content
stringlengths 35
762k
| sha1
stringlengths 40
40
| id
int64 0
3.66M
|
|---|---|---|
def start_end_epoch(graph):
"""
Start epoch of graph.
:return: (start epoch, end epoch).
"""
start = 0
end = 0
for e in graph.edges_iter():
for _, p in graph[e[0]][e[1]].items():
end = max(end, p['etime_epoch_secs'])
if start == 0:
start = p['stime_epoch_secs']
else:
start = min(start, p['stime_epoch_secs'])
return (start, end)
|
724726ec83d3a98539eed859ec584c6f1adb8567
| 3,642,100
|
def distance_metric(seg_A, seg_B, dx):
"""
Measure the distance errors between the contours of two segmentations.
The manual contours are drawn on 2D slices.
We calculate contour to contour distance for each slice.
"""
table_md = []
table_hd = []
X, Y, Z = seg_A.shape
for z in range(Z):
# Binary mask at this slice
slice_A = seg_A[:, :, z].astype(np.uint8)
slice_B = seg_B[:, :, z].astype(np.uint8)
# The distance is defined only when both contours exist on this slice
if np.sum(slice_A) > 0 and np.sum(slice_B) > 0:
# Find contours and retrieve all the points
_, contours, _ = cv2.findContours(cv2.inRange(slice_A, 1, 1),
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
pts_A = contours[0]
for i in range(1, len(contours)):
pts_A = np.vstack((pts_A, contours[i]))
_, contours, _ = cv2.findContours(cv2.inRange(slice_B, 1, 1),
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
pts_B = contours[0]
for i in range(1, len(contours)):
pts_B = np.vstack((pts_B, contours[i]))
# Distance matrix between point sets
M = np.zeros((len(pts_A), len(pts_B)))
for i in range(len(pts_A)):
for j in range(len(pts_B)):
M[i, j] = np.linalg.norm(pts_A[i, 0] - pts_B[j, 0])
# Mean distance and hausdorff distance
md = 0.5 * (np.mean(np.min(M, axis=0)) + np.mean(np.min(M, axis=1))) * dx
hd = np.max([np.max(np.min(M, axis=0)), np.max(np.min(M, axis=1))]) * dx
table_md += [md]
table_hd += [hd]
# Return the mean distance and Hausdorff distance across 2D slices
mean_md = np.mean(table_md) if table_md else None
mean_hd = np.mean(table_hd) if table_hd else None
return mean_md, mean_hd
|
4ae5de6428914c8352ae1c6cdd9e94183d9ea3f8
| 3,642,101
|
def tomographic_redshift_bin(z_s, version=default_version):
"""DES analyses work in pre-defined tomographic redshift bins. This
function returns the photometric redshift bin as a function of photometric
redshift.
Parameters
----------
z_s : numpy array
Photometric redshifts.
version : string
Which catalog version to use.
Returns
-------
z_bin : numpy array
The tomographic redshift bin corresponding to each photometric
redshift. Returns -1 in case a redshift does not fall into any bin.
"""
if version == 'Y1':
z_bins = [0.2, 0.43, 0.63, 0.9, 1.3]
else:
raise RuntimeError(
"Unkown version of DES. Supported versions are {}.".format(
known_versions))
z_bin = np.digitize(z_s, z_bins) - 1
z_bin = np.where((z_s < np.amin(z_bins)) | (z_s >= np.amax(z_bins)) |
np.isnan(z_s), -1, z_bin)
return z_bin
|
b4a21c111b8d5b5a34c018315f95cf18deb356af
| 3,642,102
|
def is_point_in_rect(point, rect):
"""Checks whether is coordinate point inside the rectangle or not.
Rectangle is defined by bounding box.
:type point: list
:param point: testing coordinate point
:type rect: list
:param rect: bounding box
:rtype: boolean
:return: boolean check result
"""
x0, y0, x1, y1 = rect
x, y = point
if x0 <= x <= x1 and y0 <= y <= y1:
return True
return False
|
d0c7a64138899f4e50b42dc75ea6030616d4dfec
| 3,642,103
|
def chinese_theorem_inv(modulo_list):
"""
Returns (x, n1*...*nk) such as
x mod mk = ak for all k, with
modulo_list = [(a1, n1), ..., (ak, nk)]
n1, ..., nk most be coprime 2 by 2.
"""
a, n = modulo_list[0]
for a2, n2 in modulo_list[1:]:
u, v = bezout(n, n2)
a, n = a*v*n2+a2*u*n, n*n2
for (a1, n1) in modulo_list:
assert a % n1 == a1
return ((n+a % n) % n, n)
|
3d1398901b75ca8b21fb97af0acdfbd65fec0a3e
| 3,642,104
|
def compute_segment_cores(split_lines_of_utt):
"""
This function returns a list of pairs (start-index, end-index) representing
the cores of segments (so if a pair is (s, e), then the core of a segment
would span (s, s+1, ... e-1).
The argument 'split_lines_of_utt' is list of lines from a ctm-edits file
corresponding to a single utterance.
By the 'core of a segment', we mean a sequence of ctm-edits lines including
at least one 'cor' line and a contiguous sequence of other lines of the
type 'cor', 'fix' and 'sil' that must be not tainted. The segment core
excludes any tainted lines at the edge of a segment, which will be added
later.
We only initiate segments when it contains something correct and not
realized as unk (i.e. ref==hyp); and we extend it with anything that is
'sil' or 'fix' or 'cor' that is not tainted. Contiguous regions of 'true'
in the resulting boolean array will then become the cores of prototype
segments, and we'll add any adjacent tainted words (or parts of them).
"""
num_lines = len(split_lines_of_utt)
line_is_in_segment_core = [False] * num_lines
# include only the correct lines
for i in range(num_lines):
if (split_lines_of_utt[i][7] == 'cor'
and split_lines_of_utt[i][4] == split_lines_of_utt[i][6]):
line_is_in_segment_core[i] = True
# extend each proto-segment forwards as far as we can:
for i in range(1, num_lines):
if line_is_in_segment_core[i - 1] and not line_is_in_segment_core[i]:
edit_type = split_lines_of_utt[i][7]
if (not is_tainted(split_lines_of_utt[i])
and (edit_type == 'cor' or edit_type == 'sil'
or edit_type == 'fix')):
line_is_in_segment_core[i] = True
# extend each proto-segment backwards as far as we can:
for i in reversed(range(0, num_lines - 1)):
if line_is_in_segment_core[i + 1] and not line_is_in_segment_core[i]:
edit_type = split_lines_of_utt[i][7]
if (not is_tainted(split_lines_of_utt[i])
and (edit_type == 'cor' or edit_type == 'sil'
or edit_type == 'fix')):
line_is_in_segment_core[i] = True
# Get contiguous regions of line in the form of a list
# of (start_index, end_index)
segment_ranges = []
cur_segment_start = None
for i in range(0, num_lines):
if line_is_in_segment_core[i]:
if cur_segment_start is None:
cur_segment_start = i
else:
if cur_segment_start is not None:
segment_ranges.append((cur_segment_start, i))
cur_segment_start = None
if cur_segment_start is not None:
segment_ranges.append((cur_segment_start, num_lines))
return segment_ranges
|
0d054d1f891127f0a27b20bfbd82ad6ce85dec39
| 3,642,105
|
import math
def local_neighborhood_nodes_for_element(index, feature_radius_pixels):
"""
local_neighborhood_nodes_for_element returns the indices of nodes which are in the local neighborhood of an
element. Note that the nodes and elements in a mesh have distinct coordinates: elements exist in the centroids
of cubes formed by the mesh of nodes.
:param index: the element for which we want the local neighborhood
:param feature_radius_pixels: minimum feature radius, in pixels
:return: the indices of the local neighborhood set
"""
# TODO: there might be an off-by-one error in here
neighbors = set()
x, y, z = elemental_index_to_nodal_index(index)
# allow our first index to vary the entire range
for i in range(math.ceil(x - feature_radius_pixels), math.floor(x + feature_radius_pixels) + 1):
# how much variability is left for the second index given the first?
leftover_y_radius = math.sqrt(feature_radius_pixels ** 2 - (x - i) ** 2)
for j in range(math.ceil(y - leftover_y_radius), math.floor(y + leftover_y_radius) + 1):
leftover_z_radius = math.sqrt(feature_radius_pixels ** 2 - (x - i) ** 2 - (y - j) ** 2)
for k in range(math.ceil(z - leftover_z_radius), math.floor(z + leftover_z_radius) + 1):
neighbors.add((i, j, k))
return neighbors
|
cf0f55b71af34cebce69cfc8fa6d201ff83c9808
| 3,642,106
|
def _classification(dataset='iris',k_range=[1,31],dist_metric='l1'):
"""
knn on classificaiton dataset
Inputs:
dataset: (str) name of dataset
k: (list) k[0]:lower bound of number of nearest neighbours; k[1]:upper bound of number of nearest neighbours
dist_metric: (str) 'l1' or 'l2'
Outputs:
validation accuracy
"""
print ('------Processing Dataset '+dataset+' ------')
x_train, x_valid, x_test, y_train, y_valid, y_test = load_dataset(dataset)
if y_train.dtype==np.dtype('bool'):
y_train = _cast_TF(y_train)
y_valid = _cast_TF(y_valid)
y_test = _cast_TF(y_test)
acc = []
predicted = _eval_knn(k_range,x_train,y_train,x_valid,y_valid,dist_metric,compute_loss=False)
for k in range(k_range[0],k_range[1]):
#print(k)
curr_predict = predicted['k='+str(k)]
#print(curr_predict)
result = np.argmax(curr_predict,axis=1)
#print(result)
gt = np.where(y_valid==True,1,0)
gt = np.argmax(gt,axis=1)
unique, counts = np.unique(result-gt, return_counts=True)
correct = dict(zip(unique, counts))[0]
#print(correct)
acc.append(correct/y_valid.shape[0])
return acc
|
ce5d0516cffcb545787abe15c46fe086ff8e4991
| 3,642,107
|
from os.path import expanduser
def parse_pgpass(hostname='scidb2.nersc.gov', username='desidev_admin'):
"""Read a ``~/.pgpass`` file.
Parameters
----------
hostname : :class:`str`, optional
Database hostname.
username : :class:`str`, optional
Database username.
Returns
-------
:class:`str`
A string suitable for creating a SQLAlchemy database engine, or None
if no matching data was found.
"""
fmt = "postgresql://{3}:{4}@{0}:{1}/{2}"
try:
with open(expanduser('~/.pgpass')) as p:
lines = p.readlines()
except FileNotFoundError:
return None
data = dict()
for l in lines:
d = l.strip().split(':')
if d[0] in data:
data[d[0]][d[3]] = fmt.format(*d)
else:
data[d[0]] = {d[3]: fmt.format(*d)}
if hostname not in data:
return None
try:
pgpass = data[hostname][username]
except KeyError:
return None
return pgpass
|
929b705fa8a753f773321e47c73d096ffb4bd171
| 3,642,108
|
def make_move(board, max_rows, max_cols, col, player):
"""Put player's piece in column COL of the board, if it is a valid move.
Return a tuple of two values:
1. If the move is valid, make_move returns the index of the row the
piece is placed in. Otherwise, it returns -1.
2. The updated board
>>> rows, columns = 2, 2
>>> board = create_board(rows, columns)
>>> row, board = make_move(board, rows, columns, 0, 'X')
>>> row
1
>>> get_piece(board, 1, 0)
'X'
>>> row, board = make_move(board, rows, columns, 0, 'O')
>>> row
0
>>> row, board = make_move(board, rows, columns, 0, 'X')
>>> row
-1
>>> row, board = make_move(board, rows, columns, -4, '0')
>>> row
-1
"""
if -1 < col and col < max_cols:
return put_piece(board, max_rows, col, player)
return (-1, board)
|
62ecffbabb83e0ee4119b8b8dbead6bdaeb24fb6
| 3,642,109
|
def convert_timestamp(ts):
"""Converts the timestamp to a format suitable for Billing.
Examples of a good timestamp for startTime, endTime, and eventTime:
'2016-05-20T00:00:00Z'
Note the trailing 'Z'. Python does not add the 'Z' so we tack it on
ourselves.
"""
return ts.isoformat() + 'Z'
|
6b8d19671cbeab69c398508fa942e36689802cdd
| 3,642,110
|
def object_id(obj, clazz=None):
"""Turn a given object into an ID that can be stored in with
the notification."""
clazz = clazz or type(obj)
if isinstance(obj, clazz):
obj = obj.id
elif is_mapping(obj):
obj = obj.get('id')
return obj
|
617ae362af894c2f27cc6e032aad7f8df4c33a7c
| 3,642,111
|
def get_email_from_request(request):
"""
Get 'Authorization' from request header,
and parse the email address using cpg-util
"""
auth_header = request.headers.get('Authorization')
if auth_header is None:
raise web.HTTPUnauthorized(reason='Missing authorization header')
try:
id_token = auth_header[7:] # Strip the 'bearer' / 'Bearer' prefix.
return email_from_id_token(id_token)
except ValueError as e:
raise web.HTTPForbidden(reason='Invalid authorization header') from e
|
353604d8021948f4cb6ed80d4fb8a9000b8457ce
| 3,642,112
|
def play(url, offset, text, card_data, response_builder):
"""Function to play audio.
Using the function to begin playing audio when:
- Play Audio Intent is invoked.
- Resuming audio when stopped / paused.
- Next / Previous commands issues.
https://developer.amazon.com/docs/custom-skills/audioplayer-interface-reference.html#play
REPLACE_ALL: Immediately begin playback of the specified stream,
and replace current and enqueued streams.
"""
# type: (str, int, str, Dict, ResponseFactory) -> Response
logger.info("play : 52 v2")
logger.info(url)
logger.info(offset)
logger.info(text)
logger.info(card_data)
if card_data:
logger.info("play : 60")
response_builder.set_card(
StandardCard(
title=card_data["title"], text=card_data["text"],
image=Image(
small_image_url=card_data["small_image_url"],
large_image_url=card_data["large_image_url"])
)
)
# Using URL as token as they are all unique
logger.info("play : 71")
response_builder.add_directive(
PlayDirective(
play_behavior=PlayBehavior.REPLACE_ALL,
audio_item=AudioItem(
stream=Stream(
token=url,
url=url,
offset_in_milliseconds=offset,
expected_previous_token=None),
metadata=add_screen_background(card_data) if card_data else None
)
)
).set_should_end_session(True)
logger.info("play : 85")
if text:
logger.info("play : 87")
response_builder.speak(text)
logger.info("play : 90")
return response_builder.response
|
1a7159adc481d86c35c9206cf8525940b6d1ece3
| 3,642,113
|
from typing import List
from typing import Dict
def all_flags_match_bombs(cells: List[List[Dict]]) -> bool:
"""
Checks whether all flags are placed correctly
and there are no flags over regular cells (not bombs)
:param cells: array of array of cells dicts
:return: True if all flags are placed correctly
"""
for row in cells:
for cell in row:
if cell["mask"] == CellMask.FLAG and cell["value"] != "*":
return False
return True
|
67cc53d8b2ea3541112245192763a6c5f8593b86
| 3,642,114
|
def household_id_list(filelist, pidp):
""" For a set of waves, obtain a list of household IDs belonging to the same individual. """
hidp_list = []
wave_list = []
wn = {1:'a', 2:'b', 3:'c', 4:'d', 5:'e', 6:'f', 7:'g'}
c=1
for name in filelist:
print("Loading wave %d data..." % c)
df = pd.read_csv(name, sep='\t')
if pidp in df['pidp'].values:
kword = wn[c]+'_hidp'
hidp = df.loc[df['pidp'] == pidp, kword].values
hidp_list.append(hidp)
wave_list.append(c)
c+=1
print("\nIndividual %d present in waves {}".format(wave_list) % pidp)
return hidp_list
|
8fd7b271034eb953c708ea38bd75ab4671f420cb
| 3,642,115
|
from typing import Tuple
def biggest_labelizer_arbitrary(metrics: dict, choice: str, *args, **kwargs) -> Tuple[str, float]:
"""Given dict of metrics result, returns (key, metrics[key]) whose value is maximal."""
metric_values = list(metrics.values())
metric_keys = list(metrics.keys())
# print(items)
big = metric_values[0]
draws = [0]
for idx, val in enumerate(metric_values[1:], start=1):
if val > big:
big = val
draws = [idx]
elif val == big:
draws.append(idx)
if len(draws) > 1 and choice in (metric_keys[idx] for idx in draws):
return choice, big
return metric_keys[draws[0]], big
|
99f4a0f5233f33d80a328cef4e43f339813371a1
| 3,642,116
|
from typing import Optional
from typing import Dict
from typing import Any
def _seaborn_viz_histogram(data, x: str, contrast: Optional[str] = None, **kwargs):
"""Plot a single histogram.
