content
stringlengths
35
762k
sha1
stringlengths
40
40
id
int64
0
3.66M
def flanking_regions_fasta_deletion(genome, dataframe, flanking_region_size): """ Makes batch processing possible, pulls down small region of genome for which to design primers around. This is based on the chromosome and position of input file. Each Fasta record will contain: >Sample_Gene_chr:posStart-posStop Seq of flanking region upstream of SV + seq of flanking region downstream of SV Args: genome (list): genome list of tuples (header, seq). dataframe (pandas object): dataframe with sample info. flanking_region_size (int): length of sequence upstream and downstream of input coordinate position to pull as sequence to design primers around. """ output = [] for headers, seqs in genome: chrm = str(headers) seq = str(seqs) for gene, sample, chrom, start, stop in zip(dataframe.Gene, dataframe.Sample, dataframe.Chr, dataframe.PosStart, dataframe.PosStop): if str(chrom) == chrm: header = str(str(sample)+"_"+str(gene)+"_"+\ str(chrom)+":"+str(start)+"-"+str(stop)+"__") flank_seq = seq[int(start)-int(flanking_region_size):int(start)+1]\ +seq[int(stop):(int(stop)+int(flanking_region_size))] output.append((header, flank_seq.upper())) return output
a20da206630d1f2fb002c5ca63eab9f240b1f1d5
12,351
import functools def numpy_episodes( train_dir, test_dir, shape, loader, preprocess_fn=None, scan_every=10, num_chunks=None, **kwargs): """Read sequences stored as compressed Numpy files as a TensorFlow dataset. Args: train_dir: Directory containing NPZ files of the training dataset. test_dir: Directory containing NPZ files of the testing dataset. shape: Tuple of batch size and chunk length for the datasets. use_cache: Boolean. Set to True to cache episodes in memory. Default is to read episodes from disk every time. **kwargs: Keyword arguments to forward to the read episodes implementation. Returns: Structured data from numpy episodes as Tensors. """ try: dtypes, shapes = _read_spec(train_dir, **kwargs) except ZeroDivisionError: dtypes, shapes = _read_spec(test_dir, **kwargs) loader = { 'scan': functools.partial(_read_episodes_scan, every=scan_every), 'reload': _read_episodes_reload, 'dummy': _read_episodes_dummy, }[loader] train = tf.data.Dataset.from_generator( functools.partial(loader, train_dir, shape[0], **kwargs), dtypes, shapes) test = tf.data.Dataset.from_generator( functools.partial(loader, test_dir, shape[0], **kwargs), dtypes, shapes) chunking = lambda x: tf.data.Dataset.from_tensor_slices( # Returns dict of image, action, reward, length tensors with num_chunks in 0 dim. chunk_sequence(x, shape[1], True, num_chunks)) def sequence_preprocess_fn(sequence): if preprocess_fn: with tf.device('/cpu:0'): sequence['image'] = preprocess_fn(sequence['image']) return sequence # This transformation (flat_map): # 1. Chunk each sequence, # 2. From each sequence one can get variable number of chunks # (first dim. of a tensor is chunks number, like with batches). # Flatten to get the dataset of chunks. train = train.flat_map(chunking) train = train.shuffle(100 * shape[0]) train = train.batch(shape[0], drop_remainder=True) train = train.map(sequence_preprocess_fn, 10).prefetch(20) test = test.flat_map(chunking) test = test.shuffle(100 * shape[0]) test = test.batch(shape[0], drop_remainder=True) test = test.map(sequence_preprocess_fn, 10).prefetch(20) return attr_dict.AttrDict(train=train, test=test)
fd9c727c64bdd725ef1615754d12b93f21568c2f
12,352
def fft_convolve(ts, query): """ Computes the sliding dot product for query over the time series using the quicker FFT convolution approach. Parameters ---------- ts : array_like The time series. query : array_like The query. Returns ------- array_like - The sliding dot product. """ n = len(ts) m = len(query) x = np.fft.fft(ts) y = np.append(np.flipud(query), np.zeros([1, n - m])) y = np.fft.fft(y) z = np.fft.ifft(x * y) return np.real(z[m - 1:n])
7e1fec2a3b30770909d7c185bbc0b4885cb7eb22
12,353
from typing import List from typing import Optional def _add_merge_gvcfs_job( b: hb.Batch, gvcfs: List[hb.ResourceGroup], output_gvcf_path: Optional[str], sample_name: str, ) -> Job: """ Combine by-interval GVCFs into a single sample GVCF file """ job_name = f'Merge {len(gvcfs)} GVCFs, {sample_name}' j = b.new_job(job_name) j.image(PICARD_IMAGE) j.cpu(2) java_mem = 7 j.memory('standard') # ~ 4G/core ~ 7.5G j.storage(f'{len(gvcfs) * 1.5 + 2}G') j.declare_resource_group( output_gvcf={ 'g.vcf.gz': '{root}-' + sample_name + '.g.vcf.gz', 'g.vcf.gz.tbi': '{root}-' + sample_name + '.g.vcf.gz.tbi', } ) input_cmd = ' '.join(f'INPUT={g["g.vcf.gz"]}' for g in gvcfs) j.command( f"""set -e (while true; do df -h; pwd; du -sh $(dirname {j.output_gvcf['g.vcf.gz']}); free -m; sleep 300; done) & java -Xms{java_mem}g -jar /usr/picard/picard.jar \ MergeVcfs {input_cmd} OUTPUT={j.output_gvcf['g.vcf.gz']} df -h; pwd; du -sh $(dirname {j.output_gvcf['g.vcf.gz']}); free -m """ ) if output_gvcf_path: b.write_output(j.output_gvcf, output_gvcf_path.replace('.g.vcf.gz', '')) return j
d89fd051cd20bef7263b600ce3513ba858acbadd
12,354
def register_permission(name, codename, ctypes=None): """Registers a permission to the framework. Returns the permission if the registration was successfully, otherwise False. **Parameters:** name The unique name of the permission. This is displayed to the customer. codename The unique codename of the permission. This is used internally to identify the permission. content_types The content type for which the permission is active. This can be used to display only reasonable permissions for an object. This must be a Django ContentType """ if ctypes is None: ctypes = [] # Permission with same codename and/or name must not exist. if Permission.objects.filter(Q(name=name) | Q(codename=codename)): return False p = Permission.objects.create(name=name, codename=codename) ctypes = [ContentType.objects.get_for_model(ctype) for ctype in ctypes] if ctypes: p.content_types = ctypes p.save() return p
f09766685ac4690bd72739450977646d521a21d0
12,355
def calculate_outliers(tile_urls, num_outliers, cache, nprocs): """ Fetch tiles and calculate the outlier tiles per layer. The number of outliers is per layer - the largest N. Cache, if true, uses a local disk cache for the tiles. This can be very useful if re-running percentile calculations. Nprocs is the number of processes to use for both fetching and aggregation. Even on a system with a single CPU, it can be worth setting this to a larger number to make concurrent nework requests for tiles. """ def factory_fn(): return LargestN(num_outliers, cache) if nprocs > 1: results = parallel( tile_urls, FactoryFunctionHolder(factory_fn), nprocs) else: results = sequential(tile_urls, factory_fn) return results
6e72820de2f954a9e349aa40d165817b3ab7c012
12,356
import random def load_trigger_dataset( fname, templatizer, limit=None, train=False, preprocessor_key=None, priming_dataset=None, max_priming_examples=64, ): """ Loads a MLM classification dataset. Parameters ========== fname : str The filename. templatizer : Templatizer Maps instances to cloze-style model inputs. limit : int (optional) Limit the amount of data loaded. train : bool Whether the data is used for training. Default: False. preprocessor_key : str Key used to lookup preprocessor for data. """ if preprocessor_key is None: preprocessor = PREPROCESSORS[fname.split('.')[-1]] else: preprocessor = PREPROCESSORS[preprocessor_key] instances = [] for x in preprocessor(fname, train=train): try: model_inputs, label_id = templatizer(x, train=train) if priming_dataset is not None: model_inputs, label_id = prime( model_inputs, label_id, priming_dataset, model_max_length=templatizer._tokenizer.model_max_length, max_priming_examples=max_priming_examples, ) except ValueError as e: logger.warning('Encountered error "%s" when processing "%s". Skipping.', e, x) continue else: instances.append((model_inputs, label_id)) if limit: limit = min(len(instances), limit) return random.sample(instances, limit) return instances
6ed4970dd0031bd33cf19414f439c69e5d5a079a
12,357
def pmu2bids(physio_files, verbose=False): """ Function to read a list of Siemens PMU physio files and save them as a BIDS physiological recording. Parameters ---------- physio_files : list of str list of paths to files with a Siemens PMU recording verbose : bool verbose flag Returns ------- physio : PhysioData PhysioData with the contents of the file """ # In case we are handled just a single file, make it a one-element list: if isinstance(physio_files, str): physio_files = [physio_files] # Init PhysioData object to hold physio signals: physio = PhysioData() # Read the files from the list, extract the relevant information and # add a new PhysioSignal to the list: for f in physio_files: physio_type, MDHTime, sampling_rate, physio_signal = readpmu(f, verbose=verbose) testSamplingRate( sampling_rate = sampling_rate, Nsamples = len(physio_signal), logTimes=MDHTime ) # specify label: if 'PULS' in physio_type: physio_label = 'cardiac' elif 'RESP' in physio_type: physio_label = 'respiratory' elif "TRIGGER" in physio_type: physio_label = 'trigger' else: physio_label = physio_type physio.append_signal( PhysioSignal( label=physio_label, units='', samples_per_second=sampling_rate, physiostarttime=MDHTime[0], signal=physio_signal ) ) return physio
41e607c80955689e5a189652ba445bf0014a3893
12,358
def add_chain(length): """Adds a chain to the network so that""" chained_works = [] chain = utils.generate_chain(length) for i in range(len(chain)-1): agent_id = get_random_agent().properties(ns.KEY_AGENT_ID).value().next() work_id = g.create_work().properties(ns.KEY_WORK_ID).value().next() g.agent(agent_id).owns_work(g.work(work_id)).next() item1 = g.create_item(chain[i]) g.agent(agent_id).works(work_id).demands(item1).next() item2 = g.create_item(chain[i+1]) g.agent(agent_id).works(work_id).offers(item2).next() chained_works.append(work_id) return chained_works
80a176fb34460404c847f00dbeab963f1a0be71e
12,359
