mirror of
https://github.com/Richard-Sti/csiborgtools_public.git
synced 2025-05-13 22:21:12 +00:00
More LOS (#137)
* Switch to CB2 * Update for extrapolation * Add 'nan' extrapolation * Update nb * Update submits * Add Rmax to the models * Update nb * Add print statement * Update script settings * Update flow model to new method * Update printing * Update path * Update so that it works * Update nb * Update submit * Add Rmin for hollow bulk flows * Update script * Update script * Update scripts back * Update scripts back * Fix normalization bug * Update script * pep8
This commit is contained in:
Richard Stiskalek
GitHub
parent
73ffffb826
commit
8d49aa071b
15 changed files with 379 additions and 198 deletions
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@ -26,10 +26,11 @@ import csiborgtools
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import numpy as np
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from astropy import units as u
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from astropy.coordinates import SkyCoord
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from astropy.io import fits
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from h5py import File
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from mpi4py import MPI
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from numba import jit
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from taskmaster import work_delegation # noqa
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from astropy.io import fits
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from utils import get_nsims
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@ -79,7 +80,7 @@ def get_los(catalogue_name, simname, comm):
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RA = f["RA"][:]
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dec = f["DEC"][:]
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elif catalogue_name == "UPGLADE":
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fname = "/mnt/users/rstiskalek/csiborgtools/data/upglade_z_0p05_all_PROCESSED.h5" # noqa
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fname = "/mnt/users/rstiskalek/csiborgtools/data/upglade_all_z0p05_new_PROCESSED.h5" # noqa
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with File(fname, 'r') as f:
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RA = f["RA"][:]
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dec = f["DEC"][:]
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@ -242,6 +243,7 @@ def combine_from_simulations(catalogue_name, simname, nsims, outfolder,
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f_out.create_dataset(f"rdist_{nsim}", data=f["rdist"][:])
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f_out.create_dataset(f"density_{nsim}", data=f["density"][:])
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f_out.create_dataset(f"velocity_{nsim}", data=f["velocity"][:])
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f_out.create_dataset(f"rmax_{nsim}", data=f["rmax"][:])
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# Remove the temporary file.
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remove(fname)
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@ -256,6 +258,30 @@ def combine_from_simulations(catalogue_name, simname, nsims, outfolder,
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# Main interpolating function #
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###############################################################################
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@jit(nopython=True)
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def find_index_of_first_nan(y):
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for n in range(1, len(y)):
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if np.isnan(y[n]):
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return n
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return None
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def replace_nan_with_last_finite(x, y, apply_decay):
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n = find_index_of_first_nan(y)
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if n is None:
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return y, x[-1]
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y[n:] = y[n-1]
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rmax = x[n-1]
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if apply_decay:
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# Optionally aply 1 / r decay
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y[n:] *= rmax / x[n:]
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return y, rmax
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def interpolate_field(pos, simname, nsim, MAS, grid, dump_folder, rmax,
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dr, smooth_scales, verbose=False):
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@ -300,6 +326,14 @@ def interpolate_field(pos, simname, nsim, MAS, grid, dump_folder, rmax,
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smooth_scales=smooth_scales, verbose=verbose,
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interpolation_method="linear")
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rmax_density = np.full((len(pos), len(smooth_scales)), np.nan)
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for i in range(len(pos)):
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for j in range(len(smooth_scales)):
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y, current_rmax = replace_nan_with_last_finite(rdist, finterp[i, :, j], False) # noqa
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finterp[i, :, j] = y
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if current_rmax is not None:
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rmax_density[i, j] = current_rmax
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print(f"Writing temporary file `{fname_out}`.")
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with File(fname_out, 'w') as f:
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f.create_dataset("rdist", data=rdist)
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@ -318,8 +352,20 @@ def interpolate_field(pos, simname, nsim, MAS, grid, dump_folder, rmax,
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smooth_scales=smooth_scales, verbose=verbose,
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interpolation_method="linear")
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rmax_velocity = np.full((3, len(pos), len(smooth_scales)), np.nan)
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for k in range(3):
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for i in range(len(pos)):
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for j in range(len(smooth_scales)):
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y, current_rmax = replace_nan_with_last_finite(rdist, finterp[k][i, :, j], True) # noqa
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finterp[k][i, :, j] = y
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if current_rmax is not None:
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rmax_velocity[k, i, j] = current_rmax
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rmax_velocity = np.min(rmax_velocity, axis=0)
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rmax = np.minimum(rmax_density, rmax_velocity)
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with File(fname_out, 'a') as f:
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f.create_dataset("velocity", data=finterp)
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f.create_dataset("rmax", data=rmax)
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###############################################################################
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@ -339,8 +385,8 @@ if __name__ == "__main__":
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parser.add_argument("--grid", type=int, help="Grid resolution.")
