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def generate_album_info(album_id, track_ids): tracks = [generate_track_info(id) for id in track_ids] album = AlbumInfo(album_id=u'album info', album=u'album info', artist=u'album info', artist_id=u'album info', tracks=tracks) for field in ALBUM_INFO_FIELDS: setattr(album, field, u'album info') return album
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return albuminfo populated with mock data .
train
false
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def project_jnap_opts(): T = current.T return {1: T('JNAP-1: Strategic Area 1: Governance'), 2: T('JNAP-2: Strategic Area 2: Monitoring'), 3: T('JNAP-3: Strategic Area 3: Disaster Management'), 4: T('JNAP-4: Strategic Area 4: Risk Reduction and Climate Change Adaptation')}
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provide the options for the jnap filter jnap : applies to cook islands only .
train
false
14,343
def read_int(s, start_position): m = _READ_INT_RE.match(s, start_position) if (not m): raise ReadError('integer', start_position) return (int(m.group()), m.end())
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reads n ints from a file .
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@api_wrapper def create_filesystem(module, system): if (not module.check_mode): filesystem = system.filesystems.create(name=module.params['name'], pool=get_pool(module, system)) if module.params['size']: size = Capacity(module.params['size']).roundup((64 * KiB)) filesystem.update_size(size) module.exit_json(changed=True)
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create filesystem .
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def unchanged_required(argument): if (argument is None): raise ValueError('argument required but none supplied') else: return argument
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return the argument text .
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@pytest.mark.parametrize('sep', [None, ' sep ']) def test_str_cat(sep): if (sep is None): expr = t_str_cat.name.str_cat(t_str_cat.comment) expected = '\n SELECT accounts2.name || accounts2.comment\n AS anon_1 FROM accounts2\n ' else: expr = t_str_cat.name.str_cat(t_str_cat.comment, sep=sep) expected = '\n SELECT accounts2.name || :name_1 || accounts2.comment\n AS anon_1 FROM accounts2\n ' result = str(compute(expr, s_str_cat, return_type='native')) assert (normalize(result) == normalize(expected))
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need at least two string columns to test str_cat .
train
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def test_classification_report_imbalanced_multiclass_with_long_string_label(): (y_true, y_pred, _) = make_prediction(binary=False) labels = np.array(['blue', ('green' * 5), 'red']) y_true = labels[y_true] y_pred = labels[y_pred] expected_report = 'pre rec spe f1 geo iba sup blue 0.83 0.79 0.92 0.81 0.86 0.74 24 greengreengreengreengreen 0.33 0.10 0.86 0.15 0.44 0.19 31 red 0.42 0.90 0.55 0.57 0.63 0.37 20 avg / total 0.51 0.53 0.80 0.47 0.62 0.41 75' report = classification_report_imbalanced(y_true, y_pred) assert_equal(_format_report(report), expected_report)
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test classification report with long string label .
train
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@with_session def config_changed(task=None, session=None): log.debug((u'Marking config for %s as changed.' % (task or u'all tasks'))) task_hash = session.query(TaskConfigHash) if task: task_hash = task_hash.filter((TaskConfigHash.task == task)) task_hash.delete()
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returns true if config has changed .
train
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def _get_decimal128(data, position, dummy0, dummy1, dummy2): end = (position + 16) return (Decimal128.from_bid(data[position:end]), end)
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decode a bson decimal128 to bson .
train
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def get_note_message(note): assert (note <= 10), ("Note is %.2f. Either you cheated, or pylint's broken!" % note) if (note < 0): msg = 'You have to do something quick !' elif (note < 1): msg = 'Hey! This is really dreadful. Or maybe pylint is buggy?' elif (note < 2): msg = "Come on! You can't be proud of this code" elif (note < 3): msg = 'Hum... Needs work.' elif (note < 4): msg = "Wouldn't you be a bit lazy?" elif (note < 5): msg = 'A little more work would make it acceptable.' elif (note < 6): msg = 'Just the bare minimum. Give it a bit more polish. ' elif (note < 7): msg = "This is okay-ish, but I'm sure you can do better." elif (note < 8): msg = 'If you commit now, people should not be making nasty comments about you on c.l.py' elif (note < 9): msg = "That's pretty good. Good work mate." elif (note < 10): msg = 'So close to being perfect...' else: msg = 'Wow ! Now this deserves our uttermost respect.\nPlease send your code to python-projects@logilab.org' return msg
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return a message according to note note is a float < 10 .
train
false
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def b64_string(input_string): return b64encode(input_string.encode('utf-8')).decode('utf-8')
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return a base64 encoded string from input_string .
train
false
14,357
def joined(subset=None, show_ipv4=False): return list_state(subset=subset, show_ipv4=show_ipv4, state='joined')
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ensure the current node joined to a cluster with node user@host name irrelevant .
train
false
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def _check_instance_uid_match(user): return (os.geteuid() == __salt__['file.user_to_uid'](user))
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returns true if running instances uid matches the specified user uid .
train
false
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def pre_begin(opt): global options options = opt for fn in pre_configure: fn(options, file_config)
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things to set up early .
train
false
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def confidence_interval_dichotomous(point_estimate, sample_size, confidence=0.95, bias=False, percentage=True, **kwargs): alpha = ppf(((confidence + 1) / 2), (sample_size - 1)) p = point_estimate if percentage: p /= 100 margin = sqrt(((p * (1 - p)) / sample_size)) if bias: margin += (0.5 / sample_size) if percentage: margin *= 100 return ((point_estimate - (alpha * margin)), (point_estimate + (alpha * margin)))
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dichotomous confidence interval from sample size and maybe a bias .
