def llhood_pbd_wcdm(model_param, ndim, nparam):
om, h0, w0, MB, od, g = [model_param[i] for i in range(6)]
wa = 0
A = []
for i in range(0, len(pb.zcmb.values)):
At = 10**(od) * attenuation(om, h0, pb.zcmb.values[i], w0, wa, g)
A.append(At)
#A_inter = interp1d(z50, A)
#DUST = A_inter(pb.zcmb.values)
dist_th = wCDM(h0, om, 1 - om,
w0).luminosity_distance(pb.zcmb.values).value
mod_th = 5 * np.log10(dist_th) + 25
hub_res = pb.mb.values - mod_th - MB - A
chisq = np.dot(hub_res.T, np.dot(pb_icm, hub_res))
if cmb == 1:
T = cmb_chisq(om, h0, w0, wa)
chisq += T
return -0.5 * chisq
def llhood_jlad_wcdm(model_param, ndim, nparam):
om, w0, a, b, MB, DM, od, g = [model_param[i] for i in range(8)]
h0 = 70
wa = 0
A = []
for i in range(0, 50):
At = 10**(od) * attenuation(om, h0, z50[i], w0, wa, g)
A.append(At)
A_inter = interp1d(z50, A)
DUST = A_inter(jla.zcmb.values)
dist_th = wCDM(h0, om, 1 - om,
w0).luminosity_distance(jla.zcmb.values).value
mod_th = 5 * np.log10(dist_th) + 25
hub_res = jla.mb.values + a * jla.x1.values - b * jla.color.values - MB - mod_th - DUST
hub_res[jla.hm.values >= 10.] += DM
C = mu_cov(a, b)
iC = np.linalg.inv(C)
chisq = np.dot(hub_res.T, np.dot(iC, hub_res))
if cmb == 1:
T = cmb_chisq(om, h0, w0, wa)
chisq += T
return -0.5 * chisq
def test_redshift_array(nz=4096): from astropy import cosmology cosmo = cosmology.wCDM(H0=0.71,Om0=0.31,Ode0=0.69,w0=-1) zmax = 10. distance = lambda z: cosmo.comoving_distance(z).value*cosmo.h redshift = DistanceToRedshiftArray(distance=distance,zmax=zmax,nz=nz) z = scipy.random.uniform(0.,2.,10000) print scipy.absolute(redshift(distance(z))-z).max()
def load_cosmology(self, handle: h5py.File) -> Cosmology:
"""
Save the (astropy) cosmology to a HDF5 dataset.
Parameters
----------
handle: h5py.File
h5py file handle for the catalogue file.
Returns
-------
astropy.cosmology.Cosmology:
Astropy cosmology instance extracted from the HDF5 file.
Also sets ``self.cosmology``.
"""
try:
group = handle["Configuration"].attrs
except:
return None
# Note that some of these are unused - commented out if not.
H0 = 100.0 * float(group["h_val"])
w_of_DE = float(group["w_of_DE"])
Omega_DE = float(group["Omega_DE"])
# Omega_Lambda = float(group["Omega_Lambda"])
Omega_b = float(group["Omega_b"])
# Omega_cdm = float(group["Omega_cdm"])
# Omega_k = float(group["Omega_k"])
Omega_m = float(group["Omega_m"])
# Omega_nu = float(group["Omega_nu"])
# Omega_r = float(group["Omega_r"])
if w_of_DE != -1.0:
cosmology = wCDM(
H0=H0,
Om0=Omega_m,
Ode0=Omega_DE,
w0=w_of_DE,
Ob0=Omega_b,
)
else:
# No EoS
cosmology = FlatLambdaCDM(
H0=H0,
Om0=Omega_m,
Ob0=Omega_b,
)
self.cosmology = cosmology
return cosmology
def HaloLensedImage(self, *args):
self.ax.clear()
self.ax.axis('off')
fileimage = CWD + "/tmp/" + self.img_filenamedir + "_image.jpg"
Simu_Dir = self.model_select() + "/Lens-Maps/"
filelens = self.simdir + "/" + Simu_Dir + self.model_name + '_Halo.fits'
