def test_scale_taxa_data_matrix(self):
coord = np.array([[1, 4, 7, 0], [2, 5, 8, 1], [3, 6, 9, 2]], float)
taxdata = {}
taxdata['prevalence'] = np.array([0, .5, 1])
taxdata['coord'] = coord
taxdata['lineages'] = np.array(['Root;A', 'Root;B', 'Root;C'])
pct_var = np.array([100, 10, 1], dtype="float")
# with scaling
res = bp.make_mage_taxa(taxdata,
3,
pct_var,
scaled=True,
scalars=None,
radius=1,
min_taxon_radius=10,
max_taxon_radius=20,
taxon_alpha=.7)
self.assertEqual(res, taxa_mage_scale)
# without scaling
res = bp.make_mage_taxa(taxdata,
3,
pct_var,
scaled=False,
scalars=None,
radius=1,
min_taxon_radius=10,
max_taxon_radius=20,
taxon_alpha=.7)
self.assertEqual(res, taxa_mage_no_scale)
def test_scale_taxa_data_matrix(self):
coord = np.array([ [1,4,7,0],
[2,5,8,1],
[3,6,9,2]],float)
taxdata = {}
taxdata['prevalence'] = np.array([0,.5,1])
taxdata['coord'] = coord
taxdata['lineages'] = np.array(['Root;A','Root;B','Root;C'])
pct_var = np.array([100,10,1],dtype="float")
# with scaling
res = bp.make_mage_taxa(taxdata,3,pct_var,scaled=True,scalars=None,\
radius=1,
min_taxon_radius=10, max_taxon_radius=20,
taxon_alpha=.7)
self.assertEqual(res, taxa_mage_scale)
# without scaling
res = bp.make_mage_taxa(taxdata,3,pct_var,scaled=False,scalars=None,\
radius=1,
min_taxon_radius=10, max_taxon_radius=20,
taxon_alpha=.7)
self.assertEqual(res, taxa_mage_no_scale)