Adds script for data visualization

src/data_visualization.py

+# Activity Recognition
+
+import matplotlib.pyplot as plt
+import statistics
+import glob
+import sys
+import numpy as np
+
+def main():
+    person = sys.argv[1] # Frida or Momina
+    visualization = sys.argv[2] # ACC or GYR
+    verbose = True if len(sys.argv) > 3 else False
+
+    data_path = '../Data_' + person + '/'
+
+    data_paths = (glob.glob(data_path + '/*.txt'))
+
+    data_x = []
+    data_y = []
+    data_z = []
+
+    all_nbr_measurements = []
+
+    for path in data_paths:
+        nbr_measurements = 0
+
+        with open(path, 'r') as f:
+            for l in f:
+                l = l.split('\t')
+                if visualization == l[1]:
+                    data_x.append(float(l[2]))
+                    data_y.append(float(l[3]))
+                    data_z.append(float(l[4][:-1])) # don't include newline
+                    nbr_measurements += 1
+
+        if verbose:
+            print("Mean value x: {:0.3f}".format(statistics.mean(data_x)))
+            print("Mean value y: {:0.3f}".format(statistics.mean(data_y)))
+            print("Mean value z: {:0.3f}".format(statistics.mean(data_z)))
+        all_nbr_measurements.append(nbr_measurements)
+        print("Mean of means for file {:s}: {:0.3f}".format(path, (statistics.mean(data_x) + statistics.mean(data_y) + statistics.mean(data_z)) / 3))
+
+    cumulative_all_nbr_measurements = np.cumsum(all_nbr_measurements)
+    plt.figure(figsize=(24, 9))
+    plt.suptitle(visualization + ' data visualization')
+    plt.subplot(311)
+    plt.plot(data_x)
+    for nbr in cumulative_all_nbr_measurements:
+        plt.axvline(nbr, color='r')
+    plt.subplot(312)
+    plt.plot(data_y)
+    for nbr in cumulative_all_nbr_measurements:
+        plt.axvline(nbr, color='r')
+    plt.subplot(313)
+    plt.plot(data_z)
+    for nbr in cumulative_all_nbr_measurements:
+        plt.axvline(nbr, color='r')
+    plt.show()
+
+if __name__ == "__main__":
+    main()
+
+
+
+