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def _exists_in_path(cmd, environ): if (u'PATH' in environ): input_environ = environ.get(u'PATH') else: input_environ = os.environ.get(u'PATH', u'') extensions = os.environ.get(u'PATHEXT', u'').split(os.pathsep) for directory in input_environ.split(os.pathsep): base = os.path.join(directory, cmd) options = ([base] + [(base + ext) for ext in extensions]) for filename in options: if os.path.exists(filename): return (True, filename) return (False, None)
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based on a code snippet from URL .
train
false
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def getEachWordCapitalized(name): withSpaces = name.lower().replace('_', ' ') words = withSpaces.split(' ') capitalizedStrings = [] for word in words: capitalizedStrings.append(word.capitalize()) return ' '.join(capitalizedStrings)
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get the capitalized name .
train
false
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def generate_pipeline_code(pipeline_tree): steps = process_operator(pipeline_tree) pipeline_text = 'make_pipeline(\n{STEPS}\n)'.format(STEPS=_indent(',\n'.join(steps), 4)) return pipeline_text
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generate code specific to the construction of the sklearn pipeline parameters pipeline_tree: list list of operators in the current optimized pipeline returns source code for the sklearn pipeline .
train
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@content_type('application/json') def pretty_json(content, **kwargs): return json(content, indent=4, separators=(',', ': '), **kwargs)
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json pretty printed and indented .
train
false
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def handle_delete(request): index_to_delete = request.POST.get('delete_index') es_indexes = [name for (name, count) in get_indexes()] if (not index_to_delete.startswith(settings.ES_INDEX_PREFIX)): raise DeleteError(('"%s" is not a valid index name.' % index_to_delete)) if (index_to_delete not in es_indexes): raise DeleteError(('"%s" does not exist.' % index_to_delete)) if (index_to_delete == read_index('default')): raise DeleteError(('"%s" is the default read index.' % index_to_delete)) delete_index(index_to_delete) return HttpResponseRedirect(request.path)
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deletes an index .
train
false
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def _weight_mean_color(graph, src, dst, n): diff = (graph.node[dst]['mean color'] - graph.node[n]['mean color']) diff = np.linalg.norm(diff) return {'weight': diff}
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callback to handle merging nodes by recomputing mean color .
train
false
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def verify_user(user): if (not user.email): raise ValidationError(("You cannot verify an account with no email set. You can set this user's email with 'pootle update_user_email %s EMAIL'" % user.username)) try: validate_email_unique(user.email, user) except ValidationError: raise ValidationError("This user's email is not unique. You can find duplicate emails with 'pootle find_duplicate_emails'") existing_primary = EmailAddress.objects.filter(user=user, primary=True) if existing_primary.exists(): existing_primary = existing_primary.first() if (not existing_primary.verified): existing_primary.verified = True existing_primary.save() return else: raise ValueError(("User '%s' is already verified" % user.username)) sync_user_email_addresses(user) email_address = EmailAddress.objects.filter(user=user, email__iexact=user.email).order_by('primary').first() email_address.verified = True email_address.primary = True email_address.save()
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verify a user account without email confirmation if the user has an existing primary allauth .
train
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def compress_kml(kml): kmz = cStringIO.StringIO() zf = zipfile.ZipFile(kmz, 'a', zipfile.ZIP_DEFLATED) zf.writestr('doc.kml', kml.encode(settings.DEFAULT_CHARSET)) zf.close() kmz.seek(0) return kmz.read()
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returns compressed kmz from the given kml string .
train
false
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def verify_task_queue_add_request(app_id, request, now): if (request.eta_usec() < 0): return taskqueue_service_pb.TaskQueueServiceError.INVALID_ETA eta = datetime.datetime.utcfromtimestamp(_usec_to_sec(request.eta_usec())) max_eta = (now + MAX_ETA) if (eta > max_eta): return taskqueue_service_pb.TaskQueueServiceError.INVALID_ETA if (request.has_crontimetable() and (app_id is None)): return taskqueue_service_pb.TaskQueueServiceError.PERMISSION_DENIED if (request.mode() == taskqueue_service_pb.TaskQueueMode.PULL): max_task_size_bytes = MAX_PULL_TASK_SIZE_BYTES else: max_task_size_bytes = MAX_PUSH_TASK_SIZE_BYTES if (request.ByteSize() > max_task_size_bytes): return taskqueue_service_pb.TaskQueueServiceError.TASK_TOO_LARGE return taskqueue_service_pb.TaskQueueServiceError.SKIPPED
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checks that a taskqueueaddrequest is valid .
train
false
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def get_properties(obj): if hasattr(obj, 'keys'): return obj.keys() elif hasattr(obj, '__dict__'): return obj.__dict__.keys() return []
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returns a list of properties for l{obj} @since: 0 .
train
true
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def validate_quotes(value): open_quotes = True for i in range(len(value)): if (value[i] == '"'): if (i and (value[(i - 1)] == '\\')): continue if open_quotes: if (i and (value[(i - 1)] != ',')): msg = (_('Invalid filter value %s. There is no comma before opening quotation mark.') % value) raise exception.InvalidParameterValue(message=msg) elif (((i + 1) != len(value)) and (value[(i + 1)] != ',')): msg = (_('Invalid filter value %s. There is no comma after closing quotation mark.') % value) raise exception.InvalidParameterValue(message=msg) open_quotes = (not open_quotes) if (not open_quotes): msg = (_('Invalid filter value %s. The quote is not closed.') % value) raise exception.InvalidParameterValue(message=msg)
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validate filter values validation opening/closing quotes in the expression .
train
false
42,956
def _is_mostly_subdomain_compatible(bucket_name): return ((bucket_name.lower() == bucket_name) and (len(bucket_name) >= 3) and (len(bucket_name) <= 63) and ('_' not in bucket_name) and ('..' not in bucket_name) and ('-.' not in bucket_name) and ('.-' not in bucket_name) and (not bucket_name.startswith('-')) and (not bucket_name.endswith('-')) and (not bucket_name.startswith('.')) and (not bucket_name.endswith('.')) and (not _is_ipv4_like(bucket_name)))
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returns true if subdomaincallingformat can be used .
train
true
42,957
def has_leading_dir(paths): common_prefix = None for path in paths: (prefix, rest) = split_leading_dir(path) if (not prefix): return False elif (common_prefix is None): common_prefix = prefix elif (prefix != common_prefix): return False return True
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returns true if all the paths have the same leading path name .
train
true
42,959
def group_snapshot_get_all(context, filters=None): return IMPL.group_snapshot_get_all(context, filters)
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get all group snapshots .
train
false
42,960
def extract_tb(tb, limit=None): if (limit is None): if hasattr(sys, 'tracebacklimit'): limit = sys.tracebacklimit list = [] n = 0 while ((tb is not None) and ((limit is None) or (n < limit))): f = tb.tb_frame lineno = tb.tb_lineno co = f.f_code filename = co.co_filename name = co.co_name linecache.checkcache(filename) line = linecache.getline(filename, lineno, f.f_globals) if line: line = line.strip() else: line = None list.append((filename, lineno, name, line)) tb = tb.tb_next n = (n + 1) return list
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return list of up to limit pre-processed entries from traceback .
train
true
42,961
def _credit_course_requirements(course_key, student): if (not (settings.FEATURES.get('ENABLE_CREDIT_ELIGIBILITY', False) and is_credit_course(course_key))): return None enrollment = CourseEnrollment.get_enrollment(student, course_key) if (enrollment and (enrollment.mode not in REQUIREMENTS_DISPLAY_MODES)): return None non_eligible_statuses = ['failed', 'declined'] requirement_statuses = get_credit_requirement_status(course_key, student.username) if is_user_eligible_for_credit(student.username, course_key): eligibility_status = 'eligible' elif any(((requirement['status'] in non_eligible_statuses) for requirement in requirement_statuses)): eligibility_status = 'not_eligible' else: eligibility_status = 'partial_eligible' return {'eligibility_status': eligibility_status, 'requirements': requirement_statuses}
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return information about which credit requirements a user has satisfied .
train
false
42,962
def get_valid_os_versions_for_breed(breed): os_versions = [] if (breed in get_valid_breeds()): os_versions = SIGNATURE_CACHE['breeds'][breed].keys() return os_versions
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return a list of valid os-versions for the given breed .
train
false
42,963
def test_not_implemented_elemwise_grad(): class TestOp(scalar.ScalarOp, ): def __init__(self): self.output_types_preference = scalar.upgrade_to_float def impl(self, n, x): return (x * n) def grad(self, inputs, gout): (n, x) = inputs (gz,) = gout dy_dx = n return [theano.gradient.grad_not_implemented(self, 0, n), (gz * dy_dx)] test_op = tensor.Elemwise(TestOp()) x = tensor.scalar() tensor.grad(test_op(2, x), x) try: tensor.grad(test_op(x, 2), x) assert False except theano.gradient.NullTypeGradError: pass
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regression test for unimplemented gradient in an elemwise op .
train
false
42,964
def overridden_settings(settings): for (name, defvalue) in iter_default_settings(): value = settings[name] if ((not isinstance(defvalue, dict)) and (value != defvalue)): (yield (name, value))
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return a dict of the settings that have been overridden .
train
false
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def _splitPrefix(name): ntok = name.split(':', 1) if (len(ntok) == 2): return ntok else: return (None, ntok[0])
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internal method for splitting a prefixed element name into its respective parts .
train
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def parse_token_response(body, scope=None): try: params = json.loads(body) except ValueError: params = dict(urlparse.parse_qsl(body)) for key in (u'expires_in', u'expires'): if (key in params): params[key] = int(params[key]) if (u'scope' in params): params[u'scope'] = scope_to_list(params[u'scope']) if (u'expires' in params): params[u'expires_in'] = params.pop(u'expires') if (u'expires_in' in params): params[u'expires_at'] = (time.time() + int(params[u'expires_in'])) params = OAuth2Token(params, old_scope=scope) validate_token_parameters(params) return params
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parse the json token response body into a dict .
