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Generate Models

Introduction#

One can create a CompNeuroPy-model using the CompNeuroModel class. The CompNeuroModel class takes as one argument the model_creation_function. In this function a classical ANNarchy model is created (populations, projections). The CompNeuroModel class only adds a framework to the model. Neccessary for a CompNeuroPy-model is to define unique names for all populations and projections. Models are created in three steps:

  1. model initialization: the initialization of the CompNeuroModel object, initializes the framework of the model without creating the ANNarchy objects (populations, projections)
  2. model creation: create the ANNarchy objects (populations, projections), i.e., run the model_creation function
  3. model compilation: compile all created models

Example#

from CompNeuroPy import CompNeuroModel
my_model = CompNeuroModel(model_creation_function=create_model,  ### the most important part, this function creates the model (populations, projections)
                          model_kwargs={'a':1, 'b':2},           ### define the two arguments a and b of function create_model
                          name='my_model',                       ### you can give the model a name
                          description='my simple example model', ### you can give the model a description
                          do_create=True,                        ### create the model directly
                          do_compile=True,                       ### let the model (and all models created before) compile directly
                          compile_folder_name='my_model')        ### name of the saved compilation folder

The following function could be the corresponding model_creation_function:

from ANNarchy import Population, Izhikevich
def create_model(a, b):
    pop = Population(geometry=a, neuron=Izhikevich, name='Izh_pop_a') ### first population, size a
    pop.b = 0                                                         ### some parameter adjustment
    Population(geometry=b, neuron=Izhikevich, name='Izh_pop_b')       ### second population, size b

Here, two populations are created (both use built-in Izhikevich neuron model of ANNarchy). The function does not require a return value. It is important that all populations and projections have unique names.

A more detailed example is available in the Examples.

CompNeuroPy.generate_model.CompNeuroModel #

Class for creating and compiling a model.

Attributes:

Name Type Description
name str

name of the model
description str

description of the model
model_creation_function function

function which creates the model
compile_folder_name str

name of the folder in which the model is compiled
model_kwargs dict

keyword arguments for model_creation_function
populations list

list of names of all populations of the model
projections list

list of names of all projections of the model
created bool

True if the model is created
compiled bool

True if the model is compiled
attribute_df pandas dataframe

dataframe containing all attributes of the model compartments
Source code in src/CompNeuroPy/generate_model.py 
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class CompNeuroModel:
    """
    Class for creating and compiling a model.

    Attributes:
        name (str):
            name of the model
        description (str):
            description of the model
        model_creation_function (function):
            function which creates the model
        compile_folder_name (str):
            name of the folder in which the model is compiled
        model_kwargs (dict):
            keyword arguments for model_creation_function
        populations (list):
            list of names of all populations of the model
        projections (list):
            list of names of all projections of the model
        created (bool):
            True if the model is created
        compiled (bool):
            True if the model is compiled
        attribute_df (pandas dataframe):
            dataframe containing all attributes of the model compartments
    """

    _initialized_models = {}
    _compiled_models = {}
    _compiled_models_updated = False

    @check_types()
    def __init__(
        self,
        model_creation_function: Callable,
        model_kwargs: dict | None = None,
        name: str = "model",
        description: str = "",
        do_create: bool = True,
        do_compile: bool = True,
        compile_folder_name: str = "annarchy",
    ):
        """
        Initializes the CompNeuroModel class.

        Args:
            model_creation_function (function):
                Function which creates the model.
            model_kwargs (dict):
                Keyword arguments for model_creation_function. Default: None.
            name (str):
                Name of the model. Default: "model".
            description (str):
                Description of the model. Default: "".
            do_create (bool):
                If True the model is created directly. Default: True.
            do_compile (bool):
                If True the model is compiled directly. Default: True.
            compile_folder_name (str):
                Name of the folder in which the model is compiled. Default: "annarchy".
        """
        self.name = name
        if name == "model":
            self.name = name + str(self._nr_models())
        self.description = description
        self.model_creation_function = model_creation_function
        self.compile_folder_name = compile_folder_name
        self.model_kwargs = model_kwargs
        self.populations = []
        self.projections = []
        self.created = False
        self.compiled = False
        self._attribute_df = None
        self._attribute_df_compiled = False
        if do_create:
            self.create(do_compile=do_compile, compile_folder_name=compile_folder_name)

