bicone_emcee-pseudo-IFU-J0930.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.3.1'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RoGfPnk31MiIiEqDBROHuX62v3MwKgd7Apuh4dAGwwcyGApVAj5jdC6JlldH7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\nohARkUD/H8WlcIqoL2MzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114e5fa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8c+5L59x2YBvQy8zaAqc751a40GTds4AhPsQgIiJhQ/Iz+eWwHDJ9OrOIK1GY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\niUJERDwpUYiIiKf/D9X+vMkzNqbBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114f51278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JQkREAilRiIhIICUKEREJpEQhIiKBlChERCSQEoWIiARSohARkUBKFCIiEkiJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1150a44e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1151f1dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "assy_error [[60.792078367, 29.276040068300006, 23.8056886096, 9.073096162799999, 8.387015118599997, 4.692570881599998, 9.227459759599995, 143.8576150096, 2.3353138272999985, 13.608557078900006, 49.95482825133, -77.1149615710001, 49.99370865199998, 64.500054573, 21.932303999999988, 20.508875558, 25.51842849800002, 18.10882816100002, 267.646783783, 45.320028500999996, 25.326146672999982, 41.569448362], [103.7650337505, 18.14188821319999, 3.5821427524999976, 5.934803476399999, 3.765976590000001, 6.469863873000001, 4.467857907499997, 2.399538384100012, 20.4953348727, 13.067430815199998, -13.796235313300002, 842.39132252, 445.595332713, 422.653243369, 469.45860169, 479.21414488000005, 484.599091462, 523.133169073, 425.719142958, 400.395861133, 407.45324047500003, 372.89929745300003]] 22\n",
      "22 22 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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g+4clKonS5IyfBHViCOdrCeEYQUwF1qrqywAisgiYBViCirKI/KXc/PBmlvy4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\n0TKLX5NEiQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1152ecda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pyfits\n",
    "import matplotlib as plt\n",
    "from pylab import *\n",
    "import numpy as np\n",
    "from decimal import *\n",
    "import numpy\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.integrate import quad\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.modeling import models, fitting\n",
    "from pylab import *                        \n",
    "import math\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~TIME TO DO THE BICONE\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "name='J0930'\n",
    "if name=='J0930':\n",
    "    PA_1='Liu032_PA21, J0930_table_contfitcoords_outflow_J0930_star.txt'\n",
    "    PA_2='Liu032_PA111, J0930_table_contfitcoords_outflow_J0930_star.txt'\n",
    "    PA_3='Liu032_PA66, J0930_table_contfitcoords_outflow_J0930_star.txt'\n",
    "    PA_4='Liu032_PA156, J0930_table_contfitcoords_outflow_J0930_star.txt'\n",
    "    \n",
    "    pixelscale_1=0.389\n",
    "    pixelscale_2=0.389\n",
    "    pixelscale_3=0.42\n",
    "    pixelscale_4=0.42\n",
    "    \n",
    "    slitwidth=1.5\n",
    "    \n",
    "    PA1=21\n",
    "    PA2=111\n",
    "    PA3=66\n",
    "    PA4=156\n",
    "    \n",
    "    \n",
    "    lower_lim=14\n",
    "    upper_lim=25\n",
    "    lower_lim_ortho=12#ws 7\n",
    "    upper_lim_ortho=25\n",
    "    lower_lim_66=16#ws 7\n",
    "    upper_lim_66=25\n",
    "    lower_lim_156=15#ws 7\n",
    "    upper_lim_156=24\n",
    "    \n",
    "    slitwidth=1.5\n",
    "    pixelscale=0.42\n",
    "    \n",
    "    #pixelscale=0.1\n",
    "    minus_one=0\n",
    "    low_1=0\n",
    "    low_2=0\n",
    "    up_1=20\n",
    "    up_2=20\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "with open(PA_1, 'r') as f:\n",
    "    data = f.readlines()\n",
    "    title=[]\n",
    "    col_1=[]\n",
    "    col_2=[]\n",
    "    col_3=[]\n",
    "    col_1_1=[]\n",
    "    col_1_2=[]\n",
    "    col_1_3=[]\n",
    "    col_2_1=[]\n",
    "    col_2_2=[]\n",
    "    col_2_3=[]\n",
    "\n",
    "    for line in data:\n",
    "        words = line.split()\n",
    "        if words[0]=='Vel':\n",
    "            title.append(words[0])\n",
    "            col_1.append(words[1])\n",
    "            col_2.append(words[3])\n",
    "            col_3.append(words[4])\n",
    "        if words[0]=='Veltwoone':\n",
    "            col_1_1.append(words[1])\n",
    "            col_1_2.append(words[3])\n",
    "            col_1_3.append(words[4])\n",
    "        if words[0]=='Veltwotwo':\n",
    "            col_2_1.append(words[1])\n",
    "            col_2_2.append(words[3])\n",
    "            col_2_3.append(words[4])\n",
    "            \n",
    "\n",
    "vel_1_gauss=[]\n",
    "vel_1_error_up=[]\n",
    "vel_1_error_down=[]\n",
    "vel_1_error=[]\n",
    "\n",
    "vel_2_1_gauss=[]\n",
    "vel_2_1_error_up=[]\n",
    "vel_2_1_error_down=[]\n",
    "vel_2_1_error=[]\n",
    "\n",
    "vel_2_2_gauss=[]\n",
    "vel_2_2_error_up=[]\n",
    "vel_2_2_error_down=[]\n",
    "vel_2_2_error=[]\n",
    "\n",
    "for j in range(len(col_1)):\n",
    "    if j < 40:\n",
    "        vel_1_gauss.append(col_1[j])\n",
    "        vel_1_error_up.append(float(col_2[j])-float(col_1[j]))\n",
    "        vel_1_error_down.append(float(col_1[j])-float(col_3[j]))\n",
    "        vel_1_error.append([float(col_2[j])-float(col_1[j]),float(col_1[j])-float(col_3[j])])\n",
    "        \n",
    "        vel_2_1_gauss.append(col_1_1[j])\n",
    "        vel_2_1_error_up.append(float(col_1_2[j])-float(col_1_1[j]))\n",
    "        vel_2_1_error_down.append(float(col_1_1[j])-float(col_1_3[j]))\n",
    "        vel_2_1_error.append([float(col_1_2[j])-float(col_1_1[j]),float(col_1_1[j])-float(col_1_3[j])])\n",
    "        \n",
    "        vel_2_2_gauss.append(col_2_1[j])\n",
    "        vel_2_2_error_up.append(float(col_2_2[j])-float(col_2_1[j]))\n",
    "        vel_2_2_error_down.append(float(col_2_1[j])-float(col_2_3[j]))\n",
    "        vel_2_2_error.append([float(col_2_2[j])-float(col_2_1[j]),float(col_2_1[j])-float(col_2_3[j])])\n",
    "\n",
    "        \n",
    "with open(PA_2, 'r') as f:\n",
    "    data = f.readlines()\n",
    "    title=[]\n",
    "    col_1=[]\n",
    "    col_2=[]\n",
    "    col_3=[]\n",
    "    col_1_1=[]\n",
    "    col_1_2=[]\n",
    "    col_1_3=[]\n",
    "    col_2_1=[]\n",
    "    col_2_2=[]\n",
    "    col_2_3=[]\n",
    "\n",
    "    for line in data:\n",
    "        words = line.split()\n",
    "        if words[0]=='Vel':\n",
    "            title.append(words[0])\n",
    "            col_1.append(words[1])\n",
    "            col_2.append(words[3])\n",
    "            col_3.append(words[4])\n",
    "        if words[0]=='Veltwoone':\n",
    "            col_1_1.append(words[1])\n",
    "            col_1_2.append(words[3])\n",
    "            col_1_3.append(words[4])\n",
    "        if words[0]=='Veltwotwo':\n",
    "            col_2_1.append(words[1])\n",
    "            col_2_2.append(words[3])\n",
    "            col_2_3.append(words[4])\n",
    "            \n",
    "\n",
    "vel_1_gauss_ortho=[]\n",
    "vel_1_error_ortho_up=[]\n",
    "vel_1_error_ortho_down=[]\n",
    "vel_1_error_ortho=[]\n",
    "\n",
    "vel_2_1_gauss_ortho=[]\n",
    "vel_2_1_error_ortho_up=[]\n",
    "vel_2_1_error_ortho_down=[]\n",
    "vel_2_1_error_ortho=[]\n",
    "\n",
    "vel_2_2_gauss_ortho=[]\n",
    "vel_2_2_error_ortho_up=[]\n",
    "vel_2_2_error_ortho_down=[]\n",
    "vel_2_2_error_ortho=[]\n",
    "\n",
    "for j in range(len(col_1)):\n",
    "    if j < 40:\n",
    "        vel_1_gauss_ortho.append(col_1[j])\n",
    "        vel_1_error_ortho_up.append(float(col_2[j])-float(col_1[j]))\n",
    "        vel_1_error_ortho_down.append(float(col_1[j])-float(col_3[j]))\n",
    "        vel_1_error_ortho.append([float(col_2[j])-float(col_1[j]),float(col_1[j])-float(col_3[j])])\n",
    "        \n",
    "        vel_2_1_gauss_ortho.append(col_1_1[j])\n",
    "        vel_2_1_error_ortho_up.append(float(col_1_2[j])-float(col_1_1[j]))\n",
    "        vel_2_1_error_ortho_down.append(float(col_1_1[j])-float(col_1_3[j]))\n",
    "        vel_2_1_error_ortho.append([float(col_1_2[j])-float(col_1_1[j]),float(col_1_1[j])-float(col_1_3[j])])\n",
    "        \n",
    "        vel_2_2_gauss_ortho.append(col_2_1[j])\n",
    "        vel_2_2_error_ortho_up.append(float(col_2_2[j])-float(col_2_1[j]))\n",
    "        vel_2_2_error_ortho_down.append(float(col_2_1[j])-float(col_2_3[j]))\n",
    "        vel_2_2_error_ortho.append([float(col_2_2[j])-float(col_2_1[j]),float(col_2_1[j])-float(col_2_3[j])])\n",
    "\n",
    "        \n",
    "        \n",
    "with open(PA_3, 'r') as f:\n",
    "    data = f.readlines()\n",
    "    title=[]\n",
    "    col_1=[]\n",
    "    col_2=[]\n",
    "    col_3=[]\n",
    "    col_1_1=[]\n",
    "    col_1_2=[]\n",
    "    col_1_3=[]\n",
    "    col_2_1=[]\n",
    "    col_2_2=[]\n",
    "    col_2_3=[]\n",
    "\n",
    "    for line in data:\n",
    "        words = line.split()\n",
    "        if words[0]=='Vel':\n",
    "            title.append(words[0])\n",
    "            col_1.append(words[1])\n",
    "            col_2.append(words[3])\n",
    "            col_3.append(words[4])\n",
    "        if words[0]=='Veltwoone':\n",
    "            col_1_1.append(words[1])\n",
    "            col_1_2.append(words[3])\n",
    "            col_1_3.append(words[4])\n",
    "        if words[0]=='Veltwotwo':\n",
    "            col_2_1.append(words[1])\n",
    "            col_2_2.append(words[3])\n",
    "            col_2_3.append(words[4])\n",
    "            \n",
    "\n",
    "vel_1_gauss_66=[]\n",
    "vel_1_error_66_up=[]\n",
    "vel_1_error_66_down=[]\n",
    "vel_1_error_66=[]\n",
    "\n",
    "vel_2_1_gauss_66=[]\n",
    "vel_2_1_error_66_up=[]\n",
    "vel_2_1_error_66_down=[]\n",
    "vel_2_1_error_66=[]\n",
    "\n",
    "vel_2_2_gauss_66=[]\n",
    "vel_2_2_error_66_up=[]\n",
    "vel_2_2_error_66_down=[]\n",
    "vel_2_2_error_66=[]\n",
    "\n",
    "for j in range(len(col_1)):\n",
    "    if j < 40:\n",
    "        vel_1_gauss_66.append(col_1[j])\n",
    "        vel_1_error_66_up.append(float(col_2[j])-float(col_1[j]))\n",
    "        vel_1_error_66_down.append(float(col_1[j])-float(col_3[j]))\n",
    "        vel_1_error_66.append([float(col_2[j])-float(col_1[j]),float(col_1[j])-float(col_3[j])])\n",
    "        \n",
    "        vel_2_1_gauss_66.append(col_1_1[j])\n",
    "        vel_2_1_error_66_up.append(float(col_1_2[j])-float(col_1_1[j]))\n",
    "        vel_2_1_error_66_down.append(float(col_1_1[j])-float(col_1_3[j]))\n",
    "        vel_2_1_error_66.append([float(col_1_2[j])-float(col_1_1[j]),float(col_1_1[j])-float(col_1_3[j])])\n",
    "        \n",
    "        vel_2_2_gauss_66.append(col_2_1[j])\n",
    "        vel_2_2_error_66_up.append(float(col_2_2[j])-float(col_2_1[j]))\n",
    "        vel_2_2_error_66_down.append(float(col_2_1[j])-float(col_2_3[j]))\n",
    "        vel_2_2_error_66.append([float(col_2_2[j])-float(col_2_1[j]),float(col_2_1[j])-float(col_2_3[j])])\n",
    "\n",
    "with open(PA_4, 'r') as f:\n",
    "    data = f.readlines()\n",
    "    title=[]\n",
    "    col_1=[]\n",
    "    col_2=[]\n",
    "    col_3=[]\n",
    "    col_1_1=[]\n",
    "    col_1_2=[]\n",
    "    col_1_3=[]\n",
    "    col_2_1=[]\n",
    "    col_2_2=[]\n",
    "    col_2_3=[]\n",
    "\n",
    "    for line in data:\n",
    "        words = line.split()\n",
    "        if words[0]=='Vel':\n",
    "            title.append(words[0])\n",
    "            col_1.append(words[1])\n",
    "            col_2.append(words[3])\n",
    "            col_3.append(words[4])\n",
    "        if words[0]=='Veltwoone':\n",
    "            col_1_1.append(words[1])\n",
    "            col_1_2.append(words[3])\n",
    "            col_1_3.append(words[4])\n",
    "        if words[0]=='Veltwotwo':\n",
    "            col_2_1.append(words[1])\n",
    "            col_2_2.append(words[3])\n",
    "            col_2_3.append(words[4])\n",
    "            \n",
    "\n",
    "vel_1_gauss_156=[]\n",
    "vel_1_error_156_up=[]\n",
    "vel_1_error_156_down=[]\n",
    "vel_1_error_156=[]\n",
    "\n",
    "vel_2_1_gauss_156=[]\n",
    "vel_2_1_error_156_up=[]\n",
    "vel_2_1_error_156_down=[]\n",
    "vel_2_1_error_156=[]\n",
    "\n",
    "vel_2_2_gauss_156=[]\n",
    "vel_2_2_error_156_up=[]\n",
    "vel_2_2_error_156_down=[]\n",
    "vel_2_2_error_156=[]\n",
    "\n",
    "for j in range(len(col_1)):\n",
    "    if j < 40:\n",
    "        vel_1_gauss_156.append(col_1[j])\n",
    "        vel_1_error_156_up.append(float(col_2[j])-float(col_1[j]))\n",
    "        vel_1_error_156_down.append(float(col_1[j])-float(col_3[j]))\n",
    "        vel_1_error_156.append([float(col_2[j])-float(col_1[j]),float(col_1[j])-float(col_3[j])])\n",
    "        \n",
    "        vel_2_1_gauss_156.append(col_1_1[j])\n",
    "        vel_2_1_error_156_up.append(float(col_1_2[j])-float(col_1_1[j]))\n",
    "        vel_2_1_error_156_down.append(float(col_1_1[j])-float(col_1_3[j]))\n",
    "        vel_2_1_error_156.append([float(col_1_2[j])-float(col_1_1[j]),float(col_1_1[j])-float(col_1_3[j])])\n",
    "        \n",
    "        vel_2_2_gauss_156.append(col_2_1[j])\n",
    "        vel_2_2_error_156_up.append(float(col_2_2[j])-float(col_2_1[j]))\n",
    "        vel_2_2_error_156_down.append(float(col_2_1[j])-float(col_2_3[j]))\n",
    "        vel_2_2_error_156.append([float(col_2_2[j])-float(col_2_1[j]),float(col_2_1[j])-float(col_2_3[j])])\n",
    "\n",
    "\n",
    "        \n",
    "xs_data=np.linspace(0, len(vel_1_gauss)-1, len(vel_1_gauss))\n",
    "\n",
    "xs_data=xs_data-len(xs_data)/2\n",
    "\n",
    "low_21=15\n",
    "high_21=25\n",
    "\n",
    "low_111=13\n",
    "high_111=25\n",
    "\n",
    "low_66=16\n",
    "high_66=24\n",
    "\n",
    "low_156=16\n",
    "high_156=24\n",
    "\n",
    "xs_data=[float(y) for y in xs_data]\n",
    "vel_1=[float(y) for y in vel_1_gauss]\n",
    "vel_2_1=[float(y) for y in vel_2_1_gauss]\n",
    "vel_2_2=[float(y) for y in vel_2_2_gauss]\n",
    "#vel_1_error=[float(y) for y in vel_1_error]\n",
    "\n",
    "xs_data=[float(y) for y in xs_data]\n",
    "vel_1_ortho=[float(y) for y in vel_1_gauss_ortho]\n",
    "vel_2_1_ortho=[float(y) for y in vel_2_1_gauss_ortho]\n",
    "vel_2_2_ortho=[float(y) for y in vel_2_2_gauss_ortho]\n",
    "\n",
    "vel_1_66=[float(y) for y in vel_1_gauss_66]\n",
    "vel_2_1_66=[float(y) for y in vel_2_1_gauss_66]\n",
    "vel_2_2_66=[float(y) for y in vel_2_2_gauss_66]\n",
    "\n",
    "vel_1_156=[float(y) for y in vel_1_gauss_156]\n",
    "vel_2_1_156=[float(y) for y in vel_2_1_gauss_156]\n",
    "vel_2_2_156=[float(y) for y in vel_2_2_gauss_156]\n",
    "#vel_1_error_ortho=[float(y) for y in vel_1_error_ortho]\n",
    "\n",
    "plt.scatter(xs_data, vel_2_1, label='vel_2_1 PA 21')\n",
    "plt.scatter(xs_data, vel_2_2, label='vel_2_2 PA 21')\n",
    "plt.scatter(xs_data[lower_lim:upper_lim], vel_2_1[lower_lim:upper_lim], label='chosen 1 PA 21')\n",
    "plt.scatter(xs_data[lower_lim:upper_lim], vel_2_2[lower_lim:upper_lim], label='chosen 2 PA 21')\n",
    "plt.legend()\n",
    "plt.xlim([-10,10])\n",
    "plt.ylim([-500,500])\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(xs_data, vel_2_1_ortho, label='vel_2_1 PA 111')\n",
    "plt.scatter(xs_data, vel_2_2_ortho, label='vel_2_2 PA 111')\n",
    "plt.scatter(xs_data[lower_lim_ortho:upper_lim_ortho], vel_2_1_ortho[lower_lim_ortho:upper_lim_ortho], label='chosen 1 PA 111')\n",
    "plt.scatter(xs_data[lower_lim_ortho:upper_lim_ortho], vel_2_2_ortho[lower_lim_ortho:upper_lim_ortho], label='chosen 2 PA 111')\n",
    "plt.legend()\n",
    "plt.xlim([-10,10])\n",
    "plt.ylim([-500,500])\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "plt.scatter(xs_data, vel_2_1_66, label='vel_2_1 PA 66')\n",
    "plt.scatter(xs_data, vel_2_2_66, label='vel_2_2 PA 66')\n",
    "plt.scatter(xs_data[lower_lim_66:upper_lim_66], vel_2_1_66[lower_lim_66:upper_lim_66], label='chosen 1 PA 66')\n",
    "plt.scatter(xs_data[lower_lim_66:upper_lim_66], vel_2_2_66[lower_lim_66:upper_lim_66], label='chosen 2 PA 66')\n",
    "plt.legend()\n",
    "plt.xlim([-10,10])\n",
    "plt.ylim([-500,500])\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "plt.scatter(xs_data, vel_2_1_156, label='vel_2_1 PA 156')\n",
    "plt.scatter(xs_data, vel_2_2_156, label='vel_2_2 PA 156')\n",
    "plt.scatter(xs_data[lower_lim_156:upper_lim_156], vel_2_1_156[lower_lim_156:upper_lim_156], label='chosen 1 PA 156')\n",
    "plt.scatter(xs_data[lower_lim_156:upper_lim_156], vel_2_2_156[lower_lim_156:upper_lim_156], label='chosen 2 PA 156')\n",
    "plt.legend()\n",
    "plt.xlim([-10,10])\n",
    "plt.ylim([-500,500])\n",
    "plt.show()\n",
    "        \n",
    "'''\n",
    "        \n",
    "xs_data=[float(y) for y in xs_data]\n",
    "vel_2_1=[float(y) for y in vel_2_1]\n",
    "vel_2_2=[float(y) for y in vel_2_2]\n",
    "vel_2_1_ortho=[float(y) for y in vel_2_1_ortho]\n",
    "vel_2_2_ortho=[float(y) for y in vel_2_2_ortho]\n",
    "vel_2_1_error=[float(y) for y in vel_2_1_error]\n",
    "vel_2_2_error=[float(y) for y in vel_2_2_error]\n",
    "vel_2_1_ortho_error=[float(y) for y in vel_2_1_error_ortho]\n",
    "vel_2_2_ortho_error=[float(y) for y in vel_2_2_error_ortho]\n",
    "#(xs_data[lower_lim:upper_lim],vel_2_1[lower_lim:upper_lim]\n",
    "if np.mean(vel_2_1[lower_lim:upper_lim])< np.mean(vel_2_1[lower_lim:upper_lim]):\n",
    "    #this means that the first set is the above side\n",
    "    v_1_above=vel_2_1[lower_lim:upper_lim]\n",
    "    v_1_below=vel_2_2[lower_lim:upper_lim]\n",
    "    vel_data_concat=vel_2_1[lower_lim:upper_lim]+vel_2_2[lower_lim:upper_lim]\n",
    "    error_data_concat=vel_2_1_error[lower_lim:upper_lim]+vel_2_2_error[lower_lim:upper_lim]\n",
    "else:\n",
    "    v_1_above=vel_2_2[lower_lim:upper_lim]\n",
    "    v_1_below=vel_2_1[lower_lim:upper_lim]\n",
    "    vel_data_concat=vel_2_2[lower_lim:upper_lim]+vel_2_1[lower_lim:upper_lim]\n",
    "    error_data_concat=vel_2_2_error[lower_lim:upper_lim]+vel_2_1_error[lower_lim:upper_lim]\n",
    "\n",
    "if np.mean(vel_2_1_ortho[lower_lim_ortho:upper_lim_ortho])< np.mean(vel_2_1_ortho[lower_lim_ortho:upper_lim_ortho]):\n",
    "    #this means that the first set is the above side\n",
    "    v_2_above=vel_2_1_ortho[lower_lim_ortho:upper_lim_ortho]\n",
    "    v_2_below=vel_2_2_ortho[lower_lim_ortho:upper_lim_ortho]\n",
    "    vel_data_concat_ortho=vel_2_1_ortho[lower_lim_ortho:upper_lim_ortho]+vel_2_2_ortho[lower_lim_ortho:upper_lim_ortho]\n",
    "    error_data_concat_ortho=vel_2_1_ortho_error[lower_lim_ortho:upper_lim_ortho]+vel_2_2_ortho_error[lower_lim_ortho:upper_lim_ortho]\n",
    "else:\n",
    "    v_2_above=vel_2_2_ortho[lower_lim_ortho:upper_lim_ortho]\n",
    "    v_2_below=vel_2_1_ortho[lower_lim_ortho:upper_lim_ortho]\n",
    "    vel_data_concat_ortho=vel_2_2_ortho[lower_lim_ortho:upper_lim_ortho]+vel_2_1_ortho[lower_lim_ortho:upper_lim_ortho]\n",
    "    error_data_concat_ortho=vel_2_2_ortho_error[lower_lim_ortho:upper_lim_ortho]+vel_2_1_ortho_error[lower_lim_ortho:upper_lim_ortho]\n",
    "\n",
    "\n",
    "xs_data_cut=xs_data[lower_lim:upper_lim]\n",
    "xs_data_concat=xs_data_cut+xs_data_cut\n",
    "xs_data_cut_ortho= xs_data[lower_lim_ortho:upper_lim_ortho]\n",
    "xs_data_concat_ortho=xs_data_cut_ortho+xs_data_cut_ortho\n",
    "\n",
    "\n",
    "\n",
    "for i in xrange(len(error_data_concat)):\n",
    "    if error_data_concat[i]==0.0:\n",
    "        error_data_concat[i]=1000\n",
    "for i in xrange(len(error_data_concat_ortho)):\n",
    "    if error_data_concat_ortho[i]==0.0:\n",
    "        error_data_concat_ortho[i]=1000\n",
    "\n",
    "\n",
    "plt.ylabel( r'Velocity [km s$^{-1}$]',  size=20)\n",
    "plt.xlabel(r'Pixel', size=20)\n",
    "plt.title(str(name), size=20)\n",
    "\n",
    "plt.errorbar(xs_data_concat, vel_data_concat,yerr=error_data_concat, color='blue',linestyle='None')\n",
    "plt.errorbar(xs_data_concat_ortho, vel_data_concat_ortho,yerr=error_data_concat_ortho, color='red', linestyle='None')\n",
    "plt.scatter(xs_data_concat, vel_data_concat, color='blue', label='PA 1')\n",
    "plt.scatter(xs_data_concat_ortho, vel_data_concat_ortho, color='red', label='PA 2')\n",
    "plt.legend()\n",
    "#plt.ylim([-400,400])\n",
    "plt.tight_layout()\n",
    "#savefig('../J0009_errors_before.pdf')\n",
    "plt.show()\n",
    "'''\n",
    "\n",
    "\n",
    "if np.mean(vel_2_1[lower_lim:upper_lim])< np.mean(vel_2_1[lower_lim:upper_lim]):\n",
    "    #this means that the first set is the above side\n",
    "    v_1_above=vel_2_1[lower_lim:upper_lim]\n",
    "    v_1_below=vel_2_2[lower_lim:upper_lim]\n",
    "    vel_data_concat=vel_2_1[lower_lim:upper_lim]+vel_2_2[lower_lim:upper_lim]\n",
    "    vel_error_up_concat=vel_2_1_error_up[lower_lim:upper_lim]+vel_2_2_error_up[lower_lim:upper_lim]\n",
