csiborgtools/notebooks/knn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5a38ed25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-12T14:25:46.519408Z",
     "start_time": "2023-04-12T14:25:03.003304Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import joblib\n",
    "from glob import glob\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "import yaml\n",
    "import scienceplots\n",
    "\n",
    "sys.path.append(\"../\")\n",
    "import csiborgtools\n",
    "\n",
    "plt.style.use([\"science\", \"notebook\"])\n",
    "%matplotlib widget \n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "68a45ea8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-12T14:25:46.554959Z",
     "start_time": "2023-04-12T14:25:46.521474Z"
    }
   },
   "outputs": [],
   "source": [
    "folder = \"/mnt/extraspace/rstiskalek/csiborg/tpcf/auto/\"\n",
    "reader = csiborgtools.read.TPCFReader()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "430044b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T22:38:52.737953Z",
     "start_time": "2023-04-09T22:38:51.453058Z"
    }
   },
   "outputs": [],
   "source": [
    "rp, wp = reader.read(\"mass002_spinlow\", folder)\n",
    "wp = reader.mean_wp(wp)\n",
    "\n",
    "rp2, wp2 = reader.read(\"mass002_spinhigh\", folder)\n",
    "wp2 = reader.mean_wp(wp2)\n",
    "\n",
    "rp3, wp3 = reader.read(\"mass002_spinmedian_perm\", folder)\n",
    "wp3 = reader.mean_wp(wp3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9a07acc5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T22:40:07.043102Z",
     "start_time": "2023-04-09T22:40:06.448027Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d33ac43bd06947f69731ba806ecd1e51",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": "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
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='data:image/png;base64,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
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
    "\n",
    "plt.figure()\n",
    "# for i in range(4):\n",
    "plt.plot(rp, wp[:, 0], c=cols[0], label=\"Below median spin\")\n",
    "plt.fill_between(rp, wp[:, 0] - wp[:, 1], wp[:, 0] + wp[:, 1], color=cols[0], alpha=0.3)\n",
    "\n",
    "plt.plot(rp2, wp2[:, 0], c=cols[1], label=\"Above median spin\")\n",
    "plt.fill_between(rp2, wp2[:, 0] - wp2[:, 1], wp2[:, 0] + wp2[:, 1], color=cols[1], alpha=0.3)\n",
    "\n",
    "plt.plot(rp3, wp3[:, 0], c=cols[2], label=\"Randomised spin\")\n",
    "plt.fill_between(rp3, wp3[:, 0] - wp3[:, 1], wp3[:, 0] + wp3[:, 1], color=cols[2], alpha=0.3)\n",
    "\n",
    "plt.legend()\n",
    "plt.xscale(\"log\")\n",
    "plt.xlabel(r\"$r~[\\mathrm{Mpc}]$\")\n",
    "plt.ylabel(r\"$\\xi(r)$\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"../plots/tpcf_spin.png\", dpi=450)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3d39187a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-12T14:25:46.878788Z",
     "start_time": "2023-04-12T14:25:46.556378Z"
    }
   },
   "outputs": [],
   "source": [
    "with open('../scripts/knn_auto.yml', 'r') as file:\n",
    "    config = yaml.safe_load(file)\n",
    "\n",
    "paths = csiborgtools.read.CSiBORGPaths()\n",
    "knnreader = csiborgtools.read.kNNCDFReader()\n",
    "\n",
    "auto_folder = \"/mnt/extraspace/rstiskalek/csiborg/knn/auto/\"\n",
    "cross_folder = \"/mnt/extraspace/rstiskalek/csiborg/knn/cross/\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d46e176f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-12T14:51:22.432093Z",
     "start_time": "2023-04-12T14:49:45.843753Z"
    }
   },
   "outputs": [],
   "source": [
    "rs, cdf = knnreader.read(\"mass001_spinlow\", auto_folder, rmin=0.5, rmax=50)\n",
    "pk = knnreader.mean_prob_k(cdf)\n",
    "\n",
    "\n",
    "rs, cdf = knnreader.read(\"mass001_spinhigh\", auto_folder, rmin=0.5, rmax=50)\n",
    "pk2 = knnreader.mean_prob_k(cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "09015847",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-12T14:51:47.023893Z",
     "start_time": "2023-04-12T14:51:46.404156Z"
    }
   },
   "outputs": [
    {
     "data": {
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   "source": [
    "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "plt.title(r\"Solid: high $\\lambda$, dashed: low $\\lambda$\")\n",
    "# plt.plot(rs, np.nansum(pk, axis=0)[:, 0], c=cols[k])\n",
    "# plt.plot(rs, np.nansum(pk_perm, axis=0)[:, 0], c=cols[k], ls=\"--\")\n",
    "for k in range(1, 5):\n",
    "    plt.plot(rs, pk[k, :, 0], c=cols[k], label=r\"$k = {}$\".format(k))\n",
    "    plt.plot(rs, pk2[k, :, 0], c=cols[k], ls=\"--\")\n",
    "#     plt.plot(rs, pk_perm[k, :, 0], c=cols[k], ls=\"--\")\n",
    "# plt.fill_between(rs, pk[k, :, 0] - pk[k, :, 1], pk[k, :, 0] + pk[k, :, 1])\n",
    "\n",
    "# plt.plot(rs, np.sum(pk, axis=0)[:, 0])\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "plt.ylabel(r\"$P(k | r)$\")\n",