    @property
    def compiled(self):
        """
        True if the model is compiled.
        """
        ### check if ANNarchy was compiled and _compiled_models is not updated yet
        if mf.annarchy_compiled() and not self._compiled_models_updated:
            self._update_compiled_models()
        return self._compiled_models[self.name]

    @compiled.setter
    def compiled(self, value):
        """
        Setter for compiled property.
        """
        self._compiled_models[self.name] = value

    @property
    def created(self):
        """
        True if the model is created.
        """
        return self._initialized_models[self.name]

    @created.setter
    def created(self, value):
        """
        Setter for created property.
        """
        self._initialized_models[self.name] = value

    @property
    def attribute_df(self):
        """
        Dataframe containing all attributes of the model compartments.
        """
        ### check if ANNarchy was compiled and _attribute_df is not updated yet
        if mf.annarchy_compiled() and not self._attribute_df_compiled:
            self._update_attribute_df_weights()
        return self._attribute_df

    def _update_compiled_models(self):
        """
        Updates _compiled_models to True for all models.
        """
        ### update _compiled_models
        for key in self._compiled_models.keys():
            self._compiled_models[key] = True
        self._compiled_models_updated = True

    def _update_attribute_df_weights(self):
        """
        Updates _attribute_df for the weights of all projections.
        """
        for proj_name in self.projections:
            values = get_projection(proj_name).w
            self._update_attribute_df(
                compartment=proj_name, parameter_name="w", parameter_value=values
            )
        self._attribute_df_compiled = True

    def compile(self, compile_folder_name=None):
        """
        Compiles a created model.

        Args:
            compile_folder_name (str, optional):
                Name of the folder in which the model is compiled. Default: value from
                initialization.
        """
        ### check if this model is created
        if self.created:
            if compile_folder_name == None:
                compile_folder_name = self.compile_folder_name

            ### check if other models were initialized but not created --> warn that they are not compiled
            not_created_model_list = self._check_if_models_created()
            if len(not_created_model_list) > 0:
                print(
                    "\nWARNING during compile of model "
                    + self.name
                    + ": There are initialized models which are not created, thus not compiled! models:\n"
                    + "\n".join(not_created_model_list)
                    + "\n"
                )
            mf.compile_in_folder(compile_folder_name, silent=True)
            self.compiled = True

            ### update attribute_df to compiled state, since weights are only available
            ### after compilation
            self._update_attribute_df_weights()
        else:
            print("\n")
            assert False, (
                "ERROR during compile of model "
                + self.name
                + ": Only compile the model after it has been created!"
            )

    def create(self, do_compile=True, compile_folder_name=None):
        """
        Creates a model and optionally compiles it directly.

        Args:
            do_compile (bool, optional):
                If True the model is compiled directly. Default: True.
            compile_folder_name (str, optional):
                Name of the folder in which the model is compiled. Default: value from
                initialization.
        """
        if self.created:
            print("model", self.name, "already created!")
        else:
            initial_existing_model = mf.get_full_model()
            ### create model populations and projections
            if self.model_kwargs != None:
                self.model_creation_function(**self.model_kwargs)
            else:
                self.model_creation_function()
            self.created = True

            ### check which populations and projections have been added
            post_existing_model = mf.get_full_model()
            ### save only added not all projections/populations
            for initial_pop in initial_existing_model["populations"]:
                post_existing_model["populations"].remove(initial_pop)
            for initial_proj in initial_existing_model["projections"]:
                post_existing_model["projections"].remove(initial_proj)
            self.populations = post_existing_model["populations"]
            self.projections = post_existing_model["projections"]

            ### check if names of populations and projections are unique
            self._check_double_compartments()

            ### create parameter dictionary
            self._attribute_df = self._get_attribute_df()

            if do_compile:
                self.compile(compile_folder_name)

    def _check_if_models_created(self):
        """
        Checks which CompNeuroPy models are created

        Returns:
            not_created_model_list (list):
                list of names of all initialized CompNeuroPy models which are not
                created yet
        """
        not_created_model_list = []
        for key in self._initialized_models.keys():
            if self._initialized_models[key] == False:
                not_created_model_list.append(key)

        return not_created_model_list

    def _nr_models(self):
        """
        Returns:
            nr_models (int):
                The current number of initialized (not considering "created")
                CompNeuroPy models
        """
        return len(list(self._initialized_models.keys()))

    def set_param(self, compartment, parameter_name, parameter_value):
        """
        Sets the specified parameter of the specified compartment.

