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def volume_detach(name, profile=None, timeout=300): conn = _auth(profile) return conn.volume_detach(name, timeout)
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detach block volume .
train
true
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def setup_user_email(request, user, addresses): from .models import EmailAddress assert (not EmailAddress.objects.filter(user=user).exists()) priority_addresses = [] adapter = get_adapter(request) stashed_email = adapter.unstash_verified_email(request) if stashed_email: priority_addresses.append(EmailAddress(user=user, email=stashed_email, primary=True, verified=True)) email = user_email(user) if email: priority_addresses.append(EmailAddress(user=user, email=email, primary=True, verified=False)) (addresses, primary) = cleanup_email_addresses(request, (priority_addresses + addresses)) for a in addresses: a.user = user a.save() EmailAddress.objects.fill_cache_for_user(user, addresses) if (primary and email and (email.lower() != primary.email.lower())): user_email(user, primary.email) user.save() return primary
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creates proper emailaddress for the user that was just signed up .
train
true
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def structured_dot(x, y): if hasattr(x, 'getnnz'): x = as_sparse_variable(x) assert (x.format in ['csr', 'csc']) if hasattr(y, 'getnnz'): y = as_sparse_variable(y) assert (y.format in ['csr', 'csc']) x_is_sparse_variable = _is_sparse_variable(x) y_is_sparse_variable = _is_sparse_variable(y) if ((not x_is_sparse_variable) and (not y_is_sparse_variable)): raise TypeError('structured_dot requires at least one sparse argument') if x_is_sparse_variable: return _structured_dot(x, y) else: assert y_is_sparse_variable return _structured_dot(y.T, x.T).T
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structured dot is like dot .
train
false
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def guess_mimetype(bin_data, default='application/octet-stream'): for entry in _mime_mappings: for signature in entry.signatures: if bin_data.startswith(signature): for discriminant in entry.discriminants: try: guess = discriminant(bin_data) if guess: return guess except Exception: _logger.getChild('guess_mimetype').warn("Sub-checker '%s' of type '%s' failed", discriminant.__name__, entry.mimetype, exc_info=True) return entry.mimetype return default
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guess the mimetype of an uploaded file .
train
false
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def stderr(a, axis=0, ddof=1): (a, axis) = _chk_asarray(a, axis) return (std(a, axis, ddof=1) / float(sqrt(a.shape[axis])))
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returns the estimated population standard error of the values in the passed array .
train
false
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def eval_number(parse_result): return super_float(''.join(parse_result))
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create a float out of its string parts .
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false
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def compare_record(old, new, truncate=None): if (old.id != new.id): raise ValueError(("'%s' vs '%s' " % (old.id, new.id))) if ((old.description != new.description) and (((old.id + ' ') + old.description).strip() != new.description) and (new.description != '<unknown description>') and (new.description != '')): raise ValueError(("'%s' vs '%s' " % (old.description, new.description))) if (len(old.seq) != len(new.seq)): raise ValueError(('%i vs %i' % (len(old.seq), len(new.seq)))) if (isinstance(old.seq, UnknownSeq) or isinstance(new.seq, UnknownSeq)): pass elif (str(old.seq) != str(new.seq)): if (len(old.seq) < 200): raise ValueError(("'%s' vs '%s'" % (old.seq, new.seq))) else: raise ValueError(("'%s...' vs '%s...'" % (old.seq[:100], new.seq[:100]))) if (('phred_quality' in old.letter_annotations) and ('phred_quality' in new.letter_annotations) and (old.letter_annotations['phred_quality'] != new.letter_annotations['phred_quality'])): if (truncate and ([min(q, truncate) for q in old.letter_annotations['phred_quality']] == [min(q, truncate) for q in new.letter_annotations['phred_quality']])): pass else: raise ValuerError('Mismatch in phred_quality') if (('solexa_quality' in old.letter_annotations) and ('solexa_quality' in new.letter_annotations) and (old.letter_annotations['solexa_quality'] != new.letter_annotations['solexa_quality'])): if (truncate and ([min(q, truncate) for q in old.letter_annotations['solexa_quality']] == [min(q, truncate) for q in new.letter_annotations['solexa_quality']])): pass else: raise ValueError('Mismatch in phred_quality') if (('phred_quality' in old.letter_annotations) and ('solexa_quality' in new.letter_annotations)): converted = [round(QualityIO.solexa_quality_from_phred(q)) for q in old.letter_annotations['phred_quality']] if truncate: converted = [min(q, truncate) for q in converted] if (converted != new.letter_annotations['solexa_quality']): print('') print(old.letter_annotations['phred_quality']) print(converted) print(new.letter_annotations['solexa_quality']) raise ValueError('Mismatch in phred_quality vs solexa_quality') if (('solexa_quality' in old.letter_annotations) and ('phred_quality' in new.letter_annotations)): converted = [round(QualityIO.phred_quality_from_solexa(q)) for q in old.letter_annotations['solexa_quality']] if truncate: converted = [min(q, truncate) for q in converted] if (converted != new.letter_annotations['phred_quality']): print(old.letter_annotations['solexa_quality']) print(converted) print(new.letter_annotations['phred_quality']) raise ValueError('Mismatch in solexa_quality vs phred_quality') return True
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this is meant to be a strict comparison for exact agreement .
