def loadin_central_fits(haloID,zl,bsz,nnn,band='ACS_F606_NEW.res'):
GroupFirstSub = il.groupcat.loadHalos(basePath,135,fields=['GroupFirstSub'])
sub_galnrs = GroupFirstSub[haloID]
my_api = "9dc591936258a12cb7192ebf79450272" # Input your own api for Illustris database
dl_base = 'http://www.illustris-project.org'
cmd = 'wget --content-disposition --header="API-Key: '+my_api+'" "'+dl_base+ \
'/api/Illustris-1/snapshots/135/subhalos/'+str(sub_galnrs)+ \
'/stellar_mocks/broadband.fits"'
if( not (os.path.isfile("./broadband_"+str(sub_galnrs)+".fits")) ):
os.system(cmd)
filename = "./broadband_"+str(sub_galnrs)+".fits"
hdulist = pyfits.open(filename)
nx1 = hdulist[3].header['NAXIS1']
nx2 = hdulist[3].header['NAXIS2']
Dcmr_zl0 = mm.cosmocalc(0.01, H0=70.4, WM=0.2726, WV=0.7274)['DL_Mpc']
Dcmr_zl1 = mm.cosmocalc(zl, H0=70.4, WM=0.2726, WV=0.7274)['DL_Mpc']
bsz_init = np.rad2deg(hdulist[3].header['FOV'])*3600.0*Dcmr_zl0/Dcmr_zl1 # arcsec
dsx_init = bsz_init/nx1
hdulist.close()
return_image = sunpy__load.load_broadband_image(filename,band)
nnx1 = int(bsz/dsx_init)
nnx2 = int(bsz/dsx_init)
return_image_new = np.zeros((nnx1,nnx2))
if bsz >= bsz_init:
return_image_new[nnx1/2-nx1/2:nnx1/2+nx1/2,nnx2/2-nx2/2:nnx2/2+nx2/2] = return_image
else:
return_image_new = return_image[nx1/2-nnx1/2:nx1/2+nnx1/2,nx2/2-nnx2/2:nx2/2+nnx2/2]
return_image_new = ssi.congrid(return_image_new,(nnn,nnn))
return return_image_new
def get_Vmax(zlo, zup):
z = zup
V1 = cosmocalc(z, H0=Ho, WM=wm)['VCM_Gpc3'] * 1e9
V2 = cosmocalc(zlo, H0=Ho, WM=wm)['VCM_Gpc3'] * 1e9
area = SURVEY_AREA * sqDeg2sr
vmax = area * (V1 - V2) / (4 * pi)
#print 'vmax',vmax,V1,V2,zup,zlo,SURVEY_AREA*sqDeg2sr,sqDeg2sr
return vmax
def __init__(self, redshift, H0=70.4, WM=0.2726, WV=0.7274):
self.H0=H0
self.WM=WM
self.WV=WV
self.redshift = redshift
self.lum_dist = (cosmocalc.cosmocalc(self.redshift, H0=self.H0, WM=self.WM, WV=self.WV))['DL_Mpc'] ## luminosity dist in mpc
self.ang_diam_dist = (cosmocalc.cosmocalc(self.redshift, H0=self.H0, WM=self.WM, WV=self.WV))['DA_Mpc'] ##
self.kpc_per_arcsec = (cosmocalc.cosmocalc(self.redshift, H0=self.H0, WM=self.WM, WV=self.WV))['PS_kpc']
def get_dL(z):
"""
Returns the comoving radial distance in Mpc
"""
if isinstance(z, float):
return cosmocalc(z, H0=Ho, WM=wm)['DL_Mpc']
else:
return [cosmocalc(zi, H0=Ho, WM=wm)['DL_Mpc'] for zi in z]
def volume(zmin,zmax,cos=[0.3,70]):
'''
Calculating dv/dz for a given redshift range and cosmology
'''
dz = zmax-zmin
vmin = cosmocalc(zmin,H0=cos[1],WM=cos[0])['VCM_Gpc3']*1.0e9 # volume in Mpc^3
vmax = cosmocalc(zmax,H0=cos[1],WM=cos[0])['VCM_Gpc3']*1.0e9 # volume in Mpc^3
dv = vmax-vmin
return dv/dz,vmax
def __init__(self, redshift, H0=70.4, WM=0.2726, WV=0.7274):
self.H0=H0
self.WM=WM
self.WV=WV
self.redshift = redshift
self.lum_dist = (cosmocalc.cosmocalc(self.redshift, H0=self.H0, WM=self.WM, WV=self.WV))['DL_Mpc'] ## luminosity dist in mpc
self.ang_diam_dist = (cosmocalc.cosmocalc(self.redshift, H0=self.H0, WM=self.WM, WV=self.WV))['DA_Mpc'] ##
self.kpc_per_arcsec = (cosmocalc.cosmocalc(self.redshift, H0=self.H0, WM=self.WM, WV=self.WV))['PS_kpc']
self.distance_modulus = 5.0 * ( np.log10(self.lum_dist*1.0e6) - 1.0 )
def NFW(r, M200, c200, zl, zs):
#this code calculates the shear at a given distance r from an object at redshift zl of mass M200 and concentration c200 assuming a background distribution of zs
#set cosmology
h = 0.7 #hubble parameter
Ho = h * 100.0 #km s^-1 Mpc-1
Om = 0.28 #cosmological parameter
Ol = 0.72 #cosmological parameter
c = 299792.0 #speed of light in km/s
Mpc = 3.0856e19 #1Mpc in km
cMpc = c / Mpc #speed of light in Mpc/s
pc = 3.0856e16 #1pc in m
Msun = 1.989e30 #1Msolar in kg
G = 6.6726e-11 #G in m^3 kg-1 s-2
GG = G * (pc**(-3.0)) * (1e-18) * Msun #G in Mpc^3 Msun^-1 s^-2
Ez = np.sqrt(Om * (1.0 + zl)**3.0 + Ol) #evolution
pcrit = 3.0 * ((Ho * Ez / Mpc)**2.0) / (8.0 * np.pi * GG)
#calculate angular diameter distances in Mpc to lens and source
dL = cosmocalc(zl, Ho, Om, Ol)['DA_Mpc']
dS = cosmocalc(zs, Ho, Om, Ol)['DA_Mpc']
dLS = (dS * (1.0 + zs) - dL * (1.0 + zl)) / (1.0 + zs)
sigmacr = cMpc**2.0 * dS / (
4.0 * GG * np.pi * dL * dLS
) #mean critical surface mass density in units Mpc^-2 Msolar
p200 = 200.0 * pcrit #density that is 200 times critical
p500 = 500.0 * pcrit #density that is 500 times critical
r200 = (3.0 * M200 / (4.0 * np.pi * p200))**(
1.0 / 3.0) #radius within which the density is 200 times critical
rs = r200 / c200 #characteristic radius in Mpc
ps = (p200 * c200**3.0) / (3.0 *
(np.log(1.0 + c200) - c200 /
(1.0 + c200))) #core density in Msolar Mpc-3
r = np.atleast_1d(r)
term = np.where(r <= rs, \
2.0*np.arctanh(np.sqrt((1.0-(r/rs))/(1.0+(r/rs))))/(np.sqrt(1.0-np.square(r/rs))), \
2.0*np.arctan(np.sqrt(((r/rs)-1.0)/(1.0+(r/rs))))/(np.sqrt(np.square(r/rs)-1.0)))
sigma = 2.0 * rs * ps * (1.0 - term) / ((np.square(r / rs)) - 1.0)
meansigma = 4.0 * rs * ps * (term + np.log(
(r / rs) / 2.0)) / (np.square(r / rs))
rhoNFW = ps / ((r / rs) * np.square(1.0 + r / rs))
kappaNFW = sigma / sigmacr #calculate convergence
meankappaNFW = meansigma / sigmacr #mean convergence
gammaNFW = meankappaNFW - kappaNFW #calculate shear
reducedshear = np.abs(gammaNFW) / (1.0 - kappaNFW
) #calculate reduced shear
return reducedshear
def volume(zmin, zmax, cos=[0.3, 70]):
'''
Calculating dv/dz for a given redshift range and cosmology
'''
dz = zmax - zmin
vmin = cosmocalc(zmin, H0=cos[1],
WM=cos[0])['VCM_Gpc3'] * 1.0e9 # volume in Mpc^3
vmax = cosmocalc(zmax, H0=cos[1],
WM=cos[0])['VCM_Gpc3'] * 1.0e9 # volume in Mpc^3
dv = vmax - vmin
return dv / dz, vmax
def __init__(self, H0=70.0, WM=0.27, WV=0.73):
self.H0 = H0
self.WM = WM
self.WV = WV
self.redshift_grid = np.logspace(-3, 2, 100)
self.comoving_mpc_grid = np.asarray([
(cc.cosmocalc(zf, H0=self.H0, WM=self.WM, WV=self.WV))['DCMR_Mpc']
for zf in self.redshift_grid
])
self.DA_mpc_grid = np.asarray([(cc.cosmocalc(zf,
H0=self.H0,
WM=self.WM,
WV=self.WV))['DA_Mpc']
for zf in self.redshift_grid])
def physicalSeparation(inputdata, redshift, H0=71.0, OmegaM=0.28):
"""
Calculates the physical distances between objects of given RAs and DECs at a given redshift.
:param inputdata: coordinates (RA and DEC) of the objects. The input data should
consist of four keys value pairs::
RA1: RA of the object from which to calculate the distance (float)
DEC1: DEC of the object from which to calculate the distance (float)
RA2: RAs of the objects (float or numpy array)
DEC2: DECs of the objects (float or numpy array)
:type inputdata: dict
:param redshift: cosmological redshift of the object
:type redshift: float
:param H0: Hubble value [71/km/s/Mpc]
:type H0: float
:param OmegaM: Matter density
:type OmegaM: float [0.28]
:return: physical distance [distance], scale at a given redshift 1 arcsec = kpc [scale],
separation on the sky in arcseconds [separation], cosmological parameters [cosmology]
:rtype: dict
"""
sep = Coords.calcAngSepDeg(inputdata["RA1"], inputdata["DEC1"], inputdata["RA2"], inputdata["DEC2"])
d = cosmocalc(redshift, H0, OmegaM)
dd = d["PS_kpc"]
pd = angularSeparationToPhysical(sep, dd)
return dict(distance=pd, scale=dd, separation=sep / DEGTOARCSEC, redshift=redshift, cosmology=d)
def Transform():
print("Formating input data to correct units")
for k in range(len(input_files_data)):
dist = (cosmocalc.cosmocalc( input_files_data[k]['red'], H0=70.4, WM=0.2726, WV=0.7274))['DL_Mpc']
#Calculate modulus distance
dist *= 1e6
#transform into parsecs
modulus_distance = 5.0 * (math.log10(dist) - 1.0)
#Calculate modulus distance
#DEBUG
#print("DIST: " + str(dist))
#print("MOD_DIST: " + str(modulus_distance))
#print("FLUX (microJs): " +str(data['fl'][i]))
input_files_data[k]['err'] = 2.5 * input_files_data[k]['err'] / input_files_data[k]['fl']
#Error from the transformation of luminosity to magnitude
input_files_data[k]['fl'] = 23.9 - 2.5* np.log10(input_files_data[k]['fl'] )
# Int Into AB magnitude
#print("Apparent AB mag: %.5e" % (input_files_data[k]['fl'][i]))
input_files_data[k]['fl'] -= modulus_distance
def Transform( red, data_file, err_file):
dist = (cosmocalc.cosmocalc( red, H0=70.4, WM=0.2726, WV=0.7274))['DL_Mpc']
#Calculate modulus distance
dist *= 1e6
#transform into parsecs
modulus_distance = 5.0 * (math.log10(dist) - 1.0)
#Calculate modulus distance
#DEBUG
#print("DIST: " + str(dist))
#print("MOD_DIST: " + str(modulus_distance))
#print("FLUX (microJs): " +str(data['fl'][i]))
err_file = 2.5 * err_file / data_file
#Error from the transformation of luminosity to magnitude
data_file = 23.9 - 2.5* np.log10(data_file )
# Int Into AB magnitude
#print("Apparent AB mag: %.5e" % (input_files_data[k]['fl'][i]))
data_file -= modulus_distance
#Into absolute magn
#print('%.5e %.5e'% (input_files_data[k]['fl'][i], input_files_data[k]['err'][i]))
#print(" ")
return (data_file, err_file)
def load_resolved_broadband_apparent_magnitudes(filename, redshift, camera=0, seed=12345, n_bands=36, **kwargs):
""" loads n_band x n_pix x n_pix image array with apparent mags for synthetic images """
mags = sunpy.sunpy__load.load_all_broadband_photometry(filename, camera=0)
for band in np.arange(n_bands):
obj = synthetic_image(filename, band=int(band), seed=seed, redshift=redshift, **kwargs)
img = obj.bg_image.return_image() # muJy / str
if band==0:
n_pixels = img.shape[0]
all_images = np.zeros( (n_bands, n_pixels, n_pixels ) )
all_images[band, :, :] = img # muJy / str
pixel_in_sr = (1e3*obj.bg_image.pixel_in_kpc /10.0)**2
all_images *= pixel_in_sr / 1e6 # in Jy
for band in np.arange(n_bands):
tot_img_in_Jy = np.sum(all_images[band,:,:]) # total image flux in Jy
abmag = -2.5 * np.log10(tot_img_in_Jy / 3631 )
if verbose:
print "the ab magnitude of band "+str(band)+" is :"+str(abmag)+" "+str(mags[band])
print abmag/mags[band], abmag - mags[band]
print " "
all_images = -2.5 * np.log10( all_images / 3631 ) # abmag in each pixel
dist = (cosmocalc.cosmocalc(redshift, H0=70.4, WM=0.2726, WV=0.7274))['DL_Mpc'] * 1e6
dist_modulus = 5.0 * ( np.log10(dist) - 1.0 )
apparent_magnitudes = dist_modulus + all_images
del mags, obj, img, n_pixels, all_images, pixel_in_sr, tot_img_in_Jy, abmag, dist, dist_modulus
gc.collect()
return apparent_magnitudes
def load_resolved_broadband_apparent_magnitudes(filename, redshift, camera=0, seed=12345, n_bands=36, **kwargs):
""" loads n_band x n_pix x n_pix image array with apparent mags for synthetic images """
mags = sunpy.sunpy__load.load_all_broadband_photometry(filename, camera=0)
for band in np.arange(n_bands):
obj = synthetic_image(filename, band=int(band), seed=seed, redshift=redshift, **kwargs)
img = obj.bg_image.return_image() # muJy / str
if band==0:
n_pixels = img.shape[0]
all_images = np.zeros( (n_bands, n_pixels, n_pixels ) )
all_images[band, :, :] = img # muJy / str
pixel_in_sr = (1e3*obj.bg_image.pixel_in_kpc /10.0)**2
all_images *= pixel_in_sr / 1e6 # in Jy
for band in np.arange(n_bands):
tot_img_in_Jy = np.sum(all_images[band,:,:]) # total image flux in Jy
abmag = -2.5 * np.log10(tot_img_in_Jy / 3631 )
if verbose:
print "the ab magnitude of band "+str(band)+" is :"+str(abmag)+" "+str(mags[band])
print abmag/mags[band], abmag - mags[band]
print " "
all_images = -2.5 * np.log10( all_images / 3631 ) # abmag in each pixel
dist = (cosmocalc.cosmocalc(redshift, H0=70.4, WM=0.2726, WV=0.7274))['DL_Mpc'] * 1e6
dist_modulus = 5.0 * ( np.log10(dist) - 1.0 )
apparent_magnitudes = dist_modulus + all_images
return apparent_magnitudes
def physicalSeparation(inputdata, redshift, H0=71.0, OmegaM=0.28):
"""
Calculates the physical distances between objects of given RAs and DECs at a given redshift.
