def test_load_horsager2009():
data = datasets.load_horsager2009(shuffle=False)
npt.assert_equal(isinstance(data, pd.DataFrame), True)
columns = [
'subject', 'implant', 'electrode', 'task', 'stim_type', 'stim_dur',
'stim_freq', 'stim_amp', 'pulse_type', 'pulse_dur', 'pulse_num',
'interphase_dur', 'delay_dur', 'ref_stim_type', 'ref_freq', 'ref_amp',
'ref_amp_factor', 'ref_pulse_dur', 'ref_interphase_dur', 'theta',
'source'
]
for expected_col in columns:
npt.assert_equal(expected_col in data.columns, True)
npt.assert_equal(data.shape, (552, 21))
npt.assert_equal(data.subject.unique(), ['S05', 'S06'])
# Shuffle dataset (index will always be range(552), but rows are shuffled):
data = datasets.load_horsager2009(shuffle=True, random_state=42)
npt.assert_equal(data.loc[0, 'subject'], 'S06')
npt.assert_equal(data.loc[0, 'electrode'], 'D1')
npt.assert_equal(data.loc[0, 'stim_type'], 'latent_addition')
npt.assert_equal(data.loc[551, 'subject'], 'S06')
npt.assert_equal(data.loc[551, 'electrode'], 'A1')
npt.assert_equal(data.loc[551, 'stim_type'], 'fixed_duration')
The model introduced in [Horsager2009]_ assumes that electrical stimulation
leads to percepts that quickly increase in brightness (over the time course
of ~100ms) and then slowly fade away (over the time course of seconds).
The model was fit to perceptual sensitivity data for a number of different
pulse trains, which are available in the :py:mod:`~pulse2percept.datasets`
subpackage.
The dataset can be loaded as follows:
"""
# sphinx_gallery_thumbnail_number = 3
from pulse2percept.datasets import load_horsager2009
data = load_horsager2009()
data.shape
###############################################################################
# Single-pulse thresholds
# -----------------------
#
# Loading the data
# ^^^^^^^^^^^^^^^^
#
# The data includes a number of thresholds measured on single-pulse stimuli.
# We can load a subset of these data; for example, for subject S05 and
# Electrode C3:
single_pulse = load_horsager2009(subjects='S05',
electrodes='C3',
def test_load_horsager2009():
data = load_horsager2009(shuffle=False)
npt.assert_equal(isinstance(data, pd.DataFrame), True)
columns = [
'subject', 'implant', 'electrode', 'task', 'stim_type', 'stim_dur',
'stim_freq', 'stim_amp', 'pulse_type', 'pulse_dur', 'pulse_num',
'interphase_dur', 'delay_dur', 'ref_stim_type', 'ref_freq', 'ref_amp',
'ref_amp_factor', 'ref_pulse_dur', 'ref_interphase_dur', 'theta',
'source'
]
for expected_col in columns:
npt.assert_equal(expected_col in data.columns, True)
npt.assert_equal(data.shape, (608, 21))
npt.assert_equal(data.subject.unique(), ['S05', 'S06'])
# Shuffle dataset (index will always be range(608), but rows are shuffled):
data = load_horsager2009(shuffle=True, random_state=42)
npt.assert_equal(data.loc[0, 'subject'], 'S05')
npt.assert_equal(data.loc[0, 'electrode'], 'B3')
npt.assert_equal(data.loc[0, 'stim_type'], 'fixed_duration')
npt.assert_equal(data.loc[607, 'subject'], 'S06')
npt.assert_equal(data.loc[607, 'electrode'], 'A1')
npt.assert_equal(data.loc[607, 'stim_type'], 'fixed_duration')
# Select subjects:
data = load_horsager2009(subjects='S05')
npt.assert_equal(data.shape, (296, 21))
npt.assert_equal(data.subject.unique(), 'S05')
data = load_horsager2009(subjects=['S05', 'S07']) # 'S07' doesnt' exist
npt.assert_equal(data.shape, (296, 21))
npt.assert_equal(data.subject.unique(), 'S05')
data = load_horsager2009(subjects=['S05', 'S06']) # same as None
npt.assert_equal(data.shape, (608, 21))
data = load_horsager2009(subjects='S6') # 'S6' doesn't exist
npt.assert_equal(data.shape, (0, 21))
npt.assert_equal(data.subject.unique(), [])
# Select electrodes:
data = load_horsager2009(electrodes='A1')
npt.assert_equal(data.shape, (114, 21))
npt.assert_equal(data.electrode.unique(), 'A1')
npt.assert_equal(data.subject.unique(), ['S06', 'S05'])
data = load_horsager2009(electrodes=['A1', 'A9']) # 'A9' doesn't exist
npt.assert_equal(data.shape, (114, 21))
npt.assert_equal(data.electrode.unique(), 'A1')
npt.assert_equal(data.subject.unique(), ['S06', 'S05'])
# Select stimulus types:
data = load_horsager2009(stim_types='single_pulse')
npt.assert_equal(data.shape, (80, 21))
npt.assert_equal(data.stim_type.unique(), 'single_pulse')
npt.assert_equal(list(data.subject.unique()), ['S05', 'S06'])
data = load_horsager2009(stim_types=['single_pulse', 'fixed_duration'])
npt.assert_equal(data.shape, (200, 21))
npt.assert_equal(list(data.stim_type.unique()),
['single_pulse', 'fixed_duration'])
npt.assert_equal(list(data.subject.unique()), ['S05', 'S06'])
# Subject + electrode + stim type:
data = load_horsager2009(subjects='S05',
electrodes=['A1', 'C3'],
stim_types='single_pulse')
npt.assert_equal(data.shape, (16, 21))
npt.assert_equal(data.subject.unique(), 'S05')
npt.assert_equal(list(data.electrode.unique()), ['C3', 'A1'])
npt.assert_equal(data.stim_type.unique(), 'single_pulse')
.. note ::
Mean threshold currents were extracted from the main publication and its
supplementary materials using Webplot Digitizer.
Therefore, these data are provided without any guarantee of correctness or
completeness.
Loading the dataset
-------------------
The dataset can be loaded as a Pandas ``DataFrame``:
"""
# sphinx_gallery_thumbnail_number = 1
from pulse2percept.datasets import load_horsager2009
data = load_horsager2009()
print(data)
###############################################################################
# Inspecting the DataFrame tells us that there are 552 threshold measurements
# (the rows) each with 21 different attributes (the columns).
#
# These attributes include specifiers such as "subject", "electrode", and
# "stim_type". We can print all column names using:
data.columns
###############################################################################
# .. note ::
#
# The meaning of all column names is explained in the docstring of
:py:class:`~pulse2percept.models.Horsager2009Model`.
The model introduced in [Horsager2009]_ assumes that electrical stimulation
leads to percepts that quickly increase in brightness (over the time course
of ~100ms) and then slowly fade away (over the time course of seconds).
The model was fit to perceptual sensitivity data for a number of different
pulse trains, which are available in the :py:mod:`~pulse2percept.datasets`
subpackage.
The dataset can be loaded as follows:
"""
# sphinx_gallery_thumbnail_number = 3
from pulse2percept.datasets import load_horsager2009
data = load_horsager2009()
data.shape
###############################################################################
# Single-pulse thresholds
# -----------------------
#
# Loading the data
# ^^^^^^^^^^^^^^^^
# The data includes a number of thresholds measured on single-pulse stimuli.
# We can load a subset of these data; for example, for subject S05 and
# Electrode C3:
single_pulse = data[(data.stim_type == 'single_pulse') &
(data.subject == 'S05') &
(data.electrode == 'C3')]
