semeru/compatible-codeparrot-small
Updated • 2
repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 75 19.8k | code_tokens sequence | docstring stringlengths 3 17.3k | docstring_tokens sequence | sha stringlengths 40 40 | url stringlengths 87 242 | partition stringclasses 1
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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; ...
:type level: str
:param level: The different level of identification are kingdom (k), phylum (p... | [
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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 retu... | python | def ensure_dir(d):
"""
Check to make sure the supplied directory path does not exist, if so, create it. The
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re-raising the error.
:type d: str
:param d: It is the full path to a directory.
:return: Does not retu... | [
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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 wh... | 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
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:param mode: The way in wh... | [
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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... | 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... | [
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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
:param unifracFN: The path to the unifrac results file
:rtype: dict
:return: A dictionary with keys: 'pcd' (principle coordinates data) which is a
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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... | python | def parse_unifrac_v1_8(unifrac, file_data):
"""
Function to parse data from older version of unifrac file obtained from Qiime version
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:type unifrac: dict
:param unifracFN: The path to the unifrac results file
:type file_data: list
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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 ... | python | 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
:param unifracFN: The path to the unifrac results file
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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):
"""
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... | [
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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()])
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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
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"""
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)
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 ... | 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.
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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.
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"""
Calculate the relative abundance of each OTUID in a Sample.
:type biomf: A BIOM file.
:param biomf: OTU table format.
:type sampleIDs: list
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smdabdoub/phylotoast | phylotoast/biom_calc.py | mean_otu_pct_abundance | def mean_otu_pct_abundance(ra, otuIDs):
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:type ra: Dict
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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.
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"""
Calculate the mean relative abundance percentage.
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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
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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 \
and os.path.exists('%s.... | python | def blastdb(fasta, maxfile = 10000000):
"""
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():
# pylint: disable=superfluous-parens
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mkouhei/bootstrap-py | bootstrap_py/control.py | print_licences | def print_licences(params, metadata):
"""Print licenses.
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"""
if hasattr(params, 'licenses'):
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sys.exit(0) | python | def print_licences(params, metadata):
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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(
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"""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()
pkg_tree.move()
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"""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()
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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()
bit = [True if i == '1' else False \
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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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"""
sort sam file
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tempdir = '%s/' % (os.path.abspath(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)]
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for line in mapping:
line = line.strip().split()
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"""
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)]
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christophertbrown/bioscripts | ctbBio/fastq2fasta.py | fq2fa | def fq2fa(fq):
"""
convert fq to fa
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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):
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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)
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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 = ''
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"""Membuat objek-objek entri dari laman yang diambil.
: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')
... | 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,
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"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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: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
:rtype: str
"""
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
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"""Mengembalikan representasi string untuk nama entri ini.
:returns: String representasi nama entri
:rtype: str
"""
hasil = self.nama
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laymonage/kbbi-python | kbbi/kbbi.py | Entri._varian | def _varian(self, varian):
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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".
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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
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kelas = makna_label.find(color='red')
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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.
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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 {
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mkouhei/bootstrap-py | bootstrap_py/docs.py | build_sphinx | def build_sphinx(pkg_data, projectdir):
"""Build sphinx documentation.
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: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
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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)
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christophertbrown/bioscripts | ctbBio/crossmap.py | bowtie | def bowtie(sam, btd, f, r, u, opt, no_shrink, threads):
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generate bowtie2 command
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"""
generate bowtie2 command
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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
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if cluster is True:
threads = '48'
btc = []
for fa in fas:
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"""
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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``.
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Redis pipelines.
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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
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crc >>= 1
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"""
return the non-direct init if the direct algorithm has been selected.
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crc = init
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bit = crc & 0x01
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scottrice/pysteam | pysteam/_crc_algorithms.py | Crc.reflect | def reflect(self, data, width):
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"""
x = data & 0x01
for i in range(width - 1):
data >>= 1
x = (x << 1) | (data & 0x01)
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"""
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"""
x = data & 0x01
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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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"""
Classic simple and slow CRC implementation. This function iterates bit
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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
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instead.
"""
table_length = 1... | python | def gen_table(self):
"""
This function generates the CRC table used for the table_driven CRC
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scottrice/pysteam | pysteam/_crc_algorithms.py | Crc.table_driven | def table_driven(self, in_data):
"""
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# 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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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]:
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nm.append(base)
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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
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"""
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
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"""
for seq in parse_fasta(fasta):
nm, masked = parse_masked(seq, min_len)
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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... | python | def get_relative_abundance(biomfile):
"""
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
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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] + \
... | 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:
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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. Expand in Data Studio
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/code-to-text/python in Semeru
CodeXGLUE -- Code-To-Text
Task Definition
The task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.
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.
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 codecode_tokens/function_tokens: tokenized version of
codedocstring: the top-level comment or docstring, if it exists in the original string
docstring_tokens: tokenized version of
docstring
Data Statistic
| Programming Language | Training | Dev | Test |
|---|---|---|---|
| Python | 251,820 | 13,914 | 14,918 |
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}
}- Downloads last month
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Size of downloaded dataset files:
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Size of the auto-converted Parquet files:
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Number of rows:
280,652
Models trained or fine-tuned on semeru/code-text-python