arm_pyqz_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pyqz\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['',\n",
       " '/home/yhwang/local/anaconda/envs/pyqz/lib/python27.zip',\n",
       " '/home/yhwang/local/anaconda/envs/pyqz/lib/python2.7',\n",
       " '/home/yhwang/local/anaconda/envs/pyqz/lib/python2.7/plat-linux2',\n",
       " '/home/yhwang/local/anaconda/envs/pyqz/lib/python2.7/lib-tk',\n",
       " '/home/yhwang/local/anaconda/envs/pyqz/lib/python2.7/lib-old',\n",
       " '/home/yhwang/local/anaconda/envs/pyqz/lib/python2.7/lib-dynload',\n",
       " '/home/yhwang/local/anaconda/envs/pyqz/lib/python2.7/site-packages',\n",
       " '/home/yhwang/local/anaconda/envs/pyqz/lib/python2.7/site-packages/pyqz-0.8.4.0-py2.7.egg',\n",
       " '/home/yhwang/local/anaconda/envs/pyqz/lib/python2.7/site-packages',\n",
       " '/home/yhwang/local/anaconda/envs/pyqz/lib/python2.7/site-packages/IPython/extensions',\n",
       " '/home/yhwang/.ipython']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8.4\n"
     ]
    }
   ],
   "source": [
    "import pyqz\n",
    "print pyqz.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[  6.50000000e+00,   8.11000000e+00,   8.00000000e+00,\n",
      "         -4.07400000e-01,  -9.85300000e-01,  -4.07400000e-01,\n",
      "         -9.85300000e-01],\n",
      "       [  6.50000000e+00,   8.48500000e+00,   8.37500000e+00,\n",
      "         -2.49400000e-01,  -1.20430000e+00,  -2.49400000e-01,\n",
      "         -1.20430000e+00],\n",
      "       [  6.50000000e+00,   8.63500000e+00,   8.52500000e+00,\n",
      "         -1.98600000e-01,  -1.43220000e+00,  -1.98600000e-01,\n",
      "         -1.43220000e+00],\n",
      "       [  6.50000000e+00,   8.83500000e+00,   8.72500000e+00,\n",
      "         -1.12000000e-01,  -1.83900000e+00,  -1.12000000e-01,\n",
      "         -1.83900000e+00],\n",
      "       [  6.50000000e+00,   8.98500000e+00,   8.87500000e+00,\n",
      "         -6.00000000e-02,  -2.43430000e+00,  -6.00000000e-02,\n",
      "         -2.43430000e+00],\n",
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      "         -2.95600000e-01,  -3.74300000e-01,  -2.95600000e-01,\n",
      "         -3.74300000e-01],\n",
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      "         -1.39800000e-01,  -6.14500000e-01,  -1.39800000e-01,\n",
      "         -6.14500000e-01],\n",
      "       [  6.75000000e+00,   8.63500000e+00,   8.52500000e+00,\n",
      "         -9.32000000e-02,  -8.59000000e-01,  -9.32000000e-02,\n",
      "         -8.59000000e-01],\n",
      "       [  6.75000000e+00,   8.83500000e+00,   8.72500000e+00,\n",
      "         -1.03000000e-02,  -1.28390000e+00,  -1.03000000e-02,\n",
      "         -1.28390000e+00],\n",
      "       [  6.75000000e+00,   8.98500000e+00,   8.87500000e+00,\n",
      "          3.26000000e-02,  -1.90740000e+00,   3.26000000e-02,\n",
      "         -1.90740000e+00],\n",
      "       [  7.00000000e+00,   8.11000000e+00,   8.00000000e+00,\n",
      "         -2.02800000e-01,   2.10900000e-01,  -2.02800000e-01,\n",
      "          2.10900000e-01],\n",
      "       [  7.00000000e+00,   8.48500000e+00,   8.37500000e+00,\n",
      "         -4.18000000e-02,  -4.64000000e-02,  -4.18000000e-02,\n",
      "         -4.64000000e-02],\n",
      "       [  7.00000000e+00,   8.63500000e+00,   8.52500000e+00,\n",
      "          7.00000000e-03,  -3.00400000e-01,   7.00000000e-03,\n",
      "         -3.00400000e-01],\n",
      "       [  7.00000000e+00,   8.83500000e+00,   8.72500000e+00,\n",
      "          9.43000000e-02,  -7.32500000e-01,   9.43000000e-02,\n",
      "         -7.32500000e-01],\n",
      "       [  7.00000000e+00,   8.98500000e+00,   8.87500000e+00,\n",
      "          1.34300000e-01,  -1.37300000e+00,   1.34300000e-01,\n",
      "         -1.37300000e+00],\n",
      "       [  7.25000000e+00,   8.11000000e+00,   8.00000000e+00,\n",
      "         -1.47500000e-01,   7.26600000e-01,  -1.47500000e-01,\n",
      "          7.26600000e-01],\n",
      "       [  7.25000000e+00,   8.48500000e+00,   8.37500000e+00,\n",
      "          2.60000000e-02,   4.58100000e-01,   2.60000000e-02,\n",
      "          4.58100000e-01],\n",
      "       [  7.25000000e+00,   8.63500000e+00,   8.52500000e+00,\n",
