{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#03---Using-dask-and-zarr-for-multithreaded-input/output\" data-toc-modified-id=\"03---Using-dask-and-zarr-for-multithreaded-input/output-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>03 - Using dask and zarr for multithreaded input/output</a></span><ul class=\"toc-item\"><li><span><a href=\"#zarr\" data-toc-modified-id=\"zarr-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>zarr</a></span></li><li><span><a href=\"#dask\" data-toc-modified-id=\"dask-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>dask</a></span><ul class=\"toc-item\"><li><span><a href=\"#Example,-write-and-read-zarr-arrays-using-multiple-threads\" data-toc-modified-id=\"Example,-write-and-read-zarr-arrays-using-multiple-threads-1.2.1\"><span class=\"toc-item-num\">1.2.1&nbsp;&nbsp;</span>Example, write and read zarr arrays using multiple threads</a></span></li><li><span><a href=\"#Create-230-Mbytes-of--fake-data\" data-toc-modified-id=\"Create-230-Mbytes-of--fake-data-1.2.2\"><span class=\"toc-item-num\">1.2.2&nbsp;&nbsp;</span>Create 230 Mbytes of  fake data</a></span></li><li><span><a href=\"#Copy-to-a-zarr-file-on-disk,-using-multiple-threads\" data-toc-modified-id=\"Copy-to-a-zarr-file-on-disk,-using-multiple-threads-1.2.3\"><span class=\"toc-item-num\">1.2.3&nbsp;&nbsp;</span>Copy to a zarr file on disk, using multiple threads</a></span></li><li><span><a href=\"#Add-some-attributes\" data-toc-modified-id=\"Add-some-attributes-1.2.4\"><span class=\"toc-item-num\">1.2.4&nbsp;&nbsp;</span>Add some attributes</a></span></li><li><span><a href=\"#Create-an-array-of-zeros----note-that-compression-shrinks-it-from-230-Mbytes-to-321-bytes\" data-toc-modified-id=\"Create-an-array-of-zeros----note-that-compression-shrinks-it-from-230-Mbytes-to-321-bytes-1.2.5\"><span class=\"toc-item-num\">1.2.5&nbsp;&nbsp;</span>Create an array of zeros -- note that compression shrinks it from 230 Mbytes to 321 bytes</a></span></li><li><span><a href=\"#copy-input-to-output-using-chunks\" data-toc-modified-id=\"copy-input-to-output-using-chunks-1.2.6\"><span class=\"toc-item-num\">1.2.6&nbsp;&nbsp;</span>copy input to output using chunks</a></span></li><li><span><a href=\"#Create-a-dask-array-from-a-zarr-disk-file\" data-toc-modified-id=\"Create-a-dask-array-from-a-zarr-disk-file-1.2.7\"><span class=\"toc-item-num\">1.2.7&nbsp;&nbsp;</span>Create a dask array from a zarr disk file</a></span></li><li><span><a href=\"#The-following-calculation-uses-numpy,-so-it-releases-the-GIL\" data-toc-modified-id=\"The-following-calculation-uses-numpy,-so-it-releases-the-GIL-1.2.8\"><span class=\"toc-item-num\">1.2.8&nbsp;&nbsp;</span>The following calculation uses numpy, so it releases the GIL</a></span></li><li><span><a href=\"#Note-that-result-hasn't-been-computed-yet\" data-toc-modified-id=\"Note-that-result-hasn't-been-computed-yet-1.2.9\"><span class=\"toc-item-num\">1.2.9&nbsp;&nbsp;</span>Note that result hasn't been computed yet</a></span></li><li><span><a href=\"#Now-do-the-calculation\" data-toc-modified-id=\"Now-do-the-calculation-1.2.10\"><span class=\"toc-item-num\">1.2.10&nbsp;&nbsp;</span>Now do the calculation</a></span></li><li><span><a href=\"#You-can-evaluate-your-own-functions-on-dask-arrays\" data-toc-modified-id=\"You-can-evaluate-your-own-functions-on-dask-arrays-1.2.11\"><span class=\"toc-item-num\">1.2.11&nbsp;&nbsp;</span>You can evaluate your own functions on dask arrays</a></span></li></ul></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    conda install zarr\n",
    "    conda install dask\n",
    "    conda install pytz  #for our timestamp timezone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import zarr\n",
    "import time\n",
    "import datetime\n",
    "import pytz\n",
    "from zarr.util import human_readable_size\n",
    "import dask\n",
    "import dask.array as da\n",
    "from dask.diagnostics import Profiler, ResourceProfiler, CacheProfiler\n",
