Satellite_Calculations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6b438979",
   "metadata": {},
   "source": [
    "### Computing and Binning Radial Distances between Sat-Host (PAI), and Time of Infall:\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "43b3a478",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from base import *\n",
    "import tqdm as tqdm\n",
    "\n",
    "from scipy import interpolate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d3ff4f3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pulling data on all tracked gas of selected satellites into df.\n",
    "allGas = pd.DataFrame()\n",
    "\n",
    "keys = ['h148_12','h148_27','h148_34','h148_38','h148_55','h148_65','h148_249',\n",
    "        'h148_251','h148_282','h229_14','h229_18','h229_20','h229_22',\n",
    "        'h229_49','h242_21','h242_38','h242_69','h329_29','h329_117']\n",
    "\n",
    "for i,key in enumerate(keys):\n",
    "    i += 1\n",
    "    allGas1 = pd.read_hdf(f'{rootPath}Stellar_Feedback_Code/SNeData/all_particles.hdf5',\n",
    "                          key=key)\n",
    "    allGas1['key'] = key\n",
    "    allGas = pd.concat([allGas, allGas1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fb26b5ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "### point at infall calculation ###\n",
    "@pynbody.derived_array\n",
    "def pai(gas):\n",
    "    return np.array(np.sqrt((gas['host_Xc'] - gas['sat_Xc'])**2 \n",
    "                            + (gas['host_Yc'] - gas['sat_Yc'])**2 \n",
    "                            + (gas['host_Zc'] - gas['sat_Zc'])**2 \n",
    "                           )/ gas['hostRvir']\n",
    "                   )\n",
    "\n",
    "### interpolation for approximating time of infall ###\n",
    "@pynbody.derived_array\n",
    "def infall(gas, key):\n",
    "    gas1 = gas[gas.key==key]  # will get errors if not using array of all gas.\n",
    "    gas1 = gas1.sort_values(by=['time'])\n",
    "    \n",
    "    pai1 = np.unique(gas1.pai)\n",
    "    time = np.unique(gas1.time)\n",
    "\n",
    "    f = interpolate.interp1d(pai1, time, fill_value='extrapolate')\n",
    "    \n",
    "    return f(1)  # finding time when at 1 Rvir of host. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58bf1bb2",
   "metadata": {},
   "source": [
    "#### Generating HDF5 of satellite data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4b2b910d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 19/19 [00:05<00:00,  3.55it/s]\n"
     ]
    }
   ],
   "source": [
    "from pickle_prot import pickle_protocol\n",
    "\n",
    "allGas['host'] = [x[:4] for x in np.array(allGas['key'])]\n",
    "allGas['sat'] = [x[5:] for x in np.array(allGas['key'])]\n",
    "allGas['pai'] = pai(allGas)\n",
    "\n",
    "keys = get_keys()\n",
    "\n",
    "for key in tqdm.tqdm(keys):\n",
    "    gas = allGas[allGas.key==key]\n",
    "    \n",
    "    time, uniq_ind = np.unique(gas.time, return_index = True)\n",
    "    gas = gas.iloc[uniq_ind]\n",
    "    \n",
    "    t_start = [gas.time.max()] * len(time)\n",
    "    t_infall = [infall(gas, key)] * len(time)\n",
    "    key1 = [key] * len(time)\n",
    "    \n",
    "    satdat = pd.DataFrame(np.array([key1, time, gas.pai, t_start,\n",
    "                                    t_infall]).T,\n",
    "                          columns=['key', 'time', 'pai', 't_start', 't_infall'])\n",
    "    \n",
    "    # saving to hdf.\n",
    "    filepath = f'{rootPath}Stellar_Feedback_Code/SNeData/satdat.hdf5'\n",
    "    with pickle_protocol(4):\n",
    "        satdat.to_hdf(filepath, key=key)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c873d568",
   "metadata": {},
   "source": [
    "## Use this cell in your notebook to import satellite data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ed26056",
   "metadata": {},
   "outputs": [],
   "source": [
    "keys = ['h148_12','h148_27','h148_34','h148_38','h148_55','h148_65','h148_249',\n",
    "        'h148_251','h148_282','h229_14','h229_18','h229_20','h229_22',\n",
    "        'h229_49','h242_21','h242_38','h242_69','h329_29','h329_117']\n",
    "\n",