:param inputdata: coordinates (RA and DEC) of the objects. The input data should
consist of four keys value pairs::
RA1: RA of the object from which to calculate the distance (float)
DEC1: DEC of the object from which to calculate the distance (float)
RA2: RAs of the objects (float or numpy array)
DEC2: DECs of the objects (float or numpy array)
:type inputdata: dict
:param redshift: cosmological redshift of the object
:type redshift: float
:param H0: Hubble value [71/km/s/Mpc]
:type H0: float
:param OmegaM: Matter density
:type OmegaM: float [0.28]
:return: physical distance [distance], scale at a given redshift 1 arcsec = kpc [scale],
separation on the sky in arcseconds [separation], cosmological parameters [cosmology]
:rtype: dict
"""
sep = Coords.calcAngSepDeg(inputdata['RA1'], inputdata['DEC1'],
inputdata['RA2'], inputdata['DEC2'])
d = cosmocalc(redshift, H0, OmegaM)
dd = d['PS_kpc']
pd = angularSeparationToPhysical(sep, dd)
return dict(distance=pd,
scale=dd,
separation=sep / DEGTOARCSEC,
redshift=redshift,
cosmology=d)
def load_resolved_broadband_apparent_magnitudes(filename, redshift, camera=0, **kwargs):
""" this is a little trickier b/c in W/m/m^2/str. First convert to abs mag, then dist correction """
images = load_all_broadband_images(filename,camera=0) # in W/m/m^2/str shape = [n_band, n_pix, n_pix]
mags = load_all_broadband_photometry(filename, camera=0)
n_pixels = images.shape[1]
hdulist = fits.open(filename)
lambda_eff = hdulist['FILTERS'].data['lambda_eff']
for index,this_lambda in enumerate(lambda_eff):
to_nu = ((this_lambda**2 ) / (speedoflight_m)) #* pixel_area_in_str
to_microjanskies = (1.0e6) * to_nu * (1.0e26) # 1 muJy/str (1Jy = 1e-26 W/m^2/Hz)
images[index,:,:] = images[index,:,:] * to_microjanskies # to microjanskies / str
pixel_in_kpc = load_fov(filename) / n_pixels
pixel_in_sr = (1e3 * pixel_in_kpc / 10.0)**2
images *= pixel_in_sr # in muJy
images /= 1e6 # in Jy
for index,this_lambda in enumerate(lambda_eff):
tot_img_in_Jy = np.sum(images[index,:,:]) # total image flux in Jy
abmag = -2.5 * np.log10(tot_img_in_Jy / 3631 )
if False:
print "the ab magnitude of this image is :"+str(abmag)+" "+str(mags[index])
print abmag/mags[index], abmag - mags[index]
print index, np.sum(images[index,:,:])
images = -2.5 * np.log10( images / 3631 ) # abmag in each pixel
dist = (cosmocalc.cosmocalc(redshift, H0=70.4, WM=0.2726, WV=0.7274))['DL_Mpc'] * 1e6
dist_modulus = 5.0 * ( np.log10(dist) - 1.0 )
apparent_magnitudes = dist_modulus + images
return apparent_magnitudes
def load_resolved_broadband_apparent_magnitudes(filename, redshift, camera=0, **kwargs):
""" this is a little trickier b/c in W/m/m^2/str. First convert to abs mag, then dist correction """
images = load_all_broadband_images(filename,camera=0) # in W/m/m^2/str shape = [n_band, n_pix, n_pix]
mags = load_all_broadband_photometry(filename, camera=0)
n_pixels = images.shape[1]
hdulist = fits.open(filename)
lambda_eff = hdulist['FILTERS'].data['lambda_eff']
for index,this_lambda in enumerate(lambda_eff):
to_nu = ((this_lambda**2 ) / (speedoflight_m)) #* pixel_area_in_str
to_microjanskies = (1.0e6) * to_nu * (1.0e26) # 1 muJy/str (1Jy = 1e-26 W/m^2/Hz)
images[index,:,:] = images[index,:,:] * to_microjanskies # to microjanskies / str
pixel_in_kpc = load_fov(filename) / n_pixels
pixel_in_sr = (1e3 * pixel_in_kpc / 10.0)**2
images *= pixel_in_sr # in muJy
images /= 1e6 # in Jy
for index,this_lambda in enumerate(lambda_eff):
tot_img_in_Jy = np.sum(images[index,:,:]) # total image flux in Jy
abmag = -2.5 * np.log10(tot_img_in_Jy / 3631 )
if True:
print "the ab magnitude of this image is :"+str(abmag)+" "+str(mags[index])
print abmag/mags[index], abmag - mags[index]
print index, np.sum(images[index,:,:])
images = -2.5 * np.log10( images / 3631 ) # abmag in each pixel
dist = (cosmocalc.cosmocalc(redshift, H0=70.4, WM=0.2726, WV=0.7274))['DL_Mpc'] * 1e6
dist_modulus = 5.0 * ( np.log10(dist) - 1.0 )
apparent_magnitudes = dist_modulus + images
return apparent_magnitudes
def NFW(r, M200, c200, zl, zs):
#this code calculates the shear at a given distance r from an object at redshift zl of mass M200 and concentration c200 assuming a background distribution of zs
#set cosmology
h = 0.7 #hubble parameter
Ho = h*100.0 #km s^-1 Mpc-1
Om = 0.28 #cosmological parameter
Ol = 0.72 #cosmological parameter
c = 299792.0 #speed of light in km/s
Mpc = 3.0856e19 #1Mpc in km
cMpc = c/Mpc #speed of light in Mpc/s
pc = 3.0856e16 #1pc in m
Msun = 1.989e30 #1Msolar in kg
G = 6.6726e-11 #G in m^3 kg-1 s-2
GG = G*(pc**(-3.0))*(1e-18)*Msun #G in Mpc^3 Msun^-1 s^-2
Ez = np.sqrt(Om*(1.0+zl)**3.0+Ol) #evolution
pcrit= 3.0*((Ho*Ez/Mpc)**2.0)/(8.0*np.pi*GG)
#calculate angular diameter distances in Mpc to lens and source
dL = cosmocalc(zl,Ho,Om,Ol)['DA_Mpc']
dS = cosmocalc(zs, Ho,Om, Ol)['DA_Mpc']
dLS = (dS*(1.0+zs)-dL*(1.0+zl))/(1.0+zs)
sigmacr=cMpc**2.0*dS/(4.0*GG*np.pi*dL*dLS) #mean critical surface mass density in units Mpc^-2 Msolar
p200 = 200.0*pcrit #density that is 200 times critical
p500 = 500.0*pcrit #density that is 500 times critical
r200 = (3.0*M200/(4.0*np.pi*p200))**(1.0/3.0) #radius within which the density is 200 times critical
rs = r200/c200 #characteristic radius in Mpc
ps = (p200*c200**3.0)/(3.0*(np.log(1.0+c200)-c200/(1.0+c200))) #core density in Msolar Mpc-3
r = np.atleast_1d(r)
term = np.where(r <= rs, \
2.0*np.arctanh(np.sqrt((1.0-(r/rs))/(1.0+(r/rs))))/(np.sqrt(1.0-np.square(r/rs))), \
2.0*np.arctan(np.sqrt(((r/rs)-1.0)/(1.0+(r/rs))))/(np.sqrt(np.square(r/rs)-1.0)))
sigma = 2.0*rs*ps*(1.0-term)/((np.square(r/rs))-1.0)
meansigma = 4.0*rs*ps*(term+np.log((r/rs)/2.0))/(np.square(r/rs))
rhoNFW = ps/((r/rs)*np.square(1.0+r/rs))
kappaNFW = sigma/sigmacr #calculate convergence
meankappaNFW = meansigma/sigmacr #mean convergence
gammaNFW = meankappaNFW-kappaNFW #calculate shear
reducedshear = np.abs(gammaNFW)/(1.0-kappaNFW) #calculate reduced shear
return reducedshear
def rr(x, pos=None):
g = cosmocalc.cosmocalc(10.0**x, H0=71.0)
if g['zage_Gyr'] < 1:
return "%.2f" % g[
'zage_Gyr'] # Return 2 dec place if age < 1; e.g 0.62
else:
return "%.1f" % g[
'zage_Gyr'] # Return 1 dec place if age > 1; e.g. 1.5
def plot_lum_rest():
'''f_{rest,V} = f_{rest_corr}*[nu_V/ ((1+z)nu_J)]^beta for flux \propto nu^beta and beta negative values'''
clf()
f_rest_corr = [
2252.14, 1626.48, 403.717, 11783.2, 913.329, 549.616, 286.863, 990.110,
14.7689, 174.540, 1419.79, 149309.80115
]
beta = [
-1.35, -0.8, -0.96, -0.22, -1.73, -0.84, -3.48, -0.42, -3.81, -0.3,
-1.7, -0.47
]
z_list_limits = [
1.1588, 2.4274, 1.51, 0.54, 1.95, 1.6, 3.036, 2.346, 3.5, 1.165, 4.8,
0.9382
]
arrf = arr(f_rest_corr)
arrb = arr(beta)
arrz = arr(z_list_limits)
nu_V = 5.444646098003629764065335753176043557e+14
nu_J = 2.398339664e+14
f_rest_V = arrf * (nu_V / ((1 + arrz) * nu_J))**(beta)
print 'f_rest_V in microjansky:'
print f_rest_V
#convert to cgs from microjansky:
f_rest_V = f_rest_V * 10**(-29)
print 'f_rest_V in cgs:'
print f_rest_V
#get luminosity distance from cosmocalc (lambdaCDM: omega_M = 0.27 and omega_lambda=0.73)
dist = []
for redshift in z_list_limits:
dist += [cosmocalc.cosmocalc(z=redshift)['DL_cm']]
arrd = arr(dist)
print 'dist:'
print arrd
L_rest_V = f_rest_V * 4 * numpy.pi * arrd**2. / (1. + arrz)
print 'L_rest_V:'
print L_rest_V
#convert to ABSOLUTE AB magnitude:
parsec = 3.085677581e18 # cm
F_10pc = L_rest_V / (4 * numpy.pi *
(10 * parsec)**2) #flux density at 10 parsecs
Absol_Mag = -2.5 * numpy.log10(F_10pc) - 48.60 #Absolute mag in AB mag
hist(Absol_Mag, 6)
xlabel('$M_v$', fontsize=27)
ylabel('Number', fontsize=28)
yticks(arr([0, 1., 2., 3., 4.]))
ax = matplotlib.pyplot.gca()
ax.set_xlim(ax.get_xlim()[::-1]) # reversing the xlimits
savefig('Lum_dist_rest.eps')
print 'Done'
return Absol_Mag
def comovingVolume(arcmin2, zmin, zmax, H0=70, WM=0.28):
"""
Calculates the comoving volume between two redshifts when
the sky survey has covered :math:`arcmin^{2}` region.
:param arcmin2: area on the sky in :math:`arcmin^{2}`
:param zmin: redshift of the front part of the volume
:param zmax: redshift of the back part of the volume
:param H0: Value of the Hubble constant
:param WM: Value of the mass density
:return: comoving volume between zmin and zmax of arcmin2
solid angle in Mpc**3
"""
front = cosmocalc(zmin, H0, WM)['VCM_Gpc3']
back = cosmocalc(zmax, H0, WM)['VCM_Gpc3']
volume = (back - front) * 1e9 * arcminSquaredToSolidAnge(arcmin2)
return volume
def comovingVolume(arcmin2, zmin, zmax, H0=70, WM=0.28):
"""
Calculates the comoving volume between two redshifts when
the sky survey has covered :math:`arcmin^{2}` region.
:param arcmin2: area on the sky in :math:`arcmin^{2}`
:param zmin: redshift of the front part of the volume
:param zmax: redshift of the back part of the volume
:param H0: Value of the Hubble constant
:param WM: Value of the mass density
:return: comoving volume between zmin and zmax of arcmin2
solid angle in Mpc**3
"""
front = cosmocalc(zmin, H0, WM)["VCM_Gpc3"]
back = cosmocalc(zmax, H0, WM)["VCM_Gpc3"]
volume = (back - front) * 1e9 * arcminSquaredToSolidAnge(arcmin2)
return volume
def calc_Vmax(zef, l, bins, zup, zlo, skyarea, getLF=True, table=False):
Vmax = []
dz = 0.1
if not type(zef) == float:
z = [min(zup, ze) for ze in zef]
else:
z = 0.5 * (zup + zlo) * (1 + numpy.zeros(len(l)))
print 'here?'
V1 = [cosmocalc(zi, H0=Ho, WM=wm)['VCM_Gpc3'] * 1e9 for zi in z]
V2 = cosmocalc(zlo, H0=Ho, WM=wm)['VCM_Gpc3'] * 1e9
#zmean = (z + zlo)/2
V1 = numpy.array(V1)
vmax_old = skyarea * (V1 - V2) / (4 * pi)
for i in range(len(z)):
zup = z[i]
z_i = zlo
vmax = 0
while z_i < zup:
V1 = cosmocalc(z_i, H0=Ho, WM=wm)['VCM_Gpc3'] * 1e9
z_i += dz
V2 = cosmocalc(z_i, H0=Ho, WM=wm)['VCM_Gpc3'] * 1e9
vmax += skyarea * (V2 - V1) / (4 * pi)
Vmax.append(vmax)
#print vmax_old[i],vmax
#for i in range(len(vmax)):
#if vmax[i]<0:
# print z[i],zef[i],zup
#sys.exit()
#for i in range(len(l)):
#print 'done with Vmax old ',numpy.mean(vmax_old),' vs new ',numpy.mean(Vmax)
Vmax = numpy.array(Vmax)
#sys.exit()
# Vmax.append(abs(vmax))
if getLF:
return lumfunc(l, bins, Vmax, table)
else:
return Vmax #vmax_old#Vmax
def __init__(self,
whichSurvey,
manifestf,
noiseZones=False,
redshiftSlices=False):
self.whichSurvey = whichSurvey
self.noiseZones = noiseZones
self.redshiftSlices = redshiftSlices
self.manifestf = manifestf
self.manifest = numpy.genfromtxt(self.manifestf)
# Extract selected z slices
self.manifest = self.manifest[[s - 1 for s in whichRedshiftSlices], :]
self.whichRedshiftSlices = whichRedshiftSlices
if self.redshiftSlices:
# self.datafiles=['%s/sdss_dr12s%i.txt'%(outdir,r) for r in self.whichRedshiftSlices] #(whichSurvey,r)
self.datafiles = [
'%s/data_cos_s%i.txt' % (outdir, r)
for r in self.whichRedshiftSlices
]
#self.datafiles= ['sims/161101b/sim.txt']
self.datafile = self.datafiles[0]
self.numRedshiftSlices = len(
whichRedshiftSlices) #self.manifest.shape[0]
self.redshifts = self.manifest[:, 3]
self.redshifts_min = self.manifest[:, 1]
self.redshifts_max = self.manifest[:, 2]
self.sliceAreasTemp = self.manifest[:, 4] # Needs to be rejigged
self.sliceNoisesTemp = self.manifest[:, 5] # Needs to be rejigged
self.zsliceData = {}
self.dls = {}
self.dlds = {}
self.Vmax = {}
self.sliceAreas = {}
self.sliceNoises = {}
self.fmag = {}
# Rejig z information
for r in range(self.numRedshiftSlices):
z = self.redshifts[r]
z_min = self.redshifts_min[r]
z_max = self.redshifts_max[r]
dataf = self.datafiles[r]
self.zsliceData[z] = self.loadData(dataf)
self.dls[z] = cosmocalc(z, H0=Ho, WM=wm)['DCMR_Mpc']
self.fmag[z] = (numpy.log10(lumfuncUtils.get_Lbins(\
[self.zsliceData[z][1][-1]],z,self.dls[z])[0]) - \
numpy.log10(lumfuncUtils.get_Lbins([self.zsliceData[z][1][-2]],z,\
self.dls[z])[0]))/0.4
self.dlds[z] = lumfuncUtils.get_dlds(z, self.dls[z])
self.Vmax[z] = lumfuncUtils.get_Vmax(z_min, z_max)
self.sliceAreas[z] = self.sliceAreasTemp[r]
self.sliceNoises[z] = self.sliceNoisesTemp[r]
elif self.noiseZones:
pass
return
def calculate_shift( value, redshift, dist, modulus_distance):
if dist == -1:
dist = (cosmocalc.cosmocalc(redshift, H0=70.4, WM=0.2726, WV=0.7274))['DL_Mpc']
dist *= 1e6
modulus_distance = 5.0*(math.log10(dist) - 1.0)
value = 23.9 - 2.5* math.log10(value)
#Into AB mag from microJks
value -= modulus_distance
#Into absolute magnitude
return value
def open_anytype(name,
cols,
zr=[],
F='mJy',
L=True,
S=False,
getz=False,
Mi=False,
ug=False,
band='L'):
if Mi:
cols.append(Mi)
if ug:
cols.append(ug)
cols.append(ug + 1)
z, s, mi, u, g = numpy.loadtxt(name, usecols=cols, unpack=True)
else:
z, s, mi = numpy.loadtxt(name, usecols=cols, unpack=True)
else:
z, s = numpy.loadtxt(name, usecols=cols, unpack=True)
if zr == []:
pass
else:
s = s[numpy.where((z > zr[0]) & (z < zr[1]))]
if Mi:
mi = mi[numpy.where((z > zr[0]) & (z < zr[1]))]
z = z[numpy.where((z > zr[0]) & (z < zr[1]))]
if S:
if Mi:
return s[s > 0], z[s > 0], dl[s > 0], mi[s > 0]
return s
if F == 'Jy':
s *= 1e-26
elif F == 'mJy':
s *= 1e-29
else: #uJy
s *= 1e-32
dl = [cosmocalc(zi, H0=Ho, WM=wm)['DCMR_Mpc'] for zi in z]
dl = numpy.array(dl) * 3.08e22
l = math.pi * 4 * s * dl**2 * (1 + z)**(specindx + 1)
if band == 'S':
l *= 1 #(1.4/3)**(-.7)#before 27Feb this was just l*=1!
if L:
if ug: return l[l > 0], z[l > 0], mi[l > 0], u[l > 0], g[l > 0]
if Mi: return l[l > 0], z[s > 0], mi[s > 0]
if getz: return l[l > 0], z[s > 0]
return l[l > 0]
print z[0], s[0], l[0], dl[0]
print len(l)
return calcu_zeff(z, s, dl, l)
def process_stars(sspa, redshift, starmet, starsft):
""" sft is in scale factor a """
#------------------------------------------------
#------------------------------------------------
#determine relations between redshift and age
aou_cz=cosmocalc.cosmocalc(redshift)['zage_Gyr']
redshifts=numpy.arange(redshift,20,0.1)
ages=[]
for z in redshifts:
aou_z=cosmocalc.cosmocalc(z)['zage_Gyr']
ages.append(aou_cz-aou_z)
ages=numpy.array(ages)
#------------------------------------------------
#------------------------------------------------
# Main Code
print 'Total Number of Stars:',len(starmet)
print '-------------------------------'
print '-------------------------------'
z =(1./starsft)-1
age = numpy.interp(z, redshifts, ages) * 1000.
Zi = numpy.abs(sspa['Z'][:, None] - starmet[None, :]).argmin(axis=0)
agei = numpy.empty(len(Zi), dtype='intp')
for k in range(len(sspa)):
mask = k == Zi
agei[mask] = \
numpy.abs(sspa['age'][k][None, :] - age[mask][:, None]).argmin(axis=-1)
# use rind to quickly access the Zi, agei from the raveled arrays)
rind = numpy.int64(numpy.ravel_multi_index((Zi, agei), sspa['age'].shape))
stellar_mass_rec_frac = numpy.float32(sspa['frac']).take(rind)
print stellar_mass_rec_frac.dtype
return rind, stellar_mass_rec_frac
def process_stars(sspa, redshift, starmet, starsft):
""" sft is in scale factor a """
#------------------------------------------------
#------------------------------------------------
#determine relations between redshift and age
aou_cz = cosmocalc.cosmocalc(redshift)['zage_Gyr']
redshifts = numpy.arange(redshift, 20, 0.1)
ages = []
for z in redshifts:
aou_z = cosmocalc.cosmocalc(z)['zage_Gyr']
ages.append(aou_cz - aou_z)
ages = numpy.array(ages)
#------------------------------------------------
#------------------------------------------------
# Main Code
print 'Total Number of Stars:', len(starmet)
print '-------------------------------'
print '-------------------------------'
z = (1. / starsft) - 1
age = numpy.interp(z, redshifts, ages) * 1000.
