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histogram.py
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histogram.py
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import sys
import numpy as np
import re
#open file gdb.out or one provided in argument
if len(sys.argv) > 1:
f = open(sys.argv[1], 'r')
else:
exit("Usage: python histogram.py <filename of ./check.exe -p -v output>")
precision = "unknown"
if "float" in sys.argv[1]:
precision = "float"
elif "double" in sys.argv[1]:
precision = "double"
optimisation = "unknown"
if "O0" in sys.argv[1]:
optimisation = "-O0"
elif "O3" in sys.argv[1]:
optimisation = "-O3"
if "fgdb" in sys.argv[1]:
optimisation += "_fortran"
#get current directory:
import os
cwd = os.getcwd()
process = cwd.split("/")[-1]
#split with respect to "_":
process = process.split("_")[-2]+"_"+process.split("_")[-1]
#get all the numbers after "Matrix element = "
matrixElement = []
matrixElementPrecision = []
matrixElementPrecisionZeros = 0
momentum = []
momentaPrecision = []
momentaPrecisionZeros = 0
def string_to_float(str):
#return Nan if string is @
if "@" in str:
return np.nan
else:
#parse a string in format -0.10831E-003
try:
return np.float64(str.replace("D", "E"))
except:
print("Error parsing string: "+str)
return np.nan
#split by spaces and minus sign that is not proceeded by "E"
def custom_split(line):
tokens = []
prev_i = 0
for i in range(len(line)):
if line[i] == " ":
tokens.append(line[prev_i:i])
prev_i = i+1
elif line[i] == "-" and line[i-1] != "E":
tokens.append(line[prev_i:i])
prev_i = i
if prev_i < len(line):
tokens.append(line[prev_i:])
return tokens
def parse_momentum(string):
split = custom_split(string)
m = []
for i in range(len(split)):
s = split[i]
if s != '' and s != ' ' and s != "-":
m.append(s)
if s == "-":
split[i+1] = "-"+split[i+1]
return np.array(list(map(string_to_float, m[1:])))
def parse_momentum_precision(string):
split = string.split()
return np.array(list(map(int, split)))
#parse the file
skipQ = True
num_iter = 0
while True:
lines = []
line = ""
while skipQ:
line = f.readline()
if "Momenta:" in line:
skipQ = False
lines.append(line)
while not "---" in line:
line = f.readline()
# print(line)
if not line:
break
lines.append(line)
num_iter += 1
if num_iter%10000 == 0:
print("Number iterations:", num_iter)
i=0
for l in lines:
#parse momenta and momenta precision
if "Momenta:" in l:
j=1
momentums=[]
momentumPrecision=[]
while not "---" in lines[i+j]:
momentums.append(parse_momentum(lines[i+j]))
if len(lines)>6:
momentumPrecision.append(parse_momentum_precision(lines[i+j+1]))
j+=1
j+=1
if "Matrix element = " in l and "Matrix element number of sig dig = " in lines[i+1]:
pos = l.find("Matrix element = ")+len("Matrix element = ")
endpos = l.find("GeV^", pos)
# matrixElement.append(int(l[endpos+4:]))
if("@" in l[pos:endpos]):
matrixElementPrecisionZeros+=1
if len(matrixElement)>0:
matrixElement.append(matrixElement[-1])
else:
matrixElement.append(1)
else:
matrixElement.append(float(l[pos:endpos]))
if "Matrix element number of sig dig = " in l:
pos = l.find("Matrix element number of sig dig = ")+len("Matrix element number of sig dig = ")
matrixElementPrecision.append(int(l[pos:pos+2]))
i+=1
if not line:
break
matrixElement = np.array(matrixElement)
matrixElementPrecision = np.array(matrixElementPrecision)
momentum = np.array(momentum)
# print(momentum)
momentaPrecision = np.array(momentaPrecision)
print("Number of matrix elements: "+str(len(matrixElementPrecision)))
print("Number of momenta: "+str(len(momentaPrecision)))
if len(momentum)>0:
print("Number of momenta in proces: "+str(len(momentum[0])))
print("Number of momenta in proces: "+str(len(momentaPrecision[0])))
#return colinearity
def colinearity(m1, m2):
# print(m1)
m1 = m1[1:]
m2 = m2[1:]
#normalize
m1 = m1/np.linalg.norm(m1)
m2 = m2/np.linalg.norm(m2)
return np.dot(m1,m2)
def energy(m):
return m[0]
colinearities = []