def convert_graph_to_db_format(input_graph: nx.Graph, with_weights=False, cast_to_directed=False): """Converts a given graph into a DB format, which consists of two or three lists 1. **Index list:** a list where the i-th position contains the index of the beginning of the list of adjacent nodes (in the second list). 2. **Node list:** for each node, we list (in order) all the nodes which are adjacent to it. 3. **Weight list:** if the weight parameter is True, includes the weights of the edges, corresponds to the nodes list **Assumptions:** The code has several preexisting assumptions: a) The nodes are labeled with numbers b) Those numbers are the sequence [0,...,num_of_nodes-1] c) If there are weights, they are floats d) If there are weights, they are initialized for all edges e) If there are weights, the weight key is 'weight' .. Note:: The code behaves differently for directed and undirected graphs. For undirected graph, every edge is actually counted twice (p->q and q->p). Example:: For the simple directed graph (0->1, 0->2,0->3,2->0,3->1,3->2): `Indices: [0, 3, 3, 4, 6]` `Neighbors: [1, 2, 3, 0, 1, 2]` Note that index[1] is the same as index[2]. That is because 1 has no neighbors, and so his neighbor list is of size 0, but we still need to have an index for the node on. For the same graph when it is undirected: `Indices: [0, 3, 5, 7, 10]` `Neighbors: [1, 2, 3, 0, 3, 0, 3, 0, 1, 2]` Note that the number of edges isn't doubled because in the directed version there is a bidirectional edge. :param graph: the nx.Graph object to convert :param with_weights: whether to create a weight list. Defaults to False. :param cast_to_directed: whether to cast the graph into a directed format :return: two or three lists: index,nodes, [weights] """ if cast_to_directed: graph = input_graph.to_directed() else: graph = input_graph.copy() if graph.is_directed(): # Color printing taken from https://www.geeksforgeeks.org/print-colors-python-terminal/ print("\033[93m {}\033[00m".format('Note that the graph is processed as a directed graph')) indices = [0] # The first neighbor list always starts at index 0 neighbor_nodes = [] nodes = [node for node in graph.nodes()] # print(nodes) nodes.sort() neighbors = [sorted([x for x in graph.neighbors(node)]) for node in nodes] # Create the indices and neighbor nodes lists for neighbor_list in neighbors: neighbor_list.sort() # print(neighbor_list) neighbor_nodes.extend(neighbor_list) indices.append(indices[-1] + len(neighbor_list)) if with_weights: try: weights = [0] * len(neighbor_nodes) current_index = 0 for node in nodes: for x in neighbors[node]: w = graph[node][x]['weight'] weights[current_index] = w current_index += 1 return indices, neighbor_nodes, weights except KeyError: # Print in red print("\033[91m {}\033[00m".format('No weights defined, returning an empty list of weights')) print() return indices, neighbor_nodes, [] return indices, neighbor_nodes
3f538f697df16b13aeb513dd60831a1252fffb6c
12,361
def auxiliary_subfields(): """Factory associated with AuxSubfieldsPoroelasticity. """ return AuxSubfieldsPoroelasticity()
bcbdaf5b6ee006a6380206ebd331f7e516593b83
12,362
def cassandra_get_unit_data(): """ Basing function to obtain units from db and return as dict :return: dictionary of units """ kpi_dict = {} cassandra_cluster = Cluster() session = cassandra_cluster.connect('pb2') query = session.prepare('SELECT * FROM kpi_units') query_data = session.execute(query) for row in query_data: kpi_dict[row[1]] = [row[0], row[2], row[3], row[4]] return kpi_dict
ab24e4e09f648a74cd16a140279da54aab3d4096
12,363
def read_cfg_float(cfgp, section, key, default): """ Read float from a config file Args: cfgp: Config parser section: [section] of the config file key: Key to be read default: Value if couldn't be read Returns: Resulting float """ if cfgp.has_option(section, key): return cfgp.getfloat(section, key) else: return default
0ed341c2d1436e3378e4e126735ac7306973ca8c
12,364
def random(website): """ 随机获取cookies :param website:查询网站给 如:weibo :return:随机获取的cookies """ g = get_conn() cookies = getattr(g, website + '_cookies').random() return cookies
6db8d81f18e57af2a7d9294481e45d4ad38962ce
12,365
import requests def get_pid(referral_data): """ Example getting PID using the same token used to query AD NOTE! to get PID the referral information must exist in the BETA(UAT) instance of TOMS """ referral_uid = referral_data['referral_uid'] url = "https://api.beta.genomics.nhs.uk/reidentification/referral-pid/{referral_uid}".format(referral_uid=referral_uid) auth_header = {'Authorization': 'Bearer {}'.format(jwt_token)} pid = requests.get(url, headers=auth_header).json() return pid
8e5e43c1a2c85826e03f0fd090fc235b0320aed7
12,366
from typing import Union from pathlib import Path from typing import Tuple from typing import List from datetime import datetime def open_events( fname: Union[Path, str], leap_sec: float, get_frame_rate: bool = False ) -> Tuple[ List[float], List[float], List[float], List[datetime], Union[List[float], None] ]: """ Parameters ---------- fname : Path or str filename of *_events.pos file leap_sec : float The current leap second used to convert GPS time to UTC time get_frame_rate : bool [default=False] Whether to return the frame rate of sequential trigger events Returns ------- lat : List[float] Latitudes (decimal degrees) of trigger events recorded by Reach M2 lon : List[float] Longitudes (decimal degrees) of trigger events recorded by Reach M2 height : List[float] Ellipsoid heights of trigger events recorded by Reach M2 dt_ls : List[datetime] datetime (UTC) of trigger events recorded by Reach M2 reach_frate : List[float] or None if get_frame_rate is True: reach_frate -> frame rate (seconds) of trigger events recorded by Reach M2 if get_frame_rate is False: reach_frate = None """ with open(fname, encoding="utf-8") as fid: contents = fid.readlines() lat, lon, height, dt_ls = [], [], [], [] reach_frate = [] if get_frame_rate else None cnt = 0 for i in range(len(contents)): if contents[i].startswith("%"): continue row = contents[i].strip().split() dt = datetime_from_event_text(row[0], row[1], leap_sec) if cnt > 0: reach_frate.append((dt - prev_dt).total_seconds()) # noqa lat.append(float(row[2])) lon.append(float(row[3])) height.append(float(row[4])) dt_ls.append(dt) prev_dt = dt # noqa cnt += 1 return lat, lon, height, dt_ls, reach_frate
973b835b1df2aafba1a535b378434b6a532584d0
12,367
def intdags_permutations(draw, min_size:int=1, max_size:int=10): """ Produce instances of a same DAG. Instances are not nesessarily topologically sorted """ return draw(lists(permutations(draw(intdags())), min_size=min_size, max_size=max_size))
50377412dbd091afa98761e673a35f44acbeb60d
12,368
def getConfiguredGraphClass(doer): """ In this class method, we must return a configured graph class """ # if options.bReified: # DU_GRAPH = Graph_MultiSinglePageXml_Segmenter_Separator_DOM if options.bSeparator: DU_GRAPH = ConjugateSegmenterGraph_MultiSinglePageXml_Separator else: DU_GRAPH = ConjugateSegmenterGraph_MultiSinglePageXml ntClass = My_ConjugateNodeType if options.bBB2: nt = ntClass("mi_clstr" #some short prefix because labels below are prefixed with it , [] # in conjugate, we accept all labels, andNone becomes "none" , [] , False # unused , BBoxDeltaFun = None , bPreserveWidth=True ) elif options.bBB31: nt = ntClass("mi_clstr" #some short prefix because labels below are prefixed with it , [] # in conjugate, we accept all labels, andNone becomes "none" , [] , False # unused , BBoxDeltaFun = (None, lambda v: v * 0.066*3) # shrink to 60% of its size , bPreserveWidth=True ) else: nt = ntClass("mi_clstr" #some short prefix because labels below are prefixed with it , [] # in conjugate, we accept all labels, andNone becomes "none" , [] , False # unused , BBoxDeltaFun =lambda v: max(v * 0.066, min(5, v/3)) #we reduce overlap in this way ) nt.setLabelAttribute("id") ## HD added 23/01/2020: needed for output generation DU_GRAPH.clusterType='paragraph' nt.setXpathExpr(( ".//pc:TextLine" , "./pc:TextEquiv") #how to get their text ) DU_GRAPH.addNodeType(nt) return DU_GRAPH
3089572eb1aa4e7db505b5211d156d3e044aaed5
12,369
def _seed(x, deg=5, seeds=None): """Seed the greedy algorithm with (deg+1) evenly spaced indices""" if seeds is None: f = lambda m, n: [ii*n//m + n//(2*m) for ii in range(m)] indices = np.sort(np.hstack([[0, len(x)-1], f(deg-1, len(x))])) else: indices = seeds errors = [] return indices, errors
7a5ff1e2e27b812f17196fbec1d7c6a2c867207c
12,371
def get_ref(cube): """Gets the 8 reflection symmetries of a nd numpy array""" L = [] L.append(cube[:,:,:]) L.append(cube[:,:,::-1]) L.append(cube[:,::-1,:]) L.append(cube[::-1,:,:]) L.append(cube[:,::-1,::-1]) L.append(cube[::-1,:,::-1]) L.append(cube[::-1,::-1,:]) L.append(cube[::-1,::-1,::-1]) return L
683ef2c7c0a312e4cf891f191452f9c29f6bc1fd
12,372
from typing import Collection from typing import Tuple from typing import Optional from typing import Mapping def get_relation_functionality( mapped_triples: Collection[Tuple[int, int, int]], add_labels: bool = True, label_to_id: Optional[Mapping[str, int]] = None, ) -> pd.DataFrame: """Calculate relation functionalities. :param mapped_triples: The ID-based triples. :return: A dataframe with columns ( functionality | inverse_functionality ) """ df = pd.DataFrame(data=mapped_triples, columns=["h", "r", "t"]) df = df.groupby(by="r").agg(dict( h=["nunique", COUNT_COLUMN_NAME], t="nunique", )) df[FUNCTIONALITY_COLUMN_NAME] = df[("h", "nunique")] / df[("h", COUNT_COLUMN_NAME)] df[INVERSE_FUNCTIONALITY_COLUMN_NAME] = df[("t", "nunique")] / df[("h", COUNT_COLUMN_NAME)] df = df[[FUNCTIONALITY_COLUMN_NAME, INVERSE_FUNCTIONALITY_COLUMN_NAME]] df.columns = df.columns.droplevel(1) df.index.name = RELATION_ID_COLUMN_NAME df = df.reset_index() return add_relation_labels(df, add_labels=add_labels, label_to_id=label_to_id)
1e6aa6d9e61ebd788d8c1726ca8a75d551b654b8
12,373
import json def df_to_vega_lite(df, path=None): """ Export a pandas.DataFrame to a vega-lite data JSON. Params ------ df : pandas.DataFrame dataframe to convert to JSON path : None or str if None, return the JSON str. Else write JSON to the file specified by path. """ chart = altair.Chart(data=df) data = chart.to_dict()['data']['values'] if path is None: return json.dumps(data, **json_dump_kwargs) with open(path, 'w') as write_file: json.dump(data, write_file, **json_dump_kwargs)
5cf5cf834d4113c05c4cc8b99aaa2a94e0a7b746
12,374
def _is_json_mimetype(mimetype): """Returns 'True' if a given mimetype implies JSON data.""" return any( [ mimetype == "application/json", mimetype.startswith("application/") and mimetype.endswith("+json"), ] )
9c2580ff4a783d9f79d6f6cac41befb516c52e9f
12,375
from datetime import datetime def make_request(action, data, token): """Make request based on passed arguments and timestamp.""" return { 'action': action, 'time': datetime.now().timestamp(), 'data': data, 'token': token }
60e511f7b067595bd698421adaafe37bbf8e59e1
12,376
