Merge branch 'population' of gitlab.eps.surrey.ac.uk:ri0005/binary_c-python into population

tests/population/scaling/plot_amdahl.py

+import matplotlib.pyplot as plt
+import numpy as np
+
+def amdahl(f,n):
+    return 1.0/((1-f) + (f/n))
+
+cores = np.arange(1, 10, 0.1)
+values_list = [] 
+par_vals = np.arange(0, 1.1, 0.1)
+
+
+for par_val in par_vals:
+    values = amdahl(par_val, cores)
+    values_list.append(values)
+
+
+for values in values_list:
+    plt.plot(cores, values, 'b-')
+plt.show()
\ No newline at end of file

tests/population/scaling/plot_scaling_new.py

@@ -6,19 +6,22 @@ import numpy as np
 import json
 import math
 
-scaling_result_dir = 'scaling_results'
+def amdahl(f,n):
+    return 1.0/((1-f) + (f/n))
 
+#################################
+# Files
+scaling_result_dir = 'scaling_results'
 filenames = [
     'astro2_2500_systems.json',
     'astro2_3000_systems.json',
 ]
-
 result_jsons = []
 for filename in filenames:
     result_jsons.append(os.path.join(os.path.abspath(scaling_result_dir), filename))
-# result_jsons.append(os.path.join(os.path.abspath(scaling_result_dir), 'david-Lenovo-IdeaPad-S340-14IWL_100_systems.json'))
-# result_jsons.append(os.path.join(os.path.abspath(scaling_result_dir), 'david-Lenovo-IdeaPad-S340-14IWL_2500_systems.json'))
 
+#################################
+# Plotting of the scaling results
 fig, ax1 = plt.subplots()
 ax2 = ax1.twinx()
 for jsonfile in result_jsons:
@@ -32,11 +35,7 @@ for jsonfile in result_jsons:
         linear_mean = np.mean(linear_data)
         linear_stdev = np.std(linear_data)
 
-        cpus = []
-        speedups = []
-        efficiencies = []
-        stddev_speedups = []
-
+        cpus, speedups, efficiencies, stddev_speedups = [], [], [], []
         for amt_cpus in result_data['mp']:
             # Get mp data
             mp_data = result_data['mp'][amt_cpus]
@@ -69,6 +68,25 @@ for jsonfile in result_jsons:
 
         # x_position_shift += 0.1
 
+
+# Do Amdahls law fitting
+# cores = np.arange(1, 48, 0.1)
+# values_list = [] 
+# par_step = 0.005
+# par_vals = np.arange(.95, 1, par_step)
+
+
+# for par_val in par_vals:
+#     values = amdahl(par_val, cores)
+#     values_list.append(values)
+
+# for i, values in enumerate(values_list):
+#     ax1.plot(cores, values, label="par_val={}".format(par_vals[i]))
+
+
+
+#################################
+# Adding plot make up
 ax1.set_title(
     "Speed up ratio vs amount of cores for different amounts of systems on {}".format(
         'name_testcase'
@@ -83,6 +101,15 @@ ax1.set_ylabel("Speed up ratio (time_linear/time_parallel)")
 # ax1.set_xlim(0, max(cpus) + 4)
 # ax2.set_ylim(0, 1)
 
+
+
+
+
+
+
+
+
+
 ax1.grid()
 ax1.legend(loc=4)
 ax1.set_xscale('log')