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rocky/python3-trepan
trepan/processor/cmdfns.py
https://github.com/rocky/python3-trepan/blob/14e91bc0acce090d67be145b1ac040cab92ac5f3/trepan/processor/cmdfns.py#L103-L118
def get_int(errmsg, arg, default=1, cmdname=None): """If arg is an int, use that otherwise take default.""" if arg: try: # eval() is used so we will allow arithmetic expressions, # variables etc. default = int(eval(arg)) except (SyntaxError, NameError, ValueError): if cmdname: errmsg("Command '%s' expects an integer; got: %s." % (cmdname, str(arg))) else: errmsg('Expecting an integer, got: %s.' % str(arg)) pass raise ValueError return default
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If arg is an int, use that otherwise take default.
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python
test
RudolfCardinal/pythonlib
cardinal_pythonlib/buildfunc.py
https://github.com/RudolfCardinal/pythonlib/blob/0b84cb35f38bd7d8723958dae51b480a829b7227/cardinal_pythonlib/buildfunc.py#L56-L70
def download_if_not_exists(url: str, filename: str, skip_cert_verify: bool = True, mkdir: bool = True) -> None: """ Downloads a URL to a file, unless the file already exists. """ if os.path.isfile(filename): log.info("No need to download, already have: {}", filename) return if mkdir: directory, basename = os.path.split(os.path.abspath(filename)) mkdir_p(directory) download(url=url, filename=filename, skip_cert_verify=skip_cert_verify)
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Downloads a URL to a file, unless the file already exists.
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python
train
mozilla-b2g/fxos-certsuite
mcts/tools/webidl/manifest_parser.py
https://github.com/mozilla-b2g/fxos-certsuite/blob/152a76c7c4c9d908524cf6e6fc25a498058f363d/mcts/tools/webidl/manifest_parser.py#L15-L36
def main(argv): """ This will generate you a manifest file, and you need to modify it! There are three category: files, untested, skipped. You can reference current manifest.json. usage: manifest_parser.py (GECKO LOCATION: /B2G/gecko/dom/webidl) The generated file can then be used with process_idl.py """ argparser = argparse.ArgumentParser() argparser.add_argument("gecko", help="/B2G/gecko/dom/webidl") args = argparser.parse_args(argv[1:]) files = [ "gecko/dom/webidl/" + f for f in listdir(args.gecko) if isfile(join(args.gecko,f)) and f.endswith("webidl") ] files.sort() with open('manifest_generated.json', 'w') as merged: merged.write('{\n "files": [\n') merged.write(" \"" + "\",\n \"".join(files) + "\"\n") merged.write(' ],\n "untested": [\n ],\n "skipped": [\n ]\n}\n')
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python
train
matrix-org/matrix-python-sdk
matrix_client/api.py
https://github.com/matrix-org/matrix-python-sdk/blob/e734cce3ccd35f2d355c6a19a7a701033472498a/matrix_client/api.py#L376-L388
def send_message(self, room_id, text_content, msgtype="m.text", timestamp=None): """Perform PUT /rooms/$room_id/send/m.room.message Args: room_id (str): The room ID to send the event in. text_content (str): The m.text body to send. timestamp (int): Set origin_server_ts (For application services only) """ return self.send_message_event( room_id, "m.room.message", self.get_text_body(text_content, msgtype), timestamp=timestamp )
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Perform PUT /rooms/$room_id/send/m.room.message Args: room_id (str): The room ID to send the event in. text_content (str): The m.text body to send. timestamp (int): Set origin_server_ts (For application services only)
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python
train
mabuchilab/QNET
src/qnet/algebra/core/algebraic_properties.py
https://github.com/mabuchilab/QNET/blob/cc20d26dad78691d34c67173e5cd67dcac94208a/src/qnet/algebra/core/algebraic_properties.py#L856-L944
def _deltasummation(term, ranges, i_range): """Partially execute a summation for `term` with a Kronecker Delta for one of the summation indices. This implements the solution to the core sub-problem in :func:`indexed_sum_over_kronecker` Args: term (QuantumExpression): term of the sum ranges (list): list of all summation index ranges (class:`IndexRangeBase` instances) i_range (int): list-index of element in `ranges` which should be eliminated Returns: ``(result, flag)`` where `result` is a list of ``(new_term, new_ranges)`` tuples and `flag` is an integer. There are three possible cases, indicated by the returned `flag`. Consider the following setup:: >>> i, j, k = symbols('i, j, k', cls=IdxSym) >>> i_range = IndexOverList(i, (0, 1)) >>> j_range = IndexOverList(j, (0, 1)) >>> ranges = [i_range, j_range] >>> def A(i, j): ... from sympy import IndexedBase ... return OperatorSymbol(StrLabel(IndexedBase('A')[i, j]), hs=0) 1. If executing the sum produces a single non-zero term, result will be ``[(new_term, new_ranges)]`` where `new_ranges` contains the input `ranges` without the eliminated range specified by `i_range`. This should be the most common case for calls to:func:`_deltasummation`:: >>> term = KroneckerDelta(i, j) * A(i, j) >>> result, flag = _deltasummation(term, [i_range, j_range], 1) >>> assert result == [(A(i, i), [i_range])] >>> assert flag == 1 2. If executing the sum for the index symbol specified via `index_range` does not reduce the sum, the result will be the list ``[(term, ranges)]`` with unchanged `term` and `ranges`:: >>> term = KroneckerDelta(j, k) * A(i, j) >>> result, flag = _deltasummation(term, [i_range, j_range], 0) >>> assert result == [(term, [i_range, j_range])] >>> assert flag == 2 This case also covers if there is no Kroncker delta in the term:: >>> term = A(i, j) >>> result, flag = _deltasummation(term, [i_range, j_range], 0) >>> assert result == [(term, [i_range, j_range])] >>> assert flag == 2 3. If `term` does not contain a Kronecker delta as a factor, but in a sum that can be expanded, the result will be a list of ``[(summand1, ranges), (summand2, ranges), ...]`` for the summands of that expansion. In this case, `:func:`_deltasummation` should be called again for every tuple in the list, with the same `i_range`:: >>> term = (KroneckerDelta(i, j) + 1) * A(i, j) >>> result, flag = _deltasummation(term, [i_range, j_range], 1) >>> assert result == [ ... (A(i, j), [i_range, j_range]), ... (KroneckerDelta(i,j) * A(i, j), [i_range, j_range])] >>> assert flag == 3 """ from qnet.algebra.core.abstract_quantum_algebra import QuantumExpression idx = ranges[i_range].index_symbol summands = _expand_delta(term, idx) if len(summands) > 1: return [(summand, ranges) for summand in summands], 3 else: delta, expr = _extract_delta(summands[0], idx) if not delta: return [(term, ranges)], 2 solns = sympy.solve(delta.args[0] - delta.args[1], idx) assert len(solns) > 0 # I can't think of an example that might cause this # if len(solns) == 0: # return [(term._zero, [])], 4 if len(solns) != 1: return [(term, ranges)], 2 value = solns[0] new_term = expr.substitute({idx: value}) if _RESOLVE_KRONECKER_WITH_PIECEWISE: new_term *= ranges[i_range].piecewise_one(value) assert isinstance(new_term, QuantumExpression) return [(new_term, ranges[:i_range] + ranges[i_range+1:])], 1
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Partially execute a summation for `term` with a Kronecker Delta for one of the summation indices. This implements the solution to the core sub-problem in :func:`indexed_sum_over_kronecker` Args: term (QuantumExpression): term of the sum ranges (list): list of all summation index ranges (class:`IndexRangeBase` instances) i_range (int): list-index of element in `ranges` which should be eliminated Returns: ``(result, flag)`` where `result` is a list of ``(new_term, new_ranges)`` tuples and `flag` is an integer. There are three possible cases, indicated by the returned `flag`. Consider the following setup:: >>> i, j, k = symbols('i, j, k', cls=IdxSym) >>> i_range = IndexOverList(i, (0, 1)) >>> j_range = IndexOverList(j, (0, 1)) >>> ranges = [i_range, j_range] >>> def A(i, j): ... from sympy import IndexedBase ... return OperatorSymbol(StrLabel(IndexedBase('A')[i, j]), hs=0) 1. If executing the sum produces a single non-zero term, result will be ``[(new_term, new_ranges)]`` where `new_ranges` contains the input `ranges` without the eliminated range specified by `i_range`. This should be the most common case for calls to:func:`_deltasummation`:: >>> term = KroneckerDelta(i, j) * A(i, j) >>> result, flag = _deltasummation(term, [i_range, j_range], 1) >>> assert result == [(A(i, i), [i_range])] >>> assert flag == 1 2. If executing the sum for the index symbol specified via `index_range` does not reduce the sum, the result will be the list ``[(term, ranges)]`` with unchanged `term` and `ranges`:: >>> term = KroneckerDelta(j, k) * A(i, j) >>> result, flag = _deltasummation(term, [i_range, j_range], 0) >>> assert result == [(term, [i_range, j_range])] >>> assert flag == 2 This case also covers if there is no Kroncker delta in the term:: >>> term = A(i, j) >>> result, flag = _deltasummation(term, [i_range, j_range], 0) >>> assert result == [(term, [i_range, j_range])] >>> assert flag == 2 3. If `term` does not contain a Kronecker delta as a factor, but in a sum that can be expanded, the result will be a list of ``[(summand1, ranges), (summand2, ranges), ...]`` for the summands of that expansion. In this case, `:func:`_deltasummation` should be called again for every tuple in the list, with the same `i_range`:: >>> term = (KroneckerDelta(i, j) + 1) * A(i, j) >>> result, flag = _deltasummation(term, [i_range, j_range], 1) >>> assert result == [ ... (A(i, j), [i_range, j_range]), ... (KroneckerDelta(i,j) * A(i, j), [i_range, j_range])] >>> assert flag == 3
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python
train
cmbruns/pyopenvr
src/openvr/__init__.py
https://github.com/cmbruns/pyopenvr/blob/68395d26bb3df6ab1f0f059c38d441f962938be6/src/openvr/__init__.py#L5727-L5742
def hookScreenshot(self, numTypes): """ Called by the running VR application to indicate that it wishes to be in charge of screenshots. If the application does not call this, the Compositor will only support VRScreenshotType_Stereo screenshots that will be captured without notification to the running app. Once hooked your application will receive a VREvent_RequestScreenshot event when the user presses the buttons to take a screenshot. """ fn = self.function_table.hookScreenshot pSupportedTypes = EVRScreenshotType() result = fn(byref(pSupportedTypes), numTypes) return result, pSupportedTypes
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Called by the running VR application to indicate that it wishes to be in charge of screenshots. If the application does not call this, the Compositor will only support VRScreenshotType_Stereo screenshots that will be captured without notification to the running app. Once hooked your application will receive a VREvent_RequestScreenshot event when the user presses the buttons to take a screenshot.
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python
train
bcbio/bcbio-nextgen
bcbio/structural/regions.py
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/structural/regions.py#L346-L364
def get_base_cnv_regions(data, work_dir, genome_default="transcripts1e4", include_gene_names=True): """Retrieve set of target regions for CNV analysis. Subsets to extended transcript regions for WGS experiments to avoid long runtimes. """ cov_interval = dd.get_coverage_interval(data) base_regions = get_sv_bed(data, include_gene_names=include_gene_names) # if we don't have a configured BED or regions to use for SV caling if not base_regions: # For genome calls, subset to regions near genes as targets if cov_interval == "genome": base_regions = get_sv_bed(data, genome_default, work_dir, include_gene_names=include_gene_names) if base_regions: base_regions = remove_exclude_regions(base_regions, base_regions, [data]) # Finally, default to the defined variant regions if not base_regions: base_regions = dd.get_variant_regions(data) or dd.get_sample_callable(data) return bedutils.clean_file(base_regions, data)
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Retrieve set of target regions for CNV analysis. Subsets to extended transcript regions for WGS experiments to avoid long runtimes.
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python
train
helgi/python-command
command/core.py
https://github.com/helgi/python-command/blob/c41fb8cdd9074b847c7bc5b5ee7f027508f52d7f/command/core.py#L212-L248
def which(program, environ=None): """ Find out if an executable exists in the supplied PATH. If so, the absolute path to the executable is returned. If not, an exception is raised. :type string :param program: Executable to be checked for :param dict :param environ: Any additional ENV variables required, specifically PATH :return string|:class:`command.CommandException` Returns the location if found, otherwise raises exception """ def is_exe(path): """ Helper method to check if a file exists and is executable """ return isfile(path) and os.access(path, os.X_OK) if program is None: raise CommandException("Invalid program name passed") fpath, fname = split(program) if fpath: if is_exe(program): return program else: if environ is None: environ = os.environ for path in environ['PATH'].split(os.pathsep): exe_file = join(path, program) if is_exe(exe_file): return exe_file raise CommandException("Could not find %s" % program)
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Find out if an executable exists in the supplied PATH. If so, the absolute path to the executable is returned. If not, an exception is raised. :type string :param program: Executable to be checked for :param dict :param environ: Any additional ENV variables required, specifically PATH :return string|:class:`command.CommandException` Returns the location if found, otherwise raises exception
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python
train
uogbuji/versa
tools/py/pipeline/core_actions.py
https://github.com/uogbuji/versa/blob/f092ffc7ed363a5b170890955168500f32de0dd5/tools/py/pipeline/core_actions.py#L185-L319
def materialize(typ, rel=None, origin=None, unique=None, links=None, inverse=False, split=None, attributes=None): ''' Create a new resource related to the origin :param typ: IRI of the type for the resource to be materialized, which becomes the target of the main link, and the origin of any additional links given in the links param :param rel: IRI of the relationship between the origin and the materialized target, or a list of relationship IRIs, each of which will be used to create a separate link, or a versa action function to derive this relationship or list of relationships at run time, or None. If none, use the action context. :param origin: Literal IRI or Versa action function for origin of the main generated link. If none, use the action context. :param unique: Versa action function to be invoked in order to derive a unique hash key input for the materialized resource, in the form of multiple key, value pairs (or key, list-of-values) :param links: Dictionary of links from the newly materialized resource. Each keys can be a relationship IRIs, a Versa action function returning a relationship IRI, a Versa action function returning a list of Versa contexts, which can be used to guide a sequence pattern of generated links, or a Versa action function returning None, which signals that the particular link is skipped entirely. :param postprocess: IRI or list of IRI queueing up actiona to be postprocessed for this materialized resource. None, the default, signals no special postprocessing For examples of all these scenarios see marcpatterns.py :return: Versa action function to do the actual work ''' links = links or [] attributes = attributes or {} def _materialize(ctx): ''' Inserts at least two main links in the context's output_model, one or more for the relationship from the origin to the materialized resource, one for the type of the materialized resource, and links according to the links parameter :param ctx: Runtime Versa context used in processing (e.g. includes the prototype link) :return: None This function is intricate in its use and shifting of Versa context, but the intricacies are all designed to make the marcpatterns mini language more natural. ''' #FIXME: Part of the datachef sorting out if not ctx.idgen: ctx.idgen = idgen _typ = typ(ctx) if callable(typ) else typ _rel = rel(ctx) if callable(rel) else rel _unique = unique(ctx) if callable(unique) else unique (o, r, t, a) = ctx.current_link #FIXME: On redesign implement split using function composition instead targets = [ sub_t.strip() for sub_t in t.split(split) ] if split else [t] #Conversions to make sure we end up with a list of relationships out of it all if _rel is None: _rel = [r] rels = _rel if isinstance(_rel, list) else ([_rel] if _rel else []) objids = [] #Botanical analogy #The stem is the relationship from the original to the materialized resource #The veins are any further relationships from materialized resource for target in targets: ctx_stem = ctx.copy(current_link=(o, r, target, a)) if origin: #Have been given enough info to derive the origin from context. Ignore origin in current link o = origin(ctx_stem) computed_unique = [] if _unique else None if _unique: # strip None values from computed