    "    vel_error_down_concat=vel_2_1_error_down[lower_lim:upper_lim]+vel_2_2_error_down[lower_lim:upper_lim]\n",
    "    #error_data_concat=vel_2_1_error[lower_lim:upper_lim]+vel_2_2_error[lower_lim:upper_lim]\n",
    "else:\n",
    "    v_1_above=vel_2_2[lower_lim:upper_lim]\n",
    "    v_1_below=vel_2_1[lower_lim:upper_lim]\n",
    "    vel_data_concat=vel_2_2[lower_lim:upper_lim]+vel_2_1[lower_lim:upper_lim]\n",
    "    vel_error_up_concat=vel_2_2_error_up[lower_lim:upper_lim]+vel_2_1_error_up[lower_lim:upper_lim]\n",
    "    vel_error_down_concat=vel_2_2_error_down[lower_lim:upper_lim]+vel_2_1_error_down[lower_lim:upper_lim]\n",
    "    #error_data_concat=vel_2_2_error[lower_lim:upper_lim]+vel_2_1_error[lower_lim:upper_lim]\n",
    "\n",
    "if np.mean(vel_2_1_ortho[lower_lim_ortho:upper_lim_ortho])< np.mean(vel_2_1_ortho[lower_lim_ortho:upper_lim_ortho]):\n",
    "    #this means that the first set is the above side\n",
    "    v_2_above=vel_2_1_ortho[lower_lim_ortho:upper_lim_ortho]\n",
    "    v_2_below=vel_2_2_ortho[lower_lim_ortho:upper_lim_ortho]\n",
    "    vel_data_concat_ortho=vel_2_1_ortho[lower_lim_ortho:upper_lim_ortho]+vel_2_2_ortho[lower_lim_ortho:upper_lim_ortho]\n",
    "    vel_error_ortho_up_concat=vel_2_1_error_ortho_up[lower_lim_ortho:upper_lim_ortho]+vel_2_2_error_ortho_up[lower_lim_ortho:upper_lim_ortho]\n",
    "    vel_error_ortho_down_concat=vel_2_1_error_ortho_down[lower_lim_ortho:upper_lim_ortho]+vel_2_2_error_ortho_down[lower_lim_ortho:upper_lim_ortho]\n",
    "    #error_data_concat_ortho=vel_2_1_ortho_error[lower_lim_ortho:upper_lim_ortho]+vel_2_2_ortho_error[lower_lim_ortho:upper_lim_ortho]\n",
    "else:\n",
    "    v_2_above=vel_2_2_ortho[lower_lim_ortho:upper_lim_ortho]\n",
    "    v_2_below=vel_2_1_ortho[lower_lim_ortho:upper_lim_ortho]\n",
    "    vel_data_concat_ortho=vel_2_2_ortho[lower_lim_ortho:upper_lim_ortho]+vel_2_1_ortho[lower_lim_ortho:upper_lim_ortho]\n",
    "    vel_error_ortho_up_concat=vel_2_2_error_ortho_up[lower_lim_ortho:upper_lim_ortho]+vel_2_1_error_ortho_up[lower_lim_ortho:upper_lim_ortho]\n",
    "    vel_error_ortho_down_concat=vel_2_2_error_ortho_down[lower_lim_ortho:upper_lim_ortho]+vel_2_1_error_ortho_down[lower_lim_ortho:upper_lim_ortho]\n",
    "\n",
    "if np.mean(vel_2_1_66[lower_lim_66:upper_lim_66])< np.mean(vel_2_1_66[lower_lim_66:upper_lim_66]):\n",
    "    #this means that the first set is the above side\n",
    "    v_2_above=vel_2_1_66[lower_lim_66:upper_lim_66]\n",
    "    v_2_below=vel_2_2_66[lower_lim_66:upper_lim_66]\n",
    "    vel_data_concat_66=vel_2_1_66[lower_lim_66:upper_lim_66]+vel_2_2_66[lower_lim_66:upper_lim_66]\n",
    "    vel_error_66_up_concat=vel_2_1_error_66_up[lower_lim_66:upper_lim_66]+vel_2_2_error_66_up[lower_lim_66:upper_lim_66]\n",
    "    vel_error_66_down_concat=vel_2_1_error_66_down[lower_lim_66:upper_lim_66]+vel_2_2_error_66_down[lower_lim_66:upper_lim_66]\n",
    "    #error_data_concat_ortho=vel_2_1_ortho_error[lower_lim_ortho:upper_lim_ortho]+vel_2_2_ortho_error[lower_lim_ortho:upper_lim_ortho]\n",
    "else:\n",
    "    v_2_above=vel_2_2_66[lower_lim_66:upper_lim_66]\n",
    "    v_2_below=vel_2_1_66[lower_lim_66:upper_lim_66]\n",
    "    vel_data_concat_66=vel_2_2_66[lower_lim_66:upper_lim_66]+vel_2_1_66[lower_lim_66:upper_lim_66]\n",
    "    vel_error_66_up_concat=vel_2_2_error_66_up[lower_lim_66:upper_lim_66]+vel_2_1_error_66_up[lower_lim_66:upper_lim_66]\n",
    "    vel_error_66_down_concat=vel_2_2_error_66_down[lower_lim_66:upper_lim_66]+vel_2_1_error_66_down[lower_lim_66:upper_lim_66]\n",
    "    \n",
    "if np.mean(vel_2_1_156[lower_lim_156:upper_lim_156])< np.mean(vel_2_1_156[lower_lim_156:upper_lim_156]):\n",
    "    #this means that the first set is the above side\n",
    "    v_2_above=vel_2_1_156[lower_lim_156:upper_lim_156]\n",
    "    v_2_below=vel_2_2_156[lower_lim_156:upper_lim_156]\n",
    "    vel_data_concat_156=vel_2_1_156[lower_lim_156:upper_lim_156]+vel_2_2_156[lower_lim_156:upper_lim_156]\n",
    "    vel_error_156_up_concat=vel_2_1_error_156_up[lower_lim_156:upper_lim_156]+vel_2_2_error_156_up[lower_lim_156:upper_lim_156]\n",
    "    vel_error_156_down_concat=vel_2_1_error_156_down[lower_lim_156:upper_lim_156]+vel_2_2_error_156_down[lower_lim_156:upper_lim_156]\n",
    "    #error_data_concat_ortho=vel_2_1_ortho_error[lower_lim_ortho:upper_lim_ortho]+vel_2_2_ortho_error[lower_lim_ortho:upper_lim_ortho]\n",
    "else:\n",
    "    v_2_above=vel_2_2_156[lower_lim_156:upper_lim_156]\n",
    "    v_2_below=vel_2_1_156[lower_lim_156:upper_lim_156]\n",
    "    vel_data_concat_156=vel_2_2_156[lower_lim_156:upper_lim_156]+vel_2_1_156[lower_lim_156:upper_lim_156]\n",
    "    vel_error_156_up_concat=vel_2_2_error_156_up[lower_lim_156:upper_lim_156]+vel_2_1_error_156_up[lower_lim_156:upper_lim_156]\n",
    "    vel_error_156_down_concat=vel_2_2_error_156_down[lower_lim_156:upper_lim_156]+vel_2_1_error_156_down[lower_lim_156:upper_lim_156]\n",
    "    \n",
    "    #error_data_concat_ortho=vel_2_2_ortho_error[lower_lim_ortho:upper_lim_ortho]+vel_2_1_ortho_error[lower_lim_ortho:upper_lim_ortho]\n",
    "\n",
    "\n",
    "xs_data_cut=xs_data[lower_lim:upper_lim]\n",
    "xs_data_concat=xs_data_cut+xs_data_cut\n",
    "xs_data_concat=[int(x) for x in xs_data_concat]\n",
    "\n",
    "xs_data_cut_ortho= xs_data[lower_lim_ortho:upper_lim_ortho]\n",
    "xs_data_concat_ortho=xs_data_cut_ortho+xs_data_cut_ortho\n",
    "xs_data_concat_ortho=[int(x) for x in xs_data_concat_ortho]\n",
    "\n",
    "xs_data_cut_66=xs_data[lower_lim_66:upper_lim_66]\n",
    "xs_data_concat_66=xs_data_cut_66+xs_data_cut_66\n",
    "xs_data_concat_66=[int(x) for x in xs_data_concat_66]\n",
    "\n",
    "xs_data_cut_156=xs_data[lower_lim_156:upper_lim_156]\n",
    "xs_data_concat_156=xs_data_cut_156+xs_data_cut_156\n",
    "xs_data_concat_156=[int(x) for x in xs_data_concat_156]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "xs_data_concat_arc=[pixelscale_1*y for y in xs_data_concat]\n",
    "xs_data_concat_ortho_arc=[pixelscale_2*y for y in xs_data_concat_ortho]\n",
    "xs_data_concat_66_arc=[pixelscale_3*y for y in xs_data_concat_66]\n",
    "xs_data_concat_156_arc=[pixelscale_4*y for y in xs_data_concat_156]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#assy_error=[vel_1_error_down[lower_lim:upper_lim],vel_1_error_up[lower_lim:upper_lim]]\n",
    "#assy_error_ortho=[vel_1_error_ortho_down[lower_lim_ortho:upper_lim_ortho],vel_1_error_ortho_up[lower_lim_ortho:upper_lim_ortho]]\n",
    "\n",
    "assy_error=[vel_error_down_concat,vel_error_up_concat]\n",
    "assy_error_ortho=[vel_error_ortho_down_concat,vel_error_ortho_up_concat]\n",
    "assy_error_66=[vel_error_66_down_concat,vel_error_66_up_concat]\n",
    "assy_error_156=[vel_error_156_down_concat,vel_error_156_up_concat]\n",
    "\n",
    "\n",
    "#error_data_concat=[vel_1_error_down[lower_lim:upper_lim],vel_1_error_up[lower_lim:upper_lim]]\n",
    "#error_data_concat_ortho=[vel_1_error_ortho_down[lower_lim_ortho:upper_lim_ortho],vel_1_error_ortho_up[lower_lim_ortho:upper_lim_ortho]]\n",
    "error_data_concat=assy_error\n",
    "error_data_concat_ortho=assy_error_ortho\n",
    "error_data_concat_66=assy_error_66\n",
    "error_data_concat_156=assy_error_156\n",
    "\n",
    "print('assy_error',assy_error, len(assy_error[0]))\n",
    "\n",
    "plt.ylabel( r'Velocity [km s$^{-1}$]',  size=20)\n",
    "plt.xlabel(r'Pixel', size=20)\n",
    "plt.title(str(name), size=20)\n",
    "\n",
    "print(len(xs_data_concat), len(vel_data_concat), len(assy_error))\n",
    "\n",
    "plt.errorbar(xs_data_concat, vel_data_concat,yerr=assy_error, color='blue',linestyle='None')#error_data_concat,\n",
    "plt.errorbar(xs_data_concat_ortho, vel_data_concat_ortho,yerr=assy_error_ortho, color='red', linestyle='None')\n",
    "plt.scatter(xs_data_concat, vel_data_concat, color='blue', label='PA 1')\n",
    "plt.scatter(xs_data_concat_ortho, vel_data_concat_ortho, color='red', label='PA 2')\n",
    "\n",
    "plt.errorbar(xs_data_concat_66, vel_data_concat_66,yerr=assy_error_66, color='pink',linestyle='None')#error_data_concat,\n",
    "plt.errorbar(xs_data_concat_156, vel_data_concat_156,yerr=assy_error_156, color='purple', linestyle='None')\n",
    "plt.scatter(xs_data_concat_66, vel_data_concat_66, color='pink', label='PA 3')\n",
    "plt.scatter(xs_data_concat_156, vel_data_concat_156, color='purple', label='PA 4')\n",
    "plt.legend()\n",
    "plt.ylim([-400,400])\n",
    "plt.xlim([-10,10])\n",
    "plt.tight_layout()\n",
    "#savefig('../J0009_errors_before.pdf')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#bicone only\n",
    "'''theta_half, h(depends on r_t), PA (do for radio = OIII), i, v_max, r_t)'''\n",
    "'''1. fit the rotating disks to the objects\n",
    "   2. double peaked - modeling for bicones is new, use your velocity peaks\n",
    "   3. use both position angles wherever you can'''\n",
    "import timeit\n",
    "import pyfits\n",
    "import matplotlib as plt\n",
    "from pylab import *\n",
    "import numpy as np\n",
    "from decimal import *\n",
    "import numpy\n",
    "from scipy.optimize import curve_fit\n",
    "#from scipy.integrate import quad\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.modeling import models, fitting\n",
    "from pylab import *                        \n",
    "import math\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~TIME TO DO THE BICONE\n",
    "\n",
    "\n",
    "clf()\n",
    "\n",
    "name_list=['Liu002_J0009','Liu014_J0803','Liu018_J0821','Liu026_J0854','Liu032_J0930','Liu040_J0959',\n",
    "           'Liu052_J1027','Liu063_J1110','Liu085_J1152','Liu107_J1315','Liu114_J1328','Wang062_J1352',\n",
    "           'Wang063_J1420','Liu139_J1524','Wang072_J1526','Wang080_J1606','Liu153_J1630','Liu159_J1720']\n",
    "name_list=['Liu052_J1027','Liu014_J0803','Liu026_J0854']\n",
    "#name_list=['Liu014_J0803']\n",
    "name_list=['Liu026_J0854']\n",
    "for i in xrange(len(name_list)):\n",
    "    name=name_list[i]\n",
    "    minus_one=0\n",
    "  \n",
    "  \n",
    "    \n",
    " \n",
    "    \n",
    "    if name=='Liu052_J1027':\n",
    "        PA_1='Liu052_PA75, J1028_table_contfitcoords_outflow.txt'\n",
    "        PA_2='Liu052_PA165, J1028_table_contfitcoords_outflow.txt'\n",
    "        PA1=75\n",
    "        PA2=165\n",
    "        lower_lim=15\n",
    "        upper_lim=24\n",
    "        lower_lim_ortho=15\n",
    "        upper_lim_ortho=24\n",
    "        low_1=0\n",
    "        up_1=9\n",
    "        low_2=0\n",
    "        up_2=9\n",
    "        pixelscale=0.42\n",
    "        slitwidth=1.5\n",
    "        minus_one=1\n",
    "        result_a =  78.73 , 54.84 , 3.68 , 38.05 , 80.76 , 594.09\n",
    "        \n",
    "        result_n =  77.1 , 32.02 , 11.77 , 33.05 , 60.81 , 536.38#orig\n",
    "        result_n =  77.38 , 32.22 , 10.72 , 31.55 , 60.76 , 527.88\n",
    "        result_s =  71.62 , 43.18 , 9.55 , 42.58 , 481.2\n",
    "        \n",
    "    if name=='Wang063_J1420':\n",
    "        PA_1='Wang063_PA79, J1420_table_contfitcoords_outflow.txt'\n",
    "        PA_2='Wang063_PA169, J1420_table_contfitcoords_outflow.txt'\n",
    "        PA1=79\n",
    "        PA2=169\n",
    "        lower_lim=17\n",
    "        upper_lim=24\n",
    "        lower_lim_ortho=16\n",
    "        upper_lim_ortho=25\n",
    "        \n",
    "        low_1=1\n",
    "        up_1=8\n",
    "        low_2=0\n",
    "        up_2=10\n",
    "        pixelscale=0.42\n",
    "        slitwidth=1.5\n",
    "        result_a =  73.18 , 35.97 , 2.78 , 44.97 , 82.13 , 412.04\n",
    "        result_n =  78.52 , 173.81 , 5.29 , 40.86 , 70.21 , 388.9\n",
    "        result_s =  80.33 , 42.42 , 4.5 , 59.08 , 251.86\n",
    "    \n",
    "    if name=='Liu139_J1524':\n",
    "        PA_1='Liu139_PA12, J1524_table_contfitcoords_outflow.txt'\n",
    "        PA_2='Liu139_PA102, J1524_table_contfitcoords_outflow.txt'\n",
    "        PA1=12\n",
    "        PA2=102\n",
    "        lower_lim=15\n",
    "        upper_lim=26\n",
    "        lower_lim_ortho=15\n",
    "        upper_lim_ortho=26\n",
    "        \n",
    "        low_1=0\n",
    "        up_1=20\n",
    "        low_2=0\n",
    "        up_2=20\n",
    "        pixelscale=0.42\n",
    "        slitwidth=1.5\n",
    "        result_a =  72.97 , 49.86 , 4.14 , 36.43 , 80.75 , 758.74\n",
    "        result_n =  84.72 , 101.26 , 8.5 , 26.17 , 59.62 , 697.43#orig\n",
    "        result_n =  85.25 , 100.58 , 8.67 , 26.45 , 60.5 , 741.8#oop\n",
    "        result_s =  35.1 , 46.41 , 1.01 , 75.26 , 524.28\n",
    "        \n",
    "        \n",
    "        result_a =  49.41 , 53.12 , 5.77 , 51.67 , 79.37 , 588.95\n",
    "        result_n =  82.78 , 100.45 , 5.17 , 34.79 , 61.77 , 721.98\n",
    "        result_s =  33.84 , 32.22 , 0.99 , 71.45 , 610.63\n",
    "    \n",
    "    if name=='Wang072_J1526':\n",
    "        PA_1='Wang072_PA38, J1526_table_contfitcoords_outflow_three.txt'\n",
    "        PA_2='Wang072_PA128, J1526_table_contfitcoords_outflow_three.txt'\n",
    "        PA1=38\n",
    "        PA2=128\n",
    "        lower_lim=14\n",
    "        upper_lim=24\n",
    "        lower_lim_ortho=14\n",
    "        upper_lim_ortho=25\n",
    "        \n",
    "        low_1=0\n",
    "        up_1=10\n",
    "        low_2=0\n",
    "        up_2=20\n",
    "        pixelscale=0.42\n",
    "        slitwidth=1.5\n",
    "        minus_one=1\n",
    "        result_a =  71.68 , 85.17 , 10.33 , 44.66 , 66.07 , 401.95\n",
    "        result_n =  73.72 , 59.31 , 10.22 , 43.99 , 62.34 , 410.83\n",
    "        result_s =  6.32 , 91.87 , 10.53 , 49.3 , 568.89\n",
    "    \n",
    "    if name=='Liu114_J1328':\n",
    "        PA_1='Liu114_PA39, J1328_table_contfitcoords_outflow.txt'\n",
    "        PA_2='Liu114_PA129, J1328_table_contfitcoords_outflow.txt'\n",
    "        PA1=39\n",
    "        PA2=129\n",
    "        lower_lim=16\n",
    "        upper_lim=25\n",
    "        lower_lim_ortho=16\n",
    "        upper_lim_ortho=25\n",
    "        \n",
    "        low_1=0\n",
    "        up_1=20\n",
    "        low_2=0\n",
    "        up_2=20\n",
    "        pixelscale=0.42\n",
    "        slitwidth=1.5\n",
    "        result_a =  78.31 , 52.13 , 5.65 , 47.5 , 80.85 , 233.38\n",
    "        result_n =  76.53 , 43.74 , 5.5 , 47.46 , 80.47 , 298.14\n",
    "        result_s =  29.78 , 11.17 , 1.41 , 76.98 , 187.02\n",
    "    \n",
    "    if name=='Liu107_J1315':\n",
    "        PA_1='Liu107_PA74, J1315_table_contfitcoords_outflow.txt'\n",
    "        PA_2='Liu107_PA164, J1315_table_contfitcoords_outflow_three.txt'\n",
    "        PA1=74\n",
    "        PA2=164\n",
    "        lower_lim=17\n",
    "        upper_lim=24\n",
    "        lower_lim_ortho=17\n",
    "        upper_lim_ortho=24\n",
    "        \n",
    "        low_1=0\n",
    "        up_1=40\n",
    "        low_2=0\n",
    "        up_2=40\n",
    "        pixelscale=0.42\n",
    "        slitwidth=1.5\n",
    "        result_a =  58.43 , 39.36 , 5.71 , 48.3 , 78.21 , 603.68\n",
    "        result_n =  55.27 , 46.84 , 5.69 , 46.47 , 76.77 , 576.03\n",
    "        result_s =  36.73 , 23.52 , 6.73 , 57.99 , 335.4\n",
    "        \n",
    "        #minus_one=1\n",
    "    \n",
    "    if name=='Liu159_J1720':\n",
    "        PA_1='Liu159_PA152, J1720_table_cont.txt'\n",
    "        PA_2='Liu159_PA242, J1720_table_cont.txt'\n",
    "        PA1=152-90\n",
    "        PA2=242-90\n",
    "        lower_lim=14\n",
    "        upper_lim=25\n",
    "        lower_lim_ortho=12\n",
    "        upper_lim_ortho=33\n",
    "        \n",
    "        low_1=6\n",
    "        up_1=17\n",
    "        low_2=4\n",
    "        up_2=40\n",
    "        pixelscale=0.1185\n",
    "        slitwidth=1.0\n",
    "        result_a =  78.84 , 37.73 , 5.63 , 46.98 , 82.55 , 425.04\n",
    "        result_n =  83.61 , 33.85 , 13.79 , 17.71 , 30.46 , 302.33\n",
    "        result_s =  32.36 , 21.08 , 9.95 , 44.65 , 391.05\n",
    "        #minus_one=1\n",
    "    \n",
    "    \n",
    "    if name=='Liu153_J1630':\n",
    "        PA_1='Liu153_PA63, J1631_table_cont.txt'\n",
    "        PA_2='Liu153_PA156, J1631_table_cont.txt'\n",
    "        PA1=63\n",
    "        PA2=153\n",
    "        lower_lim=15\n",
    "        upper_lim=29\n",
    "        lower_lim_ortho=10\n",
    "        upper_lim_ortho=26\n",
    "        \n",
    "        low_1=5\n",
    "        up_1=21\n",
    "        low_2=0\n",
    "        up_2=16\n",
    "        pixelscale=0.1185\n",
    "        slitwidth=1.0\n",
    "        minus_one=1\n",
    "        result_a =  82.6 , 37.84 , 5.9 , 40.21 , 81.93 , 291.16\n",
    "        result_n =  87.81 , 11.26 , 9.56 , 58.83 , 61.83 , 431.82\n",
    "        result_s =  49.48 , 2.84 , 7.76 , 59.25 , 435.27\n",
    "    \n",
    "    if name=='Wang080_J1606':\n",
    "        PA_1='Wang080_PA17, J1606_table_cont.txt'\n",
    "        PA_2='Wang080_PA107, J1606_table_cont.txt'\n",
    "        PA1=17\n",
    "        PA2=107\n",
    "        lower_lim=10\n",
    "        upper_lim=26\n",
    "        lower_lim_ortho=10\n",
    "        upper_lim_ortho=26\n",
    "        \n",
    "        lower_lim=11\n",
    "        upper_lim=30\n",
    "        lower_lim_ortho=11\n",
    "        upper_lim_ortho=26\n",
    "        \n",
    "        low_1=0\n",
    "        up_1=40\n",
    "        low_2=0\n",
    "        up_2=15\n",
    "        pixelscale=0.1185\n",
    "        slitwidth=1.0\n",
    "        result_a =  83.32 , 89.58 , 7.26 , 38.73 , 78.73 , 384.44\n",
    "        result_n =  80.82 , 113.77 , 8.75 , 36.65 , 74.0 , 374.26\n",
    "        result_s =  25.78 , 43.89 , 10.21 , 52.82 , 387.62\n",
    "        #minus_one=1\n",
    "    \n",
    "   \n",
    "    \n",
    "    if name=='Wang062_J1352':\n",
    "        PA_1='Wang062_PA162, J1352_table_cont.txt'\n",
    "        PA_2='Wang062_PA252, J1352_table_cont.txt'\n",
    "        PA1=162-90\n",
    "        PA2=252-90\n",
    "        lower_lim=10\n",
    "        upper_lim=32\n",
    "        lower_lim_ortho=14\n",
    "        upper_lim_ortho=25\n",
    "        \n",
    "        low_1=1\n",
    "        up_1=23\n",
    "        low_2=5\n",
    "        up_2=16\n",
    "        pixelscale=0.1185\n",
    "        slitwidth=1.0\n",
    "        result_a =  43.0 , 42.66 , 6.47 , 60.95 , 78.0 , 436.84\n",
    "        result_n =  51.24 , 49.71 , 7.59 , 59.46 , 77.39 , 395.05\n",
    "        result_s =  44.02 , 18.39 , 4.83 , 61.92 , 355.59\n",
    "    \n",
    "   \n",
    "    \n",
    "    \n",
    "        \n",
    "        \n",
    "    if name=='Liu085_J1152':\n",
    "        PA_1='Liu085_PA17, J1152_table_contfitcoords_outflow.txt'\n",
    "        PA_2='Liu085_PA107, J1152_table_contfitcoords_outflow.txt'\n",
    "        PA1=17\n",
    "        PA2=107\n",
    "        lower_lim=15\n",
    "        upper_lim=28\n",
    "        lower_lim_ortho=13\n",
    "        upper_lim_ortho=25\n",
    "        \n",
    "        low_1=2\n",
    "        up_1=19\n",
    "        low_2=0\n",
    "        up_2=13\n",
    "        pixelscale=0.389\n",
    "        slitwidth=1.5\n",
    "        result_a =  73.81 , 152.35 , 8.73 , 46.21 , 56.7 , 262.88\n",
    "        result_n =  83.64 , 42.07 , 4.2 , 46.73 , 83.79 , 242.07\n",
    "        result_s =  41.87 , 40.04 , 6.41 , 60.36 , 368.21\n",
    "    \n",
    "    if name=='Liu018_J0821':\n",
    "        PA_1='Liu018_PA43, J0821_table_contfitcoords_outflow.txt'\n",
    "        PA_2='Liu018_PA133, J0821_table_contfitcoords_outflow.txt'\n",
    "        PA1=43\n",
    "        PA2=133\n",
    "        lower_lim=16\n",
    "        upper_lim=26\n",
    "        lower_lim_ortho=15\n",
    "        upper_lim_ortho=25\n",
    "        \n",
    "        low_1=1\n",
    "        up_1=20\n",
    "        low_2=0\n",
    "        up_2=10\n",
    "        pixelscale=0.389\n",
    "        slitwidth=1.5\n",
    "        result_a =  50.83 , 6.06 , 9.26 , 49.56 , 72.05 , 361.63\n",
    "        result_n =  82.82 , 14.6 , 6.57 , 50.82 , 80.53 , 394.41\n",
    "        result_s =  54.34 , 11.18 , 7.91 , 58.51 , 358.56\n",
    "        \n",
    "    if name=='Liu002_J0009':\n",
    "        PA_1='Liu002_PA23,J0009_table_contfitcoords_outflow.txt'\n",
    "        PA_2='Liu002_PA67, J0009_table_contfitcoords_outflow.txt'\n",
    "      \n",
    "        PA1=23\n",
    "        PA2=67\n",
    "        lower_lim=15\n",
    "        upper_lim=26\n",
    "        lower_lim_ortho=13\n",
    "        upper_lim_ortho=25\n",
    "        \n",
    "        minus_one=1\n",
    "        low_1=2\n",
    "        up_1=20\n",
    "        low_2=0\n",
    "        up_2=12\n",
    "        pixelscale=0.288\n",
    "        slitwidth=1.0\n",
    "        result_a =  55.97 , 78.67 , 4.89 , 59.98 , 77.13 , 324.02\n",
