Upload 2 files
Browse files- conv_antarctis.ipynb +0 -0
- make_conv_train.ipynb +1504 -0
conv_antarctis.ipynb
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make_conv_train.ipynb
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|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 1,
|
| 6 |
+
"id": "0dd2c5d4",
|
| 7 |
+
"metadata": {},
|
| 8 |
+
"outputs": [],
|
| 9 |
+
"source": [
|
| 10 |
+
"import xarray as xr\n",
|
| 11 |
+
"import geopandas as gpd\n",
|
| 12 |
+
"from shapely.geometry import box\n",
|
| 13 |
+
"import rioxarray as rxr # Make sure you have rioxarray installed (pip install rioxarray)\n",
|
| 14 |
+
"import numpy as np\n",
|
| 15 |
+
"import ibis\n",
|
| 16 |
+
"ibis.options.interactive = True"
|
| 17 |
+
]
|
| 18 |
+
},
|
| 19 |
+
{
|
| 20 |
+
"cell_type": "code",
|
| 21 |
+
"execution_count": 2,
|
| 22 |
+
"id": "d615f835",
|
| 23 |
+
"metadata": {},
|
| 24 |
+
"outputs": [
|
| 25 |
+
{
|
| 26 |
+
"data": {
|
| 27 |
+
"text/plain": [
|
| 28 |
+
"<duckdb.duckdb.DuckDBPyConnection at 0x1c9eb17f6f0>"
|
| 29 |
+
]
|
| 30 |
+
},
|
| 31 |
+
"execution_count": 2,
|
| 32 |
+
"metadata": {},
|
| 33 |
+
"output_type": "execute_result"
|
| 34 |
+
}
|
| 35 |
+
],
|
| 36 |
+
"source": [
|
| 37 |
+
"con = ibis.duckdb.connect()\n",
|
| 38 |
+
"con.raw_sql('INSTALL spatial;')\n",
|
| 39 |
+
"con.raw_sql('LOAD spatial;')"
|
| 40 |
+
]
|
| 41 |
+
},
|
| 42 |
+
{
|
| 43 |
+
"cell_type": "markdown",
|
| 44 |
+
"id": "700cf1f9",
|
| 45 |
+
"metadata": {},
|
| 46 |
+
"source": [
|
| 47 |
+
"- The .rio accessor: https://corteva.github.io/rioxarray/html/rioxarray.html#rioxarray-rio-accessors\n",
|
| 48 |
+
"\n",
|
| 49 |
+
"- Affine( pixel_width, 0, top_left_x_coord,\n",
|
| 50 |
+
" 0, -pixel_height, top_left_y_coord)\n",
|
| 51 |
+
"\n",
|
| 52 |
+
"- Rasterio Affine Docs (https://affine.readthedocs.io/en/latest/)"
|
| 53 |
+
]
|
| 54 |
+
},
|
| 55 |
+
{
|
| 56 |
+
"cell_type": "code",
|
| 57 |
+
"execution_count": 3,
|
| 58 |
+
"id": "cf514138",
|
| 59 |
+
"metadata": {},
|
| 60 |
+
"outputs": [],
|
| 61 |
+
"source": [
|
| 62 |
+
"filename = 'BedMachineAntarctica-v3.nc'\n",
|
| 63 |
+
"sat_im = rxr.open_rasterio(filename)\n",
|
| 64 |
+
"transform = sat_im.rio.transform()"
|
| 65 |
+
]
|
| 66 |
+
},
|
| 67 |
+
{
|
| 68 |
+
"cell_type": "code",
|
| 69 |
+
"execution_count": 4,
|
| 70 |
+
"id": "7cc14869",
|
| 71 |
+
"metadata": {},
|
| 72 |
+
"outputs": [],
|
| 73 |
+
"source": [
|
| 74 |
+
"tab = con.read_parquet('bedmap_train2_30m.parquet')"
|
| 75 |
+
]
|
| 76 |
+
},
|
| 77 |
+
{
|
| 78 |
+
"cell_type": "code",
|
| 79 |
+
"execution_count": 5,
|
| 80 |
+
"id": "0fec2bb7",
|
| 81 |
+
"metadata": {},
|
| 82 |
+
"outputs": [
|
| 83 |
+
{
|
| 84 |
+
"data": {
|
| 85 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 86 |
+
"model_id": "0dae1027ada7475b8c194923c425073c",
|
| 87 |
+
"version_major": 2,
|
| 88 |
+
"version_minor": 0
|
| 89 |
+
},
|
| 90 |
+
"text/plain": [
|
| 91 |
+
"FloatProgress(value=0.0, layout=Layout(width='auto'), style=ProgressStyle(bar_color='black'))"
|
| 92 |
+
]
|
| 93 |
+
},
|
| 94 |
+
"metadata": {},
|
| 95 |
+
"output_type": "display_data"
|
| 96 |
+
}
|
| 97 |
+
],
|
| 98 |
+
"source": [
|
| 99 |
+
"# Let's create a dummy GeoPandas DataFrame for demonstration\n",
|
| 100 |
+
"num_points = 100_000\n",
|
| 101 |
+
"frac_points = num_points/30_000_000\n",
|
| 102 |
+
"# Generate random points within a reasonable Antarctica extent (approx for EPSG:3031)\n",
|
| 103 |
+
"# min_x, max_x = -2000000, 2000000\n",
|
| 104 |
+
"# min_y, max_y = -2000000, 2000000\n",
|
| 105 |
+
"# random_x = np.random.uniform(min_x, max_x, num_points)\n",
|
| 106 |
+
"# random_y = np.random.uniform(min_y, max_y, num_points)\n",
|
| 107 |
+
"# ice_thickness_data = np.random.uniform(100, 5000, num_points) # Example ice thickness\n",
|
| 108 |
+
"# v_data = np.random.uniform(0, 1, num_points) # Example velocity\n",
|
| 109 |
+
"# temp_data = np.random.uniform(0, 1000, num_points) # Example temperature\n",
|
| 110 |
+
"\n",
|
| 111 |
+
"# gdf = gpd.GeoDataFrame(\n",
|
| 112 |
+
"# {'ice_thickness': ice_thickness_data,\n",
|
| 113 |
+
"# 'v': v_data,\n",
|
| 114 |
+
"# 'temp': temp_data\n",
|
| 115 |
+
"# },\n",
|
| 116 |
+
"# geometry=gpd.points_from_xy(random_x, random_y),\n",
|
| 117 |
+
"# crs=\"EPSG:3031\"\n",
|
| 118 |
+
"# )\n",
|
| 119 |
+
"data = tab.drop(['LON','LAT'])\n",
|
| 120 |
+
"random_data = data.sample(frac_points)\n",
|
| 121 |
+
"\n",
|
| 122 |
+
"# 3.1. Create a spatial index for your GeoDataFrame\n",
|
| 123 |
+
"gdf = random_data.to_pandas()\n",
|
| 124 |
+
"gdf.crs = \"EPSG:3031\"\n",
|
| 125 |
+
"gdf_sindex = gdf.sindex"
|
| 126 |
+
]
|
| 127 |
+
},
|
| 128 |
+
{
|
| 129 |
+
"cell_type": "code",
|
| 130 |
+
"execution_count": 6,
|
| 131 |
+
"id": "ae2f315d",
|
| 132 |
+
"metadata": {},
|
| 133 |
+
"outputs": [
|
| 134 |
+
{
|
| 135 |
+
"data": {
|
| 136 |
+
"text/html": [
|
| 137 |
+
"<div>\n",
|
| 138 |
+
"<style scoped>\n",
|
| 139 |
+
" .dataframe tbody tr th:only-of-type {\n",
|
| 140 |
+
" vertical-align: middle;\n",
|
| 141 |
+
" }\n",
|
| 142 |
+
"\n",
|
| 143 |
+
" .dataframe tbody tr th {\n",
|
| 144 |
+
" vertical-align: top;\n",
|
| 145 |
+
" }\n",
|
| 146 |
+
"\n",
|
| 147 |
+
" .dataframe thead th {\n",
|
| 148 |
+
" text-align: right;\n",
|
| 149 |
+
" }\n",
|
| 150 |
+
"</style>\n",
|
| 151 |
+
"<table border=\"1\" class=\"dataframe\">\n",
|
| 152 |
+
" <thead>\n",
|
| 153 |
+
" <tr style=\"text-align: right;\">\n",
|
| 154 |
+
" <th></th>\n",
|
| 155 |
+
" <th>THICK</th>\n",
|
| 156 |
+
" <th>geometry</th>\n",
|
| 157 |
+
" <th>EAST</th>\n",
|
| 158 |
+
" <th>NORTH</th>\n",
|
| 159 |
+
" <th>vx</th>\n",
|
| 160 |
+
" <th>vy</th>\n",
|
| 161 |
+
" <th>v</th>\n",
|
| 162 |
+
" <th>ith_bm</th>\n",
|
| 163 |
+
" <th>smb</th>\n",
|
| 164 |
+
" <th>z</th>\n",
|
| 165 |
+
" <th>s</th>\n",
|
| 166 |
+
" <th>temp</th>\n",
|
| 167 |
+
" </tr>\n",
|
| 168 |
+
" </thead>\n",
|
| 169 |
+
" <tbody>\n",
|
| 170 |
+
" <tr>\n",
|
| 171 |
+
" <th>0</th>\n",
|
| 172 |
+
" <td>1064.24</td>\n",
|
| 173 |
+
" <td>POINT (-413937.178 826713.054)</td>\n",
|
| 174 |
+
" <td>-4.139372e+05</td>\n",
|
| 175 |
+
" <td>8.267131e+05</td>\n",
|
| 176 |
+
" <td>-2.619273</td>\n",
|
| 177 |
+
" <td>3.084245</td>\n",
|
| 178 |
+
" <td>4.046376</td>\n",
|
| 179 |
+
" <td>954.490873</td>\n",
|
| 180 |
+
" <td>62.644607</td>\n",
|
| 181 |
+
" <td>1162.279839</td>\n",
|
| 182 |
+
" <td>0.007977</td>\n",
|
| 183 |
+
" <td>244.021857</td>\n",
|
| 184 |
+
" </tr>\n",
|
| 185 |
+
" <tr>\n",
|
| 186 |
+
" <th>1</th>\n",
|
| 187 |
+
" <td>1193.70</td>\n",
|
| 188 |
+
" <td>POINT (1671948.79 -1896344.349)</td>\n",
|
| 189 |
+
" <td>1.671949e+06</td>\n",
|
| 190 |
+
" <td>-1.896344e+06</td>\n",
|
| 191 |
+
" <td>31.064121</td>\n",
|
| 192 |
+
" <td>-41.057273</td>\n",
|
| 193 |
+
" <td>51.484748</td>\n",
|
| 194 |
+
" <td>1211.514173</td>\n",
|
| 195 |
+
" <td>451.263755</td>\n",
|
| 196 |
+
" <td>1203.777496</td>\n",
|
| 197 |
+
" <td>0.015953</td>\n",
|
| 198 |
+
" <td>252.699878</td>\n",
|
| 199 |
+
" </tr>\n",
|
| 200 |
+
" <tr>\n",
|
| 201 |
+
" <th>2</th>\n",
|
| 202 |
+
" <td>1552.96</td>\n",
|
| 203 |
+
" <td>POINT (-1258929.617 -809646.847)</td>\n",
|
| 204 |
+
" <td>-1.258930e+06</td>\n",
|
| 205 |
+
" <td>-8.096468e+05</td>\n",
|
| 206 |
+
" <td>-2.924342</td>\n",
|
| 207 |
+
" <td>8.953849</td>\n",
|
| 208 |
+
" <td>9.419298</td>\n",
|
| 209 |
+
" <td>1663.638829</td>\n",
|
| 210 |
+
" <td>490.454161</td>\n",
|
| 211 |
+
" <td>2100.545793</td>\n",
|
| 212 |
