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def main(cfg):
"""Solve the CVRP problem."""
# Instantiate the data problem.
data = create_data_model(cfg)
print(data)
if len(data['distance_matrix'])==0:
result = {
"solution":False,
"error-message":"unable to calculate distance matrix"
}
return result
# Create the routing index manager.
manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),
data['num_vehicles'], data['depot'])
# Create Routing Model.
routing = pywrapcp.RoutingModel(manager)
# Create and register a transit callback.
def distance_callback(from_index, to_index):
"""Returns the distance between the two nodes."""
# Convert from routing variable Index to distance matrix NodeIndex.
from_node = manager.IndexToNode(from_index)
to_node = manager.IndexToNode(to_index)
return data['distance_matrix'][from_node][to_node]
transit_callback_index = routing.RegisterTransitCallback(distance_callback)
# Define cost of each arc.
routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
def demand_callback(from_index):
"""Returns the demand of the node."""
# Convert from routing variable Index to demands NodeIndex.
from_node = manager.IndexToNode(from_index)
return data['demands'][from_node]
# Add Distance constraint.
dimension_name = 'Distance'
routing.AddDimension(
transit_callback_index,
0, # no slack
7200, # vehicle maximum travel distance
True, # start cumul to zero
dimension_name)
demand_callback_index = routing.RegisterUnaryTransitCallback(demand_callback)
routing.AddDimensionWithVehicleCapacity(
demand_callback_index,
0, # null capacity slack
data['vehicle_capacities'], # vehicle maximum capacities
True, # start cumul to zero
'Capacity')
# Setting first solution heuristic.
search_parameters = pywrapcp.DefaultRoutingSearchParameters()
search_parameters.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)
# Solve the problem.
solution = routing.SolveWithParameters(search_parameters)
# Return solution dictionary
if solution:
return get_solution(data, manager, routing, solution)
else:
result = {
"solution":False
}
return result
| 18,900
|
def abs2genomic(chromsizes, start_pos, end_pos):
"""
Convert absolute coordinates to genomic coordinates
Parameters:
-----------
chromsizes: [[chrom, size],...]
A list of chromosome sizes associated with this tileset
start_pos: int
The absolute start coordinate
end_pos: int
The absolute end coordinate
"""
abs_chrom_offsets = np.r_[0, np.cumsum(chromsizes)]
cid_lo, cid_hi = (
np.searchsorted(abs_chrom_offsets, [start_pos, end_pos], side="right") - 1
)
rel_pos_lo = start_pos - abs_chrom_offsets[cid_lo]
rel_pos_hi = end_pos - abs_chrom_offsets[cid_hi]
start = rel_pos_lo
for cid in range(cid_lo, cid_hi):
yield cid, start, chromsizes[cid]
start = 0
yield cid_hi, int(start), int(rel_pos_hi)
| 18,901
|
def generate_koopman_data():
"""
Prepare Koopman data in .mat format for MATLAB Koopman code (generate_koopman_model.m)
"""
from scipy.io import savemat
from sofacontrol.utils import load_data, qv2x
from sofacontrol.measurement_models import linearModel
pod_data_name = 'pod_snapshots'
num_nodes = 1628
ee_node = [1354]
five_nodes = DEFAULT_OUTPUT_NODES
three_nodes = [1354, 726, 139]
three_nodes_row = [1354, 726, 1445]
pod_data = load_data(join(path, '{}.pkl'.format(pod_data_name)))
state = qv2x(q=pod_data['q'], v=pod_data['v'])
names = ['ee_pos']
measurement_models = [linearModel(nodes=ee_node, num_nodes=num_nodes, pos=True, vel=False)]
for i, name in enumerate(names):
mat_data_file = join(path, '{}.mat'.format(name))
y = measurement_models[i].evaluate(x=state.T)
matlab_data = dict()
matlab_data['y'] = y.T
matlab_data['u'] = np.asarray(pod_data['u'])
matlab_data['t'] = np.atleast_2d(pod_data['dt'] * np.asarray(range(len(matlab_data['u']))))
savemat(mat_data_file, matlab_data)
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|
def print_snapshot_stats(options, _fuse):
"""
@param options: Commandline options
@type options: object
@param _fuse: FUSE wrapper
@type _fuse: dedupsqlfs.fuse.dedupfs.DedupFS
"""
_fuse.setReadonly(True)
from dedupsqlfs.fuse.snapshot import Snapshot
snap = Snapshot(_fuse.operations)
snap.report_usage(options.snapshot_stats.encode('utf8'))
return
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|
def test_montage_readers(
reader, file_content, expected_dig, ext, tmpdir
):
"""Test that we have an equivalent of read_montage for all file formats."""
fname = op.join(str(tmpdir), 'test.{ext}'.format(ext=ext))
with open(fname, 'w') as fid:
fid.write(file_content)
dig_montage = reader(fname)
assert isinstance(dig_montage, DigMontage)
actual_ch_pos = dig_montage._get_ch_pos()
expected_ch_pos = expected_dig._get_ch_pos()
for kk in actual_ch_pos:
assert_allclose(actual_ch_pos[kk], expected_ch_pos[kk], atol=1e-5)
for d1, d2 in zip(dig_montage.dig, expected_dig.dig):
assert d1['coord_frame'] == d2['coord_frame']
for key in ('coord_frame', 'ident', 'kind'):
assert isinstance(d1[key], int)
assert isinstance(d2[key], int)
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|
def print_message(msg):
"""Writes a message to screen. Input: string."""
print msg
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|
def get_corners(p, fov):
"""Get corners relative to DSS coordinates. xy coords anti-clockwise"""
c = np.array([[0, 0], fov[::-1]]) # lower left, upper right xy
# corners = np.c_[c[0], c[:, 1], c[1], c[::-1, 0]].T # / clockwise yx
corners = np.c_[c[0], c[::-1, 0], c[1], c[:, 1]].T # / clockwise xy
corners = trans.rigid(corners, p)
return corners
| 18,906
|
def mixnet_m(
num_classes: int = 1000,
multiplier: float = 1.0,
divisor: int = 8,
min_depth: int = None,
dataset: str = "IMAGENET",
) -> Dict[str, Any]:
"""Build MixNet-M."""
if dataset == "IMAGENET":
medium: Tuple[List[Any], ...] = (
[24, 24, 1, 1, 1, None, False],
[24, 32, 3, 2, 6, None, False],
[32, 32, 1, 1, 3, None, False],
[32, 40, 4, 2, 6, 0.5, True],
[40, 40, 2, 1, 6, 0.5, True],
[40, 40, 2, 1, 6, 0.5, True],
[40, 40, 2, 1, 6, 0.5, True],
[40, 80, 3, 2, 6, 0.25, True],
[80, 80, 4, 1, 6, 0.25, True],
[80, 80, 4, 1, 6, 0.25, True],
[80, 80, 4, 1, 6, 0.25, True],
[80, 120, 1, 1, 6, 0.5, True],
[120, 120, 4, 1, 3, 0.5, True],
[120, 120, 4, 1, 3, 0.5, True],
[120, 120, 4, 1, 3, 0.5, True],
[120, 200, 4, 2, 6, 0.5, True],
[200, 200, 4, 1, 6, 0.5, True],
[200, 200, 4, 1, 6, 0.5, True],
[200, 200, 4, 1, 6, 0.5, True],
)
stem = round_filters(24, multiplier)
stem_stride = 2
last_out_channels = round_filters(200, multiplier)
head = round_filters(1536, multiplier=1.0)
elif dataset == "CIFAR100":
medium = (
[24, 24, 1, 1, 1, None, False],
[24, 32, 3, 1, 6, None, False],
[32, 32, 1, 1, 3, None, False],
[32, 40, 4, 2, 6, 0.5, True],
[40, 40, 2, 1, 6, 0.5, True],
[40, 40, 2, 1, 6, 0.5, True],
[40, 40, 2, 1, 6, 0.5, True],
[40, 80, 3, 2, 6, 0.25, True],
[80, 80, 4, 1, 6, 0.25, True],
[80, 80, 4, 1, 6, 0.25, True],
[80, 80, 4, 1, 6, 0.25, True],
[80, 120, 1, 1, 6, 0.5, True],
[120, 120, 4, 1, 3, 0.5, True],
[120, 120, 4, 1, 3, 0.5, True],
[120, 120, 4, 1, 3, 0.5, True],
[120, 200, 4, 2, 6, 0.5, True],
[200, 200, 4, 1, 6, 0.5, True],
[200, 200, 4, 1, 6, 0.5, True],
[200, 200, 4, 1, 6, 0.5, True],
)
stem = round_filters(24, multiplier)
stem_stride = 1
last_out_channels = round_filters(200, multiplier)
head = round_filters(1536, multiplier=1.0)
else:
raise NotImplementedError
for line in medium:
line[0] = round_filters(line[0], multiplier)
line[1] = round_filters(line[1], multiplier)
return dict(
stem=stem,
stem_stride=stem_stride,
head=head,
last_out_channels=last_out_channels,
block_args=medium,
dropout=0.25,
num_classes=num_classes,
)
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|
def no_block(func):
"""Turns a blocking function into a non-blocking coroutine function."""
@functools.wraps(func)
async def no_blocking_handler(*args, **kwargs):
partial = functools.partial(func, *args, **kwargs)
return await asyncio.get_event_loop().run_in_executor(None, partial)
return no_blocking_handler
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|
def wgan_g_loss(scores_fake):
"""
Input:
- scores_fake: Tensor of shape (N,) containing scores for fake samples
Output:
- loss: Tensor of shape (,) giving WGAN generator loss
"""
return -scores_fake.mean()
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|
def main(directory: str):
"""Export Hetionet in several BEL formats."""
click.echo(f'Using PyBEL v{pybel.get_version(with_git_hash=True)}')
click.echo('Getting hetionet')
graph = pybel.get_hetionet()
click.echo('Grounding hetionet')
graph = pybel.grounding.ground(graph)
click.echo('Exporting BEL Script')
script_gz_path = os.path.join(directory, 'hetionet-v1.0.bel.gz')
pybel.to_bel_script_gz(graph, script_gz_path)
click.echo('Exporting Nodelink')
nodelink_gz_path = os.path.join(directory, 'hetionet-v1.0.bel.nodelink.json.gz')
pybel.to_nodelink_gz(graph, nodelink_gz_path)
click.echo('Exporting GraphDati')
graphdati_gz_path = os.path.join(directory, 'hetionet-v1.0.bel.graphdati.json.gz')
pybel.to_graphdati_gz(graph, graphdati_gz_path)
click.echo('Exporting Machine Learning-ready TSV')
tsv_path = os.path.join(directory, 'hetionet-v1.0.tsv.gz')
with gzip.open(tsv_path, 'wt') as file:
pybel.to_tsv(graph, file)
| 18,910
|
def centroid_avg(stats):
"""
Read centroid X and Y 10x and return mean of centroids.
stats : stats method of ophyd camera object to use, e.g. cam_8.stats4
Examples
--------
centroid_avg(cam_8.stats4)
centroidY = centroid_avg(cam_8.stats4)[1]
"""
centroidXArr = np.zeros(10)
centroidYArr = np.zeros(10)
for i in range(0, 10):
centroidXArr[i] = stats.centroid.x.get()
centroidYArr[i] = stats.centroid.y.get()
# print('Centroid X = {:.6g} px'.format(centroidXArr[i]), ', Centroid Y = {:.6g} px'.format(centroidYArr[i]))
time.sleep(0.2)
CentroidX = centroidXArr.mean()
CentroidY = centroidYArr.mean()
print('Mean centroid X = {:.6g} px'.format(CentroidX))
print('Mean centroid Y = {:.6g} px'.format(CentroidY))
return CentroidX, CentroidY
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|
def get_address_host_port(addr, strict=False):
"""
Get a (host, port) tuple out of the given address.
For definition of strict check parse_address
ValueError is raised if the address scheme doesn't allow extracting
the requested information.
>>> get_address_host_port('tcp://1.2.3.4:80')
('1.2.3.4', 80)
"""
scheme, loc = parse_address(addr, strict=strict)
backend = registry.get_backend(scheme)
try:
return backend.get_address_host_port(loc)
except NotImplementedError:
raise ValueError(
"don't know how to extract host and port for address %r" % (addr,)
)
| 18,912
|
def get_ua_list():
"""
获取ua列表
"""
with open('zhihu_spider/misc/ua_list.txt', 'r') as f:
return [x.replace('\n', '') for x in f.readlines()]
| 18,913
|
def measure_time(func):
"""add time measure decorator to the functions"""
def func_wrapper(*args, **kwargs):
start_time = time.time()
a = func(*args, **kwargs)
end_time = time.time()
#print("time in seconds: " + str(end_time-start_time))
return end_time - start_time
return func_wrapper
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|
def generate_dummy_targets(bounds, label, n_points, field_keys=[], seed=1):
"""
Generate dummy points with randomly generated positions. Points
are generated on node 0 and distributed to other nodes if running
in parallel.
Parameters
----------
bounds : tuple of float
Bounding box to generate targets within, of format
(xmin, ymin, xmax, ymax).
label : str
Label to assign generated targets.
n_points : int
Number of points to generate
field_keys : list of str, optional
List of keys to add to `fields` property.
seed : int, optional
Random number generator seed.
Returns
-------
Targets
A collection of randomly generated targets.
"""
if mpiops.chunk_index == 0:
rnd = np.random.RandomState(seed)
def _generate_points(lower, upper, limit):
new_points = []
while len(new_points) < limit:
new_point = rnd.uniform(lower, upper)
new_points.append(new_point)
return new_points
new_lons = _generate_points(bounds[0], bounds[2], n_points)
new_lats = _generate_points(bounds[1], bounds[3], n_points)
lonlats = np.column_stack([sorted(new_lons), sorted(new_lats)])
labels = np.full(lonlats.shape[0], label)
if field_keys:
fields = {k: np.zeros(n_points) for k in field_keys}
else:
fields = {}
_logger.info("Generated %s dummy targets", len(lonlats))
# Split for distribution
lonlats = np.array_split(lonlats, mpiops.chunks)
labels = np.array_split(labels, mpiops.chunks)
split_fields = {k: np.array_split(v, mpiops.chunks) for k, v in fields.items()}
fields = [{k: v[i] for k, v in split_fields.items()} for i in range(mpiops.chunks)]
else:
lonlats, labels, fields = None, None, None
lonlats = mpiops.comm.scatter(lonlats, root=0)
labels = mpiops.comm.scatter(labels, root=0)
fields = mpiops.comm.scatter(fields, root=0)
return Targets(lonlats, labels, fields)
| 18,915
|
def assert_allclose(
actual: numpy.ndarray,
desired: List[List[Union[float, int]]],
err_msg: numpy.ndarray,
):
"""
usage.matplotlib: 1
"""
...
| 18,916
|
def save_map_data_in_nc_file(scn, chl_param, period):
"""
Save the mean values and number of data in a NetCdf file
Use the satpy method by overwriting the chl_param and mask
chl_param=mean chl data (note: it is log10 values)
mask= number of data used for the calculation of means
"""
save_path='.\\map_data\\'+period+'_eo_map_mean_data.nc'
print 'Save mean map data in nc-file: '+ save_path
scn.save_datasets(datasets=['mask', chl_param],
filename=save_path,
writer='cf')
return
#-----save_map_data_in_nc_file---------------------------------------------
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|
def load_numbers_sorted(txt: str) -> List[int]:
"""ファイルから番号を読み込みソートしてリストを返す
Args:
txt (str): ファイルのパス
Returns:
List[int]: 番号のリスト
"""
numbers = []
with open(txt) as f:
numbers = sorted(map(lambda e: int(e), f))
return numbers
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|
def start_logger(log_directory_path):
"""To set up the log file folder and default configuration give a log directory path
Args:
log_directory_path (str): The directory path to create a folder containing the log files.
Returns:
logger (object): A logger object used for the MSOrganiser software.
"""
logdirectory = os.path.join(log_directory_path,"logfiles")
try:
os.makedirs(logdirectory, exist_ok=True)
except Exception as e:
print("Unable to create log directory in " + logdirectory + " due to this error message",flush=True)
print(e,flush=True)
sys.exit(-1)
logger = logging.getLogger("MSOrganiser")
logger.setLevel(logging.INFO)
# create file handler (fh)
logfilename = os.path.join(logdirectory , 'Test_Log')
fh = TimedRotatingFileHandler(logfilename,
when='midnight',
interval=1,
backupCount=2)
# create a logging format
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
# add the handlers to the logger
logger.addHandler(fh)
return logger
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|
def translate_pt(p, offset):
"""Translates point p=(x,y) by offset=(x,y)"""
return (p[0] + offset[0], p[1] + offset[1])
| 18,920
|
def add_flag(*args, **kwargs):
"""
define a single flag.
add_flag(flagname, default_value, help='', **kwargs)
add_flag([(flagname, default_value, help), ...])
or
define flags without help message
add_flag(flagname, default_value, help='', **kwargs)
add_flag('gpu', 1, help='CUDA_VISIBLE_DEVICES')
:param args:
:param kwargs:
:return:
"""
if len(args) == 1 and isinstance(args[0], (list, tuple)):
for a in args[0]:
flag.add_flag(*a)
elif args:
flag.add_flag(*args, **kwargs)
else:
for f, v in kwargs.items():
flag.add_flag(f, v)
| 18,921
|
def inference(hypes, images, train=True):
"""Build the MNIST model up to where it may be used for inference.
Args:
images: Images placeholder, from inputs().
train: whether the network is used for train of inference
Returns:
softmax_linear: Output tensor with the computed logits.
"""
vgg16_npy_path = os.path.join(hypes['dirs']['data_dir'], 'weights',
"vgg16.npy")
vgg_fcn = fcn8_vgg.FCN8VGG(vgg16_npy_path=vgg16_npy_path)
vgg_fcn.wd = hypes['wd']
vgg_fcn.build(images, train=train, num_classes=2, random_init_fc8=True)
logits = {}
logits['images'] = images
if hypes['arch']['fcn_in'] == 'pool5':
logits['fcn_in'] = vgg_fcn.pool5
elif hypes['arch']['fcn_in'] == 'fc7':
logits['fcn_in'] = vgg_fcn.fc7
else:
raise NotImplementedError
logits['feed2'] = vgg_fcn.pool4
logits['feed4'] = vgg_fcn.pool3
logits['fcn_logits'] = vgg_fcn.upscore32
logits['deep_feat'] = vgg_fcn.pool5
logits['early_feat'] = vgg_fcn.conv4_3
return logits
| 18,922
|
def test_address_list_one():
"""Test making an address list with one address."""
email_renderer = EmailRenderer(SAMPLE_TO_ADDRESS, "")
expected_address_1 = Address(username='smith', domain='example.com')
assert email_renderer.address_list() == (expected_address_1, )
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|
def truncate(sequence):
""" Do nothing. Just a placeholder. """
string = str(sequence)
return string.split()[0]
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|
def reindex_record(pid_type, record_class, record_indexer_class):
"""Reindex records of given pid type."""
query = (x[0] for x in PersistentIdentifier.query.filter_by(
object_type='rec', status=PIDStatus.REGISTERED
).filter(
PersistentIdentifier.pid_type.in_((pid_type,))
).values(
PersistentIdentifier.pid_value
))
for pid in query:
record_indexer_class.index(record_class.get_record_by_pid(pid))
| 18,925
|
def unbind(port: int) -> dict:
"""Request browser port unbinding.
Parameters
----------
port: int
Port number to unbind.
"""
return {"method": "Tethering.unbind", "params": {"port": port}}
| 18,926
|
def get_files(dir="."):
""" Gets all the files recursivly from a given base directory.
Args:
dir (str): The base directory path.
Returns:
list: A list that contains all files.
"""
folder_queue = [dir]
files = set()
while(folder_queue):
next_folder = folder_queue.pop(0)
with os.scandir(next_folder) as it:
for entry in it:
if entry.is_file():
files.add(entry)
else:
folder_queue.append(entry.path)
files = list(files)
return files
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|
def main():
""" run the tool"""
tool = ProcessorTool()
tool.run()
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|
def test_theano_function_bad_kwarg():
"""
Passing an unknown keyword argument to theano_function() should raise an
exception.
"""
raises(Exception, lambda: theano_function_([x], [x + 1], foobar=3))
| 18,929
|
def xy_to_ellipse(x,Vx,y,Vy):
"""
Takes the Cartesian variables.
This function returns the particle's position relative to an ellipse and parameters of the ellipse.
Returns a,e,theta,theta_E
"""
# radius using x and y
r = np.sqrt(x ** 2 + y ** 2)
# speed of the particle
V = np.sqrt(Vx ** 2 + Vy ** 2)
# angular momentum per mass
h = x * Vy - y * Vx
# energy per mass
u = (V ** 2) / 2. - 4. * (np.pi ** 2) / r
# semi-major axis
a = -2. * ((np.pi) ** 2) / u
# eccentricity of the elliptical orbit, added absolute value
e = np.sqrt(np.abs(1 - ((h / (2. * np.pi)) ** 2 )/ a))
# theta
theta = np.arctan2(y,x)
# theta_E, compute e*cos(theta - thetaE) first
buff = a * (1. - e ** 2) / r - 1.
# divide buff/e and output 0 if it is a circular orbit
buff_cos = np.divide(buff, e, out=np.zeros_like(buff), where=(e > np.power(10.,-5.)))
#to make sure that arccos takes values less than 1 and greater than -1
buff_cos[buff_cos < -1.] = -1.
buff_cos[buff_cos > 1.] = 1.
delta = np.arccos(buff_cos)
# change the sign if the radial velocity is negative
delta *= np.power(-1.,(x * Vx + y * Vy) < 0.)
thetaE = theta - delta
# set thetaE to 0 if it is a circular orbit
thetaE *= (e > np.power(10.,-5.))
# fix to add 2pi or subtract 2pi if thetaE isn't between -pi and pi
thetaE -= (thetaE > np.pi) * 2 * np.pi
thetaE += (thetaE < -np.pi) * 2 * np.pi
return a,e,theta,thetaE
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|
def leak_dictionary_by_ignore_sha(
policy_breaks: List[PolicyBreak],
) -> Dict[str, List[PolicyBreak]]:
"""
leak_dictionary_by_ignore_sha sorts matches and incidents by
first appearance in file.
sort incidents by first appearance on file,
file wide matches have no index
so give it -1 so they get bumped to the top
:return: Dictionary with line number as index and a list of
matches that start on said line.
"""
policy_breaks.sort(
key=lambda x: min( # type: ignore
(match.index_start if match.index_start else -1 for match in x.matches)
)
)
sha_dict: Dict[str, List[PolicyBreak]] = OrderedDict()
for policy_break in policy_breaks:
policy_break.matches.sort(key=lambda x: x.index_start if x.index_start else -1)
ignore_sha = get_ignore_sha(policy_break)
sha_dict.setdefault(ignore_sha, []).append(policy_break)
return sha_dict
| 18,931
|
async def get_token(tkn: Token = Depends(from_authotization_header_nondyn)):
"""
Returns informations about the token currently being used. Requires a
clearance level of 0 or more.
"""
assert_has_clearance(tkn.owner, "sni.read_own_token")
return GetTokenOut.from_record(tkn)
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|
def import_as_event_history(path):
"""
Import file as event history json format.
Parameters
----------
path : str
Absolute path to file.
Returns
-------
events : list
List of historic events.
"""
# initialise output list
events = []
# import through pandas dataframe
df = pd.read_csv(path)
# verify columns existance
if not 'temperature' in df.columns or not 'unix_time' in df.columns:
print_error('Imported file should have columns \'temperature\' and \'unix_time\'.')
# extract UTC timestamps
tx = pd.to_datetime(df['unix_time'], unit='s')
# iterate events
for i in range(len(df)):
# convert unixtime to DT format
timestamp = dt_timestamp_format(tx[i])
# create event json format
json = api_json_format(timestamp, df['temperature'].iloc[i])
# append output
events.append(json)
return events
| 18,933
|
async def test_info_update(event_loop, aresponses):
"""Test getting Fumis WiRCU device information and states."""
aresponses.add(
"api.fumis.si",
"/v1/status",
"GET",
aresponses.Response(
status=200,
headers={"Content-Type": "application/json"},
text=load_fixture("info.json"),
),
)
async with aiohttp.ClientSession(loop=event_loop) as session:
fumis = Fumis(
mac="AABBCCDDEEFF", password="1234", session=session, loop=event_loop,
)
info = await fumis.update_info()
assert info
assert info.unit_id == "AABBCCDDEEFF"
assert info.unit_version == "2.0.0"
assert info.controller_version == "1.7.0"
assert info.ip == "192.168.1.2"
assert info.rssi == -48
assert info.signal_strength == 100
assert info.state == "off"
assert info.state_id == 1
assert info.status == "off"
assert info.status_id == 0
assert info.temperature == 19.9
assert info.target_temperature == 21.8
assert info.heating_time == 1823340
assert info.igniter_starts == 392
assert info.misfires == 0
assert info.overheatings == 0
assert info.uptime == 58184580
| 18,934
|
def reduce_dataset(d: pd.DataFrame, reduction_pars: dict):
"""
Reduces the data contained in a pandas DataFrame
:param d: pandas DataFrame. Each column contains lists of numbers
:param reduction_pars: dict containing 'type' and 'values'. 'type' describes the type of reduction performed on the
lists in d.
:return:
"""
p = pd.DataFrame(index=d.index)
for k in d:
if reduction_pars['type'] == 'bins':
p[k] = list(reduce_matrix(np.vstack(d[k].values), reduction_pars['values']))
if reduction_pars['type'] == 'aggregated_selection':
if np.all(reduction_pars['values'] == np.arange(len(d[k][0]))):
p[k] = d[k]
else:
p[k] = list(aggregated_reduction(np.vstack(d[k].values), reduction_pars['values']))
if reduction_pars['type'] == 'min':
p[k] = np.min(np.vstack(d[k].values), axis=1)
if reduction_pars['type'] == 'max':
p[k] = np.max(np.vstack(d[k].values), axis=1)
if reduction_pars['type'] == 'mean':
p[k] = np.mean(np.vstack(d[k].values), axis=1)
return p
| 18,935
|
def update_office(office_id):
"""Given that i am an admin i should be able to edit a specific political office
When i visit to .../api/v2/offices endpoint using PATCH method"""
if is_admin() is not True:
return is_admin()
if not request.get_json():
return make_response(jsonify({'status': 401, 'message': 'empty body'}, 401))
office_data = request.get_json()
check_missingfields = validate.missing_value_validator(['name', 'type'], office_data)
if check_missingfields is not True:
return check_missingfields
check_emptyfield = validate.empty_string_validator(['name', 'type'], office_data)
if check_emptyfield is not True:
return check_emptyfield
check_if_text_only = validate.text_arrayvalidator(['name', 'type'], office_data)
if check_if_text_only is not True:
return check_if_text_only
office_name = office_data['name']
office_type = office_data['type']
res = office.edit_office(office_id, office_name, office_type)
return res
| 18,936
|
def load_project_resource(file_path: str):
"""
Tries to load a resource:
1. directly
2. from the egg zip file
3. from the egg directory
This is necessary, because the files are bundled with the project.
:return: the file as json
"""
...
if not os.path.isfile(file_path):
try:
egg_path = __file__.split(".egg")[0] + ".egg"
if os.path.isfile(egg_path):
print(f"Try to load instances from ZIP at {egg_path}")
with zipfile.ZipFile(egg_path) as z:
f = z.open(file_path)
data = json.load(f)
else:
print(f"Try to load instances from directory at {egg_path}")
with open(egg_path + '/' + file_path) as f:
data = json.load(f)
except Exception:
raise FileNotFoundError(f"Could not find '{file_path}'. "
"Make sure you run the script from the correct directory.")
else:
with open(file_path) as f:
data = json.load(f)
return data
| 18,937
|
def test_command_line_interface():
"""Test the CLI."""
runner = CliRunner()
result = runner.invoke(cli.main)
assert result.exit_code == 0
assert 'WIP' in result.output
help_result = runner.invoke(cli.main, ['--help'])
assert help_result.exit_code == 0
assert '--help Show this message and exit.' in help_result.output
| 18,938
|
def update_column(worksheet: gspread.Worksheet, column: str, data: t.List[str]):
"""
Update a column's data
:param worksheet: the worksheet to update
:param column: the column to update
:param data: the data to replace the current cells with
"""
# Format the range and data
r = f"{column}2:{column}{len(data)+1}"
formatted = list(map(lambda v: [v], data))
worksheet.update(r, formatted)
| 18,939
|
def start_command_handler(update, context):
"""Send a message when the command /start is issued."""
add_typing(update, context)
quiz_question = QuizQuestion
quiz_question.question = "What tastes better?"
quiz_question.answers = ['water', 'ice', 'wine']
quiz_question.correct_answer_position = 2
quiz_question.correct_answer = 'wine'
add_quiz_question(update, context, quiz_question)
| 18,940
|
def phraser_on_header(row, phraser):
"""Applies phraser on cleaned header.
To be used with methods such as: `apply(func, axis=1)` or
`apply_by_multiprocessing(func, axis=1, **kwargs)`.
Parameters
----------
row : row of pd.Dataframe
phraser : Phraser instance,
Returns
-------
pd.Series
Examples
--------
>>> import pandas as pd
>>> data = pd.read_pickle('./tutorial/data/emails_anonymized.pickle')
>>> from melusine.nlp_tools.phraser import phraser_on_header
>>> from melusine.nlp_tools.phraser import Phraser
>>> # data contains a 'clean_header' column
>>> phraser = Phraser(columns='clean_header').load(filepath)
>>> data.apply(phraser_on_header, axis=1) # apply to all samples
"""
clean_header = phraser_on_text(row["clean_header"], phraser)
return clean_header
| 18,941
|
def check_c_includes(filename, includes):
""" Check whether file exist in include dirs """
from os.path import exists, isfile, join
for directory in includes:
path = join(directory, filename)
if exists(path) and isfile(path):
return path
| 18,942
|
def _delete(url, params):
"""RESTful API delete (delete entries in database)
Parameters
----------
url: str
Address for the conftrak server
params: list
Query string. Any pymongo supported mongo query
Returns
-------
bool
True if delete successful
Raises
------
requests.exceptions.HTTPError
In case delete fails, appropriate HTTPError and message string returned
"""
url_with_params = "{}?{}".format(url, ujson.dumps(params))
r = requests.delete(url_with_params)
r.raise_for_status()
| 18,943
|
def teardown(ctx):
"""Remove the VMs for current cluster."""
with _with_config(ctx) as tmpdirname:
if click.confirm('Do you really want to tear down k93s cluster, set up '
'with config {!s}'.format(ctx.obj['config'])):
k93s.vms.teardown(tmpdirname, **ctx.obj)
| 18,944
|
def hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):
"""
Args:
box_scores (N, 5): boxes in corner-form and probabilities.
iou_threshold: intersection over union threshold.
top_k: keep top_k results. If k <= 0, keep all the results.
candidate_size: only consider the candidates with the highest scores.
Returns:
picked: a list of indexes of the kept boxes
"""
scores = box_scores[:, -1]
boxes = box_scores[:, :-1]
picked = []
# _, indexes = scores.sort(descending=True)
indexes = np.argsort(scores)
# indexes = indexes[:candidate_size]
indexes = indexes[-candidate_size:]
while len(indexes) > 0:
# current = indexes[0]
current = indexes[-1]
picked.append(current)
if 0 < top_k == len(picked) or len(indexes) == 1:
break
current_box = boxes[current, :]
# indexes = indexes[1:]
indexes = indexes[:-1]
rest_boxes = boxes[indexes, :]
iou = iou_of(
rest_boxes,
np.expand_dims(current_box, axis=0),
)
indexes = indexes[iou <= iou_threshold]
return box_scores[picked, :]
| 18,945
|
def sma(data, span=100):
"""Computes and returns the simple moving average.
Note: the moving average is computed on all columns.
:Input:
:data: pandas.DataFrame with stock prices in columns
:span: int (defaul: 100), number of days/values over which
the average is computed
:Output:
:sma: pandas.DataFrame of simple moving average
"""
return data.rolling(window=span, center=False).mean()
| 18,946
|
def table_one():
"""Table 1: Training results for different neuron numbers"""
simulation_num = 1
print('\n** Running simulation {} **\n'.format(simulation_num))
table_header = ['hidden_neurons', 'learning_rate', 'max_epochs',
'activation', 'hits', 'mse']
args = INIT_ARGS.copy()
args.update({'method': 'gdm',
'activation': 'purelin',
'max_epochs': 100,
'learning_rate': 0.001})
for i in (10, 30, 50):
args['hidden_neurons'] = i
run_simulation(args, sim_num=simulation_num, header=table_header)
| 18,947
|
def parse_csd(dependencies):
"""Parse C-State Dependency"""
return _CSD_factory(len(csd_data))(csd_data)
| 18,948
|
def field_path_get_type(root: HdlType, field_path: TypePath):
"""
Get a data type of element using field path
"""
t = root
for p in field_path:
if isinstance(p, int):
t = t.element_t
else:
assert isinstance(p, str), p
t = t.field_by_name[p].dtype
return t
| 18,949
|
def reverse(rule):
"""
Given a rule X, generate its black/white reversal.
"""
#
# https://www.conwaylife.com/wiki/Black/white_reversal
#
# "The black/white reversal of a pattern is the result of
# toggling the state of each cell in the universe: bringing
# dead cells to life, and killing live cells. The black/white
# reversal of a pattern is sometimes called an anti-pattern;
# for instance, the black/white reversal of a glider (in an
# appropriate rule) is referred to as an anti-glider. The
# black/white reversal of a rule is a transformation of a
# rule in such a way that the black/white reversal of any
# pattern (in the previous sense) will behave the same way
# under the new rule as the unreversed pattern did under the
# original rule."
#
# Note that some rules are their own reversals:
#
# https://www.conwaylife.com/wiki/OCA:Day_%26_Night
#
# See also:
#
# http://golly.sourceforge.net/Help/Algorithms/QuickLife.html#b0emulation
#
# a set of the allowed numbers of neighbours
neighbours = set("012345678")
# split rule at "/"
[born, survive] = rule.split("/")
# drop "B" and "S" and make sets
born = set(born[1:])
survive = set(survive[1:])
# invert neighbour counts using set difference
# - example: B0123478 --> B56, S01234678 --> S5
born_inverse = neighbours - born
survive_inverse = neighbours - survive
# use S(8-x) for the B counts and B(8-x) for the S counts
# - example: B56 --> S23, S5 --> B3
born_complement = map(complement, survive_inverse)
survive_complement = map(complement, born_inverse)
# sort and join
born_final = "B" + "".join(sorted(born_complement))
survive_final = "S" + "".join(sorted(survive_complement))
# new rule
reverse_rule = born_final + "/" + survive_final
return reverse_rule
| 18,950
|
def hsic(k_x: torch.Tensor, k_y: torch.Tensor, centered: bool = False, unbiased: bool = True) -> torch.Tensor:
"""Compute Hilbert-Schmidt Independence Criteron (HSIC)
:param k_x: n by n values of kernel applied to all pairs of x data
:param k_y: n by n values of kernel on y data
:param centered: whether or not at least one kernel is already centered
:param unbiased: if True, use unbiased HSIC estimator of Song et al (2007), else use original estimator of Gretton et al (2005)
:return: scalar score in [0*, inf) measuring dependence of x and y
* note that if unbiased=True, it is possible to get small values below 0.
"""
if k_x.size() != k_y.size():
raise ValueError("RDMs must have the same size!")
n = k_x.size()[0]
if not centered:
h = torch.eye(n, device=k_y.device, dtype=k_y.dtype) - 1/n
k_y = h @ k_y @ h
if unbiased:
# Remove the diagonal
k_x = k_x * (1 - torch.eye(n, device=k_x.device, dtype=k_x.dtype))
k_y = k_y * (1 - torch.eye(n, device=k_y.device, dtype=k_y.dtype))
# Equation (4) from Song et al (2007)
return ((k_x *k_y).sum() - 2*(k_x.sum(dim=0)*k_y.sum(dim=0)).sum()/(n-2) + k_x.sum()*k_y.sum()/((n-1)*(n-2))) / (n*(n-3))
else:
# The original estimator from Gretton et al (2005)
return torch.sum(k_x * k_y) / (n - 1)**2
| 18,951
|
def write_msa_to_db(conn, cur, protein_id, msa):
"""
Writes a MSA to the database.
Args:
cur: Cursor object.
protein_id: Protein id of the query.
msa: MSA.
Returns:
Nothing.
"""
cur.execute('INSERT INTO alignments (id, msa) VALUES (?, ?)', (protein_id, msa))
conn.commit()
| 18,952
|
def task_group_task_ui_to_app(ui_dict):
"""Converts TaskGroupTask ui dict to App entity."""
return workflow_entity_factory.TaskGroupTaskFactory().create_empty(
obj_id=ui_dict.get("obj_id"),
title=ui_dict["title"],
assignees=emails_to_app_people(ui_dict.get("assignees")),
start_date=str_to_date(ui_dict["start_date"]),
due_date=str_to_date(ui_dict["due_date"])
)
| 18,953
|
def int_converter(value):
"""check for *int* value."""
int(value)
return str(value)
| 18,954
|
def wraps(fun, namestr="{fun}", docstr="{doc}", **kwargs):
"""Decorator for a function wrapping another.
Used when wrapping a function to ensure its name and docstring get copied
over.
Args:
fun: function to be wrapped
namestr: Name string to use for wrapped function.
docstr: Docstring to use for wrapped function.
**kwargs: additional string format values.
Return:
Wrapped function.
"""
def _wraps(f):
try:
f.__name__ = namestr.format(fun=get_name(fun), **kwargs)
f.__doc__ = docstr.format(fun=get_name(fun), doc=get_doc(fun), **kwargs)
finally:
return f
return _wraps
| 18,955
|
def test_plot_distributed_loads_fixed_left():
"""Test the plotting function for distributed loads and fixed support on the left.
Additionally, test plotting of continuity points.
"""
a = beam(L)
a.add_support(0, "fixed")
a.add_distributed_load(0, L / 2, "-q * x")
a.add_distributed_load(L / 2, L, "q * (L - x)")
a.solve()
fig, ax = a.plot(subs={"q": 1000})
return fig
| 18,956
|
def post_report(coverage):
"""Post coverage report to coveralls.io."""
response = requests.post(URL, files={'json_file': json.dumps(coverage)})
try:
result = response.json()
except ValueError:
result = {'error': 'Failure to submit data. '
'Response [%(status)s]: %(text)s' % {
'status': response.status_code,
'text': response.text}}
print(result)
if 'error' in result:
return result['error']
return 0
| 18,957
|
def parse_dialogs_per_response(lines,candid_dic,profile_size=None):
"""Parse dialogs provided in the personalized dialog tasks format.
For each dialog, every line is parsed, and the data for the dialog is made by appending
profile, user and bot responses so far, user utterance, bot answer index within candidates dictionary.
If profile is updated during the conversation due to a recognition error,
context_profile is overwritten with the new profile.
"""
data = []
context = []
context_profile = []
u = None
r = None
for line in lines:
line=line.strip()
if line:
nid, line = line.split(' ', 1)
nid = int(nid)
if nid == 1 and '\t' not in line:
# Process profile attributes
# format: isCusKnown , cusID , cusName
# format with order info: isCusKnown , cusID , cusName , prefSize , prefDrink , prefExtra (extra can be empty)
# isCusKnown is True or False
# cusID is the ID of the customer: if customer is not known, ID is 0, else starts from 1
# if isCusKnown = False then the profile will only be: False , 0
# after the customer is registered it will be False , cusID , chosenSize , chosenDrink , chosenExtra
# cusName is the name of the customer: if customer is not know, it is empty string, else it is name surname of the customer
if profile_size:
attribs = line.split(' , ')
if len(attribs) < profile_size:
# extend the attributes to the profile size so batch stacking won't be a problem
attribs.extend(['|']*(profile_size-len(attribs))) # append | for empty profile attributes, because it doesn't appear in word_index
else:
attribs = line.split(' ')
for attrib in attribs:
r=tokenize(attrib)
if r[0] != "|": # if it is a profile attribute
# Add temporal encoding, and utterance/response encoding
r.append('$r')
r.append('#'+str(nid))
context_profile.append(r)
else:
# Process conversation turns
if '\t' in line:
# Process turn containing bot response
u, r = line.split('\t')
a = candid_dic[r]
u = tokenize(u)
r = tokenize(r)
data.append((context_profile[:],context[:],u[:],a))
u.append('$u')
u.append('#'+str(nid))
r.append('$r')
r.append('#'+str(nid))
context.append(u)
context.append(r)
elif "True" in line or "False" in line:
# Process updated profile attributes (format: isCusKnown cusID cusName) - same as customer profile attributes.
# These are the true values. If the initial profile attributes are correct, there wouldn't be any updated profile attributes
# Else, it would appear after the name was given by the customer
context_profile = []
if profile_size:
attribs = line.split(' , ')
if len(attribs) < profile_size:
attribs.extend(['|']*(profile_size-len(attribs)))
else:
attribs = line.split(' ')
for attrib in attribs:
r=tokenize(attrib)
# Add temporal encoding, and utterance/response encoding
if r[0] != "|": # if it is a profile attribute
# Add temporal encoding, and utterance/response encoding
r.append('$r')
r.append('#'+str(nid))
context_profile.append(r)
else:
# Process turn without bot response
r=tokenize(line)
r.append('$r')
r.append('#'+str(nid))
context.append(r)
else:
# Clear profile and context when it is a new dialog
context=[]
context_profile=[]
return data
| 18,958
|
def get_dist():
"""
Measures the distance of the obstacle from the rover.
Uses a time.sleep call to try to prevent issues with pin writing and
reading. (See official gopigo library)
Returns error strings in the cases of measurements of -1 and 0, as -1
indicates and error, and 0 seems to also indicate a failed reading.
:return: The distance of the obstacle. (cm)
:rtype: either[int, str]
"""
time.sleep(0.01)
dist = gopigo.us_dist(gopigo.USS)
if dist == -1:
return USS_ERROR
elif dist == 0 or dist == 1:
return NOTHING_FOUND
else:
return dist
| 18,959
|
def wave_fingers(allegro_client,
finger_indices=None,
num_seconds=10):
"""
Wave one or more fingers in a sinusoidal pattern.
:param allegro_client: The client.
:param finger_indices: List of finger indices (between 0 and 3)
:param num_seconds: Total time to spend doing this.
"""
hz = 4
r = rospy.Rate(hz)
# Choose which fingers to wave, by default do all of them.
if not finger_indices:
finger_indices = [0, 1, 2, 3]
# Later we will only change the desired position for the 'active' fingers,
# so this sets the default pose for *all* fingers. This is set to the value
# 1.0, except for the first joint of each finger (the finger rotation along
# its pointing axis) which is 0.0.
position = np.ones(16)
position[[0, 4, 8, 12]] = 0.0
for t in range(hz * num_seconds):
# Generate a sinusoidal signal between 0 and 1.5
val = (np.sin(0.2 * t) + 1) * 0.75
# Set all joints for the fingers we are controlling.
for finger_idx in finger_indices:
inds = range(4*finger_idx + 1, 4*finger_idx + 4)
position[inds] = val
# Command the joint position.
allegro_client.command_joint_position(position)
r.sleep()
pass
return
| 18,960
|
def map_filter(filter_function: Callable) -> Callable:
"""
returns a version of a function that automatically maps itself across all
elements of a collection
"""
def mapped_filter(arrays, *args, **kwargs):
return [filter_function(array, *args, **kwargs) for array in arrays]
return mapped_filter
| 18,961
|
def compare_models(
champion_model: lightgbm.Booster,
challenger_model: lightgbm.Booster,
valid_df: pd.DataFrame,
comparison_metric: Literal["any", "all", "f1_score", "auc"] = "any"
) -> bool:
"""
A function to compare the performance of the Champion and Challenger models
on the validation dataset comparison metrics
"""
comparison_metrics_directions = {"f1-score": ModelDirection.HIGHER_BETTER,
"auc": ModelDirection.HIGHER_BETTER,
"accuracy": ModelDirection.HIGHER_BETTER}
# Prep datasets
features = valid_df.drop(['target', 'id'], axis=1, errors="ignore")
labels = np.array(valid_df['target'])
valid_dataset = lightgbm.Dataset(data=features, label=labels)
# Calculate Champion and Challenger metrics for each
champion_metrics = get_model_metrics(champion_model, valid_dataset, "Champion")
challenger_metrics = get_model_metrics(challenger_model, valid_dataset, "Challenger")
if comparison_metric not in ['any', 'all']:
logger.info(f"Champion performance for {comparison_metric}: {champion_metrics[comparison_metric]}")
logger.info(f"Challenger performance for {comparison_metric}: {challenger_metrics[comparison_metric]}")
register_model = challenger_metric_better(champ_metrics=champion_metrics,
challenger_metrics=challenger_metrics,
metric_name=comparison_metric,
direction=comparison_metrics_directions[comparison_metric])
else:
comparison_results = {metric: challenger_metric_better(champ_metrics=champion_metrics,
challenger_metrics=challenger_metrics,
metric_name=metric,
direction=comparison_metrics_directions[metric])
for metric in champion_metrics.keys()}
if comparison_metric == "any":
register_model = any(comparison_results.values())
if register_model:
positive_results = [metric for metric, result in comparison_results.items() if result]
for metric in positive_results:
logger.info(f"Challenger Model performed better for '{metric}' on validation data")
else:
logger.info("Champion model performed better for all metrics on validation data")
else:
register_model = all(comparison_results.values())
if register_model:
logger.info("Challenger model performed better on all metrics on validation data")
else:
negative_ressults = [metric for metric, result in comparison_results.items() if not result]
for metric in negative_ressults:
logger.info(f"Champion Model performed better for '{metric}' on validation data")
return register_model
| 18,962
|
def check_additional_args(parsedArgs, op, continueWithWarning=False):
"""
Parse additional arguments (rotation, etc.) and validate
:param additionalArgs: user input list of additional parameters e.g. [rotation, 60...]
:param op: operation object (use software_loader.getOperation('operationname')
:return: dictionary containing parsed arguments e.g. {rotation: 60}
"""
# parse additional arguments (rotation, etc.)
# http://stackoverflow.com/questions/6900955/python-convert-list-to-dictionary
if op is None:
print 'Invalid Operation Name {}'.format(op)
return {}
missing = [param for param in op.mandatoryparameters.keys() if
(param not in parsedArgs or len(str(parsedArgs[param])) == 0) and
param != 'inputmaskname' and
('source' not in op.mandatoryparameters[param] or op.mandatoryparameters[param]['source'] == 'image')]
inputmasks = [param for param in op.optionalparameters.keys() if param == 'inputmaskname' and
'purpose' in parsedArgs and parsedArgs['purpose'] == 'clone']
if ('inputmaskname' in op.mandatoryparameters.keys() or 'inputmaskname' in inputmasks) and (
'inputmaskname' not in parsedArgs or parsedArgs['inputmaskname'] is None or len(parsedArgs['inputmaskname']) == 0):
missing.append('inputmaskname')
if missing:
for m in missing:
print 'Mandatory parameter ' + m + ' is missing'
if continueWithWarning is False:
sys.exit(0)
return parsedArgs
| 18,963
|
def clean_text(text, language):
"""
text: a string
returns: modified initial string (deletes/modifies punctuation and symbols.)
"""
replace_by_blank_symbols = re.compile('\#|\u00bb|\u00a0|\u00d7|\u00a3|\u00eb|\u00fb|\u00fb|\u00f4|\u00c7|\u00ab|\u00a0\ude4c|\udf99|\udfc1|\ude1b|\ude22|\u200b|\u2b07|\uddd0|\ude02|\ud83d|\u2026|\u201c|\udfe2|\u2018|\ude2a|\ud83c|\u2018|\u201d|\u201c|\udc69|\udc97|\ud83e|\udd18|\udffb|\ude2d|\udc80|\ud83e|\udd2a|\ud83e|\udd26|\u200d|\u2642|\ufe0f|\u25b7|\u25c1|\ud83e|\udd26|\udffd|\u200d|\u2642|\ufe0f|\udd21|\ude12|\ud83e|\udd14|\ude03|\ude03|\ude03|\ude1c|\udd81|\ude03|\ude10|\u2728|\udf7f|\ude48|\udc4d|\udffb|\udc47|\ude11|\udd26|\udffe|\u200d|\u2642|\ufe0f|\udd37|\ude44|\udffb|\u200d|\u2640|\udd23|\u2764|\ufe0f|\udc93|\udffc|\u2800|\u275b|\u275c|\udd37|\udffd|\u200d|\u2640|\ufe0f|\u2764|\ude48|\u2728|\ude05|\udc40|\udf8a|\u203c|\u266a|\u203c|\u2744|\u2665|\u23f0|\udea2|\u26a1|\u2022|\u25e1|\uff3f|\u2665|\u270b|\u270a|\udca6|\u203c|\u270c|\u270b|\u270a|\ude14|\u263a|\udf08|\u2753|\udd28|\u20ac|\u266b|\ude35|\ude1a|\u2622|\u263a|\ude09|\udd20|\udd15|\ude08|\udd2c|\ude21|\ude2b|\ude18|\udd25|\udc83|\ude24|\udc3e|\udd95|\udc96|\ude0f|\udc46|\udc4a|\udc7b|\udca8|\udec5|\udca8|\udd94|\ude08|\udca3|\ude2b|\ude24|\ude23|\ude16|\udd8d|\ude06|\ude09|\udd2b|\ude00|\udd95|\ude0d|\udc9e|\udca9|\udf33|\udc0b|\ude21|\udde3|\ude37|\udd2c|\ude21|\ude09|\ude39|\ude42|\ude41|\udc96|\udd24|\udf4f|\ude2b|\ude4a|\udf69|\udd2e|\ude09|\ude01|\udcf7|\ude2f|\ude21|\ude28|\ude43|\udc4a|\uddfa|\uddf2|\udc4a|\ude95|\ude0d|\udf39|\udded|\uddf7|\udded|\udd2c|\udd4a|\udc48|\udc42|\udc41|\udc43|\udc4c|\udd11|\ude0f|\ude29|\ude15|\ude18|\ude01|\udd2d|\ude43|\udd1d|\ude2e|\ude29|\ude00|\ude1f|\udd71|\uddf8|\ude20|\udc4a|\udeab|\udd19|\ude29|\udd42|\udc4a|\udc96|\ude08|\ude0d|\udc43|\udff3|\udc13|\ude0f|\udc4f|\udff9|\udd1d|\udc4a|\udc95|\udcaf|\udd12|\udd95|\udd38|\ude01|\ude2c|\udc49|\ude01|\udf89|\udc36|\ude0f|\udfff|\udd29|\udc4f|\ude0a|\ude1e|\udd2d|\uff46|\uff41|\uff54|\uff45|\uffe3|\u300a|\u300b|\u2708|\u2044|\u25d5|\u273f|\udc8b|\udc8d|\udc51|\udd8b|\udd54|\udc81|\udd80|\uded1|\udd27|\udc4b|\udc8b|\udc51|\udd90|\ude0e')
replace_by_apostrophe_symbol = re.compile('\u2019')
replace_by_dash_symbol = re.compile('\u2014')
replace_by_u_symbols = re.compile('\u00fb|\u00f9')
replace_by_a_symbols = re.compile('\u00e2|\u00e0')
replace_by_c_symbols = re.compile('\u00e7')
replace_by_i_symbols = re.compile('\u00ee|\u00ef')
replace_by_o_symbols = re.compile('\u00f4')
replace_by_oe_symbols = re.compile('\u0153')
replace_by_e_symbols = re.compile('\u00e9|\u00ea|\u0117|\u00e8')
replace_by_blank_symbols_2 = re.compile('\/|\(|\)|\{|\}|\[|\]|\,|\;|\.|\!|\?|\:|&|\n')
text = replace_by_e_symbols.sub('e', text)
text = replace_by_a_symbols.sub('a', text)
text = replace_by_o_symbols.sub('o', text)
text = replace_by_oe_symbols.sub('oe', text)
text = replace_by_u_symbols.sub('e', text)
text = replace_by_i_symbols.sub('e', text)
text = replace_by_u_symbols.sub('e', text)
text = replace_by_apostrophe_symbol.sub("'", text)
text = replace_by_dash_symbol.sub("_", text)
text = replace_by_blank_symbols.sub('', text)
text = replace_by_blank_symbols_2.sub('', text)
#For English
#text = ''.join([c for c in text if ord(c) < 128])
text = text.replace("\\", "")
STOPWORDS = set(stopwords.words(language))#to be changed
text = text.lower() # lowercase text
text = ' '.join(word for word in text.split() if word not in STOPWORDS) # delete stopwors from text
return text
| 18,964
|
def parameters():
"""
Dictionary of parameters defining geophysical acquisition systems
"""
return {
"AeroTEM (2007)": {
"type": "time",
"flag": "Zoff",
"channel_start_index": 1,
"channels": {
"[1]": 58.1e-6,
"[2]": 85.9e-6,
"[3]": 113.7e-6,
"[4]": 141.4e-6,
"[5]": 169.2e-6,
"[6]": 197.0e-6,
"[7]": 238.7e-6,
"[8]": 294.2e-6,
"[9]": 349.8e-6,
"[10]": 405.3e-6,
"[11]": 474.8e-6,
"[12]": 558.1e-6,
"[13]": 655.3e-6,
"[14]": 794.2e-6,
"[15]": 988.7e-6,
"[16]": 1280.3e-6,
"[17]": 1738.7e-6,
},
"uncertainty": [
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
],
"waveform": [
[-1.10e-03, 1e-8],
[-8.2500e-04, 5.0e-01],
[-5.50e-04, 1.0e00],
[-2.7500e-04, 5.0e-01],
[0.0e00, 0.0e00],
[2.50e-05, 0.0e00],
[5.0e-05, 0.0e00],
[7.50e-05, 0.0e00],
[1.0e-04, 0.0e00],
[1.2500e-04, 0.0e00],
[1.50e-04, 0.0e00],
[1.7500e-04, 0.0e00],
[2.0e-04, 0.0e00],
[2.2500e-04, 0.0e00],
[2.50e-04, 0.0e00],
[3.0550e-04, 0.0e00],
[3.6100e-04, 0.0e00],
[4.1650e-04, 0.0e00],
[4.7200e-04, 0.0e00],
[5.2750e-04, 0.0e00],
[6.0750e-04, 0.0e00],
[6.8750e-04, 0.0e00],
[7.6750e-04, 0.0e00],
[8.4750e-04, 0.0e00],
[9.2750e-04, 0.0e00],
[1.1275e-03, 0.0e00],
[1.3275e-03, 0.0e00],
[1.5275e-03, 0.0e00],
[1.7275e-03, 0.0e00],
[1.9275e-03, 0.0e00],
[2.1275e-03, 0.0e00],
],
"tx_offsets": [[0, 0, 0]],
"bird_offset": [0, 0, -40],
"comment": "normalization accounts for 2.5m radius loop * 8 turns * 69 A current, nanoTesla",
"normalization": [2.9e-4, 1e-9],
"tx_specs": {"type": "CircularLoop", "a": 1.0, "I": 1.0},
"data_type": "dBzdt",
},
"AeroTEM (2010)": {
"type": "time",
"flag": "Zoff",
"channel_start_index": 1,
"channels": {
"[1]": 67.8e-6,
"[2]": 95.6e-6,
"[3]": 123.4e-6,
"[4]": 151.2e-6,
"[5]": 178.9e-6,
"[6]": 206.7e-6,
"[7]": 262.3e-6,
"[8]": 345.6e-6,
"[9]": 428.9e-6,
"[10]": 512.3e-6,
"[11]": 623.4e-6,
"[12]": 762.3e-6,
"[13]": 928.9e-6,
"[14]": 1165.0e-6,
"[15]": 1526.2e-6,
"[16]": 2081.7e-6,
"[17]": 2942.8e-6,
},
"uncertainty": [
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
],
"waveform": [
[-1.10e-03, 1e-8],
[-8.2500e-04, 5.0e-01],
[-5.50e-04, 1.0e00],
[-2.7500e-04, 5.0e-01],
[0.0e00, 0.0e00],
[2.50e-05, 0.0e00],
[5.0e-05, 0.0e00],
[7.50e-05, 0.0e00],
[1.0e-04, 0.0e00],
[1.2500e-04, 0.0e00],
[1.50e-04, 0.0e00],
[1.7500e-04, 0.0e00],
[2.0e-04, 0.0e00],
[2.2500e-04, 0.0e00],
[2.50e-04, 0.0e00],
[3.0550e-04, 0.0e00],
[3.6100e-04, 0.0e00],
[4.1650e-04, 0.0e00],
[4.7200e-04, 0.0e00],
[5.2750e-04, 0.0e00],
[6.0750e-04, 0.0e00],
[6.8750e-04, 0.0e00],
[7.6750e-04, 0.0e00],
[8.4750e-04, 0.0e00],
[9.2750e-04, 0.0e00],
[1.1275e-03, 0.0e00],
[1.3275e-03, 0.0e00],
[1.5275e-03, 0.0e00],
[1.7275e-03, 0.0e00],
[1.9275e-03, 0.0e00],
[2.1275e-03, 0.0e00],
[2.3275e-03, 0.0e00],
[2.5275e-03, 0.0e00],
[2.7275e-03, 0.0e00],
[2.9275e-03, 0.0e00],
[3.1275e-03, 0.0e00],
],
"tx_offsets": [[0, 0, 0]],
"bird_offset": [0, 0, -40],
"comment": "normalization accounts for 2.5m radius loop, 8 turns * 69 A current, nanoTesla",
"normalization": [2.9e-4, 1e-9],
"tx_specs": {"type": "CircularLoop", "a": 1.0, "I": 1.0},
"data_type": "dBzdt",
},
"DIGHEM": {
"type": "frequency",
"flag": "CPI900",
"channel_start_index": 0,
"channels": {
"CPI900": 900,
"CPI7200": 7200,
"CPI56K": 56000,
"CPQ900": 900,
"CPQ7200": 7200,
"CPQ56K": 56000,
},
"components": {
"CPI900": "real",
"CPQ900": "imag",
"CPI7200": "real",
"CPQ7200": "imag",
"CPI56K": "real",
"CPQ56K": "imag",
},
"tx_offsets": [
[8, 0, 0],
[8, 0, 0],
[6.3, 0, 0],
[8, 0, 0],
[8, 0, 0],
[6.3, 0, 0],
],
"bird_offset": [0, 0, 0],
"uncertainty": [
[0.0, 2],
[0.0, 5],
[0.0, 10],
[0.0, 2],
[0.0, 5],
[0.0, 10],
],
"tx_specs": {"type": "VMD", "a": 1.0, "I": 1.0},
"normalization": "ppm",
},
"GENESIS (2014)": {
"type": "time",
"flag": "emz_step_final",
"channel_start_index": 1,
"channels": {
"0": 9e-6,
"1": 26e-6,
"2": 52.0e-6,
"3": 95e-6,
"4": 156e-6,
"5": 243e-6,
"6": 365e-6,
"7": 547e-6,
"8": 833e-6,
"9": 1259e-6,
"10": 1858e-6,
},
"uncertainty": [
[0.05, 100],
[0.05, 100],
[0.05, 100],
[0.05, 100],
[0.05, 100],
[0.05, 100],
[0.05, 2000],
[0.05, 100],
[0.05, 100],
[0.05, 100],
[0.05, 100],
],
"waveform": "stepoff",
"tx_offsets": [[-90, 0, -43]],
"bird_offset": [-90, 0, -43],
"comment": "normalization accounts for unit dipole moment at the tx_offset, in part-per-million",
"normalization": "ppm",
"tx_specs": {"type": "VMD", "a": 1.0, "I": 1.0},
"data_type": "Bz",
},
"GEOTEM 75 Hz - 2082 Pulse": {
"type": "time",
"flag": "EM_chan",
"channel_start_index": 5,
"channels": {
"1": -1953e-6,
"2": -1562e-6,
"3": -989e-6,
"4": -416e-6,
"5": 163e-6,
"6": 235e-6,
"7": 365e-6,
"8": 521e-6,
"9": 703e-6,
"10": 912e-6,
"11": 1146e-6,
"12": 1407e-6,
"13": 1693e-6,
"14": 2005e-6,
"15": 2344e-6,
"16": 2709e-6,
"17": 3073e-6,
"18": 3464e-6,
"19": 3880e-6,
"20": 4297e-6,
},
"uncertainty": [
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
],
"waveform": [
[-2.08000000e-03, 1.22464680e-16],
[-1.83000000e-03, 3.68686212e-01],
[-1.58000000e-03, 6.85427422e-01],
[-1.33000000e-03, 9.05597273e-01],
[-1.08000000e-03, 9.98175554e-01],
[-8.30000000e-04, 9.50118712e-01],
[-5.80000000e-04, 7.68197578e-01],
[-3.30000000e-04, 4.78043417e-01],
[-8.00000000e-05, 1.20536680e-01],
[0.00000000e00, 0.00000000e00],
[1.00000000e-04, 0.00000000e00],
[2.00000000e-04, 0.00000000e00],
[3.00000000e-04, 0.00000000e00],
[4.00000000e-04, 0.00000000e00],
[5.00000000e-04, 0.00000000e00],
[6.00000000e-04, 0.00000000e00],
[7.00000000e-04, 0.00000000e00],
[8.00000000e-04, 0.00000000e00],
[9.00000000e-04, 0.00000000e00],
[1.00000000e-03, 0.00000000e00],
[1.10000000e-03, 0.00000000e00],
[1.20000000e-03, 0.00000000e00],
[1.30000000e-03, 0.00000000e00],
[1.40000000e-03, 0.00000000e00],
[1.50000000e-03, 0.00000000e00],
[1.60000000e-03, 0.00000000e00],
[1.70000000e-03, 0.00000000e00],
[1.80000000e-03, 0.00000000e00],
[1.90000000e-03, 0.00000000e00],
[2.00000000e-03, 0.00000000e00],
[2.10000000e-03, 0.00000000e00],
[2.20000000e-03, 0.00000000e00],
[2.30000000e-03, 0.00000000e00],
[2.40000000e-03, 0.00000000e00],
[2.50000000e-03, 0.00000000e00],
[2.60000000e-03, 0.00000000e00],
[2.70000000e-03, 0.00000000e00],
[2.80000000e-03, 0.00000000e00],
[2.90000000e-03, 0.00000000e00],
[3.00000000e-03, 0.00000000e00],
[3.10000000e-03, 0.00000000e00],
[3.20000000e-03, 0.00000000e00],
[3.30000000e-03, 0.00000000e00],
[3.40000000e-03, 0.00000000e00],
[3.50000000e-03, 0.00000000e00],
[3.60000000e-03, 0.00000000e00],
[3.70000000e-03, 0.00000000e00],
[3.80000000e-03, 0.00000000e00],
[3.90000000e-03, 0.00000000e00],
[4.00000000e-03, 0.00000000e00],
[4.10000000e-03, 0.00000000e00],
[4.20000000e-03, 0.00000000e00],
[4.30000000e-03, 0.00000000e00],
[4.40000000e-03, 0.00000000e00],
],
"tx_offsets": [[-123, 0, -56]],
"bird_offset": [-123, 0, -56],
"comment": "normalization accounts for unit dipole moment at the tx_offset, in part-per-million",
"normalization": "ppm",
"tx_specs": {"type": "VMD", "a": 1.0, "I": 1.0},
"data_type": "Bz",
},
"HELITEM (35C)": {
"type": "time",
"flag": "emz_db",
"channel_start_index": 5,
"channels": {
"[1]": -0.007772,
"[2]": -0.003654,
"[3]": -0.002678,
"[4]": -0.000122,
"[5]": 0.000228,
"[6]": 0.000269,
"[7]": 0.000326,
"[8]": 0.000399,
"[9]": 0.000488,
"[10]": 0.000594,
"[11]": 0.000716,
"[12]": 0.000854,
"[13]": 0.001009,
"[14]": 0.001196,
"[15]": 0.001424,
"[16]": 0.001693,
"[17]": 0.002018,
"[18]": 0.002417,
"[19]": 0.002905,
"[20]": 0.003499,
"[21]": 0.004215,
"[22]": 0.005078,
"[23]": 0.006128,
"[24]": 0.007406,
"[25]": 0.008952,
"[26]": 0.010824,
"[27]": 0.013094,
"[28]": 0.015845,
"[29]": 0.019173,
"[30]": 0.02321,
},
"uncertainty": [
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
],
"waveform": [
[-8.000e-03, 1.000e-03],
[-7.750e-03, 9.802e-02],
[-7.500e-03, 1.950e-01],
[-7.250e-03, 2.902e-01],
[-7.000e-03, 3.826e-01],
[-6.750e-03, 4.713e-01],
[-6.500e-03, 5.555e-01],
[-6.250e-03, 6.344e-01],
[-6.000e-03, 7.071e-01],
[-5.750e-03, 7.730e-01],
[-5.500e-03, 8.315e-01],
[-5.250e-03, 8.820e-01],
[-5.000e-03, 9.239e-01],
[-4.750e-03, 9.569e-01],
[-4.500e-03, 9.808e-01],
[-4.250e-03, 9.951e-01],
[-4.000e-03, 1.000e00],
[-3.750e-03, 9.951e-01],
[-3.500e-03, 9.808e-01],
[-3.250e-03, 9.569e-01],
[-3.000e-03, 9.239e-01],
[-2.750e-03, 8.820e-01],
[-2.500e-03, 8.315e-01],
[-2.250e-03, 7.730e-01],
[-2.000e-03, 7.071e-01],
[-1.750e-03, 6.344e-01],
[-1.500e-03, 5.555e-01],
[-1.250e-03, 4.713e-01],
[-1.000e-03, 3.826e-01],
[-7.500e-04, 2.902e-01],
[-5.000e-04, 1.950e-01],
[-2.500e-04, 9.802e-02],
[0.000e00, 0.000e00],
[5.000e-05, 0.000e00],
[1.000e-04, 0.000e00],
[1.500e-04, 0.000e00],
[2.000e-04, 0.000e00],
[2.500e-04, 0.000e00],
[3.000e-04, 0.000e00],
[3.500e-04, 0.000e00],
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"[10]": 6.40000e-02,
"[11]": 7.04000e-02,
"[12]": 7.68000e-02,
"[13]": 8.32000e-02,
"[14]": 9.28000e-02,
"[15]": 1.05600e-01,
"[16]": 1.18400e-01,
"[17]": 1.34400e-01,
"[18]": 1.53600e-01,
"[19]": 1.76000e-01,
"[20]": 2.01600e-01,
"[21]": 2.30400e-01,
"[22]": 2.62400e-01,
"[23]": 2.97600e-01,
"[24]": 3.39200e-01,
"[25]": 3.87200e-01,
"[26]": 4.41600e-01,
"[27]": 5.05600e-01,
"[28]": 5.82400e-01,
"[29]": 6.72000e-01,
"[30]": 7.74400e-01,
"[31]": 8.89600e-01,
"[32]": 1.02080e00,
"[33]": 1.17120e00,
"[34]": 1.34400e00,
"[35]": 1.54560e00,
"[36]": 1.77920e00,
"[37]": 2.04480e00,
"[38]": 2.34560e00,
"[39]": 2.69120e00,
"[40]": 3.08800e00,
"[41]": 3.54240e00,
"[42]": 4.06720e00,
"[43]": 4.67200e00,
"[44]": 5.36640e00,
"[45]": 6.16320e00,
"[46]": 7.07840e00,
"[47]": 8.13120e00,
"[48]": 9.33760e00,
"[49]": 1.07232e01,
"[50]": 1.24896e01,
},
"uncertainty": [
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
],
"waveform": [
[-5.50e00, 1.00000000e-08],
[-5.20e00, 5.00000000e-01],
[-4.90e00, 9.40112151e-01],
[-4.60e00, 9.40178855e-01],
[-4.30e00, 9.40245558e-01],
[-4.00e00, 9.40312262e-01],
[-3.70e00, 9.40378966e-01],
[-3.40e00, 9.40445670e-01],
[-3.10e00, 9.40512373e-01],
[-2.80e00, 9.40579077e-01],
[-2.50e00, 9.40645781e-01],
[-2.20e00, 9.40712484e-01],
[-1.90e00, 9.40779188e-01],
[-1.60e00, 9.40845892e-01],
[-1.30e00, 9.40912595e-01],
[-1.00e00, 9.40979299e-01],
[-7.00e-01, 9.41046003e-01],
[-4.00e-01, 9.41112706e-01],
[-3.50e-01, 9.41123824e-01],
[-3.00e-01, 8.98234294e-01],
[-2.50e-01, 7.89179185e-01],
[-2.00e-01, 6.47911463e-01],
[-1.50e-01, 4.80782576e-01],
[-1.00e-01, 2.95120400e-01],
[-5.00e-02, 9.92697471e-02],
[0.00e-00, 3.68628739e-18],
[5.00e-03, 0.00000000e00],
[1.00e-02, 0.00000000e00],
[1.50e-02, 0.00000000e00],
[2.00e-02, 0.00000000e00],
[2.50e-02, 0.00000000e00],
[3.00e-02, 0.00000000e00],
[3.50e-02, 0.00000000e00],
[4.00e-02, 0.00000000e00],
[4.50e-02, 0.00000000e00],
[5.00e-02, 0.00000000e00],
[5.50e-02, 0.00000000e00],
[6.00e-02, 0.00000000e00],
[6.50e-02, 0.00000000e00],
[7.00e-02, 0.00000000e00],
[7.50e-02, 0.00000000e00],
[8.00e-02, 0.00000000e00],
[8.50e-02, 0.00000000e00],
[9.00e-02, 0.00000000e00],
[9.50e-02, 0.00000000e00],
[1.00e-01, 0.00000000e00],
[1.10e-01, 0.00000000e00],
[1.20e-01, 0.00000000e00],
[1.30e-01, 0.00000000e00],
[1.40e-01, 0.00000000e00],
[1.50e-01, 0.00000000e00],
[1.60e-01, 0.00000000e00],
[1.70e-01, 0.00000000e00],
[1.80e-01, 0.00000000e00],
[1.90e-01, 0.00000000e00],
[2.00e-01, 0.00000000e00],
[2.10e-01, 0.00000000e00],
[2.20e-01, 0.00000000e00],
[2.30e-01, 0.00000000e00],
[2.40e-01, 0.00000000e00],
[2.50e-01, 0.00000000e00],
[2.60e-01, 0.00000000e00],
[2.70e-01, 0.00000000e00],
[2.80e-01, 0.00000000e00],
[2.90e-01, 0.00000000e00],
[3.00e-01, 0.00000000e00],
[3.25e-01, 0.00000000e00],
[3.50e-01, 0.00000000e00],
[3.75e-01, 0.00000000e00],
[4.00e-01, 0.00000000e00],
[4.25e-01, 0.00000000e00],
[4.50e-01, 0.00000000e00],
[4.75e-01, 0.00000000e00],
[5.00e-01, 0.00000000e00],
[5.25e-01, 0.00000000e00],
[5.50e-01, 0.00000000e00],
[5.75e-01, 0.00000000e00],
[6.00e-01, 0.00000000e00],
[7.50e-01, 0.00000000e00],
[9.00e-01, 0.00000000e00],
[1.05e00, 0.00000000e00],
[1.20e00, 0.00000000e00],
[1.35e00, 0.00000000e00],
[1.50e00, 0.00000000e00],
[1.65e00, 0.00000000e00],
[1.80e00, 0.00000000e00],
[1.95e00, 0.00000000e00],
[2.10e00, 0.00000000e00],
[2.25e00, 0.00000000e00],
[2.40e00, 0.00000000e00],
[2.55e00, 0.00000000e00],
[2.70e00, 0.00000000e00],
[2.85e00, 0.00000000e00],
[3.00e00, 0.00000000e00],
[3.15e00, 0.00000000e00],
[3.30e00, 0.00000000e00],
[3.45e00, 0.00000000e00],
[3.60e00, 0.00000000e00],
[3.75e00, 0.00000000e00],
[3.90e00, 0.00000000e00],
[4.00e00, 0.00000000e00],
[4.50e00, 0.00000000e00],
[5.00e00, 0.00000000e00],
[5.50e00, 0.00000000e00],
[6.00e00, 0.00000000e00],
[6.50e00, 0.00000000e00],
[7.00e00, 0.00000000e00],
[7.50e00, 0.00000000e00],
[8.00e00, 0.00000000e00],
[8.50e00, 0.00000000e00],
[9.00e00, 0.00000000e00],
[9.50e00, 0.00000000e00],
[1.00e01, 0.00000000e00],
[1.05e01, 0.00000000e00],
[1.10e01, 0.00000000e00],
[1.15e01, 0.00000000e00],
[1.20e01, 0.00000000e00],
[1.25e01, 0.00000000e00],
],
"tx_offsets": [[0, 0, 0]],
"bird_offset": [0, 0, 0],
"normalization": [3.1416, 1e-12],
"tx_specs": {"type": "CircularLoop", "a": 1.0, "I": 1.0},
"data_type": "dBzdt",
},
}
| 18,965
|
def generate_ngrams_and_hashit(tokens, n=3):
"""The function generates and hashes ngrams
which gets from the tokens sequence.
@param tokens - list of tokens
@param n - count of elements in sequences
"""
return [binascii.crc32(bytearray(tokens[i:i + n]))
for i in range(len(tokens) - n + 1)]
| 18,966
|
def generate_fingerprints(args: Namespace, logger: Logger = None) -> List[List[float]]:
"""
Generate the fingerprints.
:param logger:
:param args: Arguments.
:return: A list of lists of target fingerprints.
"""
# import pdb; pdb.set_trace()
checkpoint_path = args.checkpoint_paths[0]
if logger is None:
logger = create_logger('fingerprints', quiet=False)
print('Loading data')
test_data = get_data(path=args.data_path,
args=args,
use_compound_names=False,
max_data_size=float("inf"),
skip_invalid_smiles=False)
test_data = MoleculeDataset(test_data)
####
test_df = test_data.get_smiles_df()
####
logger.info(f'Total size = {len(test_data):,}')
logger.info(f'Generating...')
# Load model
# import pdb; pdb.set_trace()
model = load_checkpoint(checkpoint_path, cuda=args.cuda, current_args=args, logger=logger)
model_preds = do_generate(
model=model,
data=test_data,
args=args
)
####
test_df["fps"] = model_preds
####
return test_df
| 18,967
|
def _typecheck_input_batch_dot(x1_type, x2_type, prim_name=None):
"""
Check input tensor types to be valid and confirm they are the same type for batch dot ops.
"""
msg_prefix = f"For '{prim_name}', the" if prim_name else "The"
const_utils.check_type_valid(x1_type, [mstype.float32], 'x1')
const_utils.check_type_valid(x2_type, [mstype.float32], 'x2')
if x1_type != x2_type:
raise TypeError(f"{msg_prefix} inputs must be the same type, but got x1_type: {x1_type} and "
f"x2_type: {x2_type}.")
| 18,968
|
def transform_xlogx(mat):
"""
Args:
mat(np.array): A two-dimensional array
Returns:
np.array: Let UsV^† be the SVD of mat. Returns
Uf(s)V^†, where f(x) = -2xlogx
"""
U, s, Vd = np.linalg.svd(mat, full_matrices=False)
return (U * (-2.0*s * np.log(s))) @ Vd
| 18,969
|
def get_label_volumes(labelVolume, RefVolume, labelDictionary):
"""
Get label volumes using
1. reference volume and
2. labeldictionary
:param labelVolume:
:param RefVolume:
:param labelDictionary:
:return:
"""
import SimpleITK as sitk
import os
from collections import (
OrderedDict,
) # Need OrderedDict internally to ensure consistent ordering
labelImg = sitk.ReadImage(labelVolume, sitk.sitkInt64)
RefImg = sitk.ReadImage(RefVolume, sitk.sitkFloat64)
labelStatFilter = sitk.LabelStatisticsImageFilter()
labelStatFilter.Execute(RefImg, labelImg)
ImageSpacing = RefImg.GetSpacing()
outputLabelVolumes = list()
for value in labelStatFilter.GetLabels():
structVolume = (
ImageSpacing[0]
* ImageSpacing[1]
* ImageSpacing[2]
* labelStatFilter.GetCount(value)
)
labelVolDict = OrderedDict()
labelVolDict["Volume_mm3"] = structVolume
if value in list(labelDictionary.keys()):
print(("{0} --> {1}".format(value, labelDictionary[value])))
labelVolDict["LabelName"] = labelDictionary[value]
else:
print(("** Caution: {0} --> No name exists!".format(value)))
labelVolDict["LabelName"] = "NA"
labelVolDict["LabelCode"] = value
labelVolDict["FileName"] = os.path.abspath(labelVolume)
outputLabelVolumes.append(labelVolDict)
return outputLabelVolumes
| 18,970
|
def put_dummy_data(data_path):
"""Create a small dummy netCDF file to be cmorized."""
gen_cube = _create_sample_cube()
t_path = os.path.join(data_path, "woa13_decav81B0_t00_01.nc")
# correct var names
gen_cube.var_name = "t_an"
iris.save(gen_cube, t_path)
s_path = os.path.join(data_path, "woa13_decav81B0_s00_01.nc")
gen_cube.var_name = "s_an"
iris.save(gen_cube, s_path)
# incorrect var names
o2_path = os.path.join(data_path, "woa13_all_o00_01.nc")
gen_cube.var_name = "o2"
iris.save(gen_cube, o2_path)
no3_path = os.path.join(data_path, "woa13_all_n00_01.nc")
gen_cube.var_name = "no3"
iris.save(gen_cube, no3_path)
po4_path = os.path.join(data_path, "woa13_all_p00_01.nc")
gen_cube.var_name = "po4"
iris.save(gen_cube, po4_path)
si_path = os.path.join(data_path, "woa13_all_i00_01.nc")
gen_cube.var_name = "si"
iris.save(gen_cube, si_path)
| 18,971
|
def get_clip_name_from_unix_time(source_guid, current_clip_start_time):
"""
"""
# convert unix time to
readable_datetime = datetime.fromtimestamp(int(current_clip_start_time)).strftime('%Y_%m_%d_%H_%M_%S')
clipname = source_guid + "_" + readable_datetime
return clipname, readable_datetime
| 18,972
|
def extract_pc_in_box3d(pc, box3d):
"""Extract point cloud in box3d.
Args:
pc (np.ndarray): [N, 3] Point cloud.
box3d (np.ndarray): [8,3] 3d box.
Returns:
np.ndarray: Selected point cloud.
np.ndarray: Indices of selected point cloud.
"""
box3d_roi_inds = in_hull(pc[:, 0:3], box3d)
return pc[box3d_roi_inds, :], box3d_roi_inds
| 18,973
|
def get_list_as_str(list_to_convert: typing.List[str]) -> str:
"""Convert list into comma separated string, with each element enclosed in single quotes"""
return ", ".join(["'{}'".format(list_item) for list_item in list_to_convert])
| 18,974
|
def normalize_sides(sides):
"""
Description: Squares the sides of the rectangles and averages the points
so that they fit together
Input:
- sides - Six vertex sets representing the sides of a drawing
Returns:
- norm_sides - Squared and fit sides list
"""
sides_list = []
# Average side vertices and make perfect rectangles
def square_sides(sides):
# Find the min/max x and y values
x = []
y = []
for vert in sides:
x.append(vert[0][0])
y.append(vert[0][1])
minx = 0
miny = 0
maxx = max(x)-min(x)
maxy = max(y)-min(y)
# Construct new squared vertex set with format |1 2|
# |3 4|
squared_side = [[minx,miny],[maxx,miny],[maxx,maxy],[minx,maxy]]
#squared_side = [[minx, maxy], [maxx, maxy], [minx, miny], [maxx, miny]]
return squared_side
squared_right = square_sides(sides[0])
squared_left = square_sides(sides[1])
squared_top = square_sides(sides[2])
squared_back = square_sides(sides[3])
squared_front = square_sides(sides[4])
squared_bottom = square_sides(sides[5])
return squared_front,squared_left,squared_back,squared_right,squared_top,squared_bottom
| 18,975
|
def GetFieldInfo(column: Schema.Column,
force_nested_types: bool = False,
nested_prefix: str = 'Nested_') -> FieldInfo:
"""Returns the corresponding information for provided column.
Args:
column: the column for which to generate the dataclass FieldInfo.
force_nested_types: when True, a nested subclass is generated always,
even for known dataclass types.
nested_prefix: name prefix for nested dataclasses.
Returns:
The corresponding `FieldInfo` class for column.
"""
column.validate()
info = FieldInfo(column.info.name)
nested_name = f'{nested_prefix}{column.info.name}'
sub_nested_name = f'{nested_name}_'
if column.info.column_type in _TYPE_INFO:
info.type_info = _TYPE_INFO[column.info.column_type].copy()
_ApplyLabel(column, info)
elif column.info.column_type == Schema_pb2.ColumnInfo.TYPE_NESTED:
if column.info.message_name and not force_nested_types:
info.type_info = TypeInfo(column.info.message_name)
else:
info.type_info = TypeInfo(nested_name)
nested = NestedType(info.type_info.name)
nested.fields = [
GetFieldInfo(sub_column, force_nested_types, sub_nested_name)
for sub_column in column.fields
]
info.nested.append(nested)
_ApplyLabel(column, info)
elif column.info.column_type in (Schema_pb2.ColumnInfo.TYPE_ARRAY,
Schema_pb2.ColumnInfo.TYPE_SET):
element_info = GetFieldInfo(column.fields[0], force_nested_types,
sub_nested_name)
if column.info.column_type == Schema_pb2.ColumnInfo.TYPE_ARRAY:
name = 'typing.List'
else:
name = 'typing.Set'
info.type_info = TypeInfo(name, None, {'typing'},
[element_info.type_info])
info.nested.extend(element_info.nested)
elif column.info.column_type == Schema_pb2.ColumnInfo.TYPE_MAP:
key_info = GetFieldInfo(column.fields[0], force_nested_types,
sub_nested_name)
value_info = GetFieldInfo(column.fields[1], force_nested_types,
sub_nested_name)
info.type_info = TypeInfo('typing.Dict', None, {'typing'},
[key_info.type_info, value_info.type_info])
info.nested.extend(key_info.nested)
info.nested.extend(value_info.nested)
else:
raise ValueError(f'Unsupported type `{column.info.column_type}` '
f'for field `{column.name()}`')
info.type_info.add_annotations(_GetColumnAnnotations(column))
return info
| 18,976
|
def get_session():
"""
Get the current session.
:return: the session
:raises OutsideUnitOfWorkError: if this method is called from outside a UOW
"""
global Session
if Session is None or not Session.registry.has():
raise OutsideUnitOfWorkError
return Session()
| 18,977
|
def create_sgd_optimizers_fn(datasets, model, learning_rate, momentum=0.9, weight_decay=0, nesterov=False, scheduler_fn=None, per_step_scheduler_fn=None):
"""
Create a Stochastic gradient descent optimizer for each of the dataset with optional scheduler
Args:
datasets: a dictionary of dataset
model: a model to optimize
learning_rate: the initial learning rate
scheduler_fn: a scheduler, or `None`
momentum: the momentum of the SGD
weight_decay: the weight decay
nesterov: enables Nesterov momentum
per_step_scheduler_fn: the functor to instantiate scheduler to be run per-step (batch)
Returns:
An optimizer
"""
optimizer_fn = functools.partial(
torch.optim.SGD,
lr=learning_rate,
momentum=momentum,
weight_decay=weight_decay,
nesterov=nesterov)
return create_optimizers_fn(datasets, model,
optimizer_fn=optimizer_fn,
scheduler_fn=scheduler_fn,
per_step_scheduler_fn=per_step_scheduler_fn)
| 18,978
|
def equalize(img):
"""
Equalize the histogram of input PIL image.
Args:
img (PIL image): Image to be equalized
Returns:
img (PIL image), Equalized image.
"""
if not is_pil(img):
raise TypeError('img should be PIL image. Got {}'.format(type(img)))
return ImageOps.equalize(img)
| 18,979
|
def routemaster_serve_subprocess(unused_tcp_port):
"""
Fixture to spawn a routemaster server as a subprocess.
Yields the process reference, and the port that it can be accessed on.
"""
@contextlib.contextmanager
def _inner(*, wait_for_output=None):
env = os.environ.copy()
env.update({
'DB_HOST': os.environ.get('PG_HOST', 'localhost'),
'DB_PORT': os.environ.get('PG_PORT', '5432'),
'DB_NAME': os.environ.get('PG_DB', 'routemaster_test'),
'DB_USER': os.environ.get('PG_USER', ''),
'DB_PASS': os.environ.get('PG_PASS', ''),
})
try:
proc = subprocess.Popen(
[
'routemaster',
'--config-file=example.yaml',
'serve',
'--bind',
f'127.0.0.1:{unused_tcp_port}',
],
env=env,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
)
if wait_for_output is not None:
all_output = b''
while True:
assert proc.poll() is None, all_output.decode('utf-8')
out_line = proc.stdout.readline()
if wait_for_output in out_line:
break
all_output += out_line
yield proc, unused_tcp_port
finally:
proc.terminate()
return _inner
| 18,980
|
def ratingRange(app):
""" Get the rating range of an app. """
rating = 'Unknown'
r = app['rating']
if r >= 0 and r <= 1:
rating = '0-1'
elif r > 1 and r <= 2:
rating = '1-2'
elif r > 2 and r <= 3:
rating = '2-3'
elif r > 3 and r <= 4:
rating = '3-4'
elif r > 4 and r <= 5:
rating = '4-5'
return rating
| 18,981
|
def immediate_sister(graph, node1, node2):
"""
is node2 an immediate sister of node1?
"""
return (node2 in sister_nodes(graph, node1) and is_following(graph, node1, node2))
| 18,982
|
def get_topic_link(text: str) -> str:
"""
Generate a topic link.
A markdown link, text split with dash.
Args:
text {str} The text value to parse
Returns:
{str} The parsed text
"""
return f"{text.lower().replace(' ', '-')}"
| 18,983
|
def load_data(datapath):
"""Loads data from CSV data file.
Args:
datapath: Location of the training file
Returns:
summary dataframe containing RFM data for btyd models
actuals_df containing additional data columns for calculating error
"""
# Does not used the summary_data_from_transaction_data from the Lifetimes
# library as it wouldn't scale as well. The pre-processing done in BQ instead.
tf.logging.info('Loading data...')
ft_file = '{0}/{1}'.format(datapath, TRAINING_DATA_FILE)
#[START prob_selec]
df_ft = pd.read_csv(ft_file)
# Extracts relevant dataframes for RFM:
# - summary has aggregated values before the threshold date
# - actual_df has values of the overall period.
summary = df_ft[['customer_id', 'frequency_btyd', 'recency', 'T',
'monetary_btyd']]
#[END prob_selec]
summary.columns = ['customer_id', 'frequency', 'recency', 'T',
'monetary_value']
summary = summary.set_index('customer_id')
# additional columns needed for calculating error
actual_df = df_ft[['customer_id', 'frequency_btyd', 'monetary_dnn',
'target_monetary']]
actual_df.columns = ['customer_id', 'train_frequency', 'train_monetary',
'act_target_monetary']
tf.logging.info('Data loaded.')
return summary, actual_df
| 18,984
|
def rms(a, axis=None):
"""
Calculates the RMS of an array.
Args:
a (ndarray). A sequence of numbers to apply the RMS to.
axis (int). The axis along which to compute. If not given or None,
the RMS for the whole array is computed.
Returns:
ndarray: The RMS of the array along the desired axis or axes.
"""
a = np.array(a)
if axis is None:
div = a.size
else:
div = a.shape[axis]
ms = np.sum(a**2.0, axis=axis) / div
return np.sqrt(ms)
| 18,985
|
def alpha_114(code, end_date=None, fq="pre"):
"""
公式:
((RANK(DELAY(((HIGH - LOW) / (SUM(CLOSE, 5) / 5)), 2)) * RANK(RANK(VOLUME))) / (((HIGH - LOW) / (SUM(CLOSE, 5) / 5)) / (VWAP - CLOSE)))
Inputs:
code: 股票池
end_date: 查询日期
Outputs:
因子的值
"""
end_date = to_date_str(end_date)
func_name = sys._getframe().f_code.co_name
return JQDataClient.instance().get_alpha_191(**locals())
| 18,986
|
def event_message(iden, event):
"""Return an event message."""
return {
'id': iden,
'type': TYPE_EVENT,
'event': event.as_dict(),
}
| 18,987
|
def logout():
"""Logout."""
logout_user()
flash(lazy_gettext("You are logged out."), "info")
return redirect(url_for("public.home"))
| 18,988
|
async def test_discovery_single_instance(hass, discovery_flow_conf, source):
"""Test we not allow duplicates."""
flow = config_entries.HANDLERS["test"]()
flow.hass = hass
flow.context = {}
MockConfigEntry(domain="test").add_to_hass(hass)
result = await getattr(flow, f"async_step_{source}")({})
assert result["type"] == data_entry_flow.RESULT_TYPE_ABORT
assert result["reason"] == "single_instance_allowed"
| 18,989
|
def predicate_to_str(predicate: dict) -> str:
"""
谓词转文本
:param predicate: 谓词数据
:return: 文本
"""
result = ""
if "block" in predicate:
result += "检查方块\n\n"
block = predicate["block"]
if "nbt" in block:
result += f"检查nbt：\n``` json\n{try_pretty_json_str(block['nbt'])}\n```\n"
elif "item" in predicate:
result += "检查物品：" + try_translate(minecraft_lang, get_translate_str("item",
predicate["item"].split(':')[0],
predicate["item"].split(':')[-1:][
0])) + "\n\n"
elif "items" in predicate:
result += "检查下列物品：\n"
for item in predicate['items']:
result += " - " + try_translate(minecraft_lang, get_translate_str("item",
item.split(':')[0],
item.split(':')[-1:][
0])) + "\n"
elif "enchantments" in predicate:
result += "检查附魔\n\n"
enchantments = predicate["enchantments"]
for enchantment in enchantments:
result += enchantment_to_str(enchantment) + "\n\n"
elif "nbt" in predicate:
result += f"检查nbt：\n``` json\n{try_pretty_json_str(predicate['nbt'])}\n```\n"
elif "flags" in predicate:
flags = predicate["flags"]
if "is_on_fire" in flags:
if flags["is_on_fire"]:
result += "着火\n"
else:
result += "没有着火\n"
elif "biome" in predicate:
result += "检查生物群系：" + try_translate(minecraft_lang, get_translate_str("biome",
predicate["biome"].split(':')[0],
predicate["biome"].split(':')[-1:][
0])) + "\n"
else:
result += f"（未知的谓词）\n``` json\n{json.dumps(predicate)}\n```"
return result
| 18,990
|
def _render_export(a):
"""Renders cpp stub from python code"""
from liblolpig.export import scan_module_files
from liblolpig import Renderer
ctx = scan_module_files(a.input_filenames)
r = Renderer(ctx)
r.namespaces = a.namespaces
r.is_gccxml = a.is_gccxml
r.write_to_file(a.output_cpp, r.render_export())
| 18,991
|
def module_str_to_class(module_str):
"""Parse module class string to a class
Args:
module_str(str) Dictionary from parsed configuration file
Returns:
type: class
"""
if not validate_module_str(module_str):
raise ValueError("Module string is in wrong format")
module_path, class_name = module_str.split(":")
if os.path.isfile(module_path):
module_name = os.path.basename(module_path).replace(".py", "")
spec = importlib.util.spec_from_file_location(module_name, module_path)
module = importlib.util.module_from_spec(spec)
sys.modules[module_name] = module
spec.loader.exec_module(module)
else:
module = importlib.import_module(module_path)
return getattr(module, class_name)
| 18,992
|
async def get_postcode(postcode_like: PostCodeLike) -> Optional[Postcode]:
"""
Gets the postcode object for a given postcode string.
Acts as a middleware between us and the API, caching results.
:param postcode_like: The either a string postcode or PostCode object.
:return: The PostCode object else None if the postcode does not exist..
:raises CachingError: When the postcode is not in cache, and the API is unreachable.
"""
if isinstance(postcode_like, Postcode):
return postcode_like
postcode_like = postcode_like.replace(" ", "").upper()
try:
postcode = Postcode.get(Postcode.postcode == postcode_like)
except DoesNotExist:
logger.info(f"Postcode {postcode_like} not cached, fetching from API")
try:
postcode = await fetch_postcode_from_string(postcode_like)
except (ApiError, CircuitBreakerError):
raise CachingError(f"Requested postcode is not cached, and can't be retrieved.")
if postcode is not None:
postcode.save()
return postcode
| 18,993
|
def registerlib(path):
"""
Add KiKit's footprint library into the global footprint library table. If
the library has already been registered, update the path.
"""
if path is None:
path = getFpLibTablePath()
with open(path, "r") as f:
fpLibTable = parseSexprF(f)
rest = f.read()
kikitLib = findLib(fpLibTable, "kikit")
if kikitLib is None:
kikitLib = pushNewLib(fpLibTable)
rewriteTable = {
"name": "kikit",
"type": "KiCAD",
"uri": KIKIT_LIB,
"options": "",
"descr": "KiKit Footprint library"
}
for x in islice(kikitLib, 1, None):
x[1].value = rewriteTable[x[0].value]
ident = datetime.now().strftime("%Y-%m-%d--%H-%M:%S")
backupName = f"{path}.bak.{ident}"
shutil.copy(path, backupName)
print(f"A copy of the original {path} was made into {backupName}. ", end="")
print("You can restore it if something goes wrong.", end="\n\n")
with open(path, "w") as f:
f.write(str(fpLibTable))
f.write(rest)
print(f"KiKit footprint library successfully added to the global footprint table '{path}'. Please restart KiCAD.")
| 18,994
|
def oauth2callback():
"""
The 'flow' has this one place to call back to. We'll enter here
more than once as steps in the flow are completed, and need to keep
track of how far we've gotten. The first time we'll do the first
step, the second time we'll skip the first step and do the second,
and so on.
"""
app.logger.debug("Entering oauth2callback")
flow = client.flow_from_clientsecrets(
CLIENT_SECRET_FILE,
scope= SCOPES,
redirect_uri=flask.url_for('oauth2callback', _external=True))
## Note we are *not* redirecting above. We are noting *where*
## we will redirect to, which is this function.
## The *second* time we enter here, it's a callback
## with 'code' set in the URL parameter. If we don't
## see that, it must be the first time through, so we
## need to do step 1.
app.logger.debug("Got flow")
if 'code' not in flask.request.args:
app.logger.debug("Code not in flask.request.args")
auth_uri = flow.step1_get_authorize_url()
return flask.redirect(auth_uri)
## This will redirect back here, but the second time through
## we'll have the 'code' parameter set
else:
## It's the second time through ... we can tell because
## we got the 'code' argument in the URL.
app.logger.debug("Code was in flask.request.args")
auth_code = flask.request.args.get('code')
credentials = flow.step2_exchange(auth_code)
flask.session['credentials'] = credentials.to_json()
## Now I can build the service and execute the query,
## but for the moment I'll just log it and go back to
## the main screen
app.logger.debug("Got credentials")
return flask.redirect(flask.url_for('respond_gcal'))
| 18,995
|
def gen_cards(deck, bottom_up=True):
"""
Empties the deck!
"""
func = deck.pop if bottom_up else deck.popleft
while True:
try:
yield func()
except IndexError:
return
| 18,996
|
def get_ebv(path, specs=range(10)):
"""Lookup the EBV value for all targets from the CFRAME fibermap.
Return the median of all non-zero values.
"""
ebvs = []
for (CFRAME,), camera, spec in iterspecs(path, 'cframe', specs=specs, cameras='b'):
ebvs.append(CFRAME['FIBERMAP'].read(columns=['EBV'])['EBV'].astype(np.float32))
ebvs = np.stack(ebvs).reshape(-1)
nonzero = ebvs > 0
ebvs = ebvs[nonzero]
return np.nanmedian(ebvs)
| 18,997
|
def is_prime(i):
"""
"""
| 18,998
|
def get_results_df(
job_list: List[str],
output_dir: str,
input_dir: str = None,
) -> pd.DataFrame:
"""Get raw results in DataFrame."""
if input_dir is None:
input_dir = os.path.join(SLURM_DIR, 'inputs')
input_files = [
os.path.join(input_dir, f'{name}.json')
for name in job_list
]
output_dirs = [
os.path.join(output_dir, name)
for name in job_list
]
batches = {}
for i in range(len(input_files)):
batch_sim = read_input_json(input_files[i])
for sim in batch_sim:
sim.load_results(output_dirs[i])
batches[batch_sim.label] = batch_sim
results = []
for batch_label, batch_sim in batches.items():
batch_results = batch_sim.get_results()
# print(
# 'wall_time =',
# str(datetime.timedelta(seconds=batch_sim.wall_time))
# )
# print('n_trials = ', min(sim.n_results for sim in batch_sim))
for sim, batch_result in zip(batch_sim, batch_results):
n_logicals = batch_result['k']
# Small fix for the current situation. TO REMOVE in later versions
if n_logicals == -1:
n_logicals = 1
batch_result['label'] = batch_label
batch_result['noise_direction'] = sim.error_model.direction
eta_x, eta_y, eta_z = get_bias_ratios(sim.error_model.direction)
batch_result['eta_x'] = eta_x
batch_result['eta_y'] = eta_y
batch_result['eta_z'] = eta_z
if len(sim.results['effective_error']) > 0:
codespace = np.array(sim.results['codespace'])
x_errors = np.array(
sim.results['effective_error']
)[:, :n_logicals].any(axis=1)
batch_result['p_x'] = x_errors.mean()
batch_result['p_x_se'] = np.sqrt(
batch_result['p_x']*(1 - batch_result['p_x'])
/ (sim.n_results + 1)
)
z_errors = np.array(
sim.results['effective_error']
)[:, n_logicals:].any(axis=1)
batch_result['p_z'] = z_errors.mean()
batch_result['p_z_se'] = np.sqrt(
batch_result['p_z']*(1 - batch_result['p_z'])
/ (sim.n_results + 1)
)
batch_result['p_undecodable'] = (~codespace).mean()
if n_logicals == 1:
p_pure_x = (
np.array(sim.results['effective_error']) == [1, 0]
).all(axis=1).mean()
p_pure_y = (
np.array(sim.results['effective_error']) == [1, 1]
).all(axis=1).mean()
p_pure_z = (
np.array(sim.results['effective_error']) == [0, 1]
).all(axis=1).mean()
p_pure_x_se = np.sqrt(
p_pure_x * (1-p_pure_x) / (sim.n_results + 1)
)
p_pure_y_se = np.sqrt(
p_pure_y * (1-p_pure_y) / (sim.n_results + 1)
)
p_pure_z_se = np.sqrt(
p_pure_z * (1-p_pure_z) / (sim.n_results + 1)
)
batch_result['p_pure_x'] = p_pure_x
batch_result['p_pure_y'] = p_pure_y
batch_result['p_pure_z'] = p_pure_z
batch_result['p_pure_x_se'] = p_pure_x_se
batch_result['p_pure_y_se'] = p_pure_y_se
batch_result['p_pure_z_se'] = p_pure_z_se
else:
batch_result['p_pure_x'] = np.nan
batch_result['p_pure_y'] = np.nan
batch_result['p_pure_z'] = np.nan
batch_result['p_pure_x_se'] = np.nan
batch_result['p_pure_y_se'] = np.nan
batch_result['p_pure_z_se'] = np.nan
else:
batch_result['p_x'] = np.nan
batch_result['p_x_se'] = np.nan
batch_result['p_z'] = np.nan
batch_result['p_z_se'] = np.nan
batch_result['p_undecodable'] = np.nan
results += batch_results
results_df = pd.DataFrame(results)
return results_df
| 18,999
|
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