{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we demonstrate the experimental dask-yt particle loader in `./dask_chunking/gadget_da.py` \n",
    "\n",
    "The dask approach here attempts to wrap the loading and filtering of individual chunks with the `dask.delayed` operator (to various degress of success...), resulting in a lazy load of a `gadget` particle dataset that is automatically parallelized when running a `dask.distributed.Client`. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask_chunking import gadget_da as gda\n",
    "from dask import compute, visualize\n",
    "import yt \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's spin up a dask `Client`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask.distributed import Client "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = Client(threads_per_worker=2,n_workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:36323</li>\n",
       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>4</li>\n",
       "  <li><b>Cores: </b>8</li>\n",
       "  <li><b>Memory: </b>33.51 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:36323' processes=4 threads=8, memory=33.51 GB>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and import a `gadget` dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "yt : [WARNING  ] 2020-09-29 16:17:10,398 tqdm is not installed, progress bar can not be displayed.\n",
      "yt : [INFO     ] 2020-09-29 16:17:10,930 Files located at /home/chavlin/hdd/data/yt_data/yt_sample_sets/snapshot_033.tar.gz.untar/snapshot_033/snap_033.\n",
      "yt : [INFO     ] 2020-09-29 16:17:10,931 Default to loading snap_033.0.hdf5 for snapshot_033 dataset\n",
      "yt : [INFO     ] 2020-09-29 16:17:10,992 Parameters: current_time              = 4.343952725460923e+17 s\n"
     ]
    }
   ],
   "source": [
    "ds = yt.load_sample(\"snapshot_033\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "at present, we're going to use yt's current `chunk`ing methods to distribute each base chunk (composed of a single hdf file with start and end indices within it) to a dask-delayed read function. We'll be using the experimental `delayed_gadget` class within `dask_chunking/gadget_da.py`, which we initialize with the `ds` object, a dictionary of particle types and fields to import `ptf` and optional `subchunk_size` (explained later):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "yt : [INFO     ] 2020-09-29 16:17:11,050 Allocating for 4.194e+06 particles\n",
      "Loading particle index: 100%|██████████| 12/12 [00:00<00:00, 190.07it/s]\n"
     ]
    }
   ],
   "source": [
    "ptf = {'PartType0': ['Mass']}\n",
    "delayed_reader = gda.delayed_gadget(ds, ptf, subchunk_size = None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "within the initialization, this class will call `stage_chunks()`, which is where the dask magic happens: \n",
    "\n",
    "```\n",
    "self.delayed_chunks = []\n",
    "for df in self.data_files:\n",
    "    # dask_delayed will need to serialize all objects passed -- can't handle df object, so \n",
    "    # let's just pull out what we need for this chunk:\n",
    "    df_dict = {key:getattr(df,key) for key in ['filename','start','end','total_particles']}\n",
    "    df_dict['var_mass']=self.var_mass\n",
    "    df_dict['_element_names']=self._element_names       \n",
    "    self.delayed_chunks.append(delayed_chunk_read(self.ptf,df_dict,self.subchunk_size))\n",
    "```\n",
    "\n",
    "here, we're assembling a list of delayed objects. A single `delayed_chunk_read` will read in a single chunk and is decorated with the `@dask.delayed` decorator to signal to dask that we want to delay this function:\n",
    "\n",
    "```\n",
    "@dask.delayed\n",
    "def delayed_chunk_read(ptf,df_dict,subchunk_size):\n",
    "    return chunk_reader(ptf,df_dict,subchunk_size).read()\n",
    "```\n",
    "\n",
    "The `chunk_reader` object is a class for loading a single chunk, comprised of code from the `gadget` front end hdf loader. \n",
    "\n",
    "Note one very important point: all arguments to the delayed function **must be** pickle-serializable as dask will pickle the arguments to send to the different processors on execution. This is why the second argument to `delayed_chunk_read` is `df_dict`: `self.data_files` is a list of `ds.index.data_files[0]` objects (each is a `yt.data_objects.static_output.ParticleFile`) and `var_mas` and `_element_names` are attributes of `ds.index.io`. So far, the built in dask-pickling of `yt` objects has failed (due to cython issues -- will touch on this more below when we get to filtering....), but at this point, we just need some pretty basic data to pass (e.g., the filename, start and end indices in each file) so we store that info in a `dict` to pass to the `delayed_chunk_read`. \n",
    "\n",
    "Ok, so after initializing our `delayed_reader` object, we'll have a list of `delayed_chunks`:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Delayed('delayed_chunk_read-e80b8a5d-1f8f-4076-a36c-31e32767b910'),\n",
       " Delayed('delayed_chunk_read-330489c6-6ce8-4469-b3b5-82023af5cfd4'),\n",
       " Delayed('delayed_chunk_read-0d7fc985-65f0-4807-9b34-c654e9fe80a0'),\n",
       " Delayed('delayed_chunk_read-ef7075a8-c830-45e1-976f-8b6eb3ae67b4'),\n",
       " Delayed('delayed_chunk_read-9dbaba38-5522-49fd-bec1-cddb671fe25d'),\n",
       " Delayed('delayed_chunk_read-6f2f4385-b14c-4988-9ef0-335659a60e40'),\n",
       " Delayed('delayed_chunk_read-788e2d05-e7ca-4a1a-ae54-366a634710bf'),\n",
       " Delayed('delayed_chunk_read-6c6f217e-2cbc-4417-9743-e03a46f17df0'),\n",
       " Delayed('delayed_chunk_read-bbd70b46-8cb5-47de-ab01-bc6d6228988d'),\n",
       " Delayed('delayed_chunk_read-38554e82-f6a0-49ce-a80f-879fedd8bc1f'),\n",
       " Delayed('delayed_chunk_read-387e6bb1-b39b-4ebe-b0e1-5f9eb8956adf'),\n",
       " Delayed('delayed_chunk_read-db32222e-dfcf-442e-9493-4fde741b085a')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delayed_reader.delayed_chunks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A single delayed task can be visualized:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delayed_reader.delayed_chunks[0].visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and then computed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'PartType0': array([[ 9.0948925 , 18.5401268 , 13.50576115],\n",
       "         [ 9.09940147, 18.55133247, 13.51190567],\n",
       "         [ 9.08276558, 18.54066086, 13.49641895],\n",
       "         ...,\n",
       "         [ 9.94860458,  8.4767704 , 14.56663513],\n",
       "         [ 9.94866085,  8.47825813, 14.56705093],\n",
       "         [ 9.94791031,  8.47807693, 14.56690121]])},\n",
       " {('PartType0',\n",
       "   'Mass'): array([0.01576188, 0.01663664, 0.01871505, 0.00970176, 0.01931598,\n",
       "         0.01710295, 0.01869999, 0.00868698, 0.01191492, 0.01526247,\n",
       "         0.00870004, 0.00864736, 0.00866873, 0.01210038, 0.00866515,\n",
       "         0.00865903, 0.00893035, 0.00864386, 0.00864437, 0.0095512 ,\n",
       "         0.009749  , 0.00864466, 0.00952867, 0.00864396, 0.00865185,\n",
       "         0.00864417, 0.00865002, 0.00864616, 0.00864787, 0.0086467 ,\n",
       "         0.00864758, 0.00865183, 0.00864775, 0.00939687, 0.0086449 ,\n",
       "         0.00871795, 0.01162997, 0.01007061, 0.00868322, 0.01283415,\n",
       "         0.00864652, 0.00864493, 0.00864921, 0.00864537, 0.00867223,\n",
       "         0.00864423, 0.00864423, 0.00864427, 0.00864402, 0.00864405,\n",
       "         0.00864469, 0.00864398, 0.00864398, 0.00864617, 0.00865819,\n",
       "         0.00864479, 0.00865467, 0.00865023, 0.00864672, 0.00864549,\n",
       "         0.0086541 , 0.00864614, 0.0086621 , 0.00864856, 0.00866107,\n",
       "         0.00864497, 0.00864437, 0.00872628, 0.00865319, 0.00864782,\n",
       "         0.00867574, 0.00865113, 0.00864541, 0.00864583, 0.00865423,\n",
       "         0.00864672, 0.0094079 , 0.00865236, 0.00865042, 0.00864537,\n",
       "         0.0095771 , 0.00864497, 0.0086466 , 0.00864951, 0.00864564,\n",
       "         0.00864719, 0.00973641, 0.01123166, 0.0086561 , 0.00865673,\n",
       "         0.00867016, 0.00866977, 0.00865894, 0.00864647, 0.00864547,\n",
       "         0.00864431, 0.0111323 , 0.00864911, 0.00864662, 0.00868544,\n",
       "         0.00871773, 0.00865634, 0.0086528 , 0.0086698 , 0.00867322,\n",
       "         0.01034854, 0.00867792, 0.00864386, 0.0086446 , 0.0086485 ,\n",
       "         0.00867851, 0.00864389, 0.00864397, 0.00864393, 0.00865264,\n",
       "         0.0086439 , 0.00864573, 0.00864641, 0.00865118, 0.00884463,\n",
       "         0.00864407, 0.00871034, 0.00865471, 0.00867828, 0.008648  ,\n",
       "         0.00864427, 0.00868855, 0.00864828, 0.00867095, 0.00866964,\n",
       "         0.00866721, 0.00865462, 0.0086743 , 0.00866969, 0.00864895,\n",
       "         0.00865275, 0.00864571, 0.00865658, 0.00865294, 0.00867992,\n",
       "         0.00867557, 0.00868229, 0.00865193, 0.00865912, 0.00886692,\n",
       "         0.00868004, 0.0086862 , 0.019488  , 0.01122443, 0.02183115,\n",
       "         0.00866625, 0.00871245, 0.00867322, 0.00865103, 0.00864984,\n",
       "         0.00868173, 0.00872087, 0.02139982, 0.00966472, 0.01720018,\n",
       "         0.0188228 , 0.00870653, 0.00874153, 0.00868849, 0.02225389,\n",
       "         0.01747124, 0.01951511, 0.010462  , 0.01086188, 0.00889738,\n",
       "         0.02430551, 0.01698648, 0.00949531, 0.00874803, 0.00882819,\n",
       "         0.01417806, 0.0155667 , 0.01420717, 0.00888111, 0.00911644,\n",
       "         0.01714752, 0.01734492, 0.01464341, 0.00908168, 0.02144803,\n",
       "         0.00971754, 0.0148535 , 0.0099078 , 0.01139312, 0.01358491,\n",
       "         0.01857252, 0.01024381, 0.00927314, 0.01057635, 0.00979624,\n",
       "         0.01658888, 0.01408297, 0.00916107, 0.01534646, 0.00989786,\n",
       "         0.01286644, 0.00963002, 0.00885178, 0.00875167, 0.01189861,\n",
       "         0.01009631, 0.00870869, 0.00879007, 0.01150377, 0.00997297,\n",
       "         0.0087672 , 0.00869129, 0.00868801, 0.00867148, 0.01129076,\n",
       "         0.00868363, 0.00864965, 0.01027128, 0.00874708, 0.00876408,\n",
       "         0.0093245 , 0.00882076, 0.00898408, 0.00865777, 0.01579425,\n",
       "         0.00865272, 0.00906334, 0.00910297, 0.00948077, 0.02122764,\n",
       "         0.01258226, 0.02637874, 0.01447314, 0.00951241, 0.0102029 ,\n",
       "         0.00874167, 0.00866766, 0.00865206, 0.00881413, 0.00890438,\n",
       "         0.00881805, 0.01753351, 0.01460347, 0.01022284, 0.01129906,\n",
