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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 9 new columns ({'parameters', 'function_tokens', 'nwo', 'docstring_summary', 'return_statement', 'score', 'function', 'identifier', 'argument_list'}) and 6 missing columns ({'original_string', 'partition', 'repo', 'code', 'code_tokens', 'func_name'}).
This happened while the json dataset builder was generating data using
hf://datasets/semeru/Text-Code-CodeSearchNet-Python/valid.jsonl (at revision 8f65de8a4141f24c7a2099d21cb715cdb2b1b59a)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
url: string
sha: string
docstring_summary: string
language: string
parameters: string
return_statement: string
argument_list: string
function_tokens: list<item: string>
child 0, item: string
function: string
path: string
identifier: string
docstring: string
docstring_tokens: list<item: string>
child 0, item: string
nwo: string
score: double
idx: int64
to
{'repo': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'func_name': Value(dtype='string', id=None), 'original_string': Value(dtype='string', id=None), 'language': Value(dtype='string', id=None), 'code': Value(dtype='string', id=None), 'code_tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'docstring': Value(dtype='string', id=None), 'docstring_tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'sha': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None), 'partition': Value(dtype='string', id=None), 'idx': Value(dtype='int64', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1321, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 9 new columns ({'parameters', 'function_tokens', 'nwo', 'docstring_summary', 'return_statement', 'score', 'function', 'identifier', 'argument_list'}) and 6 missing columns ({'original_string', 'partition', 'repo', 'code', 'code_tokens', 'func_name'}).
This happened while the json dataset builder was generating data using
hf://datasets/semeru/Text-Code-CodeSearchNet-Python/valid.jsonl (at revision 8f65de8a4141f24c7a2099d21cb715cdb2b1b59a)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
repo
string | path
string | func_name
string | original_string
string | language
string | code
string | code_tokens
sequence | docstring
string | docstring_tokens
sequence | sha
string | url
string | partition
string | idx
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
smdabdoub/phylotoast | phylotoast/util.py | split_phylogeny | def split_phylogeny(p, level="s"):
"""
Return either the full or truncated version of a QIIME-formatted taxonomy string.
:type p: str
:param p: A QIIME-formatted taxonomy string: k__Foo; p__Bar; ...
:type level: str
:param level: The different level of identification are kingdom (k), phylum (p),
class (c),order (o), family (f), genus (g) and species (s). If level is
not provided, the default level of identification is species.
:rtype: str
:return: A QIIME-formatted taxonomy string up to the classification given
by param level.
"""
level = level+"__"
result = p.split(level)
return result[0]+level+result[1].split(";")[0] | python | def split_phylogeny(p, level="s"):
"""
Return either the full or truncated version of a QIIME-formatted taxonomy string.
:type p: str
:param p: A QIIME-formatted taxonomy string: k__Foo; p__Bar; ...
:type level: str
:param level: The different level of identification are kingdom (k), phylum (p),
class (c),order (o), family (f), genus (g) and species (s). If level is
not provided, the default level of identification is species.
:rtype: str
:return: A QIIME-formatted taxonomy string up to the classification given
by param level.
"""
level = level+"__"
result = p.split(level)
return result[0]+level+result[1].split(";")[0] | [
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smdabdoub/phylotoast | phylotoast/util.py | ensure_dir | def ensure_dir(d):
"""
Check to make sure the supplied directory path does not exist, if so, create it. The
method catches OSError exceptions and returns a descriptive message instead of
re-raising the error.
:type d: str
:param d: It is the full path to a directory.
:return: Does not return anything, but creates a directory path if it doesn't exist
already.
"""
if not os.path.exists(d):
try:
os.makedirs(d)
except OSError as oe:
# should not happen with os.makedirs
# ENOENT: No such file or directory
if os.errno == errno.ENOENT:
msg = twdd("""One or more directories in the path ({}) do not exist. If
you are specifying a new directory for output, please ensure
all other directories in the path currently exist.""")
return msg.format(d)
else:
msg = twdd("""An error occurred trying to create the output directory
({}) with message: {}""")
return msg.format(d, oe.strerror) | python | def ensure_dir(d):
"""
Check to make sure the supplied directory path does not exist, if so, create it. The
method catches OSError exceptions and returns a descriptive message instead of
re-raising the error.
:type d: str
:param d: It is the full path to a directory.
:return: Does not return anything, but creates a directory path if it doesn't exist
already.
"""
if not os.path.exists(d):
try:
os.makedirs(d)
except OSError as oe:
# should not happen with os.makedirs
# ENOENT: No such file or directory
if os.errno == errno.ENOENT:
msg = twdd("""One or more directories in the path ({}) do not exist. If
you are specifying a new directory for output, please ensure
all other directories in the path currently exist.""")
return msg.format(d)
else:
msg = twdd("""An error occurred trying to create the output directory
({}) with message: {}""")
return msg.format(d, oe.strerror) | [
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smdabdoub/phylotoast | phylotoast/util.py | file_handle | def file_handle(fnh, mode="rU"):
"""
Takes either a file path or an open file handle, checks validity and returns an open
file handle or raises an appropriate Exception.
:type fnh: str
:param fnh: It is the full path to a file, or open file handle
:type mode: str
:param mode: The way in which this file will be used, for example to read or write or
both. By default, file will be opened in rU mode.
:return: Returns an opened file for appropriate usage.
"""
handle = None
if isinstance(fnh, file):
if fnh.closed:
raise ValueError("Input file is closed.")
handle = fnh
elif isinstance(fnh, str):
handle = open(fnh, mode)
return handle | python | def file_handle(fnh, mode="rU"):
"""
Takes either a file path or an open file handle, checks validity and returns an open
file handle or raises an appropriate Exception.
:type fnh: str
:param fnh: It is the full path to a file, or open file handle
:type mode: str
:param mode: The way in which this file will be used, for example to read or write or
both. By default, file will be opened in rU mode.
:return: Returns an opened file for appropriate usage.
"""
handle = None
if isinstance(fnh, file):
if fnh.closed:
raise ValueError("Input file is closed.")
handle = fnh
elif isinstance(fnh, str):
handle = open(fnh, mode)
return handle | [
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smdabdoub/phylotoast | phylotoast/util.py | gather_categories | def gather_categories(imap, header, categories=None):
"""
Find the user specified categories in the map and create a dictionary to contain the
relevant data for each type within the categories. Multiple categories will have their
types combined such that each possible combination will have its own entry in the
dictionary.
:type imap: dict
:param imap: The input mapping file data keyed by SampleID
:type header: list
:param header: The header line from the input mapping file. This will be searched for
the user-specified categories
:type categories: list
:param categories: The list of user-specified category column name from mapping file
:rtype: dict
:return: A sorted dictionary keyed on the combinations of all the types found within
the user-specified categories. Each entry will contain an empty DataCategory
namedtuple. If no categories are specified, a single entry with the key
'default' will be returned
"""
# If no categories provided, return all SampleIDs
if categories is None:
return {"default": DataCategory(set(imap.keys()), {})}
cat_ids = [header.index(cat)
for cat in categories if cat in header and "=" not in cat]
table = OrderedDict()
conditions = defaultdict(set)
for i, cat in enumerate(categories):
if "=" in cat and cat.split("=")[0] in header:
cat_name = header[header.index(cat.split("=")[0])]
conditions[cat_name].add(cat.split("=")[1])
# If invalid categories or conditions identified, return all SampleIDs
if not cat_ids and not conditions:
return {"default": DataCategory(set(imap.keys()), {})}
#If only category column given, return column-wise SampleIDs
if cat_ids and not conditions:
for sid, row in imap.items():
cat_name = "_".join([row[cid] for cid in cat_ids])
if cat_name not in table:
table[cat_name] = DataCategory(set(), {})
table[cat_name].sids.add(sid)
return table
# Collect all condition names
cond_ids = set()
for k in conditions:
try:
cond_ids.add(header.index(k))
except ValueError:
continue
idx_to_test = set(cat_ids).union(cond_ids)
# If column name and condition given, return overlapping SampleIDs of column and
# condition combinations
for sid, row in imap.items():
if all([row[header.index(c)] in conditions[c] for c in conditions]):
key = "_".join([row[idx] for idx in idx_to_test])
try:
assert key in table.keys()
except AssertionError:
table[key] = DataCategory(set(), {})
table[key].sids.add(sid)
try:
assert len(table) > 0
except AssertionError:
return {"default": DataCategory(set(imap.keys()), {})}
else:
return table | python | def gather_categories(imap, header, categories=None):
"""
Find the user specified categories in the map and create a dictionary to contain the
relevant data for each type within the categories. Multiple categories will have their
types combined such that each possible combination will have its own entry in the
dictionary.
:type imap: dict
:param imap: The input mapping file data keyed by SampleID
:type header: list
:param header: The header line from the input mapping file. This will be searched for
the user-specified categories
:type categories: list
:param categories: The list of user-specified category column name from mapping file
:rtype: dict
:return: A sorted dictionary keyed on the combinations of all the types found within
the user-specified categories. Each entry will contain an empty DataCategory
namedtuple. If no categories are specified, a single entry with the key
'default' will be returned
"""
# If no categories provided, return all SampleIDs
if categories is None:
return {"default": DataCategory(set(imap.keys()), {})}
cat_ids = [header.index(cat)
for cat in categories if cat in header and "=" not in cat]
table = OrderedDict()
conditions = defaultdict(set)
for i, cat in enumerate(categories):
if "=" in cat and cat.split("=")[0] in header:
cat_name = header[header.index(cat.split("=")[0])]
conditions[cat_name].add(cat.split("=")[1])
# If invalid categories or conditions identified, return all SampleIDs
if not cat_ids and not conditions:
return {"default": DataCategory(set(imap.keys()), {})}
#If only category column given, return column-wise SampleIDs
if cat_ids and not conditions:
for sid, row in imap.items():
cat_name = "_".join([row[cid] for cid in cat_ids])
if cat_name not in table:
table[cat_name] = DataCategory(set(), {})
table[cat_name].sids.add(sid)
return table
# Collect all condition names
cond_ids = set()
for k in conditions:
try:
cond_ids.add(header.index(k))
except ValueError:
continue
idx_to_test = set(cat_ids).union(cond_ids)
# If column name and condition given, return overlapping SampleIDs of column and
# condition combinations
for sid, row in imap.items():
if all([row[header.index(c)] in conditions[c] for c in conditions]):
key = "_".join([row[idx] for idx in idx_to_test])
try:
assert key in table.keys()
except AssertionError:
table[key] = DataCategory(set(), {})
table[key].sids.add(sid)
try:
assert len(table) > 0
except AssertionError:
return {"default": DataCategory(set(imap.keys()), {})}
else:
return table | [
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:type imap: dict
:param imap: The input mapping file data keyed by SampleID
:type header: list
:param header: The header line from the input mapping file. This will be searched for
the user-specified categories
:type categories: list
:param categories: The list of user-specified category column name from mapping file
:rtype: dict
:return: A sorted dictionary keyed on the combinations of all the types found within
the user-specified categories. Each entry will contain an empty DataCategory
namedtuple. If no categories are specified, a single entry with the key
'default' will be returned | [
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smdabdoub/phylotoast | phylotoast/util.py | parse_unifrac | def parse_unifrac(unifracFN):
"""
Parses the unifrac results file into a dictionary
:type unifracFN: str
:param unifracFN: The path to the unifrac results file
:rtype: dict
:return: A dictionary with keys: 'pcd' (principle coordinates data) which is a
dictionary of the data keyed by sample ID, 'eigvals' (eigenvalues), and
'varexp' (variation explained)
"""
with open(unifracFN, "rU") as uF:
first = uF.next().split("\t")
lines = [line.strip() for line in uF]
unifrac = {"pcd": OrderedDict(), "eigvals": [], "varexp": []}
if first[0] == "pc vector number":
return parse_unifrac_v1_8(unifrac, lines)
elif first[0] == "Eigvals":
return parse_unifrac_v1_9(unifrac, lines)
else:
raise ValueError("File format not supported/recognized. Please check input "
"unifrac file.") | python | def parse_unifrac(unifracFN):
"""
Parses the unifrac results file into a dictionary
:type unifracFN: str
:param unifracFN: The path to the unifrac results file
:rtype: dict
:return: A dictionary with keys: 'pcd' (principle coordinates data) which is a
dictionary of the data keyed by sample ID, 'eigvals' (eigenvalues), and
'varexp' (variation explained)
"""
with open(unifracFN, "rU") as uF:
first = uF.next().split("\t")
lines = [line.strip() for line in uF]
unifrac = {"pcd": OrderedDict(), "eigvals": [], "varexp": []}
if first[0] == "pc vector number":
return parse_unifrac_v1_8(unifrac, lines)
elif first[0] == "Eigvals":
return parse_unifrac_v1_9(unifrac, lines)
else:
raise ValueError("File format not supported/recognized. Please check input "
"unifrac file.") | [
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smdabdoub/phylotoast | phylotoast/util.py | parse_unifrac_v1_8 | def parse_unifrac_v1_8(unifrac, file_data):
"""
Function to parse data from older version of unifrac file obtained from Qiime version
1.8 and earlier.
