def BuildDespoticInterpolator():
gmc = cloud(fileName="MilkyWayGMC.desp", verbose=True)
# gmc.setTempEq(verbose=True)
lines = gmc.lineLum("co")
T = np.linspace(10,20,5)
logn = np.linspace(1,5,10)
co10=np.zeros((T.size, logn.size))
co21=np.zeros((T.size, logn.size))
co32=np.zeros((T.size, logn.size))
for j, density in enumerate(logn):
for i,temp in enumerate(T):
gmc.Tg=temp
gmc.nH=1e1**density
lines = gmc.lineLum("co")
co10[i,j]=lines[0]["intTB"]
co21[i,j]=lines[1]["intTB"]
co32[i,j]=lines[2]["intTB"]
fx = scipy.interpolate.interp1d(T,np.arange(len(T)))
fy = scipy.interpolate.interp1d(logn,np.arange(len(logn)))
def DespoticCO10(Tvals,n):
intensity = mc(co10, [[fx(Tvals)],[fy(n)]])
return(intensity)
def DespoticCO21(Tvals,n):
intensity = mc(co21, [[fx(Tvals)],[fy(n)]])
return(intensity)
def DespoticCO32(Tvals,n):
intensity = mc(co32, [[fx(Tvals)],[fy(n)]])
return(intensity)
return(DespoticCO10,DespoticCO21,DespoticCO32)
def tkin_all(density, sigma, lengthscale, gradient, tdust, crir=1e-17*u.s**-1,
ISRF=1, tdust_rad=None, turbulence=True, gmc=gmc, reload_gmc=True,
chemistry=False):
assert density.unit.is_equivalent(u.cm**-3)
assert sigma.unit.is_equivalent(u.km/u.s)
assert lengthscale.unit.is_equivalent(u.pc)
assert gradient.unit.is_equivalent(u.km/u.s/u.pc)
assert crir.unit.is_equivalent(1/u.s)
if reload_gmc:
gmc=cloud('cloud.desp')
gmc.sigmaNT = sigma.to(u.cm/u.s).value
gmc.Td = tdust.to(u.K).value
gmc.rad.TradDust = gmc.Td if tdust_rad is None else tdust_rad.to(u.K).value
gmc.dVdr = gradient.to(u.s**-1).value
gmc.rad.chi = ISRF
# These are both per hydrogen, but we want to specify per particle, and
# we're assuming the particles are H2
gmc.rad.ionRate = crir.to(u.s**-1).value * 2
gmc.nH = density.to(u.cm**-3).value * 2
turb_heating = turb_heating_generator(lengthscale, turbulence=turbulence)
gmc.setTempEq(escapeProbGeom='LVG', PsiUser=turb_heating)
#energy_balance = gmc.dEdt()
if chemistry:
gmc.setChemEq(network=NL99)
gmc.setTempEq(escapeProbGeom='LVG', PsiUser=turb_heating)
return gmc.Tg
def tkin_all(density,
sigma,
lengthscale,
gradient,
tdust,
crir=1e-17 * u.s**-1,
ISRF=1,
tdust_rad=None,
turbulence=True,
gmc=gmc,
reload_gmc=True,
chemistry=False):
assert density.unit.is_equivalent(u.cm**-3)
assert sigma.unit.is_equivalent(u.km / u.s)
assert lengthscale.unit.is_equivalent(u.pc)
assert gradient.unit.is_equivalent(u.km / u.s / u.pc)
assert crir.unit.is_equivalent(1 / u.s)
if reload_gmc:
gmc = cloud('cloud.desp')
gmc.sigmaNT = sigma.to(u.cm / u.s).value
gmc.Td = tdust.to(u.K).value
gmc.rad.TradDust = gmc.Td if tdust_rad is None else tdust_rad.to(u.K).value
gmc.dVdr = gradient.to(u.s**-1).value
gmc.rad.chi = ISRF
# These are both per hydrogen, but we want to specify per particle, and
# we're assuming the particles are H2
gmc.rad.ionRate = crir.to(u.s**-1).value * 2
gmc.nH = density.to(u.cm**-3).value * 2
turb_heating = turb_heating_generator(lengthscale, turbulence=turbulence)
try:
gmc.setTempEq(escapeProbGeom='LVG', PsiUser=turb_heating)
except despotic.despoticError as ex:
print(ex)
return np.nan
if chemistry:
gmc.setChemEq(network=NL99)
gmc.setTempEq(escapeProbGeom='LVG', PsiUser=turb_heating)
return gmc.Tg
try:
fp = open('shockCool{:03d}.pkl'.format(i), 'rb')
stateList.append(pickle.load(fp))
fp.close()
restart=True
except IOError:
break
# Did we find any existing data?
if restart:
# Yes: copy last state to slab object to initialize it
slab = deepcopy(stateList[-1])
else:
# No
# Read the cloud initialization file
slab=cloud(fileName='cloudfiles/postShockSlab.desp', \
verbose=verbose)
# Compute initial level populations and dust temperature
slab.dEdt(overrideSkip=True, dampFactor=dampFactor, \
verbose=verbose)
slab.setDustTempEq(verbose=verbose)
# Save initial state, both locally and to disk
stateList.append(deepcopy(slab))
fp = open('shockCool000.pkl', 'wb')
pickle.dump(slab, fp)
fp.close()
# Calculate time evolution, starting at proper position
istart = len(stateList) - 1
extime = timedelta(0)
for i, t in enumerate(times[istart:-1]):
def __init__(self,
collider_densities={'ph2':990,'oh2':10},
temperature=30,
species='co',
datapath=None,
hcolumn=1e21,
abundance=1e-5,
#column=1e13,
tbackground=2.7315,
deltav=1.0,
escapeProbGeom='lvg',
outfile='radex.out',
logfile='radex.log',
debug=False,
):
"""
Interface to DESPOTIC
Parameters
----------
collider_densities: dict
Dictionary giving the volume densities of the collider(s) in units of
cm^-3. Valid entries are h2,oh2,ph2,e,He,H,H+. The keys are
case-insensitive.
