def test_ignore_type():
warnings.simplefilter("error")
with ignore_warning(category=UserWarning):
warnsA()
warnsC()
with ignore_warning(category=DeprecationWarning):
warnsB()
def test_ignore_full_message():
warnings.simplefilter("error")
with ignore_warning(message="Warning A!"):
warnsA()
with ignore_warning(message="Warning B!"):
warnsB()
with ignore_warning(message="Warning C!"):
warnsC()
def test_ignore_type():
with all_warnings():
warnings.simplefilter("error")
with ignore_warning(category=UserWarning):
warnsA()
warnsC()
with ignore_warning(category=DeprecationWarning):
warnsB()
def test_ignore_full_message():
with all_warnings():
warnings.simplefilter("error")
with ignore_warning(message="Warning A!"):
warnsA()
with ignore_warning(message="Warning B!"):
warnsB()
with ignore_warning(message="Warning C!"):
warnsC()
def update(self, force_replot=False, render_figure=True):
"""Update the current spectrum figure"""
if force_replot is True:
self.close()
self.plot(data_function_kwargs=self.data_function_kwargs,
norm=self.norm)
ydata = self._get_data()
old_xaxis = self.line.get_xdata()
if len(old_xaxis) != self.axis.size or \
np.any(np.not_equal(old_xaxis, self.axis.axis)):
self.line.set_data(self.axis.axis, ydata)
else:
self.line.set_ydata(ydata)
if 'x' in self.autoscale:
self.ax.set_xlim(self.axis.axis[0], self.axis.axis[-1])
if 'v' in self.autoscale:
self.ax.relim()
y1, y2 = np.searchsorted(self.axis.axis,
self.ax.get_xbound())
y2 += 2
y1, y2 = np.clip((y1, y2), 0, len(ydata - 1))
clipped_ydata = ydata[y1:y2]
with ignore_warning(category=RuntimeWarning):
# In case of "All-NaN slices"
y_max, y_min = (np.nanmax(clipped_ydata),
np.nanmin(clipped_ydata))
if self._plot_imag:
# Add real plot
yreal = self._get_data(real_part=True)
clipped_yreal = yreal[y1:y2]
with ignore_warning(category=RuntimeWarning):
# In case of "All-NaN slices"
y_min = min(y_min, np.nanmin(clipped_yreal))
y_max = max(y_max, np.nanmin(clipped_yreal))
if y_min == y_max:
# To avoid matplotlib UserWarning when calling `set_ylim`
y_min, y_max = y_min - 0.1, y_max + 0.1
if not np.isfinite(y_min):
y_min = None # data are -inf or all NaN
if not np.isfinite(y_max):
y_max = None # data are inf or all NaN
self.ax.set_ylim(y_min, y_max)
if self.plot_indices is True:
self.text.set_text(self.axes_manager.indices)
if render_figure:
if self.ax.figure.canvas.supports_blit:
self.ax.hspy_fig._update_animated()
else:
self.ax.figure.canvas.draw_idle()
def test_ignore_regex_message():
with all_warnings():
warnings.simplefilter("error")
with ignore_warning(message="Warning .?!"):
warnsA()
warnsB()
warnsC()
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 test_ignore_partial_message():
with all_warnings():
warnings.simplefilter("error")
with ignore_warning(message="Warning"):
warnsA()
warnsB()
warnsC()
def test_ignore_partial_message():
with all_warnings():
warnings.simplefilter("error")
with ignore_warning(message="Warning"):
warnsA()
warnsB()
warnsC()
def test_ignore_regex_message():
with all_warnings():
warnings.simplefilter("error")
with ignore_warning(message="Warning .?!"):
warnsA()
warnsB()
warnsC()
def test_quant_zeros(self):
intens = np.array([[0.5, 0.5, 0.5], [0.0, 0.5, 0.5], [0.5, 0.0, 0.5], [0.5, 0.5, 0.0], [0.5, 0.0, 0.0]]).T
with ignore_warning(message="divide by zero encountered", category=RuntimeWarning):
quant = utils_eds.quantification_cliff_lorimer(intens, [1, 1, 3]).T
np.testing.assert_allclose(
quant, np.array([[0.2, 0.2, 0.6], [0.0, 0.25, 0.75], [0.25, 0.0, 0.75], [0.5, 0.5, 0.0], [1.0, 0.0, 0.0]])
)
def test_ignore_type_fails():
warnings.simplefilter("error")
with ignore_warning(category=UserWarning):
try:
warnsB()
except DeprecationWarning as e:
assert str(e) == "Warning B!"
