{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support for dask arrays\n",
    "\n",
    "It is possible to operate on dask arrays and spare the memory (or perhaps even time)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Necessary imports\n",
    "import dask\n",
    "import dask.multiprocessing\n",
    "import physt\n",
    "import numpy as np\n",
    "\n",
    "import dask.array as da\n",
    "from physt import h1, h2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create two arrays\n",
    "np.random.seed(42)\n",
    "\n",
    "SIZE = 2 ** 21\n",
    "CHUNK = int(SIZE / 16)\n",
    "\n",
    "million = np.random.rand(SIZE)#.astype(int)\n",
    "million2 = (3 * million + np.random.normal(0., 0.3, SIZE))#.astype(int)\n",
    "\n",
    "# Chunk them for dask\n",
    "chunked = da.from_array(million, chunks=(CHUNK))\n",
    "chunked2 = da.from_array(million2, chunks=(CHUNK))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create histograms\n",
    "\n",
    "`h1`, `h2`, ... have their alternatives in `physt.dask_compat`. They should work similarly. Although, they are not complete and unexpected errors may occur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from physt.compat.dask import h1 as d1\n",
    "from physt.compat.dask import h2 as d2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check:  True\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Histogram1D(bins=(28,), total=2097152, dtype=int64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b7k6G37CS5IIkj7T6oSQbxx2vJGlpOB9nUF8GXhx5fDvwZFVtBp5sj0myBdgJXAVsB+5J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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3958062160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use chunks to create a 1D histogram\n",
    "ha = d1(chunked2, \"fixed_width\", 0.2)\n",
    "check_ha = h1(million2, \"fixed_width\", 0.2)\n",
    "ok = (ha == check_ha)\n",
    "print(\"Check: \", ok)\n",
    "ha.plot()\n",
    "ha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check:  True\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Histogram2D(bins=(5, 28), total=2097152, dtype=int64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3974c83a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use chunks to create a 2D histogram\n",
    "hb = d2(chunked, chunked2, \"fixed_width\", .2, axis_names=[\"x\", \"y\"])\n",
    "check_hb = h2(million, million2, \"fixed_width\", .2, axis_names=[\"x\", \"y\"])\n",
    "hb.plot(show_zero=False, cmap=\"rainbow\")\n",
    "ok = (hb == check_hb)\n",
    "print(\"Check: \", ok)\n",
    "hb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check:  True\n"
     ]
    },
    {
     "data": {
      "image/png": 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t7gNuATa3ZXur7wZer6qrgbuBuy5AfyVJS8BYAZVkPfCbwJdHyjuAfW19H3DjSP2Rqnqr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\nqUsGlCSpSwaUJKlLBpQkqUv/DxDzONb6DQSBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f395804a4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# And another cross-check\n",
    "hh = hb.projection(\"y\")\n",
    "hh.plot()\n",
    "print(\"Check: \", np.array_equal(hh.frequencies, ha.frequencies))   # Just frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use dask for normal arrays (will automatically split array to chunks)\n",
    "d1(million2, \"fixed_width\", 0.2) == ha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some timings\n",
    "\n",
    "Your results may vary substantially. These numbers are just for illustration, on 4-core (8-thread) machine. The real gain comes when we have data that don't fit into memory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Efficiency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.13 s, sys: 28.7 ms, total: 1.16 s\n",
      "Wall time: 1.16 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Histogram1D(bins=(28,), total=2097152, dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standard\n",
    "%time h1(million2, \"fixed_width\", 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 807 ms, sys: 4.29 ms, total: 812 ms\n",
      "Wall time: 212 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Histogram1D(bins=(28,), total=2097152, dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Same array, but using dask\n",
    "%time d1(million2, \"fixed_width\", 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 777 ms, sys: 9.89 ms, total: 787 ms\n",
      "Wall time: 178 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Histogram1D(bins=(28,), total=2097152, dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most efficient: dask with already chunked data\n",
    "%time d1(chunked2, \"fixed_width\", 0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Different scheduling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 751 ms, sys: 9.9 ms, total: 761 ms\n",
      "Wall time: 173 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Histogram1D(bins=(28,), total=2097152, dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time d1(chunked2, \"fixed_width\", 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 560 ms, sys: 14.3 ms, total: 574 ms\n",
      "Wall time: 183 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Hyper-threading or not?\n",
    "graph, name = d1(chunked2, \"fixed_width\", 0.2, compute=False)\n",
    "dask.threaded.get(graph, name, num_workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 677 ms, sys: 589 ms, total: 1.27 s\n",
      "Wall time: 4.69 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Histogram1D(bins=(28,), total=2097152, dtype=int64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multiprocessing not so efficient for small arrays?\n",
    "%time d1(chunked2, \"fixed_width\", 0.2, dask_method=dask.multiprocessing.get)"
   ]
  }
 ],
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   "codemirror_mode": {
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