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def hpsModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur, Ns, stocf): (hfreq, hmag, hphase) = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur) xr = UF.sineSubtraction(x, Ns, H, hfreq, hmag, hphase, fs) stocEnv = STM.stochasticModelAnal(xr, H, (H * 2), stocf) return (hfreq, hmag, hphase, stocEnv)
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analysis of a sound using the harmonic plus stochastic model x: input sound .
train
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def country_timezones(iso3166_code): iso3166_code = iso3166_code.upper() if (not _country_timezones_cache): zone_tab = open_resource('zone.tab') for line in zone_tab: if line.startswith('#'): continue (code, coordinates, zone) = line.split(None, 4)[:3] try: _country_timezones_cache[code].append(zone) except KeyError: _country_timezones_cache[code] = [zone] return _country_timezones_cache[iso3166_code]
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return a list of timezones used in a particular country .
train
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def _generate_cache_key(request, headerlist, key_prefix): ctx = md5.new() for header in headerlist: value = request.META.get(header, None) if (value is not None): ctx.update(value) return ('views.decorators.cache.cache_page.%s.%s.%s' % (key_prefix, request.path, ctx.hexdigest()))
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returns a cache key from the headers given in the header list .
train
false
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def greedy_partition(counts, n): buckets = [[] for i in range(n)] fill_levels = [0 for i in range(n)] for key in sorted(counts, reverse=True, key=(lambda c: counts[c])): smallest = fill_levels.index(min(fill_levels)) buckets[smallest].append(key) fill_levels[smallest] += counts[key] return (buckets, fill_levels)
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distribute k counts evenly across n buckets .
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false
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def x_server_test(f, cond, msg): if isinstance(f, (type, types.ClassType)): if (not cond): f.__unittest_skip__ = True f.__unittest_skip_why__ = msg return f else: @unittest.skipIf((not cond), msg) def wrapped_fn(*args, **kwargs): return f(*args, **kwargs) return wrapped_fn
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decorator to label test classes or instance methods as x_only .
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def dmp_add_mul(f, g, h, u, K): return dmp_add(f, dmp_mul(g, h, u, K), u, K)
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returns f + g*h where f .
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def _init_tab_completion(): log.completion.debug('Initializing tab completion.') with debug.log_time(log.completion, 'tab completion init'): model = miscmodels.TabCompletionModel() _instances[usertypes.Completion.tab] = model
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initialize the tab completion model .
train
false
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def _formatRouteBody(data, schema_store): baseSubstitutions = {u'DOMAIN': u'example.com', u'NODE_0': u'cf0f0346-17b2-4812-beca-1434997d6c3f', u'NODE_1': u'7ec3c4eb-6b1c-43da-8015-a163f7d15244'} for line in data['description']: (yield line) if ('input' in data): for line in _formatActualSchema(data['input_schema'], '+ Request JSON Schema', schema_store): (yield line) if ('output' in data): for line in _formatActualSchema(data['output_schema'], '+ Response JSON Schema', schema_store): (yield line) for example in data['examples']: substitutions = baseSubstitutions.copy() for line in _formatExample(example, substitutions): (yield line) if ('input' in data): for line in _formatSchema(data['input'], True): (yield line) if ('output' in data): for line in _formatSchema(data['output'], False): (yield line)
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generate the description of a l{klein} route .
train
false
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def test_gmail_many_folders_one_role(monkeypatch, constants): folders = constants['gmail_folders'] duplicates = [('\\HasNoChildren', '/', u'[Imap]/Trash'), ('\\HasNoChildren', '/', u'[Imap]/Sent')] folders += duplicates client = patch_gmail_client(monkeypatch, folders) raw_folders = client.folders() folder_names = client.folder_names() for role in ['inbox', 'all', 'trash', 'drafts', 'important', 'sent', 'spam', 'starred']: assert (role in folder_names) test_set = filter((lambda x: (x == role)), map((lambda y: y.role), raw_folders)) assert (len(test_set) == 1), 'assigned wrong number of {}'.format(role) names = folder_names[role] assert isinstance(names, list) assert (len(names) == 1), ('assign same role to %s folders' % len(names))
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tests that accounts with many folders with similar system folders have only one role .
train
false
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def custom_check(cmd, ignore_retcode=False): p = custom_popen(cmd) (out, _) = p.communicate() if (p.returncode and (not ignore_retcode)): raise RarExecError('Check-run failed') return out
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run command .
