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def get_ordered_patterns(order, patterns):
"""
Place the sites in the pattern dictionary into the order that they were
added.
Input:
order: a list of pattern ids providing the order
patterns: a dictionary of patterns
Output: Nested array with columns for sites, their order, and amplicon id
"""
order_dict = {pattern: order + 1 for order, pattern in enumerate(order)}
result = []
for pattern, loci in patterns.items():
for locus, data in loci.items():
for site in data['sites']:
result.append([site.strip(), order_dict[pattern], pattern])
return np.array(result, dtype=str)
|
b3622fa9b3330923da819cd923582db13199f86a
| 27,629 |
def extract_pphour(file, fecha, llat, llon):
"""
"""
l_lat = llat
l_lon = np.array(llon) % 360
i_lat, i_lon, lat, lon = get_index_lat(fecha, file, llat, llon)
tiempos = get_index_time(file, fecha)
di = tiempos[1]
di_f = (di + dt.timedelta(days=8)).replace(hour=0)
i_t1 = [i for i in range(len(tiempos)) if tiempos[i] == di][0]
i_t2 = [i for i in range(len(tiempos)) if tiempos[i] == di_f][0]
# Creamos una variable aux
res = np.empty([i_t2 - i_t1 + 1, len(lat), len(lon)])
res[:] = np.nan
fdates = [datef for datef in tiempos if datef>=di and datef<=di_f]
pp1 = file.variables['apcpsfc'][i_t1:i_t2, i_lat[0]:i_lat[1]+1,
i_lon[0]:i_lon[1]+1]
pp2 = file.variables['apcpsfc'][i_t1+1:i_t2+1, i_lat[0]:i_lat[1]+1,
i_lon[0]:i_lon[1]+1]
pphour = pp2 - pp1
return lon, lat, pphour, fdates
|
e5e2a2271e85a6d5a9f6fc4a189b1119f62d471b
| 27,630 |
def bubble_sort2(array):
"""冒泡排序的经典实现"""
n = len(array)
stop = False
#子过程运行闭区间的右端点标记定义为i,范围[n-1,1],转换为适合while惯用区间为[n-1,0)
i = n-1
while i > 0 and not stop:
stop = True
#子过程交换位置标记定义为j，范围为[0,i-1]，转换为适合while惯用区间为[0，i)
#子过程运行区间为[0,i]
j = 0
while j < i:
if array[j] > array[j+1]:
array[j],array[j+1] = array[j+1],array[j]
stop = False
j += 1
i -= 1
return array
|
c95706b40c6e328e321f68de117ee20faa5c1c31
| 27,631 |
def encriptar(texto):
"""função que recebe um texto e retorna o mesmo criptografado"""
#1. Filtro de entrada para evitar erros
#1.1 Verifica se texto é uma string
#1.1 Se texto é vazio, retorna texto
#2. remove os espaços em branco do texto
texto = texto.replace(" ","")
#3. sobre a grade
#3.1 Calcula o tamanho da grade (valor de n)
n = ceil(sqrt(len(texto)))
#3.2 Cria a grade
grade = [["" for _ in range(n)] for __ in range(n)]
#4. insere o texto na grade, no sentido vertical,
# de cima para baixo, da esquerda para a direita.
for j in range(n): #j = coluna
for i in range(n): #i = linha
try:
elemento,texto = texto[0],texto[1:]
except:
elemento = ""
finally:
#linha.append(elemento)
grade[i][j] = elemento
#5. transforma a grade em um texto
texto_criptografado = " ".join(["".join(x) for x in grade])
#6. retorna o resultado da transformação
return texto_criptografado
|
63afaf6bae94a7a9c43b3fea39bdd5df2916b195
| 27,632 |
from enum import Enum
def extend_enum(*inherited_enums: type[Enum]):
"""EXPERIMENTAL
Join multiple enums into one.
Modified version from: https://stackoverflow.com/a/64045773/14748231
"""
# All members from all enums which injected in result enum
joined_members = {}
def _add_item_if_not_exist(item) -> None:
# Add given item to joined_members dict.
# If item.name is already existing key, raise ValueError.
if item.name not in joined_members:
joined_members[item.name] = item.value
else:
raise ValueError(f"{item.name} key already in joined_members")
def wrapper(applied_enum: type[Enum]):
@wraps(applied_enum)
def inner():
# Add all items from inherited enums.
for inherited_enum in inherited_enums:
for item in inherited_enum:
_add_item_if_not_exist(item)
# Add all items from applied enum.
for item in applied_enum:
_add_item_if_not_exist(item)
# Finally, return result Enum with collected members injected.
ResEnum = Enum(applied_enum.__name__, joined_members)
ResEnum.__doc__ = applied_enum.__doc__
return ResEnum
return inner()
return wrapper
|
4ec9e36fdd584555e5c201d3a1899007fa9adb85
| 27,634 |
import requests
import csv
import io
def tsv_reader():
""" read register-like data from government-form-data TSV"""
resp = requests.get(url=url)
resp.raise_for_status()
return csv.DictReader(io.StringIO(resp.text), delimiter=sep)
|
2487f44f516afc8727d1d0fd8b253578aaaef026
| 27,635 |
import time
def GetMonotime():
"""Older tornado doesn't have monotime(); stay compatible."""
if hasattr(tornado.util, 'monotime_impl'):
return tornado.util.monotime_impl
else:
return time.time
|
e0ea587512213b2a830b4912ae67928cb8515b98
| 27,636 |
def test_multiplica():
"""
Target function returns the sum of the multilica of two given vector.
Expect output as np.float object.
"""
dict_1 = file_read.read_file('../../data/10mM_2,7-AQDS_1M_KOH_25mVs_0.5step_2.txt')
data = file_read.data_frame(dict_1, 1)
col_x1, col_x2 = baseline.split(data.Potential)
col_y1, col_y2 = baseline.split(data.Current)
a_val = baseline.multiplica(col_x1, col_y1)
assert isinstance(a_val == np.float64), ("Output should be float object,"
" but fuction is returning{}".format(type(a_val)))
b_val = np.multiply(col_x1, col_y1).sum()
(np.testing.assert_almost_equal(a_val, b_val, decimal=3),
"Calculation is incorrect")
return "Test Passed for multiplica function!"
|
7334a9500da86df959e6d6056a044d240dfbec95
| 27,637 |
def last_name_first(n):
"""Returns: copy of n in form 'last-name, first-name'
Precondition: n string in form 'first-name last-name
n has only space, separating first and last."""
assert type(n) == str, str(n) + " is not a string"
assert is_two_words(n), n+' has the wrong form'
# Compute the value
end_first = n.find(' ')
first = n[:end_first]
last = n[end_first + 1:]
return last + ', ' + first
|
448b23cc70294a28b82d36af95c2dab772b30e9d
| 27,638 |
import logging
def by_count(logger: logging.Logger, once_every: int) -> logging.Logger:
"""
The returned logger will only permit at most one print every `once_every` logging calls from the code
line this function was called from.
Usage example::
for i in range(100):
log_throttling.by_count(logger, once_every=10).info(
"This line will only log values that are multiples of 10: %s", i
)
**Notes**:
\\1. Throttling is configured per code line that this function is called from.
Changing the parameter from that used previously for that line will reset the throttling
counter for the line.
\\2. Throttling does not nest. e.g.::
log_throttling.by_time(log_throttling.by_count(logger, 10), 1).info("...")
Will simply ignore the nested `by_count`.
:param logger: A `logging.Logger` object to "wrap". The return value from this function can be used
just like a normal logger.
:param once_every: The number of logging calls for which a single call is allowed to be written.
:return: A throttled `logging.Logger`-like object.
"""
strategy = _ThrottlingByCount(once_every)
return _by_custom_strategy(logger, strategy)
|
9c67c84a4e4371a25e5947f6fcb92b860cc12e9e
| 27,639 |
def load_ps_label(frame_id):
"""
:param frame_id: file name of pseudo label
:return gt_box: loaded gt boxes (N, 9) [x, y, z, w, l, h, ry, label, scores]
"""
if frame_id in PSEUDO_LABELS:
gt_box = PSEUDO_LABELS[frame_id]['gt_boxes']
else:
raise ValueError('Cannot find pseudo label for frame: %s' % frame_id)
return gt_box
|
53b73ddfe4a51676f7e81f9e266724ccc5e494fa
| 27,641 |
import json
async def execute(
url_path: str,
body: dict,
codef,
service_type: ServiceType,
) -> dict:
"""
API 요청 실행 함수.
실제 사용자에게 제공되는 함수 내부에서 이 함수를 호출해서 사용할 것을 권장한다.
:param url_path: 요청 URL 경로
:param body: post 요청 바디
:param codef: codef 인스턴스
:param service_type: 서비스 타입
:return:
"""
req_domain = get_codef_domain(service_type)
client_id, client_secret = codef.get_client_info(service_type)
await set_token(client_id, client_secret, codef, service_type)
body = json.dumps(body, ensure_ascii=False)
return await request_product(
req_domain + url_path,
codef.get_access_token(service_type),
body,
codef.get_session(),
)
|
e0c55b38697121e3fa671fc7585fe477c977c724
| 27,642 |
def main() -> int:
""" Execute all tasks.
"""
get_googletest()
return 0
|
c2850f781245cacd77b3707881b337a113dcb048
| 27,643 |
import asyncio
async def async_unload_entry(
hass: HomeAssistantType,
config_entry: config_entries.ConfigEntry,
) -> bool:
"""Unload Energosbyt Plus entry"""
log_prefix = _make_log_prefix(config_entry, "setup")
entry_id = config_entry.entry_id
update_delegators: UpdateDelegatorsDataType = hass.data[DATA_UPDATE_DELEGATORS].pop(
entry_id
)
tasks = [
hass.config_entries.async_forward_entry_unload(config_entry, domain)
for domain in update_delegators.keys()
]
unload_ok = all(await asyncio.gather(*tasks))
if unload_ok:
hass.data[DATA_API_OBJECTS].pop(entry_id)
hass.data[DATA_FINAL_CONFIG].pop(entry_id)
cancel_listener = hass.data[DATA_UPDATE_LISTENERS].pop(entry_id)
cancel_listener()
_LOGGER.info(
log_prefix
+ (
"Интеграция выгружена"
if IS_IN_RUSSIA
else "Unloaded configuration entry"
)
)
else:
_LOGGER.warning(
log_prefix
+ (
"При выгрузке конфигурации произошла ошибка"
if IS_IN_RUSSIA
else "Failed to unload configuration entry"
)
)
return unload_ok
|
c3e7a928c801f65b4ca997fb8b3734059b95e210
| 27,644 |
def euler_vec(z, y, x, n):
"""
Return (n,3,3) tensor with each (3,3) block containing an
Euler rotation with angles z, y, x. Optionally each of z, y, x
can be a vector of length n.
"""
L = np.zeros((n, 3, 3), "d")
cosx, sinx = np.cos(x), np.sin(x)
L[:, 0, 0] = 1
L[:, 1, 1] = L[:, 2, 2] = cosx
L[:, 1, 2] = -sinx
L[:, 2, 1] = sinx
N = np.zeros((n, 3, 3), "d")
cosy, siny = np.cos(y), np.sin(y)
N[:, 0, 0] = N[:, 2, 2] = cosy
N[:, 1, 1] = 1
N[:, 0, 2] = siny
N[:, 2, 0] = -siny
ret = np.einsum("ijk,ikl->ijl", L, N)
M = np.zeros((n, 3, 3), "d")
cosz, sinz = np.cos(z), np.sin(z)
M[:, 0, 0] = M[:, 1, 1] = cosz
M[:, 0, 1] = -sinz
M[:, 1, 0] = sinz
M[:, 2, 2] = 1
ret = np.einsum("ijk,ikl->ijl", ret, M)
return ret
|
799dd414ff8fc1aa405072bb2d5d155751ad86a4
| 27,645 |
def ReadBenderEllipse( filename, dataFrame=False, headerLine=None, useDefaultColumnNames=True ):
"""Read in an ellipse fit generated by Bender/Saglia code and store it
in a dictionary (or, optionally, a ListDataFrame object). Columns are
converted to 1-D numpy arrays.
headerLine indicates which line contains the column titles (first line = 1,
etc.); the actual data is assumed to start immediately after.
