Kode til CNN'er: temp, vel_x og vel_y

make_conv_train_philip_temperature_ithbm.ipynb

@@ -0,0 +1,1107 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0dd2c5d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import geopandas as gpd\n",
+    "from shapely.geometry import box\n",
+    "import rioxarray as rxr # Make sure you have rioxarray installed (pip install rioxarray)\n",
+    "import numpy as np\n",
+    "import ibis\n",
+    "ibis.options.interactive = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d615f835",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<duckdb.duckdb.DuckDBPyConnection at 0x15589e6b0>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "con = ibis.duckdb.connect()\n",
+    "con.raw_sql('INSTALL spatial;')\n",
+    "con.raw_sql('LOAD spatial;')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "700cf1f9",
+   "metadata": {},
+   "source": [
+    "- The .rio accessor: https://corteva.github.io/rioxarray/html/rioxarray.html#rioxarray-rio-accessors\n",
+    "\n",
+    "- Affine( pixel_width, 0, top_left_x_coord,\n",
+    "          0, -pixel_height, top_left_y_coord)\n",
+    "\n",
+    "- Rasterio Affine Docs (https://affine.readthedocs.io/en/latest/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cf514138",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.DataArray 't2m' (band: 1, y: 1801, x: 3600)> Size: 52MB\n",
+      "[6483600 values with dtype=float64]\n",
+      "Coordinates:\n",
+      "  * band         (band) int64 8B 1\n",
+      "  * x            (x) float64 29kB -179.9 -179.8 -179.7 ... 179.8 179.9 180.0\n",
+      "  * y            (y) float64 14kB 90.0 89.9 89.8 89.7 ... -89.8 -89.9 -90.0\n",
+      "    spatial_ref  int64 8B 0\n",
+      "Attributes:\n",
+      "    _FillValue:    nan\n",
+      "    scale_factor:  1.0\n",
+      "    add_offset:    0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = 'era5land_era5.nc'\n",
+    "sat_im = rxr.open_rasterio(filename)\n",
+    "#sat_im = sat_im.rio.reproject(\"EPSG:3031\")\n",
+    "transform = sat_im.rio.transform()\n",
+    "print(sat_im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "106bf063",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EPSG:4326\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(sat_im.rio.crs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7cc14869",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tab = con.read_parquet('punkter_til_CNN.parquet')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0fec2bb7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's create a dummy GeoPandas DataFrame for demonstration\n",
+    "#num_points = 200_000\n",
+    "#frac_points = num_points/30_000_000\n",
+    "# Generate random points within a reasonable Antarctica extent (approx for EPSG:3031)\n",
+    "# min_x, max_x = -2000000, 2000000\n",
+    "# min_y, max_y = -2000000, 2000000\n",
+    "# random_x = np.random.uniform(min_x, max_x, num_points)\n",
+    "# random_y = np.random.uniform(min_y, max_y, num_points)\n",
+    "# ice_thickness_data = np.random.uniform(100, 5000, num_points) # Example ice thickness\n",
+    "# v_data = np.random.uniform(0, 1, num_points) # Example velocity\n",
+    "# temp_data = np.random.uniform(0, 1000, num_points) # Example temperature\n",
+    "\n",
+    "# gdf = gpd.GeoDataFrame(\n",
+    "#     {'ice_thickness': ice_thickness_data,\n",
+    "#      'v': v_data,\n",
+    "#      'temp': temp_data\n",
+    "#     },\n",
+    "#     geometry=gpd.points_from_xy(random_x, random_y),\n",
+    "#     crs=\"EPSG:3031\"\n",
+    "# )\n",
+    "#data = tab.drop(['LON','LAT'])\n",
+    "data = tab\n",
+    "#random_data = data.sample(frac_points)\n",
+    "\n",
+    "# 3.1. Create a spatial index for your GeoDataFrame\n",
+    "gdf = data.to_pandas()\n",
+    "gdf.crs = \"EPSG:4326\"\n",
+    "gdf_sindex = gdf.sindex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "88b2eb18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#gdf.to_parquet(\"punkter_til_CNN.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae2f315d",
+   "metadata": {},
+   "outputs": [
+    {
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+       "      <th></th>\n",
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+       "      <th>s</th>\n",
+       "      <th>temp</th>\n",
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+       "      <td>64.349997</td>\n",
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+       "      <td>0.002495</td>\n",
+       "      <td>230.660568</td>\n",
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+       "      <td>825.209678</td>\n",
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+       "              THICK                              geometry          EAST  \\\n",
+       "0        721.812000    POINT (2526549.10533 144908.31682)  2.526549e+06   \n",
+       "1       2486.400000  POINT (1521616.52708 -1469968.82491)  1.521617e+06   \n",
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+       "4       1390.175481   POINT (1113434.26992 1790978.98662)  1.113434e+06   \n",
+       "...             ...                                   ...           ...   \n",
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+       "199742  2781.140000    POINT (1792527.87148 317702.83796)  1.792528e+06   \n",
+       "\n",
+       "               NORTH          vx          vy           v       ith_bm  \\\n",
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+       "...              ...         ...         ...         ...          ...   \n",
+       "199738 -4.143847e+05 -301.083286 -156.749208  339.442867  1897.753276   \n",
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+       "199740 -1.049620e+06   -0.757103   -1.348858    1.546810  2749.802718   \n",
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+       "199742  3.177028e+05    0.805005    5.014312    5.078519  2736.503206   \n",
+       "\n",
+       "                smb            z         s        temp  gridCellId  \n",
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+       "...             ...          ...       ...         ...         ...  \n",
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+       "199742    79.982648  2865.923339  0.002951  236.933552         158  \n",
+       "\n",
+       "[199743 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8cd3bb9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "199743"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(gdf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3f8fbde2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/44/y59xjnbx6fqfgz896mcmxfw80000gn/T/ipykernel_4818/1954644510.py:34: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
+      "  if (x_idx - half < 0 or x_idx + half + 1 > ds.dims[\"x\"] or\n",
+      "/var/folders/44/y59xjnbx6fqfgz896mcmxfw80000gn/T/ipykernel_4818/1954644510.py:35: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
+      "  y_idx - half < 0 or y_idx + half + 1 > ds.dims[\"y\"]):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Brugte 198407 punkter, skippede 1336.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "import geopandas as gpd\n",
+    "from shapely.geometry import Point\n",
+    "\n",
+    "# Åbn temperaturdata (ingen rioxarray nødvendig)\n",
+    "ds = xr.open_dataset(\"era5land_era5.nc\")\n",
+    "t2m = ds[\"t2m\"]  # (y, x)\n",
+    "\n",
+    "# Åbn dine punkter i EPSG:4326 (forudsat det passer – ellers transformér det)\n",
+    "gdf = gpd.read_parquet(\"punkter_til_CNN.parquet\")\n",
+    "gdf = gdf.to_crs(\"EPSG:4326\")  # Sørg for at punkter er i geografisk koordinatsystem\n",
+    "\n",
+    "# Billedstørrelse (27 pixels dækker 2.7° × 2.7°)\n",
+    "size = 27\n",
+    "half = size // 2\n",
+    "pixel_deg = 0.1  # resolution pr. pixel i grader\n",
+    "\n",
+    "images = []\n",
+    "scalar_feats = ['THICK', 'vx', 'vy', 'v', 'smb', 'z', 's', 'temp', 'ith_bm', 'gridCellId']\n",
+    "im_data = {feat: [] for feat in scalar_feats}\n",
+    "\n",
+    "used = 0\n",
+    "skipped = 0\n",
+    "\n",
+    "for idx, row in gdf.iterrows():\n",
+    "    lon, lat = row.geometry.x, row.geometry.y\n",
+    "\n",
+    "    # Find indeks i datasættet tættest på punktets koordinater\n",
+    "    x_idx = int(np.argmin(np.abs(ds[\"x\"].values - lon)))\n",
+    "    y_idx = int(np.argmin(np.abs(ds[\"y\"].values - lat)))\n",
+    "\n",
+    "    # Tjek om vi kan trække et 27×27 udsnit uden at gå ud over kanter\n",
+    "    if (x_idx - half < 0 or x_idx + half + 1 > ds.dims[\"x\"] or\n",
+    "        y_idx - half < 0 or y_idx + half + 1 > ds.dims[\"y\"]):\n",
+    "        skipped += 1\n",
+    "        continue\n",
+    "\n",
+    "    patch = t2m.isel(\n",
+    "        y=slice(y_idx - half, y_idx + half + 1),\n",
+    "        x=slice(x_idx - half, x_idx + half + 1)\n",
+    "    )\n",
+    "\n",
+    "    images.append(patch.values)\n",
+    "    for feat in scalar_feats:\n",
+    "        im_data[feat].append(row[feat])\n",
+    "    used += 1\n",
+    "\n",
+    "print(f\"Brugte {used} punkter, skippede {skipped}.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0c5e8c24",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Gemte dataset med labels som 'conv_temp_ithbm_4326.nc'\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Konverter billeder til en samlet 3D-array\n",
+    "image_array = np.stack(images)\n",
+    "\n",
+    "# Vælg hvilken feature du vil bruge som label\n",
+    "label_feature = \"THICK\"  # ← Skift dette hvis du ønsker noget andet\n",
+    "\n",
+    "# Lav DataArray til billeder\n",
+    "images_da = xr.DataArray(\n",
+    "    image_array,\n",
+    "    dims=[\"sample\", \"x\", \"y\"],\n",
+    "    coords={\"sample\": np.arange(image_array.shape[0]),\n",
+    "            \"x\": np.arange(27),\n",
+    "            \"y\": np.arange(27)},\n",
+    "    name=\"images\"\n",
+    ")\n",
+    "\n",
+    "# Scalar-variabler og label\n",
+    "scalar_data = {\n",
+    "    feat: xr.DataArray(\n",
+    "        np.array(im_data[feat]),\n",
+    "        dims=[\"sample\"],\n",
+    "        coords={\"sample\": np.arange(image_array.shape[0])},\n",
+    "        name=feat\n",
+    "    )\n",
+    "    for feat in scalar_feats\n",
+    "}\n",
+    "\n",
+    "# Tilføj label som separat variabel (samme som label_feature)\n",
+    "labels_da = xr.DataArray(\n",
+    "    np.array(im_data[label_feature]),\n",
+    "    dims=[\"sample\"],\n",
+    "    coords={\"sample\": np.arange(image_array.shape[0])},\n",
+    "    name=\"labels\"\n",
+    ")\n",
+    "\n",
+    "# Saml alt i ét dataset\n",
+    "final_ds = xr.Dataset(\n",
+    "    data_vars={\n",
+    "        \"images\": images_da,\n",
+    "        \"labels\": labels_da,\n",
+    "        **scalar_data\n",
+    "    },\n",
+    "    attrs={\n",
+    "        \"description\": f\"CNN data med temperaturbilleder og '{label_feature}' som labels.\"\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "# Gem som NetCDF\n",
+    "final_ds.to_netcdf(\"conv_temp_ithbm_4326.nc\")\n",
+    "print(\"✅ Gemte dataset med labels som 'conv_temp_ithbm_4326.nc'\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6cc1c242",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'training_data_ds' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[12]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mtraining_data_ds\u001b[49m\n",
+      "\u001b[31mNameError\u001b[39m: name 'training_data_ds' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "training_data_ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0812faa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "training_data_ds.to_netcdf('conv_temp_1_01.nc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bdb4d03a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_import = xr.open_dataset('conv_temp.nc') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6bf789a6",
+   "metadata": {},
+   "outputs": [
+    {
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+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 1GB\n",
+       "Dimensions:     (x: 27, y: 27, sample: 199743)\n",
+       "Coordinates:\n",
+       "  * x           (x) int64 216B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
+       "  * y           (y) int64 216B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
+       "  * sample      (sample) int64 2MB 0 1 2 3 4 ... 199739 199740 199741 199742\n",
+       "Data variables:\n",
+       "    images      (sample, x, y) float64 1GB ...\n",
+       "    labels      (sample) float64 2MB ...\n",
+       "    vx          (sample) float64 2MB ...\n",
+       "    vy          (sample) float64 2MB ...\n",
+       "    v           (sample) float64 2MB ...\n",
+       "    smb         (sample) float64 2MB ...\n",
+       "    z           (sample) float64 2MB ...\n",
+       "    s           (sample) float64 2MB ...\n",
+       "    temp        (sample) float64 2MB ...\n",
+       "    gridCellId  (sample) int64 2MB ...\n",
+       "Attributes:\n",
