Save and load distance tracer mocks to or from file
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Deaglan Bartlett
parent
b4bf734782
commit
ad20253e29
1 changed files with 70 additions and 15 deletions
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@ -281,15 +281,32 @@ class VelocityBORGLikelihood(borg.likelihood.BaseLikelihood):
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self.vector_model_params[k] = state[k]
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# Save mock to file
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# with h5py.File(f'tracer_data_{self.run_type}.h5', 'w') as h5file:
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# for i in range(self.nsamp):
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# sample_group = h5file.create_group(f'sample_{i}')
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# sample_group.create_dataset('coord_true', data=self.coord_true[i])
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# Check that the measured coordinates all lie within the box
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# for i in range(self.nsamp):
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# if np.amax(self.r_hMpc[i]) > self.R_lim:
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# raise ValueError('All tracers must have measured coordinates within R_lim')
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if self.run_type in ['velmass', 'mock']:
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with h5py.File(f'tracer_data_{self.run_type}.h5', 'w') as h5file:
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for i in range(self.nsamp):
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sample_group = h5file.create_group(f'sample_{i}')
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sample_group.create_dataset('tracer_type', data=self.data['tracer_type'][i])
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sample_group.create_dataset('RA', data=self.data['RA'][i])
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sample_group.create_dataset('Dec', data=self.data['Dec'][i])
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sample_group.create_dataset('cz_obs', data=self.data['cz_obs'][i])
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sample_group.create_dataset('m_true', data=self.truths['m_true'][i])
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sample_group.create_dataset('m_obs', data=self.data['m_obs'][i])
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sample_group.create_dataset('sigma_m', data=self.data['sigma_m'][i])
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sample_group.create_dataset('xtrue', data=self.truths['xtrue'][i])
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if self.data['tracer_type'][i] == 'tfr':
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sample_group.create_dataset('eta_true', data=self.truths['eta_true'][i])
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sample_group.create_dataset('eta_obs', data=self.data['eta_obs'][i])
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sample_group.create_dataset('sigma_eta', data=self.data['sigma_eta'][i])
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sample_group.create_dataset('mthresh', data=self.data['mthresh'][i])
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elif self.data['tracer_type'][i] == 'sn':
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sample_group.create_dataset('stretch_true', data=self.truths['stretch_true'][i])
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sample_group.create_dataset('stretch_obs', data=self.data['stretch_obs'][i])
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sample_group.create_dataset('sigma_stretch', data=self.data['sigma_stretch'][i])
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sample_group.create_dataset('c_true', data=self.truths['c_true'][i])
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sample_group.create_dataset('c_obs', data=self.data['c_obs'][i])
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sample_group.create_dataset('c_eta', data=self.data['sigma_c'][i])
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else:
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raise ValueError(f"Unknown tracer type: {self.data['tracer_type'][i]}")
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myprint('From mock')
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self.saved_s_hat = s_hat.copy()
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@ -301,7 +318,47 @@ class VelocityBORGLikelihood(borg.likelihood.BaseLikelihood):
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myprint('Loading previously made mock data')
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raise NotImplementedError
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keys = ['tracer_type', 'RA', 'Dec', 'cz_obs', 'm_obs', 'eta_obs', 'stretch_obs', 'c_obs',
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'sigma_m', 'sigma_eta', 'sigma_stretch', 'sigma_c']
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self.data = {}
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for k in keys:
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self.data[k] = [None] * self.nsamp
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keys = ['m_true', 'eta_true', 'stretch_true', 'c_true', 'xtrue']
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self.truths = {}
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for k in keys:
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self.truths[k] = [None] * self.nsamp
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if self.run_type in ['velmass', 'mock']:
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jnp.array(f[f'sample_{i}/coord_true'][:])
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with h5py.File(f'tracer_data_{self.run_type}.h5', 'r') as h5file:
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for i in range(self.nsamp):
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self.data['tracer_type'][i] = h5file[f'sample_{i}/tracer_type']
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for k in ['RA', 'Dec', 'cz_obs', 'm_obs', 'sigma_m']:
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self.data[k][i] = np.array(h5file[f'sample_{i}/{k}'][:])
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for k in ['m_true', 'xtrue']:
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self.truths[k][i] = np.array(h5file[f'sample_{i}/{k}'][:])
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if self.data['tracer_type'][i] == 'tfr':
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for k in ['eta_obs', 'sigma_eta', 'mthresh']:
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self.data[k][i] = np.array(h5file[f'sample_{i}/{k}'][:])
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for k in ['eta_true']:
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self.truths[k][i] = np.array(h5file[f'sample_{i}/{k}'][:])
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elif self.data['tracer_type'][i] == 'sn':
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for k in ['stretch_obs', 'c_obs', 'sigma_stretch', 'sigma_c']:
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self.data[k][i] = np.array(h5file[f'sample_{i}/{k}'][:])
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for k in ['stretch_true', 'c_true']:
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self.truths[k][i] = np.array(h5file[f'sample_{i}/{k}'][:])
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else:
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raise ValueError(f"Unknown tracer type: {self.data['tracer_type'][i]}")
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else:
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raise NotImplementedError
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# Get integration points
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for i in range(self.nsamp):
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self.MB_pos[i] = utils.generateMBData(
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self.data['RA'][i], self.data['Dec'][i], self.data['cz_obs'][i], self.L[0], self.N[0],
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self.R_lim, self.Nsig, self.Nint_points, self.model_params['sig_v'], self.frac_sigma_r[i])
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def dens2like(self, output_density: np.ndarray, output_velocity: np.ndarray):
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"""
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@ -819,15 +876,13 @@ def build_likelihood(state: borg.likelihood.MarkovState, info: borg.likelihood.L
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"""
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To Do:
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- Add in the true colours etc. to the model parameter sampling
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- Sample the variables from priors
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- Sample the variables from priors and/or add prior terms
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- Check global variables are used
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- Docstrings
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- Don't want all these samplers - only gradient ones
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- Save mock to file
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- How to sample the true values now they are not in model_params?
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- How too update the vector of true values for likelihood call?
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- Commit true values to file
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- How to update the vector of true values for likelihood call
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- Add in the true colours etc. to the model parameter sampling
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- Check changes to sigma_m etc. likelihoods in original tests
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"""
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