Outflow Rate Over Time.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os # set tangos environment variable before loading tangos so we know where the data is\n",
    "os.environ['TANGOS_DB_CONNECTION'] = '/home/akinshol/Data/Timescales/TangosData/h329.db'\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import pynbody\n",
    "import tangos\n",
    "    \n",
    "%matplotlib inline\n",
    "#mpl.rcParams['font.monospace'] = 'Terminal'\n",
    "mpl.rc('font',**{'family':'serif','monospace':['Palatino']})\n",
    "mpl.rc('text', usetex=True)\n",
    "mpl.rcParams.update({'font.size': 9})\n",
    "\n",
    "pynbody.config['halo-class-priority'] =  [pynbody.halo.ahf.AHFCatalogue,\n",
    "                                          pynbody.halo.GrpCatalogue,\n",
    "                                          pynbody.halo.AmigaGrpCatalogue,\n",
    "                                          pynbody.halo.legacy.RockstarIntermediateCatalogue,\n",
    "                                          pynbody.halo.rockstar.RockstarCatalogue,\n",
    "                                          pynbody.halo.subfind.SubfindCatalogue,\n",
    "                                          pynbody.halo.hop.HOPCatalogue]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "path= '/home/christenc/Data/Sims/h329.cosmo50PLK.3072g/h329.cosmo50PLK.3072gst5HbwK1BH/snapshots/'\n",
    "snapshots = ['h329.cosmo50PLK.3072gst5HbwK1BH.004096', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.004032', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.003936', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.003840', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.003744', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.003648', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.003606', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.003552', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.003456', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.003360', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.003264', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.003195', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.003168', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.003072',\n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.002976', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.002880', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.002784', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.002688', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.002592', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.002554', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.002496', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.002400', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.002304',\n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.002208', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.002112', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.002088', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.002016', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.001920', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.001824',\n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.001740',\n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.001728',\n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.001632', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.001536', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.001475', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.001440', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.001344', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.001269', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.001248',\n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.001152', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.001106', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.001056', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.000974',\n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.000960',\n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.000864', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.000776', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.000768', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.000672', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.000637', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.000576', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.000480', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.000456', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.000384', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.000347', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.000288', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.000275', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.000225', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.000192', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.000188', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.000139', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.000107', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.000096', \n",
