def test_find_bp(self):
image_bp = tp.bandpass(self.v0_inverted, **self.bandpass_params)
actual = tp.grey_dilation(image_bp, **self.find_params)
assert_array_equal(actual, self.expected_find_bp)
def test_find_raw(self):
actual = tp.grey_dilation(self.v0_inverted, **self.find_params)
assert_array_equal(actual, self.expected_find_raw)
def test_find_bp(self):
image_bp = tp.bandpass(self.v0_inverted, **self.bandpass_params)
actual = tp.grey_dilation(image_bp, **self.find_params)
assert_array_equal(actual, self.expected_find_bp)
def test_find_raw(self):
actual = tp.grey_dilation(self.v0_inverted, **self.find_params)
assert_array_equal(actual, self.expected_find_raw)
version = 'VERSION' # adjust this
pos_columns = ['y', 'x']
char_columns = ['mass', 'size', 'ecc', 'signal', 'raw_mass', 'ep']
testpath = os.path.join(os.path.dirname(tp.__file__), 'tests')
impath = os.path.join(testpath, 'video', 'image_sequence', '*.png')
npzpath = os.path.join(testpath, 'data',
'reproducibility_v{}.npz'.format(version))
v = pims.ImageSequence(impath)
# take reader that provides uint8!
assert np.issubdtype(v.dtype, np.uint8)
v0 = tp.invert_image(v[0])
v0_bp = tp.bandpass(v0, lshort=1, llong=9)
expected_find = tp.grey_dilation(v0, separation=9)
expected_find_bandpass = tp.grey_dilation(v0_bp, separation=9)
expected_refine = tp.refine_com(v0,
v0_bp,
radius=4,
coords=expected_find_bandpass)
expected_refine = expected_refine[expected_refine['mass'] >= 140]
expected_refine_coords = expected_refine[pos_columns].values
expected_locate = tp.locate(v0, diameter=9, minmass=140)
expected_locate_coords = expected_locate[pos_columns].values
df = tp.locate(v0, diameter=9)
df = df[(df['x'] < 64) & (df['y'] < 64)]
expected_characterize = df[pos_columns + char_columns].values
f = tp.batch(tp.invert_image(v), 9, minmass=140)
f_crop = f[(f['x'] < 320) & (f['x'] > 280) & (f['y'] < 280) & (f['x'] > 240)]
version = 'VERSION' # adjust this
pos_columns = ['y', 'x']
char_columns = ['mass', 'size', 'ecc', 'signal', 'raw_mass', 'ep']
testpath = os.path.join(os.path.dirname(tp.__file__), 'tests')
impath = os.path.join(testpath, 'video', 'image_sequence', '*.png')
npzpath = os.path.join(testpath, 'data',
'reproducibility_v{}.npz'.format(version))
v = pims.ImageSequence(impath)
# take reader that provides uint8!
assert np.issubdtype(v.dtype, np.uint8)
v0 = tp.invert_image(v[0])
v0_bp = tp.bandpass(v0, lshort=1, llong=9)
expected_find = tp.grey_dilation(v0, separation=9)
expected_find_bandpass = tp.grey_dilation(v0_bp, separation=9)
expected_refine = tp.refine_com(v0, v0_bp, radius=4,
coords=expected_find_bandpass)
expected_refine = expected_refine[expected_refine['mass'] >= 140]
expected_refine_coords = expected_refine[pos_columns].values
expected_locate = tp.locate(v0, diameter=9, minmass=140)
expected_locate_coords = expected_locate[pos_columns].values
df = tp.locate(v0, diameter=9)
df = df[(df['x'] < 64) & (df['y'] < 64)]
expected_characterize = df[pos_columns + char_columns].values
f = tp.batch(tp.invert_image(v), 9, minmass=140)
f_crop = f[(f['x'] < 320) & (f['x'] > 280) & (f['y'] < 280) & (f['x'] > 240)]
f_linked = tp.link(f_crop, search_range=5, memory=0)
f_linked_memory = tp.link(f_crop, search_range=5, memory=2)