mirror of
https://github.com/Richard-Sti/csiborgtools.git
synced 2024-12-22 17:28:02 +00:00
Merge branch 'master' of github.com:Richard-Sti/csiborgtools
This commit is contained in:
rstiskalek
commit
0d1c262df0
11 changed files with 1806 additions and 307 deletions
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@ -17,7 +17,7 @@ from .density import (DensityField, PotentialField, TidalTensorField,
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overdensity_field) # noqa
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from .enclosed_mass import (particles_enclosed_mass, # noqa
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particles_enclosed_momentum, field_enclosed_mass) # noqa
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from .interp import (evaluate_cartesian, evaluate_sky, evaluate_los, # noqa
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from .interp import (evaluate_cartesian_cic, evaluate_sky, evaluate_los, # noqa
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field2rsp, fill_outside, make_sky, # noqa
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observer_peculiar_velocity, smoothen_field, # noqa
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field_at_distance) # noqa
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@ -18,6 +18,7 @@ Tools for interpolating 3D fields at arbitrary positions.
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import MAS_library as MASL
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import numpy
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import smoothing_library as SL
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from scipy.interpolate import RegularGridInterpolator
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from numba import jit
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from tqdm import tqdm, trange
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@ -30,9 +31,10 @@ from .utils import divide_nonzero, force_single_precision, nside2radec
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###############################################################################
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def evaluate_cartesian(*fields, pos, smooth_scales=None, verbose=False):
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def evaluate_cartesian_cic(*fields, pos, smooth_scales=None, verbose=False):
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"""
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Evaluate a scalar field(s) at Cartesian coordinates `pos`.
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Evaluate a scalar field(s) at Cartesian coordinates `pos` using CIC
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interpolation.
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Parameters
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----------
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@ -82,6 +84,75 @@ def evaluate_cartesian(*fields, pos, smooth_scales=None, verbose=False):
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return interp_fields
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def evaluate_cartesian_regular(*fields, pos, smooth_scales=None,
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method="linear", verbose=False):
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"""
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Evaluate a scalar field(s) at Cartesian coordinates `pos` using linear
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interpolation on a regular grid.
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Parameters
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----------
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*fields : (list of) 3-dimensional array of shape `(grid, grid, grid)`
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Fields to be interpolated.
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pos : 2-dimensional array of shape `(n_samples, 3)`
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Query positions in box units.
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smooth_scales : (list of) float, optional
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Smoothing scales in box units. If `None`, no smoothing is performed.
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method : str, optional
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Interpolation method, must be one of the methods of
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`scipy.interpolate.RegularGridInterpolator`.
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verbose : bool, optional
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Smoothing verbosity flag.
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Returns
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-------
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(list of) 2-dimensional array of shape `(n_samples, len(smooth_scales))`
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"""
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pos = force_single_precision(pos)
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if isinstance(smooth_scales, (int, float)):
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smooth_scales = [smooth_scales]
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if smooth_scales is None:
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shape = (pos.shape[0],)
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else:
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shape = (pos.shape[0], len(smooth_scales))
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ngrid = fields[0].shape[0]
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cellsize = 1. / ngrid
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X = numpy.linspace(0.5 * cellsize, 1 - 0.5 * cellsize, ngrid)
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Y, Z = numpy.copy(X), numpy.copy(X)
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interp_fields = [numpy.full(shape, numpy.nan, dtype=numpy.float32)
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for __ in range(len(fields))]
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for i, field in enumerate(fields):
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if smooth_scales is None:
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field_interp = RegularGridInterpolator(
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(X, Y, Z), field, fill_value=None, bounds_error=False,
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method=method)
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interp_fields[i] = field_interp(pos)
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else:
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desc = f"Smoothing and interpolating field {i + 1}/{len(fields)}"
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iterator = tqdm(smooth_scales, desc=desc, disable=not verbose)
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for j, scale in enumerate(iterator):
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if not scale > 0:
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fsmooth = numpy.copy(field)
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else:
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fsmooth = smoothen_field(field, scale, 1., make_copy=True)
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field_interp = RegularGridInterpolator(
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(X, Y, Z), fsmooth, fill_value=None, bounds_error=False,
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method=method)
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interp_fields[i][:, j] = field_interp(pos)
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if len(fields) == 1:
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return interp_fields[0]
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return interp_fields
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def observer_peculiar_velocity(velocity_field, smooth_scales=None,
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observer=None, verbose=True):
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"""
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@ -108,7 +179,7 @@ def observer_peculiar_velocity(velocity_field, smooth_scales=None,
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else:
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pos = numpy.asanyarray(observer).reshape(1, 3)
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vx, vy, vz = evaluate_cartesian(
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vx, vy, vz = evaluate_cartesian_cic(
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*velocity_field, pos=pos, smooth_scales=smooth_scales, verbose=verbose)
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# Reshape since we evaluated only one point
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@ -127,7 +198,7 @@ def observer_peculiar_velocity(velocity_field, smooth_scales=None,
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def evaluate_los(*fields, sky_pos, boxsize, rmax, dr, smooth_scales=None,
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verbose=False):
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interpolation_method="cic", verbose=False):
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"""
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Interpolate the fields for a set of lines of sights from the observer
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in the centre of the box.
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@ -146,6 +217,9 @@ def evaluate_los(*fields, sky_pos, boxsize, rmax, dr, smooth_scales=None,
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Radial distance step in `Mpc / h`.
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smooth_scales : (list of) float, optional
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Smoothing scales in `Mpc / h`.
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interpolation_method : str, optional
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Interpolation method. Must be one of `cic` or one of the methods of
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`scipy.interpolate.RegularGridInterpolator`.
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verbose : bool, optional
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Smoothing verbosity flag.
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@ -191,9 +265,15 @@ def evaluate_los(*fields, sky_pos, boxsize, rmax, dr, smooth_scales=None,
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smooth_scales *= mpc2box
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field_interp = evaluate_cartesian(*fields, pos=pos,
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smooth_scales=smooth_scales,
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verbose=verbose)
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if interpolation_method == "cic":
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field_interp = evaluate_cartesian_cic(
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*fields, pos=pos, smooth_scales=smooth_scales,
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verbose=verbose)
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else:
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field_interp = evaluate_cartesian_regular(
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*fields, pos=pos, smooth_scales=smooth_scales,
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method=interpolation_method, verbose=verbose)
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if len(fields) == 1:
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field_interp = [field_interp]
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@ -228,7 +308,7 @@ def evaluate_los(*fields, sky_pos, boxsize, rmax, dr, smooth_scales=None,
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def evaluate_sky(*fields, pos, mpc2box, smooth_scales=None, verbose=False):
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"""
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Evaluate a scalar field(s) at radial distance `Mpc / h`, right ascensions
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[0, 360) deg and declinations [-90, 90] deg.
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[0, 360) deg and declinations [-90, 90] deg. Uses CIC interpolation.
