mirror of
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Add better diagnostics & plotting (#67)
* Add caching functions * Add limts * Add new mass runs * Update .gitignore * Edit which CDFs are loaded * Stop saving cross hindxs * Change dist to half precision * New nearest path * Add neighbour counting * Add neighbour kwargs * Update work in progress * Add new counting * Add how dist is built * Collect dist to 1 file * Update reading routine * Delete Quijote files * Remove file * Back to float32 * Fix bugs * Rename utils * Remove neighbuor kwargs * Rename file * Fix bug * Rename plt utils * Change where nghb kwargs from * line length * Remove old notebooks * Move survey * Add white space * Update TODO * Update CDF calculation * Update temporarily plotting * Merge branch 'add_diagnostics' of github.com:Richard-Sti/csiborgtools into add_diagnostics * Start adding documentation to plotting * Remove comments * Better code documentation * Some work on tidal tensor * Better plotting * Add comment * Remove nb * Remove comment * Add documentation * Update plotting * Update submission * Update KL vs KS plots * Update the plotting routine * Update plotting * Update plotting routines
This commit is contained in:
Richard Stiskalek
GitHub
parent
004d9629a2
commit
ccbbbd24b4
20 changed files with 1075 additions and 32121 deletions
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@ -21,7 +21,7 @@ import numpy
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import healpy
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import scienceplots # noqa
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import utils
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import plt_utils
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from cache_to_disk import cache_to_disk, delete_disk_caches_for_function # noqa
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from tqdm import tqdm
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@ -35,7 +35,16 @@ except ModuleNotFoundError:
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def open_csiborg(nsim):
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"""
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Open a CSiBORG halo catalogue.
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Open a CSiBORG halo catalogue. Applies mass and distance selection.
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Parameters
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----------
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nsim : int
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Simulation index.
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Returns
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-------
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cat : csiborgtools.read.HaloCatalogue
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"""
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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bounds = {"totpartmass": (None, None), "dist": (0, 155/0.705)}
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@ -43,6 +52,20 @@ def open_csiborg(nsim):
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def open_quijote(nsim, nobs=None):
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"""
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Open a Quijote halo catalogue. Applies mass and distance selection.
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Parameters
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----------
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nsim : int
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Simulation index.
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nobs : int, optional
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Fiducial observer index.
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Returns
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-------
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cat : csiborgtools.read.QuijoteHaloCatalogue
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"""
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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cat = csiborgtools.read.QuijoteHaloCatalogue(nsim, paths, nsnap=4)
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if nobs is not None:
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@ -51,10 +74,24 @@ def open_quijote(nsim, nobs=None):
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def plot_mass_vs_ncells(nsim, pdf=False):
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"""
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Plot the halo mass vs. number of occupied cells in the initial snapshot.
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Parameters
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----------
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nsim : int
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Simulation index.
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pdf : bool, optional
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Whether to save the figure as a PDF file.
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Returns
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-------
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None
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"""
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cat = open_csiborg(nsim)
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mpart = 4.38304044e+09
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with plt.style.context(utils.mplstyle):
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with plt.style.context(plt_utils.mplstyle):
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plt.figure()
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plt.scatter(cat["totpartmass"], cat["lagpatch_ncells"], s=0.25,
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rasterized=True)
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@ -66,9 +103,9 @@ def plot_mass_vs_ncells(nsim, pdf=False):
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plt.ylabel(r"$N_{\rm cells}$")
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for ext in ["png"] if pdf is False else ["png", "pdf"]:
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fout = join(utils.fout, f"init_mass_vs_ncells_{nsim}.{ext}")
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fout = join(plt_utils.fout, f"init_mass_vs_ncells_{nsim}.{ext}")
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print(f"Saving to `{fout}`.")
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plt.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
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plt.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
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plt.close()
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@ -77,13 +114,19 @@ def plot_mass_vs_ncells(nsim, pdf=False):
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###############################################################################
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def process_counts(counts):
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mean = numpy.mean(counts, axis=0)
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std = numpy.std(counts, axis=0)
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return mean, std
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def plot_hmf(pdf=False):
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"""
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Plot the (ultimate paretn) halo mass function of CSiBORG and Quijote.
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Parameters
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----------
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pdf : bool, optional
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Whether to save the figure as a PDF file.
