adding dynamic plot

eDiscovery/plot.py

@@ -0,0 +1,49 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+file = './results/RCV1.C4.csv'
+# plot the data
+fig, axs = plt.subplots(3)
+
+while True:
+    df = pd.read_csv(file, sep='\t')
+
+    xs = df['it']
+
+    y_r = df['R']
+    y_rhat = df['Rhat']
+    y_rhatCC = df['RhatCC']
+    label='R'
+    axs[0].plot(xs, y_rhat, label='$\hat{'+label+'}$')
+    axs[0].plot(xs, y_rhatCC, label='$\hat{'+label+'}_{CC}$')
+    axs[0].plot(xs, y_r, label=label)
+    axs[0].legend()
+    axs[0].grid()
+
+    y_r = df['te-prev']
+    y_rhat = df['te-estim']
+    y_rhatCC = df['te-estimCC']
+    label='P'
+    axs[1].plot(xs, y_rhat, label='$\hat{'+label+'}$')
+    axs[1].plot(xs, y_rhatCC, label='$\hat{'+label+'}_{CC}$')
+    axs[1].plot(xs, y_r, label=label)
+    axs[1].legend()
+    axs[1].grid()
+
+    y_ae = df['AE']
+    y_ae_cc = df['AE_CC']
+    axs[2].plot(xs, y_ae, label='AE')
+    axs[2].plot(xs, y_ae_cc, label='AE-CC')
+    axs[2].legend()
+    axs[2].grid()
+
+    #plt.pause(1.0)
+    axs[0].cla()
+    axs[1].cla()
+    axs[2].cla()
+
+    plt.savefig('./plot.png')
+    break
+
+#plt.show()