/
plot_cloudy_output.py
executable file
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plot_cloudy_output.py
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"""
take in data from parse_cloudy_output data files and plot.
"""
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
#from variousutils import ion_state, int_to_roman
from collections import OrderedDict
from core import Model
import os,sys
from config import *
from subprocess import call
#roman nums are off by one bc ionization states are counted starting from "1",
#whereas I need to enter in index starting from "0"
solarHIH = -0.000177 #basically zero. solar ionization fraction is small =log(1-ionfrac)
try:
minNHI = observed['H'][0]['column'][0]
maxNHI = observed['H'][0]['column'][2]
except:
print("no constraints set for HI column", file=sys.stderr)
pass
solar={}
for item in open(paths['solar_abundance']):
item=item.split()
solar[item[0]] = float(item[1])
#plt.rc('text', usetex=True)
#plt.rc('font', family='serif')
color_map={
0:'ko',
1:'bo',
2:'co',
3:'go',
4:'yo',
5:'ro',
6:'mo',
7:'ko'
}
colors={
0:'k-',
1:'b-',
2:'c-',
3:'g-',
4:'y-',
5:'r-',
6:'m-',
7:'k-'
}
#states to plot
ionmap = {
'H':[0,1,2],
'C':[0,1,2,3,4,5,6],
'O':[0,1,2,3,4,5,6,7],
'N':[0,1,2,3,4],
'Si':[0,1,2,3,4,5,6,7],
'Fe':[0,1,2,3,4,5,6,7]
}
def makedir(string):
if not os.path.isdir(os.path.join(paths["plot_path"],string)):
try:
msg="mkdir %s"%(os.path.join(paths["plot_path"],string))
call(msg,shell=True)
except:
print("error calling:\n %s"%(msg),file=sys.stderr)
raise
def ion_state(i,elem):
"""
append ionization state to elem.
following cloudy convention, i==2 means molecular Hydrogen for elem==H
"""
if i==2 and elem=='H':
return r"H_2"
else:
return elem+roman[i]
def total_col(lst):
assert(type(lst) is list)
return np.log10(sum([10**item[1] for item in lst]))
def trim(x,y):
while len(x) > len(y):
if x[-1] == '-30.000':
del(x[-1])
while len(y) > len(x):
if y[-1] == '-30.000':
del(y[-1])
if len(y) != len(x):
raise Exception(str(x)+str(y))
return np.array(x,dtype=np.float), np.array(y, dtype=np.float)
def trim(x,y,hcol):
end = min(y.shape[0],x.shape[0])
return x[0:end], y[0:end], hcol[0:end]
def plot_NT(elem,T,N,hcol,bounds=False):
"""
plot N versus T
parameters:
-----------
elem: elem name
N: log N. should be list of lists like: [HI:[...],HII:[...],H2:[...]]
T: log T. list of lists like N
"""
hcol = np.array(hcol, dtype=np.float)
for i in to_plot[elem]:
x = np.array(T[i]) #since data is stored is min, best, max, filter out best
y = np.array(N[i])
#x,y,hcol = trim(x,y,hcol)
y = hcol[0] - y
plt.plot(x, y, color_map[i],label=ion_state(i,elem))
xlims=[np.amin(x), np.amax(x)]
ylims=[np.amin(y), np.amax(y)]
try:
if bounds:
l = minNHI - observed[elem][i]["column"][2]
if observed[elem][i]["column"][0]==-30.:
u=maxNHI
else:
u = maxNHI - observed[elem][i]["column"][0]
plt.fill( [-30.,30., 30., -30.], [l,l,u,u],
'0.50', alpha=0.2, edgecolor='b')
#ax.fill_between(xx, l, u, where=u>=l, facecolor='gray', interpolate=True)
except KeyError:
pass
plt.minorticks_on()
plt.ylabel(r"log $N_{HI}/N_{%s}$"%(elem+str(roman[i])))
plt.xlabel(r"log T (K)")
makedir('logT')
f=os.path.join(paths["plot_path"],"logT", elem+roman[i]+"NT.png")
plt.xlim([2.,5.])