       "         0.00892293, 0.01131135, 0.01149893, 0.00896504, 0.00892386,\n",
       "         0.0088651 , 0.00881195, 0.00879243, 0.00865685, 0.01000845,\n",
       "         0.00872331, 0.00981471, 0.00867905, 0.00896335, 0.00958879,\n",
       "         0.00891158, 0.0094134 , 0.01036824, 0.00924235, 0.00927313,\n",
       "         0.01221033, 0.01330525, 0.02035448, 0.00941199, 0.0100465 ,\n",
       "         0.01363682, 0.01297417, 0.01663146, 0.01457284, 0.00903581,\n",
       "         0.0101647 , 0.0157372 , 0.00878596, 0.00913168, 0.0107145 ,\n",
       "         0.00977161, 0.00956479, 0.00882804, 0.00884613, 0.01691047,\n",
       "         0.00893549, 0.00903166, 0.00894935, 0.00877558, 0.01156324,\n",
       "         0.00924913, 0.01358276, 0.01894272, 0.00955947, 0.00942666,\n",
       "         0.00875606, 0.00881323, 0.00956746, 0.00883421, 0.01629753,\n",
       "         0.0086669 , 0.00876433, 0.0096875 , 0.01188321, 0.00955884,\n",
       "         0.00873123, 0.00865462, 0.00867205, 0.00865692, 0.0086477 ,\n",
       "         0.00879077, 0.00901418, 0.00893167, 0.00881205, 0.00865754,\n",
       "         0.00935456, 0.00976939, 0.00866351, 0.00867221, 0.0143476 ,\n",
       "         0.00867235, 0.00870647, 0.01151083, 0.01523547, 0.00873427,\n",
       "         0.02577072, 0.02243926, 0.00878209, 0.00868718, 0.0086521 ,\n",
       "         0.01103147, 0.02126098, 0.00865098, 0.01706288, 0.00923983,\n",
       "         0.01008528, 0.0086549 , 0.0086628 , 0.00865432, 0.00866331,\n",
       "         0.00864874, 0.00865209, 0.02152279, 0.00866388, 0.00865532,\n",
       "         0.00864429, 0.00864659, 0.00868481, 0.01024063, 0.00865658,\n",
       "         0.00865499, 0.00864694, 0.00867339, 0.01285803, 0.00866809,\n",
       "         0.00866094, 0.0086523 , 0.00864416, 0.00865817, 0.00864684,\n",
       "         0.00865339, 0.00864393, 0.00866137, 0.0108283 , 0.00865872,\n",
       "         0.00865904, 0.01000292, 0.00865042, 0.01252308, 0.0156426 ,\n",
       "         0.00864781, 0.00865091, 0.00864583, 0.00895159, 0.00867303,\n",
       "         0.01230804, 0.01055389, 0.01355155, 0.01290767, 0.01301   ,\n",
       "         0.00865055, 0.00864436, 0.00867914, 0.00864389, 0.00864386,\n",
       "         0.00864974, 0.00864396, 0.0086496 , 0.00864521, 0.00864442,\n",
       "         0.00864768, 0.00864387, 0.00864543, 0.00865471, 0.00864542,\n",
       "         0.00885514, 0.02166013, 0.01593382, 0.03830643, 0.02097851,\n",
       "         0.02344084, 0.02224258, 0.01855106, 0.01791457, 0.01292829,\n",
       "         0.01000241, 0.01100658, 0.01275772, 0.0119765 , 0.00996338,\n",
       "         0.02194469, 0.01048275, 0.01669842, 0.01753752, 0.02722569,\n",
       "         0.01266365, 0.01107106, 0.01257765, 0.01240593], dtype=float32)},\n",
       " {'PartType0': 419},\n",
       " {'PartType0': array([0.0362274 , 0.04072027, 0.03312379, 0.02896558, 0.02205147,\n",
       "         0.00877114, 0.02944615, 0.05218949, 0.04479058, 0.04834923,\n",
       "         0.05498597, 0.04909486, 0.06056419, 0.05916185, 0.06536201,\n",
       "         0.0655411 , 0.07402892, 0.08249536, 0.06964301, 0.07268751,\n",
       "         0.07055246, 0.07013194, 0.06199848, 0.0656049 , 0.05608417,\n",
       "         0.06475606, 0.06629061, 0.07440273, 0.06142577, 0.06010155,\n",
       "         0.06914758, 0.07620735, 0.07339235, 0.07072945, 0.06843527,\n",
       "         0.0668426 , 0.07417636, 0.06568062, 0.06325129, 0.05857513,\n",
       "         0.05703314, 0.05622455, 0.06062964, 0.0561012 , 0.06240107,\n",
       "         0.05941654, 0.05907911, 0.06473345, 0.0650272 , 0.06583549,\n",
       "         0.08296753, 0.07280328, 0.07204369, 0.08683529, 0.08404635,\n",
       "         0.0888662 , 0.08750486, 0.07622379, 0.07694837, 0.08925033,\n",
       "         0.0933044 , 0.09438443, 0.09164637, 0.08713006, 0.08212744,\n",
       "         0.08015949, 0.09371071, 0.08351221, 0.07779962, 0.07042104,\n",
       "         0.07774556, 0.07544592, 0.06854992, 0.07128523, 0.0756572 ,\n",
       "         0.07094415, 0.06158028, 0.07236401, 0.07635707, 0.07759096,\n",
       "         0.08299293, 0.07545715, 0.09372611, 0.08793347, 0.08378398,\n",
       "         0.07942198, 0.09281966, 0.08754937, 0.08050711, 0.08336712,\n",
       "         0.09307314, 0.0723382 , 0.07306948, 0.07770362, 0.07114083,\n",
       "         0.07283483, 0.05471307, 0.07389059, 0.06519151, 0.06373846,\n",
       "         0.06376646, 0.07601401, 0.07296657, 0.07870615, 0.08051294,\n",
       "         0.08569593, 0.08913077, 0.07907289, 0.08016539, 0.08965013,\n",
       "         0.07939333, 0.08652394, 0.08368821, 0.09370465, 0.08823942,\n",
       "         0.09525933, 0.08731857, 0.07737144, 0.07954582, 0.07683637,\n",
       "         0.09273514, 0.08429405, 0.07646301, 0.07249814, 0.08083472,\n",
       "         0.06946927, 0.06482527, 0.06914237, 0.04874399, 0.06462259,\n",
       "         0.07467827, 0.07808919, 0.07417724, 0.07948452, 0.07750621,\n",
       "         0.07887023, 0.07469898, 0.0635175 , 0.07534111, 0.06475607,\n",
       "         0.06666034, 0.05508586, 0.07060304, 0.0638115 , 0.05223786,\n",
       "         0.06709495, 0.06123501, 0.06320112, 0.06081865, 0.06781058,\n",
       "         0.076626  , 0.08280164, 0.06663618, 0.07467308, 0.07235598,\n",
       "         0.06975453, 0.06586163, 0.03802168, 0.04314238, 0.05293893,\n",
       "         0.04910813, 0.03643692, 0.03174896, 0.01563721, 0.01464684,\n",
       "         0.00705728, 0.00844126, 0.003637  , 0.00700946, 0.00468495,\n",
       "         0.00468506, 0.00480814, 0.00876328, 0.01127337, 0.01188874,\n",
       "         0.00671419, 0.00639295, 0.0064003 , 0.00590129, 0.00600383,\n",
       "         0.00633316, 0.00669372, 0.00609435, 0.00547613, 0.00497977,\n",
       "         0.00465166, 0.00378585, 0.00572562, 0.00619436, 0.01667102,\n",
       "         0.00620233, 0.00542352, 0.00491   , 0.0037913 , 0.00595162,\n",
       "         0.00387002, 0.00459971, 0.00545381, 0.00708293, 0.00773352,\n",
       "         0.00602002, 0.00624634, 0.00616982, 0.00828608, 0.00708508,\n",
       "         0.00467211, 0.0057542 , 0.00875203, 0.00923868, 0.00805847,\n",
       "         0.0077056 , 0.00748745, 0.00805753, 0.01527414, 0.01525134,\n",
       "         0.0167823 , 0.01676001, 0.00828551, 0.00676108, 0.00768243,\n",
       "         0.00647049, 0.00640104, 0.0313032 , 0.03243444, 0.03455401,\n",
       "         0.03763789, 0.01178556, 0.00573116, 0.00720229, 0.00811889,\n",
       "         0.01672093, 0.01295587, 0.01542056, 0.01643852, 0.01129668,\n",
       "         0.00957788, 0.01151714, 0.03219613, 0.03451392, 0.01773368,\n",
       "         0.01375548, 0.01400819, 0.00457728, 0.00552251, 0.00548933,\n",
       "         0.00564196, 0.00541974, 0.00687447, 0.00584219, 0.01065555,\n",
       "         0.00739247, 0.00890108, 0.00761655, 0.01243902, 0.0103886 ,\n",
       "         0.00894922, 0.00662579, 0.01072801, 0.01050928, 0.00722813,\n",
       "         0.00763576, 0.00589884, 0.00546007, 0.00556586, 0.00624686,\n",
       "         0.00556092, 0.00500055, 0.00548144, 0.00415254, 0.00414063,\n",
       "         0.00410838, 0.00407228, 0.00462903, 0.00576377, 0.00424026,\n",
       "         0.00496545, 0.00462801, 0.00410257, 0.00458128, 0.00471121,\n",
       "         0.00435966, 0.00414989, 0.00427084, 0.00470036, 0.00563183,\n",
       "         0.00603742, 0.0070658 , 0.01065387, 0.00656062, 0.00464363,\n",
       "         0.00543656, 0.00482806, 0.006045  , 0.0047382 , 0.00456503,\n",
       "         0.00455985, 0.00497938, 0.00526886, 0.0059534 , 0.00542501,\n",
       "         0.00593828, 0.00441934, 0.00679043, 0.00768412, 0.00478962,\n",
       "         0.00497975, 0.0096066 , 0.00931533, 0.00647827, 0.01122897,\n",
       "         0.00814056, 0.00886422, 0.0085073 , 0.00892586, 0.01026801,\n",
       "         0.00671302, 0.01081401, 0.01183196, 0.0130468 , 0.01662675,\n",
       "         0.02419041, 0.01684287, 0.0181704 , 0.01506921, 0.01469795,\n",
       "         0.01510171, 0.01064657, 0.00912229, 0.01318832, 0.02469332,\n",
       "         0.02548047, 0.02698232, 0.03244451, 0.01577784, 0.03815224,\n",
       "         0.03508397, 0.04572237, 0.04062009, 0.03922674, 0.04328152,\n",
       "         0.04045059, 0.03814987, 0.03539773, 0.03825895, 0.05366944,\n",
       "         0.05259725, 0.04932721, 0.04996442, 0.05926653, 0.05825708,\n",
       "         0.04762925, 0.05079945, 0.05384446, 0.0520142 , 0.05647924,\n",
       "         0.06862377, 0.082898  , 0.06551883, 0.06457354, 0.06285927,\n",
       "         0.05532062, 0.06260139, 0.06386063, 0.05833791, 0.04712606,\n",
       "         0.05040953, 0.03517448, 0.03794625, 0.02638085, 0.04859691,\n",
       "         0.05473974, 0.05040754, 0.05122105, 0.0418124 , 0.0412233 ,\n",
       "         0.02709608, 0.03020647, 0.03555377, 0.05101901, 0.04354959,\n",
       "         0.04748858, 0.05974634, 0.04787937, 0.0653081 , 0.07386712,\n",
       "         0.05966709, 0.06532861, 0.07046203, 0.0742046 , 0.07327932,\n",
       "         0.07168894, 0.07655903, 0.08079273, 0.08997273, 0.08147647,\n",
       "         0.00367353, 0.00323596, 0.00556057, 0.00267067, 0.00220054,\n",
       "         0.00235112, 0.00239005, 0.00257686, 0.00271898, 0.00263264,\n",
       "         0.00296338, 0.00251245, 0.00264074, 0.00287286, 0.00268097,\n",
       "         0.00183029, 0.00230579, 0.00183512, 0.00167413, 0.00162726,\n",
       "         0.00196856, 0.00309351, 0.00218892, 0.00213684])})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = delayed_reader.delayed_chunks[0].compute()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PartType0': array([[ 9.0948925 , 18.5401268 , 13.50576115],\n",
       "        [ 9.09940147, 18.55133247, 13.51190567],\n",
       "        [ 9.08276558, 18.54066086, 13.49641895],\n",
       "        ...,\n",
       "        [ 9.94860458,  8.4767704 , 14.56663513],\n",
       "        [ 9.94866085,  8.47825813, 14.56705093],\n",
       "        [ 9.94791031,  8.47807693, 14.56690121]])}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the returned object is a tuple of dicts containing the coordinates (`data[0]`), field values (`data[1]`), total particles (`data[2]`) and smoothing array (`data[3]`) organized by particle type. S\n",
    "\n",
    "Now let's visualize the full list of delayed objects using `dask.visualize` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualize(*delayed_reader.delayed_chunks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which shows that each process is independent (good for parallelizing!) and we can bring the entire dataset into memory with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 76.2 ms, sys: 68.6 ms, total: 145 ms\n",
      "Wall time: 1.97 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "all_data = compute(*delayed_reader.delayed_chunks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(({'PartType0': array([[ 9.0948925 , 18.5401268 , 13.50576115],\n",
       "          [ 9.09940147, 18.55133247, 13.51190567],\n",
       "          [ 9.08276558, 18.54066086, 13.49641895],\n",
       "          ...,\n",
       "          [ 9.94860458,  8.4767704 , 14.56663513],\n",
       "          [ 9.94866085,  8.47825813, 14.56705093],\n",
       "          [ 9.94791031,  8.47807693, 14.56690121]])},\n",
       "  {('PartType0',\n",
       "    'Mass'): array([0.01576188, 0.01663664, 0.01871505, 0.00970176, 0.01931598,\n",
       "          0.01710295, 0.01869999, 0.00868698, 0.01191492, 0.01526247,\n",
       "          0.00870004, 0.00864736, 0.00866873, 0.01210038, 0.00866515,\n",
       "          0.00865903, 0.00893035, 0.00864386, 0.00864437, 0.0095512 ,\n",
       "          0.009749  , 0.00864466, 0.00952867, 0.00864396, 0.00865185,\n",
       "          0.00864417, 0.00865002, 0.00864616, 0.00864787, 0.0086467 ,\n",
       "          0.00864758, 0.00865183, 0.00864775, 0.00939687, 0.0086449 ,\n",
       "          0.00871795, 0.01162997, 0.01007061, 0.00868322, 0.01283415,\n",
       "          0.00864652, 0.00864493, 0.00864921, 0.00864537, 0.00867223,\n",
       "          0.00864423, 0.00864423, 0.00864427, 0.00864402, 0.00864405,\n",
       "          0.00864469, 0.00864398, 0.00864398, 0.00864617, 0.00865819,\n",
       "          0.00864479, 0.00865467, 0.00865023, 0.00864672, 0.00864549,\n",
       "          0.0086541 , 0.00864614, 0.0086621 , 0.00864856, 0.00866107,\n",
       "          0.00864497, 0.00864437, 0.00872628, 0.00865319, 0.00864782,\n",
       "          0.00867574, 0.00865113, 0.00864541, 0.00864583, 0.00865423,\n",
       "          0.00864672, 0.0094079 , 0.00865236, 0.00865042, 0.00864537,\n",
       "          0.0095771 , 0.00864497, 0.0086466 , 0.00864951, 0.00864564,\n",
       "          0.00864719, 0.00973641, 0.01123166, 0.0086561 , 0.00865673,\n",
       "          0.00867016, 0.00866977, 0.00865894, 0.00864647, 0.00864547,\n",
       "          0.00864431, 0.0111323 , 0.00864911, 0.00864662, 0.00868544,\n",
       "          0.00871773, 0.00865634, 0.0086528 , 0.0086698 , 0.00867322,\n",
       "          0.01034854, 0.00867792, 0.00864386, 0.0086446 , 0.0086485 ,\n",
       "          0.00867851, 0.00864389, 0.00864397, 0.00864393, 0.00865264,\n",
       "          0.0086439 , 0.00864573, 0.00864641, 0.00865118, 0.00884463,\n",
       "          0.00864407, 0.00871034, 0.00865471, 0.00867828, 0.008648  ,\n",
       "          0.00864427, 0.00868855, 0.00864828, 0.00867095, 0.00866964,\n",
       "          0.00866721, 0.00865462, 0.0086743 , 0.00866969, 0.00864895,\n",
       "          0.00865275, 0.00864571, 0.00865658, 0.00865294, 0.00867992,\n",
       "          0.00867557, 0.00868229, 0.00865193, 0.00865912, 0.00886692,\n",
       "          0.00868004, 0.0086862 , 0.019488  , 0.01122443, 0.02183115,\n",
       "          0.00866625, 0.00871245, 0.00867322, 0.00865103, 0.00864984,\n",
       "          0.00868173, 0.00872087, 0.02139982, 0.00966472, 0.01720018,\n",
       "          0.0188228 , 0.00870653, 0.00874153, 0.00868849, 0.02225389,\n",
       "          0.01747124, 0.01951511, 0.010462  , 0.01086188, 0.00889738,\n",
       "          0.02430551, 0.01698648, 0.00949531, 0.00874803, 0.00882819,\n",
       "          0.01417806, 0.0155667 , 0.01420717, 0.00888111, 0.00911644,\n",
       "          0.01714752, 0.01734492, 0.01464341, 0.00908168, 0.02144803,\n",
       "          0.00971754, 0.0148535 , 0.0099078 , 0.01139312, 0.01358491,\n",
       "          0.01857252, 0.01024381, 0.00927314, 0.01057635, 0.00979624,\n",
       "          0.01658888, 0.01408297, 0.00916107, 0.01534646, 0.00989786,\n",
       "          0.01286644, 0.00963002, 0.00885178, 0.00875167, 0.01189861,\n",
       "          0.01009631, 0.00870869, 0.00879007, 0.01150377, 0.00997297,\n",
       "          0.0087672 , 0.00869129, 0.00868801, 0.00867148, 0.01129076,\n",
       "          0.00868363, 0.00864965, 0.01027128, 0.00874708, 0.00876408,\n",
       "          0.0093245 , 0.00882076, 0.00898408, 0.00865777, 0.01579425,\n",
       "          0.00865272, 0.00906334, 0.00910297, 0.00948077, 0.02122764,\n",
       "          0.01258226, 0.02637874, 0.01447314, 0.00951241, 0.0102029 ,\n",
       "          0.00874167, 0.00866766, 0.00865206, 0.00881413, 0.00890438,\n",
       "          0.00881805, 0.01753351, 0.01460347, 0.01022284, 0.01129906,\n",
       "          0.00892293, 0.01131135, 0.01149893, 0.00896504, 0.00892386,\n",
       "          0.0088651 , 0.00881195, 0.00879243, 0.00865685, 0.01000845,\n",
       "          0.00872331, 0.00981471, 0.00867905, 0.00896335, 0.00958879,\n",
       "          0.00891158, 0.0094134 , 0.01036824, 0.00924235, 0.00927313,\n",
       "          0.01221033, 0.01330525, 0.02035448, 0.00941199, 0.0100465 ,\n",
       "          0.01363682, 0.01297417, 0.01663146, 0.01457284, 0.00903581,\n",
       "          0.0101647 , 0.0157372 , 0.00878596, 0.00913168, 0.0107145 ,\n",
       "          0.00977161, 0.00956479, 0.00882804, 0.00884613, 0.01691047,\n",
       "          0.00893549, 0.00903166, 0.00894935, 0.00877558, 0.01156324,\n",
       "          0.00924913, 0.01358276, 0.01894272, 0.00955947, 0.00942666,\n",
       "          0.00875606, 0.00881323, 0.00956746, 0.00883421, 0.01629753,\n",
       "          0.0086669 , 0.00876433, 0.0096875 , 0.01188321, 0.00955884,\n",
       "          0.00873123, 0.00865462, 0.00867205, 0.00865692, 0.0086477 ,\n",
       "          0.00879077, 0.00901418, 0.00893167, 0.00881205, 0.00865754,\n",
       "          0.00935456, 0.00976939, 0.00866351, 0.00867221, 0.0143476 ,\n",
       "          0.00867235, 0.00870647, 0.01151083, 0.01523547, 0.00873427,\n",
       "          0.02577072, 0.02243926, 0.00878209, 0.00868718, 0.0086521 ,\n",
       "          0.01103147, 0.02126098, 0.00865098, 0.01706288, 0.00923983,\n",
       "          0.01008528, 0.0086549 , 0.0086628 , 0.00865432, 0.00866331,\n",
       "          0.00864874, 0.00865209, 0.02152279, 0.00866388, 0.00865532,\n",
       "          0.00864429, 0.00864659, 0.00868481, 0.01024063, 0.00865658,\n",
       "          0.00865499, 0.00864694, 0.00867339, 0.01285803, 0.00866809,\n",
       "          0.00866094, 0.0086523 , 0.00864416, 0.00865817, 0.00864684,\n",
       "          0.00865339, 0.00864393, 0.00866137, 0.0108283 , 0.00865872,\n",
       "          0.00865904, 0.01000292, 0.00865042, 0.01252308, 0.0156426 ,\n",
       "          0.00864781, 0.00865091, 0.00864583, 0.00895159, 0.00867303,\n",
       "          0.01230804, 0.01055389, 0.01355155, 0.01290767, 0.01301   ,\n",
       "          0.00865055, 0.00864436, 0.00867914, 0.00864389, 0.00864386,\n",
       "          0.00864974, 0.00864396, 0.0086496 , 0.00864521, 0.00864442,\n",
       "          0.00864768, 0.00864387, 0.00864543, 0.00865471, 0.00864542,\n",
       "          0.00885514, 0.02166013, 0.01593382, 0.03830643, 0.02097851,\n",
       "          0.02344084, 0.02224258, 0.01855106, 0.01791457, 0.01292829,\n",
       "          0.01000241, 0.01100658, 0.01275772, 0.0119765 , 0.00996338,\n",
       "          0.02194469, 0.01048275, 0.01669842, 0.01753752, 0.02722569,\n",
       "          0.01266365, 0.01107106, 0.01257765, 0.01240593], dtype=float32)},\n",
       "  {'PartType0': 419},\n",
       "  {'PartType0': array([0.0362274 , 0.04072027, 0.03312379, 0.02896558, 0.02205147,\n",
       "          0.00877114, 0.02944615, 0.05218949, 0.04479058, 0.04834923,\n",
       "          0.05498597, 0.04909486, 0.06056419, 0.05916185, 0.06536201,\n",
       "          0.0655411 , 0.07402892, 0.08249536, 0.06964301, 0.07268751,\n",
       "          0.07055246, 0.07013194, 0.06199848, 0.0656049 , 0.05608417,\n",
       "          0.06475606, 0.06629061, 0.07440273, 0.06142577, 0.06010155,\n",
       "          0.06914758, 0.07620735, 0.07339235, 0.07072945, 0.06843527,\n",
       "          0.0668426 , 0.07417636, 0.06568062, 0.06325129, 0.05857513,\n",
       "          0.05703314, 0.05622455, 0.06062964, 0.0561012 , 0.06240107,\n",
       "          0.05941654, 0.05907911, 0.06473345, 0.0650272 , 0.06583549,\n",
       "          0.08296753, 0.07280328, 0.07204369, 0.08683529, 0.08404635,\n",
       "          0.0888662 , 0.08750486, 0.07622379, 0.07694837, 0.08925033,\n",
       "          0.0933044 , 0.09438443, 0.09164637, 0.08713006, 0.08212744,\n",
       "          0.08015949, 0.09371071, 0.08351221, 0.07779962, 0.07042104,\n",
       "          0.07774556, 0.07544592, 0.06854992, 0.07128523, 0.0756572 ,\n",
       "          0.07094415, 0.06158028, 0.07236401, 0.07635707, 0.07759096,\n",
       "          0.08299293, 0.07545715, 0.09372611, 0.08793347, 0.08378398,\n",
       "          0.07942198, 0.09281966, 0.08754937, 0.08050711, 0.08336712,\n",
       "          0.09307314, 0.0723382 , 0.07306948, 0.07770362, 0.07114083,\n",
       "          0.07283483, 0.05471307, 0.07389059, 0.06519151, 0.06373846,\n",
       "          0.06376646, 0.07601401, 0.07296657, 0.07870615, 0.08051294,\n",
       "          0.08569593, 0.08913077, 0.07907289, 0.08016539, 0.08965013,\n",
       "          0.07939333, 0.08652394, 0.08368821, 0.09370465, 0.08823942,\n",
       "          0.09525933, 0.08731857, 0.07737144, 0.07954582, 0.07683637,\n",
       "          0.09273514, 0.08429405, 0.07646301, 0.07249814, 0.08083472,\n",
       "          0.06946927, 0.06482527, 0.06914237, 0.04874399, 0.06462259,\n",
       "          0.07467827, 0.07808919, 0.07417724, 0.07948452, 0.07750621,\n",
       "          0.07887023, 0.07469898, 0.0635175 , 0.07534111, 0.06475607,\n",
       "          0.06666034, 0.05508586, 0.07060304, 0.0638115 , 0.05223786,\n",
       "          0.06709495, 0.06123501, 0.06320112, 0.06081865, 0.06781058,\n",
       "          0.076626  , 0.08280164, 0.06663618, 0.07467308, 0.07235598,\n",
       "          0.06975453, 0.06586163, 0.03802168, 0.04314238, 0.05293893,\n",
       "          0.04910813, 0.03643692, 0.03174896, 0.01563721, 0.01464684,\n",
       "          0.00705728, 0.00844126, 0.003637  , 0.00700946, 0.00468495,\n",
       "          0.00468506, 0.00480814, 0.00876328, 0.01127337, 0.01188874,\n",
