{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1446b800-7ded-4a4c-b54c-8510100c3bda",
   "metadata": {},
   "source": [
    "# Dask Array Support to AnnData"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4bd620",
   "metadata": {},
   "source": [
    "**Author:** Selman Özleyen"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb9b747f-4384-4b16-8f4f-806edfdc0b06",
   "metadata": {},
   "source": [
    "## Initializing\n",
    "\n",
    "First let’s do our imports and initialize adata objects with the help of the `adata_with_dask` function defined below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f65fb557",
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "import dask\n",
    "import dask.array as da\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import anndata as ad\n",
    "\n",
    "dask.config.set({\"visualization.engine\": \"graphviz\"});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f35076e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def adata_with_dask(M, N):\n",
    "    adata_dict = {}\n",
    "    adata_dict[\"X\"] = da.random.random((M, N))\n",
    "    adata_dict[\"dtype\"] = np.float64\n",
    "    adata_dict[\"obsm\"] = dict(\n",
    "        a=da.random.random((M, 100)),\n",
    "    )\n",
    "    adata_dict[\"layers\"] = dict(\n",
    "        a=da.random.random((M, N)),\n",
    "    )\n",
    "    adata_dict[\"obs\"] = pd.DataFrame(\n",
    "        {\"batch\": np.random.choice([\"a\", \"b\"], M)},\n",
    "        index=[f\"cell{i:03d}\" for i in range(M)],\n",
    "    )\n",
    "    adata_dict[\"var\"] = pd.DataFrame(index=[f\"gene{i:03d}\" for i in range(N)])\n",
    "\n",
    "    return ad.AnnData(**adata_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc83fbb8-ab85-40d5-81b9-c85099b1bc97",
   "metadata": {},
   "source": [
    "Here is how our adata looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "769ecca2",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 8192 × 8192\n",
       "    obs: 'batch'\n",
       "    obsm: 'a'\n",
       "    layers: 'a'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata = adata_with_dask(8192, 8192)\n",
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "621729a6",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Representation of Dask Arrays\n",
    "\n",
    "Dask arrays consists of chunks that can be distributed in clusters.\n",
    "In the figure below, each small square represents a chunk that form a dask array.\n",
    "In principle these some of these chunks could be in different machines (clusters)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "48f14de5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 512.00 MiB </td>\n",
       "                        <td> 128.00 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (8192, 8192) </td>\n",
       "                        <td> (4096, 4096) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 4 chunks in 1 graph layer </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "dask.array<random_sample, shape=(8192, 8192), dtype=float64, chunksize=(4096, 4096), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f0ad22fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.25 MiB </td>\n",
       "                        <td> 6.25 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (8192, 100) </td>\n",
       "                        <td> (8192, 100) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"76\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "dask.array<random_sample, shape=(8192, 100), dtype=float64, chunksize=(8192, 100), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obsm[\"a\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b985283b-25af-436f-aa5e-4665bfe08357",
   "metadata": {},
   "source": [
    "## The Computation Graph\n",
    "The graph layer in the `Count` row refers to the layer of the computation graph of the chunks, i.e. which operations are applied to them.\n",
    "We have this because the operations done on Dask arrays aren’t computed instantly.\n",
    "This way, Dask array can optimize the queries we issued to it.\n",
    "It also won’t keep our resources occupied for the results we expect later.\n",
    "Below is a representation of the chunks we initially created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d51e61bb-acbf-4de5-9531-de75614bd9ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.X.visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02677f13-6ad7-4e55-9d6f-1368593be9d4",
   "metadata": {},
   "source": [
    "We now show an example for this computation graph on dask arrays to understand it better.\n",
    "This part is not technically relevant to AnnData."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "56b1d1bb-221a-4df2-89a4-23d7b2e4a435",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 64.00 kiB </td>\n",
       "                        <td> 32.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (8192,) </td>\n",
       "                        <td> (4096,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >8192</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "dask.array<sum-aggregate, shape=(8192,), dtype=float64, chunksize=(4096,), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xsum = adata.X.sum(axis=1)  # do a sum on axis=1\n",
    "xsum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65dd581f-587d-4ebf-958b-49624c26b1b5",
   "metadata": {},
   "source": [
    "Note that this computation isn’t necessarily done yet, but rather saved to actually run it later.\n",
    "If we investigate the computation graph of this result, we can see that for this operation some chunks aren’t dependent on each other.\n",
    "This might give a hint to the Dask framework to store the chunks that depend on each other to the same cluster.\n",
    "For this simple exercise, all the chunks can be stored in one machine, but when it is impossible to store all the chunks into one machine this will come in handy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3cdc324d-5dc4-4299-9645-4b2ba21b5f88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xsum.visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1430611-6914-449c-a474-c3fab9eaaaee",
   "metadata": {},
   "source": [
    "But coming back to our anndata tutorial we will see that nothing changed in our adata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "415175ea-1105-482f-a574-2b845ba23feb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.X.visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28b8078d",
   "metadata": {},
   "source": [
    "## Concatenation\n",
    "\n",
    "In this section we will cover how concatenation on anndata objects that use Dask arrays looks like.\n",
    "We first create another anndata object to concatenate with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b03c9a13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 8192 × 8192\n",
       "    obs: 'batch'\n",
       "    obsm: 'a'\n",
       "    layers: 'a'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata2 = adata_with_dask(8192, 8192)\n",
    "adata2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a828bce3-d91f-4a03-94c6-b9f139ad2261",
   "metadata": {},
   "source": [
    "We can see that the X attribute of adata also consist of four chunks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "884ff189-d29b-41fb-bf2b-a1c6d61a0ca8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 512.00 MiB </td>\n",
       "                        <td> 128.00 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (8192, 8192) </td>\n",
       "                        <td> (4096, 4096) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 4 chunks in 1 graph layer </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "dask.array<random_sample, shape=(8192, 8192), dtype=float64, chunksize=(4096, 4096), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata2.X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "004872f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_concat = ad.concat([adata, adata2], index_unique=\"id\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b116dd0-9d4b-44a5-8639-11d0f328ec45",
   "metadata": {},
   "source": [
    "When we concatenate the whole object you can see that in the X of the result consists of eight chunks and they quite probably are just the source chunks aligned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2859934b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.00 GiB </td>\n",
       "                        <td> 128.00 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (16384, 8192) </td>\n",
       "                        <td> (4096, 4096) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 8 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"110\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "  <line x1=\"0\" y1=\"60\" x2=\"60\" y2=\"60\" />\n",
       "  <line x1=\"0\" y1=\"90\" x2=\"60\" y2=\"90\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"60\" y2=\"120\" style=\"stroke-width:2\" />\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
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       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "dask.array<concatenate, shape=(16384, 8192), dtype=float64, chunksize=(4096, 4096), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_concat.X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d236d2d3-74a5-4a5c-a56d-92ec023be954",
   "metadata": {},
   "source": [
    "To confirm this we look at the computation graph.\n",
    "We can confirm that this new object’s X is just the chunks of source X’s put together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e391a570",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_concat.X.visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bcc4eb3-d3ba-4830-ae77-52aa52df1127",
   "metadata": {},
   "source": [
    "Here for the obsm we also this.\n",
    "The chunk from both are just stacked on top."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "455754c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 12.50 MiB </td>\n",
       "                        <td> 6.25 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (16384, 100) </td>\n",
       "                        <td> (8192, 100) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 25.41261651458249,0.0 25.41261651458249,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >100</text>\n",
       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">16384</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "dask.array<concatenate, shape=(16384, 100), dtype=float64, chunksize=(8192, 100), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_concat.obsm[\"a\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e1c25f85-db36-4050-8560-6fd0b546810b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8/Pwwc+ZMeHt7AwDq1KmDNWvWFPripEajgVqthhCi1Hl0HQ94UdgKhaLIdqLKgnVXNbGJGcmcOXOwevVqZGdno0aNGtrtb775JsaNG4fo6GhoNBr06NED5ubmhZ67cOFC5OXloWXLlq+cR5fxACAwMBBjxozhaQ2q1Fh3VQ9PJxrJ+++/DwsLC/j5+RV5TKlUomfPnujdu3exv/jXr1/Hpk2b0KdPH53metV4YWFhiImJwZIlS/T/QYhkhHVX9bCJGUndunWxcuVKrFixAiqVSq/nJiYmYv/+/WjatKlBsnz11VeYO3cuOnToYJDxiEwV667qYRMzounTp6NXr156r5s2ZMgQuLi4GCRDcnIyHj9+jGXLlhlkPCJTx7qrWviZmJH5+/tLOr+joyPCwsIkzUBU0Vh3VQffiRERkWyxiRERkWyxiRERkWyxiRERkWyxiRERkWyxiRERkWyxiRERkWyxiRERkWyxiRERkWyxiRERkWyxiRERkWyxiRERkWyxiZm4/Px8qNVqqWMQVTnPnz+XOgLpgKvYFyMlJQU7d+6UOgYA4MKFC1AqlXB1dZU6CgDgzp07aN68udQxqJIKDw83mYM2f39/jB8/vtgbXlY0IYTUEUwWm1gxIiIiEBERIXUMk8UmRsayZs0aqSMUsm/fPqkj0CsoBFu8ycrLy0O9evWgUCiQlpYGKysrqSMRVQl79+7F2LFjMWXKFGzZskXqOFQyf34mZsJ+//13PH36FDk5OTh48KDUcYiqjIKbagYFBSEvL0/iNFQaNjET5ufnB3Nzc5ibm2P79u1SxyGqErKysrB//34AQG5uLv744w+JE1Fp2MRM1JMnT3Dw4EHk5+cjPz8f+/btw5MnT6SORVTp/frrr1CpVAAAc3NzbNu2TeJEVBo2MRO1e/du5Ofna/+en5+P3377TcJERFXDtm3boFAoALyouz/++AOPHz+WOBWVhE3MRPn5+WkLCQAUCgWPCImMLC0tDYcPHy50mb9Go8HevXslTEWlYRMzQQ8ePMDx48cLFZJarcahQ4fw4MEDCZMRVW4lfT/Uz8+vgpOQrtjETFBAQADMzIr+0ygUCuzevVuCRERVw9atW4t8sVitVuPYsWO4f/++RKmoNGxiJmjr1q3Frlqg0WiwdetWCRIRVX4pKSmIioqCRqMp8piZmZnJrOJDhbGJmZgbN27g3LlzxS4zo9FocPr0aSQnJ1d8MKJKzt/fv8QlptRqNQ8gTRSbmInZsWMHlMqSVwNTKpXYtWtXBSYiqhpKOgMCvFi78OzZs0hKSqrgVPQqbGImxs/PT/sdleKoVCoug0NkYHFxcbhy5UqpC+0qlUrs2LGjAlORLtjETEhsbCzi4+Nfud/ly5cRFxdXAYmIqoaCZaZKo1KpeErRBHEVexMSGxsLT0/PQh8sX7t2DQqFAq1bt9ZuMzMzQ0xMDJydnaWISVTpPHjwAP379y+0LSoqCm3atIGtra12m1KpRGpqKho2bFjREakEXMXexI0bNw5KpRIBAQFSRyGqMtRqNZRKJYKCguDt7S11HCoZV7EnIiL5YhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZYhMjIiLZUgghhNQhqhq1Wo179+7h3r17SE9PL/Tn2bNnyMrKQn5+PoQQOHLkCBQKBTw9PaFQKKBUKlGzZk1YWVnBzs6u0B8HBwc4ODjA3Nxc6h+RyCTl5ubi5s2bReouPT0darUajx8/BgDk5eVh9+7d6N69O9q0aQMAsLa2hpWVFWrWrAk7OzvY29tra69Ro0aws7OT8kerqvzZxIzo5s2biI2NRWxsLBITE5GcnIykpCSkpKRApVJp97O0tNQWg5WVFWrVqgVzc3MoFArUqVMHAJCZmQngRQN88uQJnj17hvT0dKSlpRUZq2nTpmjRogUcHR3RqlUrdOjQAW5ubmjatGnFvgBEElCpVLh69SpiY2Nx+fJlJCUlITk5GcnJyUhNTS20r42NDerVqwc7OzsolUrUqlULwIs6srGxwfPnz5GTkwPgRQPMy8tDVlYW0tLSkJGRUWisWrVqwdHREY6OjmjRogWcnZ3h5uYGNzc37bhkcGxihnL79m2EhYUhPDwc58+fR2xsLB49egQAaN68Odq0aVPoF7x58+ZwcHCAvb09atasWa65nzx5grS0NNy7d0/bKAuK9tq1a7h16xYAwNbWFh06dECnTp3g4eEBDw8PNGrUqNw/O5FU1Go1Ll26hFOnTiEyMhIxMTG4evUqVCoVLCws4OTkhJYtW6JFixbaA7vmzZtr30VVq1atXHMXvIu7ffu2tu6SkpKQlJSEuLg47Tu7Fi1awM3NDd26dUOfPn3QvXt32NjYGOplqMrYxMrq1q1bOHjwII4fP45Tp07h1q1bUCqV6NSpE7p27YqOHTtqj8Jq164tadZHjx4hJiYGly5dQkxMDKKjo3Hx4kXk5+fD0dERHh4eGDBgAIYOHYomTZpImpWoNBqNBtHR0QgJCcGpU6cQERGBrKws1K5dGz179kSnTp20Zx6cnZ1hYWEhad7k5GTt2ZiYmBhERkbi5s2b2v8rPDw84OXlBU9PTza1smET09Xz589x4sQJHDx4EMHBwbhy5QpsbGzQr18/9O7dGx4eHrI6usrOzkZUVBROnTqF8PBwnDx5Ek+fPoWbmxuGDh2KoUOHom/fvpL/J0CUlpamrbs///wTaWlpaNq0KQYMGKCtvfbt28PMTB7Xqd25c6dQ3V24cAGWlpbo168fhg4diuHDh8PZ2VnqmHLBJlYatVqNiIgI7Nq1CwEBAXjw4AFatmwJLy8vjBw5EoMHD4aVlZXUMQ0iPz8fkZGR+P3333Ho0CGcPXsWtra2GDlyJHx8fDB06FA2NKowmZmZ2L9/P3bt2oWQkBAIIdCjRw+MGjUKXl5e6NKlCxQKhdQxDSItLQ1Hjx7FoUOH8NtvvyE1NRUuLi7w8fHBm2++yYZWOjax4pw5cwabNm1CUFAQ0tLS4O7uDl9fX4wbNw6Ojo5Sx6sQN27cwK5du7Bz506cO3cO9evXh4+PD95++2106dJF6nhUCT179gy//vortm7ditDQUJibm2P48OHw9fXFiBEjUKNGDakjGp1arUZ4eDgCAwOxe/dupKamolOnTpg4cSKmTJmC+vXrSx3R1PhDkBBCiMzMTPHDDz+Ijh07CgDC1dVVfPPNNyIxMVHqaJJLSEgQy5YtE+3atRMARNeuXcWPP/4oHj9+LHU0qgSuXr0qPvroI1GvXj1hbm4uRo4cKbZt2yaePHkidTRJ5efniyNHjoh33nlH1KlTR1haWgofHx8REhIi1Gq11PFMxfYq38QuX74sZs6cKaytrYWNjY2YPn26CA8PlzqWyTp58qSYMmWKqF69urCxsRGzZ88WcXFxUscimVGr1WLv3r2if//+AoBwdHQUX375pbh9+7bU0UzS06dPxZYtW0SfPn0EANGiRQuxevVqHkhW5SZ26NAhMXz4cKFQKISTk5NYv369ePTokdSxZKPgnWubNm2EmZmZGDVqlDh27JjUscjE5eTkiPXr12t/b0aOHCkOHjzIdxZ6uHr1qliwYIGoWbOmqF27tvj444/FrVu3pI4llarVxDQajdi3b5/o3LmzACD69+8vfvvtNxZQORQcURccIXbv3l388ccfUsciE5OVlSW++uorUa9ePVGtWjUxe/ZscfXqValjyVpmZqZYvny5aNy4sbCwsBBTp06tih9/VJ0mFhwcLNzd3YVCoRBjxowR0dHRUkeqdCIjI8WoUaMEANGrVy9x6NAhqSORxHJycsTKlSuFvb29qFWrlvj888/F/fv3pY5VqTx79kxs3rxZtGnTRlhYWIhZs2aJmzdvSh2rolT+JhYWFiY8PDwEADFixAhx9uxZqSNVeqdPnxZDhgwRAMSAAQNEVFSU1JGogqlUKrF+/Xrh4OAgbGxsxKJFi0RaWprUsSo1lUolNm3aJBwdHYWVlZWYN2+eePjwodSxjK3yNrFbt26Jt956SygUCuHp6SkiIiKkjlTlnDx5UvTt21coFAoxZcoUcefOHakjUQUIDQ0Vrq6uwtLSUixYsECkpqZKHalKefbsmfjxxx+Fg4ODsLW1Fd9++614/vy51LGMpfI1sZycHLFkyRJhbW0tWrVqJfbs2SN1pCpv586dwtHRUdSoUUMsW7ZM5ObmSh2JjODatWti9OjRAoAYNWqUSEhIkDpSlZaVlSX+/ve/i2rVqglnZ2dx4MABqSMZQ+VqYsePHxdt27YVNjY2YsmSJfzP0oQ8e/ZMfPvtt6JWrVqiVatW/LysElGpVGL58uWiWrVqwsnJiRf2mJhbt26JyZMnC4VCIUaOHClSUlKkjmRIlaOJpaeni2nTpmkv2uBpK9N169YtMXLkSKFQKMSsWbNEZmam1JGoHKKiokSHDh1E9erVxfLly4VKpZI6EpUgJCREtGjRQtja2oqff/5ZaDQaqSMZgvyb2O7du0WDBg2Eg4OD2L17t9RxSEcBAQGiQYMGolGjRuK3336TOg7pKTc3VyxcuFCYm5sLT09Pce3aNakjkQ6ys7PFhx9+qP13S0pKkjpSecm3iWVlZYkZM2YIAGLGjBk8opeh9PR0MWXKFAFAzJkzR+Tk5EgdiXQQGxsr3NzcRK1atSrTEX2VEhUVJVxdXUXt2rXFtm3bpI5THvJsYlFRUaJt27aiTp06YseOHVLHoXLavXu3sLOzE87OzvwKhAnTaDTip59+EtbW1qJHjx589yVzubm5YtGiRcLMzEz4+PiIjIwMqSOVhbyamEajEStWrBAWFhZi0KBB/OyrErl165bw9PQUlpaW4ttvv5U6Dr0kPT1dDB8+XCiVSvGvf/1L5OfnSx2JDCQ4OFg0bNhQODo6ijNnzkgdR1/yaWJPnjwR48aNE0qlUqxcuZKnMCohtVotli1bJszNzcWECRNEdna21JFICHH+/HnRokUL0bRpUy6OXUndv39fDB48WFSrVk38/PPPUsfRhzyaWHx8vGjfvr2oV6+eCA0NlToOGdnRo0dF/fr1hbOzs7hy5YrUcaq0bdu2CWtra9GvXz9x7949qeOQEWk0GrF8+XJhZmYmJk+eLJ4+fSp1JF1sN/n7eR84cADu7u6oUaMGzp8/Dy8vL6kjkZENGDAAp0+fhrW1NXr37o3Q0FCpI1U5arUaH374ISZPnoy5c+fi8OHDaNiwodSxyIgUCgUWLVqE3bt3Y9++ffD09MT9+/eljvVqUrfR0qxfv14olUoxbdo0kZeXJ3UcqmC5ubliwoQJwsLCQm6nOGQtOztbvP7666JatWq8cKqKiouLE61btxaOjo7i8uXLUscpjWmeTtRoNGLJkiVCoVCIJUuW8POvKuyvvwvz58/nbXOM7N69e8Ld3V3UrVtXHD9+XOo4JKH09HTRr18/UbNmTREcHCx1nJKYXhN79uyZ8PHxEVZWVnL//gIZ0KZNm4SFhYWYOHFiZV7MVFJxcXHC0dFRtGnThpfPkxDixdmQ8ePHC0tLS1P9/9i0mtjTp0/FsGHDRO3atXmXYCoiNDRU1KhRQ4waNYqnlw0sJiZGNGjQQPTs2ZO3TKFCNBqN+Pjjj4WZmZn46aefpI7zMtO5sCMnJwejR49GZGQkQkJC0L9/f6kjkYnx8vLC0aNHER4ejqFDhyIrK0vqSJXCuXPnMHDgQDg5OeHPP/+EnZ2d1JHIhCgUCqxcuRJff/015syZg9WrV0sdqRCTaGKPHj3CoEGDEBsbi+PHj6NHjx5SRyIT1a1bN4SGhuLy5csYPnw4noR3wGsAACAASURBVDx5InUkWQsLC8PAgQPh7u6OgwcPombNmlJHIhO1aNEirFq1Cp988gm+/PJLqeNoKaUOkJ2djeHDhyMlJQXHjx+Hk5OT1JHIxHXu3BnHjh2Dl5cXRowYgYMHD8LGxkbqWLJz+vRpDBs2DF5eXggICIClpaXUkcjEffTRR7CxscHcuXOhVCrx97//XepI0jax3NxcjB49GtevX2cDI724uLjg2LFj6NevH15//XX8/vvvqFatmtSxZCMmJgbDhw9Hr169sGPHDjYw0tk777wDtVqN9957D5aWlli4cKGkeSQ7nfj8+XP4+Pjg/PnzCAkJQbt27aSKQjLVtm1bhISE4Pz58xg/fjxUKpXUkWQhISEBQ4YMQadOnbBv3z5YWVlJHYlk5t1338XatWvxySefYOPGjZJmkaSJaTQaTJo0CSdPnkRoaCg6d+4sRQyqBDp27Ijff/8dR44cwdtvvw0hhNSRTFpKSgoGDhyIVq1a4bfffuO7VyqzDz74AP/4xz8wd+5c7NmzR7IckpxOXLRoEX777TeEhISgW7duUkSgSqRXr17Ys2cPRowYAUdHR5P60NmUPHnyBCNGjICtrS3++OMPfo5I5bZ06VKkp6dj0qRJOHLkCHr27FnhGSq8iW3cuBGrV6/Gli1beBk9GcygQYOwYcMGzJgxA40bN8acOXOkjmRSVCoVfHx8kJaWhoiICNSuXVvqSFRJrFu3Dnfu3MGoUaMQHh6ONm3aVGyAivxW2h9//CGUSqVYunRpRU5LVchnn30mLCwsxJ9//il1FJMyY8YMYWNjI6Kjo6WOQpVQVlaW6NKli3BychLp6ekVOfV2hRAV8yFCXFwcevTogbFjx+KXX36piCmpChJCYOLEiTh48CDOnDmDVq1aSR1Jct999x0WLlyIvXv3YtSoUVLHoUrq7t276NmzJ5ydnREcHAxzc/OKmNa/QppYdnY2evTogZo1a+L48eO8GoqMKi8vD3379sWzZ88QERFRpT/7CQ8Ph6enJ/75z3+axHd6qHK7cOECevfujQ8++ABff/11RUxp/CYmhMD48eNx/PhxnD17Fk2aNDHmdEQAgFu3bqFr164YNGgQ/P39pY4jidTUVHTt2hXdunXDr7/+CoVCIXUkqgL8/PwwdepU7Ny5E+PGjTP2dP5Gv8R+1apV2Lt3LwIDA9nAqMI0a9YM27dvx86dO7Fu3Tqp41Q4lUoFb29v1KxZE35+fmxgVGEmT56MWbNmYcaMGYiPjzf6fEZ9JxYVFYU+ffrg66+/xscff2ysaYhKtGzZMixbtgyRkZHo1KmT1HEqzOLFi7Fu3TpERUXBxcVF6jhUxTx79gx9+/ZFfn4+IiMjjbkijPFOJ+bk5KBLly5o3rw5QkJCeCRIktBoNPDy8kJqaiqio6NhbW0tdSSjO3XqFAYMGID169dj9uzZUsehKioxMRGdO3fG3Llz8e9//9tY0xjvdOKCBQuQkZGBzZs3s4GRZMzMzLB161bcv38fn376qdRxjO7x48eYNGkSRo4cyQZGkmrVqhXWrl2LVatW4fDhw0abxyjvxPbu3Qtvb2/s3bsXr7/+uqGHJ9JbYGAg3nrrLezfvx8jRoyQOo7RTJgwAcePH8fFixdRr149qeMQwcfHB5GRkYiJiYGtra2hhzf86cSMjAy0b98eI0eOlHxhSKK/mjx5Mo4ePYrLly9XyhUr9u3bhzFjxiA4OBhDhgyROg4RgBc9wdXVFUOHDsWmTZsMPbzhm9jbb7+N4OBgXLlyxRhdl6jM0tPT4eLiAm9vb6xfv17qOAb15MkTtG/fHq+99ho2b94sdRyiQnbv3g0fHx+EhIRg0KBBhhzasE3s6NGjeO211xAUFISxY8caalgig/H398fkyZNx7Ngx9O3bV+o4BjNnzhzs3r0bV65cgb29vdRxiIoYO3Yszp8/j0uXLhlyAQLDNbGnT5/Czc0NnTp1wu7duw0xJJFRjBgxAjdu3MCFCxcqxeoxJ06cwIABA7B9+3a89dZbUschKtbdu3fh4uKC2bNnY8WKFYYa1nBXJ65cuRIPHz7E999/b6ghiYxi/fr1uHXrFtauXSt1lHJTq9WYN28ehg4dygZGJq1Ro0b45ptv8O233yIuLs5g4xrkndjt27fh7OyML774okpcxkzy969//QurVq1CQkICHBwcpI5TZhs2bMD8+fMRGxsLJycnqeMQlUqj0aB79+6wtbVFaGioIYY0zOnECRMm4PTp07hy5UqlOD1DlV9ubi7atWuHgQMHGuOKqQrx6NEjtG3bFpMnT8bq1auljkOkk2PHjsHT0xN//PEHhg8fXt7hyt/EIiIi4OHhwe+EkewEBARg4sSJiIiIQPfu3aWOo7ePPvoI27ZtQ0JCAurUqSN1HCKdeXt7IyYmBpcvXy7vklTlb2J9+/aFpaWlUb+RTWQMQgj07dsXVlZWsvv9vXnzJtq2bYtvv/0Wc+fOlToOkV5u3LgBFxcXfPvtt+W9C3v5mtjBgwcxbNgwREREoGfPnuUJQiSJU6dOoW/fvjhy5Ag8PT2ljqOzmTNn4vDhw4iPjzfm4qpERvP+++9j7969uH79OqpXr17WYcrXxHr27Al7e3vs37+/rEMQSc7Lywt5eXk4deqU1FF0cv36dbRr1w4///wzpk6dKnUcojK5d+8eWrdujW+++Qbz588v6zBlb2IFS9ycPn0a7u7uZQ1AJLmIiAj07t0bISEhGDx4sNRxXmny5MnaC6mUSqXUcYjK7KOPPsKOHTuQmJhY1jtMlL2Jde3aFY6OjvxiM1UKI0aMQGZmJsLDw6WOUqqEhAS4uLjAz8+P3wsj2Xvw4AFatmyJL7/8Eh9++GFZhijbl50PHz6Mc+fOYdGiRWV5OpHJWbx4MSIiIhAWFiZ1lFKtWbMGzZs3h6+vr9RRiMqtfv36mDlzJtasWQOVSlWmMcr0Tmz48OHIzc3F0aNHyzQpkSnq3bs3GjZsiD179kgdpVgZGRlo1qwZVqxYgXfffVfqOEQGkZycjDZt2mDLli2YMGGCvk/X/51YfHw8QkJCsHDhQn2fSmTSPvroI+zbt8+gS+IY0g8//AArKytezEGViqOjI7y9vcv8hX29m9iqVavQtm1bQ3zTmsikjBkzBi1atDDJ9T/z8vKwfv16zJ0715ArgBOZhIULF+LcuXM4duyY3s/Vq4k9evQI/v7+mD9/PszMDLZ2MJFJMDc3x7x58+Dn54fs7Gyp4xSyZ88eZGRk4L333pM6CpHBubu7w8PDAz/++KPez9WrE23fvh0AeFUUVVqTJ0+GSqVCYGCg1FEK+c9//oNRo0bJerFiotLMmjULv/76Kx48eKDX8/RqYv/973/x5ptvcp02qrTq1q2LsWPH4ueff5Y6ilZCQgJOnDiBWbNmSR2FyGh8fX1hbW0NPz8/vZ6ncxOLiorC+fPnWUhU6c2YMQORkZG4ePGi1FEAAD///DOaNGli6Nu6E5mU6tWr46233sLGjRuhz0XzOjexX375Ba6urlwjkSo9T09PtG7dGlu2bJE6CvLz87F161bMmDED5ubmUschMqqZM2ciPj4eEREROj9HpyaWn5+PPXv2YNKkSWUORyQXCoUCEyZMQEBAADQajaRZjh49ivv372PixImS5iCqCF26dIGLi4ten0nr1MQOHz6MBw8eYNy4cWUORyQnb731Fu7duyf5Ch6BgYHo1q0bWrduLWkOoori6+uLnTt3Qq1W67S/Tk0sMDAQ3bt3R6tWrcoVjkgunJ2d4erqKulViiqVCr/++iuXmKIq5c0330RqaqrOd5V4ZRNTqVTYt28fC4mqHF9fXwQFBel8RGhooaGhyMjIgI+PjyTzE0nByckJbm5uOh9AvrKJHT9+HBkZGfD29i53OCI58fHxwf379xEZGSnJ/Pv27UO3bt3g6OgoyfxEUvHx8cG+fft0ukrxlU0sODgY7du3ZyFRlePs7IyWLVvi4MGDkswfEhLC5d2oSho2bBju3r2L2NjYV+6rUxMbOnSoQYIRyc2QIUMQHBxc4fNevnwZN2/exLBhwyp8biKpdenSBQ0aNNCp9kptYikpKbh69SoLiaqsYcOG4dy5c7h//36FzhscHIx69eqhW7duFTovkSkwMzPD4MGDdToLUmoTCw4ORo0aNdCnTx+DhSOSk4EDB8LS0hJ//vlnhc4bEhKCwYMH8wvOVGUNGzYMYWFhePLkSan7ldrEjh07hr59+8LKysqg4YjkwsbGBr169SrTLSLKSqVSITw8HK+99lqFzUlkal577TXk5+cjPDy81P1KbWKnTp2Ch4eHQYMRyY2Hh0eFfun53LlzePr0Kc+AUJVWv359tGnT5pW1V2ITu3PnDlJSUtjEqMrz8PBAQkKC3reIKKuwsDDUq1cPbdq0qZD5iEyVh4fHK7/0XGITO3nyJJRKJT9Ypiqvd+/eMDMz02tR0vIICwuDh4cHFApFhcxHZKo8PDxw+vRpPH/+vMR9SmxiERER6NSpE2rUqGGUcERyUbt2bbi4uFTYKcWIiAj07t27QuYiMmUeHh7Izc0t9bZIJTaxc+fOwd3d3SjBiOSmW7duOH/+vNHnSU1Nxb1793gGhAgvlqCqVatWqbVXbBMTQuDSpUtwc3MzWjgiOXFzc6uQm2TGxMRo5yOq6hQKBdq3b1/qyh3FNrFbt27h0aNH6NChg9HCEcmJm5sbHj58aPSLO2JiYuDg4AB7e3ujzkMkFx06dNAe3BWn2CYWExOj7YBEBHTs2BEASi0mQ4iNjeXBI9FfuLm5ISYmpsTFgIttYpcuXULTpk1Rp04do4Yjkgt7e3s0aNBApwVJy+PSpUtwdXU16hxEcuLm5oZHjx7hzp07xT5ebBNLTExE27ZtjRqMSG5at26NGzduGHUO1h5RYQXfl0xMTCz28WKbWFJSElq0aGG8VEQy1KJFCyQlJRlt/IyMDDx+/Ji3PSL6i4YNG8La2hrJycnFPl5sE0tOTmYhEb3E2E2sYGweQBL9j0KhQLNmzUqsvSJNTK1WIyUlhU2M6CWOjo5ISkrS6W6zZZGcnAwzMzM0a9bMKOMTyVVB7RWnSBO7c+cOVCoVmxjRS1q0aIHc3FyjXWafnJwMBwcH3jWC6CUtW7bU/XRiwc3/HBwcjBqKSG4aNmwIAEZrYvfv32fdERWjQYMGJd6YtkgTS0tLAwDUq1fPuKlkIiYmBt999x0yMzOljlJpHDhwAAEBAVLH0FtBTRTUiKGlp6ez7v6CtWd4cq699PT0Yh8r0sTS09NhaWmJmjVrGj2YHJw6dQoffPABUlNTpY5SaaxYsQKffPKJ1DH0VrduXZiZmZVYTOWVlpYGOzs7o4wtR6w9w5Nr7dWrVw8ZGRnQaDRFHiu2ifFokKgoc3Nz1KlTx2hNjLVHVDw7OztoNBo8evSoyGNFmlhGRgbq1q1bIcEqg+KODKjysrOzM1oTY+3ph7VXdZR2Kr9IE8vJyTGpe4jNnz8fM2bMwO3bt/Hee+8VWhj12LFjeO+999C2bVs0bdoUb731FjZs2AC1Wq3dZ9asWXj//fdx9+5dTJgwAc2bN0erVq3w9ttvIycnp9BcZ86cgY+PD1q2bAkvLy/88MMPxV5OffXqVQwfPhz29vawsbGBu7s7du/eXWifWbNmYerUqbh+/TpmzpyJpk2bYuDAgdi2bRsAYM2aNejatSvq16+PYcOG4dq1a2V6ffLy8rBkyRK0atUKVlZWaNOmDd555x1kZWUV2k/X16o8mcePH4+vv/4a4eHhGD9+POzt7dG+fXv8+9//fuV/OI8ePcK7774LV1dXNGzYEGPHjsWBAwfK9JoYk42NTZHfG0PJycmBjY2NUcYuC9Ze6Vh7FaegLp4+fVr0QfGSefPmiX79+r28WTL9+/cXTk5OokOHDgKA6NKlixBCiCNHjghzc3NRt25d8f7774t//vOfwsPDQwAQn3zyifb53bp1E46OjqJx48aiT58+4tNPPxX9+/cXAMTYsWO1+x09elRYW1uLunXripkzZ4rZs2eLOnXqCEdHRwFAXLlyRQghxMmTJ4WNjY1wdHQUn332mVi2bJno0aOHACCWLl1aaN6GDRuKRo0aCRcXFzF58mRhaWkpFAqFGDZsmFAqlWL06NFizJgxwtLSUjRr1kyo1Wq9X5/p06cLc3NzMW3aNPHdd9+J+fPni+rVq4tevXpp99HntSpPZjs7O9GqVStRu3Zt8cYbb4jFixeLbt26CQBixowZhf5NmzRpov17SkqKcHR0FDY2NmLu3Lnib3/7m+jcubMwMzMTa9eu1fs1MaYePXqIjz/+2ChjN2zYUKxbt84oY5cFa690rL2Kc/PmTQFAREZGvvzQ9iJNbPbs2cLLy6tikumg4Jd+yJAh4urVq9rts2bNElZWViIzM1O7LTc3Vzg4OAhnZ2fttoJ/yEWLFgmNRiOEEEKtVosuXbqI2rVra/fr2LGjsLW1FUlJSdptCQkJwtraWltIGo1GdOnSRdStW1fcuXNHu9/z58/FwIEDhaWlpYiPjy8077Jly7T7HThwQAAQ1atX1+4nhBBTp04VAApt00VeXp6wsLAQo0ePLrT9u+++KzSevq9VWTPb2dkJAGLNmjXabWq1Wnh6egqFQiGio6OFEEULaeLEiUV+QZ89e6Z9TdPT0/V6XYypb9++Yt68eUYZ29bWVmzYsMEoY5cFa69krL2KlZqaKgCIEydOvPxQ0SY2bdo0MXz48IpJpoOCQoqKiiq0/erVqyImJqbQtsePH4t27dqJRo0aabd169ZNVK9eXeTm5hbad968eQKASElJEREREQKA+Oyzz4rM/84772gLKTo6WgAQ48aNK7Lf1q1bBQDxww8/aOc1NzcXz5490+5z584dAUCMGDGi0HN/+eUXAUDs3btXx1flhZycHGFhYSFq1aolzp07p92uVqtFdna2yM/PF0Lo91qVJ7OdnZ2oU6eO9j+sAqGhoQKA+Prrr4UQhQspPT1dKBQK4e7uXuTn27FjhwAgNm7cqNfrYkxeXl5i9uzZRhnbxsZGbNq0yShjlwVrr2SsvYqVkZEhAIjQ0NCXH9qufPn04rNnz2BpafmqU5QVyt7eHu7u7oW2OTs7Iz09HatXr0ZERASSk5Nx7do1PHnyBI0aNSq0b/369VGtWrVC22xtbQEA2dnZiIuLAwB06tSpyNx/vadawXno/v37F9mvS5cuAICEhATttkaNGhV6LQsyvJzP3NwcAPD8+fMi45bG2toaS5YswT/+8Q906dIF7dq1g6enJ4YPH44hQ4Zox9XntSpv5jZt2kChUBTaVvAaFrcKdXx8PIQQyM7Oxvjx4ws99uTJkxKfJxVLS0s8e/bMKGM/e/bM5FbrYO0Vj7VXsQrqorjaK3Jhx8svgikorrBXrlyJJk2a4Msvv4RKpYKXlxc2b94MDw+PIvtWr169xLGFEMjIyADwv1+Ov/prARZcGVPcklwFL+5fxyjpQ3ozs2LXXS6Tzz77DNevX8fnn38Oa2trbNiwASNHjkT79u2136/R57Uqb+biVpwoGPPl/8wAaK/0s7KygoWFRaE/dnZ2mDhxosndnNVYNaJQKIy2LmNZsfZKxtqrOKXVRZF3YpaWlsVei29KHj58iL/97W+wt7fHtWvXCn0x+6uvvtJ7vIJVw48fP44xY8YUeuyv63UV7Hfy5EmMHDmy0H4REREAXqzxVVGeP3+Op0+fwtHREUuXLsXSpUuRmpqKr776Cj/88AO+//57fPDBBwZ9rV7l+vXrRbYVvIZOTk5FHit4vdq0aaO9EquAWq1GVlYWrK2tDZ6zrJ4/f260MxWWlpZ6vyOoaKy9F1h7FaugLoo7qCrS4uVQSDdv3oRGo8HYsWML/WKkpKTgwoULeo/XrVs3WFhY4MiRI4W25+fnw9/fX/v3zp07w9LSEqGhoUXGOHbsGMzNzTFkyBC95y+rI0eOwNbWFjt27NBua9iwofYb+ZmZmQZ/rV4lISGhyOW/v/zyC4DiTxm1bt0a9vb2CAkJgUqlKvTYN998A1tbW0RFRRk8Z1kZ85Qfa+9/WHv6q8y1p1cTs7KyMvlCcnJyQo0aNRAYGIj9+/fj2rVr2Lx5M3r37o1atWohOzsb8fHxOo/XtGlTvPfee4iNjcWMGTNw7tw5nD9/HuPGjcPjx4+1+zVq1Ajvv/8+zp8/j3fffReXLl1CfHw8lixZgqCgIEyaNEl7F9KK4OHhgfr162Pp0qU4duwYHj9+jLNnz+KDDz4AAIwYMcLgr9WrqNVqvPHGG9i7dy8uX76ML7/8EuvWrYOvry/69u1bZH9LS0t88803ePLkCSZNmoRz587h+vXrWL16NZYtW4ZBgwYVe+pFKs+fPzdaE2PtsfbKozLXXsEp42Jr7+VLPRYuXCh69uxp7ItNdPbyJaEFdu7cKWrUqCEACACibt26YsuWLSIoKEjY2NgIpVIphHhx1c9fL2Ut8MUXXxT6DkpeXp6YNWuWdjwA4rXXXhN+fn6F9nv27JlYsGBBof0AiDlz5hS6sqi4edPT0wUA8c477xTaXnB1VWBgoN6vT2hoqGjUqFGhLNWqVRNfffWVQV4rfTLb2dkJLy8vMXXqVGFmZqadb8CAASItLU27X3H/puvWrRPVqlXTPkepVIo5c+aYzCW+BTp16lTslXSG0KxZM7Fq1SqjjF0WrL3SsfYqzrVr1wQAcf78+Zcf2q4QovAnZkuWLMGePXsQGxtbloZZodLT03H+/Hk4ODjAxcVF+4F7eno6MjMz0bp1a73HTElJQWxsLNq1a1fqHXYfPHiACxcuwMrKCh06dNBecSWFp0+fIiYmBrdu3UK9evXg6uqK+vXrF9rHGK/Vy+rVqwd3d3cEBwcjMzMT0dHRaNy4MVxcXHR6flZWFs6fP4/s7Gy4ubmhadOm5c5kaG3btsW0adOwePFig4/dvn17jB8/Hl988YXBxzY01t4LrL2KceHCBXTu3BkJCQkvv+P2L9LE1q1bh2+++Qb37t2r2JSk9e677+q03+TJk9GrVy8jp9HdXwupsrK1tcW///1vzJ492+Bj9+vXD506dcK6desMPjbphrVnmg4dOoRBgwYhIyPj5YMW/yJXJxbct0UIYZKX21cFnp6eOu3HGyhWrPz8fDx+/Nhot0sx5uLCpBvWnmlKS0uDubk5ateuXeSxIk3Mzs4OKpUKT548KfYJZHw+Pj5SRygTBweHSn0rkYKDO2P9jHZ2dkhJSTHK2KQb1p5pSktL097P72XFNjHgRcGyiZE+5PA5ankUvEsy1juxevXq4fz580YZmyq3qlB7JTXpIm2tQYMGAMDPxIheUrAKw8sf3BtK/fr1eRdjomLcv3+/xLor0sQK1u+6efOm0YMRyUlSUhJsbGwK3VfLkBwdHXHv3j3k5eUZZXwiuUpKSip2yTGgmCZmbm6Opk2bIikpydi5iGQlOTkZjo6ORrvgydHREUII3Lp1yyjjE8lVUlJSiV+7KHZlSUdHx0LrlhFR6YVkCAVj8wCS6H+EELh586bu78SAF8XEQiIqrLRTGoZga2uL2rVr8wCS6C8KTrHr9U6sdevWhe7NQ0QvVgk3xOoKpWHtERVWsKhxq1atin282Cbm6uqKlJQUZGZmGi8ZkYzcu3cPDx48gJubm1HncXNzq/SXSxPp4+LFi6hbty4aN25c7OPFNrEOHToAqPzfPSDSVUEtVEQTM8ZtOojkKjY2VtuTilNsE2vatCns7OzYxIj+X2xsLBwcHIx2eX2BDh064OHDh3jw4IFR5yGSizI1MeDFitpsYkQvxMbGGv1dGAB07NgRABATE2P0uYhMnUajweXLl0utvRKbWOfOnREdHW2UYERyEx0dXezdcQ3N3t4ejRs3Zu0RAYiPj0d2dnaptVdiE/Pw8MCFCxeQlZVllHBEcpGZmYmrV6+iT58+FTJf7969ERYWViFzEZmyU6dOwcbGpuxNTK1WIyoqyijhiOQiLCwMQgj07NmzQubz8PBAWFgYNBpNhcxHZKrCwsLQs2dPKJVF1qrXKrGJNWrUCI6OjjwipCovLCwMzs7ORr+oo4CHhwcyMzMRHx9fIfMRmapTp07Bw8Oj1H1KbGLAi2IKDw83aCgiuQkPD39lIRlSx44dYWNjwwNIqtJSU1ORmJiI3r17l7pfqU1swIABOHnyJFfVpiorOzsbkZGRGDBgQIXNaWFhAQ8PDxw6dKjC5iQyNYcPH4alpWX53okNHToUubm5OHnypEHDEcnF4cOHkZ+fj0GDBlXovEOHDsWff/4JtVpdofMSmYrg4GD06dMHNWrUKHW/UptYkyZN0L59ewQHBxs0HJFcBAcHo1u3bka7EWZJhg4diszMTF5YRVWSRqNBaGgohg4d+sp9S21iwItiYhOjqurgwYM6FZKhtWvXDi1btmTtUZUUHR2NBw8eYNiwYa/c95VNbNiwYYiLi8ONGzcMEo5ILi5fvoybN29K0sQAYPDgwThw4IAkcxNJ6cCBA2jSpAlcXV1fue8rm1i/fv1gb2+PXbt2GSQckVzs2rULDg4O6N69uyTzjxkzBmfPnsX169clmZ9IKrt27cKYMWN02veVTUypVOKNN97Azp07yx2MSE527doFX19fmJubSzL/wIEDUb9+fQQFBUkyP5EULl26hCtXrmD8+PE67f/KJgYA48ePx7lz57Q3JyOq7GJiYvQqJGNQKpUYM2YMAgMDJctAVNECAwPRpEkT9OrVS6f9dWpiAwYMQIMGDfhujKqMnTt3omnTphW21FRJxo8fjwsXLuDq1auS5iCqKEFBQfD19YWZmU7tSbcmZm5ujnHjxmH79u3lCkckBxqNBjt27MD48eOhUCgkzdKvXz84ODhgx44dkuYgqghnzpxBXFycXmdAdGt1AGbNmoWrV69yKRyq9A4fPowbN25gZSACHwAAFtBJREFU+vTpUkeBubk5pk6dil9++YVffKZK7+eff4arq6teF1Pp3MQ6duyILl26YOPGjWUKRyQX//3vf+Hh4QEXFxepowB4cQB59+5dHDx4UOooREaTk5ODgIAAzJgxQ6/n6dzEgBfFtHPnTmRmZuo1CZFcpKen49dff9W7kIypZcuWGDBgAA8gqVILDAxEXl4eJk6cqNfz9GpiEyZMgJmZGT8bo0pry5YtsLKygq+vr9RRCpk1axb++OMP3LlzR+ooREaxceNGjB07Vu9bHunVxGrVqoVJkyZh3bp1vGEfVTr5+fn4/vvvMWXKFNjY2Egdp5AxY8bA3t4eP/zwg9RRiAwuMjISkZGRmDt3rt7P1auJAcBHH32ExMRE/Pbbb3pPRmTKgoKCkJKSgg8++EDqKEVYWVnhvffew08//YTs7Gyp4xAZ1KpVq9CtWzf069dP7+cqhBBC3yeNHj0amZmZvEULVSo9e/ZEs2bNTPb7kJmZmWjWrBmWLVuGBQsWSB2HyCCSkpLQpk0b+Pv7l+U0vr/e78QAYOHChTh16hQiIyPL8nQik3Ps2DGcPn0aH330kdRRSmRra4tp06bh22+/RX5+vtRxiAxi9erVaNq0KcaOHVum55fpnRgAdO/eHQ0bNuRpRaoUBg8ejLy8PJw4cULqKKVKTEyEs7MzNm3ahMmTJ0sdh6hc7t27h9atW2P58uWYN29eWYbwL3MTO3DgAEaMGIHIyEj06NGjLEMQmYSwsDD06dMHR44cgaenp9RxXmn69Ok4efIkrl69CgsLC6njEJXZ/PnzERQUhMTERFSvXr0sQ5S9iQEvlsSxtrbmlzBJ1jw9PaFWq03+XViBmzdvom3btli/fr1JfZ+NSB93795F69atsXr16jJdlfj/ytfEDh06hEGDBuH48eNluqqESGqHDx+Gl5eX7H6H58yZg+DgYCQkJMDKykrqOER6M9DvcPmaGCC/o1iiAkII9OrVC3Xq1JHd2YSUlBS0bdsWK1asKOtnCUSSuX79Otq3b2+Iswnlb2Lnzp2Du7s7AgIC4OPjU56hiCqUn58fpk2bhqioKHTt2lXqOHpbtGgRNm7ciISEBNSrV0/qOEQ6Gz16NBITE3Hx4kUolcryDFX+JgYAU6dOxdGjRxEXFwdra+vyDkdkdE+fPkW7du0wbNgwbNiwQeo4ZZKVlQUnJyd4e3vj+++/lzoOkU6OHDmC1157DSEhIRg8eHB5hyvb98RetmLFCjx+/Bhr1641xHBERvfNN9/g0aNH+Oc//yl1lDKrWbMmvvzyS/z444+IjY2VOg7RK6nVanzwwQd4/fXXDdHAAJRh2aniNGjQAJ9++imWL1+OlJQUQwxJZDRJSUlYvXo1vvjiCzRs2FDqOOUyffp0dOzY0aS/pE1UYP369YiPj8eqVasMNqZBTicCQF5eHjp27AgnJyd+AZpM2pAhQ3D37l2cPXsWlpaWUscpt4iICPTp0we//PILpkyZInUcomKlpKSgffv2mD9/PpYtW2aoYQ3zmViB48ePw9PTEwEBASZ3Kwsi4MWtVt5++22cOHECHh4eUscxmHnz5mHHjh24cuUK6tevL3UcoiLeeOMNXL16FRcvXkS1atUMNaxhmxgAzJ49G3v37sXVq1d5xRSZlLS0NLi4uOCtt97Cd999J3Ucg8rJyYGbmxt69+6Nbdu2SR2HqJCAgABMmDABhw4dwsCBAw05tOGb2OPHj+Hi4oJBgwZh8+bNhhyaqFzefPNNRERE4NKlS6hZs6bUcQyuYCm4/fv3Y+TIkVLHIQLw4uDR1dUVr7/+On766SdDD2+YqxP/qnbt2tiwYQO2bt2KoKAgQw9PVCbbtm3Dzp078Z///KdSNjAAGD58OKZMmYKZM2fiwYMHUschAgDMnDkTVlZWWLFihVHGN/g7sQJz585FQEAALl68iGbNmhljCiKdJCUloVOnTnj77bcr/ddAsrOz0aVLF7Rq1QoHDhyAQqGQOhJVYRs2bMB7772HQ4cOGWtxbcOfTiyQl5cHd3d32Nra4ujRozA3NzfGNESlys/PR//+/fH48WOcOXOmrCtly8qZM2fg4eGBNWvW4P3335c6DlVR169fR+fOnbFgwQJDXo34MsOfTixQrVo1bN26FadPn8by5cuNNQ1Rqf71r3/h3LlzCAgIqBINDADc3d2xePFifPrpp7h48aLUcagKys3NhY+PD1xcXLBkyRKjzmW0d2IFvvvuOyxcuBDBwcEYNGiQMaciKuT333/H66+/jvXr1+Odd96ROk6Fys/Ph5eXF27fvo0zZ87A1tZW6khUhUyfPh379u3DmTNn0KpVK2NOZbzTiX81bdo07N+/H2fO/F979x4UdfX/cfy1u8ByEbyw4o0IEi+gEHJR2NWAkZsXTE3EC5Y2TSYFWTkjSuVYzaSVjWUqjkFeGxRTAUUuKii44qBxVURB8ILcRUBYLrv7/v1R8M1+XTSFs7ucx4wz/sH4ebJwfH9u+9kcvPTSS729OY5DaWkp3NzcEBgYiH379rHOYaK2thYuLi6ws7PDqVOn+Cl9rk/s3LkT7733HhISEjBr1qze3lzfDDGFQgGZTAa1Wg25XM4fEsz1qu7fNyKCXC7vN6cR/8qlS5fg6emJiIgIrX5OJKcdsrOz4eXlhXXr1vX6acTf9c0QA/63Zzxz5kwcOHCA3zXF9Qq1Wo3g4GCkp6fj8uXLsLa2Zp3E3I4dOxAWFoa4uDjMnz+fdQ6no+7evQt3d3c4OTkhMTERQmGv3XLxR713Y8ef2dra4tixYzhy5EhfTWiuH4qIiEB8fDwOHTrEB9jvQkNDERoaipCQEFy8eJF1DqeDWlpaEBgYiEGDBuHAgQN9NcB+Q30sOjqaAFBUVFRfb5rTcbt37yaBQEB79+5lnaJxlEolzZkzhyQSCd28eZN1DqdDlEolzZ49m4YPH04VFRV9vfmDfT7EiIjWr19P+vr6lJaWxmLznA46efIk6enp0WeffcY6RWO1tLTQpEmTyM7OjhoaGljncDrirbfeIhMTE8rJyWGx+YN9dk3sT0d/CAkJQUJCAlJTU+Hh4dHXCZwOyczMREBAABYtWoTo6GjWORrt/v378PDwwIgRI5CWlqazj+Di+kZERAS2bNmCo0ePIjAwkEVC310T+yOBQIA9e/bA29sbM2bMwJUrV1hkcDogLy8Pc+bMgb+/f288XFTnjBw5EmfOnMGdO3cQEBCA1tZW1kmclvriiy/w1VdfISoqitUAA9AHb3b+J52dnZg7dy5ycnKQkZGBCRMmsErhtFBRURG8vLzg7OyMxMREiMVi1klao/u1mzRpEhITE5/n5ztx/cAPP/yA8PBw7NixA++88w7LFDZHYt0MDAwQFxeHcePGwdfXF9euXWOZw2mRwsJC+Pj4wMHBAfHx8XyAPaWJEyciOTkZOTk5CAoKQnt7O+skTkts374d4eHh+Prrr1kPMAAA0yEGACYmJkhKSsLo0aPh5eWF3Nxc1kmchsvJyYG3tzfs7OyQmJjYr9/M/CxcXV2RnJyMrKwsBAYG8lOL3L/avHkzwsLC8OWXX+Kjjz5inQNAA4YYAJiZmSElJQWTJk2Ct7c35HI56yROQ2VmZsLHxweTJ09GUlISBgwYwDpJq7m7uyM9PR35+fnw9vbGgwcPWCdxGmrz5s1Yt24dvv32W6xdu5Z1Tg+NGGIAYGxsjISEBHh6esLf3x9paWmskzgNk5SUhICAAPj5+eH48eP8COw5cXJywrlz51BZWQk/Pz/U1NSwTuI0iFqtRnh4OCIjIxETE4PVq1ezTnqMxgwxABCLxThy5Ajmzp2LWbNmISYmhnUSpyGioqLw6quvYtGiRYiNjYWBgQHrJJ1iZ2eHzMxMNDU1wcPDA8XFxayTOA3Q1taG1157Dbt370ZsbCyWL1/OOun/Y/HutH+jVqtpw4YNBIDCw8NJpVKxTuIY6f5dEAgEtGHDBlKr1ayTdFpDQwNNmzaNBg8eTGfPnmWdwzFUV1dHMpmMhgwZQufOnWOd83fYPLHjSUVHR5O+vj4tXryY2traWOdwfezRo0e0YMECMjAwoP3797PO6TcUCgUFBQWRgYEB7dmzh3UOx0BhYSFZW1uTra0t3bhxg3XOPzmoUacT/+zNN9/EyZMnkZycDJlMhoqKCtZJXB8pLS2FVCpFeno6UlJSEBISwjqp3zA0NERsbCw++OADLF++HGFhYejq6mKdxfWRQ4cOwcPDA5aWlrh48SLGjBnDOukfafQQAwBfX1/k5eVBJBLBxcUFKSkprJO4XpaUlITJkydDJBIhJycHXl5erJP6HaFQiE2bNuH48ePYv38/vL29cf/+fdZZXC9SKpWIiIjA4sWLsWTJEpw5cwYSiYR11r9jfSz4pFpbW2np0qUkEono888/59fJdFBXVxd98sknJBAIaMWKFaRQKFgncURUVFREY8eOpREjRlBGRgbrHK4XVFZWkqenJxkbG2vbqXvNvib2V7777jsSi8Xk6elJd+7cYZ3DPSe3bt0iqVRKhoaG/GN6NNDDhw9p3rx5JBKJaP369dTZ2ck6iXtOjh8/ThKJhMaMGUN5eXmsc56WZl8T+yvh4eG4cuUKGhsb4eDggIMHD7JO4p5RXFwcXFxc0NTUhOzsbKxcuZJ1EvcnAwcOxNGjRxETE4Pvv/8eUqkUN27cYJ3FPQOFQoH3338f8+bNw4wZM/Drr7/i5ZdfZp319FiP0f+qra2NQkNDSSAQ0LJly6i+vp51EveUamtradGiRSQQCGj16tX89KGWuH79Orm4uNCAAQNo+/bt/NS+Frpw4QKNHz+ehgwZQkeOHGGd8yy073Tin508eZJGjRpFFhYW9PPPP7PO4Z7Q3r17ydzcnKysrCglJYV1DveUOjs7KTIykvT19Ukmk9G1a9dYJ3FPoKmpid59910SCoUUEBBA9+7dY530rLR/iBH99oNZtWoVCQQCmjVrFt2+fZt1Evc3ysrKyNfXl4RCIYWFhVFzczPrJO4Z5OXlkZubG4nFYtq4cSN1dHSwTuL+Rnx8PFlaWpJEItG2mzf+iW4MsW5ZWVlkb29PxsbGtHbtWmppaWGdxP2utbWVNmzYQEZGRjRmzBhKT09nncQ9JyqVinbt2kUDBgwgW1tbOnz4MOsk7g9u3LhBQUFBBICCgoKotraWddLzpFtDjIiovb2dNm3aRKampmRlZUWxsbH8UUUMqdVq2r9/P40aNYoGDhxI33zzDd9b11EVFRUUHBxMAoGA/Pz86OrVq6yT+rXGxkb68MMPSV9fnxwdHenMmTOsk3qD7g2xblVVVbRixQoSCoUkk8k0+dlfOuv06dM0ZcoUEolE9Pbbb1NNTQ3rJK4PnD9/npydnUlPT49WrVqlC9ddtIpCoaCtW7fS0KFDSSKR0M6dO0mpVLLO6i26O8S6Xb58maZPn04AyMfHh+RyOesknXf+/Hny9PQkAOTv70+5ubmsk7g+plKpKDo6mqysrMjQ0JBWr15N1dXVrLN0WkdHB+3YsYMsLS3JyMiI1qxZQ42NjayzepvuD7Fu6enpNHXqVAJAM2fOpKysLNZJOicjI4N8fX0JAHl5eVFmZibrJI6x9vZ22rZtG40YMYJMTExozZo1VFlZyTpLpygUCoqKiqIXX3yRxGIxhYWF0f3791ln9ZX+M8S6JScnk4eHBwEgd3d3iouL0+VD7V6nVCopNjaWXF1dCQBNmzZNV8+9c8+gra2NtmzZQsOHDycDAwN64403qKCggHWWVqurq6ONGzeShYUFicViWrlyZX98ilH/G2LdsrKyaN68eSQUCmn06NG0detWamhoYJ2lNerq6mjLli1kY2NDIpGIFixYQNnZ2ayzOA3X3t5O0dHRNGHCBBIIBOTv708JCQl8R/Ip5Ofn06pVq8jY2JjMzc0pMjKSqqqqWGex0n+HWLebN29SaGgomZqakqGhIS1ZsoTOnj3L72j8CyqVitLS0ig4OJjEYjGZmZlRWFgYlZWVsU7jtIxaraakpKSe9wyOGjWKPv74Y7p16xbrNI3U3NxMu3btosmTJxMAGjt2LG3bto0ePXrEOo01PsS6tbS00I8//kju7u4EgGxtbenTTz/ltwkTUUFBAUVGRpKNjQ0BIJlMRj/99BNfQNxzUVZWRuvXr6eRI0eSUCik6dOn0+7du/v9mZGOjg46ceIEvf7662RiYkKGhoYUEhJCGRkZfCf7fw4KiIhYPbdRUxUVFSEmJgaHDx9GZWUlJk6ciIULFyIoKAjjx49nndcnrl69iri4OBw+fBjFxcV44YUXEBwcjBUrVsDe3p51HqeDlEolkpKSsG/fPiQlJUGpVMLHxwcLFy7EnDlzMGTIENaJva6zsxMZGRk4dOgQjh07hocPH8Ld3R1LlizB0qVLMXjwYNaJmuZnPsT+gVqthlwuR1xcHOLi4lBVVQUbGxv4+vrCx8cH/v7+MDMzY535XLS1tUEulyMxMRHx8fG4ffs2JBIJ5s+fj2XLlkEmk0EgELDO5PoJhUKB06dPIy4uDr/88gs6Ojrg5OQEHx8fzJ49G1KpFEKh1n0Ix1+qrq5GamoqTpw4gdTUVDQ1NcHe3h5BQUEICQmBra0t60RNxofYk1KpVMjOzsapU6eQnJyM3Nxc6Ovrw93dHdOmTYOHhwekUikGDRrEOvWJNDY2Qi6X48KFC8jKysKlS5egVCrh4uKCgIAAzJgxA1OmTNGZ/yg47dXU1IS0tDQkJycjOTkZlZWVsLCwwCuvvAKZTAapVApnZ2fo6emxTn0ipaWlkMvlyMrKQmZmJq5fvw4TExNMnz69Z+1ZW1uzztQWfIj9VzU1NUhNTUV6ejrkcjlKSkogFAoxYcIEuLq6wsHBAQ4ODnB0dISFhQXT1urqahQWFqKgoABFRUXIyclBcXExiAjjx4+HVCqFt7c3/Pz8MHToUKatHPdvCgoKkJqaiszMTMjlctTX18PExARubm5wcnLqWXf29vYwNjZm1qlSqVBaWtqz9goLC5GdnY3q6moYGhrC1dUVU6dOhY+PD6ZOnQqxWMysVYvxIfa81NXV9RzZ5ObmorCwEDU1NQAACwsLjB07FjY2NrC2toaNjQ1sbGwwbNgwmJubw9zcHCKR6D9tV6VSoaGhAQ0NDaiurkZ5eTnKy8tRUVGB8vJylJSUoL6+HgAwfPhwODg4wNnZGVKpFFKpFBKJ5Lm9BhzX14gIJSUluHDhArKzs5Gfn4+rV6+ira0NIpEINjY2GD169GNrz8rKChYWFpBIJM90OUChUPSsvbt37/asve71V1JSAoVCAZFIBFtbWzg6OsLNzQ0ymQwuLi58aD0ffIj1ptra2p49sLKysp7BUl5eDoVC8djXdg8zU1NTAICZmRlEIlHPcFOpVFCpVGhubgYAtLS0oL6+Hg8ePHjs3zE2Nu4ZktbW1rC1tcXEiRPh6OjIj7K4fkGtVqOsrAz5+fkoLi7GrVu3egbLvXv3oFKper5WX18fEokE5ubmEIvFEIlEPYPNwMAAnZ2dAH4bWO3t7VCr1T2Dq7W19bHtDh069LFhOW7cuJ4jQiMjo757AfoXPsRYqa6uRl1dXc+C6P57S0sLAKC5uRkqlQpdXV0Afltsenp6PUPO1NQUEomkZwGam5vDwsICw4YNY/Y9cZym6+rqQlVVFWpra1FfX9+z/hoaGtDR0fHYjqJCoegZPkZGRjA0NIRQKOxZb3/8Y2lpCRMTE5bfWn/FhxjHcRyntX7mt55xHMdxWosPMY7jOE5r8SHGcRzHaa3/A5YChrC7D7WcAAAAAElFTkSuQmCC",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_concat.obsm[\"a\"].visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6fa473b-e033-4d49-afc5-2b902e258376",
   "metadata": {},
   "source": [
    "## Views\n",
    "\n",
    "Let’s see how our views of anndata objects play with Dask arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ad7dace-33d4-47a1-88d1-b05f360d0004",
   "metadata": {},
   "source": [
    "### Slice View\n",
    "\n",
    "\n",
    "\n",
    "We take a slice of the concatenated adatas.\n",
    "This operation returns a view of the adata which means that the resulting adata holds a view of the source adata’s Dask array, namely the `DaskArrayView` class,  which is a completely different object than Dask array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a665fa1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_slice_view = adata_concat[:500, :][:, :500]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e799501-8009-4734-a095-8a9945e036cf",
   "metadata": {},
   "source": [
    "Below, you can see the values of the X attribute of the result.\n",
    "Which implies that the resulting object isn’t “lazy” like Dask arrays. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bca1d375",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.91 MiB </td>\n",
       "                        <td> 1.91 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (500, 500) </td>\n",
       "                        <td> (500, 500) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "dask.array<getitem, shape=(500, 500), dtype=float64, chunksize=(500, 500), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_slice_view.X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d62ea78-5206-4d4d-b8a1-ef1db50f9f6c",
   "metadata": {},
   "source": [
    "But our original adata remains unchanged."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "109739e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            <table style=\"border-collapse: collapse;\">\n",
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       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.00 GiB </td>\n",
       "                        <td> 128.00 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (16384, 8192) </td>\n",
       "                        <td> (4096, 4096) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 8 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
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       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table>"
      ],
      "text/plain": [
       "dask.array<concatenate, shape=(16384, 8192), dtype=float64, chunksize=(4096, 4096), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_concat.X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ec7c71-08fa-403a-9bd4-dd0f735764a0",
   "metadata": {},
   "source": [
    "### Index List View"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cb0543c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "View of AnnData object with n_obs × n_vars = 5 × 3\n",
       "    obs: 'batch'\n",
       "    obsm: 'a'\n",
       "    layers: 'a'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_view = adata_concat[[12, 12, 3, 5, 53], [1, 2, 5]]\n",
    "small_view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8362620d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "                        <td> 120 B </td>\n",
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       "dask.array<reshape, shape=(5, 3), dtype=float64, chunksize=(5, 3), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_view.X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbdf0cfb-6554-4bd8-9fcd-3598d9bff5b1",
   "metadata": {},
   "source": [
    "### View by Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d19a9668-6603-432f-b94f-0975caf3af68",
   "metadata": {},
   "outputs": [],
   "source": [
    "categ_view = adata_concat[adata_concat.obs[\"batch\"] == \"b\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f278db22-86a7-4bbb-9738-4b9c64d9b995",
   "metadata": {},
   "outputs": [
    {
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       "                        <td> 511.62 MiB </td>\n",
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       "                        <td colspan=\"2\"> 8 chunks in 4 graph layers </td>\n",
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       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table>"
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       "dask.array<getitem, shape=(8186, 8192), dtype=float64, chunksize=(2096, 4096), chunktype=numpy.ndarray>"
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     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categ_view.X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0787478c",
   "metadata": {},
   "source": [
    "## To Memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4aadf1c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                        <td> </td>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.00 GiB </td>\n",
       "                        <td> 128.00 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (16384, 8192) </td>\n",
       "                        <td> (4096, 4096) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 8 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "            </table>\n",
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       "</table>"
      ],
      "text/plain": [
       "dask.array<concatenate, shape=(16384, 8192), dtype=float64, chunksize=(4096, 4096), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_concat.X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d5fa2e0-5231-4236-be0c-2f3a1eec7a76",
   "metadata": {},
   "source": [
    "Here no copies are made, only the result of the lazy object is asked to be materialized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "21e5d800",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.81114836, 0.6155681 , 0.71416891, ..., 0.66920919, 0.33580076,\n",
       "        0.12920279],\n",
       "       [0.00671454, 0.97405959, 0.82439798, ..., 0.13954503, 0.17895137,\n",
       "        0.79356977],\n",
