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import re
import tempfile
from pathlib import Path
async def submit_changesheet(
uploaded_file: UploadFile = File(...),
mdb: MongoDatabase = Depends(get_mongo_db),
user: User = Depends(get_current_active_user),
):
"""
Example changesheet [here](https://github.com/microbiomedata/nmdc-runtime/blob/main/metadata-translation/notebooks/data/changesheet-without-separator3.tsv).
"""
allowed_to_submit = ("dehays", "dwinston")
if user.username not in allowed_to_submit:
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail=(
f"Only users {allowed_to_submit} "
"are allowed to apply changesheets at this time."
),
)
sheet_in = await raw_changesheet_from_uploaded_file(uploaded_file)
df_change = df_from_sheet_in(sheet_in, mdb)
_ = _validate_changesheet(df_change, mdb)
# create object (backed by gridfs). use "gfs0" id shoulder for drs_object access_id.
sheet_id = generate_one_id(mdb, ns="changesheets", shoulder="gfs0")
mdb_fs = GridFS(mdb)
filename = re.sub(r"[^A-Za-z0-9\.\_\-]", "_", sheet_in.name)
PortableFilename(filename) # validates
sheet_text = sheet_in.text
drs_id = local_part(sheet_id)
DrsId(drs_id) # validates
mdb_fs.put(
sheet_text,
_id=drs_id,
filename=filename,
content_type=sheet_in.content_type,
encoding="utf-8",
)
with tempfile.TemporaryDirectory() as save_dir:
filepath = str(Path(save_dir).joinpath(filename))
with open(filepath, "w") as f:
f.write(sheet_text)
object_in = DrsObjectIn(
**drs_metadata_for(
filepath,
base={
"description": f"changesheet submitted by {user.username}",
"access_methods": [{"access_id": drs_id}],
},
)
)
self_uri = f"drs://{HOSTNAME_EXTERNAL}/{drs_id}"
drs_obj_doc = _create_object(
mdb, object_in, mgr_site="nmdc-runtime", drs_id=drs_id, self_uri=self_uri
)
doc_after = mdb.objects.find_one_and_update(
{"id": drs_obj_doc["id"]},
{"$set": {"types": ["metadata-changesheet"]}},
return_document=ReturnDocument.AFTER,
)
return doc_after
|
ebe9306aba0fef88c906c3c584f87e7c783fe9d8
| 3,645,893
|
import json
def get_notes(request, course, page=DEFAULT_PAGE, page_size=DEFAULT_PAGE_SIZE, text=None):
"""
Returns paginated list of notes for the user.
Arguments:
request: HTTP request object
course: Course descriptor
page: requested or default page number
page_size: requested or default page size
text: text to search. If None then return all results for the current logged in user.
Returns:
Paginated dictionary with these key:
start: start of the current page
current_page: current page number
next: url for next page
previous: url for previous page
count: total number of notes available for the sent query
num_pages: number of pages available
results: list with notes info dictionary. each item in this list will be a dict
"""
path = 'search' if text else 'annotations'
response = send_request(request.user, course.id, page, page_size, path, text)
try:
collection = json.loads(response.content.decode('utf-8'))
except ValueError:
log.error("Invalid JSON response received from notes api: response_content=%s", response.content)
raise EdxNotesParseError(_("Invalid JSON response received from notes api.")) # lint-amnesty, pylint: disable=raise-missing-from
# Verify response dict structure
expected_keys = ['total', 'rows', 'num_pages', 'start', 'next', 'previous', 'current_page']
keys = list(collection.keys())
if not keys or not all(key in expected_keys for key in keys):
log.error("Incorrect data received from notes api: collection_data=%s", str(collection))
raise EdxNotesParseError(_("Incorrect data received from notes api."))
filtered_results = preprocess_collection(request.user, course, collection['rows'])
# Notes API is called from:
# 1. The annotatorjs in courseware. It expects these attributes to be named "total" and "rows".
# 2. The Notes tab Javascript proxied through LMS. It expects these attributes to be called "count" and "results".
collection['count'] = collection['total']
del collection['total']
collection['results'] = filtered_results
del collection['rows']
collection['next'], collection['previous'] = construct_pagination_urls(
request,
course.id,
collection['next'],
collection['previous']
)
return collection
|
3256cacd845cf2fd07027cf6b3f2547a59cefd0f
| 3,645,894
|
import numpy
def convert_hdf_to_gaintable(f):
""" Convert HDF root to a GainTable
:param f:
:return:
"""
assert f.attrs['ARL_data_model'] == "GainTable", "Not a GainTable"
receptor_frame = ReceptorFrame(f.attrs['receptor_frame'])
frequency = numpy.array(f.attrs['frequency'])
data = numpy.array(f['data'])
gt = GainTable(data=data, receptor_frame=receptor_frame, frequency=frequency)
return gt
|
dd816eb0730b0f9993efe07c2b28db692ac6a06e
| 3,645,895
|
import pathlib
def list_files(directory):
"""Returns all files in a given directory
"""
return [f for f in pathlib.Path(directory).iterdir() if f.is_file() and not f.name.startswith('.')]
|
a8c5fea794198c17c2aff41a1a07009984a8e61f
| 3,645,896
|
def condition_conjunction(conditions):
"""Do conjuction of conditions if there are more than one, otherwise just
return the single condition."""
if not conditions:
return None
elif len(conditions) == 1:
return conditions[0]
else:
return sql.expression.and_(*conditions)
|
acf26bd9b8e47d27ad83815be70216db0e4ad091
| 3,645,897
|
def get_claimed_referrals(char):
""" Return how many claimed referrals this character has. """
return db((db.referral.referrer==char) & (db.referral.claimed==True)).count()
|
5820cdd21cbb77f6a43537ae18dc227ad4fec1b8
| 3,645,898
|
def groupsplit(X, y, valsplit):
"""
Used to split the dataset by datapoint_id into train and test sets.
The data is split to ensure all datapoints for each datapoint_id occurs completely in the respective dataset split.
Note that where there is validation set, data is split with 80% for training and 20% for test set.
Otherwise, the test set is split further with 60% as test set and 40% as validation set.
Args:
X: data excluding the target_variable
y: target variable with datapoint_id
valsplit: flag to indicate if there is a dataframe for the validation set. Accepeted values are "yes" or "no"
Returns:
X_train: X trainset
y_train: y trainset
X_test: X testset
y_test_complete: Dataframe containing the target variable with corresponding datapointid
"""
logger.info("groupsplit with valsplit: %s", valsplit)
if valsplit == 'yes':
gs = GroupShuffleSplit(n_splits=2, train_size=.7, random_state=42)
else:
gs = GroupShuffleSplit(n_splits=2, test_size=.4, random_state=42)
train_ix, test_ix = next(gs.split(X, y, groups=X.datapoint_id))
X_train = X.loc[train_ix]
y_train = y.loc[train_ix]
X_test = X.loc[test_ix]
y_test_complete = y.loc[test_ix]
return X_train, y_train, X_test, y_test_complete
|
e8ba393270a32e2464c30409a13b2c5e9528afdd
| 3,645,899
|
import urllib
def application(environ, start_response):
"""
make Passenger interpret PATH_INFO the same way that the WSGI standard
does
"""
environ["PATH_INFO"] = urllib.parse.unquote(environ["PATH_INFO"])
return app.app(environ, start_response)
|
3de57b31206a374da0378788f20e7bd8b1eca9af
| 3,645,901
|
from railrl.torch.pytorch_util import set_gpu_mode
import random
def run_experiment_here(
experiment_function,
variant=None,
exp_id=0,
seed=0,
use_gpu=True,
# Logger params:
exp_prefix="default",
snapshot_mode='last',
snapshot_gap=1,
git_infos=None,
script_name=None,
logger=default_logger,
trial_dir_suffix=None,
randomize_seed=False,
**setup_logger_kwargs
):
"""
Run an experiment locally without any serialization.
:param experiment_function: Function. `variant` will be passed in as its
only argument.
:param exp_prefix: Experiment prefix for the save file.
:param variant: Dictionary passed in to `experiment_function`.
:param exp_id: Experiment ID. Should be unique across all
experiments. Note that one experiment may correspond to multiple seeds,.
:param seed: Seed used for this experiment.
:param use_gpu: Run with GPU. By default False.
:param script_name: Name of the running script
:param log_dir: If set, set the log directory to this. Otherwise,
the directory will be auto-generated based on the exp_prefix.
