def test_contrast_stretching():
data = np.arange(100)
assert contrast_stretching(data, 1, 99) == (1, 99)
assert contrast_stretching(data, 1.0, 99.0) == (1, 99)
assert contrast_stretching(data, '1th', '99th') == (0.99, 98.01)
assert contrast_stretching(data, '0.05th', '99.95th') == (0.0495, 98.9505)
# vmin, vmax are to set in conftest.py
assert contrast_stretching(data, None, None) == (0.0, 99.0)
def optimize_contrast(self, data):
if (self._vmin_user is not None and self._vmax_user is not None):
return
if np.issubdtype(data.dtype, complex):
data = np.log(np.abs(data))
self._vmin_auto, self._vmax_auto = utils.contrast_stretching(
data, self.saturated_pixels)
def _convert_tia_single_item(self, item, suffix=''):
original_data = item.data.copy()
for extension in self.extension_list:
item.data = original_data
if extension in ['jpg', 'jpeg'] and self.contrast_streching:
if isinstance(item, hs.signals.Signal1D):
return
vmin, vmax = contrast_stretching(
item.data, self.saturated_pixels)
item.data = self.stretch_contrast(item.data, vmin, vmax)
if extension in ['tif', 'tiff']:
if isinstance(item, hs.signals.Signal1D):
return
self._set_convenient_scale(item)
if self.normalisation:
# dirty workaround to read tif file in imagej and dm...
item.data = self.normalise(item.data)
item.data *= np.iinfo(np.int32).max
item.change_dtype(np.int32)
self.fname_ext = ''.join([os.path.splitext(self.fname)[0], suffix,
'.', extension])
if os.path.exists(self.fname_ext) and self.overwrite is None:
write_answer = self._ask_confirmation_overwrite()
self._save_data(item, overwrite=write_answer)
# workaround, currently hyperspy doesn't write file is
# overwrite=False
elif not os.path.exists(self.fname_ext):
self._save_data(item)
else:
self._save_data(item, overwrite=self.overwrite)
def _calculate_vmin_max(self,
data,
auto_contrast=False,
vmin=None,
vmax=None):
# Calculate vmin and vmax using `utils.contrast_stretching` when:
# - auto_contrast is True
# - self.vmin or self.vmax is of tpye str
if (auto_contrast and
(isinstance(self.vmin, str) or isinstance(self.vmax, str))):
with ignore_warning(category=RuntimeWarning):
# In case of "All-NaN slices"
vmin, vmax = utils.contrast_stretching(data, self.vmin,
self.vmax)
else:
vmin, vmax = self._vmin_numeric, self._vmax_numeric
# provided vmin, vmax override the calculated value
if isinstance(vmin, (int, float)):
vmin = vmin
if isinstance(vmax, (int, float)):
vmax = vmax
if vmin == np.nan:
vmin = None
if vmax == np.nan:
vmax = None
return vmin, vmax
def optimize_contrast(self, data):
if (self._vmin_user is not None and self._vmax_user is not None):
return
if np.issubdtype(data.dtype, complex):
data = np.log(np.abs(data))
self._vmin_auto, self._vmax_auto = utils.contrast_stretching(
data, self.saturated_pixels)
def optimize_contrast(self, data):
if (self._vmin_user is not None and self._vmax_user is not None):
return
with ignore_warning(category=RuntimeWarning):
# In case of "All-NaN slices"
vmin, vmax = utils.contrast_stretching(data, self.saturated_pixels)
if vmin == np.nan:
vmin = None
if vmax == np.nan:
vmax = None
self._vmin_auto, self._vmax_auto = vmin, vmax
def convert(self):
original_data = self.s.data.copy()
for extension in self.extension_list:
self.s.data = original_data
if extension in ['jpg', 'jpeg'] and self.contrast_streching:
vmin, vmax = contrast_stretching(self.s.data,
self.saturated_pixels)
self.s.data = self.normalise(self.s.data, vmin, vmax)
self.fname_ext = ''.join(
[os.path.splitext(self.fname)[0], '.', extension])
if os.path.exists(self.fname_ext) and self.overwrite is None:
write_answer = self._ask_confirmation_overwrite()
self._save_data(self.s, overwrite=write_answer)
# workaround, currently hyperspy doesn't write file is
# overwrite=False
elif not os.path.exists(self.fname_ext):
self._save_data(self.s)
else:
self._save_data(self.s, overwrite=self.overwrite)
def convert(self):
original_data = self.s.data.copy()
for extension in self.extension_list:
self.s.data = original_data
if extension in ['jpg', 'jpeg'] and self.contrast_streching:
vmin, vmax = contrast_stretching(self.s.data,
self.saturated_pixels)
self.s.data = self.normalise(self.s.data, vmin, vmax)
self.fname_ext = ''.join([os.path.splitext(self.fname)[0], '.',
extension])
if os.path.exists(self.fname_ext) and self.overwrite is None:
write_answer = self._ask_confirmation_overwrite()
self._save_data(self.s, overwrite=write_answer)
# workaround, currently hyperspy doesn't write file is
# overwrite=False
elif not os.path.exists(self.fname_ext):
self._save_data(self.s)
else:
self._save_data(self.s, overwrite=self.overwrite)
def _convert_tia_single_item(self, item, suffix=''):
if self.correct_cfeg_fluctuation:
item = correct_intensity(item)
original_data = item.data.copy()
for extension in self.extension_list:
item.data = original_data
if extension in ['jpg', 'jpeg'] and self.contrast_streching:
if isinstance(item, hs.signals.Signal1D):
return
vmin, vmax = contrast_stretching(
item.data, f"{self.saturated_pixels/2}th",
f'{100-self.saturated_pixels/2}th')
item.data = self.stretch_contrast(item.data, vmin, vmax)
item.data = self.normalise(item.data)
item.data *= np.iinfo(np.uint8).max
item.change_dtype(np.uint8)
if extension in ['tif', 'tiff']:
if isinstance(item, hs.signals.Signal1D):
return
try:
# TODO: improve this
self._set_convenient_scale(item)
except:
pass
if self.normalisation:
# dirty workaround to read tif file in imagej and dm...
item.data = self.normalise(item.data)
item.data *= np.iinfo(np.int32).max
item.change_dtype(np.int32)
dname, fname = os.path.split(self.fname)
if self.use_subfolder:
dname = os.path.join(dname, extension)
fname = os.path.join(dname, os.path.splitext(fname)[0])
self.fullfname = f'{fname}{suffix}.{extension}'
if os.path.exists(self.fullfname) and self.overwrite is None:
write_answer = self._ask_confirmation_overwrite()
self._save_data(item, overwrite=write_answer)
# workaround, currently hyperspy doesn't write file is
# overwrite=False
elif not os.path.exists(self.fullfname):
self._save_data(item)
else:
self._save_data(item, overwrite=self.overwrite)
def test_out_of_range(self):
with pytest.raises(ValueError):
contrast_stretching(self.data, -1)
def test_nans(self):
data = self.data[:-1]
bounds = contrast_stretching(self.data, 1)
assert bounds == (np.percentile(data, 0.5), np.percentile(data, 99.5))
def test_no_nans(self, value):
data = self.data[:-1]
bounds = contrast_stretching(data, value[0], value[1])
assert bounds == (np.percentile(data, 0.5), np.percentile(data, 99.5))
def test_out_of_range(self):
contrast_stretching(self.data, -1)
def test_out_of_range(self):
with pytest.raises(ValueError):
contrast_stretching(self.data, -1)
def optimize_contrast(self, data):
if (self._vmin_user is not None and self._vmax_user is not None):
return
self._vmin_auto, self._vmax_auto = utils.contrast_stretching(
data, self.saturated_pixels)
def test_out_of_range(self):
contrast_stretching(self.data, -1)
def test_nans(self):
data = self.data[:-1]
bounds = contrast_stretching(self.data, 1)
nose.tools.assert_tuple_equal(bounds, (
np.percentile(data, 0.5),
np.percentile(data, 99.5)))
def optimize_contrast(self, data):
if (self._vmin_user is not None and self._vmax_user is not None):
return
self._vmin_auto, self._vmax_auto = utils.contrast_stretching(
data, self.saturated_pixels)
def test_nans(self):
data = self.data[:-1]
bounds = contrast_stretching(self.data, 1)
nose.tools.assert_tuple_equal(
bounds, (np.percentile(data, 0.5), np.percentile(data, 99.5)))
def test_nans(self):
data = self.data[:-1]
bounds = contrast_stretching(self.data, 1)
assert bounds == (
np.percentile(data, 0.5),
np.percentile(data, 99.5))
