69 lines
2.9 KiB
Python
69 lines
2.9 KiB
Python
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from config import QR_RESULTS, PLOTS_PATH, nanoseconds_to_seconds, setup
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import pandas as pd
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import matplotlib.pyplot as plt
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import matplotlib.ticker as ticker
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import seaborn as sns
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import numpy as np
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def plot_lambda(kind, smoothing_window=2):
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if kind not in ['error', 'time']:
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raise ValueError("kind must be one of 'error', 'time'")
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df = pd.read_csv(QR_RESULTS.format("statisticsQR-lambda-m300n20--{}.csv".format(kind)))
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lambda_values = df['lambda']
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if kind == 'time':
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setup(title='QR running time with respect to λ', y_label='Time (s)', x_label='λ')
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meantime_values = df['meantime']
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stdtime_values = df['stdtime']
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sns.lineplot(data=df, x='lambda', y='meantime', label='Mean Time', color='blue')
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plt.fill_between(
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lambda_values,
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meantime_values - stdtime_values,
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meantime_values + stdtime_values,
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alpha=0.3,
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label='Standard Deviation',
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color='lightblue'
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)
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plt.ylim(0, 3.5 * max(meantime_values + stdtime_values))
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plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(nanoseconds_to_seconds))
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else:
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setup(title='QR errors with respect to λ', y_label='Error', x_label='λ')
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df['relative'] = np.convolve(df['relative'], np.ones(smoothing_window) / smoothing_window, mode='same')
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df['residual'] = np.convolve(df['residual'], np.ones(smoothing_window) / smoothing_window, mode='same')
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df['stability'] = np.convolve(df['stability'], np.ones(smoothing_window) / smoothing_window, mode='same')
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sns.lineplot(data=df, x='lambda', y='relative', label='Relative Error', color='blue')
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sns.lineplot(data=df, x='lambda', y='residual', label='Residual', color='red')
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sns.lineplot(data=df, x='lambda', y='stability', label='Stability', color='green')
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plt.yscale('log')
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plt.legend(loc='upper right', fontsize=15)
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plt.xscale('log', base=10)
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plt.savefig(PLOTS_PATH.format('QR', "QR-lambda-{}.png".format(kind)))
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plt.show()
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def plot_forward_error(smoothing_window=5):
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df = pd.read_csv(QR_RESULTS.format("statisticsQR-forward-m300n20--error.csv"))
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setup(title='QR forward error with respect to λ', y_label='Error', x_label='λ')
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df['forwardQ'] = np.convolve(df['forwardQ'], np.ones(smoothing_window) / smoothing_window, mode='same')
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df['forwardR'] = np.convolve(df['forwardR'], np.ones(smoothing_window) / smoothing_window, mode='same')
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sns.lineplot(data=df, x='lambda', y='forwardQ', label='Forward Error on Q', color='blue')
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sns.lineplot(data=df, x='lambda', y='forwardR', label='Forward Error on R', color='red')
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plt.yscale('log')
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plt.xscale('log', base=10)
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plt.legend(loc='upper right', fontsize=15)
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plt.savefig(PLOTS_PATH.format('QR', "QR-forward_error.png"))
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plt.show()
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if __name__ == '__main__':
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plot_lambda('error')
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plot_lambda('time')
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plot_forward_error()
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