:return:
"""
if variant is None:
variant = {}
variant['exp_id'] = str(exp_id)
if randomize_seed or (seed is None and 'seed' not in variant):
seed = random.randint(0, 100000)
variant['seed'] = str(seed)
reset_execution_environment(logger=logger)
actual_log_dir = setup_logger(
exp_prefix=exp_prefix,
variant=variant,
exp_id=exp_id,
seed=seed,
snapshot_mode=snapshot_mode,
snapshot_gap=snapshot_gap,
git_infos=git_infos,
script_name=script_name,
logger=logger,
trial_dir_suffix=trial_dir_suffix,
**setup_logger_kwargs
)
set_seed(seed)
set_gpu_mode(use_gpu)
run_experiment_here_kwargs = dict(
variant=variant,
exp_id=exp_id,
seed=seed,
use_gpu=use_gpu,
exp_prefix=exp_prefix,
snapshot_mode=snapshot_mode,
snapshot_gap=snapshot_gap,
git_infos=git_infos,
script_name=script_name,
**setup_logger_kwargs
)
save_experiment_data(
dict(
run_experiment_here_kwargs=run_experiment_here_kwargs
),
actual_log_dir
)
return experiment_function(variant)
|
ee8b0f727027d8dcee804565606a7f82f2c77ca9
| 3,645,902
|
from typing import Tuple
def normalize_chunks(chunks: Tuple[Tuple[int, int]]) -> Tuple[Tuple[int, int]]:
"""
Minimize the amount of chunks needed to describe a smaller portion of a file.
:param chunks: A tuple with (start, end,) offsets
:return: A tuple containing as few as possible (start, end,) offsets
"""
out = []
start1, end1 = chunks[0]
if len(chunks) > 1:
for start2, end2 in chunks[1:]:
if start2 == end1:
end1 = end2
else:
out.append((start1, end1))
start1, end1 = start2, end2
out.append((start1, end1))
return tuple(out)
|
d49d1abed0573a86e0eeee5d2e5ed2e129f3274e
| 3,645,903
|
def learning_rate_schedule(adjusted_learning_rate, lr_warmup_init,
lr_warmup_step, first_lr_drop_step,
second_lr_drop_step, global_step):
"""Handles linear scaling rule, gradual warmup, and LR decay."""
# lr_warmup_init is the starting learning rate; the learning rate is linearly
# scaled up to the full learning rate after `lr_warmup_steps` before decaying.
linear_warmup = (lr_warmup_init +
(tf.cast(global_step, dtype=tf.float32) / lr_warmup_step *
(adjusted_learning_rate - lr_warmup_init)))
learning_rate = tf.where(global_step < lr_warmup_step,
linear_warmup, adjusted_learning_rate)
lr_schedule = [[1.0, lr_warmup_step],
[0.1, first_lr_drop_step],
[0.01, second_lr_drop_step]]
for mult, start_global_step in lr_schedule:
learning_rate = tf.where(global_step < start_global_step, learning_rate,
adjusted_learning_rate * mult)
return learning_rate
|
26021c7cbdb264ddc84fad94d4a01b51913f3a72
| 3,645,904
|
from typing import List
import logging
def set_power_state_server(power_state: ServerPowerState) -> List[float]:
"""Record the current power limit and set power limit using nvidia-smi."""
# Record current power limits.
if power_state.power_limit:
cmd = "nvidia-smi --query-gpu=power.limit --format=csv,noheader,nounits"
logging.info(f"Getting current GPU power limits: {cmd}")
output = run_command(cmd, get_output=True, tee=False)
current_limits = [float(line) for line in output]
# Set power limit to the specified value.
cmd = f"sudo nvidia-smi -pl {power_state.power_limit}"
logging.info(f"Setting current GPU power limits: {cmd}")
run_command(cmd)
if power_state.cpu_freq:
set_cpufreq(power_state.cpu_freq)
return ServerPowerState(current_limits, None)
|
8a9ac60dbd58dedf5f39386a7233b88b7cc5aa79
| 3,645,905
|
from typing import Union
def score_normalization(extracted_score: Union[str, None]):
"""
Sofa score normalization.
If available, returns the integer value of the SOFA score.
"""
score_range = list(range(0, 30))
if (extracted_score is not None) and (int(extracted_score) in score_range):
return int(extracted_score)
|
74501e9351296037ecc90ae647155e3c6b76ae01
| 3,645,906
|
def wrap(wrapping_key_public, plaintext):
"""
RSA-OAEP key wrapping.
Args:
wrapping_key_public: The public key of the RSA wrapping key
plaintext: The plaintext key to wrap
"""
rsa_cipher = PKCS1_OAEP.new(
key=wrapping_key_public, hashAlgo=SHA256, mgfunc=lambda x, y: pss.MGF1(x, y, SHA1))
return rsa_cipher.encrypt(plaintext)
|
171074a46440184138ccb1684754f328afc50efe
| 3,645,908
|
def compute_horizontal_vessel_purchase_cost(W, D, F_M):
"""
Return the purchase cost [Cp; in USD] of a horizontal vessel,
including thes cost of platforms and ladders.
Parameters
----------
W : float
Weight [lb].
D : float
Diameter [ft].
F_M : float
Vessel material factor.
Notes
-----
The purchase cost is given by [1]_. See source code for details.
The purchase cost is scaled according to BioSTEAM's Chemical
Plant Cost Index, `biosteam.CE`.
References
----------
.. [1] Seider, W. D., Lewin, D. R., Seader, J. D., Widagdo, S., Gani, R.,
& Ng, M. K. (2017). Product and Process Design Principles. Wiley.
Cost Accounting and Capital Cost Estimation (Chapter 16)
"""
# C_v: Vessel cost
# C_pl: Platforms and ladders cost
C_v = exp(5.6336 - 0.4599*ln(W) + 0.00582*ln(W)**2)
C_pl = 2275*D**0.20294
return bst.CE/567 * (F_M * C_v + C_pl)
|
22d38ffc38dddb992d2fd7b2c20c3dc1d0ddb53d
| 3,645,910
|
def format_dev_sub_dev_id(pciIdPair):
"""
pciIdPair (int pci device id, int pci sub device id or None)
"""
if pciIdPair[1] is None:
return "(0x%08X, None)" % pciIdPair[0]
return "(0x%08X, 0x%08X)" % pciIdPair
|
fded71eee57f4fac60175bfb015845bf1eba58f7
| 3,645,911
|
def mychats():
"""
Show Chats where I can write
:return:
{
error: 0,
chats: [...Chat]
}
"""
result = {
'error': 0,
'chats': []
}
if 'user_id' in session:
chats_rows = query_db('SELECT * FROM chats WHERE user1_id = ? OR user2_id = ?', [session['user_id'], session['user_id']])
result['chats'] = chats_rows
# for chat in query_db('select * from chats'):
# print(chat['name'])
return result
|
4af7cd34fb8649ed10723b258e7a864e3e12edc2
| 3,645,912
|
def polynom_prmzt(x, t, order):
"""
Polynomial (deterministic) parameterization of fast variables (Y).
NB: Only valid for system settings of Wilks'2005.
Note: In order to observe an improvement in DA performance w
higher orders, the EnKF must be reasonably tuned with
There is very little improvement gained above order=1.
"""
if order == 4:
# From Wilks
d = 0.262 + 1.45*x - 0.0121*x**2 - 0.00713*x**3 + 0.000296*x**4
elif order == 3:
# From Arnold
d = 0.341 + 1.30*x - 0.0136*x**2 - 0.00235*x**3
elif order == 1:
# From me -- see AdInf/illust_parameterizations.py
d = 0.74 + 0.82*x
elif order == 0:
# From me -- see AdInf/illust_parameterizations.py
d = 3.82
elif order == -1:
# Leave as dxdt_trunc
d = 0
else:
raise NotImplementedError
return d
|
80d3f9563c5f8a04a65de7d2d22f5d49d35c71fe
| 3,645,913
|
def decode(s):
"""
Deserialize an EDS object from an EDS string.
"""
lexer = _EDSLexer.lex(s.splitlines())
return _decode_eds(lexer)
|
3c7eb8ac7e570aeb1297b052e35c804dd27b0f49
| 3,645,915
|
def allowed_file(filename):
""" Verifies if file extension is compatible """
return '.' in filename and \
filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
|
75e0047eff0787e33f687e7a9b689ad8661b7501
| 3,645,917
|
def clap_convert(txt):
"""convert string of clap values on medium to actualy number
Args:
txt (str): claps values
Returns:
number on claps (int)
"""
# Medium annotation
if txt[-1] == "K":
output = int(float(txt[:-1]) * 1000)
return output
else:
return int(txt)
|
253e0e2be4f37f1994637bbfc80edfc5d72bc4e5
| 3,645,918
|
import io
def write_phase1_capsummary(inst, isStringIO=True):
"""
Write out a multiweek summary of capacity, demand, understaffing.
:param inst: Model instance
:param isStringIO: True (default) to return StringIO object, False to return string
:return: capacity summary as StringIO object or a string.
