Deviation in escape angle.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pynbody\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import pynbody.plot as pp\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import pynbody.plot.sph as sph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from Time import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%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})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "2: 2,  116.08, .003584\n",
    "3: 4,  78.68, .002816\n",
    "5: 4, 91.30, .003328\n",
    "6: 5,  80.29, .003328\n",
    "9: 9, 49.10, .002688\n",
    "10: 10, 86.53, .004096\n",
    "11: 10,  49.25, .002304\n",
    "13: 11,   40.01, .002304\n",
    "14: 12,  56.29, .003712 \n",
    "21: 17,   48.12, .003584\n",
    "27: 14,  38.71,  .001920\n",
    "28: 12,  21.30, .001920\n",
    "32: 27, 47.00, .003584\n",
    "36: 26, 41.33, .003072\n",
    "37: 23,  28.86, .002688\n",
    "41: 40,  55.76, .003968\n",
    "45: 19,  24.70, .002176\n",
    "48: 32,  32.34, .003584\n",
    "61: 42, 28.56, .002688\n",
    "68: 21, 15.33, .002432\n",
    "80: 21, 18.71, .001920\n",
    "96: 54, 19.58, .002688\n",
    "127: 37, 22.75, .003195\n",
    "128: 100, 17.74, .002816\n",
    "136: 136, 21.39, .003968\n",
    "163: 87,  15.71, .001280\n",
    "212: 57,  13.69, .001740\n",
    "265: 136,  26.12, .002048\n",
    "278: 33,  26.15, .002048\n",
    "283: 36, 28.08, .002432\n",
    "329: 79, 10.48, .001269\n",
    "386: 119,  16.11, .001536\n",
    "442: 55, 14.69, .003456\n",
    "678: 502, 9.47, .001740"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def deviation1(nums): \n",
    "    nums = [2, 4, 4, 5, 9, 10, 10, 11, 12, 17, 14, 12, 27, 26, 23, 40, 19, 32, 42, 21, 21, 54, 37, 100, 136, 87, 57, 136, 33, 36, 79, 119, 55, 502]\n",
    "    timesteps = ['.003584','.002816', '.003328', '.003328', '.002688','.004096', '.002304','.003712', '.003584', '.001920', '.001920','.003584', '.003072','.002688', '.003968', '.002176', '.003584','.002688', '.002432','.001920', '.002688', '.003195', '.002816','.003968', '.001280','.001740', '.002048', '.002432', '.001269','.001536', '.003456', '.001740']\n",
    "    rvirs = [116.08, 78.68, 91.30, 80.29, 49.10, 86.53, 49.25, 40.01, 56.29, 48.12, 38.71, 21.30, 47.00, 41.33, 28.86, 55.76, 24.70, 32.34, 28.56, 15.33, 18.71, 19.58, 22.75, 17.74, 21.39, 15.71, 13.69, 26.12, 26.15, 28.08, 10.48, 16.11, 14.69, 9.47]\n",
    "    h_mass1 = np.zeros(len(nums))\n",
    "    STD1 = np.zeros(len(nums))\n",
    "    ave_theta1 = np.zeros(len(nums))\n",
    "    i = 0\n",
    "    for num, timestep, rvir in zip(nums, timesteps, rvirs):\n",
    "        print(num,timestep)\n",
    "        sim_1 ='/home/christenc/Data/Sims/h148.cosmo50PLK.3072g/h148.cosmo50PLK.3072g3HbwK1BH/snapshots/h148.cosmo50PLK.3072g3HbwK1BH' + timestep\n",
    "        sim = pynbody.load(sim_1)\n",
    "        h = sim.halos()\n",
    "        sim.physical_units()\n",
    "# ###Loads in halo\n",
    "        halo_info = sim.halos(dummy=True)\n",
    "        halo = h.load_copy(num)\n",
    "        halo.physical_units()\n",
    "        if len(halo.gas)==0:\n",
    "            print(\"no gas\")\n",
    "            continue\n",
    "        pynbody.analysis.halo.center(halo)\n",
    "        h_mass1[i] = halo_info[num].properties['mass']\n",
    "#         ###defines sphere on outer edge of halo\n",
    "        inner_sphere = pynbody.filt.Sphere(str(.9*rvir) + ' kpc', [0,0,0])\n",
    "        outer_sphere = pynbody.filt.Sphere(str(rvir) + ' kpc', [0,0,0])\n",
    "        shell_part = sim[outer_sphere & ~inner_sphere].gas\n",
    "        if len(shell_part)==0:\n",
    "            print(\"no outer gas\")\n",
    "            continue\n",
    "#         ###defines sphere surrounding halo\n",
    "        inner_sphere_wind = pynbody.filt.Sphere(str(rvir) + ' kpc', [0,0,0])\n",
    "        outer_sphere_wind = pynbody.filt.Sphere(str(3*rvir) + ' kpc', [0,0,0])\n",
    "        shell_part_wind = sim[outer_sphere_wind & ~inner_sphere_wind].gas\n",
    "        if len(shell_part_wind)==0:\n",
    "            print(\"no wind\")\n",
    "            continue\n",
    "###Creates cutoff value for vel of gas wind\n",
    "        windv = (np.sum((shell_part_wind['vel']**2), axis=1))**.5\n",
    "        vmin = 0.5*halo_info[num].properties['v_esc']\n",
    "        Wind_mass = np.sum(shell_part_wind['mass'])        \n",
    "        Wvel = np.array(shell_part_wind['vel'])[windv > vmin]\n",
    "        Wymass = np.array(shell_part_wind['mass'])[windv > vmin]\n",
    "        windyvec = np.mean(np.array(Wymass)[np.newaxis].T * Wvel,axis=0)/Wind_mass        \n",
    "###Find Vel of gas inside the halo and creates array\n",
    "        velocity = shell_part['vel'].in_units('kpc yr**-1')\n",
    "        r = shell_part['pos'].in_units('kpc')\n",
    "        vr = np.sum((velocity*r), axis=1)\n",
    "        Vout = []\n",
    "        for x in range(len(vr)):\n",
    "            if vr[x] > 0:\n",
    "                vel = list(velocity[x])\n",
    "                Vout.append(vel)        \n",
    "    ###takes the magnitude of the gas velocity vectors\n",
    "        avecs = np.array(Vout)\n",
    "        amags = (np.sum((avecs**2), axis=1))**.5\n",
    "        bvec = windyvec\n",
    "        bmag = (np.sum(bvec**2)**.5)\n",
    "    ###Finds angle between avec and bvec\n",
    "        Theta = []\n",
    "        for avec,amag in zip(avecs,amags):\n",
    "            adotb = np.sum((avec*bvec))\n",
    "            ab = amag*bmag\n",
    "            theta = np.arccos(adotb/ab)\n",
    "            Theta = np.append(Theta, theta)\n",
    "        STD1[i] = np.std(Theta)\n",
    "        ave_theta1[i] = np.mean(Theta)\n",
    "        i = i + 1\n",
    "    return h_mass1, ave_theta1, STD1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 .003584\n",
      "4 .002816\n",
      "4 .003328\n",
      "5 .003328\n",
      "9 .002688\n",
      "no gas\n",
      "10 .004096\n",
      "10 .002304\n",
      "11 .003712\n",
      "12 .003584\n",
      "17 .001920\n",
      "14 .001920\n",
      "12 .003584\n",
      "27 .003072\n",
      "26 .002688\n",
      "23 .003968\n",
      "40 .002176\n",
      "19 .003584\n",
      "32 .002688\n",
      "42 .002432\n",
      "21 .001920\n",
      "21 .002688\n",
      "54 .003195\n",
      "no gas\n",
      "37 .002816\n",
      "100 .003968\n",
      "no gas\n",
      "136 .001280\n",
      "no gas\n",
      "87 .001740\n",
      "57 .002048\n",
      "136 .002432\n",
      "no gas\n",
      "33 .001269\n",
      "36 .001536\n",
      "79 .003456\n",
      "no gas\n",
      "119 .001740\n"
     ]
    }
   ],
   "source": [
    "h_mass1, ave_theta1, STD1 = deviation1(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "11: 7, 45.48, .003744\n",
    "31: 14, 29.37, .003195\n",
    "33: 17,  25.42, .002496\n",
    "40: 27,  28.19, .003552\n",
    "133: 37, 18.84, .001440\n",
    "137: 24, 8.34, .001152"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def deviation4(nums): \n",
    "    nums = [7, 14, 17, 27, 37, 24]\n",
    "    timesteps = ['.003744','.003195', '.002496', '.003552', '.001440','.001152']\n",
    "    rvirs = [45.48, 29.37, 25.42, 28.19, 18.84, 8.34]\n",
    "    h_mass4 = np.zeros(len(nums))\n",
    "    STD4 = np.zeros(len(nums))\n",
    "    ave_theta4 = np.zeros(len(nums))\n",
    "    i = 0\n",
    "    for num, timestep, rvir in zip(nums, timesteps, rvirs):\n",
    "        print(num,timestep)\n",
    "        sim_4 ='/home/christenc/Data/Sims/h329.cosmo50PLK.3072g/h329.cosmo50PLK.3072gst5HbwK1BH/snapshots/h329.cosmo50PLK.3072gst5HbwK1BH' + timestep\n",
    "        sim = pynbody.load(sim_4)\n",
    "        h = sim.halos()\n",
    "        sim.physical_units()\n",
    "# ###Loads in halo\n",
    "        halo_info = sim.halos(dummy=True)\n",
    "        halo = h.load_copy(num)\n",
    "        halo.physical_units()\n",
    "        if len(halo.gas)==0:\n",
    "            print(\"no gas\")\n",
    "            continue\n",
    "        pynbody.analysis.halo.center(halo)\n",
    "        h_mass4[i] = halo_info[num].properties['mass']\n",
    "#         ###defines sphere on outer edge of halo\n",
    "        inner_sphere = pynbody.filt.Sphere(str(.9*rvir) + ' kpc', [0,0,0])\n",
    "        outer_sphere = pynbody.filt.Sphere(str(rvir) + ' kpc', [0,0,0])\n",
    "        shell_part = halo[outer_sphere & ~inner_sphere].gas\n",
    "        if len(shell_part)==0:\n",
    "            print(\"no outer gas\")\n",
    "            continue\n",
    "#         ###defines sphere surrounding halo\n",
    "        inner_sphere_wind = pynbody.filt.Sphere(str(rvir) + ' kpc', [0,0,0])\n",
    "        outer_sphere_wind = pynbody.filt.Sphere(str(3*rvir) + ' kpc', [0,0,0])\n",
    "        shell_part_wind = sim[outer_sphere_wind & ~inner_sphere_wind].gas\n",
    "        if len(shell_part_wind)==0:\n",
    "            print(\"no wind\")\n",
    "            continue\n",
    "###Creates cutoff value for vel of gas wind\n",
    "        windv = (np.sum((shell_part_wind['vel']**2), axis=1))**.5\n",
    "        vmin = 0.5*halo_info[num].properties['v_esc']\n",
    "        Wind_mass = np.sum(shell_part_wind['mass'])        \n",
    "        Wvel = np.array(shell_part_wind['vel'])[windv > vmin]\n",
    "        Wymass = np.array(shell_part_wind['mass'])[windv > vmin]\n",
    "        windyvec = np.mean(np.array(Wymass)[np.newaxis].T * Wvel,axis=0)/Wind_mass        \n",
    "###Find Vel of gas inside the halo and creates array\n",
    "        velocity = shell_part['vel'].in_units('kpc yr**-1')\n",
    "        r = shell_part['pos'].in_units('kpc')\n",
    "        vr = np.sum((velocity*r), axis=1)\n",
    "        Vout = []\n",
    "        for x in range(len(vr)):\n",
    "            if vr[x] > 0:\n",
    "                vel = list(velocity[x])\n",
    "                Vout.append(vel)        \n",
    "    ###takes the magnitude of the gas velocity vectors\n",
    "        avecs = np.array(Vout)\n",
    "        amags = (np.sum((avecs**2), axis=1))**.5\n",
    "        bvec = windyvec\n",
    "        bmag = (np.sum(bvec**2)**.5)\n",
    "    ###Finds angle between avec and bvec\n",
    "        Theta = []\n",
    "        for avec,amag in zip(avecs,amags):\n",
    "            adotb = np.sum((avec*bvec))\n",
    "            ab = amag*bmag\n",
    "            theta = np.arccos(adotb/ab)\n",
    "            Theta = np.append(Theta, theta)\n",
    "        STD4[i] = np.std(Theta)\n",
    "        ave_theta4[i] = np.mean(Theta)\n",
    "        i = i + 1\n",
    "    return h_mass4, ave_theta4, STD4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 .003744\n",
      "no gas\n",
      "14 .003195\n",
      "no wind\n",
      "17 .002496\n",
      "no wind\n",
      "27 .003552\n",
      "no gas\n",
      "37 .001440\n",
      "no gas\n",
      "24 .001152\n",
      "no wind\n"
     ]
    }
   ],
   "source": [
    "h_mass4, ave_theta4, STD4 = deviation4(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "10: 6,  49.83, .001632\n",
    "12: 5, 27.66, .001728\n",
    "24: 19, 39.24, .002400\n",
    "30: 17, 18.05, .001536\n",
    "40: 41, 49.70, .003648\n",
    "41: 19, 31.87, .003360\n",
    "71: 57, 23.20, .002496\n",
    "78: 65, 54.30, .003168\n",
    "80: 14, 24.52, .002554\n",
    "86: 46, 19.93, .003072\n",
    "223: 47, 18.78, .003552\n",
    "439: 78,  6.85, .001248"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def deviation3(nums): \n",
    "    nums = [6, 5, 19, 17, 41, 19, 57, 65, 14, 46]\n",
    "    timesteps = ['.001632','.001728', '.002400', '.001536', '.003648','.003360', '.002496', '.003168', '.002554', '.003072', '003552', '.001248']\n",
    "    rvirs = [49.83, 27.66, 39.24, 18.05, 49.70, 31.87, 23.20, 54.30, 24.52, 19.93, 18.78, 6.85]\n",
    "    h_mass3 = np.zeros(len(nums))\n",
    "    STD3 = np.zeros(len(nums))\n",
    "    ave_theta3 = np.zeros(len(nums))\n",
    "    i = 0\n",
    "    for num, timestep, rvir in zip(nums, timesteps, rvirs):\n",
    "        print(num,timestep)\n",
    "        sim_3 ='/home/christenc/Data/Sims/h242.cosmo50PLK.3072g/h242.cosmo50PLK.3072gst5HbwK1BH/snapshots/h242.cosmo50PLK.3072gst5HbwK1BH' + timestep\n",
    "        sim = pynbody.load(sim_3)\n",
    "        h = sim.halos()\n",
    "        sim.physical_units()\n",
    "# ###Loads in halo\n",
    "        halo_info = sim.halos(dummy=True)\n",
    "        halo = h.load_copy(num)\n",
    "        halo.physical_units()\n",
    "        if len(halo.gas)==0:\n",
    "            print(\"no gas\")\n",
    "            continue\n",
    "        pynbody.analysis.halo.center(halo)\n",
    "        h_mass3[i] = halo_info[num].properties['mass']\n",
    "#         ###defines sphere on outer edge of halo\n",
    "        inner_sphere = pynbody.filt.Sphere(str(.9*rvir) + ' kpc', [0,0,0])\n",
    "        outer_sphere = pynbody.filt.Sphere(str(rvir) + ' kpc', [0,0,0])\n",
    "        shell_part = halo[outer_sphere & ~inner_sphere].gas\n",
    "        if len(shell_part)==0:\n",
    "            print(\"no outer gas\")\n",
    "            continue\n",
    "#         ###defines sphere surrounding halo\n",
    "        inner_sphere_wind = pynbody.filt.Sphere(str(rvir) + ' kpc', [0,0,0])\n",
    "        outer_sphere_wind = pynbody.filt.Sphere(str(3*rvir) + ' kpc', [0,0,0])\n",
    "        shell_part_wind = sim[outer_sphere_wind & ~inner_sphere_wind].gas\n",
    "        if len(shell_part_wind)==0:\n",
    "            print(\"no wind\")\n",
    "            continue\n",
    "###Creates cutoff value for vel of gas wind\n",
    "        windv = (np.sum((shell_part_wind['vel']**2), axis=1))**.5\n",
    "        vmin = 0.5*halo_info[num].properties['v_esc']\n",
    "        Wind_mass = np.sum(shell_part_wind['mass'])        \n",
    "        Wvel = np.array(shell_part_wind['vel'])[windv > vmin]\n",
    "        Wymass = np.array(shell_part_wind['mass'])[windv > vmin]\n",
    "        windyvec = np.mean(np.array(Wymass)[np.newaxis].T * Wvel,axis=0)/Wind_mass        \n",
    "###Find Vel of gas inside the halo and creates array\n",
    "        velocity = shell_part['vel'].in_units('kpc yr**-1')\n",
    "        r = shell_part['pos'].in_units('kpc')\n",
    "        vr = np.sum((velocity*r), axis=1)\n",
    "        Vout = []\n",
    "        for x in range(len(vr)):\n",
    "            if vr[x] > 0:\n",
    "                vel = list(velocity[x])\n",
    "                Vout.append(vel)        \n",
    "    ###takes the magnitude of the gas velocity vectors\n",
    "        avecs = np.array(Vout)\n",
    "        amags = (np.sum((avecs**2), axis=1))**.5\n",
    "        bvec = windyvec\n",
    "        bmag = (np.sum(bvec**2)**.5)\n",
    "    ###Finds angle between avec and bvec\n",
    "        Theta = []\n",
    "        for avec,amag in zip(avecs,amags):\n",
    "            adotb = np.sum((avec*bvec))\n",
    "            ab = amag*bmag\n",
    "            theta = np.arccos(adotb/ab)\n",
    "            Theta = np.append(Theta, theta)\n",
    "        STD3[i] = np.std(Theta)\n",
    "        ave_theta3[i] = np.mean(Theta)\n",
    "        i = i + 1\n",
    "    return h_mass3, ave_theta3, STD3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 .001632\n",
      "no wind\n",
      "5 .001728\n",
      "no wind\n",
      "19 .002400\n",
      "no gas\n",
      "17 .001536\n",
      "no gas\n",
      "41 .003648\n",
      "no gas\n",
      "19 .003360\n",
      "no wind\n",
      "57 .002496\n",
      "no gas\n",
      "65 .003168\n",
      "no gas\n",
      "14 .002554\n",
      "no wind\n",
      "46 .003072\n",
      "no gas\n"
     ]
    }
   ],
   "source": [
    "h_mass3, ave_theta3, STD3 = deviation3(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "20: 15, 36.80, .002496\n",
    "22: 9,  15.05, .001152\n",
    "23: 16,  25.93, .002208\n",
    "27: 27, 20.00, .002016\n",
    "52: 39, 22.61, .002016\n",
    "53: 27,  24.14, .001824\n",
    "55: 19, 17.97, .002400\n",
    "73: 29,  19.19, .002784\n",
    "113: 50, 16.11, .002554\n",
    "212: 34, 9.44, .002088"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def deviation2(nums): \n",
    "    nums = [15, 9, 16, 27, 39, 27, 19, 29, 50, 34]\n",
    "    timesteps = ['.002496','.001152', '.002208', '.002016', '.002016','.001824', '.002400', '.002784', '.002554', '.002088']\n",
    "    rvirs = [36.80, 15.05, 25.93, 20.00, 22.61, 24.14, 17.97, 19.19, 16.11, 9.44]\n",
    "    h_mass2 = np.zeros(len(nums))\n",
    "    STD2 = np.zeros(len(nums))\n",
    "    ave_theta2 = np.zeros(len(nums))\n",
    "    i = 0\n",
    "    for num, timestep, rvir in zip(nums, timesteps, rvirs):\n",
    "        print(num,timestep,rvir)\n",
    "        sim_2 ='/home/christenc/Data/Sims/h229.cosmo50PLK.3072g/h229.cosmo50PLK.3072gst5HbwK1BH/snapshots_200bkgdens/h229.cosmo50PLK.3072gst5HbwK1BH' + timestep\n",
    "        sim = pynbody.load(sim_2)\n",
    "        h = sim.halos()\n",
    "        sim.physical_units()\n",
    "# ###Loads in halo\n",
    "        halo_info = sim.halos(dummy=True)\n",
    "        halo = h.load_copy(num)\n",
    "        halo.physical_units()\n",
    "        if len(halo.gas)==0:\n",
    "            print('no gas')\n",
    "            continue\n",
    "        pynbody.analysis.halo.center(halo)\n",
    "        h_mass2[i] = halo_info[num].properties['mass']\n",
    "#         ###defines sphere on outer edge of halo\n",
