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halodict.py
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halodict.py
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import dust
import sigma_scat as ss
import galhalo as gh
import numpy as np
import constants as c
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d
from astropy.io import fits
## UPDATED July 10, 2013 : Rewrote ecf method in HaloDict object
## UPDATED June 11, 2013 : Make this file independent of asciidata
## July 17, 2012 : A library of objects and functions for simulating a
## full halo given an object's spectrum.
## See sim_cygx3.py for original code (and testing)
#---------------------------------------------------------------
class HaloDict( object ):
"""
A dictionary of halos where each property can be looked up with energy as a key.
"""
def __init__( self, energy, alpha=np.power(10.0, np.arange(0.0,3.01,0.05)), \
rad=dust.Grain(), scatm=ss.Scatmodel() ):
self.alpha = alpha
self.energy = energy
self.index = dict( zip(energy,range(len(energy))) )
self.rad = rad
self.scatm = scatm
self.intensity = np.zeros( shape=( len(energy), len(alpha) ) )
# The following variables get defined when htype is set
# See analytic.set_htype
self.htype = None
self.dist = None
self.taux = None
## Issues with comparing flouts, try round
## http://stackoverflow.com/questions/23721230/float-values-as-dictionary-key
def __getitem__( self, key, n=2 ):
i = self.index[round(key,n)]
return self.intensity[i,:]
## http://stackoverflow.com/questions/19151/build-a-basic-python-iterator
def __iter__( self ):
self.count = 0
return self
def next( self ):
if self.count >= len( self.energy ):
raise StopIteration
else:
self.count += 1
return self.intensity[count,:]
def __getslice__( self, i, j ):
slice = np.where( np.logical_and( self.energy>=i, self.energy<j ) )[0]
return sum( self.intensity[slice,:], 0 )
@property
def len( self ):
return len( self.energy )
@property
def hsize( self ):
return len( self.alpha )
@property
def superE( self ):
NE, NA = len(self.energy), len(self.alpha)
return np.tile( self.energy.reshape(NE,1), NA ) # NE x NA
@property
def superA( self ):
NE, NA = len(self.energy), len(self.alpha)
return np.tile( self.alpha, (NE,1) ) # NE x NA
def total_halo( self, fluxes ):
"""
Sums the halos from different energy bins to create a total halo profile.
Creates / updates the self.total parameter
"""
NE, NA = len(self.energy), len(self.alpha)
if len(fluxes) != NE:
print 'Error: Number of flux bins must equal the number of halo energy bins'
return
superflux = np.tile( fluxes.reshape(NE,1), NA )
result = np.sum( superflux * self.intensity, 0 )
self.total = result
return
# Update -- Feb 10, 2015 to match halo.ecf
def ecf( self, theta, nth=500 ):
"""
Returns the enclosed fraction for the halo surface brightness
profile, as a function of energy via
integral(theta,2pi*theta*halo)/total_halo_counts
-------------------------------------------------------------------------
theta : float : Value for which to compute enclosed fraction (arcseconds)
nth : int (500) : Number of angles to use in calculation
"""
NE, NA = len(self.energy), len(self.alpha)
result = np.zeros( NE ) # NE x NA
taux = self.taux
tharray = np.linspace( min(self.alpha), theta, nth )
if self.taux == None:
print 'ERROR: No taux is specified. Need to run halo calculation'
return result
for i in range(NE):
interpH = interp1d( self.alpha, self.intensity[i,:] )
result[i] = c.intz( tharray, interpH(tharray) * 2.0*np.pi*tharray ) / taux[i]
return result
#---------------------------------------------------------------
# Supporting functions
def get_spectrum( filename ):
data = c.read_table( filename, 2 )
energy, flux = np.array( data[0] ), np.array( data[1] )
return energy, flux
def aeff( filename ):
data = c.read_table( filename, 2 )
energy = np.array( data[0] )
aeff = np.array( data[1] )
return interp1d( energy, aeff ) # keV vs cm^2
## 2015.01.29 - Add a function that will save and load halo dicts into/from fits files
def fitsify_halodict( hd, outfile, clobber=False ):
# Set up the header
prihdr = fits.Header()
prihdr['COMMENT'] = "This is a fits file containing halo dictionary information"
prihdr['SCATM'] = hd.scatm.stype
prihdr['HTYPE'] = hd.htype.ismtype
prihdr['DUSTMASS'] = hd.dist.md
prihdr['DUSTDENS'] = hd.rad.rho
prihdr['DUSTPOWR'] = hd.rad.p
prihdu = fits.PrimaryHDU(header=prihdr)
# Create a block that contains the energy and taux info
tbhdu = fits.BinTableHDU.from_columns( \
[fits.Column(name='energy', format='1E', array=hd.energy), \
fits.Column(name='taux', format='1E', array=hd.taux)] )
dust_hdu = fits.BinTableHDU.from_columns( \
[fits.Column(name='rad', format='1E', array=hd.rad.a)] )
thdulist = fits.HDUList([prihdu, tbhdu, dust_hdu])
for EE in hd.energy:
tbhdu = fits.BinTableHDU.from_columns( \
[fits.Column(name='alpha', format='1E', array=hd.alpha), \
fits.Column(name='intensity', format='1E', array=hd[EE])] )
thdulist.append(tbhdu)
thdulist.writeto(outfile, clobber=clobber)
return
def read_halodict_fits( infile ):
hdulist = fits.open(infile)
header = hdulist[0].header
energy = hdulist[1].data['energy']
taux = hdulist[1].data['taux']
rad = hdulist[2].data['rad']
md = header['DUSTMASS']
rho = header['DUSTDENS']
powr = header['DUSTPOWR']
alpha = hdulist[3].data['alpha']
dd = dust.Dustdist( rad=rad, p=powr, rho=rho )
result = HaloDict( energy, alpha=alpha, rad=dd )
result.taux = taux
result.dist = dust.Dustspectrum( rad=dd, md=md )
for i in np.arange(len(energy))+3:
result.intensity[i-3,:] = hdulist[i].data['intensity']
return result