       "          0.00671419, 0.00639295, 0.0064003 , 0.00590129, 0.00600383,\n",
       "          0.00633316, 0.00669372, 0.00609435, 0.00547613, 0.00497977,\n",
       "          0.00465166, 0.00378585, 0.00572562, 0.00619436, 0.01667102,\n",
       "          0.00620233, 0.00542352, 0.00491   , 0.0037913 , 0.00595162,\n",
       "          0.00387002, 0.00459971, 0.00545381, 0.00708293, 0.00773352,\n",
       "          0.00602002, 0.00624634, 0.00616982, 0.00828608, 0.00708508,\n",
       "          0.00467211, 0.0057542 , 0.00875203, 0.00923868, 0.00805847,\n",
       "          0.0077056 , 0.00748745, 0.00805753, 0.01527414, 0.01525134,\n",
       "          0.0167823 , 0.01676001, 0.00828551, 0.00676108, 0.00768243,\n",
       "          0.00647049, 0.00640104, 0.0313032 , 0.03243444, 0.03455401,\n",
       "          0.03763789, 0.01178556, 0.00573116, 0.00720229, 0.00811889,\n",
       "          0.01672093, 0.01295587, 0.01542056, 0.01643852, 0.01129668,\n",
       "          0.00957788, 0.01151714, 0.03219613, 0.03451392, 0.01773368,\n",
       "          0.01375548, 0.01400819, 0.00457728, 0.00552251, 0.00548933,\n",
       "          0.00564196, 0.00541974, 0.00687447, 0.00584219, 0.01065555,\n",
       "          0.00739247, 0.00890108, 0.00761655, 0.01243902, 0.0103886 ,\n",
       "          0.00894922, 0.00662579, 0.01072801, 0.01050928, 0.00722813,\n",
       "          0.00763576, 0.00589884, 0.00546007, 0.00556586, 0.00624686,\n",
       "          0.00556092, 0.00500055, 0.00548144, 0.00415254, 0.00414063,\n",
       "          0.00410838, 0.00407228, 0.00462903, 0.00576377, 0.00424026,\n",
       "          0.00496545, 0.00462801, 0.00410257, 0.00458128, 0.00471121,\n",
       "          0.00435966, 0.00414989, 0.00427084, 0.00470036, 0.00563183,\n",
       "          0.00603742, 0.0070658 , 0.01065387, 0.00656062, 0.00464363,\n",
       "          0.00543656, 0.00482806, 0.006045  , 0.0047382 , 0.00456503,\n",
       "          0.00455985, 0.00497938, 0.00526886, 0.0059534 , 0.00542501,\n",
       "          0.00593828, 0.00441934, 0.00679043, 0.00768412, 0.00478962,\n",
       "          0.00497975, 0.0096066 , 0.00931533, 0.00647827, 0.01122897,\n",
       "          0.00814056, 0.00886422, 0.0085073 , 0.00892586, 0.01026801,\n",
       "          0.00671302, 0.01081401, 0.01183196, 0.0130468 , 0.01662675,\n",
       "          0.02419041, 0.01684287, 0.0181704 , 0.01506921, 0.01469795,\n",
       "          0.01510171, 0.01064657, 0.00912229, 0.01318832, 0.02469332,\n",
       "          0.02548047, 0.02698232, 0.03244451, 0.01577784, 0.03815224,\n",
       "          0.03508397, 0.04572237, 0.04062009, 0.03922674, 0.04328152,\n",
       "          0.04045059, 0.03814987, 0.03539773, 0.03825895, 0.05366944,\n",
       "          0.05259725, 0.04932721, 0.04996442, 0.05926653, 0.05825708,\n",
       "          0.04762925, 0.05079945, 0.05384446, 0.0520142 , 0.05647924,\n",
       "          0.06862377, 0.082898  , 0.06551883, 0.06457354, 0.06285927,\n",
       "          0.05532062, 0.06260139, 0.06386063, 0.05833791, 0.04712606,\n",
       "          0.05040953, 0.03517448, 0.03794625, 0.02638085, 0.04859691,\n",
       "          0.05473974, 0.05040754, 0.05122105, 0.0418124 , 0.0412233 ,\n",
       "          0.02709608, 0.03020647, 0.03555377, 0.05101901, 0.04354959,\n",
       "          0.04748858, 0.05974634, 0.04787937, 0.0653081 , 0.07386712,\n",
       "          0.05966709, 0.06532861, 0.07046203, 0.0742046 , 0.07327932,\n",
       "          0.07168894, 0.07655903, 0.08079273, 0.08997273, 0.08147647,\n",
       "          0.00367353, 0.00323596, 0.00556057, 0.00267067, 0.00220054,\n",
       "          0.00235112, 0.00239005, 0.00257686, 0.00271898, 0.00263264,\n",
       "          0.00296338, 0.00251245, 0.00264074, 0.00287286, 0.00268097,\n",
       "          0.00183029, 0.00230579, 0.00183512, 0.00167413, 0.00162726,\n",
       "          0.00196856, 0.00309351, 0.00218892, 0.00213684])}),\n",
       " ({'PartType0': array([[ 7.66010618, 11.80109692,  0.5202446 ],\n",
       "          [ 7.65904951, 11.80893993,  0.50201857],\n",
       "          [ 7.65729189, 11.80535507,  0.53346169],\n",
       "          ...,\n",
       "          [ 2.39935374,  4.34765959, 18.09743118],\n",
       "          [ 2.38080192,  4.43609762, 18.03917885],\n",
       "          [ 2.71342826,  4.45228958, 17.99451065]])},\n",
       "  {('PartType0',\n",
       "    'Mass'): array([0.009135  , 0.00873734, 0.0098292 , ..., 0.00864386, 0.00864386,\n",
       "          0.00864386], dtype=float32)},\n",
       "  {'PartType0': 244445},\n",
       "  {'PartType0': array([0.03090909, 0.02949233, 0.03074851, ..., 0.52712107, 0.57937199,\n",
       "          0.4726522 ])}))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "here's a screenshot of the Task Stream graph on the dask client dashboard from this execution (see the [Dask Dahboard walkthrough tutorial for details](https://www.youtube.com/watch?time_continue=21&v=N_GqzcuGLCY&feature=emb_logo)):\n",
    "\n",
    "![TaskStream](resources/daskboard_read.png)\n",
    "\n",
    "which shows the processor and thread activity throughout the `compute()`: each row is a different thread (our client is using 4 workers with 2 threads per workers here, so 8 rows) and each chunk is a different `delayed_chunk_read`. \n",
    "\n",
    "Ok, so now we have a delayed workflow to load in chunks -- but we want to be able to do at least two additional processing steps:\n",
    "1. compute derived quantities\n",
    "2. filter the chunked objects \n",
    "\n",
    "and we want to do the bulk of the computation in parallel without loading the full dataset into memory -- meaning we want to string together delayed computations to be executed across the processors. Let's start with the simpler problem: computing derived quantities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## derived quantities\n",
    "\n",
    "Calculating derived quantities on each chunk is fairly straightforward. Let's write a delayed function to return some attributes and simple function calls so that we can return some array attribute values like `min()`, `max()`, `size` given a particle type and field:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask \n",
    "@dask.delayed \n",
    "def npmeth(chunk,ptype_field,meths=['min']): \n",
    "     \n",
    "    results = []  \n",
    "    if len(chunk)>0:   \n",
    "        if ptype_field in chunk[1].keys():\n",
    "            x = chunk[1][ptype_field]\n",
    "            for meth in meths: \n",
    "                this_meth = getattr(x,meth)\n",
    "                if callable(this_meth):\n",
    "                    results.append(this_meth())\n",
    "                else:\n",
    "                    results.append(this_meth) \n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we call this method using a single delayed chunk for a given particle type and field:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_chunk_quantities = npmeth(delayed_reader.delayed_chunks[0],('PartType0','Mass'),meths=['min','max','sum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delayed('npmeth-5d8b6122-b024-45f6-839b-1fe9e00a890d')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_chunk_quantities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and when we call compute, we'll get the `min`, `max`, and `sum` of `('PartType0','Mass')` on a single chunk:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.008643857, 0.038306426, 4.4755173]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_chunk_quantities.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so to compute a derived quantity in parallel, we simply construct a list of delayed objects to calculate the quantitity on each chunk and then agreggate over the chunks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Delayed('npmeth-19dd50f9-17e7-449e-8ca2-acf718a4d2ec'),\n",
       " Delayed('npmeth-4586596f-79f5-4281-b5dd-5e9b1111076a'),\n",
       " Delayed('npmeth-cf78ce48-bd58-4d6c-8a71-842c1aeaa6ed'),\n",
       " Delayed('npmeth-22366299-9e71-4962-b684-74c2c4cf2874'),\n",
       " Delayed('npmeth-c7dfa8f8-9c81-493f-b835-73d065b6b508'),\n",
       " Delayed('npmeth-dcca79f8-3606-4593-9cb9-f2a353349798'),\n",
       " Delayed('npmeth-f6f64f02-8529-43a4-9889-9041dacd03af'),\n",
       " Delayed('npmeth-f75f00c3-0e92-4a6d-bd9f-9d5f69a2d443'),\n",
       " Delayed('npmeth-b830366c-e7e7-48da-9d52-b12fbf7e437f'),\n",
       " Delayed('npmeth-55ab6dba-aab9-4444-8b8a-a3b81952aca3'),\n",
       " Delayed('npmeth-b6b4796b-3b42-4c83-a784-3de0032ffa63'),\n",
       " Delayed('npmeth-6eb54c6b-28cd-49b2-af7e-2860312a8726')]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meths = ['min','max','sum']\n",
    "ptypefield = ('PartType0','Mass')\n",
    "derived_qs = [npmeth(chunk,ptypefield,meths=meths) for chunk in delayed_reader.delayed_chunks]\n",
    "derived_qs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualize(derived_qs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we see that after each `delayed_chunk_read` we'll be applything this `npmeth` function, so so when we call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 30.5 ms, sys: 212 µs, total: 30.8 ms\n",
      "Wall time: 44.8 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "computed_derived = compute(*derived_qs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we see the additional `npmeth` calls in the Task Stream:\n",
    "\n",
    "![TaskStream](resources/daskboard_derived.png)\n",
    "\n",
    "some of the loads do not have accompanying reads because not all the chunks contain data for the given particle type, which we can see by checking out the full data set that we've already loaded:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[419, 244445, 262144, 233206, 239908, 0, 227868, 225819, 255819, 0, 0, 251598]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[ad[2]['PartType0'] for ad in all_data]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So that, along with the 5-10ms downtime between some of the tasks indicates that there's certainly some optimization to focus on.... but we'll leave that for later. \n",
    "\n",
    "So let's return to a derived quantities question: how to calculate global derived quantities across chunks? In the case of the simple quantities we're calculating here, we can manually aggregate across chunks easily. To calculate a mean across chunks, for example we can return the sum and count for each chunk then aggregate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 25.6 ms, sys: 3.24 ms, total: 28.8 ms\n",