Zi = numpy.abs(sspa['Z'][:, None] - starmet[None, :]).argmin(axis=0)
agei = numpy.empty(len(Zi), dtype='intp')
for k in range(len(sspa)):
mask = k == Zi
agei[mask] = \
numpy.abs(sspa['age'][k][None, :] - age[mask][:, None]).argmin(axis=-1)
# use rind to quickly access the Zi, agei from the raveled arrays)
rind = numpy.int64(numpy.ravel_multi_index((Zi, agei), sspa['age'].shape))
stellar_mass_rec_frac = numpy.float32(sspa['frac']).take(rind)
print stellar_mass_rec_frac.dtype
return rind, stellar_mass_rec_frac
def plot_lum_rest():
'''f_{rest,V} = f_{rest_corr}*[nu_V/ ((1+z)nu_J)]^beta for flux \propto nu^beta and beta negative values'''
clf()
f_rest_corr = [2252.14, 1626.48, 403.717, 11783.2, 913.329, 549.616, 286.863, 990.110, 14.7689, 174.540, 1419.79, 149309.80115]
beta = [-1.35, -0.8, -0.96, -0.22, -1.73, -0.84, -3.48, -0.42, -3.81, -0.3, -1.7, -0.47]
z_list_limits = [1.1588, 2.4274, 1.51, 0.54, 1.95, 1.6, 3.036, 2.346, 3.5, 1.165, 4.8, 0.9382]
arrf = arr(f_rest_corr)
arrb = arr(beta)
arrz = arr(z_list_limits)
nu_V = 5.444646098003629764065335753176043557e+14
nu_J = 2.398339664e+14
f_rest_V = arrf * (nu_V/ ((1+arrz)*nu_J))**(beta)
print 'f_rest_V in microjansky:'
print f_rest_V
#convert to cgs from microjansky:
f_rest_V = f_rest_V*10**(-29)
print 'f_rest_V in cgs:'
print f_rest_V
#get luminosity distance from cosmocalc (lambdaCDM: omega_M = 0.27 and omega_lambda=0.73)
dist = []
for redshift in z_list_limits:
dist += [cosmocalc.cosmocalc(z=redshift)['DL_cm']]
arrd = arr(dist)
print 'dist:'
print arrd
L_rest_V = f_rest_V*4*numpy.pi*arrd**2./(1.+arrz)
print 'L_rest_V:'
print L_rest_V
#convert to ABSOLUTE AB magnitude:
parsec = 3.085677581e18 # cm
F_10pc = L_rest_V/(4 * numpy.pi * (10*parsec)**2) #flux density at 10 parsecs
Absol_Mag = -2.5*numpy.log10(F_10pc) - 48.60 #Absolute mag in AB mag
hist(Absol_Mag,6)
xlabel('$M_v$', fontsize=27)
ylabel('Number', fontsize=28)
yticks(arr([0, 1., 2., 3., 4.]))
ax = matplotlib.pyplot.gca()
ax.set_xlim(ax.get_xlim()[::-1]) # reversing the xlimits
savefig('Lum_dist_rest.eps')
print 'Done'
return Absol_Mag
def getDiameterDistances(data, redshift=0):
"""
Calculates a diameter distance in kpc / arc seconds
from data for all unique redshifts. The redshift
keyword indicates the column of redshifts.
:requires: cosmocalc
:param: data
:param: redshift column
:return: diameter distances
:rtype: dictionary
"""
out = {}
for x in set(data[:, redshift]):
out[x] = cosmocalc(x, 71.0, 0.28)['PS_kpc'] #in kpc / arc seconds
return out
def getDiameterDistances(data, redshift=0):
"""
Calculates a diameter distance in kpc / arc seconds
from data for all unique redshifts. The redshift
keyword indicates the column of redshifts.
:requires: cosmocalc
:param: data
:param: redshift column
:return: diameter distances
:rtype: dictionary
"""
out = {}
for x in set(data[:, redshift]):
out[x] = cosmocalc(x, 71.0, 0.28)['PS_kpc'] #in kpc / arc seconds
return out
def angularDiameterDistance(z, H0=70, WM=0.28):
"""
The angular diameter distance DA is defined as the ratio of
an object's physical transverse size to its angular size
(in radians). It is used to convert angular separations in
telescope images into proper separations at the source. It
is famous for not increasing indefinitely as z to inf; it turns
over at z about 1 and thereafter more distant objects actually
appear larger in angular size.
:param z: redshift
:type z: float
:param H0: value of the Hubble constant
:type H0: float
:param WM: :math:`\\Omega_{\\mathrm{matter}}`
:type WM: float
:return: angular diameter distance
:rtype: float
"""
return cosmocalc(z, H0, WM)["DA"]
def angularDiameterDistance(z, H0=70, WM=0.28):
"""
The angular diameter distance DA is defined as the ratio of
an object's physical transverse size to its angular size
(in radians). It is used to convert angular separations in
telescope images into proper separations at the source. It
is famous for not increasing indefinitely as z to inf; it turns
over at z about 1 and thereafter more distant objects actually
appear larger in angular size.
:param z: redshift
:type z: float
:param H0: value of the Hubble constant
:type H0: float
:param WM: :math:`\\Omega_{\\mathrm{matter}}`
:type WM: float
:return: angular diameter distance
:rtype: float
"""
return cosmocalc(z, H0, WM)['DA']
# kk = np.bitwise_and(obs_spec['wl'] > 3000, obs_spec['wl'] < 9000)
i_z = 0
for ccd in db_f.get(filterid).keys():
# Get the filterid filter.
f.read(filter_file, path='/%s/%s' % (filterid, ccd))
# For each defined redshift, eval the photometry and store on the database.
db_m = db.get('/%s/%s/%s' % (filterid, ccd, 'library'))
i_z = 0
for z in np.arange(z_from, z_to, z_step):
if z == 0:
d_L = 3.08567758e19 # 10 parsec in cm
else:
d_L = cosmocalc.cosmocalc(z)['DL_cm']
k_cosmo = L_sun / ( 4 * np.pi * np.power(d_L,2) )
O = zcor(obs_spec, z)
O['flux'] = O['flux'] * k_cosmo
x = spec2filterset(f.filterset, O, dlambda_eff = 3.0)
# print 'z', z
# print aux0[i_base]
#
# plt.figure(1)
# plt.clf()
# plt.plot(O['wl'][kk], O['flux'][kk])
# plt.figure(2)
def Get_fits(ClusterFile):
flexscale = True #Because of the BUG!
surveys = ['TGSS_alt'] #NVSS,WENNS,FIRST,TGSS
print '###==== Step 0: Load pointings for TGSS ====###'
pointings = []
for line in open('Surveys/TGSS_pointings.dat'):
token = line.rstrip('\n').split()
if len(token) < 2 or token[0][0] == '#':
continue
pointings.append((token[0],
SkyCoord(ra=float(token[1]) * u.degree,
dec=float(token[2]) * u.degree)))
print 'Number of TGSS pointings:', len(pointings)
print '###==== Step 1: Load cluster file and get parameters ====###'
print 'Opening the file "' + infile + '" to get the clusters for search in image databases'
with open(ClusterFile, 'rb') as csvfile:
spamreader = csv.reader(csvfile, delimiter=';', quotechar='|')
for CL in spamreader:
if CL[0] and not CL[0][0] == '#' and CL[0] not in [
o.name for o in ClList
]:
Cl_name = CL[0]
if CL[12] in ['TRUE'] and Cl_name not in [
''
]: #If Flag_INCLUDE==TRUE ; and Cl_name not in ['1RXS_J060313.4+421231']
RA = float(CL[2]) # d.XXXX
Dec = float(CL[3]) # d.XXXX
try:
z = float(CL[4])
except:
z = 0.
cosmoPara = cosmocalc(z, H0=70, WM=0.29, WV=0.71)
D_lum = cosmoPara['DL_cm'] # cm
plsc = cosmoPara['PS_kpc'] # kpc/''
#create Class object
GCl = galaxycluster(name=Cl_name, RA=RA, Dec=Dec, z=z)
ClList.append(GCl)
print '###==== Step 2: Fork fits files from servers for %s ====###' % (
Cl_name)
det_id = Cl_name
RA_h = str(RA / 15) # h.XXXX
RA_hms = str(ephem.hours(str(decdeg2hms(
float(RA))))).replace(':', ' ') # hh mm ss
Dec_dms = str(ephem.degrees(str(float(Dec)))).replace(
':', ' ') # dd mm ss
RA_hms_p = str(ephem.hours(str(decdeg2hms(
float(RA))))).replace(':', '+') # hh+mm+ss
Dec_dms_p = str(ephem.degrees(str(float(Dec)))).replace(
':', '+') # dd+mm+ss
onempc = 1e3 / plsc #Imagesize = 30 # in Bogensekunden
if onempc > 700 and flexscale:
imN = ['2+2', '15+15']
imV = ['3+3', '22.5+22.5']
else:
imN = ['1+1', '7.5+7.5']
imV = ['2+2', '15+15']
adressnvss = 'http://www.cv.nrao.edu/cgi-bin/postage.pl?ObjName=NVSS-' + det_id + '&RA=' + RA_hms_p + '&Dec=' + Dec_dms_p + '&Size=' + imN[
0] + '&Cells=' + imN[
1] + '&Equinox=2000&Type=application/octet-stream'
adressfirst = 'http://third.ucllnl.org/cgi-bin/firstcutout?RA=' + RA_h + '&Dec=' + str(
Dec
) + '&ImageSize=30&MaxInt=1&ImageType=FITSImage' #Image Size= 1 equals 1 arcmin
adressvlss = 'http://www.cv.nrao.edu/cgi-bin/newVLSSpostage.pl?ObjName=VLSS' + det_id + '&RA=' + RA_hms_p + '&Dec=' + Dec_dms_p + '&Size=' + imV[
0] + '&Cells=' + imV[
1] + '&Equinox=2000&Type=application/octet-stream' #http://www.cv.nrao.edu/vlss/VLSSpostage.shtml
#=== NVSS
if 'NVSS' in surveys:
print adressnvss
for h in range(0, 5):
try:
web_file = urllib2.urlopen(adressnvss,
timeout=18)
out_file = open(
'ClusterRegionImages/NVSS/NVSS-' + det_id +
'.fits', 'w')
out_file.write(web_file.read())
out_file.close()
except:
continue
else:
break
#=== FIRST
if 'FIRST' in surveys:
print adressfirst
for h in range(0, 5):
try:
web_file = urllib2.urlopen(adressfirst,
timeout=18)
out_file = open(
'ClusterRegionImages/FIRST/FIRST-' +
det_id + '.fits', 'w')
out_file.write(web_file.read())
out_file.close()
except:
continue
else:
break
#=== VLSS
if 'VLSS' in surveys:
print adressvlss
for h in range(0, 5):
try:
web_file = urllib2.urlopen(adressvlss,
timeout=18)
out_file = open(
'ClusterRegionImages/VLSSr/VLSSr-' +
det_id + '.fits', 'w')
out_file.write(web_file.read())
out_file.close()
except:
continue
else:
break
#=== WENSS
if 'WENNS' in surveys:
#Webside is easily callable, mayby only one request neccesairy
url = 'http://www.astron.nl/wow'
values = {
'POS': RA_hms + ' ' + Dec_dms,
'SIZE': '2.5, 2.5',
'Equinox': 'J2000',
'cutout': 1,
'surv_id': 1
}
for h in range(0, 1): #range (0,5)
try:
data = urllib.urlencode(values)
#print 'Data:',data
queurl = url + '/testcode.php?' + data
print 'WENSSurl:', queurl
response = urllib2.urlopen(queurl)
print 'Response:', response
#print response.read()
#print 'Response.geturl():',response.geturl()
#print response.info()
regionurl = ''
for line in response:
#print line.rstrip()
#== Not used until a Region larger than 1*1 deg can get choosen
#if not line.find('.fits.gz">cut') == -1:
##print line
#substr = find_between( line , 'class="testo3"><a ', '</a></span><br/>')
##print 'Found in between', substr
#regionurl = url + '/' + find_between( substr, 'href=', '.fits.gz>') + ".fits.gz"
#print 'Regionurl:',regionurl
if not line.find('.GZ</a>') == -1:
substr = find_between(
line, 'a href="survey1',
'</a></span><br/>')
#print 'Substr', substr
regionurl = url + '/survey1/' + find_between(
substr, '/', '.GZ>') + ".GZ"
print 'WENSSRegionurl:', regionurl
if not regionurl == '':
web_file = urllib2.urlopen(regionurl,
timeout=18)
filename = 'ClusterRegionImages/WENSS/WENSS-' + det_id + '.fits.gz'
out_file = open(filename, 'w')
out_file.write(web_file.read())
out_file.close()
os.system('gzip -d ' + filename)
print 'WENSS ok for', det_id, '\n'
except:
continue
else:
break
#=== TGSS
if 'TGSS' in surveys:
url = 'http://tgss.ncra.tifr.res.in/cgi-bin/tgss_postage.cgi'
values = {
'raval': RA_hms,
'decval': Dec_dms,
'szval': '1.0',
'szunit': 'deg',
'fmtval': 'fits'
}
for h in range(0, 1): #range (0,5)
try:
data = urllib.urlencode(values)
print 'Data:', data
req = urllib2.Request(url, data)
print 'Request:', req
response = urllib2.urlopen(req)
##print response.read()
print 'Response.geturl():', response.geturl()
##print response.info()
regionurl = ''
for line in response:
#print line.rstrip()
if not line.find(
'<a href="opendat/tgss_') == -1:
regionurl = 'http://tgss.ncra.tifr.res.in/' + find_between(
line, "<a href=", ">")
print 'TGSSurl:', regionurl
if not regionurl == '':
web_file = urllib2.urlopen(regionurl,
timeout=18)
out_file = open(
'ClusterRegionImages/TGSS/TGSS-' +
det_id + '.fits', 'w')
out_file.write(web_file.read())
out_file.close()
print 'TGSS ok for', det_id, '\n'
except:
continue
else:
break
#=== TGSS altenrative
if 'TGSS_alt' in surveys:
try:
size = os.path.getsize(
os.path.join(
os.path.abspath("."),
'Images_TGSS/TGSS-' + det_id + '.fits'))
except:
size = -1
if size < 10000:
if size > 0:
os.remove(
os.path.join(
os.path.abspath("."),
'Images_TGSS/TGSS-' + det_id +
'.fits'))
print 'Target ' + det_id + ' was not found in the TGSS-catalogue. It is going to be downloaded.'
ClusterPos = SkyCoord(ra=RA * u.degree,
dec=Dec * u.degree)
dist = [
p[1].separation(ClusterPos) for p in pointings
]
mindist = min(dist)
print 'TGSS-field minimal distance:', mindist
index = dist.index(mindist)
cutname = pointings[index][0]
url = 'http://vo.astron.nl/tgssadr/q_fits/imgs/form' # http://vo.astron.nl/tgssadr/q_fits/imgs/form/login?nextURL=http%3A%2F%2Fvo.astron.nl%2Ftgssadr%2Fq_fits%2Fimgs%2Fform
values = {
'hPOS': '%8.4f , %8.4f' % (RA, Dec),
'hSIZE': 0.5,
'_FORMAT': "HTML",
'submit': 'submit'
}
#_queryForm"
for h in range(0, 1): #range (0,5)
try:
#data = urllib.urlencode(values)
#print 'Data:',data
#req = urllib2.Request(url, data)
#print 'Request:',req
#response = urllib2.urlopen(req)
###print response.read()
#print 'Response.geturl():',response.geturl()
##print response.info()
#regionurl = ''
#for line in response:
##print line.rstrip()
#if not line.find('<a href="opendat/tgss_') == -1:
#regionurl = 'http://tgss.ncra.tifr.res.in/' + find_between( line , "<a href=", ">")
#print 'TGSSurl:',regionurl
regionurl = 'http://vo.astron.nl/getproduct/tgssadr/fits/TGSSADR_%s_5x5.MOSAIC.FITS' % (
cutname
) #<a href="http://vo.astron.nl/getproduct/tgssadr/fits/TGSSADR_R43D11_5x5.MOSAIC.FITS" class="productlink">TGSSADR_R43D11_5x5.MOSAIC.FITS</a>
print regionurl
if not regionurl == '':
web_file = urllib2.urlopen(regionurl,
timeout=18)
out_file = open(
'Images_TGSS/TGSS-' + det_id +
'.fits', 'w')
#out_file = open('ClusterRegionImages/TGSS/TGSS-' + det_id + '.fits', 'w')
out_file.write(web_file.read())
out_file.close()
print 'TGSS ok for', det_id, '\n'
except:
continue
else:
break
else:
print 'Target ' + det_id + ' already in the TGSS-catalogue'
#import re
#s = 'asdf=5;iwantthis123jasd'
#result = re.search('asdf=5;(.*)123jasd', s)
#print result.group(1)
#import re
#r = re.compile('Master(.*?)thon')
#m = r.search(str1)
#if m:
#lyrics = m.group(1)
#from urllib2 import Request, urlopen, URLError
#req = Request(someurl)
#try:
#response = urlopen(req)
#except URLError as e:
#if hasattr(e, 'reason'):
#print 'We failed to reach a server.'
#print 'Reason: ', e.reason
#elif hasattr(e, 'code'):
#print 'The server couldn\'t fulfill the request.'
#print 'Error code: ', e.code
#else:
## everything is fine
#Somehow Bugy
#try:
#if os.path.getsize(os.path.join(os.path.abspath("."), 'ClusterRegionImages/NVSS/NVSS-' + det_id + '.fits') ) < 500:
#os.remove(os.path.join(os.path.abspath("."), 'ClusterRegionImages/NVSS/NVSS-' + det_id + '.fits'))
#print 'Target ' + det_id + ' was not found in the NVSS-catalogue'
#except:
#print 'Target ' + det_id + ' was not found in the NVSS-catalogue, there was also no junk file'
#else:
#print 'Target ' + det_id + ' was not found in the NVSS-catalogue, there was also no junk file!'
#try:
#if os.path.getsize(os.path.join(os.path.abspath("."), 'ClusterRegionImages/FIRST/FIRST-' + det_id + '.fits') ) < 10000:
#os.remove(os.path.join(os.path.abspath("."), 'ClusterRegionImages/FIRST/FIRST-' + det_id + '.fits'))
#print 'Target ' + det_id + ' was not found in the FIRST-catalogue'
#except:
#print 'Target ' + det_id + ' was not found in the FIRST-catalogue, there was also no junk file'
#else:
#print 'Target ' + det_id + ' was not found in the FIRST-catalogue, there was also no junk file'
#try:
#if os.path.getsize(os.path.join(os.path.abspath("."), 'ClusterRegionImages/VLSSr/VLSSr-' + det_id + '.fits') ) < 500:
#os.remove(os.path.join(os.path.abspath("."), 'ClusterRegionImages/VLSSr/VLSSr-' + det_id + '.fits'))
#print 'Target ' + det_id + ' was not found in the VLSS-catalogue'
#except:
#print 'Target ' + det_id + ' was not found in the VLSS-catalogue, there was also no junk file'
#else:
#print 'Target ' + det_id + ' was not found in the VLSS-catalogue, there was also no junk file'
#if os.path.getsize(os.path.join(os.path.abspath("."), 'ClusterRegionImages/TGSS/TGSS-' + det_id + '.fits') ) < 10000:
#os.remove(os.path.join(os.path.abspath("."), 'ClusterRegionImages/TGSS/TGSS-' + det_id + '.fits'))
#print 'Target ' + det_id + ' was not found in the TGSS-catalogue'
#print >> file, "%6.0f"%det_id, "%12s"%ra_hms, "%13s"%dec_dms, "%6.2f"%z, "%8.2f"%L, "%6.2f"%scale, "%8.2f"%onempc
return
def search_sdss(blazardir='/Users/willettk/Astronomy/Research/blazars/',blazarfile='plotkin_dr8_upload.txt',savstring='',envsize=1000,ngals=1000, save=False, timing=True):
import sqlcl
import cosmocalc
import time
timestart = time.time()
# Read in the list of blazars from combined catalogs (Plotkin, BZCAT, TeVcat)
pf = open(blazardir+blazarfile,'r')
# Python dictionary for the relevant data to be returned
sdss_data_zns = { 'objid':[], 'ra':[], 'dec':[], 'type':[], 'nchild':[],
'u':[], 'g':[], 'r':[], 'i':[], 'z':[],
'p_el':[], 'p_cw':[], 'p_acw':[], 'p_edge':[], 'p_dk':[], 'p_mg':[], 'p_cs':[],
'blazar_name':[]
}
sdss_data_zs = { 'objid':[], 'ra':[], 'dec':[], 'type':[], 'nchild':[],
'u':[], 'g':[], 'r':[], 'i':[], 'z':[],
'p_el':[], 'p_cw':[], 'p_acw':[], 'p_edge':[], 'p_dk':[], 'p_mg':[], 'p_cs':[],
'blazar_name':[]
}
sdss_data_nozoo = { 'objid':[], 'ra':[], 'dec':[], 'type':[], 'nchild':[],
'u':[], 'g':[], 'r':[], 'i':[], 'z':[],
'blazar_name':[]
}
errorarr = {'type':[],'name':[]}
for line in pf:
#
blazar_name, blazar_ra, blazar_dec, blazar_z, blazar_type = line.split()
blazar_z = float(blazar_z)
#
# Compute the angular size of 1000 kpc
#
platescale = cosmocalc.cosmocalc(z=blazar_z, H0=71, WM=0.27)['PS_kpc'] # kpc per arcsec
angsize = envsize / platescale / 60.
