def app2abs(self,m_app,color,z,photo_band,color_band): ''' takes apparent magnitude of a galaxy in some photometric band (SDSS g or r or i) and converts it to an absolute magnitude via distance modulus and k correction M_abs = m_app - Dist_Mod - K_corr takes: m_app : float, apparent magnitude of galaxy color : float, color of galaxy between two SDSS bands z : float, total redshift of galaxy photo_band : str, SDSS band for magnitude. Ex: 'g' or 'r' or 'i' color_band : str, color between two SDSS bands. Ex: 'g - r' or 'g - i' or 'r - i', etc.. ''' dm = self.distance_mod(z) K_corr = calc_kcor(photo_band,z,color_band,color) return m_app - dm - K_corr
def plot_mz(x,
y,
xlim=[
0.1,
],
ylim=[0, 1],
nxbins=151,
nybins=151,
xlabel='x',
ylabel='y',
Om0=0.3,
OmL=0.7,
h=0.7,
savefig=None):
"""
plot a binned histogram showing distribution of galaxies in the apparent magnitude-redshift plane
along with the expected m-z relation expected for a "standard candle" (object of fixed luminosity)
+ the same with correction for evolution (e-korrection) and spectrum redshift (k-correction)
"""
fig, ax = plt.subplots(figsize=(2.5, 2.5))
plt.ylabel(ylabel)
plt.xlabel(xlabel)
plt.xlim(xlim[0], xlim[1])
plt.ylim(ylim[0], ylim[1])
plot_2d_dist(x,
y,
xlim,
ylim,
nxbins,
nybins,
xlabel=xlabel,
ylabel=ylabel,
fig_setup=ax)
# plot curve showing m-z relation for the constant luminosity ignoring K correction
# set cosmology to the best values from 9-year WMAP data
zd = np.linspace(x.min(), x.max(), 100)
dlum = d_l(zd, Om0, OmL) * d_H / h
# and k-correction using polynomial approximations of Chilingarian et al. 2010
# see http://kcor.sai.msu.ru/getthecode/
# this k-correction is not designed for z>0.6, so limit the z for correction calculation
grzd = np.ones_like(zd)
grzd = 0.8 * grzd
kcorr = calc_kcor('r', zd, 'g - r', grzd)
mcandle = -23. + 5. * np.log10(dlum * 1.e6 / 10.)
mcandlevol = -23. + 5. * np.log10(dlum * 1.e6 / 10.) - 1.3 * zd
mcandlevolkcorr = -23. + 5. * np.log10(
dlum * 1.e6 / 10.) - 1.3 * zd + kcorr
plt.plot(zd,
mcandle,
'--',
c='r',
lw=2.,
label=r'$\mathrm{st.\ candle}\ M_r=-23$')
plt.plot(zd,
mcandlevol,
'--',
c='darkorange',
lw=1.5,
label=r'$\mathrm{+evo\ correction}$')
plt.plot(zd,
mcandlevolkcorr,
'--',
c='y',
lw=1.,
label=r'$\mathrm{+evo+k\ corrections}$')
if savefig != None:
plt.savefig(savefig, bbox_inches='tight')
plt.legend(loc='lower right', fontsize=8, frameon=False)
plt.show()
return
def plot_Mz(x,
y,
xlim=[0, 1],
ylim=[0, 1],
nxbins=151,
nybins=151,
xlabel='x',
ylabel='y',
Om0=0.3,
OmL=0.7,
h=0.7,
savefig=None):
fig, ax = plt.subplots(figsize=(3., 3.))
plt.ylabel(ylabel)
plt.xlabel(xlabel)
plt.xlim(xlim[0], xlim[1])
plt.ylim(ylim[0], ylim[1])
#
plot_2d_dist(x,
y,
xlim,
ylim,
nxbins,
nybins,
xlabel=xlabel,
ylabel=ylabel,
fig_setup=ax)
zd = np.linspace(x.min(), x.max(), 100)
dld = d_l(zd, Om0, OmL) * d_H / h
# this k-correction is not design for z>0.5, so limit the z for correction calculation
grzd = np.ones_like(zd)
grzd = 0.8 * grzd
kcorr = calc_kcor('r', zd, 'g - r', grzd)
# main galaxy magnitude limit
Mlim = 17.77 - 5. * np.log10(dld * 1.e5)
mcandle = -23. * np.ones_like(zd)
mcandlevol = -23. - 1.3 * zd
mcandlevolkcorr = -23. - 1.3 * zd + kcorr
plt.plot(zd,
Mlim,
'--',
c='m',
lw=2.,
label=r'$\mathrm{SDSS\ spec.\ sample}\ m_{\rm lim}=17.77$')
plt.plot(zd,
mcandle,
'--',
c='r',
lw=2.,
label=r'$\mathrm{st.\ candle}\ M_r=-23$')
plt.plot(zd,
mcandlevol,
'--',
c='darkorange',
lw=1.5,
label=r'$\mathrm{+evo\ correction}$')
plt.plot(zd,
mcandlevolkcorr,
'--',
c='y',
lw=1.,
label=r'$\mathrm{+evo+k\ corrections}$')
if savefig != None:
plt.savefig(savefig, bbox_inches='tight')
plt.legend(loc='upper right', fontsize=8, frameon=False)
plt.show()
return
header = data.readline()
headers = header.split(csv_delimiter)
ids = {}
for field in required_fields:
if required_fields[field] in headers:
ids[field] = headers.index(required_fields[field])
if len(ids) != len(required_fields):
print "can't find all required fields in the catalogue."
sys.exit()
print " z(TAO) d(TAO) d(z) d(DM) diff %"
for i in range(0,n):
line = data.readline()
values = line.split(csv_delimiter)
z = float(values[ids['z']])
d = float(values[ids['d']])
b_abs = float(values[ids['b_abs']])
b_app = float(values[ids['b_app']])
r_app = float(values[ids['r_app']])
d_z = z2d(z, 1) # [Mpc/h]
color = b_app - r_app
k_corr = calc_kcor('B', z, 'B - Rc', color)
dm = b_app - b_abs - k_corr # distance modulus
d_lum = math.pow(10, 0.2*dm + 1)/1e6 # luminosity distance [Mpc]
d_m = h*d_lum/(1+z) # co-moving distance [Mpc/h]
diff = 100*d_m/d - 100; # %
print "%e\t%e\t%e\t%e\t%.2f" % (z, d, d_z, d_m, diff)
#z = data[:, 35]
data = np.loadtxt("output.csv", delimiter=",", skiprows=1)
fiber_u = data[:, 2]
fiber_g = data[:, 3]
fiber_r = data[:, 4]
fiber_i = data[:, 5]
fiber_z = data[:, 6]
petro_u = data[:, 12]
petro_g = data[:, 13]
petro_r = data[:, 14]
petro_i = data[:, 15]
petro_z = data[:, 16]
z = data[:, -4]
del mags
kcor_fu = kcor.calc_kcor("u", z, "u - r", fiber_u - fiber_r)
kcor_fg = kcor.calc_kcor("g", z, "g - r", fiber_g - fiber_r)
kcor_fr = kcor.calc_kcor("r", z, "g - r", fiber_g - fiber_r)
kcor_fi = kcor.calc_kcor("i", z, "g - i", fiber_g - fiber_i)
kcor_fz = kcor.calc_kcor("z", z, "r - z", fiber_r - fiber_z)
kcor_pu = kcor.calc_kcor("u", z, "u - r", petro_u - petro_r)
kcor_pg = kcor.calc_kcor("g", z, "g - r", petro_g - petro_r)
kcor_pr = kcor.calc_kcor("r", z, "g - r", petro_g - petro_r)
kcor_pi = kcor.calc_kcor("i", z, "g - i", petro_g - petro_i)
kcor_pz = kcor.calc_kcor("z", z, "r - z", petro_r - petro_z)
fiber_u += kcor_fu
fiber_g += kcor_fg
fiber_r += kcor_fr
fiber_i += kcor_fi
fiber_z += kcor_fz
#z = data[:, 35]
data = np.loadtxt("output.csv", delimiter = ",", skiprows = 1)
fiber_u = data[:, 2]
fiber_g = data[:, 3]
fiber_r = data[:, 4]
fiber_i = data[:, 5]
fiber_z = data[:, 6]
petro_u = data[:, 12]
petro_g = data[:, 13]
petro_r = data[:, 14]
petro_i = data[:, 15]
petro_z = data[:, 16]
z = data[:, - 4]
del mags
kcor_fu = kcor.calc_kcor("u", z, "u - r", fiber_u - fiber_r)
kcor_fg = kcor.calc_kcor("g", z, "g - r", fiber_g - fiber_r)
kcor_fr = kcor.calc_kcor("r", z, "g - r", fiber_g - fiber_r)
kcor_fi = kcor.calc_kcor("i", z, "g - i", fiber_g - fiber_i)
kcor_fz = kcor.calc_kcor("z", z, "r - z", fiber_r - fiber_z)
kcor_pu = kcor.calc_kcor("u", z, "u - r", petro_u - petro_r)
kcor_pg = kcor.calc_kcor("g", z, "g - r", petro_g - petro_r)
kcor_pr = kcor.calc_kcor("r", z, "g - r", petro_g - petro_r)
kcor_pi = kcor.calc_kcor("i", z, "g - i", petro_g - petro_i)
kcor_pz = kcor.calc_kcor("z", z, "r - z", petro_r - petro_z)
fiber_u += kcor_fu
fiber_g += kcor_fg
fiber_r += kcor_fr
fiber_i += kcor_fi
fiber_z += kcor_fz
def apply_kcor(s):
return calc_kcor("B", s["Redshift_Cosmological"], "B - Rc", s["colour"])
SDSS = CSV('data_all_2')
SDSS_data = SDSS.read_all()
# -1 because of column titles
print(SDSS.row_count_data)
calculated = np.zeros((SDSS.row_count_data, 3)) #add area
print('read data')
for index, row in enumerate(SDSS_data):
if index % 1000 == 0:
pass
z, zErr_noqso, cModelMag_r, cModelMagErr_r, cModelMag_u, cModelMagErr_u, petroRad_r, petroRadErr_r, modelMag_r, modelMag_u, petroMag_r, petroMagErr_r, petroMagErr_u, petroMag_u, Pr90 = row
dist = cosmo.luminosity_distance(z).value # in Mpc
k_corr = calc_kcor('r', z, 'u - r', (petroMag_u - petroMag_r))
M = petroMag_r - (5 * np.log10(dist)) - 25 - k_corr
M_Err = abs(petroMagErr_r)
calculated[index, 0] = M_Err
calculated[index, 1] = M # absolute mag
if index % 5000 == 0:
print(index)
array = (SDSS_data[1:, 0] * 100).astype(int)
print(array)
y = np.bincount(array)
ii = np.nonzero(y)[0]
freq = (np.vstack((ii, y[ii])).T) #.astype(float)
