# -*- coding: utf-8 -*-
# Copyright (C) 2017, 2018, 2019, 2020, 2021
# Smithsonian Astrophysical Observatory
#
#
# This program 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.
#
# This program 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 this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#
"""
Evaluate a model on a different grid to the requested one.
This is intended to support convolution-style models, where the
convolved model should be evaluated on a different grid to the
data - e.g. a larger grid, since the convolution will account
for signal outside the data range - and then be regridded to
match the desired grid.
"""
import logging
import warnings
import numpy as np
from sherpa.utils._utils import rebin
from sherpa.utils.akima import akima
from sherpa.astro.utils import reshape_2d_arrays
from sherpa.utils.err import ModelErr
warning = logging.getLogger(__name__).warning
PIXEL_RATIO_THRESHOLD = 0.1
[docs]class Axis():
"""Represent the axes of a N-D object.
This supports both "integrated" and "non-integrated" (point)
datasets.
Parameters
----------
lo : array_like or None
The starting point of the axis. If `lo` is `None` or empty
then the data axis is said to be empty. The axis can be in
ascending or descending order.
hi : array_like or None
The ending point of the axis for "integrated" axes, i.e. those
which have a low and high edge.
"""
def __init__(self, lo, hi):
self.lo = np.asarray(lo) if lo is not None else None
self.hi = np.asarray(hi) if hi is not None else None
@property
def is_empty(self):
"""Is the axis empty?
An empty axis has `lo` set to None or a zero-element array.
"""
return self.lo is None or not self.lo.size
@property
def is_integrated(self):
"""Is the axis integrated?
Is `hi` set and not a zero-element array?
"""
return self.hi is not None and self.hi.size > 0
@property
def is_ascending(self):
"""Is the axis ascending?
The axis is ascending if the elements in `lo` are sorted in
ascending order. Only the first and last elements are
checked, and it is assumed that the elements are sorted.
"""
try:
return self.lo[-1] > self.lo[0]
except TypeError:
raise ValueError("{} does not seem to be an array".format(self.lo))
@property
def start(self):
"""The starting point (lowest value) of the data axis."""
if self.is_ascending:
return self.lo[0]
return self.lo[-1]
@property
def end(self):
"""The ending point of the data axis
If the data axis is ascending the end boundary is the last
element of the `hi` array when the axis is integrated,
otherwise it's the last element of `lo`. Conversely, for
descending axes, the last element is either the first element
of the `hi` array or of the `lo` array, depending on whether
the axis is integrated or not, respectively.
"""
if self.is_ascending and self.is_integrated:
return self.hi[-1]
if self.is_ascending and not self.is_integrated:
return self.lo[-1]
if self.is_integrated:
return self.hi[0]
return self.lo[0]
@property
def size(self):
"""The size of the axis."""
return self.lo.size
[docs] def overlaps(self, other):
"""Check if this axis overlaps with another.
Parameters
----------
other : Axis
The axis to compare to.
Returns
-------
bool
True if they overlap, False if not
"""
num = max(0, min(self.end, other.end) - max(self.start, other.start))
return bool(num != 0)
[docs]class EvaluationSpace1D():
"""Class for 1D Evaluation Spaces.
An Evaluation Space is a set of data axes representing the data
space over which a model can be evaluated. A 1D Evaluation Space
has only one axis.
Parameters
----------
x : array_like or None, optional
The data array, or the low end of the data bins if the dataset is "integrated"
xhi : array_like or None, optional
The high end of the data bins for integrated datasets.
"""
def __init__(self, x=None, xhi=None):
"""The input arrays are used to instantiate a single axis."""
self.x_axis = Axis(x, xhi)
@property
def is_empty(self):
"""Is the space empty (the x axis has no elements)?"""
return self.x_axis.is_empty
@property
def is_integrated(self):
"""Is the space integrated?"""
return self.x_axis.is_integrated
@property
def is_ascending(self):
"""Is the space ascending?"""
return self.x_axis.is_empty
@property
def grid(self):
"""The grid representation of the space.
Returns
-------
tuple
A tuple representing the x axis. The tuple will contain
two arrays if the dataset is integrated, one otherwise.
"""
if self.x_axis.is_integrated:
return self.x_axis.lo, self.x_axis.hi
else:
return self.x_axis.lo,
@property
def midpoint_grid(self):
"""The mid-points of the space.
For non-integrated spaces this returns the X axis.
Returns
-------
array
Return the average point of the bins of integrated axes,
for each bin, or the non-integrated x axis array.
"""
if self.x_axis.is_integrated:
return (self.x_axis.lo + self.x_axis.hi)/2
else:
return self.x_axis.lo
@property
def start(self):
"""The start (lowest value) of the space."""
return self.x_axis.start
@property
def end(self):
"""The end (highest value) of the space."""
return self.x_axis.end
[docs] def zeros_like(self):
"""Returns zeroes for each element of the space.
Returns
-------
array
A one-dimensional array.
"""
return np.zeros(self.x_axis.lo.size)
[docs] def overlaps(self, other):
"""Check if this evaluation space overlaps with another.
Parameters
----------
other : EvaluationSpace1D
The space to compare to.
Returns
-------
bool
True if they overlap, False if not
"""
return self.x_axis.overlaps(other.x_axis)
def __contains__(self, other):
"""Are all elements of other within the range (start, end) of this space?
Parameters
----------
other : EvaluationSpace1D
The space to compare to.
Returns
-------
boolean
"""
# OL: I have mixed feelings about overriding this method. On one hand it makes the
# tests more expressive and natural, on the other this method is intended to check
# if an element is in a collection, so it's a bit of a stretch semantically.
return self.start <= other.start and self.end >= other.end
[docs]class EvaluationSpace2D():
"""Class for 2D Evaluation Spaces.
An Evaluation Space is a set of data axes representing the data
space over which a model can be evaluated. A 2D Evaluation Space
has two axes, x and y.
Parameters
----------
x, y : array_like or None, optional
The data array, or the low end of the x and y data bins if the dataset
is "integrated".
xhi, yhi : array_like or None, optional
The high end of the x and y data bins for integrated datasets.
"""
def __init__(self, x=None, y=None, xhi=None, yhi=None):
# In the 2D case the arrays are redundant, as they are flattened from a meshgrid.
# We need to clean them up first to have proper axes.
# This may happen when an EvaluationSpace2D is instantiated using the arrays passed to
# the calc method.
x_unique, y_unique, xhi_unique, yhi_unique = self._clean_arrays(x, y, xhi, yhi)
self.x_axis = Axis(x_unique, xhi_unique)
self.y_axis = Axis(y_unique, yhi_unique)
def _clean_arrays(self, x, y, xhi, yhi):
return self._clean(x), self._clean(y), self._clean(xhi), self._clean(yhi)
@staticmethod
def _clean(array):
if array is not None:
# We need to take extra care not to change the order of the arrays, hence
# the additional complexity
array_unique, indexes = np.unique(array, return_index=True)
return array_unique[indexes.argsort()]
@property
def is_empty(self):
"""Is the space empty (the x axis has no elements)?"""
return self.x_axis.is_empty or self.y_axis.is_empty
@property
def is_integrated(self):
"""Is the space integrated?"""
return (not self.is_empty) \
and self.x_axis.is_integrated \
and self.y_axis.is_integrated
@property
def is_ascending(self):
"""Is the space ascending?
Returns
-------
(xflag, yflag) : (bool, bool)
True if the axis is ascending, False otherwise, for the
x and y axes respectively.
"""
return self.x_axis.is_ascending, self.y_axis.is_ascending
@property
def start(self):
"""The start (lowest value) of the space.
Returns
-------
(xstart, ystart) : (number, number)
The start of the x and y axis arrays, respectively.
"""
return self.x_axis.start, self.y_axis.start
@property
def end(self):
"""The end (highest value) of the space.
Returns
-------
(xend, yend) : (number, number)
The end of the x and y axis arrays, respectively.
"""
return self.x_axis.end, self.y_axis.end
@property
def shape(self):
"""The sizes of the x and y axes."""
return self.x_axis.size, self.y_axis.size
[docs] def overlaps(self, other):
"""Check if this evaluation space overlaps with another.
Note that this is more stringent for 2D, as the boundaries
need to coincide in this case.
Parameters
----------
other : EvaluationSpace2D
The space to compare to.
Returns
-------
bool
True if they overlap, False if not
"""
return bool(self.x_axis.start == other.x_axis.start
and self.y_axis.start == other.y_axis.start
and self.x_axis.end == other.x_axis.end
and self.y_axis.end == other.y_axis.end)
@property
def grid(self):
"""The grid representation of the space.
The x and y arrays in the grid are one-dimensional
representations of the meshgrid obtained from the x and y axis
arrays, as in `numpy.meshgrid(x, y)[0].ravel()`
Returns
-------
tuple
A two element (x, y) or 4 element (x, y, xhi, yhi) tuple.
"""
x, y = reshape_2d_arrays(self.x_axis.lo, self.y_axis.lo)
if self.x_axis.is_integrated:
xhi, yhi = reshape_2d_arrays(self.x_axis.hi, self.y_axis.hi)
return x, y, xhi, yhi
else:
return x, y
[docs] def zeros_like(self):
"""Returns zeroes for each element of the space.
Returns
-------
array
A one dimensional array.
"""
size = self.x_axis.lo.size * self.y_axis.lo.size
return np.zeros(size)
[docs]class ModelDomainRegridder1D():
"""Allow 1D models to be evaluated on a different grid.
This class is not used directly in a model expression;
instead it creates an instance that is used to evaluate
the model.
Parameters
----------
evaluation_space : object or None, optional
name : str, optional
The default name is 'regrid1d'.
interp : callable, optional
The interpolation function: it should accept three arrays: the
output x values and the x, y values to interpolate, and return
the interpolated y values. The default is to use
`sherpa.utils.akima.akima`.
Examples
--------
The "internal" model (gaussian plus constant) will be
evaluated on the grid 0 to 10 (spacing of 0.5), and then
linearly-interpolated onto the desired grid (1 to 8,
spacing of 0.7). In this example there is no benefit to
this approach - it is easier just to evaluate
`internal_mdl` on the grid `x` - but it illustrates
the approach.
>>> from sherpa.models import Gauss1D, Const1D
>>> internal_mdl = Gauss1D() + Const1D()
>>> eval_space = EvaluationSpace1D(np.arange(0, 10, 0.5))
>>> rmdl = ModelDomainRegridder1D(eval_space)
>>> mdl = rmdl.apply_to(internal_mdl)
>>> x = np.arange(1, 8, 0.7)
>>> y = mdl(x)
"""
def __init__(self, evaluation_space=None, name='regrid1d', **kwargs):
self.name = name
self.integrate = True
self.evaluation_space = evaluation_space if evaluation_space is not None else EvaluationSpace1D()
self.method = kwargs.get("interp", akima)
@property
def method(self):
"""Interpolate the data from the internal to requested grid.
This is *only* used for point grids, as integrated grids use a
simple rebinning scheme. The default is
`sherpa.utils.akima.akima`. The callable should accept `(xout,
xin, yin)` arguments and interpolate the `(xin, yin)` data
onto the `xout` grid, returning the interpolated data.
"""
return self._method
@method.setter
def method(self, method):
if not callable(method):
raise TypeError(f"method argument '{repr(method)}' is not callable")
self._method = method
@property
def grid(self):
"""The grid of the associated evaluation space."""
return self.evaluation_space.grid
@grid.setter
def grid(self, value):
try: # value is an iterable (integrated models) to be unpacked
self.evaluation_space = EvaluationSpace1D(*value)
except TypeError: # value is a single array (non-integrated models)
self.evaluation_space = EvaluationSpace1D(value)
[docs] def apply_to(self, model):
"""Evaluate a model on a different grid."""
from sherpa.models.model import RegridWrappedModel
return RegridWrappedModel(model, self)
[docs] def calc(self, pars, modelfunc, *args, **kwargs):
"""Evaluate and regrid a model
Evaluate the model on the internal grid and then convert it
onto the desired grid either preserving flux (rebinning) or
via interpolation.
Parameters
----------
pars : sequence of numbers
The parameter values of the model.
modelfunc
The model to evaluate (the calc attribute of the model)
args
The grid to interpolate the model onto. This must match the
format of the grid attribute of the model - i.e.
non-integrate (single array) or integrated (a pair of
equal-sized arrays).
kwargs
Keyword arguments for the model.
Notes
-----
If the requested grid (i.e. that defined by args) does not overlap
the stored grid (the grid attribute) then all values are set to 0.
However, if the grids partially overlap then there will be
extrapolation (depending on the method).
It is not clear yet whether the restriction on grid type (i.e.
must match between the requested grid and the intenal grid
whether it is integrated or non-integrated) is too restrictive.
"""
if self.evaluation_space.is_empty: # Simply pass through
return modelfunc(pars, *args, **kwargs)
requested_eval_space = self._make_and_validate_grid(args)
return self._evaluate(requested_eval_space, pars, modelfunc, **kwargs)
def _make_and_validate_grid(self, args_array):
"""Validate input grid and check whether it's point or integrated.
Parameters
----------
args_array : list
The array or arguments passed to the `call` method
Returns
-------
requested_eval_space : EvaluationSpace1D
"""
nargs = len(args_array)
if nargs == 0:
raise ModelErr('nogrid')
requested_eval_space = EvaluationSpace1D(*args_array)
# Ensure the two grids match: integrated or non-integrated.
if self.evaluation_space.is_integrated and not requested_eval_space.is_integrated:
raise ModelErr('needsint')
if requested_eval_space.is_integrated and not self.evaluation_space.is_integrated:
raise ModelErr('needspoint')
if self.evaluation_space.is_integrated and requested_eval_space.is_integrated:
lo = self.evaluation_space.grid[0]
hi = self.evaluation_space.grid[1]
if np.any(lo[1:] < hi[:-1]) or np.any(lo == hi):
raise ModelErr('needsint')
return requested_eval_space
[docs] def eval_non_integrated(self, pars, modelfunc, data_grid, eval_grid,
**kwargs):
"""Interpolate the model.
Parameters
----------
pars : list of numbers
The parameters for the model.
modelfunc : callable
The model to evaluate. It is called as
`modelfunc(pars, x, **kwargs)`
data_grid : sequence of numbers
The grid on which to return the values.
eval_grid : sequence of numbers
The grid on which to evaluate the model.
kwargs
Any arguments to be sent to modelfunc.
Returns
-------
model : ndarray
The model values matching the data_grid bins.
"""
# eval_grid is out of data_grid range
if eval_grid[-1] < data_grid[0] or eval_grid[0] > data_grid[-1]:
return np.zeros(data_grid.size)
#
# join all elements of data_grid within
# eval_spaee to minimize interpolation
#
indices = np.where((data_grid > eval_grid[0]) &
(data_grid < eval_grid[-1]))
my_eval_grid = np.unique(np.append(eval_grid, data_grid[indices]))
y_tmp = modelfunc(pars, my_eval_grid, **kwargs)
y_interpolate = self.method(data_grid, my_eval_grid, y_tmp)
if y_interpolate.size == data_grid.size and \
eval_grid[0] < data_grid[0] and eval_grid[-1] > data_grid[-1]:
# data space all within eval_grid
return y_interpolate
# find indices within data_grid
indices = np.unique(data_grid.searchsorted(my_eval_grid))
indices = indices[np.where(indices < data_grid.size)]
y = np.zeros(data_grid.size)
y[indices] = y_interpolate[indices]
return y
[docs] def eval_integrated(self, pars, modelfunc, data_grid, eval_grid,
**kwargs):
"""Rebin the model onto a grid with low and high bins.
Parameters
----------
pars : list of numbers
The parameters for the model.
modelfunc : callable
The model to evaluate. It is called as
`modelfunc(pars, lo, hi, **kwargs)`
data_grid : (sequence of numbers, sequence of numbers)
The grid on which to return the values, as low and
high edges.
eval_grid : (sequence of numbers, sequence of numbers)
The grid on which to evaluate the model, as low and
high edges.
kwargs
Any arguments to be sent to modelfunc.
Returns
-------
model : ndarray
The model values matching the data_grid bins.
"""
y = modelfunc(pars, eval_grid[0], eval_grid[1],
**kwargs)
return rebin(y, eval_grid[0], eval_grid[1],
data_grid[0], data_grid[1])
def _evaluate(self, data_space, pars, modelfunc, **kwargs):
"""Evaluate the model and then convert to the requested grid.
The model is evaluated using the evaluation_space attribute and
then converted to match the data_space argument. If the data_space
is integrated and the integrate attribute is set then the conversion
is done by rebinning the data (and so preserving the signal)
otherwise the method attribute is used to interpolate the data.
Parameters
----------
data_space : EvaluationSpace1D instance
The output grid for the model.
pars : list of numbers
The parameters for the model.
modelfunc : callable
The model to evaluate. It is called as
modelfunc(pars, *args, **kwargs) where args is
either one or two arguments.
kwargs
Any arguments to be sent to modelfunc.
Notes
-----
This is based on sherpa.models.TableModel but is simplified as
we do not provide a fold method.
"""
# Not really sure I need this, but let's be safe
kwargs['integrate'] = self.integrate
eval_space = self.evaluation_space
if data_space.is_integrated and self.integrate:
return self.eval_integrated(pars, modelfunc,
data_space.grid, eval_space.grid,
**kwargs)
# We either have integrate=False or a non-integrated bin is given.
return self.eval_non_integrated(pars, modelfunc,
data_space.midpoint_grid,
eval_space.midpoint_grid,
**kwargs)
[docs]class ModelDomainRegridder2D():
"""Allow 2D models to be evaluated on a different grid.
This class is not used directly in a model expression;
instead it creates an instance that is used to evaluate
the model.
Parameters
----------
evaluation_space : object or None, optional
name : str, optional
The default name is 'regrid2d'.
Examples
--------
The "internal" model (gaussian plus constant) will be
evaluated on the grid 0 to 10 (spacing of 0.5), and then
linearly-interpolated onto the desired grid (1 to 8,
spacing of 0.7). In this example there is no benefit to
this approach - it is easier just to evaluate
``internal_mdl`` on the grid ``x, y`` - but it illustrates
the approach.
>>> from sherpa.models import Gauss2D, Const2D
>>> internal_mdl = Gauss2D() + Const2D()
>>> eval_space = EvaluationSpace2D(np.arange(0, 10, 0.5), np.arange(0, 10, 0.5))
>>> rmdl = ModelDomainRegridder2D(eval_space)
>>> mdl = rmdl.apply_to(internal_mdl)
>>> x = np.arange(1, 8, 0.7)
>>> y = np.arange(1, 8, 0.7)
>>> x, y = reshape_2d_arrays(x, y)
>>> z = mdl(x, y)
"""
def __init__(self, evaluation_space=None, name='regrid2d'):
self.name = name
self.evaluation_space = evaluation_space\
if evaluation_space is not None else EvaluationSpace2D()
@property
def grid(self):
return self.evaluation_space.grid
@grid.setter
def grid(self, value):
self.evaluation_space = EvaluationSpace2D(*value)
[docs] def apply_to(self, model):
"""Evaluate a model on a different grid."""
from sherpa.models.model import RegridWrappedModel
return RegridWrappedModel(model, self)
[docs] def calc(self, pars, modelfunc, *args, **kwargs):
"""Evaluate and regrid a model
Evaluate the model on the internal grid and then
interpolate onto the desired grid.
Parameters
----------
pars : sequence of numbers
The parameter values of the model.
modelfunc
The model to evaluate (the calc attribute of the model)
args
The grid to interpolate the model onto. This must match the
format of the grid attribute of the model - i.e.
non-integrate (x, y arrays) or integrated (xlo, ylo, xhi, yhi).
kwargs
Keyword arguments for the model.
Notes
-----
If the requested grid (i.e. that defined by args) does not overlap
the stored grid (the grid attribute) then all values are set to 0.
However, if the grids partially overlap then there will be
extrapolation (depending on the method).
It is not clear yet whether the restriction on grid type (i.e.
must match between the requested grid and the intenal grid
whether it is integrated or non-integrated) is too restrictive.
"""
if self.evaluation_space.is_empty: # Simply pass through
return modelfunc(pars, *args, **kwargs)
requested_eval_space = self._make_and_validate_grid(args)
return self._evaluate(requested_eval_space, pars, modelfunc)
def _make_and_validate_grid(self, args_array):
"""
Validate input grid and check whether it's point or integrated.
Parameters
----------
args_array : list
The array or arguments passed to the `call` method
Returns
-------
requested_eval_space : EvaluationSpace2D
"""
nargs = len(args_array)
if nargs == 0:
raise ModelErr('nogrid')
requested_eval_space = EvaluationSpace2D(*args_array)
# Ensure the two grids match: integrated or non-integrated.
if self.evaluation_space.is_integrated and not requested_eval_space.is_integrated:
raise ModelErr('needsint')
if requested_eval_space.is_integrated and not self.evaluation_space.is_integrated:
raise ModelErr('needspoint')
return requested_eval_space
def _evaluate(self, requested_space, pars, modelfunc):
# Evaluate the model on the user-defined grid and then rebin
# onto the desired grid.
if not requested_space.overlaps(self.evaluation_space):
warnings.warn("requested space and evaluation space do not overlap, evaluating model to 0")
return requested_space.zeros_like()
y = modelfunc(pars, *self.grid)
return rebin_2d(y, self.evaluation_space, requested_space).ravel()
[docs]def rebin_2d(y, from_space, to_space):
to_x_dim = to_space.x_axis.size
to_y_dim = to_space.y_axis.size
from_x_dim = from_space.x_axis.size
from_y_dim = from_space.y_axis.size
if hasattr(from_space, "data_2_psf_pixel_size_ratio"):
ratio = from_space.data_2_psf_pixel_size_ratio
scale_x, scale_y = 1/ratio[0], 1/ratio[1]
else:
scale_x = from_x_dim / to_x_dim
scale_y = from_y_dim / to_y_dim
scale = scale_x * scale_y
if scale == 1:
return y
reshaped_y = y.reshape(from_x_dim, from_y_dim)
reshaped_scaled_y = reshaped_y / scale
if (abs(scale_x - round(scale_x)) > PIXEL_RATIO_THRESHOLD
or abs(scale_y - round(scale_y)) > PIXEL_RATIO_THRESHOLD):
return rebin_no_int(reshaped_scaled_y, dimensions=(to_x_dim, to_y_dim))
return rebin_int(reshaped_scaled_y, int(round(scale_x)), int(round(scale_y)))
[docs]def rebin_int(array, scale_x, scale_y):
"""Rebin array by an integer scale on both x and y
Parameters
----------
array : array_like
The array to be rebinned
scale_x : int
The pixel ratio on the x axis
scale_y : int
The pixel ratio on the y axis
Returns
-------
array_like
"""
xedge = np.shape(array)[0] % scale_x
yedge = np.shape(array)[1] % scale_y
sub_array = array[xedge:, yedge:]
binned_x_shape = np.shape(sub_array)[0] // scale_x
binned_y_shape = np.shape(sub_array)[1] // scale_y
image = np.reshape(sub_array, (binned_x_shape, scale_x, binned_y_shape, scale_y))
image = np.sum(image, axis=3)
image = np.sum(image, axis=1)
return image
[docs]def rebin_no_int(array, dimensions=None, scale=None):
"""Rebin the array, conserving flux.
Return the array ``array`` to the new ``dimensions`` conserving flux,
so that the sum of the output matches the sum of ``array``.
Raises
------
AssertionError
If the totals of the input and result array don't agree, raise an error because computation may have gone wrong
Notes
-----
This routine is based on the example at
http://martynbristow.co.uk/wordpress/blog/rebinning-data/
which was released as GPL v3 © Martyn Bristow 2015. It has been
slightly modified for Sherpa.
Examples
--------
>>> ar = np.array([
... [0,1,2],
... [1,2,3],
... [2,3,4],
... ])
>>> rebin_no_int(ar, (2,2))
array([[1.5, 4.5],
[4.5, 7.5]])
"""
if dimensions is not None:
if isinstance(dimensions, float):
dimensions = [int(dimensions)] * len(array.shape)
elif isinstance(dimensions, int):
dimensions = [dimensions] * len(array.shape)
elif len(dimensions) != len(array.shape):
raise RuntimeError('')
elif scale is not None:
if isinstance(scale, float) or isinstance(scale, int):
dimensions = map(int, map(round, map(lambda x: x * scale, array.shape)))
elif len(scale) != len(array.shape):
raise RuntimeError('')
else:
raise RuntimeError('Incorrect parameters to rebin.\n\trebin(array, dimensions=(x,y))\n\trebin(array, scale=a')
import itertools
dY, dX = map(divmod, map(float, array.shape), dimensions)
result = np.zeros(dimensions)
for j, i in itertools.product(*map(range, array.shape)):
(J, dj) = divmod(j * dimensions[0], array.shape[0])
(I, di) = divmod(i * dimensions[1], array.shape[1])
(J1, dj1) = divmod(j + 1, array.shape[0] / float(dimensions[0]))
(I1, di1) = divmod(i + 1, array.shape[1] / float(dimensions[1]))
# Moving to new bin
# Is this a discrete bin?
dx, dy = 0, 0
if (I1 - I == 0) | ((I1 - I == 1) & (di1 == 0)):
dx = 1
else:
dx = 1 - di1
if (J1 - J == 0) | ((J1 - J == 1) & (dj1 == 0)):
dy = 1
else:
dy = 1 - dj1
# Prevent it from allocating outide the array
I_ = min(dimensions[1] - 1, I + 1)
J_ = min(dimensions[0] - 1, J + 1)
result[J, I] += array[j, i] * dx * dy
result[J_, I] += array[j, i] * (1 - dy) * dx
result[J, I_] += array[j, i] * dy * (1 - dx)
result[J_, I_] += array[j, i] * (1 - dx) * (1 - dy)
allowError = 0.001
assert array.sum() == 0 or \
(array.sum() < result.sum() * (1 + allowError)) and \
(array.sum() > result.sum() * (1 - allowError))
return result