+       "    description:  CNN data with temperature images. Scalar features are every...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-32afea32-8b45-428a-8c12-4e71e277b1d7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-32afea32-8b45-428a-8c12-4e71e277b1d7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 27</li><li><span class='xr-has-index'>y</span>: 27</li><li><span class='xr-has-index'>sample</span>: 199743</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-93ccc5b2-3e12-4732-82f2-c36ccdb2b8d5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-93ccc5b2-3e12-4732-82f2-c36ccdb2b8d5' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-454a7bea-1038-465a-90f0-9b923fa0327a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-454a7bea-1038-465a-90f0-9b923fa0327a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52fc74dc-e224-4af0-8dc6-f421e43b3ec1' class='xr-var-data-in' type='checkbox'><label for='data-52fc74dc-e224-4af0-8dc6-f421e43b3ec1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-168b7022-9a3f-496e-a859-f1ed0d6bfb94' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-168b7022-9a3f-496e-a859-f1ed0d6bfb94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13dca837-362d-4557-a8c5-e3e4371b1c49' class='xr-var-data-in' type='checkbox'><label for='data-13dca837-362d-4557-a8c5-e3e4371b1c49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 199740 199741 199742</div><input id='attrs-419a2d7c-6807-40cd-8f2a-67a6ecff8470' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-419a2d7c-6807-40cd-8f2a-67a6ecff8470' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3582f8a2-2a23-47f3-84b7-55c883c1cad0' class='xr-var-data-in' type='checkbox'><label for='data-3582f8a2-2a23-47f3-84b7-55c883c1cad0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([     0,      1,      2, ..., 199740, 199741, 199742])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9e48a2cb-873b-4224-a788-a68b8c3850a5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9e48a2cb-873b-4224-a788-a68b8c3850a5' class='xr-section-summary' >Data variables: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>images</span></div><div class='xr-var-dims'>(sample, x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b0ec4c73-a25c-4bc7-b758-6d19bc9a5f98' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0ec4c73-a25c-4bc7-b758-6d19bc9a5f98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-809d1aee-dd44-413d-8f79-a146b49131e7' class='xr-var-data-in' type='checkbox'><label for='data-809d1aee-dd44-413d-8f79-a146b49131e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[145612647 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>labels</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9a10d2f6-94ca-49fd-9369-5ae670d0abfd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a10d2f6-94ca-49fd-9369-5ae670d0abfd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f853efd-ce54-488d-be45-cc26321734bd' class='xr-var-data-in' type='checkbox'><label for='data-8f853efd-ce54-488d-be45-cc26321734bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vx</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fbcce912-9d8a-46ad-93ca-bde6aff5ac74' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fbcce912-9d8a-46ad-93ca-bde6aff5ac74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fe33aac-7116-4842-aeaa-02d5741980e0' class='xr-var-data-in' type='checkbox'><label for='data-7fe33aac-7116-4842-aeaa-02d5741980e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vy</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0518c3b0-4bf5-4c42-9f93-d63bf053031c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0518c3b0-4bf5-4c42-9f93-d63bf053031c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbf0324c-d5a1-4135-8b61-e6b36bf714ff' class='xr-var-data-in' type='checkbox'><label for='data-dbf0324c-d5a1-4135-8b61-e6b36bf714ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-260f6549-cfea-4dcb-9b34-832528731bc5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-260f6549-cfea-4dcb-9b34-832528731bc5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfd18aa1-db6f-4af7-87a1-d2624c9a564d' class='xr-var-data-in' type='checkbox'><label for='data-dfd18aa1-db6f-4af7-87a1-d2624c9a564d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smb</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-751b016c-2007-4093-b95d-5d80f53eb969' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-751b016c-2007-4093-b95d-5d80f53eb969' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16246d6e-6bd1-49a5-96d6-f56830790fd0' class='xr-var-data-in' type='checkbox'><label for='data-16246d6e-6bd1-49a5-96d6-f56830790fd0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-463545b9-a9d2-4d5e-85b0-a87d7f6cecf8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-463545b9-a9d2-4d5e-85b0-a87d7f6cecf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d74e9d7a-be0a-44cc-8b2a-732b2e8d3982' class='xr-var-data-in' type='checkbox'><label for='data-d74e9d7a-be0a-44cc-8b2a-732b2e8d3982' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>s</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d585db01-05fd-47c4-8491-497d5f8570b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d585db01-05fd-47c4-8491-497d5f8570b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-091afb11-929d-4f53-bf69-002dd449b309' class='xr-var-data-in' type='checkbox'><label for='data-091afb11-929d-4f53-bf69-002dd449b309' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-54d69425-21ea-4145-a2df-e67a99313680' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-54d69425-21ea-4145-a2df-e67a99313680' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70e2e118-1c06-4cf0-9f43-b112e323adcc' class='xr-var-data-in' type='checkbox'><label for='data-70e2e118-1c06-4cf0-9f43-b112e323adcc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gridCellId</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a4fcc5ff-8f22-46a8-bfc5-95e4b9bcc4cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a4fcc5ff-8f22-46a8-bfc5-95e4b9bcc4cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bd44784-d467-4c9f-8c88-f7231cea581f' class='xr-var-data-in' type='checkbox'><label for='data-0bd44784-d467-4c9f-8c88-f7231cea581f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-318c3fa4-ea81-4638-8d49-6abffe30f8e7' class='xr-section-summary-in' type='checkbox'  ><label for='section-318c3fa4-ea81-4638-8d49-6abffe30f8e7' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-8556ae75-116b-4231-9520-5ac6ead2852c' class='xr-index-data-in' type='checkbox'/><label for='index-8556ae75-116b-4231-9520-5ac6ead2852c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f36debc0-7425-4433-90f0-75ebf4f9d672' class='xr-index-data-in' type='checkbox'/><label for='index-f36debc0-7425-4433-90f0-75ebf4f9d672' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;y&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>sample</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-fbf8a665-4b05-4f34-bc7e-eff455d180d7' class='xr-index-data-in' type='checkbox'/><label for='index-fbf8a665-4b05-4f34-bc7e-eff455d180d7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([     0,      1,      2,      3,      4,      5,      6,      7,      8,\n",
+       "            9,\n",
+       "       ...\n",
+       "       199733, 199734, 199735, 199736, 199737, 199738, 199739, 199740, 199741,\n",
+       "       199742],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;sample&#x27;, length=199743))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1afca40d-f97a-4bab-803c-2c410a3b3c77' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1afca40d-f97a-4bab-803c-2c410a3b3c77' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>CNN data with temperature images. Scalar features are everything from our dataset punkter_til_CNN.           Images are 27x27 pixels.</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 1GB\n",
+       "Dimensions:     (x: 27, y: 27, sample: 199743)\n",
+       "Coordinates:\n",
+       "  * x           (x) int64 216B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
+       "  * y           (y) int64 216B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
+       "  * sample      (sample) int64 2MB 0 1 2 3 4 ... 199739 199740 199741 199742\n",
+       "Data variables:\n",
+       "    images      (sample, x, y) float64 1GB ...\n",
+       "    labels      (sample) float64 2MB ...\n",
+       "    vx          (sample) float64 2MB ...\n",
+       "    vy          (sample) float64 2MB ...\n",
+       "    v           (sample) float64 2MB ...\n",
+       "    smb         (sample) float64 2MB ...\n",
+       "    z           (sample) float64 2MB ...\n",
+       "    s           (sample) float64 2MB ...\n",
+       "    temp        (sample) float64 2MB ...\n",
+       "    gridCellId  (sample) int64 2MB ...\n",
+       "Attributes:\n",
+       "    description:  CNN data with temperature images. Scalar features are every..."
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_import"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "appml",
+   "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

make_conv_train_philip_velocity_x_ithbm.ipynb

@@ -0,0 +1,1719 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "0dd2c5d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import geopandas as gpd\n",
+    "from shapely.geometry import box\n",
+    "import rioxarray as rxr # Make sure you have rioxarray installed (pip install rioxarray)\n",
+    "import numpy as np\n",
+    "import ibis\n",
+    "ibis.options.interactive = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "d615f835",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<duckdb.duckdb.DuckDBPyConnection at 0x172f530f0>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "con = ibis.duckdb.connect()\n",
+    "con.raw_sql('INSTALL spatial;')\n",
+    "con.raw_sql('LOAD spatial;')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "700cf1f9",
+   "metadata": {},
+   "source": [
+    "- The .rio accessor: https://corteva.github.io/rioxarray/html/rioxarray.html#rioxarray-rio-accessors\n",
+    "\n",
+    "- Affine( pixel_width, 0, top_left_x_coord,\n",
+    "          0, -pixel_height, top_left_y_coord)\n",
+    "\n",
+    "- Rasterio Affine Docs (https://affine.readthedocs.io/en/latest/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "cf514138",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.DataArray 'VX' (band: 1, y: 12445, x: 12445)> Size: 620MB\n",
+      "[154878025 values with dtype=float32]\n",
+      "Coordinates:\n",
+      "  * band          (band) int64 8B 1\n",
+      "  * x             (x) float64 100kB -2.8e+06 -2.8e+06 ... 2.799e+06 2.8e+06\n",
+      "  * y             (y) float64 100kB 2.8e+06 2.8e+06 ... -2.799e+06 -2.8e+06\n",
+      "    spatial_ref   int64 8B 0\n",
+      "    coord_system  int64 8B 0\n",
+      "Attributes:\n",
+      "    coordinates:    lon lat\n",
+      "    long_name:      Ice velocity in x direction\n",
+      "    standard_name:  land_ice_x_velocity\n",
+      "    units:          meter/year\n",
+      "    _FillValue:     0.0\n",
+      "    scale_factor:   1.0\n",
+      "    add_offset:     0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = 'antarctic_ice_vel_phase_map_v01.nc'\n",
+    "sat_im = rxr.open_rasterio(filename)\n",
+    "sat_im = sat_im['VX']\n",
+    "#sat_im = sat_im.rio.reproject(\"EPSG:3031\")\n",
+    "transform = sat_im.rio.transform()\n",
+    "print(sat_im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "106bf063",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EPSG:3031\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(sat_im.rio.crs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "7cc14869",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tab = con.read_parquet('punkter_til_CNN.parquet')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "0fec2bb7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's create a dummy GeoPandas DataFrame for demonstration\n",
+    "#num_points = 200_000\n",
+    "#frac_points = num_points/30_000_000\n",
+    "# Generate random points within a reasonable Antarctica extent (approx for EPSG:3031)\n",
+    "# min_x, max_x = -2000000, 2000000\n",
+    "# min_y, max_y = -2000000, 2000000\n",
+    "# random_x = np.random.uniform(min_x, max_x, num_points)\n",
+    "# random_y = np.random.uniform(min_y, max_y, num_points)\n",
+    "# ice_thickness_data = np.random.uniform(100, 5000, num_points) # Example ice thickness\n",
+    "# v_data = np.random.uniform(0, 1, num_points) # Example velocity\n",
+    "# temp_data = np.random.uniform(0, 1000, num_points) # Example temperature\n",
+    "\n",
+    "# gdf = gpd.GeoDataFrame(\n",
+    "#     {'ice_thickness': ice_thickness_data,\n",
+    "#      'v': v_data,\n",
+    "#      'temp': temp_data\n",
+    "#     },\n",
+    "#     geometry=gpd.points_from_xy(random_x, random_y),\n",
+    "#     crs=\"EPSG:3031\"\n",
+    "# )\n",
+    "#data = tab.drop(['LON','LAT'])\n",
+    "data = tab\n",
+    "#random_data = data.sample(frac_points)\n",
+    "\n",
+    "# 3.1. Create a spatial index for your GeoDataFrame\n",
+    "gdf = data.to_pandas()\n",
+    "gdf.crs = \"EPSG:3031\"\n",
+    "gdf_sindex = gdf.sindex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "88b2eb18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#gdf.to_parquet(\"punkter_til_CNN.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "ae2f315d",
+   "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>THICK</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>EAST</th>\n",
+       "      <th>NORTH</th>\n",
+       "      <th>vx</th>\n",
+       "      <th>vy</th>\n",
+       "      <th>v</th>\n",
+       "      <th>ith_bm</th>\n",
+       "      <th>smb</th>\n",
+       "      <th>z</th>\n",
+       "      <th>s</th>\n",
+       "      <th>temp</th>\n",
+       "      <th>gridCellId</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>721.812000</td>\n",
+       "      <td>POINT (2526549.105 144908.317)</td>\n",
+       "      <td>2.526549e+06</td>\n",
+       "      <td>1.449083e+05</td>\n",
+       "      <td>153.634560</td>\n",
+       "      <td>2.790444</td>\n",
+       "      <td>153.659899</td>\n",
+       "      <td>721.467940</td>\n",
+       "      <td>346.598053</td>\n",
+       "      <td>77.386441</td>\n",
+       "      <td>0.007674</td>\n",
+       "      <td>260.754211</td>\n",
+       "      <td>142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2486.400000</td>\n",
+       "      <td>POINT (1521616.527 -1469968.825)</td>\n",
+       "      <td>1.521617e+06</td>\n",
+       "      <td>-1.469969e+06</td>\n",
+       "      <td>-2.169074</td>\n",
+       "      <td>-4.493365</td>\n",
+       "      <td>4.989510</td>\n",
+       "      <td>2398.450355</td>\n",
+       "      <td>134.622343</td>\n",
+       "      <td>2677.818154</td>\n",
+       "      <td>0.002828</td>\n",
+       "      <td>233.461512</td>\n",
+       "      <td>49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>802.200000</td>\n",
+       "      <td>POINT (2404674.37 -1067011.291)</td>\n",
+       "      <td>2.404674e+06</td>\n",
+       "      <td>-1.067011e+06</td>\n",
+       "      <td>60.294909</td>\n",
+       "      <td>-142.512808</td>\n",
+       "      <td>154.742937</td>\n",
+       "      <td>688.203481</td>\n",
+       "      <td>1586.881584</td>\n",
+       "      <td>249.042803</td>\n",
+       "      <td>0.011071</td>\n",
+       "      <td>261.087043</td>\n",
+       "      <td>70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3023.950000</td>\n",
+       "      <td>POINT (1699952.89 99913.876)</td>\n",
+       "      <td>1.699953e+06</td>\n",
+       "      <td>9.991388e+04</td>\n",
+       "      <td>0.934968</td>\n",
+       "      <td>2.743626</td>\n",
+       "      <td>2.898560</td>\n",
+       "      <td>3014.002473</td>\n",
+       "      <td>64.349997</td>\n",
+       "      <td>3356.340342</td>\n",
+       "      <td>0.002495</td>\n",
+       "      <td>230.660568</td>\n",
+       "      <td>140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1390.175481</td>\n",
+       "      <td>POINT (1113434.27 1790978.987)</td>\n",
+       "      <td>1.113434e+06</td>\n",
+       "      <td>1.790979e+06</td>\n",
+       "      <td>0.057320</td>\n",
+       "      <td>7.032495</td>\n",
+       "      <td>7.032729</td>\n",
+       "      <td>1235.187350</td>\n",
+       "      <td>178.636116</td>\n",
+       "      <td>892.512017</td>\n",
+       "      <td>0.002860</td>\n",
+       "      <td>253.317246</td>\n",
+       "      <td>246</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199738</th>\n",