unique list, including pairs where v is None for k, v in _unique: if None in (k, v): continue v = v if isinstance(v, list) else [v] for subitem in v: subval = subitem(ctx) if callable(subitem) else subitem if subval: subval = subval if isinstance(subval, list) else [subval] computed_unique.extend([(k, s) for s in subval]) objid = materialize_entity(ctx, _typ, unique=computed_unique) objids.append(objid) for curr_rel in rels: #e.g. scenario if passed in rel=ifexists(...) curr_rel = curr_rel(ctx) if callable(curr_rel) else curr_rel #FIXME: Fix this properly, by slugifying & making sure slugify handles all numeric case (prepend '_') curr_rel = '_' + curr_rel if curr_rel.isdigit() else curr_rel if curr_rel: if inverse: ctx.output_model.add(I(objid), I(iri.absolutize(curr_rel, ctx.base)), I(o), {}) else: ctx.output_model.add(I(o), I(iri.absolutize(curr_rel, ctx.base)), I(objid), {}) #print((objid, ctx_.existing_ids)) if objid not in ctx.existing_ids: if _typ: ctx.output_model.add(I(objid), VTYPE_REL, I(iri.absolutize(_typ, ctx.base)), {}) #FIXME: Should we be using Python Nones to mark blanks, or should Versa define some sort of null resource? #XXX: Note, links are only processed on new objects! This needs some thought for k, v in links: new_current_link = (I(objid), k, ctx.current_link[TARGET], ctx.current_link[ATTRIBUTES]) ctx_vein = ctx_stem.copy(current_link=new_current_link) k = k(ctx_vein) if callable(k) else k #If k is a list of contexts use it to dynamically execute functions if isinstance(k, list): if k and isinstance(k[0], context): for newctx in k: #The function in question will generate any needed links in the output model v(newctx) continue #import traceback; traceback.print_stack() #For looking up the call stack e.g. to debug nested materialize #Check that the links key is not None, which is a signal not to #generate the item. For example if the key is an ifexists and the #test expression result is False, it will come back as None, #and we don't want to run the v function if k: v = v(ctx_vein) if callable(v) else v #If k or v come from pipeline functions as None it signals to skip generating anything else for this link item if v is not None: v = v(ctx_vein) if callable(v) else v #FIXME: Fix properly, by slugifying & making sure slugify handles all-numeric case if k.isdigit(): k = '_' + k if isinstance(v, list): for valitems in v: if valitems: ctx.output_model.add(I(objid), I(iri.absolutize(k, ctx_vein.base)), valitems, {}) else: ctx.output_model.add(I(objid), I(iri.absolutize(k, ctx_vein.base)), v, {}) ctx.existing_ids.add(objid) return objids return _materialize
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Create a new resource related to the origin :param typ: IRI of the type for the resource to be materialized, which becomes the target of the main link, and the origin of any additional links given in the links param :param rel: IRI of the relationship between the origin and the materialized target, or a list of relationship IRIs, each of which will be used to create a separate link, or a versa action function to derive this relationship or list of relationships at run time, or None. If none, use the action context. :param origin: Literal IRI or Versa action function for origin of the main generated link. If none, use the action context. :param unique: Versa action function to be invoked in order to derive a unique hash key input for the materialized resource, in the form of multiple key, value pairs (or key, list-of-values) :param links: Dictionary of links from the newly materialized resource. Each keys can be a relationship IRIs, a Versa action function returning a relationship IRI, a Versa action function returning a list of Versa contexts, which can be used to guide a sequence pattern of generated links, or a Versa action function returning None, which signals that the particular link is skipped entirely. :param postprocess: IRI or list of IRI queueing up actiona to be postprocessed for this materialized resource. None, the default, signals no special postprocessing For examples of all these scenarios see marcpatterns.py :return: Versa action function to do the actual work
[ "Create", "a", "new", "resource", "related", "to", "the", "origin" ]
python
train
portfors-lab/sparkle
sparkle/gui/stim/stimulusview.py
https://github.com/portfors-lab/sparkle/blob/5fad1cf2bec58ec6b15d91da20f6236a74826110/sparkle/gui/stim/stimulusview.py#L549-L556
def paint(self, painter, option, index): """Uses the :meth:`paint<sparkle.gui.stim.components.qcomponents.QStimulusComponent.paint>` method of the component it represents to fill in an appropriately sized rectange. :qtdoc:`Re-implemented<QStyledItemDelegate.paint>`""" component = index.model().data(index, role=QtCore.Qt.UserRole) painter.drawRect(option.rect) component.paint(painter, option.rect, option.palette)
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Uses the :meth:`paint<sparkle.gui.stim.components.qcomponents.QStimulusComponent.paint>` method of the component it represents to fill in an appropriately sized rectange. :qtdoc:`Re-implemented<QStyledItemDelegate.paint>`
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python
train
saltstack/salt
salt/modules/win_system.py
https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/win_system.py#L676-L698
def set_hostname(hostname): ''' Set the hostname of the windows minion, requires a restart before this will be updated. .. versionadded:: 2016.3.0 Args: hostname (str): The hostname to set Returns: bool: ``True`` if successful, otherwise ``False`` CLI Example: .. code-block:: bash salt 'minion-id' system.set_hostname newhostname ''' with salt.utils.winapi.Com(): conn = wmi.WMI() comp = conn.Win32_ComputerSystem()[0] return comp.Rename(Name=hostname)
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Set the hostname of the windows minion, requires a restart before this will be updated. .. versionadded:: 2016.3.0 Args: hostname (str): The hostname to set Returns: bool: ``True`` if successful, otherwise ``False`` CLI Example: .. code-block:: bash salt 'minion-id' system.set_hostname newhostname
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python
train
TrafficSenseMSD/SumoTools
sumolib/__init__.py
https://github.com/TrafficSenseMSD/SumoTools/blob/8607b4f885f1d1798e43240be643efe6dccccdaa/sumolib/__init__.py#L198-L206
def flush(self): """flushes all file contents to disc""" for fp in self.files: fp.flush() if isinstance(fp, int) or hasattr(fp, "fileno"): try: os.fsync(fp) except OSError: pass
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flushes all file contents to disc
[ "flushes", "all", "file", "contents", "to", "disc" ]
python
train
job/aggregate6
aggregate6/aggregate6.py
https://github.com/job/aggregate6/blob/fa93046a39e397795d6258ea4c46033dee3df69b/aggregate6/aggregate6.py#L44-L61
def aggregate(l): """Aggregate a `list` of prefixes. Keyword arguments: l -- a python list of prefixes Example use: >>> aggregate(["10.0.0.0/8", "10.0.0.0/24"]) ['10.0.0.0/8'] """ tree = radix.Radix() for item in l: try: tree.add(item) except (ValueError) as err: raise Exception("ERROR: invalid IP prefix: {}".format(item)) return aggregate_tree(tree).prefixes()
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Aggregate a `list` of prefixes. Keyword arguments: l -- a python list of prefixes Example use: >>> aggregate(["10.0.0.0/8", "10.0.0.0/24"]) ['10.0.0.0/8']
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python
valid
log2timeline/plaso
plaso/cli/helpers/extraction.py
https://github.com/log2timeline/plaso/blob/9c564698d2da3ffbe23607a3c54c0582ea18a6cc/plaso/cli/helpers/extraction.py#L51-L76
def ParseOptions(cls, options, configuration_object): """Parses and validates options. Args: options (argparse.Namespace): parser options. configuration_object (CLITool): object to be configured by the argument helper. Raises: BadConfigObject: when the configuration object is of the wrong type. """ if not isinstance(configuration_object, tools.CLITool): raise errors.BadConfigObject( 'Configuration object is not an instance of CLITool') preferred_year = cls._ParseNumericOption(options, 'preferred_year') process_archives = getattr(options, 'process_archives', False) process_compressed_streams = getattr( options, 'process_compressed_streams', True) setattr(configuration_object, '_preferred_year', preferred_year) setattr(configuration_object, '_process_archives', process_archives) setattr( configuration_object, '_process_compressed_streams', process_compressed_streams)
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Parses and validates options. Args: options (argparse.Namespace): parser options. configuration_object (CLITool): object to be configured by the argument helper. Raises: BadConfigObject: when the configuration object is of the wrong type.
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python
train
cdeboever3/cdpybio
cdpybio/star.py
https://github.com/cdeboever3/cdpybio/blob/38efdf0e11d01bc00a135921cb91a19c03db5d5c/cdpybio/star.py#L137-L200
def _make_sj_out_panel(sj_outD, total_jxn_cov_cutoff=20): """Filter junctions from many sj_out files and make panel. Parameters ---------- sj_outD : dict Dict whose keys are sample names and values are sj_out dataframes total_jxn_cov_cutoff : int If the unique read coverage of a junction summed over all samples is not greater than or equal to this value, the junction will not be included in the final output. Returns ------- sj_outP : pandas.Panel Panel where each dataframe corresponds to an sj_out file filtered to remove low coverage junctions. Each dataframe has COUNT_COLS = ('unique_junction_reads', 'multimap_junction_reads', 'max_overhang') annotDF : pandas.DataFrame Dataframe with values ANNOTATION_COLS = ('chrom', 'start', 'end', 'intron_motif', 'annotated') that are otherwise duplicated in the panel. """ # num_jxns = dict() # # set of all junctions # jxnS = reduce(lambda x,y: set(x) | set(y), # [ sj_outD[k].index for k in sj_outD.keys() ]) # jxn_keepS = set() # jxn_setsD = dict() # for k in sj_outD.keys(): # jxn_setsD[k] = frozenset(sj_outD[k].index) # for j in jxnS: # if sum([ sj_outD[k].ix[j,'unique_junction_reads'] for k in sj_outD.keys() # if j in jxn_setsD[k] ]) >= total_jxn_cov_cutoff: # jxn_keepS.add(j) # for k in sj_outD.keys(): # sj_outD[k] = sj_outD[k].ix[jxn_keepS] sj_outP = pd.Panel(sj_outD) for col in ['unique_junction_reads', 'multimap_junction_reads', 'max_overhang']: sj_outP.ix[:,:,col] = sj_outP.ix[:,:,col].fillna(0) # Some dataframes will be missing information like intron_motif etc. for # junctions that were not observed in that sample. The info is somewhere in # the panel though so we can get it. annotDF = reduce(pd.DataFrame.combine_first, [ sj_outP.ix[item,:,ANNOTATION_COLS].dropna() for item in sj_outP.items ]) annotDF['start'] = annotDF['start'].astype(int) annotDF['end'] = annotDF['end'].astype(int) annotDF['annotated'] = annotDF['annotated'].astype(bool) # Sort annotation and panel annotDF = annotDF.sort_values(by=['chrom', 'start', 'end']) sj_outP = sj_outP.ix[:, annotDF.index, :] sj_outP = sj_outP.ix[:,:,COUNT_COLS].astype(int) return sj_outP, annotDF
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Filter junctions from many sj_out files and make panel. Parameters ---------- sj_outD : dict Dict whose keys are sample names and values are sj_out dataframes total_jxn_cov_cutoff : int If the unique read coverage of a junction summed over all samples is not greater than or equal to this value, the junction will not be included in the final output. Returns ------- sj_outP : pandas.Panel Panel where each dataframe corresponds to an sj_out file filtered to remove low coverage junctions. Each dataframe has COUNT_COLS = ('unique_junction_reads', 'multimap_junction_reads', 'max_overhang') annotDF : pandas.DataFrame Dataframe with values ANNOTATION_COLS = ('chrom', 'start', 'end', 'intron_motif', 'annotated') that are otherwise duplicated in the panel.
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python
train
fermiPy/fermipy
fermipy/model_utils.py
https://github.com/fermiPy/fermipy/blob/9df5e7e3728307fd58c5bba36fd86783c39fbad4/fermipy/model_utils.py#L41-L65
def get_function_spec(name): """Return a dictionary with the specification of a function: parameter names and defaults (value, bounds, scale, etc.). Returns ------- par_names : list List of parameter names for this function. norm_par : str Name of normalization parameter. default : dict Parameter defaults dictionary. """ if not hasattr(get_function_spec, 'fndict'): modelfile = os.path.join('$FERMIPY_ROOT', 'data', 'models.yaml') modelfile = os.path.expandvars(modelfile) get_function_spec.fndict = yaml.load(open(modelfile)) if not name in get_function_spec.fndict.keys(): raise Exception('Invalid Function Name: %s' % name) return get_function_spec.fndict[name]
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Return a dictionary with the specification of a function: parameter names and defaults (value, bounds, scale, etc.). Returns ------- par_names : list List of parameter names for this function. norm_par : str Name of normalization parameter. default : dict Parameter defaults dictionary.
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python
train
mikedh/trimesh
trimesh/path/exchange/misc.py
https://github.com/mikedh/trimesh/blob/25e059bf6d4caa74f62ffd58ce4f61a90ee4e518/trimesh/path/exchange/misc.py#L151-L181
def faces_to_path(mesh, face_ids=None, **kwargs): """ Given a mesh and face indices find the outline edges and turn them into a Path3D. Parameters --------- mesh : trimesh.Trimesh Triangulated surface in 3D face_ids : (n,) int Indexes referencing mesh.faces Returns --------- kwargs : dict Kwargs for Path3D constructor """ if face_ids is None: edges = mesh.edges_sorted else: # take advantage of edge ordering to index as single row edges = mesh.edges_sorted.reshape( (-1, 6))[face_ids].reshape((-1, 2)) # an edge which occurs onely once is on the boundary unique_edges = grouping.group_rows( edges, require_count=1) # add edges and vertices to kwargs kwargs.update(edges_to_path(edges=edges[unique_edges], vertices=mesh.vertices)) return kwargs
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Given a mesh and face indices find the outline edges and turn them into a Path3D. Parameters --------- mesh : trimesh.Trimesh Triangulated surface in 3D face_ids : (n,) int Indexes referencing mesh.faces Returns --------- kwargs : dict Kwargs for Path3D constructor
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python
train
marrow/WebCore
web/core/application.py
https://github.com/marrow/WebCore/blob/38d50f8022ca62976a1e5ff23f7714bd647b6532/web/core/application.py#L108-L147
def _configure(self, config): """Prepare the incoming configuration and ensure certain expected values are present. For example, this ensures BaseExtension is included in the extension list, and populates the logging config. """ config = config or dict() # We really need this to be there. if 'extensions' not in config: config['extensions'] = list() if not any(isinstance(ext, BaseExtension) for ext in config['extensions']): # Always make sure the BaseExtension is present since request/response objects are handy. config['extensions'].insert(0, BaseExtension()) if not any(isinstance(ext, arguments.ArgumentExtension) for ext in config['extensions']): # Prepare a default set of argument mutators. config['extensions'].extend([ arguments.ValidateArgumentsExtension(), arguments.ContextArgsExtension(), arguments.RemainderArgsExtension(), arguments.QueryStringArgsExtension(), arguments.FormEncodedKwargsExtension(), arguments.JSONKwargsExtension(), ]) config['extensions'].append(self) # Allow the application object itself to register callbacks. try: addLoggingLevel('trace', logging.DEBUG - 5) except AttributeError: pass # Tests are skipped on these as we have no particular need to test Python's own logging mechanism. level = config.get('logging', {}).get('level', None) if level: # pragma: no cover logging.basicConfig(level=getattr(logging, level.upper())) elif 'logging' in config: # pragma: no cover logging.config.dictConfig(config['logging']) return config
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Prepare the incoming configuration and ensure certain expected values are present. For example, this ensures BaseExtension is included in the extension list, and populates the logging config.
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python
train
CityOfZion/neo-python
neo/Wallets/Wallet.py
https://github.com/CityOfZion/neo-python/blob/fe90f62e123d720d4281c79af0598d9df9e776fb/neo/Wallets/Wallet.py#L135-L150
def AddContract(self, contract): """ Add a contract to the wallet. Args: contract (Contract): a contract of type neo.SmartContract.Contract. Raises: Exception: Invalid operation - public key mismatch. """ if not contract.PublicKeyHash.ToBytes() in self._keys.keys(): raise Exception('Invalid operation - public key mismatch') self._contracts[contract.ScriptHash.ToBytes()] = contract if contract.ScriptHash in self._watch_only: self._watch_only.remove(contract.ScriptHash)
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Add a contract to the wallet. Args: contract (Contract): a contract of type neo.SmartContract.Contract. Raises: Exception: Invalid operation - public key mismatch.