        Args:
            compartment (str):
                name of model compartment
            parameter_name (str):
                name of parameter of the compartment
            parameter_value (number or array-like with shape of compartment geometry):
                the value or values of the parameter

        Raises:
            AssertionError: if model is not created
            AssertionError: if compartment is neither a population nor a projection of
                the model
        """
        ### catch if model is not created
        assert (
            self.created == True
        ), f"ERROR set_param: model {self.name} has to be created before setting parameters!"

        ### check if compartment is in populations or projections
        comp_in_pop = compartment in self.populations
        comp_in_proj = compartment in self.projections

        if comp_in_pop:
            comp_obj = get_population(compartment)
        elif comp_in_proj:
            comp_obj = get_projection(compartment)
        else:
            assert (
                comp_in_pop or comp_in_proj
            ), f"ERROR set_param: setting parameter {parameter_name} of compartment {compartment}. The compartment is neither a population nor a projection of the model {self.name}!"

        ### set the parameter value
        setattr(comp_obj, parameter_name, parameter_value)

        ### update the model attribute_df
        self._update_attribute_df(compartment, parameter_name, parameter_value)

    def _update_attribute_df(self, compartment, parameter_name, parameter_value):
        """
        updates the attribute df for a specific paramter

        Args:
            compartment (str):
                name of model compartment
            parameter_name (str):
                name of parameter of the compartment
            parameter_value (number or array-like with shape of compartment geometry):
                the value or values of the parameter
        """
        paramter_mask = (
            (self._attribute_df["compartment_name"] == compartment).astype(int)
            * (self._attribute_df["attribute_name"] == parameter_name).astype(int)
        ).astype(bool)
        parameter_idx = np.arange(paramter_mask.size).astype(int)[paramter_mask][0]
        min_val = af.get_minimum(parameter_value)
        max_val = af.get_maximum(parameter_value)
        if min_val != max_val:
            self._attribute_df.at[parameter_idx, "value"] = f"[{min_val}, {max_val}]"
        else:
            self._attribute_df.at[parameter_idx, "value"] = str(min_val)
        self._attribute_df.at[parameter_idx, "definition"] = "modified"

    def _check_double_compartments(self):
        """
        Goes over all compartments of the model and checks if compartment is only a
        population or a projection and not both.

        Raises:
            AssertionError: if model is not created
            AssertionError: if compartment is both a population and a projection
        """
        ### cach if model is not created, only if created populations and projections are available
        assert (
            self.created == True
        ), f"ERROR model {self.name}: model has to be created before checking for double compartments!"
        ### only have to go over populations and check if they are also projections (go over projections not neccessary)
        pop_in_projections_list = []
        pop_in_projections = False
        for pop_name in self.populations:
            if pop_name in self.projections:
                pop_in_projections_list.append(pop_name)
                pop_in_projections = True

        assert (
            pop_in_projections == False
        ), f"ERROR model {self.name}: One or multiple compartments are both population and projection ({pop_in_projections_list}). Rename them!"

    def _get_attribute_df(self):
        """
        Creates a dataframe containing the attributes of all model compartments.

        Returns:
            attribute_df (pandas dataframe):
                dataframe containing all attributes of the model compartments

        Raises:
            AssertionError: if model is not created
        """
        ### cach if model is not created, only if created populations and projections are available
        assert (
            self.created == True
        ), f"ERROR model {self.name}: model has to be created before creating paramteer dictionary!"

