def _nary_dict_update(dicts, **kwargs):
"""Implementation of n-argument ``dict.update``,
with flags controlling the exact strategy.
"""
copy = kwargs['copy']
res = dicts[0].copy() if copy else dicts[0]
if len(dicts) == 1:
return res
# decide what strategy to use when updating a dictionary
# with the values from another: {(non)recursive} x {(non)overwriting}
deep = kwargs['deep']
overwrite = kwargs['overwrite']
if deep:
dict_update = curry(_recursive_dict_update, overwrite=overwrite)
else:
if overwrite:
dict_update = res.__class__.update
else:
def dict_update(dict_, other):
for k, v in iteritems(other):
dict_.setdefault(k, v)
for d in dicts[1:]:
dict_update(res, d)
return res
def _create_multitagging_metaclass(self, *tags, **kwargs):
"""Create a metaclass that applies multiple tags with given name
to every class it participates in creation of.
"""
base = kwargs.pop('base', __unit__.ObjectMetaclass)
tagging_metaclasses = map(
curry(self._create_tagging_metaclass, base=base), tags)
# sadly, the following is syntax error:
#
# class Meta(*tagging_metaclasses):
# pass
return type('Meta', tuple(tagging_metaclasses), {})
def _create_multitagging_metaclass(self, *tags, **kwargs):
"""Create a metaclass that applies multiple tags with given name
to every class it participates in creation of.
"""
base = kwargs.pop('base', __unit__.ObjectMetaclass)
tagging_metaclasses = map(
curry(self._create_tagging_metaclass, base=base), tags)
# sadly, the following is syntax error:
#
# class Meta(*tagging_metaclasses):
# pass
return type('Meta', tuple(tagging_metaclasses), {})
def approximate_coding_sessions(clustered_commits, approx_algo):
"""Approximates the coding sessions that resulted in given clustered commits.
:param clustered_commits: Dictionary mapping contributor names
to lists of Commit clusters
:param approx_algo: Name of approximation algorithm
:return: Dictionary mapping contributor names to lists of Session tuples
"""
approx_func = globals().get(approx_algo + '_approximation')
if not approx_func:
raise ValueError("Unknown approximation '%s'" % approx_algo)
return dicts.mapvalues(curry(map, approx_func), clustered_commits)
def approximate_coding_sessions(clustered_commits, approx_algo):
"""Approximates the coding sessions that resulted in given clustered commits.
:param clustered_commits: Dictionary mapping contributor names
to lists of Commit clusters
:param approx_algo: Name of approximation algorithm
:return: Dictionary mapping contributor names to lists of Session tuples
"""
approx_func = globals().get(approx_algo + '_approximation')
if not approx_func:
raise ValueError("Unknown approximation '%s'" % approx_algo)
return dicts.mapvalues(curry(map, approx_func), clustered_commits)
def cluster_commits(grouped_commits, cluster_algo, epsilon):
"""Clusters commits for every contributor in given dictionary.
:param grouped_commits: Dictionary mapping contributor names
to lists of Commit tuples
:param cluster_algo: Name of clustering algorithm
:param epsilon: Temporal distance for the epsilon-neighborhood
:return: Dictionary mapping author names to lists of coding sessions
"""
cluster_func = globals().get(cluster_algo + '_clustering')
if not cluster_func:
raise ValueError("Unknown clustering algorithm '%s'" % cluster_algo)
return dicts.mapvalues(curry(cluster_func, epsilon=epsilon),
grouped_commits)
def cluster_commits(grouped_commits, cluster_algo, epsilon):
"""Clusters commits for every contributor in given dictionary.
:param grouped_commits: Dictionary mapping contributor names
to lists of Commit tuples
:param cluster_algo: Name of clustering algorithm
:param epsilon: Temporal distance for the epsilon-neighborhood
:return: Dictionary mapping author names to lists of coding sessions
"""
cluster_func = globals().get(cluster_algo + '_clustering')
if not cluster_func:
raise ValueError("Unknown clustering algorithm '%s'" % cluster_algo)
return dicts.mapvalues(curry(cluster_func, epsilon=epsilon),
grouped_commits)