algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from zarr_rechunker import zarr_rechunker\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Source chunk memory (12) exceeds max_mem (2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-a1bbe9f7d84d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mitemsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzarr_rechunker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrechunking_plan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msource_chunks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_chunks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitemsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_mem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitemsize\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mmax_mem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Code/zarr_rechunker/zarr_rechunker/zarr_rechunker.py\u001b[0m in \u001b[0;36mrechunking_plan\u001b[0;34m(shape, source_chunks, target_chunks, itemsize, max_mem, consolidate_reads, consolidate_writes)\u001b[0m\n\u001b[1;32m    112\u001b[0m     \u001b[0mtarget_chunk_mem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitemsize\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mprod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget_chunks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msource_chunk_mem\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mmax_mem\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Source chunk memory ({source_chunk_mem}) exceeds max_mem ({max_mem})\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    115\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtarget_chunk_mem\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mmax_mem\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Target chunk memory ({target_chunk_mem}) exceeds max_mem ({max_mem})\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Source chunk memory (12) exceeds max_mem (2)"
     ]
    }
   ],
   "source": [
    "max_mem = 2\n",
    "\n",
    "shape = (16,)\n",
    "source_chunks = (3,)\n",
    "target_chunks = (7,)\n",
    "itemsize = 4\n",
    "\n",
    "rc, ic, wc = zarr_rechunker.rechunking_plan(shape, source_chunks, target_chunks, itemsize, max_mem)\n",
    "print(rc, ic, wc)\n",
    "print(itemsize * np.prod(rc) / max_mem)\n",
    "print(itemsize * np.prod(ic) / max_mem)\n",
    "print(itemsize * np.prod(wc) / max_mem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  axis 0, 1 -> 8\n",
      "  axis 1, 1 -> 8\n",
      "(8, 8) (8, 1) (8, 8)\n",
      "1.0\n",
      "0.125\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "max_mem = 256\n",
    "\n",
    "shape = (8, 8)\n",
    "source_chunks = (1, 8)\n",
    "target_chunks = (8, 1)\n",
    "itemsize = 4\n",
    "\n",
    "rc, ic, wc = zarr_rechunker.rechunking_plan(shape, source_chunks, target_chunks, itemsize, max_mem)\n",
    "print(rc, ic, wc)\n",
    "print(itemsize * np.prod(rc) / max_mem)\n",
    "print(itemsize * np.prod(ic) / max_mem)\n",
    "print(itemsize * np.prod(wc) / max_mem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "8*4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  axis 1, 7 -> 11\n",
      "  axis 0, 1 -> 14\n",
      "  axis 3, 360 -> 3600\n",
      "  axis 2, 90 -> 180\n",
      "(14, 11, 1800, 3600) (2, 11, 90, 360) (100, 11, 180, 3600)\n",
      "0.99792\n",
      "0.0007128\n",
      "0.7128\n"
     ]
    }
   ],
   "source": [
    "max_mem = 4_000_000_000\n",
    "\n",
    "source_shape = (1000, 50, 1800, 3600)\n",
    "source_chunks = (1, 7, 1800, 3600)\n",
    "target_shape = (1000, 50, 1800, 3600)\n",
    "target_chunks = (100, 11, 90, 360)\n",
    "itemsize = 4\n",
    "\n",
    "rc, ic, wc = zarr_rechunker.intermediate_chunks(source_shape, source_chunks,\n",
    "                                   target_shape, target_chunks,\n",
    "                                   itemsize, max_mem)\n",
    "print(rc, ic, wc)\n",
    "print(itemsize * np.prod(rc) / max_mem)\n",
    "print(itemsize * np.prod(ic) / max_mem)\n",
    "print(itemsize * np.prod(wc) / max_mem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.99792\n",
      "0.0007128\n",
      "0.7128\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "from math import prod\n",
    "from functools import reduce \n",
    "\n",
    "import numpy as np\n",
    "import zarr\n",
    "import dask.array as dsa\n",
    "\n",
    "from typing import Callable, Iterable, Union, Optional, List, Tuple\n",
    "\n",
    "\n",
    "def consolidate_chunks(shape: Iterable[int],\n",
    "                       chunks: Iterable[int],\n",
    "                       itemsize: Iterable[int],\n",
    "                       max_mem: int,\n",
    "                       axes: Optional[Iterable[int]]=None,\n",
    "                       chunk_limits: Optional[Iterable[int]]=None):\n",
    "    \"\"\"\n",
    "    Consolidate input chunks up to a certain memory limit. Consolidation starts on the\n",
    "    highest axis and proceeds towards axis 0.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    shape : Tuple\n",
    "        Array shape\n",
    "    chunks : Tuple\n",
    "        Original chunk shape (must be in form (5, 10, 20), no irregular chunks)\n",
    "    max_mem : Int\n",
    "        Maximum permissible chunk memory size, measured in units of itemsize\n",
    "    axes : Iterable, optional\n",
    "        List of axes on which to consolidate. Otherwise all.\n",
    "    chunk_limits : Tuple, optional\n",
    "        Maximum size of each chunk along each axis. \n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    new_chunks : tuple\n",
    "        The new chunks, size guaranteed to be <= mam_mem\n",
    "    \"\"\"\n",
    "    \n",
    "    if axes is None:\n",
    "        axes = np.arange(len(shape))\n",
    "    if chunk_limits is None:\n",
    "        chunk_limits = shape\n",
    "    \n",
    "    chunk_mem = itemsize * prod(chunks)\n",
    "    if chunk_mem > max_mem:\n",
    "        raise ValueError(f\"chunk_mem {chunk_mem} > mam_mem {max_mem}\")\n",
    "    headroom = max_mem // chunk_mem\n",
    "\n",
    "    new_chunks = list(chunks)\n",
    "    \n",
    "    for n_axis in sorted(axes)[::-1]:\n",
    "\n",
    "        c_new= min(chunks[n_axis] * headroom, shape[n_axis], chunk_limits[n_axis])\n",
    "        print(f'  axis {n_axis}, {chunks[n_axis]} -> {c_new}')\n",
    "        new_chunks[n_axis] = c_new\n",
    "        chunk_mem = itemsize * prod(new_chunks)\n",
    "        headroom = max_mem // chunk_mem\n",
    "        \n",
    "        if headroom == 1:\n",
    "            break\n",
    "    \n",
    "    return tuple(new_chunks)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  axis 1, 2 -> 8\n",
      "  axis 0, 1 -> 4\n",
      "(4, 8)\n"
     ]
    }
   ],
   "source": [
    "itemsize = 8\n",
    "shape = (8, 8)\n",
    "chunks = (1, 2)\n",
    "\n",
    "\n",
    "max_mem = 256\n",
    "new_chunks = consolidate_chunks(shape, chunks, itemsize, max_mem)\n",
    "print(new_chunks)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(chunks)\n",
    "new_chunks = consolidate_chunks(shape, chunks, itemsize, max_mem)\n",
    "print(new_chunks)\n",
    "print(itemsize * prod(new_chunks))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hypothesis import given\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 5, 1800, 3600)\n",
      "  axis 3, 3600 -> 3600\n",
      "  axis 2, 1800 -> 1800\n",
      "  axis 1, 5 -> 50\n",
      "  axis 0, 1 -> 2\n",
      "(2, 50, 1800, 3600)\n",
      "2592000000\n"
     ]
    }
   ],
   "source": [
    "max_mem = 3_000_000_000\n",
    "\n",
    "shape = (1000, 50, 1800, 3600)\n",
    "chunks = (1, 5, 1800, 3600)\n",
    "axes = (1)\n",
    "itemsize = 4\n",
    "\n",
    "print(chunks)\n",
    "new_chunks = consolidate_chunks(shape, chunks, itemsize, max_mem)\n",
    "print(new_chunks)\n",
    "print(itemsize * prod(new_chunks))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_intermediate_chunks(source_shape: Iterable[int],\n",
    "                                  source_chunks: Iterable[int],\n",
    "                                  target_shape: Iterable[int],\n",
    "                                  target_chunks: Iterable[int],\n",
    "                                  dtype: np.dtype,\n",
    "                                  max_mem: int) -> Tuple[Tuple[int]]:\n",
    "    \"\"\"\n",
    "    Calculate the chunk shape for an intermediate dataset.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    source_shape: shape of the source data\n",
    "    source_chunks: shape of the source chunks\n",
    "    target_shape: shape of the target data (must equal source_shape)\n",
    "    target_chunks: shape of the target chunks\n",
    "    dtype: input datatype\n",
    "    max_mem: maximum amount of data (nbytes) that can be held in memory at once\n",
    "    \"\"\"\n",
    "    \n",
    "    assert source_shape == target_shape\n",
    "    itemsize = dtype.itemsize\n",
    "    source_chunk_mem = itemsize * prod(source_chunks)\n",
    "    target_chunk_mem = itemsize * prod(target_chunks)\n",
    "    assert source_chunk_mem <= max_mem\n",
    "    assert target_chunk_mem <= max_mem\n",
    "\n",
    "    # Greatest common denominator chunks.\n",
    "    # These are the smallest possible chunks which evenly fit into\n",
    "    # both the source and target.\n",
    "    # Example:\n",
    "    #   source_chunks: (20, 5)\n",
    "    #   target_chunks: (4, 25)\n",
    "    #.  gcd_chunks:    (4, 5)\n",
    "    # We don't need to check their memory usage: they are guaranteed to be smaller\n",
    "    # than both source and target chunks.\n",
    "    gcd_chunks = [math.gcd(c_source, c_target) for\n",
    "                  c_source, c_target in zip(source_chunks, target_chunks)]\n",
    "\n",
    "    # first guess for the the intermdiate chunks is gcd chunks\n",
    "    temp_chunks = gcd_chunks\n",
    "    \n",
    "    # But maybe we can make  temp_chunks larger. That would be good because\n",
    "    # things will go faster. Now we see how many source chunks can be combined \n",
    "    # before we exceed the memory limit.\n",
    "\n",
    "    # input_chunks is how we will read the source data. input_chunks can potentially\n",
    "    # be larger than source_chunks\n",
    "    input_chunks = list(source_chunks)\n",
    "    chunk_mem = itemsize * prod(input_chunks)\n",
    "    # this is a key parameter; it tells us how much we can increase the chunk size\n",
    "    # beyond the greatest common denominator\n",
    "    headroom = max_mem // chunk_mem\n",
    "\n",
    "    n = 0\n",
    "    while headroom > 1:\n",
    "        n+=1\n",
    "        print(f'ITER {n}  chunk_mem {chunk_mem}, headroom {headroom}')\n",
    "\n",
    "        # start aggregating chunks\n",
    "        possible_aggregation_axes = []\n",
    "\n",
    "        # figure out which axes we might aggregate over\n",
    "        print(\"Checking axes\")\n",
    "        for n_axis, (c_temp, c_target) in enumerate(zip(temp_chunks, target_chunks)):\n",
    "            print(f\"  axis {n_axis}, ({c_temp}, {c_target})\")\n",
    "            if c_temp < c_target:\n",
    "                print(f'   adding axis {n_axis}')\n",
    "                possible_aggregation_axes.append(n_axis)\n",
    "\n",
    "            \n",
    "        # starting from last axis, aggregate into larger chunks\n",
    "        print(\"Aggregating chunks\")\n",
    "        for n_axis in possible_aggregation_axes[::-1]:\n",
    "            c_temp = min(temp_chunks[n_axis] * headroom, source_shape[n_axis], target_chunks[n_axis])\n",
    "            print(f'  axis {n_axis}, {temp_chunks[n_axis]} -> {c_temp}')\n",
    "            temp_chunks[n_axis] = c_temp\n",
    "            input_chunks[n_axis] = c_temp\n",
    "            chunk_mem = itemsize * prod(input_chunks)\n",
    "            headroom = max_mem // chunk_mem\n",
    "            \n",
    "        print(f'Final chunk_mem {chunk_mem}, headroom {headroom}')\n",
    "\n",
    "        # check to stop endless loop (in case of large max_mem)\n",
    "        if tuple(temp_chunks) == source_chunks:\n",
    "            break\n",
    "\n",
    "    return tuple(input_chunks), tuple(temp_chunks)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[a for a in range(5) if (a < 2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 <= 7 < 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list([1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ITER 1  chunk_mem 129600000, headroom 30\n",
      "Checking axes\n",
      "  axis 0, (1, 100)\n",
      "   adding axis 0\n",
      "  axis 1, (5, 10)\n",
      "   adding axis 1\n",
      "  axis 2, (90, 90)\n",
      "  axis 3, (360, 360)\n",
      "Aggregating chunks\n",
      "  axis 1, 5 -> 10\n",
      "  axis 0, 1 -> 15\n",
      "Final chunk_mem 3888000000, headroom 1\n"
     ]
    }
   ],
   "source": [
    "max_mem = 4_000_000_000\n",
    "\n",
    "source_shape = (1000, 50, 1800, 3600)\n",
    "source_chunks = (1, 5, 1800, 3600)\n",
    "target_shape = (1000, 50, 1800, 3600)\n",
    "target_chunks = (100, 10, 90, 360)\n",
    "dtype = np.dtype('f4')\n",
    "\n",
    "input_chunks, temp_chunks = calculate_intermediate_chunks(\n",
    "                                source_shape, source_chunks,\n",
    "                                target_shape, target_chunks,\n",
    "                                dtype, max_mem)\n",
    "\n",
    "\n",
    "assert input_chunks == (15, 10, 1800, 3600)\n",
    "assert temp_chunks == (15, 10, 90, 360)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "  <line x1=\"236\" y1=\"15\" x2=\"236\" y2=\"75\" />\n",
       "  <line x1=\"248\" y1=\"15\" x2=\"248\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"128.914139,15.914139 248.914139,15.914139 248.914139,75.914139 128.914139,75.914139\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"188.914139\" y=\"95.914139\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
       "  <text x=\"268.914139\" y=\"45.914139\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,268.914139,45.914139)\">1800</text>\n",
       "  <text x=\"110.957069\" y=\"87.957069\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,110.957069,87.957069)\">50</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "dask.array<from-zarr, shape=(1000, 50, 1800, 3600), dtype=float32, chunksize=(100, 10, 90, 360), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_target = dsa.from_zarr(a_temp, chunks=target_chunks)\n",
    "a_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hypothesis.strategies as st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import zarr\n",
    "import dask\n",
    "import dask.array as dsa\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import zarr\n",
    "import dask\n",
    "import dask.array as dsa\n",
    "\n",
    "store_source = {}\n",
    "shape = (8, 8)\n",
    "source_chunks = (1, 8)\n",
    "read_chunks = (2, 8)\n",
    "dtype = 'f4'\n",
    "\n",
    "a_source = zarr.ones(shape, chunks=source_chunks,\n",
    "                     dtype=dtype, store=store_source)\n",
    "a_source.attrs['foo'] = 'bar'\n",
    "d_source = dsa.from_zarr(a_source, chunks=read_chunks)\n",
    "\n",
    "target_store = {}\n",
    "a_target = zarr.empty(shape, chunks=source_chunks, dtype=dtype, store=target_store)\n",
    "a_target.attrs.update(a_source.attrs)\n",
    "store_future = dsa.store(d_source, a_target, lock=False, compute=False)\n",
    "\n",
    "\n",
    "dask.visualize(store_future)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None, None, None]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask.optimization import fuse\n",
    "dsk_fused, deps = fuse(dask.utils.ensure_dict(store_future.dask))\n",
    "delayed_fused = Delayed(store_future._key, dsk_fused)\n",
    "delayed_fused.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delayed_fused.visualize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1.]], dtype=float32)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_target[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delayed('store-0bb04ea0-98bc-11ea-a279-dca9046f1d7d')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_source = {}\n",
    "\n",
    "shape = (8, 8)\n",
    "source_chunks = (1, 8)\n",
    "read_chunks = (2, 8)\n",
    "dtype = 'f4'\n",
    "\n",
    "a_source = zarr.ones(shape, chunks=source_chunks,\n",
    "                     dtype=dtype, store=store_source)\n",
    "a_source.attrs['foo'] = 'bar'\n",
    "d_source = dsa.from_zarr(a_source, chunks=read_chunks)\n",
    "\n",
    "target_store = {}\n",
    "target_chunks = (2, 1)\n",
    "\n",
    "a_target = zarr.empty(shape, chunks=target_chunks, dtype=dtype, store=target_store)\n",
    "a_target.attrs.update(a_source.attrs)\n",
    "store_future = dsa.store(d_source, a_target, lock=False, compute=False)\n",
    "\n",
    "\n",
    "dask.visualize(store_future)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D8qHKkhPQKRhCQyfolCkBP997nhme++67j5o3b05yMF91s2UKzen0LAK3fHbu3Emvvfaa09kHJj/oFAypoBN0coKAb+892ffoeuA5NDVq1BD79u1zvaxMC5CGR1SrVk2w+2G2BegUDMWhE3RymoDH7z33vdq2b98uKleuLBYvXuw0K9fymzt3rqhatarYvXu3a2XoljF00k0R4/pAJ2Muup2FTikVcd+rrXfv3iTn6tCLL77oRMvQszz+/ve/KweIN99807My/SwIOvlJ33zZ0Mk8Kz9jQqeU9N1dq016iIkzzjhDyAU8U5o/HS/KhUdVl9uiRYt0rJ6jdYJOjuJ0LTPo5BpaRzOGTmlxutvVdvnll4t33nknbS10jfDGG2+Iq6++WtfqOVYv6OQYSlczgk6u4nUsc+iUFqV7hmfhwoXi9NNPF9KHPW0tdI2Ql5enxqd+/PFHXauYcb2gU8YIPckAOnmCOeNCoJMphIdzn5AhZW+czYuPPPIIXXfddSS3I7CZg//J2NWQJ7p+8cUX1KlTJ/8r5EINoJMLUF3IEjq5ANWFLKGTKajHXVkyhzdlk15htHbtWuVYYKoqmkbasmULNW3alHbs2EGFCxfWtJb2qgWd7HHzOhV08pq4vfKgk2lu7qxcIDdco3bt2gXe6DBG2V1I5557Ln322WemqQYlInQKhlLQCTp5TcDt954rKxdMnz6d5B47XrNyrTw5WEh8T2EL0CkYikIn6OQHATffe64YnhkzZtAll1ziBytXyuR74XsKW4BOwVAUOkEnPwi4+d5z3PBs3bqVeDVWucunH6xcKfOcc84hOROZ9u/f70r+fmQKnfygbr1M6GSdmR8poJM16o4bntWrV9NZZ51lrRZpYk+YMCFNDPcvy03raNWqVe4X5FEJ0Mkj0BkWA50yBOhRcuhkDbQrhqdhw4bWahETW3qBx21LMGvWLBowYEBMDH8O+Z744QpL4HuBTvqrCZ3014hrCJ2s6VTQWvT0sblLil2p7YTPP/9cbUsgJ2FRoUKF6LnnnqOnn35auTI//PDDdPfdd1PNmjXVtgW//voryUU8SU7ypFdeeUVttTBkyBCaNGmSisfLxn/yySdUt25dZbj27t1L1atXJ562xGvHWQ18T3xvYQnQKRhKQifoFMr3nql5phYiPfDAA+Kll16ykOJU1PPOO08cOXJEneDlajjI7bFF48aN1TH/N2LECNGrV6/o52uvvVbIiapCTvQUQ4cOFXKjOSEnfIqPP/5Y7Nq1S3Tr1k1II6Xiy0mgQhqeaForB4MGDRL//Oc/rSTROi500lqeaOWgUxSF1gfQyZI8hx3vapOLa1KZMmVs/UwpVqwY9ejRg/bs2UO8umsk5OTkRA7p9ddfp7Zt20Y/X3PNNfTRRx+R3OuHGjVqRMWLFyd2A5QGh3755Rc1LvOvf/2LnnnmGSpVqpTtcZqyZcvSgQMHouUG/QA6BUNB6ASdmEDY3nuOd7UVKVJEdX/ZeVx4CwI2JDz2MH78eGrTpk2+bNavX0+5ubnR861bt1bHGzduVNtpx17jlRPk6tj06KOPRuPbPeBZybxtd1gCdAqGktAJOjGBsL33HG/xcKuCf6XZCdyqkNsQKOPTsWNH+uOPP1Q2sS0enlH73XffRbOPLGPD4zeJoVmzZiQ3oCPZfRe9xONCdurHafjewhKgUzCUhE7QiQmE7b3niuE5ePCgraflrbfeopIlS5LcSoHk9tPE+ZQoUYLYkUD2ICpD1LNnT2JPN/7MgQ3VpZdeqhwaTp48ScePH4+WzS0nbqXcfvvtyjGA11sbOXKkLQPCdSldunQ076Af8AsNOumvInTSXyOuIXSyqJOlISETkceNGyc6d+5sImb+KNJQiHfffVdI7zYxcOBAFUF69YgmTZqIDh06iJ9//lnIVpByLrjrrruUA0GXLl3Ehg0bhPRaE7feeitbIyH7Q6PbMUjPNiGNl5AGTTkhyC65/AWbOCOXABJyvTYTMYMRBTpBJ3yfnHsG8H2yxNL5/Xh++OEHwQbETmDPNDmAr4xLbHr2dEvc10c6IIh169bFRkt6zGnZMGUSatWqJdasWZNJFlqlhU5ayZG0MtApKRqtLkAnS3IcdnxbBB5PqVChAklXZmIvtTCE3377TTkpsOdcrPNCkO8NOgVDPegEnfwi4OJ7z/ltEdgLp1WrVvT111/7xcvxcr/66iu68MILQ2N0GBB0cvwxcSVD6OQKVsczhU7WkDruXMDF8148M2fOtFYTjWPzvfA9hS1Ap2AoCp2gkx8EXH3vWeqZMxn5p59+EtL9T60mYDKJttGOHTsmKlWqJOScIG3raLdi0MkuOW/TQSdvedstDTqZJuf8ygVsmeUSNyRf1qHobpPL79CZZ56p1nzz41eHm2VCJzfpOpc3dHKOpZs5QSfzdF3pauPib775Znr77bfN10TTmMOGDVP3omn1Mq4WdMoYoScZQCdPMGdcCHQyidB048hiRLnEjJCrQIvly5dbTKlP9CVLlgg5kTW6cKk+NXOuJtDJOZZu5gSd3KTrXN7QyRTLw7lyteYnTNooS9F4WwPeifTTTz+lrl27WkqrS+R+/fqRXPmaLr74Yl2q5Hg9oJPjSF3JEDq5gtXxTKGTKaTHeekZ1wJbf7lIp5B7xrtWhlsZ8+oJcmwn1K2dCDvoFCGh91/opLc+kdpBpwiJpH+dX7kgsSjZ4hFyuwLB3mFBCXJzOWV0pk6dGpQqZ1xP6JQxQk8ygE6eYM64EOiUEqHzKxcYNbS6d++u9uiRG7UZXdbunNxoTu2AOnz4cO3q5maFoJObdJ3LGzo5x9LNnKBTUrp5rna1RWye3FJAnHXWWUJu2BY5pe1f3uGUFyXl5nK2BegUDMWhE3RymoDH7z33u9oigJYtW6a83MaMGRM5pd3fKVOmqDquWLFCu7p5VSHo5BXpzMqBTpnx8yo1dDIk7Z3h4eLnz5+vti2QSzEY1sbPk2x05J4aYt68eX5WQ4uyoZMWMqStBHRKi0iLCNApnwzeGh4uXu4IKsqXLy/kIqL5auPXCe4ClL2RYvXq1X5VQbtyoZN2khhWCDoZYtHuJHSKk8R7w8PFz507V1SpUkUMGTIkrjZ+fHjllVdE1apVxcKFC/0oXusyoZPW8kQrB52iKLQ+gE5RefwxPFz8ypUrxTnnnCN69Ogh5NbW0Rp5dcAbw3Xr1k3ILRxCuQCoUxyhk1Mk3c0HOrnL16ncoZMi6c4ioUmd6GIu1K9fn2TfJ5UrV47q1atHo0ePjrnq7qHcXpsaNGhANWvWpDlz5oRyAVCnCEInp0i6mw90cpevU7lDp/9P0ilLnkk+ixYtEi1atBAXXHCBmDx5ciZZpUz7ySefiJYtW4rWrVuLpUuXpoyLi/kJQKf8THQ8A510VCV/nbJYJ/+62hJlOHHihBg3bpxo1qyZaN68ueCxl927dydGs/x5586d4qWXXhJNmzZVRodnFJ88edJyPkjwJwHoFIwnATpBJ43fe96sXGC1mTpt2jSS831o4sSJJA0GXXLJJdS+fXvVPcZdc6mCHLshOQ+HOA+5Rhz9/PPP1KVLF+rZs2codxFNxcLta9DJbcLO5A+dnOHodi5ZpFNeDv8ucBuo3fzlmmk0e/ZsZUBmzZqlDAqv/sobsxUvXlwtwyNbLyRncpNcaYDWrFlD8pceNWzYkP7rv/5LGaw2bdoQ74eO4B4B6OQeWydzhk5O0nQvryzQSW/DYySt7DqjX375hQ4ePEh9+vShYsWK0RtvvEElS5akWrVqqZ1PjdLhnLcEoJO3vO2WBp3skvM2Xch0Cp7hicj9xx9/kFxpgHJzc1WLh1tCCPoRgE76aWJUI+hkREW/cyHRKc+1ra/dlkzul6NaO4ULF6Yvv/zS7eKQv00C0MkmOI+TQSePgdssLiw6BdbwvPXWW6qlw+M7b7/9tk0ZkcxtAtDJbcLO5A+dnOHodi5h0Ulr54JkIh44cIAqVqyottbmOOw8sG/fPuVwkCwNzntPADp5z9xOidDJDjXv04RIp2B2tcmJoMRdbJHAx5MmTYp8xF9NCEAnTYRIUw3olAaQJpfDpFMgu9p4J1P2aosE7m7jJiiCXgSgk156JKsNdEpGRq/zYdIpcF1tu3btoho1akS72SKPBnu18bWyZctGTuGvjwSgk4/wLRQNnSzA8jFqyHQKXlfb2LFjqWDBgvkeAe5uk0vu5DuPE/4QgE7+cLdaKnSySsyf+GHTKXAtngoVKhAvi2MUuCW0adMmo0s45zEB6OQxcJvFQSeb4DxOFjKd8vI3HTwGaqU4njx10UUXqaVxOB2P8+Tk5FCJEiVUNo0bN7aSHeK6RAA6uQTW4Wyhk8NAXcoujDoFrsUTq+2AAQOoaNGixH8R9CUAnfTVJrZm0CmWhr7HIdApeGM8+j4OqBkIgAAIgIAZAoF0pzZzY4gDAiAAAiCgJwEYHj11Qa1AAARAILQEYHhCKy1uDARAAAT0JADDo6cuqBUIgAAIhJZAoA0Pu1LzPwS9CUAnvfWJ1A46RUjo/TcMOgXa8PCu3Rrv3K330+th7aCTh7AzKAo6ZQDPw6Rh0CnQhsdDrVEUCIAACICAQwRgeBwCiWxAAARAAATMEYDhMccJsUAABEAABBwiAMPjEEhkAwIgAAIgYI5AoA1PgQIF4NVmTmdfY0EnX/GbLhw6mUbla8Qw6BRow3Py5El4tfn6FTBXOHQyx8nvWNDJbwXMlR8GnQJteMzJhFggAAIgAAI6EQi04QlDk1Onh8GtukAnt8g6my90cpanW7mFQadAG54wNDndejh1yhc66aRG8rpAp+RsdLoSBp0CbXh0ehhQFxAAARAAAXMEAm14wtDkNCdTsGNBp2DoB52gk1cEAm14wtDk9EpoP8uBTn7SN182dDLPys+YYdAp0IbHT/FRNgiAAAiAgD0CgTY8vDw4dw8g6E0AOumtT6R20ClCQu+/YdAp0G9tXh6cm50IehOATnrrE6kddIqQ0PtvGHQKtOHR+/FA7UAABEAABIwI5EjrKYwu2D3H2b3wwgt2k1tKN2fOHCpYsCC1bt3aUjo7kXNzc+mhhx6yk1TLNNBJS1nyVQo65UOi5QnoZEmWvIKWopuI/OSTTxL/e/jhh03EzixKw4YNVQb79u3LLCMTqdmYnjhxgh555BETsfWPAp3014hrCJ2gUyKBMLz3HDc8x48fp0GDBtGAAQMSeQX6c6FChejYsWOBvofYykOnWBr6HkMnfbWJrRl0iqWR/hhjPOkZIQYIgAAIgICDBGB4HISJrEAABEAABNITgOFJzwgxQAAEQAAEHCQAw+MgTGQFAiAAAiCQngAMT3pGiAECIAACIOAgARgeB2EiKxAAARAAgfQEYHjSM0IMEAABEAABBwnA8DgIE1mBAAiAAAikJwDDk54RYoAACIAACDhIAIbHQZjICgRAAARAID0B7QzPkSNH4mq9Y8cOtUZa3EkTHw4cOBAXKzHfuIv4YJmAEc+jR49azgc6WUZmKUGiTr/++qul9JHI0ClCwp2/iTol8jZbamK6xHzN5uN2PK0Mz8aNG2n8+PHqnrds2UJdunRR/04//XSaPHmyKRbbt2+nXr160WeffUZ33nknzZ49W6Xjl+KIESOwf48piqkjxerEMdeuXUs9evSgZ555JnXCmKvQKQaGS4exOq1YsYLatWtH1atXp8aNG0e/Z+mKhk7pCGV+PVanZLzTlZIsna7vPW0Mz8qVK2nIkCHqBcaQ58+fTx9++CHNmzeP+vTpQy+++GI69ur69ddfT506dVL5PPHEE+rv/v37qVSpUsqI3XvvvabyQSRjAok68YPN//jLY2VTPuhkzNeps4k6vfrqq2q7km3bttF5551H9913nym9oJNTihjnk6hTMt7GqU+dTZZO1/eeNoanf//+dPfdd0dJXnnllVS0aFH1uXnz5lS3bt3otWQHe/bsoW+//ZauvvpqFaVy5cpUu3Ztmjt3rvpctmxZqlChAk2ZMiVZFjifhkCiToULF6ZGjRoRszYboJNZUvbjxeq0c+dOuuWWW6hVq1ZUpkwZtbUH9yhwKyhVgE6p6DhzLVandLyTlZgunY7vPS0Mz+LFi4m/HDVr1oyyLVmypDrmX9PcbfbUU09FryU7+P7779UXq0iRItEoFStWpAULFkQ/X3bZZWqPk+gJHJgmYKST6cQxEaFTDAwXDhN14h8F3MqJBO66rlq1KkX2dYmcT/wLnRKJOPs5USczvI1qYCadbu89LQzPrFmzDFs0y5Yto6ZNm9KoUaNo9OjRRszjzi1dulS1aGJPcgtn0aJF0VM1atRQhui3336LnsOBOQLJdDKX+lQs6HSKhRtH6XTiXoGuXbtSgQKpv/7QyQ11TuWZqJMZ3qdSnzoyk063917qJ+/Uvbl6tGbNGqpUqVK+Mpo1a0ZLliyhxx57jJ5++mk6dOhQvjixJ9iDg7fCjg3cFRT7BatWrZr6vHr16thoODZBIJlOJpLGRYFOcTgc/5BKJ96ieeTIkTRw4MC05UKntIgyipCokxneRgWaSafbe08Lw8MGJdnAdPHixYmdBLjlMm3aNCPu0XPssbNr167oZz44ePAg1a9fP3qODdNpp51GXmyXHS00JAepdLJyi9DJCi3rcVPp9Nprr9EDDzxA5cqVS5sxdEqLKKMIiTqZ4W1UoJl0ur33tDA8DRo0IHYHTBXOPPNM4r7pVIH7sdmDLXaLah5EbdKkSTQZGziey3DGGWdEz+HAHAEzOpnJCTqZoWQ/TjKdZsyYQfw9atGihcqcx09TBeiUik7m1xJ1MsPbqFQz6XR772lheNgwJBqeVatWRRn//vvvxC2fli1bqnMMcejQobR58+ZoHD7gwVKeqzBx4kR1Pi8vj3hCVffu3aPx2AOkRIkSVKdOneg5HJgjYKRTJCXvOc/dOLEBOsXS8O7YSKfly5fTzJkz1Xdk3bp1xOMC3PrhAJ280ya2pESd0r2/wqRT/IBILBUPjzt06EDPPfecGsNho8CAO3fuTOydxv7p3IfJ/dKRwN1p/fr1ozlz5uRzOhgzZoyao8BdbLt376bBgwerfCJpp0+fTr179447F7mGv6kJJOrEsdnYTJgwQc27Yl2uuuqqqAcVdErN062riTrt3buXOnbsSBs2bKBBgwZFix02bJg6hk5RJJ4eJOrEhad6f4VJJy0MD/c/Pvroo8q43HXXXWrwn+cYcGuldOnS+R6GKlWqEH+ZjDzd+NrYsWMN0/JL8osvvqBnn302X544kZ5Aok6cIicnJ7rCRGIO0CmRiDefE3UqX748rV+/Pmnh0CkpGlcvJOrEhaV6f4VJJy262hg4/yLjAU/2YosEI6MTuTZp0qToKgeRc7F/jdLyr72HH36Y2MMDwR4BI51S5QSdUtFx7xp0co+tkzkn08no/cXlhuX7pI3hYag33HADsfuzmdCzZ0/lnWYmLsfhZmrfvn3VOlVm0yCeMQHoZMxFt7PQSTdFjOuTjTpp0dUWKwcPuLkRjOYJuVFOtuQJnYKhNHSCTjoS0KrFoyMg1AkEQAAEQMBZAjA8zvJEbiAAAiAAAmkIwPCkAYTLIAACIAACzhKA4XGWJ3IDARAAARBIQwCGJw0gXAYBEAABEHCWAAyPszyRGwiAAAiAQBoCMDxpAOEyCIAACICAswRgeJzlidxAAARAAATSEHB8Aimvh7Zp0yZ6/vnn0xQdrMu8EvZZZ50VrEqnqC10SgFHo0vQSSMxUlQFOqWAY3ApRwKLX8veIJKVU7zHx4ABA9TikVbS2Yn74YcfqiV2unXrZie5pTSM6Zlnnsm3w6mlTDSKDJ00EiNFVaBTCjgaXYJOlsTIc9zwWCo+g8i82Rvv0VOoUCHi/Xpyc3MzyA1J3SIAndwi62y+0MlZnm7lFhKd8gI7xjN16lQqVqyYaoHwzooIehKATnrqklgr6JRIRM/PYdEpsIaHN7Hilg5v+PbOO+/o+ZSgVgSdgvEQQCfo5CWBQHa1HTp0iHhzK96ZlEPRokXp119/xa6iXj45JsqCTiYgaRAFOmkggokqhEinYHa1TZw4UY3tRLTicZ7PP/888hF/NSEAnTQRIk01oFMaQJpcDpNOgexqGzp0qOpiizwP3OUW2T8+cg5//ScAnfzXwEwNoJMZSv7HCZNOgetq27dvH1WtWpXYfTE28M6le/bsoVKlSsWexrFPBKCTT+AtFgudLALzKXrIdApeV9u4ceMM59Kw4fnkk098eixQbCIB6JRIRM/P0ElPXRJrFTadAtfiqVWrFv3yyy+JuqjPDRs2pOXLlxtew0lvCUAnb3nbLQ062SXnbbqQ6ZTn+JI5bsqRl5dH1apVoxMnTqhi+HNOTo7yauOVBSpWrOhm8cjbJAHoZBKUz9Ggk88CmCw+jDoFrsUTqxUvzcOu1PwXQV8C0ElfbWJrBp1iaeh7HAKdgjfGo+/jgJqBAAiAAAiYIRBId2ozN4Y4IAACIAACehKA4dFTF9QKBEAABEJLAIYntNLixkAABEBATwKBNjwFChTwZN8fPaULTq2gUzC0gk7QySsCgTY8J0+eJIf3sfOKe1aVA52CITd0gk5eEQi04fEKEsoBARAAARBwjgAMj3MskRMIgAAIgIAJAjA8JiAhCgiAAAiAgHMEYHicY4mcQAAEQAAETBAItOHhddrYEwdBbwLQSW99IrWDThESev8Ng06BfmuzRxt74iDoTQA66a1PpHbQKUJC779h0CnQhkfvxwO1AwEQAAEQMCIAw2NEBedAAARAAARcIwDD4xpaZAwCIAACIGBEAIbHiArOgQAIgAAIuEYAhsc1tMgYBEAABEDAiECgDU8Y3AqNRAnbOegUDEWhE3TyikCgDU8Y3Aq9EtrPcqCTn/TNlw2dzLPyM2YYdAq04fFTfJQNAiAAAiBgjwAMjz1uSAUCIAACIGCTAAyPTXBIBgIgAAIgYI8ADI89bkgFAiAAAiBgk0COHKgSNtN6mmzbtm20adMm2rNnD+3du5cOHz5MX3/9NeXm5lKbNm2oRIkSVL58eapQoQLVrFmTqlSp4mn9UNifBKBTMJ4E6ASdfCSQp53h4UU/ly1bRnPmzKFFixap41WrVlHp0qWpTp06VK5cOapUqRIVKVKEihYtqhYJPXr0KP3xxx+0a9cu2rdvH61bt44OHjxIDRo0oGbNmlGrVq3ooosuoiZNmmA1a4eeNujkEEiXs4FOLgN2KPss00kPw5OXl0cTJkxQ/7766iuqXLkyXXjhhXT++ecrY3H22WcrI2NFY87zxx9/pJ9++onmzp1L8+bNox07dtAVV1xBnTt3pquvvtpynlbKD2Nc6BQMVaETdNL8vZdH3NXmRzhx4oSYMWOG6N27tyhTpozo2LGjePfdd8X27dtdq86WLVvE22+/La688kpRtmxZ0adPHzFr1iwhf224VmbQM4ZOwVAQOkGnZAQ0fO8d9tzwyG4x8c4774gzzzxTnHPOOeLll18WsiWSjJlr52Uft3jxxReFbE2Jhg0bivfee08cO3bMtfKCljF0CoZi0Ak6WSGgyXvPO8MjnQHE66+/LqpXry4uv/xyIbu/rPByNe7MmTPFJZdcIqRTgnjzzTeFHC9ytTydM4dOOqtzqm7Q6RQLnY+gk6E63hieyZMni1q1aomuXbsK6TBgWBMdTspxINGpUydRt25d8cUXX+hQJU/rAJ08xW27MOhkG52nCaFTUtzuGp61a9cKOYgv6tWrJ6ZPn560Frpd+M9//iOkB5249tprxcaNG3WrnuP1gU6OI3UlQ+jkClbHM4VOaZEedm0C6fDhw5VXWuvWrZV32aWXXhoMVxNZS+l8QD///DM1btyYWrZsSe+//35g6m61otDJKjF/4kMnf7hbLRU6mSSW1jZZjCDnz4gbb7xRyDkzYv369RZT6xddziES9evXF7feeqvg/tqwBOgUDCWhE3Tyg4DL7z1nWzw8b4ZbCMWKFaMFCxZQ7dq1TZo/faPJbkJavHgxSY83NRF15cqV+lbWZM2gk0lQPkeDTj4LYLJ46GQSVGw0p6zpN998IypWrCg++OADp7LULh92uZaTW8X8+fO1q5vZCkEns6T8jQed/OVvtnToZJZUXDxnnAvkqgNCrpEm5KoDcbmH8cOUKVPUvU6dOjVwtwedgiEZdIJOuhFw+L2XueFhYyNbUILnwmRL4BbPaaedJuR6coG5ZegUDKmgE3TSlYCD773MDA//MpOLdooffvhBV1au1UuO+yiDG4SWD3QiAZ1c+yo4kjG+T45gdD0Th3Syb3hmz56txnR0nhDqtgo8yZS7GHUe84FOQk0Ghk5ufxsyzx/fp8wZepGDAzrZMzzcwmFHgmwY00knJPd9ssPBihUr0kX1/Dp0OoUcOp1iofMRdNJZnVN1y1An64bn999/F2eddZYYO3bsqVpk+RF7u8nJplrN84FO+R9K6JSfiY5noJOOquSvUwY6Hba8EVyPHj2oVKlS9NZbb8V6ZWf9ca9evahQoULEM5d1CNDJWAXoZMxFt7PQSTdFjOtjUydr+/EMGzZMbSMgN5rKb/6y/AzPMOcVDkaPHu07CeiUXALolJyNTlegk05qJK+LTZ3Md7WtXr1aDaRv2LAheS2y/AovM8GD2H4uLAqd0j+E0Ck9Ix1iQCcdVEhfBxs6Hc59QgbjRlT8WW5ScffNVVddFX8Bn6IEypcvT3I9NxozZgxdd9110fNeHkCn9LShU3pGOsSATjqokL4ONnQ6bmoH0k8//VTt0sm7HSKkJsDdkLylgh/7+UCn1NrEXoVOsTT0PYZO+moTWzOLOqXvajt06JCoUaNGVq1MEAvUzjHv58ObyXm5kyl0sq4UdLLOzI8U0MkP6tbLtKBT+tWp5UC12lenbdu26dtciKEI8H4+jRo1ohEjRnhGBDpZRw2drDPzIwV08oO69TIt6ZTKrvEv9mrVqonvv/8+VTRcMyDA22jXrFlTyO0UDK46ewo62ecJneyz8zIldPKStv2yTOqUusXDv9ibN29O55xzjnXzl+UpzjvvPJLdbcrRwG0U0Mk+Yehkn52XKaGTl7Ttl2Vap2S27fjx46JWrVpCbuiWLArOpyHAK3Y3bNhQnDx5Mk1M+5ehk312kZTQKUJC77/QSW99IrUzoVPyFs/06dOpUqVKatdN+/Yvu1PyuFjBggVJbhblGgjolDla6JQ5Qy9ygE5eUM68DDM6FUhWjJyBTzfddFOyyzhvkgDPqxk1apTJ2NajQSfrzIxSQCcjKvqdg076aWJUo3Q6Ga7VJpdBoOrVq9P69euJJwch2Cewfft25eHGf4sWLWo/I4OU0MkAis1T0MkmOI+TQSePgdssLo1OeYYtnvHjx9Nll10Go2MTemyyqlWrEg+4TZo0Kfa0I8fQyRGMKhPo5BxLN3OCTm7SdS7vdDoZGh65YyV17drVuVpkeU7dunUjZup0gE7OEoVOzvJ0Kzfo5BZZZ/NNpVO+rjY574TkQpe0bt069dfZqmRnblu3bqWmTZvS7t27qUABQ1tvGQx0sowsbQLolBaRFhGgkxYypK1ECp3yd7UtWbKE5EZvMDppsZqPwONlVapUoZ9++sl8ojQxoVMaQDYuQycb0HxIAp18gG6jyFQ65fv5/e2336olcmyUgyQpCJx//vnEbJ0K0MkpkvH5QKd4Hrp+gk66KhNfr2Q65TM8CxcuhOGJZ+fIpwsuuICYrVMBOjlFMj4f6BTPQ9dP0ElXZeLrlUynfIbnhx9+oCZNmsSnxqeMCTDTZcuWZZxPJAPoFCHh7F/o5CxPt3KDTm6RdTbfZDrFORfI5VeoZMmSJJfYp9zcXGdrkOW5yf0qlHs6bxSXaYBOmRJMnh46JWej0xXopJMayeuSRKd454LNmzdT5cqVHTU6P//8M/3yyy/Ja5YlV4oVK6aM+o4dOzK+Y+iUMcKkGUCnpGi0ugCdtJIjaWWS6RTX1bZp0yaSu2cmzcTKBbkwJt199920d+9e4n6+xYsXW0keyri8WrUTRhg6uft4QCd3+TqVO3RyiqS7+RjpVDC2SJ5n4tQSOXLrZ5L7xFCbNm2IXX+dyje2vkE7LleuHO3ZsyfjakOnjBGmzAA6pcSjzUXopI0UKStipFNci4dbJ7witRNh+fLl0S477r7jVZp1CjxO4nVgtsw40wCdMiWYOj10Ss3H6Cq+T39SwXsv/9Nh9H3KfUKGSFRevp8dC3gL00zCmDFj1IrM3CXEi8WxwwKvovzII48Q9/ldeumlymW7dOnS9I9//EN1w73++uu0f/9+atmyJW3ZsoUGDhxIU6ZMIRayb9++NGvWLNV6GjBgAN122220bds2uvzyyw2ryd16d9xxB3H5fE+cXu4VQa1bt6bBgwcTj5G8+eab9N1339Ell1yiyuB8eW4M1/3BBx+k008/nfr166cMZvfu3Wnjxo1JyzOshMFJbgWy9W/VqpXBVfOnoBN0wveJCN+n+HdGgN57x/mFHA3PPvuskMYh+jmTg4ceekjccsstKosTJ06IoUOHCrk6s5APi/j444/Frl27xF//+lcxefJkFUd2QQn5Uhbjxo1TG6fde++9on79+kIaCSENkZCzYMXNN98spFeYkICFRC5+//13wyrKF7OQYynq2ssvv6y27z5w4IDKq0yZMuo851OoUCFx9OhRtT11+/btxdlnny1Wr14tpPER0glASM8+wfVYunSp4DwzDffcc4949dVXM81GQCfohO+TEPg+xb9KAvTeOxzX/8XriOXk5MSbUQc+cb6NGjWi4sWLR1sNvBbcf/7zH/roo49UCTwGdNFFF9GQIUPo2muvVS2OevXqqb8cQe7kqbbg5hbTX/7yF5IGRLVc+Hxi4Hw4cGvr8ccfp7fffptKlSqlWl7ccuIwf/58kgZR5cEOFdLIKccKXi6I/3HgNes6depEzZo1U58z/c8pvk7lk3g/0OlPIk7xdSof6JRIADoF/b0XN8bD4zBHjhwxVjnDs/wljJ0bxHv9JBo57grjLi2jwN1ysYHryj7i3C3HY0iRf9w9Fwn9+/dX3WvXX3+9OsXlyZaP6t4744wzVPlsfDhw3WLrx+e4zk6OTbGzhWxlcdYZBegUP8cMOpHaOwvfJ3tfK3yfvP8+xbV4SpQooTzR7MlnLRWPociGolpGpl27dipx4cKF1QZ0VnLihejYiEVCZLO12bNnk+zSI57hz4EHP2U3GvXp04d4bpETBiBSptm/bNS51ZdpgE6ZEkydHjrh+5T6CbF/Fe+9P9nFGR4e+GZXXScC/7rnf5HA83piPV8aNGhALVq0oJkzZ1LE8PD6Yz179lRJuCXChikSeMZ/bHrOj6/zrzx+EccGjsdziOQ4E3F3HYcPPvhAOU7IcSH1eeXKlaqrjbcX4JBYv8i5yHUVKcP/5LiWI27l0CneI5G1g074Ptn9euL75P33Ka6rrWLFio64+7K32LRp02jOnDlqHGffvn303nvvKa+1//u//1MvCTYY7EH2+eef08iRI+n5558n3rWOPdDYY427zLi18uOPP9KCBQvo+++/p4kTJ6oJmLzzJnvAsTExWoKGx4lWrFihWhfSuUB5uHF+0plBdZ9deOGFxC0iXjn1/vvvV8ZvxowZ9OWXXyrvOX6AuW47d+6kYcOGKe84uw91bDrmwONGmQboBJ3wfSLC9yn+TRKo916sX4Qc8Bc1a9aMPeX6sfy1KtauXSvk3BTXy+IC5C9jIVtEqiz+y+V7FeQ4lJCbI2VcHHTKGGHKDKBTSjxxF/F9isNh+kOWv/cOY5HQ+B8Nrn3ibkdu0hu10KwWyl2JWMzVKjVz8aGTOU5+x4JOfitgrvwkOsUvEsreHbVr11YTKs1li1hmCXCXIbtsOxGgkxMUjfOATsZcdDsLnXRTxLg+yXSKG+PhpE2bNlVeX8bZ4KxdArzttVPzgbgO0MmuEqnTQafUfHS5yp6p/B1wKuD75BTJ+HyS6ZTP8PCSNfPmzYtPjU8ZE2CmzNapAJ2cIhmfD3SK56Hrp7lz52a89FTsveH7FEvDueNkOuUzPOzxxd5oCM4SYKbM1qkAnZwiGZ8PdIrnoesn6KSrMvH1SqZTnHMBJ+H5EOyuu2HDBjrttNPic8EnWwR48zfeApbn8fAseycCdHKCYnwe0Cmeh66foJOuysTXK4VO8c4FnIxn9Ldt25amTp0anws+2SbAa9LxitxOGR2uCHSyLUfShNApKRqtLkAnreRIWplUOhn+/L7mmmvo008/TZohLlgj8Mknn1Dnzp2tJTIRGzqZgGQhCnSyAMvHqNDJR/gWik6lU76uNs6XF9KsUaOGWrAT3W0WSBtE5dUP2I2aV8rmlbWdDNDJOZrQyTmWbuYEndyk61zeaXTK39XGRfNK0Ly8zL///W/napKlOfGyPl26dHHc6EAnZx8o6OQsT7dyg05ukXU233Q6GXa1cRV69eqldhF1tjrZl9uoUaMUS7fuHDo5QxY6OcPR7Vygk9uEnck/nU5JDQ9vK8173fDinAj2CPCckIMHDypnDXs5pE8FndIzShcDOqUjpMd16KSHDulqYUan3CdkMMqIPbDYc2rEiBF0ww03GEXBuTQEeKXtv/3tb2r7hzRRbV+GTrbRRRNCpygKrQ+gk9byRCtnQqfjvKdN0iB3+BS8Uq9cbydpHFwwJrBo0SIhN6kTR48eNY7g4FnoZB8mdLLPzsuU0MlL2vbLMqnT4aQtHjZfvBglh3HjxlG3bt3UMf4zR6Bfv35qUzve88ftAJ3sE4ZO9tl5mRI6eUnbflkmdUrd4mG7J3fsVL/cv/32W/tmMMtSTp8+XdSqVUvILRA8u3PoZB01dLLOzI8U0MkP6tbLtKDT4ZRdbZGiP/roI3H22WerTdQi5/DXmMCRI0eE3G5bTJ482TiCi2ehk3m40Mk8Kz9j+q2TXOoqunGknxx0L9uiToeTerXFNrb++7//m6pUqUK8bTVCagIvvfQSNWjQgDp27Jg6ogtXoZN5qNDJPCs/Y/qtU9WqVen111/3E0Egyrask1lLunz5ciEXDxWbN282myTr4q1fv14x4q28/QrQKT156JSekQ4xoJMOKqSvgw2dzHW1RYqWLR7xl7/8RXCzCiGeAI/ncLP8nXfeib/gwyfolBw6dErORqcr0EknNZLXxaZOhw3XakvVtmPvNukmjOZnAqTbbruNpFszvf/++wlX/PkInYy5QydjLrqdhU66KWJcH5s65ZlyLoi1d7/99puoU6eOkKtXx57O6mO5LpGQ4zpCrlKgDQfolF8K6JSfiY5noJOOquSvUwY6WetqixS9ePFiUaFCBfHNN99ETmXt3ylTpohKlSqJH374QTsG0OmUJOzqCZ1O8dD1KAg6ffXVV7ri86xeGepkz/Dw3XHB7Gwg1+Xx7GZ1K4hf7EWKFBElS5YU/fv3F0uWLNGtitBJKhIEA4zvk946LVy4UNx///1CdjgJub1J1r/3Mmx42Dc8/IaV2yYoIVauXKndC9ftCnELhw3vkCFDhFwvTcjVA0SJEiXUOTl7V8ydO1ecPHnS7WqYyh86VRQTJkwwxcrPSNBJH534u8uT5u+66y5Rrlw59eMyNzdX8D923mEDlM3vvQy/T5kZHv6SfvbZZ6rbTceuJrdeIl9//bV68GbMmKGKeO6550Tx4sXVOX4g+eEsVaqUKFu2rLj99tvFzJkzfTdC0Mmtp8HZfKGTszyt5HbixAnB3Wi33nqrkHuSqe8wf5f5O83/+IelnNOjsoROVsjmi5u54eEsecY8//rPhjEffjD5XmMtPj+wjRo1Ejk5OdGHNPKwcmuIjRB3yS1dujSfAl6eyHadvGSdSVnQKRN69tJyNzkbGTY4/J2NfH8jf/lcixYt4n5AQid7rGUqZwwPF8991NzvF2Zvt7FjxyqjM3v27HzEeeIm9/1GHtTEv+XLlxfsaeZ3yHad/OZvtnzoZJaUM/H27duneigSv7eRz/zdNpoYDp1s8XfO8HDxPIjLrtY8xhGmSaa87QB3mZ111lkpvdeefPLJuC632Id22bJlthRyI1G26+QGUzfyhE5uUE2e54IFCwx/PHIX2+DBg5MmhE5J0SS7YG6tNvkCNRXkqgZqx9KtW7cSbwfAO5gGPWzcuJHOPfdckqs/k2yOk1wsNekt/fOf/6TTTz+dZJdbNI78pUQXXHAB1a9fP3rO74Ns18lv/mbLh05mSTkTT648QsxcjtdGM5RdbFS3bl267777oucSD6BTIhETn5OZpEzPs+cHj4W8/PLLgVzVmjdwe/7551X34bBhw0zj4E3zihYtqrrc+O91110nrr32WtG0aVPB3XG6hWzVSTcd0tUHOqUjlNl17pHgcdoePXoIucBv9DvMXWyrVq0ynTl0MoXK2a62xCL5Rdu+fXu1pUKQ9vNhL7SGDRuKDh06iNWrVyfeVtrPAwYMUM4EsXvyDB8+XPA4D7tf6xayVSfddEhXH+iUjpD16+w2/eqrr6ofmKNHj1YZ8N5W1apVU9/hZ555xnKm0CktMncNT6R49v7gbaD514TO22h///33qoVSo0aNjJwk2ImAJ5QmGi3+zJ4xnTp1Env27Ing0eZv0HTibdnD7MyS7MGIeFNly/cpGYdMz+/cuVNceeWVonXr1oJXWI4NbDz+8Y9/qI0wY89bOQ7a9ynT954FNt4YHq4Q/4p48cUXBb8srrnmGq1m+fOg4lVXXaV+5bzyyivi0KFDFhhai8pdeI888oiQY0Hiiy++sJbYg9hB0YlfurInWUTmUnmARpsipk6dqu79xhtvzPrvk11RJk2apL7vjz32mKsbvQXl++T2ey9BJ+8MT6Rg9hDjflC2rtIBQbz55puCXRm9Dtzi4HpIxwG1TTXX448//vCsGjwJ9YwzzhB9+/YVBw4c8KxcswXprlNkEsz+mVoAABDrSURBVC/3wX/44Ydmbyvw8UaMGKHGH9jTav78+UJ3nXQDvn//fnHTTTcp71svu/+hU9yT4L3hiRR/7Ngx8fnnn4vrr79eTdqSu2cKXu3UTSO0e/duIbctEF27dlVl8i9G/vV4/PjxSLU8/csGh9202Qh/+eWXnpZttjBddWItCxcurH75s/Hh1nTYw8CBA6PuvrxEE/+ajgRddYrUT4e//L7hsRue7uFmr0aqe4VOio71/XhMOMpZjiJfwPTxxx/TxIkTadasWXTmmWfShRdeqFyyGzduTNLbhOSsYkv5ytUE6Oeff6affvqJ5EKm6t+aNWvokksuoc6dOxPvVyMX97SUp1uRp02bRn369KG//vWvJL0ASf6adauojPLVTSc5y1y5ufNNsQssM3zttdfi3NkzumFNEssBcPrb3/5G8ocZyY23VK2kowrJVrthDXXTybCSHp6UY67KHVo6DdF7771Hbdu29bD05EVlsU55ljeCS47RmSvyFwHJlWBpzpw5tGjRIpJrwNGGDRuoSpUqJCenEn/hpJs2SVdlksvQ8H5CJCerqn+7du2ivXv3khwoJDlwqOJLN2Zq1aoVXXTRRcpHv1ChQs5U1OFc5K9XeuCBB4iN0LvvvqsMpMNFOJqdDjpF5k9EboyNz+WXX05yUJd01TlSV7N/+dnu0qWL+kEWMTqcln+Yya6itNnooFPaSroYQfZokOzOJjmuTC+88IK2P+qyTCf9DI/RMyi7wmjTpk3qn+xiIdkdR7KpTHyefw2yAeKXTsQoybETkt1XJLsjjLLT+hwbHv51Kz1tSHYfqQmpWlc4pnJe68S7H8qtxmNqQMQTdps1a0ZyKRNtXzJxFU7xgX+pt2vXjlasWEFy/DEakyco33vvvSQHhKPnrBx4rZOVujkVl3+EMiO5qgC99dZbiqNTeXuVT4h1sr4Daar+S1xzhgA7OfDyO7z2XaYunc7USM9chg4darhEES/IyuyCHtiBIDKOJV92ajyL//L+TyNHjgz67blWf/ljROn/P//zP8r5wrWCkLFdAs4umePVL4Gwl8MtuP/93/8lOZuaduzYQXKNOHrjjTeIm+MIpwjw+J9Rlxq3CJKNf5xKrf8Rt+pjl1+K1JiXceF7R4gnIFcYUF3UcqURkm729Oyzz6ou+fhY+KQDgQI6VAJ1MCYgPXDUeA93v8k5P8oASddhNa5lnCK7zvLaWrHjHty1ykabuyp54DboQbr+KocJHs+M7TZmg8QONwh/EpCuysTrJPK4FzsNsTNRqjUVwc1/Ato5F/iPRN8ayP2OSE4+pYMHD6pfc3LSq76V9ahmcrM9ZWT45cxjO+x4IicHelS6N8XI2fX066+/KkcbfslWqlRJOc94U7repcgN2ejvf/+7WoiXPULZCQlBewJ5aPFor9GpCl588cUkt9Smp556ih599FH1ZWP382wOcvkh1e3EHl7Mhp1Qxo8fHxokct6ZMjpysiixOzB3u8pFZ0Nzf3ZvZKNcNZ69/R588EHVK8Cu5jA6dmn6kM7u6BDS+U9gzJgxao+gSy+9VPCyP9kYZEsg7rZ5Yy65jL3gpYmCHni2O08ullML4m5FdiPGfc6mDzzxk5e5KVeunHj66adDte9XFukI5wIfbL1jRcr1ypSr7Q033KB+BfOvf573lE2hTJkycbcrjTDVq1eP5BJIceeD+IEnw/IcNN7PKTbIrdRjP2bNsdwBWO1rJXcCVc85t/ql11/W3H+YbhRjPCFRkz3eeL7Cc889R+eddx7J5VWydgCaV6zg+S+8UkWiYQqK3DwRmg3od999p1byCEq93ainXDWe7rnnHjV3T66vqJwI3CgHeXpGAGM8nqF2uSB2K+ZBVv41yLu/8i//7t27E7uYZltgV2OeqS73UgnsrfMPB27R8vJR2RrYJZ49FHkpqV69eqmVTNhzDSH4BOBcEHwN4+6Avbt4wHXdunXKy4uXCurdu7cySHERQ/5h0KBBxPM5Nm/eHLg75R8PcvyOHn/88cDV3YkK84x97mZs0KCBWpGEfzzxKhU8fwkhHASgZDh0zHcXvISQnLmt1q3jX83cCrr11lvV53yRQ3iCPZy4e4bHAYIWWLeHHnqI5OoLQat6xvXlOWu8vuKUKVPUWnS8LFBQu0szhhHiDDDGE2JxY2+N1/3iLzGvgMCrc/PKCLymXZgDz3lho8tzPZo3bx6IW2W3ablVCMndatVk2EBU2oFKslMMG1t2h+fFPK+++moHckUWmhLAGI+mwjheLf7V+MQTT6guN14RgVd25v5z/qKHNfCCodxdxV2PQQn333+/mqfFKzBkQ9iyZYvqCm7fvr36QcSOITA64VceXW3h1zjuDnmmPw9c8zgCd+Wcc845dMcdd4TWAPEePbzendwELI6Djh94Typehbpnz546Vs/ROvGSRnIBXNWtJreBV8/jXXfdZXnfLUcrhcw8IwDD4xlqvQpiAyQn4KkvvJyMp7qi7rzzTtq6dateFc2wNryBIG8v0b9/f+LNAXUN7A7PyyENHjzYcGFQXetttV58n+wSzV2gvHUBb9TIK3Fk69wkq/zCEh+GJyxK2rwPNjrsdsxjCmyMeGCXDVAQvcGSIeA17apWrap2n0wWx+/zcosHNe+Kd8gNa/jkk0+oYcOGynGAV48ePnw4cbcvQvYRgHNB9mme8o55kz1uIfBk1Ouuu06t+sub6gU9LFmyhNgAcRejbluLs+MHtwB43b0wrjo9e/Zs5V3Iu+zyQp48xwwhqwnAuSCr5Te4eW4B8T4mPOufj3nFZx4D4kHgIAd2prjsssuUx5Ru98EtTvY0DJvR4d0/r7jiCuU8wK3opUuXwujo9vD5VB+0eHwCH5RieekWHnfgyZi8KjIPCNesWTMo1Y+rJ3cfclcibyWty0rG7FXIDh7Lly/Xpk5x0Gx8YL4DBgxQ++Kw2z47eBht2GcjayQJBwG0eMKho3t3Ub58eTUGxC0gPuaWw+233068LH3QAncZsgs5vwx1CbyB2b333hsKo/PLL7+o1k2bNm3UhOX169er1jKMji5Pmz71gHOBPlpoXZPTTjst6gVXuXJlatGiBfXt2zdwBohXMvj000+J54v4HXjcSW7joCZO+l2XTMrfuXOnWieQJ+lya5gNDk8G5eWbEEDAiAAMjxEVnEtKgA0Qr4PGa8FVr15dLdvPS/Hw5yCE0qVLk9zPRblX+13fBx54QLHUzdnBLBfempsNOa+pxhNe2TPyySefhGu0WYBZHA+GJ4vFz+TW2fWaXzJscPhXbuvWrenmm29WTgmZ5OtFWh7o5pfkV1995UVxhmXwMj67d++mW265xfC6zid5G27urmRPPPaC5Lk4PA6YjWvL6ayTznWD4dFZnQDUjVsQvCwNd6/wtsy8aRnPvOcXu66Bxxwik0r9qOPJkyfVZNGXXnopUDP12eCw1nXq1KFt27YRe63x/CNu+SKAgBUCMDxWaCFuUgJsgNiTiQ0Q74dz8cUXq/2A2FtLx9ClSxc1BjFq1CjPq/f2228rZwLeZyYIgecZ8Tp/3MJht3oem3rnnXeoVq1aQag+6qgjgSza5xu36iGBQ4cOieeff15UqlRJSDdsIVcf9rB0c0XNnTtXyHXChFzF2lwCB2LJSZRCunIL+fJ2IDd3s5AGR0iDI6Q3o5BdgkJ2q7pbIHLPFgKH0eLR8ddACOrE+wE9/PDDtGHDBuVae/nll1PXrl2Jl7/XJfAeRbxNOG8X4VXgJf95JWadt2k4ePCgcnqoXbu28lrk7bffffdd1cXmFSeUE24CmEAabn21uTveG4cnofLYCs8F4rECdsn2O3DXIDtGrFy5Us1TcrM+vEp2kyZN1Ax+XpFZt8Bearzz57/+9S/q0KGDciCoW7eubtVEfYJPABNIg69hMO6A98bhiZL8oudlVK655hr1cuONz/wMPFB+4403KkPodj14DIznPulmdPbs2aPcoiNjOAsWLKCRI0cSjI7bT0T25o8WT/Zq7+udHz16lEaMGKFWRahXr55yzWaPOD8CuwSzRx4bQf7rRmCXY155mhcpZUcMHcL27dtVNyM7Ctxwww1qq/Sw70qrA3fUgdDiwUPgD4HChQur5Wt4HlD37t3ppptuonbt2tHMmTM9rxAvhsp74fA/twLvB8QTV3UwOrzcEc9l4kVJjx8/rubhDBkyJPRbobulLfK1TgDOBdaZIYWDBAoWLEi88gGvBceTKW+77Ta66KKLaNq0aQ6Wkj6r++67T7kJS0+39JEtxuCJqmxg+WXvZ+DFO3v37q3G1njdPW598TYF2BPHT1WytOxs8d/DfQaDgNwlVHzwwQdCLsMizj33XDFlyhTPKv7++++rMp0skO9Hbi0hxo8f72S2lvKSYzZCbrugXNvlFgyC3aQRQMBHAnCnztLfG9redoECBaITT7l7irdhYC+4iRMnkvyiuFrvHj16qK6njz76yLFyRo8erTaeY1dyrwO3tHhcqVu3bmofHO5iY546dPd5zQLl6UUAzgV66ZGyNrzUCq+PFraQm5ubcqsCNjp83zweweMk/CJlA5UYnOCzdetW+vrrr5Xx43plEri+svVGPIcpk/1/0vGJrSMz+Pjjj0lO3qUjR46ocSseQ+MuTQQQ0IRAHgyPJkqYqQbv3cITENktN0yBl2PhFa/T3ZfsdqOBAwcSL+HCi1Ty1tyxxiGb+bCRYS9Bfj54zIYdJTp16hSmxwT3Eh4CefgZFCAxc3Jy1HyTdC/oAN2SquqxY8dMVZknNfI/3sPmqaeeUix4WX5elJR/0WcjH3YFZ480/teyZUuS41RqoVZTQBEJBHwikL+/wqeKoFgQMEvgsssuU27XvNgmv2h54iOvisDdTNkSeLfPe+65R03y5GWJZsyYQZMmTYLRyZYHIOD3CcMTcAGzufpt27ZVrZ+xY8fShAkT6N///nfocSxcuFBN9uTWDW8gxy7Sw4cPV5uxhf7mcYOhIYCuttBImb03wgt98vjPHXfcEVoIbGDatGlDmzZtIp5zxMYmqDuXhlYk3JhpAjA8plEhou4EwrwD5qxZs9RkT/boi3Wo0F0T1A8EjAjA8BhRwTkQ0IwAt+bYiw8BBMJAAGM8YVAR9wACIAACASIAwxMgsVBVEAABEAgDARieMKiIewABEACBABGA4QmQWKgqCIAACISBAAxPGFTEPYAACIBAgAjA8ARILFQVBEAABMJAAIYnDCriHkAABEAgQARgeAIkFqoKAiAAAmEgAMMTBhVxDyAAAiAQIAIwPAESC1UFARAAgTAQgOEJg4q4BxAAARAIEAEYngCJhaqCAAiAQBgIwPCEQUXcAwiAAAgEiAAMT4DEQlVBAARAIAwEYHjCoCLuAQRAAAQCRACGJ0BioaogAAIgEAYCMDxhUBH3AAIgAAIBIgDDEyCxUFUQAAEQCAMBGJ4wqIh7AAEQAIEAEYDhCZBYqCoIgAAIhIEADE8YVMQ9gAAIgECACMDwBEgsVBUEQAAEwkAAhicMKuIeQAAEQCBABGB4AiQWqgoCIAACYSAAwxMGFXEPIAACIBAgAjA8ARILVQUBEACBMBCA4QmDirgHEAABEAgQARieAImFqoIACIBAGAgUDMNNZMs9nDx5ktauXUuPP/54qG756aefpkGDBmV8T+CTMUJkAAKeEMgRMnhSEgrJmABLNXDgwIzz0S2D3NxcGjBgQMbVAp+MESIDEPCCQB4MjxeYUQYIgAAIgECEQB7GeCIo8BcEQAAEQMATAjA8nmBGISAAAiAAAhEC/w/Qnnc5CdTe1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dask.visualize(store_future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'HighLevelGraph' object has no attribute 'copy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-48-bcf72b74bbd4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstore_future_optimized\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstore_future\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfuse\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimize_subgraphs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mstore_future_optimized\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisualize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/pangeo-py38/lib/python3.8/site-packages/dask/base.py\u001b[0m in \u001b[0;36moptimize\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    366\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    367\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 368\u001b[0;31m     \u001b[0mdsk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcollections_to_dsk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcollections\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    369\u001b[0m     postpersists = [\n\u001b[1;32m    370\u001b[0m         \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dask_postpersist__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_dask_collection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/pangeo-py38/lib/python3.8/site-packages/dask/base.py\u001b[0m in \u001b[0;36mcollections_to_dsk\u001b[0;34m(collections, optimize_graph, **kwargs)\u001b[0m\n\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mopt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moptimizations\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 214\u001b[0;31m             \u001b[0mgroups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdsk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdsk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgroups\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    215\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m         dsk = merge(\n",