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def text_dialog(text, title): parent = qtutils.active_window() label = QtWidgets.QLabel(parent) label.setFont(qtutils.diff_font()) label.setText(text) label.setTextInteractionFlags(Qt.NoTextInteraction) widget = QtWidgets.QDialog(parent) widget.setWindowModality(Qt.WindowModal) widget.setWindowTitle(title) layout = qtutils.hbox(defs.margin, defs.spacing, label) widget.setLayout(layout) qtutils.add_action(widget, N_(u'Close'), widget.accept, Qt.Key_Question, Qt.Key_Enter, Qt.Key_Return) widget.show() return widget
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show a wall of text in a dialog .
train
false
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def test_file_system_enumerations(): import os os_dir = os.listdir('.') os_dir.sort() enum_dir = [x[2:] for x in System.IO.Directory.EnumerateFileSystemEntries('.')] enum_dir.sort() AreEqual(os_dir, enum_dir)
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URL only minimal sanity tests should be needed .
train
false
42,984
def task_id_eq(task_id, body, message): return (body[u'id'] == task_id)
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return true if task id equals task_id .
train
false
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def make_camera(cam_type, *args, **kwargs): cam_types = {None: BaseCamera} for camType in (BaseCamera, PanZoomCamera, PerspectiveCamera, TurntableCamera, FlyCamera, ArcballCamera): cam_types[camType.__name__[:(-6)].lower()] = camType try: return cam_types[cam_type](*args, **kwargs) except KeyError: raise KeyError(('Unknown camera type "%s". Options are: %s' % (cam_type, cam_types.keys())))
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factory function for creating new cameras using a string name .
train
true
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def gemset_create(ruby, gemset, runas=None): return _rvm_do(ruby, ['rvm', 'gemset', 'create', gemset], runas=runas)
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creates a gemset .
train
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def has_override_value(name): if is_site_configuration_enabled(): return has_configuration_override(name) else: return microsite.has_override_value(name)
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returns true/false whether a microsite has a definition for the specified named value .
train
false
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def load_version(): globals_dict = {} with codecs.open(os.path.join('imblearn', 'version.py'), encoding='utf-8-sig') as fp: exec fp.read() in globals_dict return globals_dict
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executes imblearn/version .
train
false
42,990
def makescript(filename, compiler): dirname = os.path.split(filename)[0] if (not os.access(dirname, os.X_OK)): os.mkdir(dirname, 493) fp = open(filename, 'w') fp.write((SCRIPT % compiler)) fp.close() os.chmod(filename, 493) print 'fixapplepython23: Created', filename
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create a wrapper script for a compiler .
train
false
42,994
def formatRARVersion(field): return ('%u.%u' % divmod(field.value, 10))
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decodes the rar version stored on 1 byte .
train
false
42,996
def FindRegisterPythonExe(exeAlias, searchPaths, actualFileNames=None): import regutil, string (fname, ok) = FindPythonExe(exeAlias, actualFileNames, searchPaths) if (not ok): regutil.RegisterPythonExe(fname, exeAlias) return fname
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find and register a python exe assumes the core registry setup correctly .
train
false
42,997
def copy_source_sse_md5(params, **kwargs): _sse_md5(params, 'CopySourceSSECustomer')
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s3 server-side encryption requires the encryption key to be sent to the server base64 encoded .
train
false
42,998
def db_init(db, conn_name): global db_ready if (db_ready.count(conn_name) < 1): db.execute('create table if not exists seen_user(name, time, quote, chan, host, primary key(name, chan))') db.commit() db_ready.append(conn_name)
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check to see that our db has the the seen table :type db: sqlalchemy .
train
false
42,999
def render_label(content, label_for=None, label_class=None, label_title=u''): attrs = {} if label_for: attrs[u'for'] = label_for if label_class: attrs[u'class'] = label_class if label_title: attrs[u'title'] = label_title return render_tag(u'label', attrs=attrs, content=content)
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render a label with content .
train
false
43,000
def save_callback(operation, graphdef): if (operation == 'test'): for e in graphdef.expressions: if expression_ok(e): graphdef.expression = e display_graph(graphdef) return mestate.console.writeln('Invalid graph expressions', fg='red') return if (operation == 'save'): save_graph(graphdef, mestate)
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callback from save thread .
train
true
43,001
def s3_set_extension(url, extension=None): if (extension == None): extension = s3_get_extension() u = urlparse.urlparse(url) path = u.path if path: if ('.' in path): elements = [p.split('.')[0] for p in path.split('/')] else: elements = path.split('/') if (extension and elements[(-1)]): elements[(-1)] += ('.%s' % extension) path = '/'.join(elements) return urlparse.urlunparse((u.scheme, u.netloc, path, u.params, u.query, u.fragment))
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add a file extension to the path of a url .
train
false
43,003
def test_debug_logging(): with use_log_level('debug', 'Selected', True, False) as l: logger.debug('Selected foo') assert_equal(len(l), 1) assert_in('test_logging', l[0]) with use_log_level('debug', record=True, print_msg=False) as l: logger.debug('foo') assert_equal(len(l), 1) assert_in('test_logging', l[0]) with use_log_level('debug', 'foo', True, False) as l: logger.debug('bar') assert_equal(len(l), 0) with use_log_level('info', record=True, print_msg=False) as l: logger.debug('foo') logger.info('bar') assert_equal(len(l), 1) assert_not_in('unknown', l[0])
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test advanced debugging logging .
