diff --git a/workspaces/brendon/ConvLSTM2D_video_prediction_netcdf.ipynb b/workspaces/brendon/ConvLSTM2D_video_prediction_netcdf.ipynb
new file mode 100644
index 0000000..e9e4d15
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+++ b/workspaces/brendon/ConvLSTM2D_video_prediction_netcdf.ipynb
@@ -0,0 +1,1111 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "1OkqAcTNad2z"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import glob\n",
+    "import io\n",
+    "import shutil\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from datetime import datetime\n",
+    "\n",
+    "import cv2\n",
+    "from PIL import Image\n",
+    "\n",
+    "# Tensorflow\n",
+    "import tensorflow as tf\n",
+    "import keras\n",
+    "from keras import layers\n",
+    "\n",
+    "# Imaging\n",
+    "import xarray as xr\n",
+    "import imageio.v2 as imageio\n",
+    "from ipywidgets import widgets, Layout, HBox\n",
+    "\n",
+    "# Default year range\n",
+    "year_range = range(2015, 2024)\n",
+    "\n",
+    "use_existing_npy = False\n",
+    "\n",
+    "# Set seed fpr reproducible results in model fitting\n",
+    "tf.random.set_seed(31415)\n",
+    "np.random.seed(31415)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Num GPUs Available:  1\n"
+     ]
+    }
+   ],
+   "source": [
+    "gpus = tf.config.experimental.list_physical_devices(\"GPU\")\n",
+    "print(\"Num GPUs Available: \", len(gpus))\n",
+    "if gpus:\n",
+    "    for gpu in gpus:\n",
+    "        tf.config.experimental.set_memory_growth(gpu, True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Notebook functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_path(parts):\n",
+    "    out_path = \"\"\n",
+    "    for part in parts:\n",
+    "        out_path = out_path + f\"{part}{os.path.sep}\"\n",
+    "\n",
+    "    out_path = out_path.rstrip(os.path.sep)\n",
+    "    return out_path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Image reading functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a function to load a single .nc file for a given year and day\n",
+    "def load_nc_file(year, day, file_path=None) -> xr.Dataset:\n",
+    "    \"\"\"Loads the cropped, grid-corrected netcdf files on the Beaufort Sea with 74,0lat_-170,0lon\"\"\"\n",
+    "    # Generate the file path based on the year and day\n",
+    "    if file_path == None:\n",
+    "        file_path = os.path.join(\n",
+    "            data_root,\n",
+    "            str(year),\n",
+    "            f\"ims{year}{day:03d}_1km_v1.3_grid{WINDOW_SIZE}_74,0lat_-170,0lon.nc\",\n",
+    "        )\n",
+    "\n",
+    "        if not os.path.exists(file_path):\n",
+    "            # Use imputed data file\n",
+    "            file_path = os.path.join(\n",
+    "                data_root,\n",
+    "                str(year),\n",
+    "                f\"ims{year}{day:03d}_1km_v1.3#_grid{WINDOW_SIZE}_74,0lat_-170,0lon.nc\",\n",
+    "            )\n",
+    "\n",
+    "    # Load the .nc file using xarray\n",
+    "    with xr.open_dataset(file_path) as dataset:\n",
+    "        return dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def crop_to_beaufort_sea(ds: xr.Dataset, window_size: int) -> xr.Dataset:\n",
+    "    \"\"\"\n",
+    "    Center window on beaufort sea coordinates in **current** netcdf coordinate system (not quite polar stereographic) and\n",
+    "    crop to WINDOW_SIZE x WINDOW_SIZE (not 2*window size x 2*window size as was previously)\n",
+    "    \"\"\"\n",
+    "    # Beaufort Sea x, y in **current** IMS netcdf coordinate system\n",
+    "    #     x = -1652603.364653003  # meters\n",
+    "    x = -2052603.364653003  # meters\n",
+    "    #     y = -291398.56159791426  # meters\n",
+    "    y = -221398.56159791426  # meters\n",
+    "\n",
+    "    # These x, y in convert back to the below with current geolocation.py functions.\n",
+    "    ## longitude: -80.0, latitude: 74.0\n",
