{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imputation with trained STIMP in the Yangtze River Estuary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/home/mafzhang/code/STIMP/..', '/home/mafzhang/miniconda3/envs/torch/lib/python39.zip', '/home/mafzhang/miniconda3/envs/torch/lib/python3.9', '/home/mafzhang/miniconda3/envs/torch/lib/python3.9/lib-dynload', '', '/home/mafzhang/.local/lib/python3.9/site-packages', '/home/mafzhang/miniconda3/envs/torch/lib/python3.9/site-packages']\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np \n",
    "import sys\n",
    "import os\n",
    "sys.path.insert(0, os.path.join(os.getcwd(), \"..\"))\n",
    "print(sys.path)\n",
    "from dataset.dataset_imputation import PRE8dDataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import argparse\n",
    "parser = argparse.ArgumentParser(description='Imputation')\n",
    "\n",
    "# args for area and methods\n",
    "parser.add_argument('--area', type=str, default='Yangtze', help='which bay area we focus')\n",
    "\n",
    "# basic args\n",
    "parser.add_argument('--epochs', type=int, default=500, help='epochs')\n",
    "parser.add_argument('--batch_size', type=int, default=16, help='batch size')\n",
    "parser.add_argument('--lr', type=float, default=1e-3, help='learning rate')\n",
    "parser.add_argument('--wd', type=float, default=1e-4, help='weight decay')\n",
    "parser.add_argument('--test_freq', type=int, default=500, help='test per n epochs')\n",
    "parser.add_argument('--embedding_size', type=int, default=32)\n",
    "parser.add_argument('--hidden_channels', type=int, default=32)\n",
    "parser.add_argument('--diffusion_embedding_size', type=int, default=64)\n",
    "parser.add_argument('--side_channels', type=int, default=1)\n",
    "\n",
    "# args for tasks\n",
    "parser.add_argument('--in_len', type=int, default=46)\n",
    "parser.add_argument('--out_len', type=int, default=46)\n",
    "parser.add_argument('--missing_ratio', type=float, default=0.1)\n",
    "\n",
    "# args for diffusion\n",
    "parser.add_argument('--beta_start', type=float, default=0.0001, help='beta start from this')\n",
    "parser.add_argument('--beta_end', type=float, default=0.2, help='beta end to this')\n",
    "parser.add_argument('--num_steps', type=float, default=50, help='denoising steps')\n",
    "parser.add_argument('--num_samples', type=int, default=10, help='n datasets')\n",
    "parser.add_argument('--schedule', type=str, default='quad', help='noise schedule type')\n",
    "parser.add_argument('--target_strategy', type=str, default='random', help='mask')\n",
    "\n",
    "# args for mae\n",
    "parser.add_argument('--num_heads', type=int, default=8, help='n heads for self attention')\n",
    "config = parser.parse_args([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig 5g: Missing rate is equal to 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "test_dloader = DataLoader(PRE8dDataset(config, mode='test'), 1, shuffle=False)\n",
    "datas, data_ob_masks, data_gt_masks, labels, label_masks = next(iter(test_dloader))\n",
    "device = \"cuda\"\n",
    "datas, data_ob_masks, data_gt_masks, labels, label_masks = datas.float().to(device), data_ob_masks.to(device), data_gt_masks.to(device), labels.to(device), label_masks.to(device)\n",
    "\n",
    "# load model\n",
    "model = torch.load(\"./log_bak/imputation/Yangtze/STIMP/best_0.1.pt\")\n",
    "model = model.to(device)\n",
    "cond_mask = data_gt_masks\n",
    "adj = np.load(\"./data/{}/adj.npy\".format(config.area))\n",
    "adj = torch.from_numpy(adj).float().to(device)\n",
    "\n",
    "imputed_data_our = model.impute(datas, cond_mask, adj, 10)\n",
    "imputed_data_our = imputed_data_our.median(1).values\n",
    "mask = data_ob_masks - cond_mask\n",
    "imputed_our = imputed_data_our[0][mask.bool().cpu()[0]]\n",
    "truth = datas[0][mask.bool()[0]].cpu()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2025-05-08 16:42:57.570\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 21: 4.708132109954022e-05, 8.019527740543708e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:57.701\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 112: 0.0010282495059072971, 9.766197763383389e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:57.773\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 71: 0.0007443492067977786, 9.97487222775817e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:57.776\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 0: 4.399793951392894e-08, 4.399793951392894e-08\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:57.906\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 123: 0.0016074057202786207, 9.941053576767445e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:58.003\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 102: 0.0008586125331930816, 9.865849278867245e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:58.130\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 124: 0.0017285322537645698, 9.996467269957066e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:58.227\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 98: 0.0008929798495955765, 9.876443073153496e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:58.326\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 101: 0.0013608409790322185, 9.842333383858204e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:58.392\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 68: 