arm_pyqz_test-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'pyqz'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-80a7f98b826e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpyqz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pyqz'"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pyqz\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/mnt/ddnfs/data_users/yhwang/code/metallicity',\n",
       " '/home/yhwang/local/anaconda/lib/python37.zip',\n",
       " '/home/yhwang/local/anaconda/lib/python3.7',\n",
       " '/home/yhwang/local/anaconda/lib/python3.7/lib-dynload',\n",
       " '',\n",
       " '/home/yhwang/local/anaconda/lib/python3.7/site-packages',\n",
       " '/home/yhwang/local/anaconda/lib/python3.7/site-packages/IPython/extensions',\n",
       " '/home/yhwang/.ipython']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyqz.pyqz_plots as pyqzp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyqz.pyqzm.pyqz_grid_dir='/usr/local/share/mappings/data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/pyqz/ref_data'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyqz.pyqzm.pyqz_grid_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_grid = pyqz.get_grid('[NII]/[SII]+;[OIII]/[SII]+', sampling=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_grid = pyqz.get_grid('[NII]/[SII]+;[OIII]/[SII]+', struct = 'pp', Pk = 5, kappa = 'inf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/mpl-data/matplotlibrc\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "print(matplotlib.matplotlib_fname())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**********可用的中文字体**********\n"
     ]
    }
   ],
   "source": [
    "def dispFonts():\n",
    "    #显示可用的中文字体，同时支持英文的\n",
    "    from matplotlib.font_manager import FontManager\n",
    "    import subprocess\n",
    "\n",
    "    fm = FontManager()\n",
    "    mat_fonts = set(f.name for f in fm.ttflist)\n",
    "\n",
    "    output = subprocess.check_output(\n",
    "        'fc-list :lang=US -f \"%{family}\\n\"', shell=True)\n",
    "    output = output.decode('utf-8')\n",
    "    # print ‘*‘ * 10, ‘系统可用的中文字体‘, ‘*‘ * 10\n",
    "    # print output\n",
    "    zh_fonts = set(f.split(',', 1)[0] for f in output.split('\\n'))\n",
    "    available = mat_fonts & zh_fonts\n",
    "\n",
    "    print '*' * 10 +  u'可用的中文字体'+'*' * 10\n",
    "    for f in available:\n",
    "        print(f)\n",
    "dispFonts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**********可用的中文字体**********\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %matplotlib inline\n",
    "import pyqz\n",
    "\n",
    "import numpy as np\n",
    "import pyqz.pyqz_plots as pyqzp\n",
    "pyqzp.plot_grid('[NII]/[OII]+;[OIII]/[OII]+', struct = 'pp', Pk = 5, kappa = 'inf',color_mode='gas[O]+12',figname='/root/lalala.png');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pyqz\n",
    "\n",
    "import numpy as np\n",
    "import pyqz.pyqz_plots as pyqzp\n",
    "pyqz.pyqzm.pyqz_cmap_0 = 'jet'\n",
    "# pyqz.pyqzm.pyqz_cmap_0 = 'RdGnBu'\n",
    "# pyqz.pyqzm.pyqz_cmap_1 = 'RdYlBu'\n",
    "\n",
    "\n",
    "\n",
    "pyqzp.plot_grid('[NII]/[OII]+;[OIII]/[OII]+', struct = 'pp', Pk = 5, kappa = 'inf',color_mode='gas[O]+12',sampling=1);\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据横纵坐标得到金属丰度的大小。\n",
    "niioii = np.array([-0.65])\n",
    "oiiisii = np.array([-0.1])\n",
    "z = pyqz.interp_qz('Tot[O]+12',[niioii, oiiisii],'[NII]/[OII]+;[OIII]/[SII]+',\n",
    "                   sampling=1,struct='pp')\n",
    "print 'Tot[O]+12 = %.2f' % z\n",
    "\n",
    "# The result can be visualized using pyqz_plots.plot_grid()\n",
    "pyqzp.plot_grid('[NII]/[OII]+;[OIII]/[SII]+',sampling = 1, struct='pp', data = [niioii,oiiisii], interp_data=z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[ 6.5   ,  8.11  ,  8.    , -1.1183, -1.6963, -1.1183, -1.6963],\n",
       "        [ 6.5   ,  8.485 ,  8.375 , -0.8166, -1.7715, -0.8166, -1.7715],\n",
       "        [ 6.5   ,  8.635 ,  8.525 , -0.6317, -1.8652, -0.6317, -1.8652],\n",
       "        [ 6.5   ,  8.835 ,  8.725 , -0.2943, -2.0213, -0.2943, -2.0213],\n",
       "        [ 6.5   ,  8.985 ,  8.875 ,  0.091 , -2.2833,  0.091 , -2.2833],\n",
       "        [ 6.75  ,  8.11  ,  8.    , -1.1697, -1.2484, -1.1697, -1.2484],\n",
       "        [ 6.75  ,  8.485 ,  8.375 , -0.8537, -1.3284, -0.8537, -1.3284],\n",
       "        [ 6.75  ,  8.635 ,  8.525 , -0.6605, -1.4262, -0.6605, -1.4262],\n",
       "        [ 6.75  ,  8.835 ,  8.725 , -0.318 , -1.5916, -0.318 , -1.5916],\n",
       "        [ 6.75  ,  8.985 ,  8.875 ,  0.0715, -1.8686,  0.0715, -1.8686],\n",
       "        [ 7.    ,  8.11  ,  8.    , -1.2276, -0.8138, -1.2276, -0.8138],\n",
       "        [ 7.    ,  8.485 ,  8.375 , -0.8939, -0.8985, -0.8939, -0.8985],\n",
       "        [ 7.    ,  8.635 ,  8.525 , -0.691 , -0.9984, -0.691 , -0.9984],\n",
       "        [ 7.    ,  8.835 ,  8.725 , -0.3414, -1.1682, -0.3414, -1.1682],\n",
       "        [ 7.    ,  8.985 ,  8.875 ,  0.0544, -1.4529,  0.0544, -1.4529],\n",
       "        [ 7.25  ,  8.11  ,  8.    , -1.285 , -0.4108, -1.285 , -0.4108],\n",
       "        [ 7.25  ,  8.485 ,  8.375 , -0.9341, -0.502 , -0.9341, -0.502 ],\n",
       "        [ 7.25  ,  8.635 ,  8.525 , -0.7222, -0.603 , -0.7222, -0.603 ],\n",
       "        [ 7.25  ,  8.835 ,  8.725 , -0.3647, -0.7738, -0.3647, -0.7738],\n",
       "        [ 7.25  ,  8.985 ,  8.875 ,  0.0377, -1.0579,  0.0377, -1.0579],\n",
       "        [ 7.5   ,  8.11  ,  8.    , -1.3287, -0.0505, -1.3287, -0.0505],\n",
       "        [ 7.5   ,  8.485 ,  8.375 , -0.9659, -0.1539, -0.9659, -0.1539],\n",
       "        [ 7.5   ,  8.635 ,  8.525 , -0.7475, -0.2585, -0.7475, -0.2585],\n",
       "        [ 7.5   ,  8.835 ,  8.725 , -0.3824, -0.4345, -0.3824, -0.4345],\n",
