csiborgtools/notebooks/diagnostic/powerspectrum_H0.ipynb
Richard Stiskalek ee222cd010
Fix overlap runs (#125)
* Update nb

* Update script

* Update script

* Rename

* Update script

* Update script

* Remove warning

* Ignore minors when extracting MAH

* Fix paths bug

* Move notebooks

* Move files

* Rename and delete things

* Rename file

* Move file

* Rename things

* Remove old print statement

* Add basic MAH plot

* Add random MAH path

* Output snapshot numbers

* Add MAH random extraction

* Fix redshift bug

* Edit script

* Add extracting random MAH

* Little updates

* Add CB2 redshift

* Add some caching

* Add diagnostic plots

* Add caching

* Minor updates

* Update nb

* Update notebook

* Update script

* Add Sorce randoms

* Add CB2 varysmall

* Update nb

* Update nb

* Update nb

* Use catalogue HMF

* Move definition of radec2galactic

* Update nb

* Update import

* Update import

* Add galatic coords to catalogues

* Update nb
2024-04-08 11:23:21 +02:00

55 KiB

Density & velocity fields alond a LOS

In [128]:
# Copyright (C) 2024 Richard Stiskalek
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 3 of the License, or (at your
# option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General
# Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
import numpy as np
import matplotlib.pyplot as plt
import jax
from jax import numpy as jnp
from numpyro.infer import MCMC, NUTS, init_to_median

import csiborgtools


%load_ext autoreload
%autoreload 2
%matplotlib inline

paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

LOS density & radial velocity plots

In [129]:
fpath = "/mnt/extraspace/rstiskalek/catalogs/PV_compilation.hdf5"

loader_carrick = csiborgtools.flow.DataLoader("Carrick2015", [0], "Pantheon+", fpath, paths, ksmooth=0, )
# loaders_csiborg2X = [csiborgtools.flow.DataLoader("csiborg2X", i, "LOSS", fpath, paths, ksmooth=1, verbose=False) for i in range(20)]
# loaders_csiborg2 = [csiborgtools.flow.DataLoader("csiborg2_main", i, "LOSS", fpath, paths, ksmooth=1, verbose=False) for i in range(20)]

# loader_CF4 = csiborgtools.flow.DataLoader("CF4gp", [0], "LOSS", fpath, paths, ksmooth=0, )
# loader_lilow = csiborgtools.flow.DataLoader("Lilow2024", [0], "LOSS", fpath, paths, ksmooth=0, )
2024-07-17 10:04:21.726937:   reading the catalogue,
2024-07-17 10:04:21.744932:   reading the interpolated field,
2024-07-17 10:04:21.770393:   calculating the radial velocity.
/mnt/users/rstiskalek/csiborgtools/csiborgtools/flow/flow_model.py:91: UserWarning: The number of radial steps is even. Skipping the first step at 0.0 because Simpson's rule requires an odd number of steps.
  warn(f"The number of radial steps is even. Skipping the first "
In [130]:
# ks = [i for i in range(50)]
# ks = [30, 50,]
ks = np.random.choice(50, 2, replace=False)

for k in ks:
    fig, axs = plt.subplots(2, 1, figsize=(7, 7), sharex=True)
    fig.subplots_adjust(wspace=0)
    cols = plt.rcParams['axes.prop_cycle'].by_key()['color']

    # # CSiBORG2
    # x = loaders_csiborg2X[0].rdist
    # y = np.asarray([loaders_csiborg2[i].los_density[k, :] for i in range(len(loaders_csiborg2X))])
    # ylow, ymed, yhigh = np.percentile(y, [16, 50, 84], axis=0)
    # axs[0].fill_between(x, ylow, yhigh, color=cols[0], alpha=0.25)
    # axs[0].plot(x, ymed, color=cols[0], label="CSiBORG2")

    # y = np.asarray([loaders_csiborg2[i].los_radial_velocity[k, :] for i in range(len(loaders_csiborg2X))])
    # ylow, ymed, yhigh = np.percentile(y, [16, 50, 84], axis=0)
    # axs[1].fill_between(x, ylow, yhigh, color=cols[0], alpha=0.25)
    # axs[1].plot(x, ymed, color=cols[0], label="CSiBORG2")

    # # CSiBORG2X
    # x = loaders_csiborg2X[0].rdist
    # y = np.asarray([loaders_csiborg2X[i].los_density[k, :] for i in range(len(loaders_csiborg2X))])
    # ylow, ymed, yhigh = np.percentile(y, [16, 50, 84], axis=0)
    # axs[0].fill_between(x, ylow, yhigh, color=cols[1], alpha=0.25)
    # axs[0].plot(x, ymed, color=cols[1], label="CSiBORG2X")

    # y = np.asarray([loaders_csiborg2X[i].los_radial_velocity[k, :] for i in range(len(loaders_csiborg2X))])
    # ylow, ymed, yhigh = np.percentile(y, [16, 50, 84], axis=0)
    # axs[1].fill_between(x, ylow, yhigh, color=cols[1], alpha=0.25)
    # axs[1].plot(x, ymed, color=cols[1], label="CSiBORG2X")

    # Plot Carrick+2015
    axs[0].plot(loader_carrick.rdist, loader_carrick.los_density[0, k, :], color="red", label="Carrick+2015")
    axs[1].plot(loader_carrick.rdist, loader_carrick.los_radial_velocity[0, k, :] * 0.43, color="red")
    axs[1].axvline(loader_carrick._rmax[0, k], c="black", linestyle="--")

    # # Plot CF4
    # c = cols[4]
    # axs[0].plot(loader_CF4.rdist, loader_CF4.los_density[0, k, :], color=c, label="CF4")
    # axs[1].plot(loader_CF4.rdist, loader_CF4.los_radial_velocity[0, k, :], color=c)

    # # Plot Lilow2024
    # c = cols[5]
    # axs[0].plot(loader_lilow.rdist, loader_lilow.los_density[0, k, :], color=c, label="Lilow+2024")
    # axs[1].plot(loader_lilow.rdist, loader_lilow.los_radial_velocity[0, k, :], color=c)
    axs[1].axhline(0, color="black", linestyle="--")


    axs[1].set_xlabel(r"$r ~ [\mathrm{Mpc} / h]$")
    axs[0].set_ylabel(r"$\rho_{\rm LOS} / \langle \rho_{\rm matter} \rangle$")
    axs[1].set_ylabel(r"$v_{\rm LOS} ~ [\mathrm{km/s}]$")
    axs[0].set_yscale("log")

    axs[0].legend(loc="upper right")
    # axs[0].set_xlim(0, 200)

    fig.tight_layout(w_pad=0, h_pad=0)
    fig.savefig(f"../../plots/LOSS_los_{k}.png", dpi=500, bbox_inches="tight")

    fig.show()
No description has been provided for this image
No description has been provided for this image

Test running a model

In [84]:
fpath_data = "/mnt/extraspace/rstiskalek/catalogs/PV_compilation.hdf5"

simname = "Carrick2015"
catalogue = "LOSS"
loader = csiborgtools.flow.DataLoader(simname, [0, 0], catalogue, fpath_data, paths, ksmooth=0, )

SN_hyperparams = {"e_mu_mean": 0.1, "e_mu_std": 0.05,
                  "mag_cal_mean": -18.25, "mag_cal_std": 0.5,
                  "alpha_cal_mean": 0.148, "alpha_cal_std": 0.05,
                  "beta_cal_mean": 3.112, "beta_cal_std": 1.0,
                  }
calibration_hyperparams = {"Vext_std": 250,
                           "alpha_mean": 1.0, "alpha_std": 0.5,
                           "beta_mean": 1.0, "beta_std": 0.5,
                           "sigma_v_mean": 150., "sigma_v_std": 100.,
                           "sample_alpha": True, "sample_beta": True,
                           }
get_model_kwargs = {"zcmb_max": 0.05}
2024-06-29 19:40:25.229961:   reading the catalogue,
2024-06-29 19:40:25.243502:   reading the interpolated field,
2024-06-29 19:40:25.261423:   calculating the radial velocity.
/mnt/users/rstiskalek/csiborgtools/csiborgtools/flow/flow_model.py:91: UserWarning: The number of radial steps is even. Skipping the first step at 0.0 because Simpson's rule requires an odd number of steps.
  warn(f"The number of radial steps is even. Skipping the first "

Running HMC

In [85]:
model = csiborgtools.flow.get_model(loader, **get_model_kwargs)
model_kwargs = {"distmod_hyperparams": SN_hyperparams, "calibration_hyperparams": calibration_hyperparams,}
Selected 50/50 galaxies.
In [86]:
kernel = NUTS(model, init_strategy=init_to_median(num_samples=100))
mcmc = MCMC(kernel, num_warmup=500, num_samples=500)

rng_key = jax.random.PRNGKey(5)
mcmc.run(rng_key, extra_fields=("potential_energy",), **model_kwargs)
mcmc.print_summary()
samples = mcmc.get_samples()
sample: 100%|██████████| 1000/1000 [02:10<00:00,  7.68it/s, 7 steps of size 4.49e-01. acc. prob=0.90]  
                 mean       std    median      5.0%     95.0%     n_eff     r_hat
    Vext[0]     -3.71     69.92     -3.04   -123.73    103.87    469.72      1.00
    Vext[1]    -27.47     95.52    -30.48   -151.20    172.63    308.02      1.00
    Vext[2]    -59.27    131.26    -57.79   -273.64    137.55    456.29      1.00
      alpha      1.09      0.38      1.10      0.50      1.69    400.05      1.00
  alpha_cal      0.13      0.03      0.13      0.09      0.17    558.81      1.00
       beta      0.43      0.11      0.44      0.27      0.61    341.86      1.00
   beta_cal      3.54      0.18      3.54      3.23      3.81    606.77      1.00
       e_mu      0.08      0.03      0.08      0.04      0.12    330.71      1.00
    mag_cal    -18.19      0.04    -18.19    -18.25    -18.13    389.94      1.00
    sigma_v    176.93     52.05    169.93    102.74    267.56    315.30      1.00

Number of divergences: 0
In [ ]: