def test_naive_fitter_single_fits():
for fitter_class in profile_fitters.naive_fitters.values():
ground = get_ground(fitter_class)
p = np.random.poisson(fitter_class._model_function(ground, X))
lp = rois.LineProfile(distance=X, profile=p)
results = fitter_class.fit_single_profile(lp)
np.testing.assert_allclose(results['fitResults'][0].tolist(), ground, atol=5, rtol=0.15)
def _load_profiles_from_list(self, ragged):
for lp in ragged:
if isinstance(lp, rois.LineProfile):
self.add_line_profile(lp, update=False) # we might not be using a GUI
elif type(lp) is dict: # recreate the line profile
if lp['class'] == 'LineProfile':
input_lp = lp.copy()
del input_lp['class']
self.add_line_profile(rois.LineProfile(**input_lp), update=False)
def _load_profiles_from_array(self, parray):
profile_count = parray.shape[0]
for pi in range(profile_count):
lp = rois.LineProfile(parray[pi]['r1'], parray[pi]['c1'], parray[pi]['r2'], parray[pi]['c2'],
parray[pi]['slice'], parray[pi]['width'], identifier=pi)
lp.set_profile(parray[pi]['profile'])
lp.set_distance(parray[pi]['distance'])
# self.add_line_profile(lp)
# skip GUI calls and just add profile
self._rois.append(lp)
def test_non_ensemble_fitters():
for fitter_class in profile_fitters.non_ensemble_fitters.values():
ground = get_ground(fitter_class)
# leave out noise for non-ensemble fitters, otherwise results will be poor when fitting a single profile
p = fitter_class._model_function(ground, X)
lp = rois.LineProfile(distance=X, profile=p)
h = handlers.LineProfileHandler()
h.add_line_profile(lp, update=False)
fitter = fitter_class(h)
fitter.fit_profiles()
np.testing.assert_allclose(fitter.results['fitResults'][0].tolist(), ground, atol=5, rtol=0.15)
def LineProfile_from_array(self, parray, table_name=None):
lp = rois.LineProfile(parray['r1'],
parray['c1'],
parray['r2'],
parray['c2'],
parray['slice'],
parray['width'],
identifier=table_name)
lp.set_profile(parray['profile'])
lp.set_distance(parray['distance'])
return lp
def test_ensemble_fitters():
for fitter_class in profile_fitters.ensemble_fitters.values():
ground, ens = get_ground(fitter_class)
# leave out poisson sampling since we're only making one profile and with noise the lumen fitters mix psf / diam
p = fitter_class._model_function(ground, X, ens)
lp = rois.LineProfile(distance=X, profile=p)
h = handlers.LineProfileHandler()
h.add_line_profile(lp, update=False)
fitter = fitter_class(h)
fitter.ensemble_fit(ens)
np.testing.assert_allclose(fitter.results['fitResults'][0].tolist(), ground, rtol=0.1)
np.testing.assert_allclose(fitter.results['ensemble_parameter'][0].tolist(), ens, rtol=0.1)
def open_line_profiles(self, hdfFile, gui=True):
import tables
from PYME.IO import ragged
if type(hdfFile) == tables.file.File:
hdf = hdfFile
else:
hdf = tables.open_file(hdfFile)
for t in hdf.list_nodes('/'):
#print t.name, t.__class__
if isinstance(t, tables.vlarray.VLArray):
profs = ragged.RaggedVLArray(hdf, t.name)
for i in range(len(profs)): # in profs[:]:
p = profs[i]
identifier = str(
i) if not p['identifier'] else p['identifier']
if p['class'] == 'LineProfile':
lp = rois.LineProfile(p['r1'],
p['c1'],
p['r2'],
p['c2'],
p['slice'],
p['width'],
identifier=identifier,
image_name=p['image_name'])
lp.set_profile(p['profile'])
lp.set_distance(p['distance'])
else: # p['class'] == 'MultiaxisProfile':
profiles = []
positions = []
n_prof = 0
for k in p.keys():
if len(k.split('~')) > 1:
n_prof = max(n_prof, int(k.split('~')[-1]) + 1)
for pi in range(n_prof):
profiles.append(np.asarray(p['profiles~%d' % pi]))
positions.append(np.asarray(p['positions~%d' %
pi]))
lp = rois.MultiaxisProfile(profiles,
positions,
p['widths'],
identifier=identifier,
image_name=p['image_name'])
self.add_line_profile(lp, False)
if gui:
self.update_names(relabel=True)
self._on_list_changed()
# TODO - check if we need to worry about multiple profiles with the same id
hdf.close()
def _load_profiles_from_imagej(self, fn, constant_slice=None, gui=True):
"""
Read line profiles from Fiji/ImageJ.
Parameters
----------
fn : str
Filename containing line profiles.
constant_slice : int
Set slice to a constant value. Useful if ImageJ
output is inconsistent.
gui: bool
flag to update names / send signals to update the gui profile list.
"""
import zipfile
try:
import read_roi
except (ImportError):
raise ImportError(
'Please install the read_roi package (https://pypi.org/project/read-roi/).'
)
try:
imagej_rois = read_roi.read_roi_zip(fn)
except (zipfile.BadZipFile):
imagej_rois = read_roi.read_roi_file(fn)
for key in imagej_rois.keys():
roi = imagej_rois[key]
# Check for a slice val
if constant_slice is None:
try:
slice_val = roi['position'] - 1
except KeyError:
slice_val = 0
else:
slice_val = constant_slice
# x, y transposed in Fiji/ImageJ
# Position is 1-indexed in Fiji/ImageJ
self.add_line_profile(rois.LineProfile(roi['y1'],
roi['x1'],
roi['y2'],
roi['x2'],
slice=slice_val,
width=roi['width'],
identifier=roi['name'],
image_name=self.image_name),
update=False)
if gui:
self.update_names(relabel=True)
self._on_list_changed()