{ "cells": [ { "cell_type": "code", "execution_count": 68, "id": "e7e09770", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import xarray as xr\n", "from sklearn.model_selection import train_test_split\n", "import matplotlib.pyplot as plt\n", "from tensorflow.keras import layers\n", "from tensorflow import keras\n", "import keras_tuner as kt" ] }, { "cell_type": "code", "execution_count": 69, "id": "797deb25", "metadata": {}, "outputs": [], "source": [ "data = xr.open_dataset('conv_temp_ithbm_4326.nc')" ] }, { "cell_type": "code", "execution_count": 70, "id": "399483a9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(198407, 27, 27)" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.images.values.shape" ] }, { "cell_type": "code", "execution_count": 71, "id": "a92d3499", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
<xarray.Dataset> Size: 1GB\n",
"Dimensions: (sample: 198407, x: 27, y: 27)\n",
"Coordinates:\n",
" * sample (sample) int64 2MB 0 1 2 3 4 ... 198403 198404 198405 198406\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) float64 1GB 264.3 264.3 264.3 ... 234.4 234.4\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 temperaturbilleder og 'THICK' som labels.