    "plt.xlabel(r\"$r~[\\mathrm{Mpc}]$\")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig(\"../plots/autoknn.png\", dpi=450)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1279a8cb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-12T14:39:14.994393Z",
     "start_time": "2023-04-12T14:38:38.593354Z"
    }
   },
   "outputs": [],
   "source": [
    "rs, cross = knnreader.read(\"mass001\", cross_folder, rmin=1, rmax=50)\n",
    "# pk = knnreader.mean_prob_k(cdf)\n",
    "\n",
    "rs, cdf = knnreader.read(\"mass001_random\", auto_folder, rmin=1, rmax=50)\n",
    "pk = knnreader.mean_prob_k(cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9e0185ce",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-12T14:48:35.806517Z",
     "start_time": "2023-04-12T14:48:35.106183Z"
    }
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
    "\n",
    "plt.figure()\n",
    "for i in range(1, 6):\n",
    "    plt.plot(rs, cross[0, i, :], c=cols[i], label=r\"k = {}\".format(i))\n",
    "    \n",
    "    plt.plot(rs, pk[i, :, 0], c=cols[i], ls=\"--\")\n",
    "\n",
    "plt.axvline(2.65 / 0.705, c=\"red\", ls=\"--\")\n",
    "plt.xlabel(r\"$r~[\\mathrm{Mpc}]$\")\n",
    "plt.ylabel(r\"Cross-correlation\")\n",
    "plt.xscale(\"log\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"../plots/knncross.png\", dpi=450)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df973ed4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-08T15:08:50.076207Z",
     "start_time": "2023-04-08T15:08:46.683983Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "\n",
    "k = 2\n",
    "\n",
    "rs, mu, std = mean_auto(k, \"mass_003_spinhigh\")\n",
    "rs, mu_perm, std_perm = mean_auto(k, \"mass003_spinlow\")\n",
    "z = mu / mu_perm\n",
    "deltaz = z * np.sqrt((std / mu)**2 + (std_perm / mu_perm)**2)\n",
    "plt.plot(rs, z, c=cols[0])\n",
    "plt.fill_between(rs, z - deltaz, z + deltaz, color=cols[0], alpha=0.3)\n",
    "\n",
    "\n",
    "# rs, mu, std = mean_auto(k, \"mass001_spinhigh\")\n",
    "# rs, mu_perm, std_perm = mean_auto(k, \"mass001_spinhigh_perm\")\n",
    "# z = mu / mu_perm\n",
    "# deltaz = z * np.sqrt((std / mu)**2 + (std_perm / mu_perm)**2)\n",
    "# plt.plot(rs, z, c=cols[1])\n",
    "# plt.fill_between(rs, z - deltaz, z + deltaz, color=cols[1], alpha=0.3)\n",
    "\n",
    "\n",
    "plt.axhline(1, c=\"black\", ls=\"--\")\n",
    "# plt.fill_between(rs, mu - std, mu + std, color=cols[2], alpha=0.3)\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f500800",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-08T14:11:02.568517Z",
     "start_time": "2023-04-08T14:11:00.406477Z"
    }
   },
   "outputs": [],
   "source": [
    "# rs, corr = knnreader.read_auto(\"mass001_spinmedian_cross\", folder, rmin=0.5)\n",
    "# rs, corr_perm = knnreader.read_auto(\"mass001_spinmedian_cross_perm\", folder, rmin=0.5)\n",
    "\n",
    "# rs, cdf_low = knnreader.read_auto(\"mass001_spinmedian_cross_perm\", folder, rmin=0.5)\n",
    "rs, cdf_high = knnreader.read_auto(\"mass001_spinmedian_cross_perm\", folder, rmin=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6565101a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-08T14:04:17.514165Z",
     "start_time": "2023-04-08T14:04:15.555281Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84729726",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-08T15:04:01.025738Z",
     "start_time": "2023-04-08T15:03:56.792105Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "rs, mu, std = mean_auto(0, \"mass001_spinlow\")\n",
    "plt.plot(rs, mu, c=cols[0])\n",
    "plt.fill_between(rs, mu - std, mu + std, color=cols[0], alpha=0.5)\n",
    "\n",
    "rs, mu, std = mean_auto(0, \"mass001_spinlow_perm\")\n",
    "plt.plot(rs, mu, c=cols[1])\n",
    "plt.fill_between(rs, mu - std, mu + std, color=cols[1], alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f85f943",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-08T14:11:05.066794Z",
     "start_time": "2023-04-08T14:11:04.362662Z"
    }
   },
   "outputs": [],
   "source": [
    "prk_low = knnreader.prk(rs, cdf_low)\n",
    "prk_high = knnreader.prk(rs, cdf_high)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c862e8b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-08T14:11:07.074775Z",
     "start_time": "2023-04-08T14:11:05.148559Z"
    }
   },
   "outputs": [],
   "source": [
    "k = 0\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "for i in range(101):\n",
    "    plt.plot(rs, prk_low[i, k, :], lw=0.1, c=cols[0])\n",
    "    plt.plot(rs, prk_high[i, k, :], lw=0.1, c=cols[1])\n",
    "\n",
    "    \n",
    "rs, mu, std = mean_auto(0, \"mass001\")\n",
    "plt.plot(rs, mu, c=cols[2])\n",
    "plt.fill_between(rs, mu - std, mu + std, color=cols[2], alpha=0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6682fa88",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-08T14:59:29.041395Z",
     "start_time": "2023-04-08T14:59:24.875903Z"
    }
   },
   "outputs": [],
   "source": [
    "rs, corr = knnreader.read_auto(\"mass001_spinmedian_cross\", folder, rmin=0.5)\n",
