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CreatePlottingClass.py
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CreatePlottingClass.py
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import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib.colors import ListedColormap
class PlotTimes():
def __init__(self):
pass
def plot_graphs(self):
self.df_mono = pd.read_csv('others/mono.csv')
self.df_multi = pd.read_csv('others/multi.csv')
self.df = pd.concat([self.df_mono,self.df_multi],axis=1)
# print(self.df)
data = [list(self.df_mono.values.tolist())[0],
list(self.df_multi.values.tolist())[0]]
# print(data)
X = np.arange(5)
fig = plt.figure()
ax = fig.add_axes([0.03, 0.05, 0.93, 0.93])
rects1 = ax.bar(X + 0.00, data[0], color='b', width=0.25)
rects2 = ax.bar(X + 0.25, data[1], color='g', width=0.25)
def autolabel(rects):
"""Attach a text label above each bar in *rects*, displaying its height."""
for rect in rects:
height = round(rect.get_height(),2)
ax.annotate('{}'.format(height),
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points",
ha='center', va='bottom')
autolabel(rects1)
autolabel(rects2)
ax.set_ylim(0,15)
ax.set_xticks(np.arange(4), ('Mean', 'Sum', 'Count', 'StDev', 'Roll'))
ax.set_xticklabels(['Mean','Mean', 'Sum', 'Count', 'StdDev', 'Roll'])
ax.legend(labels=['Mono', 'Multi'],loc='center left')
fig.suptitle("\nRun Time of Simple Statistical functions, mono and multi processor modes")
plt.show()
def plot_simpleStatistics(self):
'''Plot Simple statistics graph, multi vs mono'''
self.df_s = pd.read_csv('csv/simpleStatistics/mono.csv')
self.df_m = pd.read_csv('csv/simpleStatistics/multi.csv')
self.df_s['value_m'] = pd.Series(self.df_m['value_m'])
self.df_s = self.df_s.set_index('index')
# print(self.df_s)
# print(df_m)
ax =self.df_s.plot(kind='bar', colormap=ListedColormap(sns.color_palette("Accent", 5)))
for p in ax.patches:
ax.annotate(str(round(p.get_height(), 2)), (p.get_x() * 1.015, p.get_height() * 1.005))
plt.title('Time in seconds taken for simple statistical functions, single vs multi core execution')
ax.legend(["Single Core", "Multi Core"]);
plt.show()
def plot_utilFunctions(self):
'''Plot UtilFunctions graph, multi vs mono'''
self.df_s = pd.read_csv('csv/utilFunctions/mono.csv')
self.df_m = pd.read_csv('csv/utilFunctions/multi.csv')
self.df_s['value_m'] = pd.Series(self.df_m['value_m'])
self.df_s = self.df_s.set_index('index')
# print(self.df_s)
# print(df_m)
ax =self.df_s.plot(kind='bar', colormap=ListedColormap(sns.color_palette("Accent", 5)))
for p in ax.patches:
ax.annotate(str(round(p.get_height(), 2)), (p.get_x() * 1.015, p.get_height() * 1.005))
plt.title('Time in seconds taken for utility functions, single vs multi core execution')
ax.legend(["Single Core", "Multi Core"]);
plt.show()
def plot_agg_loop(self):
"""Plot agg functions no loop"""
self.df_s = pd.read_csv('csv/groupbyAggLoop/mono_loop.csv')
self.df_m = pd.read_csv('csv/groupbyAggLoop/multi_loop.csv')
# print(self.df_s)
# print(self.df_m)
self.df = pd.concat([self.df_s, self.df_m], axis=1)
self.df = self.df.rename({'level_1': 'axis', 0: 'value'})
print(self.df)
self.x = np.arange(1)
self.width = 0.15
fig, ax = plt.subplots()
rects1 = ax.bar(self.x - self.width / 2, self.df['agg_s_l'].iloc[0], self.width, label='Single Core',
color='mediumseagreen')
rects2 = ax.bar(self.x + self.width / 2, self.df['agg_m_l'].iloc[0], self.width, label='Multi Core',
color='steelblue')
ax.set_ylabel('Scores')
ax.set_title('Scores by group and gender')
ax.set_xticks(self.x)
self.labels = ['Single Core']
ax.set_xticklabels(self.labels)
ax.legend()
def autolabel(rects):
"""Attach a text label above each bar in *rects*, displaying its height."""
for rect in rects:
height = rect.get_height()
ax.annotate('{}'.format(round(height, 3)),
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points",
ha='center', va='bottom')
autolabel(rects1)
autolabel(rects2)
fig.tight_layout()
plt.title('Time in seconds taken for aggs with loop, single vs multi core execution')
plt.show()
def plot_agg_no_loop(self):
self.df_s = pd.read_csv('csv/groupbyAgg/mono_no_loop.csv')
self.df_m = pd.read_csv('csv/groupbyAgg/multi_no_loop.csv')
# print(self.df_s)
# print(self.df_m)
self.df = pd.concat([self.df_s, self.df_m], axis=1)
# print(self.df.stack().reset_index())
self.df = self.df.rename({'level_1': 'axis', 0: 'value'})
print(self.df)
print(list(self.df[['agg_s']].values.tolist()))
self.x = np.arange(1)
self.width = 0.15
fig, ax = plt.subplots()
rects1 = ax.bar(self.x - self.width / 2, self.df['agg_s'].iloc[0], self.width, label='Single Core',
color='mediumseagreen')
rects2 = ax.bar(self.x + self.width / 2, self.df['agg_m'].iloc[0], self.width, label='Multi Core',
color='steelblue')
ax.set_ylabel('Scores')
ax.set_title('Scores by group and gender')
ax.set_xticks(self.x)
self.labels = ['Single Core']
ax.set_xticklabels(self.labels)
ax.legend()
def autolabel(rects):
"""Attach a text label above each bar in *rects*, displaying its height."""
for rect in rects:
height = rect.get_height()
ax.annotate('{}'.format(round(height, 3)),
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points",
ha='center', va='bottom')
autolabel(rects1)
autolabel(rects2)
fig.tight_layout()
plt.title('Time in seconds taken for aggs without loop, single vs multi core execution')
plt.show()