models/swe_cnn-lstm-dhee-aws.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "#Installing the requirements that Google Colab doesn't have\n",
    "!pip install timm \n",
    "!pip install wandb --quiet\n",
    "!pip install pytorch-lightning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#All of our imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.optim import AdamW\n",
    "from torch.nn import functional as F\n",
    "from torch.optim.lr_scheduler import CosineAnnealingLR\n",
    "\n",
    "import torchvision\n",
    "from torchvision import transforms as T\n",
    "from torchvision.io import read_image\n",
    "\n",
    "from torchmetrics import R2Score\n",
    "\n",
    "import timm\n",
    "\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "import pytorch_lightning as pl\n",
    "from pytorch_lightning.utilities.seed import seed_everything\n",
    "from pytorch_lightning.core.lightning import LightningModule\n",
    "from pytorch_lightning.loggers import WandbLogger\n",
    "from pytorch_lightning.callbacks.early_stopping import EarlyStopping\n",
    "from pytorch_lightning.callbacks import ModelCheckpoint\n",
    "from pytorch_lightning.callbacks import progress\n",
    "from pytorch_lightning.callbacks.progress import TQDMProgressBar\n",
    "from pytorch_lightning.callbacks import LearningRateMonitor\n",
    "\n",
    "import sklearn\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "import wandb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.11.1'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Need torchvision .8+ for some image reading functionality\n",
    "torchvision.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading in the data\n",
    "df = pd.read_csv(f'/home/ubuntu/Final_CNN_Dataframe.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cell_id</th>\n",
       "      <th>date</th>\n",
       "      <th>SWE</th>\n",
       "      <th>mean_inversed_swe</th>\n",
       "      <th>mean_local_swe</th>\n",
       "      <th>median_local_swe</th>\n",
       "      <th>max_local_swe</th>\n",
       "      <th>min_local_swe</th>\n",
       "      <th>mean_local_elevation</th>\n",
       "      <th>median_local_elevation</th>\n",
       "      <th>...</th>\n",
       "      <th>MOD10A1_SnowCover</th>\n",
       "      <th>MOD10A1_Albedo</th>\n",
       "      <th>MOD10A1_NDSI</th>\n",
       "      <th>MYD10A1_SnowCover</th>\n",
       "      <th>MYD10A1_Albedo</th>\n",
       "      <th>MYD10A1_NDSI</th>\n",
       "      <th>copernicus_filelocations</th>\n",
       "      <th>sentinel1_filelocation</th>\n",
       "      <th>sentinel2a_filelocation</th>\n",
       "      <th>sentinel2b_filelocation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ASO_50M_SWE_USCARC_8</td>\n",
       "      <td>2017-07-17</td>\n",
       "      <td>48.561301</td>\n",
       "      <td>4.697841</td>\n",
       "      <td>4.685963</td>\n",
       "      <td>1.381988</td>\n",
       "      <td>18.482857</td>\n",
       "      <td>0.573913</td>\n",
       "      <td>2834.64000</td>\n",
       "      <td>2880.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.761684</td>\n",
       "      <td>0.367174</td>\n",
       "      <td>0.766883</td>\n",
       "      <td>0.814966</td>\n",
       "      <td>0.345533</td>\n",
       "      <td>0.818492</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ASO_50M_SWE_USCARC_9</td>\n",
       "      <td>2017-07-17</td>\n",
       "      <td>33.924218</td>\n",
       "      <td>4.736198</td>\n",
       "      <td>4.685963</td>\n",
       "      <td>1.381988</td>\n",
       "      <td>18.482857</td>\n",
       "      <td>0.573913</td>\n",
       "      <td>2834.64000</td>\n",
       "      <td>2880.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.666698</td>\n",
       "      <td>0.326389</td>\n",
       "      <td>0.669003</td>\n",
       "      <td>0.790095</td>\n",
       "      <td>0.339398</td>\n",
       "      <td>0.792235</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ASO_50M_SWE_USCARC_26</td>\n",
       "      <td>2017-07-17</td>\n",
       "      <td>31.831418</td>\n",
       "      <td>5.221427</td>\n",
       "      <td>4.685963</td>\n",
       "      <td>1.381988</td>\n",
       "      <td>18.482857</td>\n",
       "      <td>0.573913</td>\n",
       "      <td>2834.64000</td>\n",
       "      <td>2880.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.717794</td>\n",
       "      <td>0.387605</td>\n",
       "      <td>0.721743</td>\n",
       "      <td>0.660893</td>\n",
       "      <td>0.377931</td>\n",
       "      <td>0.665706</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ASO_50M_SWE_USCARC_27</td>\n",
       "      <td>2017-07-17</td>\n",
       "      <td>41.723133</td>\n",
       "      <td>5.322858</td>\n",
       "      <td>4.685963</td>\n",
       "      <td>1.381988</td>\n",
       "      <td>18.482857</td>\n",
       "      <td>0.573913</td>\n",
       "      <td>2834.64000</td>\n",
       "      <td>2880.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.758141</td>\n",
       "      <td>0.388365</td>\n",
       "      <td>0.761231</td>\n",
       "      <td>0.754647</td>\n",
       "      <td>0.370129</td>\n",
       "      <td>0.758559</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ASO_50M_SWE_USCARC_28</td>\n",
       "      <td>2017-07-17</td>\n",
       "      <td>42.693821</td>\n",
       "      <td>5.377803</td>\n",
       "      <td>4.685963</td>\n",
       "      <td>1.381988</td>\n",
       "      <td>18.482857</td>\n",
       "      <td>0.573913</td>\n",
       "      <td>2834.64000</td>\n",
       "      <td>2880.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.732553</td>\n",
       "      <td>0.393211</td>\n",
       "      <td>0.737283</td>\n",
       "      <td>0.753537</td>\n",
       "      <td>0.346098</td>\n",
       "      <td>0.757798</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97636</th>\n",
       "      <td>ASO_50M_SWE_USCALB_79</td>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>0.474991</td>\n",
       "      <td>2.126230</td>\n",
       "      <td>1.906877</td>\n",
       "      <td>1.888571</td>\n",
       "      <td>3.574384</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2808.36624</td>\n",
       "      <td>2880.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.050713</td>\n",
       "      <td>0.056434</td>\n",
       "      <td>0.044083</td>\n",
       "      <td>0.018015</td>\n",
       "      <td>0.023185</td>\n",
       "      <td>-0.121343</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97637</th>\n",
       "      <td>ASO_50M_SWE_USCALB_87</td>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>1.828016</td>\n",
       "      <td>2.115175</td>\n",
       "      <td>1.906877</td>\n",
       "      <td>1.888571</td>\n",
       "      <td>3.574384</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2808.36624</td>\n",
       "      <td>2880.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028636</td>\n",
       "      <td>0.053653</td>\n",
       "      <td>-0.007782</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.069880</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97638</th>\n",
       "      <td>ASO_50M_SWE_USCALB_88</td>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>0.114890</td>\n",
       "      <td>2.125414</td>\n",
       "      <td>1.906877</td>\n",
       "      <td>1.888571</td>\n",
       "      <td>3.574384</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2808.36624</td>\n",
       "      <td>2880.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.143382</td>\n",
       "      <td>0.132695</td>\n",
       "      <td>0.066765</td>\n",
       "      <td>0.009245</td>\n",
       "      <td>0.012961</td>\n",
       "      <td>-0.126452</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97639</th>\n",
       "      <td>ASO_50M_SWE_USCALB_89</td>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>2.170014</td>\n",
       "      <td>2.135945</td>\n",
       "      <td>1.906877</td>\n",
       "      <td>1.888571</td>\n",
       "      <td>3.574384</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2808.36624</td>\n",
       "      <td>2880.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.115417</td>\n",
       "      <td>0.143560</td>\n",
       "      <td>0.091611</td>\n",
       "      <td>0.010301</td>\n",
       "      <td>0.015503</td>\n",
       "      <td>-0.018298</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97640</th>\n",
       "      <td>ASO_50M_SWE_USCALB_99</td>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>2.213607</td>\n",
       "      <td>2.176050</td>\n",
       "      <td>1.906877</td>\n",
       "      <td>1.888571</td>\n",
       "      <td>3.574384</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2808.36624</td>\n",
       "      <td>2880.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.235864</td>\n",
       "      <td>0.304058</td>\n",
       "      <td>0.238228</td>\n",
       "      <td>0.271857</td>\n",
       "      <td>0.234170</td>\n",
       "      <td>0.276148</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97641 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     cell_id        date        SWE  mean_inversed_swe  \\\n",
       "0       ASO_50M_SWE_USCARC_8  2017-07-17  48.561301           4.697841   \n",
       "1       ASO_50M_SWE_USCARC_9  2017-07-17  33.924218           4.736198   \n",
       "2      ASO_50M_SWE_USCARC_26  2017-07-17  31.831418           5.221427   \n",
       "3      ASO_50M_SWE_USCARC_27  2017-07-17  41.723133           5.322858   \n",
       "4      ASO_50M_SWE_USCARC_28  2017-07-17  42.693821           5.377803   \n",
       "...                      ...         ...        ...                ...   \n",
       "97636  ASO_50M_SWE_USCALB_79  2018-06-01   0.474991           2.126230   \n",
       "97637  ASO_50M_SWE_USCALB_87  2018-06-01   1.828016           2.115175   \n",
       "97638  ASO_50M_SWE_USCALB_88  2018-06-01   0.114890           2.125414   \n",
       "97639  ASO_50M_SWE_USCALB_89  2018-06-01   2.170014           2.135945   \n",
       "97640  ASO_50M_SWE_USCALB_99  2018-06-01   2.213607           2.176050   \n",
       "\n",
       "       mean_local_swe  median_local_swe  max_local_swe  min_local_swe  \\\n",
       "0            4.685963          1.381988      18.482857       0.573913   \n",
       "1            4.685963          1.381988      18.482857       0.573913   \n",
       "2            4.685963          1.381988      18.482857       0.573913   \n",
       "3            4.685963          1.381988      18.482857       0.573913   \n",
       "4            4.685963          1.381988      18.482857       0.573913   \n",
       "...               ...               ...            ...            ...   \n",