    "        inner_sphere = pynbody.filt.Sphere(str(.9*rvir) + ' kpc', [0,0,0])\n",
    "        outer_sphere = pynbody.filt.Sphere(str(rvir) + ' kpc', [0,0,0])\n",
    "        shell_part = halo[outer_sphere & ~inner_sphere].gas\n",
    "        if len(shell_part)==0:\n",
    "            print(\"no outer gas\")\n",
    "            continue\n",
    "#         ###defines sphere surrounding halo\n",
    "        inner_sphere_wind = pynbody.filt.Sphere(str(rvir) + ' kpc', [0,0,0])\n",
    "        outer_sphere_wind = pynbody.filt.Sphere(str(3*rvir) + ' kpc', [0,0,0])\n",
    "        shell_part_wind = sim[outer_sphere_wind & ~inner_sphere_wind].gas\n",
    "        if len(shell_part_wind)==0:\n",
    "            print(\"no wind\")\n",
    "            continue\n",
    "###Creates cutoff value for vel of gas wind\n",
    "        windv = (np.sum((shell_part_wind['vel']**2), axis=1))**.5\n",
    "        vmin = 0.5*halo_info[num].properties['v_esc']\n",
    "        Wind_mass = np.sum(shell_part_wind['mass'])        \n",
    "        Wvel = np.array(shell_part_wind['vel'])[windv > vmin]\n",
    "        Wymass = np.array(shell_part_wind['mass'])[windv > vmin]\n",
    "        windyvec = np.mean(np.array(Wymass)[np.newaxis].T * Wvel,axis=0)/Wind_mass        \n",
    "###Find Vel of gas inside the halo and creates array\n",
    "        velocity = shell_part['vel'].in_units('kpc yr**-1')\n",
    "        r = shell_part['pos'].in_units('kpc')\n",
    "        vr = np.sum((velocity*r), axis=1)\n",
    "        Vout = []\n",
    "        for x in range(len(vr)):\n",
    "            if vr[x] > 0:\n",
    "                vel = list(velocity[x])\n",
    "                Vout.append(vel)        \n",
    "    ###takes the magnitude of the gas velocity vectors\n",
    "        avecs = np.array(Vout)\n",
    "        amags = (np.sum((avecs**2), axis=1))**.5\n",
    "        bvec = windyvec\n",
    "        bmag = (np.sum(bvec**2)**.5)\n",
    "    ###Finds angle between avec and bvec\n",
    "        Theta = []\n",
    "        for avec,amag in zip(avecs,amags):\n",
    "            adotb = np.sum((avec*bvec))\n",
    "            ab = amag*bmag\n",
    "            theta = np.arccos(adotb/ab)\n",
    "            Theta = np.append(Theta, theta)\n",
    "        STD2[i] = np.std(Theta)\n",
    "        ave_theta2[i] = np.mean(Theta)\n",
    "        i = i + 1\n",
    "    return h_mass2, ave_theta2, STD2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 .002496 36.8\n",
      "no outer gas\n",
      "9 .001152 15.05\n",
      "no wind\n",
      "16 .002208 25.93\n",
      "no wind\n",
      "27 .002016 20.0\n",
      "no outer gas\n",
      "39 .002016 22.61\n",
      "no gas\n",
      "27 .001824 24.14\n",
      "no outer gas\n",
      "19 .002400 17.97\n",
      "no outer gas\n",
      "29 .002784 19.19\n",
      "no outer gas\n",
      "50 .002554 16.11\n",
      "no outer gas\n",
      "34 .002088 9.44\n",
      "no outer gas\n"
     ]
    }
   ],
   "source": [
    "h_mass2, ave_theta2, STD2 = deviation2(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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bl5KXfsWROuCmIGwCAABY8t3fGa6iabkfvpx893enMzfAHNmyYVNVfaKq7h57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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,4), dpi=300)\n",
    "plt.errorbar(h_mass1, ave_theta1*180/np.pi, yerr=STD1*180/np.pi, linestyle=\" \", marker='o', c='gold')#, label='h148')\n",
    "# plt.errorbar(h_mass2, ave_theta2*180/np.pi, yerr=STD2*180/np.pi, linestyle=\" \", marker='^', c='darkgreen', label='h229')\n",
    "# plt.errorbar(h_mass3, ave_theta3*180/np.pi, yerr=STD3*180/np.pi, linestyle=\" \", marker='v', c='limegreen', label='h242')\n",
    "# plt.errorbar(h_mass4, ave_theta4*180/np.pi, yerr=STD4*180/np.pi, linestyle=\" \", marker='D', c='orange', label='h329')\n",
    "plt.xlim(1e8, 3e11)\n",
    "plt.semilogx()\n",
    "\n",
    "#plt.legend(loc='upper left')\n",
    "plt.xlabel(r'Virial Mass ($M_{\\odot}$)')\n",
    "plt.ylabel('Mean Angle of Expulsion (0$^\\circ$ - 180$^\\circ$)')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('Q', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IWqNvEF7vik/T\nq3SfpeIlcsOzfN5YbhAzK54FdaDC2gAAYKFpu6Ox6wGSHr7+YS2yRanXvX/5/QRNAOoSYVO+FW8I\nXunMps/uK9lQvPgUu04zK7dvU3EgNeMJeAAAAFr6cLr1OlKuB0i6btF1+vryr6dac5Et0uM3PJ5q\nTQCIC2FTjrn7QV1ZStdZMtvoT5hZcSC1u2SscV0dXpWb3bQpeq/kFDwAALBQtfZJbXemU6ttg7Rk\nbTq1gBKPrHxEpvSWcD5w3QO6YfENqdUDgDgRNuXfjqLP28rcOzjL55nGeqjMWIUwasecdwEAAHTP\n9H876rgOMINVrat097K7U6m1xJbobz73N6nUAoAkEDblnLvv0pX9lgZKZi99Jpr1NBB97Y9mMpWO\ntU9XZjdtMbMZZzeZ2c7o477oGQAAgNm1b05+eVvHVqn9a8nWAMr47ue+m0qdh69/WNctui6VWgCQ\nBMKm+rBeV5bTHSkNicxsi6Qj0df+MgHRvboSXh2Ini0ea6ek7QqhFIvEAQBAZVb+TGrpSWbslh5p\n5QvJjA1Uoe/aPt3WfluiNa5tulbfWfmdRGsAQNIyCZvM7Edm9q/R64Eseqgn0Syl1ZL2KZw2d8DM\nxszsIzNzSXsVNvFeU24mkruPu/saXdnTaa+ZeTSeKwRNu919/UyzowAAAGbU3C3d+JbU1BXvuE1d\nYdzm7njHBeYp6SDo0ZWPalnLskRrAEDSWjKq+2OF0MQl9c91o5mNSvrI3Rf0ObdR8NMfnSK3UVdO\nnRuWdLDaYMjdt5nZYNFY45KGo03JAQAAqtfaJ33hHenkfdKlU7WP19ITgqbWvtrHAmLy5x1/rt7W\nXg1PDZe/uUqdLZ3aev3W2McFgLRlFTZJIWja5+4TZe7rUlhGNi9mdqu7/26+z+dNFCrFso9SnGMB\nAABICsHQ6mPS6aekidfmP07H1rB0jhlNyJkma9J3Vn5Hf/fx38U+9l+v/Gtd23xt7OMCQNqy2rNp\nb/R+JskiZnabruxlBAAAgDQ0d0s9r0qf+/vqn23bIN24PzxP0IScuq/rPl2/6PpYx1yxaIX6V8y5\n6AMA6kZWYdN2SROqYcYSAAAAcu6aeyq7r+0rUvePpdXHpZve4dQ55N6ipkWxL3f73ue+p9am1ljH\nBICsZLKMzt3Pmtm9kg6b2VsKS7kOK+wbNONsJzO7SdJYhSWWK+wJ9VAM7QIAACBJN/xSWrwq6y6A\nqnzjum/oF//jFzp/+XzNY31+8ef1V91/FUNXAJAPme3Z5O5DZnazwjK3TWVuN4WNsAEAAAAgc+3N\n7dpy3Rb9w+l/qHmsgRsGtKhpUe1NAUBOZLWMTpLk7sOSVkv6nUKgNNPLo9tnuz7bS0XvAAAAABCr\nb13/LS2y2kKi1a2rdf/y+2PqCADyIdOwSQonorn7ekn9CsvphhWW0xXMNzAiaAIAAACQmBWLVmjz\n8s01jfHEDU+o2Zpj6ggA8iHzsKnA3d909wfd/WZ3X+7uTe7eJOlthdlNnYWflXtJ6pK0RtLzWf5O\nAAAAABrboysflc3z77m/2PZF3dNZ4Ub6AOrCucvn9N8//e/6eOpjnb54WhOXJnRx+qLcvfzDDSSz\nPZuqsFPSPe4+UekD7n5W0lkz+5WkHyXWGQAAAIAFbVXrKt297G7917P/tepnf9DzAzVZbv7+H0AM\nTKaBDwf+5PCAJjWptalVbU1tam1q/ezVNMccoL1/3KuVi1dedX/x86Vj5SnQyn3Y5O4HzayWGUos\npwMAAACQmO9+7rtVh01fuvZL+krHVxLqCEBWZjs8YFrT+nT6U306/WnFY/3jH/6xqtqXhi9VdX+S\n6iJGd/dn5vncUYXldAAAAFgopo5LZ+b4u8rfPyL98cfShQ/S6wkNre/aPt3WfltVz/znnv8sM/5e\nHGhED1//cM2HB8zHncvuTL3mbOoibJqJmXWY2Soz65jrPncfSasnAAAAZOzUg9KJW6TxF2e/Z/K3\n0ugOaaRP+niDdP7X6fWHhvWdld+p+N7/uPQ/av3S9Ql2AyBL1y26Tl9f/vVUay6yRdqyYkuqNedS\nV2FTFC69ZGajksYkfSRpzMwum9n7ZvZcufAJAAAADWzq/erunzwkndwsnfq2dHk0mZ6wIPx5x5+r\nt7W3ont/0PODhLsBkLVHVj4y78MD5uOB6x7QikUrUqtXTt2ETWa2QyFcGlA4bc5KXuskDSqETz/M\nqk8AAADUoYnXpJFbwhI8YB6arEmPrny07H13LbtLa69dm0JHALJUODwgDUtsif7mc3+TSq1K1UXY\nZGZ7JG3Xlc2+Z9pivTh42hWdRAcAAABU5tIp6ZO7CJwwb/d33a/lLctnvW4yfb/n+yl2BCBL3/3c\nd1Op8/D1D+u6RdelUqtSuQ+bzOxpSf3FP5I0ImmfpF3RtX6FWU27JA1F9/Sb2XPpdgsAAIC6Nj0m\nnbyPJXWYl0VNi/SX3X856/U7l92pP2v7sxQ7ApCl+RweUK1rm66tas+4tOQ6bDKzZZJ2KsxkMoWA\nab273+zuD7r7M+7+ZvR6Pvp+u6TbJf1O0qCZ3ZTdbwAAAIC6c+mUdPqprLtAnbqv675Zr317xbdT\n7ARAHiQdBD268lEta1mWaI35yHXYpLA/kySNS9oUBUxHyz3k7kPuvl7SbxTCKgAAADSKT3+TfI2J\n16Tz+5Ovg4ZzTfM1s17rWdKTYicA8qCawwOq1dnSqa3Xb01k7FrlPWx6SGFWU7+7vz2P57fp6iV4\nAAAsbFPHpTPPz379949If/yxdOGD9HoCqjX+83TqjO5Kpw4AoGE1WVNis5v+euVf69rmaxMZu1Z5\nD5t6Je1z93n99ZW7D0saMbN74m0LAIA6c36/9PEG6cQt0viLs983+VtpdIc00hfuP//r9HoEKjF1\nXJp6P51ak+8SvAIAanZf1326ftH1sY65YtEK9a/I79yavIdNnZIO1DjGuEJoBQDAwnN5VDq1VTr5\ndWnyUHXPTh6STm6W/vC/JtMbMB/nXk+33kTK9QAADWdR06LYl7t973PfU2tTa6xjxinvYdO4pDM1\njrFOIbQCAGBhufB/SyO31P4vy+f/OZ5+gDhMvtfY9QAADekb131D7c3tsYz1+cWf1191/1UsYyUl\n72HTsKQvz/dhM/tm9HE8nnYAAKgjv384nKoFNAp3aWoo3ZpTR0JdAABq0N7cri3XbYllrIEbBrSo\naVEsYyUl72HTQUkb5/OgmXVIekVhg/HhOJsCAKAuTJ/NugMgXtPnpOmxlGuOSdPn060JAGhI37r+\nW1pktYVEq1tX6/7l98fUUXLyHjbtkbTezJ6r5iEzu1XSiKLlc/PdYBwAAAA54hczqnshm7oAgIay\nYtEKbV6+uaYxnrjhCTVbc0wdJSfXYZO7H5X0pqRBM3vPzL4x271m1mFm95jZHklHJHUpzGranU63\nAAAASJQtzqjukmzqAgAazqMrH5XJ5vXsF9u+qHs674m5o2S0/P/s3X1wneWZ5/nfJSu2RGQhWaEd\ntC6QZWaX3rUI2A7zB4OhwWYgTlVXEhsWh5mp2iE2oYvMH7yY0N3TU9UVjE22JkNqTDCZnqkuXpLg\nZCbVMSGxnY5xp2cLbJG12XTvZJAclyPWTWTJL0Gyx9a1f9znxAehI52X5/Wc76fq1DlHz/Pc92U6\njfHP933daRdQgfsVttKtlLTLzKSwLa7YPLxfHz5trvh/uSFJW5IpEwAAALFqWSi1dCe7la6lW2qJ\npqErAAB9bX269fJb9den/rrqZx/sfVAtluk1Q7+T+Srd/ZRC0HRUIUQyhXBphUIItazk56Xx4JCk\nO9z9dJL1AgAAICZmUtuKZOdsWxnmBQAgIv/i4/+i6mc+8dFP6KbOm2KoJh6ZD5skyd2H3H2ZQsNv\naeZwSSXXdkpa6e7DCZUIAACAJLTf2NjzAQAa3sBHB3RDxw1VPfNHvX8ky9FffuQibCpy980KvZg2\nKARKeyUNFt53SdosqdvdHyisiAIAAEAjWXhvsvN1JjwfAKAp/PPF/7zie//xwn+slQtXxlhN9PLQ\ns+kDCiHSdwsvAACQJve0K0CzaRuQ2j4pTb4Z/1ztq6UFy+OfBwDQdP5J5z9Rf1u/hiaH5rz3wd4H\nE6goWrla2QQAADLGf5t2BWhGXQ8kM08P58wAAOLRYi36Z4v/2Zz33XL5LVr+0fz9xQdhEwAAqJ2f\nT7sCNKPLEjj2uXOj1PGp+OcBADStu7rv0qLWRWWvm0xf7P1ighVFh7AJAADUzuanXQEQvdZeafEz\naVcBAGhwH2n5iP6w5w/LXr/58pv1j9r/UYIVRYewCQAA1M4+mnYFQLRauqUlr0nzetKuBADQBO7s\nvrPstc9f8fkEK4kWYRMAAKhdjo7gBebU2itdtT80IQcAIAGXzbus7LXeBb0JVhItwiYAAACgc6O0\n9DBBEwAAESBsAgAAQONou7G6+9tXS0t2S70vsnUOAICItKZdAAAAABCZ3m9LflYae1Ya3zHzPe03\nSZfdInXeKy3I33HSAABkHWETAAAAGsuC5dKiR8uHTVe+IM3vS7QkAACaCdvoAAAAAAAAEJlcrWwy\ns+sl3SOpX1KXpEPu/kSZezvd/XSS9QEAAAAAADS7XIRNhZDpeUkrSn8saZWkD4VNZna5pJNmtt7d\n/3MyVQIAkDEtl0tTp9KuAgAAAE0m89vozOx2SYcUgiYreUlhddOHuPspSd+UtMvMOpOoEwCAzOn9\nltTam3YVAAAAaDKZDpsKK5ReKX4tvA9JGpS0q3BPX5nHHys883h8FQIAkGHzr5WWHpY6N9Y3Tscf\nRlMPAAAAmkKmwyZJmxRWL5mk7ZK63f0ad1/l7ncXfj7b6qZBSZuTKhYAgMyZ1yP1vigt+YH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U+b2cXS/kvuPizpaYXAqb9w7zaFbXOmcJLduLv/JOGaAQAAACDfJg9Lx26VLoxEM96F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\nAQAUgqGlh0OPpXp0b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7p6+3W1Ur/tbJW/KOxHNZzlSlycDHw7Ik5L6AR0VBf7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\ngg==\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,4), dpi=300)\n",
    "plt.errorbar(h_mass1, ave_theta1*180/np.pi, yerr=STD1*180/np.pi, linestyle=\" \", marker='o', c='gold', label='h148')\n",
    "plt.errorbar(h_mass2, ave_theta2*180/np.pi, yerr=STD2*180/np.pi, linestyle=\" \", marker='^', c='darkgreen', label='h229')\n",
    "plt.errorbar(h_mass3, ave_theta3*180/np.pi, yerr=STD3*180/np.pi, linestyle=\" \", marker='v', c='limegreen', label='h242')\n",
    "plt.errorbar(h_mass4, ave_theta4*180/np.pi, yerr=STD4*180/np.pi, linestyle=\" \", marker='D', c='orange', label='h329')\n",
    "plt.xlim(1e8, 3e11)\n",
    "plt.semilogx()\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel(r'Virial Mass ($M_{\\odot}$)')\n",
    "plt.ylabel('Mean Angle of Expulsion (0$^\\circ$ - 180$^\\circ$)')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('Q', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6JC3STCfoS/KhJHd059X5SZKPdNs+U1X/zp7nBPojps5j9HJVvQLcyTw/MT3yHyySpCVq\nA/CnVfVEkpOBfwDO3Mv2iz4RoUGXpD7rToR3KvC97pO7AAcNer8GXZL6bz/g1e4Mor3awdQpRHZZ\nCdw9351KkvqoOz3100k+CVMnyEvy0Tme9kPg7CQruhdDz+7u65lBl6RF6k7Q96/AsUm2J1kPfBpY\nn+RnwFa6K60l+f3uJH6fBP4xyVaAqnoZ+GumzjD7APBX3X29z+HJuSSpDR6hS1IjDLokNcKgS1Ij\nDLokNcKgS1IjDLokNcKgS1IjDLokNeJ/AY1UrqtxD8k7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(h_mass4, ave_theta4*180/np.pi, yerr=STD4*180/np.pi, linestyle=\" \", marker='D', c='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5v1g+HAa/89HbeLIMAakXfQdBVf0nkAVWn9fvfqW2Ds9get/sjwwBqQeOLNaK5AymUu8M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\nqeP+D4mzKMdfA9iYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###Vmin = V_escape\n",
    "plt.errorbar(h_mass1, ave_theta1*180/np.pi, yerr=STD1*180/np.pi, linestyle=\" \", marker='D')\n",
    "plt.errorbar(h_mass2, ave_theta2*180/np.pi, yerr=STD2*180/np.pi, linestyle=\" \", marker='D')\n",
    "\n",
    "plt.xlim(1e9, 1e11)\n",
    "plt.semilogx()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from Time import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def deviation(nums): \n",
    "    ID = 33\n",