train
false
43,004
def makeCategoricalIndex(k=10, n=3, name=None): x = rands_array(nchars=4, size=n) return CategoricalIndex(np.random.choice(x, k), name=name)
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make a length k index or n categories .
train
false
43,005
def as_json(*args, **kwargs): arg = (args or kwargs) return jsonutils.dumps(arg)
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helper function for simulating xenapi plugin responses for those that are returning json .
train
false
43,006
@inspect_command(alias=u'dump_conf', signature=u'[include_defaults=False]', args=[(u'with_defaults', strtobool)]) def conf(state, with_defaults=False, **kwargs): return jsonify(state.app.conf.table(with_defaults=with_defaults), keyfilter=_wanted_config_key, unknown_type_filter=safe_repr)
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list configuration .
train
false
43,007
@register.simple_tag(takes_context=True) def expand_fragment_header_link(context, header): lines_of_context = context[u'lines_of_context'] offset = (context[u'first_line'] - header[u'line']) return render_to_string(u'reviews/expand_link.html', {u'tooltip': _(u'Expand to header'), u'text': format_html(u'<code>{0}</code>', header[u'text']), u'comment_id': context[u'comment'].id, u'expand_pos': ((lines_of_context[0] + offset), lines_of_context[1]), u'image_class': u'rb-icon-diff-expand-header'})
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render a diff comment fragment header expansion link .
train
false
43,008
def assert_no_overwrite(call, shapes, dtypes=None): if (dtypes is None): dtypes = [np.float32, np.float64, np.complex64, np.complex128] for dtype in dtypes: for order in ['C', 'F']: for faker in [_id, _FakeMatrix, _FakeMatrix2]: orig_inputs = [_get_array(s, dtype) for s in shapes] inputs = [faker(x.copy(order)) for x in orig_inputs] call(*inputs) msg = ('call modified inputs [%r, %r]' % (dtype, faker)) for (a, b) in zip(inputs, orig_inputs): np.testing.assert_equal(a, b, err_msg=msg)
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test that a call does not overwrite its input arguments .
train
false
43,010
def p_shift_expression_3(t): pass
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shift_expression : shift_expression rshift additive_expression .
train
false
43,011
def mean_variance_axis(X, axis): _raise_error_wrong_axis(axis) if isinstance(X, sp.csr_matrix): if (axis == 0): return _csr_mean_var_axis0(X) else: return _csc_mean_var_axis0(X.T) elif isinstance(X, sp.csc_matrix): if (axis == 0): return _csc_mean_var_axis0(X) else: return _csr_mean_var_axis0(X.T) else: _raise_typeerror(X)
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compute mean and variance along an axix on a csr or csc matrix parameters x : csr or csc sparse matrix .
train
false
43,012
def isLoopIntersectingLoops(loop, otherLoops): for pointIndex in xrange(len(loop)): pointBegin = loop[pointIndex] pointEnd = loop[((pointIndex + 1) % len(loop))] if isLineIntersectingLoops(otherLoops, pointBegin, pointEnd): return True return False
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determine if the loop is intersecting other loops .
train
false
43,013
@cache_permission def can_edit_flags(user, project): return check_permission(user, project, 'trans.edit_flags')
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checks whether user can edit translation flags .
train
false
43,015
def get_pool_name_filter_regex(configuration): pool_patterns = (configuration.netapp_pool_name_search_pattern or '(.+)') pool_patterns = '|'.join([(('^' + pool_pattern.strip('^$ DCTB ')) + '$') for pool_pattern in pool_patterns.split(',')]) try: return re.compile(pool_patterns) except re.error: raise exception.InvalidConfigurationValue(option='netapp_pool_name_search_pattern', value=configuration.netapp_pool_name_search_pattern)
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build the regex for filtering pools by name .
train
false
43,016
def truncate_month(dt, measure): months = ((dt.year * 12) + dt.month) months = ((months // measure) * measure) return date(((months - 1) // 12), (((months - 1) % 12) + 1), 1)
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truncate by months .
train
false
43,017
def generate_static_name(name, base=None): if base: path = os.path.join(base, name) else: path = name sha = hashlib.sha1(open(path).read()).digest() shorthash = base64.urlsafe_b64encode(sha[0:8]).rstrip('=') (name, ext) = os.path.splitext(name) return (((name + '.') + shorthash) + ext)
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generate a unique filename .
train
false
43,018
def _possibly_convert_platform(values): if isinstance(values, (list, tuple)): values = lib.list_to_object_array(list(values)) if (getattr(values, 'dtype', None) == np.object_): if hasattr(values, '_values'): values = values._values values = lib.maybe_convert_objects(values) return values
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try to do platform conversion .