        ### create empty paramteter dict
        attribute_dict = {
            "compartment_type": [],
            "compartment_name": [],
            "attribute_name": [],
            "value": [],
            "definition": [],
        }

        ### fill paramter dict with population attributes
        for pop in self.populations:
            for attribute in vars(get_population(pop))["attributes"]:
                ### store min and max of attribute
                ### create numpy array with getattr to use numpy min max function
                values = np.array(
                    [getattr(get_population(pop), attribute)]
                    + [getattr(get_population(pop), attribute)]
                )
                attribute_dict["compartment_type"].append("population")
                attribute_dict["compartment_name"].append(pop)
                attribute_dict["attribute_name"].append(attribute)
                if values.min() != values.max():
                    attribute_dict["value"].append(f"[{values.min()}, {values.max()}]")
                else:
                    attribute_dict["value"].append(str(values.min()))
                attribute_dict["definition"].append("init")

        ### fill paramter dict with projection attributes
        for proj in self.projections:
            for attribute in vars(get_projection(proj))["attributes"]:
                ### store min and max of attribute
                ### create numpy array with getattr to use numpy min max function
                values = np.array(
                    [getattr(get_projection(proj), attribute)]
                    + [getattr(get_projection(proj), attribute)]
                )
                attribute_dict["compartment_type"].append("projection")
                attribute_dict["compartment_name"].append(proj)
                attribute_dict["attribute_name"].append(attribute)
                if values.min() != values.max():
                    attribute_dict["value"].append(f"[{values.min()}, {values.max()}]")
                else:
                    attribute_dict["value"].append(values.min())
                attribute_dict["definition"].append("init")

        ### return dataframe
        return pd.DataFrame(attribute_dict)

compiled property writable #

True if the model is compiled.

created property writable #

True if the model is created.

attribute_df property #

Dataframe containing all attributes of the model compartments.

__init__(model_creation_function, model_kwargs=None, name='model', description='', do_create=True, do_compile=True, compile_folder_name='annarchy') #

Initializes the CompNeuroModel class.

Parameters:

Name Type Description Default
model_creation_function function

Function which creates the model.

 required
model_kwargs dict

Keyword arguments for model_creation_function. Default: None.

 None
name str

Name of the model. Default: "model".

 'model'
description str

Description of the model. Default: "".

 ''
do_create bool

If True the model is created directly. Default: True.

 True
do_compile bool

If True the model is compiled directly. Default: True.

 True
compile_folder_name str

Name of the folder in which the model is compiled. Default: "annarchy".

 'annarchy'
Source code in src/CompNeuroPy/generate_model.py 
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@check_types()
def __init__(
    self,
    model_creation_function: Callable,
    model_kwargs: dict | None = None,
    name: str = "model",
    description: str = "",
    do_create: bool = True,
    do_compile: bool = True,
    compile_folder_name: str = "annarchy",
):
    """
    Initializes the CompNeuroModel class.

    Args:
        model_creation_function (function):
            Function which creates the model.
        model_kwargs (dict):
            Keyword arguments for model_creation_function. Default: None.
        name (str):
            Name of the model. Default: "model".
        description (str):
            Description of the model. Default: "".
        do_create (bool):
            If True the model is created directly. Default: True.
        do_compile (bool):
            If True the model is compiled directly. Default: True.
        compile_folder_name (str):
            Name of the folder in which the model is compiled. Default: "annarchy".
    """
    self.name = name
    if name == "model":
        self.name = name + str(self._nr_models())
    self.description = description
    self.model_creation_function = model_creation_function
    self.compile_folder_name = compile_folder_name
    self.model_kwargs = model_kwargs
    self.populations = []
    self.projections = []
    self.created = False
    self.compiled = False
    self._attribute_df = None
    self._attribute_df_compiled = False
    if do_create:
        self.create(do_compile=do_compile, compile_folder_name=compile_folder_name)

compile(compile_folder_name=None) #

Compiles a created model.

Parameters:

Name Type Description Default
compile_folder_name str

Name of the folder in which the model is compiled. Default: value from initialization.

 None
Source code in src/CompNeuroPy/generate_model.py 
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def compile(self, compile_folder_name=None):
    """
    Compiles a created model.