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args = parser.parse_args()
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rmax = 200
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dr = 0.25
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rmax = 300
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dr = 0.5
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smooth_scales = [0]
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comm = MPI.COMM_WORLD
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@ -11,8 +11,7 @@ MAS="SPH"
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grid=1024
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# for catalogue in "LOSS" "Foundation" "Pantheon+" "2MTF" "SFI_gals"; do
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for catalogue in "LOSS"; do
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for catalogue in "UPGLADE"; do
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# for catalogue in "Foundation"; do
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pythoncm="$env $file --catalogue $catalogue --nsims $nsims --simname $simname --MAS $MAS --grid $grid"
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if [ $on_login -eq 1 ]; then
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@ -100,6 +100,8 @@ def get_model(paths, get_model_kwargs, verbose=True):
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ARGS.catalogue, fpath, paths,
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ksmooth=ARGS.ksmooth)
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print(f"\n{'Num. radial steps':<20} {len(loader.rdist)}\n", flush=True)
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return csiborgtools.flow.get_model(loader, **get_model_kwargs)
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@ -227,8 +229,6 @@ if __name__ == "__main__":
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nsteps = 5000
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nburn = 1000
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zcmb_max = 0.06
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sample_alpha = True
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sample_beta = True
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calculate_evidence = False
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nchains_harmonic = 10
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num_epochs = 30
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@ -237,7 +237,6 @@ if __name__ == "__main__":
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raise ValueError("The number of steps must be divisible by the number of chains.") # noqa
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main_params = {"nsteps": nsteps, "nburn": nburn, "zcmb_max": zcmb_max,
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"sample_alpha": sample_alpha, "sample_beta": sample_beta,
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"calculate_evidence": calculate_evidence,
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"nchains_harmonic": nchains_harmonic,
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"num_epochs": num_epochs}
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@ -247,10 +246,11 @@ if __name__ == "__main__":
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"Vmono_min": -1000, "Vmono_max": 1000,
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"alpha_min": -1.0, "alpha_max": 3.0,
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"beta_min": -1.0, "beta_max": 3.0,
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"sigma_v_min": 5.0, "sigma_v_max": 750.,
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"sample_Vmono": True,
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"sample_alpha": sample_alpha,
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"sample_beta": sample_beta,
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"sigma_v_min": 1.0, "sigma_v_max": 750.,
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"sample_Vmono": False,
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"sample_alpha": False,
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"sample_beta": True,
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"sample_sigma_v_ext": False,
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}
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print_variables(
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calibration_hyperparams.keys(), calibration_hyperparams.values())
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@ -280,5 +280,6 @@ if __name__ == "__main__":
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get_model_kwargs = {"zcmb_max": zcmb_max}
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model = get_model(paths, get_model_kwargs, )
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run_model(model, nsteps, nburn, model_kwargs, out_folder, sample_beta,
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calculate_evidence, nchains_harmonic, num_epochs, kwargs_print)
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run_model(model, nsteps, nburn, model_kwargs, out_folder,
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calibration_hyperparams["sample_beta"], calculate_evidence,
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nchains_harmonic, num_epochs, kwargs_print)
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@ -19,9 +19,9 @@ fi
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# Submit a job for each combination of simname, catalogue, ksim
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# for simname in "Lilow2024" "CF4" "CF4gp" "csiborg1" "csiborg2_main" "csiborg2X"; do
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for simname in "csiborg2X"; do
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for simname in "Carrick2015"; do
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# for simname in "csiborg1" "csiborg2_main" "csiborg2X"; do
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for catalogue in "Pantheon+"; do
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for catalogue in "Pantheon+_zSN"; do
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# for catalogue in "2MTF"; do
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# for ksim in 0 1 2; do
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# for ksim in 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20; do
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@ -21,19 +21,21 @@ from tqdm import tqdm
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if __name__ == "__main__":
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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simname = "csiborg1"
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simname = "csiborg2_random"
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nsims = paths.get_ics(simname)
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print(f"Number of simulations: {nsims}.")
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fname_out = f"/mnt/users/rstiskalek/csiborgtools/data/halos_{simname}.hdf5"
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fname_out = f"/mnt/users/rstiskalek/csiborgtools/data/random_halos_{simname}.hdf5" # noqa
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print(f"Writing to `{fname_out}`.")
