426 lines
No EOL
15 KiB
Python
426 lines
No EOL
15 KiB
Python
import numpy as np
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import glob
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import configparser
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import h5py as h5
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import ast
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import Pk_library as PKL
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import os
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from tqdm import tqdm
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import sys
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from contextlib import contextmanager
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@contextmanager
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def suppress_stdout():
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with open(os.devnull, 'w') as devnull:
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old_stdout = sys.stdout
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sys.stdout = devnull
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try:
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yield
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finally:
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sys.stdout = old_stdout
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def get_mcmc_steps(dirname, nframe, iter_max, iter_min=0):
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"""
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Obtain evenly-spaced sample of MCMC steps to make movie from
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"""
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all_mcmc = glob.glob(dirname + '/mcmc_*.h5')
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x = [m[len(dirname + '/mcmc_'):-3] for m in all_mcmc]
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all_mcmc = np.sort([int(m[len(dirname + '/mcmc_'):-3]) for m in all_mcmc])
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if iter_max >= 0:
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all_mcmc = all_mcmc[all_mcmc <= iter_max]
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all_mcmc = all_mcmc[all_mcmc >= iter_min]
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if nframe > 0:
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max_out = max(all_mcmc)
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min_out = min(all_mcmc)
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step = max(int((max_out - min_out+1) / nframe), 1)
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all_mcmc = all_mcmc[::step]
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if max_out not in all_mcmc:
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all_mcmc = np.concatenate([all_mcmc, [max_out]])
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return all_mcmc
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def load_param_samples(ini_name, dirname, nframe, iter_max, iter_min):
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config = configparser.ConfigParser()
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config.read(ini_name)
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to_sample = []
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for k,v in config['block_loop'].items():
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if v.strip() == 'false':
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i = k.index('_sampler')
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if k[:i] not in ['hades', 'bias', 'nmean']:
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to_sample.append(k[:i])
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print("TO SAMPLE", to_sample)
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nsamp = int(config['run']['nsamp'])
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new_to_sample = []
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for s in to_sample:
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if s in ['omega_m', 'sigma8', 'sig_v']:
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new_to_sample.append(s)
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elif s == 'bulk_flow':
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for d in ['_x', '_y', '_z']:
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new_to_sample.append(f'{s}{d}')
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else:
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for i in range(nsamp):
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new_to_sample.append(f'{s}{i}')
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# This is desired list to sample
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to_sample = new_to_sample
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# Which steps to use
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all_mcmc = get_mcmc_steps(dirname, nframe, iter_max, iter_min=iter_min)
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sampler = config['sampling']['algorithm'].lower()
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samples = np.empty((len(to_sample),len(all_mcmc)))
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print('MY SAMPLER IS', sampler)
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if sampler == 'slice':
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for i in tqdm(range(len(all_mcmc))):
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with h5.File(f'{dirname}/mcmc_{all_mcmc[i]}.h5', 'r') as f:
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for j, s in enumerate(to_sample):
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if 'model_params_' + s in f['scalars'].keys():
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samples[j,i] = f['scalars/model_params_' + s][:][0]
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elif 'model_params_cosmology.' + s in f['scalars'].keys():
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samples[j,i] = f['scalars/model_params_cosmology.' + s][:][0]
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elif s == 'sig_v':
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samples[j,i] = float(config['model'][s])
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elif s.startswith('bulk_flow'):
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if s[-1] == 'x':
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samples[j,i] = np.array(ast.literal_eval(config['model']['bulk_flow']))[0]
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elif s[-1] == 'y':
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samples[j,i] = np.array(ast.literal_eval(config['model']['bulk_flow']))[1]
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elif s[-1] == 'z':
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samples[j,i] = np.array(ast.literal_eval(config['model']['bulk_flow']))[2]
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else:
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raise NotImplementedError
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else:
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if s in config[f'cosmology'].keys():
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samples[j,i] = float(config['cosmology'][s])
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else:
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print("NOT THERE")
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samples[j,i] = float(config[f'sample_{s[-1]}'][s[:-1]])
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elif sampler in ['hmc', 'mvslice', 'transformedblackjax', 'blackjax']:
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if sampler in ['hmc', 'transformedblackjax']:
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key_name = 'attributes'
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key_name = 'model_params'
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elif sampler in ['mvslice', 'blackjax']:
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key_name = 'model_paramsattributes'
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# Get order in which model parameters are stored
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if os.path.isfile(f'{dirname}/model_params.txt'):
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with open(f'{dirname}/model_params.txt', 'r') as file:
