def test_optional_wrong_version():
with pytest.raises(ImportError):
import_optional_dependency("skimage", min_version="999")
with pytest.warns(UserWarning, match="MorphoCut requires version.+"):
import_optional_dependency("skimage",
min_version="999",
on_version="warn")
def __init__(self, path: RawOrVariable[str], **kwargs):
super().__init__()
self.path = path
self.kwargs = kwargs
import_optional_dependency("av")
self._pims = import_optional_dependency("pims")
def __init__(self,
description: Optional[RawOrVariable[str]] = None,
monitor_interval=None):
super().__init__()
self._tqdm = import_optional_dependency("tqdm")
self.description = description
self.monitor_interval = monitor_interval
def __init__(
self,
archive_fn: str,
fnames_images: MaybeList[RawOrVariable[Tuple[str, ...]]],
meta: RawOrVariable[Mapping],
meta_fn: str = "ecotaxa_export.tsv",
store_types: bool = True,
):
super().__init__()
self.archive_fn = archive_fn
if isinstance(fnames_images, tuple):
fnames_images = [fnames_images]
if not isinstance(fnames_images, list):
raise ValueError(
"Unexpected type for fnames_images: needs to be a tuple or a list of tuples"
)
self.fnames_images = fnames_images
self.meta = meta
self.meta_fn = meta_fn
self.store_types = store_types
self._pd = import_optional_dependency("pandas")
def __init__(
self,
path: RawOrVariable[str],
meta: RawOrVariable[bool],
series: Optional[RawOrVariable[int]] = None,
**kwargs,
):
super().__init__()
self.path = path
self.meta = meta
self.series = series
self.kwargs = kwargs
import_optional_dependency("jpype")
self._pims = import_optional_dependency("pims")
def __init__(
self,
archive_fn: RawOrVariable[str],
img_rank: MaybeTuple[RawOrVariable[int]] = 1,
):
super().__init__()
self.archive_fn = archive_fn
self.img_rank = img_rank
self._pd = import_optional_dependency("pandas")
def __init__(
self,
path_or_buf,
data: RawOrVariable[Mapping],
columns: Optional[Collection] = None,
drop_duplicates_subset: Optional[Collection] = None,
writer=_default_writer,
):
super().__init__()
self.path_or_buf = path_or_buf
self.data = data
self.columns = columns
self.drop_duplicates_subset = drop_duplicates_subset
self.dataframe = [] # type: List[Mapping]
self.writer = writer
self._pd = import_optional_dependency("pandas")
def __init__(self, model: Callable, image: RawOrVariable):
super().__init__()
self.model = model
self.image = image
self._torch = import_optional_dependency(
"torch", "Visit https://pytorch.org/ for instructions.", "1.2")
import torch.utils.data
self._torch_utils_data = torch.utils.data
class _StreamDataset(torch.utils.data.IterableDataset):
def __init__(self, node, stream):
self.node = node
self.stream = stream
def __iter__(self):
with closing_if_closable(self.stream) as stream:
for obj in stream:
yield (self.node.prepare_input(obj, ("image", )),
_Envelope(obj))
self._StreamDataset = _StreamDataset
def __init__(
self,
fmt: RawOrVariable[str],
string: RawOrVariable,
case_sensitive: bool = False,
):
super().__init__()
self.fmt = fmt
self.string = string
self.case_sensitive = case_sensitive
self._parse = import_optional_dependency("parse")
@self._parse.with_pattern(".*")
def parse_greedystar(text):
return text
self._extra_types = {"greedy": parse_greedystar}
if not isinstance(fmt, Variable):
self._parser = self._compile(fmt)
else:
self._parser = None
def __init__(self,
filename: str,
data: RawOrVariable[Mapping] = None,
on=None,
fields: Sequence = None):
super().__init__()
self.data = data
self.on = on
pd = import_optional_dependency("pandas")
ext = os.path.splitext(filename)[1]
if ext in (".xls", ".xlsx"):
dataframe = pd.read_excel(filename, usecols=fields)
else:
with open('example.csv', newline='') as csvfile:
dialect = csv.Sniffer().sniff(csvfile.read(1024))
dataframe = pd.read_csv(filename, dialect=dialect, usecols=fields)
dataframe.set_index(self.on, inplace=True, verify_integrity=True)
self.dataframe = dataframe
def test_optional_hit():
import_optional_dependency("morphocut")
import_optional_dependency("skimage", min_version="0.16")
def test_optional_miss():
with pytest.raises(ImportError):
import_optional_dependency("foo_bar_baz")
assert import_optional_dependency("foo_bar_baz",
raise_on_missing=False) == None
