Task90 Performance analysis timing graphs

docs/TimingPlot.py

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+import matplotlib.pyplot as plt
+
+# Timed on Matlab 2022 on intel i7, 8-core 3.8GHz 
+
+# Preprocessing plot
+m = [149.6, 377.5,1352.2]  # matlab timing. time the full constructor phase.
+p = [166.8, 904.4,4810.4]  # Python 3.10 on same PC specs. 
+
+
+N = [50, 100,200]
+                # n=50 iterates over two levels.
+                # n= 100 has three levels
+                # n= 200  has four levels
+
+
+plt.plot(N, p,marker='x', linestyle='', label='Python Implementation')
+plt.plot(N, m, marker='o', linestyle='', label='Matlab Implementation')
+
+plt.xticks([50,100,200])  # Set the positions of the ticks
+plt.gca().set_xticklabels([50,100,200])  # Set the labels of the ticks
+plt.xlim(00, 250)  # Adjust the limits of the x-axis to control the width
+
+plt.xlabel('Sqrpoisson Data set of Size N', fontsize=20)
+plt.ylabel('Runtime (ms)', fontsize=20)
+plt.title('Preprocessing Phase', fontsize=20)
+plt.legend(fontsize=18)
+plt.legend(loc='upper left')
+
+
+plt.savefig('TimePreprocessing.png', dpi=300)  # Save as PNG format with 300 dpi resolution
+plt.show()
+
+plt.clf()
+#
+# Solver Phase plot
+m1 = [31.0,44.2,91.3]  # matlab timing. time the full solving phase.
+p1 = [193.2, 1313.8,8398.9]
+
+
+
+plt.plot(N, p1,marker='x', linestyle='', label='Python Implementation')
+plt.plot(N, m1, marker='o', linestyle='', label='Matlab Implementation')
+
+plt.xticks([50,100,200])  # Set the positions of the ticks
+plt.gca().set_xticklabels([50,100,200])  # Set the labels of the ticks
+plt.xlim(00, 250)  # Adjust the limits of the x-axis to control the width
+
+plt.xlabel('Sqrpoisson Data set of Size N', fontsize=20)
+plt.ylabel('Runtime (ms)', fontsize=20)
+plt.title('Solver Phase', fontsize=20)
+plt.legend(fontsize=18)
+plt.legend(loc='upper left')
+
+
+plt.savefig('TimeSolver.png', dpi=300)  # Save as PNG format with 300 dpi resolution
+plt.show()