"""

NDWidget Spike Raster

=====================

Browse a spike raster with an ``NDWidget``. The array is ``[raster, spike, xy]``, where the value dim holds the

spike time in seconds and the raster index of each spike, so each of the 20 rasters is a separate scatter in the

collection.

The datapoints dim is time, so the one slider scrubs along it and ``display_window`` sets how many seconds of

spikes are rendered at a time. That window is what keeps a long recording viewable, only the spikes in view are

read and uploaded.

``x_range_mode="auto"`` couples the camera to that window in both directions: the x-range follows the slider,

and panning or zooming sets the display window to the new width and the index to the new center. The linear

selector marks the current time.

"""

# test_example = false

# sphinx_gallery_pygfx_docs = 'animate 6s 20fps'

import numpy as np

import fastplotlib as fpl

n_rasters = 10

n_spikes = 2_000 # spikes per raster

duration = 120.0 # seconds

rng = np.random.default_rng(0)

# a shared sequence of population events, each raster fires near every event with its own jitter

events = np.sort(rng.uniform(0, duration, n_spikes))

# [raster, spike, xy], the value dim holds the spike time in seconds and the raster index

data = np.empty((n_rasters, n_spikes, 2), dtype=np.float32)

data[:, :, 0] = events + rng.normal(0, 0.05, (n_rasters, n_spikes))

data[:, :, 1] = np.arange(n_rasters)[:, None]

data2 = np.empty((n_rasters, n_spikes, 2), dtype=np.float32)

data2[:, :, 0] = events + rng.normal(0, 0.05, (n_rasters, n_spikes))

data2[:, :, 1] = np.arange(n_rasters)[:, None]

# reference space is seconds, one step per 60 Hz frame

ranges = {"time": (0, duration, 1 / 60)}

ndw = fpl.NDWidget(ranges=ranges, size=(700, 700), shape=(2, 1))

ndw[0, 0].add_nd_timeseries(

data,

dims=("raster", "time", "xy"),

display_dims=("raster", "time", "xy"),

graphic_type=fpl.ScatterCollection,

slider_maps={"time": events}, # seconds -> index into the spike list

display_window=5.0, # seconds of spikes to render

max_display_datapoints=1_000_000, # spikes are cheap points, never decimate them

x_range_mode="auto",

cmap="viridis", # one color per raster

sizes=4,

name="raster",

)

ndw[1, 0].add_nd_timeseries(

data2,

dims=("raster", "time", "xy"),

display_dims=("raster", "time", "xy"),

graphic_type=fpl.ScatterCollection,

slider_maps={"time": events}, # seconds -> index into the spike list

display_window=5.0, # seconds of spikes to render

max_display_datapoints=1_000_000, # spikes are cheap points, never decimate them

x_range_mode="auto",

cmap="viridis", # one color per raster

sizes=4,

name="raster",

)

subplot = ndw.figure[0, 0]

ndw.show(maintain_aspect=False)

figure = ndw.figure

# NOTE: fpl.loop.run() should not be used for interactive sessions

# See the "JupyterLab and IPython" section in the user guide

if __name__ == "__main__":

print(__doc__)

fpl.loop.run()