#plt.ylim(ylims)
#plt.xlim(xlims)
#plt.ylim(ylims)
plt.show()
plt.savefig(f)
plt.close()
def plot_bT(elem,T,b,bounds=False):
"""
plot b versus T
parameters:
-----------
elem: elem name
b: b. should be list of lists like: [HI:[...],HII:[...],H2:[...]]
T: log T. list of lists like N
"""
for i in to_plot[elem]:
x = np.array(T[i]) #since data is stored is min, best, max, filter out best
y = np.array(b[i])
plt.plot(x, y, color_map[i],label=ion_state(i,elem))
xlims=[0.75*np.amin(x), 1.25*np.amax(x)]
ylims=[0.75*np.amin(y), 1.25*np.amax(y)]
try:
if bounds:
l = observed[elem][i]["b"][2]
u = observed[elem][i]["b"][0]
plt.fill( [-30.,30., 30., -30.], [l,l,u,u],
'0.50', alpha=0.2, edgecolor='b')
#ax.fill_between(xx, l, u, where=u>=l, facecolor='gray', interpolate=True)
except KeyError:
pass
plt.ylabel(r"$b_{%s})$"%(elem+str(roman[i])))
plt.xlabel(r"log T (K)")
plt.minorticks_on()
makedir('logT')
f=os.path.join(paths["plot_path"],"logT", elem+roman[i]+"bT.png")
#plt.xlim(xlims)
##plt.ylim(ylims)
#plt.xlim(xlims)
#plt.ylim(ylims)
plt.show()
plt.savefig(f)
plt.close()
def plot_NU(elem,U,N, hcol,bounds=False):
"""
plot N versus ionization parameter U
parameters:
-----------
elem: elem name
N: log N. should be list of lists like: [HI:[...],HII:[...],H2:[...]]
U: log U. list of lists like N
"""
for i in to_plot[elem]:
plt.clf()
x = np.array(U,dtype=np.float)
y = np.array(N[i])
#x,y,hcol = trim(x,y,hcol)
y = hcol[0] - y
xlims=[0.75*np.amin(x), 1.25*np.amax(x)]
ylims=[0.75*np.amin(y), 1.25*np.amax(y)]
try:
if bounds:
l = (minNHI - observed[elem][i]["column"][2])#*np.ones(x.shape[0])
u = (maxNHI - observed[elem][i]["column"][0])#*np.ones(x.shape[0])
plt.fill([-30.,30., 30., -30.], [l,l,u,u],
'0.50', alpha=0.2, edgecolor='b')
#ax.fill_between(x, l, u, where=u>=l, facecolor='gray', interpolate=True)
#plt.fill_between(np.arange(xlims[0],xlims[1]),lower,upper,color='0.50')
except KeyError:
pass
try:
plt.plot(x, y, color_map[i],label=ion_state(i,elem))
plt.minorticks_on()
except ValueError:
print(x.shape, y.shape)
print("\n\n")
raise
plt.xlim([-5.,-2.5])
#plt.ylim(ylims)
plt.ylabel(r"log $N_{HI}/N_{%s}$"%(elem+roman[i]))
plt.xlabel(r"log U")
makedir('logU')
f=os.path.join(paths["plot_path"], "logU", elem+roman[i]+"NU.png")
plt.show()
plt.savefig(f)
plt.close()
def plot_bU(elem,U,b,bounds=False):
"""
plot b versus ionization parameter U
parameters:
-----------
elem: elem name
N: log N. should be list of lists like: [HI:[...],HII:[...],H2:[...]]
U: log U. list of lists like N
"""
for i in to_plot[elem]:
x = np.array(U,dtype=np.float)
y = np.array(b[i])
try:
plt.plot(x, y, color_map[i],label=ion_state(i,elem))
except ValueError:
print(x.shape, y.shape)
print("\n\n")
raise
xlims=[0.75*np.amin(x), 1.25*np.amax(x)]
ylims=[0.75*np.amin(y), 1.25*np.amax(y)]
plt.xlim([2.0,5.0])
##plt.ylim(ylims)
plt.ylabel(r"$b_{%s}$"%(elem+roman[i]))
plt.xlabel(r"log U")
plt.minorticks_on()
try:
if bounds:
l = observed[elem][i]["b"][2]#*np.ones(x.shape[0])
u = observed[elem][i]["b"][0]#*np.ones(x.shape[0])
plt.fill([-30.,30., 30., -30.], [l,l,u,u],
'0.50', alpha=0.2, edgecolor='b')
#ax.fill_between(x, l, u, where=u>=l, facecolor='gray', interpolate=True)
#plt.fill_between(np.arange(xlims[0],xlims[1]),lower,upper,color='0.50')
except KeyError:
pass
makedir('logU')
f=os.path.join(paths["plot_path"], "logU", elem+roman[i]+"bU.png")
plt.show()
plt.savefig(f)
plt.close()
def plot_NZ(elem,Z,N, hcol,bounds=False):
"""
plot N versus T
parameters:
-----------
elem: elem name
N: log N. should be list of lists like: [HI:[...],HII:[...],H2:[...]]
