/usr/lib/python2.7/dist-packages/pyfits/diff.py is in python-pyfits 1:3.2-1build2.
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1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 | """
Facilities for diffing two FITS files. Includes objects for diffing entire
FITS files, individual HDUs, FITS headers, or just FITS data.
Used to implement the fitsdiff program.
"""
import difflib
import fnmatch
import functools
import glob
import inspect
import textwrap
from collections import defaultdict
from itertools import islice, izip
import numpy as np
import pyfits
from pyfits.card import Card, BLANK_CARD
from pyfits.header import Header
from pyfits.hdu.hdulist import fitsopen
from pyfits.hdu.table import _TableLikeHDU
from pyfits.util import StringIO, indent, reduce
__all__ = ['FITSDiff', 'HDUDiff', 'HeaderDiff', 'ImageDataDiff', 'RawDataDiff',
'TableDataDiff']
# Column attributes of interest for comparison
_COL_ATTRS = [('unit', 'units'), ('null', 'null values'), ('bscale', 'bscales'),
('bzero', 'bzeros'), ('disp', 'display formats'),
('dim', 'dimensions')]
# Smaller default shift-width for indent:
indent = functools.partial(indent, width=2)
class _BaseDiff(object):
"""
Base class for all FITS diff objects.
When instantiating a FITS diff object, the first two arguments are always
the two objects to diff (two FITS files, two FITS headers, etc.).
Instantiating a `_BaseDiff` also causes the diff itself to be executed.
The returned `_BaseDiff` instance has a number of attribute that describe
the results of the diff operation.
The most basic attribute, present on all `_BaseDiff` instances, is
`.identical` which is `True` if the two objects being compared are
identical according to the diff method for objects of that type.
"""
def __init__(self, a, b):
"""
The `_BaseDiff` class does not implement a `_diff` method and should
not be instantiated directly. Instead instantiate the appropriate
subclass of `_BaseDiff` for the objects being compared (for example,
use `HeaderDiff` to compare two `Header` objects.
"""
self.a = a
self.b = b
# For internal use in report output
self._fileobj = None
self._indent = 0
self._diff()
def __nonzero__(self):
"""
A `_BaseDiff` object acts as `True` in a boolean context if the two
objects compared are identical. Otherwise it acts as `False`.
"""
return not self.identical
@classmethod
def fromdiff(cls, other, a, b):
"""
Returns a new Diff object of a specfic subclass from an existing diff
object, passing on the values for any arguments they share in common
(such as ignore_keywords).
For example::
>>> fd = FITSDiff('a.fits', 'b.fits', ignore_keywords=['*'])
>>> hd = HeaderDiff.fromdiff(fd, header_a, header_b)
>>> hd.ignore_keywords
['*']
"""
args, _, _, _ = inspect.getargspec(cls.__init__)
# The first 3 arguments of any Diff initializer are self, a, and b.
kwargs = {}
for arg in args[3:]:
if hasattr(other, arg):
kwargs[arg] = getattr(other, arg)
return cls(a, b, **kwargs)
@property
def identical(self):
"""
`True` if all the `.diff_*` attributes on this diff instance are empty,
implying that no differences were found.
Any subclass of `_BaseDiff` must have at least one `.diff_*` attribute,
which contains a non-empty value if and only if some difference was
found between the two objects being compared.
"""
return not any(getattr(self, attr) for attr in self.__dict__
if attr.startswith('diff_'))
def report(self, fileobj=None, indent=0):
"""
Generates a text report on the differences (if any) between two
objects, and either returns it as a string or writes it to a file-like
object.
Parameters
----------
fileobj : file-like object or None (optional)
If `None`, this method returns the report as a string. Otherwise it
returns `None` and writes the report to the given file-like object
(which must have a `.write()` method at a minimum).
indent : int
The number of 4 space tabs to indent the report.
Returns
-------
report : str or None
"""
return_string = False
if fileobj is None:
fileobj = StringIO()
return_string = True
self._fileobj = fileobj
self._indent = indent # This is used internally by _writeln
self._report()
if return_string:
return fileobj.getvalue()
def _writeln(self, text):
self._fileobj.write(indent(text, self._indent) + '\n')
def _diff(self):
raise NotImplementedError
def _report(self):
raise NotImplementedError
class FITSDiff(_BaseDiff):
"""Diff two FITS files by filename, or two `HDUList` objects.
`FITSDiff` objects have the following diff attributes:
- `diff_hdu_count`: If the FITS files being compared have different numbers
of HDUs, this contains a 2-tuple of the number of HDUs in each file.
- `diff_hdus`: If any HDUs with the same index are different, this contains
a list of 2-tuples of the HDU index and the `HDUDiff` object representing
the differences between the two HDUs.
"""
def __init__(self, a, b, ignore_keywords=[], ignore_comments=[],
ignore_fields=[], numdiffs=10, tolerance=0.0,
ignore_blanks=True, ignore_blank_cards=True):
"""
Parameters
----------
a : str or `HDUList`
The filename of a FITS file on disk, or an `HDUList` object.
b : str or `HDUList`
The filename of a FITS file on disk, or an `HDUList` object to
compare to the first file.
ignore_keywords : sequence (optional)
Header keywords to ignore when comparing two headers; the presence
of these keywords and their values are ignored. Wildcard strings
may also be included in the list.
ignore_comments : sequence (optional)
A list of header keywords whose comments should be ignored in the
comparison. May contain wildcard strings as with ignore_keywords.
ignore_fields : sequence (optional)
The (case-insensitive) names of any table columns to ignore if any
table data is to be compared.
numdiffs : int (optional)
The number of pixel/table values to output when reporting HDU data
differences. Though the count of differences is the same either
way, this allows controlling the number of different values that
are kept in memory or output. If a negative value is given, then
numdifs is treated as unlimited (default: 10).
tolerance : float (optional)
The relative difference to allow when comparing two float values
either in header values, image arrays, or table columns
(default: 0.0).
ignore_blanks : bool (optional)
Ignore extra whitespace at the end of string values either in
headers or data. Extra leading whitespace is not ignored
(default: True).
"""
if isinstance(a, basestring):
try:
a = fitsopen(a)
except Exception, e:
raise IOError("error opening file a (%s): %s: %s" %
(a, e.__class.__name__, e.args[0]))
close_a = True
else:
close_a = False
if isinstance(b, basestring):
try:
b = fitsopen(b)
except Exception, e:
raise IOError("error opening file b (%s): %s: %s" %
(b, e.__class.__name__, e.args[0]))
close_b = True
else:
close_b = False
# Normalize keywords/fields to ignore to upper case
self.ignore_keywords = set(k.upper() for k in ignore_keywords)
self.ignore_comments = set(k.upper() for k in ignore_comments)
self.ignore_fields = set(k.upper() for k in ignore_fields)
self.numdiffs = numdiffs
self.tolerance = tolerance
self.ignore_blanks = ignore_blanks
self.ignore_blank_cards = ignore_blank_cards
self.diff_hdu_count = ()
self.diff_hdus = []
try:
super(FITSDiff, self).__init__(a, b)
finally:
if close_a:
a.close()
if close_b:
b.close()
def _diff(self):
if len(self.a) != len(self.b):
self.diff_hdu_count = (len(self.a), len(self.b))
# For now, just compare the extensions one by one in order...might
# allow some more sophisticated types of diffing later...
# TODO: Somehow or another simplify the passing around of diff
# options--this will become important as the number of options grows
for idx in range(min(len(self.a), len(self.b))):
hdu_diff = HDUDiff.fromdiff(self, self.a[idx], self.b[idx])
if not hdu_diff.identical:
self.diff_hdus.append((idx, hdu_diff))
def _report(self):
wrapper = textwrap.TextWrapper(initial_indent=' ',
subsequent_indent=' ')
# print out heading and parameter values
filenamea = self.a.filename()
if not filenamea:
filenamea = '<%s object at 0x%x>' % (self.a.__class__.__name__,
id(self.a))
filenameb = self.b.filename()
if not filenameb:
filenameb = '<%s object at 0x%x>' % (self.b.__class__.__name__,
id(self.b))
self._fileobj.write('\n')
self._writeln(' fitsdiff: %s' % pyfits.__version__)
self._writeln(' a: %s\n b: %s' % (filenamea, filenameb))
if self.ignore_keywords:
ignore_keywords = ' '.join(sorted(self.ignore_keywords))
self._writeln(' Keyword(s) not to be compared:\n%s' %
wrapper.fill(ignore_keywords))
if self.ignore_comments:
ignore_comments = ' '.join(sorted(self.ignore_comments))
self._writeln(' Keyword(s) whose comments are not to be compared:'
'\n%s' % wrapper.fill(ignore_comments))
if self.ignore_fields:
ignore_fields = ' '.join(sorted(self.ignore_fields))
self._writeln(' Table column(s) not to be compared:\n%s' %
wrapper.fill(ignore_fields))
self._writeln(' Maximum number of different data values to be '
'reported: %s' % self.numdiffs)
self._writeln(' Data comparison level: %s' % self.tolerance)
if self.diff_hdu_count:
self._fileobj.write('\n')
self._writeln('Files contain different numbers of HDUs:')
self._writeln(' a: %d' % self.diff_hdu_count[0])
self._writeln(' b: %d' % self.diff_hdu_count[1])
if not self.diff_hdus:
self._writeln('No differences found between common HDUs.')
return
elif not self.diff_hdus:
self._fileobj.write('\n')
self._writeln('No differences found.')
return
for idx, hdu_diff in self.diff_hdus:
# print out the extension heading
if idx == 0:
self._fileobj.write('\n')
self._writeln('Primary HDU:')
else:
self._fileobj.write('\n')
self._writeln('Extension HDU %d:' % idx)
hdu_diff.report(self._fileobj, indent=self._indent + 1)
class HDUDiff(_BaseDiff):
"""
Diff two HDU objects, including their headers and their data (but only if
both HDUs contain the same type of data (image, table, or unknown).
`HDUDiff` objects have the following diff attributes:
- `diff_extnames`: If the two HDUs have different EXTNAME values, this
contains a 2-tuple of the different extension names.
- `diff_extvers`: If the two HDUS have different EXTVER values, this
contains a 2-tuple of the different extension versions.
- `diff_extlevels`: If the two HDUs have different EXTLEVEL values, this
contains a 2-tuple of the different extension levels.
- `diff_extension_types`: If the two HDUs have different XTENSION values,
this contains a 2-tuple of the different extension types.
- `diff_headers`: Contains a `HeaderDiff` object for the headers of the two
HDUs. This will always contain an object--it may be determined whether
the headers are different through `diff_headers.identical`.
- `diff_data`: Contains either a `ImageDataDiff`, `TableDataDiff`, or
`RawDataDiff` as appropriate for the data in the HDUs, and only if the
two HDUs have non-empty data of the same type (`RawDataDiff` is used for
HDUs containing non-empty data of an indeterminate type).
"""
def __init__(self, a, b, ignore_keywords=[], ignore_comments=[],
ignore_fields=[], numdiffs=10, tolerance=0.0,
ignore_blanks=True, ignore_blank_cards=True):
"""
See `FITSDiff` for explanations of the initialization parameters.
"""
self.ignore_keywords = set(k.upper() for k in ignore_keywords)
self.ignore_comments = set(k.upper() for k in ignore_comments)
self.ignore_fields = set(k.upper() for k in ignore_fields)
self.tolerance = tolerance
self.numdiffs = numdiffs
self.ignore_blanks = ignore_blanks
self.diff_extnames = ()
self.diff_extvers = ()
self.diff_extlevels = ()
self.diff_extension_types = ()
self.diff_headers = None
self.diff_data = None
super(HDUDiff, self).__init__(a, b)
def _diff(self):
if self.a.name != self.b.name:
self.diff_extnames = (self.a.name, self.b.name)
if self.a.ver != self.b.ver:
self.diff_extvers = (self.a.ver, self.b.ver)
if self.a.level != self.b.level:
self.diff_extlevels = (self.a.level, self.b.level)
if self.a.header.get('XTENSION') != self.b.header.get('XTENSION'):
self.diff_extension_types = (self.a.header.get('XTENSION'),
self.b.header.get('XTENSION'))
self.diff_headers = HeaderDiff.fromdiff(self, self.a.header.copy(),
self.b.header.copy())
if self.a.data is None or self.b.data is None:
# TODO: Perhaps have some means of marking this case
pass
elif self.a.is_image and self.b.is_image:
self.diff_data = ImageDataDiff.fromdiff(self, self.a.data,
self.b.data)
elif (isinstance(self.a, _TableLikeHDU) and
isinstance(self.b, _TableLikeHDU)):
# TODO: Replace this if/when _BaseHDU grows a .is_table property
self.diff_data = TableDataDiff.fromdiff(self, self.a.data,
self.b.data)
elif not self.diff_extension_types:
# Don't diff the data for unequal extension types that are not
# recognized image or table types
self.diff_data = RawDataDiff.fromdiff(self, self.a.data,
self.b.data)
def _report(self):
if self.identical:
self._writeln(" No differences found.")
if self.diff_extension_types:
self._writeln(" Extension types differ:\n a: %s\n b: %s" %
self.diff_extension_types)
if self.diff_extnames:
self._writeln(" Extension names differ:\n a: %s\n b: %s" %
self.diff_extnames)
if self.diff_extvers:
self._writeln(" Extension versions differ:\n a: %s\n b: %s" %
self.diff_extvers)
if self.diff_extlevels:
self._writeln(" Extension levels differ:\n a: %s\n b: %s" %
self.diff_extlevels)
if not self.diff_headers.identical:
self._fileobj.write('\n')
self._writeln(" Headers contain differences:")
self.diff_headers.report(self._fileobj, indent=self._indent + 1)
if self.diff_data is not None and not self.diff_data.identical:
self._fileobj.write('\n')
self._writeln(" Data contains differences:")
self.diff_data.report(self._fileobj, indent=self._indent + 1)
class HeaderDiff(_BaseDiff):
"""
Diff two `Header` objects.
`HeaderDiff` objects have the following diff attributes:
- `diff_keyword_count`: If the two headers contain a different number of
keywords, this contains a 2-tuple of the keyword count for each header.
- `diff_keywords`: If either header contains one or more keywords that
don't appear at all in the other header, this contains a 2-tuple
consisting of a list of the keywords only appearing in header a, and a
list of the keywords only appearing in header b.
- `diff_duplicate_keywords`: If a keyword appears in both headers at least
once, but contains a different number of duplicates (for example, a
different number of HISTORY cards in each header), an item is added to
this dict with the keyword as the key, and a 2-tuple of the different
counts of that keyword as the value. For example::
{'HISTORY': (20, 19)}
means that header a contains 20 HISTORY cards, while header b contains
only 19 HISTORY cards.
- `diff_keyword_values`: If any of the common keyword between the two
headers have different values, they appear in this dict. It has a
structure similar to `diff_duplicate_keywords`, with the keyword as the
key, and a 2-tuple of the different values as the value. For example::
{'NAXIS': (2, 3)}
means that the NAXIS keyword has a value of 2 in header a, and a value of
3 in header b. This excludes any keywords matched by the
`ignore_keywords` list.
- `diff_keyword_comments`: Like `diff_keyword_values`, but contains
differences between keyword comments.
`HeaderDiff` objects also have a `common_keywords` attribute that lists all
keywords that appear in both headers.
"""
def __init__(self, a, b, ignore_keywords=[], ignore_comments=[],
tolerance=0.0, ignore_blanks=True, ignore_blank_cards=True):
"""
See `FITSDiff` for explanations of the initialization parameters.
"""
self.ignore_keywords = set(k.upper() for k in ignore_keywords)
self.ignore_comments = set(k.upper() for k in ignore_comments)
self.tolerance = tolerance
self.ignore_blanks = ignore_blanks
self.ignore_blank_cards = ignore_blank_cards
self.ignore_keyword_patterns = set()
self.ignore_comment_patterns = set()
for keyword in list(self.ignore_keywords):
keyword = keyword.upper()
if keyword != '*' and glob.has_magic(keyword):
self.ignore_keywords.remove(keyword)
self.ignore_keyword_patterns.add(keyword)
for keyword in list(self.ignore_comments):
keyword = keyword.upper()
if keyword != '*' and glob.has_magic(keyword):
self.ignore_comments.remove(keyword)
self.ignore_comment_patterns.add(keyword)
# Keywords appearing in each header
self.common_keywords = []
# Set to the number of keywords in each header if the counts differ
self.diff_keyword_count = ()
# Set if the keywords common to each header (excluding ignore_keywords)
# appear in different positions within the header
# TODO: Implement this
self.diff_keyword_positions = ()
# Keywords unique to each header (excluding keywords in
# ignore_keywords)
self.diff_keywords = ()
# Keywords that have different numbers of duplicates in each header
# (excluding keywords in ignore_keywords)
self.diff_duplicate_keywords = {}
# Keywords common to each header but having different values (excluding
# keywords in ignore_keywords)
self.diff_keyword_values = defaultdict(lambda: [])
# Keywords common to each header but having different comments
# (excluding keywords in ignore_keywords or in ignore_comments)
self.diff_keyword_comments = defaultdict(lambda: [])
if isinstance(a, basestring):
a = Header.fromstring(a)
if isinstance(b, basestring):
b = Header.fromstring(b)
if not (isinstance(a, Header) and isinstance(b, Header)):
raise TypeError('HeaderDiff can only diff pyfits.Header objects '
'or strings containing FITS headers.')
super(HeaderDiff, self).__init__(a, b)
# TODO: This doesn't pay much attention to the *order* of the keywords,
# except in the case of duplicate keywords. The order should be checked
# too, or at least it should be an option.
def _diff(self):
if self.ignore_blank_cards:
cardsa = [c for c in self.a.cards if str(c) != BLANK_CARD]
cardsb = [c for c in self.b.cards if str(c) != BLANK_CARD]
else:
cardsa = list(self.a.cards)
cardsb = list(self.b.cards)
# build dictionaries of keyword values and comments
def get_header_values_comments(cards):
values = {}
comments = {}
for card in cards:
value = card.value
if self.ignore_blanks and isinstance(value, basestring):
value = value.rstrip()
values.setdefault(card.keyword, []).append(value)
comments.setdefault(card.keyword, []).append(card.comment)
return values, comments
valuesa, commentsa = get_header_values_comments(cardsa)
valuesb, commentsb = get_header_values_comments(cardsb)
# Normalize all keyword to upper-case for comparison's sake;
# TODO: HIERARCH keywords should be handled case-sensitively I think
keywordsa = set(k.upper() for k in valuesa)
keywordsb = set(k.upper() for k in valuesb)
self.common_keywords = sorted(keywordsa.intersection(keywordsb))
if len(cardsa) != len(cardsb):
self.diff_keyword_count = (len(cardsa), len(cardsb))
# Any other diff attributes should exclude ignored keywords
keywordsa = keywordsa.difference(self.ignore_keywords)
keywordsb = keywordsb.difference(self.ignore_keywords)
if self.ignore_keyword_patterns:
for pattern in self.ignore_keyword_patterns:
keywordsa = keywordsa.difference(fnmatch.filter(keywordsa,
pattern))
keywordsb = keywordsb.difference(fnmatch.filter(keywordsb,
pattern))
if '*' in self.ignore_keywords:
# Any other differences between keywords are to be ignored
return
left_only_keywords = sorted(keywordsa.difference(keywordsb))
right_only_keywords = sorted(keywordsb.difference(keywordsa))
if left_only_keywords or right_only_keywords:
self.diff_keywords = (left_only_keywords, right_only_keywords)
# Compare count of each common keyword
for keyword in self.common_keywords:
if keyword in self.ignore_keywords:
continue
if self.ignore_keyword_patterns:
skip = False
for pattern in self.ignore_keyword_patterns:
if fnmatch.fnmatch(keyword, pattern):
skip = True
break
if skip:
continue
counta = len(valuesa[keyword])
countb = len(valuesb[keyword])
if counta != countb:
self.diff_duplicate_keywords[keyword] = (counta, countb)
# Compare keywords' values and comments
for a, b in zip(valuesa[keyword], valuesb[keyword]):
if diff_values(a, b, tolerance=self.tolerance):
self.diff_keyword_values[keyword].append((a, b))
else:
# If there are duplicate keywords we need to be able to
# index each duplicate; if the values of a duplicate
# are identical use None here
self.diff_keyword_values[keyword].append(None)
if not any(self.diff_keyword_values[keyword]):
# No differences found; delete the array of Nones
del self.diff_keyword_values[keyword]
if '*' in self.ignore_comments or keyword in self.ignore_comments:
continue
if self.ignore_comment_patterns:
skip = False
for pattern in self.ignore_comment_patterns:
if fnmatch.fnmatch(keyword, pattern):
skip = True
break
if skip:
continue
for a, b in zip(commentsa[keyword], commentsb[keyword]):
if diff_values(a, b):
self.diff_keyword_comments[keyword].append((a, b))
else:
self.diff_keyword_comments[keyword].append(None)
if not any(self.diff_keyword_comments[keyword]):
del self.diff_keyword_comments[keyword]
def _report(self):
if self.diff_keyword_count:
self._writeln(' Headers have different number of cards:')
self._writeln(' a: %d' % self.diff_keyword_count[0])
self._writeln(' b: %d' % self.diff_keyword_count[1])
if self.diff_keywords:
for keyword in self.diff_keywords[0]:
if keyword in Card._commentary_keywords:
val = self.a[keyword][0]
else:
val = self.a[keyword]
self._writeln(' Extra keyword %-8r in a: %r' % (keyword, val))
for keyword in self.diff_keywords[1]:
if keyword in Card._commentary_keywords:
val = self.b[keyword][0]
else:
val = self.b[keyword]
self._writeln(' Extra keyword %-8r in b: %r' % (keyword, val))
if self.diff_duplicate_keywords:
for keyword, count in sorted(self.diff_duplicate_keywords.items()):
self._writeln(' Inconsistent duplicates of keyword %-8r:' %
keyword)
self._writeln(' Occurs %d time(s) in a, %d times in (b)' %
count)
if self.diff_keyword_values or self.diff_keyword_comments:
for keyword in self.common_keywords:
report_diff_keyword_attr(self._fileobj, 'values',
self.diff_keyword_values, keyword,
ind=self._indent)
report_diff_keyword_attr(self._fileobj, 'comments',
self.diff_keyword_comments, keyword,
ind=self._indent)
# TODO: It might be good if there was also a threshold option for percentage of
# different pixels: For example ignore if only 1% of the pixels are different
# within some threshold. There are lots of possibilities here, but hold off
# for now until specific cases come up.
class ImageDataDiff(_BaseDiff):
"""
Diff two image data arrays (really any array from a PRIMARY HDU or an IMAGE
extension HDU, though the data unit is assumed to be "pixels").
`ImageDataDiff` objects have the following diff attributes:
- `diff_dimensions`: If the two arrays contain either a different number of
dimensions or different sizes in any dimension, this contains a 2-tuple
of the shapes of each array. Currently no further comparison is
performed on images that don't have the exact same dimensions.
- `diff_pixels`: If the two images contain any different pixels, this
contains a list of 2-tuples of the array index where the difference was
found, and another 2-tuple containing the different values. For example,
if the pixel at (0, 0) contains different values this would look like::
[(0, 0), (1.1, 2.2)]
where 1.1 and 2.2 are the values of that pixel in each array. This
array only contains up to `self.numdiffs` differences, for storage
efficiency.
- `diff_total`: The total number of different pixels found between the
arrays. Although `diff_pixels` does not necessarily contain all the
different pixel values, this can be used to get a count of the total
number of differences found.
- `diff_ratio`: Contains the ratio of `diff_total` to the total number of
pixels in the arrays.
"""
def __init__(self, a, b, numdiffs=10, tolerance=0.0):
"""
See `FITSDiff` for explanations of the initialization parameters.
"""
self.numdiffs = numdiffs
self.tolerance = tolerance
self.diff_dimensions = ()
self.diff_pixels = []
self.diff_ratio = 0
# self.diff_pixels only holds up to numdiffs differing pixels, but this
# self.diff_total stores the total count of differences between
# the images, but not the different values
self.diff_total = 0
super(ImageDataDiff, self).__init__(a, b)
def _diff(self):
if self.a.shape != self.b.shape:
self.diff_dimensions = (self.a.shape, self.b.shape)
# Don't do any further comparison if the dimensions differ
# TODO: Perhaps we could, however, diff just the intersection
# between the two images
return
# Find the indices where the values are not equal
# If neither a nor b are floating point, ignore self.tolerance
if not ((np.issubdtype(self.a.dtype, float) or
np.issubdtype(self.a.dtype, complex)) or
(np.issubdtype(self.b.dtype, float) or
np.issubdtype(self.b.dtype, complex))):
tolerance = 0
else:
tolerance = self.tolerance
diffs = where_not_allclose(self.a, self.b, atol=0.0, rtol=tolerance)
self.diff_total = len(diffs[0])
if self.diff_total == 0:
# Then we're done
return
if self.numdiffs < 0:
numdiffs = self.diff_total
else:
numdiffs = self.numdiffs
self.diff_pixels = [(idx, (self.a[idx], self.b[idx]))
for idx in islice(izip(*diffs), 0, numdiffs)]
self.diff_ratio = float(self.diff_total) / float(len(self.a.flat))
def _report(self):
if self.diff_dimensions:
dimsa = ' x '.join(str(d) for d in
reversed(self.diff_dimensions[0]))
dimsb = ' x '.join(str(d) for d in
reversed(self.diff_dimensions[1]))
self._writeln(' Data dimensions differ:')
self._writeln(' a: %s' % dimsa)
self._writeln(' b: %s' % dimsb)
# For now we don't do any further comparison if the dimensions
# differ; though in the future it might be nice to be able to
# compare at least where the images intersect
self._writeln(' No further data comparison performed.')
return
if not self.diff_pixels:
return
for index, values in self.diff_pixels:
index = [x + 1 for x in reversed(index)]
self._writeln(' Data differs at %s:' % index)
report_diff_values(self._fileobj, values[0], values[1],
ind=self._indent + 1)
if self.diff_total > self.numdiffs:
self._writeln(' ...')
self._writeln(' %d different pixels found (%.2f%% different).' %
(self.diff_total, self.diff_ratio * 100))
class RawDataDiff(ImageDataDiff):
"""
`RawDataDiff` is just a special case of `ImageDataDiff` where the images
are one-dimensional, and the data is treated as a 1-dimensional array of
bytes instead of pixel values. This is used to compare the data of two
non-standard extension HDUs that were not recognized as containing image or
table data.
`ImageDataDiff` objects have the following diff attributes:
- `diff_dimensions`: Same as the `diff_dimensions` attribute of
`ImageDataDiff` objects. Though the "dimension" of each array is just an
integer representing the number of bytes in the data.
- `diff_bytes`: Like the `diff_pixels` attribute of `ImageDataDiff`
objects, but renamed to reflect the minor semantic difference that these
are raw bytes and not pixel values. Also the indices are integers
instead of tuples.
- `diff_total` and `diff_ratio`: Same as `ImageDataDiff`.
"""
def __init__(self, a, b, numdiffs=10):
"""
See `FITSDiff` for explanations of the initialization parameters.
"""
self.diff_dimensions = ()
self.diff_bytes = []
super(RawDataDiff, self).__init__(a, b, numdiffs=numdiffs)
def _diff(self):
super(RawDataDiff, self)._diff()
if self.diff_dimensions:
self.diff_dimensions = (self.diff_dimensions[0][0],
self.diff_dimensions[1][0])
self.diff_bytes = [(x[0], y) for x, y in self.diff_pixels]
del self.diff_pixels
def _report(self):
if self.diff_dimensions:
self._writeln(' Data sizes differ:')
self._writeln(' a: %d bytes' % self.diff_dimensions[0])
self._writeln(' b: %d bytes' % self.diff_dimensions[1])
# For now we don't do any further comparison if the dimensions
# differ; though in the future it might be nice to be able to
# compare at least where the images intersect
self._writeln(' No further data comparison performed.')
return
if not self.diff_bytes:
return
for index, values in self.diff_bytes:
self._writeln(' Data differs at byte %d:' % index)
report_diff_values(self._fileobj, values[0], values[1],
ind=self._indent + 1)
self._writeln(' ...')
self._writeln(' %d different bytes found (%.2f%% different).' %
(self.diff_total, self.diff_ratio * 100))
class TableDataDiff(_BaseDiff):
"""
Diff two table data arrays. It doesn't matter whether the data originally
came from a binary or ASCII table--the data should be passed in as a
recarray.
`TableDataDiff` objects have the following diff attributes:
- `diff_column_count`: If the tables being compared have different numbers
of columns, this contains a 2-tuple of the column count in each table.
Even if the tables have different column counts, an attempt is still made
to compare any columns they have in common.
- `diff_columns`: If either table contains columns unique to that table,
either in name or format, this contains a 2-tuple of lists. The first
element is a list of columns (these are full `Column` objects) that
appear only in table a. The second element is a list of tables that
appear only in table b. This only lists columns with different column
definitions, and has nothing to do with the data in those columns.
- `diff_column_names`: This is like `diff_columns`, but lists only the
names of columns unique to either table, rather than the full `Column`
objects.
- `diff_column_attributes`: Lists columns that are in both tables but have
different secondard attributes, such as TUNIT or TDISP. The format is a
list of 2-tuples: The first a tuple of the column name and the attribute,
the second a tuple of the different values.
- `diff_values`: `TableDataDiff` compares the data in each table on a
column-by-column basis. If any different data is found, it is added to
this list. The format of this list is similar to the `diff_pixels`
attribute on `ImageDataDiff` objects, though the "index" consists of a
(column_name, row) tuple. For example::
[('TARGET', 0), ('NGC1001', 'NGC1002')]
shows that the tables contain different values in the 0-th row of the
'TARGET' column.
- `diff_total` and `diff_ratio`: Same as `ImageDataDiff`.
`TableDataDiff` objects also have a `common_columns` attribute that lists
the `Column` objects for columns that are identical in both tables, and a
`common_column_names` attribute which contains a set of the names of those
columns.
"""
def __init__(self, a, b, ignore_fields=[], numdiffs=10, tolerance=0.0):
"""
See `FITSDiff` for explanations of the initialization parameters.
"""
self.ignore_fields = set(ignore_fields)
self.numdiffs = numdiffs
self.tolerance = tolerance
self.common_columns = []
self.common_column_names = set()
# self.diff_columns contains columns with different column definitions,
# but not different column data. Column data is only compared in
# columns that have the same definitions
self.diff_rows = ()
self.diff_column_count = ()
self.diff_columns = ()
# If two columns have the same name+format, but other attributes are
# different (such as TUNIT or such) they are listed here
self.diff_column_attributes = []
# Like self.diff_columns, but just contains a list of the column names
# unique to each table, and in the order they appear in the tables
self.diff_column_names = ()
self.diff_values = []
self.diff_ratio = 0
self.diff_total = 0
super(TableDataDiff, self).__init__(a, b)
def _diff(self):
# Much of the code for comparing columns is similar to the code for
# comparing headers--consider refactoring
colsa = self.a.columns
colsb = self.b.columns
if len(colsa) != len(colsb):
self.diff_column_count = (len(colsa), len(colsb))
# Even if the number of columns are unequal, we still do comparison of
# any common columns
colsa = dict((c.name.lower(), c) for c in colsa)
colsb = dict((c.name.lower(), c) for c in colsb)
if '*' in self.ignore_fields:
# If all columns are to be ignored, ignore any further differences
# between the columns
return
# Keep the user's original ignore_fields list for reporting purposes,
# but internally use a case-insensitive version
ignore_fields = set([f.lower() for f in self.ignore_fields])
# It might be nice if there were a cleaner way to do this, but for now
# it'll do
for fieldname in ignore_fields:
fieldname = fieldname.lower()
if fieldname in colsa:
del colsa[fieldname]
if fieldname in colsb:
del colsb[fieldname]
colsa_set = set(colsa.values())
colsb_set = set(colsb.values())
self.common_columns = sorted(colsa_set.intersection(colsb_set),
key=lambda c: c.name)
self.common_column_names = set([col.name.lower()
for col in self.common_columns])
left_only_columns = dict((col.name.lower(), col)
for col in colsa_set.difference(colsb_set))
right_only_columns = dict((col.name.lower(), col)
for col in colsb_set.difference(colsa_set))
if left_only_columns or right_only_columns:
self.diff_columns = (left_only_columns, right_only_columns)
self.diff_column_names = ([], [])
if left_only_columns:
for col in self.a.columns:
if col.name.lower() in left_only_columns:
self.diff_column_names[0].append(col.name)
if right_only_columns:
for col in self.b.columns:
if col.name.lower() in right_only_columns:
self.diff_column_names[1].append(col.name)
# If the tables have a different number of rows, we don't compare the
# columns right now.
# TODO: It might be nice to optionally compare the first n rows where n
# is the minimum of the row counts between the two tables.
if len(self.a) != len(self.b):
self.diff_rows = (len(self.a), len(self.b))
return
# If the tables contain no rows there's no data to compare, so we're
# done at this point. (See ticket #178)
if len(self.a) == len(self.b) == 0:
return
# Like in the old fitsdiff, compare tables on a column by column basis
# The difficulty here is that, while FITS column names are meant to be
# case-insensitive, PyFITS still allows, for the sake of flexibility,
# two columns with the same name but different case. When columns are
# accessed in FITS tables, a case-sensitive is tried first, and failing
# that a case-insensitive match is made.
# It's conceivable that the same column could appear in both tables
# being compared, but with different case.
# Though it *may* lead to inconsistencies in these rare cases, this
# just assumes that there are no duplicated column names in either
# table, and that the column names can be treated case-insensitively.
for col in self.common_columns:
name_lower = col.name.lower()
if name_lower in ignore_fields:
continue
cola = colsa[name_lower]
colb = colsb[name_lower]
for attr, _ in _COL_ATTRS:
vala = getattr(cola, attr, None)
valb = getattr(colb, attr, None)
if diff_values(vala, valb):
self.diff_column_attributes.append(
((col.name.upper(), attr), (vala, valb)))
arra = self.a[col.name]
arrb = self.b[col.name]
if (np.issubdtype(arra.dtype, float) and
np.issubdtype(arrb.dtype, float)):
diffs = where_not_allclose(arra, arrb, atol=0.0,
rtol=self.tolerance)
elif 'P' in col.format:
diffs = ([idx for idx in xrange(len(arra))
if not np.allclose(arra[idx], arrb[idx], atol=0.0,
rtol=self.tolerance)],)
else:
diffs = np.where(arra != arrb)
self.diff_total += len(set(diffs[0]))
if self.numdiffs >= 0:
if len(self.diff_values) >= self.numdiffs:
# Don't save any more diff values
continue
# Add no more diff'd values than this
max_diffs = self.numdiffs - len(self.diff_values)
else:
max_diffs = len(diffs[0])
last_seen_idx = None
for idx in islice(diffs[0], 0, max_diffs):
if idx == last_seen_idx:
# Skip duplicate indices, which my occur when the column
# data contains multi-dimensional values; we're only
# interested in storing row-by-row differences
continue
last_seen_idx = idx
self.diff_values.append(((col.name, idx),
(arra[idx], arrb[idx])))
total_values = len(self.a) * len(self.a.dtype.fields)
self.diff_ratio = float(self.diff_total) / float(total_values)
def _report(self):
if self.diff_column_count:
self._writeln(' Tables have different number of columns:')
self._writeln(' a: %d' % self.diff_column_count[0])
self._writeln(' b: %d' % self.diff_column_count[1])
if self.diff_column_names:
# Show columns with names unique to either table
for name in self.diff_column_names[0]:
format = self.diff_columns[0][name.lower()].format
self._writeln(' Extra column %s of format %s in a' %
(name, format))
for name in self.diff_column_names[1]:
format = self.diff_columns[1][name.lower()].format
self._writeln(' Extra column %s of format %s in b' %
(name, format))
col_attrs = dict(_COL_ATTRS)
# Now go through each table again and show columns with common
# names but other property differences...
for col_attr, vals in self.diff_column_attributes:
name, attr = col_attr
self._writeln(' Column %s has different %s:' %
(name, col_attrs[attr]))
report_diff_values(self._fileobj, vals[0], vals[1],
ind=self._indent + 1)
if self.diff_rows:
self._writeln(' Table rows differ:')
self._writeln(' a: %s' % self.diff_rows[0])
self._writeln(' b: %s' % self.diff_rows[1])
self._writeln(' No further data comparison performed.')
return
if not self.diff_values:
return
# Finally, let's go through and report column data differences:
for indx, values in self.diff_values:
self._writeln(' Column %s data differs in row %d:' % indx)
report_diff_values(self._fileobj, values[0], values[1],
ind=self._indent + 1)
if self.diff_values and self.numdiffs < self.diff_total:
self._writeln(' ...%d additional difference(s) found.' %
(self.diff_total - self.numdiffs))
if self.diff_total > self.numdiffs:
self._writeln(' ...')
self._writeln(' %d different table data element(s) found '
'(%.2f%% different).' %
(self.diff_total, self.diff_ratio * 100))
def diff_values(a, b, tolerance=0.0):
"""
Diff two scalar values. If both values are floats they are compared to
within the given relative tolerance.
"""
if isinstance(a, float) and isinstance(b, float):
if np.isnan(a) and np.isnan(b):
return False
return not np.allclose(a, b, tolerance, 0.0)
else:
return a != b
def report_diff_values(fileobj, a, b, ind=0):
"""Write a diff between two values to the specified file-like object."""
if isinstance(a, float):
a = repr(a)
if isinstance(b, float):
b = repr(b)
if isinstance(a, np.ndarray) and isinstance(b, np.ndarray):
diff_indices = np.where(a != b)
num_diffs = reduce(lambda x, y: x * y,
(len(d) for d in diff_indices), 1)
for idx in islice(izip(*diff_indices), 3):
fileobj.write(indent(' at %r:\n' % list(idx), ind))
report_diff_values(fileobj, a[idx], b[idx], ind=ind + 1)
if num_diffs:
fileobj.write(indent(' ...and at %d more indices.\n' %
(num_diffs - 3), ind))
return
for line in difflib.ndiff(str(a).splitlines(), str(b).splitlines()):
if line[0] == '-':
line = 'a>' + line[1:]
elif line[0] == '+':
line = 'b>' + line[1:]
else:
line = ' ' + line
fileobj.write(indent(' %s\n' % line.rstrip('\n'), ind))
def report_diff_keyword_attr(fileobj, attr, diffs, keyword, ind=0):
"""
Write a diff between two header keyword values or comments to the specified
file-like object.
"""
if keyword in diffs:
vals = diffs[keyword]
for idx, val in enumerate(vals):
if val is None:
continue
if idx == 0:
dup = ''
else:
dup = '[%d]' % (idx + 1)
fileobj.write(indent(' Keyword %-8s%s has different %s:\n' %
(keyword, dup, attr), ind))
report_diff_values(fileobj, val[0], val[1], ind=ind + 1)
def where_not_allclose(a, b, rtol=1e-5, atol=1e-8):
"""
A version of numpy.allclose that returns the indices where the two arrays
differ, instead of just a boolean value.
"""
# Create fixed mask arrays to handle INF and NaN; currently INF and NaN
# are handled as equivalent
if not np.all(np.isfinite(a)):
a = np.ma.fix_invalid(a).data
if not np.all(np.isfinite(b)):
b = np.ma.fix_invalid(b).data
if atol == 0.0 and rtol == 0.0:
# Use a faster comparison for the most simple (and common) case
return np.where(a != b)
return np.where(np.abs(a - b) > (atol + rtol * np.abs(b)))
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