+    "    # Actual Beaufort Sea coordinates are closer to longitude: -140, latitude: 74.\n",
+    "\n",
+    "    beaufort_ds = ds.sel(\n",
+    "        x=slice(x - 1000 * window_size // 2, x + 1000 * window_size // 2),\n",
+    "        y=slice(y - 1000 * window_size // 2, y + 1000 * window_size // 2),\n",
+    "    )\n",
+    "    return beaufort_ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_sie_data(year, day, file_path=None) -> np.array:\n",
+    "    \"\"\"Returns a 2D numpy array copy of the IMS surface values\"\"\"\n",
+    "    return load_nc_file(year, day, file_path).IMS_Surface_Values[0].values.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_target_sie_data(year, day) -> np.array:\n",
+    "    \"\"\"Returns a 2D numpy array copy of the IMS surface values\"\"\"\n",
+    "    ds = load_nc_file(year, day)\n",
+    "    sie = ds.IMS_Surface_Values[0].values.copy()\n",
+    "    binary_sie = sie.copy()\n",
+    "    binary_sie[sie != 3] = 0\n",
+    "\n",
+    "    # Sea and Lake Ice is treated as 1\n",
+    "    binary_sie[sie == 3] = 1\n",
+    "    return binary_sie"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def binarize_data(sie: np.array) -> np.array:\n",
+    "    \"\"\"\n",
+    "    New SIE:\n",
+    "    0: Open water/out of bounds\n",
+    "    1: Sea ice or lake ice (lake mask not applied)\n",
+    "    2: Land\n",
+    "    \"\"\"\n",
+    "    binary_sie = sie.copy()\n",
+    "    binary_sie[sie != 3] = 0\n",
+    "\n",
+    "    # Sea and Lake Ice is treated as 1\n",
+    "    binary_sie[sie == 3] = 1\n",
+    "\n",
+    "    # Land and Snow-Covered Land is sent to 2.\n",
+    "    #     binary_sie[sie == 2] = 2\n",
+    "    #     binary_sie[sie == 4] = 2\n",
+    "    return binary_sie"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_n_day_chunk(year: int, day: int, n=4) -> np.array:\n",
+    "    \"\"\"\n",
+    "    Return np.array with shape (height, width, channels).\n",
+    "\n",
+    "    Does NOT wrap years or account for missing days.\n",
+    "    Starts n_day chunk at specified day, year.\n",
+    "\n",
+    "    Returns:\n",
+    "        np.array: shape (sie_y_shape, sie_x_shape, n_day)\n",
+    "    \"\"\"\n",
+    "    sie_chunk = []\n",
+    "    for day in range(day, day + n):\n",
+    "        sie = binarize_data(load_sie_data(year, day))\n",
+    "        sie_chunk.append(sie)\n",
+    "\n",
+    "    assert len(sie_chunk) == n\n",
+    "    # Use np.stack to stack the individual 2D arrays along a new third axis, resulting in (height, width, channels)\n",
+    "    return np.stack(sie_chunk, axis=-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create cropped netcdf files to Beaufort Sea"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Converting all netcdf files for 2015\n",
+      "Converting all netcdf files for 2016\n",
+      "Converting all netcdf files for 2017\n",
+      "Converting all netcdf files for 2018\n",
+      "Converting all netcdf files for 2019\n",
+      "Converting all netcdf files for 2020\n",
+      "Converting all netcdf files for 2021\n",
+      "Converting all netcdf files for 2022\n",
+      "Converting all netcdf files for 2023\n"
+     ]
+    }
+   ],
+   "source": [
+    "WINDOW_SIZE = 1000\n",
+    "overwrite = False\n",
+    "for year in range(2015, 2024):\n",
+    "    print(f\"Converting all netcdf files for {year}\")\n",
+    "    data_root = get_path([\"D:\", \"IceDyno\", \"IMS_images\"])\n",
+    "    save_dir = get_path([\"D:\", \"IceDyno\", \"IMS_images_beaufort\", year])\n",
+    "    os.makedirs(save_dir, exist_ok=True)\n",
+    "    day = 1\n",
+    "    for hdf5_file in glob.glob(get_path([data_root, year, \"*.nc\"])):\n",
+    "        resized_file = get_path(\n",
+    "            [\n",
+    "                save_dir,\n",
+    "                f\"ims{year}{day:03d}_1km_v1.3_grid{WINDOW_SIZE}_74,0lat_-170,0lon.nc\",\n",
+    "            ]\n",