image, xsize, ysize = readimage(fileimage)
image_arr = np.array(image)
alpha1, alpha2 = buildlens(filelens)
cosmo = wCDM(70.3, self.slider_dm.value, self.slider_de.value, w0=-1.0)
self.halolensedimage = deflect(image_arr, alpha1, alpha2, xsize, ysize,
self.slider_comdist.value, cosmo,
"HALO")
self.ax.imshow(self.halolensedimage)
arr_hand1 = mpimg.imread("./icons/simcode.png")
imagebox1 = OffsetImage(arr_hand1, zoom=.1)
xy = [950.0, 85.0]
ab1 = AnnotationBbox(imagebox1,
xy,
xybox=(0., 0.),
xycoords='data',
boxcoords="offset points",
pad=0.1)
self.ax.add_artist(ab1)
sim_details_text = '%s: %s\n%s: %s\n%s: %s\n%s: %s\n%s: %s\n%s: %s' % (
text_dict['t42'], self.input_name.text, text_dict['t53'],
self.SC_Type, text_dict['t20'], self.spinner_de.text,
text_dict['t24'], self.spinner_dm.text, text_dict['t27'],
self.spinner_png.text, text_dict['t31'], self.spinner_gvr.text)
print sim_details_text
self.ax.text(0.1,
0.83,
sim_details_text,
color='white',
bbox=dict(facecolor='none',
edgecolor='white',
boxstyle='round,pad=1',
alpha=0.5),
transform=self.ax.transAxes,
alpha=0.5)
self.ax.axis('off')
self.ax.get_xaxis().set_visible(False)
self.ax.get_yaxis().set_visible(False)
self.canvas.draw()
#imsave(self.savedir + "/" + self.img_filename + "_LensedHalo_Photo.jpg", self.halolensedimage)
self.fig.savefig(self.savedir + "/" + self.img_filenamedir + "/" +
self.img_filenamedir + "_LensedHalo_Photo.png")
self.LensedHalo_Photo = self.img_filename + "_LensedHalo_Photo.png"
self.showlenscluster()
def plot_hd(self, fname, f='yD', showplot=True, res=False, rowtoskip=2): cols=np.loadtxt(fname, usecols=(1,2,3), skiprows=rowtoskip) #default cosmology, flat, (om, w) = (0.27, -1) wc = wCDM(70, 0.27, 0.73) if res: hres = cols[:,1] - np.mean(cols[:,1]) else: hres = 5*np.log10(wc.luminosity_distance(cols[:,0]).value)+25 + cols[:,1] plt.errorbar(cols[:,0], hres, cols[:,2], fmt=f) plt.show()
def plot_hd(self, fname, f='yD', showplot=True, res=False, rowtoskip=2):
cols = np.loadtxt(fname, usecols=(1, 2, 3), skiprows=rowtoskip)
#default cosmology, flat, (om, w) = (0.27, -1)
wc = wCDM(70, 0.27, 0.73)
if res:
hres = cols[:, 1] - np.mean(cols[:, 1])
else:
hres = 5 * np.log10(wc.luminosity_distance(
cols[:, 0]).value) + 25 + cols[:, 1]
plt.errorbar(cols[:, 0], hres, cols[:, 2], fmt=f)
plt.show()
def define_cosmology(self, cosmology_config):
"""Define the cosmology based on `cfg.bnn_omega.cosmology`
Parameters
----------
cosmology_config : dict
Copy of cfg.bnn_omega.cosmology
Returns
-------
astropy.cosmology.wCDM object
the cosmology with which to generate all the training samples
"""
self.cosmo = wCDM(**cosmology_config)
def load_cosmology(handle: h5py.File):
"""
Save the (astropy) cosmology to a HDF5 dataset.
Parameters
----------
handle: h5py.File
h5py file handle to read the cosmology from.
Returns
-------
astropy.cosmology.Cosmology:
Astropy cosmology instance extracted from the HDF5 file.
"""
try:
group = handle["cosmology"].attrs
except:
return None
try:
cosmology = wCDM(
H0=group["H0"],
Om0=group["Om0"],
Ode0=group["Ode0"],
w0=group["w0"],
Tcmb0=group["Tcmb0"],
Neff=group["Neff"],
m_nu=Quantity(group["m_nu"], unit=group["m_nu_units"]),
Ob0=group["Ob0"],
name=group["name"],
)
except KeyError:
# No EoS
cosmology = FlatLambdaCDM(
H0=group["H0"],
Om0=group["Om0"],
Tcmb0=group["Tcmb0"],
Neff=group["Neff"],
m_nu=Quantity(group["m_nu"], unit=group["m_nu_units"]),
Ob0=group["Ob0"],
name=group["name"],
)
return cosmology
def llhood_jbc_wcdm(model_param, ndim, nparam):
om, h0, w0 = [model_param[i] for i in range(3)]
wa = 0
dist_th = wCDM(h0, om, 1 - om, w0).luminosity_distance(jb.zb.values).value
mod_th = 5 * np.log10(dist_th) + 25
hub_res = jb.mub.values - mod_th
chisq = np.dot(hub_res.T, np.dot(jb_icm, hub_res))
if cmb == 1:
T = cmb_chisq(om, h0, w0, wa)
chisq += T
return -0.5 * chisq
def set_cosmology(cosmology=None):
"""
Get an instance of a astropy.cosmology.FLRW subclass.
To avoid repeatedly instantiating the same class, test if it is the same
as the last used cosmology.
Parameters
==========
cosmology: astropy.cosmology.FLRW, str, dict
Description of cosmology, one of:
None - Use DEFAULT_COSMOLOGY
Instance of astropy.cosmology.FLRW subclass
String with name of known Astropy cosmology, e.g., "Planck13"
Dictionary with arguments required to instantiate the cosmology
class.
Returns
=======
cosmo: astropy.cosmology.FLRW
Cosmology instance
"""
from astropy import cosmology as cosmo
_set_default_cosmology()
if cosmology is None:
cosmology = DEFAULT_COSMOLOGY
elif isinstance(cosmology, cosmo.FLRW):
cosmology = cosmology
elif isinstance(cosmology, str):
cosmology = cosmo.__dict__[cosmology]
elif isinstance(cosmology, dict):
if 'Ode0' in cosmology.keys():
if 'w0' in cosmology.keys():
cosmology = cosmo.wCDM(**cosmology)
else:
cosmology = cosmo.LambdaCDM(**cosmology)
else:
cosmology = cosmo.FlatLambdaCDM(**cosmology)
COSMOLOGY[0] = cosmology
if cosmology.name is not None:
COSMOLOGY[1] = cosmology.name
else:
COSMOLOGY[1] = repr(cosmology)
def llhood_jlac_wcdm(model_param, ndim, nparam):
om, w0, a, b, MB, DM = [model_param[i] for i in range(6)]
h0 = 70
wa = 0
dist_th = wCDM(h0, om, 1 - om,
w0).luminosity_distance(jla.zcmb.values).value
mod_th = 5 * np.log10(dist_th) + 25
hub_res = jla.mb.values + a * jla.x1.values - b * jla.color.values - MB - mod_th
hub_res[jla.hm.values >= 10.] += DM
C = mu_cov(a, b)
iC = np.linalg.inv(C)
chisq = np.dot(hub_res.T, np.dot(iC, hub_res))
if cmb == 1:
T = cmb_chisq(om, h0, w0, wa)
chisq += T
return -0.5 * chisq
def sim_nonStand(self):
Simu_Dir = self.model_select() + "/Dens-Maps/"
cosmo = wCDM(70.3, self.slider_dm.value, self.slider_de.value, w0=-1.0)
filenames = sorted(glob.glob(self.simdir + "/" + Simu_Dir + '*.npy'))
lga = np.linspace(np.log(0.05), np.log(1.0), 300)
a = np.exp(lga)
z = 1. / a - 1.0
lktime = cosmo.lookback_time(z).value
def animate(filename):
image = np.load(filename)
indx = filenames.index(
filename
) #; image=ndimage.gaussian_filter(image, sigma= sigmaval, truncate=truncateval, mode='wrap')
im.set_data(image +
1) #; im.set_clim(image.min()+1.,image.max()+1.)
self.time.set_text('%s %s' %
(round(lktime[indx], 4), text_dict['t49']))
return im
dens_map = np.load(
filenames[0]
) #; dens_map=ndimage.gaussian_filter(dens_map, sigma= sigmaval, truncate=truncateval, mode='wrap') #; dens_map0 = load(filenames[-1]); #print dens_map0.min()+1, dens_map0.max()+1.
im = self.ax.imshow(dens_map + 1,
cmap=cm.magma,
norm=colors.LogNorm(vmin=1., vmax=1800.,
clip=True),
interpolation="bicubic") #, clim = (1, 1800.+1.))