Args:
data (DataFrame): The data
x (str): The name of the column to plot.
contrast (str, optional): The name of the categorical column to use for multiple contrasts.
**kwargs: Keyword arguments passed to seaborn.distplot
Raises:
ValueError: Not a numeric column.
Returns:
Seaborn Axis Object
"""
if x not in data.select_dtypes("number").columns:
raise ValueError("x must be numeric column")
default_hist_kwargs: Dict[str, Any] = {}
hist_kwargs = {**default_hist_kwargs, **(kwargs or {})}
if contrast:
data[contrast] = data[contrast].astype("category")
ax = sns.histplot(x=x, hue=contrast, data=data, **hist_kwargs)
else:
ax = sns.histplot(data[x], **hist_kwargs)
ax.set_title(f"Histogram of {x}")
return ax
|
27aed8280c372273e02e7b49647b4e2285a81fa7
| 3,642,117
|
def str2bool( s ):
"""
Description:
----------
Converting an input string to a boolean
Arguments:
----------
[NAME] [TYPE] [DESCRIPTION]
(1) s dict, str The string which
Returns:
----------
True/False depending on the given input strin gv
"""
if isinstance( s, dict ):
for key, _ in s.items():
s[ key ] = str2bool( s[ key ] )
else:
return v.lower() in ( "yes", "true", "t", "1" )
|
cb68fe0382561d69fb332b75c99c01c5a338196f
| 3,642,118
|
def im_detect(net, im, boxes=None):
"""Detect object classes in an image given object proposals.
Arguments:
net (caffe.Net): Fast R-CNN network to use
im (ndarray): color image to test (in BGR order)
boxes (ndarray): R x 4 array of object proposals or None (for RPN)
Returns:
scores (ndarray): R x K array of object class scores (K includes
background as object category 0)
boxes (ndarray): R x (4*K) array of predicted bounding boxes
"""
blobs, im_scales = _get_blobs(im, boxes)
# When mapping from image ROIs to feature map ROIs, there's some aliasing
# (some distinct image ROIs get mapped to the same feature ROI).
# Here, we identify duplicate feature ROIs, so we only compute features
# on the unique subset.
# if cfg.DEDUP_BOXES > 0 and not cfg.TEST.HAS_RPN:
# v = np.array([1, 1e3, 1e6, 1e9, 1e12])
# hashes = np.round(blobs['rois'] * cfg.DEDUP_BOXES).dot(v)
# _, index, inv_index = np.unique(hashes, return_index=True,
# return_inverse=True)
# blobs['rois'] = blobs['rois'][index, :]
# boxes = boxes[index, :]
if cfg.TEST.HAS_RPN:
im_blob = blobs['data']
blobs['im_info'] = np.array(
[[im_blob.shape[2], im_blob.shape[3], im_scales[0]]],
dtype=np.float32)
# reshape network inputs
net.blobs['data'].reshape(*(blobs['data'].shape))
if cfg.TEST.HAS_RPN:
net.blobs['im_info'].reshape(*(blobs['im_info'].shape))
else:
net.blobs['rois'].reshape(*(blobs['rois'].shape))
# do forward
forward_kwargs = {'data': blobs['data'].astype(np.float32, copy=False)}
if cfg.TEST.HAS_RPN:
forward_kwargs['im_info'] = blobs['im_info'].astype(np.float32, copy=False)
else:
forward_kwargs['rois'] = blobs['rois'].astype(np.float32, copy=False)
blobs_out = net.forward(**forward_kwargs)
if cfg.TEST.HAS_RPN:
assert len(im_scales) == 1, "Only single-image batch implemented"
rois = net.blobs['rois'].data.copy()
# unscale back to raw image space
boxes = rois[:, 1:5] / im_scales[0]
if cfg.TEST.SVM:
# use the raw scores before softmax under the assumption they
# were trained as linear SVMs
scores = net.blobs['cls_score'].data
else:
# use softmax estimated probabilities
scores = blobs_out['cls_prob']
# if cfg.TEST.BBOX_REG:
if False:
# Apply bounding-box regression deltas
box_deltas = blobs_out['bbox_pred']
pred_boxes = bbox_transform_inv(boxes, box_deltas)
pred_boxes = clip_boxes(pred_boxes, im.shape)
else:
# Simply repeat the boxes, once for each class
pred_boxes = np.tile(boxes, (1, scores.shape[1]))
# if cfg.DEDUP_BOXES > 0 and not cfg.TEST.HAS_RPN:
# # Map scores and predictions back to the original set of boxes
# scores = scores[inv_index, :]
# pred_boxes = pred_boxes[inv_index, :]
fc7 = net.blobs['fc7'].data
return net.blobs['cls_score'].data[:, :], scores, fc7, pred_boxes
|
f37cb46375f39b6baf839be5ec68388c79f47e16
| 3,642,119
|
def preCSVdatagen(xy_p, radius, nbin, PlainFirst):
"""Format the data before generating the csv input for ili'.
Args:
xy_p (str): path to the X and Y coordiantes of ablation marks .npy file.
radius (int): displayed radius of the marks in ili'.
nbin (int): bin factor used to bin the image for ili'.
PlainFirst (bool): intensity values of each datapoints are equal to 1. Used to visualize the ablation mark
coordinates on the postMALDI brighfield in ili'.
Returns:
data (list): formatted data (2D).
"""
X, Y = np.load(xy_p)
Xs = X /( nbin) # todo check relevance of Y <-> X
Ys = Y /( nbin)
Ys = Ys - np.min(Ys)
Xs = Xs - np.min(Xs)
Rs = np.ones(np.shape(Xs)) * radius
data = []
data.append(list(np.append('Num', list(range(np.shape(Xs.ravel())[0])))))
data.append(list(np.append('X', Ys.ravel())))
data.append(list(np.append('Y', Xs.ravel())))
data.append(list(np.append('Z', np.zeros(np.shape(Xs.ravel())))))
data.append(list(np.append('R', Rs.ravel())))
if PlainFirst:
data.append(list(np.append('Flat', np.ones(np.shape(Xs.ravel())))))
return data
|
2d19439eb82930e622b897a9ee1ddda364f0a04a
| 3,642,120
|
def dispatch_error_adaptor(func):
"""Construct a signature isomorphic to dispatch_error.
The actual handler will receive only arguments explicitly
declared, and a possible tg_format parameter.
"""
def adaptor(controller, tg_source,
tg_errors, tg_exceptions, *args, **kw):
tg_format = kw.pop('tg_format', None)
args, kw = inject_args(func, {"tg_source": tg_source,
"tg_errors": tg_errors, "tg_exceptions": tg_exceptions},
args, kw, 1)
args, kw = adapt_call(func, args, kw, 1)
if tg_format is not None:
kw['tg_format'] = tg_format
return func(controller, *args, **kw)
return adaptor
|
67be23f01c11d668d86f5e2b1afcfb76db79ea6c
| 3,642,121
|
import re
def address_split(address, env=None):
"""The address_split() function splits an address into its four
components. Address strings are on the form
detector-detectorID|device-deviceID, where the detectors must be in
dir(xtc.DetInfo.Detector) and device must be in
(xtc.DetInfo.Device).
@param address Full data source address of the DAQ device
@param env Optional env to dereference an alias into an address
@return Four-tuple of detector name, detector ID, device, and
device ID
"""
# pyana
m = re.match(
r"^(?P<det>\S+)\-(?P<det_id>\d+)\|(?P<dev>\S+)\-(?P<dev_id>\d+)$", address)
if m is not None:
return (m.group('det'), m.group('det_id'), m.group('dev'), m.group('dev_id'))
# psana
m = re.match(
r"^(?P<det>\S+)\.(?P<det_id>\d+)\:(?P<dev>\S+)\.(?P<dev_id>\d+)$", address)
if m is not None:
return (m.group('det'), m.group('det_id'), m.group('dev'), m.group('dev_id'))
# psana DetInfo string
m = re.match(
r"^DetInfo\((?P<det>\S+)\.(?P<det_id>\d+)\:(?P<dev>\S+)\.(?P<dev_id>\d+)\)$", address)
if m is not None:
return (m.group('det'), m.group('det_id'), m.group('dev'), m.group('dev_id'))
if env is not None:
# Try to see if this is a detector alias, and if so, dereference it. Code from psana's Detector/PyDetector.py
amap = env.aliasMap()
alias_src = amap.src(address) # string --> DAQ-style psana.Src
# if it is an alias, look up the full name
if amap.alias(alias_src) != '': # alias found
address = str(alias_src)
return address_split(address)
return (None, None, None, None)
|
c5d362c7fc6121d64ec6a660bcdb7a9b4b532553
| 3,642,122
|
import time
def solveGroth(A, n, init_val=None):
"""
...
Parameters
----------
A: np.matrix
dfajdslkf
n: int
ddddddd
init_val: float, optional
dsfdsafdasfd
Returns
-------
list of
float:
float:
float:
float:
"""
eps=0.5
eta=0.05
threshold = 1.1
N = n
Ap = A
if init_val is not None:
w=init_val
else:
w = np.ones(N)
min_val = np.sum(np.abs(Ap))
curr_y = np.zeros(N)
curr_alpha = np.zeros(N)
avg_y_val = np.zeros(N)
avg_X=np.zeros((N,N))
avg_alpha=np.zeros(N)
T=4*N
schedule_size = round(T/8) #We change eps in epochs
print("iteration bound:",T)
z = np.zeros(N)
vals = 0
g = np.random.standard_normal(T)
for i in range(T):
if (i+1)%(T/2)==0:
eps=0.01
if i%schedule_size ==0 and eps>0.01 :
eps=eps/2
if i%schedule_size == 0 and i>T/2:
eps /= 2
wtil=(1-eta)*w+eta*np.ones(N)
w1 = np.array([1/np.sqrt(j) for j in wtil])
start_time = time.time()
d = np.tile(w1, (N, 1))
M = np.multiply(Ap,d)
d = np.tile(np.array([w1]).transpose(), (1,N))
M = np.multiply(M,d)
start_time = time.time()
eigval, eigvec = lasp.eigsh(M, k=1, which='LA', tol=0.00001)
y = eigvec[:,0]
y *= np.sqrt(N)
y = np.multiply(y,w1)
avg_y_val += y**2
val = np.matmul(np.transpose(y), np.matmul(Ap,y))
avg_alpha+=val*w
if val < min_val:
min_val = val
curr_y = y
curr_alpha = w
vals += val
print("iterate", i, "val = ", val, " minval=", min_val, " linf of curr y=", np.max(np.abs(y**2)) , " infinity norm avg X =", np.max((1.0/(i+1))*avg_y_val), "SDP sol val:", vals/(i+1), "eps,eta=", eps, " , ", eta)
if checkCondition(y,threshold):
print(y,"Current iterate Condition satisfied, i : ",i)
print("min val = ", min_val)
print("curr_y = ", curr_y)
print("curr_alpha = ", curr_alpha)
print("inf norm of curr_y=", max(abs(curr_y)))
return [np.matmul(curr_y,curr_y.T),min_val, curr_alpha, avg_y_val]
elif checkCondition((1.0/(i+1))*avg_y_val, threshold):
avg_y_val=(1.0/(i+1))*avg_y_val
avg_val = vals/(i+1)
print(avg_y_val," Avg Condition satisfied, i : ",i)
print("min val = ", min_val)
print("curr val=", avg_val)
print("curr_alpha = ", (1.0/i)*avg_alpha)
print("inf norm of avg_y=", max(abs(avg_y_val)))
return [(1.0/(i+1))*avg_X,min_val, curr_alpha, avg_y_val]
if i < T/2:
w = updateWeights_2(w,y,threshold, eps, N)
else:
w = updateWeights(w,y,threshold, 2*eps, N)
u = y*g[i]
z += u
print("min val = ", min_val)
print("sum of curr_alpha = ", sum(curr_alpha))
print("sum weights at end = ", sum(w))
print("inf norm of curr_y=", max(abs(curr_y)))
return [np.matmul(curr_y, curr_y.T), min_val, curr_alpha, avg_y_val]
|
a8f4a3ea2274bd1a81565500683dea84378ccddc
| 3,642,123
|
def verify_any(func, *args, **kwargs):
"""
Assert that any of `func(*args, **kwargs)` are true.
"""
return _verify(func, 'any', *args, **kwargs)
|
618165e6a9f252ac2ddeffdb9defa34f2d281900
| 3,642,124
|
def can_create_election(user_id, user_info):
""" for now, just let it be"""
return True
|
06c8290b41b38a840b7826173fd65130d38260a7
| 3,642,125
|
import locale
def get_system_language():
""" Get system language and locale """
try:
default_locale = locale.getdefaultlocale()
except ValueError:
if IS_MAC:
# Fix for "ValueError: unknown locale: UTF-8" on Mac.
# The default English locale on Mac is set as "UTF-8" instead of "en-US.UTF-8"
# see https://bugs.python.org/issue18378
return 'en_US', 'UTF-8'
# re-throw any other issue
raise
system_lang = default_locale[0]
system_locale = default_locale[1]
return system_lang, system_locale
|
6ab566ca6274480c7af5dd65456e675ae614df7a
| 3,642,126
|
from .path import Path2D
def circle_pattern(pattern_radius,
circle_radius,
count,
center=[0.0, 0.0],
angle=None,
**kwargs):
"""
Create a Path2D representing a circle pattern.
Parameters
------------
pattern_radius : float
Radius of circle centers
circle_radius : float
The radius of each circle
count : int
Number of circles in the pattern
center : (2,) float
Center of pattern
angle : float
If defined pattern will span this angle
If None, pattern will be evenly spaced
Returns
-------------
pattern : trimesh.path.Path2D
Path containing circular pattern
"""
if angle is None:
angles = np.linspace(0.0, np.pi * 2.0, count + 1)[:-1]
elif isinstance(angle, float) or isinstance(angle, int):
angles = np.linspace(0.0, angle, count)
else:
raise ValueError('angle must be float or int!')