+    "        )\n",
+    "        if not os.path.exists(resized_file) or overwrite == True:\n",
+    "            data = load_nc_file(year, day, hdf5_file)\n",
+    "            beaufort_data = crop_to_beaufort_sea(data, WINDOW_SIZE)\n",
+    "            beaufort_data.to_netcdf(resized_file)\n",
+    "        day += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Example usage:\n",
+    "example = True\n",
+    "if example:\n",
+    "    year = 2023\n",
+    "    day = 150\n",
+    "    #     data_root = get_path([\"D:\", \"IceDyno\", \"IMS_images\"])\n",
+    "    #     multiclass_sie = load_sie_data(year, day, get_path([data_root, 2015, \"ims2015210_1km_v1.3.nc\"]))\n",
+    "    #     sie = binarize_data(multiclass_sie)\n",
+    "    #     plt.imshow(multiclass_sie)\n",
+    "    #     plt.show()\n",
+    "\n",
+    "    data_root = get_path([\"D:\", \"IceDyno\", \"IMS_images_beaufort\"])\n",
+    "    multiclass_sie = load_sie_data(\n",
+    "        year, day\n",
+    "    )  # , get_path([data_root, 2015, \"ims2015001_1km_v1.3_grid1000_74,0lat_-170,0lon.nc\"]))\n",
+    "    sie = binarize_data(multiclass_sie)\n",
+    "    plt.imshow(sie, cmap=\"gray\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load CSV files that lists dates and file names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "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>File Name</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Impute</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ims2015001_1km_v1.3.nc</td>\n",
+       "      <td>2015-01-01</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ims2015002_1km_v1.3.nc</td>\n",
+       "      <td>2015-01-02</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>ims2015003_1km_v1.3.nc</td>\n",
+       "      <td>2015-01-03</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ims2015004_1km_v1.3.nc</td>\n",
+       "      <td>2015-01-04</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ims2015005_1km_v1.3.nc</td>\n",
+       "      <td>2015-01-05</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3282</th>\n",
+       "      <td>ims2023361_1km_v1.3.nc</td>\n",
+       "      <td>2023-12-27</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3283</th>\n",
+       "      <td>ims2023362_1km_v1.3.nc</td>\n",
+       "      <td>2023-12-28</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3284</th>\n",
+       "      <td>ims2023363_1km_v1.3.nc</td>\n",
+       "      <td>2023-12-29</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3285</th>\n",
+       "      <td>ims2023364_1km_v1.3.nc</td>\n",
+       "      <td>2023-12-30</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3286</th>\n",
+       "      <td>ims2023365_1km_v1.3.nc</td>\n",
+       "      <td>2023-12-31</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3287 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   File Name       Date Impute\n",
+       "0     ims2015001_1km_v1.3.nc 2015-01-01      N\n",
+       "1     ims2015002_1km_v1.3.nc 2015-01-02      N\n",
+       "2     ims2015003_1km_v1.3.nc 2015-01-03      N\n",
+       "3     ims2015004_1km_v1.3.nc 2015-01-04      N\n",
+       "4     ims2015005_1km_v1.3.nc 2015-01-05      N\n",
+       "...                      ...        ...    ...\n",
+       "3282  ims2023361_1km_v1.3.nc 2023-12-27      N\n",
+       "3283  ims2023362_1km_v1.3.nc 2023-12-28      N\n",
+       "3284  ims2023363_1km_v1.3.nc 2023-12-29      N\n",
+       "3285  ims2023364_1km_v1.3.nc 2023-12-30      N\n",
+       "3286  ims2023365_1km_v1.3.nc 2023-12-31      N\n",
+       "\n",
+       "[3287 rows x 3 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "root_dir = get_path([\"D:\", \"IceDyno\", \"IMS_images_converted\"])\n",
+    "dfs = []\n",
+    "for yr in year_range:\n",
+    "    csv_filename = get_path([root_dir, yr, \"image_dates.csv\"])\n",
+    "    if os.path.exists(csv_filename):\n",
