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Stimulus

Functions for creating stimuli and noise inputs for models.

BaseMultipleInputs

Bases: Stimulus

Base class for stimuli consisting of multiple time series, such as summed inputs or concatenated inputs.

Source code in neurolib/utils/stimulus.py 
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class BaseMultipleInputs(Stimulus):
    """
    Base class for stimuli consisting of multiple time series, such as summed inputs or concatenated inputs.
    """

    def __init__(self, inputs):
        """
        :param inputs: List of Inputs to combine
        :type inputs: list[`Input`]
        """
        assert all(isinstance(input, Input) for input in inputs)
        self.inputs = inputs

    def __len__(self):
        """
        Return number of inputs.
        """
        return len(self.inputs)

    def __getitem__(self, index):
        """
        Return inputs by index. This also allows iteration.
        """
        return self.inputs[index]

    @property
    def n(self):
        n = set([input.n for input in self])
        assert len(n) == 1
        return next(iter(n))

    @n.setter
    def n(self, n):
        for input in self:
            input.n = n

    def get_params(self):
        """
        Get all parameters recursively for all inputs.
        """
        return {
            "type": self.__class__.__name__,
            **{f"input_{i}": input.get_params() for i, input in enumerate(self)},
        }

    def update_params(self, params_dict):
        """
        Update all parameters recursively.
        """
        for i, input in enumerate(self):
            input.update_params(params_dict.get(f"input_{i}", {}))

__getitem__(index)

Return inputs by index. This also allows iteration.

Source code in neurolib/utils/stimulus.py 
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def __getitem__(self, index):
    """
    Return inputs by index. This also allows iteration.
    """
    return self.inputs[index]

__init__(inputs)

Parameters:

Name Type Description Default
inputs list[`Input`]

List of Inputs to combine

 required
Source code in neurolib/utils/stimulus.py 
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def __init__(self, inputs):
    """
    :param inputs: List of Inputs to combine
    :type inputs: list[`Input`]
    """
    assert all(isinstance(input, Input) for input in inputs)
    self.inputs = inputs

__len__()

Return number of inputs.

Source code in neurolib/utils/stimulus.py 
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def __len__(self):
    """
    Return number of inputs.
    """
    return len(self.inputs)

get_params()

Get all parameters recursively for all inputs.

Source code in neurolib/utils/stimulus.py 
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def get_params(self):
    """
    Get all parameters recursively for all inputs.
    """
    return {
        "type": self.__class__.__name__,
        **{f"input_{i}": input.get_params() for i, input in enumerate(self)},
    }

update_params(params_dict)

Update all parameters recursively.

Source code in neurolib/utils/stimulus.py 
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def update_params(self, params_dict):
    """
    Update all parameters recursively.
    """
    for i, input in enumerate(self):
        input.update_params(params_dict.get(f"input_{i}", {}))

ConcatenatedStimulus

Bases: BaseMultipleInputs

Represents temporal concatenation of of arbitrary many stimuli.

Example: 

    summed_stimulus = SinusoidalInput(...) & OrnsteinUhlenbeckProcess(...)

Source code in neurolib/utils/stimulus.py 
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class ConcatenatedStimulus(BaseMultipleInputs):
    """
    Represents temporal concatenation of of arbitrary many stimuli.

    Example:
    ```
        summed_stimulus = SinusoidalInput(...) & OrnsteinUhlenbeckProcess(...)
    ```
    """

    def __init__(self, inputs, length_ratios=None):
        """
        :param length_ratios: Ratios of lengths of concatenated stimuli
        :type length_ratios: list[int|float]
        """
        if length_ratios is None:
            length_ratios = [1] * len(inputs)
        assert len(inputs) == len(length_ratios)
        assert all(length > 0 for length in length_ratios)
        self.length_ratios = length_ratios
        super().__init__(inputs)

    def __and__(self, other):
        assert isinstance(other, Input)
        assert self.n == other.n
        if isinstance(other, ConcatenatedStimulus):
            return ConcatenatedStimulus(
                inputs=self.inputs + other.inputs,
                length_ratios=self.length_ratios + other.length_ratios,
            )
        else:
            return ConcatenatedStimulus(inputs=self.inputs + [other], length_ratios=self.length_ratios + [1])

    def as_array(self, duration, dt):
        """
        Return concatenation of all stimuli as numpy array.
        """
        # normalize ratios to sum = 1
        ratios = [i / sum(self.length_ratios) for i in self.length_ratios]
        concat = np.concatenate(
            [input.as_array(duration * ratio, dt) for input, ratio in zip(self.inputs, ratios)],
            axis=1,
        )
        length = int(duration / dt)
        # due to rounding errors, the overall length might be longer by a few dt
        return concat[:, :length]