       "        [ 7.5   ,  8.985 ,  8.875 ,  0.0251, -0.7157,  0.0251, -0.7157],\n",
       "        [ 7.75  ,  8.11  ,  8.    , -1.3549,  0.2713, -1.3549,  0.2713],\n",
       "        [ 7.75  ,  8.485 ,  8.375 , -0.9875,  0.1481, -0.9875,  0.1481],\n",
       "        [ 7.75  ,  8.635 ,  8.525 , -0.765 ,  0.0337, -0.765 ,  0.0337],\n",
       "        [ 7.75  ,  8.835 ,  8.725 , -0.3922, -0.1631, -0.3922, -0.1631],\n",
       "        [ 7.75  ,  8.985 ,  8.875 ,  0.0197, -0.4628,  0.0197, -0.4628],\n",
       "        [ 8.    ,  8.11  ,  8.    , -1.3686,  0.5632, -1.3686,  0.5632],\n",
       "        [ 8.    ,  8.485 ,  8.375 , -1.0029,  0.4144, -1.0029,  0.4144],\n",
       "        [ 8.    ,  8.635 ,  8.525 , -0.7789,  0.2849, -0.7789,  0.2849],\n",
       "        [ 8.    ,  8.835 ,  8.725 , -0.4001,  0.0489, -0.4001,  0.0489],\n",
       "        [ 8.    ,  8.985 ,  8.875 ,  0.0117, -0.3198,  0.0117, -0.3198],\n",
       "        [ 8.25  ,  8.11  ,  8.    , -1.3758,  0.8295, -1.3758,  0.8295],\n",
       "        [ 8.25  ,  8.485 ,  8.375 , -1.0156,  0.65  , -1.0156,  0.65  ],\n",
       "        [ 8.25  ,  8.635 ,  8.525 , -0.7929,  0.5021, -0.7929,  0.5021],\n",
       "        [ 8.25  ,  8.835 ,  8.725 , -0.412 ,  0.2164, -0.412 ,  0.2164],\n",
       "        [ 8.25  ,  8.985 ,  8.875 , -0.01  , -0.2642, -0.01  , -0.2642],\n",
       "        [ 8.5   ,  8.11  ,  8.    , -1.3806,  1.0674, -1.3806,  1.0674],\n",
       "        [ 8.5   ,  8.485 ,  8.375 , -1.0273,  0.8509, -1.0273,  0.8509],\n",
       "        [ 8.5   ,  8.635 ,  8.525 , -0.8082,  0.6821, -0.8082,  0.6821],\n",
       "        [ 8.5   ,  8.835 ,  8.725 , -0.43  ,  0.3471, -0.43  ,  0.3471],\n",
       "        [ 8.5   ,  8.985 ,  8.875 , -0.0419, -0.2408, -0.0419, -0.2408]]),\n",
       " ['LogQ',\n",
       "  'Tot[O]+12',\n",
       "  'gas[O]+12',\n",
       "  '[NII]/[OII]+',\n",
       "  '[OIII]/[OII]+',\n",
       "  'Mix_x',\n",
       "  'Mix_y'],\n",
       " {'MV_id': 'MAPPINGS V HII Region Grid: QZ pp_GCZO_Pk50_kinf',\n",
       "  'columns': ['LogQ',\n",
       "   'Tot[O]+12',\n",
       "   'gas[O]+12',\n",
       "   'Tot[Fe]',\n",
       "   'gas[Fe]',\n",
       "   'LogP/k',\n",
       "   '[NII]/Ha',\n",
       "   '[NII]+/Ha',\n",
       "   '[SII]+/Ha',\n",
       "   '[NII]/[OII]+',\n",
       "   '[NII]+/[OII]+',\n",
       "   '[NII]/[SII]+',\n",
       "   '[NII]+/[SII]+',\n",
       "   '[OII]+/Hb',\n",
       "   '[OIII]/Hb',\n",
       "   '[OIII]+/Hb',\n",
       "   '[OIII]/[OII]+',\n",
       "   '[OIII]+/[OII]+',\n",
       "   '[OIII]/[SII]+',\n",
       "   '[OIII]+/[SII]+',\n",
       "   '4363/[OIII]',\n",
       "   '4363/[OIII]+',\n",
       "   '[OI]/[OII]+',\n",
       "   '[OII]+/[SII]+',\n",
       "   'R23',\n",
       "   'S23',\n",
       "   '6716/6731',\n",
       "   '3729/3726'],\n",
       "  'date': 'Thu Dec 10 14:16:36 CLST 2015',\n",
       "  'params': 'Run: pp,two,LPk=5.0,dep=Depln_Fe_1.50.txt,kap=inf,GCZFea05t2,SF=cont,a05,isp,#11,P6,Mv5.0.16,Svm802,FE15',\n",
       "  'resampled': {}}]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyqzp.pyqz.get_grid('[NII]/[OII]+;[OIII]/[OII]+', struct = 'pp', Pk = 5, kappa = 'inf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyqzp.pyqz.get_grid?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPPINGS id: MAPPINGS V HII Region Grid: QZ pp_GCZO_Pk50_kinf\n",
      "Model created: Thu Dec 10 14:16:36 CLST 2015\n",
      "Model parameters: pp,two,LPk=5.0,dep=Depln_Fe_1.50.txt,kap=inf,GCZFea05t2,SF=cont,a05,isp,#11,P6,Mv5.0.16,Svm802,FE15\n"
     ]
    }
   ],
   "source": [
    "fn = pyqz.pyqz_tools.get_MVphotogrid_fn(Pk = 5.0, calibs = 'GCZO', kappa = np.inf, struct = 'pp', sampling = 1)\n",
    "info = pyqz.pyqz_tools.get_MVphotogrid_metadata(fn)\n",
    "\n",
    "print 'MAPPINGS id: %s' % info['MV_id']\n",
    "print 'Model created: %s' % info['date']\n",
    "print 'Model parameters: %s' % info['params'].split(': ')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyqzp.plot_grid('[NII]/[OII]+;[OIII]/[OII]+', struct = 'pp', Pk = 5, kappa = 'inf', sampling=2)\n",
    "# log[O iii]5007\n",
    "# log([N ii]6584/[O ii]3726+3729)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "--> Received 1 spectrum ...\n",
      "--> Dealing with them one at a time ... be patient now !\n",
      "    (no status update until I am done ...)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/pyqz/pyqz.py:434: RuntimeWarning: invalid value encountered in greater\n",
      "  this_interp_data[this_interp_data>0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "All done in 0:00:01.449299\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[array([[  7.38831887e+00,   8.48760006e+00,   7.37681443e+00,\n",
       "           8.48372388e+00,   7.38256665e+00,   5.75221877e-03,\n",
       "           8.48566197e+00,   1.93808636e-03,   7.40791471e+00,\n",
       "           5.55948429e-02,   8.49599900e+00,   5.59232823e-02,\n",
       "           7.38019910e+00,   2.89873940e-02,   8.48326527e+00,\n",
       "           2.75916351e-02,   7.38382781e+00,   3.32681756e-02,\n",
       "           8.48403878e+00,   3.06823659e-02,   0.00000000e+00,\n",
       "           0.00000000e+00]]),\n",
       " ['[NII]/[SII]+;[OIII]/Hb|LogQ',\n",
       "  '[NII]/[SII]+;[OIII]/Hb|Tot[O]+12',\n",
       "  '[NII]/[OII]+;[OIII]/[SII]+|LogQ',\n",
       "  '[NII]/[OII]+;[OIII]/[SII]+|Tot[O]+12',\n",
       "  '<LogQ>',\n",
       "  'std(LogQ)',\n",
       "  '<Tot[O]+12>',\n",
       "  'std(Tot[O]+12)',\n",
       "  '[NII]/[SII]+;[OIII]/Hb|LogQ{KDE}',\n",
       "  'err([NII]/[SII]+;[OIII]/Hb|LogQ{KDE})',\n",
       "  '[NII]/[SII]+;[OIII]/Hb|Tot[O]+12{KDE}',\n",
       "  'err([NII]/[SII]+;[OIII]/Hb|Tot[O]+12{KDE})',\n",