train
false
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@pytest.mark.parametrize('qurl', [QUrl('http://abc123.com/this/awesome/url.html'), QUrl('https://supersecret.gov/nsa/files.txt'), None]) def test_set_url(url_widget, qurl): url_widget.set_url(qurl) if (qurl is not None): assert (url_widget.text() == qurl.toDisplayString()) else: assert (url_widget.text() == '')
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test text displayed by the widget .
train
false
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def is_module(name): for ext in CODE_FILES: if name.endswith(ext): return name[:(- len(ext))]
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is this a recognized module type? does this name end in one of the recognized code_files extensions? the file is assumed to exist .
train
false
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def validate_lms_config(settings): validate_common_config(settings) validate_marketing_site_config(settings)
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validates configurations for lms and raise valueerror if not valid .
train
false
11,015
@docfiller def generic_gradient_magnitude(input, derivative, output=None, mode='reflect', cval=0.0, extra_arguments=(), extra_keywords=None): if (extra_keywords is None): extra_keywords = {} input = numpy.asarray(input) (output, return_value) = _ni_support._get_output(output, input) axes = list(range(input.ndim)) if (len(axes) > 0): modes = _ni_support._normalize_sequence(mode, len(axes)) derivative(input, axes[0], output, modes[0], cval, *extra_arguments, **extra_keywords) numpy.multiply(output, output, output) for ii in range(1, len(axes)): tmp = derivative(input, axes[ii], output.dtype, modes[ii], cval, *extra_arguments, **extra_keywords) numpy.multiply(tmp, tmp, tmp) output += tmp numpy.sqrt(output, output, casting='unsafe') else: output[...] = input[...] return return_value
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gradient magnitude using a provided gradient function .
train
false
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def do_sync(reg=None): if (not reg): reg = registry.AppRegistry() apps = reg.get_all_apps() try: pthfile = pth.PthFile() pthfile.sync(apps) pthfile.save() build.make_syncdb() return True except (OSError, SystemError) as ex: LOG.error(("Failed to update the .pth file. Please fix any problem and run `%s --sync'\n%s" % (PROG_NAME, ex))) return False
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sync apps with virtualenv .
train
false
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def _get_hybrid_bridge_name(vif): return ('qbr' + vif['id'])[:model.NIC_NAME_LEN]
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get a bridge device name .
train
false
11,019
def authenticationAndCipheringResponse(AuthenticationParameterSRES_presence=0, MobileId_presence=0): a = TpPd(pd=3) b = MessageType(mesType=19) c = AcReferenceNumberAndSpareHalfOctets() packet = ((a / b) / c) if (AuthenticationParameterSRES_presence is 1): e = AuthenticationParameterSRES(ieiAPS=34) packet = (packet / e) if (MobileId_presence is 1): f = MobileIdHdr(ieiMI=35, eightBitMI=0) packet = (packet / f) return packet
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authentication and ciphering response section 9 .
train
true
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def instance_tag_set(context, instance_uuid, tags): return IMPL.instance_tag_set(context, instance_uuid, tags)
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replace all of the instance tags with specified list of tags .
train
false
11,022
def node_attribute_xy(G, attribute, nodes=None): if (nodes is None): nodes = set(G) else: nodes = set(nodes) node = G.node for (u, nbrsdict) in G.adjacency(): if (u not in nodes): continue uattr = node[u].get(attribute, None) if G.is_multigraph(): for (v, keys) in nbrsdict.items(): vattr = node[v].get(attribute, None) for (k, d) in keys.items(): (yield (uattr, vattr)) else: for (v, eattr) in nbrsdict.items(): vattr = node[v].get(attribute, None) (yield (uattr, vattr))
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return iterator of node-attribute pairs for all edges in g .
train
false
11,023
def sigquit_handler(sig, frame): print('Dumping stack traces for all threads in PID {}'.format(os.getpid())) id_to_name = dict([(th.ident, th.name) for th in threading.enumerate()]) code = [] for (thread_id, stack) in sys._current_frames().items(): code.append('\n# Thread: {}({})'.format(id_to_name.get(thread_id, ''), thread_id)) for (filename, line_number, name, line) in traceback.extract_stack(stack): code.append('File: "{}", line {}, in {}'.format(filename, line_number, name)) if line: code.append(' {}'.format(line.strip())) print('\n'.join(code))
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helps debug deadlocks by printing stacktraces when this gets a sigquit e .
train
true
11,024
def getAreaLoops(loops): areaLoops = 0.0 for loop in loops: areaLoops += getAreaLoop(loop) return areaLoops
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get the area of a list of complex polygons .
train
false
11,025
def dmp_convert(f, u, K0, K1): if (not u): return dup_convert(f, K0, K1) if ((K0 is not None) and (K0 == K1)): return f v = (u - 1) return dmp_strip([dmp_convert(c, v, K0, K1) for c in f], u)
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convert the ground domain of f from k0 to k1 .
train
false
11,026
def generateCoincMatrix(nCoinc=10, length=500, activity=50): coincMatrix0 = SM32(int(nCoinc), int(length)) theOnes = numpy.array(([1.0] * activity), dtype=numpy.float32) for rowIdx in xrange(nCoinc): coinc = numpy.array(random.sample(xrange(length), activity), dtype=numpy.uint32) coinc.sort() coincMatrix0.setRowFromSparse(rowIdx, coinc, theOnes) coincMatrix = SM32(int(nCoinc), int(length)) coincMatrix.initializeWithFixedNNZR(activity) return coincMatrix0
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generate a coincidence matrix .
train
true
11,027
def s2n_motorola(string): x = 0 for c in string: x = ((x << 8) | ord_(c)) return x
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extract multi-byte integer in motorola format .