train
true
14,362
def pre_prompt_hook(self): return None
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run before displaying the next prompt use this e .
train
false
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@handle_response_format @treeio_login_required def equity_add(request, response_format='html'): equities = Object.filter_by_request(request, Equity.objects, mode='r') if request.POST: if ('cancel' not in request.POST): equity = Equity() form = EquityForm(request.user.profile, request.POST, instance=equity) if form.is_valid(): equity = form.save() equity.set_user_from_request(request) return HttpResponseRedirect(reverse('finance_equity_view', args=[equity.id])) else: return HttpResponseRedirect(reverse('finance_index_equities')) else: form = EquityForm(request.user.profile) return render_to_response('finance/equity_add', {'form': form, 'equities': equities}, context_instance=RequestContext(request), response_format=response_format)
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new equity form .
train
false
14,367
@register.simple_tag(takes_context=True) def pageurl(context, page): return page.relative_url(context[u'request'].site)
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outputs a pages url as relative if its within the same site as the current page .
train
false
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def getNewRepository(): return ExportRepository()
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get the repository constructor .
train
false
14,372
def ensure_metadata_ip(): _execute('ip', 'addr', 'add', '169.254.169.254/32', 'scope', 'link', 'dev', 'lo', run_as_root=True, check_exit_code=[0, 2, 254])
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sets up local metadata ip .
train
false
14,373
def expand_db_html(html, for_editor=False): def replace_a_tag(m): attrs = extract_attrs(m.group(1)) if (u'linktype' not in attrs): return m.group(0) handler = get_link_handler(attrs[u'linktype']) return handler.expand_db_attributes(attrs, for_editor) def replace_embed_tag(m): attrs = extract_attrs(m.group(1)) handler = get_embed_handler(attrs[u'embedtype']) return handler.expand_db_attributes(attrs, for_editor) html = FIND_A_TAG.sub(replace_a_tag, html) html = FIND_EMBED_TAG.sub(replace_embed_tag, html) return html
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expand database-representation html into proper html usable in either templates or the rich text editor .
train
false
14,375
def pretty_xml(container, name, raw): root = container.parse_xml(raw) if (name == container.opf_name): pretty_opf(root) pretty_xml_tree(root) return serialize(root, u'text/xml')
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pretty print the xml represented as a string in raw .
train
false
14,376
def _notifications_from_dashboard_activity_list(user_dict, since): context = {'model': model, 'session': model.Session, 'user': user_dict['id']} activity_list = logic.get_action('dashboard_activity_list')(context, {}) activity_list = [activity for activity in activity_list if (activity['user_id'] != user_dict['id'])] strptime = datetime.datetime.strptime fmt = '%Y-%m-%dT%H:%M:%S.%f' activity_list = [activity for activity in activity_list if (strptime(activity['timestamp'], fmt) > since)] return _notifications_for_activities(activity_list, user_dict)
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return any email notifications from the given users dashboard activity list since since .
train
false
14,377
def dump_neigh_entries(ip_version, device=None, namespace=None, **kwargs): return list(privileged.dump_neigh_entries(ip_version, device, namespace, **kwargs))
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dump all neighbour entries .
train
false
14,379
def disable_share(cookie, tokens, shareid_list): url = ''.join([const.PAN_URL, 'share/cancel?channel=chunlei&clienttype=0&web=1', '&bdstoken=', tokens['bdstoken']]) data = ('shareid_list=' + encoder.encode_uri(json.dumps(shareid_list))) req = net.urlopen(url, headers={'Cookie': cookie.header_output(), 'Content-type': const.CONTENT_FORM_UTF8}, data=data.encode()) if req: content = req.data return json.loads(content.decode()) else: return None
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shareid_list 是一个list .
train
true
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def get_data_from_request(): return {'request': {'url': ('%s://%s%s' % (web.ctx['protocol'], web.ctx['host'], web.ctx['path'])), 'query_string': web.ctx.query, 'method': web.ctx.method, 'data': web.data(), 'headers': dict(get_headers(web.ctx.environ)), 'env': dict(get_environ(web.ctx.environ))}}
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returns request data extracted from web .
train
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def compute_cluster_state(node_state, additional_node_states, nonmanifest_datasets): return DeploymentState(nodes=({node_state} | additional_node_states), nonmanifest_datasets={dataset.dataset_id: dataset for dataset in nonmanifest_datasets})
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computes the cluster_state from the passed in arguments .
train
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@library.filter def datetime(value, kind=u'datetime', format=u'medium', tz=True): locale = get_current_babel_locale() if (type(value) is date): return format_date(value, format=format, locale=locale) if tz: value = localtime(value, (None if (tz is True) else tz)) if (kind == u'datetime'): return format_datetime(value, format=format, locale=locale) elif (kind == u'date'): return format_date(value, format=format, locale=locale) elif (kind == u'time'): return format_time(value, format=format, locale=locale) else: raise ValueError((u'Unknown `datetime` kind: %r' % kind))
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format a datetime for human consumption .
train
false
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def PossessiveRepeat(element, min_count, max_count): return Atomic(GreedyRepeat(element, min_count, max_count))
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builds a possessive repeat .