Normally, the function attempts to locate the header line automatically
(first line in file with same number of elements [excluding any initial "#"]
as last line in file). The headerLine keyword is mainly useful for perverse
situations (e.g., there is a line in the header that happens to have 12
words in it).
Because we (currently) don't know how the Bender code handles position
angles, we don't attempt to "correct" the PA.
"""
lines = open(filename).readlines()
nTotalLines = len(lines)
lastLine = lines[-1]
nCols = len(lastLine.split())
# find the header line -- should be first line which has same number of elements
# as the last line in the file
if headerLine is None:
headerString = None
for i in range(nTotalLines):
tmpLine = lines[i].lstrip("#")
if len(tmpLine.split()) == nCols:
headerString = tmpLine
headerLineIndex = i
break
if headerString is None:
print("Unable to find header line!\n")
return None
else:
headerLineIndex = headerLine - 1
headerString = lines[headerLineIndex]
if useDefaultColumnNames:
colheaders = DEFAULT_BENDER_COLUMNS
else:
colheaders = headerString.split()
# get rid of excess space at end, if any
colheaders[-1] = colheaders[-1].strip()
# find first data line:
firstDataLine = None
for j in range(headerLineIndex + 1, nTotalLines):
tmpLine = lines[j]
if len(tmpLine.split()) == nCols:
firstDataLine = j
break
if firstDataLine is None:
print("Unable to find first data line!\n")
return None
dataLines = [line for line in lines[firstDataLine:] if line[0] != "#"]
nDataLines = len(dataLines)
dataDict = {}
for i in range(nCols):
cname = colheaders[i]
dataDict[cname] = np.array([ float(line.split()[i]) for line in dataLines ])
dataDict["r_eq"] = EquivRadius(dataDict)
colheaders.append("r_eq")
# Convert to dataFrame, if requested:
if dataFrame is True:
frameList = []
for cname in colheaders:
frameList.append(dataDict[cname])
result = du.ListDataFrame(frameList, colheaders)
# extra conveninces
#result.AddColumnName("sma", "a")
#result.AddColumnName("intens", "i")
# add meta-data
result.tableFile = filename
else:
result = dataDict
return result
|
8d8f816f2ac7375bcc6814c34fd69a11e352255b
| 27,646 |
def getQuote(symbolStringCSV, detailFlag = 'ALL' ):
"""
Returns the live quote of a single or many companies
symbolStringCSV <str> is a comma separated value of tickers
detailFlag <'ALL' or 'INTRADAY'> specifies whether all data is returned or just a subset with intraday
sample usage:
getQuote('TVIX, GOOG', detailFlag = 'INTRADAY')
"""
url = urlRoot().format('market','quote/'+symbolStringCSV) + '?' + \
'detailFlag={}'.format(detailFlag)
# print url
return accessMethod(url)
|
b40489c5d0680126abf58d32f7e487332599ea8b
| 27,647 |
def get_user_resources_permissions_dict(user, request, resource_types=None, resource_ids=None,
inherit_groups_permissions=True, resolve_groups_permissions=False):
# type: (models.User, Request, Optional[List[Str]], Optional[List[int]], bool, bool) -> ResourcePermissionMap
"""
Creates a dictionary of resources ID with corresponding permissions of the user.
.. seealso::
:func:`regroup_permissions_by_resource`
:param user: user for which to find resources permissions
:param request: request with database session connection
:param resource_types: filter the search query with only the specified resource types
:param resource_ids: filter the search query to only the specified resource IDs
:param inherit_groups_permissions:
Whether to include group inherited permissions from user memberships or not.
If ``False``, return only user-specific resource permissions.
Otherwise, resolve inherited permissions using all groups the user is member of.
:param resolve_groups_permissions: whether to combine corresponding user/group permissions into one or not.
:return:
Only resources which the user has permissions on, or including all :term:`Inherited Permissions`, according to
:paramref:`inherit_groups_permissions` argument.
"""
ax.verify_param(user, not_none=True, http_error=HTTPNotFound,
msg_on_fail=s.UserResourcePermissions_GET_NotFoundResponseSchema.description)
# full list of user/groups permissions, filter afterwards according to flags
res_perm_tuple_list = UserService.resources_with_possible_perms(
user, resource_ids=resource_ids, resource_types=resource_types, db_session=request.db)
if not inherit_groups_permissions and not resolve_groups_permissions:
res_perm_tuple_list = filter_user_permission(res_perm_tuple_list, user)
return regroup_permissions_by_resource(res_perm_tuple_list, resolve=resolve_groups_permissions)
|
6c3c647f304167328282505f118633654b9422a2
| 27,648 |
import json
import requests
def create_new_index(index_name: str):
"""
Алгоритм внутри по шагам
:param index_name: str: название текущей версии индекса
:return: new_index_name: str: название обновленного индекса
"""
print("*" * 10)
# получаем новое имя индекса
new_index_name = up_index_version(index_name)
print(index_name, "->", new_index_name)
# создаем новый индекс с настройками старого
new_index_url = f"{ES_URL}/{new_index_name}"
# читаем settings существующего индекса
with open(f"{dir_name_settings}/{index_name}.json", "r+") as f:
settings = json.load(f)
# проверяем, есть ли изменения settings
new_settings = get_new_settings()
settings["settings"]["index"].pop("sort", None)
if new_settings:
print(f"[INFO] settings update: {new_settings}")
merge_settings(settings, new_settings)
print("[INFO] settings updated")
# сохраняем новые settings для индекса
with open(f"{dir_name}/new_settings/{new_index_name}.json", "w") as f:
json.dump(settings, f, **JSON_DUMP_PARAMS)
# читаем mapping существующего индекса
with open(f"{dir_name_mappings}/{index_name}.json", "r+") as f:
mappings = json.load(f)
# проверяем, есть ли обновления mapping ДО put'a
if RECOURSIVE_MAPPING_UPDATE:
check_mappings_for_updates(mappings, index_name)
# сохраняем новый mapping для индекса
with open(f"{dir_name_new_mappings}/{new_index_name}.json", "w") as fw:
json.dump(mappings, fw, **JSON_DUMP_PARAMS)
# put'аем новые settings и mapping
if REALLY:
print(f"request PUT {new_index_url} settings")
resp = requests.put(new_index_url, json={**settings, **mappings})
if resp.status_code == 200:
print(f"[WARN] {resp.text}")
else:
print(f"[WARN] {resp.text}")
print("*" * 10)
return new_index_name
|
797a1f2fa556f640588837c644bfbbf2b425daab
| 27,649 |
def _parse_slice_str(slice_str):
"""Parses the given string as a multidimensional array slice and returns a
list of slice objects and integer indices."""
is_valid = False
if len(slice_str) > 2:
is_valid = slice_str[0] == "[" and slice_str[-1] == "]"
sliced_inds = []
if is_valid:
slice_str_list = [x.strip() for x in slice_str[1:-1].split(",")]
for s in slice_str_list:
parts = s.split(":")
if len(parts) > 3:
is_valid = False
break
if len(parts) == 1:
try:
sliced_inds.append(int(s))
except:
is_valid = False
break
else:
try:
start = int(parts[0]) if len(parts[0]) > 0 else None
stop = int(parts[1]) if len(parts[1]) > 0 else None
if len(parts) == 3:
step = int(parts[2]) if len(parts[2]) > 0 else None
else:
step = None
except:
is_valid = False
break
sliced_inds.append(slice(start, stop, step))
if not is_valid:
raise ValueError("Invalid slice specified: %s" % slice_str)
return sliced_inds
|
6eb7a6b5d1dc2ee57e878b37be70e1e75d7d6ecc
| 27,650 |
def AverageZComparison(x, y):
""" Take the average of second and third element in an array and compare
which is bigger. To be used in conjunction with the sort function. """
xsum = x[1]+x[2]
ysum = y[1]+y[2]
if xsum < ysum:
return -1
if xsum > ysum:
return 1
return 0
|
84c9e7b92df4b3e4914c769293f71790def5e4dd
| 27,651 |
def convblock(in_channels, out_channels, kernel_size, stride=1, padding=0, use_bn=True):
"""
Returns convolution block
"""
if use_bn:
return [
nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding),
nn.BatchNorm2d(out_channels),
nn.ReLU(True)
]
else:
return [
nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding),
nn.ReLU(True)
]
|
157cc5a88e0f1e0d0f4f3afc228fd25d1a67d058
| 27,652 |
def get_caffe_op_type(layer, input_channels=1, output_channels=1):
"""
Gets the relevant Toolkit Enum for the corresponding Caffe layer stage type.
:param layer:
The particular layer field of the caffe Net msg that we want to discover the type.
:return: StageType Enum
"""
if isConvolution(layer.type):
return StageType.convolution
if isFCL(layer.type):
return StageType.fully_connected_layer
if isSoftMax(layer.type):
return StageType.soft_max
if isPooling(layer.type):
pooling_type = layer.pooling_param.pool
if pooling_type == 0: # Max
return StageType.max_pooling
if pooling_type == 1: # Average
return StageType.average_pooling
if pooling_type == 2: # Stochastic
throw_error(ErrorTable.StageTypeNotSupported, "Stochastic Pooling")
return StageType.stochastic_pooling
if isLRN(layer.type):
return StageType.LRN
if isEltwise(layer.type):
if layer.type == 'Bias':
return StageType.eltwise_sum
elif layer.eltwise_param.operation == 0:
return StageType.eltwise_prod
elif layer.eltwise_param.operation == 2:
return StageType.eltwise_max
else:
return StageType.eltwise_sum
if isBatchNorm(layer.type) or isScale(layer.type):
return StageType.scale
if isPReLU(layer.type):
return StageType.prelu
if isSigmoid(layer.type):
return StageType.sigmoid
if isTanH(layer.type):
return StageType.tanh
if isDeconvolution(layer.type):
return StageType.deconvolution
if isReshape(layer.type):
return StageType.reshape
if isFlatten(layer.type):
return StageType.toplanemajor
if isPower(layer.type):
return StageType.power
if isCrop(layer.type):
return StageType.crop
if isDepthwiseConvolution(layer, output_channels, input_channels):
return StageType.depthwise_convolution
if isPermute(layer.type):
return StageType.permute
if isNormalize(layer.type):
return StageType.normalize
if isPriorBox(layer.type):
return StageType.prior_box
if isDetectionOutput(layer.type):
return StageType.detection_output
throw_error(ErrorTable.StageTypeNotSupported, layer.type)
|
e912014862643e3724fad8f10c0b8ee7133c77e1
| 27,653 |
def comp_volumes(self):
"""Compute the Lamination volumes (Vlam, Vvent, Vslot, Vwind)
Parameters
----------
self : LamSlotWind
A LamSlotWind object
Returns
-------
V_dict: dict
Lamination volume dictionnary (Vlam, Vvent, Vslot, Vwind) [m**3]
"""
V_dict = LamSlot.comp_volumes(self)
Lf = self.comp_length() # Include radial ventilation ducts
V_dict["Vwind"] = Lf * self.slot.Zs * self.slot.comp_surface_wind()
return V_dict
|
4b9444b9ac7c78e6a7719ab73dfa5cabcd78debf
| 27,654 |
def elliptical_orbit():
"""Draw an example of a planet with an elliptical orbit around its star."""
fig, axes = plt.subplots(1, 1)
orbit = Ellipse(xy=(0, 0), width=2, height=1.5, facecolor='lightblue')
axes.add_artist(orbit)
axes.plot([-1, 0], [0, 0])
axes.annotate(
'semi-major axis',
xy=(-0.5, 0),
xytext=(-0.8, -0.2),
arrowprops=dict(arrowstyle='wedge')
)
axes.annotate(
'orbit center',
xy=(0, 0),
xytext=(-0.21, 0.115),
arrowprops=dict(arrowstyle='wedge')
)
plt.plot(
[-.75], [0.5],
marker='o', markersize=4,
color='green', label='planet'
)
plt.plot(
[0], [0],
marker='o', markersize=10,
color='orange', label='star'
)
# formatting
plt.xlim(-1.25, 1.25)
plt.ylim(-1.1, 0.75)
plt.legend(loc='lower center', ncol=2)
# remove axes
axes.xaxis.set_visible(False)
axes.yaxis.set_visible(False)
# remove box around image
for spine in axes.spines:
axes.spines[spine].set_visible(False)
return axes
|
ccb12b80111009a59cacff522a35cd41a6e73ad4
| 27,655 |
from datetime import datetime
def get_date_range_for_date(date_str, interval):
"""
Given a date string, parse it and derive a range based on the given
interval. The interval is inclusive on the lower end, and exclusve on the
higher end. For example, given a date str of 2019-03-10 and a 'month'
interval, this will return a range of 2019-03-01 -- 2019-03-31.
