Benchmark HT3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#filename = r'C:/Data/Antonio/software/src/phconvert/notebooks/data/Pre.ht3'\n",
    "filename = r'data/eitan/Pre.ht3'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "run_id = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.5.3-7-gc062ec1'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import phconvert as phc\n",
    "phc.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "class Timer:\n",
    "    def __init__(self, name):\n",
    "        self.name = name\n",
    "        \n",
    "    def __enter__(self):\n",
    "        self.start = time.clock()\n",
    "        return self\n",
    "\n",
    "    def __exit__(self, *args):\n",
    "        self.end = time.clock()\n",
    "        self.interval = self.end - self.start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Author"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "author = 'Eitan Lerner'\n",
    "author_affiliation = 'UCLA'\n",
    "creator = 'Antonino Ingargiola'\n",
    "creator_affiliation = 'UCLA'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "comment = 'A demostrative smFRET-nsALEX measurement.'\n",
    "sample_name = 'Doubly-labeled ssDNA partially hybridized to a complementary strand.'\n",
    "dye_names = 'ATTO488, ATTO647N'\n",
    "buffer_name = 'Tris20 mM Ph 7.8'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare data\n",
    "\n",
    "## Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>native_numpy_numba</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.978359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   native_numpy_numba\n",
       "0            0.978359"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with Timer('pq_read_speed_native_numba') as t:\n",
    "    timestamps, detectors, nanotimes, meta = phc.pqreader.load_ht3(filename)\n",
    "    \n",
    "read_speed_native_numpy_numba = pd.DataFrame({'native_numpy_numba': t.interval}, index=[0])\n",
    "read_speed_native_numpy_numba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOTE: You can speed-up the loading time by installing numba.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>native_numpy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.274539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   native_numpy\n",
       "0     37.274539"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with Timer('pq_read_speed_native') as t:\n",
    "    timestamps, detectors, nanotimes, meta = phc.pqreader.load_ht3(filename, ovcfunc=phc.pqreader._correct_overflow2)\n",
    "\n",
    "read_speed_native_numpy = pd.DataFrame({'native_numpy': t.interval}, index=[0])\n",
    "read_speed_native_numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20120771"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timestamps.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "899.98510079999994"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(timestamps[-1]-timestamps[0])*50e-9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d, meta = phc.loader.nsalex_ht3(filename,\n",
    "                                donor = 0,\n",
    "                                acceptor = 1,\n",
    "                                alex_period_donor = (150, 1500),\n",
    "                                alex_period_acceptor = (1540, 3050),\n",
    "                                excitation_wavelengths = (470e-9, 635e-9),\n",
    "                                detection_wavelengths = (525e-9, 690e-9),\n",
    "                                time_reversed = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Lv/Ysn4stpXq2Z91c95gxwFDgMJDrPnbQQNJGcfelDF2ewSW+x7nt6QJQHkvqon5c2e8I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\nIiIiIkjHICIiIiIigP8ChvlEwuUIKfQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xddc3eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13, 4))\n",
    "phc.plotter.alternation_hist(d, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Detector numbers: ', array([  0,   1, 127], dtype=uint8))\n"
     ]
    }
   ],
   "source": [
