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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "recorded-display", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Overview of Diffmah and DiffmahPop\n", | ||
| "\n", | ||
| "Starting from a collection of best-fitting approximations to halo merger trees, you can use the `calc_halo_history` function to compute the assembly history for every halo in the sample. Here we'll just demonstrate a few simple cases.\n", | ||
| "\n", | ||
| "Note that in these examples, we pass in arbitrary values for the early- and late-time indices. However, for real halos (and also for the results returned by the diffmah-provided MAH fitting script), $0 < \\alpha_{\\rm late} < \\alpha_{\\rm early}.$" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "8dc6f535", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import numpy as np\n", | ||
| "from matplotlib import pyplot as plt\n", | ||
| "import matplotlib.cm as cm" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "plain-arbor", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from diffmah import calc_halo_history\n", | ||
| "\n", | ||
| "n_halos, n_times = 50, 100\n", | ||
| "tarr = np.linspace(0.5, 13.8, n_times)\n", | ||
| "colors=cm.coolwarm(np.linspace(1, 0, n_halos)) # red first\n", | ||
| "\n", | ||
| "tauc = np.linspace(1, 5, n_halos)\n", | ||
| "logmp = 12\n", | ||
| "early, late = 2, 1\n", | ||
| "dmhdt, log_mah = calc_halo_history(tarr, tarr[-1], logmp, tauc, early, late)\n", | ||
| "\n", | ||
| "fig, ax = plt.subplots(1, 1)\n", | ||
| "__=ax.loglog()\n", | ||
| "for ih in range(n_halos):\n", | ||
| " __=ax.plot(tarr, 10**log_mah[ih, :], color=colors[ih])" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "completed-macro", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "tauc = 2.0\n", | ||
| "early = np.linspace(1, 3, n_halos)\n", | ||
| "late = 1\n", | ||
| "dmhdt, log_mah = calc_halo_history(tarr, tarr[-1], logmp, tauc, early, late)\n", | ||
| "\n", | ||
| "\n", | ||
| "fig, ax = plt.subplots(1, 1)\n", | ||
| "__=ax.loglog()\n", | ||
| "for ih in range(n_halos):\n", | ||
| " __=ax.plot(tarr, 10**log_mah[ih, :], color=colors[ih])" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "constant-thunder", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "tauc = 2.0\n", | ||
| "early = 3\n", | ||
| "late = np.linspace(0.01, 3, n_halos)\n", | ||
| "dmhdt, log_mah = calc_halo_history(tarr, tarr[-1], logmp, tauc, early, late)\n", | ||
| "\n", | ||
| "fig, ax = plt.subplots(1, 1)\n", | ||
| "__=ax.loglog()\n", | ||
| "for ih in range(n_halos):\n", | ||
| " __=ax.plot(tarr, 10**log_mah[ih, :], color=colors[ih])" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "acknowledged-exhibition", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Generating Monte Carlo realizations of halo MAHs with DiffmahPop" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "imported-jungle", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from diffmah import mc_halo_population\n", | ||
| "\n", | ||
| "cosmic_time = np.linspace(0.5, 13.8, 100)\n", | ||
| "\n", | ||
| "n_halos = 5_000\n", | ||
| "logmh = np.zeros(n_halos) + 12\n", | ||
| "\n", | ||
| "_res = mc_halo_population(cosmic_time, cosmic_time[-1], logmh)\n", | ||
| "dmhdt, log_mah, early, late, lgtc, mah_type_arr = _res\n", | ||
| "\n", | ||
| "fig, ax = plt.subplots(1, 1)\n", | ||
| "__=ax.loglog()\n", | ||
| "for ih in range(5):\n", | ||
| " __=ax.plot(tarr, 10**log_mah[ih, :])" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "fixed-fluid", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3 (ipykernel)", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.11.4" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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