+    "        df = pd.read_csv(csv_filename, index_col=False)\n",
+    "        dfs.append(df)\n",
+    "df = pd.concat(dfs, ignore_index=True)\n",
+    "df[\"Date\"] = pd.to_datetime(df[\"Date\"])\n",
+    "\n",
+    "display(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load ranges of dates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "def build_sequences(curr_month):\n",
+    "    file_names = {}\n",
+    "    for yr in year_range:\n",
+    "        if curr_month == 2:\n",
+    "            # Ignore extra day for leap year\n",
+    "            df_filtered = df[\n",
+    "                (df[\"Date\"] >= f\"{yr}-02-01\") & (df[\"Date\"] <= f\"{yr}-02-28\")\n",
+    "            ]\n",
+    "        else:\n",
+    "            df_filtered = df[\n",
+    "                (df[\"Date\"].dt.month == curr_month) & (df[\"Date\"].dt.year == yr)\n",
+    "            ]\n",
+    "\n",
+    "        file_names[yr] = df_filtered[\"File Name\"].tolist()\n",
+    "\n",
+    "    return file_names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Shape arrays [sequences, samples, height, width]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_array(years, days):\n",
+    "    img_width = WINDOW_SIZE\n",
+    "    img_height = WINDOW_SIZE\n",
+    "    img_array = np.zeros((len(days), len(years), img_width, img_height))\n",
+    "\n",
+    "    # Load png files\n",
+    "    for year_idx, year in enumerate(years):\n",
+    "        for day_idx, day in enumerate(days):\n",
+    "            multiclass_sie = load_sie_data(year, day)\n",
+    "            sie = binarize_data(multiclass_sie)\n",
+    "            img_array[day_idx, year_idx] = sie\n",
+    "\n",
+    "    print(f\"\\tInput data of image files: {img_array.shape}\")\n",
+    "\n",
+    "    # Swap the axes representing the number of frames and number of data samples.\n",
+    "    img_array = np.swapaxes(img_array, 0, 1)\n",
+    "    print(f\"\\tSwap axes: {img_array.shape}\")\n",
+    "\n",
+    "    # Add a channel dimension since the images are grayscale.\n",
+    "    img_array = np.expand_dims(img_array, axis=-1)\n",
+    "    print(f\"\\tReshaped array for model: {img_array.shape}\")\n",
+    "\n",
+    "    return img_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_array_weeks(year, start_day, sets=8, days_per_set=5):\n",
+    "    img_width = WINDOW_SIZE\n",
+    "    img_height = WINDOW_SIZE\n",
+    "    img_array = np.zeros((days_per_set, sets, img_width, img_height))\n",
+    "\n",
+    "    # Load png files\n",
+    "    for set_idx in range(sets):\n",
+    "        for day_idx, day in enumerate(range(start_day, start_day + days_per_set)):\n",
+    "            multiclass_sie = load_sie_data(year, day)\n",
+    "            sie = binarize_data(multiclass_sie)\n",
+    "            img_array[day_idx, set_idx] = sie\n",
+    "        start_day += days_per_set\n",
+    "\n",
+    "    print(f\"\\tInput data of image files: {img_array.shape}\")\n",
+    "\n",
+    "    # Swap the axes representing the number of frames and number of data samples.\n",
+    "    img_array = np.swapaxes(img_array, 0, 1)\n",
+    "    print(f\"\\tSwap axes: {img_array.shape}\")\n",
+    "\n",
+    "    # Add a channel dimension since the images are grayscale.\n",
+    "    img_array = np.expand_dims(img_array, axis=-1)\n",
+    "    print(f\"\\tReshaped array for model: {img_array.shape}\")\n",
+    "\n",
+    "    return img_array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create train/val/test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_datasets(png_array):\n",
+    "    # Split into train and validation sets using indexing to optimize memory.\n",
+    "    indexes = np.arange(png_array.shape[0])\n",
+    "    np.random.shuffle(indexes)\n",
+    "    train_index = indexes[: int(0.9 * png_array.shape[0])]\n",
+    "    val_index = indexes[int(0.9 * png_array.shape[0]) :]\n",
+    "    train_dataset = png_array[train_index]\n",
+    "    val_dataset = png_array[val_index]\n",
+    "\n",
+    "    #     # Normalize the data to the 0-1 range.\n",
+    "    #     train_dataset = train_dataset / 255\n",
+    "    #     val_dataset = val_dataset / 255\n",
+    "\n",
+    "    # We'll define a helper function to shift the frames, where\n",
+    "    # `x` is frames 0 to n - 1, and `y` is frames 1 to n.\n",
+    "    def create_shifted_frames(data):\n",
+    "        x = data[:, 0 : data.shape[1] - 1, :, :]\n",
+    "        y = data[:, 1 : data.shape[1], :, :]\n",
+    "        return x, y\n",
+    "\n",
+    "    # Apply the processing function to the datasets.\n",
+    "    x_train, y_train = create_shifted_frames(train_dataset)\n",
+    "    x_val, y_val = create_shifted_frames(val_dataset)\n",
+    "\n",
+    "    # Inspect the dataset.\n",
+    "    print(\n",
+    "        \"\\tTraining Dataset Shapes: \" + str(x_train.shape) + \", \" + str(y_train.shape)\n",
+    "    )\n",
+    "    print(\"\\tValidation Dataset Shapes: \" + str(x_val.shape) + \", \" + str(y_val.shape))\n",
+    "\n",
+    "    return train_dataset, val_dataset, x_train, y_train, x_val, y_val"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "def plot_orig_imgs(train_dataset):\n",
+    "    # Construct a figure on which we will visualize the images.\n",
+    "    fig, axes = plt.subplots(1, train_dataset.shape[1], figsize=(16, 6))\n",
+    "\n",
+    "    # Plot each of the sequential images for one random data example.\n",
+    "    data_choice = np.random.choice(range(len(train_dataset)), size=1)[0]\n",
+    "    for idx, ax in enumerate(axes.flat):\n",
+    "        ax.imshow(np.squeeze(train_dataset[data_choice][idx]), cmap=\"gray\")\n",
+    "        ax.set_title(f\"Frame {idx + 1}\")\n",
+    "        ax.axis(\"off\")\n",
+    "\n",
+    "    # Print information and display the figure.\n",
+    "    print(f\"\\tDisplaying frames for example {data_choice}.\")\n",
+    "    plt.tight_layout()\n",
+    "    plt.suptitle(f\"Sample Training Images\", y=1.025, fontsize=16)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Construction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_model():\n",
+    "    strategy = tf.distribute.MirroredStrategy()\n",
+    "    with strategy.scope():\n",
+    "        # Construct the input layer with no definite frame size.\n",
+    "        inp = layers.Input(shape=(None, *x_train.shape[2:]))\n",
+    "\n",
+    "        # We will construct 3 `ConvLSTM2D` layers with batch normalization,\n",
+    "        # followed by a `Conv3D` layer for the spatiotemporal outputs.\n",
+    "        x = layers.ConvLSTM2D(\n",
+    "            filters=16,\n",
+    "            kernel_size=(5, 5),\n",
+    "            padding=\"same\",\n",
+    "            return_sequences=True,\n",
+    "            activation=\"relu\",\n",
+    "        )(inp)\n",
+    "        x = layers.BatchNormalization()(x)\n",
+    "        x = layers.ConvLSTM2D(\n",
+    "            filters=16,\n",
+    "            kernel_size=(3, 3),\n",
+    "            padding=\"same\",\n",
+    "            return_sequences=True,\n",
+    "            activation=\"relu\",\n",
+    "        )(x)\n",
+    "        x = layers.BatchNormalization()(x)\n",
+    "        x = layers.ConvLSTM2D(\n",
+    "            filters=16,\n",
+    "            kernel_size=(1, 1),\n",
+    "            padding=\"same\",\n",
+    "            return_sequences=True,\n",
+    "            activation=\"relu\",\n",
+    "        )(x)\n",
+    "        x = layers.Conv3D(\n",
+    "            filters=1, kernel_size=(3, 3, 3), activation=\"sigmoid\", padding=\"same\"\n",
+    "        )(x)\n",
+    "\n",
+    "        # Next, we will build the complete model and compile it.\n",
+    "        model = keras.models.Model(inp, x)\n",
+    "        model.compile(\n",
+    "            loss=keras.losses.binary_crossentropy,\n",
