repo stringlengths 7 55 | path stringlengths 4 223 | url stringlengths 87 315 | code stringlengths 75 104k | code_tokens list | docstring stringlengths 1 46.9k | docstring_tokens list | language stringclasses 1
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UCBerkeleySETI/blimpy | blimpy/file_wrapper.py | https://github.com/UCBerkeleySETI/blimpy/blob/b8822d3e3e911944370d84371a91fa0c29e9772e/blimpy/file_wrapper.py#L282-L291 | def isheavy(self):
""" Check if the current selection is too large.
"""
selection_size_bytes = self._calc_selection_size()
if selection_size_bytes > self.MAX_DATA_ARRAY_SIZE:
return True
else:
return False | [
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carlosp420/primer-designer | primer_designer/utils.py | https://github.com/carlosp420/primer-designer/blob/586cb7fecf41fedbffe6563c8b79a3156c6066ae/primer_designer/utils.py#L4-L15 | def is_fasta(filename):
"""Check if filename is FASTA based on extension
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Boolean
"""
if re.search("\.fa*s[ta]*$", filename, flags=re.I):
return True
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SBRG/ssbio | ssbio/core/protein.py | https://github.com/SBRG/ssbio/blob/e9449e64ffc1a1f5ad07e5849aa12a650095f8a2/ssbio/core/protein.py#L1917-L2175 | def set_representative_structure(self, seq_outdir=None, struct_outdir=None, pdb_file_type=None,
engine='needle', always_use_homology=False, rez_cutoff=0.0,
seq_ident_cutoff=0.5, allow_missing_on_termini=0.2,
allow_mutants=True, allow_deletions=False,
allow_insertions=False, allow_unresolved=True,
clean=True, keep_chemicals=None, skip_large_structures=False,
force_rerun=False):
"""Set a representative structure from a structure in the structures attribute.
Each gene can have a combination of the following, which will be analyzed to set a representative structure.
* Homology model(s)
* Ranked PDBs
* BLASTed PDBs
If the ``always_use_homology`` flag is true, homology models are always set as representative when they exist.
If there are multiple homology models, we rank by the percent sequence coverage.
Args:
seq_outdir (str): Path to output directory of sequence alignment files, must be set if Protein directory
was not created initially
struct_outdir (str): Path to output directory of structure files, must be set if Protein directory
was not created initially
pdb_file_type (str): ``pdb``, ``mmCif``, ``xml``, ``mmtf`` - file type for files downloaded from the PDB
engine (str): ``biopython`` or ``needle`` - which pairwise alignment program to use.
``needle`` is the standard EMBOSS tool to run pairwise alignments.
``biopython`` is Biopython's implementation of needle. Results can differ!
always_use_homology (bool): If homology models should always be set as the representative structure
rez_cutoff (float): Resolution cutoff, in Angstroms (only if experimental structure)
seq_ident_cutoff (float): Percent sequence identity cutoff, in decimal form
allow_missing_on_termini (float): Percentage of the total length of the reference sequence which will be ignored
when checking for modifications. Example: if 0.1, and reference sequence is 100 AA, then only residues
5 to 95 will be checked for modifications.
allow_mutants (bool): If mutations should be allowed or checked for
allow_deletions (bool): If deletions should be allowed or checked for
allow_insertions (bool): If insertions should be allowed or checked for
allow_unresolved (bool): If unresolved residues should be allowed or checked for
clean (bool): If structure should be cleaned
keep_chemicals (str, list): Keep specified chemical names if structure is to be cleaned
skip_large_structures (bool): Default False -- currently, large structures can't be saved as a PDB file even
if you just want to save a single chain, so Biopython will throw an error when trying to do so. As an
alternative, if a large structure is selected as representative, the pipeline will currently point to it
and not clean it. If you don't want this to happen, set this to true.
force_rerun (bool): If sequence to structure alignment should be rerun
Returns:
StructProp: Representative structure from the list of structures. This is a not a map to the original
structure, it is copied and optionally cleaned from the original one.
Todo:
- Remedy large structure representative setting
"""
log.debug('{}: setting representative structure'.format(self.id))
if len(self.structures) == 0:
log.debug('{}: no structures available'.format(self.id))
return None
if not self.representative_sequence:
log.error('{}: no representative sequence to compare structures to'.format(self.id))
return None
if self.representative_structure and not force_rerun:
log.debug('{}: representative structure already set'.format(self.id))
return self.representative_structure
if self.representative_structure and force_rerun:
log.debug('{}: representative structure previously set, unsetting'.format(self.id))
self.representative_structure = None
if not pdb_file_type:
pdb_file_type = self.pdb_file_type
if not seq_outdir:
seq_outdir = self.sequence_dir
if not seq_outdir:
raise ValueError('Sequence output directory must be specified')
if not struct_outdir:
struct_outdir = self.structure_dir
if not struct_outdir:
raise ValueError('Structure output directory must be specified')
has_homology = False
has_pdb = False
use_homology = False
use_pdb = False
if self.num_structures_homology > 0:
has_homology = True
if self.num_structures_experimental > 0:
has_pdb = True
# If we mark to always use homology, use it if it exists
if always_use_homology:
if has_homology:
use_homology = True
elif has_pdb:
use_pdb = True
# If we don't always want to use homology, use PDB if it exists
else:
if has_homology and has_pdb:
use_pdb = True
use_homology = True
elif has_homology and not has_pdb:
use_homology = True
elif has_pdb and not has_homology:
use_pdb = True
if use_pdb:
# Put PDBs through QC/QA
all_pdbs = self.get_experimental_structures()
log.debug('{}: checking quality of {} experimental structures'.format(self.id, len(all_pdbs)))
for pdb in all_pdbs:
# Download the structure and parse it
# This will add all chains to the mapped_chains attribute if there are none
try:
pdb.download_structure_file(outdir=struct_outdir, file_type=pdb_file_type, force_rerun=force_rerun, load_header_metadata=True)
except (requests.exceptions.HTTPError, URLError):
log.error('{}: structure file could not be downloaded in {} format'.format(pdb, pdb_file_type))
continue
# TODO: add try/except to download cif file as fallback like below?
if rez_cutoff and pdb.resolution:
if pdb.resolution > rez_cutoff:
log.debug('{}: structure does not meet experimental resolution cutoff'.format(pdb, pdb_file_type))
continue
# TODO: clean up these try/except things
try:
self.align_seqprop_to_structprop(seqprop=self.representative_sequence,
structprop=pdb,
outdir=seq_outdir,
engine=engine,
parse=True,
force_rerun=force_rerun)
except (PDBConstructionException, ExtraData, KeyError) as e:
log.error('Protein {}, PDB {}: unable to parse structure file as {}. Falling back to mmCIF format.'.format(self.id, pdb, pdb_file_type))
print(e)
# Fall back to using mmCIF file if structure cannot be parsed
try:
pdb.download_structure_file(outdir=struct_outdir, file_type='mmCif',
force_rerun=force_rerun, load_header_metadata=True)
except (requests.exceptions.HTTPError, URLError):
log.error('Protein {}, PDB {}: structure file could not be downloaded'.format(self.id, pdb))
continue
try:
self.align_seqprop_to_structprop(seqprop=self.representative_sequence,
structprop=pdb,
outdir=seq_outdir,
engine=engine,
parse=True,
force_rerun=force_rerun)
except (PDBConstructionException, KeyError) as e:
log.error('Protein {}, PDB {}: unable to parse structure file as {}.'.format(self.id, pdb, 'mmCif'))
print(e)
continue
best_chain = self.find_representative_chain(seqprop=self.representative_sequence,
structprop=pdb,
seq_ident_cutoff=seq_ident_cutoff,
allow_missing_on_termini=allow_missing_on_termini,
allow_mutants=allow_mutants, allow_deletions=allow_deletions,
allow_insertions=allow_insertions, allow_unresolved=allow_unresolved)
if best_chain:
try:
self._representative_structure_setter(structprop=pdb,
clean=clean,
out_suffix='-{}_clean'.format(best_chain),
keep_chain=best_chain,
keep_chemicals=keep_chemicals,
outdir=struct_outdir,
force_rerun=force_rerun)
except TypeError:
if skip_large_structures == True:
log.warning("{}: unable to save large PDB {}-{} in PDB file format, trying next "
"structure.".format(self.id, pdb.id, best_chain))
continue
else:
log.warning("{}: unable to save large PDB {}-{} in PDB file format, setting original "
"structure as representative. Set skip_large_structures=True if you don't "
"want this to happen".format(self.id, pdb.id, best_chain))
self.representative_structure = pdb
except Exception as e:
# Try force rerunning first if there exists a corrupt clean PDB file
try:
log.debug('{}: unknown error with {}, trying force_rerun first'.format(self.id, pdb.id))
self._representative_structure_setter(structprop=pdb,
clean=clean,
out_suffix='-{}_clean'.format(best_chain),
keep_chain=best_chain,
keep_chemicals=keep_chemicals,
outdir=struct_outdir,
force_rerun=True)
except Exception as e:
# TODO: inspect causes of these errors - most common is Biopython PDBParser error
logging.exception("{}: unknown error with PDB ID {}".format(self.id, pdb.id))
print(e)
continue
log.debug('{}-{}: set as representative structure'.format(pdb.id, best_chain))
pdb.reset_chain_seq_records()
return self.representative_structure
else:
pdb.reset_chain_seq_records()
else:
log.debug('{}: no experimental structures meet cutoffs'.format(self.id))
# If we are to use homology, save its information in the representative structure field
if use_homology:
log.debug('{}: checking quality of homology models'.format(self.id))
all_models = self.get_homology_models()
# TODO: homology models are not ordered in any other way other than how they are loaded,
# rethink this for multiple homology models
for homology in all_models:
if not homology.structure_file:
log.debug('{}: no homology structure file'.format(self.id))
continue
self.align_seqprop_to_structprop(seqprop=self.representative_sequence,
structprop=homology,
outdir=seq_outdir,
engine=engine,
parse=True,
force_rerun=force_rerun)
best_chain = self.find_representative_chain(seqprop=self.representative_sequence,
structprop=homology,
seq_ident_cutoff=seq_ident_cutoff,
allow_missing_on_termini=allow_missing_on_termini,
allow_mutants=allow_mutants,
allow_deletions=allow_deletions,
allow_insertions=allow_insertions,
allow_unresolved=allow_unresolved)
if best_chain:
# If chain ID is empty (some homology models are like that), use ID "X"
if not best_chain.strip():
best_chain = 'X'
try:
self._representative_structure_setter(structprop=homology,
# new_id='{}-{}'.format(homology.id, best_chain), # 170906 Deprecated use of new_id
clean=True,
out_suffix='-{}_clean'.format(best_chain),
keep_chain=best_chain,
outdir=struct_outdir,
force_rerun=force_rerun)
except:
# TODO: inspect causes of these errors - most common is Biopython PDBParser error
logging.exception("Unknown error with homology model {}".format(homology.id))
continue
log.debug('{}-{}: set as representative structure'.format(homology.id, best_chain))
homology.reset_chain_seq_records()
return self.representative_structure
else:
homology.reset_chain_seq_records()
log.warning('{}: no structures meet quality checks'.format(self.id))
return None | [
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struct_outdir (str): Path to output directory of structure files, must be set if Protein directory
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pdb_file_type (str): ``pdb``, ``mmCif``, ``xml``, ``mmtf`` - file type for files downloaded from the PDB
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allow_insertions (bool): If insertions should be allowed or checked for
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standage/tag | tag/select.py | https://github.com/standage/tag/blob/94686adf57115cea1c5235e99299e691f80ba10b/tag/select.py#L13-L37 | def features(entrystream, type=None, traverse=False):
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saltstack/salt | salt/_compat.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/_compat.py#L90-L103 | def native_(s, encoding='latin-1', errors='strict'):
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Python 2: If ``s`` is an instance of ``text_type``, return
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GNS3/gns3-server | gns3server/controller/gns3vm/virtualbox_gns3_vm.py | https://github.com/GNS3/gns3-server/blob/a221678448fb5d24e977ef562f81d56aacc89ab1/gns3server/controller/gns3vm/virtualbox_gns3_vm.py#L304-L312 | def set_ram(self, ram):
"""
Set the RAM amount for the GNS3 VM.
:param ram: amount of memory
"""
yield from self._execute("modifyvm", [self._vmname, "--memory", str(ram)], timeout=3)
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drj11/pypng | code/iccp.py | https://github.com/drj11/pypng/blob/b8220ca9f58e4c5bc1d507e713744fcb8c049225/code/iccp.py#L147-L157 | def write(self, out):
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ChrisBeaumont/smother | smother/cli.py | https://github.com/ChrisBeaumont/smother/blob/65d1ea6ae0060d213b0dcbb983c5aa8e7fee07bb/smother/cli.py#L48-L53 | def lookup(ctx, path):
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pantsbuild/pants | src/python/pants/util/contextutil.py | https://github.com/pantsbuild/pants/blob/b72e650da0df685824ffdcc71988b8c282d0962d/src/python/pants/util/contextutil.py#L194-L206 | def temporary_file_path(root_dir=None, cleanup=True, suffix='', permissions=None):
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:API: public
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quintusdias/glymur | glymur/codestream.py | https://github.com/quintusdias/glymur/blob/8b8fb091130fff00f1028dc82219e69e3f9baf6d/glymur/codestream.py#L367-L406 | def _parse_cod_segment(cls, fptr):
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Open file object.
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The current COD segment.