    "rs, corrperm = knnreader.read_auto(\"mass001_spinmedian_cross_perm\", folder, rmin=0.5)\n",
    "\n",
    "# rs, corr_low = knnreader.read_auto(\"mass001_spinlow_cross_perm\", folder, rmin=0.5)\n",
    "# rs, corr_high = knnreader.read_auto(\"mass001_spinhigh_cross_perm\", folder, rmin=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "298f28b6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-08T14:59:47.556944Z",
     "start_time": "2023-04-08T14:59:47.103398Z"
    }
   },
   "outputs": [],
   "source": [
    "k = 2\n",
    "plt.figure()\n",
    "for i in range(101):\n",
    "    plt.plot(rs, corr[i, k, :], lw=0.1, c=cols[0])\n",
    "    plt.plot(rs, corrperm[i, k, :], lw=0.1, c=cols[1])\n",
    "#     plt.plot(rs, corr[i, k, :] - corrperm[i, k, :], lw=0.1, c=cols[0])\n",
    "#     plt.plot(rs, , lw=0.1, c=cols[1])\n",
    "    \n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a28d0290",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-08T13:59:25.401233Z",
     "start_time": "2023-04-08T13:59:25.157683Z"
    }
   },
   "outputs": [],
   "source": [
    "k = 0\n",
    "\n",
    "plt.figure()\n",
    "for i in range(101):\n",
    "    plt.plot(rs, corr[i, k, :], c=cols[0], lw=0.1)\n",
    "    \n",
    "    \n",
    "for i in range(101):\n",
    "    plt.plot(rs, corr_perm[i, k, :], c=cols[1], lw=0.1)\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e86ce611",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-08T09:30:44.330451Z",
     "start_time": "2023-04-08T09:30:44.295713Z"
    }
   },
   "outputs": [],
   "source": [
    "mean_prk.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe9a0ef3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de17207d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4cbad0c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe11b032",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "491be45e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f19accd3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T16:51:43.224223Z",
     "start_time": "2023-04-07T16:51:35.597574Z"
    }
   },
   "outputs": [],
   "source": [
    "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "rs, cdf = knnreader.read_auto(\"mass001_spinlow\", folder, rmin=0.5)\n",
    "pk = knnreader.prob_kvolume(cdf, rs, True)\n",
    "for i in range(101):\n",
    "    plt.plot(rs, pk[i, 5, :], lw=0.1, c=cols[0])\n",
    "    \n",
    "    \n",
    "rs, cdf = knnreader.read_auto(\"mass001_spinhigh\", folder, rmin=0.5)\n",
    "pk = knnreader.prob_kvolume(cdf, rs, True)\n",
    "for i in range(101):\n",
    "    plt.plot(rs, pk[i, 5, :], lw=0.1, c=cols[1])    \n",
    "    \n",
    "    \n",
    "rs, cdf = knnreader.read_auto(\"mass001_spinhigh_perm\", folder, rmin=0.5)\n",
    "pk = knnreader.prob_kvolume(cdf, rs, True)\n",
    "for i in range(101):\n",
    "    plt.plot(rs, pk[i, 5, :], lw=0.1, c=cols[2])\n",
    "\n",
    "# plt.xscale(\"log\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f735b44",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a273df53",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "900fd4f8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01974708",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T12:20:08.521290Z",
     "start_time": "2023-04-07T12:20:05.258954Z"
    }
   },
   "outputs": [],
   "source": [
    "cat = csiborgtools.read.HaloCatalogue(7444, paths, min_mass=1e12, max_dist=155/0.705)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47c494f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f02695",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T11:51:11.124583Z",
     "start_time": "2023-04-07T11:51:11.094094Z"
    }
   },
   "outputs": [],
   "source": [
    "x = \"\"\n",
    "for key in auto_config.keys():\n",
    "    x += key + \" \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62d9e837",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T11:51:12.533128Z",
     "start_time": "2023-04-07T11:51:12.499981Z"
    }
   },
   "outputs": [],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db6fc6e4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T10:28:20.801576Z",
     "start_time": "2023-04-07T10:28:20.766160Z"
    }
   },
   "outputs": [],
   "source": [
    "auto_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fdcfb01",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T10:21:14.858309Z",
     "start_time": "2023-04-07T10:21:14.826448Z"
    }
   },
   "outputs": [],
   "source": [
    "auto_folder = \"/mnt/extraspace/rstiskalek/csiborg/knn/auto/\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b5f37af",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T12:20:38.019809Z",
     "start_time": "2023-04-07T12:20:37.987281Z"
    }
   },
   "outputs": [],
   "source": [
    "np.log10(cat[\"totpartmass\"].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5b2df40",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T11:13:18.959385Z",
     "start_time": "2023-04-07T11:13:13.072536Z"
    }
   },
   "outputs": [],
   "source": [
    "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "rs, cdf = knnreader.read_auto(\"004\", auto_folder)\n",
    "pk = knnreader.prob_kvolume(cdf, rs, normalise=True)\n",
    "for i in range(101):\n",
    "    plt.plot(rs, pk[i, 0, :], c=cols[0], lw=0.1)\n",
    "\n",
    "\n",
    "rs, cdf = knnreader.read_auto(\"005\", auto_folder)\n",
    "pk = knnreader.prob_kvolume(cdf, rs, normalise=True)\n",
    "for i in range(101):\n",
    "    plt.plot(rs, pk[i, 0, :], c=cols[1], lw=0.1)\n",
    "\n",
    "    \n",
    "rs, cdf = knnreader.read_auto(\"001\", auto_folder)\n",
    "pk = knnreader.prob_kvolume(cdf, rs, normalise=True)\n",
    "for i in range(101):\n",
    "    plt.plot(rs, pk[i, 0, :], c=cols[2], lw=0.1)\n",
    "\n",
    "# plt.xscale(\"log\")\n",
    "# plt.yscale(\"log\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85c65eef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20ecf551",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ac70147",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T09:47:03.873611Z",
     "start_time": "2023-04-07T09:47:01.794363Z"
    }
   },
   "outputs": [],
   "source": [
    "cat = csiborgtools.read.HaloCatalogue(7444, paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b77b1377",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T07:49:23.539504Z",
     "start_time": "2023-04-07T07:45:50.514216Z"
    }
   },
   "outputs": [],
   "source": [
    "from tqdm import trange\n",
    "x = np.full((len(ics), 3), np.nan)\n",
    "for i in trange(len(ics)):\n",
    "    cat = csiborgtools.read.HaloCatalogue(ics[i], paths, max_dist=155 / 0.705)\n",
    "    for j, th in enumerate([1e12, 1e13, 1e14]):\n",
    "        mask = cat[\"totpartmass\"] > th\n",
    "        x[i, j] = np.nanmedian(cat[\"lambda200c\"][mask])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e55d821d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T07:51:55.598449Z",
     "start_time": "2023-04-07T07:51:55.567861Z"
    }
   },
   "outputs": [],
   "source": [
    "np.mean(x[:, 2]), np.std(x[:, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bae7373",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T11:12:14.971055Z",
     "start_time": "2023-04-07T11:12:14.938117Z"
    }
   },
   "outputs": [],
   "source": [
    "cdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "127cb78e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8368d476",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.nanmedian()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6b5a68c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-06T17:59:09.028319Z",
     "start_time": "2023-04-06T17:59:08.953783Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "\n",
    "plt.scatter(cat[\"m200\"], cat[\"lambda200c\"], s=1)\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2eb1c2e4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a79dde6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-05T13:42:26.370674Z",
     "start_time": "2023-04-05T13:42:22.862756Z"
    }
   },
   "outputs": [],
   "source": [
    "files = glob(\"/mnt/extraspace/rstiskalek/csiborg/knn/auto/*\")\n",
    "\n",
    "ks = [0, 1, 2, 3, 4, 5, 6, 7]\n",
    "rs, cdf, thresholds = knnreader.read(files, ks, rmin=0.01, rmax=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88de6882",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-05T13:42:26.943147Z",
     "start_time": "2023-04-05T13:42:26.372382Z"
    }
   },
   "outputs": [],
   "source": [
    "pk = knnreader.prob_kvolume(cdf, rs, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbb11ba2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-05T13:43:45.754506Z",
     "start_time": "2023-04-05T13:43:38.953908Z"
    }
   },
   "outputs": [],
   "source": [
    "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
    "\n",
    "plt.figure()\n",
    "n = 1\n",
    "for k in range(7):\n",
    "    plt.plot(rs, np.mean(pk[:, n, k, :], axis=0), c=cols[k], label=r\"$k = {}$\".format(k))\n",
    "    for i in range(101):\n",
    "        plt.plot(rs, pk[i, n, k, :], c=cols[k], lw=0.05)\n",
    "\n",
    "plt.legend(frameon=False)\n",
    "plt.xlabel(r\"$r~\\left[\\mathrm{Mpc}\\right]$\")\n",
    "plt.ylabel(r\"$P\\left(k | V = 4 \\pi r^3 / 3\\right)$\")\n",
    "# plt.savefig(\"../plots/test.png\", dpi=450)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6999c90d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20bbeb54",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-03T17:10:30.078450Z",
     "start_time": "2023-04-03T17:10:29.171089Z"
    }
   },
   "outputs": [],
   "source": [
    "n = 2\n",
    "k = 1\n",
    "\n",
    "x = cdf[:, n, k - 1, :] - cdf[:, n, k, :]\n",
    "\n",
    "plt.figure()\n",
    "for i in range(101):\n",
    "    plt.plot(rs, x[i, :])\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86091fcc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-03T17:13:07.495144Z",
     "start_time": "2023-04-03T17:13:06.635811Z"
    }
   },
   "outputs": [],
   "source": [