      "\u001b[0;32m~/opt/miniconda3/envs/pangeo-py38/lib/python3.8/site-packages/dask/base.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mopt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moptimizations\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 214\u001b[0;31m             \u001b[0mgroups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdsk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdsk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgroups\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    215\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m         dsk = merge(\n",
      "\u001b[0;32m~/opt/miniconda3/envs/pangeo-py38/lib/python3.8/site-packages/dask/optimization.py\u001b[0m in \u001b[0;36mfuse\u001b[0;34m(dsk, keys, dependencies, ave_width, max_width, max_height, max_depth_new_edges, rename_keys, fuse_subgraphs)\u001b[0m\n\u001b[1;32m    530\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdsk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdeps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    531\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 532\u001b[0;31m     \u001b[0mrv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdsk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    533\u001b[0m     \u001b[0mfused_trees\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    534\u001b[0m     \u001b[0;31m# These are the stacks we use to store data as we traverse the graph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'HighLevelGraph' object has no attribute 'copy'"
     ]
    }
   ],
   "source": [
    "store_future_optimized = dask.optimize(store_future, optimizations=[fuse])[0]\n",
    "store_future_optimized.visualize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask.optimization import fuse\n",
    "\n",
    "dsk = store_future.dask.dicts\n",
    "dependencies = store_future.dask.dependencies\n",
    "dsk_fused = fuse(store_future.dask.dicts)\n",
    "dask.visualize(dsk_fused)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsk_fused = fuse(dask.utils.ensure_dict(store_future.dask))\n",
    "dask.visualize(dsk_fused)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "dictionary update sequence element #0 has length 10; 2 is required",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-74-07ddfa95cb06>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelayed\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDelayed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdelayed_fused\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"read-and-store\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdsk_fused\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdelayed_fused\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/miniconda3/envs/pangeo-py38/lib/python3.8/site-packages/dask/base.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    163\u001b[0m         \u001b[0mdask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m         \"\"\"\n\u001b[0;32m--> 165\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraverse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    166\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/pangeo-py38/lib/python3.8/site-packages/dask/base.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    431\u001b[0m     )\n\u001b[1;32m    432\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 433\u001b[0;31m     \u001b[0mdsk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcollections_to_dsk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcollections\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimize_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    434\u001b[0m     \u001b[0mkeys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dask_keys__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    435\u001b[0m     \u001b[0mpostcomputes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dask_postcompute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/pangeo-py38/lib/python3.8/site-packages/dask/base.py\u001b[0m in \u001b[0;36mcollections_to_dsk\u001b[0;34m(collections, optimize_graph, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0moptimize_graph\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0mgroups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimization_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m         \u001b[0mgroups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0m_extract_graph_and_keys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgroups\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mopt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moptimizations\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/pangeo-py38/lib/python3.8/site-packages/dask/base.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0moptimize_graph\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0mgroups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimization_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m         \u001b[0mgroups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0m_extract_graph_and_keys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgroups\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mopt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moptimizations\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/pangeo-py38/lib/python3.8/site-packages/dask/base.py\u001b[0m in \u001b[0;36m_extract_graph_and_keys\u001b[0;34m(vals)\u001b[0m\n\u001b[1;32m    237\u001b[0m         \u001b[0mgraph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mHighLevelGraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mgraphs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 239\u001b[0;31m         \u001b[0mgraph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgraphs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/pangeo-py38/lib/python3.8/site-packages/dask/utils.py\u001b[0m in \u001b[0;36mensure_dict\u001b[0;34m(d)\u001b[0m\n\u001b[1;32m   1021\u001b[0m                 \u001b[0mseen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdd_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1022\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1023\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1024\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: dictionary update sequence element #0 has length 10; 2 is required"
     ]
    }
   ],
   "source": [
    "from dask.delayed import Delayed\n",
    "delayed_fused = Delayed(\"read-and-store\", dsk_fused)\n",