    def as_cubic_splines(self, duration, dt, shift_start_time=0.0):
        # normalize ratios to sum = 1
        ratios = [i / sum(self.length_ratios) for i in self.length_ratios]
        result = self.inputs[0].as_cubic_splines(duration * ratios[0], dt, shift_start_time)
        for input, ratio in zip(self.inputs[1:], ratios[1:]):
            last_time = result[-1].time
            temp = input.as_cubic_splines(duration * ratio, dt, shift_start_time=last_time)
            # `extend` adds an iteratable (whole `CubicHermiteSpline` is an
            # iterable of `Anchors`) to the current spline
            result.extend(temp)
        return result

__init__(inputs, length_ratios=None)

Parameters:

Name Type Description Default
length_ratios list[int|float]

Ratios of lengths of concatenated stimuli

 None
Source code in neurolib/utils/stimulus.py 
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def __init__(self, inputs, length_ratios=None):
    """
    :param length_ratios: Ratios of lengths of concatenated stimuli
    :type length_ratios: list[int|float]
    """
    if length_ratios is None:
        length_ratios = [1] * len(inputs)
    assert len(inputs) == len(length_ratios)
    assert all(length > 0 for length in length_ratios)
    self.length_ratios = length_ratios
    super().__init__(inputs)

as_array(duration, dt)

Return concatenation of all stimuli as numpy array.

Source code in neurolib/utils/stimulus.py 
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def as_array(self, duration, dt):
    """
    Return concatenation of all stimuli as numpy array.
    """
    # normalize ratios to sum = 1
    ratios = [i / sum(self.length_ratios) for i in self.length_ratios]
    concat = np.concatenate(
        [input.as_array(duration * ratio, dt) for input, ratio in zip(self.inputs, ratios)],
        axis=1,
    )
    length = int(duration / dt)
    # due to rounding errors, the overall length might be longer by a few dt
    return concat[:, :length]

ExponentialInput

Bases: Stimulus

Exponential rise or decay input.

Source code in neurolib/utils/stimulus.py 
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class ExponentialInput(Stimulus):
    """
    Exponential rise or decay input.
    """

    def __init__(
        self,
        inp_max,
        exp_coef=30.0,
        exp_type="rise",
        start=None,
        end=None,
        n=1,
        seed=None,
    ):
        """
        :param inp_max: Maximum of stimulus.
        :type inp_max: float
        :param exp_coeficient: Coeffiecent for the exponential (the higher the
            coefficient, the faster it rises or decays).
        :type exp_coeficient: float
        :param exp_type: Whether to "rise" or to "decay".
        :type exp_type: str
        """
        self.inp_max = inp_max
        self.exp_coef = exp_coef
        assert exp_type in ["rise", "decay"]
        self.exp_type = exp_type
        super().__init__(
            start=start,
            end=end,
            n=n,
            seed=seed,
        )

    def generate_input(self, duration, dt):
        self._get_times(duration=duration, dt=dt)
        exponential = np.exp(-(self.exp_coef / self.times[-1]) * self.times) * self.inp_max
        if self.exp_type == "rise":
            exponential = -exponential + self.inp_max
        return self._trim_stim(np.vstack([exponential] * self.n))

__init__(inp_max, exp_coef=30.0, exp_type='rise', start=None, end=None, n=1, seed=None)

Parameters:

Name Type Description Default
inp_max float

Maximum of stimulus.

 required
exp_coeficient float

Coeffiecent for the exponential (the higher the coefficient, the faster it rises or decays).

 required
exp_type str

Whether to "rise" or to "decay".

 'rise'
Source code in neurolib/utils/stimulus.py 
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def __init__(
    self,
    inp_max,
    exp_coef=30.0,
    exp_type="rise",
    start=None,
    end=None,
    n=1,
    seed=None,
):
    """
    :param inp_max: Maximum of stimulus.
    :type inp_max: float
    :param exp_coeficient: Coeffiecent for the exponential (the higher the
        coefficient, the faster it rises or decays).
    :type exp_coeficient: float
    :param exp_type: Whether to "rise" or to "decay".
    :type exp_type: str
    """
    self.inp_max = inp_max
    self.exp_coef = exp_coef
    assert exp_type in ["rise", "decay"]
    self.exp_type = exp_type
    super().__init__(
        start=start,
        end=end,
        n=n,
        seed=seed,
    )

Input

Generates input to model.