+       "      <td>1919.390000</td>\n",
+       "      <td>POINT (-1486478.853 -414384.668)</td>\n",
+       "      <td>-1.486479e+06</td>\n",
+       "      <td>-4.143847e+05</td>\n",
+       "      <td>-301.083286</td>\n",
+       "      <td>-156.749208</td>\n",
+       "      <td>339.442867</td>\n",
+       "      <td>1897.753276</td>\n",
+       "      <td>630.135663</td>\n",
+       "      <td>825.209678</td>\n",
+       "      <td>0.011550</td>\n",
+       "      <td>254.742781</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199739</th>\n",
+       "      <td>601.280000</td>\n",
+       "      <td>POINT (-1726950.887 238389.962)</td>\n",
+       "      <td>-1.726951e+06</td>\n",
+       "      <td>2.383900e+05</td>\n",
+       "      <td>-33.293658</td>\n",
+       "      <td>7.757353</td>\n",
+       "      <td>34.185438</td>\n",
+       "      <td>726.883071</td>\n",
+       "      <td>1334.391422</td>\n",
+       "      <td>632.884411</td>\n",
+       "      <td>0.025015</td>\n",
+       "      <td>256.262266</td>\n",
+       "      <td>128</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199740</th>\n",
+       "      <td>3022.010000</td>\n",
+       "      <td>POINT (1265667.68 -1049619.529)</td>\n",
+       "      <td>1.265668e+06</td>\n",
+       "      <td>-1.049620e+06</td>\n",
+       "      <td>-0.757103</td>\n",
+       "      <td>-1.348858</td>\n",
+       "      <td>1.546810</td>\n",
+       "      <td>2749.802718</td>\n",
+       "      <td>34.202691</td>\n",
+       "      <td>3065.901918</td>\n",
+       "      <td>0.000934</td>\n",
+       "      <td>225.997403</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199741</th>\n",
+       "      <td>1503.770000</td>\n",
+       "      <td>POINT (-934393.811 251856.892)</td>\n",
+       "      <td>-9.343938e+05</td>\n",
+       "      <td>2.518569e+05</td>\n",
+       "      <td>-250.500161</td>\n",
+       "      <td>186.584424</td>\n",
+       "      <td>312.352490</td>\n",
+       "      <td>1464.022580</td>\n",
+       "      <td>158.329718</td>\n",
+       "      <td>211.082870</td>\n",
+       "      <td>0.007703</td>\n",
+       "      <td>248.743671</td>\n",
+       "      <td>131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199742</th>\n",
+       "      <td>2781.140000</td>\n",
+       "      <td>POINT (1792527.871 317702.838)</td>\n",
+       "      <td>1.792528e+06</td>\n",
+       "      <td>3.177028e+05</td>\n",
+       "      <td>0.805005</td>\n",
+       "      <td>5.014312</td>\n",
+       "      <td>5.078519</td>\n",
+       "      <td>2736.503206</td>\n",
+       "      <td>79.982648</td>\n",
+       "      <td>2865.923339</td>\n",
+       "      <td>0.002951</td>\n",
+       "      <td>236.933552</td>\n",
+       "      <td>158</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>199743 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              THICK                          geometry          EAST  \\\n",
+       "0        721.812000    POINT (2526549.105 144908.317)  2.526549e+06   \n",
+       "1       2486.400000  POINT (1521616.527 -1469968.825)  1.521617e+06   \n",
+       "2        802.200000   POINT (2404674.37 -1067011.291)  2.404674e+06   \n",
+       "3       3023.950000      POINT (1699952.89 99913.876)  1.699953e+06   \n",
+       "4       1390.175481    POINT (1113434.27 1790978.987)  1.113434e+06   \n",
+       "...             ...                               ...           ...   \n",
+       "199738  1919.390000  POINT (-1486478.853 -414384.668) -1.486479e+06   \n",
+       "199739   601.280000   POINT (-1726950.887 238389.962) -1.726951e+06   \n",
+       "199740  3022.010000   POINT (1265667.68 -1049619.529)  1.265668e+06   \n",
+       "199741  1503.770000    POINT (-934393.811 251856.892) -9.343938e+05   \n",
+       "199742  2781.140000    POINT (1792527.871 317702.838)  1.792528e+06   \n",
+       "\n",
+       "               NORTH          vx          vy           v       ith_bm  \\\n",
+       "0       1.449083e+05  153.634560    2.790444  153.659899   721.467940   \n",
+       "1      -1.469969e+06   -2.169074   -4.493365    4.989510  2398.450355   \n",
+       "2      -1.067011e+06   60.294909 -142.512808  154.742937   688.203481   \n",
+       "3       9.991388e+04    0.934968    2.743626    2.898560  3014.002473   \n",
+       "4       1.790979e+06    0.057320    7.032495    7.032729  1235.187350   \n",
+       "...              ...         ...         ...         ...          ...   \n",
+       "199738 -4.143847e+05 -301.083286 -156.749208  339.442867  1897.753276   \n",
+       "199739  2.383900e+05  -33.293658    7.757353   34.185438   726.883071   \n",
+       "199740 -1.049620e+06   -0.757103   -1.348858    1.546810  2749.802718   \n",
+       "199741  2.518569e+05 -250.500161  186.584424  312.352490  1464.022580   \n",
+       "199742  3.177028e+05    0.805005    5.014312    5.078519  2736.503206   \n",
+       "\n",
+       "                smb            z         s        temp  gridCellId  \n",
+       "0        346.598053    77.386441  0.007674  260.754211         142  \n",
+       "1        134.622343  2677.818154  0.002828  233.461512          49  \n",
+       "2       1586.881584   249.042803  0.011071  261.087043          70  \n",
+       "3         64.349997  3356.340342  0.002495  230.660568         140  \n",
+       "4        178.636116   892.512017  0.002860  253.317246         246  \n",
+       "...             ...          ...       ...         ...         ...  \n",
+       "199738   630.135663   825.209678  0.011550  254.742781          93  \n",
+       "199739  1334.391422   632.884411  0.025015  256.262266         128  \n",
+       "199740    34.202691  3065.901918  0.000934  225.997403          66  \n",
+       "199741   158.329718   211.082870  0.007703  248.743671         131  \n",
+       "199742    79.982648  2865.923339  0.002951  236.933552         158  \n",
+       "\n",
+       "[199743 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "8cd3bb9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "199743"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(gdf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "3f8fbde2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/44/y59xjnbx6fqfgz896mcmxfw80000gn/T/ipykernel_96267/1356645550.py:34: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
+      "  if (x_idx - half < 0 or x_idx + half + 1 > ds.dims[\"x\"] or\n",
+      "/var/folders/44/y59xjnbx6fqfgz896mcmxfw80000gn/T/ipykernel_96267/1356645550.py:35: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
+      "  y_idx - half < 0 or y_idx + half + 1 > ds.dims[\"y\"]):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Brugte 199743 punkter, skippede 0.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "import geopandas as gpd\n",
+    "from shapely.geometry import Point\n",
+    "\n",
+    "# Åbn velocity data (ingen rioxarray nødvendig)\n",
+    "ds = xr.open_dataset(\"antarctic_ice_vel_phase_map_v01.nc\")\n",
+    "vx = ds[\"VX\"]  # (y, x)\n",
+    "\n",
+    "# Åbn dine punkter i EPSG:4326 (forudsat det passer – ellers transformér det)\n",
+    "gdf = gpd.read_parquet(\"punkter_til_CNN.parquet\")\n",
+    "gdf = gdf.to_crs(\"EPSG:3031\")  # Sørg for at punkter er i geografisk koordinatsystem\n",
+    "\n",
+    "# Billedstørrelse (27 pixels dækker 2.7° × 2.7°)\n",
+    "size = 27\n",
+    "half = size // 2\n",
+    "pixel_deg = 0.1  # resolution pr. pixel i grader\n",
+    "\n",
+    "images = []\n",
+    "scalar_feats = ['THICK', 'vx', 'vy', 'v', 'smb', 'z', 's', 'temp', 'gridCellId']\n",
+    "im_data = {feat: [] for feat in scalar_feats}\n",
+    "\n",
+    "used = 0\n",
+    "skipped = 0\n",
+    "\n",
+    "for idx, row in gdf.iterrows():\n",
+    "    lon, lat = row.geometry.x, row.geometry.y\n",
+    "\n",
+    "    # Find indeks i datasættet tættest på punktets koordinater\n",
+    "    x_idx = int(np.argmin(np.abs(ds[\"x\"].values - lon)))\n",
+    "    y_idx = int(np.argmin(np.abs(ds[\"y\"].values - lat)))\n",
+    "\n",
+    "    # Tjek om vi kan trække et 27×27 udsnit uden at gå ud over kanter\n",
+    "    if (x_idx - half < 0 or x_idx + half + 1 > ds.dims[\"x\"] or\n",
+    "        y_idx - half < 0 or y_idx + half + 1 > ds.dims[\"y\"]):\n",
+    "        skipped += 1\n",
+    "        continue\n",
+    "\n",
+    "    patch = vx.isel(\n",
+    "        y=slice(y_idx - half, y_idx + half + 1),\n",
+    "        x=slice(x_idx - half, x_idx + half + 1)\n",
+    "    )\n",
+    "\n",
+    "    images.append(patch.values)\n",
+    "    for feat in scalar_feats:\n",
+    "        im_data[feat].append(row[feat])\n",
+    "    used += 1\n",
+    "\n",
+    "print(f\"Brugte {used} punkter, skippede {skipped}.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "22da645d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Gemte dataset som 'conv_velocity_x_3031.nc'\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Konverter liste af billeder til en 3D-array (samples, x, y)\n",
+    "image_array = np.stack(images)  # shape: (N, 27, 27)\n",
+    "\n",
+    "# Lav DataArray til billederne\n",
+    "images_da = xr.DataArray(\n",
+    "    image_array,\n",
+    "    dims=[\"sample\", \"x\", \"y\"],\n",
+    "    coords={\"sample\": np.arange(image_array.shape[0]),\n",
+    "            \"x\": np.arange(27),\n",
+    "            \"y\": np.arange(27)},\n",
+    "    name=\"images\"\n",
+    ")\n",
+    "\n",
+    "# Lav DataArrays til de scalar-variabler\n",
+    "scalar_data = {\n",
+    "    feat: xr.DataArray(\n",
+    "        np.array(im_data[feat]),\n",
+    "        dims=[\"sample\"],\n",
+    "        coords={\"sample\": np.arange(image_array.shape[0])},\n",
+    "        name=feat\n",
+    "    )\n",
+    "    for feat in scalar_feats\n",
+    "}\n",
+    "\n",
+    "# Saml alt i et dataset\n",
+    "final_ds = xr.Dataset(\n",
+    "    data_vars={\n",
+    "        \"images\": images_da,\n",
+    "        **scalar_data\n",
+    "    },\n",
+    "    attrs={\n",
+    "        \"description\": \"CNN data med velocity x billeder i EPSG:3031 uden reprojektion.\"\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "# Gem til NetCDF\n",
+    "final_ds.to_netcdf(\"conv_velocity_x_3031.nc\")\n",
+    "print(\"✅ Gemte dataset som 'conv_velocity_x_3031.nc'\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "0c5e8c24",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Gemte dataset med labels som 'conv_velocity_x_3031.nc'\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Konverter billeder til en samlet 3D-array\n",
+    "image_array = np.stack(images)\n",
+    "\n",
+    "# Vælg hvilken feature du vil bruge som label\n",
+    "label_feature = \"THICK\"  # ← Skift dette hvis du ønsker noget andet\n",
+    "\n",
+    "# Lav DataArray til billeder\n",
+    "images_da = xr.DataArray(\n",
+    "    image_array,\n",
+    "    dims=[\"sample\", \"x\", \"y\"],\n",
+    "    coords={\"sample\": np.arange(image_array.shape[0]),\n",
+    "            \"x\": np.arange(27),\n",
+    "            \"y\": np.arange(27)},\n",
+    "    name=\"images\"\n",
+    ")\n",
+    "\n",
+    "# Scalar-variabler og label\n",
+    "scalar_data = {\n",
+    "    feat: xr.DataArray(\n",
+    "        np.array(im_data[feat]),\n",
+    "        dims=[\"sample\"],\n",
+    "        coords={\"sample\": np.arange(image_array.shape[0])},\n",
+    "        name=feat\n",
+    "    )\n",
+    "    for feat in scalar_feats\n",
+    "}\n",
+    "\n",
+    "# Tilføj label som separat variabel (samme som label_feature)\n",
+    "labels_da = xr.DataArray(\n",
+    "    np.array(im_data[label_feature]),\n",
+    "    dims=[\"sample\"],\n",
+    "    coords={\"sample\": np.arange(image_array.shape[0])},\n",
+    "    name=\"labels\"\n",
+    ")\n",
+    "\n",
+    "# Saml alt i ét dataset\n",
+    "final_ds = xr.Dataset(\n",
+    "    data_vars={\n",
+    "        \"images\": images_da,\n",
+    "        \"labels\": labels_da,\n",
+    "        **scalar_data\n",
+    "    },\n",
+    "    attrs={\n",
+    "        \"description\": f\"CNN data med velocity x billeder og '{label_feature}' som labels.\"\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "# Gem som NetCDF\n",
+    "final_ds.to_netcdf(\"conv_velocity_x_3031.nc\")\n",
+    "print(\"✅ Gemte dataset med labels som 'conv_velocity_x_3031.nc'\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "6cc1c242",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
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+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
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+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
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+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
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+       "\n",
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+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
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+       "  color: var(--xr-font-color2);\n",
+       "}\n",
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+       ".xr-section-summary-in + label:before {\n",
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+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
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+       "\n",
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+       "\n",
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+       "\n",
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+       "  padding-right: 10px;\n",
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+       "\n",
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+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 600MB\n",
+       "Dimensions:     (sample: 199743, x: 27, y: 27)\n",
+       "Coordinates:\n",
+       "  * sample      (sample) int64 2MB 0 1 2 3 4 ... 199739 199740 199741 199742\n",
+       "  * x           (x) int64 216B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
+       "  * y           (y) int64 216B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
+       "Data variables:\n",
+       "    images      (sample, x, y) float32 582MB 113.8 114.1 114.2 ... 1.296 1.321\n",
+       "    labels      (sample) float64 2MB 721.8 2.486e+03 ... 1.504e+03 2.781e+03\n",
+       "    THICK       (sample) float64 2MB 721.8 2.486e+03 ... 1.504e+03 2.781e+03\n",
+       "    vx          (sample) float64 2MB 153.6 -2.169 60.29 ... -0.7571 -250.5 0.805\n",
+       "    vy          (sample) float64 2MB 2.79 -4.493 -142.5 ... -1.349 186.6 5.014\n",
+       "    v           (sample) float64 2MB 153.7 4.99 154.7 ... 1.547 312.4 5.079\n",
+       "    smb         (sample) float64 2MB 346.6 134.6 1.587e+03 ... 34.2 158.3 79.98\n",
+       "    z           (sample) float64 2MB 77.39 2.678e+03 249.0 ... 211.1 2.866e+03\n",
