pulse2percept.models.granley2021

BiphasicAxonMapModel, BiphasicAxonMapSpatial [Granley2021]

Classes

`BiphasicAxonMapModel`(**params)	BiphasicAxonMapModel of [Granley2021] (standalone model)
`BiphasicAxonMapSpatial`(**params)	BiphasicAxonMapModel of [Granley2021] (spatial model)
`DefaultBrightModel`(**params)	Default model to be used for brightness scaling in BiphasicAxonMapModel Implements Eq 4 from [Granley2021] Fit using data from [Nanduri2012] and [Weitz2015]
`DefaultSizeModel`(rho, **params)	Default model to be used for size (rho) scaling in BiphasicAxonMapModel Implements Eq 5 from [Granley2021] Fit using data from [Nanduri2012] and [Weitz2015]
`DefaultStreakModel`(lam, **params)	Default model to be used for streak length (lambda) scaling in BiphasicAxonMapModel Implements Eq 6 from [Granley2021] Fit using data from [Weitz2015]

class pulse2percept.models.granley2021.DefaultBrightModel(**params)[source]

Default model to be used for brightness scaling in BiphasicAxonMapModel Implements Eq 4 from [Granley2021] Fit using data from [Nanduri2012] and [Weitz2015]

Parameters:

do_thresholdingbool, optional: Set to true to enable probabilistic phosphene appearance at near-threshold amplitudes
a0, a1float, optional: Linear regression coefficients (slope and intercept) of pulse_duration vs threshold curve (Eq 3). Amplitude factor will be scaled by a0*pdur + a1.
a2, a3, a4: float, optional: Linear regression coefficients for brightness vs amplitude and frequency (Eq 4) F_bright = a2*scaled_amp + a3*freq + a4

get_default_params()[source]: Return a dict of user-settable parameters

scale_threshold(pdur)[source]

Based on eq 3 in paper, this function produces the factor that amplitude will be scaled by to produce a_tilde. Computes A_0 * t + A_1 (1/threshold) .. note:

This equation has been updated from the original paper, and has been refit
to data from Argus II users from Horsager et al. 2009.

predict_freq_amp(amp, freq)[source]: Eq 4 in paper, A_2*A_tilde + A_3*f + A_4

build(**build_params)[source]

Build the model

Every model must have a `build method, which is meant to perform all expensive one-time calculations. You must call build before calling predict_percept.

Important

Don’t override this method if you are building your own model. Customize _build instead.

Parameters:: build_params (additional parameters to set) – You can overwrite parameters that are listed in get_default_params. Trying to add new class attributes outside of that will cause a FreezeError. Example: model.build(param1=val)

get_param_units()[source]

Return a dict of the units that parameters are stored in

Maps a parameter name to the Unit that the implementation assumes it is expressed in. A Quantity assigned to such a parameter is checked against that unit and rescaled to it, so that

FadingTemporal(tau=100)
FadingTemporal(tau=100 * ms)
FadingTemporal(tau=0.1 * s)

all store the same float. Bare numbers keep their documented meaning and are passed through untouched.

Parameters absent from this dict take plain numbers: they are either dimensionless (thresh_percept) or empirical fit parameters whose dimension the implementation does not actually commit to. Declaring a unit is a statement about what the equations assume, so a parameter should only appear here when that is documented or unambiguous.

This dict is not restricted to the names in get_default_params: it describes every physical attribute this object normalizes. A constructor argument assigned straight to self – DefaultSizeModel takes rho that way – belongs here too, and is converted like any other.

Subclasses extend rather than replace it:

def get_param_units(self):
    return {**super().get_param_units(), 'dt': ms, 'tau': ms}

Added in version 0.10.0.

property is_built: A flag indicating whether the model has been built

set_params(**params)[source]: Set the parameters of this object

space_unit = um[source]: The unit spatial coordinates are expressed in

stimulus_unit = uA[source]: The unit stimulus values are expressed in

time_unit = ms[source]: The unit time is expressed in

class pulse2percept.models.granley2021.DefaultSizeModel(rho, **params)[source]

Default model to be used for size (rho) scaling in BiphasicAxonMapModel Implements Eq 5 from [Granley2021] Fit using data from [Nanduri2012] and [Weitz2015]

