def setup(self):
"""Data from table 11.3 in Legendre & Legendre 1998
(p. 590). Loaded results as computed with vegan 2.0-8 and
compared with table 11.5 if also there."""
Y = np.loadtxt(get_data_path('example3_Y'))
X = np.loadtxt(get_data_path('example3_X'))
self.ordination = CCA(Y, X[:, :-1])
def test_Y_values(self):
X, Y = self.X, self.Y
Y[0, 0] = -1
with npt.assert_raises(ValueError):
CCA(Y, X)
Y[0] = 0
with npt.assert_raises(ValueError):
CCA(Y, X)
def setup(self):
"""Data from table 11.3 in Legendre & Legendre 1998
(p. 590). Loaded results as computed with vegan 2.0-8 and
compared with table 11.5 if also there."""
Y = np.loadtxt(get_data_path('example3_Y'))
X = np.loadtxt(get_data_path('example3_X'))
self.ordination = CCA(Y, X[:, :-1])
class TestCCAResults(object):
def setup(self):
"""Data from table 11.3 in Legendre & Legendre 1998
(p. 590). Loaded results as computed with vegan 2.0-8 and
compared with table 11.5 if also there."""
Y = np.loadtxt(get_data_path('example3_Y'))
X = np.loadtxt(get_data_path('example3_X'))
self.ordination = CCA(Y, X[:, :-1],
['Site0', 'Site1', 'Site2', 'Site3', 'Site4',
'Site5', 'Site6', 'Site7', 'Site8', 'Site9'],
['Species0', 'Species1', 'Species2', 'Species3',
'Species4', 'Species5', 'Species6', 'Species7',
'Species8'])
def test_scaling1_species(self):
scores = self.ordination.scores(1)
vegan_species = np.loadtxt(get_data_path(
'example3_species_scaling1_from_vegan'))
npt.assert_almost_equal(scores.species, vegan_species, decimal=6)
def test_scaling1_site(self):
scores = self.ordination.scores(1)
vegan_site = np.loadtxt(get_data_path(
'example3_site_scaling1_from_vegan'))
npt.assert_almost_equal(scores.site, vegan_site, decimal=4)
def test_scaling2_species(self):
scores = self.ordination.scores(2)
vegan_species = np.loadtxt(get_data_path(
'example3_species_scaling2_from_vegan'))
npt.assert_almost_equal(scores.species, vegan_species, decimal=5)
def test_scaling2_site(self):
scores = self.ordination.scores(2)
vegan_site = np.loadtxt(get_data_path(
'example3_site_scaling2_from_vegan'))
npt.assert_almost_equal(scores.site, vegan_site, decimal=4)
def setup(self):
"""Data from table 11.3 in Legendre & Legendre 1998
(p. 590). Loaded results as computed with vegan 2.0-8 and
compared with table 11.5 if also there."""
Y = np.loadtxt(get_data_path('example3_Y'))
X = np.loadtxt(get_data_path('example3_X'))
self.ordination = CCA(Y, X[:, :-1],
['Site0', 'Site1', 'Site2', 'Site3', 'Site4',
'Site5', 'Site6', 'Site7', 'Site8', 'Site9'],
['Species0', 'Species1', 'Species2', 'Species3',
'Species4', 'Species5', 'Species6', 'Species7',
'Species8'])
class TestCCAResults(object):
def setup(self):
"""Data from table 11.3 in Legendre & Legendre 1998
(p. 590). Loaded results as computed with vegan 2.0-8 and
compared with table 11.5 if also there."""
Y = np.loadtxt(get_data_path('example3_Y'))
X = np.loadtxt(get_data_path('example3_X'))
self.ordination = CCA(Y, X[:, :-1])
def test_scaling1_species(self):
scores = self.ordination.scores(1)
vegan_species = np.loadtxt(
get_data_path('example3_species_scaling1_from_vegan'))
npt.assert_almost_equal(scores.species, vegan_species, decimal=6)
def test_scaling1_site(self):
scores = self.ordination.scores(1)
vegan_site = np.loadtxt(
get_data_path('example3_site_scaling1_from_vegan'))
npt.assert_almost_equal(scores.site, vegan_site, decimal=4)
def test_scaling2_species(self):
scores = self.ordination.scores(2)
vegan_species = np.loadtxt(
get_data_path('example3_species_scaling2_from_vegan'))
npt.assert_almost_equal(scores.species, vegan_species, decimal=5)
def test_scaling2_site(self):
scores = self.ordination.scores(2)
vegan_site = np.loadtxt(
get_data_path('example3_site_scaling2_from_vegan'))
npt.assert_almost_equal(scores.site, vegan_site, decimal=4)
def test_shape(self):
X, Y = self.X, self.Y
with npt.assert_raises(ValueError):
CCA(Y, X[:-1])
import os
import numpy as np
from skbio.maths.stats.ordination import CA, RDA, CCA
path = os.path.dirname(os.path.abspath(__file__))
def get_path(fn):
return os.path.join(path, os.pardir, 'maths', 'stats', 'ordination',
'test', 'data', fn)
X = np.loadtxt(get_path('L&L_CA_data'))
ordint = CA(X)
ordint.biplot(1)
ordint.biplot(2)
Y = np.loadtxt(get_path('example2_Y'))
X = np.loadtxt(get_path('example2_X')).reshape(-1, 4, order='F')
ordint = RDA(Y, X)
ordint.biplot()
Y = np.loadtxt(get_path('example3_Y'))
X = np.loadtxt(get_path('example3_X')).reshape(-1, 4, order='F')
ordint = CCA(Y, X)
ordint.biplot()