"""
param = 'period,day,week,dmd,cap,us1,us2,ustot\n'
rows = [(i, j, w,
inst.dmd_staff[i, j, w],
inst.cov[i, j, w].value,
inst.under1[i, j, w].value,
inst.under2[i, j, w].value,
inst.under1[i, j, w].value + inst.under2[i, j, w].value)
for i in inst.PERIODS
for j in inst.DAYS
for w in inst.WEEKS
]
for row in rows:
row = [str(r) for r in row]
data_row = ','.join(row)
data_row += '\n'
param += data_row
if isStringIO:
param_out = io.StringIO()
param_out.write(param)
return param_out.getvalue()
else:
return param
|
6d6e7d083693b74ea27e7f10cec4899735f32541
| 3,645,919
|
def glorot_uniform(shape):
"""
:param shape: tuple with the shape of the wanted output (filters_amount, depth, height, width)
:return: array (it's shape=param shape) with initialized values using 'glorot uniform' initializer
"""
fan_in, fan_out = _calc_fans(shape)
scale = 1. / ((fan_in + fan_out) / 2.)
limit = np.sqrt(3.0 * scale)
return np.random.uniform(low=-limit, high=limit, size=shape)
|
0cec12b0342db827286248a722b32852cab2bdad
| 3,645,920
|
import warnings
def second_order_moments(n_components, e2, m1, alpha0):
"""Second-Order Moments
To prevent creating 2nd order moments explicitly, we construct its
decomposition with `n_components`. check reference [?] section 5.2
for details.
Parameters
----------
n_components: int
Number of components
e2: sparse matrix, shape=(n_features, n_features)
Expectation of word pairs. e2[i, j] is the expectation of word `i`
and `j` in the same document.
m1: array, shape=(n_features,)
Expectation of each words.
alpha0: double
Sum of topic topic concentration parameter
Returns
-------
m2_vals : array, shape=(n_components,)
eigen values of sencond-order moments
m2_vecs : array, shape=(n_features, n_components)
eigen values of sencond-order moments
"""
# eigen values and vectors of E2
n_features = e2.shape[0]
#print("%d ; %d" % (n_features, n_components))
if n_components == n_features:
# run full svd, convert e2 to dense array first
e2_vecs, e2_vals, _ = LA.svd(e2.toarray())
else:
#e2_vals, e2_vecs = sp.linalg.eigsh(e2, k=n_components, which='LM')
e2_vecs, e2_vals, _ = sp.linalg.svds(e2, k=n_components, which='LM',
return_singular_vectors=True)
e2_vals *= (alpha0 + 1.)
m1_p = np.dot(e2_vecs.T, m1)
# section 5.2 part 1.
m2_p = (-1. * alpha0) * (m1_p * m1_p[:, np.newaxis])
m2_p[np.diag_indices_from(m2_p)] += e2_vals
# section 5.2 part 1.
# eigen values and vectors of M2 prime
try:
m2p_vecs, m2p_vals, _ = LA.svd(m2_p)
m2_vals = m2p_vals
m2_vecs = np.dot(e2_vecs, m2p_vecs)
except LA.LinAlgError:
# In order to pass `check_estimator` test.
# convert this error to warnings.
warnings.warn("SVD in second_order_moments did not converge. "
"the algorithm will not work.",
ConvergenceWarning)
m2_vals = np.ones(m2_p.shape[0])
m2_vecs = m2_p
return (m2_vals, m2_vecs)
|
4b2ac9d43352d856875d86cd1975ec59ac5664c8
| 3,645,922
|
def remove_names(df: pd.DataFrame) -> pd.DataFrame:
"""Convert personal names to numerical values."""
df = df.reset_index()
df.drop(columns='Name', inplace=True)
return df
|
9dab1803a153d5effd2e08b6e6ff5df30fee8407
| 3,645,924
|
import torch
def handle_epoch_metrics(step_metrics, epoch_labels, epoch_predictions):
"""
Function that handles the metrics per epoch.
Inputs:
step_metrics - Dictionary containing the results of the steps of an epoch
epoch_labels - List of labels from the different steps
epoch_predictions - List of predictions from the different steps
Outputs:
epoch_merics - Dictionary containing the averaged results of an epoch
"""
# compute the loss
loss = torch.mean(torch.stack(step_metrics['losses'], dim=0), dim=0)
loss = round(loss.item(), 4)
# compute the accuracy and f1
accuracy, f1 = compute_accuracy_f1(step_metrics['predictions'], step_metrics['labels'])
# create a new epoch dictionary
epoch_metrics = {'loss': loss, 'accuracy': accuracy, 'f1': f1}
# return the epoch dictionary
return epoch_metrics
|
a1d0180095535eec641258dd921c90808aa6858f
| 3,645,925
|
def project_disk_sed(bulge_sed, disk_sed):
"""Project the disk SED onto the space where it is bluer
For the majority of observed galaxies, it appears that
the difference between the bulge and the disk SEDs is
roughly monotonic, making the disk bluer.
This projection operator projects colors that are redder onto
the same difference in color as the previous wavelength,
similar to the way monotonicity works for the morphological
`S` matrix of the model.
While a single iteration of this model is unlikely to yield
results that are as good as those in `project_disk_sed_mean`,
after many iterations it is expected to converge to a better value.
"""
new_sed = disk_sed.copy()
diff = bulge_sed - disk_sed
for s in range(1, len(diff)-1):
if diff[s]<diff[s-1]:
new_sed[s] = new_sed[s] + diff[s-1]
diff[s] = diff[s-1]
return new_sed
|
5faf8f7d8d0d780f61586f7fae39f4ba04d3752d
| 3,645,926
|
def qlog_numpy(q):
"""
Applies logarithm map to q
:param q: (4,)
:return: (3,)
"""
if all(q[1:] == 0):
q = np.zeros(3)
else:
q = np.arccos(q[0]) * q[1:] / np.linalg.norm(q[1:])
return q
|
82cf0ff2054c02e4cc3dc3a6500b1c8a0e3eb870
| 3,645,928
|
def get_ML_features(df: pd.DataFrame, protease: str='trypsin', **kwargs) -> pd.DataFrame:
"""
Uses the specified score in df to filter psms and to apply the fdr_level threshold.
Args:
df (pd.DataFrame): psms table of search results from alphapept.
protease (str, optional): string specifying the protease that was used for proteolytic digestion. Defaults to 'trypsin'.
Returns:
pd.DataFrame: df including additional scores for subsequent ML.
"""
df['decoy'] = df['sequence'].str[-1].str.islower()
df['abs_delta_m_ppm'] = np.abs(df['delta_m_ppm'])
df['naked_sequence'] = df['sequence'].apply(lambda x: ''.join([_ for _ in x if _.isupper()]))
df['n_AA']= df['naked_sequence'].str.len()
df['matched_ion_fraction'] = df['hits']/(2*df['n_AA'])
df['n_missed'] = df['naked_sequence'].apply(lambda x: count_missed_cleavages(x, protease))
df['n_internal'] = df['naked_sequence'].apply(lambda x: count_internal_cleavages(x, protease))
df['x_tandem'] = get_x_tandem_score(df)
return df
|
4ac4202fa5c86b78b1bda1a2b96d5ed4b8552b4f
| 3,645,929
|
def revcomp(sequence):
"""
Find reverse complementary sequence
:param sequence: The RNA sequence in string form
:return: The reverse complement sequence in string form
"""
complement = {"A": "U", "U": "A", "C": "G", "G": "C", "N": "N"}
revcompseq = ""
sequence_list = list(sequence)
sequence_list.reverse()
for letter in sequence_list:
revcompseq += complement[letter.upper()]
return revcompseq
|
c66b9ad967e612fa97f18bb2932e7eb4bbee8245
| 3,645,930
|
def J(X, mean, r):
"""K-meansの目的関数（最小化を目指す）"""
summation = 0.0
for n in range(len(X)):
temp = 0.0
for k in range(K):
temp += r[n, k] * np.linalg.norm(X[n] - mean[k]) ** 2
summation += temp
return summation
|
1d2dd241fc30cb5897b0224285c5c7f2f2fec675
| 3,645,931
|
import time
def timesince():
"""
Get the amount of time since 00:00 on 1 January 1970,
the raw date before formatting it.
"""
return time.time()
|
7e6944d74172947c4ac990c0fa993524ab865e18
| 3,645,933
|
def gencoords_outside(N, d, rad=None, truncmask=False, trunctype='circ'):
""" generate coordinates of all points in an NxN..xN grid with d dimensions
coords in each dimension are [-N/2, N/2)
N should be even"""
if not truncmask:
_, truncc, _ = gencoords_outside(N, d, rad, True)
return truncc
c = geometry.gencoords_base(N, d)
if rad is not None:
if trunctype == 'circ':
r2 = np.sum(c**2, axis=1)
trunkmask = r2 > (rad*N/2.0)**2
elif trunctype == 'square':
r = np.max(np.abs(c), axis=1)
trunkmask = r > (rad*N/2.0)
truncc = c[trunkmask, :]
else:
trunkmask = np.ones((c.shape[0],), dtype=np.bool8)
truncc = c
return c, truncc, trunkmask
|
0b4f3db165cb495e5d540412cb77bd36e8a42c62
| 3,645,934
|
def map_orientation(cur_orientation, cur_count):
""" . . . . . x
. . . . . x
. . . . . x
. . . . . x
. . . . . x
. . . . . x
"""
right_edge = 34905131040
""" . . . . . .
. . . . . .
. . . . . .
. . . . . .
. . . . . .
x x x x x x
"""
bottom_edge = 67645734912
""" we will check if each position of the game peice is valid
by investigating if it touches the right edge or the bottom edge
using a logica AND (&) operation. The & will be 0 if there is
no overlap and <> 0 if there is
Pass in peices positioned in the upper left corner so that this
check can walk right and down to checkk all conditions
"""
room_to_move_right = True
room_to_move_down = True
safe_down = True
while safe_down:
room_to_move_right = True
safe_right = True
row_start = cur_orientation
while safe_right:
peice_orientation_list[cur_count] = cur_orientation
cur_count += 1
""" moving piece right 1 bit is the same as multiplying by 2^1
. x . . . .
x x x . . . = 450
. . x . . .
. x x x . . = 900
"""
if room_to_move_right:
cur_orientation = cur_orientation << 1
room_to_move_right = ((cur_orientation & right_edge) == 0)
else:
safe_right = False
""" moving down is the same as shifting right 6 times or multiplying by 2^6, aka 64
. x . . . .
x x x . . . = 450
. x . . . .
x x x . . . = 28,800
"""
if room_to_move_down:
cur_orientation = row_start << 6
room_to_move_down = ((cur_orientation & bottom_edge) == 0)
else:
safe_down = False
return cur_count
|
5fa5e0c386da56cab336f33e560bf9591814060c
| 3,645,935
|
import ctypes
def glGetShaderInfoLog( baseOperation, obj ):
"""Retrieve the shader's error messages as a Python string
returns string which is '' if no message
"""
target = GLsizei()
glGetShaderiv(obj, GL_INFO_LOG_LENGTH,target)
length = target.value
if length > 0:
log = ctypes.create_string_buffer(length)
baseOperation(obj, length, None, log)
return log.value.strip(_NULL_8_BYTE) # null-termination
return ''
|
0173aa2cbeac8c8b2cb9072d0d56584285af2e0d
| 3,645,936
|
def _CheckGrdTranslations(grd_file, grd_lines, wanted_locales):
"""Check all <file> elements that correspond to an .xtb output file.
Args:
grd_file: Input .grd file path.
grd_lines: List of input .grd lines.
wanted_locales: set of wanted Chromium locale names.
Returns:
List of error message strings. Empty on success.
"""
wanted_locales = wanted_locales - set([_DEFAULT_LOCALE])
intervals = _BuildIntervalList(grd_lines, _IsTranslationGrdOutputLine)
errors = []
for start, end in intervals:
errors += _CheckGrdElementRangeLang(grd_lines, start, end, wanted_locales)
errors += _CheckGrdTranslationElementRange(grd_lines, start, end,
wanted_locales)
return errors
|
49b73187cd2ac3c7b9796a8a139d89f9a74c91a3
| 3,645,937
|
def choose_diverging_palette(as_cmap=False):
"""Launch an interactive widget to choose a diverging color palette.
This corresponds with the :func:`diverging_palette` function. This kind
of palette is good for data that range between interesting low values
and interesting high values with a meaningful midpoint. (For example,
change scores relative to some baseline value).
Requires IPython 2+ and must be used in the notebook.
Parameters
----------
as_cmap : bool
If True, the return value is a matplotlib colormap rather than a
list of discrete colors.
Returns
-------
pal or cmap : list of colors or matplotlib colormap
Object that can be passed to plotting functions.
See Also
--------
diverging_palette : Create a diverging color palette or colormap.
choose_colorbrewer_palette : Interactively choose palettes from the
colorbrewer set, including diverging palettes.
"""
pal = []
if as_cmap:
cmap = _init_mutable_colormap()
@interact
def choose_diverging_palette(
h_neg=IntSlider(min=0,
max=359,
value=220),
h_pos=IntSlider(min=0,
max=359,
value=10),
s=IntSlider(min=0, max=99, value=74),
l=IntSlider(min=0, max=99, value=50), # noqa: E741
sep=IntSlider(min=1, max=50, value=10),
n=(2, 16),
center=["light", "dark"]
):
if as_cmap:
colors = diverging_palette(h_neg, h_pos, s, l, sep, 256, center)
_update_lut(cmap, colors)
_show_cmap(cmap)
else:
pal[:] = diverging_palette(h_neg, h_pos, s, l, sep, n, center)
palplot(pal)
if as_cmap:
return cmap
return pal
|
0c2ffc8710a56e643e6c4ccdd453bd00cc59e6a2
| 3,645,938
|
def get_lm_model(args, device, config):
"""Get language model(based on GPT-2) used for sequence prediction."""
ninp = config["ninp"]
nhead = config["nhead"]
initrange = config["initrange"]
dropout = config["dropout"]
vocab_size = config["vocab_size"]
nhid = config["nhid"]
ndecoder = config["num_decoder_layers"]
if args.ssd_offload:
return transformer_lm.TransformerLM(vocab_size, ninp, nhead, nhid, dropout, initrange, ndecoder)
else:
return transformer_lm.TransformerLM(vocab_size, ninp, nhead, nhid, dropout, initrange, ndecoder).to(device)
|
071f43b9537ca8024f980271c64750e7afae864e
| 3,645,939
|
def get_meminfo():
"""
Get and format the content of /proc/meminfo
"""
buf = open('/proc/meminfo').read()
buf = ','.join([v.replace(' ', '') for v in
buf.split('\n') if v])
return buf
|
47a0d57d1b8c90b4907fab8e39154b8e4ad5b7ee
| 3,645,940
|
def effective_area(true_energy, reco_energy, simu_area):
"""
Compute the effective area from a list of simulated energy and reconstructed energy
Parameters
----------
true_energy: 1d numpy array
reco_energy: 1d numpy array
simu_area: float - area on which events are simulated
Returns
-------
float = effective area
"""
return simu_area * len(reco_energy) / len(true_energy)
|
b17efa390a1ae14bb8ecb959740bad8c391b1d2e
| 3,645,941
|
def execute(queries, arglists, fetchone=False):
"""Execute multiple queries to the sqlite3 jobtracker database.
All queries will be executed as a single transaction.
Return the result of the last query, or the ID of the last
INSERT, whichever is applicaple.
Inputs:
queries: A list of queries to be execute.
arglists: A list (same length as queries).
Each entry contains the paramters to be
substituted into the corresponding query.
fetchone: If True, fetch and return only a single row.
Otherwise, fetch and return all rows.
(Only applies for SELECT statements.
Default: fetch all rows).
Outputs:
results: Single row, or list of rows (for SELECT statements),
depending on 'fetchone'. Or, the ID of the last
entry INSERT'ed (for INSERT statements).
"""
not_connected = True
count = 0
while not_connected:
try:
db_conn = sqlite3.connect(config.background.jobtracker_db,timeout=40.0)
db_conn.isolation_level = 'DEFERRED'
db_conn.row_factory = sqlite3.Row
db_cur = db_conn.cursor()
for q, args in zip(queries, arglists):
db_cur.execute(q, args)
db_conn.commit()
if db_cur.lastrowid:
results = db_cur.lastrowid
else:
if fetchone:
results = db_cur.fetchone()
else:
results = db_cur.fetchall()
db_conn.close()
not_connected = False
except sqlite3.OperationalError, e:
try:
db_conn.rollback()
db_conn.close()
except NameError:
# Connection wasn't established, 'db_conn' is not defined.
pass
if (count % 60) == 0:
print "Couldn't connect to DB for %d seconds. Will continue trying. " \
"Error message: %s" % (count, str(e))
time.sleep(1)
count+=1
return results
|
a64e12262150514dbc5e6e7f4c193481ab8162aa
| 3,645,942
|
def physical_cpu_mhz(vir_connection):
""" Get the CPU frequency in MHz using libvirt.
:param vir_connection: A libvirt connection object.
:type vir_connection: virConnect
:return: The CPU frequency in MHz.