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with File(fname_out, 'w') as f:
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for nsim in tqdm(nsims, desc="Simulations"):
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grp = f.create_group(f"sim_{nsim}")
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cat = csiborgtools.read.CSiBORG1Catalogue(nsim, paths)
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# cat = csiborgtools.read.CSiBORG1Catalogue(nsim, paths)
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cat = csiborgtools.read.CSiBORG2Catalogue(
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nsim, 99, "random", paths, )
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grp["pos"] = cat["cartesian_pos"]
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grp["totmass"] = cat["totmass"]
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@ -23,6 +23,7 @@ from os.path import join
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import csiborgtools
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import numpy as np
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from csiborgtools import fprint
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from h5py import File
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from mpi4py import MPI
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from taskmaster import work_delegation # noqa
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@ -35,27 +36,35 @@ def t():
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return datetime.now().strftime("%H:%M:%S")
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def load_calibration(catalogue, simname, nsim, ksmooth, verbose=False):
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def load_calibration(catalogue, simname, ksmooth, sample_beta,
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verbose=False):
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"""Load the pre-computed calibration samples."""
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fname = f"/mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/flow_samples_{catalogue}_{simname}_smooth_{ksmooth}.hdf5" # noqa
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keys = ["Vext_x", "Vext_y", "Vext_z", "alpha", "beta", "sigma_v"]
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fname = f"/mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_{simname}_{catalogue}_ksmooth{ksmooth}.hdf5" # noqa
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if sample_beta:
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fname = fname.replace(".hdf5", "_sample_beta.hdf5")
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keys = ["Vext", "sigma_v", "alpha", "beta"]
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calibration_samples = {}
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with File(fname, 'r') as f:
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for key in keys:
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for n, key in enumerate(keys):
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# In case alpha wasn't sampled just set to 1
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if key == "alpha" and "alpha" not in f["samples"].keys():
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calibration_samples[key] = np.full_like(
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calibration_samples["sigma_v"], 1.0)
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continue
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# NOTE: here the posterior samples are down-sampled
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calibration_samples[key] = f[f"sim_{nsim}/{key}"][:][::10]
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calibration_samples[key] = f[f"samples/{key}"][:][::10]
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if verbose:
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k = list(calibration_samples.keys())[0]
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nsamples = len(calibration_samples[k])
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print(f"{t()}: found {nsamples} calibration posterior samples.",
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flush=True)
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if n == 0:
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num_samples_original = len(f[f"samples/{key}"])
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num_samples_final = len(calibration_samples[key])
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fprint(f"downsampling calibration samples from {num_samples_original} to {num_samples_final}.", verbose=verbose) # noqa
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return calibration_samples
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def main(loader, model, indxs, fdir, fname, num_split, verbose):
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def main(loader, nsim, model, indxs, fdir, fname, num_split, verbose):
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out = np.full(
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len(indxs), np.nan,
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dtype=[("mean_zcosmo", float), ("std_zcosmo", float)])
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@ -65,7 +74,7 @@ def main(loader, model, indxs, fdir, fname, num_split, verbose):
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disable=not verbose)):
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x, y = model.posterior_zcosmo(
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loader.cat["zcmb"][n], loader.cat["RA"][n], loader.cat["DEC"][n],
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loader.los_density[n], loader.los_radial_velocity[n],
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loader.los_density[nsim, n], loader.los_radial_velocity[nsim, n],
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extra_sigma_v=loader.cat["e_zcmb"][n] * SPEED_OF_LIGHT,
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verbose=False)
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@ -98,7 +107,7 @@ if __name__ == "__main__":
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# Galaxy sample parameters
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catalogue = "UPGLADE"
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fpath_data = "/mnt/users/rstiskalek/csiborgtools/data/upglade_z_0p05_all_PROCESSED.h5" # noqa
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fpath_data = "/mnt/users/rstiskalek/csiborgtools/data/upglade_all_z0p05_new_PROCESSED.h5" # noqa
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# Number of splits for MPI
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nsplits = 1000
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@ -112,12 +121,14 @@ if __name__ == "__main__":
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simname, nsim, catalogue, fpath_data, paths, ksmooth=ksmooth,
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verbose=rank == 0)
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calibration_samples = load_calibration(
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catalogue_calibration, simname, nsim, ksmooth, verbose=rank == 0)
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catalogue_calibration, simname, ksmooth, sample_beta=True,
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verbose=rank == 0)
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model = csiborgtools.flow.Observed2CosmologicalRedshift(
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calibration_samples, loader.rdist, loader._Omega_m)
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if rank == 0:
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print(f"{t()}: the catalogue size is {loader.cat['zcmb'].size}.")
