def filter_distance_matrix(distance_matrix: skbio.DistanceMatrix,
sample_metadata: qiime2.Metadata,
where: str = None) -> skbio.DistanceMatrix:
ids_to_keep = sample_metadata.ids(where=where)
# NOTE: there is no guaranteed ordering to output distance matrix because
# `ids_to_keep` is a set, and `DistanceMatrix.filter` uses its iteration
# order.
try:
return distance_matrix.filter(ids_to_keep, strict=False)
except skbio.stats.distance.DissimilarityMatrixError:
raise ValueError(
"All samples were filtered out of the distance matrix.")
def filter_seqs(data: pd.Series,
metadata: qiime2.Metadata,
where: str = None,
exclude_ids: bool = False) -> pd.Series:
# Note, no need to check for missing feature IDs in the metadata, because
# that is basically the point of this method.
ids_to_keep = metadata.ids(where=where)
if exclude_ids is True:
ids_to_keep = set(data.index) - set(ids_to_keep)
filtered = data[data.index.isin(ids_to_keep)]
if filtered.empty is True:
raise ValueError('All features were filtered out of the data.')
return filtered
def filter_distance_matrix(distance_matrix: skbio.DistanceMatrix,
metadata: qiime2.Metadata,
where: str=None,
exclude_ids: bool=False) -> skbio.DistanceMatrix:
ids_to_keep = metadata.ids(where=where)
if exclude_ids:
ids_to_keep = set(distance_matrix.ids) - set(ids_to_keep)
# NOTE: there is no guaranteed ordering to output distance matrix because
# `ids_to_keep` is a set, and `DistanceMatrix.filter` uses its iteration
# order.
try:
return distance_matrix.filter(ids_to_keep, strict=False)
except skbio.stats.distance.DissimilarityMatrixError:
raise ValueError(
"All samples were filtered out of the distance matrix.")
def alpha_rarefaction(output_dir: str, table: biom.Table, max_depth: int,
phylogeny: skbio.TreeNode=None, metrics: set=None,
metadata: qiime2.Metadata=None, min_depth: int=1,
steps: int=10, iterations: int=10) -> None:
if metrics is None:
metrics = {'observed_otus', 'shannon'}
if phylogeny is not None:
metrics.add('faith_pd')
elif not metrics:
raise ValueError('`metrics` was given an empty set.')
else:
phylo_overlap = phylogenetic_metrics() & metrics
if phylo_overlap and phylogeny is None:
raise ValueError('Phylogenetic metric %s was requested but '
'phylogeny was not provided.' % phylo_overlap)
if max_depth <= min_depth:
raise ValueError('Provided max_depth of %d must be greater than '
'provided min_depth of %d.' % (max_depth, min_depth))
possible_steps = max_depth - min_depth
if possible_steps < steps:
raise ValueError('Provided number of steps (%d) is greater than the '
'steps possible between min_depth and '
'max_depth (%d).' % (steps, possible_steps))
if table.is_empty():
raise ValueError('Provided table is empty.')
max_frequency = max(table.sum(axis='sample'))
if max_frequency < max_depth:
raise ValueError('Provided max_depth of %d is greater than '
'the maximum sample total frequency of the '
'feature_table (%d).' % (max_depth, max_frequency))
if metadata is not None:
metadata_ids = metadata.ids()
table_ids = set(table.ids(axis='sample'))
if not table_ids.issubset(metadata_ids):
raise ValueError('Missing samples in metadata: %r' %
table_ids.difference(metadata_ids))
filenames, categories, empty_columns = [], [], []
data = _compute_rarefaction_data(table, min_depth, max_depth,
steps, iterations, phylogeny, metrics)
for m, data in data.items():
metric_name = quote(m)
filename = '%s.csv' % metric_name
if metadata is None:
n_df = _compute_summary(data, 'sample-id')
jsonp_filename = '%s.jsonp' % metric_name
_alpha_rarefaction_jsonp(output_dir, jsonp_filename, metric_name,
n_df, '')
filenames.append(jsonp_filename)
else:
metadata_df = metadata.to_dataframe()
metadata_df = metadata_df.loc[data.index]
all_columns = metadata_df.columns
metadata_df.dropna(axis='columns', how='all', inplace=True)
empty_columns = set(all_columns) - set(metadata_df.columns)
metadata_df.columns = pd.MultiIndex.from_tuples(
[(c, '') for c in metadata_df.columns])
merged = data.join(metadata_df, how='left')
categories = metadata_df.columns.get_level_values(0)
for category in categories:
category_name = quote(category)
reindexed_df, counts = _reindex_with_metadata(category,
categories,
merged)
c_df = _compute_summary(reindexed_df, category, counts=counts)
jsonp_filename = "%s-%s.jsonp" % (metric_name, category_name)
_alpha_rarefaction_jsonp(output_dir, jsonp_filename,
metric_name, c_df, category_name)
filenames.append(jsonp_filename)
with open(os.path.join(output_dir, filename), 'w') as fh:
data.columns = ['depth-%d_iter-%d' % (t[0], t[1])
for t in data.columns.values]
if metadata is not None:
data = data.join(metadata.to_dataframe(), how='left')
data.to_csv(fh, index_label=['sample-id'])
index = os.path.join(TEMPLATES, 'alpha_rarefaction_assets', 'index.html')
q2templates.render(index, output_dir,
context={'metrics': list(metrics),
'filenames': filenames,
'categories': list(categories),
'empty_columns': sorted(empty_columns)})
shutil.copytree(os.path.join(TEMPLATES, 'alpha_rarefaction_assets',
'dist'),
os.path.join(output_dir, 'dist'))
