def test_heatmap(self):
sp = Spectrum()
sp.from_surface(surface=np.ones([10, 10]))
pl = sp.heatmap()
os.makedirs('data/test_data')
pl.savefig('data/test_data/heatmap.png')
self.assertEqual(os.path.exists('data/test_data/heatmap.png'), True)
shutil.rmtree('data/test_data/')
def plot_average_heatmaps(inputfile, outputfolder, **kwargs):
"""
Plot heatmaps for group-averaged SPHARM spectra.
Parameters
----------
inputfile : str
Path to the file with spectral data.
outputfolder : str
Output directory to save the heatmaps.
kwargs : key, value pairings
Arbitrary keyword arguments to pass to the Spectrum.heatmap function.
"""
filelib.make_folders([os.path.dirname(outputfolder), outputfolder + 'timepoints/'])
stat = pd.read_csv(inputfile, sep='\t', index_col=0)
stat.loc[:, 'Time'] = np.int_(np.round_(stat['Time'] / 10.)) * 10
if 'Group' not in stat.columns:
for name in stat['Name'].unique():
group = name.split('/')[0]
stat = stat.set_value(stat[stat['Name'] == name].index, 'Group', group)
data = stat.groupby(['degree', 'order', 'Group']).mean().reset_index()
for gr in data['Group'].unique():
curdata = data[data['Group'] == gr]
s = Spectrum()
s.harmonics_csv = curdata
pl = s.heatmap(title=gr + ' average', **kwargs)
pl.savefig(outputfolder + gr + '.png')
pl.clf()
# plot separate time points
stat = stat.groupby(['Time', 'Group', 'degree', 'order']).mean().reset_index()
for t in stat['Time'].unique():
for gr in stat['Group'].unique():
curdata = stat[(stat['Group'] == gr) & (stat['Time'] == t)]
if len(curdata) > 0:
s = Spectrum()
s.harmonics_csv = curdata
pl = s.heatmap(title=gr + ' average, time point ' + str(t), **kwargs)
pl.savefig(outputfolder + 'timepoints/' + gr + '_time=' + str(t) + '.png')
pl.clf()
def plot_individual_heatmaps(inputfolder, outputfolder, **kwargs):
"""
Plot heatmaps of individual SPHARM spectra in a given directory.
Parameters
----------
inputfolder : str
Input directory with spectra to plot.
outputfolder : str
Output directory to save the heatmaps.
kwargs : key, value pairings
Arbitrary keyword arguments to pass to the Spectrum.heatmap function.
"""
files = filelib.list_subfolders(inputfolder, extensions=['csv'])
for fn in files:
s = Spectrum(filename=inputfolder + fn)
pl = s.heatmap(title=fn[:-4], **kwargs)
filelib.make_folders([os.path.dirname(outputfolder + fn[:-4])])
pl.savefig(outputfolder + fn[:-4] + '.png')
pl.clf()