temperature: float
Local gas temperature in K
species: str
A string specifying a valid chemical species. This is used to look
up the specified molecule
hcolumn: float
The total column density of hydrogen.
abundance: float
The molecule's abundance relative to H (NOT H2 as is normally done!).
tbackground: float
Background radiation temperature (e.g., CMB)
deltav: float
The FWHM line width (really, the single-zone velocity width to
scale the column density by: this is most sensibly interpreted as a
velocity gradient (dv/dR))
sigmaNT: float
Nonthermal velocity dispersion
(this is strictly ignored - deltav IS sigmant)
datapath: str
Path to the molecular data files
"""
import despotic
self.cloud = despotic.cloud()
self.cloud.nH = float(np.sum([collider_densities[k]*2 if 'h2' in k.lower()
else collider_densities[k]
for k in collider_densities]))
for k in collider_densities.keys():
collider_densities[k.lower()] = collider_densities[k]
if 'ph2' in collider_densities:
self.cloud.comp.xpH2 = collider_densities['ph2'] / self.cloud.nH
if 'oh2' in collider_densities:
self.cloud.comp.xoH2 = collider_densities['oh2'] / self.cloud.nH
self.cloud.Td = uvalue(temperature,u.K)
self.cloud.Tg = uvalue(temperature,u.K)
self.cloud.dust.sigma10 = 0.0
self.cloud.colDen = uvalue(hcolumn,u.cm**-2)
if uvalue(tbackground,u.K) > 2.7315:
self.cloud.rad.TradDust = uvalue(tbackground,u.K)
self.species = species
if datapath is None:
emitterFile = species+'.dat'
else:
emitterFile = os.path.expanduser(os.path.join(datapath, species+'.dat'))
self.cloud.addEmitter(species, abundance, emitterFile=emitterFile)
self.cloud.comp.computeDerived(self.cloud.nH)
self.escapeProbGeom = escapeProbGeom
self.deltav = deltav
# Import the despotic library
from despotic import cloud
from despotic import emitter
# Read the Milky Way GMC cloud file
gmc = cloud(fileName='cloudfiles/MilkyWayGMC.desp')
import numpy as np
import pylab as pl
gmc.sigmaNT = 1e5 # cm/s, instead of 2 as default
gmc.Tg = 20 # start at 20 instead of 15 K
gmc.Td = 20
# add ortho-h2co
gmc.addEmitter('o-h2co_troscompt', 1e-9)
# first plot: versus density
densities = np.logspace(1,6)
gmc.colDen = 5e21 # use a moderately high column, but not as high as the default
tau11 = np.empty(densities.shape)
tau22 = np.empty(densities.shape)
for ii in xrange(tau11.size):
gmc.nH = densities[ii]
line = gmc.lineLum('o-h2co_troscompt')
tau11[ii] = line[0]['tau']
tau22[ii] = line[2]['tau']
pl.rc('font',size=20)
########################################################################
# Program code
########################################################################
# Import the despotic library
from despotic import cloud
# Import standard python libraries
from numpy import *
from matplotlib.pyplot import *
from datetime import datetime
from datetime import timedelta
# Read the Milky Way GMC cloud file
gmc = cloud(fileName='cloudfiles/MilkyWayGMC.desp')
# Read the ULIRG cloud file
ulirg = cloud(fileName='cloudfiles/ULIRG.desp')
# Compute the luminosity of the CO lines in both clouds
t1=datetime.now()
gmclines = gmc.lineLum('co')
ulirglines = ulirg.lineLum('co')
gmclines13 = gmc.lineLum('13co')
ulirglines13 = ulirg.lineLum('13co')
t2=datetime.now()
print('Execution time = '+str(t2-t1))
# Print out the CO X factor for both clouds. This is column density
# divided by velocity-integrated brightness temperature.
# Import the despotic library
from despotic import cloud
from despotic import emitter
# Read the Milky Way GMC cloud file
gmc = cloud(fileName='cloudfiles/MilkyWayGMC.desp')
import numpy as np
import pylab as pl
gmc.sigmaNT = 1e5 # cm/s, instead of 2 as default
gmc.Tg = 20 # start at 20 instead of 15 K
gmc.Td = 20
# add ortho-h2co
gmc.addEmitter('o-h2co_troscompt', 1e-9)
# first plot: versus density
densities = np.logspace(1, 6)
gmc.colDen = 5e21 # use a moderately high column, but not as high as the default
tau11 = np.empty(densities.shape)
tau22 = np.empty(densities.shape)
for ii in xrange(tau11.size):
gmc.nH = densities[ii]
line = gmc.lineLum('o-h2co_troscompt')
tau11[ii] = line[0]['tau']
tau22[ii] = line[2]['tau']
pl.rc('font', size=20)
def build_despotic_grids(gridfile='ph2co_grid_despotic.fits', ph2coAbund=1e-8,
nDens=21, logDensLower=2.0, logDensUpper=6.0,
nCol=21, logColLower=11.0, logColUpper=15.0,
nTemp=51, Tlower=10.0, Tupper=300.0,
nDv=5, DvLower=1.0, DvUpper=5.0):
"""
Generates grids of p-H2CO line intensities using Despotic. Outputs a astropy Table.