0.001529828761704266, 9.713578037917614e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:58.502\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 109: 0.0025927559472620487, 9.823590517044067e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:58.634\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 132: 0.0012813438661396503, 9.85537189990282e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:58.834\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 201: 0.001912857056595385, 9.500537998974323e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:58.927\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 89: 0.0013037541648373008, 9.968411177396774e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:59.056\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 129: 0.0009492257377132773, 9.716430213302374e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:59.190\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 135: 0.0013640134129673243, 9.834766387939453e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:59.215\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 24: 7.357676076935604e-05, 9.848386980593204e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:59.281\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 67: 0.0006225646357052028, 9.807816240936518e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:59.296\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 13: 0.0002542529546190053, 6.905669579282403e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:59.319\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 22: 0.0004059072816744447, 9.747775038704276e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:59.427\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 108: 0.001281833741813898, 9.945477358996868e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:59.545\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 118: 0.00228595151565969, 9.91998240351677e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:59.631\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 75: 0.003958098590373993, 9.817536920309067e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:59.782\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 150: 0.0019288202747702599, 9.90286935120821e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:42:59.993\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 190: 0.001833006739616394, 9.884708561003208e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:43:00.132\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 139: 0.001557467970997095, 9.996467269957066e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:43:00.192\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 60: 0.0008546045864932239, 9.848503395915031e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:43:00.291\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 86: 0.0026618584524840117, 9.825685992836952e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:43:00.496\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 192: 0.001992049627006054, 9.892042726278305e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:43:00.717\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 221: 0.0017417825292795897, 9.877607226371765e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:43:00.892\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 160: 0.0019981060177087784, 9.859679266810417e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:43:01.035\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 126: 0.0018632980063557625, 9.83639620244503e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:43:01.236\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 177: 0.0018766681896522641, 9.935698471963406e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:43:01.365\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 128: 0.0018958383006975055, 9.955023415386677e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:43:01.510\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 144: 0.0015788031741976738, 9.9594471976161e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:43:01.543\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 32: 0.00016102954396046698, 9.845243766903877e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:43:01.633\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 91: 0.0007083074888214469, 9.8293530754745e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:43:01.706\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 73: 0.0014121433487161994, 9.9940225481987e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:43:01.752\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 45: 0.0006760748801752925, 7.245165761560202e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:43:01.754\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 0: 3.099742329482069e-08, 3.099742329482069e-08\u001b[0m\n",
      "\u001b[32m2025-05-08 16:43:01.779\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 23: 0.0005124595481902361, 9.853160008788109e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:43:01.781\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 0: 1.3740951487761777e-07, 1.3740951487761777e-07\u001b[0m\n",
      "\u001b[32m2025-05-08 16:43:01.883\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 102: 0.0014482949627563357, 9.968061931431293e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:43:02.158\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 299: nan, nan\u001b[0m\n",
      "\u001b[32m2025-05-08 16:43:02.162\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 0: 2.021827896214745e-07, 2.021827896214745e-07\u001b[0m\n",
      "\u001b[32m2025-05-08 16:43:02.252\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 90: 0.0013474671868607402, 9.796698577702045e-06\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# DINEOF\n",
    "from einops import rearrange\n",
    "from model.dineof_per_step import DINEOF\n",
    "is_sea = np.load(\"./data/Yangtze/is_sea.npy\")\n",
    "H, W = is_sea.shape\n",
    "model = DINEOF(10, [H, W], keep_non_negative_only=False)\n",
    "datas_image = torch.zeros(1,46,1,H,W)\n",
    "datas_image = datas_image.to(device)\n",
    "datas_image[:,:,:,is_sea.astype(bool)]=datas\n",
    "cond_mask_image = torch.zeros(1,46,1,H,W)\n",
    "cond_mask_image = cond_mask_image.to(device)\n",
    "cond_mask_image[:,:,:,is_sea.astype(bool)]=cond_mask\n",
    "ob_mask_image = torch.zeros(1,46,1,H,W)\n",
    "ob_mask_image = ob_mask_image.to(device)\n",
    "ob_mask_image[:,:,:,is_sea.astype(bool)]=data_ob_masks\n",
    "\n",
    "impute_data_list = []\n",
    "for t in range(datas_image.shape[1]):\n",
    "    data = datas_image[:, t, :, :, :].squeeze()\n",
    "\n",
    "    tmp_data = torch.where(cond_mask_image[:,t].cpu().squeeze()==0, float(\"nan\"), data.cpu())\n",
    "    model.fit(tmp_data.numpy())\n",
    "\n",
    "    imputed_data = model.predict()\n",
    "    imputed_data = rearrange(imputed_data, \"(b t c h) w->b t c h w\", b=1, t=1, c=1, h=data.shape[-2], w=data.shape[-1])\n",
    "    impute_data_list.append(torch.from_numpy(imputed_data))\n",
    "\n",
    "imputed_data = torch.cat(impute_data_list, dim=1).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-2. , -1.5, -1. , -0.5,  0. ,  0.5,  1. ,  1.5,  2. ,  2.5]),\n",
       " [Text(0, -2.0, '−2.0'),\n",
       "  Text(0, -1.5, '−1.5'),\n",
       "  Text(0, -1.0, '−1.0'),\n",
       "  Text(0, -0.5, '−0.5'),\n",
       "  Text(0, 0.0, '0.0'),\n",
       "  Text(0, 0.5, '0.5'),\n",
       "  Text(0, 1.0, '1.0'),\n",
       "  Text(0, 1.5, '1.5'),\n",
       "  Text(0, 2.0, '2.0'),\n",
       "  Text(0, 2.5, '2.5')])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imputed_data_dineof = imputed_data[:,:,:,is_sea.astype(bool)]\n",
    "imputed_dineof = imputed_data_dineof[0][mask.bool().cpu()[0]]\n",
    "imputed_our = imputed_our[:]\n",
    "imputed_dineof = imputed_dineof[:]\n",
    "truth = truth[:]\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stat\n",
    "import pandas as pd\n",
    "method = []\n",
    "method.extend(['STIMP' for i in range(imputed_our.shape[0])])\n",
    "method.extend(['DINEOF' for i in range(imputed_our.shape[0])])\n",
    "data = {'truth': np.concatenate([truth.numpy() for i in range(2)]),\n",
    "        'imputed':np.concatenate([imputed_our.numpy(), imputed_dineof]),\n",
    "        'method':method}\n",
    "data = pd.DataFrame.from_dict(data)\n",
    "color =[\"#9F0000\",\"#576fa0\"]\n",
    "g=sns.jointplot(data=data, x=\"truth\", y=\"imputed\", hue=\"method\", kind='kde', palette=color, marginal_kws={'common_norm':False,'shade':True})\n",
    "xpoints = ypoints = plt.xlim()\n",
    "plt.plot(xpoints, ypoints, 'k-', alpha=0.75, zorder=0)\n",
    "plt.ylabel(\"imputed\", size=24)\n",
    "plt.xlabel(\"truth\", size=24)\n",
    "pcc1=stat.pearsonr(truth[:], imputed_our).statistic.item()\n",
    "pcc2=stat.pearsonr(truth[:], imputed_dineof).statistic.item()\n",
    "plt.text(0.40, 0.92, 'PCC={:.4f}'.format(pcc1), transform=plt.gca().transAxes, fontsize=12, verticalalignment='top', horizontalalignment='left', color=\"#9F0000\")\n",
    "plt.text(0.40, 0.88, 'PCC={:.4f}'.format(pcc2), transform=plt.gca().transAxes, fontsize=12, verticalalignment='top', horizontalalignment='left', color=\"#576fa0\")\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig 5g: Missing rate is equal to 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "config.missing_ratio=0.5\n",
    "from torch.utils.data import DataLoader\n",
    "test_dloader = DataLoader(PRE8dDataset(config, mode='test'), 1, shuffle=False)\n",
    "datas, data_ob_masks, data_gt_masks, labels, label_masks = next(iter(test_dloader))\n",
    "device = \"cuda\"\n",
    "datas, data_ob_masks, data_gt_masks, labels, label_masks = datas.float().to(device), data_ob_masks.to(device), data_gt_masks.to(device), labels.to(device), label_masks.to(device)\n",
    "\n",
    "model = torch.load(\"./log_bak/imputation/Yangtze/STIMP/best_0.5.pt\")\n",
    "model = model.to(device)\n",
    "cond_mask = data_gt_masks\n",
    "imputed_data_our = model.impute(datas, cond_mask, adj, 10)\n",
    "imputed_data_our = imputed_data_our.median(1).values\n",
    "mask = data_ob_masks - cond_mask\n",
    "imputed_our = imputed_data_our[0][mask.bool().cpu()[0]]\n",