    "from dask.diagnostics.profile_visualize import visualize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 03 - Using dask and zarr for multithreaded input/output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Many python programmers use [hdf5](https://support.hdfgroup.org/HDF5/) through either the [h5py](http://www.h5py.org/) or [pytables](http://www.pytables.org/) modules to store large dense arrays.  One drawback of the hdf5 implementation is that it is basically **single-threaded**, that is only one core can read or write to a dataset at any one time.  Multithreading makes data compression much more attractive, because data sections can be decompressing/compressing simultaneouly while the data is read/written.\n",
    "\n",
    "## zarr\n",
    "\n",
    "* [zarr](http://zarr.readthedocs.io/en/latest/?badge=latest) keeps the h5py interface (which is similar to numpy's), but allows different choices for file compression and is fully multithreaded.  See [Alistair Miles original blog entry](http://alimanfoo.github.io/2016/05/16/cpu-blues.html) for a discussion of the motivation behind zarr.\n",
    "\n",
    "## dask\n",
    "\n",
    "* [dask](http://dask.pydata.org/en/latest/) is a Python library that implements lazy data structures (array, dataframe, bag) and a clever thread/process scheduler.  It integrates with zarr to allow calculations on datasets that don't fit into core memory, either in a single node or across a cluster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example, write and read zarr arrays using multiple threads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create 230 Mbytes of  fake data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'228.9M'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wvel_data = np.random.normal(2000, 1000, size=[8000,7500]).astype(np.float32)\n",
    "human_readable_size(wvel_data.nbytes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Copy to a zarr file on disk, using multiple threads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "item='disk1_data'\n",
    "store = zarr.DirectoryStore(item)\n",
    "group=zarr.hierarchy.group(store=store,overwrite=True,synchronizer=zarr.ThreadSynchronizer())\n",
    "the_var='wvel'\n",
    "out_zarr1=group.zeros(the_var,shape=wvel_data.shape,dtype=wvel_data.dtype,chunks=[2000,7500])\n",
    "out_zarr1[...]=wvel_data[...]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add some attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zarr.core.Array '/wvel' (8000, 7500) float32>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now=datetime.datetime.now(pytz.UTC)\n",
    "timestamp= int(now.strftime('%s'))\n",
    "out_zarr1.attrs['history']='written for practice'\n",
    "out_zarr1.attrs['creation_date']=str(now)\n",
    "out_zarr1.attrs['gmt_timestap']=timestamp\n",
    "out_zarr1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create an array of zeros -- note that compression shrinks it from 230 Mbytes to 321 bytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zarr.core.Array '/wvel' (8000, 7500) float32>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2 = np.zeros([8000,7500],dtype=np.float32)\n",
    "item='disk2_data'\n",
    "store = zarr.DirectoryStore(item)\n",
    "group=zarr.hierarchy.group(store=store,overwrite=True,synchronizer=zarr.ThreadSynchronizer())\n",
    "the_var='wvel'\n",
    "out_zarr2=group.zeros(the_var,shape=a2.shape,dtype=a2.dtype,chunks=[2000,7500])\n",
    "out_zarr2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### copy input to output using chunks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zarr.core.Array '/wvel' (8000, 7500) float32>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item='disk2_data'\n",
    "store = zarr.DirectoryStore(item)\n",
    "group=zarr.hierarchy.group(store=store,overwrite=True,synchronizer=zarr.ThreadSynchronizer())\n",
    "the_var='wvel'\n",
    "out_zarr=group.empty(the_var,shape=wvel_data.shape,dtype=wvel_data.dtype,chunks=[2000,7500])\n",
    "out_zarr2[...]=out_zarr1[...]\n",
    "out_zarr2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a dask array from a zarr disk file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dask.array<array, shape=(8000, 7500), dtype=float32, chunksize=(2000, 7500)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da_input = da.from_array(out_zarr2, chunks=out_zarr1.chunks)\n",