    "    nums4 = np.array(datat4['haloid'][datat4['z0haloid']==ID])\n",
    "    timesteps4 = ['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",
    "    timesteps1 = ['.003584','.002816', '.003328', '.003328', '.004096','.002304', '.002304', '.003712', '.003584', '.001920', '.001920', '.003584']\n",
    "    nums2 = [15, 27, 16, 39, 47, 19]\n",
    "    timesteps2 = ['.002496', '.002208', '.003264', '.002016', '.002688', '.002304']\n",
    "    \n",
    "    h_mass4 = np.zeros(len(nums2))\n",
    "    STD4 = np.zeros(len(nums2))\n",
    "    ave_theta4 = np.zeros(len(nums2))\n",
    "    i = 0\n",
    "    for num, timestep in zip(nums4, timesteps4):\n",
    "        print(num,timestep)\n",
    "        sim_4 ='/home/christenc/Data/Sims/h329.cosmo50PLK.3072g/h329.cosmo50PLK.3072gst5HbwK1BH/snapshots_200bkgdens/' + timestep\n",
    "        sim = pynbody.load(sim_4)\n",
    "        h = sim.halos()\n",
    "        sim.physical_units()\n",
    "# ###Loads in halo\n",
    "        halo_info = sim.halos(dummy=True)\n",
    "        halo = h.load_copy(num)\n",
    "        halo.physical_units()\n",
    "        if len(halo.gas)==0:\n",
    "            print(\"No GAS!\")\n",
    "            continue\n",
    "        pynbody.analysis.halo.center(halo)\n",
    "        #rvir = halo_info[num].properties['Rvir']/halo_info[num].properties['h']*halo_info[num].properties['a']\n",
    "        index = np.where(np.array(timesteps4) == timestep)\n",
    "        print(timestep)\n",
    "        print(index)\n",
    "        rvir = (datat4['Rvir'][datat4['z0haloid']==ID].tolist())[int(index[0])]\n",
    "        print(index[0])\n",
    "        print(rvir)\n",
    "        print(halo_info[num].properties['Rvir']/halo_info[num].properties['h']*halo_info[num].properties['a'])\n",
    "        h_mass4[i] = halo_info[num].properties['mass']\n",
    "#         ###defines sphere on outer edge of halo\n",
    "        inner_sphere = pynbody.filt.Sphere(str(.9*rvir) + ' kpc', [0,0,0])\n",
    "        outer_sphere = pynbody.filt.Sphere(str(rvir) + ' kpc', [0,0,0])\n",
    "        shell_part = sim[outer_sphere & ~inner_sphere].gas\n",
    "        if len(shell_part)==0:\n",
    "            print(\"No Gas inside\")\n",
    "            continue\n",
    "#         ###defines sphere surrounding halo\n",
    "        inner_sphere_wind = pynbody.filt.Sphere(str(rvir) + ' kpc', [0,0,0])\n",
    "        outer_sphere_wind = pynbody.filt.Sphere(str(3*rvir) + ' kpc', [0,0,0])\n",
    "        shell_part_wind = sim[outer_sphere_wind & ~inner_sphere_wind].gas\n",
    "        if len(shell_part_wind)==0:\n",
    "            print(\"No Gas outside\")\n",
    "            continue\n",
    "###Creates cutoff value for vel of gas wind\n",
    "        windv = (np.sum((shell_part_wind['vel']**2), axis=1))**.5\n",
    "        vmin = 0.5*halo_info[num].properties['v_esc']\n",
    "        Wind_mass = np.sum(shell_part_wind['mass'])        \n",
    "        Wvel = np.array(shell_part_wind['vel'])[windv > vmin]\n",
    "        Wymass = np.array(shell_part_wind['mass'])[windv > vmin]\n",
    "        windyvec = np.mean(np.array(Wymass)[np.newaxis].T * Wvel,axis=0)/Wind_mass        \n",
    "###Find Vel of gas inside the halo and creates array\n",
    "        velocity = shell_part['vel'].in_units('kpc yr**-1')\n",
    "        r = shell_part['pos'].in_units('kpc')\n",
    "        vr = np.sum((velocity*r), axis=1)\n",
    "        Vout = []\n",
    "        for x in range(len(vr)):\n",
    "            if vr[x] > 0:\n",
    "                vel = list(velocity[x])\n",
    "                Vout.append(vel)        \n",
    "    ###takes the magnitude of the gas velocity vectors\n",
    "        avecs = np.array(Vout)\n",
    "        amags = (np.sum((avecs**2), axis=1))**.5\n",
    "        bvec = windyvec\n",
    "        bmag = (np.sum(bvec**2)**.5)\n",
    "    ###Finds angle between avec and bvec\n",
    "        Theta = []\n",
    "        for avec,amag in zip(avecs,amags):\n",
    "            adotb = np.sum((avec*bvec))\n",
    "            ab = amag*bmag\n",
    "            theta = np.arccos(adotb/ab)\n",
    "            Theta = np.append(Theta, theta)\n",
    "        STD4[i] = np.std(Theta)\n",
    "        ave_theta4[i] = np.mean(Theta)\n",
    "        i = i + 1\n",
    "    return h_mass4, ave_theta4, STD4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33.0 h329.cosmo50PLK.3072gst5HbwK1BH.004096\n",
      "No GAS!\n",
      "33.0 h329.cosmo50PLK.3072gst5HbwK1BH.004032\n",
      "No GAS!\n",
      "34.0 h329.cosmo50PLK.3072gst5HbwK1BH.003936\n",
      "No GAS!\n",
      "34.0 h329.cosmo50PLK.3072gst5HbwK1BH.003840\n",
      "No GAS!\n",
      "33.0 h329.cosmo50PLK.3072gst5HbwK1BH.003744\n",
      "No GAS!\n",
      "33.0 h329.cosmo50PLK.3072gst5HbwK1BH.003648\n",
      "No GAS!\n",
      "33.0 h329.cosmo50PLK.3072gst5HbwK1BH.003606\n",
      "No GAS!\n",
      "35.0 h329.cosmo50PLK.3072gst5HbwK1BH.003552\n",
      "No GAS!\n",
      "35.0 h329.cosmo50PLK.3072gst5HbwK1BH.003456\n",
      "No GAS!\n",
      "34.0 h329.cosmo50PLK.3072gst5HbwK1BH.003360\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.003360\n",
      "(array([9]),)\n",
      "[9]\n",
      "33.43\n",
      "30.185670377599273\n",
      "No Gas inside\n",
      "34.0 h329.cosmo50PLK.3072gst5HbwK1BH.003264\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.003264\n",
      "(array([10]),)\n",
      "[10]\n",
      "32.56\n",
      "29.65802175790809\n",
      "No Gas inside\n",
      "32.0 h329.cosmo50PLK.3072gst5HbwK1BH.003195\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.003195\n",
      "(array([11]),)\n",
      "[11]\n",
      "31.96\n",
      "29.370976778999566\n",
      "No Gas inside\n",
      "32.0 h329.cosmo50PLK.3072gst5HbwK1BH.003168\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.003168\n",