#
# Query the SDSS DR8 catalog
#
blazar_query_zns = """ SELECT top %i p.objID, p.ra, p.dec, p.type, p.nchild, p.u, p.g, p.r, p.i, p.z, zns.p_el, zns.p_cw, zns.p_acw, zns.p_edge, zns.p_dk, zns.p_mg, zns.p_cs FROM fGetNearbyObjEq(%f,%f,%f) n JOIN PhotoObj p on p.objid = n.objid JOIN zooNoSpec zns on zns.objid = n.objid ORDER BY p.objID """ % (ngals, float(blazar_ra), float(blazar_dec), angsize)
blazar_query_zs = """ SELECT top %i p.objID, p.ra, p.dec, p.type, p.nchild, p.u, p.g, p.r, p.i, p.z, zs.p_el, zs.p_cw, zs.p_acw, zs.p_edge, zs.p_dk, zs.p_mg, zs.p_cs FROM fGetNearbyObjEq(%f,%f,%f) n JOIN PhotoObj p on p.objid = n.objid JOIN zooSpec zs on zs.objid = n.objid ORDER BY p.objID """ % (ngals, float(blazar_ra), float(blazar_dec), angsize)
blazar_query_nozoo = """ SELECT top %i p.objID, p.ra, p.dec, p.type, p.nchild, p.u, p.g, p.r, p.i, p.z FROM fGetNearbyObjEq(%f,%f,%f) n JOIN PhotoObj p on p.objid = n.objid ORDER BY p.objID """ % (ngals, float(blazar_ra), float(blazar_dec), angsize)
#
bqtime = time.time()
#
queryreturn_zns = sqlcl.query(blazar_query_zns, fmt='csv')
queryreturn_zs = sqlcl.query(blazar_query_zs, fmt='csv')
queryreturn_nozoo = sqlcl.query(blazar_query_nozoo, fmt='csv')
#
lqtime = time.time()
#
# Make sure queries do not exceed 60 per minute (intrinsic limit of database)
#
if (lqtime - bqtime) < 3.1:
time.sleep(3.1 - (lqtime - bqtime))
#
qr_read_zns = queryreturn_zns.read()
qr_read_zs = queryreturn_zs.read()
qr_read_nozoo = queryreturn_nozoo.read()
#
# Print detections to the screen
#
if (qr_read_zns[:26] != 'No objects have been found') and (qr_read_zns[:5] != 'ERROR'):
print blazar_name
#
qrsplit = qr_read_zns.split()
print qrsplit[1:]
for n in arange(len(qrsplit[1:]))+1:
neighbor_data = qrsplit[n].split(',')
sdss_data_zns['objid'].append(neighbor_data[0])
sdss_data_zns['ra'].append(neighbor_data[1])
sdss_data_zns['dec'].append(neighbor_data[2])
sdss_data_zns['type'].append(neighbor_data[3])
sdss_data_zns['nchild'].append(neighbor_data[4])
sdss_data_zns['u'].append(neighbor_data[5])
sdss_data_zns['g'].append(neighbor_data[6])
sdss_data_zns['r'].append(neighbor_data[7])
sdss_data_zns['i'].append(neighbor_data[8])
sdss_data_zns['z'].append(neighbor_data[9])
sdss_data_zns['p_el'].append(neighbor_data[10])
sdss_data_zns['p_cw'].append(neighbor_data[11])
sdss_data_zns['p_acw'].append(neighbor_data[12])
sdss_data_zns['p_edge'].append(neighbor_data[13])
sdss_data_zns['p_dk'].append(neighbor_data[14])
sdss_data_zns['p_mg'].append(neighbor_data[15])
sdss_data_zns['p_cs'].append(neighbor_data[16])
sdss_data_zns['blazar_name'].append(blazar_name)
if (qr_read_zs[:26] != 'No objects have been found') and (qr_read_zs[:5] != 'ERROR'):
print blazar_name
#
qrsplit = qr_read_zs.split()
print qrsplit[1:]
for n in arange(len(qrsplit[1:]))+1:
neighbor_data = qrsplit[n].split(',')
sdss_data_zs['objid'].append(neighbor_data[0])
sdss_data_zs['ra'].append(neighbor_data[1])
sdss_data_zs['dec'].append(neighbor_data[2])
sdss_data_zs['type'].append(neighbor_data[3])
sdss_data_zs['nchild'].append(neighbor_data[4])
sdss_data_zs['u'].append(neighbor_data[5])
sdss_data_zs['g'].append(neighbor_data[6])
sdss_data_zs['r'].append(neighbor_data[7])
sdss_data_zs['i'].append(neighbor_data[8])
sdss_data_zs['z'].append(neighbor_data[9])
sdss_data_zs['p_el'].append(neighbor_data[10])
sdss_data_zs['p_cw'].append(neighbor_data[11])
sdss_data_zs['p_acw'].append(neighbor_data[12])
sdss_data_zs['p_edge'].append(neighbor_data[13])
sdss_data_zs['p_dk'].append(neighbor_data[14])
sdss_data_zs['p_mg'].append(neighbor_data[15])
sdss_data_zs['p_cs'].append(neighbor_data[16])
sdss_data_zs['blazar_name'].append(blazar_name)
if (qr_read_nozoo[:26] != 'No objects have been found') and (qr_read_nozoo[:5] != 'ERROR'):
print blazar_name
#
qrsplit = qr_read_nozoo.split()
print qrsplit[1:]
for n in arange(len(qrsplit[1:]))+1:
neighbor_data = qrsplit[n].split(',')
sdss_data_nozoo['objid'].append(neighbor_data[0])
sdss_data_nozoo['ra'].append(neighbor_data[1])
sdss_data_nozoo['dec'].append(neighbor_data[2])
sdss_data_nozoo['type'].append(neighbor_data[3])
sdss_data_nozoo['nchild'].append(neighbor_data[4])
sdss_data_nozoo['u'].append(neighbor_data[5])
sdss_data_nozoo['g'].append(neighbor_data[6])
sdss_data_nozoo['r'].append(neighbor_data[7])
sdss_data_nozoo['i'].append(neighbor_data[8])
sdss_data_nozoo['z'].append(neighbor_data[9])
sdss_data_nozoo['blazar_name'].append(blazar_name)
if qr_read_zns[:5] == 'ERROR':
errorarr['type'].append('zns')
errorarr['name'].append(blazar_name)
if qr_read_zs[:5] == 'ERROR':
errorarr['type'].append('zs')
errorarr['name'].append(blazar_name)
if qr_read_nozoo[:5] == 'ERROR':
errorarr['type'].append('nozoo')
errorarr['name'].append(blazar_name)
if save is True:
import pickle
# Save dictionary to file so it doesn't have to be rerun
output = open('sdss_'+str(envsize)+'kpc_zns'+str(savstring)+'.pkl', 'w')
pickle.dump(sdss_data_zns, output)
output.close()
output = open('sdss_'+str(envsize)+'kpc_zs'+str(savstring)+'.pkl', 'w')
pickle.dump(sdss_data_zs, output)
output.close()
output = open('sdss_'+str(envsize)+'kpc_nozoo'+str(savstring)+'.pkl', 'w')
pickle.dump(sdss_data_nozoo, output)
output.close()
print "Saved dictionaries to file"
endtime = time.time()
print 'Total time elapsed is %5.2f min' % ((endtime - starttime)*60)
def rr(x, pos=None):
g = cosmocalc.cosmocalc(10.0 ** x, H0=71.0)
if g["zage_Gyr"] < 1:
return "%.2f" % g["zage_Gyr"] # Return 2 dec place if age < 1; e.g 0.62
else:
return "%.1f" % g["zage_Gyr"] # Return 1 dec place if age > 1; e.g. 1.5
def plot_lum_rest():
'''f_{rest,V} = f_{rest_corr}*[nu_V/ ((1+z)nu_J)]^beta for flux \propto nu^beta and beta negative values'''
clf()
#
# those are values from the lightcurve, where I have 3minutes*(1+z) = t_0, and then I get the value of of the lightcurve at t_0
#
# me: right but what function
#
# Pierre: most of them are interpolations from Dan Perley's fits
# 10:07 AM
# some actually have values at times that are very close to 3 minutes *(1+z) and for those I just use the actual data points
# f_rest_corr = [2252.14, 1626.48, 403.717, 11783.2, 913.329, 549.616, 286.863, 990.110, 14.7689, 174.540, 1419.79, 149309.80115]
# beta = [-1.35, -0.8, -0.96, -0.22, -1.73, -0.84, -3.48, -0.42, -3.81, -0.3, -1.7, -0.47]
beta = df_grb.beta
# z_list_limits = [1.1588, 2.4274, 1.51, 0.54, 1.95, 1.6, 3.036, 2.346, 3.5, 1.165, 4.8, 0.9382]
z_list_limits = df_grb.z
arrb = arr(beta)
arrz = arr(z_list_limits)
# nu_V = 5.444646098003629764065335753176043557e+14
# nu_J = 2.398339664e+14
nu_V = qObs.V.freq
nu_J = qObs.J.freq
nu_K = qObs.Ks.freq
nu_H = qObs.H.freq
abs_mag = []
for nu_obs, f_obs in [(nu_J, jz3corrhost), (nu_H, hz3corrhost),
(nu_K, kz3corrhost)]:
arrf = arr(f_obs)
f_rest_V = arrf * (nu_V / ((1 + arrz) * nu_obs))**(beta)
print 'f_rest_V in microjansky:'
print f_rest_V
#convert to cgs from microjansky:
f_rest_V = f_rest_V * 10**(-29)
print 'f_rest_V in cgs:'
print f_rest_V
#get luminosity distance from cosmocalc (lambdaCDM: omega_M = 0.27 and omega_lambda=0.73)
dist = []
for redshift in z_list_limits:
dist += [cosmocalc.cosmocalc(z=redshift)['DL_cm']]
arrd = arr(dist)
print 'dist:'
print arrd
L_rest_V = f_rest_V * 4 * numpy.pi * arrd**2. / (1. + arrz)
print 'L_rest_V:'
print L_rest_V
#convert to ABSOLUTE AB magnitude:
parsec = 3.085677581e18 # cm
F_10pc = L_rest_V / (4 * numpy.pi *
(10 * parsec)**2) #flux density at 10 parsecs
F_10pc = F_10pc.fillna(-1).astype('float') # type problems..
Absol_Mag = -2.5 * numpy.log10(F_10pc) - 48.60 #Absolute mag in AB mag
abs_mag.append(Absol_Mag)
plt.hist(np.array(abs_mag[1]),
bins=np.linspace(-33.8, -25.5, 9),
histtype='stepfilled',
color=hc,
linewidth=0,
alpha=0.15)
plt.hist(np.array(abs_mag[0]),
bins=np.linspace(-33.8, -25.5, 9),
histtype='stepfilled',
color=jc,
linewidth=0,
alpha=0.15)
plt.hist(np.array(abs_mag[2]),
bins=np.linspace(-33.8, -25.5, 9),
histtype='stepfilled',
color=kc,
linewidth=0,
alpha=0.15)
plt.hist(np.array(abs_mag[1]),
bins=np.linspace(-33.8, -25.5, 9),
histtype='step',
color=hc,
linewidth=11,
label="$H$ detection")
plt.hist(np.array(abs_mag[0]),
bins=np.linspace(-33.8, -25.5, 9),
histtype='step',
color=jc,
linewidth=7,
label="$J$ detection")
plt.hist(np.array(abs_mag[2]),
bins=np.linspace(-33.8, -25.5, 9),
histtype='step',
color=kc,
linewidth=3,
label="$K$ detection")
switch = -0.2
for ind in Absol_Mag.index[::-1]:
# plt.text(Absol_Mag.loc[ind],1.0,ind,rotation=90,ha='center',va='center',fontsize=12)
# plt.annotate(ind,xy=(abs_mag[0].loc[ind],0),xytext=(abs_mag[0].loc[ind],0.6-switch),color=jc,arrowprops=dict(arrowstyle="->",connectionstyle="arc3",color=jc),rotation=90,ha='center',va='center',fontsize=14)
# plt.annotate(ind,xy=(abs_mag[1].loc[ind],0),xytext=(abs_mag[1].loc[ind],1.6-switch),color=hc,arrowprops=dict(arrowstyle="->",connectionstyle="arc3",color=hc),rotation=90,ha='center',va='center',fontsize=14)
# plt.annotate(ind,xy=(abs_mag[2].loc[ind],0),xytext=(abs_mag[2].loc[ind],2.6-switch),color=kc,arrowprops=dict(arrowstyle="->",connectionstyle="arc3",color=kc),rotation=90,ha='center',va='center',fontsize=14)
#
plt.annotate(ind,
xy=(abs_mag[1].loc[ind], 0),
xytext=(abs_mag[1].loc[ind], offsets[ind]),
color='dimgray',
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3",
color='gray',
lw=2),
rotation=90,
ha='center',
va='center',
bbox=dict(boxstyle="round",
fc="whitesmoke",
ec="gray",
lw=2),
fontsize=16)
switch *= -1
# hist(Absol_Mag,6,color="#88CCEE")
plt.xlabel('Absolute Magnitude $M_V$', fontsize=20)
plt.ylabel('Number', fontsize=20)
plt.yticks(arr([0, 1., 2., 3., 4., 5., 6.]))
ax = matplotlib.pyplot.gca()
ax.set_xlim(ax.get_xlim()[::-1]) # reversing the xlimits
ax.set_xlim((-24, -35))
ax.set_ylim((0, 3.5))
plt.title('$t=3$ min (rest frame)', fontsize=20)
plt.legend(loc='upper left', fontsize=18, frameon=False)
plt.savefig('Lum_dist_rest.eps')
plt.savefig('Lum_dist_rest.png')
print 'Done'
return abs_mag
def quickPlot(galaxies=170, rvir=0.105):
"""
A single halo test
:param galaxies: number of subhalo galaxies
:param rvir: radius of the main halo [Mpc]
"""
conversion = 0.000277777778 # degree to arcsecond
#redshift
z = 4.477
#get the angular diameter distance to the galaxy
dd = cosmocalc(z, 71.0, 0.28)['PS_kpc'] #in kpc / arc seconds
#position of the main galaxy
RADeg1 = 189.41432
decDeg1 = 62.166702
# get the random values
rds = rand.randomUnitSphere(galaxies)
# convert them to Cartesian coord
rd = conv.convertSphericalToCartesian(rvir / dd / 2., rds['theta'],
rds['phi'])
data = {
'CD': np.matrix('-1 0; 0 1'),
'RA': RADeg1,
'DEC': decDeg1,
'X': rd['y'],
'Y': rd['z']
}
result = conv.RAandDECfromStandardCoordinates(data)
RADeg2 = result['RA']
decDeg2 = result['DEC']
#calculate the angular separation on the sky
sep = Coords.calcAngSepDeg(RADeg1, decDeg1, RADeg2, decDeg2) / 2.
