fill_sum = 0.70
ps_stats = {}
# index = 6
for index in range(25):
k_val = k_vals[index]
delta_vals = delta_all[:, index]
delta_mean = delta_vals.mean()
delta_sigma = delta_vals.std()
distribution, bin_centers = compute_distribution(delta_vals,
n_bins_for_distribution,
log=True)
v_l, v_r, v_max, sum = get_highest_probability_interval(bin_centers,
distribution,
fill_sum,
log=True,
n_interpolate=1000)
vel = 2 * np.pi / k_val
stride = n_points * (vel / vel_max)
n_steps = int(2048 / stride)
stride = int(stride)
ids_1d = (np.arange(0, n_steps, 1) * stride).astype(np.int)
n_1d = len(ids_1d)
n_independent = n_1d**2
n_groups = stride**2
print(
f'\nk_index: {index}, stride: {stride} N_independent: {n_independent}, N_groups: {n_groups}'
)
def Sample_T0_from_Trace(param_samples,
data_grid,
SG,
hpi_sum=0.7,
n_samples=None):
print(f'\nSampling Temperateure')
temp_samples = {}
param_ids = param_samples.keys()
n_param = len(param_ids)
if not n_samples: n_samples = len(param_samples[0]['trace'])
print(f' N Samples: {n_samples}')
#
samples = []
for i in range(n_samples):
p_vals = []
for p_id in param_ids:
p_vals.append(param_samples[p_id]['trace'][i])
if n_param == 3:
temp_interp = Interpolate_3D(p_vals[0],
p_vals[1],
p_vals[2],
data_grid,
'T0',
'mean',
SG,
clip_params=True)
if n_param == 4:
temp_interp = Interpolate_4D(p_vals[0],
p_vals[1],
p_vals[2],
p_vals[3],
data_grid,
'T0',
'mean',
SG,
clip_params=True)
samples.append(temp_interp)
samples = np.array(samples).T
temp_mean = np.array([temp_vals.mean() for temp_vals in samples])
temp_sigma = []
temp_lower, temp_higher = [], []
for i in range(len(samples)):
temp_sigma.append(np.sqrt(((samples[i] - temp_mean[i])**2).mean()))
values = samples[i]
n_bins = 100
distribution, bin_centers = compute_distribution(values,
n_bins,
log=True)
fill_sum = hpi_sum
v_l, v_r, v_max, sum = get_highest_probability_interval(
bin_centers, distribution, fill_sum, log=True, n_interpolate=1000)
temp_lower.append(v_l)
temp_higher.append(v_r)
temp_sigma = np.array(temp_sigma)
temp_lower = np.array(temp_lower)
temp_higher = np.array(temp_higher)
temp_samples['mean'] = temp_mean
temp_samples['sigma'] = temp_sigma
temp_samples['z'] = data_grid[0]['z']
temp_samples['lower'] = temp_lower
temp_samples['higher'] = temp_higher
return temp_samples
def Sample_Power_Spectrum(n_samples,
params,
data_grid,
SG,
sampling='gaussian',
hpi_sum=0.7):
print(f'\nSampling Power Spectrum')
ps_samples = {}
n_param = len(params.keys())
n_z_ids = len(data_grid.keys())
for id_z in range(n_z_ids):
ps_data = data_grid[id_z]
ps_samples[id_z] = {}
ps_samples[id_z]['z'] = ps_data['z']
samples = []
for i in range(n_samples):
p_rand = []
for p_id in params.keys():
if sampling == 'gaussian':
p_rand.append(
np.random.normal(params[p_id]['mean'],
params[p_id]['sigma']))
if sampling == 'uniform':
p_min = params[p_id]['min']
p_max = params[p_id]['max']
p_rand.append(np.random.rand() * (p_max - p_min) + p_min)
if n_param == 3:
ps_interp = Interpolate_3D(p_rand[0],
p_rand[1],
p_rand[2],
ps_data,
'P(k)',
'mean',
SG,
clip_params=True)
if n_param == 4:
ps_interp = Interpolate_4D(p_rand[0],
p_rand[1],
p_rand[2],
p_rand[3],
ps_data,
'P(k)',
'mean',
SG,
clip_params=True)
samples.append(ps_interp)
samples = np.array(samples).T
ps_mean = np.array([ps_vals.mean() for ps_vals in samples])
ps_sigma = []
ps_lower, ps_higher = [], []
for i in range(len(samples)):
ps_sigma.append(np.sqrt(((samples[i] - ps_mean[i])**2).mean()))
values = samples[i]
n_bins = 100
distribution, bin_centers = compute_distribution(values,
n_bins,
log=True)
fill_sum = hpi_sum
v_l, v_r, v_max, sum = get_highest_probability_interval(
bin_centers,
distribution,
fill_sum,
log=True,
n_interpolate=1000)
ps_lower.append(v_l)
ps_higher.append(v_r)
ps_sigma = np.array(ps_sigma)
ps_lower = np.array(ps_lower)
ps_higher = np.array(ps_higher)
ps_samples[id_z]['mean'] = ps_mean
ps_samples[id_z]['sigma'] = ps_sigma
ps_samples[id_z]['k_vals'] = ps_data[0]['P(k)']['k_vals']
ps_samples[id_z]['lower'] = ps_lower
ps_samples[id_z]['higher'] = ps_higher
return ps_samples
def Sample_Power_Spectrum_from_Trace(param_samples,
data_grid,
SG,
hpi_sum=0.7,
n_samples=None):
print(f'\nSampling Power Spectrum')
ps_samples = {}
param_ids = param_samples.keys()
n_param = len(param_ids)
if not n_samples: n_samples = len(param_samples[0]['trace'])
print(f' N Samples: {n_samples}')
n_z_ids = len(data_grid.keys())
for id_z in range(n_z_ids):
ps_data = data_grid[id_z]
ps_samples[id_z] = {}
ps_samples[id_z]['z'] = ps_data['z']
samples = []
for i in range(n_samples):
p_vals = []
for p_id in param_ids:
p_vals.append(param_samples[p_id]['trace'][i])
if n_param == 3:
ps_interp = Interpolate_3D(p_vals[0],
p_vals[1],
p_vals[2],
ps_data,
'P(k)',
'mean',
SG,
clip_params=True)
if n_param == 4:
ps_interp = Interpolate_4D(p_vals[0],
p_vals[1],
p_vals[2],
p_vals[3],
ps_data,
'P(k)',
'mean',
SG,
clip_params=True)
samples.append(ps_interp)
samples = np.array(samples).T
ps_mean = np.array([ps_vals.mean() for ps_vals in samples])
ps_sigma = []
ps_lower, ps_higher = [], []
for i in range(len(samples)):
ps_sigma.append(np.sqrt(((samples[i] - ps_mean[i])**2).mean()))
values = samples[i]
n_bins = 100
distribution, bin_centers = compute_distribution(values,
n_bins,
log=True)
fill_sum = hpi_sum
v_l, v_r, v_max, sum = get_highest_probability_interval(
bin_centers,
distribution,
fill_sum,
log=True,
n_interpolate=1000)
ps_lower.append(v_l)
ps_higher.append(v_r)
ps_sigma = np.array(ps_sigma)
ps_lower = np.array(ps_lower)
ps_higher = np.array(ps_higher)
ps_samples[id_z]['mean'] = ps_mean
ps_samples[id_z]['sigma'] = ps_sigma
ps_samples[id_z]['k_vals'] = ps_data[0]['P(k)']['k_vals']
ps_samples[id_z]['lower'] = ps_lower
ps_samples[id_z]['higher'] = ps_higher
return ps_samples