    "files = knnreader.cross_files(7444, \"/mnt/extraspace/rstiskalek/csiborg/knn/cross/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0917bd5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-03T17:13:11.011391Z",
     "start_time": "2023-04-03T17:13:07.496523Z"
    }
   },
   "outputs": [],
   "source": [
    "ks = [0, 1, 2, 3, 4, 5, 6, 7]\n",
    "rs, cross, threshold = knnreader.read(files, ks, rmin=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "beba86db",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-03T17:13:11.152680Z",
     "start_time": "2023-04-03T17:13:11.013209Z"
    }
   },
   "outputs": [],
   "source": [
    "n = 0\n",
    "k = 1\n",
    "\n",
    "plt.figure()\n",
    "for i in range(100):\n",
    "    plt.plot(rs, cross[i, n, k - 1, :] - cross[i, n, k, :])\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "plt.axvline(2.65 / 0.705)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adf96c3b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-03T14:25:45.275844Z",
     "start_time": "2023-04-03T14:25:44.855207Z"
    }
   },
   "outputs": [],
   "source": [
    "\"/mnt/extraspace/hdesmond/ramses_out_7444/output_00950/clump_00950.dat\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8179c3e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f803105a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55544ddd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c7add55",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75a5e6f7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b6d813b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19115f4c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4218b673",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:27:07.868868Z",
     "start_time": "2023-04-01T08:27:04.088778Z"
    }
   },
   "outputs": [],
   "source": [
    "cat1 = csiborgtools.read.HaloCatalogue(7444, min_mass=1e13, max_dist=155 / 0.705)\n",
    "cat2 = csiborgtools.read.HaloCatalogue(7468, min_mass=1e13, max_dist=155 / 0.705)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ff7a1b6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:27:07.923418Z",
     "start_time": "2023-04-01T08:27:07.870519Z"
    }
   },
   "outputs": [],
   "source": [
    "knncdf = csiborgtools.match.kNN_CDF()\n",
    "\n",
    "\n",
    "knn1 = NearestNeighbors()\n",
    "knn1.fit(cat1.positions)\n",
    "\n",
    "knn2 = NearestNeighbors()\n",
    "knn2.fit(cat2.positions)\n",
    "\n",
    "# rs, cdf = knncdf(knn, nneighbours=2, Rmax=155 / 0.705, rmin=0.01, rmax=100,\n",
    "#                   nsamples=int(1e6), neval=int(1e4), random_state=42, batch_size=int(1e6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88a31951",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:46:56.273595Z",
     "start_time": "2023-04-01T08:46:56.088408Z"
    }
   },
   "outputs": [],
   "source": [
    "!ls /mnt/extraspace/rstiskalek/csiborg/knn/cross/knncdf_7444_7468.p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7c2d465",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T10:05:26.294670Z",
     "start_time": "2023-04-01T10:05:25.954223Z"
    }
   },
   "outputs": [],
   "source": [
    "from glob import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6083fcbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T10:05:52.758709Z",
     "start_time": "2023-04-01T10:05:52.705627Z"
    }
   },
   "outputs": [],
   "source": [
    "files = glob(\"/mnt/extraspace/rstiskalek/csiborg/knn/cross/*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d6b9d57",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T10:06:00.023427Z",
     "start_time": "2023-04-01T10:05:59.645149Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "980f74df",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T10:12:49.924557Z",
     "start_time": "2023-04-01T10:12:49.714545Z"
    }
   },
   "outputs": [],
   "source": [
    "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
    "\n",
    "plt.figure()\n",
    "for file in files:\n",
    "    d = joblib.load(file)\n",
    "    mask = d[\"rs\"] > 0.1\n",
    "    plt.plot(d[\"rs\"][mask], d[\"corr_0\"][0, mask], c=cols[0], lw=0.4)\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "plt.axvline(2.65 / 0.705, lw=0.8, c=\"red\", ls=\"--\")\n",
    "# plt.yscale(\"log\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "997e8f91",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25936419",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:51:11.896378Z",
     "start_time": "2023-04-01T08:51:11.865150Z"
    }
   },
   "outputs": [],
   "source": [
    "5500 / comb(5, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "043a93ff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:50:37.181588Z",
     "start_time": "2023-04-01T08:50:36.718438Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(d[\"rs\"], d[\"corr_0\"][1, :])\n",
    "plt.plot(d[\"rs\"], d[\"corr_1\"][1, :])\n",
    "plt.plot(d[\"rs\"], d[\"corr_2\"][1, :])\n",
    "\n",
    "# plt.yscale(\"log\")\n",
