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# ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
# ----------------------------------------------------------------------------
import io
from unittest import TestCase, main
import numpy as np
import pandas as pd
import numpy.testing as npt
from skbio import OrdinationResults
from skbio.io import OrdinationFormatError
from skbio.io.format.ordination import (
_ordination_to_ordination_results, _ordination_results_to_ordination,
_ordination_sniffer)
from skbio.util import get_data_path, assert_ordination_results_equal
class OrdinationTestData(TestCase):
def setUp(self):
self.valid_fps = map(
get_data_path,
['ordination_L&L_CA_data_scores', 'ordination_example3_scores',
'ordination_PCoA_sample_data_3_scores',
'ordination_example2_scores'])
# Store filepath, regex for matching the error message that should be
# raised when reading the file, and whether the file should be matched
# by the sniffer (True) or not (False).
self.invalid_fps = map(lambda e: (get_data_path(e[0]), e[1], e[2]), [
('empty', 'end of file.*Eigvals header', False),
('whitespace_only', 'Eigvals header not found', False),
('ordination_error1', 'Eigvals header not found', False),
('ordination_error2',
'Proportion explained header not found', False),
('ordination_error3', 'Species header not found', True),
('ordination_error4', 'Site header not found', True),
('ordination_error5', 'Biplot header not found', True),
('ordination_error6', 'Site constraints header not found', True),
('ordination_error7', 'empty line', False),
('ordination_error8', '9.*Proportion explained.*8', True),
('ordination_error9', '2 values.*1 in row 1', True),
('ordination_error10', '2 values.*1 in row 1', True),
('ordination_error11', 'Site constraints ids and site ids', True),
('ordination_error12', '9.*Eigvals.*8', True),
('ordination_error13', '9.*Proportion explained.*8', True),
('ordination_error14', 'Site is 0: 9 x 0', True),
('ordination_error15', '9 values.*8 in row 1', True),
('ordination_error16', 'Biplot is 0: 3 x 0', True),
('ordination_error17', '3 values.*2 in row 1', True),
('ordination_error18',
'proportion explained.*eigvals: 8 != 9', True),
('ordination_error19',
'coordinates.*species.*eigvals: 1 != 2', True),
('ordination_error20', 'coordinates.*site.*eigvals: 1 != 2', True),
('ordination_error21', 'one eigval', False),
('ordination_error22', 'end of file.*blank line', False),
('ordination_error23', 'end of file.*Proportion explained section',
True),
('ordination_error24', 'end of file.*row 2.*Species section', True)
])
class OrdinationResultsReaderWriterTests(OrdinationTestData):
def setUp(self):
super(OrdinationResultsReaderWriterTests, self).setUp()
# define in-memory results, one for each of the valid files in
# self.valid_fps
# CA results
axes_ids = ['CA1', 'CA2']
species_ids = ['Species1', 'Species2', 'Species3']
site_ids = ['Site1', 'Site2', 'Site3']
eigvals = pd.Series([0.0961330159181, 0.0409418140138], axes_ids)
species = pd.DataFrame([[0.408869425742, 0.0695518116298],
[-0.1153860437, -0.299767683538],
[-0.309967102571, 0.187391917117]],
index=species_ids, columns=axes_ids)
site = pd.DataFrame([[-0.848956053187, 0.882764759014],
[-0.220458650578, -1.34482000302],
[1.66697179591, 0.470324389808]],
index=site_ids, columns=axes_ids)
biplot = None
site_constraints = None
prop_explained = None
ca_scores = OrdinationResults(
'CA', 'Correspondence Analysis', eigvals=eigvals, features=species,
samples=site, biplot_scores=biplot,
sample_constraints=site_constraints,
proportion_explained=prop_explained)
# CCA results
axes_ids = ['CCA%d' % i for i in range(1, 10)]
species_ids = ['Species0', 'Species1', 'Species2', 'Species3',
'Species4', 'Species5', 'Species6', 'Species7',
'Species8']
site_ids = ['Site0', 'Site1', 'Site2', 'Site3', 'Site4', 'Site5',
'Site6', 'Site7', 'Site8', 'Site9']
eigvals = pd.Series([0.366135830393, 0.186887643052, 0.0788466514249,
0.082287840501, 0.0351348475787, 0.0233265839374,
0.0099048981912, 0.00122461669234,
0.000417454724117], axes_ids)
species = pd.DataFrame(np.loadtxt(
get_data_path('ordination_exp_Ordination_CCA_species')),
index=species_ids, columns=axes_ids)
site = pd.DataFrame(
np.loadtxt(get_data_path('ordination_exp_Ordination_CCA_site')),
index=site_ids, columns=axes_ids)
biplot = pd.DataFrame(
