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optimizely/python-sdk | optimizely/bucketer.py | Bucketer.bucket | def bucket(self, experiment, user_id, bucketing_id):
""" For a given experiment and bucketing ID determines variation to be shown to user.
Args:
experiment: Object representing the experiment for which user is to be bucketed.
user_id: ID for user.
bucketing_id: ID to be used for bucketing the user.
Returns:
Variation in which user with ID user_id will be put in. None if no variation.
"""
if not experiment:
return None
# Determine if experiment is in a mutually exclusive group
if experiment.groupPolicy in GROUP_POLICIES:
group = self.config.get_group(experiment.groupId)
if not group:
return None
user_experiment_id = self.find_bucket(bucketing_id, experiment.groupId, group.trafficAllocation)
if not user_experiment_id:
self.config.logger.info('User "%s" is in no experiment.' % user_id)
return None
if user_experiment_id != experiment.id:
self.config.logger.info('User "%s" is not in experiment "%s" of group %s.' % (
user_id,
experiment.key,
experiment.groupId
))
return None
self.config.logger.info('User "%s" is in experiment %s of group %s.' % (
user_id,
experiment.key,
experiment.groupId
))
# Bucket user if not in white-list and in group (if any)
variation_id = self.find_bucket(bucketing_id, experiment.id, experiment.trafficAllocation)
if variation_id:
variation = self.config.get_variation_from_id(experiment.key, variation_id)
self.config.logger.info('User "%s" is in variation "%s" of experiment %s.' % (
user_id,
variation.key,
experiment.key
))
return variation
self.config.logger.info('User "%s" is in no variation.' % user_id)
return None | python | def bucket(self, experiment, user_id, bucketing_id):
""" For a given experiment and bucketing ID determines variation to be shown to user.
Args:
experiment: Object representing the experiment for which user is to be bucketed.
user_id: ID for user.
bucketing_id: ID to be used for bucketing the user.
Returns:
Variation in which user with ID user_id will be put in. None if no variation.
"""
if not experiment:
return None
# Determine if experiment is in a mutually exclusive group
if experiment.groupPolicy in GROUP_POLICIES:
group = self.config.get_group(experiment.groupId)
if not group:
return None
user_experiment_id = self.find_bucket(bucketing_id, experiment.groupId, group.trafficAllocation)
if not user_experiment_id:
self.config.logger.info('User "%s" is in no experiment.' % user_id)
return None
if user_experiment_id != experiment.id:
self.config.logger.info('User "%s" is not in experiment "%s" of group %s.' % (
user_id,
experiment.key,
experiment.groupId
))
return None
self.config.logger.info('User "%s" is in experiment %s of group %s.' % (
user_id,
experiment.key,
experiment.groupId
))
# Bucket user if not in white-list and in group (if any)
variation_id = self.find_bucket(bucketing_id, experiment.id, experiment.trafficAllocation)
if variation_id:
variation = self.config.get_variation_from_id(experiment.key, variation_id)
self.config.logger.info('User "%s" is in variation "%s" of experiment %s.' % (
user_id,
variation.key,
experiment.key
))
return variation
self.config.logger.info('User "%s" is in no variation.' % user_id)
return None | [
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experiment: Object representing the experiment for which user is to be bucketed.
user_id: ID for user.
bucketing_id: ID to be used for bucketing the user.
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optimizely/python-sdk | optimizely/project_config.py | ProjectConfig._generate_key_map | def _generate_key_map(entity_list, key, entity_class):
""" Helper method to generate map from key to entity object for given list of dicts.
Args:
entity_list: List consisting of dict.
key: Key in each dict which will be key in the map.
entity_class: Class representing the entity.
Returns:
Map mapping key to entity object.
"""
key_map = {}
for obj in entity_list:
key_map[obj[key]] = entity_class(**obj)
return key_map | python | def _generate_key_map(entity_list, key, entity_class):
""" Helper method to generate map from key to entity object for given list of dicts.
Args:
entity_list: List consisting of dict.
key: Key in each dict which will be key in the map.
entity_class: Class representing the entity.
Returns:
Map mapping key to entity object.
"""
key_map = {}
for obj in entity_list:
key_map[obj[key]] = entity_class(**obj)
return key_map | [
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optimizely/python-sdk | optimizely/project_config.py | ProjectConfig._deserialize_audience | def _deserialize_audience(audience_map):
""" Helper method to de-serialize and populate audience map with the condition list and structure.
Args:
audience_map: Dict mapping audience ID to audience object.
Returns:
Dict additionally consisting of condition list and structure on every audience object.
"""
for audience in audience_map.values():
condition_structure, condition_list = condition_helper.loads(audience.conditions)
audience.__dict__.update({
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'conditionList': condition_list
})
return audience_map | python | def _deserialize_audience(audience_map):
""" Helper method to de-serialize and populate audience map with the condition list and structure.
Args:
audience_map: Dict mapping audience ID to audience object.
Returns:
Dict additionally consisting of condition list and structure on every audience object.
"""
for audience in audience_map.values():
condition_structure, condition_list = condition_helper.loads(audience.conditions)
audience.__dict__.update({
'conditionStructure': condition_structure,
'conditionList': condition_list
})
return audience_map | [
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optimizely/python-sdk | optimizely/project_config.py | ProjectConfig.get_typecast_value | def get_typecast_value(self, value, type):
""" Helper method to determine actual value based on type of feature variable.
Args:
value: Value in string form as it was parsed from datafile.
type: Type denoting the feature flag type.
Return:
Value type-casted based on type of feature variable.
"""
if type == entities.Variable.Type.BOOLEAN:
return value == 'true'
elif type == entities.Variable.Type.INTEGER:
return int(value)
elif type == entities.Variable.Type.DOUBLE:
return float(value)
else:
return value | python | def get_typecast_value(self, value, type):
""" Helper method to determine actual value based on type of feature variable.
Args:
value: Value in string form as it was parsed from datafile.
type: Type denoting the feature flag type.
Return:
Value type-casted based on type of feature variable.
"""
if type == entities.Variable.Type.BOOLEAN:
return value == 'true'
elif type == entities.Variable.Type.INTEGER:
return int(value)
elif type == entities.Variable.Type.DOUBLE:
return float(value)
else:
return value | [
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optimizely/python-sdk | optimizely/project_config.py | ProjectConfig.get_experiment_from_key | def get_experiment_from_key(self, experiment_key):
""" Get experiment for the provided experiment key.
Args:
experiment_key: Experiment key for which experiment is to be determined.
Returns:
Experiment corresponding to the provided experiment key.
"""
experiment = self.experiment_key_map.get(experiment_key)
if experiment:
return experiment
self.logger.error('Experiment key "%s" is not in datafile.' % experiment_key)
self.error_handler.handle_error(exceptions.InvalidExperimentException(enums.Errors.INVALID_EXPERIMENT_KEY_ERROR))
return None | python | def get_experiment_from_key(self, experiment_key):
""" Get experiment for the provided experiment key.
Args:
experiment_key: Experiment key for which experiment is to be determined.
Returns:
Experiment corresponding to the provided experiment key.
"""
experiment = self.experiment_key_map.get(experiment_key)
if experiment:
return experiment
self.logger.error('Experiment key "%s" is not in datafile.' % experiment_key)
self.error_handler.handle_error(exceptions.InvalidExperimentException(enums.Errors.INVALID_EXPERIMENT_KEY_ERROR))
return None | [
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optimizely/python-sdk | optimizely/project_config.py | ProjectConfig.get_experiment_from_id | def get_experiment_from_id(self, experiment_id):
""" Get experiment for the provided experiment ID.
Args:
experiment_id: Experiment ID for which experiment is to be determined.
Returns:
Experiment corresponding to the provided experiment ID.
"""
experiment = self.experiment_id_map.get(experiment_id)
if experiment:
return experiment
self.logger.error('Experiment ID "%s" is not in datafile.' % experiment_id)
self.error_handler.handle_error(exceptions.InvalidExperimentException(enums.Errors.INVALID_EXPERIMENT_KEY_ERROR))
return None | python | def get_experiment_from_id(self, experiment_id):
""" Get experiment for the provided experiment ID.
Args:
experiment_id: Experiment ID for which experiment is to be determined.
Returns:
Experiment corresponding to the provided experiment ID.
"""
experiment = self.experiment_id_map.get(experiment_id)
if experiment:
return experiment
self.logger.error('Experiment ID "%s" is not in datafile.' % experiment_id)
self.error_handler.handle_error(exceptions.InvalidExperimentException(enums.Errors.INVALID_EXPERIMENT_KEY_ERROR))
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optimizely/python-sdk | optimizely/project_config.py | ProjectConfig.get_group | def get_group(self, group_id):
""" Get group for the provided group ID.
Args:
group_id: Group ID for which group is to be determined.
Returns:
Group corresponding to the provided group ID.
"""
group = self.group_id_map.get(group_id)
if group:
return group
self.logger.error('Group ID "%s" is not in datafile.' % group_id)
self.error_handler.handle_error(exceptions.InvalidGroupException(enums.Errors.INVALID_GROUP_ID_ERROR))
return None | python | def get_group(self, group_id):
""" Get group for the provided group ID.
Args:
group_id: Group ID for which group is to be determined.
Returns:
Group corresponding to the provided group ID.
"""
group = self.group_id_map.get(group_id)
if group:
return group
self.logger.error('Group ID "%s" is not in datafile.' % group_id)
self.error_handler.handle_error(exceptions.InvalidGroupException(enums.Errors.INVALID_GROUP_ID_ERROR))
return None | [
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optimizely/python-sdk | optimizely/project_config.py | ProjectConfig.get_audience | def get_audience(self, audience_id):
""" Get audience object for the provided audience ID.
Args:
audience_id: ID of the audience.
Returns:
Dict representing the audience.
"""
audience = self.audience_id_map.get(audience_id)
if audience:
return audience
self.logger.error('Audience ID "%s" is not in datafile.' % audience_id)
self.error_handler.handle_error(exceptions.InvalidAudienceException((enums.Errors.INVALID_AUDIENCE_ERROR))) | python | def get_audience(self, audience_id):
""" Get audience object for the provided audience ID.
Args:
audience_id: ID of the audience.
Returns:
Dict representing the audience.
"""
audience = self.audience_id_map.get(audience_id)
if audience:
return audience
self.logger.error('Audience ID "%s" is not in datafile.' % audience_id)
self.error_handler.handle_error(exceptions.InvalidAudienceException((enums.Errors.INVALID_AUDIENCE_ERROR))) | [
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optimizely/python-sdk | optimizely/project_config.py | ProjectConfig.get_variation_from_key | def get_variation_from_key(self, experiment_key, variation_key):
""" Get variation given experiment and variation key.
Args:
experiment: Key representing parent experiment of variation.
variation_key: Key representing the variation.
Returns
Object representing the variation.
"""
variation_map = self.variation_key_map.get(experiment_key)
if variation_map:
variation = variation_map.get(variation_key)
if variation:
return variation
else:
self.logger.error('Variation key "%s" is not in datafile.' % variation_key)
self.error_handler.handle_error(exceptions.InvalidVariationException(enums.Errors.INVALID_VARIATION_ERROR))
return None
self.logger.error('Experiment key "%s" is not in datafile.' % experiment_key)
self.error_handler.handle_error(exceptions.InvalidExperimentException(enums.Errors.INVALID_EXPERIMENT_KEY_ERROR))
return None | python | def get_variation_from_key(self, experiment_key, variation_key):
""" Get variation given experiment and variation key.
Args:
experiment: Key representing parent experiment of variation.
variation_key: Key representing the variation.
Returns
Object representing the variation.
"""
variation_map = self.variation_key_map.get(experiment_key)
if variation_map:
variation = variation_map.get(variation_key)
if variation:
return variation
else:
self.logger.error('Variation key "%s" is not in datafile.' % variation_key)
self.error_handler.handle_error(exceptions.InvalidVariationException(enums.Errors.INVALID_VARIATION_ERROR))
return None
self.logger.error('Experiment key "%s" is not in datafile.' % experiment_key)
self.error_handler.handle_error(exceptions.InvalidExperimentException(enums.Errors.INVALID_EXPERIMENT_KEY_ERROR))
return None | [
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optimizely/python-sdk | optimizely/project_config.py | ProjectConfig.get_variation_from_id | def get_variation_from_id(self, experiment_key, variation_id):
""" Get variation given experiment and variation ID.
Args:
experiment: Key representing parent experiment of variation.
variation_id: ID representing the variation.
Returns
Object representing the variation.
"""
variation_map = self.variation_id_map.get(experiment_key)
if variation_map:
variation = variation_map.get(variation_id)
if variation:
return variation
else:
self.logger.error('Variation ID "%s" is not in datafile.' % variation_id)
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return None
self.logger.error('Experiment key "%s" is not in datafile.' % experiment_key)
self.error_handler.handle_error(exceptions.InvalidExperimentException(enums.Errors.INVALID_EXPERIMENT_KEY_ERROR))
return None | python | def get_variation_from_id(self, experiment_key, variation_id):
""" Get variation given experiment and variation ID.
Args:
experiment: Key representing parent experiment of variation.
variation_id: ID representing the variation.
Returns
Object representing the variation.
"""
variation_map = self.variation_id_map.get(experiment_key)
if variation_map:
variation = variation_map.get(variation_id)
if variation:
return variation
else:
self.logger.error('Variation ID "%s" is not in datafile.' % variation_id)
self.error_handler.handle_error(exceptions.InvalidVariationException(enums.Errors.INVALID_VARIATION_ERROR))
return None
self.logger.error('Experiment key "%s" is not in datafile.' % experiment_key)
self.error_handler.handle_error(exceptions.InvalidExperimentException(enums.Errors.INVALID_EXPERIMENT_KEY_ERROR))
return None | [
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optimizely/python-sdk | optimizely/project_config.py | ProjectConfig.get_event | def get_event(self, event_key):
""" Get event for the provided event key.
Args:
event_key: Event key for which event is to be determined.
Returns:
Event corresponding to the provided event key.
"""
event = self.event_key_map.get(event_key)
if event:
return event
self.logger.error('Event "%s" is not in datafile.' % event_key)
self.error_handler.handle_error(exceptions.InvalidEventException(enums.Errors.INVALID_EVENT_KEY_ERROR))
return None | python | def get_event(self, event_key):
""" Get event for the provided event key.
Args:
event_key: Event key for which event is to be determined.
Returns:
Event corresponding to the provided event key.
"""
event = self.event_key_map.get(event_key)
if event:
return event
self.logger.error('Event "%s" is not in datafile.' % event_key)
self.error_handler.handle_error(exceptions.InvalidEventException(enums.Errors.INVALID_EVENT_KEY_ERROR))
return None | [
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optimizely/python-sdk | optimizely/project_config.py | ProjectConfig.get_attribute_id | def get_attribute_id(self, attribute_key):
""" Get attribute ID for the provided attribute key.
Args:
attribute_key: Attribute key for which attribute is to be fetched.
Returns:
Attribute ID corresponding to the provided attribute key.
"""
attribute = self.attribute_key_map.get(attribute_key)
has_reserved_prefix = attribute_key.startswith(RESERVED_ATTRIBUTE_PREFIX)
if attribute:
if has_reserved_prefix:
self.logger.warning(('Attribute %s unexpectedly has reserved prefix %s; using attribute ID '
'instead of reserved attribute name.' % (attribute_key, RESERVED_ATTRIBUTE_PREFIX)))
return attribute.id
if has_reserved_prefix:
return attribute_key
self.logger.error('Attribute "%s" is not in datafile.' % attribute_key)
self.error_handler.handle_error(exceptions.InvalidAttributeException(enums.Errors.INVALID_ATTRIBUTE_ERROR))
return None | python | def get_attribute_id(self, attribute_key):
""" Get attribute ID for the provided attribute key.
Args:
attribute_key: Attribute key for which attribute is to be fetched.
Returns:
Attribute ID corresponding to the provided attribute key.
"""
attribute = self.attribute_key_map.get(attribute_key)
has_reserved_prefix = attribute_key.startswith(RESERVED_ATTRIBUTE_PREFIX)
if attribute:
if has_reserved_prefix:
self.logger.warning(('Attribute %s unexpectedly has reserved prefix %s; using attribute ID '
'instead of reserved attribute name.' % (attribute_key, RESERVED_ATTRIBUTE_PREFIX)))
return attribute.id
if has_reserved_prefix:
return attribute_key
self.logger.error('Attribute "%s" is not in datafile.' % attribute_key)
self.error_handler.handle_error(exceptions.InvalidAttributeException(enums.Errors.INVALID_ATTRIBUTE_ERROR))
return None | [
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optimizely/python-sdk | optimizely/project_config.py | ProjectConfig.get_feature_from_key | def get_feature_from_key(self, feature_key):
""" Get feature for the provided feature key.
Args:
feature_key: Feature key for which feature is to be fetched.
Returns:
Feature corresponding to the provided feature key.
"""
feature = self.feature_key_map.get(feature_key)
if feature:
return feature
self.logger.error('Feature "%s" is not in datafile.' % feature_key)
return None | python | def get_feature_from_key(self, feature_key):
""" Get feature for the provided feature key.
Args:
feature_key: Feature key for which feature is to be fetched.
Returns:
Feature corresponding to the provided feature key.
"""
feature = self.feature_key_map.get(feature_key)
if feature:
return feature
self.logger.error('Feature "%s" is not in datafile.' % feature_key)
return None | [
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optimizely/python-sdk | optimizely/project_config.py | ProjectConfig.get_rollout_from_id | def get_rollout_from_id(self, rollout_id):
""" Get rollout for the provided ID.
Args:
rollout_id: ID of the rollout to be fetched.
Returns:
Rollout corresponding to the provided ID.
"""
layer = self.rollout_id_map.get(rollout_id)
if layer:
return layer
self.logger.error('Rollout with ID "%s" is not in datafile.' % rollout_id)
return None | python | def get_rollout_from_id(self, rollout_id):
""" Get rollout for the provided ID.
Args:
rollout_id: ID of the rollout to be fetched.
Returns:
Rollout corresponding to the provided ID.
"""
layer = self.rollout_id_map.get(rollout_id)
if layer:
return layer
self.logger.error('Rollout with ID "%s" is not in datafile.' % rollout_id)
return None | [
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optimizely/python-sdk | optimizely/project_config.py | ProjectConfig.get_variable_value_for_variation | def get_variable_value_for_variation(self, variable, variation):
""" Get the variable value for the given variation.
Args:
variable: The Variable for which we are getting the value.
variation: The Variation for which we are getting the variable value.
Returns:
The variable value or None if any of the inputs are invalid.
"""
if not variable or not variation:
return None
if variation.id not in self.variation_variable_usage_map:
self.logger.error('Variation with ID "%s" is not in the datafile.' % variation.id)
return None
# Get all variable usages for the given variation
variable_usages = self.variation_variable_usage_map[variation.id]
# Find usage in given variation
variable_usage = None
if variable_usages:
variable_usage = variable_usages.get(variable.id)
if variable_usage:
variable_value = variable_usage.value
self.logger.info('Value for variable "%s" for variation "%s" is "%s".' % (
variable.key,
variation.key,
variable_value
))
else:
variable_value = variable.defaultValue
self.logger.info('Variable "%s" is not used in variation "%s". Assigning default value "%s".' % (
variable.key,
variation.key,
variable_value
))
return variable_value | python | def get_variable_value_for_variation(self, variable, variation):
""" Get the variable value for the given variation.
Args:
variable: The Variable for which we are getting the value.
variation: The Variation for which we are getting the variable value.
Returns:
The variable value or None if any of the inputs are invalid.
"""
if not variable or not variation:
return None
if variation.id not in self.variation_variable_usage_map:
self.logger.error('Variation with ID "%s" is not in the datafile.' % variation.id)
return None
# Get all variable usages for the given variation
variable_usages = self.variation_variable_usage_map[variation.id]
# Find usage in given variation
variable_usage = None
if variable_usages:
variable_usage = variable_usages.get(variable.id)
if variable_usage:
variable_value = variable_usage.value
self.logger.info('Value for variable "%s" for variation "%s" is "%s".' % (
variable.key,
variation.key,
variable_value
))
else:
variable_value = variable.defaultValue
self.logger.info('Variable "%s" is not used in variation "%s". Assigning default value "%s".' % (
variable.key,
variation.key,
variable_value
))
return variable_value | [
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optimizely/python-sdk | optimizely/project_config.py | ProjectConfig.get_variable_for_feature | def get_variable_for_feature(self, feature_key, variable_key):
""" Get the variable with the given variable key for the given feature.
Args:
feature_key: The key of the feature for which we are getting the variable.
variable_key: The key of the variable we are getting.
Returns:
Variable with the given key in the given variation.
"""
feature = self.feature_key_map.get(feature_key)
if not feature:
self.logger.error('Feature with key "%s" not found in the datafile.' % feature_key)
return None
if variable_key not in feature.variables:
self.logger.error('Variable with key "%s" not found in the datafile.' % variable_key)
return None
return feature.variables.get(variable_key) | python | def get_variable_for_feature(self, feature_key, variable_key):
""" Get the variable with the given variable key for the given feature.
Args:
feature_key: The key of the feature for which we are getting the variable.
variable_key: The key of the variable we are getting.
Returns:
Variable with the given key in the given variation.
"""
feature = self.feature_key_map.get(feature_key)
if not feature:
self.logger.error('Feature with key "%s" not found in the datafile.' % feature_key)
return None
if variable_key not in feature.variables:
self.logger.error('Variable with key "%s" not found in the datafile.' % variable_key)
return None
return feature.variables.get(variable_key) | [
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optimizely/python-sdk | optimizely/project_config.py | ProjectConfig.set_forced_variation | def set_forced_variation(self, experiment_key, user_id, variation_key):
""" Sets users to a map of experiments to forced variations.
Args:
experiment_key: Key for experiment.
user_id: The user ID.
variation_key: Key for variation. If None, then clear the existing experiment-to-variation mapping.
Returns:
A boolean value that indicates if the set completed successfully.
"""
experiment = self.get_experiment_from_key(experiment_key)
if not experiment:
# The invalid experiment key will be logged inside this call.
return False
experiment_id = experiment.id
if variation_key is None:
if user_id in self.forced_variation_map:
experiment_to_variation_map = self.forced_variation_map.get(user_id)
if experiment_id in experiment_to_variation_map:
del(self.forced_variation_map[user_id][experiment_id])
self.logger.debug('Variation mapped to experiment "%s" has been removed for user "%s".' % (
experiment_key,
user_id
))
else:
self.logger.debug('Nothing to remove. Variation mapped to experiment "%s" for user "%s" does not exist.' % (
experiment_key,
user_id
))
else:
self.logger.debug('Nothing to remove. User "%s" does not exist in the forced variation map.' % user_id)
return True
if not validator.is_non_empty_string(variation_key):
self.logger.debug('Variation key is invalid.')
return False
forced_variation = self.get_variation_from_key(experiment_key, variation_key)
if not forced_variation:
# The invalid variation key will be logged inside this call.
return False
variation_id = forced_variation.id
if user_id not in self.forced_variation_map:
self.forced_variation_map[user_id] = {experiment_id: variation_id}
else:
self.forced_variation_map[user_id][experiment_id] = variation_id
self.logger.debug('Set variation "%s" for experiment "%s" and user "%s" in the forced variation map.' % (
variation_id,
experiment_id,
user_id
))
return True | python | def set_forced_variation(self, experiment_key, user_id, variation_key):
""" Sets users to a map of experiments to forced variations.
Args:
experiment_key: Key for experiment.
user_id: The user ID.
variation_key: Key for variation. If None, then clear the existing experiment-to-variation mapping.
Returns:
A boolean value that indicates if the set completed successfully.
"""
experiment = self.get_experiment_from_key(experiment_key)
if not experiment:
# The invalid experiment key will be logged inside this call.
return False
experiment_id = experiment.id
if variation_key is None:
if user_id in self.forced_variation_map:
experiment_to_variation_map = self.forced_variation_map.get(user_id)
if experiment_id in experiment_to_variation_map:
del(self.forced_variation_map[user_id][experiment_id])
self.logger.debug('Variation mapped to experiment "%s" has been removed for user "%s".' % (
experiment_key,
user_id
))
else:
self.logger.debug('Nothing to remove. Variation mapped to experiment "%s" for user "%s" does not exist.' % (
experiment_key,
user_id
))
else:
self.logger.debug('Nothing to remove. User "%s" does not exist in the forced variation map.' % user_id)
return True
if not validator.is_non_empty_string(variation_key):
self.logger.debug('Variation key is invalid.')
return False
forced_variation = self.get_variation_from_key(experiment_key, variation_key)
if not forced_variation:
# The invalid variation key will be logged inside this call.
return False
variation_id = forced_variation.id
if user_id not in self.forced_variation_map:
self.forced_variation_map[user_id] = {experiment_id: variation_id}
else:
self.forced_variation_map[user_id][experiment_id] = variation_id
self.logger.debug('Set variation "%s" for experiment "%s" and user "%s" in the forced variation map.' % (
variation_id,
experiment_id,
user_id
))
return True | [
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optimizely/python-sdk | optimizely/project_config.py | ProjectConfig.get_forced_variation | def get_forced_variation(self, experiment_key, user_id):
""" Gets the forced variation key for the given user and experiment.
Args:
experiment_key: Key for experiment.
user_id: The user ID.
Returns:
The variation which the given user and experiment should be forced into.
"""
if user_id not in self.forced_variation_map:
self.logger.debug('User "%s" is not in the forced variation map.' % user_id)
return None
experiment = self.get_experiment_from_key(experiment_key)
if not experiment:
# The invalid experiment key will be logged inside this call.
return None
experiment_to_variation_map = self.forced_variation_map.get(user_id)
if not experiment_to_variation_map:
self.logger.debug('No experiment "%s" mapped to user "%s" in the forced variation map.' % (
experiment_key,
user_id
))
return None
variation_id = experiment_to_variation_map.get(experiment.id)
if variation_id is None:
self.logger.debug(
'No variation mapped to experiment "%s" in the forced variation map.' % experiment_key
)
return None
variation = self.get_variation_from_id(experiment_key, variation_id)
self.logger.debug('Variation "%s" is mapped to experiment "%s" and user "%s" in the forced variation map' % (
variation.key,
experiment_key,
user_id
))
return variation | python | def get_forced_variation(self, experiment_key, user_id):
""" Gets the forced variation key for the given user and experiment.
Args:
experiment_key: Key for experiment.
user_id: The user ID.
Returns:
The variation which the given user and experiment should be forced into.
"""
if user_id not in self.forced_variation_map:
self.logger.debug('User "%s" is not in the forced variation map.' % user_id)
return None
experiment = self.get_experiment_from_key(experiment_key)
if not experiment:
# The invalid experiment key will be logged inside this call.
return None
experiment_to_variation_map = self.forced_variation_map.get(user_id)
if not experiment_to_variation_map:
self.logger.debug('No experiment "%s" mapped to user "%s" in the forced variation map.' % (
experiment_key,
user_id
))
return None
variation_id = experiment_to_variation_map.get(experiment.id)
if variation_id is None:
self.logger.debug(
'No variation mapped to experiment "%s" in the forced variation map.' % experiment_key
)
return None
variation = self.get_variation_from_id(experiment_key, variation_id)
self.logger.debug('Variation "%s" is mapped to experiment "%s" and user "%s" in the forced variation map' % (
variation.key,
experiment_key,
user_id
))
return variation | [
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optimizely/python-sdk | optimizely/event_dispatcher.py | EventDispatcher.dispatch_event | def dispatch_event(event):
""" Dispatch the event being represented by the Event object.
Args:
event: Object holding information about the request to be dispatched to the Optimizely backend.
"""
try:
if event.http_verb == enums.HTTPVerbs.GET:
requests.get(event.url, params=event.params, timeout=REQUEST_TIMEOUT).raise_for_status()
elif event.http_verb == enums.HTTPVerbs.POST:
requests.post(
event.url, data=json.dumps(event.params), headers=event.headers, timeout=REQUEST_TIMEOUT
).raise_for_status()
except request_exception.RequestException as error:
logging.error('Dispatch event failed. Error: %s' % str(error)) | python | def dispatch_event(event):
""" Dispatch the event being represented by the Event object.
Args:
event: Object holding information about the request to be dispatched to the Optimizely backend.
"""
try:
if event.http_verb == enums.HTTPVerbs.GET:
requests.get(event.url, params=event.params, timeout=REQUEST_TIMEOUT).raise_for_status()
elif event.http_verb == enums.HTTPVerbs.POST:
requests.post(
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except request_exception.RequestException as error:
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optimizely/python-sdk | optimizely/optimizely.py | Optimizely._validate_instantiation_options | def _validate_instantiation_options(self, datafile, skip_json_validation):
""" Helper method to validate all instantiation parameters.
Args:
datafile: JSON string representing the project.
skip_json_validation: Boolean representing whether JSON schema validation needs to be skipped or not.
Raises:
Exception if provided instantiation options are valid.
"""
if not skip_json_validation and not validator.is_datafile_valid(datafile):
raise exceptions.InvalidInputException(enums.Errors.INVALID_INPUT_ERROR.format('datafile'))
if not validator.is_event_dispatcher_valid(self.event_dispatcher):
raise exceptions.InvalidInputException(enums.Errors.INVALID_INPUT_ERROR.format('event_dispatcher'))
if not validator.is_logger_valid(self.logger):
raise exceptions.InvalidInputException(enums.Errors.INVALID_INPUT_ERROR.format('logger'))
if not validator.is_error_handler_valid(self.error_handler):
raise exceptions.InvalidInputException(enums.Errors.INVALID_INPUT_ERROR.format('error_handler')) | python | def _validate_instantiation_options(self, datafile, skip_json_validation):
""" Helper method to validate all instantiation parameters.
Args:
datafile: JSON string representing the project.
skip_json_validation: Boolean representing whether JSON schema validation needs to be skipped or not.
Raises:
Exception if provided instantiation options are valid.
"""
if not skip_json_validation and not validator.is_datafile_valid(datafile):
raise exceptions.InvalidInputException(enums.Errors.INVALID_INPUT_ERROR.format('datafile'))
if not validator.is_event_dispatcher_valid(self.event_dispatcher):
raise exceptions.InvalidInputException(enums.Errors.INVALID_INPUT_ERROR.format('event_dispatcher'))
if not validator.is_logger_valid(self.logger):
raise exceptions.InvalidInputException(enums.Errors.INVALID_INPUT_ERROR.format('logger'))
if not validator.is_error_handler_valid(self.error_handler):
raise exceptions.InvalidInputException(enums.Errors.INVALID_INPUT_ERROR.format('error_handler')) | [
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optimizely/python-sdk | optimizely/optimizely.py | Optimizely._validate_user_inputs | def _validate_user_inputs(self, attributes=None, event_tags=None):
""" Helper method to validate user inputs.
Args:
attributes: Dict representing user attributes.
event_tags: Dict representing metadata associated with an event.
Returns:
Boolean True if inputs are valid. False otherwise.
"""
if attributes and not validator.are_attributes_valid(attributes):
self.logger.error('Provided attributes are in an invalid format.')
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return False
if event_tags and not validator.are_event_tags_valid(event_tags):
self.logger.error('Provided event tags are in an invalid format.')
self.error_handler.handle_error(exceptions.InvalidEventTagException(enums.Errors.INVALID_EVENT_TAG_FORMAT))
return False
return True | python | def _validate_user_inputs(self, attributes=None, event_tags=None):
""" Helper method to validate user inputs.
Args:
attributes: Dict representing user attributes.
event_tags: Dict representing metadata associated with an event.
Returns:
Boolean True if inputs are valid. False otherwise.
"""
if attributes and not validator.are_attributes_valid(attributes):
self.logger.error('Provided attributes are in an invalid format.')
self.error_handler.handle_error(exceptions.InvalidAttributeException(enums.Errors.INVALID_ATTRIBUTE_FORMAT))
return False
if event_tags and not validator.are_event_tags_valid(event_tags):
self.logger.error('Provided event tags are in an invalid format.')
self.error_handler.handle_error(exceptions.InvalidEventTagException(enums.Errors.INVALID_EVENT_TAG_FORMAT))
return False
return True | [
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optimizely/python-sdk | optimizely/optimizely.py | Optimizely._send_impression_event | def _send_impression_event(self, experiment, variation, user_id, attributes):
""" Helper method to send impression event.
Args:
experiment: Experiment for which impression event is being sent.
variation: Variation picked for user for the given experiment.
user_id: ID for user.
attributes: Dict representing user attributes and values which need to be recorded.
"""
impression_event = self.event_builder.create_impression_event(experiment,
variation.id,
user_id,
attributes)
self.logger.debug('Dispatching impression event to URL %s with params %s.' % (
impression_event.url,
impression_event.params
))
try:
self.event_dispatcher.dispatch_event(impression_event)
except:
self.logger.exception('Unable to dispatch impression event!')
self.notification_center.send_notifications(enums.NotificationTypes.ACTIVATE,
experiment, user_id, attributes, variation, impression_event) | python | def _send_impression_event(self, experiment, variation, user_id, attributes):
""" Helper method to send impression event.
Args:
experiment: Experiment for which impression event is being sent.
variation: Variation picked for user for the given experiment.
user_id: ID for user.
attributes: Dict representing user attributes and values which need to be recorded.
"""
impression_event = self.event_builder.create_impression_event(experiment,
variation.id,
user_id,
attributes)
self.logger.debug('Dispatching impression event to URL %s with params %s.' % (
impression_event.url,
impression_event.params
))
try:
self.event_dispatcher.dispatch_event(impression_event)
except:
self.logger.exception('Unable to dispatch impression event!')
self.notification_center.send_notifications(enums.NotificationTypes.ACTIVATE,
experiment, user_id, attributes, variation, impression_event) | [
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variation: Variation picked for user for the given experiment.
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optimizely/python-sdk | optimizely/optimizely.py | Optimizely._get_feature_variable_for_type | def _get_feature_variable_for_type(self, feature_key, variable_key, variable_type, user_id, attributes):
""" Helper method to determine value for a certain variable attached to a feature flag based on type of variable.
Args:
feature_key: Key of the feature whose variable's value is being accessed.
variable_key: Key of the variable whose value is to be accessed.
variable_type: Type of variable which could be one of boolean/double/integer/string.
user_id: ID for user.
attributes: Dict representing user attributes.
Returns:
Value of the variable. None if:
- Feature key is invalid.
- Variable key is invalid.
- Mismatch with type of variable.
"""
if not validator.is_non_empty_string(feature_key):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('feature_key'))
return None
if not validator.is_non_empty_string(variable_key):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('variable_key'))
return None
if not isinstance(user_id, string_types):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('user_id'))
return None
if not self._validate_user_inputs(attributes):
return None
feature_flag = self.config.get_feature_from_key(feature_key)
if not feature_flag:
return None
variable = self.config.get_variable_for_feature(feature_key, variable_key)
if not variable:
return None
# Return None if type differs
if variable.type != variable_type:
self.logger.warning(
'Requested variable type "%s", but variable is of type "%s". '
'Use correct API to retrieve value. Returning None.' % (variable_type, variable.type)
)
return None
feature_enabled = False
source_info = {}
variable_value = variable.defaultValue
decision = self.decision_service.get_variation_for_feature(feature_flag, user_id, attributes)
if decision.variation:
feature_enabled = decision.variation.featureEnabled
if feature_enabled:
variable_value = self.config.get_variable_value_for_variation(variable, decision.variation)
self.logger.info(
'Got variable value "%s" for variable "%s" of feature flag "%s".' % (
variable_value, variable_key, feature_key
)
)
else:
self.logger.info(
'Feature "%s" for variation "%s" is not enabled. '
'Returning the default variable value "%s".' % (feature_key, decision.variation.key, variable_value)
)
else:
self.logger.info(
'User "%s" is not in any variation or rollout rule. '
'Returning default value for variable "%s" of feature flag "%s".' % (user_id, variable_key, feature_key)
)
if decision.source == enums.DecisionSources.FEATURE_TEST:
source_info = {
'experiment_key': decision.experiment.key,
'variation_key': decision.variation.key
}
try:
actual_value = self.config.get_typecast_value(variable_value, variable_type)
except:
self.logger.error('Unable to cast value. Returning None.')
actual_value = None
self.notification_center.send_notifications(
enums.NotificationTypes.DECISION,
enums.DecisionNotificationTypes.FEATURE_VARIABLE,
user_id,
attributes or {},
{
'feature_key': feature_key,
'feature_enabled': feature_enabled,
'source': decision.source,
'variable_key': variable_key,
'variable_value': actual_value,
'variable_type': variable_type,
'source_info': source_info
}
)
return actual_value | python | def _get_feature_variable_for_type(self, feature_key, variable_key, variable_type, user_id, attributes):
""" Helper method to determine value for a certain variable attached to a feature flag based on type of variable.
Args:
feature_key: Key of the feature whose variable's value is being accessed.
variable_key: Key of the variable whose value is to be accessed.
variable_type: Type of variable which could be one of boolean/double/integer/string.
user_id: ID for user.
attributes: Dict representing user attributes.
Returns:
Value of the variable. None if:
- Feature key is invalid.
- Variable key is invalid.
- Mismatch with type of variable.
"""
if not validator.is_non_empty_string(feature_key):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('feature_key'))
return None
if not validator.is_non_empty_string(variable_key):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('variable_key'))
return None
if not isinstance(user_id, string_types):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('user_id'))
return None
if not self._validate_user_inputs(attributes):
return None
feature_flag = self.config.get_feature_from_key(feature_key)
if not feature_flag:
return None
variable = self.config.get_variable_for_feature(feature_key, variable_key)
if not variable:
return None
# Return None if type differs
if variable.type != variable_type:
self.logger.warning(
'Requested variable type "%s", but variable is of type "%s". '
'Use correct API to retrieve value. Returning None.' % (variable_type, variable.type)
)
return None
feature_enabled = False
source_info = {}
variable_value = variable.defaultValue
decision = self.decision_service.get_variation_for_feature(feature_flag, user_id, attributes)
if decision.variation:
feature_enabled = decision.variation.featureEnabled
if feature_enabled:
variable_value = self.config.get_variable_value_for_variation(variable, decision.variation)
self.logger.info(
'Got variable value "%s" for variable "%s" of feature flag "%s".' % (
variable_value, variable_key, feature_key
)
)
else:
self.logger.info(
'Feature "%s" for variation "%s" is not enabled. '
'Returning the default variable value "%s".' % (feature_key, decision.variation.key, variable_value)
)
else:
self.logger.info(
'User "%s" is not in any variation or rollout rule. '
'Returning default value for variable "%s" of feature flag "%s".' % (user_id, variable_key, feature_key)
)
if decision.source == enums.DecisionSources.FEATURE_TEST:
source_info = {
'experiment_key': decision.experiment.key,
'variation_key': decision.variation.key
}
try:
actual_value = self.config.get_typecast_value(variable_value, variable_type)
except:
self.logger.error('Unable to cast value. Returning None.')
actual_value = None
self.notification_center.send_notifications(
enums.NotificationTypes.DECISION,
enums.DecisionNotificationTypes.FEATURE_VARIABLE,
user_id,
attributes or {},
{
'feature_key': feature_key,
'feature_enabled': feature_enabled,
'source': decision.source,
'variable_key': variable_key,
'variable_value': actual_value,
'variable_type': variable_type,
'source_info': source_info
}
)
return actual_value | [
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Args:
feature_key: Key of the feature whose variable's value is being accessed.
variable_key: Key of the variable whose value is to be accessed.
variable_type: Type of variable which could be one of boolean/double/integer/string.
user_id: ID for user.
attributes: Dict representing user attributes.
Returns:
Value of the variable. None if:
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- Variable key is invalid.
- Mismatch with type of variable. | [
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optimizely/python-sdk | optimizely/optimizely.py | Optimizely.activate | def activate(self, experiment_key, user_id, attributes=None):
""" Buckets visitor and sends impression event to Optimizely.
Args:
experiment_key: Experiment which needs to be activated.
user_id: ID for user.
attributes: Dict representing user attributes and values which need to be recorded.
Returns:
Variation key representing the variation the user will be bucketed in.
None if user is not in experiment or if experiment is not Running.
"""
if not self.is_valid:
self.logger.error(enums.Errors.INVALID_DATAFILE.format('activate'))
return None
if not validator.is_non_empty_string(experiment_key):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('experiment_key'))
return None
if not isinstance(user_id, string_types):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('user_id'))
return None
variation_key = self.get_variation(experiment_key, user_id, attributes)
if not variation_key:
self.logger.info('Not activating user "%s".' % user_id)
return None
experiment = self.config.get_experiment_from_key(experiment_key)
variation = self.config.get_variation_from_key(experiment_key, variation_key)
# Create and dispatch impression event
self.logger.info('Activating user "%s" in experiment "%s".' % (user_id, experiment.key))
self._send_impression_event(experiment, variation, user_id, attributes)
return variation.key | python | def activate(self, experiment_key, user_id, attributes=None):
""" Buckets visitor and sends impression event to Optimizely.
