def single_object_alpha(biom_object, metrics, tree_object): """given a metric calculates alpha diversity of a biom object Inputs: biom_object: biom formatted OTU table metrics: list of alpha diversity metrics tree_object: tree object for the phylogenetic metrics Output: calculations: tab delimitted string with the calculations for the object """ calcs = [] for metric in metrics: try: metric_f = get_nonphylogenetic_metric(metric) is_phylogenetic = False except AttributeError: try: metric_f = get_phylogenetic_metric(metric) is_phylogenetic = True except AttributeError: print 'something is wrong with conf file' c = AlphaDiversityCalc(metric_f, is_phylogenetic) calcs.append(c) all_calcs = AlphaDiversityCalcs(calcs) result = all_calcs(data_path=biom_object, tree_path=tree_object, log_path=None) return all_calcs.formatResult(result)
def single_object_alpha(biom_object, metrics, tree_object):
"""given a metric calculates alpha diversity of a biom object
Inputs:
biom_object: biom formatted OTU table
metrics: list of alpha diversity metrics
tree_object: tree object for the phylogenetic metrics
Output:
calculations: tab delimitted string with the calculations for the object
"""
calcs = []
for metric in metrics:
try:
metric_f = get_nonphylogenetic_metric(metric)
is_phylogenetic = False
except AttributeError:
try:
metric_f = get_phylogenetic_metric(metric)
is_phylogenetic = True
except AttributeError:
print 'something is wrong with conf file'
c = AlphaDiversityCalc(metric_f, is_phylogenetic)
calcs.append(c)
all_calcs = AlphaDiversityCalcs(calcs)
result = all_calcs(data_path=biom_object,
tree_path=tree_object,
log_path=None)
return all_calcs.formatResult(result)
def single_file_cup(otu_filepath, metrics, outfilepath, r, alpha, f, ci_type):
"""Compute variations of the conditional uncovered probability.
otufilepath: path to otu_table file
metrics: comma separated list of required metrics
outfilepath: path to output file
r: Number of new colors that are required for the next prediction
alpha: desired confidence level
f: ratio between upper and lower bound
ci_type: type of confidence interval. One of:
ULCL: upper and lower bounds with conservative lower bound
ULCU: upper and lower woth conservative upper bound
U: Upper bound only, lower bound fixed to 0
L: Lower bound only, upper bound fixed to 1
The opposite of uncovered probability is sometimes called coverage.
"""
metrics_list = metrics.split(',')
calcs = []
params = {'r': r,
'alpha': alpha,
'f':f,
'ci_type':ci_type}
for metric in metrics_list:
try:
metric_f = get_cup_metric(metric)
except AttributeError:
stderr.write(
"Could not find metric %s.\n Known metrics are: %s\n" \
% (metric, ', '.join(list_known_metrics())))
exit(1)
c = AlphaDiversityCalc(metric_f, params=params)
calcs.append(c)
all_calcs = AlphaDiversityCalcs(calcs)
try:
result = all_calcs(data_path=otu_filepath,
result_path=outfilepath, log_path=None)
if result: #can send to stdout instead of file
print all_calcs.formatResult(result)
except IOError, e:
stderr.write("Failed because of missing files.\n")
stderr.write(str(e)+'\n')
exit(1)
def test1(self):
""" checks that output from AlphaDiversityCalcs is the right shape
when run on phylo, multiple return value nonphylo, and another nonphylo
"""
calc1 = AlphaDiversityCalc(metric=observed_otus)
calc2 = AlphaDiversityCalc(metric=PD_whole_tree, is_phylogenetic=True)
calc3 = AlphaDiversityCalc(metric=osd)
calcs = AlphaDiversityCalcs([calc1, calc2, calc3])
results = calcs(data_path=self.otu_table1_fp,
tree_path=self.tree1,
result_path=None,
log_path=None)
self.assertEqual(results[0].shape, (3, 5))
self.assertEqual(len(results[1]), 3)
self.assertEqual(len(results[2]), 5)