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ansible/tower-cli | tower_cli/cli/resource.py | https://github.com/ansible/tower-cli/blob/a2b151fed93c47725018d3034848cb3a1814bed7/tower_cli/cli/resource.py#L59-L95 | def _auto_help_text(self, help_text):
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help_text = help_text.replace('keyword argument', 'option')
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for match in re.findall(r'`([\w_]+)`', help_text):
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hcpl/xkbgroup | xkbgroup/core.py | https://github.com/hcpl/xkbgroup/blob/fcf4709a3c8221e0cdf62c09e5cccda232b0104c/xkbgroup/core.py#L228-L239 | def groups_data(self):
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PMEAL/OpenPNM | openpnm/algorithms/GenericTransport.py | https://github.com/PMEAL/OpenPNM/blob/0547b5724ffedc0a593aae48639d36fe10e0baed/openpnm/algorithms/GenericTransport.py#L244-L298 | def _set_BC(self, pores, bctype, bcvalues=None, mode='merge'):
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bctype = self._parse_mode(bctype, allowed=['value', 'rate'],
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mode = self._parse_mode(mode, allowed=['merge', 'overwrite', 'remove'],
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pores = self._parse_indices(pores)
values = np.array(bcvalues)
if values.size > 1 and values.size != pores.size:
raise Exception('The number of boundary values must match the ' +
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if ('pore.bc_'+bctype not in self.keys()) or (mode == 'overwrite'):
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inasafe/inasafe | safe/messaging/item/abstract_list.py | https://github.com/inasafe/inasafe/blob/831d60abba919f6d481dc94a8d988cc205130724/safe/messaging/item/abstract_list.py#L50-L71 | def add(self, item):
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brocade/pynos | pynos/versions/ver_6/ver_6_0_1/yang/brocade_mac_address_table.py | https://github.com/brocade/pynos/blob/bd8a34e98f322de3fc06750827d8bbc3a0c00380/pynos/versions/ver_6/ver_6_0_1/yang/brocade_mac_address_table.py#L265-L279 | def get_mac_address_table_input_request_type_get_next_request_last_mac_address_details_last_mac_address(self, **kwargs):
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pantsbuild/pants | src/python/pants/scm/git.py | https://github.com/pantsbuild/pants/blob/b72e650da0df685824ffdcc71988b8c282d0962d/src/python/pants/scm/git.py#L207-L209 | def merge_base(self, left='master', right='HEAD'):
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uw-it-aca/uw-restclients | restclients/grad/committee.py | https://github.com/uw-it-aca/uw-restclients/blob/e12dcd32bf5296b6ebdf71798031594afb7852cb/restclients/grad/committee.py#L31-L68 | def _process_json(data):
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DataDog/integrations-core | sqlserver/datadog_checks/sqlserver/sqlserver.py | https://github.com/DataDog/integrations-core/blob/ebd41c873cf9f97a8c51bf9459bc6a7536af8acd/sqlserver/datadog_checks/sqlserver/sqlserver.py#L365-L389 | def _conn_string_odbc(self, db_key, instance=None, conn_key=None, db_name=None):
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jpweiser/cuts | cuts/main.py | https://github.com/jpweiser/cuts/blob/5baf7f2e145045942ee8dcaccbc47f8f821fcb56/cuts/main.py#L34-L60 | def _parse_args(args):
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juju/charm-helpers | charmhelpers/contrib/openstack/cert_utils.py | https://github.com/juju/charm-helpers/blob/aa785c40c3b7a8c69dbfbc7921d6b9f30142e171/charmhelpers/contrib/openstack/cert_utils.py#L244-L263 | def get_requests_for_local_unit(relation_name=None):
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proteanhq/protean | src/protean/services/email/utils.py | https://github.com/proteanhq/protean/blob/0e29873f4aa634aa93cc08ed675dd749c7ed4b0f/src/protean/services/email/utils.py#L8-L15 | def get_connection(backend=None, fail_silently=False, **kwargs):
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Morrolan/surrealism | surrealism.py | https://github.com/Morrolan/surrealism/blob/7fdd2eae534410df16ee1f9d7e9bb77aa10decab/surrealism.py#L814-L841 | def __check_spaces(sentence):
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numenta/htmresearch | htmresearch/support/union_temporal_pooler_monitor_mixin.py | https://github.com/numenta/htmresearch/blob/70c096b09a577ea0432c3f3bfff4442d4871b7aa/htmresearch/support/union_temporal_pooler_monitor_mixin.py#L291-L308 | def mmGetPlotStability(self, title="Stability", showReset=False,
resetShading=0.25):
"""
Returns plot of the overlap metric between union SDRs within a sequence.
@param title an optional title for the figure
@return (Plot) plot
"""
plot = Plot(self, title)
self._mmComputeSequenceRepresentationData()
data = self._mmData["stabilityConfusion"]
plot.addGraph(sorted(data, reverse=True),
position=211,
xlabel="Time steps", ylabel="Overlap")
plot.addHistogram(data,
position=212,
bins=100,
xlabel="Overlap", ylabel="# time steps")
return plot | [
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YeoLab/anchor | anchor/infotheory.py | https://github.com/YeoLab/anchor/blob/1f9c9d6d30235b1e77b945e6ef01db5a0e55d53a/anchor/infotheory.py#L190-L215 | def binify_and_jsd(df1, df2, bins, pair=None):
"""Binify and calculate jensen-shannon divergence between two dataframes
Parameters
----------
df1, df2 : pandas.DataFrames
Dataframes to calculate JSD between columns of. Must have overlapping
column names
bins : array-like
Bins to use for transforming df{1,2} into probability distributions
pair : str, optional
Name of the pair to save as the name of the series
Returns
-------
divergence : pandas.Series
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"""
binned1 = binify(df1, bins=bins).dropna(how='all', axis=1)
binned2 = binify(df2, bins=bins).dropna(how='all', axis=1)
binned1, binned2 = binned1.align(binned2, axis=1, join='inner')
series = np.sqrt(jsd(binned1, binned2))
series.name = pair
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Bins to use for transforming df{1,2} into probability distributions
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brocade/pynos | pynos/versions/ver_6/ver_6_0_1/yang/brocade_aaa.py | https://github.com/brocade/pynos/blob/bd8a34e98f322de3fc06750827d8bbc3a0c00380/pynos/versions/ver_6/ver_6_0_1/yang/brocade_aaa.py#L631-L642 | def rule_action(self, **kwargs):
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zhanglab/psamm | psamm/balancecheck.py | https://github.com/zhanglab/psamm/blob/dc427848c4f9d109ca590f0afa024c63b685b3f4/psamm/balancecheck.py#L91-L117 | def formula_balance(model):
"""Calculate formula compositions for each reaction.
Call :func:`reaction_formula` for each reaction.
Yield (reaction, result) pairs, where result has two formula compositions
or `None`.
Args:
model: :class:`psamm.datasource.native.NativeModel`.
"""
# Mapping from compound id to formula
compound_formula = {}
for compound in model.compounds:
if compound.formula is not None:
try:
f = Formula.parse(compound.formula).flattened()
compound_formula[compound.id] = f
except ParseError as e:
msg = 'Error parsing formula for compound {}:\n{}\n{}'.format(
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if e.indicator is not None:
msg += '\n{}'.format(e.indicator)
logger.warning(msg)
for reaction in model.reactions:
yield reaction, reaction_formula(reaction.equation, compound_formula) | [
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jf-parent/brome | brome/runner/local_runner.py | https://github.com/jf-parent/brome/blob/784f45d96b83b703dd2181cb59ca8ea777c2510e/brome/runner/local_runner.py#L43-L78 | def run(self):
"""Run the test batch
"""
self.info_log("The test batch is ready.")
self.executed_tests = []
for test in self.tests:
localhost_instance = LocalhostInstance(
runner=self,
browser_config=self.browser_config,
test_name=test.Test.name
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localhost_instance.startup()
with DbSessionContext(BROME_CONFIG['database']['mongo_database_name']) as session: # noqa
test_batch = session.query(Testbatch)\
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test_batch.total_executing_tests = 1
session.save(test_batch, safe=True)
test_ = test.Test(
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browser_config=self.browser_config,
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test_batch_id=self.test_batch_id,
localhost_instance=localhost_instance,
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test_.execute()
self.executed_tests.append(test_)
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danilobellini/audiolazy | examples/play_bach_choral.py | https://github.com/danilobellini/audiolazy/blob/dba0a278937909980ed40b976d866b8e97c35dee/examples/play_bach_choral.py#L33-L40 | def ks_synth(freq):
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"""
ks_mem = (sum(lz.sinusoid(x * freq) for x in [1, 3, 9]) +
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gem/oq-engine | openquake/hazardlib/gsim/abrahamson_2014.py | https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/abrahamson_2014.py#L231-L283 | def _get_hanging_wall_term(self, C, dists, rup):
"""
Compute and return hanging wall model term, see page 1038.
"""
if rup.dip == 90.0:
return np.zeros_like(dists.rx)
else:
Fhw = np.zeros_like(dists.rx)
Fhw[dists.rx > 0] = 1.
# Compute taper t1
T1 = np.ones_like(dists.rx)
T1 *= 60./45. if rup.dip <= 30. else (90.-rup.dip)/45.0
# Compute taper t2 (eq 12 at page 1039) - a2hw set to 0.2 as
# indicated at page 1041
T2 = np.zeros_like(dists.rx)
a2hw = 0.2
if rup.mag > 6.5:
T2 += (1. + a2hw * (rup.mag - 6.5))
elif rup.mag > 5.5:
T2 += (1. + a2hw * (rup.mag - 6.5) - (1. - a2hw) *
(rup.mag - 6.5)**2)
else:
T2 *= 0.
# Compute taper t3 (eq. 13 at page 1039) - r1 and r2 specified at
# page 1040
T3 = np.zeros_like(dists.rx)
r1 = rup.width * np.cos(np.radians(rup.dip))
r2 = 3. * r1
#
idx = dists.rx < r1
T3[idx] = (np.ones_like(dists.rx)[idx] * self.CONSTS['h1'] +
self.CONSTS['h2'] * (dists.rx[idx] / r1) +
self.CONSTS['h3'] * (dists.rx[idx] / r1)**2)
#
idx = ((dists.rx >= r1) & (dists.rx <= r2))
T3[idx] = 1. - (dists.rx[idx] - r1) / (r2 - r1)
# Compute taper t4 (eq. 14 at page 1040)
T4 = np.zeros_like(dists.rx)
#
if rup.ztor <= 10.:
T4 += (1. - rup.ztor**2. / 100.)
# Compute T5 (eq 15a at page 1040) - ry1 computed according to
# suggestions provided at page 1040
T5 = np.zeros_like(dists.rx)
ry1 = dists.rx * np.tan(np.radians(20.))
#
idx = (dists.ry0 - ry1) <= 0.0
T5[idx] = 1.
#
idx = (((dists.ry0 - ry1) > 0.0) & ((dists.ry0 - ry1) < 5.0))
T5[idx] = 1. - (dists.ry0[idx] - ry1[idx]) / 5.0
# Finally, compute the hanging wall term
return Fhw*C['a13']*T1*T2*T3*T4*T5 | [
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arne-cl/discoursegraphs | src/discoursegraphs/readwrite/conll.py | https://github.com/arne-cl/discoursegraphs/blob/842f0068a3190be2c75905754521b176b25a54fb/src/discoursegraphs/readwrite/conll.py#L201-L224 | def __add_sentence_root_node(self, sent_number):
"""
adds the root node of a sentence to the graph and the list of sentences
(``self.sentences``). the node has a ``tokens` attribute, which
contains a list of the tokens (token node IDs) of this sentence.
Parameters
----------
sent_number : int
the index of the sentence within the document
Results
-------
sent_id : str
the ID of the sentence
"""
sent_id = 's{}'.format(sent_number)
self.add_node(sent_id, layers={self.ns, self.ns+':sentence'},
tokens=[])
self.add_edge(self.root, sent_id,
layers={self.ns, self.ns+':sentence'},
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self.sentences.append(sent_id)
return sent_id | [
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bitlabstudio/django-calendarium | calendarium/utils.py | https://github.com/bitlabstudio/django-calendarium/blob/cabe69eff965dff80893012fb4dfe724e995807a/calendarium/utils.py#L57-L64 | def get_occurrence(self, occ):
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pyviz/holoviews | holoviews/plotting/bokeh/sankey.py | https://github.com/pyviz/holoviews/blob/ae0dd2f3de448b0ca5e9065aabd6ef8d84c7e655/holoviews/plotting/bokeh/sankey.py#L117-L178 | def _compute_labels(self, element, data, mapping):
"""
Computes labels for the nodes and adds it to the data.
"""
if element.vdims:
edges = Dataset(element)[element[element.vdims[0].name]>0]
nodes = list(np.unique([edges.dimension_values(i) for i in range(2)]))
nodes = element.nodes.select(**{element.nodes.kdims[2].name: nodes})
else:
nodes = element
label_dim = nodes.get_dimension(self.label_index)
labels = self.labels
if label_dim and labels:
if self.label_index not in [2, None]:
self.param.warning(
"Cannot declare style mapping for 'labels' option "
"and declare a label_index; ignoring the label_index.")
elif label_dim:
labels = label_dim
if isinstance(labels, basestring):
labels = element.nodes.get_dimension(labels)
if labels is None:
text = []
if isinstance(labels, dim):
text = labels.apply(element, flat=True)
else:
text = element.nodes.dimension_values(labels)
text = [labels.pprint_value(v) for v in text]
value_dim = element.vdims[0]
text_labels = []
for i, node in enumerate(element._sankey['nodes']):
if len(text):
label = text[i]
else:
label = ''
if self.show_values:
value = value_dim.pprint_value(node['value'])
if label:
label = '%s - %s' % (label, value)
else:
label = value
if value_dim.unit:
label += ' %s' % value_dim.unit
if label:
text_labels.append(label)
ys = nodes.dimension_values(1)
nodes = element._sankey['nodes']
if nodes:
offset = (nodes[0]['x1']-nodes[0]['x0'])/4.
else:
offset = 0
if self.label_position == 'right':
xs = np.array([node['x1'] for node in nodes])+offset
else:
xs = np.array([node['x0'] for node in nodes])-offset
data['text_1'] = dict(x=xs, y=ys, text=[str(l) for l in text_labels])
align = 'left' if self.label_position == 'right' else 'right'
mapping['text_1'] = dict(text='text', x='x', y='y', text_baseline='middle', text_align=align) | [
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redhat-cip/python-dciclient | dciclient/v1/shell_commands/topic.py | https://github.com/redhat-cip/python-dciclient/blob/a4aa5899062802bbe4c30a075d8447f8d222d214/dciclient/v1/shell_commands/topic.py#L57-L76 | def create(context, name, component_types, active, product_id, data):
"""create(context, name, component_types, active, product_id, data)
Create a topic.
>>> dcictl topic-create [OPTIONS]
:param string name: Name of the topic [required]
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:param string product_id: The product the topic belongs to
:param string data: JSON data of the topic
"""
if component_types:
component_types = component_types.split(',')
state = utils.active_string(active)
result = topic.create(context, name=name, component_types=component_types,
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apache/incubator-mxnet | python/mxnet/contrib/onnx/mx2onnx/export_onnx.py | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/contrib/onnx/mx2onnx/export_onnx.py#L86-L92 | def convert_layer(node, **kwargs):
"""Convert MXNet layer to ONNX"""
op = str(node["op"])
if op not in MXNetGraph.registry_:
raise AttributeError("No conversion function registered for op type %s yet." % op)
convert_func = MXNetGraph.registry_[op]
return convert_func(node, **kwargs) | [
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onicagroup/runway | runway/module/terraform.py | https://github.com/onicagroup/runway/blob/3f3549ec3bf6e39b9f27d9738a1847f3a4369e7f/runway/module/terraform.py#L58-L64 | def get_backend_init_list(backend_vals):
"""Turn backend config dict into command line items."""
cmd_list = []
for (key, val) in backend_vals.items():
cmd_list.append('-backend-config')
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Hackerfleet/hfos | hfos/tool/user.py | https://github.com/Hackerfleet/hfos/blob/b6df14eacaffb6be5c844108873ff8763ec7f0c9/hfos/tool/user.py#L207-L225 | def add_role(ctx, role):
"""Grant a role to an existing user"""
if role is None:
log('Specify the role with --role')
return
if ctx.obj['username'] is None:
log('Specify the username with --username')
return
change_user = ctx.obj['db'].objectmodels['user'].find_one({
'name': ctx.obj['username']
})
if role not in change_user.roles:
change_user.roles.append(role)
change_user.save()
log('Done')
else:
log('User already has that role!', lvl=warn) | [
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PmagPy/PmagPy | dialogs/magic_grid2.py | https://github.com/PmagPy/PmagPy/blob/c7984f8809bf40fe112e53dcc311a33293b62d0b/dialogs/magic_grid2.py#L276-L297 | def remove_row(self, row_num=None):
"""
Remove a row from the grid
"""
#DeleteRows(self, pos, numRows, updateLabel
if not row_num and row_num != 0:
row_num = self.GetNumberRows() - 1
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self.DeleteRows(pos=row_num, numRows=1, updateLabels=True)
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try:
self.row_labels.remove(label)
except ValueError:
# if label name hasn't been saved yet, simply truncate row_labels
self.row_labels = self.row_labels[:-1]
self.row_items.pop(row_num)
if not self.changes:
self.changes = set()
self.changes.add(-1)
# fix #s for rows edited:
self.update_changes_after_row_delete(row_num) | [
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matthewdeanmartin/jiggle_version | jiggle_version/find_version_by_package.py | https://github.com/matthewdeanmartin/jiggle_version/blob/963656a0a47b7162780a5f6c8f4b8bbbebc148f5/jiggle_version/find_version_by_package.py#L58-L74 | def guess_version_by_running_live_package(
pkg_key, default="?"
): # type: (str,str) -> Any
"""Guess the version of a pkg when pip doesn't provide it.
:param str pkg_key: key of the package
:param str default: default version to return if unable to find
:returns: version
:rtype: string
"""
try:
m = import_module(pkg_key)
except ImportError:
return default
else:
return getattr(m, "__version__", default) | [
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dims/etcd3-gateway | etcd3gw/lease.py | https://github.com/dims/etcd3-gateway/blob/ad566c29cbde135aee20cfd32e0a4815ca3b5ee6/etcd3gw/lease.py#L66-L75 | def keys(self):
"""Get the keys associated with this lease.
:return:
"""
result = self.client.post(self.client.get_url("/kv/lease/timetolive"),
json={"ID": self.id,
"keys": True})
keys = result['keys'] if 'keys' in result else []
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googledatalab/pydatalab | google/datalab/storage/_object.py | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/storage/_object.py#L69-L72 | def updated_on(self):
"""The updated timestamp of the object as a datetime.datetime."""
s = self._info.get('updated', None)
return dateutil.parser.parse(s) if s else None | [
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brocade/pynos | pynos/versions/ver_7/ver_7_1_0/yang/brocade_tunnels.py | https://github.com/brocade/pynos/blob/bd8a34e98f322de3fc06750827d8bbc3a0c00380/pynos/versions/ver_7/ver_7_1_0/yang/brocade_tunnels.py#L258-L271 | def overlay_gateway_map_vlan_vni_auto(self, **kwargs):
"""Auto Generated Code
"""
config = ET.Element("config")
overlay_gateway = ET.SubElement(config, "overlay-gateway", xmlns="urn:brocade.com:mgmt:brocade-tunnels")
name_key = ET.SubElement(overlay_gateway, "name")
name_key.text = kwargs.pop('name')
map = ET.SubElement(overlay_gateway, "map")
vlan = ET.SubElement(map, "vlan")
vni = ET.SubElement(vlan, "vni")
auto = ET.SubElement(vni, "auto")
callback = kwargs.pop('callback', self._callback)
return callback(config) | [
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funkybob/knights-templater | knights/tags.py | https://github.com/funkybob/knights-templater/blob/b15cdbaae7d824d02f7f03ca04599ae94bb759dd/knights/tags.py#L225-L245 | def use(parser, token):
'''
Counterpart to `macro`, lets you render any block/macro in place.
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args, kwargs = parser.parse_args(token)
assert isinstance(args[0], ast.Str), \
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return _create_with_scope([ast.Expr(value=action)], kwargs)
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SoCo/SoCo | soco/plugins/wimp.py | https://github.com/SoCo/SoCo/blob/671937e07d7973b78c0cbee153d4f3ad68ec48c6/soco/plugins/wimp.py#L452-L482 | def _base_body(self):
"""Return the base XML body, which has the following form:
.. code :: xml
<s:Envelope xmlns:s="http://schemas.xmlsoap.org/soap/envelope/">
<s:Header>
<credentials xmlns="http://www.sonos.com/Services/1.1">
<sessionId>self._session_id</sessionId>
<deviceId>self._serial_number</deviceId>
<deviceProvider>Sonos</deviceProvider>
</credentials>
</s:Header>
</s:Envelope>
"""
item_attrib = {
'xmlns:s': 'http://schemas.xmlsoap.org/soap/envelope/',
}
xml = XML.Element('s:Envelope', item_attrib)
# Add the Header part
XML.SubElement(xml, 's:Header')
item_attrib = {
'xmlns': 'http://www.sonos.com/Services/1.1'
}
credentials = XML.SubElement(xml[0], 'credentials', item_attrib)
XML.SubElement(credentials, 'sessionId').text = self._session_id
XML.SubElement(credentials, 'deviceId').text = self._serial_number
XML.SubElement(credentials, 'deviceProvider').text = 'Sonos'
return xml | [
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<credentials xmlns="http://www.sonos.com/Services/1.1">
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pauleveritt/kaybee | kaybee/plugins/references/base_reference.py | https://github.com/pauleveritt/kaybee/blob/a00a718aaaa23b2d12db30dfacb6b2b6ec84459c/kaybee/plugins/references/base_reference.py#L23-L33 | def get_sources(self, resources):
""" Filter resources based on which have this reference """
rtype = self.rtype # E.g. category
label = self.props.label # E.g. category1
result = [
resource
for resource in resources.values()
if is_reference_target(resource, rtype, label)
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phoebe-project/phoebe2 | phoebe/parameters/constraint.py | https://github.com/phoebe-project/phoebe2/blob/e64b8be683977064e2d55dd1b3ac400f64c3e379/phoebe/parameters/constraint.py#L126-L130 | def roche_requiv_contact_L1(q, sma, compno=1):
"""
TODO: add documentation
"""
return ConstraintParameter(q._bundle, "requiv_contact_L1(%s, %d)" % (", ".join(["{%s}" % (param.uniquetwig if hasattr(param, 'uniquetwig') else param.expr) for param in (q, sma)]), compno)) | [
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tsroten/dragonmapper | dragonmapper/transcriptions.py | https://github.com/tsroten/dragonmapper/blob/68eaf43c32725f4b4923c01284cfc0112079e8ab/dragonmapper/transcriptions.py#L417-L435 | def to_pinyin(s, accented=True):
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If *accented* is ``True``, diacritics are added to the Pinyin syllables. If
it's ``False``, numbers are used to indicate tone.
"""
identity = identify(s)
if identity == PINYIN:
if _has_accented_vowels(s):
return s if accented else accented_to_numbered(s)
else:
return numbered_to_accented(s) if accented else s
elif identity == ZHUYIN:
return zhuyin_to_pinyin(s, accented=accented)
elif identity == IPA:
return ipa_to_pinyin(s, accented=accented)
else:
raise ValueError("String is not a valid Chinese transcription.") | [
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KvasirSecurity/kvasirapi-python | KvasirAPI/jsonrpc/services.py | https://github.com/KvasirSecurity/kvasirapi-python/blob/ec8c5818bd5913f3afd150f25eaec6e7cc732f4c/KvasirAPI/jsonrpc/services.py#L89-L99 | def report_list(self, service_id=None, service_port=None, hostfilter=None):
"""
Returns a list of ports with IPs, banners and vulnerabilities (warning, slow!)
:param service_id: t_services.id
:param service_port: Port (tcp/#, udp/#, info/#)
:param hostfilter: Valid hostfilter or None
:return: { 'port': [t_hosts.f_ipaddr, t_services.f_banner,
(t_vulndata.f_vulnid, t_vulndata.f_title, t_vulndata.f_severity, t_vulndata.f_cvss_score), ...}
"""
return self.send.service_report_list(service_id, service_port, hostfilter) | [
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materialsproject/pymatgen | pymatgen/analysis/magnetism/analyzer.py | https://github.com/materialsproject/pymatgen/blob/4ca558cf72f8d5f8a1f21dfdfc0181a971c186da/pymatgen/analysis/magnetism/analyzer.py#L1170-L1207 | def magnetic_deformation(structure_A, structure_B):
"""
Calculates 'magnetic deformation proxy',
a measure of deformation (norm of finite strain)
between 'non-magnetic' (non-spin-polarized) and
ferromagnetic structures.
Adapted from Bocarsly et al. 2017,
doi: 10.1021/acs.chemmater.6b04729
:param structure_A: Structure
:param structure_B: Structure
:return:
"""
# retrieve orderings of both input structures
ordering_a = CollinearMagneticStructureAnalyzer(
structure_A, overwrite_magmom_mode="none"
).ordering
ordering_b = CollinearMagneticStructureAnalyzer(
structure_B, overwrite_magmom_mode="none"
).ordering
# get a type string, this is either 'NM-FM' for between non-magnetic
# and ferromagnetic, as in Bocarsly paper, or e.g. 'FM-AFM'
type_str = "{}-{}".format(ordering_a.value, ordering_b.value)
lattice_a = structure_A.lattice.matrix.T
lattice_b = structure_B.lattice.matrix.T
lattice_a_inv = np.linalg.inv(lattice_a)
p = np.dot(lattice_a_inv, lattice_b)
eta = 0.5 * (np.dot(p.T, p) - np.identity(3))
w, v = np.linalg.eig(eta)
deformation = 100 * (1.0 / 3.0) * np.sqrt(w[0] ** 2 + w[1] ** 2 + w[2] ** 2)
MagneticDeformation = namedtuple("MagneticDeformation", "type deformation")
return MagneticDeformation(deformation=deformation, type=type_str) | [
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pgmpy/pgmpy | pgmpy/readwrite/PomdpX.py | https://github.com/pgmpy/pgmpy/blob/9381a66aba3c3871d3ccd00672b148d17d63239e/pgmpy/readwrite/PomdpX.py#L314-L328 | def get_parameter_tbl(self, parameter):
"""
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parameter
"""
par = []
for entry in parameter.findall('Entry'):
instance = defaultdict(list)
instance['Instance'] = entry.find('Instance').text.split()
if entry.find('ProbTable') is None:
instance['ValueTable'] = entry.find('ValueTable').text.split()
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instance['ProbTable'] = entry.find('ProbTable').text.split()
par.append(instance)
return par | [
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squeaky-pl/japronto | misc/cpu.py | https://github.com/squeaky-pl/japronto/blob/a526277a2f59100388c9f39d4ca22bfb4909955b/misc/cpu.py#L105-L151 | def dump():
"""
dump function
"""
try:
sensors = subprocess.check_output('sensors').decode('utf-8')
except (FileNotFoundError, subprocess.CalledProcessError):
print("Couldn't read CPU temp")
else:
cores = []
for line in sensors.splitlines():
if line.startswith('Core '):
core, rest = line.split(':')
temp = rest.strip().split()[0]
cores.append((core, temp))
for core, temp in cores:
print(core + ':', temp)
cpu_number = 0
while True:
try:
_file = open(
CPU_PREFIX + 'cpu{}/cpufreq/scaling_governor'.format(cpu_number))
except:
break
print('Core ' + str(cpu_number) + ':', _file.read().strip(), end=', ')
_file.close()
try:
_file = open(
CPU_PREFIX + 'cpu{}/cpufreq/scaling_cur_freq'.format(cpu_number))
except:
break
freq = round(int(_file.read()) / 10 ** 6, 2)
print(freq, 'GHz')
cpu_number += 1 | [
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] | python | train | 22.531915 |
saltstack/salt | salt/modules/pkg_resource.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/pkg_resource.py#L180-L214 | def version(*names, **kwargs):
'''
Common interface for obtaining the version of installed packages.