       "  '[NII]/[OII]+;[OIII]/[SII]+|LogQ{KDE}',\n",
       "  'err([NII]/[OII]+;[OIII]/[SII]+|LogQ{KDE})',\n",
       "  '[NII]/[OII]+;[OIII]/[SII]+|Tot[O]+12{KDE}',\n",
       "  'err([NII]/[OII]+;[OIII]/[SII]+|Tot[O]+12{KDE})',\n",
       "  '<LogQ{KDE}>',\n",
       "  'err(LogQ{KDE})',\n",
       "  '<Tot[O]+12{KDE}>',\n",
       "  'err(Tot[O]+12{KDE})',\n",
       "  'flag',\n",
       "  'rs_offgrid']]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyqz.get_global_qz(np.array([[ 1.00e+00, 5.00e-02, 2.38e+00, 1.19e-01, 5.07e+00, 2.53e-01,\n",
    "                               5.67e-01, 2.84e-02, 5.11e-01, 2.55e-02, 2.88e+00, 1.44e-01]]),\n",
    "                   ['Hb','stdHb','[OIII]','std[OIII]','[OII]+','std[OII]+',\n",
    "                    '[NII]','std[NII]','[SII]+','std[SII]+','Ha','stdHa'],\n",
    "                   ['[NII]/[SII]+;[OIII]/Hb','[NII]/[OII]+;[OIII]/[SII]+'],\n",
    "                   ids = ['NGC_1234'],\n",
    "                   KDE_method = 'multiv',\n",
    "                   KDE_qz_sampling = 201j,\n",
    "                   struct = 'pp',\n",
    "                   sampling = 1,\n",
    "                   verbose = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'qz' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-53-6a9e69f17cf5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpyqz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyqz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterp_qz\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mratios\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoeffs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPk\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkappa\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstruct\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'pp'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msampling\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'cubic'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'qz' is not defined"
     ]
    }
   ],
   "source": [
    "pyqz.pyqz.interp_qz(qz, data, ratios, coeffs=[[1, 0], [0, 1]], Pk=5.0, kappa=inf, struct='pp', sampling=1, method='cubic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "--> Received 1 spectrum ...\n",
      "--> Dealing with them one at a time ... be patient now !\n",
      "    (no status update until I am done ...)\n"
     ]
    },
    {
     "ename": "IOError",
     "evalue": "[Errno 2] No such file or directory: './examples/ggQZ_NGC_1234_Pk50_kinf_pp_samp1_KDE-multiv.pkl'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-16cb21d3cbeb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m                    \u001b[0mstruct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'pp'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m                    \u001b[0msampling\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m                    verbose = True)\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/pyqz/pyqz.pyc\u001b[0m in \u001b[0;36mget_global_qz\u001b[0;34m(data, data_cols, which_grids, ids, qzs, Pk, kappa, struct, sampling, error_pdf, srs, flag_level, KDE_method, KDE_qz_sampling, KDE_do_singles, KDE_pickle_loc, verbose, nproc)\u001b[0m\n\u001b[1;32m   1206\u001b[0m                 \u001b[0;32mprint\u001b[0m \u001b[0;34m'    (no status update until I am done ...)'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1207\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1208\u001b[0;31m             \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_global_qz_singlespec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1210\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/pyqz/pyqz.pyc\u001b[0m in \u001b[0;36mget_global_qz_singlespec\u001b[0;34m((j, final_cols, data, data_cols, which_grids, ids, qzs, Pk, kappa, struct, sampling, error_pdf, srs, flag_level, KDE_method, KDE_qz_sampling, KDE_do_singles, KDE_pickle_loc, verbose))\u001b[0m\n\u001b[1;32m    964\u001b[0m         \u001b[0mfn_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKDE_pickle_loc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn_out\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m         \u001b[0mftmp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn_out\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    967\u001b[0m         \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKDE_topickle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mftmp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    968\u001b[0m         \u001b[0mftmp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: './examples/ggQZ_NGC_1234_Pk50_kinf_pp_samp1_KDE-multiv.pkl'"
     ]
    }
   ],
   "source": [
    "out = pyqz.get_global_qz(np.array([[ 1.00e+00, 5.00e-02, 2.38e+00, 1.19e-01, 5.07e+00, 2.53e-01,\n",
    "                                     5.67e-01, 2.84e-02, 5.11e-01, 2.55e-02, 2.88e+00, 1.44e-01]]),\n",
    "                   ['Hb','stdHb','[OIII]','std[OIII]','[OII]+','std[OII]+',\n",
    "                    '[NII]','std[NII]','[SII]+','std[SII]+','Ha','stdHa'],\n",
    "                   ['[NII]/[SII]+;[OIII]/Hb','[NII]/[OII]+;[OIII]/[SII]+'],\n",
    "                   ids = ['NGC_1234'],\n",
    "                   KDE_method = 'multiv',\n",
    "                   KDE_qz_sampling = 201j,\n",
    "                   KDE_pickle_loc = './examples/',\n",
    "                   struct = 'pp',\n",
    "                   sampling = 1,\n",
    "                   verbose = True)\n",
    "\n",
    "import glob\n",
    "fn = glob.glob('./examples/*NGC_1234*.pkl')\n",
    "\n",
    "# pyqz_plots.get_global_qz() takes the pickle filename as argument.\n",
    "pyqzp.plot_global_qz(fn[0], show_plots=True, save_loc = './examples/', do_all_diags=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pyqz\n",