:type unifrac: dict
:param unifracFN: The path to the unifrac results file
:type file_data: list
:param file_data: Unifrac data lines after stripping whitespace characters.
"""
for line in file_data:
if line == "":
break
line = line.split("\t")
unifrac["pcd"][line[0]] = [float(e) for e in line[1:]]
unifrac["eigvals"] = [float(entry) for entry in file_data[-2].split("\t")[1:]]
unifrac["varexp"] = [float(entry) for entry in file_data[-1].split("\t")[1:]]
return unifrac | python | def parse_unifrac_v1_8(unifrac, file_data):
"""
Function to parse data from older version of unifrac file obtained from Qiime version
1.8 and earlier.
:type unifrac: dict
:param unifracFN: The path to the unifrac results file
:type file_data: list
:param file_data: Unifrac data lines after stripping whitespace characters.
"""
for line in file_data:
if line == "":
break
line = line.split("\t")
unifrac["pcd"][line[0]] = [float(e) for e in line[1:]]
unifrac["eigvals"] = [float(entry) for entry in file_data[-2].split("\t")[1:]]
unifrac["varexp"] = [float(entry) for entry in file_data[-1].split("\t")[1:]]
return unifrac | [
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:param file_data: Unifrac data lines after stripping whitespace characters. | [
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smdabdoub/phylotoast | phylotoast/util.py | parse_unifrac_v1_9 | def parse_unifrac_v1_9(unifrac, file_data):
"""
Function to parse data from newer version of unifrac file obtained from Qiime version
1.9 and later.
:type unifracFN: str
:param unifracFN: The path to the unifrac results file
:type file_data: list
:param file_data: Unifrac data lines after stripping whitespace characters.
"""
unifrac["eigvals"] = [float(entry) for entry in file_data[0].split("\t")]
unifrac["varexp"] = [float(entry)*100 for entry in file_data[3].split("\t")]
for line in file_data[8:]:
if line == "":
break
line = line.split("\t")
unifrac["pcd"][line[0]] = [float(e) for e in line[1:]]
return unifrac | python | def parse_unifrac_v1_9(unifrac, file_data):
"""
Function to parse data from newer version of unifrac file obtained from Qiime version
1.9 and later.
:type unifracFN: str
:param unifracFN: The path to the unifrac results file
:type file_data: list
:param file_data: Unifrac data lines after stripping whitespace characters.
"""
unifrac["eigvals"] = [float(entry) for entry in file_data[0].split("\t")]
unifrac["varexp"] = [float(entry)*100 for entry in file_data[3].split("\t")]
for line in file_data[8:]:
if line == "":
break
line = line.split("\t")
unifrac["pcd"][line[0]] = [float(e) for e in line[1:]]
return unifrac | [
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smdabdoub/phylotoast | phylotoast/util.py | color_mapping | def color_mapping(sample_map, header, group_column, color_column=None):
"""
Determine color-category mapping. If color_column was specified, then map the category
names to color values. Otherwise, use the palettable colors to automatically generate
a set of colors for the group values.
:type sample_map: dict
:param unifracFN: Map associating each line of the mapping file with the appropriate
sample ID (each value of the map also contains the sample ID)
:type header: tuple
:param A tuple of header line for mapping file
:type group_column: str
:param group_column: String denoting the column name for sample groups.
:type color_column: str
:param color_column: String denoting the column name for sample colors.
:type return: dict
:param return: {SampleID: Color}
"""
group_colors = OrderedDict()
group_gather = gather_categories(sample_map, header, [group_column])
if color_column is not None:
color_gather = gather_categories(sample_map, header, [color_column])
# match sample IDs between color_gather and group_gather
for group in group_gather:
for color in color_gather:
# allow incomplete assignment of colors, if group sids overlap at
# all with the color sids, consider it a match
if group_gather[group].sids.intersection(color_gather[color].sids):
group_colors[group] = color
else:
bcolors = itertools.cycle(Set3_12.hex_colors)
for group in group_gather:
group_colors[group] = bcolors.next()
return group_colors | python | def color_mapping(sample_map, header, group_column, color_column=None):
"""
Determine color-category mapping. If color_column was specified, then map the category
names to color values. Otherwise, use the palettable colors to automatically generate
a set of colors for the group values.
:type sample_map: dict
:param unifracFN: Map associating each line of the mapping file with the appropriate
sample ID (each value of the map also contains the sample ID)
:type header: tuple
:param A tuple of header line for mapping file
:type group_column: str
:param group_column: String denoting the column name for sample groups.
:type color_column: str
:param color_column: String denoting the column name for sample colors.
:type return: dict
:param return: {SampleID: Color}
"""
group_colors = OrderedDict()
group_gather = gather_categories(sample_map, header, [group_column])
if color_column is not None:
color_gather = gather_categories(sample_map, header, [color_column])
# match sample IDs between color_gather and group_gather
for group in group_gather:
for color in color_gather:
# allow incomplete assignment of colors, if group sids overlap at
# all with the color sids, consider it a match
if group_gather[group].sids.intersection(color_gather[color].sids):
group_colors[group] = color
else:
bcolors = itertools.cycle(Set3_12.hex_colors)
for group in group_gather:
group_colors[group] = bcolors.next()
return group_colors | [
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:type sample_map: dict
:param unifracFN: Map associating each line of the mapping file with the appropriate
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:type header: tuple
:param A tuple of header line for mapping file
:type group_column: str
:param group_column: String denoting the column name for sample groups.
:type color_column: str
:param color_column: String denoting the column name for sample colors.
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christophertbrown/bioscripts | ctbBio/shuffle_genome.py | rev_c | def rev_c(read):
"""
return reverse completment of read
"""
rc = []
rc_nucs = {'A':'T', 'T':'A', 'G':'C', 'C':'G', 'N':'N'}
for base in read:
rc.extend(rc_nucs[base.upper()])
return rc[::-1] | python | def rev_c(read):
"""
return reverse completment of read
"""
rc = []
rc_nucs = {'A':'T', 'T':'A', 'G':'C', 'C':'G', 'N':'N'}
for base in read:
rc.extend(rc_nucs[base.upper()])
return rc[::-1] | [
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christophertbrown/bioscripts | ctbBio/shuffle_genome.py | shuffle_genome | def shuffle_genome(genome, cat, fraction = float(100), plot = True, \
alpha = 0.1, beta = 100000, \
min_length = 1000, max_length = 200000):
"""
randomly shuffle genome
"""
header = '>randomized_%s' % (genome.name)
sequence = list(''.join([i[1] for i in parse_fasta(genome)]))
length = len(sequence)
shuffled = []
# break genome into pieces
while sequence is not False:
s = int(random.gammavariate(alpha, beta))
if s <= min_length or s >= max_length:
continue
if len(sequence) < s:
seq = sequence[0:]
else:
seq = sequence[0:s]
sequence = sequence[s:]
# if bool(random.getrandbits(1)) is True:
# seq = rev_c(seq)
# print('fragment length: %s reverse complement: True' % ('{:,}'.format(s)), file=sys.stderr)
# else:
# print('fragment length: %s reverse complement: False' % ('{:,}'.format(s)), file=sys.stderr)
shuffled.append(''.join(seq))
if sequence == []:
break
# shuffle pieces
random.shuffle(shuffled)
# subset fragments
if fraction == float(100):
subset = shuffled
else:
max_pieces = int(length * fraction/100)
subset, total = [], 0
for fragment in shuffled:
length = len(fragment)
if total + length <= max_pieces:
subset.append(fragment)
total += length
else:
diff = max_pieces - total
subset.append(fragment[0:diff])
break
# combine sequences, if requested
if cat is True:
yield [header, ''.join(subset)]
else:
for i, seq in enumerate(subset):
yield ['%s fragment:%s' % (header, i), seq] | python | def shuffle_genome(genome, cat, fraction = float(100), plot = True, \
alpha = 0.1, beta = 100000, \
min_length = 1000, max_length = 200000):
"""
randomly shuffle genome
"""
header = '>randomized_%s' % (genome.name)
sequence = list(''.join([i[1] for i in parse_fasta(genome)]))
length = len(sequence)
shuffled = []
# break genome into pieces
while sequence is not False:
s = int(random.gammavariate(alpha, beta))
if s <= min_length or s >= max_length:
continue
if len(sequence) < s:
seq = sequence[0:]
else:
seq = sequence[0:s]
sequence = sequence[s:]
# if bool(random.getrandbits(1)) is True:
# seq = rev_c(seq)
# print('fragment length: %s reverse complement: True' % ('{:,}'.format(s)), file=sys.stderr)
# else:
# print('fragment length: %s reverse complement: False' % ('{:,}'.format(s)), file=sys.stderr)
shuffled.append(''.join(seq))
if sequence == []:
break
# shuffle pieces
random.shuffle(shuffled)
# subset fragments
if fraction == float(100):
subset = shuffled
else:
max_pieces = int(length * fraction/100)
subset, total = [], 0
for fragment in shuffled:
length = len(fragment)
if total + length <= max_pieces:
subset.append(fragment)
total += length
else:
diff = max_pieces - total
subset.append(fragment[0:diff])
break
# combine sequences, if requested
if cat is True:
yield [header, ''.join(subset)]
else:
for i, seq in enumerate(subset):
yield ['%s fragment:%s' % (header, i), seq] | [
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opengridcc/opengrid | opengrid/library/regression.py | MultiVarLinReg._prune | def _prune(self, fit, p_max):
"""
If the fit contains statistically insignificant parameters, remove them.
Returns a pruned fit where all parameters have p-values of the t-statistic below p_max
Parameters
----------
fit: fm.ols fit object
Can contain insignificant parameters
p_max : float
Maximum allowed probability of the t-statistic
Returns
-------
fit: fm.ols fit object
Won't contain any insignificant parameters
"""
def remove_from_model_desc(x, model_desc):
"""
Return a model_desc without x
"""
rhs_termlist = []
for t in model_desc.rhs_termlist:
if not t.factors:
# intercept, add anyway
rhs_termlist.append(t)
elif not x == t.factors[0]._varname:
# this is not the term with x
rhs_termlist.append(t)
md = ModelDesc(model_desc.lhs_termlist, rhs_termlist)
return md
corrected_model_desc = ModelDesc(fit.model.formula.lhs_termlist[:], fit.model.formula.rhs_termlist[:])
pars_to_prune = fit.pvalues.where(fit.pvalues > p_max).dropna().index.tolist()
try:
pars_to_prune.remove('Intercept')
except:
pass
while pars_to_prune:
corrected_model_desc = remove_from_model_desc(pars_to_prune[0], corrected_model_desc)
fit = fm.ols(corrected_model_desc, data=self.df).fit()
pars_to_prune = fit.pvalues.where(fit.pvalues > p_max).dropna().index.tolist()
try:
pars_to_prune.remove('Intercept')
except:
pass
return fit | python | def _prune(self, fit, p_max):
"""
If the fit contains statistically insignificant parameters, remove them.
