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Matching paper plots (#91)
* Fix calculations of expected mass * Add paper plots * Edits to pltos * Add overlap summary * Add imports * Add import * Add binned stat * Add fit * Add more plots * Add basic env * Add histogram mode * Edit expected mass * Improve expected plots * Clean up plot * Improve separation plot * Update plots * Edit expected calculation * Update plotting * Update plots * Update plots * Update plots * Add conc fraction * Add halo maker sorting * Renaming * Add import * Add NaN treatment * add import * Move cosine smi * Update plots * Move similarity * Fix little bugs * Shorten documentation * Update plots
This commit is contained in:
Richard Stiskalek
GitHub
parent
136c552369
commit
5500fbd2b9
11 changed files with 2193 additions and 294 deletions
1632
scripts_plots/paper_match.py
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1632
scripts_plots/paper_match.py
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File diff suppressed because it is too large
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@ -66,6 +66,12 @@ def plot_mass_vs_ncells(nsim, pdf=False):
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cat = open_csiborg(nsim)
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mpart = 4.38304044e+09
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x = numpy.log10(cat["totpartmass"])
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y = numpy.log10(cat["lagpatch_ncells"])
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p = numpy.polyfit(x, y, 1)
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print("Fitted parameters are: ", p)
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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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@ -105,9 +111,9 @@ def plot_hmf(pdf=False):
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csiborg_counts = numpy.full((len(csiborg_nsims), len(bins) - 1),
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numpy.nan, dtype=numpy.float32)
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csiborg_counts[i, :] = data["counts"]
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# csiborg_counts /= numpy.diff(bins).reshape(1, -1)
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csiborg_counts /= numpy.diff(bins).reshape(1, -1)
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csiborg5511 = numpy.load(paths.halo_counts("csiborg", 5511))["counts"]
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# csiborg5511 = numpy.load(paths.halo_counts("csiborg", 5511))["counts"]
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# csiborg5511 /= numpy.diff(data["bins"])
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print("Loading Quijote halo counts.", flush=True)
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@ -121,73 +127,89 @@ def plot_hmf(pdf=False):
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(len(quijote_nsims) * nmax, len(bins) - 1), numpy.nan,
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dtype=numpy.float32)
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quijote_counts[i * nmax:(i + 1) * nmax, :] = data["counts"]
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# quijote_counts /= numpy.diff(bins).reshape(1, -1)
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quijote_counts /= numpy.diff(bins).reshape(1, -1)
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# vol = 155.5**3
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# csiborg_counts /= vol
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# quijote_counts /= vol
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vol = 4 * numpy.pi / 3 * 155.5**3
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csiborg_counts /= vol
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quijote_counts /= vol
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# csiborg5511 /= vol
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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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csiborg_counts[:, x < 10**13.1] = numpy.nan
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quijote_counts[:, x < 10**(13.1)] = numpy.nan
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# Edit upper limits
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csiborg_counts[:, x > 3e15] = numpy.nan
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quijote_counts[:, x > 3e15] = numpy.nan
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csiborg5511[x > 3e15] = numpy.nan
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# csiborg5511[x > 3e15] = numpy.nan
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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, 0.45]})
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fig.subplots_adjust(hspace=0, wspace=0)
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fig, ax = plt.subplots(nrows=1, sharex=True,
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figsize=(3.5, 2.625))
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ax = [ax]
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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, 0.25]})
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# fig.subplots_adjust(hspace=0, wspace=0)
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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", c=cols[0])
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ax[0].fill_between(x, mean_csiborg - std_csiborg,
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mean_csiborg + std_csiborg,
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alpha=0.5, color=cols[0])
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for i in range(len(csiborg_counts)):
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ax[0].plot(x, csiborg_counts[i, :], c="cornflowerblue", lw=0.5, zorder=0)
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ax[0].plot(x, mean_csiborg, label="CSiBORG", c="mediumblue", zorder=1)
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# ax[0].fill_between(x, mean_csiborg - std_csiborg,
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# mean_csiborg + std_csiborg,
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# alpha=0.5, color=cols[0])
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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", c=cols[1])
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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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color=cols[1])
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ax[0].plot(x, csiborg5511, label="CSiBORG 5511", c="k", ls="--")
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std5511 = numpy.sqrt(csiborg5511)
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ax[0].fill_between(x, csiborg5511 - std_csiborg, csiborg5511 + std5511,
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alpha=0.2, color="k")
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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[0])
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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[0])
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for i in range(len(quijote_counts)):
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ax[0].plot(x, quijote_counts[i, :], c="palegreen", lw=0.5, zorder=-1)
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ax[1].plot(x, csiborg5511 / mean_quijote, c="k", ls="--")
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ax[0].plot(x, mean_quijote, label="Quijote", c="darkgreen", zorder=1)
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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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# color=cols[1])
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# ax[0].plot(x, csiborg5511, label="CSiBORG 5511", c="k", ls="--")
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# std5511 = numpy.sqrt(csiborg5511)
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# ax[0].fill_between(x, csiborg5511 - std_csiborg, csiborg5511 + std5511,
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# alpha=0.2, color="k")
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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[0])
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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="k")
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# ax[1].plot(x, csiborg5511 / mean_quijote, c="k", ls="--")
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# Labels and accesories
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ax[1].axhline(1, color="k", ls="--",
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lw=0.5 * plt.rcParams["lines.linewidth"], zorder=0)
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# ax[1].axhline(1, color="k", ls="--",
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# lw=0.5 * plt.rcParams["lines.linewidth"], zorder=0)
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# ax[0].set_ylabel(r"$\frac{\mathrm{d}^2 N}{\mathrm{d} V \mathrm{d}\log M_{\rm tot}}~[\mathrm{dex}^{-1} (\mathrm{Mpc} / h)^{-3}]$", # noqa
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# fontsize="small")
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ax[0].set_ylabel("Counts in bins")
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ax[1].set_xlabel(r"$M_{\rm tot}~[M_\odot / h]$", fontsize="small")
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ax[1].set_ylabel(r"$\mathrm{CSiBORG} / \mathrm{Quijote}$",
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fontsize="small")
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m = numpy.isfinite(mean_quijote)
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ax[0].set_xlim(x[m].min(), x[m].max())
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ax[0].set_ylabel(r"$\mathrm{HMF}~[\mathrm{dex}^{-1} (\mathrm{Mpc} / h)^{-3}]$")
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ax[0].set_xlabel(r"$M_{\rm tot}~[M_\odot / h]$", fontsize="small")
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# ax[1].set_ylabel(r"$\mathrm{CSiBORG} / \mathrm{Quijote}$",
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# fontsize="small")
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ax[0].set_xscale("log")
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ax[0].set_yscale("log")
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ax[1].set_ylim(0.5, 2.0)
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# ax[1].set_ylim(0.5, 1.5)
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# ax[1].set_yscale("log")
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ax[0].legend(fontsize="small")
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ax[0].legend()
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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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@ -556,8 +578,8 @@ if __name__ == "__main__":
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if False:
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plot_mass_vs_ncells(7444, pdf=False)
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if False:
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plot_hmf(pdf=False)
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if True:
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plot_hmf(pdf=True)
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if False:
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plot_hmf_quijote_full(pdf=False)
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@ -569,7 +591,7 @@ if __name__ == "__main__":
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plot_groups=False, dmin=45, dmax=60,
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plot_halos=5e13, volume_weight=True)
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if True:
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if False:
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kind = "environment"
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grid = 512
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smooth_scale = 8.0
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