def get_stats_historical_prices(timestamp, horizon): """ We assume here that the price is a random variable following a normal distribution. We compute the mean and covariance of the price distribution. """ hist_prices_df = pd.read_csv(HISTORICAL_PRICES_CSV) hist_prices_df["timestamp"] = pd.to_datetime(hist_prices_df["timestamp"]) hist_prices_df = hist_prices_df.set_index("timestamp") start = pd.Timestamp(year=2018, month=6, day=2, hour=timestamp.hour, minute=timestamp.minute) end = pd.Timestamp(year=2018, month=10, day=25, hour=timestamp.hour, minute=timestamp.minute) hist_prices_df = hist_prices_df[ (hist_prices_df.index >= start) & (hist_prices_df.index < end) ] hist_prices_df['hour'] = hist_prices_df.index.hour hist_prices_df['minute'] = hist_prices_df.index.minute num_features = horizon num_samples = min(hist_prices_df.groupby( [hist_prices_df.index.hour, hist_prices_df.index.minute] ).count()['clearing_price'].values) new = hist_prices_df.groupby( [hist_prices_df.index.hour, hist_prices_df.index.minute] ).mean() new = new.set_index(pd.Index(range(48))) i = new[ (new.hour == timestamp.hour) & (new.minute == timestamp.minute) ]['clearing_price'].index.values[0] a = new[new.index >= i]['clearing_price'] b = new[new.index < i]['clearing_price'] mean_X = np.concatenate((a, b)) X = np.copy(hist_prices_df['clearing_price'].values) X = np.reshape(X, (num_samples, num_features)) cov = GaussianMixture(covariance_type='tied').fit( normalize(X)).covariances_ return mean_X, cov
bc6fdcbcb54f156d880ba2504a0ca0d50f889786
12,377
def _unflattify(values, shape): """ Unflattifies parameter values. :param values: The flattened array of values that are to be unflattified :type values: torch.Tensor :param shape: The shape of the parameter prior :type shape: torch.Size :rtype: torch.Tensor """ if len(shape) < 1 or values.shape[1:] == shape: return values return values.reshape(values.shape[0], *shape)
e885517419eb48fd1a4ebdf14a8fa3b19f3c5444
12,378
def theme_cmd(data, buffer, args): """Callback for /theme command.""" if args == '': weechat.command('', '/help ' + SCRIPT_COMMAND) return weechat.WEECHAT_RC_OK argv = args.strip().split(' ', 1) if len(argv) == 0: return weechat.WEECHAT_RC_OK if argv[0] in ('install',): weechat.prnt('', '{0}: action "{1}" not developed' ''.format(SCRIPT_NAME, argv[0])) return weechat.WEECHAT_RC_OK # check arguments if len(argv) < 2: if argv[0] in ('install', 'installfile', 'save', 'export'): weechat.prnt('', '{0}: too few arguments for action "{1}"' ''.format(SCRIPT_NAME, argv[0])) return weechat.WEECHAT_RC_OK # execute asked action if argv[0] == 'list': theme_list(argv[1] if len(argv) >= 2 else '') elif argv[0] == 'info': filename = None if len(argv) >= 2: filename = argv[1] theme = Theme(filename) if filename: theme.info('Info about theme "{0}":'.format(filename)) else: theme.info('Info about current theme:') elif argv[0] == 'show': filename = None if len(argv) >= 2: filename = argv[1] theme = Theme(filename) if filename: theme.show('Content of theme "{0}":'.format(filename)) else: theme.show('Content of current theme:') elif argv[0] == 'installfile': theme = Theme() theme.save(theme_config_get_undo()) theme = Theme(argv[1]) if theme.isok(): theme.install() elif argv[0] == 'update': theme_update() elif argv[0] == 'undo': theme = Theme(theme_config_get_undo()) if theme.isok(): theme.install() elif argv[0] == 'save': theme = Theme() theme.save(argv[1]) elif argv[0] == 'backup': theme = Theme() theme.save(theme_config_get_backup()) elif argv[0] == 'restore': theme = Theme(theme_config_get_backup()) if theme.isok(): theme.install() elif argv[0] == 'export': htheme = HtmlTheme() whitebg = False htmlfile = argv[1] argv2 = args.strip().split(' ', 2) if len(argv2) >= 3 and argv2[1] == 'white': whitebg = True htmlfile = argv2[2] htheme.save_html(htmlfile, whitebg) return weechat.WEECHAT_RC_OK
f361a56392320efac4bd1e4101b002c1e42d4b89
12,379
def get_unique_chemical_names(reagents): """Get the unique chemical species names in a list of reagents. The concentrations of these species define the vector space in which we sample possible experiments :param reagents: a list of perovskitereagent objects :return: a list of the unique chemical names in all of the reagent """ chemical_species = set() if isinstance(reagents, dict): reagents = [v for v in reagents.values()] for reagent in reagents: chemical_species.update(reagent.chemicals) return sorted(list(chemical_species))
ae5d6b3bdd8e03c47b9c19c900760c8c2b83d0a0
12,380
def get_sorted_keys(dict_to_sort): """Gets the keys from a dict and sorts them in ascending order. Assumes keys are of the form Ni, where N is a letter and i is an integer. Args: dict_to_sort (dict): dict whose keys need sorting Returns: list: list of sorted keys from dict_to_sort """ sorted_keys = list(dict_to_sort.keys()) sorted_keys.sort(key=lambda x: int(x[1:])) return sorted_keys
9614dee83723e21248381c61a60e92e78c121216
12,381
def model_3d(psrs, psd='powerlaw', noisedict=None, components=30, gamma_common=None, upper_limit=False, bayesephem=False, wideband=False): """ Reads in list of enterprise Pulsar instance and returns a PTA instantiated with model 3D from the analysis paper: per pulsar: 1. fixed EFAC per backend/receiver system 2. fixed EQUAD per backend/receiver system 3. fixed ECORR per backend/receiver system 4. Red noise modeled as a power-law with 30 sampling frequencies 5. Linear timing model. global: 1. GWB with HD correlations modeled with user defined PSD with 30 sampling frequencies. Available PSDs are ['powerlaw', 'turnover' 'spectrum'] 2. Monopole signal modeled with user defined PSD with 30 sampling frequencies. Available PSDs are ['powerlaw', 'turnover' 'spectrum'] 3. Optional physical ephemeris modeling. :param psd: PSD to use for common red noise signal. Available options are ['powerlaw', 'turnover' 'spectrum'] 'powerlaw' is default value. :param gamma_common: Fixed common red process spectral index value. By default we vary the spectral index over the range [0, 7]. :param upper_limit: Perform upper limit on common red noise amplitude. By default this is set to False. Note that when perfoming upper limits it is recommended that the spectral index also be fixed to a specific value. :param bayesephem: Include BayesEphem model. Set to False by default """ amp_prior = 'uniform' if upper_limit else 'log-uniform' # find the maximum time span to set GW frequency sampling Tspan = model_utils.get_tspan(psrs) # red noise s = red_noise_block(prior=amp_prior, Tspan=Tspan, components=components) # common red noise block s += common_red_noise_block(psd=psd, prior=amp_prior, Tspan=Tspan, components=components, gamma_val=gamma_common, orf='hd', name='gw') # monopole s += common_red_noise_block(psd=psd, prior=amp_prior, Tspan=Tspan, components=components, gamma_val=gamma_common, orf='monopole', name='monopole') # ephemeris model if bayesephem: s += deterministic_signals.PhysicalEphemerisSignal(use_epoch_toas=True) # timing model s += gp_signals.TimingModel() # adding white-noise, and acting on psr objects models = [] for p in psrs: if 'NANOGrav' in p.flags['pta'] and not wideband: s2 = s + white_noise_block(vary=False, inc_ecorr=True) models.append(s2(p)) else: s3 = s + white_noise_block(vary=False, inc_ecorr=False) models.append(s3(p)) # set up PTA pta = signal_base.PTA(models) # set white noise parameters if noisedict is None: print('No noise dictionary provided!...') else: noisedict = noisedict pta.set_default_params(noisedict) return pta
37abad1016fadd82bcff1a55e9835db28a5c4eb8
12,382
def max_votes(x): """ Return the maximum occurrence of predicted class. Notes ----- If number of class 0 prediction is equal to number of class 1 predictions, NO_VOTE will be returned. E.g. Num_preds_0 = 25, Num_preds_1 = 25, Num_preds_NO_VOTE = 0, returned vote : "NO_VOTE". """ if x['Num_preds_0'] > x['Num_preds_1'] and x['Num_preds_0'] > x['Num_preds_NO_VOTE']: return 0 elif x['Num_preds_1'] > x['Num_preds_0'] and x['Num_preds_1'] > x['Num_preds_NO_VOTE']: return 1 else: return 'NO_VOTE'
2eadafdaf9e9b4584cd81685a5c1b77a090e4f1c
12,383
def misclassification_error(y_true: np.ndarray, y_pred: np.ndarray, normalize: bool = True) -> float: """ Calculate misclassification loss Parameters ---------- y_true: ndarray of shape (n_samples, ) True response values y_pred: ndarray of shape (n_samples, ) Predicted response values normalize: bool, default = True Normalize by number of samples or not Returns ------- Misclassification of given predictions """ n = y_true.shape[-1] counter = np.ones_like(y_true) error = counter[y_true!=y_pred].sum(axis=-1) return error / n if normalize else error
676657fa4da7b4734077ba3a19878d8890f44815
12,384
from scipy.stats import uniform def dunif(x, minimum=0,maximum=1): """ Calculates the point estimate of the uniform distribution """ result=uniform.pdf(x=x,loc=minimum,scale=maximum-minimum) return result
980ffb875cefec13bb78c3a3c779c68e7f510fb7
12,385
def _generate_upsert_sql(mon_loc): """ Generate SQL to insert/update. """ mon_loc_db = [(k, _manipulate_values(v, k in TIME_COLUMNS)) for k, v in mon_loc.items()] all_columns = ','.join(col for (col, _) in mon_loc_db) all_values = ','.join(value for (_, value) in mon_loc_db) update_query = ','.join(f"{k}={v}" for (k, v) in mon_loc_db if k not in ['AGENCY_CD', 'SITE_NO']) statement = ( f"MERGE INTO GW_DATA_PORTAL.WELL_REGISTRY_STG a " f"USING (SELECT '{mon_loc['AGENCY_CD']}' AGENCY_CD, '{mon_loc['SITE_NO']}' " f"SITE_NO FROM DUAL) b ON (a.AGENCY_CD = b.AGENCY_CD AND a.SITE_NO = b.SITE_NO) " f"WHEN MATCHED THEN UPDATE SET {update_query} WHEN NOT MATCHED THEN INSERT ({all_columns}) VALUES ({all_values})" ) return statement
7cbfdc1dd8709a354e4e246324042c8cf02a703b
12,386
def dict2obj(d): """Given a dictionary, return an object with the keys mapped to attributes and the values mapped to attribute values. This is recursive, so nested dictionaries are nested objects.""" top = type('dict2obj', (object,), d) seqs = tuple, list, set, frozenset for k, v in d.items(): if isinstance(v, dict): setattr( top, k, dict2obj(v) ) elif isinstance(v, seqs): setattr( top, k, type(v)(dict2obj(sj) if isinstance(sj, dict) else sj for sj in v) ) else: setattr(top, k, v) return top
ccfa713dc130024427872eb6f2017a0383e3bc01
12,388
def customized_algorithm_plot(experiment_name='finite_simple_sanity', data_path=_DEFAULT_DATA_PATH): """Simple plot of average instantaneous regret by agent, per timestep. Args: experiment_name: string = name of experiment config. data_path: string = where to look for the files. Returns: p: ggplot plot """ df = load_data(experiment_name, data_path) plt_df = (df.groupby(['t', 'agent']) .agg({'instant_regret': np.mean}) .reset_index()) plt_df['agent_new_name'] = plt_df.agent.apply(rename_agent) custom_labels = ['Laplace TS','Langevin TS','TS','bootstrap TS'] custom_colors = ["#E41A1C","#377EB8","#4DAF4A","#984EA3"] p = (gg.ggplot(plt_df) + gg.aes('t', 'instant_regret', colour='agent_new_name') + gg.geom_line(size=1.25, alpha=0.75) + gg.xlab('time period (t)') + gg.ylab('per-period regret') + gg.scale_color_manual(name='agent', labels = custom_labels,values=custom_colors)) return p
