notebooks/model_api.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model REST API\n",
    "\n",
    "The Model REST API provides an interface for transforming models in various ways. Here are some examples. The model REST API documentation is available at [http://mira-epi-dkg-lb-dc1e19b273dedaa2.elb.us-east-1.amazonaws.com/docs#/](http://mira-epi-dkg-lb-dc1e19b273dedaa2.elb.us-east-1.amazonaws.com/docs#/)\n",
    "\n",
    "## Setup\n",
    "\n",
    "We will need `requests` to send requests to the model api."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"templates\": [{\"rate_law\": null, \"type\": \"ControlledConversion\", \"controller\": {\"name\": \"infected population\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {}}, \"subject\": {\"name\": \"susceptible population\", \"identifiers\": {\"ido\": \"0000514\"}, \"context\": {}}, \"outcome\": {\"name\": \"infected population\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {}}, \"provenance\": []}, {\"rate_law\": null, \"type\": \"NaturalConversion\", \"subject\": {\"name\": \"infected population\", \"identifiers\": {\"ido\": \"0000511\"}, \"context\": {}}, \"outcome\": {\"name\": \"immune population\", \"identifiers\": {\"ido\": \"0000592\"}, \"context\": {}}, \"provenance\": []}], \"parameters\": {}, \"initials\": {}}\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from mira.metamodel import Concept, ControlledConversion, NaturalConversion, TemplateModel\n",
    "\n",
    "# rest_url = \"http://127.0.0.1:8000\"  # Local service\n",
    "rest_url = \"http://mira-epi-dkg-lb-dc1e19b273dedaa2.elb.us-east-1.amazonaws.com\"\n",
    "\n",
    "# Example TemplateModel\n",
    "infected = Concept(name=\"infected population\", identifiers={\"ido\": \"0000511\"})\n",
    "susceptible = Concept(name=\"susceptible population\", identifiers={\"ido\": \"0000514\"})\n",
    "immune = Concept(name=\"immune population\", identifiers={\"ido\": \"0000592\"})\n",
    "controlled_conversion = ControlledConversion(\n",
    "    controller=infected,\n",
    "    subject=susceptible,\n",
    "    outcome=infected,\n",
    ")\n",
    "natural_conversion = NaturalConversion(subject=infected, outcome=immune)\n",
    "sir_template_model = TemplateModel(templates=[controlled_conversion, natural_conversion])\n",
    "sir_template_model_dict = sir_template_model.model_dump()\n",
    "print(sir_template_model.model_dump_json())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PetriNet\n",
    "\n",
    "The `/api/to_petrinet` endpoint returns a PetriNet model based on the `TemplateModel` provided:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'S': [{'sname': 'susceptible population', 'mira_ids': \"[('identity', 'ido:0000514')]\", 'mira_context': '[]'}, {'sname': 'infected population', 'mira_ids': \"[('identity', 'ido:0000511')]\", 'mira_context': '[]'}, {'sname': 'immune population', 'mira_ids': \"[('identity', 'ido:0000592')]\", 'mira_context': '[]'}], 'T': [{'tname': 't1', 'template_type': 'ControlledConversion', 'parameter_name': \"(('susceptible population', ('identity', 'ido:0000514')), ('infected population', ('identity', 'ido:0000511')), ('infected population', ('identity', 'ido:0000511')), 'ControlledConversion', 'rate')\", 'parameter_value': None}, {'tname': 't2', 'template_type': 'NaturalConversion', 'parameter_name': \"(('infected population', ('identity', 'ido:0000511')), ('immune population', ('identity', 'ido:0000592')), 'NaturalConversion', 'rate')\", 'parameter_value': None}], 'I': [{'is': 2, 'it': 1}, {'is': 1, 'it': 1}, {'is': 2, 'it': 2}], 'O': [{'os': 2, 'ot': 1}, {'os': 2, 'ot': 1}, {'os': 3, 'ot': 2}]}\n"
     ]
    }
   ],
   "source": [
    "res = requests.post(rest_url + \"/api/to_petrinet\", json=sir_template_model_dict)\n",
    "print(res.json())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Stratification\n",
    "\n",