+
" <td>0.003695</td>\n",
|
| 213 |
+
" <td>246.286919</td>\n",
|
| 214 |
+
" </tr>\n",
|
| 215 |
+
" <tr>\n",
|
| 216 |
+
" <th>3</th>\n",
|
| 217 |
+
" <td>1264.70</td>\n",
|
| 218 |
+
" <td>POINT (-844998.401 333006.309)</td>\n",
|
| 219 |
+
" <td>-8.449984e+05</td>\n",
|
| 220 |
+
" <td>3.330063e+05</td>\n",
|
| 221 |
+
" <td>-2.674579</td>\n",
|
| 222 |
+
" <td>-1.263969</td>\n",
|
| 223 |
+
" <td>2.958207</td>\n",
|
| 224 |
+
" <td>1259.694288</td>\n",
|
| 225 |
+
" <td>145.535283</td>\n",
|
| 226 |
+
" <td>345.654052</td>\n",
|
| 227 |
+
" <td>0.004805</td>\n",
|
| 228 |
+
" <td>246.782685</td>\n",
|
| 229 |
+
" </tr>\n",
|
| 230 |
+
" <tr>\n",
|
| 231 |
+
" <th>4</th>\n",
|
| 232 |
+
" <td>2000.78</td>\n",
|
| 233 |
+
" <td>POINT (-735958.864 48154.371)</td>\n",
|
| 234 |
+
" <td>-7.359589e+05</td>\n",
|
| 235 |
+
" <td>4.815437e+04</td>\n",
|
| 236 |
+
" <td>-3.450994</td>\n",
|
| 237 |
+
" <td>5.502195</td>\n",
|
| 238 |
+
" <td>6.494884</td>\n",
|
| 239 |
+
" <td>2022.433247</td>\n",
|
| 240 |
+
" <td>132.701361</td>\n",
|
| 241 |
+
" <td>1458.315269</td>\n",
|
| 242 |
+
" <td>0.002991</td>\n",
|
| 243 |
+
" <td>243.095592</td>\n",
|
| 244 |
+
" </tr>\n",
|
| 245 |
+
" <tr>\n",
|
| 246 |
+
" <th>...</th>\n",
|
| 247 |
+
" <td>...</td>\n",
|
| 248 |
+
" <td>...</td>\n",
|
| 249 |
+
" <td>...</td>\n",
|
| 250 |
+
" <td>...</td>\n",
|
| 251 |
+
" <td>...</td>\n",
|
| 252 |
+
" <td>...</td>\n",
|
| 253 |
+
" <td>...</td>\n",
|
| 254 |
+
" <td>...</td>\n",
|
| 255 |
+
" <td>...</td>\n",
|
| 256 |
+
" <td>...</td>\n",
|
| 257 |
+
" <td>...</td>\n",
|
| 258 |
+
" <td>...</td>\n",
|
| 259 |
+
" </tr>\n",
|
| 260 |
+
" <tr>\n",
|
| 261 |
+
" <th>99761</th>\n",
|
| 262 |
+
" <td>2024.41</td>\n",
|
| 263 |
+
" <td>POINT (-1070869.73 36219.135)</td>\n",
|
| 264 |
+
" <td>-1.070870e+06</td>\n",
|
| 265 |
+
" <td>3.621913e+04</td>\n",
|
| 266 |
+
" <td>-6.510958</td>\n",
|
| 267 |
+
" <td>4.801056</td>\n",
|
| 268 |
+
" <td>8.089667</td>\n",
|
| 269 |
+
" <td>1676.777041</td>\n",
|
| 270 |
+
" <td>202.611396</td>\n",
|
| 271 |
+
" <td>1739.456718</td>\n",
|
| 272 |
+
" <td>0.009819</td>\n",
|
| 273 |
+
" <td>243.995007</td>\n",
|
| 274 |
+
" </tr>\n",
|
| 275 |
+
" <tr>\n",
|
| 276 |
+
" <th>99762</th>\n",
|
| 277 |
+
" <td>2787.40</td>\n",
|
| 278 |
+
" <td>POINT (919130.388 -1668408.781)</td>\n",
|
| 279 |
+
" <td>9.191304e+05</td>\n",
|
| 280 |
+
" <td>-1.668409e+06</td>\n",
|
| 281 |
+
" <td>0.308528</td>\n",
|
| 282 |
+
" <td>-1.388530</td>\n",
|
| 283 |
+
" <td>1.422394</td>\n",
|
| 284 |
+
" <td>2820.331161</td>\n",
|
| 285 |
+
" <td>85.178935</td>\n",
|
| 286 |
+
" <td>2181.561262</td>\n",
|
| 287 |
+
" <td>0.002284</td>\n",
|
| 288 |
+
" <td>234.099728</td>\n",
|
| 289 |
+
" </tr>\n",
|
| 290 |
+
" <tr>\n",
|
| 291 |
+
" <th>99763</th>\n",
|
| 292 |
+
" <td>785.40</td>\n",
|
| 293 |
+
" <td>POINT (2292737.808 -1064316.615)</td>\n",
|
| 294 |
+
" <td>2.292738e+06</td>\n",
|
| 295 |
+
" <td>-1.064317e+06</td>\n",
|
| 296 |
+
" <td>-167.424091</td>\n",
|
| 297 |
+
" <td>-27.102104</td>\n",
|
| 298 |
+
" <td>169.603509</td>\n",
|
| 299 |
+
" <td>796.579113</td>\n",
|
| 300 |
+
" <td>1294.093330</td>\n",
|
| 301 |
+
" <td>212.071962</td>\n",
|
| 302 |
+
" <td>0.048592</td>\n",
|
| 303 |
+
" <td>258.157311</td>\n",
|
| 304 |
+
" </tr>\n",
|
| 305 |
+
" <tr>\n",
|
| 306 |
+
" <th>99764</th>\n",
|
| 307 |
+
" <td>2452.89</td>\n",
|
| 308 |
+
" <td>POINT (-1387664.803 -630194.489)</td>\n",
|
| 309 |
+
" <td>-1.387665e+06</td>\n",
|
| 310 |
+
" <td>-6.301945e+05</td>\n",
|
| 311 |
+
" <td>-43.763266</td>\n",
|
| 312 |
+
" <td>47.033471</td>\n",
|
| 313 |
+
" <td>64.244617</td>\n",
|
| 314 |
+
" <td>2506.279739</td>\n",
|
| 315 |
+
" <td>738.107499</td>\n",
|
| 316 |
+
" <td>1219.776864</td>\n",
|
| 317 |
+
" <td>0.004550</td>\n",
|
| 318 |
+
" <td>249.767054</td>\n",
|
| 319 |
+
" </tr>\n",
|
| 320 |
+
" <tr>\n",
|
| 321 |
+
" <th>99765</th>\n",
|
| 322 |
+
" <td>1680.48</td>\n",
|
| 323 |
+
" <td>POINT (539623.208 -1053727.718)</td>\n",
|
| 324 |
+
" <td>5.396232e+05</td>\n",
|
| 325 |
+
" <td>-1.053728e+06</td>\n",
|
| 326 |
+
" <td>-4.670764</td>\n",
|
| 327 |
+
" <td>-1.080457</td>\n",
|
| 328 |
+
" <td>4.794103</td>\n",
|
| 329 |
+
" <td>1509.649156</td>\n",
|
| 330 |
+
" <td>31.253687</td>\n",
|
| 331 |
+
" <td>2153.899426</td>\n",
|
| 332 |
+
" <td>0.008152</td>\n",
|
| 333 |
+
" <td>234.451772</td>\n",
|
| 334 |
+
" </tr>\n",
|
| 335 |
+
" </tbody>\n",
|
| 336 |
+
"</table>\n",
|
| 337 |
+
"<p>99766 rows × 12 columns</p>\n",
|
| 338 |
+
"</div>"
|
| 339 |
+
],
|
| 340 |
+
"text/plain": [
|
| 341 |
+
" THICK geometry EAST NORTH \\\n",
|
| 342 |
+
"0 1064.24 POINT (-413937.178 826713.054) -4.139372e+05 8.267131e+05 \n",
|
| 343 |
+
"1 1193.70 POINT (1671948.79 -1896344.349) 1.671949e+06 -1.896344e+06 \n",
|
| 344 |
+
"2 1552.96 POINT (-1258929.617 -809646.847) -1.258930e+06 -8.096468e+05 \n",
|
| 345 |
+
"3 1264.70 POINT (-844998.401 333006.309) -8.449984e+05 3.330063e+05 \n",
|
| 346 |
+
"4 2000.78 POINT (-735958.864 48154.371) -7.359589e+05 4.815437e+04 \n",
|
| 347 |
+
"... ... ... ... ... \n",
|
| 348 |
+
"99761 2024.41 POINT (-1070869.73 36219.135) -1.070870e+06 3.621913e+04 \n",
|
| 349 |
+
"99762 2787.40 POINT (919130.388 -1668408.781) 9.191304e+05 -1.668409e+06 \n",
|
| 350 |
+
"99763 785.40 POINT (2292737.808 -1064316.615) 2.292738e+06 -1.064317e+06 \n",
|
| 351 |
+
"99764 2452.89 POINT (-1387664.803 -630194.489) -1.387665e+06 -6.301945e+05 \n",
|
| 352 |
+
"99765 1680.48 POINT (539623.208 -1053727.718) 5.396232e+05 -1.053728e+06 \n",
|
| 353 |
+
"\n",
|
| 354 |
+
" vx vy v ith_bm smb \\\n",
|
| 355 |
+
"0 -2.619273 3.084245 4.046376 954.490873 62.644607 \n",
|
| 356 |
+
"1 31.064121 -41.057273 51.484748 1211.514173 451.263755 \n",
|
| 357 |
+
"2 -2.924342 8.953849 9.419298 1663.638829 490.454161 \n",
|
| 358 |
+
"3 -2.674579 -1.263969 2.958207 1259.694288 145.535283 \n",
|
| 359 |
+
"4 -3.450994 5.502195 6.494884 2022.433247 132.701361 \n",
|
| 360 |
+
"... ... ... ... ... ... \n",
|
| 361 |
+
"99761 -6.510958 4.801056 8.089667 1676.777041 202.611396 \n",
|
| 362 |
+
"99762 0.308528 -1.388530 1.422394 2820.331161 85.178935 \n",
|
| 363 |
+
"99763 -167.424091 -27.102104 169.603509 796.579113 1294.093330 \n",
|
| 364 |
+
"99764 -43.763266 47.033471 64.244617 2506.279739 738.107499 \n",
|
| 365 |
+
"99765 -4.670764 -1.080457 4.794103 1509.649156 31.253687 \n",
|
| 366 |
+
"\n",
|
| 367 |
+
" z s temp \n",
|
| 368 |
+
"0 1162.279839 0.007977 244.021857 \n",
|
| 369 |
+
"1 1203.777496 0.015953 252.699878 \n",
|
| 370 |
+
"2 2100.545793 0.003695 246.286919 \n",
|
| 371 |
+
"3 345.654052 0.004805 246.782685 \n",
|
| 372 |
+
"4 1458.315269 0.002991 243.095592 \n",
|
| 373 |
+
"... ... ... ... \n",
|
| 374 |
+
"99761 1739.456718 0.009819 243.995007 \n",
|
| 375 |
+
"99762 2181.561262 0.002284 234.099728 \n",
|
| 376 |
+
"99763 212.071962 0.048592 258.157311 \n",
|
| 377 |
+
"99764 1219.776864 0.004550 249.767054 \n",
|
| 378 |
+
"99765 2153.899426 0.008152 234.451772 \n",
|
| 379 |
+
"\n",
|
| 380 |
+
"[99766 rows x 12 columns]"
|
| 381 |
+
]
|
| 382 |
+
},
|
| 383 |
+
"execution_count": 6,
|
| 384 |
+
"metadata": {},
|
| 385 |
+
"output_type": "execute_result"
|
| 386 |
+
}
|
| 387 |
+
],
|
| 388 |
+
"source": [
|
| 389 |
+
"gdf"
|
| 390 |
+
]
|
| 391 |
+
},
|
| 392 |
+
{
|
| 393 |
+
"cell_type": "code",
|
| 394 |
+
"execution_count": 8,
|
| 395 |
+
"id": "8cd3bb9e",
|
| 396 |
+
"metadata": {},
|
| 397 |
+
"outputs": [
|
| 398 |
+
{
|
| 399 |
+
"data": {
|
| 400 |
+