    "        result_n =  51.69 , 93.22 , 7.24 , 59.79 , 70.47 , 321.84\n",
    "        result_s =  29.55 , 58.6 , 5.05 , 64.24 , 347.38\n",
    "        \n",
    "    if name=='Liu014_J0803':\n",
    "        PA_1='Liu014_PA50,J0803_table_contfitcoords_outflow_three.txt'\n",
    "        PA_2='Liu014_PA140, J0803_table_contfitcoords_outflow_three.txt'\n",
    "        PA1=50\n",
    "        PA2=140\n",
    "        lower_lim=13\n",
    "        upper_lim=26#was27\n",
    "        lower_lim_ortho=13\n",
    "        upper_lim_ortho=29\n",
    "        \n",
    "        low_1=1\n",
    "        up_1=14\n",
    "        low_2=1\n",
    "        up_2=20\n",
    "        pixelscale=0.389\n",
    "        slitwidth=1.5\n",
    "        result_a =  75.45 , 124.82 , 10.85 , 39.08 , 43.44 , 271.36\n",
    "        result_n =  68.69 , 12.57 , 5.62 , 54.41 , 80.09 , 258.82\n",
    "        result_s =  40.28 , 21.88 , 8.64 , 53.37 , 425.33\n",
    "        \n",
    "    if name=='Liu040_J0959':\n",
    "        PA_1='Liu040_PA28, J0959_table_contfitcoords_outflow_three.txt'\n",
    "        PA_2='Liu040_PA118, J0959_table_contfitcoords_outflow.txt'\n",
    "        PA1=28\n",
    "        PA2=118\n",
    "        lower_lim=14\n",
    "        upper_lim=25\n",
    "        lower_lim_ortho=13\n",
    "        upper_lim_ortho=25\n",
    "        \n",
    "        low_1=1\n",
    "        up_1=12\n",
    "        low_2=0\n",
    "        up_2=12\n",
    "        pixelscale=0.288\n",
    "        slitwidth=1.0\n",
    "        minus_one=1\n",
    "        result_a =  70.97 , 57.66 , 5.12 , 49.19 , 81.06 , 290.72\n",
    "        result_n =  77.99 , 12.6 , 6.75 , 49.43 , 70.94 , 281.46\n",
    "        result_s =  51.14 , 10.21 , 2.9 , 37.37 , 153.48\n",
    "        \n",
    "    if name=='Liu032_J0930':\n",
    "        PA_1='Liu032_PA21, J0930_table_contfitcoords_outflow.txt'\n",
    "        PA_2='Liu032_PA111, J0930_table_contfitcoords_outflow_three.txt'\n",
    "        PA1=21\n",
    "        PA2=111\n",
    "        lower_lim=15\n",
    "        upper_lim=24\n",
    "        lower_lim_ortho=16\n",
    "        upper_lim_ortho=24\n",
    "        \n",
    "        low_1=0\n",
    "        up_1=20\n",
    "        low_2=1\n",
    "        up_2=20\n",
    "        pixelscale=0.389\n",
    "        slitwidth=1.5\n",
    "        minus_one=1\n",
    "        result_a =  80.36 , 75.39 , 9.91 , 37.12 , 67.24 , 294.33\n",
    "        result_n =  71.77 , 80.93 , 4.41 , 42.69 , 82.37 , 334.1\n",
    "        result_s =  39.97 , 10.5 , 10.11 , 65.82 , 386.33\n",
    "    \n",
    "    if name=='Liu026_J0854':\n",
    "        PA_1='Liu026_PA16, J0854_table_contfitcoords_outflow.txt'\n",
    "        PA_2='Liu026_PA106, J0854_table_contfitcoords_outflow.txt'\n",
    "        PA1=16\n",
    "        PA2=106\n",
    "        lower_lim=15\n",
    "        upper_lim=23\n",
    "        lower_lim_ortho=15\n",
    "        upper_lim_ortho=27\n",
    "        \n",
    "        low_1=1\n",
    "        up_1=9#was 12\n",
    "        low_2=1\n",
    "        up_2=20\n",
    "        pixelscale=0.389\n",
    "        slitwidth=1.5\n",
    "        result_a =  60.04 , 205.0 , 6.52 , 41.83 , 74.56 , 290.89\n",
    "        result_n =  85.92 , 16.33 , 4.47 , 49.25 , 79.56 , 340.95\n",
    "        result_s =  43.24 , 17.54 , 3.79 , 60.18 , 342.36\n",
    "        \n",
    "    if name=='Liu063_J1110':\n",
    "        PA_1='Liu063_31, J1110_table_contfitcoords_outflow.txt'\n",
    "        PA_2='Liu063_PA121, J1110_table_contfitcoords_outflow.txt'\n",
    "        PA1=31\n",
    "        PA2=121\n",
    "        lower_lim=14\n",
    "        upper_lim=24\n",
    "        lower_lim_ortho=15\n",
    "        upper_lim_ortho=25\n",
    "        \n",
    "        low_1=0\n",
    "        up_1=10\n",
    "        low_2=1\n",
    "        up_2=20\n",
    "        pixelscale=0.42\n",
    "        slitwidth=1.5\n",
    "        minus_one=1\n",
    "        result_a =  48.46 , 69.45 , 6.36 , 60.14 , 77.08 , 528.55\n",
    "        result_n =  47.53 , 58.94 , 9.51 , 54.54 , 73.36 , 385.05\n",
    "        result_s =  42.4 , 6.39 , 5.26 , 59.13 , 400.71\n",
    "        \n",
    "    \n",
    "    \n",
    "        \n",
    "    \n",
    "    \n",
    "    with open(PA_1, 'r') as f:\n",
    "        data = f.readlines()\n",
    "\n",
    "        title=[]\n",
    "        col_1=[]\n",
    "        col_2=[]\n",
    "        col_3=[]\n",
    "        col_4=[]\n",
    "        col_5=[]\n",
    "        col_6=[]\n",
    "        col_7=[]\n",
    "        for line in data:\n",
    "            words = line.split()\n",
    "       \n",
    "            if words[0]=='Vel':\n",
    "                title.append(words[0])\n",
    "                col_1.append(words[1])\n",
    "                col_2.append(words[2])\n",
    "                col_3.append(words[3])\n",
    "                col_4.append(words[4])\n",
    "                \n",
    "                col_5.append(words[5])\n",
    "                col_6.append(words[6])\n",
    "                col_7.append(words[7])\n",
    "\n",
    "    vel_1_gauss=[]\n",
    "    vel_1_error=[]\n",
    "    vel_2_1=[]\n",
    "    vel_2_2=[]\n",
    "    vel_2_1_error=[]\n",
    "    vel_2_2_error=[]\n",
    "    for j in xrange(len(col_1)):\n",
    "        #if j < 40:\n",
    "        vel_1_gauss.append(col_1[j])\n",
    "        vel_1_error.append(col_3[j])\n",
    "        vel_2_1.append(col_4[j])\n",
    "        vel_2_1_error.append(col_5[j])\n",
    "        vel_2_2.append(col_6[j])\n",
    "        vel_2_2_error.append(col_7[j])\n",
    "            \n",
    "    with open(PA_2, 'r') as f:\n",
    "        data = f.readlines()\n",
    "        title=[]\n",
    "        col_1=[]\n",
    "        col_2=[]\n",
    "        col_3=[]\n",
    "        col_4=[]\n",
    "        col_5=[]\n",
    "        col_6=[]\n",
    "        col_7=[]\n",
    "        for line in data:\n",
    "            words = line.split()\n",
    "            if words[0]=='Vel':\n",
    "                title.append(words[0])\n",
    "                col_1.append(words[1])\n",
    "                col_2.append(words[2])\n",
    "                col_3.append(words[3])\n",
    "                col_4.append(words[4])\n",
    "                col_5.append(words[5])\n",
    "                col_6.append(words[6])\n",
    "                col_7.append(words[7])\n",
    " \n",
    "    vel_1_gauss_ortho=[]\n",
    "    vel_1_error_ortho=[]\n",
    "    vel_2_1_ortho=[]\n",
    "    vel_2_2_ortho=[]\n",
    "    vel_2_1_error_ortho=[]\n",
    "    vel_2_2_error_ortho=[]\n",
    "    for j in xrange(len(col_1)):\n",
    "    #if j < 40:\n",
    "        vel_1_gauss_ortho.append(col_1[j])\n",
    "        vel_1_error_ortho.append(col_3[j])\n",
    "        vel_2_1_ortho.append(col_4[j])\n",
    "        vel_2_1_error_ortho.append(col_5[j])\n",
    "        vel_2_2_ortho.append(col_6[j])\n",
    "        vel_2_2_error_ortho.append(col_7[j])\n",
    "    \n",
    "   \n",
    "    xs_data=np.linspace(0, len(vel_1_gauss)-1, len(vel_1_gauss))\n",
    "\n",
    "    xs_data=xs_data-len(xs_data)/2\n",
    " \n",
    "    xs_data=[float(y) for y in xs_data]\n",
    "    vel_2_1=[float(y) for y in vel_2_1]\n",
    "    vel_2_2=[float(y) for y in vel_2_2]\n",
    "    vel_2_1_ortho=[float(y) for y in vel_2_1_ortho]\n",
    "    vel_2_2_ortho=[float(y) for y in vel_2_2_ortho]\n",
    "    vel_2_1_error=[float(y) for y in vel_2_1_error]\n",
    "    vel_2_2_error=[float(y) for y in vel_2_2_error]\n",
    "    vel_2_1_ortho_error=[float(y) for y in vel_2_1_error_ortho]\n",
    "    vel_2_2_ortho_error=[float(y) for y in vel_2_2_error_ortho]\n",
    "    \n",
    "    xs_data=[float(y) for y in xs_data]\n",
    "    vel_2_1=[float(y) for y in vel_2_1]\n",
    "    vel_2_2=[float(y) for y in vel_2_2]\n",
    "    vel_2_1_ortho=[float(y) for y in vel_2_1_ortho]\n",
    "    vel_2_2_ortho=[float(y) for y in vel_2_2_ortho]\n",
    "    vel_2_1_error=[float(y) for y in vel_2_1_error]\n",
    "    vel_2_2_error=[float(y) for y in vel_2_2_error]\n",
    "    vel_2_1_ortho_error=[float(y) for y in vel_2_1_error_ortho]\n",
    "    vel_2_2_ortho_error=[float(y) for y in vel_2_2_error_ortho]\n",
    "    #(xs_data[lower_lim:upper_lim],vel_2_1[lower_lim:upper_lim]\n",
    "    if np.mean(vel_2_1[lower_lim:upper_lim])< np.mean(vel_2_1[lower_lim:upper_lim]):\n",
    "        #this means that the first set is the above side\n",
    "        v_1_above=vel_2_1[lower_lim:upper_lim]\n",
    "        v_1_below=vel_2_2[lower_lim:upper_lim]\n",
    "        vel_data_concat=vel_2_1[lower_lim:upper_lim]+vel_2_2[lower_lim:upper_lim]\n",
    "        error_data_concat=vel_2_1_error[lower_lim:upper_lim]+vel_2_2_error[lower_lim:upper_lim]\n",
    "    else:\n",
    "        v_1_above=vel_2_2[lower_lim:upper_lim]\n",
    "        v_1_below=vel_2_1[lower_lim:upper_lim]\n",
    "        vel_data_concat=vel_2_2[lower_lim:upper_lim]+vel_2_1[lower_lim:upper_lim]\n",
    "        error_data_concat=vel_2_2_error[lower_lim:upper_lim]+vel_2_1_error[lower_lim:upper_lim]\n",
    "        \n",
    "    if np.mean(vel_2_1_ortho[lower_lim_ortho:upper_lim_ortho])< np.mean(vel_2_1_ortho[lower_lim_ortho:upper_lim_ortho]):\n",
    "        #this means that the first set is the above side\n",
    "        v_2_above=vel_2_1_ortho[lower_lim_ortho:upper_lim_ortho]\n",
    "        v_2_below=vel_2_2_ortho[lower_lim_ortho:upper_lim_ortho]\n",
    "        vel_data_concat_ortho=vel_2_1_ortho[lower_lim_ortho:upper_lim_ortho]+vel_2_2_ortho[lower_lim_ortho:upper_lim_ortho]\n",
    "        error_data_concat_ortho=vel_2_1_ortho_error[lower_lim_ortho:upper_lim_ortho]+vel_2_2_ortho_error[lower_lim_ortho:upper_lim_ortho]\n",
    "    else:\n",
    "        v_2_above=vel_2_2_ortho[lower_lim_ortho:upper_lim_ortho]\n",
    "        v_2_below=vel_2_1_ortho[lower_lim_ortho:upper_lim_ortho]\n",
    "        vel_data_concat_ortho=vel_2_2_ortho[lower_lim_ortho:upper_lim_ortho]+vel_2_1_ortho[lower_lim_ortho:upper_lim_ortho]\n",
    "        error_data_concat_ortho=vel_2_2_ortho_error[lower_lim_ortho:upper_lim_ortho]+vel_2_1_ortho_error[lower_lim_ortho:upper_lim_ortho]\n",
    "    \n",
    "    \n",
    "    xs_data_cut=xs_data[lower_lim:upper_lim]\n",
    "    xs_data_concat=xs_data_cut+xs_data_cut\n",
    "    xs_data_cut_ortho= xs_data[lower_lim_ortho:upper_lim_ortho]\n",
    "    xs_data_concat_ortho=xs_data_cut_ortho+xs_data_cut_ortho\n",
    "    \n",
    "    \n",
    "    \n",
    "    for i in xrange(len(error_data_concat)):\n",
    "        if error_data_concat[i]==0.0:\n",
    "            error_data_concat[i]=1000\n",
    "    for i in xrange(len(error_data_concat_ortho)):\n",
    "        if error_data_concat_ortho[i]==0.0:\n",
    "            error_data_concat_ortho[i]=1000\n",
    "    \n",
    "        \n",
    "    '''plt.ylabel( r'Velocity [km s$^{-1}$]',  size=20)\n",
    "    plt.xlabel(r'Pixel', size=20)\n",
    "    plt.title(str(name), size=20)\n",
    "                 \n",
    "    plt.errorbar(xs_data_concat, vel_data_concat,yerr=error_data_concat, color='blue',linestyle='None')\n",
    "    plt.errorbar(xs_data_concat_ortho, vel_data_concat_ortho,yerr=error_data_concat_ortho, color='red', linestyle='None')\n",
    "    plt.scatter(xs_data_concat, vel_data_concat, color='blue', label='PA 1')\n",
    "    plt.scatter(xs_data_concat_ortho, vel_data_concat_ortho, color='red', label='PA 2')\n",
    "    plt.legend()\n",
    "    #plt.ylim([-400,400])\n",
    "    plt.tight_layout()\n",
    "    #savefig('../J0009_errors_before.pdf')\n",
    "    plt.show()'''\n",
    "    #savefig('Bicone_vel_check_corrected_'+str(name)+'.pdf')\n",
    "    '''now getting back to the original code'''\n",
    "\n",
    "    out_s=lnlike_bicone(result_s)\n",
    "\n",
    "    out_a=lnlike_cocone(result_a)\n",
    "\n",
    "\n",
    "    out_n=lnlike_nesting(result_n)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/beckynevin/anaconda/lib/python3.6/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.\n",
      "  warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n",
      "/Users/beckynevin/anaconda/lib/python3.6/site-packages/numpy/core/_methods.py:70: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-4.20982567958\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'What if there is a way to put in your cone for the exact spatial scale? (arcseconds as an input)'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#bicone_construction_cocone(phi,theta,r_t, half_angle,half_angle_2, vel_max)\n",
    "##y=bicone_construction_cocone(20, 0, 2, 20, 40, 100)\n",
    "result=[20,20,1,40,40,100]\n",
    "lnlike_cocone(result)\n",
    "'''So bicone_construction basically builds your bicone for a fine resolution'''\n",
    "'''What if there is a way to put in your cone for the exact spatial scale? (arcseconds as an input)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "'''This code is the masked version of a cocone, but now were making an IFU map with spatial\n",
    "direction in arcseconds\n",
    "\n",
    "-->Id like to degrade the IFU image as soon as possible to the resolution/ pixelsize of the observatory\n",
    "-->Make this code functional for MaNGA data\n",
    "'''\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def bicone_construction_cocone(phi,theta,r_t, half_angle,half_angle_2, vel_max):\n",
    "    #MaNGA pixelsize is 2\"/ pixel\n",
    "    #Do we also need a conversion to kpc?\n",
    "    \n",
    "    half_angle_r=math.radians(half_angle)\n",
    "    half_angle_r_2=math.radians(half_angle_2)\n",
    "    #h=[2*x for x in r_t]\n",
    "    h=2*r_t\n",
    "##to get the radius of the cone, you need to do some trig\n",
    "##tan half_angle_r=r/h\n",
    "\n",
    "    r=math.tan(half_angle_r)*h\n",
    "    r_2=math.tan(half_angle_r_2)*h\n",
    "    \n",
    "    theta_para=(np.asarray(np.linspace(0,2*np.pi,50)))\n",
    "    '''you dont want u1 to go all the way to -h unless that PA is totally aligned (PA90)'''\n",
    "  \n",
    "    u1_orig=np.asarray(np.linspace(-h,0,30))+h#was 50\n",
    "    u2_orig=-np.asarray(np.linspace(-h,0,30))-h\n",
    "    u=-np.asarray(np.linspace(-h,0,30))\n",
    "\n",
    "   \n",
    "   \n",
    "    \n",
    "    phi_1=math.radians(phi)\n",
    "    #this is the angle of rotation about x, so inclination\n",
    "    theta_1=math.radians(theta)\n",
    "    \n",
    "    #this is the angle of rotation about y, we don't actually need to rotate about this guy\n",
    "    psi_1=math.radians(0)\n",
    "    #this is the angle of rotation about z, so PA\n",
    "    #R is the rotation matrix\n",
    "    R=np.matrix([[np.cos(theta_1)*np.cos(psi_1),np.cos(phi_1)*np.sin(psi_1)+np.sin(phi_1)*np.sin(theta_1)*np.cos(psi_1),\n",
    "                  np.sin(phi_1)*np.sin(psi_1)-np.cos(phi_1)*np.sin(theta_1)*np.cos(psi_1)],\n",
    "                     [-np.cos(theta_1)*np.sin(psi_1), \n",
    "                      np.cos(phi_1)*np.cos(psi_1)-np.sin(phi_1)*np.sin(theta_1)*np.sin(psi_1),\n",
    "                      np.sin(phi_1)*np.cos(psi_1)+np.cos(phi_1)*np.sin(theta_1)*np.sin(psi_1)],\n",
    "                     [np.sin(theta_1), -np.sin(phi_1)*np.cos(theta_1), np.cos(phi_1)*np.cos(theta_1)]])\n",
    "    #x is going to be the front facing side (blueshifted) and x_2 is the rear cone\n",
    "    x=[]\n",
    "    x_2=[]\n",
    "    y=[]\n",
    "    y_2=[]\n",
    "    z=[]\n",
    "    z_2=[]\n",
    "    z_plane=[]\n",
    "    vel_radial_bottom=[]\n",
    "    vel_radial_top=[]\n",
    "    vel_top=[]\n",
    "    vel_bottom=[]\n",
    "    vel_totes=[]\n",
    "    x_totes=[]\n",
    "    y_totes=[]\n",
    "    z_totes=[]\n",
    "    x_plane=[]\n",
    "    y_plane=[]\n",
    "    z_plane=[]\n",
    "    x_plane_2=[]\n",
    "    y_plane_2=[]\n",
    "    z_plane_2=[]\n",
    "    for i in range(len(theta_para)):\n",
    "        for j in range(len(u)):\n",
    "            if i==0: \n",
    "                i=1\n",
    "            if j==0:\n",
    "                j=1\n",
    "        \n",
    "            #here's the parametric equation for a bicone\n",
    "            X=((h-u[j])*r*np.cos((theta_para[i])))/h\n",
    "            Y=((h-u[j])*r*np.sin((theta_para[i])))/h\n",
    "            \n",
    "            X_2=((h-u[j])*r_2*np.cos((theta_para[i])))/h\n",
    "            Y_2=((h-u[j])*r_2*np.sin((theta_para[i])))/h\n",
    "            \n",
    "            #blue cone above is Z_1\n",
    "            #red cone below is Z_2\n",
    "            Z_1=u2_orig[j]#was u2\n",
    "            Z_2=u1_orig[j]#was u1\n",
    "            \n",
    "            y_plane_into=0\n",
    "            \n",
    "            pos=np.matrix([X,Y,Z_1])\n",
    "            pos_2=np.matrix([X_2,Y_2,Z_2])\n",
    "            #pos_plane=np.matrix([X,y_plane_into,Z_1])\n",
    "            #pos_plane_2=np.matrix([X,y_plane_into, Z_2])\n",
    "            #this is taking and applying the rotation matrix\n",
    "            new_pos=np.dot(R,pos.transpose())\n",
    "            new_pos_2=np.dot(R,pos_2.transpose())\n",
    "           \n",
    "            \n",
    "            \n",
    "            #do that thing:\n",
    "            #equation: d/dw/e((x^2 + y^2)/c^2 = (z-z_0)^2)\n",
    "            #recall c=r/h\n",
    "            #<2*x,2*y,-(2*r^2*z)/h^2>\n",
    "            #plug in everything and normalize and then the z component is your v_rad\n",
    "    \n",
    "            \n",
    "            slope_x=2*np.array(new_pos[0]).ravel()\n",
    "            slope_y=2*np.array(new_pos[1]).ravel()\n",
    "            slope_z=-(2*r**2*np.array(new_pos[2]).ravel())/h**2\n",
    "            \n",
    "            norm=np.sqrt(slope_x**2+slope_y**2+slope_z**2)\n",
    "            '''applying velocity laws for a flat bicone'''\n",
    "            if np.sqrt(np.array(new_pos[0]).ravel()**2+np.array(new_pos[1]).ravel()**2+np.array(new_pos[2]).ravel()**2)<r_t:\n",
    "                #vel_max_top=-(vel_max/r_t)*np.sqrt(new_pos[0]**2+new_pos[1]**2+new_pos[2]**2)\n",
    "                vel_max_top=-vel_max\n",
    "            if np.sqrt(np.array(new_pos[0]).ravel()**2+np.array(new_pos[1]).ravel()**2+np.array(new_pos[2]).ravel()**2)>r_t:\n",
    "                vel_max_top=-(vel_max-(vel_max/r_t)*(np.sqrt(np.array(new_pos[0]).ravel()**2+np.array(new_pos[1]).ravel()**2+np.array(new_pos[2]).ravel()**2)-r_t))\n",
    "            if np.sqrt(np.array(new_pos[0]).ravel()**2+np.array(new_pos[1]).ravel()**2+np.array(new_pos[2]).ravel()**2)<2*r_t:\n",
    "                \n",
    "                x.append(np.array(new_pos[0]).ravel())\n",
    "                y.append(np.array(new_pos[1]).ravel())\n",
    "                z.append(np.array(new_pos[2]).ravel())\n",
    "                A_comp=-(slope_y/norm)\n",
    "                rad_proj=vel_max_top*np.array(A_comp).ravel()\n",
    "                vel_radial_top.append(round(rad_proj[0]))\n",
    "             \n",
    "        \n",
    "    \n",
    "            slope_x=2*np.array(new_pos_2[0]).ravel()\n",
    "            slope_y=2*np.array(new_pos_2[1]).ravel()\n",
    "            \n",
    "            slope_z_2=-(2*r_2**2*np.array(new_pos_2[2]).ravel())/h**2\n",
    "            norm=np.sqrt(slope_x**2+slope_y**2+slope_z_2**2)\n",
    "            \n",
    "            if np.sqrt(np.array(new_pos_2[0]).ravel()**2+np.array(new_pos_2[1]).ravel()**2+np.array(new_pos_2[2]).ravel()**2)<r_t:\n",
    "#                vel_max_bottom=-(vel_max/r_t)*np.sqrt(new_pos_2[0]**2+new_pos_2[1]**2+new_pos_2[2]**2)\n",
    "                vel_max_bottom=-vel_max\n",
    "            if np.sqrt(np.array(new_pos_2[0]).ravel()**2+np.array(new_pos_2[1]).ravel()**2+np.array(new_pos_2[2]).ravel()**2)>r_t:\n",
    "                \n",
    "                vel_max_bottom=-(vel_max-(vel_max/r_t)*(np.sqrt(np.array(new_pos[0]).ravel()**2+np.array(new_pos[1]).ravel()**2+np.array(new_pos[2]).ravel()**2)-r_t))\n",
    "            if np.sqrt(np.array(new_pos_2[0]).ravel()**2+np.array(new_pos_2[1]).ravel()**2+np.array(new_pos_2[2]).ravel()**2)<2*r_t:\n",
    "                \n",
    "                x_2.append(np.array(new_pos_2[0]).ravel())\n",
    "                y_2.append(np.array(new_pos_2[1]).ravel())\n",
    "                z_2.append(np.array(new_pos_2[2]).ravel())\n",
    "                #A dot x should give us the vector projection along x - divide by the magnitude of A to get cos (theta)\n",
    "                A_comp=-(slope_y/norm)\n",
    "                #vel_bottom.append(vel_max_bottom)\n",
    "                rad_proj_2=np.array(A_comp).ravel()*vel_max_bottom\n",
    "                vel_radial_bottom.append(round(rad_proj_2[0]))\n",
    "         \n",
    "    merged_x=x+x_2\n",
    "    merged_y=y+y_2\n",
    "    merged_z=z+z_2\n",
    "    \n",
    "    cone_red_x=x_2\n",
    "    cone_red_y=y_2\n",
    "    cone_red_z=z_2\n",
    "    \n",
