2],[1],[2074] -29,73,"[1, 2]","[10, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1725] -29,74,"[3, 4]","[20, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1874] -29,75,"[1, 2]","[30, 20]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[2407] -29,76,"[1, 2]","[50, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[1874] -29,77,"[1, 2]","[40, 20]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2207] -29,78,"[1, 2]","[50, 20]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[1857] -29,79,"[2, 1]","[0, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[2040] -29,80,"[3, 4]","[40, 30]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1791] -29,1,"[1, 2]","[30, 80]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[4554] -29,2,"[1, 2]","[20, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1740] -29,3,"[3, 4]","[10, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2553] -29,4,"[4, 3]","[90, 10]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[1754] -29,5,"[2, 1]","[70, 0]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1907] -29,6,"[1, 2]","[20, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[1807] -29,7,"[1, 2]","[10, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[1774] -29,8,"[2, 1]","[60, 10]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[1740] -29,9,"[2, 1]","[80, 10]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1823] -29,10,"[2, 1]","[70, 10]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1757] -29,11,"[1, 2]","[30, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[1807] -29,12,"[4, 3]","[40, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[1840] -29,13,"[4, 3]","[50, 30]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[1724] -29,14,"[4, 3]","[10, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[1741] -29,15,"[4, 3]","[0, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1641] -29,16,"[2, 1]","[20, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[1974] -29,17,"[3, 4]","[20, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1758] -29,18,"[4, 3]","[40, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1790] -29,19,"[1, 2]","[50, 20]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[1874] -29,20,"[3, 4]","[50, 10]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[1758] -29,21,"[2, 1]","[30, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2006] -29,22,"[3, 4]","[30, 20]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1774] -29,23,"[1, 2]","[30, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[1770] -29,24,"[4, 3]","[0, 30]","[2, 2]",[1],[1],[4],[0],[0],[2],[4],[1537] -29,25,"[1, 2]","[20, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[1957] -29,26,"[1, 2]","[60, 30]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[1720] -29,27,"[2, 1]","[40, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[1823] -29,28,"[2, 1]","[50, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[1740] -29,29,"[3, 4]","[30, 40]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[2023] -29,30,"[2, 1]","[70, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[1974] -29,31,"[1, 2]","[40, 70]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[2158] -29,32,"[4, 3]","[90, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2507] -29,33,"[1, 2]","[70, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2141] -29,34,"[4, 3]","[80, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2007] -29,35,"[2, 1]","[90, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2040] -29,36,"[4, 3]","[90, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1724] -29,37,"[1, 2]","[40, 70]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2057] -29,38,"[2, 1]","[70, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[3907] -29,39,"[2, 1]","[60, 20]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[1720] -29,40,"[4, 3]","[80, 10]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[1740] -29,41,"[1, 2]","[20, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[3685] -29,42,"[2, 1]","[70, 10]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1771] -29,43,"[3, 4]","[20, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[1974] -29,44,"[2, 1]","[70, 0]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[1874] -29,45,"[2, 1]","[30, 10]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1724] -29,46,"[4, 3]","[40, 10]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1740] -29,47,"[2, 1]","[30, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[1875] -29,48,"[4, 3]","[60, 30]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[1940] -29,49,"[4, 3]","[70, 10]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1787] -29,50,"[1, 2]","[10, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2040] -29,51,"[2, 1]","[40, 30]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[1840] -29,52,"[4, 3]","[20, 40]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[1906] -29,53,"[4, 3]","[30, 40]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[1824] -29,54,"[2, 1]","[20, 50]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[1754] -29,55,"[1, 2]","[60, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[1756] -29,56,"[3, 4]","[40, 20]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1741] -29,57,"[3, 4]","[50, 30]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1741] -29,58,"[3, 4]","[30, 40]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[1741] -29,59,"[1, 2]","[30, 40]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[1891] -29,60,"[4, 3]","[30, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[1706] -29,61,"[3, 4]","[40, 20]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1824] -29,62,"[3, 4]","[20, 20]","[2, 1]",[2],[1],[3],[20],[2],[1],[3],[1741] -29,63,"[4, 3]","[20, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[1840] -29,64,"[2, 1]","[40, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1790] -29,65,"[1, 2]","[80, 60]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2007] -29,66,"[1, 2]","[70, 0]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[1778] -29,67,"[4, 3]","[10, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1840] -29,68,"[4, 3]","[20, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1757] -29,69,"[3, 4]","[40, 10]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1857] -29,70,"[2, 1]","[10, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[3013] -29,71,"[4, 3]","[10, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[2011] -29,72,"[1, 2]","[70, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2007] -29,73,"[4, 3]","[10, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[1703] -29,74,"[4, 3]","[20, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1691] -29,75,"[4, 3]","[10, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1773] -29,76,"[2, 1]","[20, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1924] -29,77,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2089] -29,78,"[4, 3]","[10, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1774] -29,79,"[1, 2]","[90, 10]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[1903] -29,80,"[2, 1]","[10, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2024] -28,1,"[3, 4]","[10, 70]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[17668] -28,2,"[2, 1]","[50, 20]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[1926] -28,3,"[1, 2]","[40, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[1767] -28,4,"[2, 1]","[70, 40]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2209] -28,5,"[4, 3]","[70, 30]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[4023] -28,6,"[3, 4]","[40, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2452] -28,7,"[3, 4]","[50, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2253] -28,8,"[2, 1]","[90, 50]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[2326] -28,9,"[1, 2]","[90, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[1873] -28,10,"[2, 1]","[90, 70]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[2552] -28,11,"[4, 3]","[80, 60]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2052] -28,12,"[3, 4]","[20, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2687] -28,13,"[2, 1]","[40, 20]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2218] -28,14,"[2, 1]","[0, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2001] -28,15,"[2, 1]","[10, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[1875] -28,16,"[4, 3]","[0, 40]","[2, 2]",[1],[5],[4],[0],[0],[2],[4],[1893] -28,17,"[4, 3]","[50, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[2475] -28,18,"[4, 3]","[60, 10]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[4498] -28,19,"[3, 4]","[10, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[1976] -28,20,"[1, 2]","[30, 40]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[2786] -28,21,"[1, 2]","[30, 40]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[1926] -28,22,"[1, 2]","[30, 40]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[1759] -28,23,"[4, 3]","[30, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[3336] -28,24,"[2, 1]","[10, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2435] -28,25,"[1, 2]","[70, 10]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1875] -28,26,"[2, 1]","[20, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1801] -28,27,"[4, 3]","[20, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1927] -28,28,"[4, 3]","[20, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1776] -28,29,"[1, 2]","[70, 10]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2252] -28,30,"[3, 4]","[90, 10]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[1859] -28,31,"[4, 3]","[20, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2041] -28,32,"[4, 3]","[10, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1793] -28,33,"[2, 1]","[20, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1808] -28,34,"[3, 4]","[50, 40]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[1951] -28,35,"[3, 4]","[30, 60]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[1826] -28,36,"[1, 2]","[60, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[1816] -28,37,"[2, 1]","[40, 80]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[1925] -28,38,"[1, 2]","[80, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2024] -28,39,"[4, 3]","[60, 80]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1976] -28,40,"[2, 1]","[50, 70]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[1916] -28,41,"[1, 2]","[50, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[7904] -28,42,"[2, 1]","[80, 70]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[1809] -28,43,"[1, 2]","[70, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[1824] -28,44,"[3, 4]","[70, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[1926] -28,45,"[1, 2]","[80, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[1843] -28,46,"[4, 3]","[90, 80]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[1950] -28,47,"[1, 2]","[70, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[1827] -28,48,"[1, 2]","[80, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1836] -28,49,"[2, 1]","[80, 70]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[1906] -28,50,"[2, 1]","[50, 70]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[1911] -28,51,"[4, 3]","[50, 30]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[1816] -28,52,"[4, 3]","[40, 20]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1784] -28,53,"[2, 1]","[40, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[2151] -28,54,"[3, 4]","[0, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[4],[1966] -28,55,"[2, 1]","[20, 40]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[2210] -28,56,"[3, 4]","[30, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1884] -28,57,"[2, 1]","[60, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[1992] -28,58,"[3, 4]","[70, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1824] -28,59,"[1, 2]","[50, 80]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1817] -28,60,"[4, 3]","[80, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1960] -28,61,"[1, 2]","[80, 50]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1942] -28,62,"[1, 2]","[50, 40]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[1831] -28,63,"[1, 2]","[60, 30]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[1885] -28,64,"[4, 3]","[60, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1925] -28,65,"[2, 1]","[80, 20]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[2359] -28,66,"[3, 4]","[30, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[1782] -28,67,"[1, 2]","[30, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1993] -28,68,"[2, 1]","[50, 20]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1857] -28,69,"[3, 4]","[30, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1905] -28,70,"[2, 1]","[60, 20]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[2033] -28,71,"[2, 1]","[70, 10]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[1908] -28,72,"[2, 1]","[40, 0]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2068] -28,73,"[3, 4]","[10, 30]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[1927] -28,74,"[4, 3]","[20, 10]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[1923] -28,75,"[1, 2]","[30, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[1951] -28,76,"[2, 1]","[10, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[1927] -28,77,"[3, 4]","[60, 10]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1892] -28,78,"[1, 2]","[90, 0]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[1873] -28,79,"[4, 3]","[10, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1909] -28,80,"[2, 1]","[10, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[1800] -37,1,"[2, 1]","[10, 90]","[1, 2]",[1],[5],[2],[50],[1],[2],[2],[5126] -37,2,"[1, 2]","[80, 20]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2669] -37,3,"[4, 3]","[80, 90]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[3118] -37,4,"[1, 2]","[60, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2311] -37,5,"[4, 3]","[80, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[2443] -37,6,"[1, 2]","[70, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[1961] -37,7,"[1, 2]","[60, 0]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2380] -37,8,"[1, 2]","[70, 0]","[1, 2]",[1],[1],[2],[0],[0],[2],[1],[2562] -37,9,"[1, 2]","[90, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[3319] -37,10,"[2, 1]","[0, 70]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[2869] -37,11,"[2, 1]","[0, 80]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[2503] -37,12,"[2, 1]","[30, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1870] -37,13,"[2, 1]","[20, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2060] -37,14,"[2, 1]","[50, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[1887] -37,15,"[1, 2]","[60, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[6885] -37,16,"[1, 2]","[60, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2270] -37,17,"[3, 4]","[30, 0]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[5485] -37,18,"[1, 2]","[0, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[2],[2993] -37,19,"[4, 3]","[10, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[2877] -37,20,"[2, 1]","[0, 10]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[2237] -37,21,"[3, 4]","[20, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2436] -37,22,"[3, 4]","[10, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[1966] -37,23,"[4, 3]","[0, 20]","[2, 2]",[1],[1],[4],[0],[0],[2],[4],[1952] -37,24,"[4, 3]","[0, 20]","[2, 2]",[1],[5],[4],[0],[0],[2],[4],[2011] -37,25,"[3, 4]","[30, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[2469] -37,26,"[1, 2]","[40, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[2464] -37,27,"[2, 1]","[50, 40]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[1977] -37,28,"[1, 2]","[40, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[1919] -37,29,"[1, 2]","[70, 60]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1920] -37,30,"[4, 3]","[80, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[2277] -37,31,"[2, 1]","[70, 90]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[3334] -37,32,"[3, 4]","[90, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[3668] -37,33,"[3, 4]","[70, 90]","[2, 2]",[1],[5],[4],[450],[9],[2],[4],[2693] -37,34,"[3, 4]","[60, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2146] -37,35,"[2, 1]","[50, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[3420] -37,36,"[1, 2]","[30, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2668] -37,37,"[3, 4]","[0, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[2700] -37,38,"[1, 2]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2378] -37,39,"[3, 4]","[10, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[3011] -37,40,"[3, 4]","[20, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2071] -37,41,"[3, 4]","[0, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[3579] -37,42,"[1, 2]","[10, 30]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[2785] -37,43,"[2, 1]","[10, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2628] -37,44,"[3, 4]","[20, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2210] -37,45,"[1, 2]","[10, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[2012] -37,46,"[3, 4]","[10, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[2153] -37,47,"[3, 4]","[10, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2353] -37,48,"[4, 3]","[20, 30]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[2203] -37,49,"[3, 4]","[40, 20]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1911] -37,50,"[4, 3]","[40, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2853] -37,51,"[3, 4]","[40, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[1961] -37,52,"[3, 4]","[50, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2044] -37,53,"[1, 2]","[60, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[674829] -37,54,"[4, 3]","[50, 40]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2079] -37,55,"[2, 1]","[40, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2510] -37,56,"[3, 4]","[40, 20]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2152] -37,57,"[2, 1]","[10, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[3851] -37,58,"[2, 1]","[30, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[9000] -37,59,"[4, 3]","[20, 30]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[3117] -37,60,"[3, 4]","[20, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[4],[4976] -37,61,"[1, 2]","[20, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2227] -37,62,"[3, 4]","[20, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[2178] -37,63,"[4, 3]","[30, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[2462] -37,64,"[3, 4]","[60, 40]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1993] -37,65,"[4, 3]","[0, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1951] -37,66,"[2, 1]","[10, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2185] -37,67,"[2, 1]","[0, 80]","[1, 2]",[2],[1],[2],[0],[0],[2],[2],[2254] -37,68,"[1, 2]","[50, 10]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[2352] -37,69,"[3, 4]","[40, 0]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[3193] -37,70,"[4, 3]","[10, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[2767] -37,71,"[1, 2]","[80, 10]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[3234] -37,72,"[2, 1]","[50, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2551] -37,73,"[2, 1]","[40, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2419] -37,74,"[2, 1]","[50, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[2260] -37,75,"[2, 1]","[50, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[1968] -37,76,"[1, 2]","[10, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2618] -37,77,"[3, 4]","[20, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[2609] -37,78,"[1, 2]","[10, 30]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[2128] -37,79,"[2, 1]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2584] -37,80,"[2, 1]","[10, 70]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2453] -38,1,"[4, 3]","[60, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[10598] -38,2,"[1, 2]","[40, 50]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[5018] -38,3,"[4, 3]","[50, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[6568] -38,4,"[2, 1]","[20, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[7551] -38,5,"[3, 4]","[20, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[3516] -38,6,"[2, 1]","[30, 10]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2284] -38,7,"[3, 4]","[10, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[3500] -38,8,"[1, 2]","[10, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[3617] -38,9,"[2, 1]","[20, 10]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[3717] -38,10,"[2, 1]","[30, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[4951] -38,11,"[2, 1]","[10, 0]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[3517] -38,12,"[3, 4]","[0, 20]","[2, 1]",[1],[1],[3],[0],[0],[1],[4],[4283] -38,13,"[2, 1]","[20, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[4767] -38,14,"[4, 3]","[20, 30]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[27748] -38,15,"[1, 2]","[30, 20]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[3685] -38,16,"[4, 3]","[0, 70]","[2, 2]",[1],[1],[4],[0],[0],[2],[4],[2917] -38,17,"[2, 1]","[10, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2850] -38,18,"[1, 2]","[30, 20]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[3867] -38,19,"[4, 3]","[10, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[4668] -38,20,"[3, 4]","[50, 20]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[6001] -38,21,"[3, 4]","[60, 40]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[4300] -38,22,"[4, 3]","[10, 30]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[21353] -38,23,"[3, 4]","[20, 10]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[4101] -38,24,"[4, 3]","[20, 40]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[4451] -38,25,"[2, 1]","[0, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[3401] -38,26,"[4, 3]","[10, 40]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[3650] -38,27,"[2, 1]","[10, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[3050] -38,28,"[2, 1]","[40, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[3084] -38,29,"[3, 4]","[70, 0]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[23704] -38,30,"[1, 2]","[90, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[2985] -38,31,"[2, 1]","[0, 60]","[1, 2]",[1],[1],[2],[0],[0],[2],[2],[4818] -38,32,"[3, 4]","[50, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[5468] -38,33,"[1, 2]","[50, 20]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[6485] -38,34,"[1, 2]","[40, 20]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[2817] -38,35,"[1, 2]","[70, 10]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2601] -38,36,"[4, 3]","[20, 80]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[3701] -38,37,"[3, 4]","[70, 10]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[20686] -38,38,"[2, 1]","[30, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[7351] -38,39,"[2, 1]","[50, 40]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[3117] -38,40,"[1, 2]","[30, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[1],[4468] -38,41,"[3, 4]","[10, 30]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[52628] -38,42,"[1, 2]","[10, 70]","[1, 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40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[6317] -39,18,"[2, 1]","[70, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[6398] -39,19,"[4, 3]","[90, 30]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[4797] -39,20,"[4, 3]","[80, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[4514] -39,21,"[3, 4]","[0, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[3476] -39,22,"[3, 4]","[10, 40]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[3645] -39,23,"[2, 1]","[30, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[3356] -39,24,"[2, 1]","[10, 20]","[1, 2]",[1],[5],[2],[50],[1],[2],[2],[4005] -39,25,"[4, 3]","[10, 0]","[2, 1]",[1],[5],[3],[0],[0],[1],[3],[3603] -39,26,"[1, 2]","[10, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[4840] -39,27,"[3, 4]","[40, 0]","[2, 2]",[1],[5],[4],[0],[0],[2],[3],[3530] -39,28,"[1, 2]","[0, 0]","[1, 2]",[1],[1],[2],[0],[0],[2],[1],[5567] -39,29,"[3, 4]","[20, 40]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[3620] -39,30,"[1, 2]","[60, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[6997] -39,31,"[2, 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-40,20,"[1, 2]","[20, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[1679] -40,21,"[1, 2]","[20, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[2246] -40,22,"[3, 4]","[40, 30]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[1778] -40,23,"[3, 4]","[30, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[1661] -40,24,"[3, 4]","[0, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[1771] -40,25,"[3, 4]","[20, 10]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1645] -40,26,"[4, 3]","[10, 30]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[1688] -40,27,"[2, 1]","[0, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1697] -40,28,"[3, 4]","[70, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[1587] -40,29,"[4, 3]","[0, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1945] -40,30,"[1, 2]","[40, 10]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[1886] -40,31,"[4, 3]","[30, 60]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[1612] -40,32,"[1, 2]","[70, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[1695] -40,33,"[2, 1]","[30, 60]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[1704] -40,34,"[3, 4]","[60, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[1754] -40,35,"[2, 1]","[70, 50]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1829] -40,36,"[2, 1]","[90, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1671] -40,37,"[1, 2]","[90, 70]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[1680] -40,38,"[2, 1]","[50, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[1721] -40,39,"[4, 3]","[70, 10]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[1796] -40,40,"[4, 3]","[80, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[1679] -40,41,"[2, 1]","[60, 30]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[26089] -40,42,"[1, 2]","[20, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[1662] -40,43,"[3, 4]","[20, 70]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[2287] -40,44,"[4, 3]","[20, 0]","[2, 1]",[2],[5],[3],[0],[0],[1],[3],[1655] -40,45,"[2, 1]","[0, 0]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[1753] -40,46,"[1, 2]","[10, 0]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[1686] -40,47,"[2, 1]","[40, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[1761] -40,48,"[4, 3]","[50, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[1879] -40,49,"[4, 3]","[70, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[1687] -40,50,"[2, 1]","[70, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1704] -40,51,"[1, 2]","[80, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[1720] -40,52,"[1, 2]","[50, 80]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1712] -40,53,"[1, 2]","[40, 70]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[1721] -40,54,"[4, 3]","[90, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1728] -40,55,"[2, 1]","[60, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[1779] -40,56,"[1, 2]","[30, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[1879] -40,57,"[2, 1]","[40, 50]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[1947] -40,58,"[4, 3]","[60, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1746] -40,59,"[4, 3]","[60, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1679] -40,60,"[1, 2]","[20, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[1721] -40,61,"[1, 2]","[10, 70]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1789] -40,62,"[4, 3]","[80, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1679] -40,63,"[2, 1]","[90, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[1694] -40,64,"[2, 1]","[50, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[1721] -40,65,"[1, 2]","[0, 30]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[1720] -40,66,"[2, 1]","[50, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[1686] -40,67,"[3, 4]","[20, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[1994] -40,68,"[2, 1]","[50, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[1728] -40,69,"[1, 2]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[1820] -40,70,"[4, 3]","[10, 30]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[1812] -40,71,"[1, 2]","[40, 0]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2745] -40,72,"[3, 4]","[40, 0]","[2, 2]",[2],[5],[4],[0],[0],[2],[3],[1764] -40,73,"[2, 1]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[1863] -40,74,"[1, 2]","[0, 0]","[1, 2]",[2],[5],[2],[0],[0],[2],[1],[1821] -40,75,"[4, 3]","[20, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[1929] -40,76,"[1, 2]","[30, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[1779] -40,77,"[3, 4]","[40, 0]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[2086] -40,78,"[4, 3]","[40, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1713] -40,79,"[2, 1]","[20, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[1879] -40,80,"[1, 2]","[0, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1729] -41,1,"[2, 1]","[90, 10]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[4031] -41,2,"[4, 3]","[70, 40]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2373] -41,3,"[2, 1]","[70, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[3406] -41,4,"[3, 4]","[70, 80]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[3506] -41,5,"[4, 3]","[50, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2322] -41,6,"[3, 4]","[60, 80]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[2423] -41,7,"[4, 3]","[50, 60]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2655] -41,8,"[4, 3]","[60, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[3056] -41,9,"[3, 4]","[80, 20]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[2373] -41,10,"[4, 3]","[30, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2439] -41,11,"[3, 4]","[60, 20]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[2006] -41,12,"[4, 3]","[20, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[3122] -41,13,"[2, 1]","[40, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2539] -41,14,"[1, 2]","[30, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[1956] -41,15,"[4, 3]","[60, 40]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2523] -41,16,"[3, 4]","[60, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[1872] -41,17,"[1, 2]","[20, 50]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[2872] -41,18,"[1, 2]","[20, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[1839] -41,19,"[1, 2]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[1839] -41,20,"[1, 2]","[10, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[1772] -41,21,"[3, 4]","[30, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2189] -41,22,"[4, 3]","[20, 70]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[2391] -41,23,"[1, 2]","[70, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[2522] -41,24,"[2, 1]","[20, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2423] -41,25,"[4, 3]","[0, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[2355] -41,26,"[3, 4]","[30, 0]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[3922] -41,27,"[4, 3]","[30, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2305] -41,28,"[2, 1]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[2589] -41,29,"[1, 2]","[50, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[2822] -41,30,"[3, 4]","[30, 0]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[2606] -41,31,"[2, 1]","[20, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[3806] -41,32,"[1, 2]","[10, 20]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[1856] -41,33,"[1, 2]","[20, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[1839] -41,34,"[4, 3]","[80, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[3656] -41,35,"[4, 3]","[80, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[2039] -41,36,"[3, 4]","[10, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[3692] -41,37,"[3, 4]","[30, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[1805] -41,38,"[3, 4]","[10, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[1839] -41,39,"[2, 1]","[50, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[4455] -41,40,"[2, 1]","[50, 40]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[1822] -41,41,"[1, 2]","[30, 70]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[37668] -41,42,"[4, 3]","[80, 30]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[3189] -41,43,"[3, 4]","[30, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[4172] -41,44,"[2, 1]","[80, 10]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2289] -41,45,"[3, 4]","[20, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[1839] -41,46,"[1, 2]","[0, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[1989] -41,47,"[1, 2]","[80, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2056] -41,48,"[1, 2]","[70, 40]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[3739] -41,49,"[4, 3]","[40, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1822] -41,50,"[1, 2]","[50, 40]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[2488] -41,51,"[1, 2]","[30, 40]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[1872] -41,52,"[4, 3]","[30, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[2690] -41,53,"[1, 2]","[10, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2139] -41,54,"[2, 1]","[40, 20]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2339] -41,55,"[4, 3]","[30, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1822] -41,56,"[4, 3]","[20, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[2055] -41,57,"[2, 1]","[10, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[1805] -41,58,"[2, 1]","[10, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[1940] -41,59,"[2, 1]","[30, 10]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2038] -41,60,"[3, 4]","[10, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[2039] -41,61,"[1, 2]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[2056] -41,62,"[3, 4]","[30, 20]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[2122] -41,63,"[1, 2]","[60, 0]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1788] -41,64,"[4, 3]","[20, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2139] -41,65,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1956] -41,66,"[3, 4]","[90, 20]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[2072] -41,67,"[3, 4]","[80, 40]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1872] -41,68,"[3, 4]","[80, 40]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[1839] -41,69,"[4, 3]","[10, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[1989] -41,70,"[1, 2]","[80, 0]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2240] -41,71,"[2, 1]","[10, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[1855] -41,72,"[1, 2]","[20, 0]","[1, 2]",[2],[1],[2],[0],[0],[2],[1],[2122] -41,73,"[4, 3]","[0, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1975] -41,74,"[1, 2]","[30, 40]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[2491] -41,75,"[2, 1]","[40, 0]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[1856] -41,76,"[3, 4]","[0, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[2239] -41,77,"[2, 1]","[0, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[2339] -41,78,"[2, 1]","[40, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[2356] -41,79,"[2, 1]","[80, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[2272] -41,80,"[3, 4]","[0, 80]","[2, 1]",[2],[5],[3],[0],[0],[1],[4],[2072] -29,1,"[2, 1]","[40, 70]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[16022] -29,2,"[3, 4]","[80, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2190] -29,3,"[3, 4]","[80, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2003] -29,4,"[2, 1]","[30, 90]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[2474] -29,5,"[1, 2]","[70, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2120] -29,6,"[4, 3]","[10, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1807] -29,7,"[4, 3]","[0, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1775] -29,8,"[4, 3]","[20, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[1740] -29,9,"[4, 3]","[50, 0]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[1790] -29,10,"[3, 4]","[0, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[1924] -29,11,"[4, 3]","[80, 0]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[1757] -29,12,"[4, 3]","[70, 30]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[1824] -29,13,"[2, 1]","[90, 10]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[2174] -29,14,"[4, 3]","[90, 60]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[1841] -29,15,"[2, 1]","[80, 40]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[2074] -29,16,"[1, 2]","[20, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2190] -29,17,"[1, 2]","[30, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[1924] -29,18,"[1, 2]","[40, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[1957] -29,19,"[1, 2]","[50, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[1924] -29,20,"[3, 4]","[60, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[1907] -29,21,"[2, 1]","[50, 80]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2173] -29,22,"[1, 2]","[60, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2023] -29,23,"[2, 1]","[50, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[1940] -29,24,"[2, 1]","[10, 0]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[2473] -29,25,"[1, 2]","[0, 50]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[3057] -29,26,"[2, 1]","[60, 20]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2008] -29,27,"[2, 1]","[40, 0]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[2290] -29,28,"[2, 1]","[70, 20]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1840] -29,29,"[1, 2]","[40, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1941] -29,30,"[1, 2]","[20, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1990] -29,31,"[4, 3]","[70, 40]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2057] -29,32,"[3, 4]","[50, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1924] -29,33,"[3, 4]","[60, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1941] -29,34,"[2, 1]","[60, 80]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[1990] -29,35,"[3, 4]","[90, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[1840] -29,36,"[1, 2]","[80, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2024] -29,37,"[2, 1]","[40, 70]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[1940] -29,38,"[2, 1]","[0, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1990] -29,39,"[4, 3]","[10, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1807] -29,40,"[2, 1]","[0, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[2173] -29,41,"[3, 4]","[80, 10]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2686] -29,42,"[4, 3]","[10, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1790] -29,43,"[2, 1]","[20, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[1956] -29,44,"[1, 2]","[60, 20]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1956] -29,45,"[4, 3]","[0, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[3640] -29,46,"[4, 3]","[40, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1836] -29,47,"[1, 2]","[30, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[2108] -29,48,"[1, 2]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[1840] -29,49,"[3, 4]","[10, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[1840] -29,50,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1939] -29,51,"[2, 1]","[10, 0]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[1874] -29,52,"[1, 2]","[30, 50]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[3139] -29,53,"[2, 1]","[40, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1891] -29,54,"[2, 1]","[40, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[1874] -29,55,"[3, 4]","[80, 20]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2058] -29,56,"[2, 1]","[0, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1874] -29,57,"[4, 3]","[10, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1873] -29,58,"[3, 4]","[70, 20]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1824] -29,59,"[1, 2]","[80, 10]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[6242] -29,60,"[2, 1]","[0, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1857] -29,61,"[3, 4]","[70, 40]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[2274] -29,62,"[3, 4]","[60, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2141] -29,63,"[3, 4]","[90, 70]","[2, 1]",[2],[5],[3],[450],[9],[1],[3],[1940] -29,64,"[3, 4]","[60, 70]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1890] -29,65,"[3, 4]","[80, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1870] -29,66,"[1, 2]","[70, 80]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2057] -29,67,"[1, 2]","[90, 70]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[2007] -29,68,"[2, 1]","[50, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2140] -29,69,"[3, 4]","[90, 60]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[2074] -29,70,"[2, 1]","[30, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1991] -29,71,"[3, 4]","[80, 20]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2041] -29,72,"[4, 3]","[50, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1804] -29,73,"[1, 2]","[90, 30]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[1990] -29,74,"[2, 1]","[20, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2124] -29,75,"[1, 2]","[40, 30]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2107] -29,76,"[2, 1]","[30, 10]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[1974] -29,77,"[4, 3]","[70, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[1793] -29,78,"[1, 2]","[40, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2140] -29,79,"[4, 3]","[90, 20]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[2023] -29,80,"[1, 2]","[10, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2208] -42,1,"[4, 3]","[40, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[4694] -42,2,"[3, 4]","[80, 40]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[3631] -42,3,"[2, 1]","[50, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2302] -42,4,"[3, 4]","[80, 40]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[3400] -42,5,"[1, 2]","[70, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[2457] -42,6,"[3, 4]","[20, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[3896] -42,7,"[2, 1]","[20, 10]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[2545] -42,8,"[4, 3]","[10, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[2365] -42,9,"[2, 1]","[0, 30]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[2067] -42,10,"[4, 3]","[40, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[3226] -42,11,"[1, 2]","[10, 30]","[1, 1]",[1],[5],[1],[50],[1],[1],[2],[2128] -42,12,"[1, 2]","[20, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[1884] -42,13,"[3, 4]","[30, 10]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[2246] -42,14,"[4, 3]","[10, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2019] -42,15,"[2, 1]","[30, 60]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[2052] -42,16,"[1, 2]","[90, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[3443] -42,17,"[2, 1]","[50, 70]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[2083] -42,18,"[1, 2]","[80, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2102] -42,19,"[1, 2]","[60, 40]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2507] -42,20,"[1, 2]","[20, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[1959] -42,21,"[2, 1]","[40, 50]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[1988] -42,22,"[3, 4]","[50, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[1884] -42,23,"[1, 2]","[40, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[2126] -42,24,"[2, 1]","[30, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2108] -42,25,"[1, 2]","[50, 10]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2648] -42,26,"[4, 3]","[10, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1918] -42,27,"[1, 2]","[50, 30]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[2850] -42,28,"[2, 1]","[60, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2319] -42,29,"[4, 3]","[50, 70]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2464] -42,30,"[3, 4]","[80, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2167] -42,31,"[4, 3]","[60, 70]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1863] -42,32,"[2, 1]","[80, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2079] -42,33,"[4, 3]","[50, 80]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2132] -42,34,"[4, 3]","[60, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[1953] -42,35,"[2, 1]","[60, 70]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2101] -42,36,"[1, 2]","[90, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[5107] -42,37,"[4, 3]","[70, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[3049] -42,38,"[4, 3]","[70, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2107] -42,39,"[1, 2]","[50, 60]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[2029] -42,40,"[1, 2]","[60, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2002] -42,41,"[1, 2]","[50, 70]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[12033] -42,42,"[4, 3]","[80, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[2030] -42,43,"[1, 2]","[50, 80]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2204] -42,44,"[1, 2]","[20, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1859] -42,45,"[2, 1]","[70, 10]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2456] -42,46,"[3, 4]","[30, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[2062] -42,47,"[3, 4]","[20, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[2273] -42,48,"[2, 1]","[40, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1897] -42,49,"[4, 3]","[60, 70]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[1988] -42,50,"[3, 4]","[90, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2095] -42,51,"[1, 2]","[80, 50]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1888] -42,52,"[3, 4]","[60, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[1955] -42,53,"[1, 2]","[80, 50]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[3130] -42,54,"[3, 4]","[90, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2137] -42,55,"[4, 3]","[70, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[5186] -42,56,"[3, 4]","[60, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2010] -42,57,"[2, 1]","[60, 40]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2052] -42,58,"[2, 1]","[80, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[2003] -42,59,"[3, 4]","[20, 80]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[2047] -42,60,"[4, 3]","[80, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[2029] -42,61,"[2, 1]","[80, 0]","[1, 1]",[2],[1],[1],[0],[0],[1],[1],[2760] -42,62,"[2, 1]","[90, 30]","[1, 2]",[2],[5],[2],[450],[9],[2],[1],[2110] -42,63,"[1, 2]","[30, 90]","[1, 2]",[2],[1],[2],[90],[9],[2],[2],[2026] -42,64,"[4, 3]","[90, 10]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[1865] -42,65,"[1, 2]","[20, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[1907] -42,66,"[2, 1]","[50, 10]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[2483] -42,67,"[3, 4]","[0, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[2284] -42,68,"[1, 2]","[20, 60]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[2056] -42,69,"[2, 1]","[40, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[2147] -42,70,"[4, 3]","[60, 30]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2995] -42,71,"[1, 2]","[20, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2626] -42,72,"[2, 1]","[30, 0]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2161] -42,73,"[4, 3]","[20, 0]","[2, 1]",[2],[1],[3],[0],[0],[1],[3],[2080] -42,74,"[4, 3]","[30, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2194] -42,75,"[3, 4]","[30, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2002] -42,76,"[2, 1]","[20, 30]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[2329] -42,77,"[3, 4]","[60, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2013] -42,78,"[3, 4]","[70, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1984] -42,79,"[1, 2]","[60, 90]","[1, 2]",[2],[1],[2],[90],[9],[2],[2],[1935] -42,80,"[2, 1]","[60, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1994] -29,1,"[1, 2]","[30, 80]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[10140] -29,2,"[1, 2]","[20, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[6023] -29,3,"[3, 4]","[10, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2623] -29,4,"[4, 3]","[90, 10]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[1989] -29,5,"[2, 1]","[70, 0]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2156] -29,6,"[1, 2]","[20, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2107] -29,7,"[1, 2]","[10, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[2357] -29,8,"[2, 1]","[60, 10]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[1974] -29,9,"[2, 1]","[80, 10]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2073] -29,10,"[2, 1]","[70, 10]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1989] -29,11,"[1, 2]","[30, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2206] -29,12,"[4, 3]","[40, 20]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2553] -29,13,"[4, 3]","[50, 30]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[2090] -29,14,"[4, 3]","[10, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[2520] -29,15,"[4, 3]","[0, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1789] -29,16,"[2, 1]","[20, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[2056] -29,17,"[3, 4]","[20, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[2056] -29,18,"[4, 3]","[40, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1805] -29,19,"[1, 2]","[50, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[1972] -29,20,"[3, 4]","[50, 10]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[1956] -29,21,"[2, 1]","[30, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1939] -29,22,"[3, 4]","[30, 20]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1906] -29,23,"[1, 2]","[30, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2390] -29,24,"[4, 3]","[0, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[1789] -29,25,"[1, 2]","[20, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[1906] -29,26,"[1, 2]","[60, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[2123] -29,27,"[2, 1]","[40, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2672] -29,28,"[2, 1]","[50, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[1906] -29,29,"[3, 4]","[30, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1839] -29,30,"[2, 1]","[70, 40]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2440] -29,31,"[1, 2]","[40, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[1856] -29,32,"[4, 3]","[90, 60]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2089] -29,33,"[1, 2]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1906] -29,34,"[4, 3]","[80, 60]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[3006] -29,35,"[2, 1]","[90, 80]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[2173] -29,36,"[4, 3]","[90, 70]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2206] -29,37,"[1, 2]","[40, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2506] -29,38,"[2, 1]","[70, 30]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2392] -29,39,"[2, 1]","[60, 20]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[3190] -29,40,"[4, 3]","[80, 10]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2056] -29,41,"[1, 2]","[20, 50]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[5102] -29,42,"[2, 1]","[70, 10]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[4206] -29,43,"[3, 4]","[20, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[5273] -29,44,"[2, 1]","[70, 0]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[3239] -29,45,"[2, 1]","[30, 10]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1906] -29,46,"[4, 3]","[40, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[2473] -29,47,"[2, 1]","[30, 10]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[1990] -29,48,"[4, 3]","[60, 30]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[1838] -29,49,"[4, 3]","[70, 10]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[2221] -29,50,"[1, 2]","[10, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2340] -29,51,"[2, 1]","[40, 30]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2556] -29,52,"[4, 3]","[20, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[2139] -29,53,"[4, 3]","[30, 40]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[1939] -29,54,"[2, 1]","[20, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2872] -29,55,"[1, 2]","[60, 10]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2157] -29,56,"[3, 4]","[40, 20]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[2040] -29,57,"[3, 4]","[50, 30]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1940] -29,58,"[3, 4]","[30, 40]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[1773] -29,59,"[1, 2]","[30, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1939] -29,60,"[4, 3]","[30, 20]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2123] -29,61,"[3, 4]","[40, 20]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1989] -29,62,"[3, 4]","[20, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1739] -29,63,"[4, 3]","[20, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[1756] -29,64,"[2, 1]","[40, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1908] -29,65,"[1, 2]","[80, 60]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2023] -29,66,"[1, 2]","[70, 0]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[1889] -29,67,"[4, 3]","[10, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1789] -29,68,"[4, 3]","[20, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1755] -29,69,"[3, 4]","[40, 10]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1806] -29,70,"[2, 1]","[10, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[2040] -29,71,"[4, 3]","[10, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1706] -29,72,"[1, 2]","[70, 30]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[2041] -29,73,"[4, 3]","[10, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[1857] -29,74,"[4, 3]","[20, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1756] -29,75,"[4, 3]","[10, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1737] -29,76,"[2, 1]","[20, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2122] -29,77,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1855] -29,78,"[4, 3]","[10, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1886] -29,79,"[1, 2]","[90, 10]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[1973] -29,80,"[2, 1]","[10, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1872] -38,1,"[1, 2]","[90, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[6932] -38,2,"[2, 1]","[30, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[9717] -38,3,"[3, 4]","[50, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[132158] -38,4,"[3, 4]","[20, 70]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[2868] -38,5,"[2, 1]","[90, 30]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[2701] -38,6,"[1, 2]","[0, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[9634] -38,7,"[4, 3]","[90, 0]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[2400] -38,8,"[1, 2]","[20, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2084] -38,9,"[4, 3]","[80, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[4035] -38,10,"[3, 4]","[60, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[3368] -38,11,"[2, 1]","[60, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2784] -38,12,"[1, 2]","[60, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[4334] -38,13,"[2, 1]","[70, 40]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2118] -38,14,"[4, 3]","[90, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[2101] -38,15,"[3, 4]","[10, 50]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[2234] -38,16,"[4, 3]","[50, 40]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2518] -38,17,"[2, 1]","[20, 0]","[1, 1]",[1],[5],[1],[0],[0],[1],[1],[2435] -38,18,"[4, 3]","[30, 0]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[1951] -38,19,"[4, 3]","[50, 10]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[1867] -38,20,"[2, 1]","[50, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[3284] -38,21,"[1, 2]","[40, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[4684] -38,22,"[3, 4]","[40, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[2384] -38,23,"[3, 4]","[60, 80]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[1950] -38,24,"[3, 4]","[30, 80]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[2251] -38,25,"[2, 1]","[80, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2801] -38,26,"[2, 1]","[70, 10]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[7318] -38,27,"[2, 1]","[90, 0]","[1, 1]",[1],[5],[1],[0],[0],[1],[1],[1901] -38,28,"[4, 3]","[80, 0]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[6034] -38,29,"[2, 1]","[80, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[1984] -38,30,"[1, 2]","[30, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[4200] -38,31,"[1, 2]","[40, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[41948865] -38,32,"[4, 3]","[60, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[3351] -38,33,"[2, 1]","[60, 70]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2201] -38,34,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[4168] -38,35,"[4, 3]","[10, 60]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[2618] -38,36,"[4, 3]","[20, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2535] -38,37,"[3, 4]","[50, 80]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2884] -38,38,"[1, 2]","[70, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[2835] -38,39,"[3, 4]","[70, 40]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[4251] -38,40,"[3, 4]","[70, 40]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[6335] -38,41,"[4, 3]","[20, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[23915] -38,42,"[4, 3]","[20, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[4768] -38,43,"[4, 3]","[30, 80]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2901] -38,44,"[4, 3]","[40, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[3168] -38,45,"[1, 2]","[60, 50]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2117] -38,46,"[1, 2]","[50, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[2217] -38,47,"[1, 2]","[40, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[2085] -38,48,"[2, 1]","[20, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[6301] -38,49,"[1, 2]","[20, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[4817] -38,50,"[3, 4]","[10, 40]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[4434] -38,51,"[1, 2]","[30, 40]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[3583] -38,52,"[1, 2]","[30, 70]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[2350] -38,53,"[2, 1]","[90, 70]","[1, 2]",[2],[5],[2],[450],[9],[2],[1],[2200] -38,54,"[3, 4]","[60, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2184] -38,55,"[4, 3]","[60, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[6084] -38,56,"[1, 2]","[60, 50]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[2017] -38,57,"[1, 2]","[80, 50]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2517] -38,58,"[3, 4]","[80, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[2118] -38,59,"[3, 4]","[80, 50]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2184] -38,60,"[3, 4]","[80, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[4134] -38,61,"[4, 3]","[50, 80]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[2001] -38,62,"[3, 4]","[70, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1984] -38,63,"[4, 3]","[40, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[2251] -38,64,"[2, 1]","[30, 20]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2051] -38,65,"[4, 3]","[40, 10]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2484] -38,66,"[2, 1]","[60, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2034] -38,67,"[4, 3]","[70, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2251] -38,68,"[4, 3]","[80, 70]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2451] -38,69,"[4, 3]","[80, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[13450] -38,70,"[1, 2]","[40, 90]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[1967] -38,71,"[2, 1]","[70, 20]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2385] -38,72,"[1, 2]","[10, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1834] -38,73,"[2, 1]","[60, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[2885] -38,74,"[1, 2]","[10, 70]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1884] -38,75,"[2, 1]","[90, 10]","[1, 2]",[2],[1],[2],[90],[9],[2],[1],[1833] -38,76,"[2, 1]","[80, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[3267] -38,77,"[3, 4]","[60, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2051] -38,78,"[3, 4]","[40, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2217] -38,79,"[4, 3]","[80, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[2884] -38,80,"[4, 3]","[80, 30]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[2384] -43,1,"[4, 3]","[10, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[6371] -43,2,"[2, 1]","[0, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[1824] -43,3,"[3, 4]","[50, 20]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[2237] -43,4,"[2, 1]","[30, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[2220] -43,5,"[2, 1]","[60, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[2981] -43,6,"[2, 1]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[1757] -43,7,"[2, 1]","[40, 0]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[1757] -43,8,"[3, 4]","[0, 50]","[2, 1]",[1],[5],[3],[0],[0],[1],[4],[2748] -43,9,"[3, 4]","[20, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2467] -43,10,"[2, 1]","[40, 20]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2813] -43,11,"[4, 3]","[40, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1840] -43,12,"[1, 2]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2037] -43,13,"[1, 2]","[70, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[1823] -43,14,"[3, 4]","[80, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[1938] -43,15,"[3, 4]","[90, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1741] -43,16,"[4, 3]","[90, 90]","[2, 2]",[1],[5],[4],[450],[9],[2],[4],[1938] -43,17,"[2, 1]","[70, 90]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2005] -43,18,"[4, 3]","[90, 80]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[1759] -43,19,"[4, 3]","[70, 80]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1775] -43,20,"[4, 3]","[70, 80]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1790] -43,21,"[3, 4]","[40, 90]","[2, 2]",[1],[5],[4],[450],[9],[2],[4],[1791] -43,22,"[2, 1]","[90, 40]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[1972] -43,23,"[2, 1]","[80, 60]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[1841] -43,24,"[3, 4]","[70, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[1790] -43,25,"[2, 1]","[80, 60]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[1841] -43,26,"[2, 1]","[50, 60]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[1878] -43,27,"[1, 2]","[50, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2053] -43,28,"[3, 4]","[60, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1889] -43,29,"[4, 3]","[30, 50]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[1790] -43,30,"[4, 3]","[30, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[1839] -43,31,"[3, 4]","[30, 0]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[1906] -43,32,"[3, 4]","[30, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[1756] -43,33,"[3, 4]","[40, 10]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[1757] -43,34,"[1, 2]","[10, 0]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[1872] -43,35,"[2, 1]","[10, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[2],[1840] -43,36,"[3, 4]","[0, 40]","[2, 1]",[1],[1],[3],[0],[0],[1],[4],[2169] -43,37,"[3, 4]","[0, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[1743] -43,38,"[4, 3]","[20, 0]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1839] -43,39,"[4, 3]","[30, 10]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[1773] -43,40,"[3, 4]","[20, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1757] -43,41,"[1, 2]","[10, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[6208] -43,42,"[1, 2]","[10, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[1756] -43,43,"[4, 3]","[40, 20]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2120] -43,44,"[1, 2]","[0, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[1774] -43,45,"[3, 4]","[20, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[1741] -43,46,"[4, 3]","[60, 40]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[1822] -43,47,"[1, 2]","[80, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[1970] -43,48,"[1, 2]","[80, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[1725] -43,49,"[3, 4]","[50, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1755] -43,50,"[4, 3]","[0, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1872] -43,51,"[3, 4]","[50, 10]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1856] -43,52,"[4, 3]","[0, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[1724] -43,53,"[2, 1]","[30, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[1971] -43,54,"[4, 3]","[10, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[1724] -43,55,"[1, 2]","[0, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[2],[2038] -43,56,"[4, 3]","[10, 20]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1808] -43,57,"[4, 3]","[10, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[1725] -43,58,"[3, 4]","[50, 20]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1790] -43,59,"[2, 1]","[20, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1955] -43,60,"[4, 3]","[10, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[1741] -43,61,"[4, 3]","[20, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1674] -43,62,"[1, 2]","[50, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[1955] -43,63,"[1, 2]","[80, 20]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2070] -43,64,"[3, 4]","[70, 20]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1790] -43,65,"[3, 4]","[60, 30]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1743] -43,66,"[4, 3]","[50, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1657] -43,67,"[4, 3]","[20, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1708] -43,68,"[3, 4]","[80, 30]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[1823] -43,69,"[1, 2]","[80, 60]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1922] -43,70,"[3, 4]","[90, 30]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[1889] -43,71,"[3, 4]","[80, 40]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1757] -43,72,"[1, 2]","[60, 40]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[1790] -43,73,"[1, 2]","[70, 10]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[1772] -43,74,"[4, 3]","[20, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[2021] -43,75,"[1, 2]","[60, 0]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1789] -43,76,"[1, 2]","[30, 0]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[1741] -43,77,"[3, 4]","[10, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1790] -43,78,"[3, 4]","[50, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1707] -43,79,"[3, 4]","[60, 10]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1774] -43,80,"[1, 2]","[90, 0]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[3457] -44,1,"[4, 3]","[70, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[10387] -44,2,"[3, 4]","[20, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[4115] -44,3,"[1, 2]","[20, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2522] -44,4,"[1, 2]","[10, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1916] -44,5,"[4, 3]","[90, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1639] -44,6,"[2, 1]","[90, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[1782] -44,7,"[3, 4]","[20, 90]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[2024] -44,8,"[1, 2]","[20, 60]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[2031] -44,9,"[3, 4]","[20, 40]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[23988] -44,10,"[1, 2]","[30, 70]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[1916] -44,11,"[3, 4]","[40, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[1909] -44,12,"[3, 4]","[60, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[1941] -44,13,"[3, 4]","[30, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[1851] -44,14,"[2, 1]","[40, 50]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[1849] -44,15,"[1, 2]","[60, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[1849] -44,16,"[4, 3]","[90, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2068] -44,17,"[3, 4]","[40, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[1926] -44,18,"[3, 4]","[40, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[1898] -44,19,"[2, 1]","[10, 40]","[1, 2]",[1],[1],[2],[10],[1],[2],[2],[2158] -44,20,"[3, 4]","[80, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[1658] -44,21,"[2, 1]","[20, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1298] -44,22,"[3, 4]","[30, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[1799] -44,23,"[3, 4]","[20, 10]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1716] -44,24,"[1, 2]","[20, 10]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[1916] -44,25,"[2, 1]","[10, 30]","[1, 2]",[1],[1],[2],[10],[1],[2],[2],[2648] -44,26,"[1, 2]","[30, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[1847] -44,27,"[4, 3]","[20, 30]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[1863] -44,28,"[4, 3]","[50, 30]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[3391] -44,29,"[2, 1]","[50, 60]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[3209] -44,30,"[4, 3]","[70, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1826] -44,31,"[3, 4]","[60, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2357] -44,32,"[4, 3]","[80, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[2091] -44,33,"[4, 3]","[70, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[2299] -44,34,"[4, 3]","[70, 10]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[1850] -44,35,"[2, 1]","[60, 20]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[3882] -44,36,"[3, 4]","[10, 20]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[1975] -44,37,"[4, 3]","[50, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[2224] -44,38,"[1, 2]","[40, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[1659] -44,39,"[1, 2]","[30, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2841] -44,40,"[1, 2]","[50, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[2234] -44,41,"[2, 1]","[60, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[4197] -44,42,"[3, 4]","[50, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2441] -44,43,"[1, 2]","[50, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[14196] -44,44,"[4, 3]","[10, 90]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[3117] -44,45,"[4, 3]","[30, 60]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[3942] -44,46,"[4, 3]","[30, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[2133] -44,47,"[3, 4]","[60, 20]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[1850] -44,48,"[2, 1]","[10, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2554] -44,49,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1689] -44,50,"[4, 3]","[10, 50]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[1808] -44,51,"[4, 3]","[20, 50]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[2010] -44,52,"[4, 3]","[40, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1842] -44,53,"[1, 2]","[60, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[2100] -44,54,"[4, 3]","[30, 80]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2024] -44,55,"[4, 3]","[30, 90]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[3483] -44,56,"[1, 2]","[70, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[1908] -44,57,"[3, 4]","[40, 20]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1808] -44,58,"[1, 2]","[20, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[1649] -44,59,"[3, 4]","[20, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1809] -44,60,"[1, 2]","[20, 30]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[1755] -44,61,"[4, 3]","[0, 30]","[2, 2]",[2],[1],[4],[0],[0],[2],[4],[1912] -44,62,"[4, 3]","[30, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[1659] -44,63,"[4, 3]","[20, 0]","[2, 1]",[2],[1],[3],[0],[0],[1],[3],[2400] -44,64,"[4, 3]","[70, 10]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[1958] -44,65,"[3, 4]","[20, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[1882] -44,66,"[4, 3]","[80, 10]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1858] -44,67,"[3, 4]","[20, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[1949] -44,68,"[4, 3]","[70, 10]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[1815] -44,69,"[2, 1]","[50, 10]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[4317] -44,70,"[2, 1]","[30, 20]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1758] -44,71,"[1, 2]","[20, 30]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[1704] -44,72,"[1, 2]","[40, 20]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[1798] -44,73,"[2, 1]","[20, 0]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[1740] -44,74,"[1, 2]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[1799] -44,75,"[3, 4]","[10, 40]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[2242] -44,76,"[4, 3]","[10, 0]","[2, 1]",[2],[1],[3],[0],[0],[1],[3],[2099] -44,77,"[2, 1]","[10, 30]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[1882] -44,78,"[1, 2]","[10, 0]","[1, 2]",[2],[5],[2],[0],[0],[2],[1],[2607] -44,79,"[1, 2]","[20, 10]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[1859] -44,80,"[3, 4]","[40, 30]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2350] -45,1,"[3, 4]","[0, 60]","[2, 1]",[1],[1],[3],[0],[0],[1],[4],[3682] -45,2,"[1, 2]","[20, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[6303] -45,3,"[3, 4]","[30, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[1970] -45,4,"[3, 4]","[40, 50]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[2295] -45,5,"[4, 3]","[60, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[3995] -45,6,"[4, 3]","[50, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[6137] -45,7,"[4, 3]","[20, 80]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[4870] -45,8,"[4, 3]","[20, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[2203] -45,9,"[3, 4]","[70, 20]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[2096] -45,10,"[3, 4]","[20, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2287] -45,11,"[3, 4]","[10, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[1954] -45,12,"[2, 1]","[70, 40]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1662] -45,13,"[1, 2]","[60, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[1904] -45,14,"[2, 1]","[80, 70]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[2054] -45,15,"[3, 4]","[60, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[2179] -45,16,"[2, 1]","[80, 60]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[3229] -45,17,"[2, 1]","[80, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2129] -45,18,"[1, 2]","[50, 70]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2279] -45,19,"[3, 4]","[40, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[1762] -45,20,"[4, 3]","[40, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1737] -45,21,"[4, 3]","[70, 20]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[1937] -45,22,"[3, 4]","[20, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[1703] -45,23,"[2, 1]","[30, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[2569] -45,24,"[4, 3]","[50, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2502] -45,25,"[3, 4]","[20, 90]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[6261] -45,26,"[3, 4]","[30, 70]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[19726] -45,27,"[4, 3]","[70, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[9245] -45,28,"[4, 3]","[80, 40]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[1995] -45,29,"[3, 4]","[40, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[2329] -45,30,"[3, 4]","[20, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[2094] -45,31,"[2, 1]","[80, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[1811] -45,32,"[4, 3]","[60, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2620] -45,33,"[3, 4]","[60, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[6861] -45,34,"[2, 1]","[40, 60]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[3145] -45,35,"[1, 2]","[50, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[33501] -45,36,"[1, 2]","[40, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[3953] -45,37,"[1, 2]","[30, 20]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2353] -45,38,"[2, 1]","[50, 10]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[6261] -45,39,"[1, 2]","[20, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[4020] -45,40,"[4, 3]","[10, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[1919] -45,41,"[1, 2]","[30, 50]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[5389] -45,42,"[3, 4]","[30, 40]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[2229] -45,43,"[4, 3]","[40, 50]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2154] -45,44,"[3, 4]","[50, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[1878] -45,45,"[4, 3]","[80, 10]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[1961] -45,46,"[1, 2]","[40, 90]","[1, 2]",[2],[1],[2],[90],[9],[2],[2],[5837] -45,47,"[4, 3]","[90, 70]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[2045] -45,48,"[2, 1]","[80, 90]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[2562] -45,49,"[1, 2]","[90, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[3678] -45,50,"[2, 1]","[70, 80]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[210648] -45,51,"[4, 3]","[70, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[2095] -45,52,"[3, 4]","[80, 40]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2354] -45,53,"[3, 4]","[50, 20]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2288] -45,54,"[2, 1]","[20, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[32162] -45,55,"[2, 1]","[0, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2854] -45,56,"[1, 2]","[60, 20]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2037] -45,57,"[1, 2]","[50, 20]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[2021] -45,58,"[3, 4]","[60, 30]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[3004] -45,59,"[3, 4]","[50, 30]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1929] -45,60,"[4, 3]","[30, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[12428] -45,61,"[1, 2]","[10, 0]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[2004] -45,62,"[3, 4]","[0, 40]","[2, 1]",[2],[1],[3],[0],[0],[1],[4],[3128] -45,63,"[1, 2]","[30, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[2252] -45,64,"[1, 2]","[10, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[7262] -45,65,"[1, 2]","[0, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[3879] -45,66,"[2, 1]","[20, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[2445] -45,67,"[1, 2]","[40, 30]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[2337] -45,68,"[2, 1]","[40, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2269] -45,69,"[2, 1]","[50, 30]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[172619] -45,70,"[1, 2]","[40, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[1712] -45,71,"[4, 3]","[40, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[4],[4770] -45,72,"[2, 1]","[40, 0]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[3936] -45,73,"[2, 1]","[50, 0]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[2537] -45,74,"[3, 4]","[40, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[2353] -45,75,"[3, 4]","[40, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[1845] -45,76,"[2, 1]","[0, 60]","[1, 2]",[2],[5],[2],[0],[0],[2],[2],[2237] -45,77,"[3, 4]","[40, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1845] -45,78,"[3, 4]","[50, 30]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2095] -45,79,"[3, 4]","[70, 30]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1913] -45,80,"[4, 3]","[70, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[4895] -29,1,"[3, 4]","[70, 10]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[11504] -29,2,"[1, 2]","[60, 0]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2157] -29,3,"[2, 1]","[10, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2041] -29,4,"[4, 3]","[10, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[2173] -29,5,"[3, 4]","[70, 20]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1938] -29,6,"[3, 4]","[80, 40]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1856] -29,7,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2241] -29,8,"[1, 2]","[70, 90]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2058] -29,9,"[1, 2]","[50, 80]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1842] -29,10,"[1, 2]","[30, 90]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[1855] -29,11,"[3, 4]","[30, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[1791] -29,12,"[3, 4]","[20, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1958] -29,13,"[3, 4]","[10, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1825] -29,14,"[4, 3]","[30, 10]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[1992] -29,15,"[3, 4]","[20, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[1774] -29,16,"[3, 4]","[20, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1755] -29,17,"[1, 2]","[20, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[2041] -29,18,"[2, 1]","[20, 30]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[1975] -29,19,"[1, 2]","[40, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[1874] -29,20,"[3, 4]","[50, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[1841] -29,21,"[1, 2]","[30, 50]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[1858] -29,22,"[3, 4]","[30, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1703] -29,23,"[3, 4]","[60, 0]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1790] -29,24,"[3, 4]","[60, 30]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1875] -29,25,"[1, 2]","[70, 20]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1856] -29,26,"[2, 1]","[50, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2091] -29,27,"[4, 3]","[80, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1691] -29,28,"[3, 4]","[80, 70]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[1856] -29,29,"[2, 1]","[80, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1873] -29,30,"[1, 2]","[60, 80]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1938] -29,31,"[1, 2]","[70, 60]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1842] -29,32,"[4, 3]","[60, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1791] -29,33,"[3, 4]","[90, 60]","[2, 1]",[1],[1],[3],[90],[9],[1],[3],[1824] -29,34,"[4, 3]","[30, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[1724] -29,35,"[4, 3]","[40, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1741] -29,36,"[4, 3]","[20, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1773] -29,37,"[4, 3]","[10, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1823] -29,38,"[1, 2]","[70, 20]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1974] -29,39,"[1, 2]","[40, 20]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[1925] -29,40,"[4, 3]","[20, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[1924] -29,41,"[3, 4]","[30, 40]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[4202] -29,42,"[2, 1]","[30, 60]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2658] -29,43,"[3, 4]","[70, 40]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[3307] -29,44,"[1, 2]","[70, 60]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[1803] -29,45,"[1, 2]","[70, 60]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[1875] -29,46,"[3, 4]","[70, 90]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1755] -29,47,"[3, 4]","[80, 70]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[1725] -29,48,"[2, 1]","[90, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[2125] -29,49,"[1, 2]","[50, 70]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1823] -29,50,"[4, 3]","[70, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1675] -29,51,"[3, 4]","[80, 70]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[1725] -29,52,"[3, 4]","[60, 40]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1691] -29,53,"[4, 3]","[50, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1708] -29,54,"[4, 3]","[60, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1725] -29,55,"[1, 2]","[80, 50]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1773] -29,56,"[3, 4]","[50, 60]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[1789] -29,57,"[3, 4]","[40, 70]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[1704] -29,58,"[1, 2]","[50, 90]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2475] -29,59,"[4, 3]","[70, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[1791] -29,60,"[1, 2]","[80, 50]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1842] -29,61,"[2, 1]","[30, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1925] -29,62,"[4, 3]","[40, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1857] -29,63,"[4, 3]","[30, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1942] -29,64,"[4, 3]","[20, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[1758] -29,65,"[1, 2]","[50, 30]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[1757] -29,66,"[1, 2]","[90, 0]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[1758] -29,67,"[3, 4]","[50, 30]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1791] -29,68,"[4, 3]","[0, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[1774] -29,69,"[4, 3]","[20, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1741] -29,70,"[3, 4]","[70, 10]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1874] -29,71,"[4, 3]","[20, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[1756] -29,72,"[3, 4]","[20, 20]","[2, 1]",[2],[1],[3],[20],[2],[1],[3],[1774] -29,73,"[1, 2]","[40, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[2204] -29,74,"[3, 4]","[30, 90]","[2, 2]",[2],[5],[4],[450],[9],[2],[4],[1675] -29,75,"[2, 1]","[60, 40]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[1940] -29,76,"[4, 3]","[50, 40]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[1826] -29,77,"[3, 4]","[0, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[4],[1724] -29,78,"[1, 2]","[10, 40]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[1974] -29,79,"[4, 3]","[20, 10]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[1956] -29,80,"[3, 4]","[10, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[1925] -46,1,"[2, 1]","[20, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[6608] -46,2,"[3, 4]","[70, 40]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[3065] -46,3,"[1, 2]","[90, 70]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[2918] -46,4,"[4, 3]","[40, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2146] -46,5,"[3, 4]","[70, 50]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2432] -46,6,"[4, 3]","[70, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[2051] -46,7,"[2, 1]","[60, 10]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2978] -46,8,"[2, 1]","[40, 0]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2266] -46,9,"[4, 3]","[90, 0]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[2119] -46,10,"[4, 3]","[90, 10]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2012] -46,11,"[4, 3]","[90, 20]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[1954] -46,12,"[1, 2]","[10, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[3906] -46,13,"[3, 4]","[20, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2248] -46,14,"[2, 1]","[70, 30]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2794] -46,15,"[3, 4]","[30, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2101] -46,16,"[3, 4]","[40, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[1914] -46,17,"[1, 2]","[40, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2187] -46,18,"[4, 3]","[40, 60]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[9124] -46,19,"[3, 4]","[60, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[3033] -46,20,"[1, 2]","[70, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[2133] -46,21,"[1, 2]","[70, 0]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2336] -46,22,"[2, 1]","[20, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2315] -46,23,"[1, 2]","[80, 10]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1995] -46,24,"[4, 3]","[0, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[19617] -46,25,"[1, 2]","[70, 10]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2289] -46,26,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1931] -46,27,"[2, 1]","[50, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[2513] -46,28,"[2, 1]","[50, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[15319] -46,29,"[1, 2]","[70, 60]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1915] -46,30,"[3, 4]","[50, 70]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2235] -46,31,"[4, 3]","[80, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1981] -46,32,"[4, 3]","[60, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[2044] -46,33,"[3, 4]","[10, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2130] -46,34,"[3, 4]","[30, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[2081] -46,35,"[4, 3]","[40, 20]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2015] -46,36,"[3, 4]","[10, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[1938] -46,37,"[1, 2]","[20, 40]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[1952] -46,38,"[1, 2]","[80, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[1825] -46,39,"[2, 1]","[70, 80]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2130] -46,40,"[3, 4]","[70, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2143] -46,41,"[2, 1]","[50, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[6796] -46,42,"[3, 4]","[70, 60]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[2164] -46,43,"[4, 3]","[50, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1883] -46,44,"[1, 2]","[50, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[1997] -46,45,"[4, 3]","[60, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1926] -46,46,"[1, 2]","[10, 50]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[1770] -46,47,"[4, 3]","[60, 0]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[2213] -46,48,"[2, 1]","[60, 20]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[2868] -46,49,"[1, 2]","[10, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[4920] -46,50,"[4, 3]","[80, 30]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2032] -46,51,"[4, 3]","[70, 10]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[1890] -46,52,"[1, 2]","[10, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2410] -46,53,"[3, 4]","[20, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[1984] -46,54,"[4, 3]","[90, 10]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[2078] -46,55,"[1, 2]","[20, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2035] -46,56,"[4, 3]","[70, 20]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2204] -46,57,"[1, 2]","[10, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[1941] -46,58,"[4, 3]","[50, 20]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2011] -46,59,"[2, 1]","[50, 20]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1915] -46,60,"[1, 2]","[30, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2103] -46,61,"[3, 4]","[20, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2365] -46,62,"[3, 4]","[50, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[2003] -46,63,"[1, 2]","[60, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[1794] -46,64,"[4, 3]","[70, 60]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1989] -46,65,"[2, 1]","[90, 60]","[1, 2]",[2],[1],[2],[90],[9],[2],[1],[1945] -46,66,"[1, 2]","[40, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1871] -46,67,"[3, 4]","[40, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2056] -46,68,"[3, 4]","[20, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2400] -46,69,"[3, 4]","[50, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2001] -46,70,"[4, 3]","[30, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[2236] -46,71,"[3, 4]","[70, 0]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1773] -46,72,"[3, 4]","[70, 10]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1785] -46,73,"[4, 3]","[10, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1813] -46,74,"[4, 3]","[20, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1761] -46,75,"[4, 3]","[50, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1973] -46,76,"[3, 4]","[20, 40]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[1957] -46,77,"[4, 3]","[20, 10]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2056] -46,78,"[3, 4]","[30, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[5697] -46,79,"[2, 1]","[30, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[3318] -46,80,"[3, 4]","[30, 20]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[4226] -47,1,"[1, 2]","[30, 80]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[4070] -47,2,"[1, 2]","[20, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2207] -47,3,"[3, 4]","[10, 60]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[2314] -47,4,"[4, 3]","[90, 10]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2753] -47,5,"[2, 1]","[70, 0]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2548] -47,6,"[1, 2]","[20, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2381] -47,7,"[1, 2]","[10, 40]","[1, 1]",[1],[5],[1],[50],[1],[1],[2],[2552] -47,8,"[2, 1]","[60, 10]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[4446] -47,9,"[2, 1]","[80, 10]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2214] -47,10,"[2, 1]","[70, 10]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[3126] -47,11,"[1, 2]","[30, 40]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[1830] -47,12,"[4, 3]","[40, 20]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2297] -47,13,"[4, 3]","[50, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1729] -47,14,"[4, 3]","[10, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[1957] -47,15,"[4, 3]","[0, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[2225] -47,16,"[2, 1]","[20, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[4139] -47,17,"[3, 4]","[20, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[3856] -47,18,"[4, 3]","[40, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1997] -47,19,"[1, 2]","[50, 20]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[1919] -47,20,"[3, 4]","[50, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[1747] -47,21,"[2, 1]","[30, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1823] -47,22,"[3, 4]","[30, 20]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1846] -47,23,"[1, 2]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2303] -47,24,"[4, 3]","[0, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[2487] -47,25,"[1, 2]","[20, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[2481] -47,26,"[1, 2]","[60, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[2263] -47,27,"[2, 1]","[40, 30]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[2103] -47,28,"[2, 1]","[50, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[2430] -47,29,"[3, 4]","[30, 40]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[2775] -47,30,"[2, 1]","[70, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[1723] -47,31,"[1, 2]","[40, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[1796] -47,32,"[4, 3]","[90, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1735] -47,33,"[1, 2]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2169] -47,34,"[4, 3]","[80, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1718] -47,35,"[2, 1]","[90, 80]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[2413] -47,36,"[4, 3]","[90, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2164] -47,37,"[1, 2]","[40, 70]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1980] -47,38,"[2, 1]","[70, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[1913] -47,39,"[2, 1]","[60, 20]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[1773] -47,40,"[4, 3]","[80, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[1629] -47,41,"[1, 2]","[20, 50]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[5393] -47,42,"[2, 1]","[70, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[1796] -47,43,"[3, 4]","[20, 80]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[2230] -47,44,"[2, 1]","[70, 0]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[1930] -47,45,"[2, 1]","[30, 10]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1919] -47,46,"[4, 3]","[40, 10]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1858] -47,47,"[2, 1]","[30, 10]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2503] -47,48,"[4, 3]","[60, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1730] -47,49,"[4, 3]","[70, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[2224] -47,50,"[1, 2]","[10, 50]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[1735] -47,51,"[2, 1]","[40, 30]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[1880] -47,52,"[4, 3]","[20, 40]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[1768] -47,53,"[4, 3]","[30, 40]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2047] -47,54,"[2, 1]","[20, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[1834] -47,55,"[1, 2]","[60, 10]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1925] -47,56,"[3, 4]","[40, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[1996] -47,57,"[3, 4]","[50, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[2024] -47,58,"[3, 4]","[30, 40]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[1986] -47,59,"[1, 2]","[30, 40]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[2041] -47,60,"[4, 3]","[30, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[2235] -47,61,"[3, 4]","[40, 20]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[2358] -47,62,"[3, 4]","[20, 20]","[2, 1]",[2],[1],[3],[20],[2],[1],[3],[2114] -47,63,"[4, 3]","[20, 30]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[1879] -47,64,"[2, 1]","[40, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1796] -47,65,"[1, 2]","[80, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[1985] -47,66,"[1, 2]","[70, 0]","[1, 2]",[2],[5],[2],[0],[0],[2],[1],[1719] -47,67,"[4, 3]","[10, 80]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1808] -47,68,"[4, 3]","[20, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1746] -47,69,"[3, 4]","[40, 10]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[2336] -47,70,"[2, 1]","[10, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[1635] -47,71,"[4, 3]","[10, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1711] -47,72,"[1, 2]","[70, 30]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[1746] -47,73,"[4, 3]","[10, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[1746] -47,74,"[4, 3]","[20, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2503] -47,75,"[4, 3]","[10, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[2041] -47,76,"[2, 1]","[20, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1891] -47,77,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1929] -47,78,"[4, 3]","[10, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[2007] -47,79,"[1, 2]","[90, 10]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[1696] -47,80,"[2, 1]","[10, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2119] -48,1,"[1, 2]","[60, 40]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[16247] -48,2,"[4, 3]","[30, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[2884] -48,3,"[4, 3]","[50, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[5962] -48,4,"[1, 2]","[80, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[4327] -48,5,"[3, 4]","[70, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[7793] -48,6,"[2, 1]","[50, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[6999] -48,7,"[4, 3]","[60, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[3539] -48,8,"[1, 2]","[70, 40]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[3520] -48,9,"[2, 1]","[20, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[3604] -48,10,"[4, 3]","[0, 70]","[2, 2]",[1],[1],[4],[0],[0],[2],[4],[3150] -48,11,"[4, 3]","[30, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[5267] -48,12,"[1, 2]","[60, 40]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[6871] -48,13,"[3, 4]","[60, 30]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[3361] -48,14,"[4, 3]","[30, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[2505] -48,15,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[3252] -48,16,"[3, 4]","[80, 30]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[2498] -48,17,"[2, 1]","[60, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[2542] -48,18,"[4, 3]","[50, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[2767] -48,19,"[2, 1]","[90, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[3096] -48,20,"[4, 3]","[70, 80]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[3949] -48,21,"[3, 4]","[80, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2557] -48,22,"[3, 4]","[80, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2959] -48,23,"[4, 3]","[40, 80]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2784] -48,24,"[1, 2]","[90, 50]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[2984] -48,25,"[4, 3]","[60, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2262] -48,26,"[1, 2]","[80, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[4363] -48,27,"[1, 2]","[80, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[3901] -48,28,"[2, 1]","[60, 70]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2902] -48,29,"[4, 3]","[40, 90]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[3027] -48,30,"[2, 1]","[30, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2900] -48,31,"[2, 1]","[60, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2791] -48,32,"[2, 1]","[50, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[4042] -48,33,"[2, 1]","[40, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[3847] -48,34,"[3, 4]","[90, 50]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[2657] -48,35,"[2, 1]","[60, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[3992] -48,36,"[1, 2]","[50, 90]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2599] -48,37,"[3, 4]","[50, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[2623] -48,38,"[3, 4]","[40, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[4959] -48,39,"[2, 1]","[60, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[5193] -48,40,"[2, 1]","[70, 50]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[3674] -48,41,"[3, 4]","[80, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[6112] -48,42,"[1, 2]","[80, 70]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2692] -48,43,"[2, 1]","[70, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[2717] -48,44,"[4, 3]","[80, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[4478] -48,45,"[1, 2]","[80, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[2992] -48,46,"[4, 3]","[80, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2707] -48,47,"[2, 1]","[70, 30]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[2824] -48,48,"[3, 4]","[10, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[3600] -48,49,"[1, 2]","[20, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[3261] -48,50,"[4, 3]","[20, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[4344] -48,51,"[3, 4]","[30, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[4985] -48,52,"[1, 2]","[50, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[3598] -48,53,"[1, 2]","[50, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[3397] -48,54,"[1, 2]","[60, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2262] -48,55,"[4, 3]","[80, 70]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[4169] -48,56,"[4, 3]","[70, 80]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[2523] -48,57,"[1, 2]","[80, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[3520] -48,58,"[1, 2]","[60, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[2163] -48,59,"[3, 4]","[90, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2609] -48,60,"[2, 1]","[60, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[2926] -48,61,"[1, 2]","[80, 60]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[3278] -48,62,"[4, 3]","[90, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[2489] -48,63,"[2, 1]","[80, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[2616] -48,64,"[4, 3]","[80, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[2254] -48,65,"[3, 4]","[80, 60]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2498] -48,66,"[4, 3]","[50, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2077] -48,67,"[3, 4]","[50, 70]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[2432] -48,68,"[4, 3]","[90, 60]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[2238] -48,69,"[1, 2]","[60, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[4806] -48,70,"[1, 2]","[50, 60]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2515] -48,71,"[1, 2]","[90, 20]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[2642] -48,72,"[4, 3]","[30, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[5821] -48,73,"[3, 4]","[20, 60]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[5923] -48,74,"[3, 4]","[30, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[4512] -48,75,"[2, 1]","[70, 30]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[14537] -48,76,"[4, 3]","[80, 10]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[3346] -48,77,"[3, 4]","[10, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[6410] -48,78,"[1, 2]","[20, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[3943] -48,79,"[3, 4]","[20, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[3756] -48,80,"[2, 1]","[80, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[2757] -49,1,"[4, 3]","[30, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[22044] -49,2,"[2, 1]","[0, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1958] -49,3,"[4, 3]","[20, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1807] -49,4,"[4, 3]","[0, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[1970] -49,5,"[4, 3]","[10, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1881] -49,6,"[1, 2]","[30, 20]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2320] -49,7,"[4, 3]","[10, 20]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[2072] -49,8,"[4, 3]","[30, 60]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[1983] -49,9,"[3, 4]","[60, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[1983] -49,10,"[1, 2]","[60, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[2806] -49,11,"[3, 4]","[70, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2399] -49,12,"[2, 1]","[50, 70]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2205] -49,13,"[1, 2]","[60, 0]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[3015] -49,14,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1834] -49,15,"[1, 2]","[60, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[3229] -49,16,"[4, 3]","[40, 70]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1841] -49,17,"[1, 2]","[80, 40]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2216] -49,18,"[2, 1]","[40, 70]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[5324] -49,19,"[1, 2]","[60, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2112] -49,20,"[3, 4]","[80, 40]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[1920] -49,21,"[2, 1]","[40, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2330] -49,22,"[1, 2]","[80, 30]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2212] -49,23,"[1, 2]","[80, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2139] -49,24,"[4, 3]","[50, 80]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2024] -49,25,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2105] -49,26,"[2, 1]","[60, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[2117] -49,27,"[2, 1]","[80, 40]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[2058] -49,28,"[2, 1]","[80, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2026] -49,29,"[4, 3]","[90, 50]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[1719] -49,30,"[2, 1]","[80, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[3076] -49,31,"[4, 3]","[70, 40]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[1838] -49,32,"[3, 4]","[30, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2188] -49,33,"[3, 4]","[60, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2394] -49,34,"[4, 3]","[30, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2036] -49,35,"[4, 3]","[30, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[2102] -49,36,"[3, 4]","[20, 30]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[3880] -49,37,"[3, 4]","[20, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1813] -49,38,"[4, 3]","[20, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[1779] -49,39,"[4, 3]","[60, 70]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2301] -49,40,"[1, 2]","[70, 60]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2426] -49,41,"[2, 1]","[90, 50]","[1, 2]",[2],[5],[2],[450],[9],[2],[1],[3856] -49,42,"[3, 4]","[50, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1754] -49,43,"[3, 4]","[50, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[1799] -49,44,"[1, 2]","[50, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[2560] -49,45,"[2, 1]","[40, 30]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1844] -49,46,"[1, 2]","[50, 30]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[2785] -49,47,"[2, 1]","[40, 50]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[2320] -49,48,"[3, 4]","[20, 30]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[2290] -49,49,"[2, 1]","[40, 30]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2096] -49,50,"[2, 1]","[40, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[2278] -49,51,"[1, 2]","[20, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2172] -49,52,"[3, 4]","[30, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1764] -49,53,"[1, 2]","[30, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1852] -49,54,"[2, 1]","[80, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[2335] -49,55,"[4, 3]","[70, 10]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1907] -49,56,"[4, 3]","[80, 50]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[2180] -49,57,"[1, 2]","[70, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1930] -49,58,"[2, 1]","[60, 80]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2529] -49,59,"[1, 2]","[70, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2104] -49,60,"[1, 2]","[50, 40]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[1832] -49,61,"[4, 3]","[30, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[2136] -49,62,"[4, 3]","[10, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[2228] -49,63,"[3, 4]","[50, 10]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2232] -49,64,"[4, 3]","[10, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[2299] -49,65,"[1, 2]","[30, 0]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[2033] -49,66,"[1, 2]","[30, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1942] -49,67,"[1, 2]","[20, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[2520] -49,68,"[3, 4]","[40, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2057] -49,69,"[4, 3]","[0, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2092] -49,70,"[2, 1]","[30, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2201] -49,71,"[2, 1]","[30, 50]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[1949] -49,72,"[3, 4]","[40, 50]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[2445] -49,73,"[2, 1]","[60, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2206] -49,74,"[1, 2]","[80, 70]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2247] -49,75,"[3, 4]","[80, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2070] -49,76,"[2, 1]","[70, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[2093] -49,77,"[4, 3]","[60, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1885] -49,78,"[2, 1]","[60, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[2015] -49,79,"[4, 3]","[40, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1913] -49,80,"[1, 2]","[20, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[1881] -43,1,"[3, 4]","[70, 40]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[3451] -43,2,"[2, 1]","[20, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2302] -43,3,"[4, 3]","[30, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2038] -43,4,"[4, 3]","[50, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2202] -43,5,"[2, 1]","[80, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2152] -43,6,"[1, 2]","[40, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[1823] -43,7,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[1725] -43,8,"[2, 1]","[80, 10]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1757] -43,9,"[2, 1]","[60, 0]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[1824] -43,10,"[3, 4]","[10, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2285] -43,11,"[2, 1]","[50, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[2302] -43,12,"[1, 2]","[70, 30]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2369] -43,13,"[1, 2]","[90, 50]","[1, 1]",[1],[5],[1],[450],[9],[1],[1],[1857] -43,14,"[2, 1]","[60, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1938] -43,15,"[1, 2]","[40, 60]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1856] -43,16,"[2, 1]","[20, 50]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[1708] -43,17,"[4, 3]","[40, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1922] -43,18,"[2, 1]","[70, 40]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1857] -43,19,"[4, 3]","[90, 30]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[1774] -43,20,"[4, 3]","[80, 30]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[1889] -43,21,"[3, 4]","[0, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2054] -43,22,"[3, 4]","[10, 40]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[1691] -43,23,"[2, 1]","[30, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[1741] -43,24,"[2, 1]","[10, 20]","[1, 2]",[1],[5],[2],[50],[1],[2],[2],[1724] -43,25,"[4, 3]","[10, 0]","[2, 1]",[1],[5],[3],[0],[0],[1],[3],[1872] -43,26,"[1, 2]","[10, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[1],[1741] -43,27,"[3, 4]","[40, 0]","[2, 2]",[1],[5],[4],[0],[0],[2],[3],[1855] -43,28,"[1, 2]","[0, 0]","[1, 1]",[1],[1],[1],[0],[0],[1],[1],[1873] -43,29,"[3, 4]","[20, 40]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[1939] -43,30,"[1, 2]","[60, 30]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[1823] -43,31,"[2, 1]","[40, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1757] -43,32,"[4, 3]","[60, 30]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[1889] -43,33,"[3, 4]","[60, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1724] -43,34,"[1, 2]","[60, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2136] -43,35,"[3, 4]","[40, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[1775] -43,36,"[3, 4]","[0, 20]","[2, 1]",[1],[1],[3],[0],[0],[1],[4],[1906] -43,37,"[2, 1]","[0, 0]","[1, 2]",[1],[1],[2],[0],[0],[2],[2],[2005] -43,38,"[4, 3]","[10, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[2269] -43,39,"[3, 4]","[20, 10]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[1938] -43,40,"[4, 3]","[20, 70]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[2005] -43,41,"[2, 1]","[20, 60]","[1, 2]",[2],[1],[2],[20],[2],[2],[2],[7381] -43,42,"[1, 2]","[80, 60]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2237] -43,43,"[1, 2]","[90, 40]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[1841] -43,44,"[2, 1]","[50, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1740] -43,45,"[3, 4]","[90, 50]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[1841] -43,46,"[3, 4]","[90, 50]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[1757] -43,47,"[1, 2]","[80, 60]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1724] -43,48,"[4, 3]","[90, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2236] -43,49,"[1, 2]","[90, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[1907] -43,50,"[4, 3]","[70, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1922] -43,51,"[2, 1]","[50, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1739] -43,52,"[1, 2]","[80, 50]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1874] -43,53,"[3, 4]","[70, 50]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1823] -43,54,"[4, 3]","[50, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[2005] -43,55,"[3, 4]","[70, 40]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1907] -43,56,"[3, 4]","[50, 30]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1823] -43,57,"[3, 4]","[20, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1806] -43,58,"[3, 4]","[10, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[1774] -43,59,"[1, 2]","[0, 50]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[1804] -43,60,"[3, 4]","[20, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[1757] -43,61,"[3, 4]","[20, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1740] -43,62,"[4, 3]","[20, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[1840] -43,63,"[4, 3]","[30, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1806] -43,64,"[4, 3]","[10, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1690] -43,65,"[3, 4]","[30, 0]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[2384] -43,66,"[2, 1]","[20, 40]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[1708] -43,67,"[4, 3]","[10, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1988] -43,68,"[2, 1]","[0, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[1839] -43,69,"[2, 1]","[20, 30]","[1, 2]",[2],[1],[2],[20],[2],[2],[2],[1660] -43,70,"[3, 4]","[30, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[2417] -43,71,"[1, 2]","[60, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[1660] -43,72,"[3, 4]","[80, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[1939] -43,73,"[2, 1]","[70, 90]","[1, 1]",[2],[1],[1],[90],[9],[1],[2],[1706] -43,74,"[2, 1]","[70, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1857] -43,75,"[2, 1]","[90, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1724] -43,76,"[2, 1]","[70, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[1691] -43,77,"[2, 1]","[80, 70]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[1723] -43,78,"[1, 2]","[70, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[2005] -43,79,"[1, 2]","[90, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1725] -43,80,"[2, 1]","[30, 80]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[1922] -50,1,"[4, 3]","[20, 60]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[6150] -50,2,"[2, 1]","[30, 60]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[1901] -50,3,"[2, 1]","[40, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2401] -50,4,"[3, 4]","[50, 30]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[2101] -50,5,"[3, 4]","[50, 40]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[2068] -50,6,"[4, 3]","[30, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[2318] -50,7,"[1, 2]","[70, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[2751] -50,8,"[1, 2]","[50, 40]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[1901] -50,9,"[1, 2]","[50, 70]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2201] -50,10,"[1, 2]","[60, 90]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1768] -50,11,"[1, 2]","[50, 90]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1786] -50,12,"[2, 1]","[90, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2317] -50,13,"[2, 1]","[80, 30]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1884] -50,14,"[1, 2]","[30, 80]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[2701] -50,15,"[1, 2]","[20, 50]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[1918] -50,16,"[3, 4]","[10, 80]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[2201] -50,17,"[3, 4]","[0, 60]","[2, 1]",[1],[5],[3],[0],[0],[1],[4],[1868] -50,18,"[4, 3]","[30, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[2101] -50,19,"[3, 4]","[30, 20]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[2251] -50,20,"[1, 2]","[10, 40]","[1, 1]",[1],[5],[1],[50],[1],[1],[2],[1835] -50,21,"[1, 2]","[20, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[1884] -50,22,"[4, 3]","[50, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[2051] -50,23,"[2, 1]","[90, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[1901] -50,24,"[4, 3]","[60, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1801] -50,25,"[1, 2]","[20, 90]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[2051] -50,26,"[1, 2]","[30, 90]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[1784] -50,27,"[4, 3]","[80, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[1901] -50,28,"[2, 1]","[90, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[1801] -50,29,"[4, 3]","[70, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[1817] -50,30,"[3, 4]","[40, 70]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[2018] -50,31,"[2, 1]","[50, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1884] -50,32,"[1, 2]","[80, 10]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1918] -50,33,"[3, 4]","[70, 0]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1851] -50,34,"[3, 4]","[90, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1934] -50,35,"[4, 3]","[40, 80]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1851] -50,36,"[2, 1]","[60, 70]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[1834] -50,37,"[4, 3]","[60, 70]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1800] -50,38,"[1, 2]","[70, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[1785] -50,39,"[3, 4]","[60, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[1734] -50,40,"[3, 4]","[90, 0]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[1935] -50,41,"[1, 2]","[60, 30]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[3083] -50,42,"[3, 4]","[50, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1768] -50,43,"[2, 1]","[0, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[2001] -50,44,"[2, 1]","[20, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1768] -50,45,"[1, 2]","[90, 20]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[1934] -50,46,"[3, 4]","[90, 30]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[1984] -50,47,"[4, 3]","[20, 80]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[1951] -50,48,"[4, 3]","[20, 70]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[1968] -50,49,"[1, 2]","[50, 10]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[1818] -50,50,"[1, 2]","[60, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[1734] -50,51,"[4, 3]","[70, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1968] -50,52,"[3, 4]","[70, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[1817] -50,53,"[1, 2]","[60, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[1800] -50,54,"[4, 3]","[10, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[1951] -50,55,"[2, 1]","[10, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1751] -50,56,"[2, 1]","[10, 60]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2184] -50,57,"[1, 2]","[50, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[1900] -50,58,"[4, 3]","[0, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[1801] -50,59,"[4, 3]","[10, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1751] -50,60,"[4, 3]","[30, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1717] -50,61,"[3, 4]","[10, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[1900] -50,62,"[2, 1]","[30, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[1801] -50,63,"[3, 4]","[50, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1884] -50,64,"[4, 3]","[80, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1751] -50,65,"[2, 1]","[60, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1750] -50,66,"[4, 3]","[20, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1717] -50,67,"[4, 3]","[40, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1718] -50,68,"[3, 4]","[80, 30]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2034] -50,69,"[3, 4]","[50, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1767] -50,70,"[4, 3]","[60, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1784] -50,71,"[2, 1]","[60, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[1784] -50,72,"[4, 3]","[60, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1718] -50,73,"[2, 1]","[70, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[1834] -50,74,"[4, 3]","[90, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[1817] -50,75,"[1, 2]","[40, 70]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[2018] -50,76,"[2, 1]","[80, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[1784] -50,77,"[1, 2]","[80, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1850] -50,78,"[2, 1]","[90, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[1801] -50,79,"[4, 3]","[90, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1734] -50,80,"[1, 2]","[60, 90]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[3034] -51,1,"[1, 2]","[60, 20]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[26407] -51,2,"[4, 3]","[10, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[2688] -51,3,"[2, 1]","[10, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2888] -51,4,"[4, 3]","[30, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2744] -51,5,"[4, 3]","[50, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[2612] -51,6,"[3, 4]","[80, 60]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[2798] -51,7,"[3, 4]","[60, 40]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[3238] -51,8,"[3, 4]","[50, 10]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[3156] -51,9,"[4, 3]","[10, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2478] -51,10,"[1, 2]","[60, 20]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2511] -51,11,"[1, 2]","[60, 10]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2596] -51,12,"[1, 2]","[30, 10]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2429] -51,13,"[2, 1]","[0, 10]","[1, 2]",[1],[1],[2],[0],[0],[2],[2],[2755] -51,14,"[1, 2]","[20, 0]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[2778] -51,15,"[1, 2]","[30, 10]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[3604] -51,16,"[2, 1]","[20, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[2664] -51,17,"[4, 3]","[10, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[3799] -51,18,"[4, 3]","[20, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[2480] -51,19,"[2, 1]","[30, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[3163] -51,20,"[2, 1]","[50, 10]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2798] -51,21,"[3, 4]","[20, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2470] -51,22,"[2, 1]","[90, 40]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[2302] -51,23,"[1, 2]","[50, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2421] -51,24,"[4, 3]","[60, 50]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2311] -51,25,"[3, 4]","[40, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2087] -51,26,"[1, 2]","[20, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2720] -51,27,"[4, 3]","[80, 30]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2946] -51,28,"[4, 3]","[80, 40]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2604] -51,29,"[1, 2]","[40, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2471] -51,30,"[2, 1]","[80, 60]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[2604] -51,31,"[3, 4]","[10, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2427] -51,32,"[1, 2]","[10, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2078] -51,33,"[2, 1]","[70, 40]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2311] -51,34,"[1, 2]","[60, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2380] -51,35,"[3, 4]","[60, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2228] -51,36,"[4, 3]","[70, 60]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2353] -51,37,"[1, 2]","[60, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2222] -51,38,"[2, 1]","[80, 40]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2413] -51,39,"[3, 4]","[40, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1962] -51,40,"[2, 1]","[60, 20]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2586] -51,41,"[4, 3]","[90, 50]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[9902] -51,42,"[1, 2]","[30, 90]","[1, 2]",[2],[1],[2],[90],[9],[2],[2],[2447] -51,43,"[1, 2]","[60, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[2244] -51,44,"[2, 1]","[90, 30]","[1, 2]",[2],[1],[2],[90],[9],[2],[1],[2540] -51,45,"[1, 2]","[40, 90]","[1, 2]",[2],[1],[2],[90],[9],[2],[2],[2843] -51,46,"[2, 1]","[70, 40]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[2704] -51,47,"[1, 2]","[40, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[2689] -51,48,"[3, 4]","[90, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2261] -51,49,"[1, 2]","[90, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[2487] -51,50,"[3, 4]","[80, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[2706] -51,51,"[3, 4]","[90, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[2144] -51,52,"[3, 4]","[70, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2194] -51,53,"[3, 4]","[70, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2452] -51,54,"[2, 1]","[80, 60]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2629] -51,55,"[2, 1]","[80, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2536] -51,56,"[4, 3]","[80, 70]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2363] -51,57,"[3, 4]","[80, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2562] -51,58,"[4, 3]","[20, 90]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[3197] -51,59,"[1, 2]","[70, 40]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[12772] -51,60,"[2, 1]","[20, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[2814] -51,61,"[3, 4]","[70, 30]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[2528] -51,62,"[1, 2]","[70, 0]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2421] -51,63,"[3, 4]","[50, 40]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2871] -51,64,"[4, 3]","[10, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[2708] -51,65,"[3, 4]","[60, 10]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[2251] -51,66,"[3, 4]","[40, 50]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[2181] -51,67,"[2, 1]","[30, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2255] -51,68,"[1, 2]","[80, 70]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2296] -51,69,"[1, 2]","[70, 80]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2262] -51,70,"[2, 1]","[80, 90]","[1, 1]",[2],[1],[1],[90],[9],[1],[2],[2429] -51,71,"[3, 4]","[80, 70]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2321] -51,72,"[4, 3]","[60, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[2137] -51,73,"[1, 2]","[80, 40]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2236] -51,74,"[1, 2]","[80, 70]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2404] -51,75,"[4, 3]","[50, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[2110] -51,76,"[1, 2]","[80, 40]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1944] -51,77,"[1, 2]","[60, 0]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2211] -51,78,"[4, 3]","[20, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[2454] -51,79,"[1, 2]","[30, 20]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[2470] -51,80,"[4, 3]","[50, 40]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[3890] -52,1,"[2, 1]","[20, 70]","[1, 2]",[1],[1],[2],[20],[2],[2],[2],[18333] -52,2,"[4, 3]","[20, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[2940] -52,3,"[2, 1]","[10, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2840] -52,4,"[1, 2]","[70, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[2189] -52,5,"[3, 4]","[80, 0]","[2, 2]",[1],[5],[4],[0],[0],[2],[3],[3339] -52,6,"[3, 4]","[80, 10]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[3924] -52,7,"[3, 4]","[70, 10]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2408] -52,8,"[4, 3]","[30, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[3839] -52,9,"[2, 1]","[40, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[5658] -52,10,"[2, 1]","[50, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[2407] -52,11,"[3, 4]","[60, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[3524] -52,12,"[4, 3]","[60, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[2557] -52,13,"[3, 4]","[90, 40]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[2523] -52,14,"[2, 1]","[60, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2542] -52,15,"[2, 1]","[20, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2756] -52,16,"[1, 2]","[80, 30]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2541] -52,17,"[2, 1]","[50, 90]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[3441] -52,18,"[3, 4]","[80, 60]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2225] -52,19,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1975] -52,20,"[1, 2]","[50, 80]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[2090] -52,21,"[2, 1]","[60, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[2005] -52,22,"[2, 1]","[70, 50]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2608] -52,23,"[2, 1]","[60, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2624] -52,24,"[4, 3]","[70, 80]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[2795] -52,25,"[1, 2]","[80, 60]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2108] -52,26,"[3, 4]","[60, 70]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2791] -52,27,"[2, 1]","[80, 70]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[2656] -52,28,"[2, 1]","[80, 90]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2073] -52,29,"[2, 1]","[60, 90]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[1975] -52,30,"[1, 2]","[90, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2157] -52,31,"[3, 4]","[60, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[3224] -52,32,"[4, 3]","[70, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[3624] -52,33,"[3, 4]","[90, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2070] -52,34,"[1, 2]","[70, 60]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2174] -52,35,"[1, 2]","[50, 60]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2941] -52,36,"[4, 3]","[50, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1789] -52,37,"[3, 4]","[10, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2459] -52,38,"[4, 3]","[80, 30]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2122] -52,39,"[1, 2]","[20, 70]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[2408] -52,40,"[1, 2]","[10, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2623] -52,41,"[2, 1]","[80, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[6644] -52,42,"[2, 1]","[80, 10]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2889] -52,43,"[3, 4]","[10, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2605] -52,44,"[2, 1]","[70, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[1889] -52,45,"[4, 3]","[40, 70]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[3205] -52,46,"[4, 3]","[50, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[2255] -52,47,"[3, 4]","[50, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2406] -52,48,"[4, 3]","[70, 20]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[2106] -52,49,"[2, 1]","[70, 40]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[3157] -52,50,"[3, 4]","[40, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[2289] -52,51,"[3, 4]","[40, 70]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[2657] -52,52,"[1, 2]","[40, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[2692] -52,53,"[4, 3]","[50, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[2255] -52,54,"[1, 2]","[40, 30]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[1909] -52,55,"[3, 4]","[10, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[3091] -52,56,"[4, 3]","[10, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1839] -52,57,"[4, 3]","[10, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[4674] -52,58,"[2, 1]","[0, 50]","[1, 2]",[2],[1],[2],[0],[0],[2],[2],[3423] -52,59,"[3, 4]","[60, 10]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[3023] -52,60,"[1, 2]","[50, 10]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[3006] -52,61,"[2, 1]","[20, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[2656] -52,62,"[2, 1]","[60, 30]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2578] -52,63,"[2, 1]","[60, 40]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[1875] -52,64,"[2, 1]","[30, 50]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[1908] -52,65,"[4, 3]","[30, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[2673] -52,66,"[1, 2]","[50, 30]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[2290] -52,67,"[4, 3]","[30, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[2840] -52,68,"[3, 4]","[70, 30]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[2375] -52,69,"[2, 1]","[60, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[3041] -52,70,"[2, 1]","[70, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[2057] -52,71,"[3, 4]","[10, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[2558] -52,72,"[3, 4]","[40, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2457] -52,73,"[1, 2]","[40, 20]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[2123] -52,74,"[1, 2]","[60, 30]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2107] -52,75,"[2, 1]","[40, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[3008] -52,76,"[3, 4]","[60, 20]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2225] -52,77,"[4, 3]","[50, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[2436] -52,78,"[3, 4]","[40, 70]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[2089] -52,79,"[4, 3]","[90, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[2290] -52,80,"[3, 4]","[80, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1991] -53,1,"[2, 1]","[40, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[5202] -53,2,"[3, 4]","[70, 10]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[1868] -53,3,"[1, 2]","[80, 20]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2719] -53,4,"[3, 4]","[70, 10]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[1834] -53,5,"[3, 4]","[60, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[3235] -53,6,"[3, 4]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[2167] -53,7,"[2, 1]","[10, 50]","[1, 2]",[1],[1],[2],[10],[1],[2],[2],[2817] -53,8,"[1, 2]","[60, 20]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[5020] -53,9,"[3, 4]","[90, 30]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[2851] -53,10,"[2, 1]","[10, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2618] -53,11,"[1, 2]","[60, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[3319] -53,12,"[4, 3]","[10, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2100] -53,13,"[3, 4]","[50, 40]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[2034] -53,14,"[3, 4]","[90, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2484] -53,15,"[2, 1]","[50, 80]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[3117] -53,16,"[2, 1]","[50, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[5018] -53,17,"[4, 3]","[40, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2550] -53,18,"[3, 4]","[40, 20]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[2500] -53,19,"[2, 1]","[40, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[2168] -53,20,"[2, 1]","[30, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[2117] -53,21,"[2, 1]","[10, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[1984] -53,22,"[4, 3]","[0, 10]","[2, 2]",[1],[5],[4],[0],[0],[2],[4],[3117] -53,23,"[3, 4]","[10, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[4169] -53,24,"[2, 1]","[10, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[2536] -53,25,"[3, 4]","[10, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[3687] -53,26,"[4, 3]","[40, 10]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2453] -53,27,"[1, 2]","[10, 60]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[2053] -53,28,"[2, 1]","[50, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[3387] -53,29,"[3, 4]","[50, 60]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[2067] -53,30,"[1, 2]","[50, 80]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[2051] -53,31,"[3, 4]","[30, 70]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[3368] -53,32,"[4, 3]","[70, 30]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2217] -53,33,"[2, 1]","[60, 30]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2418] -53,34,"[2, 1]","[60, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[3002] -53,35,"[2, 1]","[90, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[2135] -53,36,"[3, 4]","[50, 60]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[2117] -53,37,"[1, 2]","[50, 80]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1885] -53,38,"[2, 1]","[40, 70]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[4638] -53,39,"[4, 3]","[50, 40]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[2186] -53,40,"[3, 4]","[40, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2170] -53,41,"[2, 1]","[80, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[3521] -53,42,"[3, 4]","[70, 80]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1736] -53,43,"[1, 2]","[60, 80]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1887] -53,44,"[1, 2]","[50, 70]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[1869] -53,45,"[2, 1]","[70, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[1786] -53,46,"[2, 1]","[30, 20]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2520] -53,47,"[2, 1]","[40, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[1970] -53,48,"[4, 3]","[60, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[1800] -53,49,"[1, 2]","[80, 40]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2417] -53,50,"[3, 4]","[70, 40]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[2834] -53,51,"[3, 4]","[60, 30]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1883] -53,52,"[3, 4]","[80, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[2217] -53,53,"[4, 3]","[60, 40]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[3937] -53,54,"[4, 3]","[60, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2067] -53,55,"[1, 2]","[40, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[3351] -53,56,"[4, 3]","[40, 20]","[2, 1]",[2],[1],[3],[20],[2],[1],[3],[2167] -53,57,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2185] -53,58,"[1, 2]","[10, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[1918] -53,59,"[4, 3]","[10, 20]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[2601] -53,60,"[3, 4]","[10, 20]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1769] -53,61,"[4, 3]","[20, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[2268] -53,62,"[2, 1]","[10, 40]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2750] -53,63,"[3, 4]","[30, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[1934] -53,64,"[4, 3]","[20, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[3336] -53,65,"[2, 1]","[40, 0]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[1751] -53,66,"[2, 1]","[50, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[2451] -53,67,"[3, 4]","[30, 60]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[2168] -53,68,"[3, 4]","[40, 70]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[1801] -53,69,"[1, 2]","[50, 60]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[3036] -53,70,"[2, 1]","[70, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[2252] -53,71,"[1, 2]","[40, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2385] -53,72,"[4, 3]","[90, 40]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[2752] -53,73,"[1, 2]","[50, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2635] -53,74,"[4, 3]","[90, 60]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[2534] -53,75,"[2, 1]","[90, 60]","[1, 2]",[2],[5],[2],[450],[9],[2],[1],[1885] -53,76,"[3, 4]","[50, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2185] -53,77,"[1, 2]","[70, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[1968] -53,78,"[4, 3]","[80, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1918] -53,79,"[1, 2]","[40, 80]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[2001] -53,80,"[4, 3]","[90, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[1767] -29,1,"[1, 2]","[30, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[8707] -29,2,"[3, 4]","[40, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[2406] -29,3,"[1, 2]","[50, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1873] -29,4,"[4, 3]","[60, 80]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1989] -29,5,"[4, 3]","[80, 90]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[1841] -29,6,"[1, 2]","[80, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2540] -29,7,"[2, 1]","[80, 90]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2307] -29,8,"[1, 2]","[90, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[1875] -29,9,"[2, 1]","[70, 40]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1887] -29,10,"[4, 3]","[30, 40]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[2523] -29,11,"[1, 2]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2973] -29,12,"[4, 3]","[60, 20]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2406] -29,13,"[2, 1]","[70, 30]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2004] -29,14,"[4, 3]","[60, 20]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[1823] -29,15,"[1, 2]","[10, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2223] -29,16,"[1, 2]","[0, 50]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[1989] -29,17,"[2, 1]","[10, 50]","[1, 2]",[1],[1],[2],[10],[1],[2],[2],[1906] -29,18,"[1, 2]","[50, 20]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[1890] -29,19,"[2, 1]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[2006] -29,20,"[4, 3]","[40, 60]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1772] -29,21,"[1, 2]","[30, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2306] -29,22,"[1, 2]","[10, 20]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[1890] -29,23,"[4, 3]","[0, 0]","[2, 2]",[1],[5],[4],[0],[0],[2],[4],[10507] -29,24,"[2, 1]","[10, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2005] -29,25,"[2, 1]","[20, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1790] -29,26,"[1, 2]","[10, 0]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[2140] -29,27,"[2, 1]","[10, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[1856] -29,28,"[4, 3]","[30, 0]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[1705] -29,29,"[1, 2]","[0, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[2222] -29,30,"[4, 3]","[20, 0]","[2, 1]",[1],[5],[3],[0],[0],[1],[3],[2187] -29,31,"[3, 4]","[40, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[1822] -29,32,"[2, 1]","[20, 10]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[1889] -29,33,"[2, 1]","[20, 10]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[1789] -29,34,"[3, 4]","[20, 30]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[1824] -29,35,"[1, 2]","[0, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[1986] -29,36,"[2, 1]","[70, 10]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1740] -29,37,"[2, 1]","[90, 30]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[1823] -29,38,"[4, 3]","[90, 30]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2190] -29,39,"[3, 4]","[20, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[1856] -29,40,"[2, 1]","[80, 10]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[2357] -29,41,"[4, 3]","[90, 0]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[4168] -29,42,"[2, 1]","[70, 10]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1989] -29,43,"[2, 1]","[70, 20]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2573] -29,44,"[3, 4]","[0, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2206] -29,45,"[1, 2]","[10, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[2340] -29,46,"[1, 2]","[30, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2123] -29,47,"[2, 1]","[60, 20]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2056] -29,48,"[3, 4]","[20, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2773] -29,49,"[2, 1]","[70, 0]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2040] -29,50,"[4, 3]","[90, 0]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[1973] -29,51,"[2, 1]","[80, 0]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[1623] -29,52,"[1, 2]","[10, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[1939] -29,53,"[4, 3]","[80, 40]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1906] -29,54,"[1, 2]","[30, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1839] -29,55,"[4, 3]","[60, 40]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[1892] -29,56,"[4, 3]","[60, 10]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[1706] -29,57,"[3, 4]","[50, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[1871] -29,58,"[3, 4]","[40, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1706] -29,59,"[2, 1]","[30, 0]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2554] -29,60,"[2, 1]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[2358] -29,61,"[1, 2]","[20, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2223] -29,62,"[3, 4]","[10, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[4],[6841] -29,63,"[2, 1]","[40, 20]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1706] -29,64,"[4, 3]","[10, 60]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[1722] -29,65,"[2, 1]","[10, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1940] -29,66,"[1, 2]","[60, 20]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[2056] -29,67,"[4, 3]","[20, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1823] -29,68,"[4, 3]","[0, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1822] -29,69,"[4, 3]","[10, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1740] -29,70,"[2, 1]","[40, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2106] -29,71,"[1, 2]","[80, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2457] -29,72,"[1, 2]","[90, 70]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[2139] -29,73,"[3, 4]","[90, 50]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[1739] -29,74,"[4, 3]","[30, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[2439] -29,75,"[4, 3]","[20, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[2040] -29,76,"[3, 4]","[90, 10]","[2, 1]",[2],[5],[3],[450],[9],[1],[3],[1756] -29,77,"[4, 3]","[20, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1706] -29,78,"[2, 1]","[40, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2589] -29,79,"[4, 3]","[50, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1956] -29,80,"[3, 4]","[70, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1989] -29,1,"[2, 1]","[20, 70]","[1, 2]",[1],[1],[2],[20],[2],[2],[2],[17723] -29,2,"[4, 3]","[20, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[3657] -29,3,"[2, 1]","[10, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2156] -29,4,"[1, 2]","[70, 20]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1923] -29,5,"[3, 4]","[80, 0]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[2606] -29,6,"[3, 4]","[80, 10]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1857] -29,7,"[3, 4]","[70, 10]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1806] -29,8,"[4, 3]","[30, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[1823] -29,9,"[2, 1]","[40, 70]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2306] -29,10,"[2, 1]","[50, 60]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[3089] -29,11,"[3, 4]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[2139] -29,12,"[4, 3]","[60, 90]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1940] -29,13,"[3, 4]","[90, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2223] -29,14,"[2, 1]","[60, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2189] -29,15,"[2, 1]","[20, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2070] -29,16,"[1, 2]","[80, 30]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1756] -29,17,"[2, 1]","[50, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[1990] -29,18,"[3, 4]","[80, 60]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[1791] -29,19,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2123] -29,20,"[1, 2]","[50, 80]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[1890] -29,21,"[2, 1]","[60, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[1956] -29,22,"[2, 1]","[70, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[2139] -29,23,"[2, 1]","[60, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1822] -29,24,"[4, 3]","[70, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1806] -29,25,"[1, 2]","[80, 60]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1939] -29,26,"[3, 4]","[60, 70]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1873] -29,27,"[2, 1]","[80, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1889] -29,28,"[2, 1]","[80, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[1906] -29,29,"[2, 1]","[60, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[1923] -29,30,"[1, 2]","[90, 70]","[1, 1]",[1],[5],[1],[450],[9],[1],[1],[1989] -29,31,"[3, 4]","[60, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[2140] -29,32,"[4, 3]","[70, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1756] -29,33,"[3, 4]","[90, 70]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[2106] -29,34,"[1, 2]","[70, 60]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[3189] -29,35,"[1, 2]","[50, 60]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1890] -29,36,"[4, 3]","[50, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[2173] -29,37,"[3, 4]","[10, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2206] -29,38,"[4, 3]","[80, 30]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[1790] -29,39,"[1, 2]","[20, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2023] -29,40,"[1, 2]","[10, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1873] -29,41,"[2, 1]","[80, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[6936] -29,42,"[2, 1]","[80, 10]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2039] -29,43,"[3, 4]","[10, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1790] -29,44,"[2, 1]","[70, 40]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1973] -29,45,"[4, 3]","[40, 70]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1873] -29,46,"[4, 3]","[50, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[2072] -29,47,"[3, 4]","[50, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[1823] -29,48,"[4, 3]","[70, 20]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1940] -29,49,"[2, 1]","[70, 40]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2156] -29,50,"[3, 4]","[40, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[1856] -29,51,"[3, 4]","[40, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[2340] -29,52,"[1, 2]","[40, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[2106] -29,53,"[4, 3]","[50, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[2156] -29,54,"[1, 2]","[40, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2290] -29,55,"[3, 4]","[10, 20]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[1739] -29,56,"[4, 3]","[10, 30]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1973] -29,57,"[4, 3]","[10, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[1705] -29,58,"[2, 1]","[0, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1989] -29,59,"[3, 4]","[60, 10]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[1806] -29,60,"[1, 2]","[50, 10]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[2123] -29,61,"[2, 1]","[20, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[2306] -29,62,"[2, 1]","[60, 30]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2023] -29,63,"[2, 1]","[60, 40]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[1857] -29,64,"[2, 1]","[30, 50]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[1973] -29,65,"[4, 3]","[30, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1723] -29,66,"[1, 2]","[50, 30]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[1974] -29,67,"[4, 3]","[30, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[1772] -29,68,"[3, 4]","[70, 30]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1974] -29,69,"[2, 1]","[60, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2139] -29,70,"[2, 1]","[70, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[2006] -29,71,"[3, 4]","[10, 70]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[2039] -29,72,"[3, 4]","[40, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1740] -29,73,"[1, 2]","[40, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[2040] -29,74,"[1, 2]","[60, 30]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[3507] -29,75,"[2, 1]","[40, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[2073] -29,76,"[3, 4]","[60, 20]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2040] -29,77,"[4, 3]","[50, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[1706] -29,78,"[3, 4]","[40, 70]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[1789] -29,79,"[4, 3]","[90, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1958] -29,80,"[3, 4]","[80, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1841] -29,1,"[2, 1]","[40, 70]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[5637] -29,2,"[3, 4]","[80, 40]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[4886] -29,3,"[3, 4]","[80, 60]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[1954] -29,4,"[2, 1]","[30, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[2207] -29,5,"[1, 2]","[70, 30]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1915] -29,6,"[4, 3]","[10, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1890] -29,7,"[4, 3]","[0, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1872] -29,8,"[4, 3]","[20, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[1873] -29,9,"[4, 3]","[50, 0]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[1720] -29,10,"[3, 4]","[0, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[1955] -29,11,"[4, 3]","[80, 0]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2122] -29,12,"[4, 3]","[70, 30]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[1923] -29,13,"[2, 1]","[90, 10]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[2256] -29,14,"[4, 3]","[90, 60]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[1889] -29,15,"[2, 1]","[80, 40]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[2189] -29,16,"[1, 2]","[20, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2055] -29,17,"[1, 2]","[30, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2088] -29,18,"[1, 2]","[40, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[2055] -29,19,"[1, 2]","[50, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[1905] -29,20,"[3, 4]","[60, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[1805] -29,21,"[2, 1]","[50, 80]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2272] -29,22,"[1, 2]","[60, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[1839] -29,23,"[2, 1]","[50, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[2123] -29,24,"[2, 1]","[10, 0]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[2086] -29,25,"[1, 2]","[0, 50]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[3139] -29,26,"[2, 1]","[60, 20]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2590] -29,27,"[2, 1]","[40, 0]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[1936] -29,28,"[2, 1]","[70, 20]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1839] -29,29,"[1, 2]","[40, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1905] -29,30,"[1, 2]","[20, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1823] -29,31,"[4, 3]","[70, 40]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2149] -29,32,"[3, 4]","[50, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[2055] -29,33,"[3, 4]","[60, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1823] -29,34,"[2, 1]","[60, 80]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2456] -29,35,"[3, 4]","[90, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[1806] -29,36,"[1, 2]","[80, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2053] -29,37,"[2, 1]","[40, 70]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2005] -29,38,"[2, 1]","[0, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1873] -29,39,"[4, 3]","[10, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1820] -29,40,"[2, 1]","[0, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[1873] -29,41,"[3, 4]","[80, 10]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[5134] -29,42,"[4, 3]","[10, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1839] -29,43,"[2, 1]","[20, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[1841] -29,44,"[1, 2]","[60, 20]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1905] -29,45,"[4, 3]","[0, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[1805] -29,46,"[4, 3]","[40, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1789] -29,47,"[1, 2]","[30, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[2422] -29,48,"[1, 2]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[1872] -29,49,"[3, 4]","[10, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[1755] -29,50,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1840] -29,51,"[2, 1]","[10, 0]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[2089] -29,52,"[1, 2]","[30, 50]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[2406] -29,53,"[2, 1]","[40, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2056] -29,54,"[2, 1]","[40, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[2155] -29,55,"[3, 4]","[80, 20]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2107] -29,56,"[2, 1]","[0, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2473] -29,57,"[4, 3]","[10, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1890] -29,58,"[3, 4]","[70, 20]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1923] -29,59,"[1, 2]","[80, 10]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2156] -29,60,"[2, 1]","[0, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1923] -29,61,"[3, 4]","[70, 40]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1937] -29,62,"[3, 4]","[60, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1855] -29,63,"[3, 4]","[90, 70]","[2, 1]",[2],[5],[3],[450],[9],[1],[3],[1873] -29,64,"[3, 4]","[60, 70]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1806] -29,65,"[3, 4]","[80, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1755] -29,66,"[1, 2]","[70, 80]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2121] -29,67,"[1, 2]","[90, 70]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[1956] -29,68,"[2, 1]","[50, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2157] -29,69,"[3, 4]","[90, 60]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[1854] -29,70,"[2, 1]","[30, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1857] -29,71,"[3, 4]","[80, 20]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2440] -29,72,"[4, 3]","[50, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1805] -29,73,"[1, 2]","[90, 30]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[1990] -29,74,"[2, 1]","[20, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1906] -29,75,"[1, 2]","[40, 30]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2008] -29,76,"[2, 1]","[30, 10]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[1973] -29,77,"[4, 3]","[70, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[2040] -29,78,"[1, 2]","[40, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2573] -29,79,"[4, 3]","[90, 20]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[1902] -29,80,"[1, 2]","[10, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[3506] -29,1,"[1, 2]","[60, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[8238] -29,2,"[3, 4]","[50, 20]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[2172] -29,3,"[3, 4]","[10, 30]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[1822] -29,4,"[2, 1]","[50, 40]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2856] -29,5,"[1, 2]","[60, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2038] -29,6,"[3, 4]","[30, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[1805] -29,7,"[4, 3]","[40, 50]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1888] -29,8,"[3, 4]","[80, 40]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[2086] -29,9,"[1, 2]","[50, 30]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2072] -29,10,"[2, 1]","[50, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[1805] -29,11,"[3, 4]","[20, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2140] -29,12,"[4, 3]","[50, 20]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[1853] -29,13,"[4, 3]","[20, 10]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1806] -29,14,"[4, 3]","[40, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[1672] -29,15,"[1, 2]","[60, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2589] -29,16,"[3, 4]","[90, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[1706] -29,17,"[3, 4]","[60, 30]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1672] -29,18,"[3, 4]","[50, 40]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[1788] -29,19,"[4, 3]","[50, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1723] -29,20,"[3, 4]","[10, 50]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[1739] -29,21,"[1, 2]","[20, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2756] -29,22,"[1, 2]","[60, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[1773] -29,23,"[1, 2]","[80, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1757] -29,24,"[3, 4]","[80, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2106] -29,25,"[4, 3]","[70, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1889] -29,26,"[4, 3]","[50, 60]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[1872] -29,27,"[2, 1]","[60, 70]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[1956] -29,28,"[3, 4]","[50, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[2123] -29,29,"[3, 4]","[80, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[1782] -29,30,"[3, 4]","[70, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1739] -29,31,"[4, 3]","[80, 50]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[1822] -29,32,"[1, 2]","[50, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2072] -29,33,"[3, 4]","[30, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[1755] -29,34,"[2, 1]","[80, 30]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[1923] -29,35,"[1, 2]","[30, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2123] -29,36,"[4, 3]","[70, 50]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2055] -29,37,"[1, 2]","[50, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[1889] -29,38,"[1, 2]","[70, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[1906] -29,39,"[2, 1]","[60, 50]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[1823] -29,40,"[2, 1]","[50, 20]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[1805] -29,41,"[1, 2]","[10, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2019] -29,42,"[3, 4]","[20, 30]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[1755] -29,43,"[2, 1]","[10, 0]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[2271] -29,44,"[3, 4]","[20, 10]","[2, 1]",[2],[1],[3],[20],[2],[1],[3],[1955] -29,45,"[4, 3]","[0, 10]","[2, 2]",[2],[1],[4],[0],[0],[2],[4],[1856] -29,46,"[2, 1]","[0, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1989] -29,47,"[1, 2]","[0, 10]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[1937] -29,48,"[2, 1]","[0, 20]","[1, 2]",[2],[1],[2],[0],[0],[2],[2],[1823] -29,49,"[3, 4]","[20, 10]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[2103] -29,50,"[1, 2]","[40, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2123] -29,51,"[4, 3]","[50, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[1722] -29,52,"[2, 1]","[70, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2022] -29,53,"[2, 1]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1790] -29,54,"[3, 4]","[80, 50]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1772] -29,55,"[2, 1]","[40, 90]","[1, 1]",[2],[1],[1],[90],[9],[1],[2],[1822] -29,56,"[4, 3]","[40, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1823] -29,57,"[2, 1]","[50, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1854] -29,58,"[3, 4]","[70, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1954] -29,59,"[1, 2]","[70, 30]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[1939] -29,60,"[1, 2]","[80, 40]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1890] -29,61,"[2, 1]","[10, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1854] -29,62,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1805] -29,63,"[1, 2]","[70, 20]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[1922] -29,64,"[3, 4]","[60, 40]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[1823] -29,65,"[1, 2]","[30, 20]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[1733] -29,66,"[1, 2]","[20, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[2089] -29,67,"[3, 4]","[10, 20]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[2606] -29,68,"[4, 3]","[0, 10]","[2, 2]",[2],[5],[4],[0],[0],[2],[4],[1789] -29,69,"[4, 3]","[30, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[2522] -29,70,"[1, 2]","[10, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[2089] -29,71,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2023] -29,72,"[2, 1]","[10, 40]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[1806] -29,73,"[1, 2]","[30, 20]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[1872] -29,74,"[2, 1]","[30, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[1739] -29,75,"[4, 3]","[30, 20]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1739] -29,76,"[3, 4]","[40, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1788] -29,77,"[4, 3]","[30, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[1756] -29,78,"[2, 1]","[50, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1722] -29,79,"[1, 2]","[90, 80]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[1789] -29,80,"[3, 4]","[30, 70]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1805] -29,1,"[4, 3]","[10, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[4503] -29,2,"[2, 1]","[0, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1872] -29,3,"[1, 2]","[70, 10]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2004] -29,4,"[1, 2]","[80, 20]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1771] -29,5,"[3, 4]","[80, 10]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1822] -29,6,"[4, 3]","[30, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1806] -29,7,"[3, 4]","[80, 0]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[1789] -29,8,"[1, 2]","[70, 0]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2073] -29,9,"[1, 2]","[70, 10]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1872] -29,10,"[2, 1]","[10, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1889] -29,11,"[1, 2]","[80, 20]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1954] -29,12,"[1, 2]","[70, 40]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1823] -29,13,"[3, 4]","[60, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1819] -29,14,"[2, 