{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modality translation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For modality translation task, you can use the unconditional model since its target is to generate out-of-distribution (ood) data. However, the Autoencoder needs a `size_factor` for the scRNA-seq decoder. Here, for unconditional model, we first run the leiden clustering algorithm to get the cluster id for each cell and use it to obtain the `size_factor`. For the ood data, we use the average of all clusters's size factor as its `size_factor`.\n",
    "\n",
    "Again, you need to first train the Autoencoder and Diffusion backbone. Note that you should set the `norm_type` in the encoder config yaml to `layer_norm` since it's an ood generation task. The `script/training_diffusion/ssh_scripts/multimodal_train_translation.sh` is an example bash file for Diffusion backbone training for this task. The training logic is the same for this bash file and multimodal_train.sh except the dataset and other hyperparameters. \n",
    "\n",
    "Then, you can translate the modality from one to another:\n",
    "```\n",
    "cd script/training_diffusion\n",
    "sh ssh_scripts/multimodal_translation.sh\n",
    "```\n",
    "You need to change the file path in both bash file to your local path. The `GEN_MODE` is the target modality (either \"rna\" or \"atac\" for current model).\n",
    "\n",
    "After that, the generated cells latent representations will be saved in the `OUT_DIR`. You can follow the below script to decode them back to original space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lep/miniconda3/envs/scmuldiff/lib/python3.8/site-packages/lightning_lite/__init__.py:29: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('lightning_lite')`.\n",
      "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
      "  __import__(\"pkg_resources\").declare_namespace(__name__)\n",
      "/home/lep/miniconda3/envs/scmuldiff/lib/python3.8/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: libtorch_cuda_cu.so: cannot open shared object file: No such file or directory\n",
      "  warn(f\"Failed to load image Python extension: {e}\")\n",
      "/home/lep/miniconda3/envs/scmuldiff/lib/python3.8/site-packages/pytorch_lightning/__init__.py:45: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pytorch_lightning')`.\n",
      "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
      "  __import__(\"pkg_resources\").declare_namespace(__name__)\n",
      "/home/lep/miniconda3/envs/scmuldiff/lib/python3.8/site-packages/umap/__init__.py:9: ImportWarning: Tensorflow not installed; ParametricUMAP will be unavailable\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '5'\n",
    "import scanpy as sc\n",
    "import anndata as ad\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from scdiffusionX.utils import MMD, LISI, random_forest, norm_total\n",
    "import pandas as pd\n",
    "from torch.distributions import Normal\n",
    "from scvi.distributions import NegativeBinomial\n",
    "from torch.distributions import Poisson, Bernoulli\n",
    "import muon as mu\n",
    "import yaml\n",
    "\n",
    "from scdiffusionX.Autoencoder.data.scrnaseq_loader import RNAseqLoader\n",
    "from scdiffusionX.Autoencoder.models.base.encoder_model import EncoderModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You need to load the training dataset to obtain the size factor. Here we combine the training and test dataset into one h5mu file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder_config = \"script/training_autoencoder/configs/encoder/encoder_multimodal_large.yaml\"\n",
    "dataset_path = \"/stor/lep/data/BABEL/all_w12878.h5mu\" \n",
    "covariate_keys = \"leiden\"\n",
    "num_class = 36  # since we use unconditional training, the num_class here represent the number of cluster\n",
    "ae_path = \"/stor/lep/workspace/multi_diffusion/CFGen/project_folder/experiments/train_autoencoder_babel_multimodal_layernorm_wd1e4/checkpoints/last.ckpt\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the test dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2004, 34861)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdata = mu.read_h5mu(dataset_path)\n",
    "mdata_rna = mdata['rna']\n",
    "real_cell = mdata_rna.X.toarray()[mdata_rna.obs['batch']=='test']  # this is the data for evaluation\n",
    "mdata_atac = mdata['atac']\n",
    "real_cell2 = mdata_atac.X.toarray()[mdata_atac.obs['batch']=='test']\n",
    "real_cell.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['0', '1', '10', '11', '12', '13', '14', '15', '16', '17', '18',\n",
       "        '19', '2', '20', '21', '22', '23', '24', '25', '26', '27', '28',\n",
       "        '29', '3', '30', '31', '32', '33', '34', '35', '4', '5', '6', '7',\n",
       "        '8', '9'], dtype=object),\n",
       " array([2901, 2004, 1311, 1287, 1218, 1095,  993,  959,  893,  837,  816,\n",
       "         787, 1914,  721,  578,  525,  465,  424,  424,  376,  373,  320,\n",
       "         281, 1759,  275,  216,  209,  172,  118,  107, 1661, 1520, 1512,\n",
       "        1460, 1379, 1378]))"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(mdata['rna'].obs['leiden'].values,return_counts=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the trained decoder and decode the latent representations back:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sys:1: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x71f26c092520>\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
     ]
    }
   ],
   "source": [
    "# get size factor for encoder. here we need all training data.\n",
    "dataset = RNAseqLoader(data_path=dataset_path,\n",
    "                            layer_key='X_counts',\n",
    "                            covariate_keys=[covariate_keys],\n",
    "                            subsample_frac=1, \n",
    "                            encoder_type='learnt_autoencoder',\n",
    "                            multimodal=True,\n",
    "                            is_binarized=True)\n",
    "\n",
    "size_factor_statistics = {\"mean\": dataset.log_size_factor_mu, \n",
    "                            \"sd\": dataset.log_size_factor_sd}\n",
    "\n",
    "def get_size_factor(type_index):\n",
    "    covariate_indices = {}\n",
    "    covariate_indices[covariate_keys] = type_index\n",
    "\n",
    "    mean_size_factor, sd_size_factor = size_factor_statistics[\"mean\"][covariate_keys], size_factor_statistics[\"sd\"][covariate_keys]\n",
    "    mean_size_factor, sd_size_factor = mean_size_factor[covariate_indices[covariate_keys]], sd_size_factor[covariate_indices[covariate_keys]]\n",
    "    size_factor_dist = Normal(loc=mean_size_factor, scale=sd_size_factor)\n",
    "    log_size_factor = size_factor_dist.sample().view(-1, 1)\n",
    "    size_factor = torch.exp(log_size_factor)\n",
    "\n",
    "    return {\"rna\": size_factor}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load encoder and decoder\n",
    "with open(encoder_config, 'r') as file:\n",
    "    yaml_content = file.read()\n",
    "autoencoder_args = yaml.safe_load(yaml_content)\n",
    "\n",
    "# Initialize encoder\n",
    "encoder_model = EncoderModel(in_dim={'atac': real_cell2.shape[1], 'rna': real_cell.shape[1]},\n",
    "                                    n_cat=num_class,\n",
    "                                    conditioning_covariate=covariate_keys, \n",
    "                                    encoder_type='learnt_autoencoder',\n",
    "                                    **autoencoder_args)\n",
    "\n",
    "# Load weights \n",
    "encoder_model.load_state_dict(torch.load(ae_path)[\"state_dict\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read generated latent representation and decode back to gene expression/atac seq. Change the data path to your own:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10020, 34861), (10020, 223897))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# only generate test set\n",
    "rna_seq = np.load('../outputs/samples_trans/babel_nocondi_layernorm/80w_atac2rna_test_x5_grad3_640/RNA_0.npz')['data']\n",
    "atac_seq = np.load('../outputs/samples_trans/babel_nocondi_layernorm/80w_rna2atac_test_x5_grad3_640/ATAC_0.npz')['data']\n",
    "type_index = np.load('../outputs/samples_trans/babel_nocondi_layernorm/80w_rna2atac_test_x5_grad3_640/RNA_0.npz')['label']\n",
    "\n",
    "# load norm factor for encoder\n",
    "npzfile = np.load('/'.join(ae_path.split('/')[:-2])+'/norm_factor.npz')\n",
    "rna_std = npzfile['rna_std']\n",
    "atac_std = npzfile['atac_std']\n",
    "z = {'rna':torch.tensor(rna_seq*rna_std).squeeze(1),'atac':torch.tensor(atac_seq*atac_std).squeeze(1)}     # open  layernorm\n",
    "\n",
    "# get size factor and decode\n",
    "size_factor = {}  # use the average of all factor in the training set as the factor of test set\n",
    "mean = torch.ones(rna_seq.shape[0])*size_factor_statistics[\"mean\"][covariate_keys][:-1].mean()  # average. [:-1] is to exclude the test set factor\n",
    "std = torch.ones(rna_seq.shape[0])*size_factor_statistics[\"sd\"][covariate_keys][:-1].mean()\n",
    "\n",
    "size_factor_dist = Normal(loc=mean, scale=std)\n",
    "log_size_factor_mean = size_factor_dist.sample().view(-1, 1)\n",
    "size_factor_mean = torch.exp(log_size_factor_mean)\n",
    "\n",
    "size_factor['rna'] = size_factor_mean  # note: if you include training set, you can use the real size factor for them instead of this average value\n",
    "\n",
    "mu_hat = encoder_model.decode(z, size_factor)\n",
    "\n",
    "sample = {}  # containing final samples \n",
    "for mod in mu_hat:\n",
    "    if mod==\"rna\":  \n",
    "        distr = NegativeBinomial(mu=mu_hat[mod], theta=torch.exp(encoder_model.theta))\n",
    "    else:  # if mod is atac\n",
    "        if not encoder_model.is_binarized:\n",
    "            distr = Poisson(rate=mu_hat[mod])\n",
    "        else:\n",
    "            distr = Bernoulli(probs=mu_hat[mod])\n",
    "    sample[mod] = distr.sample() \n",
    "\n",
    "reconstruct =  sample['rna'].detach().numpy()\n",
    "reconstruct2 = sample['atac'].detach().numpy()\n",
    "\n",
    "reconstruct.shape, reconstruct2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here one modality is translated, and the other is decode directly from the given modality latent representation. Note that we translated the same given data more than once (5 times by default), we can filter those outlier cells to reduce noise:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2004, 223897), (2004, 34861))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in only testset\n",
    "val_gen_cells = reconstruct\n",
    "real_size = (mdata_rna.obs['batch']=='test').sum()\n",
    "\n",
    "dis = np.linalg.norm((val_gen_cells-val_gen_cells.mean(axis=0)),axis=1)\n",
    "# find all the index where dis is smaller than the median value\n",
    "filtered_indices = np.where(dis<np.median(dis))[0]\n",
    "reconstruct = val_gen_cells[filtered_indices][:real_size]\n",
    "\n",
    "reconstruct2 = reconstruct2[filtered_indices][:real_size]\n",
    "reconstruct2.shape,reconstruct.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\ngen_split = []\\nreconstruct = []\\nreconstruct2 = []\\n\\nfor sp in ['val','test']:\\n    val_gen_cells = sample['rna'][mdata['rna'].obs['batch']==sp].detach().numpy()\\n    val_atac_cells = sample['atac'][mdata['atac'].obs['batch']==sp].detach().numpy()\\n    real_size = (mdata_rna.obs['batch']==sp).sum() \\n\\n    #cell filter\\n    dis = np.linalg.norm((val_gen_cells-val_gen_cells.mean(axis=0)),axis=1)\\n    # find all the index where dis is smaller than the median value\\n    filtered_indices = np.where(dis<np.median(dis))[0]\\n\\n    filtered_cells = val_gen_cells[filtered_indices][:real_size]\\n    filtered_cells_atac = val_atac_cells[filtered_indices][:real_size]\\n    reconstruct.append(filtered_cells)\\n    reconstruct2.append(filtered_cells_atac)\\n    gen_split+=[sp]*filtered_cells.shape[0]\\n\\ntrain_size = (mdata_rna.obs['batch']=='train').sum()\\nreconstruct.append(sample['rna'][mdata['rna'].obs['batch']=='train'].detach().numpy()[:train_size])\\nreconstruct2.append(sample['atac'][mdata['atac'].obs['batch']=='train'].detach().numpy()[:train_size])\\ngen_split+=['train']*sample['rna'][mdata['rna'].obs['batch']=='train'].detach().numpy()[:train_size].shape[0]\\nreconstruct = np.concatenate(reconstruct,axis=0)\\nreconstruct2 = np.concatenate(reconstruct2,axis=0)\\nreconstruct.shape,reconstruct2.shape\\n\""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter outlier cell to reduce noise \n",
    "# in both testset and validset (if you translate all train, valid and test set data)\n",
    "'''\n",
    "gen_split = []\n",
    "reconstruct = []\n",
    "reconstruct2 = []\n",
    "\n",
    "for sp in ['val','test']:\n",
    "    val_gen_cells = sample['rna'][mdata['rna'].obs['batch']==sp].detach().numpy()\n",
    "    val_atac_cells = sample['atac'][mdata['atac'].obs['batch']==sp].detach().numpy()\n",
    "    real_size = (mdata_rna.obs['batch']==sp).sum() \n",
    "\n",
    "    #cell filter\n",
    "    dis = np.linalg.norm((val_gen_cells-val_gen_cells.mean(axis=0)),axis=1)\n",
    "    # find all the index where dis is smaller than the median value\n",
    "    filtered_indices = np.where(dis<np.median(dis))[0]\n",
    "\n",
    "    filtered_cells = val_gen_cells[filtered_indices][:real_size]\n",
    "    filtered_cells_atac = val_atac_cells[filtered_indices][:real_size]\n",
    "    reconstruct.append(filtered_cells)\n",
    "    reconstruct2.append(filtered_cells_atac)\n",
    "    gen_split+=[sp]*filtered_cells.shape[0]\n",
    "\n",
    "train_size = (mdata_rna.obs['batch']=='train').sum()\n",
    "reconstruct.append(sample['rna'][mdata['rna'].obs['batch']=='train'].detach().numpy()[:train_size])\n",
    "reconstruct2.append(sample['atac'][mdata['atac'].obs['batch']=='train'].detach().numpy()[:train_size])\n",
    "gen_split+=['train']*sample['rna'][mdata['rna'].obs['batch']=='train'].detach().numpy()[:train_size].shape[0]\n",
    "reconstruct = np.concatenate(reconstruct,axis=0)\n",
    "reconstruct2 = np.concatenate(reconstruct2,axis=0)\n",
    "reconstruct.shape,reconstruct2.shape\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_cell = norm_total(real_cell)\n",
    "reconstruct = norm_total(reconstruct)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, you get the translated modality. You can evaluate them using the following script. Here we provide evaluation code for both modalities, you can choose the one you've translated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RNA\n",
      "0.28685427\n",
      "0.28685394\n",
      "spearman bulk= 0.9587163780543091\n",
      "pearson bulk= 0.9890751070159878\n",
      "\n",
      "ATAC\n",
      "0.0183027\n",
      "0.015778793\n",
      "spearman mean= 0.9328526040814712\n",
      "pearson mean= 0.9629978144727642\n"
     ]
    }
   ],
   "source": [
    "# For scRNA-seq\n",
    "print('RNA')\n",
    "print(real_cell.mean())\n",
    "print(reconstruct.mean())\n",
    "print('spearman bulk=',stats.spearmanr(real_cell.mean(axis=0), reconstruct.mean(axis=0)).correlation)\n",
    "print('pearson bulk=',np.corrcoef(real_cell.mean(axis=0), reconstruct.mean(axis=0))[0][1])\n",
    "\n",
    "# For scATAC-seq\n",
    "print('\\nATAC')\n",
    "print(real_cell2.mean())\n",
    "print(reconstruct2.mean())\n",
    "print('spearman mean=',stats.spearmanr(real_cell2.mean(axis=0), reconstruct2.mean(axis=0)).correlation)\n",
    "print('pearson mean=',np.corrcoef(real_cell2.mean(axis=0), reconstruct2.mean(axis=0))[0][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RNA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 21/21 [00:01<00:00, 11.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MMD =  tensor(0.2673)\n",
      "LISI =  0.5451907826825102\n",
      "\n",
      "ATAC\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 21/21 [00:01<00:00, 15.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MMD =  tensor(0.2004)\n",
      "LISI =  0.6503895890208606\n"
     ]
    }
   ],
   "source": [
    "# For scRNA-seq\n",
    "print('RNA')\n",
    "adata = np.concatenate((real_cell, reconstruct),axis=0)\n",
    "adata = ad.AnnData(adata, dtype=np.float32)\n",
    "adata.obs_names = [f\"true_Cell\" for i in range(real_cell.shape[0])]+[f\"gen_Cell\" for i in range(reconstruct.shape[0])]\n",
    "sc.tl.pca(adata, svd_solver='arpack')\n",
    "adata.obs['batch'] = pd.Categorical([f\"true_Cell\" for i in range(real_cell.shape[0])]+[f\"gen_Cell\" for i in range(reconstruct.shape[0])])\n",
    "print('MMD = ', MMD(adata))\n",
    "print('LISI = ', LISI(adata))\n",
    "\n",
    "# For scATAC-seq\n",
    "print('\\nATAC')\n",
    "adata = np.concatenate((real_cell2, reconstruct2),axis=0)\n",
    "adata = ad.AnnData(adata, dtype=np.float32)\n",
    "adata.obs_names = [f\"true_Cell\" for i in range(real_cell2.shape[0])]+[f\"gen_Cell\" for i in range(reconstruct2.shape[0])]\n",
    "sc.tl.pca(adata, svd_solver='arpack')\n",
    "adata.obs['batch'] = pd.Categorical([f\"true_Cell\" for i in range(real_cell2.shape[0])]+[f\"gen_Cell\" for i in range(reconstruct2.shape[0])])\n",
    "print('MMD = ', MMD(adata))\n",
    "print('LISI = ', LISI(adata))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x71f260d4a310>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot for scRNA-seq\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "plt.rcParams['ps.fonttype'] = 42\n",
    "lim = 50\n",
    "plt.ylim((-0,lim))\n",
    "plt.xlim((-0,lim))\n",
    "plt.ylabel('the gene expression of gen cells')\n",
    "plt.xlabel('the gene expression of real cells')\n",
    "plt.scatter(real_cell.mean(axis=0),reconstruct.mean(axis=0),s=9,color='#4B4B4B')\n",
    "plt.plot([0,lim],[0,lim],color='orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x71f260df70d0>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot for scATAC-seq\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "plt.rcParams['ps.fonttype'] = 42\n",
    "lim = 0.5\n",
    "plt.ylim((-0,lim))\n",
    "plt.xlim((-0,lim))\n",
    "plt.ylabel('the atac of gen cells')\n",
    "plt.xlabel('the atac of real cells')\n",
    "plt.scatter(real_cell2.mean(axis=0),reconstruct2.mean(axis=0),s=9,color='#4B4B4B')\n",
    "plt.plot([0,lim],[0,lim],color='orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gene perturbation is anthoer thing you can do once you done training the model. You can perturb the specific genes by set theirs expression value to zero (or a very high value) and then translate the scRNA-seq to scATAC-seq to see the effect of perturbations:\n",
    "```\n",
    "cd script/training_diffusion\n",
    "sh ssh_scripts/multimodal_perturb.sh\n",
    "```\n",
    "You need to change the file path in both bash file to your local path. The `GEN_MODE` is the target modality (either \"rna\" or \"atac\" for current model). The `target_gene` is the perturbed genes. In this task, you can use meta information to obtain the `size_factor` instead of leidem clustering. However, since it is still an ood generation task, we use unconditional generation mode. The `target_type` is the cell types of the target cells. The `pert` is to decide weather generate control group, True is perturb group and False is control group."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then evaluate the predicted perturbation scATAC-seq with the ground truth data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lep/miniconda3/envs/scmuldiff/lib/python3.8/site-packages/anndata/_io/specs/methods.py:584: OldFormatWarning: Element '/obs/__categories/gene' was written without encoding metadata.\n",
      "  categories = read_elem(categories_dset)\n",
      "/home/lep/miniconda3/envs/scmuldiff/lib/python3.8/site-packages/anndata/_io/specs/methods.py:587: OldFormatWarning: Element '/obs/gene' was written without encoding metadata.\n",
      "  read_elem(dataset), categories, ordered=ordered\n",
      "/home/lep/miniconda3/envs/scmuldiff/lib/python3.8/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/_index' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/lep/miniconda3/envs/scmuldiff/lib/python3.8/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/var/_index' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n"
     ]
    }
   ],
   "source": [
    "# load ground truth\n",
    "pert = sc.read_h5ad('/stor/lep/workspace/multi_diffusion/filtered.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# training data\n",
    "dataset_path = '/stor/lep/diffusion/multiome/openproblem_filtered4perturb.h5mu'\n",
    "mdata = mu.read_h5mu(dataset_path)\n",
    "mdata['atac'].var_names = [name.replace(':','-') for name in mdata['atac'].var_names]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You need to align the chromosome regions of the training data and the ground truth data first. Here we select the overlaped regions in the train and ground truth data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_region(region):\n",
    "    \"\"\"Parses chromosome region strings into tuples of (chromosome, start position, end position)\"\"\"\n",
    "    chr_id, start, end = region.split('-')\n",
    "    return chr_id, int(start), int(end)\n",
    "\n",
    "def calculate_overlap(region1, region2):\n",
    "    \"\"\"Calculate the overlap length of two chromosome regions\"\"\"\n",
    "    chr1, start1, end1 = region1\n",
    "    chr2, start2, end2 = region2\n",
    "    if chr1 == chr2:  # 只有染色体相同才计算重叠\n",
    "        overlap = max(0, min(end1, end2) - max(start1, start2))\n",
    "        return overlap\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Align chromosome regions\n",
    "results_all = []\n",
    "\n",
    "for region1 in mdata['atac'].var_names:\n",
    "    chr1, start1, end1 = parse_region(region1)\n",
    "    max_overlap = 0\n",
    "    best_match = None\n",
    "    for region2 in pert.var_names.values:\n",
    "        chr2, start2, end2 = parse_region(region2)\n",
    "        if chr2 != chr1:\n",
    "            continue\n",
    "        overlap = calculate_overlap((chr1, start1, end1), (chr2, start2, end2))\n",
    "        if overlap > max_overlap:\n",
    "            max_overlap = overlap\n",
    "            best_match = region2\n",
    "    if best_match is not None:\n",
    "        results_all.append(best_match)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocess the ground truth data, find the high variable region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sys:1: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x71f25393e520>\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 5816 × 5962\n",
       "    obs: 'gene'\n",
       "    uns: 'log1p'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the overlap chromosome regions between training data and ground truth data\n",
    "filtered_genes = pert.var.index.isin(np.unique(results_all))\n",
    "pert = pert[:,filtered_genes]\n",
    "sc.pp.normalize_total(pert, 1e4)\n",
    "sc.pp.log1p(pert)\n",
    "pert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2560x960 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate differential region\n",
    "sc.tl.rank_genes_groups(pert,'gene', method='wilcoxon',reference='CTRL',rankby_abs=True) \n",
    "sc.pl.rank_genes_groups(pert)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choose the perturb gene and calculate the change direction of differential peak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rec_arr = pert.uns['rank_genes_groups']['names']\n",
    "target_gene = 'CD3E,CD4'  #'CD3E' 'NFKB2' 'ZAP70' 'CD3E,CD4'\n",
    "de_gene = np.array([rec_arr[target_gene],])[0,:300]\n",
    "atac_pert = (pert[pert.obs['gene']==target_gene,de_gene].X.toarray()).astype(np.float32)\n",
    "atac_ctrl = (pert[pert.obs['gene']=='CTRL',de_gene].X.toarray()).astype(np.float32)\n",
    "direction_real = atac_pert.mean(0) - atac_ctrl.mean(0)\n",
    "direction_real.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_tick = [chr[:12] for chr in de_gene[:100]]\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.bar(x_tick, direction_real[:100], color='skyblue')\n",
    "plt.xticks([0,49,99],['top1','top50','top100'],rotation=90)\n",
    "plt.title('direction after perturb - real')\n",
    "plt.xlabel('top chr')\n",
    "plt.ylabel('change direction')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load AE and decode your predicted scATAC-tseq. Here, since the dataset is filtered, we use the small AE (encoder_multimodal_small)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder_config = \"encoder_multimodal_small\"\n",
    "dataset_path = \"/stor/lep/diffusion/multiome/openproblem_filtered4perturb.h5mu\" \n",
    "covariate_keys = \"cell_type\"\n",
    "num_class = 22\n",
    "ae_path = \"/stor/lep/workspace/multi_diffusion/CFGen/project_folder/experiments/train_autoencoder_openproblem_multimodal_perturb/checkpoints/last.ckpt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(69249, 2503)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdata = mu.read_h5mu(dataset_path)\n",
    "real_cell = mdata['rna'].X.toarray()\n",
    "real_cell2 = mdata['atac'].X.toarray()\n",
    "real_cell.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = RNAseqLoader(data_path=dataset_path,\n",
    "                            layer_key='X_counts',\n",
    "                            covariate_keys=[covariate_keys],\n",
    "                            subsample_frac=1, \n",
    "                            encoder_type='learnt_autoencoder',\n",
    "                            multimodal=True,\n",
    "                            is_binarized=True)\n",
    "\n",
    "size_factor_statistics = {\"mean\": dataset.log_size_factor_mu, \n",
    "                            \"sd\": dataset.log_size_factor_sd}\n",
    "\n",
    "def get_size_factor(type_index):\n",
    "    covariate_indices = {}\n",
    "    covariate_indices[covariate_keys] = type_index\n",
    "\n",
    "    mean_size_factor, sd_size_factor = size_factor_statistics[\"mean\"][covariate_keys], size_factor_statistics[\"sd\"][covariate_keys]\n",
    "    mean_size_factor, sd_size_factor = mean_size_factor[covariate_indices[covariate_keys]], sd_size_factor[covariate_indices[covariate_keys]]\n",
    "    size_factor_dist = Normal(loc=mean_size_factor, scale=sd_size_factor)\n",
    "    log_size_factor = size_factor_dist.sample().view(-1, 1)\n",
    "    size_factor = torch.exp(log_size_factor)\n",
    "\n",
    "    return {\"rna\": size_factor}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(f'{os.getcwd()}/../../Autoencoder/configs/encoder/{encoder_config}.yaml', 'r') as file:\n",
    "    yaml_content = file.read()\n",
    "autoencoder_args = yaml.safe_load(yaml_content)\n",
    "\n",
    "# Initialize encoder\n",
    "autoencoder_args['encoder_kwargs']['rna']['norm_type']='layernorm'\n",
    "autoencoder_args['encoder_kwargs']['atac']['norm_type']='layernorm'\n",
    "encoder_model = EncoderModel(in_dim={'atac': real_cell2.shape[1], 'rna': real_cell.shape[1]},\n",
    "                                    n_cat=num_class,\n",
    "                                    conditioning_covariate=covariate_keys, \n",
    "                                    encoder_type='learnt_autoencoder',\n",
    "                                    **autoencoder_args)\n",
    "\n",
    "# Load weights \n",
    "encoder_model.load_state_dict(torch.load(ae_path)[\"state_dict\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, change the path to your local path. Decode the perturb group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((564, 2503), (564, 6016))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load predicted perturb atac-seq\n",
    "rna_seq = np.load(f'../outputs/samples_trans/open_uncondi_layernorm_pert/80w_atac2rna_CTRL_{target_gene}_top5_x5/RNA_0.npz')['data']\n",
    "atac_seq = np.load(f'../outputs/samples_trans/open_uncondi_layernorm_pert/80w_rna2atac_{target_gene}_top5_x5/ATAC_0.npz')['data']\n",
    "type_index = np.load(f'../outputs/samples_trans/open_uncondi_layernorm_pert/80w_atac2rna_CTRL_{target_gene}_top5_x5/RNA_0.npz')['label']\n",
    "\n",
    "times = 5\n",
    "rna_seq = rna_seq.reshape(times,-1,rna_seq.shape[-1])[0]#.mean(0)\n",
    "# atac_seq = atac_seq.reshape(times,-1,atac_seq.shape[-1])[0]#.mean(0)\n",
    "type_index = type_index[:rna_seq.shape[0]]\n",
    "\n",
    "npzfile = np.load('/'.join(ae_path.split('/')[:-2])+'/norm_factor.npz')\n",
    "rna_std = npzfile['rna_std']\n",
    "atac_std = npzfile['atac_std']\n",
    "z = {'rna':torch.tensor(rna_seq*rna_std).squeeze(1),'atac':torch.tensor(atac_seq*atac_std).squeeze(1)}     # open  layernorm\n",
    "\n",
    "size_factor = get_size_factor(torch.tensor(type_index,dtype=torch.int))  # use ground truth factor\n",
    "mu_hat = encoder_model.decode(z, size_factor)\n",
    "\n",
    "# mu_hat['rna'] = mu_hat['rna'].reshape(times,-1,mu_hat['rna'].shape[-1]).mean(0)\n",
    "mu_hat['atac'] = mu_hat['atac'].reshape(times,-1,mu_hat['atac'].shape[-1]).mean(0)\n",
    "\n",
    "sample = {}  # containing final samples \n",
    "for mod in mu_hat:\n",
    "    if mod==\"rna\":  \n",
    "        # if not self.covariate_specific_theta:\n",
    "        distr = NegativeBinomial(mu=mu_hat[mod], theta=torch.exp(encoder_model.theta))\n",
    "    else:  # if mod is atac\n",
    "        distr = Bernoulli(probs=mu_hat[mod])\n",
    "    sample[mod] = distr.sample() \n",
    "\n",
    "reconstruct_pert = mu_hat['rna'].detach().numpy()\n",
    "reconstruct2_pert = mu_hat['atac'].detach().numpy()\n",
    "\n",
    "reconstruct_pert.shape, reconstruct2_pert.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decode the control group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((564, 2503), (564, 6016))"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# also load predicted control atac-seq\n",
    "rna_seq = np.load(f'../outputs/samples_trans/open_uncondi_layernorm_pert/80w_atac2rna_CTRL_{target_gene}_top5_x5/RNA_0.npz')['data']#[:3064]\n",
    "atac_seq = np.load(f'../outputs/samples_trans/open_uncondi_layernorm_pert/80w_rna2atac_CTRL_{target_gene}_top5_x5/ATAC_0.npz')['data']#[:3064]\n",
    "type_index = np.load(f'../outputs/samples_trans/open_uncondi_layernorm_pert/80w_atac2rna_CTRL_{target_gene}_top5_x5/RNA_0.npz')['label']\n",
    "\n",
    "times = 5\n",
    "rna_seq = rna_seq.reshape(times,-1,rna_seq.shape[-1])[0]#.mean(0)\n",
    "# atac_seq = atac_seq.reshape(times,-1,atac_seq.shape[-1])[0]#.mean(0)\n",
    "type_index = type_index[:rna_seq.shape[0]]\n",
    "\n",
    "npzfile = np.load('/'.join(ae_path.split('/')[:-2])+'/norm_factor.npz')\n",
    "rna_std = npzfile['rna_std']\n",
    "atac_std = npzfile['atac_std']\n",
    "z = {'rna':torch.tensor(rna_seq*rna_std).squeeze(1),'atac':torch.tensor(atac_seq*atac_std).squeeze(1)}     # open  layernorm\n",
    "\n",
    "size_factor = get_size_factor(torch.tensor(type_index,dtype=torch.int))  # cell type average\n",
    "\n",
    "mu_hat = encoder_model.decode(z, size_factor)\n",
    "\n",
    "# mu_hat['rna'] = mu_hat['rna'].reshape(times,-1,mu_hat['rna'].shape[-1]).mean(0)\n",
    "mu_hat['atac'] = mu_hat['atac'].reshape(times,-1,mu_hat['atac'].shape[-1]).mean(0)\n",
    "\n",
    "sample = {}  # containing final samples \n",
    "for mod in mu_hat:\n",
    "    if mod==\"rna\":  \n",
    "        # if not self.covariate_specific_theta:\n",
    "        distr = NegativeBinomial(mu=mu_hat[mod], theta=torch.exp(encoder_model.theta))\n",
    "    else:  # if mod is atac\n",
    "        distr = Bernoulli(probs=mu_hat[mod])\n",
    "    sample[mod] = distr.sample() \n",
    "\n",
    "reconstruct_ctrl = mu_hat['rna'].detach().numpy()\n",
    "reconstruct2_ctrl = mu_hat['atac'].detach().numpy()\n",
    "\n",
    "reconstruct_ctrl.shape, reconstruct2_ctrl.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize\n",
    "reconstruct2_pert=norm_total(reconstruct2_pert, 1e4)\n",
    "reconstruct2_ctrl=norm_total(reconstruct2_ctrl, 1e4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To evaluate the predict perturbation, we need to find the corresponding region align with the differential region in the ground truth data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "\n",
    "for region1 in de_gene:\n",
    "    chr1, start1, end1 = parse_region(region1)\n",
    "    max_overlap = 0\n",
    "    best_match = None\n",
    "    for region2 in mdata['atac'].var_names:\n",
    "        chr2, start2, end2 = parse_region(region2)\n",
    "        if chr2 != chr1:\n",
    "            continue\n",
    "        overlap = calculate_overlap((chr1, start1, end1), (chr2, start2, end2))\n",
    "        if overlap > max_overlap:\n",
    "            max_overlap = overlap\n",
    "            best_match = region2\n",
    "    results.append((region1, best_match, max_overlap))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate change directions of these differential regions and the accuracy to the ground truth directions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_pert = reconstruct2_pert\n",
    "pred_ctrl = reconstruct2_ctrl\n",
