mirror of
https://github.com/Richard-Sti/csiborgtools_public.git
synced 2025-05-12 13:41:13 +00:00
New matches (#69)
* Remove old file * Add velocity plotting * add smooth scale * Fix bug * Improve paths * Edit plotting * Add smoothed density * Update boundaries * Add basics * Further docs * Remove blank * Better catalog broadcasting * Update high res size * Update plotting routines * Update routine * Update plotting * Fix field saving name * Add better colormap for environemnt
This commit is contained in:
Richard Stiskalek
GitHub
parent
73687fd8cc
commit
35ccfb5c67
9 changed files with 343 additions and 169 deletions
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@ -16,6 +16,7 @@
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from os.path import join
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from argparse import ArgumentParser
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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import numpy
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import healpy
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@ -209,8 +210,8 @@ def plot_hmf(pdf=False):
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plt.close()
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def load_field(kind, nsim, grid, MAS, in_rsp=False):
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"""
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def load_field(kind, nsim, grid, MAS, in_rsp=False, smooth_scale=None):
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r"""
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Load a single field.
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Parameters
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@ -225,13 +226,16 @@ def load_field(kind, nsim, grid, MAS, in_rsp=False):
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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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smooth_scale : float, optional
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Smoothing scale in :math:`\mathrm{Mpc} / h`.
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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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return numpy.load(paths.field(kind, MAS, grid, nsim, in_rsp=in_rsp,
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smooth_scale=smooth_scale))
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###############################################################################
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@ -239,9 +243,9 @@ def load_field(kind, nsim, grid, MAS, in_rsp=False):
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###############################################################################
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def plot_projected_field(kind, nsim, grid, in_rsp, MAS="PCS",
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def plot_projected_field(kind, nsim, grid, in_rsp, smooth_scale, MAS="PCS",
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highres_only=True, slice_find=None, pdf=False):
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"""
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r"""
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Plot the mean projected field, however can also plot a single slice.
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Parameters
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@ -254,6 +258,8 @@ def plot_projected_field(kind, nsim, grid, in_rsp, MAS="PCS",
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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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smooth_scale : float
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Smoothing scale in :math:`\mathrm{Mpc} / h`.
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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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@ -273,11 +279,16 @@ def plot_projected_field(kind, nsim, grid, in_rsp, MAS="PCS",
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box = csiborgtools.read.CSiBORGBox(nsnap, nsim, paths)
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if kind == "overdensity":
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field = load_field("density", nsim, grid, MAS=MAS, in_rsp=in_rsp)
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field = load_field("density", nsim, grid, MAS=MAS, in_rsp=in_rsp,
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smooth_scale=smooth_scale)
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density_gen = csiborgtools.field.DensityField(box, MAS)
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field = density_gen.overdensity_field(field) + 2
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field = density_gen.overdensity_field(field) + 1
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else:
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field = load_field(kind, nsim, grid, MAS=MAS, in_rsp=in_rsp)
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field = load_field(kind, nsim, grid, MAS=MAS, in_rsp=in_rsp,
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smooth_scale=smooth_scale)
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if kind == "velocity":
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field = field[0, ...]