else:
raise ValueError("Expected warning to give error!")
def test_ignore_type_fails():
with all_warnings():
warnings.simplefilter("error")
with ignore_warning(category=UserWarning):
try:
warnsB()
except DeprecationWarning as e:
nt.assert_equal(str(e), "Warning B!")
else:
raise ValueError("Expected warning to give error!")
def test_quant_zeros(self):
intens = np.array([[0.5, 0.5, 0.5], [0.0, 0.5, 0.5], [0.5, 0.0, 0.5],
[0.5, 0.5, 0.0], [0.5, 0.0, 0.0]]).T
with ignore_warning(message="divide by zero encountered",
category=RuntimeWarning):
quant = utils_eds.quantification_cliff_lorimer(intens, [1, 1, 3]).T
np.testing.assert_allclose(
quant,
np.array([[0.2, 0.2, 0.6], [0.0, 0.25, 0.75], [0.25, 0.0, 0.75],
[0.5, 0.5, 0.0], [1.0, 0.0, 0.0]]))
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 test_ignore_message_fails():
warnings.simplefilter("error")
with ignore_warning(message="Warning [AB]!"):
warnsA()
warnsB()
try:
warnsC()
except UserWarning as e:
assert str(e) == "Warning C!"
else:
raise ValueError("Expected warning to give error!")
warnings.simplefilter("error")
with ignore_warning(message="Warning A! Too much"):
try:
warnsA()
except UserWarning as e:
assert str(e) == "Warning A!"
else:
raise ValueError("Expected warning to give error!")
def setup_method(self, method):
np.random.seed(1)
s = hs.signals.Signal1D(np.arange(10, 100, 0.1))
s.axes_manager[0].scale = 0.1
s.axes_manager[0].offset = 10
m = s.create_model()
with ignore_warning(message="The API of the `Polynomial` component"):
m.append(hs.model.components1D.Polynomial(1))
m.append(hs.model.components1D.Offset())
self.s = s
self.m = m
def test_pattern_binned_signal_unbinned(self):
s = self.s
s1 = self.pattern
s.metadata.Signal.binned = False
s1.metadata.Signal.binned = True
m = s.create_model()
fp = hs.model.components1D.ScalableFixedPattern(s1)
m.append(fp)
with ignore_warning(message="invalid value encountered in sqrt",
category=RuntimeWarning):
m.fit()
assert abs(fp.yscale.value - 10) <= .1
def test_pattern_binned_signal_unbinned(self):
s = self.s
s1 = self.pattern
s.metadata.Signal.binned = False
s1.metadata.Signal.binned = True
m = s.create_model()
fp = hs.model.components1D.ScalableFixedPattern(s1)
m.append(fp)
with ignore_warning(message="invalid value encountered in sqrt",
category=RuntimeWarning):
m.fit()
assert abs(fp.yscale.value - 10) <= .1
def test_pattern_unbinned_signal_binned(self):
s = self.s
s1 = self.pattern
s.metadata.Signal.binned = True
s1.metadata.Signal.binned = False
m = s.create_model()
fp = hs.model.components.ScalableFixedPattern(s1)
m.append(fp)
with ignore_warning(message="invalid value encountered in sqrt",
category=RuntimeWarning):
m.fit()
nt.assert_almost_equal(fp.yscale.value, 1000, delta=1)
def test_pattern_unbinned_signal_binned(self):
s = self.s
s1 = self.pattern
s.metadata.Signal.binned = True
s1.metadata.Signal.binned = False
m = s.create_model()
fp = hs.model.components.ScalableFixedPattern(s1)
m.append(fp)
with ignore_warning(message="invalid value encountered in sqrt",
category=RuntimeWarning):
m.fit()
nt.assert_almost_equal(fp.yscale.value, 1000, delta=1)
def test_both_unbinned(self):
s = self.s
s1 = self.pattern
s.axes_manager[-1].is_binned = False
s1.axes_manager[-1].is_binned = False
m = s.create_model()
fp = hs.model.components1D.ScalableFixedPattern(s1)
m.append(fp)
with ignore_warning(message="invalid value encountered in sqrt",
category=RuntimeWarning):
m.fit()
assert abs(fp.yscale.value - 100) <= 0.1
def test_ignore_message_fails():
with all_warnings():
warnings.simplefilter("error")
with ignore_warning(message="Warning [AB]!"):
warnsA()
warnsB()
try:
warnsC()
except UserWarning as e:
nt.assert_equal(str(e), "Warning C!")
else:
raise ValueError("Expected warning to give error!")
with all_warnings():
warnings.simplefilter("error")
with ignore_warning(message="Warning A! Too much"):
try:
warnsA()
except UserWarning as e:
nt.assert_equal(str(e), "Warning A!")
else:
raise ValueError("Expected warning to give error!")
def test_both_binned(self, uniform):
s = self.s
s1 = self.pattern
s.axes_manager[-1].is_binned = True
s1.axes_manager[-1].is_binned = True
if not uniform:
s.axes_manager[0].convert_to_non_uniform_axis()
s1.axes_manager[0].convert_to_non_uniform_axis()
m = s.create_model()
fp = hs.model.components1D.ScalableFixedPattern(s1)
m.append(fp)
with ignore_warning(message="invalid value encountered in sqrt",
category=RuntimeWarning):
m.fit()
assert abs(fp.yscale.value - 100) <= 0.1
def add_polynomial_background(self, order=6):
"""
Add a polynomial background.
the background is added to self.background_components
Parameters
----------
order: int
The order of the polynomial
"""
with ignore_warning(message="The API of the `Polynomial` component"):
background = create_component.Polynomial(order=order, legacy=False)
background.name = 'background_order_' + str(order)
background.isbackground = True
self.append(background)
self.background_components.append(background)
def test_see_fit():
# Component parameter values
A = 10
Phi = 2.5
B = 0.5
offset, scale, size = 0, 0.1, 100
x = np.linspace(offset, scale * size, size)
s = hs.signals.Signal1D(SEE(A=A, Phi=Phi, B=B).function(x))
axis = s.axes_manager[0]
axis.offset, axis.scale = offset, scale
s.add_gaussian_noise(0.1, random_state=1)
m = s.create_model()
see = SEE(A=1, Phi=1.5, B=0.5)
m.append(see)
with ignore_warning(message="divide by zero", category=RuntimeWarning):
m.fit(grad='analytical')
np.testing.assert_allclose(see.A.value, A, rtol=0.1)
np.testing.assert_allclose(see.Phi.value, Phi, rtol=0.1)
np.testing.assert_allclose(see.B.value, B, rtol=0.1)
def update(self,
force_replot=False,
render_figure=True,
update_ylimits=False):
"""Update the current spectrum figure
Parameters
----------
force_replot : bool
If True, close and open the figure. Default is False.
render_figure : bool
If True, render the figure. Useful to avoid firing matplotlib
drawing events too often. Default is True.
update_ylimits : bool
If True, update the y-limits. This is useful to avoid the figure
flickering when different lines update the y-limits consecutively,
in which case, this is done in `Signal1DFigure.update`.
Default is False.
"""
if force_replot is True:
self.close()
self.plot(data_function_kwargs=self.data_function_kwargs,
norm=self.norm)
self._y_min, self._y_max = self.ax.get_ylim()
ydata = self._get_data()
# If axis is a DataAxis instance, take the axis attribute
axis = getattr(self.axis, 'axis', self.axis)
if not np.array_equiv(self.line.get_xdata(), axis):
self.line.set_data(axis, ydata)
else:
self.line.set_ydata(ydata)
# Don't change xlim if axis has 0 length (unnecessary)
if 'x' in self.autoscale and len(axis) > 0:
x_min, x_max = axis[0], axis[-1]
if x_min == x_max:
# To avoid matplotlib UserWarning when calling `set_ylim`
x_min, x_max = (x_min - 0.1, x_min + 0.1)
self.ax.set_xlim(x_min, x_max)
# Don't change ymin if data has 0 length (unnecessary)
if 'v' in self.autoscale and len(ydata) > 0:
self.ax.relim()
# Based on the current zoom of the x axis, find the corresponding
# y range of data and calculate the y_min, y_max accordingly
i1, i2 = np.searchsorted(axis, self.ax.get_xbound())
# Make interval wider on both side and clip to allowed range
i1, i2 = np.clip((i1 - 1, i2 + 1), 0, len(ydata - 1))
ydata = ydata[i1:i2]
with ignore_warning(category=RuntimeWarning):
# In case of "All-NaN slices"
y_max, y_min = np.nanmax(ydata), np.nanmin(ydata)
if self._plot_imag:
# Add real plot
yreal = self._get_data(real_part=True)[i1:i2]
with ignore_warning(category=RuntimeWarning):
# In case of "All-NaN slices"
y_min = min(y_min, np.nanmin(yreal))
y_max = max(y_max, np.nanmin(yreal))
if y_min == y_max:
# To avoid matplotlib UserWarning when calling `set_ylim`
y_min, y_max = y_min - 0.1, y_max + 0.1
if not np.isfinite(y_min):
y_min = None # data are -inf or all NaN
if not np.isfinite(y_max):
y_max = None # data are inf or all NaN
if y_min is not None:
self._y_min = y_min
if y_max is not None:
self._y_max = y_max
if update_ylimits:
# Most of the time, we don't want to call `set_ylim` now to
# avoid flickering of the figure. However, we use the values
# `self._y_min` and `self._y_max` in `Signal1DFigure.update`
self.ax.set_ylim(self._y_min, self._y_max)
if self.plot_indices is True:
self.text.set_text(self.axes_manager.indices)
if render_figure:
self.ax.hspy_fig.render_figure()
def update(self, force_replot=False, render_figure=True,
update_ylimits=False):
"""Update the current spectrum figure
Parameters
----------
force_replot : bool
If True, close and open the figure. Default is False.
render_figure : bool
If True, render the figure. Useful to avoid firing matplotlib
drawing events too often. Default is True.
update_ylimits : bool
If True, update the y-limits. This is useful to avoid the figure
flickering when different lines update the y-limits consecutively,
in which case, this is done in `Signal1DFigure.update`.
Default is False.
"""
if force_replot is True:
self.close()
self.plot(data_function_kwargs=self.data_function_kwargs,
norm=self.norm)
self._y_min, self._y_max = self.ax.get_ylim()
ydata = self._get_data()
# If axis is a DataAxis instance, take the axis attribute
axis = getattr(self.axis, 'axis', self.axis)
if not np.array_equiv(self.line.get_xdata(), axis):
self.line.set_data(axis, ydata)
else:
self.line.set_ydata(ydata)
if 'x' in self.autoscale:
self.ax.set_xlim(axis[0], axis[-1])
if 'v' in self.autoscale:
self.ax.relim()
y1, y2 = np.searchsorted(axis, self.ax.get_xbound())
y2 += 2
y1, y2 = np.clip((y1, y2), 0, len(ydata - 1))
clipped_ydata = ydata[y1:y2]
with ignore_warning(category=RuntimeWarning):
# In case of "All-NaN slices"
y_max, y_min = (np.nanmax(clipped_ydata),
np.nanmin(clipped_ydata))
if self._plot_imag:
# Add real plot
yreal = self._get_data(real_part=True)
clipped_yreal = yreal[y1:y2]
with ignore_warning(category=RuntimeWarning):
# In case of "All-NaN slices"
y_min = min(y_min, np.nanmin(clipped_yreal))
y_max = max(y_max, np.nanmin(clipped_yreal))
if y_min == y_max:
# To avoid matplotlib UserWarning when calling `set_ylim`
y_min, y_max = y_min - 0.1, y_max + 0.1
if not np.isfinite(y_min):
y_min = None # data are -inf or all NaN
if not np.isfinite(y_max):
y_max = None # data are inf or all NaN
if y_min is not None:
self._y_min = y_min
if y_max is not None:
self._y_max = y_max
if update_ylimits:
# Most of the time, we don't want to call `set_ylim` now to
# avoid flickering of the figure. However, we use the values
# `self._y_min` and `self._y_max` in `Signal1DFigure.update`
self.ax.set_ylim(self._y_min, self._y_max)
if self.plot_indices is True:
self.text.set_text(self.axes_manager.indices)
if render_figure:
self.ax.hspy_fig.render_figure()