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model_params = [line.strip() for line in file]
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else:
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model_params = []
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print(model_params)
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for i in tqdm(range(len(all_mcmc))):
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with h5.File(f'{dirname}/mcmc_{all_mcmc[i]}.h5', 'r') as f:
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if key_name in f['scalars'].keys():
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data = f[f'scalars/{key_name}'][:]
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else:
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data = None
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for j, s in enumerate(to_sample):
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if s in model_params:
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samples[j,i] = data[model_params.index(s)]
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elif 'model_params_cosmology.' + s in f['scalars'].keys():
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samples[j,i] = f['scalars/model_params_cosmology.' + s][:][0]
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elif s == 'sig_v':
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samples[j,i] = float(config['model'][s])
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elif s in config[f'cosmology'].keys():
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samples[j,i] = float(config['cosmology'][s])
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elif s.startswith('bulk_flow'):
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idx = {'x':0, 'y':1, 'z':2}
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idx = idx[s[-1]]
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samples[j,i] = np.array(ast.literal_eval(config['model']['bulk_flow']))[idx]
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else:
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samples[j,i] = float(config[f'sample_{s[-1]}'][s[:-1]])
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else:
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raise NotImplementedError
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return to_sample, all_mcmc, samples
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def get_truths(ini_name, to_sample):
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config = configparser.ConfigParser()
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config.read(ini_name)
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truths = [None] * len(to_sample)
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for i, s in enumerate(to_sample):
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if s in config[f'cosmology'].keys():
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truths[i] = float(config['cosmology'][s])
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elif s == 'sig_v':
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truths[i] = float(config['model'][s])
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elif s.startswith('bulk_flow'):
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idx = {'x':0, 'y':1, 'z':2}
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idx = idx[s[-1]]
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truths[i] = np.array(ast.literal_eval(config['model']['bulk_flow']))[idx]
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else:
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truths[i] = float(config[f'sample_{s[-1]}'][s[:-1]])
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return truths
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def crop_field(ini_name, field):
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config = configparser.ConfigParser()
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config.read(ini_name)
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Rmax = float(config['mock']['R_max'])
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xmin = float(config['system']['corner0'])
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L = float(config['system']['L0'])
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N = int(config['system']['N0'])
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x = np.linspace(xmin, xmin+L, N)
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m = np.abs(x) < Rmax
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L = x[m].max() - x[m].min()
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return field[m][:, m][:, :, m], L
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def compute_ensemble_mean_field(ini_name, dirname, nframe, iter_max, iter_min, cut_field=True):
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"""
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Compute the mean and std deviation of the inferred density field
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"""
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print('Computing ensemble mean field')
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# Which steps to use
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all_mcmc = get_mcmc_steps(dirname, nframe, iter_max, iter_min=iter_min)
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#COMPUTE THE MEAN-DENSITY FIELD
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for i in tqdm(range(len(all_mcmc))):
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idx = all_mcmc[i]
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with h5.File(dirname + "/mcmc_%d.h5" % idx,'r') as mcmc_file:
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temp_field = np.array(mcmc_file['scalars/BORG_final_density'][...],dtype=np.float64)
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if i == 0:
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mean_field = np.array(np.full(temp_field.shape,0),dtype=np.float64)
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std_field = np.array(np.full(temp_field.shape,0),dtype=np.float64)
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mean_field += temp_field
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std_field += temp_field*temp_field
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mean_field = mean_field/np.float64(len(all_mcmc))
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std_field = std_field/np.float64(len(all_mcmc)) # < delta^2 >
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std_field = np.sqrt(std_field - mean_field **2) # < delta^2 > - < delta >^2
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# Cut the density field if needed
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if cut_field:
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mean_field, _ = crop_field(ini_name, mean_field)
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std_field, _ = crop_field(ini_name, std_field)
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return mean_field, std_field
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def get_mock_field(ini_name, dirname, which_field='delta', cut_field=True):
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with h5.File(f'{dirname}/mock_data.h5', 'r') as f:
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if which_field == 'delta':
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dens = f['scalars/BORG_final_density'][:]
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elif which_field == 'ics':
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dens = f['scalars/s_field'][:]
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if cut_field:
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dens, L = crop_field(ini_name, dens)
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else:
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config = configparser.ConfigParser()
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config.read(ini_name)
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L = float(config['system']['L0'])
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return dens, L
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def get_velmass_field(ini_file, field_type):
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"""
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Load the true field from the Velmass directory.
