_id stringlengths 2 7 | title stringlengths 1 88 | partition stringclasses 3
values | text stringlengths 31 13.1k | language stringclasses 1
value | meta_information dict |
|---|---|---|---|---|---|
q5600 | offset_limit | train | def offset_limit(func):
"""
Decorator that converts python slicing to offset and limit
"""
def func_wrapper(self, start, stop):
offset = start | python | {
"resource": ""
} |
q5601 | generate_id | train | def generate_id(*s):
"""
generates an id from one or more given strings
it uses english as the base language in case some strings
are translated, this ensures consistent ids
"""
| python | {
"resource": ""
} |
q5602 | append_query_parameter | train | def append_query_parameter(url, parameters, ignore_if_exists=True):
""" quick and dirty appending of query parameters to a url """
if ignore_if_exists:
for key in parameters.keys():
if key + "=" in url:
| python | {
"resource": ""
} |
q5603 | BetterFileInput.render | train | def render(self, name, value, attrs=None, renderer=None):
"""For django 1.10 compatibility"""
if django.VERSION >= (1, 11):
return super(BetterFileInput, self).render(name, value, attrs)
t = render_to_string(
| python | {
"resource": ""
} |
q5604 | get_table | train | def get_table(ports):
"""
This function returns a pretty table used to display the port results.
:param ports: list of found ports
:return: the table to display
"""
table = PrettyTable(["Name", "Port", "Protocol", "Description"])
| python | {
"resource": ""
} |
q5605 | run | train | def run(port, like, use_json, server):
"""Search port names and numbers."""
if not port and not server[0]:
raise click.UsageError("Please specify a port")
if server[0]:
app.run(host=server[0], port=server[1])
return
ports = get_ports(port, like)
if not ports:
sys.stderr.write("No ports | python | {
"resource": ""
} |
q5606 | RPCClient.validate_addr | train | def validate_addr(self, address, id=None, endpoint=None):
"""
returns whether or not addr string is valid
Args:
address: (str) address to lookup ( in format 'AXjaFSP23Jkbe6Pk9pPGT6NBDs1HVdqaXK')
| python | {
"resource": ""
} |
q5607 | MPIPool.map | train | def map(self, worker, tasks, callback=None):
"""Evaluate a function or callable on each task in parallel using MPI.
The callable, ``worker``, is called on each element of the ``tasks``
iterable. The results are returned in the expected order (symmetric with
``tasks``).
Parameters
----------
worker : callable
A function or callable object that is executed on each element of
the specified ``tasks`` iterable. This object must be picklable
(i.e. it can't be a function scoped within a function or a
``lambda`` function). This should accept a single positional
argument and return a single object.
tasks : iterable
A list or iterable of tasks. Each task can be itself an iterable
(e.g., tuple) of values or data to pass in to the worker function.
callback : callable, optional
An optional callback function (or callable) that is called with the
result from each worker run and is executed on the master process.
This is useful for, e.g., saving results to a file, since the
callback is only called on the master thread.
Returns
-------
results : list
A list of results from the output of each ``worker()`` call.
"""
# If not the master just wait for instructions.
if not self.is_master():
self.wait()
return
if callback is None:
callback = _dummy_callback
workerset = self.workers.copy()
tasklist = [(tid, (worker, arg)) for tid, arg in enumerate(tasks)]
resultlist = [None] * len(tasklist)
pending = len(tasklist)
while pending:
if workerset and tasklist:
worker = workerset.pop()
taskid, task = tasklist.pop()
| python | {
"resource": ""
} |
q5608 | MPIPool.close | train | def close(self):
""" Tell all the workers to quit."""
if self.is_worker():
return
| python | {
"resource": ""
} |
q5609 | update_git_devstr | train | def update_git_devstr(version, path=None):
"""
Updates the git revision string if and only if the path is being imported
directly from a git working copy. This ensures that the revision number in
the version string is accurate.
"""
try:
# Quick way to determine if we're in git or not - returns '' if not
devstr = get_git_devstr(sha=True, show_warning=False, path=path) | python | {
"resource": ""
} |
q5610 | get_git_devstr | train | def get_git_devstr(sha=False, show_warning=True, path=None):
"""
Determines the number of revisions in this repository.
Parameters
----------
sha : bool
If True, the full SHA1 hash will be returned. Otherwise, the total
count of commits in the repository will be used as a "revision
number".
show_warning : bool
If True, issue a warning if git returns an error code, otherwise errors
pass silently.
path : str or None
If a string, specifies the directory to look in to find the git
repository. If `None`, the current working directory is used, and must
be the root of the git repository.
If given a filename it uses the directory containing that file.
Returns
-------
devversion : str
Either a string with the revision number (if `sha` is False), the
SHA1 hash of the current commit (if `sha` is True), or an empty string
if git version info could not be identified.
"""
if path is None:
path = os.getcwd()
if not _get_repo_path(path, levels=0):
return ''
if not os.path.isdir(path):
path = os.path.abspath(os.path.dirname(path))
if sha:
# Faster for getting just the hash of HEAD
cmd = ['rev-parse', 'HEAD']
else:
cmd = ['rev-list', '--count', 'HEAD']
def run_git(cmd):
try:
p = subprocess.Popen(['git'] + cmd, cwd=path,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
stdin=subprocess.PIPE)
stdout, stderr = p.communicate()
except OSError as e:
if show_warning:
warnings.warn('Error running git: ' + str(e))
return (None, b'', b'')
if p.returncode == 128:
if show_warning:
warnings.warn('No git repository present at {0!r}! Using '
'default dev version.'.format(path))
return (p.returncode, b'', b'')
| python | {
"resource": ""
} |
q5611 | _get_repo_path | train | def _get_repo_path(pathname, levels=None):
"""
Given a file or directory name, determine the root of the git repository
this path is under. If given, this won't look any higher than ``levels``
(that is, if ``levels=0`` then the given path must be the root of the git
repository and is returned if so.
Returns `None` if the given path could not be determined to belong to a git
repo.
"""
if os.path.isfile(pathname):
current_dir = os.path.abspath(os.path.dirname(pathname))
elif os.path.isdir(pathname):
current_dir = os.path.abspath(pathname)
else:
return None
current_level = 0
| python | {
"resource": ""
} |
q5612 | translate | train | def translate(
nucleotide_sequence,
first_codon_is_start=True,
to_stop=True,
truncate=False):
"""Translates cDNA coding sequence into amino acid protein sequence.
Should typically start with a start codon but allowing non-methionine
first residues since the CDS we're translating might have been affected
by a start loss mutation.
The sequence may include the 3' UTR but will stop translation at the first
encountered stop codon.
Parameters
----------
nucleotide_sequence : BioPython Seq
cDNA sequence
first_codon_is_start : bool
Treat the beginning of nucleotide_sequence (translates methionin)
truncate : bool
Truncate sequence if it's not a multiple of 3 (default = False)
Returns BioPython Seq of amino acids
"""
if not isinstance(nucleotide_sequence, Seq):
nucleotide_sequence = Seq(nucleotide_sequence)
if truncate:
# if sequence isn't a multiple of 3, truncate it so BioPython
# doesn't complain
n_nucleotides = int(len(nucleotide_sequence) / 3) * 3
nucleotide_sequence = nucleotide_sequence[:n_nucleotides]
else:
n_nucleotides = len(nucleotide_sequence)
assert n_nucleotides % 3 == 0, \
("Expected nucleotide sequence to be multiple of 3"
" but got %s of length %d") % (
nucleotide_sequence,
n_nucleotides)
| python | {
"resource": ""
} |
q5613 | main | train | def main(args_list=None):
"""
Script which loads variants and annotates them with overlapping genes.
