# -*- coding: utf-8 -*-
"""
============================================================================
Beyeler et al. (2019): Focal percepts with the scoreboard model
============================================================================

This example shows how to apply the
:py:class:`~pulse2percept.models.ScoreboardModel` to a
:py:class:`~pulse2percept.implants.PRIMA75` implant.

The scoreboard model is a standard baseline model of retinal prosthesis
stimulation, which assumes that electrical stimulation leads to the percept
of focal dots of light, centered over the visual field location associated with
the stimulated retinal field location :math:`(x_{stim}, y_{stim})`, whose
spatial intensity decays with a Gaussian profile [Hayes2003]_, [Thompson2003]_:

.. math::

    I_{score}(x,y; \\rho) = \\exp \\Big(
    -\\frac{(x-x_{stim})^2 + (y-y_{stim})^2}{2 \\rho^2} \\Big)

where :math:`\\rho` is the spatial decay constant.

.. important::

    As pointed out by [Beyeler2019]_, the scoreboard model does not well
    account for percepts generated by **epiretinal** implants, where
    incidental stimulation of retinal nerve fiber bundles leads to elongated,
    'streaky' percepts.

    In that case, use :py:class:`~pulse2percept.models.AxonMapModel` instead.

The scoreboard model can be instantiated and run in three simple steps.

Creating the model
------------------

The first step is to instantiate the
:py:class:`~pulse2percept.models.ScoreboardModel` class by calling its
constructor method.

The model simulates a patch of the visual field specified by ``xrange`` and
``yrange`` (in degrees of visual angle), sampled at a step size of ``xystep``.
The grid that is created from these parameters is equivalent to calling NumPy's
``linspace(xrange[0], xrange[1], num=xystep)``.

By default, this patch is quite large, spanning 30deg x 30deg.
Because PRIMA is rather small, we want to reduce the window size to 6deg x 6deg
and sample it at 0.05deg resolution:

"""
# sphinx_gallery_thumbnail_number = 2

from pulse2percept.models import ScoreboardModel
model = ScoreboardModel(xrange=(-3, 3), yrange=(-3, 3), xystep=0.05)

##############################################################################
# Parameters you don't specify will take on default values. You can inspect
# all current model parameters as follows:

print(model)

##############################################################################
# This reveals a number of other parameters to set, such as:
#
# * ``xrange``, ``yrange``: the extent of the visual field to be simulated,
#   specified as a range of x and y coordinates (in degrees of visual angle,
#   or dva). For example, we are currently sampling x values between -20 dva
#   and +20dva, and y values between -15 dva and +15 dva.
# * ``xystep``: The resolution (in dva) at which to sample the visual field.
#   For example, we are currently sampling at 0.25 dva in both x and y
#   direction.
# * ``thresh_percept``: You can also define a brightness threshold, below which
#   the predicted output brightness will be zero. It is currently set to
#   ``1/sqrt(e)``, because that will make the radius of the predicted percept
#   equal to ``rho``.
#
# To change parameter values, either pass them directly to the constructor
# above or set them by hand, like this:

model.rho = 20

##############################################################################
# Then build the model. This is a necessary step before you can actually use
# the model to predict a percept, as it performs a number of expensive setup
# computations (e.g., building the spatial reference frame, calculating
# electric potentials):

model.build()

##############################################################################
# .. note::
#
#     You need to build a model only once. After that, you can apply any number
#     of stimuli -- or even apply the model to different implants -- without
#     having to rebuild (which takes time).
#
# Assigning a stimulus
# --------------------
# The second step is to specify a visual prosthesis from the
# :py:mod:`~pulse2percept.implants` module.
#
# In the following, we will create an
# :py:class:`~pulse2percept.implants.PRIMA75` implant. By default, the implant
# will be centered over the fovea (at x=0, y=0) and aligned with the horizontal
# meridian (rot=0):

from pulse2percept.implants import PRIMA75
implant = PRIMA75()

##############################################################################
# We can visualize the implant and verify that we are simulating the correct
# patch of retina as follows:

model.plot()
implant.plot()

##############################################################################
# The gray window indicates the extent of the grid that was created during
# ``model.build()`` using the values specified by ``xrange``, ``yrange``, and
# ``xystep``. As we can see, the window well-covers the implant that we want
# to simulate.
#
# The easiest way to assign a stimulus to the implant is to pass a NumPy array
# that specifies the current amplitude to be applied to every electrode in the
# implant.
#
# For example, the following sends 10 microamps to all 142 electrodes of the
# implant:

import numpy as np
implant.stim = 10 * np.ones(142)

##############################################################################
# .. note::
#
#     Some models can handle stimuli that have both a spatial and a temporal
#     component. The scoreboard model cannot.
#
# Predicting the percept
# ----------------------
#
# The third step is to apply the model to predict the percept resulting from
# the specified stimulus. Note that this may take some time on your machine:

percept = model.predict_percept(implant)

##############################################################################
# The resulting percept is stored in a
# :py:class:`~pulse2percept.percepts.Percept` object, which is similar in
# organization to the :py:class:`~pulse2percept.stimuli.Stimulus` object:
# the ``data`` container is a 3D NumPy array (Y, X, T) with labeled axes
# ``xdva``, ``ydva``, and ``time``.
#
# The percept can be plotted as follows:

ax = percept.plot()
ax.set_title('Predicted percept')

##############################################################################
# .. note::
#
#     Stimulation of the inferior retina leads to percepts appearing in the
#     upper visual field. This is why the percept (with axes showing the visual
#     field in degrees of visual angle) appears upside down with respect to
#     the earlier figure of the implant (with axes showing retinal coordinates
#     in microns).
#
# By default, the :py:meth:`~pulse2percept.percepts.Percept.plot` method uses
# Matplotlib's ``pcolor`` function.
# However, it can also be configured to use a hex grid (using Matplotlib's
# ``hexbin`` function). Additional parameters can be passed to ``hexbin`` as
# keyword arguments of :py:meth:`~pulse2percept.percepts.Percept.plot`:

percept = model.predict_percept(implant)
percept.plot(kind='hex', cmap='inferno')


##############################################################################
# Addressing individual electrodes
# --------------------------------
#
# Alternatively, we can address an individual electrode. To show the electrode
# names, the implant can be plotted with ``annotate=True``:

implant.plot(annotate=True)

##############################################################################
# This makes it easier to pick an electrode; e.g., F7:

implant.stim = {'F7': 10}

percept = model.predict_percept(implant)

percept.plot()