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Introduction\n", + "***\n", + "Running a simmulation with SpatialPy requires only 2 components: a model (data), and a solver (algorithm).\n", + "***\n", + "### Setup the Environment\n", + "***\n", + "'os' and 'sys' allow us to modify the directory Python searches for source code. If you wish to use an installed SpatialPy package, you do not need to run this cell. If this notebook is being run from within the SpatialPy source code directory we need to let Python know that we want to use it, not the SpatialPy package." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "sys.path.insert(1, os.path.abspath(os.path.join(os.getcwd(), '../')))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import the types that'll be needed to define your Model." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import spatialpy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Creating a SpatialPy Model\n", + "***\n", + "A SpatialPy model must include a domain, which may be fixed (not moving) or subject to fluid motion (SDPD method).\n", + "To add a biochemical network the model must also include Species, Parameters, Initial Conditions, and Reactions.\n", + "\n", + "Your model is declared and configured as a Python class. As such, the name can be of your choosing. For this example we'll be modeling Spatial Birth and Death, so lets set the name accordingly." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def create_birth_death(parameter_values=None):\n", + " # Intialize the Model with a name of your choosing.\n", + " model = spatialpy.Model(name='Spatial Birth-Death')\n", + " \n", + " # Define Domain Type IDs as constants of the Model\n", + " model.HABITAT = \"Habitat\"\n", + "\n", + " \"\"\"\n", + " A domain defines points and attributes of a regional space for simulation.\n", + "\n", + " Create a filled 2D domain\n", + " - xlim: highest and lowest coordinate in the x dimension.\n", + " - ylim: highest and lowest coordinate in the y dimension.\n", + " - numx: number of particle spacing in the x dimension.\n", + " - numy: number of particle spacing in the y dimension.\n", + " - type_id: default type ID of particles to be created. Defaults to 1.\n", + " - mass: default mass of particles to be created. Defaults to 1.0.\n", + " - nu: default viscosity of particles to be created. Defaults to 1.0.\n", + " - c: default artificial speed of sound of particles to be created. Defaults to 0.0.\n", + " - rho: default density of particles to be created.\n", + " - fixed: spatially fixed flag of particles to be created. Defaults to false.\n", + " - rho0: Background density for the system.\n", + " - c0: Speed of sound for the system.\n", + " - P0: Background pressure for the system.\n", + " - gravity: Acceleration of gravity for the system.\n", + " - apply_action: If true, apply the action, else, add the action to Domain.actions.\n", + " \"\"\"\n", + " domain = spatialpy.Domain.create_2D_domain(\n", + " xlim=(0, 1), ylim=(0, 1), numx=10, numy=10, type_id=model.HABITAT, fixed=True\n", + " )\n", + "\n", + " # Set the Models Domain\n", + " model.add_domain(domain)\n", + "\n", + " \"\"\"\n", + " Variables (SpatialPy.Species) can be anything that participates in or is produced by a reaction channel.\n", + "\n", + " - name: A user defined name for the species.\n", + " - diffusion_coefficient: Non-constant coefficient of diffusion for the Species.\n", + " - restrict_to: Restricts the movement of Species to a type or list of types within the Domain.\n", + " \"\"\"\n", + " Rabbits = spatialpy.Species(name='Rabbits', diffusion_coefficient=0.1)\n", + "\n", + " # Add the Variables to the Model.\n", + " model.add_species(Rabbits)\n", + "\n", + " \"\"\"\n", + " Scatter the initial condition for a species randomly over the list of types.\n", + " If the list of types is None scatter over all types.\n", + "\n", + " - species: The species to set the initial condition.\n", + " - count: The initial condition for the target species.\n", + " - types: Type IDs of the particles to place the initial condition.\n", + " \"\"\"\n", + " init_rabbit_pop = spatialpy.ScatterInitialCondition(species='Rabbits', count=100)\n", + "\n", + " # Add Initial Conditions to the Model\n", + " model.add_initial_condition(init_rabbit_pop)\n", + "\n", + " \"\"\"\n", + " Parameters are constant values relevant to the system, such as reaction kinetic rates.