1 files changed, 77 insertions, 0 deletions

diff --git a/Metrics/Metrics-Calculation/metrics_plot/type_analysis/src/plot_violation.py b/Metrics/Metrics-Calculation/metrics_plot/type_analysis/src/plot_violation.py
new file mode 100644
index 00000000..e5e12f42
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/type_analysis/src/plot_violation.py
@@ -0,0 +1,77 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import cdf_histogram as cdf
+import readCSV as reader
+import constants
+import csv
+from shutil import copy
+
+
+# viatra_controlled = np.genfromtxt('../statistics/violations/viatra_controlled.csv', delimiter='\n')
+# viatra_controlled_mean = np.mean(viatra_controlled)
+# viatra_nocons = np.genfromtxt('../statistics/violations/viatra_no_cons_violation.csv', delimiter='\n')
+# viatra_nocons_mean = np.mean(viatra_nocons)
+# human = np.genfromtxt('../statistics/violations/human_violation.csv', delimiter='\n')
+# human_mean = np.mean(human)
+# print('viatra controlled violation mean: ' + str(viatra_controlled_mean))
+# print('viatra no cons violation mean: ' + str(viatra_nocons_mean))
+# print('human output violation mean: ' + str(human_mean))
+
+
+def read_violation_file(file_name):
+    violation = {}
+    with open(file_name) as f:
+        for i, line in enumerate(f):
+            line = int(line.rstrip())
+            if line in violation:
+                violation[line] += 1
+            else:
+                violation[line] = 1
+    f.close()
+    return violation
+
+# violation = read_violation_file('../statistics/violations/viatra_controlled.csv')
+# print(violation)
+# fig, ax = plt.subplots()
+# rects = ax.bar(violation.keys(), violation.values())
+# cdf.autolabel(rects, ax)
+# ax.set_title('viatra controlled violation distribution')
+# ax.set_xlabel('number of violations')
+# ax.set_ylabel('number of occurrence')
+# plt.show()
+# fig.savefig('../statistics/violations/viatra_controlled_violation_distribution.png')
+
+
+def plot_violation():
+    v = np.array([0,1,1,0,0])
+    print(v)
+    (n, bins) = np.histogram(v, bins=v.size, density=True)  # NumPy version (no plot)
+    # plt.plot(.5 * (bins[1:] + bins[:-1]), n)
+    plt.hist(v, bins=50, density=1)
+    plt.show()
+
+
+def human_output_size():
+    list_of_files = reader.readmultiplefiles('../statistics/humanOutput/', 5000)
+    num_nodes_list =[]
+    human_size_dic = {}
+    for file in list_of_files:
+        contents = reader.readcsvfile(file)
+        num_of_node = contents[constants.NUMBER_NODES]
+        # human_size_dic[file] = int(num_of_node[0])
+        # num_nodes_list.append(int(num_of_node[0]))
+        if 90 <= num_of_node[0] <= 110:
+            copy(file, '../statistics/human_output_100/')
+
+    # mean = np.mean(num_nodes_list)
+    # std = np.std(num_nodes_list)
+    # with open('../statistics/human_output_size.csv', 'w', newline='') as csvfile:
+    #     spamwriter = csv.writer(csvfile, delimiter=',',
+    #                             quotechar='|', quoting=csv.QUOTE_MINIMAL)
+    #     spamwriter.writerow(['mean', mean])
+    #     spamwriter.writerow(['standard deviation', std])
+    #     for key, value in human_size_dic.items():
+    #         spamwriter.writerow([key, value])
+
+
+