mirror of
https://github.com/Richard-Sti/csiborgtools_public.git
synced 2025-05-12 13:41:13 +00:00
Update units to be consistent. (#78)
* Fix Quijote units * Updates to units * Fix how things are loaded * Updating definitions & conventions * Clear up how fiducial observers in quijote work * Refactorize array manip * Move function definition * More code refactoring * Remove unused argument * Remove `convert_from_box` * Make a note * Converting particle units * Add notes about units * Remove box constants * Add rho_crit0 * Fix spherical overdensity mass units * Refactor more code * Edit catalogue kwargs * Edit the docstring * Edit bounds * Add new checks for empty array * Remove unused import * Remove old code * Remove old function * Update real 2 redshift * Clear up the RSP conv * Add comments * Add some units
This commit is contained in:
Richard Stiskalek
GitHub
parent
fb4b4edf19
commit
acb8d9571c
23 changed files with 695 additions and 1079 deletions
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@ -49,8 +49,10 @@ def open_csiborg(nsim):
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cat : csiborgtools.read.CSiBORGHaloCatalogue
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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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return csiborgtools.read.CSiBORGHaloCatalogue(nsim, paths, bounds=bounds)
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bounds = {"totpartmass": (None, None), "dist": (0, 155)}
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return csiborgtools.read.CSiBORGHaloCatalogue(
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nsim, paths, bounds=bounds, load_fitted=True, load_initial=True,
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with_lagpatch=False)
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def open_quijote(nsim, nobs=None):
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@ -69,9 +71,11 @@ def open_quijote(nsim, nobs=None):
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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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cat = csiborgtools.read.QuijoteHaloCatalogue(
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nsim, paths, nsnap=4, load_fitted=True, load_initial=True,
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with_lagpatch=False)
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if nobs is not None:
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cat = cat.pick_fiducial_observer(nobs, rmax=155.5 / 0.705)
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cat = cat.pick_fiducial_observer(nobs, rmax=155.5)
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return cat
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@ -101,7 +105,7 @@ def plot_mass_vs_ncells(nsim, pdf=False):
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plt.yscale("log")
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for n in [1, 10, 100]:
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plt.axvline(n * 512 * mpart, c="black", ls="--", zorder=0, lw=0.8)
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plt.xlabel(r"$M_{\rm tot} / M_\odot$")
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plt.xlabel(r"$M_{\rm tot} ~ [M_\odot$ / h]")
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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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@ -198,7 +202,7 @@ def plot_hmf(pdf=False):
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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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ax[1].set_xlabel(r"$M_{\rm h}$ [$M_\odot$]")
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ax[1].set_xlabel(r"$M_{\rm h}~[M_\odot / h]$")
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ax[1].set_ylabel(r"$\mathrm{CSiBORG} / \mathrm{Quijote}$")
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ax[0].set_xscale("log")
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@ -268,7 +272,7 @@ def plot_hmf_quijote_full(pdf=False):
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lw=0.5 * plt.rcParams["lines.linewidth"], zorder=0)
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ax[0].set_ylabel(r"$\frac{\mathrm{d}^2 n}{\mathrm{d}\log M_{\rm h} \mathrm{d} V}~[\mathrm{dex}^{-1} (\mathrm{Mpc / h})^{-3}]$", # noqa
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fontsize="small")
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ax[1].set_xlabel(r"$M_{\rm h}$ [$M_\odot$]")
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ax[1].set_xlabel(r"$M_{\rm h}~[$M_\odot / h]$", fontsize="small")
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ax[1].set_ylabel(r"$\mathrm{HMF} / \langle \mathrm{HMF} \rangle$",
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fontsize="small")
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@ -99,7 +99,7 @@ def plot_knn(runname):
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# color=cols[k % len(cols)], zorder=0)
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plt.legend()
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plt.xlabel(r"$r~[\mathrm{Mpc}]$")
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plt.xlabel(r"$r~[\mathrm{Mpc} / h]$")
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plt.ylabel(r"$P(k | V = 4 \pi r^3 / 3)$")
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for ext in ["png"]:
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@ -54,7 +54,7 @@ def open_cat(nsim):
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cat : csiborgtools.read.CSiBORGHaloCatalogue
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"""
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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bounds = {"totpartmass": (1e12, None)}
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bounds = {"dist": (0, 155), "totpartmass": (1e12, None)}
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return csiborgtools.read.CSiBORGHaloCatalogue(nsim, paths, bounds=bounds)
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@ -86,7 +86,7 @@ def plot_mass_vs_pairoverlap(nsim0, nsimx):
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plt.hexbin(x, y, mincnt=1, bins="log",
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gridsize=50)
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plt.colorbar(label="Counts in bins")
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plt.xlabel(r"$\log M_{\rm tot} / M_\odot$")
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plt.xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
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plt.ylabel("Pair overlap")
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plt.ylim(0., 1.)
