def compute_chi_sq(mpv_database, fiducial_vals, id_to_load, cov_path,
r_eval_vals, Nz):
path = mpv_database['results_path'].loc[id_to_load]
success = mpv_database['successful_TF'].loc[id_to_load]
if not success:
return np.nan
vals = load_results_file(path, success)
cov = Cov.load_covariance(cov_path)
chi_sq = 0
for i in range(Nz):
inv_cov = cov.get_inverted_covariance(i)
result_vs = interp1d(vals[0], vals[1][i])(r_eval_vals)
chi_sq += np.dot((fiducial_vals[i] - result_vs)**2,
np.dot(inv_cov,
(fiducial_vals[i] - result_vs)**2)) / Nz
return chi_sq
delta_r = 2.0
r_vals = np.arange(20.0, 180.0, delta_r)
zmin = 0.1
zmax = 0.6
Nz = 5
z_step = (zmax - zmin) / Nz
z_vals = np.linspace(zmin + z_step / 2, zmax - z_step / 2, Nz)
filename = "../../data/axion_frac=0.000_matterpower_out.dat"
P_CAMB = np.loadtxt(filename)
p_interp = interpolate(P_CAMB[:, 0] * phys.h, P_CAMB[:, 1] / phys.h**3,
phys.P_cdm, phys.P_cdm)
try:
cov = Cov.load_covariance("covariance_matrix_{}.dat".format(run_code))
except (IOError, OSError) as e:
print("Covariance matrix not found. Recomputing...")
cov = Cov(p_interp,
None,
zmin,
zmax,
Nz,
r_vals,
delta_r,
10000 / 129600 * np.pi,
120.0,
kmin=min(P_CAMB[:, 0] * phys.h),
kmax=max(P_CAMB[:, 0] * phys.h),
mMin=0.6e14,
mMax=1e16,
z_step = (zmax - zmin) / Nz
z_vals = np.linspace(zmin + z_step / 2, zmax - z_step / 2, Nz)
print("Getting covariance matrix.")
start_cov = time.time()
filename = "../../data/axion_frac=0.000_matterpower_out.dat"
P_CAMB = np.loadtxt(filename)
p_interp = interpolate(P_CAMB[:, 0] * phys.h, P_CAMB[:, 1] / phys.h**3,
phys.P_cdm, phys.P_cdm)
cov_path = "covariance_matrix_{}_{}.dat".format(
['top-hat', 'gaussian', 'sharp-k',
'no-filter'][window], ["stageII", "stageIII", "stageIV"][stage])
try:
cov = Cov.load_covariance(cov_path)
except (IOError, OSError) as e:
print("Covariance matrix not found. Recomputing...")
cov = Cov(p_interp,
None,
zmin,
zmax,
Nz,
r_vals,
delta_r,
overlap_area[stage] / 129600 * np.pi,
sigma_v_vals[stage],
kmin=min(P_CAMB[:, 0] * phys.h),
kmax=max(P_CAMB[:, 0] * phys.h),
mMin=minClusterMass[stage],
mMax=1e16,
print(len(r_vals))
zmin = zmin_vals[stage]
zmax = zmax_vals[stage]
Nz = z_bin_no[stage]
z_step = (zmax - zmin) / Nz
z_vals = np.linspace(zmin + z_step / 2, zmax - z_step / 2, Nz)
print("Getting covariance matrix.")
start_cov=time.time()
filename = "../../data/axion_frac=0.000_matterpower_out.dat"
P_CAMB = np.loadtxt(filename)
p_interp = interpolate(P_CAMB[:, 0] * phys.h, P_CAMB[:, 1] / phys.h ** 3, phys.P_cdm, phys.P_cdm)
try:
cov = Cov.load_covariance("covariance_matrix_{}_{}.dat".format(['top-hat', 'gaussian', 'sharp-k', 'no-filter'][window], ["stageII", "stageIII", "stageIV"][stage]))
except (IOError, OSError) as e:
print("Covariance matrix not found. Recomputing...")
cov = Cov(p_interp, None, zmin, zmax, Nz, r_vals, delta_r, overlap_area[stage] / 129600 * np.pi, sigma_v_vals[stage], kmin=min(P_CAMB[:, 0] * phys.h), kmax=max(P_CAMB[:, 0] * phys.h), mMin=minClusterMass[stage], mMax=1e16, physics=phys, window_function=window)
cov.save_covariance("covariance_matrix_{}_{}.dat".format(['top-hat', 'gaussian', 'sharp-k', 'no-filter'][window], ["stageII", "stageIII", "stageIV"][stage]))
print("Total time take to get covariance matrices: {:.2}s".format(time.time()-start_cov))
params=[]
for m in axion_masses:
for f in axion_frac_vals:
phys = Physics.create_parameter_set(axion_mass=m, axion_frac=f)
params.append(phys)
#with MyProcessPool(12) as p:
# p.map(lambda i: run_axionCAMB("CAMB_run4plots_{}".format(i), params[i]), range(0, len(params)))