def test_mne_switch(self):
"""Test function mne_switch."""
# Download sleep file :
sleep_file = path_to_visbrain_data('excerpt2.edf')
if not os.path.isfile(sleep_file):
download_file('sleep_edf.zip', unzip=True)
to_exclude = ['VAB', 'NAF2P-A1', 'PCPAP', 'POS', 'FP2-A1', 'O2-A1',
'CZ2-A1', 'event_pneumo', 'event_pneumo_aut']
kwargs = dict(exclude=to_exclude, stim_channel=False)
mne_switch(path_to_visbrain_data('excerpt2'), '.edf', 100.,
preload=True, **kwargs)
def test_mne_switch(self):
"""Test function mne_switch."""
# Download sleep file :
sleep_file = path_to_visbrain_data('excerpt2.edf', 'example_data')
file, ext = os.path.splitext(sleep_file)
if not os.path.isfile(sleep_file):
download_file('sleep_edf.zip', unzip=True, astype='example_data')
to_exclude = ['VAB', 'NAF2P-A1', 'PCPAP', 'POS', 'FP2-A1', 'O2-A1',
'CZ2-A1', 'event_pneumo', 'event_pneumo_aut']
kwargs = dict(exclude=to_exclude, stim_channel=False)
mne_switch(file, ext, 100., preload=True, **kwargs)
def test_figure(self):
"""Test function figure."""
# Get files :
files = [path_to_visbrain_data(k) for k in _FILES]
# Titles :
titles = ['Default', 'Sources inside', 'Connectivity',
'Connectivity', 'Repartition', 'Projection']
# X-labels / Y-labels :
xlabels = [None, None, 'Nice', 'Looks good', 'Repartition', 'ROI']
ylabels = ['Dirty', 'Better', None, None, None, 'BA 4 and 6']
# Background color of each axis :
ax_bgcolor = ['slateblue', 'olive', 'black', 'darkgray', None, None]
f = Figure(files, titles=titles, xlabels=xlabels, ylabels=ylabels,
figtitle='Brain module', grid=(3, 2), ax_bgcolor=ax_bgcolor,
y=1., fig_bgcolor='white', figsize=(12, 12),
text_color='black', subspace={'wspace': 0.1, 'left': 0.})
# Colorbar to the first connectivity plot :
f.colorbar_to_axis(2, (1, 5), 'viridis', title='Color by count',
ticks='minmax', fz_ticks=12)
# Colorbar to the second connectivity plot :
f.colorbar_to_axis(3, (0., 35.), 'magma', title='Color by density',
ticks='complete', vmax=30, over='darkred',
fz_ticks=12, vmin=10., under='gray')
# Colorbar to the first projection plot :
f.colorbar_to_axis(4, (1, 6), 'viridis', title='Contributing sources',
ticks=1., fz_ticks=10, orientation='horizontal',
vmin=2, under='gray', vmax=4, over='#ab4642')
# Colorbar to the second projection plot :
f.colorbar_to_axis(5, (.1, .5), 'inferno', title='ROI projection',
ticks=[.2, .3], fz_ticks=10,
orientation='horizontal')
# Add a vertical shared colormap :
f.shared_colorbar((-10, 10), 'inferno', fz_title=30, vmin=-7, vmax=6,
under='olive', over='firebrick', position='right',
title='Shared vertical colorbar', fz_ticks=20,
pltmargin=.1, figmargin=.1)
# Add a horizontal shared colormap :
f.shared_colorbar(cmap='magma', clim=(-17, 17), fz_title=25, vmin=-11,
vmax=12, under='olive', over='firebrick',
position='bottom', title='Shared horizontal',
fz_ticks=15, pltmargin=.1)
# Save the picture :
f.save(self.to_tmp_dir('figure.png'), dpi=100)
from visbrain.objects.tests._testing_objects import _TestVolumeObject
from visbrain.objects import SourceObj, RoiObj
from visbrain.io import (download_file, path_to_visbrain_data, read_nifti,
clean_tmp)
roi_obj = RoiObj('brodmann')
roi_obj.select_roi([4, 6])
rnd = np.random.RandomState(0)
xyz = 100. * rnd.rand(50, 3)
xyz[:, 0] -= 50.