1]","[60, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1890] -29,15,"[2, 1]","[60, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2105] -29,16,"[1, 2]","[90, 70]","[1, 1]",[1],[5],[1],[450],[9],[1],[1],[2106] -29,17,"[1, 2]","[60, 80]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1872] -29,18,"[2, 1]","[90, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[1839] -29,19,"[3, 4]","[50, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[1774] -29,20,"[3, 4]","[30, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[1823] -29,21,"[3, 4]","[40, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1739] -29,22,"[3, 4]","[40, 10]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[1719] -29,23,"[4, 3]","[0, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1705] -29,24,"[1, 2]","[30, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[2325] -29,25,"[1, 2]","[50, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[1840] -29,26,"[2, 1]","[50, 70]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[1772] -29,27,"[4, 3]","[60, 50]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1889] -29,28,"[3, 4]","[0, 90]","[2, 2]",[1],[5],[4],[450],[9],[2],[4],[2039] -29,29,"[2, 1]","[70, 10]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1939] -29,30,"[4, 3]","[60, 20]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1939] -29,31,"[3, 4]","[10, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1839] -29,32,"[4, 3]","[60, 20]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1922] -29,33,"[4, 3]","[60, 20]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1839] -29,34,"[1, 2]","[20, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2073] -29,35,"[1, 2]","[10, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1972] -29,36,"[4, 3]","[80, 20]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2122] -29,37,"[1, 2]","[20, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2955] -29,38,"[2, 1]","[70, 10]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1872] -29,39,"[2, 1]","[60, 10]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[1839] -29,40,"[3, 4]","[50, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2006] -29,41,"[2, 1]","[90, 40]","[1, 2]",[2],[5],[2],[450],[9],[2],[1],[2919] -29,42,"[1, 2]","[50, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[1788] -29,43,"[4, 3]","[80, 70]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[1772] -29,44,"[2, 1]","[40, 90]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[1940] -29,45,"[4, 3]","[60, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[2072] -29,46,"[1, 2]","[90, 50]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[2039] -29,47,"[3, 4]","[80, 50]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[1789] -29,48,"[1, 2]","[70, 60]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[1922] -29,49,"[1, 2]","[60, 70]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1823] -29,50,"[1, 2]","[80, 70]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1855] -29,51,"[4, 3]","[70, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[1772] -29,52,"[1, 2]","[50, 80]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2023] -29,53,"[4, 3]","[70, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2123] -29,54,"[2, 1]","[70, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[1805] -29,55,"[4, 3]","[60, 70]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[1689] -29,56,"[1, 2]","[50, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1874] -29,57,"[3, 4]","[60, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[1789] -29,58,"[4, 3]","[60, 90]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[1988] -29,59,"[3, 4]","[70, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[1839] -29,60,"[2, 1]","[30, 90]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[1988] -29,61,"[3, 4]","[80, 10]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[1755] -29,62,"[4, 3]","[20, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1739] -29,63,"[1, 2]","[80, 40]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2172] -29,64,"[2, 1]","[0, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1856] -29,65,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1903] -29,66,"[3, 4]","[90, 40]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[1822] -29,67,"[3, 4]","[20, 90]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[1790] -29,68,"[1, 2]","[50, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[1921] -29,69,"[4, 3]","[60, 20]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[1935] -29,70,"[4, 3]","[50, 30]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1755] -29,71,"[2, 1]","[40, 60]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[1939] -29,72,"[4, 3]","[30, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1724] -29,73,"[3, 4]","[80, 40]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1756] -29,74,"[3, 4]","[70, 20]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1786] -29,75,"[3, 4]","[50, 40]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1773] -29,76,"[3, 4]","[60, 20]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[1756] -29,77,"[1, 2]","[80, 0]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1806] -29,78,"[4, 3]","[10, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1973] -29,79,"[4, 3]","[10, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1739] -29,80,"[1, 2]","[40, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[2156] -29,1,"[4, 3]","[10, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[4218] -29,2,"[2, 1]","[0, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[2019] -29,3,"[3, 4]","[50, 20]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[1806] -29,4,"[2, 1]","[30, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[1941] -29,5,"[2, 1]","[60, 30]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2122] -29,6,"[2, 1]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[1772] -29,7,"[2, 1]","[40, 0]","[1, 1]",[1],[1],[1],[0],[0],[1],[1],[1773] -29,8,"[3, 4]","[0, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2089] -29,9,"[3, 4]","[20, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[1706] -29,10,"[2, 1]","[40, 20]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2074] -29,11,"[4, 3]","[40, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[1688] -29,12,"[1, 2]","[30, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2289] -29,13,"[1, 2]","[70, 40]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1723] -29,14,"[3, 4]","[80, 50]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1836] -29,15,"[3, 4]","[90, 60]","[2, 1]",[1],[1],[3],[90],[9],[1],[3],[2089] -29,16,"[4, 3]","[90, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1722] -29,17,"[2, 1]","[70, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[1956] -29,18,"[4, 3]","[90, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[1705] -29,19,"[4, 3]","[70, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1705] -29,20,"[4, 3]","[70, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1655] -29,21,"[3, 4]","[40, 90]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1839] -29,22,"[2, 1]","[90, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[1757] -29,23,"[2, 1]","[80, 60]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[1906] -29,24,"[3, 4]","[70, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[1873] -29,25,"[2, 1]","[80, 60]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[1856] -29,26,"[2, 1]","[50, 60]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[1836] -29,27,"[1, 2]","[50, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2073] -29,28,"[3, 4]","[60, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1989] -29,29,"[4, 3]","[30, 50]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[1923] -29,30,"[4, 3]","[30, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[1739] -29,31,"[3, 4]","[30, 0]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[1723] -29,32,"[3, 4]","[30, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[1689] -29,33,"[3, 4]","[40, 10]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[1706] -29,34,"[1, 2]","[10, 0]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[1940] -29,35,"[2, 1]","[10, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[2],[1805] -29,36,"[3, 4]","[0, 40]","[2, 1]",[1],[1],[3],[0],[0],[1],[4],[2240] -29,37,"[3, 4]","[0, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[1739] -29,38,"[4, 3]","[20, 0]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1773] -29,39,"[4, 3]","[30, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[1655] -29,40,"[3, 4]","[20, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1739] -29,41,"[1, 2]","[10, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1935] -29,42,"[1, 2]","[10, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[1722] -29,43,"[4, 3]","[40, 20]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1890] -29,44,"[1, 2]","[0, 40]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[1922] -29,45,"[3, 4]","[20, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[1689] -29,46,"[4, 3]","[60, 40]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2056] -29,47,"[1, 2]","[80, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2055] -29,48,"[1, 2]","[80, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[1923] -29,49,"[3, 4]","[50, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1855] -29,50,"[4, 3]","[0, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1705] -29,51,"[3, 4]","[50, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[1724] -29,52,"[4, 3]","[0, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[1923] -29,53,"[2, 1]","[30, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2056] -29,54,"[4, 3]","[10, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[1788] -29,55,"[1, 2]","[0, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[2],[2205] -29,56,"[4, 3]","[10, 20]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[1739] -29,57,"[4, 3]","[10, 30]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[1839] -29,58,"[3, 4]","[50, 20]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1856] -29,59,"[2, 1]","[20, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1873] -29,60,"[4, 3]","[10, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[2440] -29,61,"[4, 3]","[20, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1855] -29,62,"[1, 2]","[50, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[2239] -29,63,"[1, 2]","[80, 20]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1789] -29,64,"[3, 4]","[70, 20]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1739] -29,65,"[3, 4]","[60, 30]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1739] -29,66,"[4, 3]","[50, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1737] -29,67,"[4, 3]","[20, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1691] -29,68,"[3, 4]","[80, 30]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[1739] -29,69,"[1, 2]","[80, 60]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[3056] -29,70,"[3, 4]","[90, 30]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[1707] -29,71,"[3, 4]","[80, 40]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1756] -29,72,"[1, 2]","[60, 40]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[1990] -29,73,"[1, 2]","[70, 10]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[1671] -29,74,"[4, 3]","[20, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[1723] -29,75,"[1, 2]","[60, 0]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1855] -29,76,"[1, 2]","[30, 0]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[1836] -29,77,"[3, 4]","[10, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1722] -29,78,"[3, 4]","[50, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1672] -29,79,"[3, 4]","[60, 10]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1705] -29,80,"[1, 2]","[90, 0]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[1888] -54,1,"[4, 3]","[10, 90]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[21581] -54,2,"[2, 1]","[0, 80]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[7575] -54,3,"[1, 2]","[70, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[2185] -54,4,"[1, 2]","[80, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[9422] -54,5,"[3, 4]","[80, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[5735] -54,6,"[4, 3]","[30, 70]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[3519] -54,7,"[3, 4]","[80, 0]","[2, 2]",[1],[1],[4],[0],[0],[2],[3],[2068] -54,8,"[1, 2]","[70, 0]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[3434] -54,9,"[1, 2]","[70, 10]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2106] -54,10,"[2, 1]","[10, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2768] -54,11,"[1, 2]","[80, 20]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2167] -54,12,"[1, 2]","[70, 40]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2409] -54,13,"[3, 4]","[60, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[3848] -54,14,"[2, 1]","[60, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2461] -54,15,"[2, 1]","[60, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2528] -54,16,"[1, 2]","[90, 70]","[1, 1]",[1],[5],[1],[450],[9],[1],[1],[2167] -54,17,"[1, 2]","[60, 80]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[2152] -54,18,"[2, 1]","[90, 30]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[2511] -54,19,"[3, 4]","[50, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[3029] -54,20,"[3, 4]","[30, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2085] -54,21,"[3, 4]","[40, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2166] -54,22,"[3, 4]","[40, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[2096] -54,23,"[4, 3]","[0, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[3054] -54,24,"[1, 2]","[30, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[3267] -54,25,"[1, 2]","[50, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[3071] -54,26,"[2, 1]","[50, 70]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[3632] -54,27,"[4, 3]","[60, 50]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2534] -54,28,"[3, 4]","[0, 90]","[2, 1]",[1],[5],[3],[0],[0],[1],[4],[2325] -54,29,"[2, 1]","[70, 10]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[3366] -54,30,"[4, 3]","[60, 20]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2616] -54,31,"[3, 4]","[10, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[2033] -54,32,"[4, 3]","[60, 20]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2380] -54,33,"[4, 3]","[60, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[2207] -54,34,"[1, 2]","[20, 80]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[2896] -54,35,"[1, 2]","[10, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2529] -54,36,"[4, 3]","[80, 20]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2661] -54,37,"[1, 2]","[20, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[4013] -54,38,"[2, 1]","[70, 10]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2268] -54,39,"[2, 1]","[60, 10]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2128] -54,40,"[3, 4]","[50, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[3314] -54,41,"[2, 1]","[90, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[31921] 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1]",[2],[1],[3],[80],[8],[1],[4],[2902] -54,58,"[3, 4]","[90, 60]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[2510] -54,59,"[2, 1]","[80, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[2151] -54,60,"[4, 3]","[50, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[2655] -54,61,"[1, 2]","[60, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[2263] -54,62,"[4, 3]","[30, 70]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2318] -54,63,"[1, 2]","[60, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2350] -54,64,"[2, 1]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2544] -54,65,"[1, 2]","[70, 60]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2399] -54,66,"[3, 4]","[70, 30]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[2291] -54,67,"[3, 4]","[80, 20]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2577] -54,68,"[1, 2]","[90, 0]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[3179] -54,69,"[2, 1]","[10, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[4087] -54,70,"[4, 3]","[10, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[2310] -54,71,"[2, 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3]","[10, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2402] -54,33,"[1, 2]","[40, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[2223] -54,34,"[1, 2]","[30, 60]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[2235] -54,35,"[1, 2]","[10, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[1835] -54,36,"[2, 1]","[60, 50]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2561] -54,37,"[1, 2]","[70, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2330] -54,38,"[1, 2]","[90, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[1947] -54,39,"[1, 2]","[80, 40]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2017] -54,40,"[2, 1]","[50, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[5963] -54,41,"[1, 2]","[70, 50]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[7502] -54,42,"[1, 2]","[60, 70]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2330] -54,43,"[1, 2]","[60, 80]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2082] -54,44,"[1, 2]","[80, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2044] -54,45,"[2, 1]","[90, 90]","[1, 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2]",[1],[1],[4],[70],[7],[2],[3],[2004] -54,7,"[2, 1]","[60, 10]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2781] -54,8,"[2, 1]","[40, 0]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2653] -54,9,"[4, 3]","[90, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[3],[1945] -54,10,"[4, 3]","[90, 10]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2002] -54,11,"[4, 3]","[90, 20]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2044] -54,12,"[1, 2]","[10, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2293] -54,13,"[3, 4]","[20, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2318] -54,14,"[2, 1]","[70, 30]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[4200] -54,15,"[3, 4]","[30, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2094] -54,16,"[3, 4]","[40, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[3664] -54,17,"[1, 2]","[40, 70]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[2095] -54,18,"[4, 3]","[40, 60]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[2440] -54,19,"[3, 4]","[60, 40]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2091] -54,20,"[1, 2]","[70, 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-54,75,"[4, 3]","[50, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2019] -54,76,"[3, 4]","[20, 40]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[2121] -54,77,"[4, 3]","[20, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1927] -54,78,"[3, 4]","[30, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2134] -54,79,"[2, 1]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2154] -54,80,"[3, 4]","[30, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2167] -54,1,"[4, 3]","[20, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[4034] -54,2,"[4, 3]","[20, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2778] -54,3,"[1, 2]","[40, 0]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[2300] -54,4,"[1, 2]","[80, 10]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2963] -54,5,"[4, 3]","[40, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2231] -54,6,"[4, 3]","[50, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[2128] -54,7,"[3, 4]","[30, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[3983] -54,8,"[4, 3]","[40, 50]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2216] -54,9,"[3, 4]","[60, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[2491] -54,10,"[2, 1]","[30, 60]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[2318] -54,11,"[2, 1]","[40, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[2124] -54,12,"[4, 3]","[70, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[2216] -54,13,"[4, 3]","[80, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[2288] -54,14,"[2, 1]","[80, 60]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2245] -54,15,"[4, 3]","[80, 70]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2260] -54,16,"[3, 4]","[80, 90]","[2, 2]",[1],[5],[4],[450],[9],[2],[4],[2245] -54,17,"[3, 4]","[80, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2084] -54,18,"[3, 4]","[50, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[2025] -54,19,"[1, 2]","[70, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[3727] -54,20,"[2, 1]","[80, 70]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[2887] -54,21,"[1, 2]","[50, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[2189] -54,22,"[3, 4]","[30, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2523] -54,23,"[2, 1]","[80, 10]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[2477] -54,24,"[3, 4]","[40, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2166] -54,25,"[4, 3]","[70, 50]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2375] -54,26,"[3, 4]","[20, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[2110] -54,27,"[4, 3]","[90, 50]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[2364] -54,28,"[2, 1]","[80, 60]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2453] -54,29,"[1, 2]","[30, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1997] -54,30,"[2, 1]","[60, 40]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[3017] -54,31,"[3, 4]","[60, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2108] -54,32,"[4, 3]","[20, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2069] -54,33,"[1, 2]","[50, 40]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[2408] -54,34,"[4, 3]","[50, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[2175] -54,35,"[4, 3]","[50, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[2006] 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1]",[2],[1],[1],[10],[1],[1],[1],[2052] -54,50,"[3, 4]","[30, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2583] -54,51,"[2, 1]","[30, 40]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2561] -54,52,"[4, 3]","[20, 10]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2281] -54,53,"[2, 1]","[30, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[3069] -54,54,"[3, 4]","[20, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[1900] -54,55,"[1, 2]","[0, 10]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[2371] -54,56,"[4, 3]","[0, 10]","[2, 2]",[2],[1],[4],[0],[0],[2],[4],[2295] -54,57,"[1, 2]","[10, 20]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[2514] -54,58,"[1, 2]","[10, 20]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[2035] -54,59,"[3, 4]","[10, 20]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[2353] -54,60,"[4, 3]","[30, 20]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[2130] -54,61,"[1, 2]","[10, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[1953] -54,62,"[3, 4]","[60, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2233] -54,63,"[1, 2]","[70, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[1929] -54,64,"[1, 2]","[70, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[1854] -54,65,"[4, 3]","[90, 50]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[2291] -54,66,"[4, 3]","[80, 60]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2353] -54,67,"[1, 2]","[60, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[4069] -54,68,"[1, 2]","[80, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2166] -54,69,"[2, 1]","[40, 90]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2361] -54,70,"[2, 1]","[40, 90]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2242] -54,71,"[4, 3]","[40, 60]","[2, 2]",[2],[1],[4],[40],[4],[2],[4],[2239] -54,72,"[1, 2]","[90, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[2268] -54,73,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1983] -54,74,"[3, 4]","[90, 20]","[2, 1]",[2],[5],[3],[450],[9],[1],[3],[1974] -54,75,"[1, 2]","[70, 30]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2326] -54,76,"[2, 1]","[40, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[2863] -54,77,"[4, 3]","[60, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2406] -54,78,"[3, 4]","[40, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2261] -54,79,"[4, 3]","[80, 0]","[2, 1]",[2],[1],[3],[0],[0],[1],[3],[2027] -54,80,"[2, 1]","[80, 10]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2577] -54,1,"[1, 2]","[0, 70]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[3763] -54,2,"[1, 2]","[20, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[3022] -54,3,"[3, 4]","[10, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2152] -54,4,"[4, 3]","[30, 20]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2094] -54,5,"[2, 1]","[30, 20]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[3566] -54,6,"[1, 2]","[10, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[2067] -54,7,"[4, 3]","[60, 20]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[2089] -54,8,"[2, 1]","[70, 10]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2177] -54,9,"[3, 4]","[0, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1928] -54,10,"[2, 1]","[40, 20]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2224] -54,11,"[1, 2]","[20, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2156] -54,12,"[2, 1]","[50, 40]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2090] -54,13,"[4, 3]","[40, 30]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[3492] -54,14,"[2, 1]","[40, 20]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[2706] -54,15,"[4, 3]","[30, 20]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[8255] -54,16,"[1, 2]","[10, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[1750] -54,17,"[2, 1]","[70, 10]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2088] -54,18,"[2, 1]","[50, 10]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2194] -54,19,"[4, 3]","[20, 30]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[1776] -54,20,"[2, 1]","[0, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1880] -54,21,"[2, 1]","[0, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1983] -54,22,"[3, 4]","[90, 0]","[2, 1]",[1],[1],[3],[90],[9],[1],[3],[3484] -54,23,"[4, 3]","[0, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2137] -54,24,"[1, 2]","[60, 10]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2274] -54,25,"[1, 2]","[70, 20]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1936] -54,26,"[1, 2]","[80, 20]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2033] -54,27,"[2, 1]","[0, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2248] -54,28,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2767] -54,29,"[3, 4]","[80, 30]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[1905] -54,30,"[2, 1]","[10, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2024] -54,31,"[3, 4]","[20, 0]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[2487] -54,32,"[1, 2]","[50, 20]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2227] -54,33,"[4, 3]","[60, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[2536] -54,34,"[2, 1]","[30, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2058] -54,35,"[3, 4]","[50, 60]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2667] -54,36,"[1, 2]","[60, 80]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[2089] -54,37,"[3, 4]","[80, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2070] -54,38,"[3, 4]","[80, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[2009] -54,39,"[3, 4]","[70, 60]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2490] -54,40,"[3, 4]","[60, 50]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[3149] -54,41,"[4, 3]","[0, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[4347] -54,42,"[1, 2]","[50, 10]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[2864] -54,43,"[1, 2]","[30, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[4260] -54,44,"[2, 1]","[10, 20]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[1926] -54,45,"[1, 2]","[30, 10]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[3843] -54,46,"[4, 3]","[10, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1887] -54,47,"[4, 3]","[10, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1874] -54,48,"[1, 2]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[5271] -54,49,"[3, 4]","[10, 20]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1985] -54,50,"[4, 3]","[10, 0]","[2, 1]",[2],[5],[3],[0],[0],[1],[3],[3007] -54,51,"[4, 3]","[30, 40]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[1705] -54,52,"[4, 3]","[10, 20]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[4709] -54,53,"[2, 1]","[30, 0]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2560] -54,54,"[2, 1]","[30, 20]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[6150] -54,55,"[4, 3]","[40, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[9552] -54,56,"[3, 4]","[50, 0]","[2, 2]",[2],[5],[4],[0],[0],[2],[3],[30077] -54,57,"[4, 3]","[20, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[2175] -54,58,"[4, 3]","[0, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1744] -54,59,"[1, 2]","[0, 40]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[2218] -54,60,"[4, 3]","[10, 30]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1951] -54,61,"[4, 3]","[0, 10]","[2, 2]",[2],[1],[4],[0],[0],[2],[4],[1924] -54,62,"[4, 3]","[30, 10]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[5884] -54,63,"[4, 3]","[40, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1903] -54,64,"[2, 1]","[30, 60]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[2799] -54,65,"[1, 2]","[90, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2327] -54,66,"[2, 1]","[20, 60]","[1, 2]",[2],[1],[2],[20],[2],[2],[2],[2240] -54,67,"[2, 1]","[20, 70]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[1920] -54,68,"[2, 1]","[40, 60]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[2152] -54,69,"[3, 4]","[80, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[3502] -54,70,"[1, 2]","[80, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[3344] -54,71,"[1, 2]","[70, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[1874] -54,72,"[1, 2]","[60, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[1837] -54,73,"[1, 2]","[70, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1806] -54,74,"[3, 4]","[80, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[2131] -54,75,"[2, 1]","[20, 90]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[2068] -54,76,"[2, 1]","[60, 70]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[2099] -54,77,"[3, 4]","[70, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[2135] -54,78,"[4, 3]","[40, 50]","[2, 2]",[2],[1],[4],[40],[4],[2],[4],[1996] -54,79,"[1, 2]","[60, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[2760] -54,80,"[2, 1]","[40, 70]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1949] -54,1,"[4, 3]","[30, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[4347] -54,2,"[2, 1]","[0, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2447] -54,3,"[4, 3]","[20, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1897] -54,4,"[4, 3]","[0, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[1979] -54,5,"[4, 3]","[10, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1879] -54,6,"[1, 2]","[30, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[2207] -54,7,"[4, 3]","[10, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1977] -54,8,"[4, 3]","[30, 60]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[1951] -54,9,"[3, 4]","[60, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[2108] -54,10,"[1, 2]","[60, 30]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2846] -54,11,"[3, 4]","[70, 40]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2322] -54,12,"[2, 1]","[50, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2011] -54,13,"[1, 2]","[60, 0]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2071] -54,14,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1876] -54,15,"[1, 2]","[60, 50]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[1959] -54,16,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2194] -54,17,"[1, 2]","[80, 40]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1870] -54,18,"[2, 1]","[40, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2133] -54,19,"[1, 2]","[60, 50]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2162] -54,20,"[3, 4]","[80, 40]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[1929] -54,21,"[2, 1]","[40, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2154] -54,22,"[1, 2]","[80, 30]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2165] -54,23,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[3802] -54,24,"[4, 3]","[50, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[2968] -54,25,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2143] -54,26,"[2, 1]","[60, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[2261] -54,27,"[2, 1]","[80, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[2126] -54,28,"[2, 1]","[80, 60]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2408] -54,29,"[4, 3]","[90, 50]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[2125] -54,30,"[2, 1]","[80, 40]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2094] -54,31,"[4, 3]","[70, 40]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[1836] -54,32,"[3, 4]","[30, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2092] -54,33,"[3, 4]","[60, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[1949] -54,34,"[4, 3]","[30, 70]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[4809] -54,35,"[4, 3]","[30, 40]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[2556] -54,36,"[3, 4]","[20, 30]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[2206] -54,37,"[3, 4]","[20, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1858] -54,38,"[4, 3]","[20, 60]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[1961] -54,39,"[4, 3]","[60, 70]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1941] -54,40,"[1, 2]","[70, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[3272] -54,41,"[2, 1]","[90, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[47872] -54,42,"[3, 4]","[50, 80]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[1949] -54,43,"[3, 4]","[50, 70]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[2043] -54,44,"[1, 2]","[50, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[27575] -54,45,"[2, 1]","[40, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[2240] -54,46,"[1, 2]","[50, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[1792] -54,47,"[2, 1]","[40, 50]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[1996] -54,48,"[3, 4]","[20, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2367] -54,49,"[2, 1]","[40, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[2453] -54,50,"[2, 1]","[40, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[2215] -54,51,"[1, 2]","[20, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2241] -54,52,"[3, 4]","[30, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2287] -54,53,"[1, 2]","[30, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1948] -54,54,"[2, 1]","[80, 10]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2015] -54,55,"[4, 3]","[70, 10]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[98416] -54,56,"[4, 3]","[80, 50]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[1964] -54,57,"[1, 2]","[70, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2063] -54,58,"[2, 1]","[60, 80]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2970] -54,59,"[1, 2]","[70, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[27850] -54,60,"[1, 2]","[50, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1834] -54,61,"[4, 3]","[30, 80]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2422] -54,62,"[4, 3]","[10, 70]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[2145] -54,63,"[3, 4]","[50, 10]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2040] -54,64,"[4, 3]","[10, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1889] -54,65,"[1, 2]","[30, 0]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[2202] -54,66,"[1, 2]","[30, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[2538] -54,67,"[1, 2]","[20, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[4424] -54,68,"[3, 4]","[40, 20]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[4103] -54,69,"[4, 3]","[0, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2098] -54,70,"[2, 1]","[30, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2372] -54,71,"[2, 1]","[30, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2594] -54,72,"[3, 4]","[40, 50]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[2492] -54,73,"[2, 1]","[60, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[73196] -54,74,"[1, 2]","[80, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1975] -54,75,"[3, 4]","[80, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[3234] -54,76,"[2, 1]","[70, 90]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2209] -54,77,"[4, 3]","[60, 70]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[2155] -54,78,"[2, 1]","[60, 50]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2451] -54,79,"[4, 3]","[40, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[4],[2244] -54,80,"[1, 2]","[20, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[2187] -55,1,"[2, 1]","[10, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[19085] -55,2,"[1, 2]","[80, 20]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2197] -55,3,"[4, 3]","[80, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[3096] -55,4,"[1, 2]","[60, 70]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2448] -55,5,"[4, 3]","[80, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[4128] -55,6,"[1, 2]","[70, 30]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2214] -55,7,"[1, 2]","[60, 0]","[1, 2]",[1],[1],[2],[0],[0],[2],[1],[3013] -55,8,"[1, 2]","[70, 0]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2198] -55,9,"[1, 2]","[90, 20]","[1, 1]",[1],[5],[1],[450],[9],[1],[1],[2147] -55,10,"[2, 1]","[0, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[3262] -55,11,"[2, 1]","[0, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2780] -55,12,"[2, 1]","[30, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2031] -55,13,"[2, 1]","[20, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2714] -55,14,"[2, 1]","[50, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[1997] -55,15,"[1, 2]","[60, 50]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[8688] -55,16,"[1, 2]","[60, 40]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[3013] -55,17,"[3, 4]","[30, 0]","[2, 2]",[1],[1],[4],[0],[0],[2],[3],[2829] -55,18,"[1, 2]","[0, 10]","[1, 1]",[1],[1],[1],[0],[0],[1],[2],[4211] -55,19,"[4, 3]","[10, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[2696] -55,20,"[2, 1]","[0, 10]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[3096] -55,21,"[3, 4]","[20, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[2430] -55,22,"[3, 4]","[10, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[2181] -55,23,"[4, 3]","[0, 20]","[2, 2]",[1],[1],[4],[0],[0],[2],[4],[3979] -55,24,"[4, 3]","[0, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1965] -55,25,"[3, 4]","[30, 10]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[2297] -55,26,"[1, 2]","[40, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[3262] -55,27,"[2, 1]","[50, 40]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2264] -55,28,"[1, 2]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2780] -55,29,"[1, 2]","[70, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2014] -55,30,"[4, 3]","[80, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[2780] -55,31,"[2, 1]","[70, 90]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2331] -55,32,"[3, 4]","[90, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[2264] -55,33,"[3, 4]","[70, 90]","[2, 2]",[1],[5],[4],[450],[9],[2],[4],[2381] -55,34,"[3, 4]","[60, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[3363] -55,35,"[2, 1]","[50, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2447] -55,36,"[1, 2]","[30, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2447] -55,37,"[3, 4]","[0, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[4228] -55,38,"[1, 2]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2764] -55,39,"[3, 4]","[10, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2764] -55,40,"[3, 4]","[20, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[4028] -55,41,"[3, 4]","[0, 10]","[2, 1]",[2],[1],[3],[0],[0],[1],[4],[9118] -55,42,"[1, 2]","[10, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2081] -55,43,"[2, 1]","[10, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2280] -55,44,"[3, 4]","[20, 30]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[2131] -55,45,"[1, 2]","[10, 50]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[10302] -55,46,"[3, 4]","[10, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[3777] -55,47,"[3, 4]","[10, 30]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[2530] -55,48,"[4, 3]","[20, 30]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[3029] -55,49,"[3, 4]","[40, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2348] -55,50,"[4, 3]","[40, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2380] -55,51,"[3, 4]","[40, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[1997] -55,52,"[3, 4]","[50, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2713] -55,53,"[1, 2]","[60, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2647] -55,54,"[4, 3]","[50, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[3578] -55,55,"[2, 1]","[40, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2364] -55,56,"[3, 4]","[40, 20]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[4726] -55,57,"[2, 1]","[10, 80]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[3079] -55,58,"[2, 1]","[30, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2281] -55,59,"[4, 3]","[20, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[2247] -55,60,"[3, 4]","[20, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[4],[2148] -55,61,"[1, 2]","[20, 50]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[3012] -55,62,"[3, 4]","[20, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[2098] -55,63,"[4, 3]","[30, 20]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2297] -55,64,"[3, 4]","[60, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2863] -55,65,"[4, 3]","[0, 90]","[2, 2]",[2],[5],[4],[0],[0],[2],[4],[5059] -55,66,"[2, 1]","[10, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2181] -55,67,"[2, 1]","[0, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2546] -55,68,"[1, 2]","[50, 10]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[2198] -55,69,"[3, 4]","[40, 0]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[2131] -55,70,"[4, 3]","[10, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[3246] -55,71,"[1, 2]","[80, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[2347] -55,72,"[2, 1]","[50, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2696] -55,73,"[2, 1]","[40, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1931] -55,74,"[2, 1]","[50, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[1981] -55,75,"[2, 1]","[50, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[1865] -55,76,"[1, 2]","[10, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2314] -55,77,"[3, 4]","[20, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[2314] -55,78,"[1, 2]","[10, 30]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[2097] -55,79,"[2, 1]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[3362] -55,80,"[2, 1]","[10, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[8172] -54,1,"[3, 4]","[20, 60]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[3712] -54,2,"[2, 1]","[70, 10]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[3300] -54,3,"[2, 1]","[60, 20]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2802] -54,4,"[4, 3]","[40, 30]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[1979] -54,5,"[2, 1]","[40, 30]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[2029] -54,6,"[4, 3]","[50, 60]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[1936] -54,7,"[1, 2]","[60, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[2238] -54,8,"[1, 2]","[30, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2164] -54,9,"[2, 1]","[30, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[2823] -54,10,"[4, 3]","[30, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[3],[2397] -54,11,"[1, 2]","[0, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2335] -54,12,"[1, 2]","[40, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2191] -54,13,"[4, 3]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1991] -54,14,"[4, 3]","[40, 90]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1898] -54,15,"[3, 4]","[60, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[3869] -54,16,"[2, 1]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[4478] -54,17,"[2, 1]","[70, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2045] -54,18,"[3, 4]","[30, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2214] -54,19,"[1, 2]","[40, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2595] -54,20,"[1, 2]","[10, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[2177] -54,21,"[4, 3]","[50, 10]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[2200] -54,22,"[3, 4]","[20, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2134] -54,23,"[1, 2]","[50, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[2308] -54,24,"[4, 3]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2015] -54,25,"[1, 2]","[70, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2272] -54,26,"[3, 4]","[60, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[2105] -54,27,"[2, 1]","[70, 40]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2082] -54,28,"[1, 2]","[30, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2097] -54,29,"[4, 3]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1934] -54,30,"[1, 2]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2217] -54,31,"[1, 2]","[50, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2214] -54,32,"[3, 4]","[20, 40]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[2999] -54,33,"[1, 2]","[10, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[3707] -54,34,"[2, 1]","[30, 0]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2364] -54,35,"[4, 3]","[50, 0]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[1785] -54,36,"[1, 2]","[10, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2430] -54,37,"[1, 2]","[0, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2166] -54,38,"[2, 1]","[70, 20]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[3047] -54,39,"[2, 1]","[50, 10]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2256] -54,40,"[2, 1]","[30, 20]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2374] -54,41,"[1, 2]","[10, 30]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[2317] -54,42,"[3, 4]","[0, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[2175] -54,43,"[4, 3]","[10, 0]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[2077] -54,44,"[1, 2]","[30, 20]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[2524] -54,45,"[2, 1]","[40, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[2069] -54,46,"[3, 4]","[0, 30]","[2, 1]",[2],[5],[3],[0],[0],[1],[4],[2084] -54,47,"[3, 4]","[30, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2188] -54,48,"[4, 3]","[20, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[2099] -54,49,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1885] -54,50,"[2, 1]","[30, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[1935] -54,51,"[3, 4]","[20, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[4],[2086] -54,52,"[2, 1]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2037] -54,53,"[1, 2]","[60, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[2431] -54,54,"[1, 2]","[80, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1994] -54,55,"[3, 4]","[80, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2275] -54,56,"[2, 1]","[0, 80]","[1, 2]",[2],[1],[2],[0],[0],[2],[2],[2223] -54,57,"[4, 3]","[0, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[2173] -54,58,"[4, 3]","[20, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1990] -54,59,"[2, 1]","[30, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2149] -54,60,"[3, 4]","[70, 50]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[2027] -54,61,"[4, 3]","[80, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[2532] -54,62,"[3, 4]","[50, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1949] -54,63,"[2, 1]","[50, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2065] -54,64,"[1, 2]","[60, 50]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2055] -54,65,"[3, 4]","[40, 20]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1947] -54,66,"[4, 3]","[10, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[2273] -54,67,"[1, 2]","[40, 10]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2281] -54,68,"[4, 3]","[20, 0]","[2, 1]",[2],[5],[3],[0],[0],[1],[3],[2177] -54,69,"[2, 1]","[50, 20]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2186] -54,70,"[3, 4]","[20, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[3207] -54,71,"[2, 1]","[60, 0]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[2666] -54,72,"[4, 3]","[60, 0]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2241] -54,73,"[3, 4]","[10, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[2302] -54,74,"[1, 2]","[40, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[3851] -54,75,"[2, 1]","[70, 60]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[1943] -54,76,"[2, 1]","[60, 70]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1876] -54,77,"[3, 4]","[70, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2080] -54,78,"[3, 4]","[90, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[2223] -54,79,"[3, 4]","[60, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[2905] -54,80,"[4, 3]","[70, 60]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2918] -54,1,"[4, 3]","[70, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[4125] -54,2,"[1, 2]","[10, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[1965] -54,3,"[2, 1]","[50, 0]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[1978] -54,4,"[3, 4]","[30, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2002] -54,5,"[1, 2]","[30, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[1923] -54,6,"[4, 3]","[80, 0]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2214] -54,7,"[1, 2]","[0, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2968] -54,8,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[1938] -54,9,"[2, 1]","[70, 0]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2258] -54,10,"[1, 2]","[50, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1983] -54,11,"[1, 2]","[50, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2299] -54,12,"[1, 2]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[1906] -54,13,"[3, 4]","[20, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[2186] -54,14,"[1, 2]","[40, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2434] -54,15,"[3, 4]","[50, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2990] -54,16,"[3, 4]","[0, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1992] -54,17,"[1, 2]","[40, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[1906] -54,18,"[3, 4]","[60, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1993] -54,19,"[2, 1]","[60, 80]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2329] -54,20,"[1, 2]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2178] -54,21,"[1, 2]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2182] -54,22,"[1, 2]","[70, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2385] -54,23,"[1, 2]","[70, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[1958] -54,24,"[4, 3]","[30, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2705] -54,25,"[2, 1]","[50, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2113] -54,26,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1999] -54,27,"[3, 4]","[80, 60]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2380] -54,28,"[4, 3]","[20, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2255] -54,29,"[4, 3]","[0, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1940] -54,30,"[3, 4]","[60, 30]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2152] -54,31,"[2, 1]","[40, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2115] -54,32,"[4, 3]","[50, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[1877] -54,33,"[4, 3]","[80, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1795] -54,34,"[1, 2]","[30, 70]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[2367] -54,35,"[3, 4]","[50, 70]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[2940] -54,36,"[1, 2]","[40, 60]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2467] -54,37,"[2, 1]","[80, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[3290] -54,38,"[2, 1]","[80, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2950] -54,39,"[1, 2]","[40, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2462] -54,40,"[2, 1]","[70, 20]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2619] -54,41,"[1, 2]","[50, 70]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[4488] -54,42,"[3, 4]","[50, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2007] -54,43,"[4, 3]","[70, 40]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[3691] -54,44,"[1, 2]","[0, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2299] -54,45,"[4, 3]","[80, 0]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2468] -54,46,"[3, 4]","[10, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2125] -54,47,"[2, 1]","[70, 10]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[2232] -54,48,"[4, 3]","[60, 30]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2340] -54,49,"[4, 3]","[30, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2499] -54,50,"[1, 2]","[10, 40]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[2700] -54,51,"[1, 2]","[10, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2171] -54,52,"[2, 1]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2487] -54,53,"[3, 4]","[30, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[2070] -54,54,"[3, 4]","[40, 20]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1823] -54,55,"[2, 1]","[0, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[2985] -54,56,"[1, 2]","[0, 50]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[2366] -54,57,"[2, 1]","[30, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[2387] -54,58,"[4, 3]","[30, 40]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2125] -54,59,"[1, 2]","[20, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[2476] -54,60,"[1, 2]","[30, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[1833] -54,61,"[1, 2]","[0, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[1852] -54,62,"[2, 1]","[70, 0]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2463] -54,63,"[4, 3]","[90, 50]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[2013] -54,64,"[4, 3]","[50, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[2013] -54,65,"[3, 4]","[20, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2515] -54,66,"[3, 4]","[20, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1874] -54,67,"[1, 2]","[70, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2071] -54,68,"[3, 4]","[40, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1747] -54,69,"[4, 3]","[80, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1879] -54,70,"[2, 1]","[60, 80]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[21254] -54,71,"[3, 4]","[90, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2922] -54,72,"[4, 3]","[70, 80]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[1915] -54,73,"[1, 2]","[60, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[4140] -54,74,"[3, 4]","[70, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[2354] -54,75,"[2, 1]","[80, 50]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[2505] -54,76,"[3, 4]","[60, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[2371] -54,77,"[2, 1]","[80, 90]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[9592] -54,78,"[4, 3]","[80, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1949] -54,79,"[4, 3]","[50, 60]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[2233] -54,80,"[2, 1]","[80, 90]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[2465] -54,1,"[4, 3]","[10, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[8267] -54,2,"[2, 1]","[0, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2316] -54,3,"[1, 2]","[70, 10]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2409] -54,4,"[1, 2]","[80, 20]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2305] -54,5,"[3, 4]","[80, 10]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[2090] -54,6,"[4, 3]","[30, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1920] -54,7,"[3, 4]","[80, 0]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2178] -54,8,"[1, 2]","[70, 0]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2138] -54,9,"[1, 2]","[70, 10]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2244] -54,10,"[2, 1]","[10, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2208] -54,11,"[1, 2]","[80, 20]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2409] -54,12,"[1, 2]","[70, 40]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2553] -54,13,"[3, 4]","[60, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[2153] -54,14,"[2, 1]","[60, 70]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[4765] -54,15,"[2, 1]","[60, 80]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[1855] -54,16,"[1, 2]","[90, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2136] -54,17,"[1, 2]","[60, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1869] -54,18,"[2, 1]","[90, 30]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[2781] -54,19,"[3, 4]","[50, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[2256] -54,20,"[3, 4]","[30, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2135] -54,21,"[3, 4]","[40, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2689] -54,22,"[3, 4]","[40, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[2761] -54,23,"[4, 3]","[0, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[2115] -54,24,"[1, 2]","[30, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[2187] -54,25,"[1, 2]","[50, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[2193] -54,26,"[2, 1]","[50, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2068] -54,27,"[4, 3]","[60, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[2305] -54,28,"[3, 4]","[0, 90]","[2, 1]",[1],[5],[3],[0],[0],[1],[4],[2146] -54,29,"[2, 1]","[70, 10]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2037] -54,30,"[4, 3]","[60, 20]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2629] -54,31,"[3, 4]","[10, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[3330] -54,32,"[4, 3]","[60, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[2569] -54,33,"[4, 3]","[60, 20]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2130] -54,34,"[1, 2]","[20, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2315] -54,35,"[1, 2]","[10, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2166] -54,36,"[4, 3]","[80, 20]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2258] -54,37,"[1, 2]","[20, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2661] -54,38,"[2, 1]","[70, 10]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2377] -54,39,"[2, 1]","[60, 10]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2656] -54,40,"[3, 4]","[50, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2613] -54,41,"[2, 1]","[90, 40]","[1, 2]",[2],[5],[2],[450],[9],[2],[1],[3528] -54,42,"[1, 2]","[50, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2496] -54,43,"[4, 3]","[80, 70]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[2233] -54,44,"[2, 1]","[40, 90]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[2166] -54,45,"[4, 3]","[60, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[2460] -54,46,"[1, 2]","[90, 50]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[2332] -54,47,"[3, 4]","[80, 50]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[1928] -54,48,"[1, 2]","[70, 60]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2276] -54,49,"[1, 2]","[60, 70]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2281] -54,50,"[1, 2]","[80, 70]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1809] -54,51,"[4, 3]","[70, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[2265] -54,52,"[1, 2]","[50, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2409] -54,53,"[4, 3]","[70, 50]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2215] -54,54,"[2, 1]","[70, 40]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[2045] -54,55,"[4, 3]","[60, 70]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[2440] -54,56,"[1, 2]","[50, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[2326] -54,57,"[3, 4]","[60, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[2515] -54,58,"[4, 3]","[60, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[2119] -54,59,"[3, 4]","[70, 60]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[2049] -54,60,"[2, 1]","[30, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[2523] -54,61,"[3, 4]","[80, 10]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2129] -54,62,"[4, 3]","[20, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1930] -54,63,"[1, 2]","[80, 40]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2885] -54,64,"[2, 1]","[0, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2047] -54,65,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1991] -54,66,"[3, 4]","[90, 40]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[1841] -54,67,"[3, 4]","[20, 90]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[1863] -54,68,"[1, 2]","[50, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[1770] -54,69,"[4, 3]","[60, 20]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2097] -54,70,"[4, 3]","[50, 30]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2036] -54,71,"[2, 1]","[40, 60]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[2048] -54,72,"[4, 3]","[30, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2006] -54,73,"[3, 4]","[80, 40]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1960] -54,74,"[3, 4]","[70, 20]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1914] -54,75,"[3, 4]","[50, 40]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1871] -54,76,"[3, 4]","[60, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2051] -54,77,"[1, 2]","[80, 0]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1965] -54,78,"[4, 3]","[10, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[2045] -54,79,"[4, 3]","[10, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1689] -54,80,"[1, 2]","[40, 20]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2178] -54,1,"[4, 3]","[70, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[4504] -54,2,"[1, 2]","[10, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2474] -54,3,"[2, 1]","[50, 0]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[3341] -54,4,"[3, 4]","[30, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2090] -54,5,"[1, 2]","[30, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2649] -54,6,"[4, 3]","[80, 0]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2333] -54,7,"[1, 2]","[0, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2591] -54,8,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[2532] -54,9,"[2, 1]","[70, 0]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[5209] -54,10,"[1, 2]","[50, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2058] -54,11,"[1, 2]","[50, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2038] -54,12,"[1, 2]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[1823] -54,13,"[3, 4]","[20, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[2592] -54,14,"[1, 2]","[40, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[3056] -54,15,"[3, 4]","[50, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[1970] -54,16,"[3, 4]","[0, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1806] -54,17,"[1, 2]","[40, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[2416] -54,18,"[3, 4]","[60, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1756] -54,19,"[2, 1]","[60, 80]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[3874] -54,20,"[1, 2]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[3919] -54,21,"[1, 2]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2180] -54,22,"[1, 2]","[70, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2304] -54,23,"[1, 2]","[70, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[3335] -54,24,"[4, 3]","[30, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1979] -54,25,"[2, 1]","[50, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2143] -54,26,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1827] -54,27,"[3, 4]","[80, 60]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2353] -54,28,"[4, 3]","[20, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2301] -54,29,"[4, 3]","[0, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2016] -54,30,"[3, 4]","[60, 30]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2543] -54,31,"[2, 1]","[40, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2023] -54,32,"[4, 3]","[50, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[1809] -54,33,"[4, 3]","[80, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1916] -54,34,"[1, 2]","[30, 70]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[2500] -54,35,"[3, 4]","[50, 70]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[3087] -54,36,"[1, 2]","[40, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2112] -54,37,"[2, 1]","[80, 60]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2982] -54,38,"[2, 1]","[80, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2031] -54,39,"[1, 2]","[40, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2155] -54,40,"[2, 1]","[70, 20]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2214] -54,41,"[1, 2]","[50, 70]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[5302] -54,42,"[3, 4]","[50, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2550] -54,43,"[4, 3]","[70, 40]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[2950] -54,44,"[1, 2]","[0, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[4544] -54,45,"[4, 3]","[80, 0]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2370] -54,46,"[3, 4]","[10, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2332] -54,47,"[2, 1]","[70, 10]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[2962] -54,48,"[4, 3]","[60, 30]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[7245] -54,49,"[4, 3]","[30, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2404] -54,50,"[1, 2]","[10, 40]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[2455] -54,51,"[1, 2]","[10, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1992] -54,52,"[2, 1]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2526] -54,53,"[3, 4]","[30, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[2605] -54,54,"[3, 4]","[40, 20]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1950] -54,55,"[2, 1]","[0, 10]","[1, 2]",[2],[5],[2],[0],[0],[2],[2],[2871] -54,56,"[1, 2]","[0, 50]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[2098] -54,57,"[2, 1]","[30, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[2840] -54,58,"[4, 3]","[30, 40]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2450] -54,59,"[1, 2]","[20, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[2058] -54,60,"[1, 2]","[30, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[2107] -54,61,"[1, 2]","[0, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[1733] -54,62,"[2, 1]","[70, 0]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2261] -54,63,"[4, 3]","[90, 50]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[3222] -54,64,"[4, 3]","[50, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[2306] -54,65,"[3, 4]","[20, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2022] -54,66,"[3, 4]","[20, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2055] -54,67,"[1, 2]","[70, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2494] -54,68,"[3, 4]","[40, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1925] -54,69,"[4, 3]","[80, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2117] -54,70,"[2, 1]","[60, 80]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2179] -54,71,"[3, 4]","[90, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2012] -54,72,"[4, 3]","[70, 80]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[5619] -54,73,"[1, 2]","[60, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2179] -54,74,"[3, 4]","[70, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1769] -54,75,"[2, 1]","[80, 50]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[3045] -54,76,"[3, 4]","[60, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[2170] -54,77,"[2, 1]","[80, 90]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[2684] -54,78,"[4, 3]","[80, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2890] -54,79,"[4, 3]","[50, 60]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[2200] -54,80,"[2, 1]","[80, 90]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[2083] -54,1,"[1, 2]","[60, 20]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[5452] -54,2,"[3, 4]","[50, 20]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[3576] -54,3,"[3, 4]","[10, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[3050] -54,4,"[2, 1]","[50, 40]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2601] -54,5,"[1, 2]","[60, 50]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[4041] -54,6,"[3, 4]","[30, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[4311] -54,7,"[4, 3]","[40, 50]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[3714] -54,8,"[3, 4]","[80, 40]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[4016] -54,9,"[1, 2]","[50, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2364] -54,10,"[2, 1]","[50, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[2110] -54,11,"[3, 4]","[20, 60]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[3059] -54,12,"[4, 3]","[50, 20]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[3417] -54,13,"[4, 3]","[20, 10]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2624] -54,14,"[4, 3]","[40, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[2554] -54,15,"[1, 2]","[60, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2126] -54,16,"[3, 4]","[90, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[3621] -54,17,"[3, 4]","[60, 30]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2468] -54,18,"[3, 4]","[50, 40]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[2097] -54,19,"[4, 3]","[50, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1974] -54,20,"[3, 4]","[10, 50]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[2230] -54,21,"[1, 2]","[20, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2206] -54,22,"[1, 2]","[60, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[1916] -54,23,"[1, 2]","[80, 70]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2385] -54,24,"[3, 4]","[80, 70]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2204] -54,25,"[4, 3]","[70, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2053] -54,26,"[4, 3]","[50, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[6633] -54,27,"[2, 1]","[60, 70]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2469] -54,28,"[3, 4]","[50, 70]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[2034] -54,29,"[3, 4]","[80, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2344] -54,30,"[3, 4]","[70, 60]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[2437] -54,31,"[4, 3]","[80, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[1858] -54,32,"[1, 2]","[50, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2348] -54,33,"[3, 4]","[30, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[2351] -54,34,"[2, 1]","[80, 30]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[2237] -54,35,"[1, 2]","[30, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[8096] -54,36,"[4, 3]","[70, 50]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2229] -54,37,"[1, 2]","[50, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2816] -54,38,"[1, 2]","[70, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2338] -54,39,"[2, 1]","[60, 50]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2291] -54,40,"[2, 1]","[50, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[2874] -54,41,"[1, 2]","[10, 40]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[3608] -54,42,"[3, 4]","[20, 30]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[2918] -54,43,"[2, 1]","[10, 0]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[5419] -54,44,"[3, 4]","[20, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[2655] -54,45,"[4, 3]","[0, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[1973] -54,46,"[2, 1]","[0, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[2084] -54,47,"[1, 2]","[0, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2085] -54,48,"[2, 1]","[0, 20]","[1, 2]",[2],[1],[2],[0],[0],[2],[2],[2862] -54,49,"[3, 4]","[20, 10]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[2034] -54,50,"[1, 2]","[40, 30]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[4537] -54,51,"[4, 3]","[50, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[2245] -54,52,"[2, 1]","[70, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2148] -54,53,"[2, 1]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2068] -54,54,"[3, 4]","[80, 50]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2108] -54,55,"[2, 1]","[40, 90]","[1, 1]",[2],[1],[1],[90],[9],[1],[2],[7445] -54,56,"[4, 3]","[40, 80]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[7198] -54,57,"[2, 1]","[50, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[2174] -54,58,"[3, 4]","[70, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[2649] -54,59,"[1, 2]","[70, 30]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[2450] -54,60,"[1, 2]","[80, 40]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2022] -54,61,"[2, 1]","[10, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[2387] -54,62,"[3, 4]","[80, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[2052] -54,63,"[1, 2]","[70, 20]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2011] -54,64,"[3, 4]","[60, 40]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[2303] -54,65,"[1, 2]","[30, 20]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[9427] -54,66,"[1, 2]","[20, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[1886] -54,67,"[3, 4]","[10, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[1885] -54,68,"[4, 3]","[0, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[6269] -54,69,"[4, 3]","[30, 20]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1948] -54,70,"[1, 2]","[10, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[2433] -54,71,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1827] -54,72,"[2, 1]","[10, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[1844] -54,73,"[1, 2]","[30, 20]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[2356] -54,74,"[2, 1]","[30, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[1994] -54,75,"[4, 3]","[30, 20]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2980] -54,76,"[3, 4]","[40, 30]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[5846] -54,77,"[4, 3]","[30, 50]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[5797] -54,78,"[2, 1]","[50, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2993] -54,79,"[1, 2]","[90, 80]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[2414] -54,80,"[3, 4]","[30, 70]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1951] -54,1,"[1, 2]","[60, 40]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[4417] -54,2,"[4, 3]","[30, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[1890] -54,3,"[4, 3]","[50, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2007] -54,4,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2096] -54,5,"[3, 4]","[70, 40]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2914] -54,6,"[2, 1]","[50, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[3374] -54,7,"[4, 3]","[60, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[2339] -54,8,"[1, 2]","[70, 40]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[3343] -54,9,"[2, 1]","[20, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[2009] -54,10,"[4, 3]","[0, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[5379] -54,11,"[4, 3]","[30, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1806] -54,12,"[1, 2]","[60, 40]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[3451] -54,13,"[3, 4]","[60, 30]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2685] -54,14,"[4, 3]","[30, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[2132] -54,15,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2250] -54,16,"[3, 4]","[80, 30]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1967] -54,17,"[2, 1]","[60, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[2080] -54,18,"[4, 3]","[50, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[2211] -54,19,"[2, 1]","[90, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2100] -54,20,"[4, 3]","[70, 80]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[4559] -54,21,"[3, 4]","[80, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2583] -54,22,"[3, 4]","[80, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[1978] -54,23,"[4, 3]","[40, 80]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1995] -54,24,"[1, 2]","[90, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2931] -54,25,"[4, 3]","[60, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2245] -54,26,"[1, 2]","[80, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[6781] -54,27,"[1, 2]","[80, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1900] -54,28,"[2, 1]","[60, 70]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[1925] -54,29,"[4, 3]","[40, 90]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2073] -54,30,"[2, 1]","[30, 70]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[2504] -54,31,"[2, 1]","[60, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2497] -54,32,"[2, 1]","[50, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2233] -54,33,"[2, 1]","[40, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[1873] -54,34,"[3, 4]","[90, 50]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[2446] -54,35,"[2, 1]","[60, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2739] -54,36,"[1, 2]","[50, 90]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2162] -54,37,"[3, 4]","[50, 70]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[16686] -54,38,"[3, 4]","[40, 60]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1794] -54,39,"[2, 1]","[60, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[2100] -54,40,"[2, 1]","[70, 50]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1742] -54,41,"[3, 4]","[80, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2168] -54,42,"[1, 2]","[80, 70]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[3329] -54,43,"[2, 1]","[70, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[2091] -54,44,"[4, 3]","[80, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[2332] -54,45,"[1, 2]","[80, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[1935] -54,46,"[4, 3]","[80, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[1876] -54,47,"[2, 1]","[70, 30]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[1787] -54,48,"[3, 4]","[10, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[2103] -54,49,"[1, 2]","[20, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[2380] -54,50,"[4, 3]","[20, 20]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[2015] -54,51,"[3, 4]","[30, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[1849] -54,52,"[1, 2]","[50, 40]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[1886] -54,53,"[1, 2]","[50, 60]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2074] -54,54,"[1, 2]","[60, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[1765] -54,55,"[4, 3]","[80, 70]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[1832] -54,56,"[4, 3]","[70, 80]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[1877] -54,57,"[1, 2]","[80, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2806] -54,58,"[1, 2]","[60, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[2729] -54,59,"[3, 4]","[90, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[16330] -54,60,"[2, 1]","[60, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[2418] -54,61,"[1, 2]","[80, 60]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2104] -54,62,"[4, 3]","[90, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1849] -54,63,"[2, 1]","[80, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[3029] -54,64,"[4, 3]","[80, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1699] -54,65,"[3, 4]","[80, 60]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[4170] -54,66,"[4, 3]","[50, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2365] -54,67,"[3, 4]","[50, 70]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[1825] -54,68,"[4, 3]","[90, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[28966] -54,69,"[1, 2]","[60, 50]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1758] -54,70,"[1, 2]","[50, 60]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2255] -54,71,"[1, 2]","[90, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[1581] -54,72,"[4, 3]","[30, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[1828] -54,73,"[3, 4]","[20, 60]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[1903] -54,74,"[3, 4]","[30, 50]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1901] -54,75,"[2, 1]","[70, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[2143] -54,76,"[4, 3]","[80, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1815] -54,77,"[3, 4]","[10, 60]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1878] -54,78,"[1, 2]","[20, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2650] -54,79,"[3, 4]","[20, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[1894] -54,80,"[2, 1]","[80, 30]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2478] -54,1,"[1, 2]","[50, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[3496] -54,2,"[4, 3]","[40, 70]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2314] -54,3,"[2, 1]","[50, 60]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[2579] -54,4,"[2, 1]","[50, 70]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[2457] -54,5,"[2, 1]","[40, 60]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2099] -54,6,"[1, 2]","[60, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2036] -54,7,"[4, 3]","[40, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2112] -54,8,"[3, 4]","[60, 40]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[2090] -54,9,"[3, 4]","[70, 60]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[2217] -54,10,"[2, 1]","[70, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2832] -54,11,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2618] -54,12,"[4, 3]","[70, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[4064] -54,13,"[1, 2]","[50, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2774] -54,14,"[3, 4]","[50, 60]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[10474] -54,15,"[1, 2]","[40, 90]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[13175] -54,16,"[3, 4]","[0, 80]","[2, 1]",[1],[5],[3],[0],[0],[1],[4],[10521] -54,17,"[1, 2]","[20, 90]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[6833] -54,18,"[2, 1]","[50, 10]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[3573] -54,19,"[1, 2]","[0, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2078] -54,20,"[4, 3]","[60, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[4796] -54,21,"[4, 3]","[50, 20]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[2453] -54,22,"[3, 4]","[0, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[4378] -54,23,"[4, 3]","[40, 10]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[4597] -54,24,"[2, 1]","[40, 30]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[15750] -54,25,"[1, 2]","[20, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[4619] -54,26,"[4, 3]","[50, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[2017] -54,27,"[4, 3]","[40, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[3843] -54,28,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2065] -54,29,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[5664] -54,30,"[3, 4]","[90, 50]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[5667] -54,31,"[4, 3]","[0, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[3816] -54,32,"[1, 2]","[60, 30]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2264] -54,33,"[2, 1]","[20, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[7253] -54,34,"[3, 4]","[60, 40]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[3074] -54,35,"[1, 2]","[30, 40]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[26911] -54,36,"[4, 3]","[30, 20]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2825] -54,37,"[3, 4]","[40, 30]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[7236] -54,38,"[1, 2]","[30, 10]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2283] -54,39,"[2, 1]","[20, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[2521] -54,40,"[3, 4]","[30, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1989] -54,41,"[3, 4]","[40, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[3266] -54,42,"[4, 3]","[50, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1986] -54,43,"[1, 2]","[40, 50]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[3029] -54,44,"[2, 1]","[30, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[4552] -54,45,"[2, 1]","[20, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2066] -54,46,"[2, 1]","[60, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2948] -54,47,"[4, 3]","[70, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[4640] -54,48,"[4, 3]","[90, 10]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[10919] -54,49,"[4, 3]","[70, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[10539] -54,50,"[3, 4]","[40, 60]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[52438] -54,51,"[3, 4]","[0, 30]","[2, 1]",[2],[1],[3],[0],[0],[1],[4],[4268] -54,52,"[1, 2]","[0, 30]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[3090] -54,53,"[4, 3]","[20, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[9716] -54,54,"[2, 1]","[50, 0]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[7682] -54,55,"[3, 4]","[10, 30]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[4313] -54,56,"[4, 3]","[40, 20]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[3021] -54,57,"[4, 3]","[40, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[5457] -54,58,"[2, 1]","[60, 10]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[3111] -54,59,"[2, 1]","[80, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[17766] -54,60,"[1, 2]","[30, 90]","[1, 2]",[2],[1],[2],[90],[9],[2],[2],[2713] -54,61,"[2, 1]","[70, 10]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2053] -54,62,"[1, 2]","[0, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[15811] -54,63,"[1, 2]","[0, 40]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[1884] -54,64,"[4, 3]","[50, 30]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[3034] -54,65,"[3, 4]","[40, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[2492] -54,66,"[2, 1]","[50, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[2285] -54,67,"[2, 1]","[70, 40]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1939] -54,68,"[2, 1]","[50, 40]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2101] -54,69,"[2, 1]","[30, 50]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[4441] -54,70,"[2, 1]","[20, 40]","[1, 2]",[2],[1],[2],[20],[2],[2],[2],[14070] -54,71,"[2, 1]","[20, 60]","[1, 2]",[2],[1],[2],[20],[2],[2],[2],[2082] -54,72,"[3, 4]","[40, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[2089] -54,73,"[3, 4]","[50, 10]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1871] -54,74,"[1, 2]","[40, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2121] -54,75,"[4, 3]","[30, 20]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1958] -54,76,"[4, 3]","[20, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[2038] -54,77,"[4, 3]","[10, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1870] -54,78,"[2, 1]","[30, 10]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[1990] -54,79,"[3, 4]","[20, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1627] -54,80,"[4, 3]","[30, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[1602] -54,1,"[3, 4]","[20, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[5186] -54,2,"[2, 1]","[70, 10]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2819] -54,3,"[2, 1]","[60, 20]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2715] -54,4,"[4, 3]","[40, 30]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2904] -54,5,"[2, 1]","[40, 30]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[7445] -54,6,"[4, 3]","[50, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2977] -54,7,"[1, 2]","[60, 20]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[7899] -54,8,"[1, 2]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[4019] -54,9,"[2, 1]","[30, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[4596] -54,10,"[4, 3]","[30, 0]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2668] -54,11,"[1, 2]","[0, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2891] -54,12,"[1, 2]","[40, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1996] -54,13,"[4, 3]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2686] -54,14,"[4, 3]","[40, 90]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[2732] -54,15,"[3, 4]","[60, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[4494] -54,16,"[2, 1]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[5338] -54,17,"[2, 1]","[70, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2891] -54,18,"[3, 4]","[30, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[4006] -54,19,"[1, 2]","[40, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[38061] -54,20,"[1, 2]","[10, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[37090] -54,21,"[4, 3]","[50, 10]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[2100] -54,22,"[3, 4]","[20, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[4399] -54,23,"[1, 2]","[50, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[34180] -54,24,"[4, 3]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2376] -54,25,"[1, 2]","[70, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[3669] -54,26,"[3, 4]","[60, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[4590] -54,27,"[2, 1]","[70, 40]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2860] -54,28,"[1, 2]","[30, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[3021] -54,29,"[4, 3]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[4042] -54,30,"[1, 2]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2036] -54,31,"[1, 2]","[50, 30]","[1, 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1]",[1],[1],[3],[30],[3],[1],[3],[9536] -54,34,"[4, 3]","[40, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[15161] -54,35,"[4, 3]","[60, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[2856] -54,36,"[1, 2]","[50, 80]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[9741] -54,37,"[3, 4]","[80, 50]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[9085] -54,38,"[3, 4]","[70, 50]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[4457] -54,39,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[3025] -54,40,"[2, 1]","[60, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[4584] -54,41,"[3, 4]","[70, 50]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[6444] -54,42,"[3, 4]","[60, 30]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[3048] -54,43,"[4, 3]","[60, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2377] -54,44,"[1, 2]","[50, 70]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[3032] -54,45,"[1, 2]","[50, 60]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2507] -54,46,"[1, 2]","[60, 40]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[2699] -54,47,"[2, 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1]","[20, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[2176] -54,9,"[4, 3]","[50, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[2127] -54,10,"[3, 4]","[80, 50]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2492] -54,11,"[4, 3]","[60, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[2008] -54,12,"[3, 4]","[80, 60]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2337] -54,13,"[1, 2]","[60, 80]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2780] -54,14,"[4, 3]","[60, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2234] -54,15,"[2, 1]","[50, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2214] -54,16,"[4, 3]","[50, 30]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2648] -54,17,"[3, 4]","[30, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2389] -54,18,"[1, 2]","[70, 10]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2223] -54,19,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1856] -54,20,"[3, 4]","[90, 10]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[2235] -54,21,"[4, 3]","[50, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1978] -54,22,"[2, 1]","[30, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[2478] -54,23,"[2, 1]","[0, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2753] -54,24,"[4, 3]","[10, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[3275] -54,25,"[3, 4]","[10, 0]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[2300] -54,26,"[3, 4]","[30, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[1969] -54,27,"[1, 2]","[80, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[2056] -54,28,"[4, 3]","[60, 50]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2137] -54,29,"[3, 4]","[20, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2262] -54,30,"[1, 2]","[60, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2094] -54,31,"[1, 2]","[80, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[2302] -54,32,"[3, 4]","[90, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2216] -54,33,"[2, 1]","[70, 60]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2909] -54,34,"[1, 2]","[50, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2516] -54,35,"[1, 2]","[70, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2362] -54,36,"[4, 3]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[5304] -54,37,"[4, 3]","[70, 60]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[4014] -54,38,"[2, 1]","[70, 60]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[4452] -54,39,"[3, 4]","[70, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[5697] -54,40,"[4, 3]","[70, 90]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[2405] -54,41,"[4, 3]","[70, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[5499011] -54,42,"[4, 3]","[80, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1768] -54,43,"[2, 1]","[50, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[3122] -54,44,"[3, 4]","[60, 30]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[2097] -54,45,"[1, 2]","[70, 40]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[2120] -54,46,"[1, 2]","[30, 20]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[2106] -54,47,"[2, 1]","[10, 30]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2161] -54,48,"[3, 4]","[40, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[2088] -54,49,"[1, 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-54,63,"[3, 4]","[50, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2272] -54,64,"[2, 1]","[50, 90]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[5307] -54,65,"[3, 4]","[80, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2116] -54,66,"[2, 1]","[60, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2053] -54,67,"[4, 3]","[90, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2800] -54,68,"[1, 2]","[50, 80]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[4097] -54,69,"[4, 3]","[20, 40]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[3366] -54,70,"[2, 1]","[20, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[2060] -54,71,"[2, 1]","[40, 10]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2182] -54,72,"[3, 4]","[20, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2775] -54,73,"[4, 3]","[50, 30]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[3298] -54,74,"[1, 2]","[40, 60]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[3125] -54,75,"[1, 2]","[30, 70]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[2107] -54,76,"[1, 2]","[50, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1895] -54,77,"[3, 4]","[30, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2519] -54,78,"[2, 1]","[40, 10]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[3833] -54,79,"[1, 2]","[10, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[2],[5020] -54,80,"[2, 1]","[0, 0]","[1, 2]",[2],[5],[2],[0],[0],[2],[2],[2614] -54,1,"[2, 1]","[20, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[3886] -54,2,"[3, 4]","[70, 40]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2490] -54,3,"[1, 2]","[90, 70]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[2528] -54,4,"[4, 3]","[40, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2361] -54,5,"[3, 4]","[70, 50]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2386] -54,6,"[4, 3]","[70, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[1891] -54,7,"[2, 1]","[60, 10]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2500] -54,8,"[2, 1]","[40, 0]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[6983] -54,9,"[4, 3]","[90, 0]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[3615] -54,10,"[4, 3]","[90, 10]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2593] -54,11,"[4, 3]","[90, 20]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2194] -54,12,"[1, 2]","[10, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2800] -54,13,"[3, 4]","[20, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2116] -54,14,"[2, 1]","[70, 30]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2763] -54,15,"[3, 4]","[30, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2229] -54,16,"[3, 4]","[40, 60]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[2416] -54,17,"[1, 2]","[40, 70]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[2166] -54,18,"[4, 3]","[40, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[1890] -54,19,"[3, 4]","[60, 40]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1988] -54,20,"[1, 2]","[70, 20]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2384] -54,21,"[1, 2]","[70, 0]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[4654] -54,22,"[2, 1]","[20, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2051] -54,23,"[1, 2]","[80, 10]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2059] -54,24,"[4, 3]","[0, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2500] -54,25,"[1, 2]","[70, 10]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2299] -54,26,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1907] -54,27,"[2, 1]","[50, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[2339] -54,28,"[2, 1]","[50, 60]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2471] -54,29,"[1, 2]","[70, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2326] -54,30,"[3, 4]","[50, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2322] -54,31,"[4, 3]","[80, 70]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2155] -54,32,"[4, 3]","[60, 20]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[2306] -54,33,"[3, 4]","[10, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2029] -54,34,"[3, 4]","[30, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[2443] -54,35,"[4, 3]","[40, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[2193] -54,36,"[3, 4]","[10, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[2156] -54,37,"[1, 2]","[20, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2264] -54,38,"[1, 2]","[80, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2715] -54,39,"[2, 1]","[70, 80]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2334] -54,40,"[3, 4]","[70, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2216] -54,41,"[2, 1]","[50, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[2433] -54,42,"[3, 4]","[70, 60]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[2235] -54,43,"[4, 3]","[50, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[2668] -54,44,"[1, 2]","[50, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[2406] -54,45,"[4, 3]","[60, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[2027] -54,46,"[1, 2]","[10, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2302] -54,47,"[4, 3]","[60, 0]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[3021] -54,48,"[2, 1]","[60, 20]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[2515] -54,49,"[1, 2]","[10, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[3037] -54,50,"[4, 3]","[80, 30]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[3144] -54,51,"[4, 3]","[70, 10]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2221] -54,52,"[1, 2]","[10, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[3385] -54,53,"[3, 4]","[20, 70]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[1945] -54,54,"[4, 3]","[90, 10]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[2060] -54,55,"[1, 2]","[20, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2445] -54,56,"[4, 3]","[70, 20]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2019] -54,57,"[1, 2]","[10, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[2725] -54,58,"[4, 3]","[50, 20]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2092] -54,59,"[2, 1]","[50, 20]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2723] -54,60,"[1, 2]","[30, 50]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[2553] -54,61,"[3, 4]","[20, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[1964] -54,62,"[3, 4]","[50, 40]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[21637] -54,63,"[1, 2]","[60, 50]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[2065] -54,64,"[4, 3]","[70, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2351] -54,65,"[2, 1]","[90, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2188] -54,66,"[1, 2]","[40, 50]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[2263] -54,67,"[3, 4]","[40, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2019] -54,68,"[3, 4]","[20, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1946] -54,69,"[3, 4]","[50, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[1874] -54,70,"[4, 3]","[30, 80]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2263] -54,71,"[3, 4]","[70, 0]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1938] -54,72,"[3, 4]","[70, 10]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[2146] -54,73,"[4, 3]","[10, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[2584] -54,74,"[4, 3]","[20, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1679] -54,75,"[4, 3]","[50, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2015] -54,76,"[3, 4]","[20, 40]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[2044] -54,77,"[4, 3]","[20, 10]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1934] -54,78,"[3, 4]","[30, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1992] -54,79,"[2, 1]","[30, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[1969] -54,80,"[3, 4]","[30, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[1917] -54,1,"[4, 3]","[40, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[3836] -54,2,"[3, 4]","[60, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2016] -54,3,"[1, 2]","[50, 70]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[2232] -54,4,"[2, 1]","[50, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[2842] -54,5,"[3, 4]","[0, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2234] -54,6,"[1, 2]","[10, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[1859] -54,7,"[4, 3]","[40, 0]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2197] -54,8,"[2, 1]","[80, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[2191] -54,9,"[4, 3]","[60, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[2056] -54,10,"[1, 2]","[60, 30]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2198] -54,11,"[1, 2]","[80, 20]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2058] -54,12,"[4, 3]","[40, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1963] -54,13,"[3, 4]","[90, 50]","[2, 1]",[1],[1],[3],[90],[9],[1],[3],[2209] -54,14,"[3, 4]","[90, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[2208] -54,15,"[3, 4]","[90, 70]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[3134] -54,16,"[1, 2]","[60, 70]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2558] -54,17,"[2, 1]","[40, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[2280] -54,18,"[4, 3]","[50, 30]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[2068] -54,19,"[1, 2]","[50, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2244] -54,20,"[3, 4]","[40, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2640] -54,21,"[1, 2]","[10, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2664] -54,22,"[2, 1]","[30, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[2937] -54,23,"[3, 4]","[0, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[1943] -54,24,"[2, 1]","[20, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[2056] -54,25,"[3, 4]","[30, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[2118] -54,26,"[3, 4]","[40, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[1736] -54,27,"[2, 1]","[30, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[2377] -54,28,"[4, 3]","[30, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[1919] -54,29,"[3, 4]","[0, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1960] -54,30,"[2, 1]","[20, 10]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[2463] -54,31,"[2, 1]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[2618] -54,32,"[4, 3]","[30, 50]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[3495] -54,33,"[4, 3]","[40, 80]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2206] -54,34,"[2, 1]","[30, 80]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[4297] -54,35,"[3, 4]","[80, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[5262] -54,36,"[1, 2]","[70, 10]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2713] -54,37,"[1, 2]","[60, 20]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2272] -54,38,"[2, 1]","[0, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2544] -54,39,"[1, 2]","[70, 30]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2488] -54,40,"[1, 2]","[60, 30]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[4413] -54,41,"[4, 3]","[50, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1776] -54,42,"[2, 1]","[80, 10]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[1972] -54,43,"[4, 3]","[40, 20]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[2058] -54,44,"[3, 4]","[10, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1906] -54,45,"[1, 2]","[20, 50]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[2707] -54,46,"[3, 4]","[20, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2165] -54,47,"[3, 4]","[30, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[2128] -54,48,"[3, 4]","[50, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1924] -54,49,"[1, 2]","[60, 30]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[2063] -54,50,"[2, 1]","[30, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[2822] -54,51,"[2, 1]","[40, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1910] -54,52,"[2, 1]","[40, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2326] -54,53,"[4, 3]","[50, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[4461] -54,54,"[1, 2]","[40, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[2216] -54,55,"[1, 2]","[10, 40]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[2221] -54,56,"[2, 1]","[70, 10]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1918] -54,57,"[3, 4]","[20, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[1928] -54,58,"[2, 1]","[60, 20]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[5494] -54,59,"[1, 2]","[10, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[3697] -54,60,"[3, 4]","[0, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2352] -54,61,"[4, 3]","[90, 20]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[4983] -54,62,"[1, 2]","[20, 60]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[2597] -54,63,"[2, 1]","[70, 10]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1838] -54,64,"[4, 3]","[60, 10]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2086] -54,65,"[1, 2]","[10, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[2107] -54,66,"[4, 3]","[50, 20]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2461] -54,67,"[4, 3]","[20, 30]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[2200] -54,68,"[4, 3]","[10, 20]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[2007] -54,69,"[1, 2]","[20, 10]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[2188] -54,70,"[2, 1]","[10, 40]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2394] -54,71,"[2, 1]","[30, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1872] -54,72,"[2, 1]","[10, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[4049] -54,73,"[2, 1]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1815] -54,74,"[4, 3]","[10, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[2142] -54,75,"[2, 1]","[10, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[2195] -54,76,"[2, 1]","[40, 10]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1911] -54,77,"[3, 4]","[0, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[6642] -54,78,"[4, 3]","[70, 10]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[2309] -54,79,"[3, 4]","[30, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2038] -54,80,"[4, 3]","[20, 30]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[2132] -54,1,"[1, 2]","[50, 20]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[3937] -54,2,"[4, 3]","[40, 70]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2982] -54,3,"[2, 1]","[50, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2000] -54,4,"[2, 1]","[50, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2119] -54,5,"[2, 1]","[40, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1974] -54,6,"[1, 2]","[60, 30]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2223] -54,7,"[4, 3]","[40, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[3063] -54,8,"[3, 4]","[60, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2019] -54,9,"[3, 4]","[70, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2040] -54,10,"[2, 1]","[70, 60]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2261] -54,11,"[2, 1]","[70, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2113] -54,12,"[4, 3]","[70, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[2028] -54,13,"[1, 2]","[50, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2687] -54,14,"[3, 4]","[50, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1962] -54,15,"[1, 2]","[40, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2080] -54,16,"[3, 4]","[0, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[1996] -54,17,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[1888] -54,18,"[2, 1]","[50, 10]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2898] -54,19,"[1, 2]","[0, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[4631] -54,20,"[4, 3]","[60, 10]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[3332] -54,21,"[4, 3]","[50, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[1906] -54,22,"[3, 4]","[0, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1946] -54,23,"[4, 3]","[40, 10]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2071] -54,24,"[2, 1]","[40, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[2158] -54,25,"[1, 2]","[20, 30]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[2090] -54,26,"[4, 3]","[50, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[1890] -54,27,"[4, 3]","[40, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[2085] -54,28,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1854] -54,29,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2107] -54,30,"[3, 4]","[90, 50]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[2192] -54,31,"[4, 3]","[0, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[2228] -54,32,"[1, 2]","[60, 30]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2100] -54,33,"[2, 1]","[20, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2288] -54,34,"[3, 4]","[60, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[1938] -54,35,"[1, 2]","[30, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[2099] -54,36,"[4, 3]","[30, 20]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2167] -54,37,"[3, 4]","[40, 30]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[1924] -54,38,"[1, 2]","[30, 10]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2432] -54,39,"[2, 1]","[20, 30]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[1942] -54,40,"[3, 4]","[30, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2191] -54,41,"[3, 4]","[40, 50]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[2228] -54,42,"[4, 3]","[50, 30]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2036] -54,43,"[1, 2]","[40, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[1931] -54,44,"[2, 1]","[30, 50]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[1832] -54,45,"[2, 1]","[20, 60]","[1, 2]",[2],[1],[2],[20],[2],[2],[2],[1795] -54,46,"[2, 1]","[60, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1601] -54,47,"[4, 3]","[70, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2970] -54,48,"[4, 3]","[90, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1951] -54,49,"[4, 3]","[70, 40]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[2015] -54,50,"[3, 4]","[40, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[1908] -54,51,"[3, 4]","[0, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2615] -54,52,"[1, 2]","[0, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[2912] -54,53,"[4, 3]","[20, 10]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2397] -54,54,"[2, 1]","[50, 0]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[2854] -54,55,"[3, 4]","[10, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1967] -54,56,"[4, 3]","[40, 20]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[3216] -54,57,"[4, 3]","[40, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[3241] -54,58,"[2, 1]","[60, 10]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[1810] -54,59,"[2, 1]","[80, 10]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[1943] -54,60,"[1, 2]","[30, 90]","[1, 2]",[2],[1],[2],[90],[9],[2],[2],[2865] -54,61,"[2, 1]","[70, 10]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2633] -54,62,"[1, 2]","[0, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2832] -54,63,"[1, 2]","[0, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1965] -54,64,"[4, 3]","[50, 30]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2858] -54,65,"[3, 4]","[40, 50]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[1822] -54,66,"[2, 1]","[50, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[1888] -54,67,"[2, 1]","[70, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[1975] -54,68,"[2, 1]","[50, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[1894] -54,69,"[2, 1]","[30, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2124] -54,70,"[2, 1]","[20, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[2003] -54,71,"[2, 1]","[20, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1973] -54,72,"[3, 4]","[40, 10]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2157] -54,73,"[3, 4]","[50, 10]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1947] -54,74,"[1, 2]","[40, 30]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2173] -54,75,"[4, 3]","[30, 20]","[2, 1]",[2],[1],[3],[20],[2],[1],[3],[1957] -54,76,"[4, 3]","[20, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[1767] -54,77,"[4, 3]","[10, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1621] -54,78,"[2, 1]","[30, 10]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2364] -54,79,"[3, 4]","[20, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1794] -54,80,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1923] -54,1,"[2, 1]","[20, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[4688] -54,2,"[3, 4]","[70, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[5246] -54,3,"[1, 2]","[90, 70]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[2488] -54,4,"[4, 3]","[40, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2129] -54,5,"[3, 4]","[70, 50]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2143] -54,6,"[4, 3]","[70, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[1866] -54,7,"[2, 1]","[60, 10]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2130] -54,8,"[2, 1]","[40, 0]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2732] -54,9,"[4, 3]","[90, 0]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[2270] -54,10,"[4, 3]","[90, 10]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[1960] -54,11,"[4, 3]","[90, 20]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[1896] -54,12,"[1, 2]","[10, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[7287] -54,13,"[3, 4]","[20, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2414] -54,14,"[2, 1]","[70, 30]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[3487] -54,15,"[3, 4]","[30, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2398] -54,16,"[3, 4]","[40, 60]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[2205] -54,17,"[1, 2]","[40, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2426] -54,18,"[4, 3]","[40, 60]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[2241] -54,19,"[3, 4]","[60, 40]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1871] -54,20,"[1, 2]","[70, 20]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2175] -54,21,"[1, 2]","[70, 0]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2210] -54,22,"[2, 1]","[20, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1975] -54,23,"[1, 2]","[80, 10]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[3074] -54,24,"[4, 3]","[0, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2915] -54,25,"[1, 2]","[70, 10]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2179] -54,26,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2103] -54,27,"[2, 1]","[50, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[2302] -54,28,"[2, 1]","[50, 60]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2056] -54,29,"[1, 2]","[70, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2099] -54,30,"[3, 4]","[50, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2035] -54,31,"[4, 3]","[80, 70]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2491] -54,32,"[4, 3]","[60, 20]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[2330] -54,33,"[3, 4]","[10, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2550] -54,34,"[3, 4]","[30, 40]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[3439] -54,35,"[4, 3]","[40, 20]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2072] -54,36,"[3, 4]","[10, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[2760] -54,37,"[1, 2]","[20, 40]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[1952] -54,38,"[1, 2]","[80, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2856] -54,39,"[2, 1]","[70, 80]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2185] -54,40,"[3, 4]","[70, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2191] -54,41,"[2, 1]","[50, 80]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[3366] -54,42,"[3, 4]","[70, 60]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[2050] -54,43,"[4, 3]","[50, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1934] -54,44,"[1, 2]","[50, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[2304] -54,45,"[4, 3]","[60, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[3293] -54,46,"[1, 2]","[10, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2164] -54,47,"[4, 3]","[60, 0]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[3399] -54,48,"[2, 1]","[60, 20]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[2209] -54,49,"[1, 2]","[10, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[2694] -54,50,"[4, 3]","[80, 30]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2379] -54,51,"[4, 3]","[70, 10]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2663] -54,52,"[1, 2]","[10, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2316] -54,53,"[3, 4]","[20, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[2783] -54,54,"[4, 3]","[90, 10]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[2241] -54,55,"[1, 2]","[20, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2594] -54,56,"[4, 3]","[70, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[2581] -54,57,"[1, 2]","[10, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[2193] -54,58,"[4, 3]","[50, 20]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2070] -54,59,"[2, 1]","[50, 20]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2959] -54,60,"[1, 2]","[30, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2194] -54,61,"[3, 4]","[20, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[1980] -54,62,"[3, 4]","[50, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[1910] -54,63,"[1, 2]","[60, 50]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[2162] -54,64,"[4, 3]","[70, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2341] -54,65,"[2, 1]","[90, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2195] -54,66,"[1, 2]","[40, 50]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[2283] -54,67,"[3, 4]","[40, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[3009] -54,68,"[3, 4]","[20, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[3106] -54,69,"[3, 4]","[50, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2065] -54,70,"[4, 3]","[30, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[2157] -54,71,"[3, 4]","[70, 0]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[2008] -54,72,"[3, 4]","[70, 10]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1777] -54,73,"[4, 3]","[10, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1875] -54,74,"[4, 3]","[20, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[2025] -54,75,"[4, 3]","[50, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1757] -54,76,"[3, 4]","[20, 40]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[4757] -54,77,"[4, 3]","[20, 10]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2114] -54,78,"[3, 4]","[30, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[2035] -54,79,"[2, 1]","[30, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[2062] -54,80,"[3, 4]","[30, 20]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[2202] -54,1,"[1, 2]","[10, 50]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[8768] -54,2,"[1, 2]","[0, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2581] -54,3,"[3, 4]","[0, 60]","[2, 1]",[1],[5],[3],[0],[0],[1],[4],[2963] -54,4,"[4, 3]","[70, 20]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2426] -54,5,"[4, 3]","[50, 0]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2382] -54,6,"[2, 1]","[90, 0]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[3270] -54,7,"[2, 1]","[50, 20]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2956] -54,8,"[2, 1]","[30, 10]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[3057] -54,9,"[4, 3]","[60, 10]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2683] -54,10,"[1, 2]","[10, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[4224] -54,11,"[3, 4]","[20, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[6047] -54,12,"[4, 3]","[30, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[3],[3663] -54,13,"[1, 2]","[20, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[2333] -54,14,"[2, 1]","[0, 10]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[2868] -54,15,"[4, 3]","[0, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[4],[2549] -54,16,"[2, 1]","[10, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[2],[2316] -54,17,"[1, 2]","[0, 10]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[2024] -54,18,"[3, 4]","[10, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[2380] -54,19,"[2, 1]","[20, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[2],[3281] -54,20,"[3, 4]","[20, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[5174] -54,21,"[3, 4]","[30, 0]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1968] -54,22,"[3, 4]","[50, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[2292] -54,23,"[4, 3]","[20, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2060] -54,24,"[4, 3]","[30, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1803] -54,25,"[3, 4]","[70, 40]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[1956] -54,26,"[2, 1]","[40, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2090] -54,27,"[1, 2]","[30, 20]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2222] -54,28,"[1, 2]","[50, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[2281] -54,29,"[3, 4]","[50, 20]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[2458] -54,30,"[4, 3]","[20, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2685] -54,31,"[1, 2]","[60, 10]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2293] -54,32,"[3, 4]","[40, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2599] -54,33,"[3, 4]","[70, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[2299] -54,34,"[2, 1]","[10, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[1868] -54,35,"[4, 3]","[20, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[3930] -54,36,"[4, 3]","[30, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1826] -54,37,"[4, 3]","[20, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1783] -54,38,"[2, 1]","[60, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2348] -54,39,"[1, 2]","[70, 40]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[4437] -54,40,"[2, 1]","[40, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[1968] -54,41,"[3, 4]","[70, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[2706] -54,42,"[4, 3]","[10, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2716] -54,43,"[4, 3]","[20, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[3159] -54,44,"[2, 1]","[10, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2411] -54,45,"[2, 1]","[10, 70]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2324] -54,46,"[1, 2]","[80, 20]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2188] -54,47,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2309] -54,48,"[1, 2]","[70, 30]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2253] -54,49,"[3, 4]","[50, 10]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2254] -54,50,"[2, 1]","[10, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2631] -54,51,"[2, 1]","[20, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[2021] -54,52,"[4, 3]","[30, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[3945] -54,53,"[4, 3]","[50, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[7424] -54,54,"[3, 4]","[90, 60]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[2821] -54,55,"[3, 4]","[50, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2088] -54,56,"[3, 4]","[40, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2238] -54,57,"[1, 2]","[60, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[1960] -54,58,"[4, 3]","[70, 90]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[2288] -54,59,"[3, 4]","[80, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[9365] -54,60,"[1, 2]","[70, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[2742] -54,61,"[4, 3]","[30, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[7930] -54,62,"[2, 1]","[10, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2878] -54,63,"[3, 4]","[80, 20]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2354] -54,64,"[4, 3]","[50, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1872] -54,65,"[2, 1]","[50, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[104555] -54,66,"[2, 1]","[80, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[1831] -54,67,"[2, 1]","[60, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[1950] -54,68,"[1, 2]","[50, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[3442] -54,69,"[4, 3]","[40, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[3497] -54,70,"[3, 4]","[40, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[2126] -54,71,"[4, 3]","[90, 80]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[2539] -54,72,"[3, 4]","[90, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2331] -54,73,"[4, 3]","[80, 70]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[2201] -54,74,"[4, 3]","[50, 80]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[1940] -54,75,"[1, 2]","[70, 80]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2502] -54,76,"[2, 1]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2018] -54,77,"[2, 1]","[70, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[1989] -54,78,"[1, 2]","[10, 60]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[2331] -54,79,"[4, 3]","[70, 20]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2435] -54,80,"[4, 3]","[60, 80]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[2538] -54,1,"[4, 3]","[30, 60]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[3910] -54,2,"[2, 1]","[0, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1827] -54,3,"[4, 3]","[20, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1765] -54,4,"[4, 3]","[0, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[1708] -54,5,"[4, 3]","[10, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1735] -54,6,"[1, 2]","[30, 20]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2656] -54,7,"[4, 3]","[10, 20]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[2813] -54,8,"[4, 3]","[30, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[1751] -54,9,"[3, 4]","[60, 10]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2952] -54,10,"[1, 2]","[60, 30]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2405] -54,11,"[3, 4]","[70, 40]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2002] -54,12,"[2, 1]","[50, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2586] -54,13,"[1, 2]","[60, 0]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2581] -54,14,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1874] -54,15,"[1, 2]","[60, 50]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2336] -54,16,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2053] -54,17,"[1, 2]","[80, 40]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2156] -54,18,"[2, 1]","[40, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2115] -54,19,"[1, 2]","[60, 50]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2162] -54,20,"[3, 4]","[80, 40]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2423] -54,21,"[2, 1]","[40, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2986] -54,22,"[1, 2]","[80, 30]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2232] -54,23,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1987] -54,24,"[4, 3]","[50, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1846] -54,25,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2083] -54,26,"[2, 1]","[60, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[2225] -54,27,"[2, 1]","[80, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[2674] -54,28,"[2, 1]","[80, 60]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1893] -54,29,"[4, 3]","[90, 50]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[2019] -54,30,"[2, 1]","[80, 40]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1995] -54,31,"[4, 3]","[70, 40]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2016] -54,32,"[3, 4]","[30, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2589] -54,33,"[3, 4]","[60, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[1941] -54,34,"[4, 3]","[30, 70]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[2498] -54,35,"[4, 3]","[30, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1637] -54,36,"[3, 4]","[20, 30]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[1853] -54,37,"[3, 4]","[20, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1901] -54,38,"[4, 3]","[20, 60]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[2056] -54,39,"[4, 3]","[60, 70]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1984] -54,40,"[1, 2]","[70, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2104] -54,41,"[2, 1]","[90, 50]","[1, 2]",[2],[5],[2],[450],[9],[2],[1],[2762] -54,42,"[3, 4]","[50, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1927] -54,43,"[3, 4]","[50, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[1935] -54,44,"[1, 2]","[50, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[2207] -54,45,"[2, 1]","[40, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[3651] -54,46,"[1, 2]","[50, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2064] -54,47,"[2, 1]","[40, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2095] -54,48,"[3, 4]","[20, 30]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[1837] -54,49,"[2, 1]","[40, 30]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[3320] -54,50,"[2, 1]","[40, 10]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[1896] -54,51,"[1, 2]","[20, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2636] -54,52,"[3, 4]","[30, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1882] -54,53,"[1, 2]","[30, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2482] -54,54,"[2, 1]","[80, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[2319] -54,55,"[4, 3]","[70, 10]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[3153] -54,56,"[4, 3]","[80, 50]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[2039] -54,57,"[1, 2]","[70, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2331] -54,58,"[2, 1]","[60, 80]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[1894] -54,59,"[1, 2]","[70, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2105] -54,60,"[1, 2]","[50, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1845] -54,61,"[4, 3]","[30, 80]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2057] -54,62,"[4, 3]","[10, 70]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[2989] -54,63,"[3, 4]","[50, 10]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[3056] -54,64,"[4, 3]","[10, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1704] -54,65,"[1, 2]","[30, 0]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[2256] -54,66,"[1, 2]","[30, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[2169] -54,67,"[1, 2]","[20, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[2158] -54,68,"[3, 4]","[40, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2935] -54,69,"[4, 3]","[0, 60]","[2, 2]",[2],[1],[4],[0],[0],[2],[4],[1964] -54,70,"[2, 1]","[30, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1820] -54,71,"[2, 1]","[30, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2443] -54,72,"[3, 4]","[40, 50]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[2132] -54,73,"[2, 1]","[60, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1931] -54,74,"[1, 2]","[80, 70]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2072] -54,75,"[3, 4]","[80, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2045] -54,76,"[2, 1]","[70, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1937] -54,77,"[4, 3]","[60, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[2804] -54,78,"[2, 1]","[60, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[1921] -54,79,"[4, 3]","[40, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[2447] -54,80,"[1, 2]","[20, 80]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[2192] -56,1,"[3, 4]","[70, 40]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[3799] -56,2,"[2, 1]","[20, 80]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[3727] -56,3,"[4, 3]","[30, 70]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[2011] -56,4,"[4, 3]","[50, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1949] -56,5,"[2, 1]","[80, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2452] -56,6,"[1, 2]","[40, 70]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2546] -56,7,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[2365] -56,8,"[2, 1]","[80, 10]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1854] -56,9,"[2, 1]","[60, 0]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2320] -56,10,"[3, 4]","[10, 60]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[3015] -56,11,"[2, 1]","[50, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[2348] -56,12,"[1, 2]","[70, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[2071] -56,13,"[1, 2]","[90, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2494] -56,14,"[2, 1]","[60, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2622] -56,15,"[1, 2]","[40, 60]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2279] -56,16,"[2, 1]","[20, 50]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[2380] -56,17,"[4, 3]","[40, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[2412] -56,18,"[2, 1]","[70, 40]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[3487] -56,19,"[4, 3]","[90, 30]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2295] -56,20,"[4, 3]","[80, 30]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2183] -56,21,"[3, 4]","[0, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2177] -56,22,"[3, 4]","[10, 40]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[1750] -56,23,"[2, 1]","[30, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[2405] -56,24,"[2, 1]","[10, 20]","[1, 2]",[1],[5],[2],[50],[1],[2],[2],[1988] -56,25,"[4, 3]","[10, 0]","[2, 1]",[1],[5],[3],[0],[0],[1],[3],[1891] -56,26,"[1, 2]","[10, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[1],[2287] -56,27,"[3, 4]","[40, 0]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[2198] -56,28,"[1, 2]","[0, 0]","[1, 1]",[1],[1],[1],[0],[0],[1],[1],[2210] -56,29,"[3, 4]","[20, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2022] -56,30,"[1, 2]","[60, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2053] -56,31,"[2, 1]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[2776] -56,32,"[4, 3]","[60, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[1785] -56,33,"[3, 4]","[60, 70]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2255] -56,34,"[1, 2]","[60, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2005] -56,35,"[3, 4]","[40, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[2373] -56,36,"[3, 4]","[0, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[2361] -56,37,"[2, 1]","[0, 0]","[1, 1]",[1],[1],[1],[0],[0],[1],[2],[2111] -56,38,"[4, 3]","[10, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1921] -56,39,"[3, 4]","[20, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[2098] -56,40,"[4, 3]","[20, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1810] -56,41,"[2, 1]","[20, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[3461] -56,42,"[1, 2]","[80, 60]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[3010] -56,43,"[1, 2]","[90, 40]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[2338] -56,44,"[2, 1]","[50, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2446] -56,45,"[3, 4]","[90, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[3115] -56,46,"[3, 4]","[90, 50]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[1966] -56,47,"[1, 2]","[80, 60]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2068] -56,48,"[4, 3]","[90, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[1862] -56,49,"[1, 2]","[90, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[2478] -56,50,"[4, 3]","[70, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1880] -56,51,"[2, 1]","[50, 70]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[2184] -56,52,"[1, 2]","[80, 50]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1974] -56,53,"[3, 4]","[70, 50]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1812] -56,54,"[4, 3]","[50, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[2274] -56,55,"[3, 4]","[70, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2204] -56,56,"[3, 4]","[50, 30]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1948] -56,57,"[3, 4]","[20, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1854] -56,58,"[3, 4]","[10, 20]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1968] -56,59,"[1, 2]","[0, 50]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[2254] -56,60,"[3, 4]","[20, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2007] -56,61,"[3, 4]","[20, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1809] -56,62,"[4, 3]","[20, 20]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[2180] -56,63,"[4, 3]","[30, 40]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[4173] -56,64,"[4, 3]","[10, 40]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[2715] -56,65,"[3, 4]","[30, 0]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[2010] -56,66,"[2, 1]","[20, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[3046] -56,67,"[4, 3]","[10, 60]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[2799] -56,68,"[2, 1]","[0, 20]","[1, 2]",[2],[1],[2],[0],[0],[2],[2],[5201] -56,69,"[2, 1]","[20, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[1874] -56,70,"[3, 4]","[30, 50]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[2275] -56,71,"[1, 2]","[60, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[1902] -56,72,"[3, 4]","[80, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[1759] -56,73,"[2, 1]","[70, 90]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[1794] -56,74,"[2, 1]","[70, 80]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[2033] -56,75,"[2, 1]","[90, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[1966] -56,76,"[2, 1]","[70, 40]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2190] -56,77,"[2, 1]","[80, 70]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2564] -56,78,"[1, 2]","[70, 90]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2762] -56,79,"[1, 2]","[90, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1930] -56,80,"[2, 1]","[30, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2771] -56,1,"[2, 1]","[40, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[4194] -56,2,"[3, 4]","[80, 40]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[2310] -56,3,"[3, 4]","[80, 60]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2027] -56,4,"[2, 1]","[30, 90]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[2700] -56,5,"[1, 2]","[70, 30]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[3373] -56,6,"[4, 3]","[10, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[2139] -56,7,"[4, 3]","[0, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[3026] -56,8,"[4, 3]","[20, 40]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[1894] -56,9,"[4, 3]","[50, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[3],[1857] -56,10,"[3, 4]","[0, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2372] -56,11,"[4, 3]","[80, 0]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2104] -56,12,"[4, 3]","[70, 30]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[4516] -56,13,"[2, 1]","[90, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[1969] -56,14,"[4, 3]","[90, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1825] -56,15,"[2, 1]","[80, 40]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[2439] -56,16,"[1, 2]","[20, 60]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[3923] -56,17,"[1, 2]","[30, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2061] -56,18,"[1, 2]","[40, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[1811] -56,19,"[1, 2]","[50, 60]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2031] -56,20,"[3, 4]","[60, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2106] -56,21,"[2, 1]","[50, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[3950] -56,22,"[1, 2]","[60, 30]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2080] -56,23,"[2, 1]","[50, 20]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[5324] -56,24,"[2, 1]","[10, 0]","[1, 1]",[1],[5],[1],[0],[0],[1],[1],[4827] -56,25,"[1, 2]","[0, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[2238] -56,26,"[2, 1]","[60, 20]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2916] -56,27,"[2, 1]","[40, 0]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[5039] -56,28,"[2, 1]","[70, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[2352] -56,29,"[1, 2]","[40, 80]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1949] -56,30,"[1, 2]","[20, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1734] -56,31,"[4, 3]","[70, 40]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2678] -56,32,"[3, 4]","[50, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1797] -56,33,"[3, 4]","[60, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1806] -56,34,"[2, 1]","[60, 80]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[3621] -56,35,"[3, 4]","[90, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[2790] -56,36,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2729] -56,37,"[2, 1]","[40, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1805] -56,38,"[2, 1]","[0, 80]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[2052] -56,39,"[4, 3]","[10, 80]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[2037] -56,40,"[2, 1]","[0, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[2090] -56,41,"[3, 4]","[80, 10]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[6785] -56,42,"[4, 3]","[10, 70]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[3388] -56,43,"[2, 1]","[20, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[2581] -56,44,"[1, 2]","[60, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[2232] -56,45,"[4, 3]","[0, 50]","[2, 2]",[2],[5],[4],[0],[0],[2],[4],[1839] -56,46,"[4, 3]","[40, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1963] -56,47,"[1, 2]","[30, 20]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[1904] -56,48,"[1, 2]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[2401] -56,49,"[3, 4]","[10, 50]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[1982] -56,50,"[4, 3]","[30, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[1806] -56,51,"[2, 1]","[10, 0]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[2228] -56,52,"[1, 2]","[30, 50]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[2850] -56,53,"[2, 1]","[40, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1983] -56,54,"[2, 1]","[40, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[2694] -56,55,"[3, 4]","[80, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[3161] -56,56,"[2, 1]","[0, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2314] -56,57,"[4, 3]","[10, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[3718] -56,58,"[3, 4]","[70, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2230] -56,59,"[1, 2]","[80, 10]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2011] -56,60,"[2, 1]","[0, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[3881] -56,61,"[3, 4]","[70, 40]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[3844] -56,62,"[3, 4]","[60, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2096] -56,63,"[3, 4]","[90, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2324] -56,64,"[3, 4]","[60, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[1902] -56,65,"[3, 4]","[80, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1839] -56,66,"[1, 2]","[70, 80]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2303] -56,67,"[1, 2]","[90, 70]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[2314] -56,68,"[2, 1]","[50, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[3225] -56,69,"[3, 4]","[90, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[1978] -56,70,"[2, 1]","[30, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2732] -56,71,"[3, 4]","[80, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1744] -56,72,"[4, 3]","[50, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[3149] -56,73,"[1, 2]","[90, 30]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[4004] -56,74,"[2, 1]","[20, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2839] -56,75,"[1, 2]","[40, 30]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2112] -56,76,"[2, 1]","[30, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[1993] -56,77,"[4, 3]","[70, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[4397] -56,78,"[1, 2]","[40, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[1917] -56,79,"[4, 3]","[90, 20]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[2131] -56,80,"[1, 2]","[10, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2019] -57,1,"[3, 4]","[0, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[4415] -57,2,"[1, 2]","[20, 80]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[2012] -57,3,"[3, 4]","[30, 50]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[1829] -57,4,"[3, 4]","[40, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2334] -57,5,"[4, 3]","[60, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2754] -57,6,"[4, 3]","[50, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2534] -57,7,"[4, 3]","[20, 80]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[1812] -57,8,"[4, 3]","[20, 60]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[1959] -57,9,"[3, 4]","[70, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[5152] -57,10,"[3, 4]","[20, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2702] -57,11,"[3, 4]","[10, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[1741] -57,12,"[2, 1]","[70, 40]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1864] -57,13,"[1, 2]","[60, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[1899] -57,14,"[2, 1]","[80, 70]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[1933] -57,15,"[3, 4]","[60, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[1985] -57,16,"[2, 1]","[80, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[9428] -57,17,"[2, 1]","[80, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[3137] -57,18,"[1, 2]","[50, 70]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2056] -57,19,"[3, 4]","[40, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[1906] -57,20,"[4, 3]","[40, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[2011] -57,21,"[4, 3]","[70, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[1812] -57,22,"[3, 4]","[20, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[1794] -57,23,"[2, 1]","[30, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[1812] -57,24,"[4, 3]","[50, 40]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[2221] -57,25,"[3, 4]","[20, 90]","[2, 2]",[1],[5],[4],[450],[9],[2],[4],[4115] -57,26,"[3, 4]","[30, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[3574] -57,27,"[4, 3]","[70, 20]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[6113] -57,28,"[4, 3]","[80, 40]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2152] -57,29,"[3, 4]","[40, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[5362] -57,30,"[3, 4]","[20, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[1863] -57,31,"[2, 1]","[80, 30]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[3016] -57,32,"[4, 3]","[60, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[7970] -57,33,"[3, 4]","[60, 40]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[2142] -57,34,"[2, 1]","[40, 60]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[1882] -57,35,"[1, 2]","[50, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1941] -57,36,"[1, 2]","[40, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[4325] -57,37,"[1, 2]","[30, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[2098] -57,38,"[2, 1]","[50, 10]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[1916] -57,39,"[1, 2]","[20, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[1672] -57,40,"[4, 3]","[10, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[2989] -57,41,"[1, 2]","[30, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[22370] -57,42,"[3, 4]","[30, 40]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1968] -57,43,"[4, 3]","[40, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[4709] -57,44,"[3, 4]","[50, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[4418] -57,45,"[4, 3]","[80, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[2281] -57,46,"[1, 2]","[40, 90]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[1864] -57,47,"[4, 3]","[90, 70]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[5310] -57,48,"[2, 1]","[80, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1959] -57,49,"[1, 2]","[90, 70]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[1906] -57,50,"[2, 1]","[70, 80]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[3128] -57,51,"[4, 3]","[70, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[3024] -57,52,"[3, 4]","[80, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[4961] -57,53,"[3, 4]","[50, 20]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2431] -57,54,"[2, 1]","[20, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1986] -57,55,"[2, 1]","[0, 70]","[1, 2]",[2],[5],[2],[0],[0],[2],[2],[2213] -57,56,"[1, 2]","[60, 20]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2266] -57,57,"[1, 2]","[50, 20]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[2596] -57,58,"[3, 4]","[60, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1986] -57,59,"[3, 4]","[50, 30]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1847] -57,60,"[4, 3]","[30, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2038] -57,61,"[1, 2]","[10, 0]","[1, 2]",[2],[5],[2],[0],[0],[2],[1],[2131] -57,62,"[3, 4]","[0, 40]","[2, 1]",[2],[1],[3],[0],[0],[1],[4],[1846] -57,63,"[1, 2]","[30, 40]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[25076] -57,64,"[1, 2]","[10, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[5415] -57,65,"[1, 2]","[0, 10]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[9656] -57,66,"[2, 1]","[20, 10]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[3425] -57,67,"[1, 2]","[40, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2509] -57,68,"[2, 1]","[40, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2046] -57,69,"[2, 1]","[50, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[47997] -57,70,"[1, 2]","[40, 20]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[14916] -57,71,"[4, 3]","[40, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1793] -57,72,"[2, 1]","[40, 0]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2161] -57,73,"[2, 1]","[50, 0]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[2458] -57,74,"[3, 4]","[40, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[2082] -57,75,"[3, 4]","[40, 50]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[1812] -57,76,"[2, 1]","[0, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[4483] -57,77,"[3, 4]","[40, 20]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[11244] -57,78,"[3, 4]","[50, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[6514] -57,79,"[3, 4]","[70, 30]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1759] -57,80,"[4, 3]","[70, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[1795] -58,1,"[4, 3]","[20, 60]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[3697] -58,2,"[1, 2]","[80, 30]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2107] -58,3,"[4, 3]","[20, 70]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[1805] -58,4,"[2, 1]","[20, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1623] -58,5,"[2, 1]","[50, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2557] -58,6,"[2, 1]","[70, 80]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[1707] -58,7,"[1, 2]","[80, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[1608] -58,8,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1723] -58,9,"[3, 4]","[80, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1824] -58,10,"[1, 2]","[90, 60]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[1674] -58,11,"[2, 1]","[50, 70]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[1690] -58,12,"[3, 4]","[40, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1540] -58,13,"[2, 1]","[70, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[1507] -58,14,"[4, 3]","[80, 70]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[1741] -58,15,"[4, 3]","[80, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[1873] -58,16,"[1, 2]","[70, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[1924] -58,17,"[3, 4]","[40, 90]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[1825] -58,18,"[1, 2]","[40, 80]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2805] -58,19,"[4, 3]","[80, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[1974] -58,20,"[2, 1]","[90, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[3456] -58,21,"[1, 2]","[50, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2256] -58,22,"[2, 1]","[80, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2206] -58,23,"[4, 3]","[90, 70]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[5056] -58,24,"[1, 2]","[70, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2857] -58,25,"[1, 2]","[60, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[1924] -58,26,"[2, 1]","[50, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[2340] -58,27,"[1, 2]","[80, 70]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2857] -58,28,"[2, 1]","[60, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1807] -58,29,"[3, 4]","[90, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[3290] -58,30,"[1, 2]","[90, 50]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[2224] -58,31,"[1, 2]","[80, 30]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1823] -58,32,"[4, 3]","[80, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1541] -58,33,"[3, 4]","[80, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[1741] -58,34,"[2, 1]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2474] -58,35,"[4, 3]","[70, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1474] -58,36,"[4, 3]","[60, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[1624] -58,37,"[1, 2]","[40, 60]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[3356] -58,38,"[3, 4]","[70, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[1807] -58,39,"[4, 3]","[30, 70]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[1973] -58,40,"[3, 4]","[80, 30]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1791] -58,41,"[2, 1]","[0, 80]","[1, 2]",[2],[5],[2],[0],[0],[2],[2],[6013] -58,42,"[1, 2]","[80, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[1841] -58,43,"[2, 1]","[0, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2041] -58,44,"[3, 4]","[80, 0]","[2, 2]",[2],[5],[4],[0],[0],[2],[3],[2240] -58,45,"[3, 4]","[90, 10]","[2, 1]",[2],[5],[3],[450],[9],[1],[3],[1807] -58,46,"[3, 4]","[70, 20]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1823] -58,47,"[4, 3]","[0, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[2390] -58,48,"[4, 3]","[0, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1790] -58,49,"[3, 4]","[70, 40]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1840] -58,50,"[2, 1]","[40, 70]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[1775] -58,51,"[2, 1]","[30, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[1823] -58,52,"[2, 1]","[30, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1908] -58,53,"[2, 1]","[40, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1674] -58,54,"[3, 4]","[80, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[1757] -58,55,"[1, 2]","[80, 90]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2290] -58,56,"[4, 3]","[80, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[2741] -58,57,"[4, 3]","[80, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1973] -58,58,"[3, 4]","[90, 60]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[2889] -58,59,"[2, 1]","[80, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[2207] -58,60,"[4, 3]","[50, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[1793] -58,61,"[1, 2]","[60, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2140] -58,62,"[4, 3]","[30, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[2340] -58,63,"[1, 2]","[60, 40]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[1857] -58,64,"[2, 1]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1907] -58,65,"[1, 2]","[70, 60]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[1892] -58,66,"[3, 4]","[70, 30]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1774] -58,67,"[3, 4]","[80, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[1824] -58,68,"[1, 2]","[90, 0]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[1740] -58,69,"[2, 1]","[10, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2591] -58,70,"[4, 3]","[10, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1741] -58,71,"[2, 1]","[10, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[1741] -58,72,"[2, 1]","[0, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1757] -58,73,"[2, 1]","[10, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1940] -58,74,"[4, 3]","[30, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[1756] -58,75,"[1, 2]","[0, 10]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[1774] -58,76,"[1, 2]","[10, 30]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[2422] -58,77,"[4, 3]","[0, 20]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[1524] -58,78,"[4, 3]","[10, 0]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[2623] -58,79,"[1, 2]","[20, 30]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[2390] -58,80,"[2, 1]","[20, 40]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[2623] -59,1,"[2, 1]","[20, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[4325] -59,2,"[3, 4]","[70, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2093] -59,3,"[1, 2]","[90, 70]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[1826] -59,4,"[4, 3]","[40, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2059] -59,5,"[3, 4]","[70, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2560] -59,6,"[4, 3]","[70, 20]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2218] -59,7,"[2, 1]","[60, 10]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2635] -59,8,"[2, 1]","[40, 0]","[1, 1]",[1],[5],[1],[0],[0],[1],[1],[2718] -59,9,"[4, 3]","[90, 0]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[2009] -59,10,"[4, 3]","[90, 10]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2401] -59,11,"[4, 3]","[90, 20]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2109] -59,12,"[1, 2]","[10, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2076] -59,13,"[3, 4]","[20, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1859] -59,14,"[2, 1]","[70, 30]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[3118] -59,15,"[3, 4]","[30, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2793] -59,16,"[3, 4]","[40, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2251] -59,17,"[1, 2]","[40, 70]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[1992] -59,18,"[4, 3]","[40, 60]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1976] -59,19,"[3, 4]","[60, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2001] -59,20,"[1, 2]","[70, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[2459] -59,21,"[1, 2]","[70, 0]","[1, 2]",[1],[5],[2],[0],[0],[2],[1],[2468] -59,22,"[2, 1]","[20, 80]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[2926] -59,23,"[1, 2]","[80, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[2126] -59,24,"[4, 3]","[0, 70]","[2, 2]",[1],[1],[4],[0],[0],[2],[4],[1967] -59,25,"[1, 2]","[70, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[1],[3501] -59,26,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1901] -59,27,"[2, 1]","[50, 40]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[4243] -59,28,"[2, 1]","[50, 60]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2801] -59,29,"[1, 2]","[70, 60]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[3418] -59,30,"[3, 4]","[50, 70]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2492] -59,31,"[4, 3]","[80, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2876] -59,32,"[4, 3]","[60, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[2068] -59,33,"[3, 4]","[10, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2010] -59,34,"[3, 4]","[30, 40]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[2393] -59,35,"[4, 3]","[40, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[2602] -59,36,"[3, 4]","[10, 30]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[3776] -59,37,"[1, 2]","[20, 40]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[3059] -59,38,"[1, 2]","[80, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2692] -59,39,"[2, 1]","[70, 80]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[3159] -59,40,"[3, 4]","[70, 40]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[2809] -59,41,"[2, 1]","[50, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[2375] -59,42,"[3, 4]","[70, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[1926] -59,43,"[4, 3]","[50, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[2243] -59,44,"[1, 2]","[50, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[1876] -59,45,"[4, 3]","[60, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[2051] -59,46,"[1, 2]","[10, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1884] -59,47,"[4, 3]","[60, 0]","[2, 1]",[2],[1],[3],[0],[0],[1],[3],[2467] -59,48,"[2, 1]","[60, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[1884] -59,49,"[1, 2]","[10, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[2959] -59,50,"[4, 3]","[80, 30]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2202] -59,51,"[4, 3]","[70, 10]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2293] -59,52,"[1, 2]","[10, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2234] -59,53,"[3, 4]","[20, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[1884] -59,54,"[4, 3]","[90, 10]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[2284] -59,55,"[1, 2]","[20, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1785] -59,56,"[4, 3]","[70, 20]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[1826] -59,57,"[1, 2]","[10, 90]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[2101] -59,58,"[4, 3]","[50, 20]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1926] -59,59,"[2, 1]","[50, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[3252] -59,60,"[1, 2]","[30, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2760] -59,61,"[3, 4]","[20, 60]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[1926] -59,62,"[3, 4]","[50, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[2026] -59,63,"[1, 2]","[60, 50]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[2518] -59,64,"[4, 3]","[70, 60]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[2484] -59,65,"[2, 1]","[90, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2018] -59,66,"[1, 2]","[40, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2251] -59,67,"[3, 4]","[40, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1693] -59,68,"[3, 4]","[20, 30]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[2126] -59,69,"[3, 4]","[50, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2142] -59,70,"[4, 3]","[30, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[2068] -59,71,"[3, 4]","[70, 0]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[2284] -59,72,"[3, 4]","[70, 10]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[2168] -59,73,"[4, 3]","[10, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[3343] -59,74,"[4, 3]","[20, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1926] -59,75,"[4, 3]","[50, 70]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[2084] -59,76,"[3, 4]","[20, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2117] -59,77,"[4, 3]","[20, 10]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1892] -59,78,"[3, 4]","[30, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1851] -59,79,"[2, 1]","[30, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[2743] -59,80,"[3, 4]","[30, 20]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[2201] -54,1,"[4, 3]","[10, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[3974] -54,2,"[2, 1]","[0, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2238] -54,3,"[1, 2]","[70, 10]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2241] -54,4,"[1, 2]","[80, 20]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1970] -54,5,"[3, 4]","[80, 10]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[2189] -54,6,"[4, 3]","[30, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1861] -54,7,"[3, 4]","[80, 0]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2013] -54,8,"[1, 2]","[70, 0]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2003] -54,9,"[1, 2]","[70, 10]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1888] -54,10,"[2, 1]","[10, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2355] -54,11,"[1, 2]","[80, 20]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2130] -54,12,"[1, 2]","[70, 40]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1965] -54,13,"[3, 4]","[60, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1932] -54,14,"[2, 1]","[60, 70]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2119] -54,15,"[2, 1]","[60, 80]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2165] -54,16,"[1, 2]","[90, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1950] -54,17,"[1, 2]","[60, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2337] -54,18,"[2, 1]","[90, 30]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[2825] -54,19,"[3, 4]","[50, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[2476] -54,20,"[3, 4]","[30, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[1764] -54,21,"[3, 4]","[40, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2155] -54,22,"[3, 4]","[40, 10]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2731] -54,23,"[4, 3]","[0, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1746] -54,24,"[1, 2]","[30, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[2094] -54,25,"[1, 2]","[50, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[1784] -54,26,"[2, 1]","[50, 70]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[2116] -54,27,"[4, 3]","[60, 50]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1982] -54,28,"[3, 4]","[0, 90]","[2, 2]",[1],[5],[4],[450],[9],[2],[4],[4617] -54,29,"[2, 1]","[70, 10]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2433] -54,30,"[4, 3]","[60, 20]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2681] -54,31,"[3, 4]","[10, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1905] -54,32,"[4, 3]","[60, 20]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1836] -54,33,"[4, 3]","[60, 20]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1688] -54,34,"[1, 2]","[20, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2123] -54,35,"[1, 2]","[10, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2153] -54,36,"[4, 3]","[80, 20]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2597] -54,37,"[1, 2]","[20, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2412] -54,38,"[2, 1]","[70, 10]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[3351] -54,39,"[2, 1]","[60, 10]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2669] -54,40,"[3, 4]","[50, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[1910] -54,41,"[2, 1]","[90, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[2686] -54,42,"[1, 2]","[50, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[1900] -54,43,"[4, 3]","[80, 70]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[2002] -54,44,"[2, 1]","[40, 90]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[2177] -54,45,"[4, 3]","[60, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1762] -54,46,"[1, 2]","[90, 50]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[2061] -54,47,"[3, 4]","[80, 50]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2071] -54,48,"[1, 2]","[70, 60]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2470] -54,49,"[1, 2]","[60, 70]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2076] -54,50,"[1, 2]","[80, 70]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2001] -54,51,"[4, 3]","[70, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[1938] -54,52,"[1, 2]","[50, 80]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2294] -54,53,"[4, 3]","[70, 50]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2399] -54,54,"[2, 1]","[70, 