    "truth = datas[0][mask.bool()[0]].cpu()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2025-05-08 16:44:13.178\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 60: 0.00018172245472669601, 9.632494766265154e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:13.287\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 103: 0.001003644079901278, 9.98412724584341e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:13.367\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 81: 0.00034616910852491856, 9.852286893874407e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:13.370\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 0: 3.6166316164099044e-08, 3.6166316164099044e-08\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:13.489\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 114: 0.001367044635117054, 9.751529432833195e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:13.559\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 72: 0.0005230333190411329, 9.848852641880512e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:13.679\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 114: 0.001115604885853827, 9.942566975951195e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:13.750\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 70: 0.0005284450016915798, 9.563169442117214e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:13.851\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 89: 0.0007980529335327446, 9.904848411679268e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:13.925\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 74: 0.0005747053073719144, 9.889889042824507e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:14.097\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 154: 0.0016806392231956124, 9.83488280326128e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:14.216\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 114: 0.0011925606522709131, 9.8345335572958e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:14.410\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 182: 0.0016186311841011047, 9.693787433207035e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:14.540\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 126: 0.0013545361580327153, 9.890994988381863e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:14.639\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 97: 0.0009067094069905579, 9.997456800192595e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:14.767\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 123: 0.001609492814168334, 9.93290450423956e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:14.785\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 16: 2.015665995713789e-05, 7.93514664110262e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:14.841\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 54: 0.00024294178001582623, 9.629933629184961e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:14.860\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 18: 3.5876695619663224e-05, 8.131828508339822e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:14.880\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 19: 0.0003714900813065469, 3.156892489641905e-07\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:14.992\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 110: 0.0007659235852770507, 9.788316674530506e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:15.160\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 160: 0.0017956334631890059, 9.888550266623497e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:15.345\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 172: 0.0020615363027900457, 9.840354323387146e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:15.487\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 133: 0.0015492900274693966, 9.929994121193886e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:15.633\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 136: 0.002137102885171771, 9.997747838497162e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:15.756\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 117: 0.001405011280439794, 9.96212475001812e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:15.812\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 55: 0.0003225987602490932, 9.592913556843996e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:15.950\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 125: 0.0019885250367224216, 9.818002581596375e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:16.114\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 154: 0.0015739976661279798, 9.79576725512743e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:16.260\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 134: 0.0015807206509634852, 9.996816515922546e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:16.415\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 148: 0.0014943223213776946, 9.724986739456654e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:16.602\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 172: 0.001525407424196601, 9.94314905256033e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:16.733\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 120: 0.0029906926210969687, 9.904149919748306e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:16.843\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 105: 0.0012036649277433753, 9.967712685465813e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:16.963\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 114: 0.0009628551779314876, 9.952287655323744e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:16.982\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 18: 3.529783134581521e-05, 8.971062925411388e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:17.054\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 70: 0.00032338700839318335, 9.956740541383624e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:17.123\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 68: 