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Merge pull request #164 from jlaehne/simplify-transf
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Simplify axes conversion code
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@jlaehne
jlaehne authored Dec 6, 2022
2 parents 9bc33b1 + 6aa40f2 commit 59ec862
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Showing 3 changed files with 100 additions and 258 deletions.
344 changes: 93 additions & 251 deletions lumispy/signals/luminescence_spectrum.py
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Original file line number Diff line number Diff line change
Expand Up @@ -93,6 +93,78 @@ def _reset_variance_linear_model(self):
UserWarning,
)

def _convert_data(self, newaxis, factor, inplace, jacobian, data2, var2):
"""Utility function to perform the data and variance conversion for
signal unit transformations.
"""
# convert data
if jacobian:
s2data = np.copy(
data2(
self.isig[::-1].data,
factor,
newaxis.axis,
self.axes_manager.signal_axes[0],
)
)
else:
s2data = np.copy(self.isig[::-1].data)

# inplace conversion
if inplace:
s2 = self
s2.data = s2data
# create new signal object with correct data, axes, metadata
else:
s2 = self._deepcopy_with_new_data(
s2data, copy_variance=(not jacobian), copy_learning_results=False
)
# convert axis
oldaxis = self.axes_manager.signal_axes[0]
s2.axes_manager.set_axis(
newaxis, self.axes_manager.signal_axes[0].index_in_axes_manager
)
# convert variance
if self.metadata.has_item("Signal.Noise_properties.variance"):
var = self.get_noise_variance()
if jacobian:
# if variance is a numeric value, cast into signal object
if isinstance(var, (float, int)):
var = self._deepcopy_with_new_data(
np.ones(self.data.shape) * var,
copy_variance=False,
copy_learning_results=False,
)
s2var = s2._deepcopy_with_new_data(
var2(
var.isig[::-1].data,
factor,
newaxis.axis,
oldaxis,
),
copy_variance=False,
copy_learning_results=False,
)
s2.set_noise_variance(s2var)
s2._reset_variance_linear_model()
else:
# variance left unchanged, if it is a number and Jacobian not performed
if isinstance(var, (float, int)):
if not inplace:
s2.set_noise_variance(self.get_noise_variance())
else:
s2.set_noise_variance(
s2._deepcopy_with_new_data(
var.isig[::-1].data,
copy_variance=False,
copy_learning_results=False,
)
)
if not inplace:
return s2
else:
return None

def to_eV(self, inplace=True, jacobian=True):
"""Converts signal axis of 1D signal to non-linear energy axis (eV)
using wavelength dependent refractive index of air. Assumes wavelength
Expand Down Expand Up @@ -128,120 +200,7 @@ def to_eV(self, inplace=True, jacobian=True):

evaxis, factor = axis2eV(self.axes_manager.signal_axes[0])

# in place conversion
if inplace:
# convert data
if jacobian:
self.data = data2eV(
self.isig[::-1].data,
factor,
self.axes_manager.signal_axes[0],
evaxis.axis,
)
else:
self.data = self.isig[::-1].data
# convert axis
oldaxis = self.axes_manager.signal_axes[0]
self.axes_manager.set_axis(
evaxis, self.axes_manager.signal_axes[0].index_in_axes_manager
)
# convert variance
if jacobian:
if self.metadata.has_item("Signal.Noise_properties.variance"):
var = self.get_noise_variance()
# if variance is a numeric value, cast into signal object
if isinstance(var, (float, int)):
self.set_noise_variance(
self._deepcopy_with_new_data(
np.ones(self.data.shape) * var,
copy_variance=False,
copy_learning_results=False,
)
)
var = self.get_noise_variance()
svar = self._deepcopy_with_new_data(
var2eV(
var.isig[::-1].data,
factor,
oldaxis,
evaxis.axis,
),
copy_variance=False,
copy_learning_results=False,
)
self.set_noise_variance(svar)
self._reset_variance_linear_model()
else:
if self.metadata.has_item("Signal.Noise_properties.variance"):
var = self.get_noise_variance()
# variance left unchanged, if it is a number and Jacobian not performed
if not isinstance(var, (float, int)):
self.set_noise_variance(
self._deepcopy_with_new_data(
var.isig[::-1].data,
copy_variance=False,
copy_learning_results=False,
)
)

# create and return new signal
else:
# create new data array
if jacobian:
s2data = np.copy(
data2eV(
self.isig[::-1].data,
factor,
self.axes_manager.signal_axes[0],
evaxis.axis,
)
)
else:
s2data = np.copy(self.isig[::-1].data)

# create new signal object with correct data, axes, metadata
s2 = self._deepcopy_with_new_data(
s2data, copy_variance=(not jacobian), copy_learning_results=False
)
s2.axes_manager.set_axis(
evaxis, self.axes_manager.signal_axes[0].index_in_axes_manager
)
# convert variance
if self.metadata.has_item("Signal.Noise_properties.variance"):
var = self.get_noise_variance()
if jacobian:
# if variance is a numeric value, cast into signal object
if isinstance(var, (float, int)):
var = self._deepcopy_with_new_data(
np.ones(self.data.shape) * var,
copy_variance=False,
copy_learning_results=False,
)
# convert
s2var = s2._deepcopy_with_new_data(
var2eV(
var.isig[::-1].data,
factor,
self.axes_manager.signal_axes[0],
evaxis.axis,
),
copy_variance=False,
copy_learning_results=False,
)
s2.set_noise_variance(s2var)
else:
if isinstance(var, (float, int)):
s2.set_noise_variance(self.get_noise_variance())
else:
s2.set_noise_variance(
s2._deepcopy_with_new_data(
var.isig[::-1].data,
copy_variance=False,
copy_learning_results=False,
)
)

return s2
return self._convert_data(evaxis, factor, inplace, jacobian, data2eV, var2eV)

TO_INVCM_DOCSTRING = """
The intensity is converted from counts/nm (counts/µm) to counts/cm^-1
Expand Down Expand Up @@ -284,114 +243,9 @@ def to_invcm(self, inplace=True, jacobian=True):

invcmaxis, factor = axis2invcm(self.axes_manager.signal_axes[0])

# in place conversion
if inplace:
# convert data
if jacobian:
self.data = data2invcm(
self.isig[::-1].data,
factor,
invcmaxis.axis,
)
else:
self.data = self.isig[::-1].data
# convert axis
self.axes_manager.set_axis(
invcmaxis, self.axes_manager.signal_axes[0].index_in_axes_manager
)
# convert variance
if jacobian:
if self.metadata.has_item("Signal.Noise_properties.variance"):
var = self.get_noise_variance()
# if variance is a numeric value, cast into signal object
if isinstance(var, (float, int)):
self.set_noise_variance(
self._deepcopy_with_new_data(
np.ones(self.data.shape) * var,
copy_variance=False,
copy_learning_results=False,
)
)
var = self.get_noise_variance()
# convert
svar = self._deepcopy_with_new_data(
var2invcm(
var.isig[::-1].data,
factor,
invcmaxis.axis,
),
copy_variance=False,
copy_learning_results=False,
)
self.set_noise_variance(svar)
self._reset_variance_linear_model()
else:
if self.metadata.has_item("Signal.Noise_properties.variance"):
var = self.get_noise_variance()
# variance left unchanged, if it is a number and Jacobian not performed
if not isinstance(var, (float, int)):
self.set_noise_variance(
self._deepcopy_with_new_data(
var.isig[::-1].data,
copy_variance=False,
copy_learning_results=False,
)
)

# create and return new signal
else:
# create new data array
if jacobian:
s2data = np.copy(
data2invcm(
self.isig[::-1].data,
factor,
invcmaxis.axis,
)
)
else:
s2data = np.copy(self.isig[::-1].data)

# create new signal object with correct data, axes, metadata
s2 = self._deepcopy_with_new_data(
s2data, copy_variance=(not jacobian), copy_learning_results=False
)
s2.axes_manager.set_axis(
invcmaxis, self.axes_manager.signal_axes[0].index_in_axes_manager
)
# convert variance
if self.metadata.has_item("Signal.Noise_properties.variance"):
var = self.get_noise_variance()
if jacobian:
# if variance is a numeric value, cast into signal object
if isinstance(var, (float, int)):
var = self._deepcopy_with_new_data(
np.ones(self.data.shape) * var,
copy_variance=False,
copy_learning_results=False,
)
s2var = s2._deepcopy_with_new_data(
var2invcm(
var.isig[::-1].data,
factor,
invcmaxis.axis,
),
copy_variance=False,
copy_learning_results=False,
)
s2.set_noise_variance(s2var)
else:
if isinstance(var, (float, int)):
s2.set_noise_variance(var)
else:
s2.set_noise_variance(
s2._deepcopy_with_new_data(
var.isig[::-1].data,
copy_variance=False,
copy_learning_results=False,
)
)
return s2
return self._convert_data(
invcmaxis, factor, inplace, jacobian, data2invcm, var2invcm
)

to_invcm.__doc__ %= (TO_INVCM_DOCSTRING, TO_INVCM_EXAMPLE)

