The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
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... | 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; ...
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:type d: str
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:return: Does not retu... | python | def ensure_dir(d):
"""
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smdabdoub/phylotoast | phylotoast/util.py | file_handle | def file_handle(fnh, mode="rU"):
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:type fnh: str
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smdabdoub/phylotoast | phylotoast/util.py | gather_categories | def gather_categories(imap, header, categories=None):
"""
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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 ... | python | def parse_unifrac(unifracFN):
"""
Parses the unifrac results file into a dictionary
:type unifracFN: str
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smdabdoub/phylotoast | phylotoast/util.py | parse_unifrac_v1_8 | def parse_unifrac_v1_8(unifrac, file_data):
"""
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:type unifrac: dict
:param unifracFN: The path to the unifrac results file
:type file_data: list
:param file_data: Unifrac data lines after... | python | def parse_unifrac_v1_8(unifrac, file_data):
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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
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:type unifracFN: str
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"""
Function to parse data from newer version of unifrac file obtained from Qiime version
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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... | python | def color_mapping(sample_map, header, group_column, color_column=None):
"""
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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)]))
len... | 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)]))
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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 insignif... | 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
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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)
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opengridcc/opengrid | opengrid/library/regression.py | MultiVarLinReg._predict | def _predict(self, fit, df):
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Return a df with predictions and confidence interval
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-----
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- 'predicted': the model output
- 'interval_u', 'interval_l': upper and lower confidence bounds.
The result will ... | python | def _predict(self, fit, df):
"""
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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: Retu... | 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.
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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
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"""
Calculate the mean OTU abundance percentage.
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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... | 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
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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
... | 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.
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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: M... | 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.
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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 "\
... | 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()
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return "Error setting up input arrays for Mann-Whitney U Test. Skipping "\
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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():
... | python | def print_KruskalWallisH(div_calc):
"""
Compute the Kruskal-Wallis H-test for independent samples. A typical rule is that
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"""
calc = defaultdict(list)
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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 d... | python | def handle_program_options():
"""Parses the given options passed in at the command line."""
parser = argparse.ArgumentParser(description="Calculate the alpha diversity\
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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 \
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"""
make blast db
"""
db = fasta.rsplit('.', 1)[0]
type = check_type(fasta)
if type == 'nucl':
type = ['nhr', type]
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christophertbrown/bioscripts | ctbBio/search.py | usearchdb | def usearchdb(fasta, alignment = 'local', usearch_loc = 'usearch'):
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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():
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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:
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sys.exit(0) | python | def print_licences(params, metadata):
"""Print licenses.
:param argparse.Namespace params: parameter
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if hasattr(params, 'licenses'):
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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):
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mkouhei/bootstrap-py | bootstrap_py/control.py | generate_package | def generate_package(params):
"""Generate package repository.
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pkg_data = package.PackageData(params)
pkg_tree = package.PackageTree(pkg_data)
pkg_tree.generate()
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"""Generate package repository.
:param argparse.Namespace params: parameters
"""
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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]
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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])... | 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()
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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 != '-':
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os.system("\
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"""
sort sam file
"""
tempdir = '%s/' % (os.path.abspath(sam).rsplit('/', 1)[0])
if sort is True:
mapping = '%s.sorted.sam' % (sam.rsplit('.', 1)[0])
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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].s... | 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])
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line = line.strip().split()
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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])]
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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
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"""
@wraps(f)
def wrapper(*args, **kwargs):
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"""
Converts the returned value of wrapped function to the type of the
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"""
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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)
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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').nex... | 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
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sup = BeautifulSoup(laman.text, 'html.parser')
estr = ''
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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')
... | 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:
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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.p... | python | def serialisasi(self):
"""Mengembalikan hasil serialisasi objek Entri ini.
:returns: Dictionary hasil serialisasi
:rtype: dict
"""
return {
"nama": self.nama,
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laymonage/kbbi-python | kbbi/kbbi.py | Entri._makna | def _makna(self):
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:rtype: str
"""
if len(self.makna) > 1:
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for i, makna in enumera... | python | def _makna(self):
"""Mengembalikan representasi string untuk semua makna entri ini.
:returns: String representasi makna-makna
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"""
if len(self.makna) > 1:
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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)
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"""Mengembalikan representasi string untuk nama entri ini.
:returns: String representasi nama entri
:rtype: str
"""
hasil = self.nama
if self.nomor:
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laymonage/kbbi-python | kbbi/kbbi.py | Entri._varian | def _varian(self, varian):
"""Mengembalikan representasi string untuk varian entri ini.
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:param varian: List bentuk tidak baku atau varian
:type varian: list
:returns: String representasi varian atau bentuk tidak baku
... | 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
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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')
... | 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')
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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.... | 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
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indeks = makna_label.text.find(': ')
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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
:rtype: dict
"""
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.ve... | 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
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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)):
retu... | 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:
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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
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if f... | 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)
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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... | python | def crossmap(fas, reads, options, no_shrink, keepDB, threads, cluster, nodes):
"""
map all read sets against all fasta files
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if cluster is True:
threads = '48'
btc = []
for fa in fas:
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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.
"""
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"""
Returns a connection object from the router given ``args``.
Useful in cases where a connection cannot be automatically determined
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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:
... | 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:
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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)
return x | [
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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
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... | 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.
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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... | 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
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"""
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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 <<... | 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]
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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(mas... | 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:
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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 ... | 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 ... | [
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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
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"""
Return arcsine transformed relative abundance from a BIOM format file.
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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 [":", ";"... | 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)]
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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
OTU names
"""
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] + \
... | 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)
if otu_loc is not None:
tree = tree[:otu_loc] + \
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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['#SCG... | 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['#SCG... | [
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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)', 'fragm... | 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', \
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End of preview.