def run_uclchem(phase1, phase2, species):
print("\n Running UCLCHEM..")
# Check if phase 1 exists (as n is the only important factor here)
print("\t Running phase 1...")
uclchem.wrap.run_model_to_file(phase1, species)
print("\t Phase 1 complete \n")
print("\t Running phase 2...")
uclchem.wrap.run_model_to_file(phase2, species)
print("\t Phase 2 complete \n")
print("UCLCHEM run complete \n Reading UCLCHEM output...")
data = plotfunctions.read_uclchem("{0}".format(phase2["outputFile"]))
times, dens, temp, av, coldens, abundances = list(data["Time"]), list(
data["Density"]), list(data["gasTemp"]), list(data["av"]), [], []
for spec in species.split(): # split() required as species is a string
print("spec={0}".format(spec))
print("data[spec]={0}".format(data[spec]))
abundances.append(data[spec])
coldens.append(
data[spec] * av * 1.6e21
) # See Eq. 2 of https://www.aanda.org/articles/aa/pdf/2014/07/aa23010-13.pdf
# Determine the H column density through the shock
# coldens = [phase2["rout"]*(3e18)*n_i for n_i in dens]
# Read the velocity file
vels = np.loadtxt(phase2["velocityFile"])
return {
"times": times,
"dens": dens,
"temp": temp,
"abundances": abundances,
"av": av,
"coldens": coldens,
"zn": vels[:, 1],
"vn": vels[:, 2],
"vi": vels[:, 3]
}
#aiming to have 3 panes stacked vertically
fig,axes=pf.plt.subplots(1,2,figsize=(16,9), sharey=True,num=p,clear=True)
for j , factor in enumerate(varyfactor):
#pick species, any number is fine
#title = ""
lb=False
if factor == 1 :
collfile = columnpath + "phase1-fullCtrl.dat"
#title = "Ctrl"
lb=True
else:
collfile = columnpath + "phase1-full"+e[0]+ "-" + str(factor).replace('.','_')+".dat"
#title = "Varying " + e[0]#+str(factor)
lb=False
#call read_uclchem.
time,dens,temp,abundances=pf.read_uclchem(collfile,speciesNames)
for k, s in enumerate(speciesNames):
specfactor.append(s+"_" + str(factor))
#plot species returns the axis so we can further edit
#axis.set(xscale='log',ylim=(1e-18,1e-3),xlim=(1e0,6e6))
#axis.set_title(title)
#axis.legend(loc='best')
#phase 2 display
if factor == 1 :
collfile = columnpath + "phase2-fullCtrl.dat"
#title = "Ctrl"
else:
collfile = columnpath + "phase2-full"+e[0]+ "-" + str(factor).replace('.','_')+".dat"
def plotchem(speciesNames):
p = 0
speclist = ""
factorlines = [
mlines.Line2D([], [], color='w', label="factor", linestyle='None')
]
for j, strfactor in enumerate(varyfactorstr):
factorlines.append(
mlines.Line2D([], [],
color='k',
label=strfactor,
linestyle=Linestles[j]))
for l, s in enumerate(speciesNames):
speclist += s + " "
for k, e in enumerate(elements):
i = 0
iprev = i
specfactor = []
title = "Varying " + e[0]
#aiming to have 3 panes stacked vertically
fig, axes = pf.plt.subplots(figsize=imgparams[0], num=p, clear=True)
if legendoutP1:
pf.plt.subplots_adjust(right=0.85,
left=None,
bottom=None,
top=None)
p = p + 1
#figcombo,axescombo=pf.plt.subplots(figsize=(16,9), sharey=True,num=p,clear=True)
#p=p+1
#Separate out the phase 2 graph
#figp2,axesp2=pf.plt.subplots(figsize=(16,9), sharey=True,num=p,clear=True)
phase2startt = 0
phase2endt = 0
rtists = []
for j, factor in enumerate(varyfactor):
#pick species, any number is fine
print("plotting: " + speclist + e[0] + " " + str(factor))
#title = ""
#phase 1 data extract
lb = False
if factor == 1:
collfile = columnpath + "phase1-fullCtrl.dat"
#title = "Ctrl"
lb = True
else:
collfile = columnpath + "phase1-full" + e[0] + "-" + str(
factor).replace('.', '_') + ".dat"
#title = "Varying " + e[0]#+str(factor)
lb = False
#call read_uclchem.
time, dens, temp, abundances = pf.read_uclchem(
collfile, speciesNames)
for l, s in enumerate(speciesNames):
specfactor.append(s + "_" + str(factor))
#plot species and save to test.png, alternatively send dens instead of time.