bd046c14de1598672391bbcb134dfe8bcff0b558
12,389
def _get_log_time_scale(units): """Retrieves the ``log10()`` of the scale factor for a given time unit. Args: units (str): String specifying the units (one of ``'fs'``, ``'ps'``, ``'ns'``, ``'us'``, ``'ms'``, ``'sec'``). Returns: The ``log10()`` of the scale factor for the time unit. """ scale = {"fs": -15, "ps": -12, "ns": -9, "us": -6, "ms": -3, "sec": 0} units_lwr = units.lower() if units_lwr not in scale: raise ValueError(f"Invalid unit ({units}) provided") else: return scale[units_lwr]
2371aab923aacce9159bce6ea1470ed49ef2c72f
12,390
def resolvermatch(request): """Add the name of the currently resolved pattern to the RequestContext""" match = resolve(request.path) if match: return {'resolved': match} else: return {}
41cc88633e0b207a53318c761c9849ad2d079994
12,391
def selection_sort(arr: list) -> list: """ Main sorting function. Using "find_smallest" function as part of the algorythm. :param arr: list to sort :return: sorted list """ new_arr = [] for index in range(len(arr)): smallest = find_smallest(arr) new_arr.append(arr.pop(smallest)) return new_arr
e618c5469ce77d830255dc16806f9499bed7ca9a
12,392
def get_primary_monitor(): """ Returns the primary monitor. Wrapper for: GLFWmonitor* glfwGetPrimaryMonitor(void); """ return _glfw.glfwGetPrimaryMonitor()
0bcc55f64c1b8ce6bad31323e5a4bb6ff05eab47
12,393
def query_people_and_institutions(rc, names): """Get the people and institutions names.""" people, institutions = [], [] for person_name in names: person_found = fuzzy_retrieval(all_docs_from_collection( rc.client, "people"), ["name", "aka", "_id"], person_name, case_sensitive=False) if not person_found: person_found = fuzzy_retrieval(all_docs_from_collection( rc.client, "contacts"), ["name", "aka", "_id"], person_name, case_sensitive=False) if not person_found: print( "WARNING: {} not found in contacts or people. Check aka".format( person_name)) else: people.append(person_found['name']) inst = fuzzy_retrieval(all_docs_from_collection( rc.client, "institutions"), ["name", "aka", "_id"], person_found["institution"], case_sensitive=False) if inst: institutions.append(inst["name"]) else: institutions.append(person_found.get("institution", "missing")) print("WARNING: {} missing from institutions".format( person_found["institution"])) else: people.append(person_found['name']) pinst = get_recent_org(person_found) inst = fuzzy_retrieval(all_docs_from_collection( rc.client, "institutions"), ["name", "aka", "_id"], pinst, case_sensitive=False) if inst: institutions.append(inst["name"]) else: institutions.append(pinst) print( "WARNING: {} missing from institutions".format( pinst)) return people, institutions
fd98a7557e2ee07b67ca8eddaf76c28b7b99033a
12,394
from typing import Union from typing import Tuple def add_device(overlay_id) -> Union[str, Tuple[str, int]]: """ Add device to an overlay. """ manager = get_manager() api_key = header_api_key(request) if not manager.api_key_is_valid(api_key): return jsonify(error="Not authorized"), 403 if not request.data: return jsonify(error="Send device id to add to overlay in body"), 400 if "device_id" in request.json: return manager.add_device_to_overlay(overlay_id,request.get_json()['device_id']) return jsonify(error="Send device_id as JSON"), 400
b9652b8d99672d0219df4821decebded458719bd
12,395
from math import sin, cos def pvtol(t, x, u, params={}): """Reduced planar vertical takeoff and landing dynamics""" m = params.get('m', 4.) # kg, system mass J = params.get('J', 0.0475) # kg m^2, system inertia r = params.get('r', 0.25) # m, thrust offset g = params.get('g', 9.8) # m/s, gravitational constant c = params.get('c', 0.05) # N s/m, rotational damping l = params.get('c', 0.1) # m, pivot location return np.array([ x[3], -c/m * x[1] + 1/m * cos(x[0]) * u[0] - 1/m * sin(x[0]) * u[1], -g - c/m * x[2] + 1/m * sin(x[0]) * u[0] + 1/m * cos(x[0]) * u[1], -l/J * sin(x[0]) + r/J * u[0] ])
ff3357e6e1fc1b6f878d9f16b14eba0b687642cd
12,396
from typing import List from typing import Any from typing import Callable def route( path: str, methods: List[str], **kwargs: Any ) -> Callable[[AnyCallable], AnyCallable]: """General purpose route definition. Requires you to pass an array of HTTP methods like GET, POST, PUT, etc. The remaining kwargs are exactly the same as for FastAPI's decorators like @get, @post, etc. Most users will probably want to use the shorter decorators like @get, @post, @put, etc. so they don't have to pass the list of methods. """ def marker(method: AnyCallable) -> AnyCallable: setattr( method, "_endpoint", EndpointDefinition( endpoint=method, args=RouteArgs(path=path, methods=methods, **kwargs) ), ) return method return marker
9e499d59b48a3562f46bdcbde76d87ceb199691e
12,397
import wx def canHaveGui(): """Return ``True`` if a display is available, ``False`` otherwise. """ # We cache this because calling the # IsDisplayAvailable function will cause the # application to steal focus under OSX! try: return wx.App.IsDisplayAvailable() except ImportError: return False
9a9af0f46ca22faeb5f76e350d1c831bcba95343
12,398
def syntactic_analysis(input_fd): """ Realiza análisis léxico-gráfico y sintáctico de un programa Tiger. @type input_fd: C{file} @param input_fd: Descriptor de fichero del programa Tiger al cual se le debe realizar el análisis sintáctico. @rtype: C{LanguageNode} @return: Como resultado del análsis sintáctico se obtiene el árbol de sintáxis abstracta correspondiente al programa Tiger recibido como argumento. El árbol se retorna a través del nodo de la raíz del árbol. @raise SyntacticError: Esta excepción se lanzará si se encuentra algún error de sintáxis durante el análisis del programa. La excepción contendrá información acerca del error, como por ejemplo, la línea y/o columna donde se encontró el error. """ data = input_fd.read() ast = parser.parse(data) return ast
0d0481c8ac84ac1de1ff3f756f20f33bdc8a18e0
12,399
def create_fixxation_map(eye_x, eye_y, fixxation_classifier): """ :param eye_x: an indexable datastructure with the x eye coordinates :param eye_y: an indexable datastructure with the y eye coordinates :param fixxation_classifier: a list with values which indicate if the move from the previos is a fixxations. :return: a List of circles which bound around the fixxation and witch saccades they dont bound. The List is organized Liked this [((circle1_x, circle1_y), circle1_radius), ...]) """ # process into fixxation and saccade movements points_array = [] currently_fixxation = False current_points = [] for idx, classifier in enumerate(fixxation_classifier): if classifier == 1 and currently_fixxation == False: current_points = [(eye_x[idx], eye_y[idx])] elif classifier == 1: current_points.append((eye_x[idx], eye_y[idx])) elif classifier == 0 and currently_fixxation == True: points_array.append((current_points.copy(), True)) current_points = [] currently_fixxation = False points_array.append(([(eye_x[idx], eye_y[idx])], False)) else: points_array.append(([(eye_x[idx], eye_y[idx])], True)) circles = [(make_circle(points), is_fixxation) for points, is_fixxation in points_array] circles = [((x, y), radius, is_fixxation) for ((x, y, radius), is_fixxation) in circles] return circles
bccf37777eb4d74fcb48a8316fc3d2695a209371
12,400
from re import T from typing import Any def with_metadata(obj: T, key: str, value: Any) -> T: """ Adds meta-data to an object. :param obj: The object to add meta-data to. :param key: The key to store the meta-data under. :param value: The meta-data value to store. :return: obj. """ # Create the meta-data map if not hasattr(obj, META_DATA_KEY): try: setattr(obj, META_DATA_KEY, {}) except AttributeError as e: raise ValueError(f"Cannot set meta-data against objects of type {obj.__class__.__name__}") from e # Put this mapping in the map getattr(obj, META_DATA_KEY)[key] = value return obj
566f9a2c1d083bbe44b86f0a8716e5bb44892b13
12,401
import hashlib def checksum(uploaded_file: 'SimpleUploadedFile', **options): """ Function to calculate checksum for file, can be used to verify downloaded file integrity """ hash_type = options['type'] if hash_type == ChecksumType.MD5: hasher = hashlib.md5() elif hash_type == ChecksumType.SHA256: hasher = hashlib.sha256() else: raise ValueError(f'Hash type "{hash_type}" in "checksum" function is not valid') if uploaded_file.multiple_chunks(): for data in uploaded_file.chunks(HASH_CHUNK_SIZE): hasher.update(data) else: hasher.update(uploaded_file.read()) return { 'checksum': hasher.hexdigest() }
766a288a09791242029669a63734143cf8e2c007
12,402
import types from typing import Optional from typing import Tuple def preceding_words(document: Document, position: types.Position) -> Optional[Tuple[str, str]]: """ Get the word under the cursor returning the start and end positions. """ lines = document.lines if position.line >= len(lines): return None row, col = position_from_utf16(lines, position) line = lines[row] try: word = line[:col].strip().split()[-2:] return word except ValueError: return None
9d1078084045ac468639a903c74dd24e45ed1087
12,404
def check_gpu(gpu, *args): """Move data in *args to GPU? gpu: options.gpu (None, or 0, 1, .. gpu index) """ if gpu == None: if isinstance(args[0], dict): d = args[0] #print(d.keys()) var_dict = {} for key in d: var_dict[key] = Variable(d[key]) if len(args) > 1: return [var_dict] + check_gpu(gpu, *args[1:]) else: return [var_dict] # it's a list of arguments if len(args) > 1: return [Variable(a) for a in args] else: # single argument, don't make a list return Variable(args[0]) else: if isinstance(args[0], dict): d = args[0] #print(d.keys()) var_dict = {} for key in d: var_dict[key] = Variable(d[key].cuda(gpu)) if len(args) > 1: return [var_dict] + check_gpu(gpu, *args[1:]) else: return [var_dict] # it's a list of arguments if len(args) > 1: return [Variable(a.cuda(gpu)) for a in args] else: # single argument, don't make a list return Variable(args[0].cuda(gpu))
e4849a0a99dd6ca7baeacadc130e46006dd23c3a
12,405
async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool: """Set up the SleepIQ config entry.""" conf = entry.data email = conf[CONF_USERNAME] password = conf[CONF_PASSWORD] client_session = async_get_clientsession(hass) gateway = AsyncSleepIQ(client_session=client_session) try: await gateway.login(email, password) except SleepIQLoginException: _LOGGER.error("Could not authenticate with SleepIQ server") return False except SleepIQTimeoutException as err: raise ConfigEntryNotReady( str(err) or "Timed out during authentication" ) from err try: await gateway.init_beds() except SleepIQTimeoutException as err: raise ConfigEntryNotReady( str(err) or "Timed out during initialization" ) from err except SleepIQAPIException as err: raise ConfigEntryNotReady(str(err) or "Error reading from SleepIQ API") from err coordinator = SleepIQDataUpdateCoordinator(hass, gateway, email) # Call the SleepIQ API to refresh data await coordinator.async_config_entry_first_refresh() hass.data.setdefault(DOMAIN, {})[entry.entry_id] = coordinator hass.config_entries.async_setup_platforms(entry, PLATFORMS) return True
e4a4765113c7bc1e3c50290c72f3ca8196ba2bf2
12,406
def expansion(svsal,temp,pres,salt=None,dliq=None,dvap=None, chkvals=False,chktol=_CHKTOL,salt0=None,dliq0=None,dvap0=None, chkbnd=False,useext=False,mathargs=None): """Calculate seawater-vapour thermal expansion coefficient. Calculate the thermal expansion coefficient of a seawater-vapour parcel. :arg float svsal: Total sea-vapour salinity in kg/kg. :arg float temp: Temperature in K. :arg float pres: Pressure in Pa. :arg salt: Seawater salinity in kg/kg. If unknown, pass None (default) and it will be calculated. :type salt: float or None :arg dliq: Seawater liquid water density in kg/m3. If unknown, pass None (default) and it will be calculated. :type dliq: float or None :arg dvap: Water vapour density in kg/m3. If unknown, pass None (default) and it will be calculated. :type dvap: float or None :arg bool chkvals: If True (default False) and all values are given, this function will calculate the disequilibrium and raise a warning if the results are not within a given tolerance. :arg float chktol: Tolerance to use when checking values (default _CHKTOL). :arg salt0: Initial guess for the seawater salinity in kg/kg. If None (default) then `_approx_tp` is used. :type salt0: float or None :arg dliq0: Initial guess for the seawater liquid water density in kg/m3. If None (default) then `flu3a._dliq_default` is used. :type dliq0: float or None :arg dvap0: Initial guess for the salinity in kg/kg. If None (default) then `flu3a._dvap_default` is used. :type dvap0: float or None :arg bool chkbnd: If True then warnings are raised when the given values are valid but outside the recommended bounds (default False). :arg bool useext: If False (default) then the salt contribution is calculated from _GSCOEFFS; if True, from _GSCOEFFS_EXT. :arg mathargs: Keyword arguments to the root-finder :func:`_newton <maths3.newton>` (e.g. maxiter, rtol). If None (default) then no arguments are passed and default parameters will be used. :returns: Expansion coefficient in 1/K. :raises RuntimeWarning: If the relative disequilibrium is more than chktol, if chkvals is True and all values are given. :raises RuntimeWarning: If the equilibrium seawater salinity is lower than the total parcel salinity. :Examples: >>> expansion(0.035,274.,610.) 0.4588634213 """ salt, dliq, dvap = eq_seavap(svsal,temp,pres,salt=salt,dliq=dliq, dvap=dvap,chkvals=chkvals,chktol=chktol,salt0=salt0,dliq0=dliq0, dvap0=dvap0,chkbnd=chkbnd,useext=useext,mathargs=mathargs) g_p = seavap_g(0,0,1,svsal,temp,pres,salt=salt,dliq=dliq,dvap=dvap, useext=useext) g_tp = seavap_g(0,1,1,svsal,temp,pres,salt=salt,dliq=dliq,dvap=dvap, useext=useext) alpha = g_tp / g_p return alpha
78c47eabf1d8e96c655652c3c8847b391264b05b
12,407
import yaml def yaml_to_dict(yaml_str=None, str_or_buffer=None): """ Load YAML from a string, file, or buffer (an object with a .read method). Parameters are mutually exclusive. Parameters ---------- yaml_str : str, optional A string of YAML. str_or_buffer : str or file like, optional File name or buffer from which to load YAML. Returns ------- dict Conversion from YAML. """ if not yaml_str and not str_or_buffer: raise ValueError('One of yaml_str or str_or_buffer is required.') if yaml_str: d = yaml.load(yaml_str) elif isinstance(str_or_buffer, str): with open(str_or_buffer) as f: d = yaml.load(f) else: d = yaml.load(str_or_buffer) return d
37aefe8e5b1bcc734626cbf7177e3b3dffda2416
12,408
from typing import Dict from typing import Any from typing import Tuple def verify_block_arguments( net_part: str, block: Dict[str, Any], num_block: int, ) -> Tuple[int, int]: """Verify block arguments are valid. Args: net_part: Network part, either 'encoder' or 'decoder'. block: Block parameters. num_block: Block ID. Return: block_io: Input and output dimension of the block. """ block_type = block.get("type") if block_type is None: raise ValueError( "Block %d in %s doesn't a type assigned.", (num_block, net_part) ) if block_type == "transformer": arguments = {"d_hidden", "d_ff", "heads"} elif block_type == "conformer": arguments = { "d_hidden", "d_ff", "heads", "macaron_style", "use_conv_mod", } if block.get("use_conv_mod", None) is True and "conv_mod_kernel" not in block: raise ValueError( "Block %d: 'use_conv_mod' is True but " " 'conv_mod_kernel' is not specified" % num_block ) elif block_type == "causal-conv1d": arguments = {"idim", "odim", "kernel_size"} if net_part == "encoder": raise ValueError("Encoder does not support 'causal-conv1d.'") elif block_type == "conv1d": arguments = {"idim", "odim", "kernel_size"} if net_part == "decoder": raise ValueError("Decoder does not support 'conv1d.'") else: raise NotImplementedError( "Wrong type. Currently supported: " "causal-conv1d, conformer, conv-nd or transformer." ) if not arguments.issubset(block): raise ValueError( "%s in %s in position %d: Expected block arguments : %s." " See tutorial page for more information." % (block_type, net_part, num_block, arguments) ) if block_type in ("transformer", "conformer"): block_io = (block["d_hidden"], block["d_hidden"]) else: block_io = (block["idim"], block["odim"]) return block_io
cead023afcd72d1104e02b2d67406b9c47102589
12,409
from pathlib import Path def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=()): """ Compute the average precision, given the recall and precision curves. Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. # Arguments tp: True positives (nparray, nx1 or nx10). conf: Objectness value from 0-1 (nparray). pred_cls: Predicted object classes (nparray). target_cls: True object classes (nparray). plot: Plot precision-recall curve at [email protected] save_dir: Plot save directory # Returns The average precision as computed in py-faster-rcnn. """ # Sort by objectness i = np.argsort(-conf) tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] # Find unique classes unique_classes = np.unique(target_cls) nc = unique_classes.shape[0] # number of classes, number of detections # Create Precision-Recall curve and compute AP for each class px, py = np.linspace(0, 1, 1000), [] # for plotting ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) for ci, c in enumerate(unique_classes): i = pred_cls == c n_l = (target_cls == c).sum() # number of labels n_p = i.sum() # number of predictions if n_p == 0 or n_l == 0: print("n_p: n_l:", n_p, n_l, flush=True) continue else: # Accumulate FPs and TPs fpc = (1 - tp[i]).cumsum(0) tpc = tp[i].cumsum(0) # Recall recall = tpc / (n_l + 1e-16) # recall curve #print("recall: ", recall, flush=True) #print("recall.shape: ", recall.shape, flush=True) r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases # Precision precision = tpc / (tpc + fpc) # precision curve #print("precision: ", precision, flush=True) #print("precision.shape: ", precision.shape, flush=True) p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score # AP from recall-precision curve for j in range(tp.shape[1]): ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) if plot and j == 0: py.append(np.interp(px, mrec, mpre)) # precision at [email protected] # Compute F1 (harmonic mean of precision and recall) f1 = 2 * p * r / (p + r + 1e-16) if plot: plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names) plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1') plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision') plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall') i = f1.mean(0).argmax() # max F1 index return p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype('int32')
9a41478f8b85b7d43ceeaaaf6425ece67672fc64
12,410
from typing import Optional def frame_aligned_point_error( pred_frames: r3.Rigids, target_frames: r3.Rigids, frames_mask: paddle.Tensor, pred_positions: r3.Vecs, target_positions: r3.Vecs, positions_mask: paddle.Tensor, length_scale: float, l1_clamp_distance: Optional[float] = None, epsilon=1e-4) -> paddle.Tensor: """Measure point error under different alignments. Jumper et al. (2021) Suppl. Alg. 28 "computeFAPE" Computes error between two structures with B points under A alignments derived from the given pairs of frames. Args: pred_frames: num_frames reference frames for 'pred_positions'. target_frames: num_frames reference frames for 'target_positions'. frames_mask: Mask for frame pairs to use. pred_positions: num_positions predicted positions of the structure. target_positions: num_positions target positions of the structure. positions_mask: Mask on which positions to score. length_scale: length scale to divide loss by. l1_clamp_distance: Distance cutoff on error beyond which gradients will be zero. epsilon: small value used to regularize denominator for masked average. Returns: Masked Frame Aligned Point Error. """ def unsqueeze_rigids(rigid, axis=-1): """add an axis in the axis of rot.xx and trans.x""" if axis < 0: axis_t = axis - 1 axis_r = axis - 2 else: axis_t = axis axis_r = axis rotation = paddle.unsqueeze(rigid.rot.rotation, axis=axis_r) translation = paddle.unsqueeze(rigid.trans.translation, axis=axis_t) return r3.Rigids(rot=r3.Rots(rotation), trans=r3.Vecs(translation)) def unsqueeze_vecs(vecs, axis=-1): """add an axis in the axis of rot.xx and trans.x""" if axis < 0: axis_t = axis - 1 else: axis_t = axis translation = paddle.unsqueeze(vecs.translation, axis=axis_t) return r3.Vecs(translation) # Compute array of predicted positions in the predicted frames. # r3.Vecs (num_frames, num_positions) local_pred_pos = r3.rigids_mul_vecs( unsqueeze_rigids(r3.invert_rigids(pred_frames)), unsqueeze_vecs(pred_positions, axis=1)) # Compute array of target positions in the target frames. # r3.Vecs (num_frames, num_positions) local_target_pos = r3.rigids_mul_vecs( unsqueeze_rigids(r3.invert_rigids(target_frames)), unsqueeze_vecs(target_positions, axis=1)) # Compute errors between the structures. # paddle.Tensor (num_frames, num_positions) error_dist = paddle.sqrt(r3.vecs_squared_distance(local_pred_pos, local_target_pos) + epsilon) if l1_clamp_distance: error_dist = paddle.clip(error_dist, min=0, max=l1_clamp_distance) normed_error = error_dist / length_scale normed_error *= paddle.unsqueeze(frames_mask, axis=-1) normed_error *= paddle.unsqueeze(positions_mask, axis=-2) normalization_factor = ( paddle.sum(frames_mask, axis=-1) * paddle.sum(positions_mask, axis=-1)) return (paddle.sum(normed_error, axis=[-2, -1]) / (epsilon + normalization_factor))