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Parameters
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----------
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@ -264,8 +344,9 @@ def evaluate_sky(*fields, pos, mpc2box, smooth_scales=None, verbose=False):
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smooth_scales *= mpc2box
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return evaluate_cartesian(*fields, pos=cart_pos,
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smooth_scales=smooth_scales, verbose=verbose)
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return evaluate_cartesian_cic(*fields, pos=cart_pos,
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smooth_scales=smooth_scales,
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verbose=verbose)
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def make_sky(field, angpos, dist, boxsize, verbose=True):
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@ -324,7 +405,7 @@ def make_sky(field, angpos, dist, boxsize, verbose=True):
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def field_at_distance(field, distance, boxsize, smooth_scales=None, nside=128,
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verbose=True):
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"""
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Evaluate a scalar field at uniformly spaced angular coordinates at a
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Evaluate a scalar field at uniformly spaced angular coordinates at a
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given distance from the observer
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Parameters
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@ -355,8 +436,8 @@ def field_at_distance(field, distance, boxsize, smooth_scales=None, nside=128,
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angpos])
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X = radec_to_cartesian(X) / boxsize + 0.5
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return evaluate_cartesian(field, pos=X, smooth_scales=smooth_scales,
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verbose=verbose)
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return evaluate_cartesian_cic(field, pos=X, smooth_scales=smooth_scales,
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verbose=verbose)
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###############################################################################
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@ -15,4 +15,6 @@
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from .flow_model import (DataLoader, radial_velocity_los, dist2redshift, # noqa
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dist2distmodulus, predict_zobs, project_Vext, # noqa
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SD_PV_validation_model, SN_PV_validation_model, # noqa
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radec_to_galactic) # noqa
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TF_PV_validation_model, radec_to_galactic, # noqa
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sample_prior, make_loss, get_model, # noqa
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optimize_model_with_jackknife) # noqa
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@ -20,7 +20,7 @@ References
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[1] https://arxiv.org/abs/1912.09383.
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"""
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from datetime import datetime
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from warnings import warn
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from warnings import catch_warnings, simplefilter, warn
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import numpy as np
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import numpyro
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@ -29,12 +29,18 @@ from astropy import units as u
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from astropy.coordinates import SkyCoord
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from astropy.cosmology import FlatLambdaCDM
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from h5py import File
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from jax import jit
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from jax import numpy as jnp
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from jax import vmap
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from jax.lax import cond, scan
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from jax.random import PRNGKey
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from numpyro.infer import Predictive, util
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from scipy.optimize import fmin_powell
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from sklearn.model_selection import KFold
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from tqdm import tqdm, trange
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from numdifftools import Hessian
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from ..params import simname2Omega_m
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from ..read import CSiBORG1Catalogue
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SPEED_OF_LIGHT = 299792.458 # km / s
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@ -130,19 +136,22 @@ class DataLoader:
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if not store_full_velocity:
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self._los_velocity = None
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Omega_m = simname2Omega_m(simname)
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self._Omega_m = simname2Omega_m(simname)
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# Normalize the CSiBORG density by the mean matter density
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if "csiborg" in simname:
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cosmo = FlatLambdaCDM(H0=100, Om0=Omega_m)
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cosmo = FlatLambdaCDM(H0=100, Om0=self._Omega_m)
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mean_rho_matter = cosmo.critical_density0.to("Msun/kpc^3").value
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mean_rho_matter *= Omega_m
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mean_rho_matter *= self._Omega_m
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self._los_density /= mean_rho_matter
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# Since Carrick+2015 provide `rho / <rho> - 1`
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if simname == "Carrick2015":
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self._los_density += 1
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self._mask = np.ones(len(self._cat), dtype=bool)
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self._catname = catalogue
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@property
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def cat(self):
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"""
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@ -152,7 +161,7 @@ class DataLoader:
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-------
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structured array
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"""
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return self._cat
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return self._cat[self._mask]
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@property
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def catname(self):
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@ -185,7 +194,7 @@ class DataLoader:
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----------
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3-dimensional array of shape (n_objects, n_simulations, n_steps)
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"""
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return self._los_density
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return self._los_density[self._mask]
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@property
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def los_velocity(self):
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@ -198,7 +207,7 @@ class DataLoader:
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"""
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if self._los_velocity is None:
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raise ValueError("The 3D velocities were not stored.")
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return self._los_velocity
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return self._los_velocity[self._mask]
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@property
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def los_radial_velocity(self):
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@ -209,7 +218,7 @@ class DataLoader:
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-------
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3-dimensional array of shape (n_objects, n_simulations, n_steps)
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"""
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return self._los_radial_velocity
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return self._los_radial_velocity[self._mask]
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def _read_field(self, simname, catalogue, k, paths):
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"""Read in the interpolated field."""
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@ -250,7 +259,8 @@ class DataLoader:
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arr = np.empty(len(f["RA"]), dtype=dtype)
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for key in f.keys():
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arr[key] = f[key][:]
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elif catalogue == "LOSS" or catalogue == "Foundation":
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elif catalogue in ["LOSS", "Foundation", "SFI_gals", "2MTF",
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"Pantheon+"]:
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with File(catalogue_fpath, 'r') as f:
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grp = f[catalogue]
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@ -258,28 +268,46 @@ class DataLoader:
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arr = np.empty(len(grp["RA"]), dtype=dtype)
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for key in grp.keys():
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arr[key] = grp[key][:]
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elif "csiborg1" in catalogue:
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nsim = int(catalogue.split("_")[-1])
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cat = CSiBORG1Catalogue(nsim, bounds={"totmass": (1e13, None)})
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seed = 42
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gen = np.random.default_rng(seed)
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mask = gen.choice(len(cat), size=100, replace=False)
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keys = ["r_hMpc", "RA", "DEC"]
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dtype = [(key, np.float32) for key in keys]
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arr = np.empty(len(mask), dtype=dtype)
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sph_pos = cat["spherical_pos"]
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arr["r_hMpc"] = sph_pos[mask, 0]
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arr["RA"] = sph_pos[mask, 1]
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arr["DEC"] = sph_pos[mask, 2]
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# TODO: add peculiar velocit
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else:
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raise ValueError(f"Unknown catalogue: `{catalogue}`.")
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return arr
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def make_jackknife_mask(self, i, n_splits, seed=42):
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"""
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Set the jackknife mask to exclude the `i`-th split.
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Parameters
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----------
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i : int
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Index of the split to exclude.
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n_splits : int
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Number of splits.
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seed : int, optional
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Random seed.
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Returns
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-------
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None, sets `mask` internally.
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"""
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cv = KFold(n_splits=n_splits, shuffle=True, random_state=seed)
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n = len(self._cat)
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indxs = np.arange(n)
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gen = np.random.default_rng(seed)
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gen.shuffle(indxs)
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for j, (train_index, __) in enumerate(cv.split(np.arange(n))):
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if i == j:
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self._mask = indxs[train_index]
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return
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raise ValueError("The index `i` must be in the range of `n_splits`.")
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def reset_mask(self):
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"""Reset the jackknife mask."""