    "delayed_fused.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "dictionary update sequence element #0 has length 10; 2 is required",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-73-65436cae5790>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelayed_fused\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/miniconda3/envs/pangeo-py38/lib/python3.8/site-packages/dask/base.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    431\u001b[0m     )\n\u001b[1;32m    432\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 433\u001b[0;31m     \u001b[0mdsk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcollections_to_dsk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcollections\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimize_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    434\u001b[0m     \u001b[0mkeys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dask_keys__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    435\u001b[0m     \u001b[0mpostcomputes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dask_postcompute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/pangeo-py38/lib/python3.8/site-packages/dask/base.py\u001b[0m in \u001b[0;36mcollections_to_dsk\u001b[0;34m(collections, optimize_graph, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0moptimize_graph\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0mgroups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimization_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m         \u001b[0mgroups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0m_extract_graph_and_keys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgroups\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mopt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moptimizations\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/pangeo-py38/lib/python3.8/site-packages/dask/base.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0moptimize_graph\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0mgroups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimization_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m         \u001b[0mgroups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0m_extract_graph_and_keys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgroups\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mopt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moptimizations\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/pangeo-py38/lib/python3.8/site-packages/dask/base.py\u001b[0m in \u001b[0;36m_extract_graph_and_keys\u001b[0;34m(vals)\u001b[0m\n\u001b[1;32m    237\u001b[0m         \u001b[0mgraph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mHighLevelGraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mgraphs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 239\u001b[0;31m         \u001b[0mgraph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgraphs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/miniconda3/envs/pangeo-py38/lib/python3.8/site-packages/dask/utils.py\u001b[0m in \u001b[0;36mensure_dict\u001b[0;34m(d)\u001b[0m\n\u001b[1;32m   1021\u001b[0m                 \u001b[0mseen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdd_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1022\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1023\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1024\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: dictionary update sequence element #0 has length 10; 2 is required"
     ]
    }
   ],
   "source": [
    "delayed_fused)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([None, None, None, None],)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dask.compute(store_future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1.]], dtype=float32)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_target[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{4604033152: set(), 4603945216: set(), 4603924288: set(), 4604099520: set()}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_future.dask.dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zarr.attrs.Attributes at 0x116864640>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_target.attrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mdsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msources\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtargets\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mlock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mregions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcompute\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mreturn_stored\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Store dask arrays in array-like objects, overwrite data in target\n",
       "\n",
       "This stores dask arrays into object that supports numpy-style setitem\n",
       "indexing.  It stores values chunk by chunk so that it does not have to\n",
       "fill up memory.  For best performance you can align the block size of\n",
       "the storage target with the block size of your array.\n",
       "\n",
       "If your data fits in memory then you may prefer calling\n",
       "``np.array(myarray)`` instead.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "\n",
       "sources: Array or iterable of Arrays\n",
       "targets: array-like or Delayed or iterable of array-likes and/or Delayeds\n",
       "    These should support setitem syntax ``target[10:20] = ...``\n",
       "lock: boolean or threading.Lock, optional\n",
       "    Whether or not to lock the data stores while storing.\n",
       "    Pass True (lock each file individually), False (don't lock) or a\n",
       "    particular ``threading.Lock`` object to be shared among all writes.\n",
       "regions: tuple of slices or list of tuples of slices\n",
       "    Each ``region`` tuple in ``regions`` should be such that\n",
       "    ``target[region].shape = source.shape``\n",
       "    for the corresponding source and target in sources and targets,\n",
       "    respectively. If this is a tuple, the contents will be assumed to be\n",
       "    slices, so do not provide a tuple of tuples.\n",
       "compute: boolean, optional\n",
       "    If true compute immediately, return ``dask.delayed.Delayed`` otherwise\n",
       "return_stored: boolean, optional\n",
       "    Optionally return the stored result (default False).\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> x = ...  # doctest: +SKIP\n",
       "\n",
       ">>> import h5py  # doctest: +SKIP\n",
       ">>> f = h5py.File('myfile.hdf5', mode='a')  # doctest: +SKIP\n",
       ">>> dset = f.create_dataset('/data', shape=x.shape,\n",
       "...                                  chunks=x.chunks,\n",
       "...                                  dtype='f8')  # doctest: +SKIP\n",
       "\n",
       ">>> store(x, dset)  # doctest: +SKIP\n",
       "\n",
       "Alternatively store many arrays at the same time\n",
       "\n",
       ">>> store([x, y, z], [dset1, dset2, dset3])  # doctest: +SKIP\n",
       "\u001b[0;31mFile:\u001b[0m      ~/opt/miniconda3/envs/pangeo-py38/lib/python3.8/site-packages/dask/array/core.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dsa.store?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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