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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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@ -286,10 +297,11 @@ def plot_projected_field(kind, nsim, grid, in_rsp, MAS="PCS",
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end = round(field.shape[0] * 0.73)
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field = field[start:end, start:end, start:end]
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if kind != "environment":
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cmap = "viridis"
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if kind == "environment":
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cmap = mpl.colors.ListedColormap(
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['red', 'lightcoral', 'limegreen', 'khaki'])
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else:
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cmap = "brg"
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cmap = "viridis"
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labels = [r"$y-z$", r"$x-z$", r"$x-y$"]
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with plt.style.context(plt_utils.mplstyle):
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@ -309,12 +321,15 @@ def plot_projected_field(kind, nsim, grid, in_rsp, MAS="PCS",
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else:
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ax[i].imshow(img, vmin=vmin, vmax=vmax, cmap=cmap)
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if not highres_only:
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frad = 155.5 / 677.7
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if not highres_only and 0.5 - frad < slice_find < 0.5 + frad:
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theta = numpy.linspace(0, 2 * numpy.pi, 100)
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rad = 155.5 / 677.7 * grid
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z = (slice_find - 0.5) * grid
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R = 155.5 / 677.7 * grid
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rad = R * numpy.sqrt(1 - z**2 / R**2)
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ax[i].plot(rad * numpy.cos(theta) + grid // 2,
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rad * numpy.sin(theta) + grid // 2,
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lw=plt.rcParams["lines.linewidth"], zorder=1,
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lw=0.75 * plt.rcParams["lines.linewidth"], zorder=1,
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c="red", ls="--")
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ax[i].set_title(labels[i])
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@ -343,17 +358,32 @@ def plot_projected_field(kind, nsim, grid, in_rsp, MAS="PCS",
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(xticks * size / ncells - size / 2).astype(int))
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ax[i].set_xlabel(r"$x_j ~ [\mathrm{Mpc} / h]$")
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cbar_ax = fig.add_axes([1.0, 0.1, 0.025, 0.8])
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cbar_ax = fig.add_axes([0.982, 0.155, 0.025, 0.75],
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transform=ax[2].transAxes)
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if slice_find is None:
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clabel = "Mean projected field"
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else:
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clabel = "Sliced field"
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fig.colorbar(im, cax=cbar_ax, label=clabel)
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if kind == "environment":
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bounds = [0, 1, 2, 3, 4]
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norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
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cbar = fig.colorbar(
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mpl.cm.ScalarMappable(cmap=cmap, norm=norm), cax=cbar_ax,
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ticks=[0.5, 1.5, 2.5, 3.5])
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cbar.ax.set_yticklabels(["knot", "filament", "sheet", "void"],
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rotation=90, va="center")
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else:
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fig.colorbar(im, cax=cbar_ax, label=clabel)
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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(plt_utils.fout,
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f"field_{kind}_{nsim}_rsp{in_rsp}.{ext}")
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fout = join(
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plt_utils.fout,
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f"field_{kind}_{nsim}_rsp{in_rsp}_hres{highres_only}.{ext}")
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if smooth_scale is not None and smooth_scale > 0:
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smooth_scale = float(smooth_scale)
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fout = fout.replace(f".{ext}", f"_smooth{smooth_scale}.{ext}")
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print(f"Saving to `{fout}`.")
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fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
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plt.close()
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@ -404,8 +434,8 @@ def get_sky_label(kind, volume_weight):
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return label
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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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def plot_sky_distribution(kind, nsim, grid, nside, smooth_scale, MAS="PCS",
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plot_groups=True, dmin=0, dmax=220, plot_halos=None,
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volume_weight=True, pdf=False):
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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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@ -425,6 +455,8 @@ def plot_sky_distribution(kind, nsim, grid, nside, MAS="PCS", plot_groups=True,
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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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smooth_scale : float
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Smoothing scale in :math:`\mathrm{Mpc} / h`.
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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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@ -445,11 +477,13 @@ def plot_sky_distribution(kind, nsim, grid, nside, MAS="PCS", plot_groups=True,
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box = csiborgtools.read.CSiBORGBox(nsnap, nsim, paths)
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if kind == "overdensity":
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field = load_field("density", nsim, grid, MAS=MAS, in_rsp=False)
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field = load_field("density", nsim, grid, MAS=MAS, in_rsp=False,
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smooth_scale=smooth_scale)
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density_gen = csiborgtools.field.DensityField(box, MAS)
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field = density_gen.overdensity_field(field) + 2
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field = density_gen.overdensity_field(field) + 1
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else:
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field = load_field(kind, nsim, grid, MAS=MAS, in_rsp=False)
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field = load_field(kind, nsim, grid, MAS=MAS, in_rsp=False,
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smooth_scale=smooth_scale)
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angpos = csiborgtools.field.nside2radec(nside)
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dist = numpy.linspace(dmin, dmax, 500)
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@ -519,7 +553,22 @@ if __name__ == "__main__":
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if True:
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kind = "environment"
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grid = 256
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smooth_scale = 0
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# plot_projected_field("overdensity", 7444, grid, in_rsp=True,
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# highres_only=False)
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plot_projected_field(kind, 7444, grid, in_rsp=False,
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slice_find=0.5, highres_only=False)
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smooth_scale=smooth_scale, slice_find=0.5,
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highres_only=False)
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if False:
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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d = csiborgtools.read.read_h5(paths.particles(7444))["particles"]
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plt.figure()
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plt.hist(d[:100000, 4], bins="auto")
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plt.tight_layout()
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plt.savefig("../plots/velocity_distribution.png", dpi=450,
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bbox_inches="tight")
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@ -648,7 +648,32 @@ def plot_significance(simname, runs, nsim, nobs, kind, kwargs, runs_to_mass):
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plt.close()
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def plot_significance_vs_mass(simname, runs, nsim, nobs, kind, kwargs):
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def make_binlims(run, runs_to_mass):
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"""
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Make bin limits for the 1NN distance runs, corresponding to the first half
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of the log-mass bin.