    "satdat = pd.DataFrame([])\n",
    "\n",
    "for key in keys:\n",
    "    satdat1 = pd.read_hdf(f'{rootPath}Stellar_Feedback_Code/SNeData/satdat.hdf5',\n",
    "                          key=key)\n",
    "    satdat1 = satdat1.astype({'time': float, 'pai': float, 't_start': float,\n",
    "                              't_infall': float})\n",
    "    satdat1['key'] = key\n",
    "    satdat = pd.concat([satdat, satdat1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "00334136",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>time</th>\n",
       "      <th>pai</th>\n",
       "      <th>t_start</th>\n",
       "      <th>t_infall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>h148_12</td>\n",
       "      <td>7.336092</td>\n",
       "      <td>1.700105</td>\n",
       "      <td>13.800797</td>\n",
       "      <td>8.529619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>h148_12</td>\n",
       "      <td>7.767072</td>\n",
       "      <td>1.191512</td>\n",
       "      <td>13.800797</td>\n",
       "      <td>8.529619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>h148_12</td>\n",
       "      <td>8.198053</td>\n",
       "      <td>0.689575</td>\n",
       "      <td>13.800797</td>\n",
       "      <td>8.529619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>h148_12</td>\n",
       "      <td>8.608831</td>\n",
       "      <td>0.527208</td>\n",
       "      <td>13.800797</td>\n",
       "      <td>8.529619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>h148_12</td>\n",
       "      <td>9.060013</td>\n",
       "      <td>0.755363</td>\n",
       "      <td>13.800797</td>\n",
       "      <td>8.529619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>h329_117</td>\n",
       "      <td>12.615601</td>\n",
       "      <td>1.264641</td>\n",
       "      <td>13.800797</td>\n",
       "      <td>7.071061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>h329_117</td>\n",
       "      <td>12.938837</td>\n",
       "      <td>1.132123</td>\n",
       "      <td>13.800797</td>\n",
       "      <td>7.071061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>h329_117</td>\n",
       "      <td>13.262072</td>\n",
       "      <td>0.963317</td>\n",
       "      <td>13.800797</td>\n",
       "      <td>7.071061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>h329_117</td>\n",
       "      <td>13.585307</td>\n",
       "      <td>0.762873</td>\n",
       "      <td>13.800797</td>\n",
       "      <td>7.071061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>h329_117</td>\n",
       "      <td>13.800797</td>\n",
       "      <td>0.603869</td>\n",
       "      <td>13.800797</td>\n",
       "      <td>7.071061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>463 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         key       time       pai    t_start  t_infall\n",
       "0    h148_12   7.336092  1.700105  13.800797  8.529619\n",
       "1    h148_12   7.767072  1.191512  13.800797  8.529619\n",
       "2    h148_12   8.198053  0.689575  13.800797  8.529619\n",
       "3    h148_12   8.608831  0.527208  13.800797  8.529619\n",
       "4    h148_12   9.060013  0.755363  13.800797  8.529619\n",
       "..       ...        ...       ...        ...       ...\n",
       "33  h329_117  12.615601  1.264641  13.800797  7.071061\n",
       "34  h329_117  12.938837  1.132123  13.800797  7.071061\n",
       "35  h329_117  13.262072  0.963317  13.800797  7.071061\n",
       "36  h329_117  13.585307  0.762873  13.800797  7.071061\n",
       "37  h329_117  13.800797  0.603869  13.800797  7.071061\n",
       "\n",
       "[463 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "satdat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "4477a930",
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = satdat[satdat.key=='h242_69']\n",
    "# dat[['time','pai']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "f944c13f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dat.time, dat.pai)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8fb8dcb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4183bdf",
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}