40]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[2106] -54,55,"[4, 3]","[60, 70]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[2017] -54,56,"[1, 2]","[50, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[2001] -54,57,"[3, 4]","[60, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[2032] -54,58,"[4, 3]","[60, 90]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[1861] -54,59,"[3, 4]","[70, 60]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[3182] -54,60,"[2, 1]","[30, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1995] -54,61,"[3, 4]","[80, 10]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2024] -54,62,"[4, 3]","[20, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1805] -54,63,"[1, 2]","[80, 40]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2061] -54,64,"[2, 1]","[0, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2087] -54,65,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2051] -54,66,"[3, 4]","[90, 40]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[1940] -54,67,"[3, 4]","[20, 90]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[1979] -54,68,"[1, 2]","[50, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[3957] -54,69,"[4, 3]","[60, 20]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2004] -54,70,"[4, 3]","[50, 30]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1963] -54,71,"[2, 1]","[40, 60]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[3049] -54,72,"[4, 3]","[30, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1971] -54,73,"[3, 4]","[80, 40]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1847] -54,74,"[3, 4]","[70, 20]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1844] -54,75,"[3, 4]","[50, 40]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2027] -54,76,"[3, 4]","[60, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2381] -54,77,"[1, 2]","[80, 0]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1964] -54,78,"[4, 3]","[10, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[2013] -54,79,"[4, 3]","[10, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[3615] -54,80,"[1, 2]","[40, 20]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2498] -60,1,"[1, 2]","[60, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[8668] -60,2,"[4, 3]","[40, 60]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[3678] -60,3,"[3, 4]","[40, 30]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2513] -60,4,"[4, 3]","[30, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[7238] -60,5,"[1, 2]","[80, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[2163] -60,6,"[1, 2]","[70, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[3728] -60,7,"[2, 1]","[70, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[3296] -60,8,"[4, 3]","[40, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[2830] -60,9,"[1, 2]","[70, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[2231] -60,10,"[2, 1]","[20, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2180] -60,11,"[4, 3]","[50, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[3495] -60,12,"[4, 3]","[50, 70]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2146] -60,13,"[1, 2]","[50, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[3595] -60,14,"[1, 2]","[60, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2147] -60,15,"[1, 2]","[90, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[3179] -60,16,"[1, 2]","[80, 10]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[3645] -60,17,"[4, 3]","[60, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2496] -60,18,"[4, 3]","[30, 90]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[3528] -60,19,"[2, 1]","[20, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2380] -60,20,"[4, 3]","[0, 60]","[2, 2]",[1],[5],[4],[0],[0],[2],[4],[6406] -60,21,"[2, 1]","[10, 20]","[1, 2]",[1],[1],[2],[10],[1],[2],[2],[2563] -60,22,"[3, 4]","[40, 20]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[3345] -60,23,"[2, 1]","[40, 10]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[3230] -60,24,"[3, 4]","[30, 20]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[2613] -60,25,"[4, 3]","[30, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[2663] -60,26,"[3, 4]","[10, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[1947] -60,27,"[4, 3]","[20, 10]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2114] -60,28,"[1, 2]","[10, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[1],[6906] -60,29,"[2, 1]","[0, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[1781] -60,30,"[3, 4]","[10, 30]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[8754] -60,31,"[4, 3]","[20, 30]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[2146] -60,32,"[1, 2]","[0, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[1781] -60,33,"[2, 1]","[30, 10]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[41796] -60,34,"[4, 3]","[40, 0]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[7356] -60,35,"[4, 3]","[10, 0]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[1947] -60,36,"[2, 1]","[0, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[4060] -60,37,"[2, 1]","[10, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[2330] -60,38,"[1, 2]","[40, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[2595] -60,39,"[1, 2]","[30, 0]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[1814] -60,40,"[1, 2]","[30, 10]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[5009] -60,41,"[4, 3]","[10, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[10732] -60,42,"[4, 3]","[0, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1981] -60,43,"[4, 3]","[20, 10]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1963] -60,44,"[2, 1]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[3312] -60,45,"[4, 3]","[10, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[4360] -60,46,"[3, 4]","[40, 10]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2762] -60,47,"[3, 4]","[80, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1814] -60,48,"[1, 2]","[80, 40]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[4128] -60,49,"[4, 3]","[50, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1964] -60,50,"[2, 1]","[80, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[1864] -60,51,"[3, 4]","[80, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[1931] -60,52,"[2, 1]","[80, 90]","[1, 1]",[2],[1],[1],[90],[9],[1],[2],[1781] -60,53,"[3, 4]","[80, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1947] -60,54,"[2, 1]","[60, 80]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1764] -60,55,"[4, 3]","[70, 80]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[2014] -60,56,"[2, 1]","[70, 50]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[1781] -60,57,"[2, 1]","[60, 40]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2629] -60,58,"[4, 3]","[50, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[1865] -60,59,"[2, 1]","[50, 70]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[2563] -60,60,"[3, 4]","[80, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[1848] -60,61,"[2, 1]","[50, 70]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1931] -60,62,"[4, 3]","[70, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[2213] -60,63,"[4, 3]","[80, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[4660] -60,64,"[2, 1]","[90, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[1897] -60,65,"[3, 4]","[10, 90]","[2, 2]",[2],[5],[4],[450],[9],[2],[4],[3078] -60,66,"[3, 4]","[40, 90]","[2, 2]",[2],[1],[4],[90],[9],[2],[4],[7121] -60,67,"[3, 4]","[30, 80]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[2246] -60,68,"[4, 3]","[90, 30]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[1831] -60,69,"[3, 4]","[20, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[1997] -60,70,"[3, 4]","[10, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1731] -60,71,"[4, 3]","[70, 20]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[3162] -60,72,"[2, 1]","[90, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[10283] -60,73,"[1, 2]","[10, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1781] -60,74,"[3, 4]","[20, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[3528] -60,75,"[1, 2]","[30, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[1864] -60,76,"[1, 2]","[50, 10]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[1815] -60,77,"[1, 2]","[40, 20]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2613] -60,78,"[1, 2]","[50, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[5791] -60,79,"[2, 1]","[0, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[1764] -60,80,"[3, 4]","[40, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2779] -61,1,"[4, 3]","[60, 10]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[15705] -61,2,"[1, 2]","[20, 50]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[5852] -61,3,"[3, 4]","[30, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[3261] -61,4,"[2, 1]","[50, 10]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[3028] -61,5,"[2, 1]","[30, 20]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[3676] -61,6,"[1, 2]","[20, 0]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[2148] -61,7,"[2, 1]","[30, 10]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[3991] -61,8,"[3, 4]","[20, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[3080] -61,9,"[2, 1]","[10, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[5487] -61,10,"[4, 3]","[0, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2297] -61,11,"[3, 4]","[70, 10]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2413] -61,12,"[2, 1]","[10, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[2579] -61,13,"[4, 3]","[30, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[3097] -61,14,"[2, 1]","[20, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[3143] -61,15,"[4, 3]","[30, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2280] -61,16,"[3, 4]","[60, 20]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2330] -61,17,"[4, 3]","[40, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[4024] -61,18,"[4, 3]","[60, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[3660] -61,19,"[2, 1]","[40, 70]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2729] -61,20,"[3, 4]","[70, 60]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[2597] -61,21,"[2, 1]","[60, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2546] -61,22,"[1, 2]","[70, 90]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2613] -61,23,"[1, 2]","[80, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[3028] -61,24,"[2, 1]","[70, 80]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[3061] -61,25,"[1, 2]","[70, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[3227] -61,26,"[1, 2]","[60, 40]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[1865] -61,27,"[1, 2]","[60, 10]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[3094] -61,28,"[1, 2]","[80, 10]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2131] -61,29,"[3, 4]","[70, 50]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1982] -61,30,"[4, 3]","[70, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2264] -61,31,"[3, 4]","[50, 60]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2382] -61,32,"[2, 1]","[90, 30]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[2331] -61,33,"[4, 3]","[50, 30]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[3028] -61,34,"[4, 3]","[40, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[3144] -61,35,"[4, 3]","[60, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[1999] -61,36,"[1, 2]","[50, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2880] -61,37,"[3, 4]","[80, 50]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[2147] -61,38,"[3, 4]","[70, 50]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[2052] -61,39,"[1, 2]","[80, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2696] -61,40,"[2, 1]","[60, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2596] -61,41,"[3, 4]","[70, 50]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[29233] -61,42,"[3, 4]","[60, 30]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[2215] -61,43,"[4, 3]","[60, 70]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2198] -61,44,"[1, 2]","[50, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[2580] -61,45,"[1, 2]","[50, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1898] -61,46,"[1, 2]","[60, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1882] -61,47,"[2, 1]","[30, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[3110] -61,48,"[3, 4]","[10, 30]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[3261] -61,49,"[1, 2]","[10, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[3327] -61,50,"[3, 4]","[50, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2447] -61,51,"[2, 1]","[80, 50]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[2895] -61,52,"[3, 4]","[60, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2130] -61,53,"[2, 1]","[70, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2496] -61,54,"[2, 1]","[90, 60]","[1, 2]",[2],[1],[2],[90],[9],[2],[1],[2363] -61,55,"[3, 4]","[70, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2198] -61,56,"[4, 3]","[90, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2363] -61,57,"[1, 2]","[90, 90]","[1, 2]",[2],[1],[2],[90],[9],[2],[1],[2380] -61,58,"[4, 3]","[60, 70]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[1867] -61,59,"[3, 4]","[90, 60]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[2863] -61,60,"[1, 2]","[60, 50]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2862] -61,61,"[4, 3]","[60, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[2081] -61,62,"[2, 1]","[50, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2448] -61,63,"[3, 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1]",[1],[5],[1],[400],[8],[1],[1],[2168] -63,27,"[4, 3]","[0, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1631] -63,28,"[1, 2]","[60, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[1790] -63,29,"[2, 1]","[40, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1705] -63,30,"[1, 2]","[70, 0]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2169] -63,31,"[3, 4]","[40, 30]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[1877] -63,32,"[3, 4]","[40, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[1921] -63,33,"[1, 2]","[30, 50]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[1804] -63,34,"[4, 3]","[40, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2076] -63,35,"[2, 1]","[40, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1774] -63,36,"[2, 1]","[50, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1701] -63,37,"[1, 2]","[40, 50]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[1934] -63,38,"[2, 1]","[50, 40]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[1859] -63,39,"[4, 3]","[60, 30]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[1779] -63,40,"[3, 4]","[40, 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1]",[2],[5],[3],[450],[9],[1],[3],[2002] -63,68,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1883] -63,69,"[4, 3]","[0, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1820] -63,70,"[4, 3]","[10, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1726] -63,71,"[4, 3]","[30, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1721] -63,72,"[1, 2]","[60, 10]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[2137] -63,73,"[4, 3]","[0, 70]","[2, 2]",[2],[5],[4],[0],[0],[2],[4],[1940] -63,74,"[1, 2]","[80, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[2112] -63,75,"[1, 2]","[60, 0]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[1797] -63,76,"[2, 1]","[20, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[2220] -63,77,"[4, 3]","[40, 20]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2101] -63,78,"[1, 2]","[10, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[2588] -63,79,"[1, 2]","[20, 0]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[2019] -63,80,"[4, 3]","[20, 40]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[1753] -62,1,"[1, 2]","[60, 20]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[4157] -62,2,"[4, 3]","[10, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1704] -62,3,"[2, 1]","[10, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2054] -62,4,"[4, 3]","[30, 70]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[1854] -62,5,"[4, 3]","[50, 90]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[1871] -62,6,"[3, 4]","[80, 60]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[2204] -62,7,"[3, 4]","[60, 40]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1720] -62,8,"[3, 4]","[50, 10]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[1871] -62,9,"[4, 3]","[10, 70]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[1770] -62,10,"[1, 2]","[60, 20]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[1971] -62,11,"[1, 2]","[60, 10]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2238] -62,12,"[1, 2]","[30, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[1],[2021] -62,13,"[2, 1]","[0, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[2188] -62,14,"[1, 2]","[20, 0]","[1, 2]",[1],[5],[2],[0],[0],[2],[1],[2221] -62,15,"[1, 2]","[30, 10]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[2004] -62,16,"[2, 1]","[20, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[1954] -62,17,"[4, 3]","[10, 40]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[2038] -62,18,"[4, 3]","[20, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[1653] -62,19,"[2, 1]","[30, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[2104] -62,20,"[2, 1]","[50, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[2121] -62,21,"[3, 4]","[20, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2037] -62,22,"[2, 1]","[90, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[2238] -62,23,"[1, 2]","[50, 80]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[2071] -62,24,"[4, 3]","[60, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[1870] -62,25,"[3, 4]","[40, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2087] -62,26,"[1, 2]","[20, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2321] -62,27,"[4, 3]","[80, 30]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[1970] -62,28,"[4, 3]","[80, 40]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[1887] -62,29,"[1, 2]","[40, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2538] -62,30,"[2, 1]","[80, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[3373] -62,31,"[3, 4]","[10, 70]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[6394] -62,32,"[1, 2]","[10, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2839] -62,33,"[2, 1]","[70, 40]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2421] -62,34,"[1, 2]","[60, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2171] -62,35,"[3, 4]","[60, 70]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2288] -62,36,"[4, 3]","[70, 60]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[1903] -62,37,"[1, 2]","[60, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2455] -62,38,"[2, 1]","[80, 40]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2438] -62,39,"[3, 4]","[40, 70]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[1703] -62,40,"[2, 1]","[60, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[3957] -62,41,"[4, 3]","[90, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2269] -62,42,"[1, 2]","[30, 90]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[1704] -62,43,"[1, 2]","[60, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2538] -62,44,"[2, 1]","[90, 30]","[1, 2]",[2],[1],[2],[90],[9],[2],[1],[1871] -62,45,"[1, 2]","[40, 90]","[1, 2]",[2],[1],[2],[90],[9],[2],[2],[1954] -62,46,"[2, 1]","[70, 40]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[1854] -62,47,"[1, 2]","[40, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1955] -62,48,"[3, 4]","[90, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[3089] -62,49,"[1, 2]","[90, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[2321] -62,50,"[3, 4]","[80, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[1737] -62,51,"[3, 4]","[90, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[1854] -62,52,"[3, 4]","[70, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2037] -62,53,"[3, 4]","[70, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[1770] -62,54,"[2, 1]","[80, 60]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[3340] -62,55,"[2, 1]","[80, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2438] -62,56,"[4, 3]","[80, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1937] -62,57,"[3, 4]","[80, 50]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1804] -62,58,"[4, 3]","[20, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1787] -62,59,"[1, 2]","[70, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2271] -62,60,"[2, 1]","[20, 80]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[2589] -62,61,"[3, 4]","[70, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[2171] -62,62,"[1, 2]","[70, 0]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[1937] -62,63,"[3, 4]","[50, 40]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1721] -62,64,"[4, 3]","[10, 50]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[1921] -62,65,"[3, 4]","[60, 10]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[1904] -62,66,"[3, 4]","[40, 50]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1754] -62,67,"[2, 1]","[30, 80]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2087] -62,68,"[1, 2]","[80, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[2221] -62,69,"[1, 2]","[70, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[2105] -62,70,"[2, 1]","[80, 90]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2238] -62,71,"[3, 4]","[80, 70]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2288] -62,72,"[4, 3]","[60, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1737] -62,73,"[1, 2]","[80, 40]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2055] -62,74,"[1, 2]","[80, 70]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2020] -62,75,"[4, 3]","[50, 90]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[1920] -62,76,"[1, 2]","[80, 40]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2505] -62,77,"[1, 2]","[60, 0]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2005] -62,78,"[4, 3]","[20, 50]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[1955] -62,79,"[1, 2]","[30, 20]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[1954] -62,80,"[4, 3]","[50, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1955] -64,1,"[2, 1]","[30, 80]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[5613] -64,2,"[2, 1]","[0, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[4950] -64,3,"[3, 4]","[60, 20]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[2572] -64,4,"[4, 3]","[10, 70]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[4375] -64,5,"[3, 4]","[70, 0]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[3416] -64,6,"[1, 2]","[50, 30]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[1831] -64,7,"[2, 1]","[30, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2982] -64,8,"[2, 1]","[10, 20]","[1, 2]",[1],[1],[2],[10],[1],[2],[2],[4681] -64,9,"[4, 3]","[10, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[2072] -64,10,"[4, 3]","[60, 10]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2988] -64,11,"[4, 3]","[10, 30]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[3090] -64,12,"[1, 2]","[10, 40]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[1781] -64,13,"[3, 4]","[10, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[2664] -64,14,"[4, 3]","[40, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[2615] -64,15,"[2, 1]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[6593] -64,16,"[4, 3]","[40, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[2071] -64,17,"[3, 4]","[60, 50]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2039] -64,18,"[2, 1]","[40, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2347] -64,19,"[1, 2]","[50, 40]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[6259] -64,20,"[4, 3]","[40, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[2756] -64,21,"[1, 2]","[10, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[4098] -64,22,"[4, 3]","[30, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[3639] -64,23,"[1, 2]","[10, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[3780] -64,24,"[4, 3]","[20, 10]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[3724] -64,25,"[3, 4]","[0, 50]","[2, 1]",[1],[5],[3],[0],[0],[1],[4],[2631] -64,26,"[3, 4]","[10, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[5909] -64,27,"[3, 4]","[10, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[1897] -64,28,"[3, 4]","[30, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1688] -64,29,"[4, 3]","[20, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[1897] -64,30,"[1, 2]","[10, 20]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[2456] -64,31,"[2, 1]","[0, 10]","[1, 2]",[1],[1],[2],[0],[0],[2],[2],[1747] -64,32,"[4, 3]","[60, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[1964] -64,33,"[1, 2]","[20, 40]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[2247] -64,34,"[1, 2]","[20, 30]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[3173] -64,35,"[1, 2]","[0, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[5935] -64,36,"[1, 2]","[30, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[2806] -64,37,"[4, 3]","[40, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[3406] -64,38,"[2, 1]","[40, 0]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[2539] -64,39,"[4, 3]","[80, 20]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2188] -64,40,"[1, 2]","[50, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[1963] -64,41,"[3, 4]","[60, 50]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[3919] -64,42,"[4, 3]","[80, 50]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[2257] -64,43,"[2, 1]","[60, 50]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2021] -64,44,"[3, 4]","[50, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2481] -64,45,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2456] -64,46,"[4, 3]","[50, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1655] -64,47,"[3, 4]","[30, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[1856] -64,48,"[4, 3]","[60, 0]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[1905] -64,49,"[3, 4]","[10, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[1804] -64,50,"[2, 1]","[60, 30]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[1847] -64,51,"[1, 2]","[60, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[5501] -64,52,"[2, 1]","[80, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2439] -64,53,"[4, 3]","[80, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2356] -64,54,"[1, 2]","[40, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2865] -64,55,"[3, 4]","[0, 90]","[2, 2]",[2],[5],[4],[450],[9],[2],[4],[1997] -64,56,"[2, 1]","[80, 0]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[1839] -64,57,"[2, 1]","[60, 20]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[1797] -64,58,"[3, 4]","[50, 90]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[2064] -64,59,"[3, 4]","[90, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[3],[1805] -64,60,"[1, 2]","[80, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2131] -64,61,"[2, 1]","[70, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1962] -64,62,"[4, 3]","[70, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[2206] -64,63,"[1, 2]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[1955] -64,64,"[1, 2]","[60, 80]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1738] -64,65,"[3, 4]","[60, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1689] -64,66,"[1, 2]","[90, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[3906] -64,67,"[2, 1]","[70, 90]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[2298] -64,68,"[3, 4]","[80, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2197] -64,69,"[4, 3]","[90, 70]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[2590] -64,70,"[4, 3]","[90, 80]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[2047] -64,71,"[2, 1]","[80, 70]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[1756] -64,72,"[3, 4]","[20, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[1938] -64,73,"[2, 1]","[80, 0]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[1772] -64,74,"[4, 3]","[90, 10]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[1789] -64,75,"[1, 2]","[0, 80]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[1955] -64,76,"[1, 2]","[20, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2122] -64,77,"[4, 3]","[50, 10]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[1980] -64,78,"[2, 1]","[50, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[1947] -64,79,"[4, 3]","[40, 30]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[7693] -64,80,"[2, 1]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[3415] -29,1,"[1, 2]","[60, 40]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[4605] -29,2,"[4, 3]","[30, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[1807] -29,3,"[4, 3]","[50, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1773] -29,4,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2039] -29,5,"[3, 4]","[70, 40]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1891] -29,6,"[2, 1]","[50, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1939] -29,7,"[4, 3]","[60, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[1773] -29,8,"[1, 2]","[70, 40]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1923] -29,9,"[2, 1]","[20, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[2803] -29,10,"[4, 3]","[0, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2022] -29,11,"[4, 3]","[30, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1741] -29,12,"[1, 2]","[60, 40]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[1889] -29,13,"[3, 4]","[60, 30]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1740] -29,14,"[4, 3]","[30, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1873] -29,15,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2023] -29,16,"[3, 4]","[80, 30]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1756] -29,17,"[2, 1]","[60, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[2023] -29,18,"[4, 3]","[50, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[1807] -29,19,"[2, 1]","[90, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2007] -29,20,"[4, 3]","[70, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1773] -29,21,"[3, 4]","[80, 50]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1806] -29,22,"[3, 4]","[80, 50]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1790] -29,23,"[4, 3]","[40, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1755] -29,24,"[1, 2]","[90, 50]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[2520] -29,25,"[4, 3]","[60, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1756] -29,26,"[1, 2]","[80, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1820] -29,27,"[1, 2]","[80, 70]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1856] -29,28,"[2, 1]","[60, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1856] -29,29,"[4, 3]","[40, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[2022] -29,30,"[2, 1]","[30, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1856] -29,31,"[2, 1]","[60, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1872] -29,32,"[2, 1]","[50, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1773] -29,33,"[2, 1]","[40, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[1756] -29,34,"[3, 4]","[90, 50]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[2356] -29,35,"[2, 1]","[60, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1956] -29,36,"[1, 2]","[50, 90]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2074] -29,37,"[3, 4]","[50, 70]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[2289] -29,38,"[3, 4]","[40, 60]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1856] -29,39,"[2, 1]","[60, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[1790] -29,40,"[2, 1]","[70, 50]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[3289] -29,41,"[3, 4]","[80, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[2736] -29,42,"[1, 2]","[80, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2040] -29,43,"[2, 1]","[70, 90]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2156] -29,44,"[4, 3]","[80, 60]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1972] -29,45,"[1, 2]","[80, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[2007] -29,46,"[4, 3]","[80, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[1970] -29,47,"[2, 1]","[70, 30]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[1939] -29,48,"[3, 4]","[10, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[2040] -29,49,"[1, 2]","[20, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[1839] -29,50,"[4, 3]","[20, 20]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[1787] -29,51,"[3, 4]","[30, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1756] -29,52,"[1, 2]","[50, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2124] -29,53,"[1, 2]","[50, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1773] -29,54,"[1, 2]","[60, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[1856] -29,55,"[4, 3]","[80, 70]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[1889] -29,56,"[4, 3]","[70, 80]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[1856] -29,57,"[1, 2]","[80, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[1957] -29,58,"[1, 2]","[60, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[1923] -29,59,"[3, 4]","[90, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[1873] -29,60,"[2, 1]","[60, 80]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[2155] -29,61,"[1, 2]","[80, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[1790] -29,62,"[4, 3]","[90, 70]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[1889] -29,63,"[2, 1]","[80, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[1923] -29,64,"[4, 3]","[80, 90]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1906] -29,65,"[3, 4]","[80, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[1890] -29,66,"[4, 3]","[50, 70]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[1906] -29,67,"[3, 4]","[50, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[1889] -29,68,"[4, 3]","[90, 60]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[1923] -29,69,"[1, 2]","[60, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2122] -29,70,"[1, 2]","[50, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[1957] -29,71,"[1, 2]","[90, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[1807] -29,72,"[4, 3]","[30, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[2056] -29,73,"[3, 4]","[20, 60]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[1739] -29,74,"[3, 4]","[30, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[1755] -29,75,"[2, 1]","[70, 30]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1973] -29,76,"[4, 3]","[80, 10]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[2240] -29,77,"[3, 4]","[10, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[1989] -29,78,"[1, 2]","[20, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2023] -29,79,"[3, 4]","[20, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[1804] -29,80,"[2, 1]","[80, 30]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[1989] -57,1,"[1, 2]","[30, 60]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[10265] -57,2,"[3, 4]","[40, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[1969] -57,3,"[1, 2]","[50, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2038] -57,4,"[4, 3]","[60, 80]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2248] -57,5,"[4, 3]","[80, 90]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[2074] -57,6,"[1, 2]","[80, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2144] -57,7,"[2, 1]","[80, 90]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1986] -57,8,"[1, 2]","[90, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[2022] -57,9,"[2, 1]","[70, 40]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1969] -57,10,"[4, 3]","[30, 40]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[2098] -57,11,"[1, 2]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[1952] -57,12,"[4, 3]","[60, 20]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2022] -57,13,"[2, 1]","[70, 30]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1889] -57,14,"[4, 3]","[60, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1924] -57,15,"[1, 2]","[10, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[1863] -57,16,"[1, 2]","[0, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[1819] -57,17,"[2, 1]","[10, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1794] -57,18,"[1, 2]","[50, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[1741] -57,19,"[2, 1]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[1847] -57,20,"[4, 3]","[40, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2126] -57,21,"[1, 2]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[1995] -57,22,"[1, 2]","[10, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[2],[1864] -57,23,"[4, 3]","[0, 0]","[2, 1]",[1],[5],[3],[0],[0],[1],[4],[1906] -57,24,"[2, 1]","[10, 40]","[1, 2]",[1],[5],[2],[50],[1],[2],[2],[1907] -57,25,"[2, 1]","[20, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1732] -57,26,"[1, 2]","[10, 0]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[2046] -57,27,"[2, 1]","[10, 20]","[1, 2]",[1],[1],[2],[10],[1],[2],[2],[1846] -57,28,"[4, 3]","[30, 0]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2117] -57,29,"[1, 2]","[0, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[2143] -57,30,"[4, 3]","[20, 0]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1837] -57,31,"[3, 4]","[40, 20]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[1767] -57,32,"[2, 1]","[20, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[1924] -57,33,"[2, 1]","[20, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[1819] -57,34,"[3, 4]","[20, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[1802] -57,35,"[1, 2]","[0, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[2055] -57,36,"[2, 1]","[70, 10]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1959] -57,37,"[2, 1]","[90, 30]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[1987] -57,38,"[4, 3]","[90, 30]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2064] -57,39,"[3, 4]","[20, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[2012] -57,40,"[2, 1]","[80, 10]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[2056] -57,41,"[4, 3]","[90, 0]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[4973] -57,42,"[2, 1]","[70, 10]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1942] -57,43,"[2, 1]","[70, 20]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1952] -57,44,"[3, 4]","[0, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2213] -57,45,"[1, 2]","[10, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[1697] -57,46,"[1, 2]","[30, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[1697] -57,47,"[2, 1]","[60, 20]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2866] -57,48,"[3, 4]","[20, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[1906] -57,49,"[2, 1]","[70, 0]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2082] -57,50,"[4, 3]","[90, 0]","[2, 1]",[2],[5],[3],[0],[0],[1],[3],[1690] -57,51,"[2, 1]","[80, 0]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[1855] -57,52,"[1, 2]","[10, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2038] -57,53,"[4, 3]","[80, 40]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1993] -57,54,"[1, 2]","[30, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1759] -57,55,"[4, 3]","[60, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2247] -57,56,"[4, 3]","[60, 10]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2255] -57,57,"[3, 4]","[50, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[1854] -57,58,"[3, 4]","[40, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1829] -57,59,"[2, 1]","[30, 0]","[1, 1]",[2],[1],[1],[0],[0],[1],[1],[1889] -57,60,"[2, 1]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[1994] -57,61,"[1, 2]","[20, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[1846] -57,62,"[3, 4]","[10, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[4],[1881] -57,63,"[2, 1]","[40, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[1864] -57,64,"[4, 3]","[10, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[2038] -57,65,"[2, 1]","[10, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1777] -57,66,"[1, 2]","[60, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[2046] -57,67,"[4, 3]","[20, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[2151] -57,68,"[4, 3]","[0, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1794] -57,69,"[4, 3]","[10, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1724] -57,70,"[2, 1]","[40, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1795] -57,71,"[1, 2]","[80, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2239] -57,72,"[1, 2]","[90, 70]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[2238] -57,73,"[3, 4]","[90, 50]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[1959] -57,74,"[4, 3]","[30, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1777] -57,75,"[4, 3]","[20, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1829] -57,76,"[3, 4]","[90, 10]","[2, 1]",[2],[5],[3],[450],[9],[1],[3],[2091] -57,77,"[4, 3]","[20, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1750] -57,78,"[2, 1]","[40, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1847] -57,79,"[4, 3]","[50, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1767] -57,80,"[3, 4]","[70, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1994] -59,1,"[1, 2]","[30, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[4884] -59,2,"[1, 2]","[20, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2184] -59,3,"[3, 4]","[10, 60]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[2584] -59,4,"[4, 3]","[90, 10]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[3501] -59,5,"[2, 1]","[70, 0]","[1, 1]",[1],[5],[1],[0],[0],[1],[1],[2368] -59,6,"[1, 2]","[20, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2085] -59,7,"[1, 2]","[10, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[2251] -59,8,"[2, 1]","[60, 10]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[3185] -59,9,"[2, 1]","[80, 10]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2218] -59,10,"[2, 1]","[70, 10]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2151] -59,11,"[1, 2]","[30, 40]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[2067] -59,12,"[4, 3]","[40, 20]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2268] -59,13,"[4, 3]","[50, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[2518] -59,14,"[4, 3]","[10, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[2134] -59,15,"[4, 3]","[0, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1901] -59,16,"[2, 1]","[20, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[2551] -59,17,"[3, 4]","[20, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[2368] -59,18,"[4, 3]","[40, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[2217] -59,19,"[1, 2]","[50, 20]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[2218] -59,20,"[3, 4]","[50, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[2184] -59,21,"[2, 1]","[30, 60]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[3801] -59,22,"[3, 4]","[30, 20]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[2201] -59,23,"[1, 2]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2418] -59,24,"[4, 3]","[0, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[2184] -59,25,"[1, 2]","[20, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[3068] -59,26,"[1, 2]","[60, 30]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2301] -59,27,"[2, 1]","[40, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2068] -59,28,"[2, 1]","[50, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[2251] -59,29,"[3, 4]","[30, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[2284] -59,30,"[2, 1]","[70, 40]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2150] -59,31,"[1, 2]","[40, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2418] -59,32,"[4, 3]","[90, 60]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2067] -59,33,"[1, 2]","[70, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2418] -59,34,"[4, 3]","[80, 60]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[4134] -59,35,"[2, 1]","[90, 80]","[1, 2]",[1],[1],[2],[90],[9],[2],[1],[1984] -59,36,"[4, 3]","[90, 70]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[1984] -59,37,"[1, 2]","[40, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2851] -59,38,"[2, 1]","[70, 30]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2134] -59,39,"[2, 1]","[60, 20]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2051] -59,40,"[4, 3]","[80, 10]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2034] -59,41,"[1, 2]","[20, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[1633] -59,42,"[2, 1]","[70, 10]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2268] -59,43,"[3, 4]","[20, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2184] -59,44,"[2, 1]","[70, 0]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[2718] -59,45,"[2, 1]","[30, 10]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2384] -59,46,"[4, 3]","[40, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[2417] -59,47,"[2, 1]","[30, 10]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[4235] -59,48,"[4, 3]","[60, 30]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2334] -59,49,"[4, 3]","[70, 10]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1934] -59,50,"[1, 2]","[10, 50]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[2350] -59,51,"[2, 1]","[40, 30]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2234] -59,52,"[4, 3]","[20, 40]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[3601] -59,53,"[4, 3]","[30, 40]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2101] -59,54,"[2, 1]","[20, 50]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[2085] -59,55,"[1, 2]","[60, 10]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[4268] -59,56,"[3, 4]","[40, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2418] -59,57,"[3, 4]","[50, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[2017] -59,58,"[3, 4]","[30, 40]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[2134] -59,59,"[1, 2]","[30, 40]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[1918] -59,60,"[4, 3]","[30, 20]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1901] -59,61,"[3, 4]","[40, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[1984] -59,62,"[3, 4]","[20, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2001] -59,63,"[4, 3]","[20, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[2334] -59,64,"[2, 1]","[40, 60]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[2968] -59,65,"[1, 2]","[80, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2467] -59,66,"[1, 2]","[70, 0]","[1, 2]",[2],[5],[2],[0],[0],[2],[1],[1834] -59,67,"[4, 3]","[10, 80]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[2051] -59,68,"[4, 3]","[20, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1851] -59,69,"[3, 4]","[40, 10]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[2035] -59,70,"[2, 1]","[10, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[2518] -59,71,"[4, 3]","[10, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[2984] -59,72,"[1, 2]","[70, 30]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[1901] -59,73,"[4, 3]","[10, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[1751] -59,74,"[4, 3]","[20, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2418] -59,75,"[4, 3]","[10, 80]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1967] -59,76,"[2, 1]","[20, 60]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[2018] -59,77,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[3051] -59,78,"[4, 3]","[10, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[4985] -59,79,"[1, 2]","[90, 10]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[1851] -59,80,"[2, 1]","[10, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1917] -65,1,"[2, 1]","[10, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[5502] -65,2,"[1, 2]","[80, 20]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[3397] -65,3,"[4, 3]","[80, 90]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[3246] -65,4,"[1, 2]","[60, 70]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2627] -65,5,"[4, 3]","[80, 70]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[3229] -65,6,"[1, 2]","[70, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[3564] -65,7,"[1, 2]","[60, 0]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2978] -65,8,"[1, 2]","[70, 0]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2059] -65,9,"[1, 2]","[90, 20]","[1, 1]",[1],[5],[1],[450],[9],[1],[1],[2125] -65,10,"[2, 1]","[0, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[4785] -65,11,"[2, 1]","[0, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2962] -65,12,"[2, 1]","[30, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2460] -65,13,"[2, 1]","[20, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2293] -65,14,"[2, 1]","[50, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[1690] -65,15,"[1, 2]","[60, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[1925] -65,16,"[1, 2]","[60, 40]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2175] -65,17,"[3, 4]","[30, 0]","[2, 2]",[1],[1],[4],[0],[0],[2],[3],[2861] -65,18,"[1, 2]","[0, 10]","[1, 1]",[1],[1],[1],[0],[0],[1],[2],[2226] -65,19,"[4, 3]","[10, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[2276] -65,20,"[2, 1]","[0, 10]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[2527] -65,21,"[3, 4]","[20, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[3096] -65,22,"[3, 4]","[10, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[2293] -65,23,"[4, 3]","[0, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[2209] -65,24,"[4, 3]","[0, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[2242] -65,25,"[3, 4]","[30, 10]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1940] -65,26,"[1, 2]","[40, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[2058] -65,27,"[2, 1]","[50, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[2109] -65,28,"[1, 2]","[40, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[1908] -65,29,"[1, 2]","[70, 60]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1991] -65,30,"[4, 3]","[80, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[2359] -65,31,"[2, 1]","[70, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[2393] -65,32,"[3, 4]","[90, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[1807] -65,33,"[3, 4]","[70, 90]","[2, 2]",[1],[5],[4],[450],[9],[2],[4],[1777] -65,34,"[3, 4]","[60, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1824] -65,35,"[2, 1]","[50, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1657] -65,36,"[1, 2]","[30, 50]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[2477] -65,37,"[3, 4]","[0, 20]","[2, 1]",[1],[5],[3],[0],[0],[1],[4],[2225] -65,38,"[1, 2]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2945] -65,39,"[3, 4]","[10, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1941] -65,40,"[3, 4]","[20, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[2242] -65,41,"[3, 4]","[0, 10]","[2, 1]",[2],[1],[3],[0],[0],[1],[4],[4399] -65,42,"[1, 2]","[10, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2694] -65,43,"[2, 1]","[10, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[2142] -65,44,"[3, 4]","[20, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[1857] -65,45,"[1, 2]","[10, 50]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[3012] -65,46,"[3, 4]","[10, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[2092] -65,47,"[3, 4]","[10, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2928] -65,48,"[4, 3]","[20, 30]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[2192] -65,49,"[3, 4]","[40, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1824] -65,50,"[4, 3]","[40, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[3063] -65,51,"[3, 4]","[40, 60]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[1974] -65,52,"[3, 4]","[50, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2175] -65,53,"[1, 2]","[60, 50]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[3146] -65,54,"[4, 3]","[50, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2409] -65,55,"[2, 1]","[40, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[1924] -65,56,"[3, 4]","[40, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2928] -65,57,"[2, 1]","[10, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[2459] -65,58,"[2, 1]","[30, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1807] -65,59,"[4, 3]","[20, 30]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[2376] -65,60,"[3, 4]","[20, 40]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[3313] -65,61,"[1, 2]","[20, 50]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[2058] -65,62,"[3, 4]","[20, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[4266] -65,63,"[4, 3]","[30, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[2410] -65,64,"[3, 4]","[60, 40]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2058] -65,65,"[4, 3]","[0, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[2242] -65,66,"[2, 1]","[10, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[4065] -65,67,"[2, 1]","[0, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2042] -65,68,"[1, 2]","[50, 10]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[1825] -65,69,"[3, 4]","[40, 0]","[2, 2]",[2],[5],[4],[0],[0],[2],[3],[2862] -65,70,"[4, 3]","[10, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1890] -65,71,"[1, 2]","[80, 10]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2560] -65,72,"[2, 1]","[50, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2092] -65,73,"[2, 1]","[40, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1941] -65,74,"[2, 1]","[50, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[1924] -65,75,"[2, 1]","[50, 10]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2426] -65,76,"[1, 2]","[10, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2125] -65,77,"[3, 4]","[20, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[2593] -65,78,"[1, 2]","[10, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2510] -65,79,"[2, 1]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2243] -65,80,"[2, 1]","[10, 70]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2075] -57,1,"[2, 1]","[10, 90]","[1, 2]",[1],[5],[2],[50],[1],[2],[2],[4218] -57,2,"[1, 2]","[80, 20]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1994] -57,3,"[4, 3]","[80, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[2046] -57,4,"[1, 2]","[60, 70]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[3086] -57,5,"[4, 3]","[80, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[2003] -57,6,"[1, 2]","[70, 30]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2021] -57,7,"[1, 2]","[60, 0]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2893] -57,8,"[1, 2]","[70, 0]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2884] -57,9,"[1, 2]","[90, 20]","[1, 1]",[1],[5],[1],[450],[9],[1],[1],[1749] -57,10,"[2, 1]","[0, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2082] -57,11,"[2, 1]","[0, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2161] -57,12,"[2, 1]","[30, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2361] -57,13,"[2, 1]","[20, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2622] -57,14,"[2, 1]","[50, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[3435] -57,15,"[1, 2]","[60, 50]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[1898] -57,16,"[1, 2]","[60, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[1959] -57,17,"[3, 4]","[30, 0]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[2092] -57,18,"[1, 2]","[0, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[2],[1907] -57,19,"[4, 3]","[10, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[1714] -57,20,"[2, 1]","[0, 10]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[6410] -57,21,"[3, 4]","[20, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[4970] -57,22,"[3, 4]","[10, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[6793] -57,23,"[4, 3]","[0, 20]","[2, 2]",[1],[1],[4],[0],[0],[2],[4],[3530] -57,24,"[4, 3]","[0, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1749] -57,25,"[3, 4]","[30, 10]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[9194] -57,26,"[1, 2]","[40, 10]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[27631] -57,27,"[2, 1]","[50, 40]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[1977] -57,28,"[1, 2]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2012] -57,29,"[1, 2]","[70, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[1732] -57,30,"[4, 3]","[80, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[1854] -57,31,"[2, 1]","[70, 90]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2754] -57,32,"[3, 4]","[90, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[8800] -57,33,"[3, 4]","[70, 90]","[2, 2]",[1],[5],[4],[450],[9],[2],[4],[4245] -57,34,"[3, 4]","[60, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[3244] -57,35,"[2, 1]","[50, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[4237] -57,36,"[1, 2]","[30, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[1881] -57,37,"[3, 4]","[0, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[2256] -57,38,"[1, 2]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[5887] -57,39,"[3, 4]","[10, 40]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[22947] -57,40,"[3, 4]","[20, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1907] -57,41,"[3, 4]","[0, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1728] -57,42,"[1, 2]","[10, 30]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[1760] -57,43,"[2, 1]","[10, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[1732] -57,44,"[3, 4]","[20, 30]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[1855] -57,45,"[1, 2]","[10, 50]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1864] -57,46,"[3, 4]","[10, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[1864] -57,47,"[3, 4]","[10, 30]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[1933] -57,48,"[4, 3]","[20, 30]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[1951] -57,49,"[3, 4]","[40, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1819] -57,50,"[4, 3]","[40, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1916] -57,51,"[3, 4]","[40, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2160] -57,52,"[3, 4]","[50, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[37797] -57,53,"[1, 2]","[60, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1986] -57,54,"[4, 3]","[50, 40]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2203] -57,55,"[2, 1]","[40, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[1829] -57,56,"[3, 4]","[40, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2544] -57,57,"[2, 1]","[10, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[1899] -57,58,"[2, 1]","[30, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2633] -57,59,"[4, 3]","[20, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1732] -57,60,"[3, 4]","[20, 40]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[1951] -57,61,"[1, 2]","[20, 50]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[1916] -57,62,"[3, 4]","[20, 10]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[8025] -57,63,"[4, 3]","[30, 20]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2133] -57,64,"[3, 4]","[60, 40]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1619] -57,65,"[4, 3]","[0, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1819] -57,66,"[2, 1]","[10, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1941] -57,67,"[2, 1]","[0, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1794] -57,68,"[1, 2]","[50, 10]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[1900] -57,69,"[3, 4]","[40, 0]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1794] -57,70,"[4, 3]","[10, 40]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1689] -57,71,"[1, 2]","[80, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[1645] -57,72,"[2, 1]","[50, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2639] -57,73,"[2, 1]","[40, 60]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2195] -57,74,"[2, 1]","[50, 20]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[2151] -57,75,"[2, 1]","[50, 10]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1993] -57,76,"[1, 2]","[10, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[1777] -57,77,"[3, 4]","[20, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[33225] -57,78,"[1, 2]","[10, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[1794] -57,79,"[2, 1]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1750] -57,80,"[2, 1]","[10, 70]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[1794] -57,1,"[4, 3]","[90, 30]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[4716] -57,2,"[1, 2]","[50, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1890] -57,3,"[2, 1]","[60, 50]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2004] -57,4,"[2, 1]","[80, 50]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[2283] -57,5,"[4, 3]","[80, 30]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[1855] -57,6,"[3, 4]","[0, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[2231] -57,7,"[4, 3]","[70, 20]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2151] -57,8,"[1, 2]","[0, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[1952] -57,9,"[1, 2]","[0, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2196] -57,10,"[2, 1]","[0, 10]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[1889] -57,11,"[2, 1]","[0, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[1829] -57,12,"[3, 4]","[30, 10]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1794] -57,13,"[2, 1]","[0, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[1987] -57,14,"[1, 2]","[50, 20]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[2796] -57,15,"[4, 3]","[30, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1829] -57,16,"[2, 1]","[50, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[2212] -57,17,"[1, 2]","[80, 30]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2108] -57,18,"[1, 2]","[60, 30]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[1951] -57,19,"[3, 4]","[40, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[1777] -57,20,"[4, 3]","[10, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[2509] -57,21,"[2, 1]","[30, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1899] -57,22,"[4, 3]","[10, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2021] -57,23,"[4, 3]","[10, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1907] -57,24,"[2, 1]","[20, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2605] -57,25,"[4, 3]","[20, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1933] -57,26,"[4, 3]","[10, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[1750] -57,27,"[1, 2]","[40, 10]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[1934] -57,28,"[3, 4]","[70, 10]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[1907] -57,29,"[1, 2]","[80, 30]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1811] -57,30,"[2, 1]","[30, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1941] -57,31,"[4, 3]","[10, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1837] -57,32,"[2, 1]","[10, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[1777] -57,33,"[4, 3]","[10, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1899] -57,34,"[4, 3]","[30, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[1638] -57,35,"[3, 4]","[40, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[1663] -57,36,"[3, 4]","[60, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[1655] -57,37,"[1, 2]","[80, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[1707] -57,38,"[4, 3]","[60, 90]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[1976] -57,39,"[3, 4]","[70, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[1742] -57,40,"[1, 2]","[60, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1697] -57,41,"[1, 2]","[70, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2056] -57,42,"[2, 1]","[50, 70]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1785] -57,43,"[2, 1]","[70, 90]","[1, 1]",[2],[1],[1],[90],[9],[1],[2],[1698] -57,44,"[1, 2]","[80, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1987] -57,45,"[3, 4]","[80, 60]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2231] -57,46,"[3, 4]","[80, 90]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1899] -57,47,"[1, 2]","[70, 80]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1916] -57,48,"[3, 4]","[50, 60]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[2029] -57,49,"[2, 1]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[1882] -57,50,"[1, 2]","[10, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[1907] -57,51,"[4, 3]","[20, 0]","[2, 1]",[2],[5],[3],[0],[0],[1],[3],[2841] -57,52,"[2, 1]","[0, 10]","[1, 2]",[2],[1],[2],[0],[0],[2],[2],[2030] -57,53,"[2, 1]","[10, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[2203] -57,54,"[1, 2]","[30, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[7160] -57,55,"[2, 1]","[40, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[5982] -57,56,"[4, 3]","[40, 50]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2178] -57,57,"[2, 1]","[80, 70]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2021] -57,58,"[4, 3]","[80, 60]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2169] -57,59,"[3, 4]","[40, 60]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[2684] -57,60,"[2, 1]","[60, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[1802] -57,61,"[2, 1]","[30, 10]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1836] -57,62,"[4, 3]","[40, 10]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2020] -57,63,"[2, 1]","[50, 30]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[1951] -57,64,"[3, 4]","[30, 60]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1767] -57,65,"[3, 4]","[80, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1619] -57,66,"[1, 2]","[80, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1637] -57,67,"[3, 4]","[70, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1732] -57,68,"[2, 1]","[80, 70]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[1829] -57,69,"[3, 4]","[50, 90]","[2, 2]",[2],[5],[4],[450],[9],[2],[4],[1714] -57,70,"[1, 2]","[50, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[1645] -57,71,"[4, 3]","[70, 30]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1958] -57,72,"[2, 1]","[60, 0]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[2230] -57,73,"[4, 3]","[50, 20]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2091] -57,74,"[4, 3]","[70, 20]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[1994] -57,75,"[4, 3]","[80, 0]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[1794] -57,76,"[4, 3]","[80, 20]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1958] -57,77,"[4, 3]","[90, 10]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[1777] -57,78,"[1, 2]","[10, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1872] -57,79,"[1, 2]","[30, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[1750] -57,80,"[1, 2]","[40, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1760] -60,1,"[2, 1]","[90, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[3978] -60,2,"[4, 3]","[70, 40]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2979] -60,3,"[2, 1]","[70, 40]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2563] -60,4,"[3, 4]","[70, 80]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[3261] -60,5,"[4, 3]","[50, 70]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2364] -60,6,"[3, 4]","[60, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2031] -60,7,"[4, 3]","[50, 60]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2231] -60,8,"[4, 3]","[60, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1881] -60,9,"[3, 4]","[80, 20]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1831] -60,10,"[4, 3]","[30, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2430] -60,11,"[3, 4]","[60, 20]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[2762] -60,12,"[4, 3]","[20, 40]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[1931] -60,13,"[2, 1]","[40, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2779] -60,14,"[1, 2]","[30, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[3495] -60,15,"[4, 3]","[60, 40]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[3345] -60,16,"[3, 4]","[60, 30]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[2497] -60,17,"[1, 2]","[20, 50]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[3627] -60,18,"[1, 2]","[20, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[2030] -60,19,"[1, 2]","[40, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[1864] -60,20,"[1, 2]","[10, 60]","[1, 1]",[1],[5],[1],[50],[1],[1],[2],[1864] -60,21,"[3, 4]","[30, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2063] -60,22,"[4, 3]","[20, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2047] -60,23,"[1, 2]","[70, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[3277] -60,24,"[2, 1]","[20, 70]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[2031] -60,25,"[4, 3]","[0, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[2313] -60,26,"[3, 4]","[30, 0]","[2, 2]",[1],[1],[4],[0],[0],[2],[3],[1864] -60,27,"[4, 3]","[30, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1913] -60,28,"[2, 1]","[30, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[7155] -60,29,"[1, 2]","[50, 20]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[1931] -60,30,"[3, 4]","[30, 0]","[2, 2]",[1],[1],[4],[0],[0],[2],[3],[3611] -60,31,"[2, 1]","[20, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[6638] -60,32,"[1, 2]","[10, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[2],[3395] -60,33,"[1, 2]","[20, 30]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[2496] -60,34,"[4, 3]","[80, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[4193] -60,35,"[4, 3]","[80, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[1814] -60,36,"[3, 4]","[10, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[1814] -60,37,"[3, 4]","[30, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[1764] -60,38,"[3, 4]","[10, 90]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[1964] -60,39,"[2, 1]","[50, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[2762] -60,40,"[2, 1]","[50, 40]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[1881] -60,41,"[1, 2]","[30, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[8400] -60,42,"[4, 3]","[80, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[2164] -60,43,"[3, 4]","[30, 80]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[2247] -60,44,"[2, 1]","[80, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[6555] -60,45,"[3, 4]","[20, 60]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[2430] -60,46,"[1, 2]","[0, 30]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[2496] -60,47,"[1, 2]","[80, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1864] -60,48,"[1, 2]","[70, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2463] -60,49,"[4, 3]","[40, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2363] -60,50,"[1, 2]","[50, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2047] -60,51,"[1, 2]","[30, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2978] -60,52,"[4, 3]","[30, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[2346] -60,53,"[1, 2]","[10, 50]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[2246] -60,54,"[2, 1]","[40, 20]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[1947] -60,55,"[4, 3]","[30, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2496] -60,56,"[4, 3]","[20, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[6555] -60,57,"[2, 1]","[10, 30]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2080] -60,58,"[2, 1]","[10, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[1831] -60,59,"[2, 1]","[30, 10]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[1948] -60,60,"[3, 4]","[10, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[2247] -60,61,"[1, 2]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[2130] -60,62,"[3, 4]","[30, 20]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[2147] -60,63,"[1, 2]","[60, 0]","[1, 2]",[2],[1],[2],[0],[0],[2],[1],[2381] -60,64,"[4, 3]","[20, 60]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[1948] -60,65,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2346] -60,66,"[3, 4]","[90, 20]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[2779] -60,67,"[3, 4]","[80, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[1931] -60,68,"[3, 4]","[80, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2047] -60,69,"[4, 3]","[10, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[2363] -60,70,"[1, 2]","[80, 0]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2413] -60,71,"[2, 1]","[10, 30]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2064] -60,72,"[1, 2]","[20, 0]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[1847] -60,73,"[4, 3]","[0, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[2132] -60,74,"[1, 2]","[30, 40]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[1964] -60,75,"[2, 1]","[40, 0]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2497] -60,76,"[3, 4]","[0, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1997] -60,77,"[2, 1]","[0, 20]","[1, 2]",[2],[1],[2],[0],[0],[2],[2],[1948] -60,78,"[2, 1]","[40, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[1781] -60,79,"[2, 1]","[80, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[2297] -60,80,"[3, 4]","[0, 80]","[2, 1]",[2],[5],[3],[0],[0],[1],[4],[11662] -58,1,"[2, 1]","[10, 90]","[1, 2]",[1],[5],[2],[50],[1],[2],[2],[4468] -58,2,"[1, 2]","[80, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[4906] -58,3,"[4, 3]","[80, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[3873] -58,4,"[1, 2]","[60, 70]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[3723] -58,5,"[4, 3]","[80, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[3339] -58,6,"[1, 2]","[70, 30]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2023] -58,7,"[1, 2]","[60, 0]","[1, 2]",[1],[1],[2],[0],[0],[2],[1],[2073] -58,8,"[1, 2]","[70, 0]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2007] -58,9,"[1, 2]","[90, 20]","[1, 1]",[1],[5],[1],[450],[9],[1],[1],[4239] -58,10,"[2, 1]","[0, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2473] -58,11,"[2, 1]","[0, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2307] -58,12,"[2, 1]","[30, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1873] -58,13,"[2, 1]","[20, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2040] -58,14,"[2, 1]","[50, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[2040] -58,15,"[1, 2]","[60, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2340] -58,16,"[1, 2]","[60, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2890] -58,17,"[3, 4]","[30, 0]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[2224] -58,18,"[1, 2]","[0, 10]","[1, 1]",[1],[1],[1],[0],[0],[1],[2],[3072] -58,19,"[4, 3]","[10, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[3889] -58,20,"[2, 1]","[0, 10]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[3073] -58,21,"[3, 4]","[20, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1940] -58,22,"[3, 4]","[10, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[2174] -58,23,"[4, 3]","[0, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[5689] -58,24,"[4, 3]","[0, 20]","[2, 2]",[1],[5],[4],[0],[0],[2],[4],[2606] -58,25,"[3, 4]","[30, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[2006] -58,26,"[1, 2]","[40, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[3856] -58,27,"[2, 1]","[50, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[1907] -58,28,"[1, 2]","[40, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[1873] -58,29,"[1, 2]","[70, 60]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1906] -58,30,"[4, 3]","[80, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[2890] -58,31,"[2, 1]","[70, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[2223] -58,32,"[3, 4]","[90, 60]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[2323] -58,33,"[3, 4]","[70, 90]","[2, 2]",[1],[5],[4],[450],[9],[2],[4],[1873] -58,34,"[3, 4]","[60, 70]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2407] -58,35,"[2, 1]","[50, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2941] -58,36,"[1, 2]","[30, 50]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[3023] -58,37,"[3, 4]","[0, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[3822] -58,38,"[1, 2]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2107] -58,39,"[3, 4]","[10, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[4106] -58,40,"[3, 4]","[20, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1957] -58,41,"[3, 4]","[0, 10]","[2, 1]",[2],[1],[3],[0],[0],[1],[4],[2264] -58,42,"[1, 2]","[10, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2057] -58,43,"[2, 1]","[10, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[1907] -58,44,"[3, 4]","[20, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[3090] -58,45,"[1, 2]","[10, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[3656] -58,46,"[3, 4]","[10, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[2940] -58,47,"[3, 4]","[10, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2673] -58,48,"[4, 3]","[20, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[3140] -58,49,"[3, 4]","[40, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2074] -58,50,"[4, 3]","[40, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[3040] -58,51,"[3, 4]","[40, 60]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[2589] -58,52,"[3, 4]","[50, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2306] -58,53,"[1, 2]","[60, 50]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2073] -58,54,"[4, 3]","[50, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1907] -58,55,"[2, 1]","[40, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[3039] -58,56,"[3, 4]","[40, 20]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2125] -58,57,"[2, 1]","[10, 80]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2740] -58,58,"[2, 1]","[30, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[3773] -58,59,"[4, 3]","[20, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[2408] -58,60,"[3, 4]","[20, 40]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[1790] -58,61,"[1, 2]","[20, 50]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[1973] -58,62,"[3, 4]","[20, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[7305] -58,63,"[4, 3]","[30, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[2473] -58,64,"[3, 4]","[60, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2474] -58,65,"[4, 3]","[0, 90]","[2, 2]",[2],[5],[4],[0],[0],[2],[4],[1657] -58,66,"[2, 1]","[10, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1757] -58,67,"[2, 1]","[0, 80]","[1, 2]",[2],[1],[2],[0],[0],[2],[2],[1789] -58,68,"[1, 2]","[50, 10]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[4039] -58,69,"[3, 4]","[40, 0]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[3773] -58,70,"[4, 3]","[10, 40]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[2456] -58,71,"[1, 2]","[80, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[3739] -58,72,"[2, 1]","[50, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2690] -58,73,"[2, 1]","[40, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[3306] -58,74,"[2, 1]","[50, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[1841] -58,75,"[2, 1]","[50, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[1823] -58,76,"[1, 2]","[10, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[1840] -58,77,"[3, 4]","[20, 60]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[2308] -58,78,"[1, 2]","[10, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[3340] -58,79,"[2, 1]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[3522] -58,80,"[2, 1]","[10, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1755] -57,1,"[1, 2]","[40, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[6303] -57,2,"[3, 4]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[7205] -57,3,"[2, 1]","[40, 50]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[1880] -57,4,"[2, 1]","[40, 80]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2073] -57,5,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1864] -57,6,"[4, 3]","[50, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1846] -57,7,"[2, 1]","[20, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1959] -57,8,"[2, 1]","[20, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1854] -57,9,"[4, 3]","[50, 60]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[4586] -57,10,"[3, 4]","[80, 50]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[1759] -57,11,"[4, 3]","[60, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[2283] -57,12,"[3, 4]","[80, 60]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2370] -57,13,"[1, 2]","[60, 80]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1802] -57,14,"[4, 3]","[60, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[5082] -57,15,"[2, 1]","[50, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2552] -57,16,"[4, 3]","[50, 30]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[1993] -57,17,"[3, 4]","[30, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[1934] -57,18,"[1, 2]","[70, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[2029] -57,19,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1760] -57,20,"[3, 4]","[90, 10]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[1820] -57,21,"[4, 3]","[50, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2038] -57,22,"[2, 1]","[30, 90]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[1749] -57,23,"[2, 1]","[0, 80]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[1934] -57,24,"[4, 3]","[10, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[1733] -57,25,"[3, 4]","[10, 0]","[2, 2]",[1],[1],[4],[0],[0],[2],[3],[1924] -57,26,"[3, 4]","[30, 20]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1776] -57,27,"[1, 2]","[80, 40]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1846] -57,28,"[4, 3]","[60, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[1802] -57,29,"[3, 4]","[20, 40]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[1987] -57,30,"[1, 2]","[60, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2319] -57,31,"[1, 2]","[80, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[1925] -57,32,"[3, 4]","[90, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1865] -57,33,"[2, 1]","[70, 60]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1873] -57,34,"[1, 2]","[50, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[1715] -57,35,"[1, 2]","[70, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1697] -57,36,"[4, 3]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2003] -57,37,"[4, 3]","[70, 60]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[1776] -57,38,"[2, 1]","[70, 60]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2010] -57,39,"[3, 4]","[70, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2029] -57,40,"[4, 3]","[70, 90]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1934] -57,41,"[4, 3]","[70, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[2198] -57,42,"[4, 3]","[80, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2099] -57,43,"[2, 1]","[50, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[1645] -57,44,"[3, 4]","[60, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2004] -57,45,"[1, 2]","[70, 40]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[1785] -57,46,"[1, 2]","[30, 20]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[1837] -57,47,"[2, 1]","[10, 30]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[1819] -57,48,"[3, 4]","[40, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[2056] -57,49,"[1, 2]","[60, 0]","[1, 2]",[2],[5],[2],[0],[0],[2],[1],[1724] -57,50,"[4, 3]","[20, 40]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[2021] -57,51,"[1, 2]","[90, 10]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[1889] -57,52,"[3, 4]","[60, 10]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2604] -57,53,"[4, 3]","[40, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[1969] -57,54,"[1, 2]","[50, 60]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[1907] -57,55,"[3, 4]","[70, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1820] -57,56,"[2, 1]","[60, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[2377] -57,57,"[4, 3]","[50, 60]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[1785] -57,58,"[1, 2]","[50, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[2231] -57,59,"[3, 4]","[60, 80]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1820] -57,60,"[1, 2]","[40, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[1749] -57,61,"[4, 3]","[70, 10]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[2003] -57,62,"[3, 4]","[20, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[1830] -57,63,"[3, 4]","[50, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1837] -57,64,"[2, 1]","[50, 90]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1863] -57,65,"[3, 4]","[80, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1732] -57,66,"[2, 1]","[60, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[1767] -57,67,"[4, 3]","[90, 40]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[1994] -57,68,"[1, 2]","[50, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2283] -57,69,"[4, 3]","[20, 40]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[2168] -57,70,"[2, 1]","[20, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[1900] -57,71,"[2, 1]","[40, 10]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[1778] -57,72,"[3, 4]","[20, 60]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[9281] -57,73,"[4, 3]","[50, 30]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1671] -57,74,"[1, 2]","[40, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[2370] -57,75,"[1, 2]","[30, 70]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[2151] -57,76,"[1, 2]","[50, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1785] -57,77,"[3, 4]","[30, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2125] -57,78,"[2, 1]","[40, 10]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1760] -57,79,"[1, 2]","[10, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[2],[1776] -57,80,"[2, 1]","[0, 0]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[1871] -60,1,"[4, 3]","[30, 60]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[3594] -60,2,"[2, 1]","[0, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2131] -60,3,"[4, 3]","[20, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1749] -60,4,"[4, 3]","[0, 60]","[2, 2]",[1],[5],[4],[0],[0],[2],[4],[3847] -60,5,"[4, 3]","[10, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1848] -60,6,"[1, 2]","[30, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[3214] -60,7,"[4, 3]","[10, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[2082] -60,8,"[4, 3]","[30, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[1716] -60,9,"[3, 4]","[60, 10]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2165] -60,10,"[1, 2]","[60, 30]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[1849] -60,11,"[3, 4]","[70, 40]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1865] -60,12,"[2, 1]","[50, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1881] -60,13,"[1, 2]","[60, 0]","[1, 2]",[1],[5],[2],[0],[0],[2],[1],[2347] -60,14,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1848] -60,15,"[1, 2]","[60, 50]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[3346] -60,16,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1815] -60,17,"[1, 2]","[80, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[3946] -60,18,"[2, 1]","[40, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2948] -60,19,"[1, 2]","[60, 50]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[3131] -60,20,"[3, 4]","[80, 40]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2065] -60,21,"[2, 1]","[40, 80]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[2398] -60,22,"[1, 2]","[80, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2564] -60,23,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2680] -60,24,"[4, 3]","[50, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[4296] -60,25,"[2, 1]","[70, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[5594] -60,26,"[2, 1]","[60, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[2081] -60,27,"[2, 1]","[80, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[2365] -60,28,"[2, 1]","[80, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[1882] -60,29,"[4, 3]","[90, 50]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[2981] -60,30,"[2, 1]","[80, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[2881] -60,31,"[4, 3]","[70, 40]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2848] -60,32,"[3, 4]","[30, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2082] -60,33,"[3, 4]","[60, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[1715] -60,34,"[4, 3]","[30, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1766] -60,35,"[4, 3]","[30, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1982] -60,36,"[3, 4]","[20, 30]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[2697] -60,37,"[3, 4]","[20, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2314] -60,38,"[4, 3]","[20, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[2048] -60,39,"[4, 3]","[60, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[4729] -60,40,"[1, 2]","[70, 60]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[3330] -60,41,"[2, 1]","[90, 50]","[1, 2]",[2],[5],[2],[450],[9],[2],[1],[170540] -60,42,"[3, 4]","[50, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1749] -60,43,"[3, 4]","[50, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[2049] -60,44,"[1, 2]","[50, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[2298] -60,45,"[2, 1]","[40, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[2347] -60,46,"[1, 2]","[50, 30]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[2881] -60,47,"[2, 1]","[40, 50]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[1815] -60,48,"[3, 4]","[20, 30]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[2398] -60,49,"[2, 1]","[40, 30]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2947] -60,50,"[2, 1]","[40, 10]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2182] -60,51,"[1, 2]","[20, 70]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[2847] -60,52,"[3, 4]","[30, 80]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[3130] -60,53,"[1, 2]","[30, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[1899] -60,54,"[2, 1]","[80, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[2315] -60,55,"[4, 3]","[70, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1831] -60,56,"[4, 3]","[80, 50]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[2198] -60,57,"[1, 2]","[70, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1782] -60,58,"[2, 1]","[60, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2432] -60,59,"[1, 2]","[70, 50]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[2481] -60,60,"[1, 2]","[50, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1781] -60,61,"[4, 3]","[30, 80]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2364] -60,62,"[4, 3]","[10, 70]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[2281] -60,63,"[3, 4]","[50, 10]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1949] -60,64,"[4, 3]","[10, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1848] -60,65,"[1, 2]","[30, 0]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[4395] -60,66,"[1, 2]","[30, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2930] -60,67,"[1, 2]","[20, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[1782] -60,68,"[3, 4]","[40, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2564] -60,69,"[4, 3]","[0, 60]","[2, 2]",[2],[1],[4],[0],[0],[2],[4],[2064] -60,70,"[2, 1]","[30, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[3147] -60,71,"[2, 1]","[30, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2015] -60,72,"[3, 4]","[40, 50]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[1881] -60,73,"[2, 1]","[60, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2248] -60,74,"[1, 2]","[80, 70]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2149] -60,75,"[3, 4]","[80, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[1782] -60,76,"[2, 1]","[70, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[2398] -60,77,"[4, 3]","[60, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[2298] -60,78,"[2, 1]","[60, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[2197] -60,79,"[4, 3]","[40, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[2015] -60,80,"[1, 2]","[20, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[2598] -66,1,"[2, 1]","[90, 10]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[3490] -66,2,"[4, 3]","[70, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[1803] -66,3,"[2, 1]","[70, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[1884] -66,4,"[3, 4]","[70, 80]","[2, 2]",[1],[1],[4],[80],[8],[2],[4],[1867] -66,5,"[4, 3]","[50, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1783] -66,6,"[3, 4]","[60, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[1900] -66,7,"[4, 3]","[50, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1783] -66,8,"[4, 3]","[60, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1749] -66,9,"[3, 4]","[80, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1783] -66,10,"[4, 3]","[30, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1800] -66,11,"[3, 4]","[60, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[1817] -66,12,"[4, 3]","[20, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1816] -66,13,"[2, 1]","[40, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1800] -66,14,"[1, 2]","[30, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[1883] -66,15,"[4, 3]","[60, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2133] -66,16,"[3, 4]","[60, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[1833] -66,17,"[1, 2]","[20, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[1917] -66,18,"[1, 2]","[20, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[1983] -66,19,"[1, 2]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[1918] -66,20,"[1, 2]","[10, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[1884] -66,21,"[3, 4]","[30, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[1867] -66,22,"[4, 3]","[20, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1799] -66,23,"[1, 2]","[70, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[1817] -66,24,"[2, 1]","[20, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1800] -66,25,"[4, 3]","[0, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[1799] -66,26,"[3, 4]","[30, 0]","[2, 2]",[1],[1],[4],[0],[0],[2],[3],[1929] -66,27,"[4, 3]","[30, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2000] -66,28,"[2, 1]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[1916] -66,29,"[1, 2]","[50, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[8803] -66,30,"[3, 4]","[30, 0]","[2, 2]",[1],[1],[4],[0],[0],[2],[3],[1999] -66,31,"[2, 1]","[20, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[8619] -66,32,"[1, 2]","[10, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[2],[2151] -66,33,"[1, 2]","[20, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[2000] -66,34,"[4, 3]","[80, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[2000] -66,35,"[4, 3]","[80, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[3962] -66,36,"[3, 4]","[10, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2235] -66,37,"[3, 4]","[30, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[2217] -66,38,"[3, 4]","[10, 90]","[2, 2]",[1],[1],[4],[90],[9],[2],[4],[3090] -66,39,"[2, 1]","[50, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[1967] -66,40,"[2, 1]","[50, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[2116] -66,41,"[1, 2]","[30, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[15986] -66,42,"[4, 3]","[80, 30]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1732] -66,43,"[3, 4]","[30, 80]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[1833] -66,44,"[2, 1]","[80, 10]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[1650] -66,45,"[3, 4]","[20, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[1700] -66,46,"[1, 2]","[0, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[2084] -66,47,"[1, 2]","[80, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1750] -66,48,"[1, 2]","[70, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1884] -66,49,"[4, 3]","[40, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1750] -66,50,"[1, 2]","[50, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1817] -66,51,"[1, 2]","[30, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1866] -66,52,"[4, 3]","[30, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[1884] -66,53,"[1, 2]","[10, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1867] -66,54,"[2, 1]","[40, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[1900] -66,55,"[4, 3]","[30, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[1849] -66,56,"[4, 3]","[20, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1883] -66,57,"[2, 1]","[10, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[1800] -66,58,"[2, 1]","[10, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[1833] -66,59,"[2, 1]","[30, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[1767] -66,60,"[3, 4]","[10, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[1750] -66,61,"[1, 2]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[1816] -66,62,"[3, 4]","[30, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[1933] -66,63,"[1, 2]","[60, 0]","[1, 2]",[2],[1],[2],[0],[0],[2],[1],[1933] -66,64,"[4, 3]","[20, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1950] -66,65,"[3, 4]","[80, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[2633] -66,66,"[3, 4]","[90, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[3799] -66,67,"[3, 4]","[80, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[6966] -66,68,"[3, 4]","[80, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1816] -66,69,"[4, 3]","[10, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[8384] -66,70,"[1, 2]","[80, 0]","[1, 2]",[2],[1],[2],[0],[0],[2],[1],[1717] -66,71,"[2, 1]","[10, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[1850] -66,72,"[1, 2]","[20, 0]","[1, 2]",[2],[1],[2],[0],[0],[2],[1],[1918] -66,73,"[4, 3]","[0, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1934] -66,74,"[1, 2]","[30, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[3327] -66,75,"[2, 1]","[40, 0]","[1, 1]",[2],[1],[1],[0],[0],[1],[1],[1783] -66,76,"[3, 4]","[0, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1884] -66,77,"[2, 1]","[0, 20]","[1, 2]",[2],[1],[2],[0],[0],[2],[2],[1917] -66,78,"[2, 1]","[40, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[1767] -66,79,"[2, 1]","[80, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[5403] -66,80,"[3, 4]","[0, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1850] -67,1,"[2, 1]","[20, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[4701] -67,2,"[3, 4]","[70, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2487] -67,3,"[1, 2]","[90, 70]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[2121] -67,4,"[4, 3]","[40, 60]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[4171] -67,5,"[3, 4]","[70, 50]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2421] -67,6,"[4, 3]","[70, 20]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[1970] -67,7,"[2, 1]","[60, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[1787] -67,8,"[2, 1]","[40, 0]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2545] -67,9,"[4, 3]","[90, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[3],[2404] -67,10,"[4, 3]","[90, 10]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2029] -67,11,"[4, 3]","[90, 20]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2087] -67,12,"[1, 2]","[10, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[3187] -67,13,"[3, 4]","[20, 70]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[1804] -67,14,"[2, 1]","[70, 30]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1953] -67,15,"[3, 4]","[30, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[1888] -67,16,"[3, 4]","[40, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[1787] -67,17,"[1, 2]","[40, 70]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[2445] -67,18,"[4, 3]","[40, 60]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[2053] -67,19,"[3, 4]","[60, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2070] -67,20,"[1, 2]","[70, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[1979] -67,21,"[1, 2]","[70, 0]","[1, 2]",[1],[5],[2],[0],[0],[2],[1],[2012] -67,22,"[2, 1]","[20, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2638] -67,23,"[1, 2]","[80, 10]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1703] -67,24,"[4, 3]","[0, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2671] -67,25,"[1, 2]","[70, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[1],[1920] -67,26,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1979] -67,27,"[2, 1]","[50, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[2305] -67,28,"[2, 1]","[50, 60]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[1737] -67,29,"[1, 2]","[70, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2070] -67,30,"[3, 4]","[50, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1896] -67,31,"[4, 3]","[80, 70]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2228] -67,32,"[4, 3]","[60, 20]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[1895] -67,33,"[3, 4]","[10, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[1805] -67,34,"[3, 4]","[30, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1754] -67,35,"[4, 3]","[40, 20]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[1729] -67,36,"[3, 4]","[10, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[1912] -67,37,"[1, 2]","[20, 40]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[1762] -67,38,"[1, 2]","[80, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[1753] -67,39,"[2, 1]","[70, 80]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2920] -67,40,"[3, 4]","[70, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2019] -67,41,"[2, 1]","[50, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[4509] -67,42,"[3, 4]","[70, 60]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1603] -67,43,"[4, 3]","[50, 90]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[1846] -67,44,"[1, 2]","[50, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[2012] -67,45,"[4, 3]","[60, 40]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[1878] -67,46,"[1, 2]","[10, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1795] -67,47,"[4, 3]","[60, 0]","[2, 1]",[2],[1],[3],[0],[0],[1],[3],[1704] -67,48,"[2, 1]","[60, 20]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[2622] -67,49,"[1, 2]","[10, 90]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1838] -67,50,"[4, 3]","[80, 30]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1870] -67,51,"[4, 3]","[70, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[1429] -67,52,"[1, 2]","[10, 40]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1479] -67,53,"[3, 4]","[20, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[1529] -67,54,"[4, 3]","[90, 10]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[1504] -67,55,"[1, 2]","[20, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1637] -67,56,"[4, 3]","[70, 20]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2037] -67,57,"[1, 2]","[10, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[1521] -67,58,"[4, 3]","[50, 20]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1487] -67,59,"[2, 1]","[50, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[1529] -67,60,"[1, 2]","[30, 50]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[2206] -67,61,"[3, 4]","[20, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[1412] -67,62,"[3, 4]","[50, 40]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1462] -67,63,"[1, 2]","[60, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[1437] -67,64,"[4, 3]","[70, 60]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1654] -67,65,"[2, 1]","[90, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1954] -67,66,"[1, 2]","[40, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2045] -67,67,"[3, 4]","[40, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1695] -67,68,"[3, 4]","[20, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1796] -67,69,"[3, 4]","[50, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1713] -67,70,"[4, 3]","[30, 80]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2604] -67,71,"[3, 4]","[70, 0]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1661] -67,72,"[3, 4]","[70, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[1503] -67,73,"[4, 3]","[10, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1863] -67,74,"[4, 3]","[20, 70]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[1754] -67,75,"[4, 3]","[50, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1612] -67,76,"[3, 4]","[20, 40]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[2130] -67,77,"[4, 3]","[20, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1563] -67,78,"[3, 4]","[30, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1746] -67,79,"[2, 1]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2094] -67,80,"[3, 4]","[30, 20]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1654] -56,1,"[2, 1]","[20, 60]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[3704] -56,2,"[3, 4]","[70, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2614] -56,3,"[1, 2]","[90, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1936] -56,4,"[4, 3]","[40, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1862] -56,5,"[3, 4]","[70, 50]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[3979] -56,6,"[4, 3]","[70, 20]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2030] -56,7,"[2, 1]","[60, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[1953] -56,8,"[2, 1]","[40, 0]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2168] -56,9,"[4, 3]","[90, 0]","[2, 2]",[1],[1],[4],[90],[9],[2],[3],[1971] -56,10,"[4, 3]","[90, 10]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2462] -56,11,"[4, 3]","[90, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[2384] -56,12,"[1, 2]","[10, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[2699] -56,13,"[3, 4]","[20, 70]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[1916] -56,14,"[2, 1]","[70, 30]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2077] -56,15,"[3, 4]","[30, 50]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[2023] -56,16,"[3, 4]","[40, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[1779] -56,17,"[1, 2]","[40, 70]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[1842] -56,18,"[4, 3]","[40, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[1705] -56,19,"[3, 4]","[60, 40]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1818] -56,20,"[1, 2]","[70, 20]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1747] -56,21,"[1, 2]","[70, 0]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1849] -56,22,"[2, 1]","[20, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1688] -56,23,"[1, 2]","[80, 10]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1758] -56,24,"[4, 3]","[0, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1833] -56,25,"[1, 2]","[70, 10]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1871] -56,26,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1699] -56,27,"[2, 1]","[50, 40]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2663] -56,28,"[2, 1]","[50, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1747] -56,29,"[1, 2]","[70, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2193] -56,30,"[3, 4]","[50, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2260] -56,31,"[4, 3]","[80, 70]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[1876] -56,32,"[4, 3]","[60, 20]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[1959] -56,33,"[3, 4]","[10, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[1755] -56,34,"[3, 4]","[30, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[2179] -56,35,"[4, 3]","[40, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[2379] -56,36,"[3, 4]","[10, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[2004] -56,37,"[1, 2]","[20, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2077] -56,38,"[1, 2]","[80, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2401] -56,39,"[2, 1]","[70, 80]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2011] -56,40,"[3, 4]","[70, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2374] -56,41,"[2, 1]","[50, 80]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[3235] -56,42,"[3, 4]","[70, 60]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1838] -56,43,"[4, 3]","[50, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[1759] -56,44,"[1, 2]","[50, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[1934] -56,45,"[4, 3]","[60, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1832] -56,46,"[1, 2]","[10, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2022] -56,47,"[4, 3]","[60, 0]","[2, 1]",[2],[1],[3],[0],[0],[1],[3],[1736] -56,48,"[2, 1]","[60, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[1765] -56,49,"[1, 2]","[10, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[1990] -56,50,"[4, 3]","[80, 30]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1818] -56,51,"[4, 3]","[70, 10]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[1893] -56,52,"[1, 2]","[10, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2022] -56,53,"[3, 4]","[20, 70]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[2052] -56,54,"[4, 3]","[90, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[2727] -56,55,"[1, 2]","[20, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1808] -56,56,"[4, 3]","[70, 20]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2444] -56,57,"[1, 2]","[10, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[1711] -56,58,"[4, 3]","[50, 20]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2862] -56,59,"[2, 1]","[50, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[2295] -56,60,"[1, 2]","[30, 50]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[2332] -56,61,"[3, 4]","[20, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2119] -56,62,"[3, 4]","[50, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[1745] -56,63,"[1, 2]","[60, 50]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[2190] -56,64,"[4, 3]","[70, 60]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[2058] -56,65,"[2, 1]","[90, 60]","[1, 2]",[2],[1],[2],[90],[9],[2],[1],[1746] -56,66,"[1, 2]","[40, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1777] -56,67,"[3, 4]","[40, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1974] -56,68,"[3, 4]","[20, 30]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[1998] -56,69,"[3, 4]","[50, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2655] -56,70,"[4, 3]","[30, 80]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2010] -56,71,"[3, 4]","[70, 0]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1905] -56,72,"[3, 4]","[70, 10]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1880] -56,73,"[4, 3]","[10, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1998] -56,74,"[4, 3]","[20, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1741] -56,75,"[4, 3]","[50, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1814] -56,76,"[3, 4]","[20, 40]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[1836] -56,77,"[4, 3]","[20, 10]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2064] -56,78,"[3, 4]","[30, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[4174] -56,79,"[2, 1]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2494] -56,80,"[3, 4]","[30, 20]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[5144] -68,1,"[3, 4]","[20, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[8669] -68,2,"[2, 1]","[70, 10]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2368] -68,3,"[2, 1]","[60, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[1819] -68,4,"[4, 3]","[40, 30]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[4200] -68,5,"[2, 1]","[40, 30]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[2068] -68,6,"[4, 3]","[50, 60]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[3983] -68,7,"[1, 2]","[60, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[3086] -68,8,"[1, 2]","[30, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2335] -68,9,"[2, 1]","[30, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[3320] -68,10,"[4, 3]","[30, 0]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[3353] -68,11,"[1, 2]","[0, 60]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[3103] -68,12,"[1, 2]","[40, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2268] -68,13,"[4, 3]","[60, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[2286] -68,14,"[4, 3]","[40, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[2635] -68,15,"[3, 4]","[60, 80]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2135] -68,16,"[2, 1]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[3602] -68,17,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1935] -68,18,"[3, 4]","[30, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[4254] -68,19,"[1, 2]","[40, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[9698] -68,20,"[1, 2]","[10, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[2085] -68,21,"[4, 3]","[50, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[3153] -68,22,"[3, 4]","[20, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2336] -68,23,"[1, 2]","[50, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[3286] -68,24,"[4, 3]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[4169] -68,25,"[1, 2]","[70, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[3535] -68,26,"[3, 4]","[60, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[3101] -68,27,"[2, 1]","[70, 40]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[4787] -68,28,"[1, 2]","[30, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2568] -68,29,"[4, 3]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[4002] -68,30,"[1, 2]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[4136] -68,31,"[1, 2]","[50, 30]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2069] -68,32,"[3, 4]","[20, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[2869] -68,33,"[1, 2]","[10, 60]","[1, 1]",[1],[5],[1],[50],[1],[1],[2],[4353] -68,34,"[2, 1]","[30, 0]","[1, 1]",[1],[1],[1],[0],[0],[1],[1],[1934] -68,35,"[4, 3]","[50, 0]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[4035] -68,36,"[1, 2]","[10, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2786] -68,37,"[1, 2]","[0, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1901] -68,38,"[2, 1]","[70, 20]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2635] -68,39,"[2, 1]","[50, 10]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2068] -68,40,"[2, 1]","[30, 20]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[3337] -68,41,"[1, 2]","[10, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[5900] -68,42,"[3, 4]","[0, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[2636] -68,43,"[4, 3]","[10, 0]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[2435] -68,44,"[1, 2]","[30, 20]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[5070] -68,45,"[2, 1]","[40, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[2235] -68,46,"[3, 4]","[0, 30]","[2, 1]",[2],[5],[3],[0],[0],[1],[4],[2986] -68,47,"[3, 4]","[30, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1968] -68,48,"[4, 3]","[20, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[1967] -68,49,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1868] -68,50,"[2, 1]","[30, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[2319] -68,51,"[3, 4]","[20, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[4],[2418] -68,52,"[2, 1]","[30, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[2302] -68,53,"[1, 2]","[60, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[2319] -68,54,"[1, 2]","[80, 40]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2102] -68,55,"[3, 4]","[80, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2235] -68,56,"[2, 1]","[0, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2918] -68,57,"[4, 3]","[0, 80]","[2, 2]",[2],[1],[4],[0],[0],[2],[4],[3102] -68,58,"[4, 3]","[20, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1868] -68,59,"[2, 1]","[30, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[4169] -68,60,"[3, 4]","[70, 50]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[2585] -68,61,"[4, 3]","[80, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[2151] -68,62,"[3, 4]","[50, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1968] -68,63,"[2, 1]","[50, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[2417] -68,64,"[1, 2]","[60, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1934] -68,65,"[3, 4]","[40, 20]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2201] -68,66,"[4, 3]","[10, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[1884] -68,67,"[1, 2]","[40, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[2319] -68,68,"[4, 3]","[20, 0]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2452] -68,69,"[2, 1]","[50, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[2536] -68,70,"[3, 4]","[20, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[3335] -68,71,"[2, 1]","[60, 0]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[2018] -68,72,"[4, 3]","[60, 0]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2002] -68,73,"[3, 4]","[10, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[1969] -68,74,"[1, 2]","[40, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[4754] -68,75,"[2, 1]","[70, 60]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[4037] -68,76,"[2, 1]","[60, 70]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[2167] -68,77,"[3, 4]","[70, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[5603] -68,78,"[3, 4]","[90, 60]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[2036] -68,79,"[3, 4]","[60, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[3537] -68,80,"[4, 3]","[70, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[2068] -65,1,"[2, 1]","[40, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[7262] -65,2,"[3, 4]","[70, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[3698] -65,3,"[1, 2]","[80, 20]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1941] -65,4,"[3, 4]","[70, 10]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[2344] -65,5,"[3, 4]","[60, 20]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2025] -65,6,"[3, 4]","[60, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2159] -65,7,"[2, 1]","[10, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2108] -65,8,"[1, 2]","[60, 20]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[1991] -65,9,"[3, 4]","[90, 30]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[1757] -65,10,"[2, 1]","[10, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1894] -65,11,"[1, 2]","[60, 20]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[1858] -65,12,"[4, 3]","[10, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2093] -65,13,"[3, 4]","[50, 40]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[2226] -65,14,"[3, 4]","[90, 40]","[2, 1]",[1],[1],[3],[90],[9],[1],[3],[2444] -65,15,"[2, 1]","[50, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1891] -65,16,"[2, 1]","[50, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[1874] -65,17,"[4, 3]","[40, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[2209] -65,18,"[3, 4]","[40, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2025] -65,19,"[2, 1]","[40, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[2042] -65,20,"[2, 1]","[30, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[2243] -65,21,"[2, 1]","[10, 20]","[1, 2]",[1],[1],[2],[10],[1],[2],[2],[2761] -65,22,"[4, 3]","[0, 10]","[2, 2]",[1],[5],[4],[0],[0],[2],[4],[2143] -65,23,"[3, 4]","[10, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[2175] -65,24,"[2, 1]","[10, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[2511] -65,25,"[3, 4]","[10, 30]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[3013] -65,26,"[4, 3]","[40, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[2527] -65,27,"[1, 2]","[10, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[3280] -65,28,"[2, 1]","[50, 40]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2008] -65,29,"[3, 4]","[50, 60]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[2209] -65,30,"[1, 2]","[50, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2059] -65,31,"[3, 4]","[30, 70]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[1891] -65,32,"[4, 3]","[70, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1907] -65,33,"[2, 1]","[60, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2811] -65,34,"[2, 1]","[60, 50]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2209] -65,35,"[2, 1]","[90, 30]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[2660] -65,36,"[3, 4]","[50, 60]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[2359] -65,37,"[1, 2]","[50, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2377] -65,38,"[2, 1]","[40, 70]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[2493] -65,39,"[4, 3]","[50, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2643] -65,40,"[3, 4]","[40, 50]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[2292] -65,41,"[2, 1]","[80, 60]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[9047] -65,42,"[3, 4]","[70, 80]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2159] -65,43,"[1, 2]","[60, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[1941] -65,44,"[1, 2]","[50, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[1975] -65,45,"[2, 1]","[70, 40]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1774] -65,46,"[2, 1]","[30, 20]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[1791] -65,47,"[2, 1]","[40, 10]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2058] -65,48,"[4, 3]","[60, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[1992] -65,49,"[1, 2]","[80, 40]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1925] -65,50,"[3, 4]","[70, 40]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[3999] -65,51,"[3, 4]","[60, 30]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1958] -65,52,"[3, 4]","[80, 40]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2527] -65,53,"[4, 3]","[60, 40]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[2226] -65,54,"[4, 3]","[60, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2343] -65,55,"[1, 2]","[40, 40]","[1, 1]",[2],[1],[1],[40],[4],[1],[1],[2594] -65,56,"[4, 3]","[40, 20]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2644] -65,57,"[4, 3]","[30, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[2879] -65,58,"[1, 2]","[10, 20]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[3082] -65,59,"[4, 3]","[10, 20]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[2494] -65,60,"[3, 4]","[10, 20]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[2795] -65,61,"[4, 3]","[20, 30]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[3513] -65,62,"[2, 1]","[10, 40]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2476] -65,63,"[3, 4]","[30, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[3297] -65,64,"[4, 3]","[20, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[2360] -65,65,"[2, 1]","[40, 0]","[1, 1]",[2],[1],[1],[0],[0],[1],[1],[2259] -65,66,"[2, 1]","[50, 20]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[1925] -65,67,"[3, 4]","[30, 60]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[2578] -65,68,"[3, 4]","[40, 70]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[1841] -65,69,"[1, 2]","[50, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1958] -65,70,"[2, 1]","[70, 50]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1891] -65,71,"[1, 2]","[40, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[1791] -65,72,"[4, 3]","[90, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[1858] -65,73,"[1, 2]","[50, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[1841] -65,74,"[4, 3]","[90, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1891] -65,75,"[2, 1]","[90, 60]","[1, 2]",[2],[5],[2],[450],[9],[2],[1],[1925] 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2]",[1],[5],[4],[350],[7],[2],[3],[1647] -63,10,"[3, 4]","[30, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[1747] -63,11,"[4, 3]","[80, 50]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2100] -63,12,"[1, 2]","[30, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1783] -63,13,"[3, 4]","[30, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1731] -63,14,"[4, 3]","[50, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[3],[1915] -63,15,"[4, 3]","[20, 0]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[1800] -63,16,"[3, 4]","[10, 30]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[1813] -63,17,"[3, 4]","[30, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[1662] -63,18,"[4, 3]","[50, 40]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[1738] -63,19,"[1, 2]","[10, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2347] -63,20,"[2, 1]","[70, 10]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[8689] -63,21,"[3, 4]","[30, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[10314] -63,22,"[1, 2]","[40, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[1778] -63,23,"[4, 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-63,37,"[4, 3]","[10, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1747] -63,38,"[2, 1]","[20, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1745] -63,39,"[3, 4]","[60, 20]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1912] -63,40,"[3, 4]","[60, 10]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2090] -63,41,"[2, 1]","[20, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[3220] -63,42,"[1, 2]","[90, 40]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[2004] -63,43,"[2, 1]","[50, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1917] -63,44,"[1, 2]","[80, 40]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2274] -63,45,"[1, 2]","[60, 50]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2129] -63,46,"[2, 1]","[40, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[1956] -63,47,"[4, 3]","[30, 40]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1762] -63,48,"[1, 2]","[0, 10]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[2004] -63,49,"[3, 4]","[10, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[1875] -63,50,"[1, 2]","[30, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[3729] -63,51,"[2, 1]","[10, 60]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[1825] -63,52,"[4, 3]","[0, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1778] -63,53,"[2, 1]","[30, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2925] -63,54,"[4, 3]","[30, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1837] -63,55,"[1, 2]","[10, 20]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[2006] -63,56,"[1, 2]","[20, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[1908] -63,57,"[4, 3]","[20, 0]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2762] -63,58,"[1, 2]","[0, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2449] -63,59,"[4, 3]","[20, 10]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1898] -63,60,"[1, 2]","[0, 0]","[1, 2]",[2],[5],[2],[0],[0],[2],[1],[1772] -63,61,"[3, 4]","[10, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[2074] -63,62,"[2, 1]","[10, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[2858] -63,63,"[4, 3]","[10, 0]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[1870] -63,64,"[3, 4]","[20, 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-63,78,"[2, 1]","[60, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[1938] -63,79,"[4, 3]","[60, 30]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2305] -63,80,"[1, 2]","[20, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[2190] -64,1,"[4, 3]","[70, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[5468] -64,2,"[1, 2]","[10, 60]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[2780] -64,3,"[2, 1]","[50, 0]","[1, 1]",[1],[5],[1],[0],[0],[1],[1],[2163] -64,4,"[3, 4]","[30, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[1738] -64,5,"[1, 2]","[30, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[1722] -64,6,"[4, 3]","[80, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[3],[3014] -64,7,"[1, 2]","[0, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2530] -64,8,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[2379] -64,9,"[2, 1]","[70, 0]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2146] -64,10,"[1, 2]","[50, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1763] -64,11,"[1, 2]","[50, 90]","[1, 2]",[1],[1],[2],[90],[9],[2],[2],[1665] -64,12,"[1, 2]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2047] -64,13,"[3, 4]","[20, 90]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[2421] -64,14,"[1, 2]","[40, 70]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[2088] -64,15,"[3, 4]","[50, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2713] -64,16,"[3, 4]","[0, 70]","[2, 1]",[1],[1],[3],[0],[0],[1],[4],[2505] -64,17,"[1, 2]","[40, 90]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2120] -64,18,"[3, 4]","[60, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2163] -64,19,"[2, 1]","[60, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2030] -64,20,"[1, 2]","[70, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1855] -64,21,"[1, 2]","[70, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1862] -64,22,"[1, 2]","[70, 50]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1771] -64,23,"[1, 2]","[70, 20]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1739] -64,24,"[4, 3]","[30, 80]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[2245] -64,25,"[2, 1]","[50, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2396] -64,26,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1772] -64,27,"[3, 4]","[80, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2221] -64,28,"[4, 3]","[20, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2312] -64,29,"[4, 3]","[0, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2505] -64,30,"[3, 4]","[60, 30]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2972] -64,31,"[2, 1]","[40, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2188] -64,32,"[4, 3]","[50, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[4040] -64,33,"[4, 3]","[80, 60]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2088] -64,34,"[1, 2]","[30, 70]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[3447] -64,35,"[3, 4]","[50, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[2829] -64,36,"[1, 2]","[40, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2195] -64,37,"[2, 1]","[80, 60]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1912] -64,38,"[2, 1]","[80, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[2095] -64,39,"[1, 2]","[40, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[1788] -64,40,"[2, 1]","[70, 20]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2414] -64,41,"[1, 2]","[50, 70]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[5646] -64,42,"[3, 4]","[50, 80]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[2095] -64,43,"[4, 3]","[70, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1937] -64,44,"[1, 2]","[0, 70]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[4774] -64,45,"[4, 3]","[80, 0]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2121] -64,46,"[3, 4]","[10, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1736] -64,47,"[2, 1]","[70, 10]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[2296] -64,48,"[4, 3]","[60, 30]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[1915] -64,49,"[4, 3]","[30, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1953] -64,50,"[1, 2]","[10, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[1979] -64,51,"[1, 2]","[10, 40]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1879] -64,52,"[2, 1]","[30, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[1871] -64,53,"[3, 4]","[30, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[1887] -64,54,"[3, 4]","[40, 20]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1621] -64,55,"[2, 1]","[0, 10]","[1, 2]",[2],[5],[2],[0],[0],[2],[2],[2422] -64,56,"[1, 2]","[0, 50]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[1764] -64,57,"[2, 1]","[30, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[5073] -64,58,"[4, 3]","[30, 40]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2346] -64,59,"[1, 2]","[20, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[1712] -64,60,"[1, 2]","[30, 20]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[5918] -64,61,"[1, 2]","[0, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[1846] -64,62,"[2, 1]","[70, 0]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2655] -64,63,"[4, 3]","[90, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1996] -64,64,"[4, 3]","[50, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[1597] -64,65,"[3, 4]","[20, 40]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[1720] -64,66,"[3, 4]","[20, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1889] -64,67,"[1, 2]","[70, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[1771] -64,68,"[3, 4]","[40, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1987] -64,69,"[4, 3]","[80, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[4815] -64,70,"[2, 1]","[60, 80]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2505] -64,71,"[3, 4]","[90, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2087] -64,72,"[4, 3]","[70, 80]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[2013] -64,73,"[1, 2]","[60, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[6581] -64,74,"[3, 4]","[70, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[2439] -64,75,"[2, 1]","[80, 50]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[1696] -64,76,"[3, 4]","[60, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[1778] -64,77,"[2, 1]","[80, 90]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[4548] -64,78,"[4, 3]","[80, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1862] -64,79,"[4, 3]","[50, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[1688] -64,80,"[2, 1]","[80, 90]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[1737] -62,1,"[3, 4]","[70, 40]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[11050] -62,2,"[2, 1]","[20, 80]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[2455] -62,3,"[4, 3]","[30, 70]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[2321] -62,4,"[4, 3]","[50, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1954] -62,5,"[2, 1]","[80, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2037] -62,6,"[1, 2]","[40, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[3540] -62,7,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[4492] -62,8,"[2, 1]","[80, 10]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2354] -62,9,"[2, 1]","[60, 0]","[1, 1]",[1],[5],[1],[0],[0],[1],[1],[1720] -62,10,"[3, 4]","[10, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2354] -62,11,"[2, 1]","[50, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[2188] -62,12,"[1, 2]","[70, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[2204] -62,13,"[1, 2]","[90, 50]","[1, 1]",[1],[5],[1],[450],[9],[1],[1],[2054] -62,14,"[2, 1]","[60, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1770] -62,15,"[1, 2]","[40, 60]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2204] -62,16,"[2, 1]","[20, 50]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[2689] -62,17,"[4, 3]","[40, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[2722] -62,18,"[2, 1]","[70, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[2472] -62,19,"[4, 3]","[90, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[2939] -62,20,"[4, 3]","[80, 30]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[1955] -62,21,"[3, 4]","[0, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2221] -62,22,"[3, 4]","[10, 40]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[1854] -62,23,"[2, 1]","[30, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[2271] -62,24,"[2, 1]","[10, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[2087] -62,25,"[4, 3]","[10, 0]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[2221] -62,26,"[1, 2]","[10, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[2104] -62,27,"[3, 4]","[40, 0]","[2, 2]",[1],[5],[4],[0],[0],[2],[3],[2472] -62,28,"[1, 2]","[0, 0]","[1, 1]",[1],[1],[1],[0],[0],[1],[1],[2037] -62,29,"[3, 4]","[20, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2087] -62,30,"[1, 2]","[60, 30]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2221] -62,31,"[2, 1]","[40, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1987] -62,32,"[4, 3]","[60, 30]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[2054] -62,33,"[3, 4]","[60, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2037] -62,34,"[1, 2]","[60, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2521] -62,35,"[3, 4]","[40, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[1871] -62,36,"[3, 4]","[0, 20]","[2, 1]",[1],[1],[3],[0],[0],[1],[4],[1837] -62,37,"[2, 1]","[0, 0]","[1, 1]",[1],[1],[1],[0],[0],[1],[2],[1836] -62,38,"[4, 3]","[10, 20]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[2422] -62,39,"[3, 4]","[20, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[2371] -62,40,"[4, 3]","[20, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1837] -62,41,"[2, 1]","[20, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2720] -62,42,"[1, 2]","[80, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[2088] -62,43,"[1, 2]","[90, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2438] -62,44,"[2, 1]","[50, 80]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1920] -62,45,"[3, 4]","[90, 50]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[2187] -62,46,"[3, 4]","[90, 50]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[1803] -62,47,"[1, 2]","[80, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[2120] -62,48,"[4, 3]","[90, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2188] -62,49,"[1, 2]","[90, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[2004] -62,50,"[4, 3]","[70, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[1787] -62,51,"[2, 1]","[50, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2221] -62,52,"[1, 2]","[80, 50]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2621] -62,53,"[3, 4]","[70, 50]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[2054] -62,54,"[4, 3]","[50, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[2003] -62,55,"[3, 4]","[70, 40]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1870] -62,56,"[3, 4]","[50, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1937] -62,57,"[3, 4]","[20, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1837] -62,58,"[3, 4]","[10, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[1787] -62,59,"[1, 2]","[0, 50]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[2270] -62,60,"[3, 4]","[20, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2321] -62,61,"[3, 4]","[20, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1770] -62,62,"[4, 3]","[20, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[1937] -62,63,"[4, 3]","[30, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1754] -62,64,"[4, 3]","[10, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1886] -62,65,"[3, 4]","[30, 0]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1920] -62,66,"[2, 1]","[20, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[2037] -62,67,"[4, 3]","[10, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1837] -62,68,"[2, 1]","[0, 20]","[1, 2]",[2],[1],[2],[0],[0],[2],[2],[2255] -62,69,"[2, 1]","[20, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[1754] -62,70,"[3, 4]","[30, 50]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[2005] -62,71,"[1, 2]","[60, 50]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[1987] -62,72,"[3, 4]","[80, 60]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2120] -62,73,"[2, 1]","[70, 90]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[1970] -62,74,"[2, 1]","[70, 80]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[1954] -62,75,"[2, 1]","[90, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[2004] -62,76,"[2, 1]","[70, 40]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1803] -62,77,"[2, 1]","[80, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[3156] -62,78,"[1, 2]","[70, 90]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2237] -62,79,"[1, 2]","[90, 70]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[1920] -62,80,"[2, 1]","[30, 80]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2504] -68,1,"[4, 3]","[70, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[3769] -68,2,"[3, 4]","[0, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2902] -68,3,"[2, 1]","[90, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[3054] -68,4,"[2, 1]","[80, 20]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2203] -68,5,"[3, 4]","[10, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[1836] -68,6,"[3, 4]","[10, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[3235] -68,7,"[2, 1]","[60, 0]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2654] -68,8,"[3, 4]","[0, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2486] -68,9,"[2, 1]","[40, 0]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[2602] -68,10,"[3, 4]","[10, 40]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[2269] -68,11,"[1, 2]","[10, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[3003] -68,12,"[3, 4]","[10, 20]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[2386] -68,13,"[3, 4]","[20, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2069] -68,14,"[1, 2]","[10, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2270] -68,15,"[2, 1]","[80, 20]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[3255] -68,16,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[2520] -68,17,"[4, 3]","[90, 30]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2954] -68,18,"[3, 4]","[0, 70]","[2, 1]",[1],[5],[3],[0],[0],[1],[4],[2553] -68,19,"[4, 3]","[80, 0]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2119] -68,20,"[4, 3]","[90, 20]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2003] -68,21,"[2, 1]","[80, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[4668] -68,22,"[1, 2]","[20, 90]","[1, 2]",[1],[5],[2],[450],[9],[2],[2],[3320] -68,23,"[2, 1]","[80, 20]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2837] -68,24,"[3, 4]","[30, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2487] -68,25,"[1, 2]","[40, 80]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2671] -68,26,"[1, 2]","[20, 80]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[2070] -68,27,"[4, 3]","[80, 30]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2170] -68,28,"[1, 2]","[30, 40]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[2136] -68,29,"[3, 4]","[50, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[2287] -68,30,"[3, 4]","[70, 50]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1902] -68,31,"[2, 1]","[30, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[3889] -68,32,"[4, 3]","[20, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[1869] -68,33,"[3, 4]","[50, 20]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[2336] -68,34,"[4, 3]","[40, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[3088] -68,35,"[3, 4]","[30, 60]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[3171] -68,36,"[3, 4]","[10, 60]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[2220] -68,37,"[3, 4]","[20, 50]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[2254] -68,38,"[4, 3]","[80, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[2136] -68,39,"[4, 3]","[70, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[2270] -68,40,"[4, 3]","[80, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[2253] -68,41,"[2, 1]","[50, 60]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[3503] -68,42,"[3, 4]","[60, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[2504] -68,43,"[2, 1]","[30, 70]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2686] -68,44,"[4, 3]","[30, 40]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[5655] -68,45,"[3, 4]","[70, 50]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1969] -68,46,"[1, 2]","[80, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[8932] -68,47,"[1, 2]","[70, 40]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[4251] -68,48,"[2, 1]","[40, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[3536] -68,49,"[4, 3]","[50, 90]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[7517] -68,50,"[3, 4]","[70, 40]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1986] -68,51,"[1, 2]","[30, 70]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[3653] -68,52,"[1, 2]","[0, 60]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[4133] -68,53,"[4, 3]","[40, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[3371] -68,54,"[4, 3]","[30, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[4604] -68,55,"[3, 4]","[10, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1953] -68,56,"[2, 1]","[10, 30]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[5820] -68,57,"[2, 1]","[10, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2202] -68,58,"[4, 3]","[0, 70]","[2, 2]",[2],[5],[4],[0],[0],[2],[4],[2602] -68,59,"[2, 1]","[0, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2786] -68,60,"[3, 4]","[80, 30]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1952] -68,61,"[4, 3]","[10, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[2836] -68,62,"[2, 1]","[10, 90]","[1, 1]",[2],[1],[1],[90],[9],[1],[2],[9814] -68,63,"[4, 3]","[40, 80]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[4838] -68,64,"[2, 1]","[20, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2086] -68,65,"[2, 1]","[20, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2102] -68,66,"[3, 4]","[60, 0]","[2, 2]",[2],[1],[4],[0],[0],[2],[3],[2452] -68,67,"[2, 1]","[0, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[2420] -68,68,"[2, 1]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1869] -68,69,"[4, 3]","[30, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[2236] -68,70,"[4, 3]","[30, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[2036] -68,71,"[3, 4]","[50, 10]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2319] -68,72,"[2, 1]","[20, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2087] -68,73,"[4, 3]","[40, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[2736] -68,74,"[3, 4]","[30, 50]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[2336] -68,75,"[3, 4]","[20, 10]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[2252] -68,76,"[4, 3]","[10, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[2520] -68,77,"[1, 2]","[0, 10]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[4705] -68,78,"[3, 4]","[0, 10]","[2, 1]",[2],[5],[3],[0],[0],[1],[4],[2286] -68,79,"[3, 4]","[20, 0]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[1869] -68,80,"[4, 3]","[20, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[2153] -58,1,"[1, 2]","[40, 50]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[3622] -58,2,"[4, 3]","[40, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[2123] -58,3,"[2, 1]","[30, 70]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[2205] -58,4,"[3, 4]","[70, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2606] -58,5,"[3, 4]","[80, 40]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2490] -58,6,"[1, 2]","[80, 50]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2240] -58,7,"[2, 1]","[40, 90]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[1807] -58,8,"[2, 1]","[10, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1840] -58,9,"[4, 3]","[0, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[2640] -58,10,"[2, 1]","[20, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[2374] -58,11,"[4, 3]","[20, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[2624] -58,12,"[2, 1]","[30, 0]","[1, 1]",[1],[5],[1],[0],[0],[1],[1],[1907] -58,13,"[3, 4]","[50, 20]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[1823] -58,14,"[1, 2]","[60, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2307] -58,15,"[3, 4]","[50, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[2223] -58,16,"[1, 2]","[90, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2039] -58,17,"[1, 2]","[80, 60]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1839] -58,18,"[3, 4]","[80, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2040] -58,19,"[3, 4]","[90, 90]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[1606] -58,20,"[3, 4]","[80, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1657] -58,21,"[4, 3]","[70, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1806] -58,22,"[2, 1]","[50, 10]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[6205] -58,23,"[3, 4]","[10, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[7021] -58,24,"[4, 3]","[40, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[1724] -58,25,"[4, 3]","[20, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[2290] -58,26,"[4, 3]","[40, 20]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[1690] -58,27,"[3, 4]","[20, 60]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[2323] -58,28,"[2, 1]","[70, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[3822] -58,29,"[2, 1]","[80, 10]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1974] -58,30,"[3, 4]","[30, 80]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[1574] -58,31,"[1, 2]","[30, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[3523] -58,32,"[4, 3]","[80, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[2173] -58,33,"[1, 2]","[20, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2673] -58,34,"[3, 4]","[40, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[3],[1890] -58,35,"[4, 3]","[40, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2339] -58,36,"[1, 2]","[50, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[1891] -58,37,"[1, 2]","[10, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[3423] -58,38,"[1, 2]","[0, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[1808] -58,39,"[2, 1]","[50, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[2340] -58,40,"[3, 4]","[50, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2322] -58,41,"[4, 3]","[20, 90]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[2465] -58,42,"[3, 4]","[70, 10]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1923] -58,43,"[1, 2]","[50, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[2057] -58,44,"[2, 1]","[40, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1807] -58,45,"[4, 3]","[60, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2356] -58,46,"[1, 2]","[70, 90]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1990] -58,47,"[2, 1]","[90, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[2040] -58,48,"[4, 3]","[40, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[1940] -58,49,"[3, 4]","[50, 30]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1841] -58,50,"[1, 2]","[50, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[2207] -58,51,"[2, 1]","[0, 40]","[1, 2]",[2],[5],[2],[0],[0],[2],[2],[1956] -58,52,"[4, 3]","[30, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1840] -58,53,"[1, 2]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2790] -58,54,"[2, 1]","[50, 20]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2240] -58,55,"[1, 2]","[20, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[1773] -58,56,"[1, 2]","[0, 10]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[1690] -58,57,"[3, 4]","[30, 10]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[2140] -58,58,"[3, 4]","[0, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[1990] -58,59,"[3, 4]","[10, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1773] -58,60,"[3, 4]","[40, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1824] -58,61,"[2, 1]","[40, 20]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[1658] -58,62,"[1, 2]","[60, 50]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[2357] -58,63,"[1, 2]","[70, 40]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[2023] -58,64,"[3, 4]","[50, 40]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1956] -58,65,"[2, 1]","[80, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2107] -58,66,"[2, 1]","[60, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[1773] -58,67,"[4, 3]","[40, 60]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[3023] -58,68,"[2, 1]","[50, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2057] -58,69,"[1, 2]","[70, 60]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[2041] -58,70,"[3, 4]","[70, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[1691] -58,71,"[3, 4]","[80, 60]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1740] -58,72,"[3, 4]","[60, 10]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[1673] -58,73,"[1, 2]","[50, 10]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[3306] -58,74,"[1, 2]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2506] -58,75,"[4, 3]","[30, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1823] -58,76,"[3, 4]","[70, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2174] -58,77,"[2, 1]","[30, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2423] -58,78,"[2, 1]","[10, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[2090] -58,79,"[2, 1]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2674] -58,80,"[2, 1]","[10, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[2422] -58,1,"[3, 4]","[40, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[3621] -58,2,"[2, 1]","[70, 40]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1856] -58,3,"[3, 4]","[60, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1957] -58,4,"[3, 4]","[20, 80]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[2523] -58,5,"[4, 3]","[90, 40]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[1757] -58,6,"[4, 3]","[80, 30]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2572] -58,7,"[4, 3]","[80, 50]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2139] -58,8,"[4, 3]","[60, 70]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2124] -58,9,"[2, 1]","[50, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2207] -58,10,"[4, 3]","[10, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1740] -58,11,"[3, 4]","[80, 40]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2390] -58,12,"[4, 3]","[20, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1790] -58,13,"[4, 3]","[10, 80]","[2, 2]",[1],[5],[4],[50],[1],[2],[4],[1690] -58,14,"[2, 1]","[0, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2272] -58,15,"[1, 2]","[90, 10]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[3106] -58,16,"[3, 4]","[80, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[7888] -58,17,"[4, 3]","[30, 60]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[1940] -58,18,"[1, 2]","[60, 30]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2922] -58,19,"[1, 2]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2073] -58,20,"[1, 2]","[50, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[3371] -58,21,"[2, 1]","[70, 40]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[3039] -58,22,"[1, 2]","[20, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[1939] -58,23,"[1, 2]","[40, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[4173] -58,24,"[4, 3]","[50, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1873] -58,25,"[1, 2]","[60, 50]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2973] -58,26,"[3, 4]","[80, 20]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1674] -58,27,"[3, 4]","[70, 50]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[1724] -58,28,"[3, 4]","[90, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2622] -58,29,"[4, 3]","[20, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2057] -58,30,"[2, 1]","[40, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1473] -58,31,"[3, 4]","[90, 20]","[2, 1]",[1],[1],[3],[90],[9],[1],[3],[2089] -58,32,"[4, 3]","[10, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2472] -58,33,"[1, 2]","[40, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[2040] -58,34,"[1, 2]","[30, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[1674] -58,35,"[1, 2]","[10, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[1689] -58,36,"[2, 1]","[60, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2240] -58,37,"[1, 2]","[70, 50]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1640] -58,38,"[1, 2]","[90, 40]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[1973] -58,39,"[1, 2]","[80, 40]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2190] -58,40,"[2, 1]","[50, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[1657] -58,41,"[1, 2]","[70, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[1498] -58,42,"[1, 2]","[60, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[5039] -58,43,"[1, 2]","[60, 80]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1990] -58,44,"[1, 2]","[80, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[2473] -58,45,"[2, 1]","[90, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[5488] -58,46,"[4, 3]","[50, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1856] -58,47,"[3, 4]","[80, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1790] -58,48,"[1, 2]","[50, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[2490] -58,49,"[2, 1]","[60, 40]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[1773] -58,50,"[3, 4]","[70, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1557] -58,51,"[3, 4]","[30, 80]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[2256] -58,52,"[4, 3]","[70, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[1774] -58,53,"[4, 3]","[80, 20]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2139] -58,54,"[1, 2]","[20, 80]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[2090] -58,55,"[4, 3]","[60, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1457] -58,56,"[2, 1]","[80, 30]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[1773] -58,57,"[1, 2]","[40, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[1607] -58,58,"[2, 1]","[90, 30]","[1, 2]",[2],[5],[2],[450],[9],[2],[1],[1573] -58,59,"[1, 2]","[30, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1307] -58,60,"[2, 1]","[50, 40]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2056] -58,61,"[2, 1]","[20, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1674] -58,62,"[2, 1]","[10, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1940] -58,63,"[1, 2]","[80, 0]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1856] -58,64,"[1, 2]","[90, 40]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[1624] -58,65,"[3, 4]","[80, 50]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1806] -58,66,"[4, 3]","[50, 60]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[1573] -58,67,"[4, 3]","[50, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[1607] -58,68,"[3, 4]","[80, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2174] -58,69,"[2, 1]","[80, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1758] -58,70,"[1, 2]","[80, 70]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2173] -58,71,"[1, 2]","[60, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[1940] -58,72,"[4, 3]","[80, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2207] -58,73,"[3, 4]","[60, 80]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2006] -58,74,"[2, 1]","[80, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[3673] -58,75,"[1, 2]","[90, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1973] -58,76,"[2, 1]","[20, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1940] -58,77,"[1, 2]","[80, 10]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1823] -58,78,"[1, 2]","[30, 0]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[1723] -58,79,"[3, 4]","[0, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[2139] -58,80,"[3, 4]","[0, 40]","[2, 1]",[2],[5],[3],[0],[0],[1],[4],[1823] -69,1,"[2, 1]","[20, 60]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[3999] -69,2,"[3, 4]","[70, 40]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[3087] -69,3,"[1, 2]","[90, 70]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[3220] -69,4,"[4, 3]","[40, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2068] -69,5,"[3, 4]","[70, 50]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2586] -69,6,"[4, 3]","[70, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[3],[2368] -69,7,"[2, 1]","[60, 10]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2601] -69,8,"[2, 1]","[40, 0]","[1, 1]",[1],[5],[1],[0],[0],[1],[1],[2686] -69,9,"[4, 3]","[90, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[3],[2452] -69,10,"[4, 3]","[90, 10]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[5223] -69,11,"[4, 3]","[90, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[5837] -69,12,"[1, 2]","[10, 90]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[2300] -69,13,"[3, 4]","[20, 70]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[5525] -69,14,"[2, 1]","[70, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[5084] -69,15,"[3, 4]","[30, 50]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[2167] -69,16,"[3, 4]","[40, 60]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[1884] -69,17,"[1, 2]","[40, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2101] -69,18,"[4, 3]","[40, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[2586] -69,19,"[3, 4]","[60, 40]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1984] -69,20,"[1, 2]","[70, 20]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2852] -69,21,"[1, 2]","[70, 0]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[3520] -69,22,"[2, 1]","[20, 80]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[2319] -69,23,"[1, 2]","[80, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[2119] -69,24,"[4, 3]","[0, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2269] -69,25,"[1, 2]","[70, 10]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2833] -69,26,"[4, 3]","[30, 60]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[3602] -69,27,"[2, 1]","[50, 40]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2034] -69,28,"[2, 1]","[50, 60]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[4338] -69,29,"[1, 2]","[70, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2803] -69,30,"[3, 4]","[50, 70]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[4268] -69,31,"[4, 3]","[80, 70]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2534] -69,32,"[4, 3]","[60, 20]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[2051] -69,33,"[3, 4]","[10, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[1935] -69,34,"[3, 4]","[30, 40]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[2468] -69,35,"[4, 3]","[40, 20]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2135] -69,36,"[3, 4]","[10, 30]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[4337] -69,37,"[1, 2]","[20, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2035] -69,38,"[1, 2]","[80, 60]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2284] -69,39,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2151] -69,40,"[3, 4]","[70, 40]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[1984] -69,41,"[2, 1]","[50, 80]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[2166] -69,42,"[3, 4]","[70, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[2952] -69,43,"[4, 3]","[50, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[2553] -69,44,"[1, 2]","[50, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[2319] -69,45,"[4, 3]","[60, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[2534] -69,46,"[1, 2]","[10, 50]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[3687] -69,47,"[4, 3]","[60, 0]","[2, 1]",[2],[1],[3],[0],[0],[1],[3],[2284] -69,48,"[2, 1]","[60, 20]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[3437] -69,49,"[1, 2]","[10, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[3886] -69,50,"[4, 3]","[80, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[4519] -69,51,"[4, 3]","[70, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[2967] -69,52,"[1, 2]","[10, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[3252] -69,53,"[3, 4]","[20, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[3719] -69,54,"[4, 3]","[90, 10]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[3888] -69,55,"[1, 2]","[20, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2903] -69,56,"[4, 3]","[70, 20]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2884] -69,57,"[1, 2]","[10, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[2418] -69,58,"[4, 3]","[50, 20]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2851] -69,59,"[2, 1]","[50, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[4554] -69,60,"[1, 2]","[30, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2568] -69,61,"[3, 4]","[20, 60]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[2035] -69,62,"[3, 4]","[50, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[2051] -69,63,"[1, 2]","[60, 50]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[2068] -69,64,"[4, 3]","[70, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[3452] -69,65,"[2, 1]","[90, 60]","[1, 2]",[2],[1],[2],[90],[9],[2],[1],[1818] -69,66,"[1, 2]","[40, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1868] -69,67,"[3, 4]","[40, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2752] -69,68,"[3, 4]","[20, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1817] -69,69,"[3, 4]","[50, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[3068] -69,70,"[4, 3]","[30, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[2319] -69,71,"[3, 4]","[70, 0]","[2, 2]",[2],[1],[4],[0],[0],[2],[3],[2284] -69,72,"[3, 4]","[70, 10]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1768] -69,73,"[4, 3]","[10, 80]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[2451] -69,74,"[4, 3]","[20, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[2152] -69,75,"[4, 3]","[50, 70]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[2702] -69,76,"[3, 4]","[20, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[3318] -69,77,"[4, 3]","[20, 10]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2469] -69,78,"[3, 4]","[30, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[3287] -69,79,"[2, 1]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2118] -69,80,"[3, 4]","[30, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[3403] -64,1,"[4, 3]","[30, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[4107] -64,2,"[3, 4]","[60, 10]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[2273] -64,3,"[2, 1]","[0, 40]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[2352] -64,4,"[1, 2]","[40, 0]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[2238] -64,5,"[4, 3]","[30, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2232] -64,6,"[3, 4]","[60, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2056] -64,7,"[3, 4]","[80, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[1980] -64,8,"[2, 1]","[80, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2607] -64,9,"[2, 1]","[70, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1789] -64,10,"[3, 4]","[90, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2055] -64,11,"[2, 1]","[50, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1906] -64,12,"[2, 1]","[60, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2398] -64,13,"[1, 2]","[70, 50]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[4392] -64,14,"[1, 2]","[70, 30]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[3617] -64,15,"[2, 1]","[30, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[1939] -64,16,"[1, 2]","[70, 30]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1815] -64,17,"[4, 3]","[30, 50]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[2256] -64,18,"[2, 1]","[50, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2924] -64,19,"[1, 2]","[20, 50]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[2248] -64,20,"[4, 3]","[30, 20]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[2431] -64,21,"[3, 4]","[20, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1847] -64,22,"[4, 3]","[50, 0]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[1880] -64,23,"[1, 2]","[30, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[1973] -64,24,"[4, 3]","[40, 10]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[1999] -64,25,"[1, 2]","[40, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[1865] -64,26,"[1, 2]","[60, 20]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[4652] -64,27,"[2, 1]","[20, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2073] -64,28,"[4, 3]","[40, 30]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2082] -64,29,"[2, 1]","[10, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[2189] -64,30,"[1, 2]","[10, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[1],[1771] -64,31,"[4, 3]","[20, 40]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[2223] -64,32,"[3, 4]","[50, 40]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[2324] -64,33,"[3, 4]","[50, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[1831] -64,34,"[2, 1]","[60, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[5660] -64,35,"[1, 2]","[0, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[1906] -64,36,"[1, 2]","[0, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[1988] -64,37,"[4, 3]","[70, 0]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[2106] -64,38,"[2, 1]","[80, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[1898] -64,39,"[3, 4]","[20, 70]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[2999] -64,40,"[4, 3]","[80, 60]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[1839] -64,41,"[4, 3]","[60, 80]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[4881] -64,42,"[4, 3]","[70, 90]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[1796] -64,43,"[3, 4]","[60, 90]","[2, 2]",[2],[5],[4],[450],[9],[2],[4],[1662] -64,44,"[1, 2]","[60, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[1990] -64,45,"[3, 4]","[70, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[2181] -64,46,"[4, 3]","[60, 90]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[1948] -64,47,"[4, 3]","[80, 90]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1973] -64,48,"[4, 3]","[70, 80]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[1955] -64,49,"[2, 1]","[60, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[3742] -64,50,"[3, 4]","[60, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[1897] -64,51,"[2, 1]","[50, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1680] -64,52,"[4, 3]","[60, 70]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2373] -64,53,"[1, 2]","[80, 50]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2106] -64,54,"[4, 3]","[30, 60]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2063] -64,55,"[1, 2]","[70, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2139] -64,56,"[3, 4]","[50, 0]","[2, 2]",[2],[5],[4],[0],[0],[2],[3],[2090] -64,57,"[2, 1]","[0, 20]","[1, 2]",[2],[5],[2],[0],[0],[2],[2],[2065] -64,58,"[1, 2]","[30, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[1989] -64,59,"[2, 1]","[40, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[2840] -64,60,"[3, 4]","[40, 30]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[2206] -64,61,"[1, 2]","[0, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[2306] -64,62,"[4, 3]","[10, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[2115] -64,63,"[1, 2]","[50, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[2590] -64,64,"[2, 1]","[10, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2256] -64,65,"[3, 4]","[60, 10]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[2498] -64,66,"[2, 1]","[20, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2299] -64,67,"[2, 1]","[10, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2657] -64,68,"[3, 4]","[50, 30]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2456] -64,69,"[1, 2]","[40, 60]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[2814] -64,70,"[4, 