Parameters
----------
gridfile : string
Name of grid file to output.
ph2coAbund : float
Fractional abundance of p-H2CO
nDens : int
Number of grid points in the volume density
logDensLower : float
log of volume density at lower bound of grid (log(n/cm**-3))
logDensUpper : float
log of volume density at upper bound of grid (log(n/cm**-3))
nCol : int
Number of grid points in the column density
logColLower : float
log of column density of p-H2CO at lower bound of grid (log(N/cm**-2))
logColUpper : float
log of column density of p-H2CO at upper bound of grid (log(N/cm**-2))
nTemp : int
Number of grid points in the temperature grid
Tower : float
temperature at lower bound of grid (K)
Tupper : float
temperature at upper bound of grid (K)
nDv : int
Number of grid points in the line width
DvLower : float
line width (non-thermal) at lower bound of grid (km/s)
DvUpper : float
line width (non-thermal) at upper bound of grid (km/s)
"""
if Democracy:
raise Exception("No despotic install found. Cannot build grids")
core = cloud(fileName="protostellarCore.desp", verbose=True)
nlower = logDensLower
nupper = logDensUpper
Nlower = logColLower
Nupper = logColUpper
Temps = np.linspace(Tlower, Tupper, nTemp)
Cols = 1e1**np.linspace(Nlower, Nupper, nCol)
Densities = 1e1**(np.linspace(nlower, nupper, nDens))
LineWidth = np.linspace(DvLower, DvUpper, nDv)
outtable = Table(names = ['Tex_303_202', 'Tex_322_221', 'Tex_321_220',
'tau_303_202', 'tau_322_221', 'tau_321_220',
'Temperature', 'Column', 'nH2', 'sigmaNT'])
TempArr, ColArr, DensArr, DvArr = np.meshgrid(Temps,
Cols,
Densities,
LineWidth)
for T, N, n, dv in ProgressBar(zip(TempArr.flatten(),
ColArr.flatten(),
DensArr.flatten(),
DvArr.flatten())):
core.colDen = N/ph2coAbund
core.Tg = T
core.Td = T
core.nH = n
core.sigmaNT = dv
lines = core.lineLum('p-h2co')
outtable.add_row()
outtable[-1]['Tex_303_202'] = lines[2]['Tex']
outtable[-1]['tau_303_202'] = lines[2]['tau']
outtable[-1]['Tex_322_221'] = lines[9]['Tex']
outtable[-1]['tau_322_221'] = lines[9]['tau']
outtable[-1]['Tex_321_220'] = lines[12]['Tex']
outtable[-1]['tau_321_220'] = lines[12]['tau']
outtable[-1]['Temperature'] = T
outtable[-1]['Column'] = N
outtable[-1]['nH2'] = n
outtable[-1]['sigmaNT'] = dv
outtable.write(gridfile, format='fits',overwrite=True)
from despotic import cloud import numpy import pylab specStr = 'CO' mycloud = cloud() mycloud.nH = 1.0e5 #gas density mycloud.colDen = 2.0e22 #cloud column density mycloud.sigmaNT = 4.0e5 #non-thermal velocity despersion mycloud.comp.xoH2 = 0.1 #ortho-H2 composition, xoH2 molecule per H nucleus mycloud.comp.xpH2 = 0.4 #para-H2 composition, xpH2 molecule per H nucleus mycloud.Tg = None #cloud gas kinetic temperature mycloud.Td = 0.0 #cloud dust temperature mycloud.addEmitter(specStr, 1.0e-7) #abudnace of the emitting species per H nucleus mycloud.Tg = 40.0 lines1 = mycloud.lineLum(specStr) mycloud.Tg = 400.0 lines2 = mycloud.lineLum(specStr) u1 = [l['upper'] for l in lines1] inten1 = numpy.array([l['intIntensity'] for l in lines1]) #intensity after subtracting the CMB contributuin u2 = [l['upper'] for l in lines2] inten2 = numpy.array([l['intIntensity'] for l in lines2])
def build_despotic_grids(gridfile='ph2co_grid_despotic.fits', ph2coAbund=1e-8,
nDens=21, logDensLower=2.0, logDensUpper=6.0,
nCol=21, logColLower=11.0, logColUpper=15.0,
nTemp=51, Tlower=10.0, Tupper=300.0,
nDv=5, DvLower=1.0, DvUpper=5.0):
"""
Generates grids of p-H2CO line intensities using Despotic. Outputs a astropy Table.
Parameters
----------
gridfile : string
Name of grid file to output.