:param date_str: Any ISO date or partial date. 2019, 2019-03,
2019-03-01, 2019-12-18 21:00:00
:param interval: Any interval defined in
globus_portal_framework.constants.FILTER_DATE_RANGES. Examples include:
'year', 'month', 'day', 'hour'
:return:
A date range dict. Example:
{
'from': '2019-12-18 21:00:00'
'to': '2019-12-18 21:00:01'
}
"""
dt = parse_date_filter(date_str)['datetime']
# If filtering on a month or year, chop off the extra part of the
# datetime so we don't accidentally search on the previous month
# or next month
day = datetime.timedelta(days=1)
if interval == FILTER_SECOND:
second = datetime.timedelta(seconds=1)
from_d, to_d = dt - second, dt + second
elif interval == FILTER_MINUTE:
from_d = dt.replace(second=0)
to_d = from_d + datetime.timedelta(seconds=59)
elif interval == FILTER_HOUR:
from_d = dt.replace(minute=0, second=0)
to_d = from_d + datetime.timedelta(minutes=59, seconds=59)
elif interval == FILTER_DAY:
dt = dt.replace(hour=0, minute=0, second=0)
from_d, to_d = dt, dt + day
elif interval == FILTER_MONTH:
from_d = dt.replace(day=1, hour=0, minute=0, second=0)
inc_month = 1 if dt.month == 12 else dt.month + 1
inc_year = dt.year + 1 if inc_month == 1 else dt.year
to_d = from_d.replace(month=inc_month, year=inc_year) - day
elif interval == FILTER_YEAR:
dt = dt.replace(day=1, month=1, hour=0, minute=0, second=0)
year = datetime.timedelta(days=365)
from_d, to_d = dt, dt + year
else:
raise exc.GlobusPortalException('Invalid date type {}'
''.format(interval))
# Globus search can handle any time format, so using the most precise will
# work every time.
dt_format_type = DATETIME_PARTIAL_FORMATS['time']
return {
'from': from_d.strftime(dt_format_type),
'to': to_d.strftime(dt_format_type)
}
|
4b2d1f47d4984fbd9fe590e2698b288560aa0162
| 27,656 |
def sigmaStarDFA(sigma=None):
"""
Given a alphabet S returns the minimal DFA for S*
:param sigma: set of symbols
:return: DFA
.. versionadded:: 1.2"""
if sigma is None:
raise
d = DFA()
d.setSigma(sigma)
i = d.addState()
d.setInitial(i)
d.addFinal(i)
for a in d.Sigma:
d.addTransition(i, a, i)
return d
|
51ab7fa365b356f03aab57b777bd245163ba6b02
| 27,657 |
from datetime import datetime
def timestamp_to_datetime(seconds, tz=None):
"""Returns a datetime.datetime of `seconds` in UTC
:param seconds: timestamp relative to the epoch
:param tz: timezone of the timestamp
"""
if tz is None:
tz = timezone.utc
dt = datetime.fromtimestamp(seconds, tz)
return dt.astimezone(timezone.utc)
|
2b53d69aeb2c7e5f88602af47d2d7b1ee40e0730
| 27,659 |
import re
def initialize(plugins, exclude_regex=None, rootdir='.'):
"""Scans the entire codebase for high entropy strings, and returns a
SecretsCollection object.
:type plugins: tuple of detect_secrets.plugins.base.BasePlugin
:param plugins: rules to initialize the SecretsCollection with.
:type exclude_regex: str|None
:type rootdir: str
:rtype: SecretsCollection
"""
output = SecretsCollection(plugins, exclude_regex)
git_files = _get_git_tracked_files(rootdir)
if not git_files:
return output
if exclude_regex:
regex = re.compile(exclude_regex, re.IGNORECASE)
git_files = filter(
lambda x: not regex.search(x),
git_files
)
for file in git_files:
output.scan_file(file)
return output
|
80c262041736c58f79a4356781b3ab831b6f1daa
| 27,660 |
def decompose_job_id(job_id):
"""Thin wrapper around generic decompose_job_id to use our local SPACER."""
return utils_decompose_job_id(job_id, spacer=SPACER)
|
f41553a6864940816da53982746a9f06220347e6
| 27,661 |
from re import U
from re import T
def build(P , word_size , first_hidden_size , encoding_size) :
"""
create entity and relation encoding
"""
P["W_word_left_input"] = U.initial_weights(2*word_size , first_hidden_size)
P["W_word_right_input"] = U.initial_weights(2*word_size , first_hidden_size)
P["W_encoding_output"] = U.initial_weights(2*first_hidden_size , encoding_size)
def batched_triplet_encoding(e_left , relation , e_right) :
left_input = T.concatenate([e_left , relation] , axis=1) #batched version
right_input = T.concatenate([relation , e_right] , axis=1) #batched version
left_hidden = T.tanh(T.dot(left_input , P["W_word_left_input"]))
right_hidden = T.tanh(T.dot(right_input , P["W_word_right_input"]))
all_hidden = T.concatenate([left_hidden , right_hidden] , axis = 1) #batched version
encoding = T.tanh(T.dot(all_hidden , P["W_encoding_output"]))
return encoding
def vector_triplet_encoding(e_left , relation , e_right) :
left_input = T.concatenate([e_left , relation] , axis=0) #batched version
right_input = T.concatenate([relation , e_right] , axis=0) #batched version
left_hidden = T.tanh(T.dot(left_input , P["W_word_left_input"]))
right_hidden = T.tanh(T.dot(right_input , P["W_word_right_input"]))
all_hidden = T.concatenate([left_hidden , right_hidden] , axis = 0) #batched version
encoding = T.tanh(T.dot(all_hidden , P["W_encoding_output"]))
return encoding
return batched_triplet_encoding , vector_triplet_encoding
|
9c465b9033b23aa938ea600326bc5add1000edb5
| 27,662 |
def validate_path_for_get_public_key(path: list, slip44_id: int) -> bool:
"""
Checks if path has at least three hardened items and slip44 id matches.
The path is allowed to have more than three items, but all the following
items have to be non-hardened.
"""
length = len(path)
if length < 3 or length > 5:
return False
if path[0] != 44 | HARDENED:
return False
if path[1] != slip44_id | HARDENED:
return False
if path[2] < HARDENED or path[2] > 20 | HARDENED:
return False
if length > 3 and is_hardened(path[3]):
return False
if length > 4 and is_hardened(path[4]):
return False
return True
|
1e0ef325283a2cfc6aceba748c18fa2dbc9a34c0
| 27,664 |
import base64
from datetime import datetime
def generate_result(pic_type, img_path, predicted_breed=''):
"""
Generate a result of predicted dog breed for display in Jupyter Notebook.
Args:
pic_type (str): Type of picture, either 'dog', 'human' or 'error'
img_path (str): Path to the image provided by the user
predicted_breed (str): The breed that was predicted based on the image
Returns:
str: A URI of the base64 encoded result in HTML
"""
img_content = None
with open(img_path, 'rb') as img:
img_content = base64.b64encode(img.read())
ts = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
lookup = TemplateLookup(['templates/'], output_encoding='utf-8',
strict_undefined=True)
template = lookup.get_template('dog.html.mako')
raw_html = template.render(
pic_type=pic_type,
img_content=img_content.decode(),
timestamp=ts,
breed=predicted_breed
)
return f'data:text/html;base64,{base64.b64encode(raw_html).decode()}'
|
345db6c17e0ec2a3d65963ba403200c1bde3229c
| 27,665 |
def concatSeriesMovies(dataFrame1, dataFrame2):
"""
Join, concat two dataFrames
Parameters:
dataFrame1: string
The name of the dataFrame the user wants to concat.
dataFrame2: string
The name of the dataFrame the user wants to concat.
Returns:
a new dataFrame
"""
concatedDataFrame = pd.concat([dataFrame1, dataFrame2], ignore_index=True)
return concatedDataFrame
|
cb063ca82dac809f05a60a920f77d7a53ed5d3af
| 27,666 |
def Pattern7(s):
""" Compute the correlator for this pattern:
↓ ○
↑ ↑
and symmetry-equivalent patterns
"""
res = 0.0
s = np.pad(s, ((0, 0), (2, 2), (2, 2)))
L = s.shape[-1]
for i in range(L-2):
for j in range(L-2):
res += s[0, i, j] * s[0, i+1, j] * s[1, i+1, j+1] * s[2, i+2, j+1]
res += s[0, i+1, j] * s[0, i+1, j+1] * s[1, i, j+1] * s[2, i, j+2]
res += s[2, i, j] * s[1, i+1, j] * s[0, i+1, j+1] * s[0, i+2, j+1]
res += s[2, i+1, j] * s[1, i+1, j+1] * s[0, i, j+1] * s[0, i, j+2]
res += s[2, i, j] * s[1, i, j+1] * s[0, i+1, j+1] * s[0, i+1, j+2]
res += s[0, i, j+1] * s[0, i+1, j+1] * s[1, i+1, j] * s[2, i+2, j]
res += s[0, i, j] * s[0, i, j+1] * s[1, i+1, j+1] * s[2, i+1, j+2]
res += s[2, i, j+1] * s[0, i+1, j] * s[1, i+1, j+1] * s[0, i+2, j]
res += s[1, i, j] * s[1, i+1, j] * s[0, i+1, j+1] * s[2, i+2, j+1]
res += s[1, i+1, j] * s[1, i+1, j+1] * s[0, i, j+1] * s[2, i, j+2]
res += s[2, i, j] * s[0, i+1, j] * s[1, i+1, j+1] * s[1, i+2, j+1]
res += s[2, i+1, j] * s[0, i+1, j+1] * s[1, i, j+1] * s[1, i, j+2]
res += s[2, i, j] * s[0, i, j+1] * s[1, i+1, j+1] * s[1, i+1, j+2]
res += s[1, i, j+1] * s[1, i+1, j+1] * s[0, i+1, j] * s[2, i+2, j]
res += s[1, i, j] * s[1, i, j+1] * s[0, i+1, j+1] * s[2, i+1, j+2]
res += s[2, i, j+1] * s[1, i+1, j] * s[0, i+1, j+1] * s[1, i+2, j]
return res
|
2dea3f7cae06cec65f9ee416b61367320d9893cc
| 27,667 |
def list_camera_ports():
"""
Test the ports and returns a tuple with the available ports and the ones that are working
"""
non_working_ports = []
working_ports = []
available_ports = []
dev_port = 0
while len(non_working_ports) <= 3: # If there are more than 3 non working ports stop the testing.
camera = cv2.VideoCapture(dev_port)
if not camera.isOpened():
non_working_ports.append(dev_port)
else:
is_reading, img = camera.read()
w = camera.get(cv2.CAP_PROP_FRAME_WIDTH)
h = camera.get(cv2.CAP_PROP_FRAME_HEIGHT)
if is_reading:
print("Port %s is working and reads images (%s x %s)" %(dev_port, w, h))
working_ports.append(dev_port)
else:
available_ports.append(dev_port)
dev_port +=1
return available_ports, working_ports, non_working_ports
|
192cdb62807f89a05a67f7efbbc267bcc1b1f305
| 27,668 |
from pathlib import Path
def parse_taxid_names(file_path):
"""
Parse the names.dmp file and output a dictionary mapping names to taxids
(multiple different keys) and taxids to scientific names.