fe66fea6d3d6ca418b64a2d18bdc75a6e10d6707
12,411
def remove_app_restriction_request(machine_id, comment): """Enable execution of any application on the machine. Args: machine_id (str): Machine ID comment (str): Comment to associate with the action Notes: Machine action is a collection of actions you can apply on the machine, for more info https://docs.microsoft.com/en-us/windows/security/threat-protection/microsoft-defender-atp/machineaction Returns: dict. Machine action """ cmd_url = '/machines/{}/unrestrictCodeExecution'.format(machine_id) json = { 'Comment': comment } response = http_request('POST', cmd_url, json=json) return response
f4dd44cbef6194b9fcc301fb19bb5c3ba77ad269
12,412
import torch def fix_bond_lengths( dist_mat: torch.Tensor, bond_lengths: torch.Tensor, delim: int = None, delim_value: float = ARBITRARILY_LARGE_VALUE) -> torch.Tensor: """ Replace one-offset diagonal entries with ideal bond lengths """ mat_len = dist_mat.shape[1] bond_lengths = torch.cat([bond_lengths] * (mat_len // 3))[:mat_len - 1] dist_mat[1:, :-1][torch.eye(mat_len - 1) == 1] = bond_lengths dist_mat[:-1, 1:][torch.eye(mat_len - 1) == 1] = bond_lengths # Set chain break distance to arbitrarily large value for replacement by F-W algorithm if delim is not None: dist_mat[delim * 3 + 2, (delim + 1) * 3] = delim_value dist_mat[(delim + 1) * 3, delim * 3 + 2] = delim_value return dist_mat
1112ad7019c1cb82360ad6e784f7f8262dc7b4a0
12,413
def CommandToString(command): """Returns quoted command that can be run in bash shell.""" return ' '.join(cmd_helper.SingleQuote(c) for c in command)
bcb6d3f108997b35336a68a559243931ca50a2c5
12,414
import re def version(output): """ `git --version` > git version 1.8.1.1 """ output = output.rstrip() words = re.split('\s+', output, 3) if not words or words[0] != 'git' or words[1] != 'version': raise WrongOutputError() version = words[2] parts = version.split('.') try: major = int(parts[0]) if len(parts) > 0 else None except ValueError: major = None try: minor = int(parts[1]) if len(parts) > 1 else None except ValueError: minor = None return Version(version, parts, major, minor)
21a16245cf7729b56588016f358667b210113eec
12,416
def set_up_s3_encryption_configuration(kms_arn=None): """ Use the default SSE-S3 configuration for the journal export if a KMS key ARN was not given. :type kms_arn: str :param kms_arn: The Amazon Resource Name to encrypt. :rtype: dict :return: The encryption configuration for JournalS3Export. """ if kms_arn is None: return {'ObjectEncryptionType': 'SSE_S3'} return {'ObjectEncryptionType': {'S3ObjectEncryptionType': 'SSE_KMS', 'KmsKeyArn': kms_arn}}
dd8663c17e040423a08c772fd9ca64d25abd2850
12,417
import click import json def search(dataset, node, aoi, start_date, end_date, lng, lat, dist, lower_left, upper_right, where, geojson, extended, api_key): """ Search for images. """ node = get_node(dataset, node) if aoi == "-": src = click.open_file('-') if not src.isatty(): lines = src.readlines() if len(lines) > 0: aoi = json.loads(''.join([ line.strip() for line in lines ])) bbox = map(get_bbox, aoi.get('features') or [aoi])[0] lower_left = bbox[0:2] upper_right = bbox[2:4] if where: # Query the dataset fields endpoint for queryable fields resp = api.dataset_fields(dataset, node) def format_fieldname(s): return ''.join(c for c in s if c.isalnum()).lower() field_lut = { format_fieldname(field['name']): field['fieldId'] for field in resp['data'] } where = { field_lut[format_fieldname(k)]: v for k, v in where if format_fieldname(k) in field_lut } if lower_left: lower_left = dict(zip(['longitude', 'latitude'], lower_left)) upper_right = dict(zip(['longitude', 'latitude'], upper_right)) result = api.search(dataset, node, lat=lat, lng=lng, distance=dist, ll=lower_left, ur=upper_right, start_date=start_date, end_date=end_date, where=where, extended=extended, api_key=api_key) if geojson: result = to_geojson(result) print(json.dumps(result))
309a98cf3cfc81f12631bbc15ee0325d16385338
12,418
from typing import Callable def _make_rnn_cell(spec: RNNSpec) -> Callable[[], tf.nn.rnn_cell.RNNCell]: """Return the graph template for creating RNN cells.""" return RNN_CELL_TYPES[spec.cell_type](spec.size)
48cf85bcb8d39ab7b4dd150fc890eb281d9b83d9
12,419
def run_baselines(env, seed, log_dir): """Create baselines model and training. Replace the ppo and its training with the algorithm you want to run. Args: env (gym.Env): Environment of the task. seed (int): Random seed for the trial. log_dir (str): Log dir path. Returns: str: The log file path. """ seed = seed + 1000000 set_global_seeds(seed) env.seed(seed) # Set up logger for baselines configure(dir=log_dir, format_strs=['stdout', 'log', 'csv', 'tensorboard']) baselines_logger.info('seed={}, logdir={}'.format( seed, baselines_logger.get_dir())) env = DummyVecEnv([ lambda: bench.Monitor( env, baselines_logger.get_dir(), allow_early_resets=True) ]) ddpg.learn(network='mlp', env=env, nb_epochs=params['n_epochs'], nb_epoch_cycles=params['steps_per_epoch'], normalize_observations=False, critic_l2_reg=0, actor_lr=params['policy_lr'], critic_lr=params['qf_lr'], gamma=params['discount'], nb_train_steps=params['n_train_steps'], nb_rollout_steps=params['n_rollout_steps'], nb_eval_steps=100) return osp.join(log_dir, 'progress.csv')
2a020c5efe548d3722155569fbe69cd836efeebd
12,420
def count_transitions(hypno): """ return the count for all possible transitions """ possible_transitions = [(0,1), (0,2), (0,4), # W -> S1, S2, REM (1,2), (1,0), (1,3), # S1 -> W, S2, REM (2,0), (2,1), (2,3), (2,4), # S2 -> W, S1, SWS, REM (3,0), (3,2), # SWS -> W, S2 (4,0), (4,1), (4,2)] # counts = [] for trans in possible_transitions: counts += [transition_index(hypno, trans)] return counts
4a0dc835c2e72bf46ad8d3ebe33256f32ce2ede9
12,422
def mu_ref_normal_sampler_tridiag(loc=0.0, scale=1.0, beta=2, size=10, random_state=None): """Implementation of the tridiagonal model to sample from .. math:: \\Delta(x_{1}, \\dots, x_{N})^{\\beta} \\prod_{n=1}^{N} \\exp(-\\frac{(x_i-\\mu)^2}{2\\sigma^2} ) dx_i .. seealso:: :cite:`DuEd02` II-C """ rng = check_random_state(random_state) if not (beta > 0): raise ValueError('`beta` must be positive. Given: {}'.format(beta)) # beta/2*[N-1, N-2, ..., 1] b_2_Ni = 0.5 * beta * np.arange(size - 1, 0, step=-1) alpha_coef = rng.normal(loc=loc, scale=scale, size=size) beta_coef = rng.gamma(shape=b_2_Ni, scale=scale**2) return la.eigvalsh_tridiagonal(alpha_coef, np.sqrt(beta_coef))
75e7d46ec4816bbfa46443537f66cd27043b212d
12,423
def get_pokemon(name:str) -> dict: """ Busca el pokémon dado su nombre en la base de datos y crea un diccionario con su información básica. Paramétros: name(str): Nombre del pokémon a buscar Retorna: Diccionario con la información básica del pokémon y sus evoluciones. """ try: p = Pokemon.objects.get(name=name) pokemon = { "name": p.name, "id": p.id, "weight": p.weight, "height": p.height, "stats": [], "evolutions": [] } stats = PokemonStat.objects.filter(pokemon_name=p) for stat in stats: pokemon["stats"].append({"stat": stat.stat_id, "base": stat.base}) evolutionChain = PokemonEvolution.objects.get(pokemon=p) evolutionId = evolutionChain.evolution_chain position = evolutionChain.position chain = PokemonEvolution.objects.filter(evolution_chain=evolutionId) for evolution in chain: if evolution.position > position: pokemon["evolutions"].append({"name": evolution.pokemon.name, "id": evolution.pokemon.id, "evolution_type": "Evolution"}) elif evolution.position < position: pokemon["evolutions"].append({"name": evolution.pokemon.name, "id": evolution.pokemon.id, "evolution_type": "Preevolution"}) return pokemon except ObjectDoesNotExist: return None
fa19704b2dfb6d2223a73264df6b5dc9e866fb8e
12,425
def create_cluster(module, switch, name, node1, node2): """ Method to create a cluster between two switches. :param module: The Ansible module to fetch input parameters. :param switch: Name of the local switch. :param name: The name of the cluster to create. :param node1: First node of the cluster. :param node2: Second node of the cluster. :return: String describing if cluster got created or if it's already exists. """ global CHANGED_FLAG cli = pn_cli(module) clicopy = cli cli += ' switch %s cluster-show format name no-show-headers ' % node1 cluster_list = run_cli(module, cli).split() if name not in cluster_list: cli = clicopy cli += ' switch %s cluster-create name %s ' % (switch, name) cli += ' cluster-node-1 %s cluster-node-2 %s ' % (node1, node2) if 'Success' in run_cli(module, cli): CHANGED_FLAG.append(True) return ' %s: %s created successfully \n' % (switch, name) else: return ' %s: %s already exists \n' % (switch, name)
a7f0a415d019b7fa3622d18da396879df566b365
12,426
from typing import List import random def random_terminals_for_primitive( primitive_set: dict, primitive: Primitive ) -> List[Terminal]: """ Return a list with a random Terminal for each required input to Primitive. """ return [random.choice(primitive_set[term_type]) for term_type in primitive.input]
b3160800bb5da87c0215ed4857f2596934d28c05
12,427
def where_from_pos(text, pos): """ Format a textual representation of the given position in the text. """ return "%d:%d" % (line_from_pos(text, pos), col_from_pos(text, pos))
587387f017fe32b297c06123fc3853c18a7aea46
12,429
def generateHuffmanCodes (huffsize): """ Calculate the huffman code of each length. """ huffcode = [] k = 0 code = 0 # Magic for i in range (len (huffsize)): si = huffsize[i] for k in range (si): huffcode.append ((i + 1, code)) code += 1 code <<= 1 return huffcode
60d5a2bd5524627dd5cc624dbb6b0ea09b8032d4
12,430
