examples/SimpleExample.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "87a95dcf",
   "metadata": {},
   "source": [
    "# Creating and excecuting MDF models\n",
    "\n",
    "This is a quick introduction to building MDF graphs and using them in different scenarios.\n",
    "\n",
    "1. **[Create a Model containing a Graph](#h1)**\n",
    "2. **[Generate a graph image from the model](#h2)**\n",
    "3. **[Execute the graph & print the output values](#h3)**\n",
    "\n",
    "\n",
    "## 1. Create a [Model](https://mdf.readthedocs.io/en/latest/api/Specification.html#model) containing a [Graph](https://mdf.readthedocs.io/en/latest/api/Specification.html#graph) <a class=\"anchor\" id=\"h1\"></a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "24aeb1e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from modeci_mdf.mdf import *\n",
    "\n",
    "# Create a model containing one graph\n",
    "mod = Model(id=\"AB\")\n",
    "mod_graph = Graph(id=\"ab_example\")\n",
    "mod.graphs.append(mod_graph)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e3424b7",
   "metadata": {},
   "source": [
    "\n",
    "### 1.1 Create the first [Node](https://mdf.readthedocs.io/en/latest/api/Specification.html#node)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a901b65d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ab_example:\n",
      "    nodes:\n",
      "        A:\n",
      "            metadata:\n",
      "                color: 0 .8 0\n",
      "            parameters:\n",
      "                level:\n",
      "                    value: 1.0\n",
      "            output_ports:\n",
      "                output:\n",
      "                    value: level\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Create a node\n",
    "a_node = Node(id=\"A\", metadata={\"color\": \"0 .8 0\"})\n",
    "\n",
    "# Add a parameter with a specific value\n",
    "a_node.parameters.append(Parameter(id=\"level\", value=1.0))\n",
    "\n",
    "# Add an output port\n",
    "a_node.output_ports.append(OutputPort(id=\"output\", value = \"level\"))\n",
    "\n",
    "# Add node to the graph\n",
    "mod_graph.nodes.append(a_node)\n",
    "\n",
    "# Print current structure as YAML\n",
    "print(mod_graph.to_yaml())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46c51277",
   "metadata": {},
   "source": [
    "### 1.2 Create another [Node](https://mdf.readthedocs.io/en/latest/api/Specification.html#node)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1580a79e",
   "metadata": {},
   "outputs": [],
   "source": [
    "b_node = Node(id=\"B\", metadata={\"color\": \".8 0 0\"})\n",
    "mod_graph.nodes.append(b_node)\n",
    "\n",
    "# Add an input port\n",
    "b_node.input_ports.append(InputPort(id=\"input\"))\n",
    "\n",
    "# Add some parameters\n",
    "b_node.parameters.append(Parameter(id=\"slope\", value=1.2))\n",
    "b_node.parameters.append(Parameter(id=\"intercept\", value=3))\n",
    "\n",
    "\n",
    "# Add a parameter which used the internal parameter as well as the value from the input\n",
    "p1 = Parameter(\n",
    "    id=\"linear_func\",\n",
    "    value=\"input * slope + intercept\",\n",
    ")\n",
    "b_node.parameters.append(p1)\n",
    "\n",
    "# Add an output port\n",
    "b_node.output_ports.append(OutputPort(id=\"output\", value=\"linear_func\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6c98662",
   "metadata": {},
   "source": [
    "### 1.3 Create an  [Edge](https://mdf.readthedocs.io/en/latest/api/Specification.html#edge) between the [Nodes](https://mdf.readthedocs.io/en/latest/api/Specification.html#node)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "20b8a6b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ab_example:\n",
      "    nodes:\n",
      "        A:\n",
      "            metadata:\n",
      "                color: 0 .8 0\n",
      "            parameters:\n",
      "                level:\n",
      "                    value: 1.0\n",
      "            output_ports:\n",
      "                output:\n",
      "                    value: level\n",
      "        B:\n",
      "            metadata:\n",
      "                color: .8 0 0\n",
      "            input_ports:\n",
      "                input: {}\n",
      "            parameters:\n",
      "                slope:\n",
      "                    value: 1.2\n",
      "                intercept:\n",
      "                    value: 3\n",
      "                linear_func:\n",
      "                    value: input * slope + intercept\n",
      "            output_ports:\n",
      "                output:\n",
      "                    value: linear_func\n",
      "    edges:\n",
      "        edge_A_B:\n",
      "            sender: A\n",
      "            receiver: B\n",
      "            sender_port: output\n",
      "            receiver_port: input\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# This helper method just adds an edge connecting the first output port of \n",
    "# the pre node to the first input port of the post node\n",
    "from modeci_mdf.utils import simple_connect\n",
    "\n",
    "e1 = simple_connect(a_node, b_node, mod_graph)\n",
    "\n",
    "# Print the current structure\n",
    "print(mod_graph.to_yaml())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdfc66f8",
   "metadata": {},
   "source": [
    "## 2. Generate a graph image from the model <a class=\"anchor\" id=\"h2\"></a>\n",
    "\n",
    "We will use a method on the model to generate an image representing the structure of the graph.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "69740bde",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting MDF graph: ab_example to graphviz (level: 3, format: png)\n",
      "    Node: A\n",
      "Converting 0 .8 0 to hex color\n",
      "    Node: B\n",
      "Converting .8 0 0 to hex color\n",
      "    Edge: edge_A_B connects A to B\n",
      "Written graph image to: AB.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod.to_graph_image(\n",
    "        engine=\"dot\",\n",
    "        output_format=\"png\",\n",
    "        view_on_render=False,\n",
    "        level=3,\n",
    "        filename_root=\"AB\"\n",
    "    )\n",
    "\n",
    "from IPython.display import Image\n",
    "Image(filename=\"AB.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6b8aae3",
   "metadata": {},
   "source": [
    "## 3. Execute the graph & print the output values <a class=\"anchor\" id=\"h3\"></a>\n",
    "\n",
    "The Graph is converted to an EvaluableGraph, which can be used to evaluate the current state of the graph's parameters. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ece9b9e5",
   "metadata": {},
   "source": [
    "### 3.1 Execute the graph with the current parameter values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "543006ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Init graph: ab_example\n",
      "Evaluating graph: ab_example, root nodes: ['A'], with array format numpy\n",
      "Output of A: 1\n",
      "Output of B: 4.2\n"
     ]
    }
   ],
   "source": [
    "from modeci_mdf.execution_engine import EvaluableGraph\n",
    "\n",
    "eg = EvaluableGraph(mod_graph, verbose=False)\n",
    "eg.evaluate()\n",
    "\n",
    "print('Output of A: %s'%eg.enodes[\"A\"].evaluable_outputs[\"output\"].curr_value)\n",
    "print('Output of B: %s'%eg.enodes[\"B\"].evaluable_outputs[\"output\"].curr_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87824063",
   "metadata": {},
   "source": [
    "### 3.2 Change a value to a Numpy array & execute the graph again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e9142f37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating graph: ab_example, root nodes: ['A'], with array format numpy\n",
      "Output of A:\n",
      "[[1 2]\n",
      " [3 4]]\n",
      "Output of B:\n",
      "[[4.2 5.4]\n",
      " [6.6 7.8]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "a_node.get_parameter('level').value = np.array([[1,2],[3,4]])\n",
    "\n",
    "eg.evaluate()\n",
    "\n",
    "print('Output of A:\\n%s'%eg.enodes[\"A\"].evaluable_outputs[\"output\"].curr_value)\n",
    "print('Output of B:\\n%s'%eg.enodes[\"B\"].evaluable_outputs[\"output\"].curr_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b52d116e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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