# centers of circles
centers = np.column_stack((
np.cos(angles), np.sin(angles))) * pattern_radius
vert = []
ents = []
for circle_center in centers:
# (3,3) center points of arc
three = arc.to_threepoint(angles=[0, np.pi],
center=circle_center,
radius=circle_radius)
# add a single circle entity
ents.append(
Arc(
points=np.arange(3) + len(vert),
closed=True))
# keep flat array by extend instead of append
vert.extend(three)
# translate vertices to pattern center
vert = np.array(vert) + center
pattern = Path2D(entities=ents,
vertices=vert,
**kwargs)
return pattern
|
b82d60c7a76f12349605191b16bf04d7899c3a3a
| 3,642,127
|
def boolean(input):
"""Convert the given input to a boolean value.
Intelligently handles boolean and non-string values, returning
as-is and passing to the bool builtin respectively.
This process is case-insensitive.
Acceptable values:
True
* yes
* y
* on
* true
* t
* 1
False
* no
* n
* off
* false
* f
* 0
:param input: the value to convert to a boolean
:type input: any
:returns: converted boolean value
:rtype: bool
"""
try:
input = input.strip().lower()
except AttributeError:
return bool(input)
if input in ('yes', 'y', 'on', 'true', 't', '1'):
return True
if input in ('no', 'n', 'off', 'false', 'f', '0'):
return False
raise ValueError("Unable to convert {0!r} to a boolean value.".format(input))
|
09c09206d5487bf02e3271403e2ba67358e1d148
| 3,642,128
|
def find_horizontal_up_down_links(tc, u, out_up=None, out_down=None):
"""Find indices of nodes that locate
at horizontally upcurrent and downcurrent directions
"""
if out_up is None:
out_up = np.zeros(u.shape[0], dtype=np.int)
if out_down is None:
out_down = np.zeros(u.shape[0], dtype=np.int)
out_up[:] = tc.link_west[:]
out_down[:] = tc.link_east[:]
negative_u_index = np.where(u < 0)[0]
out_up[negative_u_index] = tc.link_east[negative_u_index]
out_down[negative_u_index] = tc.link_west[negative_u_index]
return out_up, out_down
|
b61976a57d8dd850c26c7a9baff11483ccdb306f
| 3,642,129
|
def _compute_composite_beta(model, robo, j, i):
"""
Compute the composite beta wrench for link i.
Args:
model: An instance of DynModel
robo: An instance of Robot
j: link number
i: antecedent value
Returns:
An instance of DynModel that contains all the new values.
"""
i_beta_i_c = Screw()
# local variables
j_s_i = robo.geos[j].tmat.s_i_wrt_j
i_beta_i = model.composite_betas[i].val
j_beta_j_c = model.composite_betas[j].val
j_inertia_j_c = model.composite_inertias[j].val
j_zeta_j = model.zetas[j].val
# actual computation
i_beta_i_c.val = i_beta_i + (j_s_i.transpose() * j_beta_j_c) - \
(j_s_i.transpose() * j_inertia_j_c * j_zeta_j)
# store computed beta in model
model.composite_betas[i] = i_beta_i_c
return model
|
0fa80859787a4e523402d10237b63e33ca0082f4
| 3,642,130
|
def pos(x, y):
"""Returns floored and camera-offset x,y tuple.
Setting out of bounds is possible, but getting is not; mod in callers for get_at.
"""
return (flr(xo + x), flr(yo + y))
|
1a17648c074157c6164856f44cfa309923ca2226
| 3,642,131
|
from typing import List
from typing import Dict
from typing import Tuple
def _check_blockstream_for_transactions(
accounts: List[BTCAddress],
) -> Dict[BTCAddress, Tuple[bool, FVal]]:
"""May raise connection errors or KeyError"""
have_transactions = {}
for account in accounts:
url = f'https://blockstream.info/api/address/{account}'
response_data = request_get_dict(url=url, handle_429=True, backoff_in_seconds=4)
stats = response_data['chain_stats']
balance = satoshis_to_btc(int(stats['funded_txo_sum']) - int(stats['spent_txo_sum']))
have_txs = stats['tx_count'] != 0
have_transactions[account] = (have_txs, balance)
return have_transactions
|
a17a9204dc0d5f11b8c0352d15c871141e7bb09b
| 3,642,132
|
from typing import Callable
from typing import Tuple
def metropolis_hastings(
proposal: Proposal,
state: State,
step_size: float,
ns: int,
unif: float,
inverse_transform: Callable
) -> Tuple[State, Info, np.ndarray, bool]:
"""Computes the Metropolis-Hastings accept-reject criterion given a proposal, a
current state of the chain, a integration step-size, and a number of
itnegration steps. We also provide a uniform random variable for
determining the accept-reject criterion and the inverse transformation
function for transforming parameters from an unconstrained space to a
constrained space.
Args:
proposal: A proposal operator to advance the state of the Markov chain.
state: An augmented state object with the updated position and momentum
and values for the log-posterior and metric and their gradients.
step_size: The integration step-size.
num_steps: The number of integration steps.
unif: Uniform random number for determining the accept-reject decision.
inverse_transform: Inverse transformation to map samples back to the
original space.
Returns:
state: An augmented state object with the updated position and momentum
and values for the log-posterior and metric and their gradients.
info: An information object with the updated number of fixed point
iterations and boolean indicator for successful integration.
q: The position variable in the constrained space.
accept: Whether or not the proposal was accepted.
"""
ham = hamiltonian(
state.momentum,
state.log_posterior,
state.logdet_metric,
state.inv_metric)
q, fldj = inverse_transform(state.position)
ildj = -fldj
new_state, prop_info = proposal.propose(state, step_size, ns)
new_chol, new_logdet = new_state.sqrtm_metric, new_state.logdet_metric
new_q, new_fldj = inverse_transform(new_state.position)
new_ham = hamiltonian(
new_state.momentum,
new_state.log_posterior,
new_state.logdet_metric,
new_state.inv_metric)
# Notice the relevant choice of sign when the Jacobian determinant of the
# forward or inverse transform is used.
#
# Write this expression as,
# (exp(-new_ham) / exp(new_fldj)) / (exp(-ham) * exp(ildj))
#
# See the following resource for understanding the Metropolis-Hastings
# correction with a Jacobian determinant correction [1].
#
# [1] https://wiki.helsinki.fi/download/attachments/48865399/ch7-rev.pdf
logu = np.log(unif)
metropolis = logu < ham - new_ham - new_fldj - ildj + prop_info.logdet
accept = np.logical_and(metropolis, prop_info.success)
if accept:
state = new_state
q = new_q
ildj = -new_fldj
state.momentum *= -1.0
return state, prop_info, q, accept
|
b5390d8a420ebb3d62c700fe246127935b658b6c
| 3,642,133
|
import os
def continuous_future(root_symbol_str, offset=0, roll="volume", adjustment="mul", bundle=None):
"""
Return a ContinuousFuture object for the specified root symbol in the specified bundle
(or default bundle).
Parameters
----------
root_symbol_str : str
The root symbol for the future chain.
offset : int, optional
The distance from the primary contract. Default is 0.
roll : str, optional
How rolls are determined. Possible choices: 'volume',
(roll when back contract volume exceeds front contract
volume), or 'calendar' (roll on rollover date). Default
is 'volume'.
adjustment : str, optional
Method for adjusting lookback prices between rolls. Options are
'mul', 'add' or None. 'mul' calculates the ratio of front and back
contracts on the roll date ((back - front)/front) and multiplies
front contract prices by (1 + ratio). 'add' calculates the difference
between back and front contracts on the roll date (back - front)
and adds the difference to front contract prices. None concatenates
contracts without any adjustment. Default is 'mul'.
bundle : str, optional
the bundle code. If omitted, the default bundle will be used (and must be set).
Returns
-------
asset : zipline.assets.ContinuousFuture
Examples
--------
Get the continuous future object for ES and get the current chain as of
2020-09-18:
>>> es = continuous_future("ES", roll="volume", bundle="es-1min") # doctest: +SKIP
>>> data = get_data("2020-09-18 10:00:00", bundle="es-1min") # doctest: +SKIP
>>> print(data.current_chain(es)) # doctest: +SKIP
"""
if not bundle:
bundle = get_default_bundle()
if not bundle:
raise ValidationError("you must specify a bundle or set a default bundle")
bundle = bundle["default_bundle"]
load_extensions(code=bundle)
bundle_data = bundles.load(
bundle,
os.environ,
pd.Timestamp.utcnow(),
)
asset_finder = asset_finder_cache.get(bundle, bundle_data.asset_finder)
asset_finder_cache[bundle] = asset_finder
continuous_future = asset_finder.create_continuous_future(
root_symbol_str,
offset,
roll,
adjustment,
)
return continuous_future
|
e48241a8d098089f0f61c1e6587ccad0cf366f82
| 3,642,134
|
from datetime import datetime
def Now():
"""Returns a datetime.datetime instance representing the current time.
This is just a wrapper to ease testing against the datetime module.
Returns:
An instance of datetime.datetime.
"""
return datetime.datetime.now()
|
9a0657011e10b47eb755a575216944a786218f2e
| 3,642,135
|
def ndvi_list_hdf(hdf_dir, satellite=None):
"""
List all the available HDF files, grouped by tile
Args:
hdf_dir: directory containing one subdirectory per year which contains
HDF files
satellite: None to select both Tera and Aqua, 'mod13q1' for MODIS,
'myd13q1' for Aqua
Returns:
list: A dict (keyed by tilename) of list of (full filepath,
timestamp_ms) tuples, sorted by timestamp_ms
"""
files = collections.defaultdict(lambda: [])
for subdir in os.listdir(hdf_dir):
subdir = os.path.join(hdf_dir, subdir)
if not os.path.isdir(subdir):
continue
for hdf_file in os.listdir(subdir):
if not hdf_file.endswith('.hdf'):
continue
try:
full_fname = os.path.join(subdir, hdf_file)
d = parse_ndvi_filename(hdf_file)
if satellite is not None and satellite != d['satellite']:
continue
files[d['tile_name']].append((full_fname, d['timestamp_ms']))
except ValueError as e:
print e
for tile_name in files.keys():
files[tile_name] = sorted(files[tile_name], key=lambda t: t[1])
return files
|
068062bdef503b6652c62c142a1cf80d830fc8db
| 3,642,136
|
from typing import List
import os
def read_levels(dir_path: str,
progress_monitor: PyramidLevelCallback = None) -> List[xr.Dataset]:
"""
Read the of a multi-level pyramid with spatial resolution
decreasing by a factor of two in both spatial dimensions.
:param dir_path: The directory path.
:param progress_monitor: An optional progress monitor.
:return: A list of dataset instances representing the multi-level pyramid.
"""
file_paths = os.listdir(dir_path)
level_paths = {}
num_levels = -1
for filename in file_paths:
file_path = os.path.join(dir_path, filename)
basename, ext = os.path.splitext(filename)
if basename.isdigit():
index = int(basename)
num_levels = max(num_levels, index + 1)
if os.path.isfile(file_path) and ext == ".link":
level_paths[index] = (ext, file_path)
elif os.path.isdir(file_path) and ext == ".zarr":
level_paths[index] = (ext, file_path)
if num_levels != len(level_paths):
raise ValueError(f"Inconsistent pyramid directory:"
f" expected {num_levels} but found {len(level_paths)} entries:"
f" {dir_path}")
levels = []
for index in range(num_levels):
ext, file_path = level_paths[index]
if ext == ".link":
with open(file_path, "r") as fp:
link_file_path = fp.read()
dataset = xr.open_zarr(link_file_path)
else:
dataset = xr.open_zarr(file_path)
if progress_monitor is not None:
progress_monitor(dataset, index, num_levels)
levels.append(dataset)
return levels
|
5e8145c5dcca47f51e1f5190e42976b37ea433c6
| 3,642,137
|
def create_provisioned_product_name(account_name: str) -> str:
"""
Replaces all space characters in an Account Name with hyphens,
also removes all trailing and leading whitespace
"""
return account_name.strip().replace(" ", "-")
|
743e7438f421d5d42c071d27d1b0fa2a816a9b4d
| 3,642,138
|
def case34():
"""
Create the IEEE 34 bus from IEEE PES Test Feeders:
"https://site.ieee.org/pes-testfeeders/resources/”.
OUTPUT:
**net** - The pandapower format network.
"""
net = pp.create_empty_network()
# Linedata
# CF-300
line_data = {'c_nf_per_km': 3.8250977, 'r_ohm_per_km': 0.69599766,
'x_ohm_per_km': 0.5177677,
'c0_nf_per_km': 1.86976748, 'r0_ohm_per_km': 1.08727498,
'x0_ohm_per_km': 1.47374703,
'max_i_ka': 0.23, 'type': 'ol'}
pp.create_std_type(net, line_data, name='CF-300', element='line')
# CF-301
line_data = {'c_nf_per_km': 3.66884364, 'r_ohm_per_km': 1.05015841,
'x_ohm_per_km': 0.52265586,
'c0_nf_per_km': 1.82231544, 'r0_ohm_per_km': 1.48350255,
'x0_ohm_per_km': 1.60203942,
'max_i_ka': 0.18, 'type': 'ol'}
pp.create_std_type(net, line_data, name='CF-301', element='line')
# CF-302
line_data = {'c_nf_per_km': 0.8751182, 'r_ohm_per_km': 0.5798427,
'x_ohm_per_km': 0.30768221,
'c0_nf_per_km': 0.8751182, 'r0_ohm_per_km': 0.5798427,
'x0_ohm_per_km': 0.30768221,
'max_i_ka': 0.14, 'type': 'ol'}
pp.create_std_type(net, line_data, name='CF-302', element='line')
# CF-303
line_data = {'c_nf_per_km': 0.8751182, 'r_ohm_per_km': 0.5798427,
'x_ohm_per_km': 0.30768221,
'c0_nf_per_km': 0.8751182, 'r0_ohm_per_km': 0.5798427,
'x0_ohm_per_km': 0.30768221,
'max_i_ka': 0.14, 'type': 'ol'}
pp.create_std_type(net, line_data, name='CF-303', element='line')
# CF-304
line_data = {'c_nf_per_km': 0.90382554, 'r_ohm_per_km': 0.39802955,
'x_ohm_per_km': 0.29436416,
'c0_nf_per_km': 0.90382554, 'r0_ohm_per_km': 0.39802955,
'x0_ohm_per_km': 0.29436416,
'max_i_ka': 0.18, 'type': 'ol'}
pp.create_std_type(net, line_data, name='CF-304', element='line')
# Busses
# bus0 = pp.create_bus(net, name='Bus 0', vn_kv=69.0, type='n', zone='34_BUS')
bus_800 = pp.create_bus(net, name='Bus 800', vn_kv=24.9, type='n', zone='34_BUS')
bus_802 = pp.create_bus(net, name='Bus 802', vn_kv=24.9, type='n', zone='34_BUS')
bus_806 = pp.create_bus(net, name='Bus 806', vn_kv=24.9, type='n', zone='34_BUS')
bus_808 = pp.create_bus(net, name='Bus 808', vn_kv=24.9, type='n', zone='34_BUS')
bus_810 = pp.create_bus(net, name='Bus 810', vn_kv=24.9, type='n', zone='34_BUS')
bus_812 = pp.create_bus(net, name='Bus 812', vn_kv=24.9, type='n', zone='34_BUS')
bus_814 = pp.create_bus(net, name='Bus 814', vn_kv=24.9, type='n', zone='34_BUS')
bus_850 = pp.create_bus(net, name='Bus 850', vn_kv=24.9, type='n', zone='34_BUS')
bus_816 = pp.create_bus(net, name='Bus 816', vn_kv=24.9, type='n', zone='34_BUS')
bus_818 = pp.create_bus(net, name='Bus 818', vn_kv=24.9, type='n', zone='34_BUS')
bus_820 = pp.create_bus(net, name='Bus 820', vn_kv=24.9, type='n', zone='34_BUS')
bus_822 = pp.create_bus(net, name='Bus 822', vn_kv=24.9, type='n', zone='34_BUS')
bus_824 = pp.create_bus(net, name='Bus 824', vn_kv=24.9, type='n', zone='34_BUS')
bus_826 = pp.create_bus(net, name='Bus 826', vn_kv=24.9, type='n', zone='34_BUS')
bus_828 = pp.create_bus(net, name='Bus 828', vn_kv=24.9, type='n', zone='34_BUS')
bus_830 = pp.create_bus(net, name='Bus 830', vn_kv=24.9, type='n', zone='34_BUS')
bus_854 = pp.create_bus(net, name='Bus 854', vn_kv=24.9, type='n', zone='34_BUS')
bus_852 = pp.create_bus(net, name='Bus 852', vn_kv=24.9, type='n', zone='34_BUS')
bus_832 = pp.create_bus(net, name='Bus 832', vn_kv=24.9, type='n', zone='34_BUS')
bus_858 = pp.create_bus(net, name='Bus 858', vn_kv=24.9, type='n', zone='34_BUS')
bus_834 = pp.create_bus(net, name='Bus 834', vn_kv=24.9, type='n', zone='34_BUS')
bus_842 = pp.create_bus(net, name='Bus 842', vn_kv=24.9, type='n', zone='34_BUS')
bus_844 = pp.create_bus(net, name='Bus 844', vn_kv=24.9, type='n', zone='34_BUS')
bus_846 = pp.create_bus(net, name='Bus 846', vn_kv=24.9, type='n', zone='34_BUS')
bus_848 = pp.create_bus(net, name='Bus 848', vn_kv=24.9, type='n', zone='34_BUS')
bus_860 = pp.create_bus(net, name='Bus 860', vn_kv=24.9, type='n', zone='34_BUS')
bus_836 = pp.create_bus(net, name='Bus 836', vn_kv=24.9, type='n', zone='34_BUS')
bus_840 = pp.create_bus(net, name='Bus 840', vn_kv=24.9, type='n', zone='34_BUS')
bus_862 = pp.create_bus(net, name='Bus 862', vn_kv=24.9, type='n', zone='34_BUS')
bus_838 = pp.create_bus(net, name='Bus 838', vn_kv=24.9, type='n', zone='34_BUS')
bus_864 = pp.create_bus(net, name='Bus 864', vn_kv=24.9, type='n', zone='34_BUS')
bus_888 = pp.create_bus(net, name='Bus 888', vn_kv=4.16, type='n', zone='34_BUS')
bus_890 = pp.create_bus(net, name='Bus 890', vn_kv=4.16, type='n', zone='34_BUS')
bus_856 = pp.create_bus(net, name='Bus 856', vn_kv=24.9, type='n', zone='34_BUS')
# Lines
pp.create_line(net, bus_800, bus_802, length_km=0.786384, std_type='CF-300', name='Line 0')
pp.create_line(net, bus_802, bus_806, length_km=0.527304, std_type='CF-300', name='Line 1')
pp.create_line(net, bus_806, bus_808, length_km=9.823704, std_type='CF-300', name='Line 2')
pp.create_line(net, bus_808, bus_810, length_km=1.769059, std_type='CF-303', name='Line 3')
pp.create_line(net, bus_808, bus_812, length_km=11.43000, std_type='CF-300', name='Line 4')
pp.create_line(net, bus_812, bus_814, length_km=9.061704, std_type='CF-300', name='Line 5')
# pp.create_line(net, bus_814, bus_850, length_km=0.003048, std_type='CF-301', name='Line 6')
pp.create_line(net, bus_816, bus_818, length_km=0.521208, std_type='CF-302', name='Line 7')
pp.create_line(net, bus_816, bus_824, length_km=3.112008, std_type='CF-301', name='Line 8')
pp.create_line(net, bus_818, bus_820, length_km=14.67612, std_type='CF-302', name='Line 9')
pp.create_line(net, bus_820, bus_822, length_km=4.187952, std_type='CF-302', name='Line 10')
pp.create_line(net, bus_824, bus_826, length_km=0.923544, std_type='CF-303', name='Line 11')
pp.create_line(net, bus_824, bus_828, length_km=0.256032, std_type='CF-301', name='Line 12')
pp.create_line(net, bus_828, bus_830, length_km=6.230112, std_type='CF-301', name='Line 13')
pp.create_line(net, bus_830, bus_854, length_km=0.158496, std_type='CF-301', name='Line 14')
pp.create_line(net, bus_832, bus_858, length_km=1.493520, std_type='CF-301', name='Line 15')
pp.create_line(net, bus_834, bus_860, length_km=0.615696, std_type='CF-301', name='Line 16')
pp.create_line(net, bus_834, bus_842, length_km=0.085344, std_type='CF-301', name='Line 17')
pp.create_line(net, bus_836, bus_840, length_km=0.262128, std_type='CF-301', name='Line 18')
pp.create_line(net, bus_836, bus_862, length_km=0.085344, std_type='CF-301', name='Line 19')
pp.create_line(net, bus_842, bus_844, length_km=0.411480, std_type='CF-301', name='Line 20')
pp.create_line(net, bus_844, bus_846, length_km=1.109472, std_type='CF-301', name='Line 21')
pp.create_line(net, bus_846, bus_848, length_km=0.161544, std_type='CF-301', name='Line 22')
pp.create_line(net, bus_850, bus_816, length_km=0.094488, std_type='CF-301', name='Line 23')
# pp.create_line(net, bus_852, bus_832, length_km=0.003048, std_type='CF-301', name='Line 24')
pp.create_line(net, bus_854, bus_856, length_km=7.110984, std_type='CF-303', name='Line 25')
pp.create_line(net, bus_854, bus_852, length_km=11.22578, std_type='CF-301', name='Line 26')
pp.create_line(net, bus_858, bus_864, length_km=0.493776, std_type='CF-302', name='Line 27')
pp.create_line(net, bus_858, bus_834, length_km=1.776984, std_type='CF-301', name='Line 28')
pp.create_line(net, bus_860, bus_836, length_km=0.816864, std_type='CF-301', name='Line 29')
pp.create_line(net, bus_860, bus_838, length_km=1.481328, std_type='CF-304', name='Line 30')
pp.create_line(net, bus_888, bus_890, length_km=3.218688, std_type='CF-300', name='Line 31')
# Regulator 1
pp.create_transformer_from_parameters(net, bus_814, bus_850, sn_mva=2.5, vn_hv_kv=24.9,
vn_lv_kv=24.9, vkr_percent=0.320088*2.5, vk_percent=0.357539*2.5,
pfe_kw=0.0, i0_percent=0.0, shift_degree=0.0,
tap_side='lv', tap_neutral=0, tap_max=16, tap_min=-16,
tap_step_percent=0.625, tap_pos=0, tap_phase_shifter=False,
name='Regulator 1')
# Regulator 2
pp.create_transformer_from_parameters(net, bus_852, bus_832, sn_mva=2.5, vn_hv_kv=24.9,
vn_lv_kv=24.9, vkr_percent=0.320088*2.5, vk_percent=0.357539*2.5,
pfe_kw=0.0, i0_percent=0.0, shift_degree=0.0,
tap_side='lv', tap_neutral=0, tap_max=16, tap_min=-16,
tap_step_percent=0.625, tap_pos=0, tap_phase_shifter=False,
name='Regulator 2')
# # Substation
# pp.create_transformer_from_parameters(net, bus0, bus_800, sn_mva=2.5, vn_hv_kv=69.0,
# vn_lv_kv=24.9, vkr_percent=1.0, vk_percent=8.062257,
# pfe_kw=0.0, i0_percent=0.0, shift_degree=0.0,
# tap_side='lv', tap_neutral=0, tap_max=2, tap_min=-2,
# tap_step_percent=2.5, tap_pos=0, tap_phase_shifter=False,
# name='Substation')
# Traformer
pp.create_transformer_from_parameters(net, bus_832, bus_888, sn_mva=0.5, vn_hv_kv=24.9,
vn_lv_kv=4.16, vkr_percent=1.9, vk_percent=4.5,
pfe_kw=0.0, i0_percent=0.0, shift_degree=0.0,
name='Transformer 1')
# Loads
pp.create_load(net, bus_806, p_mw=0.055, q_mvar=0.029, name='Load 806')
pp.create_load(net, bus_810, p_mw=0.016, q_mvar=0.008, name='Load 810')
pp.create_load(net, bus_820, p_mw=0.034, q_mvar=0.017, name='Load 820')
pp.create_load(net, bus_822, p_mw=0.135, q_mvar=0.070, name='Load 822')
pp.create_load(net, bus_824, p_mw=0.005, q_mvar=0.002, name='Load 824')
pp.create_load(net, bus_826, p_mw=0.004, q_mvar=0.020, name='Load 826')
pp.create_load(net, bus_828, p_mw=0.004, q_mvar=0.002, name='Load 828')
pp.create_load(net, bus_830, p_mw=0.007, q_mvar=0.003, name='Load 830')
pp.create_load(net, bus_856, p_mw=0.004, q_mvar=0.002, name='Load 856')
pp.create_load(net, bus_858, p_mw=0.015, q_mvar=0.007, name='Load 858')
pp.create_load(net, bus_864, p_mw=0.002, q_mvar=0.001, name='Load 864')
pp.create_load(net, bus_834, p_mw=0.032, q_mvar=0.017, name='Load 834')
pp.create_load(net, bus_860, p_mw=0.029, q_mvar=0.073, name='Load 860')
pp.create_load(net, bus_836, p_mw=0.082, q_mvar=0.043, name='Load 836')
pp.create_load(net, bus_840, p_mw=0.040, q_mvar=0.020, name='Load 840')
pp.create_load(net, bus_838, p_mw=0.028, q_mvar=0.014, name='Load 838')
pp.create_load(net, bus_844, p_mw=0.009, q_mvar=0.005, name='Load 844')
pp.create_load(net, bus_846, p_mw=0.037, q_mvar=0.031, name='Load 846')
pp.create_load(net, bus_848, p_mw=0.023, q_mvar=0.011, name='Load 848')
pp.create_load(net, bus_860, p_mw=0.060, q_mvar=0.048, name='Load 860 spot')
pp.create_load(net, bus_840, p_mw=0.027, q_mvar=0.021, name='Load 840 spot')
pp.create_load(net, bus_844, p_mw=0.405, q_mvar=0.315, name='Load 844 spot')
pp.create_load(net, bus_848, p_mw=0.060, q_mvar=0.048, name='Load 848 spot')
pp.create_load(net, bus_890, p_mw=0.450, q_mvar=0.225, name='Load 890 spot')
pp.create_load(net, bus_830, p_mw=0.045, q_mvar=0.020, name='Load 830 spot')
# External grid
pp.create_ext_grid(net, bus_800, vm_pu=1.0, va_degree=0.0, s_sc_max_mva=10.0,
s_sc_min_mva=10.0, rx_max=1, rx_min=1, r0x0_max=1, x0x_max=1)
# Distributed generators
pp.create_sgen(net, bus_848, p_mw=0.66, q_mvar=0.500, name='DG 1', max_p_mw=0.66, min_p_mw=0, max_q_mvar=0.5, min_q_mvar=0)
pp.create_sgen(net, bus_890, p_mw=0.50, q_mvar=0.375, name='DG 2', max_p_mw=0.50, min_p_mw=0, max_q_mvar=0.375, min_q_mvar=0)
pp.create_sgen(net, bus_822, p_mw=0.1, type='PV', name='PV 1', max_p_mw=0.1, min_p_mw=0, max_q_mvar=0, min_q_mvar=0)
pp.create_sgen(net, bus_856, p_mw=0.1, type='PV', name='PV 2', max_p_mw=0.1, min_p_mw=0, max_q_mvar=0, min_q_mvar=0)
pp.create_sgen(net, bus_838, p_mw=0.1, type='PV', name='PV 3', max_p_mw=0.1, min_p_mw=0, max_q_mvar=0, min_q_mvar=0)
pp.create_sgen(net, bus_822, p_mw=0.1, type='WP', name='WP 1', max_p_mw=0.1, min_p_mw=0, max_q_mvar=0, min_q_mvar=0)
pp.create_sgen(net, bus_826, p_mw=0.1, type='WP', name='WP 2', max_p_mw=0.1, min_p_mw=0, max_q_mvar=0, min_q_mvar=0)
pp.create_sgen(net, bus_838, p_mw=0.1, type='WP', name='WP 3', max_p_mw=0.1, min_p_mw=0, max_q_mvar=0, min_q_mvar=0)
# Shunt capacity bank
pp.create_shunt(net, bus_840, q_mvar=-0.12, name='SCB 1', step=4, max_step=4)
pp.create_shunt(net, bus_864, q_mvar=-0.12, name='SCB 2', step=4, max_step=4)
# storage
pp.create_storage(net, bus_810, p_mw=0.5, max_e_mwh=2, sn_mva=1.0, soc_percent=50, min_e_mwh=0.2, name='Storage')
return net
|
8e04a125df0e0a64008724d419bafe19481f5ac1
| 3,642,139
|
def _hack_namedtuple(cls):
"""Make class generated by namedtuple picklable."""
name = cls.__name__
fields = cls._fields
def reduce(self):
return (_restore, (name, fields, tuple(self)))
cls.__reduce__ = reduce
cls._is_namedtuple_ = True
return cls
|
89468f0ffb5506ef0c9a33fec0d390576638e659
| 3,642,140
|
import os
import logging
import json
def load_config(settings_file='./test_settings.py'):
"""
Loads the config files merging the defaults
with the file defined in environ.PULLSBURY_SETTINGS if it exists.
"""
config = Config(os.getcwd())
if 'PULLSBURY_SETTINGS' in os.environ:
config.from_envvar('PULLSBURY_SETTINGS')
else:
config.from_pyfile(settings_file)
if config.get('LOGGING_CONFIG'):
logging.config.fileConfig(
config.get('LOGGING_CONFIG'),
disable_existing_loggers=False)
json_values = [
'TEAMS',
'HAPPY_SLACK_EMOJIS',
'REPO_BLACKLIST',
'SLACK_CUSTOM_EMOJI_MAPPING'
]
for value in json_values:
config.update({
value: json.loads(config.get(value, '{}'))
})
return config
|
c85060806c0045d0e4a9410de2583fad879e99f6
| 3,642,141
|
import tokenize
def build_model():
"""
Returns built and tuned model using pipeline
Parameters:
No arguments
Returns:
cv (estimator): tuned model
"""
pipeline = Pipeline([
('Features', FeatureUnion([
('text_pipeline', Pipeline([
('vect', CountVectorizer(tokenizer=tokenize)),
('tfidf', TfidfTransformer())
])),
('starting_verb', StartingVerbExtractor())
])),
('clf', MultiOutputClassifier(DecisionTreeClassifier()))
])
# now we can perform another grid search on this new estimator to be sure we have the best parameters
parameters = {
#'Features__text_pipeline__vect__max_df': [0.5,1.0],
'Features__text_pipeline__tfidf__smooth_idf': (True, False)
}
cv = GridSearchCV(pipeline, param_grid=parameters)
return cv
|
94bc0ad8a3eb48531cb6229972099369a9b9cf61
| 3,642,142
|
def INPUT_BTN(**attributes):
"""
Utility function to create a styled button
"""
return SPAN(INPUT(_class = "button-right",
**attributes),
_class = "button-left")
|
6c6610626367795518ca737b53950d3687ae4d91
| 3,642,143
|
def load_annotations(file_path):
"""Loads a file containing annotations for multiple documents.