       "       [0.38870666, 0.47573504, 0.72575821, ..., 0.96240919, 0.60595132,\n",
       "        0.22621635],\n",
       "       ...,\n",
       "       [0.34446286, 0.36435086, 0.56605972, ..., 0.72517602, 0.79974538,\n",
       "        0.89232576],\n",
       "       [0.19953605, 0.78688055, 0.66993415, ..., 0.73216844, 0.14588037,\n",
       "        0.28056473],\n",
       "       [0.48233576, 0.07140058, 0.73679539, ..., 0.9402766 , 0.98517065,\n",
       "        0.41486173]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_mem = adata_concat.to_memory(copy=False)\n",
    "adata_mem.X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11282f53-ae2f-46cf-b257-b76189517f42",
   "metadata": {},
   "source": [
    "If you want to both materialize the result and copy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "261a4d58-c238-4677-9890-e06a16f5cd22",
   "metadata": {},
   "outputs": [],
   "source": [
    "del adata_mem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6f88a1b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.81114836, 0.6155681 , 0.71416891, ..., 0.66920919, 0.33580076,\n",
       "        0.12920279],\n",
       "       [0.00671454, 0.97405959, 0.82439798, ..., 0.13954503, 0.17895137,\n",
       "        0.79356977],\n",
       "       [0.38870666, 0.47573504, 0.72575821, ..., 0.96240919, 0.60595132,\n",
       "        0.22621635],\n",
       "       ...,\n",
       "       [0.34446286, 0.36435086, 0.56605972, ..., 0.72517602, 0.79974538,\n",
       "        0.89232576],\n",
       "       [0.19953605, 0.78688055, 0.66993415, ..., 0.73216844, 0.14588037,\n",
       "        0.28056473],\n",
       "       [0.48233576, 0.07140058, 0.73679539, ..., 0.9402766 , 0.98517065,\n",
       "        0.41486173]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_mem = adata_concat.to_memory(copy=True)\n",
    "adata_mem.X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5154ceb5-8a8b-4c0d-84e9-530ee6d7e164",
   "metadata": {},
   "source": [
    "## IO operations\n",
    "\n",
    "Read/Write operations on `h5ad` and `Zarr` are supported.\n",
    "One should note that the lazy objects are materialized when this is called.\n",
    "For now, the anndata loaded from file won’t be loaded with dask arrays in it.\n",
    "`zarr` is generally much faster due to the lack of a read-lock."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4baa54cb-3a72-438e-a9f9-9337a9c9798c",
   "metadata": {},
   "source": [
    "### Write zarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "55abd05c-1d4f-4dc6-a8b4-a3f2f9772ab6",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.write_zarr(\"a2.zarr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0c0697e4-6a3a-41af-afcb-a52d269cc7bc",
   "metadata": {},
   "outputs": [
    {
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 78.12 kiB </td>\n",
       "                        <td> 78.12 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (100, 100) </td>\n",
       "                        <td> (100, 100) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
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       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
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       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >100</text>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "dask.array<random_sample, shape=(100, 100), dtype=float64, chunksize=(100, 100), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cec2f1b-73c7-4fe8-b659-6126423a1450",
   "metadata": {},
   "source": [
    "### Read zarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1aabb21c-b186-42b4-96b0-82dc0d114a0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "zarr_adata = ad.read_zarr(\"a2.zarr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5e7c26d3-8c4a-4c70-aeab-e62c2ca092a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.94530803, 0.5603623 , 0.88396408, ..., 0.5849379 , 0.83891198,\n",
       "        0.47558545],\n",
       "       [0.15451784, 0.02692963, 0.9621184 , ..., 0.74202933, 0.21178086,\n",
       "        0.04160213],\n",
       "       [0.89403029, 0.81384801, 0.82572102, ..., 0.6824308 , 0.56349224,\n",
       "        0.28945738],\n",
       "       ...,\n",
       "       [0.13949498, 0.03908582, 0.07293669, ..., 0.35253434, 0.67779358,\n",
       "        0.33458721],\n",
       "       [0.56197331, 0.69960036, 0.28042814, ..., 0.44178619, 0.55637234,\n",
       "        0.22577015],\n",
       "       [0.53397526, 0.36173157, 0.04517696, ..., 0.67863092, 0.1851758 ,\n",
       "        0.43036704]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zarr_adata.X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b729179a-6969-4a1a-aa76-a1dd189986ba",
   "metadata": {},
   "source": [
    "Notice how they are loaded as arrays rather than dask arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ebca9b-3468-4031-9c37-172fe5d06db9",
   "metadata": {},
   "source": [
    "## Dask Array Support for Other Fields\n",
    "\n",
    "This is the list of operations and in which fields they are supported, although some might have not been covered in this tutorial.\n",
    "\n",
    "The following work with operations anndata supported before are also supported now with Dask arrays:\n",
    "- anndata.concat()\n",
    "- Views\n",
    "- copy()\n",
    "- to_memory() (changed behaviour)\n",
    "- read/write on h5ad/zarr\n",
    "\n",
    "**X, obsm, varm, obsp, varp, layers, uns, and raw** attributes are all supported and tested.\n",
    "\n",
    "Note: scipy.sparse array wrapped with dask array doesn’t play well.\n",
    "This is mainly because of the inconsistent numpy api support of scipy.sparse.\n",
    "Even though not explicitly tested, a sparse array that supports the numpy api should theoretically work well."
   ]
  }
 ],
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