Source code for timelinelib.features.experimental.experimentalfeatures

# Copyright (C) 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018  Rickard Lindberg, Roger Lindberg
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"""
ExperimentalFeatures is responsible for reading and storing experimental features
   configuration, to and from the configuration file.
   It also holds a list of available experimental features.
   All experimental features are instantiated when this file is loaded.

To add a new experimental feature, do as follows:
    - Create a new file in timelinelib.features.experimental, defining a class
      that inherits from ExperimentalFeature and calls the parent __init__()-
      function with the two arguments DISPLAY_NAME and DESCRIPTION.
    - Instantiate the new experimental feature class in this file like...
        NAME = NewExperimentalFeatureClass()
    - Add the instantiated object to the FEATURES list in this file.
    - Implement the feature logic under the condition...
        if NAME.enabled():
    - If new methods are created that only are used by the new feature,
      decorate these functions with @experimental_feature(NAME)
"""


from timelinelib.features.experimental.experimentalfeaturecontainersize import ExperimentalFeatureContainerSize
from timelinelib.features.experimental.experimentalfeaturenegativejuliandays import ExperimentalFeatureNegativeJulianDays
from timelinelib.features.experimental.experimentalfeatureextendedcontainerstrategy import ExperimentalFeatureExtendedContainerStrategy


EXTENDED_CONTAINER_HEIGHT = ExperimentalFeatureContainerSize()
NEGATIVE_JULIAN_DAYS = ExperimentalFeatureNegativeJulianDays()
EXTENDED_CONTAINER_STRATEGY = ExperimentalFeatureExtendedContainerStrategy()
FEATURES = (EXTENDED_CONTAINER_HEIGHT, NEGATIVE_JULIAN_DAYS, EXTENDED_CONTAINER_STRATEGY)


[docs]class ExperimentalFeatureException(Exception): pass
[docs]class ExperimentalFeatures:
[docs] def __str__(self): """ Formats the configuration string for all experimental features, which is a semicolon separated list of feature configurations. features-configuration ::= (feature-configuration ';')* feature-configuration ::= feature-name '=' ('True'|'False') """ collector = [] for feature in FEATURES: collector.append(feature.get_config()) return "".join(collector)
[docs] def get_all_features(self): return FEATURES
[docs] def set_active_state_on_all_features_from_config_string(self, cfg_string): for item in cfg_string.split(";"): if "=" in item: name, value = item.split("=") self.set_active_state_on_feature_by_name(name.strip(), value.strip() == "True")
[docs] def set_active_state_on_feature_by_index(self, feature_index, value): FEATURES[feature_index].set_active(value)
[docs] def set_active_state_on_feature_by_name(self, name, value): for feature in FEATURES: if feature.config_name == name: feature.set_active(value) return elif feature.display_name == name: feature.set_active(value) return
[docs]def experimental_feature(feature): """ The syntax for decorators with arguments is a bit different - the decorator with arguments should return a function that will take a function and return another function. So it should really return a normal decorator. Decorator used for methods, only used by an Experimental feature. The purpose of the decorator is to simplify removal of the feature code if it is decided not to implement the feature. Example: @experimental_feature(EVENT_DONE) def foo() pass """ def deco(foo): if feature not in FEATURES: raise ExperimentalFeatureException("Feature '%s', not implemented" % feature.display_name()) return foo return deco