{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4aa723fb-6a8d-4d43-913c-a31f2316b02f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir(\"../../\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f1c3418a-3a90-41b0-baa6-c6ad340dc75f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "data = Path(\"data/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "182d566e-9678-4185-9d11-1bc7578673e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will only use them for plotting below\n",
    "import scanpy as sc\n",
    "import muon as mu"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43da885f-111d-4e07-a330-bd067d7e60b6",
   "metadata": {},
   "source": [
    "Throughout the notebook we will also track how much memory the notebook consumes after loading different objects into memory.\n",
    "\n",
    "This won't provide exact measurement but will allow to see the order of magnitude of memory consumption:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2f08adba-2d59-47f9-a8f0-aee2f31f1ed7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, psutil\n",
    "def measure_memory() -> float:\n",
    "    return psutil.Process(os.getpid()).memory_info().rss / 1024 ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "697198ed-b0ea-4ad1-a651-b2d6cc884806",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "934b8d69-b812-422f-b718-080bb8508348",
   "metadata": {},
   "source": [
    "## Shadow objects"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e70decb3-d905-4903-8e06-3e581cd7b0e5",
   "metadata": {},
   "source": [
    "A lot of exploratory and downstream analyses and visualisations require read-only access to the data.\n",
    "\n",
    "While AnnData (and MuData) [provides](https://anndata.readthedocs.io/en/latest/generated/anndata.read_h5ad.html) support to delay reading the count matrix `.X` into memory, it currently does not provide a lightweight read-only access to the file contents.\n",
    "\n",
    "This is addressed by the new classes implemented in `shadows` — *AnnDataShadow* and *MuDataShadow*.\n",
    "Briefly, they mimic AnnData and MuData interfaces while keeping a connection to the file open and loading respective arrays, matrices and tables only when they are requested.\n",
    "\n",
    "Shadow objects are currently only implemented for H5AD and H5MU files."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65462d07-01b0-4395-8891-eda01e472f38",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a38075c-8da2-4193-af1a-c52e18176f92",
   "metadata": {},
   "source": [
    "Import classes for these shadow objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "079454ed-10dc-47ef-9de2-ef70f95dbed6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from shadows import AnnDataShadow, MuDataShadow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67b6edb8-0b0f-4ae4-82f2-9f3e48640b65",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3221fae-4a1f-4b53-a680-200de544078b",
   "metadata": {},
   "source": [
    "First, let's download a multimodal dataset in the H5MU format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7397ecfb-1017-4ff0-a2f1-36d2d44b1491",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "■ File pbmc5k_citeseq_processed.h5mu from pbmc5k_citeseq has been found at data/pbmc5k_citeseq/pbmc5k_citeseq_processed.h5mu\n",
      "■ Checksum is validated (md5) for pbmc5k_citeseq_processed.h5mu\n",
      "■ Loading pbmc5k_citeseq_processed.h5mu...\n",
      "Loading MuData took about 394.68 MiB of memory\n"
     ]
    }
   ],
   "source": [
    "import mudatasets\n",
    "\n",
    "# Memory consumption before (in MiB)\n",
    "mem_before = measure_memory()\n",
    "\n",
    "mdata = mudatasets.load(\"pbmc5k_citeseq\", files=[\"pbmc5k_citeseq_processed.h5mu\"], data_dir=data, backed=False)\n",
    "# This will return a MuData objects after downloading the file,\n",
    "# but we will discard the in-memory object\n",
    "\n",
    "# Memory consumption after (in MiB)\n",
    "mem_after = measure_memory()\n",
    "\n",
    "print(f\"Loading MuData took about {(mem_after - mem_before):.2f} MiB of memory\")\n",
    "\n",
    "import gc\n",
    "del mdata\n",
    "gc.collect();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15b92533-14c1-4999-890a-0ae64bed01fc",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8ae6d73-9a74-48ed-9d41-7e92bfee8f71",
   "metadata": {},
   "source": [
    "### Access and memory consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a81a45da-352f-4d4f-9e44-530b34e6c9f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "mem_start = measure_memory()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14f6f1ef-578a-499e-913a-036ac0772574",
   "metadata": {},
   "source": [
    "Now we can initialise a new MuData-like (in reality, a MuDataShadow) object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e5ab4c4b-ff9b-4691-acd4-ee87a3fa38e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MuDataShadow took about 0.0 MiB of memory\n"
     ]
    }
   ],
   "source": [
    "file = data / \"pbmc5k_citeseq/pbmc5k_citeseq_processed.h5mu\"\n",
    "\n",
    "# Memory consumption before (in MiB)\n",
    "mem_before = measure_memory()\n",
    "\n",
    "mdata = MuDataShadow(file)\n",
    "#       ^ This will traverse the file \n",
    "#         but will not load any matrices or data frames\n",
    "\n",
    "# Memory consumption after (in MiB)\n",
    "mem_after = measure_memory()\n",
    "\n",
    "print(f\"MuDataShadow took about {(mem_after - mem_before)} MiB of memory\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "07fe84d4-6a52-476b-b008-c3d492c0c81d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "raw:\tX, var, varm\n",
      "\n",
      "raw:\tXᐁ, var, varm\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mdata = MuDataShadow(file)\n",
    "print(mdata['rna'].raw)\n",
    "mdata['rna'].raw.X[:,10]\n",
    "print(mdata['rna'].raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "58e62721-16f4-4e50-bf52-1b159c5dffbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MuData Shadow object with n_obs × n_vars = 3891 × 17838\n",
       "  obs:\t_index, leiden, leiden_wnn, louvain\n",
       "  var:\t_index, feature_types, gene_ids, highly_variable\n",
       "  obsm:\tX_mofa, X_mofa_umap, X_umap, X_wnn_umap, prot, rna\n",
       "  varm:\tLFs, prot, rna\n",
       "  obsp:\tconnectivities, distances, wnn_connectivities, wnn_distances\n",
       "  uns:\tleiden, leiden_wnn_colors, louvain, neighbors, rna:celltype_colors, umap, wnn\n",
       "  obsmap:\tprot, rna\n",
       "  varmap:\tprot, rna\n",
       "  mod:\t2 modalities\n",
       "    prot: 3891 x 32\n",
       "        X  \n",
       "        layers:\tcounts\n",
       "        obs:\t_index\n",
       "        var:\t_index, feature_types, gene_ids, highly_variable\n",
       "        obsm:\tX_pca, X_umap\n",
       "        varm:\tPCs\n",
       "        obsp:\tconnectivities, distances\n",
       "        uns:\tneighbors, pca, umap\n",
       "    rna: 3891 x 17806\n",
       "        X  \n",
       "        raw:\tXᐁ, var, varm\n",
       "        obs:\t_index, celltype, leiden, n_genes_by_counts, pct_counts_mt, total_counts, total_counts_mt\n",
       "        var:\t_index, dispersions, dispersions_norm, feature_types, gene_ids, highly_variable, mean, mean_counts, means, mt, n_cells_by_counts, pct_dropout_by_counts, std, total_counts\n",
       "        obsm:\tX_pca, X_umap\n",
       "        varm:\tPCs\n",
       "        obsp:\tconnectivities, distances\n",
       "        uns:\tcelltype_colors, hvg, leiden, leiden_colors, neighbors, pca, rank_genes_groups, umap"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0933f525-9360-423f-a538-86d7121585f4",
   "metadata": {},
   "source": [
    "Individual modalities are `AnnDataShadow` objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cdd77b19-bd5a-4dbf-893e-73ed4d2f4616",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData Shadow object with n_obs × n_vars = 3891 × 17806\n",
       "  X  \n",
       "  raw:\tXᐁ, var, varm\n",
       "  obs:\t_index, celltype, leiden, n_genes_by_counts, pct_counts_mt, total_counts, total_counts_mt\n",
       "  var:\t_index, dispersions, dispersions_norm, feature_types, gene_ids, highly_variable, mean, mean_counts, means, mt, n_cells_by_counts, pct_dropout_by_counts, std, total_counts\n",
       "  obsm:\tX_pca, X_umap\n",
       "  varm:\tPCs\n",
       "  obsp:\tconnectivities, distances\n",
       "  uns:\tcelltype_colors, hvg, leiden, leiden_colors, neighbors, pca, rank_genes_groups, umap"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdata['rna']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb50af6a-4ee2-4a8f-b022-9b0daa63e81e",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95330ef5-8434-4a44-a9e2-93105053e0c2",
   "metadata": {},
   "source": [
    "### Caching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "341a9b70-9e52-4fb8-9845-8c3941c227e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Memory consumption before reading some of the attributes\n",
    "mem_before = measure_memory()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f0054f4-5099-4f21-874d-2da7dfeacad9",
   "metadata": {},
   "source": [
    "We can use the values of the shadow object just as values in a regular MuData. They will be loaded from the object and cached:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7ce95cda-84ae-437b-80c4-24f70f3fb6d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_genes_by_counts</th>\n",
       "      <th>total_counts</th>\n",
       "      <th>total_counts_mt</th>\n",
       "      <th>pct_counts_mt</th>\n",
       "      <th>leiden</th>\n",
       "      <th>celltype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAACCCAAGAGACAAG-1</th>\n",
       "      <td>2363</td>\n",
       "      <td>7375.0</td>\n",
       "      <td>467.0</td>\n",
       "      <td>6.332204</td>\n",
       "      <td>3</td>\n",
       "      <td>intermediate mono</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCAAGGCCTAGA-1</th>\n",
       "      <td>1259</td>\n",
       "      <td>3772.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>9.093319</td>\n",
       "      <td>0</td>\n",
       "      <td>CD4+ naïve T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCAGTCGTGCCA-1</th>\n",
       "      <td>1578</td>\n",
       "      <td>4902.0</td>\n",
       "      <td>646.0</td>\n",
       "      <td>13.178295</td>\n",
       "      <td>2</td>\n",
       "      <td>CD4+ memory T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCCATCGTGCATA-1</th>\n",
       "      <td>1908</td>\n",
       "      <td>6704.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>6.354415</td>\n",
       "      <td>2</td>\n",
       "      <td>CD4+ memory T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACGAAAGACAAGCC-1</th>\n",
       "      <td>1589</td>\n",
       "      <td>3900.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>9.307693</td>\n",
       "      <td>1</td>\n",
       "      <td>CD14 mono</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGGTTGTACGAGTG-1</th>\n",
       "      <td>1450</td>\n",
       "      <td>5666.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>6.477232</td>\n",
       "      <td>0</td>\n",
       "      <td>CD4+ naïve T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTTGAGTTAACAG-1</th>\n",
       "      <td>3068</td>\n",
       "      <td>10213.0</td>\n",
       "      <td>896.0</td>\n",
       "      <td>8.773132</td>\n",
       "      <td>9</td>\n",
       "      <td>intermediate mono</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTTGCAGCACAAG-1</th>\n",
       "      <td>1649</td>\n",
       "      <td>4754.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>9.844342</td>\n",
       "      <td>4</td>\n",
       "      <td>CD8+ memory T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTTGCAGTCTTCC-1</th>\n",
       "      <td>1901</td>\n",
       "      <td>6373.0</td>\n",
       "      <td>553.0</td>\n",
       "      <td>8.677233</td>\n",
       "      <td>2</td>\n",
       "      <td>CD4+ memory T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTTGCATTGCCGG-1</th>\n",
       "      <td>3443</td>\n",
       "      <td>12220.0</td>\n",
       "      <td>1287.0</td>\n",
       "      <td>10.531915</td>\n",
       "      <td>3</td>\n",
       "      <td>intermediate mono</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3891 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    n_genes_by_counts  total_counts  total_counts_mt  \\\n",
       "AAACCCAAGAGACAAG-1               2363        7375.0            467.0   \n",
       "AAACCCAAGGCCTAGA-1               1259        3772.0            343.0   \n",
       "AAACCCAGTCGTGCCA-1               1578        4902.0            646.0   \n",
       "AAACCCATCGTGCATA-1               1908        6704.0            426.0   \n",
       "AAACGAAAGACAAGCC-1               1589        3900.0            363.0   \n",
       "...                               ...           ...              ...   \n",
       "TTTGGTTGTACGAGTG-1               1450        5666.0            367.0   \n",
       "TTTGTTGAGTTAACAG-1               3068       10213.0            896.0   \n",
       "TTTGTTGCAGCACAAG-1               1649        4754.0            468.0   \n",
       "TTTGTTGCAGTCTTCC-1               1901        6373.0            553.0   \n",
       "TTTGTTGCATTGCCGG-1               3443       12220.0           1287.0   \n",
       "\n",
       "                    pct_counts_mt leiden           celltype  \n",
       "AAACCCAAGAGACAAG-1       6.332204      3  intermediate mono  \n",
       "AAACCCAAGGCCTAGA-1       9.093319      0       CD4+ naïve T  \n",
       "AAACCCAGTCGTGCCA-1      13.178295      2      CD4+ memory T  \n",
       "AAACCCATCGTGCATA-1       6.354415      2      CD4+ memory T  \n",
       "AAACGAAAGACAAGCC-1       9.307693      1          CD14 mono  \n",
       "...                           ...    ...                ...  \n",
       "TTTGGTTGTACGAGTG-1       6.477232      0       CD4+ naïve T  \n",
       "TTTGTTGAGTTAACAG-1       8.773132      9  intermediate mono  \n",
       "TTTGTTGCAGCACAAG-1       9.844342      4      CD8+ memory T  \n",
       "TTTGTTGCAGTCTTCC-1       8.677233      2      CD4+ memory T  \n",
       "TTTGTTGCATTGCCGG-1      10.531915      3  intermediate mono  \n",
       "\n",
       "[3891 rows x 6 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rna_obs = mdata.mod[\"rna\"].obs\n",
    "rna_obs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fe2c2d0-cdc4-415d-90f9-4d0b7a2973c5",
   "metadata": {},
   "source": [
    "Individual matrices or data frames from `.obsm`, `.obsp`, `.layers`, etc. can be loaded allowing to only load e.g. a particular embedding of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2bf71f1f-3854-41be-bb95-f00d4ff725b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 20.551052  ,   0.36840764,  -1.6193684 , ...,   0.09656975,\n",
       "         -0.90912175,  -0.77955467],\n",
       "       [ -9.47144   ,  -5.5212517 ,  -5.107428  , ...,   0.64674896,\n",
       "         -0.892091  ,   1.7873902 ],\n",
       "       [ -9.913012  ,   2.766899  ,  -2.0684972 , ...,  -0.6454743 ,\n",
       "          1.615869  ,  -0.63476324],\n",
       "       ...,\n",
       "       [ -8.727723  ,   7.9196725 ,   1.3326805 , ...,   1.4592032 ,\n",
       "          0.91210324,   1.3184382 ],\n",
       "       [-10.792531  ,   3.2086673 ,  -2.0437238 , ...,   1.7311838 ,\n",
       "         -1.840564  ,   1.3253008 ],\n",
       "       [ 20.642431  ,   0.49294943,  -1.6694897 , ...,  -0.51208967,\n",
       "          0.60652566,  -0.75145006]], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rna_pca = mdata[\"rna\"].obsm[\"X_pca\"]\n",
    "rna_pca"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e5d4937-5cbd-4165-8eb8-d0ef849e1333",
   "metadata": {},
   "source": [
    "Notice the symbol `ᐁ` near the respective table (`/mod/rna/obs`) and matrix (`/mod/rna/obsm/X_pca`) indicating that the value has already been loaded into memory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d1662d05-8bb8-4428-a0c3-e979df2d2cae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MuData Shadow object with n_obs × n_vars = 3891 × 17838\n",
       "  obs:\t_index, leiden, leiden_wnn, louvain\n",
       "  var:\t_index, feature_types, gene_ids, highly_variable\n",
       "  obsm:\tX_mofa, X_mofa_umap, X_umap, X_wnn_umap, prot, rna\n",
       "  varm:\tLFs, prot, rna\n",
       "  obsp:\tconnectivities, distances, wnn_connectivities, wnn_distances\n",
       "  uns:\tleiden, leiden_wnn_colors, louvain, neighbors, rna:celltype_colors, umap, wnn\n",
       "  obsmap:\tprot, rna\n",
       "  varmap:\tprot, rna\n",
       "  mod:\t2 modalities\n",
       "    prot: 3891 x 32\n",
       "        X  \n",
       "        layers:\tcounts\n",
       "        obs:\t_index\n",
       "        var:\t_index, feature_types, gene_ids, highly_variable\n",
       "        obsm:\tX_pca, X_umap\n",
       "        varm:\tPCs\n",
       "        obsp:\tconnectivities, distances\n",
       "        uns:\tneighbors, pca, umap\n",
       "    rna: 3891 x 17806\n",
       "        X  \n",
       "        raw:\tXᐁ, var, varm\n",
       "        obs:\t_index, celltype, leiden, n_genes_by_counts, pct_counts_mt, total_counts, total_counts_mt\n",
       "        var:\t_index, dispersions, dispersions_norm, feature_types, gene_ids, highly_variable, mean, mean_counts, means, mt, n_cells_by_counts, pct_dropout_by_counts, std, total_counts\n",
       "        obsm:\tX_pcaᐁ, X_umap\n",
       "        varm:\tPCs\n",
       "        obsp:\tconnectivities, distances\n",
       "        uns:\tcelltype_colors, hvg, leiden, leiden_colors, neighbors, pca, rank_genes_groups, umap"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b27c1433-2425-400e-a842-580462656f6b",
   "metadata": {},
   "source": [
    "Let's load count matrices for a noticeable effect on memory consumption:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d13b8eb2-c7bd-4f33-b1dd-6a211a988a0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "rna_x = mdata[\"rna\"].X\n",
    "prot_x = mdata[\"prot\"].X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3aff9199-177f-4a55-804e-10b39fdd567f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Memory consumption after loading .obs, .obsm[\"X_pca\"] for RNA,\n",
    "# and count matrices for RNA and proteins\n",
    "mem_after = measure_memory()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0c0175b2-d80f-415b-b08a-64d44c7b74d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading these few objects took about 266.30 MiB of memory\n"
     ]
    }
   ],
   "source": [
    "print(f\"Reading these few objects took about {(mem_after - mem_before):.2f} MiB of memory\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "101c3a85-4cbe-4f8a-ac0a-13c9a6e47a76",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1dfcb71-37c4-4c12-ad29-3c53ea55db34",
   "metadata": {},
   "source": [
    "Cache can be cleared:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "561408e1-9766-409e-afc1-0cb74183119e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MuData Shadow object with n_obs × n_vars = 3891 × 17838\n",
       "  obs:\t_index, leiden, leiden_wnn, louvain\n",
       "  var:\t_index, feature_types, gene_ids, highly_variable\n",
       "  obsm:\tX_mofa, X_mofa_umap, X_umap, X_wnn_umap, prot, rna\n",
       "  varm:\tLFs, prot, rna\n",
       "  obsp:\tconnectivities, distances, wnn_connectivities, wnn_distances\n",
       "  uns:\tleiden, leiden_wnn_colors, louvain, neighbors, rna:celltype_colors, umap, wnn\n",
       "  obsmap:\tprot, rna\n",
       "  varmap:\tprot, rna\n",
       "  mod:\t2 modalities\n",
       "    prot: 3891 x 32\n",
       "        X  \n",
       "        layers:\tcounts\n",
       "        obs:\t_index\n",
       "        var:\t_index, feature_types, gene_ids, highly_variable\n",
       "        obsm:\tX_pca, X_umap\n",
       "        varm:\tPCs\n",
       "        obsp:\tconnectivities, distances\n",
       "        uns:\tneighbors, pca, umap\n",
       "    rna: 3891 x 17806\n",
       "        X  \n",
       "        raw:\tX, var, varm\n",
       "        obs:\t_index, celltype, leiden, n_genes_by_counts, pct_counts_mt, total_counts, total_counts_mt\n",
       "        var:\t_index, dispersions, dispersions_norm, feature_types, gene_ids, highly_variable, mean, mean_counts, means, mt, n_cells_by_counts, pct_dropout_by_counts, std, total_counts\n",
       "        obsm:\tX_pca, X_umap\n",
       "        varm:\tPCs\n",
       "        obsp:\tconnectivities, distances\n",
       "        uns:\tcelltype_colors, hvg, leiden, leiden_colors, neighbors, pca, rank_genes_groups, umap"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdata.clear_cache()\n",
    "mdata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26f73006-15ed-4cd3-bc1f-830d5ae50304",
   "metadata": {},
   "source": [
    "(No more `obsᐁ` and `X_pcaᐁ`!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "92f7b669-4932-4468-8740-1c2b0cee9e9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del rna_obs, rna_pca, rna_x, prot_x\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "908d0dca-5733-4e89-b364-2bb28d7d2688",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After clearing cache, memory consumption change is -264.33984375 MiB\n"
     ]
    }
   ],
   "source": [
    "print(f\"After clearing cache, memory consumption change is {(measure_memory() - mem_after)} MiB\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3dc75ba-72d9-476e-be4b-cc3332476f91",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e81e64d-de3f-4e58-a8a8-d02eee6a7335",
   "metadata": {},
   "source": [
    "### External tools"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17d0b9e0-652c-4dd2-b1ae-7633fb1c5741",
   "metadata": {},
   "source": [
    "As AnnDataShadow and MuDataShadow mimic AnnData and MuData interfaces, most of the read-only functionality of [the whole ecosystem of scverse tools](https://scverse.org/packages/), including [scanpy](https://scanpy.rtfd.io/) and [muon](https://muon.scverse.org/), is supported out of the box:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "984f7b43-2084-4a4d-b1ab-d45afb1a1700",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(mdata[\"rna\"], color=\"celltype\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67cf8a21-0e33-4119-9bdb-e75bcf5c6b25",
   "metadata": {},
   "source": [
    "Notice that the colours were fetched from the respective array in `.uns`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c668ce98-218c-4885-9fd1-a982a937821e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['#8000ff', '#5641fd', '#2c7ef7', '#00b5eb', '#2adddd', '#54f6cb',\n",
       "       '#80ffb4', '#abf69b', '#d4dd80', '#ffb360', '#ff7e41', '#ff4121',\n",
       "       '#ff0000'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdata['rna'].uns['celltype_colors']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "775d4d66-aa76-4e0e-aced-12107e8b16ad",
   "metadata": {},
   "source": [
    "Moreoever, they were only loaded from file when requested. We can see that an array with these value is cached now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d710e6e7-a41d-4c23-80d3-6eb52444a205",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "uns:\tcelltype_colorsᐁ, hvg, leiden, leiden_colors, neighbors, pca, rank_genes_groups, umap"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdata['rna'].uns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "500ac98b-6b59-46ac-9ab1-560698b1b214",
   "metadata": {},
   "source": [
    "The same works for muon:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c61a9057-97ed-4db2-acbf-5c6b26bd5627",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mdata.obs['celltype'] = mdata['rna'].obs.celltype\n",
    "mdata.uns['celltype_colors'] = mdata['rna'].uns['celltype_colors']\n",
    "\n",
    "mu.pl.mofa(mdata, color=\"celltype\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d38f649c-dc94-4d0c-947d-b92ebbbd2161",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c47908ef-77e3-4ec3-9c44-b0f1581601ae",
   "metadata": {},
   "source": [
    "### Adding data to the in-memory object"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25398a46-869a-41da-8d11-b24408f4f0f6",
   "metadata": {},
   "source": [
    "Shadow objects also allow to have new data added to them, for instance new embeddings:\n",
    "\n",
    "> [!NOTE]\n",
    "> Data added to in-memory shadow objects can be saved on disk as demonstrated [in another tutorial](shadows-features.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e4b128d2-6d41-4794-a74e-e09fc1a5ebce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For simplicity, we will copy an embedding\n",
