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HALLORAN2020_ReactiveBackDiffusion_Scenario_A.py
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HALLORAN2020_ReactiveBackDiffusion_Scenario_A.py
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# -*- coding: utf-8 -*-
"""
HALLORAN2020_ReactiveBackDiffusion_Scenario_A.py
Landon Halloran, 2020
www.ljsh.ca
github.com/lhalloran
Python script to process and analyse model output for Scenario A in the following paper:
L.J.S. Halloran and D. Hunkeler (2020) "Controls on the persistence of aqueous-phase
groundwater contaminants in the presence of reactive back-diffusion."
Science of the Total Environment 722, 137749.
https://doi.org/10.1016/j.scitotenv.2020.137749
"""
#%% Import necessary packages:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import StrMethodFormatter
from datetime import datetime
from matplotlib.backends.backend_pdf import PdfPages
from matplotlib.colors import LogNorm
#%%
#################################### TO BE DEFINED BY USER #######################################
dropFactor = 1.0/10 # Attenuation factor (<1).
cutOffC = 0.00001 # Cut-off normalised concentration (should be >=1E-5).
pointNumber = 5 - 1 # Observation well for analysis. Starts at 0, moving L to R (i.e., well at 100m is # 4)
##################################################################################################
#%% DEFINE CUJSTOM FUNCTIONS
# text colour chooser
def choose_colour(val,maxval):
if np.isnan(val):
colour = 'black'
elif val>maxval/2:
colour = 'black'
else:
colour = 'white'
return colour
# t,x to unitless spacetime (n pore volumes)
def tx_to_unitless(v,t,x):
return v*t/x
# convert to velocity
def to_velocity(K,epsilon,dHdx):
return (1/epsilon)*K*dHdx
#%% READ AND PRE-PROCESS DATA
fileName = 'ScenarioA.csv'
theFontSize=14
plt.rcParams["font.weight"] = "bold"
plt.rcParams["axes.labelweight"] = "bold"
nt = 211 # number of time steps
nPoints = 5
nParams = 6
nParamsCombo = 288
dHdx = 0.01 # horizontal hydraulic gradient
epsilonAquitard = 0.4 # porosity in aquitard
epsilonAquifer = 0.35 # porosity in aquifer
tRemove = 10
dataIn = pd.read_csv(fileName, header=4)
data = np.empty((nParamsCombo, nt, nPoints))
params = np.empty((nParamsCombo, nParams))
paramNames = np.array(dataIn.columns.values[1:1+nParams])
t = dataIn.values[0:nt,0]
# convert to numpy matrix...
for i in np.arange(nParamsCombo):
startInd = i*nt
data[i,:,:] = dataIn.values[startInd:startInd+nt, nParams+1:]
params[i,:] = dataIn.values[startInd, 1:1+nParams]
tDrop = np.zeros((nParamsCombo,nPoints))-1.0 # time where C drops by dropfactor from peak
maxC = np.zeros((nParamsCombo,nPoints))-1.0 # maximum concentration
for i in np.arange(nParamsCombo):
for j in np.arange(nPoints):
dataNow = data[i,:,j]
maxNow = np.max(dataNow)
if maxNow>=cutOffC:
maxC[i,j] = maxNow
else:
maxC[i,j] = cutOffC*0.1 # assign small value
if params[i,0]==5E-5: # max value is basically constant over a certain period in many of of the fastest flow cases, so define as t=10 days
indMaxNow = 10
else:
indMaxNow = np.argmax(dataNow)
if maxNow<cutOffC:
tDrop[i,j] = np.NaN # if no significant concentration is seen (i.e. down in rounding errors)
elif np.argmax(dataNow[indMaxNow:]<=maxNow*dropFactor) == 0:
tDrop[i,j] = t[-1]+1.0 # if dropFactor value is never seen, assign 200+1 years
else:
indDrop = np.argmax(dataNow[indMaxNow:]<=maxNow*dropFactor)+indMaxNow
# this now does linear approximation to estimate the inter-year point
# at which the concentration drops below threshold...
C2,t2 = dataNow[indDrop],t[indDrop]
C1,t1 = dataNow[indDrop-1],t[indDrop-1]
tDropNow = t2 - 1 + (C1-maxNow*dropFactor)/(C1-C2)
tDrop[i,j] = tDropNow - t[indMaxNow] # tDrop is now time since max
nvatt=tDrop-tDrop
thefigsize=10,10 # general plotting parameters
xPoint = np.remainder(pointNumber,5)*20 + 20 # x distance of points
#%% prep for plots
nParamUnique=[]
i=0
for row in params.transpose():
nParamUnique.append(np.unique(row).size)
print(paramNames[i]+' has '+str(nParamUnique[i])+' unique values')
i = i+1
indsOrder = np.array([2,3,0,4,5]) # indices of params for x axis, y-axis, nx plots, ny plots, param for each plot
# should be: r_1, D_F, K_aquifer, z+aquifer, f_retardation
nx,ny=nParamUnique[indsOrder[2]],nParamUnique[indsOrder[3]]
#%% when there are 5 parameters, must do everything n times, where n=# of parameter values for the 5th parameter
valsExtraParam = np.unique(params[:,indsOrder[4]])
dtNow = datetime.now().strftime("%Y.%m.%d.%H.%M.%S")
fileOutName='out/'+fileName+'_OUT_'+dtNow+'.pdf'
pp = PdfPages(fileOutName)
# begin loop
for lastParamVal in valsExtraParam: # here, this should be retardation factor
fig=plt.figure(figsize=(thefigsize))
plt.plot()
fig.patch.set_visible(False)
plt.axis('off')
textForFig = fileOutName+ '\n' + '$f_{re} =$ ' + str(lastParamVal) +'\n' + 'point # = ' +str(pointNumber+1) +' (@ '+str(xPoint)+' m)\n'+'drop factor = ' + str(dropFactor) +'\n cutOffC = '+str(cutOffC)+'\n\n order of figures:\n'+'time (years) for attenuation after peak \n peak value (mol/m$^3$) \n n pore volumes for attenuation after peak'
plt.text(0,0,textForFig,multialignment='center',ha='center')
pp.savefig(fig)
plt.close()
# make plot of tDrop vs. 4 parameters
tMin,tMax = 0,t[-1]-tRemove
fig, axes = plt.subplots(nx, ny,figsize=(thefigsize))
for ix in np.arange(nx):
valix = np.unique(params[:,indsOrder[2]])[ix]
for iy in np.arange(ny):
valiy = np.unique(params[:,indsOrder[3]])[iy]
indsNow = np.where(np.logical_and(np.logical_and(params[:,indsOrder[2]]==valix,params[:,indsOrder[3]]==valiy),
params[:,indsOrder[4]]==lastParamVal))
dataNow = tDrop[indsNow,pointNumber]
yNow=params[indsNow,indsOrder[0]]
xNow=params[indsNow,indsOrder[1]]
sizeNow=np.array([nParamUnique[indsOrder[0]],nParamUnique[indsOrder[1]]])
if ny==1:
plt.sca(axes[ix])
axNow=axes[ix]
else:
plt.sca(axes[ix,iy])
axNow=axes[ix,iy]
axNow.imshow(dataNow.reshape(sizeNow),vmax=tMax,vmin=tMin,cmap='cividis')
xTickLabels = [ '{:.2e}'.format(l) for l in xNow[0,0:np.unique(params[:,indsOrder[0]]).size] ]
yTickLabels = yNow[0,0::np.unique(params[:,indsOrder[1]]).size]
plt.gca().xaxis.set_major_formatter(StrMethodFormatter('{x:,.2f}'))
if ix==0: # ix and iy are backwards, I think....