Example usage:
varcode-genes
--vcf mutect.vcf \
--vcf strelka.vcf \
--maf tcga_brca.maf \
--variant chr1 498584 C G \
--json-variants more_variants.json
"""
print_version_info()
if args_list is None:
| python | {
"resource": ""
} |
q5614 | load_maf_dataframe | train | def load_maf_dataframe(path, nrows=None, raise_on_error=True, encoding=None):
"""
Load the guaranteed columns of a TCGA MAF file into a DataFrame
Parameters
----------
path : str
Path to MAF file
nrows : int
Optional limit to number of rows loaded
raise_on_error : bool
Raise an exception upon encountering an error or log an error
encoding : str, optional
Encoding to use for UTF when reading MAF file.
"""
require_string(path, "Path to MAF")
n_basic_columns = len(MAF_COLUMN_NAMES)
# pylint: disable=no-member
# pylint gets confused by read_csv
df = pandas.read_csv(
path,
comment="#",
sep="\t",
low_memory=False,
skip_blank_lines=True,
header=0,
encoding=encoding)
if len(df.columns) < n_basic_columns:
error_message = (
"Too few columns in MAF file %s, expected %d but got %d : %s" % (
path, n_basic_columns, len(df.columns), df.columns))
if raise_on_error:
raise ValueError(error_message)
else:
logging.warn(error_message)
# check each pair of expected/actual column names to make sure they match
for expected, actual in zip(MAF_COLUMN_NAMES, df.columns):
if expected | python | {
"resource": ""
} |
q5615 | load_maf | train | def load_maf(
path,
optional_cols=[],
sort_key=variant_ascending_position_sort_key,
distinct=True,
raise_on_error=True,
encoding=None):
"""
Load reference name and Variant objects from MAF filename.
Parameters
----------
path : str
Path to MAF (*.maf).
optional_cols : list, optional
A list of MAF columns to include as metadata if they are present in the MAF.
Does not result in an error if those columns are not present.
sort_key : fn
Function which maps each element to a sorting criterion.
Set to None to not to sort the variants.
distinct : bool
Don't keep repeated variants
raise_on_error : bool
Raise an exception upon encountering an error or just log a warning.
encoding : str, optional
Encoding to use for UTF when reading MAF file.
"""
# pylint: disable=no-member
# pylint gets confused by read_csv inside load_maf_dataframe
maf_df = load_maf_dataframe(path, raise_on_error=raise_on_error, encoding=encoding)
if len(maf_df) == 0 and raise_on_error:
raise ValueError("Empty MAF file %s" % path)
ensembl_objects = {}
variants = []
metadata = {}
for _, x in maf_df.iterrows():
contig = x.Chromosome
if isnull(contig):
error_message = "Invalid contig name: %s" % (contig,)
if raise_on_error:
raise ValueError(error_message)
else:
logging.warn(error_message)
continue
start_pos = x.Start_Position
ref = x.Reference_Allele
# it's possible in a MAF file to have multiple Ensembl releases
# mixed in a single MAF file (the genome assembly is
# specified by the NCBI_Build column)
ncbi_build = x.NCBI_Build
if ncbi_build in ensembl_objects:
ensembl = ensembl_objects[ncbi_build]
else:
if isinstance(ncbi_build, int):
reference_name = "B%d" % ncbi_build
else:
reference_name = str(ncbi_build)
ensembl = infer_genome(reference_name)
ensembl_objects[ncbi_build] = ensembl
# have to try both Tumor_Seq_Allele1 and Tumor_Seq_Allele2
# to figure out which is different from the reference allele
if x.Tumor_Seq_Allele1 != ref:
| python | {
"resource": ""
} |
q5616 | apply_to_field_if_exists | train | def apply_to_field_if_exists(effect, field_name, fn, default):
"""
Apply function to specified field of effect if it is not None,
otherwise return default.
"""
| python | {
"resource": ""
} |
q5617 | apply_to_transcript_if_exists | train | def apply_to_transcript_if_exists(effect, fn, default):
"""
Apply function to transcript associated with effect,
if it exists, otherwise return default.
"""
return | python | {
"resource": ""
} |
q5618 | gene_id_of_associated_transcript | train | def gene_id_of_associated_transcript(effect):
"""
Ensembl gene ID of transcript associated with effect, returns
None if effect does not have transcript.
"""
| python | {
"resource": ""
} |
q5619 | parse_transcript_number | train | def parse_transcript_number(effect):
"""
Try to parse the number at the end of a transcript name associated with
an effect.
e.g. TP53-001 returns the integer 1.
Parameters
----------
effect : subclass of MutationEffect
Returns int
"""
name = name_of_associated_transcript(effect)
if "-" not in | python | {
"resource": ""
} |
q5620 | select_between_exonic_splice_site_and_alternate_effect | train | def select_between_exonic_splice_site_and_alternate_effect(effect):
"""
If the given effect is an ExonicSpliceSite then it might contain
an alternate effect of higher priority. In that case, return the
alternate effect. Otherwise, this acts as an identity function.
"""
if effect.__class__ is not ExonicSpliceSite:
return effect
if effect.alternate_effect is None:
| python | {
"resource": ""
} |
q5621 | keep_max_priority_effects | train | def keep_max_priority_effects(effects):
"""
Given a list of effects, only keep the ones with the maximum priority
effect type.
Parameters
----------
effects : list of MutationEffect subclasses
Returns list of same length or shorter
| python | {
"resource": ""
} |
q5622 | filter_pipeline | train | def filter_pipeline(effects, filters):
"""
Apply each filter to the effect list sequentially. If any filter
returns zero values then ignore it. As soon as only one effect is left,
return it.
Parameters
----------
effects : list of MutationEffect subclass instances
filters : list of functions
Each function takes a list of effects and returns a list of effects
| python | {
"resource": ""
} |
q5623 | top_priority_effect_for_single_gene | train | def top_priority_effect_for_single_gene(effects):
"""
For effects which are from the same gene, check to see if there
is a canonical transcript with both the maximum length CDS
and maximum length full transcript sequence.
If not, then use number of exons and transcript name as tie-breaking
features.
Parameters
----------
effects : list of MutationEffect subclass instances
Returns single effect object
"""
# first filter effects to keep those on
# 1) maximum priority effects
# 2) protein coding genes
# 3) protein coding transcripts
# 4) complete transcripts
#
# If any of these filters drop all the effects then we move on to the next
# filtering step.
effects = filter_pipeline(
effects=effects,
filters=[
keep_max_priority_effects,
keep_effects_on_protein_coding_genes,
keep_effects_on_protein_coding_transcripts,
keep_effects_on_complete_transcripts,
],
)
if len(effects) == 1:
return effects[0]
# compare CDS length and transcript lengths of remaining effects
# if one effect has the maximum of both categories then return it
cds_lengths = [cds_length_of_associated_transcript(e) for e in effects]
max_cds_length = max(cds_lengths)
# get set of indices of all effects with maximum CDS length
max_cds_length_indices = {
i
for (i, l) in enumerate(cds_lengths)
if l == max_cds_length
}
seq_lengths = [length_of_associated_transcript(e) for e in effects]
max_seq_length = max(seq_lengths)
# get set of indices for all effects whose associated transcript
# has maximum sequence length
max_seq_length_indices = {
i
for (i, l) in | python | {
"resource": ""
} |
q5624 | VariantCollection.filter_by_transcript_expression | train | def filter_by_transcript_expression(
self,
transcript_expression_dict,
min_expression_value=0.0):
"""
Filters variants down to those which have overlap a transcript whose
expression value in the transcript_expression_dict argument is greater
than min_expression_value.
Parameters
----------
transcript_expression_dict : dict
Dictionary mapping Ensembl transcript IDs to expression estimates
(either FPKM or TPM)
min_expression_value : float
| python | {
"resource": ""
} |
q5625 | VariantCollection.filter_by_gene_expression | train | def filter_by_gene_expression(
self,
gene_expression_dict,
min_expression_value=0.0):
"""
Filters variants down to those which have overlap a gene whose
expression value in the transcript_expression_dict argument is greater
than min_expression_value.
Parameters
----------
gene_expression_dict : dict
Dictionary mapping Ensembl gene IDs to expression estimates
(either FPKM or TPM)
min_expression_value : float
| python | {
"resource": ""
} |
q5626 | VariantCollection.exactly_equal | train | def exactly_equal(self, other):
'''
Comparison between VariantCollection instances that takes into account
the info field of Variant instances.
Returns
----------
True if the variants in this collection equal the variants in the other
collection. The Variant.info fields are included in the comparison.
| python | {
"resource": ""
} |
q5627 | VariantCollection._combine_variant_collections | train | def _combine_variant_collections(cls, combine_fn, variant_collections, kwargs):
"""
Create a single VariantCollection from multiple different collections.
Parameters
----------
cls : class
Should be VariantCollection
combine_fn : function
Function which takes any number of sets of variants and returns
some combination of them (typically union or intersection).
variant_collections : tuple of VariantCollection
kwargs : dict
Optional dictionary of keyword arguments to pass to the initializer
for VariantCollection.
"""
kwargs["variants"] = combine_fn(*[set(vc) for vc in variant_collections])
kwargs["source_to_metadata_dict"] = cls._merge_metadata_dictionaries(
[vc.source_to_metadata_dict for vc in variant_collections])
kwargs["sources"] = set.union(*([vc.sources for vc in variant_collections]))
| python | {
"resource": ""
} |
q5628 | VariantCollection.union | train | def union(self, *others, **kwargs):
"""
Returns the union of variants in a several VariantCollection objects.
| python | {
"resource": ""
} |
q5629 | VariantCollection.intersection | train | def intersection(self, *others, **kwargs):
"""
Returns the intersection of variants in several VariantCollection objects.
| python | {
"resource": ""
} |
q5630 | VariantCollection.to_dataframe | train | def to_dataframe(self):
"""Build a DataFrame from this variant collection"""
def row_from_variant(variant):
return OrderedDict([
("chr", variant.contig),
("start", variant.original_start),
("ref", variant.original_ref),
("alt", variant.original_alt),
("gene_name", ";".join(variant.gene_names)),
("gene_id", ";".join(variant.gene_ids))
])
| python | {
"resource": ""
} |
q5631 | trim_shared_suffix | train | def trim_shared_suffix(ref, alt):
"""
Reuse the `trim_shared_prefix` function above to implement similar
functionality for string suffixes.
Given ref='ABC' and alt='BC', we first revese both strings:
reverse_ref = 'CBA'
reverse_alt = 'CB'
and then the result of calling trim_shared_prefix will be:
| python | {
"resource": ""
} |
q5632 | trim_shared_flanking_strings | train | def trim_shared_flanking_strings(ref, alt):
"""
Given two nucleotide or amino acid strings, identify
if they have a common prefix, a common suffix, and return
their unique components along with the prefix and suffix.
For example, if the input ref = "SYFFQGR" and alt = "SYMLLFIFQGR"
then the result will be:
| python | {
"resource": ""
} |
q5633 | main | train | def main(args_list=None):
"""
Script which loads variants and annotates them with overlapping genes
and predicted coding effects.
Example usage:
varcode
--vcf mutect.vcf \
--vcf strelka.vcf \
--maf tcga_brca.maf \
--variant chr1 498584 C G \
--json-variants more_variants.json
"""
print_version_info()
if args_list is None:
| python | {
"resource": ""
} |
q5634 | get_codons | train | def get_codons(
variant,
trimmed_cdna_ref,
trimmed_cdna_alt,
sequence_from_start_codon,
cds_offset):
"""
Returns indices of first and last reference codons affected by the variant,
as well as the actual sequence of the mutated codons which replace those
reference codons.
Parameters
----------
variant : Variant
trimmed_cdna_ref : str
Trimmed reference cDNA nucleotides affected by the variant
trimmed_cdna_alt : str
Trimmed alternate cDNA nucleotides which replace the reference
sequence_from_start_codon : str
cDNA nucleotide coding sequence
cds_offset : int
Integer offset into the coding sequence where ref is replace with alt
"""
# index (starting from 0) of first affected reference codon
ref_codon_start_offset = cds_offset // 3
# which nucleotide of the first codon got changed?
nucleotide_offset_into_first_ref_codon = cds_offset % 3
n_ref_nucleotides = len(trimmed_cdna_ref)
if n_ref_nucleotides == 0:
if nucleotide_offset_into_first_ref_codon == 2:
# if we're inserting between codons
ref_codon_end_offset = ref_codon_start_offset
else:
# inserting inside a reference codon
ref_codon_end_offset = ref_codon_start_offset + 1
ref_codons = sequence_from_start_codon[
ref_codon_start_offset * 3:ref_codon_end_offset * 3]
# split the reference codon into nucleotides before/after insertion
prefix = ref_codons[:nucleotide_offset_into_first_ref_codon + 1]
suffix = ref_codons[nucleotide_offset_into_first_ref_codon + 1:]
else:
ref_codon_end_offset = (cds_offset + n_ref_nucleotides - 1) // 3 + | python | {
"resource": ""
} |
q5635 | predict_in_frame_coding_effect | train | def predict_in_frame_coding_effect(
variant,
transcript,
trimmed_cdna_ref,
trimmed_cdna_alt,
sequence_from_start_codon,
cds_offset):
"""Coding effect of an in-frame nucleotide change
Parameters
----------
variant : Variant
transcript : Transcript
trimmed_cdna_ref : str
Reference nucleotides from the coding sequence of the transcript
trimmed_cdna_alt : str
Nucleotides to insert in place of the reference nucleotides
sequence_from_start_codon : Bio.Seq or str
Transcript sequence from the CDS start codon (including the 3' UTR).
This sequence includes the 3' UTR since a mutation may delete the stop
codon and we'll have to translate past the normal end of the CDS to
determine the new protein sequence.
cds_offset : int
Index of first ref nucleotide, starting from 0 = beginning of coding
sequence. If variant is a pure insertion (no ref nucleotides) then this
argument indicates the offset *after* which to insert the alt
nucleotides.