    "    cone_blue_x=x\n",
    "    cone_blue_y=y\n",
    "    cone_blue_z=z\n",
    "    \n",
    "    merged_x_plane=x_plane+x_plane_2\n",
    "    merged_y_plane=y_plane+y_plane_2\n",
    "    merged_z_plane=z_plane+z_plane_2\n",
    "    \n",
    "    \n",
    "    merged_vel=vel_radial_top+vel_radial_bottom\n",
    "    \n",
    "    cone_red_vel=vel_radial_bottom\n",
    "    cone_blue_vel=vel_radial_top\n",
    "    \n",
    "    \n",
    "\n",
    "    '''plt.scatter(cone_red_x, cone_red_z, c=cone_red_vel, cmap='Reds')\n",
    "    plt.colorbar()\n",
    "    plt.title('Red Cone')\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "    plt.scatter(cone_blue_x, cone_blue_z, c=cone_blue_vel, cmap='Blues')\n",
    "    plt.colorbar()\n",
    "    plt.title('Blue Cone')\n",
    "    plt.show()\n",
    "    \n",
    "    plt.scatter(merged_x, merged_z, c=merged_vel, cmap='rainbow')\n",
    "    plt.colorbar()\n",
    "    plt.title('Merged Cone')\n",
    "    plt.show()'''\n",
    "    \n",
    "    \n",
    "    return merged_x, merged_y, merged_z, merged_vel, merged_x_plane, merged_y_plane, merged_z_plane, cone_red_x, cone_red_y, cone_red_z, cone_blue_x, cone_blue_y, cone_blue_z, cone_red_vel, cone_blue_vel\n",
    "                \n",
    "    \n",
    "    \n",
    "    \n",
    "                        \n",
    "def lnlike_cocone(z):\n",
    "    \n",
    "    \n",
    "    #bicone_construction(h,phi,theta,r_t, half_angle )\n",
    "    phi_array, theta_array, thetados_array, r_t,half_angle, vel_max_array=z\n",
    "    \n",
    "    \n",
    "    h=2*r_t\n",
    "    #h=[2*x for x in r_t]\n",
    "    \n",
    "    out=bicone_construction_cocone(phi_array, theta_array,thetados_array, r_t, half_angle, vel_max_array)\n",
    "    \n",
    "    merged_x=[float(y) for y in out[0]]\n",
    "    merged_y=[float(y) for y in out[1]]\n",
    "    merged_z=[float(y) for y in out[2]]\n",
    "    merged_vel=[float(y) for y in out[3]]\n",
    "    merged_x_plane=[float(y) for y in out[4]]\n",
    "    merged_y_plane=[float(y) for y in out[5]]\n",
    "    merged_z_plane=[float(y) for y in out[6]]\n",
    "    \n",
    "    cone_red_x=[float(y) for y in out[7]]\n",
    "    cone_red_y=[float(y) for y in out[8]]\n",
    "    cone_red_z=[float(y) for y in out[9]]\n",
    "    cone_blue_x=[float(y) for y in out[10]]\n",
    "    cone_blue_y=[float(y) for y in out[11]]\n",
    "    cone_blue_z=[float(y) for y in out[12]]\n",
    "    \n",
    "    cone_red_vel=[float(y) for y in out[13]]\n",
    "    cone_blue_vel=[float(y) for y in out[14]]\n",
    "    ##vel_radial_top actually refers to the stuff going away from us on the bottom -ugh\n",
    "    #it's the projection looking down in z\n",
    "    #p=ax.scatter(x_2,y_2,z_2, c=vel_radial_bottom)\n",
    "    \n",
    "    \n",
    "    #%```````````````````````````````````\n",
    "    #I'm going to make the phi cuts according to observe PAs\n",
    "    PA_obs_1=np.radians(PA1+90)\n",
    "    PA_obs_2=np.radians(PA2+90)\n",
    "    PA_obs_3=np.radians(PA3+90)\n",
    "    PA_obs_4=np.radians(PA4+90)\n",
    " \n",
    "    x_slit_blue=np.linspace(-100*h,100*h, len(cone_blue_z))\n",
    "    x_slit_red=np.linspace(-100*h,100*h,len(cone_red_z))\n",
    "    \n",
    "    len_pts=max(upper_lim-lower_lim,upper_lim_ortho-lower_lim_ortho)\n",
    "    \n",
    "    #height is based upon the length of the overall data we have on the bicone and the slitwidth\n",
    "    height=slitwidth/pixelscale_1#(slitwidth/(pixelscale*len_pts))#*(h)#I kinda want this to be a fraction of something else#10#was 4\n",
    "    \n",
    "    z_slit_1_upper_red=[x*np.tan(PA_obs_1)-height/np.cos(PA_obs_1) for x in cone_red_x]\n",
    "    z_slit_1_lower_red=[x*np.tan(PA_obs_1)+height/np.cos(PA_obs_1) for x in cone_red_x]\n",
    "    z_slit_1_upper_blue=[x*np.tan(PA_obs_1)-height/np.cos(PA_obs_1) for x in cone_blue_x]\n",
    "    z_slit_1_lower_blue=[x*np.tan(PA_obs_1)+height/np.cos(PA_obs_1) for x in cone_blue_x]\n",
    "    \n",
    "    height=slitwidth/pixelscale_2\n",
    "    \n",
    "    z_slit_2_upper_red=[x*np.tan(PA_obs_2)-height/np.cos(PA_obs_2) for x in cone_red_x]\n",
    "    z_slit_2_lower_red=[x*np.tan(PA_obs_2)+height/np.cos(PA_obs_2) for x in cone_red_x]\n",
    "    z_slit_2_upper_blue=[x*np.tan(PA_obs_2)-height/np.cos(PA_obs_2) for x in cone_blue_x]\n",
    "    z_slit_2_lower_blue=[x*np.tan(PA_obs_2)+height/np.cos(PA_obs_2) for x in cone_blue_x]\n",
    "    \n",
    "    height=slitwidth/pixelscale_3\n",
    "    \n",
    "    #so since 66 < 90:\n",
    "    z_slit_3_upper_red=[x*np.tan(PA_obs_3)-height/np.cos(PA_obs_3) for x in cone_red_x]\n",
    "    z_slit_3_lower_red=[x*np.tan(PA_obs_3)+height/np.cos(PA_obs_3) for x in cone_red_x]\n",
    "    z_slit_3_upper_blue=[x*np.tan(PA_obs_3)-height/np.cos(PA_obs_3) for x in cone_blue_x]\n",
    "    z_slit_3_lower_blue=[x*np.tan(PA_obs_3)+height/np.cos(PA_obs_3) for x in cone_blue_x]\n",
    "    \n",
    "    height=slitwidth/pixelscale_4\n",
    "    \n",
    "    #so since 156 > 90:\n",
    "    z_slit_4_upper_red=[x*np.tan(PA_obs_4)-height/np.cos(PA_obs_4) for x in cone_red_x]\n",
    "    z_slit_4_lower_red=[x*np.tan(PA_obs_4)+height/np.cos(PA_obs_4) for x in cone_red_x]\n",
    "    z_slit_4_upper_blue=[x*np.tan(PA_obs_4)-height/np.cos(PA_obs_4) for x in cone_blue_x]\n",
    "    z_slit_4_lower_blue=[x*np.tan(PA_obs_4)+height/np.cos(PA_obs_4) for x in cone_blue_x]\n",
    "\n",
    "    \n",
    "    \n",
    "    inds_1=np.logical_and((np.asarray(cone_blue_z) > np.asarray(z_slit_1_lower_blue)), (np.asarray(cone_blue_z) < np.asarray(z_slit_1_upper_blue)))\n",
    "    inds_1_below=np.logical_and((np.asarray(cone_red_z) > np.asarray(z_slit_1_lower_red)), (np.asarray(cone_red_z) < np.asarray(z_slit_1_upper_red)))\n",
    "    \n",
    "    inds_2=np.logical_and((np.asarray(cone_blue_z) > np.asarray(z_slit_2_lower_blue)), (np.asarray(cone_blue_z) < np.asarray(z_slit_2_upper_blue)))\n",
    "    inds_2_below=np.logical_and((np.asarray(cone_red_z) > np.asarray(z_slit_2_lower_red)), (np.asarray(cone_red_z) < np.asarray(z_slit_2_upper_red)))\n",
    "    \n",
    "    inds_3=np.logical_and((np.asarray(cone_blue_z) > np.asarray(z_slit_3_lower_blue)), (np.asarray(cone_blue_z) < np.asarray(z_slit_3_upper_blue)))\n",
    "    inds_3_below=np.logical_and((np.asarray(cone_red_z) > np.asarray(z_slit_3_lower_red)), (np.asarray(cone_red_z) < np.asarray(z_slit_3_upper_red)))\n",
    "    \n",
    "    inds_4=np.logical_and((np.asarray(cone_blue_z) > np.asarray(z_slit_4_lower_blue)), (np.asarray(cone_blue_z) < np.asarray(z_slit_4_upper_blue)))\n",
    "    inds_4_below=np.logical_and((np.asarray(cone_red_z) > np.asarray(z_slit_4_lower_red)), (np.asarray(cone_red_z) < np.asarray(z_slit_4_upper_red)))\n",
    "    \n",
    "\n",
    "    '''xs_cont_1 is the blueshifted cone of PA1'''\n",
    "    xs_cont_1=np.asarray(cone_blue_x)[np.asarray(inds_1)]\n",
    "    ys_cont_1=np.asarray(cone_blue_y)[np.asarray(inds_1)]\n",
    "    zs_cont_1=np.asarray(cone_blue_z)[np.asarray(inds_1)]\n",
    "    vel_cont_1=np.asarray(cone_blue_vel)[np.asarray(inds_1)]\n",
    "    \n",
    "    '''xs_cont_1_below is the redshifted cone of PA1'''\n",
    "    xs_cont_1_below=np.asarray(cone_red_x)[np.asarray(inds_1_below)]\n",
    "    ys_cont_1_below=np.asarray(cone_red_y)[np.asarray(inds_1_below)]\n",
    "    zs_cont_1_below=np.asarray(cone_red_z)[np.asarray(inds_1_below)]\n",
    "    vel_cont_1_below=np.asarray(cone_red_vel)[np.asarray(inds_1_below)]\n",
    "    \n",
    "    xs_cont_2=np.asarray(cone_blue_x)[np.asarray(inds_2)]\n",
    "    ys_cont_2=np.asarray(cone_blue_y)[np.asarray(inds_2)]\n",
    "    zs_cont_2=np.asarray(cone_blue_z)[np.asarray(inds_2)]\n",
    "    vel_cont_2=np.asarray(cone_blue_vel)[np.asarray(inds_2)]\n",
    "    \n",
    "    xs_cont_2_below=np.asarray(cone_red_x)[np.asarray(inds_2_below)]\n",
    "    ys_cont_2_below=np.asarray(cone_red_y)[np.asarray(inds_2_below)]\n",
    "    zs_cont_2_below=np.asarray(cone_red_z)[np.asarray(inds_2_below)]\n",
    "    vel_cont_2_below=np.asarray(cone_red_vel)[np.asarray(inds_2_below)]\n",
    "    \n",
    "    xs_cont_3=np.asarray(cone_blue_x)[np.asarray(inds_3)]\n",
    "    ys_cont_3=np.asarray(cone_blue_y)[np.asarray(inds_3)]\n",
    "    zs_cont_3=np.asarray(cone_blue_z)[np.asarray(inds_3)]\n",
    "    vel_cont_3=np.asarray(cone_blue_vel)[np.asarray(inds_3)]\n",
    "    \n",
    "    xs_cont_3_below=np.asarray(cone_red_x)[np.asarray(inds_3_below)]\n",
    "    ys_cont_3_below=np.asarray(cone_red_y)[np.asarray(inds_3_below)]\n",
    "    zs_cont_3_below=np.asarray(cone_red_z)[np.asarray(inds_3_below)]\n",
    "    vel_cont_3_below=np.asarray(cone_red_vel)[np.asarray(inds_3_below)]\n",
    "    \n",
    "    xs_cont_4=np.asarray(cone_blue_x)[np.asarray(inds_4)]\n",
    "    ys_cont_4=np.asarray(cone_blue_y)[np.asarray(inds_4)]\n",
    "    zs_cont_4=np.asarray(cone_blue_z)[np.asarray(inds_4)]\n",
    "    vel_cont_4=np.asarray(cone_blue_vel)[np.asarray(inds_4)]\n",
    "    \n",
    "    xs_cont_4_below=np.asarray(cone_red_x)[np.asarray(inds_4_below)]\n",
    "    ys_cont_4_below=np.asarray(cone_red_y)[np.asarray(inds_4_below)]\n",
    "    zs_cont_4_below=np.asarray(cone_red_z)[np.asarray(inds_4_below)]\n",
    "    vel_cont_4_below=np.asarray(cone_red_vel)[np.asarray(inds_4_below)]\n",
    "    \n",
    "    \n",
    "    '''fig=plt.figure()\n",
    "    ax=fig.add_subplot(221)\n",
    "    ax.annotate('Blue Cone PA 1', xy=(0.01,0.1),xycoords='axes fraction')\n",
    "    p=ax.scatter(xs_cont_1, zs_cont_1, c=vel_cont_1, edgecolors='none', cmap=cm.winter)\n",
    "    ax.set_xlim([-h/4,h/4])\n",
    "    ax.set_ylim([-h/4,h/4])\n",
    "    #p1=ax.scatter(xs_cont_2, zs_cont_2, c=vel_cont_2, edgecolors='none', cmap=cm.rainbow)\n",
    "    cbaa0=fig.colorbar(p)\n",
    "    #cbbb=fig.colorbar(p1)\n",
    "    \n",
    "    ax1=fig.add_subplot(222)\n",
    "    ax1.annotate('Blue Cone PA 2', xy=(0.01,0.1),xycoords='axes fraction')\n",
    "    #p=ax.scatter(xs_cont_1, zs_cont_1, c=vel_cont_1, edgecolors='none', cmap=cm.rainbow)\n",
    "    p1=ax1.scatter(xs_cont_2, zs_cont_2, c=vel_cont_2, edgecolors='none', cmap=cm.winter)\n",
    "    cbaa1=fig.colorbar(p1)\n",
    "    #cbbb=fig.colorbar(p1, shrink=0.25)\n",
    "    ax1.set_xlim([-h/4,h/4])\n",
    "    ax1.set_ylim([-h/4,h/4])\n",
    "    \n",
    "    ax2=fig.add_subplot(223)\n",
    "    ax2.annotate('Red Cone PA 1', xy=(0.01,0.1),xycoords='axes fraction')\n",
    "    #p=ax.scatter(xs_cont_1, zs_cont_1, c=vel_cont_1, edgecolors='none', cmap=cm.rainbow)\n",
    "    p2=ax2.scatter(xs_cont_1_below, zs_cont_1_below, c=vel_cont_1_below, edgecolors='none', cmap=cm.Reds)\n",
    "    cbaa2=fig.colorbar(p2)\n",
    "    #cbbb=fig.colorbar(p1, shrink=0.25)\n",
    "    ax2.set_xlim([-h/4,h/4])\n",
    "    ax2.set_ylim([-h/4,h/4])\n",
    "    \n",
    "    ax3=fig.add_subplot(224)\n",
    "    ax3.annotate('Red Cone PA 2', xy=(0.01,0.1),xycoords='axes fraction')\n",
    "    #p=ax.scatter(xs_cont_1, zs_cont_1, c=vel_cont_1, edgecolors='none', cmap=cm.rainbow)\n",
    "    p3=ax3.scatter(xs_cont_2_below, zs_cont_2_below, c=vel_cont_2_below, edgecolors='none', cmap=cm.Reds)\n",
    "    cbaa3=fig.colorbar(p3)\n",
    "    #cbbb=fig.colorbar(p1, shrink=0.25)\n",
    "    ax3.set_xlim([-h/4,h/4])\n",
    "    ax3.set_ylim([-h/4,h/4])\n",
    "    \n",
    "    \n",
    "    \n",
    "    fig.tight_layout()\n",
    "    savefig('../cocone_2_J0930_PAs12.pdf')'''\n",
    "    \n",
    "    \n",
    "    '''fig=plt.figure()\n",
    "    ax=fig.add_subplot(221)\n",
    "    ax.annotate('Blue Cone PA 3', xy=(0.01,0.1),xycoords='axes fraction')\n",
    "    p=ax.scatter(xs_cont_3, zs_cont_3, c=vel_cont_3, edgecolors='none', cmap=cm.winter)\n",
    "    ax.set_xlim([-h/4,h/4])\n",
    "    ax.set_ylim([-h/4,h/4])\n",
    "    #p1=ax.scatter(xs_cont_2, zs_cont_2, c=vel_cont_2, edgecolors='none', cmap=cm.rainbow)\n",
    "    cbaa0=fig.colorbar(p)\n",
    "    #cbbb=fig.colorbar(p1)\n",
    "    \n",
    "    ax1=fig.add_subplot(222)\n",
    "    ax1.annotate('Blue Cone PA 4', xy=(0.01,0.1),xycoords='axes fraction')\n",
    "    #p=ax.scatter(xs_cont_1, zs_cont_1, c=vel_cont_1, edgecolors='none', cmap=cm.rainbow)\n",
    "    p1=ax1.scatter(xs_cont_4, zs_cont_4, c=vel_cont_4, edgecolors='none', cmap=cm.winter)\n",
    "    cbaa1=fig.colorbar(p1)\n",
    "    #cbbb=fig.colorbar(p1, shrink=0.25)\n",
    "    ax1.set_xlim([-h/4,h/4])\n",
    "    ax1.set_ylim([-h/4,h/4])\n",
    "    \n",
    "    ax2=fig.add_subplot(223)\n",
    "    ax2.annotate('Red Cone PA 3', xy=(0.01,0.1),xycoords='axes fraction')\n",
    "    #p=ax.scatter(xs_cont_1, zs_cont_1, c=vel_cont_1, edgecolors='none', cmap=cm.rainbow)\n",
    "    p2=ax2.scatter(xs_cont_3_below, zs_cont_3_below, c=vel_cont_3_below, edgecolors='none', cmap=cm.Reds)\n",
    "    cbaa2=fig.colorbar(p2)\n",
    "    #cbbb=fig.colorbar(p1, shrink=0.25)\n",
    "    ax2.set_xlim([-h/4,h/4])\n",
    "    ax2.set_ylim([-h/4,h/4])\n",
    "    \n",
    "    ax3=fig.add_subplot(224)\n",
    "    ax3.annotate('Red Cone PA 4', xy=(0.01,0.1),xycoords='axes fraction')\n",
    "    #p=ax.scatter(xs_cont_1, zs_cont_1, c=vel_cont_1, edgecolors='none', cmap=cm.rainbow)\n",
    "    p3=ax3.scatter(xs_cont_4_below, zs_cont_4_below, c=vel_cont_4_below, edgecolors='none', cmap=cm.Reds)\n",
    "    cbaa3=fig.colorbar(p3)\n",
    "    #cbbb=fig.colorbar(p1, shrink=0.25)\n",
    "    ax3.set_xlim([-h/4,h/4])\n",
    "    ax3.set_ylim([-h/4,h/4])\n",
    "    \n",
    "    \n",
    "    \n",
    "    fig.tight_layout()\n",
    "    savefig('../cocone_2_J0930_PAs34.pdf')'''\n",
    "    \n",
    "    \n",
    "    '''fig=plt.figure()\n",
    "    \n",
    "    ax1 = fig.add_subplot(111)\n",
    "    #p=ax.scatter(merged_x,merged_z,c=merged_vel,edgecolors='none')\n",
    "    \n",
    "    p=ax1.scatter(cone_red_x, cone_red_z, c=cone_red_vel, edgecolors='none',cmap=cm.Reds)\n",
    "    cba=fig.colorbar(p)\n",
    "    p1=ax1.scatter(cone_blue_x, cone_blue_z, c=cone_blue_vel, edgecolors='none', cmap=cm.winter)\n",
    "    cbb=fig.colorbar(p1)\n",
    "    #plt.xlim([-h/4,h/4])\n",
    "    #plt.ylim([-h/4,h/4])\n",
    "    plt.xlim([-1,1])\n",
    "    plt.ylim([-1,1])\n",
    "    plt.axhline(y=0)\n",
    "    plt.axvline(x=0)\n",
    "    #p1=ax.plot(merged_x,z_slit_1, c='R')\n",
    "    #p2=ax.plot(merged_x, z_slit_2, c='G')\n",
    "    #p3=ax.plot(merged_x,z_slit_2_upper)\n",
    "    #p4=ax.plot(merged_x,z_slit_2_lower)\n",
    "    #p5=ax.plot(merged_x,z_slit_1_upper)\n",
    "    #p6=ax.plot(merged_x,z_slit_1_lower)\n",
    "    \n",
    "    cba.set_label('Redshifted Cone')\n",
    "    cbb.set_label('Blueshifted Cone')'''\n",
    "    \n",
    "    \n",
    "    \n",
    "    '''Now its time to slice things up'''\n",
    "    \n",
    "    r_slit_1_above=[]\n",
    "    v_slit_1_above=[]\n",
    "    r_slit_2_above=[]\n",
    "    v_slit_2_above=[]\n",
    "    r_slit_1_below=[]\n",
    "    v_slit_1_below=[]\n",
    "    r_slit_2_below=[]\n",
    "    v_slit_2_below=[]\n",
    "    \n",
    "    mask_slit_1_above=[]\n",
    "    mask_slit_2_above=[]\n",
    "    mask_slit_1_below=[]\n",
    "    mask_slit_2_below=[]\n",
    "    \n",
    "    \n",
    "    r_slit_3_above=[]\n",
    "    v_slit_3_above=[]\n",
    "    r_slit_4_above=[]\n",
    "    v_slit_4_above=[]\n",
    "    r_slit_3_below=[]\n",
    "    v_slit_3_below=[]\n",
    "    r_slit_4_below=[]\n",
    "    v_slit_4_below=[]\n",
    "    \n",
    "    mask_slit_3_above=[]\n",
    "    mask_slit_4_above=[]\n",
    "    mask_slit_3_below=[]\n",
    "    mask_slit_4_below=[]\n",
    "    \n",
    "    \n",
    "    '''stepping needs to be pixelscale'''\n",
    "    '''slitwidth is height'''\n",
    "    '''so if were already stepping by the spatial direction just make all of these rows your data points'''\n",
    "  \n",
    "    stepping=1\n",
    "  \n",
    "    \n",
    "    length=int(max(abs(20-upper_lim),abs(20-lower_lim),abs(20-upper_lim_ortho),abs(20-lower_lim_ortho),abs(20-lower_lim_66),abs(20-upper_lim_66),abs(20-lower_lim_ortho),abs(20-lower_lim_156),abs(20-upper_lim_156)))\n",
    "    length=max((upper_lim-lower_lim),(upper_lim_ortho-lower_lim_ortho),(upper_lim_66-lower_lim_66),(upper_lim_156-lower_lim_156))\n",
    "    \n",
    "    for j in range(length):\n",
    "        #\n",
    "        width=pixelscale_1*j\n",
    "        '''z_1_perp_upper=[-x*(1/np.tan(PA_obs_1))-(width+stepping+0.5)/np.sin(math.radians(PA1-90)) for x in xs_cont_1]\n",
    "        z_1_perp_lower=[-x*(1/np.tan(PA_obs_1))-(width+0.5)/np.sin(math.radians(PA1-90)) for x in xs_cont_1]\n",
    "        z_1_perpl_upper=[-x*(1/np.tan(PA_obs_1))+(width+0.5)/np.sin(math.radians(PA1-90)) for x in xs_cont_1]\n",
    "        z_1_perpl_lower=[-x*(1/np.tan(PA_obs_1))+(width+stepping+0.5)/np.sin(math.radians(PA1-90)) for x in xs_cont_1]\n",
    "        '''\n",
    "        z_1_perp_upper=[-x*(1/np.tan(PA_obs_1))+(width+1.5*pixelscale_1)*np.sin(PA_obs_1+np.pi/2) for x in xs_cont_1]\n",
    "        z_1_perp_lower=[-x*(1/np.tan(PA_obs_1))+(width+0.5*pixelscale_1)*np.sin(PA_obs_1+np.pi/2) for x in xs_cont_1]\n",
    "        z_1_perpl_upper=[-x*(1/np.tan(PA_obs_1))-(width+0.5*pixelscale_1)*np.sin(PA_obs_1+np.pi/2) for x in xs_cont_1]\n",
    "        z_1_perpl_lower=[-x*(1/np.tan(PA_obs_1))-(width+1.5*pixelscale_1)*np.sin(PA_obs_1+np.pi/2) for x in xs_cont_1]\n",
    "        \n",
    "        \n",
    "        \n",
    "        pos_1=np.logical_and((np.asarray(zs_cont_1) > np.asarray(z_1_perp_upper)), (np.asarray(zs_cont_1) < np.asarray(z_1_perp_lower)))\n",
    "        pos_1_1=np.logical_and((np.asarray(zs_cont_1) > np.asarray(z_1_perpl_upper)), (np.asarray(zs_cont_1) < np.asarray(z_1_perpl_lower)))\n",
    "        pos_1_center=np.logical_and((np.asarray(zs_cont_1) < np.asarray(z_1_perpl_upper)), (np.asarray(zs_cont_1) > np.asarray(z_1_perp_lower)))\n",
    "        #plt.plot(xs_cont_1, z_1_perp_lower, color='black')#, label='z_1_perp_lower',lw=4,ls='--', color='red')\n",
    "        #plt.plot(xs_cont_1, z_1_perpl_upper, color='black') #label='z_1_perpl_upper',lw=4, color='pink')\n",
    "        #if j==0:\n",
    "            #plt.plot(xs_cont_2, z_2_perp_upper, label='z_1_perp_upper',lw=4, color='black')\n",
    "            #plt.plot(xs_cont_1, z_1_perp_lower, label='z_1_perp_lower',lw=4,ls='--', color='red')\n",
    "            #plt.plot(xs_cont_1, z_1_perpl_upper, label='z_1_perpl_upper',lw=4, color='pink')\n",
    "            #plt.plot(xs_cont_2, z_2_perpl_lower, label='z_1_perpl_lower',lw=4,ls='--', color='pink')\n",
    "        \n",
    "            \n",
    "        \n",
    "        if j==0:\n",
    "            xs_cont_slit_middle=np.asarray(xs_cont_1)[np.asarray(pos_1_center)]\n",
    "            zs_cont_slit_middle=np.asarray(zs_cont_1)[np.asarray(pos_1_center)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit_middle)**2+np.mean(zs_cont_slit_middle)**2))==True:\n",
    "                r_slit_1_above.append(j)\n",
    "                v_slit_1_above.append(10)\n",
    "                mask_slit_1_above.append(1)\n",
    "                \n",
    "            else:\n",
    "                #so this is the positions to the North so we'll make them negative\n",
    "                r_slit_1_above.append(j)\n",
    "                v_slit_1_above.append(np.mean(np.asarray(vel_cont_1)[np.asarray(pos_1_center)]))\n",
    "                mask_slit_1_above.append(0)\n",
    "        else:\n",
    "            xs_cont_slit=np.asarray(xs_cont_1)[np.asarray(pos_1)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_1)[np.asarray(pos_1)]\n",
    "            \n",
    "            \n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))==True:\n",
    "                \n",
    "                r_slit_1_above.append(j)\n",
    "                v_slit_1_above.append(10)\n",
    "                mask_slit_1_above.append(1)\n",
    "            else:\n",
    "                #so this is the positions to the North so we'll make them negative\n",
    "                r_slit_1_above.append(j)\n",
    "                v_slit_1_above.append(np.mean(np.asarray(vel_cont_1)[np.asarray(pos_1)]))\n",
    "                mask_slit_1_above.append(0)\n",
    "                #r_slit_1_above.append(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))\n",
    "                #v_slit_1_above.append(np.mean(np.asarray(vel_cont_1)[np.asarray(pos_1)]))\n",
    "            \n",
    "            \n",
    "            xs_cont_slit=np.asarray(xs_cont_1)[np.asarray(pos_1_1)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_1)[np.asarray(pos_1_1)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))==True:\n",
    "                \n",
    "                r_slit_1_above.append(-j)\n",
    "                v_slit_1_above.append(10)\n",
    "                mask_slit_1_above.append(1)\n",
    "            else:\n",
