Voyager Tutorial p2 Waterfall.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "blimpy2\\filterbank.py:58: UserWarning: \n",
      "This call to matplotlib.use() has no effect because the backend has already\n",
      "been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
      "or matplotlib.backends is imported for the first time.\n",
      "\n",
      "The backend was *originally* set to 'module://ipykernel.pylab.backend_inline' by the following code:\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\runpy.py\", line 174, in _run_module_as_main\n",
      "    \"__main__\", fname, loader, pkg_name)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\runpy.py\", line 72, in _run_code\n",
      "    exec code in run_globals\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n",
      "    app.launch_new_instance()\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n",
      "    app.start()\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 478, in start\n",
      "    self.io_loop.start()\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\zmq\\eventloop\\ioloop.py\", line 177, in start\n",
      "    super(ZMQIOLoop, self).start()\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\tornado\\ioloop.py\", line 888, in start\n",
      "    handler_func(fd_obj, events)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 440, in _handle_events\n",
      "    self._handle_recv()\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 472, in _handle_recv\n",
      "    self._run_callback(callback, msg)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 414, in _run_callback\n",
      "    callback(*args, **kwargs)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n",
      "    return self.dispatch_shell(stream, msg)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 233, in dispatch_shell\n",
      "    handler(stream, idents, msg)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n",
      "    user_expressions, allow_stdin)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 208, in do_execute\n",
      "    res = shell.run_cell(code, store_history=store_history, silent=silent)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 537, in run_cell\n",
      "    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2718, in run_cell\n",
      "    interactivity=interactivity, compiler=compiler, result=result)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2822, in run_ast_nodes\n",
      "    if self.run_code(code, result):\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2882, in run_code\n",
      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "  File \"<ipython-input-1-4e9c60738e0b>\", line 5, in <module>\n",
      "    import pylab as plt\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\pylab.py\", line 1, in <module>\n",
      "    from matplotlib.pylab import *\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\matplotlib\\pylab.py\", line 257, in <module>\n",
      "    from matplotlib import cbook, mlab, pyplot as plt\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\matplotlib\\pyplot.py\", line 72, in <module>\n",
      "    from matplotlib.backends import pylab_setup\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\matplotlib\\backends\\__init__.py\", line 14, in <module>\n",
      "    line for line in traceback.format_stack()\n",
      "\n",
      "\n",
      "  matplotlib.use('Agg')\n",
      "blimpy2\\guppi.py:30: UserWarning: \n",
      "This call to matplotlib.use() has no effect because the backend has already\n",
      "been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
      "or matplotlib.backends is imported for the first time.\n",
      "\n",
      "The backend was *originally* set to 'module://ipykernel.pylab.backend_inline' by the following code:\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\runpy.py\", line 174, in _run_module_as_main\n",
      "    \"__main__\", fname, loader, pkg_name)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\runpy.py\", line 72, in _run_code\n",
      "    exec code in run_globals\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n",
      "    app.launch_new_instance()\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n",
      "    app.start()\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 478, in start\n",
      "    self.io_loop.start()\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\zmq\\eventloop\\ioloop.py\", line 177, in start\n",
      "    super(ZMQIOLoop, self).start()\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\tornado\\ioloop.py\", line 888, in start\n",
      "    handler_func(fd_obj, events)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 440, in _handle_events\n",
      "    self._handle_recv()\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 472, in _handle_recv\n",
      "    self._run_callback(callback, msg)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 414, in _run_callback\n",
      "    callback(*args, **kwargs)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n",
      "    return self.dispatch_shell(stream, msg)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 233, in dispatch_shell\n",
      "    handler(stream, idents, msg)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n",
      "    user_expressions, allow_stdin)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 208, in do_execute\n",
      "    res = shell.run_cell(code, store_history=store_history, silent=silent)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 537, in run_cell\n",
      "    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2718, in run_cell\n",
      "    interactivity=interactivity, compiler=compiler, result=result)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2822, in run_ast_nodes\n",
      "    if self.run_code(code, result):\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2882, in run_code\n",
      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "  File \"<ipython-input-1-4e9c60738e0b>\", line 5, in <module>\n",
      "    import pylab as plt\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\pylab.py\", line 1, in <module>\n",
      "    from matplotlib.pylab import *\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\matplotlib\\pylab.py\", line 257, in <module>\n",
      "    from matplotlib import cbook, mlab, pyplot as plt\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\matplotlib\\pyplot.py\", line 72, in <module>\n",
      "    from matplotlib.backends import pylab_setup\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\matplotlib\\backends\\__init__.py\", line 14, in <module>\n",
      "    line for line in traceback.format_stack()\n",
      "\n",
      "\n",
      "  matplotlib.use('Agg')\n",
      "blimpy2\\waterfall.py:52: UserWarning: \n",
      "This call to matplotlib.use() has no effect because the backend has already\n",
      "been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
      "or matplotlib.backends is imported for the first time.\n",
      "\n",
      "The backend was *originally* set to 'module://ipykernel.pylab.backend_inline' by the following code:\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\runpy.py\", line 174, in _run_module_as_main\n",
      "    \"__main__\", fname, loader, pkg_name)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\runpy.py\", line 72, in _run_code\n",