Args:
experiment_key: Experiment which needs to be activated.
user_id: ID for user.
attributes: Dict representing user attributes and values which need to be recorded.
Returns:
Variation key representing the variation the user will be bucketed in.
None if user is not in experiment or if experiment is not Running.
"""
if not self.is_valid:
self.logger.error(enums.Errors.INVALID_DATAFILE.format('activate'))
return None
if not validator.is_non_empty_string(experiment_key):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('experiment_key'))
return None
if not isinstance(user_id, string_types):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('user_id'))
return None
variation_key = self.get_variation(experiment_key, user_id, attributes)
if not variation_key:
self.logger.info('Not activating user "%s".' % user_id)
return None
experiment = self.config.get_experiment_from_key(experiment_key)
variation = self.config.get_variation_from_key(experiment_key, variation_key)
# Create and dispatch impression event
self.logger.info('Activating user "%s" in experiment "%s".' % (user_id, experiment.key))
self._send_impression_event(experiment, variation, user_id, attributes)
return variation.key | [
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experiment_key: Experiment which needs to be activated.
user_id: ID for user.
attributes: Dict representing user attributes and values which need to be recorded.
Returns:
Variation key representing the variation the user will be bucketed in.
None if user is not in experiment or if experiment is not Running. | [
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optimizely/python-sdk | optimizely/optimizely.py | Optimizely.track | def track(self, event_key, user_id, attributes=None, event_tags=None):
""" Send conversion event to Optimizely.
Args:
event_key: Event key representing the event which needs to be recorded.
user_id: ID for user.
attributes: Dict representing visitor attributes and values which need to be recorded.
event_tags: Dict representing metadata associated with the event.
"""
if not self.is_valid:
self.logger.error(enums.Errors.INVALID_DATAFILE.format('track'))
return
if not validator.is_non_empty_string(event_key):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('event_key'))
return
if not isinstance(user_id, string_types):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('user_id'))
return
if not self._validate_user_inputs(attributes, event_tags):
return
event = self.config.get_event(event_key)
if not event:
self.logger.info('Not tracking user "%s" for event "%s".' % (user_id, event_key))
return
conversion_event = self.event_builder.create_conversion_event(event_key, user_id, attributes, event_tags)
self.logger.info('Tracking event "%s" for user "%s".' % (event_key, user_id))
self.logger.debug('Dispatching conversion event to URL %s with params %s.' % (
conversion_event.url,
conversion_event.params
))
try:
self.event_dispatcher.dispatch_event(conversion_event)
except:
self.logger.exception('Unable to dispatch conversion event!')
self.notification_center.send_notifications(enums.NotificationTypes.TRACK, event_key, user_id,
attributes, event_tags, conversion_event) | python | def track(self, event_key, user_id, attributes=None, event_tags=None):
""" Send conversion event to Optimizely.
Args:
event_key: Event key representing the event which needs to be recorded.
user_id: ID for user.
attributes: Dict representing visitor attributes and values which need to be recorded.
event_tags: Dict representing metadata associated with the event.
"""
if not self.is_valid:
self.logger.error(enums.Errors.INVALID_DATAFILE.format('track'))
return
if not validator.is_non_empty_string(event_key):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('event_key'))
return
if not isinstance(user_id, string_types):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('user_id'))
return
if not self._validate_user_inputs(attributes, event_tags):
return
event = self.config.get_event(event_key)
if not event:
self.logger.info('Not tracking user "%s" for event "%s".' % (user_id, event_key))
return
conversion_event = self.event_builder.create_conversion_event(event_key, user_id, attributes, event_tags)
self.logger.info('Tracking event "%s" for user "%s".' % (event_key, user_id))
self.logger.debug('Dispatching conversion event to URL %s with params %s.' % (
conversion_event.url,
conversion_event.params
))
try:
self.event_dispatcher.dispatch_event(conversion_event)
except:
self.logger.exception('Unable to dispatch conversion event!')
self.notification_center.send_notifications(enums.NotificationTypes.TRACK, event_key, user_id,
attributes, event_tags, conversion_event) | [
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optimizely/python-sdk | optimizely/optimizely.py | Optimizely.get_variation | def get_variation(self, experiment_key, user_id, attributes=None):
""" Gets variation where user will be bucketed.
Args:
experiment_key: Experiment for which user variation needs to be determined.
user_id: ID for user.
attributes: Dict representing user attributes.
Returns:
Variation key representing the variation the user will be bucketed in.
None if user is not in experiment or if experiment is not Running.
"""
if not self.is_valid:
self.logger.error(enums.Errors.INVALID_DATAFILE.format('get_variation'))
return None
if not validator.is_non_empty_string(experiment_key):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('experiment_key'))
return None
if not isinstance(user_id, string_types):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('user_id'))
return None
experiment = self.config.get_experiment_from_key(experiment_key)
variation_key = None
if not experiment:
self.logger.info('Experiment key "%s" is invalid. Not activating user "%s".' % (
experiment_key,
user_id
))
return None
if not self._validate_user_inputs(attributes):
return None
variation = self.decision_service.get_variation(experiment, user_id, attributes)
if variation:
variation_key = variation.key
if self.config.is_feature_experiment(experiment.id):
decision_notification_type = enums.DecisionNotificationTypes.FEATURE_TEST
else:
decision_notification_type = enums.DecisionNotificationTypes.AB_TEST
self.notification_center.send_notifications(
enums.NotificationTypes.DECISION,
decision_notification_type,
user_id,
attributes or {},
{
'experiment_key': experiment_key,
'variation_key': variation_key
}
)
return variation_key | python | def get_variation(self, experiment_key, user_id, attributes=None):
""" Gets variation where user will be bucketed.
Args:
experiment_key: Experiment for which user variation needs to be determined.
user_id: ID for user.
attributes: Dict representing user attributes.
Returns:
Variation key representing the variation the user will be bucketed in.
None if user is not in experiment or if experiment is not Running.
"""
if not self.is_valid:
self.logger.error(enums.Errors.INVALID_DATAFILE.format('get_variation'))
return None
if not validator.is_non_empty_string(experiment_key):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('experiment_key'))
return None
if not isinstance(user_id, string_types):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('user_id'))
return None
experiment = self.config.get_experiment_from_key(experiment_key)
variation_key = None
if not experiment:
self.logger.info('Experiment key "%s" is invalid. Not activating user "%s".' % (
experiment_key,
user_id
))
return None
if not self._validate_user_inputs(attributes):
return None
variation = self.decision_service.get_variation(experiment, user_id, attributes)
if variation:
variation_key = variation.key
if self.config.is_feature_experiment(experiment.id):
decision_notification_type = enums.DecisionNotificationTypes.FEATURE_TEST
else:
decision_notification_type = enums.DecisionNotificationTypes.AB_TEST
self.notification_center.send_notifications(
enums.NotificationTypes.DECISION,
decision_notification_type,
user_id,
attributes or {},
{
'experiment_key': experiment_key,
'variation_key': variation_key
}
)
return variation_key | [
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optimizely/python-sdk | optimizely/optimizely.py | Optimizely.is_feature_enabled | def is_feature_enabled(self, feature_key, user_id, attributes=None):
""" Returns true if the feature is enabled for the given user.
Args:
feature_key: The key of the feature for which we are determining if it is enabled or not for the given user.
user_id: ID for user.
attributes: Dict representing user attributes.
Returns:
True if the feature is enabled for the user. False otherwise.
"""
if not self.is_valid:
self.logger.error(enums.Errors.INVALID_DATAFILE.format('is_feature_enabled'))
return False
if not validator.is_non_empty_string(feature_key):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('feature_key'))
return False
if not isinstance(user_id, string_types):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('user_id'))
return False
if not self._validate_user_inputs(attributes):
return False
feature = self.config.get_feature_from_key(feature_key)
if not feature:
return False
feature_enabled = False
source_info = {}
decision = self.decision_service.get_variation_for_feature(feature, user_id, attributes)
is_source_experiment = decision.source == enums.DecisionSources.FEATURE_TEST
if decision.variation:
if decision.variation.featureEnabled is True:
feature_enabled = True
# Send event if Decision came from an experiment.
if is_source_experiment:
source_info = {
'experiment_key': decision.experiment.key,
'variation_key': decision.variation.key
}
self._send_impression_event(decision.experiment,
decision.variation,
user_id,
attributes)
if feature_enabled:
self.logger.info('Feature "%s" is enabled for user "%s".' % (feature_key, user_id))
else:
self.logger.info('Feature "%s" is not enabled for user "%s".' % (feature_key, user_id))
self.notification_center.send_notifications(
enums.NotificationTypes.DECISION,
enums.DecisionNotificationTypes.FEATURE,
user_id,
attributes or {},
{
'feature_key': feature_key,
'feature_enabled': feature_enabled,
'source': decision.source,
'source_info': source_info
}
)
return feature_enabled | python | def is_feature_enabled(self, feature_key, user_id, attributes=None):
""" Returns true if the feature is enabled for the given user.
Args:
feature_key: The key of the feature for which we are determining if it is enabled or not for the given user.
user_id: ID for user.
attributes: Dict representing user attributes.
Returns:
True if the feature is enabled for the user. False otherwise.
"""
if not self.is_valid:
self.logger.error(enums.Errors.INVALID_DATAFILE.format('is_feature_enabled'))
return False
if not validator.is_non_empty_string(feature_key):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('feature_key'))
return False
if not isinstance(user_id, string_types):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('user_id'))
return False
if not self._validate_user_inputs(attributes):
return False
feature = self.config.get_feature_from_key(feature_key)
if not feature:
return False
feature_enabled = False
source_info = {}
decision = self.decision_service.get_variation_for_feature(feature, user_id, attributes)
is_source_experiment = decision.source == enums.DecisionSources.FEATURE_TEST
if decision.variation:
if decision.variation.featureEnabled is True:
feature_enabled = True
# Send event if Decision came from an experiment.
if is_source_experiment:
source_info = {
'experiment_key': decision.experiment.key,
'variation_key': decision.variation.key
}
self._send_impression_event(decision.experiment,
decision.variation,
user_id,
attributes)
if feature_enabled:
self.logger.info('Feature "%s" is enabled for user "%s".' % (feature_key, user_id))
else:
self.logger.info('Feature "%s" is not enabled for user "%s".' % (feature_key, user_id))
self.notification_center.send_notifications(
enums.NotificationTypes.DECISION,
enums.DecisionNotificationTypes.FEATURE,
user_id,
attributes or {},
{
'feature_key': feature_key,
'feature_enabled': feature_enabled,
'source': decision.source,
'source_info': source_info
}
)
return feature_enabled | [
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user_id: ID for user.
attributes: Dict representing user attributes.
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optimizely/python-sdk | optimizely/optimizely.py | Optimizely.get_enabled_features | def get_enabled_features(self, user_id, attributes=None):
""" Returns the list of features that are enabled for the user.
Args:
user_id: ID for user.
attributes: Dict representing user attributes.
Returns:
A list of the keys of the features that are enabled for the user.
"""
enabled_features = []
if not self.is_valid:
self.logger.error(enums.Errors.INVALID_DATAFILE.format('get_enabled_features'))
return enabled_features
if not isinstance(user_id, string_types):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('user_id'))
return enabled_features
if not self._validate_user_inputs(attributes):
return enabled_features
for feature in self.config.feature_key_map.values():
if self.is_feature_enabled(feature.key, user_id, attributes):
enabled_features.append(feature.key)
return enabled_features | python | def get_enabled_features(self, user_id, attributes=None):
""" Returns the list of features that are enabled for the user.
Args:
user_id: ID for user.
attributes: Dict representing user attributes.
Returns:
A list of the keys of the features that are enabled for the user.
"""
enabled_features = []
if not self.is_valid:
self.logger.error(enums.Errors.INVALID_DATAFILE.format('get_enabled_features'))
return enabled_features
if not isinstance(user_id, string_types):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('user_id'))
return enabled_features
if not self._validate_user_inputs(attributes):
return enabled_features
for feature in self.config.feature_key_map.values():
if self.is_feature_enabled(feature.key, user_id, attributes):
enabled_features.append(feature.key)
return enabled_features | [
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optimizely/python-sdk | optimizely/optimizely.py | Optimizely.get_feature_variable_boolean | def get_feature_variable_boolean(self, feature_key, variable_key, user_id, attributes=None):
""" Returns value for a certain boolean variable attached to a feature flag.
Args:
feature_key: Key of the feature whose variable's value is being accessed.
variable_key: Key of the variable whose value is to be accessed.
user_id: ID for user.
attributes: Dict representing user attributes.
Returns:
Boolean value of the variable. None if:
- Feature key is invalid.
- Variable key is invalid.
- Mismatch with type of variable.
"""
variable_type = entities.Variable.Type.BOOLEAN
return self._get_feature_variable_for_type(feature_key, variable_key, variable_type, user_id, attributes) | python | def get_feature_variable_boolean(self, feature_key, variable_key, user_id, attributes=None):
""" Returns value for a certain boolean variable attached to a feature flag.
Args:
feature_key: Key of the feature whose variable's value is being accessed.
variable_key: Key of the variable whose value is to be accessed.
user_id: ID for user.
attributes: Dict representing user attributes.
Returns:
Boolean value of the variable. None if:
- Feature key is invalid.
- Variable key is invalid.
- Mismatch with type of variable.
"""
variable_type = entities.Variable.Type.BOOLEAN
return self._get_feature_variable_for_type(feature_key, variable_key, variable_type, user_id, attributes) | [
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optimizely/python-sdk | optimizely/optimizely.py | Optimizely.get_feature_variable_double | def get_feature_variable_double(self, feature_key, variable_key, user_id, attributes=None):
""" Returns value for a certain double variable attached to a feature flag.
Args:
feature_key: Key of the feature whose variable's value is being accessed.
variable_key: Key of the variable whose value is to be accessed.
user_id: ID for user.
attributes: Dict representing user attributes.
Returns:
Double value of the variable. None if:
- Feature key is invalid.
- Variable key is invalid.
- Mismatch with type of variable.
"""
variable_type = entities.Variable.Type.DOUBLE
return self._get_feature_variable_for_type(feature_key, variable_key, variable_type, user_id, attributes) | python | def get_feature_variable_double(self, feature_key, variable_key, user_id, attributes=None):
""" Returns value for a certain double variable attached to a feature flag.
Args:
feature_key: Key of the feature whose variable's value is being accessed.
variable_key: Key of the variable whose value is to be accessed.
user_id: ID for user.
attributes: Dict representing user attributes.
Returns:
Double value of the variable. None if:
- Feature key is invalid.
- Variable key is invalid.
- Mismatch with type of variable.
"""
variable_type = entities.Variable.Type.DOUBLE
return self._get_feature_variable_for_type(feature_key, variable_key, variable_type, user_id, attributes) | [
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optimizely/python-sdk | optimizely/optimizely.py | Optimizely.get_feature_variable_integer | def get_feature_variable_integer(self, feature_key, variable_key, user_id, attributes=None):
""" Returns value for a certain integer variable attached to a feature flag.
Args:
feature_key: Key of the feature whose variable's value is being accessed.
variable_key: Key of the variable whose value is to be accessed.
user_id: ID for user.
attributes: Dict representing user attributes.
Returns:
Integer value of the variable. None if:
- Feature key is invalid.
- Variable key is invalid.
- Mismatch with type of variable.
"""
variable_type = entities.Variable.Type.INTEGER
return self._get_feature_variable_for_type(feature_key, variable_key, variable_type, user_id, attributes) | python | def get_feature_variable_integer(self, feature_key, variable_key, user_id, attributes=None):
""" Returns value for a certain integer variable attached to a feature flag.
Args:
feature_key: Key of the feature whose variable's value is being accessed.
variable_key: Key of the variable whose value is to be accessed.
user_id: ID for user.
attributes: Dict representing user attributes.
Returns:
Integer value of the variable. None if:
- Feature key is invalid.
- Variable key is invalid.
- Mismatch with type of variable.
"""
variable_type = entities.Variable.Type.INTEGER
return self._get_feature_variable_for_type(feature_key, variable_key, variable_type, user_id, attributes) | [
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optimizely/python-sdk | optimizely/optimizely.py | Optimizely.get_feature_variable_string | def get_feature_variable_string(self, feature_key, variable_key, user_id, attributes=None):
""" Returns value for a certain string variable attached to a feature.
Args:
feature_key: Key of the feature whose variable's value is being accessed.
variable_key: Key of the variable whose value is to be accessed.
user_id: ID for user.
attributes: Dict representing user attributes.
Returns:
String value of the variable. None if:
- Feature key is invalid.
- Variable key is invalid.
- Mismatch with type of variable.
"""
variable_type = entities.Variable.Type.STRING
return self._get_feature_variable_for_type(feature_key, variable_key, variable_type, user_id, attributes) | python | def get_feature_variable_string(self, feature_key, variable_key, user_id, attributes=None):
""" Returns value for a certain string variable attached to a feature.
Args:
feature_key: Key of the feature whose variable's value is being accessed.
variable_key: Key of the variable whose value is to be accessed.
user_id: ID for user.
attributes: Dict representing user attributes.
Returns:
String value of the variable. None if:
- Feature key is invalid.
- Variable key is invalid.
- Mismatch with type of variable.
"""
variable_type = entities.Variable.Type.STRING
return self._get_feature_variable_for_type(feature_key, variable_key, variable_type, user_id, attributes) | [
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optimizely/python-sdk | optimizely/optimizely.py | Optimizely.set_forced_variation | def set_forced_variation(self, experiment_key, user_id, variation_key):
""" Force a user into a variation for a given experiment.
Args:
experiment_key: A string key identifying the experiment.
user_id: The user ID.
variation_key: A string variation key that specifies the variation which the user.
will be forced into. If null, then clear the existing experiment-to-variation mapping.
Returns:
A boolean value that indicates if the set completed successfully.
"""
if not self.is_valid:
self.logger.error(enums.Errors.INVALID_DATAFILE.format('set_forced_variation'))
return False
if not validator.is_non_empty_string(experiment_key):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('experiment_key'))
return False
if not isinstance(user_id, string_types):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('user_id'))
return False
return self.config.set_forced_variation(experiment_key, user_id, variation_key) | python | def set_forced_variation(self, experiment_key, user_id, variation_key):
""" Force a user into a variation for a given experiment.
Args:
experiment_key: A string key identifying the experiment.
user_id: The user ID.
variation_key: A string variation key that specifies the variation which the user.
will be forced into. If null, then clear the existing experiment-to-variation mapping.
Returns:
A boolean value that indicates if the set completed successfully.
"""
if not self.is_valid:
self.logger.error(enums.Errors.INVALID_DATAFILE.format('set_forced_variation'))
return False
if not validator.is_non_empty_string(experiment_key):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('experiment_key'))
return False
if not isinstance(user_id, string_types):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('user_id'))
return False
return self.config.set_forced_variation(experiment_key, user_id, variation_key) | [
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optimizely/python-sdk | optimizely/optimizely.py | Optimizely.get_forced_variation | def get_forced_variation(self, experiment_key, user_id):
""" Gets the forced variation for a given user and experiment.
Args:
experiment_key: A string key identifying the experiment.
user_id: The user ID.
Returns:
The forced variation key. None if no forced variation key.
"""
if not self.is_valid:
self.logger.error(enums.Errors.INVALID_DATAFILE.format('get_forced_variation'))
return None
if not validator.is_non_empty_string(experiment_key):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('experiment_key'))
return None
if not isinstance(user_id, string_types):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('user_id'))
return None
forced_variation = self.config.get_forced_variation(experiment_key, user_id)
return forced_variation.key if forced_variation else None | python | def get_forced_variation(self, experiment_key, user_id):
""" Gets the forced variation for a given user and experiment.
Args:
experiment_key: A string key identifying the experiment.
user_id: The user ID.
Returns:
The forced variation key. None if no forced variation key.
"""
if not self.is_valid:
self.logger.error(enums.Errors.INVALID_DATAFILE.format('get_forced_variation'))
return None
if not validator.is_non_empty_string(experiment_key):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('experiment_key'))
return None
if not isinstance(user_id, string_types):
self.logger.error(enums.Errors.INVALID_INPUT_ERROR.format('user_id'))
return None
forced_variation = self.config.get_forced_variation(experiment_key, user_id)
return forced_variation.key if forced_variation else None | [
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optimizely/python-sdk | optimizely/helpers/audience.py | is_user_in_experiment | def is_user_in_experiment(config, experiment, attributes, logger):
""" Determine for given experiment if user satisfies the audiences for the experiment.
Args:
config: project_config.ProjectConfig object representing the project.
experiment: Object representing the experiment.
attributes: Dict representing user attributes which will be used in determining
if the audience conditions are met. If not provided, default to an empty dict.
logger: Provides a logger to send log messages to.
Returns:
Boolean representing if user satisfies audience conditions for any of the audiences or not.
"""
audience_conditions = experiment.getAudienceConditionsOrIds()
logger.debug(audience_logs.EVALUATING_AUDIENCES_COMBINED.format(
experiment.key,
json.dumps(audience_conditions)
))
# Return True in case there are no audiences
if audience_conditions is None or audience_conditions == []:
logger.info(audience_logs.AUDIENCE_EVALUATION_RESULT_COMBINED.format(
experiment.key,
'TRUE'
))
return True
if attributes is None:
attributes = {}
def evaluate_custom_attr(audienceId, index):
audience = config.get_audience(audienceId)
custom_attr_condition_evaluator = condition_helper.CustomAttributeConditionEvaluator(
audience.conditionList, attributes, logger)
return custom_attr_condition_evaluator.evaluate(index)
def evaluate_audience(audienceId):
audience = config.get_audience(audienceId)
if audience is None:
return None
logger.debug(audience_logs.EVALUATING_AUDIENCE.format(audienceId, audience.conditions))
result = condition_tree_evaluator.evaluate(
audience.conditionStructure,
lambda index: evaluate_custom_attr(audienceId, index)
)
result_str = str(result).upper() if result is not None else 'UNKNOWN'
logger.info(audience_logs.AUDIENCE_EVALUATION_RESULT.format(audienceId, result_str))
return result
eval_result = condition_tree_evaluator.evaluate(
audience_conditions,
evaluate_audience
)
eval_result = eval_result or False
logger.info(audience_logs.AUDIENCE_EVALUATION_RESULT_COMBINED.format(
experiment.key,
str(eval_result).upper()
))
return eval_result | python | def is_user_in_experiment(config, experiment, attributes, logger):
""" Determine for given experiment if user satisfies the audiences for the experiment.
Args:
config: project_config.ProjectConfig object representing the project.
experiment: Object representing the experiment.
attributes: Dict representing user attributes which will be used in determining
if the audience conditions are met. If not provided, default to an empty dict.
logger: Provides a logger to send log messages to.
Returns:
Boolean representing if user satisfies audience conditions for any of the audiences or not.
"""
audience_conditions = experiment.getAudienceConditionsOrIds()
logger.debug(audience_logs.EVALUATING_AUDIENCES_COMBINED.format(
experiment.key,
json.dumps(audience_conditions)
))
# Return True in case there are no audiences
if audience_conditions is None or audience_conditions == []:
logger.info(audience_logs.AUDIENCE_EVALUATION_RESULT_COMBINED.format(
experiment.key,
'TRUE'
))
return True
if attributes is None:
attributes = {}
def evaluate_custom_attr(audienceId, index):
audience = config.get_audience(audienceId)
custom_attr_condition_evaluator = condition_helper.CustomAttributeConditionEvaluator(
audience.conditionList, attributes, logger)
return custom_attr_condition_evaluator.evaluate(index)
def evaluate_audience(audienceId):
audience = config.get_audience(audienceId)
if audience is None:
return None
logger.debug(audience_logs.EVALUATING_AUDIENCE.format(audienceId, audience.conditions))
result = condition_tree_evaluator.evaluate(
audience.conditionStructure,
lambda index: evaluate_custom_attr(audienceId, index)
)
result_str = str(result).upper() if result is not None else 'UNKNOWN'
logger.info(audience_logs.AUDIENCE_EVALUATION_RESULT.format(audienceId, result_str))
return result
eval_result = condition_tree_evaluator.evaluate(
audience_conditions,
evaluate_audience
)
eval_result = eval_result or False
logger.info(audience_logs.AUDIENCE_EVALUATION_RESULT_COMBINED.format(
experiment.key,
str(eval_result).upper()
))
return eval_result | [
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optimizely/python-sdk | optimizely/event_builder.py | BaseEventBuilder._get_common_params | def _get_common_params(self, user_id, attributes):
""" Get params which are used same in both conversion and impression events.
Args:
user_id: ID for user.
attributes: Dict representing user attributes and values which need to be recorded.
Returns:
Dict consisting of parameters common to both impression and conversion events.
"""
commonParams = {}
commonParams[self.EventParams.PROJECT_ID] = self._get_project_id()
commonParams[self.EventParams.ACCOUNT_ID] = self._get_account_id()
visitor = {}
visitor[self.EventParams.END_USER_ID] = user_id
visitor[self.EventParams.SNAPSHOTS] = []
commonParams[self.EventParams.USERS] = []
commonParams[self.EventParams.USERS].append(visitor)
commonParams[self.EventParams.USERS][0][self.EventParams.ATTRIBUTES] = self._get_attributes(attributes)
commonParams[self.EventParams.SOURCE_SDK_TYPE] = 'python-sdk'
commonParams[self.EventParams.ENRICH_DECISIONS] = True
commonParams[self.EventParams.SOURCE_SDK_VERSION] = version.__version__
commonParams[self.EventParams.ANONYMIZE_IP] = self._get_anonymize_ip()
commonParams[self.EventParams.REVISION] = self._get_revision()
return commonParams | python | def _get_common_params(self, user_id, attributes):
""" Get params which are used same in both conversion and impression events.
Args:
user_id: ID for user.
attributes: Dict representing user attributes and values which need to be recorded.
Returns:
Dict consisting of parameters common to both impression and conversion events.
"""
commonParams = {}
commonParams[self.EventParams.PROJECT_ID] = self._get_project_id()
commonParams[self.EventParams.ACCOUNT_ID] = self._get_account_id()
visitor = {}
visitor[self.EventParams.END_USER_ID] = user_id
visitor[self.EventParams.SNAPSHOTS] = []
commonParams[self.EventParams.USERS] = []
commonParams[self.EventParams.USERS].append(visitor)
commonParams[self.EventParams.USERS][0][self.EventParams.ATTRIBUTES] = self._get_attributes(attributes)
commonParams[self.EventParams.SOURCE_SDK_TYPE] = 'python-sdk'
commonParams[self.EventParams.ENRICH_DECISIONS] = True
commonParams[self.EventParams.SOURCE_SDK_VERSION] = version.__version__
commonParams[self.EventParams.ANONYMIZE_IP] = self._get_anonymize_ip()
commonParams[self.EventParams.REVISION] = self._get_revision()
return commonParams | [
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attributes: Dict representing user attributes and values which need to be recorded.
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optimizely/python-sdk | optimizely/event_builder.py | EventBuilder._get_required_params_for_impression | def _get_required_params_for_impression(self, experiment, variation_id):
""" Get parameters that are required for the impression event to register.
Args:
experiment: Experiment for which impression needs to be recorded.
variation_id: ID for variation which would be presented to user.
Returns:
Dict consisting of decisions and events info for impression event.
"""
snapshot = {}
snapshot[self.EventParams.DECISIONS] = [{
self.EventParams.EXPERIMENT_ID: experiment.id,
self.EventParams.VARIATION_ID: variation_id,
self.EventParams.CAMPAIGN_ID: experiment.layerId
}]
snapshot[self.EventParams.EVENTS] = [{
self.EventParams.EVENT_ID: experiment.layerId,
self.EventParams.TIME: self._get_time(),
self.EventParams.KEY: 'campaign_activated',
self.EventParams.UUID: str(uuid.uuid4())
}]
return snapshot | python | def _get_required_params_for_impression(self, experiment, variation_id):
""" Get parameters that are required for the impression event to register.
Args:
experiment: Experiment for which impression needs to be recorded.
variation_id: ID for variation which would be presented to user.
Returns:
Dict consisting of decisions and events info for impression event.
"""
snapshot = {}
snapshot[self.EventParams.DECISIONS] = [{
self.EventParams.EXPERIMENT_ID: experiment.id,
self.EventParams.VARIATION_ID: variation_id,
self.EventParams.CAMPAIGN_ID: experiment.layerId
}]
snapshot[self.EventParams.EVENTS] = [{
self.EventParams.EVENT_ID: experiment.layerId,
self.EventParams.TIME: self._get_time(),
self.EventParams.KEY: 'campaign_activated',
self.EventParams.UUID: str(uuid.uuid4())
}]
return snapshot | [
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optimizely/python-sdk | optimizely/event_builder.py | EventBuilder._get_required_params_for_conversion | def _get_required_params_for_conversion(self, event_key, event_tags):
""" Get parameters that are required for the conversion event to register.
Args:
event_key: Key representing the event which needs to be recorded.
event_tags: Dict representing metadata associated with the event.
Returns:
Dict consisting of the decisions and events info for conversion event.
"""
snapshot = {}
event_dict = {
self.EventParams.EVENT_ID: self.config.get_event(event_key).id,
self.EventParams.TIME: self._get_time(),
self.EventParams.KEY: event_key,
self.EventParams.UUID: str(uuid.uuid4())
}
if event_tags:
revenue_value = event_tag_utils.get_revenue_value(event_tags)
if revenue_value is not None:
event_dict[event_tag_utils.REVENUE_METRIC_TYPE] = revenue_value
numeric_value = event_tag_utils.get_numeric_value(event_tags, self.config.logger)
if numeric_value is not None:
event_dict[event_tag_utils.NUMERIC_METRIC_TYPE] = numeric_value
if len(event_tags) > 0:
event_dict[self.EventParams.TAGS] = event_tags
snapshot[self.EventParams.EVENTS] = [event_dict]
return snapshot | python | def _get_required_params_for_conversion(self, event_key, event_tags):
""" Get parameters that are required for the conversion event to register.
Args:
event_key: Key representing the event which needs to be recorded.
event_tags: Dict representing metadata associated with the event.
Returns:
Dict consisting of the decisions and events info for conversion event.
"""
snapshot = {}
event_dict = {
self.EventParams.EVENT_ID: self.config.get_event(event_key).id,
self.EventParams.TIME: self._get_time(),
self.EventParams.KEY: event_key,
self.EventParams.UUID: str(uuid.uuid4())
}
if event_tags:
revenue_value = event_tag_utils.get_revenue_value(event_tags)
if revenue_value is not None:
event_dict[event_tag_utils.REVENUE_METRIC_TYPE] = revenue_value
numeric_value = event_tag_utils.get_numeric_value(event_tags, self.config.logger)
if numeric_value is not None:
event_dict[event_tag_utils.NUMERIC_METRIC_TYPE] = numeric_value
if len(event_tags) > 0:
event_dict[self.EventParams.TAGS] = event_tags
snapshot[self.EventParams.EVENTS] = [event_dict]
return snapshot | [
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event_tags: Dict representing metadata associated with the event.
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optimizely/python-sdk | optimizely/event_builder.py | EventBuilder.create_impression_event | def create_impression_event(self, experiment, variation_id, user_id, attributes):
""" Create impression Event to be sent to the logging endpoint.
Args:
experiment: Experiment for which impression needs to be recorded.
variation_id: ID for variation which would be presented to user.
user_id: ID for user.
attributes: Dict representing user attributes and values which need to be recorded.
Returns:
Event object encapsulating the impression event.
"""
params = self._get_common_params(user_id, attributes)
impression_params = self._get_required_params_for_impression(experiment, variation_id)
params[self.EventParams.USERS][0][self.EventParams.SNAPSHOTS].append(impression_params)
return Event(self.EVENTS_URL,
params,
http_verb=self.HTTP_VERB,
headers=self.HTTP_HEADERS) | python | def create_impression_event(self, experiment, variation_id, user_id, attributes):
""" Create impression Event to be sent to the logging endpoint.
Args:
experiment: Experiment for which impression needs to be recorded.
variation_id: ID for variation which would be presented to user.
user_id: ID for user.
attributes: Dict representing user attributes and values which need to be recorded.
Returns:
Event object encapsulating the impression event.
"""
params = self._get_common_params(user_id, attributes)
impression_params = self._get_required_params_for_impression(experiment, variation_id)
params[self.EventParams.USERS][0][self.EventParams.SNAPSHOTS].append(impression_params)
return Event(self.EVENTS_URL,
params,
http_verb=self.HTTP_VERB,
headers=self.HTTP_HEADERS) | [
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experiment: Experiment for which impression needs to be recorded.
variation_id: ID for variation which would be presented to user.
user_id: ID for user.
attributes: Dict representing user attributes and values which need to be recorded.
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optimizely/python-sdk | optimizely/event_builder.py | EventBuilder.create_conversion_event | def create_conversion_event(self, event_key, user_id, attributes, event_tags):
""" Create conversion Event to be sent to the logging endpoint.
Args:
event_key: Key representing the event which needs to be recorded.
user_id: ID for user.
attributes: Dict representing user attributes and values.
event_tags: Dict representing metadata associated with the event.
Returns:
Event object encapsulating the conversion event.
"""
params = self._get_common_params(user_id, attributes)
conversion_params = self._get_required_params_for_conversion(event_key, event_tags)
params[self.EventParams.USERS][0][self.EventParams.SNAPSHOTS].append(conversion_params)
return Event(self.EVENTS_URL,
params,
http_verb=self.HTTP_VERB,
headers=self.HTTP_HEADERS) | python | def create_conversion_event(self, event_key, user_id, attributes, event_tags):
""" Create conversion Event to be sent to the logging endpoint.
Args:
event_key: Key representing the event which needs to be recorded.
user_id: ID for user.
attributes: Dict representing user attributes and values.
event_tags: Dict representing metadata associated with the event.
Returns:
Event object encapsulating the conversion event.
"""
params = self._get_common_params(user_id, attributes)
conversion_params = self._get_required_params_for_conversion(event_key, event_tags)
params[self.EventParams.USERS][0][self.EventParams.SNAPSHOTS].append(conversion_params)
return Event(self.EVENTS_URL,
params,
http_verb=self.HTTP_VERB,
headers=self.HTTP_HEADERS) | [
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optimizely/python-sdk | optimizely/helpers/condition.py | _audience_condition_deserializer | def _audience_condition_deserializer(obj_dict):
""" Deserializer defining how dict objects need to be decoded for audience conditions.
Args:
obj_dict: Dict representing one audience condition.
Returns:
List consisting of condition key with corresponding value, type and match.
"""
return [
obj_dict.get('name'),
obj_dict.get('value'),
obj_dict.get('type'),
obj_dict.get('match')
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""" Deserializer defining how dict objects need to be decoded for audience conditions.
Args:
obj_dict: Dict representing one audience condition.
Returns:
List consisting of condition key with corresponding value, type and match.
"""
return [
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optimizely/python-sdk | optimizely/helpers/condition.py | CustomAttributeConditionEvaluator._get_condition_json | def _get_condition_json(self, index):
""" Method to generate json for logging audience condition.
Args:
index: Index of the condition.
Returns:
String: Audience condition JSON.
"""
condition = self.condition_data[index]
condition_log = {
'name': condition[0],
'value': condition[1],
'type': condition[2],
'match': condition[3]
}
return json.dumps(condition_log) | python | def _get_condition_json(self, index):
""" Method to generate json for logging audience condition.
Args:
index: Index of the condition.
Returns:
String: Audience condition JSON.
"""
condition = self.condition_data[index]
condition_log = {
'name': condition[0],
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'type': condition[2],
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}
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optimizely/python-sdk | optimizely/helpers/condition.py | CustomAttributeConditionEvaluator.is_value_type_valid_for_exact_conditions | def is_value_type_valid_for_exact_conditions(self, value):
""" Method to validate if the value is valid for exact match type evaluation.
Args:
value: Value to validate.
Returns:
Boolean: True if value is a string, boolean, or number. Otherwise False.
"""
# No need to check for bool since bool is a subclass of int
if isinstance(value, string_types) or isinstance(value, (numbers.Integral, float)):
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return False | python | def is_value_type_valid_for_exact_conditions(self, value):
""" Method to validate if the value is valid for exact match type evaluation.
Args:
value: Value to validate.
Returns:
Boolean: True if value is a string, boolean, or number. Otherwise False.
"""
# No need to check for bool since bool is a subclass of int
if isinstance(value, string_types) or isinstance(value, (numbers.Integral, float)):
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optimizely/python-sdk | optimizely/helpers/condition.py | CustomAttributeConditionEvaluator.exists_evaluator | def exists_evaluator(self, index):
""" Evaluate the given exists match condition for the user attributes.
Args:
index: Index of the condition to be evaluated.
Returns:
Boolean: True if the user attributes have a non-null value for the given condition,
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"""
attr_name = self.condition_data[index][0]
return self.attributes.get(attr_name) is not None | python | def exists_evaluator(self, index):
""" Evaluate the given exists match condition for the user attributes.
Args:
index: Index of the condition to be evaluated.
Returns:
Boolean: True if the user attributes have a non-null value for the given condition,
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optimizely/python-sdk | optimizely/helpers/condition.py | CustomAttributeConditionEvaluator.greater_than_evaluator | def greater_than_evaluator(self, index):
""" Evaluate the given greater than match condition for the user attributes.
Args:
index: Index of the condition to be evaluated.
Returns:
Boolean:
- True if the user attribute value is greater than the condition value.
- False if the user attribute value is less than or equal to the condition value.
None: if the condition value isn't finite or the user attribute value isn't finite.
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condition_name = self.condition_data[index][0]
condition_value = self.condition_data[index][1]
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return None
if not validator.is_finite_number(user_value):
self.logger.warning(audience_logs.INFINITE_ATTRIBUTE_VALUE.format(
self._get_condition_json(index),
condition_name
))
return None
return user_value > condition_value | python | def greater_than_evaluator(self, index):
""" Evaluate the given greater than match condition for the user attributes.
Args:
index: Index of the condition to be evaluated.
Returns:
Boolean:
- True if the user attribute value is greater than the condition value.
- False if the user attribute value is less than or equal to the condition value.
None: if the condition value isn't finite or the user attribute value isn't finite.
"""
condition_name = self.condition_data[index][0]
condition_value = self.condition_data[index][1]
user_value = self.attributes.get(condition_name)
if not validator.is_finite_number(condition_value):
self.logger.warning(audience_logs.UNKNOWN_CONDITION_VALUE.format(
self._get_condition_json(index)
))
return None
if not self.is_value_a_number(user_value):
self.logger.warning(audience_logs.UNEXPECTED_TYPE.format(
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type(user_value),
condition_name
))
return None
if not validator.is_finite_number(user_value):
self.logger.warning(audience_logs.INFINITE_ATTRIBUTE_VALUE.format(
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condition_name
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return None
return user_value > condition_value | [
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optimizely/python-sdk | optimizely/helpers/condition.py | CustomAttributeConditionEvaluator.substring_evaluator | def substring_evaluator(self, index):
""" Evaluate the given substring match condition for the given user attributes.
Args:
index: Index of the condition to be evaluated.
Returns:
Boolean:
- True if the condition value is a substring of the user attribute value.
- False if the condition value is not a substring of the user attribute value.
None: if the condition value isn't a string or the user attribute value isn't a string.
"""
condition_name = self.condition_data[index][0]
condition_value = self.condition_data[index][1]
user_value = self.attributes.get(condition_name)
if not isinstance(condition_value, string_types):
self.logger.warning(audience_logs.UNKNOWN_CONDITION_VALUE.format(
self._get_condition_json(index),
))
return None
if not isinstance(user_value, string_types):
self.logger.warning(audience_logs.UNEXPECTED_TYPE.format(
self._get_condition_json(index),
type(user_value),
condition_name
))
return None
return condition_value in user_value | python | def substring_evaluator(self, index):
""" Evaluate the given substring match condition for the given user attributes.
Args:
index: Index of the condition to be evaluated.
Returns:
Boolean:
- True if the condition value is a substring of the user attribute value.
- False if the condition value is not a substring of the user attribute value.
None: if the condition value isn't a string or the user attribute value isn't a string.
"""
condition_name = self.condition_data[index][0]
condition_value = self.condition_data[index][1]
user_value = self.attributes.get(condition_name)
if not isinstance(condition_value, string_types):
self.logger.warning(audience_logs.UNKNOWN_CONDITION_VALUE.format(
self._get_condition_json(index),
))
return None
if not isinstance(user_value, string_types):
self.logger.warning(audience_logs.UNEXPECTED_TYPE.format(
self._get_condition_json(index),
type(user_value),
condition_name
))
return None
return condition_value in user_value | [
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optimizely/python-sdk | optimizely/helpers/condition.py | CustomAttributeConditionEvaluator.evaluate | def evaluate(self, index):
""" Given a custom attribute audience condition and user attributes, evaluate the
condition against the attributes.
Args:
index: Index of the condition to be evaluated.
Returns:
Boolean:
- True if the user attributes match the given condition.
- False if the user attributes don't match the given condition.
None: if the user attributes and condition can't be evaluated.
"""
if self.condition_data[index][2] != self.CUSTOM_ATTRIBUTE_CONDITION_TYPE:
self.logger.warning(audience_logs.UNKNOWN_CONDITION_TYPE.format(self._get_condition_json(index)))
return None
condition_match = self.condition_data[index][3]
if condition_match is None:
condition_match = ConditionMatchTypes.EXACT
if condition_match not in self.EVALUATORS_BY_MATCH_TYPE:
self.logger.warning(audience_logs.UNKNOWN_MATCH_TYPE.format(self._get_condition_json(index)))
return None
if condition_match != ConditionMatchTypes.EXISTS:
attribute_key = self.condition_data[index][0]
if attribute_key not in self.attributes:
self.logger.debug(audience_logs.MISSING_ATTRIBUTE_VALUE.format(self._get_condition_json(index), attribute_key))
return None
if self.attributes.get(attribute_key) is None:
self.logger.debug(audience_logs.NULL_ATTRIBUTE_VALUE.format(self._get_condition_json(index), attribute_key))
return None
return self.EVALUATORS_BY_MATCH_TYPE[condition_match](self, index) | python | def evaluate(self, index):
""" Given a custom attribute audience condition and user attributes, evaluate the
condition against the attributes.
Args:
index: Index of the condition to be evaluated.
Returns:
Boolean:
- True if the user attributes match the given condition.
- False if the user attributes don't match the given condition.
None: if the user attributes and condition can't be evaluated.
"""
if self.condition_data[index][2] != self.CUSTOM_ATTRIBUTE_CONDITION_TYPE:
self.logger.warning(audience_logs.UNKNOWN_CONDITION_TYPE.format(self._get_condition_json(index)))
return None
condition_match = self.condition_data[index][3]
if condition_match is None:
condition_match = ConditionMatchTypes.EXACT
if condition_match not in self.EVALUATORS_BY_MATCH_TYPE:
self.logger.warning(audience_logs.UNKNOWN_MATCH_TYPE.format(self._get_condition_json(index)))
return None
if condition_match != ConditionMatchTypes.EXISTS:
attribute_key = self.condition_data[index][0]
if attribute_key not in self.attributes:
self.logger.debug(audience_logs.MISSING_ATTRIBUTE_VALUE.format(self._get_condition_json(index), attribute_key))
return None
if self.attributes.get(attribute_key) is None:
self.logger.debug(audience_logs.NULL_ATTRIBUTE_VALUE.format(self._get_condition_json(index), attribute_key))
return None
return self.EVALUATORS_BY_MATCH_TYPE[condition_match](self, index) | [
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- False if the user attributes don't match the given condition.
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optimizely/python-sdk | optimizely/helpers/condition.py | ConditionDecoder.object_hook | def object_hook(self, object_dict):
""" Hook which when passed into a json.JSONDecoder will replace each dict
in a json string with its index and convert the dict to an object as defined
by the passed in condition_decoder. The newly created condition object is
appended to the conditions_list.
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object_dict: Dict representing an object.
Returns:
An index which will be used as the placeholder in the condition_structure
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instance = self.decoder(object_dict)
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self.index += 1
return self.index | python | def object_hook(self, object_dict):
""" Hook which when passed into a json.JSONDecoder will replace each dict
in a json string with its index and convert the dict to an object as defined
by the passed in condition_decoder. The newly created condition object is
appended to the conditions_list.
Args:
object_dict: Dict representing an object.
Returns:
An index which will be used as the placeholder in the condition_structure
"""
instance = self.decoder(object_dict)
self.condition_list.append(instance)
self.index += 1
return self.index | [
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optimizely/python-sdk | optimizely/decision_service.py | DecisionService._get_bucketing_id | def _get_bucketing_id(self, user_id, attributes):
""" Helper method to determine bucketing ID for the user.
Args:
user_id: ID for user.
attributes: Dict representing user attributes. May consist of bucketing ID to be used.
Returns:
String representing bucketing ID if it is a String type in attributes else return user ID.
"""
attributes = attributes or {}
bucketing_id = attributes.get(enums.ControlAttributes.BUCKETING_ID)
if bucketing_id is not None:
if isinstance(bucketing_id, string_types):
return bucketing_id
self.logger.warning('Bucketing ID attribute is not a string. Defaulted to user_id.')
return user_id | python | def _get_bucketing_id(self, user_id, attributes):
""" Helper method to determine bucketing ID for the user.
Args:
user_id: ID for user.
attributes: Dict representing user attributes. May consist of bucketing ID to be used.
Returns:
String representing bucketing ID if it is a String type in attributes else return user ID.
"""
attributes = attributes or {}
bucketing_id = attributes.get(enums.ControlAttributes.BUCKETING_ID)
if bucketing_id is not None:
if isinstance(bucketing_id, string_types):
return bucketing_id
self.logger.warning('Bucketing ID attribute is not a string. Defaulted to user_id.')
return user_id | [
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optimizely/python-sdk | optimizely/decision_service.py | DecisionService.get_forced_variation | def get_forced_variation(self, experiment, user_id):
""" Determine if a user is forced into a variation for the given experiment and return that variation.
Args:
experiment: Object representing the experiment for which user is to be bucketed.
user_id: ID for the user.
Returns:
Variation in which the user with ID user_id is forced into. None if no variation.
"""
forced_variations = experiment.forcedVariations
if forced_variations and user_id in forced_variations:
variation_key = forced_variations.get(user_id)
variation = self.config.get_variation_from_key(experiment.key, variation_key)
if variation:
self.logger.info('User "%s" is forced in variation "%s".' % (user_id, variation_key))
return variation
return None | python | def get_forced_variation(self, experiment, user_id):
""" Determine if a user is forced into a variation for the given experiment and return that variation.
Args:
experiment: Object representing the experiment for which user is to be bucketed.
user_id: ID for the user.
Returns:
Variation in which the user with ID user_id is forced into. None if no variation.
"""
forced_variations = experiment.forcedVariations
if forced_variations and user_id in forced_variations:
variation_key = forced_variations.get(user_id)
variation = self.config.get_variation_from_key(experiment.key, variation_key)
if variation:
self.logger.info('User "%s" is forced in variation "%s".' % (user_id, variation_key))
return variation
return None | [
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optimizely/python-sdk | optimizely/decision_service.py | DecisionService.get_stored_variation | def get_stored_variation(self, experiment, user_profile):
""" Determine if the user has a stored variation available for the given experiment and return that.
Args:
experiment: Object representing the experiment for which user is to be bucketed.
user_profile: UserProfile object representing the user's profile.
Returns:
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return variation
return None | python | def get_stored_variation(self, experiment, user_profile):
""" Determine if the user has a stored variation available for the given experiment and return that.
Args:
experiment: Object representing the experiment for which user is to be bucketed.
user_profile: UserProfile object representing the user's profile.
Returns:
Variation if available. None otherwise.
"""
user_id = user_profile.user_id
variation_id = user_profile.get_variation_for_experiment(experiment.id)
if variation_id:
variation = self.config.get_variation_from_id(experiment.key, variation_id)
if variation:
self.logger.info('Found a stored decision. User "%s" is in variation "%s" of experiment "%s".' % (
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return None | [
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optimizely/python-sdk | optimizely/decision_service.py | DecisionService.get_variation | def get_variation(self, experiment, user_id, attributes, ignore_user_profile=False):
""" Top-level function to help determine variation user should be put in.
First, check if experiment is running.
Second, check if user is forced in a variation.
Third, check if there is a stored decision for the user and return the corresponding variation.
Fourth, figure out if user is in the experiment by evaluating audience conditions if any.
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experiment: Experiment for which user variation needs to be determined.
user_id: ID for user.
attributes: Dict representing user attributes.
ignore_user_profile: True to ignore the user profile lookup. Defaults to False.
Returns:
Variation user should see. None if user is not in experiment or experiment is not running.
"""
# Check if experiment is running
if not experiment_helper.is_experiment_running(experiment):
self.logger.info('Experiment "%s" is not running.' % experiment.key)
return None
# Check if the user is forced into a variation
variation = self.config.get_forced_variation(experiment.key, user_id)
if variation:
return variation
# Check to see if user is white-listed for a certain variation
variation = self.get_forced_variation(experiment, user_id)
if variation:
return variation
# Check to see if user has a decision available for the given experiment
user_profile = UserProfile(user_id)
if not ignore_user_profile and self.user_profile_service:
try:
retrieved_profile = self.user_profile_service.lookup(user_id)
except:
self.logger.exception('Unable to retrieve user profile for user "%s" as lookup failed.' % user_id)
retrieved_profile = None
if validator.is_user_profile_valid(retrieved_profile):
user_profile = UserProfile(**retrieved_profile)
variation = self.get_stored_variation(experiment, user_profile)
if variation:
return variation
else:
self.logger.warning('User profile has invalid format.')
# Bucket user and store the new decision
if not audience_helper.is_user_in_experiment(self.config, experiment, attributes, self.logger):
self.logger.info('User "%s" does not meet conditions to be in experiment "%s".' % (
user_id,
experiment.key
))
return None
# Determine bucketing ID to be used
bucketing_id = self._get_bucketing_id(user_id, attributes)
variation = self.bucketer.bucket(experiment, user_id, bucketing_id)
if variation:
# Store this new decision and return the variation for the user
if not ignore_user_profile and self.user_profile_service:
try:
user_profile.save_variation_for_experiment(experiment.id, variation.id)
self.user_profile_service.save(user_profile.__dict__)
except:
self.logger.exception('Unable to save user profile for user "%s".' % user_id)
return variation
return None | python | def get_variation(self, experiment, user_id, attributes, ignore_user_profile=False):
""" Top-level function to help determine variation user should be put in.
First, check if experiment is running.
Second, check if user is forced in a variation.
Third, check if there is a stored decision for the user and return the corresponding variation.
Fourth, figure out if user is in the experiment by evaluating audience conditions if any.
Fifth, bucket the user and return the variation.
Args:
experiment: Experiment for which user variation needs to be determined.
user_id: ID for user.
attributes: Dict representing user attributes.
ignore_user_profile: True to ignore the user profile lookup. Defaults to False.
Returns:
Variation user should see. None if user is not in experiment or experiment is not running.
"""
# Check if experiment is running
if not experiment_helper.is_experiment_running(experiment):
self.logger.info('Experiment "%s" is not running.' % experiment.key)
return None
# Check if the user is forced into a variation
variation = self.config.get_forced_variation(experiment.key, user_id)
if variation:
return variation
# Check to see if user is white-listed for a certain variation
variation = self.get_forced_variation(experiment, user_id)
if variation:
return variation
# Check to see if user has a decision available for the given experiment
user_profile = UserProfile(user_id)
if not ignore_user_profile and self.user_profile_service:
try:
retrieved_profile = self.user_profile_service.lookup(user_id)
except:
self.logger.exception('Unable to retrieve user profile for user "%s" as lookup failed.' % user_id)
retrieved_profile = None
if validator.is_user_profile_valid(retrieved_profile):
user_profile = UserProfile(**retrieved_profile)
variation = self.get_stored_variation(experiment, user_profile)
if variation:
return variation
else:
self.logger.warning('User profile has invalid format.')
# Bucket user and store the new decision
if not audience_helper.is_user_in_experiment(self.config, experiment, attributes, self.logger):
self.logger.info('User "%s" does not meet conditions to be in experiment "%s".' % (
user_id,
experiment.key
))
return None
# Determine bucketing ID to be used
bucketing_id = self._get_bucketing_id(user_id, attributes)
variation = self.bucketer.bucket(experiment, user_id, bucketing_id)
if variation:
# Store this new decision and return the variation for the user
if not ignore_user_profile and self.user_profile_service:
try:
user_profile.save_variation_for_experiment(experiment.id, variation.id)
self.user_profile_service.save(user_profile.__dict__)
except:
self.logger.exception('Unable to save user profile for user "%s".' % user_id)
return variation
return None | [
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First, check if experiment is running.
Second, check if user is forced in a variation.
Third, check if there is a stored decision for the user and return the corresponding variation.
Fourth, figure out if user is in the experiment by evaluating audience conditions if any.
Fifth, bucket the user and return the variation.
Args:
experiment: Experiment for which user variation needs to be determined.
user_id: ID for user.
attributes: Dict representing user attributes.
ignore_user_profile: True to ignore the user profile lookup. Defaults to False.
Returns:
Variation user should see. None if user is not in experiment or experiment is not running. | [
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optimizely/python-sdk | optimizely/decision_service.py | DecisionService.get_experiment_in_group | def get_experiment_in_group(self, group, bucketing_id):
""" Determine which experiment in the group the user is bucketed into.
Args:
group: The group to bucket the user into.
bucketing_id: ID to be used for bucketing the user.
Returns:
Experiment if the user is bucketed into an experiment in the specified group. None otherwise.
"""
experiment_id = self.bucketer.find_bucket(bucketing_id, group.id, group.trafficAllocation)
if experiment_id:
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if experiment:
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experiment.key,
group.id
))
return experiment
self.logger.info('User with bucketing ID "%s" is not in any experiments of group %s.' % (
bucketing_id,
group.id
))
return None | python | def get_experiment_in_group(self, group, bucketing_id):
""" Determine which experiment in the group the user is bucketed into.
Args:
group: The group to bucket the user into.
bucketing_id: ID to be used for bucketing the user.
Returns:
Experiment if the user is bucketed into an experiment in the specified group. None otherwise.
"""
experiment_id = self.bucketer.find_bucket(bucketing_id, group.id, group.trafficAllocation)
if experiment_id:
experiment = self.config.get_experiment_from_id(experiment_id)
if experiment:
self.logger.info('User with bucketing ID "%s" is in experiment %s of group %s.' % (
bucketing_id,
experiment.key,
group.id
))
return experiment
self.logger.info('User with bucketing ID "%s" is not in any experiments of group %s.' % (
bucketing_id,
group.id
))
return None | [
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optimizely/python-sdk | optimizely/notification_center.py | NotificationCenter.add_notification_listener | def add_notification_listener(self, notification_type, notification_callback):
""" Add a notification callback to the notification center.
Args:
notification_type: A string representing the notification type from .helpers.enums.NotificationTypes
notification_callback: closure of function to call when event is triggered.
Returns:
Integer notification id used to remove the notification or -1 if the notification has already been added.
"""
if notification_type not in self.notifications:
self.notifications[notification_type] = [(self.notification_id, notification_callback)]
else:
if reduce(lambda a, b: a + 1,
filter(lambda tup: tup[1] == notification_callback, self.notifications[notification_type]),
0) > 0:
return -1
self.notifications[notification_type].append((self.notification_id, notification_callback))
ret_val = self.notification_id
self.notification_id += 1
return ret_val | python | def add_notification_listener(self, notification_type, notification_callback):
""" Add a notification callback to the notification center.
Args:
notification_type: A string representing the notification type from .helpers.enums.NotificationTypes
notification_callback: closure of function to call when event is triggered.
Returns:
Integer notification id used to remove the notification or -1 if the notification has already been added.
"""
if notification_type not in self.notifications:
self.notifications[notification_type] = [(self.notification_id, notification_callback)]
else:
if reduce(lambda a, b: a + 1,
filter(lambda tup: tup[1] == notification_callback, self.notifications[notification_type]),
0) > 0:
return -1
self.notifications[notification_type].append((self.notification_id, notification_callback))
ret_val = self.notification_id
self.notification_id += 1
return ret_val | [
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optimizely/python-sdk | optimizely/notification_center.py | NotificationCenter.remove_notification_listener | def remove_notification_listener(self, notification_id):
""" Remove a previously added notification callback.
Args:
notification_id: The numeric id passed back from add_notification_listener
Returns:
The function returns boolean true if found and removed, false otherwise.
"""
for v in self.notifications.values():
toRemove = list(filter(lambda tup: tup[0] == notification_id, v))
if len(toRemove) > 0:
v.remove(toRemove[0])
return True
return False | python | def remove_notification_listener(self, notification_id):
""" Remove a previously added notification callback.
Args:
notification_id: The numeric id passed back from add_notification_listener
Returns:
The function returns boolean true if found and removed, false otherwise.
"""
for v in self.notifications.values():
toRemove = list(filter(lambda tup: tup[0] == notification_id, v))
if len(toRemove) > 0:
v.remove(toRemove[0])
return True
return False | [
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optimizely/python-sdk | optimizely/notification_center.py | NotificationCenter.send_notifications | def send_notifications(self, notification_type, *args):
""" Fires off the notification for the specific event. Uses var args to pass in a
arbitrary list of parameter according to which notification type was fired.
Args:
notification_type: Type of notification to fire (String from .helpers.enums.NotificationTypes)
args: variable list of arguments to the callback.
"""
if notification_type in self.notifications:
for notification_id, callback in self.notifications[notification_type]:
try:
callback(*args)
except:
self.logger.exception('Problem calling notify callback!') | python | def send_notifications(self, notification_type, *args):
""" Fires off the notification for the specific event. Uses var args to pass in a
arbitrary list of parameter according to which notification type was fired.
Args:
notification_type: Type of notification to fire (String from .helpers.enums.NotificationTypes)
args: variable list of arguments to the callback.
"""
if notification_type in self.notifications:
for notification_id, callback in self.notifications[notification_type]:
try:
callback(*args)
except:
self.logger.exception('Problem calling notify callback!') | [
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optimizely/python-sdk | optimizely/helpers/condition_tree_evaluator.py | and_evaluator | def and_evaluator(conditions, leaf_evaluator):
""" Evaluates a list of conditions as if the evaluator had been applied
to each entry and the results AND-ed together.
Args:
conditions: List of conditions ex: [operand_1, operand_2].
leaf_evaluator: Function which will be called to evaluate leaf condition values.
Returns:
Boolean:
- True if all operands evaluate to True.
- False if a single operand evaluates to False.
None: if conditions couldn't be evaluated.
"""
saw_null_result = False
for condition in conditions:
result = evaluate(condition, leaf_evaluator)
if result is False:
return False
if result is None:
saw_null_result = True
return None if saw_null_result else True | python | def and_evaluator(conditions, leaf_evaluator):
""" Evaluates a list of conditions as if the evaluator had been applied
to each entry and the results AND-ed together.
Args:
conditions: List of conditions ex: [operand_1, operand_2].
leaf_evaluator: Function which will be called to evaluate leaf condition values.
Returns:
Boolean:
- True if all operands evaluate to True.
- False if a single operand evaluates to False.
None: if conditions couldn't be evaluated.
"""
saw_null_result = False
for condition in conditions:
result = evaluate(condition, leaf_evaluator)
if result is False:
return False
if result is None:
saw_null_result = True
return None if saw_null_result else True | [
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optimizely/python-sdk | optimizely/helpers/condition_tree_evaluator.py | not_evaluator | def not_evaluator(conditions, leaf_evaluator):
""" Evaluates a list of conditions as if the evaluator had been applied
to a single entry and NOT was applied to the result.
Args:
conditions: List of conditions ex: [operand_1, operand_2].
leaf_evaluator: Function which will be called to evaluate leaf condition values.
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"""
if not len(conditions) > 0:
return None
result = evaluate(conditions[0], leaf_evaluator)
return None if result is None else not result | python | def not_evaluator(conditions, leaf_evaluator):
""" Evaluates a list of conditions as if the evaluator had been applied
to a single entry and NOT was applied to the result.
Args:
conditions: List of conditions ex: [operand_1, operand_2].
leaf_evaluator: Function which will be called to evaluate leaf condition values.
Returns:
Boolean:
- True if the operand evaluates to False.
- False if the operand evaluates to True.
None: if conditions is empty or condition couldn't be evaluated.
"""
if not len(conditions) > 0:
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optimizely/python-sdk | optimizely/helpers/condition_tree_evaluator.py | evaluate | def evaluate(conditions, leaf_evaluator):
""" Top level method to evaluate conditions.
Args:
conditions: Nested array of and/or conditions, or a single leaf condition value of any type.
Example: ['and', '0', ['or', '1', '2']]
leaf_evaluator: Function which will be called to evaluate leaf condition values.
Returns:
Boolean: Result of evaluating the conditions using the operator rules and the leaf evaluator.
None: if conditions couldn't be evaluated.
"""
if isinstance(conditions, list):
if conditions[0] in list(EVALUATORS_BY_OPERATOR_TYPE.keys()):
return EVALUATORS_BY_OPERATOR_TYPE[conditions[0]](conditions[1:], leaf_evaluator)
else:
# assume OR when operator is not explicit.
return EVALUATORS_BY_OPERATOR_TYPE[ConditionOperatorTypes.OR](conditions, leaf_evaluator)
leaf_condition = conditions
return leaf_evaluator(leaf_condition) | python | def evaluate(conditions, leaf_evaluator):
""" Top level method to evaluate conditions.
Args:
conditions: Nested array of and/or conditions, or a single leaf condition value of any type.
Example: ['and', '0', ['or', '1', '2']]
leaf_evaluator: Function which will be called to evaluate leaf condition values.
Returns:
Boolean: Result of evaluating the conditions using the operator rules and the leaf evaluator.
None: if conditions couldn't be evaluated.
"""
if isinstance(conditions, list):
if conditions[0] in list(EVALUATORS_BY_OPERATOR_TYPE.keys()):
return EVALUATORS_BY_OPERATOR_TYPE[conditions[0]](conditions[1:], leaf_evaluator)
else:
# assume OR when operator is not explicit.
return EVALUATORS_BY_OPERATOR_TYPE[ConditionOperatorTypes.OR](conditions, leaf_evaluator)
leaf_condition = conditions
return leaf_evaluator(leaf_condition) | [
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Parisson/TimeSide | timeside/core/analyzer.py | data_objet_class | def data_objet_class(data_mode='value', time_mode='framewise'):
"""
Factory function for Analyzer result
"""
classes_table = {('value', 'global'): GlobalValueObject,
('value', 'event'): EventValueObject,
('value', 'segment'): SegmentValueObject,
('value', 'framewise'): FrameValueObject,
('label', 'global'): GlobalLabelObject,
('label', 'event'): EventLabelObject,
('label', 'segment'): SegmentLabelObject,
('label', 'framewise'): FrameLabelObject}
try:
return classes_table[(data_mode, time_mode)]
except KeyError as e:
raise ValueError('Wrong arguments') | python | def data_objet_class(data_mode='value', time_mode='framewise'):
"""
Factory function for Analyzer result
"""
classes_table = {('value', 'global'): GlobalValueObject,
('value', 'event'): EventValueObject,
('value', 'segment'): SegmentValueObject,
('value', 'framewise'): FrameValueObject,
('label', 'global'): GlobalLabelObject,
('label', 'event'): EventLabelObject,
('label', 'segment'): SegmentLabelObject,
('label', 'framewise'): FrameLabelObject}
try:
return classes_table[(data_mode, time_mode)]
except KeyError as e:
raise ValueError('Wrong arguments') | [
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Parisson/TimeSide | timeside/core/analyzer.py | JSON_NumpyArrayEncoder | def JSON_NumpyArrayEncoder(obj):
'''Define Specialize JSON encoder for numpy array'''
if isinstance(obj, np.ndarray):
return {'numpyArray': obj.tolist(),
'dtype': obj.dtype.__str__()}
elif isinstance(obj, np.generic):
return np.asscalar(obj)
else:
print type(obj)
raise TypeError(repr(obj) + " is not JSON serializable") | python | def JSON_NumpyArrayEncoder(obj):
'''Define Specialize JSON encoder for numpy array'''
if isinstance(obj, np.ndarray):
return {'numpyArray': obj.tolist(),
'dtype': obj.dtype.__str__()}
elif isinstance(obj, np.generic):
return np.asscalar(obj)
else:
print type(obj)
raise TypeError(repr(obj) + " is not JSON serializable") | [
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Parisson/TimeSide | timeside/core/analyzer.py | AnalyzerResult.render | def render(self):
'''Render a matplotlib figure from the analyzer result
Return the figure, use fig.show() to display if neeeded
'''
fig, ax = plt.subplots()
self.data_object._render_plot(ax)
return fig | python | def render(self):
'''Render a matplotlib figure from the analyzer result
Return the figure, use fig.show() to display if neeeded
'''
fig, ax = plt.subplots()
self.data_object._render_plot(ax)
return fig | [
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Parisson/TimeSide | timeside/core/analyzer.py | Analyzer.new_result | def new_result(self, data_mode='value', time_mode='framewise'):
'''
Create a new result
Attributes
----------
data_object : MetadataObject
id_metadata : MetadataObject
audio_metadata : MetadataObject
frame_metadata : MetadataObject
label_metadata : MetadataObject
parameters : dict
'''
from datetime import datetime
result = AnalyzerResult(data_mode=data_mode,
time_mode=time_mode)
# Automatically write known metadata
result.id_metadata.date = datetime.now().replace(
microsecond=0).isoformat(' ')
result.id_metadata.version = timeside.core.__version__
result.id_metadata.author = 'TimeSide'
result.id_metadata.id = self.id()
result.id_metadata.name = self.name()
result.id_metadata.description = self.description()
result.id_metadata.unit = self.unit()
result.id_metadata.proc_uuid = self.uuid()
result.audio_metadata.uri = self.mediainfo()['uri']
result.audio_metadata.sha1 = self.mediainfo()['sha1']
result.audio_metadata.start = self.mediainfo()['start']
result.audio_metadata.duration = self.mediainfo()['duration']
result.audio_metadata.is_segment = self.mediainfo()['is_segment']
result.audio_metadata.channels = self.channels()
result.parameters = Parameters(self.get_parameters())
if time_mode == 'framewise':
result.data_object.frame_metadata.samplerate = self.result_samplerate
result.data_object.frame_metadata.blocksize = self.result_blocksize
result.data_object.frame_metadata.stepsize = self.result_stepsize
return result | python | def new_result(self, data_mode='value', time_mode='framewise'):
'''
Create a new result
Attributes
----------
data_object : MetadataObject
id_metadata : MetadataObject
audio_metadata : MetadataObject
frame_metadata : MetadataObject
label_metadata : MetadataObject
parameters : dict
'''
from datetime import datetime
result = AnalyzerResult(data_mode=data_mode,
time_mode=time_mode)
# Automatically write known metadata
result.id_metadata.date = datetime.now().replace(
microsecond=0).isoformat(' ')
result.id_metadata.version = timeside.core.__version__
result.id_metadata.author = 'TimeSide'
result.id_metadata.id = self.id()
result.id_metadata.name = self.name()
result.id_metadata.description = self.description()
result.id_metadata.unit = self.unit()
result.id_metadata.proc_uuid = self.uuid()
result.audio_metadata.uri = self.mediainfo()['uri']
result.audio_metadata.sha1 = self.mediainfo()['sha1']
result.audio_metadata.start = self.mediainfo()['start']
result.audio_metadata.duration = self.mediainfo()['duration']
result.audio_metadata.is_segment = self.mediainfo()['is_segment']
result.audio_metadata.channels = self.channels()
result.parameters = Parameters(self.get_parameters())
if time_mode == 'framewise':
result.data_object.frame_metadata.samplerate = self.result_samplerate
result.data_object.frame_metadata.blocksize = self.result_blocksize
result.data_object.frame_metadata.stepsize = self.result_stepsize
return result | [
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Attributes
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data_object : MetadataObject
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audio_metadata : MetadataObject
frame_metadata : MetadataObject
label_metadata : MetadataObject
parameters : dict | [
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Parisson/TimeSide | timeside/core/preprocessors.py | downmix_to_mono | def downmix_to_mono(process_func):
'''
Pre-processing decorator that downmixes frames from multi-channel to mono
Downmix is achieved by averaging all channels
>>> from timeside.core.preprocessors import downmix_to_mono
>>> @downmix_to_mono
... def process(analyzer,frames,eod):
... print 'Frames, eod inside process :'
... print frames, eod
... return frames, eod
...
>>> import numpy as np
>>> frames = np.asarray([[1,2],[3,4],[5,6],[7,8],[9,10]])
>>> eod = False
>>> frames_, eod_ = process(object(),frames,eod)
Frames, eod inside process :
[1.5 3.5 5.5 7.5 9.5] False
Outside Process frames and eod are preserved :
>>> frames_
array([[ 1, 2],
[ 3, 4],
[ 5, 6],
[ 7, 8],
[ 9, 10]])
>>> eod_
False
'''
import functools
@functools.wraps(process_func)
def wrapper(analyzer, frames, eod):
# Pre-processing
if frames.ndim > 1:
downmix_frames = frames.mean(axis=-1)
else:
downmix_frames = frames
# Processing
process_func(analyzer, downmix_frames, eod)
return frames, eod
return wrapper | python | def downmix_to_mono(process_func):
'''
Pre-processing decorator that downmixes frames from multi-channel to mono
Downmix is achieved by averaging all channels
>>> from timeside.core.preprocessors import downmix_to_mono
>>> @downmix_to_mono
... def process(analyzer,frames,eod):
... print 'Frames, eod inside process :'
... print frames, eod
... return frames, eod
...
>>> import numpy as np
>>> frames = np.asarray([[1,2],[3,4],[5,6],[7,8],[9,10]])
>>> eod = False
>>> frames_, eod_ = process(object(),frames,eod)
Frames, eod inside process :
[1.5 3.5 5.5 7.5 9.5] False
Outside Process frames and eod are preserved :
>>> frames_
array([[ 1, 2],
[ 3, 4],
[ 5, 6],
[ 7, 8],
[ 9, 10]])
>>> eod_
False
'''
import functools
@functools.wraps(process_func)
def wrapper(analyzer, frames, eod):
# Pre-processing
if frames.ndim > 1:
downmix_frames = frames.mean(axis=-1)
else:
downmix_frames = frames
# Processing
process_func(analyzer, downmix_frames, eod)
return frames, eod
return wrapper | [
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Downmix is achieved by averaging all channels
>>> from timeside.core.preprocessors import downmix_to_mono
>>> @downmix_to_mono
... def process(analyzer,frames,eod):
... print 'Frames, eod inside process :'
... print frames, eod
... return frames, eod
...
>>> import numpy as np
>>> frames = np.asarray([[1,2],[3,4],[5,6],[7,8],[9,10]])
>>> eod = False
>>> frames_, eod_ = process(object(),frames,eod)
Frames, eod inside process :
[1.5 3.5 5.5 7.5 9.5] False
Outside Process frames and eod are preserved :
>>> frames_
array([[ 1, 2],
[ 3, 4],
[ 5, 6],
[ 7, 8],
[ 9, 10]])
>>> eod_
False | [
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Parisson/TimeSide | timeside/core/preprocessors.py | frames_adapter | def frames_adapter(process_func):
'''
Pre-processing decorator that adapt frames to match input_blocksize and
input_stepsize of the decorated analyzer
>>> from timeside.core.preprocessors import frames_adapter
>>> @frames_adapter
... def process(analyzer,frames,eod):
... analyzer.frames.append(frames)
... return frames, eod
>>> class Fake_Analyzer(object):
... def __init__(self):
... self.input_blocksize = 4
... self.input_stepsize = 3
... self.frames = [] # Container for the frame as viewed by process
... @staticmethod
... def id():
... return 'fake_analyzer'
>>> import numpy as np
>>> analyzer = Fake_Analyzer()
>>> frames = np.asarray(range(0,12))
>>> eod = False
>>> frames_, eod_ = process(analyzer,frames,eod)
Inside the process the frames have been adapted to match input_blocksize
and input_stepsize
>>> analyzer.frames
[array([0, 1, 2, 3]), array([3, 4, 5, 6]), array([6, 7, 8, 9])]
Outside the process, the original frames and eod are preserved:
>>> frames_
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
>>> eod_
False
Releasing the process with eod=True will zeropad the last frame if necessary
>>> frames = np.asarray(range(12,14))
>>> eod = True
>>> frames_, eod_ = process(analyzer,frames,eod)
>>> analyzer.frames
[array([0, 1, 2, 3]), array([3, 4, 5, 6]), array([6, 7, 8, 9]), array([ 9, 10, 11, 12]), array([12, 13, 0, 0])]
'''
import functools
import numpy as np
class framesBuffer(object):
def __init__(self, blocksize, stepsize):
self.blocksize = blocksize
self.stepsize = stepsize
self.buffer = None
def frames(self, frames, eod):
if self.buffer is not None:
stack = np.concatenate([self.buffer, frames])
else:
stack = frames.copy()
stack_length = len(stack)
nb_frames = (
stack_length - self.blocksize + self.stepsize) // self.stepsize
nb_frames = max(nb_frames, 0)
frames_length = nb_frames * self.stepsize + \
self.blocksize - self.stepsize
last_block_size = stack_length - frames_length
if eod:
# Final zeropadding
pad_shape = tuple(
self.blocksize - last_block_size if i == 0 else x
for i, x in enumerate(frames.shape))
stack = np.concatenate([stack, np.zeros(pad_shape,
dtype=frames.dtype)])
nb_frames += 1
self.buffer = stack[nb_frames * self.stepsize:]
eod_list = np.repeat(False, nb_frames)
if eod and len(eod_list):
eod_list[-1] = eod
for index, eod in zip(xrange(0, nb_frames * self.stepsize, self.stepsize), eod_list):
yield (stack[index:index + self.blocksize], eod)
aubio_analyzers = ['aubio_melenergy', 'aubio_mfcc', 'aubio_pitch', 'aubio_specdesc', 'aubio_temporal']
@functools.wraps(process_func)
def wrapper(analyzer, frames, eod):
# Pre-processing
if not hasattr(analyzer, 'frames_buffer'):
if analyzer.id() in aubio_analyzers:
# Aubio analyzers are waiting for stepsize length block
# and reconstructs blocksize length frames itself
# thus frames_adapter has to provide Aubio Pitch blocksize=stepsize length frames
analyzer.frames_buffer = framesBuffer(analyzer.input_stepsize,
analyzer.input_stepsize)
else:
analyzer.frames_buffer = framesBuffer(analyzer.input_blocksize,
analyzer.input_stepsize)
# Processing
for adapted_frames, adapted_eod in analyzer.frames_buffer.frames(frames, eod):
process_func(analyzer, adapted_frames, adapted_eod)
return frames, eod
return wrapper | python | def frames_adapter(process_func):
'''
Pre-processing decorator that adapt frames to match input_blocksize and
input_stepsize of the decorated analyzer
>>> from timeside.core.preprocessors import frames_adapter
>>> @frames_adapter
... def process(analyzer,frames,eod):
... analyzer.frames.append(frames)
... return frames, eod
>>> class Fake_Analyzer(object):
... def __init__(self):
... self.input_blocksize = 4
... self.input_stepsize = 3
... self.frames = [] # Container for the frame as viewed by process
... @staticmethod
... def id():
... return 'fake_analyzer'
>>> import numpy as np
>>> analyzer = Fake_Analyzer()
>>> frames = np.asarray(range(0,12))
>>> eod = False
>>> frames_, eod_ = process(analyzer,frames,eod)
Inside the process the frames have been adapted to match input_blocksize
and input_stepsize
>>> analyzer.frames
[array([0, 1, 2, 3]), array([3, 4, 5, 6]), array([6, 7, 8, 9])]
Outside the process, the original frames and eod are preserved:
>>> frames_
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
>>> eod_
False
Releasing the process with eod=True will zeropad the last frame if necessary
>>> frames = np.asarray(range(12,14))
>>> eod = True
>>> frames_, eod_ = process(analyzer,frames,eod)
>>> analyzer.frames
[array([0, 1, 2, 3]), array([3, 4, 5, 6]), array([6, 7, 8, 9]), array([ 9, 10, 11, 12]), array([12, 13, 0, 0])]
'''
import functools
import numpy as np
class framesBuffer(object):
def __init__(self, blocksize, stepsize):
self.blocksize = blocksize
self.stepsize = stepsize
self.buffer = None
def frames(self, frames, eod):
if self.buffer is not None:
stack = np.concatenate([self.buffer, frames])
else:
stack = frames.copy()
stack_length = len(stack)
nb_frames = (
stack_length - self.blocksize + self.stepsize) // self.stepsize
nb_frames = max(nb_frames, 0)
frames_length = nb_frames * self.stepsize + \
self.blocksize - self.stepsize
last_block_size = stack_length - frames_length
if eod:
# Final zeropadding
pad_shape = tuple(
self.blocksize - last_block_size if i == 0 else x
for i, x in enumerate(frames.shape))
stack = np.concatenate([stack, np.zeros(pad_shape,
dtype=frames.dtype)])
nb_frames += 1
self.buffer = stack[nb_frames * self.stepsize:]
eod_list = np.repeat(False, nb_frames)
if eod and len(eod_list):
eod_list[-1] = eod
for index, eod in zip(xrange(0, nb_frames * self.stepsize, self.stepsize), eod_list):
yield (stack[index:index + self.blocksize], eod)
aubio_analyzers = ['aubio_melenergy', 'aubio_mfcc', 'aubio_pitch', 'aubio_specdesc', 'aubio_temporal']
@functools.wraps(process_func)
def wrapper(analyzer, frames, eod):
# Pre-processing
if not hasattr(analyzer, 'frames_buffer'):
if analyzer.id() in aubio_analyzers:
# Aubio analyzers are waiting for stepsize length block
# and reconstructs blocksize length frames itself
# thus frames_adapter has to provide Aubio Pitch blocksize=stepsize length frames
analyzer.frames_buffer = framesBuffer(analyzer.input_stepsize,
analyzer.input_stepsize)
else:
analyzer.frames_buffer = framesBuffer(analyzer.input_blocksize,
analyzer.input_stepsize)
# Processing
for adapted_frames, adapted_eod in analyzer.frames_buffer.frames(frames, eod):
process_func(analyzer, adapted_frames, adapted_eod)
return frames, eod
return wrapper | [
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input_stepsize of the decorated analyzer
>>> from timeside.core.preprocessors import frames_adapter
>>> @frames_adapter
... def process(analyzer,frames,eod):
... analyzer.frames.append(frames)
... return frames, eod
>>> class Fake_Analyzer(object):
... def __init__(self):
... self.input_blocksize = 4
... self.input_stepsize = 3
... self.frames = [] # Container for the frame as viewed by process
... @staticmethod
... def id():
... return 'fake_analyzer'
>>> import numpy as np
>>> analyzer = Fake_Analyzer()
>>> frames = np.asarray(range(0,12))
>>> eod = False
>>> frames_, eod_ = process(analyzer,frames,eod)
Inside the process the frames have been adapted to match input_blocksize
and input_stepsize
>>> analyzer.frames
[array([0, 1, 2, 3]), array([3, 4, 5, 6]), array([6, 7, 8, 9])]
Outside the process, the original frames and eod are preserved:
>>> frames_
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
>>> eod_
False
Releasing the process with eod=True will zeropad the last frame if necessary
>>> frames = np.asarray(range(12,14))
>>> eod = True
>>> frames_, eod_ = process(analyzer,frames,eod)
>>> analyzer.frames
[array([0, 1, 2, 3]), array([3, 4, 5, 6]), array([6, 7, 8, 9]), array([ 9, 10, 11, 12]), array([12, 13, 0, 0])] | [
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Parisson/TimeSide | timeside/server/models.py | Item.get_uri | def get_uri(self):
"""Return the Item source"""
if self.source_file and os.path.exists(self.source_file.path):
return self.source_file.path
elif self.source_url:
return self.source_url
return None | python | def get_uri(self):
"""Return the Item source"""
if self.source_file and os.path.exists(self.source_file.path):
return self.source_file.path
elif self.source_url:
return self.source_url
return None | [
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Parisson/TimeSide | timeside/server/models.py | Item.get_audio_duration | def get_audio_duration(self):
"""
Return item audio duration
"""
decoder = timeside.core.get_processor('file_decoder')(
uri=self.get_uri())
return decoder.uri_total_duration | python | def get_audio_duration(self):
"""
Return item audio duration
"""
decoder = timeside.core.get_processor('file_decoder')(
uri=self.get_uri())
return decoder.uri_total_duration | [
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Parisson/TimeSide | timeside/server/models.py | Item.get_results_path | def get_results_path(self):
"""
Return Item result path
"""
result_path = os.path.join(RESULTS_ROOT, self.uuid)
if not os.path.exists(result_path):
os.makedirs(result_path)
return result_path | python | def get_results_path(self):
"""
Return Item result path
"""
result_path = os.path.join(RESULTS_ROOT, self.uuid)
if not os.path.exists(result_path):
os.makedirs(result_path)
return result_path | [
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Parisson/TimeSide | timeside/plugins/decoder/utils.py | get_uri | def get_uri(source):
"""
Check a media source as a valid file or uri and return the proper uri
"""
import gst
src_info = source_info(source)
if src_info['is_file']: # Is this a file?
return get_uri(src_info['uri'])
elif gst.uri_is_valid(source): # Is this a valid URI source for Gstreamer
uri_protocol = gst.uri_get_protocol(source)
if gst.uri_protocol_is_supported(gst.URI_SRC, uri_protocol):
return source
else:
raise IOError('Invalid URI source for Gstreamer')
else:
raise IOError('Failed getting uri for path %s: no such file' % source) | python | def get_uri(source):
"""
Check a media source as a valid file or uri and return the proper uri
"""
import gst
src_info = source_info(source)
if src_info['is_file']: # Is this a file?
return get_uri(src_info['uri'])
elif gst.uri_is_valid(source): # Is this a valid URI source for Gstreamer
uri_protocol = gst.uri_get_protocol(source)
if gst.uri_protocol_is_supported(gst.URI_SRC, uri_protocol):
return source
else:
raise IOError('Invalid URI source for Gstreamer')
else:
raise IOError('Failed getting uri for path %s: no such file' % source) | [
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Parisson/TimeSide | timeside/plugins/decoder/utils.py | sha1sum_file | def sha1sum_file(filename):
'''
Return the secure hash digest with sha1 algorithm for a given file
>>> from timeside.core.tools.test_samples import samples
>>> wav_file = samples["C4_scale.wav"]
>>> print sha1sum_file(wav_file)
a598e78d0b5c90da54a77e34c083abdcd38d42ba
'''
import hashlib
import io
sha1 = hashlib.sha1()
chunk_size = sha1.block_size * io.DEFAULT_BUFFER_SIZE
with open(filename, 'rb') as f:
for chunk in iter(lambda: f.read(chunk_size), b''):
sha1.update(chunk)
return sha1.hexdigest() | python | def sha1sum_file(filename):
'''
Return the secure hash digest with sha1 algorithm for a given file
>>> from timeside.core.tools.test_samples import samples
>>> wav_file = samples["C4_scale.wav"]
>>> print sha1sum_file(wav_file)
a598e78d0b5c90da54a77e34c083abdcd38d42ba
'''
import hashlib
import io
sha1 = hashlib.sha1()
chunk_size = sha1.block_size * io.DEFAULT_BUFFER_SIZE
with open(filename, 'rb') as f:
for chunk in iter(lambda: f.read(chunk_size), b''):
sha1.update(chunk)
return sha1.hexdigest() | [
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>>> from timeside.core.tools.test_samples import samples
>>> wav_file = samples["C4_scale.wav"]
>>> print sha1sum_file(wav_file)
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Parisson/TimeSide | timeside/plugins/decoder/utils.py | sha1sum_url | def sha1sum_url(url):
'''Return the secure hash digest with sha1 algorithm for a given url
>>> url = "https://github.com/yomguy/timeside-samples/raw/master/samples/guitar.wav"
>>> print sha1sum_url(url)
08301c3f9a8d60926f31e253825cc74263e52ad1
'''
import hashlib
import urllib
from contextlib import closing
sha1 = hashlib.sha1()
chunk_size = sha1.block_size * 8192
max_file_size = 10 * 1024 * 1024 # 10Mo limit in case of very large file
total_read = 0
with closing(urllib.urlopen(url)) as url_obj:
for chunk in iter(lambda: url_obj.read(chunk_size), b''):
sha1.update(chunk)
total_read += chunk_size
if total_read > max_file_size:
break
return sha1.hexdigest() | python | def sha1sum_url(url):
'''Return the secure hash digest with sha1 algorithm for a given url
>>> url = "https://github.com/yomguy/timeside-samples/raw/master/samples/guitar.wav"
>>> print sha1sum_url(url)
08301c3f9a8d60926f31e253825cc74263e52ad1
'''
import hashlib
import urllib
from contextlib import closing
sha1 = hashlib.sha1()
chunk_size = sha1.block_size * 8192
max_file_size = 10 * 1024 * 1024 # 10Mo limit in case of very large file
total_read = 0
with closing(urllib.urlopen(url)) as url_obj:
for chunk in iter(lambda: url_obj.read(chunk_size), b''):
sha1.update(chunk)
total_read += chunk_size
if total_read > max_file_size:
break
return sha1.hexdigest() | [
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>>> url = "https://github.com/yomguy/timeside-samples/raw/master/samples/guitar.wav"
>>> print sha1sum_url(url)
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Parisson/TimeSide | timeside/plugins/decoder/utils.py | sha1sum_numpy | def sha1sum_numpy(np_array):
'''
Return the secure hash digest with sha1 algorithm for a numpy array
'''
import hashlib
return hashlib.sha1(np_array.view(np.uint8)).hexdigest() | python | def sha1sum_numpy(np_array):
'''
Return the secure hash digest with sha1 algorithm for a numpy array
'''
import hashlib
return hashlib.sha1(np_array.view(np.uint8)).hexdigest() | [
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Parisson/TimeSide | timeside/core/tools/package.py | import_module_with_exceptions | def import_module_with_exceptions(name, package=None):
"""Wrapper around importlib.import_module to import TimeSide subpackage
and ignoring ImportError if Aubio, Yaafe and Vamp Host are not available"""
from timeside.core import _WITH_AUBIO, _WITH_YAAFE, _WITH_VAMP
if name.count('.server.'):
# TODO:
# Temporary skip all timeside.server submodules before check dependencies
return
try:
import_module(name, package)
except VampImportError:
# No Vamp Host
if _WITH_VAMP:
raise VampImportError
else:
# Ignore Vamp ImportError
return
except ImportError as e:
if str(e).count('yaafelib') and not _WITH_YAAFE:
# Ignore Yaafe ImportError
return
elif str(e).count('aubio') and not _WITH_AUBIO:
# Ignore Aubio ImportError
return
elif str(e).count('DJANGO_SETTINGS_MODULE'):
# Ignore module requiring DJANGO_SETTINGS_MODULE in environnement
return
else:
print (name, package)
raise e
return name | python | def import_module_with_exceptions(name, package=None):
"""Wrapper around importlib.import_module to import TimeSide subpackage
and ignoring ImportError if Aubio, Yaafe and Vamp Host are not available"""
from timeside.core import _WITH_AUBIO, _WITH_YAAFE, _WITH_VAMP
if name.count('.server.'):
# TODO:
# Temporary skip all timeside.server submodules before check dependencies
return
try:
import_module(name, package)
except VampImportError:
# No Vamp Host
if _WITH_VAMP:
raise VampImportError
else:
# Ignore Vamp ImportError
return
except ImportError as e:
if str(e).count('yaafelib') and not _WITH_YAAFE:
# Ignore Yaafe ImportError
return
elif str(e).count('aubio') and not _WITH_AUBIO:
# Ignore Aubio ImportError
return
elif str(e).count('DJANGO_SETTINGS_MODULE'):
# Ignore module requiring DJANGO_SETTINGS_MODULE in environnement
return
else:
print (name, package)
raise e
return name | [
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and ignoring ImportError if Aubio, Yaafe and Vamp Host are not available | [
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Parisson/TimeSide | timeside/core/tools/package.py | check_vamp | def check_vamp():
"Check Vamp host availability"
try:
from timeside.plugins.analyzer.externals import vamp_plugin
except VampImportError:
warnings.warn('Vamp host is not available', ImportWarning,
stacklevel=2)
_WITH_VAMP = False
else:
_WITH_VAMP = True
del vamp_plugin
return _WITH_VAMP | python | def check_vamp():
"Check Vamp host availability"
try:
from timeside.plugins.analyzer.externals import vamp_plugin
except VampImportError:
warnings.warn('Vamp host is not available', ImportWarning,
stacklevel=2)
_WITH_VAMP = False
else:
_WITH_VAMP = True
del vamp_plugin
return _WITH_VAMP | [
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Parisson/TimeSide | timeside/plugins/grapher/utils.py | im_watermark | def im_watermark(im, inputtext, font=None, color=None, opacity=.6, margin=(30, 30)):
"""imprints a PIL image with the indicated text in lower-right corner"""
if im.mode != "RGBA":
im = im.convert("RGBA")
textlayer = Image.new("RGBA", im.size, (0, 0, 0, 0))
textdraw = ImageDraw.Draw(textlayer)
textsize = textdraw.textsize(inputtext, font=font)
textpos = [im.size[i] - textsize[i] - margin[i] for i in [0, 1]]
textdraw.text(textpos, inputtext, font=font, fill=color)
if opacity != 1:
textlayer = reduce_opacity(textlayer, opacity)
return Image.composite(textlayer, im, textlayer) | python | def im_watermark(im, inputtext, font=None, color=None, opacity=.6, margin=(30, 30)):
"""imprints a PIL image with the indicated text in lower-right corner"""
if im.mode != "RGBA":
im = im.convert("RGBA")
textlayer = Image.new("RGBA", im.size, (0, 0, 0, 0))
textdraw = ImageDraw.Draw(textlayer)
textsize = textdraw.textsize(inputtext, font=font)
textpos = [im.size[i] - textsize[i] - margin[i] for i in [0, 1]]
textdraw.text(textpos, inputtext, font=font, fill=color)
if opacity != 1:
textlayer = reduce_opacity(textlayer, opacity)
return Image.composite(textlayer, im, textlayer) | [
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Parisson/TimeSide | timeside/plugins/analyzer/utils.py | nextpow2 | def nextpow2(value):
"""Compute the nearest power of two greater or equal to the input value"""
if value >= 1:
return 2**np.ceil(np.log2(value)).astype(int)
elif value > 0:
return 1
elif value == 0:
return 0
else:
raise ValueError('Value must be positive') | python | def nextpow2(value):
"""Compute the nearest power of two greater or equal to the input value"""
if value >= 1:
return 2**np.ceil(np.log2(value)).astype(int)
elif value > 0:
return 1
elif value == 0:
return 0
else:
raise ValueError('Value must be positive') | [
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Parisson/TimeSide | timeside/core/processor.py | FixedSizeInputAdapter.blocksize | def blocksize(self, input_totalframes):
"""Return the total number of frames that this adapter will output
according to the input_totalframes argument"""
blocksize = input_totalframes
if self.pad:
mod = input_totalframes % self.buffer_size
if mod:
blocksize += self.buffer_size - mod
return blocksize | python | def blocksize(self, input_totalframes):
"""Return the total number of frames that this adapter will output
according to the input_totalframes argument"""
blocksize = input_totalframes
if self.pad:
mod = input_totalframes % self.buffer_size
if mod:
blocksize += self.buffer_size - mod
return blocksize | [
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Parisson/TimeSide | timeside/core/processor.py | ProcessPipe.append_processor | def append_processor(self, proc, source_proc=None):
"Append a new processor to the pipe"
if source_proc is None and len(self.processors):
source_proc = self.processors[0]
if source_proc and not isinstance(source_proc, Processor):
raise TypeError('source_proc must be a Processor or None')
if not isinstance(proc, Processor):
raise TypeError('proc must be a Processor or None')
if proc.type == 'decoder' and len(self.processors):
raise ValueError('Only the first processor in a pipe could be a Decoder')
# TODO : check if the processor is already in the pipe
if source_proc:
for child in self._graph.neighbors_iter(source_proc.uuid()):
child_proc = self._graph.node[child]['processor']
if proc == child_proc:
proc._uuid = child_proc.uuid()
proc.process_pipe = self
break
if not self._graph.has_node(proc.uuid()):
self.processors.append(proc) # Add processor to the pipe
self._graph.add_node(proc.uuid(), processor=proc, id=proc.id())
if source_proc:
self._graph.add_edge(self.processors[0].uuid(), proc.uuid(),
type='audio_source')
proc.process_pipe = self
# Add an edge between each parent and proc
for parent in proc.parents.values():
self._graph.add_edge(parent.uuid(), proc.uuid(),
type='data_source') | python | def append_processor(self, proc, source_proc=None):
"Append a new processor to the pipe"
if source_proc is None and len(self.processors):
source_proc = self.processors[0]
if source_proc and not isinstance(source_proc, Processor):
raise TypeError('source_proc must be a Processor or None')
if not isinstance(proc, Processor):
raise TypeError('proc must be a Processor or None')
if proc.type == 'decoder' and len(self.processors):
raise ValueError('Only the first processor in a pipe could be a Decoder')
# TODO : check if the processor is already in the pipe
if source_proc:
for child in self._graph.neighbors_iter(source_proc.uuid()):
child_proc = self._graph.node[child]['processor']
if proc == child_proc:
proc._uuid = child_proc.uuid()
proc.process_pipe = self
break
if not self._graph.has_node(proc.uuid()):
self.processors.append(proc) # Add processor to the pipe
self._graph.add_node(proc.uuid(), processor=proc, id=proc.id())
if source_proc:
self._graph.add_edge(self.processors[0].uuid(), proc.uuid(),
type='audio_source')
proc.process_pipe = self
# Add an edge between each parent and proc
for parent in proc.parents.values():
self._graph.add_edge(parent.uuid(), proc.uuid(),
type='data_source') | [
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Parisson/TimeSide | timeside/plugins/analyzer/externals/vamp_plugin.py | simple_host_process | def simple_host_process(argslist):
"""Call vamp-simple-host"""
vamp_host = 'vamp-simple-host'
command = [vamp_host]
command.extend(argslist)
# try ?
stdout = subprocess.check_output(command,
stderr=subprocess.STDOUT).splitlines()
return stdout | python | def simple_host_process(argslist):
"""Call vamp-simple-host"""
vamp_host = 'vamp-simple-host'
command = [vamp_host]
command.extend(argslist)
# try ?
stdout = subprocess.check_output(command,
stderr=subprocess.STDOUT).splitlines()
return stdout | [
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Parisson/TimeSide | timeside/plugins/grapher/spectrogram_lin.py | SpectrogramLinear.set_scale | def set_scale(self):
"""generate the lookup which translates y-coordinate to fft-bin"""
f_min = float(self.lower_freq)
f_max = float(self.higher_freq)
y_min = f_min
y_max = f_max
for y in range(self.image_height):
freq = y_min + y / (self.image_height - 1.0) * (y_max - y_min)
fft_bin = freq / f_max * (self.fft_size / 2 + 1)
if fft_bin < self.fft_size / 2:
alpha = fft_bin - int(fft_bin)
self.y_to_bin.append((int(fft_bin), alpha * 255)) | python | def set_scale(self):
"""generate the lookup which translates y-coordinate to fft-bin"""
f_min = float(self.lower_freq)
f_max = float(self.higher_freq)
y_min = f_min
y_max = f_max
for y in range(self.image_height):
freq = y_min + y / (self.image_height - 1.0) * (y_max - y_min)
fft_bin = freq / f_max * (self.fft_size / 2 + 1)
if fft_bin < self.fft_size / 2:
alpha = fft_bin - int(fft_bin)
self.y_to_bin.append((int(fft_bin), alpha * 255)) | [
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Parisson/TimeSide | timeside/core/tools/hdf5.py | dict_from_hdf5 | def dict_from_hdf5(dict_like, h5group):
"""
Load a dictionnary-like object from a h5 file group
"""
# Read attributes
for name, value in h5group.attrs.items():
dict_like[name] = value | python | def dict_from_hdf5(dict_like, h5group):
"""
Load a dictionnary-like object from a h5 file group
"""
# Read attributes
for name, value in h5group.attrs.items():
dict_like[name] = value | [
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Parisson/TimeSide | timeside/plugins/decoder/array.py | ArrayDecoder.get_frames | def get_frames(self):
"Define an iterator that will return frames at the given blocksize"
nb_frames = self.input_totalframes // self.output_blocksize
if self.input_totalframes % self.output_blocksize == 0:
nb_frames -= 1 # Last frame must send eod=True
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yield (self.samples[index:index + self.output_blocksize], False)
yield (self.samples[nb_frames * self.output_blocksize:], True) | python | def get_frames(self):
"Define an iterator that will return frames at the given blocksize"
nb_frames = self.input_totalframes // self.output_blocksize
if self.input_totalframes % self.output_blocksize == 0:
nb_frames -= 1 # Last frame must send eod=True
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Parisson/TimeSide | timeside/core/component.py | implementations | def implementations(interface, recurse=True, abstract=False):
"""Returns the components implementing interface, and if recurse, any of
the descendants of interface. If abstract is True, also return the
abstract implementations."""
result = []
find_implementations(interface, recurse, abstract, result)
return result | python | def implementations(interface, recurse=True, abstract=False):
"""Returns the components implementing interface, and if recurse, any of
the descendants of interface. If abstract is True, also return the
abstract implementations."""
result = []
find_implementations(interface, recurse, abstract, result)
return result | [
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Parisson/TimeSide | timeside/core/component.py | find_implementations | def find_implementations(interface, recurse, abstract, result):
"""Find implementations of an interface or of one of its descendants and
extend result with the classes found."""
for item in MetaComponent.implementations:
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extend_unique(result, [item['class']])
if recurse:
subinterfaces = interface.__subclasses__()
if subinterfaces:
for i in subinterfaces:
find_implementations(i, recurse, abstract, result) | python | def find_implementations(interface, recurse, abstract, result):
"""Find implementations of an interface or of one of its descendants and
extend result with the classes found."""