    "import pyqz.pyqz_plots as pyqzp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MVgrid_QZ_pp_GCZO_Pk67_k10.csv\n"
     ]
    }
   ],
   "source": [
    "fn = pyqz.pyqzt.get_MVphotogrid_fn(Pk=6.7, calibs='GCZO', kappa =10, struct='pp')\n",
    "print fn.split('/')[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/scipy/interpolate/_cubic.py:306: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  m[-1] = 2. * m[-2] - m[-3]\n",
      "/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/numpy/lib/function_base.py:1768: RuntimeWarning: invalid value encountered in subtract\n",
      "  return a[slice1]-a[slice2]\n",
      "/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/scipy/interpolate/_cubic.py:317: RuntimeWarning: invalid value encountered in greater\n",
      "  ind = np.nonzero(f12 > 1e-9 * np.max(f12))\n",
      "/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/scipy/interpolate/_cubic.py:323: RuntimeWarning: invalid value encountered in subtract\n",
      "  c = (3. * m[2:-2] - 2. * t[:-1] - t[1:]) / dx\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "  Success: MVgrid_QZ_sph_GCZO_Pk50_kinf.csv resampled by a factor 2x2 and saved as MVgrid_QZ_sph_GCZO_Pk50_kinf_samp_2.csv\n"
     ]
    }
   ],
   "source": [
    "grid_fn = pyqz.pyqzt.get_MVphotogrid_fn(Pk=5.0,struct='sph', kappa='inf')\n",
    "pyqz.pyqzt.resample_MVphotogrid(grid_fn, sampling=2)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "LaTeX was not able to process the following string:\n'lp'\nHere is the full report generated by LaTeX: \n\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/IPython/core/formatters.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    332\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    335\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    248\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    546\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2424\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2425\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2426\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2428\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, dsu, suppress_composite)\u001b[0m\n\u001b[1;32m    137\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1136\u001b[0m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1137\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[0;32m-> 1138\u001b[0;31m                                                                 renderer)\n\u001b[0m\u001b[1;32m   1139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtick\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mticks_to_draw\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36m_get_tick_bboxes\u001b[0;34m(self, ticks, renderer)\u001b[0m\n\u001b[1;32m   1076\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtick\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mticks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1077\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1On\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_visible\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1078\u001b[0;31m                 \u001b[0mextent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_window_extent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1079\u001b[0m                 \u001b[0mticklabelBoxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1080\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel2On\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_visible\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36mget_window_extent\u001b[0;34m(self, renderer, dpi)\u001b[0m\n\u001b[1;32m    965\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Cannot get window extent w/o renderer'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    966\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 967\u001b[0;31m         \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    968\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_unitless_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    969\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36m_get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    351\u001b[0m         tmp, lp_h, lp_bl = renderer.get_text_width_height_descent('lp',\n\u001b[1;32m    352\u001b[0m                                                          \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fontproperties\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 353\u001b[0;31m                                                          ismath=False)\n\u001b[0m\u001b[1;32m    354\u001b[0m         \u001b[0moffsety\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlp_h\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlp_bl\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_linespacing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    228\u001b[0m             \u001b[0mfontsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_size_in_points\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m             w, h, d = texmanager.get_text_width_height_descent(s, fontsize,\n\u001b[0;32m--> 230\u001b[0;31m                                                                renderer=self)\n\u001b[0m\u001b[1;32m    231\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/texmanager.pyc\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, tex, fontsize, renderer)\u001b[0m\n\u001b[1;32m    674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    675\u001b[0m             \u001b[0;31m# use dviread. It sometimes returns a wrong descent.