Parameters
----------
file_path : str
The path to the names.dmp file.
Returns
-------
name2taxid : dict
Keys are all possible names and values are taxids.
taxid2name : dict
Keys are taxids and values are scientific names.
"""
names = Path(file_path)
with names.open() as f:
lines_processed = 0
name2taxid = {}
taxid2name = {}
for line in f:
lines_processed += 1
if lines_processed % 1000000 == 0:
print('processing line', str(lines_processed))
entries = [entry.strip() for entry in line.split('|')]
name2taxid[entries[1]] = entries[0]
if 'scientific name' in line:
taxid2name[entries[0]] = entries[1]
return name2taxid, taxid2name
|
1d136f73a56ac8d3c02fd53c6e7928a39440e27a
| 27,669 |
def dn_outfunc(e1, e2, W, V1=None, V2=None, b=None):
"""Applies a bilinear function based on given parameters.
This is a building block of Neural Tensor Network (see the reference paper
below). It takes two input variables and one or four parameters, and
outputs one variable.
To be precise, denote six input arrays mathematically by
:math:`e^1\\in \\mathbb{R}^{I\\cdot J}`,
:math:`e^2\\in \\mathbb{R}^{I\\cdot K}`,
:math:`W\\in \\mathbb{R}^{J \\cdot K \\cdot L}`,
:math:`V^1\\in \\mathbb{R}^{J \\cdot L}`,
:math:`V^2\\in \\mathbb{R}^{K \\cdot L}`, and
:math:`b\\in \\mathbb{R}^{L}`,
where :math:`I` is mini-batch size.
In this document, we call :math:`V^1`, :math:`V^2`, and :math:`b` linear
parameters.
The output of forward propagation is calculated as
.. math::
y_{il} = \\sum_{jk} e^1_{ij} e^2_{ik} W_{jkl} + \\
\\sum_{j} e^1_{ij} V^1_{jl} + \\sum_{k} e^2_{ik} V^2_{kl} + b_{l}.
Note that V1, V2, b are optional. If these are not given, then this
function omits the last three terms in the above equation.
.. note::
This function accepts an input variable ``e1`` or ``e2`` of a non-matrix
array. In this case, the leading dimension is treated as the batch
dimension, and the other dimensions are reduced to one dimension.
.. note::
In the original paper, :math:`J` and :math:`K`
must be equal and the author denotes :math:`[V^1 V^2]`
(concatenation of matrices) by :math:`V`.
Args:
e1 (~chainer.Variable): Left input variable.
e2 (~chainer.Variable): Right input variable.
W (~chainer.Variable): Quadratic weight variable.
V1 (~chainer.Variable): Left coefficient variable.
V2 (~chainer.Variable): Right coefficient variable.
b (~chainer.Variable): Bias variable.
Returns:
~chainer.Variable: Output variable.
See:
`Reasoning With Neural Tensor Networks for Knowledge Base Completion
<http://papers.nips.cc/paper/5028-reasoning-with-neural-tensor-
networks-for-knowledge-base-completion>`_ [Socher+, NIPS2013].
"""
flags = [V1 is None, V2 is None, b is None]
if any(flags):
if not all(flags):
raise ValueError('All coefficients and bias for bilinear() must '
'be None, if at least one of them is None.')
return DN_outFunction()(e1, e2, W)
else:
return DN_outFunction()(e1, e2, W, V1, V2, b)
|
f1c642b7fcaf91d49df993177b76715774fc17bf
| 27,672 |
def action_can_be_queued(action_type):
"""
test the action_type whether can be queued
Inputs: action_type, int
Outputs: true or false
"""
need_args = actions.RAW_FUNCTIONS[action_type].args
result = False
for arg in need_args:
if arg.name == 'queued':
result = True
break
return result
|
e05f56b047d5c14bc2bcb27e82e789a41b12e090
| 27,673 |
def bench4(x):
"""A benchmark function for test purposes.
f(x) = float(x) ** 2
where x is a string. It has a single minima with f(x*) = 0 at x* = "0".
This benchmark is used for checking support of categorical variables.
"""
return float(x[0]) ** 2
|
2c7bf171f917b599db6f7553b2a959cb8c691c93
| 27,674 |
def drop_columns_from_dataframe_if_all_elements_are_nan(df, elements_list=['', '']):
""" Takes two parameters:
df: Dataframe
elements_list: By default it will identify np.nan. If you want to add additional elements, as an example, you can do this ['', ' ']
"""
m = df.applymap(lambda i: i if i not in elements_list else np.nan).apply(lambda c: c.isnull().all())
columns_to_drop = df.columns[m]
df.drop(columns_to_drop, axis=1, inplace=True)
return df
|
5f66e8c33a918872e2cfeabff2116a43fda9095f
| 27,675 |
def drop_db(code, confirm_by_typing_db_code_again=None):
"""
Delete a history database.
Parameters
----------
code : str, required
the database code
confirm_by_typing_db_code_again : str, required
enter the db code again to confirm you want to drop the database, its config,
and all its data
Returns
-------
dict
status message
"""
params = {"confirm_by_typing_db_code_again": confirm_by_typing_db_code_again}
response = houston.delete("/history/databases/{0}".format(code), params=params)
houston.raise_for_status_with_json(response)
return response.json()
|
a135ad9e3abdecb891b0ef5b6a1754d01a85fda8
| 27,676 |
def _nex_group(seq, core, spatial, c_num=4, s_num=4):
"""Build spatial stream index"""
# step 1
ant_num = c_num * s_num
seq_diff = np.diff(seq)
offset = np.where(seq_diff != 0)[0]
offset = np.r_[0, offset + 1]
count = np.diff(np.r_[offset, len(seq)])
offset = offset[count == ant_num]
offset = offset[:, None] + np.r_[:ant_num]
# step 2
core = core[offset]
spatial = spatial[offset]
p = core * s_num + spatial
p = np.argsort(p, axis=-1)
offset = offset[:, :1] + p
offset = offset.reshape(-1, c_num, s_num)
return offset
|
2c9ff96b965c48aa0a0aace2cfab2e5d8b1d3a9e
| 27,677 |
import asyncio
async def test_async_init_timeout(circuit):
"""Test the async initialization time_out."""
async def w3():
await asyncio.sleep(0.1)
return 3
logger = TimeLogger('logger')
out = edzed.ValuePoll(
'out',
func=w3,
interval=10, # don't care
init_timeout=0.2,
on_output=edzed.Event(logger),
initdef='DEFAULT')
asyncio.create_task(circuit.run_forever())
await circuit.wait_init()
await circuit.shutdown()
logger.compare([(100, 3)])
|
1915a66e8e82987f878f05a4a9016037e25c7da0
| 27,678 |
def compute_diffraction(
bundle,
key,
s,
eta,
L,
nlebedev=74,
nomega=12,
mode="xray",
form="raw",
anisotropy="cos2",
print_level=False,
):
"""Compute the I(s, eta) elastic scattering signal for a Bundle.
See aimsprop/notes/ued for details on this property.
Notes:
* All frames for each initial condition (IC) in bundle should be aligned so
that the transition dipole moment from S0 -> Sex at t=0 is on z. This
is required for proper computation of anisotropy.
* All frames should be weighted by geometric considerations at the IC
(e.g., conformational wells, Wigner weights, etc), by the cross
section for the optical transition at the IC (e.g., oscillator
strength and excitation energy window), and by the frame weight due
to non-adiabatic dynamics.
Params:
bundle (Bundle) - the Bundle object to compute the property for (modified in
place)
key (str) - the name of the property.
s (np.ndarray) - list of scattering vector norms in Angstrom^-1. The
relationship between s and theta (scattering angle) is given as,
s = 4 pi / L * sin(theta / 2).
eta (np.ndarray) - list of azimuthal scattering angles in radians.
L (float) - effective wavelength of scattering particle (x-ray
wavelength or UED deBroglie wavelength) in Angstrom. Used to
convert through scattering angle theta.
nlebedev (int) - Lebedev number to use for solid angle orientation
quadrature.
nomega (int) - number of uniform quadrature points to use for plane
orientation quadrature.
mode (str) - 'xray' or 'ued' for selection of form factors
form (str) - 'raw' or 'mod' for modified/raw diffraction intensities
I(s) or M(s).
anisotropy (str) - 'none' or 'cos2' for isotropic of cos^2 (z)
anisotropty.
print_level (bool) - print progress if true (useful to track long
property computations)
Result/Return:
bundle - reference to the input Bundle object. The properties
"""
# Validity checks
if mode not in ["xray", "ued"]:
raise ValueError("Unknown mode: %s" % mode)
if form not in ["raw", "mod"]:
raise ValueError("Unknown form: %s" % form)
if anisotropy not in ["none", "cos2"]:
raise ValueError("Unknown anisotropy: %s" % anisotropy)
# Compute scattering angles via Bragg equation
theta = 2.0 * np.arcsin(s * L / (4.0 * np.pi))
tt, ee = np.meshgrid(theta, eta, indexing="ij")
ss, ee = np.meshgrid(s, eta, indexing="ij")
# Compute scattering vectors
sx = ss * np.cos(tt / 2.0) * np.sin(ee)
sy = ss * np.sin(tt / 2.0)
sz = ss * np.cos(tt / 2.0) * np.cos(ee)
# Get a rotation quadrature for the orientations of the frames
if nlebedev == 1 and nomega == 1:
# Fixed orientation
Rs = [np.eye(3)]
ws = [1.0]
else:
# Rotation quadrature
Rs, ws = rotation.rotation_quadrature(nlebedev=nlebedev, nomega=nomega)
# Get atomic form factors for appropriate x-ray/ued mode
factors = formfactor.AtomicFormFactor.build_factors(bundle.frames[0], mode=mode)
# Compute atomic scattering Iat
D = np.zeros_like(sx)
for A, factor in enumerate(factors):
F = factor.evaluate_N(qx=sx, qy=sy, qz=sz, x=0.0, y=0.0, z=0.0)
D += (np.abs(F) ** 2).real
# Compute IAM scattering, integrating over all orientation angles
for find, frame in enumerate(bundle.frames):
if print_level:
print(("Frame %5d of %5d" % (find, len(bundle.frames))))
I = np.zeros_like(sx)
for R, w in zip(Rs, ws):
# cos(z)^2 pump anisotropy
cos2 = R[2, 2] ** 2 if anisotropy == "cos2" else 1.0
# Rotated molecule
xyz = np.dot(frame.xyz, R)
# Compute diffraction
N = np.zeros_like(I, dtype=complex)
for A, factor in enumerate(factors):
x = xyz[A, 0]
y = xyz[A, 1]
z = xyz[A, 2]
N += factor.evaluate_N(qx=sx, qy=sy, qz=sz, x=x, y=y, z=z)
F = (np.abs(N) ** 2).real
if form == "mod":
F = (F - D) / D
I += w * cos2 * F
frame.properties[key] = I
return bundle
|
380043d47e6a5786ab2907a4b1bd11495784f052
| 27,679 |
def convert_dict_id_values_to_strings(dict_list):
"""This function ensures that the ``id`` keys in a list of dictionaries use string values.
:param dict_list: List (or tuple) of dictionaries (or a single dictionary) containing API object data
:type dict_list: list, tuple, dict, None
:returns: A new dictionary list with properly formatted ``id`` values
:raises: :py:exc:`TypeError`
"""
dict_list = [dict_list] if isinstance(dict_list, dict) else dict_list
new_dict_list = []
for single_dict in dict_list:
if not isinstance(single_dict, dict):
raise TypeError("The 'dict_list' argument must be a dictionary or a list of dictionaries.")
if 'id' in single_dict and not isinstance(single_dict.get('id'), str):
single_dict['id'] = str(single_dict.get('id'))
new_dict_list.append(single_dict)
return new_dict_list
|
7d1348910e5802c928b94bc74d71f3ce35770215
| 27,680 |
def delete_images(request):
"""
Deletes images which are passed via HTTP query.
"""
Image.objects.filter(pk__in=request.POST.getlist("images")).delete()
return HttpResponseRedirect(reverse("lfs_manage_global_images"))
|
29e2e3be3730a2a3552bc78feb8e8817e594dab5
| 27,681 |
import fnmatch
def is_requirements_file(location):
"""
Return True if the ``location`` is likely for a pip requirements file.
For example::
>>> is_requirements_file('dev-requirements.txt')
True
>>> is_requirements_file('requirements.txt')
True
>>> is_requirements_file('requirements.in')
True
>>> is_requirements_file('requirements.pip')
True
>>> is_requirements_file('requirements-dev.txt')
True
>>> is_requirements_file('some-requirements-dev.txt')
True
>>> is_requirements_file('reqs.txt')
False
>>> is_requirements_file('requires.txt')
True
"""
filename = fileutils.file_name(location)
req_files = (
'*requirements*.txt',
'*requirements*.pip',
'*requirements*.in',
'requires.txt',
)
return any(fnmatch.fnmatchcase(filename, rf) for rf in req_files)
|
2577595ef6d2bcb553a0354623c40589f96a5fb3
| 27,682 |
def bisect_steps_remaining():
"""Estimate of remaining steps, including the current one.
This is an approximation."""
# https://github.com/git/git/blob/566a1439f6f56c2171b8853ddbca0ad3f5098770/bisect.c#L1043
return floor(log(bisect_revisions(), 2))
|
ded51395ead2c7ea76aa4ff3c2cf7d6195f81537
| 27,683 |
def create_lstm_model(fingerprint_input, model_settings, model_size_info,
is_training):
"""Builds a model with a lstm layer (with output projection layer and
peep-hole connections)
Based on model described in https://arxiv.org/abs/1705.02411
model_size_info: [projection size, memory cells in LSTM]
"""
if is_training:
dropout_prob = tf.placeholder(tf.float32, name='dropout_prob')
input_frequency_size = model_settings['dct_coefficient_count']
input_time_size = model_settings['spectrogram_length']
fingerprint_4d = tf.reshape(fingerprint_input,
[-1, input_time_size, input_frequency_size])
num_classes = model_settings['label_count']
projection_units = model_size_info[0]
LSTM_units = model_size_info[1]
with tf.name_scope('LSTM-Layer'):
with tf.variable_scope("lstm"):
lstmcell = tf.contrib.rnn.LSTMCell(LSTM_units, use_peepholes=True,
num_proj=projection_units)
_, last = tf.nn.dynamic_rnn(cell=lstmcell, inputs=fingerprint_4d,
dtype=tf.float32)
flow = last[-1]
with tf.name_scope('Output-Layer'):
W_o = tf.get_variable('W_o', shape=[projection_units, num_classes],
initializer=tf.contrib.layers.xavier_initializer())
b_o = tf.get_variable('b_o', shape=[num_classes])
logits = tf.matmul(flow, W_o) + b_o
if is_training:
return logits, dropout_prob
else:
return logits
|
d050e3b984f2aff1b0466b5cab73d262243d6cc9
| 27,684 |
def tile(x: Tensor, count: int, dim=0) -> Tensor:
"""
Tiles x on dimension dim count times. From OpenNMT. Used for beam search.
:param x: tensor to tile
:param count: number of tiles
:param dim: dimension along which the tensor is tiled
:return: tiled tensor
"""
if isinstance(x, tuple):
h, c = x
return tile(h, count, dim=dim), tile(c, count, dim=dim)
perm = list(range(len(x.size())))
if dim != 0:
perm[0], perm[dim] = perm[dim], perm[0]
x = x.permute(perm).contiguous()
out_size = list(x.size())
out_size[0] *= count
batch = x.size(0)
x = x.view(batch, -1) \
.transpose(0, 1) \
.repeat(count, 1) \
.transpose(0, 1) \
.contiguous() \
.view(*out_size)
if dim != 0:
x = x.permute(perm).contiguous()
return x
|
04b0e507e1600f3cb3185b170ee72286c2d20a7f
| 27,685 |
from typing import Callable
from typing import Coroutine
from typing import Any
from typing import Optional
import asyncio
def cancellable_request(handler: Callable[..., Coroutine[Any, Any, Optional[Any]]]):
"""this decorator periodically checks if the client disconnected and then will cancel the request and return a 499 code (a la nginx).
Usage:
decorate the cancellable route and add request: Request as an argument
@cancellable_request
async def route(
_request: Request,
...
)
"""
@wraps(handler)
async def decorator(*args, **kwargs) -> Optional[Any]:
request = kwargs["_request"]
handler_task = asyncio.create_task(
handler(*args, **kwargs), name="cancellable_request/handler"
)
auto_cancel_task = asyncio.create_task(
_cancel_task_if_client_disconnected(request, handler_task),
name="cancellable_request/auto_cancel",
)
try:
return await handler_task
except CancelledError:
logger.warning(
"request %s was cancelled by client %s!", request.url, request.client
)
return Response("Oh No!", status_code=499)
finally:
auto_cancel_task.cancel()
return decorator
|
10e50a565f45c0e4babf68386b244557e1727bc2
| 27,686 |
import time
def add_central_server_member_delete_global_error_cert(case,
client, ss2_host,
ss2_username, ss2_password):
"""
Restores security server after member being deleted in central server
:param case:
:param cs_ssh_host: str - central server ssh host
:param cs_ssh_user: str - central server ssh username
:param cs_ssh_pass: str - central server ssh password
:param ca_ssh_host: str - ca ssh host
:param ca_ssh_user: str - ca ssh username
:param ca_ssh_pass: str - ca ssh password
:param client: dict - client info
:param ss2_host: str - security server 2 host
:param ss2_username: str - security server 2 username
:param ss2_password: str - security server 2 password
:param ss2_ssh_host: str - security server 2 ssh host
:param cert_path: str - certificate filename
:param user: dict - user, under which the changes are made
:return:
"""
self = case
sync_timeout = 120
def add_cs_member_delete_global_error_cert():
self.log('Open members page')
self.wait_until_visible(type=By.CSS_SELECTOR, element=sidebar.MEMBERS_CSS).click()
# MEMBER_10 Add new X-Road Member
self.log('MEMBER_10 Add new X-Road Member')
add_member_to_cs(self, member=client)
self.log('Wait until servers have synchronized')
time.sleep(sync_timeout)
self.log('Open security server, which was deleted from central server')
self.reload_webdriver(url=ss2_host, username=ss2_username, password=ss2_password)
self.log('Open keys and certificates')
self.wait_until_visible(type=By.CSS_SELECTOR, element=sidebar.KEYSANDCERTIFICATES_BTN_CSS).click()
self.wait_until_visible(type=By.ID, element=keys_and_certificates_table.KEYS_AND_CERTIFICATES_TABLE_ID)
self.wait_jquery()
self.log('Click on the certificate, which has global error status and delete it')
self.by_xpath(keys_and_certificates_table.GLOBAL_ERROR_CERTIFICATE_ROW_XPATH).click()
self.by_id(keys_and_certificates_table.DELETE_BTN_ID).click()
popups.confirm_dialog_click(self)
self.wait_jquery()
return add_cs_member_delete_global_error_cert
|
77ea9a9de677947285f7bda233520bc43df84e18
| 27,687 |
def get_country_models(model):
"""
Get all valid domain-specific models for a given model.
:param str model:
:return:
"""
domains = get_domain_for(model, country=None)
return ['{}{}-K9'.format(model, domain) for domain in domains]
|
48e171e304e75216ac0dd2d70613996b8ae5f9e7
| 27,688 |
def _cprint_bad_contrast3(fgcolor, bgcolor, bold, underlined):
"""Returns 1 if one of the conditions of poor contrast is matched """
# black on black with LIGHT BG
_c1 = (fgcolor == 8) and (bgcolor == 0) and (CPRINT_PAR["light_background"])
if _c1:
return 1
else:
return 0
|
89d74cfe47fecd3fd21fc3d7f47a5674df80b669
| 27,690 |
def download_from_mongo(context, sel_filter, projection):
"""
Download panda DataFrame from a mongoDB server
:param context: execution context
:param sel_filter: a SON object specifying elements which must be present for a document to be included in the
result set
:param projection: a list of field names that should be returned in the result set or a dict specifying the fields
to include or exclude. If projection is a list “_id” will always be returned.
Use a dict to exclude fields from the result (e.g. projection={‘_id’: False}).
:return: panda DataFrame or None
:rtype: panda.DataFrame
"""
df = None
client = context.resources.mongo_warehouse.get_connection(context)
if client is not None:
# get database collection
collection = client.get_collection()
# retrieve a cursor for required records
# https://api.mongodb.com/python/current/api/pymongo/collection.html#pymongo.collection.Collection.find
context.log.info(f'Document retrieval in progress')
cursor = collection.find(filter=sel_filter, projection=projection)
entries = list(cursor)
context.log.info(f'{len(entries)} documents retrieved')
context.log.info(f'DataFrame loading in progress')
df = pd.DataFrame.from_dict(entries)
# tidy up
cursor.close()
client.close_connection()
context.log.info(f'Loaded {len(df)} records')
return df
|
863a0fdc7761de38d578039c764052ecac2e57f8
| 27,691 |
def create_group(current_session, groupname, description):
"""
Creates a group and returns it
"""
return gp.create_group(current_session, groupname, description)
|
0a5adea7b0a57ec3f44d260da250b06c09eca939
| 27,692 |
import requests
def call_api(endpoint):
"""
Build the API URL and request data
:param str endpoint: specific api endpoint to hit
:return response: server's reponse to the request
"""
url = BASE_URL + endpoint
try: # try to get json data
response = requests.get(url).json()
except ValueError: # if bytes, convert to str
response = requests.get(url).content.decode('utf-8')
except Exception as e:
response = e
return handle_response(response)
|
6f2527c6eb777c2cd0ab80636865d7adda032506
| 27,693 |
import inspect
def test_callable_args(func, args):
"""
Return True when this function can be called with the given arguments.
"""
assert isinstance(args, (list, tuple))
signature = getattr(inspect, 'signature', None)
if signature is not None:
# For Python 3, use inspect.signature.
try:
sig = _signatures_cache[func]
except KeyError:
sig = signature(func)
_signatures_cache[func] = sig
try:
sig.bind(*args)
except TypeError:
return False
else:
return True
else:
# For older Python versions, fall back to using getargspec.
spec = inspect.getargspec(func)
# Drop the 'self'
def drop_self(spec):
args, varargs, varkw, defaults = spec
if args[0:1] == ['self']:
args = args[1:]
return inspect.ArgSpec(args, varargs, varkw, defaults)
spec = drop_self(spec)
# When taking *args, always return True.
if spec.varargs is not None:
return True
# Test whether the given amount of args is between the min and max
# accepted argument counts.
return len(spec.args) - len(spec.defaults or []) <= len(args) <= len(spec.args)
|
704697253a88009394297a4688a9b3179b976f0d
| 27,694 |
def get_kde_caseduration_json(df, parameters=None):
"""
Gets the estimation of KDE density for the case durations calculated on the log/dataframe
(expressed as JSON)
Parameters
--------------
df
Pandas dataframe
parameters
Possible parameters of the algorithm, including:
Parameters.GRAPH_POINTS -> number of points to include in the graph
Parameters.CASE_ID_KEY -> Column hosting the Case ID
Returns
--------------
json
JSON representing the graph points
"""
cases = get_cases_description(df, parameters=parameters)
duration_values = [x["caseDuration"] for x in cases.values()]
return case_duration_commons.get_kde_caseduration_json(duration_values, parameters=parameters)
|
54262948216c43e0bbd44ec9ed2ed691903d9a9d
| 27,695 |
def render_text(name: str,
data: str,
font_size: int = 10,
fig_width_per_char: float = 0.1,
fig_height: float = 0.4,
img_height: int = None,
img_width: int = None,
**kwargs):
"""Render a text string.