for item in MetaComponent.implementations:
if (item['interface'] == interface and (abstract or not item['abstract'])):
extend_unique(result, [item['class']])
if recurse:
subinterfaces = interface.__subclasses__()
if subinterfaces:
for i in subinterfaces:
find_implementations(i, recurse, abstract, result) | [
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Parisson/TimeSide | timeside/core/grapher.py | Grapher.draw_peaks | def draw_peaks(self, x, peaks, line_color):
"""Draw 2 peaks at x"""
y1 = self.image_height * 0.5 - peaks[0] * (self.image_height - 4) * 0.5
y2 = self.image_height * 0.5 - peaks[1] * (self.image_height - 4) * 0.5
if self.previous_y:
self.draw.line(
[self.previous_x, self.previous_y, x, y1, x, y2], line_color)
else:
self.draw.line([x, y1, x, y2], line_color)
self.draw_anti_aliased_pixels(x, y1, y2, line_color)
self.previous_x, self.previous_y = x, y2 | python | def draw_peaks(self, x, peaks, line_color):
"""Draw 2 peaks at x"""
y1 = self.image_height * 0.5 - peaks[0] * (self.image_height - 4) * 0.5
y2 = self.image_height * 0.5 - peaks[1] * (self.image_height - 4) * 0.5
if self.previous_y:
self.draw.line(
[self.previous_x, self.previous_y, x, y1, x, y2], line_color)
else:
self.draw.line([x, y1, x, y2], line_color)
self.draw_anti_aliased_pixels(x, y1, y2, line_color)
self.previous_x, self.previous_y = x, y2 | [
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Parisson/TimeSide | timeside/core/grapher.py | Grapher.draw_peaks_inverted | def draw_peaks_inverted(self, x, peaks, line_color):
"""Draw 2 inverted peaks at x"""
y1 = self.image_height * 0.5 - peaks[0] * (self.image_height - 4) * 0.5
y2 = self.image_height * 0.5 - peaks[1] * (self.image_height - 4) * 0.5
if self.previous_y and x < self.image_width - 1:
if y1 < y2:
self.draw.line((x, 0, x, y1), line_color)
self.draw.line((x, self.image_height, x, y2), line_color)
else:
self.draw.line((x, 0, x, y2), line_color)
self.draw.line((x, self.image_height, x, y1), line_color)
else:
self.draw.line((x, 0, x, self.image_height), line_color)
self.draw_anti_aliased_pixels(x, y1, y2, line_color)
self.previous_x, self.previous_y = x, y1 | python | def draw_peaks_inverted(self, x, peaks, line_color):
"""Draw 2 inverted peaks at x"""
y1 = self.image_height * 0.5 - peaks[0] * (self.image_height - 4) * 0.5
y2 = self.image_height * 0.5 - peaks[1] * (self.image_height - 4) * 0.5
if self.previous_y and x < self.image_width - 1:
if y1 < y2:
self.draw.line((x, 0, x, y1), line_color)
self.draw.line((x, self.image_height, x, y2), line_color)
else:
self.draw.line((x, 0, x, y2), line_color)
self.draw.line((x, self.image_height, x, y1), line_color)
else:
self.draw.line((x, 0, x, self.image_height), line_color)
self.draw_anti_aliased_pixels(x, y1, y2, line_color)
self.previous_x, self.previous_y = x, y1 | [
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Parisson/TimeSide | timeside/core/grapher.py | Grapher.draw_anti_aliased_pixels | def draw_anti_aliased_pixels(self, x, y1, y2, color):
""" vertical anti-aliasing at y1 and y2 """
y_max = max(y1, y2)
y_max_int = int(y_max)
alpha = y_max - y_max_int
if alpha > 0.0 and alpha < 1.0 and y_max_int + 1 < self.image_height:
current_pix = self.pixel[int(x), y_max_int + 1]
r = int((1 - alpha) * current_pix[0] + alpha * color[0])
g = int((1 - alpha) * current_pix[1] + alpha * color[1])
b = int((1 - alpha) * current_pix[2] + alpha * color[2])
self.pixel[x, y_max_int + 1] = (r, g, b)
y_min = min(y1, y2)
y_min_int = int(y_min)
alpha = 1.0 - (y_min - y_min_int)
if alpha > 0.0 and alpha < 1.0 and y_min_int - 1 >= 0:
current_pix = self.pixel[x, y_min_int - 1]
r = int((1 - alpha) * current_pix[0] + alpha * color[0])
g = int((1 - alpha) * current_pix[1] + alpha * color[1])
b = int((1 - alpha) * current_pix[2] + alpha * color[2])
self.pixel[x, y_min_int - 1] = (r, g, b) | python | def draw_anti_aliased_pixels(self, x, y1, y2, color):
""" vertical anti-aliasing at y1 and y2 """
y_max = max(y1, y2)
y_max_int = int(y_max)
alpha = y_max - y_max_int
if alpha > 0.0 and alpha < 1.0 and y_max_int + 1 < self.image_height:
current_pix = self.pixel[int(x), y_max_int + 1]
r = int((1 - alpha) * current_pix[0] + alpha * color[0])
g = int((1 - alpha) * current_pix[1] + alpha * color[1])
b = int((1 - alpha) * current_pix[2] + alpha * color[2])
self.pixel[x, y_max_int + 1] = (r, g, b)
y_min = min(y1, y2)
y_min_int = int(y_min)
alpha = 1.0 - (y_min - y_min_int)
if alpha > 0.0 and alpha < 1.0 and y_min_int - 1 >= 0:
current_pix = self.pixel[x, y_min_int - 1]
r = int((1 - alpha) * current_pix[0] + alpha * color[0])
g = int((1 - alpha) * current_pix[1] + alpha * color[1])
b = int((1 - alpha) * current_pix[2] + alpha * color[2])
self.pixel[x, y_min_int - 1] = (r, g, b) | [
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Parisson/TimeSide | timeside/plugins/grapher/spectrogram_log.py | SpectrogramLog.post_process | def post_process(self):
""" Apply last 2D transforms"""
self.image.putdata(self.pixels)
self.image = self.image.transpose(Image.ROTATE_90) | python | def post_process(self):
""" Apply last 2D transforms"""
self.image.putdata(self.pixels)
self.image = self.image.transpose(Image.ROTATE_90) | [
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Parisson/TimeSide | timeside/plugins/encoder/mp3.py | Mp3Encoder.write_metadata | def write_metadata(self):
"""Write all ID3v2.4 tags to file from self.metadata"""
import mutagen
from mutagen import id3
id3 = id3.ID3(self.filename)
for tag in self.metadata.keys():
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try:
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try:
id3.save()
except:
raise IOError('EncoderError: cannot write tags') | python | def write_metadata(self):
"""Write all ID3v2.4 tags to file from self.metadata"""
import mutagen
from mutagen import id3
id3 = id3.ID3(self.filename)
for tag in self.metadata.keys():
value = self.metadata[tag]
frame = mutagen.id3.Frames[tag](3, value)
try:
id3.add(frame)
except:
raise IOError('EncoderError: cannot tag "' + tag + '"')
try:
id3.save()
except:
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btimby/fulltext | fulltext/__main__.py | main | def main(args=sys.argv[1:]):
"""Extract text from a file.
Commands:
extract - extract text from path
check - make sure all deps are installed
Usage:
fulltext extract [-v] [-f] <path>...
fulltext check [-t]
Options:
-f, --file Open file first.
-t, --title Check deps for title.
-v, --verbose More verbose output.
"""
opt = docopt(main.__doc__.strip(), args, options_first=True)
config_logging(opt['--verbose'])
if opt['check']:
check_backends(opt['--title'])
elif opt['extract']:
handler = fulltext.get
if opt['--file']:
handler = _handle_open
for path in opt['<path>']:
print(handler(path))
else:
# we should never get here
raise ValueError("don't know how to handle cmd") | python | def main(args=sys.argv[1:]):
"""Extract text from a file.
Commands:
extract - extract text from path
check - make sure all deps are installed
Usage:
fulltext extract [-v] [-f] <path>...
fulltext check [-t]
Options:
-f, --file Open file first.
-t, --title Check deps for title.
-v, --verbose More verbose output.
"""
opt = docopt(main.__doc__.strip(), args, options_first=True)
config_logging(opt['--verbose'])
if opt['check']:
check_backends(opt['--title'])
elif opt['extract']:
handler = fulltext.get
if opt['--file']:
handler = _handle_open
for path in opt['<path>']:
print(handler(path))
else:
# we should never get here
raise ValueError("don't know how to handle cmd") | [
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btimby/fulltext | fulltext/__init__.py | is_binary | def is_binary(f):
"""Return True if binary mode."""
# NOTE: order matters here. We don't bail on Python 2 just yet. Both
# codecs.open() and io.open() can open in text mode, both set the encoding
# attribute. We must do that check first.
# If it has a decoding attribute with a value, it is text mode.
if getattr(f, "encoding", None):
return False
# Python 2 makes no further distinction.
if not PY3:
return True
# If the file has a mode, and it contains b, it is binary.
try:
if 'b' in getattr(f, 'mode', ''):
return True
except TypeError:
import gzip
if isinstance(f, gzip.GzipFile):
return True # in gzip mode is an integer
raise
# Can we sniff?
try:
f.seek(0, os.SEEK_CUR)
except (AttributeError, IOError):
return False
# Finally, let's sniff by reading a byte.
byte = f.read(1)
f.seek(-1, os.SEEK_CUR)
return hasattr(byte, 'decode') | python | def is_binary(f):
"""Return True if binary mode."""
# NOTE: order matters here. We don't bail on Python 2 just yet. Both
# codecs.open() and io.open() can open in text mode, both set the encoding
# attribute. We must do that check first.
# If it has a decoding attribute with a value, it is text mode.
if getattr(f, "encoding", None):
return False
# Python 2 makes no further distinction.
if not PY3:
return True
# If the file has a mode, and it contains b, it is binary.
try:
if 'b' in getattr(f, 'mode', ''):
return True
except TypeError:
import gzip
if isinstance(f, gzip.GzipFile):
return True # in gzip mode is an integer
raise
# Can we sniff?
try:
f.seek(0, os.SEEK_CUR)
except (AttributeError, IOError):
return False
# Finally, let's sniff by reading a byte.
byte = f.read(1)
f.seek(-1, os.SEEK_CUR)
return hasattr(byte, 'decode') | [
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btimby/fulltext | fulltext/__init__.py | handle_path | def handle_path(backend_inst, path, **kwargs):
"""
Handle a path.
Called by `get()` when provided a path. This function will prefer the
backend's `handle_path()` if one is provided Otherwise, it will open the
given path then use `handle_fobj()`.
"""
if callable(getattr(backend_inst, 'handle_path', None)):
# Prefer handle_path() if present.
LOGGER.debug("using handle_path")
return backend_inst.handle_path(path)
elif callable(getattr(backend_inst, 'handle_fobj', None)):
# Fallback to handle_fobj(). No warning here since the performance hit
# is minimal.
LOGGER.debug("using handle_fobj")
with open(path, 'rb') as f:
return backend_inst.handle_fobj(f)
else:
raise AssertionError(
'Backend %s has no _get functions' % backend_inst.__name__) | python | def handle_path(backend_inst, path, **kwargs):
"""
Handle a path.
Called by `get()` when provided a path. This function will prefer the
backend's `handle_path()` if one is provided Otherwise, it will open the
given path then use `handle_fobj()`.
"""
if callable(getattr(backend_inst, 'handle_path', None)):
# Prefer handle_path() if present.
LOGGER.debug("using handle_path")
return backend_inst.handle_path(path)
elif callable(getattr(backend_inst, 'handle_fobj', None)):
# Fallback to handle_fobj(). No warning here since the performance hit
# is minimal.
LOGGER.debug("using handle_fobj")
with open(path, 'rb') as f:
return backend_inst.handle_fobj(f)
else:
raise AssertionError(
'Backend %s has no _get functions' % backend_inst.__name__) | [
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btimby/fulltext | fulltext/__init__.py | handle_fobj | def handle_fobj(backend, f, **kwargs):
"""
Handle a file-like object.
Called by `get()` when provided a file-like. This function will prefer the
backend's `handle_fobj()` if one is provided. Otherwise, it will write the
data to a temporary file and call `handle_path()`.
"""
if not is_binary(f):
raise AssertionError('File must be opened in binary mode.')
if callable(getattr(backend, 'handle_fobj', None)):
# Prefer handle_fobj() if present.
LOGGER.debug("using handle_fobj")
return backend.handle_fobj(f)
elif callable(getattr(backend, 'handle_path', None)):
# Fallback to handle_path(). Warn user since this is potentially
# expensive.
LOGGER.debug("using handle_path")
LOGGER.warning(
"Using disk, %r backend does not provide `handle_fobj()`", backend)
ext = ''
if 'ext' in kwargs:
ext = '.' + kwargs['ext']
with fobj_to_tempfile(f, suffix=ext) as fname:
return backend.handle_path(fname, **kwargs)
else:
raise AssertionError(
'Backend %s has no _get functions' % backend.__name__) | python | def handle_fobj(backend, f, **kwargs):
"""
Handle a file-like object.
Called by `get()` when provided a file-like. This function will prefer the
backend's `handle_fobj()` if one is provided. Otherwise, it will write the
data to a temporary file and call `handle_path()`.
"""
if not is_binary(f):
raise AssertionError('File must be opened in binary mode.')
if callable(getattr(backend, 'handle_fobj', None)):
# Prefer handle_fobj() if present.
LOGGER.debug("using handle_fobj")
return backend.handle_fobj(f)
elif callable(getattr(backend, 'handle_path', None)):
# Fallback to handle_path(). Warn user since this is potentially
# expensive.
LOGGER.debug("using handle_path")
LOGGER.warning(
"Using disk, %r backend does not provide `handle_fobj()`", backend)
ext = ''
if 'ext' in kwargs:
ext = '.' + kwargs['ext']
with fobj_to_tempfile(f, suffix=ext) as fname:
return backend.handle_path(fname, **kwargs)
else:
raise AssertionError(
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btimby/fulltext | fulltext/__init__.py | backend_from_mime | def backend_from_mime(mime):
"""Determine backend module object from a mime string."""
try:
mod_name = MIMETYPE_TO_BACKENDS[mime]
except KeyError:
msg = "No handler for %r, defaulting to %r" % (mime, DEFAULT_MIME)
if 'FULLTEXT_TESTING' in os.environ:
warn(msg)
else:
LOGGER.debug(msg)
mod_name = MIMETYPE_TO_BACKENDS[DEFAULT_MIME]
mod = import_mod(mod_name)
return mod | python | def backend_from_mime(mime):
"""Determine backend module object from a mime string."""
try:
mod_name = MIMETYPE_TO_BACKENDS[mime]
except KeyError:
msg = "No handler for %r, defaulting to %r" % (mime, DEFAULT_MIME)
if 'FULLTEXT_TESTING' in os.environ:
warn(msg)
else:
LOGGER.debug(msg)
mod_name = MIMETYPE_TO_BACKENDS[DEFAULT_MIME]
mod = import_mod(mod_name)
return mod | [
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btimby/fulltext | fulltext/__init__.py | backend_from_fname | def backend_from_fname(name):
"""Determine backend module object from a file name."""
ext = splitext(name)[1]
try:
mime = EXTS_TO_MIMETYPES[ext]
except KeyError:
try:
f = open(name, 'rb')
except IOError as e:
# The file may not exist, we are being asked to determine it's type
# from it's name. Other errors are unexpected.
if e.errno != errno.ENOENT:
raise
# We will have to fall back upon the default backend.
msg = "No handler for %r, defaulting to %r" % (ext, DEFAULT_MIME)
if 'FULLTEXT_TESTING' in os.environ:
warn(msg)
else:
LOGGER.debug(msg)
mod_name = MIMETYPE_TO_BACKENDS[DEFAULT_MIME]
else:
with f:
return backend_from_fobj(f)
else:
mod_name = MIMETYPE_TO_BACKENDS[mime]
mod = import_mod(mod_name)
return mod | python | def backend_from_fname(name):
"""Determine backend module object from a file name."""
ext = splitext(name)[1]
try:
mime = EXTS_TO_MIMETYPES[ext]
except KeyError:
try:
f = open(name, 'rb')
except IOError as e:
# The file may not exist, we are being asked to determine it's type
# from it's name. Other errors are unexpected.
if e.errno != errno.ENOENT:
raise
# We will have to fall back upon the default backend.
msg = "No handler for %r, defaulting to %r" % (ext, DEFAULT_MIME)
if 'FULLTEXT_TESTING' in os.environ:
warn(msg)
else:
LOGGER.debug(msg)
mod_name = MIMETYPE_TO_BACKENDS[DEFAULT_MIME]
else:
with f:
return backend_from_fobj(f)
else:
mod_name = MIMETYPE_TO_BACKENDS[mime]
mod = import_mod(mod_name)
return mod | [
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btimby/fulltext | fulltext/__init__.py | backend_from_fobj | def backend_from_fobj(f):
"""Determine backend module object from a file object."""
if magic is None:
warn("magic lib is not installed; assuming mime type %r" % (
DEFAULT_MIME))
return backend_from_mime(DEFAULT_MIME)
else:
offset = f.tell()
try:
f.seek(0)
chunk = f.read(MAGIC_BUFFER_SIZE)
mime = magic.from_buffer(chunk, mime=True)
return backend_from_mime(mime)
finally:
f.seek(offset) | python | def backend_from_fobj(f):
"""Determine backend module object from a file object."""
if magic is None:
warn("magic lib is not installed; assuming mime type %r" % (
DEFAULT_MIME))
return backend_from_mime(DEFAULT_MIME)
else:
offset = f.tell()
try:
f.seek(0)
chunk = f.read(MAGIC_BUFFER_SIZE)
mime = magic.from_buffer(chunk, mime=True)
return backend_from_mime(mime)
finally:
f.seek(offset) | [
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btimby/fulltext | fulltext/__init__.py | backend_inst_from_mod | def backend_inst_from_mod(mod, encoding, encoding_errors, kwargs):
"""Given a mod and a set of opts return an instantiated
Backend class.
"""
kw = dict(encoding=encoding, encoding_errors=encoding_errors,
kwargs=kwargs)
try:
klass = getattr(mod, "Backend")
except AttributeError:
raise AttributeError("%r mod does not define any backend class" % mod)
inst = klass(**kw)
try:
inst.check(title=False)
except Exception as err:
bin_mod = "fulltext.backends.__bin"
warn("can't use %r due to %r; use %r backend instead" % (
mod, str(err), bin_mod))
inst = import_mod(bin_mod).Backend(**kw)
inst.check(title=False)
LOGGER.debug("using %r" % inst)
return inst | python | def backend_inst_from_mod(mod, encoding, encoding_errors, kwargs):
"""Given a mod and a set of opts return an instantiated
Backend class.
"""
kw = dict(encoding=encoding, encoding_errors=encoding_errors,
kwargs=kwargs)
try:
klass = getattr(mod, "Backend")
except AttributeError:
raise AttributeError("%r mod does not define any backend class" % mod)
inst = klass(**kw)
try:
inst.check(title=False)
except Exception as err:
bin_mod = "fulltext.backends.__bin"
warn("can't use %r due to %r; use %r backend instead" % (
mod, str(err), bin_mod))
inst = import_mod(bin_mod).Backend(**kw)
inst.check(title=False)
LOGGER.debug("using %r" % inst)
return inst | [
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btimby/fulltext | fulltext/__init__.py | get | def get(path_or_file, default=SENTINAL, mime=None, name=None, backend=None,
encoding=None, encoding_errors=None, kwargs=None,
_wtitle=False):
"""
Get document full text.
Accepts a path or file-like object.
* If given, `default` is returned instead of an error.
* `backend` is either a module object or a string specifying which
default backend to use (e.g. "doc"); take a look at backends
directory to see a list of default backends.
* `mime` and `name` should be passed if the information
is available to caller, otherwise a best guess is made.
If both are specified `mime` takes precedence.
* `encoding` and `encoding_errors` are used to handle text encoding.
They are taken into consideration mostly only by pure-python
backends which do not rely on CLI tools.
Default to "utf8" and "strict" respectively.
* `kwargs` are passed to the underlying backend.
"""
try:
text, title = _get(
path_or_file, default=default, mime=mime, name=name,
backend=backend, kwargs=kwargs, encoding=encoding,
encoding_errors=encoding_errors, _wtitle=_wtitle)
if _wtitle:
return (text, title)
else:
return text
except Exception as e:
if default is not SENTINAL:
LOGGER.exception(e)
return default
raise | python | def get(path_or_file, default=SENTINAL, mime=None, name=None, backend=None,
encoding=None, encoding_errors=None, kwargs=None,
_wtitle=False):
"""
Get document full text.
Accepts a path or file-like object.
* If given, `default` is returned instead of an error.
* `backend` is either a module object or a string specifying which
default backend to use (e.g. "doc"); take a look at backends
directory to see a list of default backends.
* `mime` and `name` should be passed if the information
is available to caller, otherwise a best guess is made.
If both are specified `mime` takes precedence.
* `encoding` and `encoding_errors` are used to handle text encoding.
They are taken into consideration mostly only by pure-python
backends which do not rely on CLI tools.
Default to "utf8" and "strict" respectively.
* `kwargs` are passed to the underlying backend.
"""
try:
text, title = _get(
path_or_file, default=default, mime=mime, name=name,
backend=backend, kwargs=kwargs, encoding=encoding,
encoding_errors=encoding_errors, _wtitle=_wtitle)
if _wtitle:
return (text, title)
else:
return text
except Exception as e:
if default is not SENTINAL:
LOGGER.exception(e)
return default
raise | [
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* `backend` is either a module object or a string specifying which
default backend to use (e.g. "doc"); take a look at backends
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* `mime` and `name` should be passed if the information
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* `encoding` and `encoding_errors` are used to handle text encoding.
They are taken into consideration mostly only by pure-python
backends which do not rely on CLI tools.
Default to "utf8" and "strict" respectively.
* `kwargs` are passed to the underlying backend. | [
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] | 9234cc1e2099209430e20317649549026de283ce | https://github.com/btimby/fulltext/blob/9234cc1e2099209430e20317649549026de283ce/fulltext/__init__.py#L585-L618 | train |
btimby/fulltext | fulltext/util.py | hilite | def hilite(s, ok=True, bold=False):
"""Return an highlighted version of 'string'."""
if not term_supports_colors():
return s
attr = []
if ok is None: # no color
pass
elif ok: # green
attr.append('32')
else: # red
attr.append('31')
if bold:
attr.append('1')
return '\x1b[%sm%s\x1b[0m' % (';'.join(attr), s) | python | def hilite(s, ok=True, bold=False):
"""Return an highlighted version of 'string'."""
if not term_supports_colors():
return s
attr = []
if ok is None: # no color
pass
elif ok: # green
attr.append('32')
else: # red
attr.append('31')
if bold:
attr.append('1')
return '\x1b[%sm%s\x1b[0m' % (';'.join(attr), s) | [
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btimby/fulltext | fulltext/util.py | fobj_to_tempfile | def fobj_to_tempfile(f, suffix=''):
"""Context manager which copies a file object to disk and return its
name. When done the file is deleted.
"""
with tempfile.NamedTemporaryFile(
dir=TEMPDIR, suffix=suffix, delete=False) as t:
shutil.copyfileobj(f, t)
try:
yield t.name
finally:
os.remove(t.name) | python | def fobj_to_tempfile(f, suffix=''):
"""Context manager which copies a file object to disk and return its
name. When done the file is deleted.
"""
with tempfile.NamedTemporaryFile(
dir=TEMPDIR, suffix=suffix, delete=False) as t:
shutil.copyfileobj(f, t)
try:
yield t.name
finally:
os.remove(t.name) | [
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