T: log T. list of lists like N
"""
for i in to_plot[elem]:
plt.clf()
x = np.array(Z,dtype=np.float)
y = np.array(N[i])
#x,y,hcol = trim(x,y,hcol)
y = hcol[0] - y #NHI-NX
#xlims=[0.75*np.amin(x), 1.25*np.amax(x)]
#ylims=[0.75*np.amin(y), 1.25*np.amax(y)]
#-3.78<float(mod.data['U'])<2.95
try:
if bounds:
l = minNHI - observed[elem][i]["column"][2]
if observed[elem][i]["column"][0]==-30.:
u=maxNHI
else:
u = maxNHI - observed[elem][i]["column"][0]
plt.fill([-30.,30., 30., -30.], [l,l,u,u], '0.50', alpha=0.2, edgecolor='b')
#plt.fill_between(np.arange(xlims[0],xlims[1]),lower,upper,color='0.50')
except KeyError:
pass
plt.plot(x, y, color_map[i],label=ion_state(i,elem))
plt.minorticks_on()
plt.xlim([-5.,-1.5])
#plt.ylim(ylims)
plt.ylabel(r"log $N_{HI}/N_{%s}$"%(elem+roman[i]))
plt.xlabel(r"log Z (solar units)")
makedir('Z')
f=os.path.join(paths["plot_path"], "Z",elem+roman[i]+"NZ.png")
plt.show()
plt.savefig(f)
plt.close()
def plot_ZU(Z,U,bounds=False):
"""
plot b versus ionization parameter U
parameters:
-----------
elem: elem name
N: log N. should be list of lists like: [HI:[...],HII:[...],H2:[...]]
U: log U. list of lists like N
"""
plt.plot(Z,U,'ko')
plt.xlabel(r'log Z')
plt.ylabel(r'log U')
plt.show()
plt.close()
def plot_ionization(elem,U,N,hcol):
plt.clf()
for i in to_plot[elem]:
x = np.array(U,dtype=np.float)
y = np.array(N[i])
#x,y,hcol = trim(x,y,hcol)
#y = hcol[0] - y #NHI-NX
plt.plot(x, y, color_map[i],label=ion_state(i,elem))
plt.minorticks_on()
plt.ylabel(r"log $N_{%s}$"%(elem))
plt.xlabel(r"log U")
plt.legend(loc='lower right', shadow=True,numpoints=1)
plt.show()
def plot_ionization_(elem,model_lst,xlims=None):
Zcolor=[ 'red','orangered','orange',
'yellow','yellowgreen','green', 'cyan',
'teal','blue','blueviolet']
Z = [-4.,-3.8,-3.6,
-3.4,-3.2,-3.0,-2.8,
-2.6,-2.4,-2.2]
for i in to_plot[elem]:
if xlims:
l,u = observed[elem][i]["column"][0], observed[elem][i]["column"][2]
if l==-30.: l=0.
plt.fill( [xlims[0], xlims[1], xlims[1], xlims[0]],
[l,l,u,u], '0.50', alpha=0.1)
plt.axhline(y=u,linestyle='dashed',linewidth=2, color='k')
for j in range(len(Z)):
mods=[item for item in model_lst if Z[j]-0.05<float(item.data['Z'])<Z[j]+0.05]
if len(mods)==0:
print("skipping Z=%lf"%(Z[j]))
continue
U=[item.data['U'] for item in mods]
N=Model.get_qty_lst(mods,elem,"column",i)
#if elem=='Si':
# N=[item+1.34 for item in N] #ISM to popII
try:
assert(len(N)>0 and len(U)>0)
except AssertionError:
print('N=',N)
print('U=',U)
raise
plt.plot(U, N, 'o', color=Zcolor[j],label="Z="+str(Z[j]))
plt.minorticks_on()
plt.ylabel(r"log $N_{%s}$"%(elem+roman[i]))
plt.xlabel(r"log U")
plt.legend(loc='lower right', shadow=True,numpoints=1)
plt.title('%s'%elem+roman[i])
plt.xlim(min(U),max(U))
plt.ylim(min(N)-1.,max(N)+1.)