       "97636        1.906877          1.888571       3.574384       0.000000   \n",
       "97637        1.906877          1.888571       3.574384       0.000000   \n",
       "97638        1.906877          1.888571       3.574384       0.000000   \n",
       "97639        1.906877          1.888571       3.574384       0.000000   \n",
       "97640        1.906877          1.888571       3.574384       0.000000   \n",
       "\n",
       "       mean_local_elevation  median_local_elevation  ...  MOD10A1_SnowCover  \\\n",
       "0                2834.64000                 2880.36  ...           0.761684   \n",
       "1                2834.64000                 2880.36  ...           0.666698   \n",
       "2                2834.64000                 2880.36  ...           0.717794   \n",
       "3                2834.64000                 2880.36  ...           0.758141   \n",
       "4                2834.64000                 2880.36  ...           0.732553   \n",
       "...                     ...                     ...  ...                ...   \n",
       "97636            2808.36624                 2880.36  ...           0.050713   \n",
       "97637            2808.36624                 2880.36  ...           0.028636   \n",
       "97638            2808.36624                 2880.36  ...           0.143382   \n",
       "97639            2808.36624                 2880.36  ...           0.115417   \n",
       "97640            2808.36624                 2880.36  ...           0.235864   \n",
       "\n",
       "       MOD10A1_Albedo  MOD10A1_NDSI  MYD10A1_SnowCover  MYD10A1_Albedo  \\\n",
       "0            0.367174      0.766883           0.814966        0.345533   \n",
       "1            0.326389      0.669003           0.790095        0.339398   \n",
       "2            0.387605      0.721743           0.660893        0.377931   \n",
       "3            0.388365      0.761231           0.754647        0.370129   \n",
       "4            0.393211      0.737283           0.753537        0.346098   \n",
       "...               ...           ...                ...             ...   \n",
       "97636        0.056434      0.044083           0.018015        0.023185   \n",
       "97637        0.053653     -0.007782           0.000000        0.000000   \n",
       "97638        0.132695      0.066765           0.009245        0.012961   \n",
       "97639        0.143560      0.091611           0.010301        0.015503   \n",
       "97640        0.304058      0.238228           0.271857        0.234170   \n",
       "\n",
       "       MYD10A1_NDSI                           copernicus_filelocations  \\\n",
       "0          0.818492  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "1          0.792235  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "2          0.665706  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "3          0.758559  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "4          0.757798  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "...             ...                                                ...   \n",
       "97636     -0.121343  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "97637     -0.069880  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "97638     -0.126452  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "97639     -0.018298  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "97640      0.276148  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "\n",
       "                                  sentinel1_filelocation  \\\n",
       "0      /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "1      /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "2      /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "3      /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "4      /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "...                                                  ...   \n",
       "97636  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "97637  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "97638  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "97639  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "97640  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "\n",
       "                                 sentinel2a_filelocation  \\\n",
       "0      /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "1      /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "2      /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "3      /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "4      /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "...                                                  ...   \n",
       "97636  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "97637  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "97638  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "97639  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "97640  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "\n",
       "                                 sentinel2b_filelocation  \n",
       "0      /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...  \n",
       "1      /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...  \n",
       "2      /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...  \n",
       "3      /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...  \n",
       "4      /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...  \n",
       "...                                                  ...  \n",
       "97636  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...  \n",
       "97637  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...  \n",
       "97638  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...  \n",
       "97639  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...  \n",
       "97640  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...  \n",
       "\n",
       "[97641 rows x 22 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get cell_id - geometry pairing and merge to df\n",
    "t = pd.read_csv(f'/home/ubuntu/traindf_allregions.csv',usecols=['cell_id','date','geometry'])\n",
    "\n",
    "df= df.merge(t,how='left',on=['cell_id','date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Designating which columns are our metadata\n",
    "feature_cols = [col for col in df.columns \n",
    "                if col not in \n",
    "                ['cell_id', 'date', 'geometry','MOD10A1_filelocations', 'MYD10A1_filelocations', \n",
    "                 'copernicus_filelocations', 'SWE','sentinel1_filelocation','sentinel2a_filelocation',\n",
    "                 'sentinel2b_filelocation','SWE_Scaled'\n",
    "                 ,'mean_inversed_swe', 'mean_local_swe', 'median_local_swe', 'max_local_swe', 'min_local_swe',\n",
    "                 'mean_local_elevation', 'median_local_elevation', 'max_local_elevation', 'min_local_elevation']]\n",
    "\n",
    "#Min max scaling the meta data\n",
    "scaler = sklearn.preprocessing.MinMaxScaler()\n",
    "df[feature_cols] =  scaler.fit_transform(df[feature_cols])\n",
    "\n",
    "#We will create a separate scaler for the targets so that we can transform them back and forth\n",
    "target_scaler = sklearn.preprocessing.MinMaxScaler()\n",
    "target_scaler.fit(np.array(df['SWE']).reshape(-1, 1))\n",
    "df['SWE_Scaled'] = target_scaler.transform(np.array(df['SWE']).reshape(-1, 1))\n",
    "\n",
    "tabluar_columns = len(feature_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Tmp drop two rows until better solution i s found\n",
    "df = df.drop([56955,82314])\n",
    "df.reset_index(inplace=True,drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cell_id</th>\n",
       "      <th>date</th>\n",
       "      <th>SWE</th>\n",
       "      <th>mean_inversed_swe</th>\n",
       "      <th>mean_local_swe</th>\n",
       "      <th>median_local_swe</th>\n",
       "      <th>max_local_swe</th>\n",
       "      <th>min_local_swe</th>\n",
       "      <th>mean_local_elevation</th>\n",
       "      <th>median_local_elevation</th>\n",
       "      <th>...</th>\n",
       "      <th>MOD10A1_NDSI</th>\n",
       "      <th>MYD10A1_SnowCover</th>\n",
       "      <th>MYD10A1_Albedo</th>\n",
       "      <th>MYD10A1_NDSI</th>\n",
       "      <th>copernicus_filelocations</th>\n",
       "      <th>sentinel1_filelocation</th>\n",
       "      <th>sentinel2a_filelocation</th>\n",
       "      <th>sentinel2b_filelocation</th>\n",
       "      <th>geometry</th>\n",
       "      <th>SWE_Scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ASO_50M_SWE_USCARC_8</td>\n",
       "      <td>2017-07-17</td>\n",
       "      <td>48.561301</td>\n",
       "      <td>4.697841</td>\n",
       "      <td>4.685963</td>\n",
       "      <td>1.381988</td>\n",
       "      <td>18.482857</td>\n",
       "      <td>0.573913</td>\n",
       "      <td>2834.64000</td>\n",
       "      <td>2880.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.873086</td>\n",
       "      <td>0.842185</td>\n",
       "      <td>0.355794</td>\n",
       "      <td>0.896260</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-119.27063272296992 37.7439075502243...</td>\n",
       "      <td>0.339387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ASO_50M_SWE_USCARC_9</td>\n",
       "      <td>2017-07-17</td>\n",
       "      <td>33.924218</td>\n",
       "      <td>4.736198</td>\n",
       "      <td>4.685963</td>\n",
       "      <td>1.381988</td>\n",
       "      <td>18.482857</td>\n",
       "      <td>0.573913</td>\n",
       "      <td>2834.64000</td>\n",
       "      <td>2880.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.807323</td>\n",
       "      <td>0.816483</td>\n",
       "      <td>0.349477</td>\n",
       "      <td>0.878423</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-119.27035750488221 37.7349013266373...</td>\n",
       "      <td>0.237091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ASO_50M_SWE_USCARC_26</td>\n",
       "      <td>2017-07-17</td>\n",
       "      <td>31.831418</td>\n",
       "      <td>5.221427</td>\n",
       "      <td>4.685963</td>\n",
       "      <td>1.381988</td>\n",
       "      <td>18.482857</td>\n",
       "      <td>0.573913</td>\n",
       "      <td>2834.64000</td>\n",
       "      <td>2880.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.842758</td>\n",
       "      <td>0.682966</td>\n",
       "      <td>0.389155</td>\n",
       "      <td>0.792468</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-119.25983912243692 37.7621381423023...</td>\n",
       "      <td>0.222465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ASO_50M_SWE_USCARC_27</td>\n",
       "      <td>2017-07-17</td>\n",
       "      <td>41.723133</td>\n",