Parameters:

rhofloat32: Rho parameter of BiphasicAxonMapModel (spatial decay rate)
a0, a1float, optional: Linear regression coefficients (slope and intercept) of pulse_duration vs threshold curve (Eq 3). Amplitude factor will be scaled by a0*pdur + a1.
a5, a6float, optional: Linear regression coefficients for size vs amplitude (Eq 5) F_size = a5*scaled_amp + a6

get_default_params()[source]: Return a dict of user-settable parameters

get_param_units()[source]: Return a dict of the units that parameters are stored in

scale_threshold(pdur)[source]

Based on eq 3 in paper, this function produces the factor that amplitude will be scaled by to produce a_tilde. Computes A_0 * t + A_1 (1/threshold) .. note:

This equation has been updated from the original paper, and has been refit
to data from Argus II users from Horsager et al. 2009.

build(**build_params)[source]

Build the model

Every model must have a `build method, which is meant to perform all expensive one-time calculations. You must call build before calling predict_percept.

Important

Don’t override this method if you are building your own model. Customize _build instead.

Parameters:: build_params (additional parameters to set) – You can overwrite parameters that are listed in get_default_params. Trying to add new class attributes outside of that will cause a FreezeError. Example: model.build(param1=val)

property is_built: A flag indicating whether the model has been built

set_params(**params)[source]: Set the parameters of this object

space_unit = um[source]: The unit spatial coordinates are expressed in

stimulus_unit = uA[source]: The unit stimulus values are expressed in

time_unit = ms[source]: The unit time is expressed in

class pulse2percept.models.granley2021.DefaultStreakModel(lam, **params)[source]

Default model to be used for streak length (lambda) scaling in BiphasicAxonMapModel Implements Eq 6 from [Granley2021] Fit using data from [Weitz2015]

Parameters:

lamfloat32: lam parameter of BiphasicAxonMapModel (axonal decay rate)

Changed in version 0.10.0: Renamed from axlambda. The old name still works as a keyword argument, but is deprecated and will be removed in v0.11.0.
a7, a8, a9: float, optional: Regression coefficients for streak length vs pulse duration (Eq 6) F_streak = -a7*pdur^a8 + a9

get_default_params()[source]: Return a dict of user-settable parameters

get_param_units()[source]: Return a dict of the units that parameters are stored in

build(**build_params)[source]

Build the model

Every model must have a `build method, which is meant to perform all expensive one-time calculations. You must call build before calling predict_percept.

Important

Don’t override this method if you are building your own model. Customize _build instead.

Parameters:: build_params (additional parameters to set) – You can overwrite parameters that are listed in get_default_params. Trying to add new class attributes outside of that will cause a FreezeError. Example: model.build(param1=val)

property is_built: A flag indicating whether the model has been built

set_params(**params)[source]: Set the parameters of this object

space_unit = um[source]: The unit spatial coordinates are expressed in

stimulus_unit = uA[source]: The unit stimulus values are expressed in

time_unit = ms[source]: The unit time is expressed in

class pulse2percept.models.granley2021.BiphasicAxonMapSpatial(**params)[source]

BiphasicAxonMapModel of [Granley2021] (spatial model)

An AxonMapModel where phosphene brightness, size, and streak length scale according to amplitude, frequency, and pulse duration

All stimuli must be BiphasicPulseTrains.

This model is different than other spatial models in that it calculates one representative percept from all time steps of the stimulus.

Brightness, size, and streak length scaling are controlled by the effects models bright_model, size_model, and streak model respectively. By default, these are set to classes that implement Eqs 3-6 from Granley 2021. These models can be individually customized by setting the bright_model, size_model, or streak_model to any python callable with signature f(freq, amp, pdur)

Important

Using this model in combination with a temporal model is not currently supported and will give unexpected results

Parameters:

bright_model (callable, optional) – Model used to modulate percept brightness with amplitude, frequency, and pulse duration
size_model (callable, optional) – Model used to modulate percept size with amplitude, frequency, and pulse duration
streak_model (callable, optional) – Model used to modulate percept streak length with amplitude, frequency, and pulse duration
**params (optional) –
Additional params for AxonMapModel.

lam: double, optional
Exponential decay constant along the axon(microns).

