def run(self, **kwargs):
json_table_str = kwargs['json_table_str']
hdf5_biom = kwargs['hdf5_table']
axis = kwargs['axis']
ids = kwargs['ids']
if axis not in self.Axes:
raise CommandError(
"Invalid axis '%s'. Must be either %s." %
(axis, ' or '.join(map(lambda e: "'%s'" % e, self.Axes))))
if hdf5_biom is None and json_table_str is None:
raise CommandError("Must specify an input table")
elif hdf5_biom is not None and json_table_str is not None:
raise CommandError("Can only specify one input table")
if json_table_str is not None:
idxs, new_axis_md = get_axis_indices(json_table_str, ids, axis)
new_data = direct_slice_data(json_table_str, idxs, axis)
# multiple walks over the string. bad form, but easy right now
# ...should add a yield_and_ignore parser or something.
def subset_generator():
yield "{"
yield direct_parse_key(json_table_str, "id")
yield ","
yield direct_parse_key(json_table_str, "format")
yield ","
yield direct_parse_key(json_table_str, "format_url")
yield ","
yield direct_parse_key(json_table_str, "type")
yield ","
yield direct_parse_key(json_table_str, "generated_by")
yield ","
yield direct_parse_key(json_table_str, "date")
yield ","
yield direct_parse_key(json_table_str, "matrix_type")
yield ","
yield direct_parse_key(json_table_str, "matrix_element_type")
yield ","
yield new_data
yield ","
yield new_axis_md
yield ","
if axis == "observation":
yield direct_parse_key(json_table_str, "columns")
else:
yield direct_parse_key(json_table_str, "rows")
yield "}"
format_ = 'json'
table = subset_generator()
else:
with biom_open(hdf5_biom) as f:
table = Table.from_hdf5(f, ids=ids, axis=axis)
format_ = 'hdf5'
return {'subsetted_table': (table, format_)}
def run(self, **kwargs):
json_table_str = kwargs['json_table_str']
hdf5_biom = kwargs['hdf5_table']
axis = kwargs['axis']
ids = kwargs['ids']
if axis not in self.Axes:
raise CommandError("Invalid axis '%s'. Must be either %s." % (
axis,
' or '.join(map(lambda e: "'%s'" % e, self.Axes))))
if hdf5_biom is None and json_table_str is None:
raise CommandError("Must specify an input table")
elif hdf5_biom is not None and json_table_str is not None:
raise CommandError("Can only specify one input table")
if json_table_str is not None:
idxs, new_axis_md = get_axis_indices(json_table_str, ids, axis)
new_data = direct_slice_data(json_table_str, idxs, axis)
# multiple walks over the string. bad form, but easy right now
# ...should add a yield_and_ignore parser or something.
def subset_generator():
yield "{"
yield direct_parse_key(json_table_str, "id")
yield ","
yield direct_parse_key(json_table_str, "format")
yield ","
yield direct_parse_key(json_table_str, "format_url")
yield ","
yield direct_parse_key(json_table_str, "type")
yield ","
yield direct_parse_key(json_table_str, "generated_by")
yield ","
yield direct_parse_key(json_table_str, "date")
yield ","
yield direct_parse_key(json_table_str, "matrix_type")
yield ","
yield direct_parse_key(json_table_str, "matrix_element_type")
yield ","
yield new_data
yield ","
yield new_axis_md
yield ","
if axis == "observation":
yield direct_parse_key(json_table_str, "columns")
else:
yield direct_parse_key(json_table_str, "rows")
yield "}"
format_ = 'json'
table = subset_generator()
else:
with biom_open(hdf5_biom) as f:
table = Table.from_hdf5(f, ids=ids, axis=axis)
format_ = 'hdf5'
return {'subsetted_table': (table, format_)}
def _subset_table(hdf5_biom, json_table_str, axis, ids):
if axis not in ['sample', 'observation']:
raise ValueError("Invalid axis '%s'. Must be either 'sample' or "
"'observation'." % axis)
if hdf5_biom is None and json_table_str is None:
raise ValueError("Must specify an input table")
elif hdf5_biom is not None and json_table_str is not None:
raise ValueError("Can only specify one input table")
if json_table_str is not None:
idxs, new_axis_md = get_axis_indices(json_table_str, ids, axis)
new_data = direct_slice_data(json_table_str, idxs, axis)