"""
ref_codon_start_offset, ref_codon_end_offset, mutant_codons = get_codons(
variant=variant,
trimmed_cdna_ref=trimmed_cdna_ref,
trimmed_cdna_alt=trimmed_cdna_alt,
sequence_from_start_codon=sequence_from_start_codon,
cds_offset=cds_offset)
mutation_affects_start_codon = (ref_codon_start_offset == 0)
if mutation_affects_start_codon and mutant_codons[:3] not in START_CODONS:
# if we changed a start codon to something else then
# we no longer know where the protein begins (or even in
# what frame).
# TODO: use the Kozak consensus sequence or a predictive model
# to identify the most likely start site
return StartLoss(
variant=variant,
transcript=transcript)
# rely on Ensembl's annotation of the protein sequence since we can't
# easily predict whether the starting nucleotide is a methionine
# (most common) or leucine
aa_ref = transcript.protein_sequence[ref_codon_start_offset:ref_codon_end_offset]
reference_protein_length = len(transcript.protein_sequence)
aa_alt, mutant_stop_codon_index, using_three_prime_utr = \
translate_in_frame_mutation(
transcript=transcript,
ref_codon_start_offset=ref_codon_start_offset,
ref_codon_end_offset=ref_codon_end_offset,
mutant_codons=mutant_codons)
mutant_codons_contain_stop = mutant_stop_codon_index != -1
# trim shared subsequences at the start and end of reference
# and mutated amino acid sequences
aa_ref, aa_alt, shared_prefix, shared_suffix = \
trim_shared_flanking_strings(
aa_ref,
aa_alt)
n_aa_ref = len(aa_ref)
n_aa_alt = len(aa_alt)
n_aa_shared = len(shared_prefix)
is_insertion = (ref_codon_start_offset == ref_codon_end_offset)
# index of first amino acid which is different from the reference
aa_mutation_start_offset = (
ref_codon_start_offset + n_aa_shared + is_insertion)
if mutant_codons_contain_stop:
mutant_stop_codon_index += n_aa_shared
if mutation_affects_start_codon and (aa_ref == aa_alt):
# Substitution between start codons gets special treatment since,
# though superficially synonymous, this could still potentially
# cause a start loss / change in reading frame and might be worth
# closer scrutiny
| python | {
"resource": ""
} |
q5636 | insert_before | train | def insert_before(sequence, offset, new_residues):
"""Mutate the given sequence by inserting the string `new_residues` before
`offset`.
Parameters
----------
sequence : sequence
String of amino acids or DNA bases
offset : int
Base 0 offset from start of sequence, after which we should insert
`new_residues`.
new_residues | python | {
"resource": ""
} |
q5637 | insert_after | train | def insert_after(sequence, offset, new_residues):
"""Mutate the given sequence by inserting the string `new_residues` after
`offset`.
Parameters
----------
sequence : sequence
String of amino acids or DNA bases
offset : int
Base 0 offset from start of sequence, after which we should insert
`new_residues`.
new_residues : | python | {
"resource": ""
} |
q5638 | substitute | train | def substitute(sequence, offset, ref, alt):
"""Mutate a sequence by substituting given `alt` at instead of `ref` at the
given `position`.
Parameters
----------
sequence : sequence
String of amino acids or DNA bases
offset : int
Base 0 offset from start of `sequence`
ref : | python | {
"resource": ""
} |
q5639 | infer_genome | train | def infer_genome(genome_object_string_or_int):
"""
If given an integer, return associated human EnsemblRelease for that
Ensembl version.
If given a string, return latest EnsemblRelease which has a reference
of the same name.
If given a PyEnsembl Genome, simply return it.
"""
if isinstance(genome_object_string_or_int, Genome):
return genome_object_string_or_int
if is_integer(genome_object_string_or_int):
| python | {
"resource": ""
} |
q5640 | Variant.genes | train | def genes(self):
"""
Return Gene object for all genes which overlap this variant.
"""
if self._genes is None:
| python | {
"resource": ""
} |
q5641 | Variant.is_insertion | train | def is_insertion(self):
"""
Does this variant represent the insertion of nucleotides into the
reference genome?
"""
# An insertion would appear in a VCF like C>CT, so that the
# alternate allele starts with the reference nucleotides.
# Since the nucleotide strings may be normalized in the constructor,
# it's worth | python | {
"resource": ""
} |
q5642 | Variant.is_deletion | train | def is_deletion(self):
"""
Does this variant represent the deletion of nucleotides from the
reference genome?
"""
# A deletion would appear in a VCF like CT>C, so that the
# reference allele starts with the alternate nucleotides.
# This is true even in the normalized | python | {
"resource": ""
} |
q5643 | Variant.is_snv | train | def is_snv(self):
"""Is the variant a single nucleotide variant"""
| python | {
"resource": ""
} |
q5644 | Variant.is_transition | train | def is_transition(self):
"""Is this variant and pyrimidine to pyrimidine change | python | {
"resource": ""
} |
q5645 | Variant.is_transversion | train | def is_transversion(self):
"""Is this variant a pyrimidine to purine change or vice versa"""
| python | {
"resource": ""
} |
q5646 | variant_overlaps_interval | train | def variant_overlaps_interval(
variant_start,
n_ref_bases,
interval_start,
interval_end):
"""
Does a variant overlap a given interval on the same chromosome?
Parameters
----------
variant_start : int
Inclusive base-1 position of variant's starting location
(or location before an insertion)
n_ref_bases : int
Number of reference bases affect by variant (used to compute
end coordinate or determine whether variant is an insertion)
interval_start : int
Interval's inclusive base-1 start position
interval_end : int
Interval's inclusive base-1 end position
"""
if n_ref_bases == 0:
# insertions only overlap intervals which start before and | python | {
"resource": ""
} |
q5647 | changes_exonic_splice_site | train | def changes_exonic_splice_site(
transcript_offset,
transcript,
transcript_ref,
transcript_alt,
exon_start_offset,
exon_end_offset,
exon_number):
"""Does the given exonic mutation of a particular transcript change a
splice site?
Parameters
----------
transcript_offset : int
Offset from start of transcript of first reference nucleotide
(or the last nucleotide before an insertion)
transcript : pyensembl.Transcript
transcript_ref : str
Reference nucleotides
transcript_alt : alt
Alternate nucleotides
exon_start_offset : int
Start offset of exon relative to beginning of transcript
exon_end_offset : int
End offset of exon relative to beginning of transcript
exon_number : int
Which exon in the order they form the transcript
"""
# first we're going to make sure the variant doesn't disrupt the
# splicing sequences we got from Divina et. al's
# Ab initio prediction of mutation-induced cryptic
# splice-site activation and exon skipping
# (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2947103/)
#
# 5' splice site: MAG|GURAGU consensus
# M is A or C; R is purine; | is the exon-intron boundary
#
# 3' splice site: YAG|R
#
if exon_number > 1 and transcript_offset == exon_start_offset:
# if this is any exon past the first, check to see if it lost
# the purine on its left side
#
# the 3' splice site sequence has just a single purine on
# the exon side
if len(transcript_ref) > 0 and transcript_ref[0] in PURINE_NUCLEOTIDES:
if len(transcript_alt) > 0:
if transcript_alt[0] not in PURINE_NUCLEOTIDES:
return True
else:
# if the mutation is a deletion, are there ref nucleotides
# afterward?
offset_after_deletion = transcript_offset + len(transcript_ref)
if len(transcript.sequence) > offset_after_deletion:
next_base = transcript.sequence[offset_after_deletion]
if next_base not in PURINE_NUCLEOTIDES:
| python | {
"resource": ""
} |
q5648 | is_purine | train | def is_purine(nucleotide, allow_extended_nucleotides=False):
"""Is the nucleotide a purine"""
if not allow_extended_nucleotides and nucleotide not in STANDARD_NUCLEOTIDES:
raise ValueError(
| python | {
"resource": ""
} |
q5649 | normalize_nucleotide_string | train | def normalize_nucleotide_string(
nucleotides,
allow_extended_nucleotides=False,
empty_chars=".-",
treat_nan_as_empty=True):
"""
Normalizes a nucleotide string by converting various ways of encoding empty
strings into "", making all letters upper case, and checking to make sure
all letters in the string are actually nucleotides.
Parameters
----------
nucleotides : str
Sequence of nucleotides, e.g. "ACCTG"
extended_nucleotides : bool
Allow non-canonical nucleotide characters like 'X' for unknown base
empty_chars : str
Characters which encode empty strings, such as "." used in VCF format
or "-" used in MAF format
treat_nan_as_empty : bool
Some MAF files represent deletions/insertions with NaN ref/alt values
"""
| python | {
"resource": ""
} |
q5650 | load_vcf | train | def load_vcf(
path,
genome=None,
reference_vcf_key="reference",
only_passing=True,
allow_extended_nucleotides=False,
include_info=True,
chunk_size=10 ** 5,
max_variants=None,
sort_key=variant_ascending_position_sort_key,
distinct=True):
"""
Load reference name and Variant objects from the given VCF filename.
Currently only local files are supported by this function (no http). If you
call this on an HTTP URL, it will fall back to `load_vcf`.
Parameters
----------
path : str
Path to VCF (*.vcf) or compressed VCF (*.vcf.gz).
genome : {pyensembl.Genome, reference name, Ensembl version int}, optional
Optionally pass in a PyEnsembl Genome object, name of reference, or
PyEnsembl release version to specify the reference associated with a
VCF (otherwise infer reference from VCF using reference_vcf_key)
reference_vcf_key : str, optional
Name of metadata field which contains path to reference FASTA
file (default = 'reference')
only_passing : boolean, optional
If true, any entries whose FILTER field is not one of "." or "PASS" is
dropped.
allow_extended_nucleotides : boolean, default False
Allow characters other that A,C,T,G in the ref and alt strings.
include_info : boolean, default True
Whether to parse the INFO and per-sample columns. If you don't need
these, set to False for faster parsing.
chunk_size: int, optional
Number of records to load in memory at once.
max_variants : int, optional
If specified, return only the first max_variants variants.
sort_key : fn
Function which maps each element to a sorting criterion.
Set to None to not to sort the variants.
distinct : boolean, default True
Don't keep repeated variants
"""
require_string(path, "Path or URL to VCF")
parsed_path = parse_url_or_path(path)
if parsed_path.scheme and parsed_path.scheme.lower() != "file":
# pandas.read_table nominally supports HTTP, but it tends to crash on
# large files and does not support gzip. Switching to the python-based
# implementation of read_table (with engine="python") helps with some
| python | {
"resource": ""
} |
q5651 | dataframes_to_variant_collection | train | def dataframes_to_variant_collection(
dataframes,
source_path,
info_parser=None,
only_passing=True,
max_variants=None,
sample_names=None,
sample_info_parser=None,
variant_kwargs={},
variant_collection_kwargs={}):
"""
Load a VariantCollection from an iterable of pandas dataframes.
This takes an iterable of dataframes instead of a single dataframe to avoid
having to load huge dataframes at once into memory. If you have a single
dataframe, just pass it in a single-element list.
Parameters
----------
dataframes
Iterable of dataframes (e.g. a generator). Expected columns are:
["CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER"]
and 'INFO' if `info_parser` is not Null. Columns must be in this
order.
source_path : str
Path of VCF file from which DataFrame chunks were generated.
info_parser : string -> object, optional
Callable to parse INFO strings.
only_passing : boolean, optional
If true, any entries whose FILTER field is not one of "." or "PASS" is
dropped.
max_variants : int, optional
If specified, return only the first max_variants variants.
sample_names : list of strings, optional
Sample names. The final columns of the dataframe should match these.
If specified, the per-sample info columns will be parsed. You must
also specify sample_info_parser.
sample_info_parser : string list * string -> dict, optional
Callable to parse per-sample info columns.
variant_kwargs : dict, optional
Additional keyword paramters to pass to Variant.__init__
variant_collection_kwargs : dict, optional
Additional keyword parameters to pass to VariantCollection.__init__.
"""
expected_columns = (
["CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER"] +
(["INFO"] if info_parser else []))
if info_parser and sample_names:
if sample_info_parser is None:
raise TypeError(
"Must specify sample_info_parser if specifying sample_names")
expected_columns.append("FORMAT")
expected_columns.extend(sample_names)
variants = []
metadata = {}
try:
for chunk in dataframes:
assert chunk.columns.tolist() == expected_columns,\
"dataframe columns (%s) do not match expected columns (%s)" % (
chunk.columns, expected_columns)
for tpl in chunk.itertuples():
(i, chrom, pos, id_, ref, alts, qual, flter) = tpl[:8]
if flter == ".":
flter = None
elif flter == "PASS":
flter = []
elif only_passing:
continue
else:
flter = flter.split(';')
if id_ == ".":
id_ = None
qual = float(qual) if qual != "." else None
alt_num = 0
info = sample_info = None
for alt in alts.split(","):
if alt != ".":
| python | {
"resource": ""
} |
q5652 | read_vcf_into_dataframe | train | def read_vcf_into_dataframe(
path,
include_info=False,
sample_names=None,
chunk_size=None):
"""
Load the data of a VCF into a pandas dataframe. All headers are ignored.
Parameters
----------
path : str
Path to local file. HTTP and other protocols are not implemented.
include_info : boolean, default False
If true, the INFO field is not parsed, but is included as a string in
the resulting data frame. If false, the INFO field is omitted.
sample_names: string list, optional
Sample names. The final columns of the dataframe should match these.
If specified (and include_info is also specified), the FORMAT and
per-sample info columns will be included in the result dataframe.
chunk_size : int, optional
If buffering is desired, the number of rows per chunk.
Returns
---------
If chunk_size is None (the default), a dataframe with the contents of the
VCF file. Otherwise, an iterable of dataframes, each with chunk_size rows.
"""
vcf_field_types = OrderedDict()
vcf_field_types['CHROM'] = str
vcf_field_types['POS'] = int
vcf_field_types['ID'] = str
vcf_field_types['REF'] = str
vcf_field_types['ALT'] = str
vcf_field_types['QUAL'] = str
vcf_field_types['FILTER'] = str
if include_info:
vcf_field_types['INFO'] = str
if sample_names:
vcf_field_types['FORMAT'] | python | {
"resource": ""
} |
q5653 | stream_gzip_decompress_lines | train | def stream_gzip_decompress_lines(stream):
"""
Uncompress a gzip stream into lines of text.
Parameters
----------
Generator of chunks of gzip compressed text.
Returns
-------
Generator of uncompressed lines.
"""
dec = zlib.decompressobj(zlib.MAX_WBITS | 16)
previous = ""
for compressed_chunk in stream:
| python | {
"resource": ""
} |
q5654 | infer_genome_from_vcf | train | def infer_genome_from_vcf(genome, vcf_reader, reference_vcf_key):
"""
Helper function to make a pyensembl.Genome instance.
"""
if genome:
return infer_genome(genome)
elif reference_vcf_key not in vcf_reader.metadata:
raise ValueError("Unable to infer reference genome for %s" % (
| python | {
"resource": ""
} |
q5655 | cdna_codon_sequence_after_insertion_frameshift | train | def cdna_codon_sequence_after_insertion_frameshift(
sequence_from_start_codon,
cds_offset_before_insertion,
inserted_nucleotides):
"""
Returns index of mutated codon and nucleotide sequence starting at the first
mutated codon.