xyz[:, 1] -= 50.
s_obj = SourceObj('S1', xyz)
download_file('MIST_ROI.zip', unzip=True, astype='example_data')
nifti_file = path_to_visbrain_data('MIST_ROI.nii.gz', 'example_data')
csv_file = path_to_visbrain_data('MIST_ROI.csv', 'example_data')
# Read the .csv file :
arr = np.genfromtxt(csv_file, delimiter=';', dtype=str)
# Get column names, labels and index :
column_names = arr[0, :]
arr = np.delete(arr, 0, 0)
n_roi = arr.shape[0]
roi_index = arr[:, 0].astype(int)
roi_labels = arr[:, [1, 2]].astype(object)
# Build the struct array :
label = np.zeros(n_roi, dtype=[('label', object), ('name', object)])
label['label'] = roi_labels[:, 0]
label['name'] = roi_labels[:, 1]
# Get the volume and the hdr transformation :
vol, _, hdr = read_nifti(nifti_file, hdr_as_array=True)
Every ROI files is downloaded to the ~/visbrain_data/roi folder
"""
import numpy as np
from visbrain.objects import RoiObj, ColorbarObj, SceneObj, SourceObj, BrainObj
from visbrain.io import download_file, path_to_visbrain_data, read_nifti
###############################################################################
# Download data
###############################################################################
# In order to work, this example need to download some data i.e coordinates of
# intracranial sources and a parcellates atlas (MIST) to illustrate how to
# define your own RoiObj
# Get the path to the ~/visbrain_data/example_data folder
vb_path = path_to_visbrain_data(folder='example_data')
# Download (x, y, z) coordinates of intracranial sources
mat = np.load(download_file('xyz_sample.npz', astype='example_data'))
xyz, subjects = mat['xyz'], mat['subjects']
data = np.random.uniform(low=-1., high=1., size=(xyz.shape[0],))
# Download the MIST parcellates
download_file('MIST_ROI.zip', unzip=True, astype='example_data')
###############################################################################
# Scene creation
###############################################################################
# First, we need to create the scene that will host objects
# Scene creation with a dark background and a custom size
sc = SceneObj(size=(1400, 1000))
# In this example, we also illustrate the use of the colorbar object. Hence, we
Every ROI files is downloaded to the ~/visbrain_data/roi folder
"""
import numpy as np
from visbrain.objects import RoiObj, ColorbarObj, SceneObj, SourceObj, BrainObj
from visbrain.io import download_file, path_to_visbrain_data, read_nifti
###############################################################################
# Download data
###############################################################################
# In order to work, this example need to download some data i.e coordinates of
# intracranial sources and a parcellates atlas (MIST) to illustrate how to
# define your own RoiObj
# Get the path to the ~/visbrain_data/example_data folder
vb_path = path_to_visbrain_data(folder='example_data')
# Download (x, y, z) coordinates of intracranial sources
mat = np.load(download_file('xyz_sample.npz', astype='example_data'))
xyz, subjects = mat['xyz'], mat['subjects']
data = np.random.uniform(low=-1., high=1., size=(xyz.shape[0], ))
# Download the MIST parcellates
download_file('MIST_ROI.zip', unzip=True, astype='example_data')
###############################################################################
# Scene creation
###############################################################################
# First, we need to create the scene that will host objects
# Scene creation with a dark background and a custom size
sc = SceneObj(size=(1400, 1000))
# In this example, we also illustrate the use of the colorbar object. Hence, we
def to_tmp_dir(self, file=None):
"""Path to a tmp dir in visbrain-data."""
vb_path = os.path.join(path_to_visbrain_data(), 'tmp')
if not os.path.exists(vb_path):
os.makedirs(vb_path)
return path_to_visbrain_data(file=file, folder='tmp')
https://www.dropbox.com/s/bj1ra95rbksukro/sleep_edf.zip?dl=1
.. image:: ../../picture/picsleep/ex_replace_detection_basic.png
"""
###############################################################################
# Load your file and create an instance of Sleep
###############################################################################
import os
import numpy as np
from visbrain import Sleep
from visbrain.io import download_file, path_to_visbrain_data
# Get data path and where to save it :
current_path = path_to_visbrain_data()
target_path = os.path.join(current_path, 'sleep_data', 'edf')
# Download the file :
download_file('sleep_edf.zip', unzip=True, to_path=target_path)
# Get data path :
dfile = os.path.join(target_path, 'excerpt2.edf') # data
hfile = os.path.join(target_path, 'Hypnogram_excerpt2.txt') # hypnogram
cfile = os.path.join(target_path, 'excerpt2_config.txt') # GUI config
# Define an instance of Sleep :
sp = Sleep(data=dfile, hypno=hfile, config_file=cfile)
###############################################################################
# Define new methods
"""Test Sleep module and related methods."""
import os
import numpy as np
from vispy.app.canvas import MouseEvent, KeyEvent
from vispy.util.keys import Key
from visbrain import Sleep
from visbrain.io import download_file, path_to_visbrain_data
from visbrain.tests._tests_visbrain import _TestVisbrain
# File to load :
sleep_file = path_to_visbrain_data('excerpt2.edf')
hypno_file = path_to_visbrain_data('Hypnogram_excerpt2.txt')
# Download sleep file :
if not os.path.isfile(sleep_file):
download_file('sleep_edf.zip', unzip=True)
onset = np.array([100, 2000, 5000])
# Create Sleep application :
sp = Sleep(data=sleep_file, hypno=hypno_file, axis=True, annotations=onset)
class TestSleep(_TestVisbrain):
"""Test sleep.py."""
###########################################################################
# TOOLS
###########################################################################
def test_reference_switch(self):
"""Test the HypnogramObj."""
import numpy as np
from visbrain.objects.hypno_obj import HypnogramObj
from visbrain.objects.tests._testing_objects import _TestObjects
from visbrain.io import path_to_visbrain_data
data = np.repeat(np.arange(6), 100) - 1.
h_obj = HypnogramObj('hypno', data)
hypno_file = path_to_visbrain_data('Hypnogram_excerpt2.txt', 'example_data')
class TestHypnogramObj(_TestObjects):
"""Test connectivity object."""
OBJ = h_obj
def test_definition(self):
"""Test function definition."""
HypnogramObj('hypno', data)
HypnogramObj(hypno_file)
def test_set_stage(self):
"""Test set stage."""
h_obj.set_stage(-1, 45, 89)
h_obj.set_stage('wake', 145, 189)
def test_properties(self):
"""Test hypnogram object properties."""
self.assert_and_test('line_width', 4.)
"""Test the HypnogramObj."""
import numpy as np
from visbrain.objects.hypno_obj import HypnogramObj
from visbrain.objects.tests._testing_objects import _TestObjects
from visbrain.io import path_to_visbrain_data
data = np.repeat(np.arange(6), 100) - 1.
h_obj = HypnogramObj('hypno', data)
hypno_file = path_to_visbrain_data('Hypnogram_excerpt2.txt')
class TestHypnogramObj(_TestObjects):
"""Test connectivity object."""
OBJ = h_obj
def test_definition(self):
"""Test function definition."""
HypnogramObj('hypno', data)
HypnogramObj(hypno_file)
def test_set_stage(self):
"""Test set stage."""
h_obj.set_stage(-1, 45, 89)
h_obj.set_stage('wake', 145, 189)
def test_properties(self):
"""Test hypnogram object properties."""
self.assert_and_test('line_width', 4.)