Returns a pruned fit where all parameters have p-values of the t-statistic below p_max
Parameters
----------
fit: fm.ols fit object
Can contain insignificant parameters
p_max : float
Maximum allowed probability of the t-statistic
Returns
-------
fit: fm.ols fit object
Won't contain any insignificant parameters
"""
def remove_from_model_desc(x, model_desc):
"""
Return a model_desc without x
"""
rhs_termlist = []
for t in model_desc.rhs_termlist:
if not t.factors:
# intercept, add anyway
rhs_termlist.append(t)
elif not x == t.factors[0]._varname:
# this is not the term with x
rhs_termlist.append(t)
md = ModelDesc(model_desc.lhs_termlist, rhs_termlist)
return md
corrected_model_desc = ModelDesc(fit.model.formula.lhs_termlist[:], fit.model.formula.rhs_termlist[:])
pars_to_prune = fit.pvalues.where(fit.pvalues > p_max).dropna().index.tolist()
try:
pars_to_prune.remove('Intercept')
except:
pass
while pars_to_prune:
corrected_model_desc = remove_from_model_desc(pars_to_prune[0], corrected_model_desc)
fit = fm.ols(corrected_model_desc, data=self.df).fit()
pars_to_prune = fit.pvalues.where(fit.pvalues > p_max).dropna().index.tolist()
try:
pars_to_prune.remove('Intercept')
except:
pass
return fit | [
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Returns a pruned fit where all parameters have p-values of the t-statistic below p_max
Parameters
----------
fit: fm.ols fit object
Can contain insignificant parameters
p_max : float
Maximum allowed probability of the t-statistic
Returns
-------
fit: fm.ols fit object
Won't contain any insignificant parameters | [
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opengridcc/opengrid | opengrid/library/regression.py | MultiVarLinReg.find_best_rsquared | def find_best_rsquared(list_of_fits):
"""Return the best fit, based on rsquared"""
res = sorted(list_of_fits, key=lambda x: x.rsquared)
return res[-1] | python | def find_best_rsquared(list_of_fits):
"""Return the best fit, based on rsquared"""
res = sorted(list_of_fits, key=lambda x: x.rsquared)
return res[-1] | [
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opengridcc/opengrid | opengrid/library/regression.py | MultiVarLinReg._predict | def _predict(self, fit, df):
"""
Return a df with predictions and confidence interval
Notes
-----
The df will contain the following columns:
- 'predicted': the model output
- 'interval_u', 'interval_l': upper and lower confidence bounds.
The result will depend on the following attributes of self:
confint : float (default=0.95)
Confidence level for two-sided hypothesis
allow_negative_predictions : bool (default=True)
If False, correct negative predictions to zero (typically for energy consumption predictions)
Parameters
----------
fit : Statsmodels fit
df : pandas DataFrame or None (default)
If None, use self.df
Returns
-------
df_res : pandas DataFrame
Copy of df with additional columns 'predicted', 'interval_u' and 'interval_l'
"""
# Add model results to data as column 'predictions'
df_res = df.copy()
if 'Intercept' in fit.model.exog_names:
df_res['Intercept'] = 1.0
df_res['predicted'] = fit.predict(df_res)
if not self.allow_negative_predictions:
df_res.loc[df_res['predicted'] < 0, 'predicted'] = 0
prstd, interval_l, interval_u = wls_prediction_std(fit,
df_res[fit.model.exog_names],
alpha=1 - self.confint)
df_res['interval_l'] = interval_l
df_res['interval_u'] = interval_u
if 'Intercept' in df_res:
df_res.drop(labels=['Intercept'], axis=1, inplace=True)
return df_res | python | def _predict(self, fit, df):
"""
Return a df with predictions and confidence interval
Notes
-----
The df will contain the following columns:
- 'predicted': the model output
- 'interval_u', 'interval_l': upper and lower confidence bounds.
The result will depend on the following attributes of self:
confint : float (default=0.95)
Confidence level for two-sided hypothesis
allow_negative_predictions : bool (default=True)
If False, correct negative predictions to zero (typically for energy consumption predictions)
Parameters
----------
fit : Statsmodels fit
df : pandas DataFrame or None (default)
If None, use self.df
Returns
-------
df_res : pandas DataFrame
Copy of df with additional columns 'predicted', 'interval_u' and 'interval_l'
"""
# Add model results to data as column 'predictions'
df_res = df.copy()
if 'Intercept' in fit.model.exog_names:
df_res['Intercept'] = 1.0
df_res['predicted'] = fit.predict(df_res)
if not self.allow_negative_predictions:
df_res.loc[df_res['predicted'] < 0, 'predicted'] = 0
prstd, interval_l, interval_u = wls_prediction_std(fit,
df_res[fit.model.exog_names],
alpha=1 - self.confint)
df_res['interval_l'] = interval_l
df_res['interval_u'] = interval_u
if 'Intercept' in df_res:
df_res.drop(labels=['Intercept'], axis=1, inplace=True)
return df_res | [
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The result will depend on the following attributes of self:
confint : float (default=0.95)
Confidence level for two-sided hypothesis
allow_negative_predictions : bool (default=True)
If False, correct negative predictions to zero (typically for energy consumption predictions)
Parameters
----------
fit : Statsmodels fit
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Returns
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df_res : pandas DataFrame
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smdabdoub/phylotoast | phylotoast/biom_calc.py | relative_abundance | def relative_abundance(biomf, sampleIDs=None):
"""
Calculate the relative abundance of each OTUID in a Sample.
:type biomf: A BIOM file.
:param biomf: OTU table format.
:type sampleIDs: list
:param sampleIDs: A list of sample id's from BIOM format OTU table.
:rtype: dict
:return: Returns a keyed on SampleIDs, and the values are dictionaries keyed on
OTUID's and their values represent the relative abundance of that OTUID in
that SampleID.
"""
if sampleIDs is None:
sampleIDs = biomf.ids()
else:
try:
for sid in sampleIDs:
assert sid in biomf.ids()
except AssertionError:
raise ValueError(
"\nError while calculating relative abundances: The sampleIDs provided do"
" not match the sampleIDs in biom file. Please double check the sampleIDs"
" provided.\n")
otuIDs = biomf.ids(axis="observation")
norm_biomf = biomf.norm(inplace=False)
return {sample: {otuID: norm_biomf.get_value_by_ids(otuID, sample)
for otuID in otuIDs} for sample in sampleIDs} | python | def relative_abundance(biomf, sampleIDs=None):
"""
Calculate the relative abundance of each OTUID in a Sample.
:type biomf: A BIOM file.
:param biomf: OTU table format.
:type sampleIDs: list
:param sampleIDs: A list of sample id's from BIOM format OTU table.
:rtype: dict
:return: Returns a keyed on SampleIDs, and the values are dictionaries keyed on
OTUID's and their values represent the relative abundance of that OTUID in
that SampleID.
"""
if sampleIDs is None:
sampleIDs = biomf.ids()
else:
try:
for sid in sampleIDs:
assert sid in biomf.ids()
except AssertionError:
raise ValueError(
"\nError while calculating relative abundances: The sampleIDs provided do"
" not match the sampleIDs in biom file. Please double check the sampleIDs"
" provided.\n")
otuIDs = biomf.ids(axis="observation")
norm_biomf = biomf.norm(inplace=False)
return {sample: {otuID: norm_biomf.get_value_by_ids(otuID, sample)
for otuID in otuIDs} for sample in sampleIDs} | [
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smdabdoub/phylotoast | phylotoast/biom_calc.py | mean_otu_pct_abundance | def mean_otu_pct_abundance(ra, otuIDs):
"""
Calculate the mean OTU abundance percentage.
:type ra: Dict
:param ra: 'ra' refers to a dictionary keyed on SampleIDs, and the values are
dictionaries keyed on OTUID's and their values represent the relative
abundance of that OTUID in that SampleID. 'ra' is the output of
relative_abundance() function.
:type otuIDs: List
:param otuIDs: A list of OTUID's for which the percentage abundance needs to be
measured.
:rtype: dict
:return: A dictionary of OTUID and their percent relative abundance as key/value pair.
"""
sids = ra.keys()
otumeans = defaultdict(int)
for oid in otuIDs:
otumeans[oid] = sum([ra[sid][oid] for sid in sids
if oid in ra[sid]]) / len(sids) * 100
return otumeans | python | def mean_otu_pct_abundance(ra, otuIDs):
"""
Calculate the mean OTU abundance percentage.
:type ra: Dict
:param ra: 'ra' refers to a dictionary keyed on SampleIDs, and the values are
dictionaries keyed on OTUID's and their values represent the relative
abundance of that OTUID in that SampleID. 'ra' is the output of
relative_abundance() function.
:type otuIDs: List
:param otuIDs: A list of OTUID's for which the percentage abundance needs to be
measured.
:rtype: dict
:return: A dictionary of OTUID and their percent relative abundance as key/value pair.
"""
sids = ra.keys()
otumeans = defaultdict(int)
for oid in otuIDs:
otumeans[oid] = sum([ra[sid][oid] for sid in sids
if oid in ra[sid]]) / len(sids) * 100
return otumeans | [
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smdabdoub/phylotoast | phylotoast/biom_calc.py | MRA | def MRA(biomf, sampleIDs=None, transform=None):
"""
Calculate the mean relative abundance percentage.
:type biomf: A BIOM file.
:param biomf: OTU table format.
:type sampleIDs: list
:param sampleIDs: A list of sample id's from BIOM format OTU table.
:param transform: Mathematical function which is used to transform smax to another
format. By default, the function has been set to None.
:rtype: dict
:return: A dictionary keyed on OTUID's and their mean relative abundance for a given
number of sampleIDs.
"""
ra = relative_abundance(biomf, sampleIDs)
if transform is not None:
ra = {sample: {otuID: transform(abd) for otuID, abd in ra[sample].items()}
for sample in ra.keys()}
otuIDs = biomf.ids(axis="observation")
return mean_otu_pct_abundance(ra, otuIDs) | python | def MRA(biomf, sampleIDs=None, transform=None):
"""
Calculate the mean relative abundance percentage.
:type biomf: A BIOM file.
:param biomf: OTU table format.
:type sampleIDs: list
:param sampleIDs: A list of sample id's from BIOM format OTU table.
:param transform: Mathematical function which is used to transform smax to another
format. By default, the function has been set to None.
:rtype: dict
:return: A dictionary keyed on OTUID's and their mean relative abundance for a given
number of sampleIDs.
"""
ra = relative_abundance(biomf, sampleIDs)
if transform is not None:
ra = {sample: {otuID: transform(abd) for otuID, abd in ra[sample].items()}
for sample in ra.keys()}
otuIDs = biomf.ids(axis="observation")
return mean_otu_pct_abundance(ra, otuIDs) | [
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smdabdoub/phylotoast | phylotoast/biom_calc.py | raw_abundance | def raw_abundance(biomf, sampleIDs=None, sample_abd=True):
"""
Calculate the total number of sequences in each OTU or SampleID.
:type biomf: A BIOM file.
:param biomf: OTU table format.
:type sampleIDs: List
:param sampleIDs: A list of column id's from BIOM format OTU table. By default, the
list has been set to None.
:type sample_abd: Boolean
:param sample_abd: A boolean operator to provide output for OTUID's or SampleID's. By
default, the output will be provided for SampleID's.
:rtype: dict
:return: Returns a dictionary keyed on either OTUID's or SampleIDs and their
respective abundance as values.
"""
results = defaultdict(int)
if sampleIDs is None:
sampleIDs = biomf.ids()
else:
try:
for sid in sampleIDs:
assert sid in biomf.ids()
except AssertionError:
raise ValueError(
"\nError while calculating raw total abundances: The sampleIDs provided "
"do not match the sampleIDs in biom file. Please double check the "
"sampleIDs provided.\n")
otuIDs = biomf.ids(axis="observation")
for sampleID in sampleIDs:
for otuID in otuIDs:
abd = biomf.get_value_by_ids(otuID, sampleID)
if sample_abd:
results[sampleID] += abd
else:
results[otuID] += abd
return results | python | def raw_abundance(biomf, sampleIDs=None, sample_abd=True):
"""
Calculate the total number of sequences in each OTU or SampleID.
:type biomf: A BIOM file.
:param biomf: OTU table format.
:type sampleIDs: List
:param sampleIDs: A list of column id's from BIOM format OTU table. By default, the
list has been set to None.