    "             'h329.cosmo50PLK.3072gst5HbwK1BH.000071']\n",
    "\n",
    "# haloids dictionary defines the major progenitor branch back through all snapshots for each z=0 halo we are \n",
    "# interest in ... read this as haloids[1] is the list containing the haloid of the major progenitors of halo 1\n",
    "# so if we have three snapshots, snapshots = [4096, 2048, 1024] then we would have haloids[1] = [1, 2, 5] \n",
    "# --- i.e. in snapshot 2048 halo 1 was actually called halo 2, and in 1024 it was called halo 5\n",
    "haloids = {\n",
    "      1: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, \n",
    "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 5, 19, 18, 11],\n",
    "    7: [7, 6, 6, 6, 6, 5, 5, 4, 4, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 6, 7, 7, 8, 7, 6, 6, 6, 6, 5, 5, 5, 5, 6, 5, 6, \n",
    "        6, 8, 8, 9, 10, 8, 9, 9, 10, 11, 10, 14, 14, 14, 16, 17, 23, 22, 22, 24, 30, 25, 24],\n",
    "    10: [10, 10, 9, 9, 9, 9, 9, 9, 11, 11, 11, 11, 13, 13, 13, 14, 14, 14, 15, 14, 16, 15, 16, 15, 15, 15, 14, 14, \n",
    "         13, 13, 13, 14, 14, 16, 16, 16, 19, 20, 22, 20, 19, 19, 19, 18, 19, 16, 24, 27, 34, 42, 44, 60, 71],\n",
    "    13: [13, 13, 13, 13, 13, 13, 13, 16, 16, 15, 15, 15, 16, 16, 16, 17, 18, 18, 17, 16, 18, 17, 18, 17, 17, 17, 17, \n",
    "         15, 16, 16, 18, 22, 24, 26, 26, 28, 25, 26, 27, 28, 24, 24, 25, 35, 38, 29, 44, 36, 54, 60, 61],\n",
    "    15: [15, 15, 15, 15, 15, 15, 16, 17, 17, 16, 17, 16, 17, 17, 17, 20, 20, 19, 22, 21, 23, 22, 22, 22, 23, 23, 23, \n",
    "         24, 25, 25, 25, 24, 23, 25, 25, 29, 33, 31, 36, 35, 37, 37, 37, 37, 37, 28, 40, 31, 41, 59],\n",
    "    19: [19, 19, 18, 18, 17, 18, 18, 19, 18, 17, 16, 17, 18, 19, 28, 31, 33, 34, 34, 33, 35, 33, 34, 31, 31, 32, 33, \n",
    "         35, 35, 36, 36, 34],\n",
    "    27: [27, 29, 29, 27, 27, 27, 27, 28, 29, 28, 29, 28, 30, 32, 31, 32, 31, 32, 32, 31, 32, 32, 42, 30, 30, 31, 30, \n",
    "         33, 32, 34, 34, 42, 39, 44, 43, 43, 47, 48, 52, 49, 48, 59, 62, 62, 61, 38],\n",
    "    29: [29, 27, 23, 21, 18, 17, 15, 14, 13, 12, 12, 12, 12, 12, 12, 13, 12, 12, 12, 11, 13, 12, 14, 13, 12, 12, 11, \n",
    "         11, 11, 11, 11, 10, 11, 10, 9, 9, 20, 22, 26, 27, 25, 39, 39, 39, 42, 42, 48, 49, 50, 50, 49, 66, 68, 74, \n",
    "         77, 97, 89],\n",
    "    31: [31, 31, 27, 25, 25, 25, 24, 25, 25, 24, 25, 25, 26, 28, 26, 27, 27, 28, 29, 27, 29, 28, 29, 28, 29, 30, 29, \n",
    "         28, 28, 29, 29, 28, 28, 30, 29, 30, 30, 28, 30, 31, 27, 23, 23, 25, 27, 23, 42, 34, 40, 45, 43, 39, 80, 90, \n",
    "         89, 78, 66, 65],\n",
    "    32: [32, 32, 32, 32, 32, 34, 34, 35, 34, 33, 32, 30, 31, 31, 27, 19, 17, 16, 25, 29, 15, 13, 13, 12, 11, 11, 12, \n",
    "         13, 12, 12, 12, 11, 13, 13, 13, 14, 14, 15, 14, 13, 14, 15, 15, 14, 17, 14, 19, 21, 55],\n",
    "    49: [49, 50, 50, 50, 51, 52, 51, 56, 56, 56, 58, 59, 60, 64, 61, 61, 62, 60, 59, 57, 60, 59, 59, 59, 59, 60],\n",
    "    55: [55, 56, 54, 54, 55, 55, 55, 55, 51, 48, 49, 48, 49, 48, 46, 43, 44, 44, 41, 41, 42, 42, 41, 42, 40, 41, 39, \n",
    "         41, 39, 40, 40, 44, 44, 43, 41, 41, 46, 46, 48, 46, 43, 42, 41, 45, 53, 34, 53, 42, 64, 159, 158, 171, 169,\n",
    "         191, 193, 148, 106, 105],\n",
    "    91: [91, 91, 93, 93, 92, 92, 90, 89, 91, 97, 110, 124, 137, 152, 125, 111, 111, 113, 111, 107, 111, 107, 110, 109, \n",
    "         111, 113],\n",
    "    94: [94, 96, 97, 99, 99, 101, 101, 100, 101, 104, 102, 98, 101, 99, 92, 103, 120, 118, 101, 97, 90, 85, 85, 110, \n",
    "         84, 84, 69, 71, 70, 73, 74, 73, 72, 70, 69, 62, 57, 50, 42, 39, 36, 30, 31, 33, 36, 27, 41, 32, 44, 52, 52, \n",
    "         56, 49, 54, 54, 52, 71, 73],\n",
    "    116: [116, 118, 119, 123, 121, 122, 119, 116, 114, 113, 111, 101, 105, 102, 100, 96, 92, 92, 91, 84, 86, 87, 84, \n",
    "          82, 80, 81, 79, 70, 62, 56, 55, 51, 43, 37, 34, 34, 34, 30, 32, 29, 28, 25, 26, 21, 28, 22, 30, 30, 33, \n",
    "          35],\n",
    "    119: [119, 123, 124, 129, 126, 126, 126, 127, 130, 131, 133, 133, 135, 136, 131, 130, 122, 104, 76, 66],\n",
    "    131: [131, 136, 133, 138, 138, 137, 138, 137, 139, 142, 143, 143, 146, 149, 147, 151, 150, 150, 151, 150, 149, \n",
    "          148, 152, 152, 155, 158, 159, 158, 157, 169, 170, 168, 168, 163, 160, 167, 171, 172, 172, 175, 170, 178, \n",
    "          176, 170, 173, 47, 169, 177, 180, 176, 171, 164, 163, 165, 163, 136, 130, 123, 71, 42, 34, 9],\n",
    "    132: [132, 119, 107, 106, 113, 151, 168, 160, 111, 83, 71, 64, 64, 58, 52, 53, 56],\n",
    "    154: [154, 154, 152, 156, 151, 162, 149, 150, 149, 150, 146, 145, 149, 150, 146, 147, 144, 139, 135, 131, 126, \n",
    "          112, 108, 96, 85, 88, 89, 88, 80, 80, 79, 79, 70, 67, 66, 58, 49, 49, 51, 58, 53, 51, 50, 55, 60, 62, \n",
    "          67, 67, 69, 68, 71, 70, 70, 76, 74, 73, 72, 69],\n",