train
true
11,028
def _paginate(request, queryset): page = int(request.GET.get('page', 1)) limit = int(request.GET.get('limit', 0)) return __paginate(page, limit, queryset)
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paginates the results from func by continuously passing in the returned marker if the results were truncated .
train
false
11,029
def render_template_with_system_context(value, context=None, prefix=None): context = (context or {}) context[SYSTEM_SCOPE] = KeyValueLookup(prefix=prefix, scope=SYSTEM_SCOPE) context[DATASTORE_PARENT_SCOPE] = {SYSTEM_SCOPE: KeyValueLookup(prefix=prefix, scope=SYSTEM_SCOPE)} rendered = render_template(value=value, context=context) return rendered
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render provided template with a default system context .
train
false
11,030
def setup_app(command, conf, vars): load_environment(conf.global_conf, conf.local_conf) from ckan import model log.debug('Creating tables') model.repo.create_db() log.info('Creating tables: SUCCESS')
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place any commands to setup ckan here .
train
false
11,031
def half_secret(d, k): l = len(d[k]) if (l > 2): d[k] = (d[k][:2] + ('*' * (l - 2))) else: d[k] = ('*' * l)
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hidden part of the secret .
train
false
11,033
def get_coord(coord_fname, method='IQR'): if (not os.path.isdir(coord_fname)): try: coord_f = open(coord_fname, 'U') except (TypeError, IOError): raise MissingFileError('Coord file required for this analysis') (coord_header, coords, eigvals, pct_var) = parse_coords(coord_f) return [coord_header, coords, eigvals, pct_var, None, None] else: (master_pcoa, support_pcoas) = load_pcoa_files(coord_fname) (coords, coords_low, coords_high, eigval_average, coord_header) = summarize_pcoas(master_pcoa, support_pcoas, method=method) pct_var = master_pcoa[3] coord_header = list(master_pcoa[0]) return [coord_header, coords, eigval_average, pct_var, coords_low, coords_high]
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opens and returns coords location matrix and metadata .
train
false
11,034
def get_log_line(req, res, trans_time, additional_info): policy_index = get_policy_index(req.headers, res.headers) return ('%s - - [%s] "%s %s" %s %s "%s" "%s" "%s" %.4f "%s" %d %s' % (req.remote_addr, time.strftime('%d/%b/%Y:%H:%M:%S +0000', time.gmtime()), req.method, req.path, res.status.split()[0], (res.content_length or '-'), (req.referer or '-'), req.headers.get('x-trans-id', '-'), (req.user_agent or '-'), trans_time, (additional_info or '-'), os.getpid(), (policy_index or '-')))
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make a line for logging that matches the documented log line format for backend servers .
train
false
11,035
@task def manylinux(ctx, vs, upload=False): manylinux = '/tmp/manylinux-builds' if (not os.path.exists(manylinux)): with cd('/tmp'): run('git clone --recursive https://github.com/minrk/manylinux-builds -b pyzmq') else: with cd(manylinux): run('git pull') run('git submodule update') run('docker pull quay.io/pypa/manylinux1_x86_64') run('docker pull quay.io/pypa/manylinux1_i686') base_cmd = "docker run --dns 8.8.8.8 --rm -e PYZMQ_VERSIONS='{vs}' -e PYTHON_VERSIONS='{pys}' -e ZMQ_VERSION='{zmq}' -v $PWD:/io".format(vs=vs, pys='2.7 3.4 3.5 3.6', zmq=libzmq_vs) with cd(manylinux): run((base_cmd + ' quay.io/pypa/manylinux1_x86_64 /io/build_pyzmqs.sh')) run((base_cmd + ' quay.io/pypa/manylinux1_i686 linux32 /io/build_pyzmqs.sh')) if upload: py = make_env('3.5', 'twine') run(['twine', 'upload', os.path.join(manylinux, 'wheelhouse', '*')])
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build manylinux wheels with matthew bretts manylinux-builds .
train
false
11,036
def runCPU(): model = ModelFactory.create(model_params.MODEL_PARAMS) model.enableInference({'predictedField': 'cpu'}) shifter = InferenceShifter() actHistory = deque(([0.0] * WINDOW), maxlen=60) predHistory = deque(([0.0] * WINDOW), maxlen=60) (actline,) = plt.plot(range(WINDOW), actHistory) (predline,) = plt.plot(range(WINDOW), predHistory) actline.axes.set_ylim(0, 100) predline.axes.set_ylim(0, 100) while True: s = time.time() cpu = psutil.cpu_percent() modelInput = {'cpu': cpu} result = shifter.shift(model.run(modelInput)) inference = result.inferences['multiStepBestPredictions'][5] if (inference is not None): actHistory.append(result.rawInput['cpu']) predHistory.append(inference) actline.set_ydata(actHistory) predline.set_ydata(predHistory) plt.draw() plt.legend(('actual', 'predicted')) try: plt.pause(SECONDS_PER_STEP) except: pass
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poll cpu usage .
train
true
11,037
def get_uploaded_file_mimetype(uploaded_file): if (uploaded_file.content_type and (len(uploaded_file.content_type.split(u'/')) == 2) and (uploaded_file.content_type != u'application/octet-stream')): mimetype = uploaded_file.content_type else: mimetype = guess_mimetype(uploaded_file) return mimetype
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return the mimetype of a file that was uploaded .
train
false
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def load_plugin(path_to_zip_file): return loader.load(path_to_zip_file)
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find and import a plugin module so that it can be registered .