#axis,rtist0=pf.plot_species(speciesNames,time,abundances,axes,ls=Linestles[j],lab=lb)#ax=axes[i])
#axis,rtist1=pf.plot_species(speciesNames,time,abundances,axes[1],ls=Linestles[j],lab=False)#ax=axes[i])
#phase 2 data notbeing used at mo so leave out
# if factor == 1 :
# collfile = columnpath + "phase2-fullCtrl.dat"
# #lb=True
# #title = "Ctrl"
# else:
# collfile = columnpath + "phase2-full"+e[0]+ "-" + str(factor).replace('.','_')+".dat"
# #lb=False
# #title = "Varying " + e[0]#+str(factor)
# #call read_uclchem.
# time2,dens2,temp,abundances2=pf.read_uclchem(collfile,speciesNames)
# #plot species returns the axis so we can further edit
# #stitch together the phase1 and phase2 series and plot
# timenp = np.asarray(time)
# timenp2 = np.asarray(time2)
# for l, s in enumerate(speciesNames):
# abundances[l].extend(abundances2[l])
# tnpp2 = timenp2 + timenp[-1]
# t = np.append(timenp,tnpp2)
axis, rtist0 = pf.plot_species(speciesNames,
time,
abundances,
axes,
ls=Linestles[j],
lab=lb,
lw=imgparams[4],
ncol=6) #ax=axes[i])
#axiscombo,rtist0=pf.plot_species(speciesNames,t,abundances,axescombo,ls=Linestles[j],lab=lb)#ax=axes[i])
rtists.append(
rtist0
) #questionable use as plotspecies doesn't return the list of handles just the last ouneat the moment
# plot phase2 on its own
#phase2startt=tnpp2[0]
#phase2endt=tnpp2[-1]
#plot species and save to test.png, alternatively send dens instead of time.
#axisp2,rtist2=pf.plot_species(speciesNames,tnpp2,abundances2,axesp2,ls=Linestles[j],lab=lb)#ax=axes[i])
#here insert the factor linestyles at the end of the legned under a heading of factor
if withfactorlegendP1:
lbls = ["species"]
hdls = [
mlines.Line2D([], [],
color='w',
label="species",
linestyle='None')
]
hndls, labls = axes.get_legend_handles_labels()
lbls.extend(labls)
lbls.append("factor")
lbls.extend(varyfactorstr)
hdls.extend(hndls)
hdls.extend(factorlines)
axes.set(xscale=xaslog,
yscale='log',
ylim=(1e-18, 1e-3),
xlim=(1e0, time[-1])) #timenp[-1]
axes.set_xlabel('Time / Years', fontsize=imgparams[2])
if xaslog == 'linear':
axes.ticklabel_format(axis='x', useMathText=True)
axes.set_ylabel('X/H', fontsize=imgparams[2])
axes.set_title(title + " phase1", fontsize=imgparams[3])
if withfactorlegendP1 and legendoutP1:
axes.legend(hdls,
lbls,
loc='upper left',
bbox_to_anchor=(1.0, 1.0),
fontsize=imgparams[5],
handlelength=1.5,
borderaxespad=0.0
) #loc='upper left',bbox_to_anchor=(0.0,0.0,1.0,1.0),
elif withfactorlegendP1:
axes.legend(hdls, lbls, loc='best', fontsize=imgparams[1])
else:
axes.legend(loc='best', fontsize=imgparams[1])
fig.savefig(plotspath + "/phase1plot" + e[0] + "_" + speciesNames[0] +
".png",
dpi=300)
# axes.set(xscale=xaslog,yscale='log',ylim=(1e-18,1e-3),xlim=(1e0,6.5e6))#t[-1]))
# if xaslog == 'linear':
# axes.ticklabel_format(axis='x',useMathText=True)
# axes.set_title(title+" phase1&2",fontsize=imgparams[3])
# fig.savefig(plotspath+"/comboplot"+e[0]+"_"+speciesNames[0]+".png",dpi=300)
# axes.set(xscale=xaslog,yscale='log',ylim=(1e-18,1e-3),xlim=(phase2startt,phase2startt+0.3e6))
# if xaslog == 'linear':
# axes.ticklabel_format(axis='x',useMathText=True)
# axes.set_title(title+" phase2",fontsize=imgparams[3])
# fig.savefig(plotspath+"/phase2plot"+e[0]+"_"+speciesNames[0]+".png",dpi=300)
p = p + 1
for k, e in enumerate(elements):
i = 0
iprev = i
specfactor = []
#aiming to have 3 panes stacked vertically
fig, axes = pf.plt.subplots(figsize=imgparams[0], num=p, clear=True)
if legendoutS:
pf.plt.subplots_adjust(right=0.85,
left=None,
bottom=None,
top=None)
for j, factor in enumerate(varyfactor):
print("plotting static: " + speclist + e[0] + " " + str(factor))
#pick species, any number is fine
title = ""
lb = False
#static model plot
if factor == 1:
collfile = columnpath + "static-fullCtrl.dat"
title = "Ctrl"
lb = True
else:
collfile = columnpath + "static-full" + e[0] + "-" + str(
factor).replace('.', '_') + ".dat"
title = "Varying " + e[0] #+str(factor)
lb = False
#call read_uclchem.
