Added plotting scripts, figures and timing results

Plots/plot_timing_histo.py

@@ -0,0 +1,89 @@
+#!/usr/bin/env python
+
+from __future__ import print_function
+
+import numpy as np
+import sys
+import matplotlib.pyplot as plt
+import matplotlib.pylab as pylab
+
+params = {'legend.fontsize': 'x-large',
+          'figure.figsize': (13, 6),
+          'figure.autolayout': True,
+         'axes.labelsize': 'x-large',
+         'axes.titlesize':'x-large',
+         'xtick.labelsize':'x-large',
+         'ytick.labelsize':'x-large'}
+pylab.rcParams.update(params)
+
+month="Sep2019"
+
+def get_timing(lang, test):
+    result = None
+    with open("../Results/timing_results_"+month+".txt", "r") as fid:
+         lines = fid.readlines()
+         for line in lines:
+             if (lang in line) and (test in line):
+                a, b, result = line.split(",")
+                break
+    return result
+
+languages = ["C", "Fortran", "Python", "Julia", "IDL", "Matlab", "R", "Java", "Scala"]
+
+test_cases = ["copy_matrix", "look_and_say", "iterative_fibonacci", "recursive_fibonacci", "matrix_multiplication", "evaluate_functions", "belief_propagation", "markov_chain", "laplace_equation", "munchauser_number"]
+
+num_lang = len(languages)
+num_test = len(test_cases)
+
+A = np.empty((num_lang,num_test,))
+B = np.zeros((num_lang,num_test,))
+A[:] = np.nan
+
+i = 0
+for lang in languages:
+    j = 0
+    for test in test_cases:
+        result = get_timing(lang, test)
+        if result:
+           A[i,j] = float(result)
+        j += 1
+    i += 1
+
+A = np.ma.masked_invalid(A)
+
+for j in range(num_test):
+    if A[0,j] == 0.0:
+       A[:,j] = np.exp(A[:,j])
+    else:
+       coef = A[0,j]
+       A[:,j] = A[:,j] / coef
+
+
+data_sets = [A[j,:] for j in range(num_lang)]
+
+colors = ["blue", "orange", "green", "purple", "red", "pink", "olive", "brown", "gray"]
+fig, ax = plt.subplots(figsize=(15.0, 7.0))
+pos = np.arange(num_test)
+bar_width = 0.085
+i = 0
+for a in data_sets:
+    ax.bar(pos + (i+1)*bar_width, a, bar_width, color=colors[i])
+    i += 1
+
+plt.yscale('log')#, nonposy='clip')
+
+ax.yaxis.grid()
+#plt.legend(loc='best')
+# Shrink current axis by 20%
+box = ax.get_position()
+ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
+
+# Put a legend to the right of the current axis
+ax.legend(languages, loc='center left', bbox_to_anchor=(1, 0.5))
+
+#plt.legend(languages, loc='upper center')
+ax.set_xticks(pos)
+ax.set_xticklabels(test_cases, rotation=45)
+
+plt.savefig("fig_languages_histo_"+month+".png", bbox_inches = "tight")
+plt.show()

Plots/plot_timing_scatter.py

@@ -0,0 +1,90 @@
+#!/usr/bin/env python
+
+from __future__ import print_function
+
+import numpy as np
+import sys
+import matplotlib.pyplot as plt
+import matplotlib.pylab as pylab
+
+params = {'legend.fontsize': 'x-large',
+          'figure.figsize': (14, 6),
+         'axes.labelsize': 'x-large',
+         'axes.titlesize':'x-large',
+         'xtick.labelsize':'x-large',
+         'ytick.labelsize':'x-large'}
+pylab.rcParams.update(params)
+
+month="Sep2019"
+
+def get_timing(lang, test):
+    result = None
+    with open("../Results/timing_results_"+month+".txt", "r") as fid:
+         lines = fid.readlines()
+         for line in lines:
+             if (lang in line) and (test in line):
+                a, b, result = line.split(",")
+                break
+    return result
+
+languages = ["C", "Fortran", "Python", "Julia", "IDL", "Matlab", "R", "Java", "Scala"]
+
+test_cases = ["copy_matrix", "look_and_say", "iterative_fibonacci", "recursive_fibonacci", "matrix_multiplication", "evaluate_functions", "belief_propagation", "markov_chain", "laplace_equation", "munchauser_number"]
+
+colors = ["blue", "orange", "green", "purple", "red", "pink", "olive", "brown", "gray", "gold"]
+
+num_lang = len(languages)
+num_test = len(test_cases)
+
+A = np.empty((num_lang,num_test,))
+B = np.zeros((num_lang,num_test,))
+A[:] = np.nan
+
+i = 0
+for lang in languages:
+    j = 0
+    for test in test_cases:
+        result = get_timing(lang, test)
+        if result:
+           A[i,j] = float(result)
+        j += 1
+    i += 1
+
+A = np.ma.masked_invalid(A)
+
+for j in range(num_test):
+    if A[0,j] == 0.0:
+       A[:,j] = np.exp(A[:,j])
+    else:
+       coef = A[0,j]
+       A[:,j] = A[:,j] / coef
+
+for j in range(num_lang):
+    B[j,:] = B[j,:] + j
+
+fig, ax = plt.subplots(1,1)
+
+
+for j in range(num_test):
+    ax.plot(B[:,j], A[:,j], "o",  color=colors[j])
+    #ax.plot(B[:,j], A[:,j], "o",  label=test_cases[j])
+
+ax.yaxis.grid()
+#plt.legend(loc='best')
+# Shrink current axis by 20%
+box = ax.get_position()
+ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
+
+# Put a legend to the right of the current axis
+ax.legend(test_cases, loc='center left', bbox_to_anchor=(1, 0.5))
+#ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+
+ax.set_yscale('log')
+
+x = np.arange(num_lang)
+
+ax.set_xticks(x)
+ax.set_xticklabels(languages) #, minor=False, rotation=45)
+
+plt.savefig("fig_languages_scatter_"+month+".png")
+plt.show()