The file should contain lines with the following format:
<DOCUMENT ID> <LINES> <SPAN START POSITIONS> <SPAN LENGTHS> <SEVERITY>
Fields are separated by tabs; LINE, SPAN START POSITIONS and SPAN LENGTHS
can have a list of values separated by white space.
Args:
file_path: path to the file.
Returns:
a dictionary mapping document id's to a list of annotations.
"""
annotations = defaultdict(list)
with open(file_path, 'r', encoding='utf8') as f:
for i, line in enumerate(f):
line = line.strip()
if not line:
continue
fields = line.split('\t')
doc_id = fields[0]
try:
annotation = Annotation.from_fields(fields[1:])
except OverlappingSpans:
msg = 'Overlapping spans when reading line %d of file %s '
msg %= (i, file_path)
print(msg)
continue
annotations[doc_id].append(annotation)
return annotations
|
0c674142ae0d99670e63959c3c00ed0ca2c8fac1
| 3,642,144
|
import re
def search(request, template_name='blog/post_search.html'):
"""
Search for blog posts.
This template will allow you to setup a simple search form that will try to return results based on
given search strings. The queries will be put through a stop words filter to remove words like
'the', 'a', or 'have' to help imporve the result set.
Template: ``blog/post_search.html``
Context:
object_list
List of blog posts that match given search term(s).
search_term
Given search term.
"""
context = {}
if request.GET:
stop_word_list = re.compile(STOP_WORDS_RE, re.IGNORECASE)
search_term = '%s' % request.GET['q']
cleaned_search_term = stop_word_list.sub('', search_term)
cleaned_search_term = cleaned_search_term.strip()
if len(cleaned_search_term) != 0:
post_list = Post.objects.published().filter(Q(title__icontains=cleaned_search_term) | Q(body__icontains=cleaned_search_term) | Q(tags__icontains=cleaned_search_term) | Q(categories__title__icontains=cleaned_search_term))
context = {'object_list': post_list, 'search_term':search_term}
else:
message = 'Search term was too vague. Please try again.'
context = {'message':message}
return render(request, template_name, context)
|
fdb72279b6ed5fe5e87c888b7a10c8a3ed8f94d0
| 3,642,145
|
import math
def orient_data (data, header, header_out=None, MLBG_rot90_flip=False, log=None,
tel=None):
"""Function to remap [data] from the CD matrix defined in [header] to
the CD matrix taken from [header_out]. If the latter is not
provided the output orientation will be North up, East left.
If [MLBG_rot90_flip] is switched on and the data is from MeerLICHT or
BlackGEM, the data will be oriented within a few degrees from
North up, East left while preserving the pixel values in the new,
*remapped* reference, D and Scorr images.
"""
# rotation matrix:
# R = [[dx * cos(theta), dy * -sin(theta)],
# [dx * sin(theta), dy * cos(theta)]]
# with theta=0: North aligned with positive y-axis
# and East with the positive x-axis (RA increases to the East)
#
# N.B.: np.dot(R, [[x], [y]]) = np.dot([x,y], R.T)
#
# matrices below are defined using the (WCS) header keywords
# CD?_?:
#
# [ CD1_1 CD2_1 ]
# [ CD1_2 CD2_2 ]
#
# orient [data] with its orientation defined in [header] to the
# orientation defined in [header_out]. If the latter is not
# provided, the output orientation will be North up, East left.
# check if input data is square; if it is not, the transformation
# will not be done properly.
assert data.shape[0] == data.shape[1]
# define data CD matrix, assumed to be in [header]
CD_data = read_CD_matrix (header, log=log)
# determine output CD matrix, either from [header_out] or North
# up, East left
if header_out is not None:
CD_out = read_CD_matrix (header_out, log=log)
else:
# define de CD matrix with North up and East left, using the
# pixel scale from the input [header]
pixscale = read_header(header, ['pixscale'])
cdelt = pixscale/3600
CD_out = np.array([[-cdelt, 0], [0, cdelt]])
# check if values of CD_data and CD_out are similar
CD_close = [math.isclose(CD_data[i,j], CD_out[i,j], rel_tol=1e-3)
for i in range(2) for j in range(2)]
#if log is not None:
# log.info ('CD_close: {}'.format(CD_close))
if np.all(CD_close):
#if log is not None:
# log.info ('data CD matrix already similar to CD_out matrix; '
# 'no need to remap data')
# if CD matrix values are all very similar, do not bother to
# do the remapping
data2return = data
elif MLBG_rot90_flip and tel in ['ML1', 'BG2', 'BG3', 'BG4']:
#if log is not None:
# log.info ('for ML/BG: rotating data by exactly 90 degrees and for '
# 'ML also flip left/right')
# rotate data by exactly 90 degrees counterclockwise (when
# viewing data with y-axis increasing to the top!) and for ML1
# also flip in the East-West direction; for ML/BG this will
# result in an image within a few degrees of the North up,
# East left orientation while preserving the original pixel
# values of the new, *remapped* reference, D and Scorr images.
data2return = np.rot90(data, k=-1)
if tel=='ML1':
data2return = np.fliplr(data2return)
# equivalent operation: data2return = np.flipud(np.rot90(data))
else:
#if log is not None:
# log.info ('remapping data from input CD matrix: {} to output CD '
# 'matrix: {}'.format(CD_data, CD_out))
# transformation matrix, which is the dot product of the
# output CD matrix and the inverse of the data CD matrix
CD_data_inv = np.linalg.inv(CD_data)
CD_trans = np.dot(CD_out, CD_data_inv)
# transpose and flip because [affine_transform] performs
# np.dot(matrix, [[y],[x]]) rather than np.dot([x,y], matrix)
matrix = np.flip(CD_trans.T)
# offset, calculated from
#
# [xi - dxi, yo - dyo] = np.dot( [xo - dxo, yo - dyo], CD_trans )
#
# where xi, yi are the input coordinates corresponding to the
# output coordinates xo, yo in data and dxi/o, dyi/o are the
# corresponding offsets from the point of
# rotation/transformation, resulting in
#
# [xi, yi] = np.dot( [xo, yo], CD_trans ) + offset
# with
# offset = -np.dot( [dxo, dyo], CD_trans ) + [dxi, dyi]
# setting [dx0, dy0] and [dxi, dyi] to the center
center = (np.array(data.shape)-1)/2
offset = -np.dot(center, np.flip(CD_trans)) + center
# infer transformed data
data2return = ndimage.affine_transform(data, matrix, offset=offset,
mode='nearest')
return data2return
|
6ef27074692f46de56e5decd7d6b315e11c4d686
| 3,642,146
|
def _batchnorm_to_groupnorm(module: nn.modules.batchnorm._BatchNorm) -> nn.Module:
"""
Converts a BatchNorm ``module`` to GroupNorm module.
This is a helper function.
Args:
module: BatchNorm module to be replaced
Returns:
GroupNorm module that can replace the BatchNorm module provided
Notes:
A default value of 32 is chosen for the number of groups based on the
paper *Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour*
https://arxiv.org/pdf/1706.02677.pdf
"""
return nn.GroupNorm(min(32, module.num_features), module.num_features, affine=True)
|
1b923a28a4727b72768acf0fc00d93d9012c5349
| 3,642,147
|
def _compute_bic(
data: np.array,
n_clusters: int
) -> BICResult:
"""Compute the BIC statistic.
Parameters
----------
data: np.array
The data to cluster.
n_clusters: int
Number of clusters to test.
Returns
-------
results: BICResult
The results as a BICResult object.
"""
gm = GaussianMixture(n_clusters)
gm.fit(data)
return BICResult(gm.bic(data), n_clusters)
|
3eb1a759a60f834f4e4fb7e364c9bce4ffb61230
| 3,642,148
|
def release_branch_name(config):
"""
build expected release branch name from current config
"""
branch_name = "{0}{1}".format(
config.gitflow_release_prefix(),
config.package_version()
)
return branch_name
|
0d97c515aca8412882c8b260405a63d20b4b0f63
| 3,642,149
|
def torch2numpy(data):
""" Transfer data from the torch tensor (on CPU) to the numpy array (on CPU). """
return data.numpy()
|
c7ca4123743c4f054d809f0e307a4de079b0af10
| 3,642,150
|
def new_schema(name, public_name, is_active=True, **options):
"""
This function adds a schema in schema model and creates physical schema.
"""
try:
schema = Schema(name=name, public_name=public_name, is_active=is_active)
schema.save()
except IntegrityError:
raise Exception('Schema already exists.')
create_schema(name, **options)
return schema
|
efd6ed2737c6a25e8beeaef9f7fffebdb9592f10
| 3,642,151
|
def find_appropriate_timestep(simulation_factory,
equilibrium_samples,
M,
midpoint_operator,
temperature,
timestep_range,
DeltaF_neq_threshold=1.0,
max_samples=10000,
batch_size=1000,
verbose=True
):
"""Perform binary search* over the timestep range, trying to find
the maximum timestep that results in DeltaF_neq that doesn't exceed threshold
or have gross instability problems.
(*Not-quite-binary-search: instead of deterministic comparisons,
it performs hypothesis tests at regular intervals.)
Sketch
------
* Maintain an interval (min_timestep, max_timestep)
* At each iteration:
* timestep <- (min_timestep + max_timestep) / 2
* Only simulate long enough to be confident that DeltaF_neq(timestep) != threshold.
* If we're confident DeltaF_neq(timestep) > threshold, reduce max_timestep to current timestep.
* If we're confident DeltaF_neq(timestep) < threshold, increase min_timestep to current timestep
Parameters
----------
simulation_factory: function
accepts a timestep argument and returns a simulation equipped with an integrator with that
timestep
equilibrium_samples: list
list of samples from the configuration distribution at equilibrium
M: int
protocol length
midpoint_operator: function
accepts a simulation as an argument, doesn't return anything
temperature: unit'd quantity
temperature used to resample velocities
timestep_range: iterable
(min_timestep, max_timestep)
DeltaF_neq_threshold: double, default=1.0
maximum allowable DeltaF_neq
max_samples: int
number of samples
verbose: boolean
if True, print a bunch of stuff to the command prompt
Returns
-------
timestep: unit'd quantity
Maximum timestep tested that doesn't exceed the DeltaF_neq_threshold
"""
max_iter = 10
alpha = 1.96 # for now hard-coded confidence level
min_timestep, max_timestep = timestep_range[0], timestep_range[-1]
for i in range(max_iter):
timestep = (min_timestep + max_timestep) / 2
if verbose:
print("Current feasible range: [{:.3f}fs, {:.3f}fs]".format(
min_timestep.value_in_unit(unit.femtosecond),
max_timestep.value_in_unit(unit.femtosecond)
))
print("Testing: {:.3f}fs".format(timestep.value_in_unit(unit.femtosecond)))
simulation = simulation_factory(timestep)
simulation_crashed = False
changed_timestep_range = False
W_shads_F, W_shads_R, W_midpoints = [], [], []
def update_lists(W_shad_F, W_midpoint, W_shad_R):
W_shads_F.append(W_shad_F)
W_midpoints.append(W_midpoint)
W_shads_R.append(W_shad_R)
# collect up to max_samples protocol samples, making a decision about whether to proceed
# every batch_size samples
for _ in range(max_samples / batch_size):
# collect another batch_size protocol samples
for _ in range(batch_size):
# draw equilibrium sample
#x, v = equilibrium_sampler()
#simulation.context.setPositions(x)
#simulation.context.setVelocities(v)
simulation.context.setPositions(equilibrium_samples[np.random.randint(len(equilibrium_samples))])
simulation.context.setVelocitiesToTemperature(temperature)
# collect and store measurements
# if the simulation crashes, set simulation_crashed flag
try:
update_lists(*apply_protocol(simulation, M, midpoint_operator))
except:
simulation_crashed = True
if verbose: print("A simulation crashed! Considering this timestep unstable...")
# if we didn't crash, update estimate of DeltaF_neq upper and lower confidence bounds
DeltaF_neq, sq_uncertainty = estimate_nonequilibrium_free_energy(np.array(W_shads_F)[:,-1], np.array(W_shads_R)[:,-1])
if np.isnan(DeltaF_neq + sq_uncertainty):
if verbose:
print("A simulation encountered NaNs!")
simulation_crashed = True
bound = alpha * np.sqrt(sq_uncertainty)
DeltaF_neq_lcb, DeltaF_neq_ucb = DeltaF_neq - bound, DeltaF_neq + bound
out_of_bounds = (DeltaF_neq_lcb > DeltaF_neq_threshold) or (DeltaF_neq_ucb < DeltaF_neq_threshold)
if verbose and (out_of_bounds or simulation_crashed):
print("After collecting {} protocol samples, DeltaF_neq is likely in the following interval: "
"[{:.3f}, {:.3f}]".format(len(W_shads_F), DeltaF_neq_lcb, DeltaF_neq_ucb))
# if (DeltaF_neq_lcb > threshold) or (nans are encountered), then we're pretty sure this timestep is too big,
# and we can move on to try a smaller one
if simulation_crashed or (DeltaF_neq_lcb > DeltaF_neq_threshold):
if verbose:
print("This timestep is probably too big!\n")
max_timestep = timestep
changed_timestep_range = True
break
# else, if (DeltaF_neq_ucb < threshold), then we're pretty sure we can get
# away with a larger timestep
elif (DeltaF_neq_ucb < DeltaF_neq_threshold):
if verbose:
print("We can probably get away with a larger timestep!\n")
min_timestep = timestep
changed_timestep_range = True
break
# else, the threshold is within the upper and lower confidence bounds, and we keep going
if (not changed_timestep_range):
timestep = (min_timestep + max_timestep) / 2
if verbose:
print("\nTerminating early: found the following timestep: ".format(timestep.value_in_unit(unit.femtosecond)))
return timestep
if verbose:
timestep = (min_timestep + max_timestep) / 2
print("\nTerminating: found the following timestep: ".format(timestep.value_in_unit(unit.femtosecond)))
return timestep
|
94000a07cfc8cf00e3440ff242c63da5c8be5d00
| 3,642,152
|
def critical_bands():
"""
Compute the Critical bands as defined in the book:
Psychoacoustics by Zwicker and Fastl. Table 6.1 p. 159
"""
# center frequencies
fc = [
50,
150,
250,
350,
450,
570,
700,
840,
1000,
1170,
1370,
1600,
1850,
2150,
2500,
2900,
3400,
4000,
4800,
5800,
7000,
8500,
10500,
13500,
]
# boundaries of the bands (e.g. the first band is from 0Hz to 100Hz
# with center 50Hz, fb[0] to fb[1], center fc[0]
fb = [
0,
100,
200,
300,
400,
510,
630,
770,
920,
1080,
1270,
1480,
1720,
2000,
2320,
2700,
3150,
3700,
4400,
5300,
6400,
7700,
9500,
12000,
15500,
]
# now just make pairs
bands = [[fb[j], fb[j + 1]] for j in range(len(fb) - 1)]
return np.array(bands), fc
|
6301a6ee86d0ea3fb588213aa8b9453b14fb7036
| 3,642,153
|
def repackage(r, amo_id, amo_file, target_version=None, sdk_dir=None):
"""Pull amo_id/amo_file.xpi, schedule xpi creation, return hashtag
"""
# validate entries
# prepare data
hashtag = get_random_string(10)
sdk = SDK.objects.all()[0]
# if (when?) choosing sdk_dir will be possible
# sdk = SDK.objects.get(dir=sdk_dir) if sdk_dir else SDK.objects.all()[0]
sdk_source_dir = sdk.get_source_dir()
# extract packages
tasks.repackage.delay(
amo_id, amo_file, sdk_source_dir, hashtag, target_version)
# call build xpi task
# respond with a hashtag which will identify downloadable xpi
# URL to check if XPI is ready:
# /xpi/check_download/{hashtag}/
# URL to download:
# /xpi/download/{hashtag}/{desired_filename}/
return HttpResponse('{"hashtag": "%s"}' % hashtag,
mimetype='application/json')
|
9527f2fbe6077e25eee72a570f2e9702cbf3b510
| 3,642,154
|
def edges_to_adj_list(edges):
"""
Transforms a set of edges in an adjacency list (represented as a dictiornary)
For UNDIRECTED graphs, i.e. if v2 in adj_list[v1], then v1 in adj_list[v2]
INPUT:
- edges : a set or list of edges
OUTPUT:
- adj_list: a dictionary with the vertices as keys, each with
a set of adjacent vertices.