Base class for other input types.

Source code in neurolib/utils/stimulus.py 
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class Input:
    """
    Generates input to model.

    Base class for other input types.
    """

    def __init__(self, n=1, seed=None):
        """
        :param n: Number of spatial dimensions / independent realizations of the input.
            For determinstic inputs, the array is just copied,
            for stociastic / noisy inputs, this means independent realizations.
        :type n: int
        :param seed: Seed for the random number generator.
        :type seed: int|None
        """
        self.n = n
        self.seed = seed
        # seed the generator
        np.random.seed(seed)
        # get parameter names
        self.param_names = inspect.getfullargspec(self.__init__).args
        self.param_names.remove("self")

    def __add__(self, other):
        """
        Sum two inputs into one SummedStimulus.
        """
        assert isinstance(other, Input)
        assert self.n == other.n
        if isinstance(other, SummedStimulus):
            return SummedStimulus(inputs=[self] + other.inputs)
        else:
            return SummedStimulus(inputs=[self, other])

    def __and__(self, other):
        """
        Concatenate two inputs into ConcatenatedStimulus.
        """
        assert isinstance(other, Input)
        assert self.n == other.n
        if isinstance(other, ConcatenatedStimulus):
            return ConcatenatedStimulus(inputs=[self] + other.inputs, length_ratios=[1] + other.length_ratios)
        else:
            return ConcatenatedStimulus(inputs=[self, other])

    def _reset(self):
        """
        Reset is called after generating an input. Can be used to reset
        intrinsic properties.
        """
        pass

    def get_params(self):
        """
        Return the parameters of the input as dict.
        """
        assert all(hasattr(self, name) for name in self.param_names), self.param_names
        params = {name: getattr(self, name) for name in self.param_names}
        return {"type": self.__class__.__name__, **params}

    def update_params(self, params_dict):
        """
        Update model input parameters.

        :param params_dict: New parameters for this input
        :type params_dict: dict
        """

        def _sanitize(value):
            """
            Change string `None` to actual None - can happen with Exploration or
            Evolution, since `pypet` does None -> "None".
            """
            if value == "None":
                return None
            else:
                return value

        for param, value in params_dict.items():
            if hasattr(self, param):
                setattr(self, param, _sanitize(value))

    def _get_times(self, duration, dt):
        """
        Generate time vector.

        :param duration: Duration of the input, in milliseconds
        :type duration: float
        :param dt: dt of input, in milliseconds
        :type dt: float
        """
        self.times = np.arange(dt, duration + dt, dt)

    def generate_input(self, duration, dt):
        """
        Function to generate input.

        :param duration: Duration of the input, in milliseconds
        :type duration: float
        :param dt: dt of input, in milliseconds
        :type dt: float
        """
        raise NotImplementedError

    def as_array(self, duration, dt):
        """
        Return input as numpy array.

        :param duration: Duration of the input, in milliseconds
        :type duration: float
        :param dt: dt of input, in milliseconds
        :type dt: float
        """
        array = self.generate_input(duration, dt)
        self._reset()
        return array

    def as_cubic_splines(self, duration, dt, shift_start_time=0.0):
        """
        Return as cubic Hermite splines.

        :param duration: Duration of the input, in milliseconds
        :type duration: float
        :param dt: dt of input, in milliseconds
        :type dt: float
        :param shift_start_time: By how much to shift the stimulus start time
        :type shift_start_time: float
        """
        self._get_times(duration, dt)
        splines = CubicHermiteSpline.from_data(self.times + shift_start_time, self.generate_input(duration, dt).T)
        self._reset()
        return splines

    def to_model(self, model):
        """
        Return numpy array of stimuli based on model parameters.

        Example:
        ```
        model.params["ext_exc_input"] = SinusoidalInput(...).to_model(model)
        ```

        :param model: neurolib's model
        :type model: `neurolib.models.Model`
        """
        assert isinstance(model, Model)
        # set number of spatial dimensions as the number of nodes in the brian network
        self.n = model.params["N"]
        return self.as_array(duration=model.params["duration"], dt=model.params["dt"])

__add__(other)

Sum two inputs into one SummedStimulus.

Source code in neurolib/utils/stimulus.py 
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def __add__(self, other):
    """
    Sum two inputs into one SummedStimulus.
    """
    assert isinstance(other, Input)
    assert self.n == other.n
    if isinstance(other, SummedStimulus):
        return SummedStimulus(inputs=[self] + other.inputs)
    else:
        return SummedStimulus(inputs=[self, other])

__and__(other)

Concatenate two inputs into ConcatenatedStimulus.