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Returns
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-------
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None
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"""
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print("Plotting the HMF...", flush=True)
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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@ -114,35 +157,39 @@ def plot_hmf(pdf=False):
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x = 10**(0.5 * (bins[1:] + bins[:-1]))
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# Edit lower limits
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csiborg_counts[:, x < 1e12] = numpy.nan
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quijote_counts[:, x < 8e12] = numpy.nan
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quijote_counts[:, x < 10**(12.4)] = numpy.nan
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# Edit upper limits
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csiborg_counts[:, x > 4e15] = numpy.nan
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quijote_counts[:, x > 4e15] = numpy.nan
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with plt.style.context(utils.mplstyle):
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with plt.style.context(plt_utils.mplstyle):
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cols = plt.rcParams["axes.prop_cycle"].by_key()["color"]
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fig, ax = plt.subplots(nrows=2, sharex=True,
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figsize=(3.5, 2.625 * 1.25),
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gridspec_kw={"height_ratios": [1, 0.65]})
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fig.subplots_adjust(hspace=0, wspace=0)
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mean_csiborg, std_csiborg = process_counts(csiborg_counts)
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# Upper panel data
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mean_csiborg = numpy.mean(csiborg_counts, axis=0)
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std_csiborg = numpy.std(csiborg_counts, axis=0)
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ax[0].plot(x, mean_csiborg, label="CSiBORG")
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ax[0].fill_between(x, mean_csiborg - std_csiborg,
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mean_csiborg + std_csiborg, alpha=0.5)
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mean_quijote, std_quijote = process_counts(quijote_counts)
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mean_quijote = numpy.mean(quijote_counts, axis=0)
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std_quijote = numpy.std(quijote_counts, axis=0)
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ax[0].plot(x, mean_quijote, label="Quijote")
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ax[0].fill_between(x, mean_quijote - std_quijote,
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mean_quijote + std_quijote, alpha=0.5)
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# Lower panel data
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log_y = numpy.log10(mean_csiborg / mean_quijote)
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err = numpy.sqrt((std_csiborg / mean_csiborg / numpy.log(10))**2
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+ (std_quijote / mean_quijote / numpy.log(10))**2)
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ax[1].plot(x, 10**log_y, c=cols[2])
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ax[1].fill_between(x, 10**(log_y - err), 10**(log_y + err), alpha=0.5,
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color=cols[2])
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# Labels and accesories
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ax[1].axhline(1, color="k", ls=plt.rcParams["lines.linestyle"],
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lw=0.5 * plt.rcParams["lines.linewidth"], zorder=0)
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ax[0].set_ylabel(r"$\frac{\mathrm{d} n}{\mathrm{d}\log M_{\rm h}}~\mathrm{dex}^{-1}$") # noqa
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@ -156,14 +203,33 @@ def plot_hmf(pdf=False):
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fig.tight_layout(h_pad=0, w_pad=0)
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for ext in ["png"] if pdf is False else ["png", "pdf"]:
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fout = join(utils.fout, f"hmf_comparison.{ext}")
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fout = join(plt_utils.fout, f"hmf_comparison.{ext}")
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print(f"Saving to `{fout}`.")
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fig.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
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fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
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plt.close()
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@cache_to_disk(7)
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def load_field(kind, nsim, grid, MAS, in_rsp=False):
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"""
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Load a single field.
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Parameters
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----------
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kind : str
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Field kind.
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nsim : int
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Simulation index.
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grid : int
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Grid size.
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MAS : str
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Mass assignment scheme.
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in_rsp : bool, optional
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Whether to load the field in redshift space.
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Returns
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-------
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field : n-dimensional array
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"""
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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return numpy.load(paths.field(kind, MAS, grid, nsim, in_rsp=in_rsp))
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@ -175,6 +241,30 @@ def load_field(kind, nsim, grid, MAS, in_rsp=False):
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def plot_projected_field(kind, nsim, grid, in_rsp, MAS="PCS",
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highres_only=True, pdf=False):
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"""
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Plot the mean projected field.
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Parameters
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----------
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kind : str
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Field kind.
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nsim : int
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Simulation index.
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grid : int
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Grid size.
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in_rsp : bool
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Whether to load the field in redshift space.
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MAS : str, optional
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Mass assignment scheme.
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highres_only : bool, optional
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Whether to only plot the high-resolution region.
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pdf : bool, optional
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Whether to save the figure as a PDF.
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Returns
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-------
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None
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"""
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print(f"Plotting projected field `{kind}`. ", flush=True)
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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nsnap = max(paths.get_snapshots(nsim))
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@ -190,14 +280,12 @@ def plot_projected_field(kind, nsim, grid, in_rsp, MAS="PCS",
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if highres_only:
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csiborgtools.field.fill_outside(field, numpy.nan, rmax=155.5,
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boxsize=677.7)
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# start = field.shape[0] // 4
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start = round(field.shape[0] * 0.27)
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end = round(field.shape[0] * 0.73)
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# end = field.shape[0] - start
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field = field[start:end, start:end, start:end]
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labels = [r"$y-z$", r"$x-z$", r"$x-y$"]
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with plt.style.context(utils.mplstyle):
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with plt.style.context(plt_utils.mplstyle):
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fig, ax = plt.subplots(figsize=(3.5 * 2, 2.625), ncols=3, sharey=True,
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sharex=True)
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fig.subplots_adjust(hspace=0, wspace=0)
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@ -216,9 +304,10 @@ def plot_projected_field(kind, nsim, grid, in_rsp, MAS="PCS",
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fig.tight_layout(h_pad=0, w_pad=0)
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for ext in ["png"] if pdf is False else ["png", "pdf"]:
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fout = join(utils.fout, f"field_{kind}_{nsim}_rsp{in_rsp}.{ext}")
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fout = join(plt_utils.fout,
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f"field_{kind}_{nsim}_rsp{in_rsp}.{ext}")
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print(f"Saving to `{fout}`.")
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fig.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
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fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
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plt.close()
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###############################################################################
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@ -227,6 +316,20 @@ def plot_projected_field(kind, nsim, grid, in_rsp, MAS="PCS",
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def get_sky_label(kind, volume_weight):
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"""
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Get the sky label for a given field kind.
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Parameters
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----------
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kind : str
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Field kind.
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volume_weight : bool
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Whether to volume weight the field.
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Returns
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-------
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label : str
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"""
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if volume_weight:
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if kind == "density":
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label = r"$\log \int_{0}^{R} r^2 \rho(r, \mathrm{RA}, \mathrm{dec}) \mathrm{d} r$" # noqa
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@ -255,8 +358,38 @@ def get_sky_label(kind, volume_weight):
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def plot_sky_distribution(kind, nsim, grid, nside, MAS="PCS", plot_groups=True,
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dmin=0, dmax=220, plot_halos=None,
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volume_weight=True, pdf=False):
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"""
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NOTE: add distance for groups.
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r"""
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Plot the sky distribution of a given field kind on the sky along with halos
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and selected observations.
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TODO
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----
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- Add distance for groups.
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Parameters
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----------
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field : str
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Field kind.
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nsim : int
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Simulation index.
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grid : int
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Grid size.
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nside : int
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Healpix nside of the sky projection.
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MAS : str, optional
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Mass assignment scheme.
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plot_groups : bool, optional
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Whether to plot the 2M++ groups.
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dmin : float, optional
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Minimum projection distance in :math:`\mathrm{Mpc}/h`.
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dmax : float, optional
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Maximum projection distance in :math:`\mathrm{Mpc}/h`.
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plot_halos : list, optional
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Minimum halo mass to plot in :math:`M_\odot`.
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volume_weight : bool, optional
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Whether to volume weight the field.
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pdf : bool, optional
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Whether to save the figure as a pdf.
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"""
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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nsnap = max(paths.get_snapshots(nsim))
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@ -274,7 +407,7 @@ def plot_sky_distribution(kind, nsim, grid, nside, MAS="PCS", plot_groups=True,
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out = csiborgtools.field.make_sky(field, angpos=angpos, dist=dist, box=box,
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volume_weight=volume_weight)
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with plt.style.context(utils.mplstyle):
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with plt.style.context(plt_utils.mplstyle):
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label = get_sky_label(kind, volume_weight)
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if kind in ["density", "overdensity"]:
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out = numpy.log10(out)
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@ -299,9 +432,9 @@ def plot_sky_distribution(kind, nsim, grid, nside, MAS="PCS", plot_groups=True,
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plt.legend(markerscale=10)
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for ext in ["png"] if pdf is False else ["png", "pdf"]:
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fout = join(utils.fout, f"sky_{kind}_{nsim}_from_{dmin}_to_{dmax}_vol{volume_weight}.{ext}") # noqa
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fout = join(plt_utils.fout, f"sky_{kind}_{nsim}_from_{dmin}_to_{dmax}_vol{volume_weight}.{ext}") # noqa
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print(f"Saving to `{fout}`.")
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plt.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
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plt.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
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plt.close()
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@ -321,12 +454,19 @@ if __name__ == "__main__":
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print(f"Cleaning cache for function {func}.")
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delete_disk_caches_for_function(func)
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# plot_mass_vs_occupancy(7444)
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# plot_mass_vs_normcells(7444 + 24 * 4, pdf=False)
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# plot_mass_vs_ncells(7444, pdf=True)
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# plot_hmf(pdf=True)
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# plot_sky_distribution("radvel", 7444, 256, nside=64,
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# plot_groups=False, dmin=50, dmax=100,
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# plot_halos=5e13, volume_weight=False)
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if False:
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plot_mass_vs_ncells(7444, pdf=False)
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plot_projected_field("potential", 7444, 256, in_rsp=True)
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if False:
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plot_hmf(pdf=False)
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if False:
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plot_sky_distribution("radvel", 7444, 256, nside=64,
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plot_groups=False, dmin=50, dmax=100,
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plot_halos=5e13, volume_weight=False)
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if True:
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plot_projected_field("overdensity", 7444, 1024, in_rsp=True,
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highres_only=False)
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plot_projected_field("overdensity", 7444, 1024, in_rsp=False,
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highres_only=False)
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@ -19,7 +19,7 @@ import matplotlib.pyplot as plt
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import numpy
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import scienceplots # noqa
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import utils
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import plt_utils
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try:
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import csiborgtools
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@ -40,7 +40,7 @@ def plot_knn(runname):
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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reader = csiborgtools.read.kNNCDFReader(paths)
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with plt.style.context(utils.mplstyle):
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with plt.style.context(plt_utils.mplstyle):
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plt.figure()
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# Quijote kNN
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@ -92,9 +92,9 @@ def plot_knn(runname):
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plt.ylabel(r"$P(k | V = 4 \pi r^3 / 3)$")
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for ext in ["png"]:
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fout = join(utils.fout, f"knn_{runname}.{ext}")
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fout = join(plt_utils.fout, f"knn_{runname}.{ext}")
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print("Saving to `{fout}`.".format(fout=fout))
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plt.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
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plt.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
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plt.close()
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