:type sample_abd: Boolean
:param sample_abd: A boolean operator to provide output for OTUID's or SampleID's. By
default, the output will be provided for SampleID's.
:rtype: dict
:return: Returns a dictionary keyed on either OTUID's or SampleIDs and their
respective abundance as values.
"""
results = defaultdict(int)
if sampleIDs is None:
sampleIDs = biomf.ids()
else:
try:
for sid in sampleIDs:
assert sid in biomf.ids()
except AssertionError:
raise ValueError(
"\nError while calculating raw total abundances: The sampleIDs provided "
"do not match the sampleIDs in biom file. Please double check the "
"sampleIDs provided.\n")
otuIDs = biomf.ids(axis="observation")
for sampleID in sampleIDs:
for otuID in otuIDs:
abd = biomf.get_value_by_ids(otuID, sampleID)
if sample_abd:
results[sampleID] += abd
else:
results[otuID] += abd
return results | [
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smdabdoub/phylotoast | phylotoast/biom_calc.py | transform_raw_abundance | def transform_raw_abundance(biomf, fn=math.log10, sampleIDs=None, sample_abd=True):
"""
Function to transform the total abundance calculation for each sample ID to another
format based on user given transformation function.
:type biomf: A BIOM file.
:param biomf: OTU table format.
:param fn: Mathematical function which is used to transform smax to another format.
By default, the function has been given as base 10 logarithm.
:rtype: dict
:return: Returns a dictionary similar to output of raw_abundance function but with
the abundance values modified by the mathematical operation. By default, the
operation performed on the abundances is base 10 logarithm.
"""
totals = raw_abundance(biomf, sampleIDs, sample_abd)
return {sid: fn(abd) for sid, abd in totals.items()} | python | def transform_raw_abundance(biomf, fn=math.log10, sampleIDs=None, sample_abd=True):
"""
Function to transform the total abundance calculation for each sample ID to another
format based on user given transformation function.
:type biomf: A BIOM file.
:param biomf: OTU table format.
:param fn: Mathematical function which is used to transform smax to another format.
By default, the function has been given as base 10 logarithm.
:rtype: dict
:return: Returns a dictionary similar to output of raw_abundance function but with
the abundance values modified by the mathematical operation. By default, the
operation performed on the abundances is base 10 logarithm.
"""
totals = raw_abundance(biomf, sampleIDs, sample_abd)
return {sid: fn(abd) for sid, abd in totals.items()} | [
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smdabdoub/phylotoast | bin/diversity.py | print_MannWhitneyU | def print_MannWhitneyU(div_calc):
"""
Compute the Mann-Whitney U test for unequal group sample sizes.
"""
try:
x = div_calc.values()[0].values()
y = div_calc.values()[1].values()
except:
return "Error setting up input arrays for Mann-Whitney U Test. Skipping "\
"significance testing."
T, p = stats.mannwhitneyu(x, y)
print "\nMann-Whitney U test statistic:", T
print "Two-tailed p-value: {}".format(2 * p) | python | def print_MannWhitneyU(div_calc):
"""
Compute the Mann-Whitney U test for unequal group sample sizes.
"""
try:
x = div_calc.values()[0].values()
y = div_calc.values()[1].values()
except:
return "Error setting up input arrays for Mann-Whitney U Test. Skipping "\
"significance testing."
T, p = stats.mannwhitneyu(x, y)
print "\nMann-Whitney U test statistic:", T
print "Two-tailed p-value: {}".format(2 * p) | [
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smdabdoub/phylotoast | bin/diversity.py | print_KruskalWallisH | def print_KruskalWallisH(div_calc):
"""
Compute the Kruskal-Wallis H-test for independent samples. A typical rule is that
each group must have at least 5 measurements.
"""
calc = defaultdict(list)
try:
for k1, v1 in div_calc.iteritems():
for k2, v2 in v1.iteritems():
calc[k1].append(v2)
except:
return "Error setting up input arrays for Kruskal-Wallis H-Test. Skipping "\
"significance testing."
h, p = stats.kruskal(*calc.values())
print "\nKruskal-Wallis H-test statistic for {} groups: {}".format(str(len(div_calc)), h)
print "p-value: {}".format(p) | python | def print_KruskalWallisH(div_calc):
"""
Compute the Kruskal-Wallis H-test for independent samples. A typical rule is that
each group must have at least 5 measurements.
"""
calc = defaultdict(list)
try:
for k1, v1 in div_calc.iteritems():
for k2, v2 in v1.iteritems():
calc[k1].append(v2)
except:
return "Error setting up input arrays for Kruskal-Wallis H-Test. Skipping "\
"significance testing."
h, p = stats.kruskal(*calc.values())
print "\nKruskal-Wallis H-test statistic for {} groups: {}".format(str(len(div_calc)), h)
print "p-value: {}".format(p) | [
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smdabdoub/phylotoast | bin/diversity.py | handle_program_options | def handle_program_options():
"""Parses the given options passed in at the command line."""
parser = argparse.ArgumentParser(description="Calculate the alpha diversity\
of a set of samples using one or more \
metrics and output a kernal density \
estimator-smoothed histogram of the \
results.")
parser.add_argument("-m", "--map_file",
help="QIIME mapping file.")
parser.add_argument("-i", "--biom_fp",
help="Path to the BIOM table")
parser.add_argument("-c", "--category",
help="Specific category from the mapping file.")
parser.add_argument("-d", "--diversity", default=["shannon"], nargs="+",
help="The alpha diversity metric. Default \
value is 'shannon', which will calculate the Shannon\
entropy. Multiple metrics can be specified (space separated).\
The full list of metrics is available at:\
http://scikit-bio.org/docs/latest/generated/skbio.diversity.alpha.html.\
Beta diversity metrics will be supported in the future.")
parser.add_argument("--x_label", default=[None], nargs="+",
help="The name of the diversity metric to be displayed on the\
plot as the X-axis label. If multiple metrics are specified,\
then multiple entries for the X-axis label should be given.")
parser.add_argument("--color_by",
help="A column name in the mapping file containing\
hexadecimal (#FF0000) color values that will\
be used to color the groups. Each sample ID must\
have a color entry.")
parser.add_argument("--plot_title", default="",
help="A descriptive title that will appear at the top \
of the output plot. Surround with quotes if there are\
spaces in the title.")
parser.add_argument("-o", "--output_dir", default=".",
help="The directory plots will be saved to.")
parser.add_argument("--image_type", default="png",
help="The type of image to save: png, svg, pdf, eps, etc...")
parser.add_argument("--save_calculations",
help="Path and name of text file to store the calculated "
"diversity metrics.")
parser.add_argument("--suppress_stats", action="store_true", help="Do not display "
"significance testing results which are shown by default.")
parser.add_argument("--show_available_metrics", action="store_true",
help="Supply this parameter to see which alpha diversity metrics "
" are available for usage. No calculations will be performed"
" if this parameter is provided.")
return parser.parse_args() | python | def handle_program_options():
"""Parses the given options passed in at the command line."""
parser = argparse.ArgumentParser(description="Calculate the alpha diversity\
of a set of samples using one or more \
metrics and output a kernal density \
estimator-smoothed histogram of the \
results.")
parser.add_argument("-m", "--map_file",
help="QIIME mapping file.")
parser.add_argument("-i", "--biom_fp",
help="Path to the BIOM table")
parser.add_argument("-c", "--category",
help="Specific category from the mapping file.")
parser.add_argument("-d", "--diversity", default=["shannon"], nargs="+",
help="The alpha diversity metric. Default \
value is 'shannon', which will calculate the Shannon\
entropy. Multiple metrics can be specified (space separated).\
The full list of metrics is available at:\
http://scikit-bio.org/docs/latest/generated/skbio.diversity.alpha.html.\
Beta diversity metrics will be supported in the future.")
parser.add_argument("--x_label", default=[None], nargs="+",
help="The name of the diversity metric to be displayed on the\
plot as the X-axis label. If multiple metrics are specified,\
then multiple entries for the X-axis label should be given.")
parser.add_argument("--color_by",
help="A column name in the mapping file containing\
hexadecimal (#FF0000) color values that will\
be used to color the groups. Each sample ID must\
have a color entry.")
parser.add_argument("--plot_title", default="",
help="A descriptive title that will appear at the top \
of the output plot. Surround with quotes if there are\
spaces in the title.")
parser.add_argument("-o", "--output_dir", default=".",
help="The directory plots will be saved to.")
parser.add_argument("--image_type", default="png",
help="The type of image to save: png, svg, pdf, eps, etc...")
parser.add_argument("--save_calculations",
help="Path and name of text file to store the calculated "
"diversity metrics.")
parser.add_argument("--suppress_stats", action="store_true", help="Do not display "
"significance testing results which are shown by default.")
parser.add_argument("--show_available_metrics", action="store_true",
help="Supply this parameter to see which alpha diversity metrics "
" are available for usage. No calculations will be performed"
" if this parameter is provided.")
return parser.parse_args() | [
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christophertbrown/bioscripts | ctbBio/search.py | blastdb | def blastdb(fasta, maxfile = 10000000):
"""
make blast db
"""
db = fasta.rsplit('.', 1)[0]
type = check_type(fasta)
if type == 'nucl':
type = ['nhr', type]
else:
type = ['phr', type]
if os.path.exists('%s.%s' % (db, type[0])) is False \
and os.path.exists('%s.00.%s' % (db, type[0])) is False:
print('# ... making blastdb for: %s' % (fasta), file=sys.stderr)
os.system('makeblastdb \
-in %s -out %s -dbtype %s -max_file_sz %s >> log.txt' \
% (fasta, db, type[1], maxfile))
else:
print('# ... database found for: %s' % (fasta), file=sys.stderr)
return db | python | def blastdb(fasta, maxfile = 10000000):
"""
make blast db
"""
db = fasta.rsplit('.', 1)[0]
type = check_type(fasta)
if type == 'nucl':
type = ['nhr', type]
else:
type = ['phr', type]
if os.path.exists('%s.%s' % (db, type[0])) is False \
and os.path.exists('%s.00.%s' % (db, type[0])) is False:
print('# ... making blastdb for: %s' % (fasta), file=sys.stderr)
os.system('makeblastdb \
-in %s -out %s -dbtype %s -max_file_sz %s >> log.txt' \
% (fasta, db, type[1], maxfile))
else:
print('# ... database found for: %s' % (fasta), file=sys.stderr)
return db | [
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christophertbrown/bioscripts | ctbBio/search.py | usearchdb | def usearchdb(fasta, alignment = 'local', usearch_loc = 'usearch'):
"""
make usearch db
"""
if '.udb' in fasta:
print('# ... database found: %s' % (fasta), file=sys.stderr)
return fasta
type = check_type(fasta)
db = '%s.%s.udb' % (fasta.rsplit('.', 1)[0], type)
if os.path.exists(db) is False:
print('# ... making usearch db for: %s' % (fasta), file=sys.stderr)
if alignment == 'local':
os.system('%s -makeudb_ublast %s -output %s >> log.txt' % (usearch_loc, fasta, db))
elif alignment == 'global':
os.system('%s -makeudb_usearch %s -output %s >> log.txt' % (usearch_loc, fasta, db))
else:
print('# ... database found for: %s' % (fasta), file=sys.stderr)
return db | python | def usearchdb(fasta, alignment = 'local', usearch_loc = 'usearch'):
"""
make usearch db
"""
if '.udb' in fasta:
print('# ... database found: %s' % (fasta), file=sys.stderr)
return fasta
type = check_type(fasta)
db = '%s.%s.udb' % (fasta.rsplit('.', 1)[0], type)
if os.path.exists(db) is False:
print('# ... making usearch db for: %s' % (fasta), file=sys.stderr)
if alignment == 'local':
os.system('%s -makeudb_ublast %s -output %s >> log.txt' % (usearch_loc, fasta, db))
elif alignment == 'global':
os.system('%s -makeudb_usearch %s -output %s >> log.txt' % (usearch_loc, fasta, db))
else:
print('# ... database found for: %s' % (fasta), file=sys.stderr)
return db | [
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mkouhei/bootstrap-py | bootstrap_py/control.py | _pp | def _pp(dict_data):
"""Pretty print."""
for key, val in dict_data.items():
# pylint: disable=superfluous-parens
print('{0:<11}: {1}'.format(key, val)) | python | def _pp(dict_data):
"""Pretty print."""
for key, val in dict_data.items():
# pylint: disable=superfluous-parens
print('{0:<11}: {1}'.format(key, val)) | [
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mkouhei/bootstrap-py | bootstrap_py/control.py | print_licences | def print_licences(params, metadata):
"""Print licenses.