CLI Example:
.. code-block:: bash
salt '*' pkg_resource.version vim
salt '*' pkg_resource.version foo bar baz
salt '*' pkg_resource.version 'python*'
'''
ret = {}
versions_as_list = \
salt.utils.data.is_true(kwargs.pop('versions_as_list', False))
pkg_glob = False
if names:
pkgs = __salt__['pkg.list_pkgs'](versions_as_list=True, **kwargs)
for name in names:
if '*' in name:
pkg_glob = True
for match in fnmatch.filter(pkgs, name):
ret[match] = pkgs.get(match, [])
else:
ret[name] = pkgs.get(name, [])
if not versions_as_list:
__salt__['pkg_resource.stringify'](ret)
# Return a string if no globbing is used, and there is one item in the
# return dict
if len(ret) == 1 and not pkg_glob:
try:
return next(six.itervalues(ret))
except StopIteration:
return ''
return ret | [
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.. code-block:: bash
salt '*' pkg_resource.version vim
salt '*' pkg_resource.version foo bar baz
salt '*' pkg_resource.version 'python*' | [
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nvdv/vprof | vprof/memory_profiler.py | https://github.com/nvdv/vprof/blob/4c3ff78f8920ab10cb9c00b14143452aa09ff6bb/vprof/memory_profiler.py#L65-L75 | def _format_obj_count(objects):
"""Formats object count."""
result = []
regex = re.compile(r'<(?P<type>\w+) \'(?P<name>\S+)\'>')
for obj_type, obj_count in objects.items():
if obj_count != 0:
match = re.findall(regex, repr(obj_type))
if match:
obj_type, obj_name = match[0]
result.append(("%s %s" % (obj_type, obj_name), obj_count))
return sorted(result, key=operator.itemgetter(1), reverse=True) | [
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] | python | test | 42.727273 |
njouanin/repool | repool/pool.py | https://github.com/njouanin/repool/blob/27102cf84cb382c0b2d935f8b8651aa7f8c2777e/repool/pool.py#L123-L133 | def release_pool(self):
"""Release pool and all its connection"""
if self._current_acquired > 0:
raise PoolException("Can't release pool: %d connection(s) still acquired" % self._current_acquired)
while not self._pool.empty():
conn = self.acquire()
conn.close()
if self._cleanup_thread is not None:
self._thread_event.set()
self._cleanup_thread.join()
self._pool = None | [
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... | Release pool and all its connection | [
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saltstack/salt | salt/modules/git.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/git.py#L63-L110 | def _config_getter(get_opt,
key,
value_regex=None,
cwd=None,
user=None,
password=None,
ignore_retcode=False,
output_encoding=None,
**kwargs):
'''
Common code for config.get_* functions, builds and runs the git CLI command
and returns the result dict for the calling function to parse.
'''
kwargs = salt.utils.args.clean_kwargs(**kwargs)
global_ = kwargs.pop('global', False)
if kwargs:
salt.utils.args.invalid_kwargs(kwargs)
if cwd is None:
if not global_:
raise SaltInvocationError(
'\'cwd\' argument required unless global=True'
)
else:
cwd = _expand_path(cwd, user)
if get_opt == '--get-regexp':
if value_regex is not None \
and not isinstance(value_regex, six.string_types):
value_regex = six.text_type(value_regex)
else:
# Ignore value_regex
value_regex = None
command = ['git', 'config']
command.extend(_which_git_config(global_, cwd, user, password,
output_encoding=output_encoding))
command.append(get_opt)
command.append(key)
if value_regex is not None:
command.append(value_regex)
return _git_run(command,
cwd=cwd,
user=user,
password=password,
ignore_retcode=ignore_retcode,
failhard=False,
output_encoding=output_encoding) | [
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toumorokoshi/sprinter | sprinter/lib/request.py | https://github.com/toumorokoshi/sprinter/blob/846697a7a087e69c61d075232e754d6975a64152/sprinter/lib/request.py#L34-L39 | def cleaned_request(request_type, *args, **kwargs):
""" Perform a cleaned requests request """
s = requests.Session()
# this removes netrc checking
s.trust_env = False
return s.request(request_type, *args, **kwargs) | [
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] | python | train | 38.333333 |
praekeltfoundation/molo | molo/core/api/endpoints.py | https://github.com/praekeltfoundation/molo/blob/57702fda4fab261d67591415f7d46bc98fa38525/molo/core/api/endpoints.py#L84-L90 | def get_queryset(self):
'''
Only serve site-specific languages
'''
request = self.request
return (Languages.for_site(request.site)
.languages.filter().order_by('pk')) | [
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sdispater/pendulum | pendulum/helpers.py | https://github.com/sdispater/pendulum/blob/94d28b0d3cb524ae02361bd1ed7ea03e2e655e4e/pendulum/helpers.py#L46-L122 | def add_duration(
dt, # type: Union[datetime, date]
years=0, # type: int
months=0, # type: int
weeks=0, # type: int
days=0, # type: int
hours=0, # type: int
minutes=0, # type: int
seconds=0, # type: int
microseconds=0,
): # type: (...) -> Union[datetime, date]
"""
Adds a duration to a date/datetime instance.
"""
days += weeks * 7
if (
isinstance(dt, date)
and not isinstance(dt, datetime)
and any([hours, minutes, seconds, microseconds])
):
raise RuntimeError("Time elements cannot be added to a date instance.")
# Normalizing
if abs(microseconds) > 999999:
s = _sign(microseconds)
div, mod = divmod(microseconds * s, 1000000)
microseconds = mod * s
seconds += div * s
if abs(seconds) > 59:
s = _sign(seconds)
div, mod = divmod(seconds * s, 60)
seconds = mod * s
minutes += div * s
if abs(minutes) > 59:
s = _sign(minutes)
div, mod = divmod(minutes * s, 60)
minutes = mod * s
hours += div * s
if abs(hours) > 23:
s = _sign(hours)
div, mod = divmod(hours * s, 24)
hours = mod * s
days += div * s
if abs(months) > 11:
s = _sign(months)
div, mod = divmod(months * s, 12)
months = mod * s
years += div * s
year = dt.year + years
month = dt.month
if months:
month += months
if month > 12:
year += 1
month -= 12
elif month < 1:
year -= 1
month += 12
day = min(DAYS_PER_MONTHS[int(is_leap(year))][month], dt.day)
dt = dt.replace(year=year, month=month, day=day)
return dt + timedelta(
days=days,
hours=hours,
minutes=minutes,
seconds=seconds,
microseconds=microseconds,
) | [
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intuition-io/intuition | intuition/api/context.py | https://github.com/intuition-io/intuition/blob/cd517e6b3b315a743eb4d0d0dc294e264ab913ce/intuition/api/context.py#L86-L103 | def _normalize_data_types(self, strategy):
''' some contexts only retrieves strings, giving back right type '''
for k, v in strategy.iteritems():
if not isinstance(v, str):
# There is probably nothing to do
continue
if v == 'true':
strategy[k] = True
elif v == 'false' or v is None:
strategy[k] = False
else:
try:
if v.find('.') > 0:
strategy[k] = float(v)
else:
strategy[k] = int(v)
except ValueError:
pass | [
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ejeschke/ginga | ginga/canvas/transform.py | https://github.com/ejeschke/ginga/blob/a78c893ec6f37a837de851947e9bb4625c597915/ginga/canvas/transform.py#L219-L241 | def from_(self, win_pts):
"""Reverse of :meth:`to_`."""
# make relative to center pixel to convert from window
# graphics space to standard X/Y coordinate space
win_pts = np.asarray(win_pts, dtype=np.float)
has_z = (win_pts.shape[-1] > 2)
ctr_pt = list(self.viewer.get_center())
if has_z:
ctr_pt.append(0.0)
mpy_pt = [1.0, -1.0]
if has_z:
mpy_pt.append(1.0)
# off_x = win_x - ctr_x
# = win_x + -ctr_x
# off_y = ctr_y - win_y
# = -win_y + ctr_y
ctr_pt[0] = -ctr_pt[0]
off_pts = np.add(np.multiply(win_pts, mpy_pt), ctr_pt)
return off_pts | [
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johnnoone/json-spec | src/jsonspec/validators/draft04.py | https://github.com/johnnoone/json-spec/blob/f91981724cea0c366bd42a6670eb07bbe31c0e0c/src/jsonspec/validators/draft04.py#L32-L251 | def compile(schema, pointer, context, scope=None):
"""
Compiles schema with `JSON Schema`_ draft-04.
:param schema: obj to compile
:type schema: Mapping
:param pointer: uri of the schema
:type pointer: Pointer, str
:param context: context of this schema
:type context: Context
.. _`JSON Schema`: http://json-schema.org
"""
schm = deepcopy(schema)
scope = urljoin(scope or str(pointer), schm.pop('id', None))
if '$ref' in schema:
return ReferenceValidator(urljoin(scope, schema['$ref']), context)
attrs = {}
if 'additionalItems' in schm:
subpointer = pointer_join(pointer, 'additionalItems')
attrs['additional_items'] = schm.pop('additionalItems')
if isinstance(attrs['additional_items'], dict):
compiled = compile(attrs['additional_items'],
subpointer,
context,
scope)
attrs['additional_items'] = compiled
elif not isinstance(attrs['additional_items'], bool):
raise CompilationError('wrong type for {}'.format('additional_items'), schema) # noqa
if 'additionalProperties' in schm:
subpointer = pointer_join(pointer, 'additionalProperties')
attrs['additional_properties'] = schm.pop('additionalProperties')
if isinstance(attrs['additional_properties'], dict):
compiled = compile(attrs['additional_properties'],
subpointer,
context,
scope)
attrs['additional_properties'] = compiled
elif not isinstance(attrs['additional_properties'], bool):
raise CompilationError('wrong type for {}'.format('additional_properties'), schema) # noqa
if 'allOf' in schm:
subpointer = pointer_join(pointer, 'allOf')
attrs['all_of'] = schm.pop('allOf')
if isinstance(attrs['all_of'], (list, tuple)):
attrs['all_of'] = [compile(element, subpointer, context, scope) for element in attrs['all_of']] # noqa
else:
# should be a boolean
raise CompilationError('wrong type for {}'.format('allOf'), schema) # noqa
if 'anyOf' in schm:
subpointer = pointer_join(pointer, 'anyOf')
attrs['any_of'] = schm.pop('anyOf')
if isinstance(attrs['any_of'], (list, tuple)):
attrs['any_of'] = [compile(element, subpointer, context, scope) for element in attrs['any_of']] # noqa
else:
# should be a boolean
raise CompilationError('wrong type for {}'.format('anyOf'), schema) # noqa
if 'default' in schm:
attrs['default'] = schm.pop('default')
if 'dependencies' in schm:
attrs['dependencies'] = schm.pop('dependencies')
if not isinstance(attrs['dependencies'], dict):
raise CompilationError('dependencies must be an object', schema)
for key, value in attrs['dependencies'].items():
if isinstance(value, dict):
subpointer = pointer_join(pointer, 'dependencies', key)
attrs['dependencies'][key] = compile(value,
subpointer,
context,
scope)
elif not isinstance(value, sequence_types):
raise CompilationError('dependencies must be an array or object', schema) # noqa
if 'enum' in schm:
attrs['enum'] = schm.pop('enum')
if not isinstance(attrs['enum'], sequence_types):
raise CompilationError('enum must be a sequence', schema)
if 'exclusiveMaximum' in schm:
attrs['exclusive_maximum'] = schm.pop('exclusiveMaximum')
if not isinstance(attrs['exclusive_maximum'], bool):
raise CompilationError('exclusiveMaximum must be a boolean', schema) # noqa
if 'exclusiveMinimum' in schm:
attrs['exclusive_minimum'] = schm.pop('exclusiveMinimum')
if not isinstance(attrs['exclusive_minimum'], bool):
raise CompilationError('exclusiveMinimum must be a boolean', schema) # noqa
if 'format' in schm:
attrs['format'] = schm.pop('format')
if not isinstance(attrs['format'], string_types):
raise CompilationError('format must be a string', schema)
if 'items' in schm:
subpointer = pointer_join(pointer, 'items')
attrs['items'] = schm.pop('items')
if isinstance(attrs['items'], (list, tuple)):
# each value must be a json schema
attrs['items'] = [compile(element, subpointer, context, scope) for element in attrs['items']] # noqa
elif isinstance(attrs['items'], dict):
# value must be a json schema
attrs['items'] = compile(attrs['items'], subpointer, context, scope) # noqa
else:
# should be a boolean
raise CompilationError('wrong type for {}'.format('items'), schema) # noqa
if 'maximum' in schm:
attrs['maximum'] = schm.pop('maximum')
if not isinstance(attrs['maximum'], number_types):
raise CompilationError('maximum must be a number', schema)
if 'maxItems' in schm:
attrs['max_items'] = schm.pop('maxItems')
if not isinstance(attrs['max_items'], integer_types):
raise CompilationError('maxItems must be integer', schema)
if 'maxLength' in schm:
attrs['max_length'] = schm.pop('maxLength')
if not isinstance(attrs['max_length'], integer_types):
raise CompilationError('maxLength must be integer', schema)
if 'maxProperties' in schm:
attrs['max_properties'] = schm.pop('maxProperties')
if not isinstance(attrs['max_properties'], integer_types):
raise CompilationError('maxProperties must be integer', schema)
if 'minimum' in schm:
attrs['minimum'] = schm.pop('minimum')
if not isinstance(attrs['minimum'], number_types):
raise CompilationError('minimum must be a number', schema)
if 'minItems' in schm:
attrs['min_items'] = schm.pop('minItems')
if not isinstance(attrs['min_items'], integer_types):
raise CompilationError('minItems must be integer', schema)
if 'minLength' in schm:
attrs['min_length'] = schm.pop('minLength')
if not isinstance(attrs['min_length'], integer_types):
raise CompilationError('minLength must be integer', schema)
if 'minProperties' in schm:
attrs['min_properties'] = schm.pop('minProperties')
if not isinstance(attrs['min_properties'], integer_types):
raise CompilationError('minProperties must be integer', schema)
if 'multipleOf' in schm:
attrs['multiple_of'] = schm.pop('multipleOf')
if not isinstance(attrs['multiple_of'], number_types):
raise CompilationError('multipleOf must be a number', schema)
if 'not' in schm:
attrs['not'] = schm.pop('not')
if not isinstance(attrs['not'], dict):
raise CompilationError('not must be an object', schema)
subpointer = pointer_join(pointer, 'not')
attrs['not'] = compile(attrs['not'], subpointer, context, scope)
if 'oneOf' in schm:
subpointer = pointer_join(pointer, 'oneOf')
attrs['one_of'] = schm.pop('oneOf')
if isinstance(attrs['one_of'], (list, tuple)):
# each value must be a json schema
attrs['one_of'] = [compile(element, subpointer, context, scope) for element in attrs['one_of']] # noqa
else:
# should be a boolean
raise CompilationError('wrong type for {}'.format('oneOf'), schema)
if 'pattern' in schm:
attrs['pattern'] = schm.pop('pattern')
if not isinstance(attrs['pattern'], string_types):
raise CompilationError('pattern must be a string', schema)
if 'properties' in schm:
attrs['properties'] = schm.pop('properties')
if not isinstance(attrs['properties'], dict):
raise CompilationError('properties must be an object', schema)
for subname, subschema in attrs['properties'].items():
subpointer = pointer_join(pointer, subname)
compiled = compile(subschema, subpointer, context, scope)
attrs['properties'][subname] = compiled
if 'patternProperties' in schm:
attrs['pattern_properties'] = schm.pop('patternProperties')
if not isinstance(attrs['pattern_properties'], dict):
raise CompilationError('patternProperties must be an object', schema) # noqa
for subname, subschema in attrs['pattern_properties'].items():
subpointer = pointer_join(pointer, 'patternProperties', subname)
compiled = compile(subschema, subpointer, context, scope)
attrs['pattern_properties'][subname] = compiled
if 'required' in schm:
attrs['required'] = schm.pop('required')
if not isinstance(attrs['required'], list):
raise CompilationError('required must be a list', schema)
if len(attrs['required']) < 1:
raise CompilationError('required cannot be empty', schema)
if 'type' in schm:
attrs['type'] = schm.pop('type')
if isinstance(attrs['type'], string_types):
attrs['type'] = [attrs['type']]
elif not isinstance(attrs['type'], sequence_types):
raise CompilationError('type must be string or sequence', schema)
if 'uniqueItems' in schm:
attrs['unique_items'] = schm.pop('uniqueItems')
if not isinstance(attrs['unique_items'], bool):
raise CompilationError('type must be boolean', schema)
return Draft04Validator(attrs, str(pointer), context.formats) | [
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] | python | train | 44 |
olt/scriptine | scriptine/command.py | https://github.com/olt/scriptine/blob/f4cfea939f2f3ad352b24c5f6410f79e78723d0e/scriptine/command.py#L241-L274 | def parse_rst_params(doc):
"""
Parse a reStructuredText docstring and return a dictionary
with parameter names and descriptions.
