From f41b15d484f847e4563477f11ca63733562f3823 Mon Sep 17 00:00:00 2001
From: Ian Bell <ian.bell@nist.gov>
Date: Sat, 5 Nov 2022 10:33:01 -0400
Subject: [PATCH] Docs for make_model
---
doc/source/models/make_model.ipynb | 113 +++++++++++++++++++++++++++++
1 file changed, 113 insertions(+)
create mode 100644 doc/source/models/make_model.ipynb
diff --git a/doc/source/models/make_model.ipynb b/doc/source/models/make_model.ipynb
new file mode 100644
index 0000000..419ea17
--- /dev/null
+++ b/doc/source/models/make_model.ipynb
@@ -0,0 +1,113 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0f7ef656",
+ "metadata": {},
+ "source": [
+ "# Constructing Models\n",
+ "\n",
+ "With a few exceptions, most models are constructed by describing the model in JSON format, and passing the JSON-formatted information to the ``make_model`` function. There are some convenience functions exposed for backwards compatibility, but as of version 0.14.0, all model construction should go via this route.\n",
+ "\n",
+ "At the C++ level, the returned value from the ``make_model`` function is a ``shared_ptr`` that wraps a pointer to an ``AbstractModel`` class. The ``AbstractModel`` class is an abstract class which defines the public C++ interface.\n",
+ "\n",
+ "In Python, construction is in two parts. First, the model is constructed, which only includes the common methods. Then, the model-specific attributes and methods are attached with the ``attach_model_specific_methods`` method.\n",
+ "\n",
+ "The JSON structure is of two parts, the ``kind`` field is a case-sensitive string defining which model kind is being constructed, and the ``model`` field contains all the information needed to build the model. In the case of hard-coded models, nothing is provided in the ``model`` field, but it must still be provided.\n",
+ "\n",
+ "Also, the argument to ``make_model`` must be valid JSON. So if you are working with numpy array datatypes, make sure to convert them to a list (which is convertible to JSON). Example below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d3ab9d9c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import teqp, numpy as np\n",
+ "teqp.__version__"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "39bd96e0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "teqp.make_model({'kind': 'vdW1', 'model': {'a': 1, 'b': 2}})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "45d41b7f",
+ "metadata": {
+ "tags": [
+ "raises-exception"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "# Fields are case-sensitive\n",
+ "teqp.make_model({'kind': 'vdW1', 'model': {'a': 1, 'B': 2}})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "907f3619",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# A hard-coded model\n",
+ "teqp.make_model({\n",
+ " 'kind': 'AmmoniaWaterTillnerRoth', \n",
+ " 'model': {}\n",
+ "})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "800b86f4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Show what to do with numpy array\n",
+ "Tc_K = np.array([100,200])\n",
+ "pc_Pa = np.array([3e6, 4e6])\n",
+ "teqp.make_model({\n",
+ " \"kind\": \"vdW\", \n",
+ " \"model\": {\n",
+ " \"Tcrit / K\": Tc_K.tolist(), \n",
+ " \"pcrit / Pa\": pc_Pa.tolist()\n",
+ " }\n",
+ "})"
+ ]
+ }
+ ],
+ "metadata": {
+ "celltoolbar": "Tags",
+ "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": "3.10.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--
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