    "# plt.xscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "279d8e58",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d08b0dc3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:27:19.271922Z",
     "start_time": "2023-04-01T08:27:07.925222Z"
    }
   },
   "outputs": [],
   "source": [
    "# rs, cdf = knncdf(knn1, nneighbours=2, Rmax=155 / 0.705, rmin=0.01, rmax=100,\n",
    "#                  nsamples=int(1e6), neval=int(1e4), random_state=42, batch_size=int(1e6))\n",
    "\n",
    "rs, cdf0, cdf1, joint_cdf = knncdf.joint(knn1, knn2, nneighbours=8, Rmax=155 / 0.705,\n",
    "                                         rmin=0.01, rmax=100, nsamples=int(1e6), neval=int(1e4),\n",
    "                                         random_state=42, batch_size=int(1e6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0866fe23",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:27:19.436097Z",
     "start_time": "2023-04-01T08:27:19.397998Z"
    }
   },
   "outputs": [],
   "source": [
    "cdf0 = knncdf.clipped_cdf(cdf0)\n",
    "cdf1 = knncdf.clipped_cdf(cdf1)\n",
    "joint_cdf = knncdf.clipped_cdf(joint_cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0649b7e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:27:59.413449Z",
     "start_time": "2023-04-01T08:27:59.244281Z"
    }
   },
   "outputs": [],
   "source": [
    "corr = knncdf.joint_to_corr(cdf0, cdf1, joint_cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4d28785",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:33:12.811065Z",
     "start_time": "2023-04-01T08:33:12.386639Z"
    }
   },
   "outputs": [],
   "source": [
    "ics = [7444, 7468, 7492, 7516, 7540, 7564, 7588, 7612, 7636, 7660, 7684,\n",
    "       7708, 7732, 7756, 7780, 7804, 7828, 7852, 7876, 7900, 7924, 7948,\n",
    "       7972, 7996, 8020, 8044, 8068, 8092, 8116, 8140, 8164, 8188, 8212,\n",
    "       8236, 8260, 8284, 8308, 8332, 8356, 8380, 8404, 8428, 8452, 8476,\n",
    "       8500, 8524, 8548, 8572, 8596, 8620, 8644, 8668, 8692, 8716, 8740,\n",
    "       8764, 8788, 8812, 8836, 8860, 8884, 8908, 8932, 8956, 8980, 9004,\n",
    "       9028, 9052, 9076, 9100, 9124, 9148, 9172, 9196, 9220, 9244, 9268,\n",
    "       9292, 9316, 9340, 9364, 9388, 9412, 9436, 9460, 9484, 9508, 9532,\n",
    "       9556, 9580, 9604, 9628, 9652, 9676, 9700, 9724, 9748, 9772, 9796,\n",
    "       9820, 9844]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaac3eff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:34:00.873304Z",
     "start_time": "2023-04-01T08:34:00.842613Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.special import comb\n",
    "\n",
    "from itertools import combinations\n",
    "# for subset in itertools.combinations(stuff, L):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "042fe713",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:34:21.387439Z",
     "start_time": "2023-04-01T08:34:21.325627Z"
    }
   },
   "outputs": [],
   "source": [
    "list(combinations(ics, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "762ec153",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c43f012",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:33:18.308219Z",
     "start_time": "2023-04-01T08:33:18.275965Z"
    }
   },
   "outputs": [],
   "source": [
    "comb()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaca64f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fb9e08e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:28:35.326570Z",
     "start_time": "2023-04-01T08:28:35.158664Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "\n",
    "# plt.plot(rs, knncdf.peaked_cdf(cdf0[0, :]))\n",
    "# plt.plot(rs, knncdf.peaked_cdf(cdf1[0, :]))\n",
    "# plt.plot(rs, knncdf.peaked_cdf(joint_cdf[0, :]))\n",
    "for i in range(8):\n",
    "    plt.plot(rs, corr[i, :])\n",
    "\n",
    "\n",
    "# plt.yscale(\"log\")\n",
    "# plt.xscale(\"log\")\n",
    "plt.axvline(2.65 / 0.705, c=\"red\", ls=\"--\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f295a0f9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d5f3d02",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T07:20:34.202874Z",
     "start_time": "2023-04-01T07:20:28.353637Z"
    }
   },
   "outputs": [],
   "source": [
    "dist1, dist2 = knncdf.joint(knn1, knn2, nneighbours=2, Rmax=155 / 0.705, rmin=0.01, rmax=100,\n",
    "             nsamples=int(1e6), neval=int(1e4), random_state=42, batch_size=int(1e6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a76c8124",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T07:20:41.074933Z",
     "start_time": "2023-04-01T07:20:41.041448Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "127b91a8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b9a8cf0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1825f00",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T06:01:29.388586Z",
     "start_time": "2023-04-01T06:01:29.321025Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(rs, knncdf.peaked_cdf(cdf[0, :]))\n",
    "\n",
    "plt.yscale(\"log\" )\n",