0.0004974923795089126, 9.769981261342764e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:17.180\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 58: 0.00024812668561935425, 9.839219273999333e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:17.186\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 0: 2.4436809908934265e-08, 2.4436809908934265e-08\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:17.220\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 33: 0.00036679705954156816, 9.863695595413446e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:17.222\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 0: 1.701689029687259e-07, 1.701689029687259e-07\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:17.334\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 108: 0.0010512343142181635, 9.797979146242142e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:17.602\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 299: nan, nan\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:17.604\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 0: 9.214258511747175e-08, 9.214258511747175e-08\u001b[0m\n",
      "\u001b[32m2025-05-08 16:44:17.712\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 104: 0.0018026444595307112, 9.951996617019176e-06\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from einops import rearrange\n",
    "from model.dineof_per_step import DINEOF\n",
    "is_sea = np.load(\"./data/Yangtze/is_sea.npy\")\n",
    "H, W = is_sea.shape\n",
    "model = DINEOF(10, [H, W], keep_non_negative_only=False)\n",
    "datas_image = torch.zeros(1,46,1,H,W)\n",
    "datas_image = datas_image.to(device)\n",
    "datas_image[:,:,:,is_sea.astype(bool)]=datas\n",
    "cond_mask_image = torch.zeros(1,46,1,H,W)\n",
    "cond_mask_image = cond_mask_image.to(device)\n",
    "cond_mask_image[:,:,:,is_sea.astype(bool)]=cond_mask\n",
    "ob_mask_image = torch.zeros(1,46,1,H,W)\n",
    "ob_mask_image = ob_mask_image.to(device)\n",
    "ob_mask_image[:,:,:,is_sea.astype(bool)]=data_ob_masks\n",
    "\n",
    "impute_data_list = []\n",
    "for t in range(datas_image.shape[1]):\n",
    "    data = datas_image[:, t, :, :, :].squeeze()\n",
    "\n",
    "    tmp_data = torch.where(cond_mask_image[:,t].cpu().squeeze()==0, float(\"nan\"), data.cpu())\n",
    "    model.fit(tmp_data.numpy())\n",
    "\n",
    "    imputed_data = model.predict()\n",
    "    imputed_data = rearrange(imputed_data, \"(b t c h) w->b t c h w\", b=1, t=1, c=1, h=data.shape[-2], w=data.shape[-1])\n",
    "    impute_data_list.append(torch.from_numpy(imputed_data))\n",
    "\n",
    "imputed_data = torch.cat(impute_data_list, dim=1).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-2. , -1.5, -1. , -0.5,  0. ,  0.5,  1. ,  1.5,  2. ,  2.5]),\n",
       " [Text(0, -2.0, '−2.0'),\n",
       "  Text(0, -1.5, '−1.5'),\n",
       "  Text(0, -1.0, '−1.0'),\n",
       "  Text(0, -0.5, '−0.5'),\n",
       "  Text(0, 0.0, '0.0'),\n",
       "  Text(0, 0.5, '0.5'),\n",
       "  Text(0, 1.0, '1.0'),\n",
       "  Text(0, 1.5, '1.5'),\n",
       "  Text(0, 2.0, '2.0'),\n",
       "  Text(0, 2.5, '2.5')])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imputed_data_dineof = imputed_data[:,:,:,is_sea.astype(bool)]\n",
    "imputed_dineof = imputed_data_dineof[0][mask.bool().cpu()[0]]\n",
    "imputed_our = imputed_our[:]\n",
    "imputed_dineof = imputed_dineof[:]\n",
    "truth = truth[:]\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stat\n",
    "import pandas as pd\n",
    "method = []\n",
    "method.extend(['STIMP' for i in range(imputed_our.shape[0])])\n",
    "method.extend(['DINEOF' for i in range(imputed_our.shape[0])])\n",
    "data = {'truth': np.concatenate([truth.numpy() for i in range(2)]),\n",
    "        'imputed':np.concatenate([imputed_our.numpy(), imputed_dineof]),\n",
    "        'method':method}\n",
    "data = pd.DataFrame.from_dict(data)\n",
    "color =[\"#9F0000\",\"#576fa0\"]\n",
    "g=sns.jointplot(data=data, x=\"truth\", y=\"imputed\", hue=\"method\", kind='kde', palette=color, marginal_kws={'common_norm':False,'shade':True})\n",
    "xpoints = ypoints = plt.xlim()\n",
    "plt.plot(xpoints, ypoints, 'k-', alpha=0.75, zorder=0)\n",
    "plt.ylabel(\"imputed\", size=24)\n",
    "plt.xlabel(\"truth\", size=24)\n",
    "pcc1=stat.pearsonr(truth[:], imputed_our).statistic.item()\n",
    "pcc2=stat.pearsonr(truth[:], imputed_dineof).statistic.item()\n",
    "plt.text(0.40, 0.92, 'PCC={:.4f}'.format(pcc1), transform=plt.gca().transAxes, fontsize=12, verticalalignment='top', horizontalalignment='left', color=\"#9F0000\")\n",
    "plt.text(0.40, 0.88, 'PCC={:.4f}'.format(pcc2), transform=plt.gca().transAxes, fontsize=12, verticalalignment='top', horizontalalignment='left', color=\"#576fa0\")\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig 5g: Missing rate is equal to 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "config.missing_ratio=0.9\n",
    "from torch.utils.data import DataLoader\n",
    "test_dloader = DataLoader(PRE8dDataset(config, mode='test'), 1, shuffle=False)\n",
    "datas, data_ob_masks, data_gt_masks, labels, label_masks = next(iter(test_dloader))\n",
    "device = \"cuda\"\n",
    "datas, data_ob_masks, data_gt_masks, labels, label_masks = datas.float().to(device), data_ob_masks.to(device), data_gt_masks.to(device), labels.to(device), label_masks.to(device)\n",
    "\n",
    "model = torch.load(\"./log_bak/imputation/Yangtze/STIMP/best_0.9.pt\")\n",