:rtype: int
"""
return vir_connection.getInfo()[3]
|
f6a404a6d531940fbc762f493e90355e2fc78690
| 3,645,943
|
def addstream(bot, input):
"""Add a stream from the notify list"""
if not input.admin: return False
if not input.group(2): return
stream = input.group(2).lower()
if not stream in bot.config.streams:
bot.config.set_add('streams', stream)
bot.reply("Added {0} to stream list".format(stream))
else:
bot.reply("{0} is already in the stream list".format(stream))
|
48465633ea58968efca31231eb5e1a47a537c979
| 3,645,944
|
def get_author(search):
"""
Queries google scholar to find an author given a
search string. If != 0 results are found it gives an error
"""
authors = list(scholarly.search_author(search))
if len(authors) > 1:
raise ValueError(f'Found >1 authors with search string: {searc}, try something more specifc')
elif not authors:
raise ValueError(f'Could not find authors with search string: {search}')
return authors[0].fill(sections=['basics', 'indices', 'publications'])
|
8fffd75f588194db0707ddd7249823fb73324549
| 3,645,945
|
import torch
def _named_tensor_generic_operation(
tensor: torch.Tensor,
tensor_ops_pre: callable = dummy,
tensor_ops_post: callable = dummy,
name_ops: callable = dummy) -> torch.Tensor:
"""
generic base function used by others
First store the names
Args:
tensor (): the named tensor to work on
tensor_ops_pre (): the operation before the name is removed
tensor_ops_post (): the operation after the name is removed that act on
the tensor
name_ops (): the operation to act on names
Returns:
"""
# Save the names in names_old and then remove the names from the tensor
tensor = tensor_ops_pre(tensor)
names_old = tensor.names
tensor = tensor.rename(None)
# operations
names_new = name_ops(names_old) # modify the names
tensor = tensor_ops_post(tensor) # change the tensor accordingly
return tensor.refine_names(*names_new)
|
8a343f2ab2c4aeaebcf64e8fc5e75cb3d8776241
| 3,645,946
|
def normalize_address_components(parsed_addr):
# type: (MutableMapping[str, str]) -> MutableMapping[str, str]
"""Normalize parsed sections of address as appropriate.
Processes parsed address through subsets of normalization rules.
:param parsed_addr: address parsed into ordereddict per usaddress.
:type parsed_addr:Mapping
:return: parsed_addr with normalization processing applied to elements.
:rtype: dict
"""
parsed_addr = normalize_numbered_streets(parsed_addr)
parsed_addr = normalize_directionals(parsed_addr)
parsed_addr = normalize_street_types(parsed_addr)
parsed_addr = normalize_occupancy_type(parsed_addr)
return parsed_addr
|
48730c85ee7930b27260b97a6ad876bcecf1b5cc
| 3,645,947
|
def is_key_in_store(loc, key):
"""
A quick check to determine whether the :class:`pandas.HDFStore`
has datA for ``key``
:ARGS:
loc: :class:`string` of path to :class:`pandas.HDFStore`
key: :class:`string` of the ticker to check if currently
available
:RETURNS:
whether ``key`` is currently a part of the data set
"""
try:
store = pandas.HDFStore(path = loc, mode = 'r')
except IOError:
print loc + " is not a valid path to an HDFStore Object"
return
store_keys = store.keys()
store.close()
return key in map(lambda x: x.strip('/'), store_keys )
|
273bd534daa0f70831e77da88808033e4f1683eb
| 3,645,949
|
def transform_rows_nonlinear06(data, **kwargs):
"""
Nonlinear row transformation 06. 12 simulated data sources; Functions: 1.0, 0.5*(x+1)^2, sin(pi*x), sin(2*pi*x), cos(pi*x), cos(2*pi*x), x^5, exp2, log10(x-x.min()), boxcox(2), boxcox(4), boxcox(6).
"""
sources_transformers = [
1.0,
lambda x: 0.5 * np.power((x+1), 2),
lambda x: np.sin(np.pi * x),
lambda x: np.sin(2.0 * np.pi * x),
lambda x: np.cos(np.pi * x),
lambda x: np.cos(2.0 * np.pi * x),
lambda x: np.power(x, 5),
lambda x: np.exp2(x),
lambda x: np.log10(x + (-1.0 * x.min()) + 0.01),
lambda x: boxcox(x + (-1.0 * x.min()) + 0.01, 2.00),
lambda x: boxcox(x + (-1.0 * x.min()) + 0.01, 4.00),
lambda x: boxcox(x + (-1.0 * x.min()) + 0.01, 6.00),
]
return _generic_data_transformation(data, sources_transformers, **kwargs)
|
dd51c838d9721fe310463fa8a0cdb0505e9c4f0f
| 3,645,950
|
def catch_parameter(opt):
"""Change the captured parameters names"""
switch = {'-h': 'help', '-o': 'one_timestamp', '-a': 'activity',
'-f': 'file_name', '-i': 'imp', '-l': 'lstm_act',
'-d': 'dense_act', '-p': 'optim', '-n': 'norm_method',
'-m': 'model_type', '-z': 'n_size', '-y': 'l_size',
'-c': 'folder', '-b': 'model_file', '-x': 'is_single_exec',
'-t': 'max_trace_size', '-e': 'splits', '-g': 'sub_group'}
try:
return switch[opt]
except:
raise Exception('Invalid option ' + opt)
|
ad3a25e3786b657947893f96a76e80f17eb3b0f0
| 3,645,951
|
def get_mgr_pods(mgr_label=constants.MGR_APP_LABEL, namespace=None):
"""
Fetches info about mgr pods in the cluster
Args:
mgr_label (str): label associated with mgr pods
(default: defaults.MGR_APP_LABEL)
namespace (str): Namespace in which ceph cluster lives
(default: defaults.ROOK_CLUSTER_NAMESPACE)
Returns:
list : of mgr pod objects
"""
namespace = namespace or config.ENV_DATA['cluster_namespace']
mgrs = get_pods_having_label(mgr_label, namespace)
mgr_pods = [Pod(**mgr) for mgr in mgrs]
return mgr_pods
|
8079d291d5e2b996547ecd615fbc00a8c70aa4e9
| 3,645,954
|
from typing import Union
def normalise_architecture(architecture: Union[str, int]):
"""Convert any valid architecture alias to either 'x86_64' or 'i686'.
Raise an error for invalid input.
"""
for (true_name, aliases) in architecture_aliases.items():
if architecture in aliases:
return true_name
raise ValueError(
f"Invalid architecture {repr(architecture)}. "
f"Legal 64 bit values are:\n {architecture_aliases['x86_64']}\n"
f"And legal 32 bit values are:\n {architecture_aliases['i686']}\n"
)
|
a7e99a2e8cc527028b82c7e628bd18f9c63c7f61
| 3,645,955
|
def process_m(filename, m, estimator):
"""Returns the list of file sizes and PSNR values for
compression method m.
"""
filesize, psnr = [], []
for q in range(0, 101, 5):
_size, _psnr = process_q(filename, q, m, estimator)
filesize.append(_size / 1024) # in kilobyte(s)
psnr.append(_psnr)
return filesize, psnr
|
7bf1bbcdf31709393788006d8d5cd1bef3bf5509
| 3,645,956
|
def fmt(n):
"""format number with a space in front if it is single digit"""
if n < 10:
return " " + str(n)
else:
return str(n)
|
976acc22cafd6d6bdb4e251853f49a114b63ec21
| 3,645,957
|
def test_registration():
"""Test registering a magic and getting a copy of it and de-registering."""
manager.MagicManager.clear_magics()
def my_magic(cell=None, line=None):
"""This is a magic."""
if not cell:
cell = 'foo'
if not line:
line = 'bar'
return f'{cell}{line}'
my_magic.magic_name = 'magical_function'
my_magic.fn = my_magic
manager.MagicManager.register_magic(my_magic)
magic_from_manager = manager.MagicManager.get_magic('magical_function')
assert magic_from_manager() == 'foobar'
my_magic.magic_name = 'other_magic'
def conditional():
return False
manager.MagicManager.register_magic(my_magic, conditional=conditional)
magic_from_manager = manager.MagicManager.get_magic('other_magic')
assert magic_from_manager is None
manager.MagicManager.register_magic(my_magic)
magic_from_manager = manager.MagicManager.get_magic('other_magic')
assert magic_from_manager() == 'foobar'
manager.MagicManager.deregister_magic('other_magic')
magic_from_manager = manager.MagicManager.get_magic('other_magic')
assert magic_from_manager is None
manager.MagicManager.deregister_magic('magical_function')
magic_from_manager = manager.MagicManager.get_magic('magical_function')
assert magic_from_manager is None
with pytest.raises(KeyError):
manager.MagicManager.deregister_magic('does_not_exist')
|
899573442f12e6e6544f32dcd472fd495eb9dc3b
| 3,645,958
|
def stop():
"""Stop cleaning
This is using docstrings for specifications.
---
definitions:
stop:
type: object
properties:
did:
type: string
siid:
type: integer
aiid:
type: integer
code:
type: integer
out:
type: array
items: {}
security:
- Bearer: []
responses:
200:
description: OK
schema:
$ref: '#/definitions/stop'
400:
description: Bad Request
401:
description: Unauthorized
"""
consoleOutput = (
popen("miiocli dreamevacuum --ip " + creds.ip + " --token " + creds.token + " play_sound")
.read()
.strip()
.rstrip("\n")
)
# 400
if consoleOutput.find("Error") != -1:
return Response(response=consoleOutput.rstrip("\n"), status=400, mimetype="text/plain")
# 200
result = consoleOutput.partition("\n")[2]
print(result)
if result.find("{'did'") != -1:
return Response(response=result.replace("'", '"'), status=200, mimetype="application/json")
|
e9d7558f4433e73a92229fbf79628eb48357e12b
| 3,645,959
|
import json
def save_key(access_key, output_filename=DEFAULT_ACCESS_KEY_FILE):
"""
saves access key to .yc json file
"""
with open(output_filename, "w+") as f:
f.write(json.dumps(access_key, indent=4))
return output_filename
|
7f15a469ad9b74a39452d8bde46223ef214300d9
| 3,645,960
|
def PutObject(object_id: str):
"""Add/replace DRS object with a user-supplied ID.