    "                r_slit_1_above.append(-j)\n",
    "                #r_slit_1_above.append(-np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))\n",
    "                v_slit_1_above.append(np.mean(np.asarray(vel_cont_1)[np.asarray(pos_1_1)]))\n",
    "                mask_slit_1_above.append(0)\n",
    "         \n",
    "        z_1_perp_upper=[-x*(1/np.tan(PA_obs_1))+(width+1.5*pixelscale_1)*np.sin(PA_obs_1+np.pi/2) for x in xs_cont_1_below]\n",
    "        z_1_perp_lower=[-x*(1/np.tan(PA_obs_1))+(width+0.5*pixelscale_1)*np.sin(PA_obs_1+np.pi/2) for x in xs_cont_1_below]\n",
    "        z_1_perpl_upper=[-x*(1/np.tan(PA_obs_1))-(width+0.5*pixelscale_1)*np.sin(PA_obs_1+np.pi/2) for x in xs_cont_1_below]\n",
    "        z_1_perpl_lower=[-x*(1/np.tan(PA_obs_1))-(width+1.5*pixelscale_1)*np.sin(PA_obs_1+np.pi/2) for x in xs_cont_1_below]\n",
    "        \n",
    "        pos_1_below=np.logical_and((np.asarray(zs_cont_1_below) > np.asarray(z_1_perp_upper)), (np.asarray(zs_cont_1_below) < np.asarray(z_1_perp_lower)))\n",
    "        pos_1_below_1=np.logical_and((np.asarray(zs_cont_1_below) > np.asarray(z_1_perpl_upper)), (np.asarray(zs_cont_1_below) < np.asarray(z_1_perpl_lower)))\n",
    "        pos_1_below_center=np.logical_and((np.asarray(zs_cont_1_below) < np.asarray(z_1_perpl_upper)), (np.asarray(zs_cont_1_below) > np.asarray(z_1_perp_lower)))\n",
    "     \n",
    "        if j==0:\n",
    "            xs_cont_slit_middle=np.asarray(xs_cont_1_below)[np.asarray(pos_1_below_center)]\n",
    "            zs_cont_slit_middle=np.asarray(zs_cont_1_below)[np.asarray(pos_1_below_center)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit_middle)**2+np.mean(zs_cont_slit_middle)**2))==True:\n",
    "                r_slit_1_below.append(j)\n",
    "                v_slit_1_below.append(10)\n",
    "                mask_slit_1_below.append(1)\n",
    "                \n",
    "            else:\n",
    "                #so this is the positions to the North so we'll make them negative\n",
    "                r_slit_1_below.append(j)\n",
    "                v_slit_1_below.append(np.mean(np.asarray(vel_cont_1_below)[np.asarray(pos_1_below_center)]))\n",
    "                mask_slit_1_below.append(0)\n",
    "        else:\n",
    "        \n",
    "            xs_cont_slit=np.asarray(xs_cont_1_below)[np.asarray(pos_1_below)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_1_below)[np.asarray(pos_1_below)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))==True:\n",
    "                r_slit_1_below.append(j)\n",
    "                v_slit_1_below.append(10)\n",
    "                mask_slit_1_below.append(1)\n",
    "                \n",
    "            else:\n",
    "                r_slit_1_below.append(j)\n",
    "                v_slit_1_below.append(np.mean(np.asarray(vel_cont_1_below)[np.asarray(pos_1_below)]))\n",
    "                mask_slit_1_below.append(0)\n",
    "            \n",
    "            xs_cont_slit=np.asarray(xs_cont_1_below)[np.asarray(pos_1_below_1)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_1_below)[np.asarray(pos_1_below_1)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2)):\n",
    "                r_slit_1_below.append(-j)\n",
    "                v_slit_1_below.append(10)\n",
    "                mask_slit_1_below.append(1)\n",
    "                \n",
    "            else:\n",
    "                r_slit_1_below.append(-j)\n",
    "                v_slit_1_below.append(np.mean(np.asarray(vel_cont_1_below)[np.asarray(pos_1_below_1)]))\n",
    "                mask_slit_1_below.append(0)\n",
    "        \n",
    "        '''now PA 66'''\n",
    "        width=pixelscale_3*j\n",
    "        '''z_1_perp_upper=[-x*(1/np.tan(PA_obs_1))-(width+stepping+0.5)/np.sin(math.radians(PA1-90)) for x in xs_cont_1]\n",
    "        z_1_perp_lower=[-x*(1/np.tan(PA_obs_1))-(width+0.5)/np.sin(math.radians(PA1-90)) for x in xs_cont_1]\n",
    "        z_1_perpl_upper=[-x*(1/np.tan(PA_obs_1))+(width+0.5)/np.sin(math.radians(PA1-90)) for x in xs_cont_1]\n",
    "        z_1_perpl_lower=[-x*(1/np.tan(PA_obs_1))+(width+stepping+0.5)/np.sin(math.radians(PA1-90)) for x in xs_cont_1]\n",
    "        '''\n",
    "        z_3_perp_upper=[-x*(1/np.tan(PA_obs_3))+(width+1.5*pixelscale_3)*np.sin(PA_obs_3+np.pi/2) for x in xs_cont_3]\n",
    "        z_3_perp_lower=[-x*(1/np.tan(PA_obs_3))+(width+pixelscale_3/2)*np.sin(PA_obs_3+np.pi/2) for x in xs_cont_3]\n",
    "        z_3_perpl_upper=[-x*(1/np.tan(PA_obs_3))-(width+pixelscale_3/2)*np.sin(PA_obs_3+np.pi/2) for x in xs_cont_3]\n",
    "        z_3_perpl_lower=[-x*(1/np.tan(PA_obs_3))-(width+1.5*pixelscale_3)*np.sin(PA_obs_3+np.pi/2) for x in xs_cont_3]\n",
    "        \n",
    "        #z_3_perp_upper=[-x*(1/np.tan(PA_obs_3))-(width+pixelscale_3)/np.sin(math.radians(PA3-90)) for x in xs_cont_3_below]\n",
    "        #z_3_perp_lower=[-x*(1/np.tan(PA_obs_3))-(width+pixelscale_3/2)/np.sin(math.radians(PA3-90)) for x in xs_cont_3_below]\n",
    "        #z_3_perpl_upper=[-x*(1/np.tan(PA_obs_3))+(width+pixelscale_3/2)/np.sin(math.radians(PA3-90)) for x in xs_cont_3_below]\n",
    "        #z_3_perpl_lower=[-x*(1/np.tan(PA_obs_3))+(width+pixelscale_3)/np.sin(math.radians(PA3-90)) for x in xs_cont_3_below]\n",
    "        \n",
    "        \n",
    "        pos_3=np.logical_and((np.asarray(zs_cont_3) > np.asarray(z_3_perp_upper)), (np.asarray(zs_cont_3) < np.asarray(z_3_perp_lower)))\n",
    "        pos_3_1=np.logical_and((np.asarray(zs_cont_3) > np.asarray(z_3_perpl_upper)), (np.asarray(zs_cont_3) < np.asarray(z_3_perpl_lower)))\n",
    "        pos_3_center=np.logical_and((np.asarray(zs_cont_3) < np.asarray(z_3_perpl_upper)), (np.asarray(zs_cont_3) > np.asarray(z_3_perp_lower)))\n",
    "        '''if j==1:\n",
    "            plt.plot(xs_cont_3, z_3_perp_lower, color='yellow')#, label='z_1_perp_lower',lw=4,ls='--', color='red')\n",
    "            plt.plot(xs_cont_3, z_3_perpl_upper, color='black') #label='z_1_perpl_upper',lw=4, color='pink')\n",
    "        if j==0:\n",
    "            #plt.plot(xs_cont_2, z_2_perp_upper, label='z_1_perp_upper',lw=4, color='black')\n",
    "            plt.plot(xs_cont_3, z_3_perp_lower, label='z_1_perp_lower',lw=4,ls='--', color='red')\n",
    "            plt.plot(xs_cont_3, z_3_perpl_upper, label='z_1_perpl_upper',lw=4, color='pink')\n",
    "            #plt.plot(xs_cont_2, z_2_perpl_lower, label='z_1_perpl_lower',lw=4,ls='--', color='pink')\n",
    "        '''\n",
    "            \n",
    "        \n",
    "        if j==0:\n",
    "            xs_cont_slit_middle=np.asarray(xs_cont_3)[np.asarray(pos_3_center)]\n",
    "            zs_cont_slit_middle=np.asarray(zs_cont_3)[np.asarray(pos_3_center)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit_middle)**2+np.mean(zs_cont_slit_middle)**2))==True:\n",
    "                r_slit_3_above.append(j)\n",
    "                v_slit_3_above.append(10)\n",
    "                mask_slit_3_above.append(1)\n",
    "                \n",
    "            else:\n",
    "                #so this is the positions to the North so we'll make them negative\n",
    "                r_slit_3_above.append(j)\n",
    "                v_slit_3_above.append(np.mean(np.asarray(vel_cont_3)[np.asarray(pos_3_center)]))\n",
    "                mask_slit_3_above.append(0)\n",
    "        else:\n",
    "            xs_cont_slit=np.asarray(xs_cont_3)[np.asarray(pos_3)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_3)[np.asarray(pos_3)]\n",
    "            \n",
    "            \n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))==True:\n",
    "                \n",
    "                r_slit_3_above.append(j)\n",
    "                v_slit_3_above.append(10)\n",
    "                mask_slit_3_above.append(1)\n",
    "            else:\n",
    "                #so this is the positions to the North so we'll make them negative\n",
    "                r_slit_3_above.append(j)\n",
    "                v_slit_3_above.append(np.mean(np.asarray(vel_cont_3)[np.asarray(pos_3)]))\n",
    "                mask_slit_3_above.append(0)\n",
    "                #r_slit_1_above.append(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))\n",
    "                #v_slit_1_above.append(np.mean(np.asarray(vel_cont_1)[np.asarray(pos_1)]))\n",
    "            \n",
    "            \n",
    "            xs_cont_slit=np.asarray(xs_cont_3)[np.asarray(pos_3_1)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_3)[np.asarray(pos_3_1)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))==True:\n",
    "                \n",
    "                r_slit_3_above.append(-j)\n",
    "                v_slit_3_above.append(10)\n",
    "                mask_slit_3_above.append(1)\n",
    "            else:\n",
    "                r_slit_3_above.append(-j)\n",
    "                #r_slit_1_above.append(-np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))\n",
    "                v_slit_3_above.append(np.mean(np.asarray(vel_cont_3)[np.asarray(pos_3_1)]))\n",
    "                mask_slit_3_above.append(0)\n",
    "         \n",
    "        z_3_perp_upper=[-x*(1/np.tan(PA_obs_3))+(width+1.5*pixelscale_3)*np.sin(PA_obs_3+np.pi/2) for x in xs_cont_3_below]\n",
    "        z_3_perp_lower=[-x*(1/np.tan(PA_obs_3))+(width+0.5*pixelscale_3)*np.sin(PA_obs_3+np.pi/2) for x in xs_cont_3_below]\n",
    "        z_3_perpl_upper=[-x*(1/np.tan(PA_obs_3))-(width+0.5*pixelscale_3)*np.sin(PA_obs_3+np.pi/2) for x in xs_cont_3_below]\n",
    "        z_3_perpl_lower=[-x*(1/np.tan(PA_obs_3))-(width+1.5*pixelscale_3)*np.sin(PA_obs_3+np.pi/2) for x in xs_cont_3_below]\n",
    "        \n",
    "        pos_3_below=np.logical_and((np.asarray(zs_cont_3_below) > np.asarray(z_3_perp_upper)), (np.asarray(zs_cont_3_below) < np.asarray(z_3_perp_lower)))\n",
    "        pos_3_below_1=np.logical_and((np.asarray(zs_cont_3_below) > np.asarray(z_3_perpl_upper)), (np.asarray(zs_cont_3_below) < np.asarray(z_3_perpl_lower)))\n",
    "        pos_3_below_center=np.logical_and((np.asarray(zs_cont_3_below) < np.asarray(z_3_perpl_upper)), (np.asarray(zs_cont_3_below) > np.asarray(z_3_perp_lower)))\n",
    "     \n",
    "        if j==0:\n",
    "            xs_cont_slit_middle=np.asarray(xs_cont_3_below)[np.asarray(pos_3_below_center)]\n",
    "            zs_cont_slit_middle=np.asarray(zs_cont_3_below)[np.asarray(pos_3_below_center)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit_middle)**2+np.mean(zs_cont_slit_middle)**2))==True:\n",
    "                r_slit_3_below.append(j)\n",
    "                v_slit_3_below.append(10)\n",
    "                mask_slit_3_below.append(1)\n",
    "                \n",
    "            else:\n",
    "                #so this is the positions to the North so we'll make them negative\n",
    "                r_slit_3_below.append(j)\n",
    "                v_slit_3_below.append(np.mean(np.asarray(vel_cont_3_below)[np.asarray(pos_3_below_center)]))\n",
    "                mask_slit_3_below.append(0)\n",
    "        else:\n",
    "        \n",
    "            xs_cont_slit=np.asarray(xs_cont_3_below)[np.asarray(pos_3_below)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_3_below)[np.asarray(pos_3_below)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))==True:\n",
    "                r_slit_3_below.append(j)\n",
    "                v_slit_3_below.append(10)\n",
    "                mask_slit_3_below.append(1)\n",
    "                \n",
    "            else:\n",
    "                r_slit_3_below.append(j)\n",
    "                v_slit_3_below.append(np.mean(np.asarray(vel_cont_3_below)[np.asarray(pos_3_below)]))\n",
    "                mask_slit_3_below.append(0)\n",
    "            \n",
    "            xs_cont_slit=np.asarray(xs_cont_3_below)[np.asarray(pos_3_below_1)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_3_below)[np.asarray(pos_3_below_1)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2)):\n",
    "                r_slit_3_below.append(-j)\n",
    "                v_slit_3_below.append(10)\n",
    "                mask_slit_3_below.append(1)\n",
    "                \n",
    "            else:\n",
    "                r_slit_3_below.append(-j)\n",
    "                v_slit_3_below.append(np.mean(np.asarray(vel_cont_3_below)[np.asarray(pos_3_below_1)]))\n",
    "                mask_slit_3_below.append(0)\n",
    "                \n",
    "                \n",
    "                \n",
    "        '''Now PA 111'''\n",
    "        width=pixelscale_2*j\n",
    "        \n",
    "        z_2_perp_upper=[-x*(1/np.tan(PA_obs_2))+(width+1.5*pixelscale_2)*np.sin(PA_obs_2+np.pi/2) for x in xs_cont_2]\n",
    "        z_2_perp_lower=[-x*(1/np.tan(PA_obs_2))+(width+0.5*pixelscale_2)*np.sin(PA_obs_2+np.pi/2) for x in xs_cont_2]\n",
    "        z_2_perpl_upper=[-x*(1/np.tan(PA_obs_2))-(width+0.5*pixelscale_2)*np.sin(PA_obs_2+np.pi/2) for x in xs_cont_2]\n",
    "        z_2_perpl_lower=[-x*(1/np.tan(PA_obs_2))-(width+1.5*pixelscale_2)*np.sin(PA_obs_2+np.pi/2) for x in xs_cont_2]\n",
    "        \n",
    "        pos_1_2=np.logical_and((np.asarray(zs_cont_2) > np.asarray(z_2_perp_upper)), (np.asarray(zs_cont_2) < np.asarray(z_2_perp_lower)))\n",
    "        pos_1_3=np.logical_and((np.asarray(zs_cont_2) > np.asarray(z_2_perpl_upper)), (np.asarray(zs_cont_2)< np.asarray(z_2_perpl_lower)))\n",
    "        \n",
    "        #plt.plot(xs_cont_2, z_2_perp_lower, color='black')#, label='z_1_perp_lower',lw=4,ls='--', color='red')\n",
    "        #plt.plot(xs_cont_2, z_2_perpl_upper, color='black') #label='z_1_perpl_upper',lw=4, color='pink')\n",
    "        pos_1_2_center=np.logical_and((np.asarray(zs_cont_2) < np.asarray(z_2_perpl_upper)), (np.asarray(zs_cont_2) > np.asarray(z_2_perp_lower)))\n",
    "        #if j==0:\n",
    "            #plt.plot(xs_cont_2, z_2_perp_upper, label='z_1_perp_upper',lw=4, color='black')\n",
    "            #plt.plot(xs_cont_2, z_2_perp_lower, label='z_2_perp_lower',lw=4,ls='--', color='red')\n",
    "            #plt.plot(xs_cont_2, z_2_perpl_upper, label='z_2_perpl_upper',lw=4, color='pink')\n",
    "            #plt.show()\n",
    "        if j==0:\n",
    "            xs_cont_slit_middle=np.asarray(xs_cont_2)[np.asarray(pos_1_2_center)]\n",
    "            zs_cont_slit_middle=np.asarray(zs_cont_2)[np.asarray(pos_1_2_center)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit_middle)**2+np.mean(zs_cont_slit_middle)**2))==True:\n",
    "                \n",
    "                r_slit_2_above.append(j)\n",
    "                v_slit_2_above.append(10)\n",
    "                mask_slit_2_above.append(1)\n",
    "                \n",
    "            else:\n",
    "                #so this is the positions to the North so we'll make them negative\n",
    "                r_slit_2_above.append(j)\n",
    "                v_slit_2_above.append(np.mean(np.asarray(vel_cont_2)[np.asarray(pos_1_2_center)]))\n",
    "                mask_slit_2_above.append(0)\n",
    "        else:\n",
    "        \n",
    "            xs_cont_slit=np.asarray(xs_cont_2)[np.asarray(pos_1_2)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_2)[np.asarray(pos_1_2)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))==True:\n",
    "                r_slit_2_above.append(j)\n",
    "                v_slit_2_above.append(10)\n",
    "                mask_slit_2_above.append(1)\n",
    "               \n",
    "            else:\n",
    "                r_slit_2_above.append(j)\n",
    "                v_slit_2_above.append(np.mean(np.asarray(vel_cont_2)[np.asarray(pos_1_2)]))\n",
    "                mask_slit_2_above.append(0)\n",
    "             \n",
    "            xs_cont_slit=np.asarray(xs_cont_2)[np.asarray(pos_1_3)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_2)[np.asarray(pos_1_3)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))==True:\n",
    "                \n",
    "                r_slit_2_above.append(-j)\n",
    "                v_slit_2_above.append(10)\n",
    "                mask_slit_2_above.append(1)\n",
    "                \n",
    "            else:\n",
    "                r_slit_2_above.append(-j)\n",
    "                v_slit_2_above.append(np.mean(np.asarray(vel_cont_2)[np.asarray(pos_1_3)]))\n",
    "                mask_slit_2_above.append(0)\n",
    "            #math.isnan(np.mean(np.asarray(vel_cont_2)[np.asarray(pos_1_3)])):\n",
    "       \n",
    "           \n",
    "            \n",
    "        z_2_perp_upper=[-x*(1/np.tan(PA_obs_2))+(width+1.5*pixelscale_2)*np.sin(PA_obs_2+np.pi/2) for x in xs_cont_2_below]\n",
    "        z_2_perp_lower=[-x*(1/np.tan(PA_obs_2))+(width+0.5*pixelscale_2)*np.sin(PA_obs_2+np.pi/2) for x in xs_cont_2_below]\n",
    "        z_2_perpl_upper=[-x*(1/np.tan(PA_obs_2))-(width+0.5*pixelscale_2)*np.sin(PA_obs_2+np.pi/2) for x in xs_cont_2_below]\n",
    "        z_2_perpl_lower=[-x*(1/np.tan(PA_obs_2))-(width+1.5*pixelscale_2)*np.sin(PA_obs_2+np.pi/2) for x in xs_cont_2_below]\n",
    "        \n",
    "        pos_1_4=np.logical_and((np.asarray(zs_cont_2_below) > np.asarray(z_2_perp_upper)), (np.asarray(zs_cont_2_below) < np.asarray(z_2_perp_lower)))\n",
    "        pos_1_5=np.logical_and((np.asarray(zs_cont_2_below) > np.asarray(z_2_perpl_upper)), (np.asarray(zs_cont_2_below) < np.asarray(z_2_perpl_lower)))\n",
    "        pos_1_4_center=np.logical_and((np.asarray(zs_cont_2_below) < np.asarray(z_2_perpl_upper)), (np.asarray(zs_cont_2_below) > np.asarray(z_2_perp_lower)))\n",
    "     \n",
    "        \n",
    "        if j==0:\n",
    "            xs_cont_slit_middle=np.asarray(xs_cont_2_below)[np.asarray(pos_1_4_center)]\n",
    "            zs_cont_slit_middle=np.asarray(zs_cont_2_below)[np.asarray(pos_1_4_center)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit_middle)**2+np.mean(zs_cont_slit_middle)**2))==True:\n",
    "                \n",
    "                r_slit_2_below.append(j)\n",
    "                v_slit_2_below.append(10)\n",
    "                mask_slit_2_below.append(1)\n",
    "            else:\n",
    "                #so this is the positions to the North so we'll make them negative\n",
    "                r_slit_2_below.append(j)\n",
    "                v_slit_2_below.append(np.mean(np.asarray(vel_cont_2_below)[np.asarray(pos_1_4_center)]))\n",
    "                mask_slit_2_below.append(0)\n",
    "        else:\n",
    "        \n",
    "            xs_cont_slit=np.asarray(xs_cont_2_below)[np.asarray(pos_1_4)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_2_below)[np.asarray(pos_1_4)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))==True:\n",
    "                r_slit_2_below.append(j)\n",
    "                v_slit_2_below.append(10)\n",
    "                mask_slit_2_below.append(1)\n",
    "                \n",
    "            else:\n",
    "                r_slit_2_below.append(j)\n",
    "                v_slit_2_below.append(np.mean(np.asarray(vel_cont_2_below)[np.asarray(pos_1_4)]))\n",
    "                mask_slit_2_below.append(0)\n",
    "            \n",
    "            xs_cont_slit=np.asarray(xs_cont_2_below)[np.asarray(pos_1_5)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_2_below)[np.asarray(pos_1_5)]\n",
    "            \n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))==True:\n",
    "                \n",
    "                r_slit_2_below.append(-j)\n",
    "                v_slit_2_below.append(10)\n",
    "                mask_slit_2_below.append(1)\n",
    "                \n",
    "            else:\n",
    "                r_slit_2_below.append(-j)\n",
    "                v_slit_2_below.append(np.mean(np.asarray(vel_cont_2_below)[np.asarray(pos_1_5)]))\n",
    "                mask_slit_2_below.append(0)\n",
    "                \n",
    "        '''Now PA 156'''\n",
    "        width=pixelscale_4*j\n",
    "        \n",
    "        z_4_perp_upper=[-x*(1/np.tan(PA_obs_4))+(width+1.5*pixelscale_4)*np.sin(PA_obs_4+np.pi/2) for x in xs_cont_4]\n",
    "        z_4_perp_lower=[-x*(1/np.tan(PA_obs_4))+(width+0.5*pixelscale_4)*np.sin(PA_obs_4+np.pi/2) for x in xs_cont_4]\n",
    "        z_4_perpl_upper=[-x*(1/np.tan(PA_obs_4))-(width+0.5*pixelscale_4)*np.sin(PA_obs_4+np.pi/2) for x in xs_cont_4]\n",
    "        z_4_perpl_lower=[-x*(1/np.tan(PA_obs_4))-(width+1.5*pixelscale_4)*np.sin(PA_obs_4+np.pi/2) for x in xs_cont_4]\n",
    "        \n",
    "        pos_4_2=np.logical_and((np.asarray(zs_cont_4) > np.asarray(z_4_perp_upper)), (np.asarray(zs_cont_4) < np.asarray(z_4_perp_lower)))\n",
    "        pos_4_3=np.logical_and((np.asarray(zs_cont_4) > np.asarray(z_4_perpl_upper)), (np.asarray(zs_cont_4)< np.asarray(z_4_perpl_lower)))\n",
    "        \n",
    "        #plt.plot(xs_cont_2, z_2_perp_lower, color='black')#, label='z_1_perp_lower',lw=4,ls='--', color='red')\n",
    "        #plt.plot(xs_cont_2, z_2_perpl_upper, color='black') #label='z_1_perpl_upper',lw=4, color='pink')\n",