    "The `/api/stratify` endpoint can stratify a model. In this example, the stratification is along two cities, effectively creating a two-city SIR model from the original SIR model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'templates': [{'rate_law': None, 'type': 'ControlledConversion', 'controller': {'name': 'infected population', 'identifiers': {'ido': '0000511'}, 'context': {'city': 'Boston'}}, 'subject': {'name': 'susceptible population', 'identifiers': {'ido': '0000514'}, 'context': {'city': 'Boston'}}, 'outcome': {'name': 'infected population', 'identifiers': {'ido': '0000511'}, 'context': {'city': 'Boston'}}, 'provenance': []}, {'rate_law': None, 'type': 'NaturalConversion', 'subject': {'name': 'infected population', 'identifiers': {'ido': '0000511'}, 'context': {'city': 'Boston'}}, 'outcome': {'name': 'immune population', 'identifiers': {'ido': '0000592'}, 'context': {'city': 'Boston'}}, 'provenance': []}, {'rate_law': None, 'type': 'ControlledConversion', 'controller': {'name': 'infected population', 'identifiers': {'ido': '0000511'}, 'context': {'city': 'New York City'}}, 'subject': {'name': 'susceptible population', 'identifiers': {'ido': '0000514'}, 'context': {'city': 'New York City'}}, 'outcome': {'name': 'infected population', 'identifiers': {'ido': '0000511'}, 'context': {'city': 'New York City'}}, 'provenance': []}, {'rate_law': None, 'type': 'NaturalConversion', 'subject': {'name': 'infected population', 'identifiers': {'ido': '0000511'}, 'context': {'city': 'New York City'}}, 'outcome': {'name': 'immune population', 'identifiers': {'ido': '0000592'}, 'context': {'city': 'New York City'}}, 'provenance': []}, {'rate_law': None, 'type': 'NaturalConversion', 'subject': {'name': 'susceptible population', 'identifiers': {'ido': '0000514'}, 'context': {'city': 'Boston'}}, 'outcome': {'name': 'susceptible population', 'identifiers': {'ido': '0000514'}, 'context': {'city': 'New York City'}}, 'provenance': []}, {'rate_law': None, 'type': 'NaturalConversion', 'subject': {'name': 'susceptible population', 'identifiers': {'ido': '0000514'}, 'context': {'city': 'New York City'}}, 'outcome': {'name': 'susceptible population', 'identifiers': {'ido': '0000514'}, 'context': {'city': 'Boston'}}, 'provenance': []}, {'rate_law': None, 'type': 'NaturalConversion', 'subject': {'name': 'infected population', 'identifiers': {'ido': '0000511'}, 'context': {'city': 'Boston'}}, 'outcome': {'name': 'infected population', 'identifiers': {'ido': '0000511'}, 'context': {'city': 'New York City'}}, 'provenance': []}, {'rate_law': None, 'type': 'NaturalConversion', 'subject': {'name': 'infected population', 'identifiers': {'ido': '0000511'}, 'context': {'city': 'New York City'}}, 'outcome': {'name': 'infected population', 'identifiers': {'ido': '0000511'}, 'context': {'city': 'Boston'}}, 'provenance': []}, {'rate_law': None, 'type': 'NaturalConversion', 'subject': {'name': 'immune population', 'identifiers': {'ido': '0000592'}, 'context': {'city': 'Boston'}}, 'outcome': {'name': 'immune population', 'identifiers': {'ido': '0000592'}, 'context': {'city': 'New York City'}}, 'provenance': []}, {'rate_law': None, 'type': 'NaturalConversion', 'subject': {'name': 'immune population', 'identifiers': {'ido': '0000592'}, 'context': {'city': 'New York City'}}, 'outcome': {'name': 'immune population', 'identifiers': {'ido': '0000592'}, 'context': {'city': 'Boston'}}, 'provenance': []}], 'parameters': {}, 'initials': {}}\n"
     ]
    }
   ],
   "source": [
    "res = requests.post(rest_url + \"/api/stratify\", json={\"template_model\": sir_template_model_dict, \"key\": \"city\", \"strata\": [\"Boston\", \"New York City\"]})\n",
    "print(res.json())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## Biomodels Model\n",
    "The `/api/biomodels/<model id>` endpoint returns a `TemplateModel` json of the biomodels model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'templates': [{'rate_law': '1.0*Infected*Susceptible*(ModelValue_2*ModelValue_5 + ModelValue_6*ModelValue_9)*(ModelValue_5*Ro_CA + ModelValue_6*Ro_NY)', 'type': 'ControlledConversion', 'controller': {'name': 'Infected', 'identifiers': {'ncit': 'C171133', 'ido': '0000511', 'biomodels.species': 'BIOMD0000000956:Infected'}, 'context': {}}, 'subject': {'name': 'Susceptible', 'identifiers': {'ido': '0000514', 'ncit': 'C171133', 'biomodels.species': 'BIOMD0000000956:Susceptible'}, 'context': {}}, 'outcome': {'name': 'Infected', 'identifiers': {'ncit': 'C171133', 'ido': '0000511', 'biomodels.species': 'BIOMD0000000956:Infected'}, 'context': {}}, 'provenance': []}, {'rate_law': '1.0*Infected*(ModelValue_2*ModelValue_5 + ModelValue_6*ModelValue_9)', 'type': 'NaturalConversion', 'subject': {'name': 'Infected', 'identifiers': {'ncit': 'C171133', 'ido': '0000511', 'biomodels.species': 'BIOMD0000000956:Infected'}, 'context': {}}, 