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def date_to_str(date): return (datetime.strftime(date, config.DATE_FORMAT) if date else None)
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converts a datetime value to the format defined in the configuration file .
train
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def s_repeat(block_name, min_reps=0, max_reps=None, step=1, variable=None, fuzzable=True, name=None): repeat = blocks.repeat(block_name, blocks.CURRENT, min_reps, max_reps, step, variable, fuzzable, name) blocks.CURRENT.push(repeat)
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repeat the rendered contents of the specified block cycling from min_reps to max_reps counting by step .
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def _compute_content_grad(F, F_content, layer): Fl = F[layer] El = (Fl - F_content[layer]) loss = ((El ** 2).sum() / 2) grad = (El * (Fl > 0)) return (loss, grad)
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computes content gradient and loss from activation features .
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def getSequenceIndexFromProcedure(procedure): craftSequence = getReadCraftSequence() if (procedure not in craftSequence): return 0 return craftSequence.index(procedure)
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get the profile sequence index of the procedure .
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def reload_rules(): return __firewall_cmd('--reload')
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reload the firewall rules .
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def HexToByte(hexStr): bytes = [] hexStr = ''.join(hexStr.split(' ')) for i in range(0, len(hexStr), 2): bytes.append(chr(int(hexStr[i:(i + 2)], 16))) return ''.join(bytes)
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convert a string hex byte values into a byte string .
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def _interfaces_config(interfaces): if (interfaces is None): interfaces = DEFAULT_INTERFACES lines = [INTERFACES_HEADER] for entry in interfaces: entry.setdefault('zone', 'net') entry.setdefault('broadcast', 'detect') entry.setdefault('options', '') lines.append((INTERFACES_FORMAT % entry)) file('/etc/shorewall/interfaces', contents=''.join(lines), use_sudo=True)
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interfaces configuration .
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@public def field_isomorphism(a, b, **args): (a, b) = (sympify(a), sympify(b)) if (not a.is_AlgebraicNumber): a = AlgebraicNumber(a) if (not b.is_AlgebraicNumber): b = AlgebraicNumber(b) if (a == b): return a.coeffs() n = a.minpoly.degree() m = b.minpoly.degree() if (n == 1): return [a.root] if ((m % n) != 0): return None if args.get('fast', True): try: result = field_isomorphism_pslq(a, b) if (result is not None): return result except NotImplementedError: pass return field_isomorphism_factor(a, b)
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construct an isomorphism between two number fields .
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def _has_db_updated_with_new_score_bwc_v2(user_id, scored_block_usage_key, expected_modified_time, score_deleted): score = get_score(user_id, scored_block_usage_key) if (score is None): return score_deleted elif (score.module_type == 'openassessment'): anon_id = anonymous_id_for_user(User.objects.get(id=user_id), scored_block_usage_key.course_key) course_id = unicode(scored_block_usage_key.course_key) item_id = unicode(scored_block_usage_key) api_score = sub_api.get_score({'student_id': anon_id, 'course_id': course_id, 'item_id': item_id, 'item_type': 'openassessment'}) if (api_score is None): return score_deleted reported_modified_time = api_score['created_at'] else: reported_modified_time = score.modified return (reported_modified_time >= expected_modified_time)
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deprecated version for backward compatibility with v2 tasks .
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def get_queue_list(queue_list=None): default_queue_list = queue_timeout.keys() if queue_list: if isinstance(queue_list, basestring): queue_list = [queue_list] for queue in queue_list: validate_queue(queue, default_queue_list) return queue_list else: return default_queue_list
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defines possible queues .
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def get_model_label(model): if isinstance(model, six.string_types): return model else: return ('%s.%s' % (model._meta.app_label, model.__name__))
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take a model class or model label and return its model label .
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def safe_version(version): return version.replace('.', '_')
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convert an arbitrary string to a standard version string spaces become dots .
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def getLevelName(level): return _levelNames.get(level, ('Level %s' % level))
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return the textual representation of logging level level .
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def shquote(arg): for c in ('"', "'", '\\', '#'): if (c in arg): return repr(arg) if (arg.split() != [arg]): return repr(arg) return arg
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quote an argument for later parsing by shlex .
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def get_name_and_placement(request, minsegs=1, maxsegs=None, rest_with_last=False): policy_index = request.headers.get('X-Backend-Storage-Policy-Index') policy = POLICIES.get_by_index(policy_index) if (not policy): raise HTTPServiceUnavailable(body=(_('No policy with index %s') % policy_index), request=request, content_type='text/plain') results = split_and_validate_path(request, minsegs=minsegs, maxsegs=maxsegs, rest_with_last=rest_with_last) results.append(policy) return results
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utility function to split and validate the request path and storage policy .
train
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def _get_plugin_specs_as_list(specs): if (specs is not None): if isinstance(specs, str): specs = (specs.split(',') if specs else []) if (not isinstance(specs, (list, tuple))): raise UsageError(("Plugin specs must be a ','-separated string or a list/tuple of strings for plugin names. Given: %r" % specs)) return list(specs) return []
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parses a list of "plugin specs" and returns a list of plugin names .
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def selWorst(individuals, k): return sorted(individuals, key=attrgetter('fitness'))[:k]
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select the *k* worst individuals among the input *individuals* .