    Args:
        compile_folder_name (str, optional):
            Name of the folder in which the model is compiled. Default: value from
            initialization.
    """
    ### check if this model is created
    if self.created:
        if compile_folder_name == None:
            compile_folder_name = self.compile_folder_name

        ### check if other models were initialized but not created --> warn that they are not compiled
        not_created_model_list = self._check_if_models_created()
        if len(not_created_model_list) > 0:
            print(
                "\nWARNING during compile of model "
                + self.name
                + ": There are initialized models which are not created, thus not compiled! models:\n"
                + "\n".join(not_created_model_list)
                + "\n"
            )
        mf.compile_in_folder(compile_folder_name, silent=True)
        self.compiled = True

        ### update attribute_df to compiled state, since weights are only available
        ### after compilation
        self._update_attribute_df_weights()
    else:
        print("\n")
        assert False, (
            "ERROR during compile of model "
            + self.name
            + ": Only compile the model after it has been created!"
        )

create(do_compile=True, compile_folder_name=None) #

Creates a model and optionally compiles it directly.

Parameters:

Name Type Description Default
do_compile bool

If True the model is compiled directly. Default: True.

 True
compile_folder_name str

Name of the folder in which the model is compiled. Default: value from initialization.

 None
Source code in src/CompNeuroPy/generate_model.py 
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def create(self, do_compile=True, compile_folder_name=None):
    """
    Creates a model and optionally compiles it directly.

    Args:
        do_compile (bool, optional):
            If True the model is compiled directly. Default: True.
        compile_folder_name (str, optional):
            Name of the folder in which the model is compiled. Default: value from
            initialization.
    """
    if self.created:
        print("model", self.name, "already created!")
    else:
        initial_existing_model = mf.get_full_model()
        ### create model populations and projections
        if self.model_kwargs != None:
            self.model_creation_function(**self.model_kwargs)
        else:
            self.model_creation_function()
        self.created = True

        ### check which populations and projections have been added
        post_existing_model = mf.get_full_model()
        ### save only added not all projections/populations
        for initial_pop in initial_existing_model["populations"]:
            post_existing_model["populations"].remove(initial_pop)
        for initial_proj in initial_existing_model["projections"]:
            post_existing_model["projections"].remove(initial_proj)
        self.populations = post_existing_model["populations"]
        self.projections = post_existing_model["projections"]

        ### check if names of populations and projections are unique
        self._check_double_compartments()

        ### create parameter dictionary
        self._attribute_df = self._get_attribute_df()

        if do_compile:
            self.compile(compile_folder_name)

set_param(compartment, parameter_name, parameter_value) #

Sets the specified parameter of the specified compartment.

Parameters:

Name Type Description Default
compartment str

name of model compartment

 required
parameter_name str

name of parameter of the compartment

 required
parameter_value number or array-like with shape of compartment geometry

the value or values of the parameter

 required

Raises:

Type Description
AssertionError

if model is not created
AssertionError

if compartment is neither a population nor a projection of the model
Source code in src/CompNeuroPy/generate_model.py 
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def set_param(self, compartment, parameter_name, parameter_value):
    """
    Sets the specified parameter of the specified compartment.

    Args:
        compartment (str):
            name of model compartment
        parameter_name (str):
            name of parameter of the compartment
        parameter_value (number or array-like with shape of compartment geometry):
            the value or values of the parameter

    Raises:
        AssertionError: if model is not created
        AssertionError: if compartment is neither a population nor a projection of
            the model
    """
    ### catch if model is not created
    assert (
        self.created == True
    ), f"ERROR set_param: model {self.name} has to be created before setting parameters!"

    ### check if compartment is in populations or projections
    comp_in_pop = compartment in self.populations
    comp_in_proj = compartment in self.projections

    if comp_in_pop:
        comp_obj = get_population(compartment)
    elif comp_in_proj:
        comp_obj = get_projection(compartment)
    else:
        assert (
            comp_in_pop or comp_in_proj
        ), f"ERROR set_param: setting parameter {parameter_name} of compartment {compartment}. The compartment is neither a population nor a projection of the model {self.name}!"

    ### set the parameter value
    setattr(comp_obj, parameter_name, parameter_value)

    ### update the model attribute_df
    self._update_attribute_df(compartment, parameter_name, parameter_value)