{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "\n",
    "import scanpy as sc\n",
    "import anndata as ad\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import anndata2ri\n",
    "import gdown\n",
    "import scipy\n",
    "import scipy.io\n",
    "from rpy2.robjects import r\n",
    "\n",
    "anndata2ri.activate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "suppressPackageStartupMessages({\n",
    "    library(SingleCellExperiment)\n",
    "    library(Seurat)\n",
    "    library(SeuratDisk)\n",
    "    library(Signac)\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Expressions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = sc.read('../data/10xpbmc10k-2020/expressions.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_ = ad.AnnData(adata.layers['count'])\n",
    "adata_.obs_names = adata.obs_names\n",
    "adata_.var_names = adata.var_names\n",
    "adata_.obs['cell_type'] = adata.obs['cell_type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "An object of class Seurat \n",
      "4000 features across 10000 samples within 1 assay \n",
      "Active assay: RNA (4000 features, 0 variable features)\n"
     ]
    }
   ],
   "source": [
    "%%R -i adata_\n",
    "rna = as.Seurat(adata_, counts='X', data=NULL)\n",
    "rna"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Peaks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = sc.read('../data/10xpbmc10k-2020/peaks.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_ = ad.AnnData(adata.layers['binary'])\n",
    "adata_.obs_names = adata.obs_names\n",
    "adata_.var_names = adata.var_names\n",
    "adata_.obs['cell_type'] = adata.obs['cell_type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "An object of class Seurat \n",
      "40086 features across 10000 samples within 1 assay \n",
      "Active assay: RNA (40086 features, 0 variable features)\n"
     ]
    }
   ],
   "source": [
    "%%R -i adata_\n",
    "peaks = as.Seurat(adata_, counts='X', data=NULL)\n",
    "peaks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run Seurat v4 WNN\n",
    "Following https://satijalab.org/seurat/v4.0/weighted_nearest_neighbor_analysis.html."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Preprocessing RNA...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Warning:\n",
      "R[write to console]:  The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric\n",
      "To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'\n",
      "This message will be shown once per session\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Preprocessing ATAC...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Calculating cell-specific modality weights\n",
      "\n",
      "R[write to console]: Finding 20 nearest neighbors for each modality.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |                                                  | 0 % ~calculating   |+++++++++++++++++++++++++                         | 50% ~04s           |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Calculating kernel bandwidths\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |                                                  | 0 % ~calculating   |+++++++++++++++++++++++++                         | 50% ~00s           |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Finding multimodal neighbors\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |                                                  | 0 % ~calculating   |+++++++++++++++++++++++++                         | 50% ~11s           |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=25s  \n",
      "  |                                                  | 0 % ~calculating   |+++++++++++++++++++++++++                         | 50% ~01s           |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Constructing multimodal KNN graph\n",
      "\n",
      "R[write to console]: Constructing multimodal SNN graph\n",
      "\n",
      "R[write to console]: Computing sPCA transformation\n",
      "\n",
      "R[write to console]: Creating h5Seurat file for version 3.1.5.9900\n",
      "\n",
      "R[write to console]: Adding counts for RNA\n",
      "\n",
      "R[write to console]: Adding data for RNA\n",
      "\n",
      "R[write to console]: No variable features found for RNA\n",
      "\n",
      "R[write to console]: No feature-level metadata found for RNA\n",
      "\n",
      "R[write to console]: Adding counts for ATAC\n",
      "\n",
      "R[write to console]: Adding data for ATAC\n",
      "\n",
      "R[write to console]: Adding variable features for ATAC\n",
      "\n",
      "R[write to console]: Adding feature-level metadata for ATAC\n",
      "\n",
      "R[write to console]: Adding counts for SCT\n",
      "\n",
      "R[write to console]: Adding data for SCT\n",
      "\n",
      "R[write to console]: Adding scale.data for SCT\n",
      "\n",
      "R[write to console]: Adding variable features for SCT\n",
      "\n",
      "R[write to console]: No feature-level metadata found for SCT\n",
      "\n",
      "R[write to console]: Writing out SCTModel.list for SCT\n",
      "\n",
      "R[write to console]: Adding cell embeddings for pca\n",
      "\n",
      "R[write to console]: Adding loadings for pca\n",
      "\n",
      "R[write to console]: No projected loadings for pca\n",
      "\n",
      "R[write to console]: Adding standard deviations for pca\n",
      "\n",
      "R[write to console]: No JackStraw data for pca\n",
      "\n",
      "R[write to console]: Adding cell embeddings for umap.rna\n",
      "\n",
      "R[write to console]: No loadings for umap.rna\n",
      "\n",
      "R[write to console]: No projected loadings for umap.rna\n",
      "\n",
      "R[write to console]: No standard deviations for umap.rna\n",
      "\n",
      "R[write to console]: No JackStraw data for umap.rna\n",
      "\n",
      "R[write to console]: Adding cell embeddings for lsi\n",
      "\n",
      "R[write to console]: Adding loadings for lsi\n",
      "\n",
      "R[write to console]: No projected loadings for lsi\n",
      "\n",
      "R[write to console]: Adding standard deviations for lsi\n",
      "\n",
      "R[write to console]: No JackStraw data for lsi\n",
      "\n",
      "R[write to console]: Adding cell embeddings for umap.atac\n",
      "\n",
      "R[write to console]: No loadings for umap.atac\n",
      "\n",
      "R[write to console]: No projected loadings for umap.atac\n",
      "\n",
      "R[write to console]: No standard deviations for umap.atac\n",
      "\n",
      "R[write to console]: No JackStraw data for umap.atac\n",
      "\n",
      "R[write to console]: Adding cell embeddings for spca\n",
      "\n",
      "R[write to console]: Adding loadings for spca\n",
      "\n",
      "R[write to console]: No projected loadings for spca\n",
      "\n",
      "R[write to console]: Adding standard deviations for spca\n",
      "\n",
      "R[write to console]: No JackStraw data for spca\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "bm <- rna\n",
    "bm[[\"ATAC\"]] <- CreateAssayObject(counts = peaks@assays$RNA@counts)\n",
    "\n",
    "rm(rna)\n",
    "rm(peaks)\n",
    "\n",
    "print('Preprocessing RNA...')\n",
    "DefaultAssay(bm) <- \"RNA\"\n",
    "bm <- SCTransform(bm, variable.features.n = 4000, verbose=FALSE)\n",
    "bm <- RunPCA(bm, verbose=FALSE)\n",
    "bm <- RunUMAP(bm, dims = 1:50, reduction.name = 'umap.rna', reduction.key = 'rnaUMAP_', verbose=FALSE)\n",
    "\n",
    "print('Preprocessing ATAC...')\n",
    "DefaultAssay(bm) <- \"ATAC\"\n",
    "bm <- RunTFIDF(bm, verbose=FALSE)\n",
    "bm <- FindTopFeatures(bm, min.cutoff = 'q0', verbose=FALSE)\n",
    "bm <- RunSVD(bm, verbose=FALSE)\n",
    "bm <- RunUMAP(bm, reduction = 'lsi', dims = 2:50, reduction.name = \"umap.atac\", \n",
    "              reduction.key = \"atacUMAP_\", verbose=FALSE)\n",
    "\n",
    "bm <- FindMultiModalNeighbors(bm, reduction.list = list(\"pca\", \"lsi\"), \n",
    "                              dims.list = list(1:50, 2:50), modality.weight.name = \"RNA.weight\")\n",
    "\n",
    "bm <- RunSPCA(bm, assay = 'SCT', graph = 'wsnn', npcs = 20)\n",
    "\n",
    "SaveH5Seurat(bm, '10xpbmc10k-seurat.h5seurat', overwrite = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Validating h5Seurat file\n",
      "\n",
      "R[write to console]: Adding scale.data from SCT as X\n",
      "\n",
      "R[write to console]: Adding data from SCT as raw\n",
      "\n",
      "R[write to console]: Transfering meta.data to obs\n",
      "\n",
      "R[write to console]: Adding dimensional reduction information for pca\n",
      "\n",
      "R[write to console]: Adding feature loadings for pca\n",
      "\n",
      "R[write to console]: Adding dimensional reduction information for spca\n",
      "\n",
      "R[write to console]: Adding feature loadings for spca\n",
      "\n",
      "R[write to console]: Adding dimensional reduction information for umap.rna\n",
      "\n",
      "R[write to console]: Adding dimensional reduction information for umap.atac (global)\n",
      "\n",
      "R[write to console]: Adding wsnn as neighbors\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "# wsnn graph is stored in SCT assay\n",
    "Convert(\"10xpbmc10k-seurat.h5seurat\", assay=\"SCT\", \"10xpbmc10k-seurat.h5ad\", overwrite=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 10000 × 3876\n",
       "    obs: 'cell_type', 'nCount_ATAC', 'nFeature_ATAC', 'nCount_SCT', 'nFeature_SCT', 'SCT.weight', 'ATAC.weight'\n",
       "    var: 'features'\n",
       "    uns: 'neighbors'\n",
       "    obsm: 'X_pca', 'X_spca', 'X_umap.atac', 'X_umap.rna'\n",
       "    varm: 'PCs', 'SPCA'\n",
       "    obsp: 'distances'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrated = sc.read('10xpbmc10k-seurat.h5ad')\n",
    "integrated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent = sc.AnnData(integrated.obsm['X_spca'])\n",
    "latent.obs = integrated.obs\n",
    "latent.obsp['connectivities'] = integrated.uns['neighbors']['distances']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent.write('../data/integrated/seurat/10xpbmc10k-seurat.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rm: cannot remove '10xpbmc10k2020-expressions.h5ad': No such file or directory\n",
      "rm: cannot remove '10xpbmc10k2020-proteins.h5ad': No such file or directory\n"
     ]
    }
   ],
   "source": [
    "!rm '10xpbmc10k2020-expressions.h5ad'\n",
    "!rm '10xpbmc10k2020-proteins.h5ad'\n",
    "!rm '10xpbmc10k-seurat.h5seurat'\n",
    "!rm '10xpbmc10k-seurat.h5ad'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R version 4.0.3 (2020-10-10)\n",
      "Platform: x86_64-conda-linux-gnu (64-bit)\n",
      "Running under: CentOS Linux 7 (Core)\n",
      "\n",
      "Matrix products: default\n",
      "BLAS/LAPACK: /mnt/home/icb/anastasia.litinetskaya/miniconda3/envs/signac/lib/libopenblasp-r0.3.10.so\n",
      "\n",
      "locale:\n",
      " [1] LC_CTYPE=C                 LC_NUMERIC=C              \n",
      " [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n",
      " [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n",
      " [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n",
      " [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n",
      "[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n",
      "\n",
      "attached base packages:\n",
      " [1] parallel  stats4    tools     stats     graphics  grDevices utils    \n",
      " [8] datasets  methods   base     \n",
      "\n",
      "other attached packages:\n",
      " [1] Matrix_1.3-2                Signac_1.1.1               \n",
      " [3] SeuratDisk_0.0.0.9018       SeuratObject_4.0.0         \n",
      " [5] Seurat_4.0.0                SingleCellExperiment_1.12.0\n",
      " [7] SummarizedExperiment_1.20.0 Biobase_2.50.0             \n",
      " [9] GenomicRanges_1.42.0        GenomeInfoDb_1.26.0        \n",
      "[11] IRanges_2.24.0              S4Vectors_0.28.0           \n",
      "[13] BiocGenerics_0.36.0         MatrixGenerics_1.2.0       \n",
      "[15] matrixStats_0.58.0         \n",
      "\n",
      "loaded via a namespace (and not attached):\n",
      "  [1] utf8_1.1.4               reticulate_1.18          tidyselect_1.1.0        \n",
      "  [4] RSQLite_2.2.3            AnnotationDbi_1.52.0     htmlwidgets_1.5.3       \n",
      "  [7] grid_4.0.3               BiocParallel_1.24.0      Rtsne_0.15              \n",
      " [10] munsell_0.5.0            codetools_0.2-18         ica_1.0-2               \n",
      " [13] future_1.21.0            miniUI_0.1.1.1           withr_2.4.1             \n",
      " [16] colorspace_2.0-0         OrganismDbi_1.32.0       knitr_1.31              \n",
      " [19] rstudioapi_0.13          ROCR_1.0-11              tensor_1.5              \n",
      " [22] listenv_0.8.0            GenomeInfoDbData_1.2.4   polyclip_1.10-0         \n",
      " [25] farver_2.1.0             bit64_4.0.5              parallelly_1.23.0       \n",
      " [28] vctrs_0.3.6              generics_0.1.0           xfun_0.20               \n",
      " [31] biovizBase_1.38.0        BiocFileCache_1.14.0     lsa_0.73.2              \n",
      " [34] ggseqlogo_0.1            R6_2.5.0                 hdf5r_1.3.3             \n",
      " [37] AnnotationFilter_1.14.0  reshape_0.8.8            bitops_1.0-6            \n",
      " [40] spatstat.utils_2.0-0     cachem_1.0.4             DelayedArray_0.16.0     \n",
      " [43] assertthat_0.2.1         promises_1.2.0.1         scales_1.1.1            \n",
      " [46] nnet_7.3-15              gtable_0.3.0             globals_0.14.0          \n",
      " [49] goftest_1.2-2            ggbio_1.38.0             ensembldb_2.14.0        \n",
      " [52] rlang_0.4.10             RcppRoll_0.3.0           splines_4.0.3           \n",
      " [55] rtracklayer_1.50.0       lazyeval_0.2.2           dichromat_2.0-0         \n",
      " [58] checkmate_2.0.0          BiocManager_1.30.10      reshape2_1.4.4          \n",
      " [61] abind_1.4-5              GenomicFeatures_1.42.0   backports_1.2.1         \n",
      " [64] httpuv_1.5.5             Hmisc_4.5-0              RBGL_1.66.0             \n",
      " [67] ggplot2_3.3.3            ellipsis_0.3.1           RColorBrewer_1.1-2      \n",
      " [70] ggridges_0.5.3           Rcpp_1.0.6               plyr_1.8.6              \n",
      " [73] base64enc_0.1-3          progress_1.2.2           zlibbioc_1.36.0         \n",
      " [76] purrr_0.3.4              RCurl_1.98-1.2           prettyunits_1.1.1       \n",
      " [79] rpart_4.1-15             openssl_1.4.3            deldir_0.2-10           \n",
      " [82] pbapply_1.4-3            cowplot_1.1.1            zoo_1.8-8               \n",
      " [85] ggrepel_0.9.1            cluster_2.1.1            magrittr_2.0.1          \n",
      " [88] RSpectra_0.16-0          data.table_1.14.0        scattermore_0.7         \n",
      " [91] lmtest_0.9-38            RANN_2.6.1               SnowballC_0.7.0         \n",
      " [94] ProtGenerics_1.22.0      fitdistrplus_1.1-3       hms_1.0.0               \n",
      " [97] patchwork_1.1.1          mime_0.10                xtable_1.8-4            \n",
      "[100] XML_3.99-0.5             jpeg_0.1-8.1             gridExtra_2.3           \n",
      "[103] compiler_4.0.3           biomaRt_2.46.0           tibble_3.1.0            \n",
      "[106] KernSmooth_2.23-18       crayon_1.4.1             htmltools_0.5.1.1       \n",
      "[109] mgcv_1.8-34              later_1.1.0.1            Formula_1.2-4           \n",
      "[112] tidyr_1.1.3              DBI_1.1.1                tweenr_1.0.1            \n",
      "[115] dbplyr_2.1.0             MASS_7.3-53.1            rappdirs_0.3.3          \n",
      "[118] cli_2.3.1                igraph_1.2.6             pkgconfig_2.0.3         \n",
      "[121] GenomicAlignments_1.26.0 foreign_0.8-81           plotly_4.9.3            \n",
      "[124] xml2_1.3.2               XVector_0.30.0           stringr_1.4.0           \n",
      "[127] VariantAnnotation_1.36.0 digest_0.6.27            sctransform_0.3.2       \n",
      "[130] RcppAnnoy_0.0.18         graph_1.68.0             spatstat.data_2.0-0     \n",
      "[133] Biostrings_2.58.0        fastmatch_1.1-0          leiden_0.3.7            \n",
      "[136] htmlTable_2.1.0          uwot_0.1.10              curl_4.3                \n",
      "[139] shiny_1.6.0              Rsamtools_2.6.0          lifecycle_1.0.0         \n",
      "[142] nlme_3.1-152             jsonlite_1.7.2           viridisLite_0.3.0       \n",
      "[145] askpass_1.1              BSgenome_1.58.0          fansi_0.4.2             \n",
      "[148] pillar_1.5.0             GGally_2.1.0             lattice_0.20-41         \n",
      "[151] fastmap_1.1.0            httr_1.4.2               survival_3.2-7          \n",
      "[154] glue_1.4.2               spatstat_1.64-1          png_0.1-7               \n",
      "[157] bit_4.0.4                ggforce_0.3.2            stringi_1.5.3           \n",
      "[160] blob_1.2.1               latticeExtra_0.6-29      memoise_2.0.0           \n",
      "[163] dplyr_1.0.4              irlba_2.3.3              future.apply_1.7.0      \n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "sessionInfo()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.neighbors(latent)\n",
    "sc.tl.umap(latent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(latent.obs_names != adata.obs_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent.obs['cell_type'] = adata.obs['cell_type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(latent, color='cell_type', frameon=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}