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def requiresOAuth(fun): def decorate(self, *args, **kwargs): if self.client.has_access_token(): try: fun(self, *args, **kwargs) except gdata.service.RequestError as error: if (error.code in [401, 403]): self.redirect('/oauth/request_token') else: raise else: self.redirect('/oauth/request_token') return decorate
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decorator for request handlers to gain authentication via oauth .
train
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def _parse_string(data, start, stop_at_equals=False, must_have_content=False): if (start == len(data)): raise ParsingError(u'Expecting string, but found end of input!') char = data[start] if ((char == u'"') or (char == u"'")): (end, value) = _parse_quoted_string(data, start) has_content = True else: (end, value) = _parse_unquoted_string(data, start, stop_at_equals) has_content = (len(value) > 0) if (must_have_content and (not has_content)): raise ParsingError(u'String starting at {0} must be non-empty!'.format(_format_position(data, start))) next_is_equals = False if (stop_at_equals and ((end + 1) < len(data))): next_is_equals = (data[end] == u'=') return (end, value, next_is_equals)
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parse a string starting at position start in data .
train
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def pop_key(data): items = sorted(data.items(), key=(lambda item: (len(item[1]), item[0]))) key = items[0][0] data.pop(key) for dep in data.values(): dep.discard(key) return key
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pop an item from the graph that has the fewest dependencies in the case of a tie the winners will be sorted alphabetically .
train
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def bw_normal_reference(x, kernel=kernels.Gaussian): C = kernel.normal_reference_constant A = _select_sigma(x) n = len(x) return ((C * A) * (n ** (-0.2)))
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plug-in bandwidth with kernel specific constant based on normal reference .
train
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def write_png(filename, data): data = np.asarray(data) if ((not (data.ndim == 3)) and (data.shape[(-1)] in (3, 4))): raise ValueError('data must be a 3D array with last dimension 3 or 4') with open(filename, 'wb') as f: f.write(_make_png(data))
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write a png file unlike imsave .
train
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def liftRepositoryDialogs(repositoryDialogs): for repositoryDialog in repositoryDialogs: repositoryDialog.root.withdraw() repositoryDialog.root.lift() repositoryDialog.root.deiconify() repositoryDialog.root.lift() repositoryDialog.root.update_idletasks()
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lift the repository dialogs .
train
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def implicit_multiplication_application(result, local_dict, global_dict): for step in (split_symbols, implicit_multiplication, implicit_application, function_exponentiation): result = step(result, local_dict, global_dict) return result
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allows a slightly relaxed syntax .
train
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def _group_activity_query(group_id): import ckan.model as model group = model.Group.get(group_id) if (not group): return model.Session.query(model.Activity).filter('0=1') dataset_ids = [dataset.id for dataset in group.packages()] q = model.Session.query(model.Activity) if dataset_ids: q = q.filter(or_((model.Activity.object_id == group_id), model.Activity.object_id.in_(dataset_ids))) else: q = q.filter((model.Activity.object_id == group_id)) return q
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return an sqlalchemy query for all activities about group_id .
train
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def gettempdir(): global tempdir if (tempdir is None): _once_lock.acquire() try: if (tempdir is None): tempdir = _get_default_tempdir() finally: _once_lock.release() return tempdir
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accessor for tempfile .
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def get_configured_provider(): return config.is_provider_configured(__opts__, (__active_provider_name__ or __virtualname__), ('user', 'tenant', 'identity_url', 'compute_region'))
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return the first configured instance .
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def matrixPoints(points, prefix, xmlElement): matrixMatrixTetragrid = matrix.getMatrixTetragrid(prefix, xmlElement) if (matrixMatrixTetragrid == None): print 'Warning, matrixMatrixTetragrid was None in matrix so nothing will be done for:' print xmlElement return for point in points: transformVector3ByMatrix(matrixMatrixTetragrid, point)
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rotate the points .
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def _set_coordinator_properties(coordinator, root, namespace): coordinator.name = root.get('name') coordinator.timezone = root.get('timezone') coordinator.start = oozie_to_django_datetime(root.get('start')) coordinator.end = oozie_to_django_datetime(root.get('end')) (coordinator.frequency_unit, coordinator.frequency_number) = oozie_to_hue_frequency(root.get('frequency'))
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get coordinator properties from coordinator xml set properties on coordinator with attributes from xml etree root .
train
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@verbose def activate_proj(projs, copy=True, verbose=None): if copy: projs = deepcopy(projs) for proj in projs: proj['active'] = True logger.info(('%d projection items activated' % len(projs))) return projs
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set all projections to active .
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def unregister_class(alias): try: x = CLASS_CACHE[alias] except KeyError: raise UnknownClassAlias(('Unknown alias %r' % (alias,))) if (not x.anonymous): del CLASS_CACHE[x.alias] del CLASS_CACHE[x.klass] return x
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unregister class instrumentation .
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@loader_option() def subqueryload(loadopt, attr): return loadopt.set_relationship_strategy(attr, {'lazy': 'subquery'})
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indicate that the given attribute should be loaded using subquery eager loading .
train
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def _shuffle(y, groups, random_state): if (groups is None): indices = random_state.permutation(len(y)) else: indices = np.arange(len(groups)) for group in np.unique(groups): this_mask = (groups == group) indices[this_mask] = random_state.permutation(indices[this_mask]) return safe_indexing(y, indices)
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return a shuffled copy of y eventually shuffle among same labels .
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def commit(): connection._commit() set_clean()
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interface to git-commit(1)_ cwd the path to the git checkout message commit message opts any additional options to add to the command line .