    "detectors = d['photon_data']['detectors']\n",
    "print('Detector numbers: ', np.unique(detectors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detector    Counts\n",
      "--------   --------\n",
      "       0    1788207\n",
      "       1     754729\n",
      "     127   17577835\n"
     ]
    }
   ],
   "source": [
    "print(\"Detector    Counts\")\n",
    "print(\"--------   --------\")\n",
    "for det, count in zip(*np.unique(detectors, return_counts=True)):\n",
    "    print(\"%8d   %8d\" % (det, count))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add author and sample info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d['comment'] = comment\n",
    "\n",
    "d['sample'] = dict(\n",
    "    sample_name=sample_name,\n",
    "    dye_names=dye_names,\n",
    "    buffer_name=buffer_name,\n",
    "    num_dyes = len(dye_names.split(',')))\n",
    "\n",
    "d['identity'] = dict(\n",
    "    author=author,\n",
    "    author_affiliation=author_affiliation,\n",
    "    creator=creator,\n",
    "    creator_affiliation=creator_affiliation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validate the Photon-HDF5 structure\n",
    "\n",
    "Before writing to disk, we assure the file structure follows the Photon-HDF5 format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "phc.hdf5.assert_valid_photon_hdf5(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save to Photon-HDF5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/eitan/Pre_zlib0.hdf5\n",
      "Saving: data/eitan/Pre_zlib0.hdf5\n",
      "data/eitan/Pre_zlib1.hdf5\n",
      "Saving: data/eitan/Pre_zlib1.hdf5\n",
      "data/eitan/Pre_zlib2.hdf5\n",
      "Saving: data/eitan/Pre_zlib2.hdf5\n",
      "data/eitan/Pre_zlib3.hdf5\n",
      "Saving: data/eitan/Pre_zlib3.hdf5\n",
      "data/eitan/Pre_zlib4.hdf5\n",
      "Saving: data/eitan/Pre_zlib4.hdf5\n",
      "data/eitan/Pre_zlib5.hdf5\n",
      "Saving: data/eitan/Pre_zlib5.hdf5\n",
      "data/eitan/Pre_zlib6.hdf5\n",
      "Saving: data/eitan/Pre_zlib6.hdf5\n",
      "data/eitan/Pre_zlib7.hdf5\n",
      "Saving: data/eitan/Pre_zlib7.hdf5\n",
      "data/eitan/Pre_zlib8.hdf5\n",
      "Saving: data/eitan/Pre_zlib8.hdf5\n",
      "data/eitan/Pre_zlib9.hdf5\n",
      "Saving: data/eitan/Pre_zlib9.hdf5\n",
      "data/eitan/Pre_blosc0.hdf5\n",
      "Saving: data/eitan/Pre_blosc0.hdf5\n",
      "data/eitan/Pre_blosc1.hdf5\n",
      "Saving: data/eitan/Pre_blosc1.hdf5\n",
      "data/eitan/Pre_blosc2.hdf5\n",
      "Saving: data/eitan/Pre_blosc2.hdf5\n",
      "data/eitan/Pre_blosc3.hdf5\n",
      "Saving: data/eitan/Pre_blosc3.hdf5\n",
      "data/eitan/Pre_blosc4.hdf5\n",
      "Saving: data/eitan/Pre_blosc4.hdf5\n",
      "data/eitan/Pre_blosc5.hdf5\n",
      "Saving: data/eitan/Pre_blosc5.hdf5\n",
      "data/eitan/Pre_blosc6.hdf5\n",
      "Saving: data/eitan/Pre_blosc6.hdf5\n",
      "data/eitan/Pre_blosc7.hdf5\n",
      "Saving: data/eitan/Pre_blosc7.hdf5\n",
      "data/eitan/Pre_blosc8.hdf5\n",
      "Saving: data/eitan/Pre_blosc8.hdf5\n",
      "data/eitan/Pre_blosc9.hdf5\n",
      "Saving: data/eitan/Pre_blosc9.hdf5\n"
     ]
    }
   ],
   "source": [
    "write_speed_ = {}\n",
    "file_size_ = {}\n",
    "for complib in ['zlib', 'blosc']:\n",
    "    for complevel in range(0, 10):\n",
    "        compression = {'complevel': complevel, 'complib': complib}\n",
    "        fn = \"%s_%s%d.hdf5\" % (filename[:-4], complib, complevel)\n",
    "        print (fn)\n",
    "        with Timer(fn) as t:\n",
    "            phc.hdf5.save_photon_hdf5(d, close=True, h5_fname=fn, compression=compression)\n",
    "        write_speed_[(complib, complevel)] = t\n",
    "        file_size_[(complib, complevel)] = os.path.getsize(fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t_blosc = [write_speed_[key].interval for key in sorted(write_speed_.keys())if key[0] == 'blosc']\n",
    "t_zlib = [write_speed_[key].interval for key in sorted(write_speed_.keys())if key[0] == 'zlib']\n",
    "\n",
    "size_blosc = [file_size_[key] for key in sorted(file_size_.keys())if key[0] == 'blosc']\n",