    "da_input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The following calculation uses numpy, so it releases the GIL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dask.array<mean_agg-aggregate, shape=(7500,), dtype=float32, chunksize=(7500,)>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result=(da_input**2. + da_input**3.).mean(axis=0)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note that result hasn't been computed yet\n",
    "\n",
    "Here is a graph of how the calculation will be split among 4 threads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask.dot import dot_graph\n",
    "dot_graph(result.dask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now do the calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "with Profiler() as prof, ResourceProfiler(dt=0.1) as rprof,\\\n",
    "              CacheProfiler() as cprof:\n",
    "    answer = result.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the cpu, memory and cache for the 4 threads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"display: table;\"><div style=\"display: table-row;\"><div style=\"display: table-cell;\"><b title=\"bokeh.models.layouts.Column\">Column</b>(</div><div style=\"display: table-cell;\">id&nbsp;=&nbsp;'84a884ae-ca4a-4418-8b27-bdee0147c3cc', <span id=\"64728ce9-daaa-4aef-9fc6-e1dc35cb4d70\" style=\"cursor: pointer;\">&hellip;)</span></div></div><div class=\"a3f00e0c-7b52-4d84-ad31-5822e2453f52\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">children&nbsp;=&nbsp;[ToolbarBox(id='52863537-2eef-4c10-8baa-821530a64b76', ...), Column(id='c93aec75-a9a1-4803-a160-b3981c757fca', ...)],</div></div><div class=\"a3f00e0c-7b52-4d84-ad31-5822e2453f52\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">css_classes&nbsp;=&nbsp;[],</div></div><div class=\"a3f00e0c-7b52-4d84-ad31-5822e2453f52\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">disabled&nbsp;=&nbsp;False,</div></div><div class=\"a3f00e0c-7b52-4d84-ad31-5822e2453f52\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">height&nbsp;=&nbsp;None,</div></div><div class=\"a3f00e0c-7b52-4d84-ad31-5822e2453f52\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">js_event_callbacks&nbsp;=&nbsp;{},</div></div><div class=\"a3f00e0c-7b52-4d84-ad31-5822e2453f52\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">js_property_callbacks&nbsp;=&nbsp;{},</div></div><div class=\"a3f00e0c-7b52-4d84-ad31-5822e2453f52\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">name&nbsp;=&nbsp;None,</div></div><div class=\"a3f00e0c-7b52-4d84-ad31-5822e2453f52\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">sizing_mode&nbsp;=&nbsp;'fixed',</div></div><div class=\"a3f00e0c-7b52-4d84-ad31-5822e2453f52\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">subscribed_events&nbsp;=&nbsp;[],</div></div><div class=\"a3f00e0c-7b52-4d84-ad31-5822e2453f52\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">tags&nbsp;=&nbsp;[],</div></div><div class=\"a3f00e0c-7b52-4d84-ad31-5822e2453f52\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">width&nbsp;=&nbsp;None)</div></div></div>\n",
       "<script>\n",
       "(function() {\n",
       "  var expanded = false;\n",
       "  var ellipsis = document.getElementById(\"64728ce9-daaa-4aef-9fc6-e1dc35cb4d70\");\n",
       "  ellipsis.addEventListener(\"click\", function() {\n",
       "    var rows = document.getElementsByClassName(\"a3f00e0c-7b52-4d84-ad31-5822e2453f52\");\n",
       "    for (var i = 0; i < rows.length; i++) {\n",
       "      var el = rows[i];\n",
       "      el.style.display = expanded ? \"none\" : \"table-row\";\n",
       "    }\n",
       "    ellipsis.innerHTML = expanded ? \"&hellip;)\" : \"&lsaquo;&lsaquo;&lsaquo;\";\n",
       "    expanded = !expanded;\n",
       "  });\n",
       "})();\n",
       "</script>\n"
      ],
      "text/plain": [
       "Column(id='84a884ae-ca4a-4418-8b27-bdee0147c3cc', ...)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualize([prof, rprof,cprof], min_border_top=15, min_border_bottom=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You can evaluate your own functions on dask arrays\n",
    "\n",
    "If your functons release the GIL, you can get multithreaded computation using [dask.delayed](http://dask.pydata.org/en/latest/delayed.html)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": false,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}