    "    443: [443, 435, 416, 375, 336, 309, 455, 336, 156, 73, 48]\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t Major progenitor halod ID: 116\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.004096, 0.0 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 0.72 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/engelhar/anaconda3/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:101: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/home/engelhar/anaconda3/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:102: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/home/engelhar/anaconda3/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:158: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/home/engelhar/anaconda3/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:159: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t Major progenitor halod ID: 118\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.004032, 0.21549019020639903 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 0.84 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 119\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.003936, 0.5387254755160633 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 0.97 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 123\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.003840, 0.8619607608256068 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 1.08 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 121\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.003744, 1.1851960461388167 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 1.14 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 122\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.003648, 1.5084313314506232 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 1.16 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 119\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.003606, 1.649846768773683 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 1.16 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 116\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.003552, 1.8316666167675404 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 1.14 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 114\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.003456, 2.1549019020950606 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 1.08 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 113\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.003360, 2.47813718746948 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 0.99 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 111\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.003264, 2.801372472779038 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 0.85 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 101\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.003195, 3.0336978340953085 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 0.72 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 105\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.003168, 3.1246077580886293 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 0.67 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 102\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.003072, 3.4478430433984677 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 0.43 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 100\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.002976, 3.7710783287080467 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 0.29 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 96\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.002880, 4.094313614017615 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 0.51 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 92\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.002784, 4.417548899327182 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 0.76 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 92\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.002688, 4.740784184636752 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 0.99 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 91\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.002592, 5.064019469946324 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 1.19 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 84\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.002554, 5.1919667703813595 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 1.26 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 86\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.002496, 5.387254755254723 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 1.33 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 87\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.002400, 5.710490040564672 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 1.42 