# multiple walks over the string. bad form, but easy right now
# ...should add a yield_and_ignore parser or something.
def subset_generator():
yield "{"
yield direct_parse_key(json_table_str, "id")
yield ","
yield direct_parse_key(json_table_str, "format")
yield ","
yield direct_parse_key(json_table_str, "format_url")
yield ","
yield direct_parse_key(json_table_str, "type")
yield ","
yield direct_parse_key(json_table_str, "generated_by")
yield ","
yield direct_parse_key(json_table_str, "date")
yield ","
yield direct_parse_key(json_table_str, "matrix_type")
yield ","
yield direct_parse_key(json_table_str, "matrix_element_type")
yield ","
yield new_data
yield ","
yield new_axis_md
yield ","
if axis == "observation":
yield direct_parse_key(json_table_str, "columns")
else:
yield direct_parse_key(json_table_str, "rows")
yield "}"
format_ = 'json'
table = subset_generator()
else:
with biom_open(hdf5_biom) as f:
table = Table.from_hdf5(f, ids=ids, axis=axis)
format_ = 'hdf5'
return table, format_
def _subset_table(hdf5_biom, json_table_str, axis, ids):
if axis not in ['sample', 'observation']:
raise ValueError("Invalid axis '%s'. Must be either 'sample' or "
"'observation'." % axis)
if hdf5_biom is None and json_table_str is None:
raise ValueError("Must specify an input table")
elif hdf5_biom is not None and json_table_str is not None:
raise ValueError("Can only specify one input table")
if json_table_str is not None:
idxs, new_axis_md = get_axis_indices(json_table_str, ids, axis)
new_data = direct_slice_data(json_table_str, idxs, axis)
# multiple walks over the string. bad form, but easy right now
# ...should add a yield_and_ignore parser or something.
def subset_generator():
yield "{"
yield direct_parse_key(json_table_str, "id")
yield ","
yield direct_parse_key(json_table_str, "format")
yield ","
yield direct_parse_key(json_table_str, "format_url")
yield ","
yield direct_parse_key(json_table_str, "type")
yield ","
yield direct_parse_key(json_table_str, "generated_by")
yield ","
yield direct_parse_key(json_table_str, "date")
yield ","
yield direct_parse_key(json_table_str, "matrix_type")
yield ","
yield direct_parse_key(json_table_str, "matrix_element_type")
yield ","
yield new_data
yield ","
yield new_axis_md
yield ","
if axis == "observation":
yield direct_parse_key(json_table_str, "columns")
else:
yield direct_parse_key(json_table_str, "rows")
yield "}"
format_ = 'json'
table = subset_generator()
else:
with biom_open(hdf5_biom) as f:
table = Table.from_hdf5(f, ids=ids, axis=axis)
format_ = 'hdf5'
return table, format_
def parse_biom_table(fp, input_is_dense=False):
try:
return Table.from_hdf5(fp)
except:
pass
if hasattr(fp, 'read'):
return Table.from_json(json.load(fp), input_is_dense=input_is_dense)
elif isinstance(fp, list):
return Table.from_json(json.loads(''.join(fp)),
input_is_dense=input_is_dense)
else:
return Table.from_json(json.loads(fp), input_is_dense=input_is_dense)
def test_rarefy_to_files2(self):
"""rarefy_to_files should write valid files with some metadata on otus
"""
maker = RarefactionMaker(self.otu_table_meta_fp, 0, 1, 1, 1)
maker.rarefy_to_files(self.rare_dir,
include_full=True,
include_lineages=False)
fname = os.path.join(self.rare_dir, "rarefaction_1_0.biom")
with biom_open(fname, 'U') as biom_file:
otu_table = Table.from_hdf5(biom_file)
self.assertItemsEqual(otu_table.ids(), self.otu_table.ids()[:2])
def test_rarefy_to_files(self):
"""rarefy_to_files should write valid files
"""
maker = RarefactionMaker(self.otu_table_fp, 0, 1, 1, 1)
maker.rarefy_to_files(
self.rare_dir,
include_full=True,
include_lineages=False)
fname = os.path.join(self.rare_dir, "rarefaction_1_0.biom")
with biom_open(fname, 'U') as biom_file:
otu_table = Table.from_hdf5(biom_file)
self.assertItemsEqual(
otu_table.sample_ids,
self.otu_table.sample_ids[:2])
def parse_biom_table(fp, ids=None, axis='sample', input_is_dense=False):
r"""Parses the biom table stored in the filepath `fp`
Parameters
----------
fp : file like
File alike object storing the BIOM table
ids : iterable
The sample/observation ids of the samples/observations that we need
to retrieve from the biom table
axis : {'sample', 'observation'}, optional
The axis to subset on
input_is_dense : boolean
Indicates if the BIOM table is dense or sparse. Valid only for JSON
tables.
Returns
-------
Table
The BIOM table stored at fp
Raises
------
ValueError
If `samples` and `observations` are provided.