:param argparse.Namespace params: parameter
:param bootstrap_py.classifier.Classifiers metadata: package metadata
"""
if hasattr(params, 'licenses'):
if params.licenses:
_pp(metadata.licenses_desc())
sys.exit(0) | python | def print_licences(params, metadata):
"""Print licenses.
:param argparse.Namespace params: parameter
:param bootstrap_py.classifier.Classifiers metadata: package metadata
"""
if hasattr(params, 'licenses'):
if params.licenses:
_pp(metadata.licenses_desc())
sys.exit(0) | [
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mkouhei/bootstrap-py | bootstrap_py/control.py | check_repository_existence | def check_repository_existence(params):
"""Check repository existence.
:param argparse.Namespace params: parameters
"""
repodir = os.path.join(params.outdir, params.name)
if os.path.isdir(repodir):
raise Conflict(
'Package repository "{0}" has already exists.'.format(repodir)) | python | def check_repository_existence(params):
"""Check repository existence.
:param argparse.Namespace params: parameters
"""
repodir = os.path.join(params.outdir, params.name)
if os.path.isdir(repodir):
raise Conflict(
'Package repository "{0}" has already exists.'.format(repodir)) | [
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mkouhei/bootstrap-py | bootstrap_py/control.py | generate_package | def generate_package(params):
"""Generate package repository.
:param argparse.Namespace params: parameters
"""
pkg_data = package.PackageData(params)
pkg_tree = package.PackageTree(pkg_data)
pkg_tree.generate()
pkg_tree.move()
VCS(os.path.join(pkg_tree.outdir, pkg_tree.name), pkg_tree.pkg_data) | python | def generate_package(params):
"""Generate package repository.
:param argparse.Namespace params: parameters
"""
pkg_data = package.PackageData(params)
pkg_tree = package.PackageTree(pkg_data)
pkg_tree.generate()
pkg_tree.move()
VCS(os.path.join(pkg_tree.outdir, pkg_tree.name), pkg_tree.pkg_data) | [
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christophertbrown/bioscripts | ctbBio/sam2fastq.py | print_single | def print_single(line, rev):
"""
print single reads to stderr
"""
if rev is True:
seq = rc(['', line[9]])[1]
qual = line[10][::-1]
else:
seq = line[9]
qual = line[10]
fq = ['@%s' % line[0], seq, '+%s' % line[0], qual]
print('\n'.join(fq), file = sys.stderr) | python | def print_single(line, rev):
"""
print single reads to stderr
"""
if rev is True:
seq = rc(['', line[9]])[1]
qual = line[10][::-1]
else:
seq = line[9]
qual = line[10]
fq = ['@%s' % line[0], seq, '+%s' % line[0], qual]
print('\n'.join(fq), file = sys.stderr) | [
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christophertbrown/bioscripts | ctbBio/sam2fastq.py | sam2fastq | def sam2fastq(sam, singles = False, force = False):
"""
convert sam to fastq
"""
L, R = None, None
for line in sam:
if line.startswith('@') is True:
continue
line = line.strip().split()
bit = [True if i == '1' else False \
for i in bin(int(line[1])).split('b')[1][::-1]]
while len(bit) < 8:
bit.append(False)
pair, proper, na, nap, rev, mrev, left, right = bit
# make sure read is paired
if pair is False:
if singles is True:
print_single(line, rev)
continue
# check if sequence is reverse-complemented
if rev is True:
seq = rc(['', line[9]])[1]
qual = line[10][::-1]
else:
seq = line[9]
qual = line[10]
# check if read is forward or reverse, return when both have been found
if left is True:
if L is not None and force is False:
print('sam file is not sorted', file = sys.stderr)
print('\te.g.: %s' % (line[0]), file = sys.stderr)
exit()
if L is not None:
L = None
continue
L = ['@%s' % line[0], seq, '+%s' % line[0], qual]
if R is not None:
yield L
yield R
L, R = None, None
if right is True:
if R is not None and force is False:
print('sam file is not sorted', file = sys.stderr)
print('\te.g.: %s' % (line[0]), file = sys.stderr)
exit()
if R is not None:
R = None
continue
R = ['@%s' % line[0], seq, '+%s' % line[0], qual]
if L is not None:
yield L
yield R
L, R = None, None | python | def sam2fastq(sam, singles = False, force = False):
"""
convert sam to fastq
"""
L, R = None, None
for line in sam:
if line.startswith('@') is True:
continue
line = line.strip().split()
bit = [True if i == '1' else False \
for i in bin(int(line[1])).split('b')[1][::-1]]
while len(bit) < 8:
bit.append(False)
pair, proper, na, nap, rev, mrev, left, right = bit
# make sure read is paired
if pair is False:
if singles is True:
print_single(line, rev)
continue
# check if sequence is reverse-complemented
if rev is True:
seq = rc(['', line[9]])[1]
qual = line[10][::-1]
else:
seq = line[9]
qual = line[10]
# check if read is forward or reverse, return when both have been found
if left is True:
if L is not None and force is False:
print('sam file is not sorted', file = sys.stderr)
print('\te.g.: %s' % (line[0]), file = sys.stderr)
exit()
if L is not None:
L = None
continue
L = ['@%s' % line[0], seq, '+%s' % line[0], qual]
if R is not None:
yield L
yield R
L, R = None, None
if right is True:
if R is not None and force is False:
print('sam file is not sorted', file = sys.stderr)
print('\te.g.: %s' % (line[0]), file = sys.stderr)
exit()
if R is not None:
R = None
continue
R = ['@%s' % line[0], seq, '+%s' % line[0], qual]
if L is not None:
yield L
yield R
L, R = None, None | [
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christophertbrown/bioscripts | ctbBio/subset_sam.py | sort_sam | def sort_sam(sam, sort):
"""
sort sam file
"""
tempdir = '%s/' % (os.path.abspath(sam).rsplit('/', 1)[0])
if sort is True:
mapping = '%s.sorted.sam' % (sam.rsplit('.', 1)[0])
if sam != '-':
if os.path.exists(mapping) is False:
os.system("\
sort -k1 --buffer-size=%sG -T %s -o %s %s\
" % (sbuffer, tempdir, mapping, sam))
else:
mapping = 'stdin-sam.sorted.sam'
p = Popen("sort -k1 --buffer-size=%sG -T %s -o %s" \
% (sbuffer, tempdir, mapping), stdin = sys.stdin, shell = True)
p.communicate()
mapping = open(mapping)
else:
if sam == '-':
mapping = sys.stdin
else:
mapping = open(sam)
return mapping | python | def sort_sam(sam, sort):
"""
sort sam file
"""
tempdir = '%s/' % (os.path.abspath(sam).rsplit('/', 1)[0])
if sort is True:
mapping = '%s.sorted.sam' % (sam.rsplit('.', 1)[0])
if sam != '-':
if os.path.exists(mapping) is False:
os.system("\
sort -k1 --buffer-size=%sG -T %s -o %s %s\
" % (sbuffer, tempdir, mapping, sam))
else:
mapping = 'stdin-sam.sorted.sam'
p = Popen("sort -k1 --buffer-size=%sG -T %s -o %s" \
% (sbuffer, tempdir, mapping), stdin = sys.stdin, shell = True)
p.communicate()
mapping = open(mapping)
else:
if sam == '-':
mapping = sys.stdin
else:
mapping = open(sam)
return mapping | [
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christophertbrown/bioscripts | ctbBio/subset_sam.py | sub_sam | def sub_sam(sam, percent, sort = True, sbuffer = False):
"""
randomly subset sam file
"""
mapping = sort_sam(sam, sort)
pool = [1 for i in range(0, percent)] + [0 for i in range(0, 100 - percent)]
c = cycle([1, 2])
for line in mapping:
line = line.strip().split()
if line[0].startswith('@'): # get the sam header
yield line
continue
if int(line[1]) <= 20: # is this from a single read?
if random.choice(pool) == 1:
yield line
else:
n = next(c)
if n == 1:
prev = line
if n == 2 and random.choice(pool) == 1:
yield prev
yield line | python | def sub_sam(sam, percent, sort = True, sbuffer = False):
"""
randomly subset sam file
"""
mapping = sort_sam(sam, sort)
pool = [1 for i in range(0, percent)] + [0 for i in range(0, 100 - percent)]
c = cycle([1, 2])
for line in mapping:
line = line.strip().split()
if line[0].startswith('@'): # get the sam header
yield line
continue
if int(line[1]) <= 20: # is this from a single read?
if random.choice(pool) == 1:
yield line
else:
n = next(c)
if n == 1:
prev = line
if n == 2 and random.choice(pool) == 1:
yield prev
yield line | [
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] | 83b2566b3a5745437ec651cd6cafddd056846240 | https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/subset_sam.py#L39-L60 | train | 30 |
christophertbrown/bioscripts | ctbBio/fastq2fasta.py | fq2fa | def fq2fa(fq):
"""
convert fq to fa
"""
c = cycle([1, 2, 3, 4])
for line in fq:
n = next(c)
if n == 1:
seq = ['>%s' % (line.strip().split('@', 1)[1])]
if n == 2:
seq.append(line.strip())
yield seq | python | def fq2fa(fq):
"""
convert fq to fa
"""
c = cycle([1, 2, 3, 4])
for line in fq:
n = next(c)
if n == 1:
seq = ['>%s' % (line.strip().split('@', 1)[1])]
if n == 2:
seq.append(line.strip())
yield seq | [
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elbow-jason/Uno-deprecated | uno/decorators.py | change_return_type | def change_return_type(f):
"""
Converts the returned value of wrapped function to the type of the
first arg or to the type specified by a kwarg key return_type's value.
"""
@wraps(f)
def wrapper(*args, **kwargs):
if kwargs.has_key('return_type'):
return_type = kwargs['return_type']
kwargs.pop('return_type')
return return_type(f(*args, **kwargs))
elif len(args) > 0:
return_type = type(args[0])
return return_type(f(*args, **kwargs))
else:
return f(*args, **kwargs)
return wrapper | python | def change_return_type(f):
"""
Converts the returned value of wrapped function to the type of the
first arg or to the type specified by a kwarg key return_type's value.
"""
@wraps(f)
def wrapper(*args, **kwargs):
if kwargs.has_key('return_type'):
return_type = kwargs['return_type']
kwargs.pop('return_type')
return return_type(f(*args, **kwargs))
elif len(args) > 0:
return_type = type(args[0])
return return_type(f(*args, **kwargs))
else:
return f(*args, **kwargs)
return wrapper | [
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elbow-jason/Uno-deprecated | uno/decorators.py | convert_args_to_sets | def convert_args_to_sets(f):
"""
Converts all args to 'set' type via self.setify function.
"""
@wraps(f)
def wrapper(*args, **kwargs):
args = (setify(x) for x in args)
return f(*args, **kwargs)
return wrapper | python | def convert_args_to_sets(f):
"""
Converts all args to 'set' type via self.setify function.
"""
@wraps(f)
def wrapper(*args, **kwargs):
args = (setify(x) for x in args)
return f(*args, **kwargs)
return wrapper | [
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laymonage/kbbi-python | kbbi/kbbi.py | KBBI._init_entri | def _init_entri(self, laman):
"""Membuat objek-objek entri dari laman yang diambil.
:param laman: Laman respons yang dikembalikan oleh KBBI daring.
:type laman: Response
"""
sup = BeautifulSoup(laman.text, 'html.parser')
estr = ''
for label in sup.find('hr').next_siblings:
if label.name == 'hr':
self.entri.append(Entri(estr))
break
if label.name == 'h2':
if estr:
self.entri.append(Entri(estr))
estr = ''
estr += str(label).strip() | python | def _init_entri(self, laman):
"""Membuat objek-objek entri dari laman yang diambil.
:param laman: Laman respons yang dikembalikan oleh KBBI daring.