    "        pos_4_2_center=np.logical_and((np.asarray(zs_cont_4) < np.asarray(z_4_perpl_upper)), (np.asarray(zs_cont_4) > np.asarray(z_4_perp_lower)))\n",
    "        #if j==0:\n",
    "            #plt.plot(xs_cont_2, z_2_perp_upper, label='z_1_perp_upper',lw=4, color='black')\n",
    "            #plt.plot(xs_cont_2, z_2_perp_lower, label='z_2_perp_lower',lw=4,ls='--', color='red')\n",
    "            #plt.plot(xs_cont_2, z_2_perpl_upper, label='z_2_perpl_upper',lw=4, color='pink')\n",
    "            #plt.show()\n",
    "        if j==0:\n",
    "            xs_cont_slit_middle=np.asarray(xs_cont_4)[np.asarray(pos_4_2_center)]\n",
    "            zs_cont_slit_middle=np.asarray(zs_cont_4)[np.asarray(pos_4_2_center)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit_middle)**2+np.mean(zs_cont_slit_middle)**2))==True:\n",
    "                \n",
    "                r_slit_4_above.append(j)\n",
    "                v_slit_4_above.append(10)\n",
    "                mask_slit_4_above.append(1)\n",
    "                \n",
    "            else:\n",
    "                #so this is the positions to the North so we'll make them negative\n",
    "                r_slit_4_above.append(j)\n",
    "                v_slit_4_above.append(np.mean(np.asarray(vel_cont_4)[np.asarray(pos_4_2_center)]))\n",
    "                mask_slit_4_above.append(0)\n",
    "        else:\n",
    "        \n",
    "            xs_cont_slit=np.asarray(xs_cont_4)[np.asarray(pos_4_2)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_4)[np.asarray(pos_4_2)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))==True:\n",
    "                r_slit_4_above.append(j)\n",
    "                v_slit_4_above.append(10)\n",
    "                mask_slit_4_above.append(1)\n",
    "               \n",
    "            else:\n",
    "                r_slit_4_above.append(j)\n",
    "                v_slit_4_above.append(np.mean(np.asarray(vel_cont_4)[np.asarray(pos_4_2)]))\n",
    "                mask_slit_4_above.append(0)\n",
    "             \n",
    "            xs_cont_slit=np.asarray(xs_cont_4)[np.asarray(pos_4_3)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_4)[np.asarray(pos_4_3)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))==True:\n",
    "                \n",
    "                r_slit_4_above.append(-j)\n",
    "                v_slit_4_above.append(10)\n",
    "                mask_slit_4_above.append(1)\n",
    "                \n",
    "            else:\n",
    "                r_slit_4_above.append(-j)\n",
    "                v_slit_4_above.append(np.mean(np.asarray(vel_cont_4)[np.asarray(pos_4_3)]))\n",
    "                mask_slit_4_above.append(0)\n",
    "            #math.isnan(np.mean(np.asarray(vel_cont_2)[np.asarray(pos_1_3)])):\n",
    "       \n",
    "           \n",
    "            \n",
    "        z_4_perp_upper=[-x*(1/np.tan(PA_obs_4))+(width+1.5*pixelscale_4)*np.sin(PA_obs_4+np.pi/2) for x in xs_cont_4_below]\n",
    "        z_4_perp_lower=[-x*(1/np.tan(PA_obs_4))+(width+0.5*pixelscale_4)*np.sin(PA_obs_4+np.pi/2) for x in xs_cont_4_below]\n",
    "        z_4_perpl_upper=[-x*(1/np.tan(PA_obs_4))-(width+0.5*pixelscale_4)*np.sin(PA_obs_4+np.pi/2) for x in xs_cont_4_below]\n",
    "        z_4_perpl_lower=[-x*(1/np.tan(PA_obs_4))-(width+1.5*pixelscale_4)*np.sin(PA_obs_4+np.pi/2) for x in xs_cont_4_below]\n",
    "        \n",
    "        pos_4_4=np.logical_and((np.asarray(zs_cont_4_below) > np.asarray(z_4_perp_upper)), (np.asarray(zs_cont_4_below) < np.asarray(z_4_perp_lower)))\n",
    "        pos_4_5=np.logical_and((np.asarray(zs_cont_4_below) > np.asarray(z_4_perpl_upper)), (np.asarray(zs_cont_4_below) < np.asarray(z_4_perpl_lower)))\n",
    "        pos_4_4_center=np.logical_and((np.asarray(zs_cont_4_below) < np.asarray(z_4_perpl_upper)), (np.asarray(zs_cont_4_below) > np.asarray(z_4_perp_lower)))\n",
    "     \n",
    "        \n",
    "        if j==0:\n",
    "            xs_cont_slit_middle=np.asarray(xs_cont_4_below)[np.asarray(pos_4_4_center)]\n",
    "            zs_cont_slit_middle=np.asarray(zs_cont_4_below)[np.asarray(pos_4_4_center)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit_middle)**2+np.mean(zs_cont_slit_middle)**2))==True:\n",
    "                \n",
    "                r_slit_4_below.append(j)\n",
    "                v_slit_4_below.append(10)\n",
    "                mask_slit_4_below.append(1)\n",
    "            else:\n",
    "                #so this is the positions to the North so we'll make them negative\n",
    "                r_slit_4_below.append(j)\n",
    "                v_slit_4_below.append(np.mean(np.asarray(vel_cont_4_below)[np.asarray(pos_4_4_center)]))\n",
    "                mask_slit_4_below.append(0)\n",
    "        else:\n",
    "        \n",
    "            xs_cont_slit=np.asarray(xs_cont_4_below)[np.asarray(pos_4_4)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_4_below)[np.asarray(pos_4_4)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))==True:\n",
    "                r_slit_4_below.append(j)\n",
    "                v_slit_4_below.append(10)\n",
    "                mask_slit_4_below.append(1)\n",
    "                \n",
    "            else:\n",
    "                r_slit_4_below.append(j)\n",
    "                v_slit_4_below.append(np.mean(np.asarray(vel_cont_4_below)[np.asarray(pos_4_4)]))\n",
    "                mask_slit_4_below.append(0)\n",
    "            \n",
    "            xs_cont_slit=np.asarray(xs_cont_4_below)[np.asarray(pos_4_5)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_4_below)[np.asarray(pos_4_5)]\n",
    "            \n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))==True:\n",
    "                \n",
    "                r_slit_4_below.append(-j)\n",
    "                v_slit_4_below.append(10)\n",
    "                mask_slit_4_below.append(1)\n",
    "                \n",
    "            else:\n",
    "                r_slit_4_below.append(-j)\n",
    "                v_slit_4_below.append(np.mean(np.asarray(vel_cont_4_below)[np.asarray(pos_4_5)]))\n",
    "                mask_slit_4_below.append(0)\n",
    "    \n",
    "    \n",
    "    '''plt.show()\n",
    "    plt.clf()\n",
    "    \n",
    "    plt.scatter(r_slit_1_above, v_slit_1_above, label='Slit 1 Above', color='orange')\n",
    "    plt.scatter(r_slit_1_below, v_slit_1_below, label='Slit 1 Below', color='pink')\n",
    "    plt.scatter(r_slit_2_above, v_slit_2_above, label='Slit 2 Above', color='blue')\n",
    "    plt.scatter(r_slit_2_below, v_slit_2_below, label='Slit 2 Below', color='purple')\n",
    "    plt.legend()\n",
    "    plt.show()'''\n",
    "    \n",
    "    \n",
    " \n",
    "    inds = np.array(r_slit_1_above).argsort()\n",
    "    sortedx_1_above = np.array(r_slit_1_above)[inds]#-minus_one\n",
    "    sortedv_1_above = np.array(v_slit_1_above)[inds]\n",
    "    \n",
    "    sorted_mask = np.array(mask_slit_1_above)[inds]\n",
    "    \n",
    "    m_sortedx_1_above = ma.masked_array(sortedx_1_above, mask=sorted_mask)\n",
    "    m_sortedv_1_above = ma.masked_array(sortedv_1_above, mask=sorted_mask)\n",
    "    \n",
    "    inds = np.array(r_slit_1_below).argsort()\n",
    "    sortedx_1_below = np.array(r_slit_1_below)[inds]\n",
    "    sortedv_1_below = np.array(v_slit_1_below)[inds]\n",
    "    \n",
    "    sorted_mask = np.array(mask_slit_1_below)[inds]\n",
    "    \n",
    "    m_sortedx_1_below = ma.masked_array(sortedx_1_below, mask=sorted_mask)\n",
    "    m_sortedv_1_below = ma.masked_array(sortedv_1_below, mask=sorted_mask)\n",
    "    \n",
    "    inds = np.array(r_slit_2_above).argsort()\n",
    "    sortedx_2_above = np.array(r_slit_2_above)[inds]\n",
    "    sortedv_2_above = np.array(v_slit_2_above)[inds]\n",
    "    \n",
    "    sorted_mask = np.array(mask_slit_2_above)[inds]\n",
    "    \n",
    "    m_sortedx_2_above = ma.masked_array(sortedx_2_above, mask=sorted_mask)\n",
    "    m_sortedv_2_above = ma.masked_array(sortedv_2_above, mask=sorted_mask)\n",
    "    \n",
    "    inds = np.array(r_slit_2_below).argsort()\n",
    "    sortedx_2_below = np.array(r_slit_2_below)[inds]\n",
    "    sortedv_2_below = np.array(v_slit_2_below)[inds]\n",
    "    \n",
    "    sorted_mask = np.array(mask_slit_2_below)[inds]\n",
    "    \n",
    "    m_sortedx_2_below = ma.masked_array(sortedx_2_below, mask=sorted_mask)\n",
    "    m_sortedv_2_below = ma.masked_array(sortedv_2_below, mask=sorted_mask)\n",
    "    \n",
    "    \n",
    "    inds = np.array(r_slit_3_above).argsort()\n",
    "    sortedx_3_above = np.array(r_slit_3_above)[inds]#-minus_one\n",
    "    sortedv_3_above = np.array(v_slit_3_above)[inds]\n",
    "                           \n",
    "    sorted_mask = np.array(mask_slit_3_above)[inds]\n",
    "    \n",
    "    m_sortedx_3_above = ma.masked_array(sortedx_3_above, mask=sorted_mask)\n",
    "    m_sortedv_3_above = ma.masked_array(sortedv_3_above, mask=sorted_mask)\n",
    "    \n",
    "    inds = np.array(r_slit_3_below).argsort()\n",
    "    sortedx_3_below = np.array(r_slit_3_below)[inds]\n",
    "    sortedv_3_below = np.array(v_slit_3_below)[inds]\n",
    "                           \n",
    "    sorted_mask = np.array(mask_slit_3_below)[inds]\n",
    "    \n",
    "    m_sortedx_3_below = ma.masked_array(sortedx_3_below, mask=sorted_mask)\n",
    "    m_sortedv_3_below = ma.masked_array(sortedv_3_below, mask=sorted_mask)\n",
    "    \n",
    "    inds = np.array(r_slit_4_above).argsort()\n",
    "    sortedx_4_above = np.array(r_slit_4_above)[inds]\n",
    "    sortedv_4_above = np.array(v_slit_4_above)[inds]\n",
    "    \n",
    "    sorted_mask = np.array(mask_slit_4_above)[inds]\n",
    "    \n",
    "    m_sortedx_4_above = ma.masked_array(sortedx_4_above, mask=sorted_mask)\n",
    "    m_sortedv_4_above = ma.masked_array(sortedv_4_above, mask=sorted_mask)\n",
    "    \n",
    "    inds = np.array(r_slit_4_below).argsort()\n",
    "    sortedx_4_below = np.array(r_slit_4_below)[inds]\n",
    "    sortedv_4_below = np.array(v_slit_4_below)[inds]\n",
    "                           \n",
    "    sorted_mask = np.array(mask_slit_4_below)[inds]\n",
    "    \n",
    "    m_sortedx_4_below = ma.masked_array(sortedx_4_below, mask=sorted_mask)\n",
    "    m_sortedv_4_below = ma.masked_array(sortedv_4_below, mask=sorted_mask)\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    '''Okay so we need to cut this according to the xs_data_concat array'''\n",
    "    first=xs_data[lower_lim:upper_lim][0]\n",
    "    last=xs_data[lower_lim:upper_lim][-1]\n",
    "    \n",
    "    '''find index where m_sortedx_1_below and same for above have the values of first and last and cut it there'''\n",
    "    for z in range(len(m_sortedx_1_below)):\n",
    "        if sortedx_1_below[z]==first:\n",
    "            i_b_first=z\n",
    "        if sortedx_1_below[z]==last:\n",
    "            i_b_last=z+1\n",
    "            \n",
    "    for z in range(len(m_sortedx_1_above)):\n",
    "        if sortedx_1_above[z]==first:\n",
    "            i_a_first=z\n",
    "        if sortedx_1_above[z]==last:\n",
    "            i_a_last=z+1\n",
    "  \n",
    "    \n",
    "    xs_fit_concat=np.ma.concatenate([m_sortedx_1_below[i_b_first:i_b_last],m_sortedx_1_above[i_a_first:i_a_last]])#xs_fit_bin+xs_fit_bin_below\n",
    "    vs_fit_concat=np.ma.concatenate([m_sortedv_1_below[i_b_first:i_b_last],m_sortedv_1_above[i_a_first:i_a_last]])#vs_fit_bin+vs_fit_bin_below\n",
    "    \n",
    "    first=xs_data[lower_lim_ortho:upper_lim_ortho][0]\n",
    "    last=xs_data[lower_lim_ortho:upper_lim_ortho][-1]\n",
    "    \n",
    "    '''find index where m_sortedx_1_below and same for above have the values of first and last and cut it there'''\n",
    "    for z in range(len(m_sortedx_2_below)):\n",
    "        if sortedx_2_below[z]==first:\n",
    "            i_b_first=z\n",
    "        if sortedx_2_below[z]==last:\n",
    "            i_b_last=z+1\n",
    "            \n",
    "    for z in range(len(m_sortedx_2_above)):\n",
    "        if sortedx_2_above[z]==first:\n",
    "            i_a_first=z\n",
    "        if sortedx_2_above[z]==last:\n",
    "            i_a_last=z+1\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    xs_fit_concat_ortho=np.ma.concatenate([m_sortedx_2_below[i_b_first:i_b_last],m_sortedx_2_above[i_a_first:i_a_last]])#xs_fit_ortho_bin+xs_fit_ortho_bin_below\n",
    "    vs_fit_concat_ortho=np.ma.concatenate([m_sortedv_2_below[i_b_first:i_b_last],m_sortedv_2_above[i_a_first:i_a_last]])#vs_fit_ortho_bin+vs_fit_ortho_bin_below\n",
    "    \n",
    "    first=xs_data[lower_lim_66:upper_lim_66][0]\n",
    "    last=xs_data[lower_lim_66:upper_lim_66][-1]\n",
    "    \n",
    "    '''find index where m_sortedx_1_below and same for above have the values of first and last and cut it there'''\n",
    "    for z in range(len(m_sortedx_3_below)):\n",
    "        if sortedx_3_below[z]==first:\n",
    "            i_b_first=z\n",
    "        if sortedx_3_below[z]==last:\n",
    "            i_b_last=z+1\n",
    "            \n",
    "    for z in range(len(m_sortedx_3_above)):\n",
    "        if sortedx_3_above[z]==first:\n",
    "            i_a_first=z\n",
    "        if sortedx_3_above[z]==last:\n",
    "            i_a_last=z+1\n",
    "    \n",
    "    xs_fit_concat_66=np.ma.concatenate([m_sortedx_3_below[i_b_first:i_b_last],m_sortedx_3_above[i_a_first:i_a_last]])#xs_fit_bin+xs_fit_bin_below\n",
    "    vs_fit_concat_66=np.ma.concatenate([m_sortedv_3_below[i_b_first:i_b_last],m_sortedv_3_above[i_a_first:i_a_last]])#vs_fit_bin+vs_fit_bin_below\n",
    "    \n",
    "    \n",
    "    first=xs_data[lower_lim_156:upper_lim_156][0]\n",
    "    last=xs_data[lower_lim_156:upper_lim_156][-1]\n",
    "    \n",
    "    '''find index where m_sortedx_1_below and same for above have the values of first and last and cut it there'''\n",
    "    for z in range(len(m_sortedx_4_below)):\n",
    "        if sortedx_4_below[z]==first:\n",
    "            i_b_first=z\n",
    "        if sortedx_4_below[z]==last:\n",
    "            i_b_last=z+1\n",
    "            \n",
    "    for z in range(len(m_sortedx_4_above)):\n",
    "        if sortedx_4_above[z]==first:\n",
    "            i_a_first=z\n",
    "        if sortedx_4_above[z]==last:\n",
    "            i_a_last=z+1\n",
    "    \n",
    "    xs_fit_concat_156=np.ma.concatenate([m_sortedx_4_below[i_b_first:i_b_last],m_sortedx_4_above[i_a_first:i_a_last]])#xs_fit_ortho_bin+xs_fit_ortho_bin_below\n",
    "    vs_fit_concat_156=np.ma.concatenate([m_sortedv_4_below[i_b_first:i_b_last],m_sortedv_4_above[i_a_first:i_a_last]])#vs_fit_ortho_bin+vs_fit_ortho_bin_below\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "  \n",
    "    \n",
    "    '''xs_data_concat_arc=[x for x in xs_data_concat]\n",
    "    xs_data_concat_ortho_arc=[x for x in xs_data_concat_ortho]\n",
    "    xs_fit_concat_arc=[x for x in xs_fit_concat]\n",
    "    xs_fit_concat_ortho_arc=[x for x in xs_fit_concat_ortho]\n",
    "    \n",
    "   \n",
    "    \n",
    "    #plt.fill_between(xs_fit_concat_ortho_arc[0:len(xs_fit_concat_ortho_arc)/2], vs_ortho_error_neg[0:len(xs_fit_concat_ortho_arc)/2], vs_ortho_error_pos[0:len(xs_fit_concat_ortho_arc)/2],alpha=0.3, color='blue')\n",
    "    #plt.fill_between(xs_fit_concat_arc[0:len(xs_fit_concat_arc)/2], vs_error_neg[0:len(xs_fit_concat_arc)/2], vs_error_pos[0:len(xs_fit_concat_arc)/2], alpha=0.3,color='red', label='PA 1 Model')\n",
    "    \n",
    "    #plt.fill_between(xs_fit_concat_ortho_arc[len(xs_fit_concat_ortho_arc)/2:], vs_ortho_error_neg[len(xs_fit_concat_ortho_arc)/2:], vs_ortho_error_pos[len(xs_fit_concat_ortho_arc)/2:], alpha=0.3,color='blue', label='PA 2 Model')\n",
    "    #plt.fill_between(xs_fit_concat_arc[len(xs_fit_concat_arc)/2:], vs_error_neg[len(xs_fit_concat_arc)/2:], vs_error_pos[len(xs_fit_concat_arc)/2:],alpha=0.3, color='red')\n",
    "    \n",
    "    plt.errorbar(xs_data_concat_arc, vel_data_concat, yerr=error_data_concat, color='orangered', linestyle='None')\n",
    "    plt.scatter(xs_data_concat_arc, vel_data_concat,  color='orangered', label='PA 1 Velocity Data', s=100, zorder=100)\n",
    "    plt.scatter(xs_fit_concat_arc, vs_fit_concat, edgecolor='orangered',marker='*', facecolor='white', s=150,label='PA 1 Model', zorder=100)\n",
    "    \n",
    "    plt.errorbar(xs_data_concat_ortho_arc, vel_data_concat_ortho, yerr=error_data_concat_ortho, color='purple', linestyle='None')\n",
    "    plt.scatter(xs_data_concat_ortho_arc, vel_data_concat_ortho, color='purple', label='PA 2 Velocity Data', s=100, zorder=100)\n",
    "    plt.scatter(xs_fit_concat_ortho_arc, vs_fit_concat_ortho, edgecolor='purple',marker='*',s=150, facecolor='white', label='PA 2 Model', zorder=100)\n",
    "    #plt.annotate(result, xy=(0.45,0.1), xycoords='axes fraction')\n",
    "    #plt.annotate(r'i   PA   r$_t$  $\\theta_{\\mathrm{half}}$  $\\theta_{\\mathrm{half2}}$  v$_{\\mathrm{max}}$', xy=(0.45,0.03), xycoords='axes fraction')\n",
    "    plt.xlabel(r'Spatial Position [$^{\\prime \\prime}$]')\n",
    "    plt.ylabel(r'Velocity Offset [km s$^{-1}$]')\n",
    "    plt.ylim([-400,400])\n",
    "    plt.annotate('J0854+5026', xy=(0.77,0.05), xycoords='axes fraction', size=15)\n",
    "    #plt.xlim([-4,4])\n",
    " \n",
    "    \n",
    "    plt.legend()#loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "    #p1 = plt.plot(xs_fit_concat_ortho_arc[len(xs_fit_concat_ortho_arc)/2:], vs_ortho_error_neg[len(xs_fit_concat_ortho_arc)/2:], color='b', linewidth=0)\n",
    "    #p2 = plt.fill_between(np.NaN, np.NaN, 'b', alpha=0.5)\n",
    "    #plt.legend([(p2[0], p1[0]), ], ['PA 2 Model'])\n",
    "    #plt.tight_layout()\n",
    "    plt.axhline(y=0, ls='--', color='black')\n",
    "    #plt.title('102.87')\n",
    "    #plt.annotate('82.84 0.52 0.7 & 65.97 2.05 0.49 & 5.52 0.13 0.17 & 40.15 0.72 1.52 & 65.84 0.35 0.28 & 304.52 4.79 7.18', xy=(0.01,0.01), xycoords='axes fraction')\n",
    "    \n",
    "    savefig('../Model_Asymmetric_Bicone_'+str(name)+'.pdf')'''\n",
    "    #plt.show()\n",
    " \n",
    "    \n",
    "    \n",
    "    resid_alt_1=[]\n",
    "    resid_alt_2=[]\n",
    "    resid_alt_3=[]\n",
    "    resid_alt_4=[]\n",
    "                           \n",
    "    sum_masked=[]\n",
    "    \n",
    "    for i in range(len(vs_fit_concat)):\n",
    "        if vs_fit_concat[i] is ma.masked:\n",
    "            sum_masked.append(1)\n",
    "        else:\n",
    "            resid_alt_1.append(-0.5*(((vs_fit_concat[i]-vel_data_concat[i])**2)/(np.sqrt((np.sqrt(error_data_concat[0][i]**2+error_data_concat[1][i]**2))**2+2.5**2))**2))\n",
    "    for i in range(len(vs_fit_concat_ortho)):\n",
    "        if vs_fit_concat_ortho[i] is ma.masked:\n",
    "            sum_masked.append(1)\n",
    "        else:\n",
    "            resid_alt_2.append(-0.5*(((vs_fit_concat_ortho[i]-vel_data_concat_ortho[i])**2)/(np.sqrt((np.sqrt(error_data_concat_ortho[0][i]**2+error_data_concat_ortho[1][i]**2))**2+2.5**2))**2))\n",
    "    for i in range(len(vs_fit_concat_66)):\n",
    "        if vs_fit_concat_66[i] is ma.masked:\n",
    "            sum_masked.append(1)\n",
    "        else:\n",
    "            resid_alt_3.append(-0.5*(((vs_fit_concat_66[i]-vel_data_concat_66[i])**2)/(np.sqrt((np.sqrt(error_data_concat_66[0][i]**2+error_data_concat_66[1][i]**2))**2+2.5**2))**2))\n",
    "    for i in range(len(vs_fit_concat_156)):\n",
    "        if vs_fit_concat_156[i] is ma.masked:\n",
    "            sum_masked.append(1)\n",
    "        else:\n",
    "            resid_alt_4.append(-0.5*(((vs_fit_concat_156[i]-vel_data_concat_156[i])**2)/(np.sqrt((np.sqrt(error_data_concat_156[0][i]**2+error_data_concat_156[1][i]**2))**2+2.5**2))**2))\n",
    "    \n",
    "    masked_elements_sum=2*np.sum(sum_masked)\n",
    "    \n",
    "    resid_alt_concat=resid_alt_1+resid_alt_2+resid_alt_3+resid_alt_4\n",
    "    #resid_concat=resid_1+resid_2\n",
    "    \n",
    "  \n",
    "    \n",
    "\n",
    "    n=2*(upper_lim-lower_lim+upper_lim_ortho-lower_lim_ortho+upper_lim_66-lower_lim_66+upper_lim_156-lower_lim_156)\n",
    "    k=6\n",
    "    \n",
    "    print(np.sum(resid_alt_concat)/(n-k-1-masked_elements_sum))\n",
    "    \n",
    "    return np.sum(resid_alt_concat)/(n-k-1-masked_elements_sum)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "comp\n"
     ]
    }
   ],
   "source": [
    "print('comp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "'''The original below'''\n",
    "\n",
    "\n",
    "\n",
    "'''This code is the masked version of a cocone, but now were making an IFU map with spatial\n",
    "direction in arcseconds\n",
    "\n",
    "-->Id like to degrade the IFU image as soon as possible to the resolution/ pixelsize of the observatory\n",
    "-->Make this code functional for MaNGA data\n",