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 84\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.002304, 6.033725325874508 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 1.53 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 82\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.002208, 6.356960611184269 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 1.56 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 80\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.002112, 6.680195896493958 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 1.57 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 81\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.002088, 6.761004717821375 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 1.56 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 79\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.002016, 7.003431181803606 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 1.50 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 70\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.001920, 7.326666467113235 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 1.37 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 62\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.001824, 7.649901752422835 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 1.17 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 56\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.001740, 7.932732627068729 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 0.91 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 55\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.001728, 7.973137037732423 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 0.87 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 51\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.001632, 8.296372323042007 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 0.50 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 43\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.001536, 8.6196076083543 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 0.46 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in 0.0\n",
      "GIN TEMP nan\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 37\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.001475, 8.824996695911537 Gyr ago\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t Distance from halo 1 (technically halo 1): 0.73 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in -5.807444202808262e-05\n",
      "GIN TEMP 20118.16684274217\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 34\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.001440, 8.942842893690639 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 0.87 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in -0.002543728245029702\n",
      "GIN TEMP 22418.057953320837\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 34\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.001344, 9.266078179025705 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 1.25 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in -0.004410747763944713\n",
      "GIN TEMP 28704.84122239182\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 34\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.001269, 9.518605745695785 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 1.59 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in -0.00631573366562934\n",
      "GIN TEMP 30106.729341231727\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 30\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.001248, 9.589313464363485 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 1.67 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in -0.008372912945167423\n",
      "GIN TEMP 27797.269935155156\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 32\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.001152, 9.912548749701417 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 2.01 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in -0.010805343503560697\n",
      "GIN TEMP 27030.782486877757\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 29\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.001106, 10.067432323925816 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 2.16 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in -0.009648965407072487\n",
      "GIN TEMP 29296.738947475435\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 28\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.001056, 10.235784035039343 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 2.33 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in -0.017333540649282962\n",
      "GIN TEMP 29095.037457027098\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 25\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.000974, 10.511880841265452 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 2.64 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in -0.02211225751668903\n",