Args:
name: name of the text
data: the string to be rendered
font_size: text font size
fig_width_per_char: the width of each character measured by ``figsize``
of ``plt.subplots()``.
fig_height: the height of the text label measured by ``figsize`` of
``plt.subplots()``.
img_height (int): height of the output image
img_width (int): width of the output image
**kwargs: extra arguments forwarded to ``ax.text``.
"""
fig, ax = plt.subplots(
figsize=(len(data) * fig_width_per_char, fig_height))
kwargs['fontsize'] = font_size
ax.text(0, 0, data, **kwargs)
ax.axis('off')
return _convert_to_image(name, fig, dpi, img_height, img_width)
|
d956b8dc885f1f582c2a54147bd42623f2a796c4
| 27,696 |
def index_closed(client, index_name):
"""Return True if index is closed"""
try:
# 1.0 params
index_metadata = client.cluster.state(
index=index_name,
metric='metadata',
)
except TypeError:
# 0.90 params:
index_metadata = client.cluster.state(
filter_blocks=True,
filter_index_templates=True,
filter_indices=index_name,
filter_nodes=True,
filter_routing_table=True,
)
return index_metadata['metadata']['indices'][index_name]['state'] == 'close'
|
9da234b5ef4b6f6c2835f3adf67aac804cc92919
| 27,697 |
def get_contributions_with_user_as_submitter(event, user):
"""Get a list of contributions in which the `user` has submission rights"""
return (_query_contributions_with_user_as_submitter(event, user)
.options(joinedload('acl_entries'))
.order_by(db.func.lower(Contribution.title))
.all())
|
ae30ded175f52aed5d50e629d5e260577bffe0f3
| 27,699 |
def rewrite_string(
searcher: AbstractSearcher,
source: Text,
path: Text,
max_iterations=1,
) -> Text:
"""Applies any replacements to the input source, and returns the result."""
return formatting.apply_substitutions(
source, find_iter(
searcher,
source,
path,
max_iterations=max_iterations,
))
|
2de75ca2511ff2496f29674ff762c7a48531c51b
| 27,700 |
def build_path_result_tests(name):
"""
Build a test for API commands that respond with ``Err`` and
``Mountpoint`` fields.
:param unicode command_name: The command in the schema to validate.
:return: ``TestCase``.
"""
return build_schema_test(
name=str(name + "Tests"),
schema={"$ref": "/endpoints.json#/definitions/" + name},
schema_store=SCHEMAS,
failing_instances={
b'additionalProperties': [
# Extra field:
{"Err": "", "Mountpoint": "/x", "extra": "y"},
# Wrong fields:
{"Result": "hello"},
],
b'required': [
# Missing field:
{}, {"Mountpoint": "/x"},
],
b'type': [
# Wrong types:
[], "", None,
],
},
passing_instances=[
{"Err": "Something went wrong."},
{"Err": "", "Mountpoint": "/x/"},
])
|
32491f1eb10d9f175522b7ea55f91a2c1e6aeb27
| 27,701 |
def get_mpls_autobw_template_detail_rpc(self, api_timeout=''):
"""
This is an auto-generated method for the PySwitchLib.
**Supported Versions**:
* SLXOS: 17r.1.01a, 17r.2.00, 17s.1.02
:type api_timeout: long or tuple(long, long)
:param api_timeout: Timeout for connection and response in seconds. If a tuple is specified, then the first value is for the connection timeout and the second value is for the response timeout.
:rtype: (*bool, list*)
:returns: Returns a tuple.
#. **api_success** (*bool*) - The success or failure of the API.
#. **details** (*list*) - List of REST request/response dictionaries, keyed by the asset's ip address.
:raises ConnectionError: If requests module connection or response timeout occurs.
:raises UnsupportedOSError: If firmware version installed on asset is not supported.
:raises RestInterfaceError: If requests module does not get a successful response from the rest URI.
:raises ValueError: If the argument value does not meet type requirements or value restrictions.
"""
operation_type = 'rpc'
compositions_list = []
bindings_list = [('pybind.slxos.v17r_1_01a.brocade_mpls_rpc.get_mpls_autobw_template_detail', 'pybind.slxos.v17r_1_01a.brocade_mpls_rpc.brocade_mpls', 'pybind.slxos.v17r_1_01a.brocade_mpls_rpc'), ('pybind.slxos.v17r_2_00.brocade_mpls_rpc.get_mpls_autobw_template_detail', 'pybind.slxos.v17r_2_00.brocade_mpls_rpc.brocade_mpls', 'pybind.slxos.v17r_2_00.brocade_mpls_rpc'), ('pybind.slxos.v17s_1_02.brocade_mpls_rpc.get_mpls_autobw_template_detail', 'pybind.slxos.v17s_1_02.brocade_mpls_rpc.brocade_mpls', 'pybind.slxos.v17s_1_02.brocade_mpls_rpc')]
composed_child_list = []
compositions_keyval_list = []
bindings_keyval = {'kwargs_key_name': '', 'keyval': '', 'extra_keyval': ''}
composed_child_leafval_list = []
leafval_map = {}
rest_leaf_name = ''
choices_kwargs_map = {}
leaf_os_support_map = {}
self._api_validation(choices_kwargs_map=choices_kwargs_map, leaf_os_support_map=leaf_os_support_map)
pybind_object = self._get_pybind_object(operation_type=operation_type, compositions_list=compositions_list, bindings_list=bindings_list, composed_child_list=composed_child_list, compositions_keyval_list=compositions_keyval_list, bindings_keyval=bindings_keyval, composed_child_leafval_list=composed_child_leafval_list, leafval_map=leafval_map)
return self._rpc_worker(operation_type=operation_type, pybind_object=pybind_object, resource_depth=1, timeout=api_timeout)
|
97e0d7ef9a43ef2335fefc6c62ad34286dbf7efb
| 27,702 |
def empty(a):
"""Test whether the slice is empty."""
return a is None or volume(a)==0
|
c1de4cfbf3bcc569b4b3cde02d518aabf32ff6fd
| 27,703 |
def nnPredict(w1, w2, data):
"""% nnPredict predicts the label of data given the parameter w1, w2 of Neural
% Network.
% Input:
% w1: matrix of weights of connections from input layer to hidden layers.
% w1(i, j) represents the weight of connection from unit i in input
% layer to unit j in hidden layer.
% w2: matrix of weights of connections from hidden layer to output layers.
% w2(i, j) represents the weight of connection from unit i in input
% layer to unit j in hidden layer.
% data: matrix of data. Each row of this matrix represents the feature
% vector of a particular image
% Output:
% label: a column vector of predicted labels"""
labels = np.array([])
# Your code here
return labels
|
564e3db0c659713de9dbfda7c279381d50d490e3
| 27,705 |
from typing import List
def count_pairs(array: List[int], difference: int) -> int:
"""
Given an array of integers, count the number of unique pairs of integers that have a given difference.
These pairs are stored in a set in order to remove duplicates.
Time complexity: O(n^2).
:param array: is the array to count.
:param difference: is the difference between two elements.
:return: the number of unique pairs of integers that have a given difference.
"""
pairs = set()
for i in range(len(array)):
for j in range(len(array)):
if array[i] - array[j] == difference:
pairs.add((array[i], array[j]))
return len(pairs)
|
e027e8885f4c4531da9b7dab7de8e84a7004c913
| 27,706 |
def decompress_deltas_18bit(buffer):
"""Parse packet deltas from 18-byte compression format."""
if bad_data_size(buffer, 18, "18-byte compressed packet"):
raise ValueError("Bad input size for byte conversion.")
deltas = np.zeros((2, 4))
# Sample 1 - Channel 1
minibuf = [(buffer[0] >> 6),
((buffer[0] & 0x3F) << 2 & 0xFF) | (buffer[1] >> 6),
((buffer[1] & 0x3F) << 2 & 0xFF) | (buffer[2] >> 6)]
deltas[0][0] = int32_from_18bit(minibuf)
# Sample 1 - Channel 2
minibuf = [(buffer[2] & 0x3F) >> 4,
(buffer[2] << 4 & 0xFF) | (buffer[3] >> 4),
(buffer[3] << 4 & 0xFF) | (buffer[4] >> 4)]
deltas[0][1] = int32_from_18bit(minibuf)
# Sample 1 - Channel 3
minibuf = [(buffer[4] & 0x0F) >> 2,
(buffer[4] << 6 & 0xFF) | (buffer[5] >> 2),
(buffer[5] << 6 & 0xFF) | (buffer[6] >> 2)]
deltas[0][2] = int32_from_18bit(minibuf)
# Sample 1 - Channel 4
minibuf = [(buffer[6] & 0x03), buffer[7], buffer[8]]
deltas[0][3] = int32_from_18bit(minibuf)
# Sample 2 - Channel 1
minibuf = [(buffer[9] >> 6),
((buffer[9] & 0x3F) << 2 & 0xFF) | (buffer[10] >> 6),
((buffer[10] & 0x3F) << 2 & 0xFF) | (buffer[11] >> 6)]
deltas[1][0] = int32_from_18bit(minibuf)
# Sample 2 - Channel 2
minibuf = [(buffer[11] & 0x3F) >> 4,
(buffer[11] << 4 & 0xFF) | (buffer[12] >> 4),
(buffer[12] << 4 & 0xFF) | (buffer[13] >> 4)]
deltas[1][1] = int32_from_18bit(minibuf)
# Sample 2 - Channel 3
minibuf = [(buffer[13] & 0x0F) >> 2,
(buffer[13] << 6 & 0xFF) | (buffer[14] >> 2),
(buffer[14] << 6 & 0xFF) | (buffer[15] >> 2)]
deltas[1][2] = int32_from_18bit(minibuf)
# Sample 2 - Channel 4
minibuf = [(buffer[15] & 0x03), buffer[16], buffer[17]]
deltas[1][3] = int32_from_18bit(minibuf)
return deltas
|
65fc1b7cc12c0d9f0a082c7df57bbae81fb1ec13
| 27,707 |
import re
def password_validate(password):
"""It validates password using regular expression
:param password: contains the users password
:returns: Boolean
:raises: ValidationError
"""
if re.match(
r"^(?=.*[a-z])(?=.*[A-Z])(?=.*\d)(?=.*[@$!%*?&])[A-Za-z\d@$!%*?&]{8,}$",
password,
):
return True
raise serializers.ValidationError(
{
"message": "Password must be at least 8 characters long, at least one"
+ " capitalized character, alphanumeric and contain special characters."
}
)
|
57cec9fd1ae1f4f2b4df887d98e65ea9f6191595
| 27,708 |
def get_emr_cluster_status(cluster_id: str, detail: bool = False):
"""
Provides cluster-level details including status, cluster_id, cluster_name and so on.
Args:
cluster_id: string, EMR cluster id
detail: bool, provided additional detail about cluster like ec2 attributes
Returns:
cluster_status(): dict, cluster level details like id, name, state
"""
cluster_status = {}
try:
response = emr.describe_cluster(ClusterId=cluster_id)
except Exception as error:
logger.error(error)
raise botocore.exceptions.ClientError(error, 'describe_cluster')
else:
cluster_status = {
"cluster_id": response.get('Cluster').get('Id'),
"cluster_name": response.get('Cluster').get('Name'),
"status": response.get('Cluster').get('Status').get('State'),
"protection": response.get('Cluster').get('TerminationProtected'),
"message": response.get('Cluster').get('Status').get('StateChangeReason').get('Message')
}
if detail:
cluster_status['ec2_attributes'] = {
'subnet_id': response.get('Cluster').get('Ec2InstanceAttributes').get('Ec2SubnetId'),
'availability_zone': response.get('Cluster').get('Ec2InstanceAttributes').get('Ec2AvailabilityZone'),
'master_sg': response.get('Cluster').get('Ec2InstanceAttributes').get('EmrManagedMasterSecurityGroup'),
'service_sg': response.get('Cluster').get('Ec2InstanceAttributes').get('ServiceAccessSecurityGroup')
}
return cluster_status
|
48da66c5aa45f0697b71924a31ea6d942bb5ce19
| 27,709 |
def get_keypair() -> KeyPairInfo:
"""Returns current keypair (ec2.KeyPairInfo)
https://boto3.readthedocs.io/en/latest/reference/services/ec2.html#keypairinfo
"""
return get_keypair_dict()[get_keypair_name()]
|
95cba4f8a81858578e831b0bdbae359791399841
| 27,710 |
def char_vectorizer(char,
custom_alphabet=False):
"""
Vectorize given nucleotide character. Convert to uppercase before
vectorizing.