Source code in neurolib/utils/stimulus.py 
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def __and__(self, other):
    """
    Concatenate two inputs into ConcatenatedStimulus.
    """
    assert isinstance(other, Input)
    assert self.n == other.n
    if isinstance(other, ConcatenatedStimulus):
        return ConcatenatedStimulus(inputs=[self] + other.inputs, length_ratios=[1] + other.length_ratios)
    else:
        return ConcatenatedStimulus(inputs=[self, other])

__init__(n=1, seed=None)

Parameters:

Name Type Description Default
n int

Number of spatial dimensions / independent realizations of the input. For determinstic inputs, the array is just copied, for stociastic / noisy inputs, this means independent realizations.

 1
seed int|None

Seed for the random number generator.

 None
Source code in neurolib/utils/stimulus.py 
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def __init__(self, n=1, seed=None):
    """
    :param n: Number of spatial dimensions / independent realizations of the input.
        For determinstic inputs, the array is just copied,
        for stociastic / noisy inputs, this means independent realizations.
    :type n: int
    :param seed: Seed for the random number generator.
    :type seed: int|None
    """
    self.n = n
    self.seed = seed
    # seed the generator
    np.random.seed(seed)
    # get parameter names
    self.param_names = inspect.getfullargspec(self.__init__).args
    self.param_names.remove("self")

as_array(duration, dt)

Return input as numpy array.

Parameters:

Name Type Description Default
duration float

Duration of the input, in milliseconds

 required
dt float

dt of input, in milliseconds

 required
Source code in neurolib/utils/stimulus.py 
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def as_array(self, duration, dt):
    """
    Return input as numpy array.

    :param duration: Duration of the input, in milliseconds
    :type duration: float
    :param dt: dt of input, in milliseconds
    :type dt: float
    """
    array = self.generate_input(duration, dt)
    self._reset()
    return array

as_cubic_splines(duration, dt, shift_start_time=0.0)

Return as cubic Hermite splines.

Parameters:

Name Type Description Default
duration float

Duration of the input, in milliseconds

 required
dt float

dt of input, in milliseconds

 required
shift_start_time float

By how much to shift the stimulus start time

 0.0
Source code in neurolib/utils/stimulus.py 
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def as_cubic_splines(self, duration, dt, shift_start_time=0.0):
    """
    Return as cubic Hermite splines.

    :param duration: Duration of the input, in milliseconds
    :type duration: float
    :param dt: dt of input, in milliseconds
    :type dt: float
    :param shift_start_time: By how much to shift the stimulus start time
    :type shift_start_time: float
    """
    self._get_times(duration, dt)
    splines = CubicHermiteSpline.from_data(self.times + shift_start_time, self.generate_input(duration, dt).T)
    self._reset()
    return splines

generate_input(duration, dt)

Function to generate input.

Parameters:

Name Type Description Default
duration float

Duration of the input, in milliseconds

 required
dt float

dt of input, in milliseconds

 required
Source code in neurolib/utils/stimulus.py 
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def generate_input(self, duration, dt):
    """
    Function to generate input.

    :param duration: Duration of the input, in milliseconds
    :type duration: float
    :param dt: dt of input, in milliseconds
    :type dt: float
    """
    raise NotImplementedError

get_params()

Return the parameters of the input as dict.

Source code in neurolib/utils/stimulus.py 
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def get_params(self):
    """
    Return the parameters of the input as dict.
    """
    assert all(hasattr(self, name) for name in self.param_names), self.param_names
    params = {name: getattr(self, name) for name in self.param_names}
    return {"type": self.__class__.__name__, **params}

to_model(model)

Return numpy array of stimuli based on model parameters.

Example: 

model.params["ext_exc_input"] = SinusoidalInput(...).to_model(model)

Parameters:

Name Type Description Default
model `neurolib.models.Model`

neurolib's model

 required
Source code in neurolib/utils/stimulus.py 
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def to_model(self, model):
    """
    Return numpy array of stimuli based on model parameters.

    Example:
    ```
    model.params["ext_exc_input"] = SinusoidalInput(...).to_model(model)
    ```

    :param model: neurolib's model
    :type model: `neurolib.models.Model`
    """
    assert isinstance(model, Model)
    # set number of spatial dimensions as the number of nodes in the brian network
    self.n = model.params["N"]
    return self.as_array(duration=model.params["duration"], dt=model.params["dt"])

update_params(params_dict)

Update model input parameters.