self.time = self.ax.text(0.1,
0.05,
text_dict['t43'] +
' %s Gyr' % round(lktime[0], 4),
horizontalalignment='left',
verticalalignment='top',
color='white',
transform=self.ax.transAxes,
fontsize=10)
arr_hand = mpimg.imread(CWD + "/tmp/" + self.img_filenamedir +
"_image.jpg")
imagebox = OffsetImage(arr_hand, zoom=.1)
xy = (0.1, 0.15) # coordinates to position this image
ab = AnnotationBbox(imagebox,
xy,
xybox=(0.1, 0.15),
xycoords='axes fraction',
boxcoords='axes fraction',
pad=0.1)
self.ax.add_artist(ab)
arr_hand1 = mpimg.imread("./icons/simcode.png")
imagebox1 = OffsetImage(arr_hand1, zoom=.1)
xy = (0.9, 0.9) # coordinates to position this image
ab1 = AnnotationBbox(imagebox1,
xy,
xybox=(0.9, 0.9),
xycoords='axes fraction',
boxcoords='axes fraction',
pad=0.1)
self.ax.add_artist(ab1)
#iMpc = lambda x: x*1024/125 #x in Mpc, return in Pixel *3.085e19
ob = AnchoredHScaleBar(size=0.1,
label="10Mpc",
loc=4,
frameon=False,
pad=0.6,
sep=2,
color="white",
linewidth=0.8)
self.ax.add_artist(ob)
sim_details_text = '%s: %s\n%s: %s\n%s: %s\n%s: %s\n%s: %s\n%s: %s' % (
text_dict['t42'], self.input_name.text, text_dict['t53'],
self.SC_Type, text_dict['t20'], self.spinner_de.text,
text_dict['t24'], self.spinner_dm.text, text_dict['t27'],
self.spinner_png.text, text_dict['t31'], self.spinner_gvr.text)
print sim_details_text
self.ax.text(0.1,
0.83,
sim_details_text,
color='white',
bbox=dict(facecolor='none',
edgecolor='white',
boxstyle='round,pad=1',
alpha=0.5),
transform=self.ax.transAxes,
alpha=0.5)
self.ani = animation.FuncAnimation(self.fig,
animate,
filenames,
repeat=False,
interval=25,
blit=False)
self.ax.axis('off')
self.ax.get_xaxis().set_visible(False)
self.ax.get_yaxis().set_visible(False)
self.canvas.draw()
raise TypeError('Angular resolution parameter must be a scalar')
fwhm_angres = fwhm_angres * U.arcmin
cosmoparms = parms['sim']['cosmo']
if cosmoparms['name'] is None:
cosmo = None
elif cosmoparms['name'].lower() == 'custom':
h = cosmoparms['h']
H0 = 100.0 * h
Om0 = cosmoparms['Om0']
Ode0 = cosmoparms['Ode0']
if Ode0 is None:
Ode0 = 1.0 - Om0
Ob0 = cosmoparms['Ob0'] / h**2
w0 = cosmoparms['w0']
cosmo = cosmology.wCDM(H0, Om0, Ode0, w0=w0, Ob0=Ob0)
elif cosmoparms['name'].lower() == 'wmap9':
cosmo = cosmology.WMAP9
else:
raise ValueError('{0} preset not currently accepted for cosmology'.format(cosmoparms['name'].lower()))
units = parms['sim']['units']
if not isinstance(units, str):
raise TypeError('Input units must be a string')
if units not in ['mK', 'K']:
raise ValueError('Supported units are "mK" and "K"')
if units == 'mK':
conv_factor = 1e-3
units = 'K'
else:
conv_factor = 1.0
def Comoving_Distance(z, Omega_M, Omega_L, w_o, w_a):
wcdm = cosmo.wCDM(H0=70., Om0=Omega_M, Ode0=Omega_L, w0=w_o)
return np.array(wcdm.comoving_distance(z))
# plot the contour positions - they're half way between the edges:
xbins = 0.5 * (xedges[:-1] + xedges[1:])
ybins = 0.5 * (yedges[:-1] + yedges[1:])
# now plot the contours
ax.hist2d(x, y, bins=150)
ax.contour(xbins, ybins, vals.T, 20, colors='k', zorder=10)
ax.set_xlim([-23, -16])
ax.set_ylim([0, 3.5])
ax.set_xlabel('M r,petro')
ax.set_ylabel('(u-r)model')
title_str = 'Observed bivariate distribution of the sample in rest-frame color vs. \
absolute magnitude. Sample size ' + str(
len(x)) + ' galaxies. Colour plot 150 bins, Contour plot 30 bins.'