"text/plain": [
|
| 401 |
+
"99766"
|
| 402 |
+
]
|
| 403 |
+
},
|
| 404 |
+
"execution_count": 8,
|
| 405 |
+
"metadata": {},
|
| 406 |
+
"output_type": "execute_result"
|
| 407 |
+
}
|
| 408 |
+
],
|
| 409 |
+
"source": [
|
| 410 |
+
"len(gdf)"
|
| 411 |
+
]
|
| 412 |
+
},
|
| 413 |
+
{
|
| 414 |
+
"cell_type": "code",
|
| 415 |
+
"execution_count": 9,
|
| 416 |
+
"id": "9f8614bd",
|
| 417 |
+
"metadata": {},
|
| 418 |
+
"outputs": [],
|
| 419 |
+
"source": [
|
| 420 |
+
"size = 27 #pixels\n",
|
| 421 |
+
"half = size // 2\n",
|
| 422 |
+
"\n",
|
| 423 |
+
"images = []\n",
|
| 424 |
+
"im_data = {}\n",
|
| 425 |
+
"\n",
|
| 426 |
+
"scalar_feats = ['THICK', 'vx', 'vy', 'v', 'smb', 'z', 's', 'temp']\n",
|
| 427 |
+
"\n",
|
| 428 |
+
"for sclr in scalar_feats:\n",
|
| 429 |
+
" im_data[sclr] = []\n",
|
| 430 |
+
"\n",
|
| 431 |
+
"\n",
|
| 432 |
+
"for idx, data_row in gdf.iterrows():\n",
|
| 433 |
+
" geom = data_row.geometry\n",
|
| 434 |
+
"\n",
|
| 435 |
+
" col, row = ~transform * (geom.x, geom.y) #Pixel coordinates\n",
|
| 436 |
+
" col, row = int(np.floor(col)), int(np.floor(row))\n",
|
| 437 |
+
"\n",
|
| 438 |
+
" patch = sat_im.surface.isel(\n",
|
| 439 |
+
" y=slice(row-half, row+half+1),\n",
|
| 440 |
+
" x=slice(col-half, col+half+1)\n",
|
| 441 |
+
" )\n",
|
| 442 |
+
"\n",
|
| 443 |
+
" image = xr.DataArray(\n",
|
| 444 |
+
" patch[0].values,\n",
|
| 445 |
+
" coords = {\n",
|
| 446 |
+
" \"x\": np.arange(size),\n",
|
| 447 |
+
" \"y\": np.arange(size)\n",
|
| 448 |
+
" },\n",
|
| 449 |
+
" dims = (\"x\", \"y\"),\n",
|
| 450 |
+
" name = \"z_image\"\n",
|
| 451 |
+
" )\n",
|
| 452 |
+
"\n",
|
| 453 |
+
" images.append(image)\n",
|
| 454 |
+
" for sclr in scalar_feats:\n",
|
| 455 |
+
" im_data[sclr].append(data_row[sclr])\n",
|
| 456 |
+
"\n",
|
| 457 |
+
"sc = np.arange(len(gdf))\n",
|
| 458 |
+
"\n",
|
| 459 |
+
"# Create a DataArray for all images\n",
|
| 460 |
+
"all_images_da = xr.concat(images, dim = 'sample')\n",
|
| 461 |
+
"all_images_da['sample'] = sc # Add sample coordinates\n",
|
| 462 |
+
"\n",
|
| 463 |
+
"scalars_ds = xr.Dataset(\n",
|
| 464 |
+
" {\n",
|
| 465 |
+
" 'vx': xr.DataArray(np.array(im_data['vx']), coords = {'sample': sc}, dims = ('sample',), name = 'vx'),\n",
|
| 466 |
+
" 'vy': xr.DataArray(np.array(im_data['vy']), coords = {'sample': sc}, dims = ('sample',), name = 'vy'),\n",
|
| 467 |
+
" 'v': xr.DataArray(np.array(im_data['v']), coords = {'sample': sc}, dims = ('sample',), name = 'v'),\n",
|
| 468 |
+
" 'smb': xr.DataArray(np.array(im_data['smb']), coords = {'sample': sc}, dims = ('sample',), name = 'smb'),\n",
|
| 469 |
+
" 'z': xr.DataArray(np.array(im_data['z']), coords = {'sample': sc}, dims = ('sample',), name = 'z'),\n",
|
| 470 |
+
" 's': xr.DataArray(np.array(im_data['s']), coords = {'sample': sc}, dims = ('sample',), name = 's'),\n",
|
| 471 |
+
" 'temp': xr.DataArray(np.array(im_data['temp']), coords = {'sample': sc}, dims = ('sample',), name = 'temp')\n",
|
| 472 |
+
" },\n",
|
| 473 |
+
" coords = {'sample': sc}\n",
|
| 474 |
+
")\n",
|
| 475 |
+
"\n",
|
| 476 |
+
"# Create DataArrays for labels and scalar features, aligning with the 'sample' dimension\n",
|
| 477 |
+
"labels_da = xr.DataArray(\n",
|
| 478 |
+
" np.array(im_data[\"THICK\"]),\n",
|
| 479 |
+
" coords={\"sample\": sc},\n",
|
| 480 |
+
" dims=(\"sample\",),\n",
|
| 481 |
+
" name=\"label\"\n",
|
| 482 |
+
")\n",
|
| 483 |
+
"\n",
|
| 484 |
+
"# Combine everything into a single Dataset\n",
|
| 485 |
+
"training_data_ds = xr.Dataset(\n",
|
| 486 |
+
" {\n",
|
| 487 |
+
" \"images\": all_images_da,\n",
|
| 488 |
+
" \"labels\": labels_da\n",
|
| 489 |
+
" },\n",
|
| 490 |
+
" coords={\"sample\": sc}, # Add sample coordinates from scalar_features_ds\n",
|
| 491 |
+
" # name = \"Elevation images, with labels and scarlars\",\n",
|
| 492 |
+
" attrs={'description': 'CNN data with elevation images. Scalar features are everything from Niccolo 30M parquet.\\\n",
|
| 493 |
+
" Images are 27x27 pixels.'}\n",
|
| 494 |
+
")\n",
|
| 495 |
+
"\n",
|
| 496 |
+
"training_data_ds = training_data_ds.merge(scalars_ds)"
|
| 497 |
+
]
|
| 498 |
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},
|
| 499 |
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{
|
| 500 |
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"cell_type": "code",
|
| 501 |
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"execution_count": 16,
|
| 502 |
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"id": "6cc1c242",
|
| 503 |
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"metadata": {},
|
| 504 |
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"outputs": [
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| 505 |
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{
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| 506 |
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" grid-column: 1;\n",
|
| 760 |
+
"}\n",
|
| 761 |
+
"\n",
|
| 762 |
+
".xr-var-dims {\n",
|
| 763 |
+
" grid-column: 2;\n",
|
| 764 |
+
"}\n",
|
| 765 |
+
"\n",
|
| 766 |
+
".xr-var-dtype {\n",
|
| 767 |
+
" grid-column: 3;\n",
|
| 768 |
+
" text-align: right;\n",
|
| 769 |
+
" color: var(--xr-font-color2);\n",
|
| 770 |
+
"}\n",
|
| 771 |
+
"\n",
|
| 772 |
+
".xr-var-preview {\n",
|
| 773 |
+
" grid-column: 4;\n",
|
| 774 |
+
"}\n",
|
| 775 |
+
"\n",
|
| 776 |
+
".xr-index-preview {\n",
|
| 777 |
+
" grid-column: 2 / 5;\n",
|
| 778 |
+
" color: var(--xr-font-color2);\n",
|
| 779 |
+
"}\n",
|
| 780 |
+
"\n",
|
| 781 |
+
".xr-var-name,\n",
|
| 782 |
+
".xr-var-dims,\n",
|
| 783 |
+
".xr-var-dtype,\n",
|
| 784 |
+
".xr-preview,\n",
|
| 785 |
+
".xr-attrs dt {\n",
|
| 786 |
+
" white-space: nowrap;\n",
|
| 787 |
+
" overflow: hidden;\n",
|
| 788 |
+
" text-overflow: ellipsis;\n",
|
| 789 |
+
" padding-right: 10px;\n",
|
| 790 |
+
"}\n",
|
| 791 |
+
"\n",
|
| 792 |
+
".xr-var-name:hover,\n",
|
| 793 |
+
".xr-var-dims:hover,\n",
|
| 794 |
+
".xr-var-dtype:hover,\n",
|
| 795 |
+
".xr-attrs dt:hover {\n",
|
| 796 |
+
" overflow: visible;\n",
|
| 797 |
+
" width: auto;\n",
|
| 798 |
+
" z-index: 1;\n",
|
| 799 |
+
"}\n",
|
| 800 |
+
"\n",
|
| 801 |
+
".xr-var-attrs,\n",
|
| 802 |
+
".xr-var-data,\n",
|
| 803 |
+
".xr-index-data {\n",
|
| 804 |
+
" display: none;\n",
|
| 805 |
+
" background-color: var(--xr-background-color) !important;\n",
|
| 806 |
+
" padding-bottom: 5px !important;\n",
|
| 807 |
+
"}\n",
|
| 808 |
+
"\n",
|
| 809 |
+
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
|
| 810 |
+
".xr-var-data-in:checked ~ .xr-var-data,\n",
|
| 811 |
+
".xr-index-data-in:checked ~ .xr-index-data {\n",
|
| 812 |
+
" display: block;\n",
|
| 813 |
+
"}\n",
|
| 814 |
+
"\n",
|
| 815 |
+
".xr-var-data > table {\n",
|
| 816 |
+
" float: right;\n",
|
| 817 |
+
"}\n",
|
| 818 |
+
"\n",
|
| 819 |
+
".xr-var-name span,\n",
|
| 820 |
+
".xr-var-data,\n",
|
| 821 |
+
".xr-index-name div,\n",
|
| 822 |
+
".xr-index-data,\n",
|
| 823 |
+
".xr-attrs {\n",
|
| 824 |
+
" padding-left: 25px !important;\n",
|
| 825 |
+
"}\n",
|
| 826 |
+
"\n",
|
| 827 |
+
".xr-attrs,\n",
|
| 828 |
+
".xr-var-attrs,\n",
|
| 829 |
+
".xr-var-data,\n",
|
| 830 |
+
".xr-index-data {\n",
|
| 831 |
+
" grid-column: 1 / -1;\n",
|
| 832 |
+
"}\n",
|
| 833 |
+
"\n",
|
| 834 |
+
"dl.xr-attrs {\n",
|
| 835 |
+
" padding: 0;\n",
|
| 836 |
+
" margin: 0;\n",
|
| 837 |
+
" display: grid;\n",
|
| 838 |
+
" grid-template-columns: 125px auto;\n",
|
| 839 |
+
"}\n",
|
| 840 |
+
"\n",
|
| 841 |
+
".xr-attrs dt,\n",
|
| 842 |
+
".xr-attrs dd {\n",
|