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python
train
Microsoft/azure-devops-python-api
azure-devops/azure/devops/v5_0/identity/identity_client.py
https://github.com/Microsoft/azure-devops-python-api/blob/4777ffda2f5052fabbaddb2abe9cb434e0cf1aa8/azure-devops/azure/devops/v5_0/identity/identity_client.py#L73-L83
def delete_group(self, group_id): """DeleteGroup. :param str group_id: """ route_values = {} if group_id is not None: route_values['groupId'] = self._serialize.url('group_id', group_id, 'str') self._send(http_method='DELETE', location_id='5966283b-4196-4d57-9211-1b68f41ec1c2', version='5.0', route_values=route_values)
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DeleteGroup. :param str group_id:
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python
train
etobella/python-xmlsig
src/xmlsig/utils.py
https://github.com/etobella/python-xmlsig/blob/120a50935a4d4c2c972cfa3f8519bbce7e30d67b/src/xmlsig/utils.py#L85-L102
def create_node(name, parent=None, ns='', tail=False, text=False): """ Creates a new node :param name: Node name :param parent: Node parent :param ns: Namespace to use :param tail: Tail to add :param text: Text of the node :return: New node """ node = etree.Element(etree.QName(ns, name)) if parent is not None: parent.append(node) if tail: node.tail = tail if text: node.text = text return node
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Creates a new node :param name: Node name :param parent: Node parent :param ns: Namespace to use :param tail: Tail to add :param text: Text of the node :return: New node
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python
train
binilinlquad/bing-search-api
bing_search_api/api.py
https://github.com/binilinlquad/bing-search-api/blob/c3a296ad7d0050dc929eda9d9760df5c15faa51a/bing_search_api/api.py#L62-L71
def search_composite(self, query, source, payload=None): '''Shortcut search with composite source''' source = '+'.join(source) if payload is None: payload = dict(Sources=quote(source)) else: payload['Sources'] = quote(source) return self.search(query, 'Composite', payload)
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Shortcut search with composite source
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python
train
bcbio/bcbio-nextgen
bcbio/cwl/defs.py
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/cwl/defs.py#L511-L602
def _variant_sv(checkpoints): """Structural variant workflow. """ if not checkpoints.get("sv"): return [], [] sv = [s("detect_sv", "batch-single", [["sv_batch_rec"]], [cwlout("sv_rec", "record", fields=[cwlout(["sv", "variantcaller"], ["string", "null"]), cwlout(["sv", "vrn_file"], ["File", "null"], [".tbi"]), cwlout(["sv", "supplemental"], {"type": "array", "items": ["File"]}), cwlout(["svvalidate", "summary"], ["File", "null"]), cwlout("inherit", exclude=[["align_bam"], ["work_bam_plus"], ["reference", "snpeff"]])])], "bcbio-vc", ["bedtools", "cnvkit", "delly", "duphold", "extract-sv-reads", "gsort", "lumpy-sv;env=python2", "manta;env=python2", "break-point-inspector", "mosdepth", "samtools", "smoove;env=python2", "pysam>=0.13.0", "seq2c", "simple_sv_annotation;env=python2", "survivor", "svtools;env=python2", "svtyper;env=python2", "r=3.5.1", "r-base", "xorg-libxt", "vawk;env=python2"], disk={"files": 2.0})] sv_batch_inputs = [["analysis"], ["genome_build"], ["work_bam_plus", "disc"], ["work_bam_plus", "sr"], ["config", "algorithm", "background", "cnv_reference"], ["config", "algorithm", "tools_on"], ["config", "algorithm", "tools_off"], ["config", "algorithm", "svprioritize"], ["config", "algorithm", "svvalidate"], ["regions", "sample_callable"], ["genome_resources", "variation", "gc_profile"], ["genome_resources", "variation", "germline_het_pon"], ["genome_resources", "aliases", "snpeff"], ["reference", "snpeff", "genome_build"], ["sv_coverage_rec"]] if checkpoints.get("vc"): sv_batch_inputs.append(["variants", "samples"]) steps = [s("calculate_sv_bins", "multi-combined", [["align_bam"], ["reference", "fasta", "base"], ["metadata", "batch"], ["metadata", "phenotype"], ["config", "algorithm", "background", "cnv_reference"], ["config", "algorithm", "callable_regions"], ["config", "algorithm", "coverage_interval"], ["config", "algorithm", "exclude_regions"], ["config", "algorithm", "sv_regions"], ["config", "algorithm", "variant_regions"], ["config", "algorithm", "variant_regions_merged"], ["config", "algorithm", "seq2c_bed_ready"], ["config", "algorithm", "svcaller"], ["depth", "variant_regions", "regions"], ["genome_resources", "variation", "lcr"], ["genome_resources", "variation", "polyx"], ["genome_resources", "variation", "encode_blacklist"], ["genome_resources", "rnaseq", "gene_bed"]], [cwlout("sv_bin_rec", "record", fields=[cwlout(["regions", "bins", "target"], ["File", "null"]), cwlout(["regions", "bins", "antitarget"], ["File", "null"]), cwlout(["regions", "bins", "gcannotated"], ["File", "null"]), cwlout(["regions", "bins", "group"], ["string", "null"]), cwlout("inherit")])], "bcbio-vc", ["bedtools", "cnvkit"], disk={"files": 1.5}, cores=1), s("calculate_sv_coverage", "multi-parallel", [["sv_bin_rec"]], [cwlout("sv_rawcoverage_rec", "record", fields=[cwlout(["depth", "bins", "target"], ["File", "null"]), cwlout(["depth", "bins", "antitarget"], ["File", "null"]), cwlout(["depth", "bins", "seq2c"], ["File", "null"]), cwlout("inherit")])], "bcbio-vc", ["mosdepth", "cnvkit", "seq2c"], disk={"files": 1.5}), s("normalize_sv_coverage", "multi-combined", [["sv_rawcoverage_rec"]], [cwlout("sv_coverage_rec", "record", fields=[cwlout(["depth", "bins", "normalized"], ["File", "null"]), cwlout(["depth", "bins", "background"], ["File", "null"]), cwlout("inherit")])], "bcbio-vc", ["cnvkit"], disk={"files": 1.5}), s("batch_for_sv", "multi-batch", sv_batch_inputs, [cwlout("sv_batch_rec", "record")], "bcbio-vc", unlist=[["config", "algorithm", "svcaller"]]), w("svcall", "multi-parallel", sv, []), s("summarize_sv", "multi-combined", [["sv_rec"]], [cwlout(["sv", "calls"], {"type": "array", "items": ["File", "null"]}), cwlout(["sv", "supplemental"], {"type": "array", "items": ["File"]}), cwlout(["sv", "prioritize", "tsv"], {"type": "array", "items": ["File", "null"]}), cwlout(["sv", "prioritize", "raw"], {"type": "array", "items": ["File", "null"]}), cwlout(["svvalidate", "grading_summary"], ["File", "null"]), cwlout(["svvalidate", "grading_plots"], {"type": "array", "items": ["File", "null"]})], "bcbio-vc", ["bcbio-prioritize"], disk={"files": 1.0}, cores=1)] final_outputs = [["sv", "calls"], ["svvalidate", "grading_summary"], ["sv", "prioritize", "tsv"], ["sv", "prioritize", "raw"], ["sv", "supplemental"]] return steps, final_outputs
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":", "\"array\"", ",", "\"items\"", ":", "[", "\"File\"", ",", "\"null\"", "]", "}", ")", "]", ",", "\"bcbio-vc\"", ",", "[", "\"bcbio-prioritize\"", "]", ",", "disk", "=", "{", "\"files\"", ":", "1.0", "}", ",", "cores", "=", "1", ")", "]", "final_outputs", "=", "[", "[", "\"sv\"", ",", "\"calls\"", "]", ",", "[", "\"svvalidate\"", ",", "\"grading_summary\"", "]", ",", "[", "\"sv\"", ",", "\"prioritize\"", ",", "\"tsv\"", "]", ",", "[", "\"sv\"", ",", "\"prioritize\"", ",", "\"raw\"", "]", ",", "[", "\"sv\"", ",", "\"supplemental\"", "]", "]", "return", "steps", ",", "final_outputs" ]
Structural variant workflow.
[ "Structural", "variant", "workflow", "." ]
python
train
cltk/cltk
cltk/lemmatize/french/lemma.py
https://github.com/cltk/cltk/blob/ed9c025b7ec43c949481173251b70e05e4dffd27/cltk/lemmatize/french/lemma.py#L46-L74
def lemmatize(self, tokens): """define list of lemmas""" entries = self.entries forms_and_lemmas = self.forms_and_lemmas lemma_list = [x[0] for x in entries] """Provide a lemma for each token""" lemmatized = [] for token in tokens: """check for a match between token and list of lemmas""" if token in lemma_list: lemmed = (token, token) lemmatized.append(lemmed) else: """if no match check for a match between token and list of lemma forms""" lemma = [k for k, v in forms_and_lemmas.items() if token in v] if lemma != []: lemmed = (token, lemma) lemmatized.append(lemmed) elif lemma == []: """if no match apply regular expressions and check for a match against the list of lemmas again""" regexed = regex(token) if regexed in lemma_list: lemmed = (token, regexed) lemmatized.append(lemmed) else: lemmed = (token, "None") lemmatized.append(lemmed) return lemmatized
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define list of lemmas
[ "define", "list", "of", "lemmas" ]
python
train
ladybug-tools/ladybug
ladybug/datacollection.py
https://github.com/ladybug-tools/ladybug/blob/c08b7308077a48d5612f644943f92d5b5dade583/ladybug/datacollection.py#L903-L916
def _check_values(self, values): """Check values whenever they come through the values setter.""" assert isinstance(values, Iterable) and not isinstance( values, (str, dict, bytes, bytearray)), \ 'values should be a list or tuple. Got {}'.format(type(values)) if self.header.analysis_period.is_annual: a_period_len = 8760 * self.header.analysis_period.timestep if self.header.analysis_period.is_leap_year is True: a_period_len = a_period_len + 24 * self.header.analysis_period.timestep else: a_period_len = len(self.header.analysis_period.moys) assert len(values) == a_period_len, \ 'Length of values does not match that expected by the '\ 'header analysis_period. {} != {}'.format(len(values), a_period_len)
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Check values whenever they come through the values setter.
[ "Check", "values", "whenever", "they", "come", "through", "the", "values", "setter", "." ]
python
train
saltstack/salt
salt/cloud/clouds/opennebula.py
https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/cloud/clouds/opennebula.py#L4586-L4605
def _xml_to_dict(xml): ''' Helper function to covert xml into a data dictionary. xml The xml data to convert. ''' dicts = {} for item in xml: key = item.tag.lower() idx = 1 while key in dicts: key += six.text_type(idx) idx += 1 if item.text is None: dicts[key] = _xml_to_dict(item) else: dicts[key] = item.text return dicts
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Helper function to covert xml into a data dictionary. xml The xml data to convert.
[ "Helper", "function", "to", "covert", "xml", "into", "a", "data", "dictionary", "." ]
python
train
openstack/horizon
horizon/tables/views.py
https://github.com/openstack/horizon/blob/5601ea9477323e599d9b766fcac1f8be742935b2/horizon/tables/views.py#L175-L188
def update_server_filter_action(self, request, table=None): """Update the table server side filter action. It is done based on the current filter. The filter info may be stored in the session and this will restore it. """ if not table: table = self.get_table() filter_info = self.get_server_filter_info(request, table) if filter_info is not None: action = filter_info['action'] setattr(action, 'filter_string', filter_info['value']) if filter_info['field_param']: setattr(action, 'filter_field', filter_info['field'])
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Update the table server side filter action. It is done based on the current filter. The filter info may be stored in the session and this will restore it.
[ "Update", "the", "table", "server", "side", "filter", "action", "." ]
python
train
grahame/dividebatur
dividebatur/counter.py
https://github.com/grahame/dividebatur/blob/adc1f6e8013943471f1679e3c94f9448a1e4a472/dividebatur/counter.py#L301-L311
def get_initial_totals(self): "determine the initial total for each candidate. only call this at the start of round 1" candidate_votes = {} # initialise to zero for every individual candidate for candidate_id in self.candidate_ids: candidate_votes[candidate_id] = 0 for candidate_id in self.candidate_ids: candidate_votes[candidate_id] = self.candidate_bundle_transactions.get_paper_count(candidate_id) for candidate_id in candidate_votes: candidate_votes[candidate_id] = int(candidate_votes[candidate_id]) return candidate_votes, 0, 0
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determine the initial total for each candidate. only call this at the start of round 1
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python
train
kennethreitz/requests-html
requests_html.py
https://github.com/kennethreitz/requests-html/blob/b59a9f2fb9333d7d467154a0fd82978efdb9d23b/requests_html.py#L541-L610
def render(self, retries: int = 8, script: str = None, wait: float = 0.2, scrolldown=False, sleep: int = 0, reload: bool = True, timeout: Union[float, int] = 8.0, keep_page: bool = False): """Reloads the response in Chromium, and replaces HTML content with an updated version, with JavaScript executed. :param retries: The number of times to retry loading the page in Chromium. :param script: JavaScript to execute upon page load (optional). :param wait: The number of seconds to wait before loading the page, preventing timeouts (optional). :param scrolldown: Integer, if provided, of how many times to page down. :param sleep: Integer, if provided, of how many long to sleep after initial render. :param reload: If ``False``, content will not be loaded from the browser, but will be provided from memory. :param keep_page: If ``True`` will allow you to interact with the browser page through ``r.html.page``. If ``scrolldown`` is specified, the page will scrolldown the specified number of times, after sleeping the specified amount of time (e.g. ``scrolldown=10, sleep=1``). If just ``sleep`` is provided, the rendering will wait *n* seconds, before returning. If ``script`` is specified, it will execute the provided JavaScript at runtime. Example: .. code-block:: python script = \"\"\" () => { return { width: document.documentElement.clientWidth, height: document.documentElement.clientHeight, deviceScaleFactor: window.devicePixelRatio, } } \"\"\" Returns the return value of the executed ``script``, if any is provided: .. code-block:: python >>> r.html.render(script=script) {'width': 800, 'height': 600, 'deviceScaleFactor': 1} Warning: the first time you run this method, it will download Chromium into your home directory (``~/.pyppeteer``). """ self.browser = self.session.browser # Automatically create a event loop and browser content = None # Automatically set Reload to False, if example URL is being used. if self.url == DEFAULT_URL: reload = False for i in range(retries): if not content: try: content, result, page = self.session.loop.run_until_complete(self._async_render(url=self.url, script=script, sleep=sleep, wait=wait, content=self.html, reload=reload, scrolldown=scrolldown, timeout=timeout, keep_page=keep_page)) except TypeError: pass else: break if not content: raise MaxRetries("Unable to render the page. Try increasing timeout") html = HTML(url=self.url, html=content.encode(DEFAULT_ENCODING), default_encoding=DEFAULT_ENCODING) self.__dict__.update(html.__dict__) self.page = page return result
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Reloads the response in Chromium, and replaces HTML content with an updated version, with JavaScript executed. :param retries: The number of times to retry loading the page in Chromium. :param script: JavaScript to execute upon page load (optional). :param wait: The number of seconds to wait before loading the page, preventing timeouts (optional). :param scrolldown: Integer, if provided, of how many times to page down. :param sleep: Integer, if provided, of how many long to sleep after initial render. :param reload: If ``False``, content will not be loaded from the browser, but will be provided from memory. :param keep_page: If ``True`` will allow you to interact with the browser page through ``r.html.page``. If ``scrolldown`` is specified, the page will scrolldown the specified number of times, after sleeping the specified amount of time (e.g. ``scrolldown=10, sleep=1``). If just ``sleep`` is provided, the rendering will wait *n* seconds, before returning. If ``script`` is specified, it will execute the provided JavaScript at runtime. Example: .. code-block:: python script = \"\"\" () => { return { width: document.documentElement.clientWidth, height: document.documentElement.clientHeight, deviceScaleFactor: window.devicePixelRatio, } } \"\"\" Returns the return value of the executed ``script``, if any is provided: .. code-block:: python >>> r.html.render(script=script) {'width': 800, 'height': 600, 'deviceScaleFactor': 1} Warning: the first time you run this method, it will download Chromium into your home directory (``~/.pyppeteer``).
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python
train
phoebe-project/phoebe2
phoebe/backend/universe.py
https://github.com/phoebe-project/phoebe2/blob/e64b8be683977064e2d55dd1b3ac400f64c3e379/phoebe/backend/universe.py#L2874-L2894
def pointing_vector(self, s, time): """ s is the spin vector in roche coordinates time is the current time """ t = time - self._t0 longitude = self._longitude + self._dlongdt * t # define the basis vectors in the spin (primed) coordinates in terms of # the Roche coordinates. # ez' = s # ex' = (ex - s(s.ex)) /|i - s(s.ex)| # ey' = s x ex' ex = np.array([1., 0., 0.]) ezp = s exp = (ex - s*np.dot(s,ex)) eyp = np.cross(s, exp) return np.sin(self._colat)*np.cos(longitude)*exp +\ np.sin(self._colat)*np.sin(longitude)*eyp +\ np.cos(self._colat)*ezp
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s is the spin vector in roche coordinates time is the current time
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python
train
sporteasy/python-poeditor
poeditor/client.py
https://github.com/sporteasy/python-poeditor/blob/e9c0a8ab08816903122f730b73ffaab46601076c/poeditor/client.py#L232-L240
def view_project_details(self, project_id): """ Returns project's details. """ data = self._run( url_path="projects/view", id=project_id ) return self._project_formatter(data['result']['project'])
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Returns project's details.
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python
train
lins05/slackbot
slackbot/dispatcher.py
https://github.com/lins05/slackbot/blob/7195d46b9e1dc4ecfae0bdcaa91461202689bfe5/slackbot/dispatcher.py#L173-L185
def unicode_compact(func): """ Make sure the first parameter of the decorated method to be a unicode object. """ @wraps(func) def wrapped(self, text, *a, **kw): if not isinstance(text, six.text_type): text = text.decode('utf-8') return func(self, text, *a, **kw) return wrapped
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Make sure the first parameter of the decorated method to be a unicode object.
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python
train
indico/indico-plugins
piwik/indico_piwik/queries/graphs.py
https://github.com/indico/indico-plugins/blob/fe50085cc63be9b8161b09539e662e7b04e4b38e/piwik/indico_piwik/queries/graphs.py#L36-L48
def get_result(self): """Perform the call and return the graph data :return: Encoded PNG graph data string to be inserted in a `src` atribute of a HTML img tag. """ png = self.call() if png is None: return if png.startswith('GD extension must be loaded'): current_plugin.logger.warning('Piwik server answered on ImageGraph.get: %s', png) return return 'data:image/png;base64,{}'.format(b64encode(png))
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Perform the call and return the graph data :return: Encoded PNG graph data string to be inserted in a `src` atribute of a HTML img tag.