3]","[60, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[2682] -64,71,"[2, 1]","[40, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[2783] -64,72,"[3, 4]","[30, 60]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[2069] -64,73,"[4, 3]","[80, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[2339] -64,74,"[3, 4]","[40, 50]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1772] -64,75,"[3, 4]","[20, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1831] -64,76,"[1, 2]","[10, 30]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[1964] -64,77,"[4, 3]","[20, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[2181] -64,78,"[1, 2]","[20, 0]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[1839] -64,79,"[2, 1]","[40, 30]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[1746] -64,80,"[2, 1]","[60, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[1762] -65,1,"[4, 3]","[30, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[3798] -65,2,"[2, 1]","[0, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[7867] -65,3,"[4, 3]","[20, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[3046] -65,4,"[4, 3]","[0, 60]","[2, 2]",[1],[5],[4],[0],[0],[2],[4],[5723] -65,5,"[4, 3]","[10, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2110] -65,6,"[1, 2]","[30, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[2176] -65,7,"[4, 3]","[10, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1842] -65,8,"[4, 3]","[30, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[1925] -65,9,"[3, 4]","[60, 10]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1858] -65,10,"[1, 2]","[60, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[2998] -65,11,"[3, 4]","[70, 40]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1908] -65,12,"[2, 1]","[50, 70]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[3349] -65,13,"[1, 2]","[60, 0]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2829] -65,14,"[4, 3]","[30, 60]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[2293] -65,15,"[1, 2]","[60, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[5656] -65,16,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[3046] -65,17,"[1, 2]","[80, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[1741] -65,18,"[2, 1]","[40, 70]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[1891] -65,19,"[1, 2]","[60, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[1743] -65,20,"[3, 4]","[80, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2560] -65,21,"[2, 1]","[40, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2543] -65,22,"[1, 2]","[80, 30]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1974] -65,23,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1757] -65,24,"[4, 3]","[50, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[2142] -65,25,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1741] -65,26,"[2, 1]","[60, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[1691] -65,27,"[2, 1]","[80, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[4736] -65,28,"[2, 1]","[80, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[6274] -65,29,"[4, 3]","[90, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[1824] -65,30,"[2, 1]","[80, 40]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1740] -65,31,"[4, 3]","[70, 40]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2476] -65,32,"[3, 4]","[30, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[3397] -65,33,"[3, 4]","[60, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[1891] -65,34,"[4, 3]","[30, 70]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[1925] -65,35,"[4, 3]","[30, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[2778] -65,36,"[3, 4]","[20, 30]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[1858] -65,37,"[3, 4]","[20, 40]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[1991] -65,38,"[4, 3]","[20, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[1891] -65,39,"[4, 3]","[60, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2360] -65,40,"[1, 2]","[70, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[10357] -65,41,"[2, 1]","[90, 50]","[1, 2]",[2],[5],[2],[450],[9],[2],[1],[2576] -65,42,"[3, 4]","[50, 80]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[2095] -65,43,"[3, 4]","[50, 70]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[2343] -65,44,"[1, 2]","[50, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[1891] -65,45,"[2, 1]","[40, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[2293] -65,46,"[1, 2]","[50, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2176] -65,47,"[2, 1]","[40, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1707] -65,48,"[3, 4]","[20, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2058] -65,49,"[2, 1]","[40, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[1941] -65,50,"[2, 1]","[40, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[2076] -65,51,"[1, 2]","[20, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[1790] -65,52,"[3, 4]","[30, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2159] -65,53,"[1, 2]","[30, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1941] -65,54,"[2, 1]","[80, 10]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[1757] -65,55,"[4, 3]","[70, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1774] -65,56,"[4, 3]","[80, 50]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[1758] -65,57,"[1, 2]","[70, 50]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[2041] -65,58,"[2, 1]","[60, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2074] -65,59,"[1, 2]","[70, 50]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[4617] -65,60,"[1, 2]","[50, 40]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[2761] -65,61,"[4, 3]","[30, 80]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1858] -65,62,"[4, 3]","[10, 70]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[3096] -65,63,"[3, 4]","[50, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[3],[1924] -65,64,"[4, 3]","[10, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[1874] -65,65,"[1, 2]","[30, 0]","[1, 2]",[2],[1],[2],[0],[0],[2],[1],[2326] -65,66,"[1, 2]","[30, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2008] -65,67,"[1, 2]","[20, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[1874] -65,68,"[3, 4]","[40, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[1841] -65,69,"[4, 3]","[0, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1958] -65,70,"[2, 1]","[30, 60]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2041] -65,71,"[2, 1]","[30, 50]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[2009] -65,72,"[3, 4]","[40, 50]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[2092] -65,73,"[2, 1]","[60, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1991] -65,74,"[1, 2]","[80, 70]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1874] -65,75,"[3, 4]","[80, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[1907] -65,76,"[2, 1]","[70, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1740] -65,77,"[4, 3]","[60, 70]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[1791] -65,78,"[2, 1]","[60, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[1958] -65,79,"[4, 3]","[40, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[4],[2443] -65,80,"[1, 2]","[20, 80]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[1840] -65,1,"[2, 1]","[30, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[5155] -65,2,"[2, 1]","[0, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[1657] -65,3,"[3, 4]","[60, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[2126] -65,4,"[4, 3]","[10, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1842] -65,5,"[3, 4]","[70, 0]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[1892] -65,6,"[1, 2]","[50, 30]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[1775] -65,7,"[2, 1]","[30, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1928] -65,8,"[2, 1]","[10, 20]","[1, 2]",[1],[1],[2],[10],[1],[2],[2],[2025] -65,9,"[4, 3]","[10, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[2512] -65,10,"[4, 3]","[60, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[3214] -65,11,"[4, 3]","[10, 30]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[1875] -65,12,"[1, 2]","[10, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2227] -65,13,"[3, 4]","[10, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[4252] -65,14,"[4, 3]","[40, 30]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2076] -65,15,"[2, 1]","[40, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1976] -65,16,"[4, 3]","[40, 50]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[3498] -65,17,"[3, 4]","[60, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[1824] -65,18,"[2, 1]","[40, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1791] -65,19,"[1, 2]","[50, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[1875] -65,20,"[4, 3]","[40, 30]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[1875] -65,21,"[1, 2]","[10, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[1675] -65,22,"[4, 3]","[30, 10]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[1658] -65,23,"[1, 2]","[10, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[1875] -65,24,"[4, 3]","[20, 10]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[1875] -65,25,"[3, 4]","[0, 50]","[2, 1]",[1],[5],[3],[0],[0],[1],[4],[1741] -65,26,"[3, 4]","[10, 40]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[1774] -65,27,"[3, 4]","[10, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[1908] -65,28,"[3, 4]","[30, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1992] -65,29,"[4, 3]","[20, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[2579] -65,30,"[1, 2]","[10, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[2],[2009] -65,31,"[2, 1]","[0, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[1658] -65,32,"[4, 3]","[60, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[1741] -65,33,"[1, 2]","[20, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[1975] -65,34,"[1, 2]","[20, 30]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[1825] -65,35,"[1, 2]","[0, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[1674] -65,36,"[1, 2]","[30, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[1523] -65,37,"[4, 3]","[40, 10]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[1658] -65,38,"[2, 1]","[40, 0]","[1, 1]",[1],[1],[1],[0],[0],[1],[1],[1841] -65,39,"[4, 3]","[80, 20]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2528] -65,40,"[1, 2]","[50, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2026] -65,41,"[3, 4]","[60, 50]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2541] -65,42,"[4, 3]","[80, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1774] -65,43,"[2, 1]","[60, 50]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[1624] -65,44,"[3, 4]","[50, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[1691] -65,45,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1674] -65,46,"[4, 3]","[50, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[2595] -65,47,"[3, 4]","[30, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[1808] -65,48,"[4, 3]","[60, 0]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[1657] -65,49,"[3, 4]","[10, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[4619] -65,50,"[2, 1]","[60, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[4300] -65,51,"[1, 2]","[60, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[4318] -65,52,"[2, 1]","[80, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1591] -65,53,"[4, 3]","[80, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[3515] -65,54,"[1, 2]","[40, 70]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[1593] -65,55,"[3, 4]","[0, 90]","[2, 2]",[2],[5],[4],[450],[9],[2],[4],[1675] -65,56,"[2, 1]","[80, 0]","[1, 1]",[2],[1],[1],[0],[0],[1],[1],[1992] -65,57,"[2, 1]","[60, 20]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[1842] -65,58,"[3, 4]","[50, 90]","[2, 2]",[2],[1],[4],[90],[9],[2],[4],[2611] -65,59,"[3, 4]","[90, 90]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[3648] -65,60,"[1, 2]","[80, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[1807] -65,61,"[2, 1]","[70, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1808] -65,62,"[4, 3]","[70, 80]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[1909] -65,63,"[1, 2]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[1942] -65,64,"[1, 2]","[60, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[2528] -65,65,"[3, 4]","[60, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1808] -65,66,"[1, 2]","[90, 80]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[1874] -65,67,"[2, 1]","[70, 90]","[1, 1]",[2],[1],[1],[90],[9],[1],[2],[1691] -65,68,"[3, 4]","[80, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[1775] -65,69,"[4, 3]","[90, 70]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[1775] -65,70,"[4, 3]","[90, 80]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[1825] -65,71,"[2, 1]","[80, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2611] -65,72,"[3, 4]","[20, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[1724] -65,73,"[2, 1]","[80, 0]","[1, 1]",[2],[1],[1],[0],[0],[1],[1],[2244] -65,74,"[4, 3]","[90, 10]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[1842] -65,75,"[1, 2]","[0, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[1691] -65,76,"[1, 2]","[20, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[1724] -65,77,"[4, 3]","[50, 10]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2209] -65,78,"[2, 1]","[50, 10]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[1925] -65,79,"[4, 3]","[40, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[2222] -65,80,"[2, 1]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1925] -56,1,"[1, 2]","[40, 50]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[3585] -56,2,"[4, 3]","[40, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[1817] -56,3,"[2, 1]","[30, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1732] -56,4,"[3, 4]","[70, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[1758] -56,5,"[3, 4]","[80, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2390] -56,6,"[1, 2]","[80, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[4183] -56,7,"[2, 1]","[40, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2468] -56,8,"[2, 1]","[10, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1812] -56,9,"[4, 3]","[0, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[3695] -56,10,"[2, 1]","[20, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[2451] -56,11,"[4, 3]","[20, 20]","[2, 1]",[1],[1],[3],[20],[2],[1],[4],[2998] -56,12,"[2, 1]","[30, 0]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[4467] -56,13,"[3, 4]","[50, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[3369] -56,14,"[1, 2]","[60, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[4024] -56,15,"[3, 4]","[50, 80]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[2684] -56,16,"[1, 2]","[90, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[3031] -56,17,"[1, 2]","[80, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2343] -56,18,"[3, 4]","[80, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2471] -56,19,"[3, 4]","[90, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[3212] -56,20,"[3, 4]","[80, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[2004] -56,21,"[4, 3]","[70, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[2328] -56,22,"[2, 1]","[50, 10]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2728] -56,23,"[3, 4]","[10, 40]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[2568] -56,24,"[4, 3]","[40, 10]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[3156] -56,25,"[4, 3]","[20, 10]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[2995] -56,26,"[4, 3]","[40, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1934] -56,27,"[3, 4]","[20, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2698] -56,28,"[2, 1]","[70, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[2708] -56,29,"[2, 1]","[80, 10]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1985] -56,30,"[3, 4]","[30, 80]","[2, 1]",[1],[5],[3],[150],[3],[1],[4],[5058] -56,31,"[1, 2]","[30, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2631] -56,32,"[4, 3]","[80, 30]","[2, 2]",[1],[5],[4],[400],[8],[2],[3],[2598] -56,33,"[1, 2]","[20, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2089] -56,34,"[3, 4]","[40, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2417] -56,35,"[4, 3]","[40, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[2041] -56,36,"[1, 2]","[50, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2064] -56,37,"[1, 2]","[10, 80]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1786] -56,38,"[1, 2]","[0, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[1761] -56,39,"[2, 1]","[50, 10]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2012] -56,40,"[3, 4]","[50, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[1738] -56,41,"[4, 3]","[20, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[2775] -56,42,"[3, 4]","[70, 10]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1891] -56,43,"[1, 2]","[50, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[1825] -56,44,"[2, 1]","[40, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2789] -56,45,"[4, 3]","[60, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2486] -56,46,"[1, 2]","[70, 90]","[1, 2]",[2],[5],[2],[450],[9],[2],[2],[3180] -56,47,"[2, 1]","[90, 70]","[1, 2]",[2],[1],[2],[90],[9],[2],[1],[3103] -56,48,"[4, 3]","[40, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[2501] -56,49,"[3, 4]","[50, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[3002] -56,50,"[1, 2]","[50, 20]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[2074] -56,51,"[2, 1]","[0, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[3418] -56,52,"[4, 3]","[30, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1908] -56,53,"[1, 2]","[30, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[2082] -56,54,"[2, 1]","[50, 20]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1893] -56,55,"[1, 2]","[20, 10]","[1, 1]",[2],[1],[1],[20],[2],[1],[1],[1976] -56,56,"[1, 2]","[0, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[1973] -56,57,"[3, 4]","[30, 10]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[2205] -56,58,"[3, 4]","[0, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[1944] -56,59,"[3, 4]","[10, 30]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[2183] -56,60,"[3, 4]","[40, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2019] -56,61,"[2, 1]","[40, 20]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[3429] -56,62,"[1, 2]","[60, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[1894] -56,63,"[1, 2]","[70, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2227] -56,64,"[3, 4]","[50, 40]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2316] -56,65,"[2, 1]","[80, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[7541] -56,66,"[2, 1]","[60, 80]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[7201] -56,67,"[4, 3]","[40, 60]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2041] -56,68,"[2, 1]","[50, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1967] -56,69,"[1, 2]","[70, 60]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[2383] -56,70,"[3, 4]","[70, 50]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[3553] -56,71,"[3, 4]","[80, 60]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1971] -56,72,"[3, 4]","[60, 10]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[2011] -56,73,"[1, 2]","[50, 10]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[1811] -56,74,"[1, 2]","[30, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[1923] -56,75,"[4, 3]","[30, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1799] -56,76,"[3, 4]","[70, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[1930] -56,77,"[2, 1]","[30, 50]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[2075] -56,78,"[2, 1]","[10, 20]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[1945] -56,79,"[2, 1]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[2188] -56,80,"[2, 1]","[10, 30]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[1920] -68,1,"[4, 3]","[30, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[3432] -68,2,"[3, 4]","[60, 10]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[2399] -68,3,"[2, 1]","[0, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2399] -68,4,"[1, 2]","[40, 0]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[1933] -68,5,"[4, 3]","[30, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2000] -68,6,"[3, 4]","[60, 50]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1950] -68,7,"[3, 4]","[80, 70]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2250] -68,8,"[2, 1]","[80, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2183] -68,9,"[2, 1]","[70, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2899] -68,10,"[3, 4]","[90, 80]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[2100] -68,11,"[2, 1]","[50, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2250] -68,12,"[2, 1]","[60, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1934] -68,13,"[1, 2]","[70, 50]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1916] -68,14,"[1, 2]","[70, 30]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2117] -68,15,"[2, 1]","[30, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[1950] -68,16,"[1, 2]","[70, 30]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2117] -68,17,"[4, 3]","[30, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[2450] -68,18,"[2, 1]","[50, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2250] -68,19,"[1, 2]","[20, 50]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[1950] -68,20,"[4, 3]","[30, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[2200] -68,21,"[3, 4]","[20, 40]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1800] -68,22,"[4, 3]","[50, 0]","[2, 1]",[1],[5],[3],[0],[0],[1],[3],[2033] -68,23,"[1, 2]","[30, 50]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[2166] -68,24,"[4, 3]","[40, 10]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2467] -68,25,"[1, 2]","[40, 30]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[2184] -68,26,"[1, 2]","[60, 20]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2283] -68,27,"[2, 1]","[20, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2683] -68,28,"[4, 3]","[40, 30]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[3899] -68,29,"[2, 1]","[10, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[2183] -68,30,"[1, 2]","[10, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[1],[2550] -68,31,"[4, 3]","[20, 40]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[2433] -68,32,"[3, 4]","[50, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[2116] -68,33,"[3, 4]","[50, 30]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[2716] -68,34,"[2, 1]","[60, 10]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[2417] -68,35,"[1, 2]","[0, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2200] -68,36,"[1, 2]","[0, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[2250] -68,37,"[4, 3]","[70, 0]","[2, 2]",[1],[1],[4],[70],[7],[2],[3],[3698] -68,38,"[2, 1]","[80, 10]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[2966] -68,39,"[3, 4]","[20, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[4],[3083] -68,40,"[4, 3]","[80, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[2267] -68,41,"[4, 3]","[60, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[8120] -68,42,"[4, 3]","[70, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[4349] -68,43,"[3, 4]","[60, 90]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[2283] -68,44,"[1, 2]","[60, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[2716] -68,45,"[3, 4]","[70, 60]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[2083] -68,46,"[4, 3]","[60, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[2350] -68,47,"[4, 3]","[80, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[2983] -68,48,"[4, 3]","[70, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[2167] -68,49,"[2, 1]","[60, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[3701] -68,50,"[3, 4]","[60, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2250] -68,51,"[2, 1]","[50, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2783] -68,52,"[4, 3]","[60, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2250] -68,53,"[1, 2]","[80, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[4034] -68,54,"[4, 3]","[30, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2284] -68,55,"[1, 2]","[70, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[3600] -68,56,"[3, 4]","[50, 0]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2200] -68,57,"[2, 1]","[0, 20]","[1, 2]",[2],[5],[2],[0],[0],[2],[2],[2834] -68,58,"[1, 2]","[30, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[2167] -68,59,"[2, 1]","[40, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[1866] -68,60,"[3, 4]","[40, 30]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[2683] -68,61,"[1, 2]","[0, 40]","[1, 1]",[2],[1],[1],[0],[0],[1],[2],[3400] -68,62,"[4, 3]","[10, 30]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[6050] -68,63,"[1, 2]","[50, 10]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[3132] -68,64,"[2, 1]","[10, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[3232] -68,65,"[3, 4]","[60, 10]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[2216] -68,66,"[2, 1]","[20, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2849] -68,67,"[2, 1]","[10, 70]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2150] -68,68,"[3, 4]","[50, 30]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[2400] -68,69,"[1, 2]","[40, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1700] -68,70,"[4, 3]","[60, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[2234] -68,71,"[2, 1]","[40, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[2500] -68,72,"[3, 4]","[30, 60]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[2116] -68,73,"[4, 3]","[80, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[3150] -68,74,"[3, 4]","[40, 50]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[2167] -68,75,"[3, 4]","[20, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1967] -68,76,"[1, 2]","[10, 30]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[2217] -68,77,"[4, 3]","[20, 10]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[3183] -68,78,"[1, 2]","[20, 0]","[1, 2]",[2],[1],[2],[0],[0],[2],[1],[2316] -68,79,"[2, 1]","[40, 30]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[1684] -68,80,"[2, 1]","[60, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[3633] -64,1,"[2, 1]","[30, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[3547] -64,2,"[2, 1]","[0, 90]","[1, 2]",[1],[1],[2],[0],[0],[2],[2],[4808] -64,3,"[3, 4]","[60, 20]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[4909] -64,4,"[4, 3]","[10, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1906] -64,5,"[3, 4]","[70, 0]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[1763] -64,6,"[1, 2]","[50, 30]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[2433] -64,7,"[2, 1]","[30, 40]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[2399] -64,8,"[2, 1]","[10, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[1681] -64,9,"[4, 3]","[10, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1914] -64,10,"[4, 3]","[60, 10]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2766] -64,11,"[4, 3]","[10, 30]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[1756] -64,12,"[1, 2]","[10, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2623] -64,13,"[3, 4]","[10, 30]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[3732] -64,14,"[4, 3]","[40, 30]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2156] -64,15,"[2, 1]","[40, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[2139] -64,16,"[4, 3]","[40, 50]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[4301] -64,17,"[3, 4]","[60, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[1798] -64,18,"[2, 1]","[40, 50]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2172] -64,19,"[1, 2]","[50, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[1673] -64,20,"[4, 3]","[40, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[1806] -64,21,"[1, 2]","[10, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[2081] -64,22,"[4, 3]","[30, 10]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[2566] -64,23,"[1, 2]","[10, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[1872] -64,24,"[4, 3]","[20, 10]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[1806] -64,25,"[3, 4]","[0, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[2072] -64,26,"[3, 4]","[10, 40]","[2, 1]",[1],[5],[3],[50],[1],[1],[4],[1873] -64,27,"[3, 4]","[10, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[1606] -64,28,"[3, 4]","[30, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[3949] -64,29,"[4, 3]","[20, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[2005] -64,30,"[1, 2]","[10, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[2],[2122] -64,31,"[2, 1]","[0, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[2248] -64,32,"[4, 3]","[60, 30]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[1821] -64,33,"[1, 2]","[20, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[1747] -64,34,"[1, 2]","[20, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[2482] -64,35,"[1, 2]","[0, 40]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[2540] -64,36,"[1, 2]","[30, 20]","[1, 1]",[1],[5],[1],[150],[3],[1],[1],[3674] -64,37,"[4, 3]","[40, 10]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[2783] -64,38,"[2, 1]","[40, 0]","[1, 1]",[1],[1],[1],[0],[0],[1],[1],[2540] -64,39,"[4, 3]","[80, 20]","[2, 2]",[1],[1],[4],[80],[8],[2],[3],[2273] -64,40,"[1, 2]","[50, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[3374] -64,41,"[3, 4]","[60, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2794] -64,42,"[4, 3]","[80, 50]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[2633] -64,43,"[2, 1]","[60, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[2923] -64,44,"[3, 4]","[50, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[1697] -64,45,"[4, 3]","[30, 10]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2115] -64,46,"[4, 3]","[50, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1989] -64,47,"[3, 4]","[30, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[1972] -64,48,"[4, 3]","[60, 0]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[2514] -64,49,"[3, 4]","[10, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[1838] -64,50,"[2, 1]","[60, 30]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[2640] -64,51,"[1, 2]","[60, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[3673] -64,52,"[2, 1]","[80, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[1864] -64,53,"[4, 3]","[80, 50]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[1947] -64,54,"[1, 2]","[40, 70]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[2081] -64,55,"[3, 4]","[0, 90]","[2, 2]",[2],[5],[4],[450],[9],[2],[4],[3241] -64,56,"[2, 1]","[80, 0]","[1, 1]",[2],[1],[1],[0],[0],[1],[1],[2055] -64,57,"[2, 1]","[60, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[1880] -64,58,"[3, 4]","[50, 90]","[2, 2]",[2],[1],[4],[90],[9],[2],[4],[2001] -64,59,"[3, 4]","[90, 90]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[2323] -64,60,"[1, 2]","[80, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2322] -64,61,"[2, 1]","[70, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[1879] -64,62,"[4, 3]","[70, 80]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[2031] -64,63,"[1, 2]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[1968] -64,64,"[1, 2]","[60, 80]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2340] -64,65,"[3, 4]","[60, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[1680] -64,66,"[1, 2]","[90, 80]","[1, 1]",[2],[1],[1],[90],[9],[1],[1],[2039] -64,67,"[2, 1]","[70, 90]","[1, 1]",[2],[1],[1],[90],[9],[1],[2],[2055] -64,68,"[3, 4]","[80, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2431] -64,69,"[4, 3]","[90, 70]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[4701] -64,70,"[4, 3]","[90, 80]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[2081] -64,71,"[2, 1]","[80, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2224] -64,72,"[3, 4]","[20, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[1790] -64,73,"[2, 1]","[80, 0]","[1, 1]",[2],[1],[1],[0],[0],[1],[1],[2172] -64,74,"[4, 3]","[90, 10]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[1955] -64,75,"[1, 2]","[0, 80]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[2189] -64,76,"[1, 2]","[20, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2235] -64,77,"[4, 3]","[50, 10]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[1984] -64,78,"[2, 1]","[50, 10]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[2097] -64,79,"[4, 3]","[40, 30]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[1906] -64,80,"[2, 1]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[4485] -70,1,"[2, 1]","[90, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[3561] -70,2,"[4, 3]","[70, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[1792] -70,3,"[2, 1]","[70, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[1710] -70,4,"[3, 4]","[70, 80]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1921] -70,5,"[4, 3]","[50, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2173] -70,6,"[3, 4]","[60, 80]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[2224] -70,7,"[4, 3]","[50, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1819] -70,8,"[4, 3]","[60, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1700] -70,9,"[3, 4]","[80, 20]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[2054] -70,10,"[4, 3]","[30, 80]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[1880] -70,11,"[3, 4]","[60, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[1832] -70,12,"[4, 3]","[20, 40]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[2076] -70,13,"[2, 1]","[40, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1827] -70,14,"[1, 2]","[30, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[1863] -70,15,"[4, 3]","[60, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[1918] -70,16,"[3, 4]","[60, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2539] -70,17,"[1, 2]","[20, 50]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[1874] -70,18,"[1, 2]","[20, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[1823] -70,19,"[1, 2]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[1736] -70,20,"[1, 2]","[10, 60]","[1, 1]",[1],[5],[1],[50],[1],[1],[2],[1839] -70,21,"[3, 4]","[30, 50]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[2060] -70,22,"[4, 3]","[20, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2461] -70,23,"[1, 2]","[70, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[1854] -70,24,"[2, 1]","[20, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2130] -70,25,"[4, 3]","[0, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[2271] -70,26,"[3, 4]","[30, 0]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1937] -70,27,"[4, 3]","[30, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2259] -70,28,"[2, 1]","[30, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[3181] -70,29,"[1, 2]","[50, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[2640] -70,30,"[3, 4]","[30, 0]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[7774] -70,31,"[2, 1]","[20, 40]","[1, 2]",[1],[1],[2],[20],[2],[2],[2],[5002] -70,32,"[1, 2]","[10, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[2],[2117] -70,33,"[1, 2]","[20, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[3255] -70,34,"[4, 3]","[80, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[1980] -70,35,"[4, 3]","[80, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[2172] -70,36,"[3, 4]","[10, 80]","[2, 2]",[1],[5],[4],[400],[8],[2],[4],[1909] -70,37,"[3, 4]","[30, 90]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[5655] -70,38,"[3, 4]","[10, 90]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[2226] -70,39,"[2, 1]","[50, 40]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2396] -70,40,"[2, 1]","[50, 40]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2014] -70,41,"[1, 2]","[30, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[2299] -70,42,"[4, 3]","[80, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[2165] -70,43,"[3, 4]","[30, 80]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[2148] -70,44,"[2, 1]","[80, 10]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2306] -70,45,"[3, 4]","[20, 60]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[3942] -70,46,"[1, 2]","[0, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[2090] -70,47,"[1, 2]","[80, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2780] -70,48,"[1, 2]","[70, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1957] -70,49,"[4, 3]","[40, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[2093] -70,50,"[1, 2]","[50, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1822] -70,51,"[1, 2]","[30, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[1925] -70,52,"[4, 3]","[30, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[1790] -70,53,"[1, 2]","[10, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1791] -70,54,"[2, 1]","[40, 20]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2379] -70,55,"[4, 3]","[30, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[1941] -70,56,"[4, 3]","[20, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[5673] -70,57,"[2, 1]","[10, 30]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2238] -70,58,"[2, 1]","[10, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[1886] -70,59,"[2, 1]","[30, 10]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[1856] -70,60,"[3, 4]","[10, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[4274] -70,61,"[1, 2]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[2584] -70,62,"[3, 4]","[30, 20]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[4541] -70,63,"[1, 2]","[60, 0]","[1, 2]",[2],[1],[2],[0],[0],[2],[1],[1913] -70,64,"[4, 3]","[20, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1746] -70,65,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2042] -70,66,"[3, 4]","[90, 20]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[2950] -70,67,"[3, 4]","[80, 40]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[5153] -70,68,"[3, 4]","[80, 40]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2260] -70,69,"[4, 3]","[10, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[2162] -70,70,"[1, 2]","[80, 0]","[1, 2]",[2],[1],[2],[0],[0],[2],[1],[2703] -70,71,"[2, 1]","[10, 30]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[1848] -70,72,"[1, 2]","[20, 0]","[1, 2]",[2],[1],[2],[0],[0],[2],[1],[2107] -70,73,"[4, 3]","[0, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1772] -70,74,"[1, 2]","[30, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2032] -70,75,"[2, 1]","[40, 0]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2074] -70,76,"[3, 4]","[0, 10]","[2, 1]",[2],[1],[3],[0],[0],[1],[4],[2618] -70,77,"[2, 1]","[0, 20]","[1, 2]",[2],[1],[2],[0],[0],[2],[2],[2125] -70,78,"[2, 1]","[40, 20]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2173] -70,79,"[2, 1]","[80, 30]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[2038] -70,80,"[3, 4]","[0, 80]","[2, 1]",[2],[5],[3],[0],[0],[1],[4],[1867] -71,1,"[4, 3]","[40, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[7403] -71,2,"[3, 4]","[80, 40]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1885] -71,3,"[2, 1]","[50, 80]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[2219] -71,4,"[3, 4]","[80, 40]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1768] -71,5,"[1, 2]","[70, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[3019] -71,6,"[3, 4]","[20, 40]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[2552] -71,7,"[2, 1]","[20, 10]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[2218] -71,8,"[4, 3]","[10, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[2969] -71,9,"[2, 1]","[0, 30]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[2235] -71,10,"[4, 3]","[40, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[2001] -71,11,"[1, 2]","[10, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[2435] -71,12,"[1, 2]","[20, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[2252] -71,13,"[3, 4]","[30, 10]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[2268] -71,14,"[4, 3]","[10, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[2169] -71,15,"[2, 1]","[30, 60]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[1968] -71,16,"[1, 2]","[90, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2402] -71,17,"[2, 1]","[50, 70]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[1918] -71,18,"[1, 2]","[80, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[2285] -71,19,"[1, 2]","[60, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[2318] -71,20,"[1, 2]","[20, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[2285] -71,21,"[2, 1]","[40, 50]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2085] -71,22,"[3, 4]","[50, 30]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[2302] -71,23,"[1, 2]","[40, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[2252] -71,24,"[2, 1]","[30, 50]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[1919] -71,25,"[1, 2]","[50, 10]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2519] -71,26,"[4, 3]","[10, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[2151] -71,27,"[1, 2]","[50, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[2635] -71,28,"[2, 1]","[60, 90]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[1951] -71,29,"[4, 3]","[50, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2219] -71,30,"[3, 4]","[80, 70]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2452] -71,31,"[4, 3]","[60, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2068] -71,32,"[2, 1]","[80, 70]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[2186] -71,33,"[4, 3]","[50, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2019] -71,34,"[4, 3]","[60, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[2118] -71,35,"[2, 1]","[60, 70]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[1852] -71,36,"[1, 2]","[90, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[1],[2169] -71,37,"[4, 3]","[70, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2185] -71,38,"[4, 3]","[70, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2052] -71,39,"[1, 2]","[50, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[2386] -71,40,"[1, 2]","[60, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[2169] -71,41,"[1, 2]","[50, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[9004] -71,42,"[4, 3]","[80, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[2268] -71,43,"[1, 2]","[50, 80]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2101] -71,44,"[1, 2]","[20, 60]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[1969] -71,45,"[2, 1]","[70, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[2019] -71,46,"[3, 4]","[30, 70]","[2, 1]",[2],[1],[3],[30],[3],[1],[4],[2101] -71,47,"[3, 4]","[20, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2152] -71,48,"[2, 1]","[40, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2119] -71,49,"[4, 3]","[60, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[2502] -71,50,"[3, 4]","[90, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2402] -71,51,"[1, 2]","[80, 50]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2336] -71,52,"[3, 4]","[60, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[1951] -71,53,"[1, 2]","[80, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[2853] -71,54,"[3, 4]","[90, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[1985] -71,55,"[4, 3]","[70, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[3402] -71,56,"[3, 4]","[60, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2168] -71,57,"[2, 1]","[60, 40]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2118] -71,58,"[2, 1]","[80, 20]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[1968] -71,59,"[3, 4]","[20, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[1935] -71,60,"[4, 3]","[80, 20]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2001] -71,61,"[2, 1]","[80, 0]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[2069] -71,62,"[2, 1]","[90, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[1852] -71,63,"[1, 2]","[30, 90]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[2034] -71,64,"[4, 3]","[90, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[2318] -71,65,"[1, 2]","[20, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2502] -71,66,"[2, 1]","[50, 10]","[1, 2]",[2],[5],[2],[250],[5],[2],[1],[2268] -71,67,"[3, 4]","[0, 50]","[2, 1]",[2],[1],[3],[0],[0],[1],[4],[1985] -71,68,"[1, 2]","[20, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[1701] -71,69,"[2, 1]","[40, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[1935] -71,70,"[4, 3]","[60, 30]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2069] -71,71,"[1, 2]","[20, 60]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[2019] -71,72,"[2, 1]","[30, 0]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[1835] -71,73,"[4, 3]","[20, 0]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1985] -71,74,"[4, 3]","[30, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2386] -71,75,"[3, 4]","[30, 20]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[2502] -71,76,"[2, 1]","[20, 30]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[2218] -71,77,"[3, 4]","[60, 50]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2369] -71,78,"[3, 4]","[70, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1886] -71,79,"[1, 2]","[60, 90]","[1, 2]",[2],[1],[2],[90],[9],[2],[2],[1969] -71,80,"[2, 1]","[60, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2502] -72,1,"[3, 4]","[10, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[3359] -72,2,"[2, 1]","[50, 20]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[2301] -72,3,"[1, 2]","[40, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[1926] -72,4,"[2, 1]","[70, 40]","[1, 1]",[1],[5],[1],[200],[4],[1],[1],[1751] -72,5,"[4, 3]","[70, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[3026] -72,6,"[3, 4]","[40, 70]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[2360] -72,7,"[3, 4]","[50, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[1776] -72,8,"[2, 1]","[90, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2210] -72,9,"[1, 2]","[90, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[1],[3493] -72,10,"[2, 1]","[90, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[3418] -72,11,"[4, 3]","[80, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[4118] -72,12,"[3, 4]","[20, 50]","[2, 2]",[1],[5],[4],[250],[5],[2],[4],[9952] -72,13,"[2, 1]","[40, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[7561] -72,14,"[2, 1]","[0, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1701] -72,15,"[2, 1]","[10, 30]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[1335] -72,16,"[4, 3]","[0, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[1743] -72,17,"[4, 3]","[50, 20]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[1843] -72,18,"[4, 3]","[60, 10]","[2, 1]",[1],[5],[3],[50],[1],[1],[3],[1585] -72,19,"[3, 4]","[10, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[1776] -72,20,"[1, 2]","[30, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[1451] -72,21,"[1, 2]","[30, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[1308] -72,22,"[1, 2]","[30, 40]","[1, 1]",[1],[1],[1],[30],[3],[1],[2],[1626] -72,23,"[4, 3]","[30, 50]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[1725] -72,24,"[2, 1]","[10, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1484] -72,25,"[1, 2]","[70, 10]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1510] -72,26,"[2, 1]","[20, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1651] -72,27,"[4, 3]","[20, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2476] -72,28,"[4, 3]","[20, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1459] -72,29,"[1, 2]","[70, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[1],[1551] -72,30,"[3, 4]","[90, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[1609] -72,31,"[4, 3]","[20, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1592] -72,32,"[4, 3]","[10, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1442] -72,33,"[2, 1]","[20, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1409] -72,34,"[3, 4]","[50, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[5969] -72,35,"[3, 4]","[30, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[1850] -72,36,"[1, 2]","[60, 30]","[1, 2]",[1],[1],[2],[30],[3],[2],[1],[1567] -72,37,"[2, 1]","[40, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1601] -72,38,"[1, 2]","[80, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[1785] -72,39,"[4, 3]","[60, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1451] -72,40,"[2, 1]","[50, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1326] -72,41,"[1, 2]","[50, 80]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1858] -72,42,"[2, 1]","[80, 70]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[1501] -72,43,"[1, 2]","[70, 50]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2251] -72,44,"[3, 4]","[70, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1535] -72,45,"[1, 2]","[80, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1584] -72,46,"[4, 3]","[90, 80]","[2, 2]",[2],[1],[4],[90],[9],[2],[3],[1459] -72,47,"[1, 2]","[70, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[2018] -72,48,"[1, 2]","[80, 70]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[3734] -72,49,"[2, 1]","[80, 70]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[1343] -72,50,"[2, 1]","[50, 70]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[1717] -72,51,"[4, 3]","[50, 30]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2592] -72,52,"[4, 3]","[40, 20]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[1399] -72,53,"[2, 1]","[40, 20]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1434] -72,54,"[3, 4]","[0, 40]","[2, 1]",[2],[1],[3],[0],[0],[1],[4],[1867] -72,55,"[2, 1]","[20, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[2018] -72,56,"[3, 4]","[30, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[1426] -72,57,"[2, 1]","[60, 40]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[3827] -72,58,"[3, 4]","[70, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[6144] -72,59,"[1, 2]","[50, 80]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[4610] -72,60,"[4, 3]","[80, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[3959] -72,61,"[1, 2]","[80, 50]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[3068] -72,62,"[1, 2]","[50, 40]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[1426] -72,63,"[1, 2]","[60, 30]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[2301] -72,64,"[4, 3]","[60, 30]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[4118] -72,65,"[2, 1]","[80, 20]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[2209] -72,66,"[3, 4]","[30, 50]","[2, 1]",[2],[5],[3],[150],[3],[1],[4],[2018] -72,67,"[1, 2]","[30, 60]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[4059] -72,68,"[2, 1]","[50, 20]","[1, 2]",[2],[1],[2],[50],[5],[2],[1],[1618] -72,69,"[3, 4]","[30, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[1617] -72,70,"[2, 1]","[60, 20]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[1776] -72,71,"[2, 1]","[70, 10]","[1, 2]",[2],[5],[2],[350],[7],[2],[1],[1593] -72,72,"[2, 1]","[40, 0]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[1410] -72,73,"[3, 4]","[10, 30]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[1676] -72,74,"[4, 3]","[20, 10]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2268] -72,75,"[1, 2]","[30, 20]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[8511] -72,76,"[2, 1]","[10, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[3001] -72,77,"[3, 4]","[60, 10]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[6211] -72,78,"[1, 2]","[90, 0]","[1, 1]",[2],[5],[1],[450],[9],[1],[1],[1526] -72,79,"[4, 3]","[10, 80]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[2450] -72,80,"[2, 1]","[10, 80]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[1492] -73,1,"[3, 4]","[70, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[3168] -73,2,"[2, 1]","[20, 80]","[1, 2]",[1],[5],[2],[100],[2],[2],[2],[2035] -73,3,"[4, 3]","[30, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1651] -73,4,"[4, 3]","[50, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1701] -73,5,"[2, 1]","[80, 50]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1952] -73,6,"[1, 2]","[40, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[1751] -73,7,"[1, 2]","[20, 90]","[1, 1]",[1],[5],[1],[100],[2],[1],[2],[18507] -73,8,"[2, 1]","[80, 10]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[2969] -73,9,"[2, 1]","[60, 0]","[1, 1]",[1],[5],[1],[0],[0],[1],[1],[17926] -73,10,"[3, 4]","[10, 60]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[1351] -73,11,"[2, 1]","[50, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[5286] -73,12,"[1, 2]","[70, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[1919] -73,13,"[1, 2]","[90, 50]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[15626] -73,14,"[2, 1]","[60, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2752] -73,15,"[1, 2]","[40, 60]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[1701] -73,16,"[2, 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1]",[1],[1],[1],[10],[1],[1],[2],[2206] -74,7,"[4, 3]","[40, 0]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[1989] -74,8,"[2, 1]","[80, 40]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[2820] -74,9,"[4, 3]","[60, 50]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2375] -74,10,"[1, 2]","[60, 30]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[3617] -74,11,"[1, 2]","[80, 20]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[2448] -74,12,"[4, 3]","[40, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[2629] -74,13,"[3, 4]","[90, 50]","[2, 1]",[1],[1],[3],[90],[9],[1],[3],[2396] -74,14,"[3, 4]","[90, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[3600] -74,15,"[3, 4]","[90, 70]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[2026] -74,16,"[1, 2]","[60, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[3063] -74,17,"[2, 1]","[40, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2615] -74,18,"[4, 3]","[50, 30]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[3152] -74,19,"[1, 2]","[50, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2452] -74,20,"[3, 4]","[40, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[4098] -74,21,"[1, 2]","[10, 40]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[3021] -74,22,"[2, 1]","[30, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[3929] -74,23,"[3, 4]","[0, 20]","[2, 1]",[1],[1],[3],[0],[0],[1],[4],[2498] -74,24,"[2, 1]","[20, 10]","[1, 2]",[1],[5],[2],[100],[2],[2],[1],[3262] -74,25,"[3, 4]","[30, 10]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[1877] -74,26,"[3, 4]","[40, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[2067] -74,27,"[2, 1]","[30, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[3719] -74,28,"[4, 3]","[30, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[1934] -74,29,"[3, 4]","[0, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[2090] -74,30,"[2, 1]","[20, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[2257] -74,31,"[2, 1]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[1943] -74,32,"[4, 3]","[30, 50]","[2, 2]",[1],[5],[4],[150],[3],[2],[4],[2439] -74,33,"[4, 3]","[40, 80]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1935] -74,34,"[2, 1]","[30, 80]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[2724] -74,35,"[3, 4]","[80, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2143] -74,36,"[1, 2]","[70, 10]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2748] -74,37,"[1, 2]","[60, 20]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2474] -74,38,"[2, 1]","[0, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2160] -74,39,"[1, 2]","[70, 30]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1875] -74,40,"[1, 2]","[60, 30]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2066] -74,41,"[4, 3]","[50, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[9387] -74,42,"[2, 1]","[80, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[2126] -74,43,"[4, 3]","[40, 20]","[2, 2]",[2],[5],[4],[200],[4],[2],[3],[1718] -74,44,"[3, 4]","[10, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[1991] -74,45,"[1, 2]","[20, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2788] -74,46,"[3, 4]","[20, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1966] -74,47,"[3, 4]","[30, 30]","[2, 1]",[2],[1],[3],[30],[3],[1],[3],[2315] -74,48,"[3, 4]","[50, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[2026] -74,49,"[1, 2]","[60, 30]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[3443] -74,50,"[2, 1]","[30, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[2],[2051] -74,51,"[2, 1]","[40, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1727] -74,52,"[2, 1]","[40, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1794] -74,53,"[4, 3]","[50, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[3],[2673] -74,54,"[1, 2]","[40, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2283] -74,55,"[1, 2]","[10, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[2],[1826] -74,56,"[2, 1]","[70, 10]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2703] -74,57,"[3, 4]","[20, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[2464] -74,58,"[2, 1]","[60, 20]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[2313] -74,59,"[1, 2]","[10, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2490] -74,60,"[3, 4]","[0, 80]","[2, 1]",[2],[1],[3],[0],[0],[1],[4],[2439] -74,61,"[4, 3]","[90, 20]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[2431] -74,62,"[1, 2]","[20, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[2857] -74,63,"[2, 1]","[70, 10]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2796] -74,64,"[4, 3]","[60, 10]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2315] -74,65,"[1, 2]","[10, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[2126] -74,66,"[4, 3]","[50, 20]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2050] -74,67,"[4, 3]","[20, 30]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[2052] -74,68,"[4, 3]","[10, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[1827] -74,69,"[1, 2]","[20, 10]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[2050] -74,70,"[2, 1]","[10, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[2589] -74,71,"[2, 1]","[30, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1842] -74,72,"[2, 1]","[10, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[1753] -74,73,"[2, 1]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2191] -74,74,"[4, 3]","[10, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[3577] -74,75,"[2, 1]","[10, 20]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[1877] -74,76,"[2, 1]","[40, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[2141] -74,77,"[3, 4]","[0, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[4],[1778] -74,78,"[4, 3]","[70, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[2599] -74,79,"[3, 4]","[30, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2146] -74,80,"[4, 3]","[20, 30]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[2325] -58,1,"[1, 2]","[10, 50]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[3522] -58,2,"[1, 2]","[0, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[1767] -58,3,"[3, 4]","[0, 60]","[2, 1]",[1],[5],[3],[0],[0],[1],[4],[2325] -58,4,"[4, 3]","[70, 20]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2373] -58,5,"[4, 3]","[50, 0]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2208] -58,6,"[2, 1]","[90, 0]","[1, 2]",[1],[5],[2],[450],[9],[2],[1],[3573] -58,7,"[2, 1]","[50, 20]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[1973] -58,8,"[2, 1]","[30, 10]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[1957] -58,9,"[4, 3]","[60, 10]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[6139] -58,10,"[1, 2]","[10, 30]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[3489] -58,11,"[3, 4]","[20, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[7388] -58,12,"[4, 3]","[30, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[3],[3140] -58,13,"[1, 2]","[20, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[3657] -58,14,"[2, 1]","[0, 10]","[1, 2]",[1],[5],[2],[0],[0],[2],[2],[2141] -58,15,"[4, 3]","[0, 0]","[2, 1]",[1],[1],[3],[0],[0],[1],[4],[3973] -58,16,"[2, 1]","[10, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[2],[1973] -58,17,"[1, 2]","[0, 10]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[2457] -58,18,"[3, 4]","[10, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[6207] -58,19,"[2, 1]","[20, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[2124] -58,20,"[3, 4]","[20, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[3],[1975] -58,21,"[3, 4]","[30, 0]","[2, 2]",[1],[1],[4],[0],[0],[2],[3],[1824] -58,22,"[3, 4]","[50, 20]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[3123] -58,23,"[4, 3]","[20, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1956] -58,24,"[4, 3]","[30, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1774] -58,25,"[3, 4]","[70, 40]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[1975] -58,26,"[2, 1]","[40, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[6855] -58,27,"[1, 2]","[30, 20]","[1, 1]",[1],[1],[1],[30],[3],[1],[1],[2090] -58,28,"[1, 2]","[50, 20]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[1857] -58,29,"[3, 4]","[50, 20]","[2, 1]",[1],[5],[3],[250],[5],[1],[3],[3056] -58,30,"[4, 3]","[20, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1989] -58,31,"[1, 2]","[60, 10]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[3490] -58,32,"[3, 4]","[40, 20]","[2, 2]",[1],[5],[4],[100],[2],[2],[3],[2140] -58,33,"[3, 4]","[70, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[2322] -58,34,"[2, 1]","[10, 90]","[1, 2]",[1],[5],[2],[50],[1],[2],[2],[1657] -58,35,"[4, 3]","[20, 90]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[2123] -58,36,"[4, 3]","[30, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[1790] -58,37,"[4, 3]","[20, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1907] -58,38,"[2, 1]","[60, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[2956] -58,39,"[1, 2]","[70, 40]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1856] -58,40,"[2, 1]","[40, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[3773] -58,41,"[3, 4]","[70, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[2147] -58,42,"[4, 3]","[10, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2757] -58,43,"[4, 3]","[20, 80]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[1774] -58,44,"[2, 1]","[10, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[3490] -58,45,"[2, 1]","[10, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2023] -58,46,"[1, 2]","[80, 20]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1957] -58,47,"[3, 4]","[80, 10]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2424] -58,48,"[1, 2]","[70, 30]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[1957] -58,49,"[3, 4]","[50, 10]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[2106] -58,50,"[2, 1]","[10, 80]","[1, 1]",[2],[1],[1],[80],[8],[1],[2],[2240] -58,51,"[2, 1]","[20, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[2005] -58,52,"[4, 3]","[30, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[2007] -58,53,"[4, 3]","[50, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[2040] -58,54,"[3, 4]","[90, 60]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[1790] -58,55,"[3, 4]","[50, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[3],[1956] -58,56,"[3, 4]","[40, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2007] -58,57,"[1, 2]","[60, 70]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1824] -58,58,"[4, 3]","[70, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[2839] -58,59,"[3, 4]","[80, 30]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[2206] -58,60,"[1, 2]","[70, 20]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[1857] -58,61,"[4, 3]","[30, 70]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2190] -58,62,"[2, 1]","[10, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2690] -58,63,"[3, 4]","[80, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[1907] -58,64,"[4, 3]","[50, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1792] -58,65,"[2, 1]","[50, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[4123] -58,66,"[2, 1]","[80, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[1893] -58,67,"[2, 1]","[60, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[1857] -58,68,"[1, 2]","[50, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[1991] -58,69,"[4, 3]","[40, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[1892] -58,70,"[3, 4]","[40, 60]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1807] -58,71,"[4, 3]","[90, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[3157] -58,72,"[3, 4]","[90, 70]","[2, 1]",[2],[5],[3],[450],[9],[1],[3],[2273] -58,73,"[4, 3]","[80, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1923] -58,74,"[4, 3]","[50, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[4],[1774] -58,75,"[1, 2]","[70, 80]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2123] -58,76,"[2, 1]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[1841] -58,77,"[2, 1]","[70, 20]","[1, 1]",[2],[5],[1],[100],[2],[1],[1],[2157] -58,78,"[1, 2]","[10, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[1892] -58,79,"[4, 3]","[70, 20]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[2340] -58,80,"[4, 3]","[60, 80]","[2, 2]",[2],[1],[4],[60],[6],[2],[4],[1990] -74,1,"[2, 1]","[10, 90]","[1, 2]",[1],[1],[2],[10],[1],[2],[2],[9087] -74,2,"[3, 4]","[70, 30]","[2, 1]",[1],[1],[3],[70],[7],[1],[3],[3318] -74,3,"[1, 2]","[80, 40]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2207] -74,4,"[2, 1]","[50, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2588] -74,5,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2158] -74,6,"[4, 3]","[20, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[1719] -74,7,"[3, 4]","[60, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[3476] -74,8,"[4, 3]","[30, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1943] -74,9,"[3, 4]","[10, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2171] -74,10,"[4, 3]","[40, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[2375] -74,11,"[2, 1]","[50, 60]","[1, 2]",[1],[5],[2],[250],[5],[2],[2],[2284] -74,12,"[2, 1]","[70, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[1799] -74,13,"[3, 4]","[80, 70]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1677] -74,14,"[1, 2]","[60, 80]","[1, 2]",[1],[1],[2],[80],[8],[2],[2],[3452] -74,15,"[2, 1]","[80, 40]","[1, 2]",[1],[1],[2],[80],[8],[2],[1],[1949] -74,16,"[1, 2]","[30, 80]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[1753] -74,17,"[4, 3]","[70, 50]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2331] -74,18,"[2, 1]","[40, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[3592] -74,19,"[1, 2]","[90, 40]","[1, 1]",[1],[5],[1],[450],[9],[1],[1],[2124] -74,20,"[2, 1]","[40, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2076] -74,21,"[1, 2]","[70, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[1794] -74,22,"[2, 1]","[0, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[1696] -74,23,"[4, 3]","[0, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[4],[2565] -74,24,"[4, 3]","[20, 30]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[2000] -74,25,"[2, 1]","[0, 0]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[2333] -74,26,"[1, 2]","[10, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[2],[4524] -74,27,"[2, 1]","[30, 20]","[1, 1]",[1],[5],[1],[100],[2],[1],[1],[4288] -74,28,"[1, 2]","[10, 10]","[1, 2]",[1],[1],[2],[10],[1],[2],[1],[2037] -74,29,"[2, 1]","[20, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[3344] -74,30,"[1, 2]","[10, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[2283] -74,31,"[1, 2]","[40, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[1916] -74,32,"[4, 3]","[30, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[1785] -74,33,"[3, 4]","[10, 30]","[2, 1]",[1],[1],[3],[10],[1],[1],[4],[1679] -74,34,"[2, 1]","[70, 0]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[2846] -74,35,"[4, 3]","[50, 0]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[2481] -74,36,"[3, 4]","[10, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[4],[2079] -74,37,"[2, 1]","[60, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[4289] -74,38,"[1, 2]","[10, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[2],[1945] -74,39,"[4, 3]","[90, 0]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[2250] -74,40,"[4, 3]","[70, 10]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[2143] -74,41,"[3, 4]","[20, 60]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[6028] -74,42,"[4, 3]","[80, 30]","[2, 2]",[2],[1],[4],[80],[8],[2],[3],[1951] -74,43,"[2, 1]","[80, 20]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2622] -74,44,"[4, 3]","[80, 10]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[3552] -74,45,"[2, 1]","[80, 20]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[2854] -74,46,"[4, 3]","[40, 30]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[3627] -74,47,"[3, 4]","[50, 40]","[2, 1]",[2],[1],[3],[50],[5],[1],[3],[1849] -74,48,"[1, 2]","[40, 30]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[2613] -74,49,"[1, 2]","[60, 70]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2365] -74,50,"[4, 3]","[70, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[2349] -74,51,"[2, 1]","[40, 70]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[2497] -74,52,"[3, 4]","[60, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[1826] -74,53,"[3, 4]","[40, 10]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[1684] -74,54,"[4, 3]","[10, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1744] -74,55,"[2, 1]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[2416] -74,56,"[2, 1]","[0, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1827] -74,57,"[3, 4]","[10, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[3],[2375] -74,58,"[3, 4]","[20, 10]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[1960] -74,59,"[2, 1]","[40, 30]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[1825] -74,60,"[3, 4]","[60, 30]","[2, 1]",[2],[5],[3],[300],[6],[1],[3],[3377] -74,61,"[3, 4]","[70, 10]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[1875] -74,62,"[1, 2]","[80, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[1],[1892] -74,63,"[3, 4]","[50, 0]","[2, 2]",[2],[5],[4],[0],[0],[2],[3],[2198] -74,64,"[1, 2]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[1],[2490] -74,65,"[2, 1]","[30, 10]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2215] -74,66,"[2, 1]","[40, 20]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[4153] -74,67,"[1, 2]","[0, 60]","[1, 2]",[2],[1],[2],[60],[6],[2],[2],[2630] -74,68,"[1, 2]","[0, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[1927] -74,69,"[4, 3]","[60, 20]","[2, 2]",[2],[1],[4],[60],[6],[2],[3],[2166] -74,70,"[4, 3]","[70, 20]","[2, 2]",[2],[5],[4],[350],[7],[2],[3],[1910] -74,71,"[3, 4]","[10, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[4],[2257] -74,72,"[1, 2]","[10, 40]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[1827] -74,73,"[1, 2]","[0, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[2176] -74,74,"[4, 3]","[40, 10]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2082] -74,75,"[2, 1]","[10, 40]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[2131] -74,76,"[2, 1]","[20, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[2864] -74,77,"[4, 3]","[10, 60]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[2131] -74,78,"[4, 3]","[10, 90]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[2032] -74,79,"[4, 3]","[10, 90]","[2, 1]",[2],[5],[3],[450],[9],[1],[4],[1804] -74,80,"[3, 4]","[70, 10]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[3682] -74,1,"[1, 2]","[60, 40]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[5582] -74,2,"[2, 1]","[30, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[1761] -74,3,"[3, 4]","[10, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[1719] -74,4,"[3, 4]","[40, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[2424] -74,5,"[2, 1]","[0, 50]","[1, 2]",[1],[1],[2],[0],[0],[2],[2],[2149] -74,6,"[4, 3]","[50, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[2091] -74,7,"[1, 2]","[80, 60]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2077] -74,8,"[4, 3]","[80, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[2084] -74,9,"[2, 1]","[50, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[1959] -74,10,"[2, 1]","[60, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1828] -74,11,"[1, 2]","[70, 60]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2366] -74,12,"[2, 1]","[60, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1802] -74,13,"[4, 3]","[70, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1966] -74,14,"[2, 1]","[70, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1785] -74,15,"[1, 2]","[80, 90]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1949] -74,16,"[2, 1]","[90, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1735] -74,17,"[2, 1]","[50, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1695] -74,18,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1718] -74,19,"[3, 4]","[40, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[2722] -74,20,"[4, 3]","[20, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[4],[1975] -74,21,"[1, 2]","[40, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[2157] -74,22,"[4, 3]","[30, 60]","[2, 2]",[1],[1],[4],[30],[3],[2],[4],[1934] -74,23,"[1, 2]","[80, 40]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[2225] -74,24,"[4, 3]","[60, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[2274] -74,25,"[4, 3]","[40, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1852] -74,26,"[4, 3]","[60, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1894] -74,27,"[4, 3]","[60, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1767] -74,28,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1944] -74,29,"[1, 2]","[80, 40]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2074] -74,30,"[4, 3]","[40, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1685] -74,31,"[3, 4]","[80, 50]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[2151] -74,32,"[3, 4]","[80, 40]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1844] -74,33,"[3, 4]","[60, 30]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1892] -74,34,"[1, 2]","[50, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2073] -74,35,"[4, 3]","[40, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2182] -74,36,"[1, 2]","[80, 50]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[2547] -74,37,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[2117] -74,38,"[3, 4]","[90, 70]","[2, 1]",[1],[1],[3],[90],[9],[1],[3],[2780] -74,39,"[2, 1]","[50, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[1903] -74,40,"[2, 1]","[70, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[1886] -74,41,"[2, 1]","[80, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[3798] -74,42,"[2, 1]","[80, 90]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[2233] -74,43,"[4, 3]","[70, 80]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[2265] -74,44,"[3, 4]","[80, 40]","[2, 2]",[2],[1],[4],[40],[4],[2],[3],[2862] -74,45,"[1, 2]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[2688] -74,46,"[2, 1]","[70, 70]","[1, 1]",[2],[1],[1],[70],[7],[1],[2],[2076] -74,47,"[3, 4]","[80, 70]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2009] -74,48,"[4, 3]","[40, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[1966] -74,49,"[1, 2]","[70, 60]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[2032] -74,50,"[3, 4]","[60, 40]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2024] -74,51,"[4, 3]","[30, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2109] -74,52,"[2, 1]","[30, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1994] -74,53,"[1, 2]","[0, 30]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[1952] -74,54,"[4, 3]","[10, 20]","[2, 2]",[2],[5],[4],[50],[1],[2],[4],[2300] -74,55,"[3, 4]","[20, 0]","[2, 1]",[2],[1],[3],[20],[2],[1],[3],[2167] -74,56,"[2, 1]","[0, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[2449] -74,57,"[1, 2]","[0, 10]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[1977] -74,58,"[1, 2]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2032] -74,59,"[4, 3]","[30, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[4],[2132] -74,60,"[4, 3]","[30, 10]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1893] -74,61,"[3, 4]","[40, 30]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[4289] -74,62,"[2, 1]","[40, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[2142] -74,63,"[1, 2]","[60, 40]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[2001] -74,64,"[1, 2]","[50, 60]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2157] -74,65,"[4, 3]","[30, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[2267] -74,66,"[4, 3]","[30, 90]","[2, 1]",[2],[1],[3],[90],[9],[1],[4],[2258] -74,67,"[2, 1]","[40, 80]","[1, 1]",[2],[5],[1],[400],[8],[1],[2],[2101] -74,68,"[3, 4]","[70, 20]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[2973] -74,69,"[4, 3]","[10, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[4],[1852] -74,70,"[1, 2]","[80, 60]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2211] -74,71,"[3, 4]","[70, 70]","[2, 1]",[2],[5],[3],[350],[7],[1],[3],[2945] -74,72,"[4, 3]","[70, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2085] -74,73,"[1, 2]","[20, 80]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[2855] -74,74,"[3, 4]","[60, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[3740] -74,75,"[3, 4]","[70, 80]","[2, 2]",[2],[5],[4],[400],[8],[2],[4],[2043] -74,76,"[2, 1]","[50, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1868] -74,77,"[1, 2]","[50, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2853] -74,78,"[4, 3]","[60, 80]","[2, 1]",[2],[1],[3],[80],[8],[1],[4],[2076] -74,79,"[4, 3]","[90, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1961] -74,80,"[1, 2]","[40, 80]","[1, 1]",[2],[1],[1],[40],[4],[1],[2],[3286] -29,1,"[1, 2]","[0, 70]","[1, 1]",[1],[5],[1],[0],[0],[1],[2],[9547] -29,2,"[1, 2]","[20, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[1839] -29,3,"[3, 4]","[10, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[4],[2325] -29,4,"[4, 3]","[30, 20]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[4756] -29,5,"[2, 1]","[30, 20]","[1, 1]",[1],[1],[1],[20],[2],[1],[1],[1989] -29,6,"[1, 2]","[10, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[2438] -29,7,"[4, 3]","[60, 20]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[1899] -29,8,"[2, 1]","[70, 10]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[1972] -29,9,"[3, 4]","[0, 40]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[2289] -29,10,"[2, 1]","[40, 20]","[1, 2]",[1],[5],[2],[200],[4],[2],[1],[1822] -29,11,"[1, 2]","[20, 60]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[2072] -29,12,"[2, 1]","[50, 40]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[1789] -29,13,"[4, 3]","[40, 30]","[2, 2]",[1],[5],[4],[200],[4],[2],[3],[1888] -29,14,"[2, 1]","[40, 20]","[1, 2]",[1],[1],[2],[40],[4],[2],[1],[1823] -29,15,"[4, 3]","[30, 20]","[2, 2]",[1],[5],[4],[150],[3],[2],[3],[1705] -29,16,"[1, 2]","[10, 50]","[1, 1]",[1],[5],[1],[50],[1],[1],[2],[2056] -29,17,"[2, 1]","[70, 10]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1956] -29,18,"[2, 1]","[50, 10]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[1756] -29,19,"[4, 3]","[20, 30]","[2, 2]",[1],[5],[4],[100],[2],[2],[4],[2356] -29,20,"[2, 1]","[0, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1921] -29,21,"[2, 1]","[0, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1939] -29,22,"[3, 4]","[90, 0]","[2, 1]",[1],[1],[3],[90],[9],[1],[3],[1856] -29,23,"[4, 3]","[0, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1738] -29,24,"[1, 2]","[60, 10]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[2056] -29,25,"[1, 2]","[70, 20]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[2372] -29,26,"[1, 2]","[80, 20]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2174] -29,27,"[2, 1]","[0, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2255] -29,28,"[4, 3]","[40, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[1873] -29,29,"[3, 4]","[80, 30]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[1822] -29,30,"[2, 1]","[10, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1823] -29,31,"[3, 4]","[20, 0]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[1673] -29,32,"[1, 2]","[50, 20]","[1, 2]",[1],[1],[2],[20],[2],[2],[1],[2073] -29,33,"[4, 3]","[60, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[4],[2050] -29,34,"[2, 1]","[30, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1887] -29,35,"[3, 4]","[50, 60]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1721] -29,36,"[1, 2]","[60, 80]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1937] -29,37,"[3, 4]","[80, 80]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[1989] -29,38,"[3, 4]","[80, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1836] -29,39,"[3, 4]","[70, 60]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1671] -29,40,"[3, 4]","[60, 50]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1723] -29,41,"[4, 3]","[0, 50]","[2, 1]",[2],[5],[3],[250],[5],[1],[4],[1976] -29,42,"[1, 2]","[50, 10]","[1, 1]",[2],[5],[1],[250],[5],[1],[1],[1804] -29,43,"[1, 2]","[30, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[1873] -29,44,"[2, 1]","[10, 20]","[1, 2]",[2],[1],[2],[10],[1],[2],[2],[2452] -29,45,"[1, 2]","[30, 10]","[1, 1]",[2],[1],[1],[30],[3],[1],[1],[1706] -29,46,"[4, 3]","[10, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1671] -29,47,"[4, 3]","[10, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1755] -29,48,"[1, 2]","[10, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[3339] -29,49,"[3, 4]","[10, 20]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1622] -29,50,"[4, 3]","[10, 0]","[2, 2]",[2],[5],[4],[50],[1],[2],[3],[2122] -29,51,"[4, 3]","[30, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[1655] -29,52,"[4, 3]","[10, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[1654] -29,53,"[2, 1]","[30, 0]","[1, 1]",[2],[1],[1],[0],[0],[1],[1],[1871] -29,54,"[2, 1]","[30, 20]","[1, 2]",[2],[1],[2],[30],[3],[2],[1],[2056] -29,55,"[4, 3]","[40, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1738] -29,56,"[3, 4]","[50, 0]","[2, 2]",[2],[5],[4],[0],[0],[2],[3],[1939] -29,57,"[4, 3]","[20, 40]","[2, 1]",[2],[5],[3],[200],[4],[1],[4],[1706] -29,58,"[4, 3]","[0, 10]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1672] -29,59,"[1, 2]","[0, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[2],[3272] -29,60,"[4, 3]","[10, 30]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1839] -29,61,"[4, 3]","[0, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[4],[1672] -29,62,"[4, 3]","[30, 10]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1637] -29,63,"[4, 3]","[40, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1773] -29,64,"[2, 1]","[30, 60]","[1, 2]",[2],[5],[2],[150],[3],[2],[2],[1956] -29,65,"[1, 2]","[90, 40]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2072] -29,66,"[2, 1]","[20, 60]","[1, 2]",[2],[1],[2],[20],[2],[2],[2],[1787] -29,67,"[2, 1]","[20, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1972] -29,68,"[2, 1]","[40, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1739] -29,69,"[3, 4]","[80, 40]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[1989] -29,70,"[1, 2]","[80, 20]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[1886] -29,71,"[1, 2]","[70, 20]","[1, 1]",[2],[1],[1],[70],[7],[1],[1],[1801] -29,72,"[1, 2]","[60, 10]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[1787] -29,73,"[1, 2]","[70, 30]","[1, 1]",[2],[5],[1],[350],[7],[1],[1],[1771] -29,74,"[3, 4]","[80, 30]","[2, 1]",[2],[1],[3],[80],[8],[1],[3],[1967] -29,75,"[2, 1]","[20, 90]","[1, 1]",[2],[5],[1],[450],[9],[1],[2],[2202] -29,76,"[2, 1]","[60, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[2007] -29,77,"[3, 4]","[70, 60]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[2005] -29,78,"[4, 3]","[40, 50]","[2, 1]",[2],[1],[3],[50],[5],[1],[4],[1755] -29,79,"[1, 2]","[60, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[1],[1872] -29,80,"[2, 1]","[40, 70]","[1, 1]",[2],[5],[1],[350],[7],[1],[2],[1921] -65,1,"[4, 3]","[40, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[3563] -65,2,"[3, 4]","[60, 40]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[2460] -65,3,"[1, 2]","[50, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[2],[2177] -65,4,"[2, 1]","[50, 40]","[1, 2]",[1],[5],[2],[250],[5],[2],[1],[2075] -65,5,"[3, 4]","[0, 50]","[2, 1]",[1],[1],[3],[0],[0],[1],[4],[2008] -65,6,"[1, 2]","[10, 60]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[2058] -65,7,"[4, 3]","[40, 0]","[2, 2]",[1],[1],[4],[40],[4],[2],[3],[1842] -65,8,"[2, 1]","[80, 40]","[1, 2]",[1],[5],[2],[400],[8],[2],[1],[2762] -65,9,"[4, 3]","[60, 50]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1942] -65,10,"[1, 2]","[60, 30]","[1, 1]",[1],[1],[1],[60],[6],[1],[1],[1690] -65,11,"[1, 2]","[80, 20]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1707] -65,12,"[4, 3]","[40, 80]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1874] -65,13,"[3, 4]","[90, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[1741] -65,14,"[3, 4]","[90, 90]","[2, 2]",[1],[5],[4],[450],[9],[2],[3],[3414] -65,15,"[3, 4]","[90, 70]","[2, 2]",[1],[5],[4],[350],[7],[2],[3],[1758] -65,16,"[1, 2]","[60, 70]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2594] -65,17,"[2, 1]","[40, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[2],[1724] -65,18,"[4, 3]","[50, 30]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[2142] -65,19,"[1, 2]","[50, 50]","[1, 2]",[1],[1],[2],[50],[5],[2],[1],[1992] -65,20,"[3, 4]","[40, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[3],[1707] -65,21,"[1, 2]","[10, 40]","[1, 1]",[1],[1],[1],[10],[1],[1],[2],[1641] -65,22,"[2, 1]","[30, 10]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[1710] -65,23,"[3, 4]","[0, 20]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[2711] -65,24,"[2, 1]","[20, 10]","[1, 1]",[1],[5],[1],[50],[1],[1],[1],[1808] -65,25,"[3, 4]","[30, 10]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[2727] -65,26,"[3, 4]","[40, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[3],[2008] -65,27,"[2, 1]","[30, 20]","[1, 2]",[1],[5],[2],[150],[3],[2],[1],[2076] -65,28,"[4, 3]","[30, 10]","[2, 1]",[1],[1],[3],[10],[1],[1],[3],[2042] -65,29,"[3, 4]","[0, 40]","[2, 2]",[1],[5],[4],[200],[4],[2],[4],[1841] -65,30,"[2, 1]","[20, 10]","[1, 1]",[1],[1],[1],[10],[1],[1],[1],[1875] -65,31,"[2, 1]","[40, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[1673] -65,32,"[4, 3]","[30, 50]","[2, 1]",[1],[5],[3],[250],[5],[1],[4],[2127] -65,33,"[4, 3]","[40, 80]","[2, 2]",[1],[1],[4],[40],[4],[2],[4],[1992] -65,34,"[2, 1]","[30, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1791] -65,35,"[3, 4]","[80, 30]","[2, 2]",[1],[1],[4],[30],[3],[2],[3],[2243] -65,36,"[1, 2]","[70, 10]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1610] -65,37,"[1, 2]","[60, 20]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[1673] -65,38,"[2, 1]","[0, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[2143] -65,39,"[1, 2]","[70, 30]","[1, 1]",[1],[1],[1],[70],[7],[1],[1],[1959] -65,40,"[1, 2]","[60, 30]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2210] -65,41,"[4, 3]","[50, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2994] -65,42,"[2, 1]","[80, 10]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[2062] -65,43,"[4, 3]","[40, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[3],[1791] -65,44,"[3, 4]","[10, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[4],[2143] -65,45,"[1, 2]","[20, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[2127] -65,46,"[3, 4]","[20, 40]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[2176] -65,47,"[3, 4]","[30, 30]","[2, 2]",[2],[1],[4],[30],[3],[2],[3],[1774] -65,48,"[3, 4]","[50, 20]","[2, 2]",[2],[1],[4],[20],[2],[2],[3],[1925] -65,49,"[1, 2]","[60, 30]","[1, 2]",[2],[5],[2],[150],[3],[2],[1],[2293] -65,50,"[2, 1]","[30, 30]","[1, 2]",[2],[1],[2],[30],[3],[2],[2],[1775] -65,51,"[2, 1]","[40, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[1641] -65,52,"[2, 1]","[40, 60]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[1942] -65,53,"[4, 3]","[50, 40]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[2108] -65,54,"[1, 2]","[40, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[1708] -65,55,"[1, 2]","[10, 40]","[1, 1]",[2],[1],[1],[10],[1],[1],[2],[1641] -65,56,"[2, 1]","[70, 10]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1959] -65,57,"[3, 4]","[20, 70]","[2, 1]",[2],[1],[3],[20],[2],[1],[4],[1891] -65,58,"[2, 1]","[60, 20]","[1, 2]",[2],[5],[2],[300],[6],[2],[1],[1925] -65,59,"[1, 2]","[10, 50]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[1859] -65,60,"[3, 4]","[0, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2210] -65,61,"[4, 3]","[90, 20]","[2, 2]",[2],[5],[4],[450],[9],[2],[3],[1658] -65,62,"[1, 2]","[20, 60]","[1, 1]",[2],[5],[1],[100],[2],[1],[2],[2779] -65,63,"[2, 1]","[70, 10]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1892] -65,64,"[4, 3]","[60, 10]","[2, 2]",[2],[5],[4],[300],[6],[2],[3],[2025] -65,65,"[1, 2]","[10, 50]","[1, 2]",[2],[5],[2],[250],[5],[2],[2],[3180] -65,66,"[4, 3]","[50, 20]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1942] -65,67,"[4, 3]","[20, 30]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[2226] -65,68,"[4, 3]","[10, 20]","[2, 1]",[2],[5],[3],[100],[2],[1],[4],[1691] -65,69,"[1, 2]","[20, 10]","[1, 2]",[2],[5],[2],[50],[1],[2],[1],[2293] -65,70,"[2, 1]","[10, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[2260] -65,71,"[2, 1]","[30, 60]","[1, 1]",[2],[5],[1],[300],[6],[1],[2],[1942] -65,72,"[2, 1]","[10, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[2],[1641] -65,73,"[2, 1]","[10, 10]","[1, 1]",[2],[5],[1],[50],[1],[1],[2],[1556] -65,74,"[4, 3]","[10, 10]","[2, 2]",[2],[1],[4],[10],[1],[2],[4],[1825] -65,75,"[2, 1]","[10, 20]","[1, 1]",[2],[1],[1],[20],[2],[1],[2],[2009] -65,76,"[2, 1]","[40, 10]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[1758] -65,77,"[3, 4]","[0, 50]","[2, 1]",[2],[5],[3],[0],[0],[1],[4],[1842] -65,78,"[4, 3]","[70, 10]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[1893] -65,79,"[3, 4]","[30, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[3330] -65,80,"[4, 3]","[20, 30]","[2, 2]",[2],[5],[4],[100],[2],[2],[4],[1826] -65,1,"[2, 1]","[20, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[3482] -65,2,"[4, 3]","[20, 90]","[2, 2]",[1],[1],[4],[20],[2],[2],[4],[1774] -65,3,"[2, 1]","[10, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1924] -65,4,"[1, 2]","[70, 20]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[1858] -65,5,"[3, 4]","[80, 0]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1843] -65,6,"[3, 4]","[80, 10]","[2, 1]",[1],[5],[3],[400],[8],[1],[3],[1908] -65,7,"[3, 4]","[70, 10]","[2, 1]",[1],[5],[3],[350],[7],[1],[3],[1808] -65,8,"[4, 3]","[30, 40]","[2, 1]",[1],[1],[3],[40],[4],[1],[4],[1942] -65,9,"[2, 1]","[40, 70]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[3953] -65,10,"[2, 1]","[50, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1674] -65,11,"[3, 4]","[60, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[3],[1991] -65,12,"[4, 3]","[60, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1942] -65,13,"[3, 4]","[90, 40]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[1724] -65,14,"[2, 1]","[60, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[2578] -65,15,"[2, 1]","[20, 70]","[1, 1]",[1],[1],[1],[70],[7],[1],[2],[1640] -65,16,"[1, 2]","[80, 30]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[1774] -65,17,"[2, 1]","[50, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[1658] -65,18,"[3, 4]","[80, 60]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[1975] -65,19,"[2, 1]","[70, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[1824] -65,20,"[1, 2]","[50, 80]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[1992] -65,21,"[2, 1]","[60, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[2260] -65,22,"[2, 1]","[70, 50]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1825] -65,23,"[2, 1]","[60, 60]","[1, 2]",[1],[5],[2],[300],[6],[2],[2],[1760] -65,24,"[4, 3]","[70, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2059] -65,25,"[1, 2]","[80, 60]","[1, 1]",[1],[5],[1],[400],[8],[1],[1],[1975] -65,26,"[3, 4]","[60, 70]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[2143] -65,27,"[2, 1]","[80, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2745] -65,28,"[2, 1]","[80, 90]","[1, 2]",[1],[5],[2],[400],[8],[2],[2],[1775] -65,29,"[2, 1]","[60, 90]","[1, 2]",[1],[1],[2],[60],[6],[2],[2],[1724] -65,30,"[1, 2]","[90, 70]","[1, 2]",[1],[5],[2],[350],[7],[2],[1],[1908] -65,31,"[3, 4]","[60, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[1925] -65,32,"[4, 3]","[70, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[1774] -65,33,"[3, 4]","[90, 70]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[1775] -65,34,"[1, 2]","[70, 60]","[1, 2]",[1],[1],[2],[60],[6],[2],[1],[1908] -65,35,"[1, 2]","[50, 60]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1659] -65,36,"[4, 3]","[50, 20]","[2, 2]",[1],[5],[4],[250],[5],[2],[3],[2762] -65,37,"[3, 4]","[10, 70]","[2, 2]",[1],[1],[4],[70],[7],[2],[4],[1591] -65,38,"[4, 3]","[80, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1574] -65,39,"[1, 2]","[20, 70]","[1, 2]",[1],[1],[2],[70],[7],[2],[2],[1793] -65,40,"[1, 2]","[10, 80]","[1, 1]",[1],[5],[1],[50],[1],[1],[2],[1859] -65,41,"[2, 1]","[80, 30]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[2126] -65,42,"[2, 1]","[80, 10]","[1, 2]",[2],[5],[2],[400],[8],[2],[1],[1724] -65,43,"[3, 4]","[10, 80]","[2, 1]",[2],[5],[3],[50],[1],[1],[4],[1725] -65,44,"[2, 1]","[70, 40]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[1774] -65,45,"[4, 3]","[40, 70]","[2, 2]",[2],[5],[4],[200],[4],[2],[4],[1824] -65,46,"[4, 3]","[50, 40]","[2, 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2]",[1],[5],[2],[100],[2],[2],[1],[2389] -74,8,"[4, 3]","[10, 10]","[2, 2]",[1],[1],[4],[10],[1],[2],[4],[2442] -74,9,"[2, 1]","[0, 30]","[1, 1]",[1],[5],[1],[150],[3],[1],[2],[2293] -74,10,"[4, 3]","[40, 20]","[2, 1]",[1],[5],[3],[100],[2],[1],[3],[2469] -74,11,"[1, 2]","[10, 30]","[1, 2]",[1],[5],[2],[150],[3],[2],[2],[2944] -74,12,"[1, 2]","[20, 40]","[1, 2]",[1],[5],[2],[200],[4],[2],[2],[2256] -74,13,"[3, 4]","[30, 10]","[2, 2]",[1],[5],[4],[50],[1],[2],[3],[2714] -74,14,"[4, 3]","[10, 50]","[2, 1]",[1],[1],[3],[50],[5],[1],[4],[1982] -74,15,"[2, 1]","[30, 60]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2096] -74,16,"[1, 2]","[90, 50]","[1, 1]",[1],[1],[1],[90],[9],[1],[1],[2154] -74,17,"[2, 1]","[50, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2144] -74,18,"[1, 2]","[80, 30]","[1, 1]",[1],[1],[1],[80],[8],[1],[1],[2016] -74,19,"[1, 2]","[60, 40]","[1, 1]",[1],[5],[1],[300],[6],[1],[1],[2021] -74,20,"[1, 2]","[20, 50]","[1, 1]",[1],[1],[1],[20],[2],[1],[2],[5287] -74,21,"[2, 1]","[40, 50]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[2238] -74,22,"[3, 4]","[50, 30]","[2, 1]",[1],[1],[3],[50],[5],[1],[3],[2325] -74,23,"[1, 2]","[40, 40]","[1, 1]",[1],[1],[1],[40],[4],[1],[1],[2031] -74,24,"[2, 1]","[30, 50]","[1, 2]",[1],[1],[2],[30],[3],[2],[2],[2266] -74,25,"[1, 2]","[50, 10]","[1, 1]",[1],[1],[1],[50],[5],[1],[1],[2121] -74,26,"[4, 3]","[10, 40]","[2, 1]",[1],[5],[3],[200],[4],[1],[4],[2087] -74,27,"[1, 2]","[50, 30]","[1, 1]",[1],[5],[1],[250],[5],[1],[1],[2114] -74,28,"[2, 1]","[60, 90]","[1, 1]",[1],[1],[1],[90],[9],[1],[2],[2125] -74,29,"[4, 3]","[50, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2622] -74,30,"[3, 4]","[80, 70]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[2352] -74,31,"[4, 3]","[60, 70]","[2, 1]",[1],[5],[3],[350],[7],[1],[4],[2147] -74,32,"[2, 1]","[80, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[1],[2144] -74,33,"[4, 3]","[50, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[2038] -74,34,"[4, 3]","[60, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[1956] -74,35,"[2, 1]","[60, 70]","[1, 1]",[1],[5],[1],[350],[7],[1],[2],[2476] -74,36,"[1, 2]","[90, 60]","[1, 1]",[1],[5],[1],[450],[9],[1],[1],[2250] -74,37,"[4, 3]","[70, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2093] -74,38,"[4, 3]","[70, 70]","[2, 1]",[1],[1],[3],[70],[7],[1],[4],[2032] -74,39,"[1, 2]","[50, 60]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[2483] -74,40,"[1, 2]","[60, 70]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[2073] -74,41,"[1, 2]","[50, 70]","[1, 2]",[2],[1],[2],[70],[7],[2],[2],[3355] -74,42,"[4, 3]","[80, 60]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1893] -74,43,"[1, 2]","[50, 80]","[1, 2]",[2],[5],[2],[400],[8],[2],[2],[2878] -74,44,"[1, 2]","[20, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1943] -74,45,"[2, 1]","[70, 10]","[1, 2]",[2],[1],[2],[70],[7],[2],[1],[2264] -74,46,"[3, 4]","[30, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[4],[2287] -74,47,"[3, 4]","[20, 20]","[2, 2]",[2],[5],[4],[100],[2],[2],[3],[2285] -74,48,"[2, 1]","[40, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[2],[2730] -74,49,"[4, 3]","[60, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1950] -74,50,"[3, 4]","[90, 50]","[2, 1]",[2],[5],[3],[450],[9],[1],[3],[1979] -74,51,"[1, 2]","[80, 50]","[1, 1]",[2],[1],[1],[80],[8],[1],[1],[2576] -74,52,"[3, 4]","[60, 50]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2150] -74,53,"[1, 2]","[80, 50]","[1, 1]",[2],[5],[1],[400],[8],[1],[1],[2437] -74,54,"[3, 4]","[90, 50]","[2, 1]",[2],[1],[3],[90],[9],[1],[3],[2445] -74,55,"[4, 3]","[70, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[4],[1884] -74,56,"[3, 4]","[60, 50]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2043] -74,57,"[2, 1]","[60, 40]","[1, 1]",[2],[5],[1],[200],[4],[1],[1],[2416] -74,58,"[2, 1]","[80, 20]","[1, 2]",[2],[1],[2],[80],[8],[2],[1],[2586] -74,59,"[3, 4]","[20, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2291] -74,60,"[4, 3]","[80, 20]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[2217] -74,61,"[2, 1]","[80, 0]","[1, 1]",[2],[1],[1],[0],[0],[1],[1],[2242] 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1]",[2],[5],[3],[150],[3],[1],[3],[2044] -74,76,"[2, 1]","[20, 30]","[1, 2]",[2],[5],[2],[100],[2],[2],[2],[2472] -74,77,"[3, 4]","[60, 50]","[2, 2]",[2],[1],[4],[50],[5],[2],[3],[2310] -74,78,"[3, 4]","[70, 70]","[2, 2]",[2],[1],[4],[70],[7],[2],[3],[2070] -74,79,"[1, 2]","[60, 90]","[1, 1]",[2],[1],[1],[60],[6],[1],[2],[2239] -74,80,"[2, 1]","[60, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[4684] -58,1,"[1, 2]","[40, 60]","[1, 1]",[1],[5],[1],[200],[4],[1],[2],[3471] -58,2,"[3, 4]","[60, 60]","[2, 2]",[1],[5],[4],[300],[6],[2],[3],[2306] -58,3,"[2, 1]","[40, 50]","[1, 1]",[1],[5],[1],[250],[5],[1],[2],[3301] -58,4,"[2, 1]","[40, 80]","[1, 2]",[1],[1],[2],[40],[4],[2],[2],[3290] -58,5,"[2, 1]","[70, 80]","[1, 1]",[1],[1],[1],[80],[8],[1],[2],[1725] -58,6,"[4, 3]","[50, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1806] -58,7,"[2, 1]","[20, 80]","[1, 1]",[1],[5],[1],[400],[8],[1],[2],[1757] -58,8,"[2, 1]","[20, 60]","[1, 1]",[1],[1],[1],[60],[6],[1],[2],[1823] -58,9,"[4, 3]","[50, 60]","[2, 1]",[1],[5],[3],[300],[6],[1],[4],[2573] -58,10,"[3, 4]","[80, 50]","[2, 2]",[1],[1],[4],[50],[5],[2],[3],[3690] -58,11,"[4, 3]","[60, 90]","[2, 1]",[1],[1],[3],[90],[9],[1],[4],[1907] -58,12,"[3, 4]","[80, 60]","[2, 1]",[1],[1],[3],[80],[8],[1],[3],[1790] -58,13,"[1, 2]","[60, 80]","[1, 1]",[1],[5],[1],[300],[6],[1],[2],[2124] -58,14,"[4, 3]","[60, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[4],[2406] -58,15,"[2, 1]","[50, 50]","[1, 1]",[1],[1],[1],[50],[5],[1],[2],[1807] -58,16,"[4, 3]","[50, 30]","[2, 1]",[1],[5],[3],[150],[3],[1],[3],[1740] -58,17,"[3, 4]","[30, 30]","[2, 1]",[1],[1],[3],[30],[3],[1],[3],[1824] -58,18,"[1, 2]","[70, 10]","[1, 2]",[1],[5],[2],[50],[1],[2],[1],[3387] -58,19,"[4, 3]","[30, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[4],[1923] -58,20,"[3, 4]","[90, 10]","[2, 1]",[1],[5],[3],[450],[9],[1],[3],[3056] -58,21,"[4, 3]","[50, 80]","[2, 1]",[1],[5],[3],[400],[8],[1],[4],[1723] -58,22,"[2, 1]","[30, 90]","[1, 1]",[1],[5],[1],[450],[9],[1],[2],[2156] 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2]",[1],[5],[4],[300],[6],[2],[4],[2390] -58,37,"[4, 3]","[70, 60]","[2, 1]",[1],[1],[3],[60],[6],[1],[3],[1790] -58,38,"[2, 1]","[70, 60]","[1, 2]",[1],[1],[2],[70],[7],[2],[1],[2524] -58,39,"[3, 4]","[70, 60]","[2, 2]",[1],[1],[4],[60],[6],[2],[3],[2073] -58,40,"[4, 3]","[70, 90]","[2, 1]",[1],[5],[3],[450],[9],[1],[4],[1790] -58,41,"[4, 3]","[70, 90]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[2381] -58,42,"[4, 3]","[80, 50]","[2, 2]",[2],[5],[4],[400],[8],[2],[3],[1939] -58,43,"[2, 1]","[50, 50]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2122] -58,44,"[3, 4]","[60, 30]","[2, 2]",[2],[5],[4],[150],[3],[2],[3],[2857] -58,45,"[1, 2]","[70, 40]","[1, 2]",[2],[1],[2],[40],[4],[2],[1],[2924] -58,46,"[1, 2]","[30, 20]","[1, 1]",[2],[5],[1],[150],[3],[1],[1],[4506] -58,47,"[2, 1]","[10, 30]","[1, 2]",[2],[5],[2],[50],[1],[2],[2],[4790] -58,48,"[3, 4]","[40, 30]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[3091] -58,49,"[1, 2]","[60, 0]","[1, 1]",[2],[5],[1],[300],[6],[1],[1],[2107] -58,50,"[4, 3]","[20, 40]","[2, 2]",[2],[1],[4],[20],[2],[2],[4],[2573] -58,51,"[1, 2]","[90, 10]","[1, 2]",[2],[1],[2],[10],[1],[2],[1],[2304] -58,52,"[3, 4]","[60, 10]","[2, 1]",[2],[1],[3],[60],[6],[1],[3],[2457] -58,53,"[4, 3]","[40, 60]","[2, 1]",[2],[5],[3],[300],[6],[1],[4],[2689] -58,54,"[1, 2]","[50, 60]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2224] -58,55,"[3, 4]","[70, 70]","[2, 1]",[2],[1],[3],[70],[7],[1],[3],[1974] -58,56,"[2, 1]","[60, 50]","[1, 1]",[2],[1],[1],[50],[5],[1],[1],[1956] -58,57,"[4, 3]","[50, 60]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1840] -58,58,"[1, 2]","[50, 90]","[1, 1]",[2],[5],[1],[250],[5],[1],[2],[2041] -58,59,"[3, 4]","[60, 80]","[2, 1]",[2],[1],[3],[60],[6],[1],[4],[1891] -58,60,"[1, 2]","[40, 90]","[1, 1]",[2],[5],[1],[200],[4],[1],[2],[2407] -58,61,"[4, 3]","[70, 10]","[2, 1]",[2],[1],[3],[10],[1],[1],[3],[1908] -58,62,"[3, 4]","[20, 70]","[2, 2]",[2],[5],[4],[350],[7],[2],[4],[4339] -58,63,"[3, 4]","[50, 50]","[2, 2]",[2],[5],[4],[250],[5],[2],[3],[1891] -58,64,"[2, 1]","[50, 90]","[1, 2]",[2],[1],[2],[50],[5],[2],[2],[3039] -58,65,"[3, 4]","[80, 80]","[2, 1]",[2],[5],[3],[400],[8],[1],[3],[2207] -58,66,"[2, 1]","[60, 40]","[1, 2]",[2],[1],[2],[60],[6],[2],[1],[3789] -58,67,"[4, 3]","[90, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[3],[2123] -58,68,"[1, 2]","[50, 80]","[1, 2]",[2],[1],[2],[80],[8],[2],[2],[1974] -58,69,"[4, 3]","[20, 40]","[2, 1]",[2],[1],[3],[40],[4],[1],[4],[2340] -58,70,"[2, 1]","[20, 10]","[1, 2]",[2],[5],[2],[100],[2],[2],[1],[3206] -58,71,"[2, 1]","[40, 10]","[1, 1]",[2],[1],[1],[10],[1],[1],[1],[2024] -58,72,"[3, 4]","[20, 60]","[2, 2]",[2],[5],[4],[300],[6],[2],[4],[2407] -58,73,"[4, 3]","[50, 30]","[2, 1]",[2],[5],[3],[150],[3],[1],[3],[1674] -58,74,"[1, 2]","[40, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1755] -58,75,"[1, 2]","[30, 70]","[1, 2]",[2],[5],[2],[350],[7],[2],[2],[1892] -58,76,"[1, 2]","[50, 60]","[1, 2]",[2],[5],[2],[300],[6],[2],[2],[1707] -58,77,"[3, 4]","[30, 80]","[2, 2]",[2],[1],[4],[80],[8],[2],[4],[2973] -58,78,"[2, 1]","[40, 10]","[1, 2]",[2],[5],[2],[200],[4],[2],[1],[2072] -58,79,"[1, 2]","[10, 20]","[1, 2]",[2],[1],[2],[20],[2],[2],[2],[1957] -58,80,"[2, 1]","[0, 0]","[1, 1]",[2],[5],[1],[0],[0],[1],[2],[3339] diff --git a/cpm/datasets/data/model_based_model_free.csv b/cpm/datasets/data/model_based_model_free.csv new file mode 100644 index 0000000..e7258f7 --- /dev/null +++ b/cpm/datasets/data/model_based_model_free.csv @@ -0,0 +1,16491 @@ +userID,trial,stimuli,rews,s,block,stake,choice,points_real,points,planet,theor_best_choice,RT +1,1,"[3, 4]","[70, 10]","[2, 2]",1,5,4,50,1,2,3,8320 +1,2,"[1, 2]","[60, 0]","[1, 2]",1,1,2,0,0,2,1,4211 +1,3,"[2, 1]","[10, 80]","[1, 1]",1,5,1,400,8,1,2,2082 +1,4,"[4, 3]","[10, 90]","[2, 2]",1,5,4,50,1,2,4,2236 +1,5,"[3, 4]","[70, 20]","[2, 2]",1,5,4,100,2,2,3,2182 +1,6,"[3, 4]","[80, 40]","[2, 2]",1,5,4,200,4,2,3,2714 +1,7,"[2, 1]","[70, 80]","[1, 1]",1,1,1,80,8,1,2,3044 +1,8,"[1, 2]","[70, 90]","[1, 1]",1,1,1,70,7,1,2,2853 +1,9,"[1, 2]","[50, 80]","[1, 2]",1,1,2,80,8,2,2,2631 +1,10,"[1, 2]","[30, 90]","[1, 2]",1,1,2,90,9,2,2,2315 +1,11,"[3, 4]","[30, 80]","[2, 1]",1,5,3,150,3,1,4,2540 +1,12,"[3, 4]","[20, 70]","[2, 1]",1,5,3,100,2,1,4,1876 +1,13,"[3, 4]","[10, 40]","[2, 1]",1,5,3,50,1,1,4,1858 +1,14,"[4, 3]","[30, 10]","[2, 2]",1,5,4,150,3,2,3,1926 +1,15,"[3, 4]","[20, 10]","[2, 1]",1,1,3,20,2,1,3,2046 +1,16,"[3, 4]","[20, 20]","[2, 2]",1,5,4,100,2,2,3,2100 +1,17,"[1, 2]","[20, 20]","[1, 2]",1,1,2,20,2,2,1,2241 +1,18,"[2, 1]","[20, 30]","[1, 2]",1,5,2,100,2,2,2,1884 +1,19,"[1, 2]","[40, 40]","[1, 2]",1,1,2,40,4,2,1,2333 +1,20,"[3, 4]","[50, 50]","[2, 2]",1,5,4,250,5,2,3,2882 +1,21,"[1, 2]","[30, 50]","[1, 2]",1,1,2,50,5,2,2,2058 +1,22,"[3, 4]","[30, 20]","[2, 2]",1,5,4,100,2,2,3,2607 +1,23,"[3, 4]","[60, 0]","[2, 1]",1,1,3,60,6,1,3,2987 +1,24,"[3, 4]","[60, 30]","[2, 1]",1,5,3,300,6,1,3,1943 +1,25,"[1, 2]","[70, 20]","[1, 1]",1,1,1,70,7,1,1,3238 +1,26,"[2, 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80]","[1, 1]",1,5,1,400,8,1,2,1723 +58,24,"[4, 3]","[10, 50]","[2, 1]",1,5,3,250,5,1,4,1674 +58,25,"[3, 4]","[10, 0]","[2, 1]",1,1,3,10,1,1,3,2089 +58,26,"[3, 4]","[30, 20]","[2, 2]",1,1,4,20,2,2,3,2540 +58,27,"[1, 2]","[80, 40]","[1, 1]",1,1,1,80,8,1,1,2223 +58,28,"[4, 3]","[60, 50]","[2, 1]",1,1,3,50,5,1,3,1757 +58,29,"[3, 4]","[20, 40]","[2, 1]",1,1,3,20,2,1,4,1807 +58,30,"[1, 2]","[60, 60]","[1, 1]",1,5,1,300,6,1,1,2041 +58,31,"[1, 2]","[80, 90]","[1, 1]",1,5,1,400,8,1,2,1988 +58,32,"[3, 4]","[90, 60]","[2, 1]",1,1,3,90,9,1,3,1774 +58,33,"[2, 1]","[70, 60]","[1, 2]",1,1,2,70,7,2,1,1840 +58,34,"[1, 2]","[50, 70]","[1, 2]",1,1,2,70,7,2,2,1924 +58,35,"[1, 2]","[70, 80]","[1, 2]",1,1,2,80,8,2,2,1756 +58,36,"[4, 3]","[60, 60]","[2, 2]",1,5,4,300,6,2,4,2390 +58,37,"[4, 3]","[70, 60]","[2, 1]",1,1,3,60,6,1,3,1790 +58,38,"[2, 1]","[70, 60]","[1, 2]",1,1,2,70,7,2,1,2524 +58,39,"[3, 4]","[70, 60]","[2, 2]",1,1,4,60,6,2,3,2073 +58,40,"[4, 3]","[70, 90]","[2, 1]",1,5,3,450,9,1,4,1790 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1]",2,5,1,250,5,1,2,2041 +58,59,"[3, 4]","[60, 80]","[2, 1]",2,1,3,60,6,1,4,1891 +58,60,"[1, 2]","[40, 90]","[1, 1]",2,5,1,200,4,1,2,2407 +58,61,"[4, 3]","[70, 10]","[2, 1]",2,1,3,10,1,1,3,1908 +58,62,"[3, 4]","[20, 70]","[2, 2]",2,5,4,350,7,2,4,4339 +58,63,"[3, 4]","[50, 50]","[2, 2]",2,5,4,250,5,2,3,1891 +58,64,"[2, 1]","[50, 90]","[1, 2]",2,1,2,50,5,2,2,3039 +58,65,"[3, 4]","[80, 80]","[2, 1]",2,5,3,400,8,1,3,2207 +58,66,"[2, 1]","[60, 40]","[1, 2]",2,1,2,60,6,2,1,3789 +58,67,"[4, 3]","[90, 40]","[2, 1]",2,1,3,40,4,1,3,2123 +58,68,"[1, 2]","[50, 80]","[1, 2]",2,1,2,80,8,2,2,1974 +58,69,"[4, 3]","[20, 40]","[2, 1]",2,1,3,40,4,1,4,2340 +58,70,"[2, 1]","[20, 10]","[1, 2]",2,5,2,100,2,2,1,3206 +58,71,"[2, 1]","[40, 10]","[1, 1]",2,1,1,10,1,1,1,2024 +58,72,"[3, 4]","[20, 60]","[2, 2]",2,5,4,300,6,2,4,2407 +58,73,"[4, 3]","[50, 30]","[2, 1]",2,5,3,150,3,1,3,1674 +58,74,"[1, 2]","[40, 60]","[1, 2]",2,5,2,300,6,2,2,1755 +58,75,"[1, 2]","[30, 70]","[1, 2]",2,5,2,350,7,2,2,1892 +58,76,"[1, 2]","[50, 60]","[1, 2]",2,5,2,300,6,2,2,1707 +58,77,"[3, 4]","[30, 80]","[2, 2]",2,1,4,80,8,2,4,2973 +58,78,"[2, 1]","[40, 10]","[1, 2]",2,5,2,200,4,2,1,2072 +58,79,"[1, 2]","[10, 20]","[1, 2]",2,1,2,20,2,2,2,1957 +58,80,"[2, 1]","[0, 0]","[1, 1]",2,5,1,0,0,1,2,3339 From 3242d384e1579de936e0ce3249e903cd863b42c0 Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Tue, 10 Jun 2025 12:45:32 +0200 Subject: [PATCH 30/54] fix error --- cpm/brainexplorer/spaceObserver.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpm/brainexplorer/spaceObserver.py b/cpm/brainexplorer/spaceObserver.py index c4809e8..efe4cb7 100644 --- a/cpm/brainexplorer/spaceObserver.py +++ b/cpm/brainexplorer/spaceObserver.py @@ -188,7 +188,7 @@ def metrics(self, mode=None): ES_4 = np.mean(bins[3]) if len(bins[3]) > 0 else np.nan user_results["ES_bins"] = np.array([ES_1, ES_2, ES_3, ES_4]) - self.results["ES_total_mean"] = np.mean(evidence_strength) + user_results["ES_total_mean"] = np.nanmean(evidence_strength) # Append user-level results self.results = pd.concat( From f102c83cf0788ea6ac927e54e2445cec0dc98f02 Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Mon, 23 Jun 2025 16:27:45 +0200 Subject: [PATCH 31/54] update example data --- cpm/datasets/data/model_based_model_free.csv | 24491 ++++++----------- 1 file changed, 8001 insertions(+), 16490 deletions(-) diff --git a/cpm/datasets/data/model_based_model_free.csv b/cpm/datasets/data/model_based_model_free.csv index e7258f7..678f199 100644 --- a/cpm/datasets/data/model_based_model_free.csv +++ b/cpm/datasets/data/model_based_model_free.csv @@ -1,16491 +1,8002 @@ userID,trial,stimuli,rews,s,block,stake,choice,points_real,points,planet,theor_best_choice,RT -1,1,"[3, 4]","[70, 10]","[2, 2]",1,5,4,50,1,2,3,8320 -1,2,"[1, 2]","[60, 0]","[1, 2]",1,1,2,0,0,2,1,4211 -1,3,"[2, 1]","[10, 80]","[1, 1]",1,5,1,400,8,1,2,2082 -1,4,"[4, 3]","[10, 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2]",2,5,2,300,6,2,2,3189 -7,75,"[1, 2]","[30, 70]","[1, 2]",2,5,2,350,7,2,2,2206 -7,76,"[1, 2]","[50, 60]","[1, 2]",2,5,2,300,6,2,2,3424 -7,77,"[3, 4]","[30, 80]","[2, 2]",2,1,4,80,8,2,4,2134 -7,78,"[2, 1]","[40, 10]","[1, 1]",2,5,1,50,1,1,1,1957 -7,79,"[1, 2]","[10, 20]","[1, 1]",2,1,1,10,1,1,2,2932 -7,80,"[2, 1]","[0, 0]","[1, 2]",2,5,2,0,0,2,2,3944 -8,1,"[4, 3]","[40, 80]","[2, 1]",1,1,3,80,8,1,4,7275 -8,2,"[2, 1]","[40, 80]","[1, 1]",1,5,1,400,8,1,2,2362 -8,3,"[4, 3]","[90, 70]","[2, 1]",1,5,3,350,7,1,3,2147 -8,4,"[3, 4]","[80, 90]","[2, 1]",1,1,3,80,8,1,4,2233 -8,5,"[4, 3]","[70, 70]","[2, 1]",1,5,3,350,7,1,4,2101 -8,6,"[2, 1]","[40, 80]","[1, 1]",1,1,1,80,8,1,2,2395 -8,7,"[4, 3]","[30, 60]","[2, 1]",1,5,3,300,6,1,4,2453 -8,8,"[3, 4]","[40, 10]","[2, 1]",1,1,3,40,4,1,3,2391 -8,9,"[2, 1]","[10, 50]","[1, 1]",1,1,1,50,5,1,2,4577 -8,10,"[3, 4]","[40, 0]","[2, 1]",1,5,3,200,4,1,3,2217 -8,11,"[3, 4]","[40, 20]","[2, 1]",1,1,3,40,4,1,3,2333 -8,12,"[4, 3]","[0, 40]","[2, 1]",1,1,3,40,4,1,4,2201 -8,13,"[1, 2]","[60, 10]","[1, 1]",1,5,1,300,6,1,1,2747 -8,14,"[1, 2]","[50, 20]","[1, 1]",1,1,1,50,5,1,1,2231 -8,15,"[3, 4]","[30, 10]","[2, 1]",1,5,3,150,3,1,3,2190 -8,16,"[1, 2]","[20, 10]","[1, 2]",1,1,2,10,1,2,1,3095 -8,17,"[2, 1]","[30, 20]","[1, 1]",1,1,1,20,2,1,1,1894 -8,18,"[1, 2]","[0, 20]","[1, 1]",1,5,1,0,0,1,2,2739 -8,19,"[3, 4]","[0, 10]","[2, 2]",1,5,4,50,1,2,4,1985 -8,20,"[1, 2]","[10, 40]","[1, 2]",1,5,2,200,4,2,2,2491 -8,21,"[3, 4]","[30, 50]","[2, 2]",1,5,4,250,5,2,4,2135 -8,22,"[4, 3]","[70, 50]","[2, 2]",1,1,4,70,7,2,3,2052 -8,23,"[4, 3]","[70, 60]","[2, 2]",1,1,4,70,7,2,3,2211 -8,24,"[1, 2]","[50, 80]","[1, 2]",1,5,2,400,8,2,2,2117 -8,25,"[2, 1]","[70, 40]","[1, 2]",1,1,2,70,7,2,1,2039 -8,26,"[3, 4]","[20, 50]","[2, 2]",1,1,4,50,5,2,4,2270 -8,27,"[4, 3]","[40, 10]","[2, 2]",1,5,4,200,4,2,3,2142 -8,28,"[4, 3]","[70, 0]","[2, 2]",1,1,4,70,7,2,3,2276 -8,29,"[4, 3]","[30, 10]","[2, 2]",1,1,4,30,3,2,3,2133 -8,30,"[3, 4]","[30, 20]","[2, 2]",1,5,4,100,2,2,3,2036 -8,31,"[1, 2]","[0, 30]","[1, 2]",1,1,2,30,3,2,2,2265 -8,32,"[2, 1]","[40, 20]","[1, 2]",1,5,2,200,4,2,1,2013 -8,33,"[1, 2]","[30, 20]","[1, 2]",1,1,2,20,2,2,1,2134 -8,34,"[2, 1]","[20, 30]","[1, 2]",1,5,2,100,2,2,2,2033 -8,35,"[4, 3]","[30, 30]","[2, 2]",1,5,4,150,3,2,4,2035 -8,36,"[1, 2]","[10, 20]","[1, 2]",1,5,2,100,2,2,2,3464 -8,37,"[2, 1]","[30, 0]","[1, 1]",1,5,1,0,0,1,1,2885 -8,38,"[2, 1]","[70, 30]","[1, 2]",1,1,2,70,7,2,1,1948 -8,39,"[3, 4]","[70, 50]","[2, 2]",1,1,4,50,5,2,3,2304 -8,40,"[1, 2]","[60, 90]","[1, 2]",1,5,2,450,9,2,2,1984 -8,41,"[2, 1]","[80, 70]","[1, 2]",2,1,2,80,8,2,1,9219 -8,42,"[1, 2]","[80, 50]","[1, 2]",2,1,2,50,5,2,1,1908 -8,43,"[2, 1]","[30, 70]","[1, 2]",2,1,2,30,3,2,2,2600 -8,44,"[1, 2]","[90, 70]","[1, 2]",2,1,2,70,7,2,1,1941 -8,45,"[4, 3]","[80, 80]","[2, 2]",2,5,4,400,8,2,4,2057 -8,46,"[4, 3]","[90, 70]","[2, 2]",2,1,4,90,9,2,3,2121 -8,47,"[2, 1]","[70, 60]","[1, 2]",2,1,2,70,7,2,1,2089 -8,48,"[2, 1]","[50, 70]","[1, 2]",2,5,2,250,5,2,2,2198 -8,49,"[2, 1]","[80, 60]","[1, 2]",2,1,2,80,8,2,1,2257 -8,50,"[4, 3]","[90, 40]","[2, 2]",2,1,4,90,9,2,3,2089 -8,51,"[4, 3]","[80, 20]","[2, 2]",2,5,4,400,8,2,3,2106 -8,52,"[3, 4]","[0, 50]","[2, 2]",2,1,4,50,5,2,4,2017 -8,53,"[2, 1]","[30, 0]","[1, 2]",2,5,2,150,3,2,1,2040 -8,54,"[1, 2]","[30, 40]","[1, 2]",2,1,2,40,4,2,2,2102 -8,55,"[1, 2]","[20, 30]","[1, 2]",2,5,2,150,3,2,2,2005 -8,56,"[3, 4]","[20, 30]","[2, 2]",2,5,4,150,3,2,4,2010 -8,57,"[2, 1]","[40, 60]","[1, 2]",2,5,2,200,4,2,2,2280 -8,58,"[4, 3]","[50, 40]","[2, 2]",2,1,4,50,5,2,3,2219 -8,59,"[2, 1]","[20, 50]","[1, 2]",2,5,2,100,2,2,2,2679 -8,60,"[4, 3]","[50, 60]","[2, 2]",2,1,4,50,5,2,4,2396 -8,61,"[2, 1]","[40, 60]","[1, 2]",2,5,2,200,4,2,2,4552 -8,62,"[1, 2]","[40, 40]","[1, 2]",2,5,2,200,4,2,1,3181 -8,63,"[1, 2]","[20, 30]","[1, 2]",2,5,2,150,3,2,2,2274 -8,64,"[4, 3]","[30, 10]","[2, 2]",2,5,4,150,3,2,3,2251 -8,65,"[3, 4]","[20, 20]","[2, 2]",2,5,4,100,2,2,3,2089 -8,66,"[2, 1]","[10, 20]","[1, 2]",2,5,2,50,1,2,2,2297 -8,67,"[3, 4]","[40, 10]","[2, 1]",2,1,3,40,4,1,3,1994 -8,68,"[3, 4]","[30, 10]","[2, 1]",2,1,3,30,3,1,3,2112 -8,69,"[2, 1]","[10, 50]","[1, 1]",2,5,1,250,5,1,2,2142 -8,70,"[4, 3]","[0, 50]","[2, 1]",2,1,3,50,5,1,4,2848 -8,71,"[2, 1]","[0, 30]","[1, 1]",2,1,1,30,3,1,2,2168 -8,72,"[2, 1]","[0, 30]","[1, 1]",2,1,1,30,3,1,2,2045 -8,73,"[4, 3]","[10, 10]","[2, 1]",2,5,3,50,1,1,4,3350 -8,74,"[4, 3]","[20, 20]","[2, 2]",2,5,4,100,2,2,4,2182 -8,75,"[3, 4]","[10, 60]","[2, 2]",2,5,4,300,6,2,4,2189 -8,76,"[4, 3]","[30, 10]","[2, 2]",2,1,4,30,3,2,3,3743 -8,77,"[1, 2]","[40, 10]","[1, 2]",2,5,2,50,1,2,1,2075 -8,78,"[3, 4]","[40, 10]","[2, 1]",2,1,3,40,4,1,3,1889 -8,79,"[4, 3]","[10, 20]","[2, 1]",2,5,3,100,2,1,4,1992 -8,80,"[1, 2]","[20, 0]","[1, 1]",2,1,1,20,2,1,1,2187 -8,1,"[4, 3]","[70, 20]","[2, 2]",1,5,4,350,7,2,3,4921 -8,2,"[1, 2]","[10, 60]","[1, 2]",1,1,2,60,6,2,2,2359 -8,3,"[2, 1]","[50, 0]","[1, 2]",1,5,2,250,5,2,1,2057 -8,4,"[3, 4]","[30, 60]","[2, 2]",1,1,4,60,6,2,4,1967 -8,5,"[1, 2]","[30, 40]","[1, 2]",1,1,2,40,4,2,2,1748 -8,6,"[4, 3]","[80, 0]","[2, 2]",1,1,4,80,8,2,3,2047 -8,7,"[1, 2]","[0, 80]","[1, 2]",1,1,2,80,8,2,2,1764 -8,8,"[1, 2]","[20, 90]","[1, 2]",1,5,2,450,9,2,2,1852 -8,9,"[2, 1]","[70, 0]","[1, 2]",1,1,2,70,7,2,1,1882 -8,10,"[1, 2]","[50, 80]","[1, 2]",1,1,2,80,8,2,2,1785 -8,11,"[1, 2]","[50, 90]","[1, 2]",1,1,2,90,9,2,2,1792 -8,12,"[1, 2]","[40, 40]","[1, 2]",1,5,2,200,4,2,1,2008 -8,13,"[3, 4]","[20, 90]","[2, 2]",1,1,4,90,9,2,4,1995 -8,14,"[1, 2]","[40, 70]","[1, 2]",1,1,2,70,7,2,2,2320 -8,15,"[3, 4]","[50, 80]","[2, 2]",1,5,4,400,8,2,4,2132 -8,16,"[3, 4]","[0, 70]","[2, 2]",1,1,4,70,7,2,4,1804 -8,17,"[1, 2]","[40, 90]","[1, 2]",1,5,2,450,9,2,2,1977 -8,18,"[3, 4]","[60, 60]","[2, 2]",1,1,4,60,6,2,3,3048 -8,19,"[2, 1]","[60, 80]","[1, 2]",1,1,2,60,6,2,2,2177 -8,20,"[1, 2]","[70, 70]","[1, 2]",1,1,2,70,7,2,1,1846 -8,21,"[1, 2]","[70, 70]","[1, 2]",1,1,2,70,7,2,1,1867 -8,22,"[1, 2]","[70, 50]","[1, 2]",1,1,2,50,5,2,1,2088 -8,23,"[1, 2]","[70, 20]","[1, 2]",1,1,2,20,2,2,1,1759 -8,24,"[4, 3]","[30, 80]","[2, 1]",1,5,3,400,8,1,4,4321 -8,25,"[2, 1]","[50, 90]","[1, 1]",1,1,1,90,9,1,2,2018 -8,26,"[2, 1]","[70, 80]","[1, 1]",1,1,1,80,8,1,2,1897 -8,27,"[3, 4]","[80, 60]","[2, 1]",1,1,3,80,8,1,3,2078 -8,28,"[4, 3]","[20, 70]","[2, 1]",1,5,3,350,7,1,4,1970 -8,29,"[4, 3]","[0, 50]","[2, 1]",1,1,3,50,5,1,4,1924 -8,30,"[3, 4]","[60, 30]","[2, 1]",1,5,3,300,6,1,3,2040 -8,31,"[2, 1]","[40, 60]","[1, 1]",1,5,1,300,6,1,2,2072 -8,32,"[4, 3]","[50, 50]","[2, 1]",1,5,3,250,5,1,4,1980 -8,33,"[4, 3]","[80, 60]","[2, 1]",1,1,3,60,6,1,3,1879 -8,34,"[1, 2]","[30, 70]","[1, 1]",1,5,1,150,3,1,2,1957 -8,35,"[3, 4]","[50, 70]","[2, 1]",1,5,3,250,5,1,4,1970 -8,36,"[1, 2]","[40, 60]","[1, 1]",1,5,1,200,4,1,2,1922 -8,37,"[2, 1]","[80, 60]","[1, 1]",1,1,1,60,6,1,1,1865 -8,38,"[2, 1]","[80, 80]","[1, 1]",1,5,1,400,8,1,2,1890 -8,39,"[1, 2]","[40, 60]","[1, 1]",1,5,1,200,4,1,2,1974 -8,40,"[2, 1]","[70, 20]","[1, 1]",1,5,1,100,2,1,1,1951 -8,41,"[1, 2]","[50, 70]","[1, 1]",2,1,1,50,5,1,2,2826 -8,42,"[3, 4]","[50, 80]","[2, 1]",2,1,3,50,5,1,4,1888 -8,43,"[4, 3]","[70, 40]","[2, 1]",2,1,3,40,4,1,3,1832 -8,44,"[1, 2]","[0, 70]","[1, 1]",2,5,1,0,0,1,2,2032 -8,45,"[4, 3]","[80, 0]","[2, 2]",2,5,4,400,8,2,3,1877 -8,46,"[3, 4]","[10, 80]","[2, 2]",2,5,4,400,8,2,4,1874 -8,47,"[2, 1]","[70, 10]","[1, 2]",2,5,2,350,7,2,1,1978 -8,48,"[4, 3]","[60, 30]","[2, 2]",2,5,4,300,6,2,3,2095 -8,49,"[4, 3]","[30, 30]","[2, 2]",2,5,4,150,3,2,4,2010 -8,50,"[1, 2]","[10, 40]","[1, 2]",2,1,2,40,4,2,2,1885 -8,51,"[1, 2]","[10, 40]","[1, 2]",2,5,2,200,4,2,2,1837 -8,52,"[2, 1]","[30, 30]","[1, 2]",2,1,2,30,3,2,2,1979 -8,53,"[3, 4]","[30, 10]","[2, 2]",2,1,4,10,1,2,3,2050 -8,54,"[3, 4]","[40, 20]","[2, 1]",2,1,3,40,4,1,3,1853 -8,55,"[2, 1]","[0, 10]","[1, 1]",2,5,1,50,1,1,2,2078 -8,56,"[1, 2]","[0, 50]","[1, 2]",2,1,2,50,5,2,2,1822 -8,57,"[2, 1]","[30, 30]","[1, 2]",2,5,2,150,3,2,2,1870 -8,58,"[4, 3]","[30, 40]","[2, 2]",2,5,4,150,3,2,4,1964 -8,59,"[1, 2]","[20, 20]","[1, 2]",2,1,2,20,2,2,1,1969 -8,60,"[1, 2]","[30, 20]","[1, 1]",2,1,1,30,3,1,1,1911 -8,61,"[1, 2]","[0, 80]","[1, 1]",2,1,1,0,0,1,2,1993 -8,62,"[2, 1]","[70, 0]","[1, 2]",2,1,2,70,7,2,1,1943 -8,63,"[4, 3]","[90, 50]","[2, 2]",2,5,4,450,9,2,3,2016 -8,64,"[4, 3]","[50, 50]","[2, 2]",2,5,4,250,5,2,4,1896 -8,65,"[3, 4]","[20, 40]","[2, 2]",2,5,4,200,4,2,4,1885 -8,66,"[3, 4]","[20, 80]","[2, 2]",2,5,4,400,8,2,4,1868 -8,67,"[1, 2]","[70, 80]","[1, 2]",2,1,2,80,8,2,2,1961 -8,68,"[3, 4]","[40, 80]","[2, 2]",2,5,4,400,8,2,4,1787 -8,69,"[4, 3]","[80, 80]","[2, 2]",2,5,4,400,8,2,4,2040 -8,70,"[2, 1]","[60, 80]","[1, 2]",2,1,2,60,6,2,2,2052 -8,71,"[3, 4]","[90, 50]","[2, 2]",2,5,4,250,5,2,3,1866 -8,72,"[4, 3]","[70, 80]","[2, 2]",2,5,4,350,7,2,4,2140 -8,73,"[1, 2]","[60, 70]","[1, 2]",2,5,2,350,7,2,2,2778 -8,74,"[3, 4]","[70, 70]","[2, 2]",2,1,4,70,7,2,3,1870 -8,75,"[2, 1]","[80, 50]","[1, 2]",2,1,2,80,8,2,1,1922 -8,76,"[3, 4]","[60, 60]","[2, 2]",2,1,4,60,6,2,3,1774 -8,77,"[2, 1]","[80, 90]","[1, 2]",2,5,2,400,8,2,2,1894 -8,78,"[4, 3]","[80, 80]","[2, 2]",2,5,4,400,8,2,4,2309 -8,79,"[4, 3]","[50, 60]","[2, 2]",2,5,4,250,5,2,4,2004 -8,80,"[2, 1]","[80, 90]","[1, 2]",2,5,2,400,8,2,2,2212 -9,1,"[1, 2]","[30, 60]","[1, 1]",1,1,1,30,3,1,2,3930 -9,2,"[3, 4]","[40, 80]","[2, 2]",1,1,4,80,8,2,4,1921 -9,3,"[1, 2]","[50, 80]","[1, 2]",1,5,2,400,8,2,2,1933 -9,4,"[4, 3]","[60, 80]","[2, 2]",1,5,4,300,6,2,4,2045 -9,5,"[4, 3]","[80, 90]","[2, 2]",1,1,4,80,8,2,4,1975 -9,6,"[1, 2]","[80, 90]","[1, 2]",1,1,2,90,9,2,2,1928 -9,7,"[2, 1]","[80, 90]","[1, 2]",1,5,2,400,8,2,2,2030 -9,8,"[1, 2]","[90, 90]","[1, 2]",1,5,2,450,9,2,1,1893 -9,9,"[2, 1]","[70, 40]","[1, 2]",1,1,2,70,7,2,1,2226 -9,10,"[4, 3]","[30, 40]","[2, 2]",1,5,4,150,3,2,4,1952 -9,11,"[1, 2]","[40, 40]","[1, 1]",1,5,1,200,4,1,1,1815 -9,12,"[4, 3]","[60, 20]","[2, 1]",1,1,3,20,2,1,3,1905 -9,13,"[2, 1]","[70, 30]","[1, 1]",1,5,1,150,3,1,1,1837 -9,14,"[4, 3]","[60, 20]","[2, 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1]",2,5,1,100,2,1,2,10015 -12,67,"[3, 4]","[40, 10]","[2, 1]",2,1,3,40,4,1,3,2179 -12,68,"[3, 4]","[30, 10]","[2, 2]",2,1,4,10,1,2,3,2004 -12,69,"[2, 1]","[10, 50]","[1, 1]",2,5,1,250,5,1,2,2421 -12,70,"[4, 3]","[0, 50]","[2, 1]",2,1,3,50,5,1,4,2146 -12,71,"[2, 1]","[0, 30]","[1, 1]",2,1,1,30,3,1,2,1880 -12,72,"[2, 1]","[0, 30]","[1, 1]",2,1,1,30,3,1,2,1737 -12,73,"[4, 3]","[10, 10]","[2, 1]",2,5,3,50,1,1,4,1962 -12,74,"[4, 3]","[20, 20]","[2, 2]",2,5,4,100,2,2,4,2095 -12,75,"[3, 4]","[10, 60]","[2, 2]",2,5,4,300,6,2,4,2011 -12,76,"[4, 3]","[30, 10]","[2, 2]",2,1,4,30,3,2,3,1903 -12,77,"[1, 2]","[40, 10]","[1, 1]",2,5,1,200,4,1,1,1989 -12,78,"[3, 4]","[40, 10]","[2, 2]",2,1,4,10,1,2,3,1805 -12,79,"[4, 3]","[10, 20]","[2, 1]",2,5,3,100,2,1,4,1963 -12,80,"[1, 2]","[20, 0]","[1, 1]",2,1,1,20,2,1,1,2146 -12,1,"[2, 1]","[90, 10]","[1, 1]",1,5,1,50,1,1,1,3377 -12,2,"[4, 3]","[70, 40]","[2, 2]",1,1,4,70,7,2,3,2521 -12,3,"[2, 1]","[70, 40]","[1, 2]",1,5,2,350,7,2,1,2212 -12,4,"[3, 4]","[70, 80]","[2, 2]",1,1,4,80,8,2,4,1953 -12,5,"[4, 3]","[50, 70]","[2, 2]",1,5,4,250,5,2,4,1921 -12,6,"[3, 4]","[60, 80]","[2, 2]",1,5,4,400,8,2,4,2053 -12,7,"[4, 3]","[50, 60]","[2, 2]",1,1,4,50,5,2,4,1954 -12,8,"[4, 3]","[60, 70]","[2, 2]",1,5,4,300,6,2,4,1830 -12,9,"[3, 4]","[80, 20]","[2, 2]",1,5,4,100,2,2,3,1713 -12,10,"[4, 3]","[30, 80]","[2, 2]",1,5,4,150,3,2,4,1923 -12,11,"[3, 4]","[60, 20]","[2, 2]",1,1,4,20,2,2,3,1763 -12,12,"[4, 3]","[20, 40]","[2, 1]",1,5,3,200,4,1,4,2079 -12,13,"[2, 1]","[40, 40]","[1, 1]",1,5,1,200,4,1,2,1946 -12,14,"[1, 2]","[30, 30]","[1, 1]",1,5,1,150,3,1,1,2039 -12,15,"[4, 3]","[60, 40]","[2, 1]",1,1,3,40,4,1,3,2071 -12,16,"[3, 4]","[60, 30]","[2, 1]",1,1,3,60,6,1,3,1897 -12,17,"[1, 2]","[20, 50]","[1, 1]",1,5,1,100,2,1,2,2104 -12,18,"[1, 2]","[20, 30]","[1, 1]",1,1,1,20,2,1,2,2003 -12,19,"[1, 2]","[40, 40]","[1, 2]",1,5,2,200,4,2,1,1978 -12,20,"[1, 2]","[10, 60]","[1, 2]",1,5,2,300,6,2,2,1804 -12,21,"[3, 4]","[30, 50]","[2, 2]",1,1,4,50,5,2,4,2645 -12,22,"[4, 3]","[20, 70]","[2, 2]",1,5,4,100,2,2,4,2005 -12,23,"[1, 2]","[70, 20]","[1, 1]",1,5,1,350,7,1,1,1996 -12,24,"[2, 1]","[20, 70]","[1, 1]",1,5,1,350,7,1,2,2270 -12,25,"[4, 3]","[0, 30]","[2, 1]",1,1,3,30,3,1,4,2163 -12,26,"[3, 4]","[30, 0]","[2, 1]",1,1,3,30,3,1,3,9064 -12,27,"[4, 3]","[30, 50]","[2, 1]",1,1,3,50,5,1,4,3213 -12,28,"[2, 1]","[30, 30]","[1, 1]",1,1,1,30,3,1,2,5497 -12,29,"[1, 2]","[50, 20]","[1, 1]",1,1,1,50,5,1,1,5446 -12,30,"[3, 4]","[30, 0]","[2, 1]",1,1,3,30,3,1,3,4579 -12,31,"[2, 1]","[20, 40]","[1, 1]",1,1,1,40,4,1,2,2045 -12,32,"[1, 2]","[10, 20]","[1, 1]",1,1,1,10,1,1,2,1894 -12,33,"[1, 2]","[20, 30]","[1, 2]",1,1,2,30,3,2,2,1745 -12,34,"[4, 3]","[80, 10]","[2, 2]",1,5,4,400,8,2,3,1816 -12,35,"[4, 3]","[80, 10]","[2, 2]",1,1,4,80,8,2,3,1870 -12,36,"[3, 4]","[10, 80]","[2, 2]",1,5,4,400,8,2,4,1920 -12,37,"[3, 4]","[30, 90]","[2, 2]",1,1,4,90,9,2,4,1922 -12,38,"[3, 4]","[10, 90]","[2, 2]",1,1,4,90,9,2,4,1804 -12,39,"[2, 1]","[50, 40]","[1, 2]",1,1,2,50,5,2,1,2229 -12,40,"[2, 1]","[50, 40]","[1, 2]",1,1,2,50,5,2,1,2705 -12,41,"[1, 2]","[30, 70]","[1, 1]",2,5,1,150,3,1,2,2799 -12,42,"[4, 3]","[80, 30]","[2, 2]",2,5,4,400,8,2,3,2486 -12,43,"[3, 4]","[30, 80]","[2, 2]",2,5,4,400,8,2,4,1970 -12,44,"[2, 1]","[80, 10]","[1, 2]",2,5,2,400,8,2,1,2420 -12,45,"[3, 4]","[20, 60]","[2, 2]",2,1,4,60,6,2,4,2105 -12,46,"[1, 2]","[0, 30]","[1, 2]",2,5,2,150,3,2,2,5855 -12,47,"[1, 2]","[80, 30]","[1, 2]",2,5,2,150,3,2,1,1962 -12,48,"[1, 2]","[70, 40]","[1, 2]",2,5,2,200,4,2,1,1762 -12,49,"[4, 3]","[40, 70]","[2, 2]",2,5,4,200,4,2,4,2095 -12,50,"[1, 2]","[50, 40]","[1, 2]",2,5,2,200,4,2,1,1519 -12,51,"[1, 2]","[30, 40]","[1, 1]",2,5,1,150,3,1,2,1895 -12,52,"[4, 3]","[30, 20]","[2, 2]",2,5,4,150,3,2,3,1921 -12,53,"[1, 2]","[10, 50]","[1, 1]",2,1,1,10,1,1,2,1879 -12,54,"[2, 1]","[40, 20]","[1, 2]",2,1,2,40,4,2,1,2779 -12,55,"[4, 3]","[30, 30]","[2, 2]",2,5,4,150,3,2,4,1945 -12,56,"[4, 3]","[20, 30]","[2, 2]",2,1,4,20,2,2,4,1913 -12,57,"[2, 1]","[10, 30]","[1, 1]",2,5,1,150,3,1,2,2362 -12,58,"[2, 1]","[10, 10]","[1, 1]",2,1,1,10,1,1,2,1895 -12,59,"[2, 1]","[30, 10]","[1, 2]",2,1,2,30,3,2,1,2203 -12,60,"[3, 4]","[10, 10]","[2, 2]",2,1,4,10,1,2,3,2154 -12,61,"[1, 2]","[10, 10]","[1, 1]",2,5,1,50,1,1,1,1930 -12,62,"[3, 4]","[30, 20]","[2, 1]",2,5,3,150,3,1,3,2078 -12,63,"[1, 2]","[60, 0]","[1, 1]",2,1,1,60,6,1,1,2153 -12,64,"[4, 3]","[20, 60]","[2, 1]",2,1,3,60,6,1,4,1813 -12,65,"[3, 4]","[80, 10]","[2, 1]",2,5,3,400,8,1,3,1887 -12,66,"[3, 4]","[90, 20]","[2, 1]",2,1,3,90,9,1,3,1836 -12,67,"[3, 4]","[80, 40]","[2, 1]",2,5,3,400,8,1,3,1954 -12,68,"[3, 4]","[80, 40]","[2, 1]",2,1,3,80,8,1,3,1729 -12,69,"[4, 3]","[10, 60]","[2, 1]",2,5,3,300,6,1,4,1913 -12,70,"[1, 2]","[80, 0]","[1, 1]",2,1,1,80,8,1,1,2063 -12,71,"[2, 1]","[10, 30]","[1, 1]",2,1,1,30,3,1,2,1955 -12,72,"[1, 2]","[20, 0]","[1, 1]",2,1,1,20,2,1,1,1703 -12,73,"[4, 3]","[0, 10]","[2, 1]",2,5,3,50,1,1,4,1970 -12,74,"[1, 2]","[30, 40]","[1, 2]",2,5,2,200,4,2,2,1879 -12,75,"[2, 1]","[40, 0]","[1, 2]",2,1,2,40,4,2,1,2137 -12,76,"[3, 4]","[0, 10]","[2, 2]",2,1,4,10,1,2,4,1836 -12,77,"[2, 1]","[0, 20]","[1, 1]",2,1,1,20,2,1,2,1687 -12,78,"[2, 1]","[40, 20]","[1, 2]",2,5,2,200,4,2,1,1912 -12,79,"[2, 1]","[80, 30]","[1, 2]",2,1,2,80,8,2,1,1837 -12,80,"[3, 4]","[0, 80]","[2, 2]",2,5,4,400,8,2,4,1911 -13,1,"[3, 4]","[20, 60]","[2, 1]",1,5,3,100,2,1,4,4348 -13,2,"[2, 1]","[70, 10]","[1, 2]",1,5,2,350,7,2,1,1978 -13,3,"[2, 1]","[60, 20]","[1, 2]",1,1,2,60,6,2,1,2195 -13,4,"[4, 3]","[40, 30]","[2, 1]",1,5,3,150,3,1,3,2186 -13,5,"[2, 1]","[40, 30]","[1, 2]",1,1,2,40,4,2,1,2277 -13,6,"[4, 3]","[50, 60]","[2, 1]",1,1,3,60,6,1,4,1837 -13,7,"[1, 2]","[60, 20]","[1, 2]",1,1,2,20,2,2,1,2760 -13,8,"[1, 2]","[30, 30]","[1, 2]",1,1,2,30,3,2,1,2794 -13,9,"[2, 1]","[30, 20]","[1, 1]",1,5,1,100,2,1,1,2645 -13,10,"[4, 3]","[30, 0]","[2, 1]",1,1,3,0,0,1,3,2602 -13,11,"[1, 2]","[0, 60]","[1, 1]",1,5,1,0,0,1,2,2703 -13,12,"[1, 2]","[40, 80]","[1, 2]",1,5,2,400,8,2,2,2303 -13,13,"[4, 3]","[60, 60]","[2, 2]",1,5,4,300,6,2,4,3927 -13,14,"[4, 3]","[40, 90]","[2, 2]",1,5,4,200,4,2,4,2928 -13,15,"[3, 4]","[60, 80]","[2, 2]",1,1,4,80,8,2,4,2878 -13,16,"[2, 1]","[70, 70]","[1, 1]",1,1,1,70,7,1,2,3902 -13,17,"[2, 1]","[70, 70]","[1, 1]",1,5,1,350,7,1,2,3202 -13,18,"[3, 4]","[30, 60]","[2, 2]",1,1,4,60,6,2,4,2586 -13,19,"[1, 2]","[40, 60]","[1, 1]",1,5,1,200,4,1,2,2778 -13,20,"[1, 2]","[10, 50]","[1, 1]",1,5,1,50,1,1,2,2385 -13,21,"[4, 3]","[50, 10]","[2, 2]",1,1,4,50,5,2,3,2761 -13,22,"[3, 4]","[20, 60]","[2, 2]",1,5,4,300,6,2,4,2454 -13,23,"[1, 2]","[50, 30]","[1, 1]",1,5,1,250,5,1,1,1937 -13,24,"[4, 3]","[60, 60]","[2, 2]",1,5,4,300,6,2,4,2295 -13,25,"[1, 2]","[70, 70]","[1, 1]",1,5,1,350,7,1,1,1912 -13,26,"[3, 4]","[60, 70]","[2, 2]",1,5,4,350,7,2,4,1887 -13,27,"[2, 1]","[70, 40]","[1, 1]",1,5,1,200,4,1,1,2128 -13,28,"[1, 2]","[30, 60]","[1, 1]",1,5,1,150,3,1,2,1971 -13,29,"[4, 3]","[60, 60]","[2, 2]",1,5,4,300,6,2,4,1886 -13,30,"[1, 2]","[70, 70]","[1, 1]",1,1,1,70,7,1,1,2229 -13,31,"[1, 2]","[50, 30]","[1, 1]",1,1,1,50,5,1,1,1920 -13,32,"[3, 4]","[20, 40]","[2, 2]",1,5,4,200,4,2,4,1827 -13,33,"[1, 2]","[10, 60]","[1, 1]",1,5,1,50,1,1,2,1812 -13,34,"[2, 1]","[30, 0]","[1, 1]",1,1,1,0,0,1,1,2002 -13,35,"[4, 3]","[50, 0]","[2, 2]",1,1,4,50,5,2,3,2062 -13,36,"[1, 2]","[10, 50]","[1, 2]",1,1,2,50,5,2,2,2395 -13,37,"[1, 2]","[0, 80]","[1, 2]",1,1,2,80,8,2,2,3236 -13,38,"[2, 1]","[70, 20]","[1, 2]",1,1,2,70,7,2,1,2353 -13,39,"[2, 1]","[50, 10]","[1, 2]",1,1,2,50,5,2,1,3012 -13,40,"[2, 1]","[30, 20]","[1, 2]",1,1,2,30,3,2,1,4594 -13,41,"[1, 2]","[10, 30]","[1, 1]",2,1,1,10,1,1,2,9382 -13,42,"[3, 4]","[0, 20]","[2, 2]",2,5,4,100,2,2,4,2286 -13,43,"[4, 3]","[10, 0]","[2, 2]",2,1,4,10,1,2,3,1812 -13,44,"[1, 2]","[30, 20]","[1, 1]",2,5,1,150,3,1,1,1861 -13,45,"[2, 1]","[40, 10]","[1, 1]",2,5,1,50,1,1,1,1920 -13,46,"[3, 4]","[0, 30]","[2, 2]",2,5,4,150,3,2,4,2220 -13,47,"[3, 4]","[30, 40]","[2, 2]",2,5,4,200,4,2,4,1695 -13,48,"[4, 3]","[20, 20]","[2, 2]",2,1,4,20,2,2,4,1869 -13,49,"[4, 3]","[30, 10]","[2, 2]",2,5,4,150,3,2,3,1978 -13,50,"[2, 1]","[30, 30]","[1, 1]",2,5,1,150,3,1,2,2479 -13,51,"[3, 4]","[20, 40]","[2, 2]",2,1,4,40,4,2,4,2551 -13,52,"[2, 1]","[30, 30]","[1, 1]",2,1,1,30,3,1,2,2128 -13,53,"[1, 2]","[60, 20]","[1, 1]",2,1,1,60,6,1,1,2144 -13,54,"[1, 2]","[80, 40]","[1, 1]",2,5,1,400,8,1,1,1754 -13,55,"[3, 4]","[80, 40]","[2, 2]",2,1,4,40,4,2,3,1962 -13,56,"[2, 1]","[0, 80]","[1, 1]",2,1,1,80,8,1,2,1770 -13,57,"[4, 3]","[0, 80]","[2, 2]",2,1,4,0,0,2,4,1944 -13,58,"[4, 3]","[20, 90]","[2, 1]",2,5,3,450,9,1,4,1829 -13,59,"[2, 1]","[30, 60]","[1, 2]",2,1,2,30,3,2,2,3062 -13,60,"[3, 4]","[70, 50]","[2, 1]",2,5,3,350,7,1,3,1954 -13,61,"[4, 3]","[80, 60]","[2, 1]",2,5,3,300,6,1,3,2070 -13,62,"[3, 4]","[50, 50]","[2, 1]",2,5,3,250,5,1,3,2103 -13,63,"[2, 1]","[50, 50]","[1, 2]",2,5,2,250,5,2,2,2328 -13,64,"[1, 2]","[60, 50]","[1, 2]",2,1,2,50,5,2,1,1919 -13,65,"[3, 4]","[40, 20]","[2, 1]",2,1,3,40,4,1,3,1828 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-17,2,"[4, 3]","[40, 50]","[2, 1]",1,5,3,250,5,1,4,3010 -17,3,"[2, 1]","[30, 70]","[1, 1]",1,1,1,70,7,1,2,2322 -17,4,"[3, 4]","[70, 70]","[2, 1]",1,5,3,350,7,1,3,1973 -17,5,"[3, 4]","[80, 40]","[2, 1]",1,1,3,80,8,1,3,2355 -17,6,"[1, 2]","[80, 50]","[1, 1]",1,1,1,80,8,1,1,1841 -17,7,"[2, 1]","[40, 90]","[1, 2]",1,1,2,40,4,2,2,2471 -17,8,"[2, 1]","[10, 60]","[1, 1]",1,1,1,60,6,1,2,2020 -17,9,"[4, 3]","[0, 40]","[2, 1]",1,5,3,200,4,1,4,3034 -17,10,"[2, 1]","[20, 20]","[1, 1]",1,5,1,100,2,1,2,2350 -17,11,"[4, 3]","[20, 20]","[2, 1]",1,1,3,20,2,1,4,1795 -17,12,"[2, 1]","[30, 0]","[1, 2]",1,5,2,150,3,2,1,2277 -17,13,"[3, 4]","[50, 20]","[2, 2]",1,1,4,20,2,2,3,2205 -17,14,"[1, 2]","[60, 60]","[1, 1]",1,5,1,300,6,1,1,2371 -17,15,"[3, 4]","[50, 80]","[2, 1]",1,5,3,250,5,1,4,2535 -17,16,"[1, 2]","[90, 70]","[1, 1]",1,1,1,90,9,1,1,1826 -17,17,"[1, 2]","[80, 60]","[1, 1]",1,5,1,400,8,1,1,2143 -17,18,"[3, 4]","[80, 80]","[2, 1]",1,1,3,80,8,1,3,2126 -17,19,"[3, 4]","[90, 90]","[2, 2]",1,5,4,450,9,2,3,1841 -17,20,"[3, 4]","[80, 80]","[2, 2]",1,5,4,400,8,2,3,2160 -17,21,"[4, 3]","[70, 20]","[2, 2]",1,5,4,350,7,2,3,2462 -17,22,"[2, 1]","[50, 10]","[1, 2]",1,5,2,250,5,2,1,2992 -17,23,"[3, 4]","[10, 40]","[2, 2]",1,1,4,40,4,2,4,2151 -17,24,"[4, 3]","[40, 10]","[2, 1]",1,5,3,50,1,1,3,3343 -17,25,"[4, 3]","[20, 10]","[2, 1]",1,1,3,10,1,1,3,2190 -17,26,"[4, 3]","[40, 20]","[2, 2]",1,5,4,200,4,2,3,1998 -17,27,"[3, 4]","[20, 60]","[2, 2]",1,1,4,60,6,2,4,2198 -17,28,"[2, 1]","[70, 20]","[1, 1]",1,1,1,20,2,1,1,2084 -17,29,"[2, 1]","[80, 10]","[1, 2]",1,1,2,80,8,2,1,2467 -17,30,"[3, 4]","[30, 80]","[2, 2]",1,5,4,400,8,2,4,1908 -17,31,"[1, 2]","[30, 80]","[1, 2]",1,1,2,80,8,2,2,2493 -17,32,"[4, 3]","[80, 30]","[2, 2]",1,5,4,400,8,2,3,4016 -17,33,"[1, 2]","[20, 50]","[1, 2]",1,1,2,50,5,2,2,2025 -17,34,"[3, 4]","[40, 40]","[2, 1]",1,5,3,200,4,1,3,2572 -17,35,"[4, 3]","[40, 40]","[2, 1]",1,1,3,40,4,1,4,1952 -17,36,"[1, 2]","[50, 70]","[1, 2]",1,1,2,70,7,2,2,2164 -17,37,"[1, 2]","[10, 80]","[1, 1]",1,5,1,50,1,1,2,8274 -17,38,"[1, 2]","[0, 50]","[1, 2]",1,1,2,50,5,2,2,1786 -17,39,"[2, 1]","[50, 10]","[1, 2]",1,1,2,50,5,2,1,1696 -17,40,"[3, 4]","[50, 20]","[2, 1]",1,5,3,250,5,1,3,2033 -17,41,"[4, 3]","[20, 90]","[2, 1]",2,5,3,450,9,1,4,5995 -17,42,"[3, 4]","[70, 10]","[2, 1]",2,5,3,350,7,1,3,2318 -17,43,"[1, 2]","[50, 20]","[1, 1]",2,1,1,50,5,1,1,1757 -17,44,"[2, 1]","[40, 70]","[1, 2]",2,5,2,200,4,2,2,1926 -17,45,"[4, 3]","[60, 70]","[2, 2]",2,5,4,300,6,2,4,2259 -17,46,"[1, 2]","[70, 90]","[1, 2]",2,5,2,450,9,2,2,2630 -17,47,"[2, 1]","[90, 70]","[1, 2]",2,1,2,90,9,2,1,3361 -17,48,"[4, 3]","[40, 40]","[2, 2]",2,5,4,200,4,2,4,2506 -17,49,"[3, 4]","[50, 30]","[2, 2]",2,1,4,30,3,2,3,1983 -17,50,"[1, 2]","[50, 20]","[1, 1]",2,5,1,250,5,1,1,2083 -17,51,"[2, 1]","[0, 40]","[1, 1]",2,5,1,200,4,1,2,1902 -17,52,"[4, 3]","[30, 10]","[2, 1]",2,1,3,10,1,1,3,8650 -17,53,"[1, 2]","[30, 30]","[1, 2]",2,1,2,30,3,2,1,1706 -17,54,"[2, 1]","[50, 20]","[1, 2]",2,1,2,50,5,2,1,2195 -17,55,"[1, 2]","[20, 10]","[1, 1]",2,1,1,20,2,1,1,2538 -17,56,"[1, 2]","[0, 10]","[1, 2]",2,1,2,10,1,2,2,2504 -17,57,"[3, 4]","[30, 10]","[2, 1]",2,5,3,150,3,1,3,1866 -17,58,"[3, 4]","[0, 20]","[2, 1]",2,5,3,0,0,1,4,1999 -17,59,"[3, 4]","[10, 30]","[2, 2]",2,5,4,150,3,2,4,2104 -17,60,"[3, 4]","[40, 20]","[2, 2]",2,1,4,20,2,2,3,2372 -17,61,"[2, 1]","[40, 20]","[1, 1]",2,1,1,20,2,1,1,1987 -17,62,"[1, 2]","[60, 50]","[1, 1]",2,5,1,300,6,1,1,2022 -17,63,"[1, 2]","[70, 40]","[1, 1]",2,1,1,70,7,1,1,1840 -17,64,"[3, 4]","[50, 40]","[2, 1]",2,1,3,50,5,1,3,2099 -17,65,"[2, 1]","[80, 70]","[1, 1]",2,5,1,350,7,1,1,2818 -17,66,"[2, 1]","[60, 80]","[1, 1]",2,5,1,400,8,1,2,1758 -17,67,"[4, 3]","[40, 60]","[2, 1]",2,5,3,300,6,1,4,2049 -17,68,"[2, 1]","[50, 70]","[1, 1]",2,1,1,70,7,1,2,2002 -17,69,"[1, 2]","[70, 60]","[1, 1]",2,1,1,70,7,1,1,3056 -17,70,"[3, 4]","[70, 50]","[2, 1]",2,1,3,70,7,1,3,1613 -17,71,"[3, 4]","[80, 60]","[2, 1]",2,5,3,400,8,1,3,1641 -17,72,"[3, 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-18,10,"[1, 2]","[90, 60]","[1, 2]",1,1,2,60,6,2,1,1839 -18,11,"[2, 1]","[50, 70]","[1, 2]",1,5,2,250,5,2,2,1851 -18,12,"[3, 4]","[40, 70]","[2, 1]",1,5,3,200,4,1,4,1753 -18,13,"[2, 1]","[70, 50]","[1, 1]",1,5,1,250,5,1,1,1651 -18,14,"[4, 3]","[80, 70]","[2, 2]",1,5,4,400,8,2,3,2319 -18,15,"[4, 3]","[80, 90]","[2, 2]",1,1,4,80,8,2,4,2189 -18,16,"[1, 2]","[70, 60]","[1, 2]",1,1,2,60,6,2,1,1771 -18,17,"[3, 4]","[40, 90]","[2, 1]",1,1,3,40,4,1,4,1732 -18,18,"[1, 2]","[40, 80]","[1, 2]",1,5,2,400,8,2,2,1753 -18,19,"[4, 3]","[80, 30]","[2, 2]",1,5,4,400,8,2,3,1783 -18,20,"[2, 1]","[90, 40]","[1, 2]",1,1,2,90,9,2,1,2252 -18,21,"[1, 2]","[50, 80]","[1, 2]",1,1,2,80,8,2,2,2110 -18,22,"[2, 1]","[80, 80]","[1, 1]",1,1,1,80,8,1,2,2471 -18,23,"[4, 3]","[90, 70]","[2, 2]",1,5,4,450,9,2,3,1680 -18,24,"[1, 2]","[70, 70]","[1, 2]",1,5,2,350,7,2,1,1636 -18,25,"[1, 2]","[60, 60]","[1, 1]",1,1,1,60,6,1,1,1731 -18,26,"[2, 1]","[50, 90]","[1, 1]",1,5,1,450,9,1,2,1647 -18,27,"[1, 2]","[80, 70]","[1, 1]",1,5,1,400,8,1,1,1611 -18,28,"[2, 1]","[60, 60]","[1, 1]",1,5,1,300,6,1,2,1713 -18,29,"[3, 4]","[90, 60]","[2, 1]",1,5,3,450,9,1,3,1702 -18,30,"[1, 2]","[90, 50]","[1, 2]",1,1,2,50,5,2,1,1730 -18,31,"[1, 2]","[80, 30]","[1, 1]",1,1,1,80,8,1,1,1741 -18,32,"[4, 3]","[80, 70]","[2, 1]",1,5,3,350,7,1,3,1735 -18,33,"[3, 4]","[80, 80]","[2, 1]",1,5,3,400,8,1,3,1690 -18,34,"[2, 1]","[70, 70]","[1, 2]",1,1,2,70,7,2,2,1881 -18,35,"[4, 3]","[70, 70]","[2, 1]",1,5,3,350,7,1,4,1898 -18,36,"[4, 3]","[60, 60]","[2, 1]",1,1,3,60,6,1,4,1861 -18,37,"[1, 2]","[40, 60]","[1, 2]",1,1,2,60,6,2,2,1595 -18,38,"[3, 4]","[70, 50]","[2, 1]",1,1,3,70,7,1,3,1869 -18,39,"[4, 3]","[30, 70]","[2, 1]",1,1,3,70,7,1,4,2637 -18,40,"[3, 4]","[80, 30]","[2, 2]",1,5,4,150,3,2,3,1705 -18,41,"[2, 1]","[0, 80]","[1, 1]",2,5,1,400,8,1,2,3935 -18,42,"[1, 2]","[80, 10]","[1, 2]",2,1,2,10,1,2,1,4524 -18,43,"[2, 1]","[0, 80]","[1, 1]",2,1,1,80,8,1,2,2076 -18,44,"[3, 4]","[80, 0]","[2, 2]",2,5,4,0,0,2,3,1397 -18,45,"[3, 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-18,63,"[1, 2]","[60, 40]","[1, 2]",2,5,2,200,4,2,1,1332 -18,64,"[2, 1]","[70, 70]","[1, 1]",2,1,1,70,7,1,2,1806 -18,65,"[1, 2]","[70, 60]","[1, 2]",2,5,2,300,6,2,1,1417 -18,66,"[3, 4]","[70, 30]","[2, 1]",2,5,3,350,7,1,3,1493 -18,67,"[3, 4]","[80, 20]","[2, 1]",2,5,3,400,8,1,3,2051 -18,68,"[1, 2]","[90, 0]","[1, 1]",2,5,1,450,9,1,1,1821 -18,69,"[2, 1]","[10, 80]","[1, 1]",2,1,1,80,8,1,2,1272 -18,70,"[4, 3]","[10, 90]","[2, 2]",2,5,4,50,1,2,4,7964 -18,71,"[2, 1]","[10, 80]","[1, 1]",2,5,1,400,8,1,2,23888 -18,72,"[2, 1]","[0, 60]","[1, 1]",2,5,1,300,6,1,2,5860 -18,73,"[2, 1]","[10, 60]","[1, 2]",2,5,2,50,1,2,2,1384 -18,74,"[4, 3]","[30, 30]","[2, 1]",2,5,3,150,3,1,4,1464 -18,75,"[1, 2]","[0, 10]","[1, 2]",2,5,2,50,1,2,2,13405 -18,76,"[1, 2]","[10, 30]","[1, 2]",2,1,2,30,3,2,2,3606 -18,77,"[4, 3]","[0, 20]","[2, 2]",2,1,4,0,0,2,4,1616 -18,78,"[4, 3]","[10, 0]","[2, 2]",2,5,4,50,1,2,3,15263 -18,79,"[1, 2]","[20, 30]","[1, 2]",2,1,2,30,3,2,2,5649 -18,80,"[2, 1]","[20, 40]","[1, 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20]","[2, 1]",1,5,3,200,4,1,3,2526 -19,19,"[2, 1]","[40, 30]","[1, 2]",1,5,2,200,4,2,1,1858 -19,20,"[2, 1]","[30, 30]","[1, 2]",1,5,2,150,3,2,2,2288 -19,21,"[2, 1]","[10, 20]","[1, 1]",1,1,1,20,2,1,2,2111 -19,22,"[4, 3]","[0, 10]","[2, 2]",1,5,4,0,0,2,4,1858 -19,23,"[3, 4]","[10, 10]","[2, 1]",1,5,3,50,1,1,3,4156 -19,24,"[2, 1]","[10, 10]","[1, 2]",1,1,2,10,1,2,2,2426 -19,25,"[3, 4]","[10, 30]","[2, 1]",1,1,3,10,1,1,4,1859 -19,26,"[4, 3]","[40, 10]","[2, 1]",1,1,3,10,1,1,3,1878 -19,27,"[1, 2]","[10, 60]","[1, 2]",1,1,2,60,6,2,2,2708 -19,28,"[2, 1]","[50, 40]","[1, 2]",1,5,2,250,5,2,1,1926 -19,29,"[3, 4]","[50, 60]","[2, 2]",1,5,4,300,6,2,4,2040 -19,30,"[1, 2]","[50, 80]","[1, 2]",1,5,2,400,8,2,2,1926 -19,31,"[3, 4]","[30, 70]","[2, 2]",1,1,4,70,7,2,4,2026 -19,32,"[4, 3]","[70, 30]","[2, 2]",1,1,4,70,7,2,3,1942 -19,33,"[2, 1]","[60, 30]","[1, 2]",1,1,2,60,6,2,1,2643 -19,34,"[2, 1]","[60, 50]","[1, 2]",1,5,2,300,6,2,1,2225 -19,35,"[2, 1]","[90, 30]","[1, 2]",1,5,2,450,9,2,1,1840 -19,36,"[3, 4]","[50, 60]","[2, 2]",1,5,4,300,6,2,4,2092 -19,37,"[1, 2]","[50, 80]","[1, 2]",1,1,2,80,8,2,2,2143 -19,38,"[2, 1]","[40, 70]","[1, 2]",1,5,2,200,4,2,2,2211 -19,39,"[4, 3]","[50, 40]","[2, 2]",1,1,4,50,5,2,3,1976 -19,40,"[3, 4]","[40, 50]","[2, 2]",1,5,4,250,5,2,4,2757 -19,41,"[2, 1]","[80, 60]","[1, 1]",2,5,1,300,6,1,1,5187 -19,42,"[3, 4]","[70, 80]","[2, 1]",2,5,3,350,7,1,4,1710 -19,43,"[1, 2]","[60, 80]","[1, 1]",2,5,1,300,6,1,2,1952 -19,44,"[1, 2]","[50, 70]","[1, 1]",2,5,1,250,5,1,2,1900 -19,45,"[2, 1]","[70, 40]","[1, 2]",2,1,2,70,7,2,1,2825 -19,46,"[2, 1]","[30, 20]","[1, 2]",2,1,2,30,3,2,1,1821 -19,47,"[2, 1]","[40, 10]","[1, 1]",2,1,1,10,1,1,1,1692 -19,48,"[4, 3]","[60, 30]","[2, 1]",2,1,3,30,3,1,3,1692 -19,49,"[1, 2]","[80, 40]","[1, 2]",2,1,2,40,4,2,1,2209 -19,50,"[3, 4]","[70, 40]","[2, 2]",2,5,4,200,4,2,3,2158 -19,51,"[3, 4]","[60, 30]","[2, 2]",2,1,4,30,3,2,3,1693 -19,52,"[3, 4]","[80, 40]","[2, 1]",2,5,3,400,8,1,3,1728 -19,53,"[4, 3]","[60, 40]","[2, 1]",2,1,3,40,4,1,3,1859 -19,54,"[4, 3]","[60, 60]","[2, 1]",2,5,3,300,6,1,4,1737 -19,55,"[1, 2]","[40, 40]","[1, 2]",2,1,2,40,4,2,1,2353 -19,56,"[4, 3]","[40, 20]","[2, 1]",2,1,3,20,2,1,3,1876 -19,57,"[4, 3]","[30, 10]","[2, 1]",2,5,3,50,1,1,3,1872 -19,58,"[1, 2]","[10, 20]","[1, 2]",2,5,2,100,2,2,2,1992 -19,59,"[4, 3]","[10, 20]","[2, 2]",2,1,4,10,1,2,4,1727 -19,60,"[3, 4]","[10, 20]","[2, 2]",2,5,4,100,2,2,4,1805 -19,61,"[4, 3]","[20, 30]","[2, 1]",2,1,3,30,3,1,4,1708 -19,62,"[2, 1]","[10, 40]","[1, 1]",2,1,1,40,4,1,2,2510 -19,63,"[3, 4]","[30, 20]","[2, 1]",2,5,3,150,3,1,3,2027 -19,64,"[4, 3]","[20, 20]","[2, 1]",2,1,3,20,2,1,4,2009 -19,65,"[2, 1]","[40, 0]","[1, 1]",2,1,1,0,0,1,1,1760 -19,66,"[2, 1]","[50, 20]","[1, 2]",2,5,2,250,5,2,1,3292 -19,67,"[3, 4]","[30, 60]","[2, 2]",2,1,4,60,6,2,4,1760 -19,68,"[3, 4]","[40, 70]","[2, 2]",2,5,4,350,7,2,4,1825 -19,69,"[1, 2]","[50, 60]","[1, 2]",2,5,2,300,6,2,2,2027 -19,70,"[2, 1]","[70, 50]","[1, 2]",2,1,2,70,7,2,1,2494 -19,71,"[1, 2]","[40, 70]","[1, 2]",2,5,2,350,7,2,2,1977 -19,72,"[4, 3]","[90, 40]","[2, 2]",2,5,4,450,9,2,3,1975 -19,73,"[1, 2]","[50, 70]","[1, 2]",2,5,2,350,7,2,2,2143 -19,74,"[4, 3]","[90, 60]","[2, 2]",2,1,4,90,9,2,3,2643 -19,75,"[2, 1]","[90, 60]","[1, 2]",2,5,2,450,9,2,1,2608 -19,76,"[3, 4]","[50, 80]","[2, 2]",2,1,4,80,8,2,4,2395 -19,77,"[1, 2]","[70, 80]","[1, 2]",2,1,2,80,8,2,2,2343 -19,78,"[4, 3]","[80, 70]","[2, 2]",2,5,4,400,8,2,3,1951 -19,79,"[1, 2]","[40, 80]","[1, 2]",2,5,2,400,8,2,2,1977 -19,80,"[4, 3]","[90, 20]","[2, 2]",2,5,4,450,9,2,3,3425 -19,1,"[1, 2]","[90, 20]","[1, 2]",1,1,2,20,2,2,1,3306 -19,2,"[2, 1]","[30, 70]","[1, 2]",1,5,2,150,3,2,2,2077 -19,3,"[3, 4]","[50, 50]","[2, 2]",1,5,4,250,5,2,3,1672 -19,4,"[3, 4]","[20, 70]","[2, 1]",1,1,3,20,2,1,4,1880 -19,5,"[2, 1]","[90, 30]","[1, 2]",1,1,2,90,9,2,1,1943 -19,6,"[1, 2]","[0, 90]","[1, 2]",1,1,2,90,9,2,2,2092 -19,7,"[4, 3]","[90, 0]","[2, 2]",1,1,4,90,9,2,3,1809 -19,8,"[1, 2]","[20, 80]","[1, 2]",1,1,2,80,8,2,2,1669 -19,9,"[4, 3]","[80, 30]","[2, 2]",1,1,4,80,8,2,3,1670 -19,10,"[3, 4]","[60, 70]","[2, 2]",1,5,4,350,7,2,4,1909 -19,11,"[2, 1]","[60, 60]","[1, 2]",1,5,2,300,6,2,2,1839 -19,12,"[1, 2]","[60, 70]","[1, 2]",1,5,2,350,7,2,2,1961 -19,13,"[2, 1]","[70, 40]","[1, 2]",1,5,2,350,7,2,1,1956 -19,14,"[4, 3]","[90, 30]","[2, 2]",1,1,4,90,9,2,3,1758 -19,15,"[3, 4]","[10, 50]","[2, 2]",1,5,4,250,5,2,4,2062 -19,16,"[4, 3]","[50, 40]","[2, 2]",1,5,4,250,5,2,3,1811 -19,17,"[2, 1]","[20, 0]","[1, 2]",1,5,2,100,2,2,1,1744 -19,18,"[4, 3]","[30, 0]","[2, 1]",1,5,3,0,0,1,3,1926 -19,19,"[4, 3]","[50, 10]","[2, 1]",1,1,3,10,1,1,3,2175 -19,20,"[2, 1]","[50, 20]","[1, 2]",1,5,2,250,5,2,1,1771 -19,21,"[1, 2]","[40, 90]","[1, 2]",1,1,2,90,9,2,2,3211 -19,22,"[3, 4]","[40, 90]","[2, 2]",1,1,4,90,9,2,4,2044 -19,23,"[3, 4]","[60, 80]","[2, 2]",1,5,4,400,8,2,4,2006 -19,24,"[3, 4]","[30, 80]","[2, 2]",1,1,4,80,8,2,4,2211 -19,25,"[2, 1]","[80, 30]","[1, 2]",1,1,2,80,8,2,1,1906 -19,26,"[2, 1]","[70, 10]","[1, 2]",1,5,2,350,7,2,1,1959 -19,27,"[2, 1]","[90, 0]","[1, 2]",1,5,2,450,9,2,1,2026 -19,28,"[4, 3]","[80, 0]","[2, 2]",1,1,4,80,8,2,3,1827 -19,29,"[2, 1]","[80, 10]","[1, 2]",1,1,2,80,8,2,1,1759 -19,30,"[1, 2]","[30, 90]","[1, 2]",1,1,2,90,9,2,2,2058 -19,31,"[1, 2]","[40, 70]","[1, 2]",1,5,2,350,7,2,2,1927 -19,32,"[4, 3]","[60, 40]","[2, 2]",1,5,4,300,6,2,3,1760 -19,33,"[2, 1]","[60, 70]","[1, 2]",1,1,2,60,6,2,2,1744 -19,34,"[4, 3]","[40, 70]","[2, 2]",1,1,4,40,4,2,4,1693 -19,35,"[4, 3]","[10, 60]","[2, 2]",1,1,4,10,1,2,4,2124 -19,36,"[4, 3]","[20, 70]","[2, 1]",1,1,3,70,7,1,4,1792 -19,37,"[3, 4]","[50, 80]","[2, 1]",1,1,3,50,5,1,4,1926 -19,38,"[1, 2]","[70, 90]","[1, 1]",1,5,1,350,7,1,2,2160 -19,39,"[3, 4]","[70, 40]","[2, 1]",1,1,3,70,7,1,3,2393 -19,40,"[3, 4]","[70, 40]","[2, 1]",1,1,3,70,7,1,3,3393 -19,41,"[4, 3]","[20, 70]","[2, 2]",2,5,4,100,2,2,4,5589 -19,42,"[4, 3]","[20, 80]","[2, 1]",2,1,3,80,8,1,4,1708 -19,43,"[4, 3]","[30, 80]","[2, 1]",2,5,3,400,8,1,4,2010 -19,44,"[4, 3]","[40, 80]","[2, 1]",2,1,3,80,8,1,4,1793 -19,45,"[1, 2]","[60, 50]","[1, 1]",2,1,1,60,6,1,1,2109 -19,46,"[1, 2]","[50, 20]","[1, 1]",2,1,1,50,5,1,1,1740 -19,47,"[1, 2]","[40, 10]","[1, 1]",2,5,1,200,4,1,1,1876 -19,48,"[2, 1]","[20, 20]","[1, 1]",2,5,1,100,2,1,2,2056 -19,49,"[1, 2]","[20, 10]","[1, 2]",2,5,2,50,1,2,1,1977 -19,50,"[3, 4]","[10, 40]","[2, 1]",2,5,3,50,1,1,4,2042 -19,51,"[1, 2]","[30, 40]","[1, 2]",2,5,2,200,4,2,2,1993 -19,52,"[1, 2]","[30, 70]","[1, 2]",2,5,2,350,7,2,2,1960 -19,53,"[2, 1]","[90, 70]","[1, 2]",2,5,2,450,9,2,1,1978 -19,54,"[3, 4]","[60, 80]","[2, 2]",2,1,4,80,8,2,4,2009 -19,55,"[4, 3]","[60, 80]","[2, 2]",2,1,4,60,6,2,4,2061 -19,56,"[1, 2]","[60, 50]","[1, 2]",2,5,2,250,5,2,1,1977 -19,57,"[1, 2]","[80, 50]","[1, 2]",2,5,2,250,5,2,1,1775 -19,58,"[3, 4]","[80, 70]","[2, 2]",2,1,4,70,7,2,3,1894 -19,59,"[3, 4]","[80, 50]","[2, 2]",2,5,4,250,5,2,3,1807 -19,60,"[3, 4]","[80, 60]","[2, 2]",2,1,4,60,6,2,3,1793 -19,61,"[4, 3]","[50, 80]","[2, 2]",2,5,4,250,5,2,4,2089 -19,62,"[3, 4]","[70, 50]","[2, 2]",2,5,4,250,5,2,3,1725 -19,63,"[4, 3]","[40, 50]","[2, 2]",2,5,4,200,4,2,4,1794 -19,64,"[2, 1]","[30, 20]","[1, 2]",2,5,2,150,3,2,1,2227 -19,65,"[4, 3]","[40, 10]","[2, 2]",2,1,4,40,4,2,3,1867 -19,66,"[2, 1]","[60, 40]","[1, 1]",2,1,1,40,4,1,1,2011 -19,67,"[4, 3]","[70, 60]","[2, 1]",2,1,3,60,6,1,3,1656 -19,68,"[4, 3]","[80, 70]","[2, 1]",2,5,3,350,7,1,3,1880 -19,69,"[4, 3]","[80, 60]","[2, 1]",2,5,3,300,6,1,3,1891 -19,70,"[1, 2]","[40, 90]","[1, 1]",2,5,1,200,4,1,2,1826 -19,71,"[2, 1]","[70, 20]","[1, 1]",2,1,1,20,2,1,1,1793 -19,72,"[1, 2]","[10, 40]","[1, 2]",2,5,2,200,4,2,2,2060 -19,73,"[2, 1]","[60, 10]","[1, 2]",2,1,2,60,6,2,1,2060 -19,74,"[1, 2]","[10, 70]","[1, 2]",2,5,2,350,7,2,2,2076 -19,75,"[2, 1]","[90, 10]","[1, 2]",2,1,2,90,9,2,1,2060 -19,76,"[2, 1]","[80, 10]","[1, 2]",2,5,2,400,8,2,1,1878 -19,77,"[3, 4]","[60, 80]","[2, 2]",2,1,4,80,8,2,4,1989 -19,78,"[3, 4]","[40, 80]","[2, 2]",2,1,4,80,8,2,4,2042 -19,79,"[4, 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-20,17,"[4, 3]","[70, 40]","[2, 1]",1,5,3,200,4,1,3,2047 -20,18,"[3, 4]","[60, 90]","[2, 1]",1,5,3,300,6,1,4,2856 -20,19,"[2, 1]","[70, 80]","[1, 2]",1,1,2,70,7,2,2,2997 -20,20,"[4, 3]","[80, 70]","[2, 2]",1,5,4,400,8,2,3,3088 -20,21,"[3, 4]","[70, 60]","[2, 2]",1,1,4,60,6,2,3,2305 -20,22,"[2, 1]","[80, 60]","[1, 2]",1,1,2,80,8,2,1,2281 -20,23,"[4, 3]","[70, 30]","[2, 2]",1,1,4,70,7,2,3,1905 -20,24,"[4, 3]","[50, 30]","[2, 1]",1,1,3,30,3,1,3,3055 -20,25,"[1, 2]","[20, 50]","[1, 1]",1,5,1,100,2,1,2,2255 -20,26,"[1, 2]","[10, 70]","[1, 2]",1,5,2,350,7,2,2,2188 -20,27,"[1, 2]","[20, 40]","[1, 1]",1,1,1,20,2,1,2,3388 -20,28,"[3, 4]","[40, 70]","[2, 2]",1,5,4,350,7,2,4,1873 -20,29,"[2, 1]","[80, 60]","[1, 2]",1,1,2,80,8,2,1,2163 -20,30,"[3, 4]","[50, 70]","[2, 2]",1,5,4,350,7,2,4,2263 -20,31,"[1, 2]","[60, 80]","[1, 2]",1,5,2,400,8,2,2,2531 -20,32,"[3, 4]","[80, 80]","[2, 2]",1,5,4,400,8,2,3,2521 -20,33,"[1, 2]","[90, 70]","[1, 2]",1,5,2,350,7,2,1,2005 -20,34,"[3, 4]","[80, 60]","[2, 2]",1,1,4,60,6,2,3,2605 -20,35,"[3, 4]","[80, 30]","[2, 2]",1,1,4,30,3,2,3,2122 -20,36,"[3, 4]","[70, 20]","[2, 1]",1,5,3,350,7,1,3,2096 -20,37,"[1, 2]","[60, 20]","[1, 1]",1,1,1,60,6,1,1,1956 -20,38,"[1, 2]","[60, 20]","[1, 1]",1,5,1,300,6,1,1,1905 -20,39,"[3, 4]","[90, 30]","[2, 1]",1,5,3,450,9,1,3,2580 -20,40,"[4, 3]","[20, 70]","[2, 1]",1,5,3,350,7,1,4,4163 -20,41,"[4, 3]","[20, 80]","[2, 1]",2,5,3,400,8,1,4,6474 -20,42,"[4, 3]","[30, 80]","[2, 1]",2,1,3,80,8,1,4,1839 -20,43,"[4, 3]","[20, 90]","[2, 2]",2,5,4,100,2,2,4,2521 -20,44,"[3, 4]","[50, 10]","[2, 1]",2,5,3,250,5,1,3,2114 -20,45,"[2, 1]","[50, 70]","[1, 1]",2,1,1,70,7,1,2,2172 -20,46,"[2, 1]","[60, 50]","[1, 1]",2,1,1,50,5,1,1,2272 -20,47,"[3, 4]","[90, 10]","[2, 1]",2,1,3,90,9,1,3,1955 -20,48,"[3, 4]","[80, 10]","[2, 1]",2,5,3,400,8,1,3,2389 -20,49,"[3, 4]","[50, 30]","[2, 2]",2,5,4,150,3,2,3,2056 -20,50,"[2, 1]","[30, 60]","[1, 1]",2,1,1,60,6,1,2,2155 -20,51,"[1, 2]","[50, 10]","[1, 1]",2,5,1,250,5,1,1,2222 -20,52,"[3, 4]","[40, 30]","[2, 1]",2,1,3,40,4,1,3,1964 -20,53,"[2, 1]","[20, 40]","[1, 1]",2,1,1,40,4,1,2,2080 -20,54,"[1, 2]","[30, 30]","[1, 2]",2,5,2,150,3,2,1,2064 -20,55,"[1, 2]","[60, 20]","[1, 1]",2,5,1,300,6,1,1,2597 -20,56,"[1, 2]","[40, 20]","[1, 1]",2,5,1,200,4,1,1,1963 -20,57,"[2, 1]","[10, 40]","[1, 1]",2,1,1,40,4,1,2,2089 -20,58,"[3, 4]","[10, 10]","[2, 2]",2,5,4,50,1,2,3,2405 -20,59,"[4, 3]","[20, 10]","[2, 1]",2,5,3,50,1,1,3,2196 -20,60,"[1, 2]","[10, 50]","[1, 2]",2,5,2,250,5,2,2,1980 -20,61,"[1, 2]","[20, 60]","[1, 2]",2,1,2,60,6,2,2,2089 -20,62,"[4, 3]","[80, 40]","[2, 2]",2,5,4,400,8,2,3,2306 -20,63,"[4, 3]","[60, 60]","[2, 2]",2,5,4,300,6,2,4,2554 -20,64,"[4, 3]","[80, 80]","[2, 2]",2,1,4,80,8,2,4,178739 -20,65,"[4, 3]","[70, 80]","[2, 2]",2,1,4,70,7,2,4,2056 -20,66,"[1, 2]","[80, 50]","[1, 2]",2,1,2,50,5,2,1,1947 -20,67,"[1, 2]","[70, 10]","[1, 2]",2,5,2,50,1,2,1,2606 -20,68,"[4, 3]","[10, 80]","[2, 1]",2,1,3,80,8,1,4,1881 -20,69,"[2, 1]","[10, 90]","[1, 1]",2,5,1,450,9,1,2,2031 -20,70,"[3, 4]","[80, 20]","[2, 1]",2,1,3,80,8,1,3,2049 -20,71,"[2, 1]","[50, 70]","[1, 1]",2,1,1,70,7,1,2,1988 -20,72,"[2, 1]","[40, 90]","[1, 1]",2,1,1,90,9,1,2,1854 -20,73,"[4, 3]","[50, 90]","[2, 1]",2,1,3,90,9,1,4,1833 -20,74,"[3, 4]","[70, 70]","[2, 1]",2,1,3,70,7,1,3,2072 -20,75,"[4, 3]","[30, 60]","[2, 1]",2,1,3,60,6,1,4,2150 -20,76,"[1, 2]","[90, 30]","[1, 1]",2,1,1,90,9,1,1,2030 -20,77,"[2, 1]","[50, 70]","[1, 1]",2,5,1,350,7,1,2,2116 -20,78,"[1, 2]","[80, 40]","[1, 1]",2,1,1,80,8,1,1,2122 -20,79,"[4, 3]","[50, 70]","[2, 1]",2,1,3,70,7,1,4,1856 -20,80,"[3, 4]","[80, 30]","[2, 1]",2,5,3,400,8,1,3,2496 -20,1,"[4, 3]","[30, 50]","[2, 1]",1,5,3,250,5,1,4,4087 -20,2,"[3, 4]","[60, 10]","[2, 1]",1,1,3,60,6,1,3,2139 -20,3,"[2, 1]","[0, 40]","[1, 1]",1,5,1,200,4,1,2,2056 -20,4,"[1, 2]","[40, 0]","[1, 1]",1,1,1,40,4,1,1,2056 -20,5,"[4, 3]","[30, 70]","[2, 2]",1,5,4,150,3,2,4,2306 -20,6,"[3, 4]","[60, 50]","[2, 2]",1,5,4,250,5,2,3,2056 -20,7,"[3, 4]","[80, 70]","[2, 2]",1,1,4,70,7,2,3,1806 -20,8,"[2, 1]","[80, 90]","[1, 2]",1,1,2,80,8,2,2,2030 -20,9,"[2, 1]","[70, 80]","[1, 2]",1,5,2,350,7,2,2,2814 -20,10,"[3, 4]","[90, 80]","[2, 2]",1,5,4,400,8,2,3,1954 -20,11,"[2, 1]","[50, 80]","[1, 2]",1,1,2,50,5,2,2,2098 -20,12,"[2, 1]","[60, 70]","[1, 1]",1,5,1,350,7,1,2,2164 -20,13,"[1, 2]","[70, 50]","[1, 1]",1,1,1,70,7,1,1,1921 -20,14,"[1, 2]","[70, 30]","[1, 1]",1,1,1,70,7,1,1,1997 -20,15,"[2, 1]","[30, 90]","[1, 1]",1,5,1,450,9,1,2,2154 -20,16,"[1, 2]","[70, 30]","[1, 1]",1,1,1,70,7,1,1,1990 -20,17,"[4, 3]","[30, 50]","[2, 1]",1,5,3,250,5,1,4,2072 -20,18,"[2, 1]","[50, 50]","[1, 1]",1,1,1,50,5,1,2,1922 -20,19,"[1, 2]","[20, 50]","[1, 2]",1,5,2,250,5,2,2,3699 -20,20,"[4, 3]","[30, 20]","[2, 2]",1,5,4,150,3,2,3,2056 -20,21,"[3, 4]","[20, 40]","[2, 1]",1,5,3,100,2,1,4,3472 -20,22,"[4, 3]","[50, 0]","[2, 2]",1,5,4,250,5,2,3,2481 -20,23,"[1, 2]","[30, 50]","[1, 2]",1,1,2,50,5,2,2,1956 -20,24,"[4, 3]","[40, 10]","[2, 1]",1,1,3,10,1,1,3,1855 -20,25,"[1, 2]","[40, 30]","[1, 2]",1,5,2,150,3,2,1,2005 -20,26,"[1, 2]","[60, 20]","[1, 2]",1,1,2,20,2,2,1,1930 -20,27,"[2, 1]","[20, 40]","[1, 1]",1,5,1,200,4,1,2,1914 -20,28,"[4, 3]","[40, 30]","[2, 1]",1,5,3,150,3,1,3,1931 -20,29,"[2, 1]","[10, 20]","[1, 2]",1,1,2,10,1,2,2,2365 -20,30,"[1, 2]","[10, 10]","[1, 1]",1,1,1,10,1,1,1,3363 -20,31,"[4, 3]","[20, 40]","[2, 2]",1,1,4,20,2,2,4,2206 -20,32,"[3, 4]","[50, 40]","[2, 1]",1,5,3,250,5,1,3,3030 -20,33,"[3, 4]","[50, 30]","[2, 1]",1,5,3,250,5,1,3,2116 -20,34,"[2, 1]","[60, 10]","[1, 1]",1,1,1,10,1,1,1,2088 -20,35,"[1, 2]","[0, 70]","[1, 2]",1,1,2,70,7,2,2,3205 -20,36,"[1, 2]","[0, 80]","[1, 2]",1,1,2,80,8,2,2,2772 -20,37,"[4, 3]","[70, 0]","[2, 2]",1,1,4,70,7,2,3,2031 -20,38,"[2, 1]","[80, 10]","[1, 2]",1,5,2,400,8,2,1,1919 -20,39,"[3, 4]","[20, 70]","[2, 2]",1,5,4,350,7,2,4,1984 -20,40,"[4, 3]","[80, 60]","[2, 2]",1,1,4,80,8,2,3,2172 -20,41,"[4, 3]","[60, 80]","[2, 1]",2,1,3,80,8,1,4,5541 -20,42,"[4, 3]","[70, 90]","[2, 1]",2,1,3,90,9,1,4,1866 -20,43,"[3, 4]","[60, 90]","[2, 1]",2,5,3,300,6,1,4,2423 -20,44,"[1, 2]","[60, 80]","[1, 1]",2,1,1,60,6,1,2,2105 -20,45,"[3, 4]","[70, 60]","[2, 1]",2,1,3,70,7,1,3,1946 -20,46,"[4, 3]","[60, 90]","[2, 1]",2,1,3,90,9,1,4,1863 -20,47,"[4, 3]","[80, 90]","[2, 1]",2,5,3,450,9,1,4,1997 -20,48,"[4, 3]","[70, 80]","[2, 1]",2,5,3,400,8,1,4,1933 -20,49,"[2, 1]","[60, 70]","[1, 1]",2,5,1,350,7,1,2,1839 -20,50,"[3, 4]","[60, 60]","[2, 1]",2,1,3,60,6,1,3,2255 -20,51,"[2, 1]","[50, 60]","[1, 1]",2,5,1,300,6,1,2,1879 -20,52,"[4, 3]","[60, 70]","[2, 1]",2,5,3,350,7,1,4,1881 -20,53,"[1, 2]","[80, 50]","[1, 1]",2,5,1,400,8,1,1,2122 -20,54,"[4, 3]","[30, 60]","[2, 1]",2,1,3,60,6,1,4,1823 -20,55,"[1, 2]","[70, 40]","[1, 1]",2,5,1,350,7,1,1,2722 -20,56,"[3, 4]","[50, 0]","[2, 1]",2,5,3,250,5,1,3,2148 -20,57,"[2, 1]","[0, 20]","[1, 2]",2,5,2,0,0,2,2,2639 -20,58,"[1, 2]","[30, 30]","[1, 1]",2,5,1,150,3,1,1,2172 -20,59,"[2, 1]","[40, 40]","[1, 1]",2,5,1,200,4,1,2,2183 -20,60,"[3, 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2]",2,1,4,20,2,2,3,1946 -20,78,"[1, 2]","[20, 0]","[1, 1]",2,1,1,20,2,1,1,1948 -20,79,"[2, 1]","[40, 30]","[1, 1]",2,1,1,30,3,1,1,3120 -20,80,"[2, 1]","[60, 30]","[1, 1]",2,1,1,30,3,1,1,2355 -21,1,"[1, 2]","[60, 40]","[1, 1]",1,1,1,60,6,1,1,4791 -21,2,"[2, 1]","[30, 10]","[1, 1]",1,1,1,10,1,1,1,2229 -21,3,"[3, 4]","[10, 10]","[2, 2]",1,1,4,10,1,2,3,1748 -21,4,"[3, 4]","[40, 10]","[2, 1]",1,5,3,200,4,1,3,2661 -21,5,"[2, 1]","[0, 50]","[1, 2]",1,1,2,0,0,2,2,3103 -21,6,"[4, 3]","[50, 60]","[2, 1]",1,5,3,300,6,1,4,1844 -21,7,"[1, 2]","[80, 60]","[1, 1]",1,1,1,80,8,1,1,2137 -21,8,"[4, 3]","[80, 70]","[2, 1]",1,5,3,350,7,1,3,1853 -21,9,"[2, 1]","[50, 90]","[1, 1]",1,1,1,90,9,1,2,1994 -21,10,"[2, 1]","[60, 80]","[1, 1]",1,5,1,400,8,1,2,3119 -21,11,"[1, 2]","[70, 60]","[1, 1]",1,1,1,70,7,1,1,2444 -21,12,"[2, 1]","[60, 70]","[1, 1]",1,5,1,350,7,1,2,2195 -21,13,"[4, 3]","[70, 60]","[2, 1]",1,1,3,60,6,1,3,1810 -21,14,"[2, 1]","[70, 80]","[1, 1]",1,5,1,400,8,1,2,1837 -21,15,"[1, 2]","[80, 90]","[1, 1]",1,1,1,80,8,1,2,2220 -21,16,"[2, 1]","[90, 70]","[1, 1]",1,5,1,350,7,1,1,2120 -21,17,"[2, 1]","[50, 80]","[1, 1]",1,5,1,400,8,1,2,2152 -21,18,"[4, 3]","[40, 70]","[2, 1]",1,1,3,70,7,1,4,1794 -21,19,"[3, 4]","[40, 40]","[2, 1]",1,1,3,40,4,1,3,2903 -21,20,"[4, 3]","[20, 20]","[2, 2]",1,5,4,100,2,2,4,2220 -21,21,"[1, 2]","[40, 20]","[1, 1]",1,1,1,40,4,1,1,2362 -21,22,"[4, 3]","[30, 60]","[2, 1]",1,1,3,60,6,1,4,1853 -21,23,"[1, 2]","[80, 40]","[1, 1]",1,1,1,80,8,1,1,1828 -21,24,"[4, 3]","[60, 90]","[2, 1]",1,5,3,450,9,1,4,1912 -21,25,"[4, 3]","[40, 70]","[2, 1]",1,5,3,350,7,1,4,2003 -21,26,"[4, 3]","[60, 90]","[2, 1]",1,5,3,450,9,1,4,1711 -21,27,"[4, 3]","[60, 70]","[2, 1]",1,1,3,70,7,1,4,1770 -21,28,"[2, 1]","[70, 70]","[1, 1]",1,5,1,350,7,1,2,3411 -21,29,"[1, 2]","[80, 40]","[1, 1]",1,5,1,400,8,1,1,2220 -21,30,"[4, 3]","[40, 80]","[2, 1]",1,5,3,400,8,1,4,1754 -21,31,"[3, 4]","[80, 50]","[2, 1]",1,5,3,400,8,1,3,1928 -21,32,"[3, 4]","[80, 40]","[2, 1]",1,5,3,400,8,1,3,2154 -21,33,"[3, 4]","[60, 30]","[2, 1]",1,5,3,300,6,1,3,2029 -21,34,"[1, 2]","[50, 50]","[1, 1]",1,1,1,50,5,1,1,1803 -21,35,"[4, 3]","[40, 80]","[2, 1]",1,5,3,400,8,1,4,1804 -21,36,"[1, 2]","[80, 50]","[1, 1]",1,5,1,400,8,1,1,1771 -21,37,"[2, 1]","[70, 80]","[1, 1]",1,1,1,80,8,1,2,1811 -21,38,"[3, 4]","[90, 70]","[2, 1]",1,1,3,90,9,1,3,1779 -21,39,"[2, 1]","[50, 60]","[1, 1]",1,5,1,300,6,1,2,1736 -21,40,"[2, 1]","[70, 90]","[1, 1]",1,5,1,450,9,1,2,1978 -21,41,"[2, 1]","[80, 80]","[1, 1]",2,5,1,400,8,1,2,4183 -21,42,"[2, 1]","[80, 90]","[1, 1]",2,5,1,450,9,1,2,2587 -21,43,"[4, 3]","[70, 80]","[2, 1]",2,1,3,80,8,1,4,2045 -21,44,"[3, 4]","[80, 40]","[2, 1]",2,1,3,80,8,1,3,2054 -21,45,"[1, 2]","[70, 70]","[1, 1]",2,1,1,70,7,1,1,2203 -21,46,"[2, 1]","[70, 70]","[1, 1]",2,1,1,70,7,1,2,2004 -21,47,"[3, 4]","[80, 70]","[2, 1]",2,5,3,400,8,1,3,1870 -21,48,"[4, 3]","[40, 60]","[2, 1]",2,5,3,300,6,1,4,1903 -21,49,"[1, 2]","[70, 60]","[1, 1]",2,5,1,350,7,1,1,2053 -21,50,"[3, 4]","[60, 40]","[2, 1]",2,1,3,60,6,1,3,1887 -21,51,"[4, 3]","[30, 60]","[2, 1]",2,1,3,60,6,1,4,1828 -21,52,"[2, 1]","[30, 70]","[1, 1]",2,5,1,350,7,1,2,1862 -21,53,"[1, 2]","[0, 30]","[1, 1]",2,5,1,0,0,1,2,2111 -21,54,"[4, 3]","[10, 20]","[2, 2]",2,5,4,50,1,2,4,1961 -21,55,"[3, 4]","[20, 0]","[2, 2]",2,1,4,0,0,2,3,1811 -21,56,"[2, 1]","[0, 10]","[1, 1]",2,5,1,50,1,1,2,2136 -21,57,"[1, 2]","[0, 10]","[1, 1]",2,5,1,0,0,1,2,2204 -21,58,"[1, 2]","[30, 30]","[1, 2]",2,1,2,30,3,2,1,2461 -21,59,"[4, 3]","[30, 30]","[2, 2]",2,1,4,30,3,2,4,1763 -21,60,"[4, 3]","[30, 10]","[2, 2]",2,1,4,30,3,2,3,1945 -21,61,"[3, 4]","[40, 30]","[2, 2]",2,1,4,30,3,2,3,2095 -21,62,"[2, 1]","[40, 60]","[1, 2]",2,5,2,200,4,2,2,2353 -21,63,"[1, 2]","[60, 40]","[1, 2]",2,1,2,40,4,2,1,1895 -21,64,"[1, 2]","[50, 60]","[1, 2]",2,5,2,300,6,2,2,1903 -21,65,"[4, 3]","[30, 60]","[2, 2]",2,1,4,30,3,2,4,1996 -21,66,"[4, 3]","[30, 90]","[2, 1]",2,1,3,90,9,1,4,1929 -21,67,"[2, 1]","[40, 80]","[1, 1]",2,5,1,400,8,1,2,1878 -21,68,"[3, 4]","[70, 20]","[2, 1]",2,1,3,70,7,1,3,3611 -21,69,"[4, 3]","[10, 70]","[2, 1]",2,5,3,350,7,1,4,1978 -21,70,"[1, 2]","[80, 60]","[1, 1]",2,5,1,400,8,1,1,2027 -21,71,"[3, 4]","[70, 70]","[2, 1]",2,5,3,350,7,1,3,1954 -21,72,"[4, 3]","[70, 40]","[2, 1]",2,1,3,40,4,1,3,1837 -21,73,"[1, 2]","[20, 80]","[1, 2]",2,1,2,80,8,2,2,2537 -21,74,"[3, 4]","[60, 80]","[2, 2]",2,5,4,400,8,2,4,2012 -21,75,"[3, 4]","[70, 80]","[2, 2]",2,5,4,400,8,2,4,1862 -21,76,"[2, 1]","[50, 50]","[1, 2]",2,1,2,50,5,2,2,2737 -21,77,"[1, 2]","[50, 30]","[1, 1]",2,1,1,50,5,1,1,2270 -21,78,"[4, 3]","[60, 80]","[2, 1]",2,1,3,80,8,1,4,1962 -21,79,"[4, 3]","[90, 70]","[2, 1]",2,1,3,70,7,1,3,1986 -21,80,"[1, 2]","[40, 80]","[1, 1]",2,1,1,40,4,1,2,2020 -21,1,"[4, 3]","[70, 10]","[2, 2]",1,1,4,70,7,2,3,3779 -21,2,"[3, 4]","[0, 80]","[2, 2]",1,5,4,400,8,2,4,2270 -21,3,"[2, 1]","[90, 10]","[1, 1]",1,1,1,10,1,1,1,1754 -21,4,"[2, 1]","[80, 20]","[1, 2]",1,1,2,80,8,2,1,2078 -21,5,"[3, 4]","[10, 70]","[2, 2]",1,5,4,350,7,2,4,2005 -21,6,"[3, 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1]",1,1,1,60,6,1,1,1274 -24,29,"[1, 2]","[30, 80]","[1, 2]",1,5,2,400,8,2,2,1281 -24,30,"[2, 1]","[60, 40]","[1, 1]",1,1,1,40,4,1,1,1290 -24,31,"[3, 4]","[60, 40]","[2, 2]",1,1,4,40,4,2,3,1270 -24,32,"[4, 3]","[20, 60]","[2, 1]",1,1,3,60,6,1,4,1257 -24,33,"[1, 2]","[50, 40]","[1, 2]",1,5,2,200,4,2,1,1272 -24,34,"[4, 3]","[50, 30]","[2, 1]",1,1,3,30,3,1,3,1264 -24,35,"[4, 3]","[50, 30]","[2, 1]",1,5,3,150,3,1,3,1277 -24,36,"[3, 4]","[20, 70]","[2, 2]",1,5,4,350,7,2,4,1273 -24,37,"[2, 1]","[70, 50]","[1, 1]",1,1,1,50,5,1,1,1277 -24,38,"[4, 3]","[70, 10]","[2, 1]",1,1,3,10,1,1,3,1266 -24,39,"[1, 2]","[40, 70]","[1, 2]",1,5,2,350,7,2,2,1287 -24,40,"[3, 4]","[30, 70]","[2, 2]",1,5,4,350,7,2,4,1602 -24,41,"[2, 1]","[70, 30]","[1, 2]",2,5,2,350,7,2,1,5684 -24,42,"[1, 2]","[20, 60]","[1, 1]",2,5,1,100,2,1,2,1271 -24,43,"[2, 1]","[30, 20]","[1, 2]",2,5,2,150,3,2,1,1257 -24,44,"[2, 1]","[40, 10]","[1, 2]",2,1,2,40,4,2,1,1271 -24,45,"[4, 3]","[30, 0]","[2, 2]",2,5,4,150,3,2,3,1249 -24,46,"[1, 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4]","[30, 20]","[2, 2]",1,1,4,20,2,2,3,1188 -24,20,"[1, 2]","[10, 40]","[1, 2]",1,5,2,200,4,2,2,1319 -24,21,"[1, 2]","[20, 60]","[1, 2]",1,5,2,300,6,2,2,3635 -24,22,"[4, 3]","[50, 30]","[2, 1]",1,5,3,150,3,1,3,1669 -24,23,"[2, 1]","[90, 10]","[1, 1]",1,1,1,10,1,1,1,6503 -24,24,"[4, 3]","[60, 20]","[2, 1]",1,5,3,100,2,1,3,1686 -24,25,"[1, 2]","[20, 90]","[1, 2]",1,5,2,450,9,2,2,1551 -24,26,"[1, 2]","[30, 90]","[1, 2]",1,1,2,90,9,2,2,1652 -24,27,"[4, 3]","[80, 10]","[2, 1]",1,5,3,50,1,1,3,3234 -24,28,"[2, 1]","[90, 20]","[1, 1]",1,5,1,100,2,1,1,4703 -24,29,"[4, 3]","[70, 50]","[2, 1]",1,1,3,50,5,1,3,3986 -24,30,"[3, 4]","[40, 70]","[2, 2]",1,1,4,70,7,2,4,1418 -24,31,"[2, 1]","[50, 60]","[1, 1]",1,5,1,300,6,1,2,1502 -24,32,"[1, 2]","[80, 10]","[1, 2]",1,1,2,10,1,2,1,1436 -24,33,"[3, 4]","[70, 0]","[2, 2]",1,5,4,0,0,2,3,9685 -24,34,"[3, 4]","[90, 20]","[2, 2]",1,5,4,100,2,2,3,2050 -24,35,"[4, 3]","[40, 80]","[2, 1]",1,1,3,80,8,1,4,2269 -24,36,"[2, 1]","[60, 70]","[1, 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-24,72,"[4, 3]","[60, 40]","[2, 1]",2,5,3,200,4,1,3,1419 -24,73,"[2, 1]","[70, 40]","[1, 1]",2,1,1,40,4,1,1,1336 -24,74,"[4, 3]","[90, 10]","[2, 1]",2,5,3,50,1,1,3,1452 -24,75,"[1, 2]","[40, 70]","[1, 1]",2,1,1,40,4,1,2,1368 -24,76,"[2, 1]","[80, 50]","[1, 2]",2,1,2,80,8,2,1,1254 -24,77,"[1, 2]","[80, 70]","[1, 1]",2,1,1,80,8,1,1,1520 -24,78,"[2, 1]","[90, 60]","[1, 1]",2,5,1,300,6,1,1,2073 -24,79,"[4, 3]","[90, 80]","[2, 1]",2,5,3,400,8,1,3,2987 -24,80,"[1, 2]","[60, 90]","[1, 2]",2,1,2,90,9,2,2,1635 -25,1,"[2, 1]","[40, 70]","[1, 1]",1,1,1,70,7,1,2,11362 -25,2,"[3, 4]","[80, 40]","[2, 1]",1,5,3,400,8,1,3,2089 -25,3,"[3, 4]","[80, 60]","[2, 1]",1,1,3,80,8,1,3,2114 -25,4,"[2, 1]","[30, 90]","[1, 2]",1,5,2,150,3,2,2,2204 -25,5,"[1, 2]","[70, 30]","[1, 2]",1,1,2,30,3,2,1,2967 -25,6,"[4, 3]","[10, 80]","[2, 2]",1,1,4,10,1,2,4,2240 -25,7,"[4, 3]","[0, 80]","[2, 1]",1,5,3,400,8,1,4,2315 -25,8,"[4, 3]","[20, 40]","[2, 1]",1,1,3,40,4,1,4,2298 -25,9,"[4, 3]","[50, 0]","[2, 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-25,45,"[4, 3]","[0, 50]","[2, 1]",2,5,3,250,5,1,4,2049 -25,46,"[4, 3]","[40, 30]","[2, 1]",2,5,3,150,3,1,3,2608 -25,47,"[1, 2]","[30, 20]","[1, 1]",2,5,1,150,3,1,1,2515 -25,48,"[1, 2]","[10, 10]","[1, 1]",2,5,1,50,1,1,1,2305 -25,49,"[3, 4]","[10, 50]","[2, 1]",2,1,3,10,1,1,4,2148 -25,50,"[4, 3]","[30, 10]","[2, 2]",2,5,4,150,3,2,3,2349 -25,51,"[2, 1]","[10, 0]","[1, 2]",2,1,2,10,1,2,1,2548 -25,52,"[1, 2]","[30, 50]","[1, 1]",2,5,1,150,3,1,2,2583 -25,53,"[2, 1]","[40, 60]","[1, 1]",2,1,1,60,6,1,2,2491 -25,54,"[2, 1]","[40, 80]","[1, 2]",2,5,2,200,4,2,2,2133 -25,55,"[3, 4]","[80, 20]","[2, 2]",2,1,4,20,2,2,3,2574 -25,56,"[2, 1]","[0, 80]","[1, 1]",2,1,1,80,8,1,2,2156 -25,57,"[4, 3]","[10, 80]","[2, 1]",2,5,3,400,8,1,4,2248 -25,58,"[3, 4]","[70, 20]","[2, 1]",2,1,3,70,7,1,3,2147 -25,59,"[1, 2]","[80, 10]","[1, 2]",2,5,2,50,1,2,1,2475 -25,60,"[2, 1]","[0, 90]","[1, 2]",2,5,2,0,0,2,2,2366 -25,61,"[3, 4]","[70, 40]","[2, 1]",2,5,3,350,7,1,3,2334 -25,62,"[3, 4]","[60, 60]","[2, 1]",2,1,3,60,6,1,3,2182 -25,63,"[3, 4]","[90, 70]","[2, 2]",2,5,4,350,7,2,3,2207 -25,64,"[3, 4]","[60, 70]","[2, 2]",2,1,4,70,7,2,4,2250 -25,65,"[3, 4]","[80, 80]","[2, 1]",2,5,3,400,8,1,3,2079 -25,66,"[1, 2]","[70, 80]","[1, 1]",2,5,1,350,7,1,2,2233 -25,67,"[1, 2]","[90, 70]","[1, 1]",2,5,1,450,9,1,1,2216 -25,68,"[2, 1]","[50, 80]","[1, 1]",2,1,1,80,8,1,2,2558 -25,69,"[3, 4]","[90, 60]","[2, 2]",2,1,4,60,6,2,3,2172 -25,70,"[2, 1]","[30, 80]","[1, 1]",2,1,1,80,8,1,2,2165 -25,71,"[3, 4]","[80, 20]","[2, 2]",2,1,4,20,2,2,3,2199 -25,72,"[4, 3]","[50, 80]","[2, 1]",2,1,3,80,8,1,4,2147 -25,73,"[1, 2]","[90, 30]","[1, 1]",2,1,1,90,9,1,1,2198 -25,74,"[2, 1]","[20, 50]","[1, 1]",2,1,1,50,5,1,2,2080 -25,75,"[1, 2]","[40, 30]","[1, 2]",2,1,2,30,3,2,1,2147 -25,76,"[2, 1]","[30, 10]","[1, 1]",2,1,1,10,1,1,1,2099 -25,77,"[4, 3]","[70, 10]","[2, 2]",2,5,4,350,7,2,3,2165 -25,78,"[1, 2]","[40, 80]","[1, 2]",2,1,2,80,8,2,2,2356 -25,79,"[4, 3]","[90, 20]","[2, 2]",2,5,4,450,9,2,3,1996 -25,80,"[1, 2]","[10, 80]","[1, 2]",2,1,2,80,8,2,2,2155 -25,1,"[1, 2]","[50, 20]","[1, 1]",1,5,1,250,5,1,1,23361 -25,2,"[4, 3]","[40, 70]","[2, 1]",1,1,3,70,7,1,4,2083 -25,3,"[2, 1]","[50, 60]","[1, 1]",1,5,1,300,6,1,2,2317 -25,4,"[2, 1]","[50, 70]","[1, 1]",1,5,1,350,7,1,2,2025 -25,5,"[2, 1]","[40, 60]","[1, 1]",1,1,1,60,6,1,2,2317 -25,6,"[1, 2]","[60, 30]","[1, 1]",1,1,1,60,6,1,1,2073 -25,7,"[4, 3]","[40, 50]","[2, 1]",1,1,3,50,5,1,4,2124 -25,8,"[3, 4]","[60, 40]","[2, 1]",1,1,3,60,6,1,3,1998 -25,9,"[3, 4]","[70, 60]","[2, 2]",1,1,4,60,6,2,3,2006 -25,10,"[2, 1]","[70, 60]","[1, 2]",1,5,2,350,7,2,1,2041 -25,11,"[2, 1]","[70, 70]","[1, 2]",1,5,2,350,7,2,2,2175 -25,12,"[4, 3]","[70, 70]","[2, 2]",1,5,4,350,7,2,4,1983 -25,13,"[1, 2]","[50, 80]","[1, 2]",1,1,2,80,8,2,2,2055 -25,14,"[3, 4]","[50, 60]","[2, 2]",1,5,4,300,6,2,4,2508 -25,15,"[1, 2]","[40, 90]","[1, 2]",1,1,2,90,9,2,2,2232 -25,16,"[3, 4]","[0, 80]","[2, 2]",1,5,4,400,8,2,4,2240 -25,17,"[1, 2]","[20, 90]","[1, 2]",1,5,2,450,9,2,2,2139 -25,18,"[2, 1]","[50, 10]","[1, 2]",1,5,2,250,5,2,1,1999 -25,19,"[1, 2]","[0, 60]","[1, 2]",1,1,2,60,6,2,2,2405 -25,20,"[4, 3]","[60, 10]","[2, 2]",1,5,4,300,6,2,3,2064 -25,21,"[4, 3]","[50, 20]","[2, 2]",1,1,4,50,5,2,3,2064 -25,22,"[3, 4]","[0, 40]","[2, 2]",1,1,4,40,4,2,4,2031 -25,23,"[4, 3]","[40, 10]","[2, 1]",1,5,3,50,1,1,3,2066 -25,24,"[2, 1]","[40, 30]","[1, 1]",1,5,1,150,3,1,1,2217 -25,25,"[1, 2]","[20, 30]","[1, 1]",1,1,1,20,2,1,2,2059 -25,26,"[4, 3]","[50, 40]","[2, 2]",1,5,4,250,5,2,3,2148 -25,27,"[4, 3]","[40, 80]","[2, 2]",1,1,4,40,4,2,4,1928 -25,28,"[4, 3]","[40, 70]","[2, 1]",1,1,3,70,7,1,4,2015 -25,29,"[1, 2]","[80, 50]","[1, 1]",1,5,1,400,8,1,1,1863 -25,30,"[3, 4]","[90, 50]","[2, 1]",1,5,3,450,9,1,3,2210 -25,31,"[4, 3]","[0, 80]","[2, 1]",1,1,3,80,8,1,4,1923 -25,32,"[1, 2]","[60, 30]","[1, 1]",1,5,1,300,6,1,1,2221 -25,33,"[2, 1]","[20, 50]","[1, 1]",1,1,1,50,5,1,2,2224 -25,34,"[3, 4]","[60, 40]","[2, 2]",1,1,4,40,4,2,3,2374 -25,35,"[1, 2]","[30, 40]","[1, 1]",1,5,1,150,3,1,2,2541 -25,36,"[4, 3]","[30, 20]","[2, 1]",1,1,3,20,2,1,3,2057 -25,37,"[3, 4]","[40, 30]","[2, 2]",1,5,4,150,3,2,3,2349 -25,38,"[1, 2]","[30, 10]","[1, 2]",1,1,2,10,1,2,1,2152 -25,39,"[2, 1]","[20, 30]","[1, 1]",1,5,1,150,3,1,2,2054 -25,40,"[3, 4]","[30, 30]","[2, 1]",1,1,3,30,3,1,3,2215 -25,41,"[3, 4]","[40, 50]","[2, 1]",2,5,3,200,4,1,4,4575 -25,42,"[4, 3]","[50, 30]","[2, 1]",2,5,3,150,3,1,3,2063 -25,43,"[1, 2]","[40, 50]","[1, 1]",2,5,1,200,4,1,2,4788 -25,44,"[2, 1]","[30, 50]","[1, 1]",2,5,1,250,5,1,2,2037 -25,45,"[2, 1]","[20, 60]","[1, 1]",2,1,1,60,6,1,2,2071 -25,46,"[2, 1]","[60, 60]","[1, 1]",2,1,1,60,6,1,2,2041 -25,47,"[4, 3]","[70, 60]","[2, 1]",2,1,3,60,6,1,3,2155 -25,48,"[4, 3]","[90, 10]","[2, 2]",2,1,4,90,9,2,3,1932 -25,49,"[4, 3]","[70, 40]","[2, 2]",2,1,4,70,7,2,3,2181 -25,50,"[3, 4]","[40, 60]","[2, 2]",2,5,4,300,6,2,4,1955 -25,51,"[3, 4]","[0, 30]","[2, 2]",2,1,4,30,3,2,4,1940 -25,52,"[1, 2]","[0, 30]","[1, 1]",2,5,1,0,0,1,2,1946 -25,53,"[4, 3]","[20, 10]","[2, 2]",2,5,4,100,2,2,3,2540 -25,54,"[2, 1]","[50, 0]","[1, 2]",2,5,2,250,5,2,1,2182 -25,55,"[3, 4]","[10, 30]","[2, 2]",2,5,4,150,3,2,4,2300 -25,56,"[4, 3]","[40, 20]","[2, 2]",2,1,4,40,4,2,3,2326 -25,57,"[4, 3]","[40, 20]","[2, 1]",2,5,3,100,2,1,3,1915 -25,58,"[2, 1]","[60, 10]","[1, 1]",2,5,1,50,1,1,1,1966 -25,59,"[2, 1]","[80, 10]","[1, 1]",2,1,1,10,1,1,1,2416 -25,60,"[1, 2]","[30, 90]","[1, 2]",2,1,2,90,9,2,2,2507 -25,61,"[2, 1]","[70, 10]","[1, 2]",2,1,2,70,7,2,1,2281 -25,62,"[1, 2]","[0, 70]","[1, 2]",2,5,2,350,7,2,2,2225 -25,63,"[1, 2]","[0, 40]","[1, 2]",2,5,2,200,4,2,2,2141 -25,64,"[4, 3]","[50, 30]","[2, 2]",2,1,4,50,5,2,3,2324 -25,65,"[3, 4]","[40, 50]","[2, 2]",2,5,4,250,5,2,4,2107 -25,66,"[2, 1]","[50, 50]","[1, 2]",2,5,2,250,5,2,2,2132 -25,67,"[2, 1]","[70, 40]","[1, 2]",2,1,2,70,7,2,1,2383 -25,68,"[2, 1]","[50, 40]","[1, 2]",2,1,2,50,5,2,1,2715 -25,69,"[2, 1]","[30, 50]","[1, 2]",2,1,2,30,3,2,2,2332 -25,70,"[2, 1]","[20, 40]","[1, 1]",2,1,1,40,4,1,2,1966 -25,71,"[2, 1]","[20, 60]","[1, 2]",2,1,2,20,2,2,2,2283 -25,72,"[3, 4]","[40, 10]","[2, 1]",2,1,3,40,4,1,3,2349 -25,73,"[3, 4]","[50, 10]","[2, 1]",2,5,3,250,5,1,3,2273 -25,74,"[1, 2]","[40, 30]","[1, 1]",2,1,1,40,4,1,1,2224 -25,75,"[4, 3]","[30, 20]","[2, 1]",2,1,3,20,2,1,3,2065 -25,76,"[4, 3]","[20, 20]","[2, 2]",2,5,4,100,2,2,4,2232 -25,77,"[4, 3]","[10, 10]","[2, 2]",2,5,4,50,1,2,4,2217 -25,78,"[2, 1]","[30, 10]","[1, 2]",2,1,2,30,3,2,1,2324 -25,79,"[3, 4]","[20, 40]","[2, 1]",2,5,3,100,2,1,4,2182 -25,80,"[4, 3]","[30, 10]","[2, 2]",2,5,4,150,3,2,3,3689 -25,1,"[4, 3]","[40, 80]","[2, 1]",1,1,3,80,8,1,4,4921 -25,2,"[2, 1]","[40, 80]","[1, 1]",1,5,1,400,8,1,2,2182 -25,3,"[4, 3]","[90, 70]","[2, 1]",1,5,3,350,7,1,3,1989 -25,4,"[3, 4]","[80, 90]","[2, 1]",1,1,3,80,8,1,4,2154 -25,5,"[4, 3]","[70, 70]","[2, 1]",1,5,3,350,7,1,4,1997 -25,6,"[2, 1]","[40, 80]","[1, 1]",1,1,1,80,8,1,2,2224 -25,7,"[4, 3]","[30, 60]","[2, 1]",1,5,3,300,6,1,4,2147 -25,8,"[3, 4]","[40, 10]","[2, 1]",1,1,3,40,4,1,3,2022 -25,9,"[2, 1]","[10, 50]","[1, 1]",1,1,1,50,5,1,2,1922 -25,10,"[3, 4]","[40, 0]","[2, 2]",1,5,4,0,0,2,3,2082 -25,11,"[3, 4]","[40, 20]","[2, 1]",1,1,3,40,4,1,3,2108 -25,12,"[4, 3]","[0, 40]","[2, 1]",1,1,3,40,4,1,4,2005 -25,13,"[1, 2]","[60, 10]","[1, 1]",1,5,1,300,6,1,1,1856 -25,14,"[1, 2]","[50, 20]","[1, 1]",1,1,1,50,5,1,1,2082 -25,15,"[3, 4]","[30, 10]","[2, 1]",1,5,3,150,3,1,3,1999 -25,16,"[1, 2]","[20, 10]","[1, 1]",1,1,1,20,2,1,1,1848 -25,17,"[2, 1]","[30, 20]","[1, 1]",1,1,1,20,2,1,1,2399 -25,18,"[1, 2]","[0, 20]","[1, 2]",1,5,2,100,2,2,2,2353 -25,19,"[3, 4]","[0, 10]","[2, 2]",1,5,4,50,1,2,4,2238 -25,20,"[1, 2]","[10, 40]","[1, 2]",1,5,2,200,4,2,2,2367 -25,21,"[3, 4]","[30, 50]","[2, 2]",1,5,4,250,5,2,4,2192 -25,22,"[4, 3]","[70, 50]","[2, 2]",1,1,4,70,7,2,3,1998 -25,23,"[4, 3]","[70, 60]","[2, 2]",1,1,4,70,7,2,3,2016 -25,24,"[1, 2]","[50, 80]","[1, 2]",1,5,2,400,8,2,2,2014 -25,25,"[2, 1]","[70, 40]","[1, 2]",1,1,2,70,7,2,1,1898 -25,26,"[3, 4]","[20, 50]","[2, 2]",1,1,4,50,5,2,4,2190 -25,27,"[4, 3]","[40, 10]","[2, 2]",1,5,4,200,4,2,3,1938 -25,28,"[4, 3]","[70, 0]","[2, 2]",1,1,4,70,7,2,3,1949 -25,29,"[4, 3]","[30, 10]","[2, 2]",1,1,4,30,3,2,3,2093 -25,30,"[3, 4]","[30, 20]","[2, 1]",1,5,3,150,3,1,3,2182 -25,31,"[1, 2]","[0, 30]","[1, 1]",1,1,1,0,0,1,2,2140 -25,32,"[2, 1]","[40, 20]","[1, 2]",1,5,2,200,4,2,1,2372 -25,33,"[1, 2]","[30, 20]","[1, 2]",1,1,2,20,2,2,1,2490 -25,34,"[2, 1]","[20, 30]","[1, 2]",1,5,2,100,2,2,2,2382 -25,35,"[4, 3]","[30, 30]","[2, 2]",1,5,4,150,3,2,4,2073 -25,36,"[1, 2]","[10, 20]","[1, 2]",1,5,2,100,2,2,2,1988 -25,37,"[2, 1]","[30, 0]","[1, 2]",1,5,2,150,3,2,1,2233 -25,38,"[2, 1]","[70, 30]","[1, 2]",1,1,2,70,7,2,1,2334 -25,39,"[3, 4]","[70, 50]","[2, 2]",1,1,4,50,5,2,3,2539 -25,40,"[1, 2]","[60, 90]","[1, 2]",1,5,2,450,9,2,2,2345 -25,41,"[2, 1]","[80, 70]","[1, 1]",2,1,1,70,7,1,1,4433 -25,42,"[1, 2]","[80, 50]","[1, 1]",2,1,1,80,8,1,1,2265 -25,43,"[2, 1]","[30, 70]","[1, 1]",2,1,1,70,7,1,2,2057 -25,44,"[1, 2]","[90, 70]","[1, 1]",2,1,1,90,9,1,1,1960 -25,45,"[4, 3]","[80, 80]","[2, 1]",2,5,3,400,8,1,4,2125 -25,46,"[4, 3]","[90, 70]","[2, 2]",2,1,4,90,9,2,3,1930 -25,47,"[2, 1]","[70, 60]","[1, 1]",2,1,1,60,6,1,1,2005 -25,48,"[2, 1]","[50, 70]","[1, 1]",2,5,1,350,7,1,2,1932 -25,49,"[2, 1]","[80, 60]","[1, 1]",2,1,1,60,6,1,1,2212 -25,50,"[4, 3]","[90, 40]","[2, 1]",2,1,3,40,4,1,3,2023 -25,51,"[4, 3]","[80, 20]","[2, 2]",2,5,4,400,8,2,3,1874 -25,52,"[3, 4]","[0, 50]","[2, 2]",2,1,4,50,5,2,4,2122 -25,53,"[2, 1]","[30, 0]","[1, 2]",2,5,2,150,3,2,1,1929 -25,54,"[1, 2]","[30, 40]","[1, 2]",2,1,2,40,4,2,2,2059 -25,55,"[1, 2]","[20, 30]","[1, 1]",2,5,1,100,2,1,2,2307 -25,56,"[3, 4]","[20, 30]","[2, 1]",2,5,3,100,2,1,4,2166 -25,57,"[2, 1]","[40, 60]","[1, 1]",2,5,1,300,6,1,2,1980 -25,58,"[4, 3]","[50, 40]","[2, 1]",2,1,3,40,4,1,3,2058 -25,59,"[2, 1]","[20, 50]","[1, 2]",2,5,2,100,2,2,2,2048 -25,60,"[4, 3]","[50, 60]","[2, 2]",2,1,4,50,5,2,4,2099 -25,61,"[2, 1]","[40, 60]","[1, 1]",2,5,1,300,6,1,2,1933 -25,62,"[1, 2]","[40, 40]","[1, 1]",2,5,1,200,4,1,1,1988 -25,63,"[1, 2]","[20, 30]","[1, 1]",2,5,1,100,2,1,2,2056 -25,64,"[4, 3]","[30, 10]","[2, 1]",2,5,3,50,1,1,3,2048 -25,65,"[3, 4]","[20, 20]","[2, 1]",2,5,3,100,2,1,3,2048 -25,66,"[2, 1]","[10, 20]","[1, 1]",2,5,1,100,2,1,2,2357 -25,67,"[3, 4]","[40, 10]","[2, 1]",2,1,3,40,4,1,3,1978 -25,68,"[3, 4]","[30, 10]","[2, 2]",2,1,4,10,1,2,3,1990 -25,69,"[2, 1]","[10, 50]","[1, 2]",2,5,2,50,1,2,2,2508 -25,70,"[4, 3]","[0, 50]","[2, 2]",2,1,4,0,0,2,4,2032 -25,71,"[2, 1]","[0, 30]","[1, 1]",2,1,1,30,3,1,2,2099 -25,72,"[2, 1]","[0, 30]","[1, 1]",2,1,1,30,3,1,2,2063 -25,73,"[4, 3]","[10, 10]","[2, 1]",2,5,3,50,1,1,4,2098 -25,74,"[4, 3]","[20, 20]","[2, 1]",2,5,3,100,2,1,4,2033 -25,75,"[3, 4]","[10, 60]","[2, 1]",2,5,3,50,1,1,4,2064 -25,76,"[4, 3]","[30, 10]","[2, 1]",2,1,3,10,1,1,3,2017 -25,77,"[1, 2]","[40, 10]","[1, 2]",2,5,2,50,1,2,1,2328 -25,78,"[3, 4]","[40, 10]","[2, 2]",2,1,4,10,1,2,3,2149 -25,79,"[4, 3]","[10, 20]","[2, 1]",2,5,3,100,2,1,4,2049 -25,80,"[1, 2]","[20, 0]","[1, 1]",2,1,1,20,2,1,1,2040 -24,1,"[2, 1]","[10, 90]","[1, 2]",1,5,2,50,1,2,2,3817 -24,2,"[1, 2]","[80, 20]","[1, 2]",1,1,2,20,2,2,1,1651 -24,3,"[4, 3]","[80, 90]","[2, 1]",1,1,3,90,9,1,4,1901 -24,4,"[1, 2]","[60, 70]","[1, 2]",1,5,2,350,7,2,2,2268 -24,5,"[4, 3]","[80, 70]","[2, 1]",1,1,3,70,7,1,3,2118 -24,6,"[1, 2]","[70, 30]","[1, 2]",1,1,2,30,3,2,1,2235 -24,7,"[1, 2]","[60, 0]","[1, 2]",1,1,2,0,0,2,1,4203 -24,8,"[1, 2]","[70, 0]","[1, 2]",1,1,2,0,0,2,1,1236 -24,9,"[1, 2]","[90, 20]","[1, 1]",1,5,1,450,9,1,1,1535 -24,10,"[2, 1]","[0, 70]","[1, 1]",1,5,1,350,7,1,2,1939 -24,11,"[2, 1]","[0, 70]","[1, 2]",1,5,2,0,0,2,2,1940 -24,12,"[2, 1]","[30, 80]","[1, 1]",1,5,1,400,8,1,2,1473 -24,13,"[2, 1]","[20, 80]","[1, 1]",1,5,1,400,8,1,2,1205 -24,14,"[2, 1]","[50, 50]","[1, 1]",1,5,1,250,5,1,2,1320 -24,15,"[1, 2]","[60, 50]","[1, 2]",1,5,2,250,5,2,1,1154 -24,16,"[1, 2]","[60, 40]","[1, 1]",1,5,1,300,6,1,1,1355 -24,17,"[3, 4]","[30, 0]","[2, 2]",1,1,4,0,0,2,3,1388 -24,18,"[1, 2]","[0, 10]","[1, 2]",1,1,2,10,1,2,2,1819 -24,19,"[4, 3]","[10, 10]","[2, 1]",1,5,3,50,1,1,4,1685 -24,20,"[2, 1]","[0, 10]","[1, 1]",1,5,1,50,1,1,2,1603 -24,21,"[3, 4]","[20, 20]","[2, 1]",1,5,3,100,2,1,3,1650 -24,22,"[3, 4]","[10, 10]","[2, 1]",1,5,3,50,1,1,3,1618 -24,23,"[4, 3]","[0, 20]","[2, 2]",1,1,4,0,0,2,4,2036 -24,24,"[4, 3]","[0, 20]","[2, 1]",1,5,3,100,2,1,4,1652 -24,25,"[3, 4]","[30, 10]","[2, 1]",1,1,3,30,3,1,3,1719 -24,26,"[1, 2]","[40, 10]","[1, 1]",1,5,1,200,4,1,1,3704 -24,27,"[2, 1]","[50, 40]","[1, 2]",1,1,2,50,5,2,1,2602 -24,28,"[1, 2]","[40, 40]","[1, 1]",1,5,1,200,4,1,1,1670 -24,29,"[1, 2]","[70, 60]","[1, 2]",1,5,2,300,6,2,1,1851 -24,30,"[4, 3]","[80, 80]","[2, 1]",1,1,3,80,8,1,4,1322 -24,31,"[2, 1]","[70, 90]","[1, 1]",1,5,1,450,9,1,2,1256 -24,32,"[3, 4]","[90, 60]","[2, 2]",1,5,4,300,6,2,3,1189 -24,33,"[3, 4]","[70, 90]","[2, 1]",1,5,3,350,7,1,4,1386 -24,34,"[3, 4]","[60, 70]","[2, 2]",1,1,4,70,7,2,4,1352 -24,35,"[2, 1]","[50, 50]","[1, 1]",1,1,1,50,5,1,2,1454 -24,36,"[1, 2]","[30, 50]","[1, 2]",1,1,2,50,5,2,2,1470 -24,37,"[3, 4]","[0, 20]","[2, 1]",1,5,3,0,0,1,4,1504 -24,38,"[1, 2]","[30, 30]","[1, 1]",1,1,1,30,3,1,1,1438 -24,39,"[1, 2]","[30, 30]","[1, 2]",1,1,2,30,3,2,1,1439 -24,40,"[3, 4]","[20, 20]","[2, 2]",1,5,4,100,2,2,3,2352 -24,41,"[3, 4]","[0, 10]","[2, 1]",2,1,3,0,0,1,4,1965 -24,42,"[1, 2]","[10, 30]","[1, 2]",2,1,2,30,3,2,2,1602 -24,43,"[2, 1]","[10, 10]","[1, 1]",2,1,1,10,1,1,2,1651 -24,44,"[3, 4]","[20, 30]","[2, 2]",2,1,4,30,3,2,4,1668 -24,45,"[1, 2]","[10, 50]","[1, 2]",2,5,2,250,5,2,2,2694 -24,46,"[3, 4]","[10, 10]","[2, 2]",2,5,4,50,1,2,3,1717 -24,47,"[3, 4]","[10, 30]","[2, 2]",2,1,4,30,3,2,4,2018 -24,48,"[4, 3]","[20, 30]","[2, 1]",2,1,3,30,3,1,4,1769 -24,49,"[3, 4]","[40, 20]","[2, 2]",2,1,4,20,2,2,3,1650 -24,50,"[4, 3]","[40, 40]","[2, 1]",2,5,3,200,4,1,4,1535 -24,51,"[3, 4]","[40, 60]","[2, 2]",2,5,4,300,6,2,4,1884 -24,52,"[3, 4]","[50, 50]","[2, 2]",2,5,4,250,5,2,3,1288 -24,53,"[1, 2]","[60, 50]","[1, 2]",2,1,2,50,5,2,1,1636 -24,54,"[4, 3]","[50, 40]","[2, 1]",2,1,3,40,4,1,3,1818 -24,55,"[2, 1]","[40, 40]","[1, 1]",2,5,1,200,4,1,2,1337 -24,56,"[3, 4]","[40, 20]","[2, 2]",2,1,4,20,2,2,3,1368 -24,57,"[2, 1]","[10, 80]","[1, 1]",2,5,1,400,8,1,2,1419 -24,58,"[2, 1]","[30, 50]","[1, 1]",2,1,1,50,5,1,2,1702 -24,59,"[4, 3]","[20, 30]","[2, 1]",2,1,3,30,3,1,4,1584 -24,60,"[3, 4]","[20, 40]","[2, 2]",2,1,4,40,4,2,4,1586 -24,61,"[1, 2]","[20, 50]","[1, 2]",2,1,2,50,5,2,2,1620 -24,62,"[3, 4]","[20, 10]","[2, 2]",2,5,4,50,1,2,3,1336 -24,63,"[4, 3]","[30, 20]","[2, 1]",2,5,3,100,2,1,3,1601 -24,64,"[3, 4]","[60, 40]","[2, 2]",2,1,4,40,4,2,3,1254 -24,65,"[4, 3]","[0, 90]","[2, 1]",2,5,3,450,9,1,4,1204 -24,66,"[2, 1]","[10, 80]","[1, 1]",2,1,1,80,8,1,2,1734 -24,67,"[2, 1]","[0, 80]","[1, 1]",2,1,1,80,8,1,2,1305 -24,68,"[1, 2]","[50, 10]","[1, 2]",2,5,2,50,1,2,1,1485 -24,69,"[3, 4]","[40, 0]","[2, 2]",2,5,4,0,0,2,3,1287 -24,70,"[4, 3]","[10, 40]","[2, 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1]",1,1,3,90,9,1,4,1670 -24,17,"[3, 4]","[40, 90]","[2, 1]",1,1,3,40,4,1,4,1755 -24,18,"[1, 2]","[40, 80]","[1, 2]",1,5,2,400,8,2,2,1521 -24,19,"[4, 3]","[80, 30]","[2, 1]",1,5,3,150,3,1,3,1671 -24,20,"[2, 1]","[90, 40]","[1, 1]",1,1,1,40,4,1,1,1704 -24,21,"[1, 2]","[50, 80]","[1, 1]",1,1,1,50,5,1,2,1819 -24,22,"[2, 1]","[80, 80]","[1, 2]",1,1,2,80,8,2,2,1753 -24,23,"[4, 3]","[90, 70]","[2, 2]",1,5,4,450,9,2,3,1586 -24,24,"[1, 2]","[70, 70]","[1, 1]",1,5,1,350,7,1,1,1738 -24,25,"[1, 2]","[60, 60]","[1, 2]",1,1,2,60,6,2,1,1685 -24,26,"[2, 1]","[50, 90]","[1, 1]",1,5,1,450,9,1,2,1737 -24,27,"[1, 2]","[80, 70]","[1, 1]",1,5,1,400,8,1,1,1703 -24,28,"[2, 1]","[60, 60]","[1, 1]",1,5,1,300,6,1,2,1520 -24,29,"[3, 4]","[90, 60]","[2, 2]",1,5,4,300,6,2,3,1669 -24,30,"[1, 2]","[90, 50]","[1, 2]",1,1,2,50,5,2,1,1671 -24,31,"[1, 2]","[80, 30]","[1, 2]",1,1,2,30,3,2,1,1703 -24,32,"[4, 3]","[80, 70]","[2, 1]",1,5,3,350,7,1,3,1637 -24,33,"[3, 4]","[80, 80]","[2, 1]",1,5,3,400,8,1,3,1603 -24,34,"[2, 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-24,13,"[1, 2]","[40, 80]","[1, 2]",1,5,2,400,8,2,2,8221 -24,14,"[4, 3]","[40, 90]","[2, 2]",1,5,4,200,4,2,4,1670 -24,15,"[3, 4]","[60, 80]","[2, 1]",1,1,3,60,6,1,4,1816 -24,16,"[2, 1]","[70, 70]","[1, 1]",1,1,1,70,7,1,2,1633 -24,17,"[2, 1]","[70, 70]","[1, 1]",1,5,1,350,7,1,2,1721 -24,18,"[3, 4]","[30, 60]","[2, 1]",1,1,3,30,3,1,4,1704 -24,19,"[1, 2]","[40, 60]","[1, 1]",1,5,1,200,4,1,2,1601 -24,20,"[1, 2]","[10, 50]","[1, 2]",1,5,2,250,5,2,2,1703 -24,21,"[1, 2]","[10, 50]","[1, 1]",1,5,1,50,1,1,2,1704 -24,22,"[3, 4]","[20, 60]","[2, 1]",1,5,3,100,2,1,4,1736 -24,23,"[1, 2]","[50, 30]","[1, 2]",1,5,2,150,3,2,1,1654 -24,24,"[4, 3]","[60, 60]","[2, 1]",1,5,3,300,6,1,4,1720 -24,25,"[1, 2]","[70, 70]","[1, 2]",1,5,2,350,7,2,1,1887 -24,26,"[3, 4]","[60, 70]","[2, 1]",1,5,3,300,6,1,4,2537 -24,27,"[2, 1]","[70, 40]","[1, 1]",1,5,1,200,4,1,1,1818 -24,28,"[1, 2]","[30, 60]","[1, 1]",1,5,1,150,3,1,2,5120 -24,29,"[4, 3]","[60, 60]","[2, 1]",1,5,3,300,6,1,4,3101 -24,30,"[1, 2]","[70, 70]","[1, 1]",1,1,1,70,7,1,1,1751 -24,31,"[1, 2]","[70, 70]","[1, 2]",1,1,2,70,7,2,1,1752 -24,32,"[3, 4]","[20, 40]","[2, 1]",1,5,3,100,2,1,4,1685 -24,33,"[3, 4]","[20, 40]","[2, 2]",1,5,4,200,4,2,4,1685 -24,34,"[2, 1]","[30, 0]","[1, 1]",1,1,1,0,0,1,1,2769 -24,35,"[4, 3]","[50, 0]","[2, 1]",1,1,3,0,0,1,3,4820 -24,36,"[1, 2]","[10, 50]","[1, 1]",1,1,1,10,1,1,2,1738 -24,37,"[1, 2]","[0, 80]","[1, 2]",1,1,2,80,8,2,2,1868 -24,38,"[2, 1]","[70, 20]","[1, 1]",1,1,1,20,2,1,1,1736 -24,39,"[2, 1]","[50, 10]","[1, 2]",1,1,2,50,5,2,1,4052 -24,40,"[2, 1]","[30, 20]","[1, 2]",1,1,2,30,3,2,1,1800 -24,41,"[1, 2]","[10, 30]","[1, 1]",2,1,1,10,1,1,2,1952 -24,42,"[3, 4]","[0, 20]","[2, 2]",2,5,4,100,2,2,4,1883 -24,43,"[4, 3]","[10, 0]","[2, 1]",2,1,3,0,0,1,3,1650 -24,44,"[1, 2]","[30, 20]","[1, 2]",2,5,2,100,2,2,1,1634 -24,45,"[2, 1]","[40, 10]","[1, 1]",2,5,1,50,1,1,1,1686 -24,46,"[3, 4]","[0, 30]","[2, 2]",2,5,4,150,3,2,4,3435 -24,47,"[3, 4]","[30, 40]","[2, 1]",2,5,3,150,3,1,4,1901 -24,48,"[4, 3]","[20, 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1]",1,5,1,400,8,1,2,1401 -24,4,"[3, 4]","[50, 30]","[2, 2]",1,1,4,30,3,2,3,1291 -24,5,"[3, 4]","[50, 40]","[2, 2]",1,5,4,200,4,2,3,1304 -24,6,"[4, 3]","[30, 50]","[2, 1]",1,5,3,250,5,1,4,1388 -24,7,"[1, 2]","[70, 20]","[1, 2]",1,1,2,20,2,2,1,1382 -24,8,"[1, 2]","[50, 40]","[1, 2]",1,5,2,200,4,2,1,1288 -24,9,"[1, 2]","[50, 70]","[1, 2]",1,1,2,70,7,2,2,1401 -24,10,"[1, 2]","[60, 90]","[1, 2]",1,5,2,450,9,2,2,1376 -24,11,"[1, 2]","[50, 90]","[1, 2]",1,1,2,90,9,2,2,1390 -24,12,"[2, 1]","[90, 60]","[1, 1]",1,1,1,60,6,1,1,1388 -24,13,"[2, 1]","[80, 30]","[1, 1]",1,1,1,30,3,1,1,1291 -24,14,"[1, 2]","[30, 80]","[1, 2]",1,5,2,400,8,2,2,1281 -24,15,"[1, 2]","[20, 50]","[1, 2]",1,1,2,50,5,2,2,1305 -24,16,"[3, 4]","[10, 80]","[2, 2]",1,5,4,400,8,2,4,1386 -24,17,"[3, 4]","[0, 60]","[2, 2]",1,5,4,300,6,2,4,1383 -24,18,"[4, 3]","[30, 20]","[2, 1]",1,5,3,100,2,1,3,1380 -24,19,"[3, 4]","[30, 20]","[2, 2]",1,1,4,20,2,2,3,1379 -24,20,"[1, 2]","[10, 40]","[1, 2]",1,5,2,200,4,2,2,1280 -24,21,"[1, 2]","[20, 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1]",1,5,1,250,5,1,1,1571 -24,35,"[4, 3]","[50, 30]","[2, 1]",1,5,3,150,3,1,3,1304 -24,36,"[3, 4]","[20, 70]","[2, 1]",1,5,3,100,2,1,4,1620 -24,37,"[3, 4]","[20, 70]","[2, 2]",1,5,4,350,7,2,4,1621 -24,38,"[4, 3]","[70, 10]","[2, 1]",1,1,3,10,1,1,3,1788 -24,39,"[1, 2]","[40, 70]","[1, 1]",1,5,1,200,4,1,2,1589 -24,40,"[3, 4]","[30, 70]","[2, 2]",1,5,4,350,7,2,4,1638 -24,41,"[2, 1]","[70, 30]","[1, 1]",2,5,1,150,3,1,1,1851 -24,42,"[1, 2]","[20, 60]","[1, 2]",2,5,2,300,6,2,2,2605 -24,43,"[2, 1]","[30, 20]","[1, 1]",2,5,1,100,2,1,1,1752 -24,44,"[2, 1]","[40, 10]","[1, 2]",2,1,2,40,4,2,1,1687 -24,45,"[4, 3]","[30, 0]","[2, 1]",2,5,3,0,0,1,3,1622 -24,46,"[1, 2]","[10, 20]","[1, 2]",2,5,2,100,2,2,2,1671 -24,47,"[2, 1]","[20, 20]","[1, 1]",2,1,1,20,2,1,2,1604 -24,48,"[4, 3]","[20, 10]","[2, 1]",2,5,3,50,1,1,3,1571 -24,49,"[2, 1]","[30, 10]","[1, 1]",2,1,1,10,1,1,1,1471 -24,50,"[3, 4]","[30, 40]","[2, 2]",2,5,4,200,4,2,4,1669 -24,51,"[2, 1]","[30, 40]","[1, 1]",2,1,1,40,4,1,2,1739 -24,52,"[4, 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-25,42,"[1, 2]","[90, 40]","[1, 1]",2,5,1,450,9,1,1,2398 -25,43,"[2, 1]","[50, 60]","[1, 1]",2,5,1,300,6,1,2,2507 -25,44,"[1, 2]","[80, 40]","[1, 1]",2,1,1,80,8,1,1,2567 -25,45,"[1, 2]","[60, 50]","[1, 1]",2,1,1,60,6,1,1,2783 -25,46,"[2, 1]","[40, 50]","[1, 1]",2,5,1,250,5,1,2,2096 -25,47,"[4, 3]","[30, 40]","[2, 1]",2,5,3,200,4,1,4,2190 -25,48,"[1, 2]","[0, 10]","[1, 1]",2,1,1,0,0,1,2,2098 -25,49,"[3, 4]","[10, 10]","[2, 2]",2,1,4,10,1,2,3,2339 -25,50,"[1, 2]","[30, 30]","[1, 2]",2,5,2,150,3,2,1,2382 -25,51,"[2, 1]","[10, 60]","[1, 2]",2,1,2,10,1,2,2,3051 -25,52,"[4, 3]","[0, 70]","[2, 2]",2,5,4,0,0,2,4,1956 -25,53,"[2, 1]","[30, 60]","[1, 1]",2,1,1,60,6,1,2,2125 -25,54,"[4, 3]","[30, 60]","[2, 1]",2,1,3,60,6,1,4,2165 -25,55,"[1, 2]","[10, 20]","[1, 1]",2,5,1,50,1,1,2,2248 -25,56,"[1, 2]","[20, 10]","[1, 1]",2,5,1,100,2,1,1,1967 -25,57,"[4, 3]","[20, 0]","[2, 1]",2,1,3,0,0,1,3,2141 -25,58,"[1, 2]","[0, 10]","[1, 2]",2,1,2,10,1,2,2,2014 -25,59,"[4, 3]","[20, 10]","[2, 2]",2,1,4,20,2,2,3,1839 -25,60,"[1, 2]","[0, 0]","[1, 2]",2,5,2,0,0,2,1,2116 -25,61,"[3, 4]","[10, 10]","[2, 1]",2,5,3,50,1,1,3,2274 -25,62,"[2, 1]","[10, 10]","[1, 1]",2,1,1,10,1,1,2,2040 -25,63,"[4, 3]","[10, 0]","[2, 2]",2,5,4,50,1,2,3,1990 -25,64,"[3, 4]","[20, 30]","[2, 2]",2,1,4,30,3,2,4,2066 -25,65,"[4, 3]","[90, 30]","[2, 2]",2,1,4,90,9,2,3,2015 -25,66,"[3, 4]","[40, 80]","[2, 2]",2,5,4,400,8,2,4,2123 -25,67,"[1, 2]","[70, 60]","[1, 2]",2,5,2,300,6,2,1,2365 -25,68,"[3, 4]","[60, 20]","[2, 2]",2,5,4,100,2,2,3,2249 -25,69,"[3, 4]","[80, 10]","[2, 2]",2,5,4,50,1,2,3,2191 -25,70,"[3, 4]","[80, 30]","[2, 2]",2,5,4,150,3,2,3,2174 -25,71,"[1, 2]","[70, 50]","[1, 2]",2,1,2,50,5,2,1,2300 -25,72,"[3, 4]","[60, 70]","[2, 2]",2,1,4,70,7,2,4,2202 -25,73,"[3, 4]","[30, 40]","[2, 2]",2,5,4,200,4,2,4,2207 -25,74,"[2, 1]","[60, 50]","[1, 2]",2,1,2,60,6,2,1,2307 -25,75,"[1, 2]","[40, 50]","[1, 2]",2,1,2,50,5,2,2,2423 -25,76,"[2, 1]","[40, 30]","[1, 2]",2,1,2,40,4,2,1,2191 -25,77,"[1, 2]","[20, 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2]",2,5,2,0,0,2,1,1637 -24,51,"[1, 2]","[90, 10]","[1, 2]",2,1,2,10,1,2,1,1621 -24,52,"[1, 2]","[90, 10]","[1, 1]",2,1,1,90,9,1,1,1621 -24,53,"[4, 3]","[40, 60]","[2, 1]",2,5,3,300,6,1,4,6239 -24,54,"[1, 2]","[50, 60]","[1, 2]",2,5,2,300,6,2,2,1687 -24,55,"[3, 4]","[70, 70]","[2, 1]",2,1,3,70,7,1,3,7289 -24,56,"[3, 4]","[70, 70]","[2, 2]",2,1,4,70,7,2,3,7290 -24,57,"[4, 3]","[50, 60]","[2, 2]",2,1,4,50,5,2,4,3004 -24,58,"[4, 3]","[50, 60]","[2, 1]",2,1,3,60,6,1,4,3005 -24,59,"[3, 4]","[60, 80]","[2, 1]",2,1,3,60,6,1,4,1738 -24,60,"[3, 4]","[60, 80]","[2, 2]",2,1,4,80,8,2,4,1739 -24,61,"[4, 3]","[70, 10]","[2, 1]",2,1,3,10,1,1,3,1571 -24,62,"[3, 4]","[20, 70]","[2, 2]",2,5,4,350,7,2,4,1686 -24,63,"[3, 4]","[50, 50]","[2, 1]",2,5,3,250,5,1,3,1571 -24,64,"[2, 1]","[50, 90]","[1, 1]",2,1,1,90,9,1,2,1702 -24,65,"[3, 4]","[80, 80]","[2, 2]",2,5,4,400,8,2,3,8702 -24,66,"[2, 1]","[60, 40]","[1, 1]",2,1,1,40,4,1,1,1649 -24,67,"[4, 3]","[90, 40]","[2, 1]",2,1,3,40,4,1,3,2238 -24,68,"[1, 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-24,5,"[2, 1]","[70, 80]","[1, 2]",1,1,2,70,7,2,2,3002 -24,6,"[2, 1]","[70, 80]","[1, 1]",1,1,1,80,8,1,2,3004 -24,7,"[2, 1]","[20, 80]","[1, 2]",1,5,2,100,2,2,2,2085 -24,8,"[2, 1]","[20, 60]","[1, 2]",1,1,2,20,2,2,2,1586 -24,9,"[2, 1]","[20, 60]","[1, 1]",1,1,1,60,6,1,2,1587 -24,10,"[3, 4]","[80, 50]","[2, 2]",1,1,4,50,5,2,3,1319 -24,11,"[4, 3]","[60, 90]","[2, 1]",1,1,3,90,9,1,4,1571 -24,12,"[3, 4]","[80, 60]","[2, 2]",1,1,4,60,6,2,3,1418 -24,13,"[1, 2]","[60, 80]","[1, 2]",1,5,2,400,8,2,2,1484 -24,14,"[4, 3]","[60, 60]","[2, 1]",1,1,3,60,6,1,4,1317 -24,15,"[2, 1]","[50, 50]","[1, 2]",1,1,2,50,5,2,2,2354 -24,16,"[4, 3]","[50, 30]","[2, 2]",1,5,4,250,5,2,3,1603 -24,17,"[4, 3]","[50, 30]","[2, 1]",1,5,3,150,3,1,3,1604 -24,18,"[1, 2]","[70, 10]","[1, 2]",1,5,2,50,1,2,1,1520 -24,19,"[4, 3]","[30, 60]","[2, 1]",1,1,3,60,6,1,4,1635 -24,20,"[3, 4]","[90, 10]","[2, 2]",1,5,4,50,1,2,3,1670 -24,21,"[4, 3]","[50, 80]","[2, 1]",1,5,3,400,8,1,4,1737 -24,22,"[2, 1]","[30, 90]","[1, 1]",1,5,1,450,9,1,2,1587 -24,23,"[2, 1]","[0, 80]","[1, 2]",1,5,2,0,0,2,2,1770 -24,24,"[2, 1]","[0, 80]","[1, 1]",1,5,1,400,8,1,2,1771 -24,25,"[3, 4]","[10, 0]","[2, 1]",1,1,3,10,1,1,3,1869 -24,26,"[3, 4]","[30, 20]","[2, 2]",1,1,4,20,2,2,3,1720 -24,27,"[3, 4]","[30, 20]","[2, 1]",1,1,3,30,3,1,3,1721 -24,28,"[4, 3]","[60, 50]","[2, 2]",1,1,4,60,6,2,3,1620 -24,29,"[4, 3]","[60, 50]","[2, 1]",1,1,3,50,5,1,3,1620 -24,30,"[1, 2]","[60, 60]","[1, 2]",1,5,2,300,6,2,1,1637 -24,31,"[1, 2]","[80, 90]","[1, 1]",1,5,1,400,8,1,2,1754 -24,32,"[3, 4]","[90, 60]","[2, 2]",1,1,4,60,6,2,3,1805 -24,33,"[2, 1]","[70, 60]","[1, 1]",1,1,1,60,6,1,1,1969 -24,34,"[1, 2]","[50, 70]","[1, 1]",1,1,1,50,5,1,2,1704 -24,35,"[1, 2]","[70, 80]","[1, 1]",1,1,1,70,7,1,2,1637 -24,36,"[4, 3]","[60, 60]","[2, 2]",1,5,4,300,6,2,4,1704 -24,37,"[4, 3]","[70, 60]","[2, 2]",1,1,4,70,7,2,3,1586 -24,38,"[4, 3]","[70, 60]","[2, 1]",1,1,3,60,6,1,3,1587 -24,39,"[3, 4]","[70, 60]","[2, 2]",1,1,4,60,6,2,3,1735 -24,40,"[4, 3]","[70, 90]","[2, 1]",1,5,3,450,9,1,4,1597 -24,41,"[4, 3]","[70, 90]","[2, 1]",2,5,3,450,9,1,4,2352 -24,42,"[4, 3]","[80, 50]","[2, 1]",2,5,3,250,5,1,3,1685 -24,43,"[2, 1]","[50, 50]","[1, 1]",2,5,1,250,5,1,2,1971 -24,44,"[3, 4]","[60, 30]","[2, 2]",2,5,4,150,3,2,3,1721 -24,45,"[1, 2]","[70, 40]","[1, 2]",2,1,2,40,4,2,1,1767 -24,46,"[1, 2]","[30, 20]","[1, 1]",2,5,1,150,3,1,1,2052 -24,47,"[2, 1]","[10, 30]","[1, 1]",2,5,1,150,3,1,2,1834 -24,48,"[2, 1]","[10, 30]","[1, 2]",2,5,2,50,1,2,2,1835 -24,49,"[1, 2]","[60, 0]","[1, 2]",2,5,2,0,0,2,1,1737 -24,50,"[4, 3]","[20, 40]","[2, 1]",2,1,3,40,4,1,4,1686 -24,51,"[1, 2]","[90, 10]","[1, 2]",2,1,2,10,1,2,1,9869 -24,52,"[3, 4]","[60, 10]","[2, 2]",2,1,4,10,1,2,3,1718 -24,53,"[4, 3]","[40, 60]","[2, 1]",2,5,3,300,6,1,4,1368 -24,54,"[1, 2]","[50, 60]","[1, 1]",2,5,1,250,5,1,2,2000 -24,55,"[3, 4]","[70, 70]","[2, 2]",2,1,4,70,7,2,3,1468 -24,56,"[3, 4]","[70, 70]","[2, 1]",2,1,3,70,7,1,3,1469 -24,57,"[4, 3]","[50, 60]","[2, 1]",2,1,3,60,6,1,4,1367 -24,58,"[1, 2]","[50, 90]","[1, 2]",2,5,2,450,9,2,2,1521 -24,59,"[1, 2]","[50, 90]","[1, 1]",2,5,1,250,5,1,2,1521 -24,60,"[1, 2]","[40, 90]","[1, 2]",2,5,2,450,9,2,2,1468 -24,61,"[4, 3]","[70, 10]","[2, 1]",2,1,3,10,1,1,3,1669 -24,62,"[3, 4]","[20, 70]","[2, 2]",2,5,4,350,7,2,4,1519 -24,63,"[3, 4]","[20, 70]","[2, 1]",2,5,3,100,2,1,4,1519 -24,64,"[2, 1]","[50, 90]","[1, 1]",2,1,1,90,9,1,2,2584 -24,65,"[2, 1]","[50, 90]","[1, 2]",2,1,2,50,5,2,2,2585 -24,66,"[2, 1]","[60, 40]","[1, 1]",2,1,1,40,4,1,1,1717 -24,67,"[4, 3]","[90, 40]","[2, 2]",2,1,4,90,9,2,3,1736 -24,68,"[1, 2]","[50, 80]","[1, 2]",2,1,2,80,8,2,2,1284 -24,69,"[1, 2]","[50, 80]","[1, 1]",2,1,1,50,5,1,2,1285 -24,70,"[2, 1]","[20, 10]","[1, 2]",2,5,2,100,2,2,1,1370 -24,71,"[2, 1]","[40, 10]","[1, 1]",2,1,1,10,1,1,1,2186 -24,72,"[3, 4]","[20, 60]","[2, 2]",2,5,4,300,6,2,4,1684 -24,73,"[4, 3]","[50, 30]","[2, 2]",2,5,4,250,5,2,3,1635 -24,74,"[1, 2]","[40, 60]","[1, 2]",2,5,2,300,6,2,2,1535 -24,75,"[1, 2]","[40, 60]","[1, 1]",2,5,1,200,4,1,2,1536 -24,76,"[1, 2]","[50, 60]","[1, 2]",2,5,2,300,6,2,2,1634 -24,77,"[3, 4]","[30, 80]","[2, 2]",2,1,4,80,8,2,4,1803 -24,78,"[3, 4]","[30, 80]","[2, 1]",2,1,3,30,3,1,4,1804 -24,79,"[1, 2]","[10, 20]","[1, 2]",2,1,2,20,2,2,2,1753 -24,80,"[2, 1]","[0, 0]","[1, 1]",2,5,1,0,0,1,2,2103 -24,81,"[2, 1]","[0, 0]","[1, 2]",2,5,2,0,0,2,2,2104 -25,1,"[4, 3]","[40, 80]","[2, 1]",1,1,3,80,8,1,4,22577 -25,2,"[2, 1]","[40, 80]","[1, 1]",1,5,1,400,8,1,2,2273 -25,3,"[4, 3]","[90, 70]","[2, 1]",1,5,3,350,7,1,3,2090 -25,4,"[3, 4]","[80, 90]","[2, 1]",1,1,3,80,8,1,4,2349 -25,5,"[4, 3]","[70, 70]","[2, 1]",1,5,3,350,7,1,4,2057 -25,6,"[2, 1]","[40, 80]","[1, 1]",1,1,1,80,8,1,2,2445 -25,7,"[4, 3]","[30, 60]","[2, 1]",1,5,3,300,6,1,4,2062 -25,8,"[3, 4]","[40, 10]","[2, 1]",1,1,3,40,4,1,3,2282 -25,9,"[2, 1]","[10, 50]","[1, 1]",1,1,1,50,5,1,2,1973 -25,10,"[3, 4]","[40, 0]","[2, 1]",1,5,3,200,4,1,3,2166 -25,11,"[3, 4]","[40, 20]","[2, 1]",1,1,3,40,4,1,3,2116 -25,12,"[4, 3]","[0, 40]","[2, 1]",1,1,3,40,4,1,4,2216 -25,13,"[1, 2]","[60, 10]","[1, 1]",1,5,1,300,6,1,1,2174 -25,14,"[1, 2]","[50, 20]","[1, 1]",1,1,1,50,5,1,1,2057 -25,15,"[3, 4]","[30, 10]","[2, 1]",1,5,3,150,3,1,3,2083 -25,16,"[1, 2]","[20, 10]","[1, 1]",1,1,1,20,2,1,1,2740 -25,17,"[2, 1]","[30, 20]","[1, 1]",1,1,1,20,2,1,1,1915 -25,18,"[1, 2]","[0, 20]","[1, 1]",1,5,1,0,0,1,2,2264 -25,19,"[3, 4]","[0, 10]","[2, 2]",1,5,4,50,1,2,4,2104 -25,20,"[1, 2]","[10, 40]","[1, 2]",1,5,2,200,4,2,2,2182 -25,21,"[3, 4]","[30, 50]","[2, 2]",1,5,4,250,5,2,4,2090 -25,22,"[4, 3]","[70, 50]","[2, 2]",1,1,4,70,7,2,3,2081 -25,23,"[4, 3]","[70, 60]","[2, 2]",1,1,4,70,7,2,3,1933 -25,24,"[1, 2]","[50, 80]","[1, 2]",1,5,2,400,8,2,2,2124 -25,25,"[2, 1]","[70, 40]","[1, 1]",1,1,1,40,4,1,1,2057 -25,26,"[3, 4]","[20, 50]","[2, 2]",1,1,4,50,5,2,4,2332 -25,27,"[4, 3]","[40, 10]","[2, 2]",1,5,4,200,4,2,3,2165 -25,28,"[4, 3]","[70, 0]","[2, 2]",1,1,4,70,7,2,3,1912 -25,29,"[4, 3]","[30, 10]","[2, 2]",1,1,4,30,3,2,3,2130 -25,30,"[3, 4]","[30, 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2]",2,5,4,100,2,2,3,2499 -25,66,"[2, 1]","[10, 20]","[1, 2]",2,5,2,50,1,2,2,2199 -25,67,"[3, 4]","[40, 10]","[2, 2]",2,1,4,10,1,2,3,2224 -25,68,"[3, 4]","[30, 10]","[2, 1]",2,1,3,30,3,1,3,2123 -25,69,"[2, 1]","[10, 50]","[1, 1]",2,5,1,250,5,1,2,2149 -25,70,"[4, 3]","[0, 50]","[2, 1]",2,1,3,50,5,1,4,2049 -25,71,"[2, 1]","[0, 30]","[1, 1]",2,1,1,30,3,1,2,1949 -25,72,"[2, 1]","[0, 30]","[1, 2]",2,1,2,0,0,2,2,2256 -25,73,"[4, 3]","[10, 10]","[2, 1]",2,5,3,50,1,1,4,1999 -25,74,"[4, 3]","[20, 20]","[2, 1]",2,5,3,100,2,1,4,2007 -25,75,"[3, 4]","[10, 60]","[2, 1]",2,5,3,50,1,1,4,2049 -25,76,"[4, 3]","[30, 10]","[2, 1]",2,1,3,10,1,1,3,2059 -25,77,"[1, 2]","[40, 10]","[1, 2]",2,5,2,50,1,2,1,2099 -25,78,"[3, 4]","[40, 10]","[2, 2]",2,1,4,10,1,2,3,2062 -25,79,"[4, 3]","[10, 20]","[2, 1]",2,5,3,100,2,1,4,2114 -25,80,"[1, 2]","[20, 0]","[1, 1]",2,1,1,20,2,1,1,1889 -25,1,"[2, 1]","[20, 60]","[1, 1]",1,5,1,300,6,1,2,5891 -25,2,"[3, 4]","[70, 40]","[2, 1]",1,5,3,350,7,1,3,2185 -25,3,"[1, 2]","[90, 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2]","[70, 0]","[1, 2]",1,5,2,0,0,2,1,2020 -25,22,"[2, 1]","[20, 80]","[1, 1]",1,5,1,400,8,1,2,2177 -25,23,"[1, 2]","[80, 10]","[1, 1]",1,5,1,400,8,1,1,2045 -25,24,"[4, 3]","[0, 70]","[2, 1]",1,1,3,70,7,1,4,1966 -25,25,"[1, 2]","[70, 10]","[1, 1]",1,1,1,70,7,1,1,1993 -25,26,"[4, 3]","[30, 60]","[2, 1]",1,1,3,60,6,1,4,2050 -25,27,"[2, 1]","[50, 40]","[1, 2]",1,1,2,50,5,2,1,2295 -25,28,"[2, 1]","[50, 60]","[1, 2]",1,1,2,50,5,2,2,2472 -25,29,"[1, 2]","[70, 60]","[1, 1]",1,5,1,350,7,1,1,2510 -25,30,"[3, 4]","[50, 70]","[2, 1]",1,1,3,50,5,1,4,2086 -25,31,"[4, 3]","[80, 70]","[2, 2]",1,5,4,400,8,2,3,2191 -25,32,"[4, 3]","[60, 20]","[2, 2]",1,5,4,300,6,2,3,2093 -25,33,"[3, 4]","[10, 60]","[2, 2]",1,1,4,60,6,2,4,1901 -25,34,"[3, 4]","[30, 40]","[2, 2]",1,5,4,200,4,2,4,2094 -25,35,"[4, 3]","[40, 20]","[2, 2]",1,5,4,200,4,2,3,1855 -25,36,"[3, 4]","[10, 30]","[2, 2]",1,5,4,150,3,2,4,1979 -25,37,"[1, 2]","[20, 40]","[1, 2]",1,1,2,40,4,2,2,2048 -25,38,"[1, 2]","[80, 60]","[1, 1]",1,1,1,80,8,1,1,1968 -25,39,"[2, 1]","[70, 80]","[1, 1]",1,1,1,80,8,1,2,2082 -25,40,"[3, 4]","[70, 40]","[2, 1]",1,1,3,70,7,1,3,2121 -25,41,"[2, 1]","[50, 80]","[1, 2]",2,5,2,250,5,2,2,3340 -25,42,"[3, 4]","[70, 60]","[2, 2]",2,1,4,60,6,2,3,2238 -25,43,"[4, 3]","[50, 90]","[2, 2]",2,1,4,50,5,2,4,2217 -25,44,"[1, 2]","[50, 50]","[1, 1]",2,1,1,50,5,1,1,2026 -25,45,"[4, 3]","[60, 40]","[2, 2]",2,5,4,300,6,2,3,1863 -25,46,"[1, 2]","[10, 50]","[1, 2]",2,1,2,50,5,2,2,2060 -25,47,"[4, 3]","[60, 0]","[2, 2]",2,1,4,60,6,2,3,1872 -25,48,"[2, 1]","[60, 20]","[1, 1]",2,1,1,20,2,1,1,2108 -25,49,"[1, 2]","[10, 90]","[1, 2]",2,5,2,450,9,2,2,2122 -25,50,"[4, 3]","[80, 30]","[2, 2]",2,5,4,400,8,2,3,1853 -25,51,"[4, 3]","[70, 10]","[2, 2]",2,5,4,350,7,2,3,1872 -25,52,"[1, 2]","[10, 40]","[1, 2]",2,5,2,200,4,2,2,2039 -25,53,"[3, 4]","[20, 70]","[2, 2]",2,5,4,350,7,2,4,1877 -25,54,"[4, 3]","[90, 10]","[2, 2]",2,5,4,450,9,2,3,1813 -25,55,"[1, 2]","[20, 50]","[1, 2]",2,1,2,50,5,2,2,1952 -25,56,"[4, 3]","[70, 20]","[2, 2]",2,5,4,350,7,2,3,1853 -25,57,"[1, 2]","[10, 90]","[1, 2]",2,5,2,450,9,2,2,1938 -25,58,"[4, 3]","[50, 20]","[2, 2]",2,5,4,250,5,2,3,1864 -25,59,"[2, 1]","[50, 20]","[1, 1]",2,1,1,20,2,1,1,2294 -25,60,"[1, 2]","[30, 50]","[1, 2]",2,1,2,50,5,2,2,2285 -25,61,"[3, 4]","[20, 60]","[2, 2]",2,5,4,300,6,2,4,1960 -25,62,"[3, 4]","[50, 40]","[2, 2]",2,5,4,200,4,2,3,1927 -25,63,"[1, 2]","[60, 50]","[1, 2]",2,5,2,250,5,2,1,1981 -25,64,"[4, 3]","[70, 60]","[2, 2]",2,1,4,70,7,2,3,1934 -25,65,"[2, 1]","[90, 60]","[1, 1]",2,1,1,60,6,1,1,2057 -25,66,"[1, 2]","[40, 50]","[1, 1]",2,1,1,40,4,1,2,1972 -25,67,"[3, 4]","[40, 40]","[2, 2]",2,1,4,40,4,2,3,1875 -25,68,"[3, 4]","[20, 30]","[2, 2]",2,5,4,150,3,2,4,1888 -25,69,"[3, 4]","[50, 50]","[2, 2]",2,1,4,50,5,2,3,1926 -25,70,"[4, 3]","[30, 80]","[2, 2]",2,1,4,30,3,2,4,1811 -25,71,"[3, 4]","[70, 0]","[2, 1]",2,1,3,70,7,1,3,1921 -25,72,"[3, 4]","[70, 10]","[2, 1]",2,5,3,350,7,1,3,1910 -25,73,"[4, 3]","[10, 80]","[2, 1]",2,5,3,400,8,1,4,1930 -25,74,"[4, 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1]",1,5,3,350,7,1,3,1905 -26,30,"[4, 3]","[70, 60]","[2, 1]",1,5,3,300,6,1,3,2537 -26,31,"[3, 4]","[50, 60]","[2, 1]",1,1,3,50,5,1,4,2279 -26,32,"[2, 1]","[90, 30]","[1, 1]",1,5,1,150,3,1,1,2315 -26,33,"[4, 3]","[50, 30]","[2, 1]",1,1,3,30,3,1,3,1905 -26,34,"[4, 3]","[40, 50]","[2, 1]",1,5,3,250,5,1,4,2654 -26,35,"[4, 3]","[60, 90]","[2, 1]",1,1,3,90,9,1,4,2049 -26,36,"[1, 2]","[50, 80]","[1, 1]",1,1,1,50,5,1,2,2075 -26,37,"[3, 4]","[80, 50]","[2, 1]",1,5,3,400,8,1,3,2475 -26,38,"[3, 4]","[70, 50]","[2, 1]",1,1,3,70,7,1,3,1972 -26,39,"[1, 2]","[80, 50]","[1, 1]",1,5,1,400,8,1,1,2157 -26,40,"[2, 1]","[60, 60]","[1, 1]",1,5,1,300,6,1,2,2412 -26,41,"[3, 4]","[70, 50]","[2, 2]",2,1,4,50,5,2,3,9185 -26,42,"[3, 4]","[60, 30]","[2, 2]",2,5,4,150,3,2,3,2897 -26,43,"[4, 3]","[60, 70]","[2, 1]",2,5,3,350,7,1,4,2600 -26,44,"[1, 2]","[50, 70]","[1, 2]",2,1,2,70,7,2,2,3396 -26,45,"[1, 2]","[50, 60]","[1, 2]",2,5,2,300,6,2,2,3277 -26,46,"[1, 2]","[60, 40]","[1, 1]",2,5,1,300,6,1,1,1920 -26,47,"[2, 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2]",2,1,2,40,4,2,1,2074 -29,55,"[4, 3]","[30, 30]","[2, 1]",2,5,3,150,3,1,4,2058 -29,56,"[4, 3]","[20, 30]","[2, 1]",2,1,3,30,3,1,4,4857 -29,57,"[2, 1]","[10, 30]","[1, 1]",2,5,1,150,3,1,2,2307 -29,58,"[2, 1]","[10, 10]","[1, 1]",2,1,1,10,1,1,2,2741 -29,59,"[2, 1]","[30, 10]","[1, 2]",2,1,2,30,3,2,1,4458 -29,60,"[3, 4]","[10, 10]","[2, 1]",2,1,3,10,1,1,3,2643 -29,61,"[1, 2]","[10, 10]","[1, 2]",2,5,2,50,1,2,1,4754 -29,62,"[3, 4]","[30, 20]","[2, 2]",2,5,4,100,2,2,3,2670 -29,63,"[1, 2]","[60, 0]","[1, 2]",2,1,2,0,0,2,1,1785 -29,64,"[4, 3]","[20, 60]","[2, 1]",2,1,3,60,6,1,4,2040 -29,65,"[3, 4]","[80, 10]","[2, 1]",2,5,3,400,8,1,3,2020 -29,66,"[3, 4]","[90, 20]","[2, 1]",2,1,3,90,9,1,3,2053 -29,67,"[3, 4]","[80, 40]","[2, 1]",2,5,3,400,8,1,3,1707 -29,68,"[3, 4]","[80, 40]","[2, 1]",2,1,3,80,8,1,3,1774 -29,69,"[4, 3]","[10, 60]","[2, 1]",2,5,3,300,6,1,4,2307 -29,70,"[1, 2]","[80, 0]","[1, 1]",2,1,1,80,8,1,1,2374 -29,71,"[2, 1]","[10, 30]","[1, 1]",2,1,1,30,3,1,2,1940 -29,72,"[1, 2]","[20, 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1]",2,5,3,400,8,1,3,1807 -29,46,"[1, 2]","[80, 30]","[1, 1]",2,1,1,80,8,1,1,1907 -29,47,"[3, 4]","[90, 40]","[2, 2]",2,5,4,200,4,2,3,1890 -29,48,"[4, 3]","[50, 50]","[2, 2]",2,1,4,50,5,2,4,1940 -29,49,"[4, 3]","[50, 40]","[2, 1]",2,1,3,40,4,1,3,1791 -29,50,"[4, 3]","[90, 20]","[2, 2]",2,1,4,90,9,2,3,1740 -29,51,"[4, 3]","[70, 30]","[2, 2]",2,1,4,70,7,2,3,1788 -29,52,"[2, 1]","[80, 40]","[1, 2]",2,5,2,400,8,2,1,1808 -29,53,"[4, 3]","[60, 60]","[2, 2]",2,5,4,300,6,2,4,1857 -29,54,"[3, 4]","[50, 40]","[2, 2]",2,1,4,40,4,2,3,1907 -29,55,"[4, 3]","[60, 40]","[2, 1]",2,5,3,200,4,1,3,1756 -29,56,"[1, 2]","[80, 90]","[1, 1]",2,5,1,400,8,1,2,1907 -29,57,"[4, 3]","[80, 60]","[2, 1]",2,5,3,300,6,1,3,2140 -29,58,"[2, 1]","[70, 60]","[1, 1]",2,5,1,300,6,1,1,2038 -29,59,"[1, 2]","[80, 60]","[1, 1]",2,5,1,400,8,1,1,1940 -29,60,"[3, 4]","[90, 60]","[2, 1]",2,1,3,90,9,1,3,1939 -29,61,"[1, 2]","[80, 50]","[1, 1]",2,5,1,400,8,1,1,1772 -29,62,"[2, 1]","[30, 90]","[1, 1]",2,5,1,450,9,1,2,2107 -29,63,"[4, 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1]",1,5,1,50,1,1,2,2689 -29,37,"[2, 1]","[30, 0]","[1, 1]",1,5,1,0,0,1,1,2306 -29,38,"[2, 1]","[70, 30]","[1, 2]",1,1,2,70,7,2,1,2124 -29,39,"[3, 4]","[70, 50]","[2, 2]",1,1,4,50,5,2,3,1840 -29,40,"[1, 2]","[60, 90]","[1, 2]",1,5,2,450,9,2,2,3857 -29,41,"[2, 1]","[80, 70]","[1, 1]",2,1,1,70,7,1,1,7769 -29,42,"[1, 2]","[80, 50]","[1, 1]",2,1,1,80,8,1,1,2189 -29,43,"[2, 1]","[30, 70]","[1, 1]",2,1,1,70,7,1,2,2004 -29,44,"[1, 2]","[90, 70]","[1, 1]",2,1,1,90,9,1,1,1851 -29,45,"[4, 3]","[80, 80]","[2, 1]",2,5,3,400,8,1,4,1739 -29,46,"[4, 3]","[90, 70]","[2, 1]",2,1,3,70,7,1,3,1906 -29,47,"[2, 1]","[70, 60]","[1, 1]",2,1,1,60,6,1,1,2323 -29,48,"[2, 1]","[50, 70]","[1, 1]",2,5,1,350,7,1,2,1857 -29,49,"[2, 1]","[80, 60]","[1, 1]",2,1,1,60,6,1,1,1739 -29,50,"[4, 3]","[90, 40]","[2, 1]",2,1,3,40,4,1,3,1787 -29,51,"[4, 3]","[80, 20]","[2, 2]",2,5,4,400,8,2,3,1840 -29,52,"[3, 4]","[0, 50]","[2, 2]",2,1,4,50,5,2,4,2306 -29,53,"[2, 1]","[30, 0]","[1, 2]",2,5,2,150,3,2,1,2056 -29,54,"[1, 2]","[30, 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-29,10,"[1, 2]","[20, 70]","[1, 1]",1,1,1,20,2,1,2,4910 -29,11,"[1, 2]","[40, 70]","[1, 2]",1,5,2,350,7,2,2,1738 -29,12,"[3, 4]","[50, 60]","[2, 2]",1,5,4,300,6,2,4,1826 -29,13,"[3, 4]","[70, 60]","[2, 2]",1,1,4,60,6,2,3,1808 -29,14,"[2, 1]","[50, 30]","[1, 2]",1,1,2,50,5,2,1,3040 -29,15,"[1, 2]","[20, 20]","[1, 1]",1,5,1,100,2,1,1,1959 -29,16,"[4, 3]","[50, 30]","[2, 2]",1,5,4,250,5,2,3,2228 -29,17,"[3, 4]","[20, 20]","[2, 2]",1,5,4,100,2,2,3,1943 -29,18,"[3, 4]","[10, 30]","[2, 2]",1,1,4,30,3,2,4,2076 -29,19,"[4, 3]","[10, 10]","[2, 1]",1,1,3,10,1,1,4,1805 -29,20,"[1, 2]","[20, 20]","[1, 2]",1,1,2,20,2,2,1,2182 -29,21,"[1, 2]","[20, 20]","[1, 1]",1,5,1,100,2,1,1,1928 -29,22,"[3, 4]","[40, 30]","[2, 1]",1,5,3,200,4,1,3,2134 -29,23,"[3, 4]","[30, 50]","[2, 1]",1,5,3,150,3,1,4,1960 -29,24,"[3, 4]","[0, 10]","[2, 1]",1,5,3,0,0,1,4,1845 -29,25,"[3, 4]","[20, 10]","[2, 2]",1,5,4,50,1,2,3,1826 -29,26,"[4, 3]","[10, 30]","[2, 2]",1,5,4,50,1,2,4,1860 -29,27,"[2, 1]","[0, 60]","[1, 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0]","[1, 2]",2,1,2,0,0,2,2,2895 -29,46,"[1, 2]","[10, 0]","[1, 1]",2,5,1,50,1,1,1,1771 -29,47,"[2, 1]","[40, 40]","[1, 1]",2,5,1,200,4,1,2,1995 -29,48,"[4, 3]","[50, 50]","[2, 1]",2,1,3,50,5,1,4,1771 -29,49,"[4, 3]","[70, 20]","[2, 1]",2,5,3,100,2,1,3,1776 -29,50,"[2, 1]","[70, 60]","[1, 1]",2,1,1,60,6,1,1,1927 -29,51,"[1, 2]","[80, 70]","[1, 1]",2,5,1,400,8,1,1,1895 -29,52,"[1, 2]","[50, 80]","[1, 1]",2,1,1,50,5,1,2,1878 -29,53,"[1, 2]","[40, 70]","[1, 1]",2,5,1,200,4,1,2,1795 -29,54,"[4, 3]","[90, 50]","[2, 1]",2,5,3,250,5,1,3,1789 -29,55,"[2, 1]","[60, 20]","[1, 1]",2,1,1,20,2,1,1,1826 -29,56,"[1, 2]","[30, 60]","[1, 2]",2,1,2,60,6,2,2,2328 -29,57,"[2, 1]","[40, 50]","[1, 2]",2,1,2,40,4,2,2,2045 -29,58,"[4, 3]","[60, 50]","[2, 1]",2,5,3,250,5,1,3,1704 -29,59,"[4, 3]","[60, 40]","[2, 1]",2,5,3,200,4,1,3,1737 -29,60,"[1, 2]","[20, 70]","[1, 1]",2,1,1,20,2,1,2,2044 -29,61,"[1, 2]","[10, 70]","[1, 2]",2,5,2,350,7,2,2,1653 -29,62,"[4, 3]","[80, 40]","[2, 2]",2,5,4,400,8,2,3,1827 -29,63,"[2, 1]","[90, 30]","[1, 2]",2,1,2,90,9,2,1,1945 -29,64,"[2, 1]","[50, 10]","[1, 2]",2,1,2,50,5,2,1,1978 -29,65,"[1, 2]","[0, 30]","[1, 2]",2,1,2,30,3,2,2,2395 -29,66,"[2, 1]","[50, 10]","[1, 1]",2,1,1,10,1,1,1,2459 -29,67,"[3, 4]","[20, 10]","[2, 2]",2,5,4,50,1,2,3,1755 -29,68,"[2, 1]","[50, 10]","[1, 2]",2,1,2,50,5,2,1,2195 -29,69,"[1, 2]","[10, 10]","[1, 2]",2,5,2,50,1,2,1,1810 -29,70,"[4, 3]","[10, 30]","[2, 2]",2,5,4,50,1,2,4,2028 -29,71,"[1, 2]","[40, 0]","[1, 2]",2,1,2,0,0,2,1,1943 -29,72,"[3, 4]","[40, 0]","[2, 1]",2,5,3,200,4,1,3,1843 -29,73,"[2, 1]","[30, 30]","[1, 1]",2,1,1,30,3,1,2,2109 -29,74,"[1, 2]","[0, 0]","[1, 1]",2,5,1,0,0,1,1,2396 -29,75,"[4, 3]","[20, 10]","[2, 2]",2,5,4,100,2,2,3,2147 -29,76,"[1, 2]","[30, 20]","[1, 2]",2,5,2,100,2,2,1,2292 -29,77,"[3, 4]","[40, 0]","[2, 2]",2,5,4,0,0,2,3,1842 -29,78,"[4, 3]","[40, 30]","[2, 1]",2,5,3,150,3,1,3,1827 -29,79,"[2, 1]","[20, 20]","[1, 1]",2,5,1,100,2,1,2,1945 -29,80,"[1, 2]","[0, 50]","[1, 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-29,36,"[1, 2]","[50, 70]","[1, 2]",1,1,2,70,7,2,2,2144 -29,37,"[1, 2]","[10, 80]","[1, 2]",1,5,2,400,8,2,2,2226 -29,38,"[1, 2]","[0, 50]","[1, 2]",1,1,2,50,5,2,2,2060 -29,39,"[2, 1]","[50, 10]","[1, 1]",1,1,1,10,1,1,1,1960 -29,40,"[3, 4]","[50, 20]","[2, 2]",1,5,4,100,2,2,3,3427 -29,41,"[4, 3]","[20, 90]","[2, 1]",2,5,3,450,9,1,4,5421 -29,42,"[3, 4]","[70, 10]","[2, 1]",2,5,3,350,7,1,3,1911 -29,43,"[1, 2]","[50, 20]","[1, 1]",2,1,1,50,5,1,1,2227 -29,44,"[2, 1]","[40, 70]","[1, 1]",2,5,1,350,7,1,2,1976 -29,45,"[4, 3]","[60, 70]","[2, 1]",2,5,3,350,7,1,4,1859 -29,46,"[1, 2]","[70, 90]","[1, 1]",2,5,1,350,7,1,2,2045 -29,47,"[2, 1]","[90, 70]","[1, 1]",2,1,1,70,7,1,1,1859 -29,48,"[4, 3]","[40, 40]","[2, 1]",2,5,3,200,4,1,4,1821 -29,49,"[3, 4]","[50, 30]","[2, 1]",2,1,3,50,5,1,3,1909 -29,50,"[1, 2]","[50, 20]","[1, 1]",2,5,1,250,5,1,1,1778 -29,51,"[2, 1]","[0, 40]","[1, 1]",2,5,1,200,4,1,2,1959 -29,52,"[4, 3]","[30, 10]","[2, 1]",2,1,3,10,1,1,3,1793 -29,53,"[1, 2]","[30, 30]","[1, 2]",2,1,2,30,3,2,1,2259 -29,54,"[2, 1]","[50, 20]","[1, 2]",2,1,2,50,5,2,1,2759 -29,55,"[1, 2]","[20, 10]","[1, 2]",2,1,2,10,1,2,1,2245 -29,56,"[1, 2]","[0, 10]","[1, 1]",2,1,1,0,0,1,2,1910 -29,57,"[3, 4]","[30, 10]","[2, 2]",2,5,4,50,1,2,3,1745 -29,58,"[3, 4]","[0, 20]","[2, 2]",2,5,4,100,2,2,4,1794 -29,59,"[3, 4]","[10, 30]","[2, 2]",2,5,4,150,3,2,4,1858 -29,60,"[3, 4]","[40, 20]","[2, 2]",2,1,4,20,2,2,3,1927 -29,61,"[2, 1]","[40, 20]","[1, 1]",2,1,1,20,2,1,1,2010 -29,62,"[1, 2]","[60, 50]","[1, 2]",2,5,2,250,5,2,1,2178 -29,63,"[1, 2]","[70, 40]","[1, 2]",2,1,2,40,4,2,1,1860 -29,64,"[3, 4]","[50, 40]","[2, 2]",2,1,4,40,4,2,3,1778 -29,65,"[2, 1]","[80, 70]","[1, 2]",2,5,2,400,8,2,1,2305 -29,66,"[2, 1]","[60, 80]","[1, 2]",2,5,2,300,6,2,2,1910 -29,67,"[4, 3]","[40, 60]","[2, 2]",2,5,4,200,4,2,4,2543 -29,68,"[2, 1]","[50, 70]","[1, 2]",2,1,2,50,5,2,2,2843 -29,69,"[1, 2]","[70, 60]","[1, 2]",2,1,2,60,6,2,1,2360 -29,70,"[3, 4]","[70, 50]","[2, 2]",2,1,4,50,5,2,3,2026 -29,71,"[3, 4]","[80, 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2]","[50, 50]","[1, 2]",1,1,2,50,5,2,1,2067 -30,10,"[2, 1]","[50, 40]","[1, 2]",1,1,2,50,5,2,1,2125 -30,11,"[1, 2]","[20, 30]","[1, 1]",1,5,1,100,2,1,2,2974 -30,12,"[1, 2]","[30, 40]","[1, 2]",1,5,2,200,4,2,2,2127 -30,13,"[3, 4]","[20, 50]","[2, 1]",1,5,3,100,2,1,4,1765 -30,14,"[3, 4]","[0, 40]","[2, 2]",1,1,4,40,4,2,4,5617 -30,15,"[4, 3]","[30, 20]","[2, 2]",1,1,4,30,3,2,3,2914 -30,16,"[4, 3]","[30, 20]","[2, 1]",1,5,3,100,2,1,3,1730 -30,17,"[2, 1]","[60, 20]","[1, 1]",1,1,1,20,2,1,1,2175 -30,18,"[2, 1]","[60, 40]","[1, 2]",1,1,2,60,6,2,1,1972 -30,19,"[4, 3]","[70, 40]","[2, 1]",1,1,3,40,4,1,3,1880 -30,20,"[3, 4]","[40, 90]","[2, 1]",1,5,3,200,4,1,4,1804 -30,21,"[2, 1]","[80, 70]","[1, 1]",1,1,1,70,7,1,1,1707 -30,22,"[2, 1]","[80, 80]","[1, 2]",1,5,2,400,8,2,2,1740 -30,23,"[1, 2]","[40, 80]","[1, 1]",1,5,1,200,4,1,2,1831 -30,24,"[3, 4]","[0, 70]","[2, 2]",1,1,4,70,7,2,4,1756 -30,25,"[3, 4]","[40, 90]","[2, 2]",1,1,4,90,9,2,4,2151 -30,26,"[3, 4]","[10, 80]","[2, 2]",1,5,4,400,8,2,4,2927 -30,27,"[2, 1]","[60, 10]","[1, 2]",1,1,2,60,6,2,1,2494 -30,28,"[3, 4]","[10, 60]","[2, 1]",1,1,3,10,1,1,4,3713 -30,29,"[4, 3]","[50, 0]","[2, 2]",1,1,4,50,5,2,3,2808 -30,30,"[1, 2]","[10, 60]","[1, 2]",1,1,2,60,6,2,2,2545 -30,31,"[1, 2]","[10, 50]","[1, 2]",1,5,2,250,5,2,2,2076 -30,32,"[3, 4]","[30, 50]","[2, 2]",1,1,4,50,5,2,4,3202 -30,33,"[1, 2]","[30, 60]","[1, 2]",1,5,2,300,6,2,2,2344 -30,34,"[2, 1]","[70, 40]","[1, 1]",1,5,1,200,4,1,1,5181 -30,35,"[2, 1]","[80, 30]","[1, 2]",1,1,2,80,8,2,1,3564 -30,36,"[1, 2]","[30, 80]","[1, 2]",1,1,2,80,8,2,2,1920 -30,37,"[3, 4]","[30, 60]","[2, 2]",1,5,4,300,6,2,4,1896 -30,38,"[1, 2]","[20, 70]","[1, 2]",1,5,2,350,7,2,2,2595 -30,39,"[2, 1]","[40, 40]","[1, 1]",1,1,1,40,4,1,2,13717 -30,40,"[1, 2]","[40, 10]","[1, 2]",1,1,2,10,1,2,1,2307 -30,41,"[3, 4]","[10, 40]","[2, 1]",2,5,3,50,1,1,4,6672 -30,42,"[3, 4]","[30, 40]","[2, 1]",2,1,3,30,3,1,4,1824 -30,43,"[3, 4]","[70, 40]","[2, 1]",2,5,3,350,7,1,3,1697 -30,44,"[3, 4]","[80, 50]","[2, 1]",2,1,3,80,8,1,3,1747 -30,45,"[3, 4]","[70, 20]","[2, 2]",2,5,4,100,2,2,3,1780 -30,46,"[1, 2]","[80, 20]","[1, 2]",2,1,2,20,2,2,1,1928 -30,47,"[3, 4]","[50, 10]","[2, 2]",2,1,4,10,1,2,3,1690 -30,48,"[4, 3]","[10, 60]","[2, 2]",2,5,4,50,1,2,4,1945 -30,49,"[1, 2]","[40, 10]","[1, 2]",2,1,2,10,1,2,1,1847 -30,50,"[1, 2]","[40, 0]","[1, 1]",2,1,1,40,4,1,1,1985 -30,51,"[1, 2]","[40, 10]","[1, 1]",2,1,1,40,4,1,1,2521 -30,52,"[1, 2]","[10, 10]","[1, 1]",2,1,1,10,1,1,1,2217 -30,53,"[4, 3]","[0, 10]","[2, 2]",2,5,4,0,0,2,4,2241 -30,54,"[1, 2]","[0, 10]","[1, 2]",2,5,2,50,1,2,2,1656 -30,55,"[2, 1]","[0, 10]","[1, 1]",2,1,1,10,1,1,2,1880 -30,56,"[1, 2]","[20, 10]","[1, 1]",2,5,1,100,2,1,1,1829 -30,57,"[2, 1]","[20, 0]","[1, 1]",2,1,1,0,0,1,1,1713 -30,58,"[1, 2]","[30, 20]","[1, 2]",2,5,2,100,2,2,1,2693 -30,59,"[3, 4]","[40, 0]","[2, 2]",2,5,4,0,0,2,3,1773 -30,60,"[4, 3]","[10, 60]","[2, 1]",2,1,3,60,6,1,4,2955 -30,61,"[2, 1]","[40, 30]","[1, 2]",2,1,2,40,4,2,1,1871 -30,62,"[2, 1]","[60, 40]","[1, 1]",2,5,1,200,4,1,1,2069 -30,63,"[4, 3]","[30, 70]","[2, 1]",2,5,3,350,7,1,4,2479 -30,64,"[4, 3]","[30, 80]","[2, 2]",2,5,4,150,3,2,4,1690 -30,65,"[1, 2]","[70, 60]","[1, 1]",2,1,1,70,7,1,1,2486 -30,66,"[2, 1]","[70, 80]","[1, 2]",2,5,2,350,7,2,2,2676 -30,67,"[2, 1]","[50, 60]","[1, 1]",2,5,1,300,6,1,2,1697 -30,68,"[4, 3]","[70, 70]","[2, 2]",2,1,4,70,7,2,4,1813 -30,69,"[3, 4]","[70, 30]","[2, 2]",2,5,4,150,3,2,3,1657 -30,70,"[1, 2]","[50, 30]","[1, 2]",2,1,2,30,3,2,1,1922 -30,71,"[4, 3]","[10, 80]","[2, 2]",2,1,4,10,1,2,4,1820 -30,72,"[1, 2]","[80, 10]","[1, 2]",2,5,2,50,1,2,1,1674 -30,73,"[3, 4]","[60, 20]","[2, 2]",2,1,4,20,2,2,3,1807 -30,74,"[3, 4]","[50, 20]","[2, 2]",2,5,4,100,2,2,3,3572 -30,75,"[4, 3]","[40, 70]","[2, 1]",2,1,3,70,7,1,4,6218 -30,76,"[1, 2]","[80, 80]","[1, 1]",2,5,1,400,8,1,1,3309 -30,77,"[2, 1]","[60, 70]","[1, 1]",2,5,1,350,7,1,2,2177 -30,78,"[1, 2]","[50, 50]","[1, 2]",2,1,2,50,5,2,1,1879 -30,79,"[1, 2]","[80, 50]","[1, 2]",2,1,2,50,5,2,1,2075 -30,80,"[3, 4]","[70, 50]","[2, 1]",2,1,3,70,7,1,3,3619 -29,1,"[4, 3]","[20, 50]","[2, 1]",1,1,3,50,5,1,4,7546 -29,2,"[2, 1]","[20, 80]","[1, 1]",1,5,1,400,8,1,2,2141 -29,3,"[4, 3]","[0, 40]","[2, 1]",1,1,3,40,4,1,4,1753 -29,4,"[3, 4]","[40, 30]","[2, 1]",1,1,3,40,4,1,3,1891 -29,5,"[1, 2]","[0, 10]","[1, 1]",1,5,1,0,0,1,2,2350 -29,6,"[2, 1]","[10, 40]","[1, 2]",1,5,2,50,1,2,2,1911 -29,7,"[2, 1]","[30, 30]","[1, 2]",1,1,2,30,3,2,2,2057 -29,8,"[4, 3]","[40, 10]","[2, 2]",1,1,4,40,4,2,3,1915 -29,9,"[4, 3]","[70, 20]","[2, 2]",1,5,4,350,7,2,3,1823 -29,10,"[3, 4]","[30, 80]","[2, 2]",1,5,4,400,8,2,4,2206 -29,11,"[4, 3]","[80, 50]","[2, 2]",1,5,4,400,8,2,3,2023 -29,12,"[1, 2]","[30, 80]","[1, 2]",1,5,2,400,8,2,2,1939 -29,13,"[3, 4]","[30, 40]","[2, 2]",1,1,4,40,4,2,4,2122 -29,14,"[4, 3]","[50, 0]","[2, 1]",1,1,3,0,0,1,3,2855 -29,15,"[4, 3]","[20, 0]","[2, 2]",1,1,4,20,2,2,3,1906 -29,16,"[3, 4]","[10, 30]","[2, 2]",1,5,4,150,3,2,4,2238 -29,17,"[3, 4]","[30, 30]","[2, 2]",1,5,4,150,3,2,3,1841 -29,18,"[4, 3]","[50, 40]","[2, 2]",1,5,4,250,5,2,3,1815 -29,19,"[1, 2]","[10, 70]","[1, 2]",1,1,2,70,7,2,2,1971 -29,20,"[2, 1]","[70, 10]","[1, 1]",1,1,1,10,1,1,1,1838 -29,21,"[3, 4]","[30, 60]","[2, 2]",1,5,4,300,6,2,4,2039 -29,22,"[1, 2]","[40, 70]","[1, 2]",1,1,2,70,7,2,2,1989 -29,23,"[4, 3]","[60, 30]","[2, 2]",1,1,4,60,6,2,3,2155 -29,24,"[1, 2]","[10, 80]","[1, 2]",1,1,2,80,8,2,2,1973 -29,25,"[3, 4]","[10, 80]","[2, 2]",1,5,4,400,8,2,4,1806 -29,26,"[3, 4]","[0, 70]","[2, 2]",1,1,4,70,7,2,4,1773 -29,27,"[2, 1]","[40, 30]","[1, 2]",1,1,2,40,4,2,1,3224 -29,28,"[1, 2]","[50, 10]","[1, 2]",1,5,2,50,1,2,1,1890 -29,29,"[3, 4]","[20, 30]","[2, 2]",1,5,4,150,3,2,4,1973 -29,30,"[4, 3]","[30, 30]","[2, 2]",1,1,4,30,3,2,4,1972 -29,31,"[4, 3]","[10, 20]","[2, 2]",1,5,4,50,1,2,4,1989 -29,32,"[4, 3]","[10, 10]","[2, 2]",1,5,4,50,1,2,4,1790 -29,33,"[4, 3]","[0, 20]","[2, 2]",1,1,4,0,0,2,4,1756 -29,34,"[1, 2]","[10, 0]","[1, 1]",1,5,1,50,1,1,1,2139 -29,35,"[2, 1]","[10, 0]","[1, 1]",1,5,1,0,0,1,1,1940 -29,36,"[3, 4]","[40, 10]","[2, 2]",1,1,4,10,1,2,3,1790 -29,37,"[4, 3]","[10, 50]","[2, 2]",1,1,4,10,1,2,4,2105 -29,38,"[2, 1]","[20, 80]","[1, 1]",1,1,1,80,8,1,2,2622 -29,39,"[3, 4]","[60, 20]","[2, 1]",1,1,3,60,6,1,3,1741 -29,40,"[3, 4]","[60, 10]","[2, 1]",1,5,3,300,6,1,3,1773 -29,41,"[2, 1]","[20, 60]","[1, 1]",2,5,1,300,6,1,2,3652 -29,42,"[1, 2]","[90, 40]","[1, 1]",2,5,1,450,9,1,1,1873 -29,43,"[2, 1]","[50, 60]","[1, 1]",2,5,1,300,6,1,2,2124 -29,44,"[1, 2]","[80, 40]","[1, 1]",2,1,1,80,8,1,1,2039 -29,45,"[1, 2]","[60, 50]","[1, 1]",2,1,1,60,6,1,1,2106 -29,46,"[2, 1]","[40, 50]","[1, 1]",2,5,1,250,5,1,2,1973 -29,47,"[4, 3]","[30, 40]","[2, 1]",2,5,3,200,4,1,4,1723 -29,48,"[1, 2]","[0, 10]","[1, 1]",2,1,1,0,0,1,2,2075 -29,49,"[3, 4]","[10, 10]","[2, 2]",2,1,4,10,1,2,3,1972 -29,50,"[1, 2]","[30, 30]","[1, 2]",2,5,2,150,3,2,1,1790 -29,51,"[2, 1]","[10, 60]","[1, 2]",2,1,2,10,1,2,2,3507 -29,52,"[4, 3]","[0, 70]","[2, 2]",2,5,4,0,0,2,4,2340 -29,53,"[2, 1]","[30, 60]","[1, 1]",2,1,1,60,6,1,2,1836 -29,54,"[4, 3]","[30, 60]","[2, 1]",2,1,3,60,6,1,4,1940 -29,55,"[1, 2]","[10, 20]","[1, 1]",2,5,1,50,1,1,2,2973 -29,56,"[1, 2]","[20, 10]","[1, 1]",2,5,1,100,2,1,1,2072 -29,57,"[4, 3]","[20, 0]","[2, 1]",2,1,3,0,0,1,3,1806 -29,58,"[1, 2]","[0, 10]","[1, 2]",2,1,2,10,1,2,2,2123 -29,59,"[4, 3]","[20, 10]","[2, 1]",2,1,3,10,1,1,3,1890 -29,60,"[1, 2]","[0, 0]","[1, 2]",2,5,2,0,0,2,1,2073 -29,61,"[3, 4]","[10, 10]","[2, 1]",2,5,3,50,1,1,3,2223 -29,62,"[2, 1]","[10, 10]","[1, 1]",2,1,1,10,1,1,2,1991 -29,63,"[4, 3]","[10, 0]","[2, 2]",2,5,4,50,1,2,3,1606 -29,64,"[3, 4]","[20, 30]","[2, 2]",2,1,4,30,3,2,4,2073 -29,65,"[4, 3]","[90, 30]","[2, 2]",2,1,4,90,9,2,3,1757 -29,66,"[3, 4]","[40, 80]","[2, 2]",2,5,4,400,8,2,4,1689 -29,67,"[1, 2]","[70, 60]","[1, 2]",2,5,2,300,6,2,1,2172 -29,68,"[3, 4]","[60, 20]","[2, 2]",2,5,4,100,2,2,3,1823 -29,69,"[3, 4]","[80, 10]","[2, 2]",2,5,4,50,1,2,3,1742 -29,70,"[3, 4]","[80, 30]","[2, 2]",2,5,4,150,3,2,3,1839 -29,71,"[1, 2]","[70, 50]","[1, 2]",2,1,2,50,5,2,1,2056 -29,72,"[3, 4]","[60, 70]","[2, 2]",2,1,4,70,7,2,4,1873 -29,73,"[3, 4]","[30, 40]","[2, 2]",2,5,4,200,4,2,4,1806 -29,74,"[2, 1]","[60, 50]","[1, 2]",2,1,2,60,6,2,1,2989 -29,75,"[1, 2]","[40, 50]","[1, 2]",2,1,2,50,5,2,2,2041 -29,76,"[2, 1]","[40, 30]","[1, 2]",2,1,2,40,4,2,1,1857 -29,77,"[1, 2]","[20, 40]","[1, 2]",2,1,2,40,4,2,2,1956 -29,78,"[2, 1]","[60, 30]","[1, 2]",2,5,2,300,6,2,1,2460 -29,79,"[4, 3]","[60, 30]","[2, 2]",2,5,4,300,6,2,3,2057 -29,80,"[1, 2]","[20, 30]","[1, 2]",2,5,2,150,3,2,2,2891 -31,1,"[1, 2]","[60, 40]","[1, 2]",1,1,2,40,4,2,1,4177 -31,2,"[2, 1]","[30, 10]","[1, 1]",1,1,1,10,1,1,1,2113 -31,3,"[3, 4]","[10, 10]","[2, 2]",1,1,4,10,1,2,3,1880 -31,4,"[3, 4]","[40, 10]","[2, 2]",1,5,4,50,1,2,3,1923 -31,5,"[2, 1]","[0, 50]","[1, 1]",1,1,1,50,5,1,2,1985 -31,6,"[4, 3]","[50, 60]","[2, 2]",1,5,4,250,5,2,4,2314 -31,7,"[1, 2]","[80, 60]","[1, 2]",1,1,2,60,6,2,1,2048 -31,8,"[4, 3]","[80, 70]","[2, 2]",1,5,4,400,8,2,3,2872 -31,9,"[2, 1]","[50, 90]","[1, 1]",1,1,1,90,9,1,2,2012 -31,10,"[2, 1]","[60, 80]","[1, 2]",1,5,2,300,6,2,2,2141 -31,11,"[1, 2]","[70, 60]","[1, 2]",1,1,2,60,6,2,1,2336 -31,12,"[2, 1]","[60, 70]","[1, 2]",1,5,2,300,6,2,2,2163 -31,13,"[4, 3]","[70, 60]","[2, 2]",1,1,4,70,7,2,3,2131 -31,14,"[2, 1]","[70, 80]","[1, 1]",1,5,1,400,8,1,2,1962 -31,15,"[1, 2]","[80, 90]","[1, 2]",1,1,2,90,9,2,2,2153 -31,16,"[2, 1]","[90, 70]","[1, 1]",1,5,1,350,7,1,1,2876 -31,17,"[2, 1]","[50, 80]","[1, 2]",1,5,2,250,5,2,2,2519 -31,18,"[4, 3]","[40, 70]","[2, 1]",1,1,3,70,7,1,4,1985 -31,19,"[3, 4]","[40, 40]","[2, 2]",1,1,4,40,4,2,3,2114 -31,20,"[4, 3]","[20, 20]","[2, 2]",1,5,4,100,2,2,4,2012 -31,21,"[1, 2]","[40, 20]","[1, 2]",1,1,2,20,2,2,1,1818 -31,22,"[4, 3]","[30, 60]","[2, 1]",1,1,3,60,6,1,4,1991 -31,23,"[1, 2]","[80, 40]","[1, 2]",1,1,2,40,4,2,1,1874 -31,24,"[4, 3]","[60, 90]","[2, 1]",1,5,3,450,9,1,4,2013 -31,25,"[4, 3]","[40, 70]","[2, 2]",1,5,4,200,4,2,4,2102 -31,26,"[4, 3]","[60, 90]","[2, 2]",1,5,4,300,6,2,4,2111 -31,27,"[4, 3]","[60, 70]","[2, 2]",1,1,4,60,6,2,4,2114 -31,28,"[2, 1]","[70, 70]","[1, 1]",1,5,1,350,7,1,2,1846 -31,29,"[1, 2]","[80, 40]","[1, 1]",1,5,1,400,8,1,1,2297 -31,30,"[4, 3]","[40, 80]","[2, 1]",1,5,3,400,8,1,4,2092 -31,31,"[3, 4]","[80, 50]","[2, 1]",1,5,3,400,8,1,3,2241 -31,32,"[3, 4]","[80, 40]","[2, 1]",1,5,3,400,8,1,3,2630 -31,33,"[3, 4]","[60, 30]","[2, 1]",1,5,3,300,6,1,3,2419 -31,34,"[1, 2]","[50, 50]","[1, 2]",1,1,2,50,5,2,1,2911 -31,35,"[4, 3]","[40, 80]","[2, 1]",1,5,3,400,8,1,4,2169 -31,36,"[1, 2]","[80, 50]","[1, 1]",1,5,1,400,8,1,1,2150 -31,37,"[2, 1]","[70, 80]","[1, 1]",1,1,1,80,8,1,2,2186 -31,38,"[3, 4]","[90, 70]","[2, 2]",1,1,4,70,7,2,3,2062 -31,39,"[2, 1]","[50, 60]","[1, 2]",1,5,2,250,5,2,2,2224 -31,40,"[2, 1]","[70, 90]","[1, 2]",1,5,2,350,7,2,2,2191 -31,41,"[2, 1]","[80, 80]","[1, 1]",2,5,1,400,8,1,2,2804607 -31,42,"[2, 1]","[80, 90]","[1, 1]",2,5,1,450,9,1,2,2287 -31,43,"[4, 3]","[70, 80]","[2, 1]",2,1,3,80,8,1,4,2007 -31,44,"[3, 4]","[80, 40]","[2, 2]",2,1,4,40,4,2,3,2024 -31,45,"[1, 2]","[70, 70]","[1, 1]",2,1,1,70,7,1,1,2258 -31,46,"[2, 1]","[70, 70]","[1, 2]",2,1,2,70,7,2,2,2365 -31,47,"[3, 4]","[80, 70]","[2, 2]",2,5,4,350,7,2,3,2047 -31,48,"[4, 3]","[40, 60]","[2, 1]",2,5,3,300,6,1,4,2348 -31,49,"[1, 2]","[70, 60]","[1, 2]",2,5,2,300,6,2,1,2427 -31,50,"[3, 4]","[60, 40]","[2, 1]",2,1,3,60,6,1,3,2046 -31,51,"[4, 3]","[30, 60]","[2, 2]",2,1,4,30,3,2,4,2000 -31,52,"[2, 1]","[30, 70]","[1, 1]",2,5,1,350,7,1,2,1925 -31,53,"[1, 2]","[0, 30]","[1, 2]",2,5,2,150,3,2,2,2196 -31,54,"[4, 3]","[10, 20]","[2, 1]",2,5,3,100,2,1,4,2169 -31,55,"[3, 4]","[20, 0]","[2, 2]",2,1,4,0,0,2,3,2621 -31,56,"[2, 1]","[0, 10]","[1, 2]",2,5,2,0,0,2,2,2553 -31,57,"[1, 2]","[0, 10]","[1, 2]",2,5,2,50,1,2,2,1975 -31,58,"[1, 2]","[30, 30]","[1, 2]",2,1,2,30,3,2,1,2136 -31,59,"[4, 3]","[30, 30]","[2, 2]",2,1,4,30,3,2,4,2539 -31,60,"[4, 3]","[30, 10]","[2, 1]",2,1,3,10,1,1,3,2054 -31,61,"[3, 4]","[40, 30]","[2, 2]",2,1,4,30,3,2,3,2012 -31,62,"[2, 1]","[40, 60]","[1, 1]",2,5,1,300,6,1,2,2042 -31,63,"[1, 2]","[60, 40]","[1, 2]",2,1,2,40,4,2,1,2142 -31,64,"[1, 2]","[50, 60]","[1, 1]",2,5,1,250,5,1,2,2121 -31,65,"[4, 3]","[30, 60]","[2, 1]",2,1,3,60,6,1,4,2136 -31,66,"[4, 3]","[30, 90]","[2, 2]",2,1,4,30,3,2,4,2328 -31,67,"[2, 1]","[40, 80]","[1, 1]",2,5,1,400,8,1,2,1986 -31,68,"[3, 4]","[70, 20]","[2, 2]",2,1,4,20,2,2,3,2230 -31,69,"[4, 3]","[10, 70]","[2, 1]",2,5,3,350,7,1,4,2136 -31,70,"[1, 2]","[80, 60]","[1, 2]",2,5,2,300,6,2,1,1995 -31,71,"[3, 4]","[70, 70]","[2, 2]",2,5,4,350,7,2,3,1958 -31,72,"[4, 3]","[70, 40]","[2, 1]",2,1,3,40,4,1,3,2152 -31,73,"[1, 2]","[20, 80]","[1, 2]",2,1,2,80,8,2,2,1946 -31,74,"[3, 4]","[60, 80]","[2, 2]",2,5,4,400,8,2,4,2041 -31,75,"[3, 4]","[70, 80]","[2, 1]",2,5,3,350,7,1,4,2358 -31,76,"[2, 1]","[50, 50]","[1, 1]",2,1,1,50,5,1,2,1960 -31,77,"[1, 2]","[50, 30]","[1, 2]",2,1,2,30,3,2,1,1834 -31,78,"[4, 3]","[60, 80]","[2, 2]",2,1,4,60,6,2,4,2138 -31,79,"[4, 3]","[90, 70]","[2, 1]",2,1,3,70,7,1,3,1879 -31,80,"[1, 2]","[40, 80]","[1, 2]",2,1,2,80,8,2,2,1808 -29,1,"[1, 2]","[60, 40]","[1, 2]",1,1,2,40,4,2,1,4372 -29,2,"[2, 1]","[30, 10]","[1, 1]",1,1,1,10,1,1,1,1891 -29,3,"[3, 4]","[10, 10]","[2, 2]",1,1,4,10,1,2,3,2423 -29,4,"[3, 4]","[40, 10]","[2, 1]",1,5,3,200,4,1,3,1852 -29,5,"[2, 1]","[0, 50]","[1, 1]",1,1,1,50,5,1,2,2239 -29,6,"[4, 3]","[50, 60]","[2, 1]",1,5,3,300,6,1,4,1857 -29,7,"[1, 2]","[80, 60]","[1, 1]",1,1,1,80,8,1,1,2140 -29,8,"[4, 3]","[80, 70]","[2, 1]",1,5,3,350,7,1,3,1790 -29,9,"[2, 1]","[50, 90]","[1, 1]",1,1,1,90,9,1,2,2189 -29,10,"[2, 1]","[60, 80]","[1, 1]",1,5,1,400,8,1,2,2204 -29,11,"[1, 2]","[70, 60]","[1, 1]",1,1,1,70,7,1,1,2140 -29,12,"[2, 1]","[60, 70]","[1, 1]",1,5,1,350,7,1,2,2007 -29,13,"[4, 3]","[70, 60]","[2, 1]",1,1,3,60,6,1,3,1824 -29,14,"[2, 1]","[70, 80]","[1, 1]",1,5,1,400,8,1,2,2041 -29,15,"[1, 2]","[80, 90]","[1, 1]",1,1,1,80,8,1,2,2140 -29,16,"[2, 1]","[90, 70]","[1, 1]",1,5,1,350,7,1,1,1969 -29,17,"[2, 1]","[50, 80]","[1, 1]",1,5,1,400,8,1,2,1856 -29,18,"[4, 3]","[40, 70]","[2, 1]",1,1,3,70,7,1,4,1790 -29,19,"[3, 4]","[40, 40]","[2, 1]",1,1,3,40,4,1,3,1840 -29,20,"[4, 3]","[20, 20]","[2, 1]",1,5,3,100,2,1,4,1740 -29,21,"[1, 2]","[40, 20]","[1, 1]",1,1,1,40,4,1,1,2056 -29,22,"[4, 3]","[30, 60]","[2, 1]",1,1,3,60,6,1,4,1804 -29,23,"[1, 2]","[80, 40]","[1, 1]",1,1,1,80,8,1,1,1891 -29,24,"[4, 3]","[60, 90]","[2, 1]",1,5,3,450,9,1,4,1758 -29,25,"[4, 3]","[40, 70]","[2, 1]",1,5,3,350,7,1,4,1774 -29,26,"[4, 3]","[60, 90]","[2, 1]",1,5,3,450,9,1,4,1754 -29,27,"[4, 3]","[60, 70]","[2, 1]",1,1,3,70,7,1,4,1720 -29,28,"[2, 1]","[70, 70]","[1, 1]",1,5,1,350,7,1,2,2023 -29,29,"[1, 2]","[80, 40]","[1, 1]",1,5,1,400,8,1,1,1990 -29,30,"[4, 3]","[40, 80]","[2, 1]",1,5,3,400,8,1,4,1740 -29,31,"[3, 4]","[80, 50]","[2, 1]",1,5,3,400,8,1,3,1841 -29,32,"[3, 4]","[80, 40]","[2, 1]",1,5,3,400,8,1,3,1723 -29,33,"[3, 4]","[60, 30]","[2, 1]",1,5,3,300,6,1,3,1707 -29,34,"[1, 2]","[50, 50]","[1, 1]",1,1,1,50,5,1,1,1841 -29,35,"[4, 3]","[40, 80]","[2, 1]",1,5,3,400,8,1,4,1820 -29,36,"[1, 2]","[80, 50]","[1, 1]",1,5,1,400,8,1,1,2058 -29,37,"[2, 1]","[70, 80]","[1, 1]",1,1,1,80,8,1,2,2223 -29,38,"[3, 4]","[90, 70]","[2, 1]",1,1,3,90,9,1,3,1857 -29,39,"[2, 1]","[50, 60]","[1, 1]",1,5,1,300,6,1,2,2274 -29,40,"[2, 1]","[70, 90]","[1, 1]",1,5,1,450,9,1,2,2324 -29,41,"[2, 1]","[80, 80]","[1, 1]",2,5,1,400,8,1,2,3519 -29,42,"[2, 1]","[80, 90]","[1, 1]",2,5,1,450,9,1,2,1873 -29,43,"[4, 3]","[70, 80]","[2, 1]",2,1,3,80,8,1,4,1840 -29,44,"[3, 4]","[80, 40]","[2, 1]",2,1,3,80,8,1,3,2023 -29,45,"[1, 2]","[70, 70]","[1, 1]",2,1,1,70,7,1,1,1873 -29,46,"[2, 1]","[70, 70]","[1, 1]",2,1,1,70,7,1,2,1841 -29,47,"[3, 4]","[80, 70]","[2, 1]",2,5,3,400,8,1,3,1936 -29,48,"[4, 3]","[40, 60]","[2, 1]",2,5,3,300,6,1,4,1938 -29,49,"[1, 2]","[70, 60]","[1, 1]",2,5,1,350,7,1,1,2022 -29,50,"[3, 4]","[60, 40]","[2, 1]",2,1,3,60,6,1,3,1805 -29,51,"[4, 3]","[30, 60]","[2, 1]",2,1,3,60,6,1,4,1939 -29,52,"[2, 1]","[30, 70]","[1, 1]",2,5,1,350,7,1,2,1841 -29,53,"[1, 2]","[0, 30]","[1, 1]",2,5,1,0,0,1,2,2006 -29,54,"[4, 3]","[10, 20]","[2, 2]",2,5,4,50,1,2,4,1889 -29,55,"[3, 4]","[20, 0]","[2, 2]",2,1,4,0,0,2,3,1920 -29,56,"[2, 1]","[0, 10]","[1, 1]",2,5,1,50,1,1,2,1973 -29,57,"[1, 2]","[0, 10]","[1, 1]",2,5,1,0,0,1,2,2090 -29,58,"[1, 2]","[30, 30]","[1, 2]",2,1,2,30,3,2,1,1823 -29,59,"[4, 3]","[30, 30]","[2, 2]",2,1,4,30,3,2,4,2154 -29,60,"[4, 3]","[30, 10]","[2, 2]",2,1,4,30,3,2,3,1752 -29,61,"[3, 4]","[40, 30]","[2, 2]",2,1,4,30,3,2,3,2498 -29,62,"[2, 1]","[40, 60]","[1, 2]",2,5,2,200,4,2,2,2274 -29,63,"[1, 2]","[60, 40]","[1, 2]",2,1,2,40,4,2,1,2325 -29,64,"[1, 2]","[50, 60]","[1, 2]",2,5,2,300,6,2,2,1805 -29,65,"[4, 3]","[30, 60]","[2, 2]",2,1,4,30,3,2,4,2157 -29,66,"[4, 3]","[30, 90]","[2, 2]",2,1,4,30,3,2,4,1757 -29,67,"[2, 1]","[40, 80]","[1, 2]",2,5,2,200,4,2,2,2090 -29,68,"[3, 4]","[70, 20]","[2, 2]",2,1,4,20,2,2,3,2087 -29,69,"[4, 3]","[10, 70]","[2, 1]",2,5,3,350,7,1,4,2374 -29,70,"[1, 2]","[80, 60]","[1, 1]",2,5,1,400,8,1,1,2057 -29,71,"[3, 4]","[70, 70]","[2, 1]",2,5,3,350,7,1,3,1687 -29,72,"[4, 3]","[70, 40]","[2, 1]",2,1,3,40,4,1,3,3790 -29,73,"[1, 2]","[20, 80]","[1, 1]",2,1,1,20,2,1,2,1873 -29,74,"[3, 4]","[60, 80]","[2, 2]",2,5,4,400,8,2,4,1790 -29,75,"[3, 4]","[70, 80]","[2, 2]",2,5,4,400,8,2,4,1973 -29,76,"[2, 1]","[50, 50]","[1, 2]",2,1,2,50,5,2,2,1974 -29,77,"[1, 2]","[50, 30]","[1, 2]",2,1,2,30,3,2,1,4541 -29,78,"[4, 3]","[60, 80]","[2, 2]",2,1,4,60,6,2,4,2090 -29,79,"[4, 3]","[90, 70]","[2, 2]",2,1,4,90,9,2,3,1757 -29,80,"[1, 2]","[40, 80]","[1, 2]",2,1,2,80,8,2,2,2607 -32,1,"[3, 4]","[60, 40]","[2, 1]",1,1,3,60,6,1,3,6267 -32,2,"[3, 4]","[60, 80]","[2, 1]",1,5,3,300,6,1,4,2639 -32,3,"[1, 2]","[30, 50]","[1, 1]",1,5,1,150,3,1,2,2313 -32,4,"[4, 3]","[50, 10]","[2, 1]",1,5,3,50,1,1,3,2518 -32,5,"[1, 2]","[10, 50]","[1, 2]",1,5,2,250,5,2,2,2759 -32,6,"[2, 1]","[50, 30]","[1, 2]",1,1,2,50,5,2,1,2216 -32,7,"[1, 2]","[40, 80]","[1, 1]",1,1,1,40,4,1,2,1904 -32,8,"[1, 2]","[60, 70]","[1, 2]",1,5,2,350,7,2,2,1988 -32,9,"[4, 3]","[90, 80]","[2, 2]",1,5,4,450,9,2,3,2284 -32,10,"[3, 4]","[80, 70]","[2, 2]",1,1,4,70,7,2,3,2467 -32,11,"[2, 1]","[70, 70]","[1, 1]",1,5,1,350,7,1,2,2517 -32,12,"[4, 3]","[70, 70]","[2, 1]",1,1,3,70,7,1,4,2865 -32,13,"[2, 1]","[70, 70]","[1, 2]",1,1,2,70,7,2,2,2721 -32,14,"[1, 2]","[80, 70]","[1, 2]",1,5,2,350,7,2,1,1948 -32,15,"[2, 1]","[60, 70]","[1, 2]",1,1,2,60,6,2,2,2958 -32,16,"[4, 3]","[50, 50]","[2, 2]",1,5,4,250,5,2,4,2200 -32,17,"[3, 4]","[40, 50]","[2, 2]",1,5,4,250,5,2,4,2109 -32,18,"[3, 4]","[40, 70]","[2, 1]",1,1,3,40,4,1,4,2807 -32,19,"[1, 2]","[30, 90]","[1, 2]",1,1,2,90,9,2,2,2296 -32,20,"[3, 4]","[20, 80]","[2, 2]",1,1,4,80,8,2,4,2018 -32,21,"[2, 1]","[80, 60]","[1, 2]",1,5,2,400,8,2,1,2334 -32,22,"[3, 4]","[70, 70]","[2, 2]",1,5,4,350,7,2,3,2109 -32,23,"[3, 4]","[70, 50]","[2, 2]",1,5,4,250,5,2,3,1986 -32,24,"[1, 2]","[70, 40]","[1, 2]",1,1,2,40,4,2,1,2883 -32,25,"[1, 2]","[70, 20]","[1, 1]",1,5,1,350,7,1,1,2200 -32,26,"[2, 1]","[30, 90]","[1, 1]",1,1,1,90,9,1,2,2396 -32,27,"[1, 2]","[60, 10]","[1, 1]",1,1,1,60,6,1,1,2201 -32,28,"[4, 3]","[10, 30]","[2, 2]",1,1,4,10,1,2,4,2616 -32,29,"[2, 1]","[20, 30]","[1, 1]",1,1,1,30,3,1,2,2103 -32,30,"[1, 2]","[10, 20]","[1, 1]",1,5,1,50,1,1,2,2506 -32,31,"[2, 1]","[10, 0]","[1, 1]",1,1,1,0,0,1,1,2144 -32,32,"[3, 4]","[0, 10]","[2, 2]",1,5,4,50,1,2,4,3775 -32,33,"[4, 3]","[0, 10]","[2, 2]",1,1,4,0,0,2,4,2188 -32,34,"[4, 3]","[20, 10]","[2, 1]",1,5,3,50,1,1,3,2220 -32,35,"[2, 1]","[20, 40]","[1, 1]",1,5,1,200,4,1,2,2311 -32,36,"[1, 2]","[30, 10]","[1, 1]",1,5,1,150,3,1,1,1945 -32,37,"[2, 1]","[10, 40]","[1, 1]",1,5,1,200,4,1,2,1976 -32,38,"[4, 3]","[0, 20]","[2, 2]",1,5,4,0,0,2,4,2613 -32,39,"[1, 2]","[10, 10]","[1, 1]",1,1,1,10,1,1,1,3846 -32,40,"[4, 3]","[20, 40]","[2, 2]",1,5,4,100,2,2,4,3850 -32,41,"[4, 3]","[30, 70]","[2, 1]",2,1,3,70,7,1,4,7845 -32,42,"[3, 4]","[60, 30]","[2, 1]",2,1,3,60,6,1,3,2341 -32,43,"[4, 3]","[60, 50]","[2, 1]",2,1,3,50,5,1,3,2035 -32,44,"[2, 1]","[40, 40]","[1, 2]",2,5,2,200,4,2,2,4208 -32,45,"[4, 3]","[20, 50]","[2, 2]",2,1,4,20,2,2,4,2503 -32,46,"[2, 1]","[40, 80]","[1, 1]",2,5,1,400,8,1,2,2426 -32,47,"[1, 2]","[50, 50]","[1, 1]",2,5,1,250,5,1,1,2410 -32,48,"[1, 2]","[50, 70]","[1, 1]",2,5,1,250,5,1,2,2190 -32,49,"[3, 4]","[50, 70]","[2, 1]",2,1,3,50,5,1,4,1887 -32,50,"[4, 3]","[50, 30]","[2, 1]",2,1,3,30,3,1,3,2002 -32,51,"[1, 2]","[30, 60]","[1, 2]",2,1,2,60,6,2,2,2826 -32,52,"[2, 1]","[50, 50]","[1, 2]",2,5,2,250,5,2,2,2118 -32,53,"[1, 2]","[40, 30]","[1, 2]",2,5,2,150,3,2,1,1853 -32,54,"[1, 2]","[20, 40]","[1, 2]",2,5,2,200,4,2,2,1785 -32,55,"[2, 1]","[70, 10]","[1, 2]",2,5,2,350,7,2,1,2094 -32,56,"[2, 1]","[90, 0]","[1, 2]",2,5,2,450,9,2,1,1870 -32,57,"[1, 2]","[10, 80]","[1, 2]",2,1,2,80,8,2,2,1835 -32,58,"[4, 3]","[80, 30]","[2, 1]",2,1,3,30,3,1,3,2773 -32,59,"[1, 2]","[30, 30]","[1, 2]",2,1,2,30,3,2,1,2381 -32,60,"[2, 1]","[30, 20]","[1, 1]",2,1,1,20,2,1,1,2184 -32,61,"[2, 1]","[60, 20]","[1, 2]",2,5,2,300,6,2,1,1770 -32,62,"[1, 2]","[30, 60]","[1, 2]",2,1,2,60,6,2,2,1812 -32,63,"[3, 4]","[40, 30]","[2, 2]",2,5,4,150,3,2,3,2302 -32,64,"[4, 3]","[40, 30]","[2, 2]",2,5,4,200,4,2,3,2100 -32,65,"[4, 3]","[10, 30]","[2, 2]",2,1,4,10,1,2,4,2299 -32,66,"[1, 2]","[40, 20]","[1, 1]",2,1,1,40,4,1,1,2490 -32,67,"[2, 1]","[30, 10]","[1, 1]",2,5,1,50,1,1,1,2218 -32,68,"[4, 3]","[10, 10]","[2, 1]",2,5,3,50,1,1,4,2569 -32,69,"[3, 4]","[20, 0]","[2, 2]",2,1,4,0,0,2,3,2330 -32,70,"[2, 1]","[10, 20]","[1, 1]",2,5,1,100,2,1,2,3352 -32,71,"[3, 4]","[20, 20]","[2, 1]",2,1,3,20,2,1,3,2009 -32,72,"[2, 1]","[80, 20]","[1, 2]",2,5,2,400,8,2,1,2621 -32,73,"[1, 2]","[20, 90]","[1, 2]",2,1,2,90,9,2,2,2462 -32,74,"[2, 1]","[80, 50]","[1, 2]",2,1,2,80,8,2,1,2475 -32,75,"[2, 1]","[80, 50]","[1, 2]",2,1,2,80,8,2,1,1790 -32,76,"[3, 4]","[60, 80]","[2, 1]",2,1,3,60,6,1,4,3270 -32,77,"[1, 2]","[70, 80]","[1, 1]",2,5,1,350,7,1,2,2625 -32,78,"[3, 4]","[70, 80]","[2, 1]",2,1,3,70,7,1,4,2445 -32,79,"[2, 1]","[80, 60]","[1, 2]",2,5,2,400,8,2,1,3159 -32,80,"[1, 2]","[40, 70]","[1, 2]",2,1,2,70,7,2,2,2351 -31,1,"[1, 2]","[30, 80]","[1, 2]",1,1,2,80,8,2,2,3446 -31,2,"[1, 2]","[20, 80]","[1, 2]",1,1,2,80,8,2,2,2930 -31,3,"[3, 4]","[10, 60]","[2, 1]",1,5,3,50,1,1,4,1985 -31,4,"[4, 3]","[90, 10]","[2, 1]",1,5,3,50,1,1,3,1931 -31,5,"[2, 1]","[70, 0]","[1, 2]",1,5,2,350,7,2,1,2169 -31,6,"[1, 2]","[20, 50]","[1, 2]",1,1,2,50,5,2,2,2023 -31,7,"[1, 2]","[10, 40]","[1, 2]",1,5,2,200,4,2,2,1846 -31,8,"[2, 1]","[60, 10]","[1, 1]",1,5,1,50,1,1,1,1954 -31,9,"[2, 1]","[80, 10]","[1, 2]",1,1,2,80,8,2,1,2024 -31,10,"[2, 1]","[70, 10]","[1, 2]",1,1,2,70,7,2,1,2313 -31,11,"[1, 2]","[30, 40]","[1, 1]",1,1,1,30,3,1,2,2297 -31,12,"[4, 3]","[40, 20]","[2, 2]",1,1,4,40,4,2,3,1929 -31,13,"[4, 3]","[50, 30]","[2, 2]",1,1,4,50,5,2,3,1811 -31,14,"[4, 3]","[10, 10]","[2, 2]",1,1,4,10,1,2,4,2438 -31,15,"[4, 3]","[0, 20]","[2, 1]",1,5,3,100,2,1,4,1815 -31,16,"[2, 1]","[20, 20]","[1, 1]",1,5,1,100,2,1,2,1918 -31,17,"[3, 4]","[20, 20]","[2, 1]",1,5,3,100,2,1,3,2154 -31,18,"[4, 3]","[40, 40]","[2, 1]",1,5,3,200,4,1,4,2091 -31,19,"[1, 2]","[50, 20]","[1, 1]",1,5,1,250,5,1,1,2603 -31,20,"[3, 4]","[50, 10]","[2, 1]",1,1,3,50,5,1,3,2125 -31,21,"[2, 1]","[30, 60]","[1, 2]",1,5,2,150,3,2,2,2203 -31,22,"[3, 4]","[30, 20]","[2, 2]",1,1,4,20,2,2,3,2262 -31,23,"[1, 2]","[30, 30]","[1, 2]",1,1,2,30,3,2,1,2073 -31,24,"[4, 3]","[0, 30]","[2, 1]",1,1,3,30,3,1,4,2019 -31,25,"[1, 2]","[20, 20]","[1, 1]",1,5,1,100,2,1,1,2164 -31,26,"[1, 2]","[60, 30]","[1, 2]",1,5,2,150,3,2,1,1986 -31,27,"[2, 1]","[40, 30]","[1, 1]",1,1,1,30,3,1,1,2377 -31,28,"[2, 1]","[50, 50]","[1, 2]",1,5,2,250,5,2,2,2164 -31,29,"[3, 4]","[30, 40]","[2, 2]",1,5,4,200,4,2,4,2104 -31,30,"[2, 1]","[70, 40]","[1, 2]",1,1,2,70,7,2,1,2752 -31,31,"[1, 2]","[40, 70]","[1, 2]",1,1,2,70,7,2,2,2839 -31,32,"[4, 3]","[90, 60]","[2, 1]",1,5,3,300,6,1,3,2153 -31,33,"[1, 2]","[70, 70]","[1, 2]",1,1,2,70,7,2,1,2276 -31,34,"[4, 3]","[80, 60]","[2, 2]",1,5,4,400,8,2,3,2104 -31,35,"[2, 1]","[90, 80]","[1, 2]",1,1,2,90,9,2,1,2588 -31,36,"[4, 3]","[90, 70]","[2, 2]",1,5,4,450,9,2,3,2190 -31,37,"[1, 2]","[40, 70]","[1, 2]",1,5,2,350,7,2,2,2690 -31,38,"[2, 1]","[70, 30]","[1, 1]",1,1,1,30,3,1,1,1931 -31,39,"[2, 1]","[60, 20]","[1, 1]",1,5,1,100,2,1,1,2603 -31,40,"[4, 3]","[80, 10]","[2, 2]",1,1,4,80,8,2,3,2320 -31,41,"[1, 2]","[20, 50]","[1, 2]",2,5,2,250,5,2,2,2654 -31,42,"[2, 1]","[70, 10]","[1, 1]",2,1,1,10,1,1,1,3157 -31,43,"[3, 4]","[20, 80]","[2, 2]",2,1,4,80,8,2,4,2041 -31,44,"[2, 1]","[70, 0]","[1, 2]",2,5,2,350,7,2,1,2187 -31,45,"[2, 1]","[30, 10]","[1, 1]",2,5,1,50,1,1,1,2079 -31,46,"[4, 3]","[40, 10]","[2, 2]",2,1,4,40,4,2,3,2111 -31,47,"[2, 1]","[30, 10]","[1, 2]",2,1,2,30,3,2,1,2383 -31,48,"[4, 3]","[60, 30]","[2, 1]",2,5,3,150,3,1,3,2114 -31,49,"[4, 3]","[70, 10]","[2, 2]",2,1,4,70,7,2,3,2763 -31,50,"[1, 2]","[10, 50]","[1, 2]",2,1,2,50,5,2,2,2299 -31,51,"[2, 1]","[40, 30]","[1, 1]",2,1,1,30,3,1,1,2344 -31,52,"[4, 3]","[20, 40]","[2, 2]",2,5,4,100,2,2,4,2346 -31,53,"[4, 3]","[30, 40]","[2, 1]",2,1,3,40,4,1,4,2761 -31,54,"[2, 1]","[20, 50]","[1, 2]",2,5,2,100,2,2,2,2187 -31,55,"[1, 2]","[60, 10]","[1, 1]",2,1,1,60,6,1,1,2121 -31,56,"[3, 4]","[40, 20]","[2, 2]",2,5,4,100,2,2,3,2632 -31,57,"[3, 4]","[50, 30]","[2, 1]",2,1,3,50,5,1,3,2748 -31,58,"[3, 4]","[30, 40]","[2, 2]",2,5,4,200,4,2,4,2493 -31,59,"[1, 2]","[30, 40]","[1, 2]",2,5,2,200,4,2,2,2371 -31,60,"[4, 3]","[30, 20]","[2, 1]",2,5,3,100,2,1,3,2153 -31,61,"[3, 4]","[40, 20]","[2, 2]",2,5,4,100,2,2,3,2194 -31,62,"[3, 4]","[20, 20]","[2, 1]",2,1,3,20,2,1,3,2711 -31,63,"[4, 3]","[20, 30]","[2, 2]",2,5,4,100,2,2,4,2329 -31,64,"[2, 1]","[40, 60]","[1, 2]",2,1,2,40,4,2,2,2476 -31,65,"[1, 2]","[80, 60]","[1, 2]",2,5,2,300,6,2,1,2287 -31,66,"[1, 2]","[70, 0]","[1, 1]",2,5,1,350,7,1,1,2410 -31,67,"[4, 3]","[10, 80]","[2, 2]",2,1,4,10,1,2,4,2565 -31,68,"[4, 3]","[20, 60]","[2, 1]",2,1,3,60,6,1,4,2091 -31,69,"[3, 4]","[40, 10]","[2, 2]",2,5,4,50,1,2,3,2292 -31,70,"[2, 1]","[10, 30]","[1, 2]",2,5,2,50,1,2,2,2119 -31,71,"[4, 3]","[10, 40]","[2, 2]",2,1,4,10,1,2,4,2536 -31,72,"[1, 2]","[70, 30]","[1, 2]",2,1,2,30,3,2,1,2186 -31,73,"[4, 3]","[10, 60]","[2, 2]",2,5,4,50,1,2,4,2126 -31,74,"[4, 3]","[20, 60]","[2, 1]",2,1,3,60,6,1,4,2281 -31,75,"[4, 3]","[10, 80]","[2, 2]",2,1,4,10,1,2,4,2077 -31,76,"[2, 1]","[20, 60]","[1, 2]",2,5,2,100,2,2,2,2380 -31,77,"[3, 4]","[80, 10]","[2, 1]",2,5,3,400,8,1,3,2215 -31,78,"[4, 3]","[10, 90]","[2, 1]",2,1,3,90,9,1,4,2757 -31,79,"[1, 2]","[90, 10]","[1, 2]",2,1,2,10,1,2,1,2116 -31,80,"[2, 1]","[10, 70]","[1, 2]",2,5,2,50,1,2,2,2023 -29,1,"[2, 1]","[20, 60]","[1, 2]",1,5,2,100,2,2,2,7256 -29,2,"[1, 2]","[90, 10]","[1, 2]",1,5,2,50,1,2,1,1987 -29,3,"[4, 3]","[10, 70]","[2, 1]",1,5,3,350,7,1,4,2656 -29,4,"[4, 3]","[40, 40]","[2, 1]",1,5,3,200,4,1,4,1906 -29,5,"[1, 2]","[50, 60]","[1, 1]",1,5,1,250,5,1,2,2224 -29,6,"[1, 2]","[60, 30]","[1, 1]",1,5,1,300,6,1,1,1889 -29,7,"[2, 1]","[30, 70]","[1, 1]",1,5,1,350,7,1,2,1857 -29,8,"[2, 1]","[30, 70]","[1, 1]",1,5,1,350,7,1,2,1870 -29,9,"[1, 2]","[50, 50]","[1, 1]",1,1,1,50,5,1,1,2056 -29,10,"[2, 1]","[50, 40]","[1, 1]",1,1,1,40,4,1,1,1872 -29,11,"[1, 2]","[20, 30]","[1, 2]",1,5,2,150,3,2,2,2000 -29,12,"[1, 2]","[30, 40]","[1, 2]",1,5,2,200,4,2,2,2040 -29,13,"[3, 4]","[20, 50]","[2, 1]",1,5,3,100,2,1,4,2040 -29,14,"[3, 4]","[0, 40]","[2, 2]",1,1,4,40,4,2,4,2220 -29,15,"[4, 3]","[30, 20]","[2, 1]",1,1,3,20,2,1,3,1773 -29,16,"[4, 3]","[30, 20]","[2, 2]",1,5,4,150,3,2,3,1761 -29,17,"[2, 1]","[60, 20]","[1, 2]",1,1,2,60,6,2,1,2005 -29,18,"[2, 1]","[60, 40]","[1, 1]",1,1,1,40,4,1,1,2021 -29,19,"[4, 3]","[70, 40]","[2, 2]",1,1,4,70,7,2,3,2224 -29,20,"[3, 4]","[40, 90]","[2, 2]",1,5,4,450,9,2,4,3238 -29,21,"[2, 1]","[80, 70]","[1, 2]",1,1,2,80,8,2,1,1840 -29,22,"[2, 1]","[80, 80]","[1, 2]",1,5,2,400,8,2,2,2959 -29,23,"[1, 2]","[40, 80]","[1, 2]",1,5,2,400,8,2,2,1989 -29,24,"[3, 4]","[0, 70]","[2, 2]",1,1,4,70,7,2,4,2203 -29,25,"[3, 4]","[40, 90]","[2, 2]",1,1,4,90,9,2,4,1940 -29,26,"[3, 4]","[10, 80]","[2, 2]",1,5,4,400,8,2,4,2089 -29,27,"[2, 1]","[60, 10]","[1, 2]",1,1,2,60,6,2,1,2073 -29,28,"[3, 4]","[10, 60]","[2, 2]",1,1,4,60,6,2,4,2241 -29,29,"[4, 3]","[50, 0]","[2, 2]",1,1,4,50,5,2,3,1990 -29,30,"[1, 2]","[10, 60]","[1, 2]",1,1,2,60,6,2,2,3565 -29,31,"[1, 2]","[10, 50]","[1, 2]",1,5,2,250,5,2,2,1991 -29,32,"[3, 4]","[30, 50]","[2, 2]",1,1,4,50,5,2,4,2021 -29,33,"[1, 2]","[30, 60]","[1, 2]",1,5,2,300,6,2,2,1971 -29,34,"[2, 1]","[70, 40]","[1, 2]",1,5,2,350,7,2,1,3574 -29,35,"[2, 1]","[80, 30]","[1, 2]",1,1,2,80,8,2,1,1823 -29,36,"[1, 2]","[30, 80]","[1, 2]",1,1,2,80,8,2,2,2207 -29,37,"[3, 4]","[30, 60]","[2, 2]",1,5,4,300,6,2,4,2023 -29,38,"[1, 2]","[20, 70]","[1, 2]",1,5,2,350,7,2,2,1756 -29,39,"[2, 1]","[40, 40]","[1, 2]",1,1,2,40,4,2,2,2906 -29,40,"[1, 2]","[40, 10]","[1, 1]",1,1,1,40,4,1,1,2357 -29,41,"[3, 4]","[10, 40]","[2, 2]",2,5,4,200,4,2,4,4986 -29,42,"[3, 4]","[30, 40]","[2, 2]",2,1,4,40,4,2,4,1973 -29,43,"[3, 4]","[70, 40]","[2, 1]",2,5,3,350,7,1,3,1707 -29,44,"[3, 4]","[80, 50]","[2, 1]",2,1,3,80,8,1,3,1757 -29,45,"[3, 4]","[70, 20]","[2, 1]",2,5,3,350,7,1,3,1757 -29,46,"[1, 2]","[80, 20]","[1, 1]",2,1,1,80,8,1,1,2274 -29,47,"[3, 4]","[50, 10]","[2, 1]",2,1,3,50,5,1,3,1873 -29,48,"[4, 3]","[10, 60]","[2, 1]",2,5,3,300,6,1,4,2073 -29,49,"[1, 2]","[40, 10]","[1, 1]",2,1,1,40,4,1,1,2271 -29,50,"[1, 2]","[40, 0]","[1, 1]",2,1,1,40,4,1,1,1907 -29,51,"[1, 2]","[40, 10]","[1, 2]",2,1,2,10,1,2,1,1773 -29,52,"[1, 2]","[10, 10]","[1, 1]",2,1,1,10,1,1,1,2073 -29,53,"[4, 3]","[0, 10]","[2, 2]",2,5,4,0,0,2,4,2290 -29,54,"[1, 2]","[0, 10]","[1, 1]",2,5,1,0,0,1,2,2907 -29,55,"[2, 1]","[0, 10]","[1, 2]",2,1,2,0,0,2,2,1990 -29,56,"[1, 2]","[20, 10]","[1, 1]",2,5,1,100,2,1,1,2138 -29,57,"[2, 1]","[20, 0]","[1, 1]",2,1,1,0,0,1,1,2257 -29,58,"[1, 2]","[30, 20]","[1, 2]",2,5,2,100,2,2,1,2089 -29,59,"[3, 4]","[40, 0]","[2, 2]",2,5,4,0,0,2,3,1957 -29,60,"[4, 3]","[10, 60]","[2, 1]",2,1,3,60,6,1,4,1724 -29,61,"[2, 1]","[40, 30]","[1, 1]",2,1,1,30,3,1,1,1855 -29,62,"[2, 1]","[60, 40]","[1, 1]",2,5,1,200,4,1,1,1871 -29,63,"[4, 3]","[30, 70]","[2, 1]",2,5,3,350,7,1,4,1990 -29,64,"[4, 3]","[30, 80]","[2, 1]",2,5,3,400,8,1,4,1789 -29,65,"[1, 2]","[70, 60]","[1, 1]",2,1,1,70,7,1,1,2090 -29,66,"[2, 1]","[70, 80]","[1, 1]",2,5,1,400,8,1,2,2024 -29,67,"[2, 1]","[50, 60]","[1, 1]",2,5,1,300,6,1,2,2890 -29,68,"[4, 3]","[70, 70]","[2, 1]",2,1,3,70,7,1,4,2257 -29,69,"[3, 4]","[70, 30]","[2, 1]",2,5,3,350,7,1,3,1823 -29,70,"[1, 2]","[50, 30]","[1, 1]",2,1,1,50,5,1,1,1773 -29,71,"[4, 3]","[10, 80]","[2, 1]",2,1,3,80,8,1,4,1773 -29,72,"[1, 2]","[80, 10]","[1, 1]",2,5,1,400,8,1,1,1823 -29,73,"[3, 4]","[60, 20]","[2, 1]",2,1,3,60,6,1,3,1790 -29,74,"[3, 4]","[50, 20]","[2, 1]",2,5,3,250,5,1,3,1840 -29,75,"[4, 3]","[40, 70]","[2, 1]",2,1,3,70,7,1,4,1823 -29,76,"[1, 2]","[80, 80]","[1, 1]",2,5,1,400,8,1,1,1990 -29,77,"[2, 1]","[60, 70]","[1, 1]",2,5,1,350,7,1,2,1857 -29,78,"[1, 2]","[50, 50]","[1, 1]",2,1,1,50,5,1,1,1955 -29,79,"[1, 2]","[80, 50]","[1, 1]",2,1,1,80,8,1,1,1890 -29,80,"[3, 4]","[70, 50]","[2, 1]",2,1,3,70,7,1,3,1837 -33,1,"[1, 2]","[60, 40]","[1, 1]",1,1,1,60,6,1,1,3877 -33,2,"[2, 1]","[30, 10]","[1, 1]",1,1,1,10,1,1,1,1842 -33,3,"[3, 4]","[10, 10]","[2, 1]",1,1,3,10,1,1,3,4112 -33,4,"[3, 4]","[40, 10]","[2, 2]",1,5,4,50,1,2,3,2019 -33,5,"[2, 1]","[0, 50]","[1, 2]",1,1,2,0,0,2,2,2116 -33,6,"[4, 3]","[50, 60]","[2, 2]",1,5,4,250,5,2,4,1973 -33,7,"[1, 2]","[80, 60]","[1, 1]",1,1,1,80,8,1,1,2173 -33,8,"[4, 3]","[80, 70]","[2, 2]",1,5,4,400,8,2,3,1997 -33,9,"[2, 1]","[50, 90]","[1, 2]",1,1,2,50,5,2,2,1950 -33,10,"[2, 1]","[60, 80]","[1, 2]",1,5,2,300,6,2,2,2111 -33,11,"[1, 2]","[70, 60]","[1, 2]",1,1,2,60,6,2,1,1884 -33,12,"[2, 1]","[60, 70]","[1, 2]",1,5,2,300,6,2,2,8109 -33,13,"[4, 3]","[70, 60]","[2, 2]",1,1,4,70,7,2,3,2094 -33,14,"[2, 1]","[70, 80]","[1, 2]",1,5,2,350,7,2,2,2903 -33,15,"[1, 2]","[80, 90]","[1, 1]",1,1,1,80,8,1,2,2253 -33,16,"[2, 1]","[90, 70]","[1, 2]",1,5,2,450,9,2,1,2349 -33,17,"[2, 1]","[50, 80]","[1, 1]",1,5,1,400,8,1,2,2235 -33,18,"[4, 3]","[40, 70]","[2, 1]",1,1,3,70,7,1,4,1960 -33,19,"[3, 4]","[40, 40]","[2, 2]",1,1,4,40,4,2,3,1981 -33,20,"[4, 3]","[20, 20]","[2, 1]",1,5,3,100,2,1,4,2466 -33,21,"[1, 2]","[40, 20]","[1, 2]",1,1,2,20,2,2,1,1869 -33,22,"[4, 3]","[30, 60]","[2, 1]",1,1,3,60,6,1,4,1728 -33,23,"[1, 2]","[80, 40]","[1, 2]",1,1,2,40,4,2,1,1777 -33,24,"[4, 3]","[60, 90]","[2, 1]",1,5,3,450,9,1,4,1962 -33,25,"[4, 3]","[40, 70]","[2, 1]",1,5,3,350,7,1,4,1693 -33,26,"[4, 3]","[60, 90]","[2, 1]",1,5,3,450,9,1,4,1753 -33,27,"[4, 3]","[60, 70]","[2, 1]",1,1,3,70,7,1,4,1826 -33,28,"[2, 1]","[70, 70]","[1, 1]",1,5,1,350,7,1,2,1659 -33,29,"[1, 2]","[80, 40]","[1, 2]",1,5,2,200,4,2,1,1651 -33,30,"[4, 3]","[40, 80]","[2, 1]",1,5,3,400,8,1,4,1593 -33,31,"[3, 4]","[80, 50]","[2, 2]",1,5,4,250,5,2,3,1678 -33,32,"[3, 4]","[80, 40]","[2, 2]",1,5,4,200,4,2,3,1668 -33,33,"[3, 4]","[60, 30]","[2, 2]",1,5,4,150,3,2,3,1726 -33,34,"[1, 2]","[50, 50]","[1, 2]",1,1,2,50,5,2,1,1710 -33,35,"[4, 3]","[40, 80]","[2, 1]",1,5,3,400,8,1,4,1685 -33,36,"[1, 2]","[80, 50]","[1, 2]",1,5,2,250,5,2,1,1685 -33,37,"[2, 1]","[70, 80]","[1, 1]",1,1,1,80,8,1,2,1703 -33,38,"[3, 4]","[90, 70]","[2, 2]",1,1,4,70,7,2,3,1686 -33,39,"[2, 1]","[50, 60]","[1, 1]",1,5,1,300,6,1,2,1752 -33,40,"[2, 1]","[70, 90]","[1, 1]",1,5,1,450,9,1,2,5461 -33,41,"[2, 1]","[80, 80]","[1, 2]",2,5,2,400,8,2,2,7881 -33,42,"[2, 1]","[80, 90]","[1, 2]",2,5,2,400,8,2,2,1717 -33,43,"[4, 3]","[70, 80]","[2, 2]",2,1,4,70,7,2,4,3152 -33,44,"[3, 4]","[80, 40]","[2, 1]",2,1,3,80,8,1,3,1668 -33,45,"[1, 2]","[70, 70]","[1, 1]",2,1,1,70,7,1,1,1743 -33,46,"[2, 1]","[70, 70]","[1, 2]",2,1,2,70,7,2,2,1693 -33,47,"[3, 4]","[80, 70]","[2, 1]",2,5,3,400,8,1,3,1700 -33,48,"[4, 3]","[40, 60]","[2, 2]",2,5,4,200,4,2,4,1650 -33,49,"[1, 2]","[70, 60]","[1, 1]",2,5,1,350,7,1,1,1705 -33,50,"[3, 4]","[60, 40]","[2, 1]",2,1,3,60,6,1,3,1709 -33,51,"[4, 3]","[30, 60]","[2, 2]",2,1,4,30,3,2,4,1799 -33,52,"[2, 1]","[30, 70]","[1, 2]",2,5,2,150,3,2,2,1678 -33,53,"[1, 2]","[0, 30]","[1, 1]",2,5,1,0,0,1,2,1685 -33,54,"[4, 3]","[10, 20]","[2, 2]",2,5,4,50,1,2,4,1690 -33,55,"[3, 4]","[20, 0]","[2, 1]",2,1,3,20,2,1,3,1627 -33,56,"[2, 1]","[0, 10]","[1, 2]",2,5,2,0,0,2,2,1659 -33,57,"[1, 2]","[0, 10]","[1, 1]",2,5,1,0,0,1,2,3210 -33,58,"[1, 2]","[30, 30]","[1, 1]",2,1,1,30,3,1,1,3827 -33,59,"[4, 3]","[30, 30]","[2, 2]",2,1,4,30,3,2,4,1709 -33,60,"[4, 3]","[30, 10]","[2, 2]",2,1,4,30,3,2,3,1801 -33,61,"[3, 4]","[40, 30]","[2, 1]",2,1,3,40,4,1,3,1677 -33,62,"[2, 1]","[40, 60]","[1, 2]",2,5,2,200,4,2,2,1716 -33,63,"[1, 2]","[60, 40]","[1, 1]",2,1,1,60,6,1,1,1643 -33,64,"[1, 2]","[50, 60]","[1, 1]",2,5,1,250,5,1,2,1668 -33,65,"[4, 3]","[30, 60]","[2, 2]",2,1,4,30,3,2,4,1692 -33,66,"[4, 3]","[30, 90]","[2, 2]",2,1,4,30,3,2,4,1760 -33,67,"[2, 1]","[40, 80]","[1, 2]",2,5,2,200,4,2,2,1686 -33,68,"[3, 4]","[70, 20]","[2, 1]",2,1,3,70,7,1,3,1742 -33,69,"[4, 3]","[10, 70]","[2, 2]",2,5,4,50,1,2,4,1676 -33,70,"[1, 2]","[80, 60]","[1, 1]",2,5,1,400,8,1,1,1703 -33,71,"[3, 4]","[70, 70]","[2, 1]",2,5,3,350,7,1,3,1760 -33,72,"[4, 3]","[70, 40]","[2, 2]",2,1,4,70,7,2,3,1693 -33,73,"[1, 2]","[20, 80]","[1, 1]",2,1,1,20,2,1,2,1716 -33,74,"[3, 4]","[60, 80]","[2, 1]",2,5,3,300,6,1,4,1691 -33,75,"[3, 4]","[70, 80]","[2, 1]",2,5,3,350,7,1,4,1640 -33,76,"[2, 1]","[50, 50]","[1, 2]",2,1,2,50,5,2,2,1736 -33,77,"[1, 2]","[50, 30]","[1, 1]",2,1,1,50,5,1,1,1689 -33,78,"[4, 3]","[60, 80]","[2, 2]",2,1,4,60,6,2,4,1602 -33,79,"[4, 3]","[90, 70]","[2, 2]",2,1,4,90,9,2,3,1661 -33,80,"[1, 2]","[40, 80]","[1, 1]",2,1,1,40,4,1,2,1794 -30,1,"[4, 3]","[60, 30]","[2, 1]",1,5,3,150,3,1,3,3849 -30,2,"[1, 2]","[40, 50]","[1, 1]",1,5,1,200,4,1,2,1763 -30,3,"[4, 3]","[50, 30]","[2, 1]",1,1,3,30,3,1,3,1705 -30,4,"[2, 1]","[20, 20]","[1, 2]",1,1,2,20,2,2,2,1803 -30,5,"[3, 4]","[20, 30]","[2, 2]",1,1,4,30,3,2,4,1640 -30,6,"[2, 1]","[30, 10]","[1, 1]",1,1,1,10,1,1,1,2026 -30,7,"[3, 4]","[10, 20]","[2, 2]",1,1,4,20,2,2,4,1831 -30,8,"[1, 2]","[10, 10]","[1, 1]",1,5,1,50,1,1,1,5452 -30,9,"[2, 1]","[20, 10]","[1, 2]",1,1,2,20,2,2,1,19239 -30,10,"[2, 1]","[30, 10]","[1, 1]",1,5,1,50,1,1,1,1780 -30,11,"[2, 1]","[10, 0]","[1, 2]",1,5,2,50,1,2,1,1699 -30,12,"[3, 4]","[0, 20]","[2, 2]",1,1,4,20,2,2,4,1715 -30,13,"[2, 1]","[20, 20]","[1, 2]",1,5,2,100,2,2,2,1829 -30,14,"[4, 3]","[20, 30]","[2, 2]",1,5,4,100,2,2,4,1978 -30,15,"[1, 2]","[30, 20]","[1, 1]",1,5,1,150,3,1,1,1878 -30,16,"[4, 3]","[0, 70]","[2, 1]",1,1,3,70,7,1,4,1675 -30,17,"[2, 1]","[10, 70]","[1, 2]",1,5,2,50,1,2,2,1740 -30,18,"[1, 2]","[30, 20]","[1, 2]",1,5,2,100,2,2,1,3391 -30,19,"[4, 3]","[10, 60]","[2, 2]",1,1,4,10,1,2,4,1830 -30,20,"[3, 4]","[50, 20]","[2, 2]",1,1,4,20,2,2,3,1723 -30,21,"[3, 4]","[60, 40]","[2, 2]",1,1,4,40,4,2,3,1994 -30,22,"[4, 3]","[10, 30]","[2, 2]",1,1,4,10,1,2,4,2009 -30,23,"[3, 4]","[20, 10]","[2, 2]",1,1,4,10,1,2,3,1696 -30,24,"[4, 3]","[20, 40]","[2, 1]",1,5,3,200,4,1,4,2010 -30,25,"[2, 1]","[0, 40]","[1, 1]",1,5,1,200,4,1,2,1715 -30,26,"[4, 3]","[10, 40]","[2, 1]",1,5,3,200,4,1,4,1782 -30,27,"[2, 1]","[10, 30]","[1, 1]",1,5,1,150,3,1,2,1608 -30,28,"[2, 1]","[40, 40]","[1, 1]",1,1,1,40,4,1,2,1961 -30,29,"[3, 4]","[70, 0]","[2, 2]",1,5,4,0,0,2,3,1821 -30,30,"[1, 2]","[90, 10]","[1, 1]",1,5,1,450,9,1,1,1813 -30,31,"[2, 1]","[0, 60]","[1, 1]",1,1,1,60,6,1,2,1896 -30,32,"[3, 4]","[50, 10]","[2, 2]",1,5,4,50,1,2,3,1815 -30,33,"[1, 2]","[50, 20]","[1, 2]",1,5,2,100,2,2,1,1763 -30,34,"[1, 2]","[40, 20]","[1, 1]",1,5,1,200,4,1,1,1722 -30,35,"[1, 2]","[70, 10]","[1, 1]",1,5,1,350,7,1,1,1727 -30,36,"[4, 3]","[20, 80]","[2, 1]",1,1,3,80,8,1,4,2034 -30,37,"[3, 4]","[70, 10]","[2, 1]",1,5,3,350,7,1,3,1655 -30,38,"[2, 1]","[30, 40]","[1, 1]",1,1,1,40,4,1,2,2504 -30,39,"[2, 1]","[50, 40]","[1, 1]",1,5,1,200,4,1,1,1798 -30,40,"[1, 2]","[30, 10]","[1, 2]",1,1,2,10,1,2,1,9962 -30,41,"[3, 4]","[10, 30]","[2, 1]",2,1,3,10,1,1,4,3499 -30,42,"[1, 2]","[10, 70]","[1, 2]",2,1,2,70,7,2,2,1848 -30,43,"[1, 2]","[20, 30]","[1, 1]",2,5,1,100,2,1,2,2140 -30,44,"[2, 1]","[50, 70]","[1, 1]",2,5,1,350,7,1,2,1706 -30,45,"[3, 4]","[40, 50]","[2, 2]",2,1,4,50,5,2,4,1748 -30,46,"[3, 4]","[40, 30]","[2, 1]",2,1,3,40,4,1,3,1823 -30,47,"[3, 4]","[0, 10]","[2, 2]",2,1,4,10,1,2,4,1797 -30,48,"[2, 1]","[0, 10]","[1, 2]",2,1,2,0,0,2,2,34067 -30,49,"[1, 2]","[10, 0]","[1, 2]",2,1,2,0,0,2,1,2033 -30,50,"[2, 1]","[0, 60]","[1, 1]",2,1,1,60,6,1,2,1838 -30,51,"[4, 3]","[10, 80]","[2, 1]",2,1,3,80,8,1,4,2019 -30,52,"[4, 3]","[10, 50]","[2, 1]",2,1,3,50,5,1,4,2142 -30,53,"[4, 3]","[10, 60]","[2, 1]",2,1,3,60,6,1,4,1724 -30,54,"[3, 4]","[60, 30]","[2, 1]",2,5,3,300,6,1,3,2143 -30,55,"[3, 4]","[80, 50]","[2, 1]",2,5,3,400,8,1,3,1782 -30,56,"[2, 1]","[60, 70]","[1, 2]",2,1,2,60,6,2,2,1865 -30,57,"[3, 4]","[90, 60]","[2, 1]",2,5,3,450,9,1,3,1936 -30,58,"[4, 3]","[80, 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2]",1,1,2,70,7,2,2,1791 -29,14,"[1, 2]","[80, 70]","[1, 2]",1,5,2,350,7,2,1,1974 -29,15,"[2, 1]","[60, 70]","[1, 2]",1,1,2,60,6,2,2,2307 -29,16,"[4, 3]","[50, 50]","[2, 2]",1,5,4,250,5,2,4,2107 -29,17,"[3, 4]","[40, 50]","[2, 2]",1,5,4,250,5,2,4,2173 -29,18,"[3, 4]","[40, 70]","[2, 2]",1,1,4,70,7,2,4,1990 -29,19,"[1, 2]","[30, 90]","[1, 2]",1,1,2,90,9,2,2,2174 -29,20,"[3, 4]","[20, 80]","[2, 2]",1,1,4,80,8,2,4,1973 -29,21,"[2, 1]","[80, 60]","[1, 2]",1,5,2,400,8,2,1,2490 -29,22,"[3, 4]","[70, 70]","[2, 2]",1,5,4,350,7,2,3,2290 -29,23,"[3, 4]","[70, 50]","[2, 2]",1,5,4,250,5,2,3,2124 -29,24,"[1, 2]","[70, 40]","[1, 2]",1,1,2,40,4,2,1,2057 -29,25,"[1, 2]","[70, 20]","[1, 1]",1,5,1,350,7,1,1,2258 -29,26,"[2, 1]","[30, 90]","[1, 1]",1,1,1,90,9,1,2,2207 -29,27,"[1, 2]","[60, 10]","[1, 1]",1,1,1,60,6,1,1,2074 -29,28,"[4, 3]","[10, 30]","[2, 1]",1,1,3,30,3,1,4,2457 -29,29,"[2, 1]","[20, 30]","[1, 2]",1,1,2,20,2,2,2,2076 -29,30,"[1, 2]","[10, 20]","[1, 1]",1,5,1,50,1,1,2,2308 -29,31,"[2, 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2]",2,5,4,50,1,2,3,2007 -29,58,"[3, 4]","[0, 20]","[2, 2]",2,5,4,100,2,2,4,1873 -29,59,"[3, 4]","[10, 30]","[2, 2]",2,5,4,150,3,2,4,1807 -29,60,"[3, 4]","[40, 20]","[2, 2]",2,1,4,20,2,2,3,1740 -29,61,"[2, 1]","[40, 20]","[1, 2]",2,1,2,40,4,2,1,2322 -29,62,"[1, 2]","[60, 50]","[1, 2]",2,5,2,250,5,2,1,1923 -29,63,"[1, 2]","[70, 40]","[1, 2]",2,1,2,40,4,2,1,2673 -29,64,"[3, 4]","[50, 40]","[2, 2]",2,1,4,40,4,2,3,2624 -29,65,"[2, 1]","[80, 70]","[1, 1]",2,5,1,350,7,1,1,2588 -29,66,"[2, 1]","[60, 80]","[1, 1]",2,5,1,400,8,1,2,2107 -29,67,"[4, 3]","[40, 60]","[2, 1]",2,5,3,300,6,1,4,2040 -29,68,"[2, 1]","[50, 70]","[1, 1]",2,1,1,70,7,1,2,1973 -29,69,"[1, 2]","[70, 60]","[1, 1]",2,1,1,70,7,1,1,2057 -29,70,"[3, 4]","[70, 50]","[2, 1]",2,1,3,70,7,1,3,1824 -29,71,"[3, 4]","[80, 60]","[2, 1]",2,5,3,400,8,1,3,1806 -29,72,"[3, 4]","[60, 10]","[2, 1]",2,5,3,300,6,1,3,1840 -29,73,"[1, 2]","[50, 10]","[1, 1]",2,1,1,50,5,1,1,2106 -29,74,"[1, 2]","[30, 30]","[1, 1]",2,1,1,30,3,1,1,1840 -29,75,"[4, 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-29,13,"[3, 4]","[20, 70]","[2, 2]",1,1,4,70,7,2,4,1857 -29,14,"[2, 1]","[70, 30]","[1, 2]",1,1,2,70,7,2,1,2124 -29,15,"[3, 4]","[30, 50]","[2, 2]",1,5,4,250,5,2,4,1940 -29,16,"[3, 4]","[40, 60]","[2, 2]",1,1,4,60,6,2,4,1824 -29,17,"[1, 2]","[40, 70]","[1, 2]",1,1,2,70,7,2,2,1736 -29,18,"[4, 3]","[40, 60]","[2, 2]",1,5,4,200,4,2,4,1974 -29,19,"[3, 4]","[60, 40]","[2, 2]",1,5,4,200,4,2,3,1871 -29,20,"[1, 2]","[70, 20]","[1, 2]",1,5,2,100,2,2,1,2940 -29,21,"[1, 2]","[70, 0]","[1, 1]",1,5,1,350,7,1,1,1907 -29,22,"[2, 1]","[20, 80]","[1, 1]",1,5,1,400,8,1,2,2040 -29,23,"[1, 2]","[80, 10]","[1, 1]",1,5,1,400,8,1,1,1891 -29,24,"[4, 3]","[0, 70]","[2, 1]",1,1,3,70,7,1,4,1740 -29,25,"[1, 2]","[70, 10]","[1, 1]",1,1,1,70,7,1,1,1873 -29,26,"[4, 3]","[30, 60]","[2, 1]",1,1,3,60,6,1,4,1724 -29,27,"[2, 1]","[50, 40]","[1, 1]",1,1,1,40,4,1,1,1923 -29,28,"[2, 1]","[50, 60]","[1, 2]",1,1,2,50,5,2,2,2123 -29,29,"[1, 2]","[70, 60]","[1, 2]",1,5,2,300,6,2,1,1957 -29,30,"[3, 4]","[50, 70]","[2, 2]",1,1,4,70,7,2,4,2408 -29,31,"[4, 3]","[80, 70]","[2, 2]",1,5,4,400,8,2,3,1960 -29,32,"[4, 3]","[60, 20]","[2, 2]",1,5,4,300,6,2,3,1740 -29,33,"[3, 4]","[10, 60]","[2, 2]",1,1,4,60,6,2,4,1806 -29,34,"[3, 4]","[30, 40]","[2, 2]",1,5,4,200,4,2,4,1824 -29,35,"[4, 3]","[40, 20]","[2, 1]",1,5,3,100,2,1,3,1690 -29,36,"[3, 4]","[10, 30]","[2, 2]",1,5,4,150,3,2,4,1823 -29,37,"[1, 2]","[20, 40]","[1, 2]",1,1,2,40,4,2,2,1740 -29,38,"[1, 2]","[80, 60]","[1, 2]",1,1,2,60,6,2,1,1755 -29,39,"[2, 1]","[70, 80]","[1, 2]",1,1,2,70,7,2,2,1856 -29,40,"[3, 4]","[70, 40]","[2, 2]",1,1,4,40,4,2,3,2423 -29,41,"[2, 1]","[50, 80]","[1, 1]",2,5,1,400,8,1,2,1985 -29,42,"[3, 4]","[70, 60]","[2, 1]",2,1,3,70,7,1,3,1774 -29,43,"[4, 3]","[50, 90]","[2, 1]",2,1,3,90,9,1,4,1807 -29,44,"[1, 2]","[50, 50]","[1, 1]",2,1,1,50,5,1,1,1974 -29,45,"[4, 3]","[60, 40]","[2, 1]",2,5,3,200,4,1,3,1773 -29,46,"[1, 2]","[10, 50]","[1, 2]",2,1,2,50,5,2,2,2871 -29,47,"[4, 3]","[60, 0]","[2, 2]",2,1,4,60,6,2,3,2123 -29,48,"[2, 1]","[60, 20]","[1, 1]",2,1,1,20,2,1,1,1936 -29,49,"[1, 2]","[10, 90]","[1, 2]",2,5,2,450,9,2,2,2090 -29,50,"[4, 3]","[80, 30]","[2, 2]",2,5,4,400,8,2,3,1907 -29,51,"[4, 3]","[70, 10]","[2, 2]",2,5,4,350,7,2,3,1737 -29,52,"[1, 2]","[10, 40]","[1, 2]",2,5,2,200,4,2,2,2739 -29,53,"[3, 4]","[20, 70]","[2, 2]",2,5,4,350,7,2,4,1790 -29,54,"[4, 3]","[90, 10]","[2, 2]",2,5,4,450,9,2,3,2040 -29,55,"[1, 2]","[20, 50]","[1, 2]",2,1,2,50,5,2,2,2755 -29,56,"[4, 3]","[70, 20]","[2, 2]",2,5,4,350,7,2,3,2023 -29,57,"[1, 2]","[10, 90]","[1, 2]",2,5,2,450,9,2,2,2324 -29,58,"[4, 3]","[50, 20]","[2, 2]",2,5,4,250,5,2,3,2107 -29,59,"[2, 1]","[50, 20]","[1, 2]",2,1,2,50,5,2,1,1973 -29,60,"[1, 2]","[30, 50]","[1, 2]",2,1,2,50,5,2,2,1857 -29,61,"[3, 4]","[20, 60]","[2, 2]",2,5,4,300,6,2,4,2190 -29,62,"[3, 4]","[50, 40]","[2, 2]",2,5,4,200,4,2,3,1924 -29,63,"[1, 2]","[60, 50]","[1, 1]",2,5,1,300,6,1,1,1824 -29,64,"[4, 3]","[70, 60]","[2, 1]",2,1,3,60,6,1,3,1821 -29,65,"[2, 1]","[90, 60]","[1, 1]",2,1,1,60,6,1,1,1906 -29,66,"[1, 2]","[40, 50]","[1, 1]",2,1,1,40,4,1,2,2023 -29,67,"[3, 4]","[40, 40]","[2, 2]",2,1,4,40,4,2,3,1911 -29,68,"[3, 4]","[20, 30]","[2, 1]",2,5,3,100,2,1,4,1691 -29,69,"[3, 4]","[50, 50]","[2, 2]",2,1,4,50,5,2,3,1673 -29,70,"[4, 3]","[30, 80]","[2, 2]",2,1,4,30,3,2,4,1757 -29,71,"[3, 4]","[70, 0]","[2, 1]",2,1,3,70,7,1,3,1924 -29,72,"[3, 4]","[70, 10]","[2, 1]",2,5,3,350,7,1,3,1741 -29,73,"[4, 3]","[10, 80]","[2, 1]",2,5,3,400,8,1,4,1891 -29,74,"[4, 3]","[20, 70]","[2, 1]",2,1,3,70,7,1,4,1740 -29,75,"[4, 3]","[50, 70]","[2, 1]",2,5,3,350,7,1,4,1757 -29,76,"[3, 4]","[20, 40]","[2, 1]",2,5,3,100,2,1,4,1940 -29,77,"[4, 3]","[20, 10]","[2, 2]",2,1,4,20,2,2,3,2388 -29,78,"[3, 4]","[30, 30]","[2, 1]",2,5,3,150,3,1,3,1757 -29,79,"[2, 1]","[30, 30]","[1, 1]",2,1,1,30,3,1,2,2023 -29,80,"[3, 4]","[30, 20]","[2, 2]",2,5,4,100,2,2,3,1741 -29,1,"[1, 2]","[50, 20]","[1, 2]",1,5,2,100,2,2,1,4024 -29,2,"[4, 3]","[40, 70]","[2, 1]",1,1,3,70,7,1,4,1790 -29,3,"[2, 1]","[50, 60]","[1, 1]",1,5,1,300,6,1,2,1923 -29,4,"[2, 1]","[50, 70]","[1, 1]",1,5,1,350,7,1,2,1757 -29,5,"[2, 1]","[40, 60]","[1, 1]",1,1,1,60,6,1,2,1874 -29,6,"[1, 2]","[60, 30]","[1, 1]",1,1,1,60,6,1,1,1839 -29,7,"[4, 3]","[40, 50]","[2, 1]",1,1,3,50,5,1,4,1774 -29,8,"[3, 4]","[60, 40]","[2, 1]",1,1,3,60,6,1,3,1723 -29,9,"[3, 4]","[70, 60]","[2, 1]",1,1,3,70,7,1,3,1724 -29,10,"[2, 1]","[70, 60]","[1, 1]",1,5,1,300,6,1,1,2006 -29,11,"[2, 1]","[70, 70]","[1, 1]",1,5,1,350,7,1,2,1873 -29,12,"[4, 3]","[70, 70]","[2, 1]",1,5,3,350,7,1,4,1739 -29,13,"[1, 2]","[50, 80]","[1, 1]",1,1,1,50,5,1,2,1854 -29,14,"[3, 4]","[50, 60]","[2, 1]",1,5,3,250,5,1,4,1706 -29,15,"[1, 2]","[40, 90]","[1, 1]",1,1,1,40,4,1,2,1889 -29,16,"[3, 4]","[0, 80]","[2, 2]",1,5,4,400,8,2,4,1707 -29,17,"[1, 2]","[20, 90]","[1, 2]",1,5,2,450,9,2,2,2140 -29,18,"[2, 1]","[50, 10]","[1, 2]",1,5,2,250,5,2,1,2240 -29,19,"[1, 2]","[0, 60]","[1, 2]",1,1,2,60,6,2,2,2006 -29,20,"[4, 3]","[60, 10]","[2, 2]",1,5,4,300,6,2,3,2090 -29,21,"[4, 3]","[50, 20]","[2, 2]",1,1,4,50,5,2,3,2320 -29,22,"[3, 4]","[0, 40]","[2, 2]",1,1,4,40,4,2,4,1990 -29,23,"[4, 3]","[40, 10]","[2, 1]",1,5,3,50,1,1,3,1857 -29,24,"[2, 1]","[40, 30]","[1, 2]",1,5,2,200,4,2,1,2122 -29,25,"[1, 2]","[20, 30]","[1, 2]",1,1,2,30,3,2,2,1956 -29,26,"[4, 3]","[50, 40]","[2, 2]",1,5,4,250,5,2,3,1906 -29,27,"[4, 3]","[40, 80]","[2, 2]",1,1,4,40,4,2,4,1790 -29,28,"[4, 3]","[40, 70]","[2, 1]",1,1,3,70,7,1,4,1690 -29,29,"[1, 2]","[80, 50]","[1, 1]",1,5,1,400,8,1,1,1990 -29,30,"[3, 4]","[90, 50]","[2, 1]",1,5,3,450,9,1,3,1806 -29,31,"[4, 3]","[0, 80]","[2, 1]",1,1,3,80,8,1,4,1823 -29,32,"[1, 2]","[60, 30]","[1, 1]",1,5,1,300,6,1,1,1990 -29,33,"[2, 1]","[20, 50]","[1, 1]",1,1,1,50,5,1,2,2290 -29,34,"[3, 4]","[60, 40]","[2, 1]",1,1,3,60,6,1,3,1889 -29,35,"[1, 2]","[30, 40]","[1, 1]",1,5,1,150,3,1,2,1973 -29,36,"[4, 3]","[30, 20]","[2, 1]",1,1,3,20,2,1,3,1773 -29,37,"[3, 4]","[40, 30]","[2, 2]",1,5,4,150,3,2,3,2123 -29,38,"[1, 2]","[30, 10]","[1, 2]",1,1,2,10,1,2,1,1856 -29,39,"[2, 1]","[20, 30]","[1, 1]",1,5,1,150,3,1,2,1940 -29,40,"[3, 4]","[30, 30]","[2, 1]",1,1,3,30,3,1,3,1909 -29,41,"[3, 4]","[40, 50]","[2, 1]",2,5,3,200,4,1,4,3185 -29,42,"[4, 3]","[50, 30]","[2, 2]",2,5,4,250,5,2,3,1974 -29,43,"[1, 2]","[40, 50]","[1, 2]",2,5,2,250,5,2,2,2323 -29,44,"[2, 1]","[30, 50]","[1, 2]",2,5,2,150,3,2,2,1840 -29,45,"[2, 1]","[20, 60]","[1, 1]",2,1,1,60,6,1,2,1823 -29,46,"[2, 1]","[60, 60]","[1, 1]",2,1,1,60,6,1,2,1890 -29,47,"[4, 3]","[70, 60]","[2, 1]",2,1,3,60,6,1,3,1807 -29,48,"[4, 3]","[90, 10]","[2, 1]",2,1,3,10,1,1,3,1723 -29,49,"[4, 3]","[70, 40]","[2, 2]",2,1,4,70,7,2,3,1873 -29,50,"[3, 4]","[40, 60]","[2, 2]",2,5,4,300,6,2,4,2157 -29,51,"[3, 4]","[0, 30]","[2, 2]",2,1,4,30,3,2,4,1923 -29,52,"[1, 2]","[0, 30]","[1, 1]",2,5,1,0,0,1,2,2140 -29,53,"[4, 3]","[20, 10]","[2, 2]",2,5,4,100,2,2,3,1940 -29,54,"[2, 1]","[50, 0]","[1, 2]",2,5,2,250,5,2,1,1907 -29,55,"[3, 4]","[10, 30]","[2, 2]",2,5,4,150,3,2,4,2124 -29,56,"[4, 3]","[40, 20]","[2, 2]",2,1,4,40,4,2,3,1839 -29,57,"[4, 3]","[40, 20]","[2, 1]",2,5,3,100,2,1,3,1774 -29,58,"[2, 1]","[60, 10]","[1, 2]",2,5,2,300,6,2,1,2673 -29,59,"[2, 1]","[80, 10]","[1, 2]",2,1,2,80,8,2,1,1873 -29,60,"[1, 2]","[30, 90]","[1, 2]",2,1,2,90,9,2,2,2023 -29,61,"[2, 1]","[70, 10]","[1, 2]",2,1,2,70,7,2,1,1856 -29,62,"[1, 2]","[0, 70]","[1, 2]",2,5,2,350,7,2,2,2039 -29,63,"[1, 2]","[0, 40]","[1, 2]",2,5,2,200,4,2,2,1803 -29,64,"[4, 3]","[50, 30]","[2, 2]",2,1,4,50,5,2,3,1987 -29,65,"[3, 4]","[40, 50]","[2, 2]",2,5,4,250,5,2,4,1953 -29,66,"[2, 1]","[50, 50]","[1, 2]",2,5,2,250,5,2,2,2191 -29,67,"[2, 1]","[70, 40]","[1, 2]",2,1,2,70,7,2,1,1886 -29,68,"[2, 1]","[50, 40]","[1, 2]",2,1,2,50,5,2,1,1937 -29,69,"[2, 1]","[30, 50]","[1, 2]",2,1,2,30,3,2,2,1839 -29,70,"[2, 1]","[20, 40]","[1, 1]",2,1,1,40,4,1,2,1841 -29,71,"[2, 1]","[20, 60]","[1, 1]",2,1,1,60,6,1,2,1974 -29,72,"[3, 4]","[40, 10]","[2, 1]",2,1,3,40,4,1,3,1873 -29,73,"[3, 4]","[50, 10]","[2, 2]",2,5,4,50,1,2,3,1706 -29,74,"[1, 2]","[40, 30]","[1, 1]",2,1,1,40,4,1,1,1823 -29,75,"[4, 3]","[30, 20]","[2, 1]",2,1,3,20,2,1,3,1824 -29,76,"[4, 3]","[20, 20]","[2, 2]",2,5,4,100,2,2,4,1905 -29,77,"[4, 3]","[10, 10]","[2, 1]",2,5,3,50,1,1,4,1707 -29,78,"[2, 1]","[30, 10]","[1, 2]",2,1,2,30,3,2,1,1989 -29,79,"[3, 4]","[20, 40]","[2, 2]",2,5,4,200,4,2,4,2157 -29,80,"[4, 3]","[30, 10]","[2, 2]",2,5,4,150,3,2,3,1907 -29,1,"[3, 4]","[10, 70]","[2, 1]",1,1,3,10,1,1,4,15739 -29,2,"[2, 1]","[50, 20]","[1, 2]",1,1,2,50,5,2,1,2207 -29,3,"[1, 2]","[40, 40]","[1, 2]",1,1,2,40,4,2,1,2023 -29,4,"[2, 1]","[70, 40]","[1, 2]",1,5,2,350,7,2,1,1873 -29,5,"[4, 3]","[70, 30]","[2, 2]",1,1,4,70,7,2,3,1956 -29,6,"[3, 4]","[40, 70]","[2, 2]",1,1,4,70,7,2,4,1891 -29,7,"[3, 4]","[50, 60]","[2, 2]",1,5,4,300,6,2,4,1824 -29,8,"[2, 1]","[90, 50]","[1, 2]",1,1,2,90,9,2,1,2036 -29,9,"[1, 2]","[90, 90]","[1, 2]",1,5,2,450,9,2,1,1907 -29,10,"[2, 1]","[90, 70]","[1, 2]",1,5,2,450,9,2,1,2010 -29,11,"[4, 3]","[80, 60]","[2, 2]",1,1,4,80,8,2,3,1807 -29,12,"[3, 4]","[20, 50]","[2, 2]",1,5,4,250,5,2,4,1756 -29,13,"[2, 1]","[40, 20]","[1, 2]",1,5,2,200,4,2,1,1973 -29,14,"[2, 1]","[0, 50]","[1, 2]",1,1,2,0,0,2,2,2057 -29,15,"[2, 1]","[10, 30]","[1, 1]",1,1,1,30,3,1,2,1724 -29,16,"[4, 3]","[0, 40]","[2, 1]",1,5,3,200,4,1,4,2005 -29,17,"[4, 3]","[50, 20]","[2, 1]",1,1,3,20,2,1,3,1790 -29,18,"[4, 3]","[60, 10]","[2, 2]",1,5,4,300,6,2,3,1673 -29,19,"[3, 4]","[10, 30]","[2, 2]",1,1,4,30,3,2,4,1724 -29,20,"[1, 2]","[30, 40]","[1, 1]",1,5,1,150,3,1,2,1874 -29,21,"[1, 2]","[30, 40]","[1, 2]",1,5,2,200,4,2,2,2040 -29,22,"[1, 2]","[30, 40]","[1, 2]",1,1,2,40,4,2,2,2074 -29,23,"[4, 3]","[30, 50]","[2, 2]",1,5,4,150,3,2,4,2276 -29,24,"[2, 1]","[10, 80]","[1, 1]",1,1,1,80,8,1,2,1973 -29,25,"[1, 2]","[70, 10]","[1, 1]",1,1,1,70,7,1,1,2023 -29,26,"[2, 1]","[20, 60]","[1, 1]",1,1,1,60,6,1,2,1873 -29,27,"[4, 3]","[20, 80]","[2, 1]",1,5,3,400,8,1,4,2656 -29,28,"[4, 3]","[20, 80]","[2, 1]",1,1,3,80,8,1,4,1707 -29,29,"[1, 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1]",1,5,1,450,9,1,2,4811 -29,2,"[1, 2]","[80, 20]","[1, 1]",1,1,1,80,8,1,1,2070 -29,3,"[4, 3]","[80, 90]","[2, 1]",1,1,3,90,9,1,4,1774 -29,4,"[1, 2]","[60, 70]","[1, 1]",1,5,1,300,6,1,2,1940 -29,5,"[4, 3]","[80, 70]","[2, 1]",1,1,3,70,7,1,3,1757 -29,6,"[1, 2]","[70, 30]","[1, 1]",1,1,1,70,7,1,1,1873 -29,7,"[1, 2]","[60, 0]","[1, 1]",1,1,1,60,6,1,1,2140 -29,8,"[1, 2]","[70, 0]","[1, 1]",1,1,1,70,7,1,1,1806 -29,9,"[1, 2]","[90, 20]","[1, 1]",1,5,1,450,9,1,1,1794 -29,10,"[2, 1]","[0, 70]","[1, 1]",1,5,1,350,7,1,2,1912 -29,11,"[2, 1]","[0, 80]","[1, 1]",1,5,1,400,8,1,2,1955 -29,12,"[2, 1]","[30, 80]","[1, 1]",1,5,1,400,8,1,2,1890 -29,13,"[2, 1]","[20, 80]","[1, 1]",1,5,1,400,8,1,2,1806 -29,14,"[2, 1]","[50, 50]","[1, 1]",1,5,1,250,5,1,2,1770 -29,15,"[1, 2]","[60, 50]","[1, 1]",1,5,1,300,6,1,1,1857 -29,16,"[1, 2]","[60, 40]","[1, 1]",1,5,1,300,6,1,1,1876 -29,17,"[3, 4]","[30, 0]","[2, 1]",1,1,3,30,3,1,3,1740 -29,18,"[1, 2]","[0, 10]","[1, 2]",1,1,2,10,1,2,2,2040 -29,19,"[4, 3]","[10, 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-29,55,"[2, 1]","[40, 40]","[1, 2]",2,5,2,200,4,2,2,2057 -29,56,"[3, 4]","[40, 20]","[2, 2]",2,1,4,20,2,2,3,1873 -29,57,"[2, 1]","[10, 80]","[1, 1]",2,5,1,400,8,1,2,2007 -29,58,"[2, 1]","[30, 50]","[1, 1]",2,1,1,50,5,1,2,2037 -29,59,"[4, 3]","[20, 30]","[2, 1]",2,1,3,30,3,1,4,1973 -29,60,"[3, 4]","[20, 40]","[2, 1]",2,1,3,20,2,1,4,1706 -29,61,"[1, 2]","[20, 50]","[1, 2]",2,1,2,50,5,2,2,2440 -29,62,"[3, 4]","[20, 10]","[2, 2]",2,5,4,50,1,2,3,2006 -29,63,"[4, 3]","[30, 20]","[2, 1]",2,5,3,100,2,1,3,2007 -29,64,"[3, 4]","[60, 40]","[2, 1]",2,1,3,60,6,1,3,1740 -29,65,"[4, 3]","[0, 90]","[2, 1]",2,5,3,450,9,1,4,1723 -29,66,"[2, 1]","[10, 80]","[1, 1]",2,1,1,80,8,1,2,1990 -29,67,"[2, 1]","[0, 80]","[1, 1]",2,1,1,80,8,1,2,1890 -29,68,"[1, 2]","[50, 10]","[1, 1]",2,5,1,250,5,1,1,1907 -29,69,"[3, 4]","[40, 0]","[2, 1]",2,5,3,200,4,1,3,1790 -29,70,"[4, 3]","[10, 40]","[2, 1]",2,1,3,40,4,1,4,1824 -29,71,"[1, 2]","[80, 10]","[1, 1]",2,5,1,400,8,1,1,1922 -29,72,"[2, 1]","[50, 70]","[1, 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0]","[2, 2]",1,1,4,30,3,2,3,2090 -29,11,"[1, 2]","[0, 60]","[1, 1]",1,5,1,0,0,1,2,1824 -29,12,"[1, 2]","[40, 80]","[1, 2]",1,5,2,400,8,2,2,2412 -29,13,"[4, 3]","[60, 60]","[2, 2]",1,5,4,300,6,2,4,1807 -29,14,"[4, 3]","[40, 90]","[2, 2]",1,5,4,200,4,2,4,1673 -29,15,"[3, 4]","[60, 80]","[2, 2]",1,1,4,80,8,2,4,1706 -29,16,"[2, 1]","[70, 70]","[1, 2]",1,1,2,70,7,2,2,1955 -29,17,"[2, 1]","[70, 70]","[1, 2]",1,5,2,350,7,2,2,2072 -29,18,"[3, 4]","[30, 60]","[2, 2]",1,1,4,60,6,2,4,2057 -29,19,"[1, 2]","[40, 60]","[1, 2]",1,5,2,300,6,2,2,1737 -29,20,"[1, 2]","[10, 50]","[1, 2]",1,5,2,250,5,2,2,1822 -29,21,"[4, 3]","[50, 10]","[2, 2]",1,1,4,50,5,2,3,3672 -29,22,"[3, 4]","[20, 60]","[2, 2]",1,5,4,300,6,2,4,1840 -29,23,"[1, 2]","[50, 30]","[1, 2]",1,5,2,150,3,2,1,2074 -29,24,"[4, 3]","[60, 60]","[2, 2]",1,5,4,300,6,2,4,1640 -29,25,"[1, 2]","[70, 70]","[1, 2]",1,5,2,350,7,2,1,1773 -29,26,"[3, 4]","[60, 70]","[2, 2]",1,5,4,350,7,2,4,1789 -29,27,"[2, 1]","[70, 40]","[1, 2]",1,5,2,350,7,2,1,2089 -29,28,"[1, 2]","[30, 60]","[1, 2]",1,5,2,300,6,2,2,2071 -29,29,"[4, 3]","[60, 60]","[2, 2]",1,5,4,300,6,2,4,2258 -29,30,"[1, 2]","[70, 70]","[1, 2]",1,1,2,70,7,2,1,2806 -29,31,"[1, 2]","[50, 30]","[1, 2]",1,1,2,30,3,2,1,1705 -29,32,"[3, 4]","[20, 40]","[2, 1]",1,5,3,100,2,1,4,1690 -29,33,"[1, 2]","[10, 60]","[1, 2]",1,5,2,300,6,2,2,1823 -29,34,"[2, 1]","[30, 0]","[1, 2]",1,1,2,30,3,2,1,1944 -29,35,"[4, 3]","[50, 0]","[2, 1]",1,1,3,0,0,1,3,1990 -29,36,"[1, 2]","[10, 50]","[1, 2]",1,1,2,50,5,2,2,2006 -29,37,"[1, 2]","[0, 80]","[1, 2]",1,1,2,80,8,2,2,1956 -29,38,"[2, 1]","[70, 20]","[1, 2]",1,1,2,70,7,2,1,1740 -29,39,"[2, 1]","[50, 10]","[1, 2]",1,1,2,50,5,2,1,1690 -29,40,"[2, 1]","[30, 20]","[1, 2]",1,1,2,30,3,2,1,1703 -29,41,"[1, 2]","[10, 30]","[1, 2]",2,1,2,30,3,2,2,2552 -29,42,"[3, 4]","[0, 20]","[2, 1]",2,5,3,0,0,1,4,1690 -29,43,"[4, 3]","[10, 0]","[2, 2]",2,1,4,10,1,2,3,1773 -29,44,"[1, 2]","[30, 20]","[1, 2]",2,5,2,100,2,2,1,2139 -29,45,"[2, 1]","[40, 10]","[1, 2]",2,5,2,200,4,2,1,2040 -29,46,"[3, 4]","[0, 30]","[2, 1]",2,5,3,0,0,1,4,2306 -29,47,"[3, 4]","[30, 40]","[2, 2]",2,5,4,200,4,2,4,1723 -29,48,"[4, 3]","[20, 20]","[2, 2]",2,1,4,20,2,2,4,1874 -29,49,"[4, 3]","[30, 10]","[2, 2]",2,5,4,150,3,2,3,1674 -29,50,"[2, 1]","[30, 30]","[1, 2]",2,5,2,150,3,2,2,1857 -29,51,"[3, 4]","[20, 40]","[2, 2]",2,1,4,40,4,2,4,2057 -29,52,"[2, 1]","[30, 30]","[1, 2]",2,1,2,30,3,2,2,1972 -29,53,"[1, 2]","[60, 20]","[1, 1]",2,1,1,60,6,1,1,2074 -29,54,"[1, 2]","[80, 40]","[1, 1]",2,5,1,400,8,1,1,1890 -29,55,"[3, 4]","[80, 40]","[2, 1]",2,1,3,80,8,1,3,1653 -29,56,"[2, 1]","[0, 80]","[1, 1]",2,1,1,80,8,1,2,2340 -29,57,"[4, 3]","[0, 80]","[2, 1]",2,1,3,80,8,1,4,1673 -29,58,"[4, 3]","[20, 90]","[2, 1]",2,5,3,450,9,1,4,1689 -29,59,"[2, 1]","[30, 60]","[1, 1]",2,1,1,60,6,1,2,1873 -29,60,"[3, 4]","[70, 50]","[2, 1]",2,5,3,350,7,1,3,1774 -29,61,"[4, 3]","[80, 60]","[2, 1]",2,5,3,300,6,1,3,1740 -29,62,"[3, 4]","[50, 50]","[2, 1]",2,5,3,250,5,1,3,1740 -29,63,"[2, 1]","[50, 50]","[1, 1]",2,5,1,250,5,1,2,1874 -29,64,"[1, 2]","[60, 50]","[1, 1]",2,1,1,60,6,1,1,1906 -29,65,"[3, 4]","[40, 20]","[2, 1]",2,1,3,40,4,1,3,1777 -29,66,"[4, 3]","[10, 30]","[2, 1]",2,1,3,30,3,1,4,1740 -29,67,"[1, 2]","[40, 10]","[1, 2]",2,1,2,10,1,2,1,1757 -29,68,"[4, 3]","[20, 0]","[2, 1]",2,5,3,0,0,1,3,2740 -29,69,"[2, 1]","[50, 20]","[1, 2]",2,1,2,50,5,2,1,1972 -29,70,"[3, 4]","[20, 50]","[2, 2]",2,5,4,250,5,2,4,1973 -29,71,"[2, 1]","[60, 0]","[1, 2]",2,1,2,60,6,2,1,1874 -29,72,"[4, 3]","[60, 0]","[2, 2]",2,5,4,300,6,2,3,2073 -29,73,"[3, 4]","[10, 70]","[2, 2]",2,1,4,70,7,2,4,1974 -29,74,"[1, 2]","[40, 70]","[1, 2]",2,1,2,70,7,2,2,1856 -29,75,"[2, 1]","[70, 60]","[1, 2]",2,5,2,350,7,2,1,3023 -29,76,"[2, 1]","[60, 70]","[1, 2]",2,5,2,300,6,2,2,1723 -29,77,"[3, 4]","[70, 50]","[2, 2]",2,1,4,50,5,2,3,1904 -29,78,"[3, 4]","[90, 60]","[2, 2]",2,1,4,60,6,2,3,1891 -29,79,"[3, 4]","[60, 60]","[2, 2]",2,1,4,60,6,2,3,1857 -29,80,"[4, 3]","[70, 60]","[2, 2]",2,5,4,350,7,2,3,1923 -29,1,"[1, 2]","[90, 20]","[1, 1]",1,1,1,90,9,1,1,4039 -29,2,"[2, 1]","[30, 70]","[1, 1]",1,5,1,350,7,1,2,1874 -29,3,"[3, 4]","[50, 50]","[2, 1]",1,5,3,250,5,1,3,1723 -29,4,"[3, 4]","[20, 70]","[2, 1]",1,1,3,20,2,1,4,1702 -29,5,"[2, 1]","[90, 30]","[1, 2]",1,1,2,90,9,2,1,1923 -29,6,"[1, 2]","[0, 90]","[1, 2]",1,1,2,90,9,2,2,2090 -29,7,"[4, 3]","[90, 0]","[2, 2]",1,1,4,90,9,2,3,2039 -29,8,"[1, 2]","[20, 80]","[1, 2]",1,1,2,80,8,2,2,1739 -29,9,"[4, 3]","[80, 30]","[2, 2]",1,1,4,80,8,2,3,1990 -29,10,"[3, 4]","[60, 70]","[2, 2]",1,5,4,350,7,2,4,1824 -29,11,"[2, 1]","[60, 60]","[1, 2]",1,5,2,300,6,2,2,2190 -29,12,"[1, 2]","[60, 70]","[1, 2]",1,5,2,350,7,2,2,1807 -29,13,"[2, 1]","[70, 40]","[1, 2]",1,5,2,350,7,2,1,1707 -29,14,"[4, 3]","[90, 30]","[2, 2]",1,1,4,90,9,2,3,2174 -29,15,"[3, 4]","[10, 50]","[2, 2]",1,5,4,250,5,2,4,1823 -29,16,"[4, 3]","[50, 40]","[2, 2]",1,5,4,250,5,2,3,1739 -29,17,"[2, 1]","[20, 0]","[1, 2]",1,5,2,100,2,2,1,1907 -29,18,"[4, 3]","[30, 0]","[2, 2]",1,5,4,150,3,2,3,1807 -29,19,"[4, 3]","[50, 10]","[2, 2]",1,1,4,50,5,2,3,1773 -29,20,"[2, 1]","[50, 20]","[1, 2]",1,5,2,250,5,2,1,1806 -29,21,"[1, 2]","[40, 90]","[1, 2]",1,1,2,90,9,2,2,2491 -29,22,"[3, 4]","[40, 90]","[2, 2]",1,1,4,90,9,2,4,2156 -29,23,"[3, 4]","[60, 80]","[2, 2]",1,5,4,400,8,2,4,1722 -29,24,"[3, 4]","[30, 80]","[2, 2]",1,1,4,80,8,2,4,1773 -29,25,"[2, 1]","[80, 30]","[1, 2]",1,1,2,80,8,2,1,1990 -29,26,"[2, 1]","[70, 10]","[1, 2]",1,5,2,350,7,2,1,1989 -29,27,"[2, 1]","[90, 0]","[1, 2]",1,5,2,450,9,2,1,1751 -29,28,"[4, 3]","[80, 0]","[2, 2]",1,1,4,80,8,2,3,1922 -29,29,"[2, 1]","[80, 10]","[1, 2]",1,1,2,80,8,2,1,1826 -29,30,"[1, 2]","[30, 90]","[1, 2]",1,1,2,90,9,2,2,2040 -29,31,"[1, 2]","[40, 70]","[1, 2]",1,5,2,350,7,2,2,1724 -29,32,"[4, 3]","[60, 40]","[2, 2]",1,5,4,300,6,2,3,1922 -29,33,"[2, 1]","[60, 70]","[1, 2]",1,1,2,60,6,2,2,1923 -29,34,"[4, 3]","[40, 70]","[2, 2]",1,1,4,40,4,2,4,1824 -29,35,"[4, 3]","[10, 60]","[2, 2]",1,1,4,10,1,2,4,1707 -29,36,"[4, 3]","[20, 70]","[2, 1]",1,1,3,70,7,1,4,1673 -29,37,"[3, 4]","[50, 80]","[2, 1]",1,1,3,50,5,1,4,1721 -29,38,"[1, 2]","[70, 90]","[1, 1]",1,5,1,350,7,1,2,2224 -29,39,"[3, 4]","[70, 40]","[2, 1]",1,1,3,70,7,1,3,1706 -29,40,"[3, 4]","[70, 40]","[2, 1]",1,1,3,70,7,1,3,2873 -29,41,"[4, 3]","[20, 70]","[2, 1]",2,5,3,350,7,1,4,1968 -29,42,"[4, 3]","[20, 80]","[2, 1]",2,1,3,80,8,1,4,1689 -29,43,"[4, 3]","[30, 80]","[2, 1]",2,5,3,400,8,1,4,2072 -29,44,"[4, 3]","[40, 80]","[2, 1]",2,1,3,80,8,1,4,1738 -29,45,"[1, 2]","[60, 50]","[1, 1]",2,1,1,60,6,1,1,2056 -29,46,"[1, 2]","[50, 20]","[1, 1]",2,1,1,50,5,1,1,1823 -29,47,"[1, 2]","[40, 10]","[1, 1]",2,5,1,200,4,1,1,1787 -29,48,"[2, 1]","[20, 20]","[1, 1]",2,5,1,100,2,1,2,1857 -29,49,"[1, 2]","[20, 10]","[1, 1]",2,5,1,100,2,1,1,2357 -29,50,"[3, 4]","[10, 40]","[2, 2]",2,5,4,200,4,2,4,1822 -29,51,"[1, 2]","[30, 40]","[1, 2]",2,5,2,200,4,2,2,1805 -29,52,"[1, 2]","[30, 70]","[1, 2]",2,5,2,350,7,2,2,1756 -29,53,"[2, 1]","[90, 70]","[1, 2]",2,5,2,450,9,2,1,1824 -29,54,"[3, 4]","[60, 80]","[2, 2]",2,1,4,80,8,2,4,1907 -29,55,"[4, 3]","[60, 80]","[2, 2]",2,1,4,60,6,2,4,1723 -29,56,"[1, 2]","[60, 50]","[1, 2]",2,5,2,250,5,2,1,2090 -29,57,"[1, 2]","[80, 50]","[1, 2]",2,5,2,250,5,2,1,1740 -29,58,"[3, 4]","[80, 70]","[2, 2]",2,1,4,70,7,2,3,2105 -29,59,"[3, 4]","[80, 50]","[2, 2]",2,5,4,250,5,2,3,1971 -29,60,"[3, 4]","[80, 60]","[2, 2]",2,1,4,60,6,2,3,1707 -29,61,"[4, 3]","[50, 80]","[2, 2]",2,5,4,250,5,2,4,2173 -29,62,"[3, 4]","[70, 50]","[2, 2]",2,5,4,250,5,2,3,1741 -29,63,"[4, 3]","[40, 50]","[2, 2]",2,5,4,200,4,2,4,1753 -29,64,"[2, 1]","[30, 20]","[1, 2]",2,5,2,150,3,2,1,1907 -29,65,"[4, 3]","[40, 10]","[2, 2]",2,1,4,40,4,2,3,1806 -29,66,"[2, 1]","[60, 40]","[1, 1]",2,1,1,40,4,1,1,1807 -29,67,"[4, 3]","[70, 60]","[2, 2]",2,1,4,70,7,2,3,1673 -29,68,"[4, 3]","[80, 70]","[2, 2]",2,5,4,400,8,2,3,1639 -29,69,"[4, 3]","[80, 60]","[2, 2]",2,5,4,400,8,2,3,1807 -29,70,"[1, 2]","[40, 90]","[1, 1]",2,5,1,200,4,1,2,1757 -29,71,"[2, 1]","[70, 20]","[1, 2]",2,1,2,70,7,2,1,2340 -29,72,"[1, 2]","[10, 40]","[1, 2]",2,5,2,200,4,2,2,1872 -29,73,"[2, 1]","[60, 10]","[1, 2]",2,1,2,60,6,2,1,1740 -29,74,"[1, 2]","[10, 70]","[1, 2]",2,5,2,350,7,2,2,1807 -29,75,"[2, 1]","[90, 10]","[1, 2]",2,1,2,90,9,2,1,1873 -29,76,"[2, 1]","[80, 10]","[1, 2]",2,5,2,400,8,2,1,1773 -29,77,"[3, 4]","[60, 80]","[2, 2]",2,1,4,80,8,2,4,1945 -29,78,"[3, 4]","[40, 80]","[2, 2]",2,1,4,80,8,2,4,1737 -29,79,"[4, 3]","[80, 20]","[2, 2]",2,5,4,400,8,2,3,1740 -29,80,"[4, 3]","[80, 30]","[2, 2]",2,1,4,80,8,2,3,1907 -29,1,"[1, 2]","[60, 40]","[1, 1]",1,1,1,60,6,1,1,3637 -29,2,"[2, 1]","[30, 10]","[1, 1]",1,1,1,10,1,1,1,1923 -29,3,"[3, 4]","[10, 10]","[2, 2]",1,1,4,10,1,2,3,2040 -29,4,"[3, 4]","[40, 10]","[2, 1]",1,5,3,200,4,1,3,1706 -29,5,"[2, 1]","[0, 50]","[1, 1]",1,1,1,50,5,1,2,3106 -29,6,"[4, 3]","[50, 60]","[2, 1]",1,5,3,300,6,1,4,1842 -29,7,"[1, 2]","[80, 60]","[1, 1]",1,1,1,80,8,1,1,1893 -29,8,"[4, 3]","[80, 70]","[2, 1]",1,5,3,350,7,1,3,1742 -29,9,"[2, 1]","[50, 90]","[1, 1]",1,1,1,90,9,1,2,1858 -29,10,"[2, 1]","[60, 80]","[1, 1]",1,5,1,400,8,1,2,1825 -29,11,"[1, 2]","[70, 60]","[1, 1]",1,1,1,70,7,1,1,1925 -29,12,"[2, 1]","[60, 70]","[1, 1]",1,5,1,350,7,1,2,1923 -29,13,"[4, 3]","[70, 60]","[2, 1]",1,1,3,60,6,1,3,4458 -29,14,"[2, 1]","[70, 80]","[1, 1]",1,5,1,400,8,1,2,1807 -29,15,"[1, 2]","[80, 90]","[1, 1]",1,1,1,80,8,1,2,1858 -29,16,"[2, 1]","[90, 70]","[1, 1]",1,5,1,350,7,1,1,1806 -29,17,"[2, 1]","[50, 80]","[1, 1]",1,5,1,400,8,1,2,1890 -29,18,"[4, 3]","[40, 70]","[2, 1]",1,1,3,70,7,1,4,1757 -29,19,"[3, 4]","[40, 40]","[2, 1]",1,1,3,40,4,1,3,1723 -29,20,"[4, 3]","[20, 20]","[2, 2]",1,5,4,100,2,2,4,1736 -29,21,"[1, 2]","[40, 20]","[1, 1]",1,1,1,40,4,1,1,1990 -29,22,"[4, 3]","[30, 60]","[2, 1]",1,1,3,60,6,1,4,1740 -29,23,"[1, 2]","[80, 40]","[1, 1]",1,1,1,80,8,1,1,1922 -29,24,"[4, 3]","[60, 90]","[2, 1]",1,5,3,450,9,1,4,1740 -29,25,"[4, 3]","[40, 70]","[2, 1]",1,5,3,350,7,1,4,1706 -29,26,"[4, 3]","[60, 90]","[2, 1]",1,5,3,450,9,1,4,1739 -29,27,"[4, 3]","[60, 70]","[2, 1]",1,1,3,70,7,1,4,1723 -29,28,"[2, 1]","[70, 70]","[1, 1]",1,5,1,350,7,1,2,1805 -29,29,"[1, 2]","[80, 40]","[1, 1]",1,5,1,400,8,1,1,1823 -29,30,"[4, 3]","[40, 80]","[2, 1]",1,5,3,400,8,1,4,1724 -29,31,"[3, 4]","[80, 50]","[2, 1]",1,5,3,400,8,1,3,1724 -29,32,"[3, 4]","[80, 40]","[2, 1]",1,5,3,400,8,1,3,1703 -29,33,"[3, 4]","[60, 30]","[2, 1]",1,5,3,300,6,1,3,1759 -29,34,"[1, 2]","[50, 50]","[1, 1]",1,1,1,50,5,1,1,1940 -29,35,"[4, 3]","[40, 80]","[2, 1]",1,5,3,400,8,1,4,1973 -29,36,"[1, 2]","[80, 50]","[1, 1]",1,5,1,400,8,1,1,2073 -29,37,"[2, 1]","[70, 80]","[1, 1]",1,1,1,80,8,1,2,1939 -29,38,"[3, 4]","[90, 70]","[2, 1]",1,1,3,90,9,1,3,1773 -29,39,"[2, 1]","[50, 60]","[1, 1]",1,5,1,300,6,1,2,1755 -29,40,"[2, 1]","[70, 90]","[1, 1]",1,5,1,450,9,1,2,1941 -29,41,"[2, 1]","[80, 80]","[1, 1]",2,5,1,400,8,1,2,2253 -29,42,"[2, 1]","[80, 90]","[1, 1]",2,5,1,450,9,1,2,1755 -29,43,"[4, 3]","[70, 80]","[2, 1]",2,1,3,80,8,1,4,1773 -29,44,"[3, 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-29,62,"[2, 1]","[40, 60]","[1, 1]",2,5,1,300,6,1,2,2007 -29,63,"[1, 2]","[60, 40]","[1, 1]",2,1,1,60,6,1,1,1873 -29,64,"[1, 2]","[50, 60]","[1, 1]",2,5,1,250,5,1,2,1856 -29,65,"[4, 3]","[30, 60]","[2, 1]",2,1,3,60,6,1,4,1790 -29,66,"[4, 3]","[30, 90]","[2, 1]",2,1,3,90,9,1,4,1740 -29,67,"[2, 1]","[40, 80]","[1, 1]",2,5,1,400,8,1,2,1923 -29,68,"[3, 4]","[70, 20]","[2, 1]",2,1,3,70,7,1,3,2023 -29,69,"[4, 3]","[10, 70]","[2, 1]",2,5,3,350,7,1,4,1758 -29,70,"[1, 2]","[80, 60]","[1, 1]",2,5,1,400,8,1,1,2041 -29,71,"[3, 4]","[70, 70]","[2, 1]",2,5,3,350,7,1,3,1757 -29,72,"[4, 3]","[70, 40]","[2, 1]",2,1,3,40,4,1,3,1806 -29,73,"[1, 2]","[20, 80]","[1, 1]",2,1,1,20,2,1,2,2340 -29,74,"[3, 4]","[60, 80]","[2, 2]",2,5,4,400,8,2,4,1740 -29,75,"[3, 4]","[70, 80]","[2, 2]",2,5,4,400,8,2,4,1856 -29,76,"[2, 1]","[50, 50]","[1, 2]",2,1,2,50,5,2,2,2023 -29,77,"[1, 2]","[50, 30]","[1, 2]",2,1,2,30,3,2,1,2023 -29,78,"[4, 3]","[60, 80]","[2, 1]",2,1,3,80,8,1,4,1823 -29,79,"[4, 3]","[90, 70]","[2, 1]",2,1,3,70,7,1,3,1757 -29,80,"[1, 2]","[40, 80]","[1, 1]",2,1,1,40,4,1,2,2090 -34,1,"[4, 3]","[40, 80]","[2, 1]",1,1,3,80,8,1,4,4339 -34,2,"[2, 1]","[40, 80]","[1, 1]",1,5,1,400,8,1,2,3377 -34,3,"[4, 3]","[90, 70]","[2, 1]",1,5,3,350,7,1,3,1926 -34,4,"[3, 4]","[80, 90]","[2, 1]",1,1,3,80,8,1,4,2603 -34,5,"[4, 3]","[70, 70]","[2, 1]",1,5,3,350,7,1,4,2231 -34,6,"[2, 1]","[40, 80]","[1, 1]",1,1,1,80,8,1,2,4112 -34,7,"[4, 3]","[30, 60]","[2, 1]",1,5,3,300,6,1,4,2527 -34,8,"[3, 4]","[40, 10]","[2, 1]",1,1,3,40,4,1,3,2200 -34,9,"[2, 1]","[10, 50]","[1, 2]",1,1,2,10,1,2,2,6159 -34,10,"[3, 4]","[40, 0]","[2, 2]",1,5,4,0,0,2,3,2660 -34,11,"[3, 4]","[40, 20]","[2, 1]",1,1,3,40,4,1,3,1928 -34,12,"[4, 3]","[0, 40]","[2, 1]",1,1,3,40,4,1,4,2318 -34,13,"[1, 2]","[60, 10]","[1, 1]",1,5,1,300,6,1,1,4842 -34,14,"[1, 2]","[50, 20]","[1, 1]",1,1,1,50,5,1,1,5390 -34,15,"[3, 4]","[30, 10]","[2, 1]",1,5,3,150,3,1,3,15047 -34,16,"[1, 2]","[20, 10]","[1, 1]",1,1,1,20,2,1,1,3834 -34,17,"[2, 1]","[30, 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-34,35,"[4, 3]","[30, 30]","[2, 1]",1,5,3,150,3,1,4,2863 -34,36,"[1, 2]","[10, 20]","[1, 2]",1,5,2,100,2,2,2,2850 -34,37,"[2, 1]","[30, 0]","[1, 2]",1,5,2,150,3,2,1,2737 -34,38,"[2, 1]","[70, 30]","[1, 2]",1,1,2,70,7,2,1,2167 -34,39,"[3, 4]","[70, 50]","[2, 2]",1,1,4,50,5,2,3,2429 -34,40,"[1, 2]","[60, 90]","[1, 1]",1,5,1,300,6,1,2,2875 -34,41,"[2, 1]","[80, 70]","[1, 1]",2,1,1,70,7,1,1,3443 -34,42,"[1, 2]","[80, 50]","[1, 2]",2,1,2,50,5,2,1,1949 -34,43,"[2, 1]","[30, 70]","[1, 1]",2,1,1,70,7,1,2,2366 -34,44,"[1, 2]","[90, 70]","[1, 2]",2,1,2,70,7,2,1,1966 -34,45,"[4, 3]","[80, 80]","[2, 2]",2,5,4,400,8,2,4,2404 -34,46,"[4, 3]","[90, 70]","[2, 2]",2,1,4,90,9,2,3,3467 -34,47,"[2, 1]","[70, 60]","[1, 2]",2,1,2,70,7,2,1,3104 -34,48,"[2, 1]","[50, 70]","[1, 1]",2,5,1,350,7,1,2,2191 -34,49,"[2, 1]","[80, 60]","[1, 1]",2,1,1,60,6,1,1,2188 -34,50,"[4, 3]","[90, 40]","[2, 1]",2,1,3,40,4,1,3,2778 -34,51,"[4, 3]","[80, 20]","[2, 1]",2,5,3,100,2,1,3,4828 -34,52,"[3, 4]","[0, 50]","[2, 2]",2,1,4,50,5,2,4,2604 -34,53,"[2, 1]","[30, 0]","[1, 1]",2,5,1,0,0,1,1,4698 -34,54,"[1, 2]","[30, 40]","[1, 2]",2,1,2,40,4,2,2,2674 -34,55,"[1, 2]","[20, 30]","[1, 2]",2,5,2,150,3,2,2,2258 -34,56,"[3, 4]","[20, 30]","[2, 1]",2,5,3,100,2,1,4,3041 -34,57,"[2, 1]","[40, 60]","[1, 2]",2,5,2,200,4,2,2,4375 -34,58,"[4, 3]","[50, 40]","[2, 1]",2,1,3,40,4,1,3,1930 -34,59,"[2, 1]","[20, 50]","[1, 2]",2,5,2,100,2,2,2,2544 -34,60,"[4, 3]","[50, 60]","[2, 2]",2,1,4,50,5,2,4,2578 -34,61,"[2, 1]","[40, 60]","[1, 2]",2,5,2,200,4,2,2,2242 -34,62,"[1, 2]","[40, 40]","[1, 2]",2,5,2,200,4,2,1,2603 -34,63,"[1, 2]","[20, 30]","[1, 2]",2,5,2,150,3,2,2,2059 -34,64,"[4, 3]","[30, 10]","[2, 1]",2,5,3,50,1,1,3,2604 -34,65,"[3, 4]","[20, 20]","[2, 1]",2,5,3,100,2,1,3,3909 -34,66,"[2, 1]","[10, 20]","[1, 2]",2,5,2,50,1,2,2,2692 -34,67,"[3, 4]","[40, 10]","[2, 1]",2,1,3,40,4,1,3,2299 -34,68,"[3, 4]","[30, 10]","[2, 1]",2,1,3,30,3,1,3,1974 -34,69,"[2, 1]","[10, 50]","[1, 1]",2,5,1,250,5,1,2,2373 -34,70,"[4, 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2]",1,1,2,30,3,2,2,3430 -35,26,"[4, 3]","[50, 40]","[2, 1]",1,5,3,200,4,1,3,3271 -35,27,"[4, 3]","[40, 80]","[2, 1]",1,1,3,80,8,1,4,1971 -35,28,"[4, 3]","[40, 70]","[2, 1]",1,1,3,70,7,1,4,1746 -35,29,"[1, 2]","[80, 50]","[1, 1]",1,5,1,400,8,1,1,4612 -35,30,"[3, 4]","[90, 50]","[2, 1]",1,5,3,450,9,1,3,2029 -35,31,"[4, 3]","[0, 80]","[2, 1]",1,1,3,80,8,1,4,1914 -35,32,"[1, 2]","[60, 30]","[1, 1]",1,5,1,300,6,1,1,2330 -35,33,"[2, 1]","[20, 50]","[1, 2]",1,1,2,20,2,2,2,3896 -35,34,"[3, 4]","[60, 40]","[2, 1]",1,1,3,60,6,1,3,2438 -35,35,"[1, 2]","[30, 40]","[1, 1]",1,5,1,150,3,1,2,2262 -35,36,"[4, 3]","[30, 20]","[2, 2]",1,1,4,30,3,2,3,2213 -35,37,"[3, 4]","[40, 30]","[2, 1]",1,5,3,200,4,1,3,1979 -35,38,"[1, 2]","[30, 10]","[1, 1]",1,1,1,30,3,1,1,2797 -35,39,"[2, 1]","[20, 30]","[1, 2]",1,5,2,100,2,2,2,2355 -35,40,"[3, 4]","[30, 30]","[2, 2]",1,1,4,30,3,2,3,2505 -35,41,"[3, 4]","[40, 50]","[2, 1]",2,5,3,200,4,1,4,2116 -35,42,"[4, 3]","[50, 30]","[2, 1]",2,5,3,150,3,1,3,1872 -35,43,"[1, 2]","[40, 50]","[1, 1]",2,5,1,200,4,1,2,2283 -35,44,"[2, 1]","[30, 50]","[1, 1]",2,5,1,250,5,1,2,3962 -35,45,"[2, 1]","[20, 60]","[1, 1]",2,1,1,60,6,1,2,2071 -35,46,"[2, 1]","[60, 60]","[1, 2]",2,1,2,60,6,2,2,2246 -35,47,"[4, 3]","[70, 60]","[2, 2]",2,1,4,70,7,2,3,2332 -35,48,"[4, 3]","[90, 10]","[2, 2]",2,1,4,90,9,2,3,1772 -35,49,"[4, 3]","[70, 40]","[2, 2]",2,1,4,70,7,2,3,1779 -35,50,"[3, 4]","[40, 60]","[2, 1]",2,5,3,200,4,1,4,2489 -35,51,"[3, 4]","[0, 30]","[2, 2]",2,1,4,30,3,2,4,3695 -35,52,"[1, 2]","[0, 30]","[1, 1]",2,5,1,0,0,1,2,2563 -35,53,"[4, 3]","[20, 10]","[2, 2]",2,5,4,100,2,2,3,2764 -35,54,"[2, 1]","[50, 0]","[1, 2]",2,5,2,250,5,2,1,3437 -35,55,"[3, 4]","[10, 30]","[2, 2]",2,5,4,150,3,2,4,2096 -35,56,"[4, 3]","[40, 20]","[2, 2]",2,1,4,40,4,2,3,1955 -35,57,"[4, 3]","[40, 20]","[2, 2]",2,5,4,200,4,2,3,2688 -35,58,"[2, 1]","[60, 10]","[1, 2]",2,5,2,300,6,2,1,2996 -35,59,"[2, 1]","[80, 10]","[1, 2]",2,1,2,80,8,2,1,1755 -35,60,"[1, 2]","[30, 90]","[1, 2]",2,1,2,90,9,2,2,1863 -35,61,"[2, 1]","[70, 10]","[1, 2]",2,1,2,70,7,2,1,2213 -35,62,"[1, 2]","[0, 70]","[1, 2]",2,5,2,350,7,2,2,2013 -35,63,"[1, 2]","[0, 40]","[1, 2]",2,5,2,200,4,2,2,1763 -35,64,"[4, 3]","[50, 30]","[2, 2]",2,1,4,50,5,2,3,4012 -35,65,"[3, 4]","[40, 50]","[2, 2]",2,5,4,250,5,2,4,2229 -35,66,"[2, 1]","[50, 50]","[1, 2]",2,5,2,250,5,2,2,2487 -35,67,"[2, 1]","[70, 40]","[1, 2]",2,1,2,70,7,2,1,1729 -35,68,"[2, 1]","[50, 40]","[1, 2]",2,1,2,50,5,2,1,1996 -35,69,"[2, 1]","[30, 50]","[1, 1]",2,1,1,50,5,1,2,1771 -35,70,"[2, 1]","[20, 40]","[1, 1]",2,1,1,40,4,1,2,1846 -35,71,"[2, 1]","[20, 60]","[1, 2]",2,1,2,20,2,2,2,1753 -35,72,"[3, 4]","[40, 10]","[2, 1]",2,1,3,40,4,1,3,2762 -35,73,"[3, 4]","[50, 10]","[2, 1]",2,5,3,250,5,1,3,2021 -35,74,"[1, 2]","[40, 30]","[1, 1]",2,1,1,40,4,1,1,2205 -35,75,"[4, 3]","[30, 20]","[2, 1]",2,1,3,20,2,1,3,1929 -35,76,"[4, 3]","[20, 20]","[2, 2]",2,5,4,100,2,2,4,1880 -35,77,"[4, 3]","[10, 10]","[2, 2]",2,5,4,50,1,2,4,2722 -35,78,"[2, 1]","[30, 10]","[1, 1]",2,1,1,10,1,1,1,2096 -35,79,"[3, 4]","[20, 40]","[2, 2]",2,5,4,200,4,2,4,2304 -35,80,"[4, 3]","[30, 10]","[2, 2]",2,5,4,150,3,2,3,2779 -35,1,"[4, 3]","[30, 60]","[2, 1]",1,5,3,300,6,1,4,3476 -35,2,"[2, 1]","[0, 60]","[1, 1]",1,5,1,300,6,1,2,2763 -35,3,"[4, 3]","[20, 70]","[2, 1]",1,5,3,350,7,1,4,1880 -35,4,"[4, 3]","[0, 60]","[2, 1]",1,5,3,300,6,1,4,1821 -35,5,"[4, 3]","[10, 60]","[2, 1]",1,1,3,60,6,1,4,3313 -35,6,"[1, 2]","[30, 20]","[1, 2]",1,1,2,20,2,2,1,4104 -35,7,"[4, 3]","[10, 20]","[2, 1]",1,5,3,100,2,1,4,1971 -35,8,"[4, 3]","[30, 60]","[2, 1]",1,5,3,300,6,1,4,3829 -35,9,"[3, 4]","[60, 10]","[2, 1]",1,5,3,300,6,1,3,1905 -35,10,"[1, 2]","[60, 30]","[1, 1]",1,5,1,300,6,1,1,1929 -35,11,"[3, 4]","[70, 40]","[2, 1]",1,5,3,350,7,1,3,1897 -35,12,"[2, 1]","[50, 70]","[1, 1]",1,1,1,70,7,1,2,2355 -35,13,"[1, 2]","[60, 0]","[1, 1]",1,5,1,300,6,1,1,2539 -35,14,"[4, 3]","[30, 60]","[2, 1]",1,1,3,60,6,1,4,2145 -35,15,"[1, 2]","[60, 50]","[1, 1]",1,5,1,300,6,1,1,2054 -35,16,"[4, 3]","[40, 70]","[2, 1]",1,1,3,70,7,1,4,2204 -35,17,"[1, 2]","[80, 40]","[1, 1]",1,1,1,80,8,1,1,2305 -35,18,"[2, 1]","[40, 70]","[1, 1]",1,5,1,350,7,1,2,6087 -35,19,"[1, 2]","[60, 50]","[1, 1]",1,1,1,60,6,1,1,2988 -35,20,"[3, 4]","[80, 40]","[2, 2]",1,1,4,40,4,2,3,2521 -35,21,"[2, 1]","[40, 80]","[1, 1]",1,5,1,400,8,1,2,3905 -35,22,"[1, 2]","[80, 30]","[1, 1]",1,1,1,80,8,1,1,3505 -35,23,"[1, 2]","[80, 50]","[1, 1]",1,5,1,400,8,1,1,1996 -35,24,"[4, 3]","[50, 80]","[2, 1]",1,1,3,80,8,1,4,3514 -35,25,"[2, 1]","[70, 70]","[1, 1]",1,5,1,350,7,1,2,2953 -35,26,"[2, 1]","[60, 60]","[1, 1]",1,1,1,60,6,1,2,2104 -35,27,"[2, 1]","[80, 40]","[1, 1]",1,5,1,200,4,1,1,2388 -35,28,"[2, 1]","[80, 60]","[1, 2]",1,1,2,80,8,2,1,1770 -35,29,"[4, 3]","[90, 50]","[2, 2]",1,1,4,90,9,2,3,1879 -35,30,"[2, 1]","[80, 40]","[1, 2]",1,1,2,80,8,2,1,2979 -35,31,"[4, 3]","[70, 40]","[2, 2]",1,5,4,350,7,2,3,1813 -35,32,"[3, 4]","[30, 80]","[2, 2]",1,5,4,400,8,2,4,2196 -35,33,"[3, 4]","[60, 40]","[2, 2]",1,1,4,40,4,2,3,1913 -35,34,"[4, 3]","[30, 70]","[2, 1]",1,1,3,70,7,1,4,1987 -35,35,"[4, 3]","[30, 40]","[2, 1]",1,5,3,200,4,1,4,1988 -35,36,"[3, 4]","[20, 30]","[2, 1]",1,1,3,20,2,1,4,1905 -35,37,"[3, 4]","[20, 40]","[2, 2]",1,1,4,40,4,2,4,1670 -35,38,"[4, 3]","[20, 60]","[2, 2]",1,5,4,100,2,2,4,1888 -35,39,"[4, 3]","[60, 70]","[2, 2]",1,5,4,300,6,2,4,2138 -35,40,"[1, 2]","[70, 60]","[1, 2]",1,5,2,300,6,2,1,3430 -35,41,"[2, 1]","[90, 50]","[1, 1]",2,5,1,250,5,1,1,3807 -35,42,"[3, 4]","[50, 80]","[2, 1]",2,5,3,250,5,1,4,1872 -35,43,"[3, 4]","[50, 70]","[2, 1]",2,1,3,50,5,1,4,1671 -35,44,"[1, 2]","[50, 50]","[1, 2]",2,5,2,250,5,2,1,2321 -35,45,"[2, 1]","[40, 30]","[1, 2]",2,5,2,200,4,2,1,1972 -35,46,"[1, 2]","[50, 30]","[1, 1]",2,1,1,50,5,1,1,2614 -35,47,"[2, 1]","[40, 50]","[1, 1]",2,1,1,50,5,1,2,2054 -35,48,"[3, 4]","[20, 30]","[2, 2]",2,1,4,30,3,2,4,3289 -35,49,"[2, 1]","[40, 30]","[1, 1]",2,5,1,150,3,1,1,3037 -35,50,"[2, 1]","[40, 10]","[1, 1]",2,1,1,10,1,1,1,2295 -35,51,"[1, 2]","[20, 70]","[1, 2]",2,5,2,350,7,2,2,3488 -35,52,"[3, 4]","[30, 80]","[2, 2]",2,5,4,400,8,2,4,1997 -35,53,"[1, 2]","[30, 30]","[1, 2]",2,5,2,150,3,2,1,2405 -35,54,"[2, 1]","[80, 10]","[1, 2]",2,5,2,400,8,2,1,2555 -35,55,"[4, 3]","[70, 10]","[2, 2]",2,1,4,70,7,2,3,4913 -35,56,"[4, 3]","[80, 50]","[2, 2]",2,1,4,80,8,2,3,2031 -35,57,"[1, 2]","[70, 50]","[1, 2]",2,1,2,50,5,2,1,15912 -35,58,"[2, 1]","[60, 80]","[1, 2]",2,1,2,60,6,2,2,2138 -35,59,"[1, 2]","[70, 50]","[1, 2]",2,1,2,50,5,2,1,1913 -35,60,"[1, 2]","[50, 40]","[1, 2]",2,5,2,200,4,2,1,1888 -35,61,"[4, 3]","[30, 80]","[2, 1]",2,5,3,400,8,1,4,2338 -35,62,"[4, 3]","[10, 70]","[2, 1]",2,1,3,70,7,1,4,1987 -35,63,"[3, 4]","[50, 10]","[2, 1]",2,1,3,50,5,1,3,2854 -35,64,"[4, 3]","[10, 80]","[2, 2]",2,1,4,10,1,2,4,1937 -35,65,"[1, 2]","[30, 0]","[1, 1]",2,1,1,30,3,1,1,2396 -35,66,"[1, 2]","[30, 30]","[1, 2]",2,5,2,150,3,2,1,3204 -35,67,"[1, 2]","[20, 20]","[1, 1]",2,1,1,20,2,1,1,2945 -35,68,"[3, 4]","[40, 20]","[2, 1]",2,5,3,200,4,1,3,2229 -35,69,"[4, 3]","[0, 60]","[2, 1]",2,1,3,60,6,1,4,3947 -35,70,"[2, 1]","[30, 60]","[1, 2]",2,1,2,30,3,2,2,2636 -35,71,"[2, 1]","[30, 50]","[1, 1]",2,1,1,50,5,1,2,1961 -35,72,"[3, 4]","[40, 50]","[2, 1]",2,5,3,200,4,1,4,3612 -35,73,"[2, 1]","[60, 60]","[1, 2]",2,5,2,300,6,2,2,13487 -35,74,"[1, 2]","[80, 70]","[1, 2]",2,1,2,70,7,2,1,1896 -35,75,"[3, 4]","[80, 80]","[2, 2]",2,1,4,80,8,2,3,2162 -35,76,"[2, 1]","[70, 90]","[1, 2]",2,5,2,350,7,2,2,3430 -35,77,"[4, 3]","[60, 70]","[2, 1]",2,1,3,70,7,1,4,4997 -35,78,"[2, 1]","[60, 50]","[1, 1]",2,5,1,250,5,1,1,32246 -35,79,"[4, 3]","[40, 40]","[2, 1]",2,1,3,40,4,1,4,1861 -35,80,"[1, 2]","[20, 80]","[1, 1]",2,5,1,100,2,1,2,1771 -36,1,"[4, 3]","[70, 20]","[2, 1]",1,5,3,100,2,1,3,4074 -36,2,"[1, 2]","[10, 60]","[1, 2]",1,1,2,60,6,2,2,2525 -36,3,"[2, 1]","[50, 0]","[1, 2]",1,5,2,250,5,2,1,2894 -36,4,"[3, 4]","[30, 60]","[2, 2]",1,1,4,60,6,2,4,2725 -36,5,"[1, 2]","[30, 40]","[1, 2]",1,1,2,40,4,2,2,2308 -36,6,"[4, 3]","[80, 0]","[2, 1]",1,1,3,0,0,1,3,2442 -36,7,"[1, 2]","[0, 80]","[1, 2]",1,1,2,80,8,2,2,2177 -36,8,"[1, 2]","[20, 90]","[1, 2]",1,5,2,450,9,2,2,1992 -36,9,"[2, 1]","[70, 0]","[1, 2]",1,1,2,70,7,2,1,2458 -36,10,"[1, 2]","[50, 80]","[1, 2]",1,1,2,80,8,2,2,1876 -36,11,"[1, 2]","[50, 90]","[1, 2]",1,1,2,90,9,2,2,1827 -36,12,"[1, 2]","[40, 40]","[1, 2]",1,5,2,200,4,2,1,1805 -36,13,"[3, 4]","[20, 90]","[2, 1]",1,1,3,20,2,1,4,4377 -36,14,"[1, 2]","[40, 70]","[1, 2]",1,1,2,70,7,2,2,1992 -36,15,"[3, 4]","[50, 80]","[2, 2]",1,5,4,400,8,2,4,3591 -36,16,"[3, 4]","[0, 70]","[2, 2]",1,1,4,70,7,2,4,2275 -36,17,"[1, 2]","[40, 90]","[1, 2]",1,5,2,450,9,2,2,1908 -36,18,"[3, 4]","[60, 60]","[2, 2]",1,1,4,60,6,2,3,2242 -36,19,"[2, 1]","[60, 80]","[1, 2]",1,1,2,60,6,2,2,2443 -36,20,"[1, 2]","[70, 70]","[1, 2]",1,1,2,70,7,2,1,1856 -36,21,"[1, 2]","[70, 70]","[1, 2]",1,1,2,70,7,2,1,1760 -36,22,"[1, 2]","[70, 50]","[1, 2]",1,1,2,50,5,2,1,1759 -36,23,"[1, 2]","[70, 20]","[1, 2]",1,1,2,20,2,2,1,1758 -36,24,"[4, 3]","[30, 80]","[2, 1]",1,5,3,400,8,1,4,2390 -36,25,"[2, 1]","[50, 90]","[1, 1]",1,1,1,90,9,1,2,2391 -36,26,"[2, 1]","[70, 80]","[1, 1]",1,1,1,80,8,1,2,2214 -36,27,"[3, 4]","[80, 60]","[2, 1]",1,1,3,80,8,1,3,2456 -36,28,"[4, 3]","[20, 70]","[2, 1]",1,5,3,350,7,1,4,1941 -36,29,"[4, 3]","[0, 50]","[2, 1]",1,1,3,50,5,1,4,1975 -36,30,"[3, 4]","[60, 30]","[2, 1]",1,5,3,300,6,1,3,2171 -36,31,"[2, 1]","[40, 60]","[1, 1]",1,5,1,300,6,1,2,2177 -36,32,"[4, 3]","[50, 50]","[2, 2]",1,5,4,250,5,2,4,2358 -36,33,"[4, 3]","[80, 60]","[2, 2]",1,1,4,80,8,2,3,2074 -36,34,"[1, 2]","[30, 70]","[1, 2]",1,5,2,350,7,2,2,2465 -36,35,"[3, 4]","[50, 70]","[2, 1]",1,5,3,250,5,1,4,1960 -36,36,"[1, 2]","[40, 60]","[1, 2]",1,5,2,300,6,2,2,2258 -36,37,"[2, 1]","[80, 60]","[1, 2]",1,1,2,80,8,2,1,2260 -36,38,"[2, 1]","[80, 80]","[1, 2]",1,5,2,400,8,2,2,1793 -36,39,"[1, 2]","[40, 60]","[1, 2]",1,5,2,300,6,2,2,1943 -36,40,"[2, 1]","[70, 20]","[1, 2]",1,5,2,350,7,2,1,1976 -36,41,"[1, 2]","[50, 70]","[1, 1]",2,1,1,50,5,1,2,3518 -36,42,"[3, 4]","[50, 80]","[2, 1]",2,1,3,50,5,1,4,2693 -36,43,"[4, 3]","[70, 40]","[2, 1]",2,1,3,40,4,1,3,2143 -36,44,"[1, 2]","[0, 70]","[1, 2]",2,5,2,350,7,2,2,3008 -36,45,"[4, 3]","[80, 0]","[2, 2]",2,5,4,400,8,2,3,2607 -36,46,"[3, 4]","[10, 80]","[2, 1]",2,5,3,50,1,1,4,1894 -36,47,"[2, 1]","[70, 10]","[1, 2]",2,5,2,350,7,2,1,2491 -36,48,"[4, 3]","[60, 30]","[2, 2]",2,5,4,300,6,2,3,2373 -36,49,"[4, 3]","[30, 30]","[2, 2]",2,5,4,150,3,2,4,2210 -36,50,"[1, 2]","[10, 40]","[1, 1]",2,1,1,10,1,1,2,2393 -36,51,"[1, 2]","[10, 40]","[1, 2]",2,5,2,200,4,2,2,2179 -36,52,"[2, 1]","[30, 30]","[1, 2]",2,1,2,30,3,2,2,2559 -36,53,"[3, 4]","[30, 10]","[2, 1]",2,1,3,30,3,1,3,2359 -36,54,"[3, 4]","[40, 20]","[2, 1]",2,1,3,40,4,1,3,2141 -36,55,"[2, 1]","[0, 10]","[1, 1]",2,5,1,50,1,1,2,2506 -36,56,"[1, 2]","[0, 50]","[1, 2]",2,1,2,50,5,2,2,2326 -36,57,"[2, 1]","[30, 30]","[1, 2]",2,5,2,150,3,2,2,2143 -36,58,"[4, 3]","[30, 40]","[2, 1]",2,5,3,200,4,1,4,2221 -36,59,"[1, 2]","[20, 20]","[1, 1]",2,1,1,20,2,1,1,2321 -36,60,"[1, 2]","[30, 20]","[1, 2]",2,1,2,20,2,2,1,2175 -36,61,"[1, 2]","[0, 80]","[1, 1]",2,1,1,0,0,1,2,2326 -36,62,"[2, 1]","[70, 0]","[1, 2]",2,1,2,70,7,2,1,2443 -36,63,"[4, 3]","[90, 50]","[2, 2]",2,5,4,450,9,2,3,2624 -36,64,"[4, 3]","[50, 50]","[2, 2]",2,5,4,250,5,2,4,2042 -36,65,"[3, 4]","[20, 40]","[2, 2]",2,5,4,200,4,2,4,2224 -36,66,"[3, 4]","[20, 80]","[2, 2]",2,5,4,400,8,2,4,2022 -36,67,"[1, 2]","[70, 80]","[1, 2]",2,1,2,80,8,2,2,2425 -36,68,"[3, 4]","[40, 80]","[2, 2]",2,5,4,400,8,2,4,1993 -36,69,"[4, 3]","[80, 80]","[2, 2]",2,5,4,400,8,2,4,2376 -36,70,"[2, 1]","[60, 80]","[1, 2]",2,1,2,60,6,2,2,2240 -36,71,"[3, 4]","[90, 50]","[2, 2]",2,5,4,250,5,2,3,3692 -36,72,"[4, 3]","[70, 80]","[2, 2]",2,5,4,350,7,2,4,1993 -36,73,"[1, 2]","[60, 70]","[1, 2]",2,5,2,350,7,2,2,2291 -36,74,"[3, 4]","[70, 70]","[2, 2]",2,1,4,70,7,2,3,2191 -36,75,"[2, 1]","[80, 50]","[1, 2]",2,1,2,80,8,2,1,2159 -36,76,"[3, 4]","[60, 60]","[2, 2]",2,1,4,60,6,2,3,3425 -36,77,"[2, 1]","[80, 90]","[1, 2]",2,5,2,400,8,2,2,2460 -36,78,"[4, 3]","[80, 80]","[2, 2]",2,5,4,400,8,2,4,2108 -36,79,"[4, 3]","[50, 60]","[2, 2]",2,5,4,250,5,2,4,1976 -36,80,"[2, 1]","[80, 90]","[1, 2]",2,5,2,400,8,2,2,2241 -29,1,"[1, 2]","[90, 20]","[1, 1]",1,1,1,90,9,1,1,4489 -29,2,"[2, 1]","[30, 70]","[1, 1]",1,5,1,350,7,1,2,2137 -29,3,"[3, 4]","[50, 50]","[2, 1]",1,5,3,250,5,1,3,1958 -29,4,"[3, 4]","[20, 70]","[2, 1]",1,1,3,20,2,1,4,1861 -29,5,"[2, 1]","[90, 30]","[1, 2]",1,1,2,90,9,2,1,2054 -29,6,"[1, 2]","[0, 90]","[1, 2]",1,1,2,90,9,2,2,1760 -29,7,"[4, 3]","[90, 0]","[2, 2]",1,1,4,90,9,2,3,2060 -29,8,"[1, 2]","[20, 80]","[1, 2]",1,1,2,80,8,2,2,1910 -29,9,"[4, 3]","[80, 30]","[2, 2]",1,1,4,80,8,2,3,2077 -29,10,"[3, 4]","[60, 70]","[2, 2]",1,5,4,350,7,2,4,1909 -29,11,"[2, 1]","[60, 60]","[1, 2]",1,5,2,300,6,2,2,2594 -29,12,"[1, 2]","[60, 70]","[1, 2]",1,5,2,350,7,2,2,1844 -29,13,"[2, 1]","[70, 40]","[1, 2]",1,5,2,350,7,2,1,1878 -29,14,"[4, 3]","[90, 30]","[2, 2]",1,1,4,90,9,2,3,2163 -29,15,"[3, 4]","[10, 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-29,67,"[3, 4]","[20, 10]","[2, 2]",2,5,4,50,1,2,3,1991 -29,68,"[2, 1]","[50, 10]","[1, 1]",2,1,1,10,1,1,1,1973 -29,69,"[1, 2]","[10, 10]","[1, 2]",2,5,2,50,1,2,1,1923 -29,70,"[4, 3]","[10, 30]","[2, 2]",2,5,4,50,1,2,4,2091 -29,71,"[1, 2]","[40, 0]","[1, 2]",2,1,2,0,0,2,1,1989 -29,72,"[3, 4]","[40, 0]","[2, 1]",2,5,3,200,4,1,3,1690 -29,73,"[2, 1]","[30, 30]","[1, 1]",2,1,1,30,3,1,2,1857 -29,74,"[1, 2]","[0, 0]","[1, 1]",2,5,1,0,0,1,1,1841 -29,75,"[4, 3]","[20, 10]","[2, 2]",2,5,4,100,2,2,3,1941 -29,76,"[1, 2]","[30, 20]","[1, 2]",2,5,2,100,2,2,1,2007 -29,77,"[3, 4]","[40, 0]","[2, 2]",2,5,4,0,0,2,3,1824 -29,78,"[4, 3]","[40, 30]","[2, 1]",2,5,3,150,3,1,3,1740 -29,79,"[2, 1]","[20, 20]","[1, 1]",2,5,1,100,2,1,2,1925 -29,80,"[1, 2]","[0, 50]","[1, 1]",2,1,1,0,0,1,2,1874 -29,1,"[1, 2]","[60, 40]","[1, 2]",1,5,2,200,4,2,1,4537 -29,2,"[4, 3]","[40, 60]","[2, 2]",1,1,4,40,4,2,4,1873 -29,3,"[3, 4]","[40, 30]","[2, 1]",1,1,3,40,4,1,3,1707 -29,4,"[4, 3]","[30, 60]","[2, 2]",1,5,4,150,3,2,4,1774 -29,5,"[1, 2]","[80, 30]","[1, 1]",1,5,1,400,8,1,1,1891 -29,6,"[1, 2]","[70, 60]","[1, 1]",1,5,1,350,7,1,1,1790 -29,7,"[2, 1]","[70, 70]","[1, 1]",1,5,1,350,7,1,2,1754 -29,8,"[4, 3]","[40, 90]","[2, 1]",1,5,3,450,9,1,4,1791 -29,9,"[1, 2]","[70, 20]","[1, 1]",1,5,1,350,7,1,1,1874 -29,10,"[2, 1]","[20, 60]","[1, 1]",1,5,1,300,6,1,2,1824 -29,11,"[4, 3]","[50, 70]","[2, 1]",1,1,3,70,7,1,4,1741 -29,12,"[4, 3]","[50, 70]","[2, 1]",1,1,3,70,7,1,4,1690 -29,13,"[1, 2]","[50, 50]","[1, 1]",1,1,1,50,5,1,1,1807 -29,14,"[1, 2]","[60, 60]","[1, 1]",1,5,1,300,6,1,1,1824 -29,15,"[1, 2]","[90, 40]","[1, 1]",1,1,1,90,9,1,1,1856 -29,16,"[1, 2]","[80, 10]","[1, 1]",1,5,1,400,8,1,1,1790 -29,17,"[4, 3]","[60, 70]","[2, 1]",1,1,3,70,7,1,4,1757 -29,18,"[4, 3]","[30, 90]","[2, 1]",1,1,3,90,9,1,4,1774 -29,19,"[2, 1]","[20, 90]","[1, 1]",1,1,1,90,9,1,2,1807 -29,20,"[4, 3]","[0, 60]","[2, 1]",1,5,3,300,6,1,4,1741 -29,21,"[2, 1]","[10, 20]","[1, 1]",1,1,1,20,2,1,2,1858 -29,22,"[3, 4]","[40, 20]","[2, 2]",1,5,4,100,2,2,3,1724 -29,23,"[2, 1]","[40, 10]","[1, 2]",1,1,2,40,4,2,1,1673 -29,24,"[3, 4]","[30, 20]","[2, 2]",1,5,4,100,2,2,3,1824 -29,25,"[4, 3]","[30, 20]","[2, 1]",1,5,3,100,2,1,3,1857 -29,26,"[3, 4]","[10, 10]","[2, 2]",1,1,4,10,1,2,3,1674 -29,27,"[4, 3]","[20, 10]","[2, 1]",1,5,3,50,1,1,3,1673 -29,28,"[1, 2]","[10, 10]","[1, 1]",1,1,1,10,1,1,1,1891 -29,29,"[2, 1]","[0, 10]","[1, 2]",1,1,2,0,0,2,2,1957 -29,30,"[3, 4]","[10, 30]","[2, 1]",1,1,3,10,1,1,4,3307 -29,31,"[4, 3]","[20, 30]","[2, 1]",1,5,3,150,3,1,4,1790 -29,32,"[1, 2]","[0, 50]","[1, 1]",1,5,1,0,0,1,2,2024 -29,33,"[2, 1]","[30, 10]","[1, 2]",1,1,2,30,3,2,1,2174 -29,34,"[4, 3]","[40, 0]","[2, 2]",1,5,4,200,4,2,3,1687 -29,35,"[4, 3]","[10, 0]","[2, 2]",1,1,4,10,1,2,3,1757 -29,36,"[2, 1]","[0, 40]","[1, 1]",1,5,1,200,4,1,2,1841 -29,37,"[2, 1]","[10, 60]","[1, 1]",1,1,1,60,6,1,2,1757 -29,38,"[1, 2]","[40, 20]","[1, 1]",1,1,1,40,4,1,1,1857 -29,39,"[1, 2]","[30, 0]","[1, 2]",1,1,2,0,0,2,1,1823 -29,40,"[1, 2]","[30, 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-29,58,"[4, 3]","[50, 50]","[2, 1]",2,5,3,250,5,1,4,4157 -29,59,"[2, 1]","[50, 70]","[1, 1]",2,5,1,350,7,1,2,1757 -29,60,"[3, 4]","[80, 50]","[2, 1]",2,1,3,80,8,1,3,2140 -29,61,"[2, 1]","[50, 70]","[1, 1]",2,1,1,70,7,1,2,1774 -29,62,"[4, 3]","[70, 80]","[2, 1]",2,1,3,80,8,1,4,1741 -29,63,"[4, 3]","[80, 60]","[2, 1]",2,1,3,60,6,1,3,1724 -29,64,"[2, 1]","[90, 30]","[1, 1]",2,1,1,30,3,1,1,1841 -29,65,"[3, 4]","[10, 90]","[2, 2]",2,5,4,450,9,2,4,2090 -29,66,"[3, 4]","[40, 90]","[2, 2]",2,1,4,90,9,2,4,1824 -29,67,"[3, 4]","[30, 80]","[2, 2]",2,1,4,80,8,2,4,1723 -29,68,"[4, 3]","[90, 30]","[2, 2]",2,1,4,90,9,2,3,1790 -29,69,"[3, 4]","[20, 80]","[2, 2]",2,1,4,80,8,2,4,1806 -29,70,"[3, 4]","[10, 80]","[2, 2]",2,5,4,400,8,2,4,1774 -29,71,"[4, 3]","[70, 20]","[2, 2]",2,1,4,70,7,2,3,1824 -29,72,"[2, 1]","[90, 20]","[1, 2]",2,1,2,90,9,2,1,2074 -29,73,"[1, 2]","[10, 50]","[1, 2]",2,1,2,50,5,2,2,1725 -29,74,"[3, 4]","[20, 30]","[2, 2]",2,5,4,150,3,2,4,1874 -29,75,"[1, 2]","[30, 20]","[1, 1]",2,1,1,30,3,1,1,2407 -29,76,"[1, 2]","[50, 10]","[1, 2]",2,5,2,50,1,2,1,1874 -29,77,"[1, 2]","[40, 20]","[1, 1]",2,1,1,40,4,1,1,2207 -29,78,"[1, 2]","[50, 20]","[1, 1]",2,1,1,50,5,1,1,1857 -29,79,"[2, 1]","[0, 40]","[1, 1]",2,5,1,200,4,1,2,2040 -29,80,"[3, 4]","[40, 30]","[2, 1]",2,5,3,200,4,1,3,1791 -29,1,"[1, 2]","[30, 80]","[1, 1]",1,1,1,30,3,1,2,4554 -29,2,"[1, 2]","[20, 80]","[1, 2]",1,1,2,80,8,2,2,1740 -29,3,"[3, 4]","[10, 60]","[2, 2]",1,5,4,300,6,2,4,2553 -29,4,"[4, 3]","[90, 10]","[2, 2]",1,5,4,450,9,2,3,1754 -29,5,"[2, 1]","[70, 0]","[1, 2]",1,5,2,350,7,2,1,1907 -29,6,"[1, 2]","[20, 50]","[1, 2]",1,1,2,50,5,2,2,1807 -29,7,"[1, 2]","[10, 40]","[1, 2]",1,5,2,200,4,2,2,1774 -29,8,"[2, 1]","[60, 10]","[1, 2]",1,5,2,300,6,2,1,1740 -29,9,"[2, 1]","[80, 10]","[1, 2]",1,1,2,80,8,2,1,1823 -29,10,"[2, 1]","[70, 10]","[1, 2]",1,1,2,70,7,2,1,1757 -29,11,"[1, 2]","[30, 40]","[1, 2]",1,1,2,40,4,2,2,1807 -29,12,"[4, 3]","[40, 20]","[2, 1]",1,1,3,20,2,1,3,1840 -29,13,"[4, 3]","[50, 30]","[2, 2]",1,1,4,50,5,2,3,1724 -29,14,"[4, 3]","[10, 10]","[2, 2]",1,1,4,10,1,2,4,1741 -29,15,"[4, 3]","[0, 20]","[2, 1]",1,5,3,100,2,1,4,1641 -29,16,"[2, 1]","[20, 20]","[1, 1]",1,5,1,100,2,1,2,1974 -29,17,"[3, 4]","[20, 20]","[2, 1]",1,5,3,100,2,1,3,1758 -29,18,"[4, 3]","[40, 40]","[2, 1]",1,5,3,200,4,1,4,1790 -29,19,"[1, 2]","[50, 20]","[1, 1]",1,5,1,250,5,1,1,1874 -29,20,"[3, 4]","[50, 10]","[2, 1]",1,1,3,50,5,1,3,1758 -29,21,"[2, 1]","[30, 60]","[1, 1]",1,5,1,300,6,1,2,2006 -29,22,"[3, 4]","[30, 20]","[2, 1]",1,1,3,30,3,1,3,1774 -29,23,"[1, 2]","[30, 30]","[1, 2]",1,1,2,30,3,2,1,1770 -29,24,"[4, 3]","[0, 30]","[2, 2]",1,1,4,0,0,2,4,1537 -29,25,"[1, 2]","[20, 20]","[1, 1]",1,5,1,100,2,1,1,1957 -29,26,"[1, 2]","[60, 30]","[1, 1]",1,5,1,300,6,1,1,1720 -29,27,"[2, 1]","[40, 30]","[1, 1]",1,1,1,30,3,1,1,1823 -29,28,"[2, 1]","[50, 50]","[1, 1]",1,5,1,250,5,1,2,1740 -29,29,"[3, 4]","[30, 40]","[2, 1]",1,5,3,150,3,1,4,2023 -29,30,"[2, 1]","[70, 40]","[1, 1]",1,1,1,40,4,1,1,1974 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2]",2,5,4,300,6,2,3,1940 -29,49,"[4, 3]","[70, 10]","[2, 2]",2,1,4,70,7,2,3,1787 -29,50,"[1, 2]","[10, 50]","[1, 2]",2,1,2,50,5,2,2,2040 -29,51,"[2, 1]","[40, 30]","[1, 2]",2,1,2,40,4,2,1,1840 -29,52,"[4, 3]","[20, 40]","[2, 2]",2,5,4,100,2,2,4,1906 -29,53,"[4, 3]","[30, 40]","[2, 2]",2,1,4,30,3,2,4,1824 -29,54,"[2, 1]","[20, 50]","[1, 2]",2,5,2,100,2,2,2,1754 -29,55,"[1, 2]","[60, 10]","[1, 2]",2,1,2,10,1,2,1,1756 -29,56,"[3, 4]","[40, 20]","[2, 1]",2,5,3,200,4,1,3,1741 -29,57,"[3, 4]","[50, 30]","[2, 1]",2,1,3,50,5,1,3,1741 -29,58,"[3, 4]","[30, 40]","[2, 1]",2,5,3,150,3,1,4,1741 -29,59,"[1, 2]","[30, 40]","[1, 1]",2,5,1,150,3,1,2,1891 -29,60,"[4, 3]","[30, 20]","[2, 1]",2,5,3,100,2,1,3,1706 -29,61,"[3, 4]","[40, 20]","[2, 1]",2,5,3,200,4,1,3,1824 -29,62,"[3, 4]","[20, 20]","[2, 1]",2,1,3,20,2,1,3,1741 -29,63,"[4, 3]","[20, 30]","[2, 1]",2,5,3,150,3,1,4,1840 -29,64,"[2, 1]","[40, 60]","[1, 1]",2,1,1,60,6,1,2,1790 -29,65,"[1, 2]","[80, 60]","[1, 1]",2,5,1,400,8,1,1,2007 -29,66,"[1, 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1]",1,5,1,150,3,1,2,1926 -28,22,"[1, 2]","[30, 40]","[1, 1]",1,1,1,30,3,1,2,1759 -28,23,"[4, 3]","[30, 50]","[2, 1]",1,5,3,250,5,1,4,3336 -28,24,"[2, 1]","[10, 80]","[1, 1]",1,1,1,80,8,1,2,2435 -28,25,"[1, 2]","[70, 10]","[1, 1]",1,1,1,70,7,1,1,1875 -28,26,"[2, 1]","[20, 60]","[1, 1]",1,1,1,60,6,1,2,1801 -28,27,"[4, 3]","[20, 80]","[2, 1]",1,5,3,400,8,1,4,1927 -28,28,"[4, 3]","[20, 80]","[2, 1]",1,1,3,80,8,1,4,1776 -28,29,"[1, 2]","[70, 10]","[1, 1]",1,1,1,70,7,1,1,2252 -28,30,"[3, 4]","[90, 10]","[2, 1]",1,5,3,450,9,1,3,1859 -28,31,"[4, 3]","[20, 80]","[2, 1]",1,5,3,400,8,1,4,2041 -28,32,"[4, 3]","[10, 80]","[2, 1]",1,1,3,80,8,1,4,1793 -28,33,"[2, 1]","[20, 70]","[1, 1]",1,1,1,70,7,1,2,1808 -28,34,"[3, 4]","[50, 40]","[2, 1]",1,5,3,250,5,1,3,1951 -28,35,"[3, 4]","[30, 60]","[2, 1]",1,1,3,30,3,1,4,1826 -28,36,"[1, 2]","[60, 30]","[1, 2]",1,1,2,30,3,2,1,1816 -28,37,"[2, 1]","[40, 80]","[1, 2]",1,1,2,40,4,2,2,1925 -28,38,"[1, 2]","[80, 40]","[1, 2]",1,5,2,200,4,2,1,2024 -28,39,"[4, 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1]",1,1,3,50,5,1,3,6001 -38,21,"[3, 4]","[60, 40]","[2, 1]",1,1,3,60,6,1,3,4300 -38,22,"[4, 3]","[10, 30]","[2, 2]",1,1,4,10,1,2,4,21353 -38,23,"[3, 4]","[20, 10]","[2, 1]",1,1,3,20,2,1,3,4101 -38,24,"[4, 3]","[20, 40]","[2, 2]",1,5,4,100,2,2,4,4451 -38,25,"[2, 1]","[0, 40]","[1, 1]",1,5,1,200,4,1,2,3401 -38,26,"[4, 3]","[10, 40]","[2, 2]",1,5,4,50,1,2,4,3650 -38,27,"[2, 1]","[10, 30]","[1, 1]",1,5,1,150,3,1,2,3050 -38,28,"[2, 1]","[40, 40]","[1, 1]",1,1,1,40,4,1,2,3084 -38,29,"[3, 4]","[70, 0]","[2, 1]",1,5,3,350,7,1,3,23704 -38,30,"[1, 2]","[90, 10]","[1, 2]",1,5,2,50,1,2,1,2985 -38,31,"[2, 1]","[0, 60]","[1, 2]",1,1,2,0,0,2,2,4818 -38,32,"[3, 4]","[50, 10]","[2, 2]",1,5,4,50,1,2,3,5468 -38,33,"[1, 2]","[50, 20]","[1, 1]",1,5,1,250,5,1,1,6485 -38,34,"[1, 2]","[40, 20]","[1, 1]",1,5,1,200,4,1,1,2817 -38,35,"[1, 2]","[70, 10]","[1, 1]",1,5,1,350,7,1,1,2601 -38,36,"[4, 3]","[20, 80]","[2, 2]",1,1,4,20,2,2,4,3701 -38,37,"[3, 4]","[70, 10]","[2, 1]",1,5,3,350,7,1,3,20686 -38,38,"[2, 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-38,56,"[2, 1]","[60, 70]","[1, 2]",2,1,2,60,6,2,2,13518 -38,57,"[3, 4]","[90, 60]","[2, 2]",2,5,4,300,6,2,3,4501 -38,58,"[4, 3]","[80, 60]","[2, 2]",2,1,4,80,8,2,3,8017 -38,59,"[3, 4]","[70, 50]","[2, 2]",2,5,4,250,5,2,3,3334 -38,60,"[2, 1]","[30, 50]","[1, 1]",2,1,1,50,5,1,2,4135 -38,61,"[2, 1]","[30, 70]","[1, 1]",2,1,1,70,7,1,2,7785 -38,62,"[2, 1]","[30, 70]","[1, 2]",2,5,2,150,3,2,2,3117 -38,63,"[3, 4]","[70, 30]","[2, 2]",2,5,4,150,3,2,3,2501 -38,64,"[1, 2]","[80, 50]","[1, 1]",2,1,1,80,8,1,1,4318 -38,65,"[3, 4]","[80, 40]","[2, 2]",2,5,4,200,4,2,3,2784 -38,66,"[4, 3]","[30, 70]","[2, 2]",2,5,4,150,3,2,4,3135 -38,67,"[2, 1]","[50, 80]","[1, 2]",2,1,2,50,5,2,2,3467 -38,68,"[2, 1]","[70, 70]","[1, 2]",2,5,2,350,7,2,2,6867 -38,69,"[4, 3]","[90, 50]","[2, 2]",2,5,4,450,9,2,3,4184 -38,70,"[4, 3]","[80, 50]","[2, 2]",2,5,4,400,8,2,3,6068 -38,71,"[1, 2]","[50, 80]","[1, 2]",2,5,2,400,8,2,2,2951 -38,72,"[2, 1]","[80, 50]","[1, 2]",2,1,2,80,8,2,1,3201 -38,73,"[4, 3]","[70, 50]","[2, 2]",2,1,4,70,7,2,3,2834 -38,74,"[3, 4]","[70, 80]","[2, 2]",2,5,4,400,8,2,4,2917 -38,75,"[2, 1]","[80, 80]","[1, 1]",2,1,1,80,8,1,2,3484 -38,76,"[4, 3]","[70, 70]","[2, 1]",2,1,3,70,7,1,4,4218 -38,77,"[2, 1]","[70, 60]","[1, 2]",2,1,2,70,7,2,1,4417 -38,78,"[1, 2]","[80, 70]","[1, 2]",2,5,2,350,7,2,1,6567 -38,79,"[2, 1]","[70, 90]","[1, 2]",2,5,2,350,7,2,2,2884 -38,80,"[1, 2]","[90, 30]","[1, 2]",2,5,2,150,3,2,1,3167 -39,1,"[3, 4]","[70, 40]","[2, 2]",1,1,4,40,4,2,3,24361 -39,2,"[2, 1]","[20, 80]","[1, 1]",1,5,1,400,8,1,2,9276 -39,3,"[4, 3]","[30, 70]","[2, 2]",1,1,4,30,3,2,4,4566 -39,4,"[4, 3]","[50, 70]","[2, 2]",1,1,4,50,5,2,4,5493 -39,5,"[2, 1]","[80, 50]","[1, 2]",1,1,2,80,8,2,1,5292 -39,6,"[1, 2]","[40, 70]","[1, 2]",1,5,2,350,7,2,2,6103 -39,7,"[1, 2]","[20, 90]","[1, 2]",1,5,2,450,9,2,2,7864 -39,8,"[2, 1]","[80, 10]","[1, 2]",1,1,2,80,8,2,1,6012 -39,9,"[2, 1]","[60, 0]","[1, 2]",1,5,2,300,6,2,1,3746 -39,10,"[3, 4]","[10, 60]","[2, 2]",1,1,4,60,6,2,4,5340 -39,11,"[2, 1]","[50, 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1]",2,1,1,60,6,1,1,1788 -41,64,"[4, 3]","[20, 60]","[2, 1]",2,1,3,60,6,1,4,2139 -41,65,"[3, 4]","[80, 10]","[2, 1]",2,5,3,400,8,1,3,1956 -41,66,"[3, 4]","[90, 20]","[2, 1]",2,1,3,90,9,1,3,2072 -41,67,"[3, 4]","[80, 40]","[2, 1]",2,5,3,400,8,1,3,1872 -41,68,"[3, 4]","[80, 40]","[2, 1]",2,1,3,80,8,1,3,1839 -41,69,"[4, 3]","[10, 60]","[2, 1]",2,5,3,300,6,1,4,1989 -41,70,"[1, 2]","[80, 0]","[1, 1]",2,1,1,80,8,1,1,2240 -41,71,"[2, 1]","[10, 30]","[1, 1]",2,1,1,30,3,1,2,1855 -41,72,"[1, 2]","[20, 0]","[1, 2]",2,1,2,0,0,2,1,2122 -41,73,"[4, 3]","[0, 10]","[2, 1]",2,5,3,50,1,1,4,1975 -41,74,"[1, 2]","[30, 40]","[1, 1]",2,5,1,150,3,1,2,2491 -41,75,"[2, 1]","[40, 0]","[1, 2]",2,1,2,40,4,2,1,1856 -41,76,"[3, 4]","[0, 10]","[2, 2]",2,1,4,10,1,2,4,2239 -41,77,"[2, 1]","[0, 20]","[1, 1]",2,1,1,20,2,1,2,2339 -41,78,"[2, 1]","[40, 20]","[1, 1]",2,5,1,100,2,1,1,2356 -41,79,"[2, 1]","[80, 30]","[1, 1]",2,1,1,30,3,1,1,2272 -41,80,"[3, 4]","[0, 80]","[2, 1]",2,5,3,0,0,1,4,2072 -29,1,"[2, 1]","[40, 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2]","[50, 60]","[1, 2]",1,1,2,60,6,2,2,1924 -29,20,"[3, 4]","[60, 80]","[2, 2]",1,5,4,400,8,2,4,1907 -29,21,"[2, 1]","[50, 80]","[1, 2]",1,1,2,50,5,2,2,2173 -29,22,"[1, 2]","[60, 30]","[1, 2]",1,1,2,30,3,2,1,2023 -29,23,"[2, 1]","[50, 20]","[1, 1]",1,5,1,100,2,1,1,1940 -29,24,"[2, 1]","[10, 0]","[1, 2]",1,5,2,50,1,2,1,2473 -29,25,"[1, 2]","[0, 50]","[1, 1]",1,5,1,0,0,1,2,3057 -29,26,"[2, 1]","[60, 20]","[1, 2]",1,1,2,60,6,2,1,2008 -29,27,"[2, 1]","[40, 0]","[1, 2]",1,1,2,40,4,2,1,2290 -29,28,"[2, 1]","[70, 20]","[1, 2]",1,5,2,350,7,2,1,1840 -29,29,"[1, 2]","[40, 80]","[1, 2]",1,5,2,400,8,2,2,1941 -29,30,"[1, 2]","[20, 80]","[1, 2]",1,5,2,400,8,2,2,1990 -29,31,"[4, 3]","[70, 40]","[2, 2]",1,5,4,350,7,2,3,2057 -29,32,"[3, 4]","[50, 70]","[2, 2]",1,5,4,350,7,2,4,1924 -29,33,"[3, 4]","[60, 60]","[2, 2]",1,1,4,60,6,2,3,1941 -29,34,"[2, 1]","[60, 80]","[1, 2]",1,5,2,300,6,2,2,1990 -29,35,"[3, 4]","[90, 60]","[2, 2]",1,5,4,300,6,2,3,1840 -29,36,"[1, 2]","[80, 50]","[1, 2]",1,5,2,250,5,2,1,2024 -29,37,"[2, 1]","[40, 70]","[1, 2]",1,1,2,40,4,2,2,1940 -29,38,"[2, 1]","[0, 80]","[1, 1]",1,5,1,400,8,1,2,1990 -29,39,"[4, 3]","[10, 80]","[2, 1]",1,1,3,80,8,1,4,1807 -29,40,"[2, 1]","[0, 90]","[1, 1]",1,5,1,450,9,1,2,2173 -29,41,"[3, 4]","[80, 10]","[2, 1]",2,1,3,80,8,1,3,2686 -29,42,"[4, 3]","[10, 70]","[2, 1]",2,5,3,350,7,1,4,1790 -29,43,"[2, 1]","[20, 40]","[1, 1]",2,1,1,40,4,1,2,1956 -29,44,"[1, 2]","[60, 20]","[1, 1]",2,1,1,60,6,1,1,1956 -29,45,"[4, 3]","[0, 50]","[2, 1]",2,5,3,250,5,1,4,3640 -29,46,"[4, 3]","[40, 30]","[2, 1]",2,5,3,150,3,1,3,1836 -29,47,"[1, 2]","[30, 20]","[1, 2]",2,5,2,100,2,2,1,2108 -29,48,"[1, 2]","[10, 10]","[1, 1]",2,5,1,50,1,1,1,1840 -29,49,"[3, 4]","[10, 50]","[2, 2]",2,1,4,50,5,2,4,1840 -29,50,"[4, 3]","[30, 10]","[2, 2]",2,5,4,150,3,2,3,1939 -29,51,"[2, 1]","[10, 0]","[1, 2]",2,1,2,10,1,2,1,1874 -29,52,"[1, 2]","[30, 50]","[1, 1]",2,5,1,150,3,1,2,3139 -29,53,"[2, 1]","[40, 60]","[1, 1]",2,1,1,60,6,1,2,1891 -29,54,"[2, 1]","[40, 80]","[1, 1]",2,5,1,400,8,1,2,1874 -29,55,"[3, 4]","[80, 20]","[2, 1]",2,1,3,80,8,1,3,2058 -29,56,"[2, 1]","[0, 80]","[1, 1]",2,1,1,80,8,1,2,1874 -29,57,"[4, 3]","[10, 80]","[2, 1]",2,5,3,400,8,1,4,1873 -29,58,"[3, 4]","[70, 20]","[2, 1]",2,1,3,70,7,1,3,1824 -29,59,"[1, 2]","[80, 10]","[1, 1]",2,5,1,400,8,1,1,6242 -29,60,"[2, 1]","[0, 90]","[1, 1]",2,5,1,450,9,1,2,1857 -29,61,"[3, 4]","[70, 40]","[2, 1]",2,5,3,350,7,1,3,2274 -29,62,"[3, 4]","[60, 60]","[2, 1]",2,1,3,60,6,1,3,2141 -29,63,"[3, 4]","[90, 70]","[2, 1]",2,5,3,450,9,1,3,1940 -29,64,"[3, 4]","[60, 70]","[2, 1]",2,1,3,60,6,1,4,1890 -29,65,"[3, 4]","[80, 80]","[2, 1]",2,5,3,400,8,1,3,1870 -29,66,"[1, 2]","[70, 80]","[1, 1]",2,5,1,350,7,1,2,2057 -29,67,"[1, 2]","[90, 70]","[1, 1]",2,5,1,450,9,1,1,2007 -29,68,"[2, 1]","[50, 80]","[1, 1]",2,1,1,80,8,1,2,2140 -29,69,"[3, 4]","[90, 60]","[2, 1]",2,1,3,90,9,1,3,2074 -29,70,"[2, 1]","[30, 80]","[1, 1]",2,1,1,80,8,1,2,1991 -29,71,"[3, 4]","[80, 20]","[2, 1]",2,1,3,80,8,1,3,2041 -29,72,"[4, 3]","[50, 80]","[2, 1]",2,1,3,80,8,1,4,1804 -29,73,"[1, 2]","[90, 30]","[1, 1]",2,1,1,90,9,1,1,1990 -29,74,"[2, 1]","[20, 50]","[1, 1]",2,1,1,50,5,1,2,2124 -29,75,"[1, 2]","[40, 30]","[1, 1]",2,1,1,40,4,1,1,2107 -29,76,"[2, 1]","[30, 10]","[1, 2]",2,1,2,30,3,2,1,1974 -29,77,"[4, 3]","[70, 10]","[2, 1]",2,5,3,50,1,1,3,1793 -29,78,"[1, 2]","[40, 80]","[1, 2]",2,1,2,80,8,2,2,2140 -29,79,"[4, 3]","[90, 20]","[2, 2]",2,5,4,450,9,2,3,2023 -29,80,"[1, 2]","[10, 80]","[1, 2]",2,1,2,80,8,2,2,2208 -42,1,"[4, 3]","[40, 80]","[2, 1]",1,5,3,400,8,1,4,4694 -42,2,"[3, 4]","[80, 40]","[2, 1]",1,5,3,400,8,1,3,3631 -42,3,"[2, 1]","[50, 80]","[1, 1]",1,5,1,400,8,1,2,2302 -42,4,"[3, 4]","[80, 40]","[2, 1]",1,5,3,400,8,1,3,3400 -42,5,"[1, 2]","[70, 30]","[1, 2]",1,5,2,150,3,2,1,2457 -42,6,"[3, 4]","[20, 40]","[2, 2]",1,5,4,200,4,2,4,3896 -42,7,"[2, 1]","[20, 10]","[1, 2]",1,5,2,100,2,2,1,2545 -42,8,"[4, 3]","[10, 10]","[2, 1]",1,1,3,10,1,1,4,2365 -42,9,"[2, 1]","[0, 30]","[1, 2]",1,5,2,0,0,2,2,2067 -42,10,"[4, 3]","[40, 20]","[2, 1]",1,5,3,100,2,1,3,3226 -42,11,"[1, 2]","[10, 30]","[1, 1]",1,5,1,50,1,1,2,2128 -42,12,"[1, 2]","[20, 40]","[1, 2]",1,5,2,200,4,2,2,1884 -42,13,"[3, 4]","[30, 10]","[2, 1]",1,5,3,150,3,1,3,2246 -42,14,"[4, 3]","[10, 50]","[2, 1]",1,1,3,50,5,1,4,2019 -42,15,"[2, 1]","[30, 60]","[1, 2]",1,5,2,150,3,2,2,2052 -42,16,"[1, 2]","[90, 50]","[1, 2]",1,1,2,50,5,2,1,3443 -42,17,"[2, 1]","[50, 70]","[1, 2]",1,5,2,250,5,2,2,2083 -42,18,"[1, 2]","[80, 30]","[1, 2]",1,1,2,30,3,2,1,2102 -42,19,"[1, 2]","[60, 40]","[1, 1]",1,5,1,300,6,1,1,2507 -42,20,"[1, 2]","[20, 50]","[1, 2]",1,1,2,50,5,2,2,1959 -42,21,"[2, 1]","[40, 50]","[1, 2]",1,1,2,40,4,2,2,1988 -42,22,"[3, 4]","[50, 30]","[2, 2]",1,1,4,30,3,2,3,1884 -42,23,"[1, 2]","[40, 40]","[1, 1]",1,1,1,40,4,1,1,2126 -42,24,"[2, 1]","[30, 50]","[1, 1]",1,1,1,50,5,1,2,2108 -42,25,"[1, 2]","[50, 10]","[1, 1]",1,1,1,50,5,1,1,2648 -42,26,"[4, 3]","[10, 40]","[2, 1]",1,5,3,200,4,1,4,1918 -42,27,"[1, 2]","[50, 30]","[1, 1]",1,5,1,250,5,1,1,2850 -42,28,"[2, 1]","[60, 90]","[1, 1]",1,1,1,90,9,1,2,2319 -42,29,"[4, 3]","[50, 70]","[2, 2]",1,5,4,250,5,2,4,2464 -42,30,"[3, 4]","[80, 70]","[2, 2]",1,1,4,70,7,2,3,2167 -42,31,"[4, 3]","[60, 70]","[2, 2]",1,5,4,300,6,2,4,1863 -42,32,"[2, 1]","[80, 70]","[1, 1]",1,5,1,350,7,1,1,2079 -42,33,"[4, 3]","[50, 80]","[2, 2]",1,5,4,250,5,2,4,2132 -42,34,"[4, 3]","[60, 90]","[2, 1]",1,1,3,90,9,1,4,1953 -42,35,"[2, 1]","[60, 70]","[1, 2]",1,5,2,300,6,2,2,2101 -42,36,"[1, 2]","[90, 60]","[1, 2]",1,5,2,300,6,2,1,5107 -42,37,"[4, 3]","[70, 70]","[2, 2]",1,1,4,70,7,2,4,3049 -42,38,"[4, 3]","[70, 70]","[2, 1]",1,1,3,70,7,1,4,2107 -42,39,"[1, 2]","[50, 60]","[1, 1]",1,5,1,250,5,1,2,2029 -42,40,"[1, 2]","[60, 70]","[1, 2]",1,1,2,70,7,2,2,2002 -42,41,"[1, 2]","[50, 70]","[1, 1]",2,1,1,50,5,1,2,12033 -42,42,"[4, 3]","[80, 60]","[2, 1]",2,5,3,300,6,1,3,2030 -42,43,"[1, 2]","[50, 80]","[1, 1]",2,5,1,250,5,1,2,2204 -42,44,"[1, 2]","[20, 60]","[1, 2]",2,5,2,300,6,2,2,1859 -42,45,"[2, 1]","[70, 10]","[1, 2]",2,1,2,70,7,2,1,2456 -42,46,"[3, 4]","[30, 70]","[2, 2]",2,1,4,70,7,2,4,2062 -42,47,"[3, 4]","[20, 20]","[2, 1]",2,5,3,100,2,1,3,2273 -42,48,"[2, 1]","[40, 50]","[1, 1]",2,1,1,50,5,1,2,1897 -42,49,"[4, 3]","[60, 70]","[2, 2]",2,1,4,60,6,2,4,1988 -42,50,"[3, 4]","[90, 50]","[2, 2]",2,5,4,250,5,2,3,2095 -42,51,"[1, 2]","[80, 50]","[1, 1]",2,1,1,80,8,1,1,1888 -42,52,"[3, 4]","[60, 50]","[2, 2]",2,1,4,50,5,2,3,1955 -42,53,"[1, 2]","[80, 50]","[1, 1]",2,5,1,400,8,1,1,3130 -42,54,"[3, 4]","[90, 50]","[2, 2]",2,1,4,50,5,2,3,2137 -42,55,"[4, 3]","[70, 70]","[2, 2]",2,1,4,70,7,2,4,5186 -42,56,"[3, 4]","[60, 50]","[2, 2]",2,1,4,50,5,2,3,2010 -42,57,"[2, 1]","[60, 40]","[1, 2]",2,5,2,300,6,2,1,2052 -42,58,"[2, 1]","[80, 20]","[1, 1]",2,1,1,20,2,1,1,2003 -42,59,"[3, 4]","[20, 80]","[2, 1]",2,1,3,20,2,1,4,2047 -42,60,"[4, 3]","[80, 20]","[2, 1]",2,5,3,100,2,1,3,2029 -42,61,"[2, 1]","[80, 0]","[1, 1]",2,1,1,0,0,1,1,2760 -42,62,"[2, 1]","[90, 30]","[1, 2]",2,5,2,450,9,2,1,2110 -42,63,"[1, 2]","[30, 90]","[1, 2]",2,1,2,90,9,2,2,2026 -42,64,"[4, 3]","[90, 10]","[2, 2]",2,5,4,450,9,2,3,1865 -42,65,"[1, 2]","[20, 60]","[1, 2]",2,1,2,60,6,2,2,1907 -42,66,"[2, 1]","[50, 10]","[1, 2]",2,5,2,250,5,2,1,2483 -42,67,"[3, 4]","[0, 50]","[2, 2]",2,1,4,50,5,2,4,2284 -42,68,"[1, 2]","[20, 60]","[1, 1]",2,1,1,20,2,1,2,2056 -42,69,"[2, 1]","[40, 30]","[1, 1]",2,1,1,30,3,1,1,2147 -42,70,"[4, 3]","[60, 30]","[2, 2]",2,5,4,300,6,2,3,2995 -42,71,"[1, 2]","[20, 60]","[1, 2]",2,1,2,60,6,2,2,2626 -42,72,"[2, 1]","[30, 0]","[1, 2]",2,5,2,150,3,2,1,2161 -42,73,"[4, 3]","[20, 0]","[2, 1]",2,1,3,0,0,1,3,2080 -42,74,"[4, 3]","[30, 30]","[2, 2]",2,1,4,30,3,2,4,2194 -42,75,"[3, 4]","[30, 20]","[2, 2]",2,5,4,100,2,2,3,2002 -42,76,"[2, 1]","[20, 30]","[1, 2]",2,5,2,100,2,2,2,2329 -42,77,"[3, 4]","[60, 50]","[2, 2]",2,1,4,50,5,2,3,2013 -42,78,"[3, 4]","[70, 70]","[2, 1]",2,1,3,70,7,1,3,1984 -42,79,"[1, 2]","[60, 90]","[1, 2]",2,1,2,90,9,2,2,1935 -42,80,"[2, 1]","[60, 60]","[1, 2]",2,5,2,300,6,2,2,1994 -29,1,"[1, 2]","[30, 80]","[1, 1]",1,1,1,30,3,1,2,10140 -29,2,"[1, 2]","[20, 80]","[1, 2]",1,1,2,80,8,2,2,6023 -29,3,"[3, 4]","[10, 60]","[2, 2]",1,5,4,300,6,2,4,2623 -29,4,"[4, 3]","[90, 10]","[2, 2]",1,5,4,450,9,2,3,1989 -29,5,"[2, 1]","[70, 0]","[1, 2]",1,5,2,350,7,2,1,2156 -29,6,"[1, 2]","[20, 50]","[1, 2]",1,1,2,50,5,2,2,2107 -29,7,"[1, 2]","[10, 40]","[1, 2]",1,5,2,200,4,2,2,2357 -29,8,"[2, 1]","[60, 10]","[1, 2]",1,5,2,300,6,2,1,1974 -29,9,"[2, 1]","[80, 10]","[1, 2]",1,1,2,80,8,2,1,2073 -29,10,"[2, 1]","[70, 10]","[1, 2]",1,1,2,70,7,2,1,1989 -29,11,"[1, 2]","[30, 40]","[1, 2]",1,1,2,40,4,2,2,2206 -29,12,"[4, 3]","[40, 20]","[2, 2]",1,1,4,40,4,2,3,2553 -29,13,"[4, 3]","[50, 30]","[2, 2]",1,1,4,50,5,2,3,2090 -29,14,"[4, 3]","[10, 10]","[2, 2]",1,1,4,10,1,2,4,2520 -29,15,"[4, 3]","[0, 20]","[2, 1]",1,5,3,100,2,1,4,1789 -29,16,"[2, 1]","[20, 20]","[1, 1]",1,5,1,100,2,1,2,2056 -29,17,"[3, 4]","[20, 20]","[2, 1]",1,5,3,100,2,1,3,2056 -29,18,"[4, 3]","[40, 40]","[2, 2]",1,5,4,200,4,2,4,1805 -29,19,"[1, 2]","[50, 20]","[1, 2]",1,5,2,100,2,2,1,1972 -29,20,"[3, 4]","[50, 10]","[2, 1]",1,1,3,50,5,1,3,1956 -29,21,"[2, 1]","[30, 60]","[1, 1]",1,5,1,300,6,1,2,1939 -29,22,"[3, 4]","[30, 20]","[2, 1]",1,1,3,30,3,1,3,1906 -29,23,"[1, 2]","[30, 30]","[1, 2]",1,1,2,30,3,2,1,2390 -29,24,"[4, 3]","[0, 30]","[2, 1]",1,1,3,30,3,1,4,1789 -29,25,"[1, 2]","[20, 20]","[1, 1]",1,5,1,100,2,1,1,1906 -29,26,"[1, 2]","[60, 30]","[1, 2]",1,5,2,150,3,2,1,2123 -29,27,"[2, 1]","[40, 30]","[1, 1]",1,1,1,30,3,1,1,2672 -29,28,"[2, 1]","[50, 50]","[1, 2]",1,5,2,250,5,2,2,1906 -29,29,"[3, 4]","[30, 40]","[2, 2]",1,5,4,200,4,2,4,1839 -29,30,"[2, 1]","[70, 40]","[1, 2]",1,1,2,70,7,2,1,2440 -29,31,"[1, 2]","[40, 70]","[1, 2]",1,1,2,70,7,2,2,1856 -29,32,"[4, 3]","[90, 60]","[2, 2]",1,5,4,450,9,2,3,2089 -29,33,"[1, 2]","[70, 70]","[1, 2]",1,1,2,70,7,2,1,1906 -29,34,"[4, 3]","[80, 60]","[2, 2]",1,5,4,400,8,2,3,3006 -29,35,"[2, 1]","[90, 80]","[1, 2]",1,1,2,90,9,2,1,2173 -29,36,"[4, 3]","[90, 70]","[2, 2]",1,5,4,450,9,2,3,2206 -29,37,"[1, 2]","[40, 70]","[1, 2]",1,5,2,350,7,2,2,2506 -29,38,"[2, 1]","[70, 30]","[1, 2]",1,1,2,70,7,2,1,2392 -29,39,"[2, 1]","[60, 20]","[1, 2]",1,5,2,300,6,2,1,3190 -29,40,"[4, 3]","[80, 10]","[2, 2]",1,1,4,80,8,2,3,2056 -29,41,"[1, 2]","[20, 50]","[1, 1]",2,5,1,100,2,1,2,5102 -29,42,"[2, 1]","[70, 10]","[1, 2]",2,1,2,70,7,2,1,4206 -29,43,"[3, 4]","[20, 80]","[2, 2]",2,1,4,80,8,2,4,5273 -29,44,"[2, 1]","[70, 0]","[1, 2]",2,5,2,350,7,2,1,3239 -29,45,"[2, 1]","[30, 10]","[1, 2]",2,5,2,150,3,2,1,1906 -29,46,"[4, 3]","[40, 10]","[2, 1]",2,1,3,10,1,1,3,2473 -29,47,"[2, 1]","[30, 10]","[1, 2]",2,1,2,30,3,2,1,1990 -29,48,"[4, 3]","[60, 30]","[2, 2]",2,5,4,300,6,2,3,1838 -29,49,"[4, 3]","[70, 10]","[2, 2]",2,1,4,70,7,2,3,2221 -29,50,"[1, 2]","[10, 50]","[1, 2]",2,1,2,50,5,2,2,2340 -29,51,"[2, 1]","[40, 30]","[1, 2]",2,1,2,40,4,2,1,2556 -29,52,"[4, 3]","[20, 40]","[2, 1]",2,5,3,200,4,1,4,2139 -29,53,"[4, 3]","[30, 40]","[2, 2]",2,1,4,30,3,2,4,1939 -29,54,"[2, 1]","[20, 50]","[1, 1]",2,5,1,250,5,1,2,2872 -29,55,"[1, 2]","[60, 10]","[1, 1]",2,1,1,60,6,1,1,2157 -29,56,"[3, 4]","[40, 20]","[2, 1]",2,5,3,200,4,1,3,2040 -29,57,"[3, 4]","[50, 30]","[2, 1]",2,1,3,50,5,1,3,1940 -29,58,"[3, 4]","[30, 40]","[2, 1]",2,5,3,150,3,1,4,1773 -29,59,"[1, 2]","[30, 40]","[1, 2]",2,5,2,200,4,2,2,1939 -29,60,"[4, 3]","[30, 20]","[2, 2]",2,5,4,150,3,2,3,2123 -29,61,"[3, 4]","[40, 20]","[2, 1]",2,5,3,200,4,1,3,1989 -29,62,"[3, 4]","[20, 20]","[2, 2]",2,1,4,20,2,2,3,1739 -29,63,"[4, 3]","[20, 30]","[2, 1]",2,5,3,150,3,1,4,1756 -29,64,"[2, 1]","[40, 60]","[1, 1]",2,1,1,60,6,1,2,1908 -29,65,"[1, 2]","[80, 60]","[1, 1]",2,5,1,400,8,1,1,2023 -29,66,"[1, 2]","[70, 0]","[1, 1]",2,5,1,350,7,1,1,1889 -29,67,"[4, 3]","[10, 80]","[2, 1]",2,1,3,80,8,1,4,1789 -29,68,"[4, 3]","[20, 60]","[2, 1]",2,1,3,60,6,1,4,1755 -29,69,"[3, 4]","[40, 10]","[2, 1]",2,5,3,200,4,1,3,1806 -29,70,"[2, 1]","[10, 30]","[1, 1]",2,5,1,150,3,1,2,2040 -29,71,"[4, 3]","[10, 40]","[2, 1]",2,1,3,40,4,1,4,1706 -29,72,"[1, 2]","[70, 30]","[1, 1]",2,1,1,70,7,1,1,2041 -29,73,"[4, 3]","[10, 60]","[2, 1]",2,5,3,300,6,1,4,1857 -29,74,"[4, 3]","[20, 60]","[2, 1]",2,1,3,60,6,1,4,1756 -29,75,"[4, 3]","[10, 80]","[2, 1]",2,1,3,80,8,1,4,1737 -29,76,"[2, 1]","[20, 60]","[1, 1]",2,5,1,300,6,1,2,2122 -29,77,"[3, 4]","[80, 10]","[2, 1]",2,5,3,400,8,1,3,1855 -29,78,"[4, 3]","[10, 90]","[2, 1]",2,1,3,90,9,1,4,1886 -29,79,"[1, 2]","[90, 10]","[1, 1]",2,1,1,90,9,1,1,1973 -29,80,"[2, 1]","[10, 70]","[1, 1]",2,5,1,350,7,1,2,1872 -38,1,"[1, 2]","[90, 20]","[1, 2]",1,1,2,20,2,2,1,6932 -38,2,"[2, 1]","[30, 70]","[1, 1]",1,5,1,350,7,1,2,9717 -38,3,"[3, 4]","[50, 50]","[2, 2]",1,5,4,250,5,2,3,132158 -38,4,"[3, 4]","[20, 70]","[2, 1]",1,1,3,20,2,1,4,2868 -38,5,"[2, 1]","[90, 30]","[1, 2]",1,1,2,90,9,2,1,2701 -38,6,"[1, 2]","[0, 90]","[1, 2]",1,1,2,90,9,2,2,9634 -38,7,"[4, 3]","[90, 0]","[2, 2]",1,1,4,90,9,2,3,2400 -38,8,"[1, 2]","[20, 80]","[1, 2]",1,1,2,80,8,2,2,2084 -38,9,"[4, 3]","[80, 30]","[2, 1]",1,1,3,30,3,1,3,4035 -38,10,"[3, 4]","[60, 70]","[2, 2]",1,5,4,350,7,2,4,3368 -38,11,"[2, 1]","[60, 60]","[1, 2]",1,5,2,300,6,2,2,2784 -38,12,"[1, 2]","[60, 70]","[1, 2]",1,5,2,350,7,2,2,4334 -38,13,"[2, 1]","[70, 40]","[1, 2]",1,5,2,350,7,2,1,2118 -38,14,"[4, 3]","[90, 30]","[2, 1]",1,1,3,30,3,1,3,2101 -38,15,"[3, 4]","[10, 50]","[2, 1]",1,5,3,50,1,1,4,2234 -38,16,"[4, 3]","[50, 40]","[2, 2]",1,5,4,250,5,2,3,2518 -38,17,"[2, 1]","[20, 0]","[1, 1]",1,5,1,0,0,1,1,2435 -38,18,"[4, 3]","[30, 0]","[2, 2]",1,5,4,150,3,2,3,1951 -38,19,"[4, 3]","[50, 10]","[2, 2]",1,1,4,50,5,2,3,1867 -38,20,"[2, 1]","[50, 20]","[1, 1]",1,5,1,100,2,1,1,3284 -38,21,"[1, 2]","[40, 90]","[1, 2]",1,1,2,90,9,2,2,4684 -38,22,"[3, 4]","[40, 90]","[2, 2]",1,1,4,90,9,2,4,2384 -38,23,"[3, 4]","[60, 80]","[2, 1]",1,5,3,300,6,1,4,1950 -38,24,"[3, 4]","[30, 80]","[2, 1]",1,1,3,30,3,1,4,2251 -38,25,"[2, 1]","[80, 30]","[1, 1]",1,1,1,30,3,1,1,2801 -38,26,"[2, 1]","[70, 10]","[1, 2]",1,5,2,350,7,2,1,7318 -38,27,"[2, 1]","[90, 0]","[1, 1]",1,5,1,0,0,1,1,1901 -38,28,"[4, 3]","[80, 0]","[2, 2]",1,1,4,80,8,2,3,6034 -38,29,"[2, 1]","[80, 10]","[1, 1]",1,1,1,10,1,1,1,1984 -38,30,"[1, 2]","[30, 90]","[1, 2]",1,1,2,90,9,2,2,4200 -38,31,"[1, 2]","[40, 70]","[1, 2]",1,5,2,350,7,2,2,41948865 -38,32,"[4, 3]","[60, 40]","[2, 1]",1,5,3,200,4,1,3,3351 -38,33,"[2, 1]","[60, 70]","[1, 2]",1,1,2,60,6,2,2,2201 -38,34,"[4, 3]","[40, 70]","[2, 1]",1,1,3,70,7,1,4,4168 -38,35,"[4, 3]","[10, 60]","[2, 2]",1,1,4,10,1,2,4,2618 -38,36,"[4, 3]","[20, 70]","[2, 1]",1,1,3,70,7,1,4,2535 -38,37,"[3, 4]","[50, 80]","[2, 1]",1,1,3,50,5,1,4,2884 -38,38,"[1, 2]","[70, 90]","[1, 2]",1,5,2,450,9,2,2,2835 -38,39,"[3, 4]","[70, 40]","[2, 1]",1,1,3,70,7,1,3,4251 -38,40,"[3, 4]","[70, 40]","[2, 1]",1,1,3,70,7,1,3,6335 -38,41,"[4, 3]","[20, 70]","[2, 1]",2,5,3,350,7,1,4,23915 -38,42,"[4, 3]","[20, 80]","[2, 1]",2,1,3,80,8,1,4,4768 -38,43,"[4, 3]","[30, 80]","[2, 2]",2,5,4,150,3,2,4,2901 -38,44,"[4, 3]","[40, 80]","[2, 1]",2,1,3,80,8,1,4,3168 -38,45,"[1, 2]","[60, 50]","[1, 1]",2,1,1,60,6,1,1,2117 -38,46,"[1, 2]","[50, 20]","[1, 2]",2,1,2,20,2,2,1,2217 -38,47,"[1, 2]","[40, 10]","[1, 2]",2,5,2,50,1,2,1,2085 -38,48,"[2, 1]","[20, 20]","[1, 2]",2,5,2,100,2,2,2,6301 -38,49,"[1, 2]","[20, 10]","[1, 2]",2,5,2,50,1,2,1,4817 -38,50,"[3, 4]","[10, 40]","[2, 1]",2,5,3,50,1,1,4,4434 -38,51,"[1, 2]","[30, 40]","[1, 1]",2,5,1,150,3,1,2,3583 -38,52,"[1, 2]","[30, 70]","[1, 1]",2,5,1,150,3,1,2,2350 -38,53,"[2, 1]","[90, 70]","[1, 2]",2,5,2,450,9,2,1,2200 -38,54,"[3, 4]","[60, 80]","[2, 2]",2,1,4,80,8,2,4,2184 -38,55,"[4, 3]","[60, 80]","[2, 1]",2,1,3,80,8,1,4,6084 -38,56,"[1, 2]","[60, 50]","[1, 1]",2,5,1,300,6,1,1,2017 -38,57,"[1, 2]","[80, 50]","[1, 1]",2,5,1,400,8,1,1,2517 -38,58,"[3, 4]","[80, 70]","[2, 2]",2,1,4,70,7,2,3,2118 -38,59,"[3, 4]","[80, 50]","[2, 1]",2,5,3,400,8,1,3,2184 -38,60,"[3, 4]","[80, 60]","[2, 2]",2,1,4,60,6,2,3,4134 -38,61,"[4, 3]","[50, 80]","[2, 2]",2,5,4,250,5,2,4,2001 -38,62,"[3, 4]","[70, 50]","[2, 2]",2,5,4,250,5,2,3,1984 -38,63,"[4, 3]","[40, 50]","[2, 1]",2,5,3,250,5,1,4,2251 -38,64,"[2, 1]","[30, 20]","[1, 2]",2,5,2,150,3,2,1,2051 -38,65,"[4, 3]","[40, 10]","[2, 2]",2,1,4,40,4,2,3,2484 -38,66,"[2, 1]","[60, 40]","[1, 1]",2,1,1,40,4,1,1,2034 -38,67,"[4, 3]","[70, 60]","[2, 1]",2,1,3,60,6,1,3,2251 -38,68,"[4, 3]","[80, 70]","[2, 2]",2,5,4,400,8,2,3,2451 -38,69,"[4, 3]","[80, 60]","[2, 1]",2,5,3,300,6,1,3,13450 -38,70,"[1, 2]","[40, 90]","[1, 1]",2,5,1,200,4,1,2,1967 -38,71,"[2, 1]","[70, 20]","[1, 2]",2,1,2,70,7,2,1,2385 -38,72,"[1, 2]","[10, 40]","[1, 2]",2,5,2,200,4,2,2,1834 -38,73,"[2, 1]","[60, 10]","[1, 1]",2,1,1,10,1,1,1,2885 -38,74,"[1, 2]","[10, 70]","[1, 1]",2,5,1,50,1,1,2,1884 -38,75,"[2, 1]","[90, 10]","[1, 2]",2,1,2,90,9,2,1,1833 -38,76,"[2, 1]","[80, 10]","[1, 1]",2,5,1,50,1,1,1,3267 -38,77,"[3, 4]","[60, 80]","[2, 2]",2,1,4,80,8,2,4,2051 -38,78,"[3, 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-43,16,"[4, 3]","[90, 90]","[2, 2]",1,5,4,450,9,2,4,1938 -43,17,"[2, 1]","[70, 90]","[1, 2]",1,5,2,350,7,2,2,2005 -43,18,"[4, 3]","[90, 80]","[2, 2]",1,1,4,90,9,2,3,1759 -43,19,"[4, 3]","[70, 80]","[2, 2]",1,1,4,70,7,2,4,1775 -43,20,"[4, 3]","[70, 80]","[2, 2]",1,1,4,70,7,2,4,1790 -43,21,"[3, 4]","[40, 90]","[2, 2]",1,5,4,450,9,2,4,1791 -43,22,"[2, 1]","[90, 40]","[1, 2]",1,1,2,90,9,2,1,1972 -43,23,"[2, 1]","[80, 60]","[1, 2]",1,5,2,400,8,2,1,1841 -43,24,"[3, 4]","[70, 70]","[2, 2]",1,1,4,70,7,2,3,1790 -43,25,"[2, 1]","[80, 60]","[1, 2]",1,5,2,400,8,2,1,1841 -43,26,"[2, 1]","[50, 60]","[1, 2]",1,1,2,50,5,2,2,1878 -43,27,"[1, 2]","[50, 70]","[1, 2]",1,5,2,350,7,2,2,2053 -43,28,"[3, 4]","[60, 70]","[2, 2]",1,5,4,350,7,2,4,1889 -43,29,"[4, 3]","[30, 50]","[2, 2]",1,5,4,150,3,2,4,1790 -43,30,"[4, 3]","[30, 50]","[2, 1]",1,5,3,250,5,1,4,1839 -43,31,"[3, 4]","[30, 0]","[2, 1]",1,5,3,150,3,1,3,1906 -43,32,"[3, 4]","[30, 20]","[2, 2]",1,1,4,20,2,2,3,1756 -43,33,"[3, 4]","[40, 10]","[2, 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1]",1,5,1,200,4,1,1,1782 -44,7,"[3, 4]","[20, 90]","[2, 1]",1,1,3,20,2,1,4,2024 -44,8,"[1, 2]","[20, 60]","[1, 1]",1,5,1,100,2,1,2,2031 -44,9,"[3, 4]","[20, 40]","[2, 1]",1,1,3,20,2,1,4,23988 -44,10,"[1, 2]","[30, 70]","[1, 1]",1,1,1,30,3,1,2,1916 -44,11,"[3, 4]","[40, 40]","[2, 2]",1,5,4,200,4,2,3,1909 -44,12,"[3, 4]","[60, 50]","[2, 2]",1,1,4,50,5,2,3,1941 -44,13,"[3, 4]","[30, 50]","[2, 2]",1,1,4,50,5,2,4,1851 -44,14,"[2, 1]","[40, 50]","[1, 2]",1,5,2,200,4,2,2,1849 -44,15,"[1, 2]","[60, 60]","[1, 2]",1,1,2,60,6,2,1,1849 -44,16,"[4, 3]","[90, 80]","[2, 1]",1,1,3,80,8,1,3,2068 -44,17,"[3, 4]","[40, 50]","[2, 2]",1,5,4,250,5,2,4,1926 -44,18,"[3, 4]","[40, 40]","[2, 2]",1,5,4,200,4,2,3,1898 -44,19,"[2, 1]","[10, 40]","[1, 2]",1,1,2,10,1,2,2,2158 -44,20,"[3, 4]","[80, 10]","[2, 2]",1,5,4,50,1,2,3,1658 -44,21,"[2, 1]","[20, 60]","[1, 1]",1,1,1,60,6,1,2,1298 -44,22,"[3, 4]","[30, 30]","[2, 1]",1,5,3,150,3,1,3,1799 -44,23,"[3, 4]","[20, 10]","[2, 1]",1,5,3,100,2,1,3,1716 -44,24,"[1, 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1]",2,1,1,50,5,1,1,4197 -44,42,"[3, 4]","[50, 20]","[2, 2]",2,1,4,20,2,2,3,2441 -44,43,"[1, 2]","[50, 20]","[1, 2]",2,5,2,100,2,2,1,14196 -44,44,"[4, 3]","[10, 90]","[2, 2]",2,1,4,10,1,2,4,3117 -44,45,"[4, 3]","[30, 60]","[2, 2]",2,1,4,30,3,2,4,3942 -44,46,"[4, 3]","[30, 80]","[2, 1]",2,5,3,400,8,1,4,2133 -44,47,"[3, 4]","[60, 20]","[2, 1]",2,5,3,300,6,1,3,1850 -44,48,"[2, 1]","[10, 80]","[1, 1]",2,1,1,80,8,1,2,2554 -44,49,"[3, 4]","[80, 10]","[2, 1]",2,5,3,400,8,1,3,1689 -44,50,"[4, 3]","[10, 50]","[2, 2]",2,5,4,50,1,2,4,1808 -44,51,"[4, 3]","[20, 50]","[2, 2]",2,1,4,20,2,2,4,2010 -44,52,"[4, 3]","[40, 60]","[2, 1]",2,1,3,60,6,1,4,1842 -44,53,"[1, 2]","[60, 20]","[1, 2]",2,5,2,100,2,2,1,2100 -44,54,"[4, 3]","[30, 80]","[2, 2]",2,1,4,30,3,2,4,2024 -44,55,"[4, 3]","[30, 90]","[2, 2]",2,1,4,30,3,2,4,3483 -44,56,"[1, 2]","[70, 10]","[1, 2]",2,1,2,10,1,2,1,1908 -44,57,"[3, 4]","[40, 20]","[2, 1]",2,1,3,40,4,1,3,1808 -44,58,"[1, 2]","[20, 30]","[1, 2]",2,5,2,150,3,2,2,1649 -44,59,"[3, 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-44,77,"[2, 1]","[10, 30]","[1, 2]",2,5,2,50,1,2,2,1882 -44,78,"[1, 2]","[10, 0]","[1, 2]",2,5,2,0,0,2,1,2607 -44,79,"[1, 2]","[20, 10]","[1, 1]",2,1,1,20,2,1,1,1859 -44,80,"[3, 4]","[40, 30]","[2, 1]",2,1,3,40,4,1,3,2350 -45,1,"[3, 4]","[0, 60]","[2, 1]",1,1,3,0,0,1,4,3682 -45,2,"[1, 2]","[20, 80]","[1, 2]",1,5,2,400,8,2,2,6303 -45,3,"[3, 4]","[30, 50]","[2, 2]",1,1,4,50,5,2,4,1970 -45,4,"[3, 4]","[40, 50]","[2, 1]",1,5,3,200,4,1,4,2295 -45,5,"[4, 3]","[60, 40]","[2, 1]",1,1,3,40,4,1,3,3995 -45,6,"[4, 3]","[50, 50]","[2, 2]",1,5,4,250,5,2,4,6137 -45,7,"[4, 3]","[20, 80]","[2, 2]",1,5,4,100,2,2,4,4870 -45,8,"[4, 3]","[20, 60]","[2, 1]",1,5,3,300,6,1,4,2203 -45,9,"[3, 4]","[70, 20]","[2, 1]",1,1,3,70,7,1,3,2096 -45,10,"[3, 4]","[20, 50]","[2, 2]",1,5,4,250,5,2,4,2287 -45,11,"[3, 4]","[10, 30]","[2, 2]",1,1,4,30,3,2,4,1954 -45,12,"[2, 1]","[70, 40]","[1, 2]",1,5,2,350,7,2,1,1662 -45,13,"[1, 2]","[60, 90]","[1, 2]",1,5,2,450,9,2,2,1904 -45,14,"[2, 1]","[80, 70]","[1, 2]",1,5,2,400,8,2,1,2054 -45,15,"[3, 4]","[60, 80]","[2, 2]",1,1,4,80,8,2,4,2179 -45,16,"[2, 1]","[80, 60]","[1, 2]",1,5,2,400,8,2,1,3229 -45,17,"[2, 1]","[80, 70]","[1, 1]",1,5,1,350,7,1,1,2129 -45,18,"[1, 2]","[50, 70]","[1, 1]",1,1,1,50,5,1,2,2279 -45,19,"[3, 4]","[40, 50]","[2, 2]",1,1,4,50,5,2,4,1762 -45,20,"[4, 3]","[40, 40]","[2, 2]",1,5,4,200,4,2,4,1737 -45,21,"[4, 3]","[70, 20]","[2, 2]",1,1,4,70,7,2,3,1937 -45,22,"[3, 4]","[20, 30]","[2, 2]",1,5,4,150,3,2,4,1703 -45,23,"[2, 1]","[30, 30]","[1, 2]",1,5,2,150,3,2,2,2569 -45,24,"[4, 3]","[50, 40]","[2, 1]",1,1,3,40,4,1,3,2502 -45,25,"[3, 4]","[20, 90]","[2, 1]",1,5,3,100,2,1,4,6261 -45,26,"[3, 4]","[30, 70]","[2, 1]",1,5,3,150,3,1,4,19726 -45,27,"[4, 3]","[70, 20]","[2, 1]",1,5,3,100,2,1,3,9245 -45,28,"[4, 3]","[80, 40]","[2, 2]",1,1,4,80,8,2,3,1995 -45,29,"[3, 4]","[40, 90]","[2, 2]",1,1,4,90,9,2,4,2329 -45,30,"[3, 4]","[20, 80]","[2, 2]",1,1,4,80,8,2,4,2094 -45,31,"[2, 1]","[80, 30]","[1, 1]",1,5,1,150,3,1,1,1811 -45,32,"[4, 3]","[60, 60]","[2, 2]",1,1,4,60,6,2,4,2620 -45,33,"[3, 4]","[60, 40]","[2, 2]",1,1,4,40,4,2,3,6861 -45,34,"[2, 1]","[40, 60]","[1, 2]",1,5,2,200,4,2,2,3145 -45,35,"[1, 2]","[50, 80]","[1, 2]",1,1,2,80,8,2,2,33501 -45,36,"[1, 2]","[40, 40]","[1, 2]",1,1,2,40,4,2,1,3953 -45,37,"[1, 2]","[30, 20]","[1, 1]",1,1,1,30,3,1,1,2353 -45,38,"[2, 1]","[50, 10]","[1, 2]",1,5,2,250,5,2,1,6261 -45,39,"[1, 2]","[20, 40]","[1, 2]",1,1,2,40,4,2,2,4020 -45,40,"[4, 3]","[10, 10]","[2, 1]",1,5,3,50,1,1,4,1919 -45,41,"[1, 2]","[30, 50]","[1, 1]",2,5,1,150,3,1,2,5389 -45,42,"[3, 4]","[30, 40]","[2, 1]",2,1,3,30,3,1,4,2229 -45,43,"[4, 3]","[40, 50]","[2, 2]",2,5,4,200,4,2,4,2154 -45,44,"[3, 4]","[50, 60]","[2, 2]",2,5,4,300,6,2,4,1878 -45,45,"[4, 3]","[80, 10]","[2, 2]",2,1,4,80,8,2,3,1961 -45,46,"[1, 2]","[40, 90]","[1, 2]",2,1,2,90,9,2,2,5837 -45,47,"[4, 3]","[90, 70]","[2, 2]",2,1,4,90,9,2,3,2045 -45,48,"[2, 1]","[80, 90]","[1, 2]",2,5,2,400,8,2,2,2562 -45,49,"[1, 2]","[90, 70]","[1, 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2]","[40, 30]","[1, 1]",2,5,1,200,4,1,1,2337 -45,68,"[2, 1]","[40, 60]","[1, 1]",2,5,1,300,6,1,2,2269 -45,69,"[2, 1]","[50, 30]","[1, 2]",2,1,2,50,5,2,1,172619 -45,70,"[1, 2]","[40, 20]","[1, 2]",2,1,2,20,2,2,1,1712 -45,71,"[4, 3]","[40, 40]","[2, 2]",2,1,4,40,4,2,4,4770 -45,72,"[2, 1]","[40, 0]","[1, 2]",2,5,2,200,4,2,1,3936 -45,73,"[2, 1]","[50, 0]","[1, 2]",2,5,2,250,5,2,1,2537 -45,74,"[3, 4]","[40, 50]","[2, 2]",2,5,4,250,5,2,4,2353 -45,75,"[3, 4]","[40, 50]","[2, 2]",2,5,4,250,5,2,4,1845 -45,76,"[2, 1]","[0, 60]","[1, 2]",2,5,2,0,0,2,2,2237 -45,77,"[3, 4]","[40, 20]","[2, 2]",2,1,4,20,2,2,3,1845 -45,78,"[3, 4]","[50, 30]","[2, 1]",2,1,3,50,5,1,3,2095 -45,79,"[3, 4]","[70, 30]","[2, 1]",2,5,3,350,7,1,3,1913 -45,80,"[4, 3]","[70, 30]","[2, 1]",2,1,3,30,3,1,3,4895 -29,1,"[3, 4]","[70, 10]","[2, 1]",1,5,3,350,7,1,3,11504 -29,2,"[1, 2]","[60, 0]","[1, 1]",1,1,1,60,6,1,1,2157 -29,3,"[2, 1]","[10, 80]","[1, 1]",1,5,1,400,8,1,2,2041 -29,4,"[4, 3]","[10, 90]","[2, 1]",1,5,3,450,9,1,4,2173 -29,5,"[3, 4]","[70, 20]","[2, 1]",1,5,3,350,7,1,3,1938 -29,6,"[3, 4]","[80, 40]","[2, 1]",1,5,3,400,8,1,3,1856 -29,7,"[2, 1]","[70, 80]","[1, 1]",1,1,1,80,8,1,2,2241 -29,8,"[1, 2]","[70, 90]","[1, 1]",1,1,1,70,7,1,2,2058 -29,9,"[1, 2]","[50, 80]","[1, 1]",1,1,1,50,5,1,2,1842 -29,10,"[1, 2]","[30, 90]","[1, 1]",1,1,1,30,3,1,2,1855 -29,11,"[3, 4]","[30, 80]","[2, 2]",1,5,4,400,8,2,4,1791 -29,12,"[3, 4]","[20, 70]","[2, 2]",1,5,4,350,7,2,4,1958 -29,13,"[3, 4]","[10, 40]","[2, 2]",1,5,4,200,4,2,4,1825 -29,14,"[4, 3]","[30, 10]","[2, 2]",1,5,4,150,3,2,3,1992 -29,15,"[3, 4]","[20, 10]","[2, 2]",1,1,4,10,1,2,3,1774 -29,16,"[3, 4]","[20, 20]","[2, 1]",1,5,3,100,2,1,3,1755 -29,17,"[1, 2]","[20, 20]","[1, 1]",1,1,1,20,2,1,1,2041 -29,18,"[2, 1]","[20, 30]","[1, 2]",1,5,2,100,2,2,2,1975 -29,19,"[1, 2]","[40, 40]","[1, 1]",1,1,1,40,4,1,1,1874 -29,20,"[3, 4]","[50, 50]","[2, 1]",1,5,3,250,5,1,3,1841 -29,21,"[1, 2]","[30, 50]","[1, 1]",1,1,1,30,3,1,2,1858 -29,22,"[3, 4]","[30, 20]","[2, 2]",1,5,4,100,2,2,3,1703 -29,23,"[3, 4]","[60, 0]","[2, 1]",1,1,3,60,6,1,3,1790 -29,24,"[3, 4]","[60, 30]","[2, 1]",1,5,3,300,6,1,3,1875 -29,25,"[1, 2]","[70, 20]","[1, 1]",1,1,1,70,7,1,1,1856 -29,26,"[2, 1]","[50, 90]","[1, 1]",1,1,1,90,9,1,2,2091 -29,27,"[4, 3]","[80, 80]","[2, 1]",1,1,3,80,8,1,4,1691 -29,28,"[3, 4]","[80, 70]","[2, 1]",1,1,3,80,8,1,3,1856 -29,29,"[2, 1]","[80, 80]","[1, 1]",1,1,1,80,8,1,2,1873 -29,30,"[1, 2]","[60, 80]","[1, 1]",1,5,1,300,6,1,2,1938 -29,31,"[1, 2]","[70, 60]","[1, 1]",1,5,1,350,7,1,1,1842 -29,32,"[4, 3]","[60, 80]","[2, 1]",1,1,3,80,8,1,4,1791 -29,33,"[3, 4]","[90, 60]","[2, 1]",1,1,3,90,9,1,3,1824 -29,34,"[4, 3]","[30, 60]","[2, 1]",1,5,3,300,6,1,4,1724 -29,35,"[4, 3]","[40, 80]","[2, 1]",1,1,3,80,8,1,4,1741 -29,36,"[4, 3]","[20, 80]","[2, 1]",1,5,3,400,8,1,4,1773 -29,37,"[4, 3]","[10, 90]","[2, 1]",1,5,3,450,9,1,4,1823 -29,38,"[1, 2]","[70, 20]","[1, 1]",1,5,1,350,7,1,1,1974 -29,39,"[1, 2]","[40, 20]","[1, 1]",1,1,1,40,4,1,1,1925 -29,40,"[4, 3]","[20, 50]","[2, 1]",1,5,3,250,5,1,4,1924 -29,41,"[3, 4]","[30, 40]","[2, 1]",2,5,3,150,3,1,4,4202 -29,42,"[2, 1]","[30, 60]","[1, 2]",2,1,2,30,3,2,2,2658 -29,43,"[3, 4]","[70, 40]","[2, 1]",2,5,3,350,7,1,3,3307 -29,44,"[1, 2]","[70, 60]","[1, 1]",2,1,1,70,7,1,1,1803 -29,45,"[1, 2]","[70, 60]","[1, 1]",2,1,1,70,7,1,1,1875 -29,46,"[3, 4]","[70, 90]","[2, 1]",2,1,3,70,7,1,4,1755 -29,47,"[3, 4]","[80, 70]","[2, 1]",2,1,3,80,8,1,3,1725 -29,48,"[2, 1]","[90, 90]","[1, 1]",2,5,1,450,9,1,2,2125 -29,49,"[1, 2]","[50, 70]","[1, 1]",2,1,1,50,5,1,2,1823 -29,50,"[4, 3]","[70, 60]","[2, 1]",2,1,3,60,6,1,3,1675 -29,51,"[3, 4]","[80, 70]","[2, 1]",2,1,3,80,8,1,3,1725 -29,52,"[3, 4]","[60, 40]","[2, 1]",2,1,3,60,6,1,3,1691 -29,53,"[4, 3]","[50, 70]","[2, 1]",2,1,3,70,7,1,4,1708 -29,54,"[4, 3]","[60, 70]","[2, 1]",2,1,3,70,7,1,4,1725 -29,55,"[1, 2]","[80, 50]","[1, 1]",2,1,1,80,8,1,1,1773 -29,56,"[3, 4]","[50, 60]","[2, 1]",2,1,3,50,5,1,4,1789 -29,57,"[3, 4]","[40, 70]","[2, 1]",2,5,3,200,4,1,4,1704 -29,58,"[1, 2]","[50, 90]","[1, 1]",2,1,1,50,5,1,2,2475 -29,59,"[4, 3]","[70, 60]","[2, 1]",2,5,3,300,6,1,3,1791 -29,60,"[1, 2]","[80, 50]","[1, 1]",2,5,1,400,8,1,1,1842 -29,61,"[2, 1]","[30, 70]","[1, 1]",2,5,1,350,7,1,2,1925 -29,62,"[4, 3]","[40, 90]","[2, 1]",2,5,3,450,9,1,4,1857 -29,63,"[4, 3]","[30, 80]","[2, 1]",2,1,3,80,8,1,4,1942 -29,64,"[4, 3]","[20, 60]","[2, 1]",2,5,3,300,6,1,4,1758 -29,65,"[1, 2]","[50, 30]","[1, 1]",2,5,1,250,5,1,1,1757 -29,66,"[1, 2]","[90, 0]","[1, 1]",2,1,1,90,9,1,1,1758 -29,67,"[3, 4]","[50, 30]","[2, 1]",2,1,3,50,5,1,3,1791 -29,68,"[4, 3]","[0, 60]","[2, 1]",2,5,3,300,6,1,4,1774 -29,69,"[4, 3]","[20, 70]","[2, 1]",2,5,3,350,7,1,4,1741 -29,70,"[3, 4]","[70, 10]","[2, 1]",2,5,3,350,7,1,3,1874 -29,71,"[4, 3]","[20, 40]","[2, 1]",2,5,3,200,4,1,4,1756 -29,72,"[3, 4]","[20, 20]","[2, 1]",2,1,3,20,2,1,3,1774 -29,73,"[1, 2]","[40, 60]","[1, 2]",2,5,2,300,6,2,2,2204 -29,74,"[3, 4]","[30, 90]","[2, 2]",2,5,4,450,9,2,4,1675 -29,75,"[2, 1]","[60, 40]","[1, 2]",2,5,2,300,6,2,1,1940 -29,76,"[4, 3]","[50, 40]","[2, 2]",2,1,4,50,5,2,3,1826 -29,77,"[3, 4]","[0, 40]","[2, 2]",2,1,4,40,4,2,4,1724 -29,78,"[1, 2]","[10, 40]","[1, 1]",2,1,1,10,1,1,2,1974 -29,79,"[4, 3]","[20, 10]","[2, 2]",2,5,4,100,2,2,3,1956 -29,80,"[3, 4]","[10, 20]","[2, 2]",2,5,4,100,2,2,4,1925 -46,1,"[2, 1]","[20, 60]","[1, 1]",1,5,1,300,6,1,2,6608 -46,2,"[3, 4]","[70, 40]","[2, 1]",1,5,3,350,7,1,3,3065 -46,3,"[1, 2]","[90, 70]","[1, 1]",1,1,1,90,9,1,1,2918 -46,4,"[4, 3]","[40, 60]","[2, 1]",1,1,3,60,6,1,4,2146 -46,5,"[3, 4]","[70, 50]","[2, 1]",1,5,3,350,7,1,3,2432 -46,6,"[4, 3]","[70, 20]","[2, 1]",1,1,3,20,2,1,3,2051 -46,7,"[2, 1]","[60, 10]","[1, 2]",1,1,2,60,6,2,1,2978 -46,8,"[2, 1]","[40, 0]","[1, 2]",1,5,2,200,4,2,1,2266 -46,9,"[4, 3]","[90, 0]","[2, 2]",1,1,4,90,9,2,3,2119 -46,10,"[4, 3]","[90, 10]","[2, 2]",1,5,4,450,9,2,3,2012 -46,11,"[4, 3]","[90, 20]","[2, 2]",1,5,4,450,9,2,3,1954 -46,12,"[1, 2]","[10, 90]","[1, 2]",1,1,2,90,9,2,2,3906 -46,13,"[3, 4]","[20, 70]","[2, 2]",1,1,4,70,7,2,4,2248 -46,14,"[2, 1]","[70, 30]","[1, 2]",1,1,2,70,7,2,1,2794 -46,15,"[3, 4]","[30, 50]","[2, 2]",1,5,4,250,5,2,4,2101 -46,16,"[3, 4]","[40, 60]","[2, 2]",1,1,4,60,6,2,4,1914 -46,17,"[1, 2]","[40, 70]","[1, 2]",1,1,2,70,7,2,2,2187 -46,18,"[4, 3]","[40, 60]","[2, 2]",1,5,4,200,4,2,4,9124 -46,19,"[3, 4]","[60, 40]","[2, 2]",1,5,4,200,4,2,3,3033 -46,20,"[1, 2]","[70, 20]","[1, 2]",1,5,2,100,2,2,1,2133 -46,21,"[1, 2]","[70, 0]","[1, 1]",1,5,1,350,7,1,1,2336 -46,22,"[2, 1]","[20, 80]","[1, 1]",1,5,1,400,8,1,2,2315 -46,23,"[1, 2]","[80, 10]","[1, 1]",1,5,1,400,8,1,1,1995 -46,24,"[4, 3]","[0, 70]","[2, 1]",1,1,3,70,7,1,4,19617 -46,25,"[1, 2]","[70, 10]","[1, 1]",1,1,1,70,7,1,1,2289 -46,26,"[4, 3]","[30, 60]","[2, 1]",1,1,3,60,6,1,4,1931 -46,27,"[2, 1]","[50, 40]","[1, 1]",1,1,1,40,4,1,1,2513 -46,28,"[2, 1]","[50, 60]","[1, 1]",1,1,1,60,6,1,2,15319 -46,29,"[1, 2]","[70, 60]","[1, 1]",1,5,1,350,7,1,1,1915 -46,30,"[3, 4]","[50, 70]","[2, 1]",1,1,3,50,5,1,4,2235 -46,31,"[4, 3]","[80, 70]","[2, 1]",1,5,3,350,7,1,3,1981 -46,32,"[4, 3]","[60, 20]","[2, 1]",1,5,3,100,2,1,3,2044 -46,33,"[3, 4]","[10, 60]","[2, 2]",1,1,4,60,6,2,4,2130 -46,34,"[3, 4]","[30, 40]","[2, 2]",1,5,4,200,4,2,4,2081 -46,35,"[4, 3]","[40, 20]","[2, 2]",1,5,4,200,4,2,3,2015 -46,36,"[3, 4]","[10, 30]","[2, 2]",1,5,4,150,3,2,4,1938 -46,37,"[1, 2]","[20, 40]","[1, 1]",1,1,1,20,2,1,2,1952 -46,38,"[1, 2]","[80, 60]","[1, 2]",1,1,2,60,6,2,1,1825 -46,39,"[2, 1]","[70, 80]","[1, 2]",1,1,2,70,7,2,2,2130 -46,40,"[3, 4]","[70, 40]","[2, 2]",1,1,4,40,4,2,3,2143 -46,41,"[2, 1]","[50, 80]","[1, 1]",2,5,1,400,8,1,2,6796 -46,42,"[3, 4]","[70, 60]","[2, 1]",2,1,3,70,7,1,3,2164 -46,43,"[4, 3]","[50, 90]","[2, 1]",2,1,3,90,9,1,4,1883 -46,44,"[1, 2]","[50, 50]","[1, 1]",2,1,1,50,5,1,1,1997 -46,45,"[4, 3]","[60, 40]","[2, 1]",2,5,3,200,4,1,3,1926 -46,46,"[1, 2]","[10, 50]","[1, 1]",2,1,1,10,1,1,2,1770 -46,47,"[4, 3]","[60, 0]","[2, 2]",2,1,4,60,6,2,3,2213 -46,48,"[2, 1]","[60, 20]","[1, 2]",2,1,2,60,6,2,1,2868 -46,49,"[1, 2]","[10, 90]","[1, 2]",2,5,2,450,9,2,2,4920 -46,50,"[4, 3]","[80, 30]","[2, 2]",2,5,4,400,8,2,3,2032 -46,51,"[4, 3]","[70, 10]","[2, 2]",2,5,4,350,7,2,3,1890 -46,52,"[1, 2]","[10, 40]","[1, 2]",2,5,2,200,4,2,2,2410 -46,53,"[3, 4]","[20, 70]","[2, 2]",2,5,4,350,7,2,4,1984 -46,54,"[4, 3]","[90, 10]","[2, 2]",2,5,4,450,9,2,3,2078 -46,55,"[1, 2]","[20, 50]","[1, 2]",2,1,2,50,5,2,2,2035 -46,56,"[4, 3]","[70, 20]","[2, 2]",2,5,4,350,7,2,3,2204 -46,57,"[1, 2]","[10, 90]","[1, 2]",2,5,2,450,9,2,2,1941 -46,58,"[4, 3]","[50, 20]","[2, 2]",2,5,4,250,5,2,3,2011 -46,59,"[2, 1]","[50, 20]","[1, 2]",2,1,2,50,5,2,1,1915 -46,60,"[1, 2]","[30, 50]","[1, 2]",2,1,2,50,5,2,2,2103 -46,61,"[3, 4]","[20, 60]","[2, 2]",2,5,4,300,6,2,4,2365 -46,62,"[3, 4]","[50, 40]","[2, 2]",2,5,4,200,4,2,3,2003 -46,63,"[1, 2]","[60, 50]","[1, 2]",2,5,2,250,5,2,1,1794 -46,64,"[4, 3]","[70, 60]","[2, 2]",2,1,4,70,7,2,3,1989 -46,65,"[2, 1]","[90, 60]","[1, 2]",2,1,2,90,9,2,1,1945 -46,66,"[1, 2]","[40, 50]","[1, 2]",2,1,2,50,5,2,2,1871 -46,67,"[3, 4]","[40, 40]","[2, 2]",2,1,4,40,4,2,3,2056 -46,68,"[3, 4]","[20, 30]","[2, 2]",2,5,4,150,3,2,4,2400 -46,69,"[3, 4]","[50, 50]","[2, 1]",2,1,3,50,5,1,3,2001 -46,70,"[4, 3]","[30, 80]","[2, 1]",2,1,3,80,8,1,4,2236 -46,71,"[3, 4]","[70, 0]","[2, 1]",2,1,3,70,7,1,3,1773 -46,72,"[3, 4]","[70, 10]","[2, 1]",2,5,3,350,7,1,3,1785 -46,73,"[4, 3]","[10, 80]","[2, 1]",2,5,3,400,8,1,4,1813 -46,74,"[4, 3]","[20, 70]","[2, 1]",2,1,3,70,7,1,4,1761 -46,75,"[4, 3]","[50, 70]","[2, 1]",2,5,3,350,7,1,4,1973 -46,76,"[3, 4]","[20, 40]","[2, 1]",2,5,3,100,2,1,4,1957 -46,77,"[4, 3]","[20, 10]","[2, 2]",2,1,4,20,2,2,3,2056 -46,78,"[3, 4]","[30, 30]","[2, 2]",2,5,4,150,3,2,3,5697 -46,79,"[2, 1]","[30, 30]","[1, 1]",2,1,1,30,3,1,2,3318 -46,80,"[3, 4]","[30, 20]","[2, 1]",2,5,3,150,3,1,3,4226 -47,1,"[1, 2]","[30, 80]","[1, 1]",1,1,1,30,3,1,2,4070 -47,2,"[1, 2]","[20, 80]","[1, 2]",1,1,2,80,8,2,2,2207 -47,3,"[3, 4]","[10, 60]","[2, 1]",1,5,3,50,1,1,4,2314 -47,4,"[4, 3]","[90, 10]","[2, 2]",1,5,4,450,9,2,3,2753 -47,5,"[2, 1]","[70, 0]","[1, 2]",1,5,2,350,7,2,1,2548 -47,6,"[1, 2]","[20, 50]","[1, 2]",1,1,2,50,5,2,2,2381 -47,7,"[1, 2]","[10, 40]","[1, 1]",1,5,1,50,1,1,2,2552 -47,8,"[2, 1]","[60, 10]","[1, 2]",1,5,2,300,6,2,1,4446 -47,9,"[2, 1]","[80, 10]","[1, 2]",1,1,2,80,8,2,1,2214 -47,10,"[2, 1]","[70, 10]","[1, 2]",1,1,2,70,7,2,1,3126 -47,11,"[1, 2]","[30, 40]","[1, 1]",1,1,1,30,3,1,2,1830 -47,12,"[4, 3]","[40, 20]","[2, 2]",1,1,4,40,4,2,3,2297 -47,13,"[4, 3]","[50, 30]","[2, 1]",1,1,3,30,3,1,3,1729 -47,14,"[4, 3]","[10, 10]","[2, 2]",1,1,4,10,1,2,4,1957 -47,15,"[4, 3]","[0, 20]","[2, 1]",1,5,3,100,2,1,4,2225 -47,16,"[2, 1]","[20, 20]","[1, 1]",1,5,1,100,2,1,2,4139 -47,17,"[3, 4]","[20, 20]","[2, 2]",1,5,4,100,2,2,3,3856 -47,18,"[4, 3]","[40, 40]","[2, 2]",1,5,4,200,4,2,4,1997 -47,19,"[1, 2]","[50, 20]","[1, 1]",1,5,1,250,5,1,1,1919 -47,20,"[3, 4]","[50, 10]","[2, 2]",1,1,4,10,1,2,3,1747 -47,21,"[2, 1]","[30, 60]","[1, 1]",1,5,1,300,6,1,2,1823 -47,22,"[3, 4]","[30, 20]","[2, 1]",1,1,3,30,3,1,3,1846 -47,23,"[1, 2]","[30, 30]","[1, 1]",1,1,1,30,3,1,1,2303 -47,24,"[4, 3]","[0, 30]","[2, 1]",1,1,3,30,3,1,4,2487 -47,25,"[1, 2]","[20, 20]","[1, 2]",1,5,2,100,2,2,1,2481 -47,26,"[1, 2]","[60, 30]","[1, 2]",1,5,2,150,3,2,1,2263 -47,27,"[2, 1]","[40, 30]","[1, 2]",1,1,2,40,4,2,1,2103 -47,28,"[2, 1]","[50, 50]","[1, 1]",1,5,1,250,5,1,2,2430 -47,29,"[3, 4]","[30, 40]","[2, 1]",1,5,3,150,3,1,4,2775 -47,30,"[2, 1]","[70, 40]","[1, 1]",1,1,1,40,4,1,1,1723 -47,31,"[1, 2]","[40, 70]","[1, 2]",1,1,2,70,7,2,2,1796 -47,32,"[4, 3]","[90, 60]","[2, 1]",1,5,3,300,6,1,3,1735 -47,33,"[1, 2]","[70, 70]","[1, 2]",1,1,2,70,7,2,1,2169 -47,34,"[4, 3]","[80, 60]","[2, 1]",1,5,3,300,6,1,3,1718 -47,35,"[2, 1]","[90, 80]","[1, 2]",1,1,2,90,9,2,1,2413 -47,36,"[4, 3]","[90, 70]","[2, 1]",1,5,3,350,7,1,3,2164 -47,37,"[1, 2]","[40, 70]","[1, 1]",1,5,1,200,4,1,2,1980 -47,38,"[2, 1]","[70, 30]","[1, 1]",1,1,1,30,3,1,1,1913 -47,39,"[2, 1]","[60, 20]","[1, 2]",1,5,2,300,6,2,1,1773 -47,40,"[4, 3]","[80, 10]","[2, 1]",1,1,3,10,1,1,3,1629 -47,41,"[1, 2]","[20, 50]","[1, 1]",2,5,1,100,2,1,2,5393 -47,42,"[2, 1]","[70, 10]","[1, 1]",2,1,1,10,1,1,1,1796 -47,43,"[3, 4]","[20, 80]","[2, 1]",2,1,3,20,2,1,4,2230 -47,44,"[2, 1]","[70, 0]","[1, 2]",2,5,2,350,7,2,1,1930 -47,45,"[2, 1]","[30, 10]","[1, 2]",2,5,2,150,3,2,1,1919 -47,46,"[4, 3]","[40, 10]","[2, 2]",2,1,4,40,4,2,3,1858 -47,47,"[2, 1]","[30, 10]","[1, 2]",2,1,2,30,3,2,1,2503 -47,48,"[4, 3]","[60, 30]","[2, 1]",2,5,3,150,3,1,3,1730 -47,49,"[4, 3]","[70, 10]","[2, 1]",2,1,3,10,1,1,3,2224 -47,50,"[1, 2]","[10, 50]","[1, 1]",2,1,1,10,1,1,2,1735 -47,51,"[2, 1]","[40, 30]","[1, 2]",2,1,2,40,4,2,1,1880 -47,52,"[4, 3]","[20, 40]","[2, 2]",2,5,4,100,2,2,4,1768 -47,53,"[4, 3]","[30, 40]","[2, 2]",2,1,4,30,3,2,4,2047 -47,54,"[2, 1]","[20, 50]","[1, 1]",2,5,1,250,5,1,2,1834 -47,55,"[1, 2]","[60, 10]","[1, 1]",2,1,1,60,6,1,1,1925 -47,56,"[3, 4]","[40, 20]","[2, 2]",2,5,4,100,2,2,3,1996 -47,57,"[3, 4]","[50, 30]","[2, 2]",2,1,4,30,3,2,3,2024 -47,58,"[3, 4]","[30, 40]","[2, 1]",2,5,3,150,3,1,4,1986 -47,59,"[1, 2]","[30, 40]","[1, 1]",2,5,1,150,3,1,2,2041 -47,60,"[4, 3]","[30, 20]","[2, 1]",2,5,3,100,2,1,3,2235 -47,61,"[3, 4]","[40, 20]","[2, 1]",2,5,3,200,4,1,3,2358 -47,62,"[3, 4]","[20, 20]","[2, 1]",2,1,3,20,2,1,3,2114 -47,63,"[4, 3]","[20, 30]","[2, 2]",2,5,4,100,2,2,4,1879 -47,64,"[2, 1]","[40, 60]","[1, 1]",2,1,1,60,6,1,2,1796 -47,65,"[1, 2]","[80, 60]","[1, 2]",2,5,2,300,6,2,1,1985 -47,66,"[1, 2]","[70, 0]","[1, 2]",2,5,2,0,0,2,1,1719 -47,67,"[4, 3]","[10, 80]","[2, 2]",2,1,4,10,1,2,4,1808 -47,68,"[4, 3]","[20, 60]","[2, 1]",2,1,3,60,6,1,4,1746 -47,69,"[3, 4]","[40, 10]","[2, 1]",2,5,3,200,4,1,3,2336 -47,70,"[2, 1]","[10, 30]","[1, 1]",2,5,1,150,3,1,2,1635 -47,71,"[4, 3]","[10, 40]","[2, 1]",2,1,3,40,4,1,4,1711 -47,72,"[1, 2]","[70, 30]","[1, 1]",2,1,1,70,7,1,1,1746 -47,73,"[4, 3]","[10, 60]","[2, 1]",2,5,3,300,6,1,4,1746 -47,74,"[4, 3]","[20, 60]","[2, 1]",2,1,3,60,6,1,4,2503 -47,75,"[4, 3]","[10, 80]","[2, 1]",2,1,3,80,8,1,4,2041 -47,76,"[2, 1]","[20, 60]","[1, 1]",2,5,1,300,6,1,2,1891 -47,77,"[3, 4]","[80, 10]","[2, 1]",2,5,3,400,8,1,3,1929 -47,78,"[4, 3]","[10, 90]","[2, 1]",2,1,3,90,9,1,4,2007 -47,79,"[1, 2]","[90, 10]","[1, 1]",2,1,1,90,9,1,1,1696 -47,80,"[2, 1]","[10, 70]","[1, 1]",2,5,1,350,7,1,2,2119 -48,1,"[1, 2]","[60, 40]","[1, 1]",1,1,1,60,6,1,1,16247 -48,2,"[4, 3]","[30, 90]","[2, 1]",1,1,3,90,9,1,4,2884 -48,3,"[4, 3]","[50, 60]","[2, 1]",1,1,3,60,6,1,4,5962 -48,4,"[1, 2]","[80, 50]","[1, 2]",1,5,2,250,5,2,1,4327 -48,5,"[3, 4]","[70, 40]","[2, 2]",1,5,4,200,4,2,3,7793 -48,6,"[2, 1]","[50, 80]","[1, 1]",1,5,1,400,8,1,2,6999 -48,7,"[4, 3]","[60, 90]","[2, 1]",1,1,3,90,9,1,4,3539 -48,8,"[1, 2]","[70, 40]","[1, 1]",1,1,1,70,7,1,1,3520 -48,9,"[2, 1]","[20, 60]","[1, 1]",1,1,1,60,6,1,2,3604 -48,10,"[4, 3]","[0, 70]","[2, 2]",1,1,4,0,0,2,4,3150 -48,11,"[4, 3]","[30, 80]","[2, 1]",1,5,3,400,8,1,4,5267 -48,12,"[1, 2]","[60, 40]","[1, 1]",1,1,1,60,6,1,1,6871 -48,13,"[3, 4]","[60, 30]","[2, 1]",1,5,3,300,6,1,3,3361 -48,14,"[4, 3]","[30, 80]","[2, 1]",1,1,3,80,8,1,4,2505 -48,15,"[1, 2]","[80, 50]","[1, 1]",1,5,1,400,8,1,1,3252 -48,16,"[3, 4]","[80, 30]","[2, 1]",1,5,3,400,8,1,3,2498 -48,17,"[2, 1]","[60, 90]","[1, 1]",1,5,1,450,9,1,2,2542 -48,18,"[4, 3]","[50, 90]","[2, 1]",1,1,3,90,9,1,4,2767 -48,19,"[2, 1]","[90, 50]","[1, 1]",1,1,1,50,5,1,1,3096 -48,20,"[4, 3]","[70, 80]","[2, 2]",1,5,4,350,7,2,4,3949 -48,21,"[3, 4]","[80, 50]","[2, 2]",1,5,4,250,5,2,3,2557 -48,22,"[3, 4]","[80, 50]","[2, 2]",1,5,4,250,5,2,3,2959 -48,23,"[4, 3]","[40, 80]","[2, 2]",1,1,4,40,4,2,4,2784 -48,24,"[1, 2]","[90, 50]","[1, 1]",1,1,1,90,9,1,1,2984 -48,25,"[4, 3]","[60, 60]","[2, 1]",1,1,3,60,6,1,4,2262 -48,26,"[1, 2]","[80, 80]","[1, 2]",1,5,2,400,8,2,1,4363 -48,27,"[1, 2]","[80, 70]","[1, 2]",1,1,2,70,7,2,1,3901 -48,28,"[2, 1]","[60, 70]","[1, 2]",1,1,2,60,6,2,2,2902 -48,29,"[4, 3]","[40, 90]","[2, 2]",1,1,4,40,4,2,4,3027 -48,30,"[2, 1]","[30, 70]","[1, 1]",1,1,1,70,7,1,2,2900 -48,31,"[2, 1]","[60, 80]","[1, 1]",1,1,1,80,8,1,2,2791 -48,32,"[2, 1]","[50, 80]","[1, 1]",1,1,1,80,8,1,2,4042 -48,33,"[2, 1]","[40, 90]","[1, 1]",1,5,1,450,9,1,2,3847 -48,34,"[3, 4]","[90, 50]","[2, 1]",1,5,3,450,9,1,3,2657 -48,35,"[2, 1]","[60, 70]","[1, 1]",1,1,1,70,7,1,2,3992 -48,36,"[1, 2]","[50, 90]","[1, 1]",1,1,1,50,5,1,2,2599 -48,37,"[3, 4]","[50, 70]","[2, 2]",1,5,4,350,7,2,4,2623 -48,38,"[3, 4]","[40, 60]","[2, 2]",1,5,4,300,6,2,4,4959 -48,39,"[2, 1]","[60, 10]","[1, 1]",1,5,1,50,1,1,1,5193 -48,40,"[2, 1]","[70, 50]","[1, 2]",1,5,2,350,7,2,1,3674 -48,41,"[3, 4]","[80, 80]","[2, 1]",2,1,3,80,8,1,3,6112 -48,42,"[1, 2]","[80, 70]","[1, 1]",2,1,1,80,8,1,1,2692 -48,43,"[2, 1]","[70, 90]","[1, 1]",2,5,1,450,9,1,2,2717 -48,44,"[4, 3]","[80, 60]","[2, 1]",2,5,3,300,6,1,3,4478 -48,45,"[1, 2]","[80, 80]","[1, 2]",2,1,2,80,8,2,1,2992 -48,46,"[4, 3]","[80, 80]","[2, 2]",2,1,4,80,8,2,4,2707 -48,47,"[2, 1]","[70, 30]","[1, 2]",2,5,2,350,7,2,1,2824 -48,48,"[3, 4]","[10, 20]","[2, 2]",2,1,4,20,2,2,4,3600 -48,49,"[1, 2]","[20, 20]","[1, 1]",2,5,1,100,2,1,1,3261 -48,50,"[4, 3]","[20, 20]","[2, 2]",2,1,4,20,2,2,4,4344 -48,51,"[3, 4]","[30, 30]","[2, 2]",2,1,4,30,3,2,3,4985 -48,52,"[1, 2]","[50, 40]","[1, 2]",2,5,2,200,4,2,1,3598 -48,53,"[1, 2]","[50, 60]","[1, 2]",2,5,2,300,6,2,2,3397 -48,54,"[1, 2]","[60, 60]","[1, 2]",2,5,2,300,6,2,1,2262 -48,55,"[4, 3]","[80, 70]","[2, 2]",2,1,4,80,8,2,3,4169 -48,56,"[4, 3]","[70, 80]","[2, 2]",2,1,4,70,7,2,4,2523 -48,57,"[1, 2]","[80, 80]","[1, 2]",2,5,2,400,8,2,1,3520 -48,58,"[1, 2]","[60, 90]","[1, 2]",2,5,2,450,9,2,2,2163 -48,59,"[3, 4]","[90, 60]","[2, 2]",2,5,4,300,6,2,3,2609 -48,60,"[2, 1]","[60, 80]","[1, 1]",2,5,1,400,8,1,2,2926 -48,61,"[1, 2]","[80, 60]","[1, 1]",2,1,1,80,8,1,1,3278 -48,62,"[4, 3]","[90, 70]","[2, 1]",2,5,3,350,7,1,3,2489 -48,63,"[2, 1]","[80, 80]","[1, 1]",2,5,1,400,8,1,2,2616 -48,64,"[4, 3]","[80, 90]","[2, 1]",2,5,3,450,9,1,4,2254 -48,65,"[3, 4]","[80, 60]","[2, 1]",2,5,3,400,8,1,3,2498 -48,66,"[4, 3]","[50, 70]","[2, 1]",2,5,3,350,7,1,4,2077 -48,67,"[3, 4]","[50, 70]","[2, 1]",2,5,3,250,5,1,4,2432 -48,68,"[4, 3]","[90, 60]","[2, 2]",2,5,4,450,9,2,3,2238 -48,69,"[1, 2]","[60, 50]","[1, 2]",2,1,2,50,5,2,1,4806 -48,70,"[1, 2]","[50, 60]","[1, 1]",2,1,1,50,5,1,2,2515 -48,71,"[1, 2]","[90, 20]","[1, 1]",2,5,1,450,9,1,1,2642 -48,72,"[4, 3]","[30, 50]","[2, 1]",2,1,3,50,5,1,4,5821 -48,73,"[3, 4]","[20, 60]","[2, 1]",2,5,3,100,2,1,4,5923 -48,74,"[3, 4]","[30, 50]","[2, 2]",2,1,4,50,5,2,4,4512 -48,75,"[2, 1]","[70, 30]","[1, 2]",2,1,2,70,7,2,1,14537 -48,76,"[4, 3]","[80, 10]","[2, 2]",2,1,4,80,8,2,3,3346 -48,77,"[3, 4]","[10, 60]","[2, 2]",2,5,4,300,6,2,4,6410 -48,78,"[1, 2]","[20, 60]","[1, 2]",2,1,2,60,6,2,2,3943 -48,79,"[3, 4]","[20, 60]","[2, 2]",2,1,4,60,6,2,4,3756 -48,80,"[2, 1]","[80, 30]","[1, 1]",2,5,1,150,3,1,1,2757 -49,1,"[4, 3]","[30, 60]","[2, 1]",1,5,3,300,6,1,4,22044 -49,2,"[2, 1]","[0, 60]","[1, 1]",1,5,1,300,6,1,2,1958 -49,3,"[4, 3]","[20, 70]","[2, 1]",1,5,3,350,7,1,4,1807 -49,4,"[4, 3]","[0, 60]","[2, 1]",1,5,3,300,6,1,4,1970 -49,5,"[4, 3]","[10, 60]","[2, 1]",1,1,3,60,6,1,4,1881 -49,6,"[1, 2]","[30, 20]","[1, 1]",1,1,1,30,3,1,1,2320 -49,7,"[4, 3]","[10, 20]","[2, 2]",1,5,4,50,1,2,4,2072 -49,8,"[4, 3]","[30, 60]","[2, 2]",1,5,4,150,3,2,4,1983 -49,9,"[3, 4]","[60, 10]","[2, 2]",1,5,4,50,1,2,3,1983 -49,10,"[1, 2]","[60, 30]","[1, 2]",1,5,2,150,3,2,1,2806 -49,11,"[3, 4]","[70, 40]","[2, 2]",1,5,4,200,4,2,3,2399 -49,12,"[2, 1]","[50, 70]","[1, 2]",1,1,2,50,5,2,2,2205 -49,13,"[1, 2]","[60, 0]","[1, 1]",1,5,1,300,6,1,1,3015 -49,14,"[4, 3]","[30, 60]","[2, 1]",1,1,3,60,6,1,4,1834 -49,15,"[1, 2]","[60, 50]","[1, 2]",1,5,2,250,5,2,1,3229 -49,16,"[4, 3]","[40, 70]","[2, 2]",1,1,4,40,4,2,4,1841 -49,17,"[1, 2]","[80, 40]","[1, 1]",1,1,1,80,8,1,1,2216 -49,18,"[2, 1]","[40, 70]","[1, 2]",1,5,2,200,4,2,2,5324 -49,19,"[1, 2]","[60, 50]","[1, 2]",1,1,2,50,5,2,1,2112 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1]",2,5,3,50,1,1,4,1769 -53,61,"[4, 3]","[20, 30]","[2, 1]",2,1,3,30,3,1,4,2268 -53,62,"[2, 1]","[10, 40]","[1, 2]",2,1,2,10,1,2,2,2750 -53,63,"[3, 4]","[30, 20]","[2, 2]",2,5,4,100,2,2,3,1934 -53,64,"[4, 3]","[20, 20]","[2, 2]",2,1,4,20,2,2,4,3336 -53,65,"[2, 1]","[40, 0]","[1, 2]",2,1,2,40,4,2,1,1751 -53,66,"[2, 1]","[50, 20]","[1, 1]",2,5,1,100,2,1,1,2451 -53,67,"[3, 4]","[30, 60]","[2, 1]",2,1,3,30,3,1,4,2168 -53,68,"[3, 4]","[40, 70]","[2, 1]",2,5,3,200,4,1,4,1801 -53,69,"[1, 2]","[50, 60]","[1, 1]",2,5,1,250,5,1,2,3036 -53,70,"[2, 1]","[70, 50]","[1, 1]",2,1,1,50,5,1,1,2252 -53,71,"[1, 2]","[40, 70]","[1, 2]",2,5,2,350,7,2,2,2385 -53,72,"[4, 3]","[90, 40]","[2, 2]",2,5,4,450,9,2,3,2752 -53,73,"[1, 2]","[50, 70]","[1, 2]",2,5,2,350,7,2,2,2635 -53,74,"[4, 3]","[90, 60]","[2, 2]",2,1,4,90,9,2,3,2534 -53,75,"[2, 1]","[90, 60]","[1, 2]",2,5,2,450,9,2,1,1885 -53,76,"[3, 4]","[50, 80]","[2, 2]",2,1,4,80,8,2,4,2185 -53,77,"[1, 2]","[70, 80]","[1, 2]",2,1,2,80,8,2,2,1968 -53,78,"[4, 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-29,16,"[1, 2]","[0, 50]","[1, 1]",1,5,1,0,0,1,2,1989 -29,17,"[2, 1]","[10, 50]","[1, 2]",1,1,2,10,1,2,2,1906 -29,18,"[1, 2]","[50, 20]","[1, 1]",1,5,1,250,5,1,1,1890 -29,19,"[2, 1]","[30, 30]","[1, 1]",1,1,1,30,3,1,2,2006 -29,20,"[4, 3]","[40, 60]","[2, 2]",1,1,4,40,4,2,4,1772 -29,21,"[1, 2]","[30, 30]","[1, 2]",1,1,2,30,3,2,1,2306 -29,22,"[1, 2]","[10, 20]","[1, 1]",1,1,1,10,1,1,2,1890 -29,23,"[4, 3]","[0, 0]","[2, 2]",1,5,4,0,0,2,4,10507 -29,24,"[2, 1]","[10, 40]","[1, 1]",1,5,1,200,4,1,2,2005 -29,25,"[2, 1]","[20, 40]","[1, 1]",1,5,1,200,4,1,2,1790 -29,26,"[1, 2]","[10, 0]","[1, 1]",1,5,1,50,1,1,1,2140 -29,27,"[2, 1]","[10, 20]","[1, 1]",1,1,1,20,2,1,2,1856 -29,28,"[4, 3]","[30, 0]","[2, 2]",1,1,4,30,3,2,3,1705 -29,29,"[1, 2]","[0, 30]","[1, 2]",1,1,2,30,3,2,2,2222 -29,30,"[4, 3]","[20, 0]","[2, 1]",1,5,3,0,0,1,3,2187 -29,31,"[3, 4]","[40, 20]","[2, 2]",1,1,4,20,2,2,3,1822 -29,32,"[2, 1]","[20, 10]","[1, 2]",1,5,2,100,2,2,1,1889 -29,33,"[2, 1]","[20, 10]","[1, 2]",1,1,2,20,2,2,1,1789 -29,34,"[3, 4]","[20, 30]","[2, 1]",1,1,3,20,2,1,4,1824 -29,35,"[1, 2]","[0, 40]","[1, 2]",1,5,2,200,4,2,2,1986 -29,36,"[2, 1]","[70, 10]","[1, 2]",1,1,2,70,7,2,1,1740 -29,37,"[2, 1]","[90, 30]","[1, 2]",1,1,2,90,9,2,1,1823 -29,38,"[4, 3]","[90, 30]","[2, 2]",1,5,4,450,9,2,3,2190 -29,39,"[3, 4]","[20, 90]","[2, 2]",1,1,4,90,9,2,4,1856 -29,40,"[2, 1]","[80, 10]","[1, 2]",1,5,2,400,8,2,1,2357 -29,41,"[4, 3]","[90, 0]","[2, 2]",2,5,4,450,9,2,3,4168 -29,42,"[2, 1]","[70, 10]","[1, 2]",2,1,2,70,7,2,1,1989 -29,43,"[2, 1]","[70, 20]","[1, 2]",2,1,2,70,7,2,1,2573 -29,44,"[3, 4]","[0, 80]","[2, 2]",2,5,4,400,8,2,4,2206 -29,45,"[1, 2]","[10, 80]","[1, 2]",2,5,2,400,8,2,2,2340 -29,46,"[1, 2]","[30, 70]","[1, 2]",2,5,2,350,7,2,2,2123 -29,47,"[2, 1]","[60, 20]","[1, 2]",2,5,2,300,6,2,1,2056 -29,48,"[3, 4]","[20, 80]","[2, 2]",2,1,4,80,8,2,4,2773 -29,49,"[2, 1]","[70, 0]","[1, 2]",2,1,2,70,7,2,1,2040 -29,50,"[4, 3]","[90, 0]","[2, 2]",2,5,4,450,9,2,3,1973 -29,51,"[2, 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-29,69,"[4, 3]","[10, 80]","[2, 1]",2,5,3,400,8,1,4,1740 -29,70,"[2, 1]","[40, 60]","[1, 1]",2,1,1,60,6,1,2,2106 -29,71,"[1, 2]","[80, 80]","[1, 1]",2,5,1,400,8,1,1,2457 -29,72,"[1, 2]","[90, 70]","[1, 1]",2,5,1,450,9,1,1,2139 -29,73,"[3, 4]","[90, 50]","[2, 1]",2,1,3,90,9,1,3,1739 -29,74,"[4, 3]","[30, 70]","[2, 1]",2,1,3,70,7,1,4,2439 -29,75,"[4, 3]","[20, 90]","[2, 1]",2,1,3,90,9,1,4,2040 -29,76,"[3, 4]","[90, 10]","[2, 1]",2,5,3,450,9,1,3,1756 -29,77,"[4, 3]","[20, 90]","[2, 1]",2,5,3,450,9,1,4,1706 -29,78,"[2, 1]","[40, 80]","[1, 1]",2,1,1,80,8,1,2,2589 -29,79,"[4, 3]","[50, 70]","[2, 1]",2,1,3,70,7,1,4,1956 -29,80,"[3, 4]","[70, 70]","[2, 1]",2,5,3,350,7,1,3,1989 -29,1,"[2, 1]","[20, 70]","[1, 2]",1,1,2,20,2,2,2,17723 -29,2,"[4, 3]","[20, 90]","[2, 1]",1,1,3,90,9,1,4,3657 -29,3,"[2, 1]","[10, 70]","[1, 1]",1,1,1,70,7,1,2,2156 -29,4,"[1, 2]","[70, 20]","[1, 1]",1,5,1,350,7,1,1,1923 -29,5,"[3, 4]","[80, 0]","[2, 1]",1,5,3,400,8,1,3,2606 -29,6,"[3, 4]","[80, 10]","[2, 1]",1,5,3,400,8,1,3,1857 -29,7,"[3, 4]","[70, 10]","[2, 1]",1,5,3,350,7,1,3,1806 -29,8,"[4, 3]","[30, 40]","[2, 1]",1,1,3,40,4,1,4,1823 -29,9,"[2, 1]","[40, 70]","[1, 2]",1,1,2,40,4,2,2,2306 -29,10,"[2, 1]","[50, 60]","[1, 2]",1,1,2,50,5,2,2,3089 -29,11,"[3, 4]","[60, 60]","[2, 2]",1,5,4,300,6,2,3,2139 -29,12,"[4, 3]","[60, 90]","[2, 2]",1,5,4,300,6,2,4,1940 -29,13,"[3, 4]","[90, 40]","[2, 2]",1,5,4,200,4,2,3,2223 -29,14,"[2, 1]","[60, 80]","[1, 1]",1,5,1,400,8,1,2,2189 -29,15,"[2, 1]","[20, 70]","[1, 1]",1,1,1,70,7,1,2,2070 -29,16,"[1, 2]","[80, 30]","[1, 1]",1,1,1,80,8,1,1,1756 -29,17,"[2, 1]","[50, 90]","[1, 1]",1,1,1,90,9,1,2,1990 -29,18,"[3, 4]","[80, 60]","[2, 1]",1,1,3,80,8,1,3,1791 -29,19,"[2, 1]","[70, 70]","[1, 1]",1,5,1,350,7,1,2,2123 -29,20,"[1, 2]","[50, 80]","[1, 1]",1,5,1,250,5,1,2,1890 -29,21,"[2, 1]","[60, 50]","[1, 1]",1,5,1,250,5,1,1,1956 -29,22,"[2, 1]","[70, 50]","[1, 1]",1,5,1,250,5,1,1,2139 -29,23,"[2, 1]","[60, 60]","[1, 1]",1,5,1,300,6,1,2,1822 -29,24,"[4, 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1]",2,5,1,150,3,1,1,6936 -29,42,"[2, 1]","[80, 10]","[1, 2]",2,5,2,400,8,2,1,2039 -29,43,"[3, 4]","[10, 80]","[2, 2]",2,5,4,400,8,2,4,1790 -29,44,"[2, 1]","[70, 40]","[1, 2]",2,1,2,70,7,2,1,1973 -29,45,"[4, 3]","[40, 70]","[2, 2]",2,5,4,200,4,2,4,1873 -29,46,"[4, 3]","[50, 40]","[2, 1]",2,5,3,200,4,1,3,2072 -29,47,"[3, 4]","[50, 50]","[2, 2]",2,1,4,50,5,2,3,1823 -29,48,"[4, 3]","[70, 20]","[2, 2]",2,1,4,70,7,2,3,1940 -29,49,"[2, 1]","[70, 40]","[1, 2]",2,1,2,70,7,2,1,2156 -29,50,"[3, 4]","[40, 50]","[2, 2]",2,5,4,250,5,2,4,1856 -29,51,"[3, 4]","[40, 70]","[2, 2]",2,1,4,70,7,2,4,2340 -29,52,"[1, 2]","[40, 70]","[1, 2]",2,1,2,70,7,2,2,2106 -29,53,"[4, 3]","[50, 50]","[2, 2]",2,1,4,50,5,2,4,2156 -29,54,"[1, 2]","[40, 30]","[1, 2]",2,1,2,30,3,2,1,2290 -29,55,"[3, 4]","[10, 20]","[2, 1]",2,1,3,10,1,1,4,1739 -29,56,"[4, 3]","[10, 30]","[2, 2]",2,1,4,10,1,2,4,1973 -29,57,"[4, 3]","[10, 50]","[2, 1]",2,5,3,250,5,1,4,1705 -29,58,"[2, 1]","[0, 50]","[1, 1]",2,1,1,50,5,1,2,1989 -29,59,"[3, 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1]",2,1,3,60,6,1,3,2040 -29,77,"[4, 3]","[50, 50]","[2, 1]",2,5,3,250,5,1,4,1706 -29,78,"[3, 4]","[40, 70]","[2, 1]",2,5,3,200,4,1,4,1789 -29,79,"[4, 3]","[90, 70]","[2, 1]",2,1,3,70,7,1,3,1958 -29,80,"[3, 4]","[80, 80]","[2, 1]",2,5,3,400,8,1,3,1841 -29,1,"[2, 1]","[40, 70]","[1, 2]",1,1,2,40,4,2,2,5637 -29,2,"[3, 4]","[80, 40]","[2, 1]",1,5,3,400,8,1,3,4886 -29,3,"[3, 4]","[80, 60]","[2, 1]",1,1,3,80,8,1,3,1954 -29,4,"[2, 1]","[30, 90]","[1, 1]",1,5,1,450,9,1,2,2207 -29,5,"[1, 2]","[70, 30]","[1, 1]",1,1,1,70,7,1,1,1915 -29,6,"[4, 3]","[10, 80]","[2, 1]",1,1,3,80,8,1,4,1890 -29,7,"[4, 3]","[0, 80]","[2, 1]",1,5,3,400,8,1,4,1872 -29,8,"[4, 3]","[20, 40]","[2, 1]",1,1,3,40,4,1,4,1873 -29,9,"[4, 3]","[50, 0]","[2, 2]",1,1,4,50,5,2,3,1720 -29,10,"[3, 4]","[0, 60]","[2, 2]",1,1,4,60,6,2,4,1955 -29,11,"[4, 3]","[80, 0]","[2, 2]",1,1,4,80,8,2,3,2122 -29,12,"[4, 3]","[70, 30]","[2, 2]",1,5,4,350,7,2,3,1923 -29,13,"[2, 1]","[90, 10]","[1, 2]",1,1,2,90,9,2,1,2256 -29,14,"[4, 3]","[90, 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-29,32,"[3, 4]","[50, 70]","[2, 2]",1,5,4,350,7,2,4,2055 -29,33,"[3, 4]","[60, 60]","[2, 2]",1,1,4,60,6,2,3,1823 -29,34,"[2, 1]","[60, 80]","[1, 2]",1,5,2,300,6,2,2,2456 -29,35,"[3, 4]","[90, 60]","[2, 2]",1,5,4,300,6,2,3,1806 -29,36,"[1, 2]","[80, 50]","[1, 2]",1,5,2,250,5,2,1,2053 -29,37,"[2, 1]","[40, 70]","[1, 2]",1,1,2,40,4,2,2,2005 -29,38,"[2, 1]","[0, 80]","[1, 1]",1,5,1,400,8,1,2,1873 -29,39,"[4, 3]","[10, 80]","[2, 1]",1,1,3,80,8,1,4,1820 -29,40,"[2, 1]","[0, 90]","[1, 1]",1,5,1,450,9,1,2,1873 -29,41,"[3, 4]","[80, 10]","[2, 1]",2,1,3,80,8,1,3,5134 -29,42,"[4, 3]","[10, 70]","[2, 1]",2,5,3,350,7,1,4,1839 -29,43,"[2, 1]","[20, 40]","[1, 1]",2,1,1,40,4,1,2,1841 -29,44,"[1, 2]","[60, 20]","[1, 1]",2,1,1,60,6,1,1,1905 -29,45,"[4, 3]","[0, 50]","[2, 1]",2,5,3,250,5,1,4,1805 -29,46,"[4, 3]","[40, 30]","[2, 1]",2,5,3,150,3,1,3,1789 -29,47,"[1, 2]","[30, 20]","[1, 2]",2,5,2,100,2,2,1,2422 -29,48,"[1, 2]","[10, 10]","[1, 1]",2,5,1,50,1,1,1,1872 -29,49,"[3, 4]","[10, 50]","[2, 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2]",2,5,2,100,2,2,1,3344 -54,71,"[1, 2]","[70, 20]","[1, 2]",2,1,2,20,2,2,1,1874 -54,72,"[1, 2]","[60, 10]","[1, 2]",2,5,2,50,1,2,1,1837 -54,73,"[1, 2]","[70, 30]","[1, 2]",2,5,2,150,3,2,1,1806 -54,74,"[3, 4]","[80, 30]","[2, 2]",2,1,4,30,3,2,3,2131 -54,75,"[2, 1]","[20, 90]","[1, 2]",2,5,2,100,2,2,2,2068 -54,76,"[2, 1]","[60, 70]","[1, 2]",2,5,2,300,6,2,2,2099 -54,77,"[3, 4]","[70, 60]","[2, 2]",2,1,4,60,6,2,3,2135 -54,78,"[4, 3]","[40, 50]","[2, 2]",2,1,4,40,4,2,4,1996 -54,79,"[1, 2]","[60, 60]","[1, 2]",2,1,2,60,6,2,1,2760 -54,80,"[2, 1]","[40, 70]","[1, 2]",2,5,2,200,4,2,2,1949 -54,1,"[4, 3]","[30, 60]","[2, 1]",1,5,3,300,6,1,4,4347 -54,2,"[2, 1]","[0, 60]","[1, 1]",1,5,1,300,6,1,2,2447 -54,3,"[4, 3]","[20, 70]","[2, 1]",1,5,3,350,7,1,4,1897 -54,4,"[4, 3]","[0, 60]","[2, 1]",1,5,3,300,6,1,4,1979 -54,5,"[4, 3]","[10, 60]","[2, 1]",1,1,3,60,6,1,4,1879 -54,6,"[1, 2]","[30, 20]","[1, 2]",1,1,2,20,2,2,1,2207 -54,7,"[4, 3]","[10, 20]","[2, 1]",1,5,3,100,2,1,4,1977 -54,8,"[4, 3]","[30, 60]","[2, 2]",1,5,4,150,3,2,4,1951 -54,9,"[3, 4]","[60, 10]","[2, 2]",1,5,4,50,1,2,3,2108 -54,10,"[1, 2]","[60, 30]","[1, 1]",1,5,1,300,6,1,1,2846 -54,11,"[3, 4]","[70, 40]","[2, 1]",1,5,3,350,7,1,3,2322 -54,12,"[2, 1]","[50, 70]","[1, 1]",1,1,1,70,7,1,2,2011 -54,13,"[1, 2]","[60, 0]","[1, 1]",1,5,1,300,6,1,1,2071 -54,14,"[4, 3]","[30, 60]","[2, 1]",1,1,3,60,6,1,4,1876 -54,15,"[1, 2]","[60, 50]","[1, 1]",1,5,1,300,6,1,1,1959 -54,16,"[4, 3]","[40, 70]","[2, 1]",1,1,3,70,7,1,4,2194 -54,17,"[1, 2]","[80, 40]","[1, 1]",1,1,1,80,8,1,1,1870 -54,18,"[2, 1]","[40, 70]","[1, 1]",1,5,1,350,7,1,2,2133 -54,19,"[1, 2]","[60, 50]","[1, 1]",1,1,1,60,6,1,1,2162 -54,20,"[3, 4]","[80, 40]","[2, 1]",1,1,3,80,8,1,3,1929 -54,21,"[2, 1]","[40, 80]","[1, 1]",1,5,1,400,8,1,2,2154 -54,22,"[1, 2]","[80, 30]","[1, 1]",1,1,1,80,8,1,1,2165 -54,23,"[1, 2]","[80, 50]","[1, 1]",1,5,1,400,8,1,1,3802 -54,24,"[4, 3]","[50, 80]","[2, 1]",1,1,3,80,8,1,4,2968 -54,25,"[2, 1]","[70, 70]","[1, 1]",1,5,1,350,7,1,2,2143 -54,26,"[2, 1]","[60, 60]","[1, 1]",1,1,1,60,6,1,2,2261 -54,27,"[2, 1]","[80, 40]","[1, 1]",1,5,1,200,4,1,1,2126 -54,28,"[2, 1]","[80, 60]","[1, 2]",1,1,2,80,8,2,1,2408 -54,29,"[4, 3]","[90, 50]","[2, 2]",1,1,4,90,9,2,3,2125 -54,30,"[2, 1]","[80, 40]","[1, 2]",1,1,2,80,8,2,1,2094 -54,31,"[4, 3]","[70, 40]","[2, 2]",1,5,4,350,7,2,3,1836 -54,32,"[3, 4]","[30, 80]","[2, 2]",1,5,4,400,8,2,4,2092 -54,33,"[3, 4]","[60, 40]","[2, 2]",1,1,4,40,4,2,3,1949 -54,34,"[4, 3]","[30, 70]","[2, 2]",1,1,4,30,3,2,4,4809 -54,35,"[4, 3]","[30, 40]","[2, 2]",1,5,4,150,3,2,4,2556 -54,36,"[3, 4]","[20, 30]","[2, 1]",1,1,3,20,2,1,4,2206 -54,37,"[3, 4]","[20, 40]","[2, 2]",1,1,4,40,4,2,4,1858 -54,38,"[4, 3]","[20, 60]","[2, 2]",1,5,4,100,2,2,4,1961 -54,39,"[4, 3]","[60, 70]","[2, 2]",1,5,4,300,6,2,4,1941 -54,40,"[1, 2]","[70, 60]","[1, 2]",1,5,2,300,6,2,1,3272 -54,41,"[2, 1]","[90, 50]","[1, 1]",2,5,1,250,5,1,1,47872 -54,42,"[3, 4]","[50, 80]","[2, 1]",2,5,3,250,5,1,4,1949 -54,43,"[3, 4]","[50, 70]","[2, 1]",2,1,3,50,5,1,4,2043 -54,44,"[1, 2]","[50, 50]","[1, 1]",2,5,1,250,5,1,1,27575 -54,45,"[2, 1]","[40, 30]","[1, 1]",2,5,1,150,3,1,1,2240 -54,46,"[1, 2]","[50, 30]","[1, 2]",2,1,2,30,3,2,1,1792 -54,47,"[2, 1]","[40, 50]","[1, 2]",2,1,2,40,4,2,2,1996 -54,48,"[3, 4]","[20, 30]","[2, 2]",2,1,4,30,3,2,4,2367 -54,49,"[2, 1]","[40, 30]","[1, 1]",2,5,1,150,3,1,1,2453 -54,50,"[2, 1]","[40, 10]","[1, 1]",2,1,1,10,1,1,1,2215 -54,51,"[1, 2]","[20, 70]","[1, 2]",2,5,2,350,7,2,2,2241 -54,52,"[3, 4]","[30, 80]","[2, 2]",2,5,4,400,8,2,4,2287 -54,53,"[1, 2]","[30, 30]","[1, 2]",2,5,2,150,3,2,1,1948 -54,54,"[2, 1]","[80, 10]","[1, 2]",2,5,2,400,8,2,1,2015 -54,55,"[4, 3]","[70, 10]","[2, 2]",2,1,4,70,7,2,3,98416 -54,56,"[4, 3]","[80, 50]","[2, 2]",2,1,4,80,8,2,3,1964 -54,57,"[1, 2]","[70, 50]","[1, 2]",2,1,2,50,5,2,1,2063 -54,58,"[2, 1]","[60, 80]","[1, 2]",2,1,2,60,6,2,2,2970 -54,59,"[1, 2]","[70, 50]","[1, 2]",2,1,2,50,5,2,1,27850 -54,60,"[1, 2]","[50, 40]","[1, 2]",2,5,2,200,4,2,1,1834 -54,61,"[4, 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2]",2,5,2,300,6,2,1,2451 -54,79,"[4, 3]","[40, 40]","[2, 2]",2,1,4,40,4,2,4,2244 -54,80,"[1, 2]","[20, 80]","[1, 2]",2,5,2,400,8,2,2,2187 -55,1,"[2, 1]","[10, 90]","[1, 1]",1,5,1,450,9,1,2,19085 -55,2,"[1, 2]","[80, 20]","[1, 1]",1,1,1,80,8,1,1,2197 -55,3,"[4, 3]","[80, 90]","[2, 1]",1,1,3,90,9,1,4,3096 -55,4,"[1, 2]","[60, 70]","[1, 1]",1,5,1,300,6,1,2,2448 -55,5,"[4, 3]","[80, 70]","[2, 1]",1,1,3,70,7,1,3,4128 -55,6,"[1, 2]","[70, 30]","[1, 1]",1,1,1,70,7,1,1,2214 -55,7,"[1, 2]","[60, 0]","[1, 2]",1,1,2,0,0,2,1,3013 -55,8,"[1, 2]","[70, 0]","[1, 1]",1,1,1,70,7,1,1,2198 -55,9,"[1, 2]","[90, 20]","[1, 1]",1,5,1,450,9,1,1,2147 -55,10,"[2, 1]","[0, 70]","[1, 1]",1,5,1,350,7,1,2,3262 -55,11,"[2, 1]","[0, 80]","[1, 1]",1,5,1,400,8,1,2,2780 -55,12,"[2, 1]","[30, 80]","[1, 1]",1,5,1,400,8,1,2,2031 -55,13,"[2, 1]","[20, 80]","[1, 1]",1,5,1,400,8,1,2,2714 -55,14,"[2, 1]","[50, 50]","[1, 1]",1,5,1,250,5,1,2,1997 -55,15,"[1, 2]","[60, 50]","[1, 1]",1,5,1,300,6,1,1,8688 -55,16,"[1, 2]","[60, 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4]","[60, 70]","[2, 2]",1,1,4,70,7,2,4,3363 -55,35,"[2, 1]","[50, 50]","[1, 2]",1,1,2,50,5,2,2,2447 -55,36,"[1, 2]","[30, 50]","[1, 2]",1,1,2,50,5,2,2,2447 -55,37,"[3, 4]","[0, 20]","[2, 2]",1,5,4,100,2,2,4,4228 -55,38,"[1, 2]","[30, 30]","[1, 1]",1,1,1,30,3,1,1,2764 -55,39,"[3, 4]","[10, 40]","[2, 2]",1,1,4,40,4,2,4,2764 -55,40,"[3, 4]","[20, 20]","[2, 2]",1,5,4,100,2,2,3,4028 -55,41,"[3, 4]","[0, 10]","[2, 1]",2,1,3,0,0,1,4,9118 -55,42,"[1, 2]","[10, 30]","[1, 2]",2,1,2,30,3,2,2,2081 -55,43,"[2, 1]","[10, 10]","[1, 2]",2,1,2,10,1,2,2,2280 -55,44,"[3, 4]","[20, 30]","[2, 1]",2,1,3,20,2,1,4,2131 -55,45,"[1, 2]","[10, 50]","[1, 1]",2,5,1,50,1,1,2,10302 -55,46,"[3, 4]","[10, 10]","[2, 2]",2,5,4,50,1,2,3,3777 -55,47,"[3, 4]","[10, 30]","[2, 1]",2,1,3,10,1,1,4,2530 -55,48,"[4, 3]","[20, 30]","[2, 2]",2,1,4,20,2,2,4,3029 -55,49,"[3, 4]","[40, 20]","[2, 2]",2,1,4,20,2,2,3,2348 -55,50,"[4, 3]","[40, 40]","[2, 2]",2,5,4,200,4,2,4,2380 -55,51,"[3, 4]","[40, 60]","[2, 2]",2,5,4,300,6,2,4,1997 -55,52,"[3, 4]","[50, 50]","[2, 2]",2,5,4,250,5,2,3,2713 -55,53,"[1, 2]","[60, 50]","[1, 2]",2,1,2,50,5,2,1,2647 -55,54,"[4, 3]","[50, 40]","[2, 1]",2,1,3,40,4,1,3,3578 -55,55,"[2, 1]","[40, 40]","[1, 2]",2,5,2,200,4,2,2,2364 -55,56,"[3, 4]","[40, 20]","[2, 1]",2,1,3,40,4,1,3,4726 -55,57,"[2, 1]","[10, 80]","[1, 2]",2,5,2,50,1,2,2,3079 -55,58,"[2, 1]","[30, 50]","[1, 1]",2,1,1,50,5,1,2,2281 -55,59,"[4, 3]","[20, 30]","[2, 1]",2,1,3,30,3,1,4,2247 -55,60,"[3, 4]","[20, 40]","[2, 2]",2,1,4,40,4,2,4,2148 -55,61,"[1, 2]","[20, 50]","[1, 1]",2,1,1,20,2,1,2,3012 -55,62,"[3, 4]","[20, 10]","[2, 2]",2,5,4,50,1,2,3,2098 -55,63,"[4, 3]","[30, 20]","[2, 2]",2,5,4,150,3,2,3,2297 -55,64,"[3, 4]","[60, 40]","[2, 2]",2,1,4,40,4,2,3,2863 -55,65,"[4, 3]","[0, 90]","[2, 2]",2,5,4,0,0,2,4,5059 -55,66,"[2, 1]","[10, 80]","[1, 1]",2,1,1,80,8,1,2,2181 -55,67,"[2, 1]","[0, 80]","[1, 1]",2,1,1,80,8,1,2,2546 -55,68,"[1, 2]","[50, 10]","[1, 1]",2,5,1,250,5,1,1,2198 -55,69,"[3, 4]","[40, 0]","[2, 1]",2,5,3,200,4,1,3,2131 -55,70,"[4, 3]","[10, 40]","[2, 1]",2,1,3,40,4,1,4,3246 -55,71,"[1, 2]","[80, 10]","[1, 2]",2,5,2,50,1,2,1,2347 -55,72,"[2, 1]","[50, 70]","[1, 1]",2,1,1,70,7,1,2,2696 -55,73,"[2, 1]","[40, 60]","[1, 1]",2,5,1,300,6,1,2,1931 -55,74,"[2, 1]","[50, 20]","[1, 1]",2,5,1,100,2,1,1,1981 -55,75,"[2, 1]","[50, 10]","[1, 1]",2,1,1,10,1,1,1,1865 -55,76,"[1, 2]","[10, 60]","[1, 2]",2,1,2,60,6,2,2,2314 -55,77,"[3, 4]","[20, 60]","[2, 2]",2,1,4,60,6,2,4,2314 -55,78,"[1, 2]","[10, 30]","[1, 1]",2,1,1,10,1,1,2,2097 -55,79,"[2, 1]","[10, 10]","[1, 2]",2,5,2,50,1,2,2,3362 -55,80,"[2, 1]","[10, 70]","[1, 1]",2,5,1,350,7,1,2,8172 -54,1,"[3, 4]","[20, 60]","[2, 1]",1,5,3,100,2,1,4,3712 -54,2,"[2, 1]","[70, 10]","[1, 2]",1,5,2,350,7,2,1,3300 -54,3,"[2, 1]","[60, 20]","[1, 2]",1,1,2,60,6,2,1,2802 -54,4,"[4, 3]","[40, 30]","[2, 2]",1,5,4,200,4,2,3,1979 -54,5,"[2, 1]","[40, 30]","[1, 2]",1,1,2,40,4,2,1,2029 -54,6,"[4, 3]","[50, 60]","[2, 2]",1,1,4,50,5,2,4,1936 -54,7,"[1, 2]","[60, 20]","[1, 2]",1,1,2,20,2,2,1,2238 -54,8,"[1, 2]","[30, 30]","[1, 2]",1,1,2,30,3,2,1,2164 -54,9,"[2, 1]","[30, 20]","[1, 1]",1,5,1,100,2,1,1,2823 -54,10,"[4, 3]","[30, 0]","[2, 1]",1,1,3,0,0,1,3,2397 -54,11,"[1, 2]","[0, 60]","[1, 2]",1,5,2,300,6,2,2,2335 -54,12,"[1, 2]","[40, 80]","[1, 2]",1,5,2,400,8,2,2,2191 -54,13,"[4, 3]","[60, 60]","[2, 2]",1,5,4,300,6,2,4,1991 -54,14,"[4, 3]","[40, 90]","[2, 2]",1,5,4,200,4,2,4,1898 -54,15,"[3, 4]","[60, 80]","[2, 2]",1,1,4,80,8,2,4,3869 -54,16,"[2, 1]","[70, 70]","[1, 2]",1,1,2,70,7,2,2,4478 -54,17,"[2, 1]","[70, 70]","[1, 2]",1,5,2,350,7,2,2,2045 -54,18,"[3, 4]","[30, 60]","[2, 2]",1,1,4,60,6,2,4,2214 -54,19,"[1, 2]","[40, 60]","[1, 2]",1,5,2,300,6,2,2,2595 -54,20,"[1, 2]","[10, 50]","[1, 2]",1,5,2,250,5,2,2,2177 -54,21,"[4, 3]","[50, 10]","[2, 2]",1,1,4,50,5,2,3,2200 -54,22,"[3, 4]","[20, 60]","[2, 2]",1,5,4,300,6,2,4,2134 -54,23,"[1, 2]","[50, 30]","[1, 2]",1,5,2,150,3,2,1,2308 -54,24,"[4, 3]","[60, 60]","[2, 2]",1,5,4,300,6,2,4,2015 -54,25,"[1, 2]","[70, 70]","[1, 2]",1,5,2,350,7,2,1,2272 -54,26,"[3, 4]","[60, 70]","[2, 2]",1,5,4,350,7,2,4,2105 -54,27,"[2, 1]","[70, 40]","[1, 2]",1,5,2,350,7,2,1,2082 -54,28,"[1, 2]","[30, 60]","[1, 2]",1,5,2,300,6,2,2,2097 -54,29,"[4, 3]","[60, 60]","[2, 2]",1,5,4,300,6,2,4,1934 -54,30,"[1, 2]","[70, 70]","[1, 2]",1,1,2,70,7,2,1,2217 -54,31,"[1, 2]","[50, 30]","[1, 2]",1,1,2,30,3,2,1,2214 -54,32,"[3, 4]","[20, 40]","[2, 1]",1,5,3,100,2,1,4,2999 -54,33,"[1, 2]","[10, 60]","[1, 2]",1,5,2,300,6,2,2,3707 -54,34,"[2, 1]","[30, 0]","[1, 2]",1,1,2,30,3,2,1,2364 -54,35,"[4, 3]","[50, 0]","[2, 2]",1,1,4,50,5,2,3,1785 -54,36,"[1, 2]","[10, 50]","[1, 2]",1,1,2,50,5,2,2,2430 -54,37,"[1, 2]","[0, 80]","[1, 2]",1,1,2,80,8,2,2,2166 -54,38,"[2, 1]","[70, 20]","[1, 2]",1,1,2,70,7,2,1,3047 -54,39,"[2, 1]","[50, 10]","[1, 2]",1,1,2,50,5,2,1,2256 -54,40,"[2, 1]","[30, 20]","[1, 2]",1,1,2,30,3,2,1,2374 -54,41,"[1, 2]","[10, 30]","[1, 1]",2,1,1,10,1,1,2,2317 -54,42,"[3, 4]","[0, 20]","[2, 2]",2,5,4,100,2,2,4,2175 -54,43,"[4, 3]","[10, 0]","[2, 2]",2,1,4,10,1,2,3,2077 -54,44,"[1, 2]","[30, 20]","[1, 1]",2,5,1,150,3,1,1,2524 -54,45,"[2, 1]","[40, 10]","[1, 1]",2,5,1,50,1,1,1,2069 -54,46,"[3, 4]","[0, 30]","[2, 1]",2,5,3,0,0,1,4,2084 -54,47,"[3, 4]","[30, 40]","[2, 2]",2,5,4,200,4,2,4,2188 -54,48,"[4, 3]","[20, 20]","[2, 2]",2,1,4,20,2,2,4,2099 -54,49,"[4, 3]","[30, 10]","[2, 2]",2,5,4,150,3,2,3,1885 -54,50,"[2, 1]","[30, 30]","[1, 2]",2,5,2,150,3,2,2,1935 -54,51,"[3, 4]","[20, 40]","[2, 2]",2,1,4,40,4,2,4,2086 -54,52,"[2, 1]","[30, 30]","[1, 2]",2,1,2,30,3,2,2,2037 -54,53,"[1, 2]","[60, 20]","[1, 2]",2,1,2,20,2,2,1,2431 -54,54,"[1, 2]","[80, 40]","[1, 2]",2,5,2,200,4,2,1,1994 -54,55,"[3, 4]","[80, 40]","[2, 2]",2,1,4,40,4,2,3,2275 -54,56,"[2, 1]","[0, 80]","[1, 2]",2,1,2,0,0,2,2,2223 -54,57,"[4, 3]","[0, 80]","[2, 1]",2,1,3,80,8,1,4,2173 -54,58,"[4, 3]","[20, 90]","[2, 1]",2,5,3,450,9,1,4,1990 -54,59,"[2, 1]","[30, 60]","[1, 1]",2,1,1,60,6,1,2,2149 -54,60,"[3, 4]","[70, 50]","[2, 1]",2,5,3,350,7,1,3,2027 -54,61,"[4, 3]","[80, 60]","[2, 1]",2,5,3,300,6,1,3,2532 -54,62,"[3, 4]","[50, 50]","[2, 1]",2,5,3,250,5,1,3,1949 -54,63,"[2, 1]","[50, 50]","[1, 1]",2,5,1,250,5,1,2,2065 -54,64,"[1, 2]","[60, 50]","[1, 1]",2,1,1,60,6,1,1,2055 -54,65,"[3, 4]","[40, 20]","[2, 1]",2,1,3,40,4,1,3,1947 -54,66,"[4, 3]","[10, 30]","[2, 1]",2,1,3,30,3,1,4,2273 -54,67,"[1, 2]","[40, 10]","[1, 1]",2,1,1,40,4,1,1,2281 -54,68,"[4, 3]","[20, 0]","[2, 1]",2,5,3,0,0,1,3,2177 -54,69,"[2, 1]","[50, 20]","[1, 2]",2,1,2,50,5,2,1,2186 -54,70,"[3, 4]","[20, 50]","[2, 2]",2,5,4,250,5,2,4,3207 -54,71,"[2, 1]","[60, 0]","[1, 2]",2,1,2,60,6,2,1,2666 -54,72,"[4, 3]","[60, 0]","[2, 2]",2,5,4,300,6,2,3,2241 -54,73,"[3, 4]","[10, 70]","[2, 2]",2,1,4,70,7,2,4,2302 -54,74,"[1, 2]","[40, 70]","[1, 2]",2,1,2,70,7,2,2,3851 -54,75,"[2, 1]","[70, 60]","[1, 2]",2,5,2,350,7,2,1,1943 -54,76,"[2, 1]","[60, 70]","[1, 2]",2,5,2,300,6,2,2,1876 -54,77,"[3, 4]","[70, 50]","[2, 2]",2,1,4,50,5,2,3,2080 -54,78,"[3, 4]","[90, 60]","[2, 2]",2,1,4,60,6,2,3,2223 -54,79,"[3, 4]","[60, 60]","[2, 2]",2,1,4,60,6,2,3,2905 -54,80,"[4, 3]","[70, 60]","[2, 2]",2,5,4,350,7,2,3,2918 -54,1,"[4, 3]","[70, 20]","[2, 1]",1,5,3,100,2,1,3,4125 -54,2,"[1, 2]","[10, 60]","[1, 2]",1,1,2,60,6,2,2,1965 -54,3,"[2, 1]","[50, 0]","[1, 2]",1,5,2,250,5,2,1,1978 -54,4,"[3, 4]","[30, 60]","[2, 2]",1,1,4,60,6,2,4,2002 -54,5,"[1, 2]","[30, 40]","[1, 2]",1,1,2,40,4,2,2,1923 -54,6,"[4, 3]","[80, 0]","[2, 2]",1,1,4,80,8,2,3,2214 -54,7,"[1, 2]","[0, 80]","[1, 2]",1,1,2,80,8,2,2,2968 -54,8,"[1, 2]","[20, 90]","[1, 2]",1,5,2,450,9,2,2,1938 -54,9,"[2, 1]","[70, 0]","[1, 2]",1,1,2,70,7,2,1,2258 -54,10,"[1, 2]","[50, 80]","[1, 2]",1,1,2,80,8,2,2,1983 -54,11,"[1, 2]","[50, 90]","[1, 2]",1,1,2,90,9,2,2,2299 -54,12,"[1, 2]","[40, 40]","[1, 2]",1,5,2,200,4,2,1,1906 -54,13,"[3, 4]","[20, 90]","[2, 2]",1,1,4,90,9,2,4,2186 -54,14,"[1, 2]","[40, 70]","[1, 2]",1,1,2,70,7,2,2,2434 -54,15,"[3, 4]","[50, 80]","[2, 2]",1,5,4,400,8,2,4,2990 -54,16,"[3, 4]","[0, 70]","[2, 2]",1,1,4,70,7,2,4,1992 -54,17,"[1, 2]","[40, 90]","[1, 2]",1,5,2,450,9,2,2,1906 -54,18,"[3, 4]","[60, 60]","[2, 2]",1,1,4,60,6,2,3,1993 -54,19,"[2, 1]","[60, 80]","[1, 2]",1,1,2,60,6,2,2,2329 -54,20,"[1, 2]","[70, 70]","[1, 2]",1,1,2,70,7,2,1,2178 -54,21,"[1, 2]","[70, 70]","[1, 2]",1,1,2,70,7,2,1,2182 -54,22,"[1, 2]","[70, 50]","[1, 2]",1,1,2,50,5,2,1,2385 -54,23,"[1, 2]","[70, 20]","[1, 2]",1,1,2,20,2,2,1,1958 -54,24,"[4, 3]","[30, 80]","[2, 1]",1,5,3,400,8,1,4,2705 -54,25,"[2, 1]","[50, 90]","[1, 1]",1,1,1,90,9,1,2,2113 -54,26,"[2, 1]","[70, 80]","[1, 1]",1,1,1,80,8,1,2,1999 -54,27,"[3, 4]","[80, 60]","[2, 1]",1,1,3,80,8,1,3,2380 -54,28,"[4, 3]","[20, 70]","[2, 1]",1,5,3,350,7,1,4,2255 -54,29,"[4, 3]","[0, 50]","[2, 1]",1,1,3,50,5,1,4,1940 -54,30,"[3, 4]","[60, 30]","[2, 1]",1,5,3,300,6,1,3,2152 -54,31,"[2, 1]","[40, 60]","[1, 1]",1,5,1,300,6,1,2,2115 -54,32,"[4, 3]","[50, 50]","[2, 1]",1,5,3,250,5,1,4,1877 -54,33,"[4, 3]","[80, 60]","[2, 1]",1,1,3,60,6,1,3,1795 -54,34,"[1, 2]","[30, 70]","[1, 1]",1,5,1,150,3,1,2,2367 -54,35,"[3, 4]","[50, 70]","[2, 1]",1,5,3,250,5,1,4,2940 -54,36,"[1, 2]","[40, 60]","[1, 1]",1,5,1,200,4,1,2,2467 -54,37,"[2, 1]","[80, 60]","[1, 1]",1,1,1,60,6,1,1,3290 -54,38,"[2, 1]","[80, 80]","[1, 2]",1,5,2,400,8,2,2,2950 -54,39,"[1, 2]","[40, 60]","[1, 2]",1,5,2,300,6,2,2,2462 -54,40,"[2, 1]","[70, 20]","[1, 2]",1,5,2,350,7,2,1,2619 -54,41,"[1, 2]","[50, 70]","[1, 1]",2,1,1,50,5,1,2,4488 -54,42,"[3, 4]","[50, 80]","[2, 2]",2,1,4,80,8,2,4,2007 -54,43,"[4, 3]","[70, 40]","[2, 2]",2,1,4,70,7,2,3,3691 -54,44,"[1, 2]","[0, 70]","[1, 2]",2,5,2,350,7,2,2,2299 -54,45,"[4, 3]","[80, 0]","[2, 2]",2,5,4,400,8,2,3,2468 -54,46,"[3, 4]","[10, 80]","[2, 2]",2,5,4,400,8,2,4,2125 -54,47,"[2, 1]","[70, 10]","[1, 2]",2,5,2,350,7,2,1,2232 -54,48,"[4, 3]","[60, 30]","[2, 2]",2,5,4,300,6,2,3,2340 -54,49,"[4, 3]","[30, 30]","[2, 2]",2,5,4,150,3,2,4,2499 -54,50,"[1, 2]","[10, 40]","[1, 1]",2,1,1,10,1,1,2,2700 -54,51,"[1, 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1]",2,1,3,40,4,1,3,5846 -54,77,"[4, 3]","[30, 50]","[2, 2]",2,1,4,30,3,2,4,5797 -54,78,"[2, 1]","[50, 70]","[1, 1]",2,5,1,350,7,1,2,2993 -54,79,"[1, 2]","[90, 80]","[1, 1]",2,5,1,450,9,1,1,2414 -54,80,"[3, 4]","[30, 70]","[2, 1]",2,1,3,30,3,1,4,1951 -54,1,"[1, 2]","[60, 40]","[1, 1]",1,1,1,60,6,1,1,4417 -54,2,"[4, 3]","[30, 90]","[2, 1]",1,1,3,90,9,1,4,1890 -54,3,"[4, 3]","[50, 60]","[2, 1]",1,1,3,60,6,1,4,2007 -54,4,"[1, 2]","[80, 50]","[1, 1]",1,5,1,400,8,1,1,2096 -54,5,"[3, 4]","[70, 40]","[2, 1]",1,5,3,350,7,1,3,2914 -54,6,"[2, 1]","[50, 80]","[1, 1]",1,5,1,400,8,1,2,3374 -54,7,"[4, 3]","[60, 90]","[2, 1]",1,1,3,90,9,1,4,2339 -54,8,"[1, 2]","[70, 40]","[1, 1]",1,1,1,70,7,1,1,3343 -54,9,"[2, 1]","[20, 60]","[1, 1]",1,1,1,60,6,1,2,2009 -54,10,"[4, 3]","[0, 70]","[2, 1]",1,1,3,70,7,1,4,5379 -54,11,"[4, 3]","[30, 80]","[2, 1]",1,5,3,400,8,1,4,1806 -54,12,"[1, 2]","[60, 40]","[1, 1]",1,1,1,60,6,1,1,3451 -54,13,"[3, 4]","[60, 30]","[2, 1]",1,5,3,300,6,1,3,2685 -54,14,"[4, 3]","[30, 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1]",2,5,1,100,2,1,2,2597 -54,63,"[2, 1]","[70, 10]","[1, 2]",2,1,2,70,7,2,1,1838 -54,64,"[4, 3]","[60, 10]","[2, 2]",2,5,4,300,6,2,3,2086 -54,65,"[1, 2]","[10, 50]","[1, 2]",2,5,2,250,5,2,2,2107 -54,66,"[4, 3]","[50, 20]","[2, 2]",2,5,4,250,5,2,3,2461 -54,67,"[4, 3]","[20, 30]","[2, 2]",2,5,4,100,2,2,4,2200 -54,68,"[4, 3]","[10, 20]","[2, 2]",2,5,4,50,1,2,4,2007 -54,69,"[1, 2]","[20, 10]","[1, 1]",2,5,1,100,2,1,1,2188 -54,70,"[2, 1]","[10, 40]","[1, 2]",2,5,2,50,1,2,2,2394 -54,71,"[2, 1]","[30, 60]","[1, 1]",2,5,1,300,6,1,2,1872 -54,72,"[2, 1]","[10, 30]","[1, 1]",2,5,1,150,3,1,2,4049 -54,73,"[2, 1]","[10, 10]","[1, 1]",2,5,1,50,1,1,2,1815 -54,74,"[4, 3]","[10, 10]","[2, 2]",2,1,4,10,1,2,4,2142 -54,75,"[2, 1]","[10, 20]","[1, 1]",2,1,1,20,2,1,2,2195 -54,76,"[2, 1]","[40, 10]","[1, 2]",2,5,2,200,4,2,1,1911 -54,77,"[3, 4]","[0, 50]","[2, 2]",2,5,4,250,5,2,4,6642 -54,78,"[4, 3]","[70, 10]","[2, 2]",2,1,4,70,7,2,3,2309 -54,79,"[3, 4]","[30, 60]","[2, 2]",2,5,4,300,6,2,4,2038 -54,80,"[4, 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-54,18,"[2, 1]","[50, 10]","[1, 2]",1,5,2,250,5,2,1,2898 -54,19,"[1, 2]","[0, 60]","[1, 2]",1,1,2,60,6,2,2,4631 -54,20,"[4, 3]","[60, 10]","[2, 2]",1,5,4,300,6,2,3,3332 -54,21,"[4, 3]","[50, 20]","[2, 1]",1,1,3,20,2,1,3,1906 -54,22,"[3, 4]","[0, 40]","[2, 2]",1,1,4,40,4,2,4,1946 -54,23,"[4, 3]","[40, 10]","[2, 2]",1,5,4,200,4,2,3,2071 -54,24,"[2, 1]","[40, 30]","[1, 1]",1,5,1,150,3,1,1,2158 -54,25,"[1, 2]","[20, 30]","[1, 1]",1,1,1,20,2,1,2,2090 -54,26,"[4, 3]","[50, 40]","[2, 1]",1,5,3,200,4,1,3,1890 -54,27,"[4, 3]","[40, 80]","[2, 1]",1,1,3,80,8,1,4,2085 -54,28,"[4, 3]","[40, 70]","[2, 1]",1,1,3,70,7,1,4,1854 -54,29,"[1, 2]","[80, 50]","[1, 1]",1,5,1,400,8,1,1,2107 -54,30,"[3, 4]","[90, 50]","[2, 1]",1,5,3,450,9,1,3,2192 -54,31,"[4, 3]","[0, 80]","[2, 1]",1,1,3,80,8,1,4,2228 -54,32,"[1, 2]","[60, 30]","[1, 1]",1,5,1,300,6,1,1,2100 -54,33,"[2, 1]","[20, 50]","[1, 1]",1,1,1,50,5,1,2,2288 -54,34,"[3, 4]","[60, 40]","[2, 2]",1,1,4,40,4,2,3,1938 -54,35,"[1, 2]","[30, 40]","[1, 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20]","[2, 2]",1,5,4,50,1,2,4,2813 -54,8,"[4, 3]","[30, 60]","[2, 1]",1,5,3,300,6,1,4,1751 -54,9,"[3, 4]","[60, 10]","[2, 1]",1,5,3,300,6,1,3,2952 -54,10,"[1, 2]","[60, 30]","[1, 1]",1,5,1,300,6,1,1,2405 -54,11,"[3, 4]","[70, 40]","[2, 1]",1,5,3,350,7,1,3,2002 -54,12,"[2, 1]","[50, 70]","[1, 1]",1,1,1,70,7,1,2,2586 -54,13,"[1, 2]","[60, 0]","[1, 1]",1,5,1,300,6,1,1,2581 -54,14,"[4, 3]","[30, 60]","[2, 1]",1,1,3,60,6,1,4,1874 -54,15,"[1, 2]","[60, 50]","[1, 1]",1,5,1,300,6,1,1,2336 -54,16,"[4, 3]","[40, 70]","[2, 1]",1,1,3,70,7,1,4,2053 -54,17,"[1, 2]","[80, 40]","[1, 1]",1,1,1,80,8,1,1,2156 -54,18,"[2, 1]","[40, 70]","[1, 1]",1,5,1,350,7,1,2,2115 -54,19,"[1, 2]","[60, 50]","[1, 1]",1,1,1,60,6,1,1,2162 -54,20,"[3, 4]","[80, 40]","[2, 1]",1,1,3,80,8,1,3,2423 -54,21,"[2, 1]","[40, 80]","[1, 1]",1,5,1,400,8,1,2,2986 -54,22,"[1, 2]","[80, 30]","[1, 1]",1,1,1,80,8,1,1,2232 -54,23,"[1, 2]","[80, 50]","[1, 1]",1,5,1,400,8,1,1,1987 -54,24,"[4, 3]","[50, 80]","[2, 1]",1,1,3,80,8,1,4,1846 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2]",2,5,4,400,8,2,4,1927 -54,43,"[3, 4]","[50, 70]","[2, 2]",2,1,4,70,7,2,4,1935 -54,44,"[1, 2]","[50, 50]","[1, 2]",2,5,2,250,5,2,1,2207 -54,45,"[2, 1]","[40, 30]","[1, 1]",2,5,1,150,3,1,1,3651 -54,46,"[1, 2]","[50, 30]","[1, 2]",2,1,2,30,3,2,1,2064 -54,47,"[2, 1]","[40, 50]","[1, 1]",2,1,1,50,5,1,2,2095 -54,48,"[3, 4]","[20, 30]","[2, 1]",2,1,3,20,2,1,4,1837 -54,49,"[2, 1]","[40, 30]","[1, 2]",2,5,2,200,4,2,1,3320 -54,50,"[2, 1]","[40, 10]","[1, 2]",2,1,2,40,4,2,1,1896 -54,51,"[1, 2]","[20, 70]","[1, 2]",2,5,2,350,7,2,2,2636 -54,52,"[3, 4]","[30, 80]","[2, 2]",2,5,4,400,8,2,4,1882 -54,53,"[1, 2]","[30, 30]","[1, 2]",2,5,2,150,3,2,1,2482 -54,54,"[2, 1]","[80, 10]","[1, 1]",2,5,1,50,1,1,1,2319 -54,55,"[4, 3]","[70, 10]","[2, 2]",2,1,4,70,7,2,3,3153 -54,56,"[4, 3]","[80, 50]","[2, 2]",2,1,4,80,8,2,3,2039 -54,57,"[1, 2]","[70, 50]","[1, 2]",2,1,2,50,5,2,1,2331 -54,58,"[2, 1]","[60, 80]","[1, 2]",2,1,2,60,6,2,2,1894 -54,59,"[1, 2]","[70, 50]","[1, 2]",2,1,2,50,5,2,1,2105 -54,60,"[1, 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-54,78,"[2, 1]","[60, 50]","[1, 1]",2,5,1,250,5,1,1,1921 -54,79,"[4, 3]","[40, 40]","[2, 1]",2,1,3,40,4,1,4,2447 -54,80,"[1, 2]","[20, 80]","[1, 1]",2,5,1,100,2,1,2,2192 -56,1,"[3, 4]","[70, 40]","[2, 1]",1,1,3,70,7,1,3,3799 -56,2,"[2, 1]","[20, 80]","[1, 2]",1,5,2,100,2,2,2,3727 -56,3,"[4, 3]","[30, 70]","[2, 2]",1,1,4,30,3,2,4,2011 -56,4,"[4, 3]","[50, 70]","[2, 1]",1,1,3,70,7,1,4,1949 -56,5,"[2, 1]","[80, 50]","[1, 1]",1,1,1,50,5,1,1,2452 -56,6,"[1, 2]","[40, 70]","[1, 1]",1,5,1,200,4,1,2,2546 -56,7,"[1, 2]","[20, 90]","[1, 2]",1,5,2,450,9,2,2,2365 -56,8,"[2, 1]","[80, 10]","[1, 2]",1,1,2,80,8,2,1,1854 -56,9,"[2, 1]","[60, 0]","[1, 2]",1,5,2,300,6,2,1,2320 -56,10,"[3, 4]","[10, 60]","[2, 1]",1,1,3,10,1,1,4,3015 -56,11,"[2, 1]","[50, 50]","[1, 2]",1,5,2,250,5,2,2,2348 -56,12,"[1, 2]","[70, 30]","[1, 2]",1,5,2,150,3,2,1,2071 -56,13,"[1, 2]","[90, 50]","[1, 2]",1,5,2,250,5,2,1,2494 -56,14,"[2, 1]","[60, 80]","[1, 1]",1,1,1,80,8,1,2,2622 -56,15,"[1, 2]","[40, 60]","[1, 1]",1,5,1,200,4,1,2,2279 -56,16,"[2, 1]","[20, 50]","[1, 2]",1,5,2,100,2,2,2,2380 -56,17,"[4, 3]","[40, 40]","[2, 1]",1,5,3,200,4,1,4,2412 -56,18,"[2, 1]","[70, 40]","[1, 2]",1,5,2,350,7,2,1,3487 -56,19,"[4, 3]","[90, 30]","[2, 2]",1,5,4,450,9,2,3,2295 -56,20,"[4, 3]","[80, 30]","[2, 2]",1,5,4,400,8,2,3,2183 -56,21,"[3, 4]","[0, 50]","[2, 2]",1,1,4,50,5,2,4,2177 -56,22,"[3, 4]","[10, 40]","[2, 1]",1,1,3,10,1,1,4,1750 -56,23,"[2, 1]","[30, 30]","[1, 2]",1,5,2,150,3,2,2,2405 -56,24,"[2, 1]","[10, 20]","[1, 2]",1,5,2,50,1,2,2,1988 -56,25,"[4, 3]","[10, 0]","[2, 1]",1,5,3,0,0,1,3,1891 -56,26,"[1, 2]","[10, 10]","[1, 2]",1,1,2,10,1,2,1,2287 -56,27,"[3, 4]","[40, 0]","[2, 1]",1,5,3,200,4,1,3,2198 -56,28,"[1, 2]","[0, 0]","[1, 1]",1,1,1,0,0,1,1,2210 -56,29,"[3, 4]","[20, 40]","[2, 2]",1,1,4,40,4,2,4,2022 -56,30,"[1, 2]","[60, 30]","[1, 2]",1,1,2,30,3,2,1,2053 -56,31,"[2, 1]","[40, 40]","[1, 2]",1,5,2,200,4,2,2,2776 -56,32,"[4, 3]","[60, 30]","[2, 1]",1,5,3,150,3,1,3,1785 -56,33,"[3, 4]","[60, 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1]",2,5,3,50,1,1,3,4397 -56,78,"[1, 2]","[40, 80]","[1, 2]",2,1,2,80,8,2,2,1917 -56,79,"[4, 3]","[90, 20]","[2, 2]",2,5,4,450,9,2,3,2131 -56,80,"[1, 2]","[10, 80]","[1, 2]",2,1,2,80,8,2,2,2019 -57,1,"[3, 4]","[0, 60]","[2, 2]",1,1,4,60,6,2,4,4415 -57,2,"[1, 2]","[20, 80]","[1, 1]",1,5,1,100,2,1,2,2012 -57,3,"[3, 4]","[30, 50]","[2, 1]",1,1,3,30,3,1,4,1829 -57,4,"[3, 4]","[40, 50]","[2, 2]",1,5,4,250,5,2,4,2334 -57,5,"[4, 3]","[60, 40]","[2, 1]",1,1,3,40,4,1,3,2754 -57,6,"[4, 3]","[50, 50]","[2, 2]",1,5,4,250,5,2,4,2534 -57,7,"[4, 3]","[20, 80]","[2, 2]",1,5,4,100,2,2,4,1812 -57,8,"[4, 3]","[20, 60]","[2, 2]",1,5,4,100,2,2,4,1959 -57,9,"[3, 4]","[70, 20]","[2, 2]",1,1,4,20,2,2,3,5152 -57,10,"[3, 4]","[20, 50]","[2, 2]",1,5,4,250,5,2,4,2702 -57,11,"[3, 4]","[10, 30]","[2, 2]",1,1,4,30,3,2,4,1741 -57,12,"[2, 1]","[70, 40]","[1, 2]",1,5,2,350,7,2,1,1864 -57,13,"[1, 2]","[60, 90]","[1, 2]",1,5,2,450,9,2,2,1899 -57,14,"[2, 1]","[80, 70]","[1, 2]",1,5,2,400,8,2,1,1933 -57,15,"[3, 4]","[60, 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2]",2,1,2,70,7,2,2,3128 -57,51,"[4, 3]","[70, 90]","[2, 1]",2,1,3,90,9,1,4,3024 -57,52,"[3, 4]","[80, 40]","[2, 2]",2,5,4,200,4,2,3,4961 -57,53,"[3, 4]","[50, 20]","[2, 1]",2,1,3,50,5,1,3,2431 -57,54,"[2, 1]","[20, 70]","[1, 1]",2,5,1,350,7,1,2,1986 -57,55,"[2, 1]","[0, 70]","[1, 2]",2,5,2,0,0,2,2,2213 -57,56,"[1, 2]","[60, 20]","[1, 1]",2,1,1,60,6,1,1,2266 -57,57,"[1, 2]","[50, 20]","[1, 1]",2,1,1,50,5,1,1,2596 -57,58,"[3, 4]","[60, 30]","[2, 2]",2,5,4,150,3,2,3,1986 -57,59,"[3, 4]","[50, 30]","[2, 1]",2,1,3,50,5,1,3,1847 -57,60,"[4, 3]","[30, 30]","[2, 2]",2,5,4,150,3,2,4,2038 -57,61,"[1, 2]","[10, 0]","[1, 2]",2,5,2,0,0,2,1,2131 -57,62,"[3, 4]","[0, 40]","[2, 1]",2,1,3,0,0,1,4,1846 -57,63,"[1, 2]","[30, 40]","[1, 1]",2,1,1,30,3,1,2,25076 -57,64,"[1, 2]","[10, 10]","[1, 1]",2,1,1,10,1,1,1,5415 -57,65,"[1, 2]","[0, 10]","[1, 1]",2,1,1,0,0,1,2,9656 -57,66,"[2, 1]","[20, 10]","[1, 2]",2,5,2,100,2,2,1,3425 -57,67,"[1, 2]","[40, 30]","[1, 2]",2,5,2,150,3,2,1,2509 -57,68,"[2, 1]","[40, 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1]",2,5,3,200,4,1,4,2705 -62,66,"[2, 1]","[50, 50]","[1, 1]",2,5,1,250,5,1,2,2171 -62,67,"[2, 1]","[70, 40]","[1, 1]",2,1,1,40,4,1,1,2438 -62,68,"[2, 1]","[50, 40]","[1, 2]",2,1,2,50,5,2,1,2337 -62,69,"[2, 1]","[30, 50]","[1, 2]",2,1,2,30,3,2,2,1854 -62,70,"[2, 1]","[20, 40]","[1, 1]",2,1,1,40,4,1,2,1637 -62,71,"[2, 1]","[20, 60]","[1, 1]",2,1,1,60,6,1,2,1971 -62,72,"[3, 4]","[40, 10]","[2, 1]",2,1,3,40,4,1,3,1753 -62,73,"[3, 4]","[50, 10]","[2, 2]",2,5,4,50,1,2,3,2255 -62,74,"[1, 2]","[40, 30]","[1, 1]",2,1,1,40,4,1,1,2204 -62,75,"[4, 3]","[30, 20]","[2, 2]",2,1,4,30,3,2,3,2155 -62,76,"[4, 3]","[20, 20]","[2, 1]",2,5,3,100,2,1,4,1670 -62,77,"[4, 3]","[10, 10]","[2, 2]",2,5,4,50,1,2,4,2087 -62,78,"[2, 1]","[30, 10]","[1, 1]",2,1,1,10,1,1,1,1988 -62,79,"[3, 4]","[20, 40]","[2, 2]",2,5,4,200,4,2,4,2088 -62,80,"[4, 3]","[30, 10]","[2, 2]",2,5,4,150,3,2,3,1821 -63,1,"[3, 4]","[30, 50]","[2, 1]",1,5,3,150,3,1,4,4195 -63,2,"[3, 4]","[30, 90]","[2, 2]",1,5,4,450,9,2,4,3329 -63,3,"[1, 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1]","[60, 70]","[1, 1]",1,1,1,70,7,1,2,1856 -29,29,"[4, 3]","[40, 90]","[2, 1]",1,1,3,90,9,1,4,2022 -29,30,"[2, 1]","[30, 70]","[1, 1]",1,1,1,70,7,1,2,1856 -29,31,"[2, 1]","[60, 80]","[1, 1]",1,1,1,80,8,1,2,1872 -29,32,"[2, 1]","[50, 80]","[1, 1]",1,1,1,80,8,1,2,1773 -29,33,"[2, 1]","[40, 90]","[1, 1]",1,5,1,450,9,1,2,1756 -29,34,"[3, 4]","[90, 50]","[2, 1]",1,5,3,450,9,1,3,2356 -29,35,"[2, 1]","[60, 70]","[1, 1]",1,1,1,70,7,1,2,1956 -29,36,"[1, 2]","[50, 90]","[1, 1]",1,1,1,50,5,1,2,2074 -29,37,"[3, 4]","[50, 70]","[2, 1]",1,5,3,250,5,1,4,2289 -29,38,"[3, 4]","[40, 60]","[2, 1]",1,5,3,200,4,1,4,1856 -29,39,"[2, 1]","[60, 10]","[1, 1]",1,5,1,50,1,1,1,1790 -29,40,"[2, 1]","[70, 50]","[1, 2]",1,5,2,350,7,2,1,3289 -29,41,"[3, 4]","[80, 80]","[2, 2]",2,1,4,80,8,2,3,2736 -29,42,"[1, 2]","[80, 70]","[1, 2]",2,1,2,70,7,2,1,2040 -29,43,"[2, 1]","[70, 90]","[1, 2]",2,5,2,350,7,2,2,2156 -29,44,"[4, 3]","[80, 60]","[2, 2]",2,5,4,400,8,2,3,1972 -29,45,"[1, 2]","[80, 80]","[1, 2]",2,1,2,80,8,2,1,2007 -29,46,"[4, 3]","[80, 80]","[2, 2]",2,1,4,80,8,2,4,1970 -29,47,"[2, 1]","[70, 30]","[1, 2]",2,5,2,350,7,2,1,1939 -29,48,"[3, 4]","[10, 20]","[2, 2]",2,1,4,20,2,2,4,2040 -29,49,"[1, 2]","[20, 20]","[1, 1]",2,5,1,100,2,1,1,1839 -29,50,"[4, 3]","[20, 20]","[2, 1]",2,1,3,20,2,1,4,1787 -29,51,"[3, 4]","[30, 30]","[2, 2]",2,1,4,30,3,2,3,1756 -29,52,"[1, 2]","[50, 40]","[1, 2]",2,5,2,200,4,2,1,2124 -29,53,"[1, 2]","[50, 60]","[1, 2]",2,5,2,300,6,2,2,1773 -29,54,"[1, 2]","[60, 60]","[1, 2]",2,5,2,300,6,2,1,1856 -29,55,"[4, 3]","[80, 70]","[2, 2]",2,1,4,80,8,2,3,1889 -29,56,"[4, 3]","[70, 80]","[2, 2]",2,1,4,70,7,2,4,1856 -29,57,"[1, 2]","[80, 80]","[1, 2]",2,5,2,400,8,2,1,1957 -29,58,"[1, 2]","[60, 90]","[1, 2]",2,5,2,450,9,2,2,1923 -29,59,"[3, 4]","[90, 60]","[2, 2]",2,5,4,300,6,2,3,1873 -29,60,"[2, 1]","[60, 80]","[1, 2]",2,5,2,300,6,2,2,2155 -29,61,"[1, 2]","[80, 60]","[1, 2]",2,1,2,60,6,2,1,1790 -29,62,"[4, 3]","[90, 70]","[2, 2]",2,5,4,450,9,2,3,1889 -29,63,"[2, 1]","[80, 80]","[1, 2]",2,5,2,400,8,2,2,1923 -29,64,"[4, 3]","[80, 90]","[2, 2]",2,5,4,400,8,2,4,1906 -29,65,"[3, 4]","[80, 60]","[2, 2]",2,5,4,300,6,2,3,1890 -29,66,"[4, 3]","[50, 70]","[2, 2]",2,5,4,250,5,2,4,1906 -29,67,"[3, 4]","[50, 70]","[2, 2]",2,5,4,350,7,2,4,1889 -29,68,"[4, 3]","[90, 60]","[2, 2]",2,5,4,450,9,2,3,1923 -29,69,"[1, 2]","[60, 50]","[1, 2]",2,1,2,50,5,2,1,2122 -29,70,"[1, 2]","[50, 60]","[1, 2]",2,1,2,60,6,2,2,1957 -29,71,"[1, 2]","[90, 20]","[1, 2]",2,5,2,100,2,2,1,1807 -29,72,"[4, 3]","[30, 50]","[2, 1]",2,1,3,50,5,1,4,2056 -29,73,"[3, 4]","[20, 60]","[2, 1]",2,5,3,100,2,1,4,1739 -29,74,"[3, 4]","[30, 50]","[2, 2]",2,1,4,50,5,2,4,1755 -29,75,"[2, 1]","[70, 30]","[1, 2]",2,1,2,70,7,2,1,1973 -29,76,"[4, 3]","[80, 10]","[2, 2]",2,1,4,80,8,2,3,2240 -29,77,"[3, 4]","[10, 60]","[2, 2]",2,5,4,300,6,2,4,1989 -29,78,"[1, 2]","[20, 60]","[1, 2]",2,1,2,60,6,2,2,2023 -29,79,"[3, 4]","[20, 60]","[2, 2]",2,1,4,60,6,2,4,1804 -29,80,"[2, 1]","[80, 30]","[1, 2]",2,5,2,400,8,2,1,1989 -57,1,"[1, 2]","[30, 60]","[1, 1]",1,1,1,30,3,1,2,10265 -57,2,"[3, 4]","[40, 80]","[2, 2]",1,1,4,80,8,2,4,1969 -57,3,"[1, 2]","[50, 80]","[1, 2]",1,5,2,400,8,2,2,2038 -57,4,"[4, 3]","[60, 80]","[2, 2]",1,5,4,300,6,2,4,2248 -57,5,"[4, 3]","[80, 90]","[2, 2]",1,1,4,80,8,2,4,2074 -57,6,"[1, 2]","[80, 90]","[1, 2]",1,1,2,90,9,2,2,2144 -57,7,"[2, 1]","[80, 90]","[1, 2]",1,5,2,400,8,2,2,1986 -57,8,"[1, 2]","[90, 90]","[1, 2]",1,5,2,450,9,2,1,2022 -57,9,"[2, 1]","[70, 40]","[1, 2]",1,1,2,70,7,2,1,1969 -57,10,"[4, 3]","[30, 40]","[2, 2]",1,5,4,150,3,2,4,2098 -57,11,"[1, 2]","[40, 40]","[1, 2]",1,5,2,200,4,2,1,1952 -57,12,"[4, 3]","[60, 20]","[2, 2]",1,1,4,60,6,2,3,2022 -57,13,"[2, 1]","[70, 30]","[1, 2]",1,5,2,350,7,2,1,1889 -57,14,"[4, 3]","[60, 20]","[2, 1]",1,5,3,100,2,1,3,1924 -57,15,"[1, 2]","[10, 40]","[1, 2]",1,1,2,40,4,2,2,1863 -57,16,"[1, 2]","[0, 50]","[1, 2]",1,5,2,250,5,2,2,1819 -57,17,"[2, 1]","[10, 50]","[1, 1]",1,1,1,50,5,1,2,1794 -57,18,"[1, 2]","[50, 20]","[1, 2]",1,5,2,100,2,2,1,1741 -57,19,"[2, 1]","[30, 30]","[1, 1]",1,1,1,30,3,1,2,1847 -57,20,"[4, 3]","[40, 60]","[2, 1]",1,1,3,60,6,1,4,2126 -57,21,"[1, 2]","[30, 30]","[1, 1]",1,1,1,30,3,1,1,1995 -57,22,"[1, 2]","[10, 20]","[1, 2]",1,1,2,20,2,2,2,1864 -57,23,"[4, 3]","[0, 0]","[2, 1]",1,5,3,0,0,1,4,1906 -57,24,"[2, 1]","[10, 40]","[1, 2]",1,5,2,50,1,2,2,1907 -57,25,"[2, 1]","[20, 40]","[1, 1]",1,5,1,200,4,1,2,1732 -57,26,"[1, 2]","[10, 0]","[1, 1]",1,5,1,50,1,1,1,2046 -57,27,"[2, 1]","[10, 20]","[1, 2]",1,1,2,10,1,2,2,1846 -57,28,"[4, 3]","[30, 0]","[2, 2]",1,1,4,30,3,2,3,2117 -57,29,"[1, 2]","[0, 30]","[1, 2]",1,1,2,30,3,2,2,2143 -57,30,"[4, 3]","[20, 0]","[2, 2]",1,5,4,100,2,2,3,1837 -57,31,"[3, 4]","[40, 20]","[2, 1]",1,1,3,40,4,1,3,1767 -57,32,"[2, 1]","[20, 10]","[1, 1]",1,5,1,50,1,1,1,1924 -57,33,"[2, 1]","[20, 10]","[1, 1]",1,1,1,10,1,1,1,1819 -57,34,"[3, 4]","[20, 30]","[2, 2]",1,1,4,30,3,2,4,1802 -57,35,"[1, 2]","[0, 40]","[1, 2]",1,5,2,200,4,2,2,2055 -57,36,"[2, 1]","[70, 10]","[1, 2]",1,1,2,70,7,2,1,1959 -57,37,"[2, 1]","[90, 30]","[1, 2]",1,1,2,90,9,2,1,1987 -57,38,"[4, 3]","[90, 30]","[2, 2]",1,5,4,450,9,2,3,2064 -57,39,"[3, 4]","[20, 90]","[2, 2]",1,1,4,90,9,2,4,2012 -57,40,"[2, 1]","[80, 10]","[1, 2]",1,5,2,400,8,2,1,2056 -57,41,"[4, 3]","[90, 0]","[2, 2]",2,5,4,450,9,2,3,4973 -57,42,"[2, 1]","[70, 10]","[1, 2]",2,1,2,70,7,2,1,1942 -57,43,"[2, 1]","[70, 20]","[1, 2]",2,1,2,70,7,2,1,1952 -57,44,"[3, 4]","[0, 80]","[2, 2]",2,5,4,400,8,2,4,2213 -57,45,"[1, 2]","[10, 80]","[1, 2]",2,5,2,400,8,2,2,1697 -57,46,"[1, 2]","[30, 70]","[1, 2]",2,5,2,350,7,2,2,1697 -57,47,"[2, 1]","[60, 20]","[1, 2]",2,5,2,300,6,2,1,2866 -57,48,"[3, 4]","[20, 80]","[2, 2]",2,1,4,80,8,2,4,1906 -57,49,"[2, 1]","[70, 0]","[1, 2]",2,1,2,70,7,2,1,2082 -57,50,"[4, 3]","[90, 0]","[2, 1]",2,5,3,0,0,1,3,1690 -57,51,"[2, 1]","[80, 0]","[1, 2]",2,5,2,400,8,2,1,1855 -57,52,"[1, 2]","[10, 80]","[1, 2]",2,1,2,80,8,2,2,2038 -57,53,"[4, 3]","[80, 40]","[2, 2]",2,5,4,400,8,2,3,1993 -57,54,"[1, 2]","[30, 50]","[1, 2]",2,1,2,50,5,2,2,1759 -57,55,"[4, 3]","[60, 40]","[2, 1]",2,1,3,40,4,1,3,2247 -57,56,"[4, 3]","[60, 10]","[2, 2]",2,5,4,300,6,2,3,2255 -57,57,"[3, 4]","[50, 60]","[2, 2]",2,5,4,300,6,2,4,1854 -57,58,"[3, 4]","[40, 40]","[2, 2]",2,1,4,40,4,2,3,1829 -57,59,"[2, 1]","[30, 0]","[1, 1]",2,1,1,0,0,1,1,1889 -57,60,"[2, 1]","[10, 10]","[1, 2]",2,5,2,50,1,2,2,1994 -57,61,"[1, 2]","[20, 30]","[1, 2]",2,1,2,30,3,2,2,1846 -57,62,"[3, 4]","[10, 40]","[2, 2]",2,1,4,40,4,2,4,1881 -57,63,"[2, 1]","[40, 20]","[1, 1]",2,5,1,100,2,1,1,1864 -57,64,"[4, 3]","[10, 60]","[2, 1]",2,5,3,300,6,1,4,2038 -57,65,"[2, 1]","[10, 70]","[1, 1]",2,1,1,70,7,1,2,1777 -57,66,"[1, 2]","[60, 20]","[1, 2]",2,5,2,100,2,2,1,2046 -57,67,"[4, 3]","[20, 80]","[2, 1]",2,1,3,80,8,1,4,2151 -57,68,"[4, 3]","[0, 70]","[2, 1]",2,5,3,350,7,1,4,1794 -57,69,"[4, 3]","[10, 80]","[2, 1]",2,5,3,400,8,1,4,1724 -57,70,"[2, 1]","[40, 60]","[1, 1]",2,1,1,60,6,1,2,1795 -57,71,"[1, 2]","[80, 80]","[1, 1]",2,5,1,400,8,1,1,2239 -57,72,"[1, 2]","[90, 70]","[1, 1]",2,5,1,450,9,1,1,2238 -57,73,"[3, 4]","[90, 50]","[2, 1]",2,1,3,90,9,1,3,1959 -57,74,"[4, 3]","[30, 70]","[2, 1]",2,1,3,70,7,1,4,1777 -57,75,"[4, 3]","[20, 90]","[2, 1]",2,1,3,90,9,1,4,1829 -57,76,"[3, 4]","[90, 10]","[2, 1]",2,5,3,450,9,1,3,2091 -57,77,"[4, 3]","[20, 90]","[2, 1]",2,5,3,450,9,1,4,1750 -57,78,"[2, 1]","[40, 80]","[1, 1]",2,1,1,80,8,1,2,1847 -57,79,"[4, 3]","[50, 70]","[2, 1]",2,1,3,70,7,1,4,1767 -57,80,"[3, 4]","[70, 70]","[2, 1]",2,5,3,350,7,1,3,1994 -59,1,"[1, 2]","[30, 80]","[1, 2]",1,1,2,80,8,2,2,4884 -59,2,"[1, 2]","[20, 80]","[1, 2]",1,1,2,80,8,2,2,2184 -59,3,"[3, 4]","[10, 60]","[2, 1]",1,5,3,50,1,1,4,2584 -59,4,"[4, 3]","[90, 10]","[2, 2]",1,5,4,450,9,2,3,3501 -59,5,"[2, 1]","[70, 0]","[1, 1]",1,5,1,0,0,1,1,2368 -59,6,"[1, 2]","[20, 50]","[1, 2]",1,1,2,50,5,2,2,2085 -59,7,"[1, 2]","[10, 40]","[1, 2]",1,5,2,200,4,2,2,2251 -59,8,"[2, 1]","[60, 10]","[1, 2]",1,5,2,300,6,2,1,3185 -59,9,"[2, 1]","[80, 10]","[1, 2]",1,1,2,80,8,2,1,2218 -59,10,"[2, 1]","[70, 10]","[1, 2]",1,1,2,70,7,2,1,2151 -59,11,"[1, 2]","[30, 40]","[1, 1]",1,1,1,30,3,1,2,2067 -59,12,"[4, 3]","[40, 20]","[2, 2]",1,1,4,40,4,2,3,2268 -59,13,"[4, 3]","[50, 30]","[2, 1]",1,1,3,30,3,1,3,2518 -59,14,"[4, 3]","[10, 10]","[2, 2]",1,1,4,10,1,2,4,2134 -59,15,"[4, 3]","[0, 20]","[2, 1]",1,5,3,100,2,1,4,1901 -59,16,"[2, 1]","[20, 20]","[1, 2]",1,5,2,100,2,2,2,2551 -59,17,"[3, 4]","[20, 20]","[2, 1]",1,5,3,100,2,1,3,2368 -59,18,"[4, 3]","[40, 40]","[2, 2]",1,5,4,200,4,2,4,2217 -59,19,"[1, 2]","[50, 20]","[1, 1]",1,5,1,250,5,1,1,2218 -59,20,"[3, 4]","[50, 10]","[2, 2]",1,1,4,10,1,2,3,2184 -59,21,"[2, 1]","[30, 60]","[1, 2]",1,5,2,150,3,2,2,3801 -59,22,"[3, 4]","[30, 20]","[2, 1]",1,1,3,30,3,1,3,2201 -59,23,"[1, 2]","[30, 30]","[1, 1]",1,1,1,30,3,1,1,2418 -59,24,"[4, 3]","[0, 30]","[2, 1]",1,1,3,30,3,1,4,2184 -59,25,"[1, 2]","[20, 20]","[1, 1]",1,5,1,100,2,1,1,3068 -59,26,"[1, 2]","[60, 30]","[1, 1]",1,5,1,300,6,1,1,2301 -59,27,"[2, 1]","[40, 30]","[1, 1]",1,1,1,30,3,1,1,2068 -59,28,"[2, 1]","[50, 50]","[1, 2]",1,5,2,250,5,2,2,2251 -59,29,"[3, 4]","[30, 40]","[2, 2]",1,5,4,200,4,2,4,2284 -59,30,"[2, 1]","[70, 40]","[1, 2]",1,1,2,70,7,2,1,2150 -59,31,"[1, 2]","[40, 70]","[1, 2]",1,1,2,70,7,2,2,2418 -59,32,"[4, 3]","[90, 60]","[2, 2]",1,5,4,450,9,2,3,2067 -59,33,"[1, 2]","[70, 70]","[1, 1]",1,1,1,70,7,1,1,2418 -59,34,"[4, 3]","[80, 60]","[2, 2]",1,5,4,400,8,2,3,4134 -59,35,"[2, 1]","[90, 80]","[1, 2]",1,1,2,90,9,2,1,1984 -59,36,"[4, 3]","[90, 70]","[2, 2]",1,5,4,450,9,2,3,1984 -59,37,"[1, 2]","[40, 70]","[1, 2]",1,5,2,350,7,2,2,2851 -59,38,"[2, 1]","[70, 30]","[1, 2]",1,1,2,70,7,2,1,2134 -59,39,"[2, 1]","[60, 20]","[1, 2]",1,5,2,300,6,2,1,2051 -59,40,"[4, 3]","[80, 10]","[2, 2]",1,1,4,80,8,2,3,2034 -59,41,"[1, 2]","[20, 50]","[1, 2]",2,5,2,250,5,2,2,1633 -59,42,"[2, 1]","[70, 10]","[1, 2]",2,1,2,70,7,2,1,2268 -59,43,"[3, 4]","[20, 80]","[2, 2]",2,1,4,80,8,2,4,2184 -59,44,"[2, 1]","[70, 0]","[1, 2]",2,5,2,350,7,2,1,2718 -59,45,"[2, 1]","[30, 10]","[1, 2]",2,5,2,150,3,2,1,2384 -59,46,"[4, 3]","[40, 10]","[2, 1]",2,1,3,10,1,1,3,2417 -59,47,"[2, 1]","[30, 10]","[1, 2]",2,1,2,30,3,2,1,4235 -59,48,"[4, 3]","[60, 30]","[2, 2]",2,5,4,300,6,2,3,2334 -59,49,"[4, 3]","[70, 10]","[2, 2]",2,1,4,70,7,2,3,1934 -59,50,"[1, 2]","[10, 50]","[1, 1]",2,1,1,10,1,1,2,2350 -59,51,"[2, 1]","[40, 30]","[1, 2]",2,1,2,40,4,2,1,2234 -59,52,"[4, 3]","[20, 40]","[2, 2]",2,5,4,100,2,2,4,3601 -59,53,"[4, 3]","[30, 40]","[2, 2]",2,1,4,30,3,2,4,2101 -59,54,"[2, 1]","[20, 50]","[1, 2]",2,5,2,100,2,2,2,2085 -59,55,"[1, 2]","[60, 10]","[1, 1]",2,1,1,60,6,1,1,4268 -59,56,"[3, 4]","[40, 20]","[2, 2]",2,5,4,100,2,2,3,2418 -59,57,"[3, 4]","[50, 30]","[2, 2]",2,1,4,30,3,2,3,2017 -59,58,"[3, 4]","[30, 40]","[2, 1]",2,5,3,150,3,1,4,2134 -59,59,"[1, 2]","[30, 40]","[1, 1]",2,5,1,150,3,1,2,1918 -59,60,"[4, 3]","[30, 20]","[2, 2]",2,5,4,150,3,2,3,1901 -59,61,"[3, 4]","[40, 20]","[2, 2]",2,5,4,100,2,2,3,1984 -59,62,"[3, 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1]",2,5,3,200,4,1,4,2589 -58,52,"[3, 4]","[50, 50]","[2, 2]",2,5,4,250,5,2,3,2306 -58,53,"[1, 2]","[60, 50]","[1, 1]",2,1,1,60,6,1,1,2073 -58,54,"[4, 3]","[50, 40]","[2, 1]",2,1,3,40,4,1,3,1907 -58,55,"[2, 1]","[40, 40]","[1, 1]",2,5,1,200,4,1,2,3039 -58,56,"[3, 4]","[40, 20]","[2, 1]",2,1,3,40,4,1,3,2125 -58,57,"[2, 1]","[10, 80]","[1, 2]",2,5,2,50,1,2,2,2740 -58,58,"[2, 1]","[30, 50]","[1, 1]",2,1,1,50,5,1,2,3773 -58,59,"[4, 3]","[20, 30]","[2, 1]",2,1,3,30,3,1,4,2408 -58,60,"[3, 4]","[20, 40]","[2, 1]",2,1,3,20,2,1,4,1790 -58,61,"[1, 2]","[20, 50]","[1, 1]",2,1,1,20,2,1,2,1973 -58,62,"[3, 4]","[20, 10]","[2, 2]",2,5,4,50,1,2,3,7305 -58,63,"[4, 3]","[30, 20]","[2, 1]",2,5,3,100,2,1,3,2473 -58,64,"[3, 4]","[60, 40]","[2, 2]",2,1,4,40,4,2,3,2474 -58,65,"[4, 3]","[0, 90]","[2, 2]",2,5,4,0,0,2,4,1657 -58,66,"[2, 1]","[10, 80]","[1, 1]",2,1,1,80,8,1,2,1757 -58,67,"[2, 1]","[0, 80]","[1, 2]",2,1,2,0,0,2,2,1789 -58,68,"[1, 2]","[50, 10]","[1, 1]",2,5,1,250,5,1,1,4039 -58,69,"[3, 4]","[40, 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1]","[20, 80]","[1, 1]",1,5,1,400,8,1,2,1959 -57,8,"[2, 1]","[20, 60]","[1, 1]",1,1,1,60,6,1,2,1854 -57,9,"[4, 3]","[50, 60]","[2, 2]",1,5,4,250,5,2,4,4586 -57,10,"[3, 4]","[80, 50]","[2, 1]",1,1,3,80,8,1,3,1759 -57,11,"[4, 3]","[60, 90]","[2, 1]",1,1,3,90,9,1,4,2283 -57,12,"[3, 4]","[80, 60]","[2, 1]",1,1,3,80,8,1,3,2370 -57,13,"[1, 2]","[60, 80]","[1, 1]",1,5,1,300,6,1,2,1802 -57,14,"[4, 3]","[60, 60]","[2, 1]",1,1,3,60,6,1,4,5082 -57,15,"[2, 1]","[50, 50]","[1, 1]",1,1,1,50,5,1,2,2552 -57,16,"[4, 3]","[50, 30]","[2, 2]",1,5,4,250,5,2,3,1993 -57,17,"[3, 4]","[30, 30]","[2, 2]",1,1,4,30,3,2,3,1934 -57,18,"[1, 2]","[70, 10]","[1, 2]",1,5,2,50,1,2,1,2029 -57,19,"[4, 3]","[30, 60]","[2, 1]",1,1,3,60,6,1,4,1760 -57,20,"[3, 4]","[90, 10]","[2, 1]",1,5,3,450,9,1,3,1820 -57,21,"[4, 3]","[50, 80]","[2, 1]",1,5,3,400,8,1,4,2038 -57,22,"[2, 1]","[30, 90]","[1, 2]",1,5,2,150,3,2,2,1749 -57,23,"[2, 1]","[0, 80]","[1, 2]",1,5,2,0,0,2,2,1934 -57,24,"[4, 3]","[10, 50]","[2, 1]",1,5,3,250,5,1,4,1733 -57,25,"[3, 4]","[10, 0]","[2, 2]",1,1,4,0,0,2,3,1924 -57,26,"[3, 4]","[30, 20]","[2, 1]",1,1,3,30,3,1,3,1776 -57,27,"[1, 2]","[80, 40]","[1, 1]",1,1,1,80,8,1,1,1846 -57,28,"[4, 3]","[60, 50]","[2, 1]",1,1,3,50,5,1,3,1802 -57,29,"[3, 4]","[20, 40]","[2, 1]",1,1,3,20,2,1,4,1987 -57,30,"[1, 2]","[60, 60]","[1, 2]",1,5,2,300,6,2,1,2319 -57,31,"[1, 2]","[80, 90]","[1, 2]",1,5,2,450,9,2,2,1925 -57,32,"[3, 4]","[90, 60]","[2, 2]",1,1,4,60,6,2,3,1865 -57,33,"[2, 1]","[70, 60]","[1, 2]",1,1,2,70,7,2,1,1873 -57,34,"[1, 2]","[50, 70]","[1, 2]",1,1,2,70,7,2,2,1715 -57,35,"[1, 2]","[70, 80]","[1, 2]",1,1,2,80,8,2,2,1697 -57,36,"[4, 3]","[60, 60]","[2, 2]",1,5,4,300,6,2,4,2003 -57,37,"[4, 3]","[70, 60]","[2, 2]",1,1,4,70,7,2,3,1776 -57,38,"[2, 1]","[70, 60]","[1, 2]",1,1,2,70,7,2,1,2010 -57,39,"[3, 4]","[70, 60]","[2, 2]",1,1,4,60,6,2,3,2029 -57,40,"[4, 3]","[70, 90]","[2, 2]",1,5,4,350,7,2,4,1934 -57,41,"[4, 3]","[70, 90]","[2, 1]",2,5,3,450,9,1,4,2198 -57,42,"[4, 3]","[80, 50]","[2, 1]",2,5,3,250,5,1,3,2099 -57,43,"[2, 1]","[50, 50]","[1, 1]",2,5,1,250,5,1,2,1645 -57,44,"[3, 4]","[60, 30]","[2, 2]",2,5,4,150,3,2,3,2004 -57,45,"[1, 2]","[70, 40]","[1, 1]",2,1,1,70,7,1,1,1785 -57,46,"[1, 2]","[30, 20]","[1, 1]",2,5,1,150,3,1,1,1837 -57,47,"[2, 1]","[10, 30]","[1, 2]",2,5,2,50,1,2,2,1819 -57,48,"[3, 4]","[40, 30]","[2, 2]",2,1,4,30,3,2,3,2056 -57,49,"[1, 2]","[60, 0]","[1, 2]",2,5,2,0,0,2,1,1724 -57,50,"[4, 3]","[20, 40]","[2, 2]",2,1,4,20,2,2,4,2021 -57,51,"[1, 2]","[90, 10]","[1, 1]",2,1,1,90,9,1,1,1889 -57,52,"[3, 4]","[60, 10]","[2, 1]",2,1,3,60,6,1,3,2604 -57,53,"[4, 3]","[40, 60]","[2, 1]",2,5,3,300,6,1,4,1969 -57,54,"[1, 2]","[50, 60]","[1, 1]",2,5,1,250,5,1,2,1907 -57,55,"[3, 4]","[70, 70]","[2, 1]",2,1,3,70,7,1,3,1820 -57,56,"[2, 1]","[60, 50]","[1, 1]",2,1,1,50,5,1,1,2377 -57,57,"[4, 3]","[50, 60]","[2, 2]",2,1,4,50,5,2,4,1785 -57,58,"[1, 2]","[50, 90]","[1, 2]",2,5,2,450,9,2,2,2231 -57,59,"[3, 4]","[60, 80]","[2, 1]",2,1,3,60,6,1,4,1820 -57,60,"[1, 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2]",1,1,4,40,4,2,3,2374 -56,41,"[2, 1]","[50, 80]","[1, 2]",2,5,2,250,5,2,2,3235 -56,42,"[3, 4]","[70, 60]","[2, 1]",2,1,3,70,7,1,3,1838 -56,43,"[4, 3]","[50, 90]","[2, 1]",2,1,3,90,9,1,4,1759 -56,44,"[1, 2]","[50, 50]","[1, 1]",2,1,1,50,5,1,1,1934 -56,45,"[4, 3]","[60, 40]","[2, 1]",2,5,3,200,4,1,3,1832 -56,46,"[1, 2]","[10, 50]","[1, 2]",2,1,2,50,5,2,2,2022 -56,47,"[4, 3]","[60, 0]","[2, 1]",2,1,3,0,0,1,3,1736 -56,48,"[2, 1]","[60, 20]","[1, 1]",2,1,1,20,2,1,1,1765 -56,49,"[1, 2]","[10, 90]","[1, 2]",2,5,2,450,9,2,2,1990 -56,50,"[4, 3]","[80, 30]","[2, 2]",2,5,4,400,8,2,3,1818 -56,51,"[4, 3]","[70, 10]","[2, 2]",2,5,4,350,7,2,3,1893 -56,52,"[1, 2]","[10, 40]","[1, 2]",2,5,2,200,4,2,2,2022 -56,53,"[3, 4]","[20, 70]","[2, 1]",2,5,3,100,2,1,4,2052 -56,54,"[4, 3]","[90, 10]","[2, 1]",2,5,3,50,1,1,3,2727 -56,55,"[1, 2]","[20, 50]","[1, 2]",2,1,2,50,5,2,2,1808 -56,56,"[4, 3]","[70, 20]","[2, 2]",2,5,4,350,7,2,3,2444 -56,57,"[1, 2]","[10, 90]","[1, 2]",2,5,2,450,9,2,2,1711 -56,58,"[4, 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1]",2,5,3,350,7,1,4,1814 -56,76,"[3, 4]","[20, 40]","[2, 1]",2,5,3,100,2,1,4,1836 -56,77,"[4, 3]","[20, 10]","[2, 2]",2,1,4,20,2,2,3,2064 -56,78,"[3, 4]","[30, 30]","[2, 1]",2,5,3,150,3,1,3,4174 -56,79,"[2, 1]","[30, 30]","[1, 2]",2,1,2,30,3,2,2,2494 -56,80,"[3, 4]","[30, 20]","[2, 1]",2,5,3,150,3,1,3,5144 -68,1,"[3, 4]","[20, 60]","[2, 2]",1,5,4,300,6,2,4,8669 -68,2,"[2, 1]","[70, 10]","[1, 2]",1,5,2,350,7,2,1,2368 -68,3,"[2, 1]","[60, 20]","[1, 1]",1,1,1,20,2,1,1,1819 -68,4,"[4, 3]","[40, 30]","[2, 2]",1,5,4,200,4,2,3,4200 -68,5,"[2, 1]","[40, 30]","[1, 2]",1,1,2,40,4,2,1,2068 -68,6,"[4, 3]","[50, 60]","[2, 2]",1,1,4,50,5,2,4,3983 -68,7,"[1, 2]","[60, 20]","[1, 2]",1,1,2,20,2,2,1,3086 -68,8,"[1, 2]","[30, 30]","[1, 1]",1,1,1,30,3,1,1,2335 -68,9,"[2, 1]","[30, 20]","[1, 1]",1,5,1,100,2,1,1,3320 -68,10,"[4, 3]","[30, 0]","[2, 2]",1,1,4,30,3,2,3,3353 -68,11,"[1, 2]","[0, 60]","[1, 1]",1,5,1,0,0,1,2,3103 -68,12,"[1, 2]","[40, 80]","[1, 2]",1,5,2,400,8,2,2,2268 -68,13,"[4, 3]","[60, 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2]",2,1,4,20,2,2,4,1967 -68,49,"[4, 3]","[30, 10]","[2, 2]",2,5,4,150,3,2,3,1868 -68,50,"[2, 1]","[30, 30]","[1, 1]",2,5,1,150,3,1,2,2319 -68,51,"[3, 4]","[20, 40]","[2, 2]",2,1,4,40,4,2,4,2418 -68,52,"[2, 1]","[30, 30]","[1, 1]",2,1,1,30,3,1,2,2302 -68,53,"[1, 2]","[60, 20]","[1, 2]",2,1,2,20,2,2,1,2319 -68,54,"[1, 2]","[80, 40]","[1, 1]",2,5,1,400,8,1,1,2102 -68,55,"[3, 4]","[80, 40]","[2, 2]",2,1,4,40,4,2,3,2235 -68,56,"[2, 1]","[0, 80]","[1, 1]",2,1,1,80,8,1,2,2918 -68,57,"[4, 3]","[0, 80]","[2, 2]",2,1,4,0,0,2,4,3102 -68,58,"[4, 3]","[20, 90]","[2, 1]",2,5,3,450,9,1,4,1868 -68,59,"[2, 1]","[30, 60]","[1, 1]",2,1,1,60,6,1,2,4169 -68,60,"[3, 4]","[70, 50]","[2, 1]",2,5,3,350,7,1,3,2585 -68,61,"[4, 3]","[80, 60]","[2, 1]",2,5,3,300,6,1,3,2151 -68,62,"[3, 4]","[50, 50]","[2, 1]",2,5,3,250,5,1,3,1968 -68,63,"[2, 1]","[50, 50]","[1, 2]",2,5,2,250,5,2,2,2417 -68,64,"[1, 2]","[60, 50]","[1, 2]",2,1,2,50,5,2,1,1934 -68,65,"[3, 4]","[40, 20]","[2, 1]",2,1,3,40,4,1,3,2201 -68,66,"[4, 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2]",2,1,4,40,4,2,3,2644 -65,57,"[4, 3]","[30, 10]","[2, 1]",2,5,3,50,1,1,3,2879 -65,58,"[1, 2]","[10, 20]","[1, 1]",2,5,1,50,1,1,2,3082 -65,59,"[4, 3]","[10, 20]","[2, 2]",2,1,4,10,1,2,4,2494 -65,60,"[3, 4]","[10, 20]","[2, 1]",2,5,3,50,1,1,4,2795 -65,61,"[4, 3]","[20, 30]","[2, 2]",2,1,4,20,2,2,4,3513 -65,62,"[2, 1]","[10, 40]","[1, 2]",2,1,2,10,1,2,2,2476 -65,63,"[3, 4]","[30, 20]","[2, 2]",2,5,4,100,2,2,3,3297 -65,64,"[4, 3]","[20, 20]","[2, 2]",2,1,4,20,2,2,4,2360 -65,65,"[2, 1]","[40, 0]","[1, 1]",2,1,1,0,0,1,1,2259 -65,66,"[2, 1]","[50, 20]","[1, 2]",2,5,2,250,5,2,1,1925 -65,67,"[3, 4]","[30, 60]","[2, 1]",2,1,3,30,3,1,4,2578 -65,68,"[3, 4]","[40, 70]","[2, 1]",2,5,3,200,4,1,4,1841 -65,69,"[1, 2]","[50, 60]","[1, 2]",2,5,2,300,6,2,2,1958 -65,70,"[2, 1]","[70, 50]","[1, 2]",2,1,2,70,7,2,1,1891 -65,71,"[1, 2]","[40, 70]","[1, 2]",2,5,2,350,7,2,2,1791 -65,72,"[4, 3]","[90, 40]","[2, 1]",2,5,3,200,4,1,3,1858 -65,73,"[1, 2]","[50, 70]","[1, 2]",2,5,2,350,7,2,2,1841 -65,74,"[4, 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1]",1,5,3,100,2,1,4,1762 -63,30,"[4, 3]","[30, 30]","[2, 1]",1,1,3,30,3,1,4,2158 -63,31,"[4, 3]","[10, 20]","[2, 1]",1,5,3,100,2,1,4,1675 -63,32,"[4, 3]","[10, 10]","[2, 2]",1,5,4,50,1,2,4,1690 -63,33,"[4, 3]","[0, 20]","[2, 1]",1,1,3,20,2,1,4,1819 -63,34,"[1, 2]","[10, 0]","[1, 1]",1,5,1,50,1,1,1,1989 -63,35,"[2, 1]","[10, 0]","[1, 2]",1,5,2,50,1,2,1,2642 -63,36,"[3, 4]","[40, 10]","[2, 2]",1,1,4,10,1,2,3,2112 -63,37,"[4, 3]","[10, 50]","[2, 1]",1,1,3,50,5,1,4,1747 -63,38,"[2, 1]","[20, 80]","[1, 1]",1,1,1,80,8,1,2,1745 -63,39,"[3, 4]","[60, 20]","[2, 1]",1,1,3,60,6,1,3,1912 -63,40,"[3, 4]","[60, 10]","[2, 1]",1,5,3,300,6,1,3,2090 -63,41,"[2, 1]","[20, 60]","[1, 1]",2,5,1,300,6,1,2,3220 -63,42,"[1, 2]","[90, 40]","[1, 1]",2,5,1,450,9,1,1,2004 -63,43,"[2, 1]","[50, 60]","[1, 1]",2,5,1,300,6,1,2,1917 -63,44,"[1, 2]","[80, 40]","[1, 1]",2,1,1,80,8,1,1,2274 -63,45,"[1, 2]","[60, 50]","[1, 1]",2,1,1,60,6,1,1,2129 -63,46,"[2, 1]","[40, 50]","[1, 1]",2,5,1,250,5,1,2,1956 -63,47,"[4, 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1]",1,1,1,70,7,1,1,1855 -64,21,"[1, 2]","[70, 70]","[1, 1]",1,1,1,70,7,1,1,1862 -64,22,"[1, 2]","[70, 50]","[1, 1]",1,1,1,70,7,1,1,1771 -64,23,"[1, 2]","[70, 20]","[1, 1]",1,1,1,70,7,1,1,1739 -64,24,"[4, 3]","[30, 80]","[2, 2]",1,5,4,150,3,2,4,2245 -64,25,"[2, 1]","[50, 90]","[1, 1]",1,1,1,90,9,1,2,2396 -64,26,"[2, 1]","[70, 80]","[1, 1]",1,1,1,80,8,1,2,1772 -64,27,"[3, 4]","[80, 60]","[2, 2]",1,1,4,60,6,2,3,2221 -64,28,"[4, 3]","[20, 70]","[2, 1]",1,5,3,350,7,1,4,2312 -64,29,"[4, 3]","[0, 50]","[2, 1]",1,1,3,50,5,1,4,2505 -64,30,"[3, 4]","[60, 30]","[2, 1]",1,5,3,300,6,1,3,2972 -64,31,"[2, 1]","[40, 60]","[1, 1]",1,5,1,300,6,1,2,2188 -64,32,"[4, 3]","[50, 50]","[2, 1]",1,5,3,250,5,1,4,4040 -64,33,"[4, 3]","[80, 60]","[2, 2]",1,1,4,80,8,2,3,2088 -64,34,"[1, 2]","[30, 70]","[1, 1]",1,5,1,150,3,1,2,3447 -64,35,"[3, 4]","[50, 70]","[2, 2]",1,5,4,350,7,2,4,2829 -64,36,"[1, 2]","[40, 60]","[1, 2]",1,5,2,300,6,2,2,2195 -64,37,"[2, 1]","[80, 60]","[1, 2]",1,1,2,80,8,2,1,1912 -64,38,"[2, 1]","[80, 80]","[1, 2]",1,5,2,400,8,2,2,2095 -64,39,"[1, 2]","[40, 60]","[1, 2]",1,5,2,300,6,2,2,1788 -64,40,"[2, 1]","[70, 20]","[1, 2]",1,5,2,350,7,2,1,2414 -64,41,"[1, 2]","[50, 70]","[1, 1]",2,1,1,50,5,1,2,5646 -64,42,"[3, 4]","[50, 80]","[2, 1]",2,1,3,50,5,1,4,2095 -64,43,"[4, 3]","[70, 40]","[2, 1]",2,1,3,40,4,1,3,1937 -64,44,"[1, 2]","[0, 70]","[1, 1]",2,5,1,0,0,1,2,4774 -64,45,"[4, 3]","[80, 0]","[2, 2]",2,5,4,400,8,2,3,2121 -64,46,"[3, 4]","[10, 80]","[2, 2]",2,5,4,400,8,2,4,1736 -64,47,"[2, 1]","[70, 10]","[1, 2]",2,5,2,350,7,2,1,2296 -64,48,"[4, 3]","[60, 30]","[2, 2]",2,5,4,300,6,2,3,1915 -64,49,"[4, 3]","[30, 30]","[2, 2]",2,5,4,150,3,2,4,1953 -64,50,"[1, 2]","[10, 40]","[1, 2]",2,1,2,40,4,2,2,1979 -64,51,"[1, 2]","[10, 40]","[1, 1]",2,5,1,50,1,1,2,1879 -64,52,"[2, 1]","[30, 30]","[1, 1]",2,1,1,30,3,1,2,1871 -64,53,"[3, 4]","[30, 10]","[2, 2]",2,1,4,10,1,2,3,1887 -64,54,"[3, 4]","[40, 20]","[2, 1]",2,1,3,40,4,1,3,1621 -64,55,"[2, 1]","[0, 10]","[1, 2]",2,5,2,0,0,2,2,2422 -64,56,"[1, 2]","[0, 50]","[1, 1]",2,1,1,0,0,1,2,1764 -64,57,"[2, 1]","[30, 30]","[1, 2]",2,5,2,150,3,2,2,5073 -64,58,"[4, 3]","[30, 40]","[2, 2]",2,5,4,150,3,2,4,2346 -64,59,"[1, 2]","[20, 20]","[1, 2]",2,1,2,20,2,2,1,1712 -64,60,"[1, 2]","[30, 20]","[1, 1]",2,1,1,30,3,1,1,5918 -64,61,"[1, 2]","[0, 80]","[1, 2]",2,1,2,80,8,2,2,1846 -64,62,"[2, 1]","[70, 0]","[1, 2]",2,1,2,70,7,2,1,2655 -64,63,"[4, 3]","[90, 50]","[2, 1]",2,5,3,250,5,1,3,1996 -64,64,"[4, 3]","[50, 50]","[2, 1]",2,5,3,250,5,1,4,1597 -64,65,"[3, 4]","[20, 40]","[2, 1]",2,5,3,100,2,1,4,1720 -64,66,"[3, 4]","[20, 80]","[2, 2]",2,5,4,400,8,2,4,1889 -64,67,"[1, 2]","[70, 80]","[1, 2]",2,1,2,80,8,2,2,1771 -64,68,"[3, 4]","[40, 80]","[2, 2]",2,5,4,400,8,2,4,1987 -64,69,"[4, 3]","[80, 80]","[2, 2]",2,5,4,400,8,2,4,4815 -64,70,"[2, 1]","[60, 80]","[1, 2]",2,1,2,60,6,2,2,2505 -64,71,"[3, 4]","[90, 50]","[2, 2]",2,5,4,250,5,2,3,2087 -64,72,"[4, 3]","[70, 80]","[2, 2]",2,5,4,350,7,2,4,2013 -64,73,"[1, 2]","[60, 70]","[1, 2]",2,5,2,350,7,2,2,6581 -64,74,"[3, 4]","[70, 70]","[2, 2]",2,1,4,70,7,2,3,2439 -64,75,"[2, 1]","[80, 50]","[1, 2]",2,1,2,80,8,2,1,1696 -64,76,"[3, 4]","[60, 60]","[2, 2]",2,1,4,60,6,2,3,1778 -64,77,"[2, 1]","[80, 90]","[1, 2]",2,5,2,400,8,2,2,4548 -64,78,"[4, 3]","[80, 80]","[2, 1]",2,5,3,400,8,1,4,1862 -64,79,"[4, 3]","[50, 60]","[2, 1]",2,5,3,300,6,1,4,1688 -64,80,"[2, 1]","[80, 90]","[1, 2]",2,5,2,400,8,2,2,1737 -62,1,"[3, 4]","[70, 40]","[2, 1]",1,1,3,70,7,1,3,11050 -62,2,"[2, 1]","[20, 80]","[1, 2]",1,5,2,100,2,2,2,2455 -62,3,"[4, 3]","[30, 70]","[2, 2]",1,1,4,30,3,2,4,2321 -62,4,"[4, 3]","[50, 70]","[2, 1]",1,1,3,70,7,1,4,1954 -62,5,"[2, 1]","[80, 50]","[1, 1]",1,1,1,50,5,1,1,2037 -62,6,"[1, 2]","[40, 70]","[1, 2]",1,5,2,350,7,2,2,3540 -62,7,"[1, 2]","[20, 90]","[1, 2]",1,5,2,450,9,2,2,4492 -62,8,"[2, 1]","[80, 10]","[1, 2]",1,1,2,80,8,2,1,2354 -62,9,"[2, 1]","[60, 0]","[1, 1]",1,5,1,0,0,1,1,1720 -62,10,"[3, 4]","[10, 60]","[2, 2]",1,1,4,60,6,2,4,2354 -62,11,"[2, 1]","[50, 50]","[1, 1]",1,5,1,250,5,1,2,2188 -62,12,"[1, 2]","[70, 30]","[1, 2]",1,5,2,150,3,2,1,2204 -62,13,"[1, 2]","[90, 50]","[1, 1]",1,5,1,450,9,1,1,2054 -62,14,"[2, 1]","[60, 80]","[1, 1]",1,1,1,80,8,1,2,1770 -62,15,"[1, 2]","[40, 60]","[1, 1]",1,5,1,200,4,1,2,2204 -62,16,"[2, 1]","[20, 50]","[1, 2]",1,5,2,100,2,2,2,2689 -62,17,"[4, 3]","[40, 40]","[2, 1]",1,5,3,200,4,1,4,2722 -62,18,"[2, 1]","[70, 40]","[1, 1]",1,5,1,200,4,1,1,2472 -62,19,"[4, 3]","[90, 30]","[2, 1]",1,5,3,150,3,1,3,2939 -62,20,"[4, 3]","[80, 30]","[2, 2]",1,5,4,400,8,2,3,1955 -62,21,"[3, 4]","[0, 50]","[2, 2]",1,1,4,50,5,2,4,2221 -62,22,"[3, 4]","[10, 40]","[2, 1]",1,1,3,10,1,1,4,1854 -62,23,"[2, 1]","[30, 30]","[1, 2]",1,5,2,150,3,2,2,2271 -62,24,"[2, 1]","[10, 20]","[1, 1]",1,5,1,100,2,1,2,2087 -62,25,"[4, 3]","[10, 0]","[2, 2]",1,5,4,50,1,2,3,2221 -62,26,"[1, 2]","[10, 10]","[1, 1]",1,1,1,10,1,1,1,2104 -62,27,"[3, 4]","[40, 0]","[2, 2]",1,5,4,0,0,2,3,2472 -62,28,"[1, 2]","[0, 0]","[1, 1]",1,1,1,0,0,1,1,2037 -62,29,"[3, 4]","[20, 40]","[2, 2]",1,1,4,40,4,2,4,2087 -62,30,"[1, 2]","[60, 30]","[1, 1]",1,1,1,60,6,1,1,2221 -62,31,"[2, 1]","[40, 40]","[1, 1]",1,5,1,200,4,1,2,1987 -62,32,"[4, 3]","[60, 30]","[2, 2]",1,5,4,300,6,2,3,2054 -62,33,"[3, 4]","[60, 70]","[2, 2]",1,1,4,70,7,2,4,2037 -62,34,"[1, 2]","[60, 40]","[1, 2]",1,5,2,200,4,2,1,2521 -62,35,"[3, 4]","[40, 40]","[2, 1]",1,5,3,200,4,1,3,1871 -62,36,"[3, 4]","[0, 20]","[2, 1]",1,1,3,0,0,1,4,1837 -62,37,"[2, 1]","[0, 0]","[1, 1]",1,1,1,0,0,1,2,1836 -62,38,"[4, 3]","[10, 20]","[2, 2]",1,5,4,50,1,2,4,2422 -62,39,"[3, 4]","[20, 10]","[2, 2]",1,1,4,10,1,2,3,2371 -62,40,"[4, 3]","[20, 70]","[2, 1]",1,1,3,70,7,1,4,1837 -62,41,"[2, 1]","[20, 60]","[1, 1]",2,1,1,60,6,1,2,2720 -62,42,"[1, 2]","[80, 60]","[1, 2]",2,1,2,60,6,2,1,2088 -62,43,"[1, 2]","[90, 40]","[1, 2]",2,5,2,200,4,2,1,2438 -62,44,"[2, 1]","[50, 80]","[1, 2]",2,1,2,50,5,2,2,1920 -62,45,"[3, 4]","[90, 50]","[2, 1]",2,1,3,90,9,1,3,2187 -62,46,"[3, 4]","[90, 50]","[2, 1]",2,1,3,90,9,1,3,1803 -62,47,"[1, 2]","[80, 60]","[1, 2]",2,1,2,60,6,2,1,2120 -62,48,"[4, 3]","[90, 80]","[2, 1]",2,1,3,80,8,1,3,2188 -62,49,"[1, 2]","[90, 90]","[1, 1]",2,5,1,450,9,1,1,2004 -62,50,"[4, 3]","[70, 60]","[2, 1]",2,1,3,60,6,1,3,1787 -62,51,"[2, 1]","[50, 70]","[1, 1]",2,5,1,350,7,1,2,2221 -62,52,"[1, 2]","[80, 50]","[1, 1]",2,5,1,400,8,1,1,2621 -62,53,"[3, 4]","[70, 50]","[2, 1]",2,5,3,350,7,1,3,2054 -62,54,"[4, 3]","[50, 90]","[2, 1]",2,5,3,450,9,1,4,2003 -62,55,"[3, 4]","[70, 40]","[2, 1]",2,1,3,70,7,1,3,1870 -62,56,"[3, 4]","[50, 30]","[2, 2]",2,5,4,150,3,2,3,1937 -62,57,"[3, 4]","[20, 40]","[2, 2]",2,5,4,200,4,2,4,1837 -62,58,"[3, 4]","[10, 20]","[2, 2]",2,5,4,100,2,2,4,1787 -62,59,"[1, 2]","[0, 50]","[1, 1]",2,1,1,0,0,1,2,2270 -62,60,"[3, 4]","[20, 60]","[2, 2]",2,5,4,300,6,2,4,2321 -62,61,"[3, 4]","[20, 40]","[2, 2]",2,5,4,200,4,2,4,1770 -62,62,"[4, 3]","[20, 20]","[2, 2]",2,1,4,20,2,2,4,1937 -62,63,"[4, 3]","[30, 40]","[2, 1]",2,1,3,40,4,1,4,1754 -62,64,"[4, 3]","[10, 40]","[2, 1]",2,1,3,40,4,1,4,1886 -62,65,"[3, 4]","[30, 0]","[2, 1]",2,5,3,150,3,1,3,1920 -62,66,"[2, 1]","[20, 40]","[1, 1]",2,5,1,200,4,1,2,2037 -62,67,"[4, 3]","[10, 60]","[2, 1]",2,1,3,60,6,1,4,1837 -62,68,"[2, 1]","[0, 20]","[1, 2]",2,1,2,0,0,2,2,2255 -62,69,"[2, 1]","[20, 30]","[1, 1]",2,1,1,30,3,1,2,1754 -62,70,"[3, 4]","[30, 50]","[2, 1]",2,5,3,150,3,1,4,2005 -62,71,"[1, 2]","[60, 50]","[1, 1]",2,5,1,300,6,1,1,1987 -62,72,"[3, 4]","[80, 60]","[2, 1]",2,1,3,80,8,1,3,2120 -62,73,"[2, 1]","[70, 90]","[1, 2]",2,1,2,70,7,2,2,1970 -62,74,"[2, 1]","[70, 80]","[1, 2]",2,1,2,70,7,2,2,1954 -62,75,"[2, 1]","[90, 90]","[1, 2]",2,5,2,450,9,2,2,2004 -62,76,"[2, 1]","[70, 40]","[1, 2]",2,1,2,70,7,2,1,1803 -62,77,"[2, 1]","[80, 70]","[1, 1]",2,5,1,350,7,1,1,3156 -62,78,"[1, 2]","[70, 90]","[1, 1]",2,5,1,350,7,1,2,2237 -62,79,"[1, 2]","[90, 70]","[1, 1]",2,1,1,90,9,1,1,1920 -62,80,"[2, 1]","[30, 80]","[1, 2]",2,1,2,30,3,2,2,2504 -68,1,"[4, 3]","[70, 10]","[2, 1]",1,1,3,10,1,1,3,3769 -68,2,"[3, 4]","[0, 80]","[2, 2]",1,5,4,400,8,2,4,2902 -68,3,"[2, 1]","[90, 10]","[1, 1]",1,1,1,10,1,1,1,3054 -68,4,"[2, 1]","[80, 20]","[1, 2]",1,1,2,80,8,2,1,2203 -68,5,"[3, 4]","[10, 70]","[2, 2]",1,5,4,350,7,2,4,1836 -68,6,"[3, 4]","[10, 50]","[2, 2]",1,5,4,250,5,2,4,3235 -68,7,"[2, 1]","[60, 0]","[1, 2]",1,5,2,300,6,2,1,2654 -68,8,"[3, 4]","[0, 50]","[2, 2]",1,1,4,50,5,2,4,2486 -68,9,"[2, 1]","[40, 0]","[1, 2]",1,1,2,40,4,2,1,2602 -68,10,"[3, 4]","[10, 40]","[2, 1]",1,1,3,10,1,1,4,2269 -68,11,"[1, 2]","[10, 30]","[1, 2]",1,1,2,30,3,2,2,3003 -68,12,"[3, 4]","[10, 20]","[2, 1]",1,1,3,10,1,1,4,2386 -68,13,"[3, 4]","[20, 60]","[2, 2]",1,5,4,300,6,2,4,2069 -68,14,"[1, 2]","[10, 70]","[1, 2]",1,5,2,350,7,2,2,2270 -68,15,"[2, 1]","[80, 20]","[1, 2]",1,1,2,80,8,2,1,3255 -68,16,"[1, 2]","[20, 90]","[1, 2]",1,5,2,450,9,2,2,2520 -68,17,"[4, 3]","[90, 30]","[2, 2]",1,5,4,450,9,2,3,2954 -68,18,"[3, 4]","[0, 70]","[2, 1]",1,5,3,0,0,1,4,2553 -68,19,"[4, 3]","[80, 0]","[2, 2]",1,1,4,80,8,2,3,2119 -68,20,"[4, 3]","[90, 20]","[2, 2]",1,5,4,450,9,2,3,2003 -68,21,"[2, 1]","[80, 20]","[1, 1]",1,1,1,20,2,1,1,4668 -68,22,"[1, 2]","[20, 90]","[1, 2]",1,5,2,450,9,2,2,3320 -68,23,"[2, 1]","[80, 20]","[1, 2]",1,1,2,80,8,2,1,2837 -68,24,"[3, 4]","[30, 70]","[2, 2]",1,1,4,70,7,2,4,2487 -68,25,"[1, 2]","[40, 80]","[1, 1]",1,5,1,200,4,1,2,2671 -68,26,"[1, 2]","[20, 80]","[1, 1]",1,1,1,20,2,1,2,2070 -68,27,"[4, 3]","[80, 30]","[2, 2]",1,1,4,80,8,2,3,2170 -68,28,"[1, 2]","[30, 40]","[1, 1]",1,1,1,30,3,1,2,2136 -68,29,"[3, 4]","[50, 70]","[2, 2]",1,1,4,70,7,2,4,2287 -68,30,"[3, 4]","[70, 50]","[2, 1]",1,5,3,350,7,1,3,1902 -68,31,"[2, 1]","[30, 60]","[1, 1]",1,5,1,300,6,1,2,3889 -68,32,"[4, 3]","[20, 30]","[2, 1]",1,1,3,30,3,1,4,1869 -68,33,"[3, 4]","[50, 20]","[2, 1]",1,5,3,250,5,1,3,2336 -68,34,"[4, 3]","[40, 40]","[2, 1]",1,1,3,40,4,1,4,3088 -68,35,"[3, 4]","[30, 60]","[2, 1]",1,5,3,150,3,1,4,3171 -68,36,"[3, 4]","[10, 60]","[2, 1]",1,5,3,50,1,1,4,2220 -68,37,"[3, 4]","[20, 50]","[2, 1]",1,1,3,20,2,1,4,2254 -68,38,"[4, 3]","[80, 40]","[2, 1]",1,5,3,200,4,1,3,2136 -68,39,"[4, 3]","[70, 50]","[2, 1]",1,5,3,250,5,1,3,2270 -68,40,"[4, 3]","[80, 60]","[2, 1]",1,5,3,300,6,1,3,2253 -68,41,"[2, 1]","[50, 60]","[1, 2]",2,1,2,50,5,2,2,3503 -68,42,"[3, 4]","[60, 30]","[2, 2]",2,1,4,30,3,2,3,2504 -68,43,"[2, 1]","[30, 70]","[1, 2]",2,1,2,30,3,2,2,2686 -68,44,"[4, 3]","[30, 40]","[2, 2]",2,1,4,30,3,2,4,5655 -68,45,"[3, 4]","[70, 50]","[2, 1]",2,1,3,70,7,1,3,1969 -68,46,"[1, 2]","[80, 20]","[1, 2]",2,1,2,20,2,2,1,8932 -68,47,"[1, 2]","[70, 40]","[1, 1]",2,5,1,350,7,1,1,4251 -68,48,"[2, 1]","[40, 80]","[1, 1]",2,1,1,80,8,1,2,3536 -68,49,"[4, 3]","[50, 90]","[2, 2]",2,1,4,50,5,2,4,7517 -68,50,"[3, 4]","[70, 40]","[2, 1]",2,5,3,350,7,1,3,1986 -68,51,"[1, 2]","[30, 70]","[1, 1]",2,5,1,150,3,1,2,3653 -68,52,"[1, 2]","[0, 60]","[1, 1]",2,1,1,0,0,1,2,4133 -68,53,"[4, 3]","[40, 10]","[2, 1]",2,5,3,50,1,1,3,3371 -68,54,"[4, 3]","[30, 10]","[2, 1]",2,5,3,50,1,1,3,4604 -68,55,"[3, 4]","[10, 30]","[2, 2]",2,5,4,150,3,2,4,1953 -68,56,"[2, 1]","[10, 30]","[1, 2]",2,1,2,10,1,2,2,5820 -68,57,"[2, 1]","[10, 50]","[1, 1]",2,1,1,50,5,1,2,2202 -68,58,"[4, 3]","[0, 70]","[2, 2]",2,5,4,0,0,2,4,2602 -68,59,"[2, 1]","[0, 80]","[1, 1]",2,1,1,80,8,1,2,2786 -68,60,"[3, 4]","[80, 30]","[2, 1]",2,5,3,400,8,1,3,1952 -68,61,"[4, 3]","[10, 80]","[2, 1]",2,5,3,400,8,1,4,2836 -68,62,"[2, 1]","[10, 90]","[1, 1]",2,1,1,90,9,1,2,9814 -68,63,"[4, 3]","[40, 80]","[2, 2]",2,5,4,200,4,2,4,4838 -68,64,"[2, 1]","[20, 70]","[1, 1]",2,1,1,70,7,1,2,2086 -68,65,"[2, 1]","[20, 70]","[1, 1]",2,1,1,70,7,1,2,2102 -68,66,"[3, 4]","[60, 0]","[2, 2]",2,1,4,0,0,2,3,2452 -68,67,"[2, 1]","[0, 30]","[1, 1]",2,5,1,150,3,1,2,2420 -68,68,"[2, 1]","[10, 10]","[1, 1]",2,5,1,50,1,1,2,1869 -68,69,"[4, 3]","[30, 20]","[2, 1]",2,5,3,100,2,1,3,2236 -68,70,"[4, 3]","[30, 40]","[2, 1]",2,5,3,200,4,1,4,2036 -68,71,"[3, 4]","[50, 10]","[2, 1]",2,5,3,250,5,1,3,2319 -68,72,"[2, 1]","[20, 60]","[1, 1]",2,1,1,60,6,1,2,2087 -68,73,"[4, 3]","[40, 50]","[2, 1]",2,1,3,50,5,1,4,2736 -68,74,"[3, 4]","[30, 50]","[2, 1]",2,5,3,150,3,1,4,2336 -68,75,"[3, 4]","[20, 10]","[2, 1]",2,5,3,100,2,1,3,2252 -68,76,"[4, 3]","[10, 30]","[2, 1]",2,1,3,30,3,1,4,2520 -68,77,"[1, 2]","[0, 10]","[1, 1]",2,5,1,0,0,1,2,4705 -68,78,"[3, 4]","[0, 10]","[2, 1]",2,5,3,0,0,1,4,2286 -68,79,"[3, 4]","[20, 0]","[2, 1]",2,5,3,100,2,1,3,1869 -68,80,"[4, 3]","[20, 40]","[2, 1]",2,1,3,40,4,1,4,2153 -58,1,"[1, 2]","[40, 50]","[1, 1]",1,5,1,200,4,1,2,3622 -58,2,"[4, 3]","[40, 50]","[2, 1]",1,5,3,250,5,1,4,2123 -58,3,"[2, 1]","[30, 70]","[1, 2]",1,1,2,30,3,2,2,2205 -58,4,"[3, 4]","[70, 70]","[2, 1]",1,5,3,350,7,1,3,2606 -58,5,"[3, 4]","[80, 40]","[2, 1]",1,1,3,80,8,1,3,2490 -58,6,"[1, 2]","[80, 50]","[1, 1]",1,1,1,80,8,1,1,2240 -58,7,"[2, 1]","[40, 90]","[1, 2]",1,1,2,40,4,2,2,1807 -58,8,"[2, 1]","[10, 60]","[1, 1]",1,1,1,60,6,1,2,1840 -58,9,"[4, 3]","[0, 40]","[2, 1]",1,5,3,200,4,1,4,2640 -58,10,"[2, 1]","[20, 20]","[1, 2]",1,5,2,100,2,2,2,2374 -58,11,"[4, 3]","[20, 20]","[2, 1]",1,1,3,20,2,1,4,2624 -58,12,"[2, 1]","[30, 0]","[1, 1]",1,5,1,0,0,1,1,1907 -58,13,"[3, 4]","[50, 20]","[2, 1]",1,1,3,50,5,1,3,1823 -58,14,"[1, 2]","[60, 60]","[1, 2]",1,5,2,300,6,2,1,2307 -58,15,"[3, 4]","[50, 80]","[2, 2]",1,5,4,400,8,2,4,2223 -58,16,"[1, 2]","[90, 70]","[1, 2]",1,1,2,70,7,2,1,2039 -58,17,"[1, 2]","[80, 60]","[1, 1]",1,5,1,400,8,1,1,1839 -58,18,"[3, 4]","[80, 80]","[2, 1]",1,1,3,80,8,1,3,2040 -58,19,"[3, 4]","[90, 90]","[2, 2]",1,5,4,450,9,2,3,1606 -58,20,"[3, 4]","[80, 80]","[2, 1]",1,5,3,400,8,1,3,1657 -58,21,"[4, 3]","[70, 20]","[2, 1]",1,5,3,100,2,1,3,1806 -58,22,"[2, 1]","[50, 10]","[1, 2]",1,5,2,250,5,2,1,6205 -58,23,"[3, 4]","[10, 40]","[2, 2]",1,1,4,40,4,2,4,7021 -58,24,"[4, 3]","[40, 10]","[2, 1]",1,5,3,50,1,1,3,1724 -58,25,"[4, 3]","[20, 10]","[2, 1]",1,1,3,10,1,1,3,2290 -58,26,"[4, 3]","[40, 20]","[2, 2]",1,5,4,200,4,2,3,1690 -58,27,"[3, 4]","[20, 60]","[2, 1]",1,1,3,20,2,1,4,2323 -58,28,"[2, 1]","[70, 20]","[1, 1]",1,1,1,20,2,1,1,3822 -58,29,"[2, 1]","[80, 10]","[1, 2]",1,1,2,80,8,2,1,1974 -58,30,"[3, 4]","[30, 80]","[2, 1]",1,5,3,150,3,1,4,1574 -58,31,"[1, 2]","[30, 80]","[1, 2]",1,1,2,80,8,2,2,3523 -58,32,"[4, 3]","[80, 30]","[2, 1]",1,5,3,150,3,1,3,2173 -58,33,"[1, 2]","[20, 50]","[1, 2]",1,1,2,50,5,2,2,2673 -58,34,"[3, 4]","[40, 40]","[2, 1]",1,5,3,200,4,1,3,1890 -58,35,"[4, 3]","[40, 40]","[2, 2]",1,1,4,40,4,2,4,2339 -58,36,"[1, 2]","[50, 70]","[1, 2]",1,1,2,70,7,2,2,1891 -58,37,"[1, 2]","[10, 80]","[1, 2]",1,5,2,400,8,2,2,3423 -58,38,"[1, 2]","[0, 50]","[1, 2]",1,1,2,50,5,2,2,1808 -58,39,"[2, 1]","[50, 10]","[1, 1]",1,1,1,10,1,1,1,2340 -58,40,"[3, 4]","[50, 20]","[2, 2]",1,5,4,100,2,2,3,2322 -58,41,"[4, 3]","[20, 90]","[2, 2]",2,5,4,100,2,2,4,2465 -58,42,"[3, 4]","[70, 10]","[2, 1]",2,5,3,350,7,1,3,1923 -58,43,"[1, 2]","[50, 20]","[1, 2]",2,1,2,20,2,2,1,2057 -58,44,"[2, 1]","[40, 70]","[1, 1]",2,5,1,350,7,1,2,1807 -58,45,"[4, 3]","[60, 70]","[2, 1]",2,5,3,350,7,1,4,2356 -58,46,"[1, 2]","[70, 90]","[1, 1]",2,5,1,350,7,1,2,1990 -58,47,"[2, 1]","[90, 70]","[1, 1]",2,1,1,70,7,1,1,2040 -58,48,"[4, 3]","[40, 40]","[2, 1]",2,5,3,200,4,1,4,1940 -58,49,"[3, 4]","[50, 30]","[2, 1]",2,1,3,50,5,1,3,1841 -58,50,"[1, 2]","[50, 20]","[1, 2]",2,5,2,100,2,2,1,2207 -58,51,"[2, 1]","[0, 40]","[1, 2]",2,5,2,0,0,2,2,1956 -58,52,"[4, 3]","[30, 10]","[2, 1]",2,1,3,10,1,1,3,1840 -58,53,"[1, 2]","[30, 30]","[1, 2]",2,1,2,30,3,2,1,2790 -58,54,"[2, 1]","[50, 20]","[1, 2]",2,1,2,50,5,2,1,2240 -58,55,"[1, 2]","[20, 10]","[1, 2]",2,1,2,10,1,2,1,1773 -58,56,"[1, 2]","[0, 10]","[1, 1]",2,1,1,0,0,1,2,1690 -58,57,"[3, 4]","[30, 10]","[2, 1]",2,5,3,150,3,1,3,2140 -58,58,"[3, 4]","[0, 20]","[2, 2]",2,5,4,100,2,2,4,1990 -58,59,"[3, 4]","[10, 30]","[2, 2]",2,5,4,150,3,2,4,1773 -58,60,"[3, 4]","[40, 20]","[2, 2]",2,1,4,20,2,2,3,1824 -58,61,"[2, 1]","[40, 20]","[1, 2]",2,1,2,40,4,2,1,1658 -58,62,"[1, 2]","[60, 50]","[1, 1]",2,5,1,300,6,1,1,2357 -58,63,"[1, 2]","[70, 40]","[1, 1]",2,1,1,70,7,1,1,2023 -58,64,"[3, 4]","[50, 40]","[2, 1]",2,1,3,50,5,1,3,1956 -58,65,"[2, 1]","[80, 70]","[1, 1]",2,5,1,350,7,1,1,2107 -58,66,"[2, 1]","[60, 80]","[1, 1]",2,5,1,400,8,1,2,1773 -58,67,"[4, 3]","[40, 60]","[2, 2]",2,5,4,200,4,2,4,3023 -58,68,"[2, 1]","[50, 70]","[1, 1]",2,1,1,70,7,1,2,2057 -58,69,"[1, 2]","[70, 60]","[1, 1]",2,1,1,70,7,1,1,2041 -58,70,"[3, 4]","[70, 50]","[2, 2]",2,1,4,50,5,2,3,1691 -58,71,"[3, 4]","[80, 60]","[2, 1]",2,5,3,400,8,1,3,1740 -58,72,"[3, 4]","[60, 10]","[2, 1]",2,5,3,300,6,1,3,1673 -58,73,"[1, 2]","[50, 10]","[1, 1]",2,1,1,50,5,1,1,3306 -58,74,"[1, 2]","[30, 30]","[1, 2]",2,1,2,30,3,2,1,2506 -58,75,"[4, 3]","[30, 10]","[2, 1]",2,1,3,10,1,1,3,1823 -58,76,"[3, 4]","[70, 30]","[2, 2]",2,5,4,150,3,2,3,2174 -58,77,"[2, 1]","[30, 50]","[1, 1]",2,5,1,250,5,1,2,2423 -58,78,"[2, 1]","[10, 20]","[1, 1]",2,1,1,20,2,1,2,2090 -58,79,"[2, 1]","[10, 10]","[1, 2]",2,5,2,50,1,2,2,2674 -58,80,"[2, 1]","[10, 30]","[1, 1]",2,1,1,30,3,1,2,2422 -58,1,"[3, 4]","[40, 70]","[2, 2]",1,1,4,70,7,2,4,3621 -58,2,"[2, 1]","[70, 40]","[1, 2]",1,1,2,70,7,2,1,1856 -58,3,"[3, 4]","[60, 60]","[2, 1]",1,5,3,300,6,1,3,1957 -58,4,"[3, 4]","[20, 80]","[2, 1]",1,5,3,100,2,1,4,2523 -58,5,"[4, 3]","[90, 40]","[2, 2]",1,5,4,450,9,2,3,1757 -58,6,"[4, 3]","[80, 30]","[2, 2]",1,1,4,80,8,2,3,2572 -58,7,"[4, 3]","[80, 50]","[2, 2]",1,5,4,400,8,2,3,2139 -58,8,"[4, 3]","[60, 70]","[2, 2]",1,1,4,60,6,2,4,2124 -58,9,"[2, 1]","[50, 80]","[1, 1]",1,5,1,400,8,1,2,2207 -58,10,"[4, 3]","[10, 70]","[2, 1]",1,5,3,350,7,1,4,1740 -58,11,"[3, 4]","[80, 40]","[2, 1]",1,1,3,80,8,1,3,2390 -58,12,"[4, 3]","[20, 40]","[2, 1]",1,5,3,200,4,1,4,1790 -58,13,"[4, 3]","[10, 80]","[2, 2]",1,5,4,50,1,2,4,1690 -58,14,"[2, 1]","[0, 70]","[1, 1]",1,5,1,350,7,1,2,2272 -58,15,"[1, 2]","[90, 10]","[1, 1]",1,1,1,90,9,1,1,3106 -58,16,"[3, 4]","[80, 10]","[2, 2]",1,5,4,50,1,2,3,7888 -58,17,"[4, 3]","[30, 60]","[2, 2]",1,5,4,150,3,2,4,1940 -58,18,"[1, 2]","[60, 30]","[1, 1]",1,1,1,60,6,1,1,2922 -58,19,"[1, 2]","[70, 70]","[1, 2]",1,1,2,70,7,2,1,2073 -58,20,"[1, 2]","[50, 40]","[1, 2]",1,1,2,40,4,2,1,3371 -58,21,"[2, 1]","[70, 40]","[1, 2]",1,1,2,70,7,2,1,3039 -58,22,"[1, 2]","[20, 70]","[1, 2]",1,5,2,350,7,2,2,1939 -58,23,"[1, 2]","[40, 60]","[1, 2]",1,1,2,60,6,2,2,4173 -58,24,"[4, 3]","[50, 50]","[2, 1]",1,1,3,50,5,1,4,1873 -58,25,"[1, 2]","[60, 50]","[1, 1]",1,5,1,300,6,1,1,2973 -58,26,"[3, 4]","[80, 20]","[2, 1]",1,5,3,400,8,1,3,1674 -58,27,"[3, 4]","[70, 50]","[2, 1]",1,1,3,70,7,1,3,1724 -58,28,"[3, 4]","[90, 60]","[2, 2]",1,1,4,60,6,2,3,2622 -58,29,"[4, 3]","[20, 70]","[2, 1]",1,5,3,350,7,1,4,2057 -58,30,"[2, 1]","[40, 60]","[1, 1]",1,1,1,60,6,1,2,1473 -58,31,"[3, 4]","[90, 20]","[2, 1]",1,1,3,90,9,1,3,2089 -58,32,"[4, 3]","[10, 70]","[2, 1]",1,5,3,350,7,1,4,2472 -58,33,"[1, 2]","[40, 40]","[1, 1]",1,5,1,200,4,1,1,2040 -58,34,"[1, 2]","[30, 60]","[1, 2]",1,1,2,60,6,2,2,1674 -58,35,"[1, 2]","[10, 60]","[1, 2]",1,1,2,60,6,2,2,1689 -58,36,"[2, 1]","[60, 50]","[1, 1]",1,1,1,50,5,1,1,2240 -58,37,"[1, 2]","[70, 50]","[1, 1]",1,1,1,70,7,1,1,1640 -58,38,"[1, 2]","[90, 40]","[1, 1]",1,1,1,90,9,1,1,1973 -58,39,"[1, 2]","[80, 40]","[1, 1]",1,5,1,400,8,1,1,2190 -58,40,"[2, 1]","[50, 90]","[1, 1]",1,5,1,450,9,1,2,1657 -58,41,"[1, 2]","[70, 50]","[1, 2]",2,5,2,250,5,2,1,1498 -58,42,"[1, 2]","[60, 70]","[1, 2]",2,1,2,70,7,2,2,5039 -58,43,"[1, 2]","[60, 80]","[1, 1]",2,5,1,300,6,1,2,1990 -58,44,"[1, 2]","[80, 80]","[1, 2]",2,1,2,80,8,2,1,2473 -58,45,"[2, 1]","[90, 90]","[1, 1]",2,5,1,450,9,1,2,5488 -58,46,"[4, 3]","[50, 70]","[2, 1]",2,1,3,70,7,1,4,1856 -58,47,"[3, 4]","[80, 80]","[2, 1]",2,5,3,400,8,1,3,1790 -58,48,"[1, 2]","[50, 80]","[1, 2]",2,5,2,400,8,2,2,2490 -58,49,"[2, 1]","[60, 40]","[1, 2]",2,1,2,60,6,2,1,1773 -58,50,"[3, 4]","[70, 70]","[2, 2]",2,1,4,70,7,2,3,1557 -58,51,"[3, 4]","[30, 80]","[2, 1]",2,5,3,150,3,1,4,2256 -58,52,"[4, 3]","[70, 10]","[2, 1]",2,5,3,50,1,1,3,1774 -58,53,"[4, 3]","[80, 20]","[2, 2]",2,5,4,400,8,2,3,2139 -58,54,"[1, 2]","[20, 80]","[1, 1]",2,5,1,100,2,1,2,2090 -58,55,"[4, 3]","[60, 30]","[2, 1]",2,5,3,150,3,1,3,1457 -58,56,"[2, 1]","[80, 30]","[1, 2]",2,1,2,80,8,2,1,1773 -58,57,"[1, 2]","[40, 70]","[1, 2]",2,5,2,350,7,2,2,1607 -58,58,"[2, 1]","[90, 30]","[1, 2]",2,5,2,450,9,2,1,1573 -58,59,"[1, 2]","[30, 60]","[1, 2]",2,5,2,300,6,2,2,1307 -58,60,"[2, 1]","[50, 40]","[1, 2]",2,1,2,50,5,2,1,2056 -58,61,"[2, 1]","[20, 60]","[1, 1]",2,1,1,60,6,1,2,1674 -58,62,"[2, 1]","[10, 90]","[1, 1]",2,5,1,450,9,1,2,1940 -58,63,"[1, 2]","[80, 0]","[1, 1]",2,1,1,80,8,1,1,1856 -58,64,"[1, 2]","[90, 40]","[1, 1]",2,1,1,90,9,1,1,1624 -58,65,"[3, 4]","[80, 50]","[2, 1]",2,5,3,400,8,1,3,1806 -58,66,"[4, 3]","[50, 60]","[2, 2]",2,1,4,50,5,2,4,1573 -58,67,"[4, 3]","[50, 60]","[2, 1]",2,5,3,300,6,1,4,1607 -58,68,"[3, 4]","[80, 80]","[2, 2]",2,5,4,400,8,2,3,2174 -58,69,"[2, 1]","[80, 80]","[1, 1]",2,1,1,80,8,1,2,1758 -58,70,"[1, 2]","[80, 70]","[1, 1]",2,1,1,80,8,1,1,2173 -58,71,"[1, 2]","[60, 80]","[1, 2]",2,1,2,80,8,2,2,1940 -58,72,"[4, 3]","[80, 50]","[2, 1]",2,5,3,250,5,1,3,2207 -58,73,"[3, 4]","[60, 80]","[2, 1]",2,1,3,60,6,1,4,2006 -58,74,"[2, 1]","[80, 80]","[1, 2]",2,1,2,80,8,2,2,3673 -58,75,"[1, 2]","[90, 40]","[1, 2]",2,5,2,200,4,2,1,1973 -58,76,"[2, 1]","[20, 80]","[1, 1]",2,1,1,80,8,1,2,1940 -58,77,"[1, 2]","[80, 10]","[1, 1]",2,1,1,80,8,1,1,1823 -58,78,"[1, 2]","[30, 0]","[1, 1]",2,1,1,30,3,1,1,1723 -58,79,"[3, 4]","[0, 10]","[2, 2]",2,5,4,50,1,2,4,2139 -58,80,"[3, 4]","[0, 40]","[2, 1]",2,5,3,0,0,1,4,1823 -69,1,"[2, 1]","[20, 60]","[1, 2]",1,5,2,100,2,2,2,3999 -69,2,"[3, 4]","[70, 40]","[2, 1]",1,5,3,350,7,1,3,3087 -69,3,"[1, 2]","[90, 70]","[1, 1]",1,1,1,90,9,1,1,3220 -69,4,"[4, 3]","[40, 60]","[2, 1]",1,1,3,60,6,1,4,2068 -69,5,"[3, 4]","[70, 50]","[2, 1]",1,5,3,350,7,1,3,2586 -69,6,"[4, 3]","[70, 20]","[2, 1]",1,1,3,20,2,1,3,2368 -69,7,"[2, 1]","[60, 10]","[1, 2]",1,1,2,60,6,2,1,2601 -69,8,"[2, 1]","[40, 0]","[1, 1]",1,5,1,0,0,1,1,2686 -69,9,"[4, 3]","[90, 0]","[2, 1]",1,1,3,0,0,1,3,2452 -69,10,"[4, 3]","[90, 10]","[2, 2]",1,5,4,450,9,2,3,5223 -69,11,"[4, 3]","[90, 20]","[2, 1]",1,5,3,100,2,1,3,5837 -69,12,"[1, 2]","[10, 90]","[1, 1]",1,1,1,10,1,1,2,2300 -69,13,"[3, 4]","[20, 70]","[2, 1]",1,1,3,20,2,1,4,5525 -69,14,"[2, 1]","[70, 30]","[1, 1]",1,1,1,30,3,1,1,5084 -69,15,"[3, 4]","[30, 50]","[2, 1]",1,5,3,150,3,1,4,2167 -69,16,"[3, 4]","[40, 60]","[2, 1]",1,1,3,40,4,1,4,1884 -69,17,"[1, 2]","[40, 70]","[1, 2]",1,1,2,70,7,2,2,2101 -69,18,"[4, 3]","[40, 60]","[2, 1]",1,5,3,300,6,1,4,2586 -69,19,"[3, 4]","[60, 40]","[2, 1]",1,5,3,300,6,1,3,1984 -69,20,"[1, 2]","[70, 20]","[1, 1]",1,5,1,350,7,1,1,2852 -69,21,"[1, 2]","[70, 0]","[1, 1]",1,5,1,350,7,1,1,3520 -69,22,"[2, 1]","[20, 80]","[1, 2]",1,5,2,100,2,2,2,2319 -69,23,"[1, 2]","[80, 10]","[1, 2]",1,5,2,50,1,2,1,2119 -69,24,"[4, 3]","[0, 70]","[2, 1]",1,1,3,70,7,1,4,2269 -69,25,"[1, 2]","[70, 10]","[1, 1]",1,1,1,70,7,1,1,2833 -69,26,"[4, 3]","[30, 60]","[2, 2]",1,1,4,30,3,2,4,3602 -69,27,"[2, 1]","[50, 40]","[1, 2]",1,1,2,50,5,2,1,2034 -69,28,"[2, 1]","[50, 60]","[1, 2]",1,1,2,50,5,2,2,4338 -69,29,"[1, 2]","[70, 60]","[1, 2]",1,5,2,300,6,2,1,2803 -69,30,"[3, 4]","[50, 70]","[2, 1]",1,1,3,50,5,1,4,4268 -69,31,"[4, 3]","[80, 70]","[2, 2]",1,5,4,400,8,2,3,2534 -69,32,"[4, 3]","[60, 20]","[2, 2]",1,5,4,300,6,2,3,2051 -69,33,"[3, 4]","[10, 60]","[2, 2]",1,1,4,60,6,2,4,1935 -69,34,"[3, 4]","[30, 40]","[2, 1]",1,5,3,150,3,1,4,2468 -69,35,"[4, 3]","[40, 20]","[2, 2]",1,5,4,200,4,2,3,2135 -69,36,"[3, 4]","[10, 30]","[2, 1]",1,5,3,50,1,1,4,4337 -69,37,"[1, 2]","[20, 40]","[1, 2]",1,1,2,40,4,2,2,2035 -69,38,"[1, 2]","[80, 60]","[1, 1]",1,1,1,80,8,1,1,2284 -69,39,"[2, 1]","[70, 80]","[1, 1]",1,1,1,80,8,1,2,2151 -69,40,"[3, 4]","[70, 40]","[2, 1]",1,1,3,70,7,1,3,1984 -69,41,"[2, 1]","[50, 80]","[1, 2]",2,5,2,250,5,2,2,2166 -69,42,"[3, 4]","[70, 60]","[2, 2]",2,1,4,60,6,2,3,2952 -69,43,"[4, 3]","[50, 90]","[2, 1]",2,1,3,90,9,1,4,2553 -69,44,"[1, 2]","[50, 50]","[1, 2]",2,1,2,50,5,2,1,2319 -69,45,"[4, 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2]",2,5,4,200,4,2,3,2051 -69,63,"[1, 2]","[60, 50]","[1, 1]",2,5,1,300,6,1,1,2068 -69,64,"[4, 3]","[70, 60]","[2, 1]",2,1,3,60,6,1,3,3452 -69,65,"[2, 1]","[90, 60]","[1, 2]",2,1,2,90,9,2,1,1818 -69,66,"[1, 2]","[40, 50]","[1, 2]",2,1,2,50,5,2,2,1868 -69,67,"[3, 4]","[40, 40]","[2, 1]",2,1,3,40,4,1,3,2752 -69,68,"[3, 4]","[20, 30]","[2, 2]",2,5,4,150,3,2,4,1817 -69,69,"[3, 4]","[50, 50]","[2, 2]",2,1,4,50,5,2,3,3068 -69,70,"[4, 3]","[30, 80]","[2, 1]",2,1,3,80,8,1,4,2319 -69,71,"[3, 4]","[70, 0]","[2, 2]",2,1,4,0,0,2,3,2284 -69,72,"[3, 4]","[70, 10]","[2, 1]",2,5,3,350,7,1,3,1768 -69,73,"[4, 3]","[10, 80]","[2, 2]",2,5,4,50,1,2,4,2451 -69,74,"[4, 3]","[20, 70]","[2, 1]",2,1,3,70,7,1,4,2152 -69,75,"[4, 3]","[50, 70]","[2, 2]",2,5,4,250,5,2,4,2702 -69,76,"[3, 4]","[20, 40]","[2, 2]",2,5,4,200,4,2,4,3318 -69,77,"[4, 3]","[20, 10]","[2, 2]",2,1,4,20,2,2,3,2469 -69,78,"[3, 4]","[30, 30]","[2, 2]",2,5,4,150,3,2,3,3287 -69,79,"[2, 1]","[30, 30]","[1, 2]",2,1,2,30,3,2,2,2118 -69,80,"[3, 4]","[30, 20]","[2, 2]",2,5,4,100,2,2,3,3403 -64,1,"[4, 3]","[30, 50]","[2, 1]",1,5,3,250,5,1,4,4107 -64,2,"[3, 4]","[60, 10]","[2, 1]",1,1,3,60,6,1,3,2273 -64,3,"[2, 1]","[0, 40]","[1, 2]",1,5,2,0,0,2,2,2352 -64,4,"[1, 2]","[40, 0]","[1, 1]",1,1,1,40,4,1,1,2238 -64,5,"[4, 3]","[30, 70]","[2, 1]",1,5,3,350,7,1,4,2232 -64,6,"[3, 4]","[60, 50]","[2, 2]",1,5,4,250,5,2,3,2056 -64,7,"[3, 4]","[80, 70]","[2, 2]",1,1,4,70,7,2,3,1980 -64,8,"[2, 1]","[80, 90]","[1, 1]",1,1,1,90,9,1,2,2607 -64,9,"[2, 1]","[70, 80]","[1, 1]",1,5,1,400,8,1,2,1789 -64,10,"[3, 4]","[90, 80]","[2, 2]",1,5,4,400,8,2,3,2055 -64,11,"[2, 1]","[50, 80]","[1, 1]",1,1,1,80,8,1,2,1906 -64,12,"[2, 1]","[60, 70]","[1, 1]",1,5,1,350,7,1,2,2398 -64,13,"[1, 2]","[70, 50]","[1, 1]",1,1,1,70,7,1,1,4392 -64,14,"[1, 2]","[70, 30]","[1, 1]",1,1,1,70,7,1,1,3617 -64,15,"[2, 1]","[30, 90]","[1, 1]",1,5,1,450,9,1,2,1939 -64,16,"[1, 2]","[70, 30]","[1, 1]",1,1,1,70,7,1,1,1815 -64,17,"[4, 3]","[30, 50]","[2, 2]",1,5,4,150,3,2,4,2256 -64,18,"[2, 1]","[50, 50]","[1, 2]",1,1,2,50,5,2,2,2924 -64,19,"[1, 2]","[20, 50]","[1, 1]",1,5,1,100,2,1,2,2248 -64,20,"[4, 3]","[30, 20]","[2, 2]",1,5,4,150,3,2,3,2431 -64,21,"[3, 4]","[20, 40]","[2, 2]",1,5,4,200,4,2,4,1847 -64,22,"[4, 3]","[50, 0]","[2, 2]",1,5,4,250,5,2,3,1880 -64,23,"[1, 2]","[30, 50]","[1, 2]",1,1,2,50,5,2,2,1973 -64,24,"[4, 3]","[40, 10]","[2, 2]",1,1,4,40,4,2,3,1999 -64,25,"[1, 2]","[40, 30]","[1, 2]",1,5,2,150,3,2,1,1865 -64,26,"[1, 2]","[60, 20]","[1, 1]",1,1,1,60,6,1,1,4652 -64,27,"[2, 1]","[20, 40]","[1, 1]",1,5,1,200,4,1,2,2073 -64,28,"[4, 3]","[40, 30]","[2, 2]",1,5,4,200,4,2,3,2082 -64,29,"[2, 1]","[10, 20]","[1, 1]",1,1,1,20,2,1,2,2189 -64,30,"[1, 2]","[10, 10]","[1, 2]",1,1,2,10,1,2,1,1771 -64,31,"[4, 3]","[20, 40]","[2, 2]",1,1,4,20,2,2,4,2223 -64,32,"[3, 4]","[50, 40]","[2, 1]",1,5,3,250,5,1,3,2324 -64,33,"[3, 4]","[50, 30]","[2, 2]",1,5,4,150,3,2,3,1831 -64,34,"[2, 1]","[60, 10]","[1, 1]",1,1,1,10,1,1,1,5660 -64,35,"[1, 2]","[0, 70]","[1, 2]",1,1,2,70,7,2,2,1906 -64,36,"[1, 2]","[0, 80]","[1, 2]",1,1,2,80,8,2,2,1988 -64,37,"[4, 3]","[70, 0]","[2, 2]",1,1,4,70,7,2,3,2106 -64,38,"[2, 1]","[80, 10]","[1, 1]",1,5,1,50,1,1,1,1898 -64,39,"[3, 4]","[20, 70]","[2, 1]",1,5,3,100,2,1,4,2999 -64,40,"[4, 3]","[80, 60]","[2, 2]",1,1,4,80,8,2,3,1839 -64,41,"[4, 3]","[60, 80]","[2, 2]",2,1,4,60,6,2,4,4881 -64,42,"[4, 3]","[70, 90]","[2, 2]",2,1,4,70,7,2,4,1796 -64,43,"[3, 4]","[60, 90]","[2, 2]",2,5,4,450,9,2,4,1662 -64,44,"[1, 2]","[60, 80]","[1, 2]",2,1,2,80,8,2,2,1990 -64,45,"[3, 4]","[70, 60]","[2, 2]",2,1,4,60,6,2,3,2181 -64,46,"[4, 3]","[60, 90]","[2, 2]",2,1,4,60,6,2,4,1948 -64,47,"[4, 3]","[80, 90]","[2, 2]",2,5,4,400,8,2,4,1973 -64,48,"[4, 3]","[70, 80]","[2, 2]",2,5,4,350,7,2,4,1955 -64,49,"[2, 1]","[60, 70]","[1, 1]",2,5,1,350,7,1,2,3742 -64,50,"[3, 4]","[60, 60]","[2, 2]",2,1,4,60,6,2,3,1897 -64,51,"[2, 1]","[50, 60]","[1, 1]",2,5,1,300,6,1,2,1680 -64,52,"[4, 3]","[60, 70]","[2, 2]",2,5,4,300,6,2,4,2373 -64,53,"[1, 2]","[80, 50]","[1, 1]",2,5,1,400,8,1,1,2106 -64,54,"[4, 3]","[30, 60]","[2, 2]",2,1,4,30,3,2,4,2063 -64,55,"[1, 2]","[70, 40]","[1, 2]",2,5,2,200,4,2,1,2139 -64,56,"[3, 4]","[50, 0]","[2, 2]",2,5,4,0,0,2,3,2090 -64,57,"[2, 1]","[0, 20]","[1, 2]",2,5,2,0,0,2,2,2065 -64,58,"[1, 2]","[30, 30]","[1, 1]",2,5,1,150,3,1,1,1989 -64,59,"[2, 1]","[40, 40]","[1, 1]",2,5,1,200,4,1,2,2840 -64,60,"[3, 4]","[40, 30]","[2, 1]",2,5,3,200,4,1,3,2206 -64,61,"[1, 2]","[0, 40]","[1, 2]",2,1,2,40,4,2,2,2306 -64,62,"[4, 3]","[10, 30]","[2, 1]",2,5,3,150,3,1,4,2115 -64,63,"[1, 2]","[50, 10]","[1, 2]",2,1,2,10,1,2,1,2590 -64,64,"[2, 1]","[10, 60]","[1, 1]",2,5,1,300,6,1,2,2256 -64,65,"[3, 4]","[60, 10]","[2, 1]",2,5,3,300,6,1,3,2498 -64,66,"[2, 1]","[20, 80]","[1, 1]",2,1,1,80,8,1,2,2299 -64,67,"[2, 1]","[10, 70]","[1, 1]",2,1,1,70,7,1,2,2657 -64,68,"[3, 4]","[50, 30]","[2, 1]",2,1,3,50,5,1,3,2456 -64,69,"[1, 2]","[40, 60]","[1, 1]",2,5,1,200,4,1,2,2814 -64,70,"[4, 3]","[60, 30]","[2, 1]",2,1,3,30,3,1,3,2682 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1]",2,1,3,50,5,1,4,2343 -65,44,"[1, 2]","[50, 50]","[1, 2]",2,5,2,250,5,2,1,1891 -65,45,"[2, 1]","[40, 30]","[1, 1]",2,5,1,150,3,1,1,2293 -65,46,"[1, 2]","[50, 30]","[1, 2]",2,1,2,30,3,2,1,2176 -65,47,"[2, 1]","[40, 50]","[1, 1]",2,1,1,50,5,1,2,1707 -65,48,"[3, 4]","[20, 30]","[2, 2]",2,1,4,30,3,2,4,2058 -65,49,"[2, 1]","[40, 30]","[1, 1]",2,5,1,150,3,1,1,1941 -65,50,"[2, 1]","[40, 10]","[1, 1]",2,1,1,10,1,1,1,2076 -65,51,"[1, 2]","[20, 70]","[1, 2]",2,5,2,350,7,2,2,1790 -65,52,"[3, 4]","[30, 80]","[2, 2]",2,5,4,400,8,2,4,2159 -65,53,"[1, 2]","[30, 30]","[1, 2]",2,5,2,150,3,2,1,1941 -65,54,"[2, 1]","[80, 10]","[1, 2]",2,5,2,400,8,2,1,1757 -65,55,"[4, 3]","[70, 10]","[2, 1]",2,1,3,10,1,1,3,1774 -65,56,"[4, 3]","[80, 50]","[2, 2]",2,1,4,80,8,2,3,1758 -65,57,"[1, 2]","[70, 50]","[1, 1]",2,1,1,70,7,1,1,2041 -65,58,"[2, 1]","[60, 80]","[1, 1]",2,1,1,80,8,1,2,2074 -65,59,"[1, 2]","[70, 50]","[1, 1]",2,1,1,70,7,1,1,4617 -65,60,"[1, 2]","[50, 40]","[1, 1]",2,5,1,250,5,1,1,2761 -65,61,"[4, 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1]",1,1,1,50,5,1,2,2160 -74,39,"[1, 2]","[70, 30]","[1, 1]",1,1,1,70,7,1,1,1875 -74,40,"[1, 2]","[60, 30]","[1, 1]",1,5,1,300,6,1,1,2066 -74,41,"[4, 3]","[50, 40]","[2, 1]",2,1,3,40,4,1,3,9387 -74,42,"[2, 1]","[80, 10]","[1, 1]",2,1,1,10,1,1,1,2126 -74,43,"[4, 3]","[40, 20]","[2, 2]",2,5,4,200,4,2,3,1718 -74,44,"[3, 4]","[10, 30]","[2, 2]",2,5,4,150,3,2,4,1991 -74,45,"[1, 2]","[20, 50]","[1, 2]",2,1,2,50,5,2,2,2788 -74,46,"[3, 4]","[20, 40]","[2, 2]",2,5,4,200,4,2,4,1966 -74,47,"[3, 4]","[30, 30]","[2, 1]",2,1,3,30,3,1,3,2315 -74,48,"[3, 4]","[50, 20]","[2, 2]",2,1,4,20,2,2,3,2026 -74,49,"[1, 2]","[60, 30]","[1, 1]",2,5,1,300,6,1,1,3443 -74,50,"[2, 1]","[30, 30]","[1, 1]",2,1,1,30,3,1,2,2051 -74,51,"[2, 1]","[40, 50]","[1, 1]",2,1,1,50,5,1,2,1727 -74,52,"[2, 1]","[40, 60]","[1, 1]",2,1,1,60,6,1,2,1794 -74,53,"[4, 3]","[50, 40]","[2, 1]",2,5,3,200,4,1,3,2673 -74,54,"[1, 2]","[40, 40]","[1, 2]",2,5,2,200,4,2,1,2283 -74,55,"[1, 2]","[10, 40]","[1, 2]",2,1,2,40,4,2,2,1826 -74,56,"[2, 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2]",2,5,2,50,1,2,2,2191 -74,74,"[4, 3]","[10, 10]","[2, 2]",2,1,4,10,1,2,4,3577 -74,75,"[2, 1]","[10, 20]","[1, 2]",2,1,2,10,1,2,2,1877 -74,76,"[2, 1]","[40, 10]","[1, 1]",2,5,1,50,1,1,1,2141 -74,77,"[3, 4]","[0, 50]","[2, 2]",2,5,4,250,5,2,4,1778 -74,78,"[4, 3]","[70, 10]","[2, 1]",2,1,3,10,1,1,3,2599 -74,79,"[3, 4]","[30, 60]","[2, 2]",2,5,4,300,6,2,4,2146 -74,80,"[4, 3]","[20, 30]","[2, 2]",2,5,4,100,2,2,4,2325 -58,1,"[1, 2]","[10, 50]","[1, 1]",1,1,1,10,1,1,2,3522 -58,2,"[1, 2]","[0, 40]","[1, 2]",1,1,2,40,4,2,2,1767 -58,3,"[3, 4]","[0, 60]","[2, 1]",1,5,3,0,0,1,4,2325 -58,4,"[4, 3]","[70, 20]","[2, 2]",1,5,4,350,7,2,3,2373 -58,5,"[4, 3]","[50, 0]","[2, 2]",1,5,4,250,5,2,3,2208 -58,6,"[2, 1]","[90, 0]","[1, 2]",1,5,2,450,9,2,1,3573 -58,7,"[2, 1]","[50, 20]","[1, 2]",1,5,2,250,5,2,1,1973 -58,8,"[2, 1]","[30, 10]","[1, 2]",1,5,2,150,3,2,1,1957 -58,9,"[4, 3]","[60, 10]","[2, 2]",1,1,4,60,6,2,3,6139 -58,10,"[1, 2]","[10, 30]","[1, 1]",1,1,1,10,1,1,2,3489 -58,11,"[3, 4]","[20, 20]","[2, 2]",1,5,4,100,2,2,3,7388 -58,12,"[4, 3]","[30, 0]","[2, 1]",1,1,3,0,0,1,3,3140 -58,13,"[1, 2]","[20, 30]","[1, 2]",1,5,2,150,3,2,2,3657 -58,14,"[2, 1]","[0, 10]","[1, 2]",1,5,2,0,0,2,2,2141 -58,15,"[4, 3]","[0, 0]","[2, 1]",1,1,3,0,0,1,4,3973 -58,16,"[2, 1]","[10, 10]","[1, 2]",1,5,2,50,1,2,2,1973 -58,17,"[1, 2]","[0, 10]","[1, 1]",1,5,1,0,0,1,2,2457 -58,18,"[3, 4]","[10, 10]","[2, 2]",1,1,4,10,1,2,3,6207 -58,19,"[2, 1]","[20, 20]","[1, 1]",1,1,1,20,2,1,2,2124 -58,20,"[3, 4]","[20, 20]","[2, 2]",1,1,4,20,2,2,3,1975 -58,21,"[3, 4]","[30, 0]","[2, 2]",1,1,4,0,0,2,3,1824 -58,22,"[3, 4]","[50, 20]","[2, 1]",1,1,3,50,5,1,3,3123 -58,23,"[4, 3]","[20, 60]","[2, 1]",1,1,3,60,6,1,4,1956 -58,24,"[4, 3]","[30, 50]","[2, 1]",1,1,3,50,5,1,4,1774 -58,25,"[3, 4]","[70, 40]","[2, 1]",1,1,3,70,7,1,3,1975 -58,26,"[2, 1]","[40, 70]","[1, 1]",1,5,1,350,7,1,2,6855 -58,27,"[1, 2]","[30, 20]","[1, 1]",1,1,1,30,3,1,1,2090 -58,28,"[1, 2]","[50, 20]","[1, 1]",1,1,1,50,5,1,1,1857 -58,29,"[3, 4]","[50, 20]","[2, 1]",1,5,3,250,5,1,3,3056 -58,30,"[4, 3]","[20, 50]","[2, 1]",1,1,3,50,5,1,4,1989 -58,31,"[1, 2]","[60, 10]","[1, 1]",1,5,1,300,6,1,1,3490 -58,32,"[3, 4]","[40, 20]","[2, 2]",1,5,4,100,2,2,3,2140 -58,33,"[3, 4]","[70, 10]","[2, 2]",1,1,4,10,1,2,3,2322 -58,34,"[2, 1]","[10, 90]","[1, 2]",1,5,2,50,1,2,2,1657 -58,35,"[4, 3]","[20, 90]","[2, 2]",1,1,4,20,2,2,4,2123 -58,36,"[4, 3]","[30, 80]","[2, 1]",1,1,3,80,8,1,4,1790 -58,37,"[4, 3]","[20, 70]","[2, 1]",1,1,3,70,7,1,4,1907 -58,38,"[2, 1]","[60, 70]","[1, 1]",1,1,1,70,7,1,2,2956 -58,39,"[1, 2]","[70, 40]","[1, 1]",1,1,1,70,7,1,1,1856 -58,40,"[2, 1]","[40, 90]","[1, 1]",1,5,1,450,9,1,2,3773 -58,41,"[3, 4]","[70, 30]","[2, 2]",2,1,4,30,3,2,3,2147 -58,42,"[4, 3]","[10, 60]","[2, 1]",2,1,3,60,6,1,4,2757 -58,43,"[4, 3]","[20, 80]","[2, 2]",2,1,4,20,2,2,4,1774 -58,44,"[2, 1]","[10, 60]","[1, 1]",2,5,1,300,6,1,2,3490 -58,45,"[2, 1]","[10, 70]","[1, 1]",2,1,1,70,7,1,2,2023 -58,46,"[1, 2]","[80, 20]","[1, 1]",2,5,1,400,8,1,1,1957 -58,47,"[3, 4]","[80, 10]","[2, 1]",2,5,3,400,8,1,3,2424 -58,48,"[1, 2]","[70, 30]","[1, 1]",2,5,1,350,7,1,1,1957 -58,49,"[3, 4]","[50, 10]","[2, 1]",2,5,3,250,5,1,3,2106 -58,50,"[2, 1]","[10, 80]","[1, 1]",2,1,1,80,8,1,2,2240 -58,51,"[2, 1]","[20, 90]","[1, 1]",2,5,1,450,9,1,2,2005 -58,52,"[4, 3]","[30, 80]","[2, 1]",2,5,3,400,8,1,4,2007 -58,53,"[4, 3]","[50, 70]","[2, 1]",2,1,3,70,7,1,4,2040 -58,54,"[3, 4]","[90, 60]","[2, 2]",2,1,4,60,6,2,3,1790 -58,55,"[3, 4]","[50, 50]","[2, 1]",2,5,3,250,5,1,3,1956 -58,56,"[3, 4]","[40, 80]","[2, 2]",2,5,4,400,8,2,4,2007 -58,57,"[1, 2]","[60, 70]","[1, 1]",2,5,1,300,6,1,2,1824 -58,58,"[4, 3]","[70, 90]","[2, 1]",2,1,3,90,9,1,4,2839 -58,59,"[3, 4]","[80, 30]","[2, 1]",2,1,3,80,8,1,3,2206 -58,60,"[1, 2]","[70, 20]","[1, 1]",2,1,1,70,7,1,1,1857 -58,61,"[4, 3]","[30, 70]","[2, 2]",2,5,4,150,3,2,4,2190 -58,62,"[2, 1]","[10, 70]","[1, 1]",2,5,1,350,7,1,2,2690 -58,63,"[3, 4]","[80, 20]","[2, 2]",2,5,4,100,2,2,3,1907 -58,64,"[4, 3]","[50, 90]","[2, 1]",2,5,3,450,9,1,4,1792 -58,65,"[2, 1]","[50, 80]","[1, 1]",2,5,1,400,8,1,2,4123 -58,66,"[2, 1]","[80, 70]","[1, 1]",2,5,1,350,7,1,1,1893 -58,67,"[2, 1]","[60, 40]","[1, 1]",2,5,1,200,4,1,1,1857 -58,68,"[1, 2]","[50, 50]","[1, 1]",2,5,1,250,5,1,1,1991 -58,69,"[4, 3]","[40, 50]","[2, 1]",2,1,3,50,5,1,4,1892 -58,70,"[3, 4]","[40, 60]","[2, 1]",2,1,3,40,4,1,4,1807 -58,71,"[4, 3]","[90, 80]","[2, 1]",2,5,3,400,8,1,3,3157 -58,72,"[3, 4]","[90, 70]","[2, 1]",2,5,3,450,9,1,3,2273 -58,73,"[4, 3]","[80, 70]","[2, 1]",2,1,3,70,7,1,3,1923 -58,74,"[4, 3]","[50, 80]","[2, 1]",2,5,3,400,8,1,4,1774 -58,75,"[1, 2]","[70, 80]","[1, 1]",2,1,1,70,7,1,2,2123 -58,76,"[2, 1]","[70, 70]","[1, 1]",2,1,1,70,7,1,2,1841 -58,77,"[2, 1]","[70, 20]","[1, 1]",2,5,1,100,2,1,1,2157 -58,78,"[1, 2]","[10, 60]","[1, 2]",2,1,2,60,6,2,2,1892 -58,79,"[4, 3]","[70, 20]","[2, 2]",2,5,4,350,7,2,3,2340 -58,80,"[4, 3]","[60, 80]","[2, 2]",2,1,4,60,6,2,4,1990 -74,1,"[2, 1]","[10, 90]","[1, 2]",1,1,2,10,1,2,2,9087 -74,2,"[3, 4]","[70, 30]","[2, 1]",1,1,3,70,7,1,3,3318 -74,3,"[1, 2]","[80, 40]","[1, 1]",1,1,1,80,8,1,1,2207 -74,4,"[2, 1]","[50, 70]","[1, 1]",1,5,1,350,7,1,2,2588 -74,5,"[4, 3]","[30, 60]","[2, 1]",1,1,3,60,6,1,4,2158 -74,6,"[4, 3]","[20, 50]","[2, 1]",1,5,3,250,5,1,4,1719 -74,7,"[3, 4]","[60, 40]","[2, 2]",1,1,4,40,4,2,3,3476 -74,8,"[4, 3]","[30, 20]","[2, 1]",1,5,3,100,2,1,3,1943 -74,9,"[3, 4]","[10, 50]","[2, 2]",1,1,4,50,5,2,4,2171 -74,10,"[4, 3]","[40, 40]","[2, 2]",1,5,4,200,4,2,4,2375 -74,11,"[2, 1]","[50, 60]","[1, 2]",1,5,2,250,5,2,2,2284 -74,12,"[2, 1]","[70, 70]","[1, 2]",1,1,2,70,7,2,2,1799 -74,13,"[3, 4]","[80, 70]","[2, 1]",1,5,3,400,8,1,3,1677 -74,14,"[1, 2]","[60, 80]","[1, 2]",1,1,2,80,8,2,2,3452 -74,15,"[2, 1]","[80, 40]","[1, 2]",1,1,2,80,8,2,1,1949 -74,16,"[1, 2]","[30, 80]","[1, 1]",1,5,1,150,3,1,2,1753 -74,17,"[4, 3]","[70, 50]","[2, 2]",1,5,4,350,7,2,3,2331 -74,18,"[2, 1]","[40, 80]","[1, 1]",1,1,1,80,8,1,2,3592 -74,19,"[1, 2]","[90, 40]","[1, 1]",1,5,1,450,9,1,1,2124 -74,20,"[2, 1]","[40, 80]","[1, 1]",1,1,1,80,8,1,2,2076 -74,21,"[1, 2]","[70, 20]","[1, 2]",1,1,2,20,2,2,1,1794 -74,22,"[2, 1]","[0, 50]","[1, 1]",1,5,1,250,5,1,2,1696 -74,23,"[4, 3]","[0, 30]","[2, 1]",1,1,3,30,3,1,4,2565 -74,24,"[4, 3]","[20, 30]","[2, 2]",1,1,4,20,2,2,4,2000 -74,25,"[2, 1]","[0, 0]","[1, 1]",1,5,1,0,0,1,2,2333 -74,26,"[1, 2]","[10, 20]","[1, 2]",1,1,2,20,2,2,2,4524 -74,27,"[2, 1]","[30, 20]","[1, 1]",1,5,1,100,2,1,1,4288 -74,28,"[1, 2]","[10, 10]","[1, 2]",1,1,2,10,1,2,1,2037 -74,29,"[2, 1]","[20, 20]","[1, 1]",1,1,1,20,2,1,2,3344 -74,30,"[1, 2]","[10, 10]","[1, 1]",1,1,1,10,1,1,1,2283 -74,31,"[1, 2]","[40, 10]","[1, 2]",1,5,2,50,1,2,1,1916 -74,32,"[4, 3]","[30, 10]","[2, 1]",1,1,3,10,1,1,3,1785 -74,33,"[3, 4]","[10, 30]","[2, 1]",1,1,3,10,1,1,4,1679 -74,34,"[2, 1]","[70, 0]","[1, 2]",1,5,2,350,7,2,1,2846 -74,35,"[4, 3]","[50, 0]","[2, 2]",1,1,4,50,5,2,3,2481 -74,36,"[3, 4]","[10, 60]","[2, 2]",1,5,4,300,6,2,4,2079 -74,37,"[2, 1]","[60, 10]","[1, 1]",1,5,1,50,1,1,1,4289 -74,38,"[1, 2]","[10, 50]","[1, 2]",1,1,2,50,5,2,2,1945 -74,39,"[4, 3]","[90, 0]","[2, 2]",1,5,4,450,9,2,3,2250 -74,40,"[4, 3]","[70, 10]","[2, 2]",1,5,4,350,7,2,3,2143 -74,41,"[3, 4]","[20, 60]","[2, 1]",2,5,3,100,2,1,4,6028 -74,42,"[4, 3]","[80, 30]","[2, 2]",2,1,4,80,8,2,3,1951 -74,43,"[2, 1]","[80, 20]","[1, 2]",2,5,2,400,8,2,1,2622 -74,44,"[4, 3]","[80, 10]","[2, 2]",2,5,4,400,8,2,3,3552 -74,45,"[2, 1]","[80, 20]","[1, 2]",2,5,2,400,8,2,1,2854 -74,46,"[4, 3]","[40, 30]","[2, 2]",2,1,4,40,4,2,3,3627 -74,47,"[3, 4]","[50, 40]","[2, 1]",2,1,3,50,5,1,3,1849 -74,48,"[1, 2]","[40, 30]","[1, 1]",2,5,1,200,4,1,1,2613 -74,49,"[1, 2]","[60, 70]","[1, 1]",2,5,1,300,6,1,2,2365 -74,50,"[4, 3]","[70, 80]","[2, 1]",2,1,3,80,8,1,4,2349 -74,51,"[2, 1]","[40, 70]","[1, 2]",2,5,2,200,4,2,2,2497 -74,52,"[3, 4]","[60, 20]","[2, 2]",2,5,4,100,2,2,3,1826 -74,53,"[3, 4]","[40, 10]","[2, 1]",2,1,3,40,4,1,3,1684 -74,54,"[4, 3]","[10, 10]","[2, 2]",2,1,4,10,1,2,4,1744 -74,55,"[2, 1]","[10, 10]","[1, 1]",2,5,1,50,1,1,2,2416 -74,56,"[2, 1]","[0, 10]","[1, 1]",2,5,1,50,1,1,2,1827 -74,57,"[3, 4]","[10, 10]","[2, 1]",2,5,3,50,1,1,3,2375 -74,58,"[3, 4]","[20, 10]","[2, 2]",2,5,4,50,1,2,3,1960 -74,59,"[2, 1]","[40, 30]","[1, 1]",2,1,1,30,3,1,1,1825 -74,60,"[3, 4]","[60, 30]","[2, 1]",2,5,3,300,6,1,3,3377 -74,61,"[3, 4]","[70, 10]","[2, 1]",2,5,3,350,7,1,3,1875 -74,62,"[1, 2]","[80, 20]","[1, 2]",2,1,2,20,2,2,1,1892 -74,63,"[3, 4]","[50, 0]","[2, 2]",2,5,4,0,0,2,3,2198 -74,64,"[1, 2]","[10, 10]","[1, 1]",2,5,1,50,1,1,1,2490 -74,65,"[2, 1]","[30, 10]","[1, 2]",2,5,2,150,3,2,1,2215 -74,66,"[2, 1]","[40, 20]","[1, 2]",2,1,2,40,4,2,1,4153 -74,67,"[1, 2]","[0, 60]","[1, 2]",2,1,2,60,6,2,2,2630 -74,68,"[1, 2]","[0, 50]","[1, 2]",2,5,2,250,5,2,2,1927 -74,69,"[4, 3]","[60, 20]","[2, 2]",2,1,4,60,6,2,3,2166 -74,70,"[4, 3]","[70, 20]","[2, 2]",2,5,4,350,7,2,3,1910 -74,71,"[3, 4]","[10, 50]","[2, 2]",2,1,4,50,5,2,4,2257 -74,72,"[1, 2]","[10, 40]","[1, 1]",2,1,1,10,1,1,2,1827 -74,73,"[1, 2]","[0, 50]","[1, 2]",2,5,2,250,5,2,2,2176 -74,74,"[4, 3]","[40, 10]","[2, 2]",2,1,4,40,4,2,3,2082 -74,75,"[2, 1]","[10, 40]","[1, 2]",2,5,2,50,1,2,2,2131 -74,76,"[2, 1]","[20, 20]","[1, 1]",2,1,1,20,2,1,2,2864 -74,77,"[4, 3]","[10, 60]","[2, 2]",2,1,4,10,1,2,4,2131 -74,78,"[4, 3]","[10, 90]","[2, 2]",2,1,4,10,1,2,4,2032 -74,79,"[4, 3]","[10, 90]","[2, 1]",2,5,3,450,9,1,4,1804 -74,80,"[3, 4]","[70, 10]","[2, 1]",2,1,3,70,7,1,3,3682 -74,1,"[1, 2]","[60, 40]","[1, 1]",1,1,1,60,6,1,1,5582 -74,2,"[2, 1]","[30, 10]","[1, 1]",1,1,1,10,1,1,1,1761 -74,3,"[3, 4]","[10, 10]","[2, 1]",1,1,3,10,1,1,3,1719 -74,4,"[3, 4]","[40, 10]","[2, 2]",1,5,4,50,1,2,3,2424 -74,5,"[2, 1]","[0, 50]","[1, 2]",1,1,2,0,0,2,2,2149 -74,6,"[4, 3]","[50, 60]","[2, 1]",1,5,3,300,6,1,4,2091 -74,7,"[1, 2]","[80, 60]","[1, 1]",1,1,1,80,8,1,1,2077 -74,8,"[4, 3]","[80, 70]","[2, 1]",1,5,3,350,7,1,3,2084 -74,9,"[2, 1]","[50, 90]","[1, 1]",1,1,1,90,9,1,2,1959 -74,10,"[2, 1]","[60, 80]","[1, 1]",1,5,1,400,8,1,2,1828 -74,11,"[1, 2]","[70, 60]","[1, 1]",1,1,1,70,7,1,1,2366 -74,12,"[2, 1]","[60, 70]","[1, 1]",1,5,1,350,7,1,2,1802 -74,13,"[4, 3]","[70, 60]","[2, 1]",1,1,3,60,6,1,3,1966 -74,14,"[2, 1]","[70, 80]","[1, 1]",1,5,1,400,8,1,2,1785 -74,15,"[1, 2]","[80, 90]","[1, 1]",1,1,1,80,8,1,2,1949 -74,16,"[2, 1]","[90, 70]","[1, 1]",1,5,1,350,7,1,1,1735 -74,17,"[2, 1]","[50, 80]","[1, 1]",1,5,1,400,8,1,2,1695 -74,18,"[4, 3]","[40, 70]","[2, 1]",1,1,3,70,7,1,4,1718 -74,19,"[3, 4]","[40, 40]","[2, 1]",1,1,3,40,4,1,3,2722 -74,20,"[4, 3]","[20, 20]","[2, 1]",1,5,3,100,2,1,4,1975 -74,21,"[1, 2]","[40, 20]","[1, 2]",1,1,2,20,2,2,1,2157 -74,22,"[4, 3]","[30, 60]","[2, 2]",1,1,4,30,3,2,4,1934 -74,23,"[1, 2]","[80, 40]","[1, 2]",1,1,2,40,4,2,1,2225 -74,24,"[4, 3]","[60, 90]","[2, 1]",1,5,3,450,9,1,4,2274 -74,25,"[4, 3]","[40, 70]","[2, 1]",1,5,3,350,7,1,4,1852 -74,26,"[4, 3]","[60, 90]","[2, 1]",1,5,3,450,9,1,4,1894 -74,27,"[4, 3]","[60, 70]","[2, 1]",1,1,3,70,7,1,4,1767 -74,28,"[2, 1]","[70, 70]","[1, 1]",1,5,1,350,7,1,2,1944 -74,29,"[1, 2]","[80, 40]","[1, 1]",1,5,1,400,8,1,1,2074 -74,30,"[4, 3]","[40, 80]","[2, 1]",1,5,3,400,8,1,4,1685 -74,31,"[3, 4]","[80, 50]","[2, 1]",1,5,3,400,8,1,3,2151 -74,32,"[3, 4]","[80, 40]","[2, 1]",1,5,3,400,8,1,3,1844 -74,33,"[3, 4]","[60, 30]","[2, 1]",1,5,3,300,6,1,3,1892 -74,34,"[1, 2]","[50, 50]","[1, 1]",1,1,1,50,5,1,1,2073 -74,35,"[4, 3]","[40, 80]","[2, 1]",1,5,3,400,8,1,4,2182 -74,36,"[1, 2]","[80, 50]","[1, 1]",1,5,1,400,8,1,1,2547 -74,37,"[2, 1]","[70, 80]","[1, 1]",1,1,1,80,8,1,2,2117 -74,38,"[3, 4]","[90, 70]","[2, 1]",1,1,3,90,9,1,3,2780 -74,39,"[2, 1]","[50, 60]","[1, 1]",1,5,1,300,6,1,2,1903 -74,40,"[2, 1]","[70, 90]","[1, 1]",1,5,1,450,9,1,2,1886 -74,41,"[2, 1]","[80, 80]","[1, 2]",2,5,2,400,8,2,2,3798 -74,42,"[2, 1]","[80, 90]","[1, 2]",2,5,2,400,8,2,2,2233 -74,43,"[4, 3]","[70, 80]","[2, 2]",2,1,4,70,7,2,4,2265 -74,44,"[3, 4]","[80, 40]","[2, 2]",2,1,4,40,4,2,3,2862 -74,45,"[1, 2]","[70, 70]","[1, 1]",2,1,1,70,7,1,1,2688 -74,46,"[2, 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-74,64,"[1, 2]","[50, 60]","[1, 1]",2,5,1,250,5,1,2,2157 -74,65,"[4, 3]","[30, 60]","[2, 1]",2,1,3,60,6,1,4,2267 -74,66,"[4, 3]","[30, 90]","[2, 1]",2,1,3,90,9,1,4,2258 -74,67,"[2, 1]","[40, 80]","[1, 1]",2,5,1,400,8,1,2,2101 -74,68,"[3, 4]","[70, 20]","[2, 1]",2,1,3,70,7,1,3,2973 -74,69,"[4, 3]","[10, 70]","[2, 1]",2,5,3,350,7,1,4,1852 -74,70,"[1, 2]","[80, 60]","[1, 1]",2,5,1,400,8,1,1,2211 -74,71,"[3, 4]","[70, 70]","[2, 1]",2,5,3,350,7,1,3,2945 -74,72,"[4, 3]","[70, 40]","[2, 1]",2,1,3,40,4,1,3,2085 -74,73,"[1, 2]","[20, 80]","[1, 1]",2,1,1,20,2,1,2,2855 -74,74,"[3, 4]","[60, 80]","[2, 2]",2,5,4,400,8,2,4,3740 -74,75,"[3, 4]","[70, 80]","[2, 2]",2,5,4,400,8,2,4,2043 -74,76,"[2, 1]","[50, 50]","[1, 1]",2,1,1,50,5,1,2,1868 -74,77,"[1, 2]","[50, 30]","[1, 2]",2,1,2,30,3,2,1,2853 -74,78,"[4, 3]","[60, 80]","[2, 1]",2,1,3,80,8,1,4,2076 -74,79,"[4, 3]","[90, 70]","[2, 1]",2,1,3,70,7,1,3,1961 -74,80,"[1, 2]","[40, 80]","[1, 1]",2,1,1,40,4,1,2,3286 -29,1,"[1, 2]","[0, 70]","[1, 1]",1,5,1,0,0,1,2,9547 -29,2,"[1, 2]","[20, 40]","[1, 2]",1,5,2,200,4,2,2,1839 -29,3,"[3, 4]","[10, 50]","[2, 2]",1,1,4,50,5,2,4,2325 -29,4,"[4, 3]","[30, 20]","[2, 2]",1,1,4,30,3,2,3,4756 -29,5,"[2, 1]","[30, 20]","[1, 1]",1,1,1,20,2,1,1,1989 -29,6,"[1, 2]","[10, 30]","[1, 2]",1,5,2,150,3,2,2,2438 -29,7,"[4, 3]","[60, 20]","[2, 2]",1,5,4,300,6,2,3,1899 -29,8,"[2, 1]","[70, 10]","[1, 2]",1,1,2,70,7,2,1,1972 -29,9,"[3, 4]","[0, 40]","[2, 2]",1,1,4,40,4,2,4,2289 -29,10,"[2, 1]","[40, 20]","[1, 2]",1,5,2,200,4,2,1,1822 -29,11,"[1, 2]","[20, 60]","[1, 1]",1,1,1,20,2,1,2,2072 -29,12,"[2, 1]","[50, 40]","[1, 2]",1,5,2,250,5,2,1,1789 -29,13,"[4, 3]","[40, 30]","[2, 2]",1,5,4,200,4,2,3,1888 -29,14,"[2, 1]","[40, 20]","[1, 2]",1,1,2,40,4,2,1,1823 -29,15,"[4, 3]","[30, 20]","[2, 2]",1,5,4,150,3,2,3,1705 -29,16,"[1, 2]","[10, 50]","[1, 1]",1,5,1,50,1,1,2,2056 -29,17,"[2, 1]","[70, 10]","[1, 2]",1,5,2,350,7,2,1,1956 -29,18,"[2, 1]","[50, 10]","[1, 2]",1,5,2,250,5,2,1,1756 -29,19,"[4, 3]","[20, 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-29,55,"[4, 3]","[40, 40]","[2, 2]",2,5,4,200,4,2,4,1738 -29,56,"[3, 4]","[50, 0]","[2, 2]",2,5,4,0,0,2,3,1939 -29,57,"[4, 3]","[20, 40]","[2, 1]",2,5,3,200,4,1,4,1706 -29,58,"[4, 3]","[0, 10]","[2, 1]",2,5,3,50,1,1,4,1672 -29,59,"[1, 2]","[0, 40]","[1, 2]",2,5,2,200,4,2,2,3272 -29,60,"[4, 3]","[10, 30]","[2, 2]",2,1,4,10,1,2,4,1839 -29,61,"[4, 3]","[0, 10]","[2, 1]",2,1,3,10,1,1,4,1672 -29,62,"[4, 3]","[30, 10]","[2, 2]",2,1,4,30,3,2,3,1637 -29,63,"[4, 3]","[40, 40]","[2, 2]",2,5,4,200,4,2,4,1773 -29,64,"[2, 1]","[30, 60]","[1, 2]",2,5,2,150,3,2,2,1956 -29,65,"[1, 2]","[90, 40]","[1, 2]",2,5,2,200,4,2,1,2072 -29,66,"[2, 1]","[20, 60]","[1, 2]",2,1,2,20,2,2,2,1787 -29,67,"[2, 1]","[20, 70]","[1, 1]",2,5,1,350,7,1,2,1972 -29,68,"[2, 1]","[40, 60]","[1, 1]",2,1,1,60,6,1,2,1739 -29,69,"[3, 4]","[80, 40]","[2, 1]",2,5,3,400,8,1,3,1989 -29,70,"[1, 2]","[80, 20]","[1, 1]",2,5,1,400,8,1,1,1886 -29,71,"[1, 2]","[70, 20]","[1, 1]",2,1,1,70,7,1,1,1801 -29,72,"[1, 2]","[60, 10]","[1, 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deletions(-) diff --git a/cpm/applications/reinforcement_learning.py b/cpm/applications/reinforcement_learning.py index 013063a..aa89870 100644 --- a/cpm/applications/reinforcement_learning.py +++ b/cpm/applications/reinforcement_learning.py @@ -237,7 +237,11 @@ def __init__( value=parameters_settings[6][0], lower=parameters_settings[6][1], upper=parameters_settings[6][2] - ) + ), + Q_MF = numpy.ones((2,2))*4.5, + Q2 = numpy.ones((2,1))*4.5, + M = numpy.zeros((2,2)), # last action matrix + R = numpy.zeros(2) # last choice structure ) @ipp.require("numpy") @@ -270,13 +274,15 @@ def MF_MB_model(parameters, trial): respst = parameters.response_stickiness # response stickiness # initialization - Q_MF = numpy.ones((2,2)) - Q2 = numpy.ones((2,1)) - TM = [numpy.eye(2), numpy.eye(2)] #transition matrix - M = numpy.zeros((2,2)) # last action matrix - R = [0, 0] # last choice structure - LL = 0 # log likelihood + # initialization + Q_MF = numpy.array(parameters.Q_MF) # Q values for model-free learning + Q2 = numpy.array(parameters.Q2) # Q values for model-based learning + + M = numpy.array(parameters.M) # last action matrix + R = numpy.array(parameters.R) # last choice structure + + TM = [numpy.eye(2), numpy.eye(2)] #transition matrix if trial["stimuli"][0] == 2 or trial["stimuli"][0] == 4: R = R = numpy.flipud(R) @@ -299,7 +305,7 @@ def MF_MB_model(parameters, trial): Q = w*Q_MB + (1-w)*Q_MF[s1-1, :].T + st * M[s1-1, :].T + respst * numpy.array(R) # calculating the probability choosing action a given Q - LL = LL + b*Q[a-1] - logsumexp(b * Q) + policy = numpy.exp(b * Q) / numpy.sum(numpy.exp(b * Q)) # update choice structure M = numpy.zeros((2,2)) @@ -321,13 +327,16 @@ def MF_MB_model(parameters, trial): Q_MF[s1-1, a-1] = Q_MF[s1-1, a-1] + lamb * lr * dtQ2 # compile results - results = pandas.DataFrame({ - "userID": trial["userID"][0], - "LL": [LL], - "Q": [Q.flatten()], - "Q_MF": [Q_MF.flatten()], - "Q_MB": [Q_MB.flatten()] - }) + results = { + "policy": numpy.array(policy), + "R": numpy.array(R), + "M": numpy.array(M), + "Q": numpy.array(Q.flatten()), + "Q2": numpy.array(Q2), + "Q_MF": numpy.array(Q_MF), + "Q_MB": numpy.array(Q_MB.flatten()), + "dependent": numpy.array([policy[1]]) + } return results From d94a4d1d8f6b5a63e223a8bb0a1eb051b545d002 Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Tue, 22 Jul 2025 16:31:14 +0200 Subject: [PATCH 33/54] Update scavenger.py --- cpm/brainexplorer/scavenger.py | 123 +++------------------------------ 1 file changed, 9 insertions(+), 114 deletions(-) diff --git a/cpm/brainexplorer/scavenger.py b/cpm/brainexplorer/scavenger.py index 03d34c9..486498d 100644 --- a/cpm/brainexplorer/scavenger.py +++ b/cpm/brainexplorer/scavenger.py @@ -1,8 +1,6 @@ import pandas as pd import numpy as np -import warnings from scipy.stats import zscore -import statsmodels.formula.api as smf class Scavenger: @@ -159,130 +157,27 @@ def metrics(self): # Group data by userID grouped_data = self.data.groupby("userID") - all_users_data = [] - # Loop through each group of user data for user_id, user_data in grouped_data: - - # Initialize a dictionary to store results for this user - user_metrics = { - "userID": user_id, - "rsk_all": [], - "t_loss_all": [], - "t_abg_all": [], - "EV_diff_chosen_all": [], - "chosen_left_all": [], - "A_EV": [], - "B_EV": [], - "chosen": [], - "all_rewards": [], - "choice_stickiness": [], - "RT": [], - "catchtrial": [], - "abovechance": [], - "chosenLeft": [] - } - - # Loop through each trial in the user's data - for _, trial_data in user_data.iterrows(): - metrics_data_trial = {} - - if trial_data["chosen"] == 0: - metrics_data_trial["chosen"] = 1 # Option A chosen (safe) - metrics_data_trial["A_perc"] = 1 - metrics_data_trial["A_magn"] = trial_data["safe_magn"] - metrics_data_trial["B_perc"] = [ - trial_data["risky_prob1"], - trial_data["risky_prob2"] - ] - metrics_data_trial["B_magn"] = [ - trial_data["risky_magn1"], - trial_data["risky_magn2"] - ] - else: - metrics_data_trial["chosen"] = 2 # Option B chosen - metrics_data_trial["A_perc"] = 1 - metrics_data_trial["A_magn"] = trial_data[ - "safe_magn" - ] - metrics_data_trial["B_perc"] = [ - trial_data["risky_prob1"], - trial_data["risky_prob2"] - ] - metrics_data_trial["B_magn"] = [ - trial_data["risky_magn1"], - trial_data["risky_magn2"] - ] - - metrics_data_trial["ambiguous"] = trial_data[ - "ambiguousTrial" - ] - - # Compute expected values - metrics_data_trial["A_EV"] = np.dot( - np.array(metrics_data_trial["A_perc"]), - np.array(metrics_data_trial["A_magn"]), - ) - metrics_data_trial["B_EV"] = np.array( - metrics_data_trial["B_perc"][0] - ) * np.array( - metrics_data_trial["B_magn"][0] - ) + np.array( - metrics_data_trial["B_perc"][1] - ) * np.array(metrics_data_trial["B_magn"][1]) - - metrics_data_trial["EV_diff_safe"] = ( - metrics_data_trial["A_EV"] - metrics_data_trial["B_EV"] - ) - - if metrics_data_trial["chosen"] == 1: - metrics_data_trial["EV_diff_chosen"] = ( - metrics_data_trial["A_EV"] - metrics_data_trial["B_EV"] - ) - elif metrics_data_trial["chosen"] == 2: - metrics_data_trial["EV_diff_chosen"] = ( - metrics_data_trial["B_EV"] - metrics_data_trial["A_EV"] - ) - - # Store the trial data for the user in the user's metrics dictionary - user_metrics["A_EV"].append(metrics_data_trial["A_EV"]) - user_metrics["B_EV"].append(metrics_data_trial["B_EV"]) - user_metrics["chosen"].append(metrics_data_trial["chosen"]) - user_metrics["rsk_all"].append(metrics_data_trial["chosen"] - 1) - user_metrics["t_loss_all"].append(metrics_data_trial["A_EV"] < 0) - user_metrics["t_abg_all"].append(bool(metrics_data_trial["ambiguous"])) - user_metrics["EV_diff_chosen_all"].append( - metrics_data_trial["EV_diff_chosen"] - ) - user_metrics["RT"].append(trial_data["RT"]) - user_metrics["all_rewards"].append(trial_data["outcome"]) - user_metrics["catchtrial"].append(trial_data["catchtrial"]) - user_metrics["abovechance"].append(trial_data["abovechance"]) - user_metrics["chosenLeft"].append(trial_data["chosenLeft"]) - - all_users_data.append(user_metrics) - - # iterate over all users - for user_data in all_users_data: - - user_id = user_data["userID"] - rsk = np.array(user_data["rsk_all"]) - t_loss = np.array(user_data["t_loss_all"]) - t_abg = np.array(user_data["t_abg_all"]) - ev_diff_chosen = np.array(user_data["EV_diff_chosen_all"], dtype=object) + print(user_data.head()) + t_abg = np.array(user_data["ambiguousTrial"], dtype=bool) catch_trials = np.array(user_data["catchtrial"], dtype=bool) abovechance = np.array(user_data["abovechance"]) chosen_left = np.array(user_data["chosenLeft"]) + ev_diff_chosen = np.array(user_data["EV_diff_chosen"], dtype=object) + rsk = np.array(user_data["chosen"]) + t_loss = np.array(user_data["safe_EV"] < 0) + user_results = {"userID": user_id} user_results["mean_RT"] = np.mean(user_data["RT"]) user_results["median_RT"] = np.median(user_data["RT"]) - user_results["total_rewards"] = np.sum(user_data["all_rewards"]) + user_results["total_rewards"] = np.sum(user_data["outcome"]) - # calculate choice stickiness - user_results["choice_stickiness"] = np.sum(user_data["chosen"][i] == 1 and user_data["chosen"][i + 1] == 1 for i in range(len(user_data["chosen"]) - 1)) + # calculate choice stickiness + user_results["choice_stickiness"] = np.sum(user_data["chosen"].iloc[i] == 1 and user_data["chosen"].iloc[i + 1] == 1 for i in range(len(user_data["chosen"]) - 1)) # Number of risky choices user_results["risky_choices"] = np.mean(rsk == 1) From 93baf5a14d148c8fac09130e9b538b0823e799f4 Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Sun, 24 Aug 2025 13:23:27 +0200 Subject: [PATCH 34/54] adapted BE metrics --- cpm/brainexplorer/milkyWay.py | 176 ++++++++++++++++++------ cpm/brainexplorer/spaceObserver.py | 211 +++++++++++++++++++---------- cpm/brainexplorer/treasurehunt.py | 103 +++++++------- 3 files changed, 314 insertions(+), 176 deletions(-) diff --git a/cpm/brainexplorer/milkyWay.py b/cpm/brainexplorer/milkyWay.py index 205d539..f3f86e5 100644 --- a/cpm/brainexplorer/milkyWay.py +++ b/cpm/brainexplorer/milkyWay.py @@ -28,6 +28,7 @@ def __init__(self, filepath): >>> results_MW = milkyWay.results_MW >>> results_PM = milkyWay.results_PM >>> milkyWay.codebook() + >>> diff_results = milkyWay.difference_metrics() Notes @@ -35,12 +36,14 @@ def __init__(self, filepath): The columns required in data: - userID: unique identifier for each participant - trial_type: the type of trial (Milky Way or Pirate Market) - - rt_adj: the adjusted reaction time of the trial - - choice: the choice made by the participant + - date: the date and time of the trial - correct: whether the choice was correct (1) or incorrect (0) + - outchosen: the outcome of the trial + - rep: whether the choice was repeated (1) or not (0) + - WSLS_v1: whether the choice was a win-stay/loose-shift choice for correct trials version 1 + - WSLS_v2: whether the choice was a win-stay/loose-shift choice for correct trials version 2 The trial-level exclusion criteria are applied: - - Reaction time < 150 or > 10000 ms - Attempts after the first attempt ("run" variable) The reaction times have to be corrected by the average time between the previous choice and new stimulus presentation which is 4200 ms. However, this correction can lead to negative reaction times, which may exclude some participants from the analysis. @@ -48,47 +51,43 @@ def __init__(self, filepath): self.data = pd.read_csv(filepath) - #average time between previous choice and new stimulus presentation (including feedback on previous choice etc) is 4.2 s, so he removed that from all RTs - #BUT: as we will see later, this correct does generate some negative RTs and thereby excludes some participants. so think about this! - self.data["rt_adj_cor"] = self.data["rt_adj"] - 4200 - #Separate into MW and PM data self.MW_data = self.data[self.data['trial_type'] == 'reward'] self.PM_data = self.data[self.data['trial_type'] == 'punish'] - #Take first attempt (i.e., not neccessarily run == 1 for PM!) - min_run_per_user = self.MW_data.groupby('userID')['run'].transform('min') - self.MW_data = self.MW_data[self.MW_data['run'] == min_run_per_user].reset_index(drop=True) - - min_run_per_user = self.PM_data.groupby('userID')['run'].transform('min') - self.PM_data = self.PM_data[self.PM_data['run'] == min_run_per_user].reset_index(drop=True) - - #Apply trial-level exclusion criteria - self.MW_data = self.MW_data[self.MW_data["rt_adj_cor"] >= 150] # only keep trials with reaction time >= 150 ms - self.MW_data = self.MW_data[self.MW_data["rt_adj_cor"] <= 10000] # only keep trials with reaction time <= 10000 ms + #Take first attempt (i.e., not neccessarily run == 1 for PM) + #min_run_per_user = self.MW_data.groupby('userID')['run'].transform('min') + #self.MW_data = self.MW_data[self.MW_data['run'] == min_run_per_user].reset_index(drop=True) - self.PM_data = self.PM_data[self.PM_data["rt_adj_cor"] >= 150] # only keep trials with reaction time > 150 ms - self.PM_data = self.PM_data[self.PM_data["rt_adj_cor"] <= 10000] # only keep trials with reaction time < 10000 ms - + #min_run_per_user = self.PM_data.groupby('userID')['run'].transform('min') + #self.PM_data = self.PM_data[self.PM_data['run'] == min_run_per_user].reset_index(drop=True) self.results_MW = pd.DataFrame() self.results_PM = pd.DataFrame() + self.results_diff = pd.DataFrame() + self.codebook = { "userID": "Unique identifier for each participant", "trial_type": "Type of trial (Milky Way or Pirate Market)", - "rt_adj": "Adjusted reaction time of the trial", - "choice": "Choice made by the participant (1 or 2)", - "correct": "Whether the choice was correct (1) or incorrect (0)", - "mean_RT_MW": "Mean reaction time for Milky Way trials", + "day_of_week": "Day of the week when the trial was conducted", + "time": "Time when the trial was conducted", + "time_of_day": "Time of day when the trial was conducted (morning, afternoon, evening, night)", + "mean_RT_MW": "Mean reaction time for Milky Way trials", "median_RT_MW": "Median reaction time for Milky Way trials", "mean_RT_PM": "Mean reaction time for Pirate Market trials", "median_RT_PM": "Median reaction time for Pirate Market trials", - "accuracy_MW": "Accuracy for Milky Way trials", - "accuracy_PM": "Accuracy for Pirate Market trials", + "accuracy_MW": "Mean accuracy for Milky Way trials", + "accuracy_PM": "Mean accuracy for Pirate Market trials", "prop_same_choice_MW": "Proportion of same choice in Milky Way trials", "prop_same_choice_PM": "Proportion of same choice in Pirate Market trials", - "prop_WSLS_1": "Proportion of win-stay/loose-shift choices for correct trials based on whether the choice was correct", - "prop_WSLS_2": "Proportion of win-stay/loose-shift choices for correct trials based on whether participant got at least 50 points (MW) or less than 50 points (PM)" + "mean_outcome_MW": "Mean outcome for Milky Way trials", + "mean_outcome_PM": "Mean outcome for Pirate Market trials", + "reward_diff_obt_forg_MW": "Mean difference between obtained and foregone reward for Milky Way trials", + "reward_diff_obt_forg_PM": "Mean difference between obtained and foregone reward for Pirate Market trials", + "prop_WSLS_1_MW": "Proportion of win-stay/loose-shift choices for correct trials version 1 for Milky Way trials", + "prop_WSLS_2_MW": "Proportion of win-stay/loose-shift choices for correct trials version 2 for Milky Way trials", + "prop_WSLS_1_PM": "Proportion of win-stay/loose-shift choices for correct trials version 1 for Pirate Market trials", + "prop_WSLS_2_PM": "Proportion of win-stay/loose-shift choices for correct trials version 2 for Pirate Market trials" } @@ -111,8 +110,15 @@ def metrics(self): - accuracy_PM: Accuracy for Pirate Market trials - prop_same_choice_MW: Proportion of same choice in Milky Way trials - prop_same_choice_PM: Proportion of same choice in Pirate Market trials - - prop_WSLS_1: Proportion of win-stay/loose-shift choices for correct trials based on whether the choice was correct - - prop_WSLS_2: Proportion of win-stay/loose-shift choices for correct trials based on whether participant got at least 50 points (MW) or less than 50 points (PM) + - mean_outcome_MW: Mean outcome for Milky Way trials + - mean_outcome_PM: Mean outcome for Pirate Market trials + - reward_diff_obt_forg_MW: Mean difference between obtained and foregone reward for Milky Way trials + - reward_diff_obt_forg_PM: Mean difference between obtained and foregone reward for Pirate Market trials + - prop_WSLS_1_MW: Proportion of win-stay/loose-shift choices for correct trials version 1 for Milky Way trials + - prop_WSLS_2_MW: Proportion of win-stay/loose-shift choices for correct trials version 2 for Milky Way trials + - prop_WSLS_1_PM: Proportion of win-stay/loose-shift choices for correct trials version 1 for Pirate Market trials + - prop_WSLS_2_PM: Proportion of win-stay/loose-shift choices for correct trials version 2 for Pirate Market trials + """ # group MilkyWay and Pirate Market data MW_data = self.MW_data.groupby("userID") @@ -123,19 +129,40 @@ def metrics(self): user_results_MW = {"userID": user_id} - user_results_MW["mean_RT_MW"] = np.mean(user_data_MW['rt_adj']) - user_results_MW["median_RT_MW"] = np.median(user_data_MW['rt_adj']) + user_results_MW["trial_type"] = "Milky Way" + + date = user_data_MW["date"].iloc[0] + if isinstance(date, str): + date = pd.to_datetime(date, format="%Y-%m-%d %H:%M:%S.%f") + user_data_MW["day_of_week"] = date.day_name() + user_data_MW["time"] = date.time() if hasattr(date, 'time') else date.strftime("%H:%M:%S.%f") + # morning 6-12, afternoon 12-18, evening 18-24, night 0-6 + user_data_MW["time_of_day"] = ( + "morning" if date.hour < 12 else + "afternoon" if date.hour < 18 else + "evening" if date.hour < 24 else + "night" + ) + + #user_results_MW["mean_RT_MW"] = np.mean(user_data_MW['rt_adj']) + #user_results_MW["median_RT_MW"] = np.median(user_data_MW['rt_adj']) + + user_results_MW["accuracy_MW"] = np.nanmean(user_data_MW["correct"]) - user_results_MW["accuracy_MW"] = np.mean(user_data_MW["correct"]) + user_results_MW["mean_outcome_MW"] = np.nanmean(user_data_MW["outchosen"]) - user_results_MW["prop_choice1_MW"] = np.mean(user_data_MW["choice"] == 1) + # Mean difference between obtained and foregone reward + user_results_MW["reward_diff_obt_forg_MW"] = np.nanmean(user_data_MW["obt_min_forg"]) + + # proportion of repeated choices + user_results_MW["prop_same_choice_MW"] = np.nanmean(user_data_MW["rep"]) # proportion of win-stay/loose-shift choices - user_results_MW["prop_WSLS_1"] = np.mean( + user_results_MW["prop_WSLS_1_MW"] = np.nanmean( user_data_MW["WSLS_v1"] ) - user_results_MW["prop_WSLS_2"] = np.mean( + user_results_MW["prop_WSLS_2_MW"] = np.nanmean( user_data_MW["WSLS_v2"] ) @@ -150,19 +177,40 @@ def metrics(self): user_results_PM = {"userID": user_id} - user_results_PM["mean_RT_PM"] = np.mean(user_data_PM['rt_adj']) - user_results_PM["median_RT_PM"] = np.median(user_data_PM['rt_adj']) + user_results_PM["trial_type"] = "Pirate Market" + + date = user_data_PM["date"].iloc[0] + if isinstance(date, str): + date = pd.to_datetime(date, format="%Y-%m-%d %H:%M:%S.%f") + user_data_PM["day_of_week"] = date.day_name() + user_data_PM["time"] = date.time() if hasattr(date, 'time') else date.strftime("%H:%M:%S.%f") + # morning 6-12, afternoon 12-18, evening 18-24, night 0-6 + user_data_PM["time_of_day"] = ( + "morning" if date.hour < 12 else + "afternoon" if date.hour < 18 else + "evening" if date.hour < 24 else + "night" + ) + + #user_results_PM["mean_RT_PM"] = np.mean(user_data_PM['rt_adj']) + #user_results_PM["median_RT_PM"] = np.median(user_data_PM['rt_adj']) + + user_results_PM["accuracy_PM"] = np.nanmean(user_data_PM["correct"]) + + user_results_PM["mean_outcome_PM"] = np.nanmean(user_data_PM["outchosen"]) - user_results_PM["accuracy_PM"] = np.mean(user_data_PM["correct"]) + # Mean difference between obtained and foregone reward + user_results_PM["reward_diff_obt_forg_PM"] = np.nanmean(user_data_PM["obt_min_forg"]) - user_results_PM["prop_choice1_PM"] = np.mean(user_data_PM["choice"] == 1) + # proportion of repeated choices + user_results_PM["prop_same_choice_PM"] = np.nanmean(user_data_PM["rep"]) # proportion of win-stay/loose-shift choices - user_results_PM["prop_WSLS_1"] = np.mean( + user_results_PM["prop_WSLS_1_PM"] = np.nanmean( user_data_PM["WSLS_v1"] ) - user_results_PM["prop_WSLS_2"] = np.mean( + user_results_PM["prop_WSLS_2_PM"] = np.nanmean( user_data_PM["WSLS_v2"] ) @@ -174,6 +222,46 @@ def metrics(self): self.results_PM = pd.DataFrame(self.results_PM) + def difference_metrics(self): + """ + Calculate the difference between the metrics of Milky Way and Pirate Market trials. + + Returns + ------- + results_diff : pd.DataFrame + A DataFrame containing the difference of the metrics between Milky Way and Pirate Market trials. + + Variables + ---------- + - accuracy_diff: Difference in accuracy between Milky Way and Pirate Market trials + - outcome_diff: Difference in mean outcome between Milky Way and Pirate Market trials + - reward_diff_obt_forg_diff: Difference in mean difference between obtained and foregone reward between Milky Way and Pirate Market trials + - prop_same_choice_diff: Difference in proportion of same choice between Milky Way and Pirate Market trials + - prop_WSLS_1_diff: Difference in proportion of win-stay/loose-shift choices for correct trials version 1 between Milky Way and Pirate Market trials + - prop_WSLS_2_diff: Difference in proportion of win-stay/loose-shift choices for correct trials version 2 between Milky Way and Pirate Market trials + """ + + # Calculate differences for each user + for user_id in pd.unique(self.results_MW["userID"]): + user_results_MW = self.results_MW[self.results_MW["userID"] == user_id] + user_results_PM = self.results_PM[self.results_PM["userID"] == user_id] + + if not user_results_MW.empty and not user_results_PM.empty: + diff_results = { + "userID": user_id, + "accuracy_diff": user_results_MW["accuracy_MW"].values[0] - user_results_PM["accuracy_PM"].values[0], + "outcome_diff": user_results_MW["mean_outcome_MW"].values[0] - user_results_PM["mean_outcome_PM"].values[0], + "reward_diff_obt_forg_diff": user_results_MW["reward_diff_obt_forg_MW"].values[0] - user_results_PM["reward_diff_obt_forg_PM"].values[0], + "prop_same_choice_diff": user_results_MW["prop_same_choice_MW"].values[0] - user_results_PM["prop_same_choice_PM"].values[0], + "prop_WSLS_1_diff": user_results_MW["prop_WSLS_1_MW"].values[0] - user_results_PM["prop_WSLS_1_PM"].values[0], + "prop_WSLS_2_diff": user_results_MW["prop_WSLS_2_MW"].values[0] - user_results_PM["prop_WSLS_2_PM"].values[0] + } + self.results_diff = pd.concat([self.results_diff, pd.DataFrame([diff_results])], ignore_index=True) + + return pd.DataFrame(self.results_diff) + + + def clean_data(self): """ Clean the data by removing participants who do not meet the participant-level inclusion criteria. @@ -203,8 +291,8 @@ def clean_data(self): self.cleanedresults_PM = self.cleanedresults_PM[self.cleanedresults_PM["userID"].isin(user_ids_to_keep)] #Remove users with at least 95% same choice - self.cleanedresults_MW = self.cleanedresults_MW[(self.cleanedresults_MW["prop_choice1_MW"] > 0.05) & (self.cleanedresults_MW["prop_choice1_MW"] < 0.95) ] - self.cleanedresults_PM = self.cleanedresults_PM[(self.cleanedresults_PM["prop_choice1_PM"] > 0.05) & (self.cleanedresults_PM["prop_choice1_PM"] < 0.95) ] + self.cleanedresults_MW = self.cleanedresults_MW[self.cleanedresults_MW["prop_same_choice_MW"] < 0.95] + self.cleanedresults_PM = self.cleanedresults_PM[self.cleanedresults_PM["prop_same_choice_PM"] < 0.95] #remove users with missing accuracy self.cleanedresults_MW = self.cleanedresults_MW[self.cleanedresults_MW["accuracy_MW"].notna()] diff --git a/cpm/brainexplorer/spaceObserver.py b/cpm/brainexplorer/spaceObserver.py index efe4cb7..6f800d9 100644 --- a/cpm/brainexplorer/spaceObserver.py +++ b/cpm/brainexplorer/spaceObserver.py @@ -43,8 +43,9 @@ def __init__(self, filepath=None): ----- The columns required in data: - - **userID**: unique identifier for each participant - - **round**: the round of the trial + - **userID: unique identifier for each participant + - **date**: the date and time of the trial + - **run**: number of attempt by participant - **accuracy**: the accuracy of the trial (1 for correct, 0 for incorrect) - **choiceRT**: the reaction time of the choice in the trial - **confidence**: the confidence rating of the trial @@ -52,7 +53,7 @@ def __init__(self, filepath=None): - **stimulus_intensity**: the evidence strength of the trial, which is the difference of evidence for each group of stimuli. Trial-level exclusion critera: - - Attempts after the first attempt ("run" variable) + - Practice trials (run == 1) - Reaction time < 150 or > 10000 ms - Reaction time of confidence < 150 or > 10000 ms - Trials with missing confidence data @@ -61,7 +62,7 @@ def __init__(self, filepath=None): ## read data self.data = pd.read_csv(filepath, header=0, na_values=["NaN", "nan"]) - self.data = self.data[self.data["run"] == 1] # only keep first attempt + self.data = self.data[self.data["run"] != 1] # exclude practice trials self.data = self.data[self.data["choiceRT"] >= 150] # only keep trials with reaction time > 150 ms self.data = self.data[self.data["choiceRT"] <= 10000] # only keep trials with reaction time < 10000 ms self.data = self.data[self.data["confidenceRT"] >= 150] # only keep trials with confidence reaction time > 150 ms @@ -77,19 +78,38 @@ def __init__(self, filepath=None): self.results = pd.DataFrame() self.codebook = { "userID": "Unique identifier for each participant", - "mean_accuracy": "Mean accuracy across all trials", + "day_of_week": "Day of the week of the trial", + "time": "Time of the trial", + "time_of_day": "Time of day of the trial (morning, afternoon, evening, night)", + "accuracy": "Mean accuracy (if the choice was the majority choice)", "mean_RT": "Mean response time across all trials", "median_RT": "Median response time across all trials", + "median_RT_correct": "Median response time for trials where the participant made the correct choice", + "median_RT_incorrect": "Median response time for trials where the participant made the incorrect", + "diff_median_RT_correct_incorrect": "Difference between the median response time for correct and incorrect choices", "mean_confidence": "Mean confidence rating across all trials", + "sd_confidence": "Standard deviation of the confidence rating across all trials", + "median_confidence": "Median confidence rating across all trials", + "median_confidence_correct": "Median confidence rating for trials where the participant made the correct choice", + "median_confidence_incorrect": "Median confidence rating for trials where the participant made the incorrect choice", + "diff_median_conf_correct_incorrect": "Difference between the median confidence rating for correct and incorrect choices", + "sd_confidence_correct": "Standard deviation of the confidence rating for trials where the participant made the correct choice", + "sd_confidence_incorrect": "Standard deviation of the confidence rating for trials where the participant made the incorrect choice", + "diff_sd_confidence_correct_incorrect": "Difference between the standard deviation of the confidence rating for correct and incorrect choices", "mean_confidenceRT": "Mean confidence response time across all trials", "median_confidenceRT": "Median confidence response time across all trials", - "mean_RT_correct": "Mean response time for correct choices", - "median_RT_correct": "Median response time for correct choices", - "mean_RT_incorrect": "Mean response time for incorrect choices", - "median_RT_incorrect": "Median response time for incorrect choices", - "conf_corr_mean": "Mean confidence rating for correct choices", - "conf_incorr_mean": "Mean confidence rating for incorrect choices", - "ES_bins": "Mean evidence strength for each of the bins (4 bins)" + "median_confidenceRT_correct": "Median confidence response time for trials where the participant made the correct choice", + "median_confidenceRT_incorrect": "Median confidence response time for trials where the participant made the incorrect choice", + "diff_median_confidenceRT_correct_incorrect": "Difference between the median confidence response time for correct and incorrect choices", + "confidence_10": "10th percentile of the confidence rating across all trials", + "confidence_25": "25th percentile of the confidence rating across all trials", + "confidence_75": "75th percentile of the confidence rating across all trials", + "confidence_90": "90th percentile of the confidence rating across all trials", + "evidence_strength_mean": "Mean evidence strength across all trials", + "evidence_strength_correct_mean": "Mean evidence strength for trials where the participant made the correct choice", + "evidence_strength_incorrect_mean": "Mean evidence strength for trials where the participant made the incorrect choice", + "diff_evidence_strength_correct_incorrect": "Difference between the mean evidence strength for correct and incorrect choices", + "ES_bins": "Mean evidence strength for each of the bins. The trials here are split into 4 quarters. The evidence strength is divided into four equal bins and the mean is calculated for each bin." } def metrics(self, mode=None): @@ -102,45 +122,39 @@ def metrics(self, mode=None): Variables --------- - - mean_accuracy : float - The mean accuracy across all trials. - - mean_RT : float - The mean response time across all trials. - - median_RT : float - The median response time across all trials. - - mean_confidence : float - The mean confidence rating across all trials. - - mean_confidenceRT : float - The mean confidence response time across all trials. - - median_confidenceRT : float - The median confidence response time across all trials. - - mean_RT_correct : float - The mean response time for trials where the participant made the correct choice. - - median_RT_correct : float - The median response time for trials where the participant made the correct choice. - - mean_RT_incorrect : float - The mean response time for trials where the participant made the incorrect choice. - - median_RT_incorrect : float - The median response time for trials where the participant made the incorrect choice. - - conf_corr_mean : float - The mean confidence rating for trials where the participant made the correct choice. - - conf_incorr_mean : float - The mean confidence rating for trials where the participant made the incorrect choice. - - ES_bins : np.array - The mean evidence strength for each of the bins. The trials here are split into 4 quarters. The evidence strength is divided into four equal bins and the mean is calculated for each bin. + - userID: unique identifier for each participant + - day_of_week: day of the week of the trial + - time: time of the trial + - time_of_day: time of day of the trial (morning, afternoon, evening, night) + - accuracy : The mean accuracy across all trials. + - mean_RT : The mean response time across all trials. + - median_RT : The median response time across all trials. + - median_RT_correct : The median response time for trials where the participant made the correct choice. + - median_RT_incorrect : The median response time for trials where the participant made the incorrect choice. + - diff_median_RT_correct_incorrect : The difference between the median response time for correct and incorrect choices. + - mean_confidence : The mean confidence rating across all trials. + - median_confidence : The median confidence rating across all trials. + - median_confidence_correct : The median confidence rating for trials where the participant made the correct choice. + - median_confidence_incorrect : The median confidence rating for trials where the participant made the incorrect choice. + - diff_median_conf_correct_incorrect : The difference between the median confidence rating for correct and incorrect choices. + - sd_confidence : The standard deviation of the confidence rating across all trials. + - sd_confidence_correct : The standard deviation of the confidence rating for trials where the participant made the correct choice. + - sd_confidence_incorrect : The standard deviation of the confidence rating for trials where the participant made the incorrect choice. + - diff_sd_confidence_correct_incorrect : The difference between the standard deviation of the confidence rating for correct and incorrect choices. + - mean_confidenceRT : The mean confidence response time across all trials. + - median_confidenceRT : The median confidence response time across all trials. + - median_confidenceRT_correct : The median confidence response time for trials where the participant made the correct choice. + - median_confidenceRT_incorrect : The median confidence response time for trials where the participant made the incorrect choice. + - diff_median_confidenceRT_correct_incorrect : The difference between the median confidence response time for correct and incorrect choices. + - confidence_10 : The 10th percentile of the confidence rating across all trials. + - confidence_25 : The 25th percentile of the confidence rating across all trials. + - confidence_75 : The 75th percentile of the confidence rating across all trials. + - confidence_90 : The 90th percentile of the confidence rating across all trials. + - evidence_strength_mean : The mean evidence strength across all trials. + - evidence_strength_correct_mean : The mean evidence strength for trials where the participant made the correct choice. + - evidence_strength_incorrect_mean : The mean evidence strength for trials where the participant made the incorrect choice. + - diff_evidence_strength_correct_incorrect : The difference between the mean evidence strength for correct and incorrect choices. + - ES_bins : The mean evidence strength for each of the bins. The trials here are split into 4 quarters. The evidence strength is divided into four equal bins and the mean is calculated for each bin. """ # Group data by subject @@ -150,34 +164,79 @@ def metrics(self, mode=None): user_results = {"userID": user_id} - user_results["mean_accuracy"] = np.mean(user_data["accuracy"]) + + user_results = {"userID": user_id} + + date = user_data["date"].iloc[0] + if isinstance(date, str): + date = pd.to_datetime(date, format="%Y-%m-%d %H:%M:%S.%f") + user_results["date"] = date + user_results["day_of_week"] = date.day_name() + user_results["time"] = date.time() if hasattr(date, 'time') else date.strftime("%H:%M:%S.%f") + # morning 6-12, afternoon 12-18, evening 18-24, night 0-6 + user_results["time_of_day"] = ( + "morning" if date.hour < 12 else + "afternoon" if date.hour < 18 else + "evening" if date.hour < 24 else + "night" + ) + + user_results["accuracy"] = np.mean(user_data["accuracy"]) user_results["mean_RT"] = np.mean(user_data["choiceRT"]) user_results["median_RT"] = np.median(user_data["choiceRT"]) + # different RT for correct and incorrect trials + user_results["median_RT_correct"] = np.median(user_data["choiceRT"][user_data["accuracy"] == 1]) + user_results["median_RT_incorrect"] = np.median(user_data["choiceRT"][user_data["accuracy"] != 1]) + user_results["diff_median_RT_correct_incorrect"] = ( + user_results["median_RT_correct"] - user_results["median_RT_incorrect"] + ) user_results["mean_confidence"] = np.nanmean(user_data["confidence"]) + user_results["sd_confidence"] = np.nanstd(user_data["confidence"]) + user_results["median_confidence"] = np.nanmedian(user_data["confidence"]) + # confidence difference for correct and incorrect trials + user_results["median_confidence_correct"] = np.nanmedian(user_data["confidence"][user_data["accuracy"] == 1]) + user_results["median_confidence_incorrect"] = np.nanmedian(user_data["confidence"][user_data["accuracy"] != 1]) + user_results["diff_median_conf_correct_incorrect"] = ( + user_results["median_confidence_correct"] - user_results["median_confidence_incorrect"] + ) + # standard deviation of confidence for correct and incorrect trials + user_results["sd_confidence_correct"] = np.nanstd(user_data["confidence"][user_data["accuracy"] == 1]) + user_results["sd_confidence_incorrect"] = np.nanstd(user_data["confidence"][user_data["accuracy"] != 1]) + user_results["diff_sd_confidence_correct_incorrect"] = ( + user_results["sd_confidence_correct"] - user_results["sd_confidence_incorrect"] + ) user_results["mean_confidenceRT"] = np.nanmean(user_data["confidenceRT"]) user_results["median_confidenceRT"] = np.nanmedian(user_data["confidenceRT"]) + # different RT for correct and incorrect trials + user_results["median_confidenceRT_correct"] = np.nanmedian(user_data["confidenceRT"][user_data["accuracy"] == 1]) + user_results["median_confidenceRT_incorrect"] = np.nanmedian(user_data["confidenceRT"][user_data["accuracy"] != 1]) + user_results["diff_median_confidenceRT_correct_incorrect"] = ( + user_results["median_confidenceRT_correct"] - user_results["median_confidenceRT_incorrect"] + ) - # confidence and RT by performance - correct = np.array(user_data["accuracy"]) - confidence = np.array(user_data["confidence"]) - - conf_corr = confidence[(correct == 1) & ~np.isnan(confidence)] - conf_incorr = confidence[(correct != 1) & ~np.isnan(confidence)] - user_results["conf_corr_mean"] = np.mean(conf_corr) if len(conf_corr) > 0 else np.nan - user_results["conf_incorr_mean"] = np.mean(conf_incorr) if len(conf_incorr) > 0 else np.nan - - user_results["mean_RT_correct"] = np.mean(np.array(user_data["choiceRT"])[correct == 1]) - user_results["median_RT_correct"] = np.median(np.array(user_data["choiceRT"])[correct == 1]) - user_results["mean_RT_incorrect"] = np.mean(np.array(user_data["choiceRT"])[correct != 1]) - user_results["median_RT_incorrect"] = np.median(np.array(user_data["choiceRT"])[correct != 1]) + # calculate confidence percentiles + confidence = user_data["confidence"] + user_results["confidence_10"] = np.nanpercentile(confidence, 10) + user_results["confidence_25"] = np.nanpercentile(confidence, 25) + user_results["confidence_75"] = np.nanpercentile(confidence, 75) + user_results["confidence_90"] = np.nanpercentile(confidence, 90) + # calculate evidence strength evidence_strength = user_data["stimulus_intensity"] - # calcualte evidence strength for each bin + user_results["evidence_strength_mean"] = np.nanmean(evidence_strength) + + # evidence strenth per correct and incorrect trials + user_results["evidence_strength_correct_mean"] = np.nanmean(evidence_strength[user_data["accuracy"] == 1]) + user_results["evidence_strength_incorrect_mean"] = np.nanmean(evidence_strength[user_data["accuracy"] != 1]) + user_results["diff_evidence_strength_correct_incorrect"] = ( + user_results["evidence_strength_correct_mean"] - user_results["evidence_strength_incorrect_mean"] + ) + # calcualte evidence strength for each bin bin_size = 4 # Divide evidence strength into 4 equal bins and calculate the mean for each bin @@ -188,7 +247,6 @@ def metrics(self, mode=None): ES_4 = np.mean(bins[3]) if len(bins[3]) > 0 else np.nan user_results["ES_bins"] = np.array([ES_1, ES_2, ES_3, ES_4]) - user_results["ES_total_mean"] = np.nanmean(evidence_strength) # Append user-level results self.results = pd.concat( @@ -211,25 +269,28 @@ def clean_data(self): - Median evidence strength > 25 - Mean confidence > 97 - Participants with more than 80 trials (due to technical error) + - Exclude participants if 10th and 25th percentile of confidence is the same AND 75th and 90th percentile of confidence is the same """ nr_part_before = len(self.results["userID"].unique()) self.cleanedresults = self.results.copy() - users_80trials = set( - self.data.loc[ - self.data.groupby("userID")["userID"].transform('count') <= 80, - "userID" - ].unique() - ) + # exclude participants who have more than 80 trials with run == 1 + users_80trials = self.cleanedresults.groupby("userID").filter(lambda x: len(x) >= 80)["userID"].unique() self.cleanedresults = self.cleanedresults[self.cleanedresults["userID"].isin(users_80trials)] self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_accuracy"] >= 0.5] self.cleanedresults = self.cleanedresults[self.cleanedresults["median_RT"] <= 3000] self.cleanedresults = self.cleanedresults[self.cleanedresults["median_confidenceRT"] <= 3000] self.cleanedresults = self.cleanedresults[self.cleanedresults["ES_total_mean"] <= 25] - self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_confidence"] <= 97] + self.cleanedresults = self.cleanedresults[self.cleanedresults["median_confidence"] <= 97] + self.cleanedresults = self.cleanedresults[self.cleanedresults["median_confidence"] >= 3] + + # exclude if 10th and 25th percentile of confidence is the same AND 75th and 90th percentile of confidence is the same + self.cleanedresults = self.cleanedresults[~( + (self.cleanedresults["confidence_10"] == self.cleanedresults["confidence_25"]) & self.cleanedresults["confidence_75"] == self.cleanedresults["confidence_90"] + )] self.deleted_participants = nr_part_before - len(self.cleanedresults["userID"].unique()) @@ -237,4 +298,4 @@ def codebook(self): """ Return a codebook describing the metrics. """ - return self.codebook + return self.codebook \ No newline at end of file diff --git a/cpm/brainexplorer/treasurehunt.py b/cpm/brainexplorer/treasurehunt.py index 66a4896..21e0eee 100644 --- a/cpm/brainexplorer/treasurehunt.py +++ b/cpm/brainexplorer/treasurehunt.py @@ -2,8 +2,8 @@ import numpy as np import warnings from scipy.stats import zscore -import statsmodels.api as sm -import statsmodels.formula.api as smf +#import statsmodels.api as sm +#import statsmodels.formula.api as smf class treasurehunt: @@ -43,12 +43,15 @@ def __init__(self, filepath=None): ----- The columns required in data: - userID: unique identifier for each participant + - "date": the date and time of the trial - run: number of attempt by participant - outcome: the outcome of the trial - confidence: the confidence of the trial - RT: the reaction time of the trial - confidenceRT: the confidence reaction time of the trial - - ev: the evidence of the trial + - draws: number of draws in the trial + - choseCurEv: whether the choice was in line with current evidence (1 = yes, 0 = no) + - ev: the evidence in the trial as a list of integers Trial-level exclusion criteria: @@ -63,20 +66,15 @@ def __init__(self, filepath=None): self.codebook = { "userID": "Unique identifier for each participant", - "mean_accuracy": "Mean accuracy across all trials", + "day_of_week": "Day of the week of the trial", + "time": "Time of the trial", + "time_of_day": "Time of day of the trial (morning, afternoon, evening, night)", + "accuracy": "Mean accuracy (if the choice was the majority choice)", "mean_points": "Mean points received across all trials", - "mean_confidence": "Mean confidence rating across all trials", - "mean_RTuntildec": "Mean reaction time until decision across all trials", - "median_RTuntildec": "Median reaction time until decision across all trials", - "mean_diffRT": "Mean difference in reaction time between the last two stimulus presentations", - "median_diffRT": "Median difference in reaction time between the last two stimulus presentations", - "mean_lastRT": "Mean reaction time after the last stimulus presentation", - "median_lastRT": "Median reaction time after the last stimulus presentation", - "mean_confidenceRT": "Mean confidence reaction time across all trials", - "median_confidenceRT": "Median confidence reaction time across all trials", "mean_n_draws": "Mean number of stimulus draws before making a decision", - "totevminus_coef": "Coefficient for the previous total evidence in the regression model", - "deltaev_coef": "Coefficient for the change in total evidence in the regression model" + "sd_n_draws": "Standard deviation of the number of stimulus draws before making a decision", + "median_RT_between_actions": "Median reaction time between actions", + "median_confidenceRT": "Median confidence reaction time across all trials" } def metrics(self): @@ -89,20 +87,16 @@ def metrics(self): Variables ---------- - - mean_accuracy: mean accuracy (if the choice was the majority choice) - - mean_points: mean points received - - mean_confidence: mean confidence of the choice - - mean_RTuntildec: mean reaction time until decision - - median_RTuntildec: median reaction time until decision - - mean_diffRT: mean difference in reaction time between the last two stimulus presentations - - median_diffRT: median difference in reaction time between the last two stimulus presentations - - mean_lastRT: mean reaction time after the last stimulus presentation - - median_lastRT: median reaction time after the last stimulus presentation - - mean_confidenceRT: mean confidence reaction time - - median_confidenceRT: median confidence reaction time - - n_draws: mean number of stimulus draws before making a decision - - totevminus_coef: coefficient for the previous total evidence in the regression model - - deltaev_coef: coefficient for the change in total evidence in the regression model + - userID: unique identifier for each participant + - day_of_week: day of the week of the trial + - time: time of the trial + - time_of_day: time of day of the trial (morning, afternoon, evening + - accuracy: mean accuracy (if the choice was the majority choice) + - mean_points: mean points received across all trials + - mean_n_draws: mean number of stimulus draws before making a decision + - sd_n_draws: standard deviation of the number of stimulus draws before making a decision + - median_RT_between_actions: median reaction time between actions + - median_confidenceRT: median confidence reaction time across all trials """ # turn string fields into list of ints self.data["ev"] = self.data["ev"].apply(lambda x: [int(i) for i in x.strip("[]").split(" ")]) @@ -115,29 +109,35 @@ def metrics(self): user_results = {"userID": user_id} - user_results["mean_points"] = np.mean(user_data["outcome"]) - - user_results["mean_accuracy"] = np.mean(user_data["correctChoice"]) + date = user_data["date"].iloc[0] + if isinstance(date, str): + date = pd.to_datetime(date, format="%Y-%m-%d %H:%M:%S.%f") + user_results["day_of_week"] = date.day_name() + user_results["time"] = date.time() if hasattr(date, 'time') else date.strftime("%H:%M:%S.%f") + # morning 6-12, afternoon 12-18, evening 18-24, night 0-6 + user_results["time_of_day"] = ( + "morning" if date.hour < 12 else + "afternoon" if date.hour < 18 else + "evening" if date.hour < 24 else + "night" + ) - user_results["mean_confidence"] = np.mean(user_data["confidence"]) + user_results["mean_points"] = np.nanmean(user_data["outcome"]) - user_results["mean_RTuntildec"] = np.mean(user_data["RTuntildec"]) - user_results["median_RTuntildec"] = np.median(user_data["RTuntildec"]) - user_results["mean_diffRT"] = np.mean(user_data["mean_diffRT"]) - user_results["median_diffRT"] = np.median(user_data["median_diffRT"]) - user_results["mean_lastRT"] = np.mean(user_data["lastRT"]) - user_results["median_lastRT"] = np.median(user_data["lastRT"]) + user_results["accuracy"] = np.nanmean(user_data["choseCurEv"]) - user_results["mean_confidenceRT"] = np.mean(user_data["confidenceRT"]) - user_results["median_confidenceRT"] = np.median(user_data["confidenceRT"]) + user_results["mean_confidence"] = np.nanmean(user_data["confidence"]) + user_results["sd_confidence"] = np.nanstd(user_data["confidence"]) - user_results["mean_n_draws"] = np.mean(user_data["draws"]) + user_results["mean_n_draws"] = np.nanmean(user_data["draws"]) + user_results["sd_n_draws"] = np.nanstd(user_data["draws"]) - # if chocie in line with current evidence - user_results["mean_chosEv"] = np.mean(user_data["chEv"]) + user_results["median_RT_between_actions"] = np.nanmedian(user_data["median_diffRT"]) - # regression for influence of recent results on current choice + user_results["median_confidenceRT"] = np.median(user_data["confidenceRT"]) + # regression for influence of recent results on current choice + """ ev = [] for idx, row in user_data.iterrows(): @@ -176,7 +176,7 @@ def metrics(self): else: user_results["totevminus_coef"] = np.nan user_results["deltaev_coef"] = np.nan - + """ # Append user-level results self.results = pd.concat( @@ -196,20 +196,12 @@ def clean_data(self): --------- - Mean number of draws < 2 or > 23 - Make choice not in line with current evidence in >= 20% of trials - - Mean confidence < 15 or > 98 - - < 3 unique values in confidence rating - < 3 unique values in number of samples """ nr_part_before = len(self.results["userID"].unique()) self.cleanedresults = self.results.copy() - # Filter out participants who have less than 3 unique values for confidence - valid_users = self.data.groupby("userID").filter( - lambda group: group["confidence"].nunique() >= 3 - )["userID"].unique() - self.cleanedresults = self.cleanedresults[self.cleanedresults["userID"].isin(valid_users)] - # Filter out participants who have less than 3 unique values for draws valid_users = self.data.groupby("userID").filter( lambda group: group["draws"].nunique() >= 3 @@ -219,9 +211,6 @@ def clean_data(self): self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_n_draws"] >= 2] # only keep participants with mean number of draws > 2 self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_n_draws"] <= 23] # only keep participants with mean number of draws < 23 - self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_confidence"] >= 15] # only keep participants with mean confidence > 15 - self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_confidence"] <= 98] # only keep participants with mean confidence < 98 - self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_chosEv"] > 0.8] # only keep participants with mean choice in line with current evidence >= 80% self.deleted_participants = nr_part_before - len(self.cleanedresults["userID"].unique()) From fdc74d96baf53f7107f164121649d9a17875a5ce Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Sun, 24 Aug 2025 13:24:24 +0200 Subject: [PATCH 35/54] new scavenger version --- cpm/brainexplorer/scavenger.py | 368 +++++++++++++++++++++++---------- 1 file changed, 261 insertions(+), 107 deletions(-) diff --git a/cpm/brainexplorer/scavenger.py b/cpm/brainexplorer/scavenger.py index 486498d..b43ed69 100644 --- a/cpm/brainexplorer/scavenger.py +++ b/cpm/brainexplorer/scavenger.py @@ -43,11 +43,16 @@ def __init__(self, filepath=None): ----- The columns required in data: - - "choice": if option A or B chosen, - - "risky_prob": Likelihood of winning for option B, - - "safe_magn": Trial Values for Option A, - - "risky_magn1" & "risky_magn2: Trial Values for Option B, - - "ambg": if the trial is ambiguous or not + - "run": number of attempt by participant + - "chosen": the option chosen by the participant (1 = safe, 2 = risky) + - "ambiguousTrial": whether the trial was ambiguous (1 = yes, 0 = no) + - "abovechance": whether the choice was above chance level (1 = yes, 0 = no) + - "EV_diff_chosen": the difference in expected value between the chosen and unchosen option + - "safe_EV": the expected value of the safe option + - "RT": the reaction time of the trial + - "date": the date and time of the trial + - "outcome": the outcome of the trial + Trial-level exclusion criteria: - Reaction time < 150 or > 10000 ms @@ -62,30 +67,57 @@ def __init__(self, filepath=None): self.results = pd.DataFrame() self.group_results = pd.DataFrame() self.codebook = { - "userID": "Unique identifier for each participant", - "mean_RT": "Mean reaction time", - "median_RT": "Median reaction time", - "total_rewards": "Total rewards earned", - "choice_stickiness": "Number of consecutive choices for the same option", - "risky_choices": "Proportion of risky choices overall", - "win_risk": "Proportion of risky wins", - "loss_risk": "Proportion of risky losses", - "win_risk_abg": "Proportion of risky wins in ambiguous trials", - "win_risk_NoAbg": "Proportion of risky wins in non-ambiguous trials", - "loss_risk_abg": "Proportion of risky losses in ambiguous trials", - "loss_risk_NoAbg": "Proportion of risky losses in non-ambiguous trials", - "rational_all": "Proportion of rational choices", - "rational_win": "Proportion of rational choices in wins", - "rational_loss": "Proportion of rational choices in losses", - "rational_all_abg": "Proportion of rational choices in ambiguous trials", - "rational_win_abg": "Proportion of rational choices in wins in ambiguous trials", - "rational_loss_abg": "Proportion of rational choices in losses in ambiguous trials", - "rational_all_NoAbg": "Proportion of rational choices in non-ambiguous trials", - "rational_win_NoAbg": "Proportion of rational choices in wins in non-ambiguous trials", - "rational_loss_NoAbg": "Proportion of rational choices in losses in non-ambiguous trials", - "rational_catchTrials": "Proportion of rational choices in catch trials", - "above_chance": "Proportion of trials with choices above chance level", - "chosen_left_all": "Proportion of trials where option A (safe) was chosen" + "userID": "Unique identifier for each participant", + "day_of_week": "Day of the week of the trial", + "time": "Time of the trial", + "time_of_day": "Time of day of the trial (morning, afternoon, evening, night)", + "mean_RT": "Mean reaction time", + "median_RT": "Median reaction time", + "median_RT_safe": "Median reaction time for safe choices", + "median_RT_risky": "Median reaction time for risky choices", + "median_RT_ambg": "Median reaction time for ambiguous trials", + "median_RT_NoAmbg":"Median reaction time for non-ambiguous trials", + "median_RT_safe_ambg": "Median reaction time for safe choices in ambiguous trials", + "median_RT_safe_NoAmbg": "Median reaction time for safe choices in non-ambiguous trials", + "median_RT_risky_ambg": "Median reaction time for risky choices in ambiguous trials", + "median_RT_risky_NoAmbg": "Median reaction time for risky choices in non-ambiguous trials", + "mean_points": "Mean points received across all trials", + "mean_points_safe": "Mean points received for safe choices", + "mean_points_risky": "Mean points received for risky choices", + "mean_points_ambg": "Mean points received in ambiguous trials", + "mean_points_NoAmbg": "Mean points received in non-ambiguous", + "mean_points_safe_ambg": "Mean points received for safe choices in ambiguous trials", + "mean_points_safe_NoAmbg": "Mean points received for safe choices in non-ambiguous trials", + "mean_points_risky_ambg": "Mean points received for risky choices in ambiguous trials", + "mean_points_risky_NoAmbg": "Mean points received for risky choices in non-ambiguous", + "risky_choices": "Proportion of risky choices overall", + "win_risk": "Proportion of risky wins", + "loss_risk": "Proportion of risky losses", + "win_risk_abg": "Proportion of risky wins in ambiguous trials", + "win_risk_NoAbg": "Proportion of risky wins in non-ambiguous trials", + "loss_risk_abg": "Proportion of risky losses in ambiguous trials", + "loss_risk_NoAbg": "Proportion of risky losses in non-ambiguous trials", + "diff_loss_win_risk": "Difference between proportion of risky losses and wins", + "diff_Ambg_NoAmbg_risk": "Difference between proportion of risky choices in ambiguous and non-ambiguous trials", + "diff_Ambg_NoAmbg_win_risk": "Difference between proportion of risky wins in ambiguous and non-ambiguous trials", + "diff_Ambg_NoAmbg_loss_risk": "Difference between proportion of risky losses in ambiguous and non-ambiguous trials", + "rational_all": "Proportion of rational choices", + "rational_win": "Proportion of rational choices in wins", + "rational_loss": "Proportion of rational choices in losses", + "rational_all_abg": "Proportion of rational choices in ambiguous trials", + "rational_win_abg": "Proportion of rational choices in wins in ambiguous trials", + "rational_loss_abg": "Proportion of rational choices in losses in ambiguous trials", + "rational_all_NoAbg": "Proportion of rational choices in non-ambiguous trials", + "diff_NoAmbg_win_loss_rational": "Difference between proportion of rational choices in wins and losses in non-ambiguous trials", + "rational_win_NoAbg": "Proportion of rational choices in wins in non-ambiguous trials", + "rational_loss_NoAbg": "Proportion of rational choices in losses in non-ambiguous trials", + "diff_Ambg_NoAmbg_rational": "Difference between proportion of rational choices in ambiguous and non-ambiguous trials", + "diff_Ambg_NoAmbg_win_rational": "Difference between proportion of rational choices in wins in ambiguous and non-ambiguous trials", + "diff_Ambg_NoAmbg_loss_rational": "Difference between proportion of rational choices in losses in ambiguous and non-ambiguous trials", + "nb_incorrect_gain": "Number of incorrect choices in gain trials", + "above_chance": "Proportion of trials with choices above chance level", + "chosen_prop_LeftRight": "Proportion of trials where option A (safe) was chosen", + "chosen_prop_SafeRisky": "Proportion of trials where the safe option was chosen" } def metrics(self): @@ -104,28 +136,59 @@ def metrics(self): results : pd.DataFrame A DataFrame containing the computed metrics. - mean_RT: Mean reaction time, - median_RT: Median reaction time, - risky_choices: Proportion of risky choices overall, - win_risk: Proportion of wins for risky choices, - loss_risk: Proportion of losses for risky choices, - risk_ambg: Proportion of risky choices in ambiguous trials, - win_risk_abg: Proportion of risky wins in ambiguous trials, - win_risk_NoAbg: Proportion of risky wins in non-ambiguous trials, - loss_risk_abg: Proportion of risky losses in ambiguous trials, - loss_risk_NoAbg: Proportion of risky losses in non-ambiguous trials, - rational_all: Proportion of rational choices, - rational_win: Proportion of rational choices in wins, - rational_loss: Proportion of rational choices in losses, - rational_all_abg: Proportion of rational choices in ambiguous trials, - rational_win_abg: Proportion of rational choices in win trials in ambiguous trials, - rational_loss_abg: Proportion of rational choices in loss trials in ambiguous trials, - rational_all_NoAbg: Proportion of rational choices in non-ambiguous trials, - rational_win_NoAbg: Proportion of rational choices in win trials in non-ambiguous trials, - rational_loss_NoAbg: Proportion of rational choices in loss trials in non-ambiguous trials - rational_catchTrials: Proportion of rational choices in catch trials, - above_chance: Proportion of trials with choices above chance level, - chosen_left_all: Proportion of trials where option A (safe) was chosen. + Variables + ---------- + - userID: Unique identifier for each participant, + - day_of_week: Day of the week of the trial, + - time: Time of the trial, + - time_of_day: Time of day of the trial (morning, afternoon, evening, night), + - mean_RT: Mean reaction time, + - median_RT: Median reaction time, + - median_RT_safe: Median reaction time for safe choices, + - median_RT_risky: Median reaction time for risky choices, + - median_RT_ambg: Median reaction time for ambiguous trials, + - median_RT_NoAmbg: Median reaction time for non-ambiguous trials, + - median_RT_safe_ambg: Median reaction time for safe choices in ambiguous trials, + - median_RT_safe_NoAmbg: Median reaction time for safe choices in non-ambiguous trials, + - median_RT_risky_ambg: Median reaction time for risky choices in ambiguous trials, + - median_RT_risky_NoAmbg: Median reaction time for risky choices in non-ambiguous trials, + - mean_points: Mean points received across all trials, + - mean_points_safe: Mean points received for safe choices, + - mean_points_risky: Mean points received for risky choices, + - mean_points_ambg: Mean points received in ambiguous trials, + - mean_points_NoAmbg: Mean points received in non-ambiguous + - mean_points_safe_ambg: Mean points received for safe choices in ambiguous trials, + - mean_points_safe_NoAmbg: Mean points received for safe choices in non-ambiguous trials, + - mean_points_risky_ambg: Mean points received for risky choices in ambiguous trials, + - mean_points_risky_NoAmbg: Mean points received for risky choices in non-ambiguous, + - risky_choices: Proportion of risky choices overall, + - win_risk: Proportion of risky wins, + - loss_risk: Proportion of risky losses, + - win_risk_abg: Proportion of risky wins in ambiguous trials, + - win_risk_NoAbg: Proportion of risky wins in non-ambiguous trials, + - loss_risk_abg: Proportion of risky losses in ambiguous trials, + - loss_risk_NoAbg: Proportion of risky losses in non-ambiguous trials, + - diff_loss_win_risk: Difference between proportion of risky losses and wins, + - diff_Ambg_NoAmbg_risk: Difference between proportion of risky choices in ambiguous and non-ambiguous trials, + - diff_Ambg_NoAmbg_win_risk: Difference between proportion of risky wins in ambiguous and non-ambiguous trials, + - diff_Ambg_NoAmbg_loss_risk: Difference between proportion of risky losses in ambiguous and non-ambiguous trials, + - rational_all: Proportion of rational choices, + - rational_win: Proportion of rational choices in wins, + - rational_loss: Proportion of rational choices in losses, + - rational_all_abg: Proportion of rational choices in ambiguous trials, + - rational_win_abg: Proportion of rational choices in wins in ambiguous trials, + - rational_loss_abg: Proportion of rational choices in losses in ambiguous trials, + - rational_all_NoAbg: Proportion of rational choices in non-ambiguous trials, + - diff_NoAmbg_win_loss_rational: Difference between proportion of rational choices in wins and losses in non-ambiguous trials, + - rational_win_NoAbg: Proportion of rational choices in wins in non-ambiguous trials, + - rational_loss_NoAbg: Proportion of rational choices in losses in non-ambiguous trials, + - diff_Ambg_NoAmbg_rational: Difference between proportion of rational choices in ambiguous and non-ambiguous trials, + - diff_Ambg_NoAmbg_win_rational: Difference between proportion of rational choices in wins in ambiguous and non-ambiguous trials, + - diff_Ambg_NoAmbg_loss_rational: Difference between proportion of rational choices in losses in ambiguous and non-ambiguous trials, + - nb_incorrect_gain: Number of incorrect choices in gain trials, + - above_chance: Proportion of trials with choices above chance level, + - chosen_prop_LeftRight: Proportion of trials where option A (safe) was chosen, + - chosen_prop_SafeRisky: Proportion of trials where the safe option was chosen Notes @@ -159,11 +222,8 @@ def metrics(self): # Loop through each group of user data for user_id, user_data in grouped_data: - print(user_data.head()) t_abg = np.array(user_data["ambiguousTrial"], dtype=bool) - catch_trials = np.array(user_data["catchtrial"], dtype=bool) abovechance = np.array(user_data["abovechance"]) - chosen_left = np.array(user_data["chosenLeft"]) ev_diff_chosen = np.array(user_data["EV_diff_chosen"], dtype=object) rsk = np.array(user_data["chosen"]) t_loss = np.array(user_data["safe_EV"] < 0) @@ -171,78 +231,172 @@ def metrics(self): user_results = {"userID": user_id} - user_results["mean_RT"] = np.mean(user_data["RT"]) - user_results["median_RT"] = np.median(user_data["RT"]) + date = user_data["date"].iloc[0] + if isinstance(date, str): + date = pd.to_datetime(date, format="%Y-%m-%d %H:%M:%S.%f") + user_results["day_of_week"] = date.day_name() + user_results["time"] = date.time() if hasattr(date, 'time') else date.strftime("%H:%M:%S.%f") + # morning 6-12, afternoon 12-18, evening 18-24, night 0-6 + user_results["time_of_day"] = ( + "morning" if date.hour < 12 else + "afternoon" if date.hour < 18 else + "evening" if date.hour < 24 else + "night" + ) + + + user_results["mean_RT"] = np.nanmean(user_data["RT"]) + user_results["median_RT"] = np.nanmedian(user_data["RT"]) + # mediean RT for each condition + user_results["median_RT_safe"] = np.nanmedian(user_data[rsk == 0]["RT"]) + user_results["median_RT_risky"] = np.nanmedian(user_data[rsk == 1]["RT"]) + user_results["median_RT_ambg"] = np.nanmedian(user_data[t_abg]["RT"]) + user_results["median_RT_NoAmbg"] = np.nanmedian(user_data[~t_abg]["RT"]) + user_results["median_RT_safe_ambg"] = np.nanmedian(user_data[t_abg & (rsk == 0)]["RT"]) + user_results["median_RT_safe_NoAmbg"] = np.nanmedian(user_data[~t_abg & (rsk == 0)]["RT"]) + user_results["median_RT_risky_ambg"] = np.nanmedian(user_data[t_abg & (rsk == 1)]["RT"]) + user_results["median_RT_risky_NoAmbg"] = np.nanmedian(user_data[~t_abg & (rsk == 1)]["RT"]) user_results["total_rewards"] = np.sum(user_data["outcome"]) + user_results["mean_points"] = np.nanmean(user_data["outcome"]) + + # mean points for risky and safe choices + user_results["mean_points_safe"] = np.nanmean(user_data[rsk == 0]["outcome"]) + user_results["mean_points_risky"] = np.nanmean(user_data[rsk == 1]["outcome"]) + + # mean points for ambigous and non-ambiguous trials + user_results["mean_points_ambg"] = np.nanmean(user_data[t_abg]["outcome"]) + user_results["mean_points_NoAmbg"] = np.nanmean(user_data[~t_abg]["outcome"]) - # calculate choice stickiness - user_results["choice_stickiness"] = np.sum(user_data["chosen"].iloc[i] == 1 and user_data["chosen"].iloc[i + 1] == 1 for i in range(len(user_data["chosen"]) - 1)) + # mean points for risky and safe choices in ambiguous and non-ambiguous trials + user_results["mean_points_safe_ambg"] = np.nanmean(user_data[t_abg & (rsk == 0)]["outcome"]) + user_results["mean_points_safe_NoAmbg"] = np.nanmean(user_data[~t_abg & (rsk == 0)]["outcome"]) + user_results["mean_points_risky_ambg"] = np.nanmean(user_data[t_abg & (rsk == 1)]["outcome"]) + user_results["mean_points_risky_NoAmbg"] = np.nanmean(user_data[~t_abg & (rsk == 1)]["outcome"]) + + # choice stickiness + user_results["choice_stickiness"] = np.sum(user_data["chosen"].iloc[i] == 1 and user_data["chosen"].iloc[i + 1] == 1 + for i in range(len(user_data["chosen"]) - 1) + ) # Number of risky choices - user_results["risky_choices"] = np.mean(rsk == 1) + user_results["risky_choices"] = np.nanmean(rsk == 1) + user_results["win_risk"] = np.sum(rsk[~t_loss]) / np.sum(~t_loss) user_results["loss_risk"] = np.sum(rsk[t_loss]) / np.sum(t_loss) - user_results["risk_ambg"] = np.sum(rsk[t_abg]) / np.sum(t_abg) - user_results["win_risk_abg"] = np.sum(rsk[~t_loss & t_abg]) / np.sum( - ~t_loss & t_abg + user_results["diff_loss_win_risk"] = user_results["loss_risk"] - user_results["win_risk"] + + user_results["risk_Ambg"] = np.sum(rsk[t_abg]) / np.sum(t_abg) + user_results["risk_NoAmbg"] = np.sum(rsk[~t_abg]) / np.sum(~t_abg) + user_results["diff_Ambg_NoAmbg_risk"] = user_results["risk_Ambg"] - user_results["risk_NoAmbg"] + + user_results["win_risk_abg"] = np.sum(rsk[~t_loss & t_abg]) / np.sum(~t_loss & t_abg) + user_results["win_risk_NoAbg"] = np.sum(rsk[~t_loss & ~t_abg]) / np.sum(~t_loss & ~t_abg) + user_results["loss_risk_abg"] = np.sum(rsk[t_loss & t_abg]) / np.sum(t_loss & t_abg) + user_results["loss_risk_NoAbg"] = np.sum(rsk[t_loss & ~t_abg]) / np.sum(t_loss & ~t_abg) + user_results["diff_Ambg_NoAmbg_win_risk"] = ( + user_results["win_risk_abg"] - user_results["win_risk_NoAbg"] ) - user_results["win_risk_NoAbg"] = np.sum(rsk[~t_loss & ~t_abg]) / np.sum( - ~t_loss & ~t_abg + user_results["diff_Ambg_NoAmbg_loss_risk"] = ( + user_results["loss_risk_abg"] - user_results["loss_risk_NoAbg"] ) - user_results["loss_risk_abg"] = np.sum(rsk[t_loss & t_abg]) / np.sum( - t_loss & t_abg + user_results["diff_Ambg_win_loss_risk"] = ( + user_results["win_risk_abg"] - user_results["loss_risk_abg"] ) - user_results["loss_risk_NoAbg"] = np.sum(rsk[t_loss & ~t_abg]) / np.sum( - t_loss & ~t_abg + user_results["diff_NoAmbg_win_loss_risk"] = ( + user_results["win_risk_NoAbg"] - user_results["loss_risk_NoAbg"] ) # Number of rational choices - user_results["rational_all"] = np.sum(ev_diff_chosen >= 0) / len( - ev_diff_chosen + user_results["rational_all"] = np.nanmean(ev_diff_chosen >= 0) + + # rational choices in win and loss trials + user_results["rational_win"] = np.sum(ev_diff_chosen[~t_loss] >= 0) / np.sum(~t_loss) + user_results["rational_loss"] = np.sum(ev_diff_chosen[t_loss] >= 0) / np.sum(t_loss) + user_results["diff_loss_win_rational"] = ( + user_results["rational_loss"] - user_results["rational_win"] + ) + + # rational choices in ambiguous and non-ambiguous trials + user_results["rational_abg"] = np.sum(ev_diff_chosen[t_abg] >= 0) / np.sum(t_abg) + user_results["rational_NoAbg"] = np.sum(ev_diff_chosen[~t_abg] >= 0) / np.sum(~t_abg) + user_results["diff_Ambg_NoAmbg_rational"] = ( + user_results["rational_abg"] - user_results["rational_NoAbg"] + ) + + # rational choices in ambiguous and non-ambiguous trials for wins and losses + user_results["rational_win_abg"] = np.sum(ev_diff_chosen[~t_loss & t_abg] >= 0) / np.sum(~t_loss & t_abg) + user_results["rational_win_NoAbg"] = np.sum(ev_diff_chosen[~t_loss & ~t_abg] >= 0) / np.sum(~t_loss & ~t_abg) + user_results["rational_loss_abg"] = np.sum(ev_diff_chosen[t_loss & t_abg] >= 0) / np.sum(t_loss & t_abg) + user_results["rational_loss_NoAbg"] = np.sum(ev_diff_chosen[t_loss & ~t_abg] >= 0) / np.sum(t_loss & ~t_abg) + user_results["diff_rational_Ambg_NoAmbg_win"] = ( + user_results["rational_win_abg"] - user_results["rational_win_NoAbg"] ) - user_results["rational_win"] = np.sum(ev_diff_chosen[~t_loss] >= 0) / np.sum( - ~t_loss + user_results["diff_rational_Ambg_NoAmbg_loss"] = ( + user_results["rational_loss_abg"] - user_results["rational_loss_NoAbg"] ) - user_results["rational_loss"] = np.sum(ev_diff_chosen[t_loss] >= 0) / np.sum( - t_loss + user_results["diff_rational_Ambg_win_loss"] = ( + user_results["rational_win_abg"] - user_results["rational_loss_abg"] + ) + user_results["diff_rational_NoAmbg_win_loss"] = ( + user_results["rational_win_NoAbg"] - user_results["rational_loss_NoAbg"] + ) + + # rational choices in safe and risky trials + user_results["rational_safe"] = np.sum(ev_diff_chosen[rsk == 0] >= 0) / np.sum(rsk == 0) + user_results["rational_risky"] = np.sum(ev_diff_chosen[rsk == 1] >= 0) / np.sum(rsk == 1) + user_results["diff_rational_safe_risky"] = ( + user_results["rational_safe"] - user_results["rational_risky"] + ) + + # rational choices in safe and risky trials for wins and losses + user_results["rational_safe_win"] = np.sum(ev_diff_chosen[~t_loss & rsk == 0] >= 0) / np.sum(~t_loss & rsk == 0) + user_results["rational_safe_loss"] = np.sum(ev_diff_chosen[t_loss & rsk == 0] >= 0) / np.sum(t_loss & rsk == 0) + user_results["rational_risky_win"] = np.sum(ev_diff_chosen[~t_loss & rsk == 1] >= 0) / np.sum(~t_loss & rsk == 1) + user_results["rational_risky_loss"] = np.sum(ev_diff_chosen[t_loss & rsk == 1] >= 0) / np.sum(t_loss & rsk == 1) + user_results["diff_rational_safe_win_risky_win"] = ( + user_results["rational_safe_win"] - user_results["rational_risky_win"] + ) + user_results["diff_rational_safe_loss_risky_loss"] = ( + user_results["rational_safe_loss"] - user_results["rational_risky_loss"] + ) + user_results["diff_rational_safe_win_risky_loss"] = ( + user_results["rational_safe_win"] - user_results["rational_risky_loss"] + ) + user_results["diff_rational_safe_loss_risky_win"] = ( + user_results["rational_safe_loss"] - user_results["rational_risky_win"] ) - user_results["rational_all_abg"] = np.sum( - ev_diff_chosen[t_abg] >= 0 - ) / np.sum(t_abg) - user_results["rational_all_NoAbg"] = np.sum( - ev_diff_chosen[~t_abg] >= 0 - ) / np.sum(~t_abg) - - user_results["rational_win_abg"] = np.sum( - ev_diff_chosen[~t_loss & t_abg] >= 0 - ) / np.sum(~t_loss & t_abg) - user_results["rational_win_NoAbg"] = np.sum( - ev_diff_chosen[~t_loss & ~t_abg] >= 0 - ) / np.sum(~t_loss & ~t_abg) - - user_results["rational_loss_abg"] = np.sum( - ev_diff_chosen[t_loss & t_abg] >= 0 - ) / np.sum(t_loss & t_abg) - user_results["rational_loss_NoAbg"] = np.sum( - ev_diff_chosen[t_loss & ~t_abg] >= 0 - ) / np.sum(t_loss & ~t_abg) - - # proportion rational chocies on catch trials - user_results["rational_catchTrials"] = np.mean( - ev_diff_chosen[catch_trials] >= 0 + # rational choices in risky and safe trials for ambiguous and non-ambiguous trials + user_results["rational_safe_abg"] = np.sum(ev_diff_chosen[t_abg & rsk == 0] >= 0) / np.sum(t_abg & rsk == 0) + user_results["rational_safe_NoAbg"] = np.sum(ev_diff_chosen[~t_abg & rsk == 0] >= 0) / np.sum(~t_abg & rsk == 0) + user_results["rational_risky_abg"] = np.sum(ev_diff_chosen[~t_abg & rsk == 1] >= 0) / np.sum(~t_abg & rsk == 1) + user_results["rational_risky_NoAbg"] = np.sum(ev_diff_chosen[t_abg & rsk == 1] >= 0) / np.sum(t_abg & rsk == 1) + user_results["diff_rational_safe_Ambg_NoAmbg"] = ( + user_results["rational_safe_abg"] - user_results["rational_safe_NoAbg"] + ) + user_results["diff_rational_risky_Ambg_NoAmbg"] = ( + user_results["rational_risky_abg"] - user_results["rational_risky_NoAbg"] + ) + user_results["diff_rational_safe_risky_Ambg"] = ( + user_results["rational_safe_abg"] - user_results["rational_risky_abg"] + ) + user_results["diff_rational_safe_risky_NoAmbg"] = ( + user_results["rational_safe_NoAbg"] - user_results["rational_risky_NoAbg"] ) + # No Brainer Trial Choices + # count how man wrong choices were made in catch trials + user_results["nb_incorrect_gain"] = user_data[user_data["nbcorrect_gain"] == 0].shape[0] + # proportion above chance level - user_results["above_chance"] = np.sum( - abovechance == 1 - ) / len(abovechance) + user_results["above_chance"] = np.nanmean(abovechance) + + # proportion of repeated choices for left/right + user_results["chosen_prop_LeftRight"] = np.mean(user_data["resp_rep"]) - # proportion chosen left - user_results["chosen_left_all"] = np.sum( - chosen_left == 1 - ) / len(chosen_left) + # proportion of repeated choices for safe/risky + user_results["chosen_prop_SafeRisky"] = np.mean(user_data["resp_rep_sr"]) # Append user-level results self.results = pd.concat( @@ -281,9 +435,9 @@ def clean_data(self): user_ids_to_keep = [uid for uid in self.data["userID"].unique() if uid not in user_ids_to_exclude] self.cleanedresults = self.cleanedresults[self.cleanedresults["userID"].isin(user_ids_to_keep)] - self.cleanedresults = self.cleanedresults[self.cleanedresults["median_RT"] < 5000] + #self.cleanedresults = self.cleanedresults[self.cleanedresults["median_RT"] < 5000] + self.cleanedresults = self.cleanedresults[self.cleanedresults["rational_catchTrials"] > 0.5] - self.cleanedresults = self.cleanedresults[self.cleanedresults["risky_choices"] < 0.95] self.cleanedresults = self.cleanedresults[self.cleanedresults["chosen_left_all"] < 0.95] self.deleted_participants = nr_part_before - len(self.cleanedresults["userID"].unique()) From 88f7d9fea9ec586f9c63870197d248130a19fcf0 Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Tue, 26 Aug 2025 08:20:40 +0200 Subject: [PATCH 36/54] fix data exclusion --- cpm/brainexplorer/scavenger.py | 13 +++++-------- cpm/brainexplorer/spaceObserver.py | 21 ++++++++++++--------- cpm/brainexplorer/treasurehunt.py | 13 ++++++++++--- 3 files changed, 27 insertions(+), 20 deletions(-) diff --git a/cpm/brainexplorer/scavenger.py b/cpm/brainexplorer/scavenger.py index b43ed69..7a08bc5 100644 --- a/cpm/brainexplorer/scavenger.py +++ b/cpm/brainexplorer/scavenger.py @@ -414,31 +414,28 @@ def clean_data(self): Exclusion criteria: ---------- - - Median reaction time > 5000 ms - - Same choice safe/risky on >= 95% of trials - Same choice left/right on >= 95% of trials - - Fail >= 50% of catch trials + - Fail >= 2 of catch trials - Participants with more than 40 trials (due to technical error) - - Participants with configID = 242 + - Participants with configID = 242 (version with 60 trials) """ nr_part_before = len(self.results["userID"].unique()) self.cleanedresults = self.results.copy() user_id_counts = self.data["userID"].value_counts() + # find user IDs with more than 40 trials user_ids_to_keep = user_id_counts[user_id_counts <= 40].index self.cleanedresults = self.cleanedresults[self.cleanedresults["userID"].isin(user_ids_to_keep)] + # find user IDs with specific configID to exclude user_ids_to_exclude = ( self.data.loc[self.data["config_id"] == 242, "userID"].unique() ) user_ids_to_keep = [uid for uid in self.data["userID"].unique() if uid not in user_ids_to_exclude] self.cleanedresults = self.cleanedresults[self.cleanedresults["userID"].isin(user_ids_to_keep)] - #self.cleanedresults = self.cleanedresults[self.cleanedresults["median_RT"] < 5000] - - self.cleanedresults = self.cleanedresults[self.cleanedresults["rational_catchTrials"] > 0.5] - self.cleanedresults = self.cleanedresults[self.cleanedresults["chosen_left_all"] < 0.95] + self.cleanedresults = self.cleanedresults[self.cleanedresults["nb_incorrect_gain"] < 2] # exclude participants who failed 2 or more catch trials self.deleted_participants = nr_part_before - len(self.cleanedresults["userID"].unique()) diff --git a/cpm/brainexplorer/spaceObserver.py b/cpm/brainexplorer/spaceObserver.py index 6f800d9..13b2290 100644 --- a/cpm/brainexplorer/spaceObserver.py +++ b/cpm/brainexplorer/spaceObserver.py @@ -247,6 +247,9 @@ def metrics(self, mode=None): ES_4 = np.mean(bins[3]) if len(bins[3]) > 0 else np.nan user_results["ES_bins"] = np.array([ES_1, ES_2, ES_3, ES_4]) + # median evidence strength + user_results["median_ES"] = np.nanmedian(evidence_strength) + # Append user-level results self.results = pd.concat( @@ -264,10 +267,10 @@ def clean_data(self): Exclusion criteria: --------- - Mean accuracy < 0.5 - - Median reaction time > 5000 ms - - Median reaction time for confidence ratings > 5000 ms + - Median reaction time > 3000 ms + - Median reaction time for confidence ratings > 3000 ms - Median evidence strength > 25 - - Mean confidence > 97 + - Mean confidence <3 or > 97 - Participants with more than 80 trials (due to technical error) - Exclude participants if 10th and 25th percentile of confidence is the same AND 75th and 90th percentile of confidence is the same """ @@ -280,12 +283,12 @@ def clean_data(self): users_80trials = self.cleanedresults.groupby("userID").filter(lambda x: len(x) >= 80)["userID"].unique() self.cleanedresults = self.cleanedresults[self.cleanedresults["userID"].isin(users_80trials)] - self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_accuracy"] >= 0.5] - self.cleanedresults = self.cleanedresults[self.cleanedresults["median_RT"] <= 3000] - self.cleanedresults = self.cleanedresults[self.cleanedresults["median_confidenceRT"] <= 3000] - self.cleanedresults = self.cleanedresults[self.cleanedresults["ES_total_mean"] <= 25] - self.cleanedresults = self.cleanedresults[self.cleanedresults["median_confidence"] <= 97] - self.cleanedresults = self.cleanedresults[self.cleanedresults["median_confidence"] >= 3] + self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_accuracy"] >= 0.5] # only keep participants with mean accuracy >= 50% + self.cleanedresults = self.cleanedresults[self.cleanedresults["median_RT"] <= 3000] # only keep participants with median reaction time <= 3000 ms + self.cleanedresults = self.cleanedresults[self.cleanedresults["median_confidenceRT"] <= 3000] # only keep participants with median confidence reaction time <= 3000 ms + self.cleanedresults = self.cleanedresults[self.cleanedresults["median_ES"] <= 25] # only keep participants with median evidence strength <= 25 + self.cleanedresults = self.cleanedresults[self.cleanedresults["median_confidence"] <= 97] # only keep participants with mean confidence <= 97 + self.cleanedresults = self.cleanedresults[self.cleanedresults["median_confidence"] >= 3] # only keep participants with mean confidence >= 3 # exclude if 10th and 25th percentile of confidence is the same AND 75th and 90th percentile of confidence is the same self.cleanedresults = self.cleanedresults[~( diff --git a/cpm/brainexplorer/treasurehunt.py b/cpm/brainexplorer/treasurehunt.py index 21e0eee..328c6d0 100644 --- a/cpm/brainexplorer/treasurehunt.py +++ b/cpm/brainexplorer/treasurehunt.py @@ -75,6 +75,7 @@ def __init__(self, filepath=None): "sd_n_draws": "Standard deviation of the number of stimulus draws before making a decision", "median_RT_between_actions": "Median reaction time between actions", "median_confidenceRT": "Median confidence reaction time across all trials" + "unique_draws": "Number of unique values in number of draws" } def metrics(self): @@ -97,6 +98,7 @@ def metrics(self): - sd_n_draws: standard deviation of the number of stimulus draws before making a decision - median_RT_between_actions: median reaction time between actions - median_confidenceRT: median confidence reaction time across all trials + - unique_draws: number of unique values in number of draws """ # turn string fields into list of ints self.data["ev"] = self.data["ev"].apply(lambda x: [int(i) for i in x.strip("[]").split(" ")]) @@ -136,6 +138,9 @@ def metrics(self): user_results["median_confidenceRT"] = np.median(user_data["confidenceRT"]) + # check if < 3 unique values in number of samples + user_results["unique_draws"] = user_data["draws"].nunique() + # regression for influence of recent results on current choice """ ev = [] @@ -208,10 +213,12 @@ def clean_data(self): )["userID"].unique() self.cleanedresults = self.cleanedresults[self.cleanedresults["userID"].isin(valid_users)] - self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_n_draws"] >= 2] # only keep participants with mean number of draws > 2 - self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_n_draws"] <= 23] # only keep participants with mean number of draws < 23 + self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_n_draws"] >= 2] # only keep participants with mean number of draws >= 2 + self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_n_draws"] <= 23] # only keep participants with mean number of draws <= 23 - self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_chosEv"] > 0.8] # only keep participants with mean choice in line with current evidence >= 80% + self.cleanedresults = self.cleanedresults[self.cleanedresults["accuracy"] > 0.8] # only keep participants with mean choice in line with current evidence > 80% + + self.cleanedresults = self.cleanedresults[self.cleanedresults["unique_draws"] >= 3] # only keep participants with at least 3 unique values in number of draws self.deleted_participants = nr_part_before - len(self.cleanedresults["userID"].unique()) From f82882e1b0bc680136f437a2df13db59ec66d1bf Mon Sep 17 00:00:00 2001 From: lucakosina <77290116+lucakosina@users.noreply.github.com> Date: Wed, 27 Aug 2025 15:23:13 +0200 Subject: [PATCH 37/54] updated metrics --- cpm/brainexplorer/milkyWay.py | 36 ++++++++++++-- cpm/brainexplorer/scavenger.py | 80 ++++++++++++++++++++++++++---- cpm/brainexplorer/spaceObserver.py | 39 ++++++++------- cpm/brainexplorer/treasurehunt.py | 11 +++- 4 files changed, 132 insertions(+), 34 deletions(-) diff --git a/cpm/brainexplorer/milkyWay.py b/cpm/brainexplorer/milkyWay.py index f3f86e5..f90b881 100644 --- a/cpm/brainexplorer/milkyWay.py +++ b/cpm/brainexplorer/milkyWay.py @@ -56,11 +56,11 @@ def __init__(self, filepath): self.PM_data = self.data[self.data['trial_type'] == 'punish'] #Take first attempt (i.e., not neccessarily run == 1 for PM) - #min_run_per_user = self.MW_data.groupby('userID')['run'].transform('min') - #self.MW_data = self.MW_data[self.MW_data['run'] == min_run_per_user].reset_index(drop=True) + min_run_per_user = self.MW_data.groupby('userID')['run'].transform('min') + self.MW_data = self.MW_data[self.MW_data['run'] == min_run_per_user].reset_index(drop=True) - #min_run_per_user = self.PM_data.groupby('userID')['run'].transform('min') - #self.PM_data = self.PM_data[self.PM_data['run'] == min_run_per_user].reset_index(drop=True) + min_run_per_user = self.PM_data.groupby('userID')['run'].transform('min') + self.PM_data = self.PM_data[self.PM_data['run'] == min_run_per_user].reset_index(drop=True) self.results_MW = pd.DataFrame() self.results_PM = pd.DataFrame() @@ -68,6 +68,7 @@ def __init__(self, filepath): self.codebook = { "userID": "Unique identifier for each participant", + "n_trials": "Number of trials completed by the participant", "trial_type": "Type of trial (Milky Way or Pirate Market)", "day_of_week": "Day of the week when the trial was conducted", "time": "Time when the trial was conducted", @@ -102,6 +103,12 @@ def metrics(self): Variables ---------- + - userID: unique identifier for each participant + - n_trials: number of trials completed by the participant + - day_of_week: day of the week of the trial + - time: time of the trial + - time_of_day: time of day of the trial (morning, afternoon, evening + - trial_type: type of trial (Milky Way or Pirate Market) - mean_RT_MW: Mean reaction time for Milky Way trials - median_RT_MW: Median reaction time for Milky Way trials - mean_RT_PM: Mean reaction time for Pirate Market trials @@ -129,6 +136,8 @@ def metrics(self): user_results_MW = {"userID": user_id} + user_results_MW["n_trials"] = len(user_data_MW) + user_results_MW["trial_type"] = "Milky Way" date = user_data_MW["date"].iloc[0] @@ -141,7 +150,8 @@ def metrics(self): "morning" if date.hour < 12 else "afternoon" if date.hour < 18 else "evening" if date.hour < 24 else - "night" + "night" if date.hour < 6 + else "-" ) #user_results_MW["mean_RT_MW"] = np.mean(user_data_MW['rt_adj']) @@ -177,6 +187,8 @@ def metrics(self): user_results_PM = {"userID": user_id} + user_results_PM["n_trials"] = len(user_data_PM) + user_results_PM["trial_type"] = "Pirate Market" date = user_data_PM["date"].iloc[0] @@ -301,6 +313,20 @@ def clean_data(self): self.deleted_participants_MW = nr_part_before_MW - len(pd.unique(self.cleanedresults_MW["userID"])) self.deleted_participants_PM = nr_part_before_PM - len(pd.unique(self.cleanedresults_PM["userID"])) + #Also clean the diff file by keeping only participants who are in both the cleanedresults_MW and in the cleanedresults_PM file + self.cleanedresults_diff = self.results_diff.copy() + + nr_part_before_diff = len(self.results_diff["userID"].unique()) + + user_ids_MW = set(self.cleanedresults_MW["userID"].unique()) # Get userIDs from both cleaned datasets + user_ids_PM = set(self.cleanedresults_PM["userID"].unique()) + valid_user_ids_diff = user_ids_MW.intersection(user_ids_PM) # Keep only userIDs that are in both + self.cleanedresults_diff = self.cleanedresults_diff[self.cleanedresults_diff["userID"].isin(valid_user_ids_diff)] + + self.deleted_participants_diff = nr_part_before_diff - len(self.cleanedresults_diff["userID"].unique()) + + return self.cleanedresults_MW, self.cleanedresults_PM, self.cleanedresults_diff + def codebook(self): """ Create a codebook for the data. diff --git a/cpm/brainexplorer/scavenger.py b/cpm/brainexplorer/scavenger.py index 7a08bc5..4a40792 100644 --- a/cpm/brainexplorer/scavenger.py +++ b/cpm/brainexplorer/scavenger.py @@ -68,6 +68,7 @@ def __init__(self, filepath=None): self.group_results = pd.DataFrame() self.codebook = { "userID": "Unique identifier for each participant", + "n_trials": "Number of trials completed by the participant", "day_of_week": "Day of the week of the trial", "time": "Time of the trial", "time_of_day": "Time of day of the trial (morning, afternoon, evening, night)", @@ -139,6 +140,7 @@ def metrics(self): Variables ---------- - userID: Unique identifier for each participant, + - n_trials: Number of trials completed by the participant, - day_of_week: Day of the week of the trial, - time: Time of the trial, - time_of_day: Time of day of the trial (morning, afternoon, evening, night), @@ -231,6 +233,8 @@ def metrics(self): user_results = {"userID": user_id} + user_results["n_trials"] = len(user_data) + date = user_data["date"].iloc[0] if isinstance(date, str): date = pd.to_datetime(date, format="%Y-%m-%d %H:%M:%S.%f") @@ -241,7 +245,8 @@ def metrics(self): "morning" if date.hour < 12 else "afternoon" if date.hour < 18 else "evening" if date.hour < 24 else - "night" + "night" if date.hour < 6 else + "-" ) @@ -250,12 +255,32 @@ def metrics(self): # mediean RT for each condition user_results["median_RT_safe"] = np.nanmedian(user_data[rsk == 0]["RT"]) user_results["median_RT_risky"] = np.nanmedian(user_data[rsk == 1]["RT"]) + user_results["median_RT_win"] = np.nanmedian(user_data[~t_loss]["RT"]) + user_results["median_RT_loss"] = np.nanmedian(user_data[t_loss]["RT"]) user_results["median_RT_ambg"] = np.nanmedian(user_data[t_abg]["RT"]) user_results["median_RT_NoAmbg"] = np.nanmedian(user_data[~t_abg]["RT"]) - user_results["median_RT_safe_ambg"] = np.nanmedian(user_data[t_abg & (rsk == 0)]["RT"]) - user_results["median_RT_safe_NoAmbg"] = np.nanmedian(user_data[~t_abg & (rsk == 0)]["RT"]) - user_results["median_RT_risky_ambg"] = np.nanmedian(user_data[t_abg & (rsk == 1)]["RT"]) - user_results["median_RT_risky_NoAmbg"] = np.nanmedian(user_data[~t_abg & (rsk == 1)]["RT"]) + user_results["median_RT_win_ambg"] = np.nanmedian(user_data[~t_loss & t_abg]["RT"]) + user_results["median_RT_win_NoAmbg"] = np.nanmedian(user_data[~t_loss & ~t_abg]["RT"]) + user_results["median_RT_loss_ambg"] = np.nanmedian(user_data[t_loss & t_abg]["RT"]) + user_results["median_RT_loss_NoAmbg"] = np.nanmedian(user_data[t_loss & ~t_abg]["RT"]) + user_results["diff_RT_win_amg_win_noAmbg"] = ( + user_results["median_RT_win_ambg"] - user_results["median_RT_win_NoAmbg"] + ) + user_results["diff_RT_loss_amg_loss_noAmbg"] = ( + user_results["median_RT_loss_ambg"] - user_results["median_RT_loss_NoAmbg"] + ) + user_results["diff_RT_win_loss_ambg"] = ( + user_results["median_RT_win_ambg"] - user_results["median_RT_loss_ambg"] + ) + user_results["diff_RT_win_loss_noAmbg"] = ( + user_results["median_RT_win_NoAmbg"] - user_results["median_RT_loss_NoAmbg"] + ) + user_results["diff_RT_win_ambg_loss_noAmbg"] = ( + user_results["median_RT_win_ambg"] - user_results["median_RT_loss_NoAmbg"] + ) + user_results["diff_RT_loss_ambg_win_noAmbg"] = ( + user_results["median_RT_loss_ambg"] - user_results["median_RT_win_NoAmbg"] + ) user_results["total_rewards"] = np.sum(user_data["outcome"]) user_results["mean_points"] = np.nanmean(user_data["outcome"]) @@ -268,11 +293,30 @@ def metrics(self): user_results["mean_points_ambg"] = np.nanmean(user_data[t_abg]["outcome"]) user_results["mean_points_NoAmbg"] = np.nanmean(user_data[~t_abg]["outcome"]) - # mean points for risky and safe choices in ambiguous and non-ambiguous trials - user_results["mean_points_safe_ambg"] = np.nanmean(user_data[t_abg & (rsk == 0)]["outcome"]) - user_results["mean_points_safe_NoAmbg"] = np.nanmean(user_data[~t_abg & (rsk == 0)]["outcome"]) - user_results["mean_points_risky_ambg"] = np.nanmean(user_data[t_abg & (rsk == 1)]["outcome"]) - user_results["mean_points_risky_NoAmbg"] = np.nanmean(user_data[~t_abg & (rsk == 1)]["outcome"]) + # mean points for win and loss trials in ambigous and non-ambiguous trials + user_results["mean_points_win_ambg"] = np.nanmean(user_data[~t_loss & t_abg]["outcome"]) + user_results["mean_points_win_NoAmbg"] = np.nanmean(user_data[~t_loss & ~t_abg]["outcome"]) + user_results["mean_points_loss_ambg"] = np.nanmean(user_data[t_loss & t_abg]["outcome"]) + user_results["mean_points_loss_NoAmbg"] = np.nanmean(user_data[t_loss & ~t_abg]["outcome"]) + user_results["diff_points_win_amg_win_noAmbg"] = ( + user_results["mean_points_win_ambg"] - user_results["mean_points_win_NoAmbg"] + ) + user_results["diff_points_loss_amg_loss_noAmbg"] = ( + user_results["mean_points_loss_ambg"] - user_results["mean_points_loss_NoAmbg"] + ) + user_results["diff_points_win_loss_ambg"] = ( + user_results["mean_points_win_ambg"] - user_results["mean_points_loss_ambg"] + ) + user_results["diff_points_win_loss_noAmbg"] = ( + user_results["mean_points_win_NoAmbg"] - user_results["mean_points_loss_NoAmbg"] + ) + user_results["diff_points_win_ambg_loss_noAmbg"] = ( + user_results["mean_points_win_ambg"] - user_results["mean_points_loss_NoAmbg"] + ) + user_results["diff_points_loss_ambg_win_noAmbg"] = ( + user_results["mean_points_loss_ambg"] - user_results["mean_points_win_NoAmbg"] + ) + # choice stickiness user_results["choice_stickiness"] = np.sum(user_data["chosen"].iloc[i] == 1 and user_data["chosen"].iloc[i + 1] == 1 @@ -286,10 +330,12 @@ def metrics(self): user_results["loss_risk"] = np.sum(rsk[t_loss]) / np.sum(t_loss) user_results["diff_loss_win_risk"] = user_results["loss_risk"] - user_results["win_risk"] + # Number of risky choices in ambiguous and non-ambiguous trials user_results["risk_Ambg"] = np.sum(rsk[t_abg]) / np.sum(t_abg) user_results["risk_NoAmbg"] = np.sum(rsk[~t_abg]) / np.sum(~t_abg) user_results["diff_Ambg_NoAmbg_risk"] = user_results["risk_Ambg"] - user_results["risk_NoAmbg"] + # Number of risky choices in ambiguous and non-ambiguous trials for wins and losses user_results["win_risk_abg"] = np.sum(rsk[~t_loss & t_abg]) / np.sum(~t_loss & t_abg) user_results["win_risk_NoAbg"] = np.sum(rsk[~t_loss & ~t_abg]) / np.sum(~t_loss & ~t_abg) user_results["loss_risk_abg"] = np.sum(rsk[t_loss & t_abg]) / np.sum(t_loss & t_abg) @@ -306,6 +352,12 @@ def metrics(self): user_results["diff_NoAmbg_win_loss_risk"] = ( user_results["win_risk_NoAbg"] - user_results["loss_risk_NoAbg"] ) + user_results["diff_amg_win_nonAmg_loss_risk"] = ( + user_results["win_risk_abg"] - user_results["loss_risk_NoAbg"] + ) + user_results["diff_amg_loss_nonAmg_win_risk"] = ( + user_results["loss_risk_abg"] - user_results["win_risk_NoAbg"] + ) # Number of rational choices user_results["rational_all"] = np.nanmean(ev_diff_chosen >= 0) @@ -341,6 +393,12 @@ def metrics(self): user_results["diff_rational_NoAmbg_win_loss"] = ( user_results["rational_win_NoAbg"] - user_results["rational_loss_NoAbg"] ) + user_results["diff_rational_Ambg_win_NoAmbg_loss"] = ( + user_results["rational_win_abg"] - user_results["rational_loss_NoAbg"] + ) + user_results["diff_rational_Ambg_loss_NoAmbg_win"] = ( + user_results["rational_loss_abg"] - user_results["rational_win_NoAbg"] + ) # rational choices in safe and risky trials user_results["rational_safe"] = np.sum(ev_diff_chosen[rsk == 0] >= 0) / np.sum(rsk == 0) @@ -439,6 +497,8 @@ def clean_data(self): self.deleted_participants = nr_part_before - len(self.cleanedresults["userID"].unique()) + return self.cleanedresults + def codebook(self): """ Return a codebook describing the metrics. diff --git a/cpm/brainexplorer/spaceObserver.py b/cpm/brainexplorer/spaceObserver.py index 13b2290..b94ef2e 100644 --- a/cpm/brainexplorer/spaceObserver.py +++ b/cpm/brainexplorer/spaceObserver.py @@ -62,22 +62,23 @@ def __init__(self, filepath=None): ## read data self.data = pd.read_csv(filepath, header=0, na_values=["NaN", "nan"]) - self.data = self.data[self.data["run"] != 1] # exclude practice trials + self.data = self.data[self.data["run"] == 1] # exclude attempts after the first one self.data = self.data[self.data["choiceRT"] >= 150] # only keep trials with reaction time > 150 ms self.data = self.data[self.data["choiceRT"] <= 10000] # only keep trials with reaction time < 10000 ms self.data = self.data[self.data["confidenceRT"] >= 150] # only keep trials with confidence reaction time > 150 ms self.data = self.data[self.data["confidenceRT"] <= 10000] # only keep trials with confidence reaction time < 10000 ms - self.data = self.data[self.data["confidence"].notna()] # only keep trials with confidence data - + self.data["confidence"] = self.data["confidence"].replace( ["NaN", "nan", "NAN", ""], pd.NA ) + self.data = self.data[self.data["confidence"].notna()] # only keep trials with confidence data because trials without confidence data are practice trials self.data["confidence"] = pd.to_numeric(self.data["confidence"], errors='coerce') self.results = pd.DataFrame() self.codebook = { "userID": "Unique identifier for each participant", + "n_trials": "Number of trials completed by the participant", "day_of_week": "Day of the week of the trial", "time": "Time of the trial", "time_of_day": "Time of day of the trial (morning, afternoon, evening, night)", @@ -123,6 +124,7 @@ def metrics(self, mode=None): Variables --------- - userID: unique identifier for each participant + - n_trials: number of trials completed by the participant - day_of_week: day of the week of the trial - time: time of the trial - time_of_day: time of day of the trial (morning, afternoon, evening, night) @@ -164,8 +166,7 @@ def metrics(self, mode=None): user_results = {"userID": user_id} - - user_results = {"userID": user_id} + user_results["n_trials"] = len(user_data) date = user_data["date"].iloc[0] if isinstance(date, str): @@ -178,16 +179,17 @@ def metrics(self, mode=None): "morning" if date.hour < 12 else "afternoon" if date.hour < 18 else "evening" if date.hour < 24 else - "night" + "night" if date.hour < 6 else + "-" ) - user_results["accuracy"] = np.mean(user_data["accuracy"]) + user_results["accuracy"] = np.nanmean(user_data["accuracy"]) - user_results["mean_RT"] = np.mean(user_data["choiceRT"]) - user_results["median_RT"] = np.median(user_data["choiceRT"]) + user_results["mean_RT"] = np.nanmean(user_data["choiceRT"]) + user_results["median_RT"] = np.nanmedian(user_data["choiceRT"]) # different RT for correct and incorrect trials - user_results["median_RT_correct"] = np.median(user_data["choiceRT"][user_data["accuracy"] == 1]) - user_results["median_RT_incorrect"] = np.median(user_data["choiceRT"][user_data["accuracy"] != 1]) + user_results["median_RT_correct"] = np.nanmedian(user_data["choiceRT"][user_data["accuracy"] == 1]) + user_results["median_RT_incorrect"] = np.nanmedian(user_data["choiceRT"][user_data["accuracy"] != 1]) user_results["diff_median_RT_correct_incorrect"] = ( user_results["median_RT_correct"] - user_results["median_RT_incorrect"] ) @@ -196,10 +198,10 @@ def metrics(self, mode=None): user_results["sd_confidence"] = np.nanstd(user_data["confidence"]) user_results["median_confidence"] = np.nanmedian(user_data["confidence"]) # confidence difference for correct and incorrect trials - user_results["median_confidence_correct"] = np.nanmedian(user_data["confidence"][user_data["accuracy"] == 1]) - user_results["median_confidence_incorrect"] = np.nanmedian(user_data["confidence"][user_data["accuracy"] != 1]) + user_results["mean_confidence_correct"] = np.nanmean(user_data["confidence"][user_data["accuracy"] == 1]) + user_results["mean_confidence_incorrect"] = np.nanmean(user_data["confidence"][user_data["accuracy"] != 1]) user_results["diff_median_conf_correct_incorrect"] = ( - user_results["median_confidence_correct"] - user_results["median_confidence_incorrect"] + user_results["mean_confidence_correct"] - user_results["mean_confidence_incorrect"] ) # standard deviation of confidence for correct and incorrect trials user_results["sd_confidence_correct"] = np.nanstd(user_data["confidence"][user_data["accuracy"] == 1]) @@ -280,10 +282,10 @@ def clean_data(self): self.cleanedresults = self.results.copy() # exclude participants who have more than 80 trials with run == 1 - users_80trials = self.cleanedresults.groupby("userID").filter(lambda x: len(x) >= 80)["userID"].unique() + users_80trials = self.data.groupby("userID").filter(lambda x: len(x) <= 80)["userID"].unique() self.cleanedresults = self.cleanedresults[self.cleanedresults["userID"].isin(users_80trials)] - self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_accuracy"] >= 0.5] # only keep participants with mean accuracy >= 50% + self.cleanedresults = self.cleanedresults[self.cleanedresults["accuracy"] >= 0.5] # only keep participants with mean accuracy >= 50% self.cleanedresults = self.cleanedresults[self.cleanedresults["median_RT"] <= 3000] # only keep participants with median reaction time <= 3000 ms self.cleanedresults = self.cleanedresults[self.cleanedresults["median_confidenceRT"] <= 3000] # only keep participants with median confidence reaction time <= 3000 ms self.cleanedresults = self.cleanedresults[self.cleanedresults["median_ES"] <= 25] # only keep participants with median evidence strength <= 25 @@ -292,11 +294,14 @@ def clean_data(self): # exclude if 10th and 25th percentile of confidence is the same AND 75th and 90th percentile of confidence is the same self.cleanedresults = self.cleanedresults[~( - (self.cleanedresults["confidence_10"] == self.cleanedresults["confidence_25"]) & self.cleanedresults["confidence_75"] == self.cleanedresults["confidence_90"] + (self.cleanedresults["confidence_10"] == self.cleanedresults["confidence_25"]) & + (self.cleanedresults["confidence_75"] == self.cleanedresults["confidence_90"]) )] self.deleted_participants = nr_part_before - len(self.cleanedresults["userID"].unique()) + return self.cleanedresults + def codebook(self): """ Return a codebook describing the metrics. diff --git a/cpm/brainexplorer/treasurehunt.py b/cpm/brainexplorer/treasurehunt.py index 328c6d0..6e4509f 100644 --- a/cpm/brainexplorer/treasurehunt.py +++ b/cpm/brainexplorer/treasurehunt.py @@ -66,6 +66,7 @@ def __init__(self, filepath=None): self.codebook = { "userID": "Unique identifier for each participant", + "n_trials": "Number of trials completed by the participant", "day_of_week": "Day of the week of the trial", "time": "Time of the trial", "time_of_day": "Time of day of the trial (morning, afternoon, evening, night)", @@ -74,7 +75,7 @@ def __init__(self, filepath=None): "mean_n_draws": "Mean number of stimulus draws before making a decision", "sd_n_draws": "Standard deviation of the number of stimulus draws before making a decision", "median_RT_between_actions": "Median reaction time between actions", - "median_confidenceRT": "Median confidence reaction time across all trials" + "median_confidenceRT": "Median confidence reaction time across all trials", "unique_draws": "Number of unique values in number of draws" } @@ -89,6 +90,7 @@ def metrics(self): Variables ---------- - userID: unique identifier for each participant + - n_trials: number of trials completed by the participant - day_of_week: day of the week of the trial - time: time of the trial - time_of_day: time of day of the trial (morning, afternoon, evening @@ -111,6 +113,8 @@ def metrics(self): user_results = {"userID": user_id} + user_results["n_trials"] = len(user_data) + date = user_data["date"].iloc[0] if isinstance(date, str): date = pd.to_datetime(date, format="%Y-%m-%d %H:%M:%S.%f") @@ -121,7 +125,8 @@ def metrics(self): "morning" if date.hour < 12 else "afternoon" if date.hour < 18 else "evening" if date.hour < 24 else - "night" + "night" if date.hour < 6 else + "-" ) user_results["mean_points"] = np.nanmean(user_data["outcome"]) @@ -222,6 +227,8 @@ def clean_data(self): self.deleted_participants = nr_part_before - len(self.cleanedresults["userID"].unique()) + return self.cleanedresults + def codebook(self): """ Return a codebook describing the metrics. From c69f687336af2b60b8b1bcadb1bd88bb1c28f1a7 Mon Sep 17 00:00:00 2001 From: lenarddome Date: Thu, 5 Mar 2026 10:42:38 +0100 Subject: [PATCH 38/54] clean: organise code contributions after main merge --- cpm/__init__.py | 1 + cpm/applications/GoblinHeist_model.py | 5 - cpm/brainexplorer/__init__.py | 6 +- cpm/brainexplorer/bandits.py | 384 +++++++++++++++++ ...easurehunt.py => information_gathering.py} | 99 +++-- cpm/brainexplorer/milkyWay.py | 334 --------------- cpm/brainexplorer/perceptual.py | 385 +++++++++++++++++ ...{scavenger.py => risky_decision_making.py} | 389 +++++++++++------- cpm/brainexplorer/riskychoices.py | 0 cpm/brainexplorer/spaceObserver.py | 309 -------------- cpm/brainexplorer/stopsignal.py | 15 - 11 files changed, 1084 insertions(+), 843 deletions(-) delete mode 100644 cpm/applications/GoblinHeist_model.py rename cpm/brainexplorer/{treasurehunt.py => information_gathering.py} (79%) delete mode 100644 cpm/brainexplorer/milkyWay.py rename cpm/brainexplorer/{scavenger.py => risky_decision_making.py} (59%) delete mode 100644 cpm/brainexplorer/riskychoices.py delete mode 100644 cpm/brainexplorer/spaceObserver.py delete mode 100644 cpm/brainexplorer/stopsignal.py diff --git a/cpm/__init__.py b/cpm/__init__.py index e8ecf09..009c706 100644 --- a/cpm/__init__.py +++ b/cpm/__init__.py @@ -21,5 +21,6 @@ from . import hierarchical from . import utils from . import datasets +from . import brainexplorer del os, sys, warnings, logging diff --git a/cpm/applications/GoblinHeist_model.py b/cpm/applications/GoblinHeist_model.py deleted file mode 100644 index 82b5e75..0000000 --- a/cpm/applications/GoblinHeist_model.py +++ /dev/null @@ -1,5 +0,0 @@ -import pandas as pd -import numpy as np -import warnings - - diff --git a/cpm/brainexplorer/__init__.py b/cpm/brainexplorer/__init__.py index 5b39fbf..c9aa211 100644 --- a/cpm/brainexplorer/__init__.py +++ b/cpm/brainexplorer/__init__.py @@ -1,4 +1,4 @@ -from cpm.brainexplorer import stopsignal -from cpm.brainexplorer import perceptual -from cpm.brainexplorer import riskychoices from cpm.brainexplorer import bandits +from cpm.brainexplorer import perceptual +from cpm.brainexplorer import risky_decision_making +from cpm.brainexplorer import information_gathering diff --git a/cpm/brainexplorer/bandits.py b/cpm/brainexplorer/bandits.py index e69de29..e74dc95 100644 --- a/cpm/brainexplorer/bandits.py +++ b/cpm/brainexplorer/bandits.py @@ -0,0 +1,384 @@ +import numpy as np +import matplotlib.pyplot as plt +import pandas as pd + + +class MilkyWay: + """ + Class to calculate metrics for a two-arm bandit task, Milky Way & Pirate Market in BrainExplorer. + + """ + + def __init__(self, filepath): + """ + Load data to be analyised and turn into Pandas DataFrame. + Trial-level exclusion criteria are applied to the data. + The results for Milky Way and Pirate Market trials are stored in separate DataFrames. + + Parameters + ---------- + filepath : str + The path to the CSV file to be processed. The column names in the data must subscribe to a prespecified convention, see Notes. + + Example + ---------- + + >>> milkyWay = milkyWay("/example/2025-02-20_SpaceObserver_Data_short.xlsx") + >>> milkyWay.metrics() + >>> milkyWay.clean_data() + >>> results_MW = milkyWay.results_MW + >>> results_PM = milkyWay.results_PM + >>> milkyWay.codebook() + >>> diff_results = milkyWay.difference_metrics() + + + Notes + ----- + The columns required in data: + - userID: unique identifier for each participant + - trial_type: the type of trial (Milky Way or Pirate Market) + - date: the date and time of the trial + - correct: whether the choice was correct (1) or incorrect (0) + - outchosen: the outcome of the trial + - rep: whether the choice was repeated (1) or not (0) + - WSLS_v1: whether the choice was a win-stay/loose-shift choice for correct trials version 1 + - WSLS_v2: whether the choice was a win-stay/loose-shift choice for correct trials version 2 + + The trial-level exclusion criteria are applied: + - Attempts after the first attempt ("run" variable) + + The reaction times have to be corrected by the average time between the previous choice and new stimulus presentation which is 4200 ms. However, this correction can lead to negative reaction times, which may exclude some participants from the analysis. + """ + + self.data = pd.read_csv(filepath) + + # Separate into MW and PM data + self.MW_data = self.data[self.data["trial_type"] == "reward"] + self.PM_data = self.data[self.data["trial_type"] == "punish"] + + # Take first attempt (i.e., not neccessarily run == 1 for PM) + min_run_per_user = self.MW_data.groupby("userID")["run"].transform("min") + self.MW_data = self.MW_data[ + self.MW_data["run"] == min_run_per_user + ].reset_index(drop=True) + + min_run_per_user = self.PM_data.groupby("userID")["run"].transform("min") + self.PM_data = self.PM_data[ + self.PM_data["run"] == min_run_per_user + ].reset_index(drop=True) + + self.results_MW = pd.DataFrame() + self.results_PM = pd.DataFrame() + self.results_diff = pd.DataFrame() + + self.codebook = { + "userID": "Unique identifier for each participant", + "n_trials": "Number of trials completed by the participant", + "trial_type": "Type of trial (Milky Way or Pirate Market)", + "day_of_week": "Day of the week when the trial was conducted", + "time": "Time when the trial was conducted", + "time_of_day": "Time of day when the trial was conducted (morning, afternoon, evening, night)", + "mean_RT_MW": "Mean reaction time for Milky Way trials", + "median_RT_MW": "Median reaction time for Milky Way trials", + "mean_RT_PM": "Mean reaction time for Pirate Market trials", + "median_RT_PM": "Median reaction time for Pirate Market trials", + "accuracy_MW": "Mean accuracy for Milky Way trials", + "accuracy_PM": "Mean accuracy for Pirate Market trials", + "prop_same_choice_MW": "Proportion of same choice in Milky Way trials", + "prop_same_choice_PM": "Proportion of same choice in Pirate Market trials", + "mean_outcome_MW": "Mean outcome for Milky Way trials", + "mean_outcome_PM": "Mean outcome for Pirate Market trials", + "reward_diff_obt_forg_MW": "Mean difference between obtained and foregone reward for Milky Way trials", + "reward_diff_obt_forg_PM": "Mean difference between obtained and foregone reward for Pirate Market trials", + "prop_WSLS_1_MW": "Proportion of win-stay/loose-shift choices for correct trials version 1 for Milky Way trials", + "prop_WSLS_2_MW": "Proportion of win-stay/loose-shift choices for correct trials version 2 for Milky Way trials", + "prop_WSLS_1_PM": "Proportion of win-stay/loose-shift choices for correct trials version 1 for Pirate Market trials", + "prop_WSLS_2_PM": "Proportion of win-stay/loose-shift choices for correct trials version 2 for Pirate Market trials", + } + + def metrics(self): + """ + Calculate the metrics for the data. + + Returns + ------- + results : pd.DataFrame + A DataFrame containing the results of the metrics. + + Variables + ---------- + - userID: unique identifier for each participant + - n_trials: number of trials completed by the participant + - day_of_week: day of the week of the trial + - time: time of the trial + - time_of_day: time of day of the trial (morning, afternoon, evening + - trial_type: type of trial (Milky Way or Pirate Market) + - mean_RT_MW: Mean reaction time for Milky Way trials + - median_RT_MW: Median reaction time for Milky Way trials + - mean_RT_PM: Mean reaction time for Pirate Market trials + - median_RT_PM: Median reaction time for Pirate Market trials + - accuracy_MW: Accuracy for Milky Way trials + - accuracy_PM: Accuracy for Pirate Market trials + - prop_same_choice_MW: Proportion of same choice in Milky Way trials + - prop_same_choice_PM: Proportion of same choice in Pirate Market trials + - mean_outcome_MW: Mean outcome for Milky Way trials + - mean_outcome_PM: Mean outcome for Pirate Market trials + - reward_diff_obt_forg_MW: Mean difference between obtained and foregone reward for Milky Way trials + - reward_diff_obt_forg_PM: Mean difference between obtained and foregone reward for Pirate Market trials + - prop_WSLS_1_MW: Proportion of win-stay/loose-shift choices for correct trials version 1 for Milky Way trials + - prop_WSLS_2_MW: Proportion of win-stay/loose-shift choices for correct trials version 2 for Milky Way trials + - prop_WSLS_1_PM: Proportion of win-stay/loose-shift choices for correct trials version 1 for Pirate Market trials + - prop_WSLS_2_PM: Proportion of win-stay/loose-shift choices for correct trials version 2 for Pirate Market trials + + """ + # group MilkyWay and Pirate Market data + MW_data = self.MW_data.groupby("userID") + PM_data = self.PM_data.groupby("userID") + + # Loop through each group of user data + for user_id, user_data_MW in MW_data: + + user_results_MW = {"userID": user_id} + + user_results_MW["n_trials"] = len(user_data_MW) + + user_results_MW["trial_type"] = "Milky Way" + + date = user_data_MW["date"].iloc[0] + if isinstance(date, str): + date = pd.to_datetime(date, format="%Y-%m-%d %H:%M:%S.%f") + user_data_MW["day_of_week"] = date.day_name() + user_data_MW["time"] = ( + date.time() if hasattr(date, "time") else date.strftime("%H:%M:%S.%f") + ) + # morning 6-12, afternoon 12-18, evening 18-24, night 0-6 + user_data_MW["time_of_day"] = ( + "morning" + if date.hour < 12 + else ( + "afternoon" + if date.hour < 18 + else ( + "evening" + if date.hour < 24 + else "night" if date.hour < 6 else "-" + ) + ) + ) + + # user_results_MW["mean_RT_MW"] = np.mean(user_data_MW['rt_adj']) + # user_results_MW["median_RT_MW"] = np.median(user_data_MW['rt_adj']) + + user_results_MW["accuracy_MW"] = np.nanmean(user_data_MW["correct"]) + + user_results_MW["mean_outcome_MW"] = np.nanmean(user_data_MW["outchosen"]) + + # Mean difference between obtained and foregone reward + user_results_MW["reward_diff_obt_forg_MW"] = np.nanmean( + user_data_MW["obt_min_forg"] + ) + + # proportion of repeated choices + user_results_MW["prop_same_choice_MW"] = np.nanmean(user_data_MW["rep"]) + + # proportion of win-stay/loose-shift choices + user_results_MW["prop_WSLS_1_MW"] = np.nanmean(user_data_MW["WSLS_v1"]) + + user_results_MW["prop_WSLS_2_MW"] = np.nanmean(user_data_MW["WSLS_v2"]) + + # Append user-level results + self.results_MW = pd.concat( + [self.results_MW, pd.DataFrame([user_results_MW])], ignore_index=True + ) + + self.results_MW = pd.DataFrame(self.results_MW) + + for user_id, user_data_PM in PM_data: + + user_results_PM = {"userID": user_id} + + user_results_PM["n_trials"] = len(user_data_PM) + + user_results_PM["trial_type"] = "Pirate Market" + + date = user_data_PM["date"].iloc[0] + if isinstance(date, str): + date = pd.to_datetime(date, format="%Y-%m-%d %H:%M:%S.%f") + user_data_PM["day_of_week"] = date.day_name() + user_data_PM["time"] = ( + date.time() if hasattr(date, "time") else date.strftime("%H:%M:%S.%f") + ) + # morning 6-12, afternoon 12-18, evening 18-24, night 0-6 + user_data_PM["time_of_day"] = ( + "morning" + if date.hour < 12 + else ( + "afternoon" + if date.hour < 18 + else "evening" if date.hour < 24 else "night" + ) + ) + + # user_results_PM["mean_RT_PM"] = np.mean(user_data_PM['rt_adj']) + # user_results_PM["median_RT_PM"] = np.median(user_data_PM['rt_adj']) + + user_results_PM["accuracy_PM"] = np.nanmean(user_data_PM["correct"]) + + user_results_PM["mean_outcome_PM"] = np.nanmean(user_data_PM["outchosen"]) + + # Mean difference between obtained and foregone reward + user_results_PM["reward_diff_obt_forg_PM"] = np.nanmean( + user_data_PM["obt_min_forg"] + ) + + # proportion of repeated choices + user_results_PM["prop_same_choice_PM"] = np.nanmean(user_data_PM["rep"]) + + # proportion of win-stay/loose-shift choices + user_results_PM["prop_WSLS_1_PM"] = np.nanmean(user_data_PM["WSLS_v1"]) + + user_results_PM["prop_WSLS_2_PM"] = np.nanmean(user_data_PM["WSLS_v2"]) + + # Append user-level results + self.results_PM = pd.concat( + [self.results_PM, pd.DataFrame([user_results_PM])], ignore_index=True + ) + + self.results_PM = pd.DataFrame(self.results_PM) + + def difference_metrics(self): + """ + Calculate the difference between the metrics of Milky Way and Pirate Market trials. + + Returns + ------- + results_diff : pd.DataFrame + A DataFrame containing the difference of the metrics between Milky Way and Pirate Market trials. + + Variables + ---------- + - accuracy_diff: Difference in accuracy between Milky Way and Pirate Market trials + - outcome_diff: Difference in mean outcome between Milky Way and Pirate Market trials + - reward_diff_obt_forg_diff: Difference in mean difference between obtained and foregone reward between Milky Way and Pirate Market trials + - prop_same_choice_diff: Difference in proportion of same choice between Milky Way and Pirate Market trials + - prop_WSLS_1_diff: Difference in proportion of win-stay/loose-shift choices for correct trials version 1 between Milky Way and Pirate Market trials + - prop_WSLS_2_diff: Difference in proportion of win-stay/loose-shift choices for correct trials version 2 between Milky Way and Pirate Market trials + """ + + # Calculate differences for each user + for user_id in pd.unique(self.results_MW["userID"]): + user_results_MW = self.results_MW[self.results_MW["userID"] == user_id] + user_results_PM = self.results_PM[self.results_PM["userID"] == user_id] + + if not user_results_MW.empty and not user_results_PM.empty: + diff_results = { + "userID": user_id, + "accuracy_diff": user_results_MW["accuracy_MW"].values[0] + - user_results_PM["accuracy_PM"].values[0], + "outcome_diff": user_results_MW["mean_outcome_MW"].values[0] + - user_results_PM["mean_outcome_PM"].values[0], + "reward_diff_obt_forg_diff": user_results_MW[ + "reward_diff_obt_forg_MW" + ].values[0] + - user_results_PM["reward_diff_obt_forg_PM"].values[0], + "prop_same_choice_diff": user_results_MW[ + "prop_same_choice_MW" + ].values[0] + - user_results_PM["prop_same_choice_PM"].values[0], + "prop_WSLS_1_diff": user_results_MW["prop_WSLS_1_MW"].values[0] + - user_results_PM["prop_WSLS_1_PM"].values[0], + "prop_WSLS_2_diff": user_results_MW["prop_WSLS_2_MW"].values[0] + - user_results_PM["prop_WSLS_2_PM"].values[0], + } + self.results_diff = pd.concat( + [self.results_diff, pd.DataFrame([diff_results])], ignore_index=True + ) + + return pd.DataFrame(self.results_diff) + + def clean_data(self): + """ + Clean the data by removing participants who do not meet the participant-level inclusion criteria. + + Exclusion Critera + ----------------- + + Participant-level: + - Same choice on >= 95% of trials + - Missing accuracy data + - Participants with more than 72 trials (due to technical error) + """ + nr_part_before_MW = len(self.results_MW["userID"].unique()) + nr_part_before_PM = len(self.results_PM["userID"].unique()) + + # created cleaned verison of results object but also keep old object for comparison + self.cleanedresults_MW = self.results_MW.copy() + self.cleanedresults_PM = self.results_PM.copy() + + # Remove users with more than 72 trials + valid_user_ids = ( + self.MW_data.groupby("userID")["userID"].transform("count") <= 72 + ) + user_ids_to_keep = self.MW_data.loc[valid_user_ids, "userID"].unique() + self.cleanedresults_MW = self.cleanedresults_MW[ + self.cleanedresults_MW["userID"].isin(user_ids_to_keep) + ] + + valid_user_ids = ( + self.PM_data.groupby("userID")["userID"].transform("count") <= 72 + ) + user_ids_to_keep = self.PM_data.loc[valid_user_ids, "userID"].unique() + self.cleanedresults_PM = self.cleanedresults_PM[ + self.cleanedresults_PM["userID"].isin(user_ids_to_keep) + ] + + # Remove users with at least 95% same choice + self.cleanedresults_MW = self.cleanedresults_MW[ + self.cleanedresults_MW["prop_same_choice_MW"] < 0.95 + ] + self.cleanedresults_PM = self.cleanedresults_PM[ + self.cleanedresults_PM["prop_same_choice_PM"] < 0.95 + ] + + # remove users with missing accuracy + self.cleanedresults_MW = self.cleanedresults_MW[ + self.cleanedresults_MW["accuracy_MW"].notna() + ] + self.cleanedresults_PM = self.cleanedresults_PM[ + self.cleanedresults_PM["accuracy_PM"].notna() + ] + + self.deleted_participants_MW = nr_part_before_MW - len( + pd.unique(self.cleanedresults_MW["userID"]) + ) + self.deleted_participants_PM = nr_part_before_PM - len( + pd.unique(self.cleanedresults_PM["userID"]) + ) + + # Also clean the diff file by keeping only participants who are in both the cleanedresults_MW and in the cleanedresults_PM file + self.cleanedresults_diff = self.results_diff.copy() + + nr_part_before_diff = len(self.results_diff["userID"].unique()) + + user_ids_MW = set( + self.cleanedresults_MW["userID"].unique() + ) # Get userIDs from both cleaned datasets + user_ids_PM = set(self.cleanedresults_PM["userID"].unique()) + valid_user_ids_diff = user_ids_MW.intersection( + user_ids_PM + ) # Keep only userIDs that are in both + self.cleanedresults_diff = self.cleanedresults_diff[ + self.cleanedresults_diff["userID"].isin(valid_user_ids_diff) + ] + + self.deleted_participants_diff = nr_part_before_diff - len( + self.cleanedresults_diff["userID"].unique() + ) + + return self.cleanedresults_MW, self.cleanedresults_PM, self.cleanedresults_diff + + def codebook(self): + """ + Create a codebook for the data. + """ + return self.codebook diff --git a/cpm/brainexplorer/treasurehunt.py b/cpm/brainexplorer/information_gathering.py similarity index 79% rename from cpm/brainexplorer/treasurehunt.py rename to cpm/brainexplorer/information_gathering.py index 6e4509f..2c286c0 100644 --- a/cpm/brainexplorer/treasurehunt.py +++ b/cpm/brainexplorer/information_gathering.py @@ -2,14 +2,15 @@ import numpy as np import warnings from scipy.stats import zscore -#import statsmodels.api as sm -#import statsmodels.formula.api as smf +# import statsmodels.api as sm +# import statsmodels.formula.api as smf -class treasurehunt: - """ + +class TreasureHunt: + """ Class to calculate metrics for the Treasure Hunt task. Includes regresssion model to predict the influence of previous evidence on current choice. - + Attributes ---------- data : pd.DataFrame @@ -76,13 +77,13 @@ def __init__(self, filepath=None): "sd_n_draws": "Standard deviation of the number of stimulus draws before making a decision", "median_RT_between_actions": "Median reaction time between actions", "median_confidenceRT": "Median confidence reaction time across all trials", - "unique_draws": "Number of unique values in number of draws" + "unique_draws": "Number of unique values in number of draws", } def metrics(self): """ - Returns + Returns ------- results : pd.DataFrame A DataFrame containing the results of the metrics. @@ -103,7 +104,9 @@ def metrics(self): - unique_draws: number of unique values in number of draws """ # turn string fields into list of ints - self.data["ev"] = self.data["ev"].apply(lambda x: [int(i) for i in x.strip("[]").split(" ")]) + self.data["ev"] = self.data["ev"].apply( + lambda x: [int(i) for i in x.strip("[]").split(" ")] + ) # Group data by userID grouped_data = self.data.groupby("userID") @@ -119,19 +122,27 @@ def metrics(self): if isinstance(date, str): date = pd.to_datetime(date, format="%Y-%m-%d %H:%M:%S.%f") user_results["day_of_week"] = date.day_name() - user_results["time"] = date.time() if hasattr(date, 'time') else date.strftime("%H:%M:%S.%f") + user_results["time"] = ( + date.time() if hasattr(date, "time") else date.strftime("%H:%M:%S.%f") + ) # morning 6-12, afternoon 12-18, evening 18-24, night 0-6 user_results["time_of_day"] = ( - "morning" if date.hour < 12 else - "afternoon" if date.hour < 18 else - "evening" if date.hour < 24 else - "night" if date.hour < 6 else - "-" + "morning" + if date.hour < 12 + else ( + "afternoon" + if date.hour < 18 + else ( + "evening" + if date.hour < 24 + else "night" if date.hour < 6 else "-" + ) + ) ) - user_results["mean_points"] = np.nanmean(user_data["outcome"]) + user_results["mean_points"] = np.nanmean(user_data["outcome"]) - user_results["accuracy"] = np.nanmean(user_data["choseCurEv"]) + user_results["accuracy"] = np.nanmean(user_data["choseCurEv"]) user_results["mean_confidence"] = np.nanmean(user_data["confidence"]) user_results["sd_confidence"] = np.nanstd(user_data["confidence"]) @@ -139,14 +150,16 @@ def metrics(self): user_results["mean_n_draws"] = np.nanmean(user_data["draws"]) user_results["sd_n_draws"] = np.nanstd(user_data["draws"]) - user_results["median_RT_between_actions"] = np.nanmedian(user_data["median_diffRT"]) + user_results["median_RT_between_actions"] = np.nanmedian( + user_data["median_diffRT"] + ) user_results["median_confidenceRT"] = np.median(user_data["confidenceRT"]) # check if < 3 unique values in number of samples user_results["unique_draws"] = user_data["draws"].nunique() - # regression for influence of recent results on current choice + # regression for influence of recent results on current choice """ ev = [] @@ -195,10 +208,10 @@ def metrics(self): # Convert results to a DataFrame self.results = pd.DataFrame(self.results) - + return self.results - - def clean_data(self): + + def clean_data(self): """ Clean the aggregated data by applying participant-level exclusion criteria. @@ -211,21 +224,35 @@ def clean_data(self): nr_part_before = len(self.results["userID"].unique()) self.cleanedresults = self.results.copy() - - # Filter out participants who have less than 3 unique values for draws - valid_users = self.data.groupby("userID").filter( - lambda group: group["draws"].nunique() >= 3 - )["userID"].unique() - self.cleanedresults = self.cleanedresults[self.cleanedresults["userID"].isin(valid_users)] - - self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_n_draws"] >= 2] # only keep participants with mean number of draws >= 2 - self.cleanedresults = self.cleanedresults[self.cleanedresults["mean_n_draws"] <= 23] # only keep participants with mean number of draws <= 23 - self.cleanedresults = self.cleanedresults[self.cleanedresults["accuracy"] > 0.8] # only keep participants with mean choice in line with current evidence > 80% - - self.cleanedresults = self.cleanedresults[self.cleanedresults["unique_draws"] >= 3] # only keep participants with at least 3 unique values in number of draws - - self.deleted_participants = nr_part_before - len(self.cleanedresults["userID"].unique()) + # Filter out participants who have less than 3 unique values for draws + valid_users = ( + self.data.groupby("userID") + .filter(lambda group: group["draws"].nunique() >= 3)["userID"] + .unique() + ) + self.cleanedresults = self.cleanedresults[ + self.cleanedresults["userID"].isin(valid_users) + ] + + self.cleanedresults = self.cleanedresults[ + self.cleanedresults["mean_n_draws"] >= 2 + ] # only keep participants with mean number of draws >= 2 + self.cleanedresults = self.cleanedresults[ + self.cleanedresults["mean_n_draws"] <= 23 + ] # only keep participants with mean number of draws <= 23 + + self.cleanedresults = self.cleanedresults[ + self.cleanedresults["accuracy"] > 0.8 + ] # only keep participants with mean choice in line with current evidence > 80% + + self.cleanedresults = self.cleanedresults[ + self.cleanedresults["unique_draws"] >= 3 + ] # only keep participants with at least 3 unique values in number of draws + + self.deleted_participants = nr_part_before - len( + self.cleanedresults["userID"].unique() + ) return self.cleanedresults @@ -233,4 +260,4 @@ def codebook(self): """ Return a codebook describing the metrics. """ - return self.codebook \ No newline at end of file + return self.codebook diff --git a/cpm/brainexplorer/milkyWay.py b/cpm/brainexplorer/milkyWay.py deleted file mode 100644 index f90b881..0000000 --- a/cpm/brainexplorer/milkyWay.py +++ /dev/null @@ -1,334 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -import pandas as pd - -class milkyWay: - """ - Class to calculate metrics for a two-arm bandit task, Milky Way & Pirate Market in BrainExplorer. - - """ - - def __init__(self, filepath): - """ - Load data to be analyised and turn into Pandas DataFrame. - Trial-level exclusion criteria are applied to the data. - The results for Milky Way and Pirate Market trials are stored in separate DataFrames. - - Parameters - ---------- - filepath : str - The path to the CSV file to be processed. The column names in the data must subscribe to a prespecified convention, see Notes. - - Example - ---------- - - >>> milkyWay = milkyWay("/example/2025-02-20_SpaceObserver_Data_short.xlsx") - >>> milkyWay.metrics() - >>> milkyWay.clean_data() - >>> results_MW = milkyWay.results_MW - >>> results_PM = milkyWay.results_PM - >>> milkyWay.codebook() - >>> diff_results = milkyWay.difference_metrics() - - - Notes - ----- - The columns required in data: - - userID: unique identifier for each participant - - trial_type: the type of trial (Milky Way or Pirate Market) - - date: the date and time of the trial - - correct: whether the choice was correct (1) or incorrect (0) - - outchosen: the outcome of the trial - - rep: whether the choice was repeated (1) or not (0) - - WSLS_v1: whether the choice was a win-stay/loose-shift choice for correct trials version 1 - - WSLS_v2: whether the choice was a win-stay/loose-shift choice for correct trials version 2 - - The trial-level exclusion criteria are applied: - - Attempts after the first attempt ("run" variable) - - The reaction times have to be corrected by the average time between the previous choice and new stimulus presentation which is 4200 ms. However, this correction can lead to negative reaction times, which may exclude some participants from the analysis. - """ - - self.data = pd.read_csv(filepath) - - #Separate into MW and PM data - self.MW_data = self.data[self.data['trial_type'] == 'reward'] - self.PM_data = self.data[self.data['trial_type'] == 'punish'] - - #Take first attempt (i.e., not neccessarily run == 1 for PM) - min_run_per_user = self.MW_data.groupby('userID')['run'].transform('min') - self.MW_data = self.MW_data[self.MW_data['run'] == min_run_per_user].reset_index(drop=True) - - min_run_per_user = self.PM_data.groupby('userID')['run'].transform('min') - self.PM_data = self.PM_data[self.PM_data['run'] == min_run_per_user].reset_index(drop=True) - - self.results_MW = pd.DataFrame() - self.results_PM = pd.DataFrame() - self.results_diff = pd.DataFrame() - - self.codebook = { - "userID": "Unique identifier for each participant", - "n_trials": "Number of trials completed by the participant", - "trial_type": "Type of trial (Milky Way or Pirate Market)", - "day_of_week": "Day of the week when the trial was conducted", - "time": "Time when the trial was conducted", - "time_of_day": "Time of day when the trial was conducted (morning, afternoon, evening, night)", - "mean_RT_MW": "Mean reaction time for Milky Way trials", - "median_RT_MW": "Median reaction time for Milky Way trials", - "mean_RT_PM": "Mean reaction time for Pirate Market trials", - "median_RT_PM": "Median reaction time for Pirate Market trials", - "accuracy_MW": "Mean accuracy for Milky Way trials", - "accuracy_PM": "Mean accuracy for Pirate Market trials", - "prop_same_choice_MW": "Proportion of same choice in Milky Way trials", - "prop_same_choice_PM": "Proportion of same choice in Pirate Market trials", - "mean_outcome_MW": "Mean outcome for Milky Way trials", - "mean_outcome_PM": "Mean outcome for Pirate Market trials", - "reward_diff_obt_forg_MW": "Mean difference between obtained and foregone reward for Milky Way trials", - "reward_diff_obt_forg_PM": "Mean difference between obtained and foregone reward for Pirate Market trials", - "prop_WSLS_1_MW": "Proportion of win-stay/loose-shift choices for correct trials version 1 for Milky Way trials", - "prop_WSLS_2_MW": "Proportion of win-stay/loose-shift choices for correct trials version 2 for Milky Way trials", - "prop_WSLS_1_PM": "Proportion of win-stay/loose-shift choices for correct trials version 1 for Pirate Market trials", - "prop_WSLS_2_PM": "Proportion of win-stay/loose-shift choices for correct trials version 2 for Pirate Market trials" - } - - - def metrics(self): - """ - Calculate the metrics for the data. - - Returns - ------- - results : pd.DataFrame - A DataFrame containing the results of the metrics. - - Variables - ---------- - - userID: unique identifier for each participant - - n_trials: number of trials completed by the participant - - day_of_week: day of the week of the trial - - time: time of the trial - - time_of_day: time of day of the trial (morning, afternoon, evening - - trial_type: type of trial (Milky Way or Pirate Market) - - mean_RT_MW: Mean reaction time for Milky Way trials - - median_RT_MW: Median reaction time for Milky Way trials - - mean_RT_PM: Mean reaction time for Pirate Market trials - - median_RT_PM: Median reaction time for Pirate Market trials - - accuracy_MW: Accuracy for Milky Way trials - - accuracy_PM: Accuracy for Pirate Market trials - - prop_same_choice_MW: Proportion of same choice in Milky Way trials - - prop_same_choice_PM: Proportion of same choice in Pirate Market trials - - mean_outcome_MW: Mean outcome for Milky Way trials - - mean_outcome_PM: Mean outcome for Pirate Market trials - - reward_diff_obt_forg_MW: Mean difference between obtained and foregone reward for Milky Way trials - - reward_diff_obt_forg_PM: Mean difference between obtained and foregone reward for Pirate Market trials - - prop_WSLS_1_MW: Proportion of win-stay/loose-shift choices for correct trials version 1 for Milky Way trials - - prop_WSLS_2_MW: Proportion of win-stay/loose-shift choices for correct trials version 2 for Milky Way trials - - prop_WSLS_1_PM: Proportion of win-stay/loose-shift choices for correct trials version 1 for Pirate Market trials - - prop_WSLS_2_PM: Proportion of win-stay/loose-shift choices for correct trials version 2 for Pirate Market trials - - """ - # group MilkyWay and Pirate Market data - MW_data = self.MW_data.groupby("userID") - PM_data = self.PM_data.groupby("userID") - - # Loop through each group of user data - for user_id, user_data_MW in MW_data: - - user_results_MW = {"userID": user_id} - - user_results_MW["n_trials"] = len(user_data_MW) - - user_results_MW["trial_type"] = "Milky Way" - - date = user_data_MW["date"].iloc[0] - if isinstance(date, str): - date = pd.to_datetime(date, format="%Y-%m-%d %H:%M:%S.%f") - user_data_MW["day_of_week"] = date.day_name() - user_data_MW["time"] = date.time() if hasattr(date, 'time') else date.strftime("%H:%M:%S.%f") - # morning 6-12, afternoon 12-18, evening 18-24, night 0-6 - user_data_MW["time_of_day"] = ( - "morning" if date.hour < 12 else - "afternoon" if date.hour < 18 else - "evening" if date.hour < 24 else - "night" if date.hour < 6 - else "-" - ) - - #user_results_MW["mean_RT_MW"] = np.mean(user_data_MW['rt_adj']) - #user_results_MW["median_RT_MW"] = np.median(user_data_MW['rt_adj']) - - user_results_MW["accuracy_MW"] = np.nanmean(user_data_MW["correct"]) - - user_results_MW["mean_outcome_MW"] = np.nanmean(user_data_MW["outchosen"]) - - # Mean difference between obtained and foregone reward - user_results_MW["reward_diff_obt_forg_MW"] = np.nanmean(user_data_MW["obt_min_forg"]) - - # proportion of repeated choices - user_results_MW["prop_same_choice_MW"] = np.nanmean(user_data_MW["rep"]) - - # proportion of win-stay/loose-shift choices - user_results_MW["prop_WSLS_1_MW"] = np.nanmean( - user_data_MW["WSLS_v1"] - ) - - user_results_MW["prop_WSLS_2_MW"] = np.nanmean( - user_data_MW["WSLS_v2"] - ) - - # Append user-level results - self.results_MW = pd.concat( - [self.results_MW, pd.DataFrame([user_results_MW])], ignore_index=True - ) - - self.results_MW = pd.DataFrame(self.results_MW) - - for user_id, user_data_PM in PM_data: - - user_results_PM = {"userID": user_id} - - user_results_PM["n_trials"] = len(user_data_PM) - - user_results_PM["trial_type"] = "Pirate Market" - - date = user_data_PM["date"].iloc[0] - if isinstance(date, str): - date = pd.to_datetime(date, format="%Y-%m-%d %H:%M:%S.%f") - user_data_PM["day_of_week"] = date.day_name() - user_data_PM["time"] = date.time() if hasattr(date, 'time') else date.strftime("%H:%M:%S.%f") - # morning 6-12, afternoon 12-18, evening 18-24, night 0-6 - user_data_PM["time_of_day"] = ( - "morning" if date.hour < 12 else - "afternoon" if date.hour < 18 else - "evening" if date.hour < 24 else - "night" - ) - - #user_results_PM["mean_RT_PM"] = np.mean(user_data_PM['rt_adj']) - #user_results_PM["median_RT_PM"] = np.median(user_data_PM['rt_adj']) - - user_results_PM["accuracy_PM"] = np.nanmean(user_data_PM["correct"]) - - user_results_PM["mean_outcome_PM"] = np.nanmean(user_data_PM["outchosen"]) - - # Mean difference between obtained and foregone reward - user_results_PM["reward_diff_obt_forg_PM"] = np.nanmean(user_data_PM["obt_min_forg"]) - - # proportion of repeated choices - user_results_PM["prop_same_choice_PM"] = np.nanmean(user_data_PM["rep"]) - - # proportion of win-stay/loose-shift choices - user_results_PM["prop_WSLS_1_PM"] = np.nanmean( - user_data_PM["WSLS_v1"] - ) - - user_results_PM["prop_WSLS_2_PM"] = np.nanmean( - user_data_PM["WSLS_v2"] - ) - - # Append user-level results - self.results_PM = pd.concat( - [self.results_PM, pd.DataFrame([user_results_PM])], ignore_index=True - ) - - self.results_PM = pd.DataFrame(self.results_PM) - - - def difference_metrics(self): - """ - Calculate the difference between the metrics of Milky Way and Pirate Market trials. - - Returns - ------- - results_diff : pd.DataFrame - A DataFrame containing the difference of the metrics between Milky Way and Pirate Market trials. - - Variables - ---------- - - accuracy_diff: Difference in accuracy between Milky Way and Pirate Market trials - - outcome_diff: Difference in mean outcome between Milky Way and Pirate Market trials - - reward_diff_obt_forg_diff: Difference in mean difference between obtained and foregone reward between Milky Way and Pirate Market trials - - prop_same_choice_diff: Difference in proportion of same choice between Milky Way and Pirate Market trials - - prop_WSLS_1_diff: Difference in proportion of win-stay/loose-shift choices for correct trials version 1 between Milky Way and Pirate Market trials - - prop_WSLS_2_diff: Difference in proportion of win-stay/loose-shift choices for correct trials version 2 between Milky Way and Pirate Market trials - """ - - # Calculate differences for each user - for user_id in pd.unique(self.results_MW["userID"]): - user_results_MW = self.results_MW[self.results_MW["userID"] == user_id] - user_results_PM = self.results_PM[self.results_PM["userID"] == user_id] - - if not user_results_MW.empty and not user_results_PM.empty: - diff_results = { - "userID": user_id, - "accuracy_diff": user_results_MW["accuracy_MW"].values[0] - user_results_PM["accuracy_PM"].values[0], - "outcome_diff": user_results_MW["mean_outcome_MW"].values[0] - user_results_PM["mean_outcome_PM"].values[0], - "reward_diff_obt_forg_diff": user_results_MW["reward_diff_obt_forg_MW"].values[0] - user_results_PM["reward_diff_obt_forg_PM"].values[0], - "prop_same_choice_diff": user_results_MW["prop_same_choice_MW"].values[0] - user_results_PM["prop_same_choice_PM"].values[0], - "prop_WSLS_1_diff": user_results_MW["prop_WSLS_1_MW"].values[0] - user_results_PM["prop_WSLS_1_PM"].values[0], - "prop_WSLS_2_diff": user_results_MW["prop_WSLS_2_MW"].values[0] - user_results_PM["prop_WSLS_2_PM"].values[0] - } - self.results_diff = pd.concat([self.results_diff, pd.DataFrame([diff_results])], ignore_index=True) - - return pd.DataFrame(self.results_diff) - - - - def clean_data(self): - """ - Clean the data by removing participants who do not meet the participant-level inclusion criteria. - - Exclusion Critera - ----------------- - - Participant-level: - - Same choice on >= 95% of trials - - Missing accuracy data - - Participants with more than 72 trials (due to technical error) - """ - nr_part_before_MW = len(self.results_MW["userID"].unique()) - nr_part_before_PM = len(self.results_PM["userID"].unique()) - - #created cleaned verison of results object but also keep old object for comparison - self.cleanedresults_MW = self.results_MW.copy() - self.cleanedresults_PM = self.results_PM.copy() - - #Remove users with more than 72 trials - valid_user_ids = self.MW_data.groupby("userID")["userID"].transform("count") <= 72 - user_ids_to_keep = self.MW_data.loc[valid_user_ids, "userID"].unique() - self.cleanedresults_MW = self.cleanedresults_MW[self.cleanedresults_MW["userID"].isin(user_ids_to_keep)] - - valid_user_ids = self.PM_data.groupby("userID")["userID"].transform("count") <= 72 - user_ids_to_keep = self.PM_data.loc[valid_user_ids, "userID"].unique() - self.cleanedresults_PM = self.cleanedresults_PM[self.cleanedresults_PM["userID"].isin(user_ids_to_keep)] - - #Remove users with at least 95% same choice - self.cleanedresults_MW = self.cleanedresults_MW[self.cleanedresults_MW["prop_same_choice_MW"] < 0.95] - self.cleanedresults_PM = self.cleanedresults_PM[self.cleanedresults_PM["prop_same_choice_PM"] < 0.95] - - #remove users with missing accuracy - self.cleanedresults_MW = self.cleanedresults_MW[self.cleanedresults_MW["accuracy_MW"].notna()] - self.cleanedresults_PM = self.cleanedresults_PM[self.cleanedresults_PM["accuracy_PM"].notna()] - - self.deleted_participants_MW = nr_part_before_MW - len(pd.unique(self.cleanedresults_MW["userID"])) - self.deleted_participants_PM = nr_part_before_PM - len(pd.unique(self.cleanedresults_PM["userID"])) - - #Also clean the diff file by keeping only participants who are in both the cleanedresults_MW and in the cleanedresults_PM file - self.cleanedresults_diff = self.results_diff.copy() - - nr_part_before_diff = len(self.results_diff["userID"].unique()) - - user_ids_MW = set(self.cleanedresults_MW["userID"].unique()) # Get userIDs from both cleaned datasets - user_ids_PM = set(self.cleanedresults_PM["userID"].unique()) - valid_user_ids_diff = user_ids_MW.intersection(user_ids_PM) # Keep only userIDs that are in both - self.cleanedresults_diff = self.cleanedresults_diff[self.cleanedresults_diff["userID"].isin(valid_user_ids_diff)] - - self.deleted_participants_diff = nr_part_before_diff - len(self.cleanedresults_diff["userID"].unique()) - - return self.cleanedresults_MW, self.cleanedresults_PM, self.cleanedresults_diff - - def codebook(self): - """ - Create a codebook for the data. - """ - return self.codebook \ No newline at end of file diff --git a/cpm/brainexplorer/perceptual.py b/cpm/brainexplorer/perceptual.py index e69de29..b176a86 100644 --- a/cpm/brainexplorer/perceptual.py +++ b/cpm/brainexplorer/perceptual.py @@ -0,0 +1,385 @@ +import pandas as pd +import numpy as np +import warnings +from scipy.stats import zscore + + +class SpaceObserver: + """ + A class to analyze and compute statistics from behavioural data from a metacognition task + in BrainExplorer, Space Observer. Participants have to choose if stimulus has more of one type of alien or the other, + and rate their confidence in their choice. + + Attributes + ---------- + data : pd.DataFrame + The data loaded from the CSV or Excel file. + results : pd.DataFrame + A DataFrame to store the computed metrics. + codebook : dict + A dictionary containing the codebook for the computed metrics. + """ + + def __init__(self, filepath=None): + """ + Load data to be analyised and turn into Pandas DataFrame. + Exlude trials not meeting the criteria. + + Parameters + ---------- + filepath : str + The path to the CSV file to be processed. The column names in the data must subscribe to a prespecified convention, see Notes. + + Example + ---------- + + >>> spaceObserver = SpaceObserver("/example/2025-02-20_SpaceObserver_Data.csv") + >>> results = spaceObserver.metrics() + >>> spaceObserver.clean_data() + >>> results = spaceObserver.results + >>> results.to_csv("/example/spaceObserver_results.csv", index=False) + >>> spaceObserver.codebook() + + Notes + ----- + The columns required in data: + + - **userID: unique identifier for each participant + - **date**: the date and time of the trial + - **run**: number of attempt by participant + - **accuracy**: the accuracy of the trial (1 for correct, 0 for incorrect) + - **choiceRT**: the reaction time of the choice in the trial + - **confidence**: the confidence rating of the trial + - **confidenceRT**: the reaction time of the confidence rating in the trial + - **stimulus_intensity**: the evidence strength of the trial, which is the difference of evidence for each group of stimuli. + + Trial-level exclusion critera: + - Practice trials (run == 1) + - Reaction time < 150 or > 10000 ms + - Reaction time of confidence < 150 or > 10000 ms + - Trials with missing confidence data + """ + + ## read data + self.data = pd.read_csv(filepath, header=0, na_values=["NaN", "nan"]) + + self.data = self.data[ + self.data["run"] == 1 + ] # exclude attempts after the first one + self.data = self.data[ + self.data["choiceRT"] >= 150 + ] # only keep trials with reaction time > 150 ms + self.data = self.data[ + self.data["choiceRT"] <= 10000 + ] # only keep trials with reaction time < 10000 ms + self.data = self.data[ + self.data["confidenceRT"] >= 150 + ] # only keep trials with confidence reaction time > 150 ms + self.data = self.data[ + self.data["confidenceRT"] <= 10000 + ] # only keep trials with confidence reaction time < 10000 ms + + self.data["confidence"] = self.data["confidence"].replace( + ["NaN", "nan", "NAN", ""], pd.NA + ) + self.data = self.data[ + self.data["confidence"].notna() + ] # only keep trials with confidence data because trials without confidence data are practice trials + + self.data["confidence"] = pd.to_numeric( + self.data["confidence"], errors="coerce" + ) + + self.results = pd.DataFrame() + self.codebook = { + "userID": "Unique identifier for each participant", + "n_trials": "Number of trials completed by the participant", + "day_of_week": "Day of the week of the trial", + "time": "Time of the trial", + "time_of_day": "Time of day of the trial (morning, afternoon, evening, night)", + "accuracy": "Mean accuracy (if the choice was the majority choice)", + "mean_RT": "Mean response time across all trials", + "median_RT": "Median response time across all trials", + "median_RT_correct": "Median response time for trials where the participant made the correct choice", + "median_RT_incorrect": "Median response time for trials where the participant made the incorrect", + "diff_median_RT_correct_incorrect": "Difference between the median response time for correct and incorrect choices", + "mean_confidence": "Mean confidence rating across all trials", + "sd_confidence": "Standard deviation of the confidence rating across all trials", + "median_confidence": "Median confidence rating across all trials", + "median_confidence_correct": "Median confidence rating for trials where the participant made the correct choice", + "median_confidence_incorrect": "Median confidence rating for trials where the participant made the incorrect choice", + "diff_median_conf_correct_incorrect": "Difference between the median confidence rating for correct and incorrect choices", + "sd_confidence_correct": "Standard deviation of the confidence rating for trials where the participant made the correct choice", + "sd_confidence_incorrect": "Standard deviation of the confidence rating for trials where the participant made the incorrect choice", + "diff_sd_confidence_correct_incorrect": "Difference between the standard deviation of the confidence rating for correct and incorrect choices", + "mean_confidenceRT": "Mean confidence response time across all trials", + "median_confidenceRT": "Median confidence response time across all trials", + "median_confidenceRT_correct": "Median confidence response time for trials where the participant made the correct choice", + "median_confidenceRT_incorrect": "Median confidence response time for trials where the participant made the incorrect choice", + "diff_median_confidenceRT_correct_incorrect": "Difference between the median confidence response time for correct and incorrect choices", + "confidence_10": "10th percentile of the confidence rating across all trials", + "confidence_25": "25th percentile of the confidence rating across all trials", + "confidence_75": "75th percentile of the confidence rating across all trials", + "confidence_90": "90th percentile of the confidence rating across all trials", + "evidence_strength_mean": "Mean evidence strength across all trials", + "evidence_strength_correct_mean": "Mean evidence strength for trials where the participant made the correct choice", + "evidence_strength_incorrect_mean": "Mean evidence strength for trials where the participant made the incorrect choice", + "diff_evidence_strength_correct_incorrect": "Difference between the mean evidence strength for correct and incorrect choices", + "ES_bins": "Mean evidence strength for each of the bins. The trials here are split into 4 quarters. The evidence strength is divided into four equal bins and the mean is calculated for each bin.", + } + + def metrics(self, mode=None): + """ + + Returns + ------- + results : pd.DataFrame + A DataFrame containing the results of the metrics. + + Variables + --------- + - userID: unique identifier for each participant + - n_trials: number of trials completed by the participant + - day_of_week: day of the week of the trial + - time: time of the trial + - time_of_day: time of day of the trial (morning, afternoon, evening, night) + - accuracy : The mean accuracy across all trials. + - mean_RT : The mean response time across all trials. + - median_RT : The median response time across all trials. + - median_RT_correct : The median response time for trials where the participant made the correct choice. + - median_RT_incorrect : The median response time for trials where the participant made the incorrect choice. + - diff_median_RT_correct_incorrect : The difference between the median response time for correct and incorrect choices. + - mean_confidence : The mean confidence rating across all trials. + - median_confidence : The median confidence rating across all trials. + - median_confidence_correct : The median confidence rating for trials where the participant made the correct choice. + - median_confidence_incorrect : The median confidence rating for trials where the participant made the incorrect choice. + - diff_median_conf_correct_incorrect : The difference between the median confidence rating for correct and incorrect choices. + - sd_confidence : The standard deviation of the confidence rating across all trials. + - sd_confidence_correct : The standard deviation of the confidence rating for trials where the participant made the correct choice. + - sd_confidence_incorrect : The standard deviation of the confidence rating for trials where the participant made the incorrect choice. + - diff_sd_confidence_correct_incorrect : The difference between the standard deviation of the confidence rating for correct and incorrect choices. + - mean_confidenceRT : The mean confidence response time across all trials. + - median_confidenceRT : The median confidence response time across all trials. + - median_confidenceRT_correct : The median confidence response time for trials where the participant made the correct choice. + - median_confidenceRT_incorrect : The median confidence response time for trials where the participant made the incorrect choice. + - diff_median_confidenceRT_correct_incorrect : The difference between the median confidence response time for correct and incorrect choices. + - confidence_10 : The 10th percentile of the confidence rating across all trials. + - confidence_25 : The 25th percentile of the confidence rating across all trials. + - confidence_75 : The 75th percentile of the confidence rating across all trials. + - confidence_90 : The 90th percentile of the confidence rating across all trials. + - evidence_strength_mean : The mean evidence strength across all trials. + - evidence_strength_correct_mean : The mean evidence strength for trials where the participant made the correct choice. + - evidence_strength_incorrect_mean : The mean evidence strength for trials where the participant made the incorrect choice. + - diff_evidence_strength_correct_incorrect : The difference between the mean evidence strength for correct and incorrect choices. + - ES_bins : The mean evidence strength for each of the bins. The trials here are split into 4 quarters. The evidence strength is divided into four equal bins and the mean is calculated for each bin. + """ + + # Group data by subject + grouped_data = self.data.groupby("userID") + + for user_id, user_data in grouped_data: + + user_results = {"userID": user_id} + + user_results["n_trials"] = len(user_data) + + date = user_data["date"].iloc[0] + if isinstance(date, str): + date = pd.to_datetime(date, format="%Y-%m-%d %H:%M:%S.%f") + user_results["date"] = date + user_results["day_of_week"] = date.day_name() + user_results["time"] = ( + date.time() if hasattr(date, "time") else date.strftime("%H:%M:%S.%f") + ) + # morning 6-12, afternoon 12-18, evening 18-24, night 0-6 + user_results["time_of_day"] = ( + "morning" + if date.hour < 12 + else ( + "afternoon" + if date.hour < 18 + else ( + "evening" + if date.hour < 24 + else "night" if date.hour < 6 else "-" + ) + ) + ) + + user_results["accuracy"] = np.nanmean(user_data["accuracy"]) + + user_results["mean_RT"] = np.nanmean(user_data["choiceRT"]) + user_results["median_RT"] = np.nanmedian(user_data["choiceRT"]) + # different RT for correct and incorrect trials + user_results["median_RT_correct"] = np.nanmedian( + user_data["choiceRT"][user_data["accuracy"] == 1] + ) + user_results["median_RT_incorrect"] = np.nanmedian( + user_data["choiceRT"][user_data["accuracy"] != 1] + ) + user_results["diff_median_RT_correct_incorrect"] = ( + user_results["median_RT_correct"] - user_results["median_RT_incorrect"] + ) + + user_results["mean_confidence"] = np.nanmean(user_data["confidence"]) + user_results["sd_confidence"] = np.nanstd(user_data["confidence"]) + user_results["median_confidence"] = np.nanmedian(user_data["confidence"]) + # confidence difference for correct and incorrect trials + user_results["mean_confidence_correct"] = np.nanmean( + user_data["confidence"][user_data["accuracy"] == 1] + ) + user_results["mean_confidence_incorrect"] = np.nanmean( + user_data["confidence"][user_data["accuracy"] != 1] + ) + user_results["diff_median_conf_correct_incorrect"] = ( + user_results["mean_confidence_correct"] + - user_results["mean_confidence_incorrect"] + ) + # standard deviation of confidence for correct and incorrect trials + user_results["sd_confidence_correct"] = np.nanstd( + user_data["confidence"][user_data["accuracy"] == 1] + ) + user_results["sd_confidence_incorrect"] = np.nanstd( + user_data["confidence"][user_data["accuracy"] != 1] + ) + user_results["diff_sd_confidence_correct_incorrect"] = ( + user_results["sd_confidence_correct"] + - user_results["sd_confidence_incorrect"] + ) + + user_results["mean_confidenceRT"] = np.nanmean(user_data["confidenceRT"]) + user_results["median_confidenceRT"] = np.nanmedian( + user_data["confidenceRT"] + ) + # different RT for correct and incorrect trials + user_results["median_confidenceRT_correct"] = np.nanmedian( + user_data["confidenceRT"][user_data["accuracy"] == 1] + ) + user_results["median_confidenceRT_incorrect"] = np.nanmedian( + user_data["confidenceRT"][user_data["accuracy"] != 1] + ) + user_results["diff_median_confidenceRT_correct_incorrect"] = ( + user_results["median_confidenceRT_correct"] + - user_results["median_confidenceRT_incorrect"] + ) + + # calculate confidence percentiles + confidence = user_data["confidence"] + user_results["confidence_10"] = np.nanpercentile(confidence, 10) + user_results["confidence_25"] = np.nanpercentile(confidence, 25) + user_results["confidence_75"] = np.nanpercentile(confidence, 75) + user_results["confidence_90"] = np.nanpercentile(confidence, 90) + + # calculate evidence strength + evidence_strength = user_data["stimulus_intensity"] + + user_results["evidence_strength_mean"] = np.nanmean(evidence_strength) + + # evidence strenth per correct and incorrect trials + user_results["evidence_strength_correct_mean"] = np.nanmean( + evidence_strength[user_data["accuracy"] == 1] + ) + user_results["evidence_strength_incorrect_mean"] = np.nanmean( + evidence_strength[user_data["accuracy"] != 1] + ) + user_results["diff_evidence_strength_correct_incorrect"] = ( + user_results["evidence_strength_correct_mean"] + - user_results["evidence_strength_incorrect_mean"] + ) + + # calcualte evidence strength for each bin + bin_size = 4 + + # Divide evidence strength into 4 equal bins and calculate the mean for each bin + bins = np.array_split(evidence_strength, bin_size) + ES_1 = np.mean(bins[0]) if len(bins[0]) > 0 else np.nan + ES_2 = np.mean(bins[1]) if len(bins[1]) > 0 else np.nan + ES_3 = np.mean(bins[2]) if len(bins[2]) > 0 else np.nan + ES_4 = np.mean(bins[3]) if len(bins[3]) > 0 else np.nan + user_results["ES_bins"] = np.array([ES_1, ES_2, ES_3, ES_4]) + + # median evidence strength + user_results["median_ES"] = np.nanmedian(evidence_strength) + + # Append user-level results + self.results = pd.concat( + [self.results, pd.DataFrame([user_results])], ignore_index=True + ) + + # Convert results to a DataFrame + self.results = pd.DataFrame(self.results) + + return self.results + + def clean_data(self): + """ + Clean aggregated data by applyign participant-level exclusion criteria. + Exclusion criteria: + --------- + - Mean accuracy < 0.5 + - Median reaction time > 3000 ms + - Median reaction time for confidence ratings > 3000 ms + - Median evidence strength > 25 + - Mean confidence <3 or > 97 + - Participants with more than 80 trials (due to technical error) + - Exclude participants if 10th and 25th percentile of confidence is the same AND 75th and 90th percentile of confidence is the same + """ + + nr_part_before = len(self.results["userID"].unique()) + + self.cleanedresults = self.results.copy() + + # exclude participants who have more than 80 trials with run == 1 + users_80trials = ( + self.data.groupby("userID") + .filter(lambda x: len(x) <= 80)["userID"] + .unique() + ) + self.cleanedresults = self.cleanedresults[ + self.cleanedresults["userID"].isin(users_80trials) + ] + + self.cleanedresults = self.cleanedresults[ + self.cleanedresults["accuracy"] >= 0.5 + ] # only keep participants with mean accuracy >= 50% + self.cleanedresults = self.cleanedresults[ + self.cleanedresults["median_RT"] <= 3000 + ] # only keep participants with median reaction time <= 3000 ms + self.cleanedresults = self.cleanedresults[ + self.cleanedresults["median_confidenceRT"] <= 3000 + ] # only keep participants with median confidence reaction time <= 3000 ms + self.cleanedresults = self.cleanedresults[ + self.cleanedresults["median_ES"] <= 25 + ] # only keep participants with median evidence strength <= 25 + self.cleanedresults = self.cleanedresults[ + self.cleanedresults["median_confidence"] <= 97 + ] # only keep participants with mean confidence <= 97 + self.cleanedresults = self.cleanedresults[ + self.cleanedresults["median_confidence"] >= 3 + ] # only keep participants with mean confidence >= 3 + + # exclude if 10th and 25th percentile of confidence is the same AND 75th and 90th percentile of confidence is the same + self.cleanedresults = self.cleanedresults[ + ~( + ( + self.cleanedresults["confidence_10"] + == self.cleanedresults["confidence_25"] + ) + & ( + self.cleanedresults["confidence_75"] + == self.cleanedresults["confidence_90"] + ) + ) + ] + + self.deleted_participants = nr_part_before - len( + self.cleanedresults["userID"].unique() + ) + + return self.cleanedresults + + def codebook(self): + """ + Return a codebook describing the metrics. + """ + return self.codebook diff --git a/cpm/brainexplorer/scavenger.py b/cpm/brainexplorer/risky_decision_making.py similarity index 59% rename from cpm/brainexplorer/scavenger.py rename to cpm/brainexplorer/risky_decision_making.py index 4a40792..fbae418 100644 --- a/cpm/brainexplorer/scavenger.py +++ b/cpm/brainexplorer/risky_decision_making.py @@ -60,79 +60,82 @@ def __init__(self, filepath=None): """ self.data = pd.read_csv(filepath, header=0) - self.data = self.data[self.data["run"] == 1] # only keep first attempt - self.data = self.data[self.data["RT"] >= 150] # only keep trials with reaction time > 150 ms - self.data = self.data[self.data["RT"] <= 10000] # only keep trials with reaction time < 10000 ms + self.data = self.data[self.data["run"] == 1] # only keep first attempt + self.data = self.data[ + self.data["RT"] >= 150 + ] # only keep trials with reaction time > 150 ms + self.data = self.data[ + self.data["RT"] <= 10000 + ] # only keep trials with reaction time < 10000 ms self.results = pd.DataFrame() self.group_results = pd.DataFrame() self.codebook = { - "userID": "Unique identifier for each participant", - "n_trials": "Number of trials completed by the participant", - "day_of_week": "Day of the week of the trial", - "time": "Time of the trial", - "time_of_day": "Time of day of the trial (morning, afternoon, evening, night)", - "mean_RT": "Mean reaction time", - "median_RT": "Median reaction time", - "median_RT_safe": "Median reaction time for safe choices", - "median_RT_risky": "Median reaction time for risky choices", - "median_RT_ambg": "Median reaction time for ambiguous trials", - "median_RT_NoAmbg":"Median reaction time for non-ambiguous trials", - "median_RT_safe_ambg": "Median reaction time for safe choices in ambiguous trials", - "median_RT_safe_NoAmbg": "Median reaction time for safe choices in non-ambiguous trials", - "median_RT_risky_ambg": "Median reaction time for risky choices in ambiguous trials", - "median_RT_risky_NoAmbg": "Median reaction time for risky choices in non-ambiguous trials", - "mean_points": "Mean points received across all trials", - "mean_points_safe": "Mean points received for safe choices", - "mean_points_risky": "Mean points received for risky choices", - "mean_points_ambg": "Mean points received in ambiguous trials", - "mean_points_NoAmbg": "Mean points received in non-ambiguous", - "mean_points_safe_ambg": "Mean points received for safe choices in ambiguous trials", - "mean_points_safe_NoAmbg": "Mean points received for safe choices in non-ambiguous trials", - "mean_points_risky_ambg": "Mean points received for risky choices in ambiguous trials", - "mean_points_risky_NoAmbg": "Mean points received for risky choices in non-ambiguous", - "risky_choices": "Proportion of risky choices overall", - "win_risk": "Proportion of risky wins", - "loss_risk": "Proportion of risky losses", - "win_risk_abg": "Proportion of risky wins in ambiguous trials", - "win_risk_NoAbg": "Proportion of risky wins in non-ambiguous trials", - "loss_risk_abg": "Proportion of risky losses in ambiguous trials", - "loss_risk_NoAbg": "Proportion of risky losses in non-ambiguous trials", - "diff_loss_win_risk": "Difference between proportion of risky losses and wins", - "diff_Ambg_NoAmbg_risk": "Difference between proportion of risky choices in ambiguous and non-ambiguous trials", - "diff_Ambg_NoAmbg_win_risk": "Difference between proportion of risky wins in ambiguous and non-ambiguous trials", - "diff_Ambg_NoAmbg_loss_risk": "Difference between proportion of risky losses in ambiguous and non-ambiguous trials", - "rational_all": "Proportion of rational choices", - "rational_win": "Proportion of rational choices in wins", - "rational_loss": "Proportion of rational choices in losses", - "rational_all_abg": "Proportion of rational choices in ambiguous trials", - "rational_win_abg": "Proportion of rational choices in wins in ambiguous trials", - "rational_loss_abg": "Proportion of rational choices in losses in ambiguous trials", - "rational_all_NoAbg": "Proportion of rational choices in non-ambiguous trials", - "diff_NoAmbg_win_loss_rational": "Difference between proportion of rational choices in wins and losses in non-ambiguous trials", - "rational_win_NoAbg": "Proportion of rational choices in wins in non-ambiguous trials", - "rational_loss_NoAbg": "Proportion of rational choices in losses in non-ambiguous trials", - "diff_Ambg_NoAmbg_rational": "Difference between proportion of rational choices in ambiguous and non-ambiguous trials", - "diff_Ambg_NoAmbg_win_rational": "Difference between proportion of rational choices in wins in ambiguous and non-ambiguous trials", - "diff_Ambg_NoAmbg_loss_rational": "Difference between proportion of rational choices in losses in ambiguous and non-ambiguous trials", - "nb_incorrect_gain": "Number of incorrect choices in gain trials", - "above_chance": "Proportion of trials with choices above chance level", - "chosen_prop_LeftRight": "Proportion of trials where option A (safe) was chosen", - "chosen_prop_SafeRisky": "Proportion of trials where the safe option was chosen" + "userID": "Unique identifier for each participant", + "n_trials": "Number of trials completed by the participant", + "day_of_week": "Day of the week of the trial", + "time": "Time of the trial", + "time_of_day": "Time of day of the trial (morning, afternoon, evening, night)", + "mean_RT": "Mean reaction time", + "median_RT": "Median reaction time", + "median_RT_safe": "Median reaction time for safe choices", + "median_RT_risky": "Median reaction time for risky choices", + "median_RT_ambg": "Median reaction time for ambiguous trials", + "median_RT_NoAmbg": "Median reaction time for non-ambiguous trials", + "median_RT_safe_ambg": "Median reaction time for safe choices in ambiguous trials", + "median_RT_safe_NoAmbg": "Median reaction time for safe choices in non-ambiguous trials", + "median_RT_risky_ambg": "Median reaction time for risky choices in ambiguous trials", + "median_RT_risky_NoAmbg": "Median reaction time for risky choices in non-ambiguous trials", + "mean_points": "Mean points received across all trials", + "mean_points_safe": "Mean points received for safe choices", + "mean_points_risky": "Mean points received for risky choices", + "mean_points_ambg": "Mean points received in ambiguous trials", + "mean_points_NoAmbg": "Mean points received in non-ambiguous", + "mean_points_safe_ambg": "Mean points received for safe choices in ambiguous trials", + "mean_points_safe_NoAmbg": "Mean points received for safe choices in non-ambiguous trials", + "mean_points_risky_ambg": "Mean points received for risky choices in ambiguous trials", + "mean_points_risky_NoAmbg": "Mean points received for risky choices in non-ambiguous", + "risky_choices": "Proportion of risky choices overall", + "win_risk": "Proportion of risky wins", + "loss_risk": "Proportion of risky losses", + "win_risk_abg": "Proportion of risky wins in ambiguous trials", + "win_risk_NoAbg": "Proportion of risky wins in non-ambiguous trials", + "loss_risk_abg": "Proportion of risky losses in ambiguous trials", + "loss_risk_NoAbg": "Proportion of risky losses in non-ambiguous trials", + "diff_loss_win_risk": "Difference between proportion of risky losses and wins", + "diff_Ambg_NoAmbg_risk": "Difference between proportion of risky choices in ambiguous and non-ambiguous trials", + "diff_Ambg_NoAmbg_win_risk": "Difference between proportion of risky wins in ambiguous and non-ambiguous trials", + "diff_Ambg_NoAmbg_loss_risk": "Difference between proportion of risky losses in ambiguous and non-ambiguous trials", + "rational_all": "Proportion of rational choices", + "rational_win": "Proportion of rational choices in wins", + "rational_loss": "Proportion of rational choices in losses", + "rational_all_abg": "Proportion of rational choices in ambiguous trials", + "rational_win_abg": "Proportion of rational choices in wins in ambiguous trials", + "rational_loss_abg": "Proportion of rational choices in losses in ambiguous trials", + "rational_all_NoAbg": "Proportion of rational choices in non-ambiguous trials", + "diff_NoAmbg_win_loss_rational": "Difference between proportion of rational choices in wins and losses in non-ambiguous trials", + "rational_win_NoAbg": "Proportion of rational choices in wins in non-ambiguous trials", + "rational_loss_NoAbg": "Proportion of rational choices in losses in non-ambiguous trials", + "diff_Ambg_NoAmbg_rational": "Difference between proportion of rational choices in ambiguous and non-ambiguous trials", + "diff_Ambg_NoAmbg_win_rational": "Difference between proportion of rational choices in wins in ambiguous and non-ambiguous trials", + "diff_Ambg_NoAmbg_loss_rational": "Difference between proportion of rational choices in losses in ambiguous and non-ambiguous trials", + "nb_incorrect_gain": "Number of incorrect choices in gain trials", + "above_chance": "Proportion of trials with choices above chance level", + "chosen_prop_LeftRight": "Proportion of trials where option A (safe) was chosen", + "chosen_prop_SafeRisky": "Proportion of trials where the safe option was chosen", } def metrics(self): - """ - Compute metrics from the loaded data: expected values of rewards when choosing safe vs. risky options, percentages of - risky and safe wins/losses as well as total rational choices. + Compute metrics from the loaded data: expected values of rewards when choosing safe vs. risky options, percentages of + risky and safe wins/losses as well as total rational choices. Parameters ---------- data : pd.DataFrame Data from all users and trials to be processed. - Returns + Returns ------- results : pd.DataFrame A DataFrame containing the computed metrics. @@ -192,15 +195,15 @@ def metrics(self): - chosen_prop_LeftRight: Proportion of trials where option A (safe) was chosen, - chosen_prop_SafeRisky: Proportion of trials where the safe option was chosen - + Notes ----- - In the game users can choose between two options with uncertain reward or loss probabilities. They reiceve information about reward probabilities for two choise options and can choose either the safe or risky option. - The data is split into two categories and options, A and B. Option A is choosing the safe option, i.e. the one which has more reward, which is labeled with 1 in "chosen" and Option B is choosing the risky option, labeled with 2 in "chosen". + In the game users can choose between two options with uncertain reward or loss probabilities. They reiceve information about reward probabilities for two choise options and can choose either the safe or risky option. + The data is split into two categories and options, A and B. Option A is choosing the safe option, i.e. the one which has more reward, which is labeled with 1 in "chosen" and Option B is choosing the risky option, labeled with 2 in "chosen". Risk values are transfromed to 0 (no risk) and 1 (risky). - - We differentiate between the percentages of winning reward visible to the users in the task vs. actual percentages. + + We differentiate between the percentages of winning reward visible to the users in the task vs. actual percentages. A_perc is the percent visible to the user for the safe option A, B_perc is the percent visible to the user for risky option B. A_magn is the trial reward or loss values for option A, B_magn is the trial reward or loss values for option B. @@ -209,7 +212,7 @@ def metrics(self): We calculate the expected value of the safe option A and the risky option B and then use these values to calculate the loss trials in which expected values are < 0. Percentages of loss and win trials are calculated for risky choice trials (by objetive risk, option B) and number of rational choices are calculated for all trials, win trials and loss trials. - + References ---------- @@ -229,7 +232,6 @@ def metrics(self): ev_diff_chosen = np.array(user_data["EV_diff_chosen"], dtype=object) rsk = np.array(user_data["chosen"]) t_loss = np.array(user_data["safe_EV"] < 0) - user_results = {"userID": user_id} @@ -239,17 +241,24 @@ def metrics(self): if isinstance(date, str): date = pd.to_datetime(date, format="%Y-%m-%d %H:%M:%S.%f") user_results["day_of_week"] = date.day_name() - user_results["time"] = date.time() if hasattr(date, 'time') else date.strftime("%H:%M:%S.%f") + user_results["time"] = ( + date.time() if hasattr(date, "time") else date.strftime("%H:%M:%S.%f") + ) # morning 6-12, afternoon 12-18, evening 18-24, night 0-6 user_results["time_of_day"] = ( - "morning" if date.hour < 12 else - "afternoon" if date.hour < 18 else - "evening" if date.hour < 24 else - "night" if date.hour < 6 else - "-" + "morning" + if date.hour < 12 + else ( + "afternoon" + if date.hour < 18 + else ( + "evening" + if date.hour < 24 + else "night" if date.hour < 6 else "-" + ) + ) ) - user_results["mean_RT"] = np.nanmean(user_data["RT"]) user_results["median_RT"] = np.nanmedian(user_data["RT"]) # mediean RT for each condition @@ -259,87 +268,133 @@ def metrics(self): user_results["median_RT_loss"] = np.nanmedian(user_data[t_loss]["RT"]) user_results["median_RT_ambg"] = np.nanmedian(user_data[t_abg]["RT"]) user_results["median_RT_NoAmbg"] = np.nanmedian(user_data[~t_abg]["RT"]) - user_results["median_RT_win_ambg"] = np.nanmedian(user_data[~t_loss & t_abg]["RT"]) - user_results["median_RT_win_NoAmbg"] = np.nanmedian(user_data[~t_loss & ~t_abg]["RT"]) - user_results["median_RT_loss_ambg"] = np.nanmedian(user_data[t_loss & t_abg]["RT"]) - user_results["median_RT_loss_NoAmbg"] = np.nanmedian(user_data[t_loss & ~t_abg]["RT"]) + user_results["median_RT_win_ambg"] = np.nanmedian( + user_data[~t_loss & t_abg]["RT"] + ) + user_results["median_RT_win_NoAmbg"] = np.nanmedian( + user_data[~t_loss & ~t_abg]["RT"] + ) + user_results["median_RT_loss_ambg"] = np.nanmedian( + user_data[t_loss & t_abg]["RT"] + ) + user_results["median_RT_loss_NoAmbg"] = np.nanmedian( + user_data[t_loss & ~t_abg]["RT"] + ) user_results["diff_RT_win_amg_win_noAmbg"] = ( - user_results["median_RT_win_ambg"] - user_results["median_RT_win_NoAmbg"] + user_results["median_RT_win_ambg"] + - user_results["median_RT_win_NoAmbg"] ) user_results["diff_RT_loss_amg_loss_noAmbg"] = ( - user_results["median_RT_loss_ambg"] - user_results["median_RT_loss_NoAmbg"] + user_results["median_RT_loss_ambg"] + - user_results["median_RT_loss_NoAmbg"] ) user_results["diff_RT_win_loss_ambg"] = ( user_results["median_RT_win_ambg"] - user_results["median_RT_loss_ambg"] ) user_results["diff_RT_win_loss_noAmbg"] = ( - user_results["median_RT_win_NoAmbg"] - user_results["median_RT_loss_NoAmbg"] + user_results["median_RT_win_NoAmbg"] + - user_results["median_RT_loss_NoAmbg"] ) user_results["diff_RT_win_ambg_loss_noAmbg"] = ( - user_results["median_RT_win_ambg"] - user_results["median_RT_loss_NoAmbg"] + user_results["median_RT_win_ambg"] + - user_results["median_RT_loss_NoAmbg"] ) user_results["diff_RT_loss_ambg_win_noAmbg"] = ( - user_results["median_RT_loss_ambg"] - user_results["median_RT_win_NoAmbg"] + user_results["median_RT_loss_ambg"] + - user_results["median_RT_win_NoAmbg"] ) user_results["total_rewards"] = np.sum(user_data["outcome"]) user_results["mean_points"] = np.nanmean(user_data["outcome"]) # mean points for risky and safe choices - user_results["mean_points_safe"] = np.nanmean(user_data[rsk == 0]["outcome"]) - user_results["mean_points_risky"] = np.nanmean(user_data[rsk == 1]["outcome"]) + user_results["mean_points_safe"] = np.nanmean( + user_data[rsk == 0]["outcome"] + ) + user_results["mean_points_risky"] = np.nanmean( + user_data[rsk == 1]["outcome"] + ) # mean points for ambigous and non-ambiguous trials user_results["mean_points_ambg"] = np.nanmean(user_data[t_abg]["outcome"]) - user_results["mean_points_NoAmbg"] = np.nanmean(user_data[~t_abg]["outcome"]) + user_results["mean_points_NoAmbg"] = np.nanmean( + user_data[~t_abg]["outcome"] + ) # mean points for win and loss trials in ambigous and non-ambiguous trials - user_results["mean_points_win_ambg"] = np.nanmean(user_data[~t_loss & t_abg]["outcome"]) - user_results["mean_points_win_NoAmbg"] = np.nanmean(user_data[~t_loss & ~t_abg]["outcome"]) - user_results["mean_points_loss_ambg"] = np.nanmean(user_data[t_loss & t_abg]["outcome"]) - user_results["mean_points_loss_NoAmbg"] = np.nanmean(user_data[t_loss & ~t_abg]["outcome"]) + user_results["mean_points_win_ambg"] = np.nanmean( + user_data[~t_loss & t_abg]["outcome"] + ) + user_results["mean_points_win_NoAmbg"] = np.nanmean( + user_data[~t_loss & ~t_abg]["outcome"] + ) + user_results["mean_points_loss_ambg"] = np.nanmean( + user_data[t_loss & t_abg]["outcome"] + ) + user_results["mean_points_loss_NoAmbg"] = np.nanmean( + user_data[t_loss & ~t_abg]["outcome"] + ) user_results["diff_points_win_amg_win_noAmbg"] = ( - user_results["mean_points_win_ambg"] - user_results["mean_points_win_NoAmbg"] + user_results["mean_points_win_ambg"] + - user_results["mean_points_win_NoAmbg"] ) user_results["diff_points_loss_amg_loss_noAmbg"] = ( - user_results["mean_points_loss_ambg"] - user_results["mean_points_loss_NoAmbg"] + user_results["mean_points_loss_ambg"] + - user_results["mean_points_loss_NoAmbg"] ) user_results["diff_points_win_loss_ambg"] = ( - user_results["mean_points_win_ambg"] - user_results["mean_points_loss_ambg"] + user_results["mean_points_win_ambg"] + - user_results["mean_points_loss_ambg"] ) user_results["diff_points_win_loss_noAmbg"] = ( - user_results["mean_points_win_NoAmbg"] - user_results["mean_points_loss_NoAmbg"] + user_results["mean_points_win_NoAmbg"] + - user_results["mean_points_loss_NoAmbg"] ) user_results["diff_points_win_ambg_loss_noAmbg"] = ( - user_results["mean_points_win_ambg"] - user_results["mean_points_loss_NoAmbg"] + user_results["mean_points_win_ambg"] + - user_results["mean_points_loss_NoAmbg"] ) user_results["diff_points_loss_ambg_win_noAmbg"] = ( - user_results["mean_points_loss_ambg"] - user_results["mean_points_win_NoAmbg"] - ) - + user_results["mean_points_loss_ambg"] + - user_results["mean_points_win_NoAmbg"] + ) - # choice stickiness - user_results["choice_stickiness"] = np.sum(user_data["chosen"].iloc[i] == 1 and user_data["chosen"].iloc[i + 1] == 1 - for i in range(len(user_data["chosen"]) - 1) - ) + # choice stickiness + user_results["choice_stickiness"] = np.sum( + user_data["chosen"].iloc[i] == 1 + and user_data["chosen"].iloc[i + 1] == 1 + for i in range(len(user_data["chosen"]) - 1) + ) # Number of risky choices user_results["risky_choices"] = np.nanmean(rsk == 1) user_results["win_risk"] = np.sum(rsk[~t_loss]) / np.sum(~t_loss) user_results["loss_risk"] = np.sum(rsk[t_loss]) / np.sum(t_loss) - user_results["diff_loss_win_risk"] = user_results["loss_risk"] - user_results["win_risk"] + user_results["diff_loss_win_risk"] = ( + user_results["loss_risk"] - user_results["win_risk"] + ) # Number of risky choices in ambiguous and non-ambiguous trials user_results["risk_Ambg"] = np.sum(rsk[t_abg]) / np.sum(t_abg) user_results["risk_NoAmbg"] = np.sum(rsk[~t_abg]) / np.sum(~t_abg) - user_results["diff_Ambg_NoAmbg_risk"] = user_results["risk_Ambg"] - user_results["risk_NoAmbg"] + user_results["diff_Ambg_NoAmbg_risk"] = ( + user_results["risk_Ambg"] - user_results["risk_NoAmbg"] + ) # Number of risky choices in ambiguous and non-ambiguous trials for wins and losses - user_results["win_risk_abg"] = np.sum(rsk[~t_loss & t_abg]) / np.sum(~t_loss & t_abg) - user_results["win_risk_NoAbg"] = np.sum(rsk[~t_loss & ~t_abg]) / np.sum(~t_loss & ~t_abg) - user_results["loss_risk_abg"] = np.sum(rsk[t_loss & t_abg]) / np.sum(t_loss & t_abg) - user_results["loss_risk_NoAbg"] = np.sum(rsk[t_loss & ~t_abg]) / np.sum(t_loss & ~t_abg) + user_results["win_risk_abg"] = np.sum(rsk[~t_loss & t_abg]) / np.sum( + ~t_loss & t_abg + ) + user_results["win_risk_NoAbg"] = np.sum(rsk[~t_loss & ~t_abg]) / np.sum( + ~t_loss & ~t_abg + ) + user_results["loss_risk_abg"] = np.sum(rsk[t_loss & t_abg]) / np.sum( + t_loss & t_abg + ) + user_results["loss_risk_NoAbg"] = np.sum(rsk[t_loss & ~t_abg]) / np.sum( + t_loss & ~t_abg + ) user_results["diff_Ambg_NoAmbg_win_risk"] = ( user_results["win_risk_abg"] - user_results["win_risk_NoAbg"] ) @@ -357,30 +412,46 @@ def metrics(self): ) user_results["diff_amg_loss_nonAmg_win_risk"] = ( user_results["loss_risk_abg"] - user_results["win_risk_NoAbg"] - ) + ) # Number of rational choices user_results["rational_all"] = np.nanmean(ev_diff_chosen >= 0) # rational choices in win and loss trials - user_results["rational_win"] = np.sum(ev_diff_chosen[~t_loss] >= 0) / np.sum(~t_loss) - user_results["rational_loss"] = np.sum(ev_diff_chosen[t_loss] >= 0) / np.sum(t_loss) + user_results["rational_win"] = np.sum( + ev_diff_chosen[~t_loss] >= 0 + ) / np.sum(~t_loss) + user_results["rational_loss"] = np.sum( + ev_diff_chosen[t_loss] >= 0 + ) / np.sum(t_loss) user_results["diff_loss_win_rational"] = ( user_results["rational_loss"] - user_results["rational_win"] ) # rational choices in ambiguous and non-ambiguous trials - user_results["rational_abg"] = np.sum(ev_diff_chosen[t_abg] >= 0) / np.sum(t_abg) - user_results["rational_NoAbg"] = np.sum(ev_diff_chosen[~t_abg] >= 0) / np.sum(~t_abg) + user_results["rational_abg"] = np.sum(ev_diff_chosen[t_abg] >= 0) / np.sum( + t_abg + ) + user_results["rational_NoAbg"] = np.sum( + ev_diff_chosen[~t_abg] >= 0 + ) / np.sum(~t_abg) user_results["diff_Ambg_NoAmbg_rational"] = ( user_results["rational_abg"] - user_results["rational_NoAbg"] ) - + # rational choices in ambiguous and non-ambiguous trials for wins and losses - user_results["rational_win_abg"] = np.sum(ev_diff_chosen[~t_loss & t_abg] >= 0) / np.sum(~t_loss & t_abg) - user_results["rational_win_NoAbg"] = np.sum(ev_diff_chosen[~t_loss & ~t_abg] >= 0) / np.sum(~t_loss & ~t_abg) - user_results["rational_loss_abg"] = np.sum(ev_diff_chosen[t_loss & t_abg] >= 0) / np.sum(t_loss & t_abg) - user_results["rational_loss_NoAbg"] = np.sum(ev_diff_chosen[t_loss & ~t_abg] >= 0) / np.sum(t_loss & ~t_abg) + user_results["rational_win_abg"] = np.sum( + ev_diff_chosen[~t_loss & t_abg] >= 0 + ) / np.sum(~t_loss & t_abg) + user_results["rational_win_NoAbg"] = np.sum( + ev_diff_chosen[~t_loss & ~t_abg] >= 0 + ) / np.sum(~t_loss & ~t_abg) + user_results["rational_loss_abg"] = np.sum( + ev_diff_chosen[t_loss & t_abg] >= 0 + ) / np.sum(t_loss & t_abg) + user_results["rational_loss_NoAbg"] = np.sum( + ev_diff_chosen[t_loss & ~t_abg] >= 0 + ) / np.sum(t_loss & ~t_abg) user_results["diff_rational_Ambg_NoAmbg_win"] = ( user_results["rational_win_abg"] - user_results["rational_win_NoAbg"] ) @@ -401,17 +472,29 @@ def metrics(self): ) # rational choices in safe and risky trials - user_results["rational_safe"] = np.sum(ev_diff_chosen[rsk == 0] >= 0) / np.sum(rsk == 0) - user_results["rational_risky"] = np.sum(ev_diff_chosen[rsk == 1] >= 0) / np.sum(rsk == 1) + user_results["rational_safe"] = np.sum( + ev_diff_chosen[rsk == 0] >= 0 + ) / np.sum(rsk == 0) + user_results["rational_risky"] = np.sum( + ev_diff_chosen[rsk == 1] >= 0 + ) / np.sum(rsk == 1) user_results["diff_rational_safe_risky"] = ( user_results["rational_safe"] - user_results["rational_risky"] ) # rational choices in safe and risky trials for wins and losses - user_results["rational_safe_win"] = np.sum(ev_diff_chosen[~t_loss & rsk == 0] >= 0) / np.sum(~t_loss & rsk == 0) - user_results["rational_safe_loss"] = np.sum(ev_diff_chosen[t_loss & rsk == 0] >= 0) / np.sum(t_loss & rsk == 0) - user_results["rational_risky_win"] = np.sum(ev_diff_chosen[~t_loss & rsk == 1] >= 0) / np.sum(~t_loss & rsk == 1) - user_results["rational_risky_loss"] = np.sum(ev_diff_chosen[t_loss & rsk == 1] >= 0) / np.sum(t_loss & rsk == 1) + user_results["rational_safe_win"] = np.sum( + ev_diff_chosen[~t_loss & rsk == 0] >= 0 + ) / np.sum(~t_loss & rsk == 0) + user_results["rational_safe_loss"] = np.sum( + ev_diff_chosen[t_loss & rsk == 0] >= 0 + ) / np.sum(t_loss & rsk == 0) + user_results["rational_risky_win"] = np.sum( + ev_diff_chosen[~t_loss & rsk == 1] >= 0 + ) / np.sum(~t_loss & rsk == 1) + user_results["rational_risky_loss"] = np.sum( + ev_diff_chosen[t_loss & rsk == 1] >= 0 + ) / np.sum(t_loss & rsk == 1) user_results["diff_rational_safe_win_risky_win"] = ( user_results["rational_safe_win"] - user_results["rational_risky_win"] ) @@ -426,26 +509,38 @@ def metrics(self): ) # rational choices in risky and safe trials for ambiguous and non-ambiguous trials - user_results["rational_safe_abg"] = np.sum(ev_diff_chosen[t_abg & rsk == 0] >= 0) / np.sum(t_abg & rsk == 0) - user_results["rational_safe_NoAbg"] = np.sum(ev_diff_chosen[~t_abg & rsk == 0] >= 0) / np.sum(~t_abg & rsk == 0) - user_results["rational_risky_abg"] = np.sum(ev_diff_chosen[~t_abg & rsk == 1] >= 0) / np.sum(~t_abg & rsk == 1) - user_results["rational_risky_NoAbg"] = np.sum(ev_diff_chosen[t_abg & rsk == 1] >= 0) / np.sum(t_abg & rsk == 1) + user_results["rational_safe_abg"] = np.sum( + ev_diff_chosen[t_abg & rsk == 0] >= 0 + ) / np.sum(t_abg & rsk == 0) + user_results["rational_safe_NoAbg"] = np.sum( + ev_diff_chosen[~t_abg & rsk == 0] >= 0 + ) / np.sum(~t_abg & rsk == 0) + user_results["rational_risky_abg"] = np.sum( + ev_diff_chosen[~t_abg & rsk == 1] >= 0 + ) / np.sum(~t_abg & rsk == 1) + user_results["rational_risky_NoAbg"] = np.sum( + ev_diff_chosen[t_abg & rsk == 1] >= 0 + ) / np.sum(t_abg & rsk == 1) user_results["diff_rational_safe_Ambg_NoAmbg"] = ( user_results["rational_safe_abg"] - user_results["rational_safe_NoAbg"] ) user_results["diff_rational_risky_Ambg_NoAmbg"] = ( - user_results["rational_risky_abg"] - user_results["rational_risky_NoAbg"] + user_results["rational_risky_abg"] + - user_results["rational_risky_NoAbg"] ) user_results["diff_rational_safe_risky_Ambg"] = ( user_results["rational_safe_abg"] - user_results["rational_risky_abg"] ) user_results["diff_rational_safe_risky_NoAmbg"] = ( - user_results["rational_safe_NoAbg"] - user_results["rational_risky_NoAbg"] + user_results["rational_safe_NoAbg"] + - user_results["rational_risky_NoAbg"] ) # No Brainer Trial Choices # count how man wrong choices were made in catch trials - user_results["nb_incorrect_gain"] = user_data[user_data["nbcorrect_gain"] == 0].shape[0] + user_results["nb_incorrect_gain"] = user_data[ + user_data["nbcorrect_gain"] == 0 + ].shape[0] # proportion above chance level user_results["above_chance"] = np.nanmean(abovechance) @@ -484,18 +579,30 @@ def clean_data(self): user_id_counts = self.data["userID"].value_counts() # find user IDs with more than 40 trials user_ids_to_keep = user_id_counts[user_id_counts <= 40].index - self.cleanedresults = self.cleanedresults[self.cleanedresults["userID"].isin(user_ids_to_keep)] + self.cleanedresults = self.cleanedresults[ + self.cleanedresults["userID"].isin(user_ids_to_keep) + ] # find user IDs with specific configID to exclude - user_ids_to_exclude = ( - self.data.loc[self.data["config_id"] == 242, "userID"].unique() - ) - user_ids_to_keep = [uid for uid in self.data["userID"].unique() if uid not in user_ids_to_exclude] - self.cleanedresults = self.cleanedresults[self.cleanedresults["userID"].isin(user_ids_to_keep)] - - self.cleanedresults = self.cleanedresults[self.cleanedresults["nb_incorrect_gain"] < 2] # exclude participants who failed 2 or more catch trials - - self.deleted_participants = nr_part_before - len(self.cleanedresults["userID"].unique()) + user_ids_to_exclude = self.data.loc[ + self.data["config_id"] == 242, "userID" + ].unique() + user_ids_to_keep = [ + uid + for uid in self.data["userID"].unique() + if uid not in user_ids_to_exclude + ] + self.cleanedresults = self.cleanedresults[ + self.cleanedresults["userID"].isin(user_ids_to_keep) + ] + + self.cleanedresults = self.cleanedresults[ + self.cleanedresults["nb_incorrect_gain"] < 2 + ] # exclude participants who failed 2 or more catch trials + + self.deleted_participants = nr_part_before - len( + self.cleanedresults["userID"].unique() + ) return self.cleanedresults @@ -503,4 +610,4 @@ def codebook(self): """ Return a codebook describing the metrics. """ - return self.codebook \ No newline at end of file + return self.codebook diff --git a/cpm/brainexplorer/riskychoices.py b/cpm/brainexplorer/riskychoices.py deleted file mode 100644 index e69de29..0000000 diff --git a/cpm/brainexplorer/spaceObserver.py b/cpm/brainexplorer/spaceObserver.py deleted file mode 100644 index b94ef2e..0000000 --- a/cpm/brainexplorer/spaceObserver.py +++ /dev/null @@ -1,309 +0,0 @@ -import pandas as pd -import numpy as np -import warnings -from scipy.stats import zscore - -class spaceObserver: - """ - A class to analyze and compute statistics from behavioural data from a metacognition task - in BrainExplorer, Space Observer. Participants have to choose if stimulus has more of one type of alien or the other, - and rate their confidence in their choice. - - Attributes - ---------- - data : pd.DataFrame - The data loaded from the CSV or Excel file. - results : pd.DataFrame - A DataFrame to store the computed metrics. - codebook : dict - A dictionary containing the codebook for the computed metrics. - """ - - def __init__(self, filepath=None): - """ - Load data to be analyised and turn into Pandas DataFrame. - Exlude trials not meeting the criteria. - - Parameters - ---------- - filepath : str - The path to the CSV file to be processed. The column names in the data must subscribe to a prespecified convention, see Notes. - - Example - ---------- - - >>> spaceObserver = SpaceObserver("/example/2025-02-20_SpaceObserver_Data.csv") - >>> results = spaceObserver.metrics() - >>> spaceObserver.clean_data() - >>> results = spaceObserver.results - >>> results.to_csv("/example/spaceObserver_results.csv", index=False) - >>> spaceObserver.codebook() - - Notes - ----- - The columns required in data: - - - **userID: unique identifier for each participant - - **date**: the date and time of the trial - - **run**: number of attempt by participant - - **accuracy**: the accuracy of the trial (1 for correct, 0 for incorrect) - - **choiceRT**: the reaction time of the choice in the trial - - **confidence**: the confidence rating of the trial - - **confidenceRT**: the reaction time of the confidence rating in the trial - - **stimulus_intensity**: the evidence strength of the trial, which is the difference of evidence for each group of stimuli. - - Trial-level exclusion critera: - - Practice trials (run == 1) - - Reaction time < 150 or > 10000 ms - - Reaction time of confidence < 150 or > 10000 ms - - Trials with missing confidence data - """ - - ## read data - self.data = pd.read_csv(filepath, header=0, na_values=["NaN", "nan"]) - - self.data = self.data[self.data["run"] == 1] # exclude attempts after the first one - self.data = self.data[self.data["choiceRT"] >= 150] # only keep trials with reaction time > 150 ms - self.data = self.data[self.data["choiceRT"] <= 10000] # only keep trials with reaction time < 10000 ms - self.data = self.data[self.data["confidenceRT"] >= 150] # only keep trials with confidence reaction time > 150 ms - self.data = self.data[self.data["confidenceRT"] <= 10000] # only keep trials with confidence reaction time < 10000 ms - - self.data["confidence"] = self.data["confidence"].replace( - ["NaN", "nan", "NAN", ""], pd.NA - ) - self.data = self.data[self.data["confidence"].notna()] # only keep trials with confidence data because trials without confidence data are practice trials - - self.data["confidence"] = pd.to_numeric(self.data["confidence"], errors='coerce') - - self.results = pd.DataFrame() - self.codebook = { - "userID": "Unique identifier for each participant", - "n_trials": "Number of trials completed by the participant", - "day_of_week": "Day of the week of the trial", - "time": "Time of the trial", - "time_of_day": "Time of day of the trial (morning, afternoon, evening, night)", - "accuracy": "Mean accuracy (if the choice was the majority choice)", - "mean_RT": "Mean response time across all trials", - "median_RT": "Median response time across all trials", - "median_RT_correct": "Median response time for trials where the participant made the correct choice", - "median_RT_incorrect": "Median response time for trials where the participant made the incorrect", - "diff_median_RT_correct_incorrect": "Difference between the median response time for correct and incorrect choices", - "mean_confidence": "Mean confidence rating across all trials", - "sd_confidence": "Standard deviation of the confidence rating across all trials", - "median_confidence": "Median confidence rating across all trials", - "median_confidence_correct": "Median confidence rating for trials where the participant made the correct choice", - "median_confidence_incorrect": "Median confidence rating for trials where the participant made the incorrect choice", - "diff_median_conf_correct_incorrect": "Difference between the median confidence rating for correct and incorrect choices", - "sd_confidence_correct": "Standard deviation of the confidence rating for trials where the participant made the correct choice", - "sd_confidence_incorrect": "Standard deviation of the confidence rating for trials where the participant made the incorrect choice", - "diff_sd_confidence_correct_incorrect": "Difference between the standard deviation of the confidence rating for correct and incorrect choices", - "mean_confidenceRT": "Mean confidence response time across all trials", - "median_confidenceRT": "Median confidence response time across all trials", - "median_confidenceRT_correct": "Median confidence response time for trials where the participant made the correct choice", - "median_confidenceRT_incorrect": "Median confidence response time for trials where the participant made the incorrect choice", - "diff_median_confidenceRT_correct_incorrect": "Difference between the median confidence response time for correct and incorrect choices", - "confidence_10": "10th percentile of the confidence rating across all trials", - "confidence_25": "25th percentile of the confidence rating across all trials", - "confidence_75": "75th percentile of the confidence rating across all trials", - "confidence_90": "90th percentile of the confidence rating across all trials", - "evidence_strength_mean": "Mean evidence strength across all trials", - "evidence_strength_correct_mean": "Mean evidence strength for trials where the participant made the correct choice", - "evidence_strength_incorrect_mean": "Mean evidence strength for trials where the participant made the incorrect choice", - "diff_evidence_strength_correct_incorrect": "Difference between the mean evidence strength for correct and incorrect choices", - "ES_bins": "Mean evidence strength for each of the bins. The trials here are split into 4 quarters. The evidence strength is divided into four equal bins and the mean is calculated for each bin." - } - - def metrics(self, mode=None): - """ - - Returns - ------- - results : pd.DataFrame - A DataFrame containing the results of the metrics. - - Variables - --------- - - userID: unique identifier for each participant - - n_trials: number of trials completed by the participant - - day_of_week: day of the week of the trial - - time: time of the trial - - time_of_day: time of day of the trial (morning, afternoon, evening, night) - - accuracy : The mean accuracy across all trials. - - mean_RT : The mean response time across all trials. - - median_RT : The median response time across all trials. - - median_RT_correct : The median response time for trials where the participant made the correct choice. - - median_RT_incorrect : The median response time for trials where the participant made the incorrect choice. - - diff_median_RT_correct_incorrect : The difference between the median response time for correct and incorrect choices. - - mean_confidence : The mean confidence rating across all trials. - - median_confidence : The median confidence rating across all trials. - - median_confidence_correct : The median confidence rating for trials where the participant made the correct choice. - - median_confidence_incorrect : The median confidence rating for trials where the participant made the incorrect choice. - - diff_median_conf_correct_incorrect : The difference between the median confidence rating for correct and incorrect choices. - - sd_confidence : The standard deviation of the confidence rating across all trials. - - sd_confidence_correct : The standard deviation of the confidence rating for trials where the participant made the correct choice. - - sd_confidence_incorrect : The standard deviation of the confidence rating for trials where the participant made the incorrect choice. - - diff_sd_confidence_correct_incorrect : The difference between the standard deviation of the confidence rating for correct and incorrect choices. - - mean_confidenceRT : The mean confidence response time across all trials. - - median_confidenceRT : The median confidence response time across all trials. - - median_confidenceRT_correct : The median confidence response time for trials where the participant made the correct choice. - - median_confidenceRT_incorrect : The median confidence response time for trials where the participant made the incorrect choice. - - diff_median_confidenceRT_correct_incorrect : The difference between the median confidence response time for correct and incorrect choices. - - confidence_10 : The 10th percentile of the confidence rating across all trials. - - confidence_25 : The 25th percentile of the confidence rating across all trials. - - confidence_75 : The 75th percentile of the confidence rating across all trials. - - confidence_90 : The 90th percentile of the confidence rating across all trials. - - evidence_strength_mean : The mean evidence strength across all trials. - - evidence_strength_correct_mean : The mean evidence strength for trials where the participant made the correct choice. - - evidence_strength_incorrect_mean : The mean evidence strength for trials where the participant made the incorrect choice. - - diff_evidence_strength_correct_incorrect : The difference between the mean evidence strength for correct and incorrect choices. - - ES_bins : The mean evidence strength for each of the bins. The trials here are split into 4 quarters. The evidence strength is divided into four equal bins and the mean is calculated for each bin. - """ - - # Group data by subject - grouped_data = self.data.groupby("userID") - - for user_id, user_data in grouped_data: - - user_results = {"userID": user_id} - - user_results["n_trials"] = len(user_data) - - date = user_data["date"].iloc[0] - if isinstance(date, str): - date = pd.to_datetime(date, format="%Y-%m-%d %H:%M:%S.%f") - user_results["date"] = date - user_results["day_of_week"] = date.day_name() - user_results["time"] = date.time() if hasattr(date, 'time') else date.strftime("%H:%M:%S.%f") - # morning 6-12, afternoon 12-18, evening 18-24, night 0-6 - user_results["time_of_day"] = ( - "morning" if date.hour < 12 else - "afternoon" if date.hour < 18 else - "evening" if date.hour < 24 else - "night" if date.hour < 6 else - "-" - ) - - user_results["accuracy"] = np.nanmean(user_data["accuracy"]) - - user_results["mean_RT"] = np.nanmean(user_data["choiceRT"]) - user_results["median_RT"] = np.nanmedian(user_data["choiceRT"]) - # different RT for correct and incorrect trials - user_results["median_RT_correct"] = np.nanmedian(user_data["choiceRT"][user_data["accuracy"] == 1]) - user_results["median_RT_incorrect"] = np.nanmedian(user_data["choiceRT"][user_data["accuracy"] != 1]) - user_results["diff_median_RT_correct_incorrect"] = ( - user_results["median_RT_correct"] - user_results["median_RT_incorrect"] - ) - - user_results["mean_confidence"] = np.nanmean(user_data["confidence"]) - user_results["sd_confidence"] = np.nanstd(user_data["confidence"]) - user_results["median_confidence"] = np.nanmedian(user_data["confidence"]) - # confidence difference for correct and incorrect trials - user_results["mean_confidence_correct"] = np.nanmean(user_data["confidence"][user_data["accuracy"] == 1]) - user_results["mean_confidence_incorrect"] = np.nanmean(user_data["confidence"][user_data["accuracy"] != 1]) - user_results["diff_median_conf_correct_incorrect"] = ( - user_results["mean_confidence_correct"] - user_results["mean_confidence_incorrect"] - ) - # standard deviation of confidence for correct and incorrect trials - user_results["sd_confidence_correct"] = np.nanstd(user_data["confidence"][user_data["accuracy"] == 1]) - user_results["sd_confidence_incorrect"] = np.nanstd(user_data["confidence"][user_data["accuracy"] != 1]) - user_results["diff_sd_confidence_correct_incorrect"] = ( - user_results["sd_confidence_correct"] - user_results["sd_confidence_incorrect"] - ) - - user_results["mean_confidenceRT"] = np.nanmean(user_data["confidenceRT"]) - user_results["median_confidenceRT"] = np.nanmedian(user_data["confidenceRT"]) - # different RT for correct and incorrect trials - user_results["median_confidenceRT_correct"] = np.nanmedian(user_data["confidenceRT"][user_data["accuracy"] == 1]) - user_results["median_confidenceRT_incorrect"] = np.nanmedian(user_data["confidenceRT"][user_data["accuracy"] != 1]) - user_results["diff_median_confidenceRT_correct_incorrect"] = ( - user_results["median_confidenceRT_correct"] - user_results["median_confidenceRT_incorrect"] - ) - - # calculate confidence percentiles - confidence = user_data["confidence"] - user_results["confidence_10"] = np.nanpercentile(confidence, 10) - user_results["confidence_25"] = np.nanpercentile(confidence, 25) - user_results["confidence_75"] = np.nanpercentile(confidence, 75) - user_results["confidence_90"] = np.nanpercentile(confidence, 90) - - # calculate evidence strength - evidence_strength = user_data["stimulus_intensity"] - - user_results["evidence_strength_mean"] = np.nanmean(evidence_strength) - - # evidence strenth per correct and incorrect trials - user_results["evidence_strength_correct_mean"] = np.nanmean(evidence_strength[user_data["accuracy"] == 1]) - user_results["evidence_strength_incorrect_mean"] = np.nanmean(evidence_strength[user_data["accuracy"] != 1]) - user_results["diff_evidence_strength_correct_incorrect"] = ( - user_results["evidence_strength_correct_mean"] - user_results["evidence_strength_incorrect_mean"] - ) - - # calcualte evidence strength for each bin - bin_size = 4 - - # Divide evidence strength into 4 equal bins and calculate the mean for each bin - bins = np.array_split(evidence_strength, bin_size) - ES_1 = np.mean(bins[0]) if len(bins[0]) > 0 else np.nan - ES_2 = np.mean(bins[1]) if len(bins[1]) > 0 else np.nan - ES_3 = np.mean(bins[2]) if len(bins[2]) > 0 else np.nan - ES_4 = np.mean(bins[3]) if len(bins[3]) > 0 else np.nan - user_results["ES_bins"] = np.array([ES_1, ES_2, ES_3, ES_4]) - - # median evidence strength - user_results["median_ES"] = np.nanmedian(evidence_strength) - - - # Append user-level results - self.results = pd.concat( - [self.results, pd.DataFrame([user_results])], ignore_index=True - ) - - # Convert results to a DataFrame - self.results = pd.DataFrame(self.results) - - return self.results - - def clean_data(self): - """ - Clean aggregated data by applyign participant-level exclusion criteria. - Exclusion criteria: - --------- - - Mean accuracy < 0.5 - - Median reaction time > 3000 ms - - Median reaction time for confidence ratings > 3000 ms - - Median evidence strength > 25 - - Mean confidence <3 or > 97 - - Participants with more than 80 trials (due to technical error) - - Exclude participants if 10th and 25th percentile of confidence is the same AND 75th and 90th percentile of confidence is the same - """ - - nr_part_before = len(self.results["userID"].unique()) - - self.cleanedresults = self.results.copy() - - # exclude participants who have more than 80 trials with run == 1 - users_80trials = self.data.groupby("userID").filter(lambda x: len(x) <= 80)["userID"].unique() - self.cleanedresults = self.cleanedresults[self.cleanedresults["userID"].isin(users_80trials)] - - self.cleanedresults = self.cleanedresults[self.cleanedresults["accuracy"] >= 0.5] # only keep participants with mean accuracy >= 50% - self.cleanedresults = self.cleanedresults[self.cleanedresults["median_RT"] <= 3000] # only keep participants with median reaction time <= 3000 ms - self.cleanedresults = self.cleanedresults[self.cleanedresults["median_confidenceRT"] <= 3000] # only keep participants with median confidence reaction time <= 3000 ms - self.cleanedresults = self.cleanedresults[self.cleanedresults["median_ES"] <= 25] # only keep participants with median evidence strength <= 25 - self.cleanedresults = self.cleanedresults[self.cleanedresults["median_confidence"] <= 97] # only keep participants with mean confidence <= 97 - self.cleanedresults = self.cleanedresults[self.cleanedresults["median_confidence"] >= 3] # only keep participants with mean confidence >= 3 - - # exclude if 10th and 25th percentile of confidence is the same AND 75th and 90th percentile of confidence is the same - self.cleanedresults = self.cleanedresults[~( - (self.cleanedresults["confidence_10"] == self.cleanedresults["confidence_25"]) & - (self.cleanedresults["confidence_75"] == self.cleanedresults["confidence_90"]) - )] - - self.deleted_participants = nr_part_before - len(self.cleanedresults["userID"].unique()) - - return self.cleanedresults - - def codebook(self): - """ - Return a codebook describing the metrics. - """ - return self.codebook \ No newline at end of file diff --git a/cpm/brainexplorer/stopsignal.py b/cpm/brainexplorer/stopsignal.py deleted file mode 100644 index d6b64df..0000000 --- a/cpm/brainexplorer/stopsignal.py +++ /dev/null @@ -1,15 +0,0 @@ -## space bus is a stop-signal task, so we can actually include several different analyses methods -## Here is an incomplete list: integration method, correlation of go and no-go RTs, - -def preprocess(data=None, id=None, trialtypes=None, rt=None, accuracy=None, exclude_method=None): - """ - Process spacebus data - - Examples - -------- - >>> import cpm.brainexplorer as be - >>> import pandas as pd - >>> spacebus = pd.read_csv('spacebus.csv') - >>> spacebus_aggregate = be.spacebus.preprocess(data=spacebus, id='userID', trialtypes='stopSignal', rt='RT_seconds', accuracy) - """ - pass \ No newline at end of file From 7f47153becb556d9966379a59358ae0c7b927704 Mon Sep 17 00:00:00 2001 From: lenarddome Date: Fri, 6 Mar 2026 09:00:34 +0100 Subject: [PATCH 39/54] quickfix: update __all__ to include HumbleTeacher in learning module and remove KernelUpdate --- cpm/models/learning.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/cpm/models/learning.py b/cpm/models/learning.py index ff4d9ea..c7b328a 100644 --- a/cpm/models/learning.py +++ b/cpm/models/learning.py @@ -1,6 +1,11 @@ import numpy as np -__all__ = ["DeltaRule", "SeparableRule", "QLearningRule", "KernelUpdate"] +__all__ = [ + "DeltaRule", + "SeparableRule", + "QLearningRule", + "HumbleTeacher", +] class DeltaRule: From 478ae1c0a773b2cda46793bcaea044eb8f1649fa Mon Sep 17 00:00:00 2001 From: lenarddome Date: Thu, 12 Mar 2026 10:48:19 +0100 Subject: [PATCH 40/54] fix: correct formatting and spacing in reinforcement learning classes --- cpm/applications/reinforcement_learning.py | 130 ++++++++++++--------- 1 file changed, 72 insertions(+), 58 deletions(-) diff --git a/cpm/applications/reinforcement_learning.py b/cpm/applications/reinforcement_learning.py index 8698a53..034d893 100644 --- a/cpm/applications/reinforcement_learning.py +++ b/cpm/applications/reinforcement_learning.py @@ -9,6 +9,7 @@ from scipy.special import logsumexp + class RLRW(Wrapper): """ The class implements a simple reinforcement learning model for a multi-armed bandit tasks using a standard update rule calculating prediction error and a Softmax decision rule. @@ -60,7 +61,7 @@ class RLRW(Wrapper): Let each stimulus have an associated value, which is the expected reward that can be obtained from selecting that stimulus. Let also $Q(a)$ be the estimated value of action $a$. In each trial, $t$, there are two stimuli present, so $Q(a)$ could be $Q(\text{left})$ or $Q(\\text{right})$, where the corresponding $Q$ values are derived from the associated value of the stimulus present on the left or right. In each trial $t$ , the Softmax choice rule (Bridle, 1990) will convert the estimated value of each action into a probability according to the following policy: $$ - P(a) = \\frac{e^{Q(a)/\\tau}}{\sum_{a'} e^{Q(a')/\\tau}} + P(a) = \\frac{e^{Q(a)/\\tau}}{\\sum_{a'} e^{Q(a')/\\tau}} $$ where $\\tau$ is the temperature parameter. After the choice is made and feedback is received, the value of the chosen stimulus is updated. The current implementation uses the variant of the delta rule (Rescorla & Wagner, 1972; Rumelhart, Hinton, & Williams, 1986) adapted for multi-armed bandit problems where each option has a single stimulus dimension (Sutton & Barto, 2020), reducing Rescorla-Wagner's summed error-term to the following equation, similar to (Bush & Mosteller, 1951): @@ -185,7 +186,7 @@ def model(parameters, trial, generate=generate): class MBMF(Wrapper): """ - This class implements a model for model-based vs model-free decision making in a decision making task. + This class implements a model for model-based vs model-free decision making in a decision making task. The model uses a combination of model-based and model-free learning to predict choices based on the rewards received for each stimulus. It is based on the model described in Kool et al. (2016) and adds choice stickiness calcualtions to the model. @@ -195,7 +196,7 @@ class MBMF(Wrapper): The individual user data to be processed. The column names in the data must subscribe to a prespecified convention, see Notes. parameters: list, optional - A list of parameters to be used in the model. If not specified, default parameters are used. + A list of parameters to be used in the model. If not specified, default parameters are used. Order of parameters: inverse temperature, learning rate, eligibility trace decay, mixing weight for low stake trials, mixing weight for high stake trials, stickiness, response stickiness. Example @@ -214,150 +215,163 @@ class MBMF(Wrapper): - userID: unique identifier for each participant - stimuli: which planets presented in stage of the trial - s: the state of the trial - - rews: the rewards for each stimulus / planet + - rews: the rewards for each stimulus / planet - choice: the choice made by the participant - - stake: the stake of the trial (1 or 5) + - stake: the stake of the trial (1 or 5) - points: the points received in the trial - parameters_settings must be a 2D array, like [[0.8, 0, 2], [0.5, 0, 1], [0.8, 0, 1], [0.8, 0, 1], [0.8, 0, 2], [-0.1, -0.5, 0.5], [-0.1, -0.5, 0.5]], where the first list specifies the inverse temperature, the second list specifies the learning rate, the third list specifies the eligibility trace decay, the fourth list specifies the mixing weight for low stake trials, the fifth list specifies the mixing weight for high stake trials, the sixth list specifies the stickiness, and the seventh list specifies the response stickiness. The first element of each list is the initial value of the parameter, the second element is the lower bound, and the third element is the upper bound. + parameters_settings must be a 2D array, like [[0.8, 0, 2], [0.5, 0, 1], [0.8, 0, 1], [0.8, 0, 1], [0.8, 0, 2], [-0.1, -0.5, 0.5], [-0.1, -0.5, 0.5]], where the first list specifies the inverse temperature, the second list specifies the learning rate, the third list specifies the eligibility trace decay, the fourth list specifies the mixing weight for low stake trials, the fifth list specifies the mixing weight for high stake trials, the sixth list specifies the stickiness, and the seventh list specifies the response stickiness. The first element of each list is the initial value of the parameter, the second element is the lower bound, and the third element is the upper bound. References - ---------- + ---------- Kool W, Cushman FA, Gershman SJ (2016) When Does Model-Based Control Pay Off? PLoS Comput Biol 12 Smid, C.R., K. Ganesan, A. Thompson, R. Cañigueral, S. Veselic, J. Royer, W. Kool, T.U. Hauser, B. Bernhardt, and N. Steinbeis. “Neurocognitive Basis of Model-Based Decision Making and Its Metacontrol in Childhood.” Developmental Cognitive Neuroscience 62 (August 1, 2023): 101269. - + """ - def __init__( - self, data=None, parameters_settings=None - ): + def __init__(self, data=None, parameters_settings=None): if parameters_settings is None: - parameters_settings = [[0.8, 0, 2], [0.5, 0, 1], [0.8, 0, 1], [0.8, 0, 1], [0.8, 0, 2], [-0.1, -0.5, 0.5], [-0.1, -0.5, 0.5]] + parameters_settings = [ + [0.8, 0, 2], + [0.5, 0, 1], + [0.8, 0, 1], + [0.8, 0, 1], + [0.8, 0, 2], + [-0.1, -0.5, 0.5], + [-0.1, -0.5, 0.5], + ] parameters = Parameters( temperature=Value( value=parameters_settings[0][0], lower=parameters_settings[0][1], - upper=parameters_settings[0][2] + upper=parameters_settings[0][2], ), learning_rate=Value( value=parameters_settings[1][0], lower=parameters_settings[1][1], - upper=parameters_settings[1][2] + upper=parameters_settings[1][2], ), eligibility_trace_decay=Value( value=parameters_settings[2][0], lower=parameters_settings[2][1], - upper=parameters_settings[2][2] + upper=parameters_settings[2][2], ), mixing_weight_low_stake=Value( value=parameters_settings[3][0], lower=parameters_settings[3][1], - upper=parameters_settings[3][2] + upper=parameters_settings[3][2], ), mixing_weight_high_stake=Value( value=parameters_settings[4][0], lower=parameters_settings[4][1], - upper=parameters_settings[4][2] + upper=parameters_settings[4][2], ), stickiness=Value( value=parameters_settings[5][0], lower=parameters_settings[5][1], - upper=parameters_settings[5][2] + upper=parameters_settings[5][2], ), response_stickiness=Value( value=parameters_settings[6][0], lower=parameters_settings[6][1], - upper=parameters_settings[6][2] + upper=parameters_settings[6][2], ), - Q_MF = numpy.ones((2,2))*4.5, - Q2 = numpy.ones((2,1))*4.5, - M = numpy.zeros((2,2)), # last action matrix - R = numpy.zeros(2) # last choice structure + Q_MF=numpy.ones((2, 2)) * 4.5, + Q2=numpy.ones((2, 1)) * 4.5, + M=numpy.zeros((2, 2)), # last action matrix + R=numpy.zeros(2), # last choice structure ) @ipp.require("numpy") - def MF_MB_model(parameters, trial): - """ Model for model-based vs model-free decision making in a decison making task Returns ------- - LL: - Log Likelihood + LL: + Log Likelihood Q: Q values for the mixing model Q_MF: Q values for model-free learning Q_MB: - Q values for model-based learning + Q values for model-based learning """ # pull out the paramters - b = parameters.temperature # softmax inverse temperature - lr = parameters.learning_rate # learning rate - lamb = parameters.eligibility_trace_decay # eligibility trace decay - w_lo = parameters.mixing_weight_low_stake # mixing weight for low stake trials - w_hi = parameters.mixing_weight_high_stake # mixing weight for high stake trials - st = parameters.stickiness # stickiness - respst = parameters.response_stickiness # response stickiness + b = parameters.temperature # softmax inverse temperature + lr = parameters.learning_rate # learning rate + lamb = parameters.eligibility_trace_decay # eligibility trace decay + w_lo = ( + parameters.mixing_weight_low_stake + ) # mixing weight for low stake trials + w_hi = ( + parameters.mixing_weight_high_stake + ) # mixing weight for high stake trials + st = parameters.stickiness # stickiness + respst = parameters.response_stickiness # response stickiness # initialization # initialization - Q_MF = numpy.array(parameters.Q_MF) # Q values for model-free learning - Q2 = numpy.array(parameters.Q2) # Q values for model-based learning + Q_MF = numpy.array(parameters.Q_MF) # Q values for model-free learning + Q2 = numpy.array(parameters.Q2) # Q values for model-based learning - M = numpy.array(parameters.M) # last action matrix - R = numpy.array(parameters.R) # last choice structure + M = numpy.array(parameters.M) # last action matrix + R = numpy.array(parameters.R) # last choice structure + + TM = [numpy.eye(2), numpy.eye(2)] # transition matrix - TM = [numpy.eye(2), numpy.eye(2)] #transition matrix - if trial["stimuli"][0] == 2 or trial["stimuli"][0] == 4: - R = R = numpy.flipud(R) + R = R = numpy.flipud(R) - s1 = trial["s"][0] # first choice stage - s2 = trial["s"][1] # second choice stage + s1 = trial["s"][0] # first choice stage + s2 = trial["s"][1] # second choice stage action = trial["choice"] a = action[0] - (s1 == 2) * 2 # choose which weight to update based on stake of trial if trial["stake"] == 1: w = w_lo - else: + else: w = w_hi # calculating the Q values for model-based learning - Q_MB = TM[s1-1].T @ Q2 + Q_MB = TM[s1 - 1].T @ Q2 # calculating the Q values for mixing model - Q = w*Q_MB + (1-w)*Q_MF[s1-1, :].T + st * M[s1-1, :].T + respst * numpy.array(R) + Q = ( + w * Q_MB + + (1 - w) * Q_MF[s1 - 1, :].T + + st * M[s1 - 1, :].T + + respst * numpy.array(R) + ) # calculating the probability choosing action a given Q policy = numpy.exp(b * Q) / numpy.sum(numpy.exp(b * Q)) # update choice structure - M = numpy.zeros((2,2)) - M[s1-1, a-1] = 1 + M = numpy.zeros((2, 2)) + M[s1 - 1, a - 1] = 1 - R = numpy.zeros((2,1)) + R = numpy.zeros((2, 1)) if action == trial["stimuli"][0]: R[0] = 1 - else: + else: R[1] = 1 # update Q values for model-free learning # first choice stage - dtQ1 = Q2[s2-1] - Q_MF[s1-1, a-1] - Q_MF[s1-1, a-1] = Q_MF[s1-1, a-1] + lr * dtQ1 + dtQ1 = Q2[s2 - 1] - Q_MF[s1 - 1, a - 1] + Q_MF[s1 - 1, a - 1] = Q_MF[s1 - 1, a - 1] + lr * dtQ1 # second choice stage - dtQ2 = trial["points"] - Q2[s2-1] - Q2[s2-1] = Q2[s2-1] + lr * dtQ2 - Q_MF[s1-1, a-1] = Q_MF[s1-1, a-1] + lamb * lr * dtQ2 + dtQ2 = trial["points"] - Q2[s2 - 1] + Q2[s2 - 1] = Q2[s2 - 1] + lr * dtQ2 + Q_MF[s1 - 1, a - 1] = Q_MF[s1 - 1, a - 1] + lamb * lr * dtQ2 # compile results results = { @@ -368,9 +382,9 @@ def MF_MB_model(parameters, trial): "Q2": numpy.array(Q2), "Q_MF": numpy.array(Q_MF), "Q_MB": numpy.array(Q_MB.flatten()), - "dependent": numpy.array([policy[1]]) + "dependent": numpy.array([policy[1]]), } - return results + return results super().__init__(data=data, model=MF_MB_model, parameters=parameters) From d4a78302acdcd590d6168d9b0fff58c61c854d4f Mon Sep 17 00:00:00 2001 From: lenarddome Date: Thu, 12 Mar 2026 10:48:32 +0100 Subject: [PATCH 41/54] feat: add perceptual decision making module and update imports --- cpm/brainexplorer/__init__.py | 2 +- ...ptual.py => perceptual_decision_making.py} | 101 +++++++++--------- 2 files changed, 51 insertions(+), 52 deletions(-) rename cpm/brainexplorer/{perceptual.py => perceptual_decision_making.py} (81%) diff --git a/cpm/brainexplorer/__init__.py b/cpm/brainexplorer/__init__.py index c9aa211..12ae622 100644 --- a/cpm/brainexplorer/__init__.py +++ b/cpm/brainexplorer/__init__.py @@ -1,4 +1,4 @@ from cpm.brainexplorer import bandits -from cpm.brainexplorer import perceptual +from cpm.brainexplorer import perceptual_decision_making from cpm.brainexplorer import risky_decision_making from cpm.brainexplorer import information_gathering diff --git a/cpm/brainexplorer/perceptual.py b/cpm/brainexplorer/perceptual_decision_making.py similarity index 81% rename from cpm/brainexplorer/perceptual.py rename to cpm/brainexplorer/perceptual_decision_making.py index b176a86..35371f3 100644 --- a/cpm/brainexplorer/perceptual.py +++ b/cpm/brainexplorer/perceptual_decision_making.py @@ -6,15 +6,14 @@ class SpaceObserver: """ - A class to analyze and compute statistics from behavioural data from a metacognition task - in BrainExplorer, Space Observer. Participants have to choose if stimulus has more of one type of alien or the other, - and rate their confidence in their choice. + A class to analyze and compute statistics from behavioural data from a perceptual decision-making task with confidence judgements in BrainExplorer, Space Observer. In these tasks, participants have to choose if stimulus has more of one type of alien or the other, + and rate their confidence in their choice. For a more complete description of the task, see Dome, Moses-Payne, and Hauser (in prep.) or Marzuki et al. (2025). Attributes ---------- - data : pd.DataFrame + data_raw : pd.DataFrame The data loaded from the CSV or Excel file. - results : pd.DataFrame + data_processed : pd.DataFrame A DataFrame to store the computed metrics. codebook : dict A dictionary containing the codebook for the computed metrics. @@ -46,51 +45,50 @@ def __init__(self, filepath=None): - **userID: unique identifier for each participant - **date**: the date and time of the trial - - **run**: number of attempt by participant + - **run**: number of sessions completed by the participant (where 1 is the practice phase) - **accuracy**: the accuracy of the trial (1 for correct, 0 for incorrect) - - **choiceRT**: the reaction time of the choice in the trial + - **RT_choice**: the reaction time of the choice in the trial - **confidence**: the confidence rating of the trial - **confidenceRT**: the reaction time of the confidence rating in the trial - **stimulus_intensity**: the evidence strength of the trial, which is the difference of evidence for each group of stimuli. - Trial-level exclusion critera: - - Practice trials (run == 1) - - Reaction time < 150 or > 10000 ms - - Reaction time of confidence < 150 or > 10000 ms - - Trials with missing confidence data + Currently, we are excluding trials that took too long or too short to respond (RT < 150 ms or RT > 10000 ms) and trials without confidence data because those are practice trials. We are only including trials with confidence data because those without confidence data are practice trials. For participant exclusion, we recommend to follow Dome, Moses-Payne, and Hauser (in prep.). + + Reference + --------- + Dome, L., Moses-Payne, M. E., & Hauser, T. U. (in prep.). Age-related shifts in metacognition reveals converging confidence for correct and error judgments across the lifespan. + + Marzuki, A., Kosina, L., Dome, L., Hewitt, S., & Hauser, T. (2025). Metacognitive antecedents to states of mental ill-health: Drops in confidence precede symptoms of OCD. Research Square. https://doi.org/10.21203/rs.3.rs-7544256/v1 """ ## read data - self.data = pd.read_csv(filepath, header=0, na_values=["NaN", "nan"]) + self.data_raw = pd.read_csv(filepath, header=0, na_values=["NaN", "nan"]) - self.data = self.data[ - self.data["run"] == 1 - ] # exclude attempts after the first one - self.data = self.data[ - self.data["choiceRT"] >= 150 + self.data_raw = self.data_raw[ + self.data_raw["RT_choice"] >= 150 ] # only keep trials with reaction time > 150 ms - self.data = self.data[ - self.data["choiceRT"] <= 10000 + self.data_raw = self.data_raw[ + self.data_raw["RT_choice"] <= 10000 ] # only keep trials with reaction time < 10000 ms - self.data = self.data[ - self.data["confidenceRT"] >= 150 + self.data_raw = self.data_raw[ + self.data_raw["confidenceRT"] >= 150 ] # only keep trials with confidence reaction time > 150 ms - self.data = self.data[ - self.data["confidenceRT"] <= 10000 + self.data_raw = self.data_raw[ + self.data_raw["confidenceRT"] <= 10000 ] # only keep trials with confidence reaction time < 10000 ms - self.data["confidence"] = self.data["confidence"].replace( + self.data_raw["confidence"] = self.data_raw["confidence"].replace( ["NaN", "nan", "NAN", ""], pd.NA ) - self.data = self.data[ - self.data["confidence"].notna() + self.data_raw = self.data_raw[ + self.data_raw["confidence"].notna() ] # only keep trials with confidence data because trials without confidence data are practice trials - self.data["confidence"] = pd.to_numeric( - self.data["confidence"], errors="coerce" + self.data_raw["confidence"] = pd.to_numeric( + self.data_raw["confidence"], errors="coerce" ) - self.results = pd.DataFrame() + self.data_processed = pd.DataFrame() self.codebook = { "userID": "Unique identifier for each participant", "n_trials": "Number of trials completed by the participant", @@ -101,7 +99,7 @@ def __init__(self, filepath=None): "mean_RT": "Mean response time across all trials", "median_RT": "Median response time across all trials", "median_RT_correct": "Median response time for trials where the participant made the correct choice", - "median_RT_incorrect": "Median response time for trials where the participant made the incorrect", + "median_RT_incorrect": "Median response time for trials where the participant made the incorrect choice", "diff_median_RT_correct_incorrect": "Difference between the median response time for correct and incorrect choices", "mean_confidence": "Mean confidence rating across all trials", "sd_confidence": "Standard deviation of the confidence rating across all trials", @@ -112,11 +110,11 @@ def __init__(self, filepath=None): "sd_confidence_correct": "Standard deviation of the confidence rating for trials where the participant made the correct choice", "sd_confidence_incorrect": "Standard deviation of the confidence rating for trials where the participant made the incorrect choice", "diff_sd_confidence_correct_incorrect": "Difference between the standard deviation of the confidence rating for correct and incorrect choices", - "mean_confidenceRT": "Mean confidence response time across all trials", - "median_confidenceRT": "Median confidence response time across all trials", - "median_confidenceRT_correct": "Median confidence response time for trials where the participant made the correct choice", - "median_confidenceRT_incorrect": "Median confidence response time for trials where the participant made the incorrect choice", - "diff_median_confidenceRT_correct_incorrect": "Difference between the median confidence response time for correct and incorrect choices", + "mean_RT_confidence": "Mean confidence response time across all trials", + "median_RT_confidence": "Median confidence response time across all trials", + "median_RT_confidence_correct": "Median confidence response time for trials where the participant made the correct choice", + "median_RT_confidence_incorrect": "Median confidence response time for trials where the participant made the incorrect choice", + "diff_median_RT_confidence_correct_incorrect": "Difference between the median confidence response time for correct and incorrect choices", "confidence_10": "10th percentile of the confidence rating across all trials", "confidence_25": "25th percentile of the confidence rating across all trials", "confidence_75": "75th percentile of the confidence rating across all trials", @@ -130,6 +128,7 @@ def __init__(self, filepath=None): def metrics(self, mode=None): """ + Compute metrics for each participant and return a DataFrame with the results. The metrics are computed for each run separately. Returns ------- @@ -175,15 +174,15 @@ def metrics(self, mode=None): """ # Group data by subject - grouped_data = self.data.groupby("userID") + grouped_data = self.data_raw.groupby(["userID", "run"]) - for user_id, user_data in grouped_data: + for (user_id, run), user_data in grouped_data: - user_results = {"userID": user_id} + user_results = {"userID": user_id, "run": run} user_results["n_trials"] = len(user_data) - date = user_data["date"].iloc[0] + date = user_data["Date"].iloc[0] if isinstance(date, str): date = pd.to_datetime(date, format="%Y-%m-%d %H:%M:%S.%f") user_results["date"] = date @@ -208,14 +207,14 @@ def metrics(self, mode=None): user_results["accuracy"] = np.nanmean(user_data["accuracy"]) - user_results["mean_RT"] = np.nanmean(user_data["choiceRT"]) - user_results["median_RT"] = np.nanmedian(user_data["choiceRT"]) + user_results["mean_RT"] = np.nanmean(user_data["RT_choice"]) + user_results["median_RT"] = np.nanmedian(user_data["RT_choice"]) # different RT for correct and incorrect trials user_results["median_RT_correct"] = np.nanmedian( - user_data["choiceRT"][user_data["accuracy"] == 1] + user_data["RT_choice"][user_data["accuracy"] == 1] ) user_results["median_RT_incorrect"] = np.nanmedian( - user_data["choiceRT"][user_data["accuracy"] != 1] + user_data["RT_choice"][user_data["accuracy"] != 1] ) user_results["diff_median_RT_correct_incorrect"] = ( user_results["median_RT_correct"] - user_results["median_RT_incorrect"] @@ -302,14 +301,14 @@ def metrics(self, mode=None): user_results["median_ES"] = np.nanmedian(evidence_strength) # Append user-level results - self.results = pd.concat( - [self.results, pd.DataFrame([user_results])], ignore_index=True + self.data_processed = pd.concat( + [self.data_processed, pd.DataFrame([user_results])], ignore_index=True ) # Convert results to a DataFrame - self.results = pd.DataFrame(self.results) + self.data_processed = pd.DataFrame(self.data_processed) - return self.results + return self.data_processed def clean_data(self): """ @@ -325,13 +324,13 @@ def clean_data(self): - Exclude participants if 10th and 25th percentile of confidence is the same AND 75th and 90th percentile of confidence is the same """ - nr_part_before = len(self.results["userID"].unique()) + nr_part_before = len(self.data_processed["userID"].unique()) - self.cleanedresults = self.results.copy() + self.cleanedresults = self.data_processed.copy() # exclude participants who have more than 80 trials with run == 1 users_80trials = ( - self.data.groupby("userID") + self.data_raw.groupby("userID") .filter(lambda x: len(x) <= 80)["userID"] .unique() ) From 56380eb9725be998212cf8de08debc8338a9282d Mon Sep 17 00:00:00 2001 From: lenarddome Date: Wed, 18 Mar 2026 09:06:59 +0100 Subject: [PATCH 42/54] feat: add tests for SpaceObserver functionality and data handling --- .../test_perceptual_decision_making.py | 189 ++++++++++++++++++ 1 file changed, 189 insertions(+) create mode 100644 test/brainexplorer/test_perceptual_decision_making.py diff --git a/test/brainexplorer/test_perceptual_decision_making.py b/test/brainexplorer/test_perceptual_decision_making.py new file mode 100644 index 0000000..acbb227 --- /dev/null +++ b/test/brainexplorer/test_perceptual_decision_making.py @@ -0,0 +1,189 @@ +import numpy as np +import pandas as pd + +from cpm.brainexplorer.perceptual_decision_making import SpaceObserver + + +def _write_space_observer_csv(tmp_path, rows, filename="space_observer.csv"): + data = pd.DataFrame(rows) + filepath = tmp_path / filename + data.to_csv(filepath, index=False) + return filepath + + +def _participant_rows( + user_id, + run, + accuracies, + rt_choices, + confidences, + confidence_rts, + stimulus_intensities, + date="2025-02-20 09:00:00.000", +): + rows = [] + for accuracy, rt_choice, confidence, confidence_rt, intensity in zip( + accuracies, + rt_choices, + confidences, + confidence_rts, + stimulus_intensities, + ): + rows.append( + { + "userID": user_id, + "run": run, + "Date": date, + "accuracy": accuracy, + "RT_choice": rt_choice, + "confidence": confidence, + "confidenceRT": confidence_rt, + "stimulus_intensity": intensity, + } + ) + return rows + + +def test_spaceobserver_init_filters_invalid_trials(tmp_path): + rows = [ + { + "userID": "u1", + "run": 1, + "Date": "2025-02-20 10:30:00.000", + "accuracy": 1, + "RT_choice": 200, + "confidence": "50", + "confidenceRT": 250, + "stimulus_intensity": 2, + }, + { + "userID": "u1", + "run": 1, + "Date": "2025-02-20 10:30:00.000", + "accuracy": 1, + "RT_choice": 120, + "confidence": "40", + "confidenceRT": 250, + "stimulus_intensity": 3, + }, + { + "userID": "u1", + "run": 1, + "Date": "2025-02-20 10:30:00.000", + "accuracy": 1, + "RT_choice": 250, + "confidence": "40", + "confidenceRT": 120, + "stimulus_intensity": 3, + }, + { + "userID": "u1", + "run": 1, + "Date": "2025-02-20 10:30:00.000", + "accuracy": 1, + "RT_choice": 250, + "confidence": "", + "confidenceRT": 250, + "stimulus_intensity": 3, + }, + ] + + filepath = _write_space_observer_csv(tmp_path, rows) + observer = SpaceObserver(str(filepath)) + + assert len(observer.data_raw) == 1 + assert observer.data_raw.iloc[0]["RT_choice"] == 200 + assert observer.data_raw.iloc[0]["confidenceRT"] == 250 + assert observer.data_raw.iloc[0]["confidence"] == 50 + assert pd.api.types.is_numeric_dtype(observer.data_raw["confidence"]) + + +def test_spaceobserver_metrics_computes_expected_values(tmp_path): + rows = _participant_rows( + user_id="u1", + run=1, + accuracies=[1, 0, 1, 0], + rt_choices=[200, 300, 400, 500], + confidences=[20, 40, 60, 80], + confidence_rts=[250, 260, 270, 280], + stimulus_intensities=[1, 2, 3, 4], + date="2025-02-20 10:30:00.000", + ) + rows += _participant_rows( + user_id="u1", + run=2, + accuracies=[1, 1], + rt_choices=[600, 700], + confidences=[90, 95], + confidence_rts=[300, 310], + stimulus_intensities=[10, 12], + date="2025-02-20 18:30:00.000", + ) + + filepath = _write_space_observer_csv(tmp_path, rows) + observer = SpaceObserver(str(filepath)) + results = observer.metrics().sort_values(["userID", "run"]).reset_index(drop=True) + + assert len(results) == 2 + run1 = results[results["run"] == 1].iloc[0] + + assert run1["n_trials"] == 4 + assert run1["time_of_day"] == "morning" + assert np.isclose(run1["accuracy"], 0.5) + assert np.isclose(run1["mean_RT"], 350) + assert np.isclose(run1["median_RT"], 350) + assert np.isclose(run1["median_RT_correct"], 300) + assert np.isclose(run1["median_RT_incorrect"], 400) + assert np.isclose(run1["mean_confidence"], 50) + assert np.isclose(run1["median_confidence"], 50) + assert np.isclose(run1["evidence_strength_mean"], 2.5) + np.testing.assert_allclose(run1["ES_bins"], np.array([1.0, 2.0, 3.0, 4.0])) + + +def test_spaceobserver_clean_data_excludes_invalid_participants(tmp_path): + rows = [] + rows += _participant_rows( + user_id="keep", + run=1, + accuracies=[1, 1, 0, 1], + rt_choices=[400, 500, 600, 700], + confidences=[20, 30, 40, 50], + confidence_rts=[500, 550, 600, 650], + stimulus_intensities=[10, 12, 14, 16], + ) + rows += _participant_rows( + user_id="drop_acc", + run=1, + accuracies=[0, 0, 1, 0], + rt_choices=[400, 500, 600, 700], + confidences=[20, 30, 40, 50], + confidence_rts=[500, 550, 600, 650], + stimulus_intensities=[10, 12, 14, 16], + ) + rows += _participant_rows( + user_id="drop_flat_conf", + run=1, + accuracies=[1, 1, 1, 1], + rt_choices=[400, 500, 600, 700], + confidences=[50, 50, 50, 50], + confidence_rts=[500, 550, 600, 650], + stimulus_intensities=[10, 12, 14, 16], + ) + rows += _participant_rows( + user_id="drop_trials", + run=1, + accuracies=[1] * 81, + rt_choices=[500] * 81, + confidences=[60] * 81, + confidence_rts=[500] * 81, + stimulus_intensities=[12] * 81, + ) + + filepath = _write_space_observer_csv(tmp_path, rows) + observer = SpaceObserver(str(filepath)) + observer.metrics() + cleaned = observer.clean_data() + + remaining_users = set(cleaned["userID"].unique()) + assert remaining_users == {"keep"} + assert observer.deleted_participants == 3 From 252f41511889779e0caf850ce8d648241730ab9e Mon Sep 17 00:00:00 2001 From: lenarddome Date: Wed, 18 Mar 2026 09:07:25 +0100 Subject: [PATCH 43/54] chore: update .gitignore to include .vscode and tmp directories --- .gitignore | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index ac40eb8..7c0ce02 100644 --- a/.gitignore +++ b/.gitignore @@ -160,4 +160,5 @@ cython_debug/ #.idea/ showcase_files/ .DS_Store -.vscode/ \ No newline at end of file +.vscode/ +tmp/ \ No newline at end of file From 36486e547cf6325971403f6053525b8a6b215d4c Mon Sep 17 00:00:00 2001 From: lenarddome Date: Thu, 19 Mar 2026 11:36:47 +0100 Subject: [PATCH 44/54] fix: improve time_of_day logic in SpaceObserver and enhance related tests --- .../perceptual_decision_making.py | 14 +- .../test_perceptual_decision_making.py | 128 +++++++++++++++++- 2 files changed, 126 insertions(+), 16 deletions(-) diff --git a/cpm/brainexplorer/perceptual_decision_making.py b/cpm/brainexplorer/perceptual_decision_making.py index 35371f3..1249a60 100644 --- a/cpm/brainexplorer/perceptual_decision_making.py +++ b/cpm/brainexplorer/perceptual_decision_making.py @@ -192,16 +192,12 @@ def metrics(self, mode=None): ) # morning 6-12, afternoon 12-18, evening 18-24, night 0-6 user_results["time_of_day"] = ( - "morning" - if date.hour < 12 + "night" + if date.hour < 6 else ( - "afternoon" - if date.hour < 18 - else ( - "evening" - if date.hour < 24 - else "night" if date.hour < 6 else "-" - ) + "morning" + if date.hour < 12 + else ("afternoon" if date.hour < 18 else "evening") ) ) diff --git a/test/brainexplorer/test_perceptual_decision_making.py b/test/brainexplorer/test_perceptual_decision_making.py index acbb227..9d2328b 100644 --- a/test/brainexplorer/test_perceptual_decision_making.py +++ b/test/brainexplorer/test_perceptual_decision_making.py @@ -4,10 +4,9 @@ from cpm.brainexplorer.perceptual_decision_making import SpaceObserver -def _write_space_observer_csv(tmp_path, rows, filename="space_observer.csv"): - data = pd.DataFrame(rows) - filepath = tmp_path / filename - data.to_csv(filepath, index=False) +def _write_space_observer_csv(tmp_path, rows): + filepath = tmp_path / "space_observer.csv" + pd.DataFrame(rows).to_csv(filepath, index=False) return filepath @@ -98,6 +97,34 @@ def test_spaceobserver_init_filters_invalid_trials(tmp_path): assert pd.api.types.is_numeric_dtype(observer.data_raw["confidence"]) +def test_spaceobserver_time_of_day(tmp_path): + base_row = { + "userID": "u1", + "run": 1, + "accuracy": 1, + "RT_choice": 300, + "confidence": "50", + "confidenceRT": 300, + "stimulus_intensity": 5, + } + cases = [ + ("2025-02-20 02:00:00.000", "night"), + ("2025-02-20 09:00:00.000", "morning"), + ("2025-02-20 14:00:00.000", "afternoon"), + ("2025-02-20 20:00:00.000", "evening"), + ] + for date_str, expected_tod in cases: + rows = [{**base_row, "Date": date_str}] + filepath = tmp_path / f"tod_{expected_tod}.csv" + pd.DataFrame(rows).to_csv(filepath, index=False) + observer = SpaceObserver(str(filepath)) + results = observer.metrics() + assert results.iloc[0]["time_of_day"] == expected_tod, ( + f"Expected time_of_day={expected_tod!r} for {date_str}, " + f"got {results.iloc[0]['time_of_day']!r}" + ) + + def test_spaceobserver_metrics_computes_expected_values(tmp_path): rows = _participant_rows( user_id="u1", @@ -126,19 +153,53 @@ def test_spaceobserver_metrics_computes_expected_values(tmp_path): assert len(results) == 2 run1 = results[results["run"] == 1].iloc[0] + run2 = results[results["run"] == 2].iloc[0] + # run 1 basic metrics assert run1["n_trials"] == 4 assert run1["time_of_day"] == "morning" assert np.isclose(run1["accuracy"], 0.5) + + # RT metrics assert np.isclose(run1["mean_RT"], 350) assert np.isclose(run1["median_RT"], 350) - assert np.isclose(run1["median_RT_correct"], 300) - assert np.isclose(run1["median_RT_incorrect"], 400) + assert np.isclose(run1["median_RT_correct"], 300) # median([200, 400]) + assert np.isclose(run1["median_RT_incorrect"], 400) # median([300, 500]) + assert np.isclose(run1["diff_median_RT_correct_incorrect"], -100) + + # confidence metrics assert np.isclose(run1["mean_confidence"], 50) assert np.isclose(run1["median_confidence"], 50) + assert np.isclose(run1["mean_confidence_correct"], 40) # mean([20, 60]) + assert np.isclose(run1["mean_confidence_incorrect"], 60) # mean([40, 80]) + assert np.isclose(run1["diff_median_conf_correct_incorrect"], -20) + assert np.isclose(run1["sd_confidence"], np.std([20, 40, 60, 80])) + + # confidence percentiles + assert np.isclose(run1["confidence_10"], np.percentile([20, 40, 60, 80], 10)) + assert np.isclose(run1["confidence_25"], np.percentile([20, 40, 60, 80], 25)) + assert np.isclose(run1["confidence_75"], np.percentile([20, 40, 60, 80], 75)) + assert np.isclose(run1["confidence_90"], np.percentile([20, 40, 60, 80], 90)) + + # confidenceRT metrics + assert np.isclose(run1["median_confidenceRT"], 265) # median([250,260,270,280]) + assert np.isclose(run1["median_confidenceRT_correct"], 260) # median([250, 270]) + assert np.isclose(run1["median_confidenceRT_incorrect"], 270) # median([260, 280]) + assert np.isclose(run1["diff_median_confidenceRT_correct_incorrect"], -10) + + # evidence strength metrics assert np.isclose(run1["evidence_strength_mean"], 2.5) + assert np.isclose(run1["evidence_strength_correct_mean"], 2.0) # mean([1, 3]) + assert np.isclose(run1["evidence_strength_incorrect_mean"], 3.0) # mean([2, 4]) + assert np.isclose(run1["diff_evidence_strength_correct_incorrect"], -1.0) + assert np.isclose(run1["median_ES"], 2.5) np.testing.assert_allclose(run1["ES_bins"], np.array([1.0, 2.0, 3.0, 4.0])) + # run 2 has time_of_day == evening (18:30) + assert run2["time_of_day"] == "evening" + assert run2["n_trials"] == 2 + assert np.isclose(run2["accuracy"], 1.0) + def test_spaceobserver_clean_data_excludes_invalid_participants(tmp_path): rows = [] @@ -151,6 +212,7 @@ def test_spaceobserver_clean_data_excludes_invalid_participants(tmp_path): confidence_rts=[500, 550, 600, 650], stimulus_intensities=[10, 12, 14, 16], ) + # accuracy < 0.5 rows += _participant_rows( user_id="drop_acc", run=1, @@ -160,6 +222,7 @@ def test_spaceobserver_clean_data_excludes_invalid_participants(tmp_path): confidence_rts=[500, 550, 600, 650], stimulus_intensities=[10, 12, 14, 16], ) + # flat confidence (10th==25th and 75th==90th) rows += _participant_rows( user_id="drop_flat_conf", run=1, @@ -169,6 +232,7 @@ def test_spaceobserver_clean_data_excludes_invalid_participants(tmp_path): confidence_rts=[500, 550, 600, 650], stimulus_intensities=[10, 12, 14, 16], ) + # more than 80 trials rows += _participant_rows( user_id="drop_trials", run=1, @@ -178,6 +242,56 @@ def test_spaceobserver_clean_data_excludes_invalid_participants(tmp_path): confidence_rts=[500] * 81, stimulus_intensities=[12] * 81, ) + # median RT > 3000 ms + rows += _participant_rows( + user_id="drop_slow_rt", + run=1, + accuracies=[1, 1, 0, 1], + rt_choices=[3200, 3100, 3300, 3400], + confidences=[30, 40, 50, 60], + confidence_rts=[500, 550, 600, 650], + stimulus_intensities=[10, 12, 14, 16], + ) + # median confidenceRT > 3000 ms + rows += _participant_rows( + user_id="drop_slow_conf_rt", + run=1, + accuracies=[1, 1, 0, 1], + rt_choices=[400, 500, 600, 700], + confidences=[30, 40, 50, 60], + confidence_rts=[3100, 3200, 3300, 3400], + stimulus_intensities=[10, 12, 14, 16], + ) + # median evidence strength > 25 + rows += _participant_rows( + user_id="drop_high_es", + run=1, + accuracies=[1, 1, 0, 1], + rt_choices=[400, 500, 600, 700], + confidences=[30, 40, 50, 60], + confidence_rts=[500, 550, 600, 650], + stimulus_intensities=[26, 28, 30, 32], + ) + # median confidence < 3 + rows += _participant_rows( + user_id="drop_low_conf", + run=1, + accuracies=[1, 1, 0, 1], + rt_choices=[400, 500, 600, 700], + confidences=[1, 2, 1, 2], + confidence_rts=[500, 550, 600, 650], + stimulus_intensities=[10, 12, 14, 16], + ) + # median confidence > 97 + rows += _participant_rows( + user_id="drop_high_conf", + run=1, + accuracies=[1, 1, 0, 1], + rt_choices=[400, 500, 600, 700], + confidences=[97, 99, 97, 99], + confidence_rts=[500, 550, 600, 650], + stimulus_intensities=[10, 12, 14, 16], + ) filepath = _write_space_observer_csv(tmp_path, rows) observer = SpaceObserver(str(filepath)) @@ -186,4 +300,4 @@ def test_spaceobserver_clean_data_excludes_invalid_participants(tmp_path): remaining_users = set(cleaned["userID"].unique()) assert remaining_users == {"keep"} - assert observer.deleted_participants == 3 + assert observer.deleted_participants == 8 From 35ca5c39141f39cccfe10fcac51751c22b21afaa Mon Sep 17 00:00:00 2001 From: lenarddome Date: Thu, 19 Mar 2026 11:55:32 +0100 Subject: [PATCH 45/54] fix: update file reading logic, fix time of day classification logic, and improve codebook descriptions in SpaceObserver and Scavenger classes, and several bug fixes --- .../perceptual_decision_making.py | 113 ++++++---- cpm/brainexplorer/risky_decision_making.py | 195 +++++++++++------- 2 files changed, 189 insertions(+), 119 deletions(-) diff --git a/cpm/brainexplorer/perceptual_decision_making.py b/cpm/brainexplorer/perceptual_decision_making.py index 1249a60..092ab92 100644 --- a/cpm/brainexplorer/perceptual_decision_making.py +++ b/cpm/brainexplorer/perceptual_decision_making.py @@ -37,7 +37,7 @@ def __init__(self, filepath=None): >>> spaceObserver.clean_data() >>> results = spaceObserver.results >>> results.to_csv("/example/spaceObserver_results.csv", index=False) - >>> spaceObserver.codebook() + >>> spaceObserver.get_codebook() Notes ----- @@ -62,7 +62,12 @@ def __init__(self, filepath=None): """ ## read data - self.data_raw = pd.read_csv(filepath, header=0, na_values=["NaN", "nan"]) + if filepath is None: + raise ValueError("filepath must be provided") + if filepath.endswith(".xlsx"): + self.data_raw = pd.read_excel(filepath, header=0, na_values=["NaN", "nan"]) + else: + self.data_raw = pd.read_csv(filepath, header=0, na_values=["NaN", "nan"]) self.data_raw = self.data_raw[ self.data_raw["RT_choice"] >= 150 @@ -90,43 +95,59 @@ def __init__(self, filepath=None): self.data_processed = pd.DataFrame() self.codebook = { + # --- Participant info --- "userID": "Unique identifier for each participant", + "run": "Session number for the participant", + "date": "Date and time of the first trial in the session", "n_trials": "Number of trials completed by the participant", - "day_of_week": "Day of the week of the trial", - "time": "Time of the trial", - "time_of_day": "Time of day of the trial (morning, afternoon, evening, night)", - "accuracy": "Mean accuracy (if the choice was the majority choice)", - "mean_RT": "Mean response time across all trials", - "median_RT": "Median response time across all trials", - "median_RT_correct": "Median response time for trials where the participant made the correct choice", - "median_RT_incorrect": "Median response time for trials where the participant made the incorrect choice", - "diff_median_RT_correct_incorrect": "Difference between the median response time for correct and incorrect choices", + "day_of_week": "Day of the week the session started", + "time": "Clock time of the first trial", + "time_of_day": "Time of day of the session (night: 0-6h, morning: 6-12h, afternoon: 12-18h, evening: 18-24h)", + # --- Accuracy --- + "accuracy": "Mean accuracy across all trials (proportion correct)", + # --- Choice reaction time --- + "mean_RT": "Mean choice response time across all trials (ms)", + "median_RT": "Median choice response time across all trials (ms)", + "median_RT_correct": "Median choice response time for correct trials (ms)", + "median_RT_incorrect": "Median choice response time for incorrect trials (ms)", + "diff_median_RT_correct_incorrect": "Difference in median choice RT: correct minus incorrect (ms)", + # --- Confidence --- "mean_confidence": "Mean confidence rating across all trials", - "sd_confidence": "Standard deviation of the confidence rating across all trials", + "sd_confidence": "Standard deviation of confidence ratings across all trials", "median_confidence": "Median confidence rating across all trials", - "median_confidence_correct": "Median confidence rating for trials where the participant made the correct choice", - "median_confidence_incorrect": "Median confidence rating for trials where the participant made the incorrect choice", - "diff_median_conf_correct_incorrect": "Difference between the median confidence rating for correct and incorrect choices", - "sd_confidence_correct": "Standard deviation of the confidence rating for trials where the participant made the correct choice", - "sd_confidence_incorrect": "Standard deviation of the confidence rating for trials where the participant made the incorrect choice", - "diff_sd_confidence_correct_incorrect": "Difference between the standard deviation of the confidence rating for correct and incorrect choices", - "mean_RT_confidence": "Mean confidence response time across all trials", - "median_RT_confidence": "Median confidence response time across all trials", - "median_RT_confidence_correct": "Median confidence response time for trials where the participant made the correct choice", - "median_RT_confidence_incorrect": "Median confidence response time for trials where the participant made the incorrect choice", - "diff_median_RT_confidence_correct_incorrect": "Difference between the median confidence response time for correct and incorrect choices", - "confidence_10": "10th percentile of the confidence rating across all trials", - "confidence_25": "25th percentile of the confidence rating across all trials", - "confidence_75": "75th percentile of the confidence rating across all trials", - "confidence_90": "90th percentile of the confidence rating across all trials", - "evidence_strength_mean": "Mean evidence strength across all trials", - "evidence_strength_correct_mean": "Mean evidence strength for trials where the participant made the correct choice", - "evidence_strength_incorrect_mean": "Mean evidence strength for trials where the participant made the incorrect choice", - "diff_evidence_strength_correct_incorrect": "Difference between the mean evidence strength for correct and incorrect choices", - "ES_bins": "Mean evidence strength for each of the bins. The trials here are split into 4 quarters. The evidence strength is divided into four equal bins and the mean is calculated for each bin.", + "mean_confidence_correct": "Mean confidence rating for correct trials", + "mean_confidence_incorrect": "Mean confidence rating for incorrect trials", + "median_confidence_correct": "Median confidence rating for correct trials", + "median_confidence_incorrect": "Median confidence rating for incorrect trials", + "diff_median_conf_correct_incorrect": "Difference in median confidence: correct minus incorrect trials", + "sd_confidence_correct": "Standard deviation of confidence ratings for correct trials", + "sd_confidence_incorrect": "Standard deviation of confidence ratings for incorrect trials", + "diff_sd_confidence_correct_incorrect": "Difference in confidence SD: correct minus incorrect trials", + # --- Confidence reaction time --- + "mean_confidenceRT": "Mean confidence response time across all trials (ms)", + "median_confidenceRT": "Median confidence response time across all trials (ms)", + "median_confidenceRT_correct": "Median confidence response time for correct trials (ms)", + "median_confidenceRT_incorrect": "Median confidence response time for incorrect trials (ms)", + "diff_median_confidenceRT_correct_incorrect": "Difference in median confidence RT: correct minus incorrect (ms)", + # --- Confidence percentiles --- + "confidence_10": "10th percentile of confidence ratings across all trials", + "confidence_25": "25th percentile of confidence ratings across all trials", + "confidence_75": "75th percentile of confidence ratings across all trials", + "confidence_90": "90th percentile of confidence ratings across all trials", + # --- Evidence strength --- + "evidence_strength_mean": "Mean evidence strength (stimulus intensity) across all trials", + "evidence_strength_correct_mean": "Mean evidence strength for correct trials", + "evidence_strength_incorrect_mean": "Mean evidence strength for incorrect trials", + "diff_evidence_strength_correct_incorrect": "Difference in mean evidence strength: correct minus incorrect trials", + "median_ES": "Median evidence strength across all trials", + # --- Evidence strength bins (trial-order quarters) --- + "ES_bin_1": "Mean evidence strength for the first quarter of trials", + "ES_bin_2": "Mean evidence strength for the second quarter of trials", + "ES_bin_3": "Mean evidence strength for the third quarter of trials", + "ES_bin_4": "Mean evidence strength for the fourth quarter of trials", } - def metrics(self, mode=None): + def metrics(self): """ Compute metrics for each participant and return a DataFrame with the results. The metrics are computed for each run separately. @@ -226,9 +247,15 @@ def metrics(self, mode=None): user_results["mean_confidence_incorrect"] = np.nanmean( user_data["confidence"][user_data["accuracy"] != 1] ) + user_results["median_confidence_correct"] = np.nanmedian( + user_data["confidence"][user_data["accuracy"] == 1] + ) + user_results["median_confidence_incorrect"] = np.nanmedian( + user_data["confidence"][user_data["accuracy"] != 1] + ) user_results["diff_median_conf_correct_incorrect"] = ( - user_results["mean_confidence_correct"] - - user_results["mean_confidence_incorrect"] + user_results["median_confidence_correct"] + - user_results["median_confidence_incorrect"] ) # standard deviation of confidence for correct and incorrect trials user_results["sd_confidence_correct"] = np.nanstd( @@ -286,12 +313,11 @@ def metrics(self, mode=None): bin_size = 4 # Divide evidence strength into 4 equal bins and calculate the mean for each bin - bins = np.array_split(evidence_strength, bin_size) - ES_1 = np.mean(bins[0]) if len(bins[0]) > 0 else np.nan - ES_2 = np.mean(bins[1]) if len(bins[1]) > 0 else np.nan - ES_3 = np.mean(bins[2]) if len(bins[2]) > 0 else np.nan - ES_4 = np.mean(bins[3]) if len(bins[3]) > 0 else np.nan - user_results["ES_bins"] = np.array([ES_1, ES_2, ES_3, ES_4]) + bins = np.array_split(evidence_strength.values, bin_size) + user_results["ES_bin_1"] = np.nanmean(bins[0]) if len(bins[0]) > 0 else np.nan + user_results["ES_bin_2"] = np.nanmean(bins[1]) if len(bins[1]) > 0 else np.nan + user_results["ES_bin_3"] = np.nanmean(bins[2]) if len(bins[2]) > 0 else np.nan + user_results["ES_bin_4"] = np.nanmean(bins[3]) if len(bins[3]) > 0 else np.nan # median evidence strength user_results["median_ES"] = np.nanmedian(evidence_strength) @@ -301,9 +327,6 @@ def metrics(self, mode=None): [self.data_processed, pd.DataFrame([user_results])], ignore_index=True ) - # Convert results to a DataFrame - self.data_processed = pd.DataFrame(self.data_processed) - return self.data_processed def clean_data(self): @@ -373,7 +396,7 @@ def clean_data(self): return self.cleanedresults - def codebook(self): + def get_codebook(self): """ Return a codebook describing the metrics. """ diff --git a/cpm/brainexplorer/risky_decision_making.py b/cpm/brainexplorer/risky_decision_making.py index fbae418..6aaedf3 100644 --- a/cpm/brainexplorer/risky_decision_making.py +++ b/cpm/brainexplorer/risky_decision_making.py @@ -37,7 +37,7 @@ def __init__(self, filepath=None): >>> scavenger.clean_data() >>> results = scavenger.results >>> results.to_csv("/example/scavenger_results.csv", index=False) - >>> scavenger.codebook() + >>> scavenger.get_codebook() Notes ----- @@ -58,7 +58,12 @@ def __init__(self, filepath=None): - Reaction time < 150 or > 10000 ms - Attempts after the first attempt ("run" variable) """ - self.data = pd.read_csv(filepath, header=0) + if filepath is None: + raise ValueError("filepath must be provided") + if filepath.endswith(".xlsx"): + self.data = pd.read_excel(filepath, header=0) + else: + self.data = pd.read_csv(filepath, header=0) self.data = self.data[self.data["run"] == 1] # only keep first attempt self.data = self.data[ @@ -71,58 +76,111 @@ def __init__(self, filepath=None): self.results = pd.DataFrame() self.group_results = pd.DataFrame() self.codebook = { + # --- Participant info --- "userID": "Unique identifier for each participant", "n_trials": "Number of trials completed by the participant", - "day_of_week": "Day of the week of the trial", - "time": "Time of the trial", - "time_of_day": "Time of day of the trial (morning, afternoon, evening, night)", - "mean_RT": "Mean reaction time", - "median_RT": "Median reaction time", - "median_RT_safe": "Median reaction time for safe choices", - "median_RT_risky": "Median reaction time for risky choices", - "median_RT_ambg": "Median reaction time for ambiguous trials", - "median_RT_NoAmbg": "Median reaction time for non-ambiguous trials", - "median_RT_safe_ambg": "Median reaction time for safe choices in ambiguous trials", - "median_RT_safe_NoAmbg": "Median reaction time for safe choices in non-ambiguous trials", - "median_RT_risky_ambg": "Median reaction time for risky choices in ambiguous trials", - "median_RT_risky_NoAmbg": "Median reaction time for risky choices in non-ambiguous trials", + "day_of_week": "Day of the week the session started", + "time": "Clock time of the first trial", + "time_of_day": "Time of day of the session (night: 0-6h, morning: 6-12h, afternoon: 12-18h, evening: 18-24h)", + # --- Reaction time --- + "mean_RT": "Mean reaction time across all trials (ms)", + "median_RT": "Median reaction time across all trials (ms)", + "median_RT_safe": "Median reaction time for safe choices (ms)", + "median_RT_risky": "Median reaction time for risky choices (ms)", + "median_RT_win": "Median reaction time for win trials (ms)", + "median_RT_loss": "Median reaction time for loss trials (ms)", + "median_RT_ambg": "Median reaction time for ambiguous trials (ms)", + "median_RT_NoAmbg": "Median reaction time for non-ambiguous trials (ms)", + "median_RT_win_ambg": "Median reaction time for win trials that were ambiguous (ms)", + "median_RT_win_NoAmbg": "Median reaction time for win trials that were non-ambiguous (ms)", + "median_RT_loss_ambg": "Median reaction time for loss trials that were ambiguous (ms)", + "median_RT_loss_NoAmbg": "Median reaction time for loss trials that were non-ambiguous (ms)", + "diff_RT_win_amg_win_noAmbg": "Difference in median RT: win-ambiguous minus win-non-ambiguous (ms)", + "diff_RT_loss_amg_loss_noAmbg": "Difference in median RT: loss-ambiguous minus loss-non-ambiguous (ms)", + "diff_RT_win_loss_ambg": "Difference in median RT: win minus loss in ambiguous trials (ms)", + "diff_RT_win_loss_noAmbg": "Difference in median RT: win minus loss in non-ambiguous trials (ms)", + "diff_RT_win_ambg_loss_noAmbg": "Difference in median RT: win-ambiguous minus loss-non-ambiguous (ms)", + "diff_RT_loss_ambg_win_noAmbg": "Difference in median RT: loss-ambiguous minus win-non-ambiguous (ms)", + # --- Points / outcomes --- + "total_rewards": "Total points received across all trials", "mean_points": "Mean points received across all trials", - "mean_points_safe": "Mean points received for safe choices", - "mean_points_risky": "Mean points received for risky choices", + "mean_points_safe": "Mean points received when choosing safe", + "mean_points_risky": "Mean points received when choosing risky", "mean_points_ambg": "Mean points received in ambiguous trials", - "mean_points_NoAmbg": "Mean points received in non-ambiguous", - "mean_points_safe_ambg": "Mean points received for safe choices in ambiguous trials", - "mean_points_safe_NoAmbg": "Mean points received for safe choices in non-ambiguous trials", - "mean_points_risky_ambg": "Mean points received for risky choices in ambiguous trials", - "mean_points_risky_NoAmbg": "Mean points received for risky choices in non-ambiguous", + "mean_points_NoAmbg": "Mean points received in non-ambiguous trials", + "mean_points_win_ambg": "Mean points received in win trials that were ambiguous", + "mean_points_win_NoAmbg": "Mean points received in win trials that were non-ambiguous", + "mean_points_loss_ambg": "Mean points received in loss trials that were ambiguous", + "mean_points_loss_NoAmbg": "Mean points received in loss trials that were non-ambiguous", + "diff_points_win_amg_win_noAmbg": "Difference in mean points: win-ambiguous minus win-non-ambiguous", + "diff_points_loss_amg_loss_noAmbg": "Difference in mean points: loss-ambiguous minus loss-non-ambiguous", + "diff_points_win_loss_ambg": "Difference in mean points: win minus loss in ambiguous trials", + "diff_points_win_loss_noAmbg": "Difference in mean points: win minus loss in non-ambiguous trials", + "diff_points_win_ambg_loss_noAmbg": "Difference in mean points: win-ambiguous minus loss-non-ambiguous", + "diff_points_loss_ambg_win_noAmbg": "Difference in mean points: loss-ambiguous minus win-non-ambiguous", + # --- Choice stickiness --- + "choice_stickiness": "Number of consecutive trial pairs where the same option (safe or risky) was chosen", + # --- Risky choice rates --- "risky_choices": "Proportion of risky choices overall", - "win_risk": "Proportion of risky wins", - "loss_risk": "Proportion of risky losses", - "win_risk_abg": "Proportion of risky wins in ambiguous trials", - "win_risk_NoAbg": "Proportion of risky wins in non-ambiguous trials", - "loss_risk_abg": "Proportion of risky losses in ambiguous trials", - "loss_risk_NoAbg": "Proportion of risky losses in non-ambiguous trials", - "diff_loss_win_risk": "Difference between proportion of risky losses and wins", - "diff_Ambg_NoAmbg_risk": "Difference between proportion of risky choices in ambiguous and non-ambiguous trials", - "diff_Ambg_NoAmbg_win_risk": "Difference between proportion of risky wins in ambiguous and non-ambiguous trials", - "diff_Ambg_NoAmbg_loss_risk": "Difference between proportion of risky losses in ambiguous and non-ambiguous trials", - "rational_all": "Proportion of rational choices", - "rational_win": "Proportion of rational choices in wins", - "rational_loss": "Proportion of rational choices in losses", - "rational_all_abg": "Proportion of rational choices in ambiguous trials", - "rational_win_abg": "Proportion of rational choices in wins in ambiguous trials", - "rational_loss_abg": "Proportion of rational choices in losses in ambiguous trials", - "rational_all_NoAbg": "Proportion of rational choices in non-ambiguous trials", - "diff_NoAmbg_win_loss_rational": "Difference between proportion of rational choices in wins and losses in non-ambiguous trials", - "rational_win_NoAbg": "Proportion of rational choices in wins in non-ambiguous trials", - "rational_loss_NoAbg": "Proportion of rational choices in losses in non-ambiguous trials", - "diff_Ambg_NoAmbg_rational": "Difference between proportion of rational choices in ambiguous and non-ambiguous trials", - "diff_Ambg_NoAmbg_win_rational": "Difference between proportion of rational choices in wins in ambiguous and non-ambiguous trials", - "diff_Ambg_NoAmbg_loss_rational": "Difference between proportion of rational choices in losses in ambiguous and non-ambiguous trials", - "nb_incorrect_gain": "Number of incorrect choices in gain trials", - "above_chance": "Proportion of trials with choices above chance level", - "chosen_prop_LeftRight": "Proportion of trials where option A (safe) was chosen", - "chosen_prop_SafeRisky": "Proportion of trials where the safe option was chosen", + "win_risk": "Proportion of risky choices in win trials", + "loss_risk": "Proportion of risky choices in loss trials", + "diff_loss_win_risk": "Difference in risky choice rate: loss minus win trials", + "risk_Ambg": "Proportion of risky choices in ambiguous trials", + "risk_NoAmbg": "Proportion of risky choices in non-ambiguous trials", + "diff_Ambg_NoAmbg_risk": "Difference in risky choice rate: ambiguous minus non-ambiguous trials", + "win_risk_abg": "Proportion of risky choices in win-ambiguous trials", + "win_risk_NoAbg": "Proportion of risky choices in win-non-ambiguous trials", + "loss_risk_abg": "Proportion of risky choices in loss-ambiguous trials", + "loss_risk_NoAbg": "Proportion of risky choices in loss-non-ambiguous trials", + "diff_Ambg_NoAmbg_win_risk": "Difference in risky win rate: ambiguous minus non-ambiguous", + "diff_Ambg_NoAmbg_loss_risk": "Difference in risky loss rate: ambiguous minus non-ambiguous", + "diff_Ambg_win_loss_risk": "Difference in risky choice rate: win minus loss in ambiguous trials", + "diff_NoAmbg_win_loss_risk": "Difference in risky choice rate: win minus loss in non-ambiguous trials", + "diff_amg_win_nonAmg_loss_risk": "Difference in risky choice rate: win-ambiguous minus loss-non-ambiguous", + "diff_amg_loss_nonAmg_win_risk": "Difference in risky choice rate: loss-ambiguous minus win-non-ambiguous", + # --- Rational choices (overall, win/loss, ambiguity) --- + "rational_all": "Proportion of rational choices across all trials", + "rational_win": "Proportion of rational choices in win trials", + "rational_loss": "Proportion of rational choices in loss trials", + "diff_loss_win_rational": "Difference in rational choice rate: loss minus win trials", + "rational_abg": "Proportion of rational choices in ambiguous trials", + "rational_NoAbg": "Proportion of rational choices in non-ambiguous trials", + "diff_Ambg_NoAmbg_rational": "Difference in rational choice rate: ambiguous minus non-ambiguous", + "rational_win_abg": "Proportion of rational choices in win-ambiguous trials", + "rational_win_NoAbg": "Proportion of rational choices in win-non-ambiguous trials", + "rational_loss_abg": "Proportion of rational choices in loss-ambiguous trials", + "rational_loss_NoAbg": "Proportion of rational choices in loss-non-ambiguous trials", + "diff_rational_Ambg_NoAmbg_win": "Difference in rational win rate: ambiguous minus non-ambiguous", + "diff_rational_Ambg_NoAmbg_loss": "Difference in rational loss rate: ambiguous minus non-ambiguous", + "diff_rational_Ambg_win_loss": "Difference in rational choice rate: win minus loss in ambiguous trials", + "diff_rational_NoAmbg_win_loss": "Difference in rational choice rate: win minus loss in non-ambiguous trials", + "diff_rational_Ambg_win_NoAmbg_loss": "Difference in rational choice rate: win-ambiguous minus loss-non-ambiguous", + "diff_rational_Ambg_loss_NoAmbg_win": "Difference in rational choice rate: loss-ambiguous minus win-non-ambiguous", + # --- Rational choices (safe/risky) --- + "rational_safe": "Proportion of rational choices when choosing safe", + "rational_risky": "Proportion of rational choices when choosing risky", + "diff_rational_safe_risky": "Difference in rational choice rate: safe minus risky choices", + "rational_safe_win": "Proportion of rational choices for safe choices in win trials", + "rational_safe_loss": "Proportion of rational choices for safe choices in loss trials", + "rational_risky_win": "Proportion of rational choices for risky choices in win trials", + "rational_risky_loss": "Proportion of rational choices for risky choices in loss trials", + "diff_rational_safe_win_risky_win": "Difference in rational win rate: safe minus risky choices", + "diff_rational_safe_loss_risky_loss": "Difference in rational loss rate: safe minus risky choices", + "diff_rational_safe_win_risky_loss": "Difference in rational choice rate: safe-win minus risky-loss", + "diff_rational_safe_loss_risky_win": "Difference in rational choice rate: safe-loss minus risky-win", + "rational_safe_abg": "Proportion of rational choices for safe choices in ambiguous trials", + "rational_safe_NoAbg": "Proportion of rational choices for safe choices in non-ambiguous trials", + "rational_risky_abg": "Proportion of rational choices for risky choices in ambiguous trials", + "rational_risky_NoAbg": "Proportion of rational choices for risky choices in non-ambiguous trials", + "diff_rational_safe_Ambg_NoAmbg": "Difference in rational safe rate: ambiguous minus non-ambiguous", + "diff_rational_risky_Ambg_NoAmbg": "Difference in rational risky rate: ambiguous minus non-ambiguous", + "diff_rational_safe_risky_Ambg": "Difference in rational choice rate: safe minus risky in ambiguous trials", + "diff_rational_safe_risky_NoAmbg": "Difference in rational choice rate: safe minus risky in non-ambiguous trials", + # --- Quality / exclusion metrics --- + "nb_incorrect_gain": "Number of incorrect choices in no-brainer gain catch trials", + "above_chance": "Proportion of trials where choice was above chance level", + "chosen_prop_LeftRight": "Proportion of trials where the same left/right response was repeated", + "chosen_prop_SafeRisky": "Proportion of trials where the same safe/risky choice was repeated", } def metrics(self): @@ -246,17 +304,10 @@ def metrics(self): ) # morning 6-12, afternoon 12-18, evening 18-24, night 0-6 user_results["time_of_day"] = ( - "morning" - if date.hour < 12 - else ( - "afternoon" - if date.hour < 18 - else ( - "evening" - if date.hour < 24 - else "night" if date.hour < 6 else "-" - ) - ) + "night" if date.hour < 6 + else "morning" if date.hour < 12 + else "afternoon" if date.hour < 18 + else "evening" ) user_results["mean_RT"] = np.nanmean(user_data["RT"]) @@ -361,8 +412,7 @@ def metrics(self): # choice stickiness user_results["choice_stickiness"] = np.sum( - user_data["chosen"].iloc[i] == 1 - and user_data["chosen"].iloc[i + 1] == 1 + user_data["chosen"].iloc[i] == user_data["chosen"].iloc[i + 1] for i in range(len(user_data["chosen"]) - 1) ) @@ -510,17 +560,17 @@ def metrics(self): # rational choices in risky and safe trials for ambiguous and non-ambiguous trials user_results["rational_safe_abg"] = np.sum( - ev_diff_chosen[t_abg & rsk == 0] >= 0 - ) / np.sum(t_abg & rsk == 0) + ev_diff_chosen[t_abg & (rsk == 0)] >= 0 + ) / np.sum(t_abg & (rsk == 0)) user_results["rational_safe_NoAbg"] = np.sum( - ev_diff_chosen[~t_abg & rsk == 0] >= 0 - ) / np.sum(~t_abg & rsk == 0) + ev_diff_chosen[~t_abg & (rsk == 0)] >= 0 + ) / np.sum(~t_abg & (rsk == 0)) user_results["rational_risky_abg"] = np.sum( - ev_diff_chosen[~t_abg & rsk == 1] >= 0 - ) / np.sum(~t_abg & rsk == 1) + ev_diff_chosen[t_abg & (rsk == 1)] >= 0 + ) / np.sum(t_abg & (rsk == 1)) user_results["rational_risky_NoAbg"] = np.sum( - ev_diff_chosen[t_abg & rsk == 1] >= 0 - ) / np.sum(t_abg & rsk == 1) + ev_diff_chosen[~t_abg & (rsk == 1)] >= 0 + ) / np.sum(~t_abg & (rsk == 1)) user_results["diff_rational_safe_Ambg_NoAmbg"] = ( user_results["rational_safe_abg"] - user_results["rational_safe_NoAbg"] ) @@ -556,9 +606,6 @@ def metrics(self): [self.results, pd.DataFrame([user_results])], ignore_index=True ) - # Convert results to a DataFrame - self.results = pd.DataFrame(self.results) - return self.results def clean_data(self): @@ -606,7 +653,7 @@ def clean_data(self): return self.cleanedresults - def codebook(self): + def get_codebook(self): """ Return a codebook describing the metrics. """ From f4bb6b60a54ca97614da0aecdf0e17f8a4b52d05 Mon Sep 17 00:00:00 2001 From: Lenard Dome <23503601+lenarddome@users.noreply.github.com> Date: Mon, 5 Oct 2026 14:13:26 +0200 Subject: [PATCH 46/54] fix(brainexplorer): fix metrics, exclusions and codebooks Recode Scavenger choices, fix time of day, and accept DataFrames. Co-Authored-By: Claude Opus 5.5 --- cpm/brainexplorer/_utils.py | 74 ++ cpm/brainexplorer/bandits.py | 517 +++++------- cpm/brainexplorer/information_gathering.py | 340 +++----- .../perceptual_decision_making.py | 488 ++++------- cpm/brainexplorer/risky_decision_making.py | 778 ++++++------------ 5 files changed, 821 insertions(+), 1376 deletions(-) create mode 100644 cpm/brainexplorer/_utils.py diff --git a/cpm/brainexplorer/_utils.py b/cpm/brainexplorer/_utils.py new file mode 100644 index 0000000..c821515 --- /dev/null +++ b/cpm/brainexplorer/_utils.py @@ -0,0 +1,74 @@ +"""Helpers shared by the BrainExplorer task classes.""" + +import os +import warnings +from contextlib import contextmanager + +import numpy as np +import pandas as pd + + +def load_data(source, name, **kwargs): + """ + Read the data of a task from a file, or copy a DataFrame. + + `source` is a pandas DataFrame, or the path to a CSV or Excel (``.xlsx``) file. + Keyword arguments are passed on to :func:`pandas.read_csv` or + :func:`pandas.read_excel`. + """ + if source is None: + raise ValueError(f"{name} needs data: a pandas DataFrame or the path to a CSV or Excel file.") + if isinstance(source, pd.DataFrame): + return source.copy() + path = os.fspath(source) + if path.endswith(".xlsx"): + return pd.read_excel(path, header=0, **kwargs) + return pd.read_csv(path, header=0, **kwargs) + + +def require(data, columns, name): + """Raise a `KeyError` that names every required column missing from `data`.""" + missing = [column for column in columns if column not in data.columns] + if missing: + raise KeyError(f"{name} needs the columns {missing}, which are missing from the data.") + + +def time_of_day(hour): + """Name the time of day of an hour: night (0-6h), morning (6-12h), afternoon (12-18h) or evening (18-24h).""" + if hour < 6: + return "night" + if hour < 12: + return "morning" + if hour < 18: + return "afternoon" + return "evening" + + +def session_time(date): + """The date, day of the week, clock time and time of day of the first trial of a session.""" + date = pd.Timestamp(pd.to_datetime(date, format="ISO8601")) if isinstance(date, str) else pd.Timestamp(date) + return { + "date": date, + "day_of_week": date.day_name(), + "time": date.time(), + "time_of_day": time_of_day(date.hour), + } + + +@contextmanager +def quiet_empty_slices(): + """Silence the warnings numpy raises for the mean, median or SD of an empty selection, which are NaN by design.""" + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="Mean of empty slice") + warnings.filterwarnings("ignore", message="All-NaN slice encountered") + warnings.filterwarnings("ignore", message="Degrees of freedom <= 0") + warnings.filterwarnings("ignore", message="invalid value encountered") + yield + + +def proportion(condition, mask): + """The proportion of trials in `mask` for which `condition` is true, or NaN if `mask` selects no trials.""" + mask = np.asarray(mask, dtype=bool) + if not mask.any(): + return np.nan + return float(np.mean(np.asarray(condition, dtype=bool)[mask])) diff --git a/cpm/brainexplorer/bandits.py b/cpm/brainexplorer/bandits.py index e74dc95..cbdc71c 100644 --- a/cpm/brainexplorer/bandits.py +++ b/cpm/brainexplorer/bandits.py @@ -1,71 +1,85 @@ import numpy as np -import matplotlib.pyplot as plt import pandas as pd +from ._utils import load_data, quiet_empty_slices, require, session_time -class MilkyWay: - """ - Class to calculate metrics for a two-arm bandit task, Milky Way & Pirate Market in BrainExplorer. - - """ - - def __init__(self, filepath): - """ - Load data to be analyised and turn into Pandas DataFrame. - Trial-level exclusion criteria are applied to the data. - The results for Milky Way and Pirate Market trials are stored in separate DataFrames. - - Parameters - ---------- - filepath : str - The path to the CSV file to be processed. The column names in the data must subscribe to a prespecified convention, see Notes. +__all__ = ["MilkyWay"] - Example - ---------- +COLUMNS = ["userID", "trial_type", "run", "date", "correct", "outchosen", "obt_min_forg", "rep", "WSLS_v1", "WSLS_v2"] - >>> milkyWay = milkyWay("/example/2025-02-20_SpaceObserver_Data_short.xlsx") - >>> milkyWay.metrics() - >>> milkyWay.clean_data() - >>> results_MW = milkyWay.results_MW - >>> results_PM = milkyWay.results_PM - >>> milkyWay.codebook() - >>> diff_results = milkyWay.difference_metrics() +## the two games, by their trial_type in the data: (name, suffix of the metrics) +GAMES = {"reward": ("Milky Way", "MW"), "punish": ("Pirate Market", "PM")} +## the metrics that are compared between the games: (name of the difference, metric without its suffix) +DIFFERENCES = [ + ("accuracy_diff", "accuracy"), + ("outcome_diff", "mean_outcome"), + ("reward_diff_obt_forg_diff", "reward_diff_obt_forg"), + ("prop_same_choice_diff", "prop_same_choice"), + ("prop_WSLS_1_diff", "prop_WSLS_1"), + ("prop_WSLS_2_diff", "prop_WSLS_2"), +] - Notes - ----- - The columns required in data: - - userID: unique identifier for each participant - - trial_type: the type of trial (Milky Way or Pirate Market) - - date: the date and time of the trial - - correct: whether the choice was correct (1) or incorrect (0) - - outchosen: the outcome of the trial - - rep: whether the choice was repeated (1) or not (0) - - WSLS_v1: whether the choice was a win-stay/loose-shift choice for correct trials version 1 - - WSLS_v2: whether the choice was a win-stay/loose-shift choice for correct trials version 2 - - The trial-level exclusion criteria are applied: - - Attempts after the first attempt ("run" variable) - - The reaction times have to be corrected by the average time between the previous choice and new stimulus presentation which is 4200 ms. However, this correction can lead to negative reaction times, which may exclude some participants from the analysis. - """ - self.data = pd.read_csv(filepath) - - # Separate into MW and PM data - self.MW_data = self.data[self.data["trial_type"] == "reward"] - self.PM_data = self.data[self.data["trial_type"] == "punish"] - - # Take first attempt (i.e., not neccessarily run == 1 for PM) - min_run_per_user = self.MW_data.groupby("userID")["run"].transform("min") - self.MW_data = self.MW_data[ - self.MW_data["run"] == min_run_per_user - ].reset_index(drop=True) +class MilkyWay: + """ + Compute descriptive statistics from the two-armed bandit tasks in BrainExplorer, *Milky Way* (reward) and *Pirate Market* (punishment). + + The metrics of the two games are computed separately, and :meth:`difference_metrics` compares them within participants. + + Parameters + ---------- + filepath : str, os.PathLike or pandas.DataFrame + The data, as a DataFrame or the path to a CSV or Excel (``.xlsx``) file. The column names must follow the convention in Notes. + + Attributes + ---------- + MW_data, PM_data : pandas.DataFrame + The Milky Way and Pirate Market trials that pass the trial-level exclusion criteria. + results_MW, results_PM : pandas.DataFrame + The metrics of each participant in each game, filled by :meth:`metrics`. + results_diff : pandas.DataFrame + The differences between the games, filled by :meth:`difference_metrics`. + cleanedresults_MW, cleanedresults_PM, cleanedresults_diff : pandas.DataFrame + The results of the participants that pass the participant-level exclusion criteria, filled by :meth:`clean_data`. + deleted_participants_MW, deleted_participants_PM, deleted_participants_diff : int + The number of participants that :meth:`clean_data` excluded from each table. + codebook : dict + The description of each column of the metrics. + + Examples + -------- + >>> from cpm.brainexplorer.bandits import MilkyWay + >>> milky_way = MilkyWay("2025-02-20_MilkyWay_Data.csv") + >>> results_MW, results_PM = milky_way.metrics() + >>> differences = milky_way.difference_metrics() + >>> cleaned_MW, cleaned_PM, cleaned_diff = milky_way.clean_data() + + Notes + ----- + The data must contain the following columns: + + - ``userID``: the unique identifier of the participant. + - ``trial_type``: the game, ``"reward"`` for Milky Way or ``"punish"`` for Pirate Market. + - ``run``: the attempt number of the participant. + - ``date``: the date and time of the trial. + - ``correct``: whether the choice was correct (1) or incorrect (0). + - ``outchosen``: the outcome of the chosen option. + - ``obt_min_forg``: the obtained minus the foregone outcome. + - ``rep``: whether the choice repeated the previous one (1) or not (0). + - ``WSLS_v1``, ``WSLS_v2``: whether the choice was a win-stay/lose-shift choice, in two versions of the definition. + + Only the first attempt of each participant in each game is kept, which is not necessarily ``run`` 1 for Pirate Market. + + Response times are not analysed. They would have to be corrected for the average time between the previous choice and the presentation of the new stimuli, 4200 ms, which can make them negative. + """ - min_run_per_user = self.PM_data.groupby("userID")["run"].transform("min") - self.PM_data = self.PM_data[ - self.PM_data["run"] == min_run_per_user - ].reset_index(drop=True) + def __init__(self, filepath=None): + data = load_data(filepath, "MilkyWay") + require(data, COLUMNS, "MilkyWay") + self.data = data + self.MW_data = _first_attempt(data[data["trial_type"] == "reward"]) + self.PM_data = _first_attempt(data[data["trial_type"] == "punish"]) self.results_MW = pd.DataFrame() self.results_PM = pd.DataFrame() @@ -75,310 +89,155 @@ def __init__(self, filepath): "userID": "Unique identifier for each participant", "n_trials": "Number of trials completed by the participant", "trial_type": "Type of trial (Milky Way or Pirate Market)", - "day_of_week": "Day of the week when the trial was conducted", - "time": "Time when the trial was conducted", - "time_of_day": "Time of day when the trial was conducted (morning, afternoon, evening, night)", - "mean_RT_MW": "Mean reaction time for Milky Way trials", - "median_RT_MW": "Median reaction time for Milky Way trials", - "mean_RT_PM": "Mean reaction time for Pirate Market trials", - "median_RT_PM": "Median reaction time for Pirate Market trials", - "accuracy_MW": "Mean accuracy for Milky Way trials", - "accuracy_PM": "Mean accuracy for Pirate Market trials", - "prop_same_choice_MW": "Proportion of same choice in Milky Way trials", - "prop_same_choice_PM": "Proportion of same choice in Pirate Market trials", - "mean_outcome_MW": "Mean outcome for Milky Way trials", - "mean_outcome_PM": "Mean outcome for Pirate Market trials", - "reward_diff_obt_forg_MW": "Mean difference between obtained and foregone reward for Milky Way trials", - "reward_diff_obt_forg_PM": "Mean difference between obtained and foregone reward for Pirate Market trials", - "prop_WSLS_1_MW": "Proportion of win-stay/loose-shift choices for correct trials version 1 for Milky Way trials", - "prop_WSLS_2_MW": "Proportion of win-stay/loose-shift choices for correct trials version 2 for Milky Way trials", - "prop_WSLS_1_PM": "Proportion of win-stay/loose-shift choices for correct trials version 1 for Pirate Market trials", - "prop_WSLS_2_PM": "Proportion of win-stay/loose-shift choices for correct trials version 2 for Pirate Market trials", + "date": "Date and time of the first trial", + "day_of_week": "Day of the week of the first trial", + "time": "Clock time of the first trial", + "time_of_day": "Time of day of the first trial (night: 0-6h, morning: 6-12h, afternoon: 12-18h, evening: 18-24h)", } + for name, suffix in GAMES.values(): + self.codebook.update( + { + f"accuracy_{suffix}": f"Mean accuracy for {name} trials", + f"mean_outcome_{suffix}": f"Mean outcome for {name} trials", + f"reward_diff_obt_forg_{suffix}": f"Mean difference between obtained and foregone reward for {name} trials", + f"prop_same_choice_{suffix}": f"Proportion of same choice in {name} trials", + f"prop_WSLS_1_{suffix}": f"Proportion of win-stay/lose-shift choices (version 1) for {name} trials", + f"prop_WSLS_2_{suffix}": f"Proportion of win-stay/lose-shift choices (version 2) for {name} trials", + } + ) + self.codebook.update( + { + "accuracy_diff": "Difference in accuracy: Milky Way minus Pirate Market", + "outcome_diff": "Difference in mean outcome: Milky Way minus Pirate Market", + "reward_diff_obt_forg_diff": "Difference in the mean obtained minus foregone reward: Milky Way minus Pirate Market", + "prop_same_choice_diff": "Difference in the proportion of same choices: Milky Way minus Pirate Market", + "prop_WSLS_1_diff": "Difference in the proportion of win-stay/lose-shift choices (version 1): Milky Way minus Pirate Market", + "prop_WSLS_2_diff": "Difference in the proportion of win-stay/lose-shift choices (version 2): Milky Way minus Pirate Market", + } + ) def metrics(self): """ - Calculate the metrics for the data. + Compute the metrics of each participant in each game. Returns ------- - results : pd.DataFrame - A DataFrame containing the results of the metrics. - - Variables - ---------- - - userID: unique identifier for each participant - - n_trials: number of trials completed by the participant - - day_of_week: day of the week of the trial - - time: time of the trial - - time_of_day: time of day of the trial (morning, afternoon, evening - - trial_type: type of trial (Milky Way or Pirate Market) - - mean_RT_MW: Mean reaction time for Milky Way trials - - median_RT_MW: Median reaction time for Milky Way trials - - mean_RT_PM: Mean reaction time for Pirate Market trials - - median_RT_PM: Median reaction time for Pirate Market trials - - accuracy_MW: Accuracy for Milky Way trials - - accuracy_PM: Accuracy for Pirate Market trials - - prop_same_choice_MW: Proportion of same choice in Milky Way trials - - prop_same_choice_PM: Proportion of same choice in Pirate Market trials - - mean_outcome_MW: Mean outcome for Milky Way trials - - mean_outcome_PM: Mean outcome for Pirate Market trials - - reward_diff_obt_forg_MW: Mean difference between obtained and foregone reward for Milky Way trials - - reward_diff_obt_forg_PM: Mean difference between obtained and foregone reward for Pirate Market trials - - prop_WSLS_1_MW: Proportion of win-stay/loose-shift choices for correct trials version 1 for Milky Way trials - - prop_WSLS_2_MW: Proportion of win-stay/loose-shift choices for correct trials version 2 for Milky Way trials - - prop_WSLS_1_PM: Proportion of win-stay/loose-shift choices for correct trials version 1 for Pirate Market trials - - prop_WSLS_2_PM: Proportion of win-stay/loose-shift choices for correct trials version 2 for Pirate Market trials - + tuple of pandas.DataFrame + The metrics of Milky Way and of Pirate Market, one row per participant, also stored in `results_MW` and `results_PM`. The columns are described in `codebook`. """ - # group MilkyWay and Pirate Market data - MW_data = self.MW_data.groupby("userID") - PM_data = self.PM_data.groupby("userID") - - # Loop through each group of user data - for user_id, user_data_MW in MW_data: - - user_results_MW = {"userID": user_id} - - user_results_MW["n_trials"] = len(user_data_MW) - - user_results_MW["trial_type"] = "Milky Way" - - date = user_data_MW["date"].iloc[0] - if isinstance(date, str): - date = pd.to_datetime(date, format="%Y-%m-%d %H:%M:%S.%f") - user_data_MW["day_of_week"] = date.day_name() - user_data_MW["time"] = ( - date.time() if hasattr(date, "time") else date.strftime("%H:%M:%S.%f") - ) - # morning 6-12, afternoon 12-18, evening 18-24, night 0-6 - user_data_MW["time_of_day"] = ( - "morning" - if date.hour < 12 - else ( - "afternoon" - if date.hour < 18 - else ( - "evening" - if date.hour < 24 - else "night" if date.hour < 6 else "-" - ) - ) - ) - - # user_results_MW["mean_RT_MW"] = np.mean(user_data_MW['rt_adj']) - # user_results_MW["median_RT_MW"] = np.median(user_data_MW['rt_adj']) - - user_results_MW["accuracy_MW"] = np.nanmean(user_data_MW["correct"]) - - user_results_MW["mean_outcome_MW"] = np.nanmean(user_data_MW["outchosen"]) - - # Mean difference between obtained and foregone reward - user_results_MW["reward_diff_obt_forg_MW"] = np.nanmean( - user_data_MW["obt_min_forg"] - ) - - # proportion of repeated choices - user_results_MW["prop_same_choice_MW"] = np.nanmean(user_data_MW["rep"]) - - # proportion of win-stay/loose-shift choices - user_results_MW["prop_WSLS_1_MW"] = np.nanmean(user_data_MW["WSLS_v1"]) - - user_results_MW["prop_WSLS_2_MW"] = np.nanmean(user_data_MW["WSLS_v2"]) - - # Append user-level results - self.results_MW = pd.concat( - [self.results_MW, pd.DataFrame([user_results_MW])], ignore_index=True - ) - - self.results_MW = pd.DataFrame(self.results_MW) - - for user_id, user_data_PM in PM_data: + self.results_MW = _game_metrics(self.MW_data, *GAMES["reward"]) + self.results_PM = _game_metrics(self.PM_data, *GAMES["punish"]) + return self.results_MW, self.results_PM - user_results_PM = {"userID": user_id} - - user_results_PM["n_trials"] = len(user_data_PM) + def difference_metrics(self): + """ + Compute the difference between the metrics of Milky Way and Pirate Market, for each participant who played both. - user_results_PM["trial_type"] = "Pirate Market" + Runs :meth:`metrics` first if it has not been run yet. - date = user_data_PM["date"].iloc[0] - if isinstance(date, str): - date = pd.to_datetime(date, format="%Y-%m-%d %H:%M:%S.%f") - user_data_PM["day_of_week"] = date.day_name() - user_data_PM["time"] = ( - date.time() if hasattr(date, "time") else date.strftime("%H:%M:%S.%f") - ) - # morning 6-12, afternoon 12-18, evening 18-24, night 0-6 - user_data_PM["time_of_day"] = ( - "morning" - if date.hour < 12 - else ( - "afternoon" - if date.hour < 18 - else "evening" if date.hour < 24 else "night" - ) - ) + Returns + ------- + pandas.DataFrame + The differences, Milky Way minus Pirate Market, one row per participant, also stored in `results_diff`. The columns are described in `codebook`. + """ + if self.results_MW.empty and self.results_PM.empty: + self.metrics() + columns = ["userID"] + [name for name, _ in DIFFERENCES] + if self.results_MW.empty or self.results_PM.empty: + self.results_diff = pd.DataFrame(columns=columns) + return self.results_diff + both = self.results_MW.merge(self.results_PM, on="userID", sort=False) + for name, metric in DIFFERENCES: + both[name] = both[f"{metric}_MW"] - both[f"{metric}_PM"] + self.results_diff = both[columns].reset_index(drop=True) + return self.results_diff - # user_results_PM["mean_RT_PM"] = np.mean(user_data_PM['rt_adj']) - # user_results_PM["median_RT_PM"] = np.median(user_data_PM['rt_adj']) + def clean_data(self): + """ + Exclude participants with the participant-level exclusion criteria. - user_results_PM["accuracy_PM"] = np.nanmean(user_data_PM["correct"]) + Runs :meth:`metrics` and :meth:`difference_metrics` first if they have not been run yet. - user_results_PM["mean_outcome_PM"] = np.nanmean(user_data_PM["outchosen"]) + Returns + ------- + tuple of pandas.DataFrame + The metrics of Milky Way, of Pirate Market, and their differences, for the participants that pass the criteria, also stored in `cleanedresults_MW`, `cleanedresults_PM` and `cleanedresults_diff`. - # Mean difference between obtained and foregone reward - user_results_PM["reward_diff_obt_forg_PM"] = np.nanmean( - user_data_PM["obt_min_forg"] - ) + Notes + ----- + A participant is excluded from the metrics of a game if - # proportion of repeated choices - user_results_PM["prop_same_choice_PM"] = np.nanmean(user_data_PM["rep"]) + - they made the same choice on at least 95% of the trials, + - their accuracy is missing, + - they played more than 72 trials of the game (due to a technical error). - # proportion of win-stay/loose-shift choices - user_results_PM["prop_WSLS_1_PM"] = np.nanmean(user_data_PM["WSLS_v1"]) + The differences are kept only for participants who pass the criteria in both games. + """ + if self.results_MW.empty and self.results_PM.empty: + self.metrics() + if self.results_diff.empty: + self.difference_metrics() - user_results_PM["prop_WSLS_2_PM"] = np.nanmean(user_data_PM["WSLS_v2"]) + self.cleanedresults_MW = _clean_game(self.results_MW, self.MW_data, "MW") + self.cleanedresults_PM = _clean_game(self.results_PM, self.PM_data, "PM") + self.deleted_participants_MW = _n_users(self.results_MW) - _n_users(self.cleanedresults_MW) + self.deleted_participants_PM = _n_users(self.results_PM) - _n_users(self.cleanedresults_PM) - # Append user-level results - self.results_PM = pd.concat( - [self.results_PM, pd.DataFrame([user_results_PM])], ignore_index=True - ) + both = set(_users(self.cleanedresults_MW)) & set(_users(self.cleanedresults_PM)) + self.cleanedresults_diff = self.results_diff[self.results_diff["userID"].isin(both)].copy() + self.deleted_participants_diff = _n_users(self.results_diff) - _n_users(self.cleanedresults_diff) - self.results_PM = pd.DataFrame(self.results_PM) + return self.cleanedresults_MW, self.cleanedresults_PM, self.cleanedresults_diff - def difference_metrics(self): + def get_codebook(self): """ - Calculate the difference between the metrics of Milky Way and Pirate Market trials. + Return the codebook, which describes each column of the metrics. Returns ------- - results_diff : pd.DataFrame - A DataFrame containing the difference of the metrics between Milky Way and Pirate Market trials. - - Variables - ---------- - - accuracy_diff: Difference in accuracy between Milky Way and Pirate Market trials - - outcome_diff: Difference in mean outcome between Milky Way and Pirate Market trials - - reward_diff_obt_forg_diff: Difference in mean difference between obtained and foregone reward between Milky Way and Pirate Market trials - - prop_same_choice_diff: Difference in proportion of same choice between Milky Way and Pirate Market trials - - prop_WSLS_1_diff: Difference in proportion of win-stay/loose-shift choices for correct trials version 1 between Milky Way and Pirate Market trials - - prop_WSLS_2_diff: Difference in proportion of win-stay/loose-shift choices for correct trials version 2 between Milky Way and Pirate Market trials + dict + The description of each column, keyed by column name. """ + return self.codebook - # Calculate differences for each user - for user_id in pd.unique(self.results_MW["userID"]): - user_results_MW = self.results_MW[self.results_MW["userID"] == user_id] - user_results_PM = self.results_PM[self.results_PM["userID"] == user_id] - - if not user_results_MW.empty and not user_results_PM.empty: - diff_results = { - "userID": user_id, - "accuracy_diff": user_results_MW["accuracy_MW"].values[0] - - user_results_PM["accuracy_PM"].values[0], - "outcome_diff": user_results_MW["mean_outcome_MW"].values[0] - - user_results_PM["mean_outcome_PM"].values[0], - "reward_diff_obt_forg_diff": user_results_MW[ - "reward_diff_obt_forg_MW" - ].values[0] - - user_results_PM["reward_diff_obt_forg_PM"].values[0], - "prop_same_choice_diff": user_results_MW[ - "prop_same_choice_MW" - ].values[0] - - user_results_PM["prop_same_choice_PM"].values[0], - "prop_WSLS_1_diff": user_results_MW["prop_WSLS_1_MW"].values[0] - - user_results_PM["prop_WSLS_1_PM"].values[0], - "prop_WSLS_2_diff": user_results_MW["prop_WSLS_2_MW"].values[0] - - user_results_PM["prop_WSLS_2_PM"].values[0], - } - self.results_diff = pd.concat( - [self.results_diff, pd.DataFrame([diff_results])], ignore_index=True - ) - - return pd.DataFrame(self.results_diff) - - def clean_data(self): - """ - Clean the data by removing participants who do not meet the participant-level inclusion criteria. - - Exclusion Critera - ----------------- - - Participant-level: - - Same choice on >= 95% of trials - - Missing accuracy data - - Participants with more than 72 trials (due to technical error) - """ - nr_part_before_MW = len(self.results_MW["userID"].unique()) - nr_part_before_PM = len(self.results_PM["userID"].unique()) - # created cleaned verison of results object but also keep old object for comparison - self.cleanedresults_MW = self.results_MW.copy() - self.cleanedresults_PM = self.results_PM.copy() +def _first_attempt(data): + """The trials of the first attempt of each participant, which is their lowest `run`.""" + first = data.groupby("userID")["run"].transform("min") + return data[data["run"] == first].reset_index(drop=True) - # Remove users with more than 72 trials - valid_user_ids = ( - self.MW_data.groupby("userID")["userID"].transform("count") <= 72 - ) - user_ids_to_keep = self.MW_data.loc[valid_user_ids, "userID"].unique() - self.cleanedresults_MW = self.cleanedresults_MW[ - self.cleanedresults_MW["userID"].isin(user_ids_to_keep) - ] - valid_user_ids = ( - self.PM_data.groupby("userID")["userID"].transform("count") <= 72 - ) - user_ids_to_keep = self.PM_data.loc[valid_user_ids, "userID"].unique() - self.cleanedresults_PM = self.cleanedresults_PM[ - self.cleanedresults_PM["userID"].isin(user_ids_to_keep) - ] - - # Remove users with at least 95% same choice - self.cleanedresults_MW = self.cleanedresults_MW[ - self.cleanedresults_MW["prop_same_choice_MW"] < 0.95 - ] - self.cleanedresults_PM = self.cleanedresults_PM[ - self.cleanedresults_PM["prop_same_choice_PM"] < 0.95 - ] - - # remove users with missing accuracy - self.cleanedresults_MW = self.cleanedresults_MW[ - self.cleanedresults_MW["accuracy_MW"].notna() - ] - self.cleanedresults_PM = self.cleanedresults_PM[ - self.cleanedresults_PM["accuracy_PM"].notna() - ] - - self.deleted_participants_MW = nr_part_before_MW - len( - pd.unique(self.cleanedresults_MW["userID"]) - ) - self.deleted_participants_PM = nr_part_before_PM - len( - pd.unique(self.cleanedresults_PM["userID"]) - ) +def _game_metrics(data, name, suffix): + """The metrics of each participant in one game.""" + rows = [] + with quiet_empty_slices(): + for user_id, user_data in data.groupby("userID"): + row = {"userID": user_id, "n_trials": len(user_data), "trial_type": name} + row.update(session_time(user_data["date"].iloc[0])) + row[f"accuracy_{suffix}"] = np.nanmean(user_data["correct"]) + row[f"mean_outcome_{suffix}"] = np.nanmean(user_data["outchosen"]) + row[f"reward_diff_obt_forg_{suffix}"] = np.nanmean(user_data["obt_min_forg"]) + row[f"prop_same_choice_{suffix}"] = np.nanmean(user_data["rep"]) + row[f"prop_WSLS_1_{suffix}"] = np.nanmean(user_data["WSLS_v1"]) + row[f"prop_WSLS_2_{suffix}"] = np.nanmean(user_data["WSLS_v2"]) + rows.append(row) + return pd.DataFrame(rows) - # Also clean the diff file by keeping only participants who are in both the cleanedresults_MW and in the cleanedresults_PM file - self.cleanedresults_diff = self.results_diff.copy() - nr_part_before_diff = len(self.results_diff["userID"].unique()) +def _clean_game(results, data, suffix): + """The metrics of one game, for the participants that pass the exclusion criteria.""" + if results.empty: + return results.copy() + trials = data.groupby("userID").size() + keep = results["userID"].isin(trials.index[trials <= 72]) + keep &= results[f"prop_same_choice_{suffix}"] < 0.95 + keep &= results[f"accuracy_{suffix}"].notna() + return results[keep].copy() - user_ids_MW = set( - self.cleanedresults_MW["userID"].unique() - ) # Get userIDs from both cleaned datasets - user_ids_PM = set(self.cleanedresults_PM["userID"].unique()) - valid_user_ids_diff = user_ids_MW.intersection( - user_ids_PM - ) # Keep only userIDs that are in both - self.cleanedresults_diff = self.cleanedresults_diff[ - self.cleanedresults_diff["userID"].isin(valid_user_ids_diff) - ] - self.deleted_participants_diff = nr_part_before_diff - len( - self.cleanedresults_diff["userID"].unique() - ) +def _users(results): + return results["userID"].unique() if "userID" in results else [] - return self.cleanedresults_MW, self.cleanedresults_PM, self.cleanedresults_diff - def codebook(self): - """ - Create a codebook for the data. - """ - return self.codebook +def _n_users(results): + return len(_users(results)) diff --git a/cpm/brainexplorer/information_gathering.py b/cpm/brainexplorer/information_gathering.py index 2c286c0..6ab6d48 100644 --- a/cpm/brainexplorer/information_gathering.py +++ b/cpm/brainexplorer/information_gathering.py @@ -1,263 +1,167 @@ -import pandas as pd +import re + import numpy as np -import warnings -from scipy.stats import zscore +import pandas as pd + +from ._utils import load_data, quiet_empty_slices, require, session_time -# import statsmodels.api as sm -# import statsmodels.formula.api as smf +__all__ = ["TreasureHunt"] + +COLUMNS = ["userID", "date", "run", "outcome", "choseCurEv", "confidence", "draws", "median_diffRT", "confidenceRT"] class TreasureHunt: """ - Class to calculate metrics for the Treasure Hunt task. Includes regresssion model to predict the influence of previous evidence on current choice. + Compute descriptive statistics from the information-gathering task in BrainExplorer, *Treasure Hunt*. + + On each trial, participants draw samples of evidence until they decide to stop and choose an option, and then rate their confidence in their choice. + + Parameters + ---------- + filepath : str, os.PathLike or pandas.DataFrame + The data, as a DataFrame or the path to a CSV or Excel (``.xlsx``) file. The column names must follow the convention in Notes. Attributes ---------- - data : pd.DataFrame - The data loaded from the CSV or Excel file. - results : pd.DataFrame - A DataFrame to store the computed metrics. + data : pandas.DataFrame + The trials that pass the trial-level exclusion criteria. + results : pandas.DataFrame + The metrics of each participant, filled by :meth:`metrics`. + cleanedresults : pandas.DataFrame + The metrics of the participants that pass the participant-level exclusion criteria, filled by :meth:`clean_data`. + deleted_participants : int + The number of participants that :meth:`clean_data` excluded. codebook : dict - A dictionary containing the codebook for the computed metrics. + The description of each column of the metrics. + + Examples + -------- + >>> from cpm.brainexplorer.information_gathering import TreasureHunt + >>> treasure_hunt = TreasureHunt("2025-02-20_TreasureHunt_Data.csv") + >>> results = treasure_hunt.metrics() + >>> cleaned = treasure_hunt.clean_data() + >>> treasure_hunt.get_codebook()["mean_n_draws"] + 'Mean number of stimulus draws before making a decision' + + Notes + ----- + The data must contain the following columns: + + - ``userID``: the unique identifier of the participant. + - ``date``: the date and time of the trial. + - ``run``: the attempt number of the participant. + - ``outcome``: the points received on the trial. + - ``confidence``: the confidence rating. + - ``confidenceRT``: the response time of the confidence rating, in ms. + - ``draws``: the number of draws on the trial. + - ``choseCurEv``: whether the choice was in line with the current evidence (1) or not (0). + - ``median_diffRT``: the median response time between the actions of the trial, in ms. + + If the data contain a column ``ev``, the evidence after each draw as a list of integers such as ``"[1 2 -1]"``, it is converted to lists of integers. + + Only the first attempt of each participant (``run`` equal to 1) is kept. """ def __init__(self, filepath=None): - """ - Load data to be analyised and turn into Pandas DataFrame - - Parameters - ---------- - filepath : str - The path to the CSV file to be processed. The column names in the data must subscribe to a prespecified convention, see Notes. - - Example - ---------- - - >>> treasurehunt = treasurehunt("/example/2025-02-20_SpaceObserver_Data_short.xlsx") - >>> treasurehunt.metrics() - >>> treasurehunt.clean_data() - >>> results = treasurehunt.results - >>> treasurehunt.codebook() - - - Notes - ----- - The columns required in data: - - userID: unique identifier for each participant - - "date": the date and time of the trial - - run: number of attempt by participant - - outcome: the outcome of the trial - - confidence: the confidence of the trial - - RT: the reaction time of the trial - - confidenceRT: the confidence reaction time of the trial - - draws: number of draws in the trial - - choseCurEv: whether the choice was in line with current evidence (1 = yes, 0 = no) - - ev: the evidence in the trial as a list of integers - - Trial-level exclusion criteria: - - - attempts after the first attempt ("run" variable) - """ - - self.data = pd.read_csv(filepath, header=0) - - self.data = self.data[self.data["run"] == 1] # only keep first attempt + data = load_data(filepath, "TreasureHunt") + require(data, COLUMNS, "TreasureHunt") + data = data[data["run"] == 1].copy() # only keep the first attempt + if "ev" in data: + data["ev"] = data["ev"].apply(_parse_evidence) + self.data = data self.results = pd.DataFrame() - self.codebook = { "userID": "Unique identifier for each participant", "n_trials": "Number of trials completed by the participant", - "day_of_week": "Day of the week of the trial", - "time": "Time of the trial", - "time_of_day": "Time of day of the trial (morning, afternoon, evening, night)", - "accuracy": "Mean accuracy (if the choice was the majority choice)", + "date": "Date and time of the first trial", + "day_of_week": "Day of the week of the first trial", + "time": "Clock time of the first trial", + "time_of_day": "Time of day of the first trial (night: 0-6h, morning: 6-12h, afternoon: 12-18h, evening: 18-24h)", "mean_points": "Mean points received across all trials", + "accuracy": "Mean accuracy (proportion of choices in line with the current evidence)", + "mean_confidence": "Mean confidence rating across all trials", + "sd_confidence": "Standard deviation of confidence ratings across all trials", "mean_n_draws": "Mean number of stimulus draws before making a decision", "sd_n_draws": "Standard deviation of the number of stimulus draws before making a decision", - "median_RT_between_actions": "Median reaction time between actions", - "median_confidenceRT": "Median confidence reaction time across all trials", + "median_RT_between_actions": "Median reaction time between actions (ms)", + "median_confidenceRT": "Median confidence reaction time across all trials (ms)", "unique_draws": "Number of unique values in number of draws", } def metrics(self): """ + Compute the metrics of each participant. Returns ------- - results : pd.DataFrame - A DataFrame containing the results of the metrics. - - Variables - ---------- - - userID: unique identifier for each participant - - n_trials: number of trials completed by the participant - - day_of_week: day of the week of the trial - - time: time of the trial - - time_of_day: time of day of the trial (morning, afternoon, evening - - accuracy: mean accuracy (if the choice was the majority choice) - - mean_points: mean points received across all trials - - mean_n_draws: mean number of stimulus draws before making a decision - - sd_n_draws: standard deviation of the number of stimulus draws before making a decision - - median_RT_between_actions: median reaction time between actions - - median_confidenceRT: median confidence reaction time across all trials - - unique_draws: number of unique values in number of draws + pandas.DataFrame + The metrics, one row per participant, also stored in `results`. The columns are described in `codebook`. """ - # turn string fields into list of ints - self.data["ev"] = self.data["ev"].apply( - lambda x: [int(i) for i in x.strip("[]").split(" ")] - ) - - # Group data by userID - grouped_data = self.data.groupby("userID") - - # Loop through each group of user data - for user_id, user_data in grouped_data: - - user_results = {"userID": user_id} - - user_results["n_trials"] = len(user_data) - - date = user_data["date"].iloc[0] - if isinstance(date, str): - date = pd.to_datetime(date, format="%Y-%m-%d %H:%M:%S.%f") - user_results["day_of_week"] = date.day_name() - user_results["time"] = ( - date.time() if hasattr(date, "time") else date.strftime("%H:%M:%S.%f") - ) - # morning 6-12, afternoon 12-18, evening 18-24, night 0-6 - user_results["time_of_day"] = ( - "morning" - if date.hour < 12 - else ( - "afternoon" - if date.hour < 18 - else ( - "evening" - if date.hour < 24 - else "night" if date.hour < 6 else "-" - ) - ) - ) - - user_results["mean_points"] = np.nanmean(user_data["outcome"]) - - user_results["accuracy"] = np.nanmean(user_data["choseCurEv"]) - - user_results["mean_confidence"] = np.nanmean(user_data["confidence"]) - user_results["sd_confidence"] = np.nanstd(user_data["confidence"]) - - user_results["mean_n_draws"] = np.nanmean(user_data["draws"]) - user_results["sd_n_draws"] = np.nanstd(user_data["draws"]) - - user_results["median_RT_between_actions"] = np.nanmedian( - user_data["median_diffRT"] - ) - - user_results["median_confidenceRT"] = np.median(user_data["confidenceRT"]) - - # check if < 3 unique values in number of samples - user_results["unique_draws"] = user_data["draws"].nunique() - - # regression for influence of recent results on current choice - """ - ev = [] - - for idx, row in user_data.iterrows(): - ev_sequence = row["ev"] - - for i in range(len(ev_sequence)): - if i == 0: - totevminus = np.nan - deltaev = np.nan - else: - totevminus = ev_sequence[i - 1] - deltaev = ev_sequence[i] - ev_sequence[i - 1] - - cont_ch = 1 if i < len(ev_sequence) - 1 else 0 # continue vs stop - - ev.append({ - "totev": ev_sequence[i], - "totevminus": totevminus, - "deltaev": deltaev, - "cont_ch": cont_ch - }) - - ev_reg_data = pd.DataFrame(ev) - - model_data = ev_reg_data.dropna(subset=["totevminus", "deltaev", "cont_ch"]) - - if not model_data.empty: - model = smf.glm( - formula="cont_ch ~ totevminus + deltaev", - data=model_data, - family=sm.families.Binomial() - ).fit() - - user_results["totevminus_coef"] = model.params.get("totevminus", np.nan) - user_results["deltaev_coef"] = model.params.get("deltaev", np.nan) - else: - user_results["totevminus_coef"] = np.nan - user_results["deltaev_coef"] = np.nan - """ - - # Append user-level results - self.results = pd.concat( - [self.results, pd.DataFrame([user_results])], ignore_index=True - ) - - # Convert results to a DataFrame - self.results = pd.DataFrame(self.results) - + rows = [] + with quiet_empty_slices(): + for user_id, user_data in self.data.groupby("userID"): + row = {"userID": user_id, "n_trials": len(user_data)} + row.update(session_time(user_data["date"].iloc[0])) + row["mean_points"] = np.nanmean(user_data["outcome"]) + row["accuracy"] = np.nanmean(user_data["choseCurEv"]) + row["mean_confidence"] = np.nanmean(user_data["confidence"]) + row["sd_confidence"] = np.nanstd(user_data["confidence"]) + row["mean_n_draws"] = np.nanmean(user_data["draws"]) + row["sd_n_draws"] = np.nanstd(user_data["draws"]) + row["median_RT_between_actions"] = np.nanmedian(user_data["median_diffRT"]) + row["median_confidenceRT"] = np.nanmedian(user_data["confidenceRT"]) + row["unique_draws"] = user_data["draws"].nunique() + rows.append(row) + self.results = pd.DataFrame(rows) return self.results def clean_data(self): """ - Clean the aggregated data by applying participant-level exclusion criteria. - - Exclusion criteria: - --------- - - Mean number of draws < 2 or > 23 - - Make choice not in line with current evidence in >= 20% of trials - - < 3 unique values in number of samples - """ - nr_part_before = len(self.results["userID"].unique()) - - self.cleanedresults = self.results.copy() + Exclude participants with the participant-level exclusion criteria. - # Filter out participants who have less than 3 unique values for draws - valid_users = ( - self.data.groupby("userID") - .filter(lambda group: group["draws"].nunique() >= 3)["userID"] - .unique() - ) - self.cleanedresults = self.cleanedresults[ - self.cleanedresults["userID"].isin(valid_users) - ] + Runs :meth:`metrics` first if it has not been run yet. - self.cleanedresults = self.cleanedresults[ - self.cleanedresults["mean_n_draws"] >= 2 - ] # only keep participants with mean number of draws >= 2 - self.cleanedresults = self.cleanedresults[ - self.cleanedresults["mean_n_draws"] <= 23 - ] # only keep participants with mean number of draws <= 23 + Returns + ------- + pandas.DataFrame + The metrics of the participants that pass the criteria, also stored in `cleanedresults`. - self.cleanedresults = self.cleanedresults[ - self.cleanedresults["accuracy"] > 0.8 - ] # only keep participants with mean choice in line with current evidence > 80% + Notes + ----- + A participant is excluded if - self.cleanedresults = self.cleanedresults[ - self.cleanedresults["unique_draws"] >= 3 - ] # only keep participants with at least 3 unique values in number of draws + - the mean number of draws is below 2 or above 23, + - at least 20% of their choices were not in line with the current evidence (an accuracy of 0.8 or below), + - the number of draws took fewer than 3 different values. + """ + if self.results.empty: + self.metrics() + results = self.results + n_before = results["userID"].nunique() - self.deleted_participants = nr_part_before - len( - self.cleanedresults["userID"].unique() - ) + keep = results["mean_n_draws"].between(2, 23) + keep &= results["accuracy"] > 0.8 + keep &= results["unique_draws"] >= 3 + self.cleanedresults = results[keep].copy() + self.deleted_participants = n_before - self.cleanedresults["userID"].nunique() return self.cleanedresults - def codebook(self): + def get_codebook(self): """ - Return a codebook describing the metrics. + Return the codebook, which describes each column of the metrics. + + Returns + ------- + dict + The description of each column, keyed by column name. """ return self.codebook + + +def _parse_evidence(value): + """Turn evidence such as ``"[1 2 -1]"`` or ``"[1, 2, -1]"`` into a list of integers; other values are kept.""" + if isinstance(value, str): + return [int(number) for number in re.findall(r"-?\d+", value)] + return value diff --git a/cpm/brainexplorer/perceptual_decision_making.py b/cpm/brainexplorer/perceptual_decision_making.py index 092ab92..f52084c 100644 --- a/cpm/brainexplorer/perceptual_decision_making.py +++ b/cpm/brainexplorer/perceptual_decision_making.py @@ -1,98 +1,82 @@ -import pandas as pd import numpy as np -import warnings -from scipy.stats import zscore +import pandas as pd + +from ._utils import load_data, quiet_empty_slices, require, session_time + +__all__ = ["SpaceObserver"] + +COLUMNS = ["userID", "run", "Date", "accuracy", "RT_choice", "confidence", "confidenceRT", "stimulus_intensity"] class SpaceObserver: """ - A class to analyze and compute statistics from behavioural data from a perceptual decision-making task with confidence judgements in BrainExplorer, Space Observer. In these tasks, participants have to choose if stimulus has more of one type of alien or the other, - and rate their confidence in their choice. For a more complete description of the task, see Dome, Moses-Payne, and Hauser (in prep.) or Marzuki et al. (2025). + Compute descriptive statistics from the perceptual decision-making task with confidence judgements in BrainExplorer, *Space Observer*. + + On each trial, participants decide which of two types of alien is more numerous, and rate their confidence in their choice. + For a more complete description of the task, see Dome, Moses-Payne, and Hauser (in prep.) or Marzuki et al. (2025). + + Parameters + ---------- + filepath : str, os.PathLike or pandas.DataFrame + The data, as a DataFrame or the path to a CSV or Excel (``.xlsx``) file. The column names must follow the convention in Notes. Attributes ---------- - data_raw : pd.DataFrame - The data loaded from the CSV or Excel file. - data_processed : pd.DataFrame - A DataFrame to store the computed metrics. + data_raw : pandas.DataFrame + The trials that pass the trial-level exclusion criteria. + data_processed : pandas.DataFrame + The metrics of each session, one row per participant and session, filled by :meth:`metrics`. + cleanedresults : pandas.DataFrame + The metrics of the sessions that pass the participant-level exclusion criteria, filled by :meth:`clean_data`. + deleted_participants : int + The number of participants that :meth:`clean_data` excluded. codebook : dict - A dictionary containing the codebook for the computed metrics. + The description of each column of the metrics. + + Examples + -------- + >>> from cpm.brainexplorer.perceptual_decision_making import SpaceObserver + >>> observer = SpaceObserver("2025-02-20_SpaceObserver_Data.csv") + >>> results = observer.metrics() + >>> cleaned = observer.clean_data() + >>> observer.get_codebook()["accuracy"] + 'Mean accuracy across all trials (proportion correct)' + + Notes + ----- + The data must contain the following columns: + + - ``userID``: the unique identifier of the participant. + - ``run``: the session number of the participant. + - ``Date``: the date and time of the trial. + - ``accuracy``: whether the choice was correct (1) or incorrect (0). + - ``RT_choice``: the response time of the choice, in ms. + - ``confidence``: the confidence rating. + - ``confidenceRT``: the response time of the confidence rating, in ms. + - ``stimulus_intensity``: the evidence strength, the difference in evidence between the two types of stimuli. + + Trials are excluded if the response time of the choice or of the confidence rating is below 150 ms or above 10000 ms, or if they have no confidence rating, since those are practice trials. + For participant-level exclusions, see :meth:`clean_data`, and Dome, Moses-Payne, and Hauser (in prep.). + + References + ---------- + Dome, L., Moses-Payne, M. E., & Hauser, T. U. (in prep.). Age-related shifts in metacognition reveals converging confidence for correct and error judgments across the lifespan. + + Marzuki, A., Kosina, L., Dome, L., Hewitt, S., & Hauser, T. (2025). Metacognitive antecedents to states of mental ill-health: Drops in confidence precede symptoms of OCD. *Research Square*. https://doi.org/10.21203/rs.3.rs-7544256/v1 """ def __init__(self, filepath=None): - """ - Load data to be analyised and turn into Pandas DataFrame. - Exlude trials not meeting the criteria. - - Parameters - ---------- - filepath : str - The path to the CSV file to be processed. The column names in the data must subscribe to a prespecified convention, see Notes. - - Example - ---------- - - >>> spaceObserver = SpaceObserver("/example/2025-02-20_SpaceObserver_Data.csv") - >>> results = spaceObserver.metrics() - >>> spaceObserver.clean_data() - >>> results = spaceObserver.results - >>> results.to_csv("/example/spaceObserver_results.csv", index=False) - >>> spaceObserver.get_codebook() - - Notes - ----- - The columns required in data: - - - **userID: unique identifier for each participant - - **date**: the date and time of the trial - - **run**: number of sessions completed by the participant (where 1 is the practice phase) - - **accuracy**: the accuracy of the trial (1 for correct, 0 for incorrect) - - **RT_choice**: the reaction time of the choice in the trial - - **confidence**: the confidence rating of the trial - - **confidenceRT**: the reaction time of the confidence rating in the trial - - **stimulus_intensity**: the evidence strength of the trial, which is the difference of evidence for each group of stimuli. - - Currently, we are excluding trials that took too long or too short to respond (RT < 150 ms or RT > 10000 ms) and trials without confidence data because those are practice trials. We are only including trials with confidence data because those without confidence data are practice trials. For participant exclusion, we recommend to follow Dome, Moses-Payne, and Hauser (in prep.). - - Reference - --------- - Dome, L., Moses-Payne, M. E., & Hauser, T. U. (in prep.). Age-related shifts in metacognition reveals converging confidence for correct and error judgments across the lifespan. - - Marzuki, A., Kosina, L., Dome, L., Hewitt, S., & Hauser, T. (2025). Metacognitive antecedents to states of mental ill-health: Drops in confidence precede symptoms of OCD. Research Square. https://doi.org/10.21203/rs.3.rs-7544256/v1 - """ + data = load_data(filepath, "SpaceObserver", na_values=["NaN", "nan"]) + require(data, COLUMNS, "SpaceObserver") - ## read data - if filepath is None: - raise ValueError("filepath must be provided") - if filepath.endswith(".xlsx"): - self.data_raw = pd.read_excel(filepath, header=0, na_values=["NaN", "nan"]) - else: - self.data_raw = pd.read_csv(filepath, header=0, na_values=["NaN", "nan"]) - - self.data_raw = self.data_raw[ - self.data_raw["RT_choice"] >= 150 - ] # only keep trials with reaction time > 150 ms - self.data_raw = self.data_raw[ - self.data_raw["RT_choice"] <= 10000 - ] # only keep trials with reaction time < 10000 ms - self.data_raw = self.data_raw[ - self.data_raw["confidenceRT"] >= 150 - ] # only keep trials with confidence reaction time > 150 ms - self.data_raw = self.data_raw[ - self.data_raw["confidenceRT"] <= 10000 - ] # only keep trials with confidence reaction time < 10000 ms - - self.data_raw["confidence"] = self.data_raw["confidence"].replace( - ["NaN", "nan", "NAN", ""], pd.NA - ) - self.data_raw = self.data_raw[ - self.data_raw["confidence"].notna() - ] # only keep trials with confidence data because trials without confidence data are practice trials - - self.data_raw["confidence"] = pd.to_numeric( - self.data_raw["confidence"], errors="coerce" - ) + data = data[data["RT_choice"].between(150, 10000)] + data = data[data["confidenceRT"].between(150, 10000)] + # trials without confidence ratings are practice trials + confidence = data["confidence"].replace(["NaN", "nan", "NAN", ""], np.nan) + data = data[confidence.notna()].copy() + data["confidence"] = pd.to_numeric(data["confidence"], errors="coerce") + self.data_raw = data self.data_processed = pd.DataFrame() self.codebook = { # --- Participant info --- @@ -149,255 +133,131 @@ def __init__(self, filepath=None): def metrics(self): """ - Compute metrics for each participant and return a DataFrame with the results. The metrics are computed for each run separately. + Compute the metrics of each session of each participant. Returns ------- - results : pd.DataFrame - A DataFrame containing the results of the metrics. - - Variables - --------- - - userID: unique identifier for each participant - - n_trials: number of trials completed by the participant - - day_of_week: day of the week of the trial - - time: time of the trial - - time_of_day: time of day of the trial (morning, afternoon, evening, night) - - accuracy : The mean accuracy across all trials. - - mean_RT : The mean response time across all trials. - - median_RT : The median response time across all trials. - - median_RT_correct : The median response time for trials where the participant made the correct choice. - - median_RT_incorrect : The median response time for trials where the participant made the incorrect choice. - - diff_median_RT_correct_incorrect : The difference between the median response time for correct and incorrect choices. - - mean_confidence : The mean confidence rating across all trials. - - median_confidence : The median confidence rating across all trials. - - median_confidence_correct : The median confidence rating for trials where the participant made the correct choice. - - median_confidence_incorrect : The median confidence rating for trials where the participant made the incorrect choice. - - diff_median_conf_correct_incorrect : The difference between the median confidence rating for correct and incorrect choices. - - sd_confidence : The standard deviation of the confidence rating across all trials. - - sd_confidence_correct : The standard deviation of the confidence rating for trials where the participant made the correct choice. - - sd_confidence_incorrect : The standard deviation of the confidence rating for trials where the participant made the incorrect choice. - - diff_sd_confidence_correct_incorrect : The difference between the standard deviation of the confidence rating for correct and incorrect choices. - - mean_confidenceRT : The mean confidence response time across all trials. - - median_confidenceRT : The median confidence response time across all trials. - - median_confidenceRT_correct : The median confidence response time for trials where the participant made the correct choice. - - median_confidenceRT_incorrect : The median confidence response time for trials where the participant made the incorrect choice. - - diff_median_confidenceRT_correct_incorrect : The difference between the median confidence response time for correct and incorrect choices. - - confidence_10 : The 10th percentile of the confidence rating across all trials. - - confidence_25 : The 25th percentile of the confidence rating across all trials. - - confidence_75 : The 75th percentile of the confidence rating across all trials. - - confidence_90 : The 90th percentile of the confidence rating across all trials. - - evidence_strength_mean : The mean evidence strength across all trials. - - evidence_strength_correct_mean : The mean evidence strength for trials where the participant made the correct choice. - - evidence_strength_incorrect_mean : The mean evidence strength for trials where the participant made the incorrect choice. - - diff_evidence_strength_correct_incorrect : The difference between the mean evidence strength for correct and incorrect choices. - - ES_bins : The mean evidence strength for each of the bins. The trials here are split into 4 quarters. The evidence strength is divided into four equal bins and the mean is calculated for each bin. - """ - - # Group data by subject - grouped_data = self.data_raw.groupby(["userID", "run"]) - - for (user_id, run), user_data in grouped_data: - - user_results = {"userID": user_id, "run": run} - - user_results["n_trials"] = len(user_data) - - date = user_data["Date"].iloc[0] - if isinstance(date, str): - date = pd.to_datetime(date, format="%Y-%m-%d %H:%M:%S.%f") - user_results["date"] = date - user_results["day_of_week"] = date.day_name() - user_results["time"] = ( - date.time() if hasattr(date, "time") else date.strftime("%H:%M:%S.%f") - ) - # morning 6-12, afternoon 12-18, evening 18-24, night 0-6 - user_results["time_of_day"] = ( - "night" - if date.hour < 6 - else ( - "morning" - if date.hour < 12 - else ("afternoon" if date.hour < 18 else "evening") - ) - ) - - user_results["accuracy"] = np.nanmean(user_data["accuracy"]) - - user_results["mean_RT"] = np.nanmean(user_data["RT_choice"]) - user_results["median_RT"] = np.nanmedian(user_data["RT_choice"]) - # different RT for correct and incorrect trials - user_results["median_RT_correct"] = np.nanmedian( - user_data["RT_choice"][user_data["accuracy"] == 1] - ) - user_results["median_RT_incorrect"] = np.nanmedian( - user_data["RT_choice"][user_data["accuracy"] != 1] - ) - user_results["diff_median_RT_correct_incorrect"] = ( - user_results["median_RT_correct"] - user_results["median_RT_incorrect"] - ) - - user_results["mean_confidence"] = np.nanmean(user_data["confidence"]) - user_results["sd_confidence"] = np.nanstd(user_data["confidence"]) - user_results["median_confidence"] = np.nanmedian(user_data["confidence"]) - # confidence difference for correct and incorrect trials - user_results["mean_confidence_correct"] = np.nanmean( - user_data["confidence"][user_data["accuracy"] == 1] - ) - user_results["mean_confidence_incorrect"] = np.nanmean( - user_data["confidence"][user_data["accuracy"] != 1] - ) - user_results["median_confidence_correct"] = np.nanmedian( - user_data["confidence"][user_data["accuracy"] == 1] - ) - user_results["median_confidence_incorrect"] = np.nanmedian( - user_data["confidence"][user_data["accuracy"] != 1] - ) - user_results["diff_median_conf_correct_incorrect"] = ( - user_results["median_confidence_correct"] - - user_results["median_confidence_incorrect"] - ) - # standard deviation of confidence for correct and incorrect trials - user_results["sd_confidence_correct"] = np.nanstd( - user_data["confidence"][user_data["accuracy"] == 1] - ) - user_results["sd_confidence_incorrect"] = np.nanstd( - user_data["confidence"][user_data["accuracy"] != 1] - ) - user_results["diff_sd_confidence_correct_incorrect"] = ( - user_results["sd_confidence_correct"] - - user_results["sd_confidence_incorrect"] - ) - - user_results["mean_confidenceRT"] = np.nanmean(user_data["confidenceRT"]) - user_results["median_confidenceRT"] = np.nanmedian( - user_data["confidenceRT"] - ) - # different RT for correct and incorrect trials - user_results["median_confidenceRT_correct"] = np.nanmedian( - user_data["confidenceRT"][user_data["accuracy"] == 1] - ) - user_results["median_confidenceRT_incorrect"] = np.nanmedian( - user_data["confidenceRT"][user_data["accuracy"] != 1] - ) - user_results["diff_median_confidenceRT_correct_incorrect"] = ( - user_results["median_confidenceRT_correct"] - - user_results["median_confidenceRT_incorrect"] - ) - - # calculate confidence percentiles - confidence = user_data["confidence"] - user_results["confidence_10"] = np.nanpercentile(confidence, 10) - user_results["confidence_25"] = np.nanpercentile(confidence, 25) - user_results["confidence_75"] = np.nanpercentile(confidence, 75) - user_results["confidence_90"] = np.nanpercentile(confidence, 90) - - # calculate evidence strength - evidence_strength = user_data["stimulus_intensity"] - - user_results["evidence_strength_mean"] = np.nanmean(evidence_strength) - - # evidence strenth per correct and incorrect trials - user_results["evidence_strength_correct_mean"] = np.nanmean( - evidence_strength[user_data["accuracy"] == 1] - ) - user_results["evidence_strength_incorrect_mean"] = np.nanmean( - evidence_strength[user_data["accuracy"] != 1] - ) - user_results["diff_evidence_strength_correct_incorrect"] = ( - user_results["evidence_strength_correct_mean"] - - user_results["evidence_strength_incorrect_mean"] - ) - - # calcualte evidence strength for each bin - bin_size = 4 - - # Divide evidence strength into 4 equal bins and calculate the mean for each bin - bins = np.array_split(evidence_strength.values, bin_size) - user_results["ES_bin_1"] = np.nanmean(bins[0]) if len(bins[0]) > 0 else np.nan - user_results["ES_bin_2"] = np.nanmean(bins[1]) if len(bins[1]) > 0 else np.nan - user_results["ES_bin_3"] = np.nanmean(bins[2]) if len(bins[2]) > 0 else np.nan - user_results["ES_bin_4"] = np.nanmean(bins[3]) if len(bins[3]) > 0 else np.nan - - # median evidence strength - user_results["median_ES"] = np.nanmedian(evidence_strength) - - # Append user-level results - self.data_processed = pd.concat( - [self.data_processed, pd.DataFrame([user_results])], ignore_index=True - ) + pandas.DataFrame + The metrics, one row per participant and session, also stored in `data_processed`. The columns are described in `codebook`. + Notes + ----- + Correct trials are those with an ``accuracy`` of 1, and incorrect trials those with an ``accuracy`` of 0. + Metrics of an empty selection, such as the median response time of incorrect trials when every choice was correct, are NaN. + The evidence strength bins split the trials of a session, in the order they were played, into four parts of (nearly) equal size. + """ + rows = [] + with quiet_empty_slices(): + for (user_id, run), user_data in self.data_raw.groupby(["userID", "run"]): + rows.append({"userID": user_id, "run": run, **_session_metrics(user_data)}) + self.data_processed = pd.DataFrame(rows) return self.data_processed def clean_data(self): """ - Clean aggregated data by applyign participant-level exclusion criteria. - Exclusion criteria: - --------- - - Mean accuracy < 0.5 - - Median reaction time > 3000 ms - - Median reaction time for confidence ratings > 3000 ms - - Median evidence strength > 25 - - Mean confidence <3 or > 97 - - Participants with more than 80 trials (due to technical error) - - Exclude participants if 10th and 25th percentile of confidence is the same AND 75th and 90th percentile of confidence is the same - """ + Exclude participants with the participant-level exclusion criteria. + + Runs :meth:`metrics` first if it has not been run yet. + + Returns + ------- + pandas.DataFrame + The metrics of the sessions that pass the criteria, also stored in `cleanedresults`. - nr_part_before = len(self.data_processed["userID"].unique()) + Notes + ----- + A session is excluded if - self.cleanedresults = self.data_processed.copy() + - the mean accuracy is below 0.5, + - the median response time of the choices is above 3000 ms, + - the median response time of the confidence ratings is above 3000 ms, + - the median evidence strength is above 25, + - the median confidence is below 3 or above 97, + - the 10th and 25th percentiles of confidence are equal, and so are the 75th and 90th percentiles. - # exclude participants who have more than 80 trials with run == 1 - users_80trials = ( - self.data_raw.groupby("userID") - .filter(lambda x: len(x) <= 80)["userID"] - .unique() - ) - self.cleanedresults = self.cleanedresults[ - self.cleanedresults["userID"].isin(users_80trials) - ] - - self.cleanedresults = self.cleanedresults[ - self.cleanedresults["accuracy"] >= 0.5 - ] # only keep participants with mean accuracy >= 50% - self.cleanedresults = self.cleanedresults[ - self.cleanedresults["median_RT"] <= 3000 - ] # only keep participants with median reaction time <= 3000 ms - self.cleanedresults = self.cleanedresults[ - self.cleanedresults["median_confidenceRT"] <= 3000 - ] # only keep participants with median confidence reaction time <= 3000 ms - self.cleanedresults = self.cleanedresults[ - self.cleanedresults["median_ES"] <= 25 - ] # only keep participants with median evidence strength <= 25 - self.cleanedresults = self.cleanedresults[ - self.cleanedresults["median_confidence"] <= 97 - ] # only keep participants with mean confidence <= 97 - self.cleanedresults = self.cleanedresults[ - self.cleanedresults["median_confidence"] >= 3 - ] # only keep participants with mean confidence >= 3 - - # exclude if 10th and 25th percentile of confidence is the same AND 75th and 90th percentile of confidence is the same - self.cleanedresults = self.cleanedresults[ - ~( - ( - self.cleanedresults["confidence_10"] - == self.cleanedresults["confidence_25"] - ) - & ( - self.cleanedresults["confidence_75"] - == self.cleanedresults["confidence_90"] - ) - ) - ] - - self.deleted_participants = nr_part_before - len( - self.cleanedresults["userID"].unique() + Participants with more than 80 trials in total, across all their sessions, are excluded entirely (due to a technical error). + """ + if self.data_processed.empty: + self.metrics() + results = self.data_processed + n_before = results["userID"].nunique() + + trials = self.data_raw.groupby("userID").size() + keep = results["userID"].isin(trials.index[trials <= 80]) + keep &= results["accuracy"] >= 0.5 + keep &= results["median_RT"] <= 3000 + keep &= results["median_confidenceRT"] <= 3000 + keep &= results["median_ES"] <= 25 + keep &= results["median_confidence"].between(3, 97) + flat = (results["confidence_10"] == results["confidence_25"]) & ( + results["confidence_75"] == results["confidence_90"] ) + keep &= ~flat + self.cleanedresults = results[keep].copy() + self.deleted_participants = n_before - self.cleanedresults["userID"].nunique() return self.cleanedresults def get_codebook(self): """ - Return a codebook describing the metrics. + Return the codebook, which describes each column of the metrics. + + Returns + ------- + dict + The description of each column, keyed by column name. """ return self.codebook + + +def _session_metrics(data): + """The metrics of one session.""" + correct = (data["accuracy"] == 1).to_numpy() + incorrect = (data["accuracy"] == 0).to_numpy() + rt = data["RT_choice"].to_numpy(dtype=float) + confidence = data["confidence"].to_numpy(dtype=float) + confidence_rt = data["confidenceRT"].to_numpy(dtype=float) + evidence = data["stimulus_intensity"].to_numpy(dtype=float) + + out = {"n_trials": len(data), **session_time(data["Date"].iloc[0])} + out["accuracy"] = np.nanmean(data["accuracy"]) + + out["mean_RT"] = np.nanmean(rt) + out["median_RT"] = np.nanmedian(rt) + out["median_RT_correct"] = np.nanmedian(rt[correct]) + out["median_RT_incorrect"] = np.nanmedian(rt[incorrect]) + out["diff_median_RT_correct_incorrect"] = out["median_RT_correct"] - out["median_RT_incorrect"] + + out["mean_confidence"] = np.nanmean(confidence) + out["sd_confidence"] = np.nanstd(confidence) + out["median_confidence"] = np.nanmedian(confidence) + out["mean_confidence_correct"] = np.nanmean(confidence[correct]) + out["mean_confidence_incorrect"] = np.nanmean(confidence[incorrect]) + out["median_confidence_correct"] = np.nanmedian(confidence[correct]) + out["median_confidence_incorrect"] = np.nanmedian(confidence[incorrect]) + out["diff_median_conf_correct_incorrect"] = out["median_confidence_correct"] - out["median_confidence_incorrect"] + out["sd_confidence_correct"] = np.nanstd(confidence[correct]) + out["sd_confidence_incorrect"] = np.nanstd(confidence[incorrect]) + out["diff_sd_confidence_correct_incorrect"] = out["sd_confidence_correct"] - out["sd_confidence_incorrect"] + + out["mean_confidenceRT"] = np.nanmean(confidence_rt) + out["median_confidenceRT"] = np.nanmedian(confidence_rt) + out["median_confidenceRT_correct"] = np.nanmedian(confidence_rt[correct]) + out["median_confidenceRT_incorrect"] = np.nanmedian(confidence_rt[incorrect]) + out["diff_median_confidenceRT_correct_incorrect"] = ( + out["median_confidenceRT_correct"] - out["median_confidenceRT_incorrect"] + ) + + for percentile in (10, 25, 75, 90): + out[f"confidence_{percentile}"] = np.nanpercentile(confidence, percentile) + + out["evidence_strength_mean"] = np.nanmean(evidence) + out["evidence_strength_correct_mean"] = np.nanmean(evidence[correct]) + out["evidence_strength_incorrect_mean"] = np.nanmean(evidence[incorrect]) + out["diff_evidence_strength_correct_incorrect"] = ( + out["evidence_strength_correct_mean"] - out["evidence_strength_incorrect_mean"] + ) + for i, part in enumerate(np.array_split(evidence, 4), start=1): + out[f"ES_bin_{i}"] = np.nanmean(part) if len(part) > 0 else np.nan + out["median_ES"] = np.nanmedian(evidence) + return out diff --git a/cpm/brainexplorer/risky_decision_making.py b/cpm/brainexplorer/risky_decision_making.py index 6aaedf3..4646e33 100644 --- a/cpm/brainexplorer/risky_decision_making.py +++ b/cpm/brainexplorer/risky_decision_making.py @@ -1,84 +1,113 @@ -import pandas as pd import numpy as np -from scipy.stats import zscore +import pandas as pd + +from ._utils import load_data, proportion, quiet_empty_slices, require, session_time + +__all__ = ["Scavenger"] + +COLUMNS = [ + "userID", + "run", + "date", + "RT", + "chosen", + "ambiguousTrial", + "abovechance", + "EV_diff_chosen", + "safe_EV", + "outcome", + "nbcorrect_gain", + "resp_rep", + "resp_rep_sr", +] class Scavenger: """ - A class to analyze and compute various descriptive statistics from behavioural data obtained in the risky decision-making - task implemented in BrainExplorer, Scavenger. The task requires participants to choose between two options: - The "safe" options returns an outcome with 100% probability, whereas the "risky" option returns one of - two potential outcomes with 50% probability each. + Compute descriptive statistics from the risky decision-making task in BrainExplorer, *Scavenger*. + + On each trial, participants choose between two options: the safe option returns its outcome with 100% probability, while the risky option returns one of two outcomes with 50% probability each. + In loss trials, the expected value of the safe option is negative. + In ambiguous trials, the probabilities of the outcomes of the risky option are only partly shown. + + Parameters + ---------- + filepath : str, os.PathLike or pandas.DataFrame + The data, as a DataFrame or the path to a CSV or Excel (``.xlsx``) file. The column names must follow the convention in Notes. Attributes ---------- - data : pd.DataFrame - The data loaded from the CSV or Excel file. - results : pd.DataFrame - A DataFrame to store the computed metrics. + data : pandas.DataFrame + The trials that pass the trial-level exclusion criteria, with an added column ``risky``, 1 for risky and 0 for safe choices. + results : pandas.DataFrame + The metrics of each participant, filled by :meth:`metrics`. + cleanedresults : pandas.DataFrame + The metrics of the participants that pass the participant-level exclusion criteria, filled by :meth:`clean_data`. + deleted_participants : int + The number of participants that :meth:`clean_data` excluded. codebook : dict - A dictionary containing the codebook for the computed metrics. + The description of each column of the metrics. + + Raises + ------ + ValueError + If ``chosen`` contains values other than 1 (safe) and 2 (risky). + + Examples + -------- + >>> from cpm.brainexplorer.risky_decision_making import Scavenger + >>> scavenger = Scavenger("2025-01-10_Scavenger.xlsx") + >>> results = scavenger.metrics() + >>> cleaned = scavenger.clean_data() + >>> scavenger.get_codebook()["risky_choices"] + 'Proportion of risky choices overall' + + Notes + ----- + The data must contain the following columns: + + - ``userID``: the unique identifier of the participant. + - ``run``: the attempt number of the participant. + - ``date``: the date and time of the trial. + - ``RT``: the response time, in ms. + - ``chosen``: the chosen option, 1 for safe and 2 for risky. + - ``ambiguousTrial``: whether the trial was ambiguous (1) or not (0). + - ``abovechance``: whether the choice was above chance level (1) or not (0). + - ``EV_diff_chosen``: the expected value of the chosen minus the unchosen option. A choice is rational if it is 0 or more. + - ``safe_EV``: the expected value of the safe option; trials where it is negative are loss trials, the others win trials. + - ``outcome``: the points received on the trial. + - ``nbcorrect_gain``: whether the choice in a no-brainer gain (catch) trial was correct (1) or not (0). + - ``resp_rep``: whether the left/right response repeated the previous one (1) or not (0). + - ``resp_rep_sr``: whether the safe/risky choice repeated the previous one (1) or not (0). + - ``config_id``: the version of the game. Only needed by :meth:`clean_data`. + + Trials are excluded if the response time is below 150 ms or above 10000 ms, and only the first attempt of each participant (``run`` equal to 1) is kept. + + References + ---------- + Habicht, J., Dubois, M., Michely, J., & Hauser, T. U. (2022). Do propranolol and amisulpride modulate confidence in risk-taking? *Wellcome Open Research*, 7, 23. """ def __init__(self, filepath=None): - """ - Load data to be analyised and turn into Pandas DataFrame - - Parameters - ---------- - filepath : str - The path to the CSV file to be processed. The column names in the data must subscribe to a prespecified convention, see Notes. - - Example - ---------- + data = load_data(filepath, "Scavenger") + require(data, COLUMNS, "Scavenger") + data = data[data["run"] == 1] # only keep the first attempt + data = data[data["RT"].between(150, 10000)].copy() - >>> scavenger = Scavenger("/example/2025-01-10_Scavenger.xlsx") - >>> scavenger.metrics() - >>> scavenger.clean_data() - >>> results = scavenger.results - >>> results.to_csv("/example/scavenger_results.csv", index=False) - >>> scavenger.get_codebook() - - Notes - ----- - The columns required in data: - - - "run": number of attempt by participant - - "chosen": the option chosen by the participant (1 = safe, 2 = risky) - - "ambiguousTrial": whether the trial was ambiguous (1 = yes, 0 = no) - - "abovechance": whether the choice was above chance level (1 = yes, 0 = no) - - "EV_diff_chosen": the difference in expected value between the chosen and unchosen option - - "safe_EV": the expected value of the safe option - - "RT": the reaction time of the trial - - "date": the date and time of the trial - - "outcome": the outcome of the trial - - - Trial-level exclusion criteria: - - Reaction time < 150 or > 10000 ms - - Attempts after the first attempt ("run" variable) - """ - if filepath is None: - raise ValueError("filepath must be provided") - if filepath.endswith(".xlsx"): - self.data = pd.read_excel(filepath, header=0) - else: - self.data = pd.read_csv(filepath, header=0) - - self.data = self.data[self.data["run"] == 1] # only keep first attempt - self.data = self.data[ - self.data["RT"] >= 150 - ] # only keep trials with reaction time > 150 ms - self.data = self.data[ - self.data["RT"] <= 10000 - ] # only keep trials with reaction time < 10000 ms + codes = set(data["chosen"].dropna().unique()) + if not codes <= {1, 2}: + raise ValueError( + f"Scavenger expects 'chosen' to be 1 (safe) or 2 (risky), but the data contain {sorted(codes)}." + ) + data["risky"] = data["chosen"] - 1 + self.data = data self.results = pd.DataFrame() - self.group_results = pd.DataFrame() self.codebook = { # --- Participant info --- "userID": "Unique identifier for each participant", "n_trials": "Number of trials completed by the participant", + "date": "Date and time of the first trial", "day_of_week": "Day of the week the session started", "time": "Clock time of the first trial", "time_of_day": "Time of day of the session (night: 0-6h, morning: 6-12h, afternoon: 12-18h, evening: 18-24h)", @@ -185,476 +214,195 @@ def __init__(self, filepath=None): def metrics(self): """ - Compute metrics from the loaded data: expected values of rewards when choosing safe vs. risky options, percentages of - risky and safe wins/losses as well as total rational choices. - - Parameters - ---------- - data : pd.DataFrame - Data from all users and trials to be processed. + Compute the metrics of each participant: response times, points, and the proportions of risky and rational choices, overall and by win/loss and ambiguous/non-ambiguous trials. Returns ------- - results : pd.DataFrame - A DataFrame containing the computed metrics. - - Variables - ---------- - - userID: Unique identifier for each participant, - - n_trials: Number of trials completed by the participant, - - day_of_week: Day of the week of the trial, - - time: Time of the trial, - - time_of_day: Time of day of the trial (morning, afternoon, evening, night), - - mean_RT: Mean reaction time, - - median_RT: Median reaction time, - - median_RT_safe: Median reaction time for safe choices, - - median_RT_risky: Median reaction time for risky choices, - - median_RT_ambg: Median reaction time for ambiguous trials, - - median_RT_NoAmbg: Median reaction time for non-ambiguous trials, - - median_RT_safe_ambg: Median reaction time for safe choices in ambiguous trials, - - median_RT_safe_NoAmbg: Median reaction time for safe choices in non-ambiguous trials, - - median_RT_risky_ambg: Median reaction time for risky choices in ambiguous trials, - - median_RT_risky_NoAmbg: Median reaction time for risky choices in non-ambiguous trials, - - mean_points: Mean points received across all trials, - - mean_points_safe: Mean points received for safe choices, - - mean_points_risky: Mean points received for risky choices, - - mean_points_ambg: Mean points received in ambiguous trials, - - mean_points_NoAmbg: Mean points received in non-ambiguous - - mean_points_safe_ambg: Mean points received for safe choices in ambiguous trials, - - mean_points_safe_NoAmbg: Mean points received for safe choices in non-ambiguous trials, - - mean_points_risky_ambg: Mean points received for risky choices in ambiguous trials, - - mean_points_risky_NoAmbg: Mean points received for risky choices in non-ambiguous, - - risky_choices: Proportion of risky choices overall, - - win_risk: Proportion of risky wins, - - loss_risk: Proportion of risky losses, - - win_risk_abg: Proportion of risky wins in ambiguous trials, - - win_risk_NoAbg: Proportion of risky wins in non-ambiguous trials, - - loss_risk_abg: Proportion of risky losses in ambiguous trials, - - loss_risk_NoAbg: Proportion of risky losses in non-ambiguous trials, - - diff_loss_win_risk: Difference between proportion of risky losses and wins, - - diff_Ambg_NoAmbg_risk: Difference between proportion of risky choices in ambiguous and non-ambiguous trials, - - diff_Ambg_NoAmbg_win_risk: Difference between proportion of risky wins in ambiguous and non-ambiguous trials, - - diff_Ambg_NoAmbg_loss_risk: Difference between proportion of risky losses in ambiguous and non-ambiguous trials, - - rational_all: Proportion of rational choices, - - rational_win: Proportion of rational choices in wins, - - rational_loss: Proportion of rational choices in losses, - - rational_all_abg: Proportion of rational choices in ambiguous trials, - - rational_win_abg: Proportion of rational choices in wins in ambiguous trials, - - rational_loss_abg: Proportion of rational choices in losses in ambiguous trials, - - rational_all_NoAbg: Proportion of rational choices in non-ambiguous trials, - - diff_NoAmbg_win_loss_rational: Difference between proportion of rational choices in wins and losses in non-ambiguous trials, - - rational_win_NoAbg: Proportion of rational choices in wins in non-ambiguous trials, - - rational_loss_NoAbg: Proportion of rational choices in losses in non-ambiguous trials, - - diff_Ambg_NoAmbg_rational: Difference between proportion of rational choices in ambiguous and non-ambiguous trials, - - diff_Ambg_NoAmbg_win_rational: Difference between proportion of rational choices in wins in ambiguous and non-ambiguous trials, - - diff_Ambg_NoAmbg_loss_rational: Difference between proportion of rational choices in losses in ambiguous and non-ambiguous trials, - - nb_incorrect_gain: Number of incorrect choices in gain trials, - - above_chance: Proportion of trials with choices above chance level, - - chosen_prop_LeftRight: Proportion of trials where option A (safe) was chosen, - - chosen_prop_SafeRisky: Proportion of trials where the safe option was chosen - + pandas.DataFrame + The metrics, one row per participant, also stored in `results`. The columns are described in `codebook`. Notes ----- - - In the game users can choose between two options with uncertain reward or loss probabilities. They reiceve information about reward probabilities for two choise options and can choose either the safe or risky option. - The data is split into two categories and options, A and B. Option A is choosing the safe option, i.e. the one which has more reward, which is labeled with 1 in "chosen" and Option B is choosing the risky option, labeled with 2 in "chosen". - Risk values are transfromed to 0 (no risk) and 1 (risky). - - We differentiate between the percentages of winning reward visible to the users in the task vs. actual percentages. - A_perc is the percent visible to the user for the safe option A, B_perc is the percent visible to the user for risky option B. - A_magn is the trial reward or loss values for option A, B_magn is the trial reward or loss values for option B. - - If visible percentages do not add up to 1 the trial is labeled as ambiguous. - - We calculate the expected value of the safe option A and the risky option B and then use these values to calculate the loss trials in which expected values are < 0. - Percentages of loss and win trials are calculated for risky choice trials (by objetive risk, option B) and number of rational choices are calculated for all trials, win trials and loss trials. - - - - References - ---------- - - Habicht J, Dubois M, Michely J and Hauser TU. Do propranolol and amisulpride modulate confidence in risk-taking? Wellcome Open Res 2022, 7:23. - - + Metrics of an empty selection, such as the proportion of risky choices in ambiguous loss trials for a participant who played none, are NaN. """ + rows = [] + with quiet_empty_slices(): + for user_id, user_data in self.data.groupby("userID"): + rows.append({"userID": user_id, **_participant_metrics(user_data)}) + self.results = pd.DataFrame(rows) + return self.results - # Group data by userID - grouped_data = self.data.groupby("userID") - - # Loop through each group of user data - for user_id, user_data in grouped_data: - t_abg = np.array(user_data["ambiguousTrial"], dtype=bool) - abovechance = np.array(user_data["abovechance"]) - ev_diff_chosen = np.array(user_data["EV_diff_chosen"], dtype=object) - rsk = np.array(user_data["chosen"]) - t_loss = np.array(user_data["safe_EV"] < 0) - - user_results = {"userID": user_id} - - user_results["n_trials"] = len(user_data) - - date = user_data["date"].iloc[0] - if isinstance(date, str): - date = pd.to_datetime(date, format="%Y-%m-%d %H:%M:%S.%f") - user_results["day_of_week"] = date.day_name() - user_results["time"] = ( - date.time() if hasattr(date, "time") else date.strftime("%H:%M:%S.%f") - ) - # morning 6-12, afternoon 12-18, evening 18-24, night 0-6 - user_results["time_of_day"] = ( - "night" if date.hour < 6 - else "morning" if date.hour < 12 - else "afternoon" if date.hour < 18 - else "evening" - ) - - user_results["mean_RT"] = np.nanmean(user_data["RT"]) - user_results["median_RT"] = np.nanmedian(user_data["RT"]) - # mediean RT for each condition - user_results["median_RT_safe"] = np.nanmedian(user_data[rsk == 0]["RT"]) - user_results["median_RT_risky"] = np.nanmedian(user_data[rsk == 1]["RT"]) - user_results["median_RT_win"] = np.nanmedian(user_data[~t_loss]["RT"]) - user_results["median_RT_loss"] = np.nanmedian(user_data[t_loss]["RT"]) - user_results["median_RT_ambg"] = np.nanmedian(user_data[t_abg]["RT"]) - user_results["median_RT_NoAmbg"] = np.nanmedian(user_data[~t_abg]["RT"]) - user_results["median_RT_win_ambg"] = np.nanmedian( - user_data[~t_loss & t_abg]["RT"] - ) - user_results["median_RT_win_NoAmbg"] = np.nanmedian( - user_data[~t_loss & ~t_abg]["RT"] - ) - user_results["median_RT_loss_ambg"] = np.nanmedian( - user_data[t_loss & t_abg]["RT"] - ) - user_results["median_RT_loss_NoAmbg"] = np.nanmedian( - user_data[t_loss & ~t_abg]["RT"] - ) - user_results["diff_RT_win_amg_win_noAmbg"] = ( - user_results["median_RT_win_ambg"] - - user_results["median_RT_win_NoAmbg"] - ) - user_results["diff_RT_loss_amg_loss_noAmbg"] = ( - user_results["median_RT_loss_ambg"] - - user_results["median_RT_loss_NoAmbg"] - ) - user_results["diff_RT_win_loss_ambg"] = ( - user_results["median_RT_win_ambg"] - user_results["median_RT_loss_ambg"] - ) - user_results["diff_RT_win_loss_noAmbg"] = ( - user_results["median_RT_win_NoAmbg"] - - user_results["median_RT_loss_NoAmbg"] - ) - user_results["diff_RT_win_ambg_loss_noAmbg"] = ( - user_results["median_RT_win_ambg"] - - user_results["median_RT_loss_NoAmbg"] - ) - user_results["diff_RT_loss_ambg_win_noAmbg"] = ( - user_results["median_RT_loss_ambg"] - - user_results["median_RT_win_NoAmbg"] - ) - - user_results["total_rewards"] = np.sum(user_data["outcome"]) - user_results["mean_points"] = np.nanmean(user_data["outcome"]) - - # mean points for risky and safe choices - user_results["mean_points_safe"] = np.nanmean( - user_data[rsk == 0]["outcome"] - ) - user_results["mean_points_risky"] = np.nanmean( - user_data[rsk == 1]["outcome"] - ) - - # mean points for ambigous and non-ambiguous trials - user_results["mean_points_ambg"] = np.nanmean(user_data[t_abg]["outcome"]) - user_results["mean_points_NoAmbg"] = np.nanmean( - user_data[~t_abg]["outcome"] - ) - - # mean points for win and loss trials in ambigous and non-ambiguous trials - user_results["mean_points_win_ambg"] = np.nanmean( - user_data[~t_loss & t_abg]["outcome"] - ) - user_results["mean_points_win_NoAmbg"] = np.nanmean( - user_data[~t_loss & ~t_abg]["outcome"] - ) - user_results["mean_points_loss_ambg"] = np.nanmean( - user_data[t_loss & t_abg]["outcome"] - ) - user_results["mean_points_loss_NoAmbg"] = np.nanmean( - user_data[t_loss & ~t_abg]["outcome"] - ) - user_results["diff_points_win_amg_win_noAmbg"] = ( - user_results["mean_points_win_ambg"] - - user_results["mean_points_win_NoAmbg"] - ) - user_results["diff_points_loss_amg_loss_noAmbg"] = ( - user_results["mean_points_loss_ambg"] - - user_results["mean_points_loss_NoAmbg"] - ) - user_results["diff_points_win_loss_ambg"] = ( - user_results["mean_points_win_ambg"] - - user_results["mean_points_loss_ambg"] - ) - user_results["diff_points_win_loss_noAmbg"] = ( - user_results["mean_points_win_NoAmbg"] - - user_results["mean_points_loss_NoAmbg"] - ) - user_results["diff_points_win_ambg_loss_noAmbg"] = ( - user_results["mean_points_win_ambg"] - - user_results["mean_points_loss_NoAmbg"] - ) - user_results["diff_points_loss_ambg_win_noAmbg"] = ( - user_results["mean_points_loss_ambg"] - - user_results["mean_points_win_NoAmbg"] - ) - - # choice stickiness - user_results["choice_stickiness"] = np.sum( - user_data["chosen"].iloc[i] == user_data["chosen"].iloc[i + 1] - for i in range(len(user_data["chosen"]) - 1) - ) - - # Number of risky choices - user_results["risky_choices"] = np.nanmean(rsk == 1) - - user_results["win_risk"] = np.sum(rsk[~t_loss]) / np.sum(~t_loss) - user_results["loss_risk"] = np.sum(rsk[t_loss]) / np.sum(t_loss) - user_results["diff_loss_win_risk"] = ( - user_results["loss_risk"] - user_results["win_risk"] - ) - - # Number of risky choices in ambiguous and non-ambiguous trials - user_results["risk_Ambg"] = np.sum(rsk[t_abg]) / np.sum(t_abg) - user_results["risk_NoAmbg"] = np.sum(rsk[~t_abg]) / np.sum(~t_abg) - user_results["diff_Ambg_NoAmbg_risk"] = ( - user_results["risk_Ambg"] - user_results["risk_NoAmbg"] - ) - - # Number of risky choices in ambiguous and non-ambiguous trials for wins and losses - user_results["win_risk_abg"] = np.sum(rsk[~t_loss & t_abg]) / np.sum( - ~t_loss & t_abg - ) - user_results["win_risk_NoAbg"] = np.sum(rsk[~t_loss & ~t_abg]) / np.sum( - ~t_loss & ~t_abg - ) - user_results["loss_risk_abg"] = np.sum(rsk[t_loss & t_abg]) / np.sum( - t_loss & t_abg - ) - user_results["loss_risk_NoAbg"] = np.sum(rsk[t_loss & ~t_abg]) / np.sum( - t_loss & ~t_abg - ) - user_results["diff_Ambg_NoAmbg_win_risk"] = ( - user_results["win_risk_abg"] - user_results["win_risk_NoAbg"] - ) - user_results["diff_Ambg_NoAmbg_loss_risk"] = ( - user_results["loss_risk_abg"] - user_results["loss_risk_NoAbg"] - ) - user_results["diff_Ambg_win_loss_risk"] = ( - user_results["win_risk_abg"] - user_results["loss_risk_abg"] - ) - user_results["diff_NoAmbg_win_loss_risk"] = ( - user_results["win_risk_NoAbg"] - user_results["loss_risk_NoAbg"] - ) - user_results["diff_amg_win_nonAmg_loss_risk"] = ( - user_results["win_risk_abg"] - user_results["loss_risk_NoAbg"] - ) - user_results["diff_amg_loss_nonAmg_win_risk"] = ( - user_results["loss_risk_abg"] - user_results["win_risk_NoAbg"] - ) - - # Number of rational choices - user_results["rational_all"] = np.nanmean(ev_diff_chosen >= 0) - - # rational choices in win and loss trials - user_results["rational_win"] = np.sum( - ev_diff_chosen[~t_loss] >= 0 - ) / np.sum(~t_loss) - user_results["rational_loss"] = np.sum( - ev_diff_chosen[t_loss] >= 0 - ) / np.sum(t_loss) - user_results["diff_loss_win_rational"] = ( - user_results["rational_loss"] - user_results["rational_win"] - ) - - # rational choices in ambiguous and non-ambiguous trials - user_results["rational_abg"] = np.sum(ev_diff_chosen[t_abg] >= 0) / np.sum( - t_abg - ) - user_results["rational_NoAbg"] = np.sum( - ev_diff_chosen[~t_abg] >= 0 - ) / np.sum(~t_abg) - user_results["diff_Ambg_NoAmbg_rational"] = ( - user_results["rational_abg"] - user_results["rational_NoAbg"] - ) - - # rational choices in ambiguous and non-ambiguous trials for wins and losses - user_results["rational_win_abg"] = np.sum( - ev_diff_chosen[~t_loss & t_abg] >= 0 - ) / np.sum(~t_loss & t_abg) - user_results["rational_win_NoAbg"] = np.sum( - ev_diff_chosen[~t_loss & ~t_abg] >= 0 - ) / np.sum(~t_loss & ~t_abg) - user_results["rational_loss_abg"] = np.sum( - ev_diff_chosen[t_loss & t_abg] >= 0 - ) / np.sum(t_loss & t_abg) - user_results["rational_loss_NoAbg"] = np.sum( - ev_diff_chosen[t_loss & ~t_abg] >= 0 - ) / np.sum(t_loss & ~t_abg) - user_results["diff_rational_Ambg_NoAmbg_win"] = ( - user_results["rational_win_abg"] - user_results["rational_win_NoAbg"] - ) - user_results["diff_rational_Ambg_NoAmbg_loss"] = ( - user_results["rational_loss_abg"] - user_results["rational_loss_NoAbg"] - ) - user_results["diff_rational_Ambg_win_loss"] = ( - user_results["rational_win_abg"] - user_results["rational_loss_abg"] - ) - user_results["diff_rational_NoAmbg_win_loss"] = ( - user_results["rational_win_NoAbg"] - user_results["rational_loss_NoAbg"] - ) - user_results["diff_rational_Ambg_win_NoAmbg_loss"] = ( - user_results["rational_win_abg"] - user_results["rational_loss_NoAbg"] - ) - user_results["diff_rational_Ambg_loss_NoAmbg_win"] = ( - user_results["rational_loss_abg"] - user_results["rational_win_NoAbg"] - ) - - # rational choices in safe and risky trials - user_results["rational_safe"] = np.sum( - ev_diff_chosen[rsk == 0] >= 0 - ) / np.sum(rsk == 0) - user_results["rational_risky"] = np.sum( - ev_diff_chosen[rsk == 1] >= 0 - ) / np.sum(rsk == 1) - user_results["diff_rational_safe_risky"] = ( - user_results["rational_safe"] - user_results["rational_risky"] - ) - - # rational choices in safe and risky trials for wins and losses - user_results["rational_safe_win"] = np.sum( - ev_diff_chosen[~t_loss & rsk == 0] >= 0 - ) / np.sum(~t_loss & rsk == 0) - user_results["rational_safe_loss"] = np.sum( - ev_diff_chosen[t_loss & rsk == 0] >= 0 - ) / np.sum(t_loss & rsk == 0) - user_results["rational_risky_win"] = np.sum( - ev_diff_chosen[~t_loss & rsk == 1] >= 0 - ) / np.sum(~t_loss & rsk == 1) - user_results["rational_risky_loss"] = np.sum( - ev_diff_chosen[t_loss & rsk == 1] >= 0 - ) / np.sum(t_loss & rsk == 1) - user_results["diff_rational_safe_win_risky_win"] = ( - user_results["rational_safe_win"] - user_results["rational_risky_win"] - ) - user_results["diff_rational_safe_loss_risky_loss"] = ( - user_results["rational_safe_loss"] - user_results["rational_risky_loss"] - ) - user_results["diff_rational_safe_win_risky_loss"] = ( - user_results["rational_safe_win"] - user_results["rational_risky_loss"] - ) - user_results["diff_rational_safe_loss_risky_win"] = ( - user_results["rational_safe_loss"] - user_results["rational_risky_win"] - ) - - # rational choices in risky and safe trials for ambiguous and non-ambiguous trials - user_results["rational_safe_abg"] = np.sum( - ev_diff_chosen[t_abg & (rsk == 0)] >= 0 - ) / np.sum(t_abg & (rsk == 0)) - user_results["rational_safe_NoAbg"] = np.sum( - ev_diff_chosen[~t_abg & (rsk == 0)] >= 0 - ) / np.sum(~t_abg & (rsk == 0)) - user_results["rational_risky_abg"] = np.sum( - ev_diff_chosen[t_abg & (rsk == 1)] >= 0 - ) / np.sum(t_abg & (rsk == 1)) - user_results["rational_risky_NoAbg"] = np.sum( - ev_diff_chosen[~t_abg & (rsk == 1)] >= 0 - ) / np.sum(~t_abg & (rsk == 1)) - user_results["diff_rational_safe_Ambg_NoAmbg"] = ( - user_results["rational_safe_abg"] - user_results["rational_safe_NoAbg"] - ) - user_results["diff_rational_risky_Ambg_NoAmbg"] = ( - user_results["rational_risky_abg"] - - user_results["rational_risky_NoAbg"] - ) - user_results["diff_rational_safe_risky_Ambg"] = ( - user_results["rational_safe_abg"] - user_results["rational_risky_abg"] - ) - user_results["diff_rational_safe_risky_NoAmbg"] = ( - user_results["rational_safe_NoAbg"] - - user_results["rational_risky_NoAbg"] - ) - - # No Brainer Trial Choices - # count how man wrong choices were made in catch trials - user_results["nb_incorrect_gain"] = user_data[ - user_data["nbcorrect_gain"] == 0 - ].shape[0] - - # proportion above chance level - user_results["above_chance"] = np.nanmean(abovechance) + def clean_data(self): + """ + Exclude participants with the participant-level exclusion criteria. - # proportion of repeated choices for left/right - user_results["chosen_prop_LeftRight"] = np.mean(user_data["resp_rep"]) + Runs :meth:`metrics` first if it has not been run yet. - # proportion of repeated choices for safe/risky - user_results["chosen_prop_SafeRisky"] = np.mean(user_data["resp_rep_sr"]) + Returns + ------- + pandas.DataFrame + The metrics of the participants that pass the criteria, also stored in `cleanedresults`. - # Append user-level results - self.results = pd.concat( - [self.results, pd.DataFrame([user_results])], ignore_index=True - ) + Raises + ------ + KeyError + If the data have no ``config_id`` column. - return self.results + Notes + ----- + A participant is excluded if - def clean_data(self): + - they repeated the same left/right response on at least 95% of the trials, + - they failed 2 or more of the no-brainer catch trials, + - they played more than 40 trials (due to a technical error), + - they played the version of the game with 60 trials (``config_id`` 242). """ - Clean the data by applying participant-level exlusion criteria. - - Exclusion criteria: - ---------- - - Same choice left/right on >= 95% of trials - - Fail >= 2 of catch trials - - Participants with more than 40 trials (due to technical error) - - Participants with configID = 242 (version with 60 trials) - """ - nr_part_before = len(self.results["userID"].unique()) - - self.cleanedresults = self.results.copy() - - user_id_counts = self.data["userID"].value_counts() - # find user IDs with more than 40 trials - user_ids_to_keep = user_id_counts[user_id_counts <= 40].index - self.cleanedresults = self.cleanedresults[ - self.cleanedresults["userID"].isin(user_ids_to_keep) - ] - - # find user IDs with specific configID to exclude - user_ids_to_exclude = self.data.loc[ - self.data["config_id"] == 242, "userID" - ].unique() - user_ids_to_keep = [ - uid - for uid in self.data["userID"].unique() - if uid not in user_ids_to_exclude - ] - self.cleanedresults = self.cleanedresults[ - self.cleanedresults["userID"].isin(user_ids_to_keep) - ] - - self.cleanedresults = self.cleanedresults[ - self.cleanedresults["nb_incorrect_gain"] < 2 - ] # exclude participants who failed 2 or more catch trials - - self.deleted_participants = nr_part_before - len( - self.cleanedresults["userID"].unique() - ) - + require(self.data, ["config_id"], "Scavenger.clean_data") + if self.results.empty: + self.metrics() + results = self.results + n_before = results["userID"].nunique() + + trials = self.data["userID"].value_counts() + long_version = self.data.loc[self.data["config_id"] == 242, "userID"].unique() + keep = results["userID"].isin(trials.index[trials <= 40]) + keep &= ~results["userID"].isin(long_version) + keep &= results["nb_incorrect_gain"] < 2 + keep &= results["chosen_prop_LeftRight"] < 0.95 + + self.cleanedresults = results[keep].copy() + self.deleted_participants = n_before - self.cleanedresults["userID"].nunique() return self.cleanedresults def get_codebook(self): """ - Return a codebook describing the metrics. + Return the codebook, which describes each column of the metrics. + + Returns + ------- + dict + The description of each column, keyed by column name. """ return self.codebook + + +def _participant_metrics(data): + """The metrics of one participant.""" + rt = data["RT"].to_numpy(dtype=float) + points = data["outcome"].to_numpy(dtype=float) + risky = data["risky"].to_numpy() == 1 + safe = data["risky"].to_numpy() == 0 + ambiguous = data["ambiguousTrial"].to_numpy(dtype=bool) + loss = (data["safe_EV"] < 0).to_numpy() + win = ~loss + rational = data["EV_diff_chosen"].to_numpy(dtype=float) >= 0 + + out = {"n_trials": len(data), **session_time(data["date"].iloc[0])} + + out["mean_RT"] = np.nanmean(rt) + out["median_RT"] = np.nanmedian(rt) + for name, mask in [ + ("safe", safe), + ("risky", risky), + ("win", win), + ("loss", loss), + ("ambg", ambiguous), + ("NoAmbg", ~ambiguous), + ("win_ambg", win & ambiguous), + ("win_NoAmbg", win & ~ambiguous), + ("loss_ambg", loss & ambiguous), + ("loss_NoAmbg", loss & ~ambiguous), + ]: + out[f"median_RT_{name}"] = np.nanmedian(rt[mask]) + out["diff_RT_win_amg_win_noAmbg"] = out["median_RT_win_ambg"] - out["median_RT_win_NoAmbg"] + out["diff_RT_loss_amg_loss_noAmbg"] = out["median_RT_loss_ambg"] - out["median_RT_loss_NoAmbg"] + out["diff_RT_win_loss_ambg"] = out["median_RT_win_ambg"] - out["median_RT_loss_ambg"] + out["diff_RT_win_loss_noAmbg"] = out["median_RT_win_NoAmbg"] - out["median_RT_loss_NoAmbg"] + out["diff_RT_win_ambg_loss_noAmbg"] = out["median_RT_win_ambg"] - out["median_RT_loss_NoAmbg"] + out["diff_RT_loss_ambg_win_noAmbg"] = out["median_RT_loss_ambg"] - out["median_RT_win_NoAmbg"] + + out["total_rewards"] = np.nansum(points) + out["mean_points"] = np.nanmean(points) + for name, mask in [ + ("safe", safe), + ("risky", risky), + ("ambg", ambiguous), + ("NoAmbg", ~ambiguous), + ("win_ambg", win & ambiguous), + ("win_NoAmbg", win & ~ambiguous), + ("loss_ambg", loss & ambiguous), + ("loss_NoAmbg", loss & ~ambiguous), + ]: + out[f"mean_points_{name}"] = np.nanmean(points[mask]) + out["diff_points_win_amg_win_noAmbg"] = out["mean_points_win_ambg"] - out["mean_points_win_NoAmbg"] + out["diff_points_loss_amg_loss_noAmbg"] = out["mean_points_loss_ambg"] - out["mean_points_loss_NoAmbg"] + out["diff_points_win_loss_ambg"] = out["mean_points_win_ambg"] - out["mean_points_loss_ambg"] + out["diff_points_win_loss_noAmbg"] = out["mean_points_win_NoAmbg"] - out["mean_points_loss_NoAmbg"] + out["diff_points_win_ambg_loss_noAmbg"] = out["mean_points_win_ambg"] - out["mean_points_loss_NoAmbg"] + out["diff_points_loss_ambg_win_noAmbg"] = out["mean_points_loss_ambg"] - out["mean_points_win_NoAmbg"] + + chosen = data["chosen"].to_numpy() + out["choice_stickiness"] = int(np.sum(chosen[1:] == chosen[:-1])) + + every = np.ones(len(data), dtype=bool) + out["risky_choices"] = proportion(risky, every) + out["win_risk"] = proportion(risky, win) + out["loss_risk"] = proportion(risky, loss) + out["diff_loss_win_risk"] = out["loss_risk"] - out["win_risk"] + out["risk_Ambg"] = proportion(risky, ambiguous) + out["risk_NoAmbg"] = proportion(risky, ~ambiguous) + out["diff_Ambg_NoAmbg_risk"] = out["risk_Ambg"] - out["risk_NoAmbg"] + out["win_risk_abg"] = proportion(risky, win & ambiguous) + out["win_risk_NoAbg"] = proportion(risky, win & ~ambiguous) + out["loss_risk_abg"] = proportion(risky, loss & ambiguous) + out["loss_risk_NoAbg"] = proportion(risky, loss & ~ambiguous) + out["diff_Ambg_NoAmbg_win_risk"] = out["win_risk_abg"] - out["win_risk_NoAbg"] + out["diff_Ambg_NoAmbg_loss_risk"] = out["loss_risk_abg"] - out["loss_risk_NoAbg"] + out["diff_Ambg_win_loss_risk"] = out["win_risk_abg"] - out["loss_risk_abg"] + out["diff_NoAmbg_win_loss_risk"] = out["win_risk_NoAbg"] - out["loss_risk_NoAbg"] + out["diff_amg_win_nonAmg_loss_risk"] = out["win_risk_abg"] - out["loss_risk_NoAbg"] + out["diff_amg_loss_nonAmg_win_risk"] = out["loss_risk_abg"] - out["win_risk_NoAbg"] + + out["rational_all"] = proportion(rational, every) + out["rational_win"] = proportion(rational, win) + out["rational_loss"] = proportion(rational, loss) + out["diff_loss_win_rational"] = out["rational_loss"] - out["rational_win"] + out["rational_abg"] = proportion(rational, ambiguous) + out["rational_NoAbg"] = proportion(rational, ~ambiguous) + out["diff_Ambg_NoAmbg_rational"] = out["rational_abg"] - out["rational_NoAbg"] + out["rational_win_abg"] = proportion(rational, win & ambiguous) + out["rational_win_NoAbg"] = proportion(rational, win & ~ambiguous) + out["rational_loss_abg"] = proportion(rational, loss & ambiguous) + out["rational_loss_NoAbg"] = proportion(rational, loss & ~ambiguous) + out["diff_rational_Ambg_NoAmbg_win"] = out["rational_win_abg"] - out["rational_win_NoAbg"] + out["diff_rational_Ambg_NoAmbg_loss"] = out["rational_loss_abg"] - out["rational_loss_NoAbg"] + out["diff_rational_Ambg_win_loss"] = out["rational_win_abg"] - out["rational_loss_abg"] + out["diff_rational_NoAmbg_win_loss"] = out["rational_win_NoAbg"] - out["rational_loss_NoAbg"] + out["diff_rational_Ambg_win_NoAmbg_loss"] = out["rational_win_abg"] - out["rational_loss_NoAbg"] + out["diff_rational_Ambg_loss_NoAmbg_win"] = out["rational_loss_abg"] - out["rational_win_NoAbg"] + + out["rational_safe"] = proportion(rational, safe) + out["rational_risky"] = proportion(rational, risky) + out["diff_rational_safe_risky"] = out["rational_safe"] - out["rational_risky"] + out["rational_safe_win"] = proportion(rational, safe & win) + out["rational_safe_loss"] = proportion(rational, safe & loss) + out["rational_risky_win"] = proportion(rational, risky & win) + out["rational_risky_loss"] = proportion(rational, risky & loss) + out["diff_rational_safe_win_risky_win"] = out["rational_safe_win"] - out["rational_risky_win"] + out["diff_rational_safe_loss_risky_loss"] = out["rational_safe_loss"] - out["rational_risky_loss"] + out["diff_rational_safe_win_risky_loss"] = out["rational_safe_win"] - out["rational_risky_loss"] + out["diff_rational_safe_loss_risky_win"] = out["rational_safe_loss"] - out["rational_risky_win"] + out["rational_safe_abg"] = proportion(rational, safe & ambiguous) + out["rational_safe_NoAbg"] = proportion(rational, safe & ~ambiguous) + out["rational_risky_abg"] = proportion(rational, risky & ambiguous) + out["rational_risky_NoAbg"] = proportion(rational, risky & ~ambiguous) + out["diff_rational_safe_Ambg_NoAmbg"] = out["rational_safe_abg"] - out["rational_safe_NoAbg"] + out["diff_rational_risky_Ambg_NoAmbg"] = out["rational_risky_abg"] - out["rational_risky_NoAbg"] + out["diff_rational_safe_risky_Ambg"] = out["rational_safe_abg"] - out["rational_risky_abg"] + out["diff_rational_safe_risky_NoAmbg"] = out["rational_safe_NoAbg"] - out["rational_risky_NoAbg"] + + out["nb_incorrect_gain"] = int((data["nbcorrect_gain"] == 0).sum()) + out["above_chance"] = np.nanmean(data["abovechance"]) + out["chosen_prop_LeftRight"] = np.nanmean(data["resp_rep"]) + out["chosen_prop_SafeRisky"] = np.nanmean(data["resp_rep_sr"]) + return out From 69c2ef1214043affcf51e69845c9818ff692bbe6 Mon Sep 17 00:00:00 2001 From: Lenard Dome <23503601+lenarddome@users.noreply.github.com> Date: Mon, 5 Oct 2026 14:13:27 +0200 Subject: [PATCH 47/54] test(brainexplorer): add unit tests for every task Co-Authored-By: Claude Opus 5.5 --- test/brainexplorer/test_bandits.py | 161 ++++++++++++++ .../brainexplorer/test_brainexplorer_utils.py | 56 +++++ .../test_information_gathering.py | 154 ++++++++++++++ .../test_perceptual_decision_making.py | 106 +++++++++- .../test_risky_decision_making.py | 200 ++++++++++++++++++ 5 files changed, 676 insertions(+), 1 deletion(-) create mode 100644 test/brainexplorer/test_bandits.py create mode 100644 test/brainexplorer/test_brainexplorer_utils.py create mode 100644 test/brainexplorer/test_information_gathering.py create mode 100644 test/brainexplorer/test_risky_decision_making.py diff --git a/test/brainexplorer/test_bandits.py b/test/brainexplorer/test_bandits.py new file mode 100644 index 0000000..286957c --- /dev/null +++ b/test/brainexplorer/test_bandits.py @@ -0,0 +1,161 @@ +import numpy as np +import pandas as pd +import pytest + +from cpm.brainexplorer.bandits import MilkyWay + + +def _game_rows( + user_id, + trial_type, + correct, + rep=None, + run=1, + outcomes=None, + date="2025-02-20 09:00:00.000", +): + n = len(correct) + rep = rep if rep is not None else [1, 0] * (n // 2) + [1] * (n % 2) + outcomes = outcomes if outcomes is not None else [10] * n + return [ + { + "userID": user_id, + "trial_type": trial_type, + "run": run, + "date": date, + "correct": c, + "outchosen": outcome, + "obt_min_forg": outcome - 5, + "rep": r, + "WSLS_v1": c, + "WSLS_v2": 1 - c, + } + for c, r, outcome in zip(correct, rep, outcomes) + ] + + +def _both_games(user_id, **kwargs): + return _game_rows(user_id, "reward", [1, 1, 0, 1], **kwargs) + _game_rows(user_id, "punish", [1, 0, 0, 1], **kwargs) + + +def test_milkyway_splits_games_and_keeps_the_first_attempt(): + rows = _game_rows("u1", "reward", [1, 1], run=1) + rows += _game_rows("u1", "reward", [0, 0], run=2) + # the first Pirate Market attempt is not necessarily run 1 + rows += _game_rows("u1", "punish", [1, 0], run=2) + rows += _game_rows("u1", "punish", [0, 0], run=3) + milky_way = MilkyWay(pd.DataFrame(rows)) + assert set(milky_way.MW_data["run"]) == {1} + assert set(milky_way.PM_data["run"]) == {2} + + +def test_milkyway_metrics_computes_expected_values(): + rows = _game_rows("u1", "reward", [1, 1, 0, 1], rep=[1, 1, 0, 0], outcomes=[10, 20, 30, 40]) + rows += _game_rows("u1", "punish", [1, 0, 0, 0], rep=[1, 0, 0, 0], outcomes=[-10, -20, -30, -40]) + milky_way = MilkyWay(pd.DataFrame(rows)) + results_MW, results_PM = milky_way.metrics() + + mw = results_MW.iloc[0] + assert mw["trial_type"] == "Milky Way" + assert mw["n_trials"] == 4 + assert np.isclose(mw["accuracy_MW"], 0.75) + assert np.isclose(mw["mean_outcome_MW"], 25) + assert np.isclose(mw["reward_diff_obt_forg_MW"], 20) + assert np.isclose(mw["prop_same_choice_MW"], 0.5) + assert np.isclose(mw["prop_WSLS_1_MW"], 0.75) + assert np.isclose(mw["prop_WSLS_2_MW"], 0.25) + + pm = results_PM.iloc[0] + assert pm["trial_type"] == "Pirate Market" + assert np.isclose(pm["accuracy_PM"], 0.25) + assert np.isclose(pm["mean_outcome_PM"], -25) + assert np.isclose(pm["prop_same_choice_PM"], 0.25) + assert results_MW is milky_way.results_MW and results_PM is milky_way.results_PM + + +def test_milkyway_results_contain_the_session_time(): + milky_way = MilkyWay(pd.DataFrame(_both_games("u1", date="2025-02-20 03:30:00.000"))) + for results in milky_way.metrics(): + row = results.iloc[0] + assert row["day_of_week"] == "Thursday" + assert str(row["time"]) == "03:30:00" + assert row["time_of_day"] == "night" + + +def test_milkyway_difference_metrics_only_for_participants_who_played_both(): + rows = _game_rows("both", "reward", [1, 1, 1, 1], outcomes=[10, 10, 10, 10]) + rows += _game_rows("both", "punish", [1, 0, 1, 0], outcomes=[-10, -10, -10, -10]) + rows += _game_rows("reward_only", "reward", [1, 0, 1, 0]) + milky_way = MilkyWay(pd.DataFrame(rows)) + differences = milky_way.difference_metrics() # also runs metrics + + assert list(differences["userID"]) == ["both"] + row = differences.iloc[0] + assert np.isclose(row["accuracy_diff"], 0.5) + assert np.isclose(row["outcome_diff"], 20) + assert np.isclose(row["reward_diff_obt_forg_diff"], 20) + assert np.isclose(row["prop_WSLS_1_diff"], 0.5) + assert np.isclose(row["prop_WSLS_2_diff"], -0.5) + + +def test_milkyway_difference_metrics_without_pirate_market(): + milky_way = MilkyWay(pd.DataFrame(_game_rows("u1", "reward", [1, 0]))) + differences = milky_way.difference_metrics() + assert differences.empty + assert "accuracy_diff" in differences.columns + + +def test_milkyway_metrics_can_be_run_twice(): + milky_way = MilkyWay(pd.DataFrame(_both_games("u1"))) + first_MW, first_PM = (results.copy() for results in milky_way.metrics()) + second_MW, second_PM = milky_way.metrics() + pd.testing.assert_frame_equal(first_MW, second_MW) + pd.testing.assert_frame_equal(first_PM, second_PM) + first_diff = milky_way.difference_metrics().copy() + pd.testing.assert_frame_equal(first_diff, milky_way.difference_metrics()) + + +def test_milkyway_clean_data_excludes_invalid_participants(): + rows = _both_games("keep") + # same choice on at least 95% of Milky Way trials + rows += _game_rows("drop_MW_same", "reward", [1] * 20, rep=[1] * 20) + rows += _game_rows("drop_MW_same", "punish", [1, 0, 0, 1]) + # more than 72 Pirate Market trials + rows += _game_rows("drop_PM_trials", "reward", [1, 1, 0, 1]) + rows += _game_rows("drop_PM_trials", "punish", [1, 0] * 37) + # missing Milky Way accuracy + rows += _game_rows("drop_MW_accuracy", "reward", [np.nan, np.nan]) + rows += _game_rows("drop_MW_accuracy", "punish", [1, 0, 0, 1]) + + milky_way = MilkyWay(pd.DataFrame(rows)) + cleaned_MW, cleaned_PM, cleaned_diff = milky_way.clean_data() # also runs metrics and difference_metrics + + assert set(cleaned_MW["userID"]) == {"keep", "drop_PM_trials"} + assert set(cleaned_PM["userID"]) == {"keep", "drop_MW_same", "drop_MW_accuracy"} + assert set(cleaned_diff["userID"]) == {"keep"} + assert milky_way.deleted_participants_MW == 2 + assert milky_way.deleted_participants_PM == 1 + assert milky_way.deleted_participants_diff == 3 + + +def test_milkyway_requires_data_and_columns(): + with pytest.raises(ValueError, match="needs data"): + MilkyWay() + data = pd.DataFrame(_both_games("u1")).drop(columns=["obt_min_forg"]) + with pytest.raises(KeyError, match="obt_min_forg"): + MilkyWay(data) + + +def test_milkyway_codebook_describes_every_metric(): + milky_way = MilkyWay(pd.DataFrame(_both_games("u1"))) + results_MW, results_PM = milky_way.metrics() + differences = milky_way.difference_metrics() + columns = set(results_MW.columns) | set(results_PM.columns) | set(differences.columns) + assert columns == set(milky_way.get_codebook()) + + +def test_milkyway_reads_csv(tmp_path): + filepath = tmp_path / "milky_way.csv" + pd.DataFrame(_both_games("u1")).to_csv(filepath, index=False) + results_MW, results_PM = MilkyWay(filepath).metrics() + assert len(results_MW) == 1 and len(results_PM) == 1 diff --git a/test/brainexplorer/test_brainexplorer_utils.py b/test/brainexplorer/test_brainexplorer_utils.py new file mode 100644 index 0000000..8d45f30 --- /dev/null +++ b/test/brainexplorer/test_brainexplorer_utils.py @@ -0,0 +1,56 @@ +import datetime + +import numpy as np +import pandas as pd +import pytest + +from cpm.brainexplorer._utils import load_data, proportion, require, session_time, time_of_day + + +@pytest.mark.parametrize( + "hour, expected", + [(0, "night"), (5, "night"), (6, "morning"), (11, "morning"), (12, "afternoon"), (17, "afternoon"), (18, "evening"), (23, "evening")], +) +def test_time_of_day(hour, expected): + assert time_of_day(hour) == expected + + +@pytest.mark.parametrize( + "date", + ["2025-02-20 19:05:30.250", "2025-02-20 19:05:30", "2025-02-20T19:05:30.250", pd.Timestamp("2025-02-20 19:05:30.250")], +) +def test_session_time(date): + out = session_time(date) + assert out["date"].floor("s") == pd.Timestamp("2025-02-20 19:05:30") + assert out["day_of_week"] == "Thursday" + assert out["time"].replace(microsecond=0) == datetime.time(19, 5, 30) + assert out["time_of_day"] == "evening" + + +def test_load_data_copies_dataframes(): + data = pd.DataFrame({"a": [1, 2]}) + loaded = load_data(data, "Task") + loaded.loc[0, "a"] = 10 + assert data.loc[0, "a"] == 1 + + +def test_load_data_reads_excel(tmp_path): + pytest.importorskip("openpyxl") + filepath = tmp_path / "data.xlsx" + pd.DataFrame({"a": [1, 2]}).to_excel(filepath, index=False) + pd.testing.assert_frame_equal(load_data(filepath, "Task"), pd.DataFrame({"a": [1, 2]})) + + +def test_load_data_needs_a_source(): + with pytest.raises(ValueError, match="Task needs data"): + load_data(None, "Task") + + +def test_require_names_every_missing_column(): + with pytest.raises(KeyError, match="'b', 'c'"): + require(pd.DataFrame({"a": [1]}), ["a", "b", "c"], "Task") + + +def test_proportion(): + assert proportion([True, False, True, True], [True, True, True, False]) == pytest.approx(2 / 3) + assert np.isnan(proportion([True, False], [False, False])) diff --git a/test/brainexplorer/test_information_gathering.py b/test/brainexplorer/test_information_gathering.py new file mode 100644 index 0000000..5d7bcc7 --- /dev/null +++ b/test/brainexplorer/test_information_gathering.py @@ -0,0 +1,154 @@ +import numpy as np +import pandas as pd +import pytest + +from cpm.brainexplorer.information_gathering import TreasureHunt + + +def _participant_rows( + user_id, + draws, + chose_current_evidence, + outcomes=None, + confidences=None, + run=1, + date="2025-02-20 09:00:00.000", +): + n = len(draws) + outcomes = outcomes if outcomes is not None else [10] * n + confidences = confidences if confidences is not None else [50] * n + return [ + { + "userID": user_id, + "date": date, + "run": run, + "outcome": outcome, + "confidence": confidence, + "RT": 1000, + "confidenceRT": 400 + 10 * i, + "draws": draw, + "choseCurEv": chose, + "median_diffRT": 200 + 10 * i, + "ev": "[1 2 -1]", + } + for i, (draw, chose, outcome, confidence) in enumerate(zip(draws, chose_current_evidence, outcomes, confidences)) + ] + + +def _keep_rows(user_id="keep", **kwargs): + return _participant_rows(user_id, draws=[3, 5, 7, 9, 11], chose_current_evidence=[1, 1, 1, 1, 1], **kwargs) + + +def test_treasurehunt_keeps_only_the_first_attempt(): + rows = _keep_rows() + _keep_rows(run=2) + hunt = TreasureHunt(pd.DataFrame(rows)) + assert len(hunt.data) == 5 + assert (hunt.data["run"] == 1).all() + + +def test_treasurehunt_metrics_computes_expected_values(): + rows = _participant_rows( + "u1", + draws=[2, 4, 6, 8], + chose_current_evidence=[1, 0, 1, 1], + outcomes=[10, 20, 30, 40], + confidences=[20, 40, 60, 80], + date="2025-02-20 14:30:00.000", + ) + hunt = TreasureHunt(pd.DataFrame(rows)) + result = hunt.metrics().iloc[0] + + assert result["n_trials"] == 4 + assert result["day_of_week"] == "Thursday" + assert result["time_of_day"] == "afternoon" + assert np.isclose(result["mean_points"], 25) + assert np.isclose(result["accuracy"], 0.75) + assert np.isclose(result["mean_confidence"], 50) + assert np.isclose(result["sd_confidence"], np.std([20, 40, 60, 80])) + assert np.isclose(result["mean_n_draws"], 5) + assert np.isclose(result["sd_n_draws"], np.std([2, 4, 6, 8])) + assert np.isclose(result["median_RT_between_actions"], 215) # median([200, 210, 220, 230]) + assert np.isclose(result["median_confidenceRT"], 415) # median([400, 410, 420, 430]) + assert result["unique_draws"] == 4 + + +@pytest.mark.parametrize( + "date, expected", + [ + ("2025-02-20 02:00:00.000", "night"), + ("2025-02-20 06:00:00.000", "morning"), + ("2025-02-20 12:00:00.000", "afternoon"), + ("2025-02-20 23:59:00.000", "evening"), + ], +) +def test_treasurehunt_time_of_day(date, expected): + hunt = TreasureHunt(pd.DataFrame(_keep_rows(date=date))) + assert hunt.metrics().iloc[0]["time_of_day"] == expected + + +def test_treasurehunt_median_confidence_rt_ignores_missing_values(): + rows = _keep_rows() + rows[0]["confidenceRT"] = np.nan + result = TreasureHunt(pd.DataFrame(rows)).metrics().iloc[0] + assert np.isclose(result["median_confidenceRT"], 425) # median([410, 420, 430, 440]) + + +@pytest.mark.parametrize("evidence", ["[1 2 -1]", "[1 2 -1]", "[1, 2, -1]", " [1 2 -1] "]) +def test_treasurehunt_parses_evidence(evidence): + rows = _keep_rows() + for row in rows: + row["ev"] = evidence + hunt = TreasureHunt(pd.DataFrame(rows)) + assert hunt.data["ev"].iloc[0] == [1, 2, -1] + hunt.metrics() + hunt.metrics() # the parsed evidence must survive a second run + assert hunt.data["ev"].iloc[0] == [1, 2, -1] + + +def test_treasurehunt_evidence_column_is_optional(): + data = pd.DataFrame(_keep_rows()).drop(columns=["ev"]) + assert len(TreasureHunt(data).metrics()) == 1 + + +def test_treasurehunt_requires_data_and_columns(): + with pytest.raises(ValueError, match="needs data"): + TreasureHunt() + data = pd.DataFrame(_keep_rows()).drop(columns=["median_diffRT"]) + with pytest.raises(KeyError, match="median_diffRT"): + TreasureHunt(data) + + +def test_treasurehunt_metrics_can_be_run_twice(): + hunt = TreasureHunt(pd.DataFrame(_keep_rows())) + first = hunt.metrics().copy() + pd.testing.assert_frame_equal(first, hunt.metrics()) + + +def test_treasurehunt_clean_data_excludes_invalid_participants(): + rows = _keep_rows() + # mean number of draws < 2 + rows += _participant_rows("drop_few_draws", draws=[1, 1, 1, 2, 3], chose_current_evidence=[1] * 5) + # mean number of draws > 23 + rows += _participant_rows("drop_many_draws", draws=[22, 24, 26, 28, 30], chose_current_evidence=[1] * 5) + # 20% of choices not in line with the evidence + rows += _participant_rows("drop_accuracy", draws=[3, 5, 7, 9, 11], chose_current_evidence=[1, 1, 1, 1, 0]) + # fewer than 3 unique numbers of draws + rows += _participant_rows("drop_unique", draws=[4, 4, 6, 6, 6], chose_current_evidence=[1] * 5) + + hunt = TreasureHunt(pd.DataFrame(rows)) + cleaned = hunt.clean_data() # also runs metrics + assert set(cleaned["userID"]) == {"keep"} + assert hunt.deleted_participants == 4 + + +def test_treasurehunt_codebook_describes_every_metric(): + hunt = TreasureHunt(pd.DataFrame(_keep_rows())) + assert set(hunt.metrics().columns) == set(hunt.get_codebook()) + + +def test_treasurehunt_reads_csv(tmp_path): + filepath = tmp_path / "treasure_hunt.csv" + pd.DataFrame(_keep_rows()).to_csv(filepath, index=False) + hunt = TreasureHunt(str(filepath)) + assert hunt.data["ev"].iloc[0] == [1, 2, -1] + assert len(hunt.metrics()) == 1 diff --git a/test/brainexplorer/test_perceptual_decision_making.py b/test/brainexplorer/test_perceptual_decision_making.py index 9d2328b..c254351 100644 --- a/test/brainexplorer/test_perceptual_decision_making.py +++ b/test/brainexplorer/test_perceptual_decision_making.py @@ -1,5 +1,6 @@ import numpy as np import pandas as pd +import pytest from cpm.brainexplorer.perceptual_decision_making import SpaceObserver @@ -193,7 +194,8 @@ def test_spaceobserver_metrics_computes_expected_values(tmp_path): assert np.isclose(run1["evidence_strength_incorrect_mean"], 3.0) # mean([2, 4]) assert np.isclose(run1["diff_evidence_strength_correct_incorrect"], -1.0) assert np.isclose(run1["median_ES"], 2.5) - np.testing.assert_allclose(run1["ES_bins"], np.array([1.0, 2.0, 3.0, 4.0])) + bins = [run1[f"ES_bin_{i}"] for i in range(1, 5)] + np.testing.assert_allclose(bins, [1.0, 2.0, 3.0, 4.0]) # run 2 has time_of_day == evening (18:30) assert run2["time_of_day"] == "evening" @@ -301,3 +303,105 @@ def test_spaceobserver_clean_data_excludes_invalid_participants(tmp_path): remaining_users = set(cleaned["userID"].unique()) assert remaining_users == {"keep"} assert observer.deleted_participants == 8 + + +def _keep_rows(user_id="keep", run=1, date="2025-02-20 09:00:00.000"): + return _participant_rows( + user_id=user_id, + run=run, + accuracies=[1, 1, 0, 1], + rt_choices=[400, 500, 600, 700], + confidences=[20, 30, 40, 50], + confidence_rts=[500, 550, 600, 650], + stimulus_intensities=[10, 12, 14, 16], + date=date, + ) + + +def test_spaceobserver_accepts_dataframe_and_path(tmp_path): + rows = _keep_rows() + filepath = _write_space_observer_csv(tmp_path, rows) + from_path = SpaceObserver(filepath).metrics() + from_frame = SpaceObserver(pd.DataFrame(rows)).metrics() + pd.testing.assert_frame_equal(from_path, from_frame) + + +def test_spaceobserver_does_not_change_the_input_dataframe(): + data = pd.DataFrame(_keep_rows()) + original = data.copy() + SpaceObserver(data).metrics() + pd.testing.assert_frame_equal(data, original) + + +def test_spaceobserver_requires_data_and_columns(): + with pytest.raises(ValueError, match="needs data"): + SpaceObserver() + data = pd.DataFrame(_keep_rows()).drop(columns=["confidenceRT"]) + with pytest.raises(KeyError, match="confidenceRT"): + SpaceObserver(data) + + +def test_spaceobserver_metrics_can_be_run_twice(): + observer = SpaceObserver(pd.DataFrame(_keep_rows())) + first = observer.metrics().copy() + second = observer.metrics() + pd.testing.assert_frame_equal(first, second) + assert len(second) == 1 + + +def test_spaceobserver_dates_without_fractional_seconds(): + observer = SpaceObserver(pd.DataFrame(_keep_rows(date="2025-02-22 23:15:00"))) + result = observer.metrics().iloc[0] + assert result["day_of_week"] == "Saturday" + assert result["time_of_day"] == "evening" + assert str(result["time"]) == "23:15:00" + + +def test_spaceobserver_all_correct_session_is_nan_for_incorrect_trials(recwarn): + rows = _participant_rows( + user_id="u1", + run=1, + accuracies=[1, 1, 1], + rt_choices=[300, 400, 500], + confidences=[50, 60, 70], + confidence_rts=[300, 300, 300], + stimulus_intensities=[5, 6, 7], + ) + result = SpaceObserver(pd.DataFrame(rows)).metrics().iloc[0] + assert np.isnan(result["median_RT_incorrect"]) + assert np.isnan(result["mean_confidence_incorrect"]) + assert np.isnan(result["diff_median_RT_correct_incorrect"]) + assert not [w for w in recwarn if issubclass(w.category, RuntimeWarning)] + + +def test_spaceobserver_clean_data_runs_metrics_first(): + observer = SpaceObserver(pd.DataFrame(_keep_rows())) + cleaned = observer.clean_data() + assert list(cleaned["userID"]) == ["keep"] + assert observer.deleted_participants == 0 + + +def test_spaceobserver_trial_limit_counts_all_sessions(): + # 4 sessions of 21 trials: no session has more than 80 trials, but the participant has 84 + rows = [] + for run in range(1, 5): + rows += _participant_rows( + user_id="many_sessions", + run=run, + accuracies=[1, 0, 1] * 7, + rt_choices=[400, 500, 600] * 7, + confidences=[20, 50, 80] * 7, + confidence_rts=[500, 550, 600] * 7, + stimulus_intensities=[10, 12, 14] * 7, + ) + rows += _keep_rows() + observer = SpaceObserver(pd.DataFrame(rows)) + cleaned = observer.clean_data() + assert set(cleaned["userID"]) == {"keep"} + assert observer.deleted_participants == 1 + + +def test_spaceobserver_codebook_describes_every_metric(): + observer = SpaceObserver(pd.DataFrame(_keep_rows())) + results = observer.metrics() + assert set(results.columns) == set(observer.get_codebook()) diff --git a/test/brainexplorer/test_risky_decision_making.py b/test/brainexplorer/test_risky_decision_making.py new file mode 100644 index 0000000..2173b46 --- /dev/null +++ b/test/brainexplorer/test_risky_decision_making.py @@ -0,0 +1,200 @@ +import numpy as np +import pandas as pd +import pytest + +from cpm.brainexplorer.risky_decision_making import Scavenger + + +def _trial(user_id, chosen, loss=False, ambiguous=False, ev_diff=1.0, rt=500, outcome=10, **extra): + row = { + "userID": user_id, + "run": 1, + "date": "2025-01-10 15:00:00.000", + "RT": rt, + "chosen": chosen, + "ambiguousTrial": int(ambiguous), + "abovechance": 1, + "EV_diff_chosen": ev_diff, + "safe_EV": -5 if loss else 5, + "outcome": outcome, + "nbcorrect_gain": np.nan, + "resp_rep": 0, + "resp_rep_sr": 0, + "config_id": 1, + } + row.update(extra) + return row + + +def _example(user_id="u1"): + """Five trials, worked out by hand in the tests below; chosen is 1 for safe and 2 for risky.""" + return [ + _trial(user_id, chosen=2, ev_diff=5, rt=300, outcome=10), + _trial(user_id, chosen=1, ambiguous=True, ev_diff=-2, rt=600, outcome=5), + _trial(user_id, chosen=1, loss=True, ev_diff=0, rt=900, outcome=-5), + _trial(user_id, chosen=2, loss=True, ambiguous=True, ev_diff=3, rt=400, outcome=0), + _trial(user_id, chosen=2, ev_diff=1, rt=500, outcome=20), + ] + + +def _keep_rows(user_id="keep"): + return [_trial(user_id, chosen=1 + i % 2, loss=i % 4 >= 2, ambiguous=i % 3 == 0, resp_rep=i % 2) for i in range(12)] + + +def test_scavenger_recodes_safe_and_risky_choices(): + scavenger = Scavenger(pd.DataFrame(_example())) + assert list(scavenger.data["risky"]) == [1, 0, 0, 1, 1] + + +@pytest.mark.parametrize("codes", [[0, 1], [1, 3]]) +def test_scavenger_rejects_other_codings(codes): + rows = [_trial("u1", chosen=code) for code in codes] + with pytest.raises(ValueError, match="1 \\(safe\\) or 2 \\(risky\\)"): + Scavenger(pd.DataFrame(rows)) + + +def test_scavenger_trial_level_exclusions(): + rows = _example() + rows.append(_trial("u1", chosen=2, rt=100)) # too fast + rows.append(_trial("u1", chosen=2, rt=12000)) # too slow + rows.append(_trial("u1", chosen=2, run=2)) # second attempt + scavenger = Scavenger(pd.DataFrame(rows)) + assert len(scavenger.data) == 5 + + +def test_scavenger_choice_metrics(): + result = Scavenger(pd.DataFrame(_example())).metrics().iloc[0] + + assert result["n_trials"] == 5 + assert result["time_of_day"] == "afternoon" + assert np.isclose(result["risky_choices"], 0.6) + assert np.isclose(result["win_risk"], 2 / 3) + assert np.isclose(result["loss_risk"], 0.5) + assert np.isclose(result["diff_loss_win_risk"], 0.5 - 2 / 3) + assert np.isclose(result["risk_Ambg"], 0.5) + assert np.isclose(result["risk_NoAmbg"], 2 / 3) + assert np.isclose(result["win_risk_abg"], 0) + assert np.isclose(result["win_risk_NoAbg"], 1) + assert np.isclose(result["loss_risk_abg"], 1) + assert np.isclose(result["loss_risk_NoAbg"], 0) + assert result["choice_stickiness"] == 2 # chosen 2, 1, 1, 2, 2 + + +def test_scavenger_rational_metrics(): + result = Scavenger(pd.DataFrame(_example())).metrics().iloc[0] + + # rational: EV_diff_chosen >= 0, which is all but the second trial + assert np.isclose(result["rational_all"], 0.8) + assert np.isclose(result["rational_win"], 2 / 3) + assert np.isclose(result["rational_loss"], 1) + assert np.isclose(result["rational_abg"], 0.5) + assert np.isclose(result["rational_NoAbg"], 1) + assert np.isclose(result["rational_safe"], 0.5) + assert np.isclose(result["rational_risky"], 1) + # safe & win is only the second trial, which was irrational + assert np.isclose(result["rational_safe_win"], 0) + assert np.isclose(result["rational_safe_loss"], 1) + assert np.isclose(result["rational_risky_win"], 1) + assert np.isclose(result["rational_risky_loss"], 1) + assert np.isclose(result["diff_rational_safe_win_risky_win"], -1) + assert np.isclose(result["rational_safe_abg"], 0) + assert np.isclose(result["rational_safe_NoAbg"], 1) + assert np.isclose(result["rational_risky_abg"], 1) + assert np.isclose(result["rational_risky_NoAbg"], 1) + + +def test_scavenger_rt_and_points_metrics(): + result = Scavenger(pd.DataFrame(_example())).metrics().iloc[0] + + assert np.isclose(result["mean_RT"], 540) + assert np.isclose(result["median_RT_safe"], 750) + assert np.isclose(result["median_RT_risky"], 400) + assert np.isclose(result["median_RT_win"], 500) + assert np.isclose(result["median_RT_loss"], 650) + assert np.isclose(result["median_RT_ambg"], 500) + assert np.isclose(result["median_RT_NoAmbg"], 500) + assert np.isclose(result["median_RT_loss_ambg"], 400) + assert np.isclose(result["diff_RT_loss_amg_loss_noAmbg"], 400 - 900) + + assert np.isclose(result["total_rewards"], 30) + assert np.isclose(result["mean_points"], 6) + assert np.isclose(result["mean_points_safe"], 0) + assert np.isclose(result["mean_points_risky"], 10) + assert np.isclose(result["mean_points_win_NoAmbg"], 15) + assert np.isclose(result["mean_points_loss_ambg"], 0) + + +def test_scavenger_quality_metrics(): + rows = _example() + rows[0].update(nbcorrect_gain=0, resp_rep=1, resp_rep_sr=1, abovechance=0) + rows[1].update(nbcorrect_gain=1, resp_rep=1) + result = Scavenger(pd.DataFrame(rows)).metrics().iloc[0] + assert result["nb_incorrect_gain"] == 1 + assert np.isclose(result["above_chance"], 0.8) + assert np.isclose(result["chosen_prop_LeftRight"], 0.4) + assert np.isclose(result["chosen_prop_SafeRisky"], 0.2) + + +def test_scavenger_metrics_of_missing_conditions_are_nan(recwarn): + rows = [_trial("u1", chosen=1 + i % 2) for i in range(4)] # win, non-ambiguous trials only + result = Scavenger(pd.DataFrame(rows)).metrics().iloc[0] + assert np.isnan(result["loss_risk"]) + assert np.isnan(result["rational_loss_abg"]) + assert np.isnan(result["median_RT_ambg"]) + assert not [w for w in recwarn if issubclass(w.category, RuntimeWarning)] + + +def test_scavenger_metrics_can_be_run_twice(): + scavenger = Scavenger(pd.DataFrame(_example())) + first = scavenger.metrics().copy() + pd.testing.assert_frame_equal(first, scavenger.metrics()) + + +def test_scavenger_clean_data_excludes_invalid_participants(): + rows = _keep_rows() + # failed 2 catch trials + catch = _keep_rows("drop_catch") + catch[0]["nbcorrect_gain"] = 0 + catch[1]["nbcorrect_gain"] = 0 + rows += catch + # repeated the same left/right response on at least 95% of trials + rows += [{**row, "resp_rep": 1} for row in _keep_rows("drop_left_right")] + # more than 40 trials + rows += [_trial("drop_trials", chosen=1 + i % 2) for i in range(41)] + # the 60-trial version of the game + rows += [{**row, "config_id": 242} for row in _keep_rows("drop_config")] + + scavenger = Scavenger(pd.DataFrame(rows)) + cleaned = scavenger.clean_data() # also runs metrics + assert set(cleaned["userID"]) == {"keep"} + assert scavenger.deleted_participants == 4 + + +def test_scavenger_clean_data_keeps_one_failed_catch_trial(): + rows = _keep_rows() + rows[0]["nbcorrect_gain"] = 0 + assert list(Scavenger(pd.DataFrame(rows)).clean_data()["userID"]) == ["keep"] + + +def test_scavenger_requires_data_and_columns(): + with pytest.raises(ValueError, match="needs data"): + Scavenger() + data = pd.DataFrame(_example()).drop(columns=["safe_EV"]) + with pytest.raises(KeyError, match="safe_EV"): + Scavenger(data) + # config_id is only needed to clean the data + scavenger = Scavenger(pd.DataFrame(_example()).drop(columns=["config_id"])) + assert len(scavenger.metrics()) == 1 + with pytest.raises(KeyError, match="config_id"): + scavenger.clean_data() + + +def test_scavenger_codebook_describes_every_metric(): + scavenger = Scavenger(pd.DataFrame(_example())) + assert set(scavenger.metrics().columns) == set(scavenger.get_codebook()) + + +def test_scavenger_reads_csv(tmp_path): + filepath = tmp_path / "scavenger.csv" + pd.DataFrame(_example()).to_csv(filepath, index=False) + assert np.isclose(Scavenger(str(filepath)).metrics().iloc[0]["risky_choices"], 0.6) From 73bb1164941f560b9fd86332c8fba5f548fbff59 Mon Sep 17 00:00:00 2001 From: Lenard Dome <23503601+lenarddome@users.noreply.github.com> Date: Mon, 5 Oct 2026 14:13:27 +0200 Subject: [PATCH 48/54] docs(api): add the brainexplorer reference page Co-Authored-By: Claude Opus 5.5 --- CHANGELOG.md | 2 ++ docs/api/brainexplorer.rst | 23 +++++++++++++++++++++++ docs/api/index.rst | 7 +++++++ docs/scripts/check_api_coverage.py | 4 ++++ 4 files changed, 36 insertions(+) create mode 100644 docs/api/brainexplorer.rst diff --git a/CHANGELOG.md b/CHANGELOG.md index 2886081..accc670 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -18,6 +18,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - Added `cpm.models.learning.SARSATrace`, a SARSA learning rule with an eligibility trace - Added test units for `HybridMBMF` and `SARSATrace` - Added a two-step task example replicating Smid et al. (2022), with its data in `cpm.datasets.load_two_step_data` +- Added `cpm.brainexplorer`, which computes descriptive statistics and applies the exclusion criteria for the BrainExplorer games Space Observer, Scavenger, Treasure Hunt, and Milky Way and Pirate Market, with a page in the API reference +- Added test units for `cpm.brainexplorer` - Added an example that fits causal ratings in a blocking experiment, recreating Figures 1 and 2 of Spicer et al. (2021), with its data in `cpm.datasets.load_blocking_data`; based on an earlier version by @chotong - Added `cpm.generators.SessionWrapper`, a `Wrapper` for models that compute all trials of a participant at once - Added `cpm.models.kernels`, the formulas of the `cpm.models` classes as functions that numba can compile, used by the classes and the built-in applications diff --git a/docs/api/brainexplorer.rst b/docs/api/brainexplorer.rst new file mode 100644 index 0000000..f06416f --- /dev/null +++ b/docs/api/brainexplorer.rst @@ -0,0 +1,23 @@ +cpm.brainexplorer +================= + +.. currentmodule:: cpm.brainexplorer + +Descriptive statistics and exclusion criteria for the games of +`BrainExplorer `__. Each class reads the data of +one game, from a pandas DataFrame or a CSV or Excel file, computes the metrics +of each participant with ``metrics()``, applies the participant-level +exclusion criteria with ``clean_data()``, and describes every metric in its +codebook, ``get_codebook()``. + +Reading Excel files needs `openpyxl `__, +which is not installed with cpm. + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + bandits.MilkyWay + information_gathering.TreasureHunt + perceptual_decision_making.SpaceObserver + risky_decision_making.Scavenger diff --git a/docs/api/index.rst b/docs/api/index.rst index 1e63c9e..ecf15f7 100644 --- a/docs/api/index.rst +++ b/docs/api/index.rst @@ -37,6 +37,12 @@ To learn how the pieces fit together, start with the :doc:`/tutorials/index`. Ready-made models for reinforcement learning, decision making and metacognition. + .. grid-item-card:: :fas:`gamepad` BrainExplorer + :link: brainexplorer + :link-type: doc + + Descriptive statistics and exclusion criteria for the BrainExplorer games. + .. grid-item-card:: :fas:`database` Datasets :link: datasets :link-type: doc @@ -57,5 +63,6 @@ To learn how the pieces fit together, start with the :doc:`/tutorials/index`. optimisation hierarchical applications + brainexplorer datasets utils diff --git a/docs/scripts/check_api_coverage.py b/docs/scripts/check_api_coverage.py index 4915a6c..dde1faa 100644 --- a/docs/scripts/check_api_coverage.py +++ b/docs/scripts/check_api_coverage.py @@ -30,6 +30,10 @@ "cpm.applications.reinforcement_learning", "cpm.applications.decision_making", "cpm.applications.signal_detection", + "cpm.brainexplorer.bandits", + "cpm.brainexplorer.information_gathering", + "cpm.brainexplorer.perceptual_decision_making", + "cpm.brainexplorer.risky_decision_making", "cpm.datasets", "cpm.utils.data", "cpm.utils.metad", From d64f86f7699efff8c8735fce6e93dc59b5653374 Mon Sep 17 00:00:00 2001 From: Lenard Dome <23503601+lenarddome@users.noreply.github.com> Date: Mon, 5 Oct 2026 14:47:35 +0200 Subject: [PATCH 49/54] fix(optimisation): infer ppt_identifier from the grouping of the data Co-Authored-By: Claude Opus 5.5 --- CHANGELOG.md | 1 + README.md | 12 ---- cpm/applications/signal_detection.py | 6 +- cpm/core/optimisers.py | 58 ++++++++++++++++++-- cpm/optimisation/bads.py | 5 +- cpm/optimisation/fmin.py | 9 +-- cpm/optimisation/free.py | 5 +- cpm/optimisation/genetic.py | 6 +- test/optimisation/test_ppt_identifier.py | 70 ++++++++++++++++++++++++ 9 files changed, 140 insertions(+), 32 deletions(-) create mode 100644 test/optimisation/test_ppt_identifier.py diff --git a/CHANGELOG.md b/CHANGELOG.md index accc670..5422f78 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -51,6 +51,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Fixed +- Fixed the optimisers and `cpm.applications.signal_detection.EstimatorMetaD` raising `UnboundLocalError` for a pandas DataFrame without `ppt_identifier`; they now raise a `ValueError` that explains what to pass, and take `ppt_identifier` from the grouping of data grouped by a single column, such as `data.groupby("ppt")`, so that the fits record the participants - Fixed `cpm.hierarchical.EmpiricalBayes` and `cpm.hierarchical.VariationalBayes` discarding the estimated population priors after the first evaluation of each fit; hierarchical results change - Fixed the random starting priors of later chains of `EmpiricalBayes` and `VariationalBayes` being length-one arrays - Fixed `numpy.random.seed` not reproducing the later chains of `EmpiricalBayes` and `VariationalBayes`, whose starting priors now also respect the lower bound and work with infinite bounds diff --git a/README.md b/README.md index 95f49eb..e1f9fe4 100644 --- a/README.md +++ b/README.md @@ -120,15 +120,3 @@ twine upload dist/* * `numpy`-style docstrings for documentation * `sphinx` for documentation generation * `pytest` for testing - -## TODO - -* [ ] MBMF parameterisation should use toolbox functions -* [ ] MBMF should be part of application (use RWRL template) -* [ ] Create example dataset for MBMF (goblin heist) -- cpm.datasets.model_based_model_free -* [ ] Example jupyter notebook for MBMF: explaining model with references, and fitting it to data, plotting parameters -* [ ] (least important for now) Parameter recovery for MBMF - * [ ] generating data with MBMF - * [ ] save parameters used to generate data - * [ ] fitting MBMF to generated data - * [ ] plotting parameters (pearson correlation, etc.) diff --git a/cpm/applications/signal_detection.py b/cpm/applications/signal_detection.py index a66eb82..e151082 100644 --- a/cpm/applications/signal_detection.py +++ b/cpm/applications/signal_detection.py @@ -11,7 +11,7 @@ from scipy.stats import norm, multivariate_normal from scipy.special import ndtr -from cpm.core.optimisers import numerical_hessian, prepare_data +from cpm.core.optimisers import group_identifier, numerical_hessian, prepare_data from cpm.core.data import detailed_pandas_compiler, decompose from cpm.core.parallel import detect_cores, execute_parallel from cpm.utils.metad import count_trials, bin_ratings @@ -428,7 +428,7 @@ class EstimatorMetaD: * 3 : display progress during iterations (more complete report). ppt_identifier : str, optional - Identifier for participants in the data. If None, the default identifier will be used. + The column that identifies the participants in the data. Required if `data` is a pandas.DataFrame. If `data` is grouped by a single column, such as `data.groupby("ppt")`, it defaults to the name of that column. ignore_invalid : bool, default False If True, invalid confidence ratings will be ignored during binning. If False, an error will be raised if invalid ratings are found. We recommend setting this to False (Default). **kwargs : additional keyword arguments @@ -464,7 +464,7 @@ def __init__( ): self.data = data self.bins = bins - self.ppt_identifier = ppt_identifier + self.ppt_identifier = group_identifier(data, ppt_identifier) self.data, self.participants, self.groups, self.__pandas__ = prepare_data( data, self.ppt_identifier ) diff --git a/cpm/core/optimisers.py b/cpm/core/optimisers.py index d5aa3fc..d26ee98 100644 --- a/cpm/core/optimisers.py +++ b/cpm/core/optimisers.py @@ -9,6 +9,7 @@ "normalise_metrics", "fit_extras", "evaluate_fit", + "group_identifier", "prepare_data", "numerical_hessian", ] @@ -281,6 +282,34 @@ def evaluate_fit(pars, function, data, loss, prior=False, metrics=None): return output +def group_identifier(data, identifier): + """ + The name of the participant identifier, taken from the grouping of the data if it is not given. + + Parameters + ---------- + data : pd.DataFrame, pd.DataFrameGroupBy, list + The data that is used to fit the model. + identifier : str or None + The name of the participant identifier given by the user. + + Returns + ------- + str or None + `identifier` if it is given. Otherwise, if `data` is grouped by a single + named column, such as ``data.groupby("ppt")``, the name of that column, and + None in all other cases. + """ + if identifier is not None or not isinstance(data, pd.api.typing.DataFrameGroupBy): + return identifier + keys = data.keys + if isinstance(keys, list) and len(keys) == 1: + keys = keys[0] + if isinstance(keys, pd.Series): + keys = keys.name + return keys if isinstance(keys, str) else None + + def prepare_data(data, identifier): """ The function extracts variables from data and converts the data to a right structure before fitting. @@ -291,23 +320,40 @@ def prepare_data(data, identifier): The `data` parameter is the data that is used to fit the model. identifier : str The `identifier` parameter is the column name that is used to group the data. + It is required if `data` is a pd.DataFrame. + Returns ------- The data, participants, groups and __pandas__ are being returned. + + Raises + ------ + ValueError + If `data` is a pd.DataFrame and `identifier` is None. + TypeError + If `data` is none of the supported types. """ + if isinstance(data, pd.DataFrame): + if identifier is None: + raise ValueError( + "The data is a pandas DataFrame, but no ppt_identifier was given. " + "Pass the name of the column that identifies the participants as ppt_identifier, " + "or group the data yourself, for example data.groupby('ppt')." + ) + data = data.groupby(identifier) if isinstance(data, pd.api.typing.DataFrameGroupBy): groups = list(data.groups.keys()) participants = data.get_group(groups[0]) __pandas__ = True - if isinstance(data, list): + elif isinstance(data, list): participants = data[0] groups = None __pandas__ = False - if isinstance(data, pd.DataFrame) and identifier is not None: - data = data.groupby(identifier) - groups = list(data.groups.keys()) - participants = data.get_group(groups[0]) - __pandas__ = True + else: + raise TypeError( + "The data must be a pandas DataFrame, a pandas DataFrameGroupBy or a list of dictionaries, " + f"not {type(data).__name__}." + ) return data, participants, groups, __pandas__ diff --git a/cpm/optimisation/bads.py b/cpm/optimisation/bads.py index a94300b..9b853cc 100644 --- a/cpm/optimisation/bads.py +++ b/cpm/optimisation/bads.py @@ -5,6 +5,7 @@ evaluate_fit, fit_extras, numerical_hessian, + group_identifier, prepare_data, ) from ..core.data import detailed_pandas_compiler, decompose @@ -93,7 +94,7 @@ class Bads: libraries : list, optional The libraries required for the parallel processing with `ipyparallel` with the IPython kernel. Default is `["numpy", "pandas"]`. ppt_identifier : str - The key in the participant data dictionary that contains the participant identifier. Default is `None`. Returned in the optimization details. + The column (or the key in the participant data dictionaries) that contains the participant identifier, returned in the optimization details. Required if `data` is a pd.DataFrame. If `data` is a pd.DataFrameGroupBy grouped by a single column, such as `data.groupby("ppt")`, it defaults to the name of that column. Default is `None`. **kwargs : dict Additional keyword arguments. See the `pybads.bads `__ documentation for what is supported. @@ -140,7 +141,7 @@ def __init__( ): self.model = copy.deepcopy(model) self.data = data - self.ppt_identifier = ppt_identifier + self.ppt_identifier = group_identifier(data, ppt_identifier) self.data, self.participants, self.groups, self.__pandas__ = prepare_data( data, self.ppt_identifier ) diff --git a/cpm/optimisation/fmin.py b/cpm/optimisation/fmin.py index f8b2271..6f445d9 100644 --- a/cpm/optimisation/fmin.py +++ b/cpm/optimisation/fmin.py @@ -4,6 +4,7 @@ evaluate_fit, fit_extras, numerical_hessian, + group_identifier, prepare_data, ) from ..core.data import detailed_pandas_compiler, decompose @@ -96,7 +97,7 @@ class Fmin: libraries : list, optional The libraries to import for parallel processing for `ipyparallel` with the IPython kernel. Default is `["numpy", "pandas"]`. ppt_identifier : str - The key in the participant data dictionary that contains the participant identifier. Default is `None`. Returned in the optimization details. + The column (or the key in the participant data dictionaries) that contains the participant identifier, returned in the optimization details. Required if `data` is a pd.DataFrame. If `data` is a pd.DataFrameGroupBy grouped by a single column, such as `data.groupby("ppt")`, it defaults to the name of that column. Default is `None`. **kwargs : dict Additional keyword arguments. See the :func:`scipy.optimize.fmin` documentation for what is supported. @@ -139,7 +140,7 @@ def __init__( ): self.model = copy.deepcopy(model) self.data = data - self.ppt_identifier = ppt_identifier + self.ppt_identifier = group_identifier(data, ppt_identifier) self.data, self.participants, self.groups, self.__pandas__ = prepare_data( data, self.ppt_identifier ) @@ -368,7 +369,7 @@ class FminBound: libraries : list, optional The libraries to import for parallel processing for `ipyparallel` with the IPython kernel. Default is `["numpy", "pandas"]`. ppt_identifier : str - The key in the participant data dictionary that contains the participant identifier. Default is `None`. Returned in the optimization details. + The column (or the key in the participant data dictionaries) that contains the participant identifier, returned in the optimization details. Required if `data` is a pd.DataFrame. If `data` is a pd.DataFrameGroupBy grouped by a single column, such as `data.groupby("ppt")`, it defaults to the name of that column. Default is `None`. **kwargs : dict Additional keyword arguments. See the :func:`scipy.optimize.fmin_l_bfgs_b` documentation for what is supported. The solver's own `disp` and `iprint` options, which `display` also sets, were removed in SciPy 1.18.0 and are ignored there. @@ -411,7 +412,7 @@ def __init__( ): self.model = copy.deepcopy(model) self.data = data - self.ppt_identifier = ppt_identifier + self.ppt_identifier = group_identifier(data, ppt_identifier) self.data, self.participants, self.groups, self.__pandas__ = prepare_data( data, self.ppt_identifier ) diff --git a/cpm/optimisation/free.py b/cpm/optimisation/free.py index c56394b..39eaf51 100644 --- a/cpm/optimisation/free.py +++ b/cpm/optimisation/free.py @@ -4,6 +4,7 @@ evaluate_fit, fit_extras, numerical_hessian, + group_identifier, prepare_data, ) from ..core.data import detailed_pandas_compiler, decompose @@ -46,7 +47,7 @@ class Minimize: libraries : list, optional The libraries to import for parallel processing for `ipyparallel` with the IPython kernel. Default is `["numpy", "pandas"]` ppt_identifier : str - The key in the participant data dictionary that contains the participant identifier. Default is `None`. Returned in the optimization details. + The column (or the key in the participant data dictionaries) that contains the participant identifier, returned in the optimization details. Required if `data` is a pd.DataFrame. If `data` is a pd.DataFrameGroupBy grouped by a single column, such as `data.groupby("ppt")`, it defaults to the name of that column. Default is `None`. **kwargs : dict Additional keyword arguments. See the :func:`scipy.optimize.minimize` documentation for what is supported. @@ -90,7 +91,7 @@ def __init__( ): self.model = copy.deepcopy(model) self.data = data - self.ppt_identifier = ppt_identifier + self.ppt_identifier = group_identifier(data, ppt_identifier) self.data, self.participants, self.groups, self.__pandas__ = prepare_data( data, self.ppt_identifier ) diff --git a/cpm/optimisation/genetic.py b/cpm/optimisation/genetic.py index 4a3a7b0..baf3a02 100644 --- a/cpm/optimisation/genetic.py +++ b/cpm/optimisation/genetic.py @@ -8,7 +8,7 @@ from . import minimise from ..core.data import decompose, detailed_pandas_compiler, extract_params_from_fit from ..generators import Simulator, Wrapper -from ..core.optimisers import objective, evaluate_fit, fit_extras, prepare_data +from ..core.optimisers import objective, evaluate_fit, fit_extras, group_identifier, prepare_data from ..core.parallel import detect_cores, execute_parallel @@ -36,7 +36,7 @@ class DifferentialEvolution: libraries : list, optional The libraries to import for parallel processing for `ipyparallel` with the IPython kernel. Default is `["numpy", "pandas"]` ppt_identifier : str - The key in the participant data dictionary that contains the participant identifier. Default is `None`. Returned in the optimization details. + The column (or the key in the participant data dictionaries) that contains the participant identifier, returned in the optimization details. Required if `data` is a pd.DataFrame. If `data` is a pd.DataFrameGroupBy grouped by a single column, such as `data.groupby("ppt")`, it defaults to the name of that column. Default is `None`. **kwargs : dict Additional keyword arguments. See the :func:`scipy.optimize.differential_evolution` documentation for what is supported. @@ -72,7 +72,7 @@ def __init__( self.parameters = [] self.display = display - self.ppt_identifier = ppt_identifier + self.ppt_identifier = group_identifier(data, ppt_identifier) self.prior = prior self.metrics = metrics diff --git a/test/optimisation/test_ppt_identifier.py b/test/optimisation/test_ppt_identifier.py new file mode 100644 index 0000000..f3deec0 --- /dev/null +++ b/test/optimisation/test_ppt_identifier.py @@ -0,0 +1,70 @@ +import numpy as np +import pandas as pd +import pytest + +from cpm.core.optimisers import group_identifier, prepare_data +from cpm.generators import Parameters, Value, Wrapper +from cpm.optimisation import FminBound, minimise + + +def simple_model(parameters, trial): + return {"dependent": np.array([trial["stimulus"] * parameters.alpha])} + + +def make_data(): + return pd.DataFrame( + { + "ppt": np.repeat([1, 2], 3), + "stimulus": np.tile([1.0, 2.0, 3.0], 2), + "observed": np.tile([0.1, 0.2, 0.3], 2), + } + ) + + +def fit(data, **kwargs): + parameters = Parameters(alpha=Value(value=0.5, lower=0.0, upper=1.0, prior="uniform")) + model = Wrapper(model=simple_model, data=make_data().query("ppt == 1"), parameters=parameters) + optimiser = FminBound(model=model, data=data, minimisation=minimise.Distance.SSE, approx_grad=True, **kwargs) + optimiser.optimise(display=False) + return optimiser + + +@pytest.mark.parametrize( + "grouping, expected", + [ + (lambda d: d.groupby("ppt"), "ppt"), + (lambda d: d.groupby(["ppt"]), "ppt"), + (lambda d: d.groupby(d["ppt"]), "ppt"), + (lambda d: d.groupby(["ppt", "stimulus"]), None), + (lambda d: d.groupby(d["ppt"].to_numpy()), None), + ], +) +def test_group_identifier_reads_a_single_named_grouping(grouping, expected): + assert group_identifier(grouping(make_data()), None) == expected + + +def test_group_identifier_keeps_a_given_identifier(): + assert group_identifier(make_data().groupby("ppt"), "participant") == "participant" + assert group_identifier(make_data(), "ppt") == "ppt" + assert group_identifier([{"observed": [1]}], None) is None + + +def test_prepare_data_needs_an_identifier_for_a_dataframe(): + with pytest.raises(ValueError, match="ppt_identifier"): + prepare_data(make_data(), None) + + +def test_prepare_data_rejects_other_types(): + with pytest.raises(TypeError, match="ndarray"): + prepare_data(np.zeros(3), None) + + +def test_fit_on_grouped_data_reports_the_participants(): + optimiser = fit(make_data().groupby("ppt")) + assert optimiser.ppt_identifier == "ppt" + assert list(optimiser.export()["ppt"]) == [1, 2] + + +def test_fit_on_a_dataframe_without_identifier_explains_the_error(): + with pytest.raises(ValueError, match="no ppt_identifier was given"): + fit(make_data()) From 032b22b486e9003f50b34a53445c1c5bfd059039 Mon Sep 17 00:00:00 2001 From: Lenard Dome <23503601+lenarddome@users.noreply.github.com> Date: Mon, 5 Oct 2026 14:47:36 +0200 Subject: [PATCH 50/54] refactor(brainexplorer): read CSV files only, like the data in cpm Co-Authored-By: Claude Opus 5.5 --- cpm/brainexplorer/_utils.py | 14 +++++--------- cpm/brainexplorer/bandits.py | 2 +- cpm/brainexplorer/information_gathering.py | 2 +- cpm/brainexplorer/perceptual_decision_making.py | 2 +- cpm/brainexplorer/risky_decision_making.py | 4 ++-- docs/api/brainexplorer.rst | 5 +---- test/brainexplorer/test_brainexplorer_utils.py | 8 ++++---- 7 files changed, 15 insertions(+), 22 deletions(-) diff --git a/cpm/brainexplorer/_utils.py b/cpm/brainexplorer/_utils.py index c821515..3fd13b6 100644 --- a/cpm/brainexplorer/_utils.py +++ b/cpm/brainexplorer/_utils.py @@ -10,20 +10,16 @@ def load_data(source, name, **kwargs): """ - Read the data of a task from a file, or copy a DataFrame. + Read the data of a task from a CSV file, or copy a DataFrame. - `source` is a pandas DataFrame, or the path to a CSV or Excel (``.xlsx``) file. - Keyword arguments are passed on to :func:`pandas.read_csv` or - :func:`pandas.read_excel`. + `source` is a pandas DataFrame, or the path to a CSV file. Keyword arguments + are passed on to :func:`pandas.read_csv`. """ if source is None: - raise ValueError(f"{name} needs data: a pandas DataFrame or the path to a CSV or Excel file.") + raise ValueError(f"{name} needs data: a pandas DataFrame or the path to a CSV file.") if isinstance(source, pd.DataFrame): return source.copy() - path = os.fspath(source) - if path.endswith(".xlsx"): - return pd.read_excel(path, header=0, **kwargs) - return pd.read_csv(path, header=0, **kwargs) + return pd.read_csv(os.fspath(source), header=0, **kwargs) def require(data, columns, name): diff --git a/cpm/brainexplorer/bandits.py b/cpm/brainexplorer/bandits.py index cbdc71c..f9be173 100644 --- a/cpm/brainexplorer/bandits.py +++ b/cpm/brainexplorer/bandits.py @@ -30,7 +30,7 @@ class MilkyWay: Parameters ---------- filepath : str, os.PathLike or pandas.DataFrame - The data, as a DataFrame or the path to a CSV or Excel (``.xlsx``) file. The column names must follow the convention in Notes. + The data, as a DataFrame or the path to a CSV file. The column names must follow the convention in Notes. Attributes ---------- diff --git a/cpm/brainexplorer/information_gathering.py b/cpm/brainexplorer/information_gathering.py index 6ab6d48..1fa7c5b 100644 --- a/cpm/brainexplorer/information_gathering.py +++ b/cpm/brainexplorer/information_gathering.py @@ -19,7 +19,7 @@ class TreasureHunt: Parameters ---------- filepath : str, os.PathLike or pandas.DataFrame - The data, as a DataFrame or the path to a CSV or Excel (``.xlsx``) file. The column names must follow the convention in Notes. + The data, as a DataFrame or the path to a CSV file. The column names must follow the convention in Notes. Attributes ---------- diff --git a/cpm/brainexplorer/perceptual_decision_making.py b/cpm/brainexplorer/perceptual_decision_making.py index f52084c..44bb774 100644 --- a/cpm/brainexplorer/perceptual_decision_making.py +++ b/cpm/brainexplorer/perceptual_decision_making.py @@ -18,7 +18,7 @@ class SpaceObserver: Parameters ---------- filepath : str, os.PathLike or pandas.DataFrame - The data, as a DataFrame or the path to a CSV or Excel (``.xlsx``) file. The column names must follow the convention in Notes. + The data, as a DataFrame or the path to a CSV file. The column names must follow the convention in Notes. Attributes ---------- diff --git a/cpm/brainexplorer/risky_decision_making.py b/cpm/brainexplorer/risky_decision_making.py index 4646e33..cd63082 100644 --- a/cpm/brainexplorer/risky_decision_making.py +++ b/cpm/brainexplorer/risky_decision_making.py @@ -33,7 +33,7 @@ class Scavenger: Parameters ---------- filepath : str, os.PathLike or pandas.DataFrame - The data, as a DataFrame or the path to a CSV or Excel (``.xlsx``) file. The column names must follow the convention in Notes. + The data, as a DataFrame or the path to a CSV file. The column names must follow the convention in Notes. Attributes ---------- @@ -56,7 +56,7 @@ class Scavenger: Examples -------- >>> from cpm.brainexplorer.risky_decision_making import Scavenger - >>> scavenger = Scavenger("2025-01-10_Scavenger.xlsx") + >>> scavenger = Scavenger("2025-01-10_Scavenger.csv") >>> results = scavenger.metrics() >>> cleaned = scavenger.clean_data() >>> scavenger.get_codebook()["risky_choices"] diff --git a/docs/api/brainexplorer.rst b/docs/api/brainexplorer.rst index f06416f..6cda4ad 100644 --- a/docs/api/brainexplorer.rst +++ b/docs/api/brainexplorer.rst @@ -5,14 +5,11 @@ cpm.brainexplorer Descriptive statistics and exclusion criteria for the games of `BrainExplorer `__. Each class reads the data of -one game, from a pandas DataFrame or a CSV or Excel file, computes the metrics +one game, from a pandas DataFrame or a CSV file, computes the metrics of each participant with ``metrics()``, applies the participant-level exclusion criteria with ``clean_data()``, and describes every metric in its codebook, ``get_codebook()``. -Reading Excel files needs `openpyxl `__, -which is not installed with cpm. - .. autosummary:: :toctree: generated/ :nosignatures: diff --git a/test/brainexplorer/test_brainexplorer_utils.py b/test/brainexplorer/test_brainexplorer_utils.py index 8d45f30..2854913 100644 --- a/test/brainexplorer/test_brainexplorer_utils.py +++ b/test/brainexplorer/test_brainexplorer_utils.py @@ -34,11 +34,11 @@ def test_load_data_copies_dataframes(): assert data.loc[0, "a"] == 1 -def test_load_data_reads_excel(tmp_path): - pytest.importorskip("openpyxl") - filepath = tmp_path / "data.xlsx" - pd.DataFrame({"a": [1, 2]}).to_excel(filepath, index=False) +def test_load_data_reads_csv(tmp_path): + filepath = tmp_path / "data.csv" + pd.DataFrame({"a": [1, 2]}).to_csv(filepath, index=False) pd.testing.assert_frame_equal(load_data(filepath, "Task"), pd.DataFrame({"a": [1, 2]})) + pd.testing.assert_frame_equal(load_data(str(filepath), "Task"), pd.DataFrame({"a": [1, 2]})) def test_load_data_needs_a_source(): From 40134c56da40ba918059a2ce393b44c926f57266 Mon Sep 17 00:00:00 2001 From: Lenard Dome <23503601+lenarddome@users.noreply.github.com> Date: Mon, 5 Oct 2026 14:53:10 +0200 Subject: [PATCH 51/54] fix(optimisation): explain a ppt_identifier that is not a column Co-Authored-By: Claude Opus 5.5 --- CHANGELOG.md | 2 +- cpm/core/optimisers.py | 7 +++++++ test/optimisation/test_ppt_identifier.py | 11 +++++++++++ 3 files changed, 19 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 5422f78..322f49b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -51,7 +51,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Fixed -- Fixed the optimisers and `cpm.applications.signal_detection.EstimatorMetaD` raising `UnboundLocalError` for a pandas DataFrame without `ppt_identifier`; they now raise a `ValueError` that explains what to pass, and take `ppt_identifier` from the grouping of data grouped by a single column, such as `data.groupby("ppt")`, so that the fits record the participants +- Fixed the optimisers and `cpm.applications.signal_detection.EstimatorMetaD` raising `UnboundLocalError` for a pandas DataFrame without `ppt_identifier`; they now raise a `ValueError` that explains what to pass, or a `KeyError` that lists the columns if `ppt_identifier` is not one of them, and take `ppt_identifier` from the grouping of data grouped by a single column, such as `data.groupby("ppt")`, so that the fits record the participants - Fixed `cpm.hierarchical.EmpiricalBayes` and `cpm.hierarchical.VariationalBayes` discarding the estimated population priors after the first evaluation of each fit; hierarchical results change - Fixed the random starting priors of later chains of `EmpiricalBayes` and `VariationalBayes` being length-one arrays - Fixed `numpy.random.seed` not reproducing the later chains of `EmpiricalBayes` and `VariationalBayes`, whose starting priors now also respect the lower bound and work with infinite bounds diff --git a/cpm/core/optimisers.py b/cpm/core/optimisers.py index d26ee98..64beefe 100644 --- a/cpm/core/optimisers.py +++ b/cpm/core/optimisers.py @@ -330,6 +330,8 @@ def prepare_data(data, identifier): ------ ValueError If `data` is a pd.DataFrame and `identifier` is None. + KeyError + If `data` is a pd.DataFrame without a column named `identifier`. TypeError If `data` is none of the supported types. """ @@ -341,6 +343,11 @@ def prepare_data(data, identifier): "Pass the name of the column that identifies the participants as ppt_identifier, " "or group the data yourself, for example data.groupby('ppt')." ) + if identifier not in data.columns: + raise KeyError( + f"The ppt_identifier {identifier!r} is not a column of the data, so the data cannot be grouped by participant. " + f"The columns are {list(data.columns)}." + ) data = data.groupby(identifier) if isinstance(data, pd.api.typing.DataFrameGroupBy): groups = list(data.groups.keys()) diff --git a/test/optimisation/test_ppt_identifier.py b/test/optimisation/test_ppt_identifier.py index f3deec0..3e57b1a 100644 --- a/test/optimisation/test_ppt_identifier.py +++ b/test/optimisation/test_ppt_identifier.py @@ -54,6 +54,17 @@ def test_prepare_data_needs_an_identifier_for_a_dataframe(): prepare_data(make_data(), None) +def test_prepare_data_needs_the_identifier_column(): + with pytest.raises(KeyError, match="'subject' is not a column of the data"): + prepare_data(make_data(), "subject") + + +def test_prepare_data_groups_a_dataframe_by_the_identifier(): + data, participants, groups, pandas = prepare_data(make_data(), "ppt") + assert pandas and groups == [1, 2] + assert list(participants["ppt"]) == [1, 1, 1] + + def test_prepare_data_rejects_other_types(): with pytest.raises(TypeError, match="ndarray"): prepare_data(np.zeros(3), None) From 28894154824801d275f2524dbdcd00d0a9ff8f10 Mon Sep 17 00:00:00 2001 From: Lenard Dome <23503601+lenarddome@users.noreply.github.com> Date: Mon, 5 Oct 2026 15:08:54 +0200 Subject: [PATCH 52/54] fix(generators): restore the log prior of values outside the support Co-Authored-By: Claude Opus 5.5 --- cpm/generators/parameters.py | 4 +--- test/generators/test_parameters.py | 9 +++++++++ 2 files changed, 10 insertions(+), 3 deletions(-) diff --git a/cpm/generators/parameters.py b/cpm/generators/parameters.py index 1584662..25fa4fe 100644 --- a/cpm/generators/parameters.py +++ b/cpm/generators/parameters.py @@ -160,13 +160,11 @@ def PDF(self, log=False): if value.prior is not None: prior += value.PDF(log=True) if np.isneginf(prior): - prior = np.finfo(np.float64).tiny - + prior = np.finfo(np.float64).min if not log: prior = np.exp(prior) if prior <= 0: prior = np.finfo(np.float64).tiny - return prior def update_prior(self, **kwargs): diff --git a/test/generators/test_parameters.py b/test/generators/test_parameters.py index 07bc536..af89c67 100644 --- a/test/generators/test_parameters.py +++ b/test/generators/test_parameters.py @@ -251,3 +251,12 @@ def test_parameters_callable_is_instance_scoped(): if __name__ == "__main__": pytest.main() + + +def test_parameters_pdf_outside_the_support_is_impossible(): + # a value outside the support must get the lowest log prior, never a value near 0 (probability 1) + params = Parameters( + a=Value(value=2.0, lower=0, upper=1, prior="truncated_normal", args={"mean": 0.5, "sd": 0.25}) + ) + assert params.PDF(log=True) == np.finfo(np.float64).min + assert params.PDF(log=False) == np.finfo(np.float64).tiny From 1166277bba493893755aea0cd1ce08ddcc1db2d0 Mon Sep 17 00:00:00 2001 From: Lenard Dome <23503601+lenarddome@users.noreply.github.com> Date: Mon, 5 Oct 2026 15:08:55 +0200 Subject: [PATCH 53/54] docs(datasets): document load_model_based_model_free Co-Authored-By: Claude Opus 5.5 --- cpm/datasets/base.py | 41 +++++++++++++++++++++++++++++------------ docs/api/datasets.rst | 1 + 2 files changed, 30 insertions(+), 12 deletions(-) diff --git a/cpm/datasets/base.py b/cpm/datasets/base.py index 9017d37..b088d13 100644 --- a/cpm/datasets/base.py +++ b/cpm/datasets/base.py @@ -122,12 +122,12 @@ def load_risky_choices(): def load_model_based_model_free(): """ - Load the model-based/model-free dataset from a CSV file. + Load the two-step task dataset from BrainExplorer. Returns ------- pd.DataFrame - A pandas DataFrame containing the model-based/model-free data. + A pandas DataFrame with one row per trial and participant. Examples -------- @@ -136,20 +136,37 @@ def load_model_based_model_free(): Notes ----- - The dataset will include the following columns: + The data are from 93 participants who played the two-step task in the BrainExplorer app (https://brainexplorer.net/), the game called "*Goblin Heist*", in up to two sessions of 80 trials. + On each trial, participants choose one of two spaceships, which takes them to one of two planets, where they receive a reward. + Like the task of Kool et al. (2016), on which it is based, it has two starting states with two spaceships each, deterministic transitions, and a stake of 1 or 5 on each trial. + The task uses the original coding, so the columns need converting before they can be passed to :class:`~cpm.applications.reinforcement_learning.HybridMBMF`. - - userID: unique identifier for each participant - - stimuli: which planets presented in stage of the trial - - s: the state of the trial - - rews: the rewards for each stimulus / planet - - choice: the choice made by the participant - - stake: the stake of the trial (1 or 5) - - points: the points received in the trial + The dataset includes the following columns: - """ - return load_csv("model_based_model_free.csv") + - ``userID``: the participant number. + - ``trial``: the trial number. + - ``block``: the block number. + - ``stimuli``: the two spaceships shown on the trial, numbered 1 to 4 across both starting states, in the order they were displayed, such as ``"[1, 2]"``. + - ``s``: the starting state and the planet reached, such as ``"[1, 2]"``. + - ``rews``: the reward values of the two planets recorded by the game on the trial, from 0 to 100, such as ``"[40, 50]"``. They are not always the reward received; that is ``points``. + - ``choice``: the chosen spaceship, numbered 1 to 4. + - ``stake``: the stake of the trial (1 or 5). + - ``points``: the reward received, from 0 to 9. + - ``points_real``: the points shown to the participant, ``points`` times 10 times ``stake``. + - ``planet``: the planet reached (1 or 2). + - ``theor_best_choice``: the spaceship that leads to the planet with the higher reward. + - ``RT``: the response time of the choice, in ms. + See Also + -------- + cpm.applications.reinforcement_learning.HybridMBMF : hybrid model-based / model-free reinforcement learning model of the two-step task. + load_two_step_data : the two-step task data of Smid et al. (2022). + References + ---------- + Kool, W., Cushman, F. A., & Gershman, S. J. (2016). When does model-based control pay off? PLoS Computational Biology, 12(8), e1005090. + """ + return load_csv("model_based_model_free.csv") def load_metacognition_data(): diff --git a/docs/api/datasets.rst b/docs/api/datasets.rst index f5851c2..56fa7a1 100644 --- a/docs/api/datasets.rst +++ b/docs/api/datasets.rst @@ -12,6 +12,7 @@ and examples. load_bandit_data load_two_step_data + load_model_based_model_free load_blocking_data load_risky_choices load_metacognition_data From 47cc34bf9f93d484735d41c3d2ae05439e781e3f Mon Sep 17 00:00:00 2001 From: Lenard Dome <23503601+lenarddome@users.noreply.github.com> Date: Mon, 5 Oct 2026 15:08:55 +0200 Subject: [PATCH 54/54] chore: bump the version to 0.26.0.dev1 Co-Authored-By: Claude Opus 5.5 --- CHANGELOG.md | 3 ++- cpm/__version__.py | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 322f49b..eb16efd 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,7 +5,7 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). -## [0.26.0.dev0] - 2026-09-28 +## [0.26.0.dev1] - 2026-10-05 ### Added @@ -20,6 +20,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - Added a two-step task example replicating Smid et al. (2022), with its data in `cpm.datasets.load_two_step_data` - Added `cpm.brainexplorer`, which computes descriptive statistics and applies the exclusion criteria for the BrainExplorer games Space Observer, Scavenger, Treasure Hunt, and Milky Way and Pirate Market, with a page in the API reference - Added test units for `cpm.brainexplorer` +- Added `cpm.datasets.load_model_based_model_free`, two-step task data from the BrainExplorer game Goblin Heist - Added an example that fits causal ratings in a blocking experiment, recreating Figures 1 and 2 of Spicer et al. (2021), with its data in `cpm.datasets.load_blocking_data`; based on an earlier version by @chotong - Added `cpm.generators.SessionWrapper`, a `Wrapper` for models that compute all trials of a participant at once - Added `cpm.models.kernels`, the formulas of the `cpm.models` classes as functions that numba can compile, used by the classes and the built-in applications diff --git a/cpm/__version__.py b/cpm/__version__.py index 38fe9be..1289812 100644 --- a/cpm/__version__.py +++ b/cpm/__version__.py @@ -1 +1 @@ -__version__ = "0.26.0.dev0" +__version__ = "0.26.0.dev1"