'outcome': {'name': 'Recovered', 'identifiers': {'ncit': 'C171133', 'biomodels.species': 'BIOMD0000000956:Recovered'}, 'context': {'property': 'ido:0000621'}}, 'provenance': []}], 'parameters': {'Pop_CA': {'name': 'Pop_CA', 'identifiers': {}, 'context': {}, 'value': 39560000.0}, 'Ro_CA': {'name': 'Ro_CA', 'identifiers': {}, 'context': {}, 'value': 2.7}, 'gamma_CA': {'name': 'gamma_CA', 'identifiers': {}, 'context': {}, 'value': 0.14}, 'Io_CA': {'name': 'Io_CA', 'identifiers': {}, 'context': {}, 'value': 5.0}, 'Time': {'name': 'Time', 'identifiers': {}, 'context': {}, 'value': 0.0}, 'Trigger_CA': {'name': 'Trigger_CA', 'identifiers': {}, 'context': {}, 'value': 1.0}, 'Trigger_NY': {'name': 'Trigger_NY', 'identifiers': {}, 'context': {}, 'value': 0.0}, 'Pop_NY': {'name': 'Pop_NY', 'identifiers': {}, 'context': {}, 'value': 19540000.0}, 'Ro_NY': {'name': 'Ro_NY', 'identifiers': {}, 'context': {}, 'value': 4.1}, 'gamma_NY': {'name': 'gamma_NY', 'identifiers': {}, 'context': {}, 'value': 0.1}, 'Io_NY': {'name': 'Io_NY', 'identifiers': {}, 'context': {}, 'value': 29.0}, 'Pop': {'name': 'Pop', 'identifiers': {}, 'context': {}, 'value': 39560000.0}, 'Ro': {'name': 'Ro', 'identifiers': {}, 'context': {}, 'value': 2.7}, 'gamma': {'name': 'gamma', 'identifiers': {}, 'context': {}, 'value': 0.14}, 'Trigger_Lockdown': {'name': 'Trigger_Lockdown', 'identifiers': {}, 'context': {}, 'value': 0.0}, 'Lockdown_CA_start': {'name': 'Lockdown_CA_start', 'identifiers': {}, 'context': {}, 'value': 27.0}, 'Lockdown_CA_end': {'name': 'Lockdown_CA_end', 'identifiers': {}, 'context': {}, 'value': 66.0}, 'Lockdown_NY_start': {'name': 'Lockdown_NY_start', 'identifiers': {}, 'context': {}, 'value': 30.0}, 'Lockdown_NY_end': {'name': 'Lockdown_NY_end', 'identifiers': {}, 'context': {}, 'value': 67.0}, 'Io': {'name': 'Io', 'identifiers': {}, 'context': {}, 'value': 5.0}, 'Peak_Time': {'name': 'Peak_Time', 'identifiers': {}, 'context': {}, 'value': 0.0}, 'ModelValue_3': {'name': 'ModelValue_3', 'identifiers': {}, 'context': {}, 'value': 5.0}, 'ModelValue_10': {'name': 'ModelValue_10', 'identifiers': {}, 'context': {}, 'value': 29.0}, 'ModelValue_16': {'name': 'ModelValue_16', 'identifiers': {}, 'context': {}, 'value': 66.0}, 'ModelValue_15': {'name': 'ModelValue_15', 'identifiers': {}, 'context': {}, 'value': 27.0}, 'ModelValue_18': {'name': 'ModelValue_18', 'identifiers': {}, 'context': {}, 'value': 67.0}, 'ModelValue_17': {'name': 'ModelValue_17', 'identifiers': {}, 'context': {}, 'value': 30.0}, 'ModelValue_0': {'name': 'ModelValue_0', 'identifiers': {}, 'context': {}, 'value': 39560000.0}, 'ModelValue_7': {'name': 'ModelValue_7', 'identifiers': {}, 'context': {}, 'value': 19540000.0}, 'ModelValue_1': {'name': 'ModelValue_1', 'identifiers': {}, 'context': {}, 'value': 2.7}, 'ModelValue_8': {'name': 'ModelValue_8', 'identifiers': {}, 'context': {}, 'value': 4.1}, 'ModelValue_5': {'name': 'ModelValue_5', 'identifiers': {}, 'context': {}, 'value': 1.0}, 'ModelValue_14': {'name': 'ModelValue_14', 'identifiers': {}, 'context': {}, 'value': 0.0}, 'ModelValue_6': {'name': 'ModelValue_6', 'identifiers': {}, 'context': {}, 'value': 0.0}, 'ModelValue_2': {'name': 'ModelValue_2', 'identifiers': {}, 'context': {}, 'value': 0.14}, 'ModelValue_9': {'name': 'ModelValue_9', 'identifiers': {}, 'context': {}, 'value': 0.1}, 'USA___CA__NY': {'name': 'USA___CA__NY', 'identifiers': {}, 'context': {}, 'value': 1.0}}, 'initials': {'Infected': 1.2639029322548e-07, 'Recovered': 0.0, 'Susceptible': 0.999999999999997}}\n"
     ]
    }
   ],
   "source": [
    "res = requests.get(rest_url + \"/api/biomodels/BIOMD0000000956\")\n",
    "print(res.json())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## Bilayer Endpoints\n",
    "The `/api/model_to_bilayer` and `/api/bilayer_to_model` endpoints can translate between a `TemplateModel` json and a `Bilayer`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Wa': [{'influx': 1, 'infusion': 2}, {'influx': 2, 'infusion': 3}], 'Win': [{'arg': 2, 'call': 1}, {'arg': 1, 'call': 1}, {'arg': 2, 'call': 2}], 'Box': [{'parameter': \"(('susceptible population', ('identity', 'ido:0000514')), ('infected population', ('identity', 'ido:0000511')), ('infected population', ('identity', 'ido:0000511')), 'ControlledConversion', 'rate')\"}, {'parameter': \"(('infected population', ('identity', 'ido:0000511')), ('immune population', ('identity', 'ido:0000592')), 'NaturalConversion', 'rate')\"}], 'Qin': [{'variable': \"('susceptible population', ('identity', 'ido:0000514'))\"}, {'variable': \"('infected population', ('identity', 'ido:0000511'))\"}, {'variable': \"('immune population', ('identity', 'ido:0000592'))\"}], 'Qout': [{'tanvar': \"('susceptible population', ('identity', 'ido:0000514'), 'derivative')\"}, {'tanvar': \"('infected population', ('identity', 'ido:0000511'), 'derivative')\"}, {'tanvar': \"('immune population', ('identity', 'ido:0000592'), 'derivative')\"}], 'Wn': [{'efflux': 1, 'effusion': 1}, {'efflux': 2, 'effusion': 2}]}\n"