Expand Down Expand Up @@ -452,40 +306,28 @@ def to_invcm_relative(self, laser=None, inplace=True, jacobian=False):
invcmaxis.name = "Raman Shift"

# replace signal axis after conversion
# in place conversion (using absolute scale for Jacobian)
# conversion (using absolute scale for Jacobian)
if inplace:
self.to_invcm(inplace=inplace, jacobian=jacobian)
self.axes_manager.set_axis(
invcmaxis, self.axes_manager.signal_axes[0].index_in_axes_manager
)
self.data = self.isig[::-1].data
# replace variance axis
if self.metadata.has_item(
"Signal.Noise_properties.variance"
) and not isinstance(self.get_noise_variance(), (float, int)):
self.metadata.Signal.Noise_properties.variance.axes_manager.set_axis(
invcmaxis, self.axes_manager.signal_axes[0].index_in_axes_manager
)
self.metadata.Signal.Noise_properties.variance.data = (
self.metadata.Signal.Noise_properties.variance.isig[::-1].data
)
# create and return new signal
s2 = self
else:
s2 = self.to_invcm(inplace=inplace, jacobian=jacobian)
s2.axes_manager.set_axis(
invcmaxis, self.axes_manager.signal_axes[0].index_in_axes_manager
# replace axis
s2.axes_manager.set_axis(
invcmaxis, self.axes_manager.signal_axes[0].index_in_axes_manager
)
s2.data = s2.isig[::-1].data
# replace variance axis
if s2.metadata.has_item("Signal.Noise_properties.variance") and not isinstance(
s2.get_noise_variance(), (float, int)
):
s2.metadata.Signal.Noise_properties.variance.axes_manager.set_axis(
invcmaxis, s2.axes_manager.signal_axes[0].index_in_axes_manager
)
s2.data = s2.isig[::-1].data
# replace variance axis
if s2.metadata.has_item(
"Signal.Noise_properties.variance"
) and not isinstance(s2.get_noise_variance(), (float, int)):
s2.metadata.Signal.Noise_properties.variance.axes_manager.set_axis(
invcmaxis, s2.axes_manager.signal_axes[0].index_in_axes_manager
)
s2.metadata.Signal.Noise_properties.variance.data = (
s2.metadata.Signal.Noise_properties.variance.isig[::-1].data
)
s2.metadata.Signal.Noise_properties.variance.data = (
s2.metadata.Signal.Noise_properties.variance.isig[::-1].data
)
if not inplace:
return s2

to_invcm_relative.__doc__ %= (TO_INVCM_DOCSTRING, TO_INVCMREL_EXAMPLE)
Expand Down
6 changes: 3 additions & 3 deletions lumispy/tests/utils/test_axis_conversion.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -75,19 +75,19 @@ def test_data2eV():
data = 100 * ones(20)
ax0 = DataAxis(axis=arange(200, 400, 10), units="nm")
evaxis, factor = axis2eV(ax0)
evdata = data2eV(data, factor, ax0, evaxis.axis)
evdata = data2eV(data, factor, evaxis.axis, ax0)
assert_allclose(evdata[0], 12.271168)
ax0 = DataAxis(axis=arange(0.2, 0.4, 0.01), units="µm")
evaxis, factor = axis2eV(ax0)
evdata = data2eV(data, factor, ax0, evaxis.axis)
evdata = data2eV(data, factor, evaxis.axis, ax0)
assert_allclose(evdata[0], 12.271168e-3)


def test_var2eV():
data = 100 * ones(20)
ax0 = DataAxis(axis=arange(200, 400, 10), units="nm")
evaxis, factor = axis2eV(ax0)
evvar = var2eV(data, factor, ax0, evaxis.axis)
evvar = var2eV(data, factor, evaxis.axis, ax0)
assert_allclose(evvar[0], 1.5058156)


Expand Down
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