physical_distance = sep * dd / conversion
text = r'Random Positions, z={0:.2f}, radius of the halo = {1:.0f} kpc'.format(
z, 1e3 * rvir)
#make a figure
fig = plt.figure(figsize=(12, 12))
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(223)
rect = fig.add_subplot(224, visible=False).get_position()
ax4 = Axes3D(fig, rect)
ax1.hist(sep * 1e4, bins=12)
ax2.hist(physical_distance, bins=12)
ax3.scatter(0, 0, c='k', s=120, marker='s')
ax3.scatter((RADeg2 - RADeg1) * 1e4, (decDeg2 - decDeg1) * 1e4,
c='b',
s=50,
marker='o',
alpha=0.45)
#generate a sphere
u = np.linspace(0, 2 * np.pi, 100)
v = np.linspace(0, np.pi, 100)
x = np.outer(np.cos(u), np.sin(v))
y = np.outer(np.sin(u), np.sin(v))
z = np.outer(np.ones(np.size(u)), np.cos(v))
#plot it as a wire frame
ax4.plot_wireframe(x * 1e4 * rvir / dd,
y * 1e4 * rvir / dd,
z * 1e4 * rvir / dd,
rstride=10,
cstride=10,
color='0.5',
alpha=0.7)
#plot the random points
ax4.scatter(rd['x'] * 2e4,
rd['y'] * 2e4,
rd['z'] * 2e4,
color='b',
s=30,
alpha=0.8)
ax1.set_xlabel(r'Projected Separation [$10^{-4}$ deg]')
ax2.set_xlabel(r'Projected Distance [kpc]')
ax3.set_xlabel(r'$\Delta$RA [$10^{-4}$ deg]')
#ax4.set_xlabel(r'$\Delta$RA [$10^{-4}$ deg]')
ax3.set_ylabel(r'$\Delta$DEC [$10^{-4}$ deg]')
#ax4.set_ylabel(r'$\Delta$DEC [$10^{-4}$ deg]')
ax3.set_xlim(-200, 200)
ax3.set_ylim(-100, 100)
ax4.set_xlim3d(-150, 150)
ax4.set_ylim3d(-150, 150)
ax4.set_zlim3d(-150, 150)
plt.annotate(text, (0.5, 0.95),
xycoords='figure fraction',
ha='center',
va='center',
fontsize=12)
plt.savefig('RandomHalo.pdf')
pos_z_d = [pos_z[i] for i in w] print len(StellarMass_d), ' galaxies diluted' # ------------------------------------- # plot stellar mass function print 'plotting SMF...' # set up figure plt.figure() ax = plt.subplot(111) if (cone == True): volume = cosmocalc(Redshift_Depth, H0=Hubble_h*100.0)['VCM_Gpc3'] * (1000.0**3.0) * (oangle/41253.0) * factor else: volume = (BoxSize / Hubble_h)**3.0 binwidth = 0.1 mass = [np.log10(StellarMass[i] * 1.0e10 / Hubble_h) for i in xrange(len(StellarMass))] # sSFR = [Sfr[i] / (StellarMass[i] * 1.0e10 / Hubble_h) for i in xrange(len(StellarMass))] mi = 5.0 ma = 15.0 NB = (ma - mi) / binwidth (counts, binedges) = np.histogram(mass, range=(mi, ma), bins=NB) # Set the x-axis values to be the centre of the bins
def plot_stellarmasses(path, database, cut, redshifts,
out_folder, obs_data,
solid_angle=1.077 * 10 ** -5,
ymin=10 ** 3, ymax=2 * 10 ** 6,
xmin=0.5, xmax=100,
nbins=15, sigma=3.0,
H0=70.0, WM=0.28, zmax=6.0,
write_out=False):
'''
160 square arcminutes in steradians =
1.354*10**-5 sr (steradians)
Simulation was 10 times the GOODS realization, so
the solid angle is 1.354*10**-4
:param sigma: sigma level of the errors to be plotted
:param nbins: number of bins (for simulated data)
:param area: actually 1 / area, used to weight galaxies
'''
#fudge factor to handle errors that are way large
fudge = ymin
#subplot numbers
columns = 2
rows = 3
#get data
query = '''select galprop.mstar from galprop, FIR where
galprop.gal_id = FIR.gal_id and
galprop.halo_id = FIR.halo_id and %s''' % cut
masses_limit = db.sqlite.get_data_sqlite(path, database, query)
query = '''select galprop.mstar from galprop, FIR where
galprop.gal_id = FIR.gal_id and
galprop.halo_id = FIR.halo_id'''
masses_total = db.sqlite.get_data_sqlite(path, database, query)
#make the figure
fig = P.figure()
P.subplots_adjust(wspace=0.0, hspace=0.0)
ax = P.subplot(rows, columns, 1)
#get the co-moving volume to the backend
vol = cosmocalc(zmax, H0, WM)['VCM_Gpc3']
#weight each galaxy
wghts = N.zeros(len(masses_total)) + (1. / (solid_angle * vol * 1e9))
#calculate the differential stellar mass function
#with log binning
b, n, nu = df.diff_function_log_binning(masses_total,
wgth=wghts,
mmax=xmax,
mmin=xmin,
nbins=nbins,
log=True)
#get the knots
x = 10 ** b
y = n
#plot the knots
mtot = ax.plot(x, y, 'k-')
#poisson error
mask = nu > 0
err = (1. / (solid_angle * vol * 1e9)) * N.sqrt(nu[mask]) * sigma
up = y[mask] + err
lw = y[mask] - err
lw[lw < ymin] = ymin
stot = ax.fill_between(x[mask], up, lw, color='#728FCE')
#limited mass
wghts = N.zeros(len(masses_limit)) + (1. / (solid_angle * vol * 1e9))
b, n, nu = df.diff_function_log_binning(masses_limit,
wgth=wghts,
mmax=xmax,
mmin=xmin,
nbins=nbins,
log=True)
#get the knots
x = 10 ** b
y = n
#plot the knots
mlim = ax.plot(x, y, 'r-')
#poisson error
mask = nu > 0
err = (1. / (solid_angle * vol * 1e9)) * N.sqrt(nu[mask]) * sigma
up = y[mask] + err
lw = y[mask] - err
lw[lw < ymin] = ymin
slim = ax.fill_between(x[mask], up, lw, color='red')
#write to the file if needed, using appending so might get long...
if write_out:
fh = open(out_folder + 'outputTotal.txt', 'a')
fh.write('#Mstellar[log10(M_sun)] dN/dlog10(M_sun) high low\n')
for aa, bb, cc, dd in zip(x[mask], y[mask], up, lw):
fh.write('%e %e %e %e\n' % (aa, bb, cc, dd))
fh.close()
#add annotation
ax.annotate('Total', (0.5, 0.9), xycoords='axes fraction',
ha='center')
#set scale
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlim(10 ** xmin, 10 ** xmax)
ax.set_ylim(ymin, ymax)
ax.set_xticklabels([])
ptot = P.Rectangle((0, 0), 1, 1, fc='#728FCE')
plim = P.Rectangle((0, 0), 1, 1, fc='red')
sline = '%i$\sigma$ errors' % sigma
P.legend((mtot, ptot, plim),
('All Galaxies', sline, '$S_{250} > 5$ mJy'))
#redshift limited plots
for i, red in enumerate(redshifts):
query = '''select galprop.mstar from galprop, FIR where
galprop.gal_id = FIR.gal_id and galprop.halo_id = FIR.halo_id
and %s''' % (red)
mtotal = db.sqlite.get_data_sqlite(path, database, query)
query = '''select galprop.mstar from galprop, FIR where
galprop.gal_id = FIR.gal_id and galprop.halo_id = FIR.halo_id
and %s and %s''' % (cut, red)
mlimit = db.sqlite.get_data_sqlite(path, database, query)
#modify redshift string
tmp = red.split()
rtitle = r'$%s < z \leq %s$' % (tmp[2], tmp[6])
#make a subplot
axs = P.subplot(rows, columns, i + 2)
#get a comoving volume to the back end
volb = cosmocalc(float(tmp[6]), H0, WM)['VCM_Gpc3']
volf = cosmocalc(float(tmp[2]), H0, WM)['VCM_Gpc3']
#weights
wgvol = (1. / (solid_angle * volb * 1e9))
# wgvol = (1./(solid_angle*(volb-volf)*1e9))
wghts = N.zeros(len(mtotal)) + wgvol
b, n, nu = df.diff_function_log_binning(mtotal,
wgth=wghts,
mmax=xmax,
mmin=xmin,
nbins=nbins,
log=True)
x = 10 ** b
y = n
#plot the knots
axs.plot(x, y, 'k-')
#poisson error
mask = nu > 0
err = wgvol * N.sqrt(nu[mask]) * sigma
up = y[mask] + err
lw = y[mask] - err
lw[lw < ymin] = ymin
axs.fill_between(x[mask], up, lw, color='#728FCE')
#limited mass
wghts = N.zeros(len(mlimit)) + wgvol
b, n, nu = df.diff_function_log_binning(mlimit,
wgth=wghts,
mmax=xmax,
mmin=xmin,
nbins=nbins,
log=True)
x = 10 ** b
y = n
#plot the knots
axs.plot(x, y, 'r-')
#poisson error
mask = nu > 0
err = wgvol * N.sqrt(nu[mask]) * sigma
up = y[mask] + err
lw = y[mask] - err
lw[lw < ymin] = ymin
axs.fill_between(x[mask], up, lw, color='red')
#write to output
if write_out:
fh = open(out_folder + 'outputredshiftbin%i.txt' % i, 'a')
fh.write('#' + rtitle + '\n')
fh.write('#Mstellar[log10(M_sun)] dN/dlog10(M_sun) high low\n')
for aa, bb, cc, dd in zip(x[mask], y[mask], up, lw):
fh.write('%e %e %e %e\n' % (aa, bb, cc, dd))
fh.close()
#add annotation
axs.annotate(rtitle, (0.5, 0.9), xycoords='axes fraction',
ha='center')
#set scales
axs.set_xscale('log')
axs.set_yscale('log')
axs.set_xlim(10 ** xmin, 10 ** xmax)
axs.set_ylim(ymin, ymax)
#remove unnecessary ticks and add units
if i % 2 == 0:
axs.set_yticklabels([])
if i == 2 or i == 3:
axs.set_xlabel(r'$M_{\star} \quad [M_{\odot}]$')
if i == 2:
axs.set_xticks(axs.get_xticks()[1:])
else:
axs.set_xticklabels([])
if i == 1:
axs.set_ylabel(r'$\frac{dN}{d \log_{10} M_{\star}} \ [Mpc^{-3} \ dex^{-1}]$')
#save figure
P.savefig(out_folder + 'stellarmassfunction.ps')
P.close()
tmp = matched[matched[:, 3] == x]
#redshift
z = tmp[0][0]
RADeg1 = tmp[0][1]
decDeg1 = tmp[0][2]
RADeg2 = tmp[1:, 1]
decDeg2 = tmp[1:, 2]
prop = tmp[:, 5]
sep = Coords.calcAngSepDeg(RADeg1, decDeg1, RADeg2, decDeg2)
dd = cosmocalc(z, 71.0, 0.28)['PS_kpc'] #dist.getDiameterDistances(data)
physical_distance = (sep / dd) / conversion
print z, RADeg1, RADeg2, decDeg1, decDeg2, prop
print sep, physical_distance
print
ax.plot([z for foo in range(len(physical_distance))], physical_distance, 'bo')
#P.show()
print
print
cursor.execute('CREATE TABLE newTable (h1, g1, h2, g2, foo)')
newqu = """INSERT INTO newTable
def plot_stellarmasses(path,
database,
cut,
redshifts,
out_folder,
obs_data,
solid_angle=1.077 * 10**-5,
ymin=10**3,
ymax=2 * 10**6,
xmin=0.5,
xmax=100,
nbins=15,
sigma=3.0,
H0=70.0,
WM=0.28,
zmax=6.0,
write_out=False):
'''
160 square arcminutes in steradians =
1.354*10**-5 sr (steradians)
Simulation was 10 times the GOODS realization, so
the solid angle is 1.354*10**-4
:param sigma: sigma level of the errors to be plotted
:param nbins: number of bins (for simulated data)
:param area: actually 1 / area, used to weight galaxies
'''
#fudge factor to handle errors that are way large
fudge = ymin
#subplot numbers
columns = 2
rows = 3
#get data
query = '''select galprop.mstar from galprop, FIR where
galprop.gal_id = FIR.gal_id and
galprop.halo_id = FIR.halo_id and %s''' % cut
masses_limit = db.sqlite.get_data_sqlite(path, database, query)
query = '''select galprop.mstar from galprop, FIR where
galprop.gal_id = FIR.gal_id and
galprop.halo_id = FIR.halo_id'''
masses_total = db.sqlite.get_data_sqlite(path, database, query)
#make the figure
fig = P.figure()
P.subplots_adjust(wspace=0.0, hspace=0.0)
ax = P.subplot(rows, columns, 1)
#get the co-moving volume to the backend
vol = cosmocalc(zmax, H0, WM)['VCM_Gpc3']
#weight each galaxy
wghts = N.zeros(len(masses_total)) + (1. / (solid_angle * vol * 1e9))
#calculate the differential stellar mass function
#with log binning
b, n, nu = df.diff_function_log_binning(masses_total,
wgth=wghts,
mmax=xmax,
mmin=xmin,
nbins=nbins,
log=True)
#get the knots
x = 10**b
y = n
#plot the knots
mtot = ax.plot(x, y, 'k-')
#poisson error
mask = nu > 0
err = (1. / (solid_angle * vol * 1e9)) * N.sqrt(nu[mask]) * sigma
up = y[mask] + err
lw = y[mask] - err
lw[lw < ymin] = ymin
stot = ax.fill_between(x[mask], up, lw, color='#728FCE')
#limited mass
wghts = N.zeros(len(masses_limit)) + (1. / (solid_angle * vol * 1e9))
b, n, nu = df.diff_function_log_binning(masses_limit,
wgth=wghts,
mmax=xmax,
mmin=xmin,
nbins=nbins,
log=True)
#get the knots
x = 10**b
y = n
#plot the knots
mlim = ax.plot(x, y, 'r-')
#poisson error
mask = nu > 0
err = (1. / (solid_angle * vol * 1e9)) * N.sqrt(nu[mask]) * sigma
up = y[mask] + err
lw = y[mask] - err
lw[lw < ymin] = ymin
slim = ax.fill_between(x[mask], up, lw, color='red')
#write to the file if needed, using appending so might get long...
if write_out:
fh = open(out_folder + 'outputTotal.txt', 'a')
fh.write('#Mstellar[log10(M_sun)] dN/dlog10(M_sun) high low\n')
for aa, bb, cc, dd in zip(x[mask], y[mask], up, lw):
fh.write('%e %e %e %e\n' % (aa, bb, cc, dd))
fh.close()
#add annotation
ax.annotate('Total', (0.5, 0.9), xycoords='axes fraction', ha='center')
#set scale
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlim(10**xmin, 10**xmax)
ax.set_ylim(ymin, ymax)
ax.set_xticklabels([])
ptot = P.Rectangle((0, 0), 1, 1, fc='#728FCE')
plim = P.Rectangle((0, 0), 1, 1, fc='red')
sline = '%i$\sigma$ errors' % sigma
P.legend((mtot, ptot, plim), ('All Galaxies', sline, '$S_{250} > 5$ mJy'))
#redshift limited plots
for i, red in enumerate(redshifts):
query = '''select galprop.mstar from galprop, FIR where
galprop.gal_id = FIR.gal_id and galprop.halo_id = FIR.halo_id
and %s''' % (red)
mtotal = db.sqlite.get_data_sqlite(path, database, query)
query = '''select galprop.mstar from galprop, FIR where
galprop.gal_id = FIR.gal_id and galprop.halo_id = FIR.halo_id
and %s and %s''' % (cut, red)
mlimit = db.sqlite.get_data_sqlite(path, database, query)
#modify redshift string
tmp = red.split()
rtitle = r'$%s < z \leq %s$' % (tmp[2], tmp[6])
#make a subplot
axs = P.subplot(rows, columns, i + 2)
#get a comoving volume to the back end
volb = cosmocalc(float(tmp[6]), H0, WM)['VCM_Gpc3']
volf = cosmocalc(float(tmp[2]), H0, WM)['VCM_Gpc3']
#weights
wgvol = (1. / (solid_angle * volb * 1e9))
# wgvol = (1./(solid_angle*(volb-volf)*1e9))
wghts = N.zeros(len(mtotal)) + wgvol
b, n, nu = df.diff_function_log_binning(mtotal,
wgth=wghts,
mmax=xmax,
mmin=xmin,
nbins=nbins,
log=True)
x = 10**b
y = n
#plot the knots
axs.plot(x, y, 'k-')
#poisson error
mask = nu > 0
err = wgvol * N.sqrt(nu[mask]) * sigma
up = y[mask] + err
lw = y[mask] - err
lw[lw < ymin] = ymin
axs.fill_between(x[mask], up, lw, color='#728FCE')
#limited mass
wghts = N.zeros(len(mlimit)) + wgvol
b, n, nu = df.diff_function_log_binning(mlimit,
wgth=wghts,
mmax=xmax,
mmin=xmin,
nbins=nbins,
log=True)
x = 10**b
y = n
#plot the knots
axs.plot(x, y, 'r-')
#poisson error
mask = nu > 0
err = wgvol * N.sqrt(nu[mask]) * sigma
up = y[mask] + err
lw = y[mask] - err
lw[lw < ymin] = ymin
axs.fill_between(x[mask], up, lw, color='red')
#write to output
if write_out:
fh = open(out_folder + 'outputredshiftbin%i.txt' % i, 'a')
fh.write('#' + rtitle + '\n')
fh.write('#Mstellar[log10(M_sun)] dN/dlog10(M_sun) high low\n')
for aa, bb, cc, dd in zip(x[mask], y[mask], up, lw):
fh.write('%e %e %e %e\n' % (aa, bb, cc, dd))
fh.close()
#add annotation
axs.annotate(rtitle, (0.5, 0.9), xycoords='axes fraction', ha='center')
#set scales
axs.set_xscale('log')
axs.set_yscale('log')
axs.set_xlim(10**xmin, 10**xmax)
axs.set_ylim(ymin, ymax)
#remove unnecessary ticks and add units
if i % 2 == 0:
axs.set_yticklabels([])
if i == 2 or i == 3:
axs.set_xlabel(r'$M_{\star} \quad [M_{\odot}]$')
if i == 2:
axs.set_xticks(axs.get_xticks()[1:])
else:
axs.set_xticklabels([])
if i == 1:
axs.set_ylabel(
r'$\frac{dN}{d \log_{10} M_{\star}} \ [Mpc^{-3} \ dex^{-1}]$')
#save figure
P.savefig(out_folder + 'stellarmassfunction.ps')
P.close()
def read_sdss(read_full_lines=True):
"""
Read MPA-JHU SDSS libraries.
If 'read_full_lines' is set, then also read the full line catalog with fluxes etc.
This is a big file so can take more time/memory.