See the original example :
https://pysurfer.github.io/auto_examples/plot_meg_inverse_solution.html#sphx-glr-auto-examples-plot-meg-inverse-solution-py
.. image:: ../../picture/picpysurfer/ex_pysurfer_meg_inverse.png
"""
from visbrain import Brain
from visbrain.objects import BrainObj
from visbrain.io import path_to_visbrain_data, download_file, read_stc
"""Download file if needed :
"""
file = 'meg_source_estimate-lh.stc'
download_file(file)
# Read the *.stc file :
file = read_stc(path_to_visbrain_data(file=file))
# Get the data and vertices from the file :
data = file['data'][:, 2]
vertices = file['vertices']
# Define a brain object and add the data to the mesh :
b_obj = BrainObj('inflated', translucent=False, hemisphere='left')
b_obj.add_activation(data=data,
vertices=vertices,
smoothing_steps=5,
clim=(13., 22.),
hide_under=13.,
cmap='plasma',
hemisphere='left')
"""
Load an existing colorbar configuration file
============================================
The configuration is in a config.txt file and is loaded to reproduce the
colorbar configuration.
Configuration files :
https://www.dropbox.com/s/5o1ph08rmpft200/cbar_config.zip?dl=0
.. image:: ../../picture/piccbar/ex_load_existing_config.png
"""
from visbrain import Colorbar
from visbrain.io import download_file, path_to_visbrain_data
download_file('cbar_config.zip', unzip=True, remove_archive=True)
Colorbar(config=path_to_visbrain_data('config_1.txt')).show()
https://pysurfer.github.io/auto_examples/plot_fmri_conjunction.html#sphx-glr-auto-examples-plot-fmri-conjunction-py
.. image:: ../../picture/picpysurfer/ex_pysurfer_conjunction_map.png
"""
from visbrain import Brain
from visbrain.objects import BrainObj
from visbrain.io import path_to_visbrain_data, download_file
"""Download files if needed
"""
file_name_1 = 'lh.sig.nii.gz'
file_name_2 = 'lh.alt_sig.nii.gz'
download_file(file_name_1)
download_file(file_name_2)
file_1 = path_to_visbrain_data(file=file_name_1)
file_2 = path_to_visbrain_data(file=file_name_2)
b_obj = BrainObj('inflated', translucent=False)
b_obj.add_activation(file=file_1,
clim=(4., 30.),
hide_under=4,
cmap='Reds_r',
hemisphere='left')
b_obj.add_activation(file=file_2,
clim=(4., 30.),
hide_under=4,
cmap='Blues_r',
hemisphere='left')
vb = Brain(brain_obj=b_obj)
"""Test the HypnogramObj."""
import numpy as np
from visbrain.objects.hypno_obj import HypnogramObj
from visbrain.objects.tests._testing_objects import _TestObjects
from visbrain.io import path_to_visbrain_data
data = np.repeat(np.arange(6), 100) - 1.
h_obj = HypnogramObj('hypno', data)
hypno_file = path_to_visbrain_data('Hypnogram_excerpt2.txt', 'example_data')
class TestHypnogramObj(_TestObjects):
"""Test connectivity object."""
OBJ = h_obj
def test_definition(self):
"""Test function definition."""
HypnogramObj('hypno', data)
HypnogramObj(hypno_file)
def test_set_stage(self):
"""Test set stage."""
h_obj.set_stage(-1, 45, 89)
h_obj.set_stage('wake', 145, 189)
def test_properties(self):
"""Test hypnogram object properties."""
self.assert_and_test('line_width', 4.)
self.assert_and_test('unicolor', True)
Display topographic plots in a grid using several plotting properties.
Download topoplot data (topoplot_data.npz) :
https://www.dropbox.com/s/m76y3p0fyj6lxht/topoplot_data.npz?dl=1
.. image:: ../../picture/pictopo/ex_topoplot_plotting_properties.png
"""
import numpy as np
from visbrain import Topo
from visbrain.io import download_file, path_to_visbrain_data
# Load the data :
download_file('topoplot_data.npz')
mat = np.load(path_to_visbrain_data('topoplot_data.npz'))
xyz, data = mat['xyz'], mat['data']
channels = [str(k) for k in range(len(data))]
kwargs = {'title_size': 3., 'cb_txt_size': 2, 'margin': 15 / 100,
'chan_offset': (0., 1.1, 0.), 'chan_size': 1.5}
# Create a topoplot instance :
t = Topo()
# Topoplot with 10 regulary spaced levels :
t.add_topoplot('Topo_1', data, xyz=xyz, channels=channels,
title='Regulary spaced levels', cmap='viridis', levels=10,
level_colors='Spectral_r', cblabel='Beta power',
title_color='#ab4642', **kwargs)
Further explanations about screenshot and transparency can be foud here :
http://visbrain.org/vbexport.html
Download source's coordinates (xyz_sample.npz) :
https://www.dropbox.com/s/whogfxutyxoir1t/xyz_sample.npz?dl=1
.. image:: ../../_static/examples/ex_brain_screenshot.png
"""
import os
import numpy as np
from visbrain.gui import Brain
from visbrain.objects import BrainObj, SourceObj, RoiObj
from visbrain.io import download_file, path_to_visbrain_data
save_pic_path = path_to_visbrain_data(folder='Example_pic')
# Load the xyz coordinates and corresponding subject name :
s_xyz = np.load(download_file('xyz_sample.npz', astype='example_data'))['xyz']
"""Create a source object with random data between [-50,50]
"""
s_data = np.random.uniform(-50, 50, s_xyz.shape[0])
s_obj = SourceObj('Sobj',
s_xyz,
data=s_data,
color='darkred',
alpha=.5,
radius_min=2.,
radius_max=8.,
edge_width=0.)
"""Create a Region of Interest Object (ROI) and display brodmann area 4 and 6
.. image:: ../../_static/examples/ex_replace_detection_basic.png
"""
###############################################################################
# Load your file and create an instance of Sleep
###############################################################################
import os
import numpy as np
from visbrain.gui import Sleep
from visbrain.io import download_file, path_to_visbrain_data
# Download the file :
download_file('sleep_edf.zip', unzip=True, astype='example_data')
target_path = path_to_visbrain_data(folder='example_data')
# Get data path :
dfile = os.path.join(target_path, 'excerpt2.edf') # data
hfile = os.path.join(target_path, 'Hypnogram_excerpt2.txt') # hypnogram
cfile = os.path.join(target_path, 'excerpt2_config.txt') # GUI config
# Define an instance of Sleep :
sp = Sleep(data=dfile, hypno=hfile, config_file=cfile)
###############################################################################
# Define new methods
###############################################################################
###############################################################################
# Spindle function
Further explanations about screenshot and transparency can be foud here :
http://visbrain.org/vbexport.html
Download source's coordinates (xyz_sample.npz) :
https://www.dropbox.com/s/whogfxutyxoir1t/xyz_sample.npz?dl=1
.. image:: ../../_static/examples/ex_brain_screenshot.png
"""
import os
import numpy as np
from visbrain.gui import Brain
from visbrain.objects import BrainObj, SourceObj, RoiObj
from visbrain.io import download_file, path_to_visbrain_data
save_pic_path = path_to_visbrain_data(folder='Example_pic')
# Load the xyz coordinates and corresponding subject name :
s_xyz = np.load(download_file('xyz_sample.npz', astype='example_data'))['xyz']
"""Create a source object with random data between [-50,50]
"""
s_data = np.random.uniform(-50, 50, s_xyz.shape[0])
s_obj = SourceObj('Sobj', s_xyz, data=s_data, color='darkred', alpha=.5,
radius_min=2., radius_max=8., edge_width=0.)
"""Create a Region of Interest Object (ROI) and display brodmann area 4 and 6
"""
roi_obj = RoiObj('brodmann')
idx_4_6 = roi_obj.where_is(['BA4', 'BA6'], exact=True)
roi_color = {idx_4_6[0]: 'red', # BA4 in red and BA6 in green
def to_tmp_dir(self, file=None):
"""Path to a tmp dir in visbrain-data."""
vb_path = os.path.join(path_to_visbrain_data(), 'tmp')
if not os.path.exists(vb_path):
os.makedirs(vb_path)
return path_to_visbrain_data(file=file, folder='tmp')
"""
from __future__ import print_function
import numpy as np
from visbrain import Brain
from visbrain.objects import SourceObj, ConnectObj
from visbrain.io import download_file, path_to_visbrain_data
# Create an empty kwargs dictionnary :
kwargs = {}
# ____________________________ DATA ____________________________
# Load the xyz coordinates and corresponding subject name :
download_file('xyz_sample.npz')
mat = np.load(path_to_visbrain_data('xyz_sample.npz'))
xyz, subjects = mat['xyz'], mat['subjects']
N = xyz.shape[0] # Number of electrodes
# Now, create some random data between [-50,50]
data = np.random.uniform(-50, 50, len(subjects))
"""Create the source object :
"""
s_obj = SourceObj('SourceObj1', xyz, data, color='crimson', alpha=.5,
edge_width=2., radius_min=2., radius_max=10.)
"""
To connect sources between them, we create a (N, N) array.
This array should be either upper or lower triangular to avoid
from visbrain.objects.tests._testing_objects import _TestVolumeObject
from visbrain.objects import SourceObj, RoiObj
from visbrain.io import (download_file, path_to_visbrain_data, read_nifti,
clean_tmp)
roi_obj = RoiObj('brodmann')
roi_obj.select_roi([4, 6])
rnd = np.random.RandomState(0)
xyz = 100. * rnd.rand(50, 3)
xyz[:, 0] -= 50.
xyz[:, 1] -= 50.
s_obj = SourceObj('S1', xyz)
download_file('MIST_ROI.zip', unzip=True, astype='example_data')
nifti_file = path_to_visbrain_data('MIST_ROI.nii.gz', 'example_data')
csv_file = path_to_visbrain_data('MIST_ROI.csv', 'example_data')
# Read the .csv file :
arr = np.genfromtxt(csv_file, delimiter=';', dtype=str)
# Get column names, labels and index :
column_names = arr[0, :]
arr = np.delete(arr, 0, 0)
n_roi = arr.shape[0]
roi_index = arr[:, 0].astype(int)
roi_labels = arr[:, [1, 2]].astype(object)
# Build the struct array :
label = np.zeros(n_roi, dtype=[('label', object), ('name', object)])
label['label'] = roi_labels[:, 0]
label['name'] = roi_labels[:, 1]
# Get the volume and the hdr transformation :
vol, _, hdr = read_nifti(nifti_file, hdr_as_array=True)
"""
Display fMRI activation
=======================
Display fMRI activations from a nii.gz file (NiBabel required).
See the original example :
https://pysurfer.github.io/auto_examples/plot_fmri_activation.html#sphx-glr-auto-examples-plot-fmri-activation-py
.. image:: ../../picture/picpysurfer/ex_pysurfer_fmri_activations.png
"""
from visbrain import Brain
from visbrain.objects import BrainObj
from visbrain.io import path_to_visbrain_data, download_file
"""Download file if needed
"""
file_name = 'lh.sig.nii.gz'
download_file(file_name)
file = path_to_visbrain_data(file=file_name)
b_obj = BrainObj('inflated', translucent=False, sulcus=True)
b_obj.add_activation(file=file, clim=(5., 20.), hide_under=5, cmap='viridis',
hemisphere='left')
vb = Brain(brain_obj=b_obj)
vb.rotate('left')
vb.show()
from visbrain.objects.tests._testing_objects import _TestVolumeObject
from visbrain.objects import SourceObj, RoiObj
from visbrain.io import (download_file, path_to_visbrain_data, read_nifti,
clean_tmp)
roi_obj = RoiObj('brodmann')
roi_obj.select_roi([4, 6])
rnd = np.random.RandomState(0)
xyz = 100. * rnd.rand(50, 3)
xyz[:, 0] -= 50.
xyz[:, 1] -= 50.
s_obj = SourceObj('S1', xyz)
download_file('MIST_ROI.zip', unzip=True)
nifti_file = path_to_visbrain_data('MIST_ROI.nii.gz')
csv_file = path_to_visbrain_data('MIST_ROI.csv')
# Read the .csv file :
arr = np.genfromtxt(csv_file, delimiter=';', dtype=str)
# Get column names, labels and index :
column_names = arr[0, :]
arr = np.delete(arr, 0, 0)
n_roi = arr.shape[0]
roi_index = arr[:, 0].astype(int)
roi_labels = arr[:, [1, 2]].astype(object)
# Build the struct array :
label = np.zeros(n_roi, dtype=[('label', object), ('name', object)])
label['label'] = roi_labels[:, 0]
label['name'] = roi_labels[:, 1]
# Get the volume and the hdr transformation :
vol, _, hdr = read_nifti(nifti_file, hdr_as_array=True)
###############################################################################
# Load your file and create an instance of Sleep
###############################################################################
import os
import numpy as np
from visbrain.gui import Sleep
from visbrain.io import download_file, path_to_visbrain_data
from wonambi.detect.spindle import DetectSpindle, detect_Moelle2011
from wonambi.detect.slowwave import DetectSlowWave, detect_Massimini2004
# Download the file :
download_file('sleep_edf.zip', unzip=True, astype='example_data')
target_path = path_to_visbrain_data(folder='example_data')
# Get data path :
dfile = os.path.join(target_path, 'excerpt2.edf') # data
hfile = os.path.join(target_path, 'Hypnogram_excerpt2.txt') # hypnogram
cfile = os.path.join(target_path, 'excerpt2_config.txt') # GUI config
# Define an instance of Sleep :
sp = Sleep(data=dfile, hypno=hfile, config_file=cfile)
###############################################################################
# Define new methods
###############################################################################
###############################################################################
# Spindle function
"""Test command lines."""
import os
from visbrain import Figure
from visbrain.io import download_file, path_to_visbrain_data
from visbrain.tests._tests_visbrain import _TestVisbrain
# List of image files to test with :
_FILES = ['default.png', 'inside.png', 'count.png', 'density.png',
'repartition.jpg', 'roi.jpg']
all_downloaded = [os.path.isfile(path_to_visbrain_data(k)) for k in _FILES]
if not all(all_downloaded):
download_file('figure.zip', unzip=True)
# Create a tmp/ directory :
dir_path = os.path.dirname(os.path.realpath(__file__))
path_to_tmp = os.path.join(*(dir_path, 'tmp'))
class TestFigure(_TestVisbrain):
"""Test figure.py."""
###########################################################################
# FIGURE
###########################################################################
def test_figure(self):
"""Test function figure."""
# Get files :
files = [path_to_visbrain_data(k) for k in _FILES]
# Titles :
Arange pictures in a grid.
Download the archive :
https://www.dropbox.com/s/jsjct54ynvdjzfq/figure.zip?dl=1
"""
from visbrain import Figure
from visbrain.io import download_file, path_to_visbrain_data
download_file("figure.zip", unzip=True)
# Files to load :
files = [
'default.png', 'inside.png', 'count.png', 'density.png', 'repartition.jpg',
'roi.jpg'
]
files = [path_to_visbrain_data(k) for k in files]
# Titles :
titles = [
'Default view', 'Select sources inside', 'Connectivity', 'Connectivity',
'Cortical repartition', 'Cortical projection'
]
# X-labels / Y-labels :
xlabels = [None, None, 'Looks nice', 'Looks good', 'Repartition', 'ROI']
ylabels = ['Dirty', 'Better', None, None, None, 'Brodmann area 4 and 6']
# Background color of each axis :
ax_bgcolor = ['slateblue', 'olive', 'black', 'darkgray', None, None]
f = Figure(files,
