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Velocity observer (#86)
* Continue if r200c not defined * Remove smooth scale * Remove smooth scale * Edit Max Matching plot * Add peculiar velocity * Add Vobs calculation * Edit docs * Add Vobs plot * Improve plotting * Edit naming convention * Make a note * Add new cat options * Update density field RSP calculation * Update field 2 rsp * Move functions and shorten documentation * Improve transforms and comments * Update docs * Update imports * Edit calculation * Add docs * Remove imports * Add Quijote flags * Edit documentation * Shorten documentation * Edit func calls * Shorten * Docs edits * Edit docs * Shorten docs * Short docs edits * Simplify docs a little bit * Save plotting * Update env
This commit is contained in:
Richard Stiskalek
GitHub
parent
8e3127f4d9
commit
ae92fd9b72
18 changed files with 761 additions and 788 deletions
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@ -191,7 +191,7 @@ def get_mtot_vs_maxpairoverlap(nsim0, simname, mass_kind, min_logmass,
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def get_max(y_):
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if len(y_) == 0:
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return 0
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return numpy.max(y_)
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return numpy.nanmax(y_)
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reader = csiborgtools.read.NPairsOverlap(cat0, catxs, paths, min_logmass)
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@ -218,7 +218,6 @@ def mtot_vs_maxpairoverlap(nsim0, simname, mass_kind, min_logmass, smoothed,
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x, y, xbins = get_mtot_vs_maxpairoverlap(nsim0, simname, mass_kind,
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min_logmass, smoothed, nbins)
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plt.close("all")
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with plt.style.context(plt_utils.mplstyle):
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plt.figure()
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plt.hexbin(x, y, mincnt=1, gridsize=50, bins="log")
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@ -252,6 +251,87 @@ def mtot_vs_maxpairoverlap(nsim0, simname, mass_kind, min_logmass, smoothed,
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plt.close()
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# --------------------------------------------------------------------------- #
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###############################################################################
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# Total DM halo mass vs maximum pair overlap consistency #
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###############################################################################
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# --------------------------------------------------------------------------- #
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@cache_to_disk(120)
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def get_mtot_vs_maxpairoverlap_consistency(nsim0, simname, mass_kind,
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min_logmass, smoothed):
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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nsimxs = csiborgtools.read.get_cross_sims(simname, nsim0, paths,
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min_logmass, smoothed=smoothed)
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cat0 = open_cat(nsim0, simname)
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catxs = open_cats(nsimxs, simname)
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reader = csiborgtools.read.NPairsOverlap(cat0, catxs, paths, min_logmass)
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x = numpy.log10(cat0[mass_kind])
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mask = x > min_logmass
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x = x[mask]
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nhalos = len(x)
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y = numpy.full((len(catxs), nhalos), numpy.nan)
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for i in trange(len(catxs), desc="Stacking catalogues"):
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overlaps = reader[i].overlap(smoothed)
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for j in range(nhalos):
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# if len(overlaps[j]) > 0:
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y[i, j] = numpy.sum(overlaps[j])
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return x, y
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def mtot_vs_maxpairoverlap_consistency(nsim0, simname, mass_kind, min_logmass,
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smoothed, ext="png"):
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left_edges = numpy.arange(min_logmass, 15, 0.1)
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# delete_disk_caches_for_function("get_mtot_vs_maxpairoverlap_consistency")
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x, y0 = get_mtot_vs_maxpairoverlap_consistency(nsim0, simname, mass_kind,
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min_logmass, smoothed)
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nsims, nhalos = y0.shape
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x_2, y0_2 = get_mtot_vs_maxpairoverlap_consistency(
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0, "quijote", "group_mass", min_logmass, smoothed)
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nsims2, nhalos = y0_2.shape
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with plt.style.context(plt_utils.mplstyle):
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plt.figure()
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yplot = numpy.full(len(left_edges), numpy.nan)
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yplot_2 = numpy.full(len(left_edges), numpy.nan)
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for ymin in [0.3]:
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y = numpy.sum(y0 > ymin, axis=0) / nsims
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y_2 = numpy.sum(y0_2 > ymin, axis=0) / nsims2
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for i, left_edge in enumerate(left_edges):
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mask = x > left_edge
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yplot[i] = numpy.mean(y[mask]) #/ nsims
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mask = x_2 > left_edge
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yplot_2[i] = numpy.mean(y_2[mask]) #/ nsims
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plt.plot(left_edges, yplot, label="CSiBORG")
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plt.plot(left_edges, yplot_2, label="Quijote")
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plt.legend()
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# y2 = numpy.concatenate(y0)
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# y2 = y2[y2 > 0]
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# m = y0 > 0
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# plt.hist(y0[m], bins=30, density=True, histtype="step")
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# m = y0_2 > 0
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# plt.hist(y0_2[m], bins=30, density=True, histtype="step")
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# plt.yscale("log")
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plt.tight_layout()
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fout = join(
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plt_utils.fout,
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f"mass_vs_max_pair_overlap_consistency_{simname}_{nsim0}.{ext}")
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print(f"Saving to `{fout}`.")