      "          8.50000000e-02,   2.04300000e-01,   8.50000000e-02,\n",
      "          2.04300000e-01],\n",
      "       [  7.25000000e+00,   8.83500000e+00,   8.72500000e+00,\n",
      "          1.88100000e-01,  -2.21100000e-01,   1.88100000e-01,\n",
      "         -2.21100000e-01],\n",
      "       [  7.25000000e+00,   8.98500000e+00,   8.87500000e+00,\n",
      "          2.36500000e-01,  -8.59100000e-01,   2.36500000e-01,\n",
      "         -8.59100000e-01],\n",
      "       [  7.50000000e+00,   8.11000000e+00,   8.00000000e+00,\n",
      "         -1.30500000e-01,   1.14780000e+00,  -1.30500000e-01,\n",
      "          1.14780000e+00],\n",
      "       [  7.50000000e+00,   8.48500000e+00,   8.37500000e+00,\n",
      "          5.83000000e-02,   8.70400000e-01,   5.83000000e-02,\n",
      "          8.70400000e-01],\n",
      "       [  7.50000000e+00,   8.63500000e+00,   8.52500000e+00,\n",
      "          1.32800000e-01,   6.21800000e-01,   1.32800000e-01,\n",
      "          6.21800000e-01],\n",
      "       [  7.50000000e+00,   8.83500000e+00,   8.72500000e+00,\n",
      "          2.61000000e-01,   2.08900000e-01,   2.61000000e-01,\n",
      "          2.08900000e-01],\n",
      "       [  7.50000000e+00,   8.98500000e+00,   8.87500000e+00,\n",
      "          3.29600000e-01,  -4.11200000e-01,   3.29600000e-01,\n",
      "         -4.11200000e-01],\n",
      "       [  7.75000000e+00,   8.11000000e+00,   8.00000000e+00,\n",
      "         -1.38700000e-01,   1.48750000e+00,  -1.38700000e-01,\n",
      "          1.48750000e+00],\n",
      "       [  7.75000000e+00,   8.48500000e+00,   8.37500000e+00,\n",
      "          6.41000000e-02,   1.19960000e+00,   6.41000000e-02,\n",
      "          1.19960000e+00],\n",
      "       [  7.75000000e+00,   8.63500000e+00,   8.52500000e+00,\n",
      "          1.55400000e-01,   9.54100000e-01,   1.55400000e-01,\n",
      "          9.54100000e-01],\n",
      "       [  7.75000000e+00,   8.83500000e+00,   8.72500000e+00,\n",
      "          3.15100000e-01,   5.44300000e-01,   3.15100000e-01,\n",
      "          5.44300000e-01],\n",
      "       [  7.75000000e+00,   8.98500000e+00,   8.87500000e+00,\n",
      "          4.13200000e-01,  -6.93000000e-02,   4.13200000e-01,\n",
      "         -6.93000000e-02],\n",
      "       [  8.00000000e+00,   8.11000000e+00,   8.00000000e+00,\n",
      "         -1.58100000e-01,   1.77370000e+00,  -1.58100000e-01,\n",
      "          1.77370000e+00],\n",
      "       [  8.00000000e+00,   8.48500000e+00,   8.37500000e+00,\n",
      "          5.49000000e-02,   1.47220000e+00,   5.49000000e-02,\n",
      "          1.47220000e+00],\n",
      "       [  8.00000000e+00,   8.63500000e+00,   8.52500000e+00,\n",
      "          1.61600000e-01,   1.22540000e+00,   1.61600000e-01,\n",
      "          1.22540000e+00],\n",
      "       [  8.00000000e+00,   8.83500000e+00,   8.72500000e+00,\n",
      "          3.56600000e-01,   8.05500000e-01,   3.56600000e-01,\n",
      "          8.05500000e-01],\n",
      "       [  8.00000000e+00,   8.98500000e+00,   8.87500000e+00,\n",
      "          4.93600000e-01,   1.62200000e-01,   4.93600000e-01,\n",
      "          1.62200000e-01],\n",
      "       [  8.25000000e+00,   8.11000000e+00,   8.00000000e+00,\n",
      "         -1.78600000e-01,   2.02670000e+00,  -1.78600000e-01,\n",
      "          2.02670000e+00],\n",
      "       [  8.25000000e+00,   8.48500000e+00,   8.37500000e+00,\n",
      "          3.99000000e-02,   1.70560000e+00,   3.99000000e-02,\n",
      "          1.70560000e+00],\n",
      "       [  8.25000000e+00,   8.63500000e+00,   8.52500000e+00,\n",
      "          1.57700000e-01,   1.45270000e+00,   1.57700000e-01,\n",
      "          1.45270000e+00],\n",
      "       [  8.25000000e+00,   8.83500000e+00,   8.72500000e+00,\n",
      "          3.86100000e-01,   1.01450000e+00,   3.86100000e-01,\n",
      "          1.01450000e+00],\n",
      "       [  8.25000000e+00,   8.98500000e+00,   8.87500000e+00,\n",
      "          5.65400000e-01,   3.11300000e-01,   5.65400000e-01,\n",
      "          3.11300000e-01],\n",
      "       [  8.50000000e+00,   8.11000000e+00,   8.00000000e+00,\n",
      "         -1.94700000e-01,   2.25340000e+00,  -1.94700000e-01,\n",
      "          2.25340000e+00],\n",