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def next_multiple(n, k): return (div_ceil(n, k) * k)
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the smallest multiple of k which is >= n .
train
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def loop_until_passed(pr_url, sleep_between, session, jenkins_session, max_retries): retry_counts = Counter() for _ in infinite_sleeps(sleep_between): resp = session.get(pr_url) if (resp.status_code != 200): print('PR not found: {}'.format(resp.content)) return (None, None) pr = resp.json() if (pr['state'] != u'open'): print('Merge request not open: {}'.format(pr['state'])) return (None, None) else: statuses = get_statuses(pr, session) if (len(statuses) < MINIMUM_STATUSES): print("Can't merge PR yet because there aren't enough statuses reporting ({} so far)".format(len(statuses))) else: needed = filter(not_success, statuses) if (not needed): return (pr, statuses) print("Can't merge PR yet because these {} checks haven't succeeded:".format(len(needed))) maybe_retry_jobs(needed, retry_counts, max_retries, jenkins_session) print('Sleeping for {} seconds and trying again.\n\n'.format(sleep_between))
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loop until all the statuses for the target pull request are green .
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def adjust_gamma(image, gamma=1, gain=1): _assert_non_negative(image) dtype = image.dtype.type if (gamma < 0): raise ValueError('Gamma should be a non-negative real number.') scale = float((dtype_limits(image, True)[1] - dtype_limits(image, True)[0])) out = ((((image / scale) ** gamma) * scale) * gain) return dtype(out)
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performs gamma correction on the input image .
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def _send_instance_update_notification(context, instance, old_vm_state=None, old_task_state=None, new_vm_state=None, new_task_state=None, service='compute', host=None): payload = info_from_instance(context, instance, None, None) if (not new_vm_state): new_vm_state = instance['vm_state'] if (not new_task_state): new_task_state = instance['task_state'] states_payload = {'old_state': old_vm_state, 'state': new_vm_state, 'old_task_state': old_task_state, 'new_task_state': new_task_state} payload.update(states_payload) (audit_start, audit_end) = audit_period_bounds(current_period=True) payload['audit_period_beginning'] = audit_start payload['audit_period_ending'] = audit_end bw = bandwidth_usage(instance, audit_start) payload['bandwidth'] = bw publisher_id = notifier_api.publisher_id(service, host) notifier_api.notify(context, publisher_id, 'compute.instance.update', notifier_api.INFO, payload)
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send compute .
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def enable_parallel(processnum=None): global pool, dt, cut, cut_for_search from multiprocessing import cpu_count if (os.name == u'nt'): raise NotImplementedError(u'jieba: parallel mode only supports posix system') else: from multiprocessing import Pool dt.check_initialized() if (processnum is None): processnum = cpu_count() pool = Pool(processnum) cut = _pcut cut_for_search = _pcut_for_search
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change the modules cut and cut_for_search functions to the parallel version .
train
true
11,087
def safe_ip_format(ip): try: if (netaddr.IPAddress(ip).version == 6): return ('[%s]' % ip) except (TypeError, netaddr.AddrFormatError): pass return ip
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transform ip string to "safe" format .
train
false
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def get_step_state(emr_connection, jobflowid, step_name, update=False): g.reset_caches() steps = get_step_states(emr_connection, jobflowid, _update=update) for (name, state, start) in sorted(steps, key=(lambda t: t[2]), reverse=True): if (name == step_name): return state else: return NOTFOUND
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return the state of a step .
train
false
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def libvlc_log_unset(p_instance): f = (_Cfunctions.get('libvlc_log_unset', None) or _Cfunction('libvlc_log_unset', ((1,),), None, None, Instance)) return f(p_instance)
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unsets the logging callback for a libvlc instance .
train
false
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def test_history_import_abspath_in_archive(): dest_parent = mkdtemp() arcname_prefix = os.path.abspath(os.path.join(dest_parent, 'insecure')) with HistoryArchive(arcname_prefix=arcname_prefix) as history_archive: history_archive.write_metafiles() history_archive.write_file('datasets/Pasted_Entry_1.txt', 'foo') history_archive.finalize() _run_unpack(history_archive, dest_parent, 'Absolute path in import archive allowed')
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ensure that a history import archive cannot reference a absolute path outside the archive .
train
false
11,091
def write_ros_handshake_header(sock, header): s = encode_ros_handshake_header(header) sock.sendall(s) return len(s)
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write ros handshake header header to socket sock .
train
false
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def test_multiset_partitions_taocp(): multiplicities = [2, 2] compare_multiset_w_baseline(multiplicities) multiplicities = [4, 3, 1] compare_multiset_w_baseline(multiplicities)
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compares the output of multiset_partitions_taocp with a baseline implementation .
train
false
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def custom_key_func(key, key_prefix, version): return (u'CUSTOM-' + u'-'.join([key_prefix, str(version), key]))
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a customized cache key function .
train
false
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def remove_prerequisite_course(course_key, milestone): if (not is_prerequisite_courses_enabled()): return None milestones_api.remove_course_milestone(course_key, milestone)
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it would remove pre-requisite course milestone for course referred by course_key .
train
false
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def getTricomplexscale(transformWords): scale = euclidean.getComplexByWords(transformWords) return [complex(scale.real, 0.0), complex(0.0, scale.imag), complex()]
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get matrixsvg by transformwords .
train
false
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def save_collection_summary(collection_summary): collection_summary_model = collection_models.CollectionSummaryModel(id=collection_summary.id, title=collection_summary.title, category=collection_summary.category, objective=collection_summary.objective, language_code=collection_summary.language_code, tags=collection_summary.tags, status=collection_summary.status, community_owned=collection_summary.community_owned, owner_ids=collection_summary.owner_ids, editor_ids=collection_summary.editor_ids, viewer_ids=collection_summary.viewer_ids, contributor_ids=collection_summary.contributor_ids, contributors_summary=collection_summary.contributors_summary, version=collection_summary.version, node_count=collection_summary.node_count, collection_model_last_updated=collection_summary.collection_model_last_updated, collection_model_created_on=collection_summary.collection_model_created_on) collection_summary_model.put()
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save a collection summary domain object as a collectionsummarymodel entity in the datastore .
train
false
11,100
def RATINGS_BY_NAME(): all_ratings = ALL_RATINGS() ratings_choices = [] for rb in RATINGS_BODIES.values(): for r in rb.ratings: ratings_choices.append((all_ratings.index(r), (u'%s - %s' % (rb.name, dehydrate_rating(r).name)))) return ratings_choices
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create a list of tuples after we know the locale since this attempts to concatenate two lazy translations in constants file .
train
false
11,101
def load_mappings(app): now = int(time.time()) cache_time = (now - (app.config.intersphinx_cache_limit * 86400)) env = app.builder.env if (not hasattr(env, 'intersphinx_cache')): env.intersphinx_cache = {} cache = env.intersphinx_cache update = False for (uri, inv) in app.config.intersphinx_mapping.iteritems(): if (not inv): inv = posixpath.join(uri, INVENTORY_FILENAME) if (('://' not in inv) or (uri not in cache) or (cache[uri][0] < cache_time)): invdata = fetch_inventory(app, uri, inv) cache[uri] = (now, invdata) update = True if update: env.intersphinx_inventory = {} for (_, invdata) in cache.itervalues(): if invdata: env.intersphinx_inventory.update(invdata)
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load name mappings encoded in command-line arguments .
train
false
11,102
def position_messages_from_tlog(filename): messages = [] mlog = mavutil.mavlink_connection(filename) while True: try: m = mlog.recv_match(type=['GLOBAL_POSITION_INT']) if (m is None): break except Exception: break if (m.lat == 0): continue messages.append(m) num_points = len(messages) keep_point_distance = 3 kept_messages = [] kept_messages.append(messages[0]) pt1num = 0 pt2num = 1 while True: if ((pt2num == (num_points - 1)) or (len(kept_messages) == 99)): kept_messages.append(messages[pt2num]) break pt1 = LocationGlobalRelative((messages[pt1num].lat / 10000000.0), (messages[pt1num].lon / 10000000.0), 0) pt2 = LocationGlobalRelative((messages[pt2num].lat / 10000000.0), (messages[pt2num].lon / 10000000.0), 0) distance_between_points = get_distance_metres(pt1, pt2) if (distance_between_points > keep_point_distance): kept_messages.append(messages[pt2num]) pt1num = pt2num pt2num = (pt2num + 1) return kept_messages
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given telemetry log .
train
true
11,103
@contextfunction def resolve_ctx(context): g._admin_render_ctx = context
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resolve current jinja2 context and store it for general consumption .
train
false
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def setDevice(dev, kind=None): if (not hasattr(backend, 'defaultOutput')): raise IOError('Attempting to SetDevice (audio) but not supported by the current audio library ({!r})'.format(audioLib)) if hasattr(dev, 'name'): dev = dev['name'] if (kind is None): backend.defaultInput = backend.defaultOutput = dev elif (kind == 'input'): backend.defaultInput = dev elif (kind == 'output'): backend.defaultOutput = dev elif travisCI: return else: raise TypeError("`kind` should be one of [None, 'output', 'input']not {!r}".format(kind))
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sets the device to be used for new streams being created .
train
false
11,105
def set_identity_providers_if_unset(facts): if ('master' in facts): deployment_type = facts['common']['deployment_type'] if ('identity_providers' not in facts['master']): identity_provider = dict(name='allow_all', challenge=True, login=True, kind='AllowAllPasswordIdentityProvider') if (deployment_type in ['enterprise', 'atomic-enterprise', 'openshift-enterprise']): identity_provider = dict(name='deny_all', challenge=True, login=True, kind='DenyAllPasswordIdentityProvider') facts['master']['identity_providers'] = [identity_provider] return facts
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set identity_providers fact if not already present in facts dict args: facts : existing facts returns: dict: the facts dict updated with the generated identity providers facts if they were not already present .
train
false
11,107
@gen.coroutine def OldRemovePhotos(client, obj_store, user_id, device_id, request): remove_episodes = [] hide_episodes = [] for ep_dict in request['episodes']: episode = (yield gen.Task(Episode.Query, client, ep_dict['episode_id'], None, must_exist=False)) if ((episode is None) or (episode.viewpoint_id == base.ViewfinderContext.current().user.private_vp_id)): remove_episodes.append(ep_dict) else: hide_episodes.append(ep_dict) hide_request = deepcopy(request) if (len(hide_episodes) > 0): hide_request['episodes'] = hide_episodes (yield HidePhotos(client, obj_store, user_id, device_id, hide_request)) remove_request = deepcopy(request) remove_request['episodes'] = remove_episodes (yield RemovePhotos(client, obj_store, user_id, device_id, remove_request)) raise gen.Return({})
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used by older clients to remove photos from showing in a users personal library .