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python
train
tensorflow/tensor2tensor
tensor2tensor/models/research/moe_experiments.py
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/moe_experiments.py#L64-L79
def xmoe_tr_1d(): """Mixture of experts (16 experts). 623M Params, einsum=1.09e13 Returns: a hparams """ hparams = xmoe_tr_dense_2k() hparams.encoder_layers = ["self_att", "moe_1d"] * 4 hparams.decoder_layers = ["self_att", "enc_att", "moe_1d"] * 4 hparams.layout = "batch:batch;experts:batch" hparams.moe_hidden_size = 2048 hparams.moe_num_experts = 16 return hparams
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Mixture of experts (16 experts). 623M Params, einsum=1.09e13 Returns: a hparams
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python
train
lrq3000/pyFileFixity
pyFileFixity/lib/profilers/visual/pympler/classtracker.py
https://github.com/lrq3000/pyFileFixity/blob/fd5ef23bb13835faf1e3baa773619b86a1cc9bdf/pyFileFixity/lib/profilers/visual/pympler/classtracker.py#L298-L324
def _inject_constructor(self, cls, func, name, resolution_level, keep, trace): """ Modifying Methods in Place - after the recipe 15.7 in the Python Cookbook by Ken Seehof. The original constructors may be restored later. """ try: constructor = cls.__init__ except AttributeError: def constructor(self, *_args, **_kwargs): pass # Possible name clash between keyword arguments of the tracked class' # constructor and the curried arguments of the injected constructor. # Therefore, the additional argument has a 'magic' name to make it less # likely that an argument name clash occurs. self._observers[cls] = _ClassObserver(constructor, name, resolution_level, keep, trace) cls.__init__ = instancemethod( lambda *args, **kwds: func(self._observers[cls], *args, **kwds), None, cls )
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Modifying Methods in Place - after the recipe 15.7 in the Python Cookbook by Ken Seehof. The original constructors may be restored later.
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python
train
shoebot/shoebot
lib/web/wikipedia.py
https://github.com/shoebot/shoebot/blob/d554c1765c1899fa25727c9fc6805d221585562b/lib/web/wikipedia.py#L775-L792
def parse_gallery_images(self, markup): """ Parses images from the <gallery></gallery> section. Images inside <gallery> tags do not have outer "[[" brackets. Add these and then parse again. """ gallery = re.search(self.re["gallery"], markup) if gallery: gallery = gallery.group(1) gallery = gallery.replace("Image:", "[[Image:") gallery = gallery.replace("\n", "]]\n") images, markup = self.parse_images(gallery) return images return []
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Parses images from the <gallery></gallery> section. Images inside <gallery> tags do not have outer "[[" brackets. Add these and then parse again.
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python
valid
ff0000/scarlet
scarlet/cms/actions.py
https://github.com/ff0000/scarlet/blob/6c37befd810916a2d7ffff2cdb2dab57bcb6d12e/scarlet/cms/actions.py#L421-L428
def get_object_url(self): """ Returns the url to link to the object The get_view_url will be called on the current bundle using 'edit` as the view name. """ return self.bundle.get_view_url('edit', self.request.user, {}, self.kwargs)
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Returns the url to link to the object The get_view_url will be called on the current bundle using 'edit` as the view name.
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python
train
cds-astro/mocpy
mocpy/tmoc/tmoc.py
https://github.com/cds-astro/mocpy/blob/09472cabe537f6bfdb049eeea64d3ea57b391c21/mocpy/tmoc/tmoc.py#L288-L300
def max_time(self): """ Get the `~astropy.time.Time` time of the tmoc last observation Returns ------- max_time : `~astropy.time.Time` time of the last observation """ max_time = Time(self._interval_set.max / TimeMOC.DAY_MICRO_SEC, format='jd', scale='tdb') return max_time
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Get the `~astropy.time.Time` time of the tmoc last observation Returns ------- max_time : `~astropy.time.Time` time of the last observation
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python
train
src-d/jgit-spark-connector
python/sourced/engine/engine.py
https://github.com/src-d/jgit-spark-connector/blob/79d05a0bcf0da435685d6118828a8884e2fe4b94/python/sourced/engine/engine.py#L549-L559
def classify_languages(self): """ Returns a new DataFrame with the language data of any blob added to its row. >>> blobs_lang_df = blobs_df.classify_languages :rtype: BlobsWithLanguageDataFrame """ return BlobsWithLanguageDataFrame(self._engine_dataframe.classifyLanguages(), self._session, self._implicits)
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Returns a new DataFrame with the language data of any blob added to its row. >>> blobs_lang_df = blobs_df.classify_languages :rtype: BlobsWithLanguageDataFrame
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python
train
JoeVirtual/KonFoo
konfoo/core.py
https://github.com/JoeVirtual/KonFoo/blob/0c62ef5c2bed4deaf908b34082e4de2544532fdc/konfoo/core.py#L1220-L1235
def container_size(self): """ Returns the accumulated bit size of all fields in the `Sequence` as a tuple in the form of ``(number of bytes, remaining number of bits)``. """ length = 0 for name, item in enumerate(self): # Container if is_container(item): byte_length, bit_length = item.container_size() length += bit_length + byte_length * 8 # Field elif is_field(item): length += item.bit_size else: raise MemberTypeError(self, item, name) return divmod(length, 8)
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Returns the accumulated bit size of all fields in the `Sequence` as a tuple in the form of ``(number of bytes, remaining number of bits)``.
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python
train
iotile/coretools
transport_plugins/websocket/iotile_transport_websocket/generic/async_server.py
https://github.com/iotile/coretools/blob/2d794f5f1346b841b0dcd16c9d284e9bf2f3c6ec/transport_plugins/websocket/iotile_transport_websocket/generic/async_server.py#L191-L209
async def send_event(self, con, name, payload): """Send an event to a client connection. This method will push an event message to the client with the given name and payload. You need to have access to the the ``connection`` object for the client, which is only available once the client has connected and passed to self.prepare_conn(connection). Args: con (websockets.Connection): The connection to use to send the event. name (str): The name of the event to send. payload (object): The msgpack-serializable object so send as the event's payload. """ message = dict(type="event", name=name, payload=payload) encoded = pack(message) await con.send(encoded)
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Send an event to a client connection. This method will push an event message to the client with the given name and payload. You need to have access to the the ``connection`` object for the client, which is only available once the client has connected and passed to self.prepare_conn(connection). Args: con (websockets.Connection): The connection to use to send the event. name (str): The name of the event to send. payload (object): The msgpack-serializable object so send as the event's payload.
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python
train
BerkeleyAutomation/perception
perception/cnn.py
https://github.com/BerkeleyAutomation/perception/blob/03d9b37dd6b66896cdfe173905c9413c8c3c5df6/perception/cnn.py#L95-L129
def _load(self): """ Loads a model into weights """ if self._model_filename is None: raise ValueError('Model filename not specified') # read the input image self._graph = tf.Graph() with self._graph.as_default(): # read in filenames reader = tf.train.NewCheckpointReader(self._model_filename) # load AlexNet weights weights = AlexNetWeights() weights.conv1W = tf.Variable(reader.get_tensor("Variable")) weights.conv1b = tf.Variable(reader.get_tensor("Variable_1")) weights.conv2W = tf.Variable(reader.get_tensor("Variable_2")) weights.conv2b = tf.Variable(reader.get_tensor("Variable_3")) weights.conv3W = tf.Variable(reader.get_tensor("Variable_4")) weights.conv3b = tf.Variable(reader.get_tensor("Variable_5")) weights.conv4W = tf.Variable(reader.get_tensor("Variable_6")) weights.conv4b = tf.Variable(reader.get_tensor("Variable_7")) weights.conv5W = tf.Variable(reader.get_tensor("Variable_8")) weights.conv5b = tf.Variable(reader.get_tensor("Variable_9")) weights.fc6W = tf.Variable(reader.get_tensor("Variable_10")) weights.fc6b = tf.Variable(reader.get_tensor("Variable_11")) weights.fc7W = tf.Variable(reader.get_tensor("Variable_12")) weights.fc7b = tf.Variable(reader.get_tensor("Variable_13")) weights.fc8W = tf.Variable(reader.get_tensor("Variable_14")) weights.fc8b = tf.Variable(reader.get_tensor("Variable_15")) # form network self._input_node = tf.placeholder(tf.float32, (self._batch_size, self._im_height, self._im_width, self._num_channels)) self._output_tensor = self.build_alexnet(weights) self._feature_tensor = self.build_alexnet(weights, output_layer=self._feature_layer) self._initialized = True
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Loads a model into weights
[ "Loads", "a", "model", "into", "weights" ]
python
train
offu/WeRoBot
werobot/client.py
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/client.py#L937-L966
def send_miniprogrampage_message( self, user_id, title, appid, pagepath, thumb_media_id, kf_account=None ): """ 发送小程序卡片(要求小程序与公众号已关联) :param user_id: 用户 ID 。 就是你收到的 `Message` 的 source :param title: 小程序卡片的标题 :param appid: 小程序的 appid,要求小程序的 appid 需要与公众号有关联关系 :param pagepath: 小程序的页面路径,跟 app.json 对齐,支持参数,比如 pages/index/index?foo=bar :param thumb_media_id: 小程序卡片图片的媒体 ID,小程序卡片图片建议大小为 520*416 :param kf_account: 需要以某个客服帐号来发消息时指定的客服账户 :return: 返回的 JSON 数据包 """ data = { "touser": user_id, "msgtype": "miniprogrampage", "miniprogrampage": { "title": title, "appid": appid, "pagepath": pagepath, "thumb_media_id": thumb_media_id } } if kf_account is not None: data["customservice"] = {"kf_account": kf_account} return self.post( url="https://api.weixin.qq.com/cgi-bin/message/custom/send", data=data )
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[ "发送小程序卡片(要求小程序与公众号已关联)" ]
python
train
saltstack/salt
salt/modules/smartos_vmadm.py
https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/smartos_vmadm.py#L752-L790
def update(vm, from_file=None, key='uuid', **kwargs): ''' Update a new vm vm : string vm to be updated from_file : string json file to update the vm with -- if present, all other options will be ignored key : string [uuid|alias|hostname] value type of 'vm' parameter kwargs : string|int|... options to update for the vm CLI Example: .. code-block:: bash salt '*' vmadm.update vm=186da9ab-7392-4f55-91a5-b8f1fe770543 from_file=/tmp/new_vm.json salt '*' vmadm.update vm=nacl key=alias from_file=/tmp/new_vm.json salt '*' vmadm.update vm=186da9ab-7392-4f55-91a5-b8f1fe770543 max_physical_memory=1024 ''' ret = {} # prepare vmcfg vmcfg = {} kwargs = salt.utils.args.clean_kwargs(**kwargs) for k, v in six.iteritems(kwargs): vmcfg[k] = v if key not in ['uuid', 'alias', 'hostname']: ret['Error'] = 'Key must be either uuid, alias or hostname' return ret uuid = lookup('{0}={1}'.format(key, vm), one=True) if 'Error' in uuid: return uuid if from_file: return _create_update_from_file('update', uuid, path=from_file) else: return _create_update_from_cfg('update', uuid, vmcfg=vmcfg)
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Update a new vm vm : string vm to be updated from_file : string json file to update the vm with -- if present, all other options will be ignored key : string [uuid|alias|hostname] value type of 'vm' parameter kwargs : string|int|... options to update for the vm CLI Example: .. code-block:: bash salt '*' vmadm.update vm=186da9ab-7392-4f55-91a5-b8f1fe770543 from_file=/tmp/new_vm.json salt '*' vmadm.update vm=nacl key=alias from_file=/tmp/new_vm.json salt '*' vmadm.update vm=186da9ab-7392-4f55-91a5-b8f1fe770543 max_physical_memory=1024
[ "Update", "a", "new", "vm" ]
python
train
astroduff/commah
commah/commah.py
https://github.com/astroduff/commah/blob/3ec70338c5123a053c79ddcf2cb3beac26bc9137/commah/commah.py#L219-L235
def _int_growth(z, **cosmo): """ Returns integral of the linear growth factor from z=200 to z=z """ zmax = 200 if hasattr(z, "__len__"): for zval in z: assert(zval < zmax) else: assert(z < zmax) y, yerr = scipy.integrate.quad( lambda z: (1 + z)/(cosmo['omega_M_0']*(1 + z)**3 + cosmo['omega_lambda_0'])**(1.5), z, zmax) return(y)
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Returns integral of the linear growth factor from z=200 to z=z
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python
train
DEIB-GECO/PyGMQL
gmql/dataset/GMQLDataset.py
https://github.com/DEIB-GECO/PyGMQL/blob/e58b2f9402a86056dcda484a32e3de0bb06ed991/gmql/dataset/GMQLDataset.py#L1133-L1182
def union(self, other, left_name="LEFT", right_name="RIGHT"): """ *Wrapper of* ``UNION`` The UNION operation is used to integrate homogeneous or heterogeneous samples of two datasets within a single dataset; for each sample of either one of the input datasets, a sample is created in the result as follows: * its metadata are the same as in the original sample; * its schema is the schema of the first (left) input dataset; new identifiers are assigned to each output sample; * its regions are the same (in coordinates and attribute values) as in the original sample. Region attributes which are missing in an input dataset sample (w.r.t. the merged schema) are set to null. :param other: a GMQLDataset :param left_name: name that you want to assign to the left dataset :param right_name: name tha t you want to assign to the right dataset :return: a new GMQLDataset Example of usage:: import gmql as gl d1 = gl.get_example_dataset("Example_Dataset_1") d2 = gl.get_example_dataset("Example_Dataset_2") result = d1.union(other=d2, left_name="D1", right_name="D2") """ if not isinstance(left_name, str) or \ not isinstance(right_name, str): raise TypeError("left_name and right_name must be strings. " "{} - {} was provided".format(type(left_name), type(right_name))) if isinstance(other, GMQLDataset): other_idx = other.__index else: raise TypeError("other must be a GMQLDataset. " "{} was provided".format(type(other))) if len(left_name) == 0 or len(right_name) == 0: raise ValueError("left_name and right_name must not be empty") new_index = self.opmng.union(self.__index, other_idx, left_name, right_name) new_local_sources, new_remote_sources = self.__combine_sources(self, other) new_location = self.__combine_locations(self, other) return GMQLDataset(index=new_index, location=new_location, local_sources=new_local_sources, remote_sources=new_remote_sources, meta_profile=self.meta_profile)
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*Wrapper of* ``UNION`` The UNION operation is used to integrate homogeneous or heterogeneous samples of two datasets within a single dataset; for each sample of either one of the input datasets, a sample is created in the result as follows: * its metadata are the same as in the original sample; * its schema is the schema of the first (left) input dataset; new identifiers are assigned to each output sample; * its regions are the same (in coordinates and attribute values) as in the original sample. Region attributes which are missing in an input dataset sample (w.r.t. the merged schema) are set to null. :param other: a GMQLDataset :param left_name: name that you want to assign to the left dataset :param right_name: name tha t you want to assign to the right dataset :return: a new GMQLDataset Example of usage:: import gmql as gl d1 = gl.get_example_dataset("Example_Dataset_1") d2 = gl.get_example_dataset("Example_Dataset_2") result = d1.union(other=d2, left_name="D1", right_name="D2")
[ "*", "Wrapper", "of", "*", "UNION" ]
python
train
glamp/bashplotlib
bashplotlib/utils/helpers.py
https://github.com/glamp/bashplotlib/blob/f7533172c4dc912b5accae42edd5c0f655d7468f/bashplotlib/utils/helpers.py#L36-L44
def printcolour(text, sameline=False, colour=get_colour("ENDC")): """ Print color text using escape codes """ if sameline: sep = '' else: sep = '\n' sys.stdout.write(get_colour(colour) + text + bcolours["ENDC"] + sep)
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Print color text using escape codes
[ "Print", "color", "text", "using", "escape", "codes" ]
python
train
facelessuser/soupsieve
soupsieve/css_parser.py
https://github.com/facelessuser/soupsieve/blob/24859cc3e756ebf46b75547d49c6b4a7bf35ee82/soupsieve/css_parser.py#L754-L794
def parse_has_combinator(self, sel, m, has_selector, selectors, rel_type, index): """Parse combinator tokens.""" combinator = m.group('relation').strip() if not combinator: combinator = WS_COMBINATOR if combinator == COMMA_COMBINATOR: if not has_selector: # If we've not captured any selector parts, the comma is either at the beginning of the pattern # or following another comma, both of which are unexpected. Commas must split selectors. raise SelectorSyntaxError( "The combinator '{}' at postion {}, must have a selector before it".format(combinator, index), self.pattern, index ) sel.rel_type = rel_type selectors[-1].relations.append(sel) rel_type = ":" + WS_COMBINATOR selectors.append(_Selector()) else: if has_selector: # End the current selector and associate the leading combinator with this selector. sel.rel_type = rel_type selectors[-1].relations.append(sel) elif rel_type[1:] != WS_COMBINATOR: # It's impossible to have two whitespace combinators after each other as the patterns # will gobble up trailing whitespace. It is also impossible to have a whitespace # combinator after any other kind for the same reason. But we could have # multiple non-whitespace combinators. So if the current combinator is not a whitespace, # then we've hit the multiple combinator case, so we should fail. raise SelectorSyntaxError( 'The multiple combinators at position {}'.format(index), self.pattern, index ) # Set the leading combinator for the next selector. rel_type = ':' + combinator sel = _Selector() has_selector = False return has_selector, sel, rel_type
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Parse combinator tokens.
