(nu_no_age, bias_no_age,
_) = cmpn.calc_seljak_warren_w_cut(1000, 0.75, cosmo)
for (t, s) in enumerate(snaps):
z = zs[t]
finaldir = '{0}Desktop/age-clustering-data/snap{1}{2}/attempt1_sub_form_jp/'.format(
home, s, snap_id) ##INPUT
agelabel = 'Sub-Root-Form. Age' ##INPUT
col_j = ['k', 'b', 'c', 'g', 'm', 'r'] ##Predefined colors for age_i
nu_res = []
for age_i in range(0, 6):
x = np.array([])
y = np.array([])
txt = np.array([], dtype=int)
if age_i == 0:
temp = cmpn.nu_eff(finaldir, (age_i, ), range(1, 7), cosmo, z,
nu_no_age, bias_no_age)
median_age = temp[5]
mass_i_median_age = temp[0]
else:
nu_res.append(
cmpn.nu_eff(finaldir, (age_i, ), range(1, 7), cosmo, z,
nu_no_age, bias_no_age))
for (idx, x_temp) in enumerate(nu_res[-1][5]):
if nu_res[-1][4][idx] > -100:
idx2 = np.where(
mass_i_median_age == nu_res[-1][0][idx])[0]
x = np.append(x, (x_temp - median_age[idx2]) /
median_age[idx2])
y = np.append(y, nu_res[-1][4][idx])
txt = np.append(txt, nu_res[-1][0][idx])
xtot.extend(x)
xtot = []
ytot = []
(nu_no_age, bias_no_age, _) = cmpn.calc_seljak_warren_w_cut(1000, 0.75, cosmo)
for (t, s) in enumerate(snaps):
z = zs[t]
finaldir = "{0}Desktop/age-clustering-data/snap{1}{2}/attempt1_sub_form_jp/".format(home, s, snap_id) ##INPUT
agelabel = "Sub-Root-Form. Age" ##INPUT
col_j = ["k", "b", "c", "g", "m", "r"] ##Predefined colors for age_i
nu_res = []
for age_i in range(0, 6):
x = np.array([])
y = np.array([])
txt = np.array([], dtype=int)
if age_i == 0:
temp = cmpn.nu_eff(finaldir, (age_i,), range(1, 7), cosmo, z, nu_no_age, bias_no_age)
median_age = temp[5]
mass_i_median_age = temp[0]
else:
nu_res.append(cmpn.nu_eff(finaldir, (age_i,), range(1, 7), cosmo, z, nu_no_age, bias_no_age))
for (idx, x_temp) in enumerate(nu_res[-1][5]):
if nu_res[-1][4][idx] > -100:
idx2 = np.where(mass_i_median_age == nu_res[-1][0][idx])[0]
x = np.append(x, (x_temp - median_age[idx2]) / median_age[idx2])
y = np.append(y, nu_res[-1][4][idx])
txt = np.append(txt, nu_res[-1][0][idx])
xtot.extend(x)
ytot.extend(y)
plt.plot(x, y, "*", color=col_j[age_i], label="{0}_{1}_{2}".format(agelabel, s, age_i))
# for (i, txt_i) in enumerate(txt):
# plt.text(x[i], y[i], txt_i)
ytot = []
(nu_no_age, bias_no_age) = cmpn.calc_seljak_warren(1000, cosmo)
for (t, s) in enumerate(snaps):
z = zs[t]
finaldir = '{0}Desktop/age-clustering-data/snap{1}/attempt1_sub_form_gao/'.format(
home, s) ##INPUT
agelabel = 'Sub-Root-Form. Age' ##INPUT
col_j = ['k', 'b', 'c', 'g', 'm', 'r'] ##Predefined colors for age_i
nu_res = []
for age_i in range(0, 6):
x = np.array([])
y = np.array([])
txt = np.array([], dtype=int)
if age_i == 0:
temp = cmpn.nu_eff(finaldir, age_i, [1, 2, 3, 4, 5, 6, 7],
cosmo, z, nu_no_age, bias_no_age)
median_age = temp[5]
mass_i_median_age = temp[0]
else:
nu_res.append(
cmpn.nu_eff(finaldir, age_i, [1, 2, 3, 4, 5, 6, 7], cosmo,
z, nu_no_age, bias_no_age))
for (idx, x_temp) in enumerate(nu_res[-1][5]):
if nu_res[-1][4][idx] > -100:
idx2 = np.where(
mass_i_median_age == nu_res[-1][0][idx])[0]
x = np.append(x, x_temp / median_age[idx2] - 1.)