Changed in version 0.10.0: Renamed from axlambda, which reads poorly next to rho. The old name still works, but is deprecated and will be removed in v0.11.0.

rho: double, optional
Exponential decay constant away from the axon(microns).

min_current_spread: float, optional
An electrode is skipped at axon segments where its current spread has decayed below this fraction of its peak. The decay is scaled per electrode by that electrode’s F_size. The default (1e-8) is a deliberate approximation: what gets dropped is the exponential times that electrode’s F_bright, summed over the skipped electrodes, so the error at a point is bounded by min_current_spread times the summed F_bright across electrodes.

eye: {‘RE’, LE’}, optional
Eye for which to generate the axon map.

xrange(x_min, x_max), optional
A tuple indicating the range of x values to simulate (in degrees of visual angle). In a right eye, negative x values correspond to the temporal retina, and positive x values to the nasal retina. In a left eye, the opposite is true.

yrangetuple, (y_min, y_max)
A tuple indicating the range of y values to simulate (in degrees of visual angle). Negative y values correspond to the superior retina, and positive y values to the inferior retina.

stepint, double, tuple
Step size for the range of (x,y) values to simulate (in degrees of visual angle). For example, to create a grid with x values [0, 0.5, 1] use xrange=(0, 1) and step=0.5. Pass a tuple to give the x and y axes different step sizes.

Changed in version 0.10.0: Renamed from xystep, which suggested that one step size applies to both axes. The old name still works, but is deprecated and will be removed in v0.11.0.

grid_type{‘rectangular’, ‘hexagonal’}
Whether to simulate points on a rectangular or hexagonal grid

vfmapVisualFieldMap, optional
An instance of a VisualFieldMap object that provides retinotopic mappings. By default, Watson2014Map is used.

n_grayint, optional
The number of gray levels to use. If an integer is given, k-means clustering is used to compress the color space of the percept into n_gray bins. If None, no compression is performed.

noisefloat or int, optional
Adds salt-and-pepper noise to each percept frame. An integer will be interpreted as the number of pixels to subject to noise in each frame. A float between 0 and 1 will be interpreted as a ratio of pixels to subject to noise in each frame.

loc_od, loc_od: (x,y), optional
Location of the optic disc in degrees of visual angle. Note that the optic disc in a left eye will be corrected to have a negative x coordinate.

n_axons: int, optional
Number of axons to generate.

axons_range: (min, max), optional
The range of angles(in degrees) at which axons exit the optic disc. This corresponds to the range of $phi_0$ values used in [Jansonius2009].

n_ax_segments: int, optional
Number of segments an axon is made of.

ax_segments_range: (min, max), optional
Lower and upper bounds for the radial position values(polar coords) for each axon.

min_ax_sensitivity: float, optional
Axon segments whose contribution to brightness is smaller than this value will be pruned to improve computational efficiency. Set to a value between 0 and 1.

axon_pickle: str, optional
File name in which to store precomputed axon maps.

ignore_pickle: bool, optional
A flag whether to ignore the pickle file in future calls to model.build().

n_threadsint, optional
Number of CPU threads to use during parallelization using OpenMP. Defaults to max number of user CPU cores.

n_jobsint, optional
Alias for n_threads; None or -1 uses every core.

get_default_params()[source]: Return a dictionary of default values for all model parameters

predict_percept(implant, t_percept=None)[source]

Predicts the spatial response Override base predict percept to have desired timesteps and remove unneccesary computation

Parameters:

implant (ProsthesisSystem) – A valid prosthesis system. A stimulus can be passed via stim().
t_percept (float or list of floats, optional) – The time points at which to output a percept (ms). This model’s numerical contract is fixed to milliseconds. If None, implant.stim.time is used. May be given as a unitful quantity (e.g. [0, 20] * ms); see pulse2percept.units.

Returns:

percept – A Percept object whose data container has dimensions Y x X x 1. Will return None if implant.stim is None.

Return type:

Percept

find_threshold(implant, bright_th, amp_range=(0, 999), amp_tol=1, bright_tol=0.1, max_iter=100)[source]

Not supported by this model

Raises:: NotImplementedError –

axlambda[source]: lam used to be called axlambda. The old name still reads and writes lam, with a DeprecationWarning:

build(**build_params)[source]

Build the model

Performs expensive one-time calculations, such as building the spatial grid used to predict a percept. You must call build before calling predict_percept.

Important

Don’t override this method if you are building your own model. Customize _build instead.

Parameters:: build_params (additional parameters to set) – You can overwrite parameters that are listed in get_default_params. Trying to add new class attributes outside of that will cause a FreezeError. Example: model.build(param1=val)

calc_axon_sensitivity(bundles)[source]

Calculate the sensitivity of each axon segment to electrical current

This function combines the x,y coordinates of each bundle segment with a sensitivity value that depends on the distance of the segment to the cell body and self.lam.

The number of bundles must equal the number of points on self.grid`. The function will then assume that the i-th bundle passes through the i-th point on the grid. This is used to determine the bundle segment that is closest to the i-th point on the grid, and to cut off all segments that extend beyond the soma. This effectively transforms a bundle into an axon, where the first axon segment now corresponds with the i-th location of the grid.