      "GIN TEMP 25461.240244332195\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 26\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.000960, 10.559019320377233 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 2.68 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in -0.0329902355165482\n",
      "GIN TEMP 24495.452680049428\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 21\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.000864, 10.882254605715236 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 3.11 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in -0.0273110959057689\n",
      "GIN TEMP 27911.536235037827\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 28\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.000776, 11.17855361727487 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 3.49 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in -0.04866597557790843\n",
      "GIN TEMP 27602.028422394793\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 22\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.000768, 11.205489891053029 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 0.36 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in -0.02422264727642243\n",
      "GIN TEMP 11487.765603779604\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 30\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.000672, 11.528725176390536 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 4.15 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in -0.06481327877499837\n",
      "GIN TEMP 25623.94931174483\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 30\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.000637, 11.646571374170062 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 5.89 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in -0.03447917821363717\n",
      "GIN TEMP 29834.334276858768\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 33\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.000576, 11.851960461728266 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 5.10 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in -0.0728308936325182\n",
      "GIN TEMP 21717.053469526738\n",
      "Shell number 2 works\n",
      "\t Major progenitor halod ID: 35\n",
      "Loaded snapshot h329.cosmo50PLK.3072gst5HbwK1BH.000480, 12.175195747065276 Gyr ago\n",
      "\t Distance from halo 1 (technically halo 1): 6.50 Rvir\n",
      "Halo is Centered\n",
      "Shell = shell_part2\n",
      "Flux in -0.110182004683983\n",
      "GIN TEMP 21082.618010018556\n",
      "Shell number 2 works\n"
     ]
    }
   ],
   "source": [
    "z0halonum = 116 # specify the halo number you're interested in (at z=0)\n",
    "\n",
    "Gin2 = []\n",
    "Gout2 = [] \n",
    "GinL = [] \n",
    "GoutL = [] \n",
    "# Gout_T = []\n",
    "# Gin_T = []\n",
    "Gout_T_25 = []\n",
    "Gin_T_25 = []\n",
    "Gout_T_67 = []\n",
    "Gin_T_67 = []\n",
    "distance = [] # e.g. distance from halo 1\n",
    "time = []\n",
    "\n",
    "for tstep in range(len(haloids[z0halonum])):\n",
    "    snapshot = snapshots[tstep]\n",
    "    \n",
    "    # load in data in pynbody\n",
    "    s = pynbody.load(path+snapshot)\n",
    "    s.physical_units()\n",
    "    h = s.halos()\n",
    "        \n",
    "    i = haloids[z0halonum][tstep]\n",
    "    halo = h.load_copy(i)\n",
    "    print('\\t Major progenitor halod ID:', i)    \n",
    "    \n",
    "    t = float(h[i].properties['time'].in_units('Gyr'))\n",
    "    print(f'Loaded snapshot {snapshot}, {13.800797497330507 - t} Gyr ago')\n",
    "    \n",
    "    #get halo 1 information and calculate distance from halo i to halo 1\n",
    "    h1id = haloids[1][tstep] # get %equivalent halo 1 id\n",
    "    h1d = np.array([h[h1id].properties['Xc'], h[h1id].properties['Yc'], h[h1id].properties['Zc']]) # position of halo 1\n",
    "    h1r = h[h1id].properties['Rvir'] # virial radius of halo 1\n",
    "    d = np.array([h[i].properties['Xc'], h[i].properties['Yc'], h[i].properties['Zc']]) # position of halo i\n",
    "    d = np.sqrt(np.sum((d - h1d)**2))/h1r # distance between the two \n",
    "    print('\\t Distance from halo 1 (technically halo %s): %.2f Rvir' % (h1id,d))\n",
    "    \n",
    "    #center on the halos\n",
    "    pynbody.analysis.halo.center(h[i])\n",
    "    pynbody.analysis.halo.center(halo)\n",
    "    \n",
    "    ### Gas Outflow RATE\n",
    "    dL = .1*h[i].properties['Rvir']/h[i].properties['h']\n",
    "    Rmag = h[i]['r']\n",
    "    \n",
    "    print(\"Halo is Centered\")\n",
    "    \n",
    "    #select the particles in a shell 0.25*Rvir\n",
    "    inner_sphere2 = pynbody.filt.Sphere(str(.2*h[i].properties['Rvir']) + ' kpc', [0,0,0])\n",
    "    outer_sphere2 = pynbody.filt.Sphere(str(.3*h[i].properties['Rvir']) + ' kpc', [0,0,0])\n",
    "    shell_part2 = halo[outer_sphere2 & ~inner_sphere2].gas\n",
    "    \n",
    "    print(\"Shell = shell_part2\")\n",
    "    \n",