:type laman: Response
"""
sup = BeautifulSoup(laman.text, 'html.parser')
estr = ''
for label in sup.find('hr').next_siblings:
if label.name == 'hr':
self.entri.append(Entri(estr))
break
if label.name == 'h2':
if estr:
self.entri.append(Entri(estr))
estr = ''
estr += str(label).strip() | [
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:param laman: Laman respons yang dikembalikan oleh KBBI daring.
:type laman: Response | [
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laymonage/kbbi-python | kbbi/kbbi.py | Entri._init_kata_dasar | def _init_kata_dasar(self, dasar):
"""Memproses kata dasar yang ada dalam nama entri.
:param dasar: ResultSet untuk label HTML dengan class="rootword"
:type dasar: ResultSet
"""
for tiap in dasar:
kata = tiap.find('a')
dasar_no = kata.find('sup')
kata = ambil_teks_dalam_label(kata)
self.kata_dasar.append(
kata + ' [{}]'.format(dasar_no.text.strip()) if dasar_no else kata
) | python | def _init_kata_dasar(self, dasar):
"""Memproses kata dasar yang ada dalam nama entri.
:param dasar: ResultSet untuk label HTML dengan class="rootword"
:type dasar: ResultSet
"""
for tiap in dasar:
kata = tiap.find('a')
dasar_no = kata.find('sup')
kata = ambil_teks_dalam_label(kata)
self.kata_dasar.append(
kata + ' [{}]'.format(dasar_no.text.strip()) if dasar_no else kata
) | [
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laymonage/kbbi-python | kbbi/kbbi.py | Entri.serialisasi | def serialisasi(self):
"""Mengembalikan hasil serialisasi objek Entri ini.
:returns: Dictionary hasil serialisasi
:rtype: dict
"""
return {
"nama": self.nama,
"nomor": self.nomor,
"kata_dasar": self.kata_dasar,
"pelafalan": self.pelafalan,
"bentuk_tidak_baku": self.bentuk_tidak_baku,
"varian": self.varian,
"makna": [makna.serialisasi() for makna in self.makna]
} | python | def serialisasi(self):
"""Mengembalikan hasil serialisasi objek Entri ini.
:returns: Dictionary hasil serialisasi
:rtype: dict
"""
return {
"nama": self.nama,
"nomor": self.nomor,
"kata_dasar": self.kata_dasar,
"pelafalan": self.pelafalan,
"bentuk_tidak_baku": self.bentuk_tidak_baku,
"varian": self.varian,
"makna": [makna.serialisasi() for makna in self.makna]
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laymonage/kbbi-python | kbbi/kbbi.py | Entri._makna | def _makna(self):
"""Mengembalikan representasi string untuk semua makna entri ini.
:returns: String representasi makna-makna
:rtype: str
"""
if len(self.makna) > 1:
return '\n'.join(
str(i) + ". " + str(makna)
for i, makna in enumerate(self.makna, 1)
)
return str(self.makna[0]) | python | def _makna(self):
"""Mengembalikan representasi string untuk semua makna entri ini.
:returns: String representasi makna-makna
:rtype: str
"""
if len(self.makna) > 1:
return '\n'.join(
str(i) + ". " + str(makna)
for i, makna in enumerate(self.makna, 1)
)
return str(self.makna[0]) | [
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laymonage/kbbi-python | kbbi/kbbi.py | Entri._nama | def _nama(self):
"""Mengembalikan representasi string untuk nama entri ini.
:returns: String representasi nama entri
:rtype: str
"""
hasil = self.nama
if self.nomor:
hasil += " [{}]".format(self.nomor)
if self.kata_dasar:
hasil = " » ".join(self.kata_dasar) + " » " + hasil
return hasil | python | def _nama(self):
"""Mengembalikan representasi string untuk nama entri ini.
:returns: String representasi nama entri
:rtype: str
"""
hasil = self.nama
if self.nomor:
hasil += " [{}]".format(self.nomor)
if self.kata_dasar:
hasil = " » ".join(self.kata_dasar) + " » " + hasil
return hasil | [
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laymonage/kbbi-python | kbbi/kbbi.py | Entri._varian | def _varian(self, varian):
"""Mengembalikan representasi string untuk varian entri ini.
Dapat digunakan untuk "Varian" maupun "Bentuk tidak baku".
:param varian: List bentuk tidak baku atau varian
:type varian: list
:returns: String representasi varian atau bentuk tidak baku
:rtype: str
"""
if varian == self.bentuk_tidak_baku:
nama = "Bentuk tidak baku"
elif varian == self.varian:
nama = "Varian"
else:
return ''
return nama + ': ' + ', '.join(varian) | python | def _varian(self, varian):
"""Mengembalikan representasi string untuk varian entri ini.
Dapat digunakan untuk "Varian" maupun "Bentuk tidak baku".
:param varian: List bentuk tidak baku atau varian
:type varian: list
:returns: String representasi varian atau bentuk tidak baku
:rtype: str
"""
if varian == self.bentuk_tidak_baku:
nama = "Bentuk tidak baku"
elif varian == self.varian:
nama = "Varian"
else:
return ''
return nama + ': ' + ', '.join(varian) | [
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laymonage/kbbi-python | kbbi/kbbi.py | Makna._init_kelas | def _init_kelas(self, makna_label):
"""Memproses kelas kata yang ada dalam makna.
:param makna_label: BeautifulSoup untuk makna yang ingin diproses.
:type makna_label: BeautifulSoup
"""
kelas = makna_label.find(color='red')
lain = makna_label.find(color='darkgreen')
info = makna_label.find(color='green')
if kelas:
kelas = kelas.find_all('span')
if lain:
self.kelas = {lain.text.strip(): lain['title'].strip()}
self.submakna = lain.next_sibling.strip()
self.submakna += ' ' + makna_label.find(color='grey').text.strip()
else:
self.kelas = {
k.text.strip(): k['title'].strip() for k in kelas
} if kelas else {}
self.info = info.text.strip() if info else '' | python | def _init_kelas(self, makna_label):
"""Memproses kelas kata yang ada dalam makna.
:param makna_label: BeautifulSoup untuk makna yang ingin diproses.
:type makna_label: BeautifulSoup
"""
kelas = makna_label.find(color='red')
lain = makna_label.find(color='darkgreen')
info = makna_label.find(color='green')
if kelas:
kelas = kelas.find_all('span')
if lain:
self.kelas = {lain.text.strip(): lain['title'].strip()}
self.submakna = lain.next_sibling.strip()
self.submakna += ' ' + makna_label.find(color='grey').text.strip()
else:
self.kelas = {
k.text.strip(): k['title'].strip() for k in kelas
} if kelas else {}
self.info = info.text.strip() if info else '' | [
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laymonage/kbbi-python | kbbi/kbbi.py | Makna._init_contoh | def _init_contoh(self, makna_label):
"""Memproses contoh yang ada dalam makna.
:param makna_label: BeautifulSoup untuk makna yang ingin diproses.
:type makna_label: BeautifulSoup
"""
indeks = makna_label.text.find(': ')
if indeks != -1:
contoh = makna_label.text[indeks + 2:].strip()
self.contoh = contoh.split('; ')
else:
self.contoh = [] | python | def _init_contoh(self, makna_label):
"""Memproses contoh yang ada dalam makna.
:param makna_label: BeautifulSoup untuk makna yang ingin diproses.
:type makna_label: BeautifulSoup
"""
indeks = makna_label.text.find(': ')
if indeks != -1:
contoh = makna_label.text[indeks + 2:].strip()
self.contoh = contoh.split('; ')
else:
self.contoh = [] | [
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laymonage/kbbi-python | kbbi/kbbi.py | Makna.serialisasi | def serialisasi(self):
"""Mengembalikan hasil serialisasi objek Makna ini.
:returns: Dictionary hasil serialisasi
:rtype: dict
"""
return {
"kelas": self.kelas,
"submakna": self.submakna,
"info": self.info,
"contoh": self.contoh
} | python | def serialisasi(self):
"""Mengembalikan hasil serialisasi objek Makna ini.
:returns: Dictionary hasil serialisasi
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"""
return {
"kelas": self.kelas,
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mkouhei/bootstrap-py | bootstrap_py/docs.py | build_sphinx | def build_sphinx(pkg_data, projectdir):
"""Build sphinx documentation.
:rtype: int
:return: subprocess.call return code
:param `bootstrap_py.control.PackageData` pkg_data: package meta data
:param str projectdir: project root directory
"""
try:
version, _minor_version = pkg_data.version.rsplit('.', 1)
except ValueError:
version = pkg_data.version
args = ' '.join(('sphinx-quickstart',
'--sep',
'-q',
'-p "{name}"',
'-a "{author}"',
'-v "{version}"',
'-r "{release}"',
'-l en',
'--suffix=.rst',
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author=pkg_data.author,
version=version,
release=pkg_data.version,
projectdir=projectdir)
if subprocess.call(shlex.split(args)) == 0:
_touch_gitkeep(projectdir) | python | def build_sphinx(pkg_data, projectdir):
"""Build sphinx documentation.