>>> doc = '''
... :param foo: foo parameter
... foo parameter
...
... :param bar: bar parameter
... :param baz: baz parameter
... baz parameter
... baz parameter
... Some text.
... '''
>>> params = parse_rst_params(doc)
>>> params['foo']
'foo parameter foo parameter'
>>> params['bar']
'bar parameter'
>>> params['baz']
'baz parameter baz parameter baz parameter'
"""
param_re = re.compile(r"""^([ \t]*):param\
(?P<param>\w+):\
(?P<body>.*\n(\1[ \t]+\w.*\n)*)""",
re.MULTILINE|re.VERBOSE)
params = {}
for match in param_re.finditer(doc):
parts = match.groupdict()
body_lines = parts['body'].strip().split('\n')
params[parts['param']] = ' '.join(s.strip() for s in body_lines)
return params | [
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pandas-dev/pandas | pandas/io/html.py | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/html.py#L498-L518 | def _handle_hidden_tables(self, tbl_list, attr_name):
"""
Return list of tables, potentially removing hidden elements
Parameters
----------
tbl_list : list of node-like
Type of list elements will vary depending upon parser used
attr_name : str
Name of the accessor for retrieving HTML attributes
Returns
-------
list of node-like
Return type matches `tbl_list`
"""
if not self.displayed_only:
return tbl_list
return [x for x in tbl_list if "display:none" not in
getattr(x, attr_name).get('style', '').replace(" ", "")] | [
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dj-stripe/dj-stripe | djstripe/models/payment_methods.py | https://github.com/dj-stripe/dj-stripe/blob/a5308a3808cd6e2baba49482f7a699f3a8992518/djstripe/models/payment_methods.py#L442-L461 | def detach(self):
"""
Detach the source from its customer.
"""
# First, wipe default source on all customers that use this.
Customer.objects.filter(default_source=self.id).update(default_source=None)
try:
# TODO - we could use the return value of sync_from_stripe_data
# or call its internals - self._sync/_attach_objects_hook etc here
# to update `self` at this point?
self.sync_from_stripe_data(self.api_retrieve().detach())
return True
except (InvalidRequestError, NotImplementedError):
# The source was already detached. Resyncing.
# NotImplementedError is an artifact of stripe-python<2.0
# https://github.com/stripe/stripe-python/issues/376
self.sync_from_stripe_data(self.api_retrieve())
return False | [
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tensorforce/tensorforce | docs/mistune.py | https://github.com/tensorforce/tensorforce/blob/520a8d992230e382f08e315ede5fc477f5e26bfb/docs/mistune.py#L817-L823 | def codespan(self, text):
"""Rendering inline `code` text.
:param text: text content for inline code.
"""
text = escape(text.rstrip(), smart_amp=False)
return '<code>%s</code>' % text | [
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numenta/nupic | scripts/profiling/tm_profile.py | https://github.com/numenta/nupic/blob/5922fafffdccc8812e72b3324965ad2f7d4bbdad/scripts/profiling/tm_profile.py#L31-L54 | def profileTM(tmClass, tmDim, nRuns):
"""
profiling performance of TemporalMemory (TM)
using the python cProfile module and ordered by cumulative time,
see how to run on command-line above.
@param tmClass implementation of TM (cpp, py, ..)
@param tmDim number of columns in TM
@param nRuns number of calls of the profiled code (epochs)
"""
# create TM instance to measure
tm = tmClass(numberOfCols=tmDim)
# generate input data
data = numpy.random.randint(0, 2, [tmDim, nRuns]).astype('float32')
for i in xrange(nRuns):
# new data every time, this is the worst case performance
# real performance would be better, as the input data would not be completely random
d = data[:,i]
# the actual function to profile!
tm.compute(d, True) | [
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saltstack/salt | salt/modules/mac_system.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/mac_system.py#L362-L387 | def set_subnet_name(name):
'''
Set the local subnet name
:param str name: The new local subnet name
.. note::
Spaces are changed to dashes. Other special characters are removed.
:return: True if successful, False if not
:rtype: bool
CLI Example:
.. code-block:: bash
The following will be set as 'Mikes-Mac'
salt '*' system.set_subnet_name "Mike's Mac"
'''
cmd = 'systemsetup -setlocalsubnetname "{0}"'.format(name)
__utils__['mac_utils.execute_return_success'](cmd)
return __utils__['mac_utils.confirm_updated'](
name,
get_subnet_name,
) | [
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kalefranz/auxlib | auxlib/collection.py | https://github.com/kalefranz/auxlib/blob/6ff2d6b57d128d0b9ed8f01ad83572e938da064f/auxlib/collection.py#L87-L117 | def first(seq, key=lambda x: bool(x), default=None, apply=lambda x: x):
"""Give the first value that satisfies the key test.
Args:
seq (iterable):
key (callable): test for each element of iterable
default: returned when all elements fail test
apply (callable): applied to element before return, but not to default value
Returns: first element in seq that passes key, mutated with optional apply
Examples:
>>> first([0, False, None, [], (), 42])
42
>>> first([0, False, None, [], ()]) is None
True
>>> first([0, False, None, [], ()], default='ohai')
'ohai'
>>> import re
>>> m = first(re.match(regex, 'abc') for regex in ['b.*', 'a(.*)'])
>>> m.group(1)
'bc'
The optional `key` argument specifies a one-argument predicate function
like that used for `filter()`. The `key` argument, if supplied, must be
in keyword form. For example:
>>> first([1, 1, 3, 4, 5], key=lambda x: x % 2 == 0)
4
"""
return next((apply(x) for x in seq if key(x)), default() if callable(default) else default) | [
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>>> first([0, False, None, [], ()]) is None
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>>> first([0, False, None, [], ()], default='ohai')
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>>> import re
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>>> m.group(1)
'bc'
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>>> first([1, 1, 3, 4, 5], key=lambda x: x % 2 == 0)
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tkf/rash | rash/init.py | https://github.com/tkf/rash/blob/585da418ec37dd138f1a4277718b6f507e9536a2/rash/init.py#L37-L77 | def init_run(shell, no_daemon, daemon_options, daemon_outfile):
"""
Configure your shell.
Add the following line in your shell RC file and then you are
ready to go::
eval $(%(prog)s)
To check if your shell is supported, simply run::
%(prog)s --no-daemon
If you want to specify shell other than $SHELL, you can give
--shell option::
eval $(%(prog)s --shell zsh)
By default, this command also starts daemon in background to
automatically index shell history records. To not start daemon,
use --no-daemon option like this::
eval $(%(prog)s --no-daemon)
To see the other methods to launch the daemon process, see
``rash daemon --help``.
"""
import sys
from .__init__ import __version__
init_file = find_init(shell)
if os.path.exists(init_file):
sys.stdout.write(INIT_TEMPLATE.format(
file=init_file, version=__version__))
else:
raise RuntimeError(
"Shell '{0}' is not supported.".format(shell_name(shell)))
if not no_daemon:
from .daemon import start_daemon_in_subprocess
start_daemon_in_subprocess(daemon_options, daemon_outfile) | [
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eval $(%(prog)s)
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%(prog)s --no-daemon
If you want to specify shell other than $SHELL, you can give
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eval $(%(prog)s --shell zsh)
By default, this command also starts daemon in background to
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eval $(%(prog)s --no-daemon)
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openstack/networking-hyperv | networking_hyperv/neutron/agent/layer2.py | https://github.com/openstack/networking-hyperv/blob/7a89306ab0586c95b99debb44d898f70834508b9/networking_hyperv/neutron/agent/layer2.py#L389-L392 | def _treat_devices_removed(self):
"""Process the removed devices."""
for device in self._removed_ports.copy():
eventlet.spawn_n(self._process_removed_port, device) | [
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log2timeline/plaso | plaso/preprocessors/macos.py | https://github.com/log2timeline/plaso/blob/9c564698d2da3ffbe23607a3c54c0582ea18a6cc/plaso/preprocessors/macos.py#L267-L311 | def _ParseFileEntry(self, knowledge_base, file_entry):
"""Parses artifact file system data for a preprocessing attribute.
Args:
knowledge_base (KnowledgeBase): to fill with preprocessing information.
file_entry (dfvfs.FileEntry): file entry that contains the artifact
value data.
Raises:
errors.PreProcessFail: if the preprocessing fails.
"""
root_key = self._GetPlistRootKey(file_entry)
if not root_key:
location = getattr(file_entry.path_spec, 'location', '')
raise errors.PreProcessFail((
'Unable to read: {0:s} plist: {1:s} with error: missing root '
'key.').format(self.ARTIFACT_DEFINITION_NAME, location))
try:
match = self._GetKeysDefaultEmpty(root_key, self._KEYS)
except KeyError as exception:
location = getattr(file_entry.path_spec, 'location', '')
raise errors.PreProcessFail(
'Unable to read: {0:s} plist: {1:s} with error: {2!s}'.format(
self.ARTIFACT_DEFINITION_NAME, location, exception))
name = match.get('name', [None])[0]
uid = match.get('uid', [None])[0]
if not name or not uid:
# TODO: add and store preprocessing errors.
return
user_account = artifacts.UserAccountArtifact(
identifier=uid, username=name)
user_account.group_identifier = match.get('gid', [None])[0]
user_account.full_name = match.get('realname', [None])[0]
user_account.shell = match.get('shell', [None])[0]
user_account.user_directory = match.get('home', [None])[0]
try:
knowledge_base.AddUserAccount(user_account)
except KeyError:
# TODO: add and store preprocessing errors.
pass | [
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saltstack/salt | salt/modules/win_groupadd.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/win_groupadd.py#L70-L82 | def _get_all_groups():
'''
A helper function that gets a list of group objects for all groups on the
machine
Returns:
iter: A list of objects for all groups on the machine
'''
with salt.utils.winapi.Com():
nt = win32com.client.Dispatch('AdsNameSpaces')
results = nt.GetObject('', 'WinNT://.')
results.Filter = ['group']
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biolink/ontobio | ontobio/util/go_utils.py | https://github.com/biolink/ontobio/blob/4e512a7831cfe6bc1b32f2c3be2ba41bc5cf7345/ontobio/util/go_utils.py#L68-L81 | def go_aspect(self, go_term):
"""
For GO terms, returns F, C, or P corresponding to its aspect
"""
if not go_term.startswith("GO:"):
return None
else:
# Check ancestors for root terms
if self.is_molecular_function(go_term):
return 'F'
elif self.is_cellular_component(go_term):
return 'C'
elif self.is_biological_process(go_term):
return 'P' | [
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PGower/PyCanvas | pycanvas/apis/content_migrations.py | https://github.com/PGower/PyCanvas/blob/68520005382b440a1e462f9df369f54d364e21e8/pycanvas/apis/content_migrations.py#L293-L321 | def update_migration_issue_users(self, id, user_id, workflow_state, content_migration_id):
"""
Update a migration issue.
Update the workflow_state of a migration issue
"""
path = {}
data = {}
params = {}
# REQUIRED - PATH - user_id
"""ID"""
path["user_id"] = user_id
# REQUIRED - PATH - content_migration_id
"""ID"""
path["content_migration_id"] = content_migration_id
# REQUIRED - PATH - id
"""ID"""
path["id"] = id
# REQUIRED - workflow_state
"""Set the workflow_state of the issue."""
self._validate_enum(workflow_state, ["active", "resolved"])
data["workflow_state"] = workflow_state
self.logger.debug("PUT /api/v1/users/{user_id}/content_migrations/{content_migration_id}/migration_issues/{id} with query params: {params} and form data: {data}".format(params=params, data=data, **path))
return self.generic_request("PUT", "/api/v1/users/{user_id}/content_migrations/{content_migration_id}/migration_issues/{id}".format(**path), data=data, params=params, single_item=True) | [
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dereneaton/ipyrad | ipyrad/assemble/cluster_within.py | https://github.com/dereneaton/ipyrad/blob/5eeb8a178160f45faf71bf47cec4abe998a575d1/ipyrad/assemble/cluster_within.py#L1429-L1508 | def run(data, samples, noreverse, maxindels, force, ipyclient):
""" run the major functions for clustering within samples """
## list of samples to submit to queue
subsamples = []
## if sample is already done skip
for sample in samples:
## If sample not in state 2 don't try to cluster it.
if sample.stats.state < 2:
print("""\
Sample not ready for clustering. First run step2 on sample: {}""".\
format(sample.name))
continue
if not force:
if sample.stats.state >= 3:
print("""\
Skipping {}; aleady clustered. Use force to re-cluster""".\
format(sample.name))
else:
if sample.stats.reads_passed_filter:
subsamples.append(sample)
else:
## force to overwrite
if sample.stats.reads_passed_filter:
subsamples.append(sample)
## run subsamples
if not subsamples:
print(" No Samples ready to be clustered. First run step2().")
else:
## arguments to apply_jobs, inst catches exceptions
try:
## make dirs that are needed including tmpdir
setup_dirs(data)
## if refmapping make filehandles that will be persistent
if not data.paramsdict["assembly_method"] == "denovo":
for sample in subsamples:
refmap_init(data, sample, force)
## set thread-count to 2 for paired-data
nthreads = 2
## set thread-count to 1 for single-end data
else:
nthreads = 1
## overwrite nthreads if value in _ipcluster dict
if "threads" in data._ipcluster.keys():
nthreads = int(data._ipcluster["threads"])
## if more CPUs than there are samples then increase threads
_ncpus = len(ipyclient)
if _ncpus > 2*len(data.samples):
nthreads *= 2
## submit jobs to be run on cluster
args = [data, subsamples, ipyclient, nthreads, maxindels, force]
new_apply_jobs(*args)
finally:
## this can fail if jobs were not stopped properly and are still
## writing to tmpdir. don't cleanup if debug is on.
try:
log_level = logging.getLevelName(LOGGER.getEffectiveLevel())
if not log_level == "DEBUG":
if os.path.exists(data.tmpdir):
shutil.rmtree(data.tmpdir)
## get all refmap_derep.fastqs
rdereps = glob.glob(os.path.join(data.dirs.edits, "*-refmap_derep.fastq"))
## Remove the unmapped fastq files
for rmfile in rdereps:
os.remove(rmfile)
except Exception as _:
LOGGER.warning("failed to cleanup files/dirs") | [
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] | python | valid | 36.3125 |
Carbonara-Project/Guanciale | guanciale/idblib.py | https://github.com/Carbonara-Project/Guanciale/blob/c239ffac6fb481d09c4071d1de1a09f60dc584ab/guanciale/idblib.py#L1002-L1014 | def name(self, id):
"""
resolves a name, both short and long names.
"""
data = self.bytes(id, 'N')
if not data:
print("%x has no name" % id)
return
if data[:1] == b'\x00':
nameid, = struct.unpack_from(">" + self.fmt, data, 1)
nameblob = self.blob(self.nodebase, 'S', nameid * 256, nameid * 256 + 32)
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return data.rstrip(b"\x00").decode('utf-8') | [
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jpscaletti/solution | solution/fields/splitted_datetime.py | https://github.com/jpscaletti/solution/blob/eabafd8e695bbb0209242e002dbcc05ffb327f43/solution/fields/splitted_datetime.py#L69-L74 | def _to_timezone(self, dt):
"""Takes a naive timezone with an utc value and return it formatted as a
local timezone."""
tz = self._get_tz()
utc_dt = pytz.utc.localize(dt)
return utc_dt.astimezone(tz) | [
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boriel/zxbasic | arch/zx48k/backend/__32bit.py | https://github.com/boriel/zxbasic/blob/23b28db10e41117805bdb3c0f78543590853b132/arch/zx48k/backend/__32bit.py#L725-L733 | def _abs32(ins):
""" Absolute value of top of the stack (32 bits in DEHL)
"""
output = _32bit_oper(ins.quad[2])
output.append('call __ABS32')
output.append('push de')
output.append('push hl')
REQUIRES.add('abs32.asm')
return output | [
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pyviz/holoviews | holoviews/core/util.py | https://github.com/pyviz/holoviews/blob/ae0dd2f3de448b0ca5e9065aabd6ef8d84c7e655/holoviews/core/util.py#L1190-L1212 | def dimension_sort(odict, kdims, vdims, key_index):
"""
Sorts data by key using usual Python tuple sorting semantics
or sorts in categorical order for any categorical Dimensions.
"""
sortkws = {}
ndims = len(kdims)
dimensions = kdims+vdims
indexes = [(dimensions[i], int(i not in range(ndims)),
i if i in range(ndims) else i-ndims)
for i in key_index]
cached_values = {d.name: [None]+list(d.values) for d in dimensions}
if len(set(key_index)) != len(key_index):
raise ValueError("Cannot sort on duplicated dimensions")
else:
sortkws['key'] = lambda x: tuple(cached_values[dim.name].index(x[t][d])
if dim.values else x[t][d]
for i, (dim, t, d) in enumerate(indexes))
if sys.version_info.major == 3:
return python2sort(odict.items(), **sortkws)
else:
return sorted(odict.items(), **sortkws) | [
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Othernet-Project/hwd | hwd/storage.py | https://github.com/Othernet-Project/hwd/blob/7f4445bac61aa2305806aa80338e2ce5baa1093c/hwd/storage.py#L114-L122 | def partitions(self):
"""
Iterable containing disk's partition objects. Objects in the iterable
are :py:class:`~hwd.storage.Partition` instances.
"""
if not self._partitions:
self._partitions = [Partition(d, self)
for d in self.device.children]
return self._partitions | [
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artefactual-labs/agentarchives | agentarchives/atom/client.py | https://github.com/artefactual-labs/agentarchives/blob/af19ade56a90c64069cf46b50972fe72b6f10a45/agentarchives/atom/client.py#L692-L747 | def add_child(
self,
parent_slug=None,
title="",
level="",
start_date=None,
end_date=None,
date_expression=None,
notes=[],
):
"""
Adds a new resource component parented within `parent`.
:param str parent_slug: The parent's slug.
:param str title: A title for the record.
:param str level: The level of description.
:return: The ID of the newly-created record.
"""
new_object = {"title": title, "level_of_description": level}
if parent_slug is not None:
new_object["parent_slug"] = parent_slug
# Optionally add date specification
new_date = {}
if start_date is not None:
new_date["start_date"] = start_date
if end_date is not None:
new_date["end_date"] = end_date
if date_expression is not None:
new_date["date"] = date_expression
if new_date != {}:
new_object["dates"] = [new_date]
# Optionally add notes
new_object["notes"] = []
for note in notes:
note_type = note.get("type", "General note")
# If there is a note, but it's an empty string, skip this;
content = note.get("content")
if not content:
continue
new_note = {"content": content, "type": note_type}
new_object["notes"].append(new_note)
return self._post(
urljoin(self.base_url, "informationobjects"),
data=json.dumps(new_object),
expected_response=201,
).json()["slug"] | [
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MSchnei/pyprf_feature | pyprf_feature/analysis/load_config.py | https://github.com/MSchnei/pyprf_feature/blob/49004ede7ae1ddee07a30afe9ce3e2776750805c/pyprf_feature/analysis/load_config.py#L12-L277 | def load_config(strCsvCnfg, lgcTest=False, lgcPrint=True):
"""
Load py_pRF_mapping config file.
Parameters
----------
strCsvCnfg : string
Absolute file path of config file.
lgcTest : Boolean
Whether this is a test (pytest). If yes, absolute path of this function
will be prepended to config file paths.
lgcPrint : Boolean
Print config parameters?
Returns
-------
dicCnfg : dict
Dictionary containing parameter names (as keys) and parameter values
(as values). For example, `dicCnfg['varTr']` contains a float, such as
`2.94`.
"""
# Dictionary with config information:
dicCnfg = {}
# Open file with parameter configuration:
# fleConfig = open(strCsvCnfg, 'r')
with open(strCsvCnfg, 'r') as fleConfig:
# Read file with ROI information:
csvIn = csv.reader(fleConfig,
delimiter='\n',
skipinitialspace=True)
# Loop through csv object to fill list with csv data:
for lstTmp in csvIn:
# Skip comments (i.e. lines starting with '#') and empty lines.
# Note: Indexing the list (i.e. lstTmp[0][0]) does not work for
# empty lines. However, if the first condition is no fullfilled
# (i.e. line is empty and 'if lstTmp' evaluates to false), the
# second logical test (after the 'and') is not actually carried
# out.
if lstTmp and not (lstTmp[0][0] == '#'):
# Name of current parameter (e.g. 'varTr'):
strParamKey = lstTmp[0].split(' = ')[0]
# print(strParamKey)
# Current parameter value (e.g. '2.94'):
strParamVlu = lstTmp[0].split(' = ')[1]
# print(strParamVlu)
# Put paramter name (key) and value (item) into dictionary:
dicCnfg[strParamKey] = strParamVlu
# Are model parameters in cartesian or polar coordinates?
# set either pol (polar) or crt (cartesian)
dicCnfg['strKwCrd'] = ast.literal_eval(dicCnfg['strKwCrd'])
if lgcPrint:
print('---Model coordinates are in: ' + str(dicCnfg['strKwCrd']))
# Number of x- or radial positions to model:
dicCnfg['varNum1'] = int(dicCnfg['varNum1'])
# Number of y- or angular positions to model:
dicCnfg['varNum2'] = int(dicCnfg['varNum2'])
if lgcPrint:
if dicCnfg['strKwCrd'] == 'crt':
print('---Number of x-positions to model: ' +
str(dicCnfg['varNum1']))
print('---Number of y-positions to model: ' +
str(dicCnfg['varNum2']))
elif dicCnfg['strKwCrd'] == 'pol':
print('---Number of radial positions to model: ' +
str(dicCnfg['varNum1']))
print('---Number of angular positions to model: ' +
str(dicCnfg['varNum2']))
# Number of pRF sizes to model:
dicCnfg['varNumPrfSizes'] = int(dicCnfg['varNumPrfSizes'])
if lgcPrint:
print('---Number of pRF sizes to model: '
+ str(dicCnfg['varNumPrfSizes']))
# Extent of visual space from centre of the screen in negative x-direction
# (i.e. from the fixation point to the left end of the screen) in degrees
# of visual angle.
dicCnfg['varExtXmin'] = float(dicCnfg['varExtXmin'])
if lgcPrint:
print('---Extent of visual space in negative x-direction: '
+ str(dicCnfg['varExtXmin']))
# Extent of visual space from centre of the screen in positive x-direction
# (i.e. from the fixation point to the right end of the screen) in degrees
# of visual angle.
dicCnfg['varExtXmax'] = float(dicCnfg['varExtXmax'])
if lgcPrint:
print('---Extent of visual space in positive x-direction: '
+ str(dicCnfg['varExtXmax']))
# Extent of visual space from centre of the screen in negative y-direction
# (i.e. from the fixation point to the lower end of the screen) in degrees
# of visual angle.
dicCnfg['varExtYmin'] = float(dicCnfg['varExtYmin'])
if lgcPrint:
print('---Extent of visual space in negative y-direction: '
+ str(dicCnfg['varExtYmin']))
# Extent of visual space from centre of the screen in positive y-direction
# (i.e. from the fixation point to the upper end of the screen) in degrees
# of visual angle.
dicCnfg['varExtYmax'] = float(dicCnfg['varExtYmax'])
if lgcPrint:
print('---Extent of visual space in positive y-direction: '
+ str(dicCnfg['varExtYmax']))
# Minimum pRF model size (standard deviation of 2D Gaussian) [degrees of
# visual angle]:
dicCnfg['varPrfStdMin'] = float(dicCnfg['varPrfStdMin'])
if lgcPrint:
print('---Minimum pRF model size: ' + str(dicCnfg['varPrfStdMin']))
# Maximum pRF model size (standard deviation of 2D Gaussian) [degrees of
# visual angle]:
dicCnfg['varPrfStdMax'] = float(dicCnfg['varPrfStdMax'])
if lgcPrint:
print('---Maximum pRF model size: ' + str(dicCnfg['varPrfStdMax']))
# Volume TR of input data [s]:
dicCnfg['varTr'] = float(dicCnfg['varTr'])
if lgcPrint:
print('---Volume TR of input data [s]: ' + str(dicCnfg['varTr']))
# Voxel resolution of fMRI data [mm]:
dicCnfg['varVoxRes'] = float(dicCnfg['varVoxRes'])
if lgcPrint:
print('---Voxel resolution of fMRI data [mm]: '
+ str(dicCnfg['varVoxRes']))
# Number of fMRI volumes and png files to load:
dicCnfg['varNumVol'] = int(dicCnfg['varNumVol'])
if lgcPrint:
print('---Total number of fMRI volumes and png files: '
+ str(dicCnfg['varNumVol']))
# Extent of temporal smoothing for fMRI data and pRF time course models
# [standard deviation of the Gaussian kernel, in seconds]:
# same temporal smoothing will be applied to pRF model time courses
dicCnfg['varSdSmthTmp'] = float(dicCnfg['varSdSmthTmp'])
if lgcPrint:
print('---Extent of temporal smoothing (Gaussian SD in [s]): '
+ str(dicCnfg['varSdSmthTmp']))
# Number of processes to run in parallel:
dicCnfg['varPar'] = int(dicCnfg['varPar'])
if lgcPrint:
print('---Number of processes to run in parallel: '
+ str(dicCnfg['varPar']))
# Size of space model in which the pRF models are
# created (x- and y-dimension).
dicCnfg['tplVslSpcSze'] = tuple([int(dicCnfg['varVslSpcSzeX']),
int(dicCnfg['varVslSpcSzeY'])])
if lgcPrint:
print('---Size of visual space model (x & y): '
+ str(dicCnfg['tplVslSpcSze']))
# Path(s) of functional data:
dicCnfg['lstPathNiiFunc'] = ast.literal_eval(dicCnfg['lstPathNiiFunc'])
if lgcPrint:
print('---Path(s) of functional data:')
for strTmp in dicCnfg['lstPathNiiFunc']:
print(' ' + str(strTmp))
# Path of mask (to restrict pRF model finding):
dicCnfg['strPathNiiMask'] = ast.literal_eval(dicCnfg['strPathNiiMask'])
if lgcPrint:
print('---Path of mask (to restrict pRF model finding):')
print(' ' + str(dicCnfg['strPathNiiMask']))
# Output basename:
dicCnfg['strPathOut'] = ast.literal_eval(dicCnfg['strPathOut'])
if lgcPrint:
print('---Output basename:')
print(' ' + str(dicCnfg['strPathOut']))
# Which version to use for pRF finding. 'numpy' or 'cython' for pRF finding
# on CPU, 'gpu' for using GPU.
dicCnfg['strVersion'] = ast.literal_eval(dicCnfg['strVersion'])
if lgcPrint:
print('---Version (numpy, cython, or gpu): '
+ str(dicCnfg['strVersion']))
# Create pRF time course models?
dicCnfg['lgcCrteMdl'] = (dicCnfg['lgcCrteMdl'] == 'True')
if lgcPrint:
print('---Create pRF time course models: '
+ str(dicCnfg['lgcCrteMdl']))
# Path to npy file with pRF time course models (to save or laod). Without
# file extension.
dicCnfg['strPathMdl'] = ast.literal_eval(dicCnfg['strPathMdl'])
if lgcPrint:
print('---Path to npy file with pRF time course models (to save '
+ 'or load):')
print(' ' + str(dicCnfg['strPathMdl']))
# switch to determine which hrf functions should be used
# 1: canonical, 2: can and temp derivative, 3: can, temp and spat deriv
dicCnfg['switchHrfSet'] = ast.literal_eval(dicCnfg['switchHrfSet'])
if lgcPrint:
print('---Switch to determine which hrf functions should be used: '
+ str(dicCnfg['switchHrfSet']))