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@ -130,7 +130,7 @@ def plot_mass_vs_maxpairoverlap(nsim0, nsimx):
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plt.hexbin(x, y, mincnt=1, bins="log",
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gridsize=50)
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plt.colorbar(label="Counts in bins")
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plt.xlabel(r"$\log M_{\rm tot} / M_\odot$")
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plt.xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
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plt.ylabel("Maximum pair overlap")
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plt.ylim(0., 1.)
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@ -214,9 +214,9 @@ def plot_mass_vsmedmaxoverlap(nsim0):
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numpy.nanstd(max_overlap, axis=1), gridsize=30,
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C=x, reduce_C_function=numpy.nanmean)
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axs[0].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
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axs[0].set_xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
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axs[0].set_ylabel(r"Mean max. pair overlap")
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axs[1].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
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axs[1].set_xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
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axs[1].set_ylabel(r"Uncertainty of max. pair overlap")
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axs[2].set_xlabel(r"Mean max. pair overlap")
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axs[2].set_ylabel(r"Uncertainty of max. pair overlap")
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@ -287,14 +287,15 @@ def plot_summed_overlap_vs_mass(nsim0):
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axs[2].plot(t, t, color="red", linestyle="--")
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axs[0].set_ylim(0.)
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axs[1].set_ylim(0.)
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axs[0].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
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axs[0].set_xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
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axs[0].set_ylabel("Mean summed overlap")
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axs[1].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
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axs[1].set_xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
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axs[1].set_ylabel("Uncertainty of summed overlap")
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axs[2].set_xlabel(r"$1 - $ mean summed overlap")
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axs[2].set_ylabel("Mean prob. of no match")
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label = ["Bin counts", "Bin counts", r"$\log M_{\rm tot} / M_\odot$"]
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label = ["Bin counts", "Bin counts",
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r"$\log M_{\rm tot} ~ [M_\odot / h]$"]
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ims = [im1, im2, im3]
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for i in range(3):
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axins = inset_axes(axs[i], width="100%", height="5%",
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@ -338,7 +339,7 @@ def plot_mass_vs_separation(nsim0, nsimx, plot_std=False, min_overlap=0.0):
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catx = open_cat(nsimx)
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reader = csiborgtools.read.PairOverlap(cat0, catx, paths,
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maxdist=155 / 0.705)
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maxdist=155)
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mass = numpy.log10(reader.cat0("totpartmass"))
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dist = reader.dist(in_initial=False, norm_kind="r200c")
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overlap = reader.overlap(True)
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@ -373,7 +374,7 @@ def plot_mass_vs_separation(nsim0, nsimx, plot_std=False, min_overlap=0.0):
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ax.plot(xrange, numpy.polyval(p, xrange), color="red",
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linestyle="--")
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fig.colorbar(cx, label="Bin counts")
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ax.set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
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ax.set_xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
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ax.set_ylabel(r"$\log \langle \Delta R / R_{\rm 200c}\rangle$")
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fig.tight_layout()
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@ -460,10 +461,10 @@ def plot_maxoverlap_mass(nsim0):
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axs[0].plot(t, t + 0.2, color="red", linestyle="--", alpha=0.5)
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axs[0].plot(t, t - 0.2, color="red", linestyle="--", alpha=0.5)
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axs[0].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
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axs[1].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
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axs[0].set_ylabel(r"Max. overlap mean of $\log M_{\rm tot} / M_\odot$")