[ "Parse", "combinator", "tokens", "." ]
python
train
CityOfZion/neo-python
neo/Core/TX/Transaction.py
https://github.com/CityOfZion/neo-python/blob/fe90f62e123d720d4281c79af0598d9df9e776fb/neo/Core/TX/Transaction.py#L419-L429
def Deserialize(self, reader): """ Deserialize full object. Args: reader (neo.IO.BinaryReader): """ self.DeserializeUnsigned(reader) self.scripts = reader.ReadSerializableArray() self.OnDeserialized()
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Deserialize full object. Args: reader (neo.IO.BinaryReader):
[ "Deserialize", "full", "object", "." ]
python
train
aleju/imgaug
imgaug/augmentables/polys.py
https://github.com/aleju/imgaug/blob/786be74aa855513840113ea523c5df495dc6a8af/imgaug/augmentables/polys.py#L950-L971
def deepcopy(self, exterior=None, label=None): """ Create a deep copy of the Polygon object. Parameters ---------- exterior : list of Keypoint or list of tuple or (N,2) ndarray, optional List of points defining the polygon. See `imgaug.Polygon.__init__` for details. label : None or str If not None, then the label of the copied object will be set to this value. Returns ------- imgaug.Polygon Deep copy. """ return Polygon( exterior=np.copy(self.exterior) if exterior is None else exterior, label=self.label if label is None else label )
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Create a deep copy of the Polygon object. Parameters ---------- exterior : list of Keypoint or list of tuple or (N,2) ndarray, optional List of points defining the polygon. See `imgaug.Polygon.__init__` for details. label : None or str If not None, then the label of the copied object will be set to this value. Returns ------- imgaug.Polygon Deep copy.
[ "Create", "a", "deep", "copy", "of", "the", "Polygon", "object", "." ]
python
valid
IAMconsortium/pyam
pyam/timeseries.py
https://github.com/IAMconsortium/pyam/blob/4077929ca6e7be63a0e3ecf882c5f1da97b287bf/pyam/timeseries.py#L10-L32
def fill_series(x, year): """Returns the value of a timeseries (indexed over years) for a year by linear interpolation. Parameters ---------- x: pandas.Series a timeseries to be interpolated year: int year of interpolation """ x = x.dropna() if year in x.index and not np.isnan(x[year]): return x[year] else: prev = [i for i in x.index if i < year] nxt = [i for i in x.index if i > year] if prev and nxt: p = max(prev) n = min(nxt) return ((n - year) * x[p] + (year - p) * x[n]) / (n - p) else: return np.nan
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Returns the value of a timeseries (indexed over years) for a year by linear interpolation. Parameters ---------- x: pandas.Series a timeseries to be interpolated year: int year of interpolation
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python
train
domainaware/parsedmarc
parsedmarc/__init__.py
https://github.com/domainaware/parsedmarc/blob/ecc9fd434c23d896ccd1f35795ccc047f946ed05/parsedmarc/__init__.py#L211-L339
def parse_aggregate_report_xml(xml, nameservers=None, timeout=2.0, parallel=False): """Parses a DMARC XML report string and returns a consistent OrderedDict Args: xml (str): A string of DMARC aggregate report XML nameservers (list): A list of one or more nameservers to use (Cloudflare's public DNS resolvers by default) timeout (float): Sets the DNS timeout in seconds parallel (bool): Parallel processing Returns: OrderedDict: The parsed aggregate DMARC report """ errors = [] try: xmltodict.parse(xml)["feedback"] except Exception as e: errors.append(e.__str__()) try: # Replace XML header (sometimes they are invalid) xml = xml_header_regex.sub("<?xml version=\"1.0\"?>", xml) # Remove invalid schema tags xml = xml_schema_regex.sub('', xml) report = xmltodict.parse(xml)["feedback"] report_metadata = report["report_metadata"] schema = "draft" if "version" in report: schema = report["version"] new_report = OrderedDict([("xml_schema", schema)]) new_report_metadata = OrderedDict() if report_metadata["org_name"] is None: if report_metadata["email"] is not None: report_metadata["org_name"] = report_metadata[ "email"].split("@")[-1] org_name = report_metadata["org_name"] if org_name is not None: org_name = get_base_domain(org_name) new_report_metadata["org_name"] = org_name new_report_metadata["org_email"] = report_metadata["email"] extra = None if "extra_contact_info" in report_metadata: extra = report_metadata["extra_contact_info"] new_report_metadata["org_extra_contact_info"] = extra new_report_metadata["report_id"] = report_metadata["report_id"] report_id = new_report_metadata["report_id"] report_id = report_id.replace("<", "").replace(">", "").split("@")[0] new_report_metadata["report_id"] = report_id date_range = report["report_metadata"]["date_range"] date_range["begin"] = timestamp_to_human(date_range["begin"]) date_range["end"] = timestamp_to_human(date_range["end"]) new_report_metadata["begin_date"] = date_range["begin"] new_report_metadata["end_date"] = date_range["end"] if "error" in report["report_metadata"]: if type(report["report_metadata"]["error"]) != list: errors = [report["report_metadata"]["error"]] else: errors = report["report_metadata"]["error"] new_report_metadata["errors"] = errors new_report["report_metadata"] = new_report_metadata records = [] policy_published = report["policy_published"] new_policy_published = OrderedDict() new_policy_published["domain"] = policy_published["domain"] adkim = "r" if "adkim" in policy_published: if policy_published["adkim"] is not None: adkim = policy_published["adkim"] new_policy_published["adkim"] = adkim aspf = "r" if "aspf" in policy_published: if policy_published["aspf"] is not None: aspf = policy_published["aspf"] new_policy_published["aspf"] = aspf new_policy_published["p"] = policy_published["p"] sp = new_policy_published["p"] if "sp" in policy_published: if policy_published["sp"] is not None: sp = report["policy_published"]["sp"] new_policy_published["sp"] = sp pct = "100" if "pct" in policy_published: if policy_published["pct"] is not None: pct = report["policy_published"]["pct"] new_policy_published["pct"] = pct fo = "0" if "fo" in policy_published: if policy_published["fo"] is not None: fo = report["policy_published"]["fo"] new_policy_published["fo"] = fo new_report["policy_published"] = new_policy_published if type(report["record"]) == list: for record in report["record"]: report_record = _parse_report_record(record, nameservers=nameservers, dns_timeout=timeout, parallel=parallel) records.append(report_record) else: report_record = _parse_report_record(report["record"], nameservers=nameservers, dns_timeout=timeout, parallel=parallel) records.append(report_record) new_report["records"] = records return new_report except expat.ExpatError as error: raise InvalidAggregateReport( "Invalid XML: {0}".format(error.__str__())) except KeyError as error: raise InvalidAggregateReport( "Missing field: {0}".format(error.__str__())) except AttributeError: raise InvalidAggregateReport("Report missing required section") except Exception as error: raise InvalidAggregateReport( "Unexpected error: {0}".format(error.__str__()))
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Parses a DMARC XML report string and returns a consistent OrderedDict Args: xml (str): A string of DMARC aggregate report XML nameservers (list): A list of one or more nameservers to use (Cloudflare's public DNS resolvers by default) timeout (float): Sets the DNS timeout in seconds parallel (bool): Parallel processing Returns: OrderedDict: The parsed aggregate DMARC report
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python
test
jaraco/jaraco.text
jaraco/text.py
https://github.com/jaraco/jaraco.text/blob/0fe070e9241cb1fdb737516a3f57da94a2618376/jaraco/text.py#L368-L375
def common_prefix(s1, s2): """ Return the common prefix of two lines. """ index = min(len(s1), len(s2)) while s1[:index] != s2[:index]: index -= 1 return s1[:index]
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Return the common prefix of two lines.
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python
train
kevinconway/venvctrl
venvctrl/venv/base.py
https://github.com/kevinconway/venvctrl/blob/36d4e0e4d5ebced6385a6ade1198f4769ff2df41/venvctrl/venv/base.py#L125-L129
def dirs(self): """Get an iter of VenvDirs within the directory.""" contents = self.paths contents = (VenvDir(path.path) for path in contents if path.is_dir) return contents
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Get an iter of VenvDirs within the directory.
[ "Get", "an", "iter", "of", "VenvDirs", "within", "the", "directory", "." ]
python
train
QUANTAXIS/QUANTAXIS
QUANTAXIS/QAARP/QAAccount.py
https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L1850-L1857
def change_cash(self, money): """ 外部操作|高危| """ res = self.cash[-1] + money if res >= 0: # 高危操作 self.cash[-1] = res
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外部操作|高危|
[ "外部操作|高危|" ]
python
train
Becksteinlab/GromacsWrapper
gromacs/fileformats/xvg.py
https://github.com/Becksteinlab/GromacsWrapper/blob/d4f9a8cb6f48292732cf7c7e4ef4a6d2ccbc51b9/gromacs/fileformats/xvg.py#L550-L635
def plot(self, **kwargs): """Plot xvg file data. The first column of the data is always taken as the abscissa X. Additional columns are plotted as ordinates Y1, Y2, ... In the special case that there is only a single column then this column is plotted against the index, i.e. (N, Y). :Keywords: *columns* : list Select the columns of the data to be plotted; the list is used as a numpy.array extended slice. The default is to use all columns. Columns are selected *after* a transform. *transform* : function function ``transform(array) -> array`` which transforms the original array; must return a 2D numpy array of shape [X, Y1, Y2, ...] where X, Y1, ... are column vectors. By default the transformation is the identity [``lambda x: x``]. *maxpoints* : int limit the total number of data points; matplotlib has issues processing png files with >100,000 points and pdfs take forever to display. Set to ``None`` if really all data should be displayed. At the moment we simply decimate the data at regular intervals. [10000] *method* method to decimate the data to *maxpoints*, see :meth:`XVG.decimate` for details *color* single color (used for all plots); sequence of colors (will be repeated as necessary); or a matplotlib colormap (e.g. "jet", see :mod:`matplotlib.cm`). The default is to use the :attr:`XVG.default_color_cycle`. *ax* plot into given axes or create new one if ``None`` [``None``] *kwargs* All other keyword arguments are passed on to :func:`matplotlib.pyplot.plot`. :Returns: *ax* axes instance """ columns = kwargs.pop('columns', Ellipsis) # slice for everything maxpoints = kwargs.pop('maxpoints', self.maxpoints_default) transform = kwargs.pop('transform', lambda x: x) # default is identity transformation method = kwargs.pop('method', "mean") ax = kwargs.pop('ax', None) if columns is Ellipsis or columns is None: columns = numpy.arange(self.array.shape[0]) if len(columns) == 0: raise MissingDataError("plot() needs at least one column of data") if len(self.array.shape) == 1 or self.array.shape[0] == 1: # special case: plot against index; plot would do this automatically but # we'll just produce our own xdata and pretend that this was X all along a = numpy.ravel(self.array) X = numpy.arange(len(a)) a = numpy.vstack((X, a)) columns = [0] + [c+1 for c in columns] else: a = self.array color = kwargs.pop('color', self.default_color_cycle) try: cmap = matplotlib.cm.get_cmap(color) colors = cmap(matplotlib.colors.Normalize()(numpy.arange(len(columns[1:]), dtype=float))) except TypeError: colors = cycle(utilities.asiterable(color)) if ax is None: ax = plt.gca() # (decimate/smooth o slice o transform)(array) a = self.decimate(method, numpy.asarray(transform(a))[columns], maxpoints=maxpoints) # now deal with infs, nans etc AFTER all transformations (needed for plotting across inf/nan) ma = numpy.ma.MaskedArray(a, mask=numpy.logical_not(numpy.isfinite(a))) # finally plot (each column separately to catch empty sets) for column, color in zip(range(1,len(columns)), colors): if len(ma[column]) == 0: warnings.warn("No data to plot for column {column:d}".format(**vars()), category=MissingDataWarning) kwargs['color'] = color ax.plot(ma[0], ma[column], **kwargs) # plot all other columns in parallel return ax
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Plot xvg file data. The first column of the data is always taken as the abscissa X. Additional columns are plotted as ordinates Y1, Y2, ... In the special case that there is only a single column then this column is plotted against the index, i.e. (N, Y). :Keywords: *columns* : list Select the columns of the data to be plotted; the list is used as a numpy.array extended slice. The default is to use all columns. Columns are selected *after* a transform. *transform* : function function ``transform(array) -> array`` which transforms the original array; must return a 2D numpy array of shape [X, Y1, Y2, ...] where X, Y1, ... are column vectors. By default the transformation is the identity [``lambda x: x``]. *maxpoints* : int limit the total number of data points; matplotlib has issues processing png files with >100,000 points and pdfs take forever to display. Set to ``None`` if really all data should be displayed. At the moment we simply decimate the data at regular intervals. [10000] *method* method to decimate the data to *maxpoints*, see :meth:`XVG.decimate` for details *color* single color (used for all plots); sequence of colors (will be repeated as necessary); or a matplotlib colormap (e.g. "jet", see :mod:`matplotlib.cm`). The default is to use the :attr:`XVG.default_color_cycle`. *ax* plot into given axes or create new one if ``None`` [``None``] *kwargs* All other keyword arguments are passed on to :func:`matplotlib.pyplot.plot`. :Returns: *ax* axes instance
[ "Plot", "xvg", "file", "data", "." ]
python
valid
zimeon/iiif
iiif/static.py
https://github.com/zimeon/iiif/blob/9d10018d01202fa2a76dfa61598dc6eca07b471f/iiif/static.py#L370-L436
def write_html(self, html_dir='/tmp', include_osd=False, osd_width=500, osd_height=500): """Write HTML test page using OpenSeadragon for the tiles generated. Assumes that the generate(..) method has already been called to set up identifier etc. Parameters: html_dir - output directory for HTML files, will be created if it does not already exist include_osd - true to include OpenSeadragon code osd_width - width of OpenSeadragon pane in pixels osd_height - height of OpenSeadragon pane in pixels """ osd_config = self.get_osd_config(self.osd_version) osd_base = osd_config['base'] osd_dir = osd_config['dir'] # relative to base osd_js = os.path.join(osd_dir, osd_config['js']) osd_images = os.path.join(osd_dir, osd_config['images']) if (os.path.isdir(html_dir)): # Exists, fine pass elif (os.path.isfile(html_dir)): raise IIIFStaticError( "Can't write to directory %s: a file of that name exists" % html_dir) else: os.makedirs(html_dir) self.logger.info("Writing HTML to %s" % (html_dir)) with open(os.path.join(self.template_dir, 'static_osd.html'), 'r') as f: template = f.read() outfile = self.identifier + '.html' outpath = os.path.join(html_dir, outfile) with open(outpath, 'w') as f: info_json_uri = '/'.join([self.identifier, 'info.json']) if (self.prefix): info_json_uri = '/'.join([self.prefix, info_json_uri]) d = dict(identifier=self.identifier, api_version=self.api_version, osd_version=self.osd_version, osd_uri=osd_js, osd_images_prefix=osd_images, osd_height=osd_width, osd_width=osd_height, info_json_uri=info_json_uri) f.write(Template(template).safe_substitute(d)) self.logger.info("%s / %s" % (html_dir, outfile)) # Do we want to copy OSD in there too? If so, do it only if # we haven't already if (include_osd): if (self.copied_osd): self.logger.info("OpenSeadragon already copied") else: # Make directory, copy JavaScript and icons (from osd_images) osd_path = os.path.join(html_dir, osd_dir) if (not os.path.isdir(osd_path)): os.makedirs(osd_path) shutil.copyfile(os.path.join(osd_base, osd_js), os.path.join(html_dir, osd_js)) self.logger.info("%s / %s" % (html_dir, osd_js)) osd_images_path = os.path.join(html_dir, osd_images) if (os.path.isdir(osd_images_path)): self.logger.warning( "OpenSeadragon images directory (%s) already exists, skipping" % osd_images_path) else: shutil.copytree(os.path.join(osd_base, osd_images), osd_images_path) self.logger.info("%s / %s/*" % (html_dir, osd_images)) self.copied_osd = True
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Write HTML test page using OpenSeadragon for the tiles generated. Assumes that the generate(..) method has already been called to set up identifier etc. Parameters: html_dir - output directory for HTML files, will be created if it does not already exist include_osd - true to include OpenSeadragon code osd_width - width of OpenSeadragon pane in pixels osd_height - height of OpenSeadragon pane in pixels
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python
train
kwikteam/phy
phy/stats/ccg.py
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/stats/ccg.py#L57-L177
def correlograms(spike_times, spike_clusters, cluster_ids=None, sample_rate=1., bin_size=None, window_size=None, symmetrize=True, ): """Compute all pairwise cross-correlograms among the clusters appearing in `spike_clusters`. Parameters ---------- spike_times : array-like Spike times in seconds. spike_clusters : array-like Spike-cluster mapping. cluster_ids : array-like The list of unique clusters, in any order. That order will be used in the output array. bin_size : float Size of the bin, in seconds. window_size : float Size of the window, in seconds. Returns ------- correlograms : array A `(n_clusters, n_clusters, winsize_samples)` array with all pairwise CCGs. """ assert sample_rate > 0. assert np.all(np.diff(spike_times) >= 0), ("The spike times must be " "increasing.") # Get the spike samples. spike_times = np.asarray(spike_times, dtype=np.float64) spike_samples = (spike_times * sample_rate).astype(np.int64) spike_clusters = _as_array(spike_clusters) assert spike_samples.ndim == 1 assert spike_samples.shape == spike_clusters.shape # Find `binsize`. bin_size = np.clip(bin_size, 1e-5, 1e5) # in seconds binsize = int(sample_rate * bin_size) # in samples assert binsize >= 1 # Find `winsize_bins`. window_size = np.clip(window_size, 1e-5, 1e5) # in seconds winsize_bins = 2 * int(.5 * window_size / bin_size) + 1 assert winsize_bins >= 1 assert winsize_bins % 2 == 1 # Take the cluster oder into account. if cluster_ids is None: clusters = _unique(spike_clusters) else: clusters = _as_array(cluster_ids) n_clusters = len(clusters) # Like spike_clusters, but with 0..n_clusters-1 indices. spike_clusters_i = _index_of(spike_clusters, clusters) # Shift between the two copies of the spike trains. shift = 1 # At a given shift, the mask precises which spikes have matching spikes # within the correlogram time window. mask = np.ones_like(spike_samples, dtype=np.bool) correlograms = _create_correlograms_array(n_clusters, winsize_bins) # The loop continues as long as there is at least one spike with # a matching spike. while mask[:-shift].any(): # Number of time samples between spike i and spike i+shift. spike_diff = _diff_shifted(spike_samples, shift) # Binarize the delays between spike i and spike i+shift. spike_diff_b = spike_diff // binsize # Spikes with no matching spikes are masked. mask[:-shift][spike_diff_b > (winsize_bins // 2)] = False # Cache the masked spike delays. m = mask[:-shift].copy() d = spike_diff_b[m] # # Update the masks given the clusters to update. # m0 = np.in1d(spike_clusters[:-shift], clusters) # m = m & m0 # d = spike_diff_b[m] d = spike_diff_b[m] # Find the indices in the raveled correlograms array that need # to be incremented, taking into account the spike clusters. indices = np.ravel_multi_index((spike_clusters_i[:-shift][m], spike_clusters_i[+shift:][m], d), correlograms.shape) # Increment the matching spikes in the correlograms array. _increment(correlograms.ravel(), indices) shift += 1 # Remove ACG peaks. correlograms[np.arange(n_clusters), np.arange(n_clusters), 0] = 0 if symmetrize: return _symmetrize_correlograms(correlograms) else: return correlograms
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Compute all pairwise cross-correlograms among the clusters appearing in `spike_clusters`. Parameters ---------- spike_times : array-like Spike times in seconds. spike_clusters : array-like Spike-cluster mapping. cluster_ids : array-like The list of unique clusters, in any order. That order will be used in the output array. bin_size : float Size of the bin, in seconds. window_size : float Size of the window, in seconds. Returns ------- correlograms : array A `(n_clusters, n_clusters, winsize_samples)` array with all pairwise CCGs.