      "Wall time: 45 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.008771999763740388"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "meths = ['size','sum']\n",
    "ptypefield = ('PartType0','Mass')\n",
    "derived_qs = [npmeth(chunk,ptypefield,meths=meths) for chunk in delayed_reader.delayed_chunks]\n",
    "derived_qs = compute(*derived_qs)\n",
    "\n",
    "# collect and compute mean\n",
    "derived_qs = np.array([l for l in derived_qs if len(l)>0]) # remove empty chunks \n",
    "global_mean = derived_qs[:,1].sum() / derived_qs[:,0].sum() \n",
    "global_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The derived quantities could be more involved, and we'll need to think more carefully about more complex quantities that require data from other chunks during computation, but let's move on to filtering...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## filtering \n",
    "\n",
    "What we want to be able to do is to use a yt selection data container, for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp = ds.sphere(ds.domain_center,(2,'code_length')) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and build a delayed process that will return only the subset of coordinates within each chunk falling within the selection. The conceptual approach is simple enough, we'd just write another delayed function to string onto the chunk reading, but in practice we run into some difficulty. The object that we really need is `sp.selector`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<yt.geometry.selection_routines.SphereSelector at 0x7f94ac423f10>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.selector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now, if we were to do something like:\n",
    "\n",
    "```\n",
    "@dask.delayed \n",
    "def get_chunk_masks(ptf,chunk,selector):\n",
    "```\n",
    "\n",
    "dask will try to serialize the selector object with pickle, which fails: \n",
    "\n",
    "```\n",
    "import pickle\n",
    "pickle.dumps(sp.selector)\n",
    "```\n",
    "\n",
    "returns the following traceback:\n",
    "\n",
    "```\n",
    "---------------------------------------------------------------------------\n",
    "TypeError                                 Traceback (most recent call last)\n",
    "<ipython-input-85-5c86acc10898> in <module>\n",
    "      1 import pickle\n",
    "----> 2 pickle.dumps(sp.selector)\n",
    "\n",
    "~/src/yt/yt/geometry/selection_routines.cpython-37m-x86_64-linux-gnu.so in yt.geometry.selection_routines.SphereSelector.__reduce_cython__()\n",
    "\n",
    "TypeError: no default __reduce__ due to non-trivial __cinit__\n",
    "```\n",
    "\n",
    "which points to an issue in serializing the cython selection routine. When trying to implement the `get_chunk_masks` above, dask returns an error message stating that the selector object cannot be serialized (`distributed.protocol.core - CRITICAL - Failed to Serialize`).  \n",
    "\n",
    "As a first approach, we'll look at what is required for initializing the `sphere` selector: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<yt.geometry.selection_routines.SphereSelector at 0x7f94ac423f10>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel = sp.selector\n",
    "sel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5109,)\n"
     ]
    }
   ],
   "source": [
    "ad = ds.all_data() \n",
    "n0 = 100000 \n",
    "n1 = 500000\n",
    "hsmls = 0 \n",
    "mask = sel.select_points(\n",
    "                ad['x'][n0:n1], ad['y'][n0:n1], ad['z'][n0:n1], hsmls\n",
    "            )\n",
    "print(mask[mask].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "YTSelectionContainer in yt/data_objects/selection_objects/data_selection_objects.py\n",
    "\n",
    "is where the selector attribute is set:\n",
    "\n",
    "```\n",
    "@property\n",
    "    def selector(self):\n",
    "        if self._selector is not None:\n",
    "            return self._selector\n",
    "        s_module = getattr(self, \"_selector_module\", yt.geometry.selection_routines)\n",
    "        sclass = getattr(s_module, f\"{self._type_name}_selector\", None)\n",
    "        if sclass is None:\n",
    "            raise YTDataSelectorNotImplemented(self._type_name)\n",
    "\n",
    "        if self._data_source is not None:\n",
    "            self._selector = compose_selector(\n",
    "                self, self._data_source.selector, sclass(self)\n",
    "            )\n",
    "        else:\n",
    "            self._selector = sclass(self)\n",
    "        return self._selector\n",
    "```        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'yt.geometry.selection_routines' from '/home/chavlin/src/yt/yt/geometry/selection_routines.cpython-37m-x86_64-linux-gnu.so'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_module = yt.geometry.selection_routines\n",
    "s_module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sphere'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp._type_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yt.geometry.selection_routines.SphereSelector"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sclass = s_module.sphere_selector\n",
    "sclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp_sel = sclass(sp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's create a `MockSphere` class that pulls out only the minimal attributes needed to initialize the sphere selector: \n",
    "\n",
    "```\n",
    "class MockDs(object):\n",
    "    def __init__(self,ds):\n",
    "        self.domain_left_edge = ds.domain_left_edge\n",
    "        self.domain_right_edge = ds.domain_right_edge\n",
    "        self.periodicity = ds.periodicity\n",
    "        \n",
    "class MockSphere(object):\n",
    "    # a stripped down sphere that records only the attributes required to initialize the sphere Selector Object\n",
    "    def __init__(self,sp):\n",
    "        self.center = sp.center \n",
    "        self.radius = sp.radius\n",
    "        self.ds = MockDs(sp.ds)            \n",
    "```        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp_M = gda.MockSphere(sp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unyt_array([12.5, 12.5, 12.5], 'code_length')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp_M.center"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's initialize our sphere selector with the mock class: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "sel_M = sclass(sp_M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5109,)\n"
     ]
    }
   ],
   "source": [
    "n0 = 100000 \n",
    "n1 = 500000\n",
    "ad = ds.all_data()\n",
    "mask_2 = sel_M.select_points(\n",
    "                ad['x'][n0:n1], ad['y'][n0:n1], ad['z'][n0:n1], hsmls\n",
    "            )\n",
    "print(mask_2[mask_2].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(mask_2==mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so looks like our sphere selector built from the mock class is behaving as expected. and our `sp_M` object should be easily pickleable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle \n",
    "sp_M_pi = pickle.dumps(sp_M)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which can be loaded back in (which dask does behind the scenes for each processor): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp_M_unpi = pickle.loads(sp_M_pi) # dask would do this in the backend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So within the delayed function for calculating masks, we can instantiate our selector using the mock class: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "sel_M = yt.geometry.selection_routines.SphereSelector(sp_M_unpi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "at present, our exploratory `delayed_gadget` class implements this approach just for the sphere selector (different selector objects required different attributes for initialization). But let's build a new `delayed_reader` supplying our mock sphere:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "delayed_reader = gda.delayed_gadget(ds, ptf, mock_selector = sp_M, subchunk_size = None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now we have two more lists of dealyed objects: `masks` and `masked_chunks`. The `masks` are delayed objects which return the boolean masks: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Delayed('get_chunk_masks-9ff69aba-e485-45bc-8159-595ef57436c1'),\n",
       " Delayed('get_chunk_masks-561607e7-4c20-4381-90fd-9481053a45b4'),\n",
       " Delayed('get_chunk_masks-bf11afeb-f947-4d7d-8574-c0d886345b75'),\n",
       " Delayed('get_chunk_masks-58f9e8ea-c177-4d07-9a46-49d49da1c0c9'),\n",
       " Delayed('get_chunk_masks-20d22784-944c-40eb-b986-0f7d14e4c77e'),\n",
       " Delayed('get_chunk_masks-c6e13077-e40c-4c32-b985-7230442a05cd'),\n",
       " Delayed('get_chunk_masks-becb6a22-ac27-4b82-8d57-8a5a61071253'),\n",
       " Delayed('get_chunk_masks-563d578f-406d-4f2d-9ab4-47566581f7c4'),\n",
       " Delayed('get_chunk_masks-e4537e80-bbed-40be-94ab-c926f302f2f4'),\n",
       " Delayed('get_chunk_masks-00872ba7-9d46-4f80-91d3-68766b5cf037'),\n",
       " Delayed('get_chunk_masks-1849634a-c79b-4a22-909b-96c07bf098d3'),\n",
       " Delayed('get_chunk_masks-6c2dbfe8-ccd8-4c8f-99a0-3ad575a2b03b')]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delayed_reader.masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PartType0': (None, None)}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chunk_1_mask = delayed_reader.masks[0].compute()\n",
    "chunk_1_mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so we could compute those masks and apply them to the delayed chunks, but the `masked_chunks` list contains delayed functions that load the chunks, masks and then returns just the masked values:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'PartType0': array([[[ 9.0948925 , 18.5401268 , 13.50576115],\n",
       "          [ 9.09940147, 18.55133247, 13.51190567],\n",
       "          [ 9.08276558, 18.54066086, 13.49641895],\n",
       "          ...,\n",
       "          [ 9.94860458,  8.4767704 , 14.56663513],\n",
       "          [ 9.94866085,  8.47825813, 14.56705093],\n",
       "          [ 9.94791031,  8.47807693, 14.56690121]]])},\n",
       " {('PartType0',\n",
       "   'Mass'): array([[0.01576188, 0.01663664, 0.01871505, 0.00970176, 0.01931598,\n",
       "          0.01710295, 0.01869999, 0.00868698, 0.01191492, 0.01526247,\n",
       "          0.00870004, 0.00864736, 0.00866873, 0.01210038, 0.00866515,\n",
       "          0.00865903, 0.00893035, 0.00864386, 0.00864437, 0.0095512 ,\n",
       "          0.009749  , 0.00864466, 0.00952867, 0.00864396, 0.00865185,\n",