+       "    s           (sample) float64 2MB 0.007674 0.002828 ... 0.007703 0.002951\n",
+       "    temp        (sample) float64 2MB 260.8 233.5 261.1 ... 226.0 248.7 236.9\n",
+       "    gridCellId  (sample) int64 2MB 142 49 70 140 246 131 ... 93 128 66 131 158\n",
+       "Attributes:\n",
+       "    description:  CNN data med velocity x billeder og &#x27;THICK&#x27; som labels.</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c31daed4-d078-4343-891e-69cf05fb9fd6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c31daed4-d078-4343-891e-69cf05fb9fd6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>sample</span>: 199743</li><li><span class='xr-has-index'>x</span>: 27</li><li><span class='xr-has-index'>y</span>: 27</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-af51c41d-f6bd-4c24-b438-3ffab1db2fc2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-af51c41d-f6bd-4c24-b438-3ffab1db2fc2' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 199740 199741 199742</div><input id='attrs-98912183-2271-4e2d-8c15-71f4561758a1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98912183-2271-4e2d-8c15-71f4561758a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5844a1c6-4d41-419d-b97f-e9bf55e7aaa2' class='xr-var-data-in' type='checkbox'><label for='data-5844a1c6-4d41-419d-b97f-e9bf55e7aaa2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([     0,      1,      2, ..., 199740, 199741, 199742])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-297a84b2-d60a-427f-96c7-3f4cceee0bc9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-297a84b2-d60a-427f-96c7-3f4cceee0bc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caadb80d-9941-401c-905f-dd79230872ef' class='xr-var-data-in' type='checkbox'><label for='data-caadb80d-9941-401c-905f-dd79230872ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-ca699560-9932-4e8d-b041-5902b4393798' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca699560-9932-4e8d-b041-5902b4393798' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba1af50b-eab0-45b7-85e1-f418358318a7' class='xr-var-data-in' type='checkbox'><label for='data-ba1af50b-eab0-45b7-85e1-f418358318a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-80a75cc5-e25b-4aeb-b1b7-d351667fb86a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-80a75cc5-e25b-4aeb-b1b7-d351667fb86a' class='xr-section-summary' >Data variables: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>images</span></div><div class='xr-var-dims'>(sample, x, y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>113.8 114.1 114.2 ... 1.296 1.321</div><input id='attrs-638831f4-8145-42ea-ae0e-2fa0345da7b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-638831f4-8145-42ea-ae0e-2fa0345da7b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3265c54-f958-4367-b2a6-de7607eeca2e' class='xr-var-data-in' type='checkbox'><label for='data-c3265c54-f958-4367-b2a6-de7607eeca2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 1.13815742e+02,  1.14090073e+02,  1.14205917e+02, ...,\n",
+       "          1.18899887e+02,  1.18346306e+02,  1.18346306e+02],\n",
+       "        [ 1.16361526e+02,  1.16609947e+02,  1.16611443e+02, ...,\n",
+       "          1.21061974e+02,  1.20903374e+02,  1.20421089e+02],\n",
+       "        [ 1.18281624e+02,  1.18281624e+02,  1.18489662e+02, ...,\n",
+       "          1.24099602e+02,  1.23985657e+02,  1.23503845e+02],\n",
+       "        ...,\n",
+       "        [ 9.14228363e+01,  9.39703217e+01,  9.62005997e+01, ...,\n",
+       "          1.50322281e+02,  1.49470535e+02,  1.49470535e+02],\n",
+       "        [ 9.01940384e+01,  9.23575592e+01,  9.47291794e+01, ...,\n",
+       "          1.47361603e+02,  1.46512024e+02,  1.46472488e+02],\n",
+       "        [ 8.95807343e+01,  9.05099335e+01,  9.29048538e+01, ...,\n",
+       "          1.43713608e+02,  1.43467789e+02,  1.43467789e+02]],\n",
+       "\n",
+       "       [[-1.87307584e+00, -1.88124275e+00, -1.88985848e+00, ...,\n",
+       "         -1.46787810e+00, -1.19338572e+00, -9.19949055e-01],\n",
+       "        [-1.87636387e+00, -1.89453006e+00, -1.90235853e+00, ...,\n",
+       "         -1.46787810e+00, -1.26956642e+00, -1.04392040e+00],\n",
+       "        [-1.90328860e+00, -1.91141713e+00, -1.92574573e+00, ...,\n",
+       "         -1.45450795e+00, -1.32607234e+00, -1.13332295e+00],\n",
+       "...\n",
+       "         -2.14296524e+02, -2.14254974e+02, -2.13557602e+02],\n",
+       "        [-2.01113632e+02, -2.03467453e+02, -2.06090622e+02, ...,\n",
+       "         -2.10541702e+02, -2.10541702e+02, -2.09905792e+02],\n",
+       "        [-1.94735641e+02, -1.97596817e+02, -2.00999390e+02, ...,\n",
+       "         -2.06227997e+02, -2.06227997e+02, -2.06227997e+02]],\n",
+       "\n",
+       "       [[ 2.98075914e-01,  3.19875926e-01,  3.48597676e-01, ...,\n",
+       "          1.48188472e+00,  1.51140320e+00,  1.53428113e+00],\n",
+       "        [ 2.08085492e-01,  2.08085492e-01,  2.24453256e-01, ...,\n",
+       "          1.51140320e+00,  1.53428113e+00,  1.55372393e+00],\n",
+       "        [ 1.26666024e-01,  1.26666024e-01,  1.31457075e-01, ...,\n",
+       "          1.52837682e+00,  1.54196572e+00,  1.55372393e+00],\n",
+       "        ...,\n",
+       "        [ 9.86931324e-02,  1.63984776e-01,  2.20422819e-01, ...,\n",
+       "          1.29952860e+00,  1.33197272e+00,  1.35958600e+00],\n",
+       "        [ 1.02333196e-01,  1.63984776e-01,  2.04046756e-01, ...,\n",
+       "          1.28426981e+00,  1.31837499e+00,  1.34536994e+00],\n",
+       "        [ 1.08834639e-01,  1.57097727e-01,  1.85580358e-01, ...,\n",
+       "          1.26059866e+00,  1.29567838e+00,  1.32105374e+00]]],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>labels</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>721.8 2.486e+03 ... 2.781e+03</div><input id='attrs-b2857638-7ac9-4920-bc25-ffef55581b69' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2857638-7ac9-4920-bc25-ffef55581b69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfa59085-4012-4f49-a5f3-beb7ef41a05d' class='xr-var-data-in' type='checkbox'><label for='data-bfa59085-4012-4f49-a5f3-beb7ef41a05d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 721.812, 2486.4  ,  802.2  , ..., 3022.01 , 1503.77 , 2781.14 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>THICK</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>721.8 2.486e+03 ... 2.781e+03</div><input id='attrs-2f379dec-c760-4c02-accb-a245a8d1c8eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2f379dec-c760-4c02-accb-a245a8d1c8eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-821560d2-3727-4649-ab89-b7538ead5348' class='xr-var-data-in' type='checkbox'><label for='data-821560d2-3727-4649-ab89-b7538ead5348' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 721.812, 2486.4  ,  802.2  , ..., 3022.01 , 1503.77 , 2781.14 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vx</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>153.6 -2.169 60.29 ... -250.5 0.805</div><input id='attrs-7b63ae9c-28df-47f5-a611-5e90828c0f97' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7b63ae9c-28df-47f5-a611-5e90828c0f97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-923566f2-e41a-4e37-9c57-692b274e5b09' class='xr-var-data-in' type='checkbox'><label for='data-923566f2-e41a-4e37-9c57-692b274e5b09' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 153.63455986,   -2.16907366,   60.29490924, ...,   -0.75710255,\n",
+       "       -250.50016102,    0.8050054 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vy</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.79 -4.493 -142.5 ... 186.6 5.014</div><input id='attrs-e9977dd6-2ad4-4b9a-a466-1bf3373a62a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9977dd6-2ad4-4b9a-a466-1bf3373a62a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef7c6952-7b36-49d2-93ca-6a6ac5f0bee3' class='xr-var-data-in' type='checkbox'><label for='data-ef7c6952-7b36-49d2-93ca-6a6ac5f0bee3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   2.79044394,   -4.49336514, -142.51280816, ...,   -1.34885798,\n",
+       "        186.58442352,    5.01431202])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>153.7 4.99 154.7 ... 312.4 5.079</div><input id='attrs-7b99edbc-b9d6-4c66-803e-b7920e5ad18a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7b99edbc-b9d6-4c66-803e-b7920e5ad18a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-100e4ed4-f9fe-4fef-bb7e-b783a48723a1' class='xr-var-data-in' type='checkbox'><label for='data-100e4ed4-f9fe-4fef-bb7e-b783a48723a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([153.659899  ,   4.98951008, 154.74293706, ...,   1.5468103 ,\n",
+       "       312.35248962,   5.07851935])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smb</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>346.6 134.6 ... 158.3 79.98</div><input id='attrs-39d22848-f898-4b40-82c6-d498cb417aec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-39d22848-f898-4b40-82c6-d498cb417aec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-871becab-bce0-43d5-8062-8f07f5c7612c' class='xr-var-data-in' type='checkbox'><label for='data-871becab-bce0-43d5-8062-8f07f5c7612c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 346.59805282,  134.62234314, 1586.88158369, ...,   34.202691  ,\n",
+       "        158.32971838,   79.98264846])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>77.39 2.678e+03 ... 211.1 2.866e+03</div><input id='attrs-02978c78-9dec-4d8c-a2a0-17bc87a7fc5a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-02978c78-9dec-4d8c-a2a0-17bc87a7fc5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef2f57db-d901-4a93-9355-601190d0ec55' class='xr-var-data-in' type='checkbox'><label for='data-ef2f57db-d901-4a93-9355-601190d0ec55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  77.38644071, 2677.81815429,  249.04280287, ..., 3065.9019181 ,\n",
+       "        211.0828696 , 2865.92333866])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>s</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.007674 0.002828 ... 0.002951</div><input id='attrs-99dc8451-5c7e-411f-93c4-2fef1a2ed872' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99dc8451-5c7e-411f-93c4-2fef1a2ed872' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6fd66ac-19e2-42f2-825f-1528a20de4d2' class='xr-var-data-in' type='checkbox'><label for='data-c6fd66ac-19e2-42f2-825f-1528a20de4d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.00767374, 0.00282843, 0.01107067, ..., 0.00093371, 0.00770311,\n",
+       "       0.00295122])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>260.8 233.5 261.1 ... 248.7 236.9</div><input id='attrs-1947e836-de4e-494b-953b-ed78ad531a58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1947e836-de4e-494b-953b-ed78ad531a58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca2bcb18-7d33-4c8e-a258-71bdaa9c49ca' class='xr-var-data-in' type='checkbox'><label for='data-ca2bcb18-7d33-4c8e-a258-71bdaa9c49ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([260.75421146, 233.4615118 , 261.08704331, ..., 225.9974035 ,\n",
+       "       248.74367106, 236.93355205])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gridCellId</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>142 49 70 140 ... 128 66 131 158</div><input id='attrs-95b9fefa-56bd-4946-9848-be597b3909ab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-95b9fefa-56bd-4946-9848-be597b3909ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3850a397-c2fa-4926-803e-919c27451029' class='xr-var-data-in' type='checkbox'><label for='data-3850a397-c2fa-4926-803e-919c27451029' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([142,  49,  70, ...,  66, 131, 158])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6f99a56e-2cdd-4f2a-96da-b973acc4ad6a' class='xr-section-summary-in' type='checkbox'  ><label for='section-6f99a56e-2cdd-4f2a-96da-b973acc4ad6a' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>sample</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-184f528c-0b41-49ca-8513-9a02571384d4' class='xr-index-data-in' type='checkbox'/><label for='index-184f528c-0b41-49ca-8513-9a02571384d4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([     0,      1,      2,      3,      4,      5,      6,      7,      8,\n",
+       "            9,\n",
+       "       ...\n",
+       "       199733, 199734, 199735, 199736, 199737, 199738, 199739, 199740, 199741,\n",
+       "       199742],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;sample&#x27;, length=199743))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f75993e9-897f-4d91-9d93-7e85a487a9bb' class='xr-index-data-in' type='checkbox'/><label for='index-f75993e9-897f-4d91-9d93-7e85a487a9bb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-492bac11-11eb-45a5-959b-347c76244472' class='xr-index-data-in' type='checkbox'/><label for='index-492bac11-11eb-45a5-959b-347c76244472' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;y&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a81631fc-7793-4169-9392-90e22aad1413' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a81631fc-7793-4169-9392-90e22aad1413' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>CNN data med velocity x billeder og &#x27;THICK&#x27; som labels.</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 600MB\n",
+       "Dimensions:     (sample: 199743, x: 27, y: 27)\n",
+       "Coordinates:\n",
+       "  * sample      (sample) int64 2MB 0 1 2 3 4 ... 199739 199740 199741 199742\n",
+       "  * x           (x) int64 216B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
+       "  * y           (y) int64 216B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
+       "Data variables:\n",
+       "    images      (sample, x, y) float32 582MB 113.8 114.1 114.2 ... 1.296 1.321\n",
+       "    labels      (sample) float64 2MB 721.8 2.486e+03 ... 1.504e+03 2.781e+03\n",
+       "    THICK       (sample) float64 2MB 721.8 2.486e+03 ... 1.504e+03 2.781e+03\n",
+       "    vx          (sample) float64 2MB 153.6 -2.169 60.29 ... -0.7571 -250.5 0.805\n",
+       "    vy          (sample) float64 2MB 2.79 -4.493 -142.5 ... -1.349 186.6 5.014\n",
+       "    v           (sample) float64 2MB 153.7 4.99 154.7 ... 1.547 312.4 5.079\n",
+       "    smb         (sample) float64 2MB 346.6 134.6 1.587e+03 ... 34.2 158.3 79.98\n",
+       "    z           (sample) float64 2MB 77.39 2.678e+03 249.0 ... 211.1 2.866e+03\n",
+       "    s           (sample) float64 2MB 0.007674 0.002828 ... 0.007703 0.002951\n",
+       "    temp        (sample) float64 2MB 260.8 233.5 261.1 ... 226.0 248.7 236.9\n",
+       "    gridCellId  (sample) int64 2MB 142 49 70 140 246 131 ... 93 128 66 131 158\n",
+       "Attributes:\n",
+       "    description:  CNN data med velocity x billeder og 'THICK' som labels."