       "      <td>5.322858</td>\n",
       "      <td>4.685963</td>\n",
       "      <td>1.381988</td>\n",
       "      <td>18.482857</td>\n",
       "      <td>0.573913</td>\n",
       "      <td>2834.64000</td>\n",
       "      <td>2880.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.869289</td>\n",
       "      <td>0.779852</td>\n",
       "      <td>0.381121</td>\n",
       "      <td>0.855546</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-119.25956503213258 37.7531318793437...</td>\n",
       "      <td>0.291596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ASO_50M_SWE_USCARC_28</td>\n",
       "      <td>2017-07-17</td>\n",
       "      <td>42.693821</td>\n",
       "      <td>5.377803</td>\n",
       "      <td>4.685963</td>\n",
       "      <td>1.381988</td>\n",
       "      <td>18.482857</td>\n",
       "      <td>0.573913</td>\n",
       "      <td>2834.64000</td>\n",
       "      <td>2880.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.853199</td>\n",
       "      <td>0.778704</td>\n",
       "      <td>0.356376</td>\n",
       "      <td>0.855029</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-119.2592910638845 37.74412560089217...</td>\n",
       "      <td>0.298380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97634</th>\n",
       "      <td>ASO_50M_SWE_USCALB_79</td>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>0.474991</td>\n",
       "      <td>2.126230</td>\n",
       "      <td>1.906877</td>\n",
       "      <td>1.888571</td>\n",
       "      <td>3.574384</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2808.36624</td>\n",
       "      <td>2880.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.387460</td>\n",
       "      <td>0.018616</td>\n",
       "      <td>0.023873</td>\n",
       "      <td>0.257797</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-118.99236337285141 37.5873385237014...</td>\n",
       "      <td>0.003320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97635</th>\n",
       "      <td>ASO_50M_SWE_USCALB_87</td>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>1.828016</td>\n",
       "      <td>2.115175</td>\n",
       "      <td>1.906877</td>\n",
       "      <td>1.888571</td>\n",
       "      <td>3.574384</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2808.36624</td>\n",
       "      <td>2880.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.352613</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.292758</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-118.9815213549945 37.60554539534471...</td>\n",
       "      <td>0.012776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97636</th>\n",
       "      <td>ASO_50M_SWE_USCALB_88</td>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>0.114890</td>\n",
       "      <td>2.125414</td>\n",
       "      <td>1.906877</td>\n",
       "      <td>1.888571</td>\n",
       "      <td>3.574384</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2808.36624</td>\n",
       "      <td>2880.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.402699</td>\n",
       "      <td>0.009553</td>\n",
       "      <td>0.013346</td>\n",
       "      <td>0.254326</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-118.98128235871765 37.5965372685042...</td>\n",
       "      <td>0.000803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97637</th>\n",
       "      <td>ASO_50M_SWE_USCALB_89</td>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>2.170014</td>\n",
       "      <td>2.135945</td>\n",
       "      <td>1.906877</td>\n",
       "      <td>1.888571</td>\n",
       "      <td>3.574384</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2808.36624</td>\n",
       "      <td>2880.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.419393</td>\n",
       "      <td>0.010645</td>\n",
       "      <td>0.015963</td>\n",
       "      <td>0.327799</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-118.98104346883868 37.5875291265839...</td>\n",
       "      <td>0.015166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97638</th>\n",
       "      <td>ASO_50M_SWE_USCALB_99</td>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>2.213607</td>\n",
       "      <td>2.176050</td>\n",
       "      <td>1.906877</td>\n",
       "      <td>1.888571</td>\n",
       "      <td>3.574384</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2808.36624</td>\n",
       "      <td>2880.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.517900</td>\n",
       "      <td>0.280937</td>\n",
       "      <td>0.241124</td>\n",
       "      <td>0.527826</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-118.96972345847816 37.5877186446765...</td>\n",
       "      <td>0.015471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97639 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     cell_id        date        SWE  mean_inversed_swe  \\\n",
       "0       ASO_50M_SWE_USCARC_8  2017-07-17  48.561301           4.697841   \n",
       "1       ASO_50M_SWE_USCARC_9  2017-07-17  33.924218           4.736198   \n",
       "2      ASO_50M_SWE_USCARC_26  2017-07-17  31.831418           5.221427   \n",
       "3      ASO_50M_SWE_USCARC_27  2017-07-17  41.723133           5.322858   \n",
       "4      ASO_50M_SWE_USCARC_28  2017-07-17  42.693821           5.377803   \n",
       "...                      ...         ...        ...                ...   \n",
       "97634  ASO_50M_SWE_USCALB_79  2018-06-01   0.474991           2.126230   \n",
       "97635  ASO_50M_SWE_USCALB_87  2018-06-01   1.828016           2.115175   \n",
       "97636  ASO_50M_SWE_USCALB_88  2018-06-01   0.114890           2.125414   \n",
       "97637  ASO_50M_SWE_USCALB_89  2018-06-01   2.170014           2.135945   \n",
       "97638  ASO_50M_SWE_USCALB_99  2018-06-01   2.213607           2.176050   \n",
       "\n",
       "       mean_local_swe  median_local_swe  max_local_swe  min_local_swe  \\\n",
       "0            4.685963          1.381988      18.482857       0.573913   \n",
       "1            4.685963          1.381988      18.482857       0.573913   \n",
       "2            4.685963          1.381988      18.482857       0.573913   \n",
       "3            4.685963          1.381988      18.482857       0.573913   \n",
       "4            4.685963          1.381988      18.482857       0.573913   \n",
       "...               ...               ...            ...            ...   \n",
       "97634        1.906877          1.888571       3.574384       0.000000   \n",
       "97635        1.906877          1.888571       3.574384       0.000000   \n",
       "97636        1.906877          1.888571       3.574384       0.000000   \n",
       "97637        1.906877          1.888571       3.574384       0.000000   \n",
       "97638        1.906877          1.888571       3.574384       0.000000   \n",
       "\n",
       "       mean_local_elevation  median_local_elevation  ...  MOD10A1_NDSI  \\\n",
       "0                2834.64000                 2880.36  ...      0.873086   \n",
       "1                2834.64000                 2880.36  ...      0.807323   \n",
       "2                2834.64000                 2880.36  ...      0.842758   \n",
       "3                2834.64000                 2880.36  ...      0.869289   \n",
       "4                2834.64000                 2880.36  ...      0.853199   \n",
       "...                     ...                     ...  ...           ...   \n",
       "97634            2808.36624                 2880.36  ...      0.387460   \n",
       "97635            2808.36624                 2880.36  ...      0.352613   \n",
       "97636            2808.36624                 2880.36  ...      0.402699   \n",
       "97637            2808.36624                 2880.36  ...      0.419393   \n",
       "97638            2808.36624                 2880.36  ...      0.517900   \n",
       "\n",
       "       MYD10A1_SnowCover  MYD10A1_Albedo  MYD10A1_NDSI  \\\n",
       "0               0.842185        0.355794      0.896260   \n",
       "1               0.816483        0.349477      0.878423   \n",
       "2               0.682966        0.389155      0.792468   \n",
       "3               0.779852        0.381121      0.855546   \n",
       "4               0.778704        0.356376      0.855029   \n",
       "...                  ...             ...           ...   \n",
       "97634           0.018616        0.023873      0.257797   \n",
       "97635           0.000000        0.000000      0.292758   \n",
       "97636           0.009553        0.013346      0.254326   \n",
       "97637           0.010645        0.015963      0.327799   \n",
       "97638           0.280937        0.241124      0.527826   \n",
       "\n",
       "                                copernicus_filelocations  \\\n",
       "0      /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "1      /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "2      /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "3      /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "4      /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "...                                                  ...   \n",
       "97634  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "97635  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "97636  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "97637  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "97638  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "\n",
       "                                  sentinel1_filelocation  \\\n",
       "0      /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "1      /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "2      /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "3      /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "4      /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "...                                                  ...   \n",
       "97634  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "97635  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "97636  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "97637  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "97638  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "\n",
       "                                 sentinel2a_filelocation  \\\n",
       "0      /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "1      /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "2      /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "3      /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "4      /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "...                                                  ...   \n",