for i in range(len(momentum)):
colinearities.append(colinearity(momentum[i][0], momentum[i][-1]))
colinearities = np.array(colinearities)
energys = momentum[:,0,0]
#flatten the arrays
matrixElement = matrixElement.flatten()
matrixElementPrecision = matrixElementPrecision.flatten()
momentum = momentum.flatten()
momentaPrecision = momentaPrecision.flatten()
print("Number of matrix elements: "+str(len(matrixElementPrecision)))
print("Number of momenta: "+str(len(momentaPrecision)))
print("Number of matrix elements prec: "+str(len(matrixElement)))
print("Number of momenta prec: "+str(len(momentum)))
#matplotlib -> default (no argument) or both
if len(sys.argv) < 3 or (len(sys.argv) > 2 and sys.argv[2] == "both"):
import matplotlib.pyplot as plt
# Colinearities vs matrix element precision and vs matrix element
# Energys vs matrix element precision and vs matrix element
if len(colinearities)>0:
#plot scatter plot of colinearities vs matrix element precision
fig, ax = plt.subplots()
plt.title("Colinearities vs matrix element precision for: "+process+" "+precision+" "+optimisation)
plt.xlabel("Colinearity")
plt.ylabel("Digits of precision")
#keep outliers
y_perturbation = np.random.uniform(-0.4, 0.4, colinearities.size)
plt.scatter(colinearities, matrixElementPrecision+y_perturbation,s=0.01)
dir = "../../../histograms/colinearities_energys_"+process
if not os.path.exists(dir):
os.makedirs(dir)
plt.savefig(dir+"/scatter_colinearities_MEP_"+process+"_"+precision+"_"+optimisation[1:]+".png" )
plt.close()
#Colinearities vs matrix element
fig, ax = plt.subplots()
plt.title("Colinearities vs matrix element precision for: "+process+" "+precision+" "+optimisation)
plt.xlabel("Colinearity")
plt.ylabel("Log10 of matrix element")
#keep outliers
plt.scatter(colinearities, np.log10(matrixElement),s=0.01)
dir = "../../../histograms/colinearities_energys_"+process
if not os.path.exists(dir):
os.makedirs(dir)
plt.savefig(dir+"/scatter_colinearities_ME_"+process+"_"+precision+"_"+optimisation[1:]+".png" )
plt.close()
#Energys vs matrix element precision
fig, ax = plt.subplots()
plt.title("Energys vs matrix element precision for: "+process+" "+precision+" "+optimisation)
plt.xlabel( "Energy")
plt.ylabel( "Digits of precision")
#keep outliers
y_perturbation = np.random.uniform(-0.4, 0.4, colinearities.size)
plt.scatter( energys, matrixElementPrecision + y_perturbation,s=0.01)
dir = "../../../histograms/colinearities_energys_"+process
if not os.path.exists(dir):
os.makedirs(dir)
plt.savefig(dir+"/scatter_energys_MEP_"+process+"_"+precision+"_"+optimisation[1:]+".png" )
plt.close()
#Energys vs matrix element
fig, ax = plt.subplots()
plt.title("Energys vs matrix element precision for: "+process+" "+precision+" "+optimisation)
plt.xlabel( "Energy")
plt.ylabel( "Log10 of matrix element")
#keep outliers
plt.scatter( energys, np.log10(matrixElement),s=0.01)
dir = "../../../histograms/colinearities_energys_"+process
if not os.path.exists(dir):
os.makedirs(dir)
plt.savefig(dir+"/scatter_energys_ME_"+process+"_"+precision+"_"+optimisation[1:]+".png" )
plt.close()
#plot histogram of matrix element precision
if len(matrixElementPrecision)>0:
fig, ax = plt.subplots()
plt.title("Matrix element precision for: "+process+" "+precision+" "+optimisation)
plt.xlabel("Digits of precision. Sum = "+str(len(matrixElementPrecision)+matrixElementPrecisionZeros))
counts, edges, bars = ax.hist(matrixElementPrecision, histtype='barstacked',bins=range(0, int(max(matrixElementPrecision))+2))
plt.bar_label(bars)
#show the mean in neat way
plt.axvline(x=sum(matrixElementPrecision)/len(matrixElementPrecision), color='c', linestyle='dashed', linewidth=1)
min_ylim, max_ylim = plt.ylim()
plt.text(sum(matrixElementPrecision)/len(matrixElementPrecision), +max_ylim*0.1, 'Mean: {:.2f}'.format(sum(matrixElementPrecision)/len(matrixElementPrecision)),color='black')
#show the median in neat way