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self._mask = np.ones(len(self._cat), dtype=bool)
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###############################################################################
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# Supplementary flow functions #
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|
|
@ -405,6 +433,19 @@ def dist2distmodulus(dist, Omega_m):
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return 5 * jnp.log10(luminosity_distance) + 25
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# def distmodulus2dist(distmodulus, Omega_m):
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# """
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||||
# Copied from Supranta. Make sure this actually works.
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#
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||||
#
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||||
# """
|
||||
# dL = 10 ** ((distmodulus - 25.) / 5.)
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# r_hMpc = dL
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# for i in range(4):
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# r_hMpc = dL / (1.0 + dist2redshift(r_hMpc, Omega_m))
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# return r_hMpc
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||||
|
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def project_Vext(Vext_x, Vext_y, Vext_z, RA, dec):
|
||||
"""
|
||||
Project the external velocity onto the line of sight along direction
|
||||
|
|
@ -459,8 +500,8 @@ def predict_zobs(dist, beta, Vext_radial, vpec_radial, Omega_m):
|
|||
# Flow validation models #
|
||||
###############################################################################
|
||||
|
||||
|
||||
def calculate_ptilde_wo_bias(xrange, mu, err, r_squared_xrange=None):
|
||||
def calculate_ptilde_wo_bias(xrange, mu, err, r_squared_xrange=None,
|
||||
is_err_squared=False):
|
||||
"""
|
||||
Calculate `ptilde(r)` without any bias.
|
||||
|
||||
|
|
@ -475,12 +516,17 @@ def calculate_ptilde_wo_bias(xrange, mu, err, r_squared_xrange=None):
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r_squared_xrange : 1-dimensional array, optional
|
||||
Radial distances squared where the field was interpolated for each
|
||||
object. If not provided, the `r^2` correction is not applied.
|
||||
is_err_squared : bool, optional
|
||||
Whether the error is already squared.
|
||||
|
||||
Returns
|
||||
-------
|
||||
1-dimensional array
|
||||
"""
|
||||
ptilde = jnp.exp(-0.5 * ((xrange - mu) / err)**2)
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||||
if is_err_squared:
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ptilde = jnp.exp(-0.5 * (xrange - mu)**2 / err)
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||||
else:
|
||||
ptilde = jnp.exp(-0.5 * ((xrange - mu) / err)**2)
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||||
|
||||
if r_squared_xrange is not None:
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||||
ptilde *= r_squared_xrange
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||||
|
|
@ -548,7 +594,7 @@ class SD_PV_validation_model:
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|||
self._z_obs = jnp.asarray(z_obs, dtype=dt)
|
||||
|
||||
self._r_hMpc = jnp.asarray(r_hMpc, dtype=dt)
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||||
self._e_rhMpc = jnp.asarray(e_r_hMpc, dtype=dt)
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self._e2_rhMpc = jnp.asarray(e_r_hMpc**2, dtype=dt)
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||||
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||||
# Get radius squared
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||||
r2_xrange = r_xrange**2
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||||
|
|
@ -560,22 +606,23 @@ class SD_PV_validation_model:
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|||
raise ValueError("The radial step size must be constant.")
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||||
dr = dr[0]
|
||||
|
||||
self._r_xrange = r_xrange
|
||||
|
||||
# Get the various vmapped functions
|
||||
self._vmap_ptilde_wo_bias = vmap(lambda mu, err: calculate_ptilde_wo_bias(r_xrange, mu, err, r2_xrange)) # noqa
|
||||
self._vmap_ptilde_wo_bias = vmap(lambda mu, err: calculate_ptilde_wo_bias(r_xrange, mu, err, r2_xrange, True)) # noqa
|
||||
self._vmap_simps = vmap(lambda y: simps(y, dr))
|
||||
self._vmap_zobs = vmap(lambda beta, Vr, vpec_rad: predict_zobs(r_xrange, beta, Vr, vpec_rad, Omega_m), in_axes=(None, 0, 0)) # noqa
|
||||
self._vmap_ll_zobs = vmap(lambda zobs, zobs_pred, sigma_v: calculate_ll_zobs(zobs, zobs_pred, sigma_v), in_axes=(0, 0, None)) # noqa
|
||||
|
||||
# Distribution of external velocity components
|
||||
self._Vext = dist.Uniform(-1000, 1000)
|
||||
self._Vext = dist.Uniform(-500, 500)
|
||||
# Distribution of density, velocity and location bias parameters
|
||||
self._alpha = dist.LogNormal(*lognorm_mean_std_to_loc_scale(1.0, 0.5)) # noqa
|
||||
self._beta = dist.Normal(1., 0.5)
|
||||
self._h = dist.LogNormal(*lognorm_mean_std_to_loc_scale(1.0, 0.5))
|
||||
# Distribution of velocity uncertainty sigma_v
|
||||
self._sv = dist.LogNormal(*lognorm_mean_std_to_loc_scale(150, 100))
|
||||
|
||||
def __call__(self, sample_alpha=False, scale_distance=False):
|
||||
def __call__(self, sample_alpha=False):
|
||||
"""
|
||||
The simple distance NumPyro PV validation model.
|
||||
|
||||
|
|
@ -584,21 +631,19 @@ class SD_PV_validation_model:
|
|||
sample_alpha : bool, optional
|
||||
Whether to sample the density bias parameter `alpha`, otherwise
|
||||
it is fixed to 1.
|
||||
scale_distance : bool, optional
|
||||
Whether to scale the distance by `h`, otherwise it is fixed to 1.
