{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imputation with trained STIMP in the Northern Gulf of Mexico"
   ]
  },
  {
   "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='MEXICO', 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 5a: Missing rate is equal to 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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/imputation/MEXICO/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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2025-05-08 16:00:05.038\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 167: 0.0024114695843309164, 9.750714525580406e-06\u001b[0m\n",
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      "\u001b[32m2025-05-08 16:00:11.248\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 171: 0.0022447442170232534, 9.74978320300579e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:00:11.286\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 40: 0.00034438399598002434, 9.817042155191302e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:00:11.424\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 151: 0.002266534138470888, 9.848037734627724e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:00:11.590\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 179: 0.002390695968642831, 9.997515007853508e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:00:11.727\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: 0.0019407444633543491, 9.64419450610876e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:00:11.876\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 162: 0.0019273994257673621, 9.737559594213963e-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/MEXICO/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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-3., -2., -1.,  0.,  1.,  2.,  3.]),\n",
       " [Text(0, -3.0, '−3'),\n",
       "  Text(0, -2.0, '−2'),\n",
       "  Text(0, -1.0, '−1'),\n",
       "  Text(0, 0.0, '0'),\n",
       "  Text(0, 1.0, '1'),\n",
       "  Text(0, 2.0, '2'),\n",
       "  Text(0, 3.0, '3')])"
      ]
     },
     "execution_count": 9,
     "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 5a: Missing rate is equal to 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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/imputation/MEXICO/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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2025-05-08 16:04:03.602\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 139: 0.0015157636953517795, 9.98715404421091e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:03.784\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 184: 0.0016669080359861255, 9.927665814757347e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:03.949\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 163: 0.0015358608216047287, 9.816023521125317e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:04.065\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: 0.001035201014019549, 9.757932275533676e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:04.186\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 120: 0.0011178131680935621, 9.990064427256584e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:04.319\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 131: 0.0015064311446622014, 9.89111140370369e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:04.440\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 121: 0.0011072386987507343, 9.85502265393734e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:04.547\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 107: 0.0014296036679297686, 9.90403350442648e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:04.655\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: 0.0009885813342407346, 9.844312444329262e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:04.755\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: 0.0008216698770411313, 9.978015441447496e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:04.846\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 92: 0.0009396987152285874, 9.939365554600954e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:04.992\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 146: 0.0013311404036357999, 9.838840924203396e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:05.037\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 44: 0.00015673169400542974, 9.80004551820457e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:05.133\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: 0.0007812091498635709, 9.964627679437399e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:05.246\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: 0.0012790574692189693, 9.900424629449844e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:05.376\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: 0.00124261318705976, 9.820330888032913e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:05.488\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: 0.0011830134317278862, 9.74640715867281e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:05.622\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 130: 0.0012919403379783034, 9.897281415760517e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:05.726\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: 0.0009124678908847272, 9.811134077608585e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:05.876\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: 0.0017269201343879104, 9.869690984487534e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:06.055\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 173: 0.0018341108225286007, 9.76654700934887e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:06.206\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 137: 0.0014894654741510749, 9.950948879122734e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:06.373\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 161: 0.001414332538843155, 9.828130714595318e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:06.518\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 145: 0.0014833847526460886, 9.912648238241673e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:06.656\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: 0.0013469243422150612, 9.737210348248482e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:06.805\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 149: 0.0015318260993808508, 9.987270459532738e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:06.955\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: 0.0014535944210365415, 9.937095455825329e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:07.113\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 159: 0.0015478064306080341, 9.857816621661186e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:07.237\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: 0.001629698439501226, 9.963172487914562e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:07.397\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: 0.0018093626713380218, 9.837327525019646e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:07.540\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 145: 0.001903114141896367, 9.949319064617157e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:07.696\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 157: 0.0018188718240708113, 9.960262104868889e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:07.846\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 151: 0.0018710327567532659, 9.846757166087627e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:08.001\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: 0.0015902334125712514, 9.818468242883682e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:08.147\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 149: 0.002163328230381012, 9.551877155900002e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:08.301\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 157: 0.0014534150250256062, 9.987270459532738e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:08.434\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: 0.0013811803655698895, 9.677489288151264e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:08.572\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 143: 0.0014524502912536263, 9.977375157177448e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:08.719\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: 0.0017091295449063182, 9.905779734253883e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:08.850\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: 0.0019908843096345663, 9.979819878935814e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:08.986\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 140: 0.00110264599788934, 9.981915354728699e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:09.028\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 44: 0.0001312033273279667, 9.238312486559153e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:09.168\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: 0.0019513461738824844, 9.915558621287346e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:09.325\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 161: 0.0015245361719280481, 9.820330888032913e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:09.468\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 145: 0.0016904419753700495, 9.934534318745136e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:04:09.604\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 139: 0.0016243739519268274, 9.85502265393734e-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/MEXICO/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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-3., -2., -1.,  0.,  1.,  2.,  3.]),\n",