time3, dens3, temp3, abundances3 = pf.read_uclchem(
collfile, speciesNames)
#plot species and save to test.png, alternatively send dens instead of time.
axis, rtist = pf.plot_species(speciesNames,
time3,
abundances3,
axes,
ls=Linestles[j],
lab=lb,
lw=imgparams[4],
ncol=6) #ax=axes[i])
#plot species returns the axis so we can further edit
if xaslog == 'linear':
axis.set(xscale=xaslog,
yscale='log',
ylim=(1e-18, 1e-3),
xlim=(1e0, time3[-1]))
axis.ticklabel_format(axis='x', useMathText=True)
else:
axis.set(xscale=xaslog,
yscale='log',
ylim=(1e-18, 1e-3),
xlim=(1e0, time3[-1]))
axis.set_ylabel('X/H', fontsize=imgparams[2])
axis.set_xlabel('Time / Years', fontsize=imgparams[2])
axis.set_title(title + " static cloud", fontsize=imgparams[3])
#axis.set_title(title)
#here insert the factor linestyles at the end of the legned under a heading of factor
if withfactorlegendS:
lbls = ["species"]
hdls = [
mlines.Line2D([], [],
color='w',
label="species",
linestyle='None')
]
hndls, labls = axes.get_legend_handles_labels()
lbls.extend(labls)
lbls.append("factor")
lbls.extend(varyfactorstr)
hdls.extend(hndls)
hdls.extend(factorlines)
if withfactorlegendS and legendoutS:
axes.legend(
hdls,
lbls,
loc='upper left',
bbox_to_anchor=(1.0, 1.0),
fontsize=imgparams[5],
handlelength=1.5,
borderaxespad=0.0
) #loc='upper left',bbox_to_anchor=(0.0,0.0,1.0,1.0),
elif withfactorlegendS:
axes.legend(hdls, lbls, loc='best', fontsize=imgparams[1])
else:
axes.legend(loc='best', fontsize=imgparams[1])
i = i + 1
#axes[0].text(.02,0.98,e[0],horizontalalignment="left",verticalalignment="top",transform=axes[0].transAxes)
#the single plot per page version
# axes.text(.02,0.98,e[0],horizontalalignment="left",verticalalignment="top",transform=axes.transAxes)
#axes[3].text(.02,0.98,"Your Row",horizontalalignment="left",verticalalignment="top",transform=axes[3].transAxes)
fig.savefig(plotspath + "/staticplot" + e[0] + "_" + speciesNames[0] +
".png",
dpi=300)
pf.plt.clf()
def plotchem(speciesGroup):
#speciesGroup=speciesNameLists[0]#use this to just do one element for a test
speciesNames = speciesGroup[1]
speciesDisplay = speciesGroup[2]
groupname = speciesGroup[0]
p=0
for k, e in enumerate(elements):
i=0
iprev = i
specfactor = []
time = []
abundances = []
species = []
abundscale = []
varying=[]#willl be a different plot "varying" may be able to put in a grid of plots
model = []
timelim = 5.4e6
title = modelname + " Varying " + e[0]
#aiming to have 3 panes stacked vertically
fig,axes=plt.subplots(figsize=imgparams[0], num=p,clear=True)
plt.rcParams["axes.labelsize"]=imgparams[5]
plt.rcParams["axes.titlesize"]="large"
plt.rcParams["axes.formatter.use_mathtext"]=True
plt.rcParams["font.size"]=8.0
plt.rcParams["legend.fontsize"]="small"
plt.rcParams["legend.borderaxespad"]=0.0
plt.rcParams["legend.handletextpad"]=0.1
#plt.rcParams["figure.autolayout"]=True
p=p+1
#figcombo,axescombo=pf.plt.subplots(figsize=(16,9), sharey=True,num=p,clear=True)
#p=p+1
#Separate out the phase 2 graph
#figp2,axesp2=pf.plt.subplots(figsize=(16,9), sharey=True,num=p,clear=True)
timeadded = False
# phase2startt=0
# phase2endt=0
#iterate over all the multipliers held in varyfactor
for j , factor in enumerate(varyfactor):
#pick species, any number is fine
#title = ""
lb=False
#phase 1 plot
if factor == 1 :
collfile = columnpath + "phase1-fullCtrl.dat"
#title = "Ctrl"
lb=True
else:
collfile = columnpath + "phase1-full"+e[0]+ "-" + str(factor).replace('.','_')+".dat"
#title = "Varying " + e[0]#+str(factor)
lb=False
#call read_uclchem.
