def _make_groups(self, trajectory_categories, sort_category):
r"""Groups the sample ids in `self._metadata_map` by the values in
`trajectory_categories`
Creates `self._groups`, a dictionary keyed by category and values are
dictionaries in which the keys represent the group name within the
category and values are ordered lists of sample ids
If `sort_category` is not None, the sample ids are sorted based on the
values under this category in the metadata map. Otherwise, they are
sorted using the sample id.
Parameters
----------
trajectory_categories : list of str
A list of metadata categories to use to create the groups.
Default: None, compute all of them
sort_category : str or None
The category from self._metadata_map to use to sort groups
"""
# If sort_category is provided, we used the value of such category to
# sort. Otherwise, we use the sample id.
if sort_category:
sort_val = lambda sid: self._metadata_map[sort_category][sid]
else:
sort_val = lambda sid: sid
self._groups = defaultdict(dict)
for cat in trajectory_categories:
# Group samples by category
gb = self._metadata_map.groupby(cat)
for g, df in gb:
sorted_list = signed_natsort([(sort_val(sid), sid)
for sid in df.index])
self._groups[cat][g] = [val[1] for val in sorted_list]
def _make_groups(self, trajectory_categories, sort_category):
r"""Groups the sample ids in `self._metadata_map` by the values in
`trajectory_categories`
Creates `self._groups`, a dictionary keyed by category and values are
dictionaries in which the keys represent the group name within the
category and values are ordered lists of sample ids
If `sort_category` is not None, the sample ids are sorted based on the
values under this category in the metadata map. Otherwise, they are
sorted using the sample id.
Parameters
----------
trajectory_categories : list of str
A list of metadata categories to use to create the groups.
Default: None, compute all of them
sort_category : str or None
The category from self._metadata_map to use to sort groups
"""
# If sort_category is provided, we used the value of such category to
# sort. Otherwise, we use the sample id.
if sort_category:
sort_val = lambda sid: self._metadata_map[sort_category][sid]
else:
sort_val = lambda sid: sid
self._groups = defaultdict(dict)
for cat in trajectory_categories:
# Group samples by category
gb = self._metadata_map.groupby(cat)
for g, df in gb:
sorted_list = signed_natsort([(sort_val(sid), sid)
for sid in df.index])
self._groups[cat][g] = [val[1] for val in sorted_list]
def test_signed_sort(self):
"""Test correct sorting of different data types"""
# an empty list must be returned when an empty list needs to be sorted
self.assertEqual(signed_natsort([]), [])
# tuples that can be sorted by type-casting the first element
test_list = [('9', 'SampleA'), ('-1', 'SampleD'), ('7', 'SampleC'),
('-2', 'SampleE'), ('-0.11', 'SampleF'),
('17.11', 'SampleB'), ('100', 'SampleG'),
('13', 'SampleH')]
expected_result = [('-2', 'SampleE'), ('-1', 'SampleD'),
('-0.11', 'SampleF'), ('7', 'SampleC'),
('9', 'SampleA'), ('13', 'SampleH'),
('17.11', 'SampleB'), ('100', 'SampleG')]
output = signed_natsort(test_list)
self.assertEquals(output, expected_result)
# tuples that must be sorted alphabetically
test_list = [('Cygnus', 'SampleA'), ('Cepheus', 'SampleD'),
('Auriga', 'SampleC'), ('Grus', 'SampleE'),
('Hydra', 'SampleF'), ('Carina', 'SampleB'),
('Orion', 'SampleG'), ('Lynx', 'SampleH')]
expected_result = [('Auriga', 'SampleC'), ('Carina', 'SampleB'),
('Cepheus', 'SampleD'), ('Cygnus', 'SampleA'),
('Grus', 'SampleE'), ('Hydra', 'SampleF'),
('Lynx', 'SampleH'), ('Orion', 'SampleG')]
output = signed_natsort(test_list)
self.assertEquals(output, expected_result)
# mixed case, tuples will be sorted alpha-numerically
test_list = [('Cygnus', 'SampleA'), ('Cepheus', 'SampleD'),
('Auriga', 'SampleC'), ('Grus', 'SampleE'),
('-0.11', 'SampleF'), ('17.11', 'SampleB'),
('100', 'SampleG'), ('Lynx', 'SampleH')]
