Update graph plotting technique and make the graph succinct

graphs_and_analysis/Graphs_v3/graph_plotter_v3.py

@@ -3,57 +3,120 @@
 import matplotlib.pyplot as plt
 import seaborn as sns
 
+
+def add_bar_size(ax):
+    for p in ax.patches:
+        ax.annotate(f"{p.get_height():.0f}", (p.get_x() * 1.005, p.get_height() * 1.005))
+
+
 # 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]
+comparison_type = ["Light comparison", "Heavy comparison"]
+names_init = np.array(["std::sort", "pdqsort", "std::stable_sort", "spinsort", "flat_stable_sort", "spreadsort", "timsort", "skasort", "fm_sort\noptimization"])
+names_rev = names_init[::-1]
+averages_init = [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]
+averages_rev = averages_init[::-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:
+for i in names_rev:
     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()
+v1_light_init = np.array(averages_rev[::2])
+v2_heavy_init = np.array(averages_rev[1::2])
+v1_light_norm = v1_light_init * range_limit / v1_light_init.max()
+v2_heavy_norm = v2_heavy_init * range_limit / v2_heavy_init.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
+p1 = pd.DataFrame([names_rev, v1_light_norm]).T
+p2 = pd.DataFrame([names_rev, v2_heavy_norm]).T
+p3 = pd.DataFrame([names2, averages_rev, len(names_rev) * ["Light comparison", "Heavy comparison"]],
+                  index=["Sorting technique", "Average execution time", "Data comparison 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)])
+x_pos = range(len(names_rev))
+plt.bar(x_pos, v1_light_norm)
+plt.xticks(x_pos, names_rev)
+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
+def change_width(ax, new_value):
+    for patch in ax.patches:
+        current_width = patch.get_width()
+        diff = current_width - new_value
+
+        # we change the bar width
+        patch.set_width(new_value)
+
+        # we recenter the bar
+        patch.set_x(patch.get_x() + diff * .5)
+
+
+# https://seaborn.pydata.org/generated/seaborn.barplot.html
 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 = sns.barplot(x="Data comparison type", y="Average execution time", hue="Sorting technique", data=p3,
+                 palette=sns.color_palette())
 # 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')
+add_bar_size(ax)
+change_width(ax, 0.15)
 
-plt.yticks(range(0, range_limit+1, range_limit//10), [str(i) for i in range(0, range_limit + 1, range_limit//10)])
+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.xlabel("")
 plt.title(graph_title)
-plt.tight_layout(h_pad=2)
+# plt.tight_layout(h_pad=2)
+plt.show()
+# plt.savefig("Figure_1.png", bbox_inches='tight', dpi=600)
+
+##########################################################################
+# GRAPH type 3 - BEST
+# import matplotlib.pyplot as plt
+# import matplotlib.cm as mplcm
+# import matplotlib.colors as colors
+# NUM_COLORS = len(names_rev)
+# cm = plt.get_cmap('gist_rainbow')
+# cNorm  = colors.Normalize(vmin=0, vmax=NUM_COLORS-1)
+# scalarMap = mplcm.ScalarMappable(norm=cNorm, cmap=cm)
+# # ax.set_color_cycle([scalarMap.to_rgba(i) for i in range(NUM_COLORS)])
+# plt.colorbar(scalarMap)
+
+df3 = pd.DataFrame([v1_light_norm, v2_heavy_norm], index=comparison_type, columns=names_rev)
+index = np.arange(len(df3))
+bar_width = 0.095
+opacity = 1.0
+
+fig, ax = plt.subplots()
+
+for i in range(len(df3.columns)):
+    ax.bar(index + i * bar_width, df3.values[:, i], bar_width, alpha=opacity, label=df3.columns[i])
+
+add_bar_size(ax)
+
+ax.set_xticks(index + len(df3.columns)*bar_width/2 - bar_width/2)
+ax.set_xticklabels(comparison_type)
+ax.set_xlabel("")
+ax.set_ylabel(y_label_name)
+ax.set_title(graph_title)
+ax.legend()
+
+current_palette = sns.color_palette()
+# sns.palplot(current_palette)
+plt.legend(current_palette)
+
+fig.tight_layout()
 plt.show()
 
+##########################################################################
 
 # REFER: https://seaborn.pydata.org/tutorial/color_palettes.html
 # REFER: https://seaborn.pydata.org/examples/color_palettes.html