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def getn_hidden_area(*args):
"""getn_hidden_area(int n) -> hidden_area_t"""
return _idaapi.getn_hidden_area(*args)
|
3265d4258ce6717e8ca23bd10754e1b1648d4217
| 3,640,067
|
def cdist(X: DNDarray, Y: DNDarray = None, quadratic_expansion: bool = False) -> DNDarray:
"""
Calculate Euclidian distance between two DNDarrays:
.. math:: d(x,y) = \\sqrt{(|x-y|^2)}
Returns 2D DNDarray of size :math: `m \\times n`
Parameters
----------
X : DNDarray
2D array of size :math: `m \\times f`
Y : DNDarray
2D array of size :math: `n \\times f`
quadratic_expansion : bool
Whether to use quadratic expansion for :math:`\\sqrt{(|x-y|^2)}` (Might yield speed-up)
"""
if quadratic_expansion:
return _dist(X, Y, _euclidian_fast)
else:
return _dist(X, Y, _euclidian)
|
14a2368ff0717ff04e0477699ff13d20f359ba0d
| 3,640,068
|
def popcount_u8(x: np.ndarray):
"""Return the total bit count of a uint8 array"""
if x.dtype != np.uint8:
raise ValueError("input dtype must be uint8")
count = 0
# for each item look-up the number of bits in the LUT
for elem in x.flat:
count += u8_count_lut[elem]
return count
|
e85c07b3df7dcd993c0f1cc7f9dbecd97e8be317
| 3,640,069
|
from scipy import stats
def split_errorRC(tr, t1, t2, q, Emat, maxdt, ddt, dphi):
"""
Calculates error bars based on a F-test and
a given confidence interval q.
Note
----
This version uses a Fisher transformation for
correlation-type misfit.
Parameters
----------
tr : :class:`~obspy.core.Trace`
Seismogram
t1 : :class:`~obspy.core.utcdatetime.UTCDateTime`
Start time of picking window
t2 : :class:`~obspy.core.utcdatetime.UTCDateTime`
End time of picking window
q : float
Confidence level
Emat : :class:`~numpy.ndarray`
Energy minimization matrix
Returns
-------
err_dtt : float
Error in dt estimate (sec)
err_phi : float
Error in phi estimate (degrees)
err_contour : :class:`~numpy.ndarray`
Error contour for plotting
"""
phi = np.arange(-90.0, 90.0, dphi)*np.pi/180.
dtt = np.arange(0., maxdt, ddt)
# Copy trace to avoid overriding
tr_tmp = tr.copy()
tr_tmp.trim(t1, t2)
# Get degrees of freedom
dof = split_dof(tr_tmp)
if dof <= 3:
dof = 3.01
print(
"Degrees of freedom < 3. Fixing to DOF = 3, which may " +
"result in inaccurate errors")
n_par = 2
# Fisher transformation
vmin = np.arctanh(Emat.min())
# Error contour
zrr_contour = vmin + (vmin*np.sign(vmin)*n_par/(dof - n_par) *
stats.f.ppf(1. - q, n_par, dof - n_par)) *\
np.sqrt(1./(dof-3))
# Back transformation
err_contour = np.tanh(zrr_contour)
# Estimate uncertainty (q confidence interval)
err = np.where(Emat < err_contour)
err_phi = max(
0.25*(phi[max(err[0])] - phi[min(err[0])])*180./np.pi, 0.25*dphi)
err_dtt = max(0.25*(dtt[max(err[1])] - dtt[min(err[1])]), 0.25*ddt)
return err_dtt, err_phi, err_contour
|
3155031382c881a15a8a300d6656cae1fc0fee64
| 3,640,070
|
import copy
def filter_parts(settings):
"""
Remove grouped components and glyphs that have been deleted or split.
"""
parts = []
temp = copy.copy(settings['glyphs'])
for glyph in settings['glyphs']:
name = glyph['class_name']
if name.startswith("_split") or name.startswith("_group") or name.startswith("_delete"):
parts.append(glyph)
temp.remove(glyph)
settings['glyphs'] = temp
# Remove from the training glyphs as well
temp2 = copy.copy(settings['training_glyphs'])
for glyph in settings['training_glyphs']:
name = glyph['class_name']
if name.startswith("_split") or name.startswith("_group") or name.startswith("_delete"):
temp2.remove(glyph)
settings['training_glyphs'] = temp2
return parts
|
f8d6a59eeeb314619fd4c332e2594dee3543ee9c
| 3,640,071
|
def kernel_zz(Y, X, Z):
"""
Kernel zz for second derivative of the potential generated by a sphere
"""
radius = np.sqrt(Y ** 2 + X ** 2 + Z ** 2)
r2 = radius*radius
r5 = r2*r2*radius
kernel = (3*Z**2 - r2)/r5
return kernel
|
14f36fe23531994cd40c74d26b91477d266ca21c
| 3,640,072
|
def getAccentedVocal(vocal, acc_type="g"):
"""
It returns given vocal with grave or acute accent
"""
vocals = {'a': {'g': u'\xe0', 'a': u'\xe1'},
'e': {'g': u'\xe8', 'a': u'\xe9'},
'i': {'g': u'\xec', 'a': u'\xed'},
'o': {'g': u'\xf2', 'a': u'\xf3'},
'u': {'g': u'\xf9', 'a': u'\xfa'}}
return vocals[vocal][acc_type]
|
cfec276dac32e6ff092eee4f1fc84b412c5c915c
| 3,640,073
|
def env_initialize(env, train_mode=True, brain_idx=0, idx=0, verbose=False):
""" Setup environment and return info """
# get the default brain
brain_name = env.brain_names[brain_idx]
brain = env.brains[brain_name]
# reset the environment
env_info = env.reset(train_mode=train_mode)[brain_name]
# examine the state space and action space
state = env_info.vector_observations[idx]
state_size = len(state)
action_size = brain.vector_action_space_size
if verbose:
# number of agents in the environment
print(f'Number of agents: {len(env_info.agents)}')
print(f'Number of actions: {action_size}')
print(f'States have length: {state_size}')
print(f'States look like: {state}')
return (brain, brain_name, state, action_size, state_size)
|
3c951a77009cca8c876c36965ec33781dd2c08dd
| 3,640,074
|
def lorentzianfit(x, y, parent=None, name=None):
"""Compute Lorentzian fit
Returns (yfit, params), where yfit is the fitted curve and params are
the fitting parameters"""
dx = np.max(x) - np.min(x)
dy = np.max(y) - np.min(y)
sigma = dx * 0.1
amp = fit.LorentzianModel.get_amp_from_amplitude(dy, sigma)
a = FitParam(_("Amplitude"), amp, 0.0, amp * 1.2)
b = FitParam(_("Base line"), np.min(y), np.min(y) - 0.1 * dy, np.max(y))
sigma = FitParam(_("Std-dev") + " (σ)", sigma, sigma * 0.2, sigma * 10)
mu = FitParam(_("Mean") + " (μ)", xpeak(x, y), np.min(x), np.max(x))
params = [a, sigma, mu, b]
def fitfunc(x, params):
return fit.LorentzianModel.func(x, *params)
values = guifit(
x, y, fitfunc, params, parent=parent, wintitle=_("Lorentzian fit"), name=name
)
if values:
return fitfunc(x, values), params
|
cd221c3483ee7f54ac49baaeaf617ef8ec2b7fa7
| 3,640,075
|
def tf_quat(T):
""" Return quaternion from 4x4 homogeneous transform """
assert T.shape == (4, 4)
return rot2quat(tf_rot(T))
|
7fb2a7b136201ec0e6a92faf2cc030830df46fa5
| 3,640,076
|
def solve2(lines):
"""Solve the problem."""
result = 0
for group in parse_answers2(lines):
result += len(group)
return result
|
5990b61e713733ba855937b8191b8a8a4f503873
| 3,640,077
|
def get_contract_type(timestamp: int, due_timestamp: int) -> str:
"""Get the contract_type
Input the timestamp and due_timestamp. Return which contract_type is.
Args:
timestamp: The target timestamp, you want to know.
due_timestamp: The due timestamp of the contract.
Returns:
The contract_type name.
Raises:
RuntimeError: An error occurred timestamp gt due_timestamp.
"""
minus = due_timestamp - timestamp
if minus < 0:
raise RuntimeError("the timestamp more than due_timestamp")
if minus < 7 * 24 * 60 * 60 * 1000:
return CONTRACT_TYPE_THIS_WEEK
elif minus < 14 * 24 * 60 * 60 * 1000:
return CONTRACT_TYPE_NEXT_WEEK
else:
return CONTRACT_TYPE_QUARTER
|
3b3a084f786c82a5fc1b2a7a051e9005b3df5f0a
| 3,640,078
|
from typing import Any
from typing import Optional
from typing import Union
from typing import Type
from typing import Tuple
from typing import Sequence
from typing import cast
def is_sequence_of(obj: Any,
types: Optional[Union[Type[object],
Tuple[Type[object], ...]]] = None,
depth: Optional[int] = None,
shape: Optional[Sequence[int]] = None
) -> bool:
"""
Test if object is a sequence of entirely certain class(es).
Args:
obj: The object to test.
types: Allowed type(s). If omitted, we just test the depth/shape.
depth: Level of nesting, ie if ``depth=2`` we expect a sequence of
sequences. Default 1 unless ``shape`` is supplied.
shape: The shape of the sequence, ie its length in each dimension.
If ``depth`` is omitted, but ``shape`` included, we set
``depth = len(shape)``.
Returns:
bool: ``True`` if every item in ``obj`` matches ``types``.
"""
if not is_sequence(obj):
return False
if shape is None or shape == ():
next_shape: Optional[Tuple[int]] = None
if depth is None:
depth = 1
else:
if depth is None:
depth = len(shape)
elif depth != len(shape):
raise ValueError('inconsistent depth and shape')
if len(obj) != shape[0]:
return False
next_shape = cast(Tuple[int], shape[1:])
for item in obj:
if depth > 1:
if not is_sequence_of(item, types, depth=depth - 1,
shape=next_shape):
return False
elif types is not None and not isinstance(item, types):
return False
return True
|
3762454785563c7787451efad143547f97ae8994
| 3,640,079
|
def _parse_tree_height(sent):
"""
Gets the height of the parse tree for a sentence.
"""
children = list(sent._.children)
if not children:
return 0
else:
return max(_parse_tree_height(child) for child in children) + 1
|
d6de5c1078701eeeb370c917478d93e7653d7f4f
| 3,640,081
|
def pandas_loss_p_g_i_t(c_m, lgd, ead, new):
""" Distribution of losses at time t.
long format (N_MC, G, K, T)."""
mat_4D = loss_g_i_t(c_m, lgd, ead, new)
names = ['paths', 'group_ID', 'credit_rating_rank', 'time_steps']
index = pds.MultiIndex.from_product([range(s)for s in mat_4D.shape], names=names)
df = pds.DataFrame({'loss_p_g_i_t': mat_4D.flatten()}, index=index)['loss_p_g_i_t']
df = pds.Series.to_frame(df)
df['loss_p_g_i_t_ID'] = np.arange(len(df))
df.insert(0, 'portfolio_ID', 'pilot 1 Bank A', allow_duplicates=False)
return df
|
65e9db48eab0a40596b205a7304bd225eb5c93d0
| 3,640,082
|
def find_available_pacs(pacs, pac_to_unstuck=None, pac_to_super=None, pac_to_normal=None):
"""
Finds the available pacs that are not assigned
"""
available_pacs = pacs['mine']
if pac_to_unstuck is not None:
available_pacs = [x for x in available_pacs if x['id'] not in pac_to_unstuck.keys()]
if pac_to_super is not None:
available_pacs = [x for x in available_pacs if x['id'] not in pac_to_super.keys()]
if pac_to_normal is not None:
available_pacs = [x for x in available_pacs if x['id'] not in pac_to_normal.keys()]
return available_pacs
|
4b6674fd87db2127d5fffa781431ccc9a9ff775a
| 3,640,084
|
async def login_for_access_token(
form_data: OAuth2PasswordRequestForm = Depends(),
):
"""
Log in to your account using oauth2 authorization.
In response we get an jwt authorization token
which is used for granting access to data
"""
is_auth, scope = await authenticate_authority(
form_data.username, form_data.password
)
if not is_auth:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Incorrect username or password",
)
access_token_expires = timedelta(
minutes=security_config.ACCESS_TOKEN_EXPIRE_MINUTES
)
access_token = create_access_token(
data={"sub": form_data.username, "scopes": [scope]},
expires_time=access_token_expires,
)
return {"access_token": access_token, "token_type": "bearer"}
|
441326317f0f13275ad33e369efe419a605ac4eb
| 3,640,085
|
def get_plain_expressions(s):
"""Return a list of plain, non-nested shell expressions found in the shell
string s. These are shell expressions that do not further contain a nested
expression and can therefore be resolved indenpendently.
For example::
>>> get_plain_expressions("${_pyname%${_pyname#?}}")
['${_pyname#?}']
"""
return _get_non_nested_expressions(s)
|
a3b0f6812ffe361e291b28c4273ca7cc975eb1e7
| 3,640,086
|
def create_indices(dims):
"""Create lists of indices"""
return [range(1,dim+1) for dim in dims]
|
1a83b59eb1ca2b24b9db3c9eec05db7335938cae
| 3,640,087
|
def observed_property(property_name, default, cast=None):
"""Default must be immutable."""
hidden_property_name = "_" + property_name
if cast is None:
if cast is False:
cast = lambda x: x
else:
cast = type(default)
def getter(self):
try:
return getattr(self, hidden_property_name)
except AttributeError:
return default
def deleter(self):
try:
delattr(self, hidden_property_name)
except AttributeError:
pass
def setter(self, value):
value = cast(value)
if value == default:
try:
delattr(self, hidden_property_name)
except AttributeError:
pass
else:
setattr(self, hidden_property_name, value)
return property(getter, observed(setter), observed(deleter))
|
7358557b221b5d4fa18fbd29cd02b47823cfdfe0
| 3,640,088
|
from typing import Callable
from io import StringIO
def query_helper(
source: S3Ref, query: str, dest: S3Ref = None, transform: Callable = None
) -> StringIO:
"""
query_helper runs the given s3_select query on the given object.
- The results are saved in a in memory file (StringIO) and returned.
- If dest is specified, the file is copied to the provided S3Ref
- If transform callable is specified, tranform is called first with the
temp file before uploading to the destination s3.
"""
event_stream = s3.select_object_content(
Bucket=source.bucket,
Key=source.key,
ExpressionType="SQL",
Expression=query,
InputSerialization={"JSON": {"Type": "LINES"}},
OutputSerialization={"JSON": {}},
)
# Iterate over events in the event stream as they come
output = StringIO()
for s3_select_event in event_stream["Payload"]:
if "Records" in s3_select_event:
data = s3_select_event["Records"]["Payload"]
output.write(data.decode("utf-8"))
if transform:
output.seek(0)
output = transform(output)
if dest is not None:
upload(output, dest)
output.seek(0)
return output
|
3670734c76f615fe6deb3dfed8305cfc1740b124
| 3,640,089
|
def indicator_selector(row, indicator, begin, end):
"""Return Tons of biomass loss."""
dasy = {}
if indicator == 4:
return row[2]['value']
for i in range(len(row)):
if row[i]['indicator_id'] == indicator and row[i]['year'] >= int(begin) and row[i]['year'] <= int(end):
dasy[str(row[i]['year'])] = row[i]['value']
return dasy
|
329411837633f4e28bea4b2b261b6f4149b92fb1
| 3,640,090
|
import math
def xy_from_range_bearing(range: float, bearing: float) -> map_funcs.Point:
"""Given a range in metres and a bearing from the camera this returns the x, y position in metres relative to the runway
start."""
theta_deg = bearing - google_earth.RUNWAY_HEADING_DEG
x = CAMERA_POSITION_XY.x + range * math.cos(math.radians(theta_deg))
y = CAMERA_POSITION_XY.y + range * math.sin(math.radians(theta_deg))
return map_funcs.Point(x, y)
|
a2575437b52003660d83b241da13f10687fa4241
| 3,640,091
|
def flask_get_modules():
"""Return the list of all modules
---
tags:
- Modules
responses:
200:
description: A list of modules
"""
db_list = db.session.query(Module).all()
return jsonify(db_list)
|
21352458773143f785658488e34f9e486c7f818d
| 3,640,092
|
def create_user(username, password):
"""Registra um novo usuario caso nao esteja cadastrado"""
if User.query.filter_by(username=username).first():
raise RuntimeError(f'{username} ja esta cadastrado')
user = User(username=username, password=generate_password_hash(password))
db.session.add(user)
db.session.commit()
return user
|
1a50d31b764cce10d0db78141041deafc15f7c40
| 3,640,093
|
import numpy
def _get_mesh_colour_scheme():
"""Returns colour scheme for MESH (maximum estimated size of hail).
:return: colour_map_object: Instance of `matplotlib.colors.ListedColormap`.
:return: colour_norm_object: Instance of `matplotlib.colors.BoundaryNorm`.
"""
colour_list = [
[152, 152, 152], [152, 203, 254], [0, 152, 254], [0, 45, 254],
[0, 101, 0], [0, 152, 0], [0, 203, 0], [254, 254, 50],
[254, 203, 0], [254, 152, 0], [254, 0, 0], [254, 0, 152],
[152, 50, 203]
]
for i in range(len(colour_list)):
colour_list[i] = numpy.array(colour_list[i], dtype=float) / 255
colour_map_object = matplotlib.colors.ListedColormap(colour_list)
colour_map_object.set_under(numpy.full(3, 1))
colour_bounds_mm = numpy.array([
0.1, 15.9, 22.2, 28.6, 34.9, 41.3, 47.6, 54, 60.3, 65, 70, 75, 80, 85
])
colour_norm_object = matplotlib.colors.BoundaryNorm(
colour_bounds_mm, colour_map_object.N)
return colour_map_object, colour_norm_object
|
4301822297d069a6cc289e72b5bf388ffae01cf4
| 3,640,094
|
def _manually_create_user(username, pw):
"""
Create an *active* user, its server directory, and return its userdata dictionary.
:param username: str
:param pw: str
:return: dict
"""
enc_pass = server._encrypt_password(pw)
# Create user directory with default structure (use the server function)
user_dir_state = server.init_user_directory(username)
single_user_data = user_dir_state
single_user_data[server.USER_IS_ACTIVE] = True
single_user_data[server.PWD] = enc_pass
single_user_data[server.USER_CREATION_TIME] = server.now_timestamp()
single_user_data['shared_with_me'] = {}
single_user_data['shared_with_others'] = {}
single_user_data['shared_files'] = {}
server.userdata[username] = single_user_data
return single_user_data
|
21d523ae29121697e63460302d8027499b4d896d
| 3,640,097
|
def update_geoscale(df, to_scale):
"""
Updates df['Location'] based on specified to_scale
:param df: df, requires Location column
:param to_scale: str, target geoscale
:return: df, with 5 digit fips
"""
# code for when the "Location" is a FIPS based system
if to_scale == 'state':
df.loc[:, 'Location'] = df['Location'].apply(lambda x: str(x[0:2]))
# pad zeros
df.loc[:, 'Location'] = df['Location'].apply(lambda x:
x.ljust(3 + len(x), '0')
if len(x) < 5 else x)
elif to_scale == 'national':
df.loc[:, 'Location'] = US_FIPS
return df
|
e62083f176cd749a88b2e73774e70140c6c5b9ac
| 3,640,098
|
import json
def translate(text, from_lang="auto", to_lang="zh-CN"):
"""translate text, return the result as json"""
url = 'https://translate.googleapis.com/translate_a/single?'
params = []
params.append('client=gtx')
params.append('sl=' + from_lang)
params.append('tl=' + to_lang)
params.append('hl=en-US')
params.append('dt=t')
params.append('dt=bd')
params.append('dj=1')
params.append('source=input')
params.append(urlencode({'q': text}))
url += '&'.join(params)
request = urllib2.Request(url)
browser = "Mozilla/5.0 (X11; Linux x86_64; rv:45.0) Gecko/20100101 Firefox/45.0"
request.add_header('User-Agent', browser)
response = urllib2.urlopen(request)
return json.loads(response.read().decode('utf8'))
|
944a5a90f60d8e54c402100e512bbce2bbb407c5
| 3,640,099
|
def backproject_to_plane(cam, img_pt, plane):
"""Back an image point to a specified world plane"""
# map to normalized image coordinates
npt = np.matrix(npl.solve(cam[0], np.array(list(img_pt)+[1.0])))
M = cam[1].transpose()
n = np.matrix(plane[:3]).flatten()
d = plane.flat[3]
Mt = M * cam[2]
Mp = M * npt.transpose()
return Mp * (np.dot(n, Mt) - d) / np.dot(n, Mp) - Mt
|
47ae45103460db5a5447900dda10783c8f92362e
| 3,640,100
|
from datetime import datetime
def format_cell(cell, datetime_fmt=None):
"""Format a cell."""
if datetime_fmt and isinstance(cell, datetime):
return cell.strftime(datetime_fmt)
return cell
|
8d3fb41bb3d7d3f3b341482e2d050d32092118bf
| 3,640,101
|
def optimize(gradients, optim, global_step, summaries, global_norm=None, global_norm_clipped=None, appendix=''):
"""Modified from sugartensor"""
# Add Summary
if summaries is None:
summaries = ["loss", "learning_rate"]
# if "gradient_norm" in summaries:
# if global_norm is None:
# tf.summary.scalar("global_norm/gradient_norm" + appendix,
# clip_ops.global_norm(list(zip(*gradients))[0]))
# else:
# tf.summary.scalar("global_norm/gradient_norm" + appendix,
# global_norm)
# if global_norm_clipped is not None:
# tf.summary.scalar("global_norm/gradient_norm_clipped" + appendix,
# global_norm_clipped)
# Add histograms for variables, gradients and gradient norms.
for gradient, variable in gradients:
if isinstance(gradient, ops.IndexedSlices):
grad_values = gradient.values
else:
grad_values = gradient
if grad_values is not None:
var_name = variable.name.replace(":", "_")
# if "gradients" in summaries:
# tf.summary.histogram("gradients/%s" % var_name, grad_values)
# if "gradient_norm" in summaries:
# tf.summary.scalar("gradient_norm/%s" % var_name,
# clip_ops.global_norm([grad_values]))
# Gradient Update OP
return optim.apply_gradients(gradients, global_step=global_step)
|
4887d45d5b9eb5a96008daeab5d11c97afed27fd
| 3,640,102
|
def _get_desired_asg_capacity(region, stack_name):
"""Retrieve the desired capacity of the autoscaling group for a specific cluster."""
asg_conn = boto3.client("autoscaling", region_name=region)
tags = asg_conn.describe_tags(Filters=[{"Name": "value", "Values": [stack_name]}])
asg_name = tags.get("Tags")[0].get("ResourceId")
response = asg_conn.describe_auto_scaling_groups(AutoScalingGroupNames=[asg_name])
return response["AutoScalingGroups"][0]["DesiredCapacity"]
|
cdcec8493333a001fe3883b0c815da521c571f7a
| 3,640,103
|
def _default_geo_type_precision():
""" default digits after decimal for geo types """
return 4
|
eef082c8a8b38f4ede7bfb5d631b2679041b650c
| 3,640,104
|
import scipy
def load_movietimes(filepath_timestamps, filepath_daq):
"""Load daq and cam time stamps, create muxer"""
df = pd.read_csv(filepath_timestamps)
# DAQ time stamps
with h5py.File(filepath_daq, 'r') as f:
daq_stamps = f['systemtime'][:]
daq_sampleinterval = f['samplenumber'][:]
# remove trailing zeros - may be left over if recording didn't finish properly
if 0 in daq_stamps:
last_valid_idx = np.argmax(daq_stamps == 0)
else:
last_valid_idx = len(daq_stamps) - 1 # in case there are no trailing zeros
daq_samplenumber = np.cumsum(daq_sampleinterval)[:last_valid_idx, np.newaxis]
last_sample = daq_samplenumber[-1, 0]
nb_seconds_per_interval, _ = scipy.stats.mode(np.diff(daq_stamps[:last_valid_idx, 0])) # seconds - using mode here to be more robust
nb_seconds_per_interval = nb_seconds_per_interval[0]
nb_samples_per_interval = np.mean(np.diff(daq_samplenumber[:last_valid_idx, 0]))
sampling_rate_Hz = np.around(nb_samples_per_interval / nb_seconds_per_interval, -3) # round to 1000s of Hz
# ss = SampStamp(sample_times=daq_stamps[:last_valid_idx, 0], frame_times=shutter_times, sample_numbers=daq_samplenumber[:, 0], auto_monotonize=False)
ss = SampStamp(sample_times=daq_stamps[:last_valid_idx, 0], sample_numbers=daq_samplenumber[:, 0],
frame_samples=df['sample'], frame_numbers=df['movie_frame'], auto_monotonize=False)
# # different refs:
#
# # first sample is 0 seconds
# s0 = ss.sample_time(0)
# ss = SampStamp(sample_times=daq_stamps[:, 0] - s0, frame_times=cam_stamps[:, 0] - s0, sample_numbers=daq_samplenumber[:, 0])
#
# # first frame is 0 seconds - for no-resample-video-data
# f0 = ss.frame_time(0)
# ss = SampStamp(sample_times=daq_stamps[:, 0] - f0, frame_times=cam_stamps[:, 0] - f0, sample_numbers=daq_samplenumber[:, 0])
return ss, last_sample, sampling_rate_Hz
|
4d54f7378f3d189a5bea4c14f68d5556958ba4f3
| 3,640,105
|
def is_valid_hotkey(hotkey: str) -> bool:
"""Returns True if hotkey string is valid."""
mode_opts = ["press", "click", "wheel"]
btn_opts = [b.name for b in Button]
wheel_opts = ["up", "down"]
hotkeylist = hotkey[2:].split("_")
if len(hotkeylist) == 0 or len(hotkeylist) % 2 != 0:
return False
for i in range(0, len(hotkeylist), 2):
mode = hotkeylist[i]
btn = hotkeylist[i + 1]
if mode not in mode_opts:
return False
if mode == "wheel" and btn not in wheel_opts:
return False
elif mode in ("press", "click") and btn not in btn_opts:
return False
if hotkey[-2] == "press":
return False
return True
|
2fb47b3f77b4cb3da2b70340b6ed96bd03c0bd14
| 3,640,106
|
def find_range_with_sum(values : list[int], target : int) -> tuple[int, int]:
"""Given a list of positive integers, find a range which sums to a target
value."""
i = j = acc = 0
while j < len(values):
if acc == target:
return i, j
elif acc < target:
acc += values[j]
j += 1
else:
acc -= values[i]
i += 1
return -1, -1
|
d54f185c98c03f985724a29471ecb1e301c14df5
| 3,640,107
|
def output_AR1(outfile, fmri_image, clobber=False):
"""
Create an output file of the AR1 parameter from the OLS pass of
fmristat.
Parameters
----------
outfile :
fmri_image : ``FmriImageList`` or 4D image
object such that ``object[0]`` has attributes ``coordmap`` and ``shape``
clobber : bool
if True, overwrite previous output
Returns
-------
regression_output : ``RegressionOutput`` instance
"""
outim = ModelOutputImage(outfile, fmri_image[0].coordmap,
fmri_image[0].shape, clobber=clobber)
return outputters.RegressionOutput(outim, outputters.output_AR1)
|
b805e73992a51045378d5e8f86ccf780d049002b
| 3,640,108
|
def feature_bit_number(current):
"""Fuzz bit number field of a feature name table header extension."""
constraints = UINT8_V
return selector(current, constraints)
|
4a2103f399765aec9d84c8152922db3801e4a718
| 3,640,109
|
def render_page(context, slot, payload): # pylint: disable=R0201,W0613
""" Base template slot """
chapter = request.args.get('chapter', '')
module = request.args.get('module', '')
page = request.args.get('page', '')
try:
if page:
return render_template(f"{chapter.lower()}/{module.lower()}/{page.lower()}.html", active_chapter=chapter,
config=payload)
return render_template(f"{chapter.lower()}/{module.lower()}.html", active_chapter=chapter, config=payload)
except:
return render_template(f"common/empty.html", active_chapter=chapter, config=payload)
|
69e5a837d90084b4c215ff75fb08a47aab1cff97
| 3,640,110
|
def add_stripe_customer_if_not_existing(f):
"""
Decorator which creates user as a customer if not already existing before making a request to the Stripe API
"""
@wraps(f)
def wrapper(user: DjangoUserProtocol, *args, **kwargs):
user = create_customer(user)
return f(user, *args, **kwargs)
return wrapper
|
676e9fad5de545a2627d52942917a49af3c6539d
| 3,640,111
|
def noisify_patternnet_asymmetric(y_train, noise, random_state=None):
""" mistakes in labelling the land cover classes in PatternNet dataset
cemetery -> christmas_tree_fram
harbor <--> ferry terminal
Den.Res --> costal home
overpass <--> intersection
park.space --> park.lot
runway_mark --> park.space
costal home <--> sparse Res
swimming pool --> costal home
"""
nb_classes = 38
P = np.eye(nb_classes)
n = noise
if n>0.0:
P[5,5], P[5,7] = 1.-n, n
P[9,9], P[9,32] =1.-n, n
P[11,11], P[11,9] = 1.-n, n
P[17,17], P[17,12] = 1.-n, n
P[12,12], P[12,17] = 1.-n, n
P[18,18], P[18,23] = 1.-n, n
P[23,23], P[23,18] = 1.-n, n
P[25,25], P[25, 24] = 1.-n, n
P[29,29], P[29,25] = 1.-n, n
P[32,32], P[32,9] = 1.-n, n
P[34,34], P[34,9] = 1.-n, n
y_train_noisy = multiclass_noisify(y_train, P=P,
random_state=random_state)
actual_noise = (y_train_noisy != y_train).mean()
assert actual_noise > 0.0
print('Actual noise %.2f' % actual_noise)
y_train = y_train_noisy
return y_train
|
ef37d5c39081cba489076956c0cd948a93ba0387
| 3,640,114
|
def group_superset_counts(pred, label):
"""
Return TP if all label spans appear within pred spans
:param pred, label: A group, represeted as a dict
:return: A Counts namedtuple with TP, FP and FN counts
"""
if (pred["label"] != label["label"]):
return Counts(0, 1, 1)
for label_span in label["spans"]:
for pred_span in pred["spans"]:
if (pred_span["start"] <= label_span["start"] and
pred_span["end"] >= label_span["end"]):
break
else:
return Counts(0, 1, 1)
return Counts(1, 0, 0)
|
8fddd5cfdb0050e97ec60d37e4d939b40cf5d891
| 3,640,115
|
def other():
""" Queries all of the logged in user's Campaigns
and plugs them into the campaigns template """
entities = db.session.query(Entity)
entities = [e.to_dict() for e in entities]
return render_template('other.html', entities=entities)
|
7c7613f919bf5eecc223cf90715e9bd2ae6eb130
| 3,640,116
|
def rel_angle(vec_set1, vec_set2):
"""
Calculate the relative angle between two vector sets
Args:
vec_set1(array[array]): an array of two vectors
vec_set2(array[array]): second array of two vectors
"""
return vec_angle(vec_set2[0], vec_set2[1]) / vec_angle(vec_set1[0], vec_set1[1]) - 1
|
af89a10e26968f53200294919d8b72b532aa3522
| 3,640,117
|
def check_position_axes(chgcar1: CHGCAR, chgcar2: CHGCAR) -> bool:
"""Check the cell vectors and atom positions are same in two CHGCAR.
Parameters
-----------
chgcar1, chgcar2: vaspy.CHGCAR
Returns
-------
bool
"""
cell1 = chgcar1.poscar.cell_vecs
cell2 = chgcar2.poscar.cell_vecs
pos1 = np.array(chgcar1.poscar.positions)
pos2 = np.array(chgcar2.poscar.positions)
assert np.allclose(cell1, cell2), "UnitCells are inconsistent. Abort."
assert np.allclose(pos1, pos2), "Atom positions are inconsistent!!! Abort."
return True
|
29eabfd72a664c77d55164953b6819f3eabd72f1
| 3,640,118
|
def path_shortest(graph, start):
""" Pythonic minheap implementation of dijkstra's algorithm """
# Initialize all distances to infinity but the start one.
distances = {node: float('infinity') for node in graph}
distances[start] = 0
paths = [(0, start)]
while paths:
current_distance, current_node = heap.heappop(paths)
neighbors = graph[current_node].items()
for neighbor, weight in neighbors:
distance = current_distance + weight
if distance < distances[neighbor]:
distances[neighbor] = distance
heap.heappush(paths, (distance, neighbor))
return distances
|
32fe7df3fb02c3a0c3882f5cc5135417c5193985
| 3,640,119
|
import urllib
import json
def request(url, *args, **kwargs):
"""Requests a single JSON resource from the Wynncraft API.
:param url: The URL of the resource to fetch
:type url: :class:`str`
:param args: Positional arguments to pass to the URL
:param kwargs: Keyword arguments (:class:`str`) to pass to the URL
:returns: The returned JSON object as a :class:`dict`
:rtype: :class:`dict`
"""
parsedArgs = (urllib.parse.quote(a) for a in args)
parsedKwargs = {}
for k,v in kwargs.items():
parsedKwargs[k] = urllib.parse.quote(v)
response = urllib.request.urlopen(url.format(*parsedArgs, **parsedKwargs))
data = json.load(response)
response.close()
return data
|
66f23e5a15b44b5c9bc0777c717154749d25987e
| 3,640,120
|
def destagger(var, stagger_dim, meta=False):
"""Return the variable on the unstaggered grid.
This function destaggers the variable by taking the average of the
values located on either side of the grid box.
Args:
var (:class:`xarray.DataArray` or :class:`numpy.ndarray`): A variable
on a staggered grid.
stagger_dim (:obj:`int`): The dimension index to destagger.
Negative values can be used to choose dimensions referenced
from the right hand side (-1 is the rightmost dimension).
meta (:obj:`bool`, optional): Set to False to disable metadata and
return :class:`numpy.ndarray` instead of
:class:`xarray.DataArray`. Default is False.
Returns:
:class:`xarray.DataArray` or :class:`numpy.ndarray`:
The destaggered variable. If xarray is enabled and
the *meta* parameter is True, then the result will be a
:class:`xarray.DataArray` object. Otherwise, the result will be a
:class:`numpy.ndarray` object with no metadata.
"""
var_shape = var.shape
num_dims = var.ndim
stagger_dim_size = var_shape[stagger_dim]
# Dynamically building the range slices to create the appropriate
# number of ':'s in the array accessor lists.
# For example, for a 3D array, the calculation would be
# result = .5 * (var[:,:,0:stagger_dim_size-2]
# + var[:,:,1:stagger_dim_size-1])
# for stagger_dim=2. So, full slices would be used for dims 0 and 1, but
# dim 2 needs the special slice.
full_slice = slice(None)
slice1 = slice(0, stagger_dim_size - 1, 1)
slice2 = slice(1, stagger_dim_size, 1)
# default to full slices
dim_ranges_1 = [full_slice] * num_dims
dim_ranges_2 = [full_slice] * num_dims
# for the stagger dim, insert the appropriate slice range
dim_ranges_1[stagger_dim] = slice1
dim_ranges_2[stagger_dim] = slice2
result = .5*(var[tuple(dim_ranges_1)] + var[tuple(dim_ranges_2)])
return result
|
89bb08618fa8890001f72a43da06ee8b15b328be
| 3,640,121
|
def create_blueprint(request_manager: RequestManager, cache: Cache,
dataset_factory: DatasetFactory):
"""
Creates an instance of the blueprint.
"""
blueprint = Blueprint('metadata', __name__, url_prefix='/metadata')
@cache.memoize()
def _get_method_types_per_approach():
frame = dataset_factory.get_prepared_data_frame()
return metadata.get_method_type_count_per_approach(frame)
@cache.memoize()
def _get_approach_type_counts():
frame = dataset_factory.get_prepared_data_frame()
return metadata.get_approach_type_count(frame)
# pylint: disable=unused-variable
@blueprint.route('method/count')
def get_method_count():
"""
Triggers calculation of number of method types per approach.
---
response:
200:
description: The retrieved result will be a JSON object
representing the number of different method types per
approach.
application/json:
schema:
$ref: '#/definitions/RequestResponse'
"""
ticket = request_manager.submit_ticketed(
_get_method_types_per_approach)
return get_state_response(ticket)
@blueprint.route('approaches/count')
def get_approach_count():
"""
Computes which approach types are present in the available data and how
many sessions each of them was used in.
---
response:
200:
description: The retrieved result will be a JSON object
representing the number of sessions each approach was
used in.
application/json:
schema:
$ref: '#/definitions/RequestResponse'
"""
ticket = request_manager.submit_ticketed(_get_approach_type_counts)
return get_state_response(ticket)
return blueprint
|
32693a6286e4ffb15e4820dbd7ad5fdbe6632e95
| 3,640,123
|
def sides(function_ast, parameters, function_callback):
"""
Given an ast, parses both sides of an expression.
sides(b != c) => None
"""
left = side(function_ast['leftExpression'], parameters, function_callback)
right = side(function_ast['rightExpression'], parameters, function_callback)
return (left, right)
|
9ed00100122f821340a0db37e77bfcf786eacdf9
| 3,640,124
|
def print_url(host, port, datasets):
"""
Prints a list of available dataset URLs, if any. Otherwise, prints a
generic URL.
"""
def url(path = None):
return colored(
"blue",
"http://{host}:{port}/{path}".format(
host = host,
port = port,
path = path if path is not None else ""))
horizontal_rule = colored("green", "—" * 78)
print()
print(horizontal_rule)
if len(datasets):
print(" The following datasets should be available in a moment:")
for path in sorted(datasets, key = str.casefold):
print(" • %s" % url(path))
else:
print(" Open <%s> in your browser." % url())
print()
print(" ", colored("yellow", "Warning: No datasets detected."))
print(horizontal_rule)
print()
|
37d58dce1672f60d72936d6e1b9644fdd5ab689f
| 3,640,125
|
def get_default_sample_path_random(data_path):
"""Return path to sample with default parameters as suffix"""
extra_suffix = get_default_extra_suffix(related_docs=False)
return get_default_sample_path(data_path, sample_suffix=extra_suffix)
|
54220840dc6ef1831859a60058506e7503effcb7
| 3,640,126
|
def VectorShadersAddMaterialDesc(builder, materialDesc):
"""This method is deprecated. Please switch to AddMaterialDesc."""
return AddMaterialDesc(builder, materialDesc)
|
0aaec1d3e14536a65c9cb876075d12348176096c
| 3,640,127
|
import math
def phase_randomize(D, random_state=0):
"""Randomly shift signal phases
For each timecourse (from each voxel and each subject), computes its DFT
and then randomly shifts the phase of each frequency before inverting
back into the time domain. This yields timecourses with the same power
spectrum (and thus the same autocorrelation) as the original timecourses,
but will remove any meaningful temporal relationships between the
timecourses.
This procedure is described in:
Simony E, Honey CJ, Chen J, Lositsky O, Yeshurun Y, Wiesel A, Hasson U
(2016) Dynamic reconfiguration of the default mode network during narrative
comprehension. Nat Commun 7.
Parameters
----------
D : voxel by time by subject ndarray
fMRI data to be phase randomized
random_state : RandomState or an int seed (0 by default)
A random number generator instance to define the state of the
random permutations generator.
Returns
----------
ndarray of same shape as D
phase randomized timecourses
"""
random_state = check_random_state(random_state)
F = fft(D, axis=1)
if D.shape[1] % 2 == 0:
pos_freq = np.arange(1, D.shape[1] // 2)
neg_freq = np.arange(D.shape[1] - 1, D.shape[1] // 2, -1)
else:
pos_freq = np.arange(1, (D.shape[1] - 1) // 2 + 1)
neg_freq = np.arange(D.shape[1] - 1, (D.shape[1] - 1) // 2, -1)
shift = random_state.rand(D.shape[0], len(pos_freq),
D.shape[2]) * 2 * math.pi
# Shift pos and neg frequencies symmetrically, to keep signal real
F[:, pos_freq, :] *= np.exp(1j * shift)
F[:, neg_freq, :] *= np.exp(-1j * shift)
return np.real(ifft(F, axis=1))
|
d8f3230acdf8b3df98995adaadc92f41497a27ea
| 3,640,128
|
def monospaced(text):
"""
Convert all contiguous whitespace into single space and strip leading and
trailing spaces.
Parameters
----------
text : str
Text to be re-spaced
Returns
-------
str
Copy of input string with all contiguous white space replaced with
single space " ".
"""
return REGEX_SPACE.sub(' ', text).strip()
|
51f07908dde10ef67bd70b5eb65e03ee832c3755
| 3,640,129
|
def molecule_block(*args, **kwargs):
"""
Generates the TRIPOS Mol2 block for a given molecule, returned as a string
"""
mol = Molecule(*args, **kwargs)
block = mol.molecule_block() + mol.atom_block() + mol.bond_block() + '\n'
return block
|
79ebf821e105666fb81396197fa0f218b2cf3e48
| 3,640,130
|
def setup_test_env(settings_key='default'):
"""Allows easier integration testing by creating RPC and HTTP clients
:param settings_key: Desired server to use
:return: Tuple of RPC client, HTTP client, and thrift module
"""
return RpcClient(handler), HttpClient(), load_module(settings_key)
|
827a71692dd2eb9946db34289dcf48d5b5d4415b
| 3,640,131
|
from typing import List
from typing import Tuple
def calculateCentroid(
pointCloud : List[Tuple[float, float, float]]
) -> Tuple[float, float, float]:
"""Calculate centroid of point cloud.
Arguments
--------------------------------------------------------------------------
pointCloud (float 3-tuple list) -- list of xyz coordinates.
Returns
--------------------------------------------------------------------------
centroid (float 3-tuple) -- centroid of points in point cloud.
"""
numPoints = len(pointCloud)
x, y, z = [], [], []
for point in pointCloud:
x.append(point[0])
y.append(point[1])
z.append(point[2])
x, y, z = sum(x) / numPoints, sum(y) / numPoints, sum(z) / numPoints
return x, y, z
|
0e8d6d578a0a983fe1e68bff22c5cc613503ee76
| 3,640,132
|
def get_num_uniq_users(csv_file, userid_col):
"""
A Helper function to help get the number of unique users
:param csv_file: path to CSV file
:param userid_col: Column for user ID
:return:
"""
# Read the CSV file using pandas
df = pd.read_csv(csv_file)
# Use the nunique() method to get number of unique users
num = len(np.unique(df[userid_col]))
return num
|
ade25596bb308414c80e1aea87d412bd5a340288
| 3,640,134
|
from zooniverse_web.models import Survey, QuestionResponse, Response, QuestionOption
from zooniverse_web.utility.survey import generate_new_survey
def administration(request):
"""Administration actions ((re)train acton predictor for a new survey)
Parameters
----------
request:
POST request
Returns
-------
render:
django.shortcuts.render (a page to be rendered)
"""
message = None
message_class = None
if request.method == 'POST':
next_action = request.POST.get('submit', None)
if next_action == '(Re)Train Recommender':
previous_survey = Survey.objects.filter(active=True).order_by('-creation_date').first()
if not previous_survey:
survey_created = generate_new_survey()
message_class = 'success'
message = 'New survey created on {}!'.format(survey_created.creation_date)
else:
# Are there any responses for this survey?
try:
for option in QuestionOption.objects.all():
QuestionResponse.objects.filter(
response=Response.objects.get(
status=Response.FINISHED,
survey=previous_survey
),
answer=option.option
)
survey_created = generate_new_survey()
message_class = 'success'
message = 'New survey created on {}!'.format(survey_created.creation_date.date())
except (QuestionOption.DoesNotExist, QuestionResponse.DoesNotExist, Response.DoesNotExist):
message = 'You do not have enough question responses saved yet for the current survey! ' \
'Try again later.'
message_class = 'warning'
except:
message = 'Something went wrong while generating the survey. Please try again. <br />' \
'If the problem keeps on occuring, please contact your system administrator.'
message_class = 'danger'
else:
message = ''
message_class = ''
return render(
request,
'administration/administration.html',
{
'message': message,
'message_class': message_class,
}
)
|
7d5a08450c9058f6fd33a13fc4cf6b714bc7e657
| 3,640,136
|
from typing import Counter
def checkout(skus):
"""
Calculate the total amount for the checkout based on the SKUs entered in
:param skus: string, each char is an item
:return: int, total amount of the cart, including special offers
"""
total = 0
counter = Counter(skus)
# got through the offers (biggest first), and calculate the line total, and any free offers...
for item in counter:
print('item: {}'.format(item))
if item not in ITEMS:
return -1
line_total = 0
free_offer = 0
qty = counter[item]
ordered_offers = sorted(ITEMS[item]['special_offers'], key=lambda k: (k['min_quantity']), reverse=True)
# does this item have an specials?
for offer in ordered_offers:
# how many can we get of the biggest offer
number_of_offers = qty // offer['min_quantity']
if 'price' in offer:
# how many are left, put in qty for next offer...
number_of_items_in_offer = number_of_offers * offer['min_quantity']
qty -= number_of_items_in_offer
# update the line total
line_total += number_of_offers * offer['price']
elif 'other_free' in offer:
if offer['other_free'] in counter:
# make sure we have the min required items
if counter[item] >= offer['min_quantity']:
other_free = offer['other_free']
# is this full price the correct value? what if we used a multi price?
free_offer = number_of_offers * ITEMS[other_free]['price']
# add any remaining qty as full price to the line_total
line_total += qty * ITEMS[item]['price']
# add the line total, and the free offers to the checkout total
total += line_total
total -= free_offer
return total
|
ad00a9c3e3cd4f34cfd7b5b306d3863decc0751b
| 3,640,137
|
def func_tradeg(filename, hdulist=None, whichhdu=None):
"""Return the fits header value TELRA in degrees.
"""
hdulist2 = None
if hdulist is None:
hdulist2 = fits.open(filename, 'readonly')
else:
hdulist2 = hdulist
telra = fitsutils.get_hdr_value(hdulist2, 'TELRA')
if hdulist is None:
hdulist2.close()
return spmeta.convert_ra_to_deg(telra)
|
4e6751d2eb0ac9e6264f768e932cbd42c2fc2c4e
| 3,640,138
|
def column_indexes(column_names, row_header):
"""項目位置の取得
Args:
column_names (str): column name
row_header (dict): row header info.
Returns:
[type]: [description]
"""
column_indexes = {}
for idx in column_names:
column_indexes[idx] = row_header.index(column_names[idx])
return column_indexes
|
4205e31e91cd64f833abd9ad87a02d91eebc8c61
| 3,640,139
|
import logging
import pickle
def dmx_psrs(caplog):
"""Sample pytest fixture.
See more at: http://doc.pytest.org/en/latest/fixture.html
"""
caplog.set_level(logging.CRITICAL)
psrs = []
for p in psr_names:
with open(datadir+'/{0}_ng9yr_dmx_DE436_epsr.pkl'.format(p), 'rb') as fin:
psrs.append(pickle.load(fin))
return psrs
|
6bbb5df017374f207d7c9338a737212f5c7e5b23
| 3,640,140
|
def fit_stats(act_map, param, func=KentFunc):
"""Generate fitting statistics from scipy's curve fitting"""
phi_grid, theta_grid = meshgrid(phi_arr, theta_arr)
Xin = np.array([theta_grid.flatten(), phi_grid.flatten()]).T
fval = act_map.flatten()
fpred = func(Xin, *param) # KentFunc
res = fval - fpred
rsquare = 1 - (res**2).mean() / fval.var()
return res.reshape(act_map.shape), rsquare
|
97e4223daf3e140f1a091491f18012840b8c006a
| 3,640,141
|
def conv2d_for_hpool_valid_width_wrapper(inputs,filters,strides,padding,**kwargs):
"""
Wraps tf.layers.conv2d to allow valid convolution across signal width and
'same' convolution across signal height when padding is set to "valid_time"
Arguments:
inputs (TF Tensor): Tensor input.
filters (TF Tensor): Must have the same type as input.
A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
strides (int or tuple/list) : An integer or tuple/list of 2 integers,
specifying the strides of the convolution along the height and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding (string): One of `"valid"`, `"same"`, or `"valid_time"` (case-insensitive).
kwargs (dictionary): Specifies all other arguments required by
tf.layers.conv2d. Passes these directly to function without modification.
See Tensorflow documentation for further details.
Returns:
(TF Tensor): Output of tf.layers.conv2d.
"""
#Collects relvant parameters
size=inputs.get_shape()
kernel_size = filters.get_shape()
filter_height = int(kernel_size[0])
in_height = int(size[1])
#Calculates according to SAME padding formula
if (in_height % strides[0] == 0):
pad_along_height = max(filter_height - strides[0], 0)
else:
pad_along_height = max(filter_height - (in_height % strides[0]), 0)
pad_top = pad_along_height // 2
pad_bottom = pad_along_height - pad_top
#Pads signal if VALID_TIME is selected and padding is necessary
#Otherwise, pass inputs through and allow specified convolutioon
if pad_along_height == 0 or padding.upper() != 'VALID_TIME':
padding = 'VALID' if padding.upper() == 'VALID_TIME' else padding
output_tensor = tf.nn.conv2d(inputs,filter=filters,
strides=strides,padding=padding,
**kwargs)
else:
#Pads input tensor and moves conv2d to valid padding
paddings = tf.constant([[0,0],[pad_top, pad_bottom], [0, 0],[0,0]])
input_padded = tf.pad(inputs,paddings)
output_tensor=tf.nn.conv2d(input_padded,filter=filters,
strides=strides, padding="VALID",
**kwargs)
return output_tensor
|
9b4438c687232245e645ea5714e7ad7899ecd98b
| 3,640,142
|
import copy
def resample_cells(tree, params, current_node = 'root', inplace = False):
"""
Runs a new simulation of the cell evolution on a fixed tree
"""
if not inplace:
tree = copy.deepcopy(tree)
for child in tree.successors(current_node):
initial_cell = tree.nodes[current_node]['cell'].deepcopy()
initial_cell.reset_seed()
tree.nodes[child]['cell'] = sim.evolve_cell(initial_cell,
tree.nodes[child]['time_to_parent'],
params)
resample_cells(tree, params, current_node = child, inplace = True)
return tree
|
10cde9abdf3a6271aa20276c3e193b1c93ca7908
| 3,640,143
|
def get_sql_query(table_name:str) -> str:
"""Fetch SQL query file for generation of dim or fact table(s)"""
f = open(f'./models/sql/{table_name}.sql')
f_sql_query = f.read()
f.close()
return f_sql_query
|
fc3308eae51b7d10667a50a0f4ee4e295bfea8d0
| 3,640,144
|
def _map_args(call_node, function):
"""Maps AST call nodes to the actual function's arguments.
Args:
call_node: ast.Call
function: Callable[..., Any], the actual function matching call_node
Returns:
Dict[Text, ast.AST], mapping each of the function's argument names to
the respective AST node.
"""
args = call_node.args
kwds = {kwd.arg: kwd.value for kwd in call_node.keywords}
return tf_inspect.getcallargs(function, *args, **kwds)
|
b19befded386e6081be9858c7eb31ffd45c96ef3
| 3,640,145
|
def sub_bases( motif ):
"""
Return all possible specifications of a motif with degenerate bases.
"""
subs = {"W":"[AT]", \
"S":"[CG]", \
"M":"[AC]", \
"K":"[GT]", \
"R":"[AG]", \
"Y":"[CT]", \
"B":"[CGT]", \
"D":"[AGT]", \
"H":"[ACT]", \
"V":"[ACG]", \
"N":"[ACGTN]"}
for symbol,sub in subs.iteritems():
if motif.find(symbol) > -1:
motif = motif.replace(symbol, sub)
return motif
|
10ff2ea1959aba103f1956398afb5f1d8801edd7
| 3,640,146
|
import logging
def parse(input_file_path):
"""
Parse input file
:param input_file_path: input file path
:return: Image list
"""
verticals, horizontals = 0, 0
logging.info("parsing %s", input_file_path)
with open(input_file_path, 'r') as input_file:
nb = int(input_file.readline()) # images nb
images = []
for i, img_txt in enumerate(input_file.readlines()):
data = img_txt.rstrip().split(' ')
orientation = data[0]
tags = data[2:]
images.append(Image(i, orientation, set(tags)))
if orientation == 'V':
verticals += 1
else: # H
horizontals += 1
logging.info('parsing %s done', input_file_path)
logging.info('%d images found (%d V,%d H)', nb, verticals, horizontals)
return images
|
ecd4fd066d1128f385da59965a93e59c038052bd
| 3,640,147
|
def char(ctx, number):
"""
Returns the character specified by a number
"""
return chr(conversions.to_integer(number, ctx))
|
5c5254978055f690b6801479b180ff39b31e2248
| 3,640,148
|
import re
import logging
async def get_character_name(gear_url, message):
"""
It is *sometimes* the case that discord users don't update their username
to be their character name (eg for alts).
This method renders the gear_url in an HTML session and parses the page
to attempt to find the character's name.
This assumes a specific format of the page: player names are nested in
an h3 element with css class named 'class-[player class]'
Returns the character's name if successful, otherwise returns the message sender's
display name in discord.
"""
name = message.author.display_name
if not re.match(SIXTY_UPGRADES_REGEX, gear_url):
return name
for i in range(MAX_FETCH_CHARACTER_NAME_RETRIES):
try:
asession = AsyncHTMLSession()
webpage = await asession.get(gear_url)
await webpage.html.arender()
query_selector = "h3[class^='class-']"
name = webpage.html.find(query_selector, first=True).text
break
except Exception as e:
logging.error(e)
finally:
await asession.close()
return name
|
cdd18e0123f226d2c59d41bbf39e0dfc02188d73
| 3,640,149
|
import pandas
def get_treant_df(tags, path='.'):
"""Get treants as a Pandas DataFrame
Args:
tags: treant tags to identify the treants
path: the path to search for treants
Returns:
a Pandas DataFrame with the treant name, tags and categories
>>> from click.testing import CliRunner
>>> from toolz.curried import do
>>> with CliRunner().isolated_filesystem() as dir_:
... assert pipe(
... dir_,
... dtr.Treant,
... do(lambda x: x.__setattr__('tags', ['atag'])),
... lambda x: x.uuid[:8],
... lambda x: x == get_treant_df(['atag'], path=dir_).uuid[0]
... )
"""
return pipe(
tags,
get_by_tags(path=path),
lambda x: x.map(get_treant_data),
pandas.DataFrame,
)
|
a5972646e27ffd88d18f1c0d212a2ae081ebe4f1
| 3,640,150
|
def gather_keypoints(keypoints_1, keypoints_2, matches):
"""
Gather matched keypoints in a (n x 4) array,
where each row correspond to a pair of matching
keypoints' coordinates in two images.
"""
res = []
for m in matches:
idx_1 = m.queryIdx
idx_2 = m.trainIdx
pt_1 = keypoints_1[idx_1].pt
pt_2 = keypoints_2[idx_2].pt
row = [pt_1[0], pt_1[1], pt_2[0], pt_2[1]]
res.append(row)
return np.array(res)
|
5abef87c570493b57e81dcddc2732ed541aa6a08
| 3,640,151
|
async def stop():
""" Stop any playing audio. """
Sound.stop()
return Sound.get_state()
|
3c7ea7aae3e8dd7e3b33ddd9beed0ce2182800bc
| 3,640,152
|
def is_numeric(X, compress=True):
"""
Determine whether input is numeric array
Parameters
----------
X: Numpy array
compress: Boolean
Returns
-------
V: Numpy Boolean array if compress is False, otherwise Boolean Value
"""
def is_float(val):
try:
float(val)
except ValueError:
return False
else:
return True
isnumeric = np.vectorize(is_float, otypes=[bool]) # return numpy array
V = isnumeric(X)
if compress:
return np.all(V)
return V
|
ad28657f51680cd193671a6a8a8da6a91390dc15
| 3,640,153
|
def figure_ellipse_fitting(img, seg, ellipses, centers, crits, fig_size=9):
""" show figure with result of the ellipse fitting
:param ndarray img:
:param ndarray seg:
:param [(int, int, int, int, float)] ellipses:
:param [(int, int)] centers:
:param [float] crits:
:param float fig_size:
:return:
>>> img = np.random.random((100, 150, 3))
>>> seg = np.random.randint(0, 2, (100, 150))
>>> ells = np.random.random((3, 5)) * 25
>>> centers = np.random.random((3, 2)) * 25
>>> crits = np.random.random(3)
>>> fig = figure_ellipse_fitting(img[:, :, 0], seg, ells, centers, crits)
>>> isinstance(fig, matplotlib.figure.Figure)
True
"""
assert len(ellipses) == len(centers) == len(crits), \
'number of ellipses (%i) and centers (%i) and criteria (%i) ' \
'should match' % (len(ellipses), len(centers), len(crits))
fig, ax = create_figure_by_image(img.shape[:2], fig_size)
assert img.ndim == 2, \
'required image dimension is 2 to instead %s' % repr(img.shape)
ax.imshow(img, cmap=plt.cm.Greys_r)
for i, params in enumerate(ellipses):
c1, c2, h, w, phi = params
rr, cc = ellipse_perimeter(int(c1), int(c2), int(h), int(w), phi)
ax.plot(cc, rr, '.', color=COLORS[i % len(COLORS)],
label='#%i with crit=%d' % ((i + 1), int(crits[i])))
ax.legend(loc='lower right')
# plt.plot(centers[:, 1], centers[:, 0], 'ow')
for i in range(len(centers)):
ax.plot(centers[i, 1], centers[i, 0], 'o',
color=COLORS[i % len(COLORS)])
ax.set_xlim([0, seg.shape[1]])
ax.set_ylim([seg.shape[0], 0])
ax.axis('off')
fig.subplots_adjust(left=0, right=1, top=1, bottom=0)
return fig
|
de6b58a01a64c3123f5aad4dfb6935c6c19a041c
| 3,640,154
|
def fmt_bytesize(num: float, suffix: str = "B") -> str:
"""Change a number of bytes in a human readable format.
Args:
num: number to format
suffix: (Default value = 'B')
Returns:
The value formatted in human readable format (e.g. KiB).
"""
for unit in ["", "Ki", "Mi", "Gi", "Ti", "Pi", "Ei", "Zi"]:
if abs(num) < 1024.0:
return "{:3.1f} {}{}".format(num, unit, suffix)
num /= 1024.0
return "{:.1f} {}{}".format(num, "Yi", suffix)
|
09b36d229856004b6df108ab1ce4ef0a9c1e6289
| 3,640,155
|
def get_kpoint_mesh(structure: Structure, cutoff_length: float, force_odd: bool = True):
"""Calculate reciprocal-space sampling with real-space cut-off."""
reciprocal_lattice = structure.lattice.reciprocal_lattice_crystallographic
# Get reciprocal cell vector magnitudes
abc_recip = np.array(reciprocal_lattice.abc)
mesh = np.ceil(abc_recip * 2 * cutoff_length).astype(int)
if force_odd:
mesh += (mesh + 1) % 2
return mesh
|
0536b5e2c37b7ba98d240fc3099fad93d246f730
| 3,640,156
|
def resnet_v1_101(inputs,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
spatial_squeeze=True,
reuse=None,
scope='resnet_v1_101', **kwargs):
"""ResNet-101 model of [1]. See resnet_v1() for arg and return description."""
blocks = [
resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
resnet_v1_block('block3', base_depth=256, num_units=23, stride=2),
resnet_v1_block('block4', base_depth=512, num_units=3, stride=1),
]
return resnet_v1(inputs, blocks, num_classes, is_training,
global_pool=global_pool, output_stride=output_stride,
include_root_block=True, spatial_squeeze=spatial_squeeze,
reuse=reuse, scope=scope, **kwargs)
|
138289084d48edd9d9c1096bd790b1479d902ec1
| 3,640,157
|
def is_package_authorized(package_name):
"""
get user information if it is authorized user in the package config
Returns:
[JSON string]: [user information session]
"""
authorized_users = get_package_admins(package_name)
user_info = get_user_info()
user_dict = j.data.serializers.json.loads(user_info)
username = user_dict["username"]
# if the package doesn't include admins then allow any authenticated user
if authorized_users and not any([username in authorized_users, username in j.core.identity.me.admins]):
return abort(403)
return user_info
|
59b89ebb9c8579d61a18a194e7f5f4bd41d738b6
| 3,640,158
|
def submit_search_query(query_string, query_limit, query_offset,
class_resource):
"""
Submit a search query request to the RETS API
"""
search_result = class_resource.search(
query='%s' % query_string, limit=query_limit, offset=query_offset)
return search_result
|
f8c30c86f7ff7c33fc96b26b1491ddaa48710fbc
| 3,640,159
|
def one_hot_encode(df):
"""
desc : one hot encodes categorical cols
args:
df (pd.DataFrame) : stroke dataframe
returns:
df (pd.DataFrame) : stroke dataframe with one_hot_encoded columns
"""
# extract categorical columns
stroke_data = df.copy()
cat_cols = stroke_data.select_dtypes(include = ["object"])
cat_vals = cat_cols.values
cat_cols_names = cat_cols.columns
enc = OneHotEncoder(sparse = False)
encoded_vals = enc.fit_transform(cat_vals)
encoded_cols = enc.get_feature_names(cat_cols_names)
encoded_cols = pd.DataFrame(encoded_vals,columns = encoded_cols,index = cat_cols.index)
#drop non one hot encoded cols
stroke_data.drop(columns = cat_cols_names, axis = 1, inplace = True)
#add encoded columns
stroke_data = pd.concat([stroke_data,encoded_cols], axis = 1)
#print(stroke_data.shape)
print(stroke_data)
return stroke_data
|
6895dfbc4bb57d5e8d9a5552e2ac7fcb94e07434
| 3,640,160
|
def assert_increasing(a):
"""Utility function for enforcing ascending values.
This function's handle can be supplied as :py:kwarg:`post_method` to a
:py:func:`processed_proprty <pyproprop>` to enforce values within a
:py:type:`ndarray <numpy>` are in ascending order. This is useful for
enforcing time guesses to be sequential.
"""
if (a is not None) and (not np.all(np.diff(a) >= 0)):
msg = f"Elements in {a} must be in ascending numerical order."
raise ValueError(msg)
return a
|
f1ded37b40686cf400da23f567880e73180a78fe
| 3,640,161
|
def copy_to_device(device,
remote_path,
local_path='harddisk:',
server=None,
protocol='http',
vrf=None,
timeout=300,
compact=False,
use_kstack=False,
fu=None,
http_auth=True,
**kwargs):
"""
Copy file from linux server to the device.
Args:
device (Device): Device object
remote_path (str): remote file path on the server
local_path (str): local file path to copy to on the device (default: harddisk:)
server (str): hostname or address of the server (default: None)
protocol(str): file transfer protocol to be used (default: http)
vrf (str): vrf to use (optional)
timeout(int): timeout value in seconds, default 300
compact(bool): compress image option for n9k, defaults False
fu(obj): FileUtils object to use instead of creating one. Defaults to None.
use_kstack(bool): Use faster version of copy, defaults False
Not supported with a file transfer protocol
prompting for a username and password
http_auth (bool): Use http authentication (default: True)
Returns:
None
If the server is not specified, a HTTP server will be spawned
on the local system and serve the directory of the file
specified via remote_path and the copy operation will use http.
If the device is connected via CLI proxy (unix jump host) and the proxy has
'socat' installed, the transfer will be done via the proxy automatically.
"""
return generic_copy_to_device(device=device,
remote_path=remote_path,
local_path=local_path,
server=server,
protocol=protocol,
vrf=vrf,
timeout=timeout,
compact=compact,
use_kstack=use_kstack,
fu=fu,
http_auth=http_auth,
**kwargs)
|
762ef928656473458e0fee8dc47c1a581103ed0e
| 3,640,162
|
def decode_replay_header(contents):
"""Decodes and return the replay header from the contents byte string."""
decoder = VersionedDecoder(contents, protocol.typeinfos)
return decoder.instance(protocol.replay_header_typeid)
|
1fcee7900a5c0c310e67afe31a154b8310da7089
| 3,640,164
|
def _generate_indexed(array: IndexedArray) -> str:
"""Generate an indexed Bash array."""
return (
"("
+ " ".join(
f"[{index}]={_generate_string(value)}"
for index, value in enumerate(array)
if value is not None
)
+ ")"
)
|
2443b3c6be74684360c395995b3d16d4ebecf1d8
| 3,640,165
|
from datetime import datetime
def up_date(dte, r_quant, str_unit, bln_post_colon):
""" Adjust a date in the light of a (quantity, unit) tuple,
taking account of any recent colon
"""
if str_unit == 'w':
dte += timedelta(weeks=r_quant)
elif str_unit == 'd':
dte += timedelta(days=r_quant)
elif str_unit == 'h':
dte += timedelta(hours=r_quant)
elif str_unit == 'm':
dte += timedelta(minutes=r_quant)
elif str_unit in ('Y', 'y'):
if r_quant > 500: # jul 2019 vs jul 17
r_year = r_quant
else:
r_year = datetime.now().year + r_quant
try:
dte = datetime.replace(dte, year=int(r_year))
except ValueError:
dte = datetime.replace(dte, day=28, month=2,
year=int(datetime.now().year + r_quant))
elif str_unit == 'H':
dte = datetime.replace(dte, hour=int(r_quant), second=0, microsecond=0)
elif str_unit == 'M':
dte = datetime.replace(dte, minute=int(r_quant),
second=0, microsecond=0)
elif str_unit == 'a':
if not bln_post_colon:
dte = datetime.replace(dte, hour=int(r_quant), minute=0,
second=0, microsecond=0)
elif str_unit == 'p':
if bln_post_colon: # adjust by 12 hours if necessary
if dte.hour < 12:
dte = datetime.replace(dte, hour=dte.hour+12)
else:
p_quant = r_quant
if p_quant < 12:
p_quant += 12
dte = datetime.replace(dte, hour=int(p_quant), minute=0,
second=0, microsecond=0)
elif (len(str_unit) >= 3) and (STR_MONTHS.find(str_unit) != -1):
dte = datetime.replace(dte, month=(STR_MONTHS.index(str_unit) + 3)/3,
day=int(r_quant), second=0, microsecond=0)
# refers to this year or next year ? (assume not past)
dte_today = datetime.today().replace(hour=0, minute=0, \
second=0, microsecond=0)
if dte < dte_today:
dte = dte.replace(year=(dte_today.year+1))
return dte
|
684b09e5d37bf0d3445262b886c73188d35425ef
| 3,640,166
|
from typing import MutableMapping
def read_options() -> Options:
"""
read command line arguments and options
Returns:
option class(Options)
Raises:
NotInspectableError: the file or the directory does not exists.
"""
args: MutableMapping = docopt(__doc__)
schema = Schema({
"<path>": And(Use(get_path), lambda path: path.is_file() or path.is_dir(),
error=f"The specified path {args['<path>']}"
" does not exist.\n")
})
try:
args = schema.validate(args)
except SchemaError as e:
raise NotInspectableError(e.args[0])
return Options(args["<path>"])
|
25cd3c29f6e206fd97334f7a48d267680a9e553c
| 3,640,167
|
def test_generator_single_input_2():
"""
Feature: Test single str input
Description: input str
Expectation: success
"""
def generator_str():
for i in range(64):
yield chr(ord('a') + i)
class RandomAccessDatasetInner:
def __init__(self):
self.__data = [i for i in range(64)]
def __getitem__(self, item):
return chr(ord('a') + self.__data[item])
def __len__(self):
return 64
class SequentialAccessDataset:
def __init__(self):
self.__data = [i for i in range(64)]
self.__index = 0
def __next__(self):
if self.__index >= 64:
raise StopIteration
item = chr(ord('a') + self.__data[self.__index])
self.__index += 1
return item
def __iter__(self):
self.__index = 0
return self
def __len__(self):
return 64
def assert_generator_single_input_2(data):
# apply dataset operations
data1 = ds.GeneratorDataset(data, ["data"], shuffle=False)
i = 0
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
s = chr(ord('a') + i)
golden = np.array(bytes(s, encoding='utf8'))
np.testing.assert_array_equal(item["data"], golden)
i = i + 1
assert_generator_single_input_2(generator_str)
assert_generator_single_input_2(RandomAccessDatasetInner())
assert_generator_single_input_2(SequentialAccessDataset())
|
d251279c0740b52c9d32c2ae572f6dbdf32f36ea
| 3,640,168
|
def SLINK(Dataset, d):
"""function to execute SLINK algo
Args:
Dataset(List) :- list of data points, who are also lists
d(int) :- dimension of data points
Returns:
res(Iterables) :- list of triples sorted by the second element,
first element is index of point,
the other two are pointer representations of dendrograms noting the
lowest level at which i is no longer the last point in his cluster and
the last point in the cluster which i then joins
Heights(Iterables) :- list of the second element of res' triples
"""
n = len(Dataset)
A = [inf for i in range(n)]
B = [0 for i in range(n)]
# initialisation
A[0] = inf
B[0] = 0
for k in range(1, n):
B[k] = k
A[k] = inf
M = [0 for i in range(k + 1)]
for i in range(k):
M[i] = metrics(Dataset[i], Dataset[k])
for i in range(k):
if(A[i] >= M[i]):
M[B[i]] = min(M[B[i]], A[i])
A[i] = M[i]
B[i] = k
if(A[i] < M[i]):
M[B[i]] = min(M[B[i]], M[i])
for i in range(k):
if(A[i] >= A[B[i]]):
B[i] = k
res = [(index, i, j) for index, (i, j) in enumerate(zip(A, B))]
res = sorted(res, key=lambda x: x[1])
Heights = [triple[1] for triple in res]
return(res, Heights)
|
d10a3f8cb3e6d81649bebd4a45f5be79d206f1be
| 3,640,169
|
def static_file(path='index.html'):
"""static_file"""
return app.send_static_file(path)
|
5c3f2d423d029a8e7bb8db5fbe3c557f7a6aa9c3
| 3,640,170
|
def lazy_property(function):
""" Decorator to make a lazily executed property """
attribute = '_' + function.__name__
@property
@wraps(function)
def wrapper(self):
if not hasattr(self, attribute):
setattr(self, attribute, function(self))
return getattr(self, attribute)
return wrapper
|
db1d62eb66a018bc166b67fe9c2e25d671261f77
| 3,640,171
|
from pathlib import Path
def basename(fname):
"""
Return file name without path.
Examples
--------
>>> fname = '../test/data/FSI.txt.zip'
>>> print('{}, {}, {}'.format(*basename(fname)))
../test/data, FSI.txt, .zip
"""
if not isinstance(fname, path_type):
fname = Path(fname)
path, name, ext = fname.parent, fname.stem, fname.suffix
return path, name, ext
|
55cd53ec71e4e914493129e40fa216ddcdbe8083
| 3,640,172
|
def validate_lockstring(lockstring):
"""
Validate so lockstring is on a valid form.
Args:
lockstring (str): Lockstring to validate.
Returns:
is_valid (bool): If the lockstring is valid or not.
error (str or None): A string describing the error, or None
if no error was found.
"""
global _LOCK_HANDLER
if not _LOCK_HANDLER:
_LOCK_HANDLER = LockHandler(_ObjDummy())
return _LOCK_HANDLER.validate(lockstring)
|
0feb67597e31667013ab182159c8433ae4a80346
| 3,640,173
|
from typing import Iterable
def decode_geohash_collection(geohashes: Iterable[str]):
"""
Return collection of geohashes decoded into location coordinates.
Parameters
----------
geohashes: Iterable[str]
Collection of geohashes to be decoded
Returns
-------
Iterable[Tuple[float, float]]
Collection of location coordinates in Latitude/Longitude
"""
locations = []
for geohash in geohashes:
exact_location = decode_geo_hash(geohash)
locations.append((exact_location[0], exact_location[1]))
return locations
|
2e673c852c7ac2775fd29b32243bdc8b1aa83d77
| 3,640,174
|
def renormalize_sparse(A: sp.spmatrix) -> sp.spmatrix:
"""Get (D**-0.5) * A * (D ** -0.5), where D is the diagonalized row sum."""
A = sp.coo_matrix(A)
A.eliminate_zeros()
rowsum = np.array(A.sum(1))
assert np.all(rowsum >= 0)
d_inv_sqrt = np.power(rowsum, -0.5).flatten()
d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.0
d_mat_inv_sqrt = sp.diags(d_inv_sqrt)
return d_mat_inv_sqrt.dot(A).dot(d_mat_inv_sqrt)
# return A.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()
|
33122bcf018dba842f04e044cf9e799860a56042
| 3,640,175
|
def _fetch(
self,
targets=None,
jobs=None,
remote=None,
all_branches=False,
show_checksums=False,
with_deps=False,
all_tags=False,
recursive=False,
):
"""Download data items from a cloud and imported repositories
Returns:
int: number of successfully downloaded files
Raises:
DownloadError: thrown when there are failed downloads, either
during `cloud.pull` or trying to fetch imported files
config.NoRemoteError: thrown when downloading only local files and no
remote is configured
"""
used = self.used_cache(
targets,
all_branches=all_branches,
all_tags=all_tags,
with_deps=with_deps,
force=True,
remote=remote,
jobs=jobs,
recursive=recursive,
)
downloaded = 0
failed = 0
try:
downloaded += self.cloud.pull(
used, jobs, remote=remote, show_checksums=show_checksums
)
except NoRemoteError:
if not used.external and used["local"]:
raise
except DownloadError as exc:
failed += exc.amount
for (repo_url, repo_rev), files in used.external.items():
d, f = _fetch_external(self, repo_url, repo_rev, files)
downloaded += d
failed += f
if failed:
raise DownloadError(failed)
return downloaded
|
18238bb1c4c5bd0772757013173e26645d5cdf5a
| 3,640,178
|
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