      "       [  8.50000000e+00,   8.48500000e+00,   8.37500000e+00,\n",
      "          2.50000000e-02,   1.90310000e+00,   2.50000000e-02,\n",
      "          1.90310000e+00],\n",
      "       [  8.50000000e+00,   8.63500000e+00,   8.52500000e+00,\n",
      "          1.48300000e-01,   1.63860000e+00,   1.48300000e-01,\n",
      "          1.63860000e+00],\n",
      "       [  8.50000000e+00,   8.83500000e+00,   8.72500000e+00,\n",
      "          4.00000000e-01,   1.17710000e+00,   4.00000000e-01,\n",
      "          1.17710000e+00],\n",
      "       [  8.50000000e+00,   8.98500000e+00,   8.87500000e+00,\n",
      "          6.13400000e-01,   4.14500000e-01,   6.13400000e-01,\n",
      "          4.14500000e-01]]), ['LogQ', 'Tot[O]+12', 'gas[O]+12', '[NII]/[SII]+', '[OIII]/[SII]+', 'Mix_x', 'Mix_y'], {'date': 'Thu Dec 10 14:16:36 CLST 2015', 'columns': ['LogQ', 'Tot[O]+12', 'gas[O]+12', 'Tot[Fe]', 'gas[Fe]', 'LogP/k', '[NII]/Ha', '[NII]+/Ha', '[SII]+/Ha', '[NII]/[OII]+', '[NII]+/[OII]+', '[NII]/[SII]+', '[NII]+/[SII]+', '[OII]+/Hb', '[OIII]/Hb', '[OIII]+/Hb', '[OIII]/[OII]+', '[OIII]+/[OII]+', '[OIII]/[SII]+', '[OIII]+/[SII]+', '4363/[OIII]', '4363/[OIII]+', '[OI]/[OII]+', '[OII]+/[SII]+', 'R23', 'S23', '6716/6731', '3729/3726'], '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', 'resampled': {}, 'MV_id': 'MAPPINGS V HII Region Grid: QZ pp_GCZO_Pk50_kinf'}]\n"
     ]
    }
   ],
   "source": [
    "a_grid = pyqz.get_grid('[NII]/[SII]+;[OIII]/[SII]+', sampling=1)\n",
    "print a_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyqz.pyqzm.pyqz_grid_dir='/usr/local/share/mappings/data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/yhwang/local/anaconda/envs/pyqz/lib/python2.7/site-packages/pyqz/ref_data'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyqz.pyqzm.pyqz_grid_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/yhwang/local/anaconda/envs/pyqz/lib/python2.7/site-packages/pyqz'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyqz.pyqzm.pyqz_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_grid = pyqz.get_grid('[NII]/[SII]+;[OIII]/[SII]+', sampling=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_grid = pyqz.get_grid('[NII]/[SII]+;[OIII]/[SII]+', struct = 'pp', Pk = 5, kappa = 'inf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyqz.pyqz_plots as pyqzp"
   ]
  },
  {
   "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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 关闭tex，恢复正常\n",
    "from matplotlib import rc\n",
    "rc(\"text\", usetex=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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y7adLp1XSnGLYmu0RkkZLWqQa2UfbbZOUdD/ZHmB7iaRWSQ9K+j9Jb0XExtJDCjWvP8a7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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "a = np.array(range(30))\n",
    "plt.hist(a,bins=5,label=\"${test^{\\odot}-1.4}$\");\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3bqw5duvWLf73v/8RFRVFaGgo48eP58aNG9SqVStbiHRxcZHJGo+4urqyYsWKPL2nra0t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\n3wFvSCmr9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1a0atVKdyw7O5sNGzZQWFjIli1bmDNnDkePHsXf379UJbJJkyZ63V+8SZMm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\nJDAKIYQQQogSSWAUQgghhBAl+j9gnxzxvQqqewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KkW5ubnKP5FcoX758jkxmKVWqFP3796d///4kJCSwefNmNmzYwKBBg6hRowatW7emdevW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TJ+/3MNyw84YWVd9Jt3eF/oN86aZ596h9Cn2hMcHKzULar436KXmkgIga+vL76+vrz22mtkZmZq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\nQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KkW5ubnKP5FcoX758jkxmKVWqFP3796d///4kJCSwefNmNmzYwKBBg6hRowatW7emdevW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TJ+/3MNyw84YWVd9Jt3eF/oN86aZ596h9Cn2hMcHKzULar436KXmkgIga+vL76+vrz22mtkZmZq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\nQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyqzp.plot_grid('[NII]/[OII]+;[OIII]/[OII]+', struct = 'pp', Pk = 5, kappa = 'inf',color_mode='gas[O]+12',sampling=1)"
   ]
  },
  {
   "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()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
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 "nbformat": 4,
 "nbformat_minor": 4
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