ph2coAbund : float
Fractional abundance of p-H2CO
nDens : int
Number of grid points in the volume density
logDensLower : float
log of volume density at lower bound of grid (log(n/cm**-3))
logDensUpper : float
log of volume density at upper bound of grid (log(n/cm**-3))
nCol : int
Number of grid points in the column density
logColLower : float
log of column density of p-H2CO at lower bound of grid (log(N/cm**-2))
logColUpper : float
log of column density of p-H2CO at upper bound of grid (log(N/cm**-2))
nTemp : int
Number of grid points in the temperature grid
Tower : float
temperature at lower bound of grid (K)
Tupper : float
temperature at upper bound of grid (K)
nDv : int
Number of grid points in the line width
DvLower : float
line width (non-thermal) at lower bound of grid (km/s)
DvUpper : float
line width (non-thermal) at upper bound of grid (km/s)
"""
if Democracy:
raise Exception("No despotic install found. Cannot build grids")
core = cloud(fileName="protostellarCore.desp", verbose=True)
nlower = logDensLower
nupper = logDensUpper
Nlower = logColLower
Nupper = logColUpper
Temps = np.linspace(Tlower, Tupper, nTemp)
Cols = 1e1**np.linspace(Nlower, Nupper, nCol)
Densities = 1e1**(np.linspace(nlower, nupper, nDens))
LineWidth = np.linspace(DvLower, DvUpper, nDv)
outtable = Table(names = ['Tex_303_202', 'Tex_322_221', 'Tex_321_220',
'tau_303_202', 'tau_322_221', 'tau_321_220',
'Temperature', 'Column', 'nH2', 'sigmaNT'])
TempArr, ColArr, DensArr, DvArr = np.meshgrid(Temps,
Cols,
Densities,
LineWidth)
for T, N, n, dv in ProgressBar(zip(TempArr.flatten(),
ColArr.flatten(),
DensArr.flatten(),
DvArr.flatten())):
core.colDen = N/ph2coAbund
core.Tg = T
core.Td = T
core.nH = n
core.sigmaNT = dv
lines = core.lineLum('p-h2co')
outtable.add_row()
outtable[-1]['Tex_303_202'] = lines[2]['Tex']
outtable[-1]['tau_303_202'] = lines[2]['tau']
outtable[-1]['Tex_322_221'] = lines[9]['Tex']
outtable[-1]['tau_322_221'] = lines[9]['tau']
outtable[-1]['Tex_321_220'] = lines[12]['Tex']
outtable[-1]['tau_321_220'] = lines[12]['tau']
outtable[-1]['Temperature'] = T
outtable[-1]['Column'] = N
outtable[-1]['nH2'] = n
outtable[-1]['sigmaNT'] = dv
outtable.write(gridfile, format='fits',overwrite=True)
def __init__(self,
emitter_abundances,
emitter_lines,
dens_func,
ps,
Reff=1.,
sigmaNT=2.0e5,
Tg=10.,
xoH2=0.1,
xpH2=0.4,
xHe=0.1,
min_col=19,
max_col=24,
steps=11):
""" __init__(emitter_abundances, emitter_lines, dens_func, ps,
Reff=None, sigmaNT=2.0e5, Tg=10., xoH2=0.1, xpH2=0.4,
xHe=0.1, min_col=19, max_col=24, steps=11)
Initialise a CoGObj
Attributes
----------
emitter_abundances : dict
A dictionary whose keys are species name strings and whose
values are relative abundances
emitter_lines : dict
A dictionary whose keys are species name strings and whose
values are lists of lines needed (ordered by freq for each
species.
dens_func : DensityFunc or derived
The mean density function of the cloud
ps : IsmPowerspec or derived
The power spectrum within the cloud.
Reff : float
The effective radius of the cloud used when estimating
escape probabilities. Default is None, which implies
size set by column density and nH.
sigmaNT : float
Non-thermal velocity dispersion
Tg : float
Gas temperature in Kelvin
xoH2 : float
relative abundance of ortho-H2
xpH2 : float
relative abundance of para-H2
xHe : float
relative abundance of He
min_col : float
The minimum log10 of the column density of H nuclei in cm^-2
to be used
max_col : float
The maximum log10 of the column density of H nuclei in cm^-2
to be used
steps : int
The number of steps used when finding the CoG
"""
# setup cloud
self.cloud = dp.cloud()
# check dens_func and ps types
if not isinstance(dens_func, density.UniformDensityFunc):
raise NotImplementedError("Currently only implemented with "
"UniformDensityFunc density function "
"instances")
if not isinstance(ps, powerspec.SM14Powerspec):
raise NotImplementedError("Currently only implemented with"
"SM14Powerspec power-spectrum instances")
self.dens_func = dens_func
self.ps = ps
self.cloud.sigmaNT = sigmaNT
self.cloud.Tg = Tg
self.cloud.comp.xoH2 = xoH2
self.cloud.comp.xpH2 = xpH2
self.cloud.comp.xHe = xHe
# Set some derived cloud params
self.cloud.nH = self.dens_func.dens_0
self.depth = self.dens_func.half_width * 2.
self.cloud.colDen = self.dens_func.integral() * parsec
if Reff is not None:
self.cloud.Reff = Reff
else:
self.cloud.Reff = self.depth
var_R = ps.outer_integral(1. / self.cloud.Reff)
self.cloud.cfac = var_R / pow(self.cloud.nH, 2) + 1.
# add emitters
for emitter in emitter_abundances:
self.cloud.addEmitter(emitter, emitter_abundances[emitter])
# set up dicts and arrays needed
cols = np.linspace(min_col, max_col, steps)
TB_dict = {}
emitter_trans = {}
for emitter in emitter_lines:
TB_dict[emitter] = {}
emitter_trans[emitter] = [
np.array(emitter_lines[emitter]) + 1,
np.array(emitter_lines[emitter])
]
for line in emitter_lines[emitter]:
TB_dict[emitter][line] = np.zeros(steps)
# Find values
for i, col in enumerate(cols):
self.cloud.colDen = math.pow(10, col)
self.cloud.nH = self.cloud.colDen / (self.depth * parsec)
for emitter in emitter_lines:
lines_dicts = self.cloud.lineLum(
emitter, kt07=True, transition=emitter_trans[emitter])
for line in emitter_lines[emitter]:
TB_dict[emitter][line][i] = (lines_dicts[
emitter_lines[emitter].index(line)]["intTB"])
# Fit splines
self.splines = {}
for emitter in emitter_lines:
self.splines[emitter] = {}
for line in emitter_lines[emitter]:
self.splines[emitter][line] = (InterpolatedUnivariateSpline(
cols, TB_dict[emitter][line]))