train
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def submit_detailed_enrollment_features_csv(request, course_key): task_type = 'detailed_enrollment_report' task_class = enrollment_report_features_csv task_input = {} task_key = '' return submit_task(request, task_type, task_class, course_key, task_input, task_key)
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submits a task to generate a csv containing detailed enrollment info .
train
false
14,388
def longest_line_length(code): return max((len(line) for line in code.splitlines()))
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return length of longest line .
train
false
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def combine_xyz(vec, square=False): if (vec.ndim != 2): raise ValueError('Input must be 2D') if ((vec.shape[0] % 3) != 0): raise ValueError('Input must have 3N rows') (n, p) = vec.shape if np.iscomplexobj(vec): vec = np.abs(vec) comb = (vec[0::3] ** 2) comb += (vec[1::3] ** 2) comb += (vec[2::3] ** 2) if (not square): comb = np.sqrt(comb) return comb
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compute the three cartesian components of a vector or matrix together .
train
false
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def ssh_main(): ip = raw_input('Enter IP Address: ') username = 'pyclass' password = getpass() test_device = NetworkDevice(ip, username, password) (remote_conn_pre, remote_conn, _) = ssh.establish_connection(ip, username, password) ssh.disable_paging(remote_conn) remote_conn.send('\n') remote_conn.send('show version\n') test_device.show_version = ssh.read_ssh_data(remote_conn) remote_conn_pre.close() process_show_version(test_device) net_device_verification(test_device) with open('ssh_file.pkl', 'wb') as f: pickle.dump(test_device, f)
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process show version using ssh .
train
false
14,391
def naughty_strings(filepath=FILEPATH): strings = [] with open(filepath, 'r') as f: strings = f.readlines() strings = [x.strip(u'\n') for x in strings] strings = [x for x in strings if (x and (not x.startswith(u'#')))] strings.insert(0, u'') return strings
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get the list of naughty_strings .
train
false
14,392
def trimmed_mean_ci(data, limits=(0.2, 0.2), inclusive=(True, True), alpha=0.05, axis=None): data = ma.array(data, copy=False) trimmed = mstats.trimr(data, limits=limits, inclusive=inclusive, axis=axis) tmean = trimmed.mean(axis) tstde = mstats.trimmed_stde(data, limits=limits, inclusive=inclusive, axis=axis) df = (trimmed.count(axis) - 1) tppf = t.ppf((1 - (alpha / 2.0)), df) return np.array(((tmean - (tppf * tstde)), (tmean + (tppf * tstde))))
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selected confidence interval of the trimmed mean along the given axis .
train
false
14,393
def Delete(keys, **kwargs): return DeleteAsync(keys, **kwargs).get_result()
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deletes one or more entities from the datastore .
train
false
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def p_initializer_2(t): pass
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initializer : lbrace initializer_list rbrace | lbrace initializer_list comma rbrace .
train
false
14,396
def underscore(text): return UNDERSCORE[1].sub('\\1_\\2', UNDERSCORE[0].sub('\\1_\\2', text)).lower()
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convert dots to underscore in a string .
train
true
14,397
def _invert(eq, *symbols, **kwargs): eq = sympify(eq) free = eq.free_symbols if (not symbols): symbols = free if (not (free & set(symbols))): return (eq, S.Zero) dointpow = bool(kwargs.get('integer_power', False)) lhs = eq rhs = S.Zero while True: was = lhs while True: (indep, dep) = lhs.as_independent(*symbols) if lhs.is_Add: if (indep is S.Zero): break lhs = dep rhs -= indep else: if (indep is S.One): break lhs = dep rhs /= indep if lhs.is_Add: terms = {} for a in lhs.args: (i, d) = a.as_independent(*symbols) terms.setdefault(d, []).append(i) if any(((len(v) > 1) for v in terms.values())): args = [] for (d, i) in terms.items(): if (len(i) > 1): args.append((Add(*i) * d)) else: args.append((i[0] * d)) lhs = Add(*args) if (lhs.is_Add and (not rhs) and (len(lhs.args) == 2) and (not lhs.is_polynomial(*symbols))): (a, b) = ordered(lhs.args) (ai, ad) = a.as_independent(*symbols) (bi, bd) = b.as_independent(*symbols) if any((_ispow(i) for i in (ad, bd))): (a_base, a_exp) = ad.as_base_exp() (b_base, b_exp) = bd.as_base_exp() if (a_base == b_base): lhs = powsimp(powdenest((ad / bd))) rhs = ((- bi) / ai) else: rat = (ad / bd) _lhs = powsimp((ad / bd)) if (_lhs != rat): lhs = _lhs rhs = ((- bi) / ai) if ((ai * bi) is S.NegativeOne): if (all((isinstance(i, Function) for i in (ad, bd))) and (ad.func == bd.func) and (len(ad.args) == len(bd.args))): if (len(ad.args) == 1): lhs = (ad.args[0] - bd.args[0]) else: raise NotImplementedError('equal function with more than 1 argument') elif (lhs.is_Mul and any((_ispow(a) for a in lhs.args))): lhs = powsimp(powdenest(lhs)) if lhs.is_Function: if (hasattr(lhs, 'inverse') and (len(lhs.args) == 1)): rhs = lhs.inverse()(rhs) lhs = lhs.args[0] elif (lhs.func is atan2): (y, x) = lhs.args lhs = (2 * atan((y / (sqrt(((x ** 2) + (y ** 2))) + x)))) if (rhs and lhs.is_Pow and lhs.exp.is_Integer and (lhs.exp < 0)): lhs = (1 / lhs) rhs = (1 / rhs) if (lhs.is_Pow and ((lhs.exp.is_Integer and dointpow) or ((not lhs.exp.is_Integer) and (len(symbols) > 1) and (len((lhs.base.free_symbols & set(symbols))) > 1)))): rhs = (rhs ** (1 / lhs.exp)) lhs = lhs.base if (lhs == was): break return (rhs, lhs)
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"rhs", ",", "lhs", ")" ]
reduce the complex valued equation f(x) = y to a set of equations {g(x) = h_1(y) .