| 843 |
+
" padding: 0;\n",
|
| 844 |
+
" margin: 0;\n",
|
| 845 |
+
" float: left;\n",
|
| 846 |
+
" padding-right: 10px;\n",
|
| 847 |
+
" width: auto;\n",
|
| 848 |
+
"}\n",
|
| 849 |
+
"\n",
|
| 850 |
+
".xr-attrs dt {\n",
|
| 851 |
+
" font-weight: normal;\n",
|
| 852 |
+
" grid-column: 1;\n",
|
| 853 |
+
"}\n",
|
| 854 |
+
"\n",
|
| 855 |
+
".xr-attrs dt:hover span {\n",
|
| 856 |
+
" display: inline-block;\n",
|
| 857 |
+
" background: var(--xr-background-color);\n",
|
| 858 |
+
" padding-right: 10px;\n",
|
| 859 |
+
"}\n",
|
| 860 |
+
"\n",
|
| 861 |
+
".xr-attrs dd {\n",
|
| 862 |
+
" grid-column: 2;\n",
|
| 863 |
+
" white-space: pre-wrap;\n",
|
| 864 |
+
" word-break: break-all;\n",
|
| 865 |
+
"}\n",
|
| 866 |
+
"\n",
|
| 867 |
+
".xr-icon-database,\n",
|
| 868 |
+
".xr-icon-file-text2,\n",
|
| 869 |
+
".xr-no-icon {\n",
|
| 870 |
+
" display: inline-block;\n",
|
| 871 |
+
" vertical-align: middle;\n",
|
| 872 |
+
" width: 1em;\n",
|
| 873 |
+
" height: 1.5em !important;\n",
|
| 874 |
+
" stroke-width: 0;\n",
|
| 875 |
+
" stroke: currentColor;\n",
|
| 876 |
+
" fill: currentColor;\n",
|
| 877 |
+
"}\n",
|
| 878 |
+
"</style><pre class='xr-text-repr-fallback'><xarray.Dataset> Size: 298MB\n",
|
| 879 |
+
"Dimensions: (x: 27, y: 27, sample: 99766)\n",
|
| 880 |
+
"Coordinates:\n",
|
| 881 |
+
" * x (x) int64 216B 0 1 2 3 4 5 6 7 8 9 ... 18 19 20 21 22 23 24 25 26\n",
|
| 882 |
+
" * y (y) int64 216B 0 1 2 3 4 5 6 7 8 9 ... 18 19 20 21 22 23 24 25 26\n",
|
| 883 |
+
" * sample (sample) int64 798kB 0 1 2 3 4 5 ... 99761 99762 99763 99764 99765\n",
|
| 884 |
+
"Data variables:\n",
|
| 885 |
+
" images (sample, x, y) float32 291MB 1.093e+03 1.093e+03 ... 2.133e+03\n",
|
| 886 |
+
" labels (sample) float64 798kB 1.064e+03 1.194e+03 ... 2.453e+03 1.68e+03\n",
|
| 887 |
+
" vx (sample) float64 798kB -2.619 31.06 -2.924 ... -167.4 -43.76 -4.671\n",
|
| 888 |
+
" vy (sample) float64 798kB 3.084 -41.06 8.954 ... -27.1 47.03 -1.08\n",
|
| 889 |
+
" v (sample) float64 798kB 4.046 51.48 9.419 ... 169.6 64.24 4.794\n",
|
| 890 |
+
" smb (sample) float64 798kB 62.64 451.3 490.5 ... 1.294e+03 738.1 31.25\n",
|
| 891 |
+
" z (sample) float64 798kB 1.162e+03 1.204e+03 ... 1.22e+03 2.154e+03\n",
|
| 892 |
+
" s (sample) float64 798kB 0.007977 0.01595 ... 0.00455 0.008152\n",
|
| 893 |
+
" temp (sample) float64 798kB 244.0 252.7 246.3 ... 258.2 249.8 234.5\n",
|
| 894 |
+
"Attributes:\n",
|
| 895 |
+
" description: CNN data with elevation images. Scalar features are everyth...</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-c6bff10c-0fa0-44fe-81c3-d48f65cd0b8d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c6bff10c-0fa0-44fe-81c3-d48f65cd0b8d' 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>: 99766</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8c708d47-4082-49f7-8b9d-0adf32231f41' class='xr-section-summary-in' type='checkbox' checked><label for='section-8c708d47-4082-49f7-8b9d-0adf32231f41' 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-8ba6949c-95bf-4ad5-b045-057cf0fcea5f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8ba6949c-95bf-4ad5-b045-057cf0fcea5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f01e4341-92ec-4cd1-9afc-28d64ffa0de1' class='xr-var-data-in' type='checkbox'><label for='data-f01e4341-92ec-4cd1-9afc-28d64ffa0de1' 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",
|
| 896 |
+
" 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-b72a2d45-a067-45c8-ab80-a5a16d6c01b3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b72a2d45-a067-45c8-ab80-a5a16d6c01b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7d140bc-5b5a-4ebb-b5cc-855944628a93' class='xr-var-data-in' type='checkbox'><label for='data-a7d140bc-5b5a-4ebb-b5cc-855944628a93' 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",
|
| 897 |
+
" 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 ... 99762 99763 99764 99765</div><input id='attrs-4e3a3fa9-69cf-4f4e-824e-0a65ea1b3cec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4e3a3fa9-69cf-4f4e-824e-0a65ea1b3cec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-299f325f-f837-4761-902b-c9d5d3e65d4c' class='xr-var-data-in' type='checkbox'><label for='data-299f325f-f837-4761-902b-c9d5d3e65d4c' 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, ..., 99763, 99764, 99765])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-708909ab-3818-4ebc-b388-491bdea6d7c2' class='xr-section-summary-in' type='checkbox' checked><label for='section-708909ab-3818-4ebc-b388-491bdea6d7c2' class='xr-section-summary' >Data variables: <span>(9)</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'>1.093e+03 1.093e+03 ... 2.133e+03</div><input id='attrs-4b3d836a-fa09-4c7a-9f59-340894318a61' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b3d836a-fa09-4c7a-9f59-340894318a61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfa2ec7b-dead-4863-a5f7-3b98e99dc490' class='xr-var-data-in' type='checkbox'><label for='data-cfa2ec7b-dead-4863-a5f7-3b98e99dc490' 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([[[1092.5057 , 1093.3561 , 1094.1313 , ..., 1181.6246 ,\n",
|
| 898 |
+
" 1187.6576 , 1193.0239 ],\n",
|
| 899 |
+
" [1094.4419 , 1095.7927 , 1096.984 , ..., 1180.3619 ,\n",
|
| 900 |
+
" 1187.452 , 1193.2283 ],\n",
|
| 901 |
+
" [1096.7919 , 1098.3004 , 1099.7814 , ..., 1180.863 ,\n",
|
| 902 |
+
" 1188.2043 , 1192.8219 ],\n",
|
| 903 |
+
" ...,\n",
|
| 904 |
+
" [1165.3557 , 1161.6156 , 1162.1578 , ..., 1213.3615 ,\n",
|
| 905 |
+
" 1240.3538 , 1267.7552 ],\n",
|
| 906 |
+
" [1162.5809 , 1159.64 , 1158.411 , ..., 1217.8882 ,\n",
|
| 907 |
+
" 1221.593 , 1262.5428 ],\n",
|
| 908 |
+
" [1162.889 , 1158.089 , 1152.5607 , ..., 1216.3546 ,\n",
|
| 909 |
+
" 1219.4219 , 1261.0667 ]],\n",
|
| 910 |
+
"\n",
|
| 911 |
+
" [[1284.6726 , 1281.334 , 1280.9316 , ..., 1233.7749 ,\n",
|
| 912 |
+
" 1228.7972 , 1223.2537 ],\n",
|
| 913 |
+
" [1283.8816 , 1283.3535 , 1283.7786 , ..., 1230.4524 ,\n",
|
| 914 |
+
" 1225.3945 , 1219.3942 ],\n",
|
| 915 |
+
" [1284.984 , 1285.9851 , 1285.9116 , ..., 1226.6727 ,\n",
|
| 916 |
+
" 1221.5719 , 1215.9447 ],\n",
|
| 917 |
+
"...\n",
|
| 918 |
+
" [1208.358 , 1208.8232 , 1208.9432 , ..., 1249.744 ,\n",
|
| 919 |
+
" 1252.31 , 1254.7693 ],\n",
|
| 920 |
+
" [1210.8943 , 1210.7345 , 1210.8956 , ..., 1254.7004 ,\n",
|
| 921 |
+
" 1256.9983 , 1259.825 ],\n",
|
| 922 |
+
" [1213.844 , 1213.547 , 1213.1089 , ..., 1259.0881 ,\n",
|
| 923 |
+
" 1261.5992 , 1264.7634 ]],\n",
|
| 924 |
+
"\n",
|
| 925 |
+
" [[2119.1958 , 2120.359 , 2127.3264 , ..., 2161.8188 ,\n",
|
| 926 |
+
" 2161.1162 , 2157.5664 ],\n",
|
| 927 |
+
" [2121.8757 , 2118.1497 , 2121.682 , ..., 2160.6697 ,\n",
|
| 928 |
+
" 2159.7742 , 2155.9841 ],\n",
|
| 929 |
+
" [2128.5696 , 2122.7732 , 2120.0735 , ..., 2161.4866 ,\n",
|
| 930 |
+
" 2160.421 , 2155.841 ],\n",
|
| 931 |
+
" ...,\n",
|
| 932 |
+
" [2089.4995 , 2092.1367 , 2094.292 , ..., 2135.124 ,\n",
|
| 933 |
+
" 2133.6853 , 2133.2725 ],\n",
|
| 934 |
+
" [2091.6492 , 2094.2234 , 2097.71 , ..., 2132.7346 ,\n",
|
| 935 |
+
" 2132.212 , 2132.3438 ],\n",
|
| 936 |
+
" [2090.6233 , 2094.323 , 2098.8733 , ..., 2131.1018 ,\n",
|
| 937 |
+