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plt.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
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plt.close()
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# --------------------------------------------------------------------------- #
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###############################################################################
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# Total DM halo mass vs summed pair overlaps #
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@ -876,8 +956,7 @@ def get_matching_max_vs_overlap(simname, nsim0, min_logmass, mult):
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def matching_max_vs_overlap(simname, nsim0, min_logmass):
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left_edges = numpy.arange(min_logmass, 15, 0.1)
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delete_disk_caches_for_function("get_matching_max_vs_overlap")
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nsims = 100 if simname == "csiborg" else 9
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with plt.style.context("science"):
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fig, axs = plt.subplots(ncols=2, figsize=(3.5 * 2, 2.625))
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@ -891,34 +970,36 @@ def matching_max_vs_overlap(simname, nsim0, min_logmass):
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success = x["success"]
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nbins = len(left_edges)
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y = numpy.full((nbins, 100), numpy.nan)
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y2 = numpy.full((nbins, 100), numpy.nan)
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y = numpy.full((nbins, nsims), numpy.nan)
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y2 = numpy.full(nbins, numpy.nan)
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y2err = numpy.full(nbins, numpy.nan)
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for i in range(nbins):
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m = mass0 > left_edges[i]
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for j in range(100):
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for j in range(nsims):
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y[i, j] = numpy.sum(
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max_overlap[m, j] == match_overlap[m, j])
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y[i, j] /= numpy.sum(success[m, j])
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y2[i, j] = numpy.sum(success[m, j]) / numpy.sum(m)
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offset = numpy.random.normal(0, 0.01)
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y2[i] = numpy.mean(numpy.sum(success[m, :], axis=1) / nsims)
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y2err[i] = numpy.std(numpy.sum(success[m, :], axis=1) / nsims)
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offset = numpy.random.normal(0, 0.015)
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ysummary = numpy.percentile(y, [16, 50, 84], axis=1)
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axs[0].errorbar(
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left_edges + offset, ysummary[1],
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yerr=[ysummary[1] - ysummary[0], ysummary[2] - ysummary[1]],
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capsize=3, c=cols[n],
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capsize=4, c=cols[n], ls="dashed",
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label=r"$\leq {}~R_{{\rm 200c}}$".format(mult), errorevery=2)
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ysummary = numpy.percentile(y2, [16, 50, 84], axis=1)
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axs[1].errorbar(
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left_edges + offset, ysummary[1], ls="--",
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yerr=[ysummary[1] - ysummary[0], ysummary[2] - ysummary[1]],
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capsize=3, c=cols[n], errorevery=2)
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axs[1].errorbar(left_edges + offset, y2, yerr=y2err,
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capsize=4, errorevery=2, c=cols[n], ls="dashed")
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axs[0].legend(ncols=2, fontsize="small")
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for i in range(2):
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axs[i].set_xlabel(r"$\log M_{\rm tot, min} ~ [M_\odot / h]$")
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axs[1].set_ylim(0)
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axs[0].set_ylabel(r"$f_{\rm agreement}$")
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axs[1].set_ylabel(r"$f_{\rm match}$")
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@ -1048,7 +1129,7 @@ if __name__ == "__main__":
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smoothed = True
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nbins = 10
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ext = "png"
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plot_quijote = False
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plot_quijote = True
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min_maxoverlap = 0.
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funcs = [
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@ -1128,5 +1209,19 @@ if __name__ == "__main__":
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mtot_vs_maxoverlap_property(0, "quijote", min_logmass, key,
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min_maxoverlap, smoothed)
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if False:
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matching_max_vs_overlap("csiborg", 7444, min_logmass)
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if plot_quijote:
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matching_max_vs_overlap("quijote", 0, min_logmass)
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if True:
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matching_max_vs_overlap(7444, min_logmass)
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mtot_vs_maxpairoverlap_consistency(
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7444, "csiborg", "fof_totpartmass", min_logmass, smoothed,
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ext="png")
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# if plot_quijote:
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# mtot_vs_maxpairoverlap_consistency(
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# 0, "quijote", "group_mass", min_logmass, smoothed,
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# ext="png")
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