Added Project and Report

project/plotting/QR.py

@@ -0,0 +1,68 @@
+from config import QR_RESULTS, PLOTS_PATH, nanoseconds_to_seconds, setup
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+import seaborn as sns
+import numpy as np
+
+
+def plot_lambda(kind, smoothing_window=2):
+    if kind not in ['error', 'time']:
+        raise ValueError("kind must be one of 'error', 'time'")
+
+    df = pd.read_csv(QR_RESULTS.format("statisticsQR-lambda-m300n20--{}.csv".format(kind)))
+    lambda_values = df['lambda']
+
+    if kind == 'time':
+
+        setup(title='QR running time with respect to λ', y_label='Time (s)', x_label='λ')
+        meantime_values = df['meantime']
+        stdtime_values = df['stdtime']
+        sns.lineplot(data=df, x='lambda', y='meantime', label='Mean Time', color='blue')
+        plt.fill_between(
+            lambda_values,
+            meantime_values - stdtime_values,
+            meantime_values + stdtime_values,
+            alpha=0.3,
+            label='Standard Deviation',
+            color='lightblue'
+        )
+        plt.ylim(0, 3.5 * max(meantime_values + stdtime_values))
+        plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(nanoseconds_to_seconds))
+    else:
+        setup(title='QR errors with respect to λ', y_label='Error', x_label='λ')
+
+        df['relative'] = np.convolve(df['relative'], np.ones(smoothing_window) / smoothing_window, mode='same')
+        df['residual'] = np.convolve(df['residual'], np.ones(smoothing_window) / smoothing_window, mode='same')
+        df['stability'] = np.convolve(df['stability'], np.ones(smoothing_window) / smoothing_window, mode='same')
+
+        sns.lineplot(data=df, x='lambda', y='relative', label='Relative Error', color='blue')
+        sns.lineplot(data=df, x='lambda', y='residual', label='Residual', color='red')
+        sns.lineplot(data=df, x='lambda', y='stability', label='Stability', color='green')
+        plt.yscale('log')
+
+    plt.legend(loc='upper right', fontsize=15)
+    plt.xscale('log', base=10)
+
+    plt.savefig(PLOTS_PATH.format('QR', "QR-lambda-{}.png".format(kind)))
+    plt.show()
+
+
+def plot_forward_error(smoothing_window=5):
+    df = pd.read_csv(QR_RESULTS.format("statisticsQR-forward-m300n20--error.csv"))
+    setup(title='QR forward error with respect to λ', y_label='Error', x_label='λ')
+    df['forwardQ'] = np.convolve(df['forwardQ'], np.ones(smoothing_window) / smoothing_window, mode='same')
+    df['forwardR'] = np.convolve(df['forwardR'], np.ones(smoothing_window) / smoothing_window, mode='same')
+    sns.lineplot(data=df, x='lambda', y='forwardQ', label='Forward Error on Q', color='blue')
+    sns.lineplot(data=df, x='lambda', y='forwardR', label='Forward Error on R', color='red')
+    plt.yscale('log')
+    plt.xscale('log', base=10)
+    plt.legend(loc='upper right', fontsize=15)
+    plt.savefig(PLOTS_PATH.format('QR', "QR-forward_error.png"))
+    plt.show()
+
+
+if __name__ == '__main__':
+    plot_lambda('error')
+    plot_lambda('time')
+    plot_forward_error()