     ]
    }
   ],
   "source": [
    "# Get a bilayer json representation from a TemplateModel json\n",
    "res = requests.post(rest_url + \"/api/model_to_bilayer\", json=sir_template_model_dict)\n",
    "print(res.json())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'templates': [{'rate_law': \"('infected population', ('identity', 'ido:0000511'))*('susceptible population', ('identity', 'ido:0000514'))*(('susceptible population', ('identity', 'ido:0000514')), ('infected population', ('identity', 'ido:0000511')), ('infected population', ('identity', 'ido:0000511')), 'ControlledConversion', 'rate')\", 'type': 'ControlledConversion', 'controller': {'name': \"('infected population', ('identity', 'ido:0000511'))\", 'identifiers': {}, 'context': {}}, 'subject': {'name': \"('susceptible population', ('identity', 'ido:0000514'))\", 'identifiers': {}, 'context': {}}, 'outcome': {'name': \"('infected population', ('identity', 'ido:0000511'))\", 'identifiers': {}, 'context': {}}, 'provenance': []}, {'rate_law': \"('infected population', ('identity', 'ido:0000511'))*(('infected population', ('identity', 'ido:0000511')), ('immune population', ('identity', 'ido:0000592')), 'NaturalConversion', 'rate')\", 'type': 'NaturalConversion', 'subject': {'name': \"('infected population', ('identity', 'ido:0000511'))\", 'identifiers': {}, 'context': {}}, 'outcome': {'name': \"('immune population', ('identity', 'ido:0000592'))\", 'identifiers': {}, 'context': {}}, 'provenance': []}], 'parameters': {\"(('susceptible population', ('identity', 'ido:0000514')), ('infected population', ('identity', 'ido:0000511')), ('infected population', ('identity', 'ido:0000511')), 'ControlledConversion', 'rate')\": {'name': \"(('susceptible population', ('identity', 'ido:0000514')), ('infected population', ('identity', 'ido:0000511')), ('infected population', ('identity', 'ido:0000511')), 'ControlledConversion', 'rate')\", 'identifiers': {}, 'context': {}, 'value': 1.0}, \"(('infected population', ('identity', 'ido:0000511')), ('immune population', ('identity', 'ido:0000592')), 'NaturalConversion', 'rate')\": {'name': \"(('infected population', ('identity', 'ido:0000511')), ('immune population', ('identity', 'ido:0000592')), 'NaturalConversion', 'rate')\", 'identifiers': {}, 'context': {}, 'value': 1.0}}, 'initials': {}}\n"
     ]
    }
   ],
   "source": [
    "# Reverse the process by providing the bilayer json and get back the SIR TemplateModel json\n",
    "bilayer_json = res.json()\n",
    "res = requests.post(rest_url + \"/api/bilayer_to_model\", json=bilayer_json)\n",
    "print(res.json())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## SBML XML To Model\n",
    "For the `/api/sbml_xml_to_model` endpoint, you can provide an SBML model in the form of a string of the XML and get back a model.\n",
    "\n",
    "First, get an XML string. Here, done by the `get_sbml_model` function from the `biomodels` submodule in MIRA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<?xml version='1.0' encoding='UTF-8' standalone='no'?>\n",
      "<sbml xmlns=\"http://www.sbml.org/sbml/level2/version4\" level=\"2\" version=\"4\">\n",
      "  <model id=\"Bertozzi2020___SIR_model_of_scenarios_of_COVID_19_spread_in_CA\" metaid=\"COPASI0\" name=\"Bertozzi2020 - SI\n"
     ]
    }
   ],
   "source": [
    "from mira.sources.biomodels import get_sbml_model\n",
    "biomd951_xml_str = get_sbml_model(\"BIOMD0000000956\")\n",