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Parameters
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----------
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run : str
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Run name.
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runs_to_mass : dict
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Dictionary mapping run names to total halo mass range.
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Returns
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-------
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xmin, xmax : floats
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"""
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xmin, xmax = runs_to_mass[run]
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xmax = xmin + (xmax - xmin) / 2
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xmin, xmax = 10**xmin, 10**xmax
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if run == "mass009":
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xmax = numpy.infty
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return xmin, xmax
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def plot_significance_vs_mass(simname, runs, nsim, nobs, kind, kwargs,
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runs_to_mass):
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"""
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Plot significance of the 1NN distance as a function of the total mass.
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@ -667,6 +692,8 @@ def plot_significance_vs_mass(simname, runs, nsim, nobs, kind, kwargs):
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(Kolmogorov-Smirnov p-value).
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kwargs : dict
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Nearest neighbour reader keyword arguments.
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runs_to_mass : dict
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Dictionary mapping run names to total halo mass range.
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Returns
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-------
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@ -686,8 +713,12 @@ def plot_significance_vs_mass(simname, runs, nsim, nobs, kind, kwargs):
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y = make_kl(simname, run, nsim, nobs, kwargs)
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else:
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y = numpy.log10(make_ks(simname, run, nsim, nobs, kwargs))
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xs.append(x)
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ys.append(y)
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xmin, xmax = make_binlims(run, runs_to_mass)
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mask = (x >= xmin) & (x < xmax)
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xs.append(x[mask])
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ys.append(y[mask])
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xs = numpy.concatenate(xs)
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ys = numpy.concatenate(ys)
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@ -715,7 +746,7 @@ def plot_significance_vs_mass(simname, runs, nsim, nobs, kind, kwargs):
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plt.close()
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def plot_kl_vs_ks(simname, runs, nsim, nobs, kwargs):
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def plot_kl_vs_ks(simname, runs, nsim, nobs, kwargs, runs_to_mass):
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"""
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Plot Kullback-Leibler divergence vs Kolmogorov-Smirnov statistic p-value.
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@ -731,6 +762,8 @@ def plot_kl_vs_ks(simname, runs, nsim, nobs, kwargs):
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Fiducial Quijote observer index. For CSiBORG must be set to `None`.
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kwargs : dict
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Nearest neighbour reader keyword arguments.
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runs_to_mass : dict
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Dictionary mapping run names to total halo mass range.
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Returns
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-------
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@ -741,9 +774,16 @@ def plot_kl_vs_ks(simname, runs, nsim, nobs, kwargs):
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xs, ys, cs = [], [], []
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for run in runs:
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cs.append(reader.read_single(simname, run, nsim, nobs)["mass"])
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xs.append(make_kl(simname, run, nsim, nobs, kwargs))
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ys.append(make_ks(simname, run, nsim, nobs, kwargs))
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c = reader.read_single(simname, run, nsim, nobs)["mass"]
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x = make_kl(simname, run, nsim, nobs, kwargs)
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y = make_ks(simname, run, nsim, nobs, kwargs)
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cmin, cmax = make_binlims(run, runs_to_mass)
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mask = (c >= cmin) & (c < cmax)
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xs.append(x[mask])
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ys.append(y[mask])
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cs.append(c[mask])
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xs = numpy.concatenate(xs)
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ys = numpy.log10(numpy.concatenate(ys))
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cs = numpy.log10(numpy.concatenate(cs))
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@ -768,7 +808,7 @@ def plot_kl_vs_ks(simname, runs, nsim, nobs, kwargs):
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plt.close()
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def plot_kl_vs_overlap(runs, nsim, kwargs):
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def plot_kl_vs_overlap(runs, nsim, kwargs, runs_to_mass):
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"""
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Plot KL divergence vs overlap for CSiBORG.
