# pulse2percept.models.nanduri2012¶

Classes

 Nanduri2012Model(**params) [Nanduri2012] Model Nanduri2012Spatial(**params) Spatial response model of [Nanduri2012] Nanduri2012Temporal(**params) Temporal model of [Nanduri2012]
class pulse2percept.models.nanduri2012.Nanduri2012Model(**params)[source]

[Nanduri2012] Model

Implements the model described in [Nanduri2012], where percepts are circular and their brightness evolves over time.

The model combines two parts:

• Nanduri2012Spatial is used to calculate the spatial activation function, which is assumed to be equivalent to the “current spread” described as a function of distance from the center of the stimulating electrode (see Eq.2 in the paper).
• Nanduri2012Temporal is used to calculate the temporal activation function, which is assumed to be the output of a linear-nonlinear cascade model (see Fig.6 in the paper).
Parameters: atten_a (float, optional) – Nominator of the attentuation function (Eq.2 in the paper) atten_n (float32, optional) – Exponent of the attenuation function’s denominator (Eq.2 in the paper) dt (float, optional) – Sampling time step (ms) tau1 (float, optional) – Time decay constant for the fast leaky integrater. tau2 (float, optional) – Time decay constant for the charge accumulation. tau3 (float, optional) – Time decay constant for the slow leaky integrator. eps (float, optional) – Scaling factor applied to charge accumulation. asymptote (float, optional) – Asymptote of the logistic function used in the stationary nonlinearity stage. slope (float, optional) – Slope of the logistic function in the stationary nonlinearity stage. shift (float, optional) – Shift of the logistic function in the stationary nonlinearity stage. scale_out (float32, optional) – A scaling factor applied to the output of the model thresh_percept (float, optional) – Below threshold, the percept has brightness zero. retinotopy (VisualFieldMap, optional) – An instance of a VisualFieldMap object that provides ret2dva and dva2ret methods. By default, Curcio1990Map 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.
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) 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 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 (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 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 (ms). If None, implant.stim.time is used. amp_th (float) – Threshold current (uA), estimated so that the output of model.predict_percept(stim(amp_th)) is within bright_tol of bright_th.
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 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 (ms). If None, implant.stim.time is used. percept (Percept) – A Percept object whose data container has dimensions Y x X x T. Will return None if implant.stim is None.
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.nanduri2012.Nanduri2012Spatial(**params)[source]

Spatial response model of [Nanduri2012]

Implements the spatial response model described in [Nanduri2012], which assumes that the spatial activation of retinal tissue is equivalent to the “current spread” $$I$$, described as a function of distance $$r$$ from the center of the stimulating electrode:

$\begin{split}I(r) = \begin{cases} \frac{\verb!atten_a!}{\verb!atten_a! + (r-a)^\verb!atten_n!} & r > a \\ 1 & r \leq a \end{cases}\end{split}$

where $$a$$ is the radius of the electrode (see Eq.2 in the paper).

Note

Use this class if you just want the spatial response model. Use Nanduri2012Model if you want both the spatial and temporal model.

Parameters: atten_a (float, optional) – Nominator of the attentuation function atten_n (float32, optional) – Exponent of the attenuation function’s denominator retinotopy (VisualFieldMap, optional) – An instance of a VisualFieldMap object that provides ret2dva and dva2ret methods. By default, Curcio1990Map 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.
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)
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 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 (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 of bright_th max_iter (int, optional) – Search will stop after max_iter iterations amp_th (float) – Threshold current (uA), estimated so that the output of model.predict_percept(stim(amp_th)) is within bright_tol of bright_th.
get_default_params()[source]

Returns all settable parameters of the Nanduri model

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 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 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 via stim. 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. percept (Percept) – A Percept object whose data container has dimensions Y x X x T. Will return None if implant.stim is None.
set_params(**params)[source]

Set the parameters of this model

class pulse2percept.models.nanduri2012.Nanduri2012Temporal(**params)[source]

Temporal model of [Nanduri2012]

Implements the temporal response model described in [Nanduri2012], which assumes that the temporal activation of retinal tissue is the output of a linear-nonlinear model cascade (see Fig.6 in the paper).

Note

Use this class if you just want the temporal response model. Use Nanduri2012Model if you want both the spatial and temporal model.

Parameters: dt (float, optional) – Sampling time step (ms) tau1 (float, optional) – Time decay constant for the fast leaky integrater. tau2 (float, optional) – Time decay constant for the charge accumulation. tau3 (float, optional) – Time decay constant for the slow leaky integrator. eps (float, optional) – Scaling factor applied to charge accumulation. asymptote (float, optional) – Asymptote of the logistic function used in the stationary nonlinearity stage. slope (float, optional) – Slope of the logistic function in the stationary nonlinearity stage. shift (float, optional) – Shift of the logistic function in the stationary nonlinearity stage. scale_out (float32, optional) – A scaling factor applied to the output of the model thresh_percept (float, optional) – Below threshold, the percept has brightness zero. n_threads (int, optional) – Number of CPU threads to use during parallelization using OpenMP. Defaults to max number of user CPU cores.
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)
find_threshold(stim, 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 of model.predict_percept(stim(amp_th)) is approximately bright_th.

Parameters: stim (Stimulus) – The 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 (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 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 (ms). If None, implant.stim.time is used. amp_th (float) – Threshold current (uA), estimated so that the output of model.predict_percept(stim(amp_th)) is within bright_tol of bright_th.
get_default_params()[source]

Return a dictionary of default values for all model parameters

is_built

A flag indicating whether the model has been built

predict_percept(stim, t_percept=None)[source]

Predict the temporal response

Important

Don’t override this method if you are creating your own model. Customize _predict_temporal instead.

Parameters: stim (: py: class: ~pulse2percept.stimuli.Stimulus or) – : py: class: ~pulse2percept.models.Percept Either a Stimulus or a Percept object. The temporal model will be applied to each spatial location in the stimulus/percept. t_percept (float or list of floats, optional) – The time points at which to output a percept (ms). If None, the percept will be output once very 20 ms (50 Hz frame rate). Note If your stimulus is shorter than 20 ms, you should specify the desired time points manually. percept (Percept) – A Percept object whose data container has dimensions Y x X x T. Will return None if stim is None.

Notes

• If a list of time points is provided for t_percept, the values will automatically be sorted.
set_params`(**params)[source]

Set the parameters of this model