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "bdb4d03a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_import = xr.open_dataset('conv_velocity_x_3031.nc') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "6bf789a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 600MB\n",
+       "Dimensions:     (sample: 199743, x: 27, y: 27)\n",
+       "Coordinates:\n",
+       "  * sample      (sample) int64 2MB 0 1 2 3 4 ... 199739 199740 199741 199742\n",
+       "  * x           (x) int64 216B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
+       "  * y           (y) int64 216B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
+       "Data variables:\n",
+       "    images      (sample, x, y) float32 582MB ...\n",
+       "    labels      (sample) float64 2MB ...\n",
+       "    THICK       (sample) float64 2MB ...\n",
+       "    vx          (sample) float64 2MB ...\n",
+       "    vy          (sample) float64 2MB ...\n",
+       "    v           (sample) float64 2MB ...\n",
+       "    smb         (sample) float64 2MB ...\n",
+       "    z           (sample) float64 2MB ...\n",
+       "    s           (sample) float64 2MB ...\n",
+       "    temp        (sample) float64 2MB ...\n",
+       "    gridCellId  (sample) int64 2MB ...\n",
+       "Attributes:\n",
+       "    description:  CNN data med velocity x billeder og &#x27;THICK&#x27; som labels.</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c5a736f4-2f6b-4aff-9873-dae737c98646' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c5a736f4-2f6b-4aff-9873-dae737c98646' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>sample</span>: 199743</li><li><span class='xr-has-index'>x</span>: 27</li><li><span class='xr-has-index'>y</span>: 27</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-897a309e-35c8-48a0-9b8c-ce02d45b2af7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-897a309e-35c8-48a0-9b8c-ce02d45b2af7' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 199740 199741 199742</div><input id='attrs-f7428a0c-7524-4cdd-b292-1743e5193ffa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f7428a0c-7524-4cdd-b292-1743e5193ffa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5070d733-9008-40e7-8d61-c9f35c4eec8b' class='xr-var-data-in' type='checkbox'><label for='data-5070d733-9008-40e7-8d61-c9f35c4eec8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([     0,      1,      2, ..., 199740, 199741, 199742])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-32d2352f-1ee8-485e-9c14-0f22628b7c44' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-32d2352f-1ee8-485e-9c14-0f22628b7c44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61f90fa4-09e6-4958-91bd-62b2d9a78aa8' class='xr-var-data-in' type='checkbox'><label for='data-61f90fa4-09e6-4958-91bd-62b2d9a78aa8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-5c356178-ee07-4c92-9007-607a1e8bb466' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c356178-ee07-4c92-9007-607a1e8bb466' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-846a80a8-da19-4d6e-a147-baa352a7856c' class='xr-var-data-in' type='checkbox'><label for='data-846a80a8-da19-4d6e-a147-baa352a7856c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3c38f167-3f0b-486d-b895-c48b3d7ab043' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3c38f167-3f0b-486d-b895-c48b3d7ab043' class='xr-section-summary' >Data variables: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>images</span></div><div class='xr-var-dims'>(sample, x, y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2d135129-57a2-4eb7-9ece-286e45bd9958' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d135129-57a2-4eb7-9ece-286e45bd9958' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a52de7ad-a583-4ebc-bfd1-c6b7733100be' class='xr-var-data-in' type='checkbox'><label for='data-a52de7ad-a583-4ebc-bfd1-c6b7733100be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[145612647 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>labels</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-805bd29c-9a49-4d5c-bac9-c520abed4a40' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-805bd29c-9a49-4d5c-bac9-c520abed4a40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4d7f96e-86df-42da-9709-19e1e6cc0f58' class='xr-var-data-in' type='checkbox'><label for='data-a4d7f96e-86df-42da-9709-19e1e6cc0f58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>THICK</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8a315509-476a-4e6e-95e4-f93fd1a77d2b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8a315509-476a-4e6e-95e4-f93fd1a77d2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a90aa3c-3639-4f7d-a7c7-051ac3fe84d8' class='xr-var-data-in' type='checkbox'><label for='data-8a90aa3c-3639-4f7d-a7c7-051ac3fe84d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vx</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b8d4351b-1b1a-4be6-a045-98d201da4322' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b8d4351b-1b1a-4be6-a045-98d201da4322' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4a34e47-7f7b-4aa8-aceb-5269ea8d8c92' class='xr-var-data-in' type='checkbox'><label for='data-f4a34e47-7f7b-4aa8-aceb-5269ea8d8c92' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vy</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-37a9b40d-0dfb-4007-b5ab-9dff423f24d7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-37a9b40d-0dfb-4007-b5ab-9dff423f24d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-440eccea-e341-482d-acfe-80f75f862541' class='xr-var-data-in' type='checkbox'><label for='data-440eccea-e341-482d-acfe-80f75f862541' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1d6e9d11-7646-4153-b50e-bf80b537763e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d6e9d11-7646-4153-b50e-bf80b537763e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f7c6484-a32a-4e97-a3c7-c0c31650f6e9' class='xr-var-data-in' type='checkbox'><label for='data-8f7c6484-a32a-4e97-a3c7-c0c31650f6e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smb</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-086ab6dd-0ecc-41bf-a0bd-90f26e5436a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-086ab6dd-0ecc-41bf-a0bd-90f26e5436a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-531ce6ff-c2bb-4a28-8380-a9f06edfc8a6' class='xr-var-data-in' type='checkbox'><label for='data-531ce6ff-c2bb-4a28-8380-a9f06edfc8a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-61184683-910d-4415-80f9-381ecfedda48' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-61184683-910d-4415-80f9-381ecfedda48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b33cd99-f9df-4d10-b73e-f629633bccc8' class='xr-var-data-in' type='checkbox'><label for='data-4b33cd99-f9df-4d10-b73e-f629633bccc8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>s</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d84f024c-8f3e-439b-bbc7-8a709cc2d57e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d84f024c-8f3e-439b-bbc7-8a709cc2d57e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09a9a457-45b8-4611-b3d0-4c8cfdaa388f' class='xr-var-data-in' type='checkbox'><label for='data-09a9a457-45b8-4611-b3d0-4c8cfdaa388f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e24df826-acc7-4d1e-a0cc-650884e62fdd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e24df826-acc7-4d1e-a0cc-650884e62fdd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f295995e-0815-40b5-8c2d-b510f2baf790' class='xr-var-data-in' type='checkbox'><label for='data-f295995e-0815-40b5-8c2d-b510f2baf790' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gridCellId</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5b773d11-0f51-4933-99a1-1af8fcf5b63b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5b773d11-0f51-4933-99a1-1af8fcf5b63b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6f6c147-573f-4d8a-a236-5f85765ff7c4' class='xr-var-data-in' type='checkbox'><label for='data-d6f6c147-573f-4d8a-a236-5f85765ff7c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-981494bc-80ea-47ee-9f10-1672256f9b1d' class='xr-section-summary-in' type='checkbox'  ><label for='section-981494bc-80ea-47ee-9f10-1672256f9b1d' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>sample</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-08ee7b96-8e66-4157-bd93-414c1d933d69' class='xr-index-data-in' type='checkbox'/><label for='index-08ee7b96-8e66-4157-bd93-414c1d933d69' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([     0,      1,      2,      3,      4,      5,      6,      7,      8,\n",
+       "            9,\n",
+       "       ...\n",
+       "       199733, 199734, 199735, 199736, 199737, 199738, 199739, 199740, 199741,\n",
+       "       199742],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;sample&#x27;, length=199743))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-d2ee535b-6fff-4259-a858-0ff453f74b5b' class='xr-index-data-in' type='checkbox'/><label for='index-d2ee535b-6fff-4259-a858-0ff453f74b5b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-87c261a9-f46a-4816-bc29-3fbce8437f58' class='xr-index-data-in' type='checkbox'/><label for='index-87c261a9-f46a-4816-bc29-3fbce8437f58' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;y&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d432f0c6-fba7-4be8-9e68-6a7e871b1894' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d432f0c6-fba7-4be8-9e68-6a7e871b1894' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>CNN data med velocity x billeder og &#x27;THICK&#x27; som labels.</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 600MB\n",
+       "Dimensions:     (sample: 199743, x: 27, y: 27)\n",
+       "Coordinates:\n",
+       "  * sample      (sample) int64 2MB 0 1 2 3 4 ... 199739 199740 199741 199742\n",
+       "  * x           (x) int64 216B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
+       "  * y           (y) int64 216B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
+       "Data variables:\n",
+       "    images      (sample, x, y) float32 582MB ...\n",
+       "    labels      (sample) float64 2MB ...\n",
+       "    THICK       (sample) float64 2MB ...\n",
+       "    vx          (sample) float64 2MB ...\n",
+       "    vy          (sample) float64 2MB ...\n",
+       "    v           (sample) float64 2MB ...\n",
+       "    smb         (sample) float64 2MB ...\n",
+       "    z           (sample) float64 2MB ...\n",
+       "    s           (sample) float64 2MB ...\n",
+       "    temp        (sample) float64 2MB ...\n",
+       "    gridCellId  (sample) int64 2MB ...\n",
+       "Attributes:\n",
+       "    description:  CNN data med velocity x billeder og 'THICK' som labels."
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_import"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "4a1e044e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2526549.105328181 144908.31682322195\n",
+      "<xarray.DataArray 'x' ()> Size: 8B\n",
+      "array(2458435.62499475)\n",
+      "Coordinates:\n",
+      "    x            float64 8B 2.458e+06\n",
+      "    y            float64 8B 1.364e+05\n",
+      "    band         int64 8B 1\n",
+      "    spatial_ref  int64 8B 0\n",
+      "Attributes:\n",
+      "    axis:           X\n",
+      "    long_name:      x coordinate of projection\n",
+      "    standard_name:  projection_x_coordinate\n",
+      "    units:          metre <xarray.DataArray 'y' ()> Size: 8B\n",
+      "array(136396.0348264)\n",
+      "Coordinates:\n",
+      "    x            float64 8B 2.458e+06\n",
+      "    y            float64 8B 1.364e+05\n",
+      "    band         int64 8B 1\n",
+      "    spatial_ref  int64 8B 0\n",
+      "Attributes:\n",
+      "    axis:           Y\n",
+      "    long_name:      y coordinate of projection\n",
+      "    standard_name:  projection_y_coordinate\n",
+      "    units:          metre\n"
+     ]
+    }
+   ],
+   "source": [
+    "p = gdf.iloc[0].geometry\n",
+    "\n",
+    "transform = sat_im.rio.transform()\n",
+    "\n",
+    "col, row = ~transform * (p.x, p.y)\n",
+    "\n",
+    "col, row = int(np.floor(col)), int(np.floor(row))\n",
+    "\n",
+    "print(p.x, p.y)\n",
+    "\n",
+    "print(sat_im[0][row][col].x, sat_im[0][row][col].y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ac2f290",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Source file path for Affine class: c:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.geoMLvenv\\Lib\\site-packages\\affine\\__init__.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "import inspect\n",
+    "\n",
+    "source_file_path = inspect.getsourcefile(type(sat_im.surface.rio.transform()))\n",
+    "print(f\"\\nSource file path for Affine class: {source_file_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "5faf0630",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(199594.11626899874, -199594.11626899874)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(sat_im.rio.resolution())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "appml",
+   "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

make_conv_train_philip_velocity_y_ithbm.ipynb

@@ -0,0 +1,1578 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0dd2c5d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import geopandas as gpd\n",
+    "from shapely.geometry import box\n",
+    "import rioxarray as rxr # Make sure you have rioxarray installed (pip install rioxarray)\n",
+    "import numpy as np\n",
+    "import ibis\n",
+    "ibis.options.interactive = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d615f835",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<duckdb.duckdb.DuckDBPyConnection at 0x16aa47070>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "con = ibis.duckdb.connect()\n",
+    "con.raw_sql('INSTALL spatial;')\n",
+    "con.raw_sql('LOAD spatial;')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "700cf1f9",
+   "metadata": {},
+   "source": [
+    "- The .rio accessor: https://corteva.github.io/rioxarray/html/rioxarray.html#rioxarray-rio-accessors\n",
+    "\n",
+    "- Affine( pixel_width, 0, top_left_x_coord,\n",
+    "          0, -pixel_height, top_left_y_coord)\n",
+    "\n",
+    "- Rasterio Affine Docs (https://affine.readthedocs.io/en/latest/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cf514138",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.DataArray 'VY' (band: 1, y: 12445, x: 12445)> Size: 620MB\n",
+      "[154878025 values with dtype=float32]\n",
+      "Coordinates:\n",
+      "  * band          (band) int64 8B 1\n",
+      "  * x             (x) float64 100kB -2.8e+06 -2.8e+06 ... 2.799e+06 2.8e+06\n",
+      "  * y             (y) float64 100kB 2.8e+06 2.8e+06 ... -2.799e+06 -2.8e+06\n",
+      "    spatial_ref   int64 8B 0\n",
+      "    coord_system  int64 8B 0\n",
+      "Attributes:\n",
+      "    coordinates:    lon lat\n",
+      "    long_name:      Ice velocity in y direction\n",