    "plt.xscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "289549a0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-31T22:55:20.690887Z",
     "start_time": "2023-03-31T22:55:20.656550Z"
    }
   },
   "outputs": [],
   "source": [
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a8c5202",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-31T22:54:52.330633Z",
     "start_time": "2023-03-31T22:54:52.299548Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46f54897",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-31T22:54:25.138813Z",
     "start_time": "2023-03-31T22:54:25.105044Z"
    }
   },
   "outputs": [],
   "source": [
    "dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58806ab9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c59b3a19",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e345945c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-31T09:35:49.059172Z",
     "start_time": "2023-03-31T09:35:42.817291Z"
    }
   },
   "outputs": [],
   "source": [
    "m1 = (rs > 1) & (rs < 35)\n",
    "\n",
    "fig, axs = plt.subplots(ncols=3, figsize=(6.4 * 1.5, 4.8), sharey=True)\n",
    "fig.subplots_adjust(wspace=0)\n",
    "for k in range(3):\n",
    "    for n in range(len(ics)):\n",
    "        m = m1 & (cdfs[n, k, :] > 1e-3)\n",
    "        axs[k].plot(rs[m], cdfs[n, k, m], c=\"black\", lw=0.05)\n",
    "\n",
    "    axs[k].set_xscale(\"log\")\n",
    "    axs[k].set_yscale(\"log\")\n",
    "    axs[k].set_title(r\"$k = {}$\".format(k))\n",
    "    axs[k].set_xlabel(r\"$r~\\left[\\mathrm{Mpc}\\right]$\")\n",
    "\n",
    "axs[0].set_ylabel(r\"Peaked CDF\")\n",
    "\n",
    "plt.tight_layout(w_pad=0)\n",
    "fig.savefig(\"../plots/peaked_cdf.png\", dpi=450)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f8786c0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-31T09:50:10.103650Z",
     "start_time": "2023-03-31T09:50:02.221741Z"
    }
   },
   "outputs": [],
   "source": [
    "m = (rs > 0.5) & (rs < 35)\n",
    "\n",
    "fig, axs = plt.subplots(ncols=3, figsize=(6.4 * 1.5, 4.8), sharey=True)\n",
    "fig.subplots_adjust(wspace=0)\n",
    "for k in range(3):\n",
    "    mu = np.nanmean(cdfs[:, k, :], axis=0)\n",
    "\n",
    "    for n in range(len(ics)):\n",
    "        axs[k].plot(rs[m], (cdfs[n, k, :] / mu)[m], c=\"black\", lw=0.1)\n",
    "\n",
    "    axs[k].set_ylim(0.5, 1.5)\n",
    "    axs[k].axhline(1, ls=\"--\", c=\"red\", zorder=0)\n",
    "    axs[k].axvline(2.65 / 0.705, ls=\"--\", c=\"red\", zorder=0)\n",
    "    axs[k].set_xscale(\"log\")\n",
    "    axs[k].set_xlabel(r\"$r~\\left[\\mathrm{Mpc}\\right]$\")\n",
    "    axs[k].set_title(r\"$k = {}$\".format(k))\n",
    "    \n",
    "axs[0].set_ylabel(r\"Relative peaked CDF\")\n",
    "plt.tight_layout(w_pad=0)\n",
    "fig.savefig(\"../plots/peaked_cdf_ratios.png\", dpi=450)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f64cec1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T15:46:31.532259Z",
     "start_time": "2023-03-30T15:46:30.977449Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "k = 2\n",
    "mu = np.nanmean(cdfs[:, k, :], axis=0)\n",
    "# plt.plot(rs, mu, c=\"black\")\n",
    "for i in range(len(ics)):\n",
    "    plt.plot(rs, cdfs[i, k, :] / mu)\n",
    "\n",
    "\n",
    "plt.ylim(0.75, 1.25)\n",
    "plt.axhline(1, ls=\"--\", c=\"black\")\n",
    "plt.xscale(\"log\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6784766",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b416efb3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e650fe2c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1311187d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03e49a11",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T14:58:29.937514Z",
     "start_time": "2023-03-30T14:58:29.530552Z"
    }
   },
   "outputs": [],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24578cba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0024bbf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6dc55410",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T14:41:24.290602Z",
     "start_time": "2023-03-30T14:41:16.204679Z"
    }
   },
   "outputs": [],
   "source": [
    "dist0, __ = knn0.kneighbors(X, 3)\n",
    "distx, __ = knnx.kneighbors(X, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11508c3c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T14:41:24.560538Z",
     "start_time": "2023-03-30T14:41:24.292674Z"
    }
   },
   "outputs": [],
   "source": [
    "x0, y0 = knncdf.peaked_cdf_from_samples(dist0[:, 0], 0.5, 20, neval=10000)\n",
    "xx, yx = knncdf.peaked_cdf_from_samples(distx[:, 0], 0.5, 20, neval=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "404501ad",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T14:41:24.598933Z",
     "start_time": "2023-03-30T14:41:24.562062Z"
    }
   },
   "outputs": [],
   "source": [
    "distx[:, 0].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43e08969",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T14:46:10.262865Z",
     "start_time": "2023-03-30T14:46:09.486658Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(x0, y0)\n",