# plots graphical representations of the matrices at various points in
# the procedues DESPOTIC uses to render them calculable.
#
########################################################################
# Import libraries
from despotic import cloud
import numpy as np
import matplotlib.pyplot as plt
########################################################################
# Program code
########################################################################
# Construct a test cloud
cloud = cloud()
# Assign density, gas temperature, abundances; they're all we need for
# this test. Just use pure para-H2 for simplicity.
cloud.nH = 1e3
cloud.comp.xpH2 = 0.5
cloud.Tg = 10.0
# Add three exmaple emitters; abundance values don't matter for this
# example, so just set them to 1
cloud.addEmitter('co', 1.0)
cloud.addEmitter('c+', 1.0, extrap=True, emitterURL='*****@*****.**')
cloud.addEmitter('o-nh3', 1.0, extrap=True)
# Compute level populations for optically thin cloud with no clumping;
# get back the dict containing diagnostic information
# Import the despotic library from despotic import cloud # Import standard python libraries from numpy import * from matplotlib.pyplot import * from datetime import datetime from datetime import timedelta #from despotic.chemistry import NL99 from despotic.chemistry import NL99_GC ### # Read the Test Cloud file #testcloud = cloud(fileName='../cloudfiles/MilkyWayGMC.desp') testcloud = cloud(fileName='../cloudfiles/testcloud.desp') #if want interactive mode: #import code #code.interact(local=locals()) #from despotic.chemistry import abundanceDict # Lower the CR ionization rate so that a fully CO composition becomes # possible testcloud.rad.ionRate = 2e-17 # Raise the temperature to 20 K testcloud.Tg = 20.0
def __init__(
self,
collider_densities={
'ph2': 990,
'oh2': 10
},
temperature=30,
species='co',
datapath=None,
hcolumn=1e21,
abundance=1e-5,
#column=1e13,
tbackground=2.7315,
deltav=1.0,
escapeProbGeom='lvg',
outfile='radex.out',
logfile='radex.log',
debug=False,
):
"""
Interface to DESPOTIC
Parameters
----------
collider_densities: dict
Dictionary giving the volume densities of the collider(s) in units of
cm^-3. Valid entries are h2,oh2,ph2,e,He,H,H+. The keys are
case-insensitive.
temperature: float
Local gas temperature in K
species: str
A string specifying a valid chemical species. This is used to look
up the specified molecule
hcolumn: float
The total column density of hydrogen.
abundance: float
The molecule's abundance relative to H (NOT H2 as is normally done!).
tbackground: float
Background radiation temperature (e.g., CMB)
deltav: float
The FWHM line width (really, the single-zone velocity width to
scale the column density by: this is most sensibly interpreted as a
velocity gradient (dv/dR))
sigmaNT: float
Nonthermal velocity dispersion
(this is strictly ignored - deltav IS sigmant)
datapath: str
Path to the molecular data files
"""
import despotic
self.cloud = despotic.cloud()
self.cloud.nH = float(
np.sum([
collider_densities[k] *
2 if 'h2' in k.lower() else collider_densities[k]
for k in collider_densities
]))
for k in collider_densities.keys():
collider_densities[k.lower()] = collider_densities[k]
if 'ph2' in collider_densities:
self.cloud.comp.xpH2 = collider_densities['ph2'] / self.cloud.nH
if 'oh2' in collider_densities:
self.cloud.comp.xoH2 = collider_densities['oh2'] / self.cloud.nH
self.cloud.Td = uvalue(temperature, u.K)
self.cloud.Tg = uvalue(temperature, u.K)
self.cloud.dust.sigma10 = 0.0
self.cloud.colDen = uvalue(hcolumn, u.cm**-2)
if uvalue(tbackground, u.K) > 2.7315:
self.cloud.rad.TradDust = uvalue(tbackground, u.K)
self.species = species
if datapath is None:
emitterFile = species + '.dat'
else:
emitterFile = os.path.expanduser(
os.path.join(datapath, species + '.dat'))
self.cloud.addEmitter(species, abundance, emitterFile=emitterFile)
self.cloud.comp.computeDerived(self.cloud.nH)
self.escapeProbGeom = escapeProbGeom
self.deltav = deltav
def despotify(pcube, vcube, vgrid, voxel_size=3.08e18, species='o-h2co',
cloud=None, cloudfile='MilkyWayGMC.desp', cloudfile_path=None,
output_linenumbers=[0,2],
output_properties=['tau','Tex','intTB']):
"""
Turn a simulated ppp cube into a ppv cube using despotic for the radiative
transfer
Note that it is "despot-ify", not "de-spotify".