    "print(biomd951_xml_str[:250])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "Next, submit this string to the endpoint and get the corresponding model back:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'templates': [{'rate_law': '1.0*Infected*Susceptible*(ModelValue_2*ModelValue_5 + ModelValue_6*ModelValue_9)*(ModelValue_5*Ro_CA + ModelValue_6*Ro_NY)', 'type': 'ControlledConversion', 'controller': {'name': 'Infected', 'identifiers': {'ncit': 'C171133', 'ido': '0000511'}, 'context': {}}, 'subject': {'name': 'Susceptible', 'identifiers': {'ido': '0000514', 'ncit': 'C171133'}, 'context': {}}, 'outcome': {'name': 'Infected', 'identifiers': {'ncit': 'C171133', 'ido': '0000511'}, 'context': {}}, 'provenance': []}, {'rate_law': '1.0*Infected*(ModelValue_2*ModelValue_5 + ModelValue_6*ModelValue_9)', 'type': 'NaturalConversion', 'subject': {'name': 'Infected', 'identifiers': {'ncit': 'C171133', 'ido': '0000511'}, 'context': {}}, 'outcome': {'name': 'Recovered', 'identifiers': {'ncit': 'C171133'}, 'context': {'property': 'ido:0000621'}}, 'provenance': []}], 'parameters': {'Pop_CA': {'name': 'Pop_CA', 'identifiers': {}, 'context': {}, 'value': 39560000.0}, 'Ro_CA': {'name': 'Ro_CA', 'identifiers': {}, 'context': {}, 'value': 2.7}, 'gamma_CA': {'name': 'gamma_CA', 'identifiers': {}, 'context': {}, 'value': 0.14}, 'Io_CA': {'name': 'Io_CA', 'identifiers': {}, 'context': {}, 'value': 5.0}, 'Time': {'name': 'Time', 'identifiers': {}, 'context': {}, 'value': 0.0}, 'Trigger_CA': {'name': 'Trigger_CA', 'identifiers': {}, 'context': {}, 'value': 1.0}, 'Trigger_NY': {'name': 'Trigger_NY', 'identifiers': {}, 'context': {}, 'value': 0.0}, 'Pop_NY': {'name': 'Pop_NY', 'identifiers': {}, 'context': {}, 'value': 19540000.0}, 'Ro_NY': {'name': 'Ro_NY', 'identifiers': {}, 'context': {}, 'value': 4.1}, 'gamma_NY': {'name': 'gamma_NY', 'identifiers': {}, 'context': {}, 'value': 0.1}, 'Io_NY': {'name': 'Io_NY', 'identifiers': {}, 'context': {}, 'value': 29.0}, 'Pop': {'name': 'Pop', 'identifiers': {}, 'context': {}, 'value': 39560000.0}, 'Ro': {'name': 'Ro', 'identifiers': {}, 'context': {}, 'value': 2.7}, 'gamma': {'name': 'gamma', 'identifiers': {}, 'context': {}, 'value': 0.14}, 'Trigger_Lockdown': {'name': 'Trigger_Lockdown', 'identifiers': {}, 'context': {}, 'value': 0.0}, 'Lockdown_CA_start': {'name': 'Lockdown_CA_start', 'identifiers': {}, 'context': {}, 'value': 27.0}, 'Lockdown_CA_end': {'name': 'Lockdown_CA_end', 'identifiers': {}, 'context': {}, 'value': 66.0}, 'Lockdown_NY_start': {'name': 'Lockdown_NY_start', 'identifiers': {}, 'context': {}, 'value': 30.0}, 'Lockdown_NY_end': {'name': 'Lockdown_NY_end', 'identifiers': {}, 'context': {}, 'value': 67.0}, 'Io': {'name': 'Io', 'identifiers': {}, 'context': {}, 'value': 5.0}, 'Peak_Time': {'name': 'Peak_Time', 'identifiers': {}, 'context': {}, 'value': 0.0}, 'ModelValue_3': {'name': 'ModelValue_3', 'identifiers': {}, 'context': {}, 'value': 5.0}, 'ModelValue_10': {'name': 'ModelValue_10', 'identifiers': {}, 'context': {}, 'value': 29.0}, 'ModelValue_16': {'name': 'ModelValue_16', 'identifiers': {}, 'context': {}, 'value': 66.0}, 'ModelValue_15': {'name': 'ModelValue_15', 'identifiers': {}, 'context': {}, 'value': 27.0}, 'ModelValue_18': {'name': 'ModelValue_18', 'identifiers': {}, 'context': {}, 'value': 67.0}, 'ModelValue_17': {'name': 'ModelValue_17', 'identifiers': {}, 'context': {}, 'value': 30.0}, 'ModelValue_0': {'name': 'ModelValue_0', 'identifiers': {}, 'context': {}, 'value': 39560000.0}, 'ModelValue_7': {'name': 'ModelValue_7', 'identifiers': {}, 'context': {}, 'value': 19540000.0}, 'ModelValue_1': {'name': 'ModelValue_1', 'identifiers': {}, 'context': {}, 'value': 2.7}, 'ModelValue_8': {'name': 'ModelValue_8', 'identifiers': {}, 'context': {}, 'value': 4.1}, 'ModelValue_5': {'name': 'ModelValue_5', 'identifiers': {}, 'context': {}, 'value': 1.0}, 'ModelValue_14': {'name': 'ModelValue_14', 'identifiers': {}, 'context': {}, 'value': 0.0}, 'ModelValue_6': {'name': 'ModelValue_6', 'identifiers': {}, 'context': {}, 'value': 0.0}, 'ModelValue_2': {'name': 'ModelValue_2', 'identifiers': {}, 'context': {}, 'value': 0.14}, 'ModelValue_9': {'name': 'ModelValue_9', 'identifiers': {}, 'context': {}, 'value': 0.1}, 'USA___CA__NY': {'name': 'USA___CA__NY', 'identifiers': {}, 'context': {}, 'value': 1.0}}, 'initials': {'Infected': 1.2639029322548e-07, 'Recovered': 0.0, 'Susceptible': 0.999999999999997}}\n"
     ]
    }
   ],
   "source": [
    "res = requests.post(rest_url + \"/api/sbml_xml_to_model\", json={\"xml_string\": biomd951_xml_str})\n",
    "print(res.json())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graphviz dot File\n",
    "\n",
    "The `/api/viz/to_dot_file` endpoint takes a `TemplateModel` and returns a graphviz dotfile of the provided model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "strict digraph \"\" {\n",
      "\tgraph [bb=\"0,0,178,313\"];\n",
      "\tnode [label=\"\\N\"];\n",
      "\t\"('susceptible population', ('identity', 'ido:0000514'))\"\t[height=0.65278,\n",
      "\t\tlabel=\"{susceptible population | {identity | ido:0000514}}\",\n",
      "\t\tpos=\"89,289.5\",\n",
      "\t\trects=\"-1.4211e-14,289.5,178,312.5 -1.4211e-14,266.5,72,289.5 72,266.5,178,289.5\",\n",
      "\t\tshape=record,\n",
      "\t\twidth=2.4722];\n",
      "\tT0\t[color=blue,\n",
      "\t\tfillcolor=blue,\n",
      "\t\tfixedsize=true,\n",
      "\t\theight=0.19444,\n",
      "\t\tlabel=\"\",\n",
      "\t\tpos=\"89,223\",\n",
      "\t\tshape=square,\n",
      "\t\tstyle=filled,\n",
      "\t\twidth=0.19444];\n",
      "\t\"('susceptible population', ('identity', 'ido:0000514'))\" -> T0\t[pos=\"e,89,230.05 89,266.3 89,257.68 89,248 89,240.08\"];\n",
      "\t\"('infected population', ('identity', 'ido:0000511'))\"\t[height=0.65278,\n",
      "\t\tlabel=\"{infected population | {identity | ido:0000511}}\",\n",
      "\t\tpos=\"89,156.5\",\n",
      "\t\trects=\"-1.4211e-14,156.5,178,179.5 -1.4211e-14,133.5,72,156.5 72,133.5,178,156.5\",\n",
      "\t\tshape=record,\n",
      "\t\twidth=2.4722];\n",
      "\tT0 -> \"('infected population', ('identity', 'ido:0000511'))\"\t[pos=\"e,82.56,179.55 84.757,215.58 83.08,209.38 82.245,199.52 82.25,189.66\"];\n",
      "\t\"('infected population', ('identity', 'ido:0000511'))\" -> T0\t[color=blue,\n",
      "\t\tpos=\"e,93.243,215.58 95.44,179.55 95.974,188.06 95.818,197.63 94.974,205.53\"];\n",
      "\tT1\t[color=blue,\n",
      "\t\tfillcolor=blue,\n",
      "\t\tfixedsize=true,\n",
      "\t\theight=0.19444,\n",
      "\t\tlabel=\"\",\n",
      "\t\tpos=\"89,90\",\n",
      "\t\tshape=square,\n",
      "\t\tstyle=filled,\n",
      "\t\twidth=0.19444];\n",
      "\t\"('infected population', ('identity', 'ido:0000511'))\" -> T1\t[pos=\"e,89,97.054 89,133.3 89,124.68 89,115 89,107.08\"];\n",
      "\t\"('immune population', ('identity', 'ido:0000592'))\"\t[height=0.65278,\n",
      "\t\tlabel=\"{immune population | {identity | ido:0000592}}\",\n",
      "\t\tpos=\"89,23.5\",\n",
      "\t\trects=\"-1.4211e-14,23.5,178,46.5 -1.4211e-14,0.5,72,23.5 72,0.5,178,23.5\",\n",
      "\t\tshape=record,\n",
      "\t\twidth=2.4722];\n",
      "\tT1 -> \"('immune population', ('identity', 'ido:0000592'))\"\t[pos=\"e,89,46.548 89,82.58 89,76.384 89,66.516 89,56.66\"];\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res = requests.post(rest_url + \"/api/viz/to_dot_file\", json=sir_template_model_dict)\n",
    "print(res.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph image\n",
    "\n",
    "The `/api/viz/to_image` endpoint returns an image of the model as a graph structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = requests.post(rest_url + \"/api/viz/to_image\", json=sir_template_model_dict)\n",
    "with open(\"./graph.png\", \"wb\") as fio:\n",
    "    fio.write(res.content)\n",
    "\n",
    "from IPython.display import Image\n",
    "Image(filename=\"./graph.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing Two Models\n",
    "The endpoints `/api/models_to_delta_graph` and `/api/models_to_delta_image` returns a graph and a png image of the delta, respectively, of the comparison between two models.\n",
    "First, let's create a modified version of the SIR model that has location specified in the context of its templates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Add a location to the templates that are not NaturalConversion (Note that this has no meaning outside as an illustration in this example)\n",
    "templates_w_context = [templ.with_context(location=\"geonames:5128581\") if not isinstance(templ, NaturalConversion) else templ for templ in sir_template_model.templates]\n",
    "sir_w_context = TemplateModel(templates=templates_w_context, parameters=sir_template_model.parameters, initials=sir_template_model.initials)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "Submit the two models to `/api/models_to_delta_image` to get an image of the delta graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = requests.post(rest_url + \"/api/models_to_delta_image\", json={\"template_model1\": sir_template_model_dict, \"template_model2\": sir_w_context.model_dump()})\n",
