{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from h5py import File\n",
    "from scipy.stats import spearmanr\n",
    "\n",
    "import csiborgtools\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)\n",
    "\n",
    "# d = np.load(paths.field_interpolated(\"SDSS\", \"csiborg2_main\", 16817, \"density\", \"SPH\", 1024))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey = csiborgtools.SDSS()(apply_selection=False)\n",
    "# survey = csiborgtools.SDSSxALFALFA()(apply_selection=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Reading fields:   0%|          | 0/20 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Reading fields: 100%|██████████| 20/20 [00:11<00:00,  1.80it/s]\n",
      "Reading fields: 100%|██████████| 20/20 [00:10<00:00,  1.86it/s]\n"
     ]
    }
   ],
   "source": [
    "for kind in [\"main\", \"random\"]:\n",
    "    x, smooth = csiborgtools.summary.read_interpolated_field(survey, f\"csiborg2_{kind}\", \"density\", \"SPH\", 1024, paths)\n",
    "    np .savez(f\"../data/{survey.name}_{kind}_density_SPH_1024.npz\", val=x, smooth_scales=smooth)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 641409, 5)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan]]], dtype=float32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.load(\"../data/SDSS_main_density_SPH_1024.npz\")[\"val\"]"
   ]
  },
  {
   "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,
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}