{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install and import `sme`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EtqqtL8VYdA5"
   },
   "outputs": [],
   "source": [
    "!pip install -q sme\n",
    "import sme\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "print(\"sme version:\", sme.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Importing a model\n",
    "- to load an existing sme or xml file: `sme.open_file('model_filename.xml')`\n",
    "- to load a built-in example model: `sme.open_example_model()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LLPnn1h1Yee7"
   },
   "outputs": [],
   "source": [
    "my_model = sme.open_example_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Getting help\n",
    "- to see the type of an object: `type(object)`\n",
    "- to print a one line description of an object: `repr(object)`\n",
    "- to print a multi-line description of an object: `print(object)`\n",
    "- to get help on an object, its methods and properties: `help(object)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(my_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "repr(my_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(my_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "help(my_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Viewing model contents\n",
    "- the compartments in a model can be accessed as a list: `model.compartments`\n",
    "- the list can be iterated over, or an item looked up by index or name\n",
    "- other lists of objects, such as species in a compartment, or parameters in a reaction, behave in the same way\n",
    "\n",
    "### Iterating over compartments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for compartment in my_model.compartments:\n",
    "    print(repr(compartment))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get compartment by name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_compartment = my_model.compartments[\"Cell\"]\n",
    "print(repr(cell_compartment))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get compartment by list index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_compartment = my_model.compartments[-1]\n",
    "print(repr(last_compartment))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display geometry of compartments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(nrows=1, ncols=len(my_model.compartments), figsize=(18, 12))\n",
    "for ax, compartment in zip(axs, my_model.compartments):\n",
    "    ax.imshow(compartment.geometry_mask[0], interpolation=\"none\")\n",
    "    ax.set_title(f\"{compartment.name}\")\n",
    "    ax.set_xlabel(\"x\")\n",
    "    ax.set_ylabel(\"y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display parameter names and values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_reac = my_model.compartments[\"Nucleus\"].reactions[\"A to B conversion\"]\n",
    "print(my_reac)\n",
    "for param in my_reac.parameters:\n",
    "    print(param)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Editing model contents\n",
    "- Parameter values and object names can be changed by assigning new values to them"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Model name: {my_model.name}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model.name = \"New model name!\"\n",
    "print(f\"Model name: {my_model.name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "param = my_model.parameters[0]\n",
    "print(f\"{param.name} = {param.value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "param.value = \"2.5\"\n",
    "print(f\"{param.name} = {param.value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reaction Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "k1 = my_model.compartments[\"Nucleus\"].reactions[\"A to B conversion\"].parameters[\"k1\"]\n",
    "print(f\"{k1.name} = {k1.value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "k1.value = 0.72\n",
    "print(f\"{k1.name} = {k1.value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Species Initial Concentrations\n",
    "- can be Uniform (`float`), Analytic (`str`) or Image (`np.ndarray`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "species = my_model.compartments[\"Cell\"].species[0]\n",
    "print(\n",
    "    f\"Species '{species.name}' has initial concentration of type '{species.concentration_type}', value '{species.uniform_concentration}'\"\n",
    ")\n",
    "species.uniform_concentration = 1.3\n",
    "print(\n",
    "    f\"Species '{species.name}' has initial concentration of type '{species.concentration_type}', value '{species.uniform_concentration}'\"\n",
    ")\n",
    "plt.imshow(species.concentration_image[0])\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "species.analytic_concentration = \"3 + 2*cos(x/2)+sin(y/3)\"\n",
    "print(\n",
    "    f\"Species '{species.name}' has initial concentration of type '{species.concentration_type}', expression '{species.analytic_concentration}'\"\n",
    ")\n",
    "plt.imshow(species.concentration_image[0])\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate concentration image with concentration = x + y\n",
    "new_concentration_image = np.zeros(species.concentration_image.shape)\n",
    "for index, _ in np.ndenumerate(new_concentration_image):\n",
    "    new_concentration_image[index] = index[0] + index[1]\n",
    "\n",
    "species.concentration_image = new_concentration_image\n",
    "print(\n",
    "    f\"Species '{species.name}' has initial concentration of type '{species.concentration_type}'\"\n",
    ")\n",
    "plt.imshow(species.concentration_image[0])\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Exporting a model\n",
    "- to save the model, including any simulation results: `model.export_sme_file('model_filename.sme')`\n",
    "- to export the model as an SBML file (no simulation results): `model.export_sbml_file('model_filename.xml')`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LLPnn1h1Yee7"
   },
   "outputs": [],
   "source": [
    "my_model.export_sme_file(\"model.sme\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "sme_getting_started.ipynb",
   "provenance": []
  },
  "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}