      "(array([12]),)\n",
      "[12]\n",
      "31.73\n",
      "29.2427283687523\n",
      "No Gas inside\n",
      "32.0 h329.cosmo50PLK.3072gst5HbwK1BH.003072\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.003072\n",
      "(array([13]),)\n",
      "[13]\n",
      "30.81\n",
      "28.907620023409258\n",
      "No Gas inside\n",
      "31.0 h329.cosmo50PLK.3072gst5HbwK1BH.002976\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.002976\n",
      "(array([14]),)\n",
      "[14]\n",
      "29.9\n",
      "28.6341223938872\n",
      "No Gas inside\n",
      "31.0 h329.cosmo50PLK.3072gst5HbwK1BH.002880\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.002880\n",
      "(array([15]),)\n",
      "[15]\n",
      "29.01\n",
      "28.394062638534603\n",
      "No Gas inside\n",
      "27.0 h329.cosmo50PLK.3072gst5HbwK1BH.002784\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.002784\n",
      "(array([16]),)\n",
      "[16]\n",
      "28.1\n",
      "28.214314257444794\n",
      "No Gas inside\n",
      "27.0 h329.cosmo50PLK.3072gst5HbwK1BH.002688\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.002688\n",
      "(array([17]),)\n",
      "[17]\n",
      "27.24\n",
      "27.544595968359207\n",
      "No Gas inside\n",
      "28.0 h329.cosmo50PLK.3072gst5HbwK1BH.002592\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.002592\n",
      "(array([18]),)\n",
      "[18]\n",
      "26.34\n",
      "14.68983126232624\n",
      "No Gas inside\n",
      "32.0 h329.cosmo50PLK.3072gst5HbwK1BH.002554\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.002554\n",
      "(array([19]),)\n",
      "[19]\n",
      "25.96\n",
      "11.51977862641299\n",
      "No Gas inside\n",
      "17.0 h329.cosmo50PLK.3072gst5HbwK1BH.002496\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.002496\n",
      "(array([20]),)\n",
      "[20]\n",
      "25.42\n",
      "29.750610466005845\n",
      "No Gas inside\n",
      "16.0 h329.cosmo50PLK.3072gst5HbwK1BH.002400\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.002400\n",
      "(array([21]),)\n",
      "[21]\n",
      "24.52\n",
      "29.30024513224669\n",
      "No Gas inside\n",
      "16.0 h329.cosmo50PLK.3072gst5HbwK1BH.002304\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.002304\n",
      "(array([22]),)\n",
      "[22]\n",
      "23.56\n",
      "28.306214337813564\n",
      "No Gas inside\n",
      "16.0 h329.cosmo50PLK.3072gst5HbwK1BH.002208\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.002208\n",
      "(array([23]),)\n",
      "[23]\n",
      "22.65\n",
      "27.22163441415152\n",
      "No Gas inside\n",
      "15.0 h329.cosmo50PLK.3072gst5HbwK1BH.002112\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.002112\n",
      "(array([24]),)\n",
      "[24]\n",
      "21.77\n",
      "26.208218089968145\n",
      "No Gas inside\n",
      "14.0 h329.cosmo50PLK.3072gst5HbwK1BH.002088\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.002088\n",
      "(array([25]),)\n",
      "[25]\n",
      "21.56\n",
      "26.00232104421996\n",
      "No Gas inside\n",
      "13.0 h329.cosmo50PLK.3072gst5HbwK1BH.002016\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.002016\n",
      "(array([26]),)\n",
      "[26]\n",
      "20.91\n",
      "25.22604419802483\n",
      "No Gas inside\n",
      "15.0 h329.cosmo50PLK.3072gst5HbwK1BH.001920\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.001920\n",
      "(array([27]),)\n",
      "[27]\n",
      "20.09\n",
      "23.66110691895783\n",
      "No Gas inside\n",
      "14.0 h329.cosmo50PLK.3072gst5HbwK1BH.001824\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.001824\n",
      "(array([28]),)\n",
      "[28]\n",
      "19.35\n",
      "22.752816559757655\n",
      "No Gas inside\n",
      "14.0 h329.cosmo50PLK.3072gst5HbwK1BH.001740\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.001740\n",
      "(array([29]),)\n",
      "[29]\n",
      "18.65\n",
      "21.966685003696824\n",
      "No Gas inside\n",
      "13.0 h329.cosmo50PLK.3072gst5HbwK1BH.001728\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.001728\n",
      "(array([30]),)\n",
      "[30]\n",
      "18.54\n",
      "21.84261594910936\n",
      "No Gas inside\n",
      "12.0 h329.cosmo50PLK.3072gst5HbwK1BH.001632\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.001632\n",
      "(array([31]),)\n",
      "[31]\n",
      "17.71\n",
      "20.879865004225813\n",
      "No Gas inside\n",
      "12.0 h329.cosmo50PLK.3072gst5HbwK1BH.001536\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.001536\n",
      "(array([32]),)\n",
      "[32]\n",
      "16.79\n",
      "19.882869258763844\n",
      "No Gas inside\n",
      "12.0 h329.cosmo50PLK.3072gst5HbwK1BH.001475\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.001475\n",
      "(array([33]),)\n",
      "[33]\n",
      "16.21\n",
      "19.235550727149096\n",
      "No Gas inside\n",
      "12.0 h329.cosmo50PLK.3072gst5HbwK1BH.001440\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.001440\n",
      "(array([34]),)\n",
      "[34]\n",
      "15.88\n",
      "18.82880683895757\n",
      "No Gas inside\n",
      "15.0 h329.cosmo50PLK.3072gst5HbwK1BH.001344\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.001344\n",
      "(array([35]),)\n",
      "[35]\n",
      "14.98\n",
      "17.73652246299793\n",
      "No Gas inside\n",
      "14.0 h329.cosmo50PLK.3072gst5HbwK1BH.001269\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.001269\n",
      "(array([36]),)\n",
      "[36]\n",
      "14.24\n",
      "16.916603310411695\n",
      "No Gas inside\n",
      "16.0 h329.cosmo50PLK.3072gst5HbwK1BH.001248\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.001248\n",
      "(array([37]),)\n",
      "[37]\n",
      "14.03\n",