Notes
-----
Subsetting from the BIOM table is only supported in one axis
Examples
--------
Parse a hdf5 biom table
>>> from h5py import File # doctest: +SKIP
>>> from biom.parse import parse_biom_table
>>> f = File('rich_sparse_otu_table_hdf5.biom') # doctest: +SKIP
>>> t = parse_biom_table(f) # doctest: +SKIP
Parse a hdf5 biom table subsetting observations
>>> from h5py import File # doctest: +SKIP
>>> from biom.parse import parse_biom_table
>>> f = File('rich_sparse_otu_table_hdf5.biom') # doctest: +SKIP
>>> t = parse_biom_table(f, ids=["GG_OTU_1"],
... axis='observation') # doctest: +SKIP
"""
if axis not in ['observation', 'sample']:
UnknownAxisError(axis)
try:
return Table.from_hdf5(fp, ids=ids, axis=axis)
except ValueError:
pass
except RuntimeError:
pass
if hasattr(fp, 'read'):
old_pos = fp.tell()
# Read in characters until first non-whitespace
# If it is a {, then this is (most likely) JSON
c = fp.read(1)
while c.isspace():
c = fp.read(1)
if c == '{':
fp.seek(old_pos)
t = Table.from_json(json.load(fp, object_pairs_hook=OrderedDict),
input_is_dense=input_is_dense)
else:
fp.seek(old_pos)
t = Table.from_tsv(fp, None, None, lambda x: x)
elif isinstance(fp, list):
try:
t = Table.from_json(json.loads(''.join(fp),
object_pairs_hook=OrderedDict),
input_is_dense=input_is_dense)
except ValueError:
t = Table.from_tsv(fp, None, None, lambda x: x)
else:
t = Table.from_json(json.loads(fp, object_pairs_hook=OrderedDict),
input_is_dense=input_is_dense)
def subset_ids(data, id_, md):
return id_ in ids
def gt_zero(vals, id_, md):
return np.any(vals)
if ids is not None:
t.filter(subset_ids, axis=axis)
axis = 'observation' if axis == 'sample' else 'sample'
t.filter(gt_zero, axis=axis)
return t
def parse_biom_table(fp, ids=None, axis='sample', input_is_dense=False):
r"""Parses the biom table stored in the filepath `fp`
Parameters
----------
fp : file like
File alike object storing the BIOM table
ids : iterable
The sample/observation ids of the samples/observations that we need
to retrieve from the biom table
axis : {'sample', 'observation'}, optional
The axis to subset on
input_is_dense : boolean
Indicates if the BIOM table is dense or sparse. Valid only for JSON
tables.
Returns
-------
Table
The BIOM table stored at fp
Raises
------
ValueError
If `samples` and `observations` are provided.
Notes
-----
Subsetting from the BIOM table is only supported in one axis
Examples
--------
Parse a hdf5 biom table
>>> from h5py import File # doctest: +SKIP
>>> from biom.parse import parse_biom_table
>>> f = File('rich_sparse_otu_table_hdf5.biom') # doctest: +SKIP
>>> t = parse_biom_table(f) # doctest: +SKIP
Parse a hdf5 biom table subsetting observations
>>> from h5py import File # doctest: +SKIP
>>> from biom.parse import parse_biom_table
>>> f = File('rich_sparse_otu_table_hdf5.biom') # doctest: +SKIP
>>> t = parse_biom_table(f, ids=["GG_OTU_1"],
... axis='observation') # doctest: +SKIP
"""
if axis not in ['observation', 'sample']:
UnknownAxisError(axis)
try:
return Table.from_hdf5(fp, ids=ids, axis=axis)
except:
pass
if hasattr(fp, 'read'):
old_pos = fp.tell()
try:
t = Table.from_json(json.load(fp), input_is_dense=input_is_dense)
except ValueError:
fp.seek(old_pos)
t = Table.from_tsv(fp, None, None, lambda x: x)
elif isinstance(fp, list):
try:
t = Table.from_json(json.loads(''.join(fp)),
input_is_dense=input_is_dense)
except ValueError:
t = Table.from_tsv(fp, None, None, lambda x: x)
else:
t = Table.from_json(json.loads(fp), input_is_dense=input_is_dense)
if ids is not None:
f = lambda data, id_, md: id_ in ids
t.filter(f, axis=axis)
axis = 'observation' if axis == 'sample' else 'sample'
f = lambda vals, id_, md: np.any(vals)
t.filter(f, axis=axis)
return t
def parse_biom_table(fp, ids=None, axis='sample', input_is_dense=False):
r"""Parses the biom table stored in the filepath `fp`
Parameters
----------
fp : file like
File alike object storing the BIOM table
ids : iterable
The sample/observation ids of the samples/observations that we need
to retrieve from the biom table
axis : {'sample', 'observation'}, optional
The axis to subset on
input_is_dense : boolean
Indicates if the BIOM table is dense or sparse. Valid only for JSON
tables.