+      "    standard_name:  land_ice_y_velocity\n",
+      "    units:          meter/year\n",
+      "    _FillValue:     0.0\n",
+      "    scale_factor:   1.0\n",
+      "    add_offset:     0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = 'antarctic_ice_vel_phase_map_v01.nc'\n",
+    "sat_im = rxr.open_rasterio(filename)\n",
+    "sat_im = sat_im['VY']\n",
+    "#sat_im = sat_im.rio.reproject(\"EPSG:3031\")\n",
+    "transform = sat_im.rio.transform()\n",
+    "print(sat_im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "106bf063",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EPSG:3031\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(sat_im.rio.crs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7cc14869",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tab = con.read_parquet('punkter_til_CNN.parquet')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0fec2bb7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's create a dummy GeoPandas DataFrame for demonstration\n",
+    "#num_points = 200_000\n",
+    "#frac_points = num_points/30_000_000\n",
+    "# Generate random points within a reasonable Antarctica extent (approx for EPSG:3031)\n",
+    "# min_x, max_x = -2000000, 2000000\n",
+    "# min_y, max_y = -2000000, 2000000\n",
+    "# random_x = np.random.uniform(min_x, max_x, num_points)\n",
+    "# random_y = np.random.uniform(min_y, max_y, num_points)\n",
+    "# ice_thickness_data = np.random.uniform(100, 5000, num_points) # Example ice thickness\n",
+    "# v_data = np.random.uniform(0, 1, num_points) # Example velocity\n",
+    "# temp_data = np.random.uniform(0, 1000, num_points) # Example temperature\n",
+    "\n",
+    "# gdf = gpd.GeoDataFrame(\n",
+    "#     {'ice_thickness': ice_thickness_data,\n",
+    "#      'v': v_data,\n",
+    "#      'temp': temp_data\n",
+    "#     },\n",
+    "#     geometry=gpd.points_from_xy(random_x, random_y),\n",
+    "#     crs=\"EPSG:3031\"\n",
+    "# )\n",
+    "#data = tab.drop(['LON','LAT'])\n",
+    "data = tab\n",
+    "#random_data = data.sample(frac_points)\n",
+    "\n",
+    "# 3.1. Create a spatial index for your GeoDataFrame\n",
+    "gdf = data.to_pandas()\n",
+    "gdf.crs = \"EPSG:3031\"\n",
+    "gdf_sindex = gdf.sindex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "88b2eb18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#gdf.to_parquet(\"punkter_til_CNN.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae2f315d",
+   "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>THICK</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>EAST</th>\n",
+       "      <th>NORTH</th>\n",
+       "      <th>vx</th>\n",
+       "      <th>vy</th>\n",
+       "      <th>v</th>\n",
+       "      <th>ith_bm</th>\n",
+       "      <th>smb</th>\n",
+       "      <th>z</th>\n",
+       "      <th>s</th>\n",
+       "      <th>temp</th>\n",
+       "      <th>gridCellId</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>721.812000</td>\n",
+       "      <td>POINT (2526549.105 144908.317)</td>\n",
+       "      <td>2.526549e+06</td>\n",
+       "      <td>1.449083e+05</td>\n",
+       "      <td>153.634560</td>\n",
+       "      <td>2.790444</td>\n",
+       "      <td>153.659899</td>\n",
+       "      <td>721.467940</td>\n",
+       "      <td>346.598053</td>\n",
+       "      <td>77.386441</td>\n",
+       "      <td>0.007674</td>\n",
+       "      <td>260.754211</td>\n",
+       "      <td>142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2486.400000</td>\n",
+       "      <td>POINT (1521616.527 -1469968.825)</td>\n",
+       "      <td>1.521617e+06</td>\n",
+       "      <td>-1.469969e+06</td>\n",
+       "      <td>-2.169074</td>\n",
+       "      <td>-4.493365</td>\n",
+       "      <td>4.989510</td>\n",
+       "      <td>2398.450355</td>\n",
+       "      <td>134.622343</td>\n",
+       "      <td>2677.818154</td>\n",
+       "      <td>0.002828</td>\n",
+       "      <td>233.461512</td>\n",
+       "      <td>49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>802.200000</td>\n",
+       "      <td>POINT (2404674.37 -1067011.291)</td>\n",
+       "      <td>2.404674e+06</td>\n",
+       "      <td>-1.067011e+06</td>\n",
+       "      <td>60.294909</td>\n",
+       "      <td>-142.512808</td>\n",
+       "      <td>154.742937</td>\n",
+       "      <td>688.203481</td>\n",
+       "      <td>1586.881584</td>\n",
+       "      <td>249.042803</td>\n",
+       "      <td>0.011071</td>\n",
+       "      <td>261.087043</td>\n",
+       "      <td>70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3023.950000</td>\n",
+       "      <td>POINT (1699952.89 99913.876)</td>\n",
+       "      <td>1.699953e+06</td>\n",
+       "      <td>9.991388e+04</td>\n",
+       "      <td>0.934968</td>\n",
+       "      <td>2.743626</td>\n",
+       "      <td>2.898560</td>\n",
+       "      <td>3014.002473</td>\n",
+       "      <td>64.349997</td>\n",
+       "      <td>3356.340342</td>\n",
+       "      <td>0.002495</td>\n",
+       "      <td>230.660568</td>\n",
+       "      <td>140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1390.175481</td>\n",
+       "      <td>POINT (1113434.27 1790978.987)</td>\n",
+       "      <td>1.113434e+06</td>\n",
+       "      <td>1.790979e+06</td>\n",
+       "      <td>0.057320</td>\n",
+       "      <td>7.032495</td>\n",
+       "      <td>7.032729</td>\n",
+       "      <td>1235.187350</td>\n",
+       "      <td>178.636116</td>\n",
+       "      <td>892.512017</td>\n",
+       "      <td>0.002860</td>\n",
+       "      <td>253.317246</td>\n",
+       "      <td>246</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199738</th>\n",
+       "      <td>1919.390000</td>\n",
+       "      <td>POINT (-1486478.853 -414384.668)</td>\n",
+       "      <td>-1.486479e+06</td>\n",
+       "      <td>-4.143847e+05</td>\n",
+       "      <td>-301.083286</td>\n",
+       "      <td>-156.749208</td>\n",
+       "      <td>339.442867</td>\n",
+       "      <td>1897.753276</td>\n",
+       "      <td>630.135663</td>\n",
+       "      <td>825.209678</td>\n",
+       "      <td>0.011550</td>\n",
+       "      <td>254.742781</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199739</th>\n",
+       "      <td>601.280000</td>\n",
+       "      <td>POINT (-1726950.887 238389.962)</td>\n",
+       "      <td>-1.726951e+06</td>\n",
+       "      <td>2.383900e+05</td>\n",
+       "      <td>-33.293658</td>\n",
+       "      <td>7.757353</td>\n",
+       "      <td>34.185438</td>\n",
+       "      <td>726.883071</td>\n",
+       "      <td>1334.391422</td>\n",
+       "      <td>632.884411</td>\n",
+       "      <td>0.025015</td>\n",
+       "      <td>256.262266</td>\n",
+       "      <td>128</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199740</th>\n",
+       "      <td>3022.010000</td>\n",
+       "      <td>POINT (1265667.68 -1049619.529)</td>\n",
+       "      <td>1.265668e+06</td>\n",
+       "      <td>-1.049620e+06</td>\n",
+       "      <td>-0.757103</td>\n",
+       "      <td>-1.348858</td>\n",
+       "      <td>1.546810</td>\n",
+       "      <td>2749.802718</td>\n",
+       "      <td>34.202691</td>\n",
+       "      <td>3065.901918</td>\n",
+       "      <td>0.000934</td>\n",
+       "      <td>225.997403</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199741</th>\n",
+       "      <td>1503.770000</td>\n",
+       "      <td>POINT (-934393.811 251856.892)</td>\n",
+       "      <td>-9.343938e+05</td>\n",
+       "      <td>2.518569e+05</td>\n",
+       "      <td>-250.500161</td>\n",
+       "      <td>186.584424</td>\n",
+       "      <td>312.352490</td>\n",
+       "      <td>1464.022580</td>\n",
+       "      <td>158.329718</td>\n",
+       "      <td>211.082870</td>\n",
+       "      <td>0.007703</td>\n",
+       "      <td>248.743671</td>\n",
+       "      <td>131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199742</th>\n",
+       "      <td>2781.140000</td>\n",
+       "      <td>POINT (1792527.871 317702.838)</td>\n",
+       "      <td>1.792528e+06</td>\n",
+       "      <td>3.177028e+05</td>\n",
+       "      <td>0.805005</td>\n",
+       "      <td>5.014312</td>\n",
+       "      <td>5.078519</td>\n",
+       "      <td>2736.503206</td>\n",
+       "      <td>79.982648</td>\n",
+       "      <td>2865.923339</td>\n",
+       "      <td>0.002951</td>\n",
+       "      <td>236.933552</td>\n",
+       "      <td>158</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>199743 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              THICK                          geometry          EAST  \\\n",
+       "0        721.812000    POINT (2526549.105 144908.317)  2.526549e+06   \n",
+       "1       2486.400000  POINT (1521616.527 -1469968.825)  1.521617e+06   \n",
+       "2        802.200000   POINT (2404674.37 -1067011.291)  2.404674e+06   \n",
+       "3       3023.950000      POINT (1699952.89 99913.876)  1.699953e+06   \n",
+       "4       1390.175481    POINT (1113434.27 1790978.987)  1.113434e+06   \n",
+       "...             ...                               ...           ...   \n",
+       "199738  1919.390000  POINT (-1486478.853 -414384.668) -1.486479e+06   \n",
+       "199739   601.280000   POINT (-1726950.887 238389.962) -1.726951e+06   \n",
+       "199740  3022.010000   POINT (1265667.68 -1049619.529)  1.265668e+06   \n",
+       "199741  1503.770000    POINT (-934393.811 251856.892) -9.343938e+05   \n",
+       "199742  2781.140000    POINT (1792527.871 317702.838)  1.792528e+06   \n",
+       "\n",
+       "               NORTH          vx          vy           v       ith_bm  \\\n",
+       "0       1.449083e+05  153.634560    2.790444  153.659899   721.467940   \n",
+       "1      -1.469969e+06   -2.169074   -4.493365    4.989510  2398.450355   \n",
+       "2      -1.067011e+06   60.294909 -142.512808  154.742937   688.203481   \n",
+       "3       9.991388e+04    0.934968    2.743626    2.898560  3014.002473   \n",
+       "4       1.790979e+06    0.057320    7.032495    7.032729  1235.187350   \n",
+       "...              ...         ...         ...         ...          ...   \n",
+       "199738 -4.143847e+05 -301.083286 -156.749208  339.442867  1897.753276   \n",
+       "199739  2.383900e+05  -33.293658    7.757353   34.185438   726.883071   \n",
+       "199740 -1.049620e+06   -0.757103   -1.348858    1.546810  2749.802718   \n",
+       "199741  2.518569e+05 -250.500161  186.584424  312.352490  1464.022580   \n",
+       "199742  3.177028e+05    0.805005    5.014312    5.078519  2736.503206   \n",
+       "\n",
+       "                smb            z         s        temp  gridCellId  \n",
+       "0        346.598053    77.386441  0.007674  260.754211         142  \n",
+       "1        134.622343  2677.818154  0.002828  233.461512          49  \n",
+       "2       1586.881584   249.042803  0.011071  261.087043          70  \n",
+       "3         64.349997  3356.340342  0.002495  230.660568         140  \n",
+       "4        178.636116   892.512017  0.002860  253.317246         246  \n",
+       "...             ...          ...       ...         ...         ...  \n",
+       "199738   630.135663   825.209678  0.011550  254.742781          93  \n",
+       "199739  1334.391422   632.884411  0.025015  256.262266         128  \n",
+       "199740    34.202691  3065.901918  0.000934  225.997403          66  \n",
+       "199741   158.329718   211.082870  0.007703  248.743671         131  \n",
+       "199742    79.982648  2865.923339  0.002951  236.933552         158  \n",
+       "\n",
+       "[199743 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8cd3bb9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "199743"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(gdf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3f8fbde2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/44/y59xjnbx6fqfgz896mcmxfw80000gn/T/ipykernel_3814/2297924111.py:34: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
+      "  if (x_idx - half < 0 or x_idx + half + 1 > ds.dims[\"x\"] or\n",
+      "/var/folders/44/y59xjnbx6fqfgz896mcmxfw80000gn/T/ipykernel_3814/2297924111.py:35: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
+      "  y_idx - half < 0 or y_idx + half + 1 > ds.dims[\"y\"]):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Brugte 199743 punkter, skippede 0.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "import geopandas as gpd\n",
+    "from shapely.geometry import Point\n",
+    "\n",
+    "# Åbn velocity data (ingen rioxarray nødvendig)\n",
+    "ds = xr.open_dataset(\"antarctic_ice_vel_phase_map_v01.nc\")\n",
+    "vy = ds[\"VY\"]  # (y, x)\n",
+    "\n",
+    "# Åbn dine punkter i EPSG:4326 (forudsat det passer – ellers transformér det)\n",
+    "gdf = gpd.read_parquet(\"punkter_til_CNN.parquet\")\n",
+    "gdf = gdf.to_crs(\"EPSG:3031\")  # Sørg for at punkter er i geografisk koordinatsystem\n",
+    "\n",
+    "# Billedstørrelse (27 pixels dækker 2.7° × 2.7°)\n",
+    "size = 27\n",
+    "half = size // 2\n",
+    "pixel_deg = 0.1  # resolution pr. pixel i grader\n",
+    "\n",
+    "images = []\n",
+    "scalar_feats = ['THICK', 'vx', 'vy', 'v', 'smb', 'z', 's', 'temp', 'ith_bm', 'gridCellId']\n",
+    "im_data = {feat: [] for feat in scalar_feats}\n",
+    "\n",
+    "used = 0\n",
+    "skipped = 0\n",
+    "\n",
+    "for idx, row in gdf.iterrows():\n",
+    "    lon, lat = row.geometry.x, row.geometry.y\n",
+    "\n",
+    "    # Find indeks i datasættet tættest på punktets koordinater\n",
+    "    x_idx = int(np.argmin(np.abs(ds[\"x\"].values - lon)))\n",
+    "    y_idx = int(np.argmin(np.abs(ds[\"y\"].values - lat)))\n",
+    "\n",
+    "    # Tjek om vi kan trække et 27×27 udsnit uden at gå ud over kanter\n",
+    "    if (x_idx - half < 0 or x_idx + half + 1 > ds.dims[\"x\"] or\n",
+    "        y_idx - half < 0 or y_idx + half + 1 > ds.dims[\"y\"]):\n",
+    "        skipped += 1\n",
+    "        continue\n",
+    "\n",
+    "    patch = vy.isel(\n",
+    "        y=slice(y_idx - half, y_idx + half + 1),\n",
+    "        x=slice(x_idx - half, x_idx + half + 1)\n",
+    "    )\n",
+    "\n",
+    "    images.append(patch.values)\n",
+    "    for feat in scalar_feats:\n",
+    "        im_data[feat].append(row[feat])\n",
+    "    used += 1\n",
+    "\n",
+    "print(f\"Brugte {used} punkter, skippede {skipped}.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0c5e8c24",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Gemte dataset med labels som 'conv_velocity_y_ithbm_3031.nc'\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Konverter billeder til en samlet 3D-array\n",
+    "image_array = np.stack(images)\n",
+    "\n",
+    "# Vælg hvilken feature du vil bruge som label\n",
+    "label_feature = \"THICK\"  # ← Skift dette hvis du ønsker noget andet\n",
+    "\n",
+    "# Lav DataArray til billeder\n",