\n", + "\n", + " - name: A user defined name for reference.\n", + " - expression: Some constant value.\n", + " \"\"\"\n", + " kb = spatialpy.Parameter(name='k_birth', expression=10)\n", + " kd = spatialpy.Parameter(name='k_death', expression=0.1)\n", + "\n", + " # Add the Parameters to the Model.\n", + " model.add_parameter([kb, kd])\n", + "\n", + " \"\"\"\n", + " Reactions are the reaction channels which cause the system to change over time.\n", + "\n", + " - name: A user defined name for the reaction.\n", + " - reactants: A dictionary with participant reactants as keys, and consumed per reaction as value.\n", + " - products: A dictionary with reaction products as keys, and number formed per reaction as value.\n", + " - rate: A parameter rate constant to be applied to the propensity of this reaction firing.\n", + " - propensity_function: Can be used instead of rate in order to declare a custom propensity function\n", + " in string format.\n", + " - restrict_to: Restrict reaction channels to a type or list of types within the Domain.\n", + " \"\"\"\n", + " birth = spatialpy.Reaction(\n", + " name='birth',\n", + " reactants={},\n", + " products={\"Rabbits\":1},\n", + " rate=\"k_birth\"\n", + " )\n", + "\n", + " death = spatialpy.Reaction(\n", + " name='death',\n", + " reactants={\"Rabbits\":1},\n", + " products={},\n", + " rate=\"k_death\"\n", + " )\n", + "\n", + " # Add the Reactions to the Model.\n", + " model.add_reaction([birth, death])\n", + "\n", + " # Define the Timespan of the Model\n", + " tspan = spatialpy.TimeSpan.linspace(t=10, num_points=11, timestep_size=1)\n", + " \n", + " # Set the Timespan of the Model\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_birth_death()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Running the Simulation\n", + "***\n", + "Run a spatial stochastic simulation on the Model and store the results in the 'results' variable." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "results = model.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### You can find a thorough list of `model.run()` arguments in the [documentation](https://stochss.github.io/SpatialPy/docs/build/html/classes/spatialpy.core.html#spatialpy.core.model.Model.run) to customize the behavior of your simulations.\"\n", + "***\n", + "## Visualizations\n", + "***\n", + "Plot the results of the simulation.\n", + "\n", + "There are a multitude of arguments that can be set to tweak the behavior and visuals of the plot. \n", + "For now though, lets plot the populations of `Rabbits` at the end of the simulation using MatPlotLib." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "results.plot_species(\n", + " # Set the target species to visualize\n", + " species='Rabbits',\n", + " \n", + " # Set to True to use Plotly animation\n", + " animated=True,\n", + " \n", + " # Set the size of the Particles\n", + " size=20\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### A more complete list of `results.plot_species()` arguments can be found in the [documentation](https://stochss.github.io/SpatialPy/docs/build/html/classes/spatialpy.core.html#spatialpy.core.result.Result.plot_species).\n", + "***\n", + "## Where to go now?\n", + "***\n", + "For a more advanced example on reaction diffusion follow the link below:\n", + "\n", + "- [3D Cylinder Demo](./3D_Cylinder_Demo.ipynb)\n", + "\n", + "For examples on Fluid Dynamic models follow the links below:\n", + "\n", + "- [Gravity](./Gravity.ipynb)\n", + "- [Weir](./weir.ipynb)" + ] + } + ], + "metadata": { + "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": "3.9.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/spatialpy/core/timespan.py b/spatialpy/core/timespan.py index d32ea1ee..f467bb86 100644 --- a/spatialpy/core/timespan.py +++ b/spatialpy/core/timespan.py @@ -25,10 +25,10 @@ class TimeSpan(Iterator): the species populations during the simulation. :param items: Evenly-spaced list of times at which to sample the species populations during the simulation. - Best to use the form np.linspace(, , ) + Best to use the form np.linspace(0, , ) :type items: list, tuple, range, or numpy.ndarray - :param timestep_size: Size of each timestep in seconds + :param timestep_size: Size of each timestep :type timestep_size: int | float :raises TimespanError: items is an invalid type.