    "mdata.obsm[\"X_mofa_copy\"] = mdata.obsm[\"X_mofa\"].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84bc7370-6f53-4cec-bb12-1f218e83851b",
   "metadata": {},
   "source": [
    "Notice a new symbol (`▲`) to signify an in-memory object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2e84f3ae-bd69-4f6e-93a8-e3447a0bc999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MuData Shadow object with n_obs × n_vars = 3891 × 17838\n",
       "  obs:\t_index, leiden, leiden_wnn, louvain\n",
       "  var:\t_index, feature_types, gene_ids, highly_variable\n",
       "  obsm:\tX_mofaᐁ, X_mofa_umapᐁ, X_umapᐁ, X_wnn_umapᐁ, protᐁ, rnaᐁ, X_mofa_copy▲\n",
       "  varm:\tLFs, prot, rna\n",
       "  obsp:\tconnectivitiesᐁ, distancesᐁ, wnn_connectivitiesᐁ, wnn_distancesᐁ\n",
       "  uns:\tleiden, leiden_wnn_colors, louvain, neighbors, rna:celltype_colors, umap, wnn, celltype_colors▲\n",
       "  obsmap:\tprot, rna\n",
       "  varmap:\tprot, rna\n",
       "  mod:\t2 modalities\n",
       "    prot: 3891 x 32\n",
       "        X  \n",
       "        layers:\tcounts\n",
       "        obs:\t_index\n",
       "        var:\t_index, feature_types, gene_ids, highly_variable\n",
       "        obsm:\tX_pca, X_umap\n",
       "        varm:\tPCs\n",
       "        obsp:\tconnectivities, distances\n",
       "        uns:\tneighbors, pca, umap\n",
       "    rna: 3891 x 17806\n",
       "        X  \n",
       "        raw:\tX, var, varm\n",
       "        obs:\t_index, celltype, leiden, n_genes_by_counts, pct_counts_mt, total_counts, total_counts_mt\n",
       "        var:\t_index, dispersions, dispersions_norm, feature_types, gene_ids, highly_variable, mean, mean_counts, means, mt, n_cells_by_counts, pct_dropout_by_counts, std, total_counts\n",
       "        obsm:\tX_pca, X_umapᐁ\n",
       "        varm:\tPCs\n",
       "        obsp:\tconnectivities, distances\n",
       "        uns:\tcelltype_colorsᐁ, hvg, leiden, leiden_colors, neighbors, pca, rank_genes_groups, umap"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cdd16dc-9a2b-44c3-a5d7-005cdac190cd",
   "metadata": {},
   "source": [
    "> [!NOTE] \n",
    "> Adding columns to data frames or altering objects otherwise is currently not being registered by shadow objects."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3143a80e-44a6-480d-aab3-3086cecdfd11",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f91166e6-4f3b-40ae-9395-449e0de0c632",
   "metadata": {},
   "source": [
    "### Wind down"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "81ba3b67-bb8f-4731-9c86-b91fdbf8abef",
   "metadata": {},
   "outputs": [],
   "source": [
    "mdata.clear_cache()\n",
    "mdata.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53604373-44ec-42f6-8235-9dd9ff0950d4",
   "metadata": {},
   "source": [
    "`mdata` cannot be used after the connection is closed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a5a2dd74-13ae-4510-94fb-88d656376864",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MuData is not available: Invalid location identifier (invalid location identifier)\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    print(mdata)\n",
    "except ValueError as e:\n",
    "    print(\"MuData is not available:\", e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "465c1a71-19f9-4ae5-90fc-f642a694b030",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9378"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del mdata\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6a2a970e-924a-4fb5-ba9e-98a7e0cc95e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In the end, memory consumption is -1.2421875 MiB up\n"
     ]
    }
   ],
   "source": [
    "print(f\"In the end, memory consumption is {(measure_memory() - mem_start)} MiB up\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "752bd981-1cbd-43ec-b707-9308afb7e55f",
   "metadata": {},
   "source": [
    " "
   ]
  }
 ],
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