+    "            optimizer=keras.optimizers.Adam(),\n",
+    "        )\n",
+    "\n",
+    "    #         model.summary()\n",
+    "\n",
+    "    # Free up GPU resources\n",
+    "    tf.keras.backend.clear_session()\n",
+    "\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "def fit_model(model, month):\n",
+    "    print(f\"\\tTraining model for {month}\")\n",
+    "    # Define some callbacks to improve training.\n",
+    "    early_stopping = keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=10)\n",
+    "    reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor=\"val_loss\", patience=5)\n",
+    "\n",
+    "    # Define modifiable training hyperparameters.\n",
+    "    epochs = 100\n",
+    "    batch_size = 1\n",
+    "\n",
+    "    # Fit the model to the training data.\n",
+    "    history = model.fit(\n",
+    "        x_train,\n",
+    "        y_train,\n",
+    "        batch_size=batch_size,\n",
+    "        epochs=epochs,\n",
+    "        validation_data=(x_val, y_val),\n",
+    "        callbacks=[early_stopping, reduce_lr],\n",
+    "        verbose=0,\n",
+    "    )\n",
+    "\n",
+    "    # Plot losses\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    plt.plot(history.history[\"loss\"], label=\"Training Loss\")\n",
+    "    plt.plot(history.history[\"val_loss\"], label=\"Validation Loss\")\n",
+    "    plt.xlabel(\"Epochs\")\n",
+    "    plt.ylabel(\"Loss\")\n",
+    "    plt.title(\"Training and Validation Loss\")\n",
+    "    plt.legend()\n",
+    "    # plt.ylim(0.035, 0.11)\n",
+    "    plt.show()\n",
+    "\n",
+    "    print(\n",
+    "        f\"\\tMin loss: {(history.history['loss'][np.argmin(history.history['loss'])]):.4f}\"\n",
+    "    )\n",
+    "    print(\n",
+    "        f\"\\tMin val_loss: {(history.history['val_loss'][np.argmin(history.history['val_loss'])]):.4f}\"\n",
+    "    )\n",
+    "\n",
+    "    today = datetime.today()\n",
+    "    formatted_date = today.strftime(\"%m%d%y\")\n",
+    "    model.save(f\"{formatted_date}_{month}.h5py\")\n",
+    "\n",
+    "    # Free up GPU resources\n",
+    "    tf.keras.backend.clear_session()\n",
+    "\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Frame Prediction Visualizations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(model, val_dataset, month):\n",
+    "    # Select a random example from the validation dataset.\n",
+    "    example = val_dataset[np.random.choice(range(len(val_dataset)), size=1)[0]]\n",
+    "\n",
+    "    offset = len(val_dataset) // 2\n",
+    "    # Pick the first/last ten frames from the example.\n",
+    "    frames = example[:offset, ...]\n",
+    "    original_frames = example[offset:, ...]\n",
+    "\n",
+    "    # Predict a new set of 10 frames.\n",
+    "    for _ in range(len(val_dataset)):\n",
+    "        # Extract the model's prediction and post-process it.\n",
+    "        new_prediction = model.predict(np.expand_dims(frames, axis=0), verbose=0)\n",
+    "        new_prediction = np.squeeze(new_prediction, axis=0)\n",
+    "        predicted_frame = np.expand_dims(new_prediction[-1, ...], axis=0)\n",
+    "\n",
+    "        # Extend the set of prediction frames.\n",
+    "        frames = np.concatenate((frames, predicted_frame), axis=0)\n",
+    "\n",
+    "    # Construct a figure for the original and new frames.\n",
+    "    fig, axes = plt.subplots(2, 2, figsize=(16, 6))\n",
+    "\n",
+    "    # Plot the original frames.\n",
+    "    for idx, ax in enumerate(axes[0]):\n",
+    "        ax.imshow(np.squeeze(original_frames[idx]), cmap=\"gray\")\n",
+    "        ax.set_title(f\"Actual Frame {idx}\")\n",
+    "        ax.axis(\"off\")\n",
+    "\n",
+    "    # Plot the new frames.\n",
+    "    new_frames = frames[:offset, ...]\n",
+    "    for idx, ax in enumerate(axes[1]):\n",
+    "        test_frames = (new_frames[0] * 255).astype(int)\n",
