def plot_3D_surfaces(inputfolder, outputfolder, points=True, gridsize=100):
"""
Plot 3D views of surfaces located in a given directory.
Parameters
----------
inputfolder : str
Input directory with surfaces.
outputfolder : str
Output directory to save the plots.
points : bool, optional
If True, surface points will be displayed.
Default is True.
gridsize : int, optional
Dimension of the square grid to interpolate the surface points.
Default is 100.
"""
files = filelib.list_subfolders(inputfolder, extensions=['csv'])
for fn in files:
s = Surface(filename=inputfolder + fn)
s.centrate()
s.to_spherical()
s.Rgrid = s.interpolate(grid_size=gridsize)
mesh = s.plot_surface(points=points)
mesh.magnification = 3
filelib.make_folders([os.path.dirname(outputfolder + fn[:-4])])
mesh.save(outputfolder + fn[:-4] + '.png', size=(200, 200))
def compute_frequency_spectra(**kwargs):
"""
Compute frequency spectra for a given surface.
Keyword arguments
-----------------
*inputfolder* : str
Directory with the input surface.
*item* : str
File name of the input surface.
*outputfolder* : str
Directory to save the computed spectra.
*grid_size* : int
Dimension of the square grid to interpolate the surface points.
*normalize* : bool
If True, the values of the spectrum will be normalized according to the `normalization_method`.
*normalization_method* : str, optional
If 'mean-radius', the grid values will be divided by the mean grid value prior to the SPHARM transform.
If 'zero-component', all spectral components will be divided by the value of the first component (m=0, n=0).
Default is 'zero-component'.
"""
inputfolder = kwargs.get('inputfolder')
outputfolder = kwargs.get('outputfolder', inputfolder + '../spharm/')
filename = kwargs.get('item')
if not os.path.exists(outputfolder + filename):
surface = Surface(filename=inputfolder + filename)
surface.centrate()
surface.to_spherical()
surface.compute_spharm(
grid_size=kwargs.get('grid_size'),
normalize=kwargs.get('normalize'),
normalization_method=kwargs.get('normalization_method'))
surface.spharm.save_to_csv(outputfolder + filename)
def test_transforms(self):
img = ImageStack(filename='', load=False)
img.data = np.zeros([10, 100, 100])
img.data[2:7, 10:-10, 10:-10] = 1
voxel_size = [4, 0.3824, 0.3824]
img.extract_surfaces('data/test_data/surfaces/',
voxel_size=voxel_size,
reconstruct=False)
surf = Surface(filename='data/test_data/surfaces/_Cell00001.csv')
surf.to_spherical()
x, y, z = tr.spherical_to_cart(surf.R, surf.theta, surf.phi)
self.assertAlmostEqual(np.sum(np.abs(surf.x - x)), 0, 7)
self.assertAlmostEqual(np.sum(np.abs(surf.y - y)), 0, 7)
self.assertAlmostEqual(np.sum(np.abs(surf.z - z)), 0, 7)
shutil.rmtree('data/test_data/')
def test_spharm_transform(self):
img = ImageStack(filename='', load=False)
img.data = np.zeros([100, 100, 100])
img.data[48:52, 48:52, 48:52] = 1.
voxel_size = 0.3
img.extract_surfaces('data/test_data/surfaces/',
voxel_size=voxel_size,
reconstruct=True)
surf = Surface(filename='data/test_data/surfaces/_Cell00001.csv')
surf.centrate()
surf.to_spherical()
grid = surf.interpolate(grid_size=10)
surf.compute_spharm(grid_size=10)
ngrid = surf.inverse_spharm()
self.assertAlmostEqual(np.mean(np.abs(ngrid - grid)), 0, 1)
shutil.rmtree('data/test_data/')
def test_read_surface_and_save_as_stack(self):
img = ImageStack(filename='', load=False)
img.data = np.zeros([10, 100, 100])
img.data[2:7, 10:-10, 10:-10] = 1
voxel_size = [4, 0.3824, 0.3824]
img.extract_surfaces('data/test_data/surfaces/',
voxel_size=voxel_size,
reconstruct=False)
surf = Surface(filename='data/test_data/surfaces/_Cell00001.csv')
for coord in ['x', 'y', 'z']:
self.assertIsNotNone(surf.__dict__[coord])
surf.save_as_stack(filename='data/test_data/stack.tif', voxel_size=0.5)
self.assertEqual(os.path.exists('data/test_data/stack.tif'), True)
self.assertEqual(os.path.exists('data/test_data/stack.txt'), True)
shutil.rmtree('data/test_data/')
def test_add_spectrum_and_plotting(self):
ms = MovingSurface()
surf = Surface(grid=np.ones([10, 10]))
for i in range(3):
ms.add_surface(surf, timepoint=i * 20)
surf = Surface(grid=np.ones([10, 10]))
surf.Rgrid[3:4, 4:8] = 10
for i in range(2):
ms.add_surface(surf, timepoint=i * 20 + 60)
surf = Surface(grid=np.ones([10, 10]))
for i in range(3):
ms.add_surface(surf, timepoint=i * 20 + 100)
self.assertEqual(len(ms.timespectrum.spectra), 0)
ms.compute_timespectrum(gridsize=10)
self.assertEqual(len(ms.timespectrum.spectra), 8)
ms.plot_surfaces('data/test_data/surfaces/')
files = os.listdir('data/test_data/surfaces/')
self.assertEqual(len(files), 8)
ms.plot_max_projections(outputfolder='data/test_data/maxprojections/',
voxel_size=0.1)
files = os.listdir('data/test_data/maxprojections/')
self.assertEqual(len(files), 24)
shutil.rmtree('data/test_data/')
def test_init_and_save(self, coords):
os.makedirs('data/test_data')
f = open('data/test_data/surface.txt', 'w')
f.write(coords)
f.close()
surf = Surface(filename='data/test_data/surface.txt')
for coord in ['x', 'y', 'z']:
self.assertIsNotNone(surf.__dict__[coord])
shutil.rmtree('data/test_data/')
def convert_to_tiff(**kwargs):
"""
Save the surface as a 3D image stack.
Keyword arguments
-----------------
*inputfolder* : str
Directory with the input surface.
*item* : str
File name of the input surface.
*outputfolder* : str
Directory to save the output 3D stack.
"""
inputfolder = kwargs.get('inputfolder')
outputfolder = kwargs.get('outputfolder', inputfolder + '../stacks/')
filename = kwargs.get('item')
surface = Surface(filename=inputfolder + filename, **kwargs)
surface.save_as_stack(outputfolder + filename + '.tif',
voxel_size=kwargs.get('voxel_size'))
def convert_surfaces(**kwargs):
"""
Convert surface file from txt to a csv format.
Keyword arguments
-----------------
*inputfolder* : str
Directory with the input surface.
*item* : str
File name of the input surface.
*outputfolder* : str
Directory to save the converted surface.
"""
inputfolder = kwargs.get('inputfolder')
outputfolder = kwargs.get('outputfolder', inputfolder + '../surfaces/')
filename = kwargs.get('item')
surface = Surface(filename=inputfolder + filename, **kwargs)
surface.save(outputfolder + filename + '.csv')
def test_interpolate(self, grid_size):
img = ImageStack(filename='', load=False)
img.data = np.zeros([10, 100, 100])
img.data[2:7, 10:-10, 10:-10] = 1
voxel_size = [4, 0.3824, 0.3824]
img.extract_surfaces('data/test_data/surfaces/',
voxel_size=voxel_size,
reconstruct=False)
surf = Surface(filename='data/test_data/surfaces/_Cell00001.csv')
surf.centrate()
surf.to_spherical()
grid = surf.interpolate(grid_size=grid_size)
self.assertEqual(len(grid), grid_size)
shutil.rmtree('data/test_data/')
files = os.listdir(path + 'stacks/' + gr + '/')
for fn in files:
print(fn)
stack = ImageStack(path + 'stacks/' + gr + '/' + fn)
stack.filename = fn
stack.extract_surfaces(path + 'surfaces/' + gr + '/',
voxel_size=0.3)
groups = os.listdir(path + 'surfaces/')
for gr in groups:
print(gr)
files = os.listdir(path + 'surfaces/' + gr + '/')
files.sort()
for fn in files:
print(fn)
surface = Surface(filename=path + 'surfaces/' + gr + '/' + fn,
voxel_size=0.3)
mesh = mlab.points3d(surface.x,
surface.y,
surface.z,
surface.x,
scale_mode='none',
scale_factor=0.5,
mode='sphere',
colormap='jet').scene
mesh.background = (1, 1, 1)
mesh.magnification = 10
filelib.make_folders([
os.path.dirname(path + 'surface_plots/' + gr + '_' +
fn[:-4])
])
mesh.save(path + 'surface_plots/' + gr + '_' + fn[:-4] +
def compute_spharm(**kwargs):
"""
Compute spherical harmonics spectra for a given surface.
Keyword arguments
-----------------
*inputfolder* : str
Directory with the input surface.
*item* : str
File name of the input surface.
*outputfolder* : str
Directory to save the computed spectra.
*grid_size* : int
Dimension of the square grid to interpolate the surface points.
*normalize* : bool
If True, the values of the spectrum will be normalized according to the `normalization_method`.
*normalization_method* : str, optional
If 'mean-radius', the grid values will be divided by the mean grid value prior to the SPHARM transform.
If 'zero-component', all spectral components will be divided by the value of the first component (m=0, n=0).
Default is 'zero-component'.
"""
inputfolder = kwargs.get('inputfolder')
outputfolder = kwargs.get('outputfolder', inputfolder + '../spharm/')
filename = kwargs.get('item')
combined_tracks = kwargs.get('combined_tracks', False)
rotate = kwargs.get('rotate', False)
filelib.make_folders([os.path.dirname(outputfolder[:-1] + '_kwargs.csv')])
pd.Series(kwargs).to_csv(outputfolder[:-1] + '_kwargs.csv',
sep='\t',
header=False)
if not (os.path.exists(outputfolder + filename)
or os.path.exists(outputfolder + filename[:-4] +
'_Time_%03d.csv' % 1)):
if combined_tracks:
stat = pd.read_csv(inputfolder + filename, sep='\t', index_col=0)
stat.at[:,
'Time'] = np.array(stat['Time']).astype(float).astype(int)
stat = stat.sort_values('Time').reset_index()
t_surface = MovingSurface()
if not filename.endswith('.csv'):
filename += '.csv'
times = stat['Time'].unique()
if len(times) > 2:
for t in times:
curstat = stat[stat['Time'] == t]
if len(curstat) > 4:
surface = Surface(data=curstat)
surface.metadata[
'Name'] = filename[:-4] + '_Time_%03d.csv' % t
if 'TrackID' in curstat.columns:
surface.metadata['TrackID'] = curstat.iloc[0][
'TrackID']
surface.centrate()
if len(t_surface.surfaces) > 0:
x, y, z = surface.center - t_surface.surfaces[
-1].center # direction of the previous interval
surface.migration_angles = tr.cart_to_spherical(
x, y, z)[1:]
t_surface.add_surface(surface)
else:
print(filename, times, t, len(curstat))
t_surface.surfaces[0].migration_angles = t_surface.surfaces[
1].migration_angles
for surface in t_surface.surfaces:
if rotate:
surface.rotate(surface.migration_angles[0],
surface.migration_angles[1])
surface.to_spherical()
surface.compute_spharm(grid_size=kwargs.get('grid_size'),
normalize=kwargs.get('normalize'),
normalization_method=kwargs.get(
'normalization_method',
'zero-component'))
surface.spharm.save_to_csv(outputfolder +
surface.metadata['Name'])
else:
surface = Surface(filename=inputfolder + filename)
surface.centrate()
surface.to_spherical()
surface.compute_spharm(grid_size=kwargs.get('grid_size'),
normalize=kwargs.get('normalize'),
normalization_method=kwargs.get(
'normalization_method',
'zero-component'))
if not filename.endswith('.csv'):
filename += '.csv'
surface.spharm.save_to_csv(outputfolder + filename)
if not path.endswith('/'):
path += '/'
path += 'output/'
track_stat = pd.DataFrame()
summary_stat = pd.DataFrame()
groups = os.listdir(path + 'surfaces/')
for gr in groups:
print(gr)
files = os.listdir(path + 'surfaces/' + gr + '/')
for fn in files:
stat = pd.read_csv(path + 'surfaces/' + gr + '/' + fn, sep='\t', index_col=0)
t1 = stat['Time'].unique()[0]
stat = stat[stat['Time'] == t1]
print(fn, len(stat))
surface = Surface(data=stat)
mesh = surface.plot_points(scale_factor=0.2)
filelib.make_folders([os.path.dirname(path + 'surface_plots/' + gr + '_' + fn[:-4])])
mesh.save(path + 'surface_plots/' + gr + '_' + fn[:-4] + '_init.png', size=(100, 100))
mlab.clf()
surface.centrate()
surface.to_spherical()
surface.compute_spharm(grid_size=120, normalize=True)
mesh = surface.plot_surface(points=False)
filelib.make_folders([os.path.dirname(path + 'surface_plots/' + gr + '_' + fn[:-4])])
mesh.save(path + 'surface_plots/' + gr + '_' + fn[:-4] + '_grid.png', size=(100, 100))
mlab.clf()
surface.inverse_spharm(lmax=10)
mesh = surface.plot_surface(points=False)
def plot_inverse_shapes(inputfile, outputfolder, group='Group'):
"""
Plot average cells shapes obtained by inverse SPHARM.
Parameters
----------
inputfile : str
Path to the file with spectral data.
outputfolder : str
Directory to save the plotted distributions.
group : str, optional
Column in the input data sheet to use for grouping.
Default is 'Group'.
"""
filelib.make_folders([os.path.dirname(outputfolder)])
stat = pd.read_csv(inputfile, sep='\t', index_col=0)
stat['value'] = stat['real'] + stat['imag']*1j
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()
groups = data[group].unique()
for gr in groups:
curdata = data[data[group] == gr]
sp = Spectrum()
sp.harmonics_csv = curdata
sp.convert_to_shtools_array()
surf = Surface()
surf.spharm = sp
maxdegree = np.max(sp.harmonics_csv['degree'])
for lmax in np.arange(5, maxdegree + 1, 5):
surf.inverse_spharm(lmax=lmax)
surf.plot_surface(points=False).save(outputfolder + '_' + gr + '_inverse_lmax=' + str(lmax) + '.png',
size=(200, 200))
surf.inverse_spharm(lmax=None)
surf.plot_surface(points=False).save(outputfolder + '_' + gr + '_inverse_full.png',
size=(200, 200))
def test07_spharm_inverse_less(self):
path = site.getsitepackages()[0] + '/SPHARM/tests/'
fn = path + 'data/synthetic_cell.txt'
surf = Surface(filename=fn)
surf.centrate()
surf.to_spherical()
surf.compute_spharm(grid_size=100)
surf.inverse_spharm()
os.makedirs('data/test_data')
with warnings.catch_warnings():
warnings.simplefilter("ignore")
surf.plot_surface(points=True).save(
'data/test_data/surface_inverse_all.png', size=(200, 200))
surf.inverse_spharm(lmax=30)
surf.plot_surface(points=True).save(
'data/test_data/surface_inverse_30.png', size=(200, 200))
surf.inverse_spharm(lmax=10)
surf.plot_surface(points=True).save(
'data/test_data/surface_inverse_10.png', size=(200, 200))
self.assertEqual(
os.path.exists('data/test_data/surface_inverse_all.png'), True)
self.assertEqual(
os.path.exists('data/test_data/surface_inverse_30.png'), True)
self.assertEqual(
os.path.exists('data/test_data/surface_inverse_10.png'), True)
shutil.rmtree('data/test_data/')
def test05_plot(self):
path = site.getsitepackages()[0] + '/SPHARM/tests/'
fn = path + 'data/synthetic_cell.txt'
surf = Surface(filename=fn)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
mesh = surf.plot_points()
os.makedirs('data/test_data')
mesh.save('data/test_data/points_3D.png', size=(200, 200))
surf.centrate()
surf.to_spherical()
surf.Rgrid = surf.interpolate(grid_size=100)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
mesh = surf.plot_surface(points=False)
mesh.save('data/test_data/surface_3D.png', size=(200, 200))
with warnings.catch_warnings():
warnings.simplefilter("ignore")
mesh = surf.plot_surface(points=True)
mesh.save('data/test_data/surface_with_points_3D.png',
size=(200, 200))
self.assertEqual(os.path.exists('data/test_data/surface_3D.png'), True)
self.assertEqual(os.path.exists('data/test_data/points_3D.png'), True)
self.assertEqual(
os.path.exists('data/test_data/surface_with_points_3D.png'), True)
shutil.rmtree('data/test_data/')
