def create_opts(sample_type, chp_path, gradient_color_by, barchart_categories):
"""Create a dict of options for processing functions
Parameters
----------
sample_type : str, {fecal, oral, skin}
The sample type.
chp_path : str
The path to the current processing chapter.
gradient_color_by : str
The taxon to color by for the gradient PCoA.
barchart_categories : Iterable of str
The relevant barchart categories to use.
Returns
-------
dict
The paths and desired options.
"""
opts = {k: agu.get_path(v) for k, v in agenv.paths.items()}
opts['sample_type'] = sample_type
opts['gradient_color_by'] = gradient_color_by
opts['chp-path'] = chp_path
opts['rarefaction-depth'] = agenv.get_rarefaction_depth()
barchart_map = {
'diet': 'DIET_TYPE',
'sex': 'SEX',
'age': 'AGE_CAT',
'bmi': 'BMI_CAT',
'flossing': 'FLOSSING_FREQUENCY',
'cosmetics': 'COSMETICS_FREQUENCY',
'hand': 'DOMINANT_HAND'
}
fmt = []
for cat in barchart_categories:
if cat not in barchart_map:
raise KeyError("%s is not known" % cat)
cat_path = opts['ag-100nt-1k-%s-%s-biom' % (sample_type, cat)]
fmt.append("%s:%s" % (barchart_map[cat], cat_path))
opts['barchart_categories'] = '"%s"' % ', '.join(fmt)
return opts
def create_opts(sample_type, chp_path, gradient_color_by, barchart_categories):
"""Create a dict of options for processing functions
Parameters
----------
sample_type : str, {fecal, oral, skin}
The sample type.
chp_path : str
The path to the current processing chapter.
gradient_color_by : str
The taxon to color by for the gradient PCoA.
barchart_categories : Iterable of str
The relevant barchart categories to use.
Returns
-------
dict
The paths and desired options.
"""
opts = {k: agu.get_path(v) for k, v in agenv.paths.items()}
opts['sample_type'] = sample_type
opts['gradient_color_by'] = gradient_color_by
opts['chp-path'] = chp_path
opts['rarefaction-depth'] = agenv.get_rarefaction_depth()
barchart_map = {'diet': 'DIET_TYPE',
'sex': 'SEX',
'age': 'AGE_CAT',
'bmi': 'BMI_CAT',
'flossing': 'FLOSSING_FREQUENCY',
'cosmetics': 'COSMETICS_FREQUENCY',
'hand': 'DOMINANT_HAND'}
fmt = []
for cat in barchart_categories:
if cat not in barchart_map:
raise KeyError("%s is not known" % cat)
cat_path = opts['ag-100nt-1k-%s-%s-biom' % (sample_type, cat)]
fmt.append("%s:%s" % (barchart_map[cat], cat_path))
opts['barchart_categories'] = '"%s"' % ', '.join(fmt)
return opts
def create_opts(sample_type, chp_path, gradient_color_by, barchart_categories):
"""Create a dict of options for processing functions
Parameters
----------
sample_type : str, {fecal, oral, skin}
The sample type.
chp_path : str
The path to the current processing chapter.
gradient_color_by : str
The taxon to color by for the gradient PCoA.
barchart_categories : Iterable of str
The relevant barchart categories to use.
Returns
-------
dict
The paths and desired options.
"""
def attach_dict(d, items):
return [(d, i) for i in items]
opts = {}
queue = attach_dict(opts, agenv.paths.items())
while queue:
d, (key, value) = queue.pop()
if isinstance(value, dict):
# get the relevant dict or a new dict
nested_d = d.get(key, {})
d[key] = nested_d
queue.extend(attach_dict(nested_d, value.items()))
else:
d[key] = agu.get_path(value)
opts['sample_type'] = sample_type
opts['gradient_color_by'] = gradient_color_by
opts['chp-path'] = chp_path
opts['rarefaction-depth'] = int(agenv.get_rarefaction_depth()[0])
barchart_map = {'diet': 'DIET_TYPE',
'sex': 'SEX',
'age': 'AGE_CAT',
'bmi': 'BMI_CAT',
'flossing': 'FLOSSING_FREQUENCY',
'cosmetics': 'COSMETICS_FREQUENCY',
'hand': 'DOMINANT_HAND'}
fmt = []
for cat in barchart_categories:
if cat not in barchart_map:
raise KeyError("%s is not known" % cat)
table_key = 'ag-%s-%s-biom' % (sample_type, cat)
cat_path = opts['collapsed']['100nt']['1k'][table_key]
fmt.append("%s:%s" % (barchart_map[cat], cat_path))
opts['barchart_categories'] = '"%s"' % ', '.join(fmt)
return opts
def create_opts(sample_type, chp_path, gradient_color_by, barchart_categories):
"""Create a dict of options for processing functions
Parameters
----------
sample_type : str, {fecal, oral, skin}
The sample type.
chp_path : str
The path to the current processing chapter.
gradient_color_by : str
The taxon to color by for the gradient PCoA.
barchart_categories : Iterable of str
The relevant barchart categories to use.
Returns
-------
dict
The paths and desired options.
"""
def attach_dict(d, items):
return [(d, i) for i in items]
opts = {}
queue = attach_dict(opts, agenv.paths.items())
while queue:
d, (key, value) = queue.pop()
if isinstance(value, dict):
# get the relevant dict or a new dict
nested_d = d.get(key, {})
d[key] = nested_d
queue.extend(attach_dict(nested_d, value.items()))
else:
d[key] = agu.get_path(value)
opts['sample_type'] = sample_type
opts['gradient_color_by'] = gradient_color_by
opts['chp-path'] = chp_path
opts['rarefaction-depth'] = int(agenv.get_rarefaction_depth()[0])
barchart_map = {
'diet': 'DIET_TYPE',
'sex': 'SEX',
'age': 'AGE_CAT',
'bmi': 'BMI_CAT',
'flossing': 'FLOSSING_FREQUENCY',
'cosmetics': 'COSMETICS_FREQUENCY',
'hand': 'DOMINANT_HAND'
}
fmt = []
for cat in barchart_categories:
if cat not in barchart_map:
raise KeyError("%s is not known" % cat)
table_key = 'ag-%s-%s-biom' % (sample_type, cat)
cat_path = opts['collapsed']['100nt']['1k'][table_key]
fmt.append("%s:%s" % (barchart_map[cat], cat_path))
opts['barchart_categories'] = '"%s"' % ', '.join(fmt)
return opts