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axs[1].set_ylabel(r"Max. overlap std. of $\log M_{\rm tot} / M_\odot$")
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axs[0].set_xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
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axs[1].set_xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
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axs[0].set_ylabel(r"Max. overlap mean of $\log M_{\rm tot} ~ [M_\odot / h]$")
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axs[1].set_ylabel(r"Max. overlap std. of $\log M_{\rm tot} ~ [M_\odot / h]$")
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ims = [im0, im1]
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for i in range(2):
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@ -518,9 +519,9 @@ def plot_maxoverlapstat(nsim0, key):
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m = numpy.isfinite(key_val) & numpy.isfinite(mu)
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print("True to expectation corr: ", kendalltau(key_val[m], mu[m]))
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axs[0].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
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axs[0].set_xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
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axs[0].set_ylabel(r"Max. overlap mean of ${}$".format(key_label))
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axs[1].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
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axs[1].set_xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
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axs[1].set_ylabel(r"Max. overlap std. of ${}$".format(key_label))
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axs[2].set_xlabel(r"${}$".format(key_label))
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axs[2].set_ylabel(r"Max. overlap mean of ${}$".format(key_label))
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@ -622,10 +623,9 @@ def plot_mass_vs_expected_mass(nsim0, min_overlap=0, max_prob_nomatch=1):
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gridsize=50, C=mass[mask],
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reduce_C_function=numpy.nanmedian)
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axs[2].axhline(0, color="red", linestyle="--", alpha=0.5)
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axs[0].set_xlabel(r"True $\log M_{\rm tot} / M_\odot$")
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axs[0].set_ylabel(r"Expected $\log M_{\rm tot} / M_\odot$")
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axs[1].set_xlabel(r"True $\log M_{\rm tot} / M_\odot$")
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axs[0].set_xlabel(r"True $\log M_{\rm tot} ~ [M_\odot / h]$")
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axs[0].set_ylabel(r"Expected $\log M_{\rm tot} ~ [M_\odot / h]$")
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axs[1].set_xlabel(r"True $\log M_{\rm tot} ~ [M_\odot / h]$")
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axs[1].set_ylabel(r"Std. of $\sigma_{\log M_{\rm tot}}$")
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axs[2].set_xlabel(r"1 - median prob. of no match")
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axs[2].set_ylabel(r"$\log M_{\rm tot} - \log M_{\rm tot, exp}$")
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@ -636,7 +636,8 @@ def plot_mass_vs_expected_mass(nsim0, min_overlap=0, max_prob_nomatch=1):
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axs[0].plot(t, t - 0.2, color="red", linestyle="--", alpha=0.5)
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ims = [im0, im1, im2]
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labels = ["Bin counts", "Bin counts", r"$\log M_{\rm tot}$"]
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labels = ["Bin counts", "Bin counts",
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r"$\log M_{\rm tot} ~ [M_\odot / h]$"]
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for i in range(3):
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axins = inset_axes(axs[i], width="100%", height="5%",
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loc='upper center', borderpad=-0.75)
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@ -758,10 +759,10 @@ def plot_dist(run, kind, kwargs, runs_to_mass, pulled_cdf=False, r200=None):
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fig, ax = plt.subplots()
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if run != "mass009":
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ax.set_title(r"${} \leq \log M_{{\rm tot}} / M_\odot < {}$"
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ax.set_title(r"${} \leq \log M_{{\rm tot}} / (M_\odot h) < {}$"
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.format(*runs_to_mass[run]), fontsize="small")
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else:
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ax.set_title(r"$\log M_{{\rm tot}} / M_\odot \geq {}$"
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ax.set_title(r"$\log M_{{\rm tot}} / (M_\odot h) \geq {}$"
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.format(runs_to_mass[run][0]), fontsize="small")
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# Plot data
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nrad = y_csiborg.shape[0]
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@ -778,12 +779,12 @@ def plot_dist(run, kind, kwargs, runs_to_mass, pulled_cdf=False, r200=None):
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ax.plot(x2, y2, c="gray", ls="--",
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label="Quijote" if i == 0 else None)
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fig.colorbar(cmap, ax=ax, label=r"$R_{\rm dist}~[\mathrm{Mpc}]$")