train
false
14,398
def _CheckStatus(status): if (status.code() != search_service_pb.SearchServiceError.OK): if (status.code() in _ERROR_MAP): raise _ERROR_MAP[status.code()](status.error_detail()) else: raise InternalError(status.error_detail())
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checks whether a requeststatus has a value of ok .
train
false
14,399
def addVector3Loop(loop, loops, vertexes, z): vector3Loop = [] for point in loop: vector3Index = Vector3Index(len(vertexes), point.real, point.imag, z) vector3Loop.append(vector3Index) vertexes.append(vector3Index) if (len(vector3Loop) > 0): loops.append(vector3Loop)
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add vector3loop to loops if there is something in it .
train
false
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def setup_console_logger(log_level='error', log_format=None, date_format=None): if is_console_configured(): logging.getLogger(__name__).warning('Console logging already configured') return __remove_temp_logging_handler() if (log_level is None): log_level = 'warning' level = LOG_LEVELS.get(log_level.lower(), logging.ERROR) setLogRecordFactory(SaltColorLogRecord) handler = None for handler in logging.root.handlers: if (handler is LOGGING_STORE_HANDLER): continue if (not hasattr(handler, 'stream')): continue if (handler.stream is sys.stderr): break else: handler = StreamHandler(sys.stderr) handler.setLevel(level) if (not log_format): log_format = '[%(levelname)-8s] %(message)s' if (not date_format): date_format = '%H:%M:%S' formatter = logging.Formatter(log_format, datefmt=date_format) handler.setFormatter(formatter) logging.root.addHandler(handler) global __CONSOLE_CONFIGURED global __LOGGING_CONSOLE_HANDLER __CONSOLE_CONFIGURED = True __LOGGING_CONSOLE_HANDLER = handler
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setup the console logger .
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true
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def load_soups(config): soups = {} for (page, path) in config[u'sources'].items(): with open(path, u'rb') as orig_file: soups[page] = beautiful_soup(orig_file.read().decode(u'utf-8')) return soups
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generate beautifulsoup ast for each page listed in config .
train
false
14,404
def must_answer_survey(course_descriptor, user): if (not is_survey_required_for_course(course_descriptor)): return False survey = SurveyForm.get(course_descriptor.course_survey_name) has_staff_access = has_access(user, 'staff', course_descriptor) answered_survey = SurveyAnswer.do_survey_answers_exist(survey, user) return ((not answered_survey) and (not has_staff_access))
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returns whether a user needs to answer a required survey .
train
false
14,405
def device_exists(device): (_out, err) = _execute('ip', 'link', 'show', 'dev', device, check_exit_code=False, run_as_root=True) return (not err)
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return true if the device exists in the namespace .
train
false
14,406
def print_duration(method): def timed(*args, **kw): ts = time.time() result = method(*args, **kw) te = time.time() print ('%r %2.2f sec' % (method.__name__, (te - ts))) return result return timed
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prints out the runtime duration of a method in seconds .
train
true
14,407
def _runMultiple(tupleList): for (f, args, kwargs) in tupleList: f(*args, **kwargs)
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run a list of functions .
train
false
14,408
def _string_to_record_type(string): string = string.upper() record_type = getattr(RecordType, string) return record_type
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return a string representation of a dns record type to a libcloud recordtype enum .
train
true
14,409
def _restore_str(unused_name, value): return (None if (value == 'None') else value)
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restores an string key-value pair from a renewal config file .
train
false
14,410
def test_saving_state_exclude_domains(hass_recorder): hass = hass_recorder({'exclude': {'domains': 'test'}}) states = _add_entities(hass, ['test.recorder', 'test2.recorder']) assert (len(states) == 1) assert (hass.states.get('test2.recorder') == states[0])
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test saving and restoring a state .
train
false
14,411
def test_make_imbalance_bad_ratio(): min_c_ = 1 ratio = 0.0 assert_raises(ValueError, make_imbalance, X, Y, ratio, min_c_) ratio = (-2.0) assert_raises(ValueError, make_imbalance, X, Y, ratio, min_c_) ratio = 2.0 assert_raises(ValueError, make_imbalance, X, Y, ratio, min_c_) ratio = [0.5, 0.5] assert_raises(ValueError, make_imbalance, X, Y, ratio, min_c_)
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test either if an error is raised with bad ratio argument .
train
false
14,412
def multiset_partitions_baseline(multiplicities, components): canon = [] for (ct, elem) in zip(multiplicities, components): canon.extend(([elem] * ct)) cache = set() n = len(canon) for (nc, q) in _set_partitions(n): rv = [[] for i in range(nc)] for i in range(n): rv[q[i]].append(canon[i]) canonical = tuple(sorted([tuple(p) for p in rv])) cache.add(canonical) return cache
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enumerates partitions of a multiset parameters multiplicities list of integer multiplicities of the components of the multiset .