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 676\u001b[0;31m             \u001b[0mdvifile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_dvi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    677\u001b[0m             \u001b[0mdvi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdviread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDvi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdvifile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m72\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdpi_fraction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    678\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/texmanager.pyc\u001b[0m in \u001b[0;36mmake_dvi\u001b[0;34m(self, tex, fontsize)\u001b[0m\n\u001b[1;32m    421\u001b[0m                      \u001b[0;34m'string:\\n%s\\nHere is the full report generated by '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    422\u001b[0m                      \u001b[0;34m'LaTeX: \\n\\n'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mrepr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'unicode_escape'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 423\u001b[0;31m                      report))\n\u001b[0m\u001b[1;32m    424\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    425\u001b[0m                 \u001b[0mmpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'debug'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: LaTeX was not able to process the following string:\n'lp'\nHere is the full report generated by LaTeX: \n\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a10b29c50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyqzp.plot_grid('[NII]/[SII]+;[NII]/Ha;[OIII]/Hb',\n",
    "                     coeffs = [[1.0,0.264,0.0],[0.242,-0.910,0.342]],\n",
    "                     struct='pp',\n",
    "                     sampling=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/font_manager.py:1331: UserWarning: findfont: Font family [u'sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "ename": "OSError",
     "evalue": "[Errno 2] No such file or directory: 'latex'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/IPython/core/formatters.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    332\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    335\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    248\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/backend_bases.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2210\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2211\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2212\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2213\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2214\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    516\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 517\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    518\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    519\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    435\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m             \u001b[0;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 437\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    438\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1491\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1492\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1493\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1495\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2633\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2634\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2635\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2637\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1190\u001b[0m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1191\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[0;32m-> 1192\u001b[0;31m                                                                 renderer)\n\u001b[0m\u001b[1;32m   1193\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1194\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtick\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mticks_to_draw\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36m_get_tick_bboxes\u001b[0;34m(self, ticks, renderer)\u001b[0m\n\u001b[1;32m   1128\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtick\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mticks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1129\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1On\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_visible\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1130\u001b[0;31m                 \u001b[0mextent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_window_extent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1131\u001b[0m                 \u001b[0mticklabelBoxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel2On\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_visible\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36mget_window_extent\u001b[0;34m(self, renderer, dpi)\u001b[0m\n\u001b[1;32m    920\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Cannot get window extent w/o renderer'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    921\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 922\u001b[0;31m         \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    923\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_unitless_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    924\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36m_get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    307\u001b[0m                 w, h, d = renderer.get_text_width_height_descent(clean_line,\n\u001b[1;32m    308\u001b[0m                                                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fontproperties\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 309\u001b[0;31m                                                         