README.md

@@ -192,3 +192,8 @@ It also has sample text files for the **Count Unique Words in a File** test case
        The goal is to be able to do a generate the three-diemsional arrays
        (year/level/value) and carry out a contour plot.
 
+## <font color="red">Results</font>
+
+![Histogram (Sep2019](Plots/fig_languages_histo_Sep2019.png)
+
+![Scatter Plot (Sep2019](Plots/fig_languages_scatter_Sep2019.png)

Results/timing_results_Sep2019.txt

@@ -0,0 +1,97 @@
+
+C, copy_matrix, 0.29
+C, look_and_say, 0.40
+C, iterative_fibonacci, 0.00
+C, recursive_fibonacci, 2.20
+C, matrix_multiplication, 4.06
+C, evaluate_functions, 116.06
+C, belief_propagation, 9.72
+C, markov_chain, 0.00
+C, laplace_equation, 3.18
+C, munchauser_number, 3.86
+
+Fortran, copy_matrix, 0.2240
+Fortran, look_and_say, 0.120
+Fortran, iterative_fibonacci, 0.00
+Fortran, recursive_fibonacci, 0.00
+Fortran, matrix_multiplication, 0.9281
+Fortran, evaluate_functions, 62.167885
+Fortran, belief_propagation, 15.86499
+Fortran, markov_chain, 0.00
+Fortran, laplace_equation, 5.136
+Fortran, munchauser_number, 21.2933
+
+Python, copy_matrix, 1.6055
+Python, look_and_say, 220.9076
+Python, iterative_fibonacci, 0.00
+Python, recursive_fibonacci, 1735.2580
+Python, matrix_multiplication, 0.3308
+Python, evaluate_functions, 18.2397
+Python, belief_propagation, 13.6831
+Python, markov_chain, 0.1089
+Python, laplace_equation, 40.5679
+Python, munchauser_number, 1121.7358
+
+Julia, copy_matrix, 0.292153
+Julia, look_and_say, 0.40
+Julia, iterative_fibonacci, 0.00
+Julia, recursive_fibonacci, 4.126
+Julia, matrix_multiplication, 0.350375
+Julia, evaluate_functions, 69.587
+Julia, belief_propagation, 19.207
+Julia, markov_chain, 0.00
+Julia, laplace_equation, 9.5005
+Julia, munchauser_number, 106.269
+
+IDL, copy_matrix, 1.2643
+IDL, look_and_say, 1612.4277
+IDL, iterative_fibonacci, 0.0
+IDL, recursive_fibonacci, 304.2198
+IDL, matrix_multiplication, 0.3258
+IDL, evaluate_functions, 35.2387 
+IDL, belief_propagation, 65.7071
+IDL, markov_chain, 0.0157
+IDL, laplace_equation, 28.0683
+
+Matlab, copy_matrix, 0.846718
+Matlab, look_and_say, 4431.342780
+Matlab, iterative_fibonacci, 0.002642
+Matlab, recursive_fibonacci, 150.716018
+Matlab, matrix_multiplication, 0.282908
+Matlab, evaluate_functions, 6.001189
+Matlab, belief_propagation, 7.383720
+Matlab, markov_chain, 0.043713
+Matlab, laplace_equation, 8.6276
+Matlab, munchauser_number, 373.785635
+
+R, copy_matrix, 11.402
+R, iterative_fibonacci, 0.032
+R, recursive_fibonacci, 0.009
+R, matrix_multiplication, 0.364
+R, evaluate_functions, 113.929
+R, belief_propagation, 89.831
+R, markov_chain, 0.228
+R, laplace_equation, 340.271
+
+Java, copy_matrix, 0.590
+Java, look_and_say, 0.1211
+Java, iterative_fibonacci, 0.00
+Java, recursive_fibonacci, 4.82112
+Java, matrix_multiplication, 34.356
+Java, evaluate_functions, 865.057
+Java, belief_propagation, 254.377
+Java, markov_chain, 0.006
+Java, laplace_equation, 5.219
+Java, munchauser_number, 4.8953
+
+Scala, copy_matrix, 0.768
+Scala, look_and_say, 0.189
+Scala, iterative_fibonacci, 0.00
+Scala, recursive_fibonacci, 5.083
+Scala, matrix_multiplication, 32.1310
+Scala, evaluate_functions, 446.708
+Scala, belief_propagation, 212.37
+Scala, markov_chain, 0.011
+Scala, laplace_equation, 5.72
+Scala, munchauser_number, 73.964
+