       "          0.00864417, 0.00865002, 0.00864616, 0.00864787, 0.0086467 ,\n",
       "          0.00864758, 0.00865183, 0.00864775, 0.00939687, 0.0086449 ,\n",
       "          0.00871795, 0.01162997, 0.01007061, 0.00868322, 0.01283415,\n",
       "          0.00864652, 0.00864493, 0.00864921, 0.00864537, 0.00867223,\n",
       "          0.00864423, 0.00864423, 0.00864427, 0.00864402, 0.00864405,\n",
       "          0.00864469, 0.00864398, 0.00864398, 0.00864617, 0.00865819,\n",
       "          0.00864479, 0.00865467, 0.00865023, 0.00864672, 0.00864549,\n",
       "          0.0086541 , 0.00864614, 0.0086621 , 0.00864856, 0.00866107,\n",
       "          0.00864497, 0.00864437, 0.00872628, 0.00865319, 0.00864782,\n",
       "          0.00867574, 0.00865113, 0.00864541, 0.00864583, 0.00865423,\n",
       "          0.00864672, 0.0094079 , 0.00865236, 0.00865042, 0.00864537,\n",
       "          0.0095771 , 0.00864497, 0.0086466 , 0.00864951, 0.00864564,\n",
       "          0.00864719, 0.00973641, 0.01123166, 0.0086561 , 0.00865673,\n",
       "          0.00867016, 0.00866977, 0.00865894, 0.00864647, 0.00864547,\n",
       "          0.00864431, 0.0111323 , 0.00864911, 0.00864662, 0.00868544,\n",
       "          0.00871773, 0.00865634, 0.0086528 , 0.0086698 , 0.00867322,\n",
       "          0.01034854, 0.00867792, 0.00864386, 0.0086446 , 0.0086485 ,\n",
       "          0.00867851, 0.00864389, 0.00864397, 0.00864393, 0.00865264,\n",
       "          0.0086439 , 0.00864573, 0.00864641, 0.00865118, 0.00884463,\n",
       "          0.00864407, 0.00871034, 0.00865471, 0.00867828, 0.008648  ,\n",
       "          0.00864427, 0.00868855, 0.00864828, 0.00867095, 0.00866964,\n",
       "          0.00866721, 0.00865462, 0.0086743 , 0.00866969, 0.00864895,\n",
       "          0.00865275, 0.00864571, 0.00865658, 0.00865294, 0.00867992,\n",
       "          0.00867557, 0.00868229, 0.00865193, 0.00865912, 0.00886692,\n",
       "          0.00868004, 0.0086862 , 0.019488  , 0.01122443, 0.02183115,\n",
       "          0.00866625, 0.00871245, 0.00867322, 0.00865103, 0.00864984,\n",
       "          0.00868173, 0.00872087, 0.02139982, 0.00966472, 0.01720018,\n",
       "          0.0188228 , 0.00870653, 0.00874153, 0.00868849, 0.02225389,\n",
       "          0.01747124, 0.01951511, 0.010462  , 0.01086188, 0.00889738,\n",
       "          0.02430551, 0.01698648, 0.00949531, 0.00874803, 0.00882819,\n",
       "          0.01417806, 0.0155667 , 0.01420717, 0.00888111, 0.00911644,\n",
       "          0.01714752, 0.01734492, 0.01464341, 0.00908168, 0.02144803,\n",
       "          0.00971754, 0.0148535 , 0.0099078 , 0.01139312, 0.01358491,\n",
       "          0.01857252, 0.01024381, 0.00927314, 0.01057635, 0.00979624,\n",
       "          0.01658888, 0.01408297, 0.00916107, 0.01534646, 0.00989786,\n",
       "          0.01286644, 0.00963002, 0.00885178, 0.00875167, 0.01189861,\n",
       "          0.01009631, 0.00870869, 0.00879007, 0.01150377, 0.00997297,\n",
       "          0.0087672 , 0.00869129, 0.00868801, 0.00867148, 0.01129076,\n",
       "          0.00868363, 0.00864965, 0.01027128, 0.00874708, 0.00876408,\n",
       "          0.0093245 , 0.00882076, 0.00898408, 0.00865777, 0.01579425,\n",
       "          0.00865272, 0.00906334, 0.00910297, 0.00948077, 0.02122764,\n",
       "          0.01258226, 0.02637874, 0.01447314, 0.00951241, 0.0102029 ,\n",
       "          0.00874167, 0.00866766, 0.00865206, 0.00881413, 0.00890438,\n",
       "          0.00881805, 0.01753351, 0.01460347, 0.01022284, 0.01129906,\n",
       "          0.00892293, 0.01131135, 0.01149893, 0.00896504, 0.00892386,\n",
       "          0.0088651 , 0.00881195, 0.00879243, 0.00865685, 0.01000845,\n",
       "          0.00872331, 0.00981471, 0.00867905, 0.00896335, 0.00958879,\n",
       "          0.00891158, 0.0094134 , 0.01036824, 0.00924235, 0.00927313,\n",
       "          0.01221033, 0.01330525, 0.02035448, 0.00941199, 0.0100465 ,\n",
       "          0.01363682, 0.01297417, 0.01663146, 0.01457284, 0.00903581,\n",
       "          0.0101647 , 0.0157372 , 0.00878596, 0.00913168, 0.0107145 ,\n",
       "          0.00977161, 0.00956479, 0.00882804, 0.00884613, 0.01691047,\n",
       "          0.00893549, 0.00903166, 0.00894935, 0.00877558, 0.01156324,\n",
       "          0.00924913, 0.01358276, 0.01894272, 0.00955947, 0.00942666,\n",
       "          0.00875606, 0.00881323, 0.00956746, 0.00883421, 0.01629753,\n",
       "          0.0086669 , 0.00876433, 0.0096875 , 0.01188321, 0.00955884,\n",
       "          0.00873123, 0.00865462, 0.00867205, 0.00865692, 0.0086477 ,\n",
       "          0.00879077, 0.00901418, 0.00893167, 0.00881205, 0.00865754,\n",
       "          0.00935456, 0.00976939, 0.00866351, 0.00867221, 0.0143476 ,\n",
       "          0.00867235, 0.00870647, 0.01151083, 0.01523547, 0.00873427,\n",
       "          0.02577072, 0.02243926, 0.00878209, 0.00868718, 0.0086521 ,\n",
       "          0.01103147, 0.02126098, 0.00865098, 0.01706288, 0.00923983,\n",
       "          0.01008528, 0.0086549 , 0.0086628 , 0.00865432, 0.00866331,\n",
       "          0.00864874, 0.00865209, 0.02152279, 0.00866388, 0.00865532,\n",
       "          0.00864429, 0.00864659, 0.00868481, 0.01024063, 0.00865658,\n",
       "          0.00865499, 0.00864694, 0.00867339, 0.01285803, 0.00866809,\n",
       "          0.00866094, 0.0086523 , 0.00864416, 0.00865817, 0.00864684,\n",
       "          0.00865339, 0.00864393, 0.00866137, 0.0108283 , 0.00865872,\n",
       "          0.00865904, 0.01000292, 0.00865042, 0.01252308, 0.0156426 ,\n",
       "          0.00864781, 0.00865091, 0.00864583, 0.00895159, 0.00867303,\n",
       "          0.01230804, 0.01055389, 0.01355155, 0.01290767, 0.01301   ,\n",
       "          0.00865055, 0.00864436, 0.00867914, 0.00864389, 0.00864386,\n",
       "          0.00864974, 0.00864396, 0.0086496 , 0.00864521, 0.00864442,\n",
       "          0.00864768, 0.00864387, 0.00864543, 0.00865471, 0.00864542,\n",
       "          0.00885514, 0.02166013, 0.01593382, 0.03830643, 0.02097851,\n",
       "          0.02344084, 0.02224258, 0.01855106, 0.01791457, 0.01292829,\n",
       "          0.01000241, 0.01100658, 0.01275772, 0.0119765 , 0.00996338,\n",
       "          0.02194469, 0.01048275, 0.01669842, 0.01753752, 0.02722569,\n",
       "          0.01266365, 0.01107106, 0.01257765, 0.01240593]], dtype=float32)})"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masked_chunk_1 = delayed_reader.masked_chunks[0].compute()\n",
    "masked_chunk_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and now when we compute the full list, dask will distribute the chunk reading and masking to individual processes: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualize(delayed_reader.masked_chunks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualize(delayed_reader.masked_chunks[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_subset = compute(*delayed_reader.masked_chunks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we now see the reading (purple), retrieving of masks (green) and applying the masks (yellow) in the task stream:\n",
    "\n",
    "![TaskStream](resources/daskboard_masking.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and our `data_subset` contains only the masked data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(({'PartType0': array([[[ 9.0948925 , 18.5401268 , 13.50576115],\n",
       "           [ 9.09940147, 18.55133247, 13.51190567],\n",
       "           [ 9.08276558, 18.54066086, 13.49641895],\n",
       "           ...,\n",
       "           [ 9.94860458,  8.4767704 , 14.56663513],\n",
       "           [ 9.94866085,  8.47825813, 14.56705093],\n",
       "           [ 9.94791031,  8.47807693, 14.56690121]]])},\n",
       "  {('PartType0',\n",
       "    'Mass'): array([[0.01576188, 0.01663664, 0.01871505, 0.00970176, 0.01931598,\n",
       "           0.01710295, 0.01869999, 0.00868698, 0.01191492, 0.01526247,\n",
       "           0.00870004, 0.00864736, 0.00866873, 0.01210038, 0.00866515,\n",
       "           0.00865903, 0.00893035, 0.00864386, 0.00864437, 0.0095512 ,\n",
       "           0.009749  , 0.00864466, 0.00952867, 0.00864396, 0.00865185,\n",
       "           0.00864417, 0.00865002, 0.00864616, 0.00864787, 0.0086467 ,\n",
       "           0.00864758, 0.00865183, 0.00864775, 0.00939687, 0.0086449 ,\n",
       "           0.00871795, 0.01162997, 0.01007061, 0.00868322, 0.01283415,\n",
       "           0.00864652, 0.00864493, 0.00864921, 0.00864537, 0.00867223,\n",
       "           0.00864423, 0.00864423, 0.00864427, 0.00864402, 0.00864405,\n",
       "           0.00864469, 0.00864398, 0.00864398, 0.00864617, 0.00865819,\n",
       "           0.00864479, 0.00865467, 0.00865023, 0.00864672, 0.00864549,\n",
       "           0.0086541 , 0.00864614, 0.0086621 , 0.00864856, 0.00866107,\n",
       "           0.00864497, 0.00864437, 0.00872628, 0.00865319, 0.00864782,\n",
       "           0.00867574, 0.00865113, 0.00864541, 0.00864583, 0.00865423,\n",
       "           0.00864672, 0.0094079 , 0.00865236, 0.00865042, 0.00864537,\n",
       "           0.0095771 , 0.00864497, 0.0086466 , 0.00864951, 0.00864564,\n",
       "           0.00864719, 0.00973641, 0.01123166, 0.0086561 , 0.00865673,\n",
       "           0.00867016, 0.00866977, 0.00865894, 0.00864647, 0.00864547,\n",
       "           0.00864431, 0.0111323 , 0.00864911, 0.00864662, 0.00868544,\n",
       "           0.00871773, 0.00865634, 0.0086528 , 0.0086698 , 0.00867322,\n",
       "           0.01034854, 0.00867792, 0.00864386, 0.0086446 , 0.0086485 ,\n",
       "           0.00867851, 0.00864389, 0.00864397, 0.00864393, 0.00865264,\n",
       "           0.0086439 , 0.00864573, 0.00864641, 0.00865118, 0.00884463,\n",
       "           0.00864407, 0.00871034, 0.00865471, 0.00867828, 0.008648  ,\n",
       "           0.00864427, 0.00868855, 0.00864828, 0.00867095, 0.00866964,\n",
       "           0.00866721, 0.00865462, 0.0086743 , 0.00866969, 0.00864895,\n",
       "           0.00865275, 0.00864571, 0.00865658, 0.00865294, 0.00867992,\n",
       "           0.00867557, 0.00868229, 0.00865193, 0.00865912, 0.00886692,\n",
       "           0.00868004, 0.0086862 , 0.019488  , 0.01122443, 0.02183115,\n",
       "           0.00866625, 0.00871245, 0.00867322, 0.00865103, 0.00864984,\n",
       "           0.00868173, 0.00872087, 0.02139982, 0.00966472, 0.01720018,\n",
       "           0.0188228 , 0.00870653, 0.00874153, 0.00868849, 0.02225389,\n",
       "           0.01747124, 0.01951511, 0.010462  , 0.01086188, 0.00889738,\n",
