def test_reduce_to_segments(self):
"""Tests reduce_to_segments function."""
test1 = [('ULSA', 1.0), ('UENG', 2.0), ('UKIN', 3.0), ('ULSA', 1.0)]
expected1 = {
'ULSA': numpy.array([1.0, 1.0]),
'UENG': numpy.array([2.0]),
'UKIN': numpy.array([3.0])
}
actual1 = vertex_analysis.reduce_to_segments(test1)
self._test_dict_to_numpy_array_equal(actual1, expected1)
test2 = []
expected2 = dict()
actual2 = vertex_analysis.reduce_to_segments(test2)
self._test_dict_to_numpy_array_equal(actual2, expected2)
test3 = [('ULSA', 1.0), ('ULSA', 2.0), ('ULSA', 3.0), ('ULSA', 1.0)]
expected3 = {
'ULSA': numpy.array([1.0, 2.0, 3.0, 1.0]),
}
actual3 = vertex_analysis.reduce_to_segments(test3)
self._test_dict_to_numpy_array_equal(actual3, expected3)
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:
ax1 = fig.add_subplot(2, 1, 1)
