def process_smbh_cluster(H0, WM, WV):
smbh_cluster_file = 'SMBH_cluster'
smbh_cluster = get_cluster_file(os.path.join(cluster_folder, smbh_cluster_file))
for i, line in enumerate(smbh_cluster):
sys.stdout.write(''.join(['\rConverting z to age in SMBH Cluster for #', str(i+1), ' of ',
str(len(smbh_cluster))]))
sys.stdout.flush()
z = float(line[0])
input_params= [z, H0, WM, WV]
age = CC.get_output_params(input_params)
line.insert(1, age)
print
smbh_cluster = np.array(smbh_cluster, dtype=object)
smbh_cluster = sorted(smbh_cluster, key=lambda l:l[2])
F = open(os.path.join(cluster_folder, 'smbh_cluster.pkl'), 'wb')
pickle.dump(smbh_cluster, F)
F.close()
print('Saved SMBH Cluster array to pickle')
print
print(smbh_cluster[0])
print(smbh_cluster[int(len(smbh_cluster)/4)])
print(smbh_cluster[int(len(smbh_cluster)/2)])
print(smbh_cluster[3*int(len(smbh_cluster)/4)])
print(smbh_cluster[len(smbh_cluster)-1])
return smbh_cluster
def process_galaxies_cluster(H0, WM, WV):
galaxies_cluster_file = 'galaxies_cluster'
galaxies_cluster = get_cluster_file(os.path.join(cluster_folder, galaxies_cluster_file))
galaxies_cluster_length = len(galaxies_cluster)
for i, line in enumerate(galaxies_cluster):
if len(line) >= 6:
line[5] = line[5:]
if len(line) >= 7:
line[6:] = []
sys.stdout.write(''.join(['\rConverting z to age in Galaxy Cluster for #', str(i+1), ' of ',
str(galaxies_cluster_length)]))
sys.stdout.flush()
z = float(line[0])
input_params= [z, H0, WM, WV]
age = CC.get_output_params(input_params)
line.insert(1, age)
print
#galaxies_cluster = np.array([np.array(line, dtype=object) for line in galaxies_cluster], dtype=object)
galaxies_cluster = np.array(galaxies_cluster, dtype=object)
galaxies_cluster = sorted(galaxies_cluster, key=lambda l:l[2])
# print(galaxies_cluster)
F = open(os.path.join(cluster_folder, 'galaxies_cluster.pkl'), 'wb')
pickle.dump(galaxies_cluster, F)
F.close()
print('Saved Galaxies Cluster array to pickle')
return galaxies_cluster
def run():
# z_s = [4, 3.1, 2.8, 2.5, 2.2, 2.0, 1.9, 1.75, 1.6, 1.5, 1.35, 1.3, 1.25, 1.2, 1.15, 1.1, 1.05, 1.0, 0.95, 0.9, 0.85,
# 0.8, 0.75, 0.7, 0.65, 0.6, 0.55, 0.5, 0.45, 0.4, 0.35, 0.25, 0.2, 0.15, 0.1, 0.05, 0.0]
z_s = np.linspace(4., 0., num=1000)
print z_s
age = []
for z in z_s:
input_params= [z, H0, WM, WV]
age.append(CC.get_output_params(input_params))
print age
sigma = np.ones(len(age))
sigma[[0, -1]] = 0.02
popt, pcov = scipy.optimize.curve_fit(func, age, z_s, sigma=sigma)
print popt
z_est = np.array([func(x, *tuple(popt)) for x in age])
# print zip(age, z_est)
plt.figure(1)
plt.plot(age, z_s, label='t-to-z func')
plt.plot(age, z_est, label='curve fit')
locs, labels = plt.xticks()
plt.setp(labels, rotation=45)
# plt.xticks(np.arange(1.5, 14., 0.5))
plt.yticks(np.arange(0., 4.5, 0.5))
plt.legend()
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.xlabel('Age (Gyr)')
plt.ylabel('Z')
plt.figure(2)
diff = [(zest-zs)/zs for (zest, zs) in zip(z_est, z_s)]
plt.plot(age, diff)
plt.xlabel('Age (Gyr)')
plt.ylabel('(z_est-z_s)/z_s')
locs, labels = plt.xticks()
plt.setp(labels, rotation=45)
plt.xticks(np.arange(1.5, 14., 0.5))
plt.yticks(np.arange(-0.5, 9., 0.5))
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.title('Difference ratio between curve fit and z_function')
plt.show()