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python
train
numenta/nupic
src/nupic/algorithms/backtracking_tm_cpp.py
https://github.com/numenta/nupic/blob/5922fafffdccc8812e72b3324965ad2f7d4bbdad/src/nupic/algorithms/backtracking_tm_cpp.py#L625-L646
def getSegmentOnCell(self, c, i, segIdx): """ Overrides :meth:`nupic.algorithms.backtracking_tm.BacktrackingTM.getSegmentOnCell`. """ segList = self.cells4.getNonEmptySegList(c,i) seg = self.cells4.getSegment(c, i, segList[segIdx]) numSyn = seg.size() assert numSyn != 0 # Accumulate segment information result = [] result.append([int(segIdx), bool(seg.isSequenceSegment()), seg.getPositiveActivations(), seg.getTotalActivations(), seg.getLastActiveIteration(), seg.getLastPosDutyCycle(), seg.getLastPosDutyCycleIteration()]) for s in xrange(numSyn): sc, si = self.getColCellIdx(seg.getSrcCellIdx(s)) result.append([int(sc), int(si), seg.getPermanence(s)]) return result
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Overrides :meth:`nupic.algorithms.backtracking_tm.BacktrackingTM.getSegmentOnCell`.
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python
valid
ghcollin/multitables
multitables.py
https://github.com/ghcollin/multitables/blob/9654a45800289a20e66d2b0e0666149f0d370f93/multitables.py#L202-L217
def get(self): """ Fetch the next item in the queue. Blocks until an item is ready. :return: The next unsigned integer in the queue. """ with self.cvar: while True: if self.size.value > 0: rval = self.vals[self.tail.value] self.tail.value = (self.tail.value + 1) % len(self.vals) self.size.value -= 1 if rval == -2: return QueueClosed assert(rval >= 0) return rval self.cvar.wait()
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Fetch the next item in the queue. Blocks until an item is ready. :return: The next unsigned integer in the queue.
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python
test
QuantEcon/QuantEcon.py
quantecon/game_theory/vertex_enumeration.py
https://github.com/QuantEcon/QuantEcon.py/blob/26a66c552f2a73967d7efb6e1f4b4c4985a12643/quantecon/game_theory/vertex_enumeration.py#L88-L123
def _vertex_enumeration_gen(labelings_bits_tup, equations_tup, trans_recips): """ Main body of `vertex_enumeration_gen`. Parameters ---------- labelings_bits_tup : tuple(ndarray(np.uint64, ndim=1)) Tuple of ndarrays of integers representing labelings of the vertices of the best response polytopes. equations_tup : tuple(ndarray(float, ndim=2)) Tuple of ndarrays containing the hyperplane equations of the polar polytopes. trans_recips : tuple(scalar(float)) Tuple of the reciprocals of the translations. """ m, n = equations_tup[0].shape[1] - 1, equations_tup[1].shape[1] - 1 num_vertices0, num_vertices1 = \ equations_tup[0].shape[0], equations_tup[1].shape[0] ZERO_LABELING0_BITS = (np.uint64(1) << np.uint64(m)) - np.uint64(1) COMPLETE_LABELING_BITS = (np.uint64(1) << np.uint64(m+n)) - np.uint64(1) for i in range(num_vertices0): if labelings_bits_tup[0][i] == ZERO_LABELING0_BITS: continue for j in range(num_vertices1): xor = labelings_bits_tup[0][i] ^ labelings_bits_tup[1][j] if xor == COMPLETE_LABELING_BITS: yield _get_mixed_actions( labelings_bits_tup[0][i], (equations_tup[0][i], equations_tup[1][j]), trans_recips ) break
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Main body of `vertex_enumeration_gen`. Parameters ---------- labelings_bits_tup : tuple(ndarray(np.uint64, ndim=1)) Tuple of ndarrays of integers representing labelings of the vertices of the best response polytopes. equations_tup : tuple(ndarray(float, ndim=2)) Tuple of ndarrays containing the hyperplane equations of the polar polytopes. trans_recips : tuple(scalar(float)) Tuple of the reciprocals of the translations.
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python
train
heikomuller/sco-client
scocli/modelrun.py
https://github.com/heikomuller/sco-client/blob/c4afab71297f73003379bba4c1679be9dcf7cef8/scocli/modelrun.py#L311-L365
def create(url, model_id, name, arguments, properties=None): """Create a new model run using the given SCO-API create model run Url. Parameters ---------- url : string Url to POST model run create model run request model_id : string Unique model identifier name : string User-defined name for model run arguments : Dictionary Dictionary of arguments for model run properties : Dictionary, optional Set of additional properties for created mode run. Returns ------- string Url of created model run resource """ # Create list of model run arguments. Catch TypeErrors if arguments is # not a list. obj_args = [] try: for arg in arguments: obj_args.append({'name' : arg, 'value' : arguments[arg]}) except TypeError as ex: raise ValueError('invalid argument set') # Create request body and send POST request to given Url body = { 'model' : model_id, 'name' : name, 'arguments' : obj_args, } # Create list of properties if given. Catch TypeErrors if properties is # not a list. if not properties is None: obj_props = [] try: for key in properties: if key != 'name': obj_props.append({'key':key, 'value':properties[key]}) except TypeError as ex: raise ValueError('invalid property set') body['properties'] = obj_props # POST create model run request try: req = urllib2.Request(url) req.add_header('Content-Type', 'application/json') response = urllib2.urlopen(req, json.dumps(body)) except urllib2.URLError as ex: raise ValueError(str(ex)) # Get model run self reference from successful response return references_to_dict(json.load(response)['links'])[REF_SELF]
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python
train
datastax/python-driver
cassandra/cluster.py
https://github.com/datastax/python-driver/blob/30a80d0b798b1f45f8cb77163b1fa791f3e3ca29/cassandra/cluster.py#L4386-L4400
def one(self): """ Return a single row of the results or None if empty. This is basically a shortcut to `result_set.current_rows[0]` and should only be used when you know a query returns a single row. Consider using an iterator if the ResultSet contains more than one row. """ row = None if self._current_rows: try: row = self._current_rows[0] except TypeError: # generator object is not subscriptable, PYTHON-1026 row = next(iter(self._current_rows)) return row
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Return a single row of the results or None if empty. This is basically a shortcut to `result_set.current_rows[0]` and should only be used when you know a query returns a single row. Consider using an iterator if the ResultSet contains more than one row.
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python
train
sorgerlab/indra
indra/sources/biopax/processor.py
https://github.com/sorgerlab/indra/blob/79a70415832c5702d7a820c7c9ccc8e25010124b/indra/sources/biopax/processor.py#L1398-L1404
def _is_small_molecule(pe): """Return True if the element is a small molecule""" val = isinstance(pe, _bp('SmallMolecule')) or \ isinstance(pe, _bpimpl('SmallMolecule')) or \ isinstance(pe, _bp('SmallMoleculeReference')) or \ isinstance(pe, _bpimpl('SmallMoleculeReference')) return val
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Return True if the element is a small molecule
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python
train
census-instrumentation/opencensus-python
opencensus/trace/stack_trace.py
https://github.com/census-instrumentation/opencensus-python/blob/992b223f7e34c5dcb65922b7d5c827e7a1351e7d/opencensus/trace/stack_trace.py#L86-L103
def format_stack_frame_json(self): """Convert StackFrame object to json format.""" stack_frame_json = {} stack_frame_json['function_name'] = get_truncatable_str( self.func_name) stack_frame_json['original_function_name'] = get_truncatable_str( self.original_func_name) stack_frame_json['file_name'] = get_truncatable_str(self.file_name) stack_frame_json['line_number'] = self.line_num stack_frame_json['column_number'] = self.col_num stack_frame_json['load_module'] = { 'module': get_truncatable_str(self.load_module), 'build_id': get_truncatable_str(self.build_id), } stack_frame_json['source_version'] = get_truncatable_str( self.source_version) return stack_frame_json
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Convert StackFrame object to json format.
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python
train
googleapis/google-cloud-python
bigtable/google/cloud/bigtable/client.py
https://github.com/googleapis/google-cloud-python/blob/85e80125a59cb10f8cb105f25ecc099e4b940b50/bigtable/google/cloud/bigtable/client.py#L230-L251
def instance_admin_client(self): """Getter for the gRPC stub used for the Table Admin API. For example: .. literalinclude:: snippets.py :start-after: [START bigtable_instance_admin_client] :end-before: [END bigtable_instance_admin_client] :rtype: :class:`.bigtable_admin_pb2.BigtableInstanceAdmin` :returns: A BigtableInstanceAdmin instance. :raises: :class:`ValueError <exceptions.ValueError>` if the current client is not an admin client or if it has not been :meth:`start`-ed. """ if self._instance_admin_client is None: if not self._admin: raise ValueError("Client is not an admin client.") self._instance_admin_client = _create_gapic_client( bigtable_admin_v2.BigtableInstanceAdminClient )(self) return self._instance_admin_client
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Getter for the gRPC stub used for the Table Admin API. For example: .. literalinclude:: snippets.py :start-after: [START bigtable_instance_admin_client] :end-before: [END bigtable_instance_admin_client] :rtype: :class:`.bigtable_admin_pb2.BigtableInstanceAdmin` :returns: A BigtableInstanceAdmin instance. :raises: :class:`ValueError <exceptions.ValueError>` if the current client is not an admin client or if it has not been :meth:`start`-ed.
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python
train
pennersr/django-allauth
allauth/socialaccount/providers/base.py
https://github.com/pennersr/django-allauth/blob/f70cb3d622f992f15fe9b57098e0b328445b664e/allauth/socialaccount/providers/base.py#L65-L99
def sociallogin_from_response(self, request, response): """ Instantiates and populates a `SocialLogin` model based on the data retrieved in `response`. The method does NOT save the model to the DB. Data for `SocialLogin` will be extracted from `response` with the help of the `.extract_uid()`, `.extract_extra_data()`, `.extract_common_fields()`, and `.extract_email_addresses()` methods. :param request: a Django `HttpRequest` object. :param response: object retrieved via the callback response of the social auth provider. :return: A populated instance of the `SocialLogin` model (unsaved). """ # NOTE: Avoid loading models at top due to registry boot... from allauth.socialaccount.models import SocialLogin, SocialAccount adapter = get_adapter(request) uid = self.extract_uid(response) extra_data = self.extract_extra_data(response) common_fields = self.extract_common_fields(response) socialaccount = SocialAccount(extra_data=extra_data, uid=uid, provider=self.id) email_addresses = self.extract_email_addresses(response) self.cleanup_email_addresses(common_fields.get('email'), email_addresses) sociallogin = SocialLogin(account=socialaccount, email_addresses=email_addresses) user = sociallogin.user = adapter.new_user(request, sociallogin) user.set_unusable_password() adapter.populate_user(request, sociallogin, common_fields) return sociallogin
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python
train
Azure/azure-uamqp-python
uamqp/client.py
https://github.com/Azure/azure-uamqp-python/blob/b67e4fcaf2e8a337636947523570239c10a58ae2/uamqp/client.py#L633-L651
def redirect(self, redirect, auth): """Redirect the client endpoint using a Link DETACH redirect response. :param redirect: The Link DETACH redirect details. :type redirect: ~uamqp.errors.LinkRedirect :param auth: Authentication credentials to the redirected endpoint. :type auth: ~uamqp.authentication.common.AMQPAuth """ if self._ext_connection: raise ValueError( "Clients with a shared connection cannot be " "automatically redirected.") if self.message_handler: self.message_handler.destroy() self.message_handler = None self._pending_messages = [] self._remote_address = address.Target(redirect.address) self._redirect(redirect, auth)
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Redirect the client endpoint using a Link DETACH redirect response. :param redirect: The Link DETACH redirect details. :type redirect: ~uamqp.errors.LinkRedirect :param auth: Authentication credentials to the redirected endpoint. :type auth: ~uamqp.authentication.common.AMQPAuth
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python
train
rosenbrockc/fortpy
fortpy/isense/context.py
https://github.com/rosenbrockc/fortpy/blob/1ed0757c52d549e41d9d44bdea68cb89529293a5/fortpy/isense/context.py#L348-L360
def _match_exec(self, i): """Looks at line 'i' for a subroutine or function definition.""" self.col_match = self.RE_EXEC.match(self._source[i]) if self.col_match is not None: if self.col_match.group("codetype") == "function": self.el_type = Function else: self.el_type = Subroutine self.el_name = self.col_match.group("name") return True else: return False
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Looks at line 'i' for a subroutine or function definition.