t,dens,temp,abund=pf.read_uclchem(collfile,speciesNames)
if t[-1] > timelim:
timelim = t[-1]
for l, s in enumerate(speciesDisplay):
if len(abund[l]) != len(t):
print("Species "+s+"no values a=" +str(len(abund))+" t="+str(len(t)))
continue
abundances.extend(abund[l])
time.extend(t)
species.extend([s]*len(t)) #.replace('ice','*')
abundscale.extend([str(factor)]*len(t))
varying.extend([e[0]]*len(t))#varying element means we could construct a page of plots
model.extend(["collapse"]*len(t))
#static model plot
if factor == 1 :
collfile = columnpath + "static-fullCtrl.dat"
title = "Ctrl"
lb = True
else:
collfile = columnpath + "static-full"+e[0]+ "-" + str(factor).replace('.','_')+".dat"
title = modelname + "Varying " + e[0]#+str(factor)
lb = False
#call read_uclchem.
t,dens,temp,abund=pf.read_uclchem(collfile,speciesNames)
for l, s in enumerate(speciesDisplay):
if len(abund[l]) != len(t):
print("Species "+s+"no values a=" +str(len(abund))+" t="+str(len(t)))
continue
abundances.extend(abund[l])
time.extend(t)
species.extend([s]*len(t)) #.replace('ice','*')
abundscale.extend([str(factor)]*len(t))
varying.extend([e[0]]*len(t))#varying element means we could construct a page of plots
model.extend(["static"]*len(t))
# timenp = np.asarray(time)
# if not timeadded:
# p1df["time"] = time
# timeadded = True
# for l, s in enumerate(speciesNames):
# colname=s+str(factor)
# colname = s.replace('#','ice')
# specfactor.append(colname)
# p1df = pd.concat([p1df,pd.DataFrame({colname:abundances[l]})],axis=1)#column for each species with
#
#plot species and save to test.png, alternatively send dens instead of time.
#axis,rtist0=pf.plot_species(speciesNames,time,abundances,axes,ls=Linestles[j],lab=lb)#ax=axes[i])
#axis,rtist1=pf.plot_species(speciesNames,time,abundances,axes[1],ls=Linestles[j],lab=False)#ax=axes[i])
#p1 = p1df.transpose()
p1df = pd.DataFrame({"time":time,"abundances":abundances,"species":species,"factor":abundscale,"varying":varying,"model":model})
g = sns.FacetGrid(p1df,col="model",row="varying",aspect = 1.0)#,legend_out=True)
g.map_dataframe(sns.lineplot, x="time",y="abundances",hue="species",style="factor",dashes=Linestyles,linewidth=imgparams[4],ci=None) #data=p1df,legend="brief",ax=axes,
g.set(xscale=xaslog,yscale='log',ylim=(1e-18,5e-3),xlim=(1e0,timelim))
g.set_axis_labels('Time years','X/H')
g.add_legend(fontsize=imgparams[5])#,loc='upper left',bbox_to_anchor=(1.0,1.0))
g.fig.suptitle("Abundances "+modelname, fontsize=8)
g.fig.subplots_adjust(top=0.9,left=0.1,right=0.9)
#axes.set(xscale=xaslog,yscale='log',ylim=(1e-18,1e-3),xlim=(1e0,t[-1]))
#axes.set_xlabel('Time / Years',fontsize=imgparams[2])
#if xaslog == 'linear':
# axes.ticklabel_format(axis='x',useMathText=True)
# axes.set_ylabel('X/H',fontsize=imgparams[2])
#axes.set_title(title+" static cloud",fontsize=imgparams[3])
#axes.legend(loc='best',fontsize=imgparams[1])
plt.savefig(plotspath+"/facetplot"+e[0]+"_"+groupname+".png",dpi=300)
def plotchem(speciesGroup):
#speciesGroup=speciesNameLists[0]#use this to just do one element for a test
speciesNames = speciesGroup[1]
speciesDisplay = speciesGroup[2]
groupname = speciesGroup[0]
if bulk is False:
print(speciesNames)
p=0
for k, e in enumerate(elements):
i=0
iprev = i
specfactor = []
time = []
abundances = []
species = []
abundscale = []
varying=[]#willl be a different plot "varying" may be able to put in a grid of plots
model = []
timelim = 5.4e6
title = "Varying " + e[0]
#aiming to have 3 panes stacked vertically