"""
adj_list = {} # store in dictionary
for v1, v2 in edges:
if v1 in adj_list: # edge already in it
adj_list[v1].add(v2)
else:
adj_list[v1] = set([v2])
if v2 in adj_list: # edge already in it
adj_list[v2].add(v1)
else:
adj_list[v2] = set([v1])
return adj_list
|
683f10e9a0a9b8a29d63b276b2e550ebe8287a05
| 3,642,155
|
from typing import Optional
def _get_lookups(
name: str,
project: interface.Project,
base: Optional[str] = None) -> list[str]:
"""[summary]
Args:
name (str): [description]
design (Optional[str]): [description]
kind (Optional[str]): [description]
Returns:
list[str]: [description]
"""
lookups = [name]
if name in project.outline.designs:
lookups.append(project.outline.designs[name])
if name in project.outline.kinds:
lookups.append(project.outline.kinds[name])
if base is not None:
lookups.append(base)
return lookups
|
c8f700a19bbae0167c8474f033d625a763743db8
| 3,642,156
|
def unwrap(value):
"""
Unwraps the given Document or DocumentList as applicable.
"""
if isinstance(value, Document):
return value.to_dict()
elif isinstance(value, DocumentList):
return value.to_list()
else:
return value
|
7e25c2935ff0a467e51097c4291e8d5f751c34db
| 3,642,157
|
def home_all():
"""Home page view.
On this page a summary campaign manager view will shown with all campaigns.
"""
context = dict(
oauth_consumer_key=OAUTH_CONSUMER_KEY,
oauth_secret=OAUTH_SECRET,
all=True,
map_provider=map_provider()
)
# noinspection PyUnresolvedReferences
return render_template('index.html', **context)
|
d987486f30cc5a8f6e697d9ccb92741b2d2067e4
| 3,642,158
|
import math
def _sqrt(x):
"""_sqrt."""
isnumpy = isinstance(x, np.ndarray)
isscalar = np.isscalar(x)
return np.sqrt(x) if isnumpy else math.sqrt(x) if isscalar else x.sqrt()
|
16f566493deeaaf35841548e6db89408ca686bfe
| 3,642,159
|
def update_subnet(context, id, subnet):
"""Update values of a subnet.
: param context: neutron api request context
: param id: UUID representing the subnet to update.
: param subnet: dictionary with keys indicating fields to update.
valid keys are those that have a value of True for 'allow_put'
as listed in the RESOURCE_ATTRIBUTE_MAP object in
neutron/api/v2/attributes.py.
"""
LOG.info("update_subnet %s for tenant %s" %
(id, context.tenant_id))
with context.session.begin():
subnet_db = db_api.subnet_find(context, id=id, scope=db_api.ONE)
if not subnet_db:
raise exceptions.SubnetNotFound(id=id)
s = subnet["subnet"]
always_pop = ["_cidr", "cidr", "first_ip", "last_ip", "ip_version",
"segment_id", "network_id"]
admin_only = ["do_not_use", "created_at", "tenant_id",
"next_auto_assign_ip", "enable_dhcp"]
utils.filter_body(context, s, admin_only, always_pop)
dns_ips = utils.pop_param(s, "dns_nameservers", [])
host_routes = utils.pop_param(s, "host_routes", [])
gateway_ip = utils.pop_param(s, "gateway_ip", None)
allocation_pools = utils.pop_param(s, "allocation_pools", None)
if not CONF.QUARK.allow_allocation_pool_update:
if allocation_pools:
raise exceptions.BadRequest(
resource="subnets",
msg="Allocation pools cannot be updated.")
alloc_pools = allocation_pool.AllocationPools(
subnet_db["cidr"],
policies=models.IPPolicy.get_ip_policy_cidrs(subnet_db))
else:
alloc_pools = allocation_pool.AllocationPools(subnet_db["cidr"],
allocation_pools)
if gateway_ip:
alloc_pools.validate_gateway_excluded(gateway_ip)
default_route = None
for route in host_routes:
netaddr_route = netaddr.IPNetwork(route["destination"])
if netaddr_route.value == routes.DEFAULT_ROUTE.value:
default_route = route
break
if default_route is None:
route_model = db_api.route_find(
context, cidr=str(routes.DEFAULT_ROUTE), subnet_id=id,
scope=db_api.ONE)
if route_model:
db_api.route_update(context, route_model,
gateway=gateway_ip)
else:
db_api.route_create(context,
cidr=str(routes.DEFAULT_ROUTE),
gateway=gateway_ip, subnet_id=id)
if dns_ips:
subnet_db["dns_nameservers"] = []
for dns_ip in dns_ips:
subnet_db["dns_nameservers"].append(db_api.dns_create(
context,
ip=netaddr.IPAddress(dns_ip)))
if host_routes:
subnet_db["routes"] = []
for route in host_routes:
subnet_db["routes"].append(db_api.route_create(
context, cidr=route["destination"], gateway=route["nexthop"]))
if CONF.QUARK.allow_allocation_pool_update:
if isinstance(allocation_pools, list):
cidrs = alloc_pools.get_policy_cidrs()
ip_policies.ensure_default_policy(cidrs, [subnet_db])
subnet_db["ip_policy"] = db_api.ip_policy_update(
context, subnet_db["ip_policy"], exclude=cidrs)
subnet = db_api.subnet_update(context, subnet_db, **s)
return v._make_subnet_dict(subnet)
|
f1ac159f612d3b8a5459ee3b70c440cf7cf84cd5
| 3,642,160
|
def validate_params():
"""@rtype bool"""
def validate_single_param(param_name, required_type):
"""@rtype bool"""
inner_result = True
if not rospy.has_param(param_name):
rospy.logfatal('Parameter {} is not defined but needed'.format(param_name))
inner_result = False
else:
if type(required_type) is list and len(required_type) > 0:
if type(rospy.get_param(param_name)) in required_type:
rospy.logfatal('Parameter {} is not any of type {}'.format(param_name, required_type))
inner_result = False
else:
if type(rospy.get_param(param_name)) is not required_type:
rospy.logfatal('Parameter {} is not of type {}'.format(param_name, required_type))
inner_result = False
return inner_result
result = True
result = result and validate_single_param('~update_frequency', int)
result = result and validate_single_param('~do_cpu', bool)
result = result and validate_single_param('~do_memory', bool)
result = result and validate_single_param('~do_network', bool)
return result
|
8734d9db7e29b8b6c30dc8a3ae72b0cf18c85310
| 3,642,161
|
import os
def run_test_general_base_retrieval_methods(query_dic, query_types, trec_cast_eval, similarity, string_params,
searcher: SimpleSearcher, reranker,
write_to_trec_eval, write_results_to_file, reranker_query_config,
reranking_threshold, use_rrf):
"""
Run topics in trec_cast_eval
query_dic is a dict with string keys and a QueryConfig object
query_types is a list of strings that denote keys we want to use that are in query_dic
trec_cast_eval - object of type ConvSearchEvaluationGeneral
If write_to_trec_eval writes the results in trec eval format
If write_results_to_file writes the results in tsv format (including the
query and document's content for later use)
"""
metric_results = {}
doc_results = {}
for query_type in query_types:
print(similarity + " " + query_type + " " + string_params)
current_key = similarity + "_" + query_type + "_" + string_params
metric_results[current_key], _, doc_results[current_key] = \
run_topics_general(trec_cast_eval=trec_cast_eval,
query_config=query_dic[query_type], searcher=searcher,
reranker=reranker,
reranker_query_config=reranker_query_config,
reranking_threshold=reranking_threshold,
use_rrf=use_rrf)
index_name = os.path.basename(os.path.normpath(searcher.index_dir))
run_file_name = index_name + "_" + current_key
run_name = query_type
if searcher.is_using_rm3():
run_file_name += "_rm3"
if reranker:
run_file_name += "_" + reranker.RERANKER_TYPE + "_" + str(reranking_threshold)
run_name += "_" + reranker.RERANKER_TYPE + "_" + str(reranking_threshold)
if write_to_trec_eval:
write_trec_results(file_name=run_file_name, result=doc_results[current_key],
run_name=run_name)
if write_results_to_file:
write_doc_results_to_file(file_name=run_file_name + ".tsv", result=doc_results[current_key])
return metric_results, doc_results
|
c0320231f65955b65f447bf0303f4443dcc5110d
| 3,642,162
|
def user_exists(username):
"""Return True if the username exists, or False if it doesn't."""
try:
adobe_api.AdobeAPIObject(username)
except adobe_api.AdobeAPINoUserException:
return False
return True
|
3767bec38c8058e7bd193e5532e4150ca501a96a
| 3,642,163
|
def bags_containing_bag(bag: str, rules: dict[str, list]) -> int:
"""Returns the bags that have bag in their rules."""
return {r_bag
for r_bag, r_rule in rules.items()
for _, r_color in r_rule
if bag in r_color}
|
f9e67a4ade4dd9bdf25e05669741c71270007215
| 3,642,164
|
def default_mutable_arguments():
"""Explore default mutable arguments, which are a dangerous game in themselves.
Why do mutable default arguments suffer from this apparent problem? A function's
default values are evaluated at the point of function definition in the defining
scope. In particular, we can examine these bindings by printing
append_twice.__defaults__ after append_twice has been defined. For this function,
we have
print(append_twice.__defaults__) # ([],)
If a binding for `lst` is not supplied, then the `lst` name inside append_twice
falls back to the array object that lives inside append_twice.__defaults__.
In particular, if we update `lst` in place during one function call, we have changed
the value of the default argument. That is,
print(append_twice.__defaults__) # ([], )
append_twice(1)
print(append_twice.__defaults__) # ([1, 1], )
append_twice(2)
print(append_twice.__defaults__) # ([1, 1, 2, 2], )
In each case where a user-supplied binding for `lst is not given, we modify the
single (mutable) default value, which leads to this crazy behavior.
"""
def append_twice(a, lst=[]):
"""Append a value to a list twice."""
lst.append(a)
lst.append(a)
return lst
print(append_twice(1, lst=[4])) # => [4, 1, 1]
print(append_twice(11, lst=[2, 3, 5, 7])) # => [2, 3, 5, 7, 11, 11]
print(append_twice(1)) # => [1, 1]
print(append_twice(2)) # => [1, 1, 2, 2]
print(append_twice(3))
|
a58a8c2807e29af68d501aa5ad4b33ad1aa80252
| 3,642,165
|
def is_text_file(file_):
"""
detect if file is of type text
:param file_: file to be tested
:returns: `bool` of whether the file is text
"""
with open(file_, 'rb') as ff:
data = ff.read(1024)
return not is_binary_string(data)
|
d064b51ea239f34ed97d47416b1f411650ce8a1a
| 3,642,166
|
from typing import Union
from datetime import datetime
from typing import List
import pytz
def soft_update_datetime_field(
model_inst: models.Model,
field_name: str,
warehouse_field_value: Union[datetime, None],
) -> List[str]:
"""
Uses Django ORM to update DateTime field of model instance if the field value is null and the warehouse data is non-null.
"""
model_name: str = model_inst.__class__.__name__
current_field_value: Union[datetime, None] = getattr(model_inst, field_name)
# Skipping update if the field already has a value, provided by a previous cron run or administrator
if current_field_value is not None:
logger.info(
f'Skipped update of {field_name} for {model_name} instance ({model_inst.id}); existing value was found')
else:
if warehouse_field_value:
warehouse_field_value = warehouse_field_value.replace(tzinfo=pytz.UTC)
setattr(model_inst, field_name, warehouse_field_value)
logger.info(f'Updated {field_name} for {model_name} instance ({model_inst.id})')
return [field_name]
return []
|
33034a548ee572706cd1e6e696d5a9249ad0b528
| 3,642,167
|
import itertools
def plot_confusion_matrix(
y_true,
y_pred,
normalize=False,
cmap=plt.cm.Blues,
label_list = None,
visible=True,
savepath=None):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
cm = confusion_matrix(y_true, y_pred)
acc = accuracy_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred, average="micro")
title = f"Confusion Matrix, Acc: {acc:.2f}, F1: {f1:.2f}"
if label_list == None:
classes = range(0, max(y_true))
else:
classes = label_list
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
plt.figure(figsize=(13,13))
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
if savepath is not None:
plt.savefig(savepath)
if visible:
plt.show()
return acc, f1
|
f15d2170ba0e869cb47e554ea374f93b05dbcab8
| 3,642,168
|
def _test_pressure_reconstruction(self, g, recon_p, point_val, point_coo):
"""
Testing pressure reconstruction. This function uses the reconstructed
pressure local polynomial and perform an evaluation at the Lagrangian
points, and checks if the those values are equal to the point_val array.
Parameters
----------
g : PorePy object
Grid.
recon_p : NumPy nd-Array
Reconstructed pressure polynomial.
point_val : NumPy nd-Array
Pressure avlues at the Lagrangian nodes.
point_coo : NumPy array
Coordinates at the Lagrangian nodes.
Returns
-------
None.
"""
def assert_reconp(eval_poly, point_val):
np.testing.assert_allclose(
eval_poly,
point_val,
rtol=1e-6,
atol=1e-3,
err_msg="Pressure reconstruction has failed"
)
eval_poly = utils.eval_P1(recon_p, point_coo)
assert_reconp(eval_poly, point_val)
return None
|
b70b202cc21ba632f18af2f5fcf72f7b6d509e91
| 3,642,169
|
def logout():
"""Logout
:return: Function used to log out the current user
"""
logout_user()
return redirect(url_for('index'))
|
f5e2ef30b47c645ba5671395a115eb6d6c9425f1
| 3,642,170
|
import json
import base64
import os
import requests
def get_authorization_url(
app_id, redirect_uri, scope='all', state='', extra_data='', **params):
"""
Get the url to start the first leg of OAuth flow.