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Uses the ini file given in dirname
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field_type can be one of 'delta', 'vx', 'vy', 'vz', 'ics'
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"""
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config = configparser.ConfigParser()
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config.read(ini_file)
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dirname = config['mock']['velmass_dirname']
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if field_type == 'ics':
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f = np.load('/data101/bartlett/fsigma8/VELMASS/velmass_density.npy')
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else:
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if field_type == 'delta':
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key = 'd'
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else:
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key = field_type
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f = np.load(dirname + '/../density.npz')[key]
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if field_type == 'delta':
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f = f / np.mean(f) - 1
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return f
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def get_velmass_Lbox(ini_file):
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"""
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Load the true box length from the Velmass directory.
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Uses the ini file given in dirname
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"""
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config = configparser.ConfigParser()
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config.read(ini_file)
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velmass_dirname = config['mock']['velmass_dirname']
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# Get box size
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with open(velmass_dirname + '/auto-rockstar.cfg') as f:
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data = [r for r in f]
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Lbox = [r for r in data if r.startswith('BOX_SIZE')][0].strip()
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Lbox = float(Lbox.split('=')[1])
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return Lbox
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def get_borg_Lbox(ini_file):
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"""
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Load the box length used by BORG
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Uses the ini file given in dirname
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"""
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config = configparser.ConfigParser()
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config.read(ini_file)
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Lbox = float(config['system']['L0'])
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return Lbox
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def get_borg_N(ini_file):
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"""
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Load the box length used by BORG
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Uses the ini file given in dirname
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"""
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config = configparser.ConfigParser()
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config.read(ini_file)
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N = int(config['system']['N0'])
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return N
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def get_borg_Rmax(ini_file):
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"""
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Load the Rmax used by BORG
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Uses the ini file given in dirname
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"""
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config = configparser.ConfigParser()
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config.read(ini_file)
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R_max = float(config['mock']['R_max'])
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return R_max
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def get_borg_corner(ini_file):
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"""
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Load the corner used by BORG
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Uses the ini file given in dirname
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"""
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config = configparser.ConfigParser()
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config.read(ini_file)
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corn = float(config['system']['corner0'])
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return corn
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def crop_velmass_to_borg(ini_file, field_type):
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"""
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Load the true field from the Velmass directory.
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Then crop the field to only contain the region used by BORG
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Uses the ini file given in dirname
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field_type can be one of 'delta', 'vx', 'vy', 'vz', or 'ics'
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"""
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true_field = get_velmass_field(ini_file, field_type)
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Lbox_true = get_velmass_Lbox(ini_file)
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Lbox_borg = get_borg_Lbox(ini_file)
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N = true_field.shape[0]
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imin = int((1 - Lbox_borg / Lbox_true) * N / 2)
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imax = int((1 + Lbox_borg / Lbox_true) * N / 2)
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true_field = true_field[imin:imax,imin:imax,imin:imax]
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return true_field
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def get_spectra(ini_name, dirname, nframe, iter_max, iter_min, which_field='delta', cut_field=True, mock_type='borg', MAS=''):
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# Which steps to use
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all_mcmc = get_mcmc_steps(dirname, nframe, iter_max, iter_min=iter_min)
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if MAS == '':
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if which_field == 'delta':
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MAS = "CIC"
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elif which_field == 'ics':
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MAS = None
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else:
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raise NotImplementedError
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# Compute original power spectrum
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if mock_type == 'borg':
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delta1, boxsize = get_mock_field(ini_name, dirname, which_field=which_field, cut_field=cut_field)
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elif mock_type == 'velmass':
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delta1 = crop_velmass_to_borg(ini_name, which_field)
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if cut_field:
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delta1, boxsize = crop_field(ini_name, delta1)
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else:
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boxsize = get_borg_Lbox(ini_name)
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else:
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raise NotImplementedError
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# Turn to overdensity
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if which_field == 'delta':
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delta1 = (1. + delta1) / (1. + delta1).mean() - 1.