plt.show()
def plot_TZ(T,Z,bounds=False):
"""
plot b versus ionization parameter U
parameters:
-----------
elem: elem name
N: log N. should be list of lists like: [HI:[...],HII:[...],H2:[...]]
U: log U. list of lists like N
"""
plt.plot(T,Z,'ko')
plt.xlabel(r'log Temperature (K)')
plt.ylabel(r'log Z (solar units)')
plt.minorticks_on()
plt.show()
plt.close()
def plot_nhU(nh,U,bounds=False):
"""
plot b versus ionization parameter U
parameters:
-----------
elem: elem name
N: log N. should be list of lists like: [HI:[...],HII:[...],H2:[...]]
U: log U. list of lists like N
"""
plt.plot(nh,U,'ko')
plt.xlabel(r'log $n_{H}$')
plt.ylabel(r'log U')
plt.minorticks_on()
plt.show()
plt.close()
def plot_NZ(elem,Z,N, hcol,bounds=False):
"""
plot N versus T
parameters:
-----------
elem: elem name
N: log N. should be list of lists like: [HI:[...],HII:[...],H2:[...]]
T: log T. list of lists like N
"""
for i in to_plot[elem]:
plt.clf()
x = np.array(Z,dtype=np.float)
y = np.array(N[i])
#x,y,hcol = trim(x,y,hcol)
y = hcol[0] - y #NHI-NX
xlims=[0.75*np.amin(x), 1.25*np.amax(x)]
ylims=[0.75*np.amin(y), 1.25*np.amax(y)]
try:
if bounds:
l = minNHI - observed[elem][i]["column"][2]
if observed[elem][i]["column"][0]==-30.:
u=maxNHI
else:
u = maxNHI - observed[elem][i]["column"][0]
plt.fill([-30.,30., 30., -30.], [l,l,u,u], '0.50', alpha=0.2, edgecolor='b')
#plt.fill_between(np.arange(xlims[0],xlims[1]),lower,upper,color='0.50')
except KeyError:
pass
plt.plot(x, y, color_map[i],label=ion_state(i,elem))
plt.minorticks_on()
#plt.xlim(xlims)
#plt.ylim(ylims)
plt.ylabel(r"log $N_{HI}/N_{%s}$"%(elem+roman[i]))
plt.xlabel(r"log Z (solar)")
makedir('Z')
f=os.path.join(paths["plot_path"], "Z",elem+roman[i]+"NZ.png")
plt.show()
plt.savefig(f)
plt.close()
def plot_N(elem,data,trans=0,bounds=True):
"""plot logNX versus [X/H]
elem: string. elem name
N:
hcol
trans
bounds
"""
plt.clf()
trans=int(trans)
y = np.array( [ item.data[elem][trans]["column"][1] for item in data ] )
xbounds=[] #i-indices of datapts within bounds
n=[] #number density of elem X
for i in range(0,y.shape[0]):
#j = transition
num=0.
for j in range(0,ions[elem]): #note that for most temperature ranges, ionmap may cut off very high ionizations. not important for my range of interest.