    "size_zlib = [file_size_[key] for key in sorted(file_size_.keys())if key[0] == 'zlib']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "write_speed = pd.DataFrame({'zlib': t_zlib, 'blosc': t_blosc})\n",
    "file_size = pd.DataFrame({'zlib': size_zlib, 'blosc': size_blosc})\n",
    "file_size['native'] = os.path.getsize(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x107e10f0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe305ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Zhiw5xn/mbTbp+o2dQzvviDhxDvucOt9gZmZWnd8eb602DfN525QBcuTIkAHGPM/bzMzy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdc1ee48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "write_speed.plot(kind='bar')\n",
    "file_size.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OLw9zTpj/0GPO7flOrGfC/Pet3by4+XdNn39uP49Dc06Y/9Ccj/Ofx4b/fW75z2Nk2LR6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\nSR36/ytUt4lISoJ7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdcc2358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "write_speed.plot(kind='bar')\n",
    "plt.ylim(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x104652e8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x3d5b438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "write_speed.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OLw9zTpj/0GPO7flOrGfC/Pet3by4+XdNn39uP49Dc06Y/9Ccj/Ofx4b/fW75z2Nk2LR6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\nSR36/ytUt4lISoJ7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdcc2828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "write_speed.plot(kind='bar')\n",
    "plt.ylim(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "write_speed.to_hdf('benchmark%d.h5' % run_id, key='ht3_write_speed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_size.to_hdf('benchmark%d.h5' % run_id, key='ht3_file_size')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/eitan/Pre_zlib9.hdf5\n",
      "data/eitan/Pre_zlib8.hdf5\n",
      "data/eitan/Pre_zlib7.hdf5\n",
      "data/eitan/Pre_zlib6.hdf5\n",
      "data/eitan/Pre_zlib5.hdf5\n",
      "data/eitan/Pre_zlib4.hdf5\n",
      "data/eitan/Pre_zlib3.hdf5\n",
      "data/eitan/Pre_zlib2.hdf5\n",
      "data/eitan/Pre_zlib1.hdf5\n",
      "data/eitan/Pre_zlib0.hdf5\n",
      "data/eitan/Pre_blosc9.hdf5\n",
      "data/eitan/Pre_blosc8.hdf5\n",
      "data/eitan/Pre_blosc7.hdf5\n",
      "data/eitan/Pre_blosc6.hdf5\n",
      "data/eitan/Pre_blosc5.hdf5\n",
      "data/eitan/Pre_blosc4.hdf5\n",
      "data/eitan/Pre_blosc3.hdf5\n",
      "data/eitan/Pre_blosc2.hdf5\n",
      "data/eitan/Pre_blosc1.hdf5\n",
      "data/eitan/Pre_blosc0.hdf5\n"
     ]
    }
   ],
   "source": [
    "read_speed_ = {}\n",
    "cache = []\n",
    "for complib in ['zlib', 'blosc']:\n",
    "    for complevel in range(9, -1, -1):\n",
    "        fn = \"%s_%s%d.hdf5\" % (filename[:-4], complib, complevel)\n",
    "        print (fn)\n",
    "        assert os.path.exists(fn)\n",
    "        with Timer(fn) as t:\n",
    "            \n",
    "            with tables.open_file(fn) as h5:\n",
    "                a = h5.root.photon_data.timestamps.read()\n",
    "                b = h5.root.photon_data.detectors.read()\n",
    "                c = h5.root.photon_data.nanotimes.read()\n",
    "        \n",
    "        cache.append((a,b,c))\n",
    "        read_speed_[(complib, complevel)] = t\n",
    "del cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#h5 = tables.open_file('data/eitan/Pre.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# h5.root.photon_data.timestamps.chunkshape\n",
    "# h5.root.photon_data.detectors.chunkshape\n",
    "# h5.root.photon_data.nanotimes.chunkshape\n",
    "# h5.root.photon_data.timestamps.size_in_memory\n",
    "# h5.root.photon_data.timestamps.size_on_disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t_blosc = [read_speed_[key].interval for key in sorted(read_speed_.keys())if key[0] == 'blosc']\n",
