Content

codes/frequency_deriv/frequency_deriv.py

@@ -1,5 +1,6 @@
 #!/usr/bin/env python3
 
+import numpy as np
 import statistics
 from collections import defaultdict
 from typing import Dict
@@ -26,7 +27,8 @@ def plot_devi(data: Dict[datetime, str]):
     # c = 0
     per_diff = defaultdict(list)
     for prev, next in zip(sor, sor[1:]):
-        diff = abs(2.5 - (next[0].timestamp() - prev[0].timestamp()))
+        # diff = abs(2.5 - (next[0].timestamp() - prev[0].timestamp()))
+        diff = ((next[0].timestamp() - prev[0].timestamp()) - 2.5)
         diffs.append(diff)
         per_crawler[prev[1]].append(prev[0])
         per_diff[prev[1]].append(diff)
@@ -72,16 +74,20 @@ def plot_devi(data: Dict[datetime, str]):
         t = per_crawler[c]
         devi = []
         for pre, nex in zip(t, t[1:]):
-            devi.append(abs(10 - (nex.timestamp() - pre.timestamp())))
-        x = [10 * x for x in range(len(devi))]
+            # devi.append(abs(10 - (nex.timestamp() - pre.timestamp())))
+            devi.append(((nex.timestamp() - pre.timestamp()) - 10))
+        x = np.array([10 * x for x in range(len(devi))])
+        devi = np.array(devi)
         fig, ax = plt.subplots()
         ax.scatter(x, devi, s=10)
+        m, b = np.polyfit(x, devi, 1)
+        plt.plot(x, m*x+b, color='red')
         ax.set_title(f'Timedeviation for {c}')
         ax.set_xlabel('Time passed in seconds')
         ax.set_ylabel('Deviation in seconds')
         plt.savefig(f'./time_devi_{c}.png')
         plt.close()
-        print(f'{c}: {statistics.mean(devi)}')
+        print(f'{c} & \\num{{{statistics.mean(devi)}}} \\\\')
         # for ts in per_crawler[c]: