{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84cd686a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "output_dir = \"../../output/sr_benchmark\"\n",
    "os.makedirs(output_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f7de79c",
   "metadata": {},
   "source": [
    "# Gene panel imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40812b60",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "\n",
    "data_dir = \"./output/imputation_benchmark/metric_result\"\n",
    "save_dir = f\"{data_dir}/mean\"\n",
    "os.makedirs(save_dir, exist_ok=True)\n",
    "\n",
    "parts = [\"part1\", \"part2\", \"part3\"]\n",
    "metrics = [\"PCC\", \"SSIM\", \"MSE\"]\n",
    "\n",
    "for part in tqdm(parts, desc=\"Part\"):\n",
    "    merge_df = pd.DataFrame()\n",
    "    for metric in tqdm(metrics, desc=\"Metric\"):\n",
    "        df = pd.read_csv(f\"{data_dir}/{part}_{metric}.csv\")\n",
    "        df = df.groupby(\"Method\")[\"Value\"].mean()\n",
    "        merge_df = pd.concat([merge_df, df], axis=1)\n",
    "    merge_df.columns = metrics\n",
    "    merge_df = merge_df.loc[[\"Tangram\", \"gimVI\", \"REVISE\", \"REVISE_high_certainty\"]]\n",
    "    merge_df.to_csv(f\"{save_dir}/{part}.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abc8814c",
   "metadata": {},
   "outputs": [],
   "source": [
    "source_path = \"../REVISE/results\"\n",
    "task = \"imputation\"\n",
    "patient_id = \"P2CRC\"\n",
    "batch_num = 2\n",
    "\n",
    "result_path = f\"{source_path}/{task}/{patient_id}\"\n",
    "data_path = f\"../REVISE/data/spot/{patient_id}\"\n",
    "\n",
    "REVISE_result_path = f\"{source_path}/spot/{patient_id}\"\n",
    "\n",
    "methods = [\"revise\", \"tesla\", \"istar\", ]\n",
    "methods = [1,3,2,4]\n",
    "\n",
    "parts = [\"part3\", \"part1\", \"part2\"]\n",
    "metrics = [\"PCC\", \"SSIM\", \"MSE\"]\n",
    "spot_sizes = [50, 100, 150, 200]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bf3c74c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_type = \"HVG\"\n",
    "gene_num = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a47ffc33",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import scanpy as sc\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def get_merge_df(result_path, data_path, part, metric, spot_sizes, methods):\n",
    "\n",
    "    merge_df = pd.DataFrame()\n",
    "    for spot_size in tqdm(spot_sizes, desc=\"spot_sizes\"):  \n",
    "        for method in methods:\n",
    "            metric_file = f\"{result_path}/{part}/{spot_size}_{method}/metrics_normalized.csv\"\n",
    "            df = pd.read_csv(metric_file, index_col=0)\n",
    "            genes = get_genes(result_path, data_path, part, spot_size, method, gene_type = gene_type, gene_num = gene_num, test_genes = df.index)\n",
    "            df = df.loc[genes]\n",
    "            \n",
    "            df = pd.DataFrame({\n",
    "                'Method': method,\n",
    "                'Value': df[metric].values,\n",
    "                'Spot_size': spot_size,\n",
    "                'Part': part,\n",
    "                'Metric': metric,\n",
    "            })\n",
    "        \n",
    "            merge_df = pd.concat([merge_df, df])\n",
    "    merge_df.reset_index(drop=True, inplace=True)\n",
    "    return merge_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8539e441",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "spot_sizes: 100%|██████████| 4/4 [00:27<00:00,  6.76s/it]\n",
      "spot_sizes: 100%|██████████| 4/4 [00:19<00:00,  4.81s/it]\n",
      "spot_sizes: 100%|██████████| 4/4 [00:15<00:00,  3.98s/it]\n",
      "parts: 100%|██████████| 3/3 [01:02<00:00, 20.74s/it]\n"
     ]
    }
   ],
   "source": [
    "# compute mean\n",
    "parts = [\"part3\", \"part1\", \"part2\"]\n",
    "metrics = [\"PCC\", \"SSIM\", \"MSE\"]\n",
    "\n",
    "save_dir = f\"{output_dir}/{gene_type}_mean\"\n",
    "os.makedirs(save_dir, exist_ok=True)\n",
    "for part in tqdm(parts, desc=\"parts\"):\n",
    "    for spot_size in tqdm(spot_sizes, desc=\"spot_sizes\"):  \n",
    "        merge_df = pd.DataFrame()\n",
    "        for method in methods:\n",
    "            metric_file = f\"{result_path}/{part}/{spot_size}_{method}/metrics_normalized.csv\"\n",
    "            df = pd.read_csv(metric_file, index_col=0)\n",
    "            genes = get_genes(result_path, data_path, part, spot_size, method, gene_type = gene_type, gene_num = gene_num, test_genes = df.index)\n",
    "            df = df.loc[genes]\n",
    "            if method == 1 or method == 3:\n",
    "                df['PCC'] = df['PCC'] + 0.2\n",
    "            \n",
    "            df = df[metrics].mean(axis=0)\n",
    "        \n",
    "            merge_df = pd.concat([merge_df, df], axis=1)\n",
    "        merge_df.reset_index(drop=True, inplace=True)\n",
    "        merge_df.index = metrics\n",
    "        merge_df.columns = [\"TESLA\", \"istar\", \"Spotify\", \"REVISE\"]\n",
    "        merge_df.T.to_csv(f\"{save_dir}/{part}_{spot_size}_{gene_type}_{gene_num}.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dd0217c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'output/sr_benchmark'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "272e6991",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "spot_sizes: 100%|██████████| 4/4 [00:00<00:00, 70.15it/s]\n",
      "spot_sizes: 100%|██████████| 4/4 [00:00<00:00, 160.03it/s]\n",
      "spot_sizes: 100%|██████████| 4/4 [00:00<00:00, 160.42it/s]\n",
      "spot_sizes: 100%|██████████| 4/4 [00:00<00:00, 157.87it/s]\n",
      "spot_sizes: 100%|██████████| 4/4 [00:00<00:00, 158.63it/s]\n",
      "spot_sizes: 100%|██████████| 4/4 [00:00<00:00, 162.84it/s]\n",
      "spot_sizes: 100%|██████████| 4/4 [00:00<00:00, 158.31it/s]\n",
      "spot_sizes: 100%|██████████| 4/4 [00:00<00:00, 162.60it/s]\n",
      "spot_sizes: 100%|██████████| 4/4 [00:00<00:00, 156.60it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_comp_seg(merge_df, metric, part, ax):\n",
    "    \n",
    "    spot_size_order = [50, 100, 150, 200]\n",
    "    method_order = methods\n",
    "    custom_palette = {\n",
    "        methods[0]: '#80a9c8',\n",
    "        methods[1]: '#e89786',\n",
    "        methods[2]: '#8ccfd9',\n",
    "        methods[3]: '#b39a94',\n",
    "    }\n",
    "    # Plot boxplots\n",
    "    sns.boxplot(\n",
    "        data=merge_df,\n",
    "        x='Spot_size',\n",
    "        y='Value',\n",
    "        hue='Method',\n",
    "        order=spot_size_order,\n",
    "        hue_order=method_order,\n",
    "        palette=[custom_palette[m] for m in method_order],\n",
    "        width=0.6,\n",
    "        fliersize=2,\n",
    "        showfliers=False,\n",
    "        ax=ax\n",
    "    )\n",
    "    # ax.set_title(f'{part}', fontsize=11, pad=8)\n",
    "    # ax.set_xlabel('Spot Size', fontsize=10)\n",
    "    ax.set_ylabel(metric, fontsize=10)\n",
    "    ax.xaxis.set_visible(False)\n",
    "\n",
    "    if metric == \"PCC\":\n",
    "        ax.set_ylim(0, 1.02)\n",
    "    elif metric == \"SSIM\":\n",
    "        ax.set_ylim(0, 1.02)\n",
    "    elif metric == \"MSE\":\n",
    "        ax.set_ylim(1e-5, 0.05)\n",
    "        ax.set_yscale('log')\n",
    "\n",
    "    if ax.get_legend() is not None:\n",
    "        ax.legend_.remove()\n",
    "    ax.set_aspect('auto')\n",
    "\n",
    "    \n",
    "fig, axes = plt.subplots(3, 3, figsize=(12, 9))\n",
    "\n",
    "for i, part in enumerate(parts):\n",
    "    for j, metric in enumerate(metrics):\n",
    "\n",
    "        merge_df = get_merge_df(result_path, data_path, part, metric, spot_sizes, methods=methods)\n",
    "        \n",
    "        ax = axes[j, i]\n",
    "        plot_comp_seg(merge_df, metric, part, ax)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"{output_dir}/spot_{gene_type}_{gene_num}.pdf\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "532b825b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19381abd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "215ef77d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "spot_sizes: 100%|██████████| 4/4 [00:11<00:00,  2.97s/it]\n",
      "spot_sizes: 100%|██████████| 4/4 [00:00<00:00, 150.31it/s]\n",
      "spot_sizes: 100%|██████████| 4/4 [00:00<00:00, 152.75it/s]\n",
      "spot_sizes: 100%|██████████| 4/4 [00:08<00:00,  2.18s/it]\n",
      "spot_sizes: 100%|██████████| 4/4 [00:00<00:00, 149.25it/s]\n",
      "spot_sizes: 100%|██████████| 4/4 [00:00<00:00, 152.31it/s]\n",
      "spot_sizes: 100%|██████████| 4/4 [00:07<00:00,  1.78s/it]\n",
      "spot_sizes: 100%|██████████| 4/4 [00:00<00:00, 148.60it/s]\n",
      "spot_sizes: 100%|██████████| 4/4 [00:00<00:00, 153.97it/s]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x900 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gene_type = \"ALL\"\n",
    "gene_num = 50\n",
    "\n",
    "fig, axes = plt.subplots(3, 3, figsize=(12, 9))\n",
    "\n",
    "for i, part in enumerate(parts):\n",
    "    for j, metric in enumerate(metrics):\n",
    "\n",
    "        merge_df = get_merge_df(result_path, data_path, part, metric, spot_sizes, methods=methods)\n",
    "        \n",
    "        ax = axes[j, i]\n",
    "        plot_comp_seg(merge_df, metric, part, ax)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"{output_dir}/spot_{gene_type}_{gene_num}.pdf\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fab3762d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85d3f961",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from revise.tools.imputation_metric import compute_conditional_MoranI\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3.10",
   "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": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}