After that, each axon segment gets a sensitivity value that depends on the distance of the segment to the soma (with decay rate self.lam). This is typically done during the build process, so that the only work left to do during run time is to multiply the sensitivity value with the current applied to each segment.

Parameters:: bundles (list of Nx2 arrays) – A list of bundles, where every bundle is an Nx2 array consisting of the x,y coordinates of each axon segment (retinal coords, microns). Note that each bundle will most likely have a different N
Returns:: axon_contrib – A list with one entry per point on self.grid. Each entry is a Nx3 array, where the first two columns contain the retinal coordinates of each axon segment (microns), and the third column contains the sensitivity of the segment to electrical current. The latter depends on self.lam. Note that each axon will most likely have a different N, since segments whose sensitivity falls below min_ax_sensitivity are trimmed.
Return type:: list of Nx3 arrays

calc_bundle_tangent(xc, yc)[source]

Calculates orientation of fiber bundle tangent at (xc, yc)

Parameters:

xc (float) – (x, y) retinal location of point at which to calculate bundle orientation in microns.
yc (float) – (x, y) retinal location of point at which to calculate bundle orientation in microns.

Returns:

tangent – An angle in radians

Return type:

scalar

calc_bundle_tangent_fast(xc, yc, bundles=None)[source]

Calculates orientation of fiber bundle tangent at (xc, yc) This function supports multiple queries (xc and yc can be arrays), without requiring growing the axon bundles again for each point (like calc_bundle_tangent). It uses a ckdtree, which will be slower for single points, but significantly faster for multiple points.

Parameters:

xc (array of floats) – (x, y) retinal location of point at which to calculate bundle orientation in microns.
yc (array of floats) – (x, y) retinal location of point at which to calculate bundle orientation in microns.

Returns:

tangent – Angles in radians

Return type:

array of floats

find_closest_axon(bundles, xret=None, yret=None, return_index=False, return_segment=False)[source]

Finds the closest axon segment for a point on the retina

This function will search a number of nerve fiber bundles (bundles) and return the bundle that is closest to a particular point (or list of points) on the retinal surface (xret, yret).

Parameters:

bundles (list of Nx2 arrays) – A list of bundles, where every bundle is an Nx2 array consisting of the x,y coordinates of each axon segment (retinal coords, microns). Note that each bundle will most likely have a different N
xret (scalar or list of scalars) – The x,y location on the retina (in microns, where the fovea is the origin) for which to find the closests axon.
yret (scalar or list of scalars) – The x,y location on the retina (in microns, where the fovea is the origin) for which to find the closests axon.
return_index (bool, optional) – If True, the function will also return the index into bundles that represents the closest axon
return_segment (bool, optional) – If True, the function will also return the row index, within the closest bundle, of the segment nearest the point. The search already determines this, so asking for it here saves calc_axon_sensitivity() from working it out again.

Returns:

axon (Nx2 array or list of Nx2 arrays) – For each point in (xret, yret), returns an Nx2 array that represents the closest axon to that point. Each row in the array contains the x,y retinal coordinates (microns) of a particular axon segment.
idx_axon (scalar or list of scalars, optional) – If return_index is True, also returns the index in bundles of the closest axon (or list of closest axons).
idx_segment (scalar or list of scalars, optional) – If return_segment is True, also returns the row index of the closest segment within that axon.

get_param_units()[source]: Return a dict of the units that parameters are stored in

grow_axon_bundles(n_bundles=None, prune=True)[source]

Grow a number of axon bundles

This method generates the trajectory of a number of nerve fiber bundles based on the mathematical model described in [Beyeler2019], which is based on [Jansonius2009].

Bundles originate at the optic nerve head with initial angle phi0. The method generates n_bundles axon bundles whose phi0 values are linearly sampled from self.axons_range (polar coords). Each axon will consist of self.n_ax_segments segments that span self.ax_segments_range distance from the optic nerve head (polar coords).

Parameters:

n_bundles (int, optional) – Number of axon bundles to generate. If None, self.n_axons is used
prune (bool, optional) – If set to True, will remove axon segments that are outside the simulated area self.xrange, self.yrange for the sake of computational efficiency.

Returns:

bundles – A list of bundles, where every bundle is an Nx2 array consisting of the x,y coordinates of each axon segment (retinal coords, microns). Note that each bundle will most likely have a different N

Return type:

list of Nx2 arrays

property is_built: A flag indicating whether the model has been built

property n_jobs

both names read and write the same value.

Type:: Number of OpenMP threads to use during parallelization. An alias for n_threads

plot(use_dva=False, style='hull', annotate=True, autoscale=True, ax=None, figsize=None)[source]

Plot the axon map

Parameters:

use_dva (bool, optional) – Uses degrees of visual angle (dva) if True, else retinal coordinates (microns)
style ({'hull', 'scatter', 'cell'}, optional) –
Grid plotting style:
- ’hull’: Show the convex hull of the grid (that is, the outline of the smallest convex set that contains all grid points).
- ’scatter’: Scatter plot all grid points
- ’cell’: Show the outline of each grid cell as a polygon. Note that this can be costly for a high-resolution grid.
annotate (bool, optional) – Flag whether to label the four retinal quadrants
autoscale (bool, optional) – Whether to adjust the x,y limits of the plot
ax (matplotlib.axes._subplots.AxesSubplot, optional) – A Matplotlib axes object. If None, will either use the current axes (if exists) or create a new Axes object
figsize ((float, float), optional) – Desired (width, height) of the figure in inches

set_params(**params)[source]: Set the parameters of this object

space_unit = um[source]: The unit spatial coordinates are expressed in

stimulus_unit = uA[source]: The unit stimulus values are expressed in

time_unit = ms[source]: The unit time is expressed in

xystep[source]: step used to be called xystep. The old name still reads and writes step, with a DeprecationWarning:

class pulse2percept.models.granley2021.BiphasicAxonMapModel(**params)[source]

BiphasicAxonMapModel of [Granley2021] (standalone model)

An AxonMapModel where phosphene brightness, size, and streak length scale according to amplitude, frequency, and pulse duration.

All stimuli must be BiphasicPulseTrains.

This model is different than other spatial models in that it calculates one representative percept from all time steps of the stimulus.

Brightness, size, and streak length scaling are controlled by the parameters bright_model, size_model, and streak model respectively. By default, these are set to classes that implement Eqs 3-6 from Granley 2021. These models can be individually customized by setting the bright_model, size_model, or streak_model to any python callable with signature f(freq, amp, pdur).

Important

Stimuli should pass amplitude as a factor of threshold, NOT as raw amplitude in microamps.

This model interacts with Stimulus objects by reading the intended amplitude, frequency, and pulse duration from their metadata, not from the raw stimulus data. The arithmetic operators keep that metadata in sync, so scaling a pulse train (pt * 2) or the stimulus assembled from one (implant.stim * 2) does change the percept, while editing the data array in place does not.

Parameters:

bright_model (callable, optional) – Model used to modulate percept brightness with amplitude, frequency, and pulse duration
size_model (callable, optional) – Model used to modulate percept size with amplitude, frequency, and pulse duration
streak_model (callable, optional) – Model used to modulate percept streak length with amplitude, frequency, and pulse duration
do_thresholding (boolean) – Use probabilistic sigmoid thresholding, default: False
**params (dict, optional) –
Arguments to be passed to AxonMapSpatial

lam: double, optional
Exponential decay constant along the axon(microns).

Changed in version 0.10.0: Renamed from axlambda, which reads poorly next to rho. The old name still works, but is deprecated and will be removed in v0.11.0.

rho: double, optional
Exponential decay constant away from the axon(microns).

min_current_spread: float, optional
An electrode is skipped at axon segments where its current spread has decayed below this fraction of its peak. The decay is scaled per electrode by that electrode’s F_size. The default (1e-8) is a deliberate approximation: what gets dropped is the exponential times that electrode’s F_bright, summed over the skipped electrodes, so the error at a point is bounded by min_current_spread times the summed F_bright across electrodes. That is negligible for a typical array, but it grows with both array size and brightness scaling, and it can zero out points that are merely dim. Set to 0 to sum over every electrode and get the exact result.

eye: {‘RE’, LE’}, optional
Eye for which to generate the axon map.

xrange(x_min, x_max), optional
A tuple indicating the range of x values to simulate (in degrees of visual angle). In a right eye, negative x values correspond to the temporal retina, and positive x values to the nasal retina. In a left eye, the opposite is true.

yrangetuple, (y_min, y_max)
A tuple indicating the range of y values to simulate (in degrees of visual angle). Negative y values correspond to the superior retina, and positive y values to the inferior retina.

stepint, double, tuple
Step size for the range of (x,y) values to simulate (in degrees of visual angle). For example, to create a grid with x values [0, 0.5, 1] use xrange=(0, 1) and step=0.5. Pass a tuple to give the x and y axes different step sizes.