       "97634  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "97635  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "97636  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "97637  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "97638  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "\n",
       "                                 sentinel2b_filelocation  \\\n",
       "0      /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "1      /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "2      /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "3      /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "4      /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "...                                                  ...   \n",
       "97634  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "97635  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "97636  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "97637  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "97638  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "\n",
       "                                                geometry SWE_Scaled  \n",
       "0      POLYGON ((-119.27063272296992 37.7439075502243...   0.339387  \n",
       "1      POLYGON ((-119.27035750488221 37.7349013266373...   0.237091  \n",
       "2      POLYGON ((-119.25983912243692 37.7621381423023...   0.222465  \n",
       "3      POLYGON ((-119.25956503213258 37.7531318793437...   0.291596  \n",
       "4      POLYGON ((-119.2592910638845 37.74412560089217...   0.298380  \n",
       "...                                                  ...        ...  \n",
       "97634  POLYGON ((-118.99236337285141 37.5873385237014...   0.003320  \n",
       "97635  POLYGON ((-118.9815213549945 37.60554539534471...   0.012776  \n",
       "97636  POLYGON ((-118.98128235871765 37.5965372685042...   0.000803  \n",
       "97637  POLYGON ((-118.98104346883868 37.5875291265839...   0.015166  \n",
       "97638  POLYGON ((-118.96972345847816 37.5877186446765...   0.015471  \n",
       "\n",
       "[97639 rows x 24 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cell_id</th>\n",
       "      <th>date</th>\n",
       "      <th>SWE</th>\n",
       "      <th>mean_inversed_swe</th>\n",
       "      <th>mean_local_swe</th>\n",
       "      <th>median_local_swe</th>\n",
       "      <th>max_local_swe</th>\n",
       "      <th>min_local_swe</th>\n",
       "      <th>mean_local_elevation</th>\n",
       "      <th>median_local_elevation</th>\n",
       "      <th>...</th>\n",
       "      <th>MOD10A1_NDSI</th>\n",
       "      <th>MYD10A1_SnowCover</th>\n",
       "      <th>MYD10A1_Albedo</th>\n",
       "      <th>MYD10A1_NDSI</th>\n",
       "      <th>copernicus_filelocations</th>\n",
       "      <th>sentinel1_filelocation</th>\n",
       "      <th>sentinel2a_filelocation</th>\n",
       "      <th>sentinel2b_filelocation</th>\n",
       "      <th>geometry</th>\n",
       "      <th>SWE_Scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>90555</th>\n",
       "      <td>ASO_50M_SWE_USCAKC_1755</td>\n",
       "      <td>2019-06-11</td>\n",
       "      <td>39.395702</td>\n",
       "      <td>26.869506</td>\n",
       "      <td>21.684</td>\n",
       "      <td>25.11</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.82</td>\n",
       "      <td>2916.936</td>\n",
       "      <td>2956.56</td>\n",
       "      <td>...</td>\n",
       "      <td>0.866739</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.807571</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-118.89230654533912 37.1906678512196...</td>\n",
       "      <td>0.27533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       cell_id        date        SWE  mean_inversed_swe  \\\n",
       "90555  ASO_50M_SWE_USCAKC_1755  2019-06-11  39.395702          26.869506   \n",
       "\n",
       "       mean_local_swe  median_local_swe  max_local_swe  min_local_swe  \\\n",
       "90555          21.684             25.11           36.0           0.82   \n",
       "\n",
       "       mean_local_elevation  median_local_elevation  ...  MOD10A1_NDSI  \\\n",
       "90555              2916.936                 2956.56  ...      0.866739   \n",
       "\n",
       "       MYD10A1_SnowCover  MYD10A1_Albedo  MYD10A1_NDSI  \\\n",
       "90555                0.0             0.0      0.807571   \n",
       "\n",
       "                                copernicus_filelocations  \\\n",
       "90555  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "\n",
       "                                  sentinel1_filelocation  \\\n",
       "90555  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "\n",
       "                                 sentinel2a_filelocation  \\\n",
       "90555  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "\n",
       "                                 sentinel2b_filelocation  \\\n",
       "90555  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "\n",
       "                                                geometry SWE_Scaled  \n",
       "90555  POLYGON ((-118.89230654533912 37.1906678512196...    0.27533  \n",
       "\n",
       "[1 rows x 24 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['cell_id']=='ASO_50M_SWE_USCAKC_1755')& (df['date']== '2019-06-11')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Join weather data\n",
    "#a = pd.read_csv('/home/ubuntu/SnowData/Unique_CellIDs_byDate__ASO_50M_SWE_USCALB__with_HRRR_TMP_surface_12h.csv',index_col=0)\n",
    "#b = pd.read_csv('/home/ubuntu/SnowData/Unique_CellIDs_byDate__ASO_50M_SWE_USCALB__with_HRRR_PRATE_surface_12h.csv',index_col=0)\n",
    "#weather = pd.merge(a, b,  how='left', on = ['index','cell_id','geometry','date','month_year'])\n",
    "weather = pd.read_csv('/home/ubuntu/SnowData/GRIDMET_Weather_Data_V3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#weather[weather.duplicated(subset=['cell_id', 'date'],keep=False)].sort_values(['cell_id', 'date']).head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop any entries with duplicate cell_id and date combination\n",
    "#weather = weather.drop_duplicates(subset=['cell_id', 'date'], keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#weather[(weather['cell_id']=='ASO_50M_SWE_USCARC_8')&(weather['geometry']==a)&(weather['date']=='2017-07-17')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = []\n",
    "for i in df['date'].values:\n",
    "    if pd.to_datetime(i).strftime('%Y') in year:\n",
    "        continue\n",
    "    else:\n",
    "        year.append(pd.to_datetime(i).strftime('%Y'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2017', '2019', '2016', '2018']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cell_id</th>\n",
       "      <th>date</th>\n",
       "      <th>SWE</th>\n",
       "      <th>mean_inversed_swe</th>\n",
       "      <th>mean_local_swe</th>\n",
       "      <th>median_local_swe</th>\n",
       "      <th>max_local_swe</th>\n",
       "      <th>min_local_swe</th>\n",
       "      <th>mean_local_elevation</th>\n",
       "      <th>median_local_elevation</th>\n",
       "      <th>...</th>\n",
       "      <th>MOD10A1_NDSI</th>\n",
       "      <th>MYD10A1_SnowCover</th>\n",
       "      <th>MYD10A1_Albedo</th>\n",
       "      <th>MYD10A1_NDSI</th>\n",
       "      <th>copernicus_filelocations</th>\n",
       "      <th>sentinel1_filelocation</th>\n",
       "      <th>sentinel2a_filelocation</th>\n",
       "      <th>sentinel2b_filelocation</th>\n",
       "      <th>geometry</th>\n",
       "      <th>SWE_Scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7536</th>\n",
       "      <td>ASO_50M_SWE_USCATB_2267</td>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.035490</td>\n",
       "      <td>2.174248</td>\n",
       "      <td>0.426335</td>\n",
       "      <td>9.612795</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2322.454088</td>\n",
       "      <td>2331.72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.413564</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.433309</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-119.53128775945261 37.9910717933107...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13982</th>\n",
       "      <td>ASO_50M_SWE_USCATB_2267</td>\n",
       "      <td>2016-04-26</td>\n",
       "      <td>29.657914</td>\n",
       "      <td>24.970221</td>\n",
       "      <td>25.838000</td>\n",
       "      <td>32.650000</td>\n",
       "      <td>55.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2441.326088</td>\n",
       "      <td>2331.72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.570611</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.767115</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-119.53128775945261 37.9910717933107...</td>\n",
       "      <td>0.207274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15107</th>\n",
       "      <td>ASO_50M_SWE_USCATB_2267</td>\n",
       "      <td>2017-01-29</td>\n",
       "      <td>39.044215</td>\n",
       "      <td>32.621191</td>\n",
       "      <td>29.960000</td>\n",
       "      <td>33.354286</td>\n",
       "      <td>42.864286</td>\n",
       "      <td>15.700000</td>\n",
       "      <td>2322.454088</td>\n",
       "      <td>2331.72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.872834</td>\n",
       "      <td>0.612198</td>\n",
       "      <td>0.348205</td>\n",
       "      <td>0.846093</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-119.53128775945261 37.9910717933107...</td>\n",
       "      <td>0.272874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24903</th>\n",
       "      <td>ASO_50M_SWE_USCATB_2267</td>\n",
       "      <td>2017-07-27</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.186704</td>\n",
       "      <td>2.390149</td>\n",
       "      <td>0.351801</td>\n",
       "      <td>10.991677</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2322.454088</td>\n",
       "      <td>2331.72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.176000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045450</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-119.53128775945261 37.9910717933107...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26029</th>\n",
       "      <td>ASO_50M_SWE_USCATB_2267</td>\n",
       "      <td>2016-03-26</td>\n",
       "      <td>34.362943</td>\n",
       "      <td>9.228537</td>\n",
       "      <td>10.195728</td>\n",
       "      <td>1.927619</td>\n",
       "      <td>44.894286</td>\n",
       "      <td>0.971429</td>\n",
       "      <td>2322.454088</td>\n",
       "      <td>2331.72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.865329</td>\n",
       "      <td>0.763864</td>\n",
       "      <td>0.313841</td>\n",
       "      <td>0.848192</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-119.53128775945261 37.9910717933107...</td>\n",