Parameters:

Name Type Description Default
params_dict dict

New parameters for this input

 required
Source code in neurolib/utils/stimulus.py 
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def update_params(self, params_dict):
    """
    Update model input parameters.

    :param params_dict: New parameters for this input
    :type params_dict: dict
    """

    def _sanitize(value):
        """
        Change string `None` to actual None - can happen with Exploration or
        Evolution, since `pypet` does None -> "None".
        """
        if value == "None":
            return None
        else:
            return value

    for param, value in params_dict.items():
        if hasattr(self, param):
            setattr(self, param, _sanitize(value))

LinearRampInput

Bases: Stimulus

Linear ramp input.

Source code in neurolib/utils/stimulus.py 
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class LinearRampInput(Stimulus):
    """
    Linear ramp input.
    """

    def __init__(
        self,
        inp_max,
        ramp_length,
        start=None,
        end=None,
        n=1,
        seed=None,
    ):
        """
        :param inp_max: Maximum of stimulus.
        :type inp_max: float
        :param ramp_length: Duration of linear ramp, in milliseconds
        :type ramp_length: float
        """
        self.inp_max = inp_max
        self.ramp_length = ramp_length
        super().__init__(
            start=start,
            end=end,
            n=n,
            seed=seed,
        )

    def generate_input(self, duration, dt):
        self._get_times(duration=duration, dt=dt)
        linear_inp = (self.inp_max / self.ramp_length) * self.times * (self.times < self.ramp_length) + self.inp_max * (
            self.times >= self.ramp_length
        )
        return self._trim_stim(np.vstack([linear_inp] * self.n))

__init__(inp_max, ramp_length, start=None, end=None, n=1, seed=None)

Parameters:

Name Type Description Default
inp_max float

Maximum of stimulus.

 required
ramp_length float

Duration of linear ramp, in milliseconds

 required
Source code in neurolib/utils/stimulus.py 
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def __init__(
    self,
    inp_max,
    ramp_length,
    start=None,
    end=None,
    n=1,
    seed=None,
):
    """
    :param inp_max: Maximum of stimulus.
    :type inp_max: float
    :param ramp_length: Duration of linear ramp, in milliseconds
    :type ramp_length: float
    """
    self.inp_max = inp_max
    self.ramp_length = ramp_length
    super().__init__(
        start=start,
        end=end,
        n=n,
        seed=seed,
    )

OrnsteinUhlenbeckProcess

Bases: Input

Ornstein–Uhlenbeck input, i.e. dX = (mu - X)/tau * dt + sigma*dW

Source code in neurolib/utils/stimulus.py 
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class OrnsteinUhlenbeckProcess(Input):
    """
    Ornstein–Uhlenbeck input, i.e.
        dX = (mu - X)/tau * dt + sigma*dW
    """

    def __init__(
        self,
        mu,
        sigma,
        tau,
        n=1,
        seed=None,
    ):
        """
        :param mu: Drift of the OU process
        :type mu: float
        :param sigma: Standard deviation of the Wiener process, i.e. strength of the noise
        :type sigma: float
        :param tau: Timescale of the OU process, in ms
        :type tau: float
        """
        self.mu = mu
        self.sigma = sigma
        self.tau = tau
        super().__init__(
            n=n,
            seed=seed,
        )

    def generate_input(self, duration, dt):
        self._get_times(duration=duration, dt=dt)
        x = np.random.rand(self.n, self.times.shape[0]) * self.mu
        return self.numba_ou(x, self.times, dt, self.mu, self.sigma, self.tau, self.n)

    @staticmethod
    @numba.njit()
    def numba_ou(x, times, dt, mu, sigma, tau, n):
        """
        Generation of Ornstein-Uhlenback input - wrapped in numba's jit for
        speed.
        """
        for i in range(times.shape[0] - 1):
            x[:, i + 1] = x[:, i] + dt * ((mu - x[:, i]) / tau) + sigma * np.sqrt(dt) * np.random.randn(n)
        return x

__init__(mu, sigma, tau, n=1, seed=None)

Parameters:

Name Type Description Default
mu float

Drift of the OU process

 required
sigma float

Standard deviation of the Wiener process, i.e. strength of the noise

 required
tau float

Timescale of the OU process, in ms

 required
Source code in neurolib/utils/stimulus.py 
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def __init__(
    self,
    mu,
    sigma,
    tau,
    n=1,
    seed=None,
):
    """
    :param mu: Drift of the OU process
    :type mu: float
    :param sigma: Standard deviation of the Wiener process, i.e. strength of the noise
    :type sigma: float
    :param tau: Timescale of the OU process, in ms
    :type tau: float
    """
    self.mu = mu
    self.sigma = sigma
    self.tau = tau
    super().__init__(
        n=n,
        seed=seed,
    )

numba_ou(x, times, dt, mu, sigma, tau, n) staticmethod

Generation of Ornstein-Uhlenback input - wrapped in numba's jit for speed.