Args:
object_id: Identifier of DRS object to be created/updated.
Returns:
Identifier of created/updated DRS object.
"""
return register_object(
data=request.json,
object_id=object_id,
)
|
faf0aa633ef149c34f3fe0e80d8fdcc9df68dfec
| 3,645,961
|
def get_handler_name(method: str, url_path: str, path_params: dict):
"""
Возвращает имя необходимого хендлера для рефлексифного вызова метода
:param method: Метод
:param url_path: URL
:param path_params: Параметры
:return:
"""
handler = url_path.replace('/', '_')
for key, value in path_params.items():
handler = handler.replace(value, key)
return method.lower() + handler
|
e8060538a6bf73e6291ecbcbec14f11997a53507
| 3,645,962
|
import json
def plot_AP(file_path: str):
""" 绘制 AP 柱状图 """
with open(file_path, encoding='utf-8') as f:
result = json.load(f)
AP = []
classes = []
for k, v in result.items():
if k!='mAP':
AP.append(v['AP'])
classes.append(k)
fig, ax = plt.subplots(1, 1, num='AP 柱状图')
ax.barh(range(len(AP)), AP, height=0.6, tick_label=classes)
ax.set(xlabel='AP', title=f'mAP: {result["mAP"]:.2%}')
return fig, ax
|
7d691161d07d5f4a70c2b46b8971f54c93972a7b
| 3,645,964
|
def partner_data_ingest_new_files(source, destination):
"""
:param source : list of files to process:
:param destination: destination to copy validated files
check s3 path for new file, trigger partner_data_ingest for new files.
"""
hook = S3SyncHook(aws_conn_id="aws_default", verify=True)
diff = hook.diff(source, destination)
return partner_data_ingest(new_files=diff, destination=destination)
|
49685cac11c12af7aa3e2e9ecc152dc46f1b2c5e
| 3,645,965
|
import six
def _mofval(value, indent, maxline, line_pos=0, end_space=0):
"""
Low level function that returns the MOF representation of a non-string
value (i.e. a value that cannot not be split into multiple parts, for
example a numeric or boolean value).
If the MOF representation of the value does not fit into the remaining
space of the current line, it is put into a new line, considering the
specified indentation.
NOTE: This method is derived from pywbem mofval but differs in that we
want to output even if we violate the maxline limit on the new line. This
method favors outputting data over exceptions.
Parameters:
value (:term:`unicode string`): The non-string value. Must not be `None`.
indent (:term:`integer`): Number of spaces to indent any new lines that
are generated.
maxline (:term:`integer`): Maximum line length for the generated MOF.
line_pos (:term:`integer`): Length of content already on the current
line.
end_space (:term:`integer`): Length of space to be left free on the last
line.
Returns:
tuple of
* :term:`unicode string`: MOF string.
* new line_pos
"""
assert isinstance(value, six.text_type)
# Check for output on current line
# if fits or this is first entry on the line
avl_len = maxline - line_pos - end_space
if len(value) <= avl_len or line_pos == 0:
line_pos += len(value)
return value, line_pos
mof_str = u'\n' + _indent_str(indent) + value
line_pos = indent + len(value)
return mof_str, line_pos
|
964e788a228ac88305fb8d82e7e9b9a4a8cd1a2f
| 3,645,966
|
def to_vector(texto,model,idf):
""" Receives a sentence string along with a word embedding model and
returns the vector representation of the sentence"""
tokens = normalizer(texto).split() # splits the text by space and returns a list of words
vec = np.zeros(300) # creates an empty vector of 300 dimensions
for word in tokens: # iterates over the sentence
if (word in model) & (word in idf): # checks if the word is both in the word embedding and the tf-idf model
vec += model[word]*idf[word] # adds every word embedding to the vector
if np.linalg.norm(vec) > 0:
return vec / np.linalg.norm(vec) # divides the vector by their normal
else:
return vec
|
24f811110f9b6d9b0fc8a0f6ffcf2d37e1cd6feb
| 3,645,968
|
def evaluate_interval_detection(labels, predictions, event_val, def_val, seq_length, other_vals=[]):
"""Evaluate interval detection for sequences by calculating
tp, fp, and fn.
Extends the metric outlined by Kyritsis et al. (2019) in
Modeling wrist micromovements to measure in-meal eating behavior from
inertial sensor data
https://ieeexplore.ieee.org/abstract/document/8606156/
by introducing additional possible events.
Args:
labels: The ground truth [batch_size, seq_length], encoding relevant
sequences using the vals given in parameters.
predictions: The predictions [batch_size, seq_length], encoding relevant
sequences using the vals given in parameters.
event_val: The value for true events.
def_val: The default value for non-events.
other_vals: List or 1-D tensor of vals for other events.
seq_length: The sequence length.
Returns:
tp: True positives (number of true sequences of event_vals predicted
with at least one predicting event_val) - scalar
fp_1: False positives type 1 (number of excess predicting event_vals
matching a true sequence of event_val in excess) - scalar
fp_2: False positives type 2 (number of predicting event_vals matching
def_val instead of event_val) - scalar
fp_3: False positives type 3 (number of predicting event_vals matching
other_vals instead of event_val) - 1D tensor with value for each
element in other_vals
fn: False negatives (number of true sequences of event_vals not matched
by at least one predicting event_val)
"""
def sequence_masks(labels, event_val, def_val, batch_size, seq_length):
"""Generate masks [labels, max_seq_count, seq_length] for all event sequences in the labels"""
# Mask non-event elements as False and event elements as True
event_mask = tf.equal(labels, event_val)
# Mask elements that are not equal to previous elements
diff_mask = tf.not_equal(event_mask[:, 1:], event_mask[:, :-1])
prev_mask = tf.concat([tf.ones_like(labels[:, :1], tf.bool), diff_mask], axis=1)
next_mask = tf.concat([diff_mask, tf.ones_like(labels[:, :1], tf.bool)], axis=1)
# Test if there are no sequences
empty = tf.equal(tf.reduce_sum(tf.cast(event_mask, tf.int32)), 0)
# Mask sequence starts and ends
seq_start_mask = tf.logical_and(prev_mask, event_mask)
seq_end_mask = tf.logical_and(next_mask, event_mask)
# Scatter seq_val
seq_count_per_batch = tf.reduce_sum(tf.cast(seq_start_mask, tf.int32), axis=[1])
max_seq_count = tf.reduce_max(seq_count_per_batch)
seq_val_idx_mask = tf.reshape(tf.sequence_mask(seq_count_per_batch, maxlen=max_seq_count), [-1])
seq_val_idx = tf.boolean_mask(tf.range(tf.size(seq_val_idx_mask)), seq_val_idx_mask)
seq_vals = tf.boolean_mask(labels, seq_start_mask)
seq_val = tf.scatter_nd(
indices=tf.expand_dims(seq_val_idx, axis=1),
updates=seq_vals,
shape=tf.shape(seq_val_idx_mask))
seq_val = tf.reshape(seq_val, [batch_size, max_seq_count])
# Set elements of seq_val that are not event_val to def_val
seq_val = tf.where(
tf.not_equal(seq_val, tf.fill(tf.shape(seq_val), event_val)),
x=tf.fill(tf.shape(seq_val), def_val), y=seq_val)
# Scatter seq_start
seq_start_idx = tf.where(seq_start_mask)[:,1]
seq_start = tf.scatter_nd(
indices=tf.expand_dims(seq_val_idx, axis=1),
updates=seq_start_idx,
shape=tf.shape(seq_val_idx_mask))
seq_start = tf.reshape(seq_start, [batch_size, max_seq_count])
# Scatter seq_end
seq_end_idx = tf.where(seq_end_mask)[:,1]
seq_end = tf.scatter_nd(
indices=tf.expand_dims(seq_val_idx, axis=1),
updates=seq_end_idx,
shape=tf.shape(seq_val_idx_mask))
seq_end = tf.reshape(seq_end, [batch_size, max_seq_count])
def batch_seq_masks(starts, ends, length, vals, def_val):
"""Return seq masks for one batch"""
def seq_mask(start, end, length, val, def_val):
"""Return one seq mask"""
return tf.concat([
tf.fill([start], def_val),
tf.fill([end-start+1], val),
tf.fill([length-end-1], def_val)], axis=0)
return tf.map_fn(
fn=lambda x: seq_mask(x[0], x[1], length, x[2], def_val),
elems=(starts, ends, vals),
dtype=tf.int32)
seq_masks = tf.cond(empty,
lambda: tf.fill([batch_size, 0, seq_length], def_val),
lambda: tf.map_fn(
fn=lambda x: batch_seq_masks(x[0], x[1], seq_length, x[2], def_val),
elems=(seq_start, seq_end, seq_val),
dtype=tf.int32))
return seq_masks, max_seq_count
labels = tf.cast(labels, dtype=tf.int32)
predictions = tf.cast(predictions, dtype=tf.int32)
def_val = tf.cast(def_val, dtype=tf.int32)
event_val = tf.cast(event_val, dtype=tf.int32)
# Dimensions
batch_size = labels.get_shape()[0]
# Compute whether labels are empty (no event_val sequences)
event_mask = tf.equal(labels, event_val)
empty = tf.equal(tf.reduce_sum(tf.cast(event_mask, tf.int32)), 0)
# Derive positive ground truth mask; reshape to [n_gt_seq, seq_length]
pos_mask, max_seq_count = sequence_masks(labels, event_val=event_val,
def_val=def_val, batch_size=batch_size, seq_length=seq_length)
