metavar = "filename",
help = "[REQUIRED] outfile name",
required = True)
options = parser.parse_args()
#############################
# Import json formatted OTU #
#############################
import json
jsondata = open(options.biominputfile)
biom = json.load(jsondata)
jsondata.close()
from biom import Table
table = Table.from_json(biom)
print("")
print("Original OTU Table (without taxonomy)")
print("-------------------------------------")
print("")
print(table)
print("")
min_samplesize = int(min(table.sum(axis='sample')))
print("Subsampling to the smallest sample size: " + str(min_samplesize))
# Subsample
table_ss = table.subsample(min_samplesize)
# Output
def setUpClass(cls):
_table1 = [
'a\ta\t1\t0.0\t0.5\t0.1', 'a\ta\t1\t1.0\t1.0\t0.2',
'a\ta\t1\t2.0\t1.5\t0.2', 'a\tb\t1\t3.0\t2.0\t8.',
'a\tb\t1\t4.0\t2.5\t9.', 'a\tb\t1\t5.0\t3.0\t10.',
'b\ta\t1\t0.0\t2.0\t0.1', 'b\ta\t1\t1.0\t3.0\t0.3',
'b\ta\t1\t2.0\t4.0\t0.1', 'b\tb\t1\t3.0\t5.0\t9.',
'b\tb\t1\t4.0\t6.0\t11.', 'b\tb\t1\t5.0\t7.0\t10.'
]
cls.table1 = pd.DataFrame(
[(n.split('\t')) for n in _table1],
columns=['group', 'dataset', 'level', 'x', 'y', 'c'],
dtype=float)
cls.table2 = """{"id": "None",
"format": "Biological Observation Matrix 1.0.0",
"format_url": "http:\/\/biom-format.org",
"type": "OTU table",
"generated_by": "greg",
"date": "2013-08-22T13:10:23.907145",
"matrix_type": "sparse",
"matrix_element_type": "float",
"shape": [
3,
4
],
"data": [
[
0,
0,
1
],
[
0,
1,
2
],
[
0,
2,
3
],
[
0,
3,
4
],
[
1,
0,
2
],
[
1,
1,
0
],
[
1,
2,
7
],
[
1,
3,
8
],
[
2,
0,
9
],
[
2,
1,
10
],
[
2,
2,
11
],
[
2,
3,
12
]
],
"rows": [
{
"id": "o1",
"metadata": {
"domain": "Archaea"
}
},
{
"id": "o2",
"metadata": {
"domain": "Bacteria"
}
},
{
"id": "o3",
"metadata": {
"domain": "Bacteria"
}
}
],
"columns": [
{
"id": "s1",
"metadata": {
"method": "A",
"Sample": "A",
"parameters": "A"
}
},
{
"id": "s2",
"metadata": {
"method": "A",
"Sample": "A",
"parameters": "B"
}
},
{
"id": "s3",
"metadata": {
"method": "A",
"Sample": "A",
"parameters": "C"
}
},
{
"id": "s4",
"metadata": {
"method": "B",
"Sample": "A",
"parameters": "D"
}
}
]
}"""
# table 2
# OTU ID s1 s2 s3 s4
# o1 1.0 2.0 3.0 4.0
# o2 2.0 0.0 7.0 8.0
# o3 9.0 10.0 11.0 12.0
cls.tmpdir = mkdtemp()
cls.table2 = Table.from_json(json.loads(cls.table2))
write_biom_table(cls.table2, 'hdf5', join(cls.tmpdir, 'table2.biom'))
cls.dm, cls.s_md = make_distance_matrix(join(cls.tmpdir,
'table2.biom'),
method="braycurtis")
cls.dist = per_method_distance(cls.dm,
cls.s_md,
group_by='method',
standard='B',
metric='distance',
sample='Sample')
metavar="filename",
help="[REQUIRED] outfile name",
required=True)
options = parser.parse_args()
#############################
# Import json formatted OTU #
#############################
import json
jsondata = open(options.biominputfile)
biom = json.load(jsondata)
jsondata.close()
from biom import Table
table = Table.from_json(biom)
print("")
print("Original OTU Table (without taxonomy)")
print("-------------------------------------")
print("")
print(table)
print("")
min_samplesize = int(min(table.sum(axis='sample')))
print("Subsampling to the smallest sample size: " + str(min_samplesize))
# Subsample
table_ss = table.subsample(min_samplesize)
# Output
def setUp(self):
self.table1 = Table.from_json(json.load(StringIO(table1)))
self.table2 = Table.from_json(json.load(StringIO(table2)))
self.table3 = Table.from_json(json.load(StringIO(table3)))
self.mock_result_table1 = pd.DataFrame.from_csv(StringIO(mock_result_table1))
def setUp(self):
_table1 = """{"id": "None",
"format": "Biological Observation Matrix 1.0.0",
"format_url": "http://biom-format.org",
"type": "OTU table",
"generated_by": "greg",
"date": "2013-08-22T13:10:23.907145",
"matrix_type": "sparse",
"matrix_element_type": "float",
"shape": [
3,
4
],
"data": [
[
0,
0,
1
],
[
0,
1,
2
],
[
0,
2,
3
],
[
0,
3,
4
],
[
1,
0,
2
],
[
1,
1,
0
],
[
1,
2,
7
],
[
1,
3,
8
],
[
2,
0,
9
],
[
2,
1,
10
],
[
2,
2,
11
],
[
2,
3,
12
]
],
"rows": [
{
"id": "k__Bacteria",
"metadata": {
"domain": "Bacteria"
}
},
{
"id": "k__Archaea",
"metadata": {
"domain": "Archaea"
}
},
{
"id": "k__[Fungi]",
"metadata": {
"domain": "[Fungi]"
}
}
],
"columns": [
{
"id": "s1",
"metadata": {
"country": "Peru",
"pH": 4.2
}
},
{
"id": "s2",
"metadata": {
"country": "Peru",
"pH": 5.2
}
},
{
"id": "s3",
"metadata": {
"country": "Peru",
"pH": 5
}
},
{
"id": "s4",
"metadata": {
"country": "Peru",
"pH": 4.9
}
}
]
}"""
# table 1
# OTU ID s1 s2 s3 s4
# k__Archaea 1.0 2.0 3.0 4.0
# k__Bacteria 2.0 0.0 7.0 8.0
# k__[Fungi] 9.0 10.0 11.0 12.0
self.table1 = Table.from_json(json.loads(_table1))
def setUp(self):
self.table1 = Table.from_json(json.load(StringIO(table1)))
self.table2 = Table.from_json(json.load(StringIO(table2)))