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print(f"{t()}: loaded calibration samples and model.", flush=True)
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fprint(f"catalogue size is {loader.cat['zcmb'].size}.", verbose=rank == 0)
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fprint("loaded calibration samples and model.", verbose=rank == 0)
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# Decide how to split up the job
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if rank == 0:
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@ -131,7 +142,8 @@ if __name__ == "__main__":
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# Process all splits with MPI, the rank 0 delegates the jobs.
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def main_wrapper(n):
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main(loader, model, split_indxs[n], fdir, fname, n, verbose=size == 1)
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main(loader, nsim, model, split_indxs[n], fdir, fname, n,
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verbose=size == 1)
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comm.Barrier()
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work_delegation(
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@ -1,6 +1,6 @@
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nthreads=${1}
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on_login=${2}
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memory=4
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memory=12
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queue="redwood"
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env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_csiborg/bin/python"
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file="post_upglade.py"
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@ -15,6 +15,9 @@
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"""
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A script to calculate the bulk flow in Quijote simulations from either
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particles or FoF haloes and to also save the resulting smaller halo catalogues.
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If `Rmin > 0` the bulk flows computed from projected radial velocities are
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wrong, but the 3D volume average bulk flows are still correct.
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"""
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from datetime import datetime
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from os.path import join
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@ -70,7 +73,7 @@ def volume_bulk_flow(rdist, mass, vel, distances):
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###############################################################################
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def main(nsim, folder, fname_basis, Rmax, subtract_observer_velocity,
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def main(nsim, folder, fname_basis, Rmin, Rmax, subtract_observer_velocity,
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verbose=True):
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boxsize = csiborgtools.simname2boxsize("quijote")
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observers = csiborgtools.read.fiducial_observers(boxsize, Rmax)
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@ -100,6 +103,11 @@ def main(nsim, folder, fname_basis, Rmax, subtract_observer_velocity,
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return_distance=True, sort_results=True)
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rdist_part, indxs = rdist_part[0], indxs[0]
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# And only the ones that are above Rmin
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mask = rdist_part > Rmin
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rdist_part = rdist_part[mask]
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indxs = indxs[mask]
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part_pos_current = part_pos[indxs] - observers[i]
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part_vel_current = part_vel[indxs]
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# Quijote particle masses are all equal
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@ -110,13 +118,16 @@ def main(nsim, folder, fname_basis, Rmax, subtract_observer_velocity,
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np.asarray(observers[i]).reshape(1, -1), Rmax,
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return_distance=True, sort_results=True)
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rdist_halo, indxs = rdist_halo[0], indxs[0]
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mask = rdist_halo > Rmin
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rdist_halo = rdist_halo[mask]
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indxs = indxs[mask]
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halo_pos_current = halo_pos[indxs] - observers[i]
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halo_vel_current = halo_vel[indxs]
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halo_mass_current = halo_mass[indxs]
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# Subtract the observer velocity
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rscale = 0.5 # Mpc / h
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rscale = 2.0 # Mpc / h
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weights = np.exp(-0.5 * (rdist_part / rscale)**2)
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obs_vel_x = np.average(part_vel_current[:, 0], weights=weights)
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obs_vel_y = np.average(part_vel_current[:, 1], weights=weights)
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@ -183,6 +194,7 @@ def main(nsim, folder, fname_basis, Rmax, subtract_observer_velocity,
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if __name__ == "__main__":
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Rmin = 0
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Rmax = 150
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subtract_observer_velocity = True
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||||
folder = "/mnt/extraspace/rstiskalek/quijote/BulkFlow_fiducial"
|
||||
|
|
@ -195,7 +207,7 @@ if __name__ == "__main__":
|
|||
nsims = list(paths.get_ics("quijote"))
|
||||
|
||||
def main_wrapper(nsim):
|
||||
main(nsim, folder, fname_basis, Rmax, subtract_observer_velocity,
|
||||
main(nsim, folder, fname_basis, Rmin, Rmax, subtract_observer_velocity,
|
||||
verbose=rank == 0)
|
||||
|
||||
if rank == 0:
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
nthreads=20
|
||||
nthreads=12
|
||||
memory=24
|
||||
on_login=0
|
||||
queue="berg"
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue