#
# LSST Data Management System
# Copyright 2008-2017 LSST/AURA.
#
# This product includes software developed by the
# LSST Project (http://www.lsst.org/).
#
# 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 LSST License Statement and
# the GNU General Public License along with this program. If not,
# see <http://www.lsstcorp.org/LegalNotices/>.
#
# the asserts are automatically imported so unit tests can find them without special imports;
# the other functions are hidden unless explicitly asked for
__all__ = ["assertImagesAlmostEqual", "assertImagesEqual", "assertMasksEqual",
"assertMaskedImagesAlmostEqual", "assertMaskedImagesEqual",
"assertImagesNearlyEqual", "assertMaskedImagesNearlyEqual"]
import warnings
import numpy as np
import lsst.utils.tests
from .image import ImageF
from .basicUtils import makeMaskedImageFromArrays
def makeGaussianNoiseMaskedImage(dimensions, sigma, variance=1.0):
"""Make a gaussian noise MaskedImageF
Inputs:
- dimensions: dimensions of output array (cols, rows)
- sigma; sigma of image plane's noise distribution
- variance: constant value for variance plane
"""
npSize = (dimensions[1], dimensions[0])
image = np.random.normal(loc=0.0, scale=sigma,
size=npSize).astype(np.float32)
mask = np.zeros(npSize, dtype=np.int32)
variance = np.zeros(npSize, dtype=np.float32) + variance
return makeMaskedImageFromArrays(image, mask, variance)
def makeRampImage(bbox, start=0, stop=None, imageClass=ImageF):
"""!Make an image whose values are a linear ramp
@param[in] bbox bounding box of image (an lsst.afw.geom.Box2I)
@param[in] start starting ramp value, inclusive
@param[in] stop ending ramp value, inclusive; if None, increase by integer values
@param[in] imageClass type of image (e.g. lsst.afw.image.ImageF)
"""
im = imageClass(bbox)
imDim = im.getDimensions()
numPix = imDim[0]*imDim[1]
imArr = im.getArray()
if stop is None:
# increase by integer values
stop = start + numPix - 1
rampArr = np.linspace(start=start, stop=stop,
endpoint=True, num=numPix, dtype=imArr.dtype)
# numpy arrays are transposed w.r.t. afwImage
imArr[:] = np.reshape(rampArr, (imDim[1], imDim[0]))
return im
@lsst.utils.tests.inTestCase
def assertImagesAlmostEqual(testCase, image0, image1, skipMask=None,
rtol=1.0e-05, atol=1e-08, msg="Images differ"):
"""!Assert that two images are almost equal, including non-finite values
@param[in] testCase unittest.TestCase instance the test is part of;
an object supporting one method: fail(self, msgStr)
@param[in] image0 image 0, an lsst.afw.image.Image, lsst.afw.image.Mask,
or transposed numpy array (see warning)
@param[in] image1 image 1, an lsst.afw.image.Image, lsst.afw.image.Mask,
or transposed numpy array (see warning)
@param[in] skipMask mask of pixels to skip, or None to compare all pixels;
an lsst.afw.image.Mask, lsst.afw.image.Image, or transposed numpy array (see warning);
all non-zero pixels are skipped
@param[in] rtol maximum allowed relative tolerance; more info below
@param[in] atol maximum allowed absolute tolerance; more info below
@param[in] msg exception message prefix; details of the error are appended after ": "
The images are nearly equal if all pixels obey:
|val1 - val0| <= rtol*|val1| + atol
or, for float types, if nan/inf/-inf pixels match.
@warning the comparison equation is not symmetric, so in rare cases the assertion
may give different results depending on which image comes first.
@warning the axes of numpy arrays are transposed with respect to Image and Mask data.
Thus for example if image0 and image1 are both lsst.afw.image.ImageD with dimensions (2, 3)
and skipMask is a numpy array, then skipMask must have shape (3, 2).
@throw self.failureException (usually AssertionError) if any of the following are true
for un-skipped pixels:
- non-finite values differ in any way (e.g. one is "nan" and another is not)
- finite values differ by too much, as defined by atol and rtol
@throw TypeError if the dimensions of image0, image1 and skipMask do not match,
or any are not of a numeric data type.
"""
errStr = imagesDiffer(
image0, image1, skipMask=skipMask, rtol=rtol, atol=atol)
if errStr:
testCase.fail("%s: %s" % (msg, errStr))
@lsst.utils.tests.inTestCase
def assertImagesEqual(*args, **kwds):
"""!Assert that two images are exactly equal, including non-finite values.
All arguments are forwarded to assertAnglesAlmostEqual aside from atol and rtol,
which are set to zero.
"""
return assertImagesAlmostEqual(*args, atol=0, rtol=0, **kwds)
@lsst.utils.tests.inTestCase
def assertMasksEqual(testCase, mask0, mask1, skipMask=None, msg="Masks differ"):
"""!Assert that two masks are equal
@param[in] testCase unittest.TestCase instance the test is part of;
an object supporting one method: fail(self, msgStr)
@param[in] mask0 mask 0, an lsst.afw.image.Mask, lsst.afw.image.Image,
or transposed numpy array (see warning)
@param[in] mask1 mask 1, an lsst.afw.image.Mask, lsst.afw.image.Image,
or transposed numpy array (see warning)
@param[in] skipMask mask of pixels to skip, or None to compare all pixels;
an lsst.afw.image.Mask, lsst.afw.image.Image, or transposed numpy array (see warning);
all non-zero pixels are skipped
@param[in] msg exception message prefix; details of the error are appended after ": "
@warning the axes of numpy arrays are transposed with respect to Mask and Image.
Thus for example if mask0 and mask1 are both lsst.afw.image.Mask with dimensions (2, 3)
and skipMask is a numpy array, then skipMask must have shape (3, 2).
@throw self.failureException (usually AssertionError) if any any un-skipped pixels differ
@throw TypeError if the dimensions of mask0, mask1 and skipMask do not match,
or any are not of a numeric data type.
"""
errStr = imagesDiffer(mask0, mask1, skipMask=skipMask, rtol=0, atol=0)
if errStr:
testCase.fail("%s: %s" % (msg, errStr))
@lsst.utils.tests.inTestCase
def assertMaskedImagesAlmostEqual(
testCase, maskedImage0, maskedImage1,
doImage=True, doMask=True, doVariance=True, skipMask=None,
rtol=1.0e-05, atol=1e-08, msg="Masked images differ",
):
"""!Assert that two masked images are nearly equal, including non-finite values
@param[in] testCase unittest.TestCase instance the test is part of;
an object supporting one method: fail(self, msgStr)
@param[in] maskedImage0 masked image 0 (an lsst.afw.image.MaskedImage or
collection of three transposed numpy arrays: image, mask, variance)
@param[in] maskedImage1 masked image 1 (an lsst.afw.image.MaskedImage or
collection of three transposed numpy arrays: image, mask, variance)
@param[in] doImage compare image planes if True
@param[in] doMask compare mask planes if True
@param[in] doVariance compare variance planes if True
@param[in] skipMask mask of pixels to skip, or None to compare all pixels;
an lsst.afw.image.Mask, lsst.afw.image.Image, or transposed numpy array;
all non-zero pixels are skipped
@param[in] rtol maximum allowed relative tolerance; more info below
@param[in] atol maximum allowed absolute tolerance; more info below
@param[in] msg exception message prefix; details of the error are appended after ": "
The mask planes must match exactly. The image and variance planes are nearly equal if all pixels obey:
|val1 - val0| <= rtol*|val1| + atol
or, for float types, if nan/inf/-inf pixels match.
@warning the comparison equation is not symmetric, so in rare cases the assertion
may give different results depending on which masked image comes first.
@warning the axes of numpy arrays are transposed with respect to MaskedImage data.
Thus for example if maskedImage0 and maskedImage1 are both lsst.afw.image.MaskedImageD
with dimensions (2, 3) and skipMask is a numpy array, then skipMask must have shape (3, 2).
@throw self.failureException (usually AssertionError) if any of the following are true
for un-skipped pixels:
- non-finite image or variance values differ in any way (e.g. one is "nan" and another is not)
- finite values differ by too much, as defined by atol and rtol
- mask pixels differ at all
@throw TypeError if the dimensions of maskedImage0, maskedImage1 and skipMask do not match,
either image or variance plane is not of a numeric data type,
either mask plane is not of an integer type (unsigned or signed),
or skipMask is not of a numeric data type.
"""
maskedImageArrList0 = maskedImage0.getArrays() if hasattr(
maskedImage0, "getArrays") else maskedImage0
maskedImageArrList1 = maskedImage1.getArrays() if hasattr(
maskedImage1, "getArrays") else maskedImage1
for arrList, arg, name in (
(maskedImageArrList0, maskedImage0, "maskedImage0"),
(maskedImageArrList1, maskedImage1, "maskedImage1"),
):
try:
assert len(arrList) == 3
# check that array shapes are all identical
# check that image and variance are float or int of some kind
# and mask is int of some kind
for i in (0, 2):
assert arrList[i].shape == arrList[1].shape
assert arrList[i].dtype.kind in ("b", "i", "u", "f", "c")
assert arrList[1].dtype.kind in ("b", "i", "u")
except Exception:
raise TypeError("%s=%r is not a supported type" % (name, arg))
errStrList = []
for ind, (doPlane, planeName) in enumerate(((doImage, "image"),
(doMask, "mask"),
(doVariance, "variance"))):
if not doPlane:
continue
if planeName == "mask":
errStr = imagesDiffer(maskedImageArrList0[ind], maskedImageArrList1[ind], skipMask=skipMask,
rtol=0, atol=0)
if errStr:
errStrList.append(errStr)
else:
errStr = imagesDiffer(maskedImageArrList0[ind], maskedImageArrList1[ind],
skipMask=skipMask, rtol=rtol, atol=atol)
if errStr:
errStrList.append("%s planes differ: %s" % (planeName, errStr))
if errStrList:
testCase.fail("%s: %s" % (msg, "; ".join(errStrList)))
@lsst.utils.tests.inTestCase
def assertMaskedImagesEqual(*args, **kwds):
"""!Assert that two masked images are exactly equal, including non-finite values.
All arguments are forwarded to assertMaskedImagesAlmostEqual aside from atol and rtol,
which are set to zero.
"""
return assertMaskedImagesAlmostEqual(*args, atol=0, rtol=0, **kwds)
def imagesDiffer(image0, image1, skipMask=None, rtol=1.0e-05, atol=1e-08):
"""!Compare the pixels of two image or mask arrays; return True if close, False otherwise
@param[in] image0 image 0, an lsst.afw.image.Image, lsst.afw.image.Mask,
or transposed numpy array (see warning)
@param[in] image1 image 1, an lsst.afw.image.Image, lsst.afw.image.Mask,
or transposed numpy array (see warning)
@param[in] skipMask mask of pixels to skip, or None to compare all pixels;
an lsst.afw.image.Mask, lsst.afw.image.Image, or transposed numpy array (see warning);
all non-zero pixels are skipped
@param[in] rtol maximum allowed relative tolerance; more info below
@param[in] atol maximum allowed absolute tolerance; more info below
The images are nearly equal if all pixels obey:
|val1 - val0| <= rtol*|val1| + atol
or, for float types, if nan/inf/-inf pixels match.
@warning the comparison equation is not symmetric, so in rare cases the assertion
may give different results depending on which image comes first.
@warning the axes of numpy arrays are transposed with respect to Image and Mask data.
Thus for example if image0 and image1 are both lsst.afw.image.ImageD with dimensions (2, 3)
and skipMask is a numpy array, then skipMask must have shape (3, 2).
@return a string which is non-empty if the images differ
@throw TypeError if the dimensions of image0, image1 and skipMask do not match,
or any are not of a numeric data type.
"""
errStrList = []
imageArr0 = image0.getArray() if hasattr(image0, "getArray") else image0
imageArr1 = image1.getArray() if hasattr(image1, "getArray") else image1
skipMaskArr = skipMask.getArray() if hasattr(skipMask, "getArray") else skipMask
# check the inputs
arrArgNameList = [
(imageArr0, image0, "image0"),
(imageArr1, image1, "image1"),
]
if skipMask is not None:
arrArgNameList.append((skipMaskArr, skipMask, "skipMask"))
for i, (arr, arg, name) in enumerate(arrArgNameList):
try:
assert arr.dtype.kind in ("b", "i", "u", "f", "c")
except Exception:
raise TypeError("%r=%r is not a supported type" % (name, arg))
if i != 0:
if arr.shape != imageArr0.shape:
raise TypeError("%s shape = %s != %s = image0 shape" %
(name, arr.shape, imageArr0.shape))
# np.allclose mis-handled unsigned ints in numpy 1.8
# and subtraction doesn't give the desired answer in any case
# so cast unsigned arrays into int64 (there may be a simple
# way to safely use a smaller data type but I've not found it)
if imageArr0.dtype.kind == "u":
imageArr0 = imageArr0.astype(
np.promote_types(imageArr0.dtype, np.int8))
if imageArr1.dtype.kind == "u":
imageArr1 = imageArr1.astype(
np.promote_types(imageArr1.dtype, np.int8))
if skipMaskArr is not None:
skipMaskArr = np.array(skipMaskArr, dtype=bool)
maskedArr0 = np.ma.array(imageArr0, copy=False, mask=skipMaskArr)
maskedArr1 = np.ma.array(imageArr1, copy=False, mask=skipMaskArr)
filledArr0 = maskedArr0.filled(0.0)
filledArr1 = maskedArr1.filled(0.0)
else:
skipMaskArr = None
filledArr0 = imageArr0
filledArr1 = imageArr1
try:
np.array([np.nan], dtype=imageArr0.dtype)
np.array([np.nan], dtype=imageArr1.dtype)
except Exception:
# one or both images does not support non-finite values (nan, etc.)
# so just use value comparison
valSkipMaskArr = skipMaskArr
else:
# both images support non-finite values, of which numpy has exactly three: nan, +inf and -inf;
# compare those individually in order to give useful diagnostic output
nan0 = np.isnan(filledArr0)
nan1 = np.isnan(filledArr1)
if np.any(nan0 != nan1):
errStrList.append("NaNs differ")
posinf0 = np.isposinf(filledArr0)
posinf1 = np.isposinf(filledArr1)
if np.any(posinf0 != posinf1):
errStrList.append("+infs differ")
neginf0 = np.isneginf(filledArr0)
neginf1 = np.isneginf(filledArr1)
if np.any(neginf0 != neginf1):
errStrList.append("-infs differ")
valSkipMaskArr = nan0 | nan1 | posinf0 | posinf1 | neginf0 | neginf1
if skipMaskArr is not None:
valSkipMaskArr |= skipMaskArr
# compare values that should be comparable (are finite and not masked)
valMaskedArr1 = np.ma.array(imageArr0, copy=False, mask=valSkipMaskArr)
valMaskedArr2 = np.ma.array(imageArr1, copy=False, mask=valSkipMaskArr)
valFilledArr1 = valMaskedArr1.filled(0.0)
valFilledArr2 = valMaskedArr2.filled(0.0)
if not np.allclose(valFilledArr1, valFilledArr2, rtol=rtol, atol=atol):
errArr = np.abs(valFilledArr1 - valFilledArr2)
maxErr = errArr.max()
maxPosInd = np.where(errArr == maxErr)
maxPosTuple = (maxPosInd[1][0], maxPosInd[0][0])
errStr = "maxDiff=%s at position %s; value=%s vs. %s" % \
(maxErr, maxPosTuple,
valFilledArr1[maxPosInd][0], valFilledArr2[maxPosInd][0])
errStrList.insert(0, errStr)
return "; ".join(errStrList)
@lsst.utils.tests.inTestCase
def assertImagesNearlyEqual(*args, **kwargs):
warnings.warn("Deprecated. Use assertImagesAlmostEqual",
DeprecationWarning, 2)
assertImagesAlmostEqual(*args, **kwargs)
@lsst.utils.tests.inTestCase
def assertMaskedImagesNearlyEqual(*args, **kwargs):
warnings.warn("Deprecated. Use assertMaskedImagesAlmostEqual",
DeprecationWarning, 2)
assertMaskedImagesAlmostEqual(*args, **kwargs)