" 2131.7812 , 2132.9963 ]]], 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'>1.064e+03 1.194e+03 ... 1.68e+03</div><input id='attrs-0b2a6aaf-bbf5-4569-bbab-5cd4c76e45d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b2a6aaf-bbf5-4569-bbab-5cd4c76e45d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2751c73e-9bc1-4bd2-ab32-fa6db880d242' class='xr-var-data-in' type='checkbox'><label for='data-2751c73e-9bc1-4bd2-ab32-fa6db880d242' 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([1064.24, 1193.7 , 1552.96, ..., 785.4 , 2452.89, 1680.48])</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'>-2.619 31.06 ... -43.76 -4.671</div><input id='attrs-9685dc8c-8951-44cf-bae6-53b0d83d855a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9685dc8c-8951-44cf-bae6-53b0d83d855a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d252b01b-062f-4041-9a16-8343931d3b81' class='xr-var-data-in' type='checkbox'><label for='data-d252b01b-062f-4041-9a16-8343931d3b81' 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([ -2.6192728 , 31.0641206 , -2.92434162, ..., -167.42409072,\n",
|
| 938 |
+
" -43.7632661 , -4.6707643 ])</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'>3.084 -41.06 8.954 ... 47.03 -1.08</div><input id='attrs-1a3ed6a2-2a79-4364-b376-983ff1ea5b8f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a3ed6a2-2a79-4364-b376-983ff1ea5b8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51d222f9-5e7d-4a9c-8493-d38895b5e283' class='xr-var-data-in' type='checkbox'><label for='data-51d222f9-5e7d-4a9c-8493-d38895b5e283' 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([ 3.08424518, -41.05727307, 8.95384872, ..., -27.10210423,\n",
|
| 939 |
+
" 47.03347077, -1.0804574 ])</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'>4.046 51.48 9.419 ... 64.24 4.794</div><input id='attrs-ea9ca126-fa9b-4cf6-9dfa-3199efe5c7da' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea9ca126-fa9b-4cf6-9dfa-3199efe5c7da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dbcf236-ac57-4081-8e9f-be29effc3556' class='xr-var-data-in' type='checkbox'><label for='data-4dbcf236-ac57-4081-8e9f-be29effc3556' 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([ 4.04637595, 51.48474784, 9.41929832, ..., 169.60350883,\n",
|
| 940 |
+
" 64.24461715, 4.79410339])</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'>62.64 451.3 490.5 ... 738.1 31.25</div><input id='attrs-df405866-562e-4446-b785-f53bf9297f74' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df405866-562e-4446-b785-f53bf9297f74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30795d36-bf4f-4b3a-ac66-9830b2ba2778' class='xr-var-data-in' type='checkbox'><label for='data-30795d36-bf4f-4b3a-ac66-9830b2ba2778' 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([ 62.64460724, 451.26375545, 490.45416098, ..., 1294.09333047,\n",
|
| 941 |
+
" 738.10749937, 31.25368722])</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'>1.162e+03 1.204e+03 ... 2.154e+03</div><input id='attrs-c95266fb-86df-4413-a1cf-704aeeb8fd4b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c95266fb-86df-4413-a1cf-704aeeb8fd4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c5724b8-cb1a-4e83-861e-45ca2f7aa255' class='xr-var-data-in' type='checkbox'><label for='data-2c5724b8-cb1a-4e83-861e-45ca2f7aa255' 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([1162.27983927, 1203.77749618, 2100.54579323, ..., 212.0719624 ,\n",
|
| 942 |
+
" 1219.77686434, 2153.89942609])</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'>0.007977 0.01595 ... 0.008152</div><input id='attrs-c5800ab8-4adb-4733-b8d0-2e75653a33f5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c5800ab8-4adb-4733-b8d0-2e75653a33f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64b426eb-1397-4a5c-a661-5950f91042e1' class='xr-var-data-in' type='checkbox'><label for='data-64b426eb-1397-4a5c-a661-5950f91042e1' 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([0.00797659, 0.01595318, 0.0036955 , ..., 0.04859182, 0.00454957,\n",
|
| 943 |
+
" 0.00815192])</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'>244.0 252.7 246.3 ... 249.8 234.5</div><input id='attrs-c3f17c06-3fc4-4c3a-88a8-90afc650ec43' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c3f17c06-3fc4-4c3a-88a8-90afc650ec43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe9a5422-2446-4de1-acd5-a8609fd88623' class='xr-var-data-in' type='checkbox'><label for='data-fe9a5422-2446-4de1-acd5-a8609fd88623' 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([244.02185692, 252.69987787, 246.28691873, ..., 258.1573107 ,\n",
|
| 944 |
+
" 249.76705444, 234.45177178])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bca8d97e-9a3c-4c68-a2f3-e34a91add4d6' class='xr-section-summary-in' type='checkbox' ><label for='section-bca8d97e-9a3c-4c68-a2f3-e34a91add4d6' 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-0a34b61e-bb99-49b4-8018-4ef2aef54de4' class='xr-index-data-in' type='checkbox'/><label for='index-0a34b61e-bb99-49b4-8018-4ef2aef54de4' 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",
|
| 945 |
+
" 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
|
| 946 |
+
" dtype='int64', name='x'))</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-b8fda282-8e4c-4368-9582-4f0ceb9423b1' class='xr-index-data-in' type='checkbox'/><label for='index-b8fda282-8e4c-4368-9582-4f0ceb9423b1' 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",
|
| 947 |
+
" 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
|
| 948 |
+
" dtype='int64', name='y'))</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-a3e75d4e-77de-4c54-a0e9-ef4a78c3bd36' class='xr-index-data-in' type='checkbox'/><label for='index-a3e75d4e-77de-4c54-a0e9-ef4a78c3bd36' 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,\n",
|
| 949 |
+
" ...\n",
|
| 950 |
+
" 99756, 99757, 99758, 99759, 99760, 99761, 99762, 99763, 99764, 99765],\n",
|
| 951 |
+
" dtype='int64', name='sample', length=99766))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a27dcb76-c8e0-40c3-93f3-da85eef62b1d' class='xr-section-summary-in' type='checkbox' checked><label for='section-a27dcb76-c8e0-40c3-93f3-da85eef62b1d' 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 elevation images. Scalar features are everything from Niccolo 30M parquet. Images are 27x27 pixels.</dd></dl></div></li></ul></div></div>"
|
| 952 |
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],
|
| 953 |
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"text/plain": [
|
| 954 |
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"<xarray.Dataset> Size: 298MB\n",
|
| 955 |
+
"Dimensions: (x: 27, y: 27, sample: 99766)\n",
|
| 956 |
+
"Coordinates:\n",
|
| 957 |
+
" * x (x) int64 216B 0 1 2 3 4 5 6 7 8 9 ... 18 19 20 21 22 23 24 25 26\n",
|
| 958 |
+
" * y (y) int64 216B 0 1 2 3 4 5 6 7 8 9 ... 18 19 20 21 22 23 24 25 26\n",
|
| 959 |
+
" * sample (sample) int64 798kB 0 1 2 3 4 5 ... 99761 99762 99763 99764 99765\n",
|
| 960 |
+
"Data variables:\n",
|
| 961 |
+
" images (sample, x, y) float32 291MB 1.093e+03 1.093e+03 ... 2.133e+03\n",
|
| 962 |
+
" labels (sample) float64 798kB 1.064e+03 1.194e+03 ... 2.453e+03 1.68e+03\n",
|
| 963 |
+
" vx (sample) float64 798kB -2.619 31.06 -2.924 ... -167.4 -43.76 -4.671\n",
|
| 964 |
+
" vy (sample) float64 798kB 3.084 -41.06 8.954 ... -27.1 47.03 -1.08\n",
|
| 965 |
+
" v (sample) float64 798kB 4.046 51.48 9.419 ... 169.6 64.24 4.794\n",
|
| 966 |
+
" smb (sample) float64 798kB 62.64 451.3 490.5 ... 1.294e+03 738.1 31.25\n",
|
| 967 |
+
" z (sample) float64 798kB 1.162e+03 1.204e+03 ... 1.22e+03 2.154e+03\n",
|
| 968 |
+
" s (sample) float64 798kB 0.007977 0.01595 ... 0.00455 0.008152\n",
|
| 969 |
+
" temp (sample) float64 798kB 244.0 252.7 246.3 ... 258.2 249.8 234.5\n",
|
| 970 |
+
"Attributes:\n",
|
| 971 |
+
" description: CNN data with elevation images. Scalar features are everyth..."