|
||||
"""
|
||||
Vx = numpyro.sample("Vext_x", self._Vext)
|
||||
Vy = numpyro.sample("Vext_y", self._Vext)
|
||||
Vz = numpyro.sample("Vext_z", self._Vext)
|
||||
|
||||
alpha = numpyro.sample("alpha", self._alpha) if sample_alpha else 1.0
|
||||
beta = numpyro.sample("beta", self._beta)
|
||||
h = numpyro.sample("h", self._h) if scale_distance else 1.0
|
||||
sigma_v = numpyro.sample("sigma_v", self._sv)
|
||||
|
||||
Vext_rad = project_Vext(Vx, Vy, Vz, self._RA, self._dec)
|
||||
|
||||
# Calculate p(r) and multiply it by the galaxy bias
|
||||
ptilde = self._vmap_ptilde_wo_bias(h * self._r_hMpc, h * self._e_rhMpc)
|
||||
ptilde = self._vmap_ptilde_wo_bias(self._r_hMpc, self._e2_rhMpc)
|
||||
ptilde *= self._los_density**alpha
|
||||
|
||||
# Normalization of p(r)
|
||||
|
|
@ -667,26 +712,25 @@ class SN_PV_validation_model:
|
|||
dr = dr[0]
|
||||
|
||||
# Get the various vmapped functions
|
||||
self._vmap_ptilde_wo_bias = vmap(lambda mu, err: calculate_ptilde_wo_bias(mu_xrange, mu, err, r2_xrange)) # noqa
|
||||
self._vmap_simps = vmap(lambda y: simps(y, dr))
|
||||
self._vmap_zobs = vmap(lambda beta, Vr, vpec_rad: predict_zobs(r_xrange, beta, Vr, vpec_rad, Omega_m), in_axes=(None, 0, 0)) # noqa
|
||||
self._vmap_ll_zobs = vmap(lambda zobs, zobs_pred, sigma_v: calculate_ll_zobs(zobs, zobs_pred, sigma_v), in_axes=(0, 0, None)) # noqa
|
||||
self._f_ptilde_wo_bias = lambda mu, err: calculate_ptilde_wo_bias(mu_xrange, mu, err, r2_xrange, True) # noqa
|
||||
self._f_simps = lambda y: simps(y, dr) # noqa
|
||||
self._f_zobs = lambda beta, Vr, vpec_rad: predict_zobs(r_xrange, beta, Vr, vpec_rad, Omega_m) # noqa
|
||||
|
||||
# Distribution of external velocity components
|
||||
self._dist_Vext = dist.Uniform(-1000, 1000)
|
||||
self._Vext = dist.Uniform(-500, 500)
|
||||
# Distribution of velocity and density bias parameters
|
||||
self._dist_alpha = dist.LogNormal(*lognorm_mean_std_to_loc_scale(1.0, 0.5)) # noqa
|
||||
self._dist_beta = dist.Normal(1., 0.5)
|
||||
self._alpha = dist.LogNormal(*lognorm_mean_std_to_loc_scale(1.0, 0.5))
|
||||
self._beta = dist.Normal(1., 0.5)
|
||||
# Distribution of velocity uncertainty
|
||||
self._dist_sigma_v = dist.LogNormal(*lognorm_mean_std_to_loc_scale(150, 100)) # noqa
|
||||
self._sigma_v = dist.LogNormal(*lognorm_mean_std_to_loc_scale(150, 100)) # noqa
|
||||
|
||||
# Distribution of light curve calibration parameters
|
||||
self._dist_mag_cal = dist.Normal(-18.25, 1.0)
|
||||
self._dist_alpha_cal = dist.Normal(0.1, 0.5)
|
||||
self._dist_beta_cal = dist.Normal(3.0, 1.0)
|
||||
self._dist_e_mu = dist.LogNormal(*lognorm_mean_std_to_loc_scale(0.1, 0.05)) # noqa
|
||||
self._mag_cal = dist.Normal(-18.25, 0.5)
|
||||
self._alpha_cal = dist.Normal(0.148, 0.05)
|
||||
self._beta_cal = dist.Normal(3.112, 1.0)
|
||||
self._e_mu = dist.LogNormal(*lognorm_mean_std_to_loc_scale(0.1, 0.05))
|
||||
|
||||
def __call__(self, sample_alpha=False, fix_calibration=False):
|
||||
def __call__(self, sample_alpha=True, fix_calibration=False):
|
||||
"""
|
||||
The supernova NumPyro PV validation model with SALT2 calibration.
|
||||
|
||||
|
|
@ -695,23 +739,25 @@ class SN_PV_validation_model:
|
|||
sample_alpha : bool, optional
|
||||
Whether to sample the density bias parameter `alpha`, otherwise
|
||||
it is fixed to 1.
|
||||
fix_calibration : str, optional
|
||||
Whether to fix the calibration parameters. If not provided, they
|
||||
are sampled. If "Foundation" or "LOSS" is provided, the parameters
|
||||
are fixed to the best inverse parameters for the Foundation or LOSS
|
||||
catalogues.
|
||||
"""
|
||||
Vx = numpyro.sample("Vext_x", self._dist_Vext)
|
||||
Vy = numpyro.sample("Vext_y", self._dist_Vext)
|
||||
Vz = numpyro.sample("Vext_z", self._dist_Vext)
|
||||
if sample_alpha:
|
||||
alpha = numpyro.sample("alpha", self._dist_alpha)
|
||||
else:
|
||||
alpha = 1.0
|
||||
beta = numpyro.sample("beta", self._dist_beta)
|
||||
sigma_v = numpyro.sample("sigma_v", self._dist_sigma_v)
|
||||
Vx = numpyro.sample("Vext_x", self._Vext)
|
||||
Vy = numpyro.sample("Vext_y", self._Vext)
|
||||
Vz = numpyro.sample("Vext_z", self._Vext)
|
||||
alpha = numpyro.sample("alpha", self._alpha) if sample_alpha else 1.0
|
||||
beta = numpyro.sample("beta", self._beta)
|
||||
sigma_v = numpyro.sample("sigma_v", self._sigma_v)
|
||||
|
||||
if fix_calibration == "Foundation":
|
||||
# Foundation inverse best parameters
|
||||
e_mu_intrinsic = 0.064
|
||||
alpha_cal = 0.135
|
||||
beta_cal = 2.9
|
||||
sigma_v = 140
|
||||
sigma_v = 149
|
||||
mag_cal = -18.555
|
||||
elif fix_calibration == "LOSS":
|
||||
# LOSS inverse best parameters
|
||||
|
|
@ -719,31 +765,399 @@ class SN_PV_validation_model:
|
|||
alpha_cal = 0.123
|
||||
beta_cal = 3.52
|
||||
mag_cal = -18.195
|
||||
sigma_v = 140
|
||||
sigma_v = 149
|
||||
else:
|
||||
e_mu_intrinsic = numpyro.sample("e_mu_intrinsic", self._dist_e_mu)
|
||||
mag_cal = numpyro.sample("mag_cal", self._dist_mag_cal)
|
||||
alpha_cal = numpyro.sample("alpha_cal", self._dist_alpha_cal)
|
||||
beta_cal = numpyro.sample("beta_cal", self._dist_beta_cal)
|
||||
e_mu_intrinsic = numpyro.sample("e_mu_intrinsic", self._e_mu)
|
||||
mag_cal = numpyro.sample("mag_cal", self._mag_cal)
|
||||
alpha_cal = numpyro.sample("alpha_cal", self._alpha_cal)
|
||||
beta_cal = numpyro.sample("beta_cal", self._beta_cal)
|
||||
|
||||
Vext_rad = project_Vext(Vx, Vy, Vz, self._RA, self._dec)
|
||||
|
||||
mu = self._mB - mag_cal + alpha_cal * self._x1 - beta_cal * self._c
|
||||
squared_e_mu = (self._e2_mB
|
||||
+ alpha_cal**2 * self._e2_x1
|
||||
+ beta_cal**2 * self._e2_c)
|
||||
squared_e_mu += e_mu_intrinsic**2
|
||||
squared_e_mu = (self._e2_mB + alpha_cal**2 * self._e2_x1
|
||||
+ beta_cal**2 * self._e2_c + e_mu_intrinsic**2)
|
||||
|
||||
# Calculate p(r) and multiply it by the galaxy bias
|
||||
ptilde = self._vmap_ptilde_wo_bias(mu, squared_e_mu**0.5)
|
||||
ptilde *= self._los_density**alpha
|
||||
def scan_body(ll, i):
|
||||
# Calculate p(r) and multiply it by the galaxy bias
|
||||
ptilde = self._f_ptilde_wo_bias(mu[i], squared_e_mu[i])
|
||||
ptilde *= self._los_density[i]**alpha
|
||||
|
||||
# Normalization of p(r)
|
||||
pnorm = self._vmap_simps(ptilde)
|
||||
# Normalization of p(r)
|
||||
pnorm = self._f_simps(ptilde)
|
||||
|
||||
# Calculate p(z_obs) and multiply it by p(r)
|
||||
zobs_pred = self._vmap_zobs(beta, Vext_rad, self._los_velocity)
|
||||
ptilde *= self._vmap_ll_zobs(self._z_obs, zobs_pred, sigma_v)
|
||||
# Calculate p(z_obs) and multiply it by p(r)
|
||||
zobs_pred = self._f_zobs(beta, Vext_rad[i], self._los_velocity[i])
|
||||
ptilde *= calculate_ll_zobs(self._z_obs[i], zobs_pred, sigma_v)
|
||||
|
||||
ll = jnp.sum(jnp.log(self._vmap_simps(ptilde) / pnorm))
|
||||
return ll + jnp.log(self._f_simps(ptilde) / pnorm), None
|
||||
|
||||
ll = 0.
