def test_PulseTrain():
# All zeros:
npt.assert_almost_equal(PulseTrain(10, Stimulus(np.zeros((1, 5)))).data, 0)
# Simple fake pulse:
pulse = Stimulus([[0, -1, 0]], time=[0, 0.1, 0.2])
for n_pulses in [2, 3, 10]:
pt = PulseTrain(10, pulse, n_pulses=n_pulses)
npt.assert_equal(np.sum(np.isclose(pt.data, -1)), n_pulses)
# stim_dur too short:
npt.assert_almost_equal(PulseTrain(2, pulse, stim_dur=10).data, 0)
# Invalid calls:
with pytest.raises(TypeError):
# Wrong stimulus type:
PulseTrain(10, {'a': 1})
with pytest.raises(ValueError):
# Pulse does not fit:
PulseTrain(100000, pulse)
with pytest.raises(ValueError):
# n_pulses does not fit:
PulseTrain(10, pulse, n_pulses=100000)
with pytest.raises(ValueError):
# No time component:
PulseTrain(10, Stimulus(1))
with pytest.raises(ValueError):
# Empty stim:
pulse = Stimulus([[0, 0, 0]], time=[0, 0.1, 0.2], compress=True)
PulseTrain(10, pulse)
def test_PulseTrain():
# All zeros:
npt.assert_almost_equal(PulseTrain(10, Stimulus(np.zeros((1, 5)))).data,
0)
# Simple fake pulse:
pulse = Stimulus([[0, -1, 0]], time=[0, 0.1, 0.2])
for n_pulses in [2, 3, 10]:
pt = PulseTrain(10, pulse, n_pulses=n_pulses, electrode='A4')
npt.assert_equal(np.sum(np.isclose(pt.data, -1)), n_pulses)
npt.assert_equal(pt.electrodes, 'A4')
# PulseTrains can cut off/trim individual pulses if necessary:
pt = PulseTrain(3, pulse, stim_dur=11)
npt.assert_almost_equal(pt.time[-1], 11)
npt.assert_almost_equal(pt[0, 11], 0)
# Invalid calls:
with pytest.raises(TypeError):
# Wrong stimulus type:
PulseTrain(10, {'a': 1})
with pytest.raises(ValueError):
# Pulse does not fit:
PulseTrain(100000, pulse)
with pytest.raises(ValueError):
# n_pulses does not fit:
PulseTrain(10, pulse, n_pulses=100000)
with pytest.raises(ValueError):
# No time component:
PulseTrain(10, Stimulus(1))
with pytest.raises(ValueError):
# Empty stim:
pulse = Stimulus([[0, 0, 0]], time=[0, 0.1, 0.2], compress=True)
PulseTrain(10, pulse)
pt.plot()
###############################################################################
# Generic pulse trains
# --------------------
#
# Finally, you can concatenate any :py:class:`~pulse2percept.stimuli.Stimulus`
# object into a pulse train.
#
# For example, let's define a single ramp stimulus:
import numpy as np
from pulse2percept.stimuli import Stimulus, PulseTrain
# Single ramp:
dt = 1e-3
ramp = Stimulus([[0, 0, 1, 1, 2, 2, 0, 0]],
time=[0, 1, 1 + dt, 2, 2 + dt, 3, 3 + dt, 5 - dt])
ramp.plot()
# Ramp train:
PulseTrain(20, ramp, stim_dur=200).plot()
# Biphasic ramp:
biphasic_ramp = Stimulus(np.concatenate((ramp.data, -ramp.data), axis=1),
time=np.concatenate((ramp.time, ramp.time + 5)))
biphasic_ramp.plot()
# Biphasic ramp train:
PulseTrain(20, biphasic_ramp, stim_dur=200).plot()
# ----------------------------------
# The easiest way to generate a pulse train is to use the
# :py:class:`~pulse2percept.stimuli.PulseTrain` object, which allows for
# various stimulus attributes to be specified:
time_step = 0.1 / 1000 # temporal sampling in seconds
freq = 20 # frequency in Hz
amp = 100 # maximum amplitude of the pulse train in microAmps
dur = 0.2 # total duration of the pulse train in seconds
pulse_type = 'cathodicfirst' # whether the first phase is positive or negative
pulse_order = 'gapfirst' # whether the train starts with gap or a pulse.
# Define the pulse train with given parameters
ptrain = PulseTrain(tsample=time_step,
freq=freq,
dur=dur,
amp=amp,
pulsetype=pulse_type,
pulseorder=pulse_order)
# Create a new stimulus where the pulse train is the source
ptrain_stim = Stimulus(ptrain)
# Visualize:
fig, ax = plt.subplots(figsize=(8, 5))
ax.plot(ptrain_stim.time, ptrain_stim.data[0, :])
ax.set_xlabel('Time (s)')
ax.set_ylabel('Amplitude ($\mu$A)')
###############################################################################
# Alternatively, we are free to specify a discrete set of points in time and
# the current amplitude we would like to apply at those times.