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DataViz_NFsim.py
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DataViz_NFsim.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Nov 4 18:19:28 2021
@author: Ani Chattaraj
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import json
from numpy import array
font = {'family' : 'Arial',
'size' : 16}
plt.rc('font', **font)
def getColumns(txtfile):
# name of observables in gdat file
with open(txtfile,'r') as tf:
lines = tf.readlines()
columns = lines[0].replace('#','').split()
return columns
def plotTimeCourse(path, obsList=[]):
# plotting the observable time course
txtfile = path + '/pyStat/Mean_Observable_Counts.txt'
mean_data = np.loadtxt(path + '/pyStat/Mean_Observable_Counts.txt')
std_data = np.loadtxt(path + '/pyStat/Stdev_Observable_Counts.txt')
_, numVar = mean_data.shape
colNames = getColumns(txtfile)
if len(obsList) == 0:
for i in range(1, numVar):
x, y, yerr = mean_data[:,0], mean_data[:,int(i)], std_data[:,int(i)]
plt.plot(x,y, label=f'{colNames[i]}')
plt.fill_between(x, y-yerr, y+yerr, alpha=0.2)
else:
for i in obsList:
x, y, yerr = mean_data[:,0], mean_data[:,int(i)], std_data[:,int(i)]
plt.plot(x,y, label=f'{colNames[i]}')
plt.fill_between(x, y-yerr, y+yerr, alpha=0.2)
plt.legend()
plt.xlabel('Time (seconds)')
plt.ylabel('Observable Counts')
plt.show()
def plotClusterDist(path, sizeRange=[]):
# plotting the cluster size distribution (ACO: average cluster occupancy)
plt.subplots(figsize=(7,4))
df = pd.read_csv(path + '/pyStat/SteadyState_distribution.csv')
cs, foTM = df['Cluster size'], df['foTM']
if len(sizeRange) == 0:
aco = sum(cs*foTM)
plt.bar(cs, height=foTM, fc='grey',ec='k', label=f'ACO = {aco:.2f}')
plt.axvline(aco, ls='dashed', lw=1.5, color='k')
plt.xlabel('Cluster Size (molecules)')
plt.ylabel('Fraction of total molecules')
plt.legend()
plt.show()
else:
# sizeRange = [1,10,20]
# clusters : 1-10, 10-20, >20
idList = [0]
#xbar = np.arange(1, len(sizeRange)+1, 1)
xLab = [f'{sizeRange[i]} - {sizeRange[i+1]}' for i in range(len(sizeRange) - 1)]
xLab.append(f'> {sizeRange[-1]}')
for size in sizeRange[1:]:
i = 0
while cs[i] < size:
i += 1
if cs[i] == size:
idList.append(i+1)
else:
idList.append(i)
foTM_binned = [sum(foTM[idList[i]: idList[i+1]]) for i in range(len(idList)-1)]
foTM_binned.append(sum(foTM[idList[-1]:]))
try:
plt.bar(xLab, foTM_binned, color='grey', ec='k')
plt.xlabel('Cluster size range (molecules)')
plt.ylabel('Fraction of total molecules')
plt.ylim(0,1)
plt.show()
except:
print('Invalid size range!! Maximal size range might be higher than largest cluster!')
def plotBondsPerMolecule(path):
# plotting the bond count distribution per molecule
df = pd.read_csv(path + '/pyStat/Bonds_per_single_molecule.csv')
fig, ax = plt.subplots(figsize=(7,4))
bonds, freq = df['BondCounts'], df['frequency']
m_bf = sum(bonds*freq)
ax.bar(bonds, freq, width=0.3, color='b')
ax.axvline(m_bf, ls='dashed', c='k', lw=2, label=f'Mean = {m_bf:.2f}')
plt.legend()
ax.set_xlabel('Bonds per molecule')
ax.set_ylabel('Frequency')
plt.show()
def plotBondCounts(path, molecules=[]):
if len(molecules) > 0:
for mol in molecules:
df = pd.read_csv(path + f'/pyStat/{mol}_bonds_per_molecule.csv')
plt.bar(df['BondCounts'], df['frequency'], width=0.3, color='b')
plt.xlabel('Bonds per molecule')
plt.ylabel('Frequency')
plt.title(mol)
plt.ylim(0,1)
plt.show()
else:
print('Please pass on the molecular names!')
