def test_fourier(self):
sp = Spectrum()
sp.from_surface(surface=np.ones([10, 10]))
sp.convert_to_csv()
tsp = TimeSpectrum()
for i in range(10):
tsp.add_spectrum(sp)
tsp.fourier_analysis()
tsp.save_frequencies_to_csv('data/test_data/time_spectrum_freq.csv')
self.assertEqual(os.path.exists('data/test_data/time_spectrum_freq.csv'), True)
pl = tsp.frequency_heatmap()
pl.savefig('data/test_data/frequency_heatmap.png')
self.assertEqual(os.path.exists('data/test_data/frequency_heatmap.png'), True)
shutil.rmtree('data/test_data/')
def plot_individual_time_heatmaps(inputfile, outputfolder, group='Group', cutoff=None,
logscale=False, id_col='TrackID'):
"""
Plot the amplitude of spectral components over time for different groups as a heatmap.
Parameters
----------
inputfile : str
Path to the file with spectral data.
outputfolder : str
Directory to save the plotted heat maps.
group : str, optional
Column in the input data sheet to use for grouping.
Default is 'Group'.
cutoff : int, optional
The number of degrees to display.
If None, all degrees will be displayed.
logscale : bool, optional
If True, the natural logarithm of the value will be displayed.
Default is False.
id_col : str
Column in the input data sheet to group connected time points.
Default is 'TrackID'
"""
filelib.make_folders([os.path.dirname(outputfolder)])
stat = pd.read_csv(inputfile, sep='\t', index_col=0)
stat['Time'] = np.int_(np.round_(stat['Time'] / 10.)) * 10
if cutoff is not None:
stat = stat[stat['degree'] <= cutoff]
for gr in stat[group].unique():
substat = stat[stat[group] == gr]
for id in substat[id_col].unique():
subsubstat = substat[substat[id_col] == id]
subsubstat = subsubstat.sort_values('Time').reset_index()
time_spectrum = TimeSpectrum()
for t in subsubstat['Time'].unique():
sp = Spectrum()
sp.harmonics_csv = subsubstat[subsubstat['Time'] == t]
time_spectrum.add_spectrum(sp, timepoint=t)
pl = time_spectrum.time_heatmap(value='amplitude', logscale=logscale)
if pl is not None:
pl.savefig(outputfolder + '_' + gr + '_' + 'track_' + str(id) + '.png')
def __init__(self, name=None):
"""
Initiate the time series for the surface.
Parameters
----------
name : str, optional
Name of the surface to add to the output file when saving results.
Default is None.
"""
if name is None:
self.name = ''
else:
self.name = name
self.surfaces = []
self.times = []
self.timespectrum = TimeSpectrum(name=name)
self.minmax = np.array([[1000., -1000.], [1000., -1000.],
[1000., -1000.]])
self.centers = []
def plot_mean_abs_derivative(inputfile, outputfolder, group='Group', cutoff=None, id_col='TrackID'):
"""
Plot the derivative of each spectral component of different groups over time.
Parameters
----------
inputfile : str
Path to the file with spectral data.
outputfolder : str
Directory to save the plotted derivative.
group : str, optional
Column in the input data sheet to use for grouping.
Default is 'Group'.
cutoff : int, optional
The number of degrees to display.
If None, all degrees will be displayed.
id_col : str
Column in the input data sheet to group connected time points.
Default is 'TrackID'
"""
filelib.make_folders([os.path.dirname(outputfolder)])
if not os.path.exists(inputfile[:-4] + '_mean_abs_derivative.csv'):
stat = pd.read_csv(inputfile, sep='\t', index_col=0)
if id_col == 'CellID':
stat.loc[:, 'Time'] = np.int_(np.round_(stat['Time'] / 10.)) * 10
nstat = pd.DataFrame()
if cutoff is not None:
stat = stat[stat['degree'] <= cutoff]
for gr in stat[group].unique():
substat = stat[stat[group] == gr]
for id in substat[id_col].unique():
subsubstat = substat[substat[id_col] == id]
subsubstat = subsubstat.sort_values('Time')
time_spectrum = TimeSpectrum()
for t in subsubstat['Time'].unique():
sp = Spectrum()
sp.harmonics_csv = subsubstat[subsubstat['Time'] == t]
time_spectrum.add_spectrum(sp, timepoint=t)
time_spectrum.compute_derivative()
meanderivstat = time_spectrum.mean_abs_derivative
meanderivstat['Group'] = gr
meanderivstat['TrackID'] = id
nstat = pd.concat([nstat, meanderivstat], ignore_index=True)
nstat.to_csv(inputfile[:-4] + '_mean_abs_derivative.csv', sep='\t')
nstat = pd.read_csv(inputfile[:-4] + '_mean_abs_derivative.csv', sep='\t', index_col=0)
nstat = nstat.sort_values(['harmonic', group])
plt.clf()
plt.figure(figsize=(20, 5))
sns.barplot(x='harmonic', y='absolute amplitude', data=nstat, hue=group)
plt.ylabel('Mean absolute derivative of amplitude')
labels = nstat['harmonic'].unique()
plt.xticks(np.arange(len(labels)) + 0.6, labels, rotation='vertical')
margins = {'left': 0.07, 'right': 0.98, 'top': 0.93, 'bottom': 0.25}
plt.subplots_adjust(**margins)
plt.savefig(outputfolder + 'mean_abs_derivative.png')
plt.close()
class MovingSurface(object):
"""
Class for storing the dynamics of an object surface.
"""
def __init__(self, name=None):
"""
Initiate the time series for the surface.
Parameters
----------
name : str, optional
Name of the surface to add to the output file when saving results.
Default is None.
"""
if name is None:
self.name = ''
else:
self.name = name
self.surfaces = []
self.times = []
self.timespectrum = TimeSpectrum(name=name)
self.minmax = np.array([[1000., -1000.], [1000., -1000.],
[1000., -1000.]])
self.centers = []
def add_surface(self, surface, timepoint=None):
"""
Add new surface to the time series.
Parameters
----------
surface : Surface
Object surface at one time point.
timepoint : scalar, optional
Time point to assign to the surface.
If None, the number of previously added surfaces will be used to label the time point
(e.g. 0 if none were added).
Default is None.
"""
self.surfaces.append(surface)
if timepoint is None:
self.times.append(len(self.surfaces))
else:
self.times.append(timepoint)
self.minmax[0, 0] = min(self.minmax[0, 0], np.min(surface.z))
self.minmax[1, 0] = min(self.minmax[1, 0], np.min(surface.y))
self.minmax[2, 0] = min(self.minmax[2, 0], np.min(surface.x))
self.minmax[0, 1] = max(self.minmax[0, 1], np.max(surface.z))
self.minmax[1, 1] = max(self.minmax[1, 1], np.max(surface.y))
self.minmax[2, 1] = max(self.minmax[2, 1], np.max(surface.x))
self.centers.append(surface.center)
def compute_timespectrum(self, gridsize):
"""
Compute a SPHARM spectrum for each surface and generate a TimeSpectrum object.
Parameters
----------
gridsize : int, optional
Dimension of the square grid to interpolate the surface points.
Will be used to interpolate the surface coordinates if self.Rgrid is None
(in this case it is a mandatory parameter).
Default is None.
"""
for i, surface in enumerate(self.surfaces):
surface.compute_spharm(grid_size=gridsize)
self.timespectrum.add_spectrum(surface.spharm,
timepoint=self.times[i])
def plot_surfaces(self, outputfolder, points=False):
"""
Plot 3D views of all surfaces with mayavi and save to png files.
Parameters
----------
outputfolder : str
Directory to save the plots.
points : bool, optional
If True, surface points will be displayed.
Default is False.
"""
filelib.make_folders([outputfolder])
extent = np.array([self.minmax[2], self.minmax[1],
self.minmax[0]]).flatten()
for i, surface in enumerate(self.surfaces):
mesh = surface.plot_surface(points=points, extent=extent)
mesh.magnification = 3
mesh.save(outputfolder + self.name + '_%03d.png' % self.times[i],
size=(200, 200))
def plot_max_projections(self, outputfolder, voxel_size):
"""
Plot maxium projections of all surfaces and save to png files.
Parameters
----------
outputfolder : str
Directory to save the plots.
voxel_size : scalar or sequence of scalars
Voxel size of the image.
Specified either by individual value for each axis, or by one value for all axes.
"""
filelib.make_folders([outputfolder])
voxel_size = np.array([voxel_size]).flatten()
if len(voxel_size) == 1:
voxel_size = np.ones(3) * voxel_size
for i, surface in enumerate(self.surfaces):
stack = surface.as_stack(voxel_size=voxel_size, minmax=self.minmax)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
io.imsave(
outputfolder + 'xy_' + self.name +
'_%03d.png' % self.times[i],
stack.max(0).astype(np.uint8))
io.imsave(
outputfolder + 'xz_' + self.name +
'_%03d.png' % self.times[i],
stack.max(1).astype(np.uint8))
io.imsave(
outputfolder + 'yz_' + self.name +
'_%03d.png' % self.times[i],
stack.max(2).astype(np.uint8))
def plot_average_frequency_heatmaps(inputfile, outputfolder, group='Group', cutoff=None,
logscale=False, id_col='TrackID'):
"""
Plot the Fourier frequencies of spectral components of different groups as a heatmap.
Parameters
----------
inputfile : str
Path to the file with spectral data.
outputfolder : str
Directory to save the plotted heat maps.
group : str, optional
Column in the input data sheet to use for grouping.
Default is 'Group'.
cutoff : int, optional
The number of degrees to display.
If None, all degrees will be displayed.
logscale : bool, optional
If True, the natural logarithm of the value will be displayed.
Default is False.
id_col : str
Column in the input data sheet to group connected time points.
Default is 'TrackID'
"""
filelib.make_folders([os.path.dirname(outputfolder)])
stat = pd.read_csv(inputfile, sep='\t', index_col=0)
stat.loc[:, 'Time'] = np.int_(np.round_(stat['Time'] / 10.)) * 10
if cutoff is not None:
stat = stat[stat['degree'] <= cutoff]
frequency_stat = pd.DataFrame()
for gr in stat[group].unique():
substat = stat[stat[group] == gr]
for id in substat[id_col].unique():
subsubstat = substat[substat[id_col] == id]
time_spectrum = TimeSpectrum()
for t in subsubstat['Time'].unique():
sp = Spectrum()
sp.harmonics_csv = subsubstat[subsubstat['Time'] == t]
time_spectrum.add_spectrum(sp, timepoint=t)
time_spectrum.fourier_analysis(value='amplitude')
time_spectrum.frequencies['Group'] = gr
frequency_stat = pd.concat([frequency_stat, time_spectrum.frequencies], ignore_index=True)
frequency_stat = frequency_stat.groupby(['Group', 'frequency', 'harmonic']).mean().reset_index()
for gr in stat[group].unique():
time_spectrum = TimeSpectrum()
time_spectrum.frequencies = frequency_stat[frequency_stat['Group'] == gr]
pl = time_spectrum.frequency_heatmap(value='amplitude', logscale=logscale)
if pl is not None:
pl.savefig(outputfolder + gr + '.png')
def test_feature_vector(self):
surf = np.ones([10, 10])
sp = Spectrum()
sp.from_surface(surface=surf)
sp.convert_to_csv()
surf[3:4, 4:8] = 10
sp2 = Spectrum()
sp2.from_surface(surface=surf)
sp2.convert_to_csv()
tsp = TimeSpectrum()
tsp.add_spectrum(sp)
tsp.add_spectrum(sp2)
tsp.add_spectrum(sp)
tsp.compute_derivative()
self.assertEqual(len(tsp.return_feature_vector(cutoff=2)), 3)
def test_add_spectrum_and_plotting(self):
tsp = TimeSpectrum()
sp = Spectrum()
sp.from_surface(surface=np.ones([10, 10]))
sp.convert_to_csv()
for i in range(3):
tsp.add_spectrum(sp, timepoint=i*20)
sp = Spectrum()
surf = np.ones([10, 10])
surf[3:4, 4:8] = 10
sp.from_surface(surface=surf)
sp.convert_to_csv()
for i in range(2):
tsp.add_spectrum(sp, timepoint=i*20+60)
sp = Spectrum()
sp.from_surface(surface=np.ones([10, 10]))
sp.convert_to_csv()
for i in range(3):
tsp.add_spectrum(sp, timepoint=i*20+100)
self.assertEqual(len(tsp.spectra), 8)
tsp.save_to_csv('data/test_data/time_spectrum.csv')
self.assertEqual(os.path.exists('data/test_data/time_spectrum.csv'), True)
pl = tsp.time_heatmap()
pl.savefig('data/test_data/time_heatmap.png')
self.assertEqual(os.path.exists('data/test_data/time_heatmap.png'), True)
tsp.compute_derivative()
pl = tsp.derivative_heatmap()
pl.savefig('data/test_data/derivative_heatmap.png')
self.assertEqual(os.path.exists('data/test_data/derivative_heatmap.png'), True)
pl = tsp.plot_mean_abs_derivative()
pl.savefig('data/test_data/mean_abs_derivative.png')
self.assertEqual(os.path.exists('data/test_data/mean_abs_derivative.png'), True)
shutil.rmtree('data/test_data/')