    "direction_pred_pred = pred_pert.mean(0) - pred_ctrl.mean(0)\n",
    "direction = []\n",
    "for item in results:\n",
    "    if item[1] is not None:\n",
    "        direction.append(direction_pred_pred[np.where(mdata['atac'].var_names == item[1])[0][0]])\n",
    "    else:\n",
    "        direction.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "top 20 total acc:  0.8\n",
      "positive acc:  1.0\n",
      "negative acc:  0.7894736842105263\n",
      "top 20 total acc random:  0.4\n",
      "positive acc random:  0.0\n",
      "negative acc random:  0.42105263157894735\n",
      "top 50 total acc:  0.82\n",
      "positive acc:  1.0\n",
      "negative acc:  0.8125\n",
      "top 50 total acc random:  0.4\n",
      "positive acc random:  0.5\n",
      "negative acc random:  0.3958333333333333\n",
      "top 100 total acc:  0.77\n",
      "positive acc:  1.0\n",
      "negative acc:  0.7578947368421053\n",
      "top 100 total acc random:  0.45\n",
      "positive acc random:  0.6\n",
      "negative acc random:  0.4421052631578947\n",
      "top 200 total acc:  0.73\n",
      "positive acc:  0.8181818181818182\n",
      "negative acc:  0.7248677248677249\n",
      "top 200 total acc random:  0.48\n",
      "positive acc random:  0.36363636363636365\n",
      "negative acc random:  0.48677248677248675\n",
      "top 300 total acc:  0.6833333333333333\n",
      "positive acc:  0.7647058823529411\n",
      "negative acc:  0.6784452296819788\n",
      "top 300 total acc random:  0.49\n",
      "positive acc random:  0.47058823529411764\n",
      "negative acc random:  0.4911660777385159\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "direction_random = np.random.randint(0, 2, size=300)\n",
    "\n",
    "total_acc = []\n",
    "posi_acc = []\n",
    "nega_acc = []\n",
    "total_acc_random = []\n",
    "posi_acc_random = []\n",
    "nega_acc_random = []\n",
    "for num in [20,50,100,200,300]:\n",
    "    accuracy = accuracy_score(np.array(direction_real[:num])>0, np.array(direction[:num])>0)\n",
    "    total_acc.append(accuracy)\n",
    "    print(f'top {num} total acc: ', accuracy)\n",
    "    index_posi = np.where(np.array(direction_real[:num])>0)[0]\n",
    "    accuracy = accuracy_score(np.array(direction_real[:num])[index_posi]>0, np.array(direction[:num])[index_posi]>0)\n",
    "    posi_acc.append(accuracy)\n",
    "    print('positive acc: ', accuracy)\n",
    "    index_posi = np.where(np.array(direction_real[:num])<0)[0]\n",
    "    accuracy = accuracy_score(np.array(direction_real[:num])[index_posi]>0, np.array(direction[:num])[index_posi]>0)\n",
    "    nega_acc.append(accuracy)\n",
    "    print('negative acc: ', accuracy)    \n",
    "\n",
    "    # accuracy = accuracy_score(np.array(direction_real[:num])>0, np.array(direction_random[:num])>0)\n",
    "    # total_acc_random.append(accuracy)\n",
    "    # print(f'top {num} total acc random: ', accuracy)\n",
    "    # index_posi = np.where(np.array(direction_real[:num])>0)[0]\n",
    "    # accuracy = accuracy_score(np.array(direction_real[:num])[index_posi]>0, np.array(direction_random[:num])[index_posi]>0)\n",
    "    # posi_acc_random.append(accuracy)\n",
    "    # print('positive acc random: ', accuracy)\n",
    "    # index_posi = np.where(np.array(direction_real[:num])<0)[0]\n",
    "    # accuracy = accuracy_score(np.array(direction_real[:num])[index_posi]>0, np.array(direction_random[:num])[index_posi]>0)\n",
    "    # nega_acc_random.append(accuracy)\n",
    "    # print('negative acc random: ', accuracy)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "plt.rcParams['ps.fonttype'] = 42\n",
    "plt.figure(figsize=(10, 6))  # 设置图形大小\n",
    "x = np.linspace(0, 4, 5)\n",
    "plt.plot(x, total_acc, label='total accuracy') \n",
    "plt.plot(x, posi_acc, label='positive accuracy') \n",
    "plt.plot(x, nega_acc, label='negative accuracy') \n",
    "\n",
    "\n",
    "# 设置图形属性\n",
    "plt.title(f'random direction accuracy - {target_gene}')        # 设置标题\n",
    "plt.xlabel('top k peak')                    # 设置 x 轴标签\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.xticks(x, ['20','50','100','200','300'])\n",
    "# plt.yticks([])\n",
    "# plt.ylabel('Y-axis')                    # 设置 y 轴标签\n",
    "# plt.ylim(0.2, 0.65)                       # 设置 y 轴范围\n",
    "plt.axhline(0, color='black', linewidth=0.8, linestyle='--')  # 添加水平参考线\n",
    "plt.axvline(0, color='black', linewidth=0.8, linestyle='--')  # 添加垂直参考线\n",
    "plt.grid()                              # 添加网格\n",
    "# plt.legend()                            # 显示图例\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "test",
   "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": "3.8.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}