fig,axes=plt.subplots(figsize=imgparams[0], num=p,clear=True)
p=p+1
#figcombo,axescombo=pf.plt.subplots(figsize=(16,9), sharey=True,num=p,clear=True)
#p=p+1
#Separate out the phase 2 graph
#figp2,axesp2=pf.plt.subplots(figsize=(16,9), sharey=True,num=p,clear=True)
timeadded = False
# phase2startt=0
# phase2endt=0
collfilepf1 = columnpath + "phase1-fullCtrl.dat"
tpf1,denspf1,temppf1,abundpf1=pf.read_uclchem(collfilepf1,speciesNames)
collfilesf1 = columnpath + "static-fullCtrl.dat"
tsf1,denssf1,tempsf1,abundsf1=pf.read_uclchem(collfilesf1,speciesNames)
#iterate over all the multipliers held in varyfactor
for j , factor in enumerate(varyfactor):
#pick species, any number is fine
#title = ""
lb=False
#phase 1 plot
if factor == 1 : #we have the abundances for factor 1 the base level
continue
lb=True
collfile = columnpath + "phase1-full"+e[0]+ "-" + str(factor).replace('.','_')+".dat"
#title = "Varying " + e[0]#+str(factor)
lb=False
#call read_uclchem. Fetches a list of abundances for each species requested.
t,dens,temp,abund=pf.read_uclchem(collfile,speciesNames)
if t[-1] > timelim:
timelim = t[-1]
for l, s in enumerate(speciesDisplay):
if (len(abund[l]) != len(t)) | (len(abundpf1[l]) != len(tpf1)):
print("Collapse Species "+s+"no values a=" +str(len(abund[l]))+" t="+str(len(t)) + "a1=" +str(len(abundpf1[l]))+" t1="+str(len(tpf1)))
else:
#extract the abundance and subtract the base factor 1 level first get the lists the same length as for factor 1
if len(t) > len(tpf1):
aabundf = np.asarray(abund[l][:len(tpf1)])
aabpf = np.asarray(abundpf1[l][:len(tpf1)])
aab = aabundf - aabpf
abundances.extend(aab.tolist())
time.extend(tpf1)
species.extend([s.replace('#','ice')]*len(tpf1))
abundscale.extend([str(factor)]*len(tpf1))
varying.extend([e[0]]*len(tpf1))#varying element means we could construct a page of plots
model.extend(["collapse"]*len(tpf1))
#calculate the parallel track for the current factor
#This should be a function to insert a track of abundance and time
# npx = aabundf * (factor - 1.0)#rebase so factor 1 is teh new zero
# abundances.extend(npx.tolist())
# time.extend(tpf1)
# species.extend([s.replace('#','ice')+"pll"]*len(tpf1))
# abundscale.extend([str(factor)]*len(tpf1))
# varying.extend([e[0]]*len(tpf1))#varying element means we could construct a page of plots
# model.extend(["collapse"]*len(tpf1))
else:
aabundf = np.asarray(abund[l][:len(t)])
aabpf = np.asarray(abundpf1[l][:len(t)])
aab = aabundf - aabpf
abundances.extend(aab.tolist())
time.extend(t)
species.extend([s.replace('#','ice')]*len(t))
abundscale.extend([str(factor)]*len(t))
varying.extend([e[0]]*len(t))#varying element means we could construct a page of plots
model.extend(["collapse"]*len(t))
#calculate the parallel track for the current factor
# npx = aabundf * (factor - 1.0)
# abundances.extend(npx.tolist())
# time.extend(t)
# species.extend([s.replace('#','ice')+"pll"]*len(t))
# abundscale.extend([str(factor)]*len(t))
# varying.extend([e[0]]*len(t))#varying element means we could construct a page of plots
# model.extend(["collapse"]*len(t))
#print("time "+str(len(time))+" abundances ",str(len(abundances))+" species "+str(len(species))+" factor "+str(len(abundscale))+" varying "+str(len(varying))+" model "+str(len(model)))
#static model plot
title = "Ctrl"
lb = True
collfile = columnpath + "static-full"+e[0]+ "-" + str(factor).replace('.','_')+".dat"
title = "Varying " + e[0]#+str(factor)
lb = False
#call read_uclchem.