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python
train
pypa/pipenv
pipenv/vendor/pathlib2/__init__.py
https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/vendor/pathlib2/__init__.py#L1647-L1656
def expanduser(self): """ Return a new path with expanded ~ and ~user constructs (as returned by os.path.expanduser) """ if (not (self._drv or self._root) and self._parts and self._parts[0][:1] == '~'): homedir = self._flavour.gethomedir(self._parts[0][1:]) return self._from_parts([homedir] + self._parts[1:]) return self
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Return a new path with expanded ~ and ~user constructs (as returned by os.path.expanduser)
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python
train
dslackw/slpkg
slpkg/main.py
https://github.com/dslackw/slpkg/blob/dd2e08a80e944d337d157b992167ba631a4343de/slpkg/main.py#L665-L682
def pkg_find(self): """Find packages from all enabled repositories """ flag = [] options = [ "-F", "--FIND" ] additional_options = ["--case-ins"] for arg in self.args: if arg in additional_options: flag.append(arg) self.args.remove(arg) packages = self.args[1:] if len(self.args) > 1 and self.args[0] in options: FindFromRepos().find(packages, flag) else: usage("")
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Find packages from all enabled repositories
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python
train
inasafe/inasafe
safe/gui/tools/help/peta_bencana_help.py
https://github.com/inasafe/inasafe/blob/831d60abba919f6d481dc94a8d988cc205130724/safe/gui/tools/help/peta_bencana_help.py#L13-L26
def peta_bencana_help(): """Help message for PetaBencana dialog. .. versionadded:: 3.2.1 :returns: A message object containing helpful information. :rtype: messaging.message.Message """ message = m.Message() message.add(m.Brand()) message.add(heading()) message.add(content()) return message
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Help message for PetaBencana dialog. .. versionadded:: 3.2.1 :returns: A message object containing helpful information. :rtype: messaging.message.Message
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python
train
Clinical-Genomics/scout
scout/server/blueprints/cases/views.py
https://github.com/Clinical-Genomics/scout/blob/90a551e2e1653a319e654c2405c2866f93d0ebb9/scout/server/blueprints/cases/views.py#L277-L311
def matchmaker_delete(institute_id, case_name): """Remove a case from MatchMaker""" # check that only authorized users can delete patients from MME user_obj = store.user(current_user.email) if 'mme_submitter' not in user_obj['roles']: flash('unauthorized request', 'warning') return redirect(request.referrer) institute_obj, case_obj = institute_and_case(store, institute_id, case_name) # Required params for sending a delete request to MME: mme_base_url = current_app.config.get('MME_URL') mme_token = current_app.config.get('MME_TOKEN') if not mme_base_url or not mme_token: flash('An error occurred reading matchmaker connection parameters. Please check config file!', 'danger') return redirect(request.referrer) delete_result = controllers.mme_delete(case_obj, mme_base_url, mme_token) n_deleted = 0 category = 'warning' for resp in delete_result: if resp['status_code'] == 200: n_deleted += 1 else: flash(resp['message'], category) if n_deleted: category = 'success' # update case by removing mme submission # and create events for patients deletion from MME user_obj = store.user(current_user.email) store.case_mme_delete(case_obj=case_obj, user_obj=user_obj) flash('Number of patients deleted from Matchmaker: {} out of {}'.format(n_deleted, len(delete_result)), category) return redirect(request.referrer)
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Remove a case from MatchMaker
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python
test
KelSolaar/Umbra
umbra/ui/visual_accelerators.py
https://github.com/KelSolaar/Umbra/blob/66f45f08d9d723787f1191989f8b0dda84b412ce/umbra/ui/visual_accelerators.py#L68-L102
def highlight_occurences(editor): """ Highlights given editor current line. :param editor: Document editor. :type editor: QWidget :return: Method success. :rtype: bool """ format = editor.language.theme.get("accelerator.occurence") if not format: return False extra_selections = editor.extraSelections() or [] if not editor.isReadOnly(): word = editor.get_word_under_cursor() if not word: return False block = editor.document().findBlock(0) cursor = editor.document().find(word, block.position(), QTextDocument.FindCaseSensitively | QTextDocument.FindWholeWords) while block.isValid() and cursor.position() != -1: selection = QTextEdit.ExtraSelection() selection.format.setBackground(format.background()) selection.cursor = cursor extra_selections.append(selection) cursor = editor.document().find(word, cursor.position(), QTextDocument.FindCaseSensitively | QTextDocument.FindWholeWords) block = block.next() editor.setExtraSelections(extra_selections) return True
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Highlights given editor current line. :param editor: Document editor. :type editor: QWidget :return: Method success. :rtype: bool
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python
train
NICTA/revrand
revrand/basis_functions.py
https://github.com/NICTA/revrand/blob/4c1881b6c1772d2b988518e49dde954f165acfb6/revrand/basis_functions.py#L109-L152
def apply_grad(fun, grad): """ Apply a function that takes a gradient matrix to a sequence of 2 or 3 dimensional gradients. This is partucularly useful when the gradient of a basis concatenation object is quite complex, eg. >>> X = np.random.randn(100, 3) >>> y = np.random.randn(100) >>> N, d = X.shape >>> base = RandomRBF(Xdim=d, nbases=5) + RandomRBF(Xdim=d, nbases=5, ... lenscale=Parameter(np.ones(d), Positive())) >>> Phi = base.transform(X, 1., np.ones(d)) >>> dffun = lambda dPhi: y.dot(Phi).dot(dPhi.T).dot(y) >>> df = apply_grad(dffun, base.grad(X, 1., np.ones(d))) >>> np.isscalar(df[0]) True >>> df[1].shape (3,) Parameters ---------- fun: callable the function too apply to the (2d) gradient. grad: ndarray or generator the gradient of the basis function (output of base.grad). Returns ------- scalar, ndarray or sequence: the result of applying fun(grad) for a structured grad. """ if issequence(grad): fgrad = [apply_grad(fun, g) for g in grad] return fgrad if len(fgrad) != 1 else fgrad[0] elif len(grad) == 0: return [] elif (grad.ndim == 1) or (grad.ndim == 2): return fun(grad) elif grad.ndim == 3: return np.array([fun(grad[:, :, i]) for i in range(grad.shape[2])]) else: raise ValueError("Only up to 3d gradients allowed!")
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Apply a function that takes a gradient matrix to a sequence of 2 or 3 dimensional gradients. This is partucularly useful when the gradient of a basis concatenation object is quite complex, eg. >>> X = np.random.randn(100, 3) >>> y = np.random.randn(100) >>> N, d = X.shape >>> base = RandomRBF(Xdim=d, nbases=5) + RandomRBF(Xdim=d, nbases=5, ... lenscale=Parameter(np.ones(d), Positive())) >>> Phi = base.transform(X, 1., np.ones(d)) >>> dffun = lambda dPhi: y.dot(Phi).dot(dPhi.T).dot(y) >>> df = apply_grad(dffun, base.grad(X, 1., np.ones(d))) >>> np.isscalar(df[0]) True >>> df[1].shape (3,) Parameters ---------- fun: callable the function too apply to the (2d) gradient. grad: ndarray or generator the gradient of the basis function (output of base.grad). Returns ------- scalar, ndarray or sequence: the result of applying fun(grad) for a structured grad.
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python
train
gwpy/gwpy
setup_utils.py
https://github.com/gwpy/gwpy/blob/7a92b917e7dd2d99b15895293a1fa1d66cdb210a/setup_utils.py#L110-L134
def get_gitpython_version(): """Determine the required version of GitPython Because of target systems running very, very old versions of setuptools, we only specify the actual version we need when we need it. """ # if not in git clone, it doesn't matter if not in_git_clone(): return 'GitPython' # otherwise, call out to get the git version try: gitv = subprocess.check_output('git --version', shell=True) except (OSError, IOError, subprocess.CalledProcessError): # no git installation, most likely git_version = '0.0.0' else: if isinstance(gitv, bytes): gitv = gitv.decode('utf-8') git_version = gitv.strip().split()[2] # if git>=2.15, we need GitPython>=2.1.8 if LooseVersion(git_version) >= '2.15': return 'GitPython>=2.1.8' return 'GitPython'
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Determine the required version of GitPython Because of target systems running very, very old versions of setuptools, we only specify the actual version we need when we need it.
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python
train
spoqa/tsukkomi
tsukkomi/typed.py
https://github.com/spoqa/tsukkomi/blob/c67bd28a5211cdd11f8ac81f109c915f3b780445/tsukkomi/typed.py#L145-L161
def check_union(data: typing.Union, hint: type) -> bool: """Check argument type & return type of :class:`typing.Union`. since it raises check :class:`typing.Union` using `isinstance`, so compare in diffrent way :param data: union given as a argument :param hint: assumed type of given ``data`` """ r = any(check_type(data, t)[1] for t in hint.__union_params__) if not r: raise TypeError( 'expected one of {0!r}, found: {1!r}'.format( hint.__union_params__, type(data) ) ) return hint, r
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Check argument type & return type of :class:`typing.Union`. since it raises check :class:`typing.Union` using `isinstance`, so compare in diffrent way :param data: union given as a argument :param hint: assumed type of given ``data``
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python
train
not-na/peng3d
peng3d/window.py
https://github.com/not-na/peng3d/blob/1151be665b26cc8a479f6307086ba919e4d32d85/peng3d/window.py#L215-L222
def addMenu(self,menu): """ Adds a menu to the list of menus. """ # If there is no menu selected currently, this menu will automatically be made active. # Add the line above to the docstring if fixed self.menus[menu.name]=menu self.peng.sendEvent("peng3d:window.menu.add",{"peng":self.peng,"window":self,"menu":menu})
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Adds a menu to the list of menus.
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python
test
mitsei/dlkit
dlkit/json_/osid/metadata.py
https://github.com/mitsei/dlkit/blob/445f968a175d61c8d92c0f617a3c17dc1dc7c584/dlkit/json_/osid/metadata.py#L1025-L1039
def supports_heading_type(self, heading_type): """Tests if the given heading type is supported. arg: heading_type (osid.type.Type): a heading Type return: (boolean) - ``true`` if the type is supported, ``false`` otherwise raise: IllegalState - syntax is not a ``HEADING`` raise: NullArgument - ``heading_type`` is ``null`` *compliance: mandatory -- This method must be implemented.* """ # Implemented from template for osid.Metadata.supports_coordinate_type if self._kwargs['syntax'] not in ['``HEADING``']: raise errors.IllegalState() return heading_type in self.get_heading_types
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Tests if the given heading type is supported. arg: heading_type (osid.type.Type): a heading Type return: (boolean) - ``true`` if the type is supported, ``false`` otherwise raise: IllegalState - syntax is not a ``HEADING`` raise: NullArgument - ``heading_type`` is ``null`` *compliance: mandatory -- This method must be implemented.*
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python
train
noxdafox/vminspect
vminspect/comparator.py
https://github.com/noxdafox/vminspect/blob/e685282564877e2d1950f1e09b292f4f4db1dbcd/vminspect/comparator.py#L128-L153
def compare_registry(self, concurrent=False): """Compares the Windows Registry contained within the two File Systems. It parses all the registry hive files contained within the disks and generates the following report. {'created_keys': {'\\Reg\\Key': (('Key', 'Type', 'Value'))} 'deleted_keys': ['\\Reg\\Key', ...], 'created_values': {'\\Reg\\Key': (('Key', 'Type', 'NewValue'))}, 'deleted_values': {'\\Reg\\Key': (('Key', 'Type', 'OldValue'))}, 'modified_values': {'\\Reg\\Key': (('Key', 'Type', 'NewValue'))}} Only registry hives which are contained in both disks are compared. If the second disk contains a new registry hive, its content can be listed using winreg.RegistryHive.registry() method. If the concurrent flag is True, two processes will be used speeding up the comparison on multiple CPUs. """ self.logger.debug("Comparing Windows registries.") self._assert_windows() return compare_registries(self.filesystems[0], self.filesystems[1], concurrent=concurrent)
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Compares the Windows Registry contained within the two File Systems. It parses all the registry hive files contained within the disks and generates the following report. {'created_keys': {'\\Reg\\Key': (('Key', 'Type', 'Value'))} 'deleted_keys': ['\\Reg\\Key', ...], 'created_values': {'\\Reg\\Key': (('Key', 'Type', 'NewValue'))}, 'deleted_values': {'\\Reg\\Key': (('Key', 'Type', 'OldValue'))}, 'modified_values': {'\\Reg\\Key': (('Key', 'Type', 'NewValue'))}} Only registry hives which are contained in both disks are compared. If the second disk contains a new registry hive, its content can be listed using winreg.RegistryHive.registry() method. If the concurrent flag is True, two processes will be used speeding up the comparison on multiple CPUs.
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python
train
midasplatform/pydas
pydas/api.py
https://github.com/midasplatform/pydas/blob/e5f9e96e754fb2dc5da187b05e4abc77a9b2affd/pydas/api.py#L331-L346
def _create_folder(local_folder, parent_folder_id): """ Function for creating a remote folder and returning the id. This should be a building block for user-level functions. :param local_folder: full path to a local folder :type local_folder: string :param parent_folder_id: id of parent folder on the Midas Server instance, where the new folder will be added :type parent_folder_id: int | long :returns: id of the remote folder that was created :rtype: int | long """ new_folder = session.communicator.create_folder( session.token, os.path.basename(local_folder), parent_folder_id) return new_folder['folder_id']
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Function for creating a remote folder and returning the id. This should be a building block for user-level functions. :param local_folder: full path to a local folder :type local_folder: string :param parent_folder_id: id of parent folder on the Midas Server instance, where the new folder will be added :type parent_folder_id: int | long :returns: id of the remote folder that was created :rtype: int | long
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python
valid
limodou/uliweb
uliweb/contrib/jsonql/__init__.py
https://github.com/limodou/uliweb/blob/34472f25e4bc0b954a35346672f94e84ef18b076/uliweb/contrib/jsonql/__init__.py#L313-L336
def parse_entry(self, name): """ Parse query entry name, just like: { 'User[]:user' } 'User[]:user' is an entry name. :param name: :return: """ # calculate schema mode # if ':name' or '' or '[]:name' or '[]' found, it'll be treat as multiple Schema query alias = name if ':' in name: name, alias = name.split(':') if name.endswith('[]'): need_list = True name = name[:-2] else: need_list = False return alias, name, need_list
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Parse query entry name, just like: { 'User[]:user' } 'User[]:user' is an entry name. :param name: :return:
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python
train
OpenTreeOfLife/peyotl
peyotl/phylesystem/helper.py
https://github.com/OpenTreeOfLife/peyotl/blob/5e4e52a0fdbd17f490aa644ad79fda6ea2eda7c0/peyotl/phylesystem/helper.py#L64-L76
def create_id2study_info(path, tag): """Searchers for *.json files in this repo and returns a map of study id ==> (`tag`, dir, study filepath) where `tag` is typically the shard name """ d = {} for triple in os.walk(path): root, files = triple[0], triple[2] for filename in files: if filename.endswith('.json'): study_id = filename[:-5] d[study_id] = (tag, root, os.path.join(root, filename)) return d
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Searchers for *.json files in this repo and returns a map of study id ==> (`tag`, dir, study filepath) where `tag` is typically the shard name
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python
train
amzn/ion-python
amazon/ion/reader_binary.py
https://github.com/amzn/ion-python/blob/0b21fa3ba7755f55f745e4aa970d86343b82449d/amazon/ion/reader_binary.py#L334-L387
def _read_data_handler(length, whence, ctx, skip=False, stream_event=ION_STREAM_INCOMPLETE_EVENT): """Creates a co-routine for retrieving data up to a requested size. Args: length (int): The minimum length requested. whence (Coroutine): The co-routine to return to after the data is satisfied. ctx (_HandlerContext): The context for the read. skip (Optional[bool]): Whether the requested number of bytes should be skipped. stream_event (Optional[IonEvent]): The stream event to return if no bytes are read or available. """ trans = None queue = ctx.queue if length > ctx.remaining: raise IonException('Length overrun: %d bytes, %d remaining' % (length, ctx.remaining)) # Make sure to check the queue first. queue_len = len(queue) if queue_len > 0: # Any data available means we can only be incomplete. stream_event = ION_STREAM_INCOMPLETE_EVENT length -= queue_len if skip: # For skipping we need to consume any remnant in the buffer queue. if length >= 0: queue.skip(queue_len) else: queue.skip(queue_len + length) while True: data_event, self = (yield trans) if data_event is not None and data_event.data is not None: data = data_event.data data_len = len(data) if data_len > 0: # We got something so we can only be incomplete. stream_event = ION_STREAM_INCOMPLETE_EVENT length -= data_len if not skip: queue.extend(data) else: pos_adjustment = data_len if length < 0: pos_adjustment += length # More data than we need to skip, so make sure to accumulate that remnant. queue.extend(data[length:]) queue.position += pos_adjustment if length <= 0: # We got all the data we need, go back immediately yield Transition(None, whence) trans = Transition(stream_event, self)
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Creates a co-routine for retrieving data up to a requested size. Args: length (int): The minimum length requested. whence (Coroutine): The co-routine to return to after the data is satisfied. ctx (_HandlerContext): The context for the read. skip (Optional[bool]): Whether the requested number of bytes should be skipped. stream_event (Optional[IonEvent]): The stream event to return if no bytes are read or available.