titlestr=paramNames[indsOrder[3]]+' = ' +str(valiy)
plt.title(titlestr,fontsize=theFontSize,weight='bold')
if iy==ny-1:
titlestr=paramNames[indsOrder[2]]+' = ' +str(valix)
axNow.yaxis.set_label_position("right")
plt.ylabel(titlestr,rotation=270,fontsize=theFontSize,ha='center', va='bottom')
if ix==nx-1:
plt.xticks(np.arange(np.unique(params[:,indsOrder[0]]).size), xTickLabels,rotation=15)
else:
plt.xticks(np.arange(np.unique(params[:,indsOrder[0]]).size),[])
if iy==0:
plt.yticks(np.arange(np.unique(params[:,indsOrder[1]]).size), yTickLabels)
else:
plt.yticks(np.arange(np.unique(params[:,indsOrder[1]]).size), [])
# add value labels to plot
x_positions = np.arange(nParamUnique[indsOrder[0]])
y_positions = np.arange(nParamUnique[indsOrder[1]])
for y_index, y in enumerate(y_positions):
for x_index, x in enumerate(x_positions):
tNow=dataNow.reshape(sizeNow)[y_index,x_index]
if tNow>t[-1]-tRemove:
label='>'+str(int(t[-1]-tRemove)) # for when attenuation takes longer than simulation results
elif np.isnan(tNow):
label='*' # for when concentration never exceeds cutoff
else:
label = "{:2.2f}".format(tNow)
text_x = x
text_y = y
thecolour=choose_colour(tNow,t[-1]-tRemove)
axNow.text(text_x, text_y, label, color=thecolour, ha='center', va='center', name='ITC Avant Garde Gothic',rotation=45)
fig.text(0.5, 0.05, paramNames[indsOrder[1]], ha='center',fontsize=theFontSize)
fig.text(0.05, 0.5, paramNames[indsOrder[0]], ha='center',fontsize=theFontSize,rotation='vertical')
pp.savefig(fig)
# make plot of maximum concentration value:
CMin,CMax = np.min(maxC[:,pointNumber]),np.max(maxC[:,pointNumber])
fig, axes = plt.subplots(nx, ny,figsize=(thefigsize))
for ix in np.arange(nx):
valix = np.unique(params[:,indsOrder[2]])[ix]
for iy in np.arange(ny):
valiy = np.unique(params[:,indsOrder[3]])[iy]
indsNow = np.where(np.logical_and(np.logical_and(params[:,indsOrder[2]]==valix,params[:,indsOrder[3]]==valiy),
params[:,indsOrder[4]]==lastParamVal))
dataNow = maxC[indsNow,pointNumber]
yNow=params[indsNow,indsOrder[0]]
xNow=params[indsNow,indsOrder[1]]
sizeNow=np.array([nParamUnique[indsOrder[0]],nParamUnique[indsOrder[1]]])
if ny==1:
plt.sca(axes[ix])
axNow=axes[ix]
else:
plt.sca(axes[ix,iy])
axNow=axes[ix,iy]
axNow.imshow(dataNow.reshape(sizeNow),vmax=CMax,vmin=CMin)
xTickLabels = [ '{:.2e}'.format(l) for l in xNow[0,0:np.unique(params[:,indsOrder[0]]).size] ]
yTickLabels = yNow[0,0::np.unique(params[:,indsOrder[1]]).size]
plt.gca().xaxis.set_major_formatter(StrMethodFormatter('{x:,.2f}'))
if ix==0: # ix and iy are backwards, I think....
titlestr=paramNames[indsOrder[3]]+' = ' +str(valiy)
plt.title(titlestr,fontsize=theFontSize,weight='bold')
if iy==ny-1:
titlestr=paramNames[indsOrder[2]]+' = ' +str(valix)
axNow.yaxis.set_label_position("right")
plt.ylabel(titlestr,rotation=270,fontsize=theFontSize,ha='center', va='bottom')
if ix==nx-1:
plt.xticks(np.arange(np.unique(params[:,indsOrder[0]]).size), xTickLabels,rotation=15)
else:
plt.xticks(np.arange(np.unique(params[:,indsOrder[0]]).size),[])
if iy==0:
plt.yticks(np.arange(np.unique(params[:,indsOrder[1]]).size), yTickLabels)
else:
plt.yticks(np.arange(np.unique(params[:,indsOrder[1]]).size), [])
# add value labels to plot
x_positions = np.arange(nParamUnique[indsOrder[0]])
y_positions = np.arange(nParamUnique[indsOrder[1]])
for y_index, y in enumerate(y_positions):
for x_index, x in enumerate(x_positions):
CNow=dataNow.reshape(sizeNow)[y_index,x_index]
if CNow>1:
label='1.000'
elif CNow<cutOffC:
label='*' # for when concentration never exceeds cutoff
else:
label = "{:2.3f}".format(CNow)
text_x = x
text_y = y
thecolour=choose_colour(CNow,CMax)