# should model fitting be based on k-fold cross-validation?
# if not, set to 1
dicCnfg['varNumXval'] = ast.literal_eval(dicCnfg['varNumXval'])
if lgcPrint:
print('---Model fitting will have this number of folds for xval: '
+ str(dicCnfg['varNumXval']))
# If we create new pRF time course models, the following parameters have to
# be provided:
if dicCnfg['lgcCrteMdl']:
# Name of the npy that holds spatial info about conditions
dicCnfg['strSptExpInf'] = ast.literal_eval(dicCnfg['strSptExpInf'])
if lgcPrint:
print('---Path to npy file with spatial condition info: ')
print(' ' + str(dicCnfg['strSptExpInf']))
# Name of the npy that holds temporal info about conditions
dicCnfg['strTmpExpInf'] = ast.literal_eval(dicCnfg['strTmpExpInf'])
if lgcPrint:
print('---Path to npy file with temporal condition info: ')
print(' ' + str(dicCnfg['strTmpExpInf']))
# Factor by which time courses and HRF will be upsampled for the
# convolutions
dicCnfg['varTmpOvsmpl'] = ast.literal_eval(dicCnfg['varTmpOvsmpl'])
if lgcPrint:
print('---Factor by which time courses and HRF will be upsampled: '
+ str(dicCnfg['varTmpOvsmpl']))
# Is this a test?
if lgcTest:
# Prepend absolute path of this file to config file paths:
dicCnfg['strPathNiiMask'] = (strDir + dicCnfg['strPathNiiMask'])
dicCnfg['strPathOut'] = (strDir + dicCnfg['strPathOut'])
dicCnfg['strPathMdl'] = (strDir + dicCnfg['strPathMdl'])
dicCnfg['strSptExpInf'] = (strDir + dicCnfg['strSptExpInf'])
dicCnfg['strTmpExpInf'] = (strDir + dicCnfg['strTmpExpInf'])
# Loop through functional runs:
varNumRun = len(dicCnfg['lstPathNiiFunc'])
for idxRun in range(varNumRun):
dicCnfg['lstPathNiiFunc'][idxRun] = (
strDir
+ dicCnfg['lstPathNiiFunc'][idxRun]
)
return dicCnfg | [
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"o... | Load py_pRF_mapping config file.
Parameters
----------
strCsvCnfg : string
Absolute file path of config file.
lgcTest : Boolean
Whether this is a test (pytest). If yes, absolute path of this function
will be prepended to config file paths.
lgcPrint : Boolean
Print config parameters?
Returns
-------
dicCnfg : dict
Dictionary containing parameter names (as keys) and parameter values
(as values). For example, `dicCnfg['varTr']` contains a float, such as
`2.94`. | [
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] | python | train | 39.349624 |
StackStorm/pybind | pybind/nos/v7_2_0/interface/hundredgigabitethernet/__init__.py | https://github.com/StackStorm/pybind/blob/44c467e71b2b425be63867aba6e6fa28b2cfe7fb/pybind/nos/v7_2_0/interface/hundredgigabitethernet/__init__.py#L1672-L1693 | def _set_bpdu_drop(self, v, load=False):
"""
Setter method for bpdu_drop, mapped from YANG variable /interface/hundredgigabitethernet/bpdu_drop (container)
If this variable is read-only (config: false) in the
source YANG file, then _set_bpdu_drop is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_bpdu_drop() directly.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=bpdu_drop.bpdu_drop, is_container='container', presence=False, yang_name="bpdu-drop", rest_name="bpdu-drop", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Drop received BPDUs', u'callpoint': u'phy-stp-config', u'sort-priority': u'105', u'cli-compact-syntax': None, u'cli-sequence-commands': None, u'cli-incomplete-no': None, u'display-when': u'/vcsmode/vcs-mode = "true"'}}, namespace='urn:brocade.com:mgmt:brocade-xstp', defining_module='brocade-xstp', yang_type='container', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """bpdu_drop must be of a type compatible with container""",
'defined-type': "container",
'generated-type': """YANGDynClass(base=bpdu_drop.bpdu_drop, is_container='container', presence=False, yang_name="bpdu-drop", rest_name="bpdu-drop", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Drop received BPDUs', u'callpoint': u'phy-stp-config', u'sort-priority': u'105', u'cli-compact-syntax': None, u'cli-sequence-commands': None, u'cli-incomplete-no': None, u'display-when': u'/vcsmode/vcs-mode = "true"'}}, namespace='urn:brocade.com:mgmt:brocade-xstp', defining_module='brocade-xstp', yang_type='container', is_config=True)""",
})
self.__bpdu_drop = t
if hasattr(self, '_set'):
self._set() | [
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qiniu/python-sdk | qiniu/services/compute/qcos_api.py | https://github.com/qiniu/python-sdk/blob/a69fbef4e3e6ea1ebe09f4610a5b18bb2c17de59/qiniu/services/compute/qcos_api.py#L623-L638 | def disable_ap_port(self, apid, port):
"""临时关闭接入点端口
临时关闭接入点端口，仅对公网域名，公网ip有效。
Args:
- apid: 接入点ID
- port: 要设置的端口号
Returns:
返回一个tuple对象，其格式为(<result>, <ResponseInfo>)
- result 成功返回空dict{}，失败返回{"error": "<errMsg string>"}
- ResponseInfo 请求的Response信息
"""
url = '{0}/v3/aps/{1}/{2}/disable'.format(self.host, apid, port)
return self.__post(url) | [
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- port: 要设置的端口号
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] | python | train | 28.5 |
ladybug-tools/ladybug | ladybug/datatype/mass.py | https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/datatype/mass.py#L59-L66 | def to_si(self, values, from_unit):
"""Return values in SI and the units to which the values have been converted."""
if from_unit in self.si_units:
return values, from_unit
elif from_unit == 'ton':
return self.to_unit(values, 'tonne', from_unit), 'tonne'
else:
return self.to_unit(values, 'kg', from_unit), 'kg' | [
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dmlc/gluon-nlp | src/gluonnlp/model/sampled_block.py | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/model/sampled_block.py#L52-L95 | def hybrid_forward(self, F, x, sampled_values, label, w_all, b_all):
"""Forward computation."""
sampled_candidates, expected_count_sampled, expected_count_true = sampled_values
# (num_sampled, in_unit)
w_sampled = w_all.slice(begin=(0, 0), end=(self._num_sampled, None))
w_true = w_all.slice(begin=(self._num_sampled, 0), end=(None, None))
b_sampled = b_all.slice(begin=(0,), end=(self._num_sampled,))
b_true = b_all.slice(begin=(self._num_sampled,), end=(None,))
# true pred
# (batch_size, 1)
x = x.reshape((-1, self._in_unit))
pred_true = (w_true * x).sum(axis=1) + b_true
# samples pred
# (batch_size, num_sampled)
b_sampled = F.reshape(b_sampled, (-1,))
pred_sampled = F.FullyConnected(x, weight=w_sampled, bias=b_sampled,
num_hidden=self._num_sampled)
# remove accidental hits
if self._remove_accidental_hits:
label_vec = F.reshape(label, (-1, 1))
sample_vec = F.reshape(sampled_candidates, (1, -1))
mask = F.broadcast_equal(label_vec, sample_vec) * -1e37
pred_sampled = pred_sampled + mask
# subtract log(q)
expected_count_sampled = F.reshape(expected_count_sampled,
shape=(1, self._num_sampled))
expected_count_true = expected_count_true.reshape((-1,))
pred_true = pred_true - F.log(expected_count_true)
pred_true = pred_true.reshape((-1, 1))
pred_sampled = F.broadcast_sub(pred_sampled, F.log(expected_count_sampled))
# pred and new_labels
# (batch_size, 1+num_sampled)
pred = F.concat(pred_true, pred_sampled, dim=1)
if self._sparse_label:
new_label = F.zeros_like(label)
else:
label_vec = F.reshape(label, (-1, 1))
new_label_true = F.ones_like(label_vec)
new_label_sampled = F.zeros_like(pred_sampled)
new_label = F.Concat(new_label_true, new_label_sampled, dim=1)
return pred, new_label | [
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etingof/pyasn1 | pyasn1/type/namedtype.py | https://github.com/etingof/pyasn1/blob/25cf116ef8d11bb0e08454c0f3635c9f4002c2d6/pyasn1/type/namedtype.py#L406-L437 | def getPositionNearType(self, tagSet, idx):
"""Return the closest field position where given ASN.1 type is allowed.
Some ASN.1 serialisation allow for skipping optional and defaulted fields.
Some constructed ASN.1 types allow reordering of the fields. When recovering
such objects it may be important to know at which field position, in field set,
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Parameters
----------
tagSet: :class:`~pyasn1.type.tag.TagSet`
ASN.1 type which field position to look up
idx: :py:class:`int`
Field position at or past which to perform ASN.1 type look up
Returns
-------
: :py:class:`int`
Field position in fields set
Raises
------
: :class:`~pyasn1.error.PyAsn1Error`
If *tagSet* is not present or not unique within callee *NamedTypes*
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"""
try:
return idx + self.__ambiguousTypes[idx].getPositionByType(tagSet)
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raise error.PyAsn1Error('Type position out of range') | [
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ASN.1 type which field position to look up
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Field position at or past which to perform ASN.1 type look up
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Field position in fields set
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PythonSanSebastian/docstamp | docstamp/file_utils.py | https://github.com/PythonSanSebastian/docstamp/blob/b43808f2e15351b0b2f0b7eade9c7ef319c9e646/docstamp/file_utils.py#L138-L170 | def csv_to_json(csv_filepath, json_filepath, fieldnames, ignore_first_line=True):
""" Convert a CSV file in `csv_filepath` into a JSON file in `json_filepath`.
Parameters
----------
csv_filepath: str
Path to the input CSV file.
json_filepath: str
Path to the output JSON file. Will be overwritten if exists.
fieldnames: List[str]
Names of the fields in the CSV file.
ignore_first_line: bool
"""
import csv
import json
csvfile = open(csv_filepath, 'r')
jsonfile = open(json_filepath, 'w')
reader = csv.DictReader(csvfile, fieldnames)
rows = []
if ignore_first_line:
next(reader)
for row in reader:
rows.append(row)
json.dump(rows, jsonfile)
jsonfile.close()
csvfile.close() | [
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Path to the output JSON file. Will be overwritten if exists.
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ThomasChiroux/attowiki | src/attowiki/rst_directives.py | https://github.com/ThomasChiroux/attowiki/blob/6c93c420305490d324fdc95a7b40b2283a222183/src/attowiki/rst_directives.py#L30-L49 | def add_node(node, **kwds):
"""add_node from Sphinx
"""
nodes._add_node_class_names([node.__name__])
for key, val in kwds.iteritems():
try:
visit, depart = val
except ValueError:
raise ValueError('Value for key %r must be a '
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if key == 'html':
from docutils.writers.html4css1 import HTMLTranslator as translator
elif key == 'latex':
from docutils.writers.latex2e import LaTeXTranslator as translator
else:
# ignore invalid keys for compatibility
continue
setattr(translator, 'visit_'+node.__name__, visit)
if depart:
setattr(translator, 'depart_'+node.__name__, depart) | [
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tango-controls/pytango | tango/asyncio_executor.py | https://github.com/tango-controls/pytango/blob/9cf78c517c9cdc1081ff6d080a9646a740cc1d36/tango/asyncio_executor.py#L88-L94 | def execute(self, fn, *args, **kwargs):
"""Execute an operation and return the result."""
if self.in_executor_context():
corofn = asyncio.coroutine(lambda: fn(*args, **kwargs))
return corofn()
future = self.submit(fn, *args, **kwargs)
return future.result() | [
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boriel/zxbasic | arch/zx48k/optimizer.py | https://github.com/boriel/zxbasic/blob/23b28db10e41117805bdb3c0f78543590853b132/arch/zx48k/optimizer.py#L641-L653 | def rl(self, r):
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if self.C is None or not is_number(self.regs[r]):
self.set(r, None)
self.set_flag(None)
return
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tmp = self.C
v_ = self.getv(self.regs[r])
self.C = v_ & 1
self.regs[r] = str((v_ & 0xFE) | tmp) | [
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selenol/selenol-python | selenol_python/params.py | https://github.com/selenol/selenol-python/blob/53775fdfc95161f4aca350305cb3459e6f2f808d/selenol_python/params.py#L40-L51 | def _get_value(data_structure, key):
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:param data_structure: Dictionary, list or subscriptable object.
:param key: Array with the defined path ordered.
"""
if len(key) == 0:
raise KeyError()
value = data_structure[key[0]]
if len(key) > 1:
return _get_value(value, key[1:])
return value | [
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madprime/vcf2clinvar | vcf2clinvar/clinvar.py | https://github.com/madprime/vcf2clinvar/blob/d5bbf6df2902c6cabe9ef1894cfac527e90fa32a/vcf2clinvar/clinvar.py#L91-L122 | def _parse_allele_data(self):
"""Parse alleles for ClinVar VCF, overrides parent method."""