    "'''\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def bicone_construction_cocone(phi,theta,r_t, half_angle,half_angle_2, vel_max):\n",
    "    #MaNGA pixelsize is 2\"/ pixel\n",
    "    #Do we also need a conversion to kpc?\n",
    "    \n",
    "    half_angle_r=math.radians(half_angle)\n",
    "    half_angle_r_2=math.radians(half_angle_2)\n",
    "    #h=[2*x for x in r_t]\n",
    "    h=2*r_t\n",
    "##to get the radius of the cone, you need to do some trig\n",
    "##tan half_angle_r=r/h\n",
    "\n",
    "    r=math.tan(half_angle_r)*h\n",
    "    r_2=math.tan(half_angle_r_2)*h\n",
    "    \n",
    "    theta_para=(np.asarray(np.linspace(0,2*np.pi,50)))\n",
    "    '''you dont want u1 to go all the way to -h unless that PA is totally aligned (PA90)'''\n",
    "  \n",
    "    u1_orig=np.asarray(np.linspace(-h,0,30))+h#was 50\n",
    "    u2_orig=-np.asarray(np.linspace(-h,0,30))-h\n",
    "    u=-np.asarray(np.linspace(-h,0,30))\n",
    "\n",
    "   \n",
    "   \n",
    "    \n",
    "    phi_1=math.radians(phi)\n",
    "    #this is the angle of rotation about x, so inclination\n",
    "    theta_1=math.radians(theta)\n",
    "    \n",
    "    #this is the angle of rotation about y, we don't actually need to rotate about this guy\n",
    "    psi_1=math.radians(0)\n",
    "    #this is the angle of rotation about z, so PA\n",
    "    #R is the rotation matrix\n",
    "    R=np.matrix([[np.cos(theta_1)*np.cos(psi_1),np.cos(phi_1)*np.sin(psi_1)+np.sin(phi_1)*np.sin(theta_1)*np.cos(psi_1),\n",
    "                  np.sin(phi_1)*np.sin(psi_1)-np.cos(phi_1)*np.sin(theta_1)*np.cos(psi_1)],\n",
    "                     [-np.cos(theta_1)*np.sin(psi_1), \n",
    "                      np.cos(phi_1)*np.cos(psi_1)-np.sin(phi_1)*np.sin(theta_1)*np.sin(psi_1),\n",
    "                      np.sin(phi_1)*np.cos(psi_1)+np.cos(phi_1)*np.sin(theta_1)*np.sin(psi_1)],\n",
    "                     [np.sin(theta_1), -np.sin(phi_1)*np.cos(theta_1), np.cos(phi_1)*np.cos(theta_1)]])\n",
    "    #x is going to be the front facing side (blueshifted) and x_2 is the rear cone\n",
    "    x=[]\n",
    "    x_2=[]\n",
    "    y=[]\n",
    "    y_2=[]\n",
    "    z=[]\n",
    "    z_2=[]\n",
    "    z_plane=[]\n",
    "    vel_radial_bottom=[]\n",
    "    vel_radial_top=[]\n",
    "    vel_top=[]\n",
    "    vel_bottom=[]\n",
    "    vel_totes=[]\n",
    "    x_totes=[]\n",
    "    y_totes=[]\n",
    "    z_totes=[]\n",
    "    x_plane=[]\n",
    "    y_plane=[]\n",
    "    z_plane=[]\n",
    "    x_plane_2=[]\n",
    "    y_plane_2=[]\n",
    "    z_plane_2=[]\n",
    "    for i in range(len(theta_para)):\n",
    "        for j in range(len(u)):\n",
    "            if i==0: \n",
    "                i=1\n",
    "            if j==0:\n",
    "                j=1\n",
    "        \n",
    "            #here's the parametric equation for a bicone\n",
    "            X=((h-u[j])*r*np.cos((theta_para[i])))/h\n",
    "            Y=((h-u[j])*r*np.sin((theta_para[i])))/h\n",
    "            \n",
    "            X_2=((h-u[j])*r_2*np.cos((theta_para[i])))/h\n",
    "            Y_2=((h-u[j])*r_2*np.sin((theta_para[i])))/h\n",
    "            \n",
    "            #blue cone above is Z_1\n",
    "            #red cone below is Z_2\n",
    "            Z_1=u2_orig[j]#was u2\n",
    "            Z_2=u1_orig[j]#was u1\n",
    "            \n",
    "            y_plane_into=0\n",
    "            \n",
    "            pos=np.matrix([X,Y,Z_1])\n",
    "            pos_2=np.matrix([X_2,Y_2,Z_2])\n",
    "            #pos_plane=np.matrix([X,y_plane_into,Z_1])\n",
    "            #pos_plane_2=np.matrix([X,y_plane_into, Z_2])\n",
    "            #this is taking and applying the rotation matrix\n",
    "            new_pos=np.dot(R,pos.transpose())\n",
    "            new_pos_2=np.dot(R,pos_2.transpose())\n",
    "           \n",
    "            \n",
    "            \n",
    "            #do that thing:\n",
    "            #equation: d/dw/e((x^2 + y^2)/c^2 = (z-z_0)^2)\n",
    "            #recall c=r/h\n",
    "            #<2*x,2*y,-(2*r^2*z)/h^2>\n",
    "            #plug in everything and normalize and then the z component is your v_rad\n",
    "    \n",
    "            \n",
    "            slope_x=2*np.array(new_pos[0]).ravel()\n",
    "            slope_y=2*np.array(new_pos[1]).ravel()\n",
    "            slope_z=-(2*r**2*np.array(new_pos[2]).ravel())/h**2\n",
    "            \n",
    "            norm=np.sqrt(slope_x**2+slope_y**2+slope_z**2)\n",
    "            '''applying velocity laws for a flat bicone'''\n",
    "            if np.sqrt(np.array(new_pos[0]).ravel()**2+np.array(new_pos[1]).ravel()**2+np.array(new_pos[2]).ravel()**2)<r_t:\n",
    "                #vel_max_top=-(vel_max/r_t)*np.sqrt(new_pos[0]**2+new_pos[1]**2+new_pos[2]**2)\n",
    "                vel_max_top=-vel_max\n",
    "            if np.sqrt(np.array(new_pos[0]).ravel()**2+np.array(new_pos[1]).ravel()**2+np.array(new_pos[2]).ravel()**2)>r_t:\n",
    "                vel_max_top=-(vel_max-(vel_max/r_t)*(np.sqrt(np.array(new_pos[0]).ravel()**2+np.array(new_pos[1]).ravel()**2+np.array(new_pos[2]).ravel()**2)-r_t))\n",
    "            if np.sqrt(np.array(new_pos[0]).ravel()**2+np.array(new_pos[1]).ravel()**2+np.array(new_pos[2]).ravel()**2)<2*r_t:\n",
    "                \n",
    "                x.append(np.array(new_pos[0]).ravel())\n",
    "                y.append(np.array(new_pos[1]).ravel())\n",
    "                z.append(np.array(new_pos[2]).ravel())\n",
    "                A_comp=-(slope_y/norm)\n",
    "                rad_proj=vel_max_top*np.array(A_comp).ravel()\n",
    "                vel_radial_top.append(round(rad_proj[0]))\n",
    "             \n",
    "        \n",
    "    \n",
    "            slope_x=2*np.array(new_pos_2[0]).ravel()\n",
    "            slope_y=2*np.array(new_pos_2[1]).ravel()\n",
    "            \n",
    "            slope_z_2=-(2*r_2**2*np.array(new_pos_2[2]).ravel())/h**2\n",
    "            norm=np.sqrt(slope_x**2+slope_y**2+slope_z_2**2)\n",
    "            \n",
    "            if np.sqrt(np.array(new_pos_2[0]).ravel()**2+np.array(new_pos_2[1]).ravel()**2+np.array(new_pos_2[2]).ravel()**2)<r_t:\n",
    "#                vel_max_bottom=-(vel_max/r_t)*np.sqrt(new_pos_2[0]**2+new_pos_2[1]**2+new_pos_2[2]**2)\n",
    "                vel_max_bottom=-vel_max\n",
    "            if np.sqrt(np.array(new_pos_2[0]).ravel()**2+np.array(new_pos_2[1]).ravel()**2+np.array(new_pos_2[2]).ravel()**2)>r_t:\n",
    "                \n",
    "                vel_max_bottom=-(vel_max-(vel_max/r_t)*(np.sqrt(np.array(new_pos[0]).ravel()**2+np.array(new_pos[1]).ravel()**2+np.array(new_pos[2]).ravel()**2)-r_t))\n",
    "            if np.sqrt(np.array(new_pos_2[0]).ravel()**2+np.array(new_pos_2[1]).ravel()**2+np.array(new_pos_2[2]).ravel()**2)<2*r_t:\n",
    "                \n",
    "                x_2.append(np.array(new_pos_2[0]).ravel())\n",
    "                y_2.append(np.array(new_pos_2[1]).ravel())\n",
    "                z_2.append(np.array(new_pos_2[2]).ravel())\n",
    "                #A dot x should give us the vector projection along x - divide by the magnitude of A to get cos (theta)\n",
    "                A_comp=-(slope_y/norm)\n",
    "                #vel_bottom.append(vel_max_bottom)\n",
    "                rad_proj_2=np.array(A_comp).ravel()*vel_max_bottom\n",
    "                vel_radial_bottom.append(round(rad_proj_2[0]))\n",
    "         \n",
    "    merged_x=x+x_2\n",
    "    merged_y=y+y_2\n",
    "    merged_z=z+z_2\n",
    "    \n",
    "    cone_red_x=x_2\n",
    "    cone_red_y=y_2\n",
    "    cone_red_z=z_2\n",
    "    \n",
    "    cone_blue_x=x\n",
    "    cone_blue_y=y\n",
    "    cone_blue_z=z\n",
    "    \n",
    "    merged_x_plane=x_plane+x_plane_2\n",
    "    merged_y_plane=y_plane+y_plane_2\n",
    "    merged_z_plane=z_plane+z_plane_2\n",
    "    \n",
    "    \n",
    "    merged_vel=vel_radial_top+vel_radial_bottom\n",
    "    \n",
    "    cone_red_vel=vel_radial_bottom\n",
    "    cone_blue_vel=vel_radial_top\n",
    "    \n",
    "    \n",
    "\n",
    "    plt.scatter(cone_red_x, cone_red_z, c=cone_red_vel, cmap='Reds')\n",
    "    plt.colorbar()\n",
    "    plt.title('Red Cone')\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "    plt.scatter(cone_blue_x, cone_blue_z, c=cone_blue_vel, cmap='Blues')\n",
    "    plt.colorbar()\n",
    "    plt.title('Blue Cone')\n",
    "    plt.show()\n",
    "    \n",
    "    plt.scatter(merged_x, merged_z, c=merged_vel, cmap='rainbow')\n",
    "    plt.colorbar()\n",
    "    plt.title('Merged Cone')\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "    return merged_x, merged_y, merged_z, merged_vel, merged_x_plane, merged_y_plane, merged_z_plane, cone_red_x, cone_red_y, cone_red_z, cone_blue_x, cone_blue_y, cone_blue_z, cone_red_vel, cone_blue_vel\n",
    "                \n",
    "    \n",
    "    \n",
    "    \n",
    "                        \n",
    "def lnlike_cocone(z):\n",
    "    \n",
    "    \n",
    "    #bicone_construction(h,phi,theta,r_t, half_angle )\n",
    "    phi_array, theta_array, thetados_array, r_t,half_angle, vel_max_array=z\n",
    "    \n",
    "    \n",
    "    h=2*r_t\n",
    "    #h=[2*x for x in r_t]\n",
    "    \n",
    "    out=bicone_construction_cocone(phi_array, theta_array,thetados_array, r_t, half_angle, vel_max_array)\n",
    "    \n",
    "    merged_x=[float(y) for y in out[0]]\n",
    "    merged_y=[float(y) for y in out[1]]\n",
    "    merged_z=[float(y) for y in out[2]]\n",
    "    merged_vel=[float(y) for y in out[3]]\n",
    "    merged_x_plane=[float(y) for y in out[4]]\n",
    "    merged_y_plane=[float(y) for y in out[5]]\n",
    "    merged_z_plane=[float(y) for y in out[6]]\n",
    "    \n",
    "    cone_red_x=[float(y) for y in out[7]]\n",
    "    cone_red_y=[float(y) for y in out[8]]\n",
    "    cone_red_z=[float(y) for y in out[9]]\n",
    "    cone_blue_x=[float(y) for y in out[10]]\n",
    "    cone_blue_y=[float(y) for y in out[11]]\n",
    "    cone_blue_z=[float(y) for y in out[12]]\n",
    "    \n",
    "    cone_red_vel=[float(y) for y in out[13]]\n",
    "    cone_blue_vel=[float(y) for y in out[14]]\n",
    "    ##vel_radial_top actually refers to the stuff going away from us on the bottom -ugh\n",
    "    #it's the projection looking down in z\n",
    "    #p=ax.scatter(x_2,y_2,z_2, c=vel_radial_bottom)\n",
    "    \n",
    "    \n",
    "    #%```````````````````````````````````\n",
    "    #I'm going to make the phi cuts according to observe PAs\n",
    "    PA_obs_1=np.radians(PA1+90)\n",
    "    PA_obs_2=np.radians(PA2+90)\n",
    "    PA_obs_3=np.radians(PA3+90)\n",
    "    PA_obs_4=np.radians(PA4+90)\n",
    " \n",
    "    x_slit_blue=np.linspace(-100*h,100*h, len(cone_blue_z))\n",
    "    x_slit_red=np.linspace(-100*h,100*h,len(cone_red_z))\n",
    "    \n",
    "    len_pts=max(upper_lim-lower_lim,upper_lim_ortho-lower_lim_ortho)\n",
    "    \n",
    "    #height is based upon the length of the overall data we have on the bicone and the slitwidth\n",
    "    height=slitwidth/pixelscale#(slitwidth/(pixelscale*len_pts))#*(h)#I kinda want this to be a fraction of something else#10#was 4\n",
    "    \n",
    "    if PA1 < 90:\n",
    "        z_slit_1_upper_red=[x*np.tan(PA_obs_1)-height/np.cos(PA_obs_1) for x in cone_red_x]\n",
    "        z_slit_1_lower_red=[x*np.tan(PA_obs_1)+height/np.cos(PA_obs_1) for x in cone_red_x]\n",
    "        z_slit_1_upper_blue=[x*np.tan(PA_obs_1)-height/np.cos(PA_obs_1) for x in cone_blue_x]\n",
    "        z_slit_1_lower_blue=[x*np.tan(PA_obs_1)+height/np.cos(PA_obs_1) for x in cone_blue_x]\n",
    "    else:\n",
    "        z_slit_1_upper_red=[x*np.tan(PA_obs_1)+height/np.cos(PA_obs_1) for x in cone_red_x]\n",
    "        z_slit_1_lower_red=[x*np.tan(PA_obs_1)-height/np.cos(PA_obs_1) for x in cone_red_x]\n",
    "        z_slit_1_upper_blue=[x*np.tan(PA_obs_1)+height/np.cos(PA_obs_1) for x in cone_blue_x]\n",
    "        z_slit_1_lower_blue=[x*np.tan(PA_obs_1)-height/np.cos(PA_obs_1) for x in cone_blue_x]\n",
    "        \n",
    "    if PA2 > 90:\n",
    "        z_slit_2_upper_red=[x*np.tan(PA_obs_2)-height/np.cos(PA_obs_2) for x in cone_red_x]\n",
    "        z_slit_2_lower_red=[x*np.tan(PA_obs_2)+height/np.cos(PA_obs_2) for x in cone_red_x]\n",
    "        z_slit_2_upper_blue=[x*np.tan(PA_obs_2)-height/np.cos(PA_obs_2) for x in cone_blue_x]\n",
    "        z_slit_2_lower_blue=[x*np.tan(PA_obs_2)+height/np.cos(PA_obs_2) for x in cone_blue_x]\n",
    "    else: \n",
    "        z_slit_2_upper_red=[x*np.tan(PA_obs_2)-height/np.cos(PA_obs_2) for x in cone_red_x]\n",
    "        z_slit_2_lower_red=[x*np.tan(PA_obs_2)+height/np.cos(PA_obs_2) for x in cone_red_x]\n",
    "        z_slit_2_upper_blue=[x*np.tan(PA_obs_2)-height/np.cos(PA_obs_2) for x in cone_blue_x]\n",
    "        z_slit_2_lower_blue=[x*np.tan(PA_obs_2)+height/np.cos(PA_obs_2) for x in cone_blue_x]\n",
    "    \n",
    "    plt.clf()\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    p1=ax.scatter(cone_red_x, cone_red_z, c=cone_red_vel, edgecolors='none',  cmap=cm.Reds, vmin=0)\n",
    "    cbb=fig.colorbar(p1)#, shrink=0.5)\n",
    "    \n",
    "    \n",
    "    p=ax.scatter(cone_blue_x, cone_blue_z,c=cone_blue_vel,edgecolors='none',  cmap=cm.winter, vmax=0)\n",
    "    cba=fig.colorbar(p)#,shrink=0.5)\n",
    "    \n",
    "    \n",
    "    #p3=ax.plot(x_slit,z_slit_1, color='red', label='PA1 = '+str(PA1))\n",
    "    #p4=ax.plot(x_slit,z_slit_2, color='blue', label='PA2 = '+str(PA2))\n",
    "   \n",
    "    '''p5=ax.plot(cone_blue_x,z_slit_1_upper_blue, color='black', ls='-.', label='PA 1 = '+str(PA1+add))\n",
    "    p6=ax.plot(cone_blue_x,z_slit_1_lower_blue, color='black', ls='-.')\n",
    "    p7=ax.plot(cone_blue_x,z_slit_2_upper_blue, color='black', ls='-', label='PA 2 = '+str(PA2+add))\n",
    "    p8=ax.plot(cone_blue_x,z_slit_2_lower_blue, color='black', ls='-')'''\n",
    "    \n",
    "    #p9=ax.plot(cone_red_x,z_slit_1_upper_red, color='black', ls='-.')#, label='PA1 = '+str(PA1))\n",
    "    #p10=ax.plot(cone_red_x,z_slit_1_lower_red, color='black', ls='-.')\n",
    "    #p11=ax.plot(cone_red_x,z_slit_2_upper_red, color='black', ls='-')#, label='PA2 = '+str(PA2))\n",
    "    #p12=ax.plot(cone_red_x,z_slit_2_lower_red, color='black', ls='-')\n",
    "    plt.xlim([-h/4,h/4])\n",
    "    plt.ylim([-h/4,h/4])\n",
    "    plt.legend()\n",
    "    #fig.colorbar(p)\n",
    "    \n",
    "    cba.set_label('Blueshifted Cone')\n",
    "    cbb.set_label('Redshifted Cone')\n",
    "    \n",
    "    savefig('../cocone_J0930.pdf')\n",
    "    \n",
    "    inds_1=np.logical_and((np.asarray(cone_blue_z) > np.asarray(z_slit_1_lower_blue)), (np.asarray(cone_blue_z) < np.asarray(z_slit_1_upper_blue)))\n",
    "    inds_1_below=np.logical_and((np.asarray(cone_red_z) > np.asarray(z_slit_1_lower_red)), (np.asarray(cone_red_z) < np.asarray(z_slit_1_upper_red)))\n",
    "    \n",
    "    inds_2=np.logical_and((np.asarray(cone_blue_z) > np.asarray(z_slit_2_lower_blue)), (np.asarray(cone_blue_z) < np.asarray(z_slit_2_upper_blue)))\n",
    "    inds_2_below=np.logical_and((np.asarray(cone_red_z) > np.asarray(z_slit_2_lower_red)), (np.asarray(cone_red_z) < np.asarray(z_slit_2_upper_red)))\n",
    "    \n",
    "\n",
    "    '''xs_cont_1 is the blueshifted cone of PA1'''\n",
    "    xs_cont_1=np.asarray(cone_blue_x)[np.asarray(inds_1)]\n",
    "    ys_cont_1=np.asarray(cone_blue_y)[np.asarray(inds_1)]\n",
    "    zs_cont_1=np.asarray(cone_blue_z)[np.asarray(inds_1)]\n",
    "    vel_cont_1=np.asarray(cone_blue_vel)[np.asarray(inds_1)]\n",
    "    \n",
    "    '''xs_cont_1_below is the redshifted cone of PA1'''\n",
    "    xs_cont_1_below=np.asarray(cone_red_x)[np.asarray(inds_1_below)]\n",
    "    ys_cont_1_below=np.asarray(cone_red_y)[np.asarray(inds_1_below)]\n",
    "    zs_cont_1_below=np.asarray(cone_red_z)[np.asarray(inds_1_below)]\n",
    "    vel_cont_1_below=np.asarray(cone_red_vel)[np.asarray(inds_1_below)]\n",
    "    \n",
    "    xs_cont_2=np.asarray(cone_blue_x)[np.asarray(inds_2)]\n",
    "    ys_cont_2=np.asarray(cone_blue_y)[np.asarray(inds_2)]\n",
    "    zs_cont_2=np.asarray(cone_blue_z)[np.asarray(inds_2)]\n",
    "    vel_cont_2=np.asarray(cone_blue_vel)[np.asarray(inds_2)]\n",
    "    \n",
    "    xs_cont_2_below=np.asarray(cone_red_x)[np.asarray(inds_2_below)]\n",
    "    ys_cont_2_below=np.asarray(cone_red_y)[np.asarray(inds_2_below)]\n",
    "    zs_cont_2_below=np.asarray(cone_red_z)[np.asarray(inds_2_below)]\n",
    "    vel_cont_2_below=np.asarray(cone_red_vel)[np.asarray(inds_2_below)]\n",
    "    \n",
    "    fig=plt.figure()\n",
    "    ax=fig.add_subplot(221)\n",
    "    ax.annotate('Blue Cone PA 1', xy=(0.01,0.1),xycoords='axes fraction')\n",
    "    p=ax.scatter(xs_cont_1, zs_cont_1, c=vel_cont_1, edgecolors='none', cmap=cm.winter)\n",
    "    ax.set_xlim([-h/4,h/4])\n",
    "    ax.set_ylim([-h/4,h/4])\n",
    "    #p1=ax.scatter(xs_cont_2, zs_cont_2, c=vel_cont_2, edgecolors='none', cmap=cm.rainbow)\n",
    "    cbaa0=fig.colorbar(p)\n",
    "    #cbbb=fig.colorbar(p1)\n",
    "    \n",
    "    ax1=fig.add_subplot(222)\n",
    "    ax1.annotate('Blue Cone PA 2', xy=(0.01,0.1),xycoords='axes fraction')\n",
    "    #p=ax.scatter(xs_cont_1, zs_cont_1, c=vel_cont_1, edgecolors='none', cmap=cm.rainbow)\n",
    "    p1=ax1.scatter(xs_cont_2, zs_cont_2, c=vel_cont_2, edgecolors='none', cmap=cm.winter)\n",
    "    cbaa1=fig.colorbar(p1)\n",
    "    #cbbb=fig.colorbar(p1, shrink=0.25)\n",
    "    ax1.set_xlim([-h/4,h/4])\n",
    "    ax1.set_ylim([-h/4,h/4])\n",
    "    \n",
    "    ax2=fig.add_subplot(223)\n",
    "    ax2.annotate('Red Cone PA 1', xy=(0.01,0.1),xycoords='axes fraction')\n",
    "    #p=ax.scatter(xs_cont_1, zs_cont_1, c=vel_cont_1, edgecolors='none', cmap=cm.rainbow)\n",
    "    p2=ax2.scatter(xs_cont_1_below, zs_cont_1_below, c=vel_cont_1_below, edgecolors='none', cmap=cm.Reds)\n",
    "    cbaa2=fig.colorbar(p2)\n",
    "    #cbbb=fig.colorbar(p1, shrink=0.25)\n",
    "    ax2.set_xlim([-h/4,h/4])\n",
    "    ax2.set_ylim([-h/4,h/4])\n",
    "    \n",
    "    ax3=fig.add_subplot(224)\n",
    "    ax3.annotate('Red Cone PA 2', xy=(0.01,0.1),xycoords='axes fraction')\n",
    "    #p=ax.scatter(xs_cont_1, zs_cont_1, c=vel_cont_1, edgecolors='none', cmap=cm.rainbow)\n",
    "    p3=ax3.scatter(xs_cont_2_below, zs_cont_2_below, c=vel_cont_2_below, edgecolors='none', cmap=cm.Reds)\n",
    "    cbaa3=fig.colorbar(p3)\n",
    "    #cbbb=fig.colorbar(p1, shrink=0.25)\n",
    "    ax3.set_xlim([-h/4,h/4])\n",
    "    ax3.set_ylim([-h/4,h/4])\n",
    "    fig.tight_layout()\n",
    "    savefig('../cocone_2_J0930.pdf')\n",
    "    \n",
    "    fig=plt.figure()\n",
    "    \n",
    "    ax1 = fig.add_subplot(111)\n",
    "    #p=ax.scatter(merged_x,merged_z,c=merged_vel,edgecolors='none')\n",
    "    \n",
    "    p=ax1.scatter(cone_red_x, cone_red_z, c=cone_red_vel, edgecolors='none',cmap=cm.Reds)\n",
    "    cba=fig.colorbar(p)\n",
    "    p1=ax1.scatter(cone_blue_x, cone_blue_z, c=cone_blue_vel, edgecolors='none', cmap=cm.winter)\n",
    "    cbb=fig.colorbar(p1)\n",
    "    plt.xlim([-h/4,h/4])\n",
    "    plt.ylim([-h/4,h/4])\n",
    "    #p1=ax.plot(merged_x,z_slit_1, c='R')\n",
    "    #p2=ax.plot(merged_x, z_slit_2, c='G')\n",
    "    #p3=ax.plot(merged_x,z_slit_2_upper)\n",
    "    #p4=ax.plot(merged_x,z_slit_2_lower)\n",
    "    #p5=ax.plot(merged_x,z_slit_1_upper)\n",
    "    #p6=ax.plot(merged_x,z_slit_1_lower)\n",
    "    \n",
    "    cba.set_label('Redshifted Cone')\n",
    "    cbb.set_label('Blueshifted Cone')\n",
    "    \n",
    "    \n",
    "    \n",
    "    '''Now its time to slice things up'''\n",
    "    \n",
    "    r_slit_1_above=[]\n",
    "    v_slit_1_above=[]\n",
    "    r_slit_2_above=[]\n",
    "    v_slit_2_above=[]\n",
    "    r_slit_1_below=[]\n",
    "    v_slit_1_below=[]\n",
    "    r_slit_2_below=[]\n",
    "    v_slit_2_below=[]\n",
    "    \n",
    "    \n",
    "    '''stepping needs to be pixelscale'''\n",
    "    '''slitwidth is height'''\n",
    "    '''so if were already stepping by the spatial direction just make all of these rows your data points'''\n",
    "  \n",
    "    stepping=1\n",
    "    length_data=len(vel_1_gauss)/2\n",
    "    \n",
    "    length=max(abs(length_data-upper_lim),abs(length_data-lower_lim),abs(20-upper_lim_ortho),abs(20-lower_lim_ortho))\n",
    "    \n",
    "    for j in range(length):\n",
    "        #\n",
    "        width=stepping*j\n",
    "        z_1_perp_upper=[-x*(1/np.tan(PA_obs_1))+(width+stepping+0.5)*np.sin(PA_obs_1+np.pi/2) for x in xs_cont_1]\n",
    "        z_1_perp_lower=[-x*(1/np.tan(PA_obs_1))+(width+0.5)*np.sin(PA_obs_1+np.pi/2) for x in xs_cont_1]\n",
    "        z_1_perpl_upper=[-x*(1/np.tan(PA_obs_1))-(width+0.5)*np.sin(PA_obs_1+np.pi/2) for x in xs_cont_1]\n",