    "    #Perform calculations\n",
    "    velocity2 = shell_part2['vel'].in_units('kpc yr**-1')\n",
    "    r2 = shell_part2['pos'].in_units('kpc')\n",
    "    Mg2 = shell_part2['mass'].in_units('Msol')\n",
    "    r_mag2 = shell_part2['r'].in_units('kpc')\n",
    "    temp2 = shell_part2['temp']\n",
    "\n",
    "    VR2 = np.sum((velocity2*r2), axis=1)\n",
    "    \n",
    "    #### First, the Mas Flux within the Shell ####\n",
    "    \n",
    "    gin2 = []\n",
    "    gout2 = []\n",
    "    \n",
    "    for y in range(len(VR2)):\n",
    "        if VR2[y] < 0: \n",
    "            gflowin2 = np.array(((VR2[y]/r_mag2[y])*Mg2[y])/dL)\n",
    "            gin2 = np.append(gin2, gflowin2)\n",
    "        else: \n",
    "            gflowout2 = np.array(((VR2[y]/r_mag2[y])*Mg2[y])/dL)\n",
    "            gout2 = np.append(gout2, gflowout2)\n",
    "    GIN2 = np.sum(gin2)\n",
    "    GOUT2 = np.sum(gout2)\n",
    "    \n",
    "    print(\"Flux in\", GIN2)\n",
    "    \n",
    "    ##### Now the temperature of particles within the Shell ####\n",
    "    \n",
    "    tin2 = []\n",
    "    min_2 = []\n",
    "    tout2 = []\n",
    "    mout_2 = []\n",
    "    \n",
    "    for y in range(len(VR2)):\n",
    "        if VR2[y] < 0: \n",
    "            intemp2 = np.array(temp2[y]*Mg2[y])\n",
    "            tin2 = np.append(tin2, intemp2)\n",
    "            min2 = np.array(Mg2[y])\n",
    "            min_2 = np.append(min_2, min2)\n",
    "        else: \n",
    "            outemp2 = np.array(temp2[y]*Mg2[y])\n",
    "            tout2 = np.append(tout2, outemp2)\n",
    "            mout2 = np.array(Mg2[y])\n",
    "            mout_2 = np.append(mout_2, mout2)\n",
    "            \n",
    "    in_2T = np.sum(tin2)/np.sum(min_2)\n",
    "    out_2T = np.sum(tout2)/np.sum(mout_2)    \n",
    "    GIN_T_25 = np.sum(in_2T)\n",
    "    GOUT_T_25 = np.sum(out_2T)\n",
    "    \n",
    "    print(\"GIN TEMP\", GIN_T_25)\n",
    "    \n",
    "    \n",
    "    #select the particles in a shell\n",
    "    inner_sphereL = pynbody.filt.Sphere(str(.6*h[i].properties['Rvir']) + ' kpc', [0,0,0])\n",
    "    outer_sphereL = pynbody.filt.Sphere(str(h[i].properties['Rvir']) + ' kpc', [0,0,0])\n",
    "    shell_partL = halo[outer_sphereL & ~inner_sphereL].gas\n",
    "    \n",
    "    #Perform calculations\n",
    "    DD = .4*h[i].properties['Rvir']/h[i].properties['h']\n",
    "    velocityL = shell_partL['vel'].in_units('kpc yr**-1')\n",
    "    rL = shell_partL['pos'].in_units('kpc')\n",
    "    MgL = shell_partL['mass'].in_units('Msol')\n",
    "    r_magL = shell_partL['r'].in_units('kpc')\n",
    "    tempL = shell_partL['temp']\n",
    "\n",
    "    VRL = np.sum((velocityL*rL), axis=1)\n",
    "    \n",
    "    #### First, the Mas Flux within a Shell ####\n",
    "    \n",
    "    ginL = []\n",
    "    goutL = []\n",
    "    \n",
    "    for y in range(len(VRL)):\n",
    "        if VRL[y] < 0: \n",
    "            gflowinL = np.array(((VRL[y]/r_magL[y])*MgL[y])/DD)\n",
    "            ginL = np.append(ginL, gflowinL)\n",
    "        else: \n",
    "            gflowoutL = np.array(((VRL[y]/r_magL[y])*MgL[y])/DD)\n",
    "            goutL = np.append(goutL, gflowoutL)   \n",
    "    GIN2 = np.sum(ginL)\n",
    "    GOUT2 = np.sum(goutL)\n",
    "    \n",
    "    ##### Now the temperature of particles within the Shell ####\n",
    "    \n",
    "    tin67 = []\n",
    "    min_67 = []\n",
    "    tout67 = []\n",
    "    mout_67 = []\n",
    "    \n",
    "    for y in range(len(VRL)):\n",
    "        if VRL[y] < 0: \n",
    "            intemp67 = np.array(tempL[y]*MgL[y])\n",
    "            tin67 = np.append(tin67, intemp67)\n",
    "            min67 = np.array(MgL[y])\n",
    "            min_67 = np.append(min_67, min67)\n",
    "        else: \n",
    "            outemp67 = np.array(tempL[y]*MgL[y])\n",
    "            tout67 = np.append(tout67, outemp67)\n",
    "            mout67 = np.array(MgL[y])\n",
    "            mout_67 = np.append(mout_67, mout67)\n",
    "            \n",
    "    in_67T = np.sum(tin67)/np.sum(min_67)\n",
    "    out_67T = np.sum(tout67)/np.sum(mout_67)    \n",
    "    GIN_T_67 = np.sum(in_67T)\n",
    "    GOUT_T_67 = np.sum(out_67T)\n",
    "    VR2 = np.sum((velocity2*r2), axis=1)\n",
    "    print(\"Shell number 2 works\")\n",
    "            \n",
    "#     ### Gas Outflow TEMP\n",
    "#     VEL = halo.gas['vel'].in_units('kpc yr**-1')\n",
    "#     R = halo.gas['pos'].in_units('kpc')\n",
    "#     GM = halo.gas['mass'].in_units('Msol')\n",
    "#     R_mag = halo.gas['r'].in_units('kpc')\n",
    "#     T = halo.gas['temp']\n",
    "    \n",
    "#     out_T = []\n",
    "#     mm_p = []\n",
    "\n",
    "#     VR = np.sum((VEL*R), axis=1)\n",
    "    \n",
    "#     for x in range(len(VR)):\n",
    "#         if VR[x] > 0:\n",
    "#             goutmp = np.sum(T[x]*GM[x])\n",
    "#             pmout = np.sum(GM[x])\n",
    "#         else: \n",