t,dens,temp,abund=pf.read_uclchem(collfile,speciesNames)
#note this time pf1 replaced by sf1
for l, s in enumerate(speciesDisplay):
if (len(abund[l]) != len(t)) | (len(abundsf1[l]) != len(tsf1)):
print("Static Species "+s+" no values a=" +str(len(abund[l]))+" t="+str(len(t)) + " a1=" +str(len(abundsf1[l]))+" t1="+str(len(tsf1)))
else:
#extract the abundance and subtract the base factor 1 level first get the lists the same length as for factor 1
if len(t) > len(tsf1):
aabundf = np.asarray(abund[l][:len(tsf1)])
aabpf = np.asarray(abundsf1[l][:len(tsf1)])
aab = aabundf - aabpf
abundances.extend(aab.tolist())
time.extend(tsf1)
species.extend([s.replace('#','ice')]*len(tsf1))
abundscale.extend([str(factor)]*len(tsf1))
varying.extend([e[0]]*len(tsf1))#varying element means we could construct a page of plots
model.extend(["static"]*len(tsf1))
#calculate the parallel track for the current factor
# npx = aabundf * (factor - 1.0)#rebase so factor 1 is teh new zero
# abundances.extend(npx.tolist())
# time.extend(tsf1)
# species.extend([s.replace('#','ice')+"pll"]*len(tsf1))
# abundscale.extend([str(factor)]*len(tsf1))
# varying.extend([e[0]]*len(tsf1))#varying element means we could construct a page of plots
# model.extend(["static"]*len(tsf1))
else:
aabundf = np.asarray(abund[l][:len(t)])
aabpf = np.asarray(abundsf1[l][:len(t)])
aab = aabundf - aabpf
abundances.extend(aab.tolist())
time.extend(t)
species.extend([s.replace('#','ice')]*len(t))
abundscale.extend([str(factor)]*len(t))
varying.extend([e[0]]*len(t))#varying element means we could construct a page of plots
model.extend(["static"]*len(t))
#calculate the parallel track for the current factor
# npx = aabundf * (factor - 1.0)
# abundances.extend(npx.tolist())
# time.extend(t)
# species.extend([s.replace('#','ice')+"pll"]*len(t))
# abundscale.extend([str(factor)]*len(t))
# varying.extend([e[0]]*len(t))#varying element means we could construct a page of plots
# model.extend(["static"]*len(t))
#print("time "+str(len(time))+" abundances ",str(len(abundances))+" species "+str(len(species))+" factor "+str(len(abundscale))+" varying "+str(len(varying))+" model "+str(len(model)))
# timenp = np.asarray(time)
# if not timeadded:
# p1df["time"] = time
# timeadded = True
# for l, s in enumerate(speciesNames):
# colname=s+str(factor)
# colname = s.replace('#','ice')
# specfactor.append(colname)
# p1df = pd.concat([p1df,pd.DataFrame({colname:abundances[l]})],axis=1)#column for each species with
#
#plot species and save to test.png, alternatively send dens instead of time.
