Add python program to extract data and plot graphs

graphs_and_analysis/Graphs_v3/data_extractor_v3.py

@@ -0,0 +1,35 @@
+arr = [input().split("|") for i in range(15)]
+
+import re
+res = []
+for i in arr:
+    res.append([])
+    res[-1].append(i[0].strip())
+    for j in range(1, len(i)):
+        res[-1].extend(re.split(r"\s",i[j].strip()))
+
+print("                        |", end="")
+for i in range(1, 10):
+    print(f"      {i}      ", end="|")
+
+print()
+print("                        |", end="")
+for i in range(1, 10):
+    print(f"  H     L    ", end="|")
+
+print()
+for i in res:
+    print(f"{i[0]:24}", end=",")
+    for j in range(1, len(i)):
+        if i[j] != "": print(f"{i[j]:6}", end=",")
+    print()
+
+
+########################################################################################
+### EXCEL formula ###
+
+# =SUM(B26:B40)
+# =SUM(C26:C40)
+
+# =MIN(B42,D42,F42,H42,J42,L42,N42,P42,R42)
+# =MIN(C42,E42,G42,I42,K42,M42,O42,Q42,S42)

graphs_and_analysis/Graphs_v3/graph_plotter_v3.py

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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# INPUT
+names = np.array(["std::sort", "pdqsort", "std::stable_sort", "spinsort", "flat_stable_sort", "spreadsort", "timsort", "skasort", "fm_sort\noptimization"])[::-1]
+Averages = [174.19, 30.06, 104.68, 20.04, 152.71, 151.56, 86.94, 59.96, 70.32, 50.75, 61.38, 17.42, 77.88, 67.69, 42.41, 27.85, 27.55, 10.08][::-1]
+range_limit = 1000
+graph_title = 'Array size = 2.98 GB\nArray length = 781250\nObject size = 4 K bytes = vector<uint64_t>[512]'
+y_label_name = f'Running time - normalized to range[0, {range_limit}]'
+
+names2 = []
+for i in names:
+    names2.append(i)
+    names2.append(i)
+
+v1 = np.array(Averages[::2])
+v2 = np.array(Averages[1::2])
+v1 = v1 * range_limit / v1.max()
+v2 = v2 * range_limit / v2.max()
+
+p1 = pd.DataFrame([names, v1]).T
+p2 = pd.DataFrame([names, v2]).T
+p3 = pd.DataFrame([names2, Averages, len(names)*["Light comparison","Heavy comparison"]], index=["Sorting technique", "Average execution time" ,"Data comparision type"]).T
+p3["Average execution time"] = p3["Average execution time"] * range_limit / p3["Average execution time"].max()
+
+# GRAPH type 1
+x_pos = range(len(names))
+plt.bar(x_pos, v1)
+plt.xticks(x_pos, names)
+plt.yticks(range(0, range_limit+1, range_limit//10), [str(i) for i in range(0, range_limit+1, range_limit//10)])
+plt.ylabel(y_label_name)
+plt.title(graph_title)
+plt.show()
+
+# GRAPH type 2
+sns.set(style="whitegrid")
+ax = sns.barplot(x="Sorting technique", y="Average execution time", hue="Data comparision type", data=p3, palette=sns.color_palette("coolwarm", 17)[0::16] )
+# ax.axhline(0, color="k", clip_on=False)
+
+for p in ax.patches:
+    ax.annotate(f"{p.get_height():.0f}", (p.get_x() * 1.005, p.get_height() * 1.005))
+
+# for ticks in ax.xaxis.get_major_ticks():
+#     if ticks.label1.get_text() == names[-1]:
+#         # ticks.label1.set_facecolor("red")
+#         # ax.patches[p3.index.get_indexer([ticks.label1.get_text()])[0]].set_facecolor('red')
+#         ax.patches[p3.index.get_indexer([ticks.label1.get_text()])[0]].set_facecolor('red')
+
+plt.yticks(range(0, range_limit+1, range_limit//10), [str(i) for i in range(0, range_limit + 1, range_limit//10)])
+plt.ylabel(y_label_name)
+plt.title(graph_title)
+plt.tight_layout(h_pad=2)
+plt.show()
+
+
+# REFER: https://seaborn.pydata.org/tutorial/color_palettes.html
+# REFER: https://seaborn.pydata.org/examples/color_palettes.html
+# default_color_palette = sns.color_palette()
+# cool_warm_tuple = sns.color_palette("coolwarm", 17)
+# sns.palplot(cool_warm_tuple)
+# plt.show()