>>> char_vectorizer("C")
[0, 1, 0, 0]
>>> char_vectorizer("g")
[0, 0, 1, 0]
>>> char_vectorizer("M", ['E', 'H', 'I', 'M', 'S'])
[0, 0, 0, 1, 0]
"""
alphabet = ['A','C','G','U']
if custom_alphabet:
alphabet = custom_alphabet
char = char.upper()
l = len(char)
vector = []
assert l == 1, "given char length != 1 (given char: \"%s\")" % (l)
for c in alphabet:
if c == char:
vector.append(1)
else:
vector.append(0)
return vector
|
53c306d814807299b7bac7801f4b7d2a663b6f99
| 27,711 |
def orbit_from_name(name):
"""Return :py:class:`~poliastro.twobody.orbit.Orbit` given a name.
Retrieve info from JPL DASTCOM5 database.
Parameters
----------
name : str
NEO name.
Returns
-------
orbit : list (~poliastro.twobody.orbit.Orbit)
NEO orbits.
"""
records = record_from_name(name)
orbits = []
for record in records:
orbits.append(orbit_from_record(record))
return orbits
|
145c1ef503743e9066ecf8e2f5e06c0526f8ab62
| 27,712 |
def make_snowflake(timestamp_ms, datacenter_id, worker_id, sequence_id, twepoch=twepoch):
"""generate a twitter-snowflake id, based on
https://github.com/twitter/snowflake/blob/master/src/main/scala/com/twitter/service/snowflake/IdWorker.scala
:param: timestamp_ms time since UNIX epoch in milliseconds"""
timestamp_ms = int(timestamp_ms)
sid = ((timestamp_ms - twepoch) % max_timestamp) << datacenter_id_bits << worker_id_bits << sequence_id_bits
sid += (datacenter_id % max_datacenter_id) << worker_id_bits << sequence_id_bits
sid += (worker_id % max_worker_id) << sequence_id_bits
sid += sequence_id % max_sequence_id
return sid
|
473d97589dbe90949cb69ecdbeba719cbea5c2ee
| 27,713 |
from typing import Sequence
def compute_committee(indices: Sequence[ValidatorIndex],
seed: Bytes32,
index: uint64,
count: uint64) -> Sequence[ValidatorIndex]:
"""
Return the committee corresponding to ``indices``, ``seed``, ``index``, and committee ``count``.
"""
start = (len(indices) * index) // count
end = (len(indices) * (index + 1)) // count
return [indices[compute_shuffled_index(i, len(indices), seed)] for i in range(start, end)]
|
168fc69ef7d4962c089cd6e497cd94ccad062c73
| 27,714 |
def get_default_graph():
""" Setting graphviz graph global options """
graph = Digraph('AWS', engine='dot')
graph.body.append('splines=line')
graph.body.append('rankdir=LR')
graph.body.append('outputorder=edgesfirst')
graph.node_attr.update(shape='rectangle', style='filled', color='black')
return graph
|
25ae7376b31d280722fb2791dbf9478a6b4cb2d1
| 27,715 |
from json import dumps
def GetJson(data):
"""
将对象转换为JSON
@data 被转换的对象(dict/list/str/int...)
"""
if data == bytes: data = data.decode('utf-8')
return dumps(data)
|
372b501f5ada7254efab10447dcbdc91c8799408
| 27,716 |
def binary_tail(n: int) -> int:
""" The last 1 digit and the following 0s of a binary representation, as a number """
return ((n ^ (n - 1)) + 1) >> 1
|
63460cef7b39b7e7ee2ec880810ff71d82be01e9
| 27,717 |
from datetime import datetime
def time_left(expire_date):
"""Return remaining days before feature expiration or 0 if expired."""
today_dt = datetime.today()
expire_dt = datetime.strptime(expire_date, "%d-%b-%Y")
# Calculate remaining days before expiration
days_left_td = expire_dt - today_dt
days_left = days_left_td.days
if days_left <= 0:
days_left = 0
return days_left
|
652acd27b0d4fa9b21321df4ff8ce6ce15b97ed6
| 27,718 |
import yaml
def load_config():
"""
importer_config.yaml must be in the current working directory
"""
with open(_PATH) as fd:
config = yaml.safe_load(fd)
# Remove trailing slashes from URLs
config['kbase_endpoint'] = config['kbase_endpoint'].strip('/')
return config
|
0137c59cd2ea7b5b8ba57a82fb2997e6e7330751
| 27,719 |
def get_package_version():
"""Return the Review Board version as a Python package version string.
Returns:
unicode:
The Review Board package version.
"""
version = '%s.%s' % (VERSION[0], VERSION[1])
if VERSION[2] or VERSION[3]:
version = '%s.%s' % (version, VERSION[2])
if VERSION[3]:
version = '%s.%s' % (version, VERSION[3])
tag = VERSION[4]
if tag != 'final':
if tag == 'alpha':
tag = 'a'
elif tag == 'beta':
tag = 'b'
version = '%s%s%s' % (version, tag, VERSION[5])
return version
|
418eef843dd3647cffdeb879781e3599487cd885
| 27,720 |
def fit(x, y, sigy=None, error=False):
""" Perform a linear fit on a range of x and y values.
This function fits a set of data points x, y with individual standard
deviations sigy for the y values to a straight line y = a + bx by
minimizing chi-square.
If the 'error' parameter is False the function returns only the found
values for 'a' and 'b' as a tuple. If 'error' is True then a third tuple
item is returned containing a dictionary of error statistics ('siga' the
standard deviation of 'a', 'sigb' the standard deviation of 'b', 'chi2'
the value of chi-square, and 'q' the goodness of fit probability).
If 'sigy' is 'None' then no standard deviations are used, 'q' will be 1.0
and the normalization of chi-square is to unit standard deviation for all
points.
Example:
>>> a, b, err = fit([1.1, 1.95, 3.05], [1, 2.01, 2.95], error=True)
"""
# This routine is based on an algorithm from Numerical Recipes
x = np.asarray(x).astype(float)
y = np.asarray(y).astype(float)
if x.size != y.size:
ValueError("Arrays 'x' and 'y' have different length")
if x.size < 2:
raise ValueError("Arrays 'x' and 'y' should have at least 2 elements")
useWeights = False
if sigy is not None:
sigy = np.asarray(sigy)
if sigy.size != y.size:
raise ValueError("Arrays 'sigy' and 'y' have different length")
useWeights = True
# We need to minimize:
#
# chi2 = sum( ((y[i] - a - b * x[i]) / sigy[i])^2 ; i=1..N)
#
# When taking derivatives with respect to a and b we get
#
# dchi2/da = 0 = -2 * sum( (y[i] - a - b * x[i]) / sig[i]^2 ; i=1..N)
# dchi2/db = 0 = -2 * sum( x[i] * (y[i] - a - b * x[i]) / sig[i]^2 ; i=1..N)
#
# which provides us with a linear equation that we can use to solve a and b
if useWeights:
weights = 1 / (np.square(sigy.flat))
S = np.sum(weights)
Sx = np.dot(x.flat, weights)
Sy = np.dot(y.flat, weights)
t = x.flat - Sx / S
tw = t * weights
Stt = np.dot(tw, t)
b = np.dot(tw, y.flat) / Stt
else:
S = x.size
Sx = np.sum(x.flat)
Sy = np.sum(y.flat)
t = x.flat - Sx / S
Stt = np.dot(t, t)
b = np.dot(t, y.flat) / Stt
a = (Sy - Sx * b) / S
if not error:
return (a, b)
siga = np.sqrt((1 + Sx * Sx / (S * Stt)) / S)
sigb = np.sqrt(1 / Stt)
chi2 = 0.0
q = 1.0
if useWeights:
chi = (y - a - b * x.flat) / sigy.flat
chi2 = np.dot(chi, chi)
if x.size > 2:
q = gammaq(0.5 * (x.size - 2), 0.5 * chi2)
else:
chi = y.flat - a - b * x.flat
chi2 = np.dot(chi, chi)
if x.size > 2:
sigdat = np.sqrt(chi2 / (x.size - 2))
siga *= sigdat
sigb *= sigdat
return (a, b, {'siga': siga, 'sigb': sigb, 'chi2': chi2, 'q': float(q)})
|
f02db36a6e7395d3f955cafc6f506e3a42984abf
| 27,722 |
def eval_metrics_offline(cfg,
pred_folder,
palette,
metrics=['mFscoreCD', 'mFscore']
):
"""Calculate evaluation metrics from offline GT and prediction images, maily use the evaluate of dataset class
Args:
cfg (Config): config dict, must have the necessary items to build a
dataset
palette (list | ndarray): palette of the label
metrics ([str]): metrics to be calculated. Default:
['mFscoreCD', 'mFscore']
Returns:
returns of the evaluate method of the dataset class
"""
assert osp.isdir(pred_folder)
dataset = build_dataset(cfg)
preds = []
for img_info in dataset.img_infos:
pred_path = osp.join(pred_folder, img_info['ann']['seg_map'])
pred = read_s2looking_label(pred_path, palette)
preds.append(pred)
return dataset.evaluate(preds, metric=metrics)
|
7d5e44a1cda8d7bbb92a56cedf18e940c51663e5
| 27,723 |
def zero_pad_and_crop(img, amount=4):
"""Zero pad by `amount` zero pixels on each side then take a random crop.
Args:
img: numpy image that will be zero padded and cropped.
amount: amount of zeros to pad `img` with horizontally and verically.
Returns:
The cropped zero padded img. The returned numpy array will be of the same
shape as `img`.
"""
padded_img = np.zeros((img.shape[0] + amount * 2, img.shape[1] + amount * 2,
img.shape[2]))
padded_img[amount:img.shape[0] + amount, amount:
img.shape[1] + amount, :] = img
top = np.random.randint(low=0, high=2 * amount)
left = np.random.randint(low=0, high=2 * amount)
new_img = padded_img[top:top + img.shape[0], left:left + img.shape[1], :]
return new_img
|
63ac80cb9759fd06032afd97f03e0057aed86784
| 27,724 |
def video_id(video_id_or_url):
"""
Returns video id from given video id or url
Parameters:
-----------
video_id_or_url: str - either a video id or url
Returns:
--------
the video id
"""
if 'watch?v=' in video_id_or_url:
return video_id_or_url.split('watch?v=')[1]
else:
# assume we already have an video id
return video_id_or_url
|
9f680ac621e1f5c6314a6a3e97093d786fa7ea33
| 27,725 |
def check_permission(resource: Resource, permission_name: str) -> dict:
"""
Check if requester has sufficient permissions to do something on specific resource.
Raises if not.