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@ -780,6 +820,8 @@ def plot_kl_vs_overlap(runs, nsim, kwargs):
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Simulation index.
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kwargs : dict
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Nearest neighbour reader keyword arguments.
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runs_to_mass : dict
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Dictionary mapping run names to total halo mass range.
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Returns
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-------
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@ -802,12 +844,15 @@ def plot_kl_vs_overlap(runs, nsim, kwargs):
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prob_nomatch = prob_nomatch[mask]
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mass = mass[mask]
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kl = make_kl("csiborg", run, nsim, nobs=None, kwargs=kwargs)
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xs.append(kl)
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ys1.append(1 - numpy.mean(prob_nomatch, axis=1))
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ys2.append(numpy.std(prob_nomatch, axis=1))
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cs.append(numpy.log10(mass))
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x = make_kl("csiborg", run, nsim, nobs=None, kwargs=kwargs)
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y1 = 1 - numpy.mean(prob_nomatch, axis=1)
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y2 = numpy.std(prob_nomatch, axis=1)
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cmin, cmax = make_binlims(run, runs_to_mass)
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mask = (mass >= cmin) & (mass < cmax)
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xs.append(x[mask])
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ys1.append(y1[mask])
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ys2.append(y2[mask])
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cs.append(numpy.log10(mass[mask]))
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xs = numpy.concatenate(xs)
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ys1 = numpy.concatenate(ys1)
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@ -877,7 +922,7 @@ if __name__ == "__main__":
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delete_disk_caches_for_function(func)
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# Plot 1NN distance distributions.
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if False:
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if True:
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for i in range(1, 10):
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run = f"mass00{i}"
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for pulled_cdf in [True, False]:
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@ -886,12 +931,12 @@ if __name__ == "__main__":
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plot_dist(run, "pdf", neighbour_kwargs, runs_to_mass)
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# Plot 1NN CDF differences.
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if False:
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if True:
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runs = [f"mass00{i}" for i in range(1, 10)]
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for pulled_cdf in [True, False]:
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plot_cdf_diff(runs, neighbour_kwargs, pulled_cdf=pulled_cdf,
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runs_to_mass=runs_to_mass)
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if False:
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if True:
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runs = [f"mass00{i}" for i in range(1, 9)]
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for kind in ["kl", "ks"]:
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plot_significance("csiborg", runs, 7444, nobs=None, kind=kind,
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@ -902,12 +947,14 @@ if __name__ == "__main__":
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runs = [f"mass00{i}" for i in range(1, 10)]
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for kind in ["kl", "ks"]:
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plot_significance_vs_mass("csiborg", runs, 7444, nobs=None,
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kind=kind, kwargs=neighbour_kwargs)
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kind=kind, kwargs=neighbour_kwargs,
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runs_to_mass=runs_to_mass)
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if False:
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if True:
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runs = [f"mass00{i}" for i in range(1, 10)]
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plot_kl_vs_ks("csiborg", runs, 7444, None, kwargs=neighbour_kwargs)
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plot_kl_vs_ks("csiborg", runs, 7444, None, kwargs=neighbour_kwargs,
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runs_to_mass=runs_to_mass)
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if False:
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if True:
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runs = [f"mass00{i}" for i in range(1, 10)]
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plot_kl_vs_overlap(runs, 7444, neighbour_kwargs)
|
||||
plot_kl_vs_overlap(runs, 7444, neighbour_kwargs, runs_to_mass)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue