How to use run_4 method in SeleniumBase

Best Python code snippet using SeleniumBase

final_project.py

Source: final_project.py Github

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...68lastW = W69run_4 = False70run_5 = False71run_6 = False72def run_4(b4):73 global run_474 run_4 = not run_475b4 = button(text = "air",pos = scene1.title_anchor,bind=run_4)76def run_5(b5):77 global run_578 run_5 = not run_579b5 = button(text = "water",pos = scene1.title_anchor,bind=run_5)80def run_6(b6):81 global run_682 run_6 = not run_683b6 = button(text = "glass",pos = scene1.title_anchor,bind=run_6)84#scene1.bind('keydown', keyinput) # setting for the binding function85#scene2.bind('keydown', keyinput) # setting for the binding function86def get_E_field(shape1 , medium):...

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run-analysis.py

Source: run-analysis.py Github

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1import matplotlib.pyplot as plt2import numpy as np3import pandas as pd4import seaborn as sns5def ra_plot(run_1, run_2, run_3, run_4, window=10):6 run_avg_1 = run_1['average'].rolling(window).mean()7 run_avg_2 = run_2['average'].rolling(window).mean()8 run_avg_3 = run_3['average'].rolling(window).mean()9 run_avg_4 = run_4['average'].rolling(window).mean()10 run_avg_combo = pd.concat([run_avg_1,11 run_avg_2.reindex(run_avg_1.index),12 run_avg_3.reindex(run_avg_1.index),13 run_avg_4 .reindex(run_avg_1.index)], axis=1)14 run_avg_combo.set_axis(['average_1', 'average_2', 'average_3', 'average_4'], axis=1, inplace=True)15 run_avg_combo['min'] = run_avg_combo.min(axis=1)16 run_avg_combo['max'] = run_avg_combo.max(axis=1)17 run_avg_combo['average'] = run_avg_combo.mean(axis=1)18 plt.plot(run_avg_combo['average_1'], label='Run 1')19 plt.plot(run_avg_combo['average_2'], label='Run 2')20 plt.plot(run_avg_combo['average_3'], label='Run 3')21 plt.plot(run_avg_combo['average_4'], label='Run 4')22 plt.plot(run_avg_combo['average'], color='black', label='Average', linestyle='dashed')23 plt.fill_between(run_avg_combo.index, run_avg_combo['min'], run_avg_combo['max'], alpha=0.5, label='Range')24 plt.xlabel('Episode')25 plt.ylabel('Reward')26 # plt.title('Total Average Episode Reward')27 plt.legend()28 plt.savefig('graphs/​total-average-episode-reward-window{}-new.png'.format(window))29 plt.show()30def sd_plot(run_1, run_2, run_3, run_4):31 run_1 = run_1.drop([' distance_to_target'], axis=1)32 run_combo = pd.concat([run_1,33 run_2['average'].reindex(run_1.index),34 run_3['average'].reindex(run_1.index),35 run_4['average'].reindex(run_1.index)], axis=1)36 run_combo.set_axis(['average_1', 'average_2', 'average_3', 'average_4'], axis=1, inplace=True)37 run_combo['min'] = run_combo.min(axis=1)38 run_combo['max'] = run_combo.max(axis=1)39 run_combo['average'] = run_combo.mean(axis=1)40 plt.plot(run_combo['average_1'], label='Run 1')41 plt.plot(run_combo['average_2'], label='Run 2')42 plt.plot(run_combo['average_3'], label='Run 3')43 plt.plot(run_combo['average_4'], label='Run 4')44 plt.plot(run_combo['average'], color='black', label='Average', linestyle='dashed')45 plt.fill_between(run_combo.index, run_combo['min'], run_combo['max'], alpha=0.5, label='Range')46 plt.xlabel('Episode')47 plt.ylabel('Reward')48 # plt.title('Total Average Episode Reward')49 plt.legend(loc='lower right')50 plt.savefig('graphs/​total-average-episode-reward-minmax.png')51 plt.show()52def sd_plot_2(run_1, run_2, run_3, run_4):53 run_1 = run_1.drop(['episode'], axis=1)54 run_combo = pd.concat([run_1,55 run_2['average'].reindex(run_1.index),56 run_3['average'].reindex(run_1.index),57 run_4['average'].reindex(run_1.index)], axis=1)58 run_combo.set_axis(['average_1', 'average_2', 'average_3', 'average_4'], axis=1, inplace=True)59 run_combo['min'] = run_combo.min(axis=1)60 