train
false
43,019
def train_op_fun(total_loss, global_step): nb_ex_per_train_epoch = int((60000 / FLAGS.nb_teachers)) num_batches_per_epoch = (nb_ex_per_train_epoch / FLAGS.batch_size) decay_steps = int((num_batches_per_epoch * FLAGS.epochs_per_decay)) initial_learning_rate = (float(FLAGS.learning_rate) / 100.0) lr = tf.train.exponential_decay(initial_learning_rate, global_step, decay_steps, LEARNING_RATE_DECAY_FACTOR, staircase=True) tf.scalar_summary('learning_rate', lr) loss_averages_op = moving_av(total_loss) with tf.control_dependencies([loss_averages_op]): opt = tf.train.GradientDescentOptimizer(lr) grads = opt.compute_gradients(total_loss) apply_gradient_op = opt.apply_gradients(grads, global_step=global_step) for var in tf.trainable_variables(): tf.histogram_summary(var.op.name, var) variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step) variables_averages_op = variable_averages.apply(tf.trainable_variables()) with tf.control_dependencies([apply_gradient_op, variables_averages_op]): train_op = tf.no_op(name='train') return train_op
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train model .
train
false
43,021
@conf.commands.register def arping(net, timeout=2, cache=0, verbose=None, **kargs): if (verbose is None): verbose = conf.verb (ans, unans) = srp((Ether(dst='ff:ff:ff:ff:ff:ff') / ARP(pdst=net)), verbose=verbose, filter='arp and arp[7] = 2', timeout=timeout, iface_hint=net, **kargs) ans = ARPingResult(ans.res) if (cache and (ans is not None)): for pair in ans: arp_cache[pair[1].psrc] = (pair[1].hwsrc, time.time()) if verbose: ans.show() return (ans, unans)
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send arp who-has requests to determine which hosts are up arping -> none set cache=true if you want arping to modify internal arp-cache .
train
true
43,022
def quote_from_bytes(bs, safe='/'): if (not isinstance(bs, (bytes, bytearray))): raise TypeError('quote_from_bytes() expected bytes') if (not bs): return '' if isinstance(safe, str): safe = safe.encode('ascii', 'ignore') else: safe = bytes([c for c in safe if (c < 128)]) if (not bs.rstrip((_ALWAYS_SAFE_BYTES + safe))): return bs.decode() try: quoter = _safe_quoters[safe] except KeyError: _safe_quoters[safe] = quoter = Quoter(safe).__getitem__ return ''.join([quoter(char) for char in bs])
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like quote() .
train
true
43,023
def p_command_print_empty(p): p[0] = ('PRINT', [], None)
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command : print .
train
false
43,024
def transformVector3ByMatrix(tetragrid, vector3): if getIsIdentityTetragridOrNone(tetragrid): return transformVector3Blindly(tetragrid, vector3)
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transform the vector3 by a matrix .
train
false
43,026
def IsDebug(): import imp for suffix_item in imp.get_suffixes(): if (suffix_item[0] == '_d.pyd'): return '_d' return ''
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return "_d" if were running a debug version .
train
false
43,027
def make_pool(max_concurrency, max_members): return worker.TarUploadPool(FakeUploader(), max_concurrency, max_members)
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set up a pool with a fakeuploader .
train
false
43,028
def is_open_quote(sql): parsed = sqlparse.parse(sql) return any((_parsed_is_open_quote(p) for p in parsed))
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returns true if the query contains an unclosed quote .
train
false
43,030
def set_selected_item(widget, idx): if (type(widget) is QtWidgets.QTreeWidget): item = widget.topLevelItem(idx) if item: item.setSelected(True) widget.setCurrentItem(item)
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sets a the currently selected item to the item at index idx .
train
false
43,031
def binary_blobs(length=512, blob_size_fraction=0.1, n_dim=2, volume_fraction=0.5, seed=None): rs = np.random.RandomState(seed) shape = tuple(([length] * n_dim)) mask = np.zeros(shape) n_pts = max((int((1.0 / blob_size_fraction)) ** n_dim), 1) points = (length * rs.rand(n_dim, n_pts)).astype(np.int) mask[[indices for indices in points]] = 1 mask = gaussian(mask, sigma=((0.25 * length) * blob_size_fraction)) threshold = np.percentile(mask, (100 * (1 - volume_fraction))) return np.logical_not((mask < threshold))
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generate synthetic binary image with several rounded blob-like objects .
train
false
43,032
def _meijerint_definite_3(f, x): res = _meijerint_definite_4(f, x) if (res and (res[1] != False)): return res if f.is_Add: _debug('Expanding and evaluating all terms.') ress = [_meijerint_definite_4(g, x) for g in f.args] if all(((r is not None) for r in ress)): conds = [] res = S(0) for (r, c) in ress: res += r conds += [c] c = And(*conds) if (c != False): return (res, c)
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try to integrate f dx from zero to infinity .
train
false
43,035
def get_tensor_parents(tensor): parents_list = [] parents_list.append(tensor) if tensor.op: for t in tensor.op.inputs: if (not ('read:0' in t.name)): parents_list += get_tensor_parents(t) return parents_list
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get all calculation and data parent tensors .
train
false
43,036
def pixel_wise_softmax(output, name='pixel_wise_softmax'): with tf.name_scope(name) as scope: return tf.nn.softmax(output)
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return the softmax outputs of images .
train
false
43,038
def test_ast_non_decoratable(): cant_compile(u'(with-decorator (foo) (* x x))')
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ensure decorating garbage breaks .
