def plot_beta_alt_mpl(dataset,
beta_metric,
named_sample_set,
sample_id=None,
category=None):
pcoa_repo = _get_pcoa_repo(dataset)
metadata_repo = _get_metadata_repo(dataset, get_resources)
if not pcoa_repo.has_pcoa(named_sample_set, beta_metric):
raise UnknownResource(f"No PCoA for named_sample_set="
f"'{named_sample_set}',beta_metric="
f"'{beta_metric}'")
# check metadata repo for the requested categories
has_category = metadata_repo.has_category([category])
missing_categories = [
cat for i, cat in enumerate([
category,
]) if not has_category[i]
]
if len(missing_categories) > 0:
raise UnknownResource(f"Missing specified metadata categories: "
f"{missing_categories}")
pcoa = pcoa_repo.get_pcoa(named_sample_set, beta_metric)
metadata = metadata_repo.get_metadata(category)
x = pcoa.samples[0]
y = pcoa.samples[1]
response = _make_mpl_fig(metadata, x, y, sample_id)
return send_file(response,
mimetype='image/png',
as_attachment=True,
attachment_filename='pcoa.png',
conditional=True)
def plot_alpha_filtered_json_query_alt(dataset,
body,
alpha_metric=None,
percentiles=None,
sample_id=None):
repo = _get_metadata_repo(dataset, get_resources)
alpha_repo_getter = _alpha_repo_getter_alt(dataset)
return _plot_alpha_percentiles_querybuilder(
alpha_metric,
percentiles,
body,
repo,
sample_id,
alpha_repo_getter,
)
def plot_alpha_filtered_alt(dataset,
alpha_metric=None,
percentiles=None,
sample_id=None,
**kwargs):
repo = _get_metadata_repo(dataset, get_resources)
query = _format_query(kwargs)
is_invalid = _validate_query(kwargs, repo)
if is_invalid:
return is_invalid
alpha_repo_getter = _alpha_repo_getter_alt(dataset)
return _plot_alpha_percentiles_querybuilder(
alpha_metric,
percentiles,
query,
repo,
sample_id,
alpha_repo_getter,
)
def dataset_sample_exists(dataset, sample_id):
metadata = _get_metadata_repo(dataset)
return metadata.has_sample_id(sample_id)