Source code in neurolib/utils/stimulus.py 
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@staticmethod
@numba.njit()
def numba_ou(x, times, dt, mu, sigma, tau, n):
    """
    Generation of Ornstein-Uhlenback input - wrapped in numba's jit for
    speed.
    """
    for i in range(times.shape[0] - 1):
        x[:, i + 1] = x[:, i] + dt * ((mu - x[:, i]) / tau) + sigma * np.sqrt(dt) * np.random.randn(n)
    return x

SinusoidalInput

Bases: Stimulus

Sinusoidal input.

Source code in neurolib/utils/stimulus.py 
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class SinusoidalInput(Stimulus):
    """
    Sinusoidal input.
    """

    def __init__(
        self,
        amplitude,
        frequency,
        dc_bias=False,
        start=None,
        end=None,
        n=1,
        seed=None,
    ):
        """
        :param amplitude: Amplitude of the sinusoid.
        :type amplitude: float
        :param frequency: Frequency of the sinus oscillation, in Hz
        :type frequency: float
        :param dc_bias: Whether the sinusoid oscillates around 0
            (False), or has a positive DC bias, thus non-negative (True).
        :type dc_bias: bool
        """
        self.amplitude = amplitude
        self.frequency = frequency
        self.dc_bias = dc_bias
        super().__init__(
            start=start,
            end=end,
            n=n,
            seed=seed,
        )

    def generate_input(self, duration, dt):
        self._get_times(duration=duration, dt=dt)
        sinusoid = self.amplitude * np.sin(2 * np.pi * self.times * (self.frequency / 1000.0))
        if self.dc_bias:
            sinusoid += self.amplitude
        return self._trim_stim(np.vstack([sinusoid] * self.n))

__init__(amplitude, frequency, dc_bias=False, start=None, end=None, n=1, seed=None)

Parameters:

Name Type Description Default
amplitude float

Amplitude of the sinusoid.

 required
frequency float

Frequency of the sinus oscillation, in Hz

 required
dc_bias bool

Whether the sinusoid oscillates around 0 (False), or has a positive DC bias, thus non-negative (True).

 False
Source code in neurolib/utils/stimulus.py 
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def __init__(
    self,
    amplitude,
    frequency,
    dc_bias=False,
    start=None,
    end=None,
    n=1,
    seed=None,
):
    """
    :param amplitude: Amplitude of the sinusoid.
    :type amplitude: float
    :param frequency: Frequency of the sinus oscillation, in Hz
    :type frequency: float
    :param dc_bias: Whether the sinusoid oscillates around 0
        (False), or has a positive DC bias, thus non-negative (True).
    :type dc_bias: bool
    """
    self.amplitude = amplitude
    self.frequency = frequency
    self.dc_bias = dc_bias
    super().__init__(
        start=start,
        end=end,
        n=n,
        seed=seed,
    )

SquareInput

Bases: Stimulus

Oscillatory square input.

Source code in neurolib/utils/stimulus.py 
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class SquareInput(Stimulus):
    """
    Oscillatory square input.
    """

    def __init__(
        self,
        amplitude,
        frequency,
        dc_bias=False,
        start=None,
        end=None,
        n=1,
        seed=None,
    ):
        """
        :param amplitude: Amplitude of the square
        :type amplitude: float
        :param frequency: Frequency of the square oscillation, in Hz
        :type frequency: float
        :param dc_bias: Whether the square oscillates around 0
            (False), or has a positive DC bias, thus non-negative (True).
        :type dc_bias: bool
        """
        self.amplitude = amplitude
        self.frequency = frequency
        self.dc_bias = dc_bias
        super().__init__(
            start=start,
            end=end,
            n=n,
            seed=seed,
        )

    def generate_input(self, duration, dt):
        self._get_times(duration=duration, dt=dt)
        square_inp = self.amplitude * square(2 * np.pi * self.times * (self.frequency / 1000.0))
        if self.dc_bias:
            square_inp += self.amplitude
        return self._trim_stim(np.vstack([square_inp] * self.n))

__init__(amplitude, frequency, dc_bias=False, start=None, end=None, n=1, seed=None)

Parameters:

Name Type Description Default
amplitude float

Amplitude of the square

 required
frequency float

Frequency of the square oscillation, in Hz

 required
dc_bias bool

Whether the square oscillates around 0 (False), or has a positive DC bias, thus non-negative (True).