train
false
43,040
def cut_from_dict(desc, cube=None): cut_type = desc['type'].lower() dim = desc.get('dimension') if (dim and cube): dim = cube.dimension(dim) if (cut_type == 'point'): return PointCut(dim, desc.get('path'), desc.get('hierarchy'), desc.get('invert', False)) elif (cut_type == 'set'): return SetCut(dim, desc.get('paths'), desc.get('hierarchy'), desc.get('invert', False)) elif (cut_type == 'range'): return RangeCut(dim, desc.get('from'), desc.get('to'), desc.get('hierarchy'), desc.get('invert', False)) else: raise ArgumentError(('Unknown cut type %s' % cut_type))
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returns a cut from desc dictionary .
train
false
43,041
def os_upgrade(): print green(('%s: Updating OS' % env.host)) run('apt-get update', pty=True) run('apt-get upgrade -y', pty=True)
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update os .
train
false
43,042
def RenderNormalCdf(mu, sigma, low, high, n=101): xs = np.linspace(low, high, n) ps = stats.norm.cdf(xs, mu, sigma) return (xs, ps)
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generates sequences of xs and ps for a normal cdf .
train
false
43,045
def almostEqualF(a, b, rel_err=2e-15, abs_err=5e-323): if math.isnan(a): return math.isnan(b) if math.isinf(a): return (a == b) if ((not a) and (not b)): return (math.copysign(1.0, a) == math.copysign(1.0, b)) try: absolute_error = abs((b - a)) except OverflowError: return False else: return (absolute_error <= max(abs_err, (rel_err * abs(a))))
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determine whether floating-point values a and b are equal to within a rounding error .
train
false
43,047
def _get_arg_list(name, fobj): trunc = 20 argspec = inspect.getargspec(fobj) arg_list = [] if argspec.args: for arg in argspec.args: arg_list.append(str(arg)) arg_list.reverse() if argspec.defaults: for i in range(len(argspec.defaults)): arg_list[i] = ((str(arg_list[i]) + '=') + str(argspec.defaults[(- i)])) arg_list.reverse() if argspec.varargs: arg_list.append(argspec.varargs) if argspec.keywords: arg_list.append(argspec.keywords) arg_list = [x[:trunc] for x in arg_list] str_param = ('%s(%s)' % (name, ', '.join(arg_list))) return str_param
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given a function object .
train
false
43,049
def send_prowl(title, msg, gtype, force=False, test=None): if test: apikey = test.get('prowl_apikey') else: apikey = sabnzbd.cfg.prowl_apikey() if (not apikey): return T('Cannot send, missing required data') title = Tx(NOTIFICATION.get(gtype, 'other')) title = urllib2.quote(title.encode('utf8')) msg = urllib2.quote(msg.encode('utf8')) prio = get_prio(gtype, 'prowl') if force: prio = 0 if (prio > (-3)): url = ('https://api.prowlapp.com/publicapi/add?apikey=%s&application=SABnzbd&event=%s&description=%s&priority=%d' % (apikey, title, msg, prio)) try: urllib2.urlopen(url) return '' except: logging.warning(T('Failed to send Prowl message')) logging.info('Traceback: ', exc_info=True) return T('Failed to send Prowl message') return ''
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send message to prowl .
train
false
43,050
def _codeStatusSplit(line): parts = line.split(' ', 1) if (len(parts) == 1): return (parts[0], '') return parts
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parse the first line of a multi-line server response .
train
false
43,052
def add_neigh_entry(ip_address, mac_address, device, namespace=None, **kwargs): ip_version = get_ip_version(ip_address) privileged.add_neigh_entry(ip_version, ip_address, mac_address, device, namespace, **kwargs)
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add a neighbour entry .
train
false
43,053
def samples_from_table(table, start=0, stop=(-1), rate=44100): samples = np.array(table.getTable()) if ((start, stop) != (0, (-1))): if (stop > start): samples = samples[(start * rate):(stop * rate)] elif start: samples = samples[(start * rate):] return samples
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return samples as a np .
train
false
43,054
def _get_xywh(bb): w = ((bb[:, BB_XMAX_IDX] - bb[:, BB_XMIN_IDX]) + FRCN_EPS) h = ((bb[:, BB_YMAX_IDX] - bb[:, BB_YMIN_IDX]) + FRCN_EPS) x = (bb[:, BB_XMIN_IDX] + (0.5 * w)) y = (bb[:, BB_YMIN_IDX] + (0.5 * h)) return (x, y, w, h)
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given bounding boxes with coordinates .
train
false
43,055
def configure_cli_command(cmdline): args = shlex.split(cmdline) cli = CommandLineInterface() cmdinst = cli._configure_command(cmdname=args[0], argv=args[1:]) return cmdinst
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helper to configure a command class .
train
false
43,056
@contextfilter def do_rejectattr(*args, **kwargs): return select_or_reject(args, kwargs, (lambda x: (not x)), True)
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filters a sequence of objects by applying a test to the specified attribute of each object .