       "           0.02430551, 0.01698648, 0.00949531, 0.00874803, 0.00882819,\n",
       "           0.01417806, 0.0155667 , 0.01420717, 0.00888111, 0.00911644,\n",
       "           0.01714752, 0.01734492, 0.01464341, 0.00908168, 0.02144803,\n",
       "           0.00971754, 0.0148535 , 0.0099078 , 0.01139312, 0.01358491,\n",
       "           0.01857252, 0.01024381, 0.00927314, 0.01057635, 0.00979624,\n",
       "           0.01658888, 0.01408297, 0.00916107, 0.01534646, 0.00989786,\n",
       "           0.01286644, 0.00963002, 0.00885178, 0.00875167, 0.01189861,\n",
       "           0.01009631, 0.00870869, 0.00879007, 0.01150377, 0.00997297,\n",
       "           0.0087672 , 0.00869129, 0.00868801, 0.00867148, 0.01129076,\n",
       "           0.00868363, 0.00864965, 0.01027128, 0.00874708, 0.00876408,\n",
       "           0.0093245 , 0.00882076, 0.00898408, 0.00865777, 0.01579425,\n",
       "           0.00865272, 0.00906334, 0.00910297, 0.00948077, 0.02122764,\n",
       "           0.01258226, 0.02637874, 0.01447314, 0.00951241, 0.0102029 ,\n",
       "           0.00874167, 0.00866766, 0.00865206, 0.00881413, 0.00890438,\n",
       "           0.00881805, 0.01753351, 0.01460347, 0.01022284, 0.01129906,\n",
       "           0.00892293, 0.01131135, 0.01149893, 0.00896504, 0.00892386,\n",
       "           0.0088651 , 0.00881195, 0.00879243, 0.00865685, 0.01000845,\n",
       "           0.00872331, 0.00981471, 0.00867905, 0.00896335, 0.00958879,\n",
       "           0.00891158, 0.0094134 , 0.01036824, 0.00924235, 0.00927313,\n",
       "           0.01221033, 0.01330525, 0.02035448, 0.00941199, 0.0100465 ,\n",
       "           0.01363682, 0.01297417, 0.01663146, 0.01457284, 0.00903581,\n",
       "           0.0101647 , 0.0157372 , 0.00878596, 0.00913168, 0.0107145 ,\n",
       "           0.00977161, 0.00956479, 0.00882804, 0.00884613, 0.01691047,\n",
       "           0.00893549, 0.00903166, 0.00894935, 0.00877558, 0.01156324,\n",
       "           0.00924913, 0.01358276, 0.01894272, 0.00955947, 0.00942666,\n",
       "           0.00875606, 0.00881323, 0.00956746, 0.00883421, 0.01629753,\n",
       "           0.0086669 , 0.00876433, 0.0096875 , 0.01188321, 0.00955884,\n",
       "           0.00873123, 0.00865462, 0.00867205, 0.00865692, 0.0086477 ,\n",
       "           0.00879077, 0.00901418, 0.00893167, 0.00881205, 0.00865754,\n",
       "           0.00935456, 0.00976939, 0.00866351, 0.00867221, 0.0143476 ,\n",
       "           0.00867235, 0.00870647, 0.01151083, 0.01523547, 0.00873427,\n",
       "           0.02577072, 0.02243926, 0.00878209, 0.00868718, 0.0086521 ,\n",
       "           0.01103147, 0.02126098, 0.00865098, 0.01706288, 0.00923983,\n",
       "           0.01008528, 0.0086549 , 0.0086628 , 0.00865432, 0.00866331,\n",
       "           0.00864874, 0.00865209, 0.02152279, 0.00866388, 0.00865532,\n",
       "           0.00864429, 0.00864659, 0.00868481, 0.01024063, 0.00865658,\n",
       "           0.00865499, 0.00864694, 0.00867339, 0.01285803, 0.00866809,\n",
       "           0.00866094, 0.0086523 , 0.00864416, 0.00865817, 0.00864684,\n",
       "           0.00865339, 0.00864393, 0.00866137, 0.0108283 , 0.00865872,\n",
       "           0.00865904, 0.01000292, 0.00865042, 0.01252308, 0.0156426 ,\n",
       "           0.00864781, 0.00865091, 0.00864583, 0.00895159, 0.00867303,\n",
       "           0.01230804, 0.01055389, 0.01355155, 0.01290767, 0.01301   ,\n",
       "           0.00865055, 0.00864436, 0.00867914, 0.00864389, 0.00864386,\n",
       "           0.00864974, 0.00864396, 0.0086496 , 0.00864521, 0.00864442,\n",
       "           0.00864768, 0.00864387, 0.00864543, 0.00865471, 0.00864542,\n",
       "           0.00885514, 0.02166013, 0.01593382, 0.03830643, 0.02097851,\n",
       "           0.02344084, 0.02224258, 0.01855106, 0.01791457, 0.01292829,\n",
       "           0.01000241, 0.01100658, 0.01275772, 0.0119765 , 0.00996338,\n",
       "           0.02194469, 0.01048275, 0.01669842, 0.01753752, 0.02722569,\n",
       "           0.01266365, 0.01107106, 0.01257765, 0.01240593]], dtype=float32)}),\n",
       " ({'PartType0': array([[13.43093872, 11.19056606, 12.9906044 ],\n",
       "          [13.43668842, 11.18202591, 13.00140095],\n",
       "          [13.44595337, 11.1965723 , 13.00923061],\n",
       "          ...,\n",
       "          [11.85178089, 10.12987709, 12.46292496],\n",
       "          [13.38823414, 11.19161415, 12.92356396],\n",
       "          [13.38854218, 11.19038105, 12.92326355]])},\n",
       "  {('PartType0',\n",
       "    'Mass'): array([0.00884863, 0.00882724, 0.00864468, ..., 0.00945342, 0.02861722,\n",
       "          0.01246947], dtype=float32)}),\n",
       " ({'PartType0': array([[13.28926563, 11.24862957, 12.7988348 ],\n",
       "          [13.30125618, 11.25296021, 12.79409981],\n",
       "          [13.2896452 , 11.23968029, 12.84126377],\n",
       "          ...,\n",
       "          [13.47682667, 11.07727623, 12.63766289],\n",
       "          [13.45073509, 11.04586411, 12.60851288],\n",
       "          [13.46500587, 11.05492592, 12.64425945]])},\n",
       "  {('PartType0',\n",
       "    'Mass'): array([0.00896836, 0.00868008, 0.00865726, ..., 0.00876143, 0.00889264,\n",
       "          0.0086753 ], dtype=float32)}),\n",
       " ({'PartType0': array([[12.40897083, 11.28258801, 11.81107712],\n",
       "          [12.41020012, 11.28173542, 11.81221294],\n",
       "          [12.41038513, 11.28312969, 11.81461811],\n",
       "          ...,\n",
       "          [13.11116409, 11.26555443, 12.98612309],\n",
       "          [13.38894558, 11.18954372, 12.92433167],\n",
       "          [13.38833237, 11.18933868, 12.92260551]])},\n",
       "  {('PartType0',\n",
       "    'Mass'): array([0.01105031, 0.01259088, 0.01301601, ..., 0.00866676, 0.03573753,\n",
       "          0.0319961 ], dtype=float32)}),\n",
       " ({'PartType0': array([[13.43947315, 11.1077652 , 12.9115057 ],\n",
       "          [13.43997383, 11.1078558 , 12.90948868],\n",
       "          [13.43338394, 11.11088848, 12.91359043],\n",
       "          ...,\n",
       "          [13.42452526, 11.17721558, 12.9738245 ],\n",
       "          [13.40465546, 11.17043495, 12.97396851],\n",
       "          [13.40785408, 11.17961884, 12.97327042]])},\n",
       "  {('PartType0',\n",
       "    'Mass'): array([0.01265707, 0.03210822, 0.0094103 , ..., 0.00870406, 0.00879111,\n",
       "          0.00881443], dtype=float32)}),\n",
       " ({'PartType0': array([], shape=(1, 0, 3), dtype=float64)}, {}),\n",
       " ({'PartType0': array([[13.39521313, 11.19988251, 12.92331028],\n",
       "          [13.39372635, 11.20076847, 12.91552067],\n",
       "          [13.38780212, 11.19334316, 12.9240427 ],\n",
       "          ...,\n",
       "          [13.42351151, 11.19343662, 13.0007782 ],\n",
       "          [13.40781403, 11.18992615, 12.98962116],\n",
       "          [13.39047337, 11.1887331 , 12.92683125]])},\n",
       "  {('PartType0',\n",
       "    'Mass'): array([0.01203093, 0.00882288, 0.01812755, 0.09704798, 0.01313368,\n",
       "          0.03285612, 0.01957441, 0.02674876, 0.01098251, 0.00923867,\n",
       "          0.00899523, 0.00899033, 0.00893849, 0.00891161, 0.00882139,\n",
       "          0.00944025, 0.00870398, 0.00877806, 0.00871626, 0.00870836,\n",
       "          0.00880212, 0.00930818, 0.00886443, 0.00928235, 0.00875006,\n",
       "          0.00870678, 0.00884044, 0.01033679, 0.0088478 , 0.00866768,\n",
       "          0.00874628, 0.0089016 , 0.01014718, 0.00944654, 0.0086832 ,\n",
       "          0.00875447, 0.00893992, 0.00884991, 0.0089059 , 0.00875688,\n",
       "          0.00883372, 0.01142054, 0.00956721, 0.01628112, 0.0091363 ,\n",
       "          0.00897317, 0.01035234, 0.00872913, 0.00967621, 0.00966599,\n",
       "          0.00869202, 0.01048123, 0.00889965, 0.00958413, 0.00875794,\n",
       "          0.00909611, 0.00890253, 0.008756  , 0.00880695, 0.00869773,\n",
       "          0.0088725 , 0.00873408, 0.01006394, 0.00963769, 0.01023244,\n",
       "          0.00903017, 0.00868421, 0.00877307, 0.00871271, 0.00869814,\n",
       "          0.00938541, 0.00870607, 0.00874158, 0.00987726, 0.00868072,\n",
       "          0.00889448, 0.00887321, 0.00867826, 0.00907872, 0.00866725,\n",
       "          0.00874164, 0.00882037, 0.00869153, 0.01020411, 0.00866115,\n",
       "          0.00867086, 0.00871024, 0.00880474, 0.00886343, 0.00890287,\n",
       "          0.00865637, 0.00866516, 0.00865121, 0.00864927, 0.00871735,\n",
       "          0.00884125, 0.00926972, 0.0088592 , 0.00873864, 0.00964638,\n",
       "          0.00908021, 0.00883721, 0.00866603, 0.00870935, 0.00867811,\n",
       "          0.00864657, 0.00865668, 0.00878043, 0.00865264, 0.00868016,\n",
       "          0.00866103, 0.00866239, 0.00870312, 0.00865389, 0.0086508 ,\n",
       "          0.00989787, 0.00865483, 0.00864632, 0.00867351, 0.00865269,\n",
       "          0.00864494, 0.00864656, 0.00865772, 0.00866503, 0.0086741 ,\n",
       "          0.0086517 , 0.00864655, 0.00866203, 0.00864669, 0.00890101,\n",
       "          0.00864809, 0.00961686, 0.00864914, 0.008694  , 0.00868662,\n",
       "          0.00896892, 0.00870718, 0.008682  , 0.00870205, 0.00874101,\n",
       "          0.00873744, 0.00916183, 0.00955473, 0.00873588, 0.00901161,\n",
       "          0.008939  , 0.00880357, 0.00968182, 0.01290366, 0.00881046,\n",
       "          0.00911532, 0.00901558, 0.00882433, 0.00883096, 0.00876668,\n",
       "          0.00905134, 0.00864699, 0.00882495, 0.01073011, 0.00936707,\n",
       "          0.00874935, 0.00907302, 0.00879584, 0.00892107, 0.00866073,\n",
       "          0.00868277, 0.00874908, 0.00869675, 0.00878325, 0.00865738,\n",
       "          0.00933214, 0.00868655, 0.00867728, 0.00879737, 0.00882823,\n",
       "          0.00873398, 0.0087144 , 0.00879571, 0.00914419, 0.00887893,\n",
       "          0.00867258, 0.0091319 , 0.008701  , 0.00893433, 0.00873664,\n",
       "          0.00886017, 0.00876832, 0.00876004, 0.00876043, 0.00874676,\n",
       "          0.00864896, 0.00865531, 0.00876568, 0.00866495, 0.00866952,\n",
       "          0.00869993, 0.00867675, 0.00916402, 0.00905003, 0.00866342,\n",
       "          0.00868235, 0.00896684, 0.00876822, 0.00924851, 0.00867193,\n",
       "          0.00982375, 0.00870061, 0.00865871, 0.00876008, 0.00866068,\n",
       "          0.00864587, 0.00864438, 0.00865156, 0.00864443, 0.00866326,\n",
       "          0.00866363, 0.00866585, 0.00869578, 0.00864615, 0.00865266,\n",