Refer to `Authentication Docs <https://developers.kloudless.com/docs/latest/
authentication#oauth-2.0>`_ for more information.
:param str app_id: Application ID
:param str redirect_uri: Redirect URI to your application server
:param str scope: A space-delimited string of scopes that indicate which
services a user can connect, and which permissions to request
:param str state: An arbitrary string which would be redirected back via
``redirect_uri`` as query parameter. Random url-safe Base64 string would
be generated by default
:param str extra_data: A URL-encoded JSON object containing data used to
pre-fill default values for fields in the Kloudless authentication
forms. For example, the domain of a WebDAV server
:param params: Additional query parameters
:returns: `tuple(url, state)`: Redirect the user to ``url`` to start
authorization. Saved ``state`` in user's session for future validation
:rtype: `tuple(str, str)`
"""
if extra_data and isinstance(extra_data, dict):
extra_data = json.dumps(extra_data)
if not state:
state = base64.urlsafe_b64encode(os.urandom(12)).decode('utf8')
params.update({
'client_id': app_id,
'response_type': 'code',
'redirect_uri': redirect_uri,
'scope': scope,
'state': state,
'extra_data': extra_data,
})
endpoint = construct_kloudless_endpoint('oauth',
api_version=OAUTH_API_VERSION)
url = requests.Request('GET', endpoint, params=params).prepare().url
return url, state
|
27abfc9c9c14887f1a3a963f7eb07d92706e288a
| 3,642,171
|
def get_user_messages(user, index=0, number=0):
"""
返回指定user按时间倒序的从index索引开始的number个message
"""
if not user or user.is_anonymous or index < 0 or number < 0:
return tuple()
# noinspection PyBroadException
try:
if index == 0 and number == 0:
all_message = user.messages.all()
else:
all_message = user.messages.all()[index:index+number]
except Exception as e:
all_message = tuple()
return all_message
|
bb0c499e5ca8ec650d2ebca12852d2345733e882
| 3,642,172
|
def third_party_apps_default_dc_modules_and_settings(klass):
"""
Decorator for DefaultDcSettingsSerializer class.
Updates modules and settings fields defined in installed third party apps.
"""
logger.info('Loading third party apps DEFAULT DC modules and settings.')
for third_party_app, app_dc_settings in get_third_party_apps_serializer_settings():
try:
app_dc_settings.DEFAULT_DC_MODULES
except AttributeError:
logger.info('Skipping app: %s does not have any DEFAULT DC modules defined.', third_party_app)
else:
_update_serializer_modules(third_party_app, app_dc_settings.DEFAULT_DC_MODULES, klass, default_dc=True)
try:
app_dc_settings.DEFAULT_DC_SETTINGS
except AttributeError:
logger.info('Skipping app: %s does not have any DEFAULT DC settings defined.', third_party_app)
else:
_update_serializer_settings(third_party_app, app_dc_settings, klass, default_dc=True)
return klass
|
59be03a271e60352b429d45ecff647100388f9ab
| 3,642,173
|
from typing import Union
from pathlib import Path
def split_lvis(
n_experiences: int,
train_transform=None,
eval_transform=None,
shuffle=True,
root_path: Union[str, Path] = None,
):
"""
Creates the example Split LVIS benchmark.
This is a toy benchmark created only to show how a detection benchmark can
be created. It was not meant to be used for research purposes!
:param n_experiences: The number of train experiences to create.
:param train_transform: The train transformation.
:param eval_transform: The eval transformation.
:param shuffle: If True, the dataset will be split randomly
:param root_path: The root path of the dataset. Defaults to None,
which means that the default path will be used.
:return: A :class:`DetectionScenario` instance.
"""
train_dataset = LvisDataset(root=root_path, train=True)
val_dataset = LvisDataset(root=root_path, train=False)
all_cat_ids = set(train_dataset.lvis_api.get_cat_ids())
all_cat_ids.union(val_dataset.lvis_api.get_cat_ids())
return split_detection_benchmark(
n_experiences=n_experiences,
train_dataset=train_dataset,
test_dataset=val_dataset,
n_classes=len(all_cat_ids),
train_transform=train_transform,
eval_transform=eval_transform,
shuffle=shuffle,
)
|
efece586ec6bfbc45911ed9f4f2ad5ead2cfd88b
| 3,642,174
|
def compute_log_ksi_normalized(log_edge_pot, #'(t-1,t)',
log_node_pot, # '(t, label)',
T,
n_labels,
log_alpha,
log_beta,
temp_array_1,
temp_array_2):
""" to obtain the two-slice posterior marginals p(y_t = i, y_t+1 = j| X_1:T) = normalized ksi_t,t+1(i,j) """
# in the following, will index log_ksi only with t, to stand for log_ksi[t,t+1]. including i,j: log_ksi[t,i,j]
log_alpha = compute_log_alpha(log_edge_pot, log_node_pot, T, n_labels, log_alpha, temp_array_1, temp_array_2)
log_beta = compute_log_beta(log_edge_pot, log_node_pot, T, n_labels, log_beta, temp_array_1, temp_array_2)
log_ksi = np.empty((T-1, n_labels, n_labels))
for t in range(T-1):
psi_had_beta = log_node_pot[t+1,:] + log_beta[t+1, :] # represents psi_t+1 \hadamard beta_t+1 in MLAPP eq 17.67
log_ksi[t,:,:] = log_edge_pot
for c in range(n_labels):
for d in range(n_labels):
log_ksi[t,c,d] += log_alpha[t,d] + psi_had_beta[c]
# normalize current ksi[t,:,:] over both dimensions. This is not required of ksi, strictly speaking, but the output of the function needs to be normalized, and it's cheaper to do it in-place on ksi than to create a fresh variable to hold the normalized values
log_ksi[t,:,:] -= lse_numba_2d(log_ksi[t,:,:])
return log_ksi
|
e4e6ea464851ba64d640e14fd7c88e9c52f28f50
| 3,642,175
|
import argparse
from pathlib import Path
def parse_args():
"""Parse input arguments."""
parser = argparse.ArgumentParser(description="Map gleason data to standard format.")
parser.add_argument("-d", "--data_path", type=Path, help="Path to folder with the data.", required=True)
parser.add_argument("-n", "--n_jobs", type=int, help="Number of jobs to run in parallel.", required=True)
return parser.parse_args()
|
0a1a1cb404d74c48d550642e31399410d1bd13c3
| 3,642,176
|
from typing import Any
from typing import Type
def _deserialize_union(x: Any, field_type: Type) -> Any:
"""Deserialize values for Union typed fields
Args:
x (Any): value to be deserialized.
field_type (Type): field type.
Returns:
[Any]: desrialized value.
"""
for arg in field_type.__args__:
# stop after first matching type in Union
try:
x = _deserialize(x, arg)
break
except ValueError:
pass
return x
|
01793983a0a82fc16c03adbe57f52de9be5c81ea
| 3,642,177
|
def read_simplest_expandable(expparams, config):
"""
Read expandable parameters from config file of the type `param_1`.
Parameters
----------
expparams : dict, dict.keys, set, or alike
The parameter names that should be considered as expandable.
Usually, this is a module subdictionary of `type_simplest_ep`.
config : dict, dict.keys, set, or alike
The user configuration file.
Returns
-------
set of str
The parameters in `config` that comply with `expparams`.
"""
new = set()
for param in config:
try:
name, idx = param.split("_")
except ValueError:
continue
if idx.isdigit() and name in expparams:
new.add(param)
return new
|
4e2068e4a6cbca050da6a33a24b5fb0d2477e4e3
| 3,642,178
|
from typing import Callable
from typing import Iterable
from typing import Any
def rec_map_reduce_array_container(
reduce_func: Callable[[Iterable[Any]], Any],
map_func: Callable[[Any], Any],
ary: ArrayOrContainerT) -> "DeviceArray":
"""Perform a map-reduce over array containers recursively.
:param reduce_func: callable used to reduce over the components of *ary*
(and those of its sub-containers) if *ary* is a
:class:`~arraycontext.ArrayContainer`. Must be associative.
:param map_func: callable used to map a single array of type
:class:`arraycontext.ArrayContext.array_types`. Returns an array of the
same type or a scalar.
.. note::
The traversal order is unspecified. *reduce_func* must be associative in
order to guarantee a sensible result. This is because *reduce_func* may be
called on subsets of the component arrays, and then again (potentially
multiple times) on the results. As an example, consider a container made up
of two sub-containers, *subcontainer0* and *subcontainer1*, that each
contain two component arrays, *array0* and *array1*. The same result must be
computed whether traversing recursively::
reduce_func([
reduce_func([
map_func(subcontainer0.array0),
map_func(subcontainer0.array1)]),
reduce_func([
map_func(subcontainer1.array0),
map_func(subcontainer1.array1)])])
reducing all of the arrays at once::
reduce_func([
map_func(subcontainer0.array0),
map_func(subcontainer0.array1),
map_func(subcontainer1.array0),
map_func(subcontainer1.array1)])
or any other such traversal.
"""
def rec(_ary: ArrayOrContainerT) -> ArrayOrContainerT:
try:
iterable = serialize_container(_ary)
except NotAnArrayContainerError:
return map_func(_ary)
else:
return reduce_func([
rec(subary) for _, subary in iterable
])
return rec(ary)
|
885862371ece1e1f041a44693704300945d8d4a0
| 3,642,179
|
def load_data_test(test_path, diagnoses_list, baseline=True, multi_cohort=False):
"""
Load data not managed by split_manager.
Args:
test_path (str): path to the test TSV files / split directory / TSV file for multi-cohort
diagnoses_list (List[str]): list of the diagnoses wanted in case of split_dir or multi-cohort
baseline (bool): If True baseline sessions only used (split_dir handling only).
multi_cohort (bool): If True considers multi-cohort setting.
"""
# TODO: computes baseline sessions on-the-fly to manager TSV file case
if multi_cohort:
if not test_path.endswith(".tsv"):
raise ValueError(
"If multi_cohort is given, the tsv_path argument should be a path to a TSV file."
)
else:
tsv_df = pd.read_csv(test_path, sep="\t")
check_multi_cohort_tsv(tsv_df, "labels")
test_df = pd.DataFrame()
found_diagnoses = set()
for idx in range(len(tsv_df)):
cohort_name = tsv_df.loc[idx, "cohort"]
cohort_path = tsv_df.loc[idx, "path"]
cohort_diagnoses = (
tsv_df.loc[idx, "diagnoses"].replace(" ", "").split(",")
)
if bool(set(cohort_diagnoses) & set(diagnoses_list)):
target_diagnoses = list(set(cohort_diagnoses) & set(diagnoses_list))
cohort_test_df = load_data_test_single(
cohort_path, target_diagnoses, baseline=baseline
)
cohort_test_df["cohort"] = cohort_name
test_df = pd.concat([test_df, cohort_test_df])
found_diagnoses = found_diagnoses | (
set(cohort_diagnoses) & set(diagnoses_list)
)
if found_diagnoses != set(diagnoses_list):
raise ValueError(
f"The diagnoses found in the multi cohort dataset {found_diagnoses} "
f"do not correspond to the diagnoses wanted {set(diagnoses_list)}."
)
test_df.reset_index(inplace=True, drop=True)
else:
if test_path.endswith(".tsv"):
tsv_df = pd.read_csv(test_path, sep="\t")
multi_col = {"cohort", "path"}
if multi_col.issubset(tsv_df.columns.values):
raise ValueError(
"To use multi-cohort framework, please add --multi_cohort flag."
)
test_df = load_data_test_single(test_path, diagnoses_list, baseline=baseline)
test_df["cohort"] = "single"
return test_df
|
50b739425ca76a3d170688f19e230a0d84c21221
| 3,642,180
|
import json
def load_augmentations_config(
placeholder_params: dict, path_to_config: str = "configs/augmentations.json"
) -> dict:
"""Load the json config with params of all transforms
Args:
placeholder_params (dict): dict with values of placeholders
path_to_config (str): path to the json config file
"""
with open(path_to_config, "r") as config_file:
augmentations = json.load(config_file)
for name, params in augmentations.items():
params = [fill_placeholders(param, placeholder_params) for param in params]
return augmentations
|
49f3170033411418e7e5468aecdcdc612a677e66
| 3,642,181
|
import numpy
def simplify_mask(mask, r_ids, r_p_zip, replace=True):
"""Simplify the mask by replacing all `region_ids` with their `root_parent_id`
The `region_ids` and `parent_ids` are paired from which a tree is inferred. The root
of this tree is value `0`. `region_ids` that have a corresponding `parent_id` of 0
are penultimate roots. This method replaces each `region_id` with its penultimate `parent_id`.
It *simplifies* the volume.
:param mask: a 3D volume
:type mask: `numpy.array`
:param r_id: sequence of `region_id`
:type r_id: iterable
:param r_p_zip: sequence of 2-tuples with `region_id` and `parent_id`
:type r_p_zip: iterable
:param bool replace: if `True` then the returned `mask` will have values; `False` will leave the `mask` unchanged (useful for running tests to speed things up)
:return: `simplified_mask`, `segment_colours`, `segment_ids`
:rtype: tuple
"""
simplified_mask = numpy.ndarray(mask.shape, dtype=int) # @UnusedVariable
simplified_mask = 0
# group regions_ids by parent_id
root_parent_id_group = dict()
for r in r_ids:
p = get_root(r_p_zip, r)
if p not in root_parent_id_group:
root_parent_id_group[p] = [r]
else:
root_parent_id_group[p] += [r]
if replace:
# It is vastly faster to use multiple array-wide comparisons than to do
# comparisons element-wise. Therefore, we generate a string to be executed
# that will do hundreds of array-wide comparisons at a time.
# Each comparison is for all region_ids for a parent_id which will
# then get assigned the parent_id.
for parent_id, region_id_list in root_parent_id_group.items():
# check whether any element in the mask has a value == r0 OR r1 ... OR rN
# e.g. (mask == r0) | (mask == r1) | ... | (mask == rN)
comp = ' | '.join(['( mask == %s )' % r for r in region_id_list])
# set those that satisfy the above to have the parent_id
# Because parent_ids are non-overlapping (i.e. no region_id has two parent_ids)
# we can do successive summation instead of assignments.
full_op = 'simplified_mask += (' + comp + ') * %s' % parent_id
exec(full_op)
else:
simplified_mask = mask
segment_ids = root_parent_id_group.keys()
# segment_colors = [r_c_zip[s] for s in segment_ids]
return simplified_mask, segment_ids
|
b8344a893319ad7a26f931b2edbc6ef452b82c24
| 3,642,182
|
def getStops(ll):
"""
getStops
Returns a list of stops based off of a lat long pair
:param: ll { lat : float, lng : float }
:return: list
"""
if not ll:
return None
url = "%sstops?appID=%s&ll=%s,%s" % (BASE_URI, APP_ID, ll['lat'], ll['lng'])
try:
f = urlopen(url)
except HTTPError:
return None
response = f.read()
dom = parseString(response)
stopElems = dom.getElementsByTagName("location")
stops = []
for se in stopElems:
locid = se.getAttribute("locid")
desc = se.getAttribute("desc")
direction = se.getAttribute("dir")
stops.append("ID: %s, %s on %s" % (locid, direction, desc))
return stops
|
eedfc49a02ab6c2ccf45e241262236804007a156
| 3,642,183
|
import warnings
import six
def rws(log_joint, observed, latent, axis=None):
"""
Implements Reweighted Wake-sleep from (Bornschein, 2015). This works for
both continuous and discrete latent `StochasticTensor` s.