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print("BOXSIZE", boxsize)
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Pk = PKL.Pk(delta1.astype(np.float32), boxsize, axis=0, MAS=MAS, threads=1, verbose=True)
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k = Pk.k3D
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Pk_true = Pk.Pk[:,0]
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# Get other spectra
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all_pk = np.zeros((len(all_mcmc), len(k)))
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all_r = np.zeros((len(all_mcmc), len(k)))
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for i in tqdm(range(len(all_mcmc))):
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idx = all_mcmc[i]
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with h5.File(dirname + "/mcmc_%d.h5" % idx,'r') as mcmc_file:
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if which_field == 'delta':
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delta2= np.array(mcmc_file['scalars/BORG_final_density'][...],dtype=np.float64)
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elif which_field == 'ics':
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delta2 = np.array(mcmc_file['scalars/s_field'][...],dtype=np.float64)
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else:
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raise NotImplementedError
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if cut_field:
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delta2, _ = crop_field(ini_name, delta2)
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with suppress_stdout():
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Pk = PKL.XPk([delta1.astype(np.float32),delta2.astype(np.float32)], boxsize, axis=0, MAS=[MAS, MAS], threads=1)
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all_pk[i,:] = Pk.Pk[:,0,1] #monopole of field 2
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all_r[i,:] = Pk.XPk[:,0,0] / np.sqrt(Pk.Pk[:,0,1] * Pk.Pk[:,0,0])
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return k, Pk_true, all_pk, all_r
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def get_both_fields(ini_name, dirname, step, which_field='delta', cut_field=True, mock_type='borg'):
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# Mock
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if mock_type == 'borg':
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delta1, boxsize = get_mock_field(ini_name, dirname, which_field=which_field, cut_field=cut_field)
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elif mock_type == 'velmass':
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delta1 = crop_velmass_to_borg(ini_name, which_field)
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if cut_field:
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delta1, boxsize = crop_field(ini_name, delta1)
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else:
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boxsize = get_borg_Lbox(ini_name)
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else:
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raise NotImplementedError
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# Step
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with h5.File(dirname + "/mcmc_%d.h5" % step,'r') as mcmc_file:
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if which_field == 'delta':
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delta2= np.array(mcmc_file['scalars/BORG_final_density'][...],dtype=np.float64)
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elif which_field == 'ics':
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delta2 = np.array(mcmc_file['scalars/s_field'][...],dtype=np.float64)
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else:
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raise NotImplementedError
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if cut_field:
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delta2, _ = crop_field(ini_name, delta2)
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return delta1, delta2
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def get_likelihood_values(dirname, nframe, iter_max, iter_min):
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all_mcmc = get_mcmc_steps(dirname, nframe, iter_max, iter_min=iter_min)
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all_logL = np.zeros(len(all_mcmc))
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all_logprior = np.zeros(len(all_mcmc))
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for i in tqdm(range(len(all_mcmc))):
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with h5.File(f'{dirname}/mcmc_{all_mcmc[i]}.h5', 'r') as f:
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s_hat = f['scalars/s_hat_field'][:]
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all_logL[i] = f['scalars/hmc_Elh'][:]
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all_logprior[i] = f['scalars/hmc_Eprior'][:]
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return all_logL, all_logprior |