+    "images_da = xr.DataArray(\n",
+    "    image_array,\n",
+    "    dims=[\"sample\", \"x\", \"y\"],\n",
+    "    coords={\"sample\": np.arange(image_array.shape[0]),\n",
+    "            \"x\": np.arange(27),\n",
+    "            \"y\": np.arange(27)},\n",
+    "    name=\"images\"\n",
+    ")\n",
+    "\n",
+    "# Scalar-variabler og label\n",
+    "scalar_data = {\n",
+    "    feat: xr.DataArray(\n",
+    "        np.array(im_data[feat]),\n",
+    "        dims=[\"sample\"],\n",
+    "        coords={\"sample\": np.arange(image_array.shape[0])},\n",
+    "        name=feat\n",
+    "    )\n",
+    "    for feat in scalar_feats\n",
+    "}\n",
+    "\n",
+    "# Tilføj label som separat variabel (samme som label_feature)\n",
+    "labels_da = xr.DataArray(\n",
+    "    np.array(im_data[label_feature]),\n",
+    "    dims=[\"sample\"],\n",
+    "    coords={\"sample\": np.arange(image_array.shape[0])},\n",
+    "    name=\"labels\"\n",
+    ")\n",
+    "\n",
+    "# Saml alt i ét dataset\n",
+    "final_ds = xr.Dataset(\n",
+    "    data_vars={\n",
+    "        \"images\": images_da,\n",
+    "        \"labels\": labels_da,\n",
+    "        **scalar_data\n",
+    "    },\n",
+    "    attrs={\n",
+    "        \"description\": f\"CNN data med velocity y billeder og '{label_feature}' som labels.\"\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "# Gem som NetCDF\n",
+    "final_ds.to_netcdf(\"conv_velocity_y_ithbm_3031.nc\")\n",
+    "print(\"✅ Gemte dataset med labels som 'conv_velocity_y_ithbm_3031.nc'\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "6cc1c242",
+   "metadata": {},
+   "outputs": [
+    {
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+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 602MB\n",
+       "Dimensions:     (sample: 199743, x: 27, y: 27)\n",
+       "Coordinates:\n",
+       "  * sample      (sample) int64 2MB 0 1 2 3 4 ... 199739 199740 199741 199742\n",
+       "  * x           (x) int64 216B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
+       "  * y           (y) int64 216B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
+       "Data variables:\n",
+       "    images      (sample, x, y) float32 582MB -24.8 -26.14 -26.14 ... 5.298 5.325\n",
+       "    labels      (sample) float64 2MB 721.8 2.486e+03 ... 1.504e+03 2.781e+03\n",
+       "    THICK       (sample) float64 2MB 721.8 2.486e+03 ... 1.504e+03 2.781e+03\n",
+       "    vx          (sample) float64 2MB 153.6 -2.169 60.29 ... -0.7571 -250.5 0.805\n",
+       "    vy          (sample) float64 2MB 2.79 -4.493 -142.5 ... -1.349 186.6 5.014\n",
+       "    v           (sample) float64 2MB 153.7 4.99 154.7 ... 1.547 312.4 5.079\n",
+       "    smb         (sample) float64 2MB 346.6 134.6 1.587e+03 ... 34.2 158.3 79.98\n",
+       "    z           (sample) float64 2MB 77.39 2.678e+03 249.0 ... 211.1 2.866e+03\n",
+       "    s           (sample) float64 2MB 0.007674 0.002828 ... 0.007703 0.002951\n",
+       "    temp        (sample) float64 2MB 260.8 233.5 261.1 ... 226.0 248.7 236.9\n",
+       "    ith_bm      (sample) float64 2MB 721.5 2.398e+03 ... 1.464e+03 2.737e+03\n",
+       "    gridCellId  (sample) int64 2MB 142 49 70 140 246 131 ... 93 128 66 131 158\n",
+       "Attributes:\n",
+       "    description:  CNN data med velocity y billeder og &#x27;THICK&#x27; som labels.</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5c623644-bace-4097-a215-a8308b6d960c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5c623644-bace-4097-a215-a8308b6d960c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>sample</span>: 199743</li><li><span class='xr-has-index'>x</span>: 27</li><li><span class='xr-has-index'>y</span>: 27</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-efb5316e-dec7-44bc-9870-4421776f66ec' class='xr-section-summary-in' type='checkbox'  checked><label for='section-efb5316e-dec7-44bc-9870-4421776f66ec' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 199740 199741 199742</div><input id='attrs-53bc0625-257b-4d22-97b5-ab8a3159d52e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53bc0625-257b-4d22-97b5-ab8a3159d52e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c302518-afa7-48f6-9309-542b695542c1' class='xr-var-data-in' type='checkbox'><label for='data-6c302518-afa7-48f6-9309-542b695542c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([     0,      1,      2, ..., 199740, 199741, 199742])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-dd9f6d72-27be-4d91-9aa8-4cc48dde33b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd9f6d72-27be-4d91-9aa8-4cc48dde33b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da9f4b31-a278-40a2-8143-bc833a31eed9' class='xr-var-data-in' type='checkbox'><label for='data-da9f4b31-a278-40a2-8143-bc833a31eed9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-9ac61c33-3913-4fd6-80e1-f4ef823d685b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ac61c33-3913-4fd6-80e1-f4ef823d685b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91374cfe-0e8c-4bf4-976b-90856562daec' class='xr-var-data-in' type='checkbox'><label for='data-91374cfe-0e8c-4bf4-976b-90856562daec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-46f7426d-993d-4ff4-8679-5b987f58d4b9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-46f7426d-993d-4ff4-8679-5b987f58d4b9' class='xr-section-summary' >Data variables: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>images</span></div><div class='xr-var-dims'>(sample, x, y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-24.8 -26.14 -26.14 ... 5.298 5.325</div><input id='attrs-7e065e17-a993-40ad-82ec-868dc5ace902' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e065e17-a993-40ad-82ec-868dc5ace902' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3956b88e-6a3e-4f93-9f4b-82df4f89f57c' class='xr-var-data-in' type='checkbox'><label for='data-3956b88e-6a3e-4f93-9f4b-82df4f89f57c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ -24.799807 ,  -26.135286 ,  -26.135286 , ...,   13.455515 ,\n",
+       "           13.98404  ,   14.545469 ],\n",
+       "        [ -26.135286 ,  -27.155676 ,  -27.343094 , ...,   13.131123 ,\n",
+       "           13.698674 ,   13.98404  ],\n",
+       "        [ -27.155676 ,  -28.43307  ,  -28.43307  , ...,   13.131123 ,\n",
+       "           13.591631 ,   13.816688 ],\n",
+       "        ...,\n",
+       "        [  26.812248 ,   29.012577 ,   32.59299  , ...,    8.254437 ,\n",
+       "            5.7658763,    3.4984245],\n",
+       "        [  27.45772  ,   32.59299  ,   36.64383  , ...,    8.315132 ,\n",
+       "            5.794109 ,    3.969142 ],\n",
+       "        [  29.077293 ,   34.10318  ,   39.19159  , ...,    8.900702 ,\n",
+       "            6.3087554,    4.178577 ]],\n",
+       "\n",
+       "       [[  -4.963394 ,   -4.9265018,   -4.897467 , ...,   -3.7544744,\n",
+       "           -3.6070323,   -3.6070323],\n",
+       "        [  -4.987354 ,   -4.963394 ,   -4.926109 , ...,   -3.7544744,\n",
+       "           -3.6167762,   -3.6167762],\n",
+       "        [  -5.0174165,   -4.9848547,   -4.943872 , ...,   -3.7544744,\n",
+       "           -3.73255  ,   -3.6752112],\n",
+       "...\n",
+       "        [ 161.14764  ,  162.54997  ,  163.3627   , ...,  195.00664  ,\n",
+       "          196.68521  ,  197.9451   ],\n",
+       "        [ 158.69185  ,  160.31963  ,  161.49341  , ...,  194.14435  ,\n",
+       "          195.94392  ,  197.45715  ],\n",
+       "        [ 154.92838  ,  156.8253   ,  158.25111  , ...,  193.19746  ,\n",
+       "          194.94556  ,  196.63599  ]],\n",
+       "\n",
+       "       [[   4.7060533,    4.754837 ,    4.803118 , ...,    5.5028234,\n",
+       "            5.5390887,    5.5763907],\n",
+       "        [   4.7060533,    4.7525525,    4.803118 , ...,    5.528059 ,\n",
+       "            5.562705 ,    5.5991898],\n",
+       "        [   4.714839 ,    4.7525525,    4.807532 , ...,    5.5432553,\n",
+       "            5.5751224,    5.602625 ],\n",
+       "        ...,\n",
+       "        [   4.4667544,    4.4911814,    4.5288105, ...,    5.3275647,\n",
+       "            5.352216 ,    5.3788004],\n",
+       "        [   4.426362 ,    4.456049 ,    4.4911814, ...,    5.300851 ,\n",
+       "            5.3275647,    5.352216 ],\n",
+       "        [   4.385785 ,    4.4232802,    4.456049 , ...,    5.268609 ,\n",
+       "            5.2975974,    5.325445 ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>labels</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>721.8 2.486e+03 ... 2.781e+03</div><input id='attrs-9a4cd1bd-032d-4143-bcc0-f26541c0d1f3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a4cd1bd-032d-4143-bcc0-f26541c0d1f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e40a390c-343d-45c3-8702-630aeed7ed74' class='xr-var-data-in' type='checkbox'><label for='data-e40a390c-343d-45c3-8702-630aeed7ed74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 721.812, 2486.4  ,  802.2  , ..., 3022.01 , 1503.77 , 2781.14 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>THICK</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>721.8 2.486e+03 ... 2.781e+03</div><input id='attrs-ee1a7d3d-5f56-42dc-a0ef-d82c1d17c036' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee1a7d3d-5f56-42dc-a0ef-d82c1d17c036' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87516c43-b341-400b-8cc7-c511053b1d7f' class='xr-var-data-in' type='checkbox'><label for='data-87516c43-b341-400b-8cc7-c511053b1d7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 721.812, 2486.4  ,  802.2  , ..., 3022.01 , 1503.77 , 2781.14 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vx</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>153.6 -2.169 60.29 ... -250.5 0.805</div><input id='attrs-0235d6dd-4db7-439e-9992-fde0d2f844c8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0235d6dd-4db7-439e-9992-fde0d2f844c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bde91db-2a18-4bc2-999e-6cc5bd4bdb47' class='xr-var-data-in' type='checkbox'><label for='data-0bde91db-2a18-4bc2-999e-6cc5bd4bdb47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 153.63455986,   -2.16907366,   60.29490924, ...,   -0.75710255,\n",
+       "       -250.50016102,    0.8050054 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vy</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.79 -4.493 -142.5 ... 186.6 5.014</div><input id='attrs-dc3529c2-d2b3-446a-adf6-ca8791e70238' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc3529c2-d2b3-446a-adf6-ca8791e70238' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d03c4057-0497-4de6-beba-39abf877ae78' class='xr-var-data-in' type='checkbox'><label for='data-d03c4057-0497-4de6-beba-39abf877ae78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   2.79044394,   -4.49336514, -142.51280816, ...,   -1.34885798,\n",
+       "        186.58442352,    5.01431202])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>153.7 4.99 154.7 ... 312.4 5.079</div><input id='attrs-2aaf86d2-32a5-4b3e-b474-d93c7b5307d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2aaf86d2-32a5-4b3e-b474-d93c7b5307d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31399727-5428-4ed3-842a-9940471b8a90' class='xr-var-data-in' type='checkbox'><label for='data-31399727-5428-4ed3-842a-9940471b8a90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([153.659899  ,   4.98951008, 154.74293706, ...,   1.5468103 ,\n",
+       "       312.35248962,   5.07851935])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smb</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>346.6 134.6 ... 158.3 79.98</div><input id='attrs-ddf7f438-ddab-48f6-81d1-d802fec28c13' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ddf7f438-ddab-48f6-81d1-d802fec28c13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51f3ad48-f465-44d4-b754-21ba11043941' class='xr-var-data-in' type='checkbox'><label for='data-51f3ad48-f465-44d4-b754-21ba11043941' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 346.59805282,  134.62234314, 1586.88158369, ...,   34.202691  ,\n",
+       "        158.32971838,   79.98264846])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>77.39 2.678e+03 ... 211.1 2.866e+03</div><input id='attrs-d3113e65-6bc0-4648-a4fe-58215c142de8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d3113e65-6bc0-4648-a4fe-58215c142de8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c77f03eb-d74a-42a8-9de3-18ecb3e78edf' class='xr-var-data-in' type='checkbox'><label for='data-c77f03eb-d74a-42a8-9de3-18ecb3e78edf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  77.38644071, 2677.81815429,  249.04280287, ..., 3065.9019181 ,\n",
+       "        211.0828696 , 2865.92333866])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>s</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.007674 0.002828 ... 0.002951</div><input id='attrs-32407f2b-e0db-4e33-8b98-e7c54bb87724' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-32407f2b-e0db-4e33-8b98-e7c54bb87724' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e230e8df-cffd-4645-9efb-19a1d91d2436' class='xr-var-data-in' type='checkbox'><label for='data-e230e8df-cffd-4645-9efb-19a1d91d2436' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.00767374, 0.00282843, 0.01107067, ..., 0.00093371, 0.00770311,\n",
+       "       0.00295122])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>260.8 233.5 261.1 ... 248.7 236.9</div><input id='attrs-cac70d76-d9d4-4890-b147-802174c01023' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cac70d76-d9d4-4890-b147-802174c01023' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c4070e0-5638-4127-808c-7cb1db85d342' class='xr-var-data-in' type='checkbox'><label for='data-6c4070e0-5638-4127-808c-7cb1db85d342' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([260.75421146, 233.4615118 , 261.08704331, ..., 225.9974035 ,\n",
+       "       248.74367106, 236.93355205])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ith_bm</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>721.5 2.398e+03 ... 2.737e+03</div><input id='attrs-3458bf79-d8c5-4579-884a-fe5e975505ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3458bf79-d8c5-4579-884a-fe5e975505ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c2e218d-95b8-4455-893c-88bccad785e3' class='xr-var-data-in' type='checkbox'><label for='data-9c2e218d-95b8-4455-893c-88bccad785e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 721.46793968, 2398.45035469,  688.20348145, ..., 2749.80271761,\n",