#create a directory for the histograms if it doesn't exist
dir = "../../../histograms"
if not os.path.exists(dir):
os.makedirs(dir)
#save histogram
plt.savefig(dir+"/histogram_"+process+"_"+precision+"_"+optimisation[1:]+".png" )
plt.close()
#plot histogram of momenta precision
if len(momentaPrecision)>0:
fig, ax = plt.subplots()
plt.title("Momenta precision for: "+process+" "+precision+" "+optimisation)
plt.xlabel("Digits of precision. Sum = "+str(len(momentaPrecision)+momentaPrecisionZeros))
counts, edges, bars = ax.hist(momentaPrecision, histtype='barstacked',color='orange',bins=range(0, int(max(momentaPrecision))+2))
plt.bar_label(bars)
#show the mean in neat way
plt.axvline(x=sum(momentaPrecision)/len(momentaPrecision), color='c', linestyle='dashed', linewidth=1)
min_ylim, max_ylim = plt.ylim()
plt.text(sum(momentaPrecision)/len(momentaPrecision), +max_ylim*0.1, 'Mean: {:.2f}'.format(sum(momentaPrecision)/len(momentaPrecision)),color='black')
#show the median in neat way
#create a directory for the histograms if it doesn't exist
dir = "../../../histograms"
if not os.path.exists(dir):
os.makedirs(dir)
#save histogram
plt.savefig(dir+"/histogram_momenta_"+process+"_"+precision+"_"+optimisation[1:]+".png" )
plt.close()
#scatter plot of matrix element precision vs matrix element
if len(matrixElementPrecision)>0:
fig, ax = plt.subplots()
zeros = matrixElementPrecisionZeros
plt.title("Matrix element precision vs matrix element for: "+process+" "+precision+" "+optimisation+"\n Plus "+str(zeros)+" zero precision.")
plt.xlabel("log10(Matrix element)")
plt.ylabel("Digits of precision")
#delete outliers. Outlier is a value that is more than 5 standard deviations away from the mean
matrixElementPrecision = np.array(matrixElementPrecision,dtype=float)
matrixElement = np.array(matrixElement)
variation = np.random.uniform(-0.4, 0.4, matrixElementPrecision.size)
matrixElementPrecision += variation
plt.scatter(np.log10(matrixElement), matrixElementPrecision,s=0.1)
plt.savefig(dir+"/scatter_"+process+"_"+precision+"_"+optimisation[1:]+".png" )
#scatter plot of momenta precision vs momentum
if len(momentaPrecision)>0:
fig, ax = plt.subplots()
zeros = momentaPrecisionZeros
plt.title("Momenta precision vs momentum for: "+process+" "+precision+" "+optimisation+"\n Plus "+str(zeros)+" zero precision.")
plt.xlabel("momentum")
plt.ylabel("Digits of precision")
#delete outliers. Outlier is a value that is more than 5 standard deviations away from the mean
momentaPrecision = np.array(momentaPrecision,dtype=float)
momentum = np.array(momentum)
variation = np.random.uniform(-0.4, 0.4, momentaPrecision.size)
momentaPrecision += variation
plt.scatter(momentum, momentaPrecision,s=0.01,color='black')
plt.savefig(dir+"/scatter_momentum_"+process+"_"+precision+"_"+optimisation[1:]+".png" )
#Plotly only plots the last two plots and histograms:
# - histogram of matrix element precision
# - scatter plot of matrix element precision vs matrix element
# - histogram of momenta precision
# - scatter plot of momenta precision vs momentum
#plotly -> plotly or both
if len(sys.argv) > 2 and (sys.argv[2] == "plotly" or sys.argv[2] == "both"):
#do the same using plotly
import plotly.express as px
# fig = px.histogram(df, x="total_bill")
# fig.show()
fig = px.histogram(x=matrixElementPrecision , title="Matrix element precision for: "+process+" "+precision+" "+optimisation)
fig.show()
#scatter plot of matrix element precision vs matrix element
fig = px.scatter(x=np.log10(matrixElement), y=matrixElementPrecision, title="Matrix element precision vs matrix element for: "+process+" "+precision+" "+optimisation, labels={"x": "log10(Matrix element)", "y": "Digits of precision"})
fig.show()
if len(sys.argv) > 2 and (sys.argv[2] != "plotly" and sys.argv[2] != "both"):
exit("Usage: python histogram.py <filename of ./check.exe -p -v output> <plotly or both or nothing(for matplotlib)>")