Returns
-------
Table
The BIOM table stored at fp
Raises
------
ValueError
If `samples` and `observations` are provided.
Notes
-----
Subsetting from the BIOM table is only supported in one axis
Examples
--------
Parse a hdf5 biom table
>>> from h5py import File # doctest: +SKIP
>>> from biom.parse import parse_biom_table
>>> f = File('rich_sparse_otu_table_hdf5.biom') # doctest: +SKIP
>>> t = parse_biom_table(f) # doctest: +SKIP
Parse a hdf5 biom table subsetting observations
>>> from h5py import File # doctest: +SKIP
>>> from biom.parse import parse_biom_table
>>> f = File('rich_sparse_otu_table_hdf5.biom') # doctest: +SKIP
>>> t = parse_biom_table(f, ids=["GG_OTU_1"],
... axis='observation') # doctest: +SKIP
"""
if axis not in ['observation', 'sample']:
UnknownAxisError(axis)
try:
return Table.from_hdf5(fp, ids=ids, axis=axis)
except:
pass
if hasattr(fp, 'read'):
old_pos = fp.tell()
try:
t = Table.from_json(json.load(fp), input_is_dense=input_is_dense)
except ValueError:
fp.seek(old_pos)
t = Table.from_tsv(fp, None, None, lambda x: x)
elif isinstance(fp, list):
try:
t = Table.from_json(json.loads(''.join(fp)),
input_is_dense=input_is_dense)
except ValueError:
t = Table.from_tsv(fp, None, None, lambda x: x)
else:
t = Table.from_json(json.loads(fp), input_is_dense=input_is_dense)
if ids is not None:
f = lambda data, id_, md: id_ in ids
t.filter(f, axis=axis)
axis = 'observation' if axis == 'sample' else 'sample'
f = lambda vals, id_, md: np.any(vals)
t.filter(f, axis=axis)
return t
def parse_biom_table(file_obj, ids=None, axis='sample', input_is_dense=False):
r"""Parses the biom table stored in `file_obj`
Parameters
----------
file_obj : file-like object, or list
file-like object storing the BIOM table (tab-delimited or JSON), or
a list of lines of the BIOM table in tab-delimited or JSON format
ids : iterable
The sample/observation ids of the samples/observations that we need
to retrieve from the biom table
axis : {'sample', 'observation'}, optional
The axis to subset on
input_is_dense : boolean
Indicates if the BIOM table is dense or sparse. Valid only for JSON
tables.
Returns
-------
Table
The BIOM table stored at file_obj
Raises
------
ValueError
If `samples` and `observations` are provided.
Notes
-----
Subsetting from the BIOM table is only supported in one axis
Examples
--------
Parse a hdf5 biom table
>>> from h5py import File # doctest: +SKIP
>>> from biom.parse import parse_biom_table
>>> f = File('rich_sparse_otu_table_hdf5.biom') # doctest: +SKIP
>>> t = parse_biom_table(f) # doctest: +SKIP
Parse a hdf5 biom table subsetting observations
>>> from h5py import File # doctest: +SKIP
>>> from biom.parse import parse_biom_table
>>> f = File('rich_sparse_otu_table_hdf5.biom') # doctest: +SKIP
>>> t = parse_biom_table(f, ids=["GG_OTU_1"],
... axis='observation') # doctest: +SKIP
"""
if axis not in ['observation', 'sample']:
UnknownAxisError(axis)
try:
return Table.from_hdf5(file_obj, ids=ids, axis=axis)
except ValueError:
pass
except RuntimeError:
pass
if hasattr(file_obj, 'read'):
old_pos = file_obj.tell()
# Read in characters until first non-whitespace
# If it is a {, then this is (most likely) JSON
c = file_obj.read(1)
while c.isspace():
c = file_obj.read(1)
if c == '{':
file_obj.seek(old_pos)
t = Table.from_json(json.load(file_obj,
object_pairs_hook=OrderedDict),
input_is_dense=input_is_dense)
else:
file_obj.seek(old_pos)
t = Table.from_tsv(file_obj, None, None, lambda x: x)
elif isinstance(file_obj, list):
try:
t = Table.from_json(json.loads(''.join(file_obj),
object_pairs_hook=OrderedDict),
input_is_dense=input_is_dense)
except ValueError:
t = Table.from_tsv(file_obj, None, None, lambda x: x)
else:
t = Table.from_json(json.loads(file_obj,
object_pairs_hook=OrderedDict),
input_is_dense=input_is_dense)
def subset_ids(data, id_, md):
return id_ in ids
def gt_zero(vals, id_, md):
return np.any(vals)
if ids is not None:
t.filter(subset_ids, axis=axis)
axis = 'observation' if axis == 'sample' else 'sample'
t.filter(gt_zero, axis=axis)
return t