:param log_joint: A function that accepts a dictionary argument of
``(string, Tensor)`` pairs, which are mappings from all
`StochasticTensor` names in the model to their observed values. The
function should return a Tensor, representing the log joint likelihood
of the model.
:param observed: A dictionary of ``(string, Tensor)`` pairs. Mapping from
names of observed `StochasticTensor` s to their values.
:param latent: A dictionary of ``(string, (Tensor, Tensor))``) pairs.
Mapping from names of latent `StochasticTensor` s to their samples and
log probabilities.
:param axis: The sample dimension(s) to reduce when computing the
outer expectation in log likelihood and in the cost for adapting
proposals. If `None`, no dimension is reduced.
:return: A Tensor. The surrogate cost to minimize.
:return: A Tensor. Estimated log likelihoods.
"""
warnings.warn("rws(): This function will be deprecated in the coming "
"version (0.3.1). Variational utilities are moving to "
"`zs.variational`. Features of the original rws() can be "
"achieved by two new variational objectives. For learning "
"model parameters, please use the importance weighted "
"objective: `zs.variational.iw_objective()`. For adapting "
"the proposal, the new rws gradient estimator can be "
"accessed by first constructing the inclusive KL divergence "
"objective using `zs.variational.klpq` and then calling "
"its rws() method.", category=FutureWarning)
latent_k, latent_v = map(list, zip(*six.iteritems(latent)))
latent_outputs = dict(zip(latent_k, map(lambda x: x[0], latent_v)))
latent_logpdfs = map(lambda x: x[1], latent_v)
joint_obs = merge_dicts(observed, latent_outputs)
log_joint_value = log_joint(joint_obs)
entropy = -sum(latent_logpdfs)
log_w = log_joint_value + entropy
if axis is not None:
log_w_max = tf.reduce_max(log_w, axis, keep_dims=True)
w_u = tf.exp(log_w - log_w_max)
w_tilde = tf.stop_gradient(
w_u / tf.reduce_sum(w_u, axis, keep_dims=True))
log_likelihood = log_mean_exp(log_w, axis)
fake_log_joint_cost = -tf.reduce_sum(w_tilde * log_joint_value, axis)
fake_proposal_cost = tf.reduce_sum(w_tilde * entropy, axis)
cost = fake_log_joint_cost + fake_proposal_cost
else:
cost = log_w
log_likelihood = log_w
return cost, log_likelihood
|
eb6278919dd484884b3110681680e67d3ee17d2f
| 3,642,184
|
def fit_svr(X, y, kernel: str = 'rbf') -> LinearSVR:
"""
Fit support vector regression for the given input X and expected labes y.
:param X: Feature data
:param y: Labels that should be correctly computed
:param kernel: type of kernel used by the SVR {‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’}, default=’rbf’
:return: SVR that is fitted to X and y
"""
svr = LinearSVR()
svr.fit(X=X, y=y)
return svr
|
10a38fca990c4ab058d582fbe38bd05df7456660
| 3,642,185
|
import typing
def process_get_namespaces_from_accounts(
status: int,
json: list,
network_type: models.NetworkType,
) -> typing.Sequence[models.NamespaceInfo]:
"""
Process the "/account/namespaces" HTTP response.
:param status: Status code for HTTP response.
:param json: JSON data for response message.
"""
assert status == 200
return [models.NamespaceInfo.create_from_dto(i, network_type) for i in json]
|
748bdca72db0640e75f8a0c063f7968ea9583e94
| 3,642,186
|
from typing import Optional
import os
def _initialize_pydataverse(DATAVERSE_URL: Optional[str], API_TOKEN: Optional[str]):
"""Sets up a pyDataverse API for upload."""
# Get environment variables
if DATAVERSE_URL is None:
try:
DATAVERSE_URL = os.environ["DATAVERSE_URL"]
except KeyError:
raise MissingURLException
if API_TOKEN is None:
try:
API_TOKEN = os.environ["DATAVERSE_API_TOKEN"]
except KeyError:
raise MissingCredentialsException
return NativeApi(DATAVERSE_URL, API_TOKEN), DataAccessApi(DATAVERSE_URL, API_TOKEN)
|
716ce3a3e589cc9323159e6285194ab74706dc92
| 3,642,187
|
def _hexsplit(string):
""" Split a hex string into 8-bit/2-hex-character groupings separated by spaces"""
return ' '.join([string[i:i+2] for i in range(0, len(string), 2)])
|
672e475edeaafaa08254845e620b0a771b294fa8
| 3,642,188
|
def get_analysis_id(analysis_id):
"""
Get the new analysis id
:param analysis_id: analysis_index DataFrame
:return: new analysis_id
"""
if analysis_id.size == 0:
analysis_id = 0
else:
analysis_id = np.nanmax(analysis_id.values) + 1
return int(analysis_id)
|
3318764daadca6c1e1921847f623fcac169e2cb5
| 3,642,189
|
from typing import Union
def get_station_pqr(station_name: str, rcu_mode: Union[str, int], db):
"""
Get PQR coordinates for the relevant subset of antennas in a station.
Args:
station_name: Station name, e.g. 'DE603LBA' or 'DE603'
rcu_mode: RCU mode (0 - 6, can be string)
db: instance of LofarAntennaDatabase from lofarantpos
Example:
>>> from lofarantpos.db import LofarAntennaDatabase
>>> db = LofarAntennaDatabase()
>>> pqr = get_station_pqr("DE603", "outer", db)
>>> pqr.shape
(96, 3)
>>> pqr[0, 0]
1.7434713
>>> pqr = get_station_pqr("LV614", "5", db)
>>> pqr.shape
(96, 3)
"""
full_station_name = get_full_station_name(station_name, rcu_mode)
station_type = get_station_type(full_station_name)
if 'LBA' in station_name or str(rcu_mode) in ('1', '2', '3', '4', 'inner', 'outer'):
# Get the PQR positions for an individual station
station_pqr = db.antenna_pqr(full_station_name)
# Exception: for Dutch stations (sparse not yet accommodated)
if (station_type == 'core' or station_type == 'remote') and int(rcu_mode) in (3, 4):
station_pqr = station_pqr[0:48, :]
elif (station_type == 'core' or station_type == 'remote') and int(rcu_mode) in (1, 2):
station_pqr = station_pqr[48:, :]
elif 'HBA' in station_name or str(rcu_mode) in ('5', '6', '7', '8'):
selected_dipole_config = {
'intl': GENERIC_INT_201512, 'remote': GENERIC_REMOTE_201512, 'core': GENERIC_CORE_201512
}
selected_dipoles = selected_dipole_config[station_type] + \
np.arange(len(selected_dipole_config[station_type])) * 16
station_pqr = db.hba_dipole_pqr(full_station_name)[selected_dipoles]
else:
raise RuntimeError("Station name did not contain LBA or HBA, could not load antenna positions")
return station_pqr.astype('float32')
|
d796639866421876bc58a7621d37bbe7239da6df
| 3,642,190
|
from typing import List
def hello_world(cities: List[str] = ["Berlin", "Paris"]) -> bool:
"""
Hello world function.
Arguments:
- cities: List of cities in which 'hello world' is posted.
Return:
- success: Whether or not function completed successfully.
"""
try:
[print("Hello {}!".format(c)) for c in cities] # for loop one-liner
return True
except KeyboardInterrupt:
return False
finally:
pass
|
a24f0f47c9b44c97f46524d354fff0ed9a735fe3
| 3,642,191
|
import os
import re
def parse_dir(directory, default_settings, oldest_revision, newest_revision,
rep):
"""Parses bench data from files like bench_r<revision>_<scalar>.
(str, {str, str}, Number, Number) -> {int:[BenchDataPoints]}"""
revision_data_points = {} # {revision : [BenchDataPoints]}
file_list = os.listdir(directory)
file_list.sort()
for bench_file in file_list:
file_name_match = re.match('bench_r(\d+)_(\S+)', bench_file)
if (file_name_match is None):
continue
revision = int(file_name_match.group(1))
scalar_type = file_name_match.group(2)
if (revision < oldest_revision or revision > newest_revision):
continue
file_handle = open(directory + '/' + bench_file, 'r')
if (revision not in revision_data_points):
revision_data_points[revision] = []
default_settings['scalar'] = scalar_type
revision_data_points[revision].extend(
bench_util.parse(default_settings, file_handle, rep))
file_handle.close()
return revision_data_points
|
8a802eec0c528f67bffdfc239079eba3729eb2f3
| 3,642,192
|
import random
def random_samples(traj_obs, expert, num_sample):
"""Randomly sample a subset of states to collect expert feedback.
Args:
traj_obs: observations from a list of trajectories.
expert: an expert policy.
num_sample: the number of samples to collect.
Returns:
new expert data.
"""
expert_data = []
for i in range(len(traj_obs)):
obs = traj_obs[i]
random.shuffle(obs)
new_expert_data = []
chosen = np.random.choice(range(len(obs)),
size=min(num_sample, len(obs)),
replace=False)
for ch in chosen:
state = obs[ch].observation
action_step = expert.action(obs[ch])
action = action_step.action
new_expert_data.append((state, action))
expert_data.extend(new_expert_data)
return expert_data
|
55aa4312c095ce97b8cf2840ff9ca61e393dff63
| 3,642,193
|
import os
def makeFolder(path):
"""Build a folder.
Args:
path (str): Folder path.
Returns:
bool: Creation status.
"""
if(not os.path.isdir(path)):
try:
os.makedirs(path)
except OSError as error:
print("Directory %s can't be created (%s)" % (path, error))
return False
else:
return True
else:
return False
|
4bd1535fb3ffc69f5638b6cfbeaf90a1ccbdf2f9
| 3,642,194
|
def get_p2_vector(img):
"""
Returns a p2 vector.
We calculate the p2 vector by taking the radial mean of
the autocorrelation of the input image.
"""
radvars = []
dimX = img.shape[0]
dimY = img.shape[1]
fftimage = np.fft.fft2(img)
final_image = np.fft.ifft2(fftimage*np.conj(fftimage))
finImg = np.abs(final_image)/(dimX*dimY)
centrdImg = np.fft.fftshift(finImg)
center = [int(dimX/2), int(dimY/2)]
radvar, _ = radial_profile(centrdImg, center, (dimX, dimY))
radvars.append(radvar)
p2_vec = np.array(radvars)
return p2_vec[0]
|
7544751bf268d6eea432e21efe3d3a7703b16c1b
| 3,642,195
|
import os
def start_replica_cmd(builddir, replica_id):
"""
Return a command that starts an skvbc replica when passed to
subprocess.Popen.
Note each arguments is an element in a list.
"""
statusTimerMilli = "500"
viewChangeTimeoutMilli = "10000"
path = os.path.join(builddir, "tests", "simpleKVBC",
"TesterReplica", "skvbc_replica")
return [path,
"-k", KEY_FILE_PREFIX,
"-i", str(replica_id),
"-s", statusTimerMilli,
"-v", viewChangeTimeoutMilli,
"-e", str(True)
]
|
5f30b328dd0e583581310afeb3de0af3bc79c17c
| 3,642,196
|
def multiplex(n, q, **kwargs):
""" Convert one queue into several equivalent Queues
>>> q1, q2, q3 = multiplex(3, in_q)
"""
out_queues = [Queue(**kwargs) for i in range(n)]
def f():
while True:
x = q.get()
for out_q in out_queues:
out_q.put(x)
t = Thread(target=f)
t.daemon = True
t.start()
return out_queues
|
4de6fa4fd495c2b320c4cdf28aa56df4411b7aa9
| 3,642,197
|
def stack(arrays, axis=0):
"""
Join a sequence of arrays along a new axis.
The `axis` parameter specifies the index of the new axis in the dimensions
of the result. For example, if ``axis=0`` it will be the first dimension
and if ``axis=-1`` it will be the last dimension.
.. versionadded:: 1.10.0
Parameters
----------
arrays : sequence of array_like
Each array must have the same shape.
axis : int, optional
The axis in the result array along which the input arrays are stacked.
Returns
-------
stacked : ndarray
The stacked array has one more dimension than the input arrays.
See Also
--------
concatenate : Join a sequence of arrays along an existing axis.
split : Split array into a list of multiple sub-arrays of equal size.
Examples
--------
>>> arrays = [np.random.randn(3, 4) for _ in range(10)]
>>> np.stack(arrays, axis=0).shape
(10, 3, 4)
>>> np.stack(arrays, axis=1).shape
(3, 10, 4)
>>> np.stack(arrays, axis=2).shape
(3, 4, 10)
>>> a = np.array_create.array([1, 2, 3])
>>> b = np.array_create.array([2, 3, 4])
>>> np.stack((a, b))
array_create.array([[1, 2, 3],
[2, 3, 4]])
>>> np.stack((a, b), axis=-1)
array_create.array([[1, 2],
[2, 3],
[3, 4]])
"""
arrays = [array_create.array(arr) for arr in arrays]
if not arrays:
raise ValueError('need at least one array to stack')
shapes = set(arr.shape for arr in arrays)
if len(shapes) != 1:
raise ValueError('all input arrays must have the same shape')
result_ndim = arrays[0].ndim + 1
if not -result_ndim <= axis < result_ndim:
msg = 'axis {0} out of bounds [-{1}, {1})'.format(axis, result_ndim)
raise IndexError(msg)
if axis < 0:
axis += result_ndim
sl = (slice(None),) * axis + (None,)
expanded_arrays = [arr[sl] for arr in arrays]
return concatenate(expanded_arrays, axis=axis)
|
ba8a2b514c32a1dc7a15215e5e26a90f2ace9a26
| 3,642,198
|
import logging
import sys
def get_runs(runs, selected_runs, cmdline):
"""Selects which run(s) to execute based on parts of the command-line.
Will return an iterable of run numbers. Might also fail loudly or exit
after printing the original command-line.
"""
name_map = dict((r['id'], i) for i, r in enumerate(runs) if 'id' in r)
run_list = []
def parse_run(s):
try:
r = int(s)
except ValueError:
logging.critical("Error: Unknown run %s", s)
raise UsageError
if r < 0 or r >= len(runs):
logging.critical("Error: Expected 0 <= run <= %d, got %d",
len(runs) - 1, r)
sys.exit(1)
return r
if selected_runs is None:
run_list = list(irange(len(runs)))
else:
for run_item in selected_runs.split(','):
run_item = run_item.strip()
if run_item in name_map:
run_list.append(name_map[run_item])
continue
sep = run_item.find('-')
if sep == -1:
run_list.append(parse_run(run_item))
else:
if sep > 0:
first = parse_run(run_item[:sep])
else:
first = 0
if sep + 1 < len(run_item):
last = parse_run(run_item[sep + 1:])
else:
last = len(runs) - 1
if last < first:
logging.critical("Error: Last run number should be "
"greater than the first")
sys.exit(1)
run_list.extend(irange(first, last + 1))
# --cmdline without arguments: display the original command-line
if cmdline == []:
print("Original command-lines:")
for run in run_list:
print(' '.join(shell_escape(arg)
for arg in runs[run]['argv']))
sys.exit(0)
return run_list
|
fefeb7ec2e9c11767e2e15856321bf68aaad8a83
| 3,642,199
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.