"""
import unicorn.paper1
import cosmocalc
sdss_path = unicorn.GRISM_HOME + 'ANALYSIS/SDSS/'
print 'SDSS ICLASS...'
if unicorn.paper1.sdss_iclass is None:
unicorn.paper1.sdss_iclass = pyfits.open(
sdss_path + 'gal_iclass_dr7_v5_2.fits')[0].data
print 'SDSS Info, redshifts etc....'
if unicorn.paper1.sdss_info is None:
unicorn.paper1.sdss_info = pyfits.open(
sdss_path + 'gal_info_dr7_v5_2.fits')[1].data
if read_full_lines:
print 'SDSS Full line data...'
if unicorn.paper1.sdss_line is None:
unicorn.paper1.sdss_line = pyfits.open(
sdss_path + 'gal_line_dr7_v5_2.fits')[1].data
print 'SDSS Total SFR...'
if unicorn.paper1.sdss_totsfr is None:
unicorn.paper1.sdss_totsfr = pyfits.open(
sdss_path + 'gal_totsfr_dr7_v5_2.fits')[1].data
print 'SDSS Total Specific SFR...'
if unicorn.paper1.sdss_totspecsfr is None:
unicorn.paper1.sdss_totspecsfr = pyfits.open(
sdss_path + 'gal_totspecsfr_dr7_v5_2.fits')[1].data
print 'SDSS Total stellar mass...'
if unicorn.paper1.sdss_logm is None:
unicorn.paper1.sdss_logm = pyfits.open(sdss_path +
'totlgm_dr7_v5_2.fits')[1].data
print 'SDSS O/H abundances...'
if unicorn.paper1.sdss_fiboh is None:
unicorn.paper1.sdss_fiboh = pyfits.open(
sdss_path + 'gal_fiboh_dr7_v5_2.fits')[1].data
print 'SDSS-VAGC Sersic fits...'
if unicorn.paper1.sdss_sersic is None:
unicorn.paper1.sdss_sersic = pyfits.open(sdss_path +
'sersic_catalog.fits')[1].data
#
print 'SDSS-VAGC Coords...'
if unicorn.paper1.sdss_vagc is None:
unicorn.paper1.sdss_vagc = pyfits.open(sdss_path +
'object_catalog.fits')[1].data
print 'Match between catalogs'
if unicorn.paper1.idx_mpa_vagc is None:
unicorn.paper1.idx_mpa_vagc = np.loadtxt(sdss_path + 'mpa_vagc.match',
dtype=np.int)
#### Keep only matches from VAGC
unicorn.paper1.sdss_sersic = unicorn.paper1.sdss_sersic[
unicorn.paper1.idx_mpa_vagc]
#### Get R50 in kpc
zgrid = np.arange(100) / 100. * 4 + 1. / 100
scale = zgrid * 0.
for i in range(100):
cc = cosmocalc.cosmocalc(zgrid[i])
scale[i] = cc['PS_kpc']
unicorn.paper1.sdss_sersic.SERSIC_R50_i_KPC = unicorn.paper1.sdss_sersic.SERSIC_R50[:, 3] * np.interp(
unicorn.paper1.sdss_info.Z, zgrid, scale)
def testRandomizer():
#calculate a test case
conversion = 0.000277777778 # degree to arcsecond
#random distance between z = 0 and 5
z = np.random.rand() * 5.0
#random separation of the main galaxy and the subhalo
physical_distance = np.random.rand() * 1e3 #in kpc
#get the angular diameter distance to the galaxy
dd1 = cosmocalc(z, 71.0, 0.28)['PS_kpc'] #in kpc / arc seconds
dd = (physical_distance / dd1) * conversion # to degrees
# RA and DEC of the main galaxy, first one in GOODS south
ra_main = 52.904892 #in degrees
dec_main = -27.757082 #in degrees
# get the random position
rds = rand.randomUnitSphere(points=1)
# convert the position to Cartesian coord
# when dd is the radius of the sphere coordinates
# the dd is in units of degrees here so the results
# are also in degrees.
rd = conv.convertSphericalToCartesian(dd, rds['theta'], rds['phi'])
# Make the assumption that x is towards the observer
# z is north and y is west. Now if we assume that our z and y
# are Standard Coordinates, i.e., the projection of the RA and DEC
# of an object onto the tangent plane of the sky. We can now
# assume that the y coordinate is aligned with RA and the z coordinate
# is aligned with DEC. The origin of this system is at the tangent point
# in the dark matter halo.
# Poor man's solution to the problem would be:
new_ra = ra_main - (rd['y'][0] / np.cos(rd['z'][0]))
new_dec = dec_main + rd['z'][0]
# However, this only works if one is away from the pole.
# More general solution can be derived using spherical geometry:
data = {}
data['CD'] = np.matrix('-1 0; 0 1')
data['RA'] = ra_main
data['DEC'] = dec_main
data['X'] = rd['y'][0]
data['Y'] = rd['z'][0]
result = conv.RAandDECfromStandardCoordinates(data)
#print the output
print 'Redshift of the galaxy is %.3f while the subhaloes distance is %0.2f kpc' % (
z, physical_distance)
print '\nCoordinates of the main halo galaxy are (RA and DEC):'
print '%.7f %.7f' % (ra_main, dec_main)
print astCoords.decimal2hms(ra_main,
':'), astCoords.decimal2dms(dec_main, ':')
print '\nCoordinates for the subhalo galaxy are (RA and DEC):'
print '%.7f %.7f' % (new_ra, new_dec)
print astCoords.decimal2hms(new_ra,
':'), astCoords.decimal2dms(new_dec, ':')
print 'or when using more accurate technique'
print '%.7f %.7f' % (result['RA'], result['DEC'])
print astCoords.decimal2hms(result['RA'], ':'), astCoords.decimal2dms(
result['DEC'], ':')
#print 'Shift in RA and DEC [degrees]:'
#print rd['y'][0], (rd['z'][0]/np.cos(rd['z'][0]))
print '\nShift in RA and DEC [seconds]:'
print -rd['y'][0] / np.cos(
rd['z'][0]) / conversion, rd['z'][0] / conversion
print 'or again with the better method:'
print(result['RA'] - ra_main) / conversion, (result['DEC'] -
dec_main) / conversion
def quickPlot(galaxies=170, rvir=0.105):
"""
A single halo test
:param galaxies: number of subhalo galaxies
:param rvir: radius of the main halo [Mpc]
"""
conversion = 0.000277777778 # degree to arcsecond
#redshift
z = 4.477
#get the angular diameter distance to the galaxy
dd = cosmocalc(z, 71.0, 0.28)['PS_kpc'] #in kpc / arc seconds
#position of the main galaxy
RADeg1 = 189.41432
decDeg1 = 62.166702
# get the random values
rds = rand.randomUnitSphere(galaxies)
# convert them to Cartesian coord
rd = conv.convertSphericalToCartesian(rvir/dd/2., rds['theta'], rds['phi'])
data = {'CD': np.matrix('-1 0; 0 1'), 'RA': RADeg1, 'DEC': decDeg1, 'X': rd['y'], 'Y': rd['z']}
result = conv.RAandDECfromStandardCoordinates(data)
RADeg2 = result['RA']
decDeg2 = result['DEC']
#calculate the angular separation on the sky
sep = Coords.calcAngSepDeg(RADeg1, decDeg1, RADeg2, decDeg2) / 2.
physical_distance = sep * dd / conversion
text = r'Random Positions, z={0:.2f}, radius of the halo = {1:.0f} kpc'.format(z, 1e3 * rvir)
#make a figure
fig = plt.figure(figsize=(12,12))
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(223)
rect = fig.add_subplot(224, visible=False).get_position()
ax4 = Axes3D(fig, rect)
ax1.hist(sep * 1e4, bins=12)
ax2.hist(physical_distance, bins=12)
ax3.scatter(0, 0, c='k', s=120, marker='s')
ax3.scatter((RADeg2 - RADeg1) * 1e4, (decDeg2 - decDeg1) * 1e4, c='b', s=50, marker='o', alpha=0.45)
#generate a sphere
u = np.linspace(0, 2 * np.pi, 100)
v = np.linspace(0, np.pi, 100)
x = np.outer(np.cos(u), np.sin(v))
y = np.outer(np.sin(u), np.sin(v))
z = np.outer(np.ones(np.size(u)), np.cos(v))
#plot it as a wire frame
ax4.plot_wireframe(x*1e4*rvir/dd, y*1e4*rvir/dd, z*1e4*rvir/dd,
rstride=10, cstride=10, color='0.5', alpha=0.7)
#plot the random points
ax4.scatter(rd['x']*2e4, rd['y']*2e4, rd['z']*2e4, color='b', s=30, alpha=0.8)
ax1.set_xlabel(r'Projected Separation [$10^{-4}$ deg]')
ax2.set_xlabel(r'Projected Distance [kpc]')
ax3.set_xlabel(r'$\Delta$RA [$10^{-4}$ deg]')
#ax4.set_xlabel(r'$\Delta$RA [$10^{-4}$ deg]')
ax3.set_ylabel(r'$\Delta$DEC [$10^{-4}$ deg]')
#ax4.set_ylabel(r'$\Delta$DEC [$10^{-4}$ deg]')
ax3.set_xlim(-200, 200)
ax3.set_ylim(-100, 100)
ax4.set_xlim3d(-150, 150)
ax4.set_ylim3d(-150, 150)
ax4.set_zlim3d(-150, 150)
plt.annotate(text, (0.5, 0.95), xycoords='figure fraction', ha='center', va='center', fontsize=12)
plt.savefig('RandomHalo.pdf')
flux_sdss_synth = np.array(flux_sdss_synth)
goodness = s.chisquare(flux_sdss_corr[1:], flux_sdss_synth)
absolute_goodness = goodness[0] / 3
# UV Synthetic equivalent fluxes -------------------------------------------------------------------------------
## Only necessary for the example plot (with arrows)
# k = np.abs(wavelengths_galex[0] - synthetic_wavelengths).argmin()
# fuv_synth_flux = synthetic_fluxes[k]
#
# m = np.abs(wavelengths_galex[1] - synthetic_wavelengths).argmin()
# nuv_synth_flux = synthetic_fluxes[m]
# Absolute Magnitudes using Luminosity Distance ----------------------------------------------------------------
# This is optional to calculate,so for now it will be kept commented -------------------------------------------
luminosity_distance = cosmo.cosmocalc(redshift[index])["DL_Mpc"]
# mag_fuv_abs = mag_fuv_dered_fm - 5 * np.log10(luminosity_distance) - 25
# mag_nuv_abs = mag_nuv_dered_fm - 5 * np.log10(luminosity_distance) - 25
mag_u_abs = mag_ab_u[index] - 5 * np.log10(luminosity_distance) - 25
mag_g_abs = mag_ab_g[index] - 5 * np.log10(luminosity_distance) - 25
mag_r_abs = mag_ab_r[index] - 5 * np.log10(luminosity_distance) - 25
mag_i_abs = mag_ab_i[index] - 5 * np.log10(luminosity_distance) - 25
mag_z_abs = mag_ab_z[index] - 5 * np.log10(luminosity_distance) - 25
# SDSS Fluxes Uncertainty --------------------------------------------------------------------------------------
model_mag_err_sdss_index = model_mag_err_sdss[index][0]
sigma_flux = []
for i in range(sdss_mag_dered[index].size):
if fiber_correction == 1:
sigma_fiber_corr_square = 0.0
else:
def _get_angdist(self):
if self._angdist is None:
cc = cosmocalc(self.redshift)
self._angdist = cc['DA_cm']
return self._angdist
#!/usr/bin/env python
from pylab import*
from matplotlib.ticker import AutoMinorLocator
import numpy
from lumfuncUtils import get_dl, get_Vmax,get_dsdl,get_z
from utils import *
from cosmocalc import cosmocalc
Ho=70.0
wm=0.3
zbins =[0.1, 0.4, 0.6, 0.8, 1.0, 1.3, 1.6, 2.0, 2.5, 3.2, 4.0]
t= [cosmocalc(zi,H0=70,WM = 0.3)['zage_Gyr'] for zi in zbins]
BIN_COL = -1
SURVEY_NOISE=2.5 # uJy
rc('legend', fontsize=23)
def sfr(m,z,So=2.8,a1=0.23,a2=0.13,mo=10.8):
return So - a1*z-numpy.log10(1+(10**(mo-a2*z)/10**(m)))
def phi(m,m_star,phi1,phi2,a,b):
m_m=10**(m-m_star)
#print m_star, phi1,phi2,a,b
sk=numpy.log(10)* (phi1*(m_m)**(a+1) + phi2*(m_m)**(1+b))
mf=numpy.exp(-m_m)*sk
#print numpy.exp(-m_m)
return mf
#5 0.28<z<0.36, 7 0.45<z<0.56 9 0.68<z<0.82 10 0.82<z<1 11 1<z<1.2 13 1.45<z<1.75
#14 1.75<z<2.2 15 2.2<z<2.6 16 2.6z3.25 17 3.25z3.75
w_18=[[10.88 , -0.55 , -1.536, -2.555, -3.4 , 8.388, 1.592],\
def fried_plot(blazardir='/Users/willettk/Astronomy/Research/blazars/',blazarfile='plotkin_dr8_upload.txt',loaddata=True, envsize = 1000):
from pylab import *
import pickle
import coords
import cosmocalc
# Retrieve data from saved file
if loaddata is True:
pkl_file = open(blazardir+'sdss_'+str(envsize)+'kpc_zns.pkl', 'r')
sdss_data_zns = pickle.load(pkl_file)
pkl_file.close()
pkl_file = open(blazardir+'sdss_'+str(envsize)+'kpc_zs.pkl', 'r')
sdss_data_zs = pickle.load(pkl_file)
pkl_file.close()
pkl_file = open(blazardir+'sdss_'+str(envsize)+'kpc_nozoo.pkl', 'r')
sdss_data_nozoo = pickle.load(pkl_file)
pkl_file.close()
print "Loaded data from saved files."
nz = sdss_data_nozoo
zns = sdss_data_zns
zs = sdss_data_zs
# Reproduce Figure 1 in Fried et al. (1993), determining whether galaxy surface density for BL Lacs changes with galactocentric radius
# Bin by redshift; start with the Fried+ bins at median z of 0.28, 0.65, 0.97 (bin widths of z ~ 0.30)
# Quantities to plot:
# - distance from host [kpc]
# Dictionaries have the Python ID number, RA, and dec
# Reload the original list of blazars; assume closest object to the center (choose cone radius and redshift) is the host galaxy.
# For the remainder, convert angular distance to linear. Still assuming no redshift dependence?
# Make a cut in apparent magnitude at r < 22 (identical to Fried paper)
r = array(nz['r'],dtype='f')
rind = where(r < 22)
sdss_name = array(nz['blazar_name'])[rind[0]]
sdss_ra = array(nz['ra'])[rind[0]]
sdss_dec = array(nz['dec'])[rind[0]]
angseparr = []
physseparr = []
angseparr_low = []
angseparr_mid = []
angseparr_hi = []
physseparr_low = []
physseparr_mid = []
physseparr_hi = []
b_ra = []
b_dec = []
z_low = []
z_mid = []
z_hi = []
pf = open(blazardir+blazarfile,'r')
for line in pf:
#
blazar_name, blazar_ra, blazar_dec, blazar_z = line.split()
if blazar_name != 'Plotkin_BLLac637':
blazar_z = float(blazar_z)
ind1 = list(sdss_name).index(blazar_name)
ind2 = list(sdss_name).index(blazar_name[:-3]+'%(var)03d' % {'var':float(blazar_name[-3:])+1})
ra = sdss_ra
dec = sdss_dec
b_ra.append(blazar_ra)
b_dec.append(blazar_dec)
b_radec = (float(blazar_ra),float(blazar_dec))
for galaxy in arange(ind1,ind2):
s_radec = (float(ra[galaxy]),float(dec[galaxy]))
sdss_coords = coords.Position(s_radec)
blazar_coords = coords.Position(b_radec)
sep = sdss_coords.angsep(blazar_coords)
platescale = cosmocalc.cosmocalc(z=blazar_z, H0=71, WM=0.27)['PS_kpc'] # kpc per arcsec
sep_arcmin = sep.arcsec() / 60
sep_kpc = platescale*sep.arcsec()
angseparr.append(sep_arcmin)
physseparr.append(sep_kpc)
if blazar_z < 0.46:
angseparr_low.append(sep_arcmin)
physseparr_low.append(sep_kpc)
z_low.append(blazar_z)
elif (blazar_z > 0.46) and (blazar_z < 0.81):
angseparr_mid.append(sep_arcmin)
physseparr_mid.append(sep_kpc)
z_mid.append(blazar_z)
elif (blazar_z > 0.81 and blazar_z < 1.048):
angseparr_hi.append(sep_arcmin)
physseparr_hi.append(sep_kpc)
z_hi.append(blazar_z)
# - galaxy surface density [N_gal / sq. arcmin]
# Bin galaxies into bins of 100 kpc each (galactocentric distance)
# For the galaxies at each distance from the host object, find the average number of galaxies per square arcminute in that field
bins_kpc = (arange(10)+1)*100
bins_arcmin_low = zeros(10)
bins_arcmin_mid = zeros(10)
bins_arcmin_hi = zeros(10)
for b in arange(10):
bins_arcmin_low[b] = (bins_kpc[b]/cosmocalc.cosmocalc(z=0.28,H0=71, WM=0.27)['PS_kpc'] / 60)
bins_arcmin_mid[b] = (bins_kpc[b]/cosmocalc.cosmocalc(z=0.65,H0=71, WM=0.27)['PS_kpc'] / 60)
bins_arcmin_hi[b] = (bins_kpc[b]/cosmocalc.cosmocalc(z=0.97,H0=71, WM=0.27)['PS_kpc'] / 60)
hist_low = np.histogram(angseparr_low,bins=bins_arcmin_low)
hist_mid = np.histogram(angseparr_mid,bins=bins_arcmin_mid)
hist_hi = np.histogram(angseparr_hi ,bins=bins_arcmin_hi )
# Assume the plate scale is for the median redshift in each distance class
area_low = np.pi * ((array(bins_arcmin_low[1:10]))**2 - (array(bins_arcmin_low[0:9]))**2)
area_mid = np.pi * ((array(bins_arcmin_mid[1:10]))**2 - (array(bins_arcmin_mid[0:9]))**2)
area_hi = np.pi * ((array(bins_arcmin_hi[1:10]))**2 - (array(bins_arcmin_hi[0:9]))**2)
density_low = hist_low[0] / area_low
density_mid = hist_mid[0] / area_mid
density_hi = hist_hi[0] / area_hi
fig = figure(2)
clf()
###############
# Sort r-band magnitudes into redshift bins
###############
subplot(321)
hist_physsep = hist(r[:ind2],bins=arange(10,30),color='k')
hist_physsep = hist(r[rind][:ind2],bins=arange(10,30),color='w')
xlabel(r'$m_r$')
ylabel(r'$N_{gal}$')
subplot(323)
hist_physsep_low = hist(physseparr_low,color='r')
hist_physsep_mid = hist(physseparr_mid,color='g')
hist_physsep_hi = hist(physseparr_hi,color='b')
xlabel('Physical separation [kpc]')
ylabel(r'$N_{gal}$')
subplot(325)
plot(b_ra, b_dec, 'kx')
xlabel('RA')
ylabel('Dec')
ylim(-30,90)
subplot(322)
errorbar(bins_kpc[1:10], density_low/len(density_low), yerr=sqrt(density_low), color='r', fmt='o')
xlabel('Physical separation [kpc]')
ylabel(r'$N_{gal}/sq. arcmin$')
xlim(0,1000)
title(r'$\langle z\rangle = %f$' % mean(z_low))