    "plt.plot(xx, yx)\n",
    "\n",
    "plt.yscale(\"log\")\n",
    "plt.xscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39547a75",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e160b38",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T13:02:02.033125Z",
     "start_time": "2023-03-30T13:02:00.674878Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "\n",
    "for i in range(3):\n",
    "    plt.plot(*knncdf.cdf_from_samples(dist0[:, i], 1, 25))\n",
    "    plt.plot(*knncdf.cdf_from_samples(distx[:, i], 1, 25))\n",
    "\n",
    "# plt.xlim(0.5, 25)\n",
    "\n",
    "plt.yscale(\"log\")\n",
    "plt.xscale(\"log\")\n",
    "plt.xlabel(r\"$r~\\left[\\mathrm{Mpc}\\right]$\")\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bfb65d8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4703d81c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T12:13:35.958444Z",
     "start_time": "2023-03-30T12:13:35.924241Z"
    }
   },
   "outputs": [],
   "source": [
    "x = dist[:, 0]\n",
    "q = np.linspace(0, 100, int(x.size / 5))\n",
    "\n",
    "p = np.percentile(x, q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b054c6df",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T12:16:50.052225Z",
     "start_time": "2023-03-30T12:16:50.020395Z"
    }
   },
   "outputs": [],
   "source": [
    "y = np.sort(x)\n",
    "\n",
    "yy = np.arange(y.size) / y.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5445c964",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T12:16:53.599925Z",
     "start_time": "2023-03-30T12:16:53.521266Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(p, q / 100)\n",
    "\n",
    "plt.plot(y, yy)\n",
    "\n",
    "# plt.yscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87fe5874",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb0ad6b9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T12:03:34.387625Z",
     "start_time": "2023-03-30T12:03:34.290961Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.hist(dist[:, 0], bins=\"auto\", histtype=\"step\")\n",
    "plt.hist(dist[:, 1], bins=\"auto\", histtype=\"step\")\n",
    "plt.hist(dist[:, 2], bins=\"auto\", histtype=\"step\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2aba833",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f70f238",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03bcb191",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T11:38:04.906150Z",
     "start_time": "2023-03-30T11:38:04.758107Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.hist(cat0[\"dec\"], bins=\"auto\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5ad4722",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T11:53:23.004853Z",
     "start_time": "2023-03-30T11:53:22.971967Z"
    }
   },
   "outputs": [],
   "source": [
    "gen = np.random.default_rng(22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "785b530a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T11:53:23.330397Z",
     "start_time": "2023-03-30T11:53:23.296612Z"
    }
   },
   "outputs": [],
   "source": [
    "gen.normal()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3d3b5e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "464b606d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T11:36:13.649124Z",
     "start_time": "2023-03-30T11:36:12.995693Z"
    }
   },
   "outputs": [],
   "source": [
    "theta = np.linspace( t, np.pi, 100)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(theta, np.sin(theta))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c29049f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd2a3295",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af9abf04",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T11:10:11.104389Z",
     "start_time": "2023-03-30T11:10:11.070499Z"
    }
   },
   "outputs": [],
   "source": [
    "X = np.array([-3.9514747, -0.6966991,  2.97158]).reshape(1, -1)\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e181b3c3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T11:32:17.840355Z",
     "start_time": "2023-03-30T11:32:17.351883Z"
    }
   },
   "outputs": [],
   "source": [
    "dist, indxs = knn0.kneighbors(X, n_neighbors=1)\n",
    "\n",
    "dist, indxs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d38fd960",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T11:10:18.182326Z",
     "start_time": "2023-03-30T11:10:18.145629Z"
    }
   },
   "outputs": [],
   "source": [
    "cat0.positions[indxs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a16ddc2f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbbe8fb6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "759a0149",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "312c96c9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b097637b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ced23cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be26cbcc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
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