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.ScoreboardSpatial(**params)

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).

• min_current_spread (float, optional) – An electrode is skipped at grid points where its Gaussian current spread has decayed below this fraction of its peak. The default (1e-8, about 6.1 rho away) drops the Gaussian times the stimulus amplitude, summed over the skipped electrodes, so the error at a point is bounded by min_current_spread times the summed amplitude across electrodes.

• 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.

• step (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) 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'}, optional) – Whether to simulate points on a rectangular or hexagonal grid

• vfmap (VisualFieldMap, optional) – An instance of a VisualFieldMap that provides retinotopic mappings. By default, Watson2014Map is used.

• n_gray (int, 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.

• noise (float 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.

• n_threads (int, optional) – Number of CPU threads to use during parallelization using OpenMP. Defaults to max number of user CPU cores.

• n_jobs (int, optional) – Alias for n_threads; None or -1 uses every core.

• :: (.. important) – If you change important model parameters outside the constructor (e.g., by directly setting model.xrange = (-10, 10)), you will have to call model.build() again for your changes to take effect.

get_default_params()

Returns all settable parameters of the scoreboard model

get_param_units()

Return a dict of the units that parameters are stored in

build(**build_params)

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)

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

Find the threshold current for a certain stimulus

Estimates amp_th such that the output of model.predict_percept(stim(amp_th)) is approximately bright_th.

Parameters:

• implant (ProsthesisSystem) – The implant and its stimulus to use. Stimulus amplitude will be up and down regulated until amp_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, counted in this model’s stimulus_unit (microamps, for every model p2p ships).

• amp_tol (float, optional) – Search will stop if candidate range of amplitudes is within amp_tol, in stimulus_unit

• bright_tol (float, optional) – Search will stop if model brightness is within bright_tol of bright_th

• max_iter (int, optional) – Search will stop after max_iter iterations

Returns:

amp_th – Threshold current, in stimulus_unit, estimated so that the output of model.predict_percept(stim(amp_th)) is within bright_tol of bright_th.

Return type:

float

Notes

• amp_range and amp_tol may be given as unitful quantities (e.g. amp_range=(0, 1 * mA)); the answer comes back as a plain number of microamps. bright_th and bright_tol are model output, which is not a physical quantity and carries no unit. See pulse2percept.units.

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', autoscale=True, ax=None, figsize=None)

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 high-resolution 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 – Returns the axis object of the plot

Return type:

matplotlib.axes.Axes

predict_percept(implant, t_percept=None)

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 via stim().

• t_percept (float or list of floats, optional) – The time points at which to output a percept, counted in this model’s time_unit (milliseconds, for every model p2p ships). 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, and whose time axis is labelled in time_unit. Will return None if implant.stim is None.

Return type:

Percept

set_params(**params)

Set the parameters of this object

space_unit = um

The unit spatial coordinates are expressed in

stimulus_unit = uA

The unit stimulus values are expressed in

time_unit = ms

The unit time is expressed in

xystep

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

class pulse2percept.models.beyeler2019.ScoreboardModel(**params)

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).

• min_current_spread (float, optional) – An electrode is skipped at grid points where its Gaussian current spread has decayed below this fraction of its peak. The default (1e-8, about 6.1 rho away) drops the Gaussian times the stimulus amplitude, summed over the skipped electrodes, so the error at a point is bounded by min_current_spread times the summed amplitude across electrodes.

• 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.

• step (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) 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'}, optional) – Whether to simulate points on a rectangular or hexagonal grid

• vfmap (VisualFieldMap, optional) – An instance of a VisualFieldMap object that provides retinotopic mappings. By default, Watson2014Map is used.

• n_gray (int, 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.

• noise (float 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.

• n_threads (int, optional) – Number of CPU threads to use during parallelization using OpenMP. Defaults to max number of user CPU cores.

• n_jobs (int, optional) – Alias for n_threads; None or -1 uses every core.

• :: (.. important) – If you change important model parameters outside the constructor (e.g., by directly setting model.xrange = (-10, 10)), you will have to call model.build() again for your changes to take effect.

build(**build_params)

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

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

Find the threshold current for a certain stimulus

Estimates amp_th such that the output of model.predict_percept(stim(amp_th)) is approximately bright_th.

Parameters:

• implant (ProsthesisSystem) – The implant and its stimulus to use. Stimulus amplitude will be up and down regulated until amp_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, counted in this model’s stimulus_unit (microamps, for every model p2p ships).

• 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 of bright_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, counted in this model’s time_unit (milliseconds, for every model p2p ships). If None, implant.stim.time is used. May be given as a unitful quantity (e.g. [0, 20] * ms); see pulse2percept.units.

Returns:

amp_th – Threshold current, in stimulus_unit, estimated so that the output of model.predict_percept(stim(amp_th)) is within bright_tol of bright_th.

Return type:

float

Notes

• amp_range, amp_tol and t_percept may be given as unitful quantities; the answer comes back as a plain number of microamps. bright_th and bright_tol are model output, which is not a physical quantity and carries no unit. See pulse2percept.units.

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

predict_percept(implant, t_percept=None)

Predict a percept

Important

You must call build before calling predict_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, counted in this model’s time_unit (milliseconds, for every model p2p ships). 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

set_params(params)

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.

class pulse2percept.models.beyeler2019.AxonMapSpatial(**params)

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:

• 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 Gaussian current spread has decayed below this fraction of its peak. The default (1e-8, about 6.1 rho away) drops the Gaussian times the stimulus amplitude, summed over the skipped electrodes, so the error at a point is bounded by min_current_spread times the summed amplitude 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.

• 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.

• step (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) 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'}, optional) – Whether to simulate points on a rectangular or hexagonal grid

• vfmap (VisualFieldMap, optional) – An instance of a VisualFieldMap object that provides retinotopic mappings. By default, Watson2014Map is used.

• n_gray (int, 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.

• noise (float 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 ((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.

• 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.

• 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.

• n_jobs (int, optional) – Alias for n_threads; None or -1 uses every core.

• :: (.. important) – If you change important model parameters outside the constructor (e.g., by directly setting model.lam = 100), you will have to call model.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.

axlambda

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

get_default_params()

Return a dictionary of default values for all model parameters

get_param_units()

Return a dict of the units that parameters are stored in

grow_axon_bundles(n_bundles=None, prune=True)

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

find_closest_axon(bundles, xret=None, yret=None, return_index=False, return_segment=False)

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.

calc_axon_sensitivity(bundles)

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)

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)

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

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

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

build(**build_params)

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)

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

Find the threshold current for a certain stimulus

Estimates amp_th such that the output of model.predict_percept(stim(amp_th)) is approximately bright_th.

Parameters:

• implant (ProsthesisSystem) – The implant and its stimulus to use. Stimulus amplitude will be up and down regulated until amp_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, counted in this model’s stimulus_unit (microamps, for every model p2p ships).

• amp_tol (float, optional) – Search will stop if candidate range of amplitudes is within amp_tol, in stimulus_unit

• bright_tol (float, optional) – Search will stop if model brightness is within bright_tol of bright_th

• max_iter (int, optional) – Search will stop after max_iter iterations

Returns:

amp_th – Threshold current, in stimulus_unit, estimated so that the output of model.predict_percept(stim(amp_th)) is within bright_tol of bright_th.

Return type:

float

Notes

• amp_range and amp_tol may be given as unitful quantities (e.g. amp_range=(0, 1 * mA)); the answer comes back as a plain number of microamps. bright_th and bright_tol are model output, which is not a physical quantity and carries no unit. See pulse2percept.units.

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

predict_percept(implant, t_percept=None)

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 via stim().

• t_percept (float or list of floats, optional) – The time points at which to output a percept, counted in this model’s time_unit (milliseconds, for every model p2p ships). 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, and whose time axis is labelled in time_unit. Will return None if implant.stim is None.

Return type:

Percept

set_params(**params)

Set the parameters of this object

space_unit = um

The unit spatial coordinates are expressed in

stimulus_unit = uA

The unit stimulus values are expressed in

time_unit = ms

The unit time is expressed in

xystep

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

class pulse2percept.models.beyeler2019.AxonMapModel(**params)

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:

• 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 Gaussian current spread has decayed below this fraction of its peak. The default (1e-8, about 6.1 rho away) drops the Gaussian times the stimulus amplitude, summed over the skipped electrodes, so the error at a point is bounded by min_current_spread times the summed amplitude 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.

• 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.

• step (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) 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'}, optional) – Whether to simulate points on a rectangular or hexagonal grid

• vfmap (VisualFieldMap, optional) – An instance of a VisualFieldMap object that provides retinotopic mappings. By default, Watson2014Map is used.

• n_gray (int, 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.

• noise (float 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 ((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.

• 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.

• 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.

• n_jobs (int, optional) – Alias for n_threads; None or -1 uses every core.

• :: (.. important) – If you change important model parameters outside the constructor (e.g., by directly setting model.lam = 100), you will have to call model.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.

predict_percept(implant, t_percept=None)

Predict a percept

Important

You must call build before calling predict_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, counted in this model’s time_unit (milliseconds, for every model p2p ships). 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

build(**build_params)

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

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

Find the threshold current for a certain stimulus

Estimates amp_th such that the output of model.predict_percept(stim(amp_th)) is approximately bright_th.

Parameters:

• implant (ProsthesisSystem) – The implant and its stimulus to use. Stimulus amplitude will be up and down regulated until amp_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, counted in this model’s stimulus_unit (microamps, for every model p2p ships).

• 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 of bright_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, counted in this model’s time_unit (milliseconds, for every model p2p ships). If None, implant.stim.time is used. May be given as a unitful quantity (e.g. [0, 20] * ms); see pulse2percept.units.

Returns:

amp_th – Threshold current, in stimulus_unit, estimated so that the output of model.predict_percept(stim(amp_th)) is within bright_tol of bright_th.

Return type:

float

Notes

• amp_range, amp_tol and t_percept may be given as unitful quantities; the answer comes back as a plain number of microamps. bright_th and bright_tol are model output, which is not a physical quantity and carries no unit. See pulse2percept.units.

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)

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.