train
false
14,413
def config_java(bin=None, options=None, verbose=False): global _java_bin, _java_options _java_bin = find_binary('java', bin, env_vars=['JAVAHOME', 'JAVA_HOME'], verbose=verbose, binary_names=['java.exe']) if (options is not None): if isinstance(options, compat.string_types): options = options.split() _java_options = list(options)
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configure nltks java interface .
train
false
14,415
def membership_type(): if (not auth.s3_has_role(ADMIN)): s3.filter = auth.filter_by_root_org(s3db.member_membership_type) output = s3_rest_controller() return output
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rest controller .
train
false
14,417
def populate_project_info(attributes): if (('tenant_id' in attributes) and ('project_id' not in attributes)): attributes['project_id'] = attributes['tenant_id'] elif (('project_id' in attributes) and ('tenant_id' not in attributes)): attributes['tenant_id'] = attributes['project_id'] if (attributes.get('project_id') != attributes.get('tenant_id')): msg = _("'project_id' and 'tenant_id' do not match") raise webob.exc.HTTPBadRequest(msg) return attributes
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ensure that both project_id and tenant_id attributes are present .
train
false
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def enable_color(): global LIGHT_GREEN LIGHT_GREEN = '\x1b[1;32m' global LIGHT_RED LIGHT_RED = '\x1b[1;31m' global LIGHT_BLUE LIGHT_BLUE = '\x1b[1;34m' global DARK_RED DARK_RED = '\x1b[0;31m' global END_COLOR END_COLOR = '\x1b[0m'
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enable colors by setting colour code constants to ansi color codes .
train
false
14,420
def prop_show(prop, extra_args=None, cibfile=None): return item_show(item='property', item_id=prop, extra_args=extra_args, cibfile=cibfile)
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show the value of a cluster property prop name of the property extra_args additional options for the pcs property command cibfile use cibfile instead of the live cib cli example: .
train
true
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def check_net_address(addr, family): if (enum and PY3): assert isinstance(family, enum.IntEnum), family if (family == AF_INET): octs = [int(x) for x in addr.split('.')] assert (len(octs) == 4), addr for num in octs: assert (0 <= num <= 255), addr if (not PY3): addr = unicode(addr) ipaddress.IPv4Address(addr) elif (family == AF_INET6): assert isinstance(addr, str), addr if (not PY3): addr = unicode(addr) ipaddress.IPv6Address(addr) elif (family == psutil.AF_LINK): assert (re.match('([a-fA-F0-9]{2}[:|\\-]?){6}', addr) is not None), addr else: raise ValueError('unknown family %r', family)
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check a net address validity .
train
false
14,422
def getUserContact(master, contact_types, uid): d = master.db.users.getUser(uid) d.addCallback(_extractContact, contact_types, uid) return d
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this is a simple getter function that returns a user attribute that matches the contact_types argument .
train
true
14,423
def check_dna_chars_primers(header, mapping_data, errors, disable_primer_check=False): valid_dna_chars = DNASequence.iupac_characters() valid_dna_chars.add(',') header_fields_to_check = ['ReversePrimer'] if (not disable_primer_check): header_fields_to_check.append('LinkerPrimerSequence') check_indices = [] for curr_field in range(len(header)): if (header[curr_field] in header_fields_to_check): check_indices.append(curr_field) correction_ix = 1 for curr_data in range(len(mapping_data)): for curr_ix in check_indices: if (len(mapping_data[curr_data][curr_ix]) == 0): errors.append(('Missing expected DNA sequence DCTB %d,%d' % ((curr_data + correction_ix), curr_ix))) for curr_data in range(len(mapping_data)): for curr_ix in check_indices: for curr_nt in mapping_data[curr_data][curr_ix]: if (curr_nt not in valid_dna_chars): errors.append(('Invalid DNA sequence detected: %s DCTB %d,%d' % (mapping_data[curr_data][curr_ix], (curr_data + correction_ix), curr_ix))) continue return errors
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checks for valid dna characters in primer fields also flags empty fields as errors unless flags are passed to suppress barcode or primer checks .
train
false
14,425
def denormalize_form_dict(data_dict, form, attr_list): assert isinstance(form, forms.Form) res = django.http.QueryDict('', mutable=True) for attr in attr_list: try: res[str(form.add_prefix(attr))] = data_dict[attr] except KeyError: pass return res
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denormalize_form_dict -> a querydict with the attributes set .
train
false
14,426
def find_python_files(path): try: return sorted([file[:(-3)] for file in os.listdir(path) if ((not file.startswith(('_', '.'))) and file.endswith('.py'))]) except OSError: return []
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return a list of the python files in a directory .
train
false
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@treeio_login_required @handle_response_format def service_record_edit(request, service_record_id, response_format='html'): service_record = get_object_or_404(ItemServicing, pk=service_record_id) if (not request.user.profile.has_permission(service_record, mode='w')): return user_denied(request, message="You don't have write access to this ServiceRecord", response_format=response_format) if request.POST: if ('cancel' not in request.POST): form = ServiceRecordForm(request.user.profile, None, request.POST, instance=service_record) if form.is_valid(): service_record = form.save(request) return HttpResponseRedirect(reverse('infrastructure_service_record_view', args=[service_record.id])) else: return HttpResponseRedirect(reverse('infrastructure_service_record_view', args=[service_record.id])) else: form = ServiceRecordForm(request.user.profile, None, instance=service_record) context = _get_default_context(request) context.update({'service_record': service_record, 'form': form}) return render_to_response('infrastructure/service_record_edit', context, context_instance=RequestContext(request), response_format=response_format)
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servicerecord edit page .
train
false
14,428
def topological_sort(graph, key=None): (V, E) = graph L = [] S = set(V) E = list(E) for (v, u) in E: S.discard(u) if (key is None): key = (lambda value: value) S = sorted(S, key=key, reverse=True) while S: node = S.pop() L.append(node) for (u, v) in list(E): if (u == node): E.remove((u, v)) for (_u, _v) in E: if (v == _v): break else: kv = key(v) for (i, s) in enumerate(S): ks = key(s) if (kv > ks): S.insert(i, v) break else: S.append(v) if E: raise ValueError('cycle detected') else: return L
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returns a depth first sorted order if depth_first is true .
train
false
14,429
@login_required def edxnotes(request, course_id): course_key = CourseKey.from_string(course_id) course = get_course_with_access(request.user, 'load', course_key) if (not is_feature_enabled(course)): raise Http404 notes_info = get_notes(request, course) has_notes = (len(notes_info.get('results')) > 0) context = {'course': course, 'notes_endpoint': reverse('notes', kwargs={'course_id': course_id}), 'notes': notes_info, 'page_size': DEFAULT_PAGE_SIZE, 'debug': settings.DEBUG, 'position': None, 'disabled_tabs': settings.NOTES_DISABLED_TABS, 'has_notes': has_notes} if (not has_notes): field_data_cache = FieldDataCache.cache_for_descriptor_descendents(course.id, request.user, course, depth=2) course_module = get_module_for_descriptor(request.user, request, course, field_data_cache, course_key, course=course) position = get_course_position(course_module) if position: context.update({'position': position}) return render_to_response('edxnotes/edxnotes.html', context)
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decorator that makes components annotatable .
train
false
14,431
def get_num_cpus(): return multiprocessing.cpu_count()
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get the number of cpu processes on the current machine .
train
false
14,432
def isProfileSetting(name): global settingsDictionary if ((name in settingsDictionary) and settingsDictionary[name].isProfile()): return True return False
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check if a certain key name is actually a profile value .
train
false
14,433
def SizeToReadableString(filesize): for x in ['bytes', 'KiB', 'MiB', 'GiB', 'TiB']: if (filesize < 1000.0): return ('%3.1f %s' % (filesize, x)) filesize /= 1000.0
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turn a filesize int into a human readable filesize .
train
false
14,434
def _make_query(client, query, submission_type='Execute', udfs=None, settings=None, resources=[], wait=False, name=None, desc=None, local=True, is_parameterized=True, max=30.0, database='default', email_notify=False, **kwargs): res = make_query(client, query, submission_type, udfs, settings, resources, wait, name, desc, local, is_parameterized, max, database, email_notify, **kwargs) if (submission_type == 'Execute'): fragment = collapse_whitespace(smart_str(query[:20])) verify_history(client, fragment=fragment) return res
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wrapper around the real make_query .
train
false
14,436
def write_float(fid, kind, data): data_size = 4 data = np.array(data, dtype='>f4').T _write(fid, data, kind, data_size, FIFF.FIFFT_FLOAT, '>f4')
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write a single-precision floating point tag to a fif file .
train
false
14,439
def downsize_quota_delta(context, instance): old_flavor = instance.get_flavor('old') new_flavor = instance.get_flavor('new') return resize_quota_delta(context, new_flavor, old_flavor, 1, (-1))
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calculate deltas required to adjust quota for an instance downsize .
train
false
14,441
def get_server_messages(app): messages = [] for (basepath, folders, files) in os.walk(frappe.get_pymodule_path(app)): for dontwalk in (u'.git', u'public', u'locale'): if (dontwalk in folders): folders.remove(dontwalk) for f in files: if (f.endswith(u'.py') or f.endswith(u'.html') or f.endswith(u'.js')): messages.extend(get_messages_from_file(os.path.join(basepath, f))) return messages
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extracts all translatable strings from python modules inside an app .
train
false
14,442
def butter_lp(n, Wn): zeros = [] poles = _butter_analog_poles(n) k = 1 fs = 2 warped = ((2 * fs) * mpmath.tan(((mpmath.pi * Wn) / fs))) (z, p, k) = _zpklp2lp(zeros, poles, k, wo=warped) (z, p, k) = _zpkbilinear(z, p, k, fs=fs) return (z, p, k)
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lowpass butterworth digital filter design .
train
false
14,444
def read_packed_refs_with_peeled(f): last = None for l in f: if (l[0] == '#'): continue l = l.rstrip('\r\n') if l.startswith('^'): if (not last): raise PackedRefsException('unexpected peeled ref line') if (not valid_hexsha(l[1:])): raise PackedRefsException(('Invalid hex sha %r' % l[1:])) (sha, name) = _split_ref_line(last) last = None (yield (sha, name, l[1:])) else: if last: (sha, name) = _split_ref_line(last) (yield (sha, name, None)) last = l if last: (sha, name) = _split_ref_line(last) (yield (sha, name, None))
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read a packed refs file including peeled refs .
train
false
14,445
def is_hex_digits(entry, count): try: if (len(entry) != count): return False int(entry, 16) return True except (ValueError, TypeError): return False
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checks if a string is the given number of hex digits .