y = np.append(
y, (nu_res[-1][4][idx] - nu_res[-1][2][idx]) /
nu_res[-1][2][idx])
txt = np.append(txt, nu_res[-1][0][idx])
p1 = np.empty(len(snaps), dtype = np.object)
#Properties for the mass-age sample across all redshifts
best_fits = []
fit_age = np.empty(0, dtype = float)
fit_nu = np.empty(0, dtype = float)
fit_nueff = np.empty(0, dtype = float)
fig = plt.figure()
axi = (fig.add_subplot(131, projection = '3d'),
fig.add_subplot(132, projection = '3d'),
fig.add_subplot(133, projection = '3d'))
for (t, s) in enumerate(snaps): #Step through redshift
z = zs[t]
pnt = z_points[t]
finaldir = '{0}Desktop/age-clustering-data/snap{1}{2}/attempt1_sub_form_jp/'.format(home, s, snap_identifier) ##INPUT
agelabel = 'Sub-Max-Form. Age' ##INPUT ##Label for age definition
nu_res = cmpn.nu_eff(finaldir, range(0, 6), range(1, 8), cosmo, z, nu_no_age, bias_no_age)
for age_i in range(1, 6): #Step through mass_i and enumerate the age
tot_agei = np.empty(0, dtype= int)
tot_massi = np.empty(0, dtype = int)
tot_age = np.empty(0, dtype = float)
tot_nueff = np.empty(0, dtype = float)
tot_nu = np.empty(0, dtype = float)
tot_z = np.empty(0, dtype = float)
color = col_j[age_i]
for mass_i in range(1, 8):
idx = index_nu_eff(nu_res, [age_i], [mass_i])[0]
if nu_res[4][idx] > -100: ##If there is a nu_eff calculated for this object
idx2 = index_nu_eff(nu_res, [0], [mass_i])[0]
#Calculated values for mass_i and age_i sample
tot_agei = np.append(tot_agei, nu_res[1][idx])
tot_massi = np.append(tot_massi, nu_res[0][idx])
#Properties for the mass-age sample across all redshifts
best_fits = []
fit_age = np.empty(0, dtype=float)
fit_nu = np.empty(0, dtype=float)
fit_nueff = np.empty(0, dtype=float)
fig = plt.figure()
axi = (fig.add_subplot(131, projection='3d'),
fig.add_subplot(132, projection='3d'),
fig.add_subplot(133, projection='3d'))
for (t, s) in enumerate(snaps): #Step through redshift
z = zs[t]
pnt = z_points[t]
finaldir = '{0}Desktop/age-clustering-data/snap{1}{2}/attempt1_sub_form_jp/'.format(
home, s, snap_identifier) ##INPUT
agelabel = 'Sub-Max-Form. Age' ##INPUT ##Label for age definition
nu_res = cmpn.nu_eff(finaldir, range(0, 6), range(1, 8), cosmo, z,
nu_no_age, bias_no_age)
for age_i in range(1, 6): #Step through mass_i and enumerate the age
tot_agei = np.empty(0, dtype=int)
tot_massi = np.empty(0, dtype=int)
tot_age = np.empty(0, dtype=float)
tot_nueff = np.empty(0, dtype=float)
tot_nu = np.empty(0, dtype=float)
tot_z = np.empty(0, dtype=float)
color = col_j[age_i]
for mass_i in range(1, 8):
idx = index_nu_eff(nu_res, [age_i], [mass_i])[0]
if nu_res[4][
idx] > -100: ##If there is a nu_eff calculated for this object
idx2 = index_nu_eff(nu_res, [0], [mass_i])[0]
#Calculated values for mass_i and age_i sample
tot_agei = np.append(tot_agei, nu_res[1][idx])
xtot = []
ytot = []
(nu_no_age, bias_no_age) = cmpn.calc_seljak_warren(1000, cosmo)
for (t, s) in enumerate(snaps):
z = zs[t]
finaldir = '{0}Desktop/age-clustering-data/snap{1}/attempt1_sub_form_gao/'.format(home, s) ##INPUT
agelabel = 'Sub-Root-Form. Age' ##INPUT
col_j = ['k', 'b', 'c', 'g', 'm', 'r'] ##Predefined colors for age_i
nu_res = []
for age_i in range(0,6):
x = np.array([])
y = np.array([])
txt = np.array([],dtype = int)
if age_i == 0:
temp = cmpn.nu_eff(finaldir, age_i, [1, 2, 3, 4, 5, 6, 7], cosmo, z, nu_no_age, bias_no_age)
median_age = temp[5]
mass_i_median_age = temp[0]
else:
nu_res.append(cmpn.nu_eff(finaldir, age_i, [1, 2, 3, 4, 5, 6, 7], cosmo, z, nu_no_age, bias_no_age))
for (idx, x_temp) in enumerate(nu_res[-1][5]):
if nu_res[-1][4][idx] > -100:
idx2 = np.where(mass_i_median_age == nu_res[-1][0][idx])[0]
x = np.append(x, x_temp / median_age[idx2] - 1.)