|
| 972 |
+
]
|
| 973 |
+
},
|
| 974 |
+
"execution_count": 16,
|
| 975 |
+
"metadata": {},
|
| 976 |
+
"output_type": "execute_result"
|
| 977 |
+
}
|
| 978 |
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],
|
| 979 |
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"source": [
|
| 980 |
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"training_data_ds"
|
| 981 |
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]
|
| 982 |
+
},
|
| 983 |
+
{
|
| 984 |
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"cell_type": "code",
|
| 985 |
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"execution_count": null,
|
| 986 |
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"id": "c0812faa",
|
| 987 |
+
"metadata": {},
|
| 988 |
+
"outputs": [],
|
| 989 |
+
"source": [
|
| 990 |
+
"# training_data_ds.to_netcdf('conv_train_1.nc')"
|
| 991 |
+
]
|
| 992 |
+
},
|
| 993 |
+
{
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| 995 |
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| 996 |
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"id": "bdb4d03a",
|
| 997 |
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"metadata": {},
|
| 998 |
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"outputs": [],
|
| 999 |
+
"source": [
|
| 1000 |
+
"test_import = xr.open_dataset('conv_train_1.nc') "
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]
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| 1002 |
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{
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"id": "6bf789a6",
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"metadata": {},
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| 1088 |
+
" color: var(--xr-font-color2);\n",
|
| 1089 |
+
"}\n",
|
| 1090 |
+
"\n",
|
| 1091 |
+
".xr-sections {\n",
|
| 1092 |
+
" padding-left: 0 !important;\n",
|
| 1093 |
+
" display: grid;\n",
|
| 1094 |
+
" grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
|
| 1095 |
+
"}\n",
|
| 1096 |
+
"\n",
|
| 1097 |
+
".xr-section-item {\n",
|
| 1098 |
+
" display: contents;\n",
|
| 1099 |
+
"}\n",
|
| 1100 |
+
"\n",
|
| 1101 |
+
".xr-section-item input {\n",
|
| 1102 |
+
" display: inline-block;\n",
|
| 1103 |
+
" opacity: 0;\n",
|
| 1104 |
+
" height: 0;\n",
|
| 1105 |
+
"}\n",
|
| 1106 |
+
"\n",
|
| 1107 |
+
".xr-section-item input + label {\n",
|
| 1108 |
+
" color: var(--xr-disabled-color);\n",
|
| 1109 |
+
"}\n",
|
| 1110 |
+
"\n",
|
| 1111 |
+
".xr-section-item input:enabled + label {\n",
|
| 1112 |
+
" cursor: pointer;\n",
|
| 1113 |
+
" color: var(--xr-font-color2);\n",
|
| 1114 |
+
"}\n",
|
| 1115 |
+
"\n",
|
| 1116 |
+
".xr-section-item input:focus + label {\n",
|
| 1117 |
+
" border: 2px solid var(--xr-font-color0);\n",
|
| 1118 |
+
"}\n",
|
| 1119 |
+
"\n",
|
| 1120 |
+
".xr-section-item input:enabled + label:hover {\n",
|
| 1121 |
+
" color: var(--xr-font-color0);\n",
|
| 1122 |
+
"}\n",
|
| 1123 |
+
"\n",
|
| 1124 |
+
".xr-section-summary {\n",
|
| 1125 |
+
" grid-column: 1;\n",
|
| 1126 |
+
" color: var(--xr-font-color2);\n",
|
| 1127 |
+
" font-weight: 500;\n",
|
| 1128 |
+
"}\n",
|
| 1129 |
+
"\n",
|
| 1130 |
+
".xr-section-summary > span {\n",
|
| 1131 |
+
" display: inline-block;\n",
|
| 1132 |
+
" padding-left: 0.5em;\n",
|
| 1133 |
+
"}\n",
|
| 1134 |
+
"\n",
|
| 1135 |
+
".xr-section-summary-in:disabled + label {\n",
|
| 1136 |
+
" color: var(--xr-font-color2);\n",
|
| 1137 |
+
"}\n",
|
| 1138 |
+
"\n",
|
| 1139 |
+
".xr-section-summary-in + label:before {\n",
|
| 1140 |
+
" display: inline-block;\n",
|
| 1141 |
+
" content: \"►\";\n",
|
| 1142 |
+
" font-size: 11px;\n",
|
| 1143 |
+
" width: 15px;\n",
|
| 1144 |
+
" text-align: center;\n",
|
| 1145 |
+
"}\n",
|
| 1146 |
+
"\n",
|
| 1147 |
+
".xr-section-summary-in:disabled + label:before {\n",
|
| 1148 |
+
" color: var(--xr-disabled-color);\n",
|
| 1149 |
+
"}\n",
|
| 1150 |
+
"\n",
|
| 1151 |
+
".xr-section-summary-in:checked + label:before {\n",
|
| 1152 |
+
" content: \"▼\";\n",
|
| 1153 |
+
"}\n",
|
| 1154 |
+
"\n",
|
| 1155 |
+
".xr-section-summary-in:checked + label > span {\n",
|
| 1156 |
+
" display: none;\n",
|
| 1157 |
+
"}\n",
|
| 1158 |
+
"\n",
|
| 1159 |
+
".xr-section-summary,\n",
|
| 1160 |
+
".xr-section-inline-details {\n",
|
| 1161 |
+
" padding-top: 4px;\n",
|
| 1162 |
+
" padding-bottom: 4px;\n",
|
| 1163 |
+
"}\n",
|
| 1164 |
+
"\n",
|
| 1165 |
+
".xr-section-inline-details {\n",
|
| 1166 |
+
" grid-column: 2 / -1;\n",
|
| 1167 |
+
"}\n",
|
| 1168 |
+
"\n",
|
| 1169 |
+
".xr-section-details {\n",
|
| 1170 |
+
" display: none;\n",
|
| 1171 |
+
" grid-column: 1 / -1;\n",
|
| 1172 |
+
" margin-bottom: 5px;\n",
|
| 1173 |
+
"}\n",
|
| 1174 |
+
"\n",
|
| 1175 |
+
".xr-section-summary-in:checked ~ .xr-section-details {\n",
|
| 1176 |
+
" display: contents;\n",
|
| 1177 |
+
"}\n",
|
| 1178 |
+
"\n",
|
| 1179 |
+
".xr-array-wrap {\n",
|
| 1180 |
+
" grid-column: 1 / -1;\n",
|
| 1181 |
+
" display: grid;\n",
|
| 1182 |
+
" grid-template-columns: 20px auto;\n",
|
| 1183 |
+
"}\n",
|
| 1184 |
+
"\n",
|
| 1185 |
+
".xr-array-wrap > label {\n",
|
| 1186 |
+
" grid-column: 1;\n",
|
| 1187 |
+
" vertical-align: top;\n",
|
| 1188 |
+
"}\n",
|
| 1189 |
+
"\n",
|
| 1190 |
+
".xr-preview {\n",
|
| 1191 |
+
" color: var(--xr-font-color3);\n",
|
| 1192 |
+
"}\n",
|
| 1193 |
+
"\n",
|
| 1194 |
+
".xr-array-preview,\n",
|
| 1195 |
+
".xr-array-data {\n",
|
| 1196 |
+
" padding: 0 5px !important;\n",
|
| 1197 |
+
" grid-column: 2;\n",
|
| 1198 |
+
"}\n",
|
| 1199 |
+
"\n",
|
| 1200 |
+
".xr-array-data,\n",
|
| 1201 |
+
".xr-array-in:checked ~ .xr-array-preview {\n",
|
| 1202 |
+
" display: none;\n",
|
| 1203 |
+
"}\n",
|
| 1204 |
+
"\n",
|
| 1205 |
+
".xr-array-in:checked ~ .xr-array-data,\n",
|
| 1206 |
+
".xr-array-preview {\n",
|
| 1207 |
+
" display: inline-block;\n",
|
| 1208 |
+
"}\n",
|
| 1209 |
+
"\n",
|
| 1210 |
+
".xr-dim-list {\n",
|
| 1211 |
+
" display: inline-block !important;\n",
|
| 1212 |
+
" list-style: none;\n",
|
| 1213 |
+
" padding: 0 !important;\n",
|
| 1214 |
+
" margin: 0;\n",
|
| 1215 |
+
"}\n",
|
| 1216 |
+
"\n",
|
| 1217 |
+
".xr-dim-list li {\n",
|
| 1218 |
+
" display: inline-block;\n",
|
| 1219 |
+
" padding: 0;\n",
|
| 1220 |
+
" margin: 0;\n",
|
| 1221 |
+
"}\n",
|
| 1222 |
+
"\n",
|
| 1223 |
+
".xr-dim-list:before {\n",
|
| 1224 |
+
" content: \"(\";\n",
|
| 1225 |
+
"}\n",
|
| 1226 |
+
"\n",
|
| 1227 |
+
".xr-dim-list:after {\n",
|
| 1228 |
+
" content: \")\";\n",
|
| 1229 |
+
"}\n",
|
| 1230 |
+
"\n",
|
| 1231 |
+
".xr-dim-list li:not(:last-child):after {\n",
|
| 1232 |
+
" content: \",\";\n",
|
| 1233 |
+
" padding-right: 5px;\n",
|
| 1234 |
+
"}\n",
|
| 1235 |
+
"\n",
|
| 1236 |
+
".xr-has-index {\n",
|
| 1237 |
+
" font-weight: bold;\n",
|
| 1238 |
+
"}\n",
|
| 1239 |
+
"\n",
|
| 1240 |
+
".xr-var-list,\n",
|
| 1241 |
+
".xr-var-item {\n",
|
| 1242 |
+
" display: contents;\n",
|
| 1243 |
+
"}\n",
|
| 1244 |
+
"\n",
|
| 1245 |
+
".xr-var-item > div,\n",
|
| 1246 |
+
".xr-var-item label,\n",
|
| 1247 |
+
".xr-var-item > .xr-var-name span {\n",
|
| 1248 |
+
" background-color: var(--xr-background-color-row-even);\n",
|
| 1249 |
+
" margin-bottom: 0;\n",
|
| 1250 |
+
"}\n",
|
| 1251 |
+
"\n",
|
| 1252 |
+
".xr-var-item > .xr-var-name:hover span {\n",
|
| 1253 |
+
" padding-right: 5px;\n",
|
| 1254 |
+
"}\n",
|
| 1255 |
+
"\n",
|
| 1256 |
+
".xr-var-list > li:nth-child(odd) > div,\n",
|
| 1257 |
+
".xr-var-list > li:nth-child(odd) > label,\n",
|
| 1258 |
+
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
|
| 1259 |
+
" background-color: var(--xr-background-color-row-odd);\n",
|
| 1260 |
+
"}\n",
|
| 1261 |
+
"\n",
|
| 1262 |
+
".xr-var-name {\n",
|
| 1263 |
+
" grid-column: 1;\n",
|
| 1264 |
+
"}\n",
|
| 1265 |
+
"\n",
|
| 1266 |
+
".xr-var-dims {\n",
|
| 1267 |
+
" grid-column: 2;\n",
|
| 1268 |
+
"}\n",
|
| 1269 |
+
"\n",
|
| 1270 |
+
".xr-var-dtype {\n",
|
| 1271 |
+
" grid-column: 3;\n",
|
| 1272 |
+
" text-align: right;\n",
|
| 1273 |
+
" color: var(--xr-font-color2);\n",