    "model = model.to(device)\n",
    "cond_mask = data_gt_masks\n",
    "imputed_data_our = model.impute(datas, cond_mask, adj, 10)\n",
    "imputed_data_our = imputed_data_our.median(1).values\n",
    "mask = data_ob_masks - cond_mask\n",
    "imputed_our = imputed_data_our[0][mask.bool().cpu()[0]]\n",
    "truth = datas[0][mask.bool()[0]].cpu()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2025-05-08 16:49:09.361\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 0: 7.668309365271853e-08, 7.668309365271853e-08\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2025-05-08 16:49:09.375\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.05715912580490112, 0.022987596690654755\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.386\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.03161339461803436, 0.02170996367931366\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.403\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: inf, nan\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.419\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.0353475846350193, 0.013851732015609741\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.428\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.019878100603818893, 0.016625836491584778\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.439\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.025722039863467216, 0.011239001527428627\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.448\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.015220475383102894, 0.004366486333310604\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.456\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.017662154510617256, 0.003993446007370949\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.464\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.021306386217474937, 0.012515569105744362\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.474\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.055746566504240036, 0.01922396942973137\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.483\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.03499658405780792, 0.013764653354883194\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.492\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.058387160301208496, 0.02401900291442871\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.501\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.026054155081510544, 0.010700307786464691\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.510\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.021644258871674538, 0.008256109431385994\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.515\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.03321734815835953, 0.014978978782892227\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.518\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 0: 2.3848681252047754e-08, 2.3848681252047754e-08\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.523\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.009581639431416988, 0.004611657932400703\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.525\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 0: 2.346368255246034e-08, 2.346368255246034e-08\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.527\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 0: 3.7093514038133435e-08, 3.7093514038133435e-08\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.533\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.022515250369906425, 0.009211746975779533\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.538\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.06756709516048431, 0.028331279754638672\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.544\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.09469026327133179, 0.03785631060600281\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.550\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.03914198279380798, 0.010798364877700806\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.556\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.04378948733210564, 0.019532058387994766\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.561\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.03323502838611603, 0.014274928718805313\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.567\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.011877693235874176, 0.0035524489358067513\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.573\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.046788524836301804, 0.019438933581113815\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.579\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.03796656057238579, 0.015091147273778915\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.584\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.04216518625617027, 0.016477413475513458\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.590\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.03814290836453438, 0.016480151563882828\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.596\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.055455874651670456, 0.024559568613767624\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.602\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.05383404344320297, 