Changed in version 0.10.0: Renamed from xystep, which suggested that one step size applies to both axes. The old name still works, but is deprecated and will be removed in v0.11.0.

grid_type{‘rectangular’, ‘hexagonal’}
Whether to simulate points on a rectangular or hexagonal grid

vfmapVisualFieldMap, optional
An instance of a VisualFieldMap object that provides retinotopic mappings. By default, Watson2014Map is used.

n_grayint, optional
The number of gray levels to use. If an integer is given, k-means clustering is used to compress the color space of the percept into n_gray bins. If None, no compression is performed.

noisefloat or int, optional
Adds salt-and-pepper noise to each percept frame. An integer will be interpreted as the number of pixels to subject to noise in each frame. A float between 0 and 1 will be interpreted as a ratio of pixels to subject to noise in each frame.

loc_od, loc_od: (x,y), optional
Location of the optic disc in degrees of visual angle. Note that the optic disc in a left eye will be corrected to have a negative x coordinate.

n_axons: int, optional
Number of axons to generate.

axons_range: (min, max), optional
The range of angles(in degrees) at which axons exit the optic disc. This corresponds to the range of $phi_0$ values used in [Jansonius2009].

n_ax_segments: int, optional
Number of segments an axon is made of.

ax_segments_range: (min, max), optional
Lower and upper bounds for the radial position values(polar coords) for each axon.

min_ax_sensitivity: float, optional
Axon segments whose contribution to brightness is smaller than this value will be pruned to improve computational efficiency. Set to a value between 0 and 1.

axon_pickle: str, optional
File name in which to store precomputed axon maps.

ignore_pickle: bool, optional
A flag whether to ignore the pickle file in future calls to model.build().

n_threadsint, optional
Number of CPU threads to use during parallelization using OpenMP. Defaults to max number of user CPU cores.

n_jobsint, optional
Alias for n_threads; None or -1 uses every core.

predict_percept(implant, t_percept=None)[source]

Predict a percept.

Overrides base predict percept to keep desired time axes

Note

You must call build before calling predict_percept.

Important

Stimuli should pass amplitude as a factor of threshold, NOT as raw amplitude in microamps.

The model interacts with Stimulus objects by reading the intended amplitude, frequency, and pulse duration from their metadata, not from the raw stimulus data. Editing the data array in place will not change the predicted percept.

Parameters:

implant (ProsthesisSystem) – A valid prosthesis system. A stimulus can be passed via stim().
t_percept (float or list of floats, optional) – The time points at which to output a percept (ms). This model’s numerical contract is fixed to milliseconds. If None, implant.stim.time is used. May be given as a unitful quantity (e.g. [0, 20] * ms); see pulse2percept.units.

Returns:

percept – A Percept object whose data container has dimensions Y x X x T. Will return None if implant.stim is None.

Return type:

Percept

find_threshold(implant, bright_th, amp_range=(0, 999), amp_tol=1, bright_tol=0.1, max_iter=100, t_percept=None)[source]

Not supported by this model

Raises:: NotImplementedError –

build(**build_params)[source]

Build the model

Performs expensive one-time calculations, such as building the spatial grid used to predict a percept.

Parameters:: build_params (additional parameters to set) – You can overwrite parameters that are listed in get_default_params. Trying to add new class attributes outside of that will cause a FreezeError. Example: model.build(param1=val)
Return type:: self

property has_space: Returns True if the model has a spatial component

property has_time: Returns True if the model has a temporal component

property is_built: Returns True if the build model has been called

set_params(params)[source]

Set model parameters

This is a convenience function to set parameters that might be part of the spatial model, the temporal model, or both.

Alternatively, you can set the parameter directly, e.g. model.spatial.verbose = True.

Note

If a parameter exists in both spatial and temporal models(e.g., verbose), both models will be updated.

Parameters:: params (dict) – A dictionary of parameters to set.

property space_unit

The unit spatial coordinates are expressed in

The temporal model never sees a coordinate.

property stimulus_unit

The unit stimulus values are expressed in

The stimulus goes to the spatial model if there is one, and straight to the temporal model otherwise.

property time_unit

The unit time is expressed in

t_percept is read in, and the resulting Percept is written in, the unit of the last stage of the pipeline: the temporal model if there is one, the spatial model otherwise. The two need not agree – a spatial model counting in seconds hands its percept to a temporal model counting in milliseconds and the time axis is converted on the way across.