pulse2percept.models.beyeler2019¶
AxonMapModel
, AxonMapSpatial
[Beyeler2019]
Classes
AxonMapModel (**params) 
Axon map model of [Beyeler2019] (standalone model) 
AxonMapSpatial (**params) 
Axon map model of [Beyeler2019] (spatial module only) 
ScoreboardModel (**params) 
Scoreboard model of [Beyeler2019] (standalone model) 
ScoreboardSpatial (**params) 
Scoreboard model of [Beyeler2019] (spatial module only) 

class
pulse2percept.models.beyeler2019.
AxonMapModel
(**params)[source]¶ Axon map model of [Beyeler2019] (standalone model)
Implements the axon map model described in [Beyeler2019], where percepts are elongated along nerve fiber bundle trajectories of the retina.
Parameters:  axlambda (double, optional) – Exponential decay constant along the axon(microns).
 rho (double, optional) – Exponential decay constant away from the axon(microns).
 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.
 yrange ((y_min, y_max), optional) – 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.
 xystep (int or double or tuple, optional) – 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)
andxystep=0.5
.  grid_type ({'rectangular', 'hexagonal'}, optional) – Whether to simulate points on a rectangular or hexagonal grid
 retinotopy (
VisualFieldMap
, optional) – An instance of aVisualFieldMap
object that providesret2dva
anddva2ret
methods. By default,Watson2014Map
is used.  n_gray (int, optional) – The number of gray levels to use. If an integer is given, kmeans
clustering is used to compress the color space of the percept into
n_gray
bins. If None, no compression is performed.  noise (float or int, optional) – Adds saltandpepper 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,) – 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. If engine is jax, all other axons will be padded to the length enforced by this constraint.
 engine (string, optional) – Engine to use for computation. Options are ‘serial’, ‘cython’, and ‘jax’. Defaults to ‘cython’
 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_threads (int, optional) – Number of CPU threads to use during parallelization using OpenMP. Defaults to max number of user CPU cores.
 important :: (.) – If you change important model parameters outside the constructor (e.g.,
by directly setting
model.axlambda = 100
), you will have to callmodel.build()
again for your changes to take effect.
Notes
 The axon map is not very accurate when the upper bound of
ax_segments_range
is greater than 90 deg.

build
(**build_params)[source]¶ Build the model
Performs expensive onetime 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 aFreezeError
. Example:model.build(param1=val)
Returns: self

find_threshold
(implant, bright_th, amp_range=(0, 999), amp_tol=1, bright_tol=0.1, max_iter=100, t_percept=None)[source]¶ Find the threshold current for a certain stimulus
Estimates
amp_th
such that the output ofmodel.predict_percept(stim(amp_th))
is approximatelybright_th
.Parameters:  implant (
ProsthesisSystem
) – The implant and its stimulus to use. Stimulus amplitude will be up and down regulated untilamp_th
is found.  bright_th (float) – Model output (brightness) that’s considered “at threshold”.
 amp_range ((amp_lo, amp_hi), optional) – Range of amplitudes to search (uA).
 amp_tol (float, optional) – Search will stop if candidate range of amplitudes is within
amp_tol
 bright_tol (float, optional) – Search will stop if model brightness is within
bright_tol
ofbright_th
 max_iter (int, optional) – Search will stop after
max_iter
iterations  t_percept (float or list of floats, optional) – The time points at which to output a percept (ms).
If None,
implant.stim.time
is used.
Returns: amp_th (float) – Threshold current (uA), estimated so that the output of
model.predict_percept(stim(amp_th))
is withinbright_tol
ofbright_th
. implant (

has_space
¶ Returns True if the model has a spatial component

has_time
¶ Returns True if the model has a temporal component

is_built
¶ Returns True if the
build
model has been called

predict_percept
(implant, t_percept=None)[source]¶ Predict a percept
Important
You must call
build
before callingpredict_percept
.Parameters:  implant (
ProsthesisSystem
) – A valid prosthesis system. A stimulus can be passed viastim
.  t_percept (float or list of floats, optional) – The time points at which to output a percept (ms).
If None,
implant.stim.time
is used.
Returns: percept (
Percept
) – A Percept object whosedata
container has dimensions Y x X x T. Will return None ifimplant.stim
is None. implant (