 False
Source code in neurolib/utils/stimulus.py 
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def __init__(
    self,
    amplitude,
    frequency,
    dc_bias=False,
    start=None,
    end=None,
    n=1,
    seed=None,
):
    """
    :param amplitude: Amplitude of the square
    :type amplitude: float
    :param frequency: Frequency of the square oscillation, in Hz
    :type frequency: float
    :param dc_bias: Whether the square oscillates around 0
        (False), or has a positive DC bias, thus non-negative (True).
    :type dc_bias: bool
    """
    self.amplitude = amplitude
    self.frequency = frequency
    self.dc_bias = dc_bias
    super().__init__(
        start=start,
        end=end,
        n=n,
        seed=seed,
    )

StepInput

Bases: Stimulus

Step input.

Source code in neurolib/utils/stimulus.py 
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class StepInput(Stimulus):
    """
    Step input.
    """

    def __init__(
        self,
        step_size,
        start=None,
        end=None,
        n=1,
        seed=None,
    ):
        """
        :param step_size: Size of the step, i.e., the amplitude.
        :type step_size: float
        """
        self.step_size = step_size
        super().__init__(
            start=start,
            end=end,
            n=n,
            seed=seed,
        )

    def generate_input(self, duration, dt):
        self._get_times(duration=duration, dt=dt)
        return self._trim_stim(np.ones((self.n, self.times.shape[0])) * self.step_size)

__init__(step_size, start=None, end=None, n=1, seed=None)

Parameters:

Name Type Description Default
step_size float

Size of the step, i.e., the amplitude.

 required
Source code in neurolib/utils/stimulus.py 
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def __init__(
    self,
    step_size,
    start=None,
    end=None,
    n=1,
    seed=None,
):
    """
    :param step_size: Size of the step, i.e., the amplitude.
    :type step_size: float
    """
    self.step_size = step_size
    super().__init__(
        start=start,
        end=end,
        n=n,
        seed=seed,
    )

Stimulus

Bases: Input

Generates a stimulus with optional start and end times.

Source code in neurolib/utils/stimulus.py 
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class Stimulus(Input):
    """
    Generates a stimulus with optional start and end times.
    """

    def __init__(
        self,
        start=None,
        end=None,
        n=1,
        seed=None,
    ):
        """
        :param start: start of the stimulus, in milliseconds
        :type start: float
        :param end: end of the stimulus, in milliseconds
        :type end: float
        """
        self.start = start
        self.end = end
        self._default_start = start
        self._default_end = end
        super().__init__(
            n=n,
            seed=seed,
        )

    def _reset(self):
        self.start = self._default_start
        self.end = self._default_end

    def _get_times(self, duration, dt):
        super()._get_times(duration=duration, dt=dt)
        self.start = self.start or 0.0
        self.end = self.end or duration + dt
        assert self.start < duration
        assert self.end <= duration + dt

    def _trim_stim(self, stim_input):
        """
        Trim stimulus. Translate the start of the stimulus by
        padding the beginning and replace the end with zeros.
        """
        # trim start
        how_much = int(np.sum(self.times <= self.start))
        # translate start of the stim by padding the beginning with zeros
        stim_input = np.pad(stim_input, ((0, 0), (how_much, 0)), mode="constant")
        if how_much > 0:
            stim_input = stim_input[:, :-how_much]
        # trim end
        stim_input[:, self.times > self.end] = 0.0
        return stim_input

__init__(start=None, end=None, n=1, seed=None)

Parameters:

Name Type Description Default
start float

start of the stimulus, in milliseconds

 None
end float

end of the stimulus, in milliseconds

 None
Source code in neurolib/utils/stimulus.py 
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def __init__(
    self,
    start=None,
    end=None,
    n=1,
    seed=None,
):
    """
    :param start: start of the stimulus, in milliseconds
    :type start: float
    :param end: end of the stimulus, in milliseconds
    :type end: float
    """
    self.start = start
    self.end = end
    self._default_start = start
    self._default_end = end
    super().__init__(
        n=n,
        seed=seed,
    )

SummedStimulus

Bases: BaseMultipleInputs

Represents the summation of arbitrary many stimuli.

Example: 

    summed_stimulus = SinusoidalInput(...) + OrnsteinUhlenbeckProcess(...)