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fig.colorbar(cmap, ax=ax, label=r"$R_{\rm dist}~[\mathrm{Mpc} / h]$")
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ax.grid(alpha=0.5, lw=0.4)
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# Plot labels
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if pulled_cdf:
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if r200 is None:
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ax.set_xlabel(r"$\tilde{r}_{1\mathrm{NN}}~[\mathrm{Mpc}]$")
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ax.set_xlabel(r"$\tilde{r}_{1\mathrm{NN}}~[\mathrm{Mpc} / h]$")
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if kind == "pdf":
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ax.set_ylabel(r"$p(\tilde{r}_{1\mathrm{NN}})$")
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else:
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@ -796,7 +797,7 @@ def plot_dist(run, kind, kwargs, runs_to_mass, pulled_cdf=False, r200=None):
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ax.set_ylabel(r"$\mathrm{CDF}(\tilde{r}_{1\mathrm{NN}} / R_{200c})$") # noqa
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else:
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if r200 is None:
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ax.set_xlabel(r"$r_{1\mathrm{NN}}~[\mathrm{Mpc}]$")
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ax.set_xlabel(r"$r_{1\mathrm{NN}}~[\mathrm{Mpc} / h]$")
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if kind == "pdf":
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ax.set_ylabel(r"$p(r_{1\mathrm{NN}})$")
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else:
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@ -901,7 +902,7 @@ def plot_cdf_diff(runs, kwargs, pulled_cdf, runs_to_mass):
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ax.fill_between(r, *numpy.percentile(dy, [16, 84], axis=0),
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alpha=0.5, color=cmap.to_rgba(runs_to_mass[i]))
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fig.colorbar(cmap, ax=ax, ticks=runs_to_mass,
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label=r"$\log M_{\rm tot} / M_\odot$")
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label=r"$\log M_{\rm tot} ~ [M_\odot / h]$")
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ax.set_xlim(0.0, 55)
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ax.set_ylim(0)
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@ -909,17 +910,17 @@ def plot_cdf_diff(runs, kwargs, pulled_cdf, runs_to_mass):
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# Plot labels
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if pulled_cdf:
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ax.set_xlabel(r"$\tilde{r}_{1\mathrm{NN}}~[\mathrm{Mpc}]$")
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ax.set_xlabel(r"$\tilde{r}_{1\mathrm{NN}}~[\mathrm{Mpc} / h]$")
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else:
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ax.set_xlabel(r"$r_{1\mathrm{NN}}~[\mathrm{Mpc}]$")
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ax.set_xlabel(r"$r_{1\mathrm{NN}}~[\mathrm{Mpc} / h]$")
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ax.set_ylabel(r"$\Delta \mathrm{CDF}(r_{1\mathrm{NN}})$")
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# Plot labels
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if pulled_cdf:
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ax.set_xlabel(r"$\tilde{r}_{1\mathrm{NN}}~[\mathrm{Mpc}]$")
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ax.set_xlabel(r"$\tilde{r}_{1\mathrm{NN}}~[\mathrm{Mpc} / h]$")
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ax.set_ylabel(r"$\Delta \mathrm{CDF}(\tilde{r}_{1\mathrm{NN}})$")
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else:
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ax.set_xlabel(r"$r_{1\mathrm{NN}}~[\mathrm{Mpc}]$")
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ax.set_xlabel(r"$r_{1\mathrm{NN}}~[\mathrm{Mpc} / h]$")
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ax.set_ylabel(r"$\Delta \mathrm{CDF}(r_{1\mathrm{NN}})$")
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fig.tight_layout()
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@ -1104,7 +1105,7 @@ def plot_significance(simname, runs, nsim, nobs, kind, kwargs, runs_to_mass):
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cbar_ax = fig.add_axes([1.0, 0.125, 0.035, 0.85])
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fig.colorbar(cmap, cax=cbar_ax, ticks=runs_to_mass,
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label=r"$\log M_{\rm tot} / M_\odot$")
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label=r"$\log M_{\rm tot} ~ [M_\odot / h]$")
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ax[0].set_xlim(z[0], z[-1])
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ax[0].set_ylim(1e-5, 1.)
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@ -1216,7 +1217,7 @@ def plot_significance_vs_mass(simname, runs, nsim, nobs, kind, kwargs,
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corr = plt_utils.latex_float(*kendalltau(xs[mask], ys[mask]))
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plt.title(r"$\tau = {}, p = {}$".format(*corr), fontsize="small")
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plt.xlabel(r"$\log M_{\rm tot} / M_\odot$")
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plt.xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
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if kind == "ks":
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plt.ylabel(r"$\log p$-value of $r_{1\mathrm{NN}}$ distribution")
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plt.ylim(top=0)
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