CDF for nearest neighbour (#63)

* Updat ebounds * fix mistake * add plot script * fix which sims * Add Poisson * Just docs * Hide things to __main__ * Rename paths * Move old script * Remove radpos * Paths renaming * Paths renaming * Remove trunk stuff * Add import * Add nearest neighbour search * Add Quijote fiducial indices * Add final snapshot matching * Add fiducial observer selection * add boxsizes * Add reading functions * Little stuff * Bring back the fiducial observer * Add arguments * Add quijote paths * Add notes * Get this running * Add yaml * Remove Poisson stuff * Get the 2PCF script running * Add not finished htings * Remove comment * Verbosity only on 0th rank! * Update plotting style * Add nearest neighbour CDF * Save radial distance too * Add centres * Add basic plotting
2025-05-14 06:31:11 +00:00 · 2023-05-21 22:46:28 +01:00 · 2023-05-21 22:46:28 +01:00 · 2185846e90
commit 2185846e90
parent 369438f881
34 changed files with 1254 additions and 351 deletions
--- a/scripts_plots/plot_knn.py
+++ b/scripts_plots/plot_knn.py
@ -0,0 +1,103 @@
+# Copyright (C) 2023 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.
+
+from os.path import join
+
+import matplotlib.pyplot as plt
+import numpy
+
+import scienceplots  # noqa
+import utils
+
+try:
+    import csiborgtools
+except ModuleNotFoundError:
+    import sys
+    sys.path.append("../")
+    import csiborgtools
+
+
+###############################################################################
+#                Probability of matching a reference simulation halo          #
+###############################################################################
+
+
+def plot_knn(runname):
+    print(f"Plotting kNN CDF for {runname}.")
+    cols = plt.rcParams["axes.prop_cycle"].by_key()["color"]
+    paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
+    reader = csiborgtools.read.kNNCDFReader(paths)
+
+    with plt.style.context(utils.mplstyle):
+        plt.figure()
+
+        # Quijote kNN
+        rs, cdf, ndensity = reader.read("quijote", runname, kind="auto")
+        pk = reader.prob_k(cdf)
+        pk_poisson = reader.poisson_prob_k(rs, numpy.arange(pk.shape[1]),
+                                           ndensity)
+
+        for k in range(3):
+            mu = numpy.mean(pk[:, k, :], axis=0)
+            std = numpy.std(pk[:, k, :], axis=0)
+            plt.plot(rs, mu, label=r"$k = {}$, Quijote".format(k + 1),
+                     c=cols[k % len(cols)])
+            # plt.fill_between(rs, mu - std, mu + std, alpha=0.15,
+            #                  color=cols[k % len(cols)], zorder=0)
+
+            mu = numpy.mean(pk_poisson[:, k, :], axis=0)
+            std = numpy.std(pk_poisson[:, k, :], axis=0)
+            plt.plot(rs, mu, c=cols[k % len(cols)], ls="dashed",
+                     label=r"$k = {}$, Poisson analytical".format(k + 1))
+            # plt.fill_between(rs, mu - std, mu + std, alpha=0.15,
+            #                  color=cols[k % len(cols)], zorder=0, hatch="\\")
+
+        # Quijote poisson kNN
+        rs, cdf, ndensity = reader.read("quijote", "mass003_poisson",
+                                        kind="auto")
+        pk = reader.prob_k(cdf)
+
+        for k in range(3):
+            mu = numpy.mean(pk[:, k, :], axis=0)
+            std = numpy.std(pk[:, k, :], axis=0)
+            plt.plot(rs, mu, label=r"$k = {}$, Poisson Quijote".format(k + 1),
+                     c=cols[k % len(cols)], ls="dotted")
+            # plt.fill_between(rs, mu - std, mu + std, alpha=0.15,
+            #                  color=cols[k % len(cols)], zorder=0)
+
+#         # CSiBORG kNN
+#         rs, cdf, ndensity = reader.read("csiborg", runname, kind="auto")
+#         pk = reader.mean_prob_k(cdf)
+#         for k in range(2):
+#             mu = pk[k, :, 0]
+#             std = pk[k, :, 1]
+#             plt.plot(rs, mu, ls="--", c=cols[k % len(cols)])
+#             plt.fill_between(rs, mu - std, mu + std, alpha=0.15, hatch="\\",
+#                              color=cols[k % len(cols)], zorder=0)
+
+        plt.legend()
+        plt.xlabel(r"$r~[\mathrm{Mpc}]$")
+        plt.ylabel(r"$P(k | V = 4 \pi r^3 / 3)$")
+
+        for ext in ["png"]:
+            fout = join(utils.fout, f"knn_{runname}.{ext}")
+            print("Saving to `{fout}`.".format(fout=fout))
+            plt.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
+        plt.close()
+
+
+if __name__ == "__main__":
+
+    plot_knn("mass003")