def test_pcoa_fsvd(self):
# Run fsvd, computing all dimensions.
fsvd_result = pcoa(self.dm,
number_of_dimensions=self.dm.data.shape[0])
# Run eigh, which computes all dimensions by default.
eigh_result = pcoa(self.dm)
assert_ordination_results_equal(fsvd_result, eigh_result,
ignore_directionality=True,
ignore_method_names=True)
def test_pcoa_fsvd(self):
# Run fsvd, computing all dimensions.
fsvd_result = pcoa(self.dm, number_of_dimensions=self.dm.data.shape[0])
# Run eigh, which computes all dimensions by default.
eigh_result = pcoa(self.dm)
assert_ordination_results_equal(fsvd_result,
eigh_result,
ignore_directionality=True,
ignore_method_names=True)
def test_pcoa(self):
dm = skbio.DistanceMatrix([[0.0000000, 0.3333333, 0.6666667],
[0.3333333, 0.0000000, 0.4285714],
[0.6666667, 0.4285714, 0.0000000]],
ids=['S1', 'S2', 'S3'])
actual = pcoa(dm)
expected = skbio.stats.ordination.pcoa(dm)
skbio.util.assert_ordination_results_equal(
actual, expected, ignore_directionality=True)
def core_metrics(table: biom.Table, phylogeny: skbio.TreeNode,
sampling_depth: int) -> (pd.Series,
pd.Series,
pd.Series,
pd.Series,
skbio.DistanceMatrix,
skbio.DistanceMatrix,
skbio.DistanceMatrix,
skbio.DistanceMatrix,
skbio.OrdinationResults,
skbio.OrdinationResults,
skbio.OrdinationResults,
skbio.OrdinationResults):
rarefied_table = rarefy(table=table, sampling_depth=sampling_depth)
faith_pd_vector = alpha_phylogenetic(
table=rarefied_table, phylogeny=phylogeny, metric='faith_pd')
observed_otus_vector = alpha(table=rarefied_table, metric='observed_otus')
shannon_vector = alpha(table=rarefied_table, metric='shannon')
evenness_vector = alpha(table=rarefied_table, metric='pielou_e')
unweighted_unifrac_distance_matrix = beta_phylogenetic(
table=rarefied_table, phylogeny=phylogeny, metric='unweighted_unifrac')
weighted_unifrac_distance_matrix = beta_phylogenetic(
table=rarefied_table, phylogeny=phylogeny, metric='weighted_unifrac')
jaccard_distance_matrix = beta(table=rarefied_table, metric='jaccard')
bray_curtis_distance_matrix = beta(
table=rarefied_table, metric='braycurtis')
unweighted_unifrac_pcoa_results = pcoa(
distance_matrix=unweighted_unifrac_distance_matrix)
weighted_unifrac_pcoa_results = pcoa(
distance_matrix=weighted_unifrac_distance_matrix)
jaccard_pcoa_results = pcoa(distance_matrix=jaccard_distance_matrix)
bray_curtis_pcoa_results = pcoa(
distance_matrix=bray_curtis_distance_matrix)
return (
faith_pd_vector, observed_otus_vector, shannon_vector, evenness_vector,
unweighted_unifrac_distance_matrix, weighted_unifrac_distance_matrix,
jaccard_distance_matrix, bray_curtis_distance_matrix,
unweighted_unifrac_pcoa_results, weighted_unifrac_pcoa_results,
jaccard_pcoa_results, bray_curtis_pcoa_results
)
def test_pcoa(self):
dm = skbio.DistanceMatrix([[0.0000000, 0.3333333, 0.6666667],
[0.3333333, 0.0000000, 0.4285714],
[0.6666667, 0.4285714, 0.0000000]],
ids=['S1', 'S2', 'S3'])
actual = pcoa(dm)
expected = skbio.stats.ordination.pcoa(dm)
skbio.util.assert_ordination_results_equal(actual,
expected,
ignore_directionality=True)
def core_metrics(
table: biom.Table, phylogeny: skbio.TreeNode, sampling_depth: int
) -> (pd.Series, pd.Series, pd.Series, pd.Series, skbio.DistanceMatrix,
skbio.DistanceMatrix, skbio.DistanceMatrix, skbio.DistanceMatrix,
skbio.OrdinationResults, skbio.OrdinationResults,
skbio.OrdinationResults, skbio.OrdinationResults):
rarefied_table = rarefy(table=table, sampling_depth=sampling_depth)
faith_pd_vector = alpha_phylogenetic(table=rarefied_table,
phylogeny=phylogeny,
metric='faith_pd')
observed_otus_vector = alpha(table=rarefied_table, metric='observed_otus')
shannon_vector = alpha(table=rarefied_table, metric='shannon')
evenness_vector = alpha(table=rarefied_table, metric='pielou_e')
unweighted_unifrac_distance_matrix = beta_phylogenetic(
table=rarefied_table, phylogeny=phylogeny, metric='unweighted_unifrac')
weighted_unifrac_distance_matrix = beta_phylogenetic(
table=rarefied_table, phylogeny=phylogeny, metric='weighted_unifrac')
jaccard_distance_matrix = beta(table=rarefied_table, metric='jaccard')
bray_curtis_distance_matrix = beta(table=rarefied_table,
metric='braycurtis')
unweighted_unifrac_pcoa_results = pcoa(
distance_matrix=unweighted_unifrac_distance_matrix)
weighted_unifrac_pcoa_results = pcoa(
distance_matrix=weighted_unifrac_distance_matrix)
jaccard_pcoa_results = pcoa(distance_matrix=jaccard_distance_matrix)
bray_curtis_pcoa_results = pcoa(
distance_matrix=bray_curtis_distance_matrix)
return (faith_pd_vector, observed_otus_vector, shannon_vector,
evenness_vector, unweighted_unifrac_distance_matrix,
weighted_unifrac_distance_matrix, jaccard_distance_matrix,
bray_curtis_distance_matrix, unweighted_unifrac_pcoa_results,
weighted_unifrac_pcoa_results, jaccard_pcoa_results,
bray_curtis_pcoa_results)
def test_pcoa(self):
observed = pcoa(self.dm)
skbio.util.assert_ordination_results_equal(
observed, self.ordination, ignore_directionality=True)
def test_pcoa(self):
observed = pcoa(self.dm)
skbio.util.assert_ordination_results_equal(observed,
self.ordination,
ignore_directionality=True)
