updating scaling routine

tests/population/scaling/plot_scaling_new.py

+import matplotlib
+import os
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+import json
+
+scaling_result_dir = 'scaling_results'
+
+
+result_jsons = []
+result_jsons.append(os.path.join(os.path.abspath(scaling_result_dir), 'david-Lenovo-IdeaPad-S340-14IWL_100_systems.json'))
+
+
+
+fig, ax1 = plt.subplots()
+ax2 = ax1.twinx()
+for jsonfile in result_jsons:
+    print(jsonfile)
+
+    with open(jsonfile, 'r') as f:
+        result_data = json.loads(f.read())
+
+        # Get linear data
+        linear_data = result_data['linear']
+        linear_mean = np.mean(linear_data)
+        linear_stdev = np.std(linear_data)
+
+        cpus = []
+        mp_speedups = []
+        mp_efficiencies = []
+        stddevs = []
+
+        for amt_cpus in result_data['mp']:
+            # Get mp data
+            mp_data = result_data['mp'][amt_cpus]
+            mp_mean = np.mean(mp_data)
+            mp_stdev = np.std(mp_data)
+
+            # Calc and append info
+            amt_cpus = int(amt_cpus)
+            speedup = linear_mean/mp_mean
+            efficiency = speedup/int(amt_cpus)
+
+            cpus.append(amt_cpus)
+            mp_speedups.append(speedup)
+            mp_efficiencies.append(efficiency)
+            stddevs.append(mp_stdev)
+
+        # Plot
+        ax1.errorbar(
+            cpus,
+            mp_speedups,
+            stddevs,
+            linestyle="None",
+            marker="^",
+            label="Speed up & efficiency of {} systems".format(result_data['amt_systems']),
+        )
+
+
+ax1.set_title(
+    "Speed up ratio vs amount of cores for different amounts of systems on {}".format(
+        'name_testcase'
+    )
+)
+ax1.set_xlabel("Amount of cores used")
+ax1.set_ylabel("Speed up ratio (time_linear/time_parallel)")
+
+ax1.set_xlim(0, max(cpus) + 4)
+# ax1.set_ylim(0, max_speedup + 2)
+ax2.set_ylim(0, 1)
+
+ax1.grid()
+ax1.legend(loc=4)
+plt.show()

tests/population/scaling/scaling_script.py

@@ -31,7 +31,7 @@ for i in range(1, int(amount_of_cpus/stepsize) + 1 ):
 amt_repeats = 5
 
 resolution = {'M_1': 10, 'per': 10}
-total_systems = np.prod([el for el in resolution.values()])
+total_systems = int(np.prod([el for el in resolution.values()]))
 result_dir = 'scaling_results'