+    "        threshold = int(np.mean(np.unique(new_frames[0] * 255)))\n",
+    "        test_frames[test_frames < threshold] = 0\n",
+    "        test_frames[test_frames >= threshold] = 255\n",
+    "        ax.imshow(np.squeeze(test_frames), cmap=\"gray\")\n",
+    "        ax.set_title(f\"Predicted Frame {idx}\")\n",
+    "        ax.axis(\"off\")\n",
+    "\n",
+    "    # Display the figure.\n",
+    "    plt.suptitle(f\"Predicted and Actual Images for {month}\", y=1.025, fontsize=16)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict_test(model, test_dataset):\n",
+    "    pred = model.predict(test_dataset)\n",
+    "\n",
+    "    fig, axs = plt.subplots(2, 4, figsize=(10, 6))\n",
+    "    axs = axs.flatten()\n",
+    "    offset = 0\n",
+    "\n",
+    "    for idx, ax in enumerate(axs):\n",
+    "        if idx < 4:\n",
+    "            ax.imshow(pred[0][idx], cmap=\"gray\")\n",
+    "            ax.set_title(f\"{idx + 1} days out\")\n",
+    "        else:\n",
+    "            ax.imshow(test_dataset[0][idx - 4], cmap=\"gray\")\n",
+    "            ax.set_title(f\"Actual image {offset + 1} days out\")\n",
+    "            offset += 1\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.suptitle(\"Prediction and Actual images for 2023 Starting at Day 210\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Predicted Videos"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "# Select a few random examples from the dataset.\n",
+    "examples = val_dataset[np.random.choice(range(len(val_dataset)), size=5)]\n",
+    "\n",
+    "# Iterate over the examples and predict the frames.\n",
+    "predicted_videos = []\n",
+    "for example in examples:\n",
+    "    # Pick the first/last ten frames from the example.\n",
+    "    frames = example[:10, ...]\n",
+    "    original_frames = example[10:, ...]\n",
+    "    new_predictions = np.zeros(shape=(10, *frames[0].shape))\n",
+    "\n",
+    "    # Predict a new set of 10 frames.\n",
+    "    for i in range(10):\n",
+    "        # Extract the model's prediction and post-process it.\n",
+    "        frames = example[: 10 + i + 1, ...]\n",
+    "        new_prediction = model.predict(np.expand_dims(frames, axis=0), verbose=0)\n",
+    "        new_prediction = np.squeeze(new_prediction, axis=0)\n",
+    "        predicted_frame = np.expand_dims(new_prediction[-1, ...], axis=0)\n",
+    "\n",
+    "        # Extend the set of prediction frames.\n",
+    "        new_predictions[i] = predicted_frame\n",
+    "\n",
+    "    # Set back to 0 or 255\n",
+    "    new_predictions = (new_predictions*255).astype(int)\n",
+    "    new_predictions[new_predictions < 127] = 0\n",
+    "    new_predictions[new_predictions >= 127] = 255\n",
+    "    \n",
+    "    # Create and save GIFs for each of the ground truth/prediction images.\n",
+    "    for idx, frame_set in enumerate([original_frames, new_predictions]):\n",
+    "        # Construct a GIF from the selected video frames.\n",
+    "        current_frames = np.squeeze(frame_set)\n",
+    "        current_frames = current_frames[..., np.newaxis] * np.ones(3)\n",
+    "        current_frames = (current_frames * 255).astype(np.uint8)\n",
+    "        current_frames = list(current_frames)\n",
+    "\n",
+    "\n",
+    "            \n",
+    "        # Construct a GIF from the frames.\n",
+    "        with io.BytesIO() as gif:\n",
+    "            imageio.mimsave(gif, current_frames, \"GIF\", duration=200)\n",
+    "            predicted_videos.append(gif.getvalue())\n",
+    "\n",
+    "# Display the videos.\n",
+    "print(\" Truth\\tPrediction\")\n",
+    "for i in range(0, len(predicted_videos), 2):\n",
+    "    # Construct and display an `HBox` with the ground truth and prediction.\n",
+    "    box = HBox(\n",
+    "        [\n",
+    "            widgets.Image(value=predicted_videos[i]),\n",
+    "            widgets.Image(value=predicted_videos[i + 1]),\n",
+    "        ]\n",
