csiborgtools/notebooks/test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import numpy\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "import csiborgtools\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "d0 = np.load(\"/mnt/extraspace/rstiskalek/csiborg_postprocessing/field_interpolated/TNG300-1_TNG300-1_density_PCS_00000_1024_scatter0.0.npz\")\n",
    "d1 = np.load(\"/mnt/extraspace/rstiskalek/csiborg_postprocessing/field_interpolated/TNG300-1_TNG300-1_density_PCS_00000_1024_scatter1.0.npz\")\n",
    "d2 = np.load(\"/mnt/extraspace/rstiskalek/csiborg_postprocessing/field_interpolated/TNG300-1_TNG300-1_density_PCS_00000_1024_scatter2.0.npz\")\n",
    "\n",
    "val0 = d0[\"val\"]\n",
    "val1 = d1[\"val\"]\n",
    "val2 = d2[\"val\"]\n",
    "\n",
    "\n",
    "with h5py.File(\"/mnt/extraspace/rstiskalek/TNG300-1/postprocessing/subhalo_catalogue_099.hdf5\", 'r') as f:\n",
    "    mstar = f[\"SubhaloMassType\"][:, 4] * 1e10\n",
    "    mhi = f[\"m_neutral_H\"][:]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SignificanceResult(statistic=-0.1217955937334526, pvalue=0.0)\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import spearmanr\n",
    "\n",
    "k = 1\n",
    "\n",
    "x = mhi\n",
    "y = val1[:, k]\n",
    "\n",
    "m = (x > 0) & (y > 0)\n",
    "x = x[m]\n",
    "y = y[m]\n",
    "\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "print(spearmanr(x, y))\n",
    "plt.scatter(x, y, s=0.1)\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv_csiborg",
   "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": 2
}