def plotBoundFraction(path):
#df = pd.read_csv(path + '/pyStat/Cluster_composition.csv')
jdict = json.load(open(path + '/pyStat/BoundFraction.json'))
csList, bfList, freqList = [], [], []
for cs, bf in jdict.items():
for item, freq in bf.items():
csList.append(float(cs))
bfList.append(float(item))
freqList.append(float(freq))
plt.subplots(figsize=(7,4))
cm = plt.cm.get_cmap('rainbow')
sc = plt.scatter(csList, bfList, c = freqList, cmap=cm)
cbar = plt.colorbar(sc)
cbar.ax.set_ylabel('Frequency')
plt.xlabel('Cluster size (molecules)')
plt.ylabel('Bound fraction')
plt.show()
def plotBarGraph(xdata, yList, yLabels, title='', width=0.1, alpha=0.5):
N_entry = len(yList)
midVarId = N_entry//2
if N_entry % 2 == 1:
# odd number
plt.bar(xdata, yList[midVarId], width=width, alpha=alpha, label=yLabels[midVarId])
idx = 1
for id_lh in range(0, midVarId):
plt.bar(xdata - 0.15*idx, yList[id_lh], width=width, alpha=alpha, label=yLabels[id_lh])
idx += 1
idx = 1
for id_rh in range(midVarId+1, N_entry):
plt.bar(xdata + 0.15*idx, yList[id_rh], width=width, alpha=alpha, label=yLabels[id_rh])
idx += 1
else:
# even number
shiftIndex = [0.06] + [0.1]*midVarId
idx = 1
for id_lh in range(0, midVarId):
plt.bar(xdata - idx*shiftIndex[idx-1], yList[id_lh], width=width, alpha=alpha, label=yLabels[id_lh])
idx += 1
idx = 1
for id_rh in range(midVarId, N_entry):
plt.bar(xdata + idx*shiftIndex[idx-1], yList[id_rh], width=width, alpha=alpha, label=yLabels[id_rh])
idx += 1
pass
plt.legend(ncol=2)
plt.xlabel('Cluster size (molecules)')
plt.ylabel('Frequency')
plt.title(title, pad=12)
plt.show()
def plotMolecularDistribution(path, molecules=[], width=0.1, alpha=0.6):
df = pd.read_csv(path + '/pyStat/Molecular_distribution.csv')
csList = df['Clusters']
if len(molecules) == 0:
mols = df.columns[2:]
freqList = [df[mol] for mol in mols]
plotBarGraph(csList, freqList, mols, width=width, alpha=alpha, title='Molecular Distribution')
else:
freqList = [df[mol] for mol in molecules]
plotBarGraph(csList, freqList, molecules, width=width, alpha=alpha, title='Molecular Distribution')
def plotClusterComposition(path, specialClusters=[], width=0.1, alpha=0.6):
df = pd.read_csv(path + '/pyStat/Cluster_composition.csv')
csList = df['Clusters']
if len(specialClusters) == 0:
mols = df.columns[2:]
freqList = [df[mol] for mol in mols]
plotBarGraph(csList, freqList, mols, width=width, alpha=alpha, title='Cluster Composition')
else:
idx = [i for i in range(len(csList)) if csList[i] in specialClusters]
df2 = df.iloc[idx]
mols = df.columns[2:]
freqList = [df2[mol] for mol in mols]
plotBarGraph(df2['Clusters'], freqList, mols, width=width, alpha=alpha, title='Cluster Composition')