       "      <td>0.240157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33788</th>\n",
       "      <td>ASO_50M_SWE_USCATB_2267</td>\n",
       "      <td>2018-04-23</td>\n",
       "      <td>20.860187</td>\n",
       "      <td>10.663129</td>\n",
       "      <td>9.042857</td>\n",
       "      <td>10.668571</td>\n",
       "      <td>18.158571</td>\n",
       "      <td>0.014286</td>\n",
       "      <td>2322.454088</td>\n",
       "      <td>2331.72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.780527</td>\n",
       "      <td>0.501567</td>\n",
       "      <td>0.255780</td>\n",
       "      <td>0.757579</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-119.53128775945261 37.9910717933107...</td>\n",
       "      <td>0.145789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35492</th>\n",
       "      <td>ASO_50M_SWE_USCATB_2267</td>\n",
       "      <td>2016-04-07</td>\n",
       "      <td>25.710933</td>\n",
       "      <td>8.163772</td>\n",
       "      <td>8.914702</td>\n",
       "      <td>1.961143</td>\n",
       "      <td>40.367143</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2322.454088</td>\n",
       "      <td>2331.72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.676421</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.621020</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-119.53128775945261 37.9910717933107...</td>\n",
       "      <td>0.179690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38484</th>\n",
       "      <td>ASO_50M_SWE_USCATB_2267</td>\n",
       "      <td>2018-05-28</td>\n",
       "      <td>2.257307</td>\n",
       "      <td>0.075943</td>\n",
       "      <td>0.066000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2322.454088</td>\n",
       "      <td>2331.72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270596</td>\n",
       "      <td>0.073802</td>\n",
       "      <td>0.074757</td>\n",
       "      <td>0.395168</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-119.53128775945261 37.9910717933107...</td>\n",
       "      <td>0.015776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54559</th>\n",
       "      <td>ASO_50M_SWE_USCATB_2267</td>\n",
       "      <td>2016-05-09</td>\n",
       "      <td>26.732759</td>\n",
       "      <td>5.312869</td>\n",
       "      <td>5.628157</td>\n",
       "      <td>2.050540</td>\n",
       "      <td>23.761429</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2322.454088</td>\n",
       "      <td>2331.72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.853569</td>\n",
       "      <td>0.704663</td>\n",
       "      <td>0.257716</td>\n",
       "      <td>0.806836</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-119.53128775945261 37.9910717933107...</td>\n",
       "      <td>0.186831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68672</th>\n",
       "      <td>ASO_50M_SWE_USCATB_2267</td>\n",
       "      <td>2016-05-27</td>\n",
       "      <td>18.472615</td>\n",
       "      <td>3.007747</td>\n",
       "      <td>4.172379</td>\n",
       "      <td>3.542857</td>\n",
       "      <td>9.405714</td>\n",
       "      <td>1.951771</td>\n",
       "      <td>2840.736000</td>\n",
       "      <td>2834.64</td>\n",
       "      <td>...</td>\n",
       "      <td>0.907529</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.724002</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-119.31499791313145 37.8226861044099...</td>\n",
       "      <td>0.129102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89443</th>\n",
       "      <td>ASO_50M_SWE_USCATB_2267</td>\n",
       "      <td>2017-07-17</td>\n",
       "      <td>0.019484</td>\n",
       "      <td>2.262310</td>\n",
       "      <td>2.498099</td>\n",
       "      <td>0.314534</td>\n",
       "      <td>11.681118</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2322.454088</td>\n",
       "      <td>2331.72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.085894</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.149528</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-119.53128775945261 37.9910717933107...</td>\n",
       "      <td>0.000136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91302</th>\n",
       "      <td>ASO_50M_SWE_USCATB_2267</td>\n",
       "      <td>2016-04-01</td>\n",
       "      <td>29.916373</td>\n",
       "      <td>8.648106</td>\n",
       "      <td>9.479644</td>\n",
       "      <td>1.944381</td>\n",
       "      <td>42.995714</td>\n",
       "      <td>0.114286</td>\n",
       "      <td>2322.454088</td>\n",
       "      <td>2331.72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.854208</td>\n",
       "      <td>0.751667</td>\n",
       "      <td>0.362223</td>\n",
       "      <td>0.837303</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-119.53128775945261 37.9910717933107...</td>\n",
       "      <td>0.209081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95721</th>\n",
       "      <td>ASO_50M_SWE_USCATB_2267</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>25.368814</td>\n",
       "      <td>7.343333</td>\n",
       "      <td>7.969004</td>\n",
       "      <td>1.986286</td>\n",
       "      <td>35.590000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2322.454088</td>\n",
       "      <td>2331.72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.889146</td>\n",
       "      <td>0.797243</td>\n",
       "      <td>0.272609</td>\n",
       "      <td>0.866137</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-119.53128775945261 37.9910717933107...</td>\n",
       "      <td>0.177299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97428</th>\n",
       "      <td>ASO_50M_SWE_USCATB_2267</td>\n",
       "      <td>2016-07-08</td>\n",
       "      <td>0.055945</td>\n",
       "      <td>1.275564</td>\n",
       "      <td>1.013280</td>\n",
       "      <td>0.380062</td>\n",
       "      <td>2.485551</td>\n",
       "      <td>0.018634</td>\n",
       "      <td>2840.736000</td>\n",
       "      <td>2834.64</td>\n",
       "      <td>...</td>\n",
       "      <td>0.188762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.109188</td>\n",
       "      <td>/home/ubuntu/SnowData/CopernicusData/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...</td>\n",
       "      <td>/home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...</td>\n",
       "      <td>POLYGON ((-119.31499791313145 37.8226861044099...</td>\n",
       "      <td>0.000391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       cell_id        date        SWE  mean_inversed_swe  \\\n",
       "7536   ASO_50M_SWE_USCATB_2267  2017-08-16   0.000000           2.035490   \n",
       "13982  ASO_50M_SWE_USCATB_2267  2016-04-26  29.657914          24.970221   \n",
       "15107  ASO_50M_SWE_USCATB_2267  2017-01-29  39.044215          32.621191   \n",
       "24903  ASO_50M_SWE_USCATB_2267  2017-07-27   0.000000           2.186704   \n",
       "26029  ASO_50M_SWE_USCATB_2267  2016-03-26  34.362943           9.228537   \n",
       "33788  ASO_50M_SWE_USCATB_2267  2018-04-23  20.860187          10.663129   \n",
       "35492  ASO_50M_SWE_USCATB_2267  2016-04-07  25.710933           8.163772   \n",
       "38484  ASO_50M_SWE_USCATB_2267  2018-05-28   2.257307           0.075943   \n",
       "54559  ASO_50M_SWE_USCATB_2267  2016-05-09  26.732759           5.312869   \n",
       "68672  ASO_50M_SWE_USCATB_2267  2016-05-27  18.472615           3.007747   \n",
       "89443  ASO_50M_SWE_USCATB_2267  2017-07-17   0.019484           2.262310   \n",
       "91302  ASO_50M_SWE_USCATB_2267  2016-04-01  29.916373           8.648106   \n",
       "95721  ASO_50M_SWE_USCATB_2267  2016-04-16  25.368814           7.343333   \n",
       "97428  ASO_50M_SWE_USCATB_2267  2016-07-08   0.055945           1.275564   \n",
       "\n",
       "       mean_local_swe  median_local_swe  max_local_swe  min_local_swe  \\\n",
       "7536         2.174248          0.426335       9.612795       0.000000   \n",
       "13982       25.838000         32.650000      55.800000       0.000000   \n",
       "15107       29.960000         33.354286      42.864286      15.700000   \n",
       "24903        2.390149          0.351801      10.991677       0.000000   \n",
       "26029       10.195728          1.927619      44.894286       0.971429   \n",
       "33788        9.042857         10.668571      18.158571       0.014286   \n",
       "35492        8.914702          1.961143      40.367143       0.000000   \n",
       "38484        0.066000          0.000000       0.240000       0.000000   \n",
       "54559        5.628157          2.050540      23.761429       0.000000   \n",
       "68672        4.172379          3.542857       9.405714       1.951771   \n",
       "89443        2.498099          0.314534      11.681118       0.000000   \n",
       "91302        9.479644          1.944381      42.995714       0.114286   \n",
       "95721        7.969004          1.986286      35.590000       0.000000   \n",
       "97428        1.013280          0.380062       2.485551       0.018634   \n",
       "\n",
       "       mean_local_elevation  median_local_elevation  ...  MOD10A1_NDSI  \\\n",
       "7536            2322.454088                 2331.72  ...      0.413564   \n",
       "13982           2441.326088                 2331.72  ...      0.570611   \n",
       "15107           2322.454088                 2331.72  ...      0.872834   \n",
       "24903           2322.454088                 2331.72  ...      0.176000   \n",
       "26029           2322.454088                 2331.72  ...      0.865329   \n",
       "33788           2322.454088                 2331.72  ...      0.780527   \n",
       "35492           2322.454088                 2331.72  ...      0.676421   \n",
       "38484           2322.454088                 2331.72  ...      0.270596   \n",
       "54559           2322.454088                 2331.72  ...      0.853569   \n",
       "68672           2840.736000                 2834.64  ...      0.907529   \n",
       "89443           2322.454088                 2331.72  ...      0.085894   \n",
       "91302           2322.454088                 2331.72  ...      0.854208   \n",
       "95721           2322.454088                 2331.72  ...      0.889146   \n",
       "97428           2840.736000                 2834.64  ...      0.188762   \n",
       "\n",
       "       MYD10A1_SnowCover  MYD10A1_Albedo  MYD10A1_NDSI  \\\n",
       "7536            0.000000        0.000000      0.433309   \n",
       "13982           0.000000        0.000000      0.767115   \n",
       "15107           0.612198        0.348205      0.846093   \n",
       "24903           0.000000        0.000000      0.045450   \n",