Parameters
----------
pcube : np.ndarray
3-dimensional array containing values with units of density in n(H2) cm^-3
vcube : np.ndarray
3-dimensional array containing Z-velocity values, i.e. the velocity
should be in the direction of the 0'th axis (because python arrays are
inverted). Expected unit is km/s, but it doesn't matter as long as the
velocity units match the vgrid units
vgrid : np.ndarray
1-dimensional array containing the output velocity grid. Must have
same units as vcube.
voxel_size : float
1-dimensional size of a voxel in cm. Used to convert from density to
column
species : str
A string identifying the LAMDA species name, e.g. 'o-h2co', 'co', etc.
cloud : None or despotic.cloud
Can pass in a despotic cloud instance that will be modified by the
specified cube density. Otherwise, will be read from file.
cloudfile : str
The filename specifying the default cloud file to use
cloudfile_path : str or None
If none, defaults to despotic.__path__/cloudfiles/
output_linenumbers : iterable
A list of integer indices for which line numbers should be output as
cubes
output_properties : iterable
A list of strings identifying the line properties to output as cubes
Returns
-------
A data cube of dimensions [velocity,position,position] for each line in
output_linenumbers for each property in output_properties
"""
if pcube.shape != vcube.shape:
raise ValueError('Cube Size mismatch: {0},{1}'.format(str(pcube.shape),
str(vcube.shape)))
if vgrid.ndim > 1:
raise ValueError('Velocity grid must be 1-dimensional')
imshape = pcube.shape[1:]
outcubeshape = (vgrid.size,) + imshape
nelts = vgrid.size
vinds = np.empty(vcube.shape, dtype='int64')
# not needed
# volume_spectra = np.empty(outcubeshape)
# dens_spectra = np.empty(outcubeshape)
for jj,kk in np.ndindex(imshape):
vinds[:,jj,kk] = np.digitize(vcube[:,jj,kk], vgrid)
# volume_spectra[:,jj,kk] = np.bincount(vinds[:,jj,kk], minlength=nelts)
# dens_spectra[:,jj,kk] = np.bincount(vinds[:,jj,kk],
# weights=pcube[:,jj,kk],
# minlength=nelts)
cloudfile_path = cloudfile_path or despotic.__path__[0]+"/cloudfiles/"
if cloud is None:
cloud = despotic.cloud(fileName="{0}/{1}".format(cloudfile_path,
cloudfile))
try:
from progressbar import ProgressBar,Percentage,Bar
from progressbar import AdaptiveETA as ETA
except ImportError:
from progressbar import ProgressBar,Percentage,Bar
from progressbar import ETA
pb = ProgressBar(widgets=[Percentage(), ETA(), Bar()],
maxval=pcube.size).start()
# property cubes prior to gridding have same shape as input cubes
# use dict() instead of {} for python2.6 compatibility
prop_cubes = dict([
("{0}{1}".format(pr,ln), np.empty(pcube.shape))
for ln,pr in itertools.product(output_linenumbers, output_properties)])
for (zi,yi,xi),nH in np.ndenumerate(pcube):
cloud.nH = pcube[zi,yi,xi]
cloud.colDen = cloud.nH * voxel_size
line = cloud.lineLum(species)
for ln,pr in itertools.product(output_linenumbers,
output_properties):
key = "{0}{1}".format(pr,ln)
prop_cubes[key][zi,yi,xi] = line[ln][pr]
pb.update(pb.currval+1)
pb.finish()
# spectral cubes have outcubeshape
spectra_cubes = {}
spectra_cubes = dict([
("{0}{1}".format(pr,ln), np.empty(outcubeshape))
for ln,pr in itertools.product(output_linenumbers, output_properties)])
for key in prop_cubes:
for jj,kk in itertools.product(*map(xrange,imshape)):
spectra_cubes[key][:,jj,kk] = \
np.bincount(vinds[:,jj,kk],
weights=prop_cubes[key][:,jj,kk],
minlength=nelts)
return spectra_cubes,prop_cubes
# Program code
########################################################################
# Constants
import scipy.constants as physcons
kB = physcons.k / physcons.erg
mH = physcons.m_p / physcons.gram
G = physcons.G * 1e3
# Only recompute if we haven't already done the computation
try:
gmc
except NameError:
# Use the Milky Way GMC file as a base, but add C+ and O as emitting species
gmc = cloud('../cloudfiles/MilkyWayGMC.desp')
gmc.addEmitter('c+', 1e-10)
gmc.addEmitter('o', 1e-4)
# Set CR ionization rate
gmc.rad.ionRate = 3e-17
# Set IR temp to 10 K
gmc.rad.TradDust = 10.0
gmc.Td = 10.0
# Set column density to 10^22 cm^-2
gmc.colDen = 1.0e22
# Set initial temperature guess to 20 K
gmc.Tg = 20.0
def despotify(pcube,
vcube,
vgrid,
voxel_size=3.08e18,
species='o-h2co',
cloud=None,
cloudfile='MilkyWayGMC.desp',
cloudfile_path=None,
output_linenumbers=[0, 2],
output_properties=['tau', 'Tex', 'intTB']):
"""
Turn a simulated ppp cube into a ppv cube using despotic for the radiative
transfer
Note that it is "despot-ify", not "de-spotify".
Parameters
----------
pcube : np.ndarray
3-dimensional array containing values with units of density in n(H2) cm^-3
vcube : np.ndarray
3-dimensional array containing Z-velocity values, i.e. the velocity
should be in the direction of the 0'th axis (because python arrays are
inverted). Expected unit is km/s, but it doesn't matter as long as the
velocity units match the vgrid units
vgrid : np.ndarray
1-dimensional array containing the output velocity grid. Must have
same units as vcube.
voxel_size : float
1-dimensional size of a voxel in cm. Used to convert from density to
column
species : str
A string identifying the LAMDA species name, e.g. 'o-h2co', 'co', etc.
cloud : None or despotic.cloud
Can pass in a despotic cloud instance that will be modified by the
specified cube density. Otherwise, will be read from file.
