本文整理汇总了Python中param.parameterized.ParamOverrides.param_keywords方法的典型用法代码示例。如果您正苦于以下问题：Python ParamOverrides.param_keywords方法的具体用法？Python ParamOverrides.param_keywords怎么用？Python ParamOverrides.param_keywords使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类param.parameterized.ParamOverrides的用法示例。
在下文中一共展示了ParamOverrides.param_keywords方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from param.parameterized import ParamOverrides [as 别名]
# 或者: from param.parameterized.ParamOverrides import param_keywords [as 别名]
def __init__(self,inherent_features={},**params):
"""
If a dataset already and inherently includes certain features, a dictionary
with feature-name:code-to-access-the-feature pairs should be supplied
specifying how to select (e.g. from a set of images) the appropriate
feature value.
Any extra parameter values supplied here will be passed down to the
feature_coordinators requested in features_to_vary.
"""
p=ParamOverrides(self,params,allow_extra_keywords=True)
super(PatternCoordinator, self).__init__(**p.param_keywords())
self._feature_params = p.extra_keywords()
self._inherent_features = inherent_features
# And also, this key must be in feature_coordinators because _inherent_features
# can have additional features such as i to support multiple images
# TFALERT: Once spatial frequency (sf) is added, this will
# cause warnings, because all image datasets will have a
# spatial frequency inherent feature, but mostly we just
# ignore that by having only a single size of DoG, which
# discards all but a narrow range of sf. So the dataset will
# have sf inherently, but that won't be an error or even
# worthy of a warning.
if(len((set(self._inherent_features.keys()) - set(self.features_to_vary)) & set(self.feature_coordinators.keys()))):
self.warning('Inherent feature present which is not requested in features')
self._feature_coordinators_to_apply = []
for feature, feature_coordinator in self.feature_coordinators.iteritems():
if feature in self.features_to_vary and feature not in self._inherent_features:
# if it is a list, append each list item individually
if isinstance(feature_coordinator,list):
for individual_feature_coordinator in feature_coordinator:
self._feature_coordinators_to_apply.append(individual_feature_coordinator)
else:
self._feature_coordinators_to_apply.append(feature_coordinator)示例2: __init__
# 需要导入模块: from param.parameterized import ParamOverrides [as 别名]
# 或者: from param.parameterized.ParamOverrides import param_keywords [as 别名]
def __init__(self,inherent_features=[],**params):
"""
If a dataset already and inherently includes certain features,
a list with the inherent feature names should be supplied.
Any extra parameter values supplied here will be passed down
to the feature_coordinators requested in features_to_vary.
"""
p=ParamOverrides(self,params,allow_extra_keywords=True)
super(PatternCoordinator, self).__init__(**p.param_keywords())
self._feature_params = p.extra_keywords()
self._inherent_features = inherent_features
# TFALERT: Once spatial frequency (sf) is added, this will
# cause warnings, because all image datasets will have a
# spatial frequency inherent feature, but mostly we just
# ignore that by having only a single size of DoG, which
# discards all but a narrow range of sf. So the dataset will
# have sf inherently, but that won't be an error or even
# worthy of a warning.
if(len(set(self._inherent_features) - set(self.features_to_vary))):
self.warning('Inherent feature present which is not requested in features')
self._feature_coordinators_to_apply = []
for feature, feature_coordinator in self.feature_coordinators.items():
if feature in self.features_to_vary and feature not in self._inherent_features:
# if it is a list, append each list item individually
if isinstance(feature_coordinator,list):
for individual_feature_coordinator in feature_coordinator:
self._feature_coordinators_to_apply.append(individual_feature_coordinator)
else:
self._feature_coordinators_to_apply.append(feature_coordinator)