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python
train
mongodb/motor
motor/motor_gridfs.py
https://github.com/mongodb/motor/blob/6af22720723bde7c78eb8cb126962cfbfc034b2c/motor/motor_gridfs.py#L457-L514
def find(self, *args, **kwargs): """Find and return the files collection documents that match ``filter``. Returns a cursor that iterates across files matching arbitrary queries on the files collection. Can be combined with other modifiers for additional control. For example:: cursor = bucket.find({"filename": "lisa.txt"}, no_cursor_timeout=True) while (yield cursor.fetch_next): grid_out = cursor.next_object() data = yield grid_out.read() This iterates through all versions of "lisa.txt" stored in GridFS. Note that setting no_cursor_timeout to True may be important to prevent the cursor from timing out during long multi-file processing work. As another example, the call:: most_recent_three = fs.find().sort("uploadDate", -1).limit(3) would return a cursor to the three most recently uploaded files in GridFS. Follows a similar interface to :meth:`~motor.MotorCollection.find` in :class:`~motor.MotorCollection`. :Parameters: - `filter`: Search query. - `batch_size` (optional): The number of documents to return per batch. - `limit` (optional): The maximum number of documents to return. - `no_cursor_timeout` (optional): The server normally times out idle cursors after an inactivity period (10 minutes) to prevent excess memory use. Set this option to True prevent that. - `skip` (optional): The number of documents to skip before returning. - `sort` (optional): The order by which to sort results. Defaults to None. - `session` (optional): a :class:`~pymongo.client_session.ClientSession`, created with :meth:`~MotorClient.start_session`. If a :class:`~pymongo.client_session.ClientSession` is passed to :meth:`find`, all returned :class:`MotorGridOut` instances are associated with that session. .. versionchanged:: 1.2 Added session parameter. """ cursor = self.delegate.find(*args, **kwargs) grid_out_cursor = create_class_with_framework( AgnosticGridOutCursor, self._framework, self.__module__) return grid_out_cursor(cursor, self.collection)
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Find and return the files collection documents that match ``filter``. Returns a cursor that iterates across files matching arbitrary queries on the files collection. Can be combined with other modifiers for additional control. For example:: cursor = bucket.find({"filename": "lisa.txt"}, no_cursor_timeout=True) while (yield cursor.fetch_next): grid_out = cursor.next_object() data = yield grid_out.read() This iterates through all versions of "lisa.txt" stored in GridFS. Note that setting no_cursor_timeout to True may be important to prevent the cursor from timing out during long multi-file processing work. As another example, the call:: most_recent_three = fs.find().sort("uploadDate", -1).limit(3) would return a cursor to the three most recently uploaded files in GridFS. Follows a similar interface to :meth:`~motor.MotorCollection.find` in :class:`~motor.MotorCollection`. :Parameters: - `filter`: Search query. - `batch_size` (optional): The number of documents to return per batch. - `limit` (optional): The maximum number of documents to return. - `no_cursor_timeout` (optional): The server normally times out idle cursors after an inactivity period (10 minutes) to prevent excess memory use. Set this option to True prevent that. - `skip` (optional): The number of documents to skip before returning. - `sort` (optional): The order by which to sort results. Defaults to None. - `session` (optional): a :class:`~pymongo.client_session.ClientSession`, created with :meth:`~MotorClient.start_session`. If a :class:`~pymongo.client_session.ClientSession` is passed to :meth:`find`, all returned :class:`MotorGridOut` instances are associated with that session. .. versionchanged:: 1.2 Added session parameter.
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python
train
bitlabstudio/django-subscribe
subscribe/templatetags/subscribe_tags.py
https://github.com/bitlabstudio/django-subscribe/blob/313de63fb4acda172e88b65c3327c793f98e8aa9/subscribe/templatetags/subscribe_tags.py#L35-L54
def is_subscribed(user, obj): """ Returns ``True`` if the user is subscribed to the given object. :param user: A ``User`` instance. :param obj: Any object. """ if not user.is_authenticated(): return False ctype = ContentType.objects.get_for_model(obj) try: Subscription.objects.get( user=user, content_type=ctype, object_id=obj.pk) except Subscription.DoesNotExist: return False return True
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Returns ``True`` if the user is subscribed to the given object. :param user: A ``User`` instance. :param obj: Any object.
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python
train
datastax/python-driver
cassandra/cqlengine/query.py
https://github.com/datastax/python-driver/blob/30a80d0b798b1f45f8cb77163b1fa791f3e3ca29/cassandra/cqlengine/query.py#L940-L946
def allow_filtering(self): """ Enables the (usually) unwise practive of querying on a clustering key without also defining a partition key """ clone = copy.deepcopy(self) clone._allow_filtering = True return clone
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Enables the (usually) unwise practive of querying on a clustering key without also defining a partition key
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python
train
zhanglab/psamm
psamm/gapfill.py
https://github.com/zhanglab/psamm/blob/dc427848c4f9d109ca590f0afa024c63b685b3f4/psamm/gapfill.py#L52-L140
def gapfind(model, solver, epsilon=0.001, v_max=1000, implicit_sinks=True): """Identify compounds in the model that cannot be produced. Yields all compounds that cannot be produced. This method assumes implicit sinks for all compounds in the model so the only factor that influences whether a compound can be produced is the presence of the compounds needed to produce it. Epsilon indicates the threshold amount of reaction flux for the products to be considered non-blocked. V_max indicates the maximum flux. This method is implemented as a MILP-program. Therefore it may not be efficient for larger models. Args: model: :class:`MetabolicModel` containing core reactions and reactions that can be added for gap-filling. solver: MILP solver instance. epsilon: Threshold amount of a compound produced for it to not be considered blocked. v_max: Maximum flux. implicit_sinks: Whether implicit sinks for all compounds are included when gap-filling (traditional GapFill uses implicit sinks). """ prob = solver.create_problem() # Set integrality tolerance such that w constraints are correct min_tol = prob.integrality_tolerance.min int_tol = _find_integer_tolerance(epsilon, v_max, min_tol) if int_tol < prob.integrality_tolerance.value: prob.integrality_tolerance.value = int_tol # Define flux variables v = prob.namespace() for reaction_id in model.reactions: lower, upper = model.limits[reaction_id] v.define([reaction_id], lower=lower, upper=upper) # Define constraints on production of metabolites in reaction w = prob.namespace(types=lp.VariableType.Binary) binary_cons_lhs = {compound: 0 for compound in model.compounds} for spec, value in iteritems(model.matrix): compound, reaction_id = spec if value != 0: w.define([spec]) w_var = w(spec) lower, upper = (float(x) for x in model.limits[reaction_id]) if value > 0: dv = v(reaction_id) else: dv = -v(reaction_id) lower, upper = -upper, -lower prob.add_linear_constraints( dv <= upper * w_var, dv >= epsilon + (lower - epsilon) * (1 - w_var)) binary_cons_lhs[compound] += w_var xp = prob.namespace(model.compounds, types=lp.VariableType.Binary) objective = xp.sum(model.compounds) prob.set_objective(objective) for compound, lhs in iteritems(binary_cons_lhs): prob.add_linear_constraints(lhs >= xp(compound)) # Define mass balance constraints massbalance_lhs = {compound: 0 for compound in model.compounds} for spec, value in iteritems(model.matrix): compound, reaction_id = spec massbalance_lhs[compound] += v(reaction_id) * value for compound, lhs in iteritems(massbalance_lhs): if implicit_sinks: # The constraint is merely >0 meaning that we have implicit sinks # for all compounds. prob.add_linear_constraints(lhs >= 0) else: prob.add_linear_constraints(lhs == 0) # Solve try: result = prob.solve(lp.ObjectiveSense.Maximize) except lp.SolverError as e: raise_from(GapFillError('Failed to solve gapfill: {}'.format(e), e)) for compound in model.compounds: if result.get_value(xp(compound)) < 0.5: yield compound
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Identify compounds in the model that cannot be produced. Yields all compounds that cannot be produced. This method assumes implicit sinks for all compounds in the model so the only factor that influences whether a compound can be produced is the presence of the compounds needed to produce it. Epsilon indicates the threshold amount of reaction flux for the products to be considered non-blocked. V_max indicates the maximum flux. This method is implemented as a MILP-program. Therefore it may not be efficient for larger models. Args: model: :class:`MetabolicModel` containing core reactions and reactions that can be added for gap-filling. solver: MILP solver instance. epsilon: Threshold amount of a compound produced for it to not be considered blocked. v_max: Maximum flux. implicit_sinks: Whether implicit sinks for all compounds are included when gap-filling (traditional GapFill uses implicit sinks).
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python
train
mar10/wsgidav
wsgidav/dav_provider.py
https://github.com/mar10/wsgidav/blob/cec0d84222fc24bea01be1cea91729001963f172/wsgidav/dav_provider.py#L809-L814
def remove_all_properties(self, recursive): """Remove all associated dead properties.""" if self.provider.prop_manager: self.provider.prop_manager.remove_properties( self.get_ref_url(), self.environ )
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Remove all associated dead properties.
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python
valid
manns/pyspread
pyspread/src/lib/_grid_cairo_renderer.py
https://github.com/manns/pyspread/blob/0e2fd44c2e0f06605efc3058c20a43a8c1f9e7e0/pyspread/src/lib/_grid_cairo_renderer.py#L962-L971
def draw(self): """Draws cell background to context""" self.context.set_source_rgb(*self._get_background_color()) self.context.rectangle(*self.rect) self.context.fill() # If show frozen is active, show frozen pattern if self.view_frozen and self.cell_attributes[self.key]["frozen"]: self._draw_frozen_pattern()
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Draws cell background to context
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python
train
O365/python-o365
O365/utils/attachment.py
https://github.com/O365/python-o365/blob/02a71cf3775cc6a3c042e003365d6a07c8c75a73/O365/utils/attachment.py#L140-L154
def to_api_data(self): """ Returns a dict to communicate with the server :rtype: dict """ data = {'@odata.type': self._gk( '{}_attachment_type'.format(self.attachment_type)), self._cc('name'): self.name} if self.attachment_type == 'file': data[self._cc('contentBytes')] = self.content else: data[self._cc('item')] = self.content return data
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Returns a dict to communicate with the server :rtype: dict
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python
train
andresriancho/w3af-api-client
w3af_api_client/connection.py
https://github.com/andresriancho/w3af-api-client/blob/adeb79bad75264d754de69f0bb981b366da96f32/w3af_api_client/connection.py#L156-L178
def get_scans(self): """ :return: A list with all the Scan instances available in the remote API """ code, data = self.send_request('/scans/', method='GET') if code != 200: msg = 'Failed to retrieve scans. Unexpected code %s' raise APIException(msg % code) scans = data.get('items', None) if scans is None: raise APIException('Failed to retrieve scans, no "items" in JSON.') scan_instances = [] for scan_json in scans: scan_id = scan_json['id'] scan_status = scan_json['status'] scan = Scan(self, scan_id=scan_id, status=scan_status) scan_instances.append(scan) return scan_instances
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:return: A list with all the Scan instances available in the remote API
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python
train
mamrhein/specification
specification/_extd_ast_expr.py
https://github.com/mamrhein/specification/blob/a4c09a0d286cda7a04e8a189f12e23edd97f64ea/specification/_extd_ast_expr.py#L124-L130
def wrap_expr(self, src: str, dfltChaining: bool) -> str: """Wrap `src` in parentheses if neccessary.""" diff_binding = self.op_man.diff_binding() if diff_binding < 0 or diff_binding == 0 and not dfltChaining: return self.parenthesize(src) else: return src
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Wrap `src` in parentheses if neccessary.
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python
train
ARMmbed/mbed-cloud-sdk-python
src/mbed_cloud/_backends/update_service/models/update_campaign_put_request.py
https://github.com/ARMmbed/mbed-cloud-sdk-python/blob/c0af86fb2cdd4dc7ed26f236139241067d293509/src/mbed_cloud/_backends/update_service/models/update_campaign_put_request.py#L174-L184
def root_manifest_id(self, root_manifest_id): """ Sets the root_manifest_id of this UpdateCampaignPutRequest. :param root_manifest_id: The root_manifest_id of this UpdateCampaignPutRequest. :type: str """ if root_manifest_id is not None and len(root_manifest_id) > 32: raise ValueError("Invalid value for `root_manifest_id`, length must be less than or equal to `32`") self._root_manifest_id = root_manifest_id
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Sets the root_manifest_id of this UpdateCampaignPutRequest. :param root_manifest_id: The root_manifest_id of this UpdateCampaignPutRequest. :type: str
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python
train
KvasirSecurity/kvasirapi-python
KvasirAPI/jsonrpc/services.py
https://github.com/KvasirSecurity/kvasirapi-python/blob/ec8c5818bd5913f3afd150f25eaec6e7cc732f4c/KvasirAPI/jsonrpc/services.py#L44-L54
def info(self, svc_rec=None, ipaddr=None, proto=None, port=None): """ Information about a service. :param svc_rec: t_services.id :param ipaddr: IP Address :param proto: Protocol (tcp, udp, info) :param port: Port (0-65535) :return: [ service_id, host_id, ipv4, ipv6, hostname, proto, number, status, name, banner ] """ return self.send.service_info(svc_rec, ipaddr, proto, port)
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Information about a service. :param svc_rec: t_services.id :param ipaddr: IP Address :param proto: Protocol (tcp, udp, info) :param port: Port (0-65535) :return: [ service_id, host_id, ipv4, ipv6, hostname, proto, number, status, name, banner ]
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python
train
aio-libs/aiodocker
aiodocker/services.py
https://github.com/aio-libs/aiodocker/blob/88d0285ddba8e606ff684278e0a831347209189c/aiodocker/services.py#L168-L182
async def inspect(self, service_id: str) -> Mapping[str, Any]: """ Inspect a service Args: service_id: ID or name of the service Returns: a dict with info about a service """ response = await self.docker._query_json( "services/{service_id}".format(service_id=service_id), method="GET" ) return response
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Inspect a service Args: service_id: ID or name of the service Returns: a dict with info about a service
[ "Inspect", "a", "service" ]
python
train
econ-ark/HARK
HARK/ConsumptionSaving/ConsGenIncProcessModel.py
https://github.com/econ-ark/HARK/blob/3d184153a189e618a87c9540df1cd12044039cc5/HARK/ConsumptionSaving/ConsGenIncProcessModel.py#L836-L868
def solve(self): ''' Solves a one period consumption saving problem with risky income, with persistent income explicitly tracked as a state variable. Parameters ---------- None Returns ------- solution : ConsumerSolution The solution to the one period problem, including a consumption function (defined over market resources and persistent income), a marginal value function, bounding MPCs, and human wealth as a func- tion of persistent income. Might also include a value function and marginal marginal value function, depending on options selected. ''' aLvl,pLvl = self.prepareToCalcEndOfPrdvP() EndOfPrdvP = self.calcEndOfPrdvP() if self.vFuncBool: self.makeEndOfPrdvFunc(EndOfPrdvP) if self.CubicBool: interpolator = self.makeCubiccFunc else: interpolator = self.makeLinearcFunc solution = self.makeBasicSolution(EndOfPrdvP,aLvl,pLvl,interpolator) solution = self.addMPCandHumanWealth(solution) if self.vFuncBool: solution.vFunc = self.makevFunc(solution) if self.CubicBool: solution = self.addvPPfunc(solution) return solution
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python
train
suds-community/suds
suds/wsdl.py
https://github.com/suds-community/suds/blob/6fb0a829337b5037a66c20aae6f89b41acd77e40/suds/wsdl.py#L70-L82
def resolve(self, definitions): """ Resolve named references to other WSDL objects. Can be safely called multiple times. @param definitions: A definitions object. @type definitions: L{Definitions} """ if not self.__resolved: self.do_resolve(definitions) self.__resolved = True
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Resolve named references to other WSDL objects. Can be safely called multiple times. @param definitions: A definitions object. @type definitions: L{Definitions}
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python
train
oxalorg/Stab
stab/watchman.py
https://github.com/oxalorg/Stab/blob/8f0ded780fd7a53a674835c9cb1b7ca08b98f562/stab/watchman.py#L24-L35
def should_build(self, fpath, meta): """ Checks if the file should be built or not Only skips layouts which are tagged as INCREMENTAL Rebuilds only those files with mtime changed since previous build """ if meta.get('layout', self.default_template) in self.inc_layout: if self.prev_mtime.get(fpath, 0) == os.path.getmtime(fpath): return False else: return True return True
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Checks if the file should be built or not Only skips layouts which are tagged as INCREMENTAL Rebuilds only those files with mtime changed since previous build
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python
train
etoccalino/django-rest-framework-httpsignature
rest_framework_httpsignature/authentication.py
https://github.com/etoccalino/django-rest-framework-httpsignature/blob/03ac3c213153ae6084c84b8ff61e101798b342a4/rest_framework_httpsignature/authentication.py#L46-L56
def build_dict_to_sign(self, request, signature_headers): """Build a dict with headers and values used in the signature. "signature_headers" is a list of lowercase header names. """ d = {} for header in signature_headers: if header == '(request-target)': continue d[header] = request.META.get(self.header_canonical(header)) return d
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Build a dict with headers and values used in the signature. "signature_headers" is a list of lowercase header names.
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python
train