pos_mask = tf.reshape(pos_mask, [-1, seq_length])
# Mask of default events
def_mask = tf.equal(labels, def_val)
# Masks for other events
other_masks = tf.map_fn(fn=lambda x: tf.equal(labels, x),
elems=tf.convert_to_tensor(other_vals, dtype=tf.int32), dtype=tf.bool)
# Retain only event_val in predictions
predictions = tf.where(
tf.not_equal(predictions, tf.fill(tf.shape(predictions), event_val)),
x=tf.fill(tf.shape(predictions), def_val), y=predictions)
# Stack predictions accordingly
pred_stacked = tf.reshape(tf.tile(tf.expand_dims(predictions, axis=1), [1, max_seq_count, 1]), [-1, seq_length])
# Remove empty masks and according preds
keep_mask = tf.greater(tf.reduce_sum(tf.cast(tf.not_equal(pos_mask, def_val), tf.int32), axis=1), 0)
pos_mask = tf.cond(empty,
lambda: pos_mask,
lambda: tf.boolean_mask(pos_mask, keep_mask))
pred_stacked = tf.cond(empty,
lambda: pred_stacked,
lambda: tf.boolean_mask(pred_stacked, keep_mask))
# Calculate number predictions per pos sequence
# Reduce predictions to elements in pos_mask that equal event_val, then count them
pred_sums = tf.map_fn(
fn=lambda x: tf.reduce_sum(tf.cast(tf.equal(tf.boolean_mask(x[0], tf.equal(x[1], event_val)), event_val), tf.int32)),
elems=(pred_stacked, pos_mask), dtype=tf.int32)
# Calculate true positive, false positive and false negative count
tp = tf.reduce_sum(tf.map_fn(lambda count: tf.cond(count > 0, lambda: 1, lambda: 0), pred_sums))
fn = tf.reduce_sum(tf.map_fn(lambda count: tf.cond(count > 0, lambda: 0, lambda: 1), pred_sums))
fp_1 = tf.cond(empty,
lambda: 0,
lambda: tf.reduce_sum(tf.map_fn(lambda count: tf.cond(count > 1, lambda: count-1, lambda: 0), pred_sums)))
# False positives of type 2 are any detections on default events
fp_2 = tf.reduce_sum(tf.cast(tf.equal(tf.boolean_mask(predictions, def_mask), event_val), tf.int32))
# False positives of type 3 are any detections on other events
fp_3 = tf.map_fn(
fn=lambda x: tf.reduce_sum(tf.cast(tf.equal(tf.boolean_mask(predictions, x), event_val), tf.int32)),
elems=other_masks, dtype=tf.int32)
tp = tf.cast(tp, tf.float32)
fp_1 = tf.cast(fp_1, tf.float32)
fp_2 = tf.cast(fp_2, tf.float32)
fp_3 = tf.cast(fp_3, tf.float32)
fn = tf.cast(fn, tf.float32)
return tp, fp_1, fp_2, fp_3, fn
|
4149eaf357d28236077e9cfac7d7ed8ee113818c
| 3,645,969
|
def _div(v):
"""Pure spatial divergence"""
return _div_id(np.vstack((v, [np.zeros_like(v[0])])), l1_ratio=0.)
|
706a97e4d5930067b6524210738fce5b27f407c5
| 3,645,970
|
def _always_run(*args, **kwargs) -> bool:
""" This returns False to indicate that the step is not already completed. """
return False
|
db31e0ac20ac0eef410fb051928308ce7414f5b6
| 3,645,972
|
def generate_urls(search):
"""Generates a URLS in the correct format that brings to Google Image seearch page"""
return [(BASE_URL+quote(word)+GOOGLE_PICTURE_ID) for word in search]
|
4d7d13cdf15fb3e029f11bb2e3f28920cf7c2f97
| 3,645,973
|
def batch_provider(data, batch_size, processor=None, worker_count=1, queue_size=16, report_progress=True):
""" Return an object that produces a sequence of batches from input data
Input data is split into batches of size :attr:`batch_size` which are processed with function :attr:`processor`
Data is split and processed by separate threads and dumped into a queue allowing continuous
provision of data. The main purpose of this primitive is to provide easy to use tool for parallel batch
processing/generation in background while main thread runs the main algorithm.
Batches are processed in parallel, allowing better utilization of CPU cores and disk that may improve
GPU utilization for DL tasks with Storage/IO bottleneck.
This primitive can be used in various ways. For small datasets, the input :attr:`data` list may contain actual
dataset, while :attr:`processor` function does from small to no data processing. For larger datasets, :attr:`data`
list may contain just filenames or keys while :attr:`processor` function reads data from disk or db.
There are many purposes that function :attr:`processor` can be used for, depending on your use case.
- Reading data from disk or db
- Data decoding, e.g. from JPEG.
- Augmenting data, flipping, rotating adding nose, etc.
- Concatenation of data, stacking to single ndarray, conversion to a tensor, uploading to GPU.
- Data generation.
Note:
Sequential order of batches is guaranteed only if number of workers is 1 (Default), otherwise batches might
be supplied out of order.
Args:
data (list): Input data, each entry in the list should be a separate data point.
batch_size (int): Size of a batch. If size of data is not divisible by :attr:`batch_size`, then
the last batch will have smaller size.
processor (Callable[[list], Any], optional): Function for processing batches. Receives slice of the :attr:`data`
list as input. Can return object of any type. Defaults to None.
worker_count (int, optional): Number of workers, should be greater or equal to one. To process data in parallel
and fully load CPU :attr:`worker_count` should be close to the number of CPU cores. Defaults to one.
queue_size (int, optional): Maximum size of the queue, which is number of batches to buffer. Should be larger
than :attr:`worker_count`. Typically, one would want this to be as large as possible to amortize all disk
IO and computational costs. Downside of large value is increased RAM consumption. Defaults to 16.
report_progress (bool, optional): Print a progress bar similar to `tqdm`. You still may use `tqdm` if you set
:attr:`report_progress` to False. To use `tqdm` just do
::
for x in tqdm(batch_provider(...)):
...
Defaults to True.
Returns:
Iterator: An object that produces a sequence of batches. :meth:`next()` method of the iterator will return
object that was produced by :attr:`processor` function
Raises:
StopIteration: When all data was iterated through. Stops the for loop.
Example:
::
def process(batch):
images = [misc.imread(x[0]) for x in batch]
images = np.asarray(images, dtype=np.float32)
images = images.transpose((0, 3, 1, 2))
labeles = [x[1] for x in batch]
labeles = np.asarray(labeles, np.int)
return torch.from_numpy(images) / 255.0, torch.from_numpy(labeles)
data = [('some_list.jpg', 1), ('of_filenames.jpg', 2), ('etc.jpg', 4), ...] # filenames and labels
batches = dlutils.batch_provider(data, 32, process)
for images, labeles in batches:
result = model(images)
loss = F.nll_loss(result, labeles)
loss.backward()
optimizer.step()
"""
class State:
def __init__(self):
self.current_batch = 0
self.lock = Lock()
self.data_len = len(data)
self.batch_count = self.data_len // batch_size + (1 if self.data_len % batch_size != 0 else 0)
self.quit_event = Event()
self.queue = Queue(queue_size)
self.batches_done_count = 0
self.progress_bar = None
if report_progress:
self.progress_bar = ProgressBar(self.batch_count)
def get_next_batch_it(self):
try:
self.lock.acquire()
if self.quit_event.is_set() or self.current_batch == self.batch_count:
raise StopIteration
cb = self.current_batch
self.current_batch += 1
return cb
finally:
self.lock.release()
def push_done_batch(self, batch):
try:
self.lock.acquire()
self.queue.put(batch)
self.batches_done_count += 1
finally:
self.lock.release()
def all_done(self):
return self.batches_done_count == self.batch_count and self.queue.empty()
if processor is None:
def processor(x):
return x
def _worker(state):
while not state.quit_event.is_set():
try:
cb = state.get_next_batch_it()
data_slice = data[cb * batch_size:min((cb + 1) * batch_size, state.data_len)]
b = processor(data_slice)
state.push_done_batch(b)
except StopIteration:
break
class Iterator:
def __init__(self):
self.state = State()
self.workers = []
for i in range(worker_count):
worker = Thread(target=_worker, args=(self.state, ))
worker.daemon = True
worker.start()
self.workers.append(worker)
def __len__(self):
return self.state.batch_count
def __iter__(self):
return self
def __next__(self):
if not self.state.quit_event.is_set() and not self.state.all_done():
item = self.state.queue.get()
self.state.queue.task_done()
if self.state.progress_bar is not None:
self.state.progress_bar.increment()
return item
else:
self.state.quit_event.set()
raise StopIteration
def __del__(self):
self.state.quit_event.set()
while not self.state.queue.empty():
self.state.queue.get(False)
self.state.queue.task_done()
for worker in self.workers:
worker.join()
return Iterator()
|
2760e9bc9977f4fcdc07624bf896d6b48ce1276d
| 3,645,974
|
import random
def simplex(key, log_L_constraint, live_points_U,
loglikelihood_from_constrained,
prior_transform, sampler_state, replace_id):
"""
Samples from the prior restricted to the likelihood constraint.