[[-0.169746767979, 0.63069090084, 0.760769036049],
[-0.994016563505, 0.0609533148724, -0.0449369418179],
[0.184352565909, -0.974867543612, 0.0309865007541]],
columns=axes_ids[:3])
site_constraints = pd.DataFrame(np.loadtxt(
get_data_path('ordination_exp_Ordination_CCA_site_constraints')),
index=site_ids, columns=axes_ids)
prop_explained = None
cca_scores = OrdinationResults('CCA',
'Canonical Correspondence Analysis',
eigvals=eigvals, features=species,
samples=site, biplot_scores=biplot,
sample_constraints=site_constraints,
proportion_explained=prop_explained)
# PCoA results
axes_ids = ['PC%d' % i for i in range(1, 10)]
species_ids = None
site_ids = ['PC.636', 'PC.635', 'PC.356', 'PC.481', 'PC.354', 'PC.593',
'PC.355', 'PC.607', 'PC.634']
eigvals = pd.Series([0.512367260461, 0.300719094427, 0.267912066004,
0.208988681078, 0.19169895326, 0.16054234528,
0.15017695712, 0.122457748167, 0.0], axes_ids)
species = None
site = pd.DataFrame(
np.loadtxt(get_data_path('ordination_exp_Ordination_PCoA_site')),
index=site_ids, columns=axes_ids)
biplot = None
site_constraints = None
prop_explained = pd.Series([0.267573832777, 0.15704469605,
0.139911863774, 0.109140272454,
0.100111048503, 0.0838401161912,
0.0784269939011, 0.0639511763509, 0.0],
axes_ids)
pcoa_scores = OrdinationResults('PCoA',
'Principal Coordinate Analysis',
eigvals=eigvals, features=species,
samples=site, biplot_scores=biplot,
sample_constraints=site_constraints,
proportion_explained=prop_explained)
# RDA results
axes_ids = ['RDA%d' % i for i in range(1, 8)]
species_ids = ['Species0', 'Species1', 'Species2', 'Species3',
'Species4', 'Species5']
site_ids = ['Site0', 'Site1', 'Site2', 'Site3', 'Site4', 'Site5',
'Site6', 'Site7', 'Site8', 'Site9']
eigvals = pd.Series([25.8979540892, 14.9825779819, 8.93784077262,
6.13995623072, 1.68070536498, 0.57735026919,
0.275983624351], axes_ids)
species = pd.DataFrame(np.loadtxt(
get_data_path('ordination_exp_Ordination_RDA_species')),
index=species_ids, columns=axes_ids)
site = pd.DataFrame(
np.loadtxt(get_data_path('ordination_exp_Ordination_RDA_site')),
index=site_ids, columns=axes_ids)
biplot = pd.DataFrame(
[[0.422650019179, -0.559142585857, -0.713250678211],
[0.988495963777, 0.150787422017, -0.0117848614073],
[-0.556516618887, 0.817599992718, 0.147714267459],
[-0.404079676685, -0.9058434809, -0.127150316558]],
columns=axes_ids[:3])
site_constraints = pd.DataFrame(np.loadtxt(
get_data_path('ordination_exp_Ordination_RDA_site_constraints')),
index=site_ids, columns=axes_ids)
prop_explained = None
rda_scores = OrdinationResults(
'RDA', 'Redundancy Analysis', eigvals=eigvals, features=species,
samples=site, biplot_scores=biplot,
sample_constraints=site_constraints,
proportion_explained=prop_explained)
self.ordination_results_objs = [ca_scores, cca_scores, pcoa_scores,
rda_scores]
def test_read_valid_files(self):
for fp, obj in zip(self.valid_fps, self.ordination_results_objs):
obs = _ordination_to_ordination_results(fp)
assert_ordination_results_equal(
obs, obj, ignore_method_names=True,
ignore_axis_labels=True)
def test_read_invalid_files(self):
for invalid_fp, error_msg_regexp, _ in self.invalid_fps:
with self.assertRaisesRegex(OrdinationFormatError,
error_msg_regexp):
_ordination_to_ordination_results(invalid_fp)
def test_write(self):
for fp, obj in zip(self.valid_fps, self.ordination_results_objs):
fh = io.StringIO()
_ordination_results_to_ordination(obj, fh)
obs = fh.getvalue()
fh.close()
with io.open(fp) as fh:
exp = fh.read()
npt.assert_equal(obs, exp)
def test_roundtrip_read_write(self):
for fp in self.valid_fps:
# Read.
obj1 = _ordination_to_ordination_results(fp)
# Write.
fh = io.StringIO()
_ordination_results_to_ordination(obj1, fh)
fh.seek(0)
# Read.
obj2 = _ordination_to_ordination_results(fh)
fh.close()
assert_ordination_results_equal(obj1, obj2)
class SnifferTests(OrdinationTestData):
def setUp(self):
super(SnifferTests, self).setUp()
def test_matches_and_nonmatches(self):
# Sniffer should match all valid files, and will match some invalid
# ones too because it doesn't exhaustively check the entire file.
for fp in self.valid_fps:
self.assertEqual(_ordination_sniffer(fp), (True, {}))
for fp, _, expected_sniffer_match in self.invalid_fps:
self.assertEqual(_ordination_sniffer(fp),
(expected_sniffer_match, {}))
if __name__ == '__main__':
main()
|