      "    exec code in run_globals\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n",
      "    app.launch_new_instance()\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n",
      "    app.start()\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 478, in start\n",
      "    self.io_loop.start()\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\zmq\\eventloop\\ioloop.py\", line 177, in start\n",
      "    super(ZMQIOLoop, self).start()\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\tornado\\ioloop.py\", line 888, in start\n",
      "    handler_func(fd_obj, events)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 440, in _handle_events\n",
      "    self._handle_recv()\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 472, in _handle_recv\n",
      "    self._run_callback(callback, msg)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 414, in _run_callback\n",
      "    callback(*args, **kwargs)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n",
      "    return self.dispatch_shell(stream, msg)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 233, in dispatch_shell\n",
      "    handler(stream, idents, msg)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n",
      "    user_expressions, allow_stdin)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 208, in do_execute\n",
      "    res = shell.run_cell(code, store_history=store_history, silent=silent)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 537, in run_cell\n",
      "    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2718, in run_cell\n",
      "    interactivity=interactivity, compiler=compiler, result=result)\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2822, in run_ast_nodes\n",
      "    if self.run_code(code, result):\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2882, in run_code\n",
      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "  File \"<ipython-input-1-4e9c60738e0b>\", line 5, in <module>\n",
      "    import pylab as plt\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\pylab.py\", line 1, in <module>\n",
      "    from matplotlib.pylab import *\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\matplotlib\\pylab.py\", line 257, in <module>\n",
      "    from matplotlib import cbook, mlab, pyplot as plt\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\matplotlib\\pyplot.py\", line 72, in <module>\n",
      "    from matplotlib.backends import pylab_setup\n",
      "  File \"C:\\Users\\chris\\Anaconda2\\lib\\site-packages\\matplotlib\\backends\\__init__.py\", line 14, in <module>\n",
      "    line for line in traceback.format_stack()\n",
      "\n",
      "\n",
      "  matplotlib.use('Agg')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- File Info ---\n",
      "    telescope_id :                                6\n",
      "     rawdatafile : ./blc3_2bit_guppi_57386_VOYAGER1_0004.0000.raw\n",
      "     source_name :                         VOYAGER1\n",
      "          tstart :                    57386.8649074\n",
      "       data_type :                                1\n",
      "          nchans :                         66060288\n",
      "           ibeam :                                1\n",
      "           tsamp :                      105.6964608\n",
      "            foff :               -2.83831641788e-06\n",
      "         src_raj :                     17:11:58.728\n",
      "         src_dej :                      11:56:57.48\n",
      "          nbeams :                                1\n",
      "        az_start :                              0.0\n",
      "            fch1 :                    8588.96484233\n",
      "        za_start :                              0.0\n",
      "      machine_id :                               20\n",
      "            nifs :                                1\n",
      "           nbits :                               32\n",
      "\n",
      "Num ints in file :                                2\n",
      "      File shape :                (2L, 1, 66060288)\n",
      "--- Selection Info ---\n",
      "Data selection shape :                 (2, 1, 66060288)\n",
      "Minimum freq (MHz) :                    8401.46484233\n",
      "Maximum freq (MHz) :                    8588.96484233\n"
     ]
    }
   ],
   "source": [
    "#working directly from breakthrough/GBT/voyager/voyager.ipynb\n",
    "#Using Python 2 and the Waterfall.py method of importing filterbank files\n",
    "#https://github.com/UCBerkeleySETI/breakthrough/blob/master/GBT/voyager/voyager.ipynb\n",
    "\n",
    "import pylab as plt\n",
    "from blimpy2 import Waterfall\n",
    "%matplotlib inline\n",
    "\n",
    "obs = Waterfall('voyager_f1032192_t300_v2.fil')\n",
    "obs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'telescope_id': 6, 'rawdatafile': './blc3_2bit_guppi_57386_VOYAGER1_0004.0000.raw', 'source_name': 'VOYAGER1', 'tstart': 57386.864907407406, 'data_type': 1, 'nchans': 66060288, 'ibeam': 1, 'tsamp': 105.6964608, 'foff': -2.838316417875744e-06, 'src_raj': <Angle 17.19964667 hourangle>, 'src_dej': <Angle 11.9493 deg>, 'nbeams': 1, 'az_start': 0.0, 'fch1': 8588.964842330843, 'za_start': 0.0, 'machine_id': 20, 'nifs': 1, 'nbits': 32}\n",
      "(2L, 1L, 66060288L)\n"
     ]
    }
   ],
   "source": [
    "print obs.header\n",
    "print obs.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "extracting integration 0...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8556f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot_spectrum works here.\n",
    "\n",
    "obs.plot_spectrum()\n",
    "#fig = plt.gcf()\n",
    "#fig.savefig(\"justwork5.png\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "extracting integration 0...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe6da710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot_spectrum can't zoom (doesn't zoom in Waterfall case)\n",
    "\n",
    "obs.plot_spectrum(f_start=8420.18, f_stop=8420.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "extracting integration 0...\n",
      "extracting integration 0...\n",
      "extracting integration 0...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe918c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot_spectrum can't zoom (doesn't zoom in Waterfall case)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.subplot(3,1,1)\n",
    "obs.plot_spectrum(f_start=8420.193, f_stop=8420.195) # left sideband\n",
    "plt.subplot(3,1,2)\n",
    "obs.plot_spectrum(f_start=8420.2163, f_stop=8420.2166) # carrier\n",
    "plt.subplot(3,1,3)\n",
    "obs.plot_spectrum(f_start=8420.238, f_stop=8420.24) # right sideband\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe918780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot_waterfall works\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "obs.plot_waterfall() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb9d91d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot_waterfall can't zoom. Plot remains totally unchanged in Waterfall case\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.subplot(3,1,1)\n",
    "obs.plot_waterfall(f_start=8420.193, f_stop=8420.195) # left sideband\n",
    "plt.subplot(3,1,2)\n",
    "obs.plot_waterfall(f_start=8420.2163, f_stop=8420.2166) # carrier\n",
    "plt.subplot(3,1,3)\n",
    "obs.plot_waterfall(f_start=8420.238, f_stop=8420.24) # right sideband\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}