run_combo['max'] = run_combo.max(axis=1)61 run_combo['average'] = run_combo.mean(axis=1)62 plt.plot(run_combo['average_1'], label='Run 1')63 plt.plot(run_combo['average_2'], label='Run 2')64 plt.plot(run_combo['average_3'], label='Run 3')65 plt.plot(run_combo['average_4'], label='Run 4')66 plt.plot(run_combo['average'], color='black', label='Average', linestyle='dashed')67 plt.fill_between(run_combo.index, run_combo['min'], run_combo['max'], alpha=0.5, label='Range')68 plt.xlabel('Episode')69 plt.ylabel('Reward')70 # plt.title('Total Average Episode Reward')71 plt.legend(loc='lower right')72 plt.savefig('graphs/​total-average-episode-reward-minmax-new.png')73 plt.show()74if __name__ == "__main__":75 sns.set()76 sns.set_style('whitegrid')77 run1 = pd.read_csv('data-files/​run1a.csv')78 run2 = pd.read_csv('data-files/​run2a.csv')79 run3 = pd.read_csv('data-files/​run3a.csv')80 run4 = pd.read_csv('data-files/​run4a.csv')81 runs = [run1, run2, run3, run4]82 run1 = run1.drop(['Steps_In_Episode',83 'Total_Steps',84 'Mean_Episode_Reward',85 'Mean_Reward_All',86 ' Memory_used',87 'Time_Elapsed',88 'Solved',89 'Solved.1'], axis=1)90 run1.set_axis(['episode', 'average'], axis=1, inplace=True)91 run2 = run2.drop(['Steps_In_Episode',92 'Total_Steps',93 'Mean_Episode_Reward',94 'Mean_Reward_All',95 ' Memory_used',96 'Time_Elapsed',97 'Solved',98 'Solved.1'], axis=1)99 run2.set_axis(['episode', 'average'], axis=1, inplace=True)100 run3 = run3.drop(['Steps_In_Episode',101 'Total_Steps',102 'Mean_Episode_Reward',103 'Mean_Reward_All',104 ' Memory_used',105 'Time_Elapsed',106 'Solved',107 'Solved.1'], axis=1)108 run3.set_axis(['episode', 'average'], axis=1, inplace=True)109 run4 = run4.drop(['Steps_In_Episode',110 'Total_Steps',111 'Mean_Episode_Reward',112 'Mean_Reward_All',113 ' Memory_used',114 'Time_Elapsed',115 'Solved',116 'Solved.1'], axis=1)117 run4.set_axis(['episode', 'average'], axis=1, inplace=True)118 sd_plot_2(run1, run2, run3, run4)119 ra_plot(run1, run2, run3, run4)120 ra_plot(run1, run2, run3, run4, window=100)121 '''122 run1 = pd.read_csv('data-files/​run1.csv')123 run2 = pd.read_csv('data-files/​run2.csv')124 run3 = pd.read_csv('data-files/​run3.csv')125 run4 = pd.read_csv('data-files/​run4.csv')126 frames = [run1, run2, run3, run4]127 long_file = pd.concat(frames)128 columns = long_file.columns129 mean = long_file[columns[2]].mean()130 print(mean)131 std = long_file[columns[2]].std()132 print(std)133 min = long_file[columns[2]].min()134 print(min)135 run1 = run1.drop(['total_timesteps'], axis=1)136 run1 = run1.groupby('episode').mean()137 run1['average'] = run1.expanding().mean()138 run2 = run2.drop(['total_timesteps'], axis=1)139 run2 = run2.groupby('episode').mean()140 run2['average'] = run2.expanding().mean()141 run3 = run3.drop(['total_timesteps'], axis=1)142 run3 = run3.groupby('episode').mean()143 run3['average'] = run3.expanding().mean()144 run4 = run4.drop(['total_timesteps'], axis=1)145 run4 = run4.groupby('episode').mean()146 run4['average'] = run4.expanding().mean()147 # ra_plot(run1, run2, run3, run4)148 # ra_plot(run1, run2, run3, run4, window=100)149 # sd_plot(run1, run2, run3, run4)...

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part2.py

Source: part2.py Github

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1f = open("input.txt", "r")2data = f.read().split("\n")3data.pop()4def countTrees(data,right,down):5 num_rows = len(data)6 num_cols = len(data[0])7 num_trees = 08 x_pos = 09 x = 010 while x < num_rows - 1:11 x += down12 x_pos += right13 x_pos_mod = x_pos % num_cols14 if data[x][x_pos_mod] == '#':15 num_trees += 116 return num_trees17run_1 = countTrees(data,1,1)18run_2 = countTrees(data,3,1)19run_3 = countTrees(data,5,1)20run_4 = countTrees(data,7,1)21run_5 = countTrees(data,1,2)...

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