axNow.text(text_x, text_y, label, color=thecolour, ha='center', va='center', name='ITC Avant Garde Gothic',rotation=45)
fig.text(0.5, 0.05, paramNames[indsOrder[1]], ha='center',fontsize=theFontSize)
fig.text(0.05, 0.5, paramNames[indsOrder[0]], ha='center',fontsize=theFontSize,rotation='vertical')
pp.savefig(fig)
# make plot of n volumes (normalised time) vs. 4 parameters
nParamUnique=[]
i=0
for row in params.transpose():
nParamUnique.append(np.unique(row).size)
print(paramNames[i]+' has '+str(nParamUnique[i])+' unique values')
i = i+1
tnMin,tnMax = min(tx_to_unitless(365*24*3600*to_velocity(params[:,indsOrder[2]],epsilonAquifer,dHdx),tDrop[:,pointNumber],xPoint)),max(tx_to_unitless(365*24*3600*to_velocity(params[:,indsOrder[2]],epsilonAquifer,dHdx),tDrop[:,pointNumber],xPoint))
fig, axes = plt.subplots(nx, ny,figsize=(thefigsize))
for ix in np.arange(nx):
valix = np.unique(params[:,indsOrder[2]])[ix]
for iy in np.arange(ny):
valiy = np.unique(params[:,indsOrder[3]])[iy]
#print(str(valix)+','+str(valiy))
indsNow = np.where(np.logical_and(np.logical_and(params[:,indsOrder[2]]==valix,params[:,indsOrder[3]]==valiy),
params[:,indsOrder[4]]==lastParamVal))
dataNowtDrop = tDrop[indsNow,pointNumber]
dataNow = tx_to_unitless(365*24*3600*to_velocity(valix,epsilonAquifer,dHdx),dataNowtDrop,xPoint) # option convert to n volumes
nvatt[indsNow,pointNumber] = dataNow
yNow=params[indsNow,indsOrder[0]]
xNow=params[indsNow,indsOrder[1]]
sizeNow=np.array([nParamUnique[indsOrder[0]],nParamUnique[indsOrder[1]]])
if ny==1:
plt.sca(axes[ix])
axNow=axes[ix]
else:
plt.sca(axes[ix,iy])
axNow=axes[ix,iy]
axNow.imshow(dataNow.reshape(sizeNow),vmax=tnMax,vmin=tnMin,cmap='cividis',norm=LogNorm(vmin=tnMin, vmax=tnMax))
xTickLabels = [ '{:.2e}'.format(l) for l in xNow[0,0:np.unique(params[:,indsOrder[0]]).size] ]
yTickLabels = yNow[0,0::np.unique(params[:,indsOrder[1]]).size]
plt.gca().xaxis.set_major_formatter(StrMethodFormatter('{x:,.2f}'))
if ix==0:
titlestr=paramNames[indsOrder[3]]+' = ' +str(valiy)
plt.title(titlestr,fontsize=theFontSize,weight='bold')
if iy==ny-1:
titlestr=paramNames[indsOrder[2]]+' = ' +str(valix)
axNow.yaxis.set_label_position("right")
plt.ylabel(titlestr,rotation=270,fontsize=theFontSize,ha='center', va='bottom')
if ix==nx-1:
plt.xticks(np.arange(np.unique(params[:,indsOrder[0]]).size), xTickLabels,rotation=15)
else:
plt.xticks(np.arange(np.unique(params[:,indsOrder[0]]).size),[])
if iy==0:
plt.yticks(np.arange(np.unique(params[:,indsOrder[1]]).size), yTickLabels)
else:
plt.yticks(np.arange(np.unique(params[:,indsOrder[1]]).size), [])
# add value labels to plot
x_positions = np.arange(nParamUnique[indsOrder[0]])
y_positions = np.arange(nParamUnique[indsOrder[1]])
for y_index, y in enumerate(y_positions):
for x_index, x in enumerate(x_positions):
tnNow = dataNow.reshape(sizeNow)[y_index,x_index]
if dataNowtDrop.reshape(sizeNow)[y_index,x_index]>t[-1]-tRemove:
label='>'+"{:2.2f}".format(tnNow) # for when attenuation takes longer than simulation results
elif np.isnan(tnNow):
label='*' # for when concentration never exceeds cutoff
else:
label = "{:2.2f}".format(tnNow)
text_x = x
text_y = y
thecolour=choose_colour(np.log(tnNow/tnMin),np.log(tnMax))
axNow.text(text_x, text_y, label, color=thecolour, ha='center', va='center', name='ITC Avant Garde Gothic',rotation=45)
fig.text(0.5, 0.05, paramNames[indsOrder[1]], ha='center',fontsize=theFontSize)
fig.text(0.05, 0.5, paramNames[indsOrder[0]], ha='center',fontsize=theFontSize,rotation='vertical')
pp.savefig(fig)
#%%
fig=plt.figure(figsize=(thefigsize))
plt.plot()
fig.patch.set_visible(False)
plt.axis('off')
textForFig = fileOutName+ '\n Plots involving combined parameters $Pi_1$, $Pi_2$, and $\eta$...'
