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How to use run_4 method in SeleniumBase

Best Python code snippet using SeleniumBase

final_project.py

Source:final_project.py

...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):...

run-analysis.py

Source:run-analysis.py

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)...

part2.py

Source:part2.py

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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