def test_pip_merge():
pip_list = [
"package>=1.1,==1.4", "package2", "package2>=1.5",
"package>=1.1,==1.4,==1.5", "package5"
]
assert normalize_pip(pip_list) == [
'package>=1.1,==1.4,==1.5', 'package2>=1.5', 'package5'
]
def normalized(self):
"""Normalize the list of dependencies
"""
channels, packages = self._get_channels_packages()
if isinstance(packages, related.types.TypedSequence):
packages = packages.list
if isinstance(channels, related.types.TypedSequence):
channels = channels.list
return Dependencies(conda=packages,
pip=kconda.normalize_pip(list(self.pip)),
conda_channels=channels)
def to_env_dict(self, env_name):
deps = self.normalized()
channels, packages = deps._get_channels_packages()
if isinstance(packages, related.types.TypedSequence):
packages = packages.list
if isinstance(channels, related.types.TypedSequence):
channels = channels.list
env_dict = OrderedDict(
name=env_name,
channels=channels,
dependencies=packages +
[OrderedDict(pip=kconda.normalize_pip(deps.pip))])
return env_dict
def merge(self, dependencies):
"""Merge one dependencies with another one
Use case: merging the dependencies of model and dataloader
Args:
dependencies: Dependencies instance
Returns:
new Dependencies instance
"""
return Dependencies(
conda=unique_list(list(self.conda) + list(dependencies.conda)),
pip=kconda.normalize_pip(list(self.pip) + list(dependencies.pip)),
conda_channels=unique_list(
list(self.conda_channels) + list(dependencies.conda_channels)))
