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ElapsedTimeStudy.py
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from ROOT import TTree
from ROOT import TFile
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib import colors
import seaborn as sns
import pandas as pd
import numpy as np
material_palette = ["#4CAF50", "#2196F3", "#9E9E9E", "#FF9800", "#607D8B", "#9C27B0"]
sns.set_palette(material_palette)
sns.set(style='ticks', palette='Spectral', font_scale=1.5)
nAPA = range(1,13)
#nAPA = [1,12]
infile_stable = []
intree_stable = []
infile_sort = []
intree_sort = []
timing_timesort_df=pd.DataFrame()
timings_timesort = []
Sort_timesort = []
APAs_timesort = []
nhit_timesort = []
timing_spacesort_df=pd.DataFrame()
timings_spacesort = []
Sort_spacesort = []
APAs_spacesort = []
nhit_spacesort = []
timing_clustering_df=pd.DataFrame()
timings_clustering = []
Sort_clustering = []
APAs_clustering = []
nhit_clustering = []
Config = 5
for i in range(0,len(nAPA)):
print("Considering APA"+str(nAPA[i]))
infile_stable.append(TFile("build/OutputPDS_time_"+str(nAPA[i])+"APA_1Sort.root","READ"))
intree_stable.append(infile_stable[i].Get("TimingInfo"))
for entry in intree_stable[i]:
if entry.Config is not Config:
continue
totaltime=0.
for time in entry.TimeOrdering_WireClustTime:
totaltime = totaltime+time
timings_timesort.append(totaltime*1000.)
APAs_timesort .append(nAPA[i])
Sort_timesort .append("std::stable_sort")
nhit_timesort .append(entry.nHit)
totaltime=0.
for time in entry.SpaceOrdering_WireClustTime:
totaltime = totaltime+time
timings_spacesort.append(totaltime*1000.)
APAs_spacesort .append(nAPA[i])
Sort_spacesort .append("std::stable_sort")
nhit_spacesort .append(entry.nHit)
totaltime=0.
for time in entry.Clustering_WireClustTime:
totaltime = totaltime+time
timings_clustering.append(totaltime*1000.)
APAs_clustering .append(nAPA[i])
Sort_clustering .append("std::stable_sort")
nhit_clustering .append(entry.nHit)
infile_sort.append(TFile("build/OutputPDS_time_"+str(nAPA[i])+"APA_0Sort.root","READ"))
intree_sort.append(infile_sort[i].Get("TimingInfo"))
for entry in intree_sort[i]:
if entry.Config is not Config:
continue
totaltime=0.
for time in entry.TimeOrdering_WireClustTime:
totaltime = totaltime+time
timings_timesort.append(totaltime*1000.)
APAs_timesort .append(nAPA[i])
Sort_timesort .append("std::sort")
nhit_timesort .append(entry.nHit)
totaltime=0.
for time in entry.SpaceOrdering_WireClustTime:
totaltime = totaltime+time
timings_spacesort.append(totaltime*1000.)
APAs_spacesort .append(nAPA[i])
Sort_spacesort .append("std::sort")
nhit_spacesort .append(entry.nHit)
totaltime=0.
for time in entry.Clustering_WireClustTime:
totaltime = totaltime+time
timings_clustering.append(totaltime*1000.)
APAs_clustering .append(nAPA[i])
Sort_clustering .append("std::sort")
nhit_clustering .append(entry.nHit)
timing_timesort_df['n APAs'] = APAs_timesort
timing_timesort_df['Time sorting Time'] = np.float32(timings_timesort)
timing_timesort_df['Sort algorithm'] = Sort_timesort
timing_timesort_df['n hits'] = nhit_timesort
timing_spacesort_df['n APAs'] = APAs_spacesort
timing_spacesort_df['Space sorting Time'] = np.float32(timings_spacesort)
timing_spacesort_df['Sort algorithm'] = Sort_spacesort
timing_spacesort_df['n hits'] = nhit_spacesort
timing_clustering_df['n APAs'] = APAs_clustering
timing_clustering_df['Clustering Time'] = np.float32(timings_clustering)
timing_clustering_df['Sort algorithm'] = Sort_clustering
timing_clustering_df['n hits'] = nhit_clustering
print(timing_timesort_df .describe())
print(timing_spacesort_df .describe())
print(timing_clustering_df.describe())
fig, ax = plt.subplots(figsize=(20, 12))
fig.suptitle("Timing")
ax = sns.boxplot(data=timing_clustering_df, x="n APAs", y="Clustering Time", hue="Sort algorithm",whis=[1.,99.])
ax.set_xlabel('number of APAs')
ax.set_ylabel('Time [ms]')
ax.yaxis.set_major_locator(ticker.MultipleLocator(0.1))
ax.yaxis.set_major_formatter(ticker.ScalarFormatter())
plt.grid(True)
fig.savefig("Clustering_timeplot.pdf")
fig, ax = plt.subplots(figsize=(20, 12))
fig.suptitle("Timing")
ax = sns.boxplot(data=timing_timesort_df, x="n APAs", y="Time sorting Time", hue="Sort algorithm",whis=[1.,99.])