"""
# special logic for insertions
coding_sequence_after_insertion = \
sequence_from_start_codon[cds_offset_before_insertion + 1:]
if cds_offset_before_insertion % 3 == 2:
# insertion happens after last nucleotide in a codon,
# doesn't disrupt the existing codon from cds_offset-2 to cds_offset
mutated_codon_index = cds_offset_before_insertion // 3 + 1
nucleotides_before = ""
elif cds_offset_before_insertion % 3 == 1:
# insertion happens after 2nd nucleotide of a codon
# codon positions:
# 1) cds_offset - 1
# 2) cds_offset
# <----- Insertsion
# 3) cds_offset + 1
mutated_codon_index = cds_offset_before_insertion // 3
# the first codon in the returned sequence will contain two reference
# nucleotides before the insertion
nucleotides_before = sequence_from_start_codon[
cds_offset_before_insertion - 1:cds_offset_before_insertion + 1]
| python | {
"resource": ""
} |
q5656 | cdna_codon_sequence_after_deletion_or_substitution_frameshift | train | def cdna_codon_sequence_after_deletion_or_substitution_frameshift(
sequence_from_start_codon,
cds_offset,
trimmed_cdna_ref,
trimmed_cdna_alt):
"""
Logic for any frameshift which isn't an insertion.
We have insertions as a special case since our base-inclusive
indexing means something different for insertions:
cds_offset = base before insertion
Whereas in this case:
cds_offset = first reference base affected by a variant
Returns index of first modified codon and sequence from that codon
onward.
"""
mutated_codon_index = cds_offset // 3
# get the sequence starting from the first modified codon until the end
# of the transcript.
sequence_after_mutated_codon = \
sequence_from_start_codon[mutated_codon_index * 3:]
| python | {
"resource": ""
} |
q5657 | predict_frameshift_coding_effect | train | def predict_frameshift_coding_effect(
variant,
transcript,
trimmed_cdna_ref,
trimmed_cdna_alt,
cds_offset,
sequence_from_start_codon):
"""
Coding effect of a frameshift mutation.
Parameters
----------
variant : Variant
transcript : Transcript
trimmed_cdna_ref : nucleotide sequence
Reference nucleotides in the coding sequence of the given transcript.
trimmed_cdna_alt : nucleotide sequence
Alternate nucleotides introduced by mutation
cds_offset : int
Offset into the CDS of first ref nucleotide. For insertions, this
is the offset of the last ref nucleotide before the insertion.
sequence_from_start_codon | python | {
"resource": ""
} |
q5658 | predict_variant_effects | train | def predict_variant_effects(variant, raise_on_error=False):
"""Determine the effects of a variant on any transcripts it overlaps.
Returns an EffectCollection object.
Parameters
----------
variant : Variant
raise_on_error : bool
Raise an exception if we encounter an error while trying to
determine the effect of this variant on a transcript, or simply
log the error and continue.
"""
# if this variant isn't overlapping any genes, return a
# Intergenic effect
# TODO: look for nearby genes and mark those as Upstream and Downstream
# effects
if len(variant.gene_ids) == 0:
effects = [Intergenic(variant)]
else:
# list of all MutationEffects for all genes & transcripts
effects = []
# group transcripts by their gene ID
transcripts_grouped_by_gene = groupby_field(variant.transcripts, 'gene_id')
# want effects in the list grouped by the gene they come from
for gene_id in sorted(variant.gene_ids):
if gene_id not in transcripts_grouped_by_gene:
# intragenic variant overlaps a gene but not any transcripts
gene = variant.ensembl.gene_by_id(gene_id)
effects.append(Intragenic(variant, gene))
| python | {
"resource": ""
} |
q5659 | predict_variant_effect_on_transcript_or_failure | train | def predict_variant_effect_on_transcript_or_failure(variant, transcript):
"""
Try predicting the effect of a variant on a particular transcript but
suppress raised exceptions by converting them into `Failure` effect
values.
"""
try:
return predict_variant_effect_on_transcript(
variant=variant,
| python | {
"resource": ""
} |
q5660 | choose_intronic_effect_class | train | def choose_intronic_effect_class(
variant,
nearest_exon,
distance_to_exon):
"""
Infer effect of variant which does not overlap any exon of
the given transcript.
"""
assert distance_to_exon > 0, \
"Expected intronic effect to have distance_to_exon > 0, got %d" % (
distance_to_exon,)
if nearest_exon.strand == "+":
# if exon on positive strand
start_before = variant.trimmed_base1_start < nearest_exon.start
start_same = variant.trimmed_base1_start == nearest_exon.start
before_exon = start_before or (variant.is_insertion and start_same)
else:
# if exon on negative strand
end_after = variant.trimmed_base1_end > nearest_exon.end
end_same = variant.trimmed_base1_end == nearest_exon.end
before_exon = end_after or (variant.is_insertion and end_same)
# distance cutoffs based on consensus splice sequences from
# http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2947103/
# 5' splice site: MAG|GURAGU consensus
# M is A or C; R is purine; | is the exon-intron boundary
# 3' splice site: YAG|R
if distance_to_exon <= 2:
if before_exon:
# 2 last nucleotides of intron before exon are the splice
# acceptor site, typically "AG"
return SpliceAcceptor
else: | python | {
"resource": ""
} |
q5661 | exonic_transcript_effect | train | def exonic_transcript_effect(variant, exon, exon_number, transcript):
"""Effect of this variant on a Transcript, assuming we already know
that this variant overlaps some exon of the transcript.
Parameters
----------
variant : Variant
exon : pyensembl.Exon
Exon which this variant overlaps
exon_number : int
Index (starting from 1) of the given exon in the transcript's
sequence of exons.
transcript : pyensembl.Transcript
"""
genome_ref = variant.trimmed_ref
genome_alt = variant.trimmed_alt
variant_start = variant.trimmed_base1_start
variant_end = variant.trimmed_base1_end
# clip mutation to only affect the current exon
if variant_start < exon.start:
# if mutation starts before current exon then only look
# at nucleotides which overlap the exon
logger.info('Mutation in variant %s starts before exon %s', variant, exon)
assert len(genome_ref) > 0, "Unexpected insertion into intron"
n_skip_start = exon.start - variant_start
genome_ref = genome_ref[n_skip_start:]
genome_alt = genome_alt[n_skip_start:]
genome_start = exon.start
else:
genome_start = variant_start
if variant_end > exon.end:
# if mutation goes past exon end then only look at nucleotides
# which overlap the exon
logger.info('Mutation in variant %s ends after exon %s', variant, exon)
n_skip_end = variant_end - exon.end
genome_ref = genome_ref[:-n_skip_end]
genome_alt = genome_alt[:len(genome_ref)]
genome_end = exon.end
else:
genome_end = variant_end
transcript_offset = interval_offset_on_transcript(
genome_start, genome_end, transcript)
if transcript.on_backward_strand:
cdna_ref = reverse_complement(genome_ref)
cdna_alt = reverse_complement(genome_alt)
else:
cdna_ref = genome_ref
cdna_alt = genome_alt
n_ref = len(cdna_ref)
expected_ref = str(
transcript.sequence[transcript_offset:transcript_offset + n_ref])
if cdna_ref != expected_ref:
raise ValueError(
("Found ref nucleotides '%s' in sequence"
" of %s at offset %d (chromosome positions %d:%d)"
" but variant %s has '%s'") % (
expected_ref,
transcript,
transcript_offset,
genome_start,
genome_end,
variant,
cdna_ref))
utr5_length = min(transcript.start_codon_spliced_offsets)
# does the variant start inside the 5' | python | {
"resource": ""
} |
q5662 | apply_groupby | train | def apply_groupby(records, fn, skip_none=False):
"""
Given a list of objects, group them into a dictionary by
applying fn to each one and using returned values as a dictionary
key.
Parameters
----------
records : list
fn : function
skip_none : bool
If False, then None can be a key in the returned dictionary,
otherwise records whose key value is | python | {
"resource": ""
} |
q5663 | groupby_field | train | def groupby_field(records, field_name, skip_none=True):
"""
Given a list of objects, group them into a dictionary by
the unique values of a given field name.
""" | python | {
"resource": ""
} |
q5664 | memoize | train | def memoize(fn):
"""
Simple memoization decorator for functions and methods,
assumes that all arguments to the function can be hashed and
compared.
"""
memoized_values = {}
@wraps(fn)
def wrapped_fn(*args, **kwargs):
key = (args, tuple(sorted(kwargs.items())))
try:
| python | {
"resource": ""
} |
q5665 | EffectCollection.filter_by_transcript_expression | train | def filter_by_transcript_expression(
self,
transcript_expression_dict,
min_expression_value=0.0):
"""
Filters effects to those which have an associated transcript whose
expression value in the transcript_expression_dict argument is greater
than min_expression_value.
Parameters
----------
transcript_expression_dict : dict
Dictionary mapping Ensembl transcript IDs to expression estimates
(either FPKM or TPM)
min_expression_value : float
| python | {
"resource": ""
} |
q5666 | EffectCollection.filter_by_gene_expression | train | def filter_by_gene_expression(
self,
gene_expression_dict,
min_expression_value=0.0):
"""
Filters effects to those which have an associated gene whose
expression value in the gene_expression_dict argument is greater
than min_expression_value.
Parameters
----------
gene_expression_dict : dict
Dictionary mapping Ensembl gene IDs to expression estimates
(either FPKM or TPM)
min_expression_value : float
| python | {
"resource": ""
} |
q5667 | EffectCollection.filter_by_effect_priority | train | def filter_by_effect_priority(self, min_priority_class):
"""
Create a new EffectCollection containing only effects whose priority
| python | {
"resource": ""
} |
q5668 | EffectCollection.top_priority_effect_per_variant | train | def top_priority_effect_per_variant(self):
"""Highest priority effect for each unique variant"""
return OrderedDict(
(variant, top_priority_effect(variant_effects))
| python | {
"resource": ""
} |
q5669 | EffectCollection.top_priority_effect_per_transcript_id | train | def top_priority_effect_per_transcript_id(self):
"""Highest priority effect for each unique transcript ID"""
return OrderedDict(
(transcript_id, top_priority_effect(variant_effects))
| python | {
"resource": ""
} |
q5670 | EffectCollection.top_priority_effect_per_gene_id | train | def top_priority_effect_per_gene_id(self):
"""Highest priority effect for each unique gene ID"""
return OrderedDict(
(gene_id, top_priority_effect(variant_effects))
| python | {
"resource": ""
} |
q5671 | EffectCollection.top_expression_effect | train | def top_expression_effect(self, expression_levels):
"""
Return effect whose transcript has the highest expression level.
If none of the effects are expressed or have associated transcripts,
then return None. In case of ties, add lexicographical sorting by
effect priority and transcript length.
"""
effect_expression_dict = self.effect_expression(expression_levels)
if len(effect_expression_dict) == 0:
return None
def key_fn(effect_fpkm_pair):
"""
Sort effects primarily by their expression level
| python | {
"resource": ""
} |
q5672 | EffectCollection.to_dataframe | train | def to_dataframe(self):
"""Build a dataframe from the effect collection"""
# list of properties to extract from Variant objects if they're
# not None
variant_properties = [
"contig",
"start",
"ref",
"alt",
"is_snv",
"is_transversion",
"is_transition"
]
def row_from_effect(effect):
row = OrderedDict()
row['variant'] = str(effect.variant.short_description)
for field_name in variant_properties:
# if effect.variant is None then this column value will be None
row[field_name] = getattr(effect.variant, field_name, None)
| python | {
"resource": ""
} |
q5673 | random_variants | train | def random_variants(
count,
genome_name="GRCh38",
deletions=True,
insertions=True,
random_seed=None):
"""
Generate a VariantCollection with random variants that overlap
at least one complete coding transcript.
"""
rng = random.Random(random_seed)
ensembl = genome_for_reference_name(genome_name)
if ensembl in _transcript_ids_cache:
transcript_ids = _transcript_ids_cache[ensembl]
else:
transcript_ids = ensembl.transcript_ids()
_transcript_ids_cache[ensembl] = transcript_ids
variants = []
# we should finish way before this loop is over but just in case
# something is wrong with PyEnsembl we want to avoid an infinite loop
for _ in range(count * 100):
if len(variants) < count:
transcript_id = rng.choice(transcript_ids)
transcript = ensembl.transcript_by_id(transcript_id)
if not transcript.complete:
continue
exon = rng.choice(transcript.exons)
base1_genomic_position = rng.randint(exon.start, exon.end)
transcript_offset = transcript.spliced_offset(base1_genomic_position)
seq = transcript.sequence
ref = str(seq[transcript_offset])
if transcript.on_backward_strand:
ref = reverse_complement(ref)
| python | {
"resource": ""
} |
q5674 | MSLDAP.get_server_info | train | def get_server_info(self, anonymous = True):
"""
Performs bind on the server and grabs the DSA info object.
If anonymous is set to true, then it will perform anonymous bind, not using user credentials
Otherwise it will use the credentials set in the object constructor.
"""
if anonymous == True:
logger.debug('Getting server info via Anonymous BIND on server %s' % self.target_server.get_host())
server = Server(self.target_server.get_host(), use_ssl=self.target_server.is_ssl(), get_info=ALL)
conn = Connection(server, auto_bind=True)
logger.debug('Got server info')
else:
logger.debug('Getting server info via credentials supplied on server %s' % self.target_server.get_host())
server = Server(self.target_server.get_host(), use_ssl=self.target_server.is_ssl(), get_info=ALL)
if self.use_sspi == True:
| python | {
"resource": ""
} |
q5675 | MSLDAP.get_all_user_objects | train | def get_all_user_objects(self):
"""
Fetches all user objects from the AD, and returns MSADUser object
"""
logger.debug('Polling AD for all user objects')
ldap_filter = r'(objectClass=user)'
attributes = MSADUser.ATTRS
for entry in self.pagedsearch(ldap_filter, attributes):
| python | {
"resource": ""
} |
q5676 | MSLDAP.get_all_knoreq_user_objects | train | def get_all_knoreq_user_objects(self, include_machine = False):
"""
Fetches all user objects with useraccountcontrol DONT_REQ_PREAUTH flag set from the AD, and returns MSADUser object.
"""
logger.debug('Polling AD for all user objects, machine accounts included: %s'% include_machine)
if include_machine == True:
ldap_filter = r'(userAccountControl:1.2.840.113556.1.4.803:=4194304)'
else:
| python | {
"resource": ""
} |
q5677 | vn | train | def vn(x):
"""
value or none, returns none if x is an empty list
"""
if x == []:
return None
if isinstance(x, list):
| python | {
"resource": ""
} |
q5678 | BotoSqliteEngine.load_tables | train | def load_tables(self, query, meta):
"""
Load necessary resources tables into db to execute given query.
"""
try:
for table in meta.tables:
self.load_table(table)
except NoCredentialsError:
help_link = 'http://boto3.readthedocs.io/en/latest/guide/configuration.html'
| python | {
"resource": ""
} |
q5679 | BotoSqliteEngine.load_table | train | def load_table(self, table):
"""
Load resources as specified by given table into our db.
"""
region = table.database if table.database else self.default_region
resource_name, collection_name = table.table.split('_', 1)
# we use underscore "_" instead of dash "-" for region name but boto3 need dash
boto_region_name = region.replace('_', '-')
resource = self.boto3_session.resource(resource_name, region_name=boto_region_name)
| python | {
"resource": ""
} |
q5680 | json_serialize | train | def json_serialize(obj):
"""
Simple generic JSON serializer for common objects.
"""
if isinstance(obj, datetime):
return obj.isoformat()
if hasattr(obj, 'id'):
return | python | {
"resource": ""
} |
q5681 | json_get | train | def json_get(serialized_object, field):
"""
This emulates the HSTORE `->` get value operation.
It get value from JSON serialized column by given key and return `null` if not present.
Key can be either an integer for array index access or a string for object field access.
:return: JSON serialized value of key in object
"""
# return null if serialized_object is null or "serialized null"
if serialized_object is None:
return None
obj = json.loads(serialized_object)
if obj is None:
return None
if isinstance(field, int):
| python | {
"resource": ""
} |
q5682 | create_table | train | def create_table(db, schema_name, table_name, columns):
"""
Create a table, schema_name.table_name, in given database with given list of column names.
"""
table = '{0}.{1}'.format(schema_name, table_name) if schema_name else table_name
db.execute('DROP TABLE | python | {
"resource": ""
} |
q5683 | insert_all | train | def insert_all(db, schema_name, table_name, columns, items):
"""
Insert all item in given items list into the specified table, schema_name.table_name.
"""
table = '{0}.{1}'.format(schema_name, table_name) if schema_name else table_name | python | {
"resource": ""
} |
q5684 | Creator.get_events | train | def get_events(self, *args, **kwargs):
"""
Returns a full EventDataWrapper object for this creator.
/creators/{creatorId}/events
:returns: EventDataWrapper -- A new request to API. Contains full results set.
"""
| python | {
"resource": ""
} |
q5685 | Creator.get_series | train | def get_series(self, *args, **kwargs):
"""
Returns a full SeriesDataWrapper object for this creator.
/creators/{creatorId}/series
:returns: SeriesDataWrapper -- A new request to API. Contains full results set.
"""
| python | {
"resource": ""
} |
q5686 | Creator.get_stories | train | def get_stories(self, *args, **kwargs):
"""
Returns a full StoryDataWrapper object for this creator.
/creators/{creatorId}/stories
:returns: StoriesDataWrapper -- A new request to API. Contains full results set.
"""
| python | {
"resource": ""
} |
q5687 | MarvelObject.list_to_instance_list | train | def list_to_instance_list(_self, _list, _Class):
"""
Takes a list of resource dicts and returns a list
of resource instances, defined by the _Class param.
:param _self: Original resource calling the method
:type _self: core.MarvelObject
:param _list: List of dicts describing a Resource.
:type _list: list
:param _Class: The Resource class to | python | {
"resource": ""
} |
q5688 | Marvel._call | train | def _call(self, resource_url, params=None):
"""
Calls the Marvel API endpoint
:param resource_url: url slug of the resource
:type resource_url: str
:param params: query params to add to endpoint
| python | {
"resource": ""
} |
q5689 | Marvel.get_character | train | def get_character(self, id):
"""Fetches a single character by id.
get /v1/public/characters
:param id: ID of Character
:type params: int
:returns: CharacterDataWrapper
>>> m = Marvel(public_key, private_key)
>>> cdw = m.get_character(1009718)
>>> print cdw.data.count
1
| python | {
"resource": ""
} |
q5690 | Marvel.get_characters | train | def get_characters(self, *args, **kwargs):
"""Fetches lists of comic characters with optional filters.
get /v1/public/characters/{characterId}
:returns: CharacterDataWrapper
>>> m = Marvel(public_key, private_key)
>>> cdw = m.get_characters(orderBy="name,-modified", limit="5", offset="15")
>>> print cdw.data.count
1401
>>> for result in cdw.data.results:
... print result.name
Aginar
Air-Walker (Gabriel Lan)
| python | {
"resource": ""
} |
q5691 | Marvel.get_comics | train | def get_comics(self, *args, **kwargs):
"""
Fetches list of comics.
get /v1/public/comics
:returns: ComicDataWrapper
>>> m = Marvel(public_key, private_key)
>>> cdw = m.get_comics(orderBy="issueNumber,-modified", limit="10", offset="15")
>>> print cdw.data.count
10
| python | {
"resource": ""
} |
q5692 | Marvel.get_creator | train | def get_creator(self, id):
"""Fetches a single creator by id.
get /v1/public/creators/{creatorId}
:param id: ID of Creator
:type params: int
:returns: CreatorDataWrapper
>>> m = Marvel(public_key, private_key)
>>> cdw = m.get_creator(30)
>>> print cdw.data.count
| python | {
"resource": ""
} |
q5693 | Marvel.get_event | train | def get_event(self, id):
"""Fetches a single event by id.
get /v1/public/event/{eventId}
:param id: ID of Event
:type params: int
:returns: EventDataWrapper
>>> m = Marvel(public_key, private_key)
>>> response = m.get_event(253)
>>> print response.data.result.title
| python | {
"resource": ""
} |
q5694 | Marvel.get_single_series | train | def get_single_series(self, id):
"""Fetches a single comic series by id.
get /v1/public/series/{seriesId}
:param id: ID of Series
:type params: int
:returns: SeriesDataWrapper
>>> m = Marvel(public_key, private_key)
>>> response = m.get_single_series(12429)
| python | {
"resource": ""
} |
q5695 | Marvel.get_story | train | def get_story(self, id):
"""Fetches a single story by id.
get /v1/public/stories/{storyId}
:param id: ID of Story
:type params: int
:returns: StoryDataWrapper
>>> m = Marvel(public_key, private_key)
>>> response = m.get_story(29)
>>> print response.data.result.title
Caught in the heart | python | {
"resource": ""
} |
q5696 | Marvel.get_stories | train | def get_stories(self, *args, **kwargs):
"""Fetches lists of stories.
get /v1/public/stories
:returns: StoryDataWrapper
>>> #Find all the stories that involved both Hulk and Wolverine
>>> #hulk's id: 1009351
>>> #wolverine's id: 1009718
>>> m = Marvel(public_key, private_key)
>>> response = m.get_stories(characters="1009351,1009718")
>>> | python | {
"resource": ""
} |
q5697 | Story.get_creators | train | def get_creators(self, *args, **kwargs):
"""
Returns a full CreatorDataWrapper object for this story.
/stories/{storyId}/creators
| python | {
"resource": ""
} |
q5698 | Story.get_characters | train | def get_characters(self, *args, **kwargs):
"""
Returns a full CharacterDataWrapper object for this story.
/stories/{storyId}/characters
| python | {
"resource": ""
} |
q5699 | ffmpeg_version | train | def ffmpeg_version():
"""Returns the available ffmpeg version
Returns
----------
version : str
version number as string
"""
cmd = [
'ffmpeg',
'-version'
]
output = sp.check_output(cmd)
aac_codecs = [
x for x in
output.splitlines() if "ffmpeg version " in str(x) | python | {
"resource": ""
} |
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