       "26029           0.763864        0.313841      0.848192   \n",
       "33788           0.501567        0.255780      0.757579   \n",
       "35492           0.000000        0.000000      0.621020   \n",
       "38484           0.073802        0.074757      0.395168   \n",
       "54559           0.704663        0.257716      0.806836   \n",
       "68672           0.000000        0.000000      0.724002   \n",
       "89443           0.000000        0.000000      0.149528   \n",
       "91302           0.751667        0.362223      0.837303   \n",
       "95721           0.797243        0.272609      0.866137   \n",
       "97428           0.000000        0.000000      0.109188   \n",
       "\n",
       "                                copernicus_filelocations  \\\n",
       "7536   /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "13982  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "15107  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "24903  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "26029  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "33788  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "35492  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "38484  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "54559  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "68672  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "89443  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "91302  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "95721  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "97428  /home/ubuntu/SnowData/CopernicusData/ASO_50M_S...   \n",
       "\n",
       "                                  sentinel1_filelocation  \\\n",
       "7536   /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "13982  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "15107  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "24903  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "26029  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "33788  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "35492  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "38484  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "54559  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "68672  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "89443  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "91302  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "95721  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "97428  /home/ubuntu/SnowData/Sen1_Data_poly/ASO_50M_S...   \n",
       "\n",
       "                                 sentinel2a_filelocation  \\\n",
       "7536   /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "13982  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "15107  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "24903  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "26029  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "33788  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "35492  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "38484  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "54559  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "68672  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "89443  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "91302  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "95721  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "97428  /home/ubuntu/SnowData/Sen2_DataA_poly/ASO_50M_...   \n",
       "\n",
       "                                 sentinel2b_filelocation  \\\n",
       "7536   /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "13982  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "15107  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "24903  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "26029  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "33788  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "35492  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "38484  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "54559  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "68672  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "89443  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "91302  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "95721  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "97428  /home/ubuntu/SnowData/Sen2_DataB_poly/ASO_50M_...   \n",
       "\n",
       "                                                geometry SWE_Scaled  \n",
       "7536   POLYGON ((-119.53128775945261 37.9910717933107...   0.000000  \n",
       "13982  POLYGON ((-119.53128775945261 37.9910717933107...   0.207274  \n",
       "15107  POLYGON ((-119.53128775945261 37.9910717933107...   0.272874  \n",
       "24903  POLYGON ((-119.53128775945261 37.9910717933107...   0.000000  \n",
       "26029  POLYGON ((-119.53128775945261 37.9910717933107...   0.240157  \n",
       "33788  POLYGON ((-119.53128775945261 37.9910717933107...   0.145789  \n",
       "35492  POLYGON ((-119.53128775945261 37.9910717933107...   0.179690  \n",
       "38484  POLYGON ((-119.53128775945261 37.9910717933107...   0.015776  \n",
       "54559  POLYGON ((-119.53128775945261 37.9910717933107...   0.186831  \n",
       "68672  POLYGON ((-119.31499791313145 37.8226861044099...   0.129102  \n",
       "89443  POLYGON ((-119.53128775945261 37.9910717933107...   0.000136  \n",
       "91302  POLYGON ((-119.53128775945261 37.9910717933107...   0.209081  \n",
       "95721  POLYGON ((-119.53128775945261 37.9910717933107...   0.177299  \n",
       "97428  POLYGON ((-119.31499791313145 37.8226861044099...   0.000391  \n",
       "\n",
       "[14 rows x 24 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['cell_id'] == 'ASO_50M_SWE_USCATB_2267']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 1212\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1212"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class args:\n",
    "    #Overall Args\n",
    "    folder_name = \"/home/ubuntu/SnowData\"\n",
    "\n",
    "    #Keep track of features used in wandb\n",
    "    features = feature_cols\n",
    "    lstm_features = ['precip_daily','wind_dir_avg','temp_min','temp_max','wind_vel']\n",
    "\n",
    "    #Setting the number of CPU workers we are using\n",
    "    num_workers = 12\n",
    "\n",
    "    #Setting the seed so we can replicate\n",
    "    seed = 1212\n",
    "\n",
    "    #Toggle for whether or not we want our model pretrained on imagenet\n",
    "    pretrained = True\n",
    "\n",
    "    #Next we pick the model name with the appropriate shape, img size and output\n",
    "    model_name1 = 'mixnet_s'\n",
    "    model_shape1 = 1536\n",
    "    model_name2 = 'tf_efficientnet_b2_ns'\n",
    "    model_shape2 = 1408 #768 for swin small 1536 for swin large 1792 for efficientnet b4 768 for cait-m-36\n",
    "    imagesize = 224\n",
    "    num_classes = 1\n",
    "    img_channels = 3\n",
    "    \n",
    "    dropout1 = .1\n",
    "    dropout2 = .2\n",
    "\n",
    "    #LSTM variables\n",
    "    lstm_hidden = 128\n",
    "    lstm_layers = 4\n",
    "    lstm_seqlen = 10\n",
    "\n",
    "    #Training Args\n",
    "    train_batch_size = 28\n",
    "    val_batch_size = 28\n",
    "    test_batch_size = 28\n",
    "\n",
    "    #Max epochs and number of folds\n",
    "    max_epochs = 100\n",
    "    n_splits = 2\n",
    "\n",
    "    #Optimizer and Scheduler args\n",
    "    loss = 'nn.BCEWithLogitsLoss'\n",
    "    lr = 3e-4\n",
    "    warmup_epochs = 5\n",
    "    weight_decay = 3e-6\n",
    "    eta_min = 0.000001\n",
    "    n_accumulate = 1\n",
    "    T_0 = 50\n",
    "    T_max = 3000\n",
    "\n",
    "    #Callback args\n",
    "    #Minimum number amount of improvement to not trigger patience\n",
    "    min_delta = 0.0\n",
    "    #Number of epochs in a row to wait for improvement\n",
    "    patience = 30\n",
    "\n",
    "#Dataloader Args\n",
    "loaderargs = {'num_workers' : args.num_workers, 'pin_memory': False, 'drop_last': False}\n",
    "device = torch.device(\"cuda:0\")\n",
    "\n",
    "seed_everything(args.seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Datasets are how pytorch knows how to read in the data\n",
    "class SWEDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, df,ts, test = False, seq_len = 10):\n",
    "        self.df = df\n",
    "        self.seq_len = seq_len\n",
    "        #First we must specify the path to the images\n",
    "        #self.MOD10A1_file_names = df['MOD10A1_filelocations'].values\n",
    "        #self.MYD10A1_file_names = df['MYD10A1_filelocations'].values\n",
    "        self.copernicus_file_names = df['copernicus_filelocations'].values\n",
    "        self.sentinel1_file_names = df['sentinel1_filelocation'].values\n",
    "        self.sentinel2a_file_names = df['sentinel2a_filelocation'].values\n",
    "        self.sentinel2b_file_names = df['sentinel2b_filelocation'].values\n",
    "        #Variables to query time series and output\n",
    "        self.cell_id = df['cell_id'].values\n",
    "        self.date = df['date'].values\n",
    "        self.geometry = df['geometry'].values\n",
    "        ts['date'] = pd.to_datetime(ts['date'])\n",
    "        self.timeseries = ts\n",
    "        #The only transform we want to do right now is the resizing\n",
    "        self._transform = T.Resize(size= (args.imagesize, args.imagesize))\n",
    "        #We specify the tabular feature columns\n",
    "        self.meta = df[feature_cols].values\n",
    "        #Now we specify the targets\n",
    "        self.targets = df['SWE_Scaled'].values\n",
    "        #Finally we specify if this is training or test\n",
    "        self.test = test\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.df)\n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        #Get the image, scale it to between 0-1 and resize it\n",
    "        copernicus_img_path = self.copernicus_file_names[index]\n",
    "        copernicus_img = read_image(copernicus_img_path,mode = torchvision.io.image.ImageReadMode.RGB) / 255\n",
    "        copernicus_img = self._transform(copernicus_img)\n",
    "        \n",
    "        sentinel1_img_path = self.sentinel1_file_names[index]\n",
    "        sentinel1_img = read_image(sentinel1_img_path, mode = torchvision.io.image.ImageReadMode.RGB) / 255\n",
    "        sentinel1_img = self._transform(sentinel1_img)\n",
    "        \n",
    "        sentinel2a_img_path = self.sentinel2a_file_names[index]\n",
    "        sentinel2a_img = read_image(sentinel2a_img_path, mode = torchvision.io.image.ImageReadMode.RGB) / 255\n",
    "        sentinel2a_img = self._transform(sentinel2a_img)\n",
    "        \n",