    "with open(\"./delta_graph.png\", \"wb\") as f:\n",
    "    f.write(res.content)\n",
    "Image(filename=\"./delta_graph.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "From the image, it can be seen that the NaturalConversion of the two models are equals, while the ControlledConversion in one model is a refinement of the other, in this case because the context was added.\n",
    "\n",
    "The other endpoint provides a way to download the graph seen in the image as a node-link data json:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'directed': True, 'multigraph': False, 'graph': {'rankdir': 'LR'}, 'nodes': [{'tags': ['1'], 'type': 'ControlledConversion', 'template_key': ['ControlledConversion', [['ido', '0000514'], []], [['ido', '0000511'], []], [['ido', '0000511'], []]], 'label': 'ControlledConversion', 'color': 'orange', 'shape': 'record', 'id': ['ControlledConversion', ['infected population', ['identity', 'ido:0000511']], ['infected population', ['identity', 'ido:0000511']], ['susceptible population', ['identity', 'ido:0000514']], '1']}, {'tags': ['1', '2'], 'label': 'infected population\\n(ido:0000511)', 'color': 'red', 'concept_identity_key': [['ido', '0000511'], []], 'id': ['infected population', ['identity', 'ido:0000511']]}, {'tags': ['1'], 'label': 'susceptible population\\n(ido:0000514)', 'color': 'orange', 'concept_identity_key': [['ido', '0000514'], []], 'id': ['susceptible population', ['identity', 'ido:0000514']]}, {'tags': ['1'], 'type': 'NaturalConversion', 'template_key': ['NaturalConversion', [['ido', '0000511'], []], [['ido', '0000592'], []]], 'label': 'NaturalConversion', 'color': 'orange', 'shape': 'record', 'id': ['NaturalConversion', ['immune population', ['identity', 'ido:0000592']], ['infected population', ['identity', 'ido:0000511']], '1']}, {'tags': ['1', '2'], 'label': 'immune population\\n(ido:0000592)', 'color': 'red', 'concept_identity_key': [['ido', '0000592'], []], 'id': ['immune population', ['identity', 'ido:0000592']]}, {'type': 'ControlledConversion', 'template_key': ['ControlledConversion', [['ido', '0000514'], [['location', 'geonames:5128581']]], [['ido', '0000511'], [['location', 'geonames:5128581']]], [['ido', '0000511'], [['location', 'geonames:5128581']]]], 'label': 'ControlledConversion', 'color': 'blue', 'shape': 'record', 'tags': ['2'], 'id': ['ControlledConversion', ['infected population', ['identity', 'ido:0000511'], ['location', 'geonames:5128581']], ['infected population', ['identity', 'ido:0000511'], ['location', 'geonames:5128581']], ['susceptible population', ['identity', 'ido:0000514'], ['location', 'geonames:5128581']], '2']}, {'label': 'infected population\\n(ido:0000511)\\nlocation-geonames:5128581', 'color': 'blue', 'concept_identity_key': [['ido', '0000511'], [['location', 'geonames:5128581']]], 'tags': ['2'], 'id': ['infected population', ['identity', 'ido:0000511'], ['location', 'geonames:5128581']]}, {'label': 'susceptible population\\n(ido:0000514)\\nlocation-geonames:5128581', 'color': 'blue', 'concept_identity_key': [['ido', '0000514'], [['location', 'geonames:5128581']]], 'tags': ['2'], 'id': ['susceptible population', ['identity', 'ido:0000514'], ['location', 'geonames:5128581']]}, {'type': 'NaturalConversion', 'template_key': ['NaturalConversion', [['ido', '0000511'], []], [['ido', '0000592'], []]], 'label': 'NaturalConversion', 'color': 'blue', 'shape': 'record', 'tags': ['2'], 'id': ['NaturalConversion', ['immune population', ['identity', 'ido:0000592']], ['infected population', ['identity', 'ido:0000511']], '2']}], 'links': [{'label': 'outcome', 'color': 'orange', 'source': ['ControlledConversion', ['infected population', ['identity', 'ido:0000511']], ['infected population', ['identity', 'ido:0000511']], ['susceptible population', ['identity', 'ido:0000514']], '1'], 'target': ['infected population', ['identity', 'ido:0000511']]}, {'label': 'controller', 'color': 'orange', 'source': ['infected population', ['identity', 'ido:0000511']], 'target': ['ControlledConversion', ['infected population', ['identity', 'ido:0000511']], ['infected population', ['identity', 'ido:0000511']], ['susceptible population', ['identity', 'ido:0000514']], '1']}, {'label': 'subject', 'color': 'orange', 