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)
def test_dict__some_object(self):
with self.assertRaises(TypeError):
__unit__.mapvalues(MapValues.FUNCTION, object())
def test_dict__none(self):
with self.assertRaises(TypeError):
__unit__.mapvalues(MapValues.FUNCTION, None)
def test_function__non_function(self):
with self.assertRaises(TypeError):
__unit__.mapvalues(object(), self.DICT)
def test_function__none(self):
self.assertEquals(self.DICT, __unit__.mapvalues(None, self.DICT))
def write_contrib(contrib, parent, tag):
ET.SubElement(parent, tag, attrib=dicts.mapvalues(str_, contrib))
def test_function__none(self):
self.assertEquals(self.DICT, __unit__.mapvalues(None, self.DICT))
def test_map(self):
self.assertEquals(self.MAPPED_DICT,
__unit__.mapvalues(MapValues.FUNCTION, self.DICT))
def test_dict__empty(self):
self.assertEquals({}, __unit__.mapvalues(None, {}))
self.assertEquals({}, __unit__.mapvalues(MapValues.FUNCTION, {}))
def test_dict__some_object(self):
with self.assertRaises(TypeError):
__unit__.mapvalues(MapValues.FUNCTION, object())
def test_dict__none(self):
with self.assertRaises(TypeError):
__unit__.mapvalues(MapValues.FUNCTION, None)
def test_function__non_function(self):
with self.assertRaises(TypeError):
__unit__.mapvalues(object(), self.DICT)
def test_dict__empty(self):
self.assertEquals({}, __unit__.mapvalues(None, {}))
self.assertEquals({}, __unit__.mapvalues(MapValues.FUNCTION, {}))
def test_map(self):
self.assertEquals(self.MAPPED_DICT,
__unit__.mapvalues(MapValues.FUNCTION, self.DICT))
def write_contrib(contrib, parent, tag):
ET.SubElement(parent, tag, attrib=dicts.mapvalues(str_, contrib))