if j in data[i].data[elem].keys():
"""if j in observed[elem].keys() and j==trans:
try:
if observed[elem][j]["column"][0]<= data[i].data[elem][j]["column"][1] <=observed[elem][j]["column"][2]:
xbounds.append(i)
except:
print(observed[elem][j].keys())
print(data[i].data[elem][j].keys())
raise
"""
num+=10.**data[i].data[elem][j]["column"][1]
else:
break
n.append(np.log10(num))
nH = np.array(
[np.log10(10.**item.data["H"][0]["column"][1] +\
10.**item.data["H"][1]["column"][1] +\
10.**item.data["H"][2]["column"][1]) for item in data]
) #get total column for HI, HI and H2
x = np.array(np.array(n) - nH -(solar[elem] - solar['H']),dtype=np.float)
assert(x.shape[0]==y.shape[0]>2)
xlims=[np.amin(x), np.amax(x)]
ylims=[np.amin(y), np.amax(y)]
if bounds:
try:
ly = observed[elem][trans]["column"][2]
uy = observed[elem][trans]["column"][0]
#lx= min(list(x[xbounds]))
#ux= max(list(x[xbounds]))
lx= min(xlims)
ux= max(xlims)
plt.fill([lx,ux,ux,lx], [ly,ly,uy,uy], '0.50', alpha=0.2, edgecolor='b')
except:
pass
plt.plot(x, y, 'ko')
plt.minorticks_on()
#plt.xlim(xlims)
#plt.ylim(ylims)
#4.07E-4, -3.39
plt.ylabel(r"$log(N_{"+elem+roman[trans]+r"})$")
plt.xlabel(r"$["+elem+r"/H]$")
f=os.path.join(paths["plot_path"], "N"+elem+roman[trans]+"_NH.png")
plt.show()
plt.savefig(f)
plt.close()
def plot_NHIH(data,bounds=True):
"""plot logNHI versus [HI/H]
elem: string. elem name
N:
hcol
trans
bounds
"""
plt.clf()
trans=0
y = np.array( [ item.data["H"][0]["column"][1] for item in data ] )
xbounds=[] #i-indices of datapts within bounds
n=[] #number density of elem X
nH = np.array( [np.log10(10.**item.data["H"][0]["column"][1] + \
10.**item.data["H"][1]["column"][1] + \
10.**item.data["H"][2]["column"][1]) for item in data]) #get total column for HI, HI and H2
nHI = np.array( [item.data["H"][0]["column"][1] for item in data])
x = np.array(( (y-nH)-solarHIH),dtype=np.float)
assert(x.shape[0]==y.shape[0]>2)
xlims=[np.amin(x), np.amax(x)]
ylims=[np.amin(y), np.amax(y)]
if bounds:
ly = observed["H"][0]["column"][2]
uy = observed["H"][0]["column"][0]
#lx= min(list(x[xbounds]))
#ux= max(list(x[xbounds]))
lx= min(xlims)
ux= max(xlims)
plt.fill([lx,ux,ux,lx], [ly,ly,uy,uy], '0.50', alpha=0.2, edgecolor='b')
plt.plot(x, y, 'ko')
plt.minorticks_on()
##plt.xlim(xlims)
##plt.ylim(ylims)
plt.ylabel(r"log($N_{HI}$)")
plt.xlabel(r"[HI/H]")
f=os.path.join(paths["plot_path"], "N_HI_NH.png")
plt.savefig(f)
plt.show()
plt.close()
def plot_Nhden(elem,N,hcol,hden,bounds=False):
for i in to_plot[elem]:
plt.clf()
x = np.array(hden,dtype=np.float)
y = np.array(N[i])
#x,y,hcol = trim(x,y,hcol)
y = hcol[0] - y
xlims=[0.75*np.amin(x), 1.25*np.amax(x)]
ylims=[0.75*np.amin(y), 1.25*np.amax(y)]
try:
if bounds:
l = minNHI - observed[elem][i]["column"][2]
if observed[elem][i]["column"][0]==-30.:
u=maxNHI
else:
u = maxNHI - observed[elem][i]["column"][0]
plt.fill([-30.,30., 30., -30.], [l,l,u,u], '0.50', alpha=0.2, edgecolor='b')
#plt.fill_between(np.arange(xlims[0],xlims[1]),lower,upper,color='0.50')
except KeyError:
pass
plt.plot(x, y, color_map[i],label=ion_state(i,elem))
plt.ylabel(r"log $N_{HI}/N_{%s}$"%(str(elem)+str(roman[i])))
plt.xlabel("log $n_{H}$")
plt.minorticks_on()
makedir('hden')
f=os.path.join(paths["plot_path"],"hden", elem+roman[i]+"N_Nhden.png")
plt.xlim([-3.,0.])
#plt.ylim(ylims)
plt.savefig(f)
plt.show()
plt.close()