'source': ['infected population', ['identity', 'ido:0000511']], 'target': ['NaturalConversion', ['immune population', ['identity', 'ido:0000592']], ['infected population', ['identity', 'ido:0000511']], '1']}, {'label': 'subject', 'color': 'blue', 'source': ['infected population', ['identity', 'ido:0000511']], 'target': ['NaturalConversion', ['immune population', ['identity', 'ido:0000592']], ['infected population', ['identity', 'ido:0000511']], '2']}, {'label': 'subject', 'color': 'orange', 'source': ['susceptible population', ['identity', 'ido:0000514']], 'target': ['ControlledConversion', ['infected population', ['identity', 'ido:0000511']], ['infected population', ['identity', 'ido:0000511']], ['susceptible population', ['identity', 'ido:0000514']], '1']}, {'label': 'outcome', 'color': 'orange', 'source': ['NaturalConversion', ['immune population', ['identity', 'ido:0000592']], ['infected population', ['identity', 'ido:0000511']], '1'], 'target': ['immune population', ['identity', 'ido:0000592']]}, {'label': 'is_equal', 'color': 'red', 'weight': 2, 'source': ['NaturalConversion', ['immune population', ['identity', 'ido:0000592']], ['infected population', ['identity', 'ido:0000511']], '1'], 'target': ['NaturalConversion', ['immune population', ['identity', 'ido:0000592']], ['infected population', ['identity', 'ido:0000511']], '2']}, {'label': 'outcome', 'color': 'blue', 'source': ['ControlledConversion', ['infected population', ['identity', 'ido:0000511'], ['location', 'geonames:5128581']], ['infected population', ['identity', 'ido:0000511'], ['location', 'geonames:5128581']], ['susceptible population', ['identity', 'ido:0000514'], ['location', 'geonames:5128581']], '2'], 'target': ['infected population', ['identity', 'ido:0000511'], ['location', 'geonames:5128581']]}, {'label': 'refinement_of', 'color': 'red', 'weight': 2, 'source': ['ControlledConversion', ['infected population', ['identity', 'ido:0000511'], ['location', 'geonames:5128581']], ['infected population', ['identity', 'ido:0000511'], ['location', 'geonames:5128581']], ['susceptible population', ['identity', 'ido:0000514'], ['location', 'geonames:5128581']], '2'], 'target': ['ControlledConversion', ['infected population', ['identity', 'ido:0000511']], ['infected population', ['identity', 'ido:0000511']], ['susceptible population', ['identity', 'ido:0000514']], '1']}, {'label': 'controller', 'color': 'blue', 'source': ['infected population', ['identity', 'ido:0000511'], ['location', 'geonames:5128581']], 'target': ['ControlledConversion', ['infected population', ['identity', 'ido:0000511'], ['location', 'geonames:5128581']], ['infected population', ['identity', 'ido:0000511'], ['location', 'geonames:5128581']], ['susceptible population', ['identity', 'ido:0000514'], ['location', 'geonames:5128581']], '2']}, {'label': 'subject', 'color': 'blue', 'source': ['susceptible population', ['identity', 'ido:0000514'], ['location', 'geonames:5128581']], 'target': ['ControlledConversion', ['infected population', ['identity', 'ido:0000511'], ['location', 'geonames:5128581']], ['infected population', ['identity', 'ido:0000511'], ['location', 'geonames:5128581']], ['susceptible population', ['identity', 'ido:0000514'], ['location', 'geonames:5128581']], '2']}, {'label': 'refinement_of', 'color': 'red', 'weight': 2, 'source': ['susceptible population', ['identity', 'ido:0000514'], ['location', 'geonames:5128581']], 'target': ['susceptible population', ['identity', 'ido:0000514']]}, {'label': 'outcome', 'color': 'blue', 'source': ['NaturalConversion', ['immune population', ['identity', 'ido:0000592']], ['infected population', ['identity', 'ido:0000511']], '2'], 'target': ['immune population', ['identity', 'ido:0000592']]}, {'label': 'is_equal', 'color': 'red', 'weight': 2, 'source': ['NaturalConversion', ['immune population', ['identity', 'ido:0000592']], ['infected population', ['identity', 'ido:0000511']], '2'], 'target': ['NaturalConversion', ['immune population', ['identity', 'ido:0000592']], ['infected population', ['identity', 'ido:0000511']], '1']}]}\n"
     ]
    }
   ],
   "source": [
    "res = requests.post(rest_url + \"/api/models_to_delta_graph\", json={\"template_model1\": sir_template_model_dict, \"template_model2\": sir_w_context.model_dump()})\n",
    "print(res.json())"
   ]
  }
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