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# coding: utf-8
# /*##########################################################################
# Copyright (C) 2018 European Synchrotron Radiation Facility
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
# ############################################################################*/
"""
This module provides classes to calibrate data.
Classes
-------
- :class:`NoCalibration`
- :class:`LinearCalibration`
- :class:`ArrayCalibration`
"""
import numpy
class AbstractCalibration(object):
"""A calibration is a transformation to be applied to an axis (i.e. a 1D array).
"""
def __init__(self):
super(AbstractCalibration, self).__init__()
def __call__(self, x):
"""Apply calibration to an axis or to a value.
:param x: Axis (1-D array), or value"""
raise NotImplementedError(
"AbstractCalibration can not be used directly. " +
"You must subclass it and implement __call__")
def is_affine(self):
"""Returns True for an affine calibration of the form
:math:`x \mapsto a + b * x`, or else False.
"""
return False
def get_slope(self):
raise NotImplementedError(
"get_slope is implemented only for affine calibrations")
class NoCalibration(AbstractCalibration):
"""No calibration :math:`x \mapsto x`
"""
def __init__(self):
super(NoCalibration, self).__init__()
def __call__(self, x):
return x
def is_affine(self):
return True
def get_slope(self):
return 1.
class LinearCalibration(AbstractCalibration):
"""Linear calibration :math:`x \mapsto a + b x`,
where *a* is the y-intercept and *b* is the slope.
:param y_intercept: y-intercept
:param slope: Slope of the affine transformation
"""
def __init__(self, y_intercept, slope):
super(LinearCalibration, self).__init__()
self.constant = y_intercept
self.slope = slope
def __call__(self, x):
return self.constant + self.slope * x
def is_affine(self):
return True
def get_slope(self):
return self.slope
class ArrayCalibration(AbstractCalibration):
"""One-to-one mapping calibration, defined by an array *x'*,
such as :math:`x \mapsto x'`*.
This calibration can only be applied to x arrays of the same length as the
calibration array *x'*.
It is typically applied to an axis of indices or
channels (:math:`0, 1, ..., n-1`).
:param x1: Calibration array"""
def __init__(self, x1):
super(ArrayCalibration, self).__init__()
if not isinstance(x1, (list, tuple)) and not hasattr(x1, "shape"):
raise TypeError(
"The calibration array must be a sequence (list, dataset, array)")
self.calibration_array = numpy.array(x1)
self._is_affine = None
def __call__(self, x):
# calibrate the entire axis
if isinstance(x, (list, tuple, numpy.ndarray)) and \
len(self.calibration_array) == len(x):
return self.calibration_array
# calibrate one value, by index
if isinstance(x, int) and x < len(self.calibration_array):
return self.calibration_array[x]
raise ValueError("ArrayCalibration must be applied to array of same size "
"or to index.")
def is_affine(self):
"""If all values in the calibration array are regularly spaced,
return True."""
if self._is_affine is None:
delta_x = self.calibration_array[1:] - self.calibration_array[:-1]
# use a less strict relative tolerance to account for rounding errors
# e.g. when using float64 into float32 (see #1823)
if not numpy.isclose(delta_x, delta_x[0], rtol=1e-4).all():
self._is_affine = False
else:
self._is_affine = True
return self._is_affine
def get_slope(self):
"""If the calibration array is regularly spaced, return the spacing."""
if not self.is_affine():
raise AttributeError(
"get_slope only makes sense for affine transformations"
)
return self.calibration_array[1] - self.calibration_array[0]
class FunctionCalibration(AbstractCalibration):
"""Calibration defined by a function *f*, such as :math:`x \mapsto f(x)`*.
:param function: Calibration function"""
def __init__(self, function, is_affine=False):
super(FunctionCalibration, self).__init__()
if not hasattr(function, "__call__"):
raise TypeError("The calibration function must be a callable")
self.function = function
self._is_affine = is_affine
def __call__(self, x):
return self.function(x)
def is_affine(self):
"""Return True if calibration is affine.
This is False by default, unless the object is instantiated with
``is_affine=True``."""
return self._is_affine
def get_slope(self):
"""If the calibration array is regularly spaced, return the spacing."""
if not self.is_affine():
raise AttributeError(
"get_slope only makes sense for affine transformations"
)
# fixme: what if function is not defined at x=1 or x=2?
return self.function(2) - self.function(1)
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