|
||||
ll, __ = scan(scan_body, ll, jnp.arange(len(self._RA)))
|
||||
numpyro.factor("ll", ll)
|
||||
|
||||
|
||||
class TF_PV_validation_model:
|
||||
"""
|
||||
Tully-Fisher peculiar velocity (PV) validation model that includes the
|
||||
calibration of the Tully-Fisher distance `mu = m - (a + b * eta)`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
los_density : 2-dimensional array of shape (n_objects, n_steps)
|
||||
LOS density field.
|
||||
los_velocity : 3-dimensional array of shape (n_objects, n_steps)
|
||||
LOS radial velocity field.
|
||||
RA, dec : 1-dimensional arrays of shape (n_objects)
|
||||
Right ascension and declination in degrees.
|
||||
z_obs : 1-dimensional array of shape (n_objects)
|
||||
Observed redshifts.
|
||||
mag, eta : 1-dimensional arrays of shape (n_objects)
|
||||
Apparent magnitude and `eta` parameter.
|
||||
e_mag, e_eta : 1-dimensional arrays of shape (n_objects)
|
||||
Errors on the apparent magnitude and `eta` parameter.
|
||||
r_xrange : 1-dimensional array
|
||||
Radial distances where the field was interpolated for each object.
|
||||
Omega_m : float
|
||||
Matter density parameter.
|
||||
"""
|
||||
|
||||
def __init__(self, los_density, los_velocity, RA, dec, z_obs,
|
||||
mag, eta, e_mag, e_eta, r_xrange, Omega_m):
|
||||
dt = jnp.float32
|
||||
# Convert everything to JAX arrays.
|
||||
self._los_density = jnp.asarray(los_density, dtype=dt)
|
||||
self._los_velocity = jnp.asarray(los_velocity, dtype=dt)
|
||||
|
||||
self._RA = jnp.asarray(np.deg2rad(RA), dtype=dt)
|
||||
self._dec = jnp.asarray(np.deg2rad(dec), dtype=dt)
|
||||
self._z_obs = jnp.asarray(z_obs, dtype=dt)
|
||||
|
||||
self._mag = jnp.asarray(mag, dtype=dt)
|
||||
self._eta = jnp.asarray(eta, dtype=dt)
|
||||
self._e2_mag = jnp.asarray(e_mag**2, dtype=dt)
|
||||
self._e2_eta = jnp.asarray(e_eta**2, dtype=dt)
|
||||
|
||||
# Get radius squared
|
||||
r2_xrange = r_xrange**2
|
||||
r2_xrange /= r2_xrange.mean()
|
||||
mu_xrange = dist2distmodulus(r_xrange, Omega_m)
|
||||
|
||||
# Get the stepsize, we need it to be constant for Simpson's rule.
|
||||
dr = np.diff(r_xrange)
|
||||
if not np.all(np.isclose(dr, dr[0], atol=1e-5)):
|
||||
raise ValueError("The radial step size must be constant.")
|
||||
dr = dr[0]
|
||||
|
||||
# Get the various vmapped functions
|
||||
self._f_ptilde_wo_bias = lambda mu, err: calculate_ptilde_wo_bias(mu_xrange, mu, err, r2_xrange, True) # noqa
|
||||
self._f_simps = lambda y: simps(y, dr) # noqa
|
||||
self._f_zobs = lambda beta, Vr, vpec_rad: predict_zobs(r_xrange, beta, Vr, vpec_rad, Omega_m) # noqa
|
||||
|
||||
# Distribution of external velocity components
|
||||
self._Vext = dist.Uniform(-1000, 1000)
|
||||
# Distribution of velocity and density bias parameters
|
||||
self._alpha = dist.LogNormal(*lognorm_mean_std_to_loc_scale(1.0, 0.5)) # noqa
|
||||
self._beta = dist.Normal(1., 0.5)
|
||||
# Distribution of velocity uncertainty
|
||||
self._sigma_v = dist.LogNormal(*lognorm_mean_std_to_loc_scale(150, 100)) # noqa
|
||||
|
||||
# Distribution of Tully-Fisher calibration parameters
|
||||
self._a = dist.Normal(-21., 0.5)
|
||||
self._b = dist.Normal(-5.95, 0.1)
|
||||
self._e_mu = dist.LogNormal(*lognorm_mean_std_to_loc_scale(0.3, 0.1)) # noqa
|
||||
|
||||
def __call__(self, sample_alpha=True):
|
||||
"""
|
||||
The Tully-Fisher NumPyro PV validation model.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
sample_alpha : bool, optional
|
||||
Whether to sample the density bias parameter `alpha`, otherwise
|
||||
it is fixed to 1.
|
||||
"""
|
||||
Vx = numpyro.sample("Vext_x", self._Vext)
|
||||
Vy = numpyro.sample("Vext_y", self._Vext)
|
||||
Vz = numpyro.sample("Vext_z", self._Vext)
|
||||
alpha = numpyro.sample("alpha", self._alpha) if sample_alpha else 1.0
|
||||
beta = numpyro.sample("beta", self._beta)
|
||||
sigma_v = numpyro.sample("sigma_v", self._sigma_v)
|
||||
|
||||
e_mu_intrinsic = numpyro.sample("e_mu_intrinsic", self._e_mu)
|
||||
a = numpyro.sample("a", self._a)
|
||||
b = numpyro.sample("b", self._b)
|
||||
|
||||
Vext_rad = project_Vext(Vx, Vy, Vz, self._RA, self._dec)
|
||||
|
||||
mu = self._mag - (a + b * self._eta)
|
||||
squared_e_mu = (self._e2_mag + b**2 * self._e2_eta
|
||||
+ e_mu_intrinsic**2)
|
||||
|
||||
def scan_body(ll, i):
|
||||
# Calculate p(r) and multiply it by the galaxy bias
|
||||
ptilde = self._f_ptilde_wo_bias(mu[i], squared_e_mu[i])
|
||||
ptilde *= self._los_density[i]**alpha
|
||||
|
||||
# Normalization of p(r)
|
||||
pnorm = self._f_simps(ptilde)
|
||||
|
||||
# Calculate p(z_obs) and multiply it by p(r)
|
||||
zobs_pred = self._f_zobs(beta, Vext_rad[i], self._los_velocity[i])
|
||||
ptilde *= calculate_ll_zobs(self._z_obs[i], zobs_pred, sigma_v)
|
||||
|
||||
return ll + jnp.log(self._f_simps(ptilde) / pnorm), None
|
||||
|
||||
ll = 0.