ismath=ismath)\n\u001b[0m\u001b[1;32m    310\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    311\u001b[0m                 \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    234\u001b[0m             \u001b[0mfontsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_size_in_points\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m             w, h, d = texmanager.get_text_width_height_descent(\n\u001b[0;32m--> 236\u001b[0;31m                 s, fontsize, renderer=self)\n\u001b[0m\u001b[1;32m    237\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/texmanager.pyc\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, tex, fontsize, renderer)\u001b[0m\n\u001b[1;32m    499\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    500\u001b[0m             \u001b[0;31m# use dviread. It sometimes returns a wrong descent.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 501\u001b[0;31m             \u001b[0mdvifile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_dvi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    502\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mdviread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDvi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdvifile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m72\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdpi_fraction\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdvi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    503\u001b[0m                 \u001b[0mpage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdvi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/texmanager.pyc\u001b[0m in \u001b[0;36mmake_dvi\u001b[0;34m(self, tex, fontsize)\u001b[0m\n\u001b[1;32m    363\u001b[0m                 self._run_checked_subprocess(\n\u001b[1;32m    364\u001b[0m                     [\"latex\", \"-interaction=nonstopmode\", \"--halt-on-error\",\n\u001b[0;32m--> 365\u001b[0;31m                      texfile], tex)\n\u001b[0m\u001b[1;32m    366\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mfname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasefile\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'*'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    367\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dvi'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'tex'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/texmanager.pyc\u001b[0m in \u001b[0;36m_run_checked_subprocess\u001b[0;34m(self, command, tex)\u001b[0m\n\u001b[1;32m    333\u001b[0m             report = subprocess.check_output(command,\n\u001b[1;32m    334\u001b[0m                                              \u001b[0mcwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtexcache\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 335\u001b[0;31m                                              stderr=subprocess.STDOUT)\n\u001b[0m\u001b[1;32m    336\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0msubprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCalledProcessError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    337\u001b[0m             raise RuntimeError(\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/subprocess32.pyc\u001b[0m in \u001b[0;36mcheck_output\u001b[0;34m(*popenargs, **kwargs)\u001b[0m\n\u001b[1;32m    332\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'stdout'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'stdout argument not allowed, it will be overridden.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m     \u001b[0mprocess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mPIPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mpopenargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    335\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m         \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munused_err\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommunicate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/subprocess32.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds)\u001b[0m\n\u001b[1;32m    612\u001b[0m                                     \u001b[0mc2pread\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc2pwrite\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m                                     \u001b[0merrread\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrwrite\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 614\u001b[0;31m                                     restore_signals, start_new_session)\n\u001b[0m\u001b[1;32m    615\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    616\u001b[0m                 \u001b[0;31m# The cleanup is performed within the finally block rather\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/subprocess32.pyc\u001b[0m in \u001b[0;36m_execute_child\u001b[0;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, universal_newlines, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, restore_signals, start_new_session)\u001b[0m\n\u001b[1;32m   1391\u001b[0m                             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1392\u001b[0m                                 \u001b[0merr_msg\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m': '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mrepr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_executable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1393\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno_num\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1394\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1395\u001b[0m                     \u001b[0mexception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: [Errno 2] No such file or directory: 'latex'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import matplotlib\n",