train
false
14,446
def dmp_degree(f, u): if dmp_zero_p(f, u): return (- oo) else: return (len(f) - 1)
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return the leading degree of f in x_0 in k[x] .
train
false
14,447
def MakeCASignedCert(common_name, private_key, ca_cert, ca_private_key, serial_number=2): public_key = private_key.GetPublicKey() builder = x509.CertificateBuilder() builder = builder.issuer_name(ca_cert.GetIssuer()) subject = x509.Name([x509.NameAttribute(oid.NameOID.COMMON_NAME, common_name)]) builder = builder.subject_name(subject) valid_from = (rdfvalue.RDFDatetime.Now() - rdfvalue.Duration('1d')) valid_until = (rdfvalue.RDFDatetime.Now() + rdfvalue.Duration('3650d')) builder = builder.not_valid_before(valid_from.AsDatetime()) builder = builder.not_valid_after(valid_until.AsDatetime()) builder = builder.serial_number(serial_number) builder = builder.public_key(public_key.GetRawPublicKey()) builder = builder.add_extension(x509.BasicConstraints(ca=False, path_length=None), critical=True) certificate = builder.sign(private_key=ca_private_key.GetRawPrivateKey(), algorithm=hashes.SHA256(), backend=openssl.backend) return rdf_crypto.RDFX509Cert(certificate)
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make a cert and sign it with the cas private key .
train
true
14,448
@pytest.mark.network def test_install_with_ignoreinstalled_requested(script): script.pip('install', 'INITools==0.1', expect_error=True) result = script.pip('install', '-I', 'INITools==0.3', expect_error=True) assert result.files_created, 'pip install -I did not install' assert os.path.exists(((script.site_packages_path / 'INITools-0.1-py%s.egg-info') % pyversion)) assert os.path.exists(((script.site_packages_path / 'INITools-0.3-py%s.egg-info') % pyversion))
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test old conflicting package is completely ignored .
train
false
14,452
def call_tadm(args): if isinstance(args, compat.string_types): raise TypeError(u'args should be a list of strings') if (_tadm_bin is None): config_tadm() cmd = ([_tadm_bin] + args) p = subprocess.Popen(cmd, stdout=sys.stdout) (stdout, stderr) = p.communicate() if (p.returncode != 0): print() print(stderr) raise OSError(u'tadm command failed!')
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call the tadm binary with the given arguments .
train
false
14,456
def expm(A, q=None): if (q is not None): msg = 'argument q=... in scipy.linalg.expm is deprecated.' warnings.warn(msg, DeprecationWarning) import scipy.sparse.linalg return scipy.sparse.linalg.expm(A)
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compute the matrix exponential using pade approximation .
train
false
14,457
def setup_default_session(**kwargs): global DEFAULT_SESSION DEFAULT_SESSION = Session(**kwargs)
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set up a default session .
train
false
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def displayFiles(filenames): for filename in filenames: displayFile(filename)
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parse gcode files and display the commands .
train
false
14,461
def can_create_more(data): data = dict(data) user = data['user'] course_key = data['usage_key'].course_key if (Bookmark.objects.filter(user=user, course_key=course_key).count() >= settings.MAX_BOOKMARKS_PER_COURSE): return False return True
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determine if a new bookmark can be created for the course based on limit defined in django .
train
false
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def plat_specific_errors(*errnames): errno_names = dir(errno) nums = [getattr(errno, k) for k in errnames if (k in errno_names)] return list(dict.fromkeys(nums).keys())
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return error numbers for all errors in errnames on this platform .
train
true
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def assert_table_name_col_equal(t, name, col): if isinstance(col, coordinates.SkyCoord): assert np.all((t[name].ra == col.ra)) assert np.all((t[name].dec == col.dec)) elif isinstance(col, u.Quantity): if (type(t) is QTable): assert np.all((t[name].value == col.value)) elif isinstance(col, table_helpers.ArrayWrapper): assert np.all((t[name].data == col.data)) else: assert np.all((t[name] == col))
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assert all .
train
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def get_email_backend(real_email=False): if (real_email or settings.SEND_REAL_EMAIL): backend = None else: backend = 'olympia.amo.mail.DevEmailBackend' return django.core.mail.get_connection(backend)
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get a connection to an email backend .
train
false
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def _doc_link(rawtext, text, options={}, content=[]): (has_explicit_title, title, slug) = split_explicit_title(text) twin_slugs = False post = None for p in doc_role.site.timeline: if (p.meta('slug') == slug): if (post is None): post = p else: twin_slugs = True break try: if (post is None): raise ValueError except ValueError: return (False, False, None, None, slug) if (not has_explicit_title): title = post.title() permalink = post.permalink() return (True, twin_slugs, title, permalink, slug)
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handle the doc role .
train
false
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def rich_text_publisher(registry, xml_parent, data): parsers = ['HTML', 'Confluence', 'WikiText'] reporter = XML.SubElement(xml_parent, 'org.korosoft.jenkins.plugin.rtp.RichTextPublisher') reporter.set('plugin', 'rich-text-publisher-plugin') mappings = [('stable-text', 'stableText', None), ('unstable-text', 'unstableText', ''), ('failed-text', 'failedText', ''), ('unstable-as-stable', 'unstableAsStable', True), ('failed-as-stable', 'failedAsStable', True), ('parser-name', 'parserName', 'WikiText', parsers)] helpers.convert_mapping_to_xml(reporter, data, mappings, fail_required=True)
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yaml: rich-text-publisher this plugin puts custom rich text message to the build pages and job main page .
