How to use run_1 method in SeleniumBase

Best Python code snippet using SeleniumBase

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)...

test_unit.py

Source:test_unit.py

1# -----------------------------------------------------------2# Unit tests for overflow problem utilising pytest3# email jozsef.la.kepes@gmail.com4# -----------------------------------------------------------5from problem import Problem6def test_unit():7    """Perform unit tests for water overflow problem"""8    # Test 1: Use 2.4 litres as an input9    run_1 = Problem()10    run_1.pour(2.4)11    # Test 2.4L input for all final glass volumes12    assert run_1.get_glass_content(0, 0) == 25013    assert run_1.get_glass_content(1, 0) == 25014    assert run_1.get_glass_content(1, 1) == 25015    assert run_1.get_glass_content(2, 0) == 25016    assert run_1.get_glass_content(2, 1) == 25017    assert run_1.get_glass_content(2, 2) == 25018    assert run_1.get_glass_content(3, 0) == 112.519    assert run_1.get_glass_content(3, 1) == 25020    assert run_1.get_glass_content(3, 2) == 25021    assert run_1.get_glass_content(3, 3) == 112.522    assert run_1.get_glass_content(4, 0) == 023    assert run_1.get_glass_content(4, 1) == 43.7524    assert run_1.get_glass_content(4, 2) == 87.525    assert run_1.get_glass_content(4, 3) == 43.7526    assert run_1.get_glass_content(4, 4) == 027    # Test final total glass volume is the same is the input volume28    assert run_1.sum_glass_content() == 2.429    # Test 2: Use 15 litres as an input30    run_2 = Problem()31    run_2.pour(15)32    assert run_2.sum_glass_content() == 1533    # Test 3: Use a float as an input34    run_3 = Problem()35    run_3.pour(15.01)...

workouts.py

Source:workouts.py

1import os2from typing import TextIO3from app.models import Track, User, Workout, db4def seed_workouts():5    base_dir = os.path.dirname(__file__)6    # Read GPX files from the 'gpx_files' directory7    run_1: TextIO = open(os.path.join(base_dir, 'gpx_files', 'run_1.gpx'))8    run_2: TextIO = open(os.path.join(base_dir, 'gpx_files', 'boulder.gpx'))9    # Find a user10    demo_user = User.query.filter_by(username='demo').first()11    # Create workout objects12    workout_1 = Workout.create_workout_from_gpx(13        run_1, "First Run", demo_user.id)14    track_1 = Track.create_track_from_gpx_track(15        run_2, "Boulder Skyline", demo_user.id)16    # Commit the session17    db.session.add(workout_1)18    db.session.add(track_1)19    db.session.commit()20def undo_workouts():21    db.session.execute(22        'TRUNCATE track_points, tracks, workouts RESTART IDENTITY;')...

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