axhline(y=mean(density_low),color='r',linewidth=2)
subplot(324)
errorbar(bins_kpc[1:10], density_mid/len(density_mid), yerr=sqrt(density_mid), color='g', fmt='o')
xlabel('Physical separation [kpc]')
ylabel(r'$N_{gal}/sq. arcmin$')
xlim(0,1000)
title(r'$\langle z\rangle = %f$' % mean(z_mid))
axhline(y=mean(density_mid),color='g',linewidth=2)
subplot(326)
errorbar(bins_kpc[1:10], density_hi/len(density_hi), yerr = sqrt(density_hi), color='b', fmt='o')
xlabel('Physical separation [kpc]')
ylabel(r'$N_{gal}/sq. arcmin$')
xlim(0,1000)
title(r'$\langle z\rangle = %f$' % mean(z_hi))
axhline(y=mean(density_hi),color='b',linewidth=2)
subplots_adjust(hspace=0.5)
os.system('mkdir '+p.output_dir+'/neb')
os.system('mkdir '+p.output_dir+'/SEDs')
os.system('mkdir '+p.output_dir+'/StellarSEDs')
#-------------------
# for nebula emission
nebula_lines = {'Peta': {0.0040000000000000001: 0.0184, 0.050000000000000003: 0.0184, 0.0080000000000000002: 0.0184, 0.00040000000000000002: 0.0184, 'l': 9014.8999999999996, 0.02: 0.0184}, '[OIII]4363.00': {0.0040000000000000001: 0.066000000000000003, 0.050000000000000003: 0.01, 0.0080000000000000002: 0.01, 0.00040000000000000002: 0.109, 'l': 4363.0, 0.02: 0.01}, '[NII]6583.45': {0.0040000000000000001: 0.17499999999999999, 0.050000000000000003: 0.40400000000000003, 0.0080000000000000002: 0.40400000000000003, 0.00040000000000000002: 0.014999999999999999, 'l': 6583.4499999999998, 0.02: 0.40400000000000003}, '[NII]5755.00': {0.0040000000000000001: 0.0, 0.050000000000000003: 0.01, 0.0080000000000000002: 0.01, 0.00040000000000000002: 0.0, 'l': 5755.0, 0.02: 0.01}, 'Hkappa': {0.0040000000000000001: 0.030550000000000001, 0.050000000000000003: 0.030550000000000001, 0.0080000000000000002: 0.030550000000000001, 0.00040000000000000002: 0.030550000000000001, 'l': 3750.0, 0.02: 0.030550000000000001}, 'Ptheta': {0.0040000000000000001: 0.0138, 0.050000000000000003: 0.0138, 0.0080000000000000002: 0.0138, 0.00040000000000000002: 0.0138, 'l': 8862.7999999999993, 0.02: 0.0138}, '[NII]6548.05': {0.0040000000000000001: 0.058999999999999997, 0.050000000000000003: 0.13600000000000001, 0.0080000000000000002: 0.13600000000000001, 0.00040000000000000002: 0.0050000000000000001, 'l': 6548.0500000000002, 0.02: 0.13600000000000001}, '[SII]6730.00': {0.0040000000000000001: 0.13800000000000001, 0.050000000000000003: 0.20999999999999999, 0.0080000000000000002: 0.20999999999999999, 0.00040000000000000002: 0.029000000000000001, 'l': 6730.0, 0.02: 0.20999999999999999}, '[OIII]1663.0': {0.0040000000000000001: 0.058000000000000003, 0.050000000000000003: 0.01, 0.0080000000000000002: 0.01, 0.00040000000000000002: 0.0, 'l': 1663.0, 0.02: 0.01}, '[OI]': {0.0040000000000000001: 0.041000000000000002, 0.050000000000000003: 0.13, 0.0080000000000000002: 0.13, 0.00040000000000000002: 0.0080000000000000002, 'l': 6300.0, 0.02: 0.13}, 'Htheta': {0.0040000000000000001: 0.053039999999999997, 0.050000000000000003: 0.053039999999999997, 0.0080000000000000002: 0.053039999999999997, 0.00040000000000000002: 0.053039999999999997, 'l': 3797.0, 0.02: 0.053039999999999997}, 'Hiota': {0.0040000000000000001: 0.039750000000000001, 0.050000000000000003: 0.039750000000000001, 0.0080000000000000002: 0.039750000000000001, 0.00040000000000000002: 0.039750000000000001, 'l': 3770.0, 0.02: 0.039750000000000001}, '[NI]': {0.0040000000000000001: 0.01, 0.050000000000000003: 0.029999999999999999, 0.0080000000000000002: 0.029999999999999999, 0.00040000000000000002: 0.0030000000000000001, 'l': 5199.0, 0.02: 0.029999999999999999}, '[CII]1335.0': {0.0040000000000000001: 0.0, 0.050000000000000003: 0.11, 0.0080000000000000002: 0.11, 0.00040000000000000002: 0.0, 'l': 1335.0, 0.02: 0.11}, '[SII]10336.41': {0.0040000000000000001: 0.0, 0.050000000000000003: 0.050000000000000003, 0.0080000000000000002: 0.050000000000000003, 0.00040000000000000002: 0.0, 'l': 10336.41, 0.02: 0.050000000000000003}, 'Heta': {0.0040000000000000001: 0.07306, 0.050000000000000003: 0.07306, 0.0080000000000000002: 0.07306, 0.00040000000000000002: 0.07306, 'l': 3835.0, 0.02: 0.07306}, 'Pgamma': {0.0040000000000000001: 0.090399999999999994, 0.050000000000000003: 0.090399999999999994, 0.0080000000000000002: 0.090399999999999994, 0.00040000000000000002: 0.090399999999999994, 'l': 10938.1, 0.02: 0.090399999999999994}, 'P12': {0.0040000000000000001: 0.0106, 0.050000000000000003: 0.0106, 0.0080000000000000002: 0.0106, 0.00040000000000000002: 0.0106, 'l': 8750.5, 0.02: 0.0106}, '[OIII]5006.84': {0.0040000000000000001: 4.7519999999999998, 0.050000000000000003: 4.0810000000000004, 0.0080000000000000002: 4.0810000000000004, 0.00040000000000000002: 3.1589999999999998, 'l': 5006.8400000000001, 0.02: 4.0810000000000004}, '[OII]3727.0': {0.0040000000000000001: 1.7909999999999999, 0.050000000000000003: 3.0099999999999998, 0.0080000000000000002: 3.0099999999999998, 0.00040000000000000002: 0.48899999999999999, 'l': 3727.0, 0.02: 3.0099999999999998}, '[NII]2141.0': {0.0040000000000000001: 0.0, 0.050000000000000003: 0.01, 0.0080000000000000002: 0.01, 0.00040000000000000002: 0.0, 'l': 2141.0, 0.02: 0.01}, '[NeIII]': {0.0040000000000000001: 0.41599999999999998, 0.050000000000000003: 0.29999999999999999, 0.0080000000000000002: 0.29999999999999999, 0.00040000000000000002: 0.29499999999999998, 'l': 3869.0, 0.02: 0.29999999999999999}, '[ArIII]7751.11': {0.0040000000000000001: 0.17599999999999999, 0.050000000000000003: 0.085999999999999993, 0.0080000000000000002: 0.085999999999999993, 0.00040000000000000002: 0.067000000000000004, 'l': 7751.1099999999997, 0.02: 0.085999999999999993}, 'Hxi': {0.0040000000000000001: 0.1051, 0.050000000000000003: 0.1051, 0.0080000000000000002: 0.1051, 0.00040000000000000002: 0.1051, 'l': 3889.0, 0.02: 0.1051}, '[HeI]6678.00': {0.0040000000000000001: 0.029999999999999999, 0.050000000000000003: 0.029999999999999999, 0.0080000000000000002: 0.029999999999999999, 0.00040000000000000002: 0.025999999999999999, 'l': 6678.0, 0.02: 0.029999999999999999}, 'H18': {0.0040000000000000001: 0.0090600000000000003, 0.050000000000000003: 0.0090600000000000003, 0.0080000000000000002: 0.0090600000000000003, 0.00040000000000000002: 0.0090600000000000003, 'l': 3692.0, 0.02: 0.0090600000000000003}, 'Pbeta': {0.0040000000000000001: 0.16300000000000001, 0.050000000000000003: 0.16300000000000001, 0.0080000000000000002: 0.16300000000000001, 0.00040000000000000002: 0.16300000000000001, 'l': 12818.1, 0.02: 0.16300000000000001}, 'H_epsilon + [NeIII]': {0.0040000000000000001: 0.28299999999999997, 0.050000000000000003: 0.159, 0.0080000000000000002: 0.159, 0.00040000000000000002: 0.27000000000000002, 'l': 3970.0, 0.02: 0.159}, 'Bdelta': {0.0040000000000000001: 0.01915, 0.050000000000000003: 0.01915, 0.0080000000000000002: 0.01915, 0.00040000000000000002: 0.01915, 'l': 19445.0, 0.02: 0.01915}, ' H_zeta+ [HeI]': {0.0040000000000000001: 0.192, 0.050000000000000003: 0.107, 0.0080000000000000002: 0.107, 0.00040000000000000002: 0.20300000000000001, 'l': 3889.0, 0.02: 0.107}, '[HeI]}': {0.0040000000000000001: 0.035999999999999997, 0.050000000000000003: 0.050000000000000003, 0.0080000000000000002: 0.050000000000000003, 0.00040000000000000002: 0.035999999999999997, 'l': 4471.0, 0.02: 0.050000000000000003}, '[SIII]6312.0': {0.0040000000000000001: 0.017000000000000001, 0.050000000000000003: 0.029999999999999999, 0.0080000000000000002: 0.029999999999999999, 0.00040000000000000002: 0.0089999999999999993, 'l': 6312.0, 0.02: 0.029999999999999999}, 'H13': {0.0040000000000000001: 0.02401, 0.050000000000000003: 0.02401, 0.0080000000000000002: 0.02401, 0.00040000000000000002: 0.02401, 'l': 3735.0, 0.02: 0.02401}, 'H14': {0.0040000000000000001: 0.019210000000000001, 0.050000000000000003: 0.019210000000000001, 0.0080000000000000002: 0.019210000000000001, 0.00040000000000000002: 0.019210000000000001, 'l': 3723.0, 0.02: 0.019210000000000001}, 'H15': {0.0040000000000000001: 0.01562, 0.050000000000000003: 0.01562, 0.0080000000000000002: 0.01562, 0.00040000000000000002: 0.01562, 'l': 3713.0, 0.02: 0.01562}, 'H16': {0.0040000000000000001: 0.012880000000000001, 0.050000000000000003: 0.012880000000000001, 0.0080000000000000002: 0.012880000000000001, 0.00040000000000000002: 0.012880000000000001, 'l': 3704.0, 0.02: 0.012880000000000001}, 'H17': {0.0040000000000000001: 0.01074, 0.050000000000000003: 0.01074, 0.0080000000000000002: 0.01074, 0.00040000000000000002: 0.01074, 'l': 3698.0, 0.02: 0.01074}, '[OIII]4958.91': {0.0040000000000000001: 1.617, 0.050000000000000003: 1.399, 0.0080000000000000002: 1.399, 0.00040000000000000002: 1.097, 'l': 4958.9099999999999, 0.02: 1.399}, '[CII]2326.00': {0.0040000000000000001: 0.0, 0.050000000000000003: 0.28999999999999998, 0.0080000000000000002: 0.28999999999999998, 0.00040000000000000002: 0.0, 'l': 2326.0, 0.02: 0.28999999999999998}, 'Lalpha': {0.0040000000000000001: 23.699999999999999, 0.050000000000000003: 23.699999999999999, 0.0080000000000000002: 23.699999999999999, 0.00040000000000000002: 23.699999999999999, 'l': 1216.0, 0.02: 23.699999999999999}, '[HeI]5876.00': {0.0040000000000000001: 0.108, 0.050000000000000003: 0.14000000000000001, 0.0080000000000000002: 0.14000000000000001, 0.00040000000000000002: 0.096000000000000002, 'l': 5876.0, 0.02: 0.14000000000000001}, '[ArIV]+[HeI]': {0.0040000000000000001: 0.014, 0.050000000000000003: 0.0, 0.0080000000000000002: 0.0, 0.00040000000000000002: 0.01, 'l': 4711.0, 0.02: 0.0}, '[CIII]': {0.0040000000000000001: 0.0, 0.050000000000000003: 0.17999999999999999, 0.0080000000000000002: 0.17999999999999999, 0.00040000000000000002: 0.0, 'l': 1909.0, 0.02: 0.17999999999999999}, 'Hbeta': {0.0040000000000000001: 1.0, 0.050000000000000003: 1.0, 0.0080000000000000002: 1.0, 0.00040000000000000002: 1.0, 'l': 4861.0, 0.02: 1.0}, 'Hdelta': {0.0040000000000000001: 0.2591, 0.050000000000000003: 0.2591, 0.0080000000000000002: 0.2591, 0.00040000000000000002: 0.2591, 'l': 4101.0, 0.02: 0.2591}, '[SIII]9530.85': {0.0040000000000000001: 0.0, 0.050000000000000003: 0.36499999999999999, 0.0080000000000000002: 0.36499999999999999, 0.00040000000000000002: 0.0, 'l': 9530.8500000000004, 0.02: 0.36499999999999999}, '[SII]6716.00': {0.0040000000000000001: 0.188, 0.050000000000000003: 0.29999999999999999, 0.0080000000000000002: 0.29999999999999999, 0.00040000000000000002: 0.036999999999999998, 'l': 6716.0, 0.02: 0.29999999999999999}, 'Bgamma': {0.0040000000000000001: 0.02928, 0.050000000000000003: 0.02928, 0.0080000000000000002: 0.02928, 0.00040000000000000002: 0.02928, 'l': 21656.0, 0.02: 0.02928}, 'Pdelta': {0.0040000000000000001: 0.055300000000000002, 0.050000000000000003: 0.055300000000000002, 0.0080000000000000002: 0.055300000000000002, 0.00040000000000000002: 0.055300000000000002, 'l': 10049.4, 0.02: 0.055300000000000002}, '[SII]10320.49': {0.0040000000000000001: 0.0, 0.050000000000000003: 0.058000000000000003, 0.0080000000000000002: 0.058000000000000003, 0.00040000000000000002: 0.0, 'l': 10320.49, 0.02: 0.058000000000000003}, '[SIII]9068.60': {0.0040000000000000001: 0.51000000000000001, 0.050000000000000003: 0.94499999999999995, 0.0080000000000000002: 0.94499999999999995, 0.00040000000000000002: 0.0, 'l': 9068.6000000000004, 0.02: 0.94499999999999995}, 'Palpha': {0.0040000000000000001: 0.30099999999999999, 0.050000000000000003: 0.30099999999999999, 0.0080000000000000002: 0.30099999999999999, 0.00040000000000000002: 0.30099999999999999, 'l': 18751.0, 0.02: 0.30099999999999999}, 'Balpha': {0.0040000000000000001: 0.089940000000000006, 0.050000000000000003: 0.089940000000000006, 0.0080000000000000002: 0.089940000000000006, 0.00040000000000000002: 0.089940000000000006, 'l': 40512.0, 0.02: 0.089940000000000006}, '[ArIII]7135.79': {0.0040000000000000001: 0.070999999999999994, 0.050000000000000003: 0.035000000000000003, 0.0080000000000000002: 0.035000000000000003, 0.00040000000000000002: 0.027, 'l': 7135.79, 0.02: 0.035000000000000003}, 'Hepsilon': {0.0040000000000000001: 0.15909999999999999, 0.050000000000000003: 0.15909999999999999, 0.0080000000000000002: 0.15909999999999999, 0.00040000000000000002: 0.15909999999999999, 'l': 3970.0, 0.02: 0.15909999999999999}, '[MgII]': {0.0040000000000000001: 0.31, 0.050000000000000003: 0.070000000000000007, 0.0080000000000000002: 0.070000000000000007, 0.00040000000000000002: 0.0, 'l': 2798.0, 0.02: 0.070000000000000007}, 'Pxi': {0.0040000000000000001: 0.025399999999999999, 0.050000000000000003: 0.025399999999999999, 0.0080000000000000002: 0.025399999999999999, 0.00040000000000000002: 0.025399999999999999, 'l': 9229.0, 0.02: 0.025399999999999999}, '[SII]4076.35': {0.0040000000000000001: 0.0070000000000000001, 0.050000000000000003: 0.010999999999999999, 0.0080000000000000002: 0.010999999999999999, 0.00040000000000000002: 0.002, 'l': 4076.3499999999999, 0.02: 0.010999999999999999}, 'Hgamma': {0.0040000000000000001: 0.46100000000000002, 0.050000000000000003: 0.46100000000000002, 0.0080000000000000002: 0.46100000000000002, 0.00040000000000000002: 0.46100000000000002, 'l': 4340.0, 0.02: 0.46100000000000002}, 'Pepsilon': {0.0040000000000000001: 0.036499999999999998, 0.050000000000000003: 0.036499999999999998, 0.0080000000000000002: 0.036499999999999998, 0.00040000000000000002: 0.036499999999999998, 'l': 9546.0, 0.02: 0.036499999999999998}, 'Bbeta': {0.0040000000000000001: 0.048509999999999998, 0.050000000000000003: 0.048509999999999998, 0.0080000000000000002: 0.048509999999999998, 0.00040000000000000002: 0.048509999999999998, 'l': 26252.0, 0.02: 0.048509999999999998}, '[SII]4068.6': {0.0040000000000000001: 0.017000000000000001, 0.050000000000000003: 0.029000000000000001, 0.0080000000000000002: 0.029000000000000001, 0.00040000000000000002: 0.0050000000000000001, 'l': 4068.5999999999999, 0.02: 0.029000000000000001}, 'Halpha': {0.0040000000000000001: 2.915, 0.050000000000000003: 2.915, 0.0080000000000000002: 2.915, 0.00040000000000000002: 2.915, 'l': 6562.0, 0.02: 2.915}, '[OII]7319.99': {0.0040000000000000001: 0.027, 0.050000000000000003: 0.025999999999999999, 0.0080000000000000002: 0.025999999999999999, 0.00040000000000000002: 0.012, 'l': 7319.9899999999998, 0.02: 0.025999999999999999}, '[SII]10286.73': {0.0040000000000000001: 0.0, 0.050000000000000003: 0.048000000000000001, 0.0080000000000000002: 0.048000000000000001, 0.00040000000000000002: 0.0, 'l': 10286.73, 0.02: 0.048000000000000001}, '[HeI]4026.0': {0.0040000000000000001: 0.014999999999999999, 0.050000000000000003: 0.014999999999999999, 0.0080000000000000002: 0.014999999999999999, 0.00040000000000000002: 0.014999999999999999, 'l': 4026.0, 0.02: 0.014999999999999999}, '[OII]7330.73': {0.0040000000000000001: 0.014, 0.050000000000000003: 0.014, 0.0080000000000000002: 0.014, 0.00040000000000000002: 0.0070000000000000001, 'l': 7330.7299999999996, 0.02: 0.014}, '[HeI]7065.00': {0.0040000000000000001: 0.023, 0.050000000000000003: 0.040000000000000001, 0.0080000000000000002: 0.040000000000000001, 0.00040000000000000002: 0.028000000000000001, 'l': 7065.0, 0.02: 0.040000000000000001}}
velocity=1000.