       " [Text(0, -3.0, '−3'),\n",
       "  Text(0, -2.0, '−2'),\n",
       "  Text(0, -1.0, '−1'),\n",
       "  Text(0, 0.0, '0'),\n",
       "  Text(0, 1.0, '1'),\n",
       "  Text(0, 2.0, '2'),\n",
       "  Text(0, 3.0, '3')])"
      ]
     },
     "execution_count": 12,
     "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 5a: Missing rate is equal to 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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/imputation/MEXICO/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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2025-05-08 16:07:26.927\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 58: 0.00015187902317848057, 9.929281077347696e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:27.000\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: 0.0002237727167084813, 9.703464456833899e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:27.057\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 57: 0.00017613488307688385, 9.938885341398418e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:27.113\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 57: 8.697685552760959e-05, 9.286239219363779e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:27.158\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 44: 0.00036218654713593423, 8.104776497930288e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:27.228\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: 4.801901013706811e-05, 7.680551789235324e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:27.270\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 42: 0.0001388771052006632, 5.718000466004014e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:27.326\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 57: 0.00025041893240995705, 9.762414265424013e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:27.359\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: 0.0004084364336449653, 3.939931048080325e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:27.407\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 49: 0.00017096057126764208, 9.526629582978785e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:27.444\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 37: 0.00011079287651227787, 9.134848369285464e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:27.510\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 69: 0.0001042808944475837, 9.451672667637467e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:27.548\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 39: 0.0001796404249034822, 8.186689228750765e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:27.592\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: 0.000145958925713785, 9.630588465370238e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:27.636\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 44: 0.0001175681027234532, 9.213843441102654e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:27.681\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: 0.00022565377003047615, 9.378723916597664e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:27.724\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 43: 0.00015027915651444346, 9.112394764088094e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:27.766\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 43: 9.944717749021947e-05, 9.269810107070953e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:27.808\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 41: 9.884594328468665e-05, 9.866300388239324e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:27.848\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 40: 0.0005300716729834676, 6.018788553774357e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:27.923\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: 0.000187698271474801, 9.872252121567726e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:27.962\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 37: 0.0007283929153345525, 4.24310564994812e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:28.018\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 57: 0.00020205293549224734, 9.961382602341473e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:28.064\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 46: 0.0005271050613373518, 7.425551302731037e-07\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:28.121\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: 0.00021120367455296218, 9.574097930453718e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:28.186\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 67: 0.0001723091263556853, 9.87622479442507e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:28.227\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 40: 0.00039949396159499884, 3.246619598940015e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:28.284\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 59: 0.00012777798110619187, 9.618699550628662e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:28.339\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 56: 0.00020881296950392425, 9.725990821607411e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:28.397\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 59: 0.00014268478844314814, 9.572409908287227e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:28.454\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: 0.0001750936935422942, 9.582261554896832e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:28.521\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: 0.00024027546169236302, 9.76555747911334e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:28.580\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: 0.00017896997451316565, 9.778246749192476e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:28.637\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 58: 0.000167188816703856, 9.616589522920549e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:28.695\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 59: 0.00041574350325390697, 9.605544619262218e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:28.762\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 69: 0.00020687095820903778, 9.370458428747952e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:28.824\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 64: 0.0001603587734280154, 9.606665116734803e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:28.880\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 57: 0.0002463874698150903, 9.479583241045475e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:28.945\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 63: 0.0001788117951946333, 9.628449333831668e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:28.999\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 51: 0.0001592903572600335, 9.383860742673278e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:29.055\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 56: 0.00020430688164196908, 9.535520803183317e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:29.074\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: 3.636835390352644e-05, 8.21973299025558e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:29.127\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: 0.0001306253398070112, 9.472758392803371e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:29.178\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 52: 0.00018380387336947024, 9.648618288338184e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:29.241\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 64: 0.0001010614141705446, 9.077521099243313e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:07:29.290\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 48: 0.00022896577138453722, 3.043169272132218e-06\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from einops import rearrange\n",
    "from model.dineof_per_step import DINEOF\n",
    "is_sea = np.load(\"./data/MEXICO/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": 16,
   "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": 16,
     "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)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch",
   "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": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}