plt.text(0,0,textForFig,multialignment='center',ha='center')
pp.savefig(fig)
plt.close()
#%% plots vs combined parameters
tDropMasked = np.ma.masked_where(tDrop==201,tDrop)
nDrop = tx_to_unitless(365*24*3600*to_velocity(params[:,0],epsilonAquifer,dHdx),tDropMasked[:,pointNumber],xPoint)
# pis are from application of Buckingham Pi Theorem:
pi1=params[:,4]*np.sqrt(params[:,2]/(params[:,3]*epsilonAquitard**(4/3)))
pi2=params[:,0]*np.sqrt(params[:,3]*epsilonAquitard**(4/3))/params[:,2]**(3/2)
alpher=0.6
nvatt_masked = np.ma.masked_where(tDrop==201,nvatt)
#%% n_v,att vs. Pis
fig = plt.figure(figsize=(12,6))
ylimNow=[0.1,100]
legendNow = ['$z_{a}=1$m, $K_{a}=2$x$10^{-6}$ m/s','$z_{a}=1$m, $K_{a}=1$x$10^{-5}$ m/s','$z_{a}=1$m, $K_{a}=5$x$10^{-5}$ m/s',
'$z_{a}=0.2$m, $K_{a}=2$x$10^{-6}$ m/s','$z_{a}=0.2$m, $K_{a}=1$x$10^{-5}$ m/s','$z_{a}=0.2$m, $K_{a}=5$x$10^{-5}$ m/s',
'$z_{a}=5$m, $K_{a}=2$x$10^{-6}$ m/s','$z_{a}=5$m, $K_{a}=1$x$10^{-5}$ m/s','$z_{a}=5$m, $K_{a}=5$x$10^{-5}$ m/s']
# nvatt vs. Pi1
pltNow=plt.subplot(1,2,1)
loopNow=zip(np.arange(0,288,32),['gv','go','g^','bv','bo','b^','rv','ro','r^'])
for i,c in loopNow:
plt.loglog(pi1[i:i+32],nvatt_masked[i:i+32,pointNumber],c,alpha=alpher)#
pltNow.grid(True,which="both",ls=":",linewidth=0.5,c='grey')
plt.xlabel('$\Pi_1$ [-]',fontsize=theFontSize)
plt.ylabel('$n_{v,att}$',fontsize=theFontSize)
plt.ylim(ylimNow)
plt.legend(legendNow,fontsize=theFontSize-6)
# nvatt vs. Pi2
pltNow = plt.subplot(1,2,2)
loopNow=zip(np.arange(0,288,32),['gv','go','g^','bv','bo','b^','rv','ro','r^'])
for i,c in loopNow:
plt.loglog(pi2[i:i+32],nvatt_masked[i:i+32,pointNumber],c,alpha=alpher)#
pltNow.grid(True,which="both",ls=":",linewidth=0.5,c='grey')
plt.xlabel('$\Pi_2$ [-]',fontsize=theFontSize)
plt.ylabel('$n_{v,att}$',fontsize=theFontSize)
plt.ylim(ylimNow)
pp.savefig(fig)
#%% C'max vs. Pis
fig = plt.figure(figsize=(12,6))
ylimNow=[0,1]
legendNow = ['$z_{a}=1$m, $K_{a}=2$x$10^{-6}$ m/s','$z_{a}=1$m, $K_{a}=1$x$10^{-5}$ m/s','$z_{a}=1$m, $K_{a}=5$x$10^{-5}$ m/s',
'$z_{a}=0.2$m, $K_{a}=2$x$10^{-6}$ m/s','$z_{a}=0.2$m, $K_{a}=1$x$10^{-5}$ m/s','$z_{a}=0.2$m, $K_{a}=5$x$10^{-5}$ m/s',
'$z_{a}=5$m, $K_{a}=2$x$10^{-6}$ m/s','$z_{a}=5$m, $K_{a}=1$x$10^{-5}$ m/s','$z_{a}=5$m, $K_{a}=5$x$10^{-5}$ m/s']
# nvatt vs. Pi1
pltNow=plt.subplot(1,2,1)
loopNow=zip(np.arange(0,288,32),['gv','go','g^','bv','bo','b^','rv','ro','r^'])
for i,c in loopNow:
plt.semilogx(pi1[i:i+32],maxC[i:i+32,pointNumber],c,alpha=alpher)#
pltNow.grid(True,which="both",ls=":",linewidth=0.5,c='grey')