    "#mpl.use('Agg')\n",
    "#%matplotlib inline\n",
    "#import matplotlib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pyqz\n",
    "import numpy as np\n",
    "import pyqz.pyqz_plots as pyqzp\n",
    "#a_grid = pyqz.get_grid('[NII]/[SII]+;[OIII]/[SII]+', sampling=1)\n",
    "\n",
    "#%matplotlib inline\n",
    "pyqzp.plot_grid('[NII]/[OII]+;[OIII]/[OII]+', struct = 'pp', Pk = 5, kappa = 'inf', sampling=2, show_plot=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/pyqz/ref_data/MVgrid_QZ_pp_GCZO_Pk50_kinf.csv\n"
     ]
    }
   ],
   "source": [
    "fn = pyqz.pyqzt.get_MVphotogrid_fn(Pk=5, calibs='GCZO', kappa ='inf', struct='pp')\n",
    "#print fn.split('/')[-1]\n",
    "print fn.split()[-0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/font_manager.py:1331: UserWarning: findfont: Font family [u'sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "ename": "OSError",
     "evalue": "[Errno 2] No such file or directory: 'latex'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/IPython/core/formatters.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    332\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    335\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    248\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/backend_bases.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2210\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2211\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2212\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2213\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2214\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    516\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 517\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    518\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    519\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    435\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m             \u001b[0;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 437\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    438\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1491\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1492\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1493\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1495\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2633\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2634\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2635\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2637\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1190\u001b[0m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1191\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[0;32m-> 1192\u001b[0;31m                                                                 renderer)\n\u001b[0m\u001b[1;32m   1193\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1194\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtick\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mticks_to_draw\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36m_get_tick_bboxes\u001b[0;34m(self, ticks, renderer)\u001b[0m\n\u001b[1;32m   1128\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtick\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mticks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1129\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1On\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_visible\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1130\u001b[0;31m                 \u001b[0mextent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_window_extent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1131\u001b[0m                 \u001b[0mticklabelBoxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel2On\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_visible\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36mget_window_extent\u001b[0;34m(self, renderer, dpi)\u001b[0m\n\u001b[1;32m    920\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Cannot get window extent w/o renderer'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    921\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 922\u001b[0;31m         \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    923\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_unitless_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    924\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36m_get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    307\u001b[0m                 w, h, d = renderer.get_text_width_height_descent(clean_line,\n\u001b[1;32m    308\u001b[0m                                                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fontproperties\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 309\u001b[0;31m                                                         ismath=ismath)\n\u001b[0m\u001b[1;32m    310\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    311\u001b[0m                 \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    234\u001b[0m             \u001b[0mfontsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_size_in_points\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m             w, h, d = texmanager.get_text_width_height_descent(\n\u001b[0;32m--> 236\u001b[0;31m                 s, fontsize, renderer=self)\n\u001b[0m\u001b[1;32m    237\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/texmanager.pyc\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, tex, fontsize, renderer)\u001b[0m\n\u001b[1;32m    499\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    500\u001b[0m             \u001b[0;31m# use dviread. It sometimes returns a wrong descent.