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scalable_fixed_pattern.py
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scalable_fixed_pattern.py
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# -*- coding: utf-8 -*-
# Copyright 2007-2023 The HyperSpy developers
#
# This file is part of HyperSpy.
#
# HyperSpy is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# HyperSpy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with HyperSpy. If not, see <https://www.gnu.org/licenses/#GPL>.
import numpy as np
from scipy.interpolate import make_interp_spline
from hyperspy.component import Component
from hyperspy.ui_registry import add_gui_method
from hyperspy.docstrings.parameters import FUNCTION_ND_DOCSTRING
@add_gui_method(toolkey="hyperspy.ScalableFixedPattern_Component")
class ScalableFixedPattern(Component):
r"""Fixed pattern component with interpolation support.
.. math::
f(x) = a \cdot s \left(b \cdot x - x_0\right) + c
============ =============
Variable Parameter
============ =============
:math:`a` yscale
:math:`b` xscale
:math:`x_0` shift
============ =============
Parameters
----------
yscale : Float
xscale : Float
shift : Float
interpolate : Bool
If False no interpolation is performed and only a y-scaled spectrum is
returned.
Methods
-------
prepare_interpolator : method to fine tune the interpolation
Examples
--------
The fixed pattern is defined by a single spectrum which must be provided to
the ScalableFixedPattern constructor, e.g.:
>>> s = hs.load('my_spectrum.hspy')
>>> my_fixed_pattern = hs.model.components1D.ScalableFixedPattern(s)
"""
def __init__(self, signal1D, yscale=1.0, xscale=1.0,
shift=0.0, interpolate=True):
Component.__init__(self, ['yscale', 'xscale', 'shift'], ['yscale'])
self._position = self.shift
self._whitelist['signal1D'] = ('init,sig', signal1D)
self._whitelist['interpolate'] = None
self.signal = signal1D
self.yscale.free = True
self.yscale.value = yscale
self.xscale.value = xscale
self.shift.value = shift
self.prepare_interpolator()
# Options
self.isbackground = True
self.convolved = False
self.interpolate = interpolate
@property
def interpolate(self):
return self._interpolate
@interpolate.setter
def interpolate(self, value):
self._interpolate = value
self.xscale.free = value
self.shift.free = value
def prepare_interpolator(self, **kwargs):
"""Prepare interpolation.
Parameters
----------
x : array
The spectral axis of the fixed pattern
**kwargs : dict
Keywords argument are passed to
:py:func:`scipy.interpolate.make_interp_spline`
"""
self.f = make_interp_spline(
self.signal.axes_manager.signal_axes[0].axis,
self.signal.data.squeeze(),
**kwargs
)
def _function(self, x, xscale, yscale, shift):
if self.interpolate is True:
result = yscale * self.f(x * xscale - shift)
else:
result = yscale * self.signal.data
axis = self.signal.axes_manager.signal_axes[0]
if axis.is_binned:
if axis.is_uniform:
return result / axis.scale
else:
return result / np.gradient(axis.axis)
else:
return result
def function(self, x):
return self._function(x, self.xscale.value, self.yscale.value,
self.shift.value)
def function_nd(self, axis):
"""%s
"""
if self._is_navigation_multidimensional:
x = axis[np.newaxis, :]
xscale = self.xscale.map['values'][..., np.newaxis]
yscale = self.yscale.map['values'][..., np.newaxis]
shift = self.shift.map['values'][..., np.newaxis]
return self._function(x, xscale, yscale, shift)
else:
return self.function(axis)
function_nd.__doc__ %= FUNCTION_ND_DOCSTRING
def grad_yscale(self, x):
return self.function(x) / self.yscale.value