|
||||
ll, __ = scan(scan_body, ll, jnp.arange(len(self._RA)))
|
||||
|
||||
numpyro.factor("ll", ll)
|
||||
|
||||
|
||||
###############################################################################
|
||||
# Shortcut to create a model #
|
||||
###############################################################################
|
||||
|
||||
|
||||
def get_model(loader, k, zcmb_max=None, verbose=True):
|
||||
"""
|
||||
Get a model and extract the relevant data from the loader.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
loader : DataLoader
|
||||
DataLoader instance.
|
||||
k : int
|
||||
Simulation index.
|
||||
zcmb_max : float, optional
|
||||
Maximum observed redshift in the CMB frame to include.
|
||||
verbose : bool, optional
|
||||
Verbosity flag.
|
||||
|
||||
Returns
|
||||
-------
|
||||
model : NumPyro model
|
||||
"""
|
||||
zcmb_max = np.infty if zcmb_max is None else zcmb_max
|
||||
|
||||
if k > loader.los_density.shape[1]:
|
||||
raise ValueError(f"Simulation index `{k}` out of range.")
|
||||
|
||||
los_overdensity = loader.los_density[:, k, :]
|
||||
los_velocity = loader.los_radial_velocity[:, k, :]
|
||||
kind = loader._catname
|
||||
|
||||
if kind in ["LOSS", "Foundation"]:
|
||||
keys = ["RA", "DEC", "z_CMB", "mB", "x1", "c", "e_mB", "e_x1", "e_c"]
|
||||
RA, dec, zCMB, mB, x1, c, e_mB, e_x1, e_c = (loader.cat[k] for k in keys) # noqa
|
||||
|
||||
mask = (zCMB < zcmb_max)
|
||||
model = SN_PV_validation_model(
|
||||
los_overdensity[mask], los_velocity[mask], RA[mask], dec[mask],
|
||||
zCMB[mask], mB[mask], x1[mask], c[mask], e_mB[mask], e_x1[mask],
|
||||
e_c[mask], loader.rdist, loader._Omega_m)
|
||||
elif kind == "Pantheon+":
|
||||
keys = ["RA", "DEC", "zCMB", "mB", "x1", "c", "biasCor_m_b", "mBERR",
|
||||
"x1ERR", "cERR", "biasCorErr_m_b"]
|
||||
|
||||
RA, dec, zCMB, mB, x1, c, bias_corr_mB, e_mB, e_x1, e_c, e_bias_corr_mB = (loader.cat[k] for k in keys) # noqa
|
||||
mB -= bias_corr_mB
|
||||
e_mB = np.sqrt(e_mB**2 + e_bias_corr_mB**2)
|
||||
|
||||
mask = (zCMB < zcmb_max)
|
||||
model = SN_PV_validation_model(
|
||||
los_overdensity[mask], los_velocity[mask], RA[mask], dec[mask],
|
||||
zCMB[mask], mB[mask], x1[mask], c[mask], e_mB[mask], e_x1[mask],
|
||||
e_c[mask], loader.rdist, loader._Omega_m)
|
||||
elif kind in ["SFI_gals", "2MTF"]:
|
||||
keys = ["RA", "DEC", "z_CMB", "mag", "eta", "e_mag", "e_eta"]
|
||||
RA, dec, zCMB, mag, eta, e_mag, e_eta = (loader.cat[k] for k in keys)
|
||||
|
||||
mask = (zCMB < zcmb_max)
|
||||
if kind == "SFI_gals":
|
||||
mask &= (eta > -0.15) & (eta < 0.2)
|
||||
if verbose:
|
||||
print("Emplyed eta cut for SFI galaxies.", flush=True)
|
||||
model = TF_PV_validation_model(
|
||||
los_overdensity[mask], los_velocity[mask], RA[mask], dec[mask],
|
||||
zCMB[mask], mag[mask], eta[mask], e_mag[mask], e_eta[mask],
|
||||
loader.rdist, loader._Omega_m)
|
||||
else:
|
||||
raise ValueError(f"Catalogue `{kind}` not recognized.")
|
||||
|
||||
if verbose:
|
||||
print(f"Selected {np.sum(mask)}/{len(mask)} galaxies.", flush=True)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
###############################################################################
|
||||
# Maximizing likelihood of a NumPyro model #
|
||||
###############################################################################
|
||||
|
||||
|
||||
def sample_prior(model, seed, sample_alpha, as_dict=False):
|
||||
"""
|
||||
Sample a single set of parameters from the prior of the model.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
model : NumPyro model
|
||||
NumPyro model.
|
||||
seed : int
|
||||
Random seed.
|
||||
sample_alpha : bool
|
||||
Whether to sample the density bias parameter `alpha`.
|
||||
as_dict : bool, optional
|
||||
Whether to return the parameters as a dictionary or a list of
|
||||
parameters.
|
||||
|
||||
Returns
|
||||
-------
|
||||
x, keys : tuple
|
||||
Tuple of parameters and their names. If `as_dict` is True, returns
|
||||
only a dictionary.
|
||||
"""
|
||||
predictive = Predictive(model, num_samples=1)
|
||||
samples = predictive(PRNGKey(seed), sample_alpha=sample_alpha)
|
||||
|
||||
if as_dict:
|
||||
return samples
|
||||
|
||||
keys = list(samples.keys())
|
||||
if "ll" in keys:
|
||||
keys.remove("ll")
|
||||
|
||||
x = np.asarray([samples[key][0] for key in keys])
|
||||
return x, keys
|
||||
|
||||
|
||||
def make_loss(model, keys, sample_alpha=True, to_jit=True):
|
||||
"""
|
||||
Generate a loss function for the NumPyro model, that is the negative
|
||||
log-likelihood. Note that this loss function cannot be automatically
|
||||
differentiated.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
model : NumPyro model
|
||||
NumPyro model.
|
||||
keys : list
|
||||
List of parameter names.
|
||||
sample_alpha : bool, optional
|
||||
Whether to sample the density bias parameter `alpha`.
|
||||
to_jit : bool, optional
|
||||
Whether to JIT the loss function.
|
||||
|
||||
Returns
|
||||
-------
|
||||
loss : function
|
||||
Loss function `f(x)` where `x` is a list of parameters ordered
|
||||
according to `keys`.