ax.set_xlabel('number of APAs')
ax.set_ylabel('Time [ms]')
ax.yaxis.set_major_locator(ticker.MultipleLocator(0.1))
ax.yaxis.set_major_formatter(ticker.ScalarFormatter())
plt.grid(True)
fig.savefig("Time_sorting_timeplot.pdf")
fig, ax = plt.subplots(figsize=(20, 12))
fig.suptitle("Timing")
ax = sns.boxplot(data=timing_spacesort_df, x="n APAs", y="Space sorting Time", hue="Sort algorithm",whis=[1.,99.])
ax.set_xlabel('number of APAs')
ax.set_ylabel('Time [ms]')
ax.yaxis.set_major_locator(ticker.MultipleLocator(0.1))
ax.yaxis.set_major_formatter(ticker.ScalarFormatter())
plt.grid(True)
fig.savefig("Space_sorting_timeplot.pdf")
fig, ax = plt.subplots(figsize=(20, 12))
fig.suptitle("Timing")
ax.set_xlabel('number of hits')
ax.set_ylabel('Time [ms]')
plt.hist2d(timing_spacesort_df.loc[timing_spacesort_df['Sort algorithm']=="std::sort"]["n hits"],
timing_spacesort_df.loc[timing_spacesort_df['Sort algorithm']=="std::sort"]["Space sorting Time"],
bins=100, range=[[0,500],[0,0.6]], norm=colors.LogNorm())
plt.colorbar()
plt.grid(True)
fig.savefig("Space_sorting_timeplot_nhits_sort.pdf")
fig, ax = plt.subplots(figsize=(20, 12))
fig.suptitle("Timing")
ax.set_xlabel('number of hits')
ax.set_ylabel('Time [ms]')
plt.hist2d(timing_timesort_df.loc[timing_timesort_df['Sort algorithm']=="std::sort"]["n hits"],
timing_timesort_df.loc[timing_timesort_df['Sort algorithm']=="std::sort"]["Time sorting Time"],
bins=100, range=[[0,500],[0,0.2]], norm=colors.LogNorm())
plt.colorbar()
plt.grid(True)
fig.savefig("Time_sorting_timeplot_nhits_sort.pdf")
fig, ax = plt.subplots(figsize=(20, 12))
fig.suptitle("Timing")
ax.set_xlabel('number of hits')
ax.set_ylabel('Time [ms]')
plt.hist2d(timing_clustering_df.loc[timing_clustering_df['Sort algorithm']=="std::sort"]["n hits"],
timing_clustering_df.loc[timing_clustering_df['Sort algorithm']=="std::sort"]["Clustering Time"],
bins=100, range=[[0,500],[0,1]], norm=colors.LogNorm())
plt.colorbar()
plt.grid(True)
fig.savefig("Clustering_timeplot_nhits_sort.pdf")
fig, ax = plt.subplots(figsize=(20, 12))
fig.suptitle("Timing")
ax.set_xlabel('number of hits')
ax.set_ylabel('Time [ms]')
plt.hist2d(timing_spacesort_df.loc[timing_spacesort_df['Sort algorithm']=="std::stable_sort"]["n hits"],
timing_spacesort_df.loc[timing_spacesort_df['Sort algorithm']=="std::stable_sort"]["Space sorting Time"],
bins=100, range=[[0,500],[0,0.4]], norm=colors.LogNorm())
plt.colorbar()
plt.grid(True)
fig.savefig("Space_sorting_timeplot_nhits_stablesort.pdf")
fig, ax = plt.subplots(figsize=(20, 12))
fig.suptitle("Timing")
ax.set_xlabel('number of hits')
ax.set_ylabel('Time [ms]')
plt.hist2d(timing_timesort_df.loc[timing_timesort_df['Sort algorithm']=="std::stable_sort"]["n hits"],
timing_timesort_df.loc[timing_timesort_df['Sort algorithm']=="std::stable_sort"]["Time sorting Time"],
bins=100, range=[[0,500],[0,0.4]], norm=colors.LogNorm())
plt.colorbar()
plt.grid(True)
fig.savefig("Time_sorting_timeplot_nhits_stablesort.pdf")
fig, ax = plt.subplots(figsize=(20, 12))
fig.suptitle("Timing")
ax.set_xlabel('number of hits')
ax.set_ylabel('Time [ms]')
plt.hist2d(timing_clustering_df.loc[timing_clustering_df['Sort algorithm']=="std::stable_sort"]["n hits"],
timing_clustering_df.loc[timing_clustering_df['Sort algorithm']=="std::stable_sort"]["Clustering Time"],
bins=100, range=[[0,500],[0,1]], norm=colors.LogNorm())
plt.colorbar()
plt.grid(True)
fig.savefig("Clustering_timeplot_nhits_stablesort.pdf")