cloudfile : str
The filename specifying the default cloud file to use
cloudfile_path : str or None
If none, defaults to despotic.__path__/cloudfiles/
output_linenumbers : iterable
A list of integer indices for which line numbers should be output as
cubes
output_properties : iterable
A list of strings identifying the line properties to output as cubes
Returns
-------
A data cube of dimensions [velocity,position,position] for each line in
output_linenumbers for each property in output_properties
"""
if pcube.shape != vcube.shape:
raise ValueError('Cube Size mismatch: {0},{1}'.format(
str(pcube.shape), str(vcube.shape)))
if vgrid.ndim > 1:
raise ValueError('Velocity grid must be 1-dimensional')
imshape = pcube.shape[1:]
outcubeshape = (vgrid.size, ) + imshape
nelts = vgrid.size
vinds = np.empty(vcube.shape, dtype='int64')
# not needed
# volume_spectra = np.empty(outcubeshape)
# dens_spectra = np.empty(outcubeshape)
for jj, kk in np.ndindex(imshape):
vinds[:, jj, kk] = np.digitize(vcube[:, jj, kk], vgrid)
# volume_spectra[:,jj,kk] = np.bincount(vinds[:,jj,kk], minlength=nelts)
# dens_spectra[:,jj,kk] = np.bincount(vinds[:,jj,kk],
# weights=pcube[:,jj,kk],
# minlength=nelts)
cloudfile_path = cloudfile_path or despotic.__path__[0] + "/cloudfiles/"
if cloud is None:
cloud = despotic.cloud(
fileName="{0}/{1}".format(cloudfile_path, cloudfile))
try:
from progressbar import ProgressBar, Percentage, Bar
from progressbar import AdaptiveETA as ETA
except ImportError:
from progressbar import ProgressBar, Percentage, Bar
from progressbar import ETA
pb = ProgressBar(widgets=[Percentage(), ETA(), Bar()],
maxval=pcube.size).start()
# property cubes prior to gridding have same shape as input cubes
# use dict() instead of {} for python2.6 compatibility
prop_cubes = dict([
("{0}{1}".format(pr, ln), np.empty(pcube.shape))
for ln, pr in itertools.product(output_linenumbers, output_properties)
])
for (zi, yi, xi), nH in np.ndenumerate(pcube):
cloud.nH = pcube[zi, yi, xi]
cloud.colDen = cloud.nH * voxel_size
line = cloud.lineLum(species)
for ln, pr in itertools.product(output_linenumbers, output_properties):
key = "{0}{1}".format(pr, ln)
prop_cubes[key][zi, yi, xi] = line[ln][pr]
pb.update(pb.currval + 1)
pb.finish()
# spectral cubes have outcubeshape
spectra_cubes = {}
spectra_cubes = dict([
("{0}{1}".format(pr, ln), np.empty(outcubeshape))
for ln, pr in itertools.product(output_linenumbers, output_properties)
])
for key in prop_cubes:
for jj, kk in itertools.product(*map(xrange, imshape)):
spectra_cubes[key][:,jj,kk] = \
np.bincount(vinds[:,jj,kk],
weights=prop_cubes[key][:,jj,kk],
minlength=nelts)
return spectra_cubes, prop_cubes
pl.switch_backend('Qt4Agg')
from astropy import units as u
from astropy import constants
import paths
from paths import fpath
from astropy.utils.console import ProgressBar
import pprint
# Import the despotic library and the NL99 network; also import numpy
from despotic import cloud
import despotic
import os
import numpy as np
# Use the Milky Way GMC file as a base
gmc=cloud('cloud.desp')
from despotic.chemistry import NL99
# gmc.setChemEq(network=NL99)
def turb_heating_generator(lengthscale=1*u.pc, turbulence=True):
def turb_heating(cloud, lengthscale=lengthscale):
""" Turbulent heating rate depends on cloud linewidth
(sigma_nonthermal) and driving scale of the turbulence
DESPOTIC wants units of erg/s/H (per hydrogen), so the turbulent
heating rate n sigma^3 / L is divided by n to get just sigma^3/L
"""
if turbulence:
gamturb = (1.4 * constants.m_p *
(0.5*3**1.5 * (cloud.sigmaNT*u.cm/u.s)**3 / (lengthscale)))
return [(gamturb).to(u.erg/u.s).value, 0]
from astropy import units as u
from astropy import constants
from astropy.table import Table
import paths
from paths import fpath
from astropy.utils.console import ProgressBar
import pprint
# Import the despotic library and the NL99 network; also import numpy
from despotic import cloud
import despotic
import os
import numpy as np
# Use the Milky Way GMC file as a base
gmc = cloud('cloud.desp')
from despotic.chemistry import NL99
# gmc.setChemEq(network=NL99)
def turb_heating_generator(lengthscale=1 * u.pc, turbulence=True):
def turb_heating(cloud, lengthscale=lengthscale):
""" Turbulent heating rate depends on cloud linewidth
(sigma_nonthermal) and driving scale of the turbulence
DESPOTIC wants units of erg/s/H (per hydrogen), so the turbulent
heating rate n sigma^3 / L is divided by n to get just sigma^3/L
MacLow 1999, 2002, 2004 gives exactly:
3e-27 erg cm^-3 s^-1 (n/1 cm^3) (v/10 km/s)**3 (L/100 pc)**-1
Ours is higher by the factor 3^1.5 = 5, which is the conversion from 1D
cloud_mass = 1e4 # msun
box_area = 10. # pc
vox_length = box_area * 3.08e18 / 256.
total_density = pppcube.sum()
# H2 cm^-3
cloud_mean_density = cloud_mass * 2e33/2.8/1.67e-24 / (total_density * vox_length**3)
# start with simple case
x,y = 128,128
nelts = 100
expand = 1
vgrid = np.linspace(ppvcube.min(),ppvcube.max(),nelts)
vdata = ppvcube[:,y-expand:y+expand+1,x-expand:x+expand+1]
pdata = pppcube[:,y-expand:y+expand+1,x-expand:x+expand+1] * cloud_mean_density
gmc = cloud(fileName='/Users/adam/repos/despotic/cloudfiles/MilkyWayGMC.desp')
gmc.sigmaNT = 1e5 # cm/s, instead of 2 as default
gmc.Tg = 20. # start at 20 instead of 15 K
gmc.Td = 20.