"""
base_permission_policy = resource.get_guest_authorization()
if (authorization_header := resource.request.headers.get("Authorization")) is None:
if not base_permission_policy:
raise Unauthorized("Authorization header missing or empty")
authorizer = Authorizer(
auth_jwt=authorization_header,
resource_name=resource.get_name(),
permission_name=permission_name,
base_permission_policy=base_permission_policy,
)
authorizer.check_access()
return authorizer.restrictions
|
b632fbb5f3429c1540da35c3b82e6fa562b81c87
| 27,726 |
from datetime import datetime
import json
def makePalRecordsConsistent(pal_records, low_frequency, high_frequency,
user_id, fcc_channel_id="1",
start_date=None, end_date=None):
"""Make Pal object consistent with the inputs
Args:
pal_records: (list) A list of PAL Records in the form of dictionary.
low_frequency: (number) The Primary Low Frequency in Hz for PAL.
high_frequency: (number) The Primary High Frequency in Hz for PAL.
user_id: (string) The userId to put in PAL Records.
fcc_channel_id: (string) The FCC-supplied frequency channel identifier.
start_date: (string) PAL license start date, generally set as one year
before the current date
end_date: (string) PAL license expiration date, generally set as more than
one year after the current date
Returns:
A list containing individual PAL records in the form of dictionary
Note: The PAL Dictionary must contain censusYear(number) and
fipsCode(number)
"""
start_date = datetime.now().replace(year=datetime.now().year - 1) \
if start_date is None else start_date
end_date = datetime.now().replace(year=datetime.now().year + 1) \
if end_date is None else end_date
for index, pal_rec in enumerate(pal_records):
pal_fips_code = pal_rec['fipsCode']
pal_census_year = pal_rec['censusYear']
del pal_rec['fipsCode'], pal_rec['censusYear']
pal_rec = defaultdict(lambda: defaultdict(dict), pal_rec)
# Change the FIPS Code and Registration Date-Year in Pal Id
pal_rec['palId'] = '/'.join(['pal', '%s-%d' %
('{:02d}'.format(start_date.month),
start_date.year),
str(pal_fips_code), fcc_channel_id])
pal_rec['userId'] = user_id
# Make the date consistent in Pal Record for Registration and License
pal_rec['registrationInformation']['registrationDate'] = \
start_date.strftime('%Y-%m-%dT%H:%M:%SZ')
# Change License Information in Pal
pal_rec['license']['licenseAreaIdentifier'] = str(pal_fips_code)
pal_rec['license']['licenseAreaExtent'] = \
'zone/census_tract/census/{}/{}'.format(pal_census_year, pal_fips_code)
pal_rec['license']['licenseDate'] = start_date.strftime('%Y-%m-%dT%H:%M:%SZ')
pal_rec['license']['licenseExpiration'] = end_date.strftime('%Y-%m-%dT%H:%M:%SZ')
pal_rec['license']['licenseFrequencyChannelId'] = fcc_channel_id
# Change Frequency Information in Pal
pal_rec['channelAssignment']['primaryAssignment']['lowFrequency'] = low_frequency
pal_rec['channelAssignment']['primaryAssignment']['highFrequency'] = high_frequency
# Converting from defaultdict to dict
pal_records[index] = json.loads(json.dumps(pal_rec))
return pal_records
|
3029ee761afb17428a6a2b0b1e85c0f2f3fdc6d6
| 27,727 |
import yaml
def read_config():
"""Read from rotest.yml config elrados segment."""
config_path = search_config_file()
if config_path is not None:
with open(config_path, "r") as config_file:
configuration_content = config_file.read()
yaml_configuration = yaml.load(configuration_content)
else:
yaml_configuration = {}
return AttrDict(yaml_configuration.get("elrados", {}))
|
5fb81ef5609074c029203021895ba55654760a60
| 27,728 |
import typing
def cast_to_str(some_value: typing.Any, from_type: typing.Any) -> typing.Any:
"""Just helper for creating suitable test assets."""
if from_type == bytes:
return some_value.decode()
return str(some_value)
|
9157873a74d0d02b919d047710c1d4ccee4121a6
| 27,729 |
def downsample_by_group(df,min_distance=1.,message_callback=print):
"""Group and down-sample a DataFrame of xrsd records.
Parameters
----------
df : pandas.DataFrame
dataframe containing xrsd samples
min_distance : float
the minimum allowed nearest-neighbor distance
for continuing to downsample after 10 or more samples
have been selected
Returns
-------
data_sample : pandas.DataFrame
DataFrame containing all of the down-sampled data from
each group in the input dataframe.
Features in this DataFrame are not scaled:
the correct scaler should be applied before training models.
"""
data_sample = pd.DataFrame(columns=df.columns)
group_cols = ['experiment_id','system_class']
all_groups = df.groupby(group_cols)
# downsample each group independently
for group_labels,grp in all_groups.groups.items():
group_df = df.iloc[grp].copy()
if message_callback:
message_callback('Downsampling data for group: {}'.format(group_labels))
#lbl_df = _filter_by_labels(data,lbls)
dsamp = downsample(df.iloc[grp].copy(), min_distance)
if message_callback:
message_callback('Finished downsampling: kept {}/{}'.format(len(dsamp),len(group_df)))
data_sample = data_sample.append(dsamp)
return data_sample
|
bbcdbab6c1bf5e42554e102bda8d245263a47bfd
| 27,730 |
from typing import Union
def get_pure_ratings(
ratings: Union[str, pd.Series, pd.DataFrame]
) -> Union[str, pd.Series, pd.DataFrame]:
"""Removes rating watches/outlooks.
Parameters
----------
ratings : str, pd.Series, or pd.DataFrame
Rating may contain watch, such as `AA- *+`, `BBB+ (CwNegative)`.
Outlook/watch should be seperated by a blank from the actual rating.
Returns
-------
Union[str, pd.Series, pd.DataFrame]
String, Series, or DataFrame with regular ratings stripped off of watches.
The name of the resulting Series or the columns of the returning DataFrame will
be suffixed with `_clean`.
Examples
--------
Cleaning a single rating:
>>> get_pure_ratings("AA- *+")
'AA-'
>>> get_pure_ratings("Au")
'A'
Cleaning a `pd.Series`:
>>> import pandas as pd
>>> rating_series=pd.Series(
... data=[
... "BB+ *-",
... "BBB *+",
... np.nan,
... "AA- (Developing)",
... np.nan,
... "CCC+ (CwPositive)",
... "BB+u",
... ],
... name="rtg_SP",
... )
>>> get_pure_ratings(rating_series)
0 BB+
1 BBB
2 NaN
3 AA-
4 NaN
5 CCC+
6 BB+
Name: rtg_SP_clean, dtype: object
Cleaning a `pd.DataFrame`:
>>> rtg_df = pd.DataFrame(
... data={
... "rtg_SP": [
... "BB+ *-",
... "BBB *+",
... np.nan,
... "AA- (Developing)",
... np.nan,
... "CCC+ (CwPositive)",
... "BB+u",
... ],
... "rtg_Fitch": [
... "BB+ *-",
... "BBB *+",
... pd.NA,
... "AA- (Developing)",
... np.nan,
... "CCC+ (CwPositive)",
... "BB+u",
... ],
... },
... )
>>> get_pure_ratings(rtg_df)
rtg_SP_clean rtg_Fitch_clean
0 BB+ BB+
1 BBB BBB
2 NaN <NA>
3 AA- AA-
4 NaN NaN
5 CCC+ CCC+
6 BB+ BB+
"""
if isinstance(ratings, str):
ratings = ratings.split()[0]
ratings = ratings.rstrip("uU")
return ratings
elif isinstance(ratings, pd.Series):
# identify string occurrences
isstring = ratings.apply(type).eq(str)
# strip string after occurrence of very first blank and strip character 'u',
# which has usually been added without a blank
ratings[isstring] = ratings[isstring].str.split().str[0]
ratings[isstring] = ratings[isstring].str.rstrip("uU")
ratings.name = f"{ratings.name}_clean"
return ratings
elif isinstance(ratings, pd.DataFrame):
# Recursive call of `get_pure_ratings`
return pd.concat(
[get_pure_ratings(ratings=ratings[col]) for col in ratings.columns], axis=1
)
|
a834b3f33f12d8d3b6c109021a4d075c32160544
| 27,731 |
def export_grid(outname,resh,ignore=[255,0]):
"""[Exports given grid to a point cloud]
Args:
outname ([str]): [Output file name]
resh ([np.array]): [3D Volume in grid representation]
ignore (list, optional): [values to ignore]. Defaults to [255,0].
Returns:
Grid as a point cloud and its colors
"""
pts = []
colors = []
grid_shape = resh.shape
# print(grid_shape)
colorMap = np.array([[255, 0, 0],
[0, 255, 0],
[0, 0, 255],
[80, 128, 255],
[255, 230, 180],
[255, 0, 255],
[0, 255, 255],
[100, 0, 0],
[0, 100, 0],
[255, 255, 0],
[50, 150, 0],
[200, 255, 255],
[255, 200, 255],
[128, 128, 80],
[0, 50, 128],
[0, 100, 100],
[0, 255, 128],
[0, 128, 255],
[255, 0, 128],
[128, 0, 255],
[255, 128, 0],
[128, 255, 0],
])
# resh = np.load("results_overfit_scannet_corrected/0000.npy").reshape(grid_shape)
for x in range(grid_shape[0]):
for y in range(grid_shape[1]):
for z in range(grid_shape[2]):
if resh[x,y,z] not in ignore:
colors.append(colorMap[resh[x,y,z]])
pts.append([x,y,z])
a = trimesh.points.PointCloud(pts,colors).export(outname)
return np.array(pts),np.array(colors)
|
27fd06c37d57f0eb320d0d81908efc1acf6bc8ee
| 27,732 |
def sum_errors(dic):
"""Helper function to sum up number of failed jobs per host.
Assumes that dic is in the form
:param dict dic: {"error_code1":count1, "error_code2":count2, etc.}
:return int: Sum of all values in dic
"""
return sum(value for key, value in dic.iteritems())
|
0d2bc9df58e5bf9639a331d64061de4c0a5aa4ed
| 27,733 |
def checkFriends(new_user, usernameOfFriend):
""" Check if users are friends or not"""
# Check if users are friends first
viewF = viewFriends(new_user, usernameOfFriend)
# Check if users have any pending requests
viewP = searchPendingRequests(new_user, usernameOfFriend)
# Logic
if viewF:
return True
elif viewP:
# The friendship has not been approved yet
return False
else:
return False
|
6268d180a768ad858d5e1c5c69c92837a027f07f
| 27,734 |
def update(instance, **data):
"""Update instance with data directly by using ``update()``
skipping calling ``save()`` method.
Usage: ``instance = update(instance, some_field=some_value)``
"""
instance.__class__.objects.filter(pk=instance.pk).update(**data)
return refresh(instance)
|
8462d5459ba02d11ef3edd0c1d3c152d4b682634
| 27,735 |
def cast_distance_matrix_to_optimal_integer_type(D_X):
"""
Cast distance matrix to smallest signed integer type, sufficient
to hold all its distances.
Parameters
-----------
D_X: np.array (|X|×|X|)
Distance matrix of a compact metric space X with integer
distances.
Returns
--------
D: np.array (|X|×|X|)
Distance matrix of the metric space, cast to optimal type.
"""
max_distance = np.max(D_X)
# Type is signed integer to allow subtractions.
optimal_int_type = determine_optimal_int_type(max_distance)
D_X = D_X.astype(optimal_int_type)
return D_X
|
01c8e43259d5edc7282a9c01269277f34495ff9d
| 27,736 |
def getObject(name):
"""Get reference to single Rhino object, checking for failure"""
rc, obRef = Rhino.Input.RhinoGet.GetOneObject("Select " + name, True, Rhino.DocObjects.ObjectType.AnyObject)
if rc != Rhino.Commands.Result.Success or not obRef : raise NameError(rc)
return obRef
|
20d562dd90dec82a479bdedabef62f17129cecf0
| 27,737 |
def get_falsecolor(input):
"""
picks false color bands from the 12 Sentinel bands (for visual interpretation of vegetation)
:param input: 12-band image tensor
:return: 3-band NIR-RED-GREEN tensor
"""
rgb_band_idxs = [bands.index(b) for b in ["S2B8", "S2B4", "S2B3"]]
return input[rgb_band_idxs]
|
a4e80bb61211456794b34c99ec0ad92f1b1f567d
| 27,738 |
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