scrna/query_hlca_knn.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "ny_p9QIakW9A"
   },
   "source": [
    "# Querying the Human Lung Cell Atlas\n",
    "\n",
    "Here we demonstrate how to query the Human Lung Cell Atlas using scANVI, scArches, and scvi-hub.\n",
    "\n",
    "- Sikkema, Lisa, et al. \"An integrated cell atlas of the human lung in health and disease.\" [bioRxiv](https://www.biorxiv.org/content/10.1101/2022.03.10.483747v1) (2022).\n",
    "\n",
    "If you use this tutorial in your research we recommend citing the HLCA as well as scANVI, scArches, and scvi-tools, which can be found on the [references page](https://docs.scvi-tools.org/en/stable/references.html) at Gayoso22, Lotfollahi21, Xu21 respectively.\n",
    "\n",
    "This tutorial is adapted from a [similar one](https://github.com/LungCellAtlas/mapping_data_to_the_HLCA/blob/main/notebooks/LCA_scArches_mapping_new_data_to_hlca.ipynb) presented by the HLCA authors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note}\n",
    "Running the following cell will install tutorial dependencies on Google Colab only. It will have no effect on environments other than Google Colab.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-22T13:42:54.270645Z",
     "iopub.status.busy": "2024-09-22T13:42:54.270546Z",
     "iopub.status.idle": "2024-09-22T13:42:55.280099Z",
     "shell.execute_reply": "2024-09-22T13:42:55.279715Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.12/site-packages/scvi_colab/_core.py:41: UserWarning: \n",
      "                Not currently in Google Colab environment.\n",
      "\n",
      "                Please run with `run_outside_colab=True` to override.\n",
      "\n",
      "                Returning with no further action.\n",
      "                \n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "!pip install --quiet scvi-colab\n",
    "from scvi_colab import install\n",
    "\n",
    "install()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aghHRoYY1HKM",
    "outputId": "f1177a7d-42ad-454f-f89b-b73566c2c77c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/access/.conda/envs/scvi/lib/python3.12/site-packages/anndata/utils.py:429: FutureWarning: Importing read_csv from `anndata` is deprecated. Import anndata.io.read_csv instead.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/access/.conda/envs/scvi/lib/python3.12/site-packages/anndata/utils.py:429: FutureWarning: Importing read_loom from `anndata` is deprecated. Import anndata.io.read_loom instead.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/access/.conda/envs/scvi/lib/python3.12/site-packages/anndata/utils.py:429: FutureWarning: Importing read_text from `anndata` is deprecated. Import anndata.io.read_text instead.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/access/.conda/envs/scvi/lib/python3.12/site-packages/anndata/utils.py:429: FutureWarning: Importing CSCDataset from `anndata.experimental` is deprecated. Import anndata.abc.CSCDataset instead.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/access/.conda/envs/scvi/lib/python3.12/site-packages/anndata/utils.py:429: FutureWarning: Importing CSRDataset from `anndata.experimental` is deprecated. Import anndata.abc.CSRDataset instead.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/access/.conda/envs/scvi/lib/python3.12/site-packages/anndata/utils.py:429: FutureWarning: Importing read_elem from `anndata.experimental` is deprecated. Import anndata.io.read_elem instead.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import tempfile\n",
    "\n",
    "import anndata\n",
    "import numba\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pooch\n",
    "import pynndescent\n",
    "import scanpy as sc\n",
    "import scvi\n",
    "import seaborn as sns\n",
    "import torch\n",
    "from scvi.hub import HubModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Seed set to 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last run with scvi-tools version: 1.2.1\n"
     ]
    }
   ],
   "source": [
    "scvi.settings.seed = 0\n",
    "print(\"Last run with scvi-tools version:\", scvi.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note}\n",
    "You can modify `save_dir` below to change where the data files for this tutorial are saved.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "aVDljNeyKYPA"
   },
   "outputs": [],
   "source": [
    "sc.set_figure_params(figsize=(6, 6), frameon=False)\n",
    "sns.set_theme()\n",
    "torch.set_float32_matmul_precision(\"high\")\n",
    "save_dir = tempfile.TemporaryDirectory()\n",
    "\n",
    "%config InlineBackend.print_figure_kwargs={\"facecolor\": \"w\"}\n",
    "%config InlineBackend.figure_format=\"retina\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "2C23xxVWrnzk"
   },
   "source": [
    "## Download the reference files\n",
    "\n",
    "First we download the pre-trained scANVI model from the HuggingFace [repo](https://huggingface.co/scvi-tools/human-lung-cell-atlas)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3463960/39342736.py:1: UserWarning: No revision was passed, so the default (latest) revision will be used.\n",
      "  hubmodel = HubModel.pull_from_huggingface_hub(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c8c9c5ee41bc448d8706df7c14e50ef5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Fetching 3 files:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mINFO    \u001b[0m Reading adata\u001b[33m...\u001b[0m                                                                                          \n",
      "\u001b[34mINFO    \u001b[0m Loading model\u001b[33m...\u001b[0m                                                                                          \n",
      "\u001b[34mINFO    \u001b[0m File                                                                                                      \n",
      "         \u001b[35m/tmp/tmpd0b597to/models--scvi-tools--human-lung-cell-atlas-scanvi/snapshots/dcb6d4947c81824af166b645fa4024\u001b[0m\n",
      "         \u001b[35m4d0cb7ea6c/\u001b[0m\u001b[95mmodel.pt\u001b[0m already downloaded                                                                    \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/access/.conda/envs/scvi/lib/python3.12/site-packages/scvi/model/base/_save_load.py:76: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  model = torch.load(model_path, map_location=map_location)\n",
      "/home/access/.conda/envs/scvi/lib/python3.12/site-packages/scvi/data/fields/_dataframe_field.py:224: UserWarning: Category 18 in adata.obs['_scvi_labels'] has fewer than 3 cells. Models may not train properly.\n",
      "  new_mapping = _make_column_categorical(\n",
      "/home/access/.conda/envs/scvi/lib/python3.12/site-packages/scvi/data/fields/_scanvi.py:55: UserWarning: Category 18 in adata.obs['_scvi_labels'] has fewer than 3 cells. Models may not train properly.\n",
      "  mapping = _make_column_categorical(\n"
     ]
    }
   ],
   "source": [
    "hubmodel = HubModel.pull_from_huggingface_hub(\n",
    "    \"scvi-tools/human-lung-cell-atlas-scanvi\", cache_dir=save_dir.name\n",
    ")\n",
    "adata = hubmodel.adata\n",
    "model = hubmodel.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mC441rKarlNw",
    "outputId": "517dc350-2665-4adc-89d3-d18304da92ea"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 584944 × 2000\n",
       "    obs: 'suspension_type', 'donor_id', 'is_primary_data', 'assay_ontology_term_id', 'cell_type_ontology_term_id', 'development_stage_ontology_term_id', 'disease_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'tissue_ontology_term_id', 'organism_ontology_term_id', 'sex_ontology_term_id', 'BMI', 'age_or_mean_of_age_range', 'age_range', 'anatomical_region_ccf_score', 'ann_coarse_for_GWAS_and_modeling', 'ann_finest_level', 'ann_level_1', 'ann_level_2', 'ann_level_3', 'ann_level_4', 'ann_level_5', 'cause_of_death', 'dataset', 'entropy_dataset_leiden_3', 'entropy_original_ann_level_1_leiden_3', 'entropy_original_ann_level_2_clean_leiden_3', 'entropy_original_ann_level_3_clean_leiden_3', 'entropy_subject_ID_leiden_3', 'fresh_or_frozen', 'leiden_1', 'leiden_2', 'leiden_3', 'leiden_4', 'leiden_5', 'log10_total_counts', 'lung_condition', 'mixed_ancestry', 'n_genes_detected', 'original_ann_highest_res', 'original_ann_level_1', 'original_ann_level_2', 'original_ann_level_3', 'original_ann_level_4', 'original_ann_level_5', 'original_ann_nonharmonized', 'reannotation_type', 'reference_genome', 'sample', 'scanvi_label', 'sequencing_platform', 'size_factors', 'smoking_status', 'study', 'subject_type', 'tissue_dissociation_protocol', 'tissue_level_2', 'tissue_level_3', 'tissue_sampling_method', 'tissue_type', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid', '_scvi_batch', '_scvi_labels', 'cv_cell', 'observed_lib_size'\n",
       "    var: 'feature_name', 'feature_length', 'cv_gene'\n",
       "    uns: '_scvi_adata_minify_type', '_scvi_manager_uuid', '_scvi_uuid'\n",
       "    obsm: 'X_scanvi_emb', 'X_umap', 'scanvi_latent_qzm', 'scanvi_latent_qzv'\n",
       "    varm: 'lfc_model', 'lfc_raw'\n",
       "    obsp: 'connectivities', 'distances'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ScanVI Model with the following params: \n",
       "unlabeled_category: unlabeled, n_hidden: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">128</span>, n_latent: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span>, n_layers: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>, dropout_rate: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.1</span>, dispersion: gene, \n",
       "gene_likelihood: nb\n",
       "Training status: Trained\n",
       "Model's adata is minified?: <span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "ScanVI Model with the following params: \n",
       "unlabeled_category: unlabeled, n_hidden: \u001b[1;36m128\u001b[0m, n_latent: \u001b[1;36m30\u001b[0m, n_layers: \u001b[1;36m2\u001b[0m, dropout_rate: \u001b[1;36m0.1\u001b[0m, dispersion: gene, \n",
       "gene_likelihood: nb\n",
       "Training status: Trained\n",
       "Model's adata is minified?: \u001b[3;92mTrue\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Anndata setup with scvi-tools version <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.2</span>.<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Anndata setup with scvi-tools version \u001b[1;36m1.2\u001b[0m.\u001b[1;36m1\u001b[0m.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Setup via `SCANVI.setup_anndata` with arguments:\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Setup via `SCANVI.setup_anndata` with arguments:\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #008000; text-decoration-color: #008000\">'labels_key'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'scanvi_label'</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #008000; text-decoration-color: #008000\">'unlabeled_category'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'unlabeled'</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #008000; text-decoration-color: #008000\">'layer'</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #008000; text-decoration-color: #008000\">'batch_key'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'dataset'</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #008000; text-decoration-color: #008000\">'size_factor_key'</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #008000; text-decoration-color: #008000\">'categorical_covariate_keys'</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #008000; text-decoration-color: #008000\">'continuous_covariate_keys'</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>,\n",
       "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #008000; text-decoration-color: #008000\">'use_minified'</span>: <span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>\n",
       "<span style=\"font-weight: bold\">}</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m{\u001b[0m\n",
       "\u001b[2;32m│   \u001b[0m\u001b[32m'labels_key'\u001b[0m: \u001b[32m'scanvi_label'\u001b[0m,\n",
       "\u001b[2;32m│   \u001b[0m\u001b[32m'unlabeled_category'\u001b[0m: \u001b[32m'unlabeled'\u001b[0m,\n",
       "\u001b[2;32m│   \u001b[0m\u001b[32m'layer'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
       "\u001b[2;32m│   \u001b[0m\u001b[32m'batch_key'\u001b[0m: \u001b[32m'dataset'\u001b[0m,\n",
       "\u001b[2;32m│   \u001b[0m\u001b[32m'size_factor_key'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
       "\u001b[2;32m│   \u001b[0m\u001b[32m'categorical_covariate_keys'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
       "\u001b[2;32m│   \u001b[0m\u001b[32m'continuous_covariate_keys'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
       "\u001b[2;32m│   \u001b[0m\u001b[32m'use_minified'\u001b[0m: \u001b[3;92mTrue\u001b[0m\n",
       "\u001b[1m}\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">         Summary Statistics          </span>\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\">     Summary Stat Key     </span>┃<span style=\"font-weight: bold\"> Value  </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">         n_batch          </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">   14   </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">         n_cells          </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\"> 584944 </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\"> n_extra_categorical_covs </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">   0    </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\"> n_extra_continuous_covs  </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">   0    </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">         n_labels         </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">   29   </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">       n_latent_qzm       </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">   30   </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">       n_latent_qzv       </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">   30   </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">          n_vars          </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">  2000  </span>│\n",
       "└──────────────────────────┴────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[3m         Summary Statistics          \u001b[0m\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m    Summary Stat Key    \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mValue \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m        n_batch         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m  14  \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m        n_cells         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m584944\u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33mn_extra_categorical_covs\u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m  0   \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33mn_extra_continuous_covs \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m  0   \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m        n_labels        \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m  29  \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m      n_latent_qzm      \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m  30  \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m      n_latent_qzv      \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m  30  \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m         n_vars         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 2000 \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "└──────────────────────────┴────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                       Data Registry                        </span>\n",
       "┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\">   Registry Key    </span>┃<span style=\"font-weight: bold\">         scvi-tools Location          </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">         X         </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">               adata.X                </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">       batch       </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">       adata.obs['_scvi_batch']       </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">      labels       </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">      adata.obs['_scvi_labels']       </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">    latent_qzm     </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">   adata.obsm['scanvi_latent_qzm']    </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">    latent_qzv     </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">   adata.obsm['scanvi_latent_qzv']    </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">    minify_type    </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\"> adata.uns['_scvi_adata_minify_type'] </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\"> observed_lib_size </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">    adata.obs['observed_lib_size']    </span>│\n",
       "└───────────────────┴──────────────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[3m                       Data Registry                        \u001b[0m\n",
       "┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m  Registry Key   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        scvi-tools Location         \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m        X        \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m              adata.X               \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m      batch      \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m      adata.obs['_scvi_batch']      \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m     labels      \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m     adata.obs['_scvi_labels']      \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m   latent_qzm    \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m  adata.obsm['scanvi_latent_qzm']   \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m   latent_qzv    \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m  adata.obsm['scanvi_latent_qzv']   \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m   minify_type   \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128madata.uns['_scvi_adata_minify_type']\u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33mobserved_lib_size\u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m   adata.obs['observed_lib_size']   \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "└───────────────────┴──────────────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                           batch State Registry                           </span>\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\">   Source Location    </span>┃<span style=\"font-weight: bold\">        Categories         </span>┃<span style=\"font-weight: bold\"> scvi-tools Encoding </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\"> adata.obs['dataset'] </span>│<span style=\"color: #008000; text-decoration-color: #008000\">   Banovich_Kropski_2020   </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">          0          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                      </span>│<span style=\"color: #008000; text-decoration-color: #008000\">     Barbry_Leroy_2020     </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">          1          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                      </span>│<span style=\"color: #008000; text-decoration-color: #008000\"> Jain_Misharin_2021_10Xv1  </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">          2          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                      </span>│<span style=\"color: #008000; text-decoration-color: #008000\"> Jain_Misharin_2021_10Xv2  </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">          3          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                      </span>│<span style=\"color: #008000; text-decoration-color: #008000\">       Krasnow_2020        </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">          4          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                      </span>│<span style=\"color: #008000; text-decoration-color: #008000\"> Lafyatis_Rojas_2019_10Xv1 </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">          5          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                      </span>│<span style=\"color: #008000; text-decoration-color: #008000\"> Lafyatis_Rojas_2019_10Xv2 </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">          6          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                      </span>│<span style=\"color: #008000; text-decoration-color: #008000\">        Meyer_2019         </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">          7          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                      </span>│<span style=\"color: #008000; text-decoration-color: #008000\">       Misharin_2021       </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">          8          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                      </span>│<span style=\"color: #008000; text-decoration-color: #008000\">  Misharin_Budinger_2018   </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">          9          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                      </span>│<span style=\"color: #008000; text-decoration-color: #008000\">        Nawijn_2021        </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         10          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                      </span>│<span style=\"color: #008000; text-decoration-color: #008000\">    Seibold_2020_10Xv2     </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         11          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                      </span>│<span style=\"color: #008000; text-decoration-color: #008000\">    Seibold_2020_10Xv3     </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         12          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                      </span>│<span style=\"color: #008000; text-decoration-color: #008000\">   Teichmann_Meyer_2019    </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         13          </span>│\n",
       "└──────────────────────┴───────────────────────────┴─────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[3m                           batch State Registry                           \u001b[0m\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m  Source Location   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m       Categories        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mscvi-tools Encoding\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33madata.obs['dataset']\u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m  Banovich_Kropski_2020  \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m         0         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                    \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m    Barbry_Leroy_2020    \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m         1         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                    \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32mJain_Misharin_2021_10Xv1 \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m         2         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                    \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32mJain_Misharin_2021_10Xv2 \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m         3         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                    \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m      Krasnow_2020       \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m         4         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                    \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32mLafyatis_Rojas_2019_10Xv1\u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m         5         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                    \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32mLafyatis_Rojas_2019_10Xv2\u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m         6         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                    \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m       Meyer_2019        \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m         7         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                    \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m      Misharin_2021      \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m         8         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                    \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m Misharin_Budinger_2018  \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m         9         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                    \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m       Nawijn_2021       \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        10         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                    \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m   Seibold_2020_10Xv2    \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        11         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                    \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m   Seibold_2020_10Xv3    \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        12         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                    \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m  Teichmann_Meyer_2019   \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        13         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "└──────────────────────┴───────────────────────────┴─────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                            labels State Registry                            </span>\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\">      Source Location      </span>┃<span style=\"font-weight: bold\">       Categories        </span>┃<span style=\"font-weight: bold\"> scvi-tools Encoding </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\"> adata.obs['scanvi_label'] </span>│<span style=\"color: #008000; text-decoration-color: #008000\">           AT1           </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">          0          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">           AT2           </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">          1          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">       Arterial EC       </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">          2          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">     B cell lineage      </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">          3          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">          Basal          </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">          4          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">   Bronchial Vessel 1    </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">          5          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">   Bronchial Vessel 2    </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">          6          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">        Capillary        </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">          7          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">        Ciliated         </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">          8          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">     Dendritic cells     </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">          9          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">   Fibroblast lineage    </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         10          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\"> KRT5- KRT17+ epithelial </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         11          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">      Lymphatic EC       </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         12          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">       Macrophages       </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         13          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">       Mast cells        </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         14          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">     Megakaryocytes      </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         15          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">       Mesothelium       </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         16          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">        Monocytes        </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         17          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">      Neutrophilic       </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         18          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">      Non-T/B cells      </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         19          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">   Proliferating cells   </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         20          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">          Rare           </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         21          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">        Secretory        </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         22          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">      Smooth Muscle      </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         23          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">        Squamous         </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         24          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">  Submucosal Secretory   </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         25          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">     T cell lineage      </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         26          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">         Venous          </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         27          </span>│\n",
       "│<span style=\"color: #0087ff; text-decoration-color: #0087ff\">                           </span>│<span style=\"color: #008000; text-decoration-color: #008000\">        unlabeled        </span>│<span style=\"color: #af00d7; text-decoration-color: #af00d7\">         28          </span>│\n",
       "└───────────────────────────┴─────────────────────────┴─────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[3m                            labels State Registry                            \u001b[0m\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m     Source Location     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Categories       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mscvi-tools Encoding\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33madata.obs['scanvi_label']\u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m          AT1          \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m         0         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m          AT2          \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m         1         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m      Arterial EC      \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m         2         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m    B cell lineage     \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m         3         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m         Basal         \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m         4         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m  Bronchial Vessel 1   \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m         5         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m  Bronchial Vessel 2   \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m         6         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m       Capillary       \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m         7         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m       Ciliated        \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m         8         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m    Dendritic cells    \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m         9         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m  Fibroblast lineage   \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        10         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32mKRT5- KRT17+ epithelial\u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        11         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m     Lymphatic EC      \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        12         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m      Macrophages      \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        13         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m      Mast cells       \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        14         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m    Megakaryocytes     \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        15         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m      Mesothelium      \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        16         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m       Monocytes       \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        17         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m     Neutrophilic      \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        18         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m     Non-T/B cells     \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        19         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m  Proliferating cells  \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        20         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m         Rare          \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        21         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m       Secretory       \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        22         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m     Smooth Muscle     \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        23         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m       Squamous        \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        24         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m Submucosal Secretory  \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        25         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m    T cell lineage     \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        26         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m        Venous         \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        27         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m                         \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m       unlabeled       \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m        28         \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
       "└───────────────────────────┴─────────────────────────┴─────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.view_anndata_setup()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lOp1pqbBARMy"
   },
   "source": [
    "## Learn a neighbors index on reference latent space"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "8aLtbvVXBvco"
   },
   "source": [
    "Here we create the neighbors index using `PyNNDescent`. We will use this later to classify query cells. `PyNNDescent` is an extremely fast approximate neighbors technique.\n",
    "\n",
    "In the case of the `HubModel` instance above, we see that the data is in minified mode, meaning the count data is not actually in the object, and we only store a minified representation of the data. We see that we can access the mean of the embedding (`latent_qzm`) above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9A5tMYRvaiwk",
    "outputId": "2bd7b768-df78-45ff-9768-3f6e1bf5ffbb"
   },
   "outputs": [],
   "source": [
    "X_train = adata.obsm[\"scanvi_latent_qzm\"]\n",
    "ref_nn_index = pynndescent.NNDescent(X_train)\n",
    "ref_nn_index.prepare()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kZXbZT5Ar-LN"
   },
   "source": [
    "## Download query data\n",
    "\n",
    "In this tutorial we use the fresh, single-cell sample from the following publication:\n",
    "\n",
    "- Delorey, Toni M., et al. \"COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets.\" Nature 595.7865 (2021): 107-113.\n",
    "\n",
    "In principle at this stage you may load your own data. There are few important notes though:\n",
    "\n",
    "- Using the HLCA requires using Gene IDs for the query data\n",
    "- The query data should include batches in `query_data.obs[\"dataset\"]`\n",
    "- It's necessary to run `query_data.obs[\"scanvi_label\"] = \"unlabeled\"` so that scvi-tools can properly register the query data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def download_data(save_path: str):\n",
    "    \"\"\"Download and cache the query data.\"\"\"\n",
    "    data_path = pooch.retrieve(\n",
    "        url=\"https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5230nnn/GSM5230027/suppl/GSM5230027_04-P103142-S149-R01_raw_feature_bc_matrix.h5.gz\",\n",
    "        known_hash=\"3b7f8318059d655ea774752b6d8b13381323f5018e9f3868ffc53674f94f537f\",\n",
    "        fname=\"query_data.h5.gz\",\n",
    "        path=save_path,\n",
    "        processor=pooch.Decompress(),\n",
    "        progressbar=True,\n",
    "    )\n",
    "    metadata_path = pooch.retrieve(\n",
    "        url=\"https://ftp.ncbi.nlm.nih.gov/geo/series/GSE171nnn/GSE171668/suppl/GSE171668_lung_metadata.csv.gz\",\n",
    "        known_hash=\"290b0ac86e85183e65eefb68670ad27fc5156866144d9ac6f2eb27f34e31e79e\",\n",
    "        fname=\"query_metadata.csv.gz\",\n",
    "        path=save_path,\n",
    "        processor=pooch.Decompress(),\n",
    "        progressbar=True,\n",
    "    )\n",
    "    return data_path, metadata_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/access/.conda/envs/scvi/lib/python3.12/site-packages/anndata/_core/anndata.py:1758: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "  utils.warn_names_duplicates(\"var\")\n",
      "/home/access/.conda/envs/scvi/lib/python3.12/site-packages/anndata/_core/anndata.py:1758: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "  utils.warn_names_duplicates(\"var\")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "  0%|▏                                     | 99.3k/27.4M [00:00<01:32, 295kB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "  1%|▎                                      | 214k/27.4M [00:00<00:53, 506kB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "  2%|▌                                      | 427k/27.4M [00:00<00:30, 880kB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "  3%|█▏                                    | 886k/27.4M [00:00<00:15, 1.71MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "  6%|██▎                                  | 1.69M/27.4M [00:01<00:14, 1.83MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 17%|██████                               | 4.52M/27.4M [00:01<00:03, 5.94MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 20%|███████▎                             | 5.37M/27.4M [00:01<00:03, 5.99MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 23%|████████▍                            | 6.23M/27.4M [00:01<00:03, 6.04MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 26%|█████████▌                           | 7.08M/27.4M [00:01<00:03, 6.07MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 29%|██████████▋                          | 7.93M/27.4M [00:01<00:03, 6.09MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 32%|███████████▊                         | 8.78M/27.4M [00:02<00:03, 6.11MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 35%|█████████████                        | 9.65M/27.4M [00:02<00:02, 6.16MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 38%|██████████████▏                      | 10.5M/27.4M [00:02<00:02, 6.17MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 42%|███████████████▎                     | 11.4M/27.4M [00:02<00:02, 6.19MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 45%|████████████████▌                    | 12.2M/27.4M [00:02<00:02, 6.18MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 48%|█████████████████▋                   | 13.1M/27.4M [00:02<00:02, 6.21MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 51%|██████████████████▉                  | 14.0M/27.4M [00:02<00:02, 6.24MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 54%|████████████████████                 | 14.8M/27.4M [00:03<00:02, 6.25MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 57%|█████████████████████▎               | 15.7M/27.4M [00:03<00:01, 6.33MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 61%|██████████████████████▍              | 16.6M/27.4M [00:03<00:01, 6.35MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 64%|███████████████████████▋             | 17.5M/27.4M [00:03<00:01, 6.43MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 67%|████████████████████████▉            | 18.5M/27.4M [00:03<00:01, 6.53MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 71%|██████████████████████████▏          | 19.4M/27.4M [00:03<00:01, 6.59MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 74%|███████████████████████████▌         | 20.4M/27.4M [00:03<00:01, 6.67MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 78%|████████████████████████████▊        | 21.3M/27.4M [00:04<00:00, 6.80MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 82%|██████████████████████████████▏      | 22.3M/27.4M [00:04<00:00, 6.90MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 85%|███████████████████████████████▌     | 23.3M/27.4M [00:04<00:00, 7.00MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 89%|████████████████████████████████▉    | 24.3M/27.4M [00:04<00:00, 7.09MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 93%|██████████████████████████████████▎  | 25.4M/27.4M [00:04<00:00, 7.24MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 97%|███████████████████████████████████▋ | 26.4M/27.4M [00:04<00:00, 7.34MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "  0%|                                              | 0.00/27.4M [00:00<?, ?B/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "100%|█████████████████████████████████████| 27.4M/27.4M [00:00<00:00, 47.0GB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Decompressing '/tmp/tmppp7v4nri/query_data.h5.gz' to '/tmp/tmppp7v4nri/query_data.h5.gz.decomp' using method 'auto'.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading data from 'https://ftp.ncbi.nlm.nih.gov/geo/series/GSE171nnn/GSE171668/suppl/GSE171668_lung_metadata.csv.gz' to file '/tmp/tmppp7v4nri/query_metadata.csv.gz'.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "  0%|                                              | 0.00/1.95M [00:00<?, ?B/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "  0%|                                     | 1.02k/1.95M [00:00<04:18, 7.54kB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "  2%|▋                                     | 33.8k/1.95M [00:00<00:13, 144kB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "  5%|█▉                                    | 99.3k/1.95M [00:00<00:06, 296kB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 11%|████▎                                  | 214k/1.95M [00:00<00:03, 509kB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 23%|████████▉                              | 443k/1.95M [00:00<00:01, 926kB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 46%|█████████████████▌                    | 902k/1.95M [00:00<00:00, 1.74MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 84%|███████████████████████████████▏     | 1.64M/1.95M [00:00<00:00, 2.88MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      " 99%|████████████████████████████████████▌| 1.92M/1.95M [00:01<00:00, 2.65MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "  0%|                                              | 0.00/1.95M [00:00<?, ?B/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "100%|█████████████████████████████████████| 1.95M/1.95M [00:00<00:00, 6.70GB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Decompressing '/tmp/tmppp7v4nri/query_metadata.csv.gz' to '/tmp/tmppp7v4nri/query_metadata.csv.gz.decomp' using method 'auto'.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.12/site-packages/anndata/_core/anndata.py:1756: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "  utils.warn_names_duplicates(\"var\")\n",
      "/usr/local/lib/python3.12/site-packages/anndata/_core/anndata.py:1756: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "  utils.warn_names_duplicates(\"var\")\n"
     ]
    }
   ],
   "source": [
    "query_data_path, query_metadata_path = download_data(save_dir.name)\n",
    "\n",
    "query_adata = sc.read_10x_h5(query_data_path)\n",
    "query_metadata = pd.read_csv(query_metadata_path, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pJJ4OQIXtaUa",
    "outputId": "45f75b92-39a7-4df1-8ec1-7d43e45372bc"
   },
   "outputs": [],
   "source": [
    "# clean up .var.index (gene names)\n",
    "query_adata.var[\"gene_names\"] = query_adata.var.index\n",
    "query_adata.var.index = [idx.split(\"___\")[-1] for idx in query_adata.var.gene_ids]\n",
    "# clean up cell barcodes:\n",
    "query_adata.obs.index = query_adata.obs.index.str.rstrip(\"-1\")\n",
    "# read in metadata (to select only cells of interest and remove empty drops)\n",
    "# subset to cells from our sample\n",
    "query_metadata = query_metadata.loc[query_metadata.donor == \"D12_4\", :].copy()\n",
    "# clean up barcodes:\n",
    "query_metadata.index = [idx.split(\"-\")[-1] for idx in query_metadata.index]\n",
    "# subset adata to cells in metadata:\n",
    "query_adata = query_adata[query_metadata.index, :].copy()\n",
    "# add dataset information:\n",
    "query_adata.obs[\"dataset\"] = \"test_dataset_delorey_regev\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XwLjM6XHU4x2"
   },
   "source": [
    "## Loading the query model from the reference files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NdEB0tCWvSSZ"
   },
   "source": [
    "Here we run `prepare_query_anndata`, which reorders the genes and pads any missing genes with 0s. This should generally be run before reference mapping with scArches to ensure data correctness."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{important}\n",
    "Below we use the path to the model we downloaded from HuggingFace. While in most cases the model instance can be used instead of the path, here the reference model's adata is in minified mode.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_KysYnn4uoqx",
    "outputId": "bf5c9d3d-dfa7-4ee9-dd08-569f2e0fe0d0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mINFO    \u001b[0m Found \u001b[1;36m99.65\u001b[0m% reference vars in query data.                                                                \n"
     ]
    }
   ],
   "source": [
    "scvi.model.SCANVI.prepare_query_anndata(query_adata, model)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above, we see that the model is expecting a labels key with the name `\"scanvi_label\"`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "HSEppSf1vqzz"
   },
   "outputs": [],
   "source": [
    "query_adata.obs[\"scanvi_label\"] = \"unlabeled\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AU7GN0ofUse0",
    "outputId": "468d8ca4-fd69-42d7-cf85-dc2ca9318246"
   },
   "outputs": [],
   "source": [
    "query_model = scvi.model.SCANVI.load_query_data(query_adata, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o5YzJ5o4OCuV"
   },
   "source": [
    "Here we use scArches/scANVI-specific query training arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "kiwK69r65ThW"
   },
   "outputs": [],
   "source": [
    "surgery_epochs = 500\n",
    "train_kwargs_surgery = {\n",
    "    \"early_stopping\": True,\n",
    "    \"early_stopping_monitor\": \"elbo_train\",\n",
    "    \"early_stopping_patience\": 10,\n",
    "    \"early_stopping_min_delta\": 0.001,\n",
    "    \"plan_kwargs\": {\"weight_decay\": 0.0},\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "M7pMhiB88c78",
    "outputId": "c2ae6bea-9f84-4c5b-a3ab-d6af8c20b661"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mINFO    \u001b[0m Training for \u001b[1;36m500\u001b[0m epochs.                                                                                  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "/home/access/.conda/envs/scvi/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=63` in the `DataLoader` to improve performance.\n",
      "/home/access/.conda/envs/scvi/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=63` in the `DataLoader` to improve performance.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "82b4780892a8462e9ff7032201a1d4bc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training:   0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Monitored metric elbo_train did not improve in the last 10 records. Best score: 513.520. Signaling Trainer to stop.\n"
     ]
    }
   ],
   "source": [
    "query_model.train(max_epochs=surgery_epochs, **train_kwargs_surgery)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "query_model_path = os.path.join(save_dir.name, \"query_model\")\n",
    "query_model.save(query_model_path, overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "l-8bGlNnFeOg"
   },
   "outputs": [],
   "source": [
    "query_emb = anndata.AnnData(query_model.get_latent_representation())\n",
    "query_emb.obs_names = query_adata.obs_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jyHeDAynGBUZ"
   },
   "source": [
    "Now let's store the predictions in the query embedding object. We reuse the PyNNDescent index from before, converting distances to affinities, and weighting the predictions using these affinities.\n",
    "\n",
    "This follows the same approach used in the HLCA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "aKqTRnKsawyn"
   },
   "outputs": [],
   "source": [
    "ref_neighbors, ref_distances = ref_nn_index.query(query_emb.X)\n",
    "\n",
    "# convert distances to affinities\n",
    "stds = np.std(ref_distances, axis=1)\n",
    "stds = (2.0 / stds) ** 2\n",
    "stds = stds.reshape(-1, 1)\n",
    "ref_distances_tilda = np.exp(-np.true_divide(ref_distances, stds))\n",
    "weights = ref_distances_tilda / np.sum(ref_distances_tilda, axis=1, keepdims=True)\n",
    "\n",
    "\n",
    "@numba.njit\n",
    "def weighted_prediction(weights, ref_cats):\n",
    "    \"\"\"Get highest weight category.\"\"\"\n",
    "    N = len(weights)\n",
    "    predictions = np.zeros((N,), dtype=ref_cats.dtype)\n",
    "    uncertainty = np.zeros((N,))\n",
    "    for i in range(N):\n",
    "        obs_weights = weights[i]\n",
    "        obs_cats = ref_cats[i]\n",
    "        best_prob = 0\n",
    "        for c in np.unique(obs_cats):\n",
    "            cand_prob = np.sum(obs_weights[obs_cats == c])\n",
    "            if cand_prob > best_prob:\n",
    "                best_prob = cand_prob\n",
    "                predictions[i] = c\n",
    "                uncertainty[i] = max(1 - best_prob, 0)\n",
    "\n",
    "    return predictions, uncertainty\n",
    "\n",
    "\n",
    "# for each annotation level, get prediction and uncertainty\n",
    "label_keys = [f\"ann_level_{i}\" for i in range(1, 6)] + [\"ann_finest_level\"]\n",
    "for l in label_keys:\n",
    "    ref_cats = adata.obs[l].cat.codes.to_numpy()[ref_neighbors]\n",
    "    p, u = weighted_prediction(weights, ref_cats)\n",
    "    p = np.asarray(adata.obs[l].cat.categories)[p]\n",
    "    query_emb.obs[l + \"_pred\"], query_emb.obs[l + \"_uncertainty\"] = p, u"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q1T5CVeFRtdh"
   },
   "source": [
    "Now let's filter our predictions on the uncertainty threshold, which is discussed in the HLCA manuscript."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HhD20VMERtB7",
    "outputId": "bf3a3839-ae85-4dff-8f9a-8f5d14514797"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ann_level_1: 0.004479283314669653 unknown\n",
      "ann_level_2: 0.007838745800671893 unknown\n",
      "ann_level_3: 0.07334826427771557 unknown\n",
      "ann_level_4: 0.36114221724524076 unknown\n",
      "ann_level_5: 0.009518477043673012 unknown\n",
      "ann_finest_level: 0.38353863381858905 unknown\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3463960/642663619.py:5: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "  query_emb.obs[l + \"_pred\"].loc[mask] = \"Unknown\"\n",
      "/tmp/ipykernel_3463960/642663619.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  query_emb.obs[l + \"_pred\"].loc[mask] = \"Unknown\"\n",
      "/tmp/ipykernel_3463960/642663619.py:5: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "  query_emb.obs[l + \"_pred\"].loc[mask] = \"Unknown\"\n",
      "/tmp/ipykernel_3463960/642663619.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  query_emb.obs[l + \"_pred\"].loc[mask] = \"Unknown\"\n",
      "/tmp/ipykernel_3463960/642663619.py:5: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "  query_emb.obs[l + \"_pred\"].loc[mask] = \"Unknown\"\n",
      "/tmp/ipykernel_3463960/642663619.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  query_emb.obs[l + \"_pred\"].loc[mask] = \"Unknown\"\n",
      "/tmp/ipykernel_3463960/642663619.py:5: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "  query_emb.obs[l + \"_pred\"].loc[mask] = \"Unknown\"\n",
      "/tmp/ipykernel_3463960/642663619.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  query_emb.obs[l + \"_pred\"].loc[mask] = \"Unknown\"\n",
      "/tmp/ipykernel_3463960/642663619.py:5: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "  query_emb.obs[l + \"_pred\"].loc[mask] = \"Unknown\"\n",
      "/tmp/ipykernel_3463960/642663619.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  query_emb.obs[l + \"_pred\"].loc[mask] = \"Unknown\"\n",
      "/tmp/ipykernel_3463960/642663619.py:5: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "  query_emb.obs[l + \"_pred\"].loc[mask] = \"Unknown\"\n",
      "/tmp/ipykernel_3463960/642663619.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  query_emb.obs[l + \"_pred\"].loc[mask] = \"Unknown\"\n"
     ]
    }
   ],
   "source": [
    "uncertainty_threshold = 0.2\n",
    "for l in label_keys:\n",
    "    mask = query_emb.obs[l + \"_uncertainty\"] > 0.2\n",
    "    print(f\"{l}: {sum(mask) / len(mask)} unknown\")\n",
    "    query_emb.obs[l + \"_pred\"].loc[mask] = \"Unknown\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "SZ-lEoW2M-2D"
   },
   "outputs": [],
   "source": [
    "query_emb.obs[\"dataset\"] = \"test_dataset_delorey_regev\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "88oPw-HrI8K4"
   },
   "source": [
    "## Combine embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oODNEnliMR29",
    "outputId": "640e93b7-a3f3-4576-b163-825bb63bae97"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 584944 × 30\n",
       "    obs: 'suspension_type', 'donor_id', 'is_primary_data', 'assay_ontology_term_id', 'cell_type_ontology_term_id', 'development_stage_ontology_term_id', 'disease_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'tissue_ontology_term_id', 'organism_ontology_term_id', 'sex_ontology_term_id', 'BMI', 'age_or_mean_of_age_range', 'age_range', 'anatomical_region_ccf_score', 'ann_coarse_for_GWAS_and_modeling', 'ann_finest_level', 'ann_level_1', 'ann_level_2', 'ann_level_3', 'ann_level_4', 'ann_level_5', 'cause_of_death', 'dataset', 'entropy_dataset_leiden_3', 'entropy_original_ann_level_1_leiden_3', 'entropy_original_ann_level_2_clean_leiden_3', 'entropy_original_ann_level_3_clean_leiden_3', 'entropy_subject_ID_leiden_3', 'fresh_or_frozen', 'leiden_1', 'leiden_2', 'leiden_3', 'leiden_4', 'leiden_5', 'log10_total_counts', 'lung_condition', 'mixed_ancestry', 'n_genes_detected', 'original_ann_highest_res', 'original_ann_level_1', 'original_ann_level_2', 'original_ann_level_3', 'original_ann_level_4', 'original_ann_level_5', 'original_ann_nonharmonized', 'reannotation_type', 'reference_genome', 'sample', 'scanvi_label', 'sequencing_platform', 'size_factors', 'smoking_status', 'study', 'subject_type', 'tissue_dissociation_protocol', 'tissue_level_2', 'tissue_level_3', 'tissue_sampling_method', 'tissue_type', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid', '_scvi_batch', '_scvi_labels', 'cv_cell', 'observed_lib_size'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_emb = anndata.AnnData(X_train, obs=adata.obs)\n",
    "ref_emb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AMdJ5SovMhXg",
    "outputId": "357078c1-a83a-4e01-d3ca-298d58b3e09f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 1786 × 30\n",
       "    obs: 'ann_level_1_pred', 'ann_level_1_uncertainty', 'ann_level_2_pred', 'ann_level_2_uncertainty', 'ann_level_3_pred', 'ann_level_3_uncertainty', 'ann_level_4_pred', 'ann_level_4_uncertainty', 'ann_level_5_pred', 'ann_level_5_uncertainty', 'ann_finest_level_pred', 'ann_finest_level_uncertainty', 'dataset'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_emb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "g0tBSVL2MrUw",
    "outputId": "0e911246-c409-441d-8d80-027378daabad"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3463960/852954867.py:1: FutureWarning: Use anndata.concat instead of AnnData.concatenate, AnnData.concatenate is deprecated and will be removed in the future. See the tutorial for concat at: https://anndata.readthedocs.io/en/latest/concatenation.html\n",
      "  combined_emb = ref_emb.concatenate(query_emb)\n"
     ]
    }
   ],
   "source": [
    "combined_emb = ref_emb.concatenate(query_emb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "48iqJ7cI-YQF"
   },
   "source": [
    "## Visualize embeddings and predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order for the notebook to run faster here, we recomment to swtich to RAPIDS (see https://docs.rapids.ai/install/). In this tutorial we will show how to visualrize the embeddings using UMAPs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "LATENT_KEY = \"X_scanVI\"\n",
    "combined_emb.obsm[LATENT_KEY] = combined_emb.X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.neighbors(combined_emb, use_rep=LATENT_KEY)\n",
    "sc.tl.umap(combined_emb, min_dist=0.3)\n",
    "colors = [l + \"_uncertainty\" for l in label_keys]\n",
    "sc.pl.umap(\n",
    "    combined_emb,\n",
    "    color=colors,\n",
    "    ncols=2,\n",
    "    frameon=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = [l + \"_pred\" for l in label_keys]\n",
    "sc.pl.umap(\n",
    "    combined_emb,\n",
    "    color=colors,\n",
    "    ncols=1,\n",
    "    size=0.5,\n",
    "    frameon=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pl.umap(\n",
    "    combined_emb,\n",
    "    color=\"ann_finest_level\",\n",
    "    ncols=1,\n",
    "    size=0.5,\n",
    "    frameon=False,\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "machine_shape": "hm",
   "name": "Copy of query_hlca_knn.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "b5142939ddaa1787bd1bfcf4c0ad4d35be0fa2237c553f986d37efcb39f03f79"
   }
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "00572a5f67164f9ca1ae8a95b12fd681": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "0305b4003d0c4d8abf455e62d9eb3c14": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "ProgressView",
       "bar_style": "danger",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_6159a33ceced49f7b277758e5a9da68b",
       "max": 500,
       "min": 0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_66db0b66881a4eab9ff4fde798875b82",
       "tabbable": null,
       "tooltip": null,
       "value": 220
      }
     },
     "03c18d38247b4c9e900e31eb58d20995": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_366b537cab274edfa8e8564b177936a7",
       "placeholder": "​",
       "style": "IPY_MODEL_4db728b689424b62b964ce2daaefb6aa",
       "tabbable": null,
       "tooltip": null,
       "value": " 5.83M/5.83M [00:00&lt;00:00, 7.96MB/s]"
      }
     },
     "059c6e6f6dee4a379a464e08667d063f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "0ec8eee7d02a4aa5b4ca9fc0e11c30f2": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_4c8486e1c3d64b9caa5effb8ef5600df",
        "IPY_MODEL_560f77767fa5483abf560cf27b1e663a",
        "IPY_MODEL_1b0c64d9ef9843ba9ac3af12fa3706fd"
       ],
       "layout": "IPY_MODEL_7f409e9055e247219fe2525fe40e1362",
       "tabbable": null,
       "tooltip": null
      }
     },
     "0ed5ecd5ef5046a290032b53103f7c15": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_30de741c0b174b068e1aceb0eee86bd4",
       "placeholder": "​",
       "style": "IPY_MODEL_e7c8fe7c44844abcb0392394be704f54",
       "tabbable": null,
       "tooltip": null,
       "value": " 247/247 [00:00&lt;00:00, 74.0kB/s]"
      }
     },
     "0f876d3e82164014b05b9aadccf3d723": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "0ff2aec84f654d0e8e5a27b6ad22ffbb": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "10533a17f04e4e97bf002000e023ba24": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "17d87bfa71c94b9bb1d8fa9e37785a91": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "1b0c64d9ef9843ba9ac3af12fa3706fd": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_3e31581e7c724ba09f64f8c709ed13cc",
       "placeholder": "​",
       "style": "IPY_MODEL_00572a5f67164f9ca1ae8a95b12fd681",
       "tabbable": null,
       "tooltip": null,
       "value": " 4.46k/4.46k [00:00&lt;00:00, 1.19MB/s]"
      }
     },
     "1d2a7e8d17ec4a319a5c14df82f38418": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "1e25139547464743a75d7a77f538261c": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "212e613491a042a0af83c32417be43c4": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_2a94777896b7481182bedccfe263d559",
       "placeholder": "​",
       "style": "IPY_MODEL_ca0990c35e8c4eda99a6fb6103c1be85",
       "tabbable": null,
       "tooltip": null,
       "value": "Fetching 3 files: 100%"
      }
     },
     "2217efc7c4af4e2fa429c90bd3989528": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_b34099c264f44efca431c9cf835539b5",
        "IPY_MODEL_0305b4003d0c4d8abf455e62d9eb3c14",
        "IPY_MODEL_a7cc1c93d79647f2b26f93b4f2484fef"
       ],
       "layout": "IPY_MODEL_c52991dcebac4b08819e034f5233684a",
       "tabbable": null,
       "tooltip": null
      }
     },
     "26532929f0e648089362837e5d295971": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_17d87bfa71c94b9bb1d8fa9e37785a91",
       "placeholder": "​",
       "style": "IPY_MODEL_29718161b24e4c3481b00ccc7574538b",
       "tabbable": null,
       "tooltip": null,
       "value": "adata.h5ad: 100%"
      }
     },
     "28260a063af149d2bde564d493578e53": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "ProgressView",
       "bar_style": "success",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_af9b97e54a7942e69fa6b6fa4dbd5edb",
       "max": 904829808,
       "min": 0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_362d910328ed41cc9b10616d80d180a7",
       "tabbable": null,
       "tooltip": null,
       "value": 904829808
      }
     },
     "28a7c0ba8dfb4ea288376a35c8ba4591": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "29718161b24e4c3481b00ccc7574538b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "2a94777896b7481182bedccfe263d559": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "2ad2311a7eae4c6ca0327ff8f85cc92b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "2e072b88b43e42d48cb28c6ff4a67705": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "3086a05a50844d16b651066b88df51ed": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "ProgressView",
       "bar_style": "success",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_0f876d3e82164014b05b9aadccf3d723",
       "max": 247,
       "min": 0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_4d483c5530be4e3ab29fd19958f6699f",
       "tabbable": null,
       "tooltip": null,
       "value": 247
      }
     },
     "30de741c0b174b068e1aceb0eee86bd4": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "3182e5c7b00349ac999c6c5db8a82fc3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "33e37fc3f2a846e08662d2794c6553a3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "361137da45fa426289bbb25980259f8f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "362d910328ed41cc9b10616d80d180a7": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
     },
     "366b537cab274edfa8e8564b177936a7": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "3e31581e7c724ba09f64f8c709ed13cc": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "438afda6c7604b1abe98a229e68bfe49": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "4c8486e1c3d64b9caa5effb8ef5600df": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_1e25139547464743a75d7a77f538261c",
       "placeholder": "​",
       "style": "IPY_MODEL_c9376e919e9d4f78a482d3aa1aa763d2",
       "tabbable": null,
       "tooltip": null,
       "value": "README.md: 100%"
      }
     },
     "4d483c5530be4e3ab29fd19958f6699f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
     },
     "4db728b689424b62b964ce2daaefb6aa": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "507770ebf0484b46b897dec6ad6562d8": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "557126644420401ea3c4dac599557ca8": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_b24c5804ff6e425ca7fa86b570abd718",
        "IPY_MODEL_cadcca1838524fd294418b27fe4c338e",
        "IPY_MODEL_03c18d38247b4c9e900e31eb58d20995"
       ],
       "layout": "IPY_MODEL_fc7fca017fdb4d93ab6b1f1b7c06e7d2",
       "tabbable": null,
       "tooltip": null
      }
     },
     "560f77767fa5483abf560cf27b1e663a": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "ProgressView",
       "bar_style": "success",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_2e072b88b43e42d48cb28c6ff4a67705",
       "max": 4462,
       "min": 0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_e9c3c79c43874a41a8de372a9babaef4",
       "tabbable": null,
       "tooltip": null,
       "value": 4462
      }
     },
     "6159a33ceced49f7b277758e5a9da68b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "66db0b66881a4eab9ff4fde798875b82": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
     },
     "6b986430efab47af9522784fe3165209": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "ProgressView",
       "bar_style": "success",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_507770ebf0484b46b897dec6ad6562d8",
       "max": 3,
       "min": 0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_92a614cf429e41749879f59f7b9560c4",
       "tabbable": null,
       "tooltip": null,
       "value": 3
      }
     },
     "6f19aff9e59d44e4ba98cfe08f99ee40": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "7f409e9055e247219fe2525fe40e1362": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "857b51bc547c472d84e0e3aca4cddcc1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "8f1620fcc8ac4a0a9bcc8cbc659efa4b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "92a614cf429e41749879f59f7b9560c4": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
     },
     "a7cc1c93d79647f2b26f93b4f2484fef": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_33e37fc3f2a846e08662d2794c6553a3",
       "placeholder": "​",
       "style": "IPY_MODEL_8f1620fcc8ac4a0a9bcc8cbc659efa4b",
       "tabbable": null,
       "tooltip": null,
       "value": " 220/500 [00:25&lt;00:31,  9.03it/s, v_num=1, train_loss_step=463, train_loss_epoch=470]"
      }
     },
     "af9b97e54a7942e69fa6b6fa4dbd5edb": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "b0180d5251ba49aa8f9bd2cc7a010960": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_212e613491a042a0af83c32417be43c4",
        "IPY_MODEL_6b986430efab47af9522784fe3165209",
        "IPY_MODEL_d65a401ca6c749ada62ce587e49f965c"
       ],
       "layout": "IPY_MODEL_3182e5c7b00349ac999c6c5db8a82fc3",
       "tabbable": null,
       "tooltip": null
      }
     },
     "b047d0356fc54c8f9154c04f2f57f3c6": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_361137da45fa426289bbb25980259f8f",
       "placeholder": "​",
       "style": "IPY_MODEL_6f19aff9e59d44e4ba98cfe08f99ee40",
       "tabbable": null,
       "tooltip": null,
       "value": "_scvi_required_metadata.json: 100%"
      }
     },
     "b24c5804ff6e425ca7fa86b570abd718": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_28a7c0ba8dfb4ea288376a35c8ba4591",
       "placeholder": "​",
       "style": "IPY_MODEL_f5ead2eae1de4cffb430e03d12e6dbf9",
       "tabbable": null,
       "tooltip": null,
       "value": "model.pt: 100%"
      }
     },
     "b34099c264f44efca431c9cf835539b5": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_438afda6c7604b1abe98a229e68bfe49",
       "placeholder": "​",
       "style": "IPY_MODEL_857b51bc547c472d84e0e3aca4cddcc1",
       "tabbable": null,
       "tooltip": null,
       "value": "Epoch 220/500:  44%"
      }
     },
     "c3a7e893f5d24080810fb8cfc32d9c81": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_b047d0356fc54c8f9154c04f2f57f3c6",
        "IPY_MODEL_3086a05a50844d16b651066b88df51ed",
        "IPY_MODEL_0ed5ecd5ef5046a290032b53103f7c15"
       ],
       "layout": "IPY_MODEL_1d2a7e8d17ec4a319a5c14df82f38418",
       "tabbable": null,
       "tooltip": null
      }
     },
     "c52991dcebac4b08819e034f5233684a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "c9376e919e9d4f78a482d3aa1aa763d2": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "ca0990c35e8c4eda99a6fb6103c1be85": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "cadcca1838524fd294418b27fe4c338e": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "ProgressView",
       "bar_style": "success",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_10533a17f04e4e97bf002000e023ba24",
       "max": 5827102,
       "min": 0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_d56e773aa35c4c71a01c59b7cd7ed4a9",
       "tabbable": null,
       "tooltip": null,
       "value": 5827102
      }
     },
     "cb8dbca79381453fb414a38fd1219513": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_0ff2aec84f654d0e8e5a27b6ad22ffbb",
       "placeholder": "​",
       "style": "IPY_MODEL_f08107a50a9e4cbb9e2cbed21bee5b82",
       "tabbable": null,
       "tooltip": null,
       "value": " 905M/905M [01:31&lt;00:00, 7.43MB/s]"
      }
     },
     "d56e773aa35c4c71a01c59b7cd7ed4a9": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
     },
     "d65a401ca6c749ada62ce587e49f965c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_059c6e6f6dee4a379a464e08667d063f",
       "placeholder": "​",
       "style": "IPY_MODEL_ffef1909d8ad4a818cde13f5c71baa35",
       "tabbable": null,
       "tooltip": null,
       "value": " 3/3 [01:33&lt;00:00, 54.92s/it]"
      }
     },
     "e7c8fe7c44844abcb0392394be704f54": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "e9c3c79c43874a41a8de372a9babaef4": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
     },
     "f08107a50a9e4cbb9e2cbed21bee5b82": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "f43ccead59564e52966bc7ccf007c4eb": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_26532929f0e648089362837e5d295971",
        "IPY_MODEL_28260a063af149d2bde564d493578e53",
        "IPY_MODEL_cb8dbca79381453fb414a38fd1219513"
       ],
       "layout": "IPY_MODEL_2ad2311a7eae4c6ca0327ff8f85cc92b",
       "tabbable": null,
       "tooltip": null
      }
     },
     "f5ead2eae1de4cffb430e03d12e6dbf9": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "fc7fca017fdb4d93ab6b1f1b7c06e7d2": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "ffef1909d8ad4a818cde13f5c71baa35": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}