This undoes the shrinkage at each step to approximate a bound on the contours.
First it does a scaling on each dimension.
Args:
key:
log_L_constraint:
live_points_U:
loglikelihood_from_constrained:
Returns:
"""
N,D = live_points_U.shape
key, width_key = random.split(key, 2)
def body(state):
(key, i, u_test, x_test, log_L_test) = state
key, sample_key, select_key, R_key = random.split(key, 4)
i = random.randint(select_key, shape=(), minval=0, maxval=N + 1)
# M,M
R = random_ortho_matrix(R_key, D)
# initial L, R for each direction
# t_R[i] = max_(k) (points[k,j] - spawn_point_U[j]) @ R[j,i]
# t_L[i] = max_(k) (points[k,j] - spawn_point_U[j]) @ -R[j,i]
# N, M
dx = live_points_U[sampler_state.knn_indices[i, :], :] - live_points_U[i, :]
# [N, M]
t = dx @ R
# [M]
t_R = jnp.maximum(jnp.max(t, axis=0), 0.)
t_L = jnp.minimum(jnp.min(t, axis=0), 0.)
u_test = live_points_U[i,:] + R @ random.uniform(sample_key, shape=[D], minval=t_L, maxval=t_R)
u_test = jnp.clip(u_test, 0., 1.)
x_test = prior_transform(u_test)
log_L_test = loglikelihood_from_constrained(**x_test)
return (key, i + 1, u_test, x_test, log_L_test)
(key, num_likelihood_evaluations, u_new, x_new, log_L_new) = while_loop(lambda state: state[-1] <= log_L_constraint,
body,
(key, 0, live_points_U[0, :],
prior_transform(live_points_U[0, :]),
log_L_constraint))
new_dist = jnp.linalg.norm(u_new - dynamic_update_slice(live_points_U, u_new[None, :], [replace_id,0]), axis=1)
new_dist = jnp.where(new_dist == 0., jnp.inf, new_dist)
new_indices = jnp.argsort(new_dist)[:D+1]
knn_indices = dynamic_update_slice(sampler_state.knn_indices,
new_indices[None, :],
[replace_id, 0])
sampler_state = sampler_state._replace(knn_indices=knn_indices)
CubesResults = namedtuple('CubesResults',
['key', 'num_likelihood_evaluations', 'u_new', 'x_new', 'log_L_new',
'sampler_state'])
return CubesResults(key, num_likelihood_evaluations, u_new, x_new, log_L_new, sampler_state)
|
f88038eca201b87fdc8f4b4722357a4eafd0366e
| 3,645,975
|
def has_anonymous_link(node, auth):
"""check if the node is anonymous to the user
:param Node node: Node which the user wants to visit
:param str link: any view-only link in the current url
:return bool anonymous: Whether the node is anonymous to the user or not
"""
if auth.private_link:
return auth.private_link.anonymous
return False
|
c5941bce3f0110dfcd5e9bbb19bae0682c5e731f
| 3,645,976
|
def lstm(c_prev, x):
"""Long Short-Term Memory units as an activation function.
This function implements LSTM units with forget gates. Let the previous
cell state :math:`c_{\\text{prev}}` and the incoming signal :math:`x`.
First, the incoming signal :math:`x` is split into four arrays
:math:`a, i, f, o` of the same shapes along the second axis.
It means that :math:`x` 's second axis must have 4 times the length of
:math:`c_{\\text{prev}}`.
The splitted input signals are corresponding to:
- :math:`a` : sources of cell input
- :math:`i` : sources of input gate
- :math:`f` : sources of forget gate
- :math:`o` : sources of output gate
Second, it computes outputs as:
.. math::
c &= \\tanh(a) \\text{sigmoid}(i)
+ c_{\\text{prev}} \\text{sigmoid}(f), \\\\
h &= \\tanh(c) \\text{sigmoid}(o).
These are returned as a tuple of two variables.
Args:
c_prev (~chainer.Variable): Variable that holds the previous cell
state. The cell state should be a zero array or the output of the
previous call of LSTM.
x (~chainer.Variable): Variable that holds the incoming signal. It must
have the second dimension four times of that of the cell state,
Returns:
tuple: Two :class:`~chainer.Variable` objects ``c`` and ``h``. ``c`` is
the updated cell state. ``h`` indicates the outgoing signal.
See the original paper proposing LSTM with forget gates:
`Long Short-Term Memory in Recurrent Neural Networks \
<http://www.felixgers.de/papers/phd.pdf>`_.
.. admonition:: Example
Assuming ``y`` is the current input signal, ``c`` is the previous cell
state, and ``h`` is the previous output signal from an ``lstm``
function. Each of ``y``, ``c`` and ``h`` has ``n_units`` channels.
Most typical preparation of ``x`` is:
>>> model = FunctionSet(w=F.Linear(n_units, 4 * n_units),
... v=F.Linear(n_units, 4 * n_units),
... ...)
>>> x = model.w(y) + model.v(h)
>>> c, h = F.lstm(c, x)
It corresponds to calculate the input sources :math:`a, i, f, o` from
the current input ``y`` and the previous output ``h``. Different
parameters are used for different kind of input sources.
"""
return LSTM()(c_prev, x)
|
795fb92554c04be29a75f770fe0fb88d4224f94a
| 3,645,977
|
import torch
import time
def hals(video,
video_factorization,
maxiter_hals=30,
nnt=False,
verbose=False,
indent='',
device='cuda',
**kwargs):
"""Perform maxiter HALS updates To Temporal & Spatial Components
Parameter:
video: LowRankVideo class object
video_factorization: localized NMF factors
maxiter_hals: maximum number of iterations to tune hals
nnt: whether or not temporal components should be constrained to be nonnegative
verbose: whether or not print status update
indent: previous identation for printing status update
device: computation device
**kwargs: optional additional input arguments
Return:
hals iteration counter
"""
for itr in range(maxiter_hals):
if verbose:
if device=='cuda': torch.cuda.synchronize()
print(indent + '|--v HALS Iteration {:g}'.format(itr+1))
itr_t0 = time()
step_t0 = itr_t0
# Spatial Update Step
video_factorization.update_spatial(video)
if verbose:
if device=='cuda': torch.cuda.synchronize()
print(indent + '| |--> Spatial update took {:g} seconds'.format(time()-step_t0))
step_t0 = itr_t0
# Remove Empty Components
video_factorization.prune_empty_components()
video_factorization.normalize_spatial()
if verbose:
if device=='cuda': torch.cuda.synchronize()
print(indent + '| |--> Component prune after spatial update took {:g} seconds'.format(time()-step_t0))
step_t0 = itr_t0
# Temporal Update Step
video_factorization.update_temporal(video, nonnegative=nnt)
if verbose:
if device=='cuda': torch.cuda.synchronize()
print(indent + '| |--> Temporal update took {:g} seconds'.format(time()-step_t0))
print(indent + '| \'-total : {:g} seconds'.format(time()-itr_t0))
# Remove Empty Components
video_factorization.prune_empty_components()
if verbose:
if device=='cuda': torch.cuda.synchronize()
print(indent + '| |--> Component prune after temporal update took {:g} seconds'.format(time()-step_t0))
step_t0 = itr_t0
return itr + 1
|
a993f9e434c3196110c0569cb124d9edbb794dec
| 3,645,978
|
def generate_random_sd(error, seq = None):
""" generates random sd with error% error rate
If seq is specified, random sd is generated from a substring of it."""
if seq == None:
seq1 = randSeq(rand(minLen, maxLen))
else:
length = rand(minLen, maxLen)
start = rand(0, len(seq) - length - 1)
seq1 = seq[start:start + length]
sED = rand(max(0, error - maxLED),min(maxSED, error))
seq2 = makeSmall(seq1, sED)[0]
seq2 = makeLarge(seq2, error-sED)[0]
return seq1, seq2, sED
|
34694895cd37e5714666c3f9f80ae5a010310d3c
| 3,645,979
|
def is_successful(gsm