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.

class
pulse2percept.models.beyeler2019.
AxonMapSpatial
(**params)[source]¶ Axon map model of [Beyeler2019] (spatial module only)
Implements the axon map model described in [Beyeler2019], where percepts are elongated along nerve fiber bundle trajectories of the retina.
Parameters:  axlambda (double, optional) – Exponential decay constant along the axon(microns).
 rho (double, optional) – Exponential decay constant away from the axon(microns).
 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.
 yrange ((y_min, y_max), optional) – 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.
 xystep (int or double or tuple, optional) – 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)
andxystep=0.5
.  grid_type ({'rectangular', 'hexagonal'}, optional) – Whether to simulate points on a rectangular or hexagonal grid
 retinotopy (
VisualFieldMap
, optional) – An instance of aVisualFieldMap
object that providesret2dva
anddva2ret
methods. By default,Watson2014Map
is used.  n_gray (int, optional) – The number of gray levels to use. If an integer is given, kmeans
clustering is used to compress the color space of the percept into
n_gray
bins. If None, no compression is performed.  noise (float or int, optional) – Adds saltandpepper 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,) – 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. If engine is jax, all other axons will be padded to the length enforced by this constraint.
 engine (string, optional) – Engine to use for computation. Options are ‘serial’, ‘cython’, and ‘jax’. Defaults to ‘cython’
 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_threads (int, optional) – Number of CPU threads to use during parallelization using OpenMP. Defaults to max number of user CPU cores.
 important :: (.) – If you change important model parameters outside the constructor (e.g.,
by directly setting
model.axlambda = 100
), you will have to callmodel.build()
again for your changes to take effect.
Notes
 The axon map is not very accurate when the upper bound of
ax_segments_range
is greater than 90 deg.

build
(**build_params)[source]¶ Build the model
Performs expensive onetime calculations, such as building the spatial grid used to predict a percept. You must call
build
before callingpredict_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 aFreezeError
. Example:model.build(param1=val)

calc_axon_sensitivity
(bundles, pad=False)[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.axlambda
.The number of
bundles
must equal the number of points onself.grid`
. The function will then assume that the ith bundle passes through the ith point on the grid. This is used to determine the bundle segment that is closest to the ith 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 ith 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.axlambda
). 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.If pad is True (set when engine is ‘jax’), axons are padded to all have the same length as the longest axon
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 (numpy array with shape (n_points, axon_length, 3)) – An array of axon segments and sensitivity values. Each entry in the array 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.axlambda
. axon_length is set to the maximum length of any axon after being trimmed due to min_sensitivity

calc_bundle_tangent
(xc, yc)[source]¶ Calculates orientation of fiber bundle tangent at (xc, yc)
Parameters: yc (xc,) – (x, y) retinal location of point at which to calculate bundle orientation in microns. Returns: tangent (scalar) – An angle in radians

find_closest_axon
(bundles, xret=None, yret=None, return_index=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
 yret (xret,) – 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
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 inbundles
of the closest axon (or list of closest axons).

find_threshold
(implant, bright_th, amp_range=(0, 999), amp_tol=1, bright_tol=0.1, max_iter=100)[source]¶ Find the threshold current for a certain stimulus
Estimates
amp_th
such that the output ofmodel.predict_percept(stim(amp_th))
is approximatelybright_th
.Parameters:  implant (
ProsthesisSystem
) – The implant and its stimulus to use. Stimulus amplitude will be up and down regulated untilamp_th
is found.  bright_th (float) – Model output (brightness) that’s considered “at threshold”.
 amp_range ((amp_lo, amp_hi), optional) – Range of amplitudes to search (uA).
 amp_tol (float, optional) – Search will stop if candidate range of amplitudes is within
amp_tol
 bright_tol (float, optional) – Search will stop if model brightness is within
bright_tol
ofbright_th
 max_iter (int, optional) – Search will stop after
max_iter
iterations
Returns: amp_th (float) – Threshold current (uA), estimated so that the output of
model.predict_percept(stim(amp_th))
is withinbright_tol
ofbright_th
. implant (