def one_hot_df(df, cat_col_list): """ Make one hot encoding on categoric columns. Returns a dataframe for the categoric columns provided. ------------------------- inputs - df: original input DataFrame - cat_col_list: list of categorical columns to encode. outputs - df_hot: one hot encoded subset of the original DataFrame. """ df_hot = pd.DataFrame() for col in cat_col_list: encoded_matrix = col_encoding(df, col) df_ = pd.DataFrame(encoded_matrix, columns = [col+ ' ' + str(int(i))\ for i in range(encoded_matrix.shape[1])]) df_hot = pd.concat([df_hot, df_], axis = 1) return df_hot
d47978a551edbc11f93f9a2e87dbe1598e39161b
12,431
from typing import List from typing import Optional import select from typing import Dict async def load_users_by_id(user_ids: List[int]) -> List[Optional[User]]: """ Batch-loads users by their IDs. """ query = select(User).filter(User.id.in_(user_ids)) async with get_session() as session: result: Result = await session.execute(query) user_map: Dict[int, User] = {user.id: user for user in result.scalars()} return [user_map.get(user_id) for user_id in user_ids]
ac9d0a16a40d478ed7fec590bf591aa0124270d9
12,432
def create_timetravel_model(for_model): """ Returns the newly created timetravel model class for the model given. """ if for_model._meta.proxy: _tt_model = for_model._meta.concrete_model._tt_model for_model._tt_model = _tt_model for_model._meta._tt_model = _tt_model return opt = for_model._meta name = 'tt_%s' % opt.db_table class Meta: app_label = get_migration_app() db_table = name index_together = [[OK, VU]] verbose_name = name[:39] attrs = {'Meta': Meta, '_tt_is_timetravel_model': True, '__module__': for_model.__module__} fields = copy_fields(for_model) attrs.update(fields) for_model._tt_has_history = True ret = type(str(name), (Model,), attrs) for_model._tt_model = ret for_model._meta._tt_model = ret return ret
6a2557f3737ce014e14ba9dd36cd7a6d9c8c78b7
12,433
def public_route_server_has_read(server_id, user_id=None): """ check if current user has read access to the given server """ user = user_id and User.query.get_or_404(user_id) or current_user server = DockerServer.query.get_or_404(server_id) if server.has_group_read(user): return Response("read access", 200) abort(403)
b9f812feac7c7e951f8c37178fd1dc2913601631
12,434
def isValidPublicAddress(address: str) -> bool: """Check if address is a valid NEO address""" valid = False if len(address) == 34 and address[0] == 'A': try: base58.b58decode_check(address.encode()) valid = True except ValueError: # checksum mismatch valid = False return valid
a99f08c289f9d3136adf7e17697645131e785ecb
12,435
def cost_to_go_np(cost_seq, gamma_seq): """ Calculate (discounted) cost to go for given cost sequence """ # if np.any(gamma_seq == 0): # return cost_seq cost_seq = gamma_seq * cost_seq # discounted reward sequence cost_seq = np.cumsum(cost_seq[:, ::-1], axis=-1)[:, ::-1] # cost to go (but scaled by [1 , gamma, gamma*2 and so on]) cost_seq /= gamma_seq # un-scale it to get true discounted cost to go return cost_seq
bea4de4cb32c3a346ebe8ea532c2c94589893e65
12,436
import re def parse_args(): """ Parses the command line arguments. """ # Override epilog formatting OptionParser.format_epilog = lambda self, formatter: self.epilog parser = OptionParser(usage="usage: %prog -f secret.txt | --file secret.txt | --folder allmysecrets", epilog=EXAMPLES) parser.add_option("-p", "--password", dest="password", help="set password file for AES decryption") parser.add_option("-f", "--file", dest="file", help="encrypt/decrypt this file") parser.add_option("-F", "--folder", dest="folder", help="encrypt/decrypt all files in this folder") parser.add_option("--encrypt", action="store_true", dest="encrypt", help="encrypt file(s)") parser.add_option("--decrypt", action="store_true", dest="decrypt", help="decrypt file(s)") parser.add_option("--recursive", action="store_true", dest="recursive", help="encrypt/decrypt files in folder recursively") parser.formatter.store_option_strings(parser) parser.formatter.store_option_strings = lambda _: None for option, value in parser.formatter.option_strings.items(): value = re.sub(r"\A(-\w+) (\w+), (--[\w-]+=(\2))\Z", r"\g<1>/\g<3>", value) value = value.replace(", ", '/') if len(value) > MAX_HELP_OPTION_LENGTH: value = ("%%.%ds.." % (MAX_HELP_OPTION_LENGTH - parser.formatter.indent_increment)) % value parser.formatter.option_strings[option] = value args = parser.parse_args()[0] if not any((args.file, args.folder)): parser.error("Required argument is missing. Use '-h' for help.") if not any((args.encrypt, args.decrypt)): parser.error("Required argument is missing. Use '-h' for help.") if args.decrypt and not args.password: parser.error("Required password file is missing. Use '-h' for help.") return args
8f696bb8b419269766bceadb42b36f2a3e052e5b
12,437
def x_to_ggsg(seq): """replace Xs with a Serine-Glycine linker (GGSG pattern) seq and return value are strings """ if "X" not in seq: return seq replacement = [] ggsg = _ggsg_generator() for aa in seq: if aa != "X": replacement.append(aa) # restart linker iterator for next stretch of Xs ggsg = _ggsg_generator() else: replacement.append(next(ggsg)) return "".join(replacement)
53885ca76484f25a04ffc4220af0d7b0e56defd4
12,438
from typing import OrderedDict def gini_pairwise(idadf, target=None, features=None, ignore_indexer=True): """ Compute the conditional gini coefficients between a set of features and a set of target in an IdaDataFrame. Parameters ---------- idadf : IdaDataFrame target : str or list of str, optional A column or list of columns against to be used as target. Per default, consider all columns features : str or list of str, optional A column or list of columns to be used as features. Per default, consider all columns. ignore_indexer : bool, default: True Per default, ignore the column declared as indexer in idadf Returns ------- Pandas.DataFrame or Pandas.Series if only one target Notes ----- Input columns as target and features should be categorical, otherwise this measure does not make much sense. Examples -------- >>> idadf = IdaDataFrame(idadb, "IRIS") >>> gini_pairwise(idadf) """ # Check input target, features = _check_input(idadf, target, features, ignore_indexer) gini_dict = OrderedDict() length = len(idadf) for t in target: gini_dict[t] = OrderedDict() features_notarget = [x for x in features if (x != t)] for feature in features_notarget: if t not in gini_dict: gini_dict[t] = OrderedDict() query = ("SELECT SUM((POWER(c,2) - gini)/c)/%s FROM "+ "(SELECT SUM(POWER(count,2)) as gini, SUM(count) as c FROM "+ "(SELECT CAST(COUNT(*) AS FLOAT) AS count, \"%s\" FROM %s GROUP BY \"%s\",\"%s\") "+ "GROUP BY \"%s\")") query0 = query%(length, feature, idadf.name, t, feature, feature) gini_dict[t][feature] = idadf.ida_scalar_query(query0) result = pd.DataFrame(gini_dict).fillna(np.nan) if len(result.columns) > 1: order = [x for x in result.columns if x in features] + [x for x in features if x not in result.columns] result = result.reindex(order) result = result.dropna(axis=1, how="all") if len(result.columns) == 1: if len(result) == 1: result = result.iloc[0,0] else: result = result[result.columns[0]].copy() result.sort_values(ascending = True) else: result = result.fillna(0) return result
aa886c8d44e54597e86f0736ea383671bda2e13f
12,439
def init_isolated_80(): """ Real Name: b'init Isolated 80' Original Eqn: b'0' Units: b'person' Limits: (None, None) Type: constant b'' """ return 0
5511cac38bf9bd68446fcb1dc41ac96807ea57a2
12,440
def xcom_api_setup(): """Instantiate api""" return XComApi(API_CLIENT)
1a47066f389ab2846f1aa31ce8338389def07e6d
12,441
def zeros_tensor(*args, **kwargs): """Construct a tensor of a given shape with every entry equal to zero.""" labels = kwargs.pop("labels", []) dtype = kwargs.pop("dtype", np.float) base_label = kwargs.pop("base_label", "i") return Tensor(np.zeros(*args, dtype=dtype), labels=labels, base_label=base_label)
3baba23ba763afb51c715a85aa6f84c8c2d99c43
12,442
from typing import Tuple def reg_split_from( splitted_mappings: np.ndarray, splitted_sizes: np.ndarray, splitted_weights: np.ndarray, ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """ When creating the regularization matrix of a source pixelization, this function assumes each source pixel has been split into a cross of four points (the size of which is based on the area of the source pixel). This cross of points represents points which together can evaluate the gradient of the pixelization's reconstructed values. This function takes each cross of points and determines the regularization weights of every point on the cross, to construct a regulariaztion matrix based on the gradient of each pixel. The size of each cross depends on the Voronoi pixel area, thus this regularization scheme and its weights depend on the pixel area (there are larger weights for bigger pixels). This ensures that bigger pixels are regularized more. The number of pixel neighbors over which regularization is 4 * the total number of source pixels. This contrasts other regularization schemes, where the number of neighbors changes depending on, for example, the Voronoi mesh geometry. By having a fixed number of neighbors this removes stochasticty in the regularization that is applied to a solution. There are cases where a grid has over 100 neighbors, corresponding to very coordinate transformations. In such extreme cases, we raise a `exc.FitException`. Parameters ---------- splitted_mappings splitted_sizes splitted_weights Returns ------- """ max_j = np.shape(splitted_weights)[1] - 1 splitted_weights *= -1.0 for i in range(len(splitted_mappings)): pixel_index = i // 4 flag = 0 for j in range(splitted_sizes[i]): if splitted_mappings[i][j] == pixel_index: splitted_weights[i][j] += 1.0 flag = 1 if j >= max_j: raise exc.PixelizationException( "the number of Voronoi natural neighbours exceeds 100." ) if flag == 0: splitted_mappings[i][j + 1] = pixel_index splitted_sizes[i] += 1 splitted_weights[i][j + 1] = 1.0 return splitted_mappings, splitted_sizes, splitted_weights
545f0bd7345a8ab908d2338eaa7cb4c3562f4234
12,443
def get_initiator_IP(json_isessions): """ pull the IP from the host session """ print("-" * 20 + " get_initiator started") for session in json_isessions['sessions']: session_array[session['initiatorIP']] = session['initiatorName'] return session_array
4140b9f32727d1e5e1e98fd6714e8d91276b2272
12,444
def get_data_for_recent_jobs(recency_msec=DEFAULT_RECENCY_MSEC): """Get a list containing data about recent jobs. This list is arranged in descending order based on the time the job was enqueued. At most NUM_JOBS_IN_DASHBOARD_LIMIT job descriptions are returned. Args: - recency_secs: the threshold for a recent job, in seconds. """ recent_job_models = job_models.JobModel.get_recent_jobs( NUM_JOBS_IN_DASHBOARD_LIMIT, recency_msec) return [_get_job_dict_from_job_model(model) for model in recent_job_models]