+       "       1464.02257956, 2736.50320639])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gridCellId</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>142 49 70 140 ... 128 66 131 158</div><input id='attrs-e11236b1-22bd-4ff1-b428-af2b3296b65b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e11236b1-22bd-4ff1-b428-af2b3296b65b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab1fa7c5-3ef6-49e7-b5db-100ae413cf64' class='xr-var-data-in' type='checkbox'><label for='data-ab1fa7c5-3ef6-49e7-b5db-100ae413cf64' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([142,  49,  70, ...,  66, 131, 158])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1b75d912-12c4-45e1-ab5f-d2f40bef0eb5' class='xr-section-summary-in' type='checkbox'  ><label for='section-1b75d912-12c4-45e1-ab5f-d2f40bef0eb5' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>sample</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-c680bf37-f218-424d-a518-bd39f8d3aa58' class='xr-index-data-in' type='checkbox'/><label for='index-c680bf37-f218-424d-a518-bd39f8d3aa58' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([     0,      1,      2,      3,      4,      5,      6,      7,      8,\n",
+       "            9,\n",
+       "       ...\n",
+       "       199733, 199734, 199735, 199736, 199737, 199738, 199739, 199740, 199741,\n",
+       "       199742],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;sample&#x27;, length=199743))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-7d1d4789-f714-4c30-a1d0-b9ed3785ab64' class='xr-index-data-in' type='checkbox'/><label for='index-7d1d4789-f714-4c30-a1d0-b9ed3785ab64' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-79ca11d8-eb8d-4f22-af1f-d42777e4504a' class='xr-index-data-in' type='checkbox'/><label for='index-79ca11d8-eb8d-4f22-af1f-d42777e4504a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;y&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-255d52d4-f521-45bb-9530-c4e2af4212fa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-255d52d4-f521-45bb-9530-c4e2af4212fa' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>CNN data med velocity y billeder og &#x27;THICK&#x27; som labels.</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 602MB\n",
+       "Dimensions:     (sample: 199743, x: 27, y: 27)\n",
+       "Coordinates:\n",
+       "  * sample      (sample) int64 2MB 0 1 2 3 4 ... 199739 199740 199741 199742\n",
+       "  * x           (x) int64 216B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
+       "  * y           (y) int64 216B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
+       "Data variables:\n",
+       "    images      (sample, x, y) float32 582MB -24.8 -26.14 -26.14 ... 5.298 5.325\n",
+       "    labels      (sample) float64 2MB 721.8 2.486e+03 ... 1.504e+03 2.781e+03\n",
+       "    THICK       (sample) float64 2MB 721.8 2.486e+03 ... 1.504e+03 2.781e+03\n",
+       "    vx          (sample) float64 2MB 153.6 -2.169 60.29 ... -0.7571 -250.5 0.805\n",
+       "    vy          (sample) float64 2MB 2.79 -4.493 -142.5 ... -1.349 186.6 5.014\n",
+       "    v           (sample) float64 2MB 153.7 4.99 154.7 ... 1.547 312.4 5.079\n",
+       "    smb         (sample) float64 2MB 346.6 134.6 1.587e+03 ... 34.2 158.3 79.98\n",
+       "    z           (sample) float64 2MB 77.39 2.678e+03 249.0 ... 211.1 2.866e+03\n",
+       "    s           (sample) float64 2MB 0.007674 0.002828 ... 0.007703 0.002951\n",
+       "    temp        (sample) float64 2MB 260.8 233.5 261.1 ... 226.0 248.7 236.9\n",
+       "    ith_bm      (sample) float64 2MB 721.5 2.398e+03 ... 1.464e+03 2.737e+03\n",
+       "    gridCellId  (sample) int64 2MB 142 49 70 140 246 131 ... 93 128 66 131 158\n",
+       "Attributes:\n",
+       "    description:  CNN data med velocity y billeder og 'THICK' som labels."
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "bdb4d03a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_import = xr.open_dataset('conv_velocity_y_ithbm_3031.nc') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "6bf789a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<defs>\n",
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1f1f1f;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 602MB\n",
+       "Dimensions:     (sample: 199743, x: 27, y: 27)\n",
+       "Coordinates:\n",
+       "  * sample      (sample) int64 2MB 0 1 2 3 4 ... 199739 199740 199741 199742\n",
+       "  * x           (x) int64 216B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
+       "  * y           (y) int64 216B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
+       "Data variables:\n",
+       "    images      (sample, x, y) float32 582MB ...\n",
+       "    labels      (sample) float64 2MB ...\n",
+       "    THICK       (sample) float64 2MB ...\n",
+       "    vx          (sample) float64 2MB ...\n",
+       "    vy          (sample) float64 2MB ...\n",
+       "    v           (sample) float64 2MB ...\n",
+       "    smb         (sample) float64 2MB ...\n",
+       "    z           (sample) float64 2MB ...\n",
+       "    s           (sample) float64 2MB ...\n",
+       "    temp        (sample) float64 2MB ...\n",
+       "    ith_bm      (sample) float64 2MB ...\n",
+       "    gridCellId  (sample) int64 2MB ...\n",
+       "Attributes:\n",
+       "    description:  CNN data med velocity y billeder og &#x27;THICK&#x27; som labels.</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-335875ea-7809-414f-9d3a-10ac4606072b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-335875ea-7809-414f-9d3a-10ac4606072b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>sample</span>: 199743</li><li><span class='xr-has-index'>x</span>: 27</li><li><span class='xr-has-index'>y</span>: 27</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-db5434eb-d27d-4347-ab0f-fad4bce58dad' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db5434eb-d27d-4347-ab0f-fad4bce58dad' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 199740 199741 199742</div><input id='attrs-8baedae4-32c3-4b6a-bfe3-8e6e0c404c3b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8baedae4-32c3-4b6a-bfe3-8e6e0c404c3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b06a7142-bcdc-4d3b-ab63-85dc346bb3dd' class='xr-var-data-in' type='checkbox'><label for='data-b06a7142-bcdc-4d3b-ab63-85dc346bb3dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([     0,      1,      2, ..., 199740, 199741, 199742])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-5f1cb7b6-7e1c-472d-9f2c-3c4b526ae57a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f1cb7b6-7e1c-472d-9f2c-3c4b526ae57a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da4cc508-faa2-46b8-956b-32ca8d0055fa' class='xr-var-data-in' type='checkbox'><label for='data-da4cc508-faa2-46b8-956b-32ca8d0055fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-86bb660d-16bc-446f-813a-8a6c7e2aa9a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-86bb660d-16bc-446f-813a-8a6c7e2aa9a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fec05039-de5c-4655-9262-8c6a0ac59452' class='xr-var-data-in' type='checkbox'><label for='data-fec05039-de5c-4655-9262-8c6a0ac59452' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-16c1a110-53cd-4ea4-9d22-b64adc53f8de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-16c1a110-53cd-4ea4-9d22-b64adc53f8de' class='xr-section-summary' >Data variables: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>images</span></div><div class='xr-var-dims'>(sample, x, y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e784adca-220a-4768-a31a-a014025d489d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e784adca-220a-4768-a31a-a014025d489d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1da3a75b-00c0-4341-ac66-1d2a98d565cb' class='xr-var-data-in' type='checkbox'><label for='data-1da3a75b-00c0-4341-ac66-1d2a98d565cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[145612647 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>labels</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cb070b6e-df07-48f1-a7ba-e4a556960ca6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cb070b6e-df07-48f1-a7ba-e4a556960ca6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a33756f7-d323-44df-ae1d-4e3848bdd9a1' class='xr-var-data-in' type='checkbox'><label for='data-a33756f7-d323-44df-ae1d-4e3848bdd9a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>THICK</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7176c23c-20a0-4f39-83fb-72ffca7fb590' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7176c23c-20a0-4f39-83fb-72ffca7fb590' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c48e521b-5d63-4533-9206-2bd92a85a939' class='xr-var-data-in' type='checkbox'><label for='data-c48e521b-5d63-4533-9206-2bd92a85a939' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vx</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e1ff81ce-11db-4578-be59-3a2dccbbc8f3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e1ff81ce-11db-4578-be59-3a2dccbbc8f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ee77473-7695-49e4-ba35-64c60218097e' class='xr-var-data-in' type='checkbox'><label for='data-5ee77473-7695-49e4-ba35-64c60218097e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vy</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-acf2a83b-5e41-4966-abe2-c6d65db39459' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-acf2a83b-5e41-4966-abe2-c6d65db39459' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d47b999-6a80-4365-a3f1-b1056bcb926a' class='xr-var-data-in' type='checkbox'><label for='data-6d47b999-6a80-4365-a3f1-b1056bcb926a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-717f79db-2d66-4397-96f1-958e5ad8a04a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-717f79db-2d66-4397-96f1-958e5ad8a04a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d37fcc1e-b992-4a96-b69e-06eaaea42161' class='xr-var-data-in' type='checkbox'><label for='data-d37fcc1e-b992-4a96-b69e-06eaaea42161' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smb</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-95b5e6c4-f5a6-437c-87a2-ef922f6ee901' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-95b5e6c4-f5a6-437c-87a2-ef922f6ee901' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6baeea75-2fe2-4d63-8f75-07db4b5fab79' class='xr-var-data-in' type='checkbox'><label for='data-6baeea75-2fe2-4d63-8f75-07db4b5fab79' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dcc66aec-d71c-4338-a66b-053477f1e79a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dcc66aec-d71c-4338-a66b-053477f1e79a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d16bc7ac-7690-4da6-99d4-a829fbf42a4d' class='xr-var-data-in' type='checkbox'><label for='data-d16bc7ac-7690-4da6-99d4-a829fbf42a4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>s</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1b79655d-bb50-4b29-a0dc-2c11433774ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1b79655d-bb50-4b29-a0dc-2c11433774ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70a6ac45-d945-426f-910c-26f13e7cacae' class='xr-var-data-in' type='checkbox'><label for='data-70a6ac45-d945-426f-910c-26f13e7cacae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a923326a-7661-4582-be40-8f2f1243de15' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a923326a-7661-4582-be40-8f2f1243de15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20bdcfc7-fa92-4189-82a9-2096cd81f7c3' class='xr-var-data-in' type='checkbox'><label for='data-20bdcfc7-fa92-4189-82a9-2096cd81f7c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ith_bm</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-89536c41-74fa-4576-af5b-04abefd0d841' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-89536c41-74fa-4576-af5b-04abefd0d841' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a3a5d12-1b80-4d86-9c5f-b2960f49dcf8' class='xr-var-data-in' type='checkbox'><label for='data-2a3a5d12-1b80-4d86-9c5f-b2960f49dcf8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gridCellId</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bc313217-0f04-4964-a114-96c42d9f17e4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bc313217-0f04-4964-a114-96c42d9f17e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35c82e4b-bec9-4c7c-825a-63cb8b3b3a1b' class='xr-var-data-in' type='checkbox'><label for='data-35c82e4b-bec9-4c7c-825a-63cb8b3b3a1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[199743 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f9ba1bd7-8415-4115-94ad-f3b494b8890c' class='xr-section-summary-in' type='checkbox'  ><label for='section-f9ba1bd7-8415-4115-94ad-f3b494b8890c' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>sample</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-6ed47c76-b204-42b0-9b23-579e3343c142' class='xr-index-data-in' type='checkbox'/><label for='index-6ed47c76-b204-42b0-9b23-579e3343c142' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([     0,      1,      2,      3,      4,      5,      6,      7,      8,\n",
+       "            9,\n",
+       "       ...\n",
+       "       199733, 199734, 199735, 199736, 199737, 199738, 199739, 199740, 199741,\n",
+       "       199742],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;sample&#x27;, length=199743))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-6ed60ee4-840c-4c44-b4f8-72368e33737a' class='xr-index-data-in' type='checkbox'/><label for='index-6ed60ee4-840c-4c44-b4f8-72368e33737a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b58bff00-a6fc-4329-984f-3c1aba501a1e' class='xr-index-data-in' type='checkbox'/><label for='index-b58bff00-a6fc-4329-984f-3c1aba501a1e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;y&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a757c469-3905-4ad3-a706-4ff4877652e5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a757c469-3905-4ad3-a706-4ff4877652e5' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>CNN data med velocity y billeder og &#x27;THICK&#x27; som labels.</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 602MB\n",
+       "Dimensions:     (sample: 199743, x: 27, y: 27)\n",
+       "Coordinates:\n",
+       "  * sample      (sample) int64 2MB 0 1 2 3 4 ... 199739 199740 199741 199742\n",
+       "  * x           (x) int64 216B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
+       "  * y           (y) int64 216B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
+       "Data variables:\n",
+       "    images      (sample, x, y) float32 582MB ...\n",
+       "    labels      (sample) float64 2MB ...\n",
+       "    THICK       (sample) float64 2MB ...\n",
+       "    vx          (sample) float64 2MB ...\n",
+       "    vy          (sample) float64 2MB ...\n",
+       "    v           (sample) float64 2MB ...\n",
+       "    smb         (sample) float64 2MB ...\n",
+       "    z           (sample) float64 2MB ...\n",
+       "    s           (sample) float64 2MB ...\n",
+       "    temp        (sample) float64 2MB ...\n",
+       "    ith_bm      (sample) float64 2MB ...\n",
+       "    gridCellId  (sample) int64 2MB ...\n",
+       "Attributes:\n",
+       "    description:  CNN data med velocity y billeder og 'THICK' som labels."
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_import"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "appml",
+   "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}