expected_result = [('17.11', 'SampleB'), ('100', 'SampleG'),
('-0.11', 'SampleF'), ('Auriga', 'SampleC'),
('Cepheus', 'SampleD'), ('Cygnus', 'SampleA'),
('Grus', 'SampleE'), ('Lynx', 'SampleH')]
output = signed_natsort(test_list)
self.assertEquals(output, expected_result)
# mixed case just a list
test_list = ['foo', 'bar', '-100', '12', 'spam', '4', '-1']
expected_result = ['4', '12', '-1', '-100', 'bar', 'foo', 'spam']
output = signed_natsort(test_list)
self.assertEquals(output, expected_result)
# list of elements that can be type-casted
test_list = ['0', '1', '14', '12', '-15', '4', '-1']
expected_result = ['-15', '-1', '0', '1', '4', '12', '14']
output = signed_natsort(test_list)
self.assertEquals(output, expected_result)
# mixed dict case
test_dict = {'foo': 'a', 'bar': 'b', '-100': '1', '12': '11',
'spam': 'q', '4': '11', '-1': 'e'}
expected_result = ['4', '12', '-1', '-100', 'bar', 'foo', 'spam']
output = signed_natsort(test_dict)
self.assertEquals(output, expected_result)
# dict where the keys can be type-casted
test_dict = {'0': 'foo', '1': 'bar', '14': 'stand', '12': 'eggs',
'-15': 'q', '4': 'b', '-1': 'h'}
expected_result = ['-15', '-1', '0', '1', '4', '12', '14']
output = signed_natsort(test_dict)
self.assertEquals(output, expected_result)
def test_signed_sort(self):
"""Test correct sorting of different data types"""
# an empty list must be returned when an empty list needs to be sorted
self.assertEqual(signed_natsort([]), [])
# tuples that can be sorted by type-casting the first element
test_list = [('9', 'SampleA'), ('-1', 'SampleD'), ('7', 'SampleC'),
('-2', 'SampleE'), ('-0.11', 'SampleF'),
('17.11', 'SampleB'), ('100', 'SampleG'),
('13', 'SampleH')]
expected_result = [('-2', 'SampleE'), ('-1', 'SampleD'),
('-0.11', 'SampleF'), ('7', 'SampleC'),
('9', 'SampleA'), ('13', 'SampleH'),
('17.11', 'SampleB'), ('100', 'SampleG')]
output = signed_natsort(test_list)
self.assertEquals(output, expected_result)
# tuples that must be sorted alphabetically
test_list = [('Cygnus', 'SampleA'), ('Cepheus', 'SampleD'),
('Auriga', 'SampleC'), ('Grus', 'SampleE'),
('Hydra', 'SampleF'), ('Carina', 'SampleB'),
('Orion', 'SampleG'), ('Lynx', 'SampleH')]
expected_result = [('Auriga', 'SampleC'), ('Carina', 'SampleB'),
('Cepheus', 'SampleD'), ('Cygnus', 'SampleA'),
('Grus', 'SampleE'), ('Hydra', 'SampleF'),
('Lynx', 'SampleH'), ('Orion', 'SampleG')]
output = signed_natsort(test_list)
self.assertEquals(output, expected_result)
# mixed case, tuples will be sorted alpha-numerically
test_list = [('Cygnus', 'SampleA'), ('Cepheus', 'SampleD'),
('Auriga', 'SampleC'), ('Grus', 'SampleE'),
('-0.11', 'SampleF'), ('17.11', 'SampleB'),
('100', 'SampleG'), ('Lynx', 'SampleH')]
expected_result = [('17.11', 'SampleB'), ('100', 'SampleG'),
('-0.11', 'SampleF'), ('Auriga', 'SampleC'),
('Cepheus', 'SampleD'), ('Cygnus', 'SampleA'),
('Grus', 'SampleE'), ('Lynx', 'SampleH')]
output = signed_natsort(test_list)
self.assertEquals(output, expected_result)
# mixed case just a list
test_list = ['foo', 'bar', '-100', '12', 'spam', '4', '-1']
expected_result = ['4', '12', '-1', '-100', 'bar', 'foo', 'spam']
output = signed_natsort(test_list)
self.assertEquals(output, expected_result)
# list of elements that can be type-casted
test_list = ['0', '1', '14', '12', '-15', '4', '-1']
expected_result = ['-15', '-1', '0', '1', '4', '12', '14']
output = signed_natsort(test_list)
self.assertEquals(output, expected_result)
# mixed dict case
test_dict = {
'foo': 'a',
'bar': 'b',
'-100': '1',
'12': '11',
'spam': 'q',
'4': '11',
'-1': 'e'
}
expected_result = ['4', '12', '-1', '-100', 'bar', 'foo', 'spam']
output = signed_natsort(test_dict)
self.assertEquals(output, expected_result)
# dict where the keys can be type-casted
test_dict = {
'0': 'foo',
'1': 'bar',
'14': 'stand',
'12': 'eggs',
'-15': 'q',
'4': 'b',
'-1': 'h'
}
expected_result = ['-15', '-1', '0', '1', '4', '12', '14']
output = signed_natsort(test_dict)
self.assertEquals(output, expected_result)
