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scalable_fixed_pattern.py
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scalable_fixed_pattern.py
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# -*- coding: utf-8 -*-
# Copyright 2007-2016 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 <http://www.gnu.org/licenses/>.
from scipy.interpolate import interp1d
from hyperspy.component import Component
from hyperspy.ui_registry import add_gui_method
@add_gui_method(toolkey="ScalableFixedPattern_Component")
class ScalableFixedPattern(Component):
"""Fixed pattern component with interpolation support.
f(x) = a*s(b*x-x0) + c
+------------+-----------+
| Parameter | Attribute |
+------------+-----------+
+------------+-----------+
| a | yscale |
+------------+-----------+
| b | xscale |
+------------+-----------+
| x0 | shift |
+------------+-----------+
The fixed pattern is defined by a single spectrum which must be provided to
the ScalableFixedPattern constructor, e.g.:
.. code-block:: ipython
In [1]: s = load('my_spectrum.hspy')
In [2]: my_fixed_pattern = components.ScalableFixedPattern(s))
Attributes
----------
yscale, xscale, 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
"""
def __init__(self, signal1D, yscale=1.0, xscale=1.0,
shift=0.0, interpolate=True):
Component.__init__(self, ['yscale', 'xscale', 'shift'])
self._position = self.shift
self._whitelist['signal1D'] = ('init,sig', signal1D)
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
def prepare_interpolator(self, kind='linear', fill_value=0, **kwargs):
"""Prepare interpolation.
Parameters
----------
x : array
The spectral axis of the fixed pattern
kind : str or int, optional
Specifies the kind of interpolation as a string
('linear', 'nearest', 'zero', 'slinear', 'quadratic, 'cubic')
or as an integer specifying the order of the spline interpolator
to use. Default is 'linear'.
fill_value : float, optional
If provided, then this value will be used to fill in for requested
points outside of the data range. If not provided, then the default
is NaN.
Notes
-----
Any extra keyword argument is passed to `scipy.interpolate.interp1d`
"""
self.f = interp1d(
self.signal.axes_manager.signal_axes[0].axis,
self.signal.data.squeeze(),
kind=kind,
bounds_error=False,
fill_value=fill_value,
**kwargs)
def function(self, x):
if self.interpolate is True:
result = self.yscale.value * self.f(
x * self.xscale.value - self.shift.value)
else:
result = self.yscale.value * self.signal.data
if self.signal.metadata.Signal.binned is True:
return result / self.signal.axes_manager.signal_axes[0].scale
else:
return result
def grad_yscale(self, x):
return self.function(x) / self.yscale.value