train
false
43,060
def dsplit(ary, indices_or_sections): if (ary.ndim <= 2): raise ValueError('Cannot dsplit an array with less than 3 dimensions') return split(ary, indices_or_sections, 2)
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splits an array into multiple sub arrays along the third axis .
train
false
43,061
def switch(): try: target = g['stuff'].split()[0] args = parse_arguments() try: if (g['stuff'].split()[(-1)] == '-f'): guide = 'To ignore an option, just hit Enter key.' printNicely(light_magenta(guide)) only = raw_input('Only nicks [Ex: @xxx,@yy]: ') ignore = raw_input('Ignore nicks [Ex: @xxx,@yy]: ') args.filter = list(filter(None, only.split(','))) args.ignore = list(filter(None, ignore.split(','))) except: printNicely(red('Sorry, wrong format.')) return g['stream_stop'] = True try: stuff = g['stuff'].split()[1] except: stuff = None spawn_dict = {'public': spawn_public_stream, 'list': spawn_list_stream, 'mine': spawn_personal_stream} spawn_dict.get(target)(args, stuff) except: debug_option() printNicely(red("Sorry I can't understand."))
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switch stream .
train
false
43,062
def continue_training(path): with change_recursion_limit(config.recursion_limit): with open(path, 'rb') as f: main_loop = load(f) main_loop.run()
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continues training using checkpoint .
train
false
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@pytest.fixture(scope='session') def pootle_content_type(): from django.contrib.contenttypes.models import ContentType args = {'app_label': 'pootle_app', 'model': 'directory'} return ContentType.objects.get(**args)
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require the pootle contenttype .
train
false
43,064
def testsearchandreplace(): (document, docbody, relationships) = simpledoc() docbody = document.xpath('/w:document/w:body', namespaces=nsprefixes)[0] assert search(docbody, 'ing 1') assert search(docbody, 'ing 2') assert search(docbody, 'graph 3') assert search(docbody, 'ist Item') assert search(docbody, 'A1') if search(docbody, 'Paragraph 2'): docbody = replace(docbody, 'Paragraph 2', 'Whacko 55') assert search(docbody, 'Whacko 55')
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ensure search and replace functions work .
train
false
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def set_vif_host_backend_ovs_config(conf, brname, interfaceid, tapname=None): conf.net_type = 'bridge' conf.source_dev = brname conf.vporttype = 'openvswitch' conf.add_vport_param('interfaceid', interfaceid) if tapname: conf.target_dev = tapname conf.script = ''
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populate a libvirtconfigguestinterface instance with host backend details for an openvswitch bridge .
train
false
43,066
def center_matrix(M, dim=0): M = np.asarray(M, float) if dim: M = ((M - M.mean(axis=0)) / M.std(axis=0)) else: M = (M - M.mean(axis=1)[:, np.newaxis]) M = (M / M.std(axis=1)[:, np.newaxis]) return M
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return the matrix *m* with each row having zero mean and unit std .
train
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43,067
@pytest.fixture(scope='session', autouse=True) def delete_pattern(): from django.core.cache.backends.locmem import LocMemCache LocMemCache.delete_pattern = (lambda x, y: 0)
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adds the no-op delete_pattern() method to locmemcache .
train
false
43,069
def splitfactor_sqf(p, DE, coefficientD=False, z=None, basic=False): kkinv = ([(1 / x) for x in DE.T[:DE.level]] + DE.T[:DE.level]) if z: kkinv = [z] S = [] N = [] p_sqf = p.sqf_list_include() if p.is_zero: return (((p, 1),), ()) for (pi, i) in p_sqf: Si = pi.as_poly(*kkinv).gcd(derivation(pi, DE, coefficientD=coefficientD, basic=basic).as_poly(*kkinv)).as_poly(DE.t) pi = Poly(pi, DE.t) Si = Poly(Si, DE.t) Ni = pi.exquo(Si) if (not Si.is_one): S.append((Si, i)) if (not Ni.is_one): N.append((Ni, i)) return (tuple(N), tuple(S))
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splitting square-free factorization given a derivation d on k[t] and p in k[t] .
train
false
43,070
def get_user_obj_perms_model(obj): from guardian.models import UserObjectPermissionBase from guardian.models import UserObjectPermission return get_obj_perms_model(obj, UserObjectPermissionBase, UserObjectPermission)
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returns model class that connects given obj and user class .
train
false
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def clean_extra_output_destination(): global extra_print_dests extra_print_dests = []
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clean extra print destination(s) :rtype: none .
train
false
43,073
def bson_ts_to_long(timestamp): return ((timestamp.time << 32) + timestamp.inc)
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convert bson timestamp into integer .
train
false
43,074
def volume_data_get_for_host(context, host, count_only=False): return IMPL.volume_data_get_for_host(context, host, count_only)
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get for project .
train
false
43,076
def _parse_single_node(text): return lxml.html.html5parser.fragment_fromstring(text, parser=PARSER)
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parse a single html node from a string into a tree .
train
false