train
false
11,108
def codeDescription(status_code): if (status_code in http_error_code): return http_error_code[status_code] else: sickrage.srCore.srLogger.error((u'Unknown error code: %s. Please submit an issue' % status_code)) return u'unknown'
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returns the description of the url error code .
train
false
11,110
def adapt_rgb(apply_to_rgb): def decorator(image_filter): @functools.wraps(image_filter) def image_filter_adapted(image, *args, **kwargs): if is_rgb_like(image): return apply_to_rgb(image_filter, image, *args, **kwargs) else: return image_filter(image, *args, **kwargs) return image_filter_adapted return decorator
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return decorator that adapts to rgb images to a gray-scale filter .
train
false
11,111
def _get_allocated_vnc_ports(session): vnc_ports = set() result = session._call_method(vim_util, 'get_objects', 'VirtualMachine', [VNC_CONFIG_KEY]) while result: for obj in result.objects: if (not hasattr(obj, 'propSet')): continue dynamic_prop = obj.propSet[0] option_value = dynamic_prop.val vnc_port = option_value.value vnc_ports.add(int(vnc_port)) result = session._call_method(vutil, 'continue_retrieval', result) return vnc_ports
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return an integer set of all allocated vnc ports .
train
false
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def has_ccx_coach_role(user, course_key): if hasattr(course_key, 'ccx'): ccx_id = course_key.ccx role = CourseCcxCoachRole(course_key) if role.has_user(user): list_ccx = CustomCourseForEdX.objects.filter(course_id=course_key.to_course_locator(), coach=user) if list_ccx.exists(): coach_ccx = list_ccx[0] return (str(coach_ccx.id) == ccx_id) else: raise CCXLocatorValidationException('Invalid CCX key. To verify that user is a coach on CCX, you must provide key to CCX') return False
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check if user is a coach on this ccx .
train
false
11,113
def fixed_ip_get_all(context): return IMPL.fixed_ip_get_all(context)
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get all defined fixed ips .
train
false
11,114
def unicode_to_html(text): return ''.join([(u'&#%s;' % ord(i)) for i in text])
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turns all unicode into html entities .
train
false
11,115
def _from_soap(in_envelope_xml, xmlids=None, **kwargs): ns_soap = kwargs.pop('ns', ns.soap11_env) if xmlids: resolve_hrefs(in_envelope_xml, xmlids) if (in_envelope_xml.tag != ('{%s}Envelope' % ns_soap)): raise Fault('Client.SoapError', ('No {%s}Envelope element was found!' % ns_soap)) header_envelope = in_envelope_xml.xpath('e:Header', namespaces={'e': ns_soap}) body_envelope = in_envelope_xml.xpath('e:Body', namespaces={'e': ns_soap}) if ((len(header_envelope) == 0) and (len(body_envelope) == 0)): raise Fault('Client.SoapError', 'Soap envelope is empty!') header = None if (len(header_envelope) > 0): header = header_envelope[0].getchildren() body = None if ((len(body_envelope) > 0) and (len(body_envelope[0]) > 0)): body = body_envelope[0][0] return (header, body)
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parses the xml string into the header and payload .
train
false
11,116
def format_header_param(name, value): if (not any(((ch in value) for ch in '"\\\r\n'))): result = ('%s="%s"' % (name, value)) try: result.encode('ascii') except (UnicodeEncodeError, UnicodeDecodeError): pass else: return result if ((not six.PY3) and isinstance(value, six.text_type)): value = value.encode('utf-8') value = email.utils.encode_rfc2231(value, 'utf-8') value = ('%s*=%s' % (name, value)) return value
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helper function to format and quote a single header parameter .
train
true
11,117
def clear_info_cache(app, env, account, container=None): set_info_cache(app, env, account, container, None)
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clear the cached info in both memcache and env .
train
false
11,118
def ne_chunk(tagged_tokens, binary=False): if binary: chunker_pickle = _BINARY_NE_CHUNKER else: chunker_pickle = _MULTICLASS_NE_CHUNKER chunker = load(chunker_pickle) return chunker.parse(tagged_tokens)
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use nltks currently recommended named entity chunker to chunk the given list of tagged tokens .
train
false
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def is_fcntl_available(check_sunos=False): if (check_sunos and is_sunos()): return False return HAS_FCNTL
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simple function to check if the fcntl module is available or not .
train
false
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def computed_values(d, *args, **kwargs): result = {} for (k, v) in six.iteritems(d): if callable(v): v = v(*args, **kwargs) if isinstance(v, dict): v = computed_values(v, *args, **kwargs) result[k] = v return result
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returns a new dict that has callable values replaced with the return values .
train
false
11,121
def _GetDatastoreStats(kinds_list, use_stats_kinds=False): global_stat = stats.GlobalStat.all().fetch(1) if (not global_stat): return _KindsListToTuple(kinds_list) global_ts = global_stat[0].timestamp kind_stats = stats.KindStat.all().filter('timestamp =', global_ts).fetch(1000) if (not kind_stats): return _KindsListToTuple(kinds_list) results = {} for kind_ent in kind_stats: if ((not kind_ent.kind_name.startswith('__')) and (use_stats_kinds or (kind_ent.kind_name in kinds_list)) and (kind_ent.count > 0)): results[kind_ent.kind_name] = _PresentatableKindStats(kind_ent) utils.CacheStats(results.values()) for kind_str in (kinds_list or []): if (kind_str not in results): results[kind_str] = {'kind_name': kind_str} return (global_ts, sorted(results.values(), key=(lambda x: x['kind_name'])))
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retrieves stats for kinds .
train
false