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 501\u001b[0;31m             \u001b[0mdvifile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_dvi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    502\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mdviread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDvi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdvifile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m72\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdpi_fraction\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdvi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    503\u001b[0m                 \u001b[0mpage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdvi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/texmanager.pyc\u001b[0m in \u001b[0;36mmake_dvi\u001b[0;34m(self, tex, fontsize)\u001b[0m\n\u001b[1;32m    363\u001b[0m                 self._run_checked_subprocess(\n\u001b[1;32m    364\u001b[0m                     [\"latex\", \"-interaction=nonstopmode\", \"--halt-on-error\",\n\u001b[0;32m--> 365\u001b[0;31m                      texfile], tex)\n\u001b[0m\u001b[1;32m    366\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mfname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasefile\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'*'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    367\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dvi'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'tex'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/matplotlib/texmanager.pyc\u001b[0m in \u001b[0;36m_run_checked_subprocess\u001b[0;34m(self, command, tex)\u001b[0m\n\u001b[1;32m    333\u001b[0m             report = subprocess.check_output(command,\n\u001b[1;32m    334\u001b[0m                                              \u001b[0mcwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtexcache\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 335\u001b[0;31m                                              stderr=subprocess.STDOUT)\n\u001b[0m\u001b[1;32m    336\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0msubprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCalledProcessError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    337\u001b[0m             raise RuntimeError(\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/subprocess32.pyc\u001b[0m in \u001b[0;36mcheck_output\u001b[0;34m(*popenargs, **kwargs)\u001b[0m\n\u001b[1;32m    332\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'stdout'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'stdout argument not allowed, it will be overridden.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m     \u001b[0mprocess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mPIPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mpopenargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    335\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m         \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munused_err\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommunicate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/subprocess32.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds)\u001b[0m\n\u001b[1;32m    612\u001b[0m                                     \u001b[0mc2pread\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc2pwrite\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m                                     \u001b[0merrread\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrwrite\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 614\u001b[0;31m                                     restore_signals, start_new_session)\n\u001b[0m\u001b[1;32m    615\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    616\u001b[0m                 \u001b[0;31m# The cleanup is performed within the finally block rather\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/envs/pyqzz/lib/python2.7/site-packages/subprocess32.pyc\u001b[0m in \u001b[0;36m_execute_child\u001b[0;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, universal_newlines, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, restore_signals, start_new_session)\u001b[0m\n\u001b[1;32m   1391\u001b[0m                             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1392\u001b[0m                                 \u001b[0merr_msg\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m': '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mrepr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_executable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1393\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno_num\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1394\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1395\u001b[0m                     \u001b[0mexception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: [Errno 2] No such file or directory: 'latex'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import matplotlib\n",
    "#mpl.use('Agg')\n",
    "#import matplotlib.pyplot as plt\n",
    "import pyqz\n",
    "import numpy as np\n",
    "import pyqz.pyqz_plots as pyqzp\n",
    "a_grid = pyqz.get_grid('[NII]/[SII]+;[OIII]/[SII]+', sampling=1)\n",
    "\n",
    "\n",
    "pyqzp.plot_grid('[NII]/[OII]+;[OIII]/[OII]+', struct = 'pp', Pk = 5, kappa = 'inf', sampling=2, show_plot=True)\n",
    "#pyqzp.plt.imshow(lala)\n",
    "#pyqzp.plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'module://ipykernel.pylab.backend_inline'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.get_backend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyqzp.plot_grid?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.figure.\n",
    "pyqzp.\n",
    "pyqzp.plt.imshow()\n",
    "pyqzp.plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "math.pi"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}