plt.xlabel('$\Pi_1$ [-]',fontsize=theFontSize)
plt.ylabel('$C\'_{max}$',fontsize=theFontSize)
plt.ylim(ylimNow)
plt.legend(legendNow,fontsize=theFontSize-6)
# nvatt vs. Pi2
pltNow = plt.subplot(1,2,2)
loopNow=zip(np.arange(0,288,32),['gv','go','g^','bv','bo','b^','rv','ro','r^'])
for i,c in loopNow:
plt.semilogx(pi2[i:i+32],maxC[i:i+32,pointNumber],c,alpha=alpher)#
pltNow.grid(True,which="both",ls=":",linewidth=0.5,c='grey')
plt.xlabel('$\Pi_2$ [-]',fontsize=theFontSize)
plt.ylabel('$C\'_{max}$',fontsize=theFontSize)
plt.ylim(ylimNow)
pp.savefig(fig)
#%% C'max vs. n_v,att
fig = plt.figure(figsize=(6,6))
pltNow=plt.subplot(1,1,1)
loopNow=zip(np.arange(0,288,32),['gv','go','g^','bv','bo','b^','rv','ro','r^'])
for i,c in loopNow:
plt.semilogx(nvatt_masked[i:i+32,pointNumber],maxC[i:i+32,pointNumber],c,alpha=alpher)#
plt.xlabel('$n_{v,att}$',fontsize=theFontSize)
plt.ylabel('$C\'_{max}$',fontsize=theFontSize)
plt.ylim([0,1])
plt.xlim([0.1,100])
pltNow.grid(True,which="both",ls=":",linewidth=0.5,c='grey')
#plt.legend(['1 m','0.2 m','5 m'])
legendNow = ['$z_{a}=1$m, $K_{a}=2$x$10^{-6}$ m/s','$z_{a}=1$m, $K_{a}=1$x$10^{-5}$ m/s','$z_{a}=1$m, $K_{a}=5$x$10^{-5}$ m/s',
'$z_{a}=0.2$m, $K_{a}=2$x$10^{-6}$ m/s','$z_{a}=0.2$m, $K_{a}=1$x$10^{-5}$ m/s','$z_{a}=0.2$m, $K_{a}=5$x$10^{-5}$ m/s',
'$z_{a}=5$m, $K_{a}=2$x$10^{-6}$ m/s','$z_{a}=5$m, $K_{a}=1$x$10^{-5}$ m/s','$z_{a}=5$m, $K_{a}=5$x$10^{-5}$ m/s']
plt.legend(legendNow,fontsize=theFontSize-6)
pp.savefig(fig)
#%% ETA PARAMETER
# n_v,att and C'max vs eta
eta = np.sqrt(params[:,3]*params[:,2]*epsilonAquitard**(4/3))/(params[:,0]*params[:,4])
fig = plt.figure(figsize=(12,6))
pltNow=plt.subplot(1,2,1)
loopNow=zip(np.arange(0,nParamsCombo,32),['gv','go','g^','bv','bo','b^','rv','ro','r^'])
for i,c in loopNow:
plt.loglog(eta[i:i+32],nvatt_masked[i:i+32,pointNumber],c,alpha=alpher)#
pltNow.grid(True,which="both",ls=":",linewidth=0.5,c='grey')
plt.xlabel('$\eta$ $[m^{-1}]$',fontsize=theFontSize)
plt.ylabel('$n_{v,att}$',fontsize=theFontSize)
pltNow=plt.subplot(1,2,2)
loopNow=zip(np.arange(0,nParamsCombo,32),['gv','go','g^','bv','bo','b^','rv','ro','r^'])
for i,c in loopNow:
plt.semilogx(eta[i:i+32],maxC[i:i+32,pointNumber],c,alpha=alpher)#
pltNow.grid(True,which="both",ls=":",linewidth=0.5,c='grey')
plt.xlabel('$\eta$ $[m^{-1}]$',fontsize=theFontSize)
plt.ylabel('$C\'_{max}$',fontsize=theFontSize)
pp.savefig(fig)
#%% ...and we're done!
pp.close()
print('Figures successfully written to file: '+fileOutName)
plt.close('all')