    "        sentinel2b_img_path = self.sentinel2b_file_names[index]\n",
    "        sentinel2b_img = read_image(sentinel2b_img_path, mode = torchvision.io.image.ImageReadMode.RGB) / 255\n",
    "        sentinel2b_img = self._transform(sentinel2b_img)\n",
    "\n",
    "        #Pull from weather data and generate time-series\n",
    "        #Variables = precip_daily|wind_dir_avg|temp_min|temp_max|wind_vel\n",
    "        date_range = pd.date_range(end=self.date[index], periods=self.seq_len)\n",
    "        \n",
    "        sequence = self.timeseries.loc[(self.timeseries['cell_id'] == self.cell_id[index]) \n",
    "                                       & (self.timeseries['geometry'] == self.geometry[index]) \n",
    "                                       & (self.timeseries.date.isin(date_range))\n",
    "                                      ].sort_values('date')\n",
    "        #fill na values\n",
    "        sequence = sequence.fillna(-1)\n",
    "        if list(sequence.shape) != [10,8]:\n",
    "            missing_dates = [[self.cell_id[index],-1,i,-1,-1,-1,-1,-1] for i in date_range if i not in list(sequence.date)]\n",
    "            missing_dates = pd.DataFrame(data = missing_dates,columns=['cell_id','geometry','date','precip_daily','wind_dir_avg','temp_min','temp_max','wind_vel'])\n",
    "            sequence = pd.concat([sequence,missing_dates],axis=0,ignore_index=True).sort_values('date')\n",
    "        #Drop non-tabular data columns\n",
    "        sequence.drop(['cell_id','geometry','date'],axis=1,inplace=True)\n",
    "        \n",
    "        ts = torch.tensor(sequence.values.tolist(),dtype=torch.float32)\n",
    "\n",
    "        \n",
    "        #Pull in the features for our batch\n",
    "        meta = self.meta[index, :]\n",
    "        \n",
    "        #Specify the target based on whether this is training or test\n",
    "        if self.test:\n",
    "          target = 0\n",
    "        else:\n",
    "          target = self.targets[index]\n",
    "            \n",
    "        return copernicus_img, sentinel1_img, sentinel2a_img, sentinel2b_img, target, meta , ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pytorch Lightning Requires that the dataset be formatted as a module\n",
    "class SWEDataModule(pl.LightningDataModule):\n",
    "    def __init__(self, traindf, valdf,ts,args, loaderargs):\n",
    "        super().__init__()\n",
    "        #Import our training and validation set, which we will define later\n",
    "        self._train_df = traindf\n",
    "        self._val_df = valdf\n",
    "        self.ts = ts\n",
    "\n",
    "        #Makesure we bring in our args so we can use them\n",
    "        self.args = args\n",
    "        self.loaderargs = loaderargs\n",
    "\n",
    "    #Building the datasets\n",
    "    def __create_dataset(self, train=True):\n",
    "        if train == 'train':\n",
    "          return SWEDataset(self._train_df,self.ts)\n",
    "        else:\n",
    "          return SWEDataset(self._val_df, self.ts)\n",
    "\n",
    "    #Using the datasets to return a dataloader\n",
    "    def train_dataloader(self):\n",
    "        SWE_train = self.__create_dataset(\"train\")\n",
    "        return DataLoader(SWE_train, **self.loaderargs, batch_size=self.args.train_batch_size)\n",
    "\n",
    "    def val_dataloader(self):\n",
    "        SWE_val = self.__create_dataset(\"val\")\n",
    "        return DataLoader(SWE_val, **self.loaderargs, batch_size=self.args.val_batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_default_transforms():\n",
    "    transform = {\n",
    "        \"train\": T.Compose(\n",
    "            [\n",
    "                #T.RandomHorizontalFlip(),\n",
    "                #T.RandomVerticalFlip(),\n",
    "                #T.RandomAffine(15, translate=(0.1, 0.1), scale=(0.9, 1.1)),\n",
    "                T.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3),\n",
    "                T.ConvertImageDtype(torch.float),\n",
    "                T.Normalize(mean = (0.485, 0.456, 0.406), \n",
    "                            std = (0.229, 0.224, 0.225))\n",
    "                \n",
    "            ]\n",
    "        ),\n",
    "        \"val\": T.Compose(\n",
    "            [\n",
    "                T.ConvertImageDtype(torch.float),\n",
    "                T.Normalize(mean = (0.485, 0.456, 0.406), \n",
    "                            std = (0.229, 0.224, 0.225))\n",
    "            ]\n",
    "        ),\n",
    "    }\n",
    "    return transform\n",
    "  \n",
    "\n",
    "def mixup(x1: torch.Tensor, x2: torch.Tensor, x3: torch.Tensor,x4: torch.Tensor,\n",
    "          y: torch.Tensor, \n",
    "          z = torch.Tensor, alpha: float = 1.0):\n",
    "    assert alpha > 0, \"alpha should be larger than 0\"\n",
    "    assert x1.size(0) > 1, \"Mixup cannot be applied to a single instance.\"\n",
    "\n",
    "    lam = np.random.beta(alpha, alpha)\n",
    "    rand_index = torch.randperm(x1.size()[0])\n",
    "    mixed_x1 = lam * x1 + (1 - lam) * x1[rand_index, :]\n",
    "    mixed_x2 = lam * x2 + (1 - lam) * x2[rand_index, :]\n",
    "    mixed_x3 = lam * x3 + (1 - lam) * x3[rand_index, :]\n",
    "    mixed_x4 = lam * x4 + (1 - lam) * x4[rand_index, :]\n",
    "    mixed_meta = lam * z + (1 - lam) * z[rand_index, :]\n",
    "    target_a, target_b = y, y[rand_index]\n",
    "    return mixed_x1,mixed_x2,mixed_x3, mixed_x4,mixed_meta, target_a, target_b,  lam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model\n",
    "class CNNLSTM(LightningModule):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.args = args\n",
    "        self.scaler = target_scaler\n",
    "        self.tabular_columns = tabluar_columns\n",
    "        self._criterion = eval(self.args.loss)()\n",
    "        self.transform = get_default_transforms()\n",
    "        \n",
    "        self.relu = nn.ReLU()\n",
    "        self.dropout1 = nn.Dropout(p=args.dropout1)\n",
    "        self.dropout2 = nn.Dropout(p=args.dropout2)\n",
    "        self.bn = nn.BatchNorm1d(6)\n",
    "                \n",
    "        #Tracking\n",
    "        self.trainr2 = R2Score()\n",
    "        self.valr2 = R2Score()\n",
    "        \n",
    "        #Image Models\n",
    "        self.model1 = timm.create_model(args.model_name1, \n",
    "                                       pretrained=args.pretrained, \n",
    "                                       num_classes=0,\n",
    "                                       in_chans = 3,\n",
    "                                       #global_pool=''\n",
    "                                       )\n",
    "        self.model2 = timm.create_model(args.model_name2, \n",
    "                                       pretrained=args.pretrained, \n",
    "                                       num_classes=0,\n",
    "                                       in_chans = 3,\n",
    "                                       #global_pool=''\n",
    "                                       )\n",
    "        self.model3 = timm.create_model(args.model_name2, \n",
    "                                       pretrained=args.pretrained, \n",
    "                                       num_classes=0,\n",
    "                                       in_chans = 3,\n",
    "                                       #global_pool=''\n",
    "                                       )\n",
    "        self.model4 = timm.create_model(args.model_name2, \n",
    "                                       pretrained=args.pretrained, \n",
    "                                       num_classes=0,\n",
    "                                       in_chans = 3,\n",
    "                                       #global_pool=''\n",
    "                                       )\n",
    "        #LSTM\n",
    "        self.lstm = nn.LSTM(input_size = 5,\n",
    "                            hidden_size = args.lstm_hidden,\n",
    "                            num_layers = args.lstm_layers,\n",
    "                            batch_first=True,dropout=.2)\n",
    "        #Possible multiple LSTM layers?\n",
    "        #self.lstm = nn.LSTM(input_size = args.lstm_hidden,\n",
    "        #                    hidden_size = self.hidden_size,\n",
    "        ##                    num_layers = args.lstm_layers,\n",
    "        #                    batch_first=True,dropout=.1)\n",
    "        \n",
    "        #Linear regression layer\n",
    "        self.linear1 = nn.Linear(7046,1024)\n",
    "        self.linear2 = nn.Linear(1024,256)\n",
    "        self.linear3 = nn.Linear(256,args.num_classes)\n",
    "    \n",
    "        \n",
    "    def forward(self,features1,features2,features3,features4,meta,ts):\n",
    "        \n",
    "        \n",
    "        \n",
    "        #Image Convolution\n",
    "        #Image Models\n",
    "        features1 = self.model1(features1)                 \n",
    "        features1 = self.relu(features1)\n",
    "        features1 = self.dropout2(features1)\n",
    "        \n",
    "        features2 = self.model2(features2)                 \n",
    "        features2 = self.relu(features2)\n",
    "        features2 = self.dropout2(features2)\n",
    "        \n",
    "        features3 = self.model3(features3)                 \n",
    "        features3 = self.relu(features3)\n",
    "        features3 = self.dropout2(features3)\n",
    "        \n",
    "        features4 = self.model4(features4)                 \n",
    "        features4 = self.relu(features4)\n",
    "        features4 = self.dropout2(features4)\n",
    "        \n",
    "\n",
    "        meta = self.bn(meta)\n",
    "        \n",
    "        #LSTM\n",
    "        batch_size, seq_len, feature_len = ts.size()\n",
    "        # Initialize hidden state with zeros\n",
    "        \n",
    "        h_0 = torch.zeros(args.lstm_layers, batch_size, args.lstm_hidden,requires_grad=True).cuda()\n",
    "        c_0 = torch.zeros(args.lstm_layers, batch_size, args.lstm_hidden,requires_grad=True).cuda()\n",
    "        \n",
    "        f_ts, (final_hidden,final_cell) = self.lstm(ts, (h_0,c_0))\n",
    "        f_ts = f_ts.contiguous().view(batch_size,-1)\n",
    "        \n",
    "        #*************************************************************\n",
    "        #Concatenate meta and image features\n",
    "        features = torch.cat([features1,features2,features3,features4,f_ts,meta],dim=1)\n",
    "        #*************************************************************\n",
    "        \n",
    "        #Linear\n",
    "        features = self.linear1(features)\n",
    "        features = self.relu(features)\n",
    "        features = self.dropout1(features)\n",
    "        \n",
    "        features = self.linear2(features)\n",
    "        features = self.relu(features)\n",