ax.set_title(title_str)
plt.show()
cosmo = FlatLambdaCDM(H0=70 * u.km / u.s / u.Mpc, Om0=0.3)
cosmo3 = wCDM(H0=70, Om0=0.3, Ode0=0.7, w0=-0.9)
cosmo4 = LambdaCDM(H0=70, Om0=0.3, Ode0=0.7)
cosmo5 = wCDM(H0=70, Om0=0, Ode0=0.7, w0=-0.9)
#choose_cosmo_cm_diagram(cosmo)
#choose_cosmo_cm_diagram(cosmo1)
choose_cosmo_cm_diagram(cosmo2)
#choose_cosmo_cm_diagram(cosmo3)
#choose_cosmo_cm_diagram(cosmo4)
#choose_cosmo_cm_diagram(cosmo5)
def generate_elg_random(mjd, chunk, area, output_path, random_path, out_random,
out_catalog, out_dndz, z_min, z_max, binwidth_z,
weights, tiling_compl_cut, redshift_compl_cut,
down_sample_fact, H0, omega_m, omega_lam, w_DE):
run_info = open(output_path + '/run.info', 'a')
# Read plate centers
tilelist = ascii.read('data/ebosstiles.dat')
tiles = tilelist["TILE"]
racen = tilelist["racen"]
deccen = tilelist["deccen"]
obs_tiles = np.loadtxt(output_path + '/tiles.txt')
rac = []
decc = []
i = 0
for tile in tiles:
if tile in obs_tiles:
rac.append(racen[i])
decc.append(deccen[i])
i += 1
rac = np.asarray(rac)
rac = np.where(rac > 270., rac - 360., rac)
decc = np.asarray(decc)
nplates = len(rac)
#----------------------
# open random fits catalog
hdu = fits.open(random_path)
data = hdu[1].data
tmp = data['ra']
randra = np.where(tmp > 270., tmp - 360., tmp)
randdec = data['dec']
#----------------------
# Modify the random number density
idx = np.random.choice(len(randra),
size=int(len(randra) / down_sample_fact))
new_density = (10000. / down_sample_fact)
info_line = ("Number density in the random catalog: %.2f deg^-2\n\n" %
new_density)
print info_line
run_info.write(info_line)
randra = randra[idx]
randdec = randdec[idx]
nrand = len(randra)
randSkyCoord = SkyCoord(ra=randra * u.deg,
dec=randdec * u.deg,
frame='fk5')
#--------------------
# open data ascii file created by function 'generate_elg_catalog'
tmp = ascii.read(out_catalog + '.dat')
datara = tmp['ra']
datadec = tmp['dec']
dataz = tmp['z']
dataw_compl = tmp['w_compl']
dataw_sys = tmp['w_sys']
ndata = len(datara)
#--------------------
# selecting randoms within the plate
inplate = np.zeros(nrand, dtype='bool')
tile_rad = 1.49
for k in range(nplates):
cenSkyCoord = SkyCoord(ra=rac[k] * u.deg,
dec=decc[k] * u.deg,
frame='fk5')
tmp = (randSkyCoord.separation(cenSkyCoord).deg <= tile_rad)
inplate[tmp] = True
randra = np.asarray(randra[inplate])
randdec = np.asarray(randdec[inplate])
#-------------------------------------
# Remove randoms with a completeness below a certain treshold
randc = rand_compl('tiling', randra, randdec, mjd, chunk)
if tiling_compl_cut > 0.:
tmp = (randc >= tiling_compl_cut)
randra = randra[tmp]
randdec = randdec[tmp]
randc = rand_compl('redshift', randra, randdec, mjd, chunk)
if redshift_compl_cut > 0.:
tmp = (randc >= redshift_compl_cut)
randra = randra[tmp]
randdec = randdec[tmp]
nrand = len(randra)
# assign random redshift to random objects,
# following the same redshift distribution as in data
dataw_tot = dataw_compl * dataw_sys
randz = np.random.choice(dataz, size=nrand, p=dataw_tot / sum(dataw_tot))
#------------------------------------
mbins = my_arange(z_min, z_max, binwidth_z)
###---------------###
### TOTAL WEIGHTS ###
###---------------###
# To be assigned to data and random
randw = np.ones(nrand)
dataw_FKP = np.ones(ndata)
if 3 in weights: # The FKP weights
def weight_FKP(x):
return 1. / (1. + x * 10000.)