    "t_zlib = [read_speed_[key].interval for key in sorted(read_speed_.keys())if key[0] == 'zlib']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "read_speed = pd.DataFrame({'zlib': t_zlib, 'blosc': t_blosc})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "read_speed = read_speed.join(read_speed_native_numpy)\n",
    "read_speed = read_speed.join(read_speed_native_numpy_numba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1058c748>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vk3SzpLslnVxbv0TSqbXyfEnrauUR4M2SrirOiyXNkvR9SfepGv91l5a63inpNkm3Szqx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e82e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "read_speed.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1.2)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y9PO1eR5FmxLgC8CeFM/LY8D5fcrwKeA/JP1Q0n+X9Mw+bbfqfOAY4H2SLgT+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\nVNDMzCbm4m1mVkMu3mZmNeTibWZWQ/8fZGRnP+LswaIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112e7908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "read_speed.plot(kind='bar')\n",
    "plt.ylim(0, 1.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1.2)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y9PO1eR5FmxLgC8CeFM/LY8D5fcrwKeA/JP1Q0n+X9Mw+bbfqfOAY4H2SLgT+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\nVNDMzCbm4m1mVkMu3mZmNeTibWZWQ/8fZGRnP+LswaIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11507748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "read_speed.plot(kind='bar')\n",
    "plt.ylim(0, 1.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1.2)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y9PO1eR5FmxLgC8CeFM/LY8D5fcrwKeA/JP1Q0n+X9Mw+bbfqfOAY4H2SLgT+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\nVNDMzCbm4m1mVkMu3mZmNeTibWZWQ/8fZGRnP+LswaIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112b8f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "read_speed.plot(kind='bar')\n",
    "plt.ylim(0, 1.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>blosc</th>\n",
       "      <th>zlib</th>\n",
       "      <th>native_numpy</th>\n",
       "      <th>native_numpy_numba</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.154712</td>\n",
       "      <td>0.148167</td>\n",
       "      <td>37.274539</td>\n",
       "      <td>0.978359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.155966</td>\n",
       "      <td>0.584412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.153241</td>\n",
       "      <td>0.563229</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.152987</td>\n",
       "      <td>0.538936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.225491</td>\n",
       "      <td>0.533664</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.224690</td>\n",
       "      <td>0.499392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.262756</td>\n",
       "      <td>0.474334</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.333553</td>\n",
       "      <td>0.466075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.333610</td>\n",
       "      <td>0.467009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.393065</td>\n",
       "      <td>0.452551</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      blosc      zlib  native_numpy  native_numpy_numba\n",
       "0  0.154712  0.148167     37.274539            0.978359\n",
       "1  0.155966  0.584412           NaN                 NaN\n",
       "2  0.153241  0.563229           NaN                 NaN\n",
       "3  0.152987  0.538936           NaN                 NaN\n",
       "4  0.225491  0.533664           NaN                 NaN\n",
       "5  0.224690  0.499392           NaN                 NaN\n",
       "6  0.262756  0.474334           NaN                 NaN\n",
       "7  0.333553  0.466075           NaN                 NaN\n",
       "8  0.333610  0.467009           NaN                 NaN\n",
       "9  0.393065  0.452551           NaN                 NaN"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "read_speed.to_hdf('benchmark%d.h5' % run_id, key='ht3_read_speed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "2.7.9"
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}