Update examples

examples/custom_model.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "235c92cd-cc05-42b8-a516-1185eeac5f0c",
+   "metadata": {},
+   "source": [
+    "# Creating a Custom LUME-model\n",
+    "Custom models that are compatible with LUME tools can be created by inhereting from the `LUMEBaseModel`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "56725817-2b21-4bea-98b0-151dea959f77",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from lume_model.base import LUMEBaseModel\n",
+    "from lume_model.variables import ScalarInputVariable, ScalarOutputVariable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79c62b18-7dc1-44ca-b578-4dea5cc4a4b4",
+   "metadata": {},
+   "source": [
+    "## Model Definition\n",
+    "The minimum requirement for creating a custom LUME-model is to implement the abstract `evaluate` method inherited from `LUMEBaseModel`. Here, we simply return the squared input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f96d9863-269c-49d8-9671-cc73a783bcbc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ExampleModel(LUMEBaseModel):\n",
+    "    def evaluate(self, input_dict):\n",
+    "        output_dict = {\n",
+    "            \"output1\": input_dict[self.input_variables[0].name] ** 2,\n",
+    "            \"output2\": input_dict[self.input_variables[1].name] ** 2,\n",
+    "        }\n",
+    "        return output_dict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "868fff4d-1f46-48e2-8bd0-c9d831df79e6",
+   "metadata": {},
+   "source": [
+    "## Model Instantiation and Execution\n",
+    "Instantiation requires specification of the input and output variables of the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "97946e64-062d-47d4-8d0c-d7e02a335a56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_variables = [\n",
+    "    ScalarInputVariable(name=\"input1\", default=0.1, value_range=[0.0, 1.0]),\n",
+    "    ScalarInputVariable(name=\"input2\", default=0.2, value_range=[0.0, 1.0]),\n",
+    "]\n",
+    "output_variables = [\n",
+    "    ScalarOutputVariable(name=\"output1\"),\n",
+    "    ScalarOutputVariable(name=\"output2\"),\n",
+    "]\n",
+    "\n",
+    "m = ExampleModel(input_variables=input_variables, output_variables=output_variables)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "50aae4be-0d6e-456f-83e8-3a84d6d78f84",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output1': 0.09, 'output2': 0.36}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "input_dict = {\n",
+    "    \"input1\": 0.3,\n",
+    "    \"input2\": 0.6,\n",
+    "}\n",
+    "m.evaluate(input_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a547f3c-1706-4b32-bab6-9687627f6a78",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:lume-model-dev]",
+   "language": "python",
+   "name": "conda-env-lume-model-dev-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}