def test_map_to_segments(self):
"""Tests map_to_segments function."""
segmenter = None
test1 = [(1, 1.0), (2, 2.0), (3, 3.0)]
expected1 = [('ULSA', 1.0), ('UENG', 2.0), ('UKIN', 3.0)]
mock_student_wrapper1 = unittest.mock.MagicMock()
mock_student_wrapper1.segment_student = unittest.mock.MagicMock(
side_effect=['ULSA', 'UENG', 'UKIN'])
actual1 = list(
vertex_analysis.map_to_segments(test1, segmenter,
mock_student_wrapper1))
self.assertEqual(actual1, expected1)
test2 = []
expected2 = []
mock_student_wrapper2 = unittest.mock.MagicMock()
actual2 = list(
vertex_analysis.map_to_segments(test2, segmenter,
mock_student_wrapper2))
self.assertEqual(actual2, expected2)
help='Directory containing the swig modules.',
action=ReadableDirectory, required=True)
parser.set_defaults()
args = parser.parse_args()
show_unnormalized = 'frequency' in args.distributions
show_normalized = 'normalized' in args.distributions
# segment the data
vertex_lines = vertex_analysis.get_id_values(args.file)
students_wrapper = StudentContainerWrapper(
args.swig_module_path, args.student_archive_path)
segmenter = StudentContainerWrapper.SEGMENTERS[args.segmenter]
mapped_lines = vertex_analysis.map_to_segments(
vertex_lines, segmenter, students_wrapper)
segments = vertex_analysis.reduce_to_segments(mapped_lines)
# filter out small segments
filtered_segments = {segment: values for segment, values in segments.items()
if values.size >= args.threshold}
# set up the histogram for various colors
size = ((12, 8) if show_unnormalized and show_normalized
else (12, 4))
fig = matplotlib.pyplot.figure(figsize=size)
#fig.subplots_adjust(hspace=0.4)
# Create the non-normalized histogram.
if show_unnormalized:
if show_normalized:
'--swig-module-path', dest='swig_module_path',
help='Directory containing the swig modules.',
action=readable_directory.ReadableDirectory, required=True)
args = parser.parse_args()
unweighted = args.weightedness == 'unweighted'
weighted = args.weightedness == 'weighted'
# get container of students
students = StudentContainerWrapper(
args.swig_module_path, args.student_archive_path)
# get the weights of individual segments
lines = list(vertex_analysis.get_id_values(args.file))
mapped_to_segments = vertex_analysis.map_to_segments(
lines, StudentContainerWrapper.SEGMENTERS[args.field], students)
reduced_data = vertex_analysis.reduce_to_in_out(
mapped_to_segments, args.in_segment)
if unweighted:
actual_segments = {key: len(data) for key, data in reduced_data.items()}
elif weighted:
actual_segments = {key: sum(data) for key, data in reduced_data.items()}
print(actual_segments)
population_segments = segment.get_segment_counts(students, args.field)
print(population_segments[args.in_segment])
population = len(students)
total_weight = sum(line[1] for line in lines) if weighted else len(lines)
