Merge pull request #6 from s-kganz/main

Create notebooks for function definitions and testing

Author: s-kganz

notebooks/functions.ipynb

@@ -0,0 +1,81 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a4a784f3-7130-4c1d-aa11-eb02d614ee76",
+   "metadata": {},
+   "source": [
+    "# Functions for other notebooks to use"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "405f62a3-f187-436b-a94c-1658014c9aae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict_on_array(\n",
+    "    dataset: MapDataset,\n",
+    "    model: torch.nn.Module,\n",
+    "    output_tensor_dim: dict[str, int],\n",
+    "    new_dim: list[str],\n",
+    "    resample_dim: list[str],\n",
+    "    batch_size: int=16\n",
+    "):\n",
+    "    # TODO set up output array\n",
+    "    output_size = {}\n",
+    "    for key, size in output_tensor_dim.items():\n",
+    "        if key in new_dim:\n",
+    "            # This is a new axis, size is determined\n",
+    "            # by the tensor size.\n",
+    "            output_size[key] = output_tensor_dim[key]\n",
+    "        else:\n",
+    "            # This is a resampled axis, determine the new size\n",
+    "            # by the ratio of the batchgen window to the tensor size.\n",
+    "            window_size = ds.X_generator.input_dims[key]\n",
+    "            tensor_size = output_tensor_dim[key]\n",
+    "            resample_ratio = tensor_size / window_size\n",
+    "\n",
+    "            temp_output_size = ds.X_generator.ds.sizes[key] * resample_ratio\n",
+    "            assert temp_output_size.is_integer()\n",
+    "            output_size[key] = int(temp_output_size)\n",
+    "            \n",
+    "    output_array = np.zeros(tuple(output_size.values()))\n",
+    "    output_n     = np.zeros(output_array.shape)\n",
+    "\n",
+    "    '''\n",
+    "    # Prepare data laoder\n",
+    "    loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size)\n",
+    "    for batch in loader:\n",
+    "        out_batch = model(batch).detach().numpy()\n",
+    "        # TODO write each example to the output array\n",
+    "    '''\n",
+    "\n",
+    "    # TODO aggregate output\n",
+    "    return output_array"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}