0.02300872653722763\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.607\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.03453878313302994, 0.013379789888858795\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.613\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.02408694103360176, 0.012295190244913101\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.615\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 0: 2.2849956593518073e-08, 2.2849956593518073e-08\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.620\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.015276920981705189, 0.008797475136816502\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.626\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.02445455826818943, 0.01103702001273632\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.631\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.00716039165854454, 0.004828255623579025\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.633\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 0: 7.721340011812572e-09, 7.721340011812572e-09\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.637\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.001410915981978178, 0.0010338590946048498\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.640\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 0: 3.007425064538438e-08, 3.007425064538438e-08\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.645\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.0503329262137413, 0.020350046455860138\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.649\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: nan, nan\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.654\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: inf, nan\u001b[0m\n",
      "\u001b[32m2025-05-08 16:49:09.660\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 2: 0.08589810878038406, 0.04171190410852432\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from einops import rearrange\n",
    "from model.dineof_per_step import DINEOF\n",
    "is_sea = np.load(\"./data/Yangtze/is_sea.npy\")\n",
    "H, W = is_sea.shape\n",
    "model = DINEOF(10, [H, W], keep_non_negative_only=False, nitemax=3)\n",
    "datas_image = torch.zeros(1,46,1,H,W)\n",
    "datas_image = datas_image.to(device)\n",
    "datas_image[:,:,:,is_sea.astype(bool)]=datas\n",
    "cond_mask_image = torch.zeros(1,46,1,H,W)\n",
    "cond_mask_image = cond_mask_image.to(device)\n",
    "cond_mask_image[:,:,:,is_sea.astype(bool)]=cond_mask\n",
    "ob_mask_image = torch.zeros(1,46,1,H,W)\n",
    "ob_mask_image = ob_mask_image.to(device)\n",
    "ob_mask_image[:,:,:,is_sea.astype(bool)]=data_ob_masks\n",
    "\n",
    "impute_data_list = []\n",
    "for t in range(datas_image.shape[1]):\n",
    "    data = datas_image[:, t, :, :, :].squeeze()\n",
    "\n",
    "    tmp_data = torch.where(cond_mask_image[:,t].cpu().squeeze()==0, float(\"nan\"), data.cpu())\n",
    "    model.fit(tmp_data.numpy())\n",
    "\n",
    "    imputed_data = model.predict()\n",
    "    imputed_data = rearrange(imputed_data, \"(b t c h) w->b t c h w\", b=1, t=1, c=1, h=data.shape[-2], w=data.shape[-1])\n",
    "    impute_data_list.append(torch.from_numpy(imputed_data))\n",
    "\n",
    "imputed_data = torch.cat(impute_data_list, dim=1).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-1.5, -1. , -0.5,  0. ,  0.5,  1. ,  1.5,  2. ,  2.5]),\n",
       " [Text(0, -1.5, '−1.5'),\n",
       "  Text(0, -1.0, '−1.0'),\n",
       "  Text(0, -0.5, '−0.5'),\n",
       "  Text(0, 0.0, '0.0'),\n",
       "  Text(0, 0.5, '0.5'),\n",
       "  Text(0, 1.0, '1.0'),\n",
       "  Text(0, 1.5, '1.5'),\n",
       "  Text(0, 2.0, '2.0'),\n",
       "  Text(0, 2.5, '2.5')])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imputed_data_dineof = imputed_data[:,:,:,is_sea.astype(bool)]\n",
    "imputed_dineof = imputed_data_dineof[0][mask.bool().cpu()[0]]\n",
    "imputed_our = imputed_our[:]\n",
    "imputed_dineof = imputed_dineof[:]\n",
    "truth = truth[:]\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stat\n",
    "import pandas as pd\n",
    "method = []\n",
    "method.extend(['STIMP' for i in range(imputed_our.shape[0])])\n",
    "method.extend(['DINEOF' for i in range(imputed_our.shape[0])])\n",
    "data = {'truth': np.concatenate([truth.numpy() for i in range(2)]),\n",
    "        'imputed':np.concatenate([imputed_our.numpy(), imputed_dineof]),\n",
    "        'method':method}\n",
    "data = pd.DataFrame.from_dict(data)\n",
    "color =[\"#9F0000\",\"#576fa0\"]\n",
    "g=sns.jointplot(data=data, x=\"truth\", y=\"imputed\", hue=\"method\", kind='kde', palette=color, marginal_kws={'common_norm':False,'shade':True})\n",
    "xpoints = ypoints = plt.xlim()\n",
    "plt.plot(xpoints, ypoints, 'k-', alpha=0.75, zorder=0)\n",
    "plt.ylabel(\"imputed\", size=24)\n",
    "plt.xlabel(\"truth\", size=24)\n",
    "pcc1=stat.pearsonr(truth[:], imputed_our).statistic.item()\n",
    "pcc2=stat.pearsonr(truth[:], imputed_dineof).statistic.item()\n",
    "plt.text(0.40, 0.92, 'PCC={:.4f}'.format(pcc1), transform=plt.gca().transAxes, fontsize=12, verticalalignment='top', horizontalalignment='left', color=\"#9F0000\")\n",
    "plt.text(0.40, 0.88, 'PCC={:.4f}'.format(pcc2), transform=plt.gca().transAxes, fontsize=12, verticalalignment='top', horizontalalignment='left', color=\"#576fa0\")\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)"
   ]
  }
 ],
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