# open the data redshift distribution
tmp = ascii.read(out_dndz + '.dat')
bincenters = tmp['z']
nz = tmp['nz']
# Calculation of the comoving volume
cosmo = wCDM(H0=H0, Om0=omega_m, Ode0=omega_lam, w0=w_DE)
# sky surface = 4pi sr = 129600/pi deg^2
tmp = cosmo.comoving_volume(mbins).value * (
np.pi / 129600.) * cosmo.h**3. # h^-3 * Mpc^3 / deg^2
dV = tmp[1:] - tmp[:-1]
nz = nz / area / dV
#-----------------------------------
# Fitting of the redshift distribution with a skew gaussian
# mu, sigmag, alpha, a, c1, c2, c3
skew_init = [np.mean(dataz),
np.std(dataz), 0.,
max(nz), 0., 0., 0.] # initial guess
popt = fit_redshift_dist(bincenters, nz,
skew_init) # optimal parameters of the fit
info_line = ("Initial guess for the fit of n(z):\n")
info_line += ("mu = %.2f\n" % skew_init[0])
info_line += ("sigma = %.2f\n" % skew_init[1])
info_line += ("alpha = %.2f\n" % skew_init[2])
info_line += ("amp = %.2e\n" % skew_init[3])
info_line += ("c1 = %.2e\n" % skew_init[4])
info_line += ("c2 = %.2e\n" % skew_init[5])
info_line += ("c3 = %.2e\n\n" % skew_init[6])
print info_line
run_info.write(info_line)
info_line = ("Output parameters for the fit of n(z):\n")
info_line += ("mu = %.2f\n" % popt[0])
info_line += ("sigma = %.2f\n" % popt[1])
info_line += ("alpha = %.2f\n" % popt[2])
info_line += ("amp = %.2e\n" % popt[3])
info_line += ("c1 = %.2e\n" % popt[4])
info_line += ("c2 = %.2e\n" % popt[5])
info_line += ("c3 = %.2e\n\n" % popt[6])
print info_line
run_info.write(info_line)
#-------------------------------------------------------
# Calculate the FKP for data and randoms
nz_fit = skew_gauss(randz, *popt)
randw = weight_FKP(nz_fit)
nz_fit = skew_gauss(dataz, *popt)
dataw_FKP = weight_FKP(nz_fit)
#---------------------------------------
# print n(z)
plt.figure()
tmp = np.linspace(z_min, z_max, 1000)
plt.plot(bincenters, nz * 1.e4, 'bo', label='data')
plt.plot(tmp,
skew_gauss(tmp, *skew_init) * 1.e4,
'g-',
label='inital guess')
plt.plot(tmp, skew_gauss(tmp, *popt) * 1.e4, 'r-', label='final fit')
plt.legend(loc='upper right')
plt.xlabel(r'$z$', size=18)
plt.ylabel(r'$n(z)$ [$10^{-4}h^3$Mpc$^{-3}$]', size=18)
plt.savefig(output_path + '/nz_fit.pdf', format='pdf')
# Calculate the total weights to assign to data
dataw_tot = np.ones(ndata)
if 1 in weights:
dataw_tot *= dataw_compl
info_line = ('Using completeness weights\n')
print info_line
run_info.write(info_line)
if 2 in weights:
dataw_tot *= dataw_sys
info_line = ('Using systematic weights\n')
print info_line
run_info.write(info_line)
if 3 in weights:
dataw_tot *= dataw_FKP
info_line = ('Using FKP weights\n')
print info_line
run_info.write(info_line)
if len(weights) == 0:
info_line = ('No weights are used\n')
print info_line
run_info.write(info_line)
# The effective redshift
tmp = np.average(dataz, weights=dataw_tot)
info_line = ("\n")
info_line += ("Effective redshift: %.2f\n" % tmp)
# The effective number of data
tmp = sum(dataw_tot)
info_line += ("Effective number of data: %.6f\n" % tmp)
# The effective number of randoms
tmp = sum(randw)
info_line += ("Effective number of randoms: %.6f\n\n" % tmp)
print info_line
run_info.write(info_line)