|
||||
"""
|
||||
def f(x):
|
||||
samples = {key: x[i] for i, key in enumerate(keys)}
|
||||
|
||||
loss = -util.log_likelihood(
|
||||
model, samples, sample_alpha=sample_alpha)["ll"]
|
||||
|
||||
loss += cond(samples["sigma_v"] > 0, lambda: 0., lambda: jnp.inf)
|
||||
loss += cond(samples["e_mu_intrinsic"] > 0, lambda: 0., lambda: jnp.inf) # noqa
|
||||
|
||||
return cond(jnp.isfinite(loss), lambda: loss, lambda: jnp.inf)
|
||||
|
||||
if to_jit:
|
||||
return jit(f)
|
||||
|
||||
return f
|
||||
|
||||
|
||||
def optimize_model_with_jackknife(loader, k, n_splits=5, sample_alpha=True,
|
||||
get_model_kwargs={}, seed=42):
|
||||
"""
|
||||
Optimize the log-likelihood of a model for `n_splits` jackknifes.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
loader : DataLoader
|
||||
DataLoader instance.
|
||||
k : int
|
||||
Simulation index.
|
||||
n_splits : int, optional
|
||||
Number of jackknife splits.
|
||||
sample_alpha : bool, optional
|
||||
Whether to sample the density bias parameter `alpha`.
|
||||
get_model_kwargs : dict, optional
|
||||
Additional keyword arguments to pass to the `get_model` function.
|
||||
seed : int, optional
|
||||
Random seed.
|
||||
|
||||
Returns
|
||||
-------
|
||||
samples : dict
|
||||
Dictionary of optimized parameters for each jackknife split.
|
||||
stats : dict
|
||||
Dictionary of mean and standard deviation for each parameter.
|
||||
fmin : 1-dimensional array
|
||||
Minimum negative log-likelihood for each jackknife split.
|
||||
logz : 1-dimensional array
|
||||
Log-evidence for each jackknife split.
|
||||
bic : 1-dimensional array
|
||||
Bayesian information criterion for each jackknife split.
|
||||
"""
|
||||
mask = np.zeros(n_splits, dtype=bool)
|
||||
x0 = None
|
||||
|
||||
# Loop over the CV splits.
|
||||
for i in trange(n_splits):
|
||||
loader.make_jackknife_mask(i, n_splits, seed=seed)
|
||||
model = get_model(loader, k, verbose=False, **get_model_kwargs)
|
||||
|
||||
if x0 is None:
|
||||
x0, keys = sample_prior(model, seed, sample_alpha)
|
||||
x = np.full((n_splits, len(x0)), np.nan)
|
||||
fmin = np.full(n_splits, np.nan)
|
||||
logz = np.full(n_splits, np.nan)
|
||||
bic = np.full(n_splits, np.nan)
|
||||
|
||||
loss = make_loss(model, keys, sample_alpha=sample_alpha,
|
||||
to_jit=True)
|
||||
for j in range(100):
|
||||
if np.isfinite(loss(x0)):
|
||||
break
|
||||
x0, __ = sample_prior(model, seed + 1, sample_alpha)
|
||||
else:
|
||||
raise ValueError("Failed to find finite initial loss.")
|
||||
|
||||
else:
|
||||
loss = make_loss(model, keys, sample_alpha=sample_alpha,
|
||||
to_jit=True)
|
||||
|
||||
with catch_warnings():
|
||||
simplefilter("ignore")
|
||||
res = fmin_powell(loss, x0, disp=False)
|
||||
|
||||
if np.all(np.isfinite(res)):
|
||||
x[i] = res
|
||||
mask[i] = True
|
||||
x0 = res
|
||||
fmin[i] = loss(res)
|
||||
|
||||
f_hess = Hessian(loss, method="forward", richardson_terms=1)
|
||||
hess = f_hess(res)
|
||||
D = len(keys)
|
||||
logz[i] = (
|
||||
- fmin[i]
|
||||
+ 0.5 * np.log(np.abs(np.linalg.det(np.linalg.inv(hess))))
|
||||
+ D / 2 * np.log(2 * np.pi))
|
||||
|
||||
bic[i] = len(keys) * np.log(len(loader.cat["RA"])) + 2 * fmin[i]
|
||||
|
||||
samples = {key: x[:, i][mask] for i, key in enumerate(keys)}
|
||||
|
||||
mean = [np.mean(samples[key]) for key in keys]
|
||||
std = [(len(samples[key] - 1) * np.var(samples[key], ddof=0))**0.5
|
||||
for key in keys]
|
||||
stats = {key: (mean[i], std[i]) for i, key in enumerate(keys)}
|
||||
|
||||
return samples, stats, fmin, logz, bic
|
||||
|
|
|
|||
|
|
@ -63,6 +63,8 @@ def simname2Omega_m(simname):
|
|||
Omega_m: float
|
||||
"""
|
||||
d = {"csiborg1": 0.307,
|
||||
"csiborg2_main": 0.3111,
|
||||
"csiborg2_random": 0.3111,
|
||||
"borg1": 0.307,
|
||||
"Carrick2015": 0.3,
|
||||
}
|
||||
|
|
|
|||
223
notebooks/field_velocity_fof_sph.ipynb
Normal file
223
notebooks/field_velocity_fof_sph.ipynb
Normal file
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
|
|
@ -58,7 +58,8 @@ def get_los(catalogue_name, simname, comm):
|
|||
if comm.Get_rank() == 0:
|
||||
folder = "/mnt/extraspace/rstiskalek/catalogs"
|
||||
|
||||
if catalogue_name == "LOSS" or catalogue_name == "Foundation":
|
||||
if catalogue_name in ["LOSS", "Foundation", "SFI_gals", "2MTF",
|
||||
"Pantheon+"]:
|
||||
fpath = join(folder, "PV_compilation_Supranta2019.hdf5")
|
||||
with File(fpath, 'r') as f:
|
||||
grp = f[catalogue_name]
|
||||
|
|
@ -69,18 +70,6 @@ def get_los(catalogue_name, simname, comm):
|
|||
with File(fpath, 'r') as f:
|
||||
RA = f["RA"][:]
|
||||
dec = f["DEC"][:]
|
||||
elif "csiborg1" in catalogue_name:
|
||||
nsim = int(catalogue_name.split("_")[-1])
|
||||
cat = csiborgtools.read.CSiBORG1Catalogue(
|
||||
nsim, bounds={"totmass": (1e13, None)})
|
||||
|
||||
seed = 42
|
||||
gen = np.random.default_rng(seed)
|
||||
mask = gen.choice(len(cat), size=100, replace=False)
|
||||
|
||||
sph_pos = cat["spherical_pos"]
|
||||
RA = sph_pos[mask, 1]
|
||||
dec = sph_pos[mask, 2]
|
||||
else:
|
||||
raise ValueError(f"Unknown field name: `{catalogue_name}`.")