#axis,rtist0=pf.plot_species(speciesNames,time,abundances,axes,ls=Linestles[j],lab=lb)#ax=axes[i])
#axis,rtist1=pf.plot_species(speciesNames,time,abundances,axes[1],ls=Linestles[j],lab=False)#ax=axes[i])
#p1 = p1df.transpose()
if bulk is False:
print("time "+str(len(time))+" abundances ",str(len(abundances))+" species "+str(len(species))+" factor "+str(len(abundscale))+" varying "+str(len(varying))+" model "+str(len(model)))
p1df = pd.DataFrame({"time":time,"abundances":abundances,"species":species,"factor":abundscale,"varying":varying,"model":model})
g = sns.FacetGrid(p1df,col="model",row="varying")
g.map_dataframe(sns.lineplot, x="time",y="abundances",hue="species",style="factor",dashes=Linestyles,linewidth=imgparams[4],ci=None) #data=p1df,legend="brief",ax=axes,
g.set(xscale=xaslog,yscale='linear',xlim=(1e0,timelim)) #,ylim=(-1e-6,1e-6)
g.set_axis_labels('Time / Years','X/H')
g.add_legend()
#axes.set(xscale=xaslog,yscale='log',ylim=(1e-18,1e-3),xlim=(1e0,t[-1]))
#axes.set_xlabel('Time / Years',fontsize=imgparams[2])
#if xaslog == 'linear':
# axes.ticklabel_format(axis='x',useMathText=True)
# axes.set_ylabel('X/H',fontsize=imgparams[2])
#axes.set_title(title+" static cloud",fontsize=imgparams[3])
#axes.legend(loc='best',fontsize=imgparams[1])
plt.savefig(plotspath+"/facetplot"+e[0]+"_"+groupname+".png",dpi=300)
def plotchem(speciesGroup):
#speciesNames=speciesNameLists[0]#use this to just do one element for a test
speciesNames = speciesGroup[1]
speciesDisplay = speciesGroup[2]
groupname = speciesGroup[0]
p=0
for k, e in enumerate(elements):
i=0
iprev = i
specfactor = []
time = []
abundances = []
species = []
abundscale = []
varying=[]#willl be a different plot "varying" may be able to put in a grid of plots
title = "Varying " + e[0]
#aiming to have 3 panes stacked vertically
fig,axes=plt.subplots(figsize=imgparams[0], num=p,clear=True)
p=p+1
#figcombo,axescombo=pf.plt.subplots(figsize=(16,9), sharey=True,num=p,clear=True)
#p=p+1
#Separate out the phase 2 graph
#figp2,axesp2=pf.plt.subplots(figsize=(16,9), sharey=True,num=p,clear=True)
timeadded = False
# phase2startt=0
# phase2endt=0
#iterate over all the multipliers held in varyfactor
for j , factor in enumerate(varyfactor):
#pick species, any number is fine
#title = ""
lb=False
if factor == 1 :
collfile = columnpath + "phase1-fullCtrl.dat"
#title = "Ctrl"
lb=True
else:
collfile = columnpath + "phase1-full"+e[0]+ "-" + str(factor).replace('.','_')+".dat"
#title = "Varying " + e[0]#+str(factor)
lb=False
#call read_uclchem.
t,dens,temp,abund=pf.read_uclchem(collfile,speciesNames)
for l, s in enumerate(speciesDisplay):
if len(abund[l]) != len(t):
print("Species "+s+"no values a=" +str(len(abund))+" t="+str(len(t)))
continue
abundances.extend(abund[l])
time.extend(t)
species.extend([s.replace('#','ice')]*len(t))
abundscale.extend([str(factor)]*len(t))
varying.extend([e[0]]*len(t))#varying element means we could construct a page of plots
if factor == 1:
npabund = np.asarray(abund[l])
for q, f in enumerate(varyfactor):
if f == 1:
continue#just the outliier tracks
npx = npabund * f
abundances.extend(npx.tolist())
time.extend(t)
species.extend([s.replace('#','ice')+"pll"]*len(t))
abundscale.extend([str(f)]*len(t))
varying.extend([e[0]]*len(t))#varying element means we could construct a page of plots
# timenp = np.asarray(time)
# if not timeadded:
# p1df["time"] = time
# timeadded = True
# for l, s in enumerate(speciesNames):
# colname=s+str(factor)
# colname = s.replace('#','ice')
# specfactor.append(colname)
# p1df = pd.concat([p1df,pd.DataFrame({colname:abundances[l]})],axis=1)#column for each species with
#
#plot species and save to test.png, alternatively send dens instead of time.
