Hello,
I am still trying to package[2] dxtbx[1], and now I end up with something
strange. When I run the test suite during the build I have a failure like this
tests/test_dataset_as_flex.py ..F..F..F [ 2%]
I put the error message bellow, it is quite long
now If I execute by hand only the failing test like this, it works
$ pytest-3 tests/test_dataset_as_flex.py
=================================================================================================================================
test session starts
=================================================================================================================================
platform linux -- Python 3.10.6, pytest-7.1.2, pluggy-1.0.0+repack
rootdir: /home/picca/debian/science-team/dxtbx, configfile: pytest.ini
plugins: requests-mock-1.9.3, forked-1.4.0, xdist-2.5.0, mock-3.8.2,
dials-data-2.4.0
collected 9 items
tests/test_dataset_as_flex.py .........
[100%]
==================================================================================================================================
9 passed in 0.61s
==================================================================================================================================
before investigating further, I would like your advice in order to debug this
sort of issue.
first what is the difference between
pytest and pytest <file>
thanks for your help
Frederic
[1] https://github.com/cctbx/dxtbx
[2] https://salsa.debian.org/science-team/dxtbx
full error message
___________ test_dataset_as_flex[int-dataset_as_flex_int-bshuf_lz4] ____________
type_name = 'int', creator = <function bshuf_lz4 at 0x7fccddd35120>
converter = <Boost.Python.function object at 0x556b03602bb0>
@pytest.mark.parametrize(
"creator",
[
uncompressed,
gzip,
bshuf_lz4,
],
)
@pytest.mark.parametrize(
"type_name,converter",
[
("int", dataset_as_flex_int),
("float", dataset_as_flex_float),
("double", dataset_as_flex_double),
],
)
def test_dataset_as_flex(type_name, creator, converter):
# Create an in-memory HDF5 dataset with unique name
f = h5py.File(type_name + ".h5", "w", driver="core",
backing_store=False)
shape = (20, 20, 20)
> dataset = creator(f, shape, type_name)
tests/test_dataset_as_flex.py:64:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/test_dataset_as_flex.py:34: in bshuf_lz4
return file.create_dataset(
/usr/lib/python3/dist-packages/h5py/_debian_h5py_serial/_hl/group.py:161: in
create_dataset
dsid = dataset.make_new_dset(group, shape, dtype, data, name, **kwds)
/usr/lib/python3/dist-packages/h5py/_debian_h5py_serial/_hl/dataset.py:106: in
make_new_dset
dcpl = filters.fill_dcpl(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
plist = <h5py._debian_h5py_serial.h5p.PropDCID object at 0x7fccdbbd9760>
shape = (20, 20, 20), dtype = dtype('int64'), chunks = (10, 10, 10)
compression = 32008, compression_opts = (0, 2), shuffle = None
fletcher32 = None, maxshape = None, scaleoffset = None, external = []
allow_unknown_filter = False
def fill_dcpl(plist, shape, dtype, chunks, compression, compression_opts,
shuffle, fletcher32, maxshape, scaleoffset, external,
allow_unknown_filter=False):
""" Generate a dataset creation property list.
Undocumented and subject to change without warning.
"""
if shape is None or shape == ():
shapetype = 'Empty' if shape is None else 'Scalar'
if any((chunks, compression, compression_opts, shuffle, fletcher32,
scaleoffset is not None)):
raise TypeError(
f"{shapetype} datasets don't support chunk/filter options"
)
if maxshape and maxshape != ():
raise TypeError(f"{shapetype} datasets cannot be extended")
return h5p.create(h5p.DATASET_CREATE)
def rq_tuple(tpl, name):
""" Check if chunks/maxshape match dataset rank """
if tpl in (None, True):
return
try:
tpl = tuple(tpl)
except TypeError:
raise TypeError('"%s" argument must be None or a sequence
object' % name)
if len(tpl) != len(shape):
raise ValueError('"%s" must have same rank as dataset shape' %
name)
rq_tuple(chunks, 'chunks')
rq_tuple(maxshape, 'maxshape')
if compression is not None:
if isinstance(compression, FilterRefBase):
compression_opts = compression.filter_options
compression = compression.filter_id
if compression not in encode and not isinstance(compression, int):
raise ValueError('Compression filter "%s" is unavailable' %
compression)
if compression == 'gzip':
if compression_opts is None:
gzip_level = DEFAULT_GZIP
elif compression_opts in range(10):
gzip_level = compression_opts
else:
raise ValueError("GZIP setting must be an integer from 0-9,
not %r" % compression_opts)
elif compression == 'lzf':
if compression_opts is not None:
raise ValueError("LZF compression filter accepts no
options")
elif compression == 'szip':
if compression_opts is None:
compression_opts = DEFAULT_SZIP
err = "SZIP options must be a 2-tuple ('ec'|'nn', even integer
0-32)"
try:
szmethod, szpix = compression_opts
except TypeError:
raise TypeError(err)
if szmethod not in ('ec', 'nn'):
raise ValueError(err)
if not (0<szpix<=32 and szpix%2 == 0):
raise ValueError(err)
elif compression_opts is not None:
# Can't specify just compression_opts by itself.
raise TypeError("Compression method must be specified")
if scaleoffset is not None:
# scaleoffset must be an integer when it is not None or False,
# except for integral data, for which scaleoffset == True is
# permissible (will use SO_INT_MINBITS_DEFAULT)
if scaleoffset < 0:
raise ValueError('scale factor must be >= 0')
if dtype.kind == 'f':
if scaleoffset is True:
raise ValueError('integer scaleoffset must be provided for '
'floating point types')
elif dtype.kind in ('u', 'i'):
if scaleoffset is True:
scaleoffset = h5z.SO_INT_MINBITS_DEFAULT
else:
raise TypeError('scale/offset filter only supported for integer
'
'and floating-point types')
# Scale/offset following fletcher32 in the filter chain will
(almost?)
# always triggers a read error, as most scale/offset settings are
# lossy. Since fletcher32 must come first (see comment below) we
# simply prohibit the combination of fletcher32 and scale/offset.
if fletcher32:
raise ValueError('fletcher32 cannot be used with potentially
lossy'
' scale/offset filter')
external = _normalize_external(external)
# End argument validation
if (chunks is True) or \
(chunks is None and any((shuffle, fletcher32, compression, maxshape,
scaleoffset is not None))):
chunks = guess_chunk(shape, maxshape, dtype.itemsize)
if maxshape is True:
maxshape = (None,)*len(shape)
if chunks is not None:
plist.set_chunk(chunks)
plist.set_fill_time(h5d.FILL_TIME_ALLOC) # prevent resize glitch
# scale-offset must come before shuffle and compression
if scaleoffset is not None:
if dtype.kind in ('u', 'i'):
plist.set_scaleoffset(h5z.SO_INT, scaleoffset)
else: # dtype.kind == 'f'
plist.set_scaleoffset(h5z.SO_FLOAT_DSCALE, scaleoffset)
for item in external:
plist.set_external(*item)
if shuffle:
plist.set_shuffle()
if compression == 'gzip':
plist.set_deflate(gzip_level)
elif compression == 'lzf':
plist.set_filter(h5z.FILTER_LZF, h5z.FLAG_OPTIONAL)
elif compression == 'szip':
opts = {'ec': h5z.SZIP_EC_OPTION_MASK, 'nn':
h5z.SZIP_NN_OPTION_MASK}
plist.set_szip(opts[szmethod], szpix)
elif isinstance(compression, int):
if not allow_unknown_filter and not h5z.filter_avail(compression):
> raise ValueError("Unknown compression filter number: %s" %
> compression)
E ValueError: Unknown compression filter number: 32008
/usr/lib/python3/dist-packages/h5py/_debian_h5py_serial/_hl/filters.py:281:
ValueError