032f27b55c70947a44cd6ed244291118e3660f77
12,445
def construct_outgoing_multicast_answers(answers: _AnswerWithAdditionalsType) -> DNSOutgoing: """Add answers and additionals to a DNSOutgoing.""" out = DNSOutgoing(_FLAGS_QR_RESPONSE | _FLAGS_AA, multicast=True) _add_answers_additionals(out, answers) return out
65f0a2a42f9d3f1bd8fbc74e8303248adf01e65d
12,446
import struct def load_analog_binary_v1(filename): """Load analog traces stored in the binary format by Logic 1.2.0+ The format is documented at https://support.saleae.com/faq/technical-faq/data-export-format-analog-binary Returns (data, period) where data is a numpy array of 32-bit floats of shape (nchannels, nsamples) and period is the sampling period in seconds. """ with open(filename, 'rb') as f: nsamples, nchannels, period = struct.unpack('<QId', f.read(20)) if nchannels > 16: raise RuntimeError(f'Invalid nchannels={nchannels}. Are you sure this is binary analog data from v1.2.0+?') if period < 1 / 50e6 or period > 1: raise RuntimeError(f'Invalid period={period}. Are you sure this is binary analog data from v1.2.0+?') data = np.fromfile(f, dtype=np.dtype('<f'), count=nsamples * nchannels).reshape(nchannels, nsamples).astype('=f') return data, period
5fcb97c4da367a8abeb12d7dc2852dbb7412956d
12,447
import click def setup_phantomjs(): """Create and return a PhantomJS browser object.""" try: # Setup capabilities for the PhantomJS browser phantomjs_capabilities = DesiredCapabilities.PHANTOMJS # Some basic creds to use against an HTTP Basic Auth prompt phantomjs_capabilities['phantomjs.page.settings.userName'] = 'none' phantomjs_capabilities['phantomjs.page.settings.password'] = 'none' # Flags to ignore SSL problems and get screenshots service_args = [] service_args.append('--ignore-ssl-errors=true') service_args.append('--web-security=no') service_args.append('--ssl-protocol=any') # Create the PhantomJS browser and set the window size browser = webdriver.PhantomJS(desired_capabilities=phantomjs_capabilities,service_args=service_args) browser.set_window_size(1920,1080) except Exception as error: click.secho("[!] Bad news: PhantomJS failed to load (not installed?), so activities \ requiring a web browser will be skipped.",fg="red") click.secho("L.. Details: {}".format(error),fg="red") browser = None return browser
5a8e536850e2a3c39adaf3228fc1a1f7ad4694dd
12,448
def normal_pdf(x, mu, cov, log=True): """ Calculate the probability density of Gaussian (Normal) distribution. Parameters ---------- x : float, 1-D array_like (K, ), or 2-D array_like (K, N) The variable for calculating the probability density. mu : float or 1-D array_like, (K, ) The mean of the Gaussian distribution. cov : float or 2-D array_like, (K, K) The variance or the covariance matrix of the Gaussian distribution. log : bool If true, the return value is at log scale. Returns ------- pdf : numpy float The probability density of x. if N==1, return a float elif N>1, return an array """ if len(np.array(mu).shape) == 0: x = np.array(x).reshape(-1,1) elif len(np.array(x).shape) <= 1: x = np.array(x).reshape(1, -1) x = x - np.array(mu) N, K = x.shape if len(np.array(cov).shape) < 2: cov = np.array(cov).reshape(-1,1) cov_inv = np.linalg.inv(cov) cov_det = np.linalg.det(cov) if cov_det <= 0: print("Warning: the det of covariance is not positive!") return None pdf_all = np.zeros(N) pdf_part1 = -(K*np.log(2*np.pi) + np.log(cov_det)) / 2.0 for i in range(N): pdf_all[i] = pdf_part1 - np.dot(np.dot(x[i,:], cov_inv), x[i,:]) / 2.0 if log == False: pdf_all = np.exp(pdf_all) if N == 1: pdf_all = pdf_all[0] return pdf_all
4cdb573e1283a5740cb8d5b518b69c02bc013fe6
12,449
import sqlite3 from datetime import datetime def get_quiz(id, user): """Get Quiz""" conn = sqlite3.connect(DBNAME) cursor = conn.cursor() if user == 'admin' or user == 'fabioja': cursor.execute( "SELECT id, release, expire, problem, tests, results, diagnosis, numb from QUIZ where id = {0}".format(id)) else: cursor.execute("SELECT id, release, expire, problem, tests, results, diagnosis, numb from QUIZ where id = {0} and release < '{1}'".format( id, datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) info = [reg for reg in cursor.fetchall()] conn.close() return info
7e517e2ca84ebd320883950d4c3d6e572f82c226
12,450
def filesystem_entry(filesystem): """ Filesystem tag {% filesystem_entry filesystem %} is used to display a single filesystem. Arguments --------- filesystem: filesystem object Returns ------- A context which maps the filesystem object to filesystem. """ return {'filesystem': filesystem}
3afbd0b8ee9e72ab8841ca5c5517396650d2a898
12,451
def haversine(lat1, lon1, lat2, lon2, units='miles'): """ Calculates arc length distance between two lat_lon points (must be in radians) lat2 & and lon2 can be numpy arrays units can be 'miles' or 'km' (kilometers) """ earth_radius = {'miles': 3959., 'km': 6371.} a = np.square(np.sin((lat2 - lat1)/2.)) + np.cos(lat1) * np.cos(lat2) * np.square(np.sin((lon2 - lon1)/2.)) return 2 * earth_radius[units] * np.arcsin(np.sqrt(a))
cadfa496f39e0a02115140d827bebfa6ff96a2dd
12,452
from typing import Optional def OptionalDateField(description='',validators=[]): """ A custom field that makes the DateField optional """ validators.append(Optional()) field = DateField(description,validators) return field
66695ca94ff7d7283ff5508b4ef3f78efba9a988
12,453
def init_brats_metrics(): """Initialize dict for BraTS Dice metrics""" metrics = {} metrics['ET'] = {'labels': [3]} metrics['TC'] = {'labels': [1, 3]} metrics['WT'] = {'labels': [1, 2, 3]} for _, value in metrics.items(): value.update({'tp':0, 'tot':0}) return metrics
755dc706f7090d78dac18a989745041b8617a9d6
12,454
def add_rse(rse, issuer, vo='def', deterministic=True, volatile=False, city=None, region_code=None, country_name=None, continent=None, time_zone=None, ISP=None, staging_area=False, rse_type=None, latitude=None, longitude=None, ASN=None, availability=None): """ Creates a new Rucio Storage Element(RSE). :param rse: The RSE name. :param issuer: The issuer account. :param vo: The VO to act on. :param deterministic: Boolean to know if the pfn is generated deterministically. :param volatile: Boolean for RSE cache. :param city: City for the RSE. :param region_code: The region code for the RSE. :param country_name: The country. :param continent: The continent. :param time_zone: Timezone. :param staging_area: staging area. :param ISP: Internet service provider. :param rse_type: RSE type. :param latitude: Latitude coordinate of RSE. :param longitude: Longitude coordinate of RSE. :param ASN: Access service network. :param availability: Availability. """ validate_schema(name='rse', obj=rse, vo=vo) kwargs = {'rse': rse} if not permission.has_permission(issuer=issuer, vo=vo, action='add_rse', kwargs=kwargs): raise exception.AccessDenied('Account %s can not add RSE' % (issuer)) return rse_module.add_rse(rse, vo=vo, deterministic=deterministic, volatile=volatile, city=city, region_code=region_code, country_name=country_name, staging_area=staging_area, continent=continent, time_zone=time_zone, ISP=ISP, rse_type=rse_type, latitude=latitude, longitude=longitude, ASN=ASN, availability=availability)
3b41e227ea64c5f03d80ae8734c29b24f9c3bed9
12,455
from typing import Dict from typing import Tuple from typing import List def multi_graph_partition(costs: Dict, probs: Dict, p_t: np.ndarray, idx2nodes: Dict, ot_hyperpara: Dict, weights: Dict = None, predefine_barycenter: bool = False) -> \ Tuple[List[Dict], List[Dict], List[Dict], Dict, np.ndarray]: """ Achieve multi-graph partition via calculating Gromov-Wasserstein barycenter between the target graphs and a proposed one Args: costs: a dictionary of graphs {key: graph idx, value: (n_s, n_s) adjacency matrix of source graph} probs: a dictionary of graphs {key: graph idx, value: (n_s, 1) the distribution of source nodes} p_t: (n_t, 1) the distribution of target nodes idx2nodes: a dictionary of graphs {key: graph idx, value: a dictionary {key: idx of row in cost, value: name of node}} ot_hyperpara: a dictionary of hyperparameters weights: a dictionary of graph {key: graph idx, value: the weight of the graph} predefine_barycenter: False: learn barycenter, True: use predefined barycenter Returns: sub_costs_all: a list of graph dictionary: a dictionary {key: graph idx, value: sub cost matrices}} sub_idx2nodes: a list of graph dictionary: a dictionary {key: graph idx, value: a dictionary mapping indices to nodes' names}} trans: a dictionary {key: graph idx, value: an optimal transport between the graph and the barycenter} cost_t: the reference graph corresponding to partition result """ sub_costs_cluster = [] sub_idx2nodes_cluster = [] sub_probs_cluster = [] sub_costs_all = {} sub_idx2nodes_all = {} sub_probs_all = {} if predefine_barycenter is True: cost_t = csr_matrix(np.diag(p_t[:, 0])) trans = {} for n in costs.keys(): sub_costs_all[n], sub_probs_all[n], sub_idx2nodes_all[n], trans[n] = graph_partition(costs[n], probs[n], p_t, idx2nodes[n], ot_hyperpara) else: cost_t, trans, _ = Gwl.gromov_wasserstein_barycenter(costs, probs, p_t, ot_hyperpara, weights) for n in costs.keys(): sub_costs, sub_probs, sub_idx2nodes = node_cluster_assignment(costs[n], trans[n], probs[n], p_t, idx2nodes[n]) sub_costs_all[n] = sub_costs sub_idx2nodes_all[n] = sub_idx2nodes sub_probs_all[n] = sub_probs for i in range(p_t.shape[0]): sub_costs = {} sub_idx2nodes = {} sub_probs = {} for n in costs.keys(): if i in sub_costs_all[n].keys(): sub_costs[n] = sub_costs_all[n][i] sub_idx2nodes[n] = sub_idx2nodes_all[n][i] sub_probs[n] = sub_probs_all[n][i] sub_costs_cluster.append(sub_costs) sub_idx2nodes_cluster.append(sub_idx2nodes) sub_probs_cluster.append(sub_probs) return sub_costs_cluster, sub_probs_cluster, sub_idx2nodes_cluster, trans, cost_t
a3743cd9cc9e7f9a10eb84992fb74e7fe57f5792
12,456
def TDataStd_BooleanArray_Set(*args): """ * Finds or creates an attribute with the array. :param label: :type label: TDF_Label & :param lower: :type lower: int :param upper: :type upper: int :rtype: Handle_TDataStd_BooleanArray """ return _TDataStd.TDataStd_BooleanArray_Set(*args)
c458a1182474432d2df049ae3126a6b6b2b49a8e
12,457
def py_list_to_tcl_list(py_list): """ Convert Python list to Tcl list using Tcl interpreter. :param py_list: Python list. :type py_list: list :return: string representing the Tcl string equivalent to the Python list. """ py_list_str = [str(s) for s in py_list] return tcl_str(tcl_interp_g.eval('split' + tcl_str('\t'.join(py_list_str)) + '\\t'))
7f42044b8a0b28089abf453e7a1b65d5cb1fb399
12,458