#axis,rtist0=pf.plot_species(speciesNames,time,abundances,axes,ls=Linestles[j],lab=lb)#ax=axes[i])
#axis,rtist1=pf.plot_species(speciesNames,time,abundances,axes[1],ls=Linestles[j],lab=False)#ax=axes[i])
#p1 = p1df.transpose()
p1df = pd.DataFrame({"time":time,"abundances":abundances,"species":species,"factor":abundscale,"varying":varying})
sns.lineplot(x="time",y="abundances",hue="species",style="factor",dashes=Linestyles,data=p1df,legend="brief",ax=axes,linewidth=imgparams[4])
axes.set(xscale=xaslog,yscale='log',ylim=(1e-18,1e-3),xlim=(1e0,t[-1]))
axes.set_xlabel('Time / Years',fontsize=imgparams[2])
if xaslog == 'linear':
axes.ticklabel_format(axis='x',useMathText=True)
axes.set_ylabel('X/H',fontsize=imgparams[2])
axes.set_title(title+" phase1",fontsize=imgparams[3])
axes.legend(loc='best',fontsize=imgparams[1])
plt.savefig(plotspath+"/phase1plot"+e[0]+"_"+groupname+".png",dpi=300)
#plt.show()
# axes.legend(loc='best',fontsize=imgparams[1])
# # fig.savefig(plotspath+"/phase1plot"+e[0]+"_"+speciesNames[0]+".png",dpi=300)
# axes.set(xscale=xaslog,yscale='log',ylim=(1e-18,1e-3),xlim=(1e0,6.5e6))#t[-1]))
# if xaslog == 'linear':
# axes.ticklabel_format(axis='x',useMathText=True)
# axes.set_title(title+" phase1&2",fontsize=imgparams[3])
# fig.savefig(plotspath+"/comboplot"+e[0]+"_"+speciesNames[0]+".png",dpi=300)
# axes.set(xscale=xaslog,yscale='log',ylim=(1e-18,1e-3),xlim=(phase2startt,phase2startt+0.3e6))
# if xaslog == 'linear':
# axes.ticklabel_format(axis='x',useMathText=True)
# axes.set_title(title+" phase2",fontsize=imgparams[3])
# fig.savefig(plotspath+"/phase2plot"+e[0]+"_"+speciesNames[0]+".png",dpi=300)
p=p+1
for k, e in enumerate(elements):
i=0
iprev = i
specfactor = []
time = []
abundances = []
species = []
abundscale = []
varying=[]#willl be a different plot "varying" may be able to put in a grid of plots
#aiming to have 3 panes stacked vertically
fig,axes=plt.subplots(figsize=imgparams[0],num=p,clear=True)
for j , factor in enumerate(varyfactor):
#pick species, any number is fine
title = ""
lb=False
#static model plot
if factor == 1 :
collfile = columnpath + "static-fullCtrl.dat"
title = "Ctrl"
lb = True
else:
collfile = columnpath + "static-full"+e[0]+ "-" + str(factor).replace('.','_')+".dat"
title = "Varying " + e[0]#+str(factor)
lb = False
#call read_uclchem.
t,dens,temp,abund=pf.read_uclchem(collfile,speciesNames)
for l, s in enumerate(speciesDisplay):
if len(abund[l]) != len(t):
print("Species "+s+"no values a=" +str(len(abund))+" t="+str(len(t)))
continue
abundances.extend(abund[l])
time.extend(t)
species.extend([s.replace('#','ice')]*len(t))
abundscale.extend([str(factor)]*len(t))
varying.extend([e[0]]*len(t))#varying element means we could construct a page of plots
if factor == 1:#produce parralell tracks and label as a related species
npabund = np.asarray(abund[l])
for q, f in enumerate(varyfactor):
if f == 1:
continue#just the outliier tracks
npx = npabund * f
abundances.extend(npx.tolist())
time.extend(t)
species.extend([s.replace('#','ice')+"pll"]*len(t))
abundscale.extend([str(f)]*len(t))
varying.extend([e[0]]*len(t))#varying element means we could construct a page of plots
# timenp = np.asarray(time)
# if not timeadded:
# p1df["time"] = time
# timeadded = True
# for l, s in enumerate(speciesNames):
# colname=s+str(factor)
# colname = s.replace('#','ice')
# specfactor.append(colname)
# p1df = pd.concat([p1df,pd.DataFrame({colname:abundances[l]})],axis=1)#column for each species with
#
#plot species and save to test.png, alternatively send dens instead of time.
#axis,rtist0=pf.plot_species(speciesNames,time,abundances,axes,ls=Linestles[j],lab=lb)#ax=axes[i])
#axis,rtist1=pf.plot_species(speciesNames,time,abundances,axes[1],ls=Linestles[j],lab=False)#ax=axes[i])
#p1 = p1df.transpose()
p1df = pd.DataFrame({"time":time,"abundances":abundances,"species":species,"factor":abundscale,"varying":varying})
sns.lineplot(x="time",y="abundances",hue="species",style="factor",dashes=Linestyles,data=p1df,legend="brief",ax=axes,linewidth=imgparams[4])
axes.set(xscale=xaslog,yscale='log',ylim=(1e-18,1e-3),xlim=(1e0,t[-1]))
axes.set_xlabel('Time / Years',fontsize=imgparams[2])
if xaslog == 'linear':
axes.ticklabel_format(axis='x',useMathText=True)
axes.set_ylabel('X/H',fontsize=imgparams[2])
axes.set_title(title+" static cloud",fontsize=imgparams[3])
axes.legend(loc='best',fontsize=imgparams[1])
plt.savefig(plotspath+"/staticplot"+e[0]+"_"+groupname+".png",dpi=300)