#----------------------------------------------
# writing the catalog for data
tmp = np.transpose(np.vstack((datara, datadec, dataz, dataw_tot)))
ascii.write(tmp,
out_catalog + '.dat',
format='no_header',
names=['ra', 'dec', 'z', 'w'],
formats={
'ra': '%.6f',
'dec': '%.6f',
'z': '%.6f',
'w': '%.6f'
})
col1 = fits.Column(name='ra', format='D', array=datara)
col2 = fits.Column(name='dec', format='D', array=datadec)
col3 = fits.Column(name='z', format='D', array=dataz)
col4 = fits.Column(name='w_FKP', format='D', array=dataw_FKP)
col5 = fits.Column(name='w_compl', format='D', array=dataw_compl)
col6 = fits.Column(name='w_sys', format='D', array=dataw_sys)
cols = fits.ColDefs([col1, col2, col3, col4, col5, col6])
tbhdu = fits.BinTableHDU.from_columns(cols)
tbhdu.writeto(out_catalog + '.fits', clobber=True)
# writing the random
tmp = np.transpose(np.array([randra, randdec, randz, randw]))
ascii.write(tmp,
out_random + '.dat',
format='no_header',
names=['randra', 'randdec', 'randz', 'randw'],
formats={
'randra': '%.6f',
'randdec': '%.6f',
'randz': '%.6f',
'randw': '%.6f'
})
col1 = fits.Column(name='randra', format='D', array=randra)
col2 = fits.Column(name='randdec', format='D', array=randdec)
col3 = fits.Column(name='randz', format='D', array=randz)
col4 = fits.Column(name='randw', format='D', array=randw)
cols = fits.ColDefs([col1, col2, col3, col4])
tbhdu = fits.BinTableHDU.from_columns(cols)
tbhdu.writeto(out_random + '.fits', clobber=True)
#---- Figures ----
if chunk == 'eboss21':
plt.figure(figsize=(20, 8.8))
footprint_ra = np.array([-43.0, 0.0, 0.0, -43.0, -43.0])
footprint_dec = np.array([-2.0, -2.0, 2.0, 2.0, -2.0])
xlim = [-48.5, 5]
ylim = [-2.5, 2.5]
elif chunk == 'eboss22':
plt.figure(figsize=(20, 8.8))
footprint_ra = np.array([0.0, 45.0, 45.0, 0.0, 0.0])
footprint_dec = np.array([-5.0, -5.0, 5.0, 5.0, -5.0])
xlim = [-3, 48]
ylim = [-6, 6]
elif chunk == 'eboss23':
plt.figure(figsize=(16, 8))
footprint_ra = np.array(
[126, 126, 142.5, 142.5, 157, 157, 136.5, 136.5, 126])
footprint_dec = np.array([16, 29, 29, 27, 27, 13.8, 13.8, 16, 16])
xlim = [125, 158]
ylim = [13, 30]
# ra, dec
plt.scatter(datara,
datadec,
s=1,
color='b',
alpha=0.4,
zorder=2,
label='Data : ' + str(ndata) + ' objects')
plt.scatter(randra,
randdec,
s=0.2,
color='r',
zorder=1,
label='Randoms : ' + str(nrand) + ' objects')
plt.plot(footprint_ra, footprint_dec, c='k', zorder=10, linewidth=2)
plt.xlabel(r'R.A. [deg]', size=16)
plt.ylabel(r'DEC. [deg]', size=16)
plt.xlim(xlim)
plt.ylim(ylim)
plt.tight_layout()
plt.grid(True, linestyle='-', alpha=0.3)
plt.savefig(output_path + '/footprint.png', format='png')
# z histogram of randoms
plt.figure()
plt.hist(dataz,
bins=mbins,
color='b',
edgecolor='none',
normed=True,
histtype='bar',
label='Data : ' + str(ndata) + ' galaxies')
plt.hist(randz,
bins=mbins,
color='r',
linewidth=2,
normed=True,
histtype='step',
label='Randoms : ' + str(nrand) + ' objects')
plt.xlabel(r'$z$', size=18)
plt.ylabel(r'$n(z)$', size=18)
plt.xlim(z_min, z_max)
plt.legend()
plt.grid(True, linestyle='-', alpha=0.3)
plt.savefig(output_path + '/hist_rand.pdf', format='pdf')
run_info.close()