    "        z_1_perpl_lower=[-x*(1/np.tan(PA_obs_1))-(width+stepping+0.5)*np.sin(PA_obs_1+np.pi/2) for x in xs_cont_1]\n",
    "        \n",
    "        #if j==0:\n",
    "        \n",
    "        #order of lines top to bottom: z_1_perpl_upper, z1_perpl_lower\n",
    "        #z_1_perp_lower, z_1_perp_upper\n",
    "        #so lowers are closer to the center\n",
    "        \n",
    "        pos_1=np.logical_and((np.asarray(zs_cont_1) > np.asarray(z_1_perp_upper)), (np.asarray(zs_cont_1) < np.asarray(z_1_perp_lower)))\n",
    "        pos_1_1=np.logical_and((np.asarray(zs_cont_1) > np.asarray(z_1_perpl_upper)), (np.asarray(zs_cont_1) < np.asarray(z_1_perpl_lower)))\n",
    "        pos_1_center=np.logical_and((np.asarray(zs_cont_1) < np.asarray(z_1_perpl_upper)), (np.asarray(zs_cont_1) > np.asarray(z_1_perp_lower)))\n",
    "        plt.plot(xs_cont_1, z_1_perp_lower, color='black')#, label='z_1_perp_lower',lw=4,ls='--', color='red')\n",
    "        plt.plot(xs_cont_1, z_1_perpl_upper, color='black') #label='z_1_perpl_upper',lw=4, color='pink')\n",
    "        if j==0:\n",
    "            #plt.plot(xs_cont_2, z_2_perp_upper, label='z_1_perp_upper',lw=4, color='black')\n",
    "            plt.plot(xs_cont_1, z_1_perp_lower, label='z_1_perp_lower',lw=4,ls='--', color='red')\n",
    "            plt.plot(xs_cont_1, z_1_perpl_upper, label='z_1_perpl_upper',lw=4, color='pink')\n",
    "            #plt.plot(xs_cont_2, z_2_perpl_lower, label='z_1_perpl_lower',lw=4,ls='--', color='pink')\n",
    "        \n",
    "            plt.plot(cone_blue_x,z_slit_1_upper_blue, color='black', ls='-.', label='PA1 = '+str(PA1))\n",
    "            plt.plot(cone_blue_x,z_slit_1_lower_blue, color='black', ls='-.')\n",
    "            plt.plot(cone_blue_x,z_slit_2_upper_blue, color='black', ls='-', label='PA2 = '+str(PA2))\n",
    "            plt.plot(cone_blue_x,z_slit_2_lower_blue, color='black', ls='-')\n",
    "            #plt.show()\n",
    "        \n",
    "        if j==0:\n",
    "            xs_cont_slit_middle=np.asarray(xs_cont_1)[np.asarray(pos_1_center)]\n",
    "            zs_cont_slit_middle=np.asarray(zs_cont_1)[np.asarray(pos_1_center)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit_middle)**2+np.mean(zs_cont_slit_middle)**2))==True:\n",
    "                r_slit_1_above.append(j)\n",
    "                v_slit_1_above.append(0)\n",
    "                \n",
    "            else:\n",
    "                #so this is the positions to the North so we'll make them negative\n",
    "                r_slit_1_above.append(j)\n",
    "                v_slit_1_above.append(np.mean(np.asarray(vel_cont_1)[np.asarray(pos_1_center)]))\n",
    "        else:\n",
    "            xs_cont_slit=np.asarray(xs_cont_1)[np.asarray(pos_1)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_1)[np.asarray(pos_1)]\n",
    "            \n",
    "            \n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))==True:\n",
    "                \n",
    "                r_slit_1_above.append(j)\n",
    "                v_slit_1_above.append(100000)\n",
    "            else:\n",
    "                #so this is the positions to the North so we'll make them negative\n",
    "                r_slit_1_above.append(j)\n",
    "                v_slit_1_above.append(np.mean(np.asarray(vel_cont_1)[np.asarray(pos_1)]))\n",
    "                #r_slit_1_above.append(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))\n",
    "                #v_slit_1_above.append(np.mean(np.asarray(vel_cont_1)[np.asarray(pos_1)]))\n",
    "            \n",
    "            \n",
    "            xs_cont_slit=np.asarray(xs_cont_1)[np.asarray(pos_1_1)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_1)[np.asarray(pos_1_1)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))==True:\n",
    "                \n",
    "                r_slit_1_above.append(-j)\n",
    "                v_slit_1_above.append(100000)\n",
    "            else:\n",
    "                r_slit_1_above.append(-j)\n",
    "                #r_slit_1_above.append(-np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))\n",
    "                v_slit_1_above.append(np.mean(np.asarray(vel_cont_1)[np.asarray(pos_1_1)]))\n",
    "         \n",
    "        z_1_perp_upper=[-x*(1/np.tan(PA_obs_1))+(width+stepping+0.5)*np.sin(PA_obs_1+np.pi/2) for x in xs_cont_1_below]\n",
    "        z_1_perp_lower=[-x*(1/np.tan(PA_obs_1))+(width+0.5)*np.sin(PA_obs_1+np.pi/2) for x in xs_cont_1_below]\n",
    "        z_1_perpl_upper=[-x*(1/np.tan(PA_obs_1))-(width+0.5)*np.sin(PA_obs_1+np.pi/2) for x in xs_cont_1_below]\n",
    "        z_1_perpl_lower=[-x*(1/np.tan(PA_obs_1))-(width+stepping+0.5)*np.sin(PA_obs_1+np.pi/2) for x in xs_cont_1_below]\n",
    "        \n",
    "        pos_1_below=np.logical_and((np.asarray(zs_cont_1_below) > np.asarray(z_1_perp_upper)), (np.asarray(zs_cont_1_below) < np.asarray(z_1_perp_lower)))\n",
    "        pos_1_below_1=np.logical_and((np.asarray(zs_cont_1_below) > np.asarray(z_1_perpl_upper)), (np.asarray(zs_cont_1_below) < np.asarray(z_1_perpl_lower)))\n",
    "        pos_1_below_center=np.logical_and((np.asarray(zs_cont_1_below) < np.asarray(z_1_perpl_upper)), (np.asarray(zs_cont_1_below) > np.asarray(z_1_perp_lower)))\n",
    "     \n",
    "        if j==0:\n",
    "            xs_cont_slit_middle=np.asarray(xs_cont_1_below)[np.asarray(pos_1_below_center)]\n",
    "            zs_cont_slit_middle=np.asarray(zs_cont_1_below)[np.asarray(pos_1_below_center)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit_middle)**2+np.mean(zs_cont_slit_middle)**2))==True:\n",
    "                r_slit_1_below.append(j)\n",
    "                v_slit_1_below.append(0)\n",
    "                \n",
    "            else:\n",
    "                #so this is the positions to the North so we'll make them negative\n",
    "                r_slit_1_below.append(j)\n",
    "                v_slit_1_below.append(np.mean(np.asarray(vel_cont_1_below)[np.asarray(pos_1_below_center)]))\n",
    "        else:\n",
    "        \n",
    "            xs_cont_slit=np.asarray(xs_cont_1_below)[np.asarray(pos_1_below)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_1_below)[np.asarray(pos_1_below)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))==True:\n",
    "                r_slit_1_below.append(-j)\n",
    "                v_slit_1_below.append(100000)\n",
    "                \n",
    "            else:\n",
    "                r_slit_1_below.append(j)\n",
    "                v_slit_1_below.append(np.mean(np.asarray(vel_cont_1_below)[np.asarray(pos_1_below)]))\n",
    "            \n",
    "            xs_cont_slit=np.asarray(xs_cont_1_below)[np.asarray(pos_1_below_1)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_1_below)[np.asarray(pos_1_below_1)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2)):\n",
    "                r_slit_1_below.append(-j)\n",
    "                v_slit_1_below.append(100000)\n",
    "                \n",
    "            else:\n",
    "                r_slit_1_below.append(-j)\n",
    "                v_slit_1_below.append(np.mean(np.asarray(vel_cont_1_below)[np.asarray(pos_1_below_1)]))\n",
    "        \n",
    "        z_2_perp_upper=[-x*(1/np.tan(PA_obs_2))+(width+stepping+0.5)*np.sin(PA_obs_2+np.pi/2) for x in xs_cont_2]\n",
    "        z_2_perp_lower=[-x*(1/np.tan(PA_obs_2))+(width+0.5)*np.sin(PA_obs_2+np.pi/2) for x in xs_cont_2]\n",
    "        z_2_perpl_upper=[-x*(1/np.tan(PA_obs_2))-(width+0.5)*np.sin(PA_obs_2+np.pi/2) for x in xs_cont_2]\n",
    "        z_2_perpl_lower=[-x*(1/np.tan(PA_obs_2))-(width+stepping+0.5)*np.sin(PA_obs_2+np.pi/2) for x in xs_cont_2]\n",
    "        \n",
    "        pos_1_2=np.logical_and((np.asarray(zs_cont_2) > np.asarray(z_2_perp_upper)), (np.asarray(zs_cont_2) < np.asarray(z_2_perp_lower)))\n",
    "        pos_1_3=np.logical_and((np.asarray(zs_cont_2) > np.asarray(z_2_perpl_upper)), (np.asarray(zs_cont_2)< np.asarray(z_2_perpl_lower)))\n",
    "        \n",
    "        plt.plot(xs_cont_2, z_2_perp_lower, color='black')#, label='z_1_perp_lower',lw=4,ls='--', color='red')\n",
    "        plt.plot(xs_cont_2, z_2_perpl_upper, color='black') #label='z_1_perpl_upper',lw=4, color='pink')\n",
    "        pos_1_2_center=np.logical_and((np.asarray(zs_cont_2) < np.asarray(z_2_perpl_upper)), (np.asarray(zs_cont_2) > np.asarray(z_2_perp_lower)))\n",
    "        if j==0:\n",
    "            #plt.plot(xs_cont_2, z_2_perp_upper, label='z_1_perp_upper',lw=4, color='black')\n",
    "            plt.plot(xs_cont_2, z_2_perp_lower, label='z_2_perp_lower',lw=4,ls='--', color='red')\n",
    "            plt.plot(xs_cont_2, z_2_perpl_upper, label='z_2_perpl_upper',lw=4, color='pink')\n",
    "            #plt.show()\n",
    "        if j==0:\n",
    "            xs_cont_slit_middle=np.asarray(xs_cont_2)[np.asarray(pos_1_2_center)]\n",
    "            zs_cont_slit_middle=np.asarray(zs_cont_2)[np.asarray(pos_1_2_center)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit_middle)**2+np.mean(zs_cont_slit_middle)**2))==True:\n",
    "                \n",
    "                r_slit_2_above.append(j)\n",
    "                v_slit_2_above.append(0)\n",
    "                \n",
    "            else:\n",
    "                #so this is the positions to the North so we'll make them negative\n",
    "                r_slit_2_above.append(j)\n",
    "                v_slit_2_above.append(np.mean(np.asarray(vel_cont_2)[np.asarray(pos_1_2_center)]))\n",
    "        else:\n",
    "        \n",
    "            xs_cont_slit=np.asarray(xs_cont_2)[np.asarray(pos_1_2)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_2)[np.asarray(pos_1_2)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))==True:\n",
    "                r_slit_2_above.append(j)\n",
    "                v_slit_2_above.append(100000)\n",
    "               \n",
    "            else:\n",
    "                r_slit_2_above.append(j)\n",
    "                v_slit_2_above.append(np.mean(np.asarray(vel_cont_2)[np.asarray(pos_1_2)]))\n",
    "                \n",
    "             \n",
    "            xs_cont_slit=np.asarray(xs_cont_2)[np.asarray(pos_1_3)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_2)[np.asarray(pos_1_3)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))==True:\n",
    "                \n",
    "                r_slit_2_above.append(-j)\n",
    "                v_slit_2_above.append(100000)\n",
    "                \n",
    "            else:\n",
    "                r_slit_2_above.append(-j)\n",
    "                v_slit_2_above.append(np.mean(np.asarray(vel_cont_2)[np.asarray(pos_1_3)]))\n",
    "            #math.isnan(np.mean(np.asarray(vel_cont_2)[np.asarray(pos_1_3)])):\n",
    "       \n",
    "           \n",
    "            \n",
    "        z_2_perp_upper=[-x*(1/np.tan(PA_obs_2))+(width+stepping+0.5)*np.sin(PA_obs_2+np.pi/2) for x in xs_cont_2_below]\n",
    "        z_2_perp_lower=[-x*(1/np.tan(PA_obs_2))+(width+0.5)*np.sin(PA_obs_2+np.pi/2) for x in xs_cont_2_below]\n",
    "        z_2_perpl_upper=[-x*(1/np.tan(PA_obs_2))-(width+0.5)*np.sin(PA_obs_2+np.pi/2) for x in xs_cont_2_below]\n",
    "        z_2_perpl_lower=[-x*(1/np.tan(PA_obs_2))-(width+stepping+0.5)*np.sin(PA_obs_2+np.pi/2) for x in xs_cont_2_below]\n",
    "        pos_1_4=np.logical_and((np.asarray(zs_cont_2_below) > np.asarray(z_2_perp_upper)), (np.asarray(zs_cont_2_below) < np.asarray(z_2_perp_lower)))\n",
    "        pos_1_5=np.logical_and((np.asarray(zs_cont_2_below) > np.asarray(z_2_perpl_upper)), (np.asarray(zs_cont_2_below) < np.asarray(z_2_perpl_lower)))\n",
    "        pos_1_4_center=np.logical_and((np.asarray(zs_cont_2_below) < np.asarray(z_2_perpl_upper)), (np.asarray(zs_cont_2_below) > np.asarray(z_2_perp_lower)))\n",
    "     \n",
    "        \n",
    "        if j==0:\n",
    "            xs_cont_slit_middle=np.asarray(xs_cont_2_below)[np.asarray(pos_1_4_center)]\n",
    "            zs_cont_slit_middle=np.asarray(zs_cont_2_below)[np.asarray(pos_1_4_center)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit_middle)**2+np.mean(zs_cont_slit_middle)**2))==True:\n",
    "                \n",
    "                r_slit_2_below.append(j)\n",
    "                v_slit_2_below.append(0)\n",
    "            else:\n",
    "                #so this is the positions to the North so we'll make them negative\n",
    "                r_slit_2_below.append(j)\n",
    "                v_slit_2_below.append(np.mean(np.asarray(vel_cont_2_below)[np.asarray(pos_1_4_center)]))\n",
    "        else:\n",
    "        \n",
    "            xs_cont_slit=np.asarray(xs_cont_2_below)[np.asarray(pos_1_4)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_2_below)[np.asarray(pos_1_4)]\n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))==True:\n",
    "                r_slit_2_below.append(j)\n",
    "                v_slit_2_below.append(100000)\n",
    "                \n",
    "            else:\n",
    "                r_slit_2_below.append(j)\n",
    "                v_slit_2_below.append(np.mean(np.asarray(vel_cont_2_below)[np.asarray(pos_1_4)]))\n",
    "                \n",
    "            \n",
    "            xs_cont_slit=np.asarray(xs_cont_2_below)[np.asarray(pos_1_5)]\n",
    "            zs_cont_slit=np.asarray(zs_cont_2_below)[np.asarray(pos_1_5)]\n",
    "            \n",
    "            if math.isnan(np.sqrt(np.mean(xs_cont_slit)**2+np.mean(zs_cont_slit)**2))==True:\n",
    "                \n",
    "                r_slit_2_below.append(-j)\n",
    "                v_slit_2_below.append(100000)\n",
    "                \n",
    "            else:\n",
    "                r_slit_2_below.append(-j)\n",
    "                v_slit_2_below.append(np.mean(np.asarray(vel_cont_2_below)[np.asarray(pos_1_5)]))\n",
    "        \n",
    "    \n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "    plt.scatter(r_slit_1_above, v_slit_1_above, label='Slit 1 Above', color='orange')\n",
    "    plt.scatter(r_slit_1_below, v_slit_1_below, label='Slit 1 Below', color='pink')\n",
    "    plt.scatter(r_slit_2_above, v_slit_2_above, label='Slit 2 Above', color='blue')\n",
    "    plt.scatter(r_slit_2_below, v_slit_2_below, label='Slit 2 Below', color='purple')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    " \n",
    "    inds = np.array(r_slit_1_above).argsort()\n",
    "    sortedx_1_above = np.array(r_slit_1_above)[inds]-minus_one\n",
    "    sortedv_1_above = np.array(v_slit_1_above)[inds]\n",
    "    \n",
    "    inds = np.array(r_slit_1_below).argsort()\n",
    "    sortedx_1_below = np.array(r_slit_1_below)[inds]-minus_one\n",
    "    sortedv_1_below = np.array(v_slit_1_below)[inds]\n",
    "    \n",
    "    inds = np.array(r_slit_2_above).argsort()\n",
    "    sortedx_2_above = np.array(r_slit_2_above)[inds]-minus_one\n",
    "    sortedv_2_above = np.array(v_slit_2_above)[inds]\n",
    "    \n",
    "    inds = np.array(r_slit_2_below).argsort()\n",
    "    sortedx_2_below = np.array(r_slit_2_below)[inds]-minus_one\n",
    "    sortedv_2_below = np.array(v_slit_2_below)[inds]\n",
    "    \n",
    "    \n",
    "    \n",
    "    xs_fit_concat=np.concatenate([sortedx_1_below[low_1:up_1],sortedx_1_above[low_1:up_1]])#xs_fit_bin+xs_fit_bin_below\n",
    "    vs_fit_concat=np.concatenate([sortedv_1_below[low_1:up_1],sortedv_1_above[low_1:up_1]])#vs_fit_bin+vs_fit_bin_below\n",
    "    xs_fit_concat_ortho=np.concatenate([sortedx_2_below[low_2:up_2],sortedx_2_above[low_2:up_2]])#xs_fit_ortho_bin+xs_fit_ortho_bin_below\n",
    "    vs_fit_concat_ortho=np.concatenate([sortedv_2_below[low_2:up_2],sortedv_2_above[low_2:up_2]])#vs_fit_ortho_bin+vs_fit_ortho_bin_below\n",
    "    \n",
    "    \n",
    "  \n",
    "    \n",
    "    xs_data_concat_arc=[x*pixelscale for x in xs_data_concat]\n",
    "    xs_data_concat_ortho_arc=[x*pixelscale for x in xs_data_concat_ortho]\n",
    "    xs_fit_concat_arc=[x*pixelscale for x in xs_fit_concat]\n",
    "    xs_fit_concat_ortho_arc=[x*pixelscale for x in xs_fit_concat_ortho]\n",
    "    \n",
    "    vs_ortho_error_pos=vs_fit_concat_ortho+error_data_concat_ortho\n",
    "    vs_ortho_error_neg=vs_fit_concat_ortho-error_data_concat_ortho\n",
    "    \n",
    "    vs_error_pos=vs_fit_concat+error_data_concat\n",
    "    vs_error_neg=vs_fit_concat-error_data_concat\n",
    "    \n",
    "    #plt.fill_between(xs_fit_concat_ortho_arc[0:len(xs_fit_concat_ortho_arc)/2], vs_ortho_error_neg[0:len(xs_fit_concat_ortho_arc)/2], vs_ortho_error_pos[0:len(xs_fit_concat_ortho_arc)/2],alpha=0.3, color='blue')\n",
    "    #plt.fill_between(xs_fit_concat_arc[0:len(xs_fit_concat_arc)/2], vs_error_neg[0:len(xs_fit_concat_arc)/2], vs_error_pos[0:len(xs_fit_concat_arc)/2], alpha=0.3,color='red', label='PA 1 Model')\n",
    "    \n",
    "    #plt.fill_between(xs_fit_concat_ortho_arc[len(xs_fit_concat_ortho_arc)/2:], vs_ortho_error_neg[len(xs_fit_concat_ortho_arc)/2:], vs_ortho_error_pos[len(xs_fit_concat_ortho_arc)/2:], alpha=0.3,color='blue', label='PA 2 Model')\n",
    "    #plt.fill_between(xs_fit_concat_arc[len(xs_fit_concat_arc)/2:], vs_error_neg[len(xs_fit_concat_arc)/2:], vs_error_pos[len(xs_fit_concat_arc)/2:],alpha=0.3, color='red')\n",
    "    \n",
    "    plt.errorbar(xs_data_concat_arc, vel_data_concat, yerr=error_data_concat, color='orangered', linestyle='None')\n",
    "    plt.scatter(xs_data_concat_arc, vel_data_concat,  color='orangered', label='PA 1 Velocity Data', s=100, zorder=100)\n",
    "    plt.scatter(xs_fit_concat_arc, vs_fit_concat, edgecolor='orangered',marker='*', facecolor='white', s=150,label='PA 1 Model', zorder=100)\n",
    "    \n",
    "    plt.errorbar(xs_data_concat_ortho_arc, vel_data_concat_ortho, yerr=error_data_concat_ortho, color='purple', linestyle='None')\n",
    "    plt.scatter(xs_data_concat_ortho_arc, vel_data_concat_ortho, color='purple', label='PA 2 Velocity Data', s=100, zorder=100)\n",
    "    plt.scatter(xs_fit_concat_ortho_arc, vs_fit_concat_ortho, edgecolor='purple',marker='*',s=150, facecolor='white', label='PA 2 Model', zorder=100)\n",
    "    #plt.annotate(result, xy=(0.45,0.1), xycoords='axes fraction')\n",
    "    #plt.annotate(r'i   PA   r$_t$  $\\theta_{\\mathrm{half}}$  $\\theta_{\\mathrm{half2}}$  v$_{\\mathrm{max}}$', xy=(0.45,0.03), xycoords='axes fraction')\n",
    "    plt.xlabel(r'Spatial Position [$^{\\prime \\prime}$]')\n",
    "    plt.ylabel(r'Velocity Offset [km s$^{-1}$]')\n",
    "    plt.ylim([-400,400])\n",
    "    plt.annotate('J0854+5026', xy=(0.77,0.05), xycoords='axes fraction', size=15)\n",
    "    #plt.xlim([-4,4])\n",
    "    '''error = np.random.normal(0.1, 0.02, size=y.shape)\n",
    "    y += np.random.normal(0, 0.1, size=y.shape)\n",
    "\n",
    "    pl.plot(x, y, 'k-')\n",
    "    pl.fill_between(x, y-error, y+error)'''\n",
    "    \n",
    "    plt.legend()#loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "    #p1 = plt.plot(xs_fit_concat_ortho_arc[len(xs_fit_concat_ortho_arc)/2:], vs_ortho_error_neg[len(xs_fit_concat_ortho_arc)/2:], color='b', linewidth=0)\n",
    "    #p2 = plt.fill_between(np.NaN, np.NaN, 'b', alpha=0.5)\n",
    "    #plt.legend([(p2[0], p1[0]), ], ['PA 2 Model'])\n",
    "    #plt.tight_layout()\n",
    "    plt.axhline(y=0, ls='--', color='black')\n",
    "    #plt.title('102.87')\n",
    "    #plt.annotate('82.84 0.52 0.7 & 65.97 2.05 0.49 & 5.52 0.13 0.17 & 40.15 0.72 1.52 & 65.84 0.35 0.28 & 304.52 4.79 7.18', xy=(0.01,0.01), xycoords='axes fraction')\n",
    "    \n",
    "    savefig('../Model_Asymmetric_Bicone_'+str(name)+'.pdf')\n",
    "    #plt.show()\n",
    " \n",
    "    \n",
    "    resid_1=[]\n",
    "    resid_2=[]\n",
    "    resid_alt_1=[]\n",
    "    resid_alt_2=[]\n",
    "    \n",
    "    \n",
    "    for i in range(len(xs_data_concat)):\n",
    "        resid_1.append((((vs_fit_concat[i]-vel_data_concat[i])**2/error_data_concat[i]**2)+np.log(2*np.pi*error_data_concat[i]**2)))\n",
    "        resid_alt_1.append((((vs_fit_concat[i]-vel_data_concat[i])**2)/(np.sqrt(error_data_concat[i]**2+2.5**2))**2))\n",
    "        \n",
    "    for i in range(len(xs_data_concat_ortho)):\n",
    "        resid_alt_2.append((((vs_fit_concat_ortho[i]-vel_data_concat_ortho[i])**2)/(np.sqrt(error_data_concat_ortho[i]**2+2.5**2)**2)))\n",
    "        resid_2.append((((vs_fit_concat_ortho[i]-vel_data_concat_ortho[i])**2/error_data_concat_ortho[i]**2)+np.log(2*np.pi*error_data_concat_ortho[i]**2)))\n",
    "        \n",
    "    \n",
    "    resid_alt_concat=resid_alt_1+resid_alt_2\n",
    "    resid_concat=resid_1+resid_2\n",
    "    \n",
    "      \n",
    "    \n",
    "\n",
    "    n=2*(upper_lim-lower_lim+upper_lim_ortho-lower_lim_ortho)\n",
    "    k=6\n",
    "    \n",
    "    return np.sum(resid_alt_concat)/(n-k-1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    " "
   ]
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