+    "    )\n",
+    "    display(box)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running model for 120 Days in 2023 starting at day 90\n",
+      "\tInput data of image files: (4, 30, 1000, 1000)\n",
+      "\tSwap axes: (30, 4, 1000, 1000)\n",
+      "\tReshaped array for model: (30, 4, 1000, 1000, 1)\n",
+      "\tTraining Dataset Shapes: (27, 3, 1000, 1000, 1), (27, 3, 1000, 1000, 1)\n",
+      "\tValidation Dataset Shapes: (3, 3, 1000, 1000, 1), (3, 3, 1000, 1000, 1)\n",
+      "\tDisplaying frames for example 19.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\n",
+      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
+      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
+      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
+      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
+      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
+      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
+      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
+      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
+      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
+      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
+      "\tTraining model for 120 Days in 2023 starting at day 90\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\tMin loss: 0.0147\n",
+      "\tMin val_loss: 0.0039\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: 040824_120 Days in 2023 starting at day 90.h5py\\assets\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: 040824_120 Days in 2023 starting at day 90.h5py\\assets\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\tInput data of image files: (4, 1, 1000, 1000)\n",
+      "\tSwap axes: (1, 4, 1000, 1000)\n",
+      "\tReshaped array for model: (1, 4, 1000, 1000, 1)\n",
+      "1/1 [==============================] - 2s 2s/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: total: 20min 53s\n",
+      "Wall time: 28min 7s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "day_range = [\n",
+    "    (1, \"January\"),\n",
+    "    (2, \"February\"),\n",
+    "    (3, \"March\"),\n",
+    "    (4, \"April\"),\n",
+    "    (5, \"May\"),\n",
+    "    (6, \"June\"),\n",
+    "    (7, \"July\"),\n",
+    "    (8, \"August\"),\n",
+    "    (9, \"September\"),\n",
+    "    (10, \"October\"),\n",
+    "    (11, \"November\"),\n",
+    "    (12, \"December\"),\n",
+    "]\n",
+    "\n",
+    "# for mon_ord, mon_text in day_range:\n",
+    "#     print(f\"Running model for range {mon_text}\")\n",
+    "#     img_array = build_array_weeks(2023, 121, weeks=8)\n",
+    "# #     img_array = build_array(years = range(2015, 2024), days = range(1, 5))\n",
+    "#     train_dataset, val_dataset, x_train, y_train, x_val, y_val = create_datasets(img_array)\n",
+    "#     plot_orig_imgs(train_dataset, mon_text)\n",
+    "#     model = build_model()\n",
+    "#     model = fit_model(model, mon_text)\n",
+    "#     predict(model, val_dataset, mon_text)\n",
+    "\n",
+    "WINDOW_SIZE = 1000\n",
+    "root_dir = get_path([\"D:\", \"IceDyno\", \"IMS_images_beaufort\"])\n",
+    "mon_text = \"120 Days in 2023 starting at day 90\"\n",
+    "print(f\"Running model for 120 Days in 2023 starting at day 90\")\n",
+    "\n",
+    "img_array = build_array_weeks(year=2023, start_day=90, sets=30, days_per_set=4)\n",
+    "train_dataset, val_dataset, x_train, y_train, x_val, y_val = create_datasets(img_array)\n",
+    "plot_orig_imgs(train_dataset)\n",
+    "model = build_model()\n",
+    "model = fit_model(model, mon_text)\n",
+    "test_dataset = build_array_weeks(\n",
+    "    year=2023, start_day=210, sets=1, days_per_set=4\n",
+    ")  # Next four days after training/validation\n",
+    "predict_test(model, test_dataset)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "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": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}