# Get allele frequencies if they exist.
pref_freq, frequencies = self._parse_frequencies()
info_clnvar_single_tags = ['ALLELEID', 'CLNSIG', 'CLNHGVS']
cln_data = {x.lower(): self.info[x] if x in self.info else None
for x in info_clnvar_single_tags}
cln_data.update(
{'clndisdb': [x.split(',') for x in
self.info['CLNDISDB'].split('|')]
if 'CLNDISDB' in self.info else []})
cln_data.update({'clndn': self.info['CLNDN'].split('|') if
'CLNDN' in self.info else []})
cln_data.update({'clnvi': self.info['CLNVI'].split(',')
if 'CLNVI' in self.info else []})
try:
sequence = self.alt_alleles[0]
except IndexError:
sequence = self.ref_allele
allele = ClinVarAllele(frequency=pref_freq, sequence=sequence,
**cln_data)
# A few ClinVar variants are only reported as a combination with
# other variants, and no single-variant effect is proposed. Skip these.
if not cln_data['clnsig']:
return []
return [allele] | [
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ynop/audiomate | audiomate/corpus/subset/selection.py | https://github.com/ynop/audiomate/blob/61727920b23a708293c3d526fa3000d4de9c6c21/audiomate/corpus/subset/selection.py#L107-L141 | def random_subsets(self, relative_sizes, by_duration=False, balance_labels=False, label_list_ids=None):
"""
Create a bunch of subsets with the given sizes relative to the size or duration of the full corpus.
Basically the same as calling ``random_subset`` or ``random_subset_by_duration`` multiple times
with different values. But this method makes sure that every subset contains only utterances,
that are also contained in the next bigger subset.
Args:
relative_sizes (list): A list of numbers between 0 and 1 indicating the sizes of the desired subsets,
relative to the full corpus.
by_duration (bool): If True the size measure is the duration of all utterances in a subset/corpus.
balance_labels (bool): If True the labels contained in a subset are chosen to be balanced
as far as possible.
label_list_ids (list): List of label-list ids. If none is given, all label-lists are considered
for balancing. Otherwise only the ones that are in the list are considered.
Returns:
dict : A dictionary containing all subsets with the relative size as key.
"""
resulting_sets = {}
next_bigger_subset = self.corpus
for relative_size in reversed(relative_sizes):
generator = SubsetGenerator(next_bigger_subset, random_seed=self.random_seed)
if by_duration:
sv = generator.random_subset_by_duration(relative_size, balance_labels=balance_labels,
label_list_ids=label_list_ids)
else:
sv = generator.random_subset(relative_size, balance_labels=balance_labels,
label_list_ids=label_list_ids)
resulting_sets[relative_size] = sv
return resulting_sets | [
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"corpus",... | Create a bunch of subsets with the given sizes relative to the size or duration of the full corpus.
Basically the same as calling ``random_subset`` or ``random_subset_by_duration`` multiple times
with different values. But this method makes sure that every subset contains only utterances,
that are also contained in the next bigger subset.
Args:
relative_sizes (list): A list of numbers between 0 and 1 indicating the sizes of the desired subsets,
relative to the full corpus.
by_duration (bool): If True the size measure is the duration of all utterances in a subset/corpus.
balance_labels (bool): If True the labels contained in a subset are chosen to be balanced
as far as possible.
label_list_ids (list): List of label-list ids. If none is given, all label-lists are considered
for balancing. Otherwise only the ones that are in the list are considered.
Returns:
dict : A dictionary containing all subsets with the relative size as key. | [
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"random_subset",
"or",
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"m... | python | train | 55.685714 |
edx/edx-enterprise | enterprise/utils.py | https://github.com/edx/edx-enterprise/blob/aea91379ab0a87cd3bc798961fce28b60ee49a80/enterprise/utils.py#L414-L443 | def get_course_track_selection_url(course_run, query_parameters):
"""
Return track selection url for the given course.
Arguments:
course_run (dict): A dictionary containing course run metadata.
query_parameters (dict): A dictionary containing query parameters to be added to course selection url.
Raises:
(KeyError): Raised when course run dict does not have 'key' key.
Returns:
(str): Course track selection url.
"""
try:
course_root = reverse('course_modes_choose', kwargs={'course_id': course_run['key']})
except KeyError:
LOGGER.exception(
"KeyError while parsing course run data.\nCourse Run: \n[%s]", course_run,
)
raise
url = '{}{}'.format(
settings.LMS_ROOT_URL,
course_root
)
course_run_url = update_query_parameters(url, query_parameters)
return course_run_url | [
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Arguments:
course_run (dict): A dictionary containing course run metadata.
query_parameters (dict): A dictionary containing query parameters to be added to course selection url.
Raises:
(KeyError): Raised when course run dict does not have 'key' key.
Returns:
(str): Course track selection url. | [
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] | python | valid | 29.566667 |
UpCloudLtd/upcloud-python-api | upcloud_api/server.py | https://github.com/UpCloudLtd/upcloud-python-api/blob/954b0ad7c4b932b2be31a95d88975f6b0eeac8ed/upcloud_api/server.py#L272-L278 | def remove_tags(self, tags):
"""
Add tags to a server. Accepts tags as strings or Tag objects.
"""
if self.cloud_manager.remove_tags(self, tags):
new_tags = [tag for tag in self.tags if tag not in tags]
object.__setattr__(self, 'tags', new_tags) | [
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] | python | train | 42.142857 |
astropy/pyregion | pyregion/region_to_filter.py | https://github.com/astropy/pyregion/blob/913af7ea4917855cb2e43d5086d1c8dd99c31363/pyregion/region_to_filter.py#L6-L117 | def as_region_filter(shape_list, origin=1):
"""
Often, the regions files implicitly assume the lower-left corner
of the image as a coordinate (1,1). However, the python convetion
is that the array index starts from 0. By default (origin = 1),
coordinates of the returned mpl artists have coordinate shifted by
(1, 1). If you do not want this shift, use origin=0.
"""
filter_list = []
for shape in shape_list:
if shape.name == "composite":
continue
if shape.name == "polygon":
xy = np.array(shape.coord_list) - origin
f = region_filter.Polygon(xy[::2], xy[1::2])
elif shape.name == "rotbox" or shape.name == "box":
xc, yc, w, h, rot = shape.coord_list
# -1 for change origin to 0,0
xc, yc = xc - origin, yc - origin
f = region_filter.Rotated(region_filter.Box(xc, yc, w, h),
rot, xc, yc)
elif shape.name == "ellipse":
xc, yc = shape.coord_list[:2]
# -1 for change origin to 0,0
xc, yc = xc - origin, yc - origin
angle = shape.coord_list[-1]
maj_list, min_list = shape.coord_list[2:-1:2], shape.coord_list[3:-1:2]
if len(maj_list) > 1:
w1, h1 = max(maj_list), max(min_list)
w2, h2 = min(maj_list), min(min_list)
f1 = region_filter.Ellipse(xc, yc, w1, h1) \
& ~region_filter.Ellipse(xc, yc, w2, h2)
f = region_filter.Rotated(f1, angle, xc, yc)
else:
w, h = maj_list[0], min_list[0]
f = region_filter.Rotated(region_filter.Ellipse(xc, yc, w, h),
angle, xc, yc)
elif shape.name == "annulus":
xc, yc = shape.coord_list[:2]
# -1 for change origin to 0,0
xc, yc = xc - origin, yc - origin
r_list = shape.coord_list[2:]
r1 = max(r_list)
r2 = min(r_list)
f = region_filter.Circle(xc, yc, r1) & ~region_filter.Circle(xc, yc, r2)
elif shape.name == "circle":
xc, yc, r = shape.coord_list
# -1 for change origin to 0,0
xc, yc = xc - origin, yc - origin
f = region_filter.Circle(xc, yc, r)
elif shape.name == "panda":
xc, yc, a1, a2, an, r1, r2, rn = shape.coord_list
# -1 for change origin to 0,0
xc, yc = xc - origin, yc - origin
f1 = region_filter.Circle(xc, yc, r2) & ~region_filter.Circle(xc, yc, r1)
f = f1 & region_filter.AngleRange(xc, yc, a1, a2)
elif shape.name == "pie":
xc, yc, r1, r2, a1, a2 = shape.coord_list
# -1 for change origin to 0,0
xc, yc = xc - origin, yc - origin
f1 = region_filter.Circle(xc, yc, r2) & ~region_filter.Circle(xc, yc, r1)
f = f1 & region_filter.AngleRange(xc, yc, a1, a2)
elif shape.name == "epanda":
xc, yc, a1, a2, an, r11, r12, r21, r22, rn, angle = shape.coord_list
# -1 for change origin to 0,0
xc, yc = xc - origin, yc - origin
f1 = region_filter.Ellipse(xc, yc, r21, r22) & ~region_filter.Ellipse(xc, yc, r11, r12)
f2 = f1 & region_filter.AngleRange(xc, yc, a1, a2)
f = region_filter.Rotated(f2, angle, xc, yc)
# f = f2 & region_filter.AngleRange(xc, yc, a1, a2)
elif shape.name == "bpanda":
xc, yc, a1, a2, an, r11, r12, r21, r22, rn, angle = shape.coord_list
# -1 for change origin to 0,0
xc, yc = xc - origin, yc - origin
f1 = region_filter.Box(xc, yc, r21, r22) & ~region_filter.Box(xc, yc, r11, r12)
f2 = f1 & region_filter.AngleRange(xc, yc, a1, a2)
f = region_filter.Rotated(f2, angle, xc, yc)
# f = f2 & region_filter.AngleRange(xc, yc, a1, a2)
else:
warnings.warn("'as_region_filter' does not know how to convert {0}"
" to a region filter.".format(shape.name))
continue
if shape.exclude:
filter_list = [region_filter.RegionOrList(*filter_list) & ~f]
else:
filter_list.append(f)
return region_filter.RegionOrList(*filter_list) | [
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is that the array index starts from 0. By default (origin = 1),
coordinates of the returned mpl artists have coordinate shifted by
(1, 1). If you do not want this shift, use origin=0. | [
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"st... | python | train | 38.25 |
rstoneback/pysat | pysat/instruments/cosmic2013_gps.py | https://github.com/rstoneback/pysat/blob/4ae1afd80e15e4449397d39dce8c3e969c32c422/pysat/instruments/cosmic2013_gps.py#L172-L224 | def load_files(files, tag=None, sat_id=None, altitude_bin=None):
'''Loads a list of COSMIC data files, supplied by user.
Returns a list of dicts, a dict for each file.
'''
output = [None]*len(files)
drop_idx = []
for (i,file) in enumerate(files):
try:
#data = netCDF4.Dataset(file)
data = netcdf_file(file, mode='r', mmap=False)
# build up dictionary will all ncattrs
new = {}
# get list of file attributes
#ncattrsList = data.ncattrs()
ncattrsList = data._attributes.keys()
for d in ncattrsList:
new[d] = data._attributes[d] #data.getncattr(d)
# load all of the variables in the netCDF
loadedVars={}
keys = data.variables.keys()
for key in keys:
if data.variables[key][:].dtype.byteorder != '=':
loadedVars[key] = data.variables[key][:].byteswap().newbyteorder()
else:
loadedVars[key] = data.variables[key][:]
new['profiles'] = pysat.DataFrame(loadedVars)
output[i] = new
data.close()
except RuntimeError:
# some of the files have zero bytes, which causes a read error
# this stores the index of these zero byte files so I can drop
# the Nones the gappy file leaves behind
drop_idx.append(i)
# drop anything that came from the zero byte files
drop_idx.reverse()
for i in drop_idx:
del output[i]
if tag == 'ionprf':
if altitude_bin is not None:
for out in output:
out['profiles'].index = (out['profiles']['MSL_alt']/altitude_bin).round().values*altitude_bin
out['profiles'] = out['profiles'].groupby(out['profiles'].index.values).mean()
else:
for out in output:
out['profiles'].index = out['profiles']['MSL_alt']
return output | [
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Returns a list of dicts, a dict for each file. | [
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] | python | train | 38.056604 |
uuazed/numerapi | numerapi/numerapi.py | https://github.com/uuazed/numerapi/blob/fc9dcc53b32ede95bfda1ceeb62aec1d67d26697/numerapi/numerapi.py#L412-L435 | def get_nmr_prize_pool(self, round_num=0, tournament=1):
"""Get NMR prize pool for the given round and tournament.
Args:
round_num (int, optional): The round you are interested in,
defaults to current round.
tournament (int, optional): ID of the tournament, defaults to 1
Returns:
decimal.Decimal: prize pool in NMR
Raises:
Value Error: in case of invalid round number
"""
tournaments = self.get_competitions(tournament)
tournaments.sort(key=lambda t: t['number'])
if round_num == 0:
t = tournaments[-1]
else:
tournaments = [t for t in tournaments if t['number'] == round_num]
if len(tournaments) == 0:
raise ValueError("invalid round number")
t = tournaments[0]
return t['prizePoolNmr'] | [
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"t"... | Get NMR prize pool for the given round and tournament.
Args:
round_num (int, optional): The round you are interested in,
defaults to current round.
tournament (int, optional): ID of the tournament, defaults to 1
Returns:
decimal.Decimal: prize pool in NMR
Raises:
Value Error: in case of invalid round number | [
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] | python | train | 36.458333 |
JarryShaw/DictDumper | src/json.py | https://github.com/JarryShaw/DictDumper/blob/430efcfdff18bb2421c3f27059ff94c93e621483/src/json.py#L237-L253 | def _append_bytes(self, value, _file): # pylint: disable=no-self-use
"""Call this function to write bytes contents.
Keyword arguments:
* value - dict, content to be dumped
* _file - FileIO, output file
"""
# binascii.b2a_base64(value) -> plistlib.Data
# binascii.a2b_base64(Data) -> value(bytes)
_text = ' '.join(textwrap.wrap(value.hex(), 2))
# _data = [H for H in iter(
# functools.partial(io.StringIO(value.hex()).read, 2), '')
# ] # to split bytes string into length-2 hex string list
_labs = ' "{text}"'.format(text=_text)
_file.write(_labs) | [
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Keyword arguments:
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* _file - FileIO, output file | [
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"."
] | python | train | 39 |
jplusplus/statscraper | statscraper/base_scraper.py | https://github.com/jplusplus/statscraper/blob/932ec048b23d15b3dbdaf829facc55fd78ec0109/statscraper/base_scraper.py#L493-L497 | def shape(self):
"""Compute the shape of the dataset as (rows, cols)."""
if not self.data:
return (0, 0)
return (len(self.data), len(self.dimensions)) | [
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] | python | train | 36.4 |
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