#------------------------------------------------
#------------------------------------------------
#determine relations between redshift and age
aou_cz=cosmocalc.cosmocalc(p.snap_z)['zage_Gyr']
redshifts=np.arange(p.snap_z,20,0.1)
ages=[]
for z in redshifts:
aou_z=cosmocalc.cosmocalc(z)['zage_Gyr']
ages.append(aou_cz-aou_z)
ages=np.array(ages)
#------------------------------------------------
#------------------------------------------------
# read in SSPs
mets=[0.05,0.02,0.008,0.004,0.0004]
metsl={0.05:'005',0.02:'002',0.008:'0008',0.004:'0004',0.0004:'00004'}
ssp={}
def find_image(self,mstar,redshift,sfr,seed,xpix,ypix,hmag):
sim_simname = self.simdata['col1']
sim_expfact = self.simdata['col2']
sim_sfr = self.simdata['col54']
sim_mstar = self.simdata['col56']
sim_redshift = 1.0/sim_expfact - 1.0
metalmass = self.simdata['col53']
sim_res_pc = self.simdata['col62']
sim_string = self.simdata['col60']
simage_loc = '/Users/gsnyder/Documents/Projects/HydroART_Morphology/Hyades_Data/images_rsync/'
self.mstar_list.append(mstar)
self.redshift_list.append(redshift)
adjust_size=False
print " "
print "Searching for simulation with mstar,z,seed : ", mstar, redshift, seed
wide_i = np.where(np.logical_and(np.logical_and(np.abs(sim_redshift-redshift)<0.3,np.abs(np.log10(sim_mstar)-mstar)<0.1),sim_sfr > -1))[0]
Nwi = wide_i.shape[0]
if Nwi==0:
wide_i = np.where(np.logical_and(np.logical_and(np.abs(sim_redshift-redshift)<0.5,np.abs(np.log10(sim_mstar)-mstar)<0.4),sim_sfr > -1))[0]
Nwi = wide_i.shape[0]
if Nwi==0 and (mstar < 7.1):
print " Can't find good sim, adjusting image parameters to get low mass things "
wide_i = np.where(np.abs(sim_redshift-redshift)<0.3)[0] #wide_i is a z range
llmi = np.argmin(np.log10(sim_mstar[wide_i])) #the lowest mass in this z range
wlmi = np.where(np.abs(np.log10(sim_mstar[wide_i]) - np.log10(sim_mstar[wide_i[llmi]])) < 0.3)[0] #search within 0.3 dex of lowest available sims
print " ", wide_i.shape, llmi, wlmi.shape
wide_i = wide_i[wlmi]
Nwi = wide_i.shape[0]
print " ", Nwi
adjust_size=True
#assert(wide_i.shape[0] > 0)
if Nwi==0:
print " Could not find roughly appropriate simulation for mstar,z: ", mstar, redshift
print " "
self.image_files.append('')
return 0#np.zeros(shape=(600,600)), -1
print " Found N candidates: ", wide_i.shape
np.random.seed(seed)
#choose random example and camera
rps = np.random.random_integers(0,Nwi-1,1)[0]
cn = str(np.random.random_integers(5,8,1)[0])
prefix = os.path.basename(sim_string[wide_i[rps]])
sim_realmstar = np.log10(sim_mstar[wide_i[rps]]) #we picked a sim with this log mstar
mstar_factor = sim_realmstar - mstar
rad_factor = 1.0
lum_factor = 1.0
if adjust_size==True:
rad_factor = 10.0**(mstar_factor*0.5) #must **shrink** images by this factor, total flux by mstar factor
lum_factor = 10.0**(mstar_factor)
print ">>>FACTORS<<< ", prefix, sim_realmstar, mstar_factor, rad_factor, lum_factor
im_folder = simage_loc + prefix +'_skipir/images'
im_file = os.path.join(im_folder, prefix+'_skipir_CAMERA'+cn+'-BROADBAND_'+self.filter_string+'_simulation.fits')
cn_file = os.path.join(im_folder, prefix+'_skipir_CAMERA'+cn+'-BROADBAND_'+self.filter_string+'_candelized_noise.fits')
req1 = os.path.join(im_folder, prefix+'_skipir_CAMERA'+cn+'-BROADBAND_'+self.req_filters[0]+'_simulation.fits')
req2 = os.path.join(im_folder, prefix+'_skipir_CAMERA'+cn+'-BROADBAND_'+self.req_filters[1]+'_simulation.fits')
req3 = os.path.join(im_folder, prefix+'_skipir_CAMERA'+cn+'-BROADBAND_'+self.req_filters[2]+'_simulation.fits')
req4 = os.path.join(im_folder, prefix+'_skipir_CAMERA'+cn+'-BROADBAND_'+self.req_filters[3]+'_simulation.fits')
req5 = os.path.join(im_folder, prefix+'_skipir_CAMERA'+cn+'-BROADBAND_'+self.req_filters[4]+'_simulation.fits')
## Actually, probably want to keep trying some possible galaxies/files...
is_file = os.path.lexists(im_file) and os.path.lexists(cn_file) and os.path.lexists(req1) and os.path.lexists(req2) and os.path.lexists(req3) and os.path.lexists(req4) and os.path.lexists(req5)
#is_file = os.path.lexists(im_file) and os.path.lexists(cn_file) #and os.path.lexists(req1) and os.path.lexists(req2) and os.path.lexists(req3)
if is_file==False:
print " Could not find appropriate files: ", im_file, cn_file
print " "
self.image_files.append('')
return 0 #np.zeros(shape=(600,600)), -1
self.image_files.append(im_file)
cn_header = pyfits.open(cn_file)[0].header
im_hdu = pyfits.open(im_file)[0]
scalesim = cn_header.get('SCALESIM') #pc/pix
Ps = cosmocalc.cosmocalc(redshift)['PS_kpc'] #kpc/arcsec
print " Simulation pixel size at z: ", scalesim
print " Plate scale for z: ", Ps
print " Desired Kpc/pix at z: ", Ps*self.Pix_arcsec
sunrise_image = np.float32(im_hdu.data) #W/m/m^2/Sr
Sim_Npix = sunrise_image.shape[0]
New_Npix = int( Sim_Npix*(scalesim/(1000.0*Ps*self.Pix_arcsec))/rad_factor ) #rad_factor reduces number of pixels (total size) desired
if New_Npix==0:
New_Npix=1
print " New galaxy pixel count: ", New_Npix
rebinned_image = congrid.congrid(sunrise_image,(New_Npix,New_Npix)) #/lum_factor #lum_factor shrinks surface brightness by mass factor... but we're shrinking size first, so effective total flux already adjusted by this; may need to ^^ SB instead??? or fix size adjust SB?
print " New galaxy image shape: ", rebinned_image.shape
print " New galaxy image max: ", np.max(rebinned_image)
#finite_bool = np.isfinite(rebinned_image)
#num_infinite = np.where(finite_bool==False)[0].shape[0]
#print " Number of INF pixels: ", num_infinite, prefix
#self.N_inf.append(num_infinite)
if xpix==-1:
xpix = int( (self.Npix-1)*np.random.rand()) #np.random.random_integers(0,self.Npix-1,1)[0]
ypix = int( (self.Npix-1)*np.random.rand()) #np.random.random_integers(0,self.Npix-1,1)[0]
self.x_array.append(xpix)
self.y_array.append(ypix)
x1_choice = np.asarray([int(xpix-float(New_Npix)/2.0),0])
x1i = np.argmax(x1_choice)
x1 = x1_choice[x1i]
diff=0
if x1==0:
diff = x1_choice[1]-x1_choice[0]
x2_choice = np.asarray([x1 + New_Npix - diff,self.Npix])
x2i = np.argmin(x2_choice)
x2 = int(x2_choice[x2i])
x1sim = abs(np.min(x1_choice))
x2sim = min(New_Npix,self.Npix-x1)
y1_choice = np.asarray([int(ypix-float(New_Npix)/2.0),0])
y1i = np.argmax(y1_choice)
y1 = y1_choice[y1i]
diff=0
if y1==0:
diff = y1_choice[1]-y1_choice[0]
y2_choice = np.asarray([y1 + New_Npix - diff,self.Npix])
y2i = np.argmin(y2_choice)
y2 = int(y2_choice[y2i])
y1sim = abs(np.min(y1_choice))
y2sim = min(New_Npix,self.Npix-y1)
print " Placing new image at x,y in x1:x2, y1:y2 from xsim,ysim, ", xpix, ypix, x1,x2,y1,y2, x1sim, x2sim, y1sim, y2sim
#image_slice = np.zeros_like(self.blank_array)
print " done creating image slice"
#bool_slice = np.int32( np.zeros(shape=(self.Npix,self.Npix)))
image_cutout = rebinned_image[x1sim:x2sim,y1sim:y2sim]
print " New image shape: ", image_cutout.shape
pixel_Sr = (self.Pix_arcsec**2)/sq_arcsec_per_sr #pixel area in steradians: Sr/pixel
to_nJy_per_Sr = (1.0e9)*(1.0e14)*(self.eff_lambda_microns**2)/c #((pixscale/206265.0)^2)*
#sigma_nJy = 0.3*(2.0**(-0.5))*((1.0e9)*(3631.0/5.0)*10.0**(-0.4*self.maglim))*self.Pix_arcsec*(3.0*self.FWHM_arcsec)
to_Jy_per_pix = to_nJy_per_Sr*(1.0e-9)*pixel_Sr
#b = b*(to_nJy_per_Sr_b*fluxscale*bluefact) # + np.random.randn(Npix,Npix)*sigma_nJy/pixel_Sr
image_cutout = image_cutout*to_Jy_per_pix #image_cutout*to_nJy_per_Sr
#image_slice[x1:x2,y1:y2] = image_cutout*1.0
#bool_slice[x1:x2,y1:y2]=1
print " done slicing"
#self.final_array += image_slice
self.final_array[x1:x2,y1:y2] += image_cutout
print " done adding image to final array"
#finite_bool = np.isfinite(self.final_array)
#num_infinite = np.where(finite_bool==False)[0].shape[0]
#print " Final array INF count and max:", num_infinite, np.max(self.final_array)
print " "
return 1 #sunrise_image,scalesim
import continuum
from sys import argv
snap = readsubhalo.SnapDir(argv[1], p.snapshot_dir)
#------------------------------------------------
# make output directories
output_dir = snap.subhalodir + '/SED'
os.system('mkdir '+ output_dir)
#------------------------------------------------
#------------------------------------------------
#determine relations between redshift and age
aou_cz=cosmocalc.cosmocalc(snap.redshift)['zage_Gyr']
redshifts=numpy.arange(snap.redshift,20,0.1)
ages=[]
for z in redshifts:
aou_z=cosmocalc.cosmocalc(z)['zage_Gyr']
ages.append(aou_cz-aou_z)
ages=numpy.array(ages)
#------------------------------------------------
#------------------------------------------------
# read in SSPs
mets=numpy.array([0.05,0.02,0.008,0.004,0.0004])
metsl={0.05:'005',0.02:'002',0.008:'0008',0.004:'0004',0.0004:'00004'}
ssp={}
flux_nuv_corr = flux_nuv_fitzpatrick * correction_factor
flux_fuv_corr = flux_fuv_fitzpatrick * correction_factor
flux_galex_corr = np.array([float(flux_fuv_corr), float(flux_nuv_corr)])
flux_all_corr = np.array([float(flux_sdss_corr[0]), float(flux_sdss_corr[1]), float(flux_sdss_corr[2]),
float(flux_sdss_corr[3]), float(flux_sdss_corr[4]), float(flux_fuv_corr),
float(flux_nuv_corr)])
# UV Synthetic equivalent fluxes -------------------------------------------------------------------------------
k = np.abs(fuv_obs_wavelength - synthetic_wavelengths).argmin()
fuv_synth_flux = synthetic_fluxes[k]
m = np.abs(nuv_obs_wavelength - synthetic_wavelengths).argmin()
nuv_synth_flux = synthetic_fluxes[m]
# Absolute Magnitudes using Luminosity Distance ----------------------------------------------------------------
luminosity_distance = cosmo.cosmocalc(z[index])['DL_Mpc']
mag_fuv_abs = mag_fuv_dered_fm - 5 * np.log10(luminosity_distance) - 25
mag_nuv_abs = mag_nuv_dered_fm - 5 * np.log10(luminosity_distance) - 25
mag_u_abs = mag_ab_u[index] - 5 * np.log10(luminosity_distance) - 25
mag_g_abs = mag_ab_g[index] - 5 * np.log10(luminosity_distance) - 25
mag_r_abs = mag_ab_r[index] - 5 * np.log10(luminosity_distance) - 25
mag_i_abs = mag_ab_i[index] - 5 * np.log10(luminosity_distance) - 25
mag_z_abs = mag_ab_z[index] - 5 * np.log10(luminosity_distance) - 25
# Chi^2 selection ---------------------------------------------------------------------------------------------
flux_sdss_synth = []
for n in range(wavelengths_sdss_griz.size):
index_flux_synth = np.abs(wavelengths_sdss_griz[n] - synthetic_wavelengths).argmin()
flux_synth = synthetic_fluxes[index_flux_synth]
flux_sdss_synth.append(flux_synth)
flux_sdss_synth = np.array(flux_sdss_synth)
def getDataSlow(db='database.db', path='/Users/sammy/Research/CANDELS/v2/'):
"""
Get data from the lcone table in db.
:param db: name of the SQLite database file.
:type db: string
:param path: full path of the database file
:type path: string
:Warning: This is extremely slow way to pull out data from a database.
Should be rewritten using table joins etc. so that less queries
could be performed.
:return: redshift, halo mass and radius, projected distances of subhalo galaxies [kpc],
halo_id of the main halo
:rtype: dict
"""
conversion = 0.000277777778 # degree to arcsecond
redshifts = [
'redshift < 0.5 and', 'redshift >= 0.5 and redshift < 1.0 and',
'redshift >= 1.0 and redshift < 2.0 and',
'redshift >= 2.0 and redshift < 3.0 and',
'redshift >= 3.0 and redshift < 4.0 and',
'redshift >= 4.0 and redshift < 5.0 and',
'redshift >= 5.0 and redshift < 6.0 and',
'redshift >= 6.0 and redshift < 7.0 and'
]
for i, red in enumerate(redshifts):
print red
qr = 'select halo_id from lcone where %s gal_id > 1' % red
#qr = 'select halo_id from lcone where %s mhalo > 12.7' % red
#pull out data
ids = sq.get_data_sqliteSMNfunctions(path, db, qr)
uids = np.unique(ids)
print len(uids), 'haloes'
saveid = []
saveorig = []
savedist = []
savemhalo = []
saverhalo = []
saveredshift = []
#we should now look for each unique id
for id in uids:
query = 'select redshift, ra, dec, halo_id, mhalo, rhalo from lcone where halo_id = {0:d}'.format(
id)
#print query
data = sq.get_data_sqliteSMNfunctions(path, db, query)
#if multiples, then don't take it
if len(set(data[:, 0])) > 1:
print 'skipping', id
continue
if len(data[:, 1]) < 2:
print 'no subhaloes', id, data[:, 1]
continue
#redshift
z = data[0, 0]
#look the diameter distance from the lookup table
dd = cosmocalc(z, 71.0, 0.28)['PS_kpc']
#the first halo, assume it to be the main halo
RADeg1 = data[0, 1]
decDeg1 = data[0, 2]
#the following haloes, assume to be subhaloes
RADeg2 = data[1:, 1]
decDeg2 = data[1:, 2]
#calculate the angular separation on the sky
sep = Coords.calcAngSepDeg(RADeg1, decDeg1, RADeg2, decDeg2)
physical_distance = sep * dd / conversion
#these are all the main halo parameters
saveredshift.append(z)
saveid.append(int(data[0, 3]))
savemhalo.append(data[0, 4])
saverhalo.append(data[0, 5])
savedist.append(physical_distance)
saveorig.append(id)
out = dict(halo_ids=saveid,
distances=savedist,
original=saveorig,
mhalo=savemhalo,
rhalo=saverhalo,
redshift=saveredshift)
wr.cPickleDumpDictionary(out, 'distances%i.pickle' % (i + 1))
tmp = matched[matched[:, 3] == x]
#redshift
z = tmp[0][0]
RADeg1 = tmp[0][1]
decDeg1 = tmp[0][2]
RADeg2 = tmp[1:, 1]
decDeg2 = tmp[1:, 2]
prop = tmp[:, 5]
sep = Coords.calcAngSepDeg(RADeg1, decDeg1, RADeg2, decDeg2)
dd = cosmocalc(z, 71.0,
0.28)['PS_kpc'] #dist.getDiameterDistances(data)
physical_distance = (sep / dd) / conversion
print z, RADeg1, RADeg2, decDeg1, decDeg2, prop
print sep, physical_distance
print
ax.plot([z for foo in range(len(physical_distance))],
physical_distance, 'bo')
#P.show()
print
print
cursor.execute('CREATE TABLE newTable (h1, g1, h2, g2, foo)')
#DEBUG
#print(data['red'])
#for i in range(len(data['wl'])):
#print('%.5e %.5e %.5e\n' %(data['wl'][i], data['fl'][i], data['err'][i]))
###################################################################
#
# TRANSFORM INTO ABSOLUTE MAGNITUDES
#
###################################################################
for k in range(len(input_files_data)):
dist = (cosmocalc.cosmocalc( input_files_data[k]['red'], H0=70.4, WM=0.2726, WV=0.7274))['DL_Mpc']
#Calculate modulus distance
dist *= 1e6
#transform into parsecs
modulus_distance = 5.0 * (math.log10(dist) - 1.0)
#Calculate modulus distance
#DEBUG
#print("DIST: " + str(dist))
#print("MOD_DIST: " + str(modulus_distance))
for i in range(len(input_files_data[k]['wl'])):
#print("FLUX (microJs): " +str(data['fl'][i]))
input_files_data[k]['err'][i] = 2.5 * input_files_data[k]['err'][i] / input_files_data[k]['fl'][i]
from deproject import Deproject
from cosmocalc import cosmocalc
redshift = 0.004233
from math import pi
dep = Deproject(numpy.arange(30., 640., 30)*0.492)
dep.theta = 75
dep.angdist = cosmocalc(redshift)['DA_cm'] * 0.892
set_method("levmar")
set_stat("chi2gehrels")
for ann in range(dep.nshell):
dep.load_pha('m87/r%dgrspec.pha' % (ann+1), annulus=ann)
dep.set_source('xswabs*xsmekal')
dep.ignore(None, 0.5)
dep.ignore(1.8, 2.2)
dep.ignore(7, None)
dep.set_par('xswabs.nh', 0.0255)
dep.freeze("xswabs.nh")
dep.set_par('xsmekal.abundanc', 0.5)
dep.thaw('xsmekal.abundanc')
dep.set_par('xsmekal.redshift', redshift)
dep.subtract()
dep.fit()
LSLOPE2_MAX = 1
LSTAR_2_MIN = 20.0
LSTAR_2_MAX = 24.9
LSLOPE_2_MIN = -5.0
LSLOPE_2_MAX = 5.0
LSLOPE2_2_MIN = -5.0
LSLOPE2_2_MAX = 5.0
LNORM_2_MIN = -5.5
LNORM_2_MAX = 2
LSIGMA_MIN = 0.6
LSIGMA_MAX = 0.1
if setBreaks:
dl = cosmocalc(redshifts[0], H0=Ho, WM=wm)['DL_Mpc']
l = math.pi * 4 * (Breaks[0] * 1e-32) * (dl * 3.08e22)**2 * (
1 + redshifts[0])**((LF_SPEC_INDEX) + 1)
LSTAR_MIN = LSTAR_MAX = 23.1
#LMAX_MIN=LMAX_MAX=24.6
print 'setting break'
LSTAR_PRIOR = 'DELTA'
#LMAX_PRIOR = 'DELTA'
# LF redshift evolution (if > 1 z slice only)
LZEVOL_MIN = -5.0
LZEVOL_MAX = 5.0
if len(whichRedshiftSlices) <= 1:
LZEVOL_MIN = LZEVOL_MAX = 0.0 # delta fn on LZEVOL
#S0_MIN=S0_MAX=S0_TRUE # delta fn on S0