       "          0.00910017, 0.0087096 , 0.00865182, 0.00866087, 0.00866448,\n",
       "          0.00864516, 0.00865118, 0.00864631, 0.00864779, 0.00864549,\n",
       "          0.00865328, 0.00864611, 0.00864808, 0.00865296, 0.00875413,\n",
       "          0.00869133, 0.00866995, 0.00865028, 0.00901158, 0.0087286 ,\n",
       "          0.00864713, 0.00868909, 0.00874365, 0.00864559, 0.00877111,\n",
       "          0.0086518 , 0.00871893, 0.00869103, 0.00871341, 0.00867477,\n",
       "          0.00878077, 0.00876209, 0.00864886, 0.00882232, 0.00868635,\n",
       "          0.0087137 , 0.00869438, 0.00867599, 0.00876006, 0.00867494,\n",
       "          0.00864447, 0.00869653, 0.00864598, 0.00892699, 0.00888338,\n",
       "          0.00959347, 0.00864403, 0.00872051, 0.00868824, 0.00865858,\n",
       "          0.00864397, 0.00864481, 0.00865195, 0.00864596, 0.00876382,\n",
       "          0.00879695, 0.00864803, 0.00868434, 0.00864664, 0.00870971,\n",
       "          0.0086554 , 0.00864534, 0.00864753, 0.00888581, 0.00967348,\n",
       "          0.00883506, 0.00868367, 0.00866216, 0.00871572, 0.00869022,\n",
       "          0.00871313, 0.00874807, 0.00876788, 0.00923704, 0.00871691,\n",
       "          0.00899592, 0.00869392, 0.00875113, 0.00882208, 0.00878247,\n",
       "          0.0087584 , 0.00871955, 0.0086804 , 0.00876417, 0.00869908,\n",
       "          0.00877622, 0.0086981 , 0.00883226, 0.00867931, 0.00883125,\n",
       "          0.00866077, 0.00885404, 0.00871046, 0.00877254, 0.00873536,\n",
       "          0.00877649, 0.00868185, 0.00918181, 0.0086527 , 0.0087715 ,\n",
       "          0.00891042, 0.00870223, 0.00868787, 0.00887731, 0.00886218,\n",
       "          0.00880006, 0.00867987, 0.00884889, 0.0088745 , 0.00864824,\n",
       "          0.00865565, 0.0086617 , 0.00867107, 0.00876228, 0.00865519,\n",
       "          0.00864736, 0.00869823, 0.0086486 , 0.00864438, 0.00867763,\n",
       "          0.00865534, 0.00864471, 0.0086469 , 0.00876905, 0.00866138,\n",
       "          0.00864426, 0.0086503 , 0.00866874, 0.00870558, 0.00868498,\n",
       "          0.0092262 , 0.00868068, 0.00867059, 0.00864974, 0.00869882,\n",
       "          0.00885761, 0.00872128, 0.00882509, 0.00865995, 0.0086457 ,\n",
       "          0.00865564, 0.00868505, 0.00959161, 0.00892541, 0.00866263,\n",
       "          0.00864892, 0.00878631, 0.00976356, 0.00870429, 0.00881173,\n",
       "          0.00869607, 0.00883245, 0.00866649, 0.00866251, 0.0086637 ,\n",
       "          0.00879855, 0.00864934, 0.00866011, 0.00872501, 0.00867285,\n",
       "          0.00868581, 0.00879146, 0.00868083, 0.00894334, 0.00865406,\n",
       "          0.00869168, 0.00867943, 0.00865867, 0.00873563, 0.00870161,\n",
       "          0.00871691, 0.00940083, 0.00867154, 0.00876287, 0.00882982,\n",
       "          0.00873961, 0.00895397, 0.00866214, 0.00873045, 0.00905387,\n",
       "          0.00866149, 0.00873944, 0.00876095, 0.00864876, 0.00869037,\n",
       "          0.00950852, 0.00865531, 0.00881079, 0.00869398, 0.00887109,\n",
       "          0.00870613, 0.00879226, 0.00902033, 0.01184652, 0.01101478,\n",
       "          0.0150399 , 0.01014263, 0.01091627, 0.00873162, 0.00950557,\n",
       "          0.00880134, 0.00865875, 0.008763  , 0.0091825 , 0.01004407,\n",
       "          0.00892587, 0.00865865, 0.00870995, 0.00865768, 0.00878465,\n",
       "          0.00870174, 0.00873989, 0.0086711 , 0.00895921, 0.00868311,\n",
       "          0.00867799, 0.00867407, 0.00869631, 0.00925512, 0.0090023 ,\n",
       "          0.00873555, 0.00866125, 0.00867573, 0.00865635, 0.00870393,\n",
       "          0.00866756, 0.01000759, 0.00868898, 0.0096409 , 0.00864618,\n",
       "          0.00867007, 0.00865202, 0.00866651, 0.00868688, 0.00869466,\n",
       "          0.00896448, 0.00866669, 0.00872678, 0.00866407, 0.00866447,\n",
       "          0.00898271, 0.00904266, 0.00866892, 0.00894086, 0.00874062,\n",
       "          0.00866783, 0.00904381, 0.0087747 , 0.00866502, 0.00907539,\n",
       "          0.00879642, 0.00865868, 0.00889166, 0.00915072, 0.00865272,\n",
       "          0.00864481, 0.00867476, 0.00872816, 0.00878476, 0.00912634,\n",
       "          0.00894622, 0.00882892, 0.00864542, 0.00865445, 0.00864524,\n",
       "          0.00864627, 0.00864562, 0.00868555, 0.00876571, 0.00871714,\n",
       "          0.00872101, 0.00864781, 0.00867894, 0.00864451, 0.00864505,\n",
       "          0.00874473, 0.00865246, 0.00864729, 0.00865292, 0.00874277,\n",
       "          0.00869719, 0.00866853, 0.00869315, 0.01085332, 0.00876114,\n",
       "          0.00968153, 0.00886534, 0.00869854, 0.00873785, 0.00883327,\n",
       "          0.00866658, 0.00897269, 0.01576388, 0.00874511, 0.00878712,\n",
       "          0.00914866, 0.00874413, 0.0086578 , 0.00874343, 0.00882145,\n",
       "          0.00888411, 0.00946762, 0.00874286, 0.00866455, 0.00903582,\n",
       "          0.00865258, 0.00866445, 0.00874822, 0.00884121, 0.0095798 ,\n",
       "          0.00866635, 0.00882196, 0.00864599, 0.00864687, 0.008701  ,\n",
       "          0.01287949, 0.00886486, 0.00873135, 0.00880416, 0.00864825,\n",
       "          0.00864657, 0.00880771, 0.00895446, 0.00881167, 0.00871066,\n",
       "          0.00869427, 0.00864491, 0.0088043 , 0.00864845, 0.00864613,\n",
       "          0.0086487 , 0.00865456, 0.0086541 , 0.00865372, 0.00872631,\n",
       "          0.0087514 , 0.06022028], dtype=float32)}),\n",
       " ({'PartType0': array([[12.776124  , 11.70685959, 12.95094872],\n",
       "          [12.77933407, 11.65993309, 12.96052647],\n",
       "          [12.74176502, 11.59843731, 12.9240551 ],\n",
       "          ...,\n",
       "          [12.50014687, 11.03889656, 10.83100605],\n",
       "          [12.63121414, 11.09275723, 10.91062832],\n",
       "          [12.58743954, 10.9895792 , 10.86112881]])},\n",
       "  {('PartType0',\n",
       "    'Mass'): array([0.00864398, 0.00865749, 0.00864481, ..., 0.0088496 , 0.00895973,\n",
       "          0.00864386], dtype=float32)}),\n",
       " ({'PartType0': array([[13.4385376 , 11.04317665, 12.57679367],\n",
       "          [13.4385128 , 11.05965042, 12.59449577],\n",
       "          [13.47732162, 11.07425117, 12.56700706],\n",
       "          ...,\n",
       "          [13.38940811, 11.19235039, 12.9252739 ],\n",
       "          [13.38981056, 11.19226837, 12.92547035],\n",
       "          [13.3897171 , 11.19199467, 12.92544365]])},\n",
       "  {('PartType0',\n",
       "    'Mass'): array([0.0089119 , 0.00896248, 0.00876433, ..., 0.01497461, 0.01153182,\n",
       "          0.02685248], dtype=float32)}),\n",
       " ({'PartType0': array([], shape=(1, 0, 3), dtype=float64)}, {}),\n",
       " ({'PartType0': array([], shape=(1, 0, 3), dtype=float64)}, {}),\n",
       " ({'PartType0': array([[12.57719803, 10.98426628, 10.982687  ],\n",
       "          [12.50411797, 11.02719498, 11.00050163],\n",
       "          [12.57489491, 10.9623003 , 11.12458515],\n",
       "          ...,\n",
       "          [13.41440487, 11.1604948 , 12.90496349],\n",
       "          [13.42183781, 11.17457771, 12.8966856 ],\n",
       "          [13.4048357 , 11.17023563, 12.89961433]])},\n",
       "  {('PartType0',\n",
       "    'Mass'): array([0.00892981, 0.00864386, 0.00864386, ..., 0.00867115, 0.00939662,\n",
       "          0.00985094], dtype=float32)}))"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_subset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## an updated, pickleable selector object \n",
    "\n",
    "The [pickleAbleSelects] branch contains a draft of how to make the cython selector objects pickleable. At present, it uses the pickle hooks `__setstate__` and `__getstate__` to record and apply the selector object hashes. The implementation currently requires all hash attributes to be public cython objects, so right now only works with the sphere selector, but after building that branch, we no longer need the `MockSphere` object and can pass the full `selector` object:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp = ds.sphere(ds.domain_center,(2,'code_length')) \n",
    "delayed_reader = gda.delayed_gadget(ds, ptf, mock_selector = sp.selector, subchunk_size = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_subset = compute(*delayed_reader.masked_chunks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The base `SelectorObject` is also pickleable, but the other selector objects will require adjustments depending on decisions for h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## misc notes \n",
    "\n",
    "What's this `subchunk_size`? When reading a single chunk, we're reading an index range in a single hdf file. In the `delayed_gadget` class, if `subchunk_size` is None, we just read the full index range into a numpy array. If it is not None, it will return dask arrays split by `subchunk_size`. So far, this generally slows things down:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "delayed_reader = gda.delayed_gadget(ds, ptf, subchunk_size = 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 56.8 ms, sys: 89.2 ms, total: 146 ms\n",
      "Wall time: 181 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "all_data = compute(*delayed_reader.delayed_chunks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which is slower than without the subchunking by about 30 ms. The derived quantity calculate also slows down as dask has a bit more overhead communication to manage the different chunks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 53.6 ms, sys: 7.55 ms, total: 61.1 ms\n",
      "Wall time: 82.3 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "meths = ['size','sum']\n",
    "ptypefield = ('PartType0','Mass')\n",
    "derived_qs = [npmeth(chunk,ptypefield,meths=meths) for chunk in delayed_reader.delayed_chunks]\n",
    "derived_qs = compute(*derived_qs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so for the **present** test gadget dataset and current exploratory loader, we don't get any speed up (though if we had memory issues in loading single chunks, the subchunking should help that).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an additional aside, I made one failed attempt to use dask's hdf dataframe reader to import the gadget hdf files. The underlying pandas hdf reader has a limited expected structure for hdf files and can't handle the gadget h5py hdf files, so we need `h5py` to load and access data....\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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