    "#             gintmp = np.sum(T[x]*GM[x])\n",
    "#             pmin = np.sum(GM[x])\n",
    "#     gout_T = goutmp/pmout\n",
    "#     gin_T = gintmp/pmin\n",
    "    \n",
    "\n",
    "    \n",
    "#     Gout_T.append(gout_T)\n",
    "#     Gin_T.append(gin_T)\n",
    "    Gin2.append(np.sum(gin2))\n",
    "    Gout2.append(np.sum(gout2))\n",
    "    GinL.append(np.sum(ginL))\n",
    "    GoutL.append(np.sum(goutL))\n",
    "    Gout_T_25.append(GOUT_T_25)\n",
    "    Gin_T_25.append(GIN_T_25)\n",
    "    Gout_T_67.append(GOUT_T_67)\n",
    "    Gin_T_67.append(GIN_T_67)\n",
    "    distance.append(d)\n",
    "    time.append(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create some mock data\n",
    "t = np.arange(0.01, 10.0, 0.01)\n",
    "data1 = np.exp(t)\n",
    "data2 = np.sin(2 * np.pi * t)\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "color = 'tab:red'\n",
    "ax1.set_xlabel('time (s)')\n",
    "ax1.set_ylabel('exp', color=color)\n",
    "ax1.plot(t, data1, color=color)\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "color = 'tab:blue'\n",
    "ax2.set_ylabel('sin', color=color)  # we already handled the x-label with ax1\n",
    "ax2.plot(t, data2, color=color)\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "halo = h4.load_copy(7)\n",
    "halo.physical_units()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 504x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.set_xlabel('Gyr')\n",
    "ax1.set_ylabel('Distance', color='black')\n",
    "ax1.plot(time, distance, c='black')\n",
    "ax1.tick_params(axis='y', labelcolor='black')\n",
    "\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "ax2.set_ylabel('Flow Rates', color='b')  # we already handled the x-label with ax1\n",
    "ax2.plot(time, np.abs(Gin2), c='blue', label=r'M${in}$')\n",
    "ax2.plot(time, Gout2, c='red', label=r'M${out}$')\n",
    "ax2.tick_params(axis='y', labelcolor='b')\n",
    "\n",
    "# plt.plot(time, distance, c='black')\n",
    "#\n",
    "# plt.legend()\n",
    "# plt.semilogy()\n",
    "# plt.title(\"Mass flux at 0.25*Rvir\")\n",
    "# plt.xlabel(\"Gyr\")\n",
    "\n",
    "# pynbody.plot.stars.sfh(halo, filename=None, massform=True, clear=False, legend=False, subplot=False, trange=False, bins=60)\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Gyr')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "plt.plot(time, distance, c='black')\n",
    "plt.plot(time, np.abs(GinL),c='blue', label=r'M${in}$')\n",
    "plt.plot(time, GoutL, c='red', label=r'M${out}$')\n",
    "#plt.semilogy()\n",
    "plt.title(\"Mass flux at (0.6-1.0)*Rvir\")\n",
    "plt.xlabel(\"Gyr\")\n",
    "\n",
    "# pynbody.plot.stars.sfh(halo, filename=None, massform=True, clear=False, legend=False, subplot=False, trange=False, bins=60)\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Gyr')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "plt.plot(time, np.abs(Gin2), c='blue', label=r'M${in}$ 0.25Rvir')\n",
    "plt.plot(time, Gout2, c='red', label=r'M${out}$ 0.25Rvir')\n",
    "plt.plot(time, np.abs(GinL), c='cyan', label=r'M${in}$ (.6-1)Rvir')\n",
    "plt.plot(time, GoutL, c='tomato', label=r'M${out}$ (.6-1)Rvir')\n",
    "plt.semilogy()\n",
    "plt.title(\"Mass flux\")\n",
    "plt.xlabel(\"Gyr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Gyr')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "#plt.plot(time, distance, c='black')\n",
    "plt.plot(time, Gin_T_25, c='blue', label=r'T${in}$')\n",
    "plt.plot(time, Gout_T_25, c='red', label=r'T${out}$')\n",
    "plt.semilogy()\n",
    "plt.title(\"Temperature at 0.25Rvir\")\n",
    "plt.xlabel(\"Gyr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Gyr')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "#plt.plot(time, distance, c='black')\n",
    "plt.plot(time, Gin_T_67, c='blue', label=r'T${in}$')\n",
    "plt.plot(time, Gout_T_67, c='red', label=r'T${out}$')\n",
    "plt.semilogy()\n",
    "plt.title(\"Temperature at (0.6-1)Rvir\")\n",
    "plt.xlabel(\"Gyr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Gyr')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "plt.plot(time, np.abs(Gin2), c='blue', label=r'M${in}$ 0.25Rvir')\n",
    "plt.plot(time, Gout2, c='red', label=r'M${out}$ 0.25Rvir')\n",
    "plt.plot(time, Gin_T_25, c='tomato', label=r'T${in}$ 0.25Rvir')\n",
    "plt.plot(time, Gout_T_25, c='cyan', label=r'T${out}$ 0.25Rvir')\n",
    "plt.semilogy()\n",
    "plt.title(\"Mass Temp at 0.25 Rvir\")\n",
    "plt.xlabel(\"Gyr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Gyr')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "plt.plot(time, np.abs(GinL), c='blue', label=r'M${in}$')\n",
    "plt.plot(time, GoutL, c='red', label=r'M${out}$')\n",
    "plt.plot(time, Gin_T_67, c='tomato', label=r'T${in}$ (0.6-1)Rvir')\n",
    "plt.plot(time, Gout_T_67, c='cyan', label=r'T${out}$ (0.6-1)Rvir')\n",
    "plt.semilogy()\n",
    "plt.title(\"Mass Temp at (0.6-1)Rvir\")\n",
    "plt.xlabel(\"Gyr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}