# add ortho-h2co
gmc.addEmitter('o-h2co', 1e-9)
spectra,props = despotify(pdata, vdata, vgrid, vox_length, cloud=gmc)
pl.figure()
onedshape = vgrid.shape + (np.prod(spectra[spectra.keys()[0]].shape[1:]),)
for ii,key in enumerate(spectra):
pl.subplot(2,3,ii+1)
pl.plot(vgrid, spectra[key].reshape(onedshape), label=key)
########################################################################
# User-settable options
########################################################################
# Set up a range of densities
lognHgrid = np.arange(2, 6.01, 0.2)
# Specify whether verbose printing while running is desired
verbose = True
########################################################################
# Program code
########################################################################
# Read the protostellar core file
core = cloud(fileName='cloudfiles/protostellarCore.desp', verbose=True)
# Check if we have saved work
try:
# Load pickle files
inFile = open('coreTemp_Tg.pkl', 'rb')
Tg = pickle.load(inFile)
inFile.close()
inFile = open('coreTemp_Td.pkl', 'rb')
Td = pickle.load(inFile)
inFile.close()
inFile = open('coreTemp_rates.pkl', 'rb')
rates = pickle.load(inFile)
inFile.close()
startIdx = len(Tg) # Point at which to restart
except IOError:
# Program code
########################################################################
# Constants
import scipy.constants as physcons
kB = physcons.k/physcons.erg
mH = physcons.m_p/physcons.gram
G = physcons.G*1e3
# Only recompute if we haven't already done the computation
try:
gmc
except NameError:
# Use the Milky Way GMC file as a base, but add C+ and O as emitting species
gmc=cloud('../cloudfiles/MilkyWayGMC.desp')
gmc.addEmitter('c+', 1e-10)
gmc.addEmitter('o', 1e-4)
# Set CR ionization rate
gmc.rad.ionRate = 3e-17
# Set IR temp to 10 K
gmc.rad.TradDust = 10.0
gmc.Td = 10.0
# Set column density to 10^22 cm^-2
gmc.colDen = 1.0e22
# Set initial temperature guess to 20 K
gmc.Tg = 20.0
# User-settable options
########################################################################
# Set up a range of densities
lognHgrid = np.arange(2, 6.01, 0.2)
# Specify whether verbose printing while running is desired
verbose = True
########################################################################
# Program code
########################################################################
# Read the protostellar core file
core = cloud(fileName="cloudfiles/protostellarCore.desp", verbose=True)
# Check if we have saved work
try:
# Load pickle files
inFile = open("coreTemp_Tg.pkl", "rb")
Tg = pickle.load(inFile)
inFile.close()
inFile = open("coreTemp_Td.pkl", "rb")
Td = pickle.load(inFile)
inFile.close()
inFile = open("coreTemp_rates.pkl", "rb")
rates = pickle.load(inFile)
inFile.close()
startIdx = len(Tg) # Point at which to restart
except IOError:
"""
Created on Wed Sep 20 07:14:25 2017
@author: kaytemori
"""
#Automate despotic GMC property finding to calculate the CO(1-0), CO(2-1) line
#brightness ('intTb' in the despotic lines) and plot these brightnesses for
#the despotic GMC model as a function of temperature for T= 5 K to 100 K
#(T = 5, 10, 15, ... 100 K).
from despotic import cloud
import numpy as np
import matplotlib.pyplot as plt
gmc = cloud(fileName="MilkyWayGMC.desp", verbose=True)
gmc.setTempEq(verbose=True)
lines = gmc.lineLum("co")
T = np.linspace(5, 100, 20)
co10 = np.zeros(T.size)
co21 = np.zeros(T.size)
for i, temp in enumerate(T):
gmc.Tg = temp
lines = gmc.lineLum("co")
co10[i] = lines[0]["intTB"]
co21[i] = lines[1]["intTB"]
vox_length = box_area * 3.08e18 / 256.
total_density = pppcube.sum()
# H2 cm^-3
cloud_mean_density = cloud_mass * 2e33 / 2.8 / 1.67e-24 / (total_density *
vox_length**3)
# start with simple case
x, y = 128, 128
nelts = 100
expand = 0
vgrid = np.linspace(ppvcube.min(), ppvcube.max(), nelts)
vdata = ppvcube[:100, y - expand:y + expand + 1, x - expand:x + expand + 1]
pdata = pppcube[:100, y - expand:y + expand + 1,
x - expand:x + expand + 1] * cloud_mean_density
gmc = cloud(fileName='/Users/adam/repos/despotic/cloudfiles/MilkyWayGMC.desp')
gmc.sigmaNT = 1e5 # cm/s, instead of 2 as default
gmc.Tg = 20. # start at 20 instead of 15 K
gmc.Td = 20.
# add ortho-h2co
gmc.addEmitter('o-h2co', 1e-9)
spectra, props = despotify(pdata, vdata, vgrid, vox_length, cloud=gmc)
onedshape = vgrid.shape + (np.prod(spectra[spectra.keys()[0]].shape[1:]), )
for key in spectra:
pl.figure()
pl.plot(vgrid, spectra[key].reshape(onedshape), label=key)
pl.legend(loc='best')