Source code in neurolib/utils/stimulus.py 
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class SummedStimulus(BaseMultipleInputs):
    """
    Represents the summation of arbitrary many stimuli.

    Example:
    ```
        summed_stimulus = SinusoidalInput(...) + OrnsteinUhlenbeckProcess(...)
    ```
    """

    def __add__(self, other):
        assert isinstance(other, Input)
        assert self.n == other.n
        if isinstance(other, SummedStimulus):
            return SummedStimulus(inputs=self.inputs + other.inputs)
        else:
            return SummedStimulus(inputs=self.inputs + [other])

    def as_array(self, duration, dt):
        """
        Return sum of all inputes as numpy array.
        """
        return np.sum(
            np.stack([input.as_array(duration, dt) for input in self.inputs]),
            axis=0,
        )

    def as_cubic_splines(self, duration, dt, shift_start_time=0.0):
        """
        Return sum of all inputes as cubic Hermite splines.
        """
        result = self.inputs[0].as_cubic_splines(duration, dt, shift_start_time)
        for input in self.inputs[1:]:
            result.plus(input.as_cubic_splines(duration, dt, shift_start_time))
        return result

as_array(duration, dt)

Return sum of all inputes as numpy array.

Source code in neurolib/utils/stimulus.py 
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def as_array(self, duration, dt):
    """
    Return sum of all inputes as numpy array.
    """
    return np.sum(
        np.stack([input.as_array(duration, dt) for input in self.inputs]),
        axis=0,
    )

as_cubic_splines(duration, dt, shift_start_time=0.0)

Return sum of all inputes as cubic Hermite splines.

Source code in neurolib/utils/stimulus.py 
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def as_cubic_splines(self, duration, dt, shift_start_time=0.0):
    """
    Return sum of all inputes as cubic Hermite splines.
    """
    result = self.inputs[0].as_cubic_splines(duration, dt, shift_start_time)
    for input in self.inputs[1:]:
        result.plus(input.as_cubic_splines(duration, dt, shift_start_time))
    return result

WienerProcess

Bases: Input

Stimulus sampled from a Wiener process, i.e. drawn from standard normal distribution N(0, sqrt(dt)).

Source code in neurolib/utils/stimulus.py 
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class WienerProcess(Input):
    """
    Stimulus sampled from a Wiener process, i.e. drawn from standard normal distribution N(0, sqrt(dt)).
    """

    def generate_input(self, duration, dt):
        self._get_times(duration=duration, dt=dt)
        return np.random.normal(0.0, np.sqrt(dt), (self.n, self.times.shape[0]))

ZeroInput

Bases: Input

No stimulus, i.e. all zeros. Can be used to add a delay between two stimuli.

Source code in neurolib/utils/stimulus.py 
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class ZeroInput(Input):
    """
    No stimulus, i.e. all zeros. Can be used to add a delay between two stimuli.
    """

    def generate_input(self, duration, dt):
        self._get_times(duration=duration, dt=dt)
        return np.zeros((self.n, self.times.shape[0]))

RectifiedInput(amplitude, n=1)

Return rectified input with exponential decay, i.e. a negative step followed by a slow decay to zero, followed by a positive step and again a slow decay to zero. Can be used for bistablity detection.

Parameters:

Name Type Description Default
amplitude float

Amplitude (both negative and positive) for the step

 required
n int

Number of realizations (spatial dimension)

 1

Returns:

Type Description
`ConctatenatedInput`

Concatenated input which represents the rectified stimulus with exponential decay
Source code in neurolib/utils/stimulus.py 
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def RectifiedInput(amplitude, n=1):
    """
    Return rectified input with exponential decay, i.e. a negative step followed by a
    slow decay to zero, followed by a positive step and again a slow decay to zero.
    Can be used for bistablity detection.

    :param amplitude: Amplitude (both negative and positive) for the step
    :type amplitude: float
    :param n: Number of realizations (spatial dimension)
    :type n: int
    :return: Concatenated input which represents the rectified stimulus with exponential decay
    :rtype: `ConctatenatedInput`
    """

    return ConcatenatedStimulus(
        [
            StepInput(step_size=-amplitude, n=n),
            ExponentialInput(inp_max=amplitude, exp_type="rise", exp_coef=12.5, n=n)
            + StepInput(step_size=-amplitude, n=n),
            StepInput(step_size=amplitude, n=n),
            ExponentialInput(amplitude, exp_type="decay", exp_coef=7.5, n=n),
            StepInput(step_size=0.0, n=n),
        ],
        length_ratios=[0.5, 2.5, 0.5, 1.5, 1.0],
    )