    "        features = self.dropout1(features)\n",
    "        \n",
    "        output = self.linear3(features)\n",
    "        return output\n",
    "    \n",
    "###I DIDN\"T MIX UP TS data\n",
    "    def __share_step(self, batch, mode):\n",
    "        copernicus_img, sentinel1_img, sentinel2a_img, sentinel2b_img, labels, meta,ts = batch\n",
    "        labels = labels.float()\n",
    "        meta = meta.float()\n",
    "        ts = ts.float()\n",
    "        copernicus_img = self.transform[mode](copernicus_img)\n",
    "        sentinel1_img = self.transform[mode](sentinel1_img)\n",
    "        sentinel2a_img = self.transform[mode](sentinel2a_img)\n",
    "        sentinel2b_img = self.transform[mode](sentinel2b_img)\n",
    "\n",
    "        rand_index = torch.rand(1)[0]\n",
    "        \n",
    "        #This is a mixup function\n",
    "        if rand_index < 0.5 and mode == 'train':\n",
    "            copernicus_mixed,sentinel1_mixed,sentinel2a_mixed,sentinel2b_mixed, mixed_meta, target_a, target_b, lam = mixup(\n",
    "                                                          copernicus_img,sentinel1_img,sentinel2a_img,sentinel2b_img,\n",
    "                                                          labels, meta, alpha=0.5)\n",
    "            logits = self.forward(copernicus_mixed,sentinel1_mixed,sentinel2a_mixed,sentinel2b_mixed, mixed_meta,ts).squeeze(1)\n",
    "            loss = self._criterion(logits, target_a) * lam + \\\n",
    "                (1 - lam) * self._criterion(logits, target_b)\n",
    "\n",
    "        else:  \n",
    "          logits = self.forward(copernicus_img,sentinel1_img,sentinel2a_img,sentinel2b_img, meta,ts).squeeze(1)\n",
    "          loss = self._criterion(logits, labels)\n",
    "\n",
    "        pred = torch.from_numpy(self.scaler \\\n",
    "            .inverse_transform(np.array(logits.sigmoid().detach().cpu()) \\\n",
    "            .reshape(-1, 1)))\n",
    "        labels = torch.from_numpy(self.scaler \\\n",
    "            .inverse_transform(np.array(labels.detach().cpu()) \\\n",
    "            .reshape(-1, 1)))\n",
    "        \n",
    "        '''\n",
    "        #This is random noise\n",
    "        elif rand_index > 0.8 and mode == 'train':\n",
    "            images = images + (torch.randn(images.size(0),3,args.imagesize,args.imagesize, \n",
    "                                           dtype = torch.float, device = device)*10)/100\n",
    "            logits = self.forward(images, meta).squeeze(1)\n",
    "            loss = self._criterion(logits, labels)\n",
    "        '''\n",
    "\n",
    "        return loss, pred, labels\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        loss, pred, labels = self.__share_step(batch, 'train')\n",
    "        self.trainr2(pred.cuda(),labels.cuda())\n",
    "        self.log('train_loss', loss, on_step=True, on_epoch=True, logger=True)\n",
    "        return {'loss': loss, 'pred': pred, 'labels': labels}\n",
    "    \n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        loss, pred, labels = self.__share_step(batch, 'val')\n",
    "        self.valr2(pred.cuda(),labels.cuda())\n",
    "        self.log('val_loss', loss, on_step=False, on_epoch=True, prog_bar=True)\n",
    "        return {'pred': pred, 'labels': labels}\n",
    "\n",
    "    def training_epoch_end(self, outputs):\n",
    "        self.log('train_r2_epoch',self.trainr2)\n",
    "        self.__share_epoch_end(outputs, 'train')\n",
    "\n",
    "    def validation_epoch_end(self, outputs):\n",
    "        self.log('val_r2_epoch',self.valr2)\n",
    "        self.__share_epoch_end(outputs, 'val')\n",
    "\n",
    "    def __share_epoch_end(self, outputs, mode):\n",
    "        preds = []\n",
    "        labels = []\n",
    "        for out in outputs:\n",
    "            pred, label = out['pred'], out['labels']\n",
    "            preds.append(pred)\n",
    "            labels.append(label)\n",
    "        preds = torch.cat(preds)\n",
    "        labels = torch.cat(labels)\n",
    "        metrics = torch.sqrt(((labels - preds) ** 2).mean())\n",
    "        self.log(f'{mode}_RMSE', metrics)    \n",
    "\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        optimizer = AdamW(self.parameters(), lr=args.lr, weight_decay = args.weight_decay)\n",
    "        \n",
    "        return {\n",
    "        \"optimizer\": optimizer,\n",
    "        \"lr_scheduler\": {\n",
    "            \"scheduler\": CosineAnnealingLR(optimizer, T_max = args.T_max, eta_min= args.eta_min),\n",
    "            \"interval\": \"step\",\n",
    "            \"monitor\": \"train_loss\",\n",
    "            \"frequency\": 1}\n",
    "            }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33msnowcastshowdown\u001b[0m (use `wandb login --relogin` to force relogin)\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: wandb version 0.12.12 is available!  To upgrade, please run:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:  $ pip install wandb --upgrade\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/snowcastshowdown/cnn-lstm_Modeling/runs/7di7flbt\" target=\"_blank\">cnn-lstm-gridv3-fillna-nosnotel-v2</a></strong> to <a href=\"https://wandb.ai/snowcastshowdown/cnn-lstm_Modeling\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "\n",
       "                "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/envs/pytorch_p38/lib/python3.8/site-packages/pytorch_lightning/loggers/wandb.py:341: UserWarning: There is a wandb run already in progress and newly created instances of `WandbLogger` will reuse this run. If this is not desired, call `wandb.finish()` before instantiating `WandbLogger`.\n",
      "  rank_zero_warn(\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: logging graph, to disable use `wandb.watch(log_graph=False)`\n",
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "   | Name       | Type              | Params\n",
      "--------------------------------------------------\n",
      "0  | _criterion | BCEWithLogitsLoss | 0     \n",
      "1  | relu       | ReLU              | 0     \n",
      "2  | dropout1   | Dropout           | 0     \n",
      "3  | dropout2   | Dropout           | 0     \n",
      "4  | bn         | BatchNorm1d       | 12    \n",
      "5  | trainr2    | R2Score           | 0     \n",
      "6  | valr2      | R2Score           | 0     \n",
      "7  | model1     | EfficientNet      | 2.6 M \n",
      "8  | model2     | EfficientNet      | 7.7 M \n",
      "9  | model3     | EfficientNet      | 7.7 M \n",
      "10 | model4     | EfficientNet      | 7.7 M \n",
      "11 | lstm       | LSTM              | 465 K \n",
      "12 | linear1    | Linear            | 7.2 M \n",
      "13 | linear2    | Linear            | 262 K \n",
      "14 | linear3    | Linear            | 257   \n",
      "--------------------------------------------------\n",
      "33.6 M    Trainable params\n",
      "0         Non-trainable params\n",
      "33.6 M    Total params\n",
      "134.579   Total estimated model params size (MB)\n",
      "/home/ubuntu/anaconda3/envs/pytorch_p38/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:631: UserWarning: Checkpoint directory /home/ubuntu/snowcap/weights exists and is not empty.\n",
      "  rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation sanity check: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 1212\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "98bc6b231a3b46c9a5366cdf6e7a1ec3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "wandb: Network error (ReadTimeout), entering retry loop.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/envs/pytorch_p38/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:688: UserWarning: Detected KeyboardInterrupt, attempting graceful shutdown...\n",
      "  rank_zero_warn(\"Detected KeyboardInterrupt, attempting graceful shutdown...\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<br/>Waiting for W&B process to finish, PID 29262... <strong style=\"color:green\">(success).</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "275b5dbf1abb4602bddefc55886d5a00",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Label(value=' 387.37MB of 387.37MB uploaded (0.00MB deduped)\\r'), FloatProgress(value=1.0, max=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Control-C detected -- Run data was not synced\n"
     ]
    }
   ],
   "source": [
    "#Not doing kfold, instead separating by year\n",
    "traindf = df[(pd.to_datetime(df['date']).dt.year == int('2019'))|\n",
    "            (pd.to_datetime(df['date']).dt.year == int('2018'))].copy().reset_index(drop=True)\n",
    "valdf = df[(pd.to_datetime(df['date']).dt.year == int('2016'))|\n",
    "            (pd.to_datetime(df['date']).dt.year == int('2017'))].copy().reset_index(drop=True)\n",
    "\n",
    "model = CNNLSTM()\n",
    "\n",
    "modelname = 'cnn-lstm-gridv3-fillna-nosnotel-v2'\n",
    "\n",
    "#Callbacks\n",
    "early_stop_callback = EarlyStopping(monitor=\"val_RMSE\", min_delta=args.min_delta, patience=args.patience, \n",
    "                                    verbose=False, mode=\"min\")\n",
    "progressbar = TQDMProgressBar(refresh_rate = 10)\n",
    "checkpoint_callback = ModelCheckpoint(dirpath='/home/ubuntu/snowcap/weights', \n",
    "                                      filename= f\"{modelname}_best_weights\", save_top_k=1, monitor=\"val_RMSE\")\n",
    "lr_monitor = LearningRateMonitor(logging_interval='step')\n",
    "\n",
    "#Initialize wandb()\n",
    "wandb.init(name=modelname,project = \"cnn-lstm_Modeling\", entity = \"snowcastshowdown\", job_type='train')\n",
    "\n",
    "#Log model parameters into wandb (args variable dictionary)\n",
    "args_dict = dict(args.__dict__)\n",
    "#pop out non-json-able variables\n",
    "for key in ['__module__','__dict__','__weakref__','__doc__']:\n",
    "    args_dict.pop(key,None)\n",
    "wandb.config.update(args_dict)\n",
    "\n",
    "\n",
    "wandb_logger = WandbLogger(log_model = 'all')\n",
    "\n",
    "wandb_logger.watch(model)\n",
    "\n",
    "trainer = pl.Trainer(max_epochs=args.max_epochs, \n",
    "                    gpus=1, \n",
    "                    logger=wandb_logger,\n",
    "                    callbacks=[early_stop_callback, \n",
    "                                progressbar, \n",
    "                                checkpoint_callback,\n",
    "                                lr_monitor])\n",
    "\n",
    "SWE_Datamodule = SWEDataModule(traindf, valdf, weather, args = args, loaderargs = loaderargs)\n",
    "\n",
    "trainer.fit(model, SWE_Datamodule)\n",
    "\n",
    "wandb.finish()\n",
    "\n",
    "del model\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "wandb.finish()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}