y = np.append(y, (nu_res[-1][4][idx] - nu_res[-1][2][idx]) / nu_res[-1][2][idx])
txt = np.append(txt, nu_res[-1][0][idx])
xtot.extend(x)
ytot.extend(y)
plt.plot(x, y, '+',
color = col_j[age_i], label = '{0}_{1}_{2}'.format(agelabel, s, age_i))
for (i, txt_i) in enumerate(txt):
xtot = []
ytot = []
(nu_no_age, bias_no_age, _) = calc_seljak_warren_w_cut(1000, 0.75, cosmo)
for (t, s) in enumerate(snaps):
z = zs[t]
finaldir = '{0}Desktop/age-clustering-data/snap{1}{2}/attempt1_sub_form_jp/'.format(home, s, suffix) ##INPUT
agelabel = 'Sub-Max-Form. Age' ##INPUT
col_j = ['k', 'b', 'c', 'g', 'm', 'r'] ##Predefined colors for age_i
nu_res = []
for age_i in range(0,6):
x = np.array([])
y = np.array([])
txt = np.array([],dtype = int)
if age_i == 0:
temp = nu_eff(finaldir, (age_i,), range(1, 9), cosmo, z, nu_no_age, bias_no_age)
median_age = temp[5]
mass_i_median_age = temp[0]
else:
nu_res.append(nu_eff(finaldir, (age_i,), range(1, 9), cosmo, z, nu_no_age, bias_no_age))
for (idx, x_temp) in enumerate(nu_res[-1][5]):
idx2 = np.where(mass_i_median_age == nu_res[-1][0][idx])[0]
x = np.append(x, (x_temp - median_age[idx2]) / median_age[idx2])
y = np.append(y, nu_res[-1][2][idx])
txt = np.append(txt, nu_res[-1][0][idx])
xtot.extend(x)
ytot.extend(y)
plt.plot(x, y, '+',
color = col_j[age_i], label = '{0}_{1}_{2}'.format(agelabel, s, age_i))
# for (i, txt_i) in enumerate(txt):
# plt.text(x[i], y[i], txt_i)
ytot = []
(nu_no_age, bias_no_age, _) = calc_seljak_warren_w_cut(1000, 0.75, cosmo)
for (t, s) in enumerate(snaps):
z = zs[t]
finaldir = '{0}Desktop/age-clustering-data/snap{1}{2}/attempt1_sub_form_jp/'.format(
home, s, suffix) ##INPUT
agelabel = 'Sub-Max-Form. Age' ##INPUT
col_j = ['k', 'b', 'c', 'g', 'm', 'r'] ##Predefined colors for age_i
nu_res = []
for age_i in range(0, 6):
x = np.array([])
y = np.array([])
txt = np.array([], dtype=int)
if age_i == 0:
temp = nu_eff(finaldir, (age_i, ), range(1, 9), cosmo, z,
nu_no_age, bias_no_age)
median_age = temp[5]
mass_i_median_age = temp[0]
else:
nu_res.append(
nu_eff(finaldir, (age_i, ), range(1, 9), cosmo, z,
nu_no_age, bias_no_age))
for (idx, x_temp) in enumerate(nu_res[-1][5]):
idx2 = np.where(mass_i_median_age == nu_res[-1][0][idx])[0]
x = np.append(x, (x_temp - median_age[idx2]) /
median_age[idx2])
y = np.append(y, nu_res[-1][2][idx])
txt = np.append(txt, nu_res[-1][0][idx])
xtot.extend(x)
ytot.extend(y)
plt.plot(x,