      "16.674926703982035\n",
      "No Gas inside\n",
      "15.0 h329.cosmo50PLK.3072gst5HbwK1BH.001152\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.001152\n",
      "(array([38]),)\n",
      "[38]\n",
      "13.1\n",
      "15.610756456647467\n",
      "No Gas inside\n",
      "14.0 h329.cosmo50PLK.3072gst5HbwK1BH.001106\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.001106\n",
      "(array([39]),)\n",
      "[39]\n",
      "12.67\n",
      "15.114213008425104\n",
      "No Gas inside\n",
      "14.0 h329.cosmo50PLK.3072gst5HbwK1BH.001056\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.001056\n",
      "(array([40]),)\n",
      "[40]\n",
      "12.26\n",
      "14.584316646655358\n",
      "No Gas inside\n",
      "15.0 h329.cosmo50PLK.3072gst5HbwK1BH.000974\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.000974\n",
      "(array([41]),)\n",
      "[41]\n",
      "11.72\n",
      "13.765236262745699\n",
      "No Gas inside\n",
      "15.0 h329.cosmo50PLK.3072gst5HbwK1BH.000960\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.000960\n",
      "(array([42]),)\n",
      "[42]\n",
      "11.64\n",
      "13.620184505827027\n",
      "No Gas inside\n",
      "14.0 h329.cosmo50PLK.3072gst5HbwK1BH.000864\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.000864\n",
      "(array([43]),)\n",
      "[43]\n",
      "10.7\n",
      "12.51040637823159\n",
      "No Gas inside\n",
      "17.0 h329.cosmo50PLK.3072gst5HbwK1BH.000776\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.000776\n",
      "(array([44]),)\n",
      "[44]\n",
      "9.45\n",
      "11.156410768804315\n",
      "No Gas inside\n",
      "17.0 h329.cosmo50PLK.3072gst5HbwK1BH.000768\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.000768\n",
      "(array([45]),)\n",
      "[45]\n",
      "9.29\n",
      "11.032534029253142\n",
      "No Gas inside\n",
      "22.0 h329.cosmo50PLK.3072gst5HbwK1BH.000672\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.000672\n",
      "(array([46]),)\n",
      "[46]\n",
      "7.35\n",
      "9.911004092687238\n",
      "No Gas inside\n",
      "69.0 h329.cosmo50PLK.3072gst5HbwK1BH.000637\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.000637\n",
      "(array([47]),)\n",
      "[47]\n",
      "6.95\n",
      "3.698941011731312\n",
      "No Gas inside\n",
      "52.0 h329.cosmo50PLK.3072gst5HbwK1BH.000576\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.000576\n",
      "(array([48]),)\n",
      "[48]\n",
      "6.35\n",
      "5.303588308256865\n",
      "50.0 h329.cosmo50PLK.3072gst5HbwK1BH.000480\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.000480\n",
      "(array([49]),)\n",
      "[49]\n",
      "5.79\n",
      "5.3121703422142295\n",
      "52.0 h329.cosmo50PLK.3072gst5HbwK1BH.000456\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.000456\n",
      "(array([50]),)\n",
      "[50]\n",
      "5.53\n",
      "5.145350244722666\n",
      "65.0 h329.cosmo50PLK.3072gst5HbwK1BH.000384\n",
      "h329.cosmo50PLK.3072gst5HbwK1BH.000384\n",
      "(array([51]),)\n",
      "[51]\n",
      "4.38\n",
      "4.104784155732517\n"
     ]
    },
    {
     "ename": "AxisError",
     "evalue": "axis 1 is out of bounds for array of dimension 1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAxisError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-41-859303e7d982>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mh_mass4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mave_theta4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSTD4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeviation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-40-7934388a8f90>\u001b[0m in \u001b[0;36mdeviation\u001b[0;34m(nums)\u001b[0m\n\u001b[1;32m    127\u001b[0m     \u001b[0;31m###takes the magnitude of the gas velocity vectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m         \u001b[0mavecs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mVout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 129\u001b[0;31m         \u001b[0mamags\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mavecs\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    130\u001b[0m         \u001b[0mbvec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwindyvec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m         \u001b[0mbmag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbvec\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/engelhar/.local/lib/python3.5/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36msum\u001b[0;34m(a, axis, dtype, out, keepdims, initial)\u001b[0m\n\u001b[1;32m   2074\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2075\u001b[0m     return _wrapreduction(a, np.add, 'sum', axis, dtype, out, keepdims=keepdims,\n\u001b[0;32m-> 2076\u001b[0;31m                           initial=initial)\n\u001b[0m\u001b[1;32m   2077\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2078\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/engelhar/.local/lib/python3.5/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapreduction\u001b[0;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001b[0m\n\u001b[1;32m     84\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpasskwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mufunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpasskwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAxisError\u001b[0m: axis 1 is out of bounds for array of dimension 1"
     ]
    }
   ],
   "source": [
    "h_mass4, ave_theta4, STD4 = deviation(1)"
   ]
  },
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   "execution_count": null,
   "metadata": {
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   },
   "outputs": [],
   "source": []
  }
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