|
| 1274 |
+
"}\n",
|
| 1275 |
+
"\n",
|
| 1276 |
+
".xr-var-preview {\n",
|
| 1277 |
+
" grid-column: 4;\n",
|
| 1278 |
+
"}\n",
|
| 1279 |
+
"\n",
|
| 1280 |
+
".xr-index-preview {\n",
|
| 1281 |
+
" grid-column: 2 / 5;\n",
|
| 1282 |
+
" color: var(--xr-font-color2);\n",
|
| 1283 |
+
"}\n",
|
| 1284 |
+
"\n",
|
| 1285 |
+
".xr-var-name,\n",
|
| 1286 |
+
".xr-var-dims,\n",
|
| 1287 |
+
".xr-var-dtype,\n",
|
| 1288 |
+
".xr-preview,\n",
|
| 1289 |
+
".xr-attrs dt {\n",
|
| 1290 |
+
" white-space: nowrap;\n",
|
| 1291 |
+
" overflow: hidden;\n",
|
| 1292 |
+
" text-overflow: ellipsis;\n",
|
| 1293 |
+
" padding-right: 10px;\n",
|
| 1294 |
+
"}\n",
|
| 1295 |
+
"\n",
|
| 1296 |
+
".xr-var-name:hover,\n",
|
| 1297 |
+
".xr-var-dims:hover,\n",
|
| 1298 |
+
".xr-var-dtype:hover,\n",
|
| 1299 |
+
".xr-attrs dt:hover {\n",
|
| 1300 |
+
" overflow: visible;\n",
|
| 1301 |
+
" width: auto;\n",
|
| 1302 |
+
" z-index: 1;\n",
|
| 1303 |
+
"}\n",
|
| 1304 |
+
"\n",
|
| 1305 |
+
".xr-var-attrs,\n",
|
| 1306 |
+
".xr-var-data,\n",
|
| 1307 |
+
".xr-index-data {\n",
|
| 1308 |
+
" display: none;\n",
|
| 1309 |
+
" background-color: var(--xr-background-color) !important;\n",
|
| 1310 |
+
" padding-bottom: 5px !important;\n",
|
| 1311 |
+
"}\n",
|
| 1312 |
+
"\n",
|
| 1313 |
+
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
|
| 1314 |
+
".xr-var-data-in:checked ~ .xr-var-data,\n",
|
| 1315 |
+
".xr-index-data-in:checked ~ .xr-index-data {\n",
|
| 1316 |
+
" display: block;\n",
|
| 1317 |
+
"}\n",
|
| 1318 |
+
"\n",
|
| 1319 |
+
".xr-var-data > table {\n",
|
| 1320 |
+
" float: right;\n",
|
| 1321 |
+
"}\n",
|
| 1322 |
+
"\n",
|
| 1323 |
+
".xr-var-name span,\n",
|
| 1324 |
+
".xr-var-data,\n",
|
| 1325 |
+
".xr-index-name div,\n",
|
| 1326 |
+
".xr-index-data,\n",
|
| 1327 |
+
".xr-attrs {\n",
|
| 1328 |
+
" padding-left: 25px !important;\n",
|
| 1329 |
+
"}\n",
|
| 1330 |
+
"\n",
|
| 1331 |
+
".xr-attrs,\n",
|
| 1332 |
+
".xr-var-attrs,\n",
|
| 1333 |
+
".xr-var-data,\n",
|
| 1334 |
+
".xr-index-data {\n",
|
| 1335 |
+
" grid-column: 1 / -1;\n",
|
| 1336 |
+
"}\n",
|
| 1337 |
+
"\n",
|
| 1338 |
+
"dl.xr-attrs {\n",
|
| 1339 |
+
" padding: 0;\n",
|
| 1340 |
+
" margin: 0;\n",
|
| 1341 |
+
" display: grid;\n",
|
| 1342 |
+
" grid-template-columns: 125px auto;\n",
|
| 1343 |
+
"}\n",
|
| 1344 |
+
"\n",
|
| 1345 |
+
".xr-attrs dt,\n",
|
| 1346 |
+
".xr-attrs dd {\n",
|
| 1347 |
+
" padding: 0;\n",
|
| 1348 |
+
" margin: 0;\n",
|
| 1349 |
+
" float: left;\n",
|
| 1350 |
+
" padding-right: 10px;\n",
|
| 1351 |
+
" width: auto;\n",
|
| 1352 |
+
"}\n",
|
| 1353 |
+
"\n",
|
| 1354 |
+
".xr-attrs dt {\n",
|
| 1355 |
+
" font-weight: normal;\n",
|
| 1356 |
+
" grid-column: 1;\n",
|
| 1357 |
+
"}\n",
|
| 1358 |
+
"\n",
|
| 1359 |
+
".xr-attrs dt:hover span {\n",
|
| 1360 |
+
" display: inline-block;\n",
|
| 1361 |
+
" background: var(--xr-background-color);\n",
|
| 1362 |
+
" padding-right: 10px;\n",
|
| 1363 |
+
"}\n",
|
| 1364 |
+
"\n",
|
| 1365 |
+
".xr-attrs dd {\n",
|
| 1366 |
+
" grid-column: 2;\n",
|
| 1367 |
+
" white-space: pre-wrap;\n",
|
| 1368 |
+
" word-break: break-all;\n",
|
| 1369 |
+
"}\n",
|
| 1370 |
+
"\n",
|
| 1371 |
+
".xr-icon-database,\n",
|
| 1372 |
+
".xr-icon-file-text2,\n",
|
| 1373 |
+
".xr-no-icon {\n",
|
| 1374 |
+
" display: inline-block;\n",
|
| 1375 |
+
" vertical-align: middle;\n",
|
| 1376 |
+
" width: 1em;\n",
|
| 1377 |
+
" height: 1.5em !important;\n",
|
| 1378 |
+
" stroke-width: 0;\n",
|
| 1379 |
+
" stroke: currentColor;\n",
|
| 1380 |
+
" fill: currentColor;\n",
|
| 1381 |
+
"}\n",
|
| 1382 |
+
"</style><pre class='xr-text-repr-fallback'><xarray.Dataset> Size: 298MB\n",
|
| 1383 |
+
"Dimensions: (sample: 99766, x: 27, y: 27)\n",
|
| 1384 |
+
"Coordinates:\n",
|
| 1385 |
+
" * sample (sample) int32 399kB 0 1 2 3 4 5 ... 99761 99762 99763 99764 99765\n",
|
| 1386 |
+
" * x (x) int32 108B 0 1 2 3 4 5 6 7 8 9 ... 18 19 20 21 22 23 24 25 26\n",
|
| 1387 |
+
" * y (y) int32 108B 0 1 2 3 4 5 6 7 8 9 ... 18 19 20 21 22 23 24 25 26\n",
|
| 1388 |
+
"Data variables:\n",
|
| 1389 |
+
" images (sample, x, y) float32 291MB ...\n",
|
| 1390 |
+
" labels (sample) float64 798kB ...\n",
|
| 1391 |
+
" vx (sample) float64 798kB ...\n",
|
| 1392 |
+
" vy (sample) float64 798kB ...\n",
|
| 1393 |
+
" v (sample) float64 798kB ...\n",
|
| 1394 |
+
" smb (sample) float64 798kB ...\n",
|
| 1395 |
+
" z (sample) float64 798kB ...\n",
|
| 1396 |
+
" s (sample) float64 798kB ...\n",
|
| 1397 |
+
" temp (sample) float64 798kB ...\n",
|
| 1398 |
+
"Attributes:\n",
|
| 1399 |
+
" description: CNN data with elevation images. Scalar features are everyth...</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-0e2f7c7e-2cd2-4982-a8a2-0bc8b9bdc4d5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0e2f7c7e-2cd2-4982-a8a2-0bc8b9bdc4d5' 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>: 99766</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-0c1ef4d1-7113-4b9d-82de-d462fcbdb769' class='xr-section-summary-in' type='checkbox' checked><label for='section-0c1ef4d1-7113-4b9d-82de-d462fcbdb769' 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'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 99762 99763 99764 99765</div><input id='attrs-6335aaac-619c-46bd-a686-c15a4c7d776f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6335aaac-619c-46bd-a686-c15a4c7d776f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55395f74-69a2-47d9-84fb-22982cbcac96' class='xr-var-data-in' type='checkbox'><label for='data-55395f74-69a2-47d9-84fb-22982cbcac96' 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, ..., 99763, 99764, 99765], dtype=int32)</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'>int32</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-017600b7-76db-43a5-bdc3-a67e4038cccc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-017600b7-76db-43a5-bdc3-a67e4038cccc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57d16ef2-e532-4204-850d-269b006c35be' class='xr-var-data-in' type='checkbox'><label for='data-57d16ef2-e532-4204-850d-269b006c35be' 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",
|
| 1400 |
+
" 18, 19, 20, 21, 22, 23, 24, 25, 26], dtype=int32)</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'>int32</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-0aef4d4b-2f9b-4851-a3cf-0f248066e9d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0aef4d4b-2f9b-4851-a3cf-0f248066e9d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cceb419-823d-4b59-a48d-b28a0def925f' class='xr-var-data-in' type='checkbox'><label for='data-0cceb419-823d-4b59-a48d-b28a0def925f' 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",
|
| 1401 |
+
" 18, 19, 20, 21, 22, 23, 24, 25, 26], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e8a55884-30f5-49e3-ad00-7fff9b2f51f7' class='xr-section-summary-in' type='checkbox' checked><label for='section-e8a55884-30f5-49e3-ad00-7fff9b2f51f7' class='xr-section-summary' >Data variables: <span>(9)</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-e37fe593-a0ef-4148-b41c-2017b8d8020c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e37fe593-a0ef-4148-b41c-2017b8d8020c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdede8f0-6b50-41e7-ac8e-8554066f621d' class='xr-var-data-in' type='checkbox'><label for='data-fdede8f0-6b50-41e7-ac8e-8554066f621d' 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>[72729414 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-038b948d-503f-4771-95d0-9270fa2dcba0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-038b948d-503f-4771-95d0-9270fa2dcba0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b029294f-474c-450a-bfd1-b9bb91dfe938' class='xr-var-data-in' type='checkbox'><label for='data-b029294f-474c-450a-bfd1-b9bb91dfe938' 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>[99766 