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 generatesn_bundles
axon bundles whosephi0
values are linearly sampled fromself.axons_range
(polar coords). Each axon will consist ofself.n_ax_segments
segments that spanself.ax_segments_range
distance from the optic nerve head (polar coords).Parameters: Returns: 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

is_built
¶ A flag indicating whether the model has been built

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 highresolution 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

predict_percept
(implant, t_percept=None)[source]¶ Predict the spatial response
Important
Don’t override this method if you are creating your own model. Customize
_predict_spatial
instead.Parameters:  implant (
ProsthesisSystem
) – A valid prosthesis system. A stimulus can be passed viastim
.  t_percept (float or list of floats, optional) – The time points at which to output a percept (ms).
If None,
implant.stim.time
is used.
Returns: percept (
Percept
) – A Percept object whosedata
container has dimensions Y x X x T. Will return None ifimplant.stim
is None. implant (

class
pulse2percept.models.beyeler2019.
ScoreboardModel
(**params)[source]¶ Scoreboard model of [Beyeler2019] (standalone model)
Implements the scoreboard model described in [Beyeler2019], where all percepts are Gaussian blobs.
Note
Use this class if you want a standalone model. Use
ScoreboardSpatial
if you want to combine the spatial model with a temporal model.Parameters:  rho (double, optional) – Exponential decay constant describing phosphene size (microns).
 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.
 yrange (tuple, (y_min, y_max), optional) – 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.
 xystep (int, double, tuple, optional) – 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)
andxystep=0.5
.  grid_type ({'rectangular', 'hexagonal'}, optional) – Whether to simulate points on a rectangular or hexagonal grid
 retinotopy (
VisualFieldMap
, optional) – An instance of aVisualFieldMap
object that providesret2dva
anddva2ret
methods. By default,Watson2014Map
is used.  n_gray (int, optional) – The number of gray levels to use. If an integer is given, kmeans
clustering is used to compress the color space of the percept into
n_gray
bins. If None, no compression is performed.  noise (float or int, optional) – Adds saltandpepper 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.
 n_threads (int, optional) – Number of CPU threads to use during parallelization using OpenMP. Defaults to max number of user CPU cores.
 important :: (.) – If you change important model parameters outside the constructor (e.g.,
by directly setting
model.xrange = (10, 10)
), you will have to callmodel.build()
again for your changes to take effect.

build
(**build_params)[source]¶ Build the model
Performs expensive onetime 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 aFreezeError
. Example:model.build(param1=val)
Returns: self

find_threshold
(implant, bright_th, amp_range=(0, 999), amp_tol=1, bright_tol=0.1, max_iter=100, t_percept=None)[source]¶ Find the threshold current for a certain stimulus
Estimates
amp_th
such that the output ofmodel.predict_percept(stim(amp_th))
is approximatelybright_th
.Parameters:  implant (
ProsthesisSystem
) – The implant and its stimulus to use. Stimulus amplitude will be up and down regulated untilamp_th
is found.  bright_th (float) – Model output (brightness) that’s considered “at threshold”.
 amp_range ((amp_lo, amp_hi), optional) – Range of amplitudes to search (uA).
 amp_tol (float, optional) – Search will stop if candidate range of amplitudes is within
amp_tol
 bright_tol (float, optional) – Search will stop if model brightness is within
bright_tol
ofbright_th
 max_iter (int, optional) – Search will stop after
max_iter
iterations  t_percept (float or list of floats, optional) – The time points at which to output a percept (ms).
If None,
implant.stim.time
is used.
Returns: amp_th (float) – Threshold current (uA), estimated so that the output of
model.predict_percept(stim(amp_th))
is withinbright_tol
ofbright_th
. implant (

has_space
¶ Returns True if the model has a spatial component

has_time
¶ Returns True if the model has a temporal component

is_built
¶ Returns True if the
build
model has been called

predict_percept
(implant, t_percept=None)[source]¶ Predict a percept
Important
You must call
build
before callingpredict_percept
.Parameters:  implant (
ProsthesisSystem
) – A valid prosthesis system. A stimulus can be passed viastim
.  t_percept (float or list of floats, optional) – The time points at which to output a percept (ms).
If None,
implant.stim.time
is used.
Returns: percept (
Percept
) – A Percept object whosedata
container has dimensions Y x X x T. Will return None ifimplant.stim
is None. implant (

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.

class
pulse2percept.models.beyeler2019.
ScoreboardSpatial
(**params)[source]¶ Scoreboard model of [Beyeler2019] (spatial module only)
Implements the scoreboard model described in [Beyeler2019], where all percepts are Gaussian blobs.
Note
Use this class if you want to combine the spatial model with a temporal model. Use
ScoreboardModel
if you want a a standalone model.Parameters:  rho (double, optional) – Exponential decay constant describing phosphene size (microns).
 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.
 yrange (tuple, (y_min, y_max), optional) – 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.
 xystep (int, double, tuple, optional) – 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)
andxystep=0.5
.  grid_type ({'rectangular', 'hexagonal'}, optional) – Whether to simulate points on a rectangular or hexagonal grid
 retinotopy (
VisualFieldMap
, optional) – An instance of aVisualFieldMap
object that providesret2dva
anddva2ret
methods. By default,Watson2014Map
is used.  n_gray (int, optional) – The number of gray levels to use. If an integer is given, kmeans
clustering is used to compress the color space of the percept into
n_gray
bins. If None, no compression is performed.  noise (float or int, optional) – Adds saltandpepper 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.
 n_threads (int, optional) – Number of CPU threads to use during parallelization using OpenMP. Defaults to max number of user CPU cores.
 important :: (.) – If you change important model parameters outside the constructor (e.g.,
by directly setting
model.xrange = (10, 10)
), you will have to callmodel.build()
again for your changes to take effect.

build
(**build_params)[source]¶ Build the model
Performs expensive onetime calculations, such as building the spatial grid used to predict a percept. You must call
build
before callingpredict_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 aFreezeError
. Example:model.build(param1=val)

find_threshold
(implant, bright_th, amp_range=(0, 999), amp_tol=1, bright_tol=0.1, max_iter=100)[source]¶ Find the threshold current for a certain stimulus
Estimates
amp_th
such that the output ofmodel.predict_percept(stim(amp_th))
is approximatelybright_th
.Parameters:  implant (
ProsthesisSystem
) – The implant and its stimulus to use. Stimulus amplitude will be up and down regulated untilamp_th
is found.  bright_th (float) – Model output (brightness) that’s considered “at threshold”.
 amp_range ((amp_lo, amp_hi), optional) – Range of amplitudes to search (uA).
 amp_tol (float, optional) – Search will stop if candidate range of amplitudes is within
amp_tol
 bright_tol (float, optional) – Search will stop if model brightness is within
bright_tol
ofbright_th
 max_iter (int, optional) – Search will stop after
max_iter
iterations
Returns: amp_th (float) – Threshold current (uA), estimated so that the output of
model.predict_percept(stim(amp_th))
is withinbright_tol
ofbright_th
. implant (

is_built
¶ A flag indicating whether the model has been built

plot
(use_dva=False, style='hull', autoscale=True, ax=None, figsize=None)[source]¶ Plot the model
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 highresolution grid.
 autoscale (bool, optional) – Whether to adjust the x,y limits of the plot to fit the implant
 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
Returns: ax (
matplotlib.axes.Axes
) – Returns the axis object of the plot

predict_percept
(implant, t_percept=None)[source]¶ Predict the spatial response
Important
Don’t override this method if you are creating your own model. Customize
_predict_spatial
instead.Parameters:  implant (
ProsthesisSystem
) – A valid prosthesis system. A stimulus can be passed viastim
.  t_percept (float or list of floats, optional) – The time points at which to output a percept (ms).
If None,
implant.stim.time
is used.
Returns: percept (
Percept
) – A Percept object whosedata
container has dimensions Y x X x T. Will return None ifimplant.stim
is None. implant (