|
||||
|
||||
|
|
@ -122,6 +111,9 @@ def get_field(simname, nsim, kind, MAS, grid):
|
|||
# Open the field reader.
|
||||
if simname == "csiborg1":
|
||||
field_reader = csiborgtools.read.CSiBORG1Field(nsim)
|
||||
elif "csiborg2" in simname:
|
||||
simkind = simname.split("_")[-1]
|
||||
field_reader = csiborgtools.read.CSiBORG2Field(nsim, simkind)
|
||||
elif simname == "Carrick2015":
|
||||
folder = "/mnt/extraspace/rstiskalek/catalogs"
|
||||
warn(f"Using local paths from `{folder}`.", RuntimeWarning)
|
||||
|
|
@ -287,7 +279,7 @@ if __name__ == "__main__":
|
|||
|
||||
rmax = 200
|
||||
dr = 0.5
|
||||
smooth_scales = [0, 2, 4, 6]
|
||||
smooth_scales = [0, 2]
|
||||
|
||||
comm = MPI.COMM_WORLD
|
||||
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
||||
|
|
|
|||
|
|
@ -1,14 +1,13 @@
|
|||
nthreads=11
|
||||
memory=64
|
||||
nthreads=4
|
||||
memory=32
|
||||
on_login=${1}
|
||||
queue="berg"
|
||||
env="/mnt/users/rstiskalek/csiborgtools/venv_csiborg/bin/python"
|
||||
file="field_los.py"
|
||||
|
||||
catalogue="LOSS"
|
||||
# catalogue="csiborg1_9844"
|
||||
catalogue=${2}
|
||||
nsims="-1"
|
||||
simname="csiborg1"
|
||||
simname="csiborg2_main"
|
||||
MAS="SPH"
|
||||
grid=1024
|
||||
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ import jax
|
|||
import numpy as np
|
||||
from h5py import File
|
||||
from mpi4py import MPI
|
||||
from numpyro.infer import MCMC, NUTS
|
||||
from numpyro.infer import MCMC, NUTS, init_to_sample
|
||||
from taskmaster import work_delegation # noqa
|
||||
|
||||
|
||||
|
|
@ -49,7 +49,7 @@ def get_model(args, nsim_iterator):
|
|||
if args.catalogue == "A2":
|
||||
fpath = join(folder, "A2.h5")
|
||||
elif args.catalogue == "LOSS" or args.catalogue == "Foundation":
|
||||
raise NotImplementedError("To be implemented..")
|
||||
fpath = join(folder, "PV_compilation_Supranta2019.hdf5")
|
||||
else:
|
||||
raise ValueError(f"Unknown catalogue: `{args.catalogue}`.")
|
||||
|
||||
|
|
@ -61,16 +61,48 @@ def get_model(args, nsim_iterator):
|
|||
los_overdensity = loader.los_density[:, nsim_iterator, :]
|
||||
los_velocity = loader.los_radial_velocity[:, nsim_iterator, :]
|
||||
|
||||
RA = loader.cat["RA"]
|
||||
dec = loader.cat["DEC"]
|
||||
z_obs = loader.cat["z_obs"]
|
||||
if args.catalogue == "A2":
|
||||
RA = loader.cat["RA"]
|
||||
dec = loader.cat["DEC"]
|
||||
z_obs = loader.cat["z_obs"]
|
||||
|
||||
r_hMpc = loader.cat["r_hMpc"]
|
||||
e_r_hMpc = loader.cat["e_rhMpc"]
|
||||
r_hMpc = loader.cat["r_hMpc"]
|
||||
e_r_hMpc = loader.cat["e_rhMpc"]
|
||||
|
||||
return csiborgtools.flow.SD_PV_validation_model(
|
||||
los_overdensity, los_velocity, RA, dec, z_obs, r_hMpc, e_r_hMpc,
|
||||
loader.rdist, Omega_m)
|
||||
return csiborgtools.flow.SD_PV_validation_model(
|
||||
los_overdensity, los_velocity, RA, dec, z_obs, r_hMpc, e_r_hMpc,
|
||||
loader.rdist, Omega_m)
|
||||
elif args.catalogue == "LOSS" or args.catalogue == "Foundation":
|
||||
RA = loader.cat["RA"]
|
||||
dec = loader.cat["DEC"]
|
||||
zCMB = loader.cat["z_CMB"]
|
||||
|
||||
mB = loader.cat["mB"]
|
||||
x1 = loader.cat["x1"]
|
||||
c = loader.cat["c"]
|
||||
|
||||
e_mB = loader.cat["e_mB"]
|
||||
e_x1 = loader.cat["e_x1"]
|
||||
e_c = loader.cat["e_c"]
|
||||
|
||||
return csiborgtools.flow.SN_PV_validation_model(
|
||||
los_overdensity, los_velocity, RA, dec, zCMB, mB, x1, c,
|
||||
e_mB, e_x1, e_c, loader.rdist, Omega_m)
|
||||
elif args.catalogue in ["SFI_gals", "2MTF"]:
|
||||
RA = loader.cat["RA"]
|
||||
dec = loader.cat["DEC"]
|
||||
zCMB = loader.cat["z_CMB"]
|
||||
|
||||
mag = loader.cat["mag"]
|
||||
eta = loader.cat["eta"]
|
||||
e_mag = loader.cat["e_mag"]
|
||||
e_eta = loader.cat["e_eta"]
|
||||
|
||||
return csiborgtools.flow.TF_PV_validation_model(
|
||||
los_overdensity, los_velocity, RA, dec, zCMB, mag, eta,
|
||||
e_mag, e_eta, loader.rdist, Omega_m)
|
||||
else:
|
||||
raise ValueError(f"Unknown catalogue: `{args.catalogue}`.")
|
||||
|
||||
|
||||
def run_model(model, nsteps, nchains, nsim, dump_folder, show_progress=True):
|
||||
|
|
@ -96,8 +128,8 @@ def run_model(model, nsteps, nchains, nsim, dump_folder, show_progress=True):
|
|||
-------
|
||||
None
|
||||
"""
|
||||
nuts_kernel = NUTS(model)
|
||||
mcmc = MCMC(nuts_kernel, num_warmup=nsteps // 2, num_samples=nsteps // 2,
|
||||
nuts_kernel = NUTS(model, init_strategy=init_to_sample)
|
||||
mcmc = MCMC(nuts_kernel, num_warmup=500, num_samples=nsteps,
|
||||
chain_method="sequential", num_chains=nchains,
|
||||
progress_bar=show_progress)
|
||||
rng_key = jax.random.PRNGKey(42)
|
||||
|
|
@ -185,8 +217,8 @@ if __name__ == "__main__":
|
|||
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
||||
nsims = paths.get_ics(args.simname)
|
||||
|
||||
nsteps = 5000
|
||||
nchains = 4
|
||||
nsteps = 2000
|
||||
nchains = 2
|
||||
|
||||
# Create the dumping folder.
|
||||
if comm.Get_rank() == 0:
|
||||
|
|
|
|||
|
|
@ -7,8 +7,8 @@ queue="berg"
|
|||
env="/mnt/users/rstiskalek/csiborgtools/venv_csiborg/bin/python"
|
||||
file="flow_validation.py"
|
||||
|
||||
catalogue="A2"
|
||||
simname="Carrick2015"
|
||||
catalogue="Foundation"
|
||||
simname="csiborg2_random"
|
||||
|
||||
|
||||
pythoncm="$env $file --catalogue $catalogue --simname $simname --ksmooth $ksmooth"
|
||||
|
|
|
|||
Loading…
Reference in a new issue