import os
import pandas as pd
import matplotlib.pyplot as plt
from time import gmtime, strftime
import sys
import numpy as np
import os

if(len(sys.argv)!=2):
	print("Please provide path to csv table")
	sys.exit(1);
path=sys.argv[1]
no_filename=path.split(".")[0]
splits=no_filename.split("_")
meas_type=splits[1]
img_path=splits[1]+"/plots/"
if not os.path.exists(img_path):
    os.makedirs(img_path)
    
df = pd.read_csv(path)
df.sort_values(by=['threads'])
df_means=df.groupby("threads").agg({"runtime":['mean','std']})
df_means=df_means.reset_index()
df_means.columns=["threads","mean_runtime","std_runtime"]
df_means.to_csv(meas_type+"/evaluation_"+meas_type+"_threads-to-runtime")
fig, ax = plt.subplots(1,1)
fig.autofmt_xdate()
plt.errorbar(df_means["threads"], df_means['mean_runtime'], yerr=df_means['std_runtime'], marker='x')
plt.ylabel('Mean Runtime')
plt.xlabel('Number of Used Threads')
plt.xticks(df_means["threads"])
plt.margins(0.05, 0.1)
plt.title('Number of Used Threads to Mean Runtime:\n n=10^6, t=1 ')
plt.savefig(img_path+strftime("%Y-%m-%d_%H:%M:%S", gmtime())+"_mean-runtime-vs-theads_"+meas_type)
plt.show()



fe6bf80862a5889309e7303726c15b334180618f
<mask token>

<mask token>
if len(sys.argv) != 2:
    print('Please provide path to csv table')
    sys.exit(1)
<mask token>
if not os.path.exists(img_path):
    os.makedirs(img_path)
<mask token>
df.sort_values(by=['threads'])
<mask token>
df_means.to_csv(meas_type + '/evaluation_' + meas_type + '_threads-to-runtime')
<mask token>
fig.autofmt_xdate()
plt.errorbar(df_means['threads'], df_means['mean_runtime'], yerr=df_means[
    'std_runtime'], marker='x')
plt.ylabel('Mean Runtime')
plt.xlabel('Number of Used Threads')
plt.xticks(df_means['threads'])
plt.margins(0.05, 0.1)
plt.title("""Number of Used Threads to Mean Runtime:
 n=10^6, t=1 """)
plt.savefig(img_path + strftime('%Y-%m-%d_%H:%M:%S', gmtime()) +
    '_mean-runtime-vs-theads_' + meas_type)
plt.show()

<mask token>
if len(sys.argv) != 2:
    print('Please provide path to csv table')
    sys.exit(1)
path = sys.argv[1]
no_filename = path.split('.')[0]
splits = no_filename.split('_')
meas_type = splits[1]
img_path = splits[1] + '/plots/'
if not os.path.exists(img_path):
    os.makedirs(img_path)
df = pd.read_csv(path)
df.sort_values(by=['threads'])
df_means = df.groupby('threads').agg({'runtime': ['mean', 'std']})
df_means = df_means.reset_index()
df_means.columns = ['threads', 'mean_runtime', 'std_runtime']
df_means.to_csv(meas_type + '/evaluation_' + meas_type + '_threads-to-runtime')
fig, ax = plt.subplots(1, 1)
fig.autofmt_xdate()
plt.errorbar(df_means['threads'], df_means['mean_runtime'], yerr=df_means[
    'std_runtime'], marker='x')
plt.ylabel('Mean Runtime')
plt.xlabel('Number of Used Threads')
plt.xticks(df_means['threads'])
plt.margins(0.05, 0.1)
plt.title("""Number of Used Threads to Mean Runtime:
 n=10^6, t=1 """)
plt.savefig(img_path + strftime('%Y-%m-%d_%H:%M:%S', gmtime()) +
    '_mean-runtime-vs-theads_' + meas_type)
plt.show()

import os
import pandas as pd
import matplotlib.pyplot as plt
from time import gmtime, strftime
import sys
import numpy as np
import os
if len(sys.argv) != 2:
    print('Please provide path to csv table')
    sys.exit(1)
path = sys.argv[1]
no_filename = path.split('.')[0]
splits = no_filename.split('_')
meas_type = splits[1]
img_path = splits[1] + '/plots/'
if not os.path.exists(img_path):
    os.makedirs(img_path)
df = pd.read_csv(path)
df.sort_values(by=['threads'])
df_means = df.groupby('threads').agg({'runtime': ['mean', 'std']})
df_means = df_means.reset_index()
df_means.columns = ['threads', 'mean_runtime', 'std_runtime']
df_means.to_csv(meas_type + '/evaluation_' + meas_type + '_threads-to-runtime')
fig, ax = plt.subplots(1, 1)
fig.autofmt_xdate()
plt.errorbar(df_means['threads'], df_means['mean_runtime'], yerr=df_means[
    'std_runtime'], marker='x')
plt.ylabel('Mean Runtime')
plt.xlabel('Number of Used Threads')
plt.xticks(df_means['threads'])
plt.margins(0.05, 0.1)
plt.title("""Number of Used Threads to Mean Runtime:
 n=10^6, t=1 """)
plt.savefig(img_path + strftime('%Y-%m-%d_%H:%M:%S', gmtime()) +
    '_mean-runtime-vs-theads_' + meas_type)
plt.show()

import os
import pandas as pd
import matplotlib.pyplot as plt
from time import gmtime, strftime
import sys
import numpy as np
import os

if(len(sys.argv)!=2):
	print("Please provide path to csv table")
	sys.exit(1);
path=sys.argv[1]
no_filename=path.split(".")[0]
splits=no_filename.split("_")
meas_type=splits[1]
img_path=splits[1]+"/plots/"
if not os.path.exists(img_path):
    os.makedirs(img_path)
    
df = pd.read_csv(path)
df.sort_values(by=['threads'])
df_means=df.groupby("threads").agg({"runtime":['mean','std']})
df_means=df_means.reset_index()
df_means.columns=["threads","mean_runtime","std_runtime"]
df_means.to_csv(meas_type+"/evaluation_"+meas_type+"_threads-to-runtime")
fig, ax = plt.subplots(1,1)
fig.autofmt_xdate()
plt.errorbar(df_means["threads"], df_means['mean_runtime'], yerr=df_means['std_runtime'], marker='x')
plt.ylabel('Mean Runtime')
plt.xlabel('Number of Used Threads')
plt.xticks(df_means["threads"])
plt.margins(0.05, 0.1)
plt.title('Number of Used Threads to Mean Runtime:\n n=10^6, t=1 ')
plt.savefig(img_path+strftime("%Y-%m-%d_%H:%M:%S", gmtime())+"_mean-runtime-vs-theads_"+meas_type)
plt.show()


