"""Define a dictionary with interface customization entries
"""
kwargs = {
'xlabel': 'xlabel',
'ylabel': 'ylabel',
'title': 'title',
'color': 'lightgray',
'marker_symbol': 'x',
'title_font_size': 20,
'hist_nbins': 100,
'line_lw': 2.5,
'tf_norm': 3,
'tf_interp': 'nearest',
'tf_cmap': 'Spectral_r',
'tf_baseline': (250, 750),
'tf_av_window': 100,
'tf_av_overlap': .5,
'tf_clim': (-.5, .5),
'axis_font_size': 18,
'tick_font_size': 8,
'axis_color': 'white',
'bgcolor': (.1, .1, .1),
'form': 'marker',
'grid_titles': gtitles,
'grid_font_size': gfz,
'grid_titles_color': 'white'
}
sg = Signal(data, sf=sf, axis=-1, **kwargs)
sg.show()
noise=100)
time += 8. # force the time vector to start at 8 seconds
time *= 1000. # millisecond conversion
"""The data have a shape of (125 channels, 4000 time points). In order to work,
the program need to know that the time axis is not the first dimension. Hence,
we use the `axis` parameter to specify that the time axis is the second one
`axis=1`
"""
axis = 1 # localization of the time axis
"""Add a label to the x-axis (xlabel), y-axis (ylabel) and a title
"""
xlabel = 'Time (ms)'
ylabel = 'Amplitude (uV)'
title = 'Plot of a 1-d signal'
"""Build the title to add to each time-series in the grid
"""
gtitles = ['Trial ' + str(k) for k in range(n_trials)] # grid titles
gfz = 8. # grid titles font-size
glw = 2. # grid line width
Signal(data,
sf=sf,
axis=axis,
time=time,
xlabel=xlabel,
ylabel=ylabel,
title=title,
grid_titles=gtitles,
grid_font_size=gfz,
grid_lw=glw).show()
from visbrain.gui import Signal
sf = 1000. # Sampling-frequency
n_pts = 4000 # Number of time points
n_sines = 10 # Number of sines
"""Create artificial sines :
"""
time = np.arange(n_pts) / sf
f_sines = np.linspace(13, 30, n_sines)
data = np.sin(2 * np.pi * f_sines.reshape(-1, 1) * time.reshape(1, -1))
data += np.random.randn(*data.shape)
"""Create the signal instance and pass data to it
"""
sig = Signal(data, axis=1, sf=sf, time=time, form='tf', tf_cmap='Spectral_r',
psd_nperseg=n_pts)
"""Start by exporting the grid of signals.
"""
sig.screenshot('grid.tiff', canvas='grid', dpi=600, autocrop=True)
"""Loop over all of the signals and export all time-frequency maps.
"""
for k in sig:
# Set the signal index :
sig.set_signal_index(k)
# Set final exported image to be the power spectrum density
if k == n_sines - 2:
sig.set_signal_form('psd')
# Export image :
sig.screenshot('tf_' + str(k) + '.png', dpi=600, autocrop=True)
from visbrain.gui import Signal
from visbrain.utils import generate_eeg
sf = 512. # sampling frequency
n_pts = 4000 # number of time points
n_trials = 125 # number of trials in the dataset
"""Generate a random EEG vector of shape (n_trials, n_pts). This time, we
smooth signals and decrease the noise on it.
"""
data, _ = generate_eeg(sf=sf, n_pts=n_pts, n_trials=n_trials, smooth=200,
noise=1000)
gtitles = ['Trial ' + str(k) for k in range(n_trials)] # grid titles
gfz = 8. # grid titles font-size
"""Define a dictionary with interface customization entries
"""
kwargs = {'xlabel': 'xlabel', 'ylabel': 'ylabel', 'title': 'title',
'color': 'lightgray', 'marker_symbol': 'x', 'title_font_size': 20,
'hist_nbins': 100, 'line_lw': 2.5, 'tf_norm': 3,
'tf_interp': 'nearest', 'tf_cmap': 'Spectral_r',
'tf_baseline': (250, 750), 'tf_av_window': 100, 'tf_av_overlap': .5,
'tf_clim': (-.5, .5), 'axis_font_size': 18, 'tick_font_size': 8,
'axis_color': 'white', 'bgcolor': (.1, .1, .1), 'form': 'marker',
'grid_titles': gtitles, 'grid_font_size': gfz,
'grid_titles_color': 'white'}
sg = Signal(data, sf=sf, axis=-1, **kwargs)
sg.show()
'title': 'My title',
'display_grid': True,
'color': 'darkgray',
'symbol': 'x',
'title_font_size': 20,
'axis_font_size': 18,
'tick_font_size': 8,
'axis_color': 'blue',
'bgcolor': 'white',
'enable_grid': True,
'display_signal': True,
'form': 'psd'
}
# ---------------- Application ----------------
sig = Signal(data, sf=sf, axis=-1, time=time)
class TestSignal(_TestVisbrain):
"""Test signal.py."""
###########################################################################
# LABELS
###########################################################################
def test_xlabel(self):
"""Test setting xlabel."""
# Text :
sig._sig_xlab.setText('New x label')
sig._fcn_axis_xlab()
# Size :
sig._sig_lab_fz.setValue(24.)
Import annotations from a text file and annotate trials.
All annotations are added to the Annotations/ tab. Then, select a row of the
table to jump to it.
Download an example of annotation file :
https://drive.google.com/file/d/0B6vtJiCQZUBvMG95RHNXbDEwaGs/view?usp=sharing
.. image:: ../../_static/examples/ex_annotations.png
"""
from visbrain.gui import Signal
from visbrain.utils import generate_eeg
sf = 512. # sampling frequency
n_pts = 4000 # number of time points
n_trials = 125 # number of trials in the dataset
"""Generate a random EEG vector of shape (n_trials, n_pts). This time, we
smooth signals and decrease the noise on it.
"""
data, _ = generate_eeg(sf=sf,
n_pts=n_pts,
n_trials=n_trials,
smooth=200,
noise=1000)
"""Specify the path to the annotation file :
"""
annotations = 'signal_annotations.txt'
Signal(data, sf=sf, axis=-1, line_lw=2., annotations=annotations).show()
Superimposition of all the signals.
.. image:: ../../_static/examples/ex_butterfly.png
"""
import numpy as np
from visbrain.gui import Signal
sf = 1024. # Sampling frequency
n_pts = 1000 # Number of time points
n_sines = 100 # Number of sines
f_sines = (.4, .6) # (Min, Max) sines frequencies
"""Generate the time vector
"""
time_2d = np.mgrid[0:n_sines, 0:n_pts][1] / sf
time = time_2d[0, :] * 1000.
"""Generate a random dataset to illustrate the butterfly
"""
phy = np.random.uniform(0., np.pi / 2., n_sines)
f_sines = np.random.uniform(f_sines[0], f_sines[1], (n_sines, )).reshape(-1, 1)
data = np.sin(2 * np.pi * f_sines * time_2d + phy.reshape(-1, 1))
data += np.random.rand(*data.shape) / 100.
data *= np.random.uniform(.7, 1.3, (n_sines, )).reshape(-1, 1)
Signal(data,
time=time,
form='butterfly',
xlabel='Time (ms)',
ylabel='Amplitude (uV)',
color='darkgray').show()
sf = 1000. # Sampling-frequency
n_pts = 4000 # Number of time points
n_sines = 10 # Number of sines
"""Create artificial sines :
"""
time = np.arange(n_pts) / sf
f_sines = np.linspace(13, 30, n_sines)
data = np.sin(2 * np.pi * f_sines.reshape(-1, 1) * time.reshape(1, -1))
data += np.random.randn(*data.shape)
"""Create the signal instance and pass data to it
"""
sig = Signal(data,
axis=1,
sf=sf,
time=time,
form='tf',
tf_cmap='Spectral_r',
psd_nperseg=n_pts)
"""Start by exporting the grid of signals.
"""
sig.screenshot('grid.tiff', canvas='grid', dpi=600, autocrop=True)
"""Loop over all of the signals and export all time-frequency maps.
"""
for k in sig:
# Set the signal index :
sig.set_signal_index(k)
# Set final exported image to be the power spectrum density
if k == n_sines - 2:
sig.set_signal_form('psd')
# Export image :
Shortcuts
---------
* Mouse wheel : to zoom over the canvas
* Mouse press and hold : to move the center of the camera
* Double click : insert an annotation under the mouse cursor
* <delete> : reset the camera
.. image:: ../../_static/examples/ex_1d_signal.png
"""
from visbrain.gui import Signal
from visbrain.utils import generate_eeg
sf = 512. # sampling frequency
n_pts = 4000 # number of time points
"""Generate a random EEG vector of shape (n_pts,). Also get the associated
time vector with the same length as the data.
"""
data, time = generate_eeg(sf=sf, n_pts=n_pts)
time += 8. # force the time vector to start at 8 seconds
time *= 1000. # millisecond conversion
"""Add a label to the x-axis (xlabel), y-axis (ylabel) and a title
"""
xlabel = 'Time (ms)'
ylabel = 'Amplitude (uV)'
title = 'Plot of a 1-d signal'
Signal(data, sf=sf, time=time, xlabel=xlabel, ylabel=ylabel,
title=title).show()