def biplot(output_dir: str,
biplot: skbio.OrdinationResults,
sample_metadata: qiime2.Metadata,
feature_metadata: qiime2.Metadata = None,
ignore_missing_samples: bool = False,
invert: bool = False,
number_of_features: int = 5) -> None:
if invert:
biplot.samples, biplot.features = biplot.features, biplot.samples
sample_metadata, feature_metadata = feature_metadata, sample_metadata
# select the top N most important features based on the vector's magnitude
feats = biplot.features.copy()
origin = np.zeros_like(feats.columns)
feats['importance'] = feats.apply(euclidean, axis=1, args=(origin, ))
feats.sort_values('importance', inplace=True, ascending=False)
feats.drop(['importance'], inplace=True, axis=1)
biplot.features = feats[:number_of_features].copy()
generic_plot(output_dir,
master=biplot,
other_pcoa=None,
ignore_missing_samples=ignore_missing_samples,
metadata=sample_metadata,
feature_metadata=feature_metadata,
plot_name='biplot')
def plot(output_dir: str, pcoa: skbio.OrdinationResults,
metadata: qiime2.Metadata, custom_axes: str = None,
ignore_missing_samples: bool = False,
ignore_pcoa_features: bool = False) -> None:
if ignore_pcoa_features:
pcoa.features = None
if pcoa.features is not None:
raise ValueError("Arrows cannot be visualized with the 'plot' method, "
"use 'biplot' instead, or enable "
"`ignore_pcoa_features`.")
generic_plot(output_dir, master=pcoa, metadata=metadata, other_pcoa=None,
ignore_missing_samples=ignore_missing_samples,
custom_axes=custom_axes, plot_name='plot')
def biplot(output_dir: str, biplot: skbio.OrdinationResults,
sample_metadata: qiime2.Metadata, feature_metadata:
qiime2.Metadata=None,
number_of_features: int=5) -> None:
# select the top N most important features based on the vector's magnitude
feats = biplot.features.copy()
origin = np.zeros_like(feats.columns)
feats['importance'] = feats.apply(euclidean, axis=1, args=(origin,))
feats.sort_values('importance', inplace=True, ascending=False)
feats.drop(['importance'], inplace=True, axis=1)
biplot.features = feats[:number_of_features].copy()
_generic_plot(output_dir, master=biplot, other_pcoa=None,
metadata=sample_metadata, feature_metadata=feature_metadata,
plot_name='biplot')
def plot(output_dir: str,
tree: NewickFormat,
feature_table: pd.DataFrame,
sample_metadata: qiime2.Metadata,
pcoa: OrdinationResults = None,
feature_metadata: qiime2.Metadata = None,
ignore_missing_samples: bool = False,
filter_missing_features: bool = False,
number_of_features: int = 5,
filter_unobserved_features_from_phylogeny: bool = True) -> None:
if pcoa is not None and pcoa.features is not None:
# select the top N most important features based on the vector's
# magnitude (coped from q2-emperor)
feats = pcoa.features.copy()
origin = np.zeros_like(feats.columns)
feats['importance'] = feats.apply(euclidean, axis=1, args=(origin, ))
feats.sort_values('importance', inplace=True, ascending=False)
feats.drop(['importance'], inplace=True, axis=1)
pcoa.features = feats[:number_of_features].copy()
sample_metadata = sample_metadata.to_dataframe()
if feature_metadata is not None:
feature_metadata = feature_metadata.to_dataframe()
# path to the actual newick file
with open(str(tree)) as file:
t = parse_newick(file.readline())
trim_tree = filter_unobserved_features_from_phylogeny
viz = Empress(tree=t,
table=feature_table,
sample_metadata=sample_metadata,
feature_metadata=feature_metadata,
ordination=pcoa,
ignore_missing_samples=ignore_missing_samples,
filter_missing_features=filter_missing_features,
filter_unobserved_features_from_phylogeny=trim_tree)
with open(os.path.join(output_dir, 'empress.html'), 'w') as file:
file.write(str(viz))
viz.copy_support_files(output_dir)
index = os.path.join(TEMPLATES, 'index.html')
q2templates.render(index, output_dir)
def community_plot(output_dir: str,
tree: NewickFormat,
feature_table: biom.Table,
sample_metadata: qiime2.Metadata,
pcoa: OrdinationResults = None,
feature_metadata: qiime2.Metadata = None,
ignore_missing_samples: bool = False,
filter_extra_samples: bool = False,
filter_missing_features: bool = False,
number_of_features: int = 5,
shear_tree: bool = True) -> None:
"""Visualizes a tree alongside community-level data.
The functionality available in this visualization is a superset of the
functionality in tree_plot() -- including sample metadata coloring /
barplots, animations, and Emperor integration support.
"""
if pcoa is not None and pcoa.features is not None:
# select the top N most important features based on the vector's
# magnitude (coped from q2-emperor)
feats = pcoa.features.copy()
# in cases where the axes are all zero there might be all-NA
# columns
feats.fillna(0, inplace=True)
origin = np.zeros_like(feats.columns)
feats['importance'] = feats.apply(euclidean, axis=1, args=(origin, ))
feats.sort_values('importance', inplace=True, ascending=False)
feats.drop(['importance'], inplace=True, axis=1)
pcoa.features = feats[:number_of_features].copy()
sample_metadata = sample_metadata.to_dataframe()
if feature_metadata is not None:
feature_metadata = feature_metadata.to_dataframe()
t = get_bp(tree)
viz = Empress(tree=t,
table=feature_table,
sample_metadata=sample_metadata,
feature_metadata=feature_metadata,
ordination=pcoa,
ignore_missing_samples=ignore_missing_samples,
filter_extra_samples=filter_extra_samples,
filter_missing_features=filter_missing_features,
shear_tree=shear_tree)
save_viz(viz, output_dir)