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-514bce6b-50c5-4b51-be89-a8539e39703c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-514bce6b-50c5-4b51-be89-a8539e39703c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0ad5996-2707-4e27-8527-84cc954a1193' class='xr-var-data-in' type='checkbox'><label for='data-a0ad5996-2707-4e27-8527-84cc954a1193' 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>[99766 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-5f0eab0b-41eb-40c6-95fc-b9c3b9f52de4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f0eab0b-41eb-40c6-95fc-b9c3b9f52de4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-928269d4-3753-4740-9c65-64552639bf90' class='xr-var-data-in' type='checkbox'><label for='data-928269d4-3753-4740-9c65-64552639bf90' 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>[99766 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-74e1d1e3-887c-4d66-9e2b-0b6cd18d5a8d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-74e1d1e3-887c-4d66-9e2b-0b6cd18d5a8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4f50b80-4e4e-477e-9444-150ab3df8050' class='xr-var-data-in' type='checkbox'><label for='data-b4f50b80-4e4e-477e-9444-150ab3df8050' 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>[99766 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-620958f0-24af-47d3-8c98-8d148ac5e878' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-620958f0-24af-47d3-8c98-8d148ac5e878' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4847a9ae-aae0-4826-beda-9e66abf11b65' class='xr-var-data-in' type='checkbox'><label for='data-4847a9ae-aae0-4826-beda-9e66abf11b65' 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>[99766 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-3d9aefec-49f1-4203-b22e-dbfa9050900d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d9aefec-49f1-4203-b22e-dbfa9050900d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc2ce70c-f1da-4fc9-8542-a7e2645c7ab2' class='xr-var-data-in' type='checkbox'><label for='data-bc2ce70c-f1da-4fc9-8542-a7e2645c7ab2' 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>[99766 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-93510040-c969-4a01-a924-47df518e586f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93510040-c969-4a01-a924-47df518e586f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c917ecb-f7f4-4692-872c-5eeb60bead45' class='xr-var-data-in' type='checkbox'><label for='data-3c917ecb-f7f4-4692-872c-5eeb60bead45' 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>[99766 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-fdfc0af3-4e34-4783-bdf2-b2dabba05d31' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fdfc0af3-4e34-4783-bdf2-b2dabba05d31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7f830f2-7802-4932-8fbc-e3b8a1fddca1' class='xr-var-data-in' type='checkbox'><label for='data-d7f830f2-7802-4932-8fbc-e3b8a1fddca1' 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>[99766 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-949cf030-21e2-49bb-94dd-b4da96ac9b38' class='xr-section-summary-in' type='checkbox' ><label for='section-949cf030-21e2-49bb-94dd-b4da96ac9b38' 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-8e8e183f-f1b9-474e-bd07-1b44f26b18d4' class='xr-index-data-in' type='checkbox'/><label for='index-8e8e183f-f1b9-474e-bd07-1b44f26b18d4' 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,\n",
|
| 1402 |
+
" ...\n",
|
| 1403 |
+
" 99756, 99757, 99758, 99759, 99760, 99761, 99762, 99763, 99764, 99765],\n",
|
| 1404 |
+
" dtype='int32', name='sample', length=99766))</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-7778756e-c0a5-46ff-b2b8-22d1512fcf9e' class='xr-index-data-in' type='checkbox'/><label for='index-7778756e-c0a5-46ff-b2b8-22d1512fcf9e' 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",
|
| 1405 |
+
" 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
|
| 1406 |
+
" dtype='int32', name='x'))</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-65f25132-8c31-4309-a3e5-9d43eec4fbd0' class='xr-index-data-in' type='checkbox'/><label for='index-65f25132-8c31-4309-a3e5-9d43eec4fbd0' 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",
|
| 1407 |
+
" 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
|
| 1408 |
+
" dtype='int32', name='y'))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a5029b4a-40fe-4041-9186-6209cf7587d0' class='xr-section-summary-in' type='checkbox' checked><label for='section-a5029b4a-40fe-4041-9186-6209cf7587d0' 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 elevation images. Scalar features are everything from Niccolo 30M parquet. Images are 27x27 pixels.</dd></dl></div></li></ul></div></div>"
|
| 1409 |
+
],
|
| 1410 |
+
"text/plain": [
|
| 1411 |
+
"<xarray.Dataset> Size: 298MB\n",
|
| 1412 |
+
"Dimensions: (sample: 99766, x: 27, y: 27)\n",
|
| 1413 |
+
"Coordinates:\n",
|
| 1414 |
+
" * sample (sample) int32 399kB 0 1 2 3 4 5 ... 99761 99762 99763 99764 99765\n",
|
| 1415 |
+
" * x (x) int32 108B 0 1 2 3 4 5 6 7 8 9 ... 18 19 20 21 22 23 24 25 26\n",
|
| 1416 |
+
" * y (y) int32 108B 0 1 2 3 4 5 6 7 8 9 ... 18 19 20 21 22 23 24 25 26\n",
|
| 1417 |
+
"Data variables:\n",
|
| 1418 |
+
" images (sample, x, y) float32 291MB ...\n",
|
| 1419 |
+
" labels (sample) float64 798kB ...\n",
|
| 1420 |
+
" vx (sample) float64 798kB ...\n",
|
| 1421 |
+
" vy (sample) float64 798kB ...\n",
|
| 1422 |
+
" v (sample) float64 798kB ...\n",
|
| 1423 |
+
" smb (sample) float64 798kB ...\n",
|
| 1424 |
+
" z (sample) float64 798kB ...\n",
|
| 1425 |
+
" s (sample) float64 798kB ...\n",
|
| 1426 |
+
" temp (sample) float64 798kB ...\n",
|
| 1427 |
+
"Attributes:\n",
|
| 1428 |
+
" description: CNN data with elevation images. Scalar features are everyth..."
|
| 1429 |
+
]
|
| 1430 |
+
},
|
| 1431 |
+
"execution_count": 19,
|
| 1432 |
+
"metadata": {},
|
| 1433 |
+
"output_type": "execute_result"
|
| 1434 |
+
}
|
| 1435 |
+
],
|
| 1436 |
+
"source": [
|
| 1437 |
+
"test_import"
|
| 1438 |
+
]
|
| 1439 |
+
},
|
| 1440 |
+
{
|
| 1441 |
+
"cell_type": "code",
|
| 1442 |
+
"execution_count": null,
|
| 1443 |
+
"id": "4a1e044e",
|
| 1444 |
+
"metadata": {},
|
| 1445 |
+
"outputs": [],
|
| 1446 |
+
"source": [
|
| 1447 |
+
"p = gdf.iloc[0].geometry\n",
|
| 1448 |
+
"\n",
|
| 1449 |
+
"transform = sat_im.rio.transform()\n",
|
| 1450 |
+
"\n",
|
| 1451 |
+
"col, row = ~transform * (p.x, p.y)\n",
|
| 1452 |
+
"\n",
|
| 1453 |
+
"col, row = int(np.floor(col)), int(np.floor(row))\n",
|
| 1454 |
+
"\n",
|
| 1455 |
+
"print(p.x, p.y)\n",
|
| 1456 |
+
"\n",
|
| 1457 |
+
"print(sat_im.surface[0][row][col].x, sat_im.surface[0][row][col].y)"
|
| 1458 |
+
]
|
| 1459 |
+
},
|
| 1460 |
+
{
|
| 1461 |
+
"cell_type": "code",
|
| 1462 |
+
"execution_count": 25,
|
| 1463 |
+
"id": "9ac2f290",
|
| 1464 |
+
"metadata": {},
|
| 1465 |
+
"outputs": [
|
| 1466 |
+
{
|
| 1467 |
+
"name": "stdout",
|
| 1468 |
+
"output_type": "stream",
|
| 1469 |
+
"text": [
|
| 1470 |
+
"\n",
|
| 1471 |
+
"Source file path for Affine class: c:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.geoMLvenv\\Lib\\site-packages\\affine\\__init__.py\n"
|
| 1472 |
+
]
|
| 1473 |
+
}
|
| 1474 |
+
],
|
| 1475 |
+
"source": [
|
| 1476 |
+
"import inspect\n",
|
| 1477 |
+
"\n",
|
| 1478 |
+
"source_file_path = inspect.getsourcefile(type(sat_im.surface.rio.transform()))\n",
|
| 1479 |
+
"print(f\"\\nSource file path for Affine class: {source_file_path}\")"
|
| 1480 |
+
]
|
| 1481 |
+
}
|
| 1482 |
+
],
|
| 1483 |
+
"metadata": {
|
| 1484 |
+
"kernelspec": {
|
| 1485 |
+
"display_name": ".geoMLvenv",
|
| 1486 |
+
"language": "python",
|
| 1487 |
+
"name": "python3"
|
| 1488 |
+
},
|
| 1489 |
+
"language_info": {
|
| 1490 |
+
"codemirror_mode": {
|
| 1491 |
+
"name": "ipython",
|
| 1492 |
+
"version": 3
|
| 1493 |
+
},
|
| 1494 |
+
"file_extension": ".py",
|
| 1495 |
+
"mimetype": "text/x-python",
|
| 1496 |
+
"name": "python",
|
| 1497 |
+
"nbconvert_exporter": "python",
|
| 1498 |
+
"pygments_lexer": "ipython3",
|
| 1499 |
+
"version": "3.11.9"
|
| 1500 |
+
}
|
| 1501 |
+
},
|
| 1502 |
+
"nbformat": 4,
|
| 1503 |
+
"nbformat_minor": 5
|
| 1504 |
+
}
|