:rtype: int
:return: subprocess.call return code
:param `bootstrap_py.control.PackageData` pkg_data: package meta data
:param str projectdir: project root directory
"""
try:
version, _minor_version = pkg_data.version.rsplit('.', 1)
except ValueError:
version = pkg_data.version
args = ' '.join(('sphinx-quickstart',
'--sep',
'-q',
'-p "{name}"',
'-a "{author}"',
'-v "{version}"',
'-r "{release}"',
'-l en',
'--suffix=.rst',
'--master=index',
'--ext-autodoc',
'--ext-viewcode',
'--makefile',
'{projectdir}')).format(name=pkg_data.name,
author=pkg_data.author,
version=version,
release=pkg_data.version,
projectdir=projectdir)
if subprocess.call(shlex.split(args)) == 0:
_touch_gitkeep(projectdir) | [
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:rtype: int
:return: subprocess.call return code
:param `bootstrap_py.control.PackageData` pkg_data: package meta data
:param str projectdir: project root directory | [
"Build",
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] | 95d56ed98ef409fd9f019dc352fd1c3711533275 | https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/docs.py#L8-L40 | train | 43 |
christophertbrown/bioscripts | ctbBio/crossmap.py | bowtiedb | def bowtiedb(fa, keepDB):
"""
make bowtie db
"""
btdir = '%s/bt2' % (os.getcwd())
# make directory for
if not os.path.exists(btdir):
os.mkdir(btdir)
btdb = '%s/%s' % (btdir, fa.rsplit('/', 1)[-1])
if keepDB is True:
if os.path.exists('%s.1.bt2' % (btdb)):
return btdb
p = subprocess.Popen('bowtie2-build -q %s %s' \
% (fa, btdb), shell = True)
p.communicate()
return btdb | python | def bowtiedb(fa, keepDB):
"""
make bowtie db
"""
btdir = '%s/bt2' % (os.getcwd())
# make directory for
if not os.path.exists(btdir):
os.mkdir(btdir)
btdb = '%s/%s' % (btdir, fa.rsplit('/', 1)[-1])
if keepDB is True:
if os.path.exists('%s.1.bt2' % (btdb)):
return btdb
p = subprocess.Popen('bowtie2-build -q %s %s' \
% (fa, btdb), shell = True)
p.communicate()
return btdb | [
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christophertbrown/bioscripts | ctbBio/crossmap.py | bowtie | def bowtie(sam, btd, f, r, u, opt, no_shrink, threads):
"""
generate bowtie2 command
"""
bt2 = 'bowtie2 -x %s -p %s ' % (btd, threads)
if f is not False:
bt2 += '-1 %s -2 %s ' % (f, r)
if u is not False:
bt2 += '-U %s ' % (u)
bt2 += opt
if no_shrink is False:
if f is False:
bt2 += ' | shrinksam -u -k %s-shrunk.sam ' % (sam)
else:
bt2 += ' | shrinksam -k %s-shrunk.sam ' % (sam)
else:
bt2 += ' > %s.sam' % (sam)
return bt2 | python | def bowtie(sam, btd, f, r, u, opt, no_shrink, threads):
"""
generate bowtie2 command
"""
bt2 = 'bowtie2 -x %s -p %s ' % (btd, threads)
if f is not False:
bt2 += '-1 %s -2 %s ' % (f, r)
if u is not False:
bt2 += '-U %s ' % (u)
bt2 += opt
if no_shrink is False:
if f is False:
bt2 += ' | shrinksam -u -k %s-shrunk.sam ' % (sam)
else:
bt2 += ' | shrinksam -k %s-shrunk.sam ' % (sam)
else:
bt2 += ' > %s.sam' % (sam)
return bt2 | [
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christophertbrown/bioscripts | ctbBio/crossmap.py | crossmap | def crossmap(fas, reads, options, no_shrink, keepDB, threads, cluster, nodes):
"""
map all read sets against all fasta files
"""
if cluster is True:
threads = '48'
btc = []
for fa in fas:
btd = bowtiedb(fa, keepDB)
F, R, U = reads
if F is not False:
if U is False:
u = False
for i, f in enumerate(F):
r = R[i]
if U is not False:
u = U[i]
sam = '%s/%s-vs-%s' % (os.getcwd(), \
fa.rsplit('/', 1)[-1], f.rsplit('/', 1)[-1].rsplit('.', 3)[0])
btc.append(bowtie(sam, btd, f, r, u, options, no_shrink, threads))
else:
f = False
r = False
for u in U:
sam = '%s/%s-vs-%s' % (os.getcwd(), \
fa.rsplit('/', 1)[-1], u.rsplit('/', 1)[-1].rsplit('.', 3)[0])
btc.append(bowtie(sam, btd, f, r, u, options, no_shrink, threads))
if cluster is False:
for i in btc:
p = subprocess.Popen(i, shell = True)
p.communicate()
else:
ID = ''.join(random.choice([str(i) for i in range(0, 9)]) for _ in range(5))
for node, commands in enumerate(chunks(btc, nodes), 1):
bs = open('%s/crossmap-qsub.%s.%s.sh' % (os.getcwd(), ID, node), 'w')
print('\n'.join(commands), file=bs)
bs.close()
p = subprocess.Popen(\
'qsub -V -N crossmap %s' \
% (bs.name), \
shell = True)
p.communicate() | python | def crossmap(fas, reads, options, no_shrink, keepDB, threads, cluster, nodes):
"""
map all read sets against all fasta files
"""
if cluster is True:
threads = '48'
btc = []
for fa in fas:
btd = bowtiedb(fa, keepDB)
F, R, U = reads
if F is not False:
if U is False:
u = False
for i, f in enumerate(F):
r = R[i]
if U is not False:
u = U[i]
sam = '%s/%s-vs-%s' % (os.getcwd(), \
fa.rsplit('/', 1)[-1], f.rsplit('/', 1)[-1].rsplit('.', 3)[0])
btc.append(bowtie(sam, btd, f, r, u, options, no_shrink, threads))
else:
f = False
r = False
for u in U:
sam = '%s/%s-vs-%s' % (os.getcwd(), \
fa.rsplit('/', 1)[-1], u.rsplit('/', 1)[-1].rsplit('.', 3)[0])
btc.append(bowtie(sam, btd, f, r, u, options, no_shrink, threads))
if cluster is False:
for i in btc:
p = subprocess.Popen(i, shell = True)
p.communicate()
else:
ID = ''.join(random.choice([str(i) for i in range(0, 9)]) for _ in range(5))
for node, commands in enumerate(chunks(btc, nodes), 1):
bs = open('%s/crossmap-qsub.%s.%s.sh' % (os.getcwd(), ID, node), 'w')
print('\n'.join(commands), file=bs)
bs.close()
p = subprocess.Popen(\
'qsub -V -N crossmap %s' \
% (bs.name), \
shell = True)
p.communicate() | [
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disqus/nydus | nydus/db/base.py | BaseCluster.get_conn | def get_conn(self, *args, **kwargs):
"""
Returns a connection object from the router given ``args``.
Useful in cases where a connection cannot be automatically determined
during all steps of the process. An example of this would be
Redis pipelines.
"""
connections = self.__connections_for('get_conn', args=args, kwargs=kwargs)
if len(connections) is 1:
return connections[0]
else:
return connections | python | def get_conn(self, *args, **kwargs):
"""
Returns a connection object from the router given ``args``.
Useful in cases where a connection cannot be automatically determined
during all steps of the process. An example of this would be
Redis pipelines.
"""
connections = self.__connections_for('get_conn', args=args, kwargs=kwargs)
if len(connections) is 1:
return connections[0]
else:
return connections | [
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scottrice/pysteam | pysteam/_crc_algorithms.py | Crc.__get_nondirect_init | def __get_nondirect_init(self, init):
"""
return the non-direct init if the direct algorithm has been selected.
"""
crc = init
for i in range(self.Width):
bit = crc & 0x01
if bit:
crc^= self.Poly
crc >>= 1
if bit:
crc |= self.MSB_Mask
return crc & self.Mask | python | def __get_nondirect_init(self, init):
"""
return the non-direct init if the direct algorithm has been selected.
"""
crc = init
for i in range(self.Width):
bit = crc & 0x01
if bit:
crc^= self.Poly
crc >>= 1
if bit:
crc |= self.MSB_Mask
return crc & self.Mask | [
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scottrice/pysteam | pysteam/_crc_algorithms.py | Crc.reflect | def reflect(self, data, width):
"""
reflect a data word, i.e. reverts the bit order.
"""
x = data & 0x01
for i in range(width - 1):
data >>= 1
x = (x << 1) | (data & 0x01)
return x | python | def reflect(self, data, width):
"""
reflect a data word, i.e. reverts the bit order.
"""
x = data & 0x01
for i in range(width - 1):
data >>= 1
x = (x << 1) | (data & 0x01)
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scottrice/pysteam | pysteam/_crc_algorithms.py | Crc.bit_by_bit | def bit_by_bit(self, in_data):
"""
Classic simple and slow CRC implementation. This function iterates bit
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value at the end.
"""
# If the input data is a string, convert to bytes.
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in_data = [ord(c) for c in in_data]
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if topbit:
register ^= self.Poly
if self.ReflectOut:
register = self.reflect(register, self.Width)
return register ^ self.XorOut | python | def bit_by_bit(self, in_data):
"""
Classic simple and slow CRC implementation. This function iterates bit
by bit over the augmented input message and returns the calculated CRC
value at the end.
"""
# If the input data is a string, convert to bytes.
if isinstance(in_data, str):
in_data = [ord(c) for c in in_data]
register = self.NonDirectInit
for octet in in_data:
if self.ReflectIn:
octet = self.reflect(octet, 8)
for i in range(8):
topbit = register & self.MSB_Mask
register = ((register << 1) & self.Mask) | ((octet >> (7 - i)) & 0x01)
if topbit:
register ^= self.Poly
for i in range(self.Width):
topbit = register & self.MSB_Mask
register = ((register << 1) & self.Mask)
if topbit:
register ^= self.Poly
if self.ReflectOut:
register = self.reflect(register, self.Width)
return register ^ self.XorOut | [
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scottrice/pysteam | pysteam/_crc_algorithms.py | Crc.gen_table | def gen_table(self):
"""
This function generates the CRC table used for the table_driven CRC
algorithm. The Python version cannot handle tables of an index width
other than 8. See the generated C code for tables with different sizes
instead.
"""
table_length = 1 << self.TableIdxWidth
tbl = [0] * table_length
for i in range(table_length):
register = i
if self.ReflectIn:
register = self.reflect(register, self.TableIdxWidth)
register = register << (self.Width - self.TableIdxWidth + self.CrcShift)
for j in range(self.TableIdxWidth):
if register & (self.MSB_Mask << self.CrcShift) != 0:
register = (register << 1) ^ (self.Poly << self.CrcShift)
else:
register = (register << 1)
if self.ReflectIn:
register = self.reflect(register >> self.CrcShift, self.Width) << self.CrcShift
tbl[i] = register & (self.Mask << self.CrcShift)
return tbl | python | def gen_table(self):
"""
This function generates the CRC table used for the table_driven CRC
algorithm. The Python version cannot handle tables of an index width
other than 8. See the generated C code for tables with different sizes
instead.
"""
table_length = 1 << self.TableIdxWidth
tbl = [0] * table_length
for i in range(table_length):
register = i
if self.ReflectIn:
register = self.reflect(register, self.TableIdxWidth)
register = register << (self.Width - self.TableIdxWidth + self.CrcShift)
for j in range(self.TableIdxWidth):
if register & (self.MSB_Mask << self.CrcShift) != 0:
register = (register << 1) ^ (self.Poly << self.CrcShift)
else:
register = (register << 1)
if self.ReflectIn:
register = self.reflect(register >> self.CrcShift, self.Width) << self.CrcShift
tbl[i] = register & (self.Mask << self.CrcShift)
return tbl | [
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scottrice/pysteam | pysteam/_crc_algorithms.py | Crc.table_driven | def table_driven(self, in_data):
"""
The Standard table_driven CRC algorithm.
"""
# If the input data is a string, convert to bytes.
if isinstance(in_data, str):
in_data = [ord(c) for c in in_data]
tbl = self.gen_table()
register = self.DirectInit << self.CrcShift
if not self.ReflectIn:
for octet in in_data:
tblidx = ((register >> (self.Width - self.TableIdxWidth + self.CrcShift)) ^ octet) & 0xff
register = ((register << (self.TableIdxWidth - self.CrcShift)) ^ tbl[tblidx]) & (self.Mask << self.CrcShift)
register = register >> self.CrcShift
else:
register = self.reflect(register, self.Width + self.CrcShift) << self.CrcShift
for octet in in_data:
tblidx = ((register >> self.CrcShift) ^ octet) & 0xff
register = ((register >> self.TableIdxWidth) ^ tbl[tblidx]) & (self.Mask << self.CrcShift)
register = self.reflect(register, self.Width + self.CrcShift) & self.Mask
if self.ReflectOut:
register = self.reflect(register, self.Width)
return register ^ self.XorOut | python | def table_driven(self, in_data):
"""
The Standard table_driven CRC algorithm.
"""
# If the input data is a string, convert to bytes.
if isinstance(in_data, str):
in_data = [ord(c) for c in in_data]
tbl = self.gen_table()
register = self.DirectInit << self.CrcShift
if not self.ReflectIn:
for octet in in_data:
tblidx = ((register >> (self.Width - self.TableIdxWidth + self.CrcShift)) ^ octet) & 0xff
register = ((register << (self.TableIdxWidth - self.CrcShift)) ^ tbl[tblidx]) & (self.Mask << self.CrcShift)
register = register >> self.CrcShift
else:
register = self.reflect(register, self.Width + self.CrcShift) << self.CrcShift
for octet in in_data:
tblidx = ((register >> self.CrcShift) ^ octet) & 0xff
register = ((register >> self.TableIdxWidth) ^ tbl[tblidx]) & (self.Mask << self.CrcShift)
register = self.reflect(register, self.Width + self.CrcShift) & self.Mask
if self.ReflectOut:
register = self.reflect(register, self.Width)
return register ^ self.XorOut | [
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christophertbrown/bioscripts | ctbBio/strip_masked.py | parse_masked | def parse_masked(seq, min_len):
"""
parse masked sequence into non-masked and masked regions
"""
nm, masked = [], [[]]
prev = None
for base in seq[1]:
if base.isupper():
nm.append(base)
if masked != [[]] and len(masked[-1]) < min_len:
nm.extend(masked[-1])
del masked[-1]
prev = False
elif base.islower():
if prev is False:
masked.append([])
masked[-1].append(base)
prev = True
return nm, masked | python | def parse_masked(seq, min_len):
"""
parse masked sequence into non-masked and masked regions
"""
nm, masked = [], [[]]
prev = None
for base in seq[1]:
if base.isupper():
nm.append(base)
if masked != [[]] and len(masked[-1]) < min_len:
nm.extend(masked[-1])
del masked[-1]
prev = False
elif base.islower():
if prev is False:
masked.append([])
masked[-1].append(base)
prev = True
return nm, masked | [
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christophertbrown/bioscripts | ctbBio/strip_masked.py | strip_masked | def strip_masked(fasta, min_len, print_masked):
"""
remove masked regions from fasta file as long as
they are longer than min_len
"""
for seq in parse_fasta(fasta):
nm, masked = parse_masked(seq, min_len)
nm = ['%s removed_masked >=%s' % (seq[0], min_len), ''.join(nm)]
yield [0, nm]
if print_masked is True:
for i, m in enumerate([i for i in masked if i != []], 1):
m = ['%s insertion:%s' % (seq[0], i), ''.join(m)]
yield [1, m] | python | def strip_masked(fasta, min_len, print_masked):
"""
remove masked regions from fasta file as long as
they are longer than min_len
"""
for seq in parse_fasta(fasta):
nm, masked = parse_masked(seq, min_len)
nm = ['%s removed_masked >=%s' % (seq[0], min_len), ''.join(nm)]
yield [0, nm]
if print_masked is True:
for i, m in enumerate([i for i in masked if i != []], 1):
m = ['%s insertion:%s' % (seq[0], i), ''.join(m)]
yield [1, m] | [
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smdabdoub/phylotoast | bin/network_plots_gephi.py | get_relative_abundance | def get_relative_abundance(biomfile):
"""
Return arcsine transformed relative abundance from a BIOM format file.
:type biomfile: BIOM format file
:param biomfile: BIOM format file used to obtain relative abundances for each OTU in
a SampleID, which are used as node sizes in network plots.
:type return: Dictionary of dictionaries.
:return: Dictionary keyed on SampleID whose value is a dictionarykeyed on OTU Name
whose value is the arc sine tranfsormed relative abundance value for that
SampleID-OTU Name pair.
"""
biomf = biom.load_table(biomfile)
norm_biomf = biomf.norm(inplace=False)
rel_abd = {}
for sid in norm_biomf.ids():
rel_abd[sid] = {}
for otuid in norm_biomf.ids("observation"):
otuname = oc.otu_name(norm_biomf.metadata(otuid, axis="observation")["taxonomy"])
otuname = " ".join(otuname.split("_"))
abd = norm_biomf.get_value_by_ids(otuid, sid)
rel_abd[sid][otuname] = abd
ast_rel_abd = bc.arcsine_sqrt_transform(rel_abd)
return ast_rel_abd | python | def get_relative_abundance(biomfile):
"""
Return arcsine transformed relative abundance from a BIOM format file.
:type biomfile: BIOM format file
:param biomfile: BIOM format file used to obtain relative abundances for each OTU in
a SampleID, which are used as node sizes in network plots.
:type return: Dictionary of dictionaries.
:return: Dictionary keyed on SampleID whose value is a dictionarykeyed on OTU Name
whose value is the arc sine tranfsormed relative abundance value for that
SampleID-OTU Name pair.
"""
biomf = biom.load_table(biomfile)
norm_biomf = biomf.norm(inplace=False)
rel_abd = {}
for sid in norm_biomf.ids():
rel_abd[sid] = {}
for otuid in norm_biomf.ids("observation"):
otuname = oc.otu_name(norm_biomf.metadata(otuid, axis="observation")["taxonomy"])
otuname = " ".join(otuname.split("_"))
abd = norm_biomf.get_value_by_ids(otuid, sid)
rel_abd[sid][otuname] = abd
ast_rel_abd = bc.arcsine_sqrt_transform(rel_abd)
return ast_rel_abd | [
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:type biomfile: BIOM format file
:param biomfile: BIOM format file used to obtain relative abundances for each OTU in
a SampleID, which are used as node sizes in network plots.
:type return: Dictionary of dictionaries.
:return: Dictionary keyed on SampleID whose value is a dictionarykeyed on OTU Name
whose value is the arc sine tranfsormed relative abundance value for that
SampleID-OTU Name pair. | [
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smdabdoub/phylotoast | bin/iTol.py | find_otu | def find_otu(otuid, tree):
"""
Find an OTU ID in a Newick-format tree.
Return the starting position of the ID or None if not found.
"""
for m in re.finditer(otuid, tree):
before, after = tree[m.start()-1], tree[m.start()+len(otuid)]
if before in ["(", ",", ")"] and after in [":", ";"]:
return m.start()
return None | python | def find_otu(otuid, tree):
"""
Find an OTU ID in a Newick-format tree.
Return the starting position of the ID or None if not found.
"""
for m in re.finditer(otuid, tree):
before, after = tree[m.start()-1], tree[m.start()+len(otuid)]
if before in ["(", ",", ")"] and after in [":", ";"]:
return m.start()
return None | [
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smdabdoub/phylotoast | bin/iTol.py | newick_replace_otuids | def newick_replace_otuids(tree, biomf):
"""
Replace the OTU ids in the Newick phylogenetic tree format with truncated
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"""
for val, id_, md in biomf.iter(axis="observation"):
otu_loc = find_otu(id_, tree)
if otu_loc is not None:
tree = tree[:otu_loc] + \
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tree[otu_loc + len(id_):]
return tree | python | def newick_replace_otuids(tree, biomf):
"""
Replace the OTU ids in the Newick phylogenetic tree format with truncated
OTU names
"""
for val, id_, md in biomf.iter(axis="observation"):
otu_loc = find_otu(id_, tree)
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tree = tree[:otu_loc] + \
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return tree | [
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christophertbrown/bioscripts | ctbBio/cluster_ani.py | genome_info | def genome_info(genome, info):
"""
return genome info for choosing representative
if ggKbase table provided - choose rep based on SCGs and genome length
- priority for most SCGs - extra SCGs, then largest genome
otherwise, based on largest genome
"""
try:
scg = info['#SCGs']
dups = info['#SCG duplicates']
length = info['genome size (bp)']
return [scg - dups, length, genome]
except:
return [False, False, info['genome size (bp)'], genome] | python | def genome_info(genome, info):
"""
return genome info for choosing representative
if ggKbase table provided - choose rep based on SCGs and genome length
- priority for most SCGs - extra SCGs, then largest genome
otherwise, based on largest genome
"""
try:
scg = info['#SCGs']
dups = info['#SCG duplicates']
length = info['genome size (bp)']
return [scg - dups, length, genome]
except:
return [False, False, info['genome size (bp)'], genome] | [
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christophertbrown/bioscripts | ctbBio/cluster_ani.py | print_clusters | def print_clusters(fastas, info, ANI):
"""
choose represenative genome and
print cluster information
*if ggKbase table is provided, use SCG info to choose best genome
"""
header = ['#cluster', 'num. genomes', 'rep.', 'genome', '#SCGs', '#SCG duplicates', \
'genome size (bp)', 'fragments', 'list']
yield header
in_cluster = []
for cluster_num, cluster in enumerate(connected_components(ANI)):
cluster = sorted([genome_info(genome, info[genome]) \
for genome in cluster], \
key = lambda x: x[0:], reverse = True)
rep = cluster[0][-1]
cluster = [i[-1] for i in cluster]
size = len(cluster)
for genome in cluster:
in_cluster.append(genome)
try:
stats = [size, rep, genome, \
info[genome]['#SCGs'], info[genome]['#SCG duplicates'], \
info[genome]['genome size (bp)'], info[genome]['# contigs'], cluster]
except:
stats = [size, rep, genome, \
'n/a', 'n/a', \
info[genome]['genome size (bp)'], info[genome]['# contigs'], cluster]
if rep == genome:
stats = ['*%s' % (cluster_num)] + stats
else:
stats = [cluster_num] + stats
yield stats
# print singletons
try:
start = cluster_num + 1
except:
start = 0
fastas = set([i.rsplit('.', 1)[0].rsplit('/', 1)[-1].rsplit('.contigs')[0] for i in fastas])
for cluster_num, genome in \
enumerate(fastas.difference(set(in_cluster)), start):
try:
stats = ['*%s' % (cluster_num), 1, genome, genome, \
info[genome]['#SCGs'], info[genome]['#SCG duplicates'], \
info[genome]['genome size (bp)'], info[genome]['# contigs'], [genome]]
except:
stats = ['*%s' % (cluster_num), 1, genome, genome, \
'n/a', 'n/a', \
info[genome]['genome size (bp)'], info[genome]['# contigs'], [genome]]
yield stats | python | def print_clusters(fastas, info, ANI):
"""
choose represenative genome and
print cluster information
*if ggKbase table is provided, use SCG info to choose best genome
"""
header = ['#cluster', 'num. genomes', 'rep.', 'genome', '#SCGs', '#SCG duplicates', \
'genome size (bp)', 'fragments', 'list']
yield header
in_cluster = []
for cluster_num, cluster in enumerate(connected_components(ANI)):
cluster = sorted([genome_info(genome, info[genome]) \
for genome in cluster], \
key = lambda x: x[0:], reverse = True)
rep = cluster[0][-1]
cluster = [i[-1] for i in cluster]
size = len(cluster)
for genome in cluster:
in_cluster.append(genome)
try:
stats = [size, rep, genome, \
info[genome]['#SCGs'], info[genome]['#SCG duplicates'], \
info[genome]['genome size (bp)'], info[genome]['# contigs'], cluster]
except:
stats = [size, rep, genome, \
'n/a', 'n/a', \
info[genome]['genome size (bp)'], info[genome]['# contigs'], cluster]
if rep == genome:
stats = ['*%s' % (cluster_num)] + stats
else:
stats = [cluster_num] + stats
yield stats
# print singletons
try:
start = cluster_num + 1
except:
start = 0
fastas = set([i.rsplit('.', 1)[0].rsplit('/', 1)[-1].rsplit('.contigs')[0] for i in fastas])
for cluster_num, genome in \
enumerate(fastas.difference(set(in_cluster)), start):
try:
stats = ['*%s' % (cluster_num), 1, genome, genome, \
info[genome]['#SCGs'], info[genome]['#SCG duplicates'], \
info[genome]['genome size (bp)'], info[genome]['# contigs'], [genome]]
except:
stats = ['*%s' % (cluster_num), 1, genome, genome, \
'n/a', 'n/a', \
info[genome]['genome size (bp)'], info[genome]['# contigs'], [genome]]
yield stats | [
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*if ggKbase table is provided, use SCG info to choose best genome | [
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End of preview.
Dataset is imported from CodeXGLUE and pre-processed using their script.
Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/text-to-code/codesearchnet/python in Semeru
CodeXGLUE -- Code Search (AdvTest)
Task Definition
Given a natural language, the task is to search source code that matches the natural language. To test the generalization ability of a model, function names and variables in test sets are replaced by special tokens.
Dataset
The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
- Remove examples that codes cannot be parsed into an abstract syntax tree.
- Remove examples that #tokens of documents is < 3 or >256
- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
- Remove examples that documents are not English.
Besides, to test the generalization ability of a model, function names and variables in test sets are replaced by special tokens.
Data Format
After preprocessing dataset, you can obtain three .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
- repo: the owner/repo
- path: the full path to the original file
- func_name: the function or method name
- original_string: the raw string before tokenization or parsing
- language: the programming language
- code/function: the part of the
original_stringthat is code - code_tokens/function_tokens: tokenized version of
code - docstring: the top-level comment or docstring, if it exists in the original string
- docstring_tokens: tokenized version of
docstring - url: the url for the example (identify natural language)
- idx: the index of code (identify code)
Data Statistics
Data statistics of the dataset are shown in the below table:
| #Examples | |
|---|---|
| Train | 251,820 |
| Dev | 9,604 |
| Test | 19,210 |
Example
Given a text-code file evaluator/test.jsonl:
{"url": "url0", "docstring": "doc0","function": "fun0", "idx": 10}
{"url": "url1", "docstring": "doc1","function": "fun1", "idx": 11}
{"url": "url2", "docstring": "doc2","function": "fun2", "idx": 12}
{"url": "url3", "docstring": "doc3","function": "fun3", "idx": 13}
{"url": "url4", "docstring": "doc4","function": "fun4", "idx": 14}
Input Predictions
For each url for natural language, descending sort candidate codes and return their idx in order. For example:
{"url": "url0", "answers": [10,11,12,13,14]}
{"url": "url1", "answers": [10,12,11,13,14]}
{"url": "url2", "answers": [13,11,12,10,14]}
{"url": "url3", "answers": [10,14,12,13,11]}
{"url": "url4", "answers": [10,11,12,13,14]}
Reference
@article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
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