train
false
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def mplot2d(f, var, show=True): import warnings warnings.filterwarnings('ignore', 'Could not match \\S') p = import_module('pylab') if (not p): sys.exit('Matplotlib is required to use mplot2d.') if (not is_sequence(f)): f = [f] for f_i in f: (x, y) = sample(f_i, var) p.plot(x, y) p.draw() if show: p.show()
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plot a 2d function using matplotlib/tk .
train
false
14,469
@pytest.fixture def po_directory(request, po_test_dir, settings): from pootle_store.models import fs translation_directory = settings.POOTLE_TRANSLATION_DIRECTORY settings.POOTLE_TRANSLATION_DIRECTORY = po_test_dir fs.location = po_test_dir def _cleanup(): settings.POOTLE_TRANSLATION_DIRECTORY = translation_directory request.addfinalizer(_cleanup)
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sets up a tmp directory for po files .
train
false
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def customConvertJson(value): if isinstance(value, float): return int(value) elif isinstance(value, unicode): return str(value) elif isinstance(value, list): return [customConvertJson(item) for item in value] elif isinstance(value, dict): new_dict = {} for (key, val) in value.iteritems(): new_key = customConvertJson(key) new_val = customConvertJson(val) new_dict[new_key] = new_val return new_dict else: return value
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recursively process json values and do type conversions .
train
false
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@utils.arg('flavor', metavar='<flavor>', help=_('Name or ID of flavor.')) def do_flavor_show(cs, args): flavor = _find_flavor(cs, args.flavor) _print_flavor(flavor)
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show details about the given flavor .
train
false
14,474
def idd_diffsnorm(m, n, matvect, matvect2, matvec, matvec2, its=20): return _id.idd_diffsnorm(m, n, matvect, matvect2, matvec, matvec2, its)
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estimate spectral norm of the difference of two real matrices by the randomized power method .
train
false
14,475
def encrypt_header_val(crypto, value, key): if (not value): raise ValueError('empty value is not acceptable') crypto_meta = crypto.create_crypto_meta() crypto_ctxt = crypto.create_encryption_ctxt(key, crypto_meta['iv']) enc_val = base64.b64encode(crypto_ctxt.update(value)) return (enc_val, crypto_meta)
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encrypt a header value using the supplied key .
train
false
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def authen(): twitter_credential = ((os.environ.get('HOME', os.environ.get('USERPROFILE', '')) + os.sep) + '.rainbow_oauth') if (not os.path.exists(twitter_credential)): oauth_dance('Rainbow Stream', CONSUMER_KEY, CONSUMER_SECRET, twitter_credential) (oauth_token, oauth_token_secret) = read_token_file(twitter_credential) return OAuth(oauth_token, oauth_token_secret, CONSUMER_KEY, CONSUMER_SECRET)
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authenticate with twitter oauth .
train
false
14,477
def getUserDocumentsPath(): if sys.platform.startswith('win'): if sys.platform.startswith('win32'): try: from win32com.shell import shell alt = False except ImportError: try: import ctypes dll = ctypes.windll.shell32 alt = True except: raise Exception("Could not find 'My Documents'") else: alt = True if (not alt): df = shell.SHGetDesktopFolder() pidl = df.ParseDisplayName(0, None, '::{450d8fba-ad25-11d0-98a8-0800361b1103}')[1] path = shell.SHGetPathFromIDList(pidl) else: buf = ctypes.create_string_buffer(300) dll.SHGetSpecialFolderPathA(None, buf, 5, False) path = buf.value elif sys.platform.startswith('darwin'): from Carbon import Folder, Folders folderref = Folder.FSFindFolder(Folders.kUserDomain, Folders.kDocumentsFolderType, False) path = folderref.as_pathname() else: path = os.getenv('HOME') return path
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find the users "documents" directory .
train
false
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def checkAlias(alias): if (',' in alias): raise AXError(('Alias %r must not contain comma' % (alias,))) if ('.' in alias): raise AXError(('Alias %r must not contain period' % (alias,)))
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check an alias for invalid characters; raise axerror if any are found .
train
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def _preprocess_for_cut(x): x_is_series = isinstance(x, Series) series_index = None name = None if x_is_series: series_index = x.index name = x.name x = np.asarray(x) return (x_is_series, series_index, name, x)
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handles preprocessing for cut where we convert passed input to array .
train
false
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def GetAllClients(token=None): index = client_index.CreateClientIndex(token=token) return index.LookupClients(['.'])
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return a list of all client urns .
train
false
14,484
def gpke(bw, data, data_predict, var_type, ckertype='gaussian', okertype='wangryzin', ukertype='aitchisonaitken', tosum=True): kertypes = dict(c=ckertype, o=okertype, u=ukertype) Kval = np.empty(data.shape) for (ii, vtype) in enumerate(var_type): func = kernel_func[kertypes[vtype]] Kval[:, ii] = func(bw[ii], data[:, ii], data_predict[ii]) iscontinuous = np.array([(c == 'c') for c in var_type]) dens = (Kval.prod(axis=1) / np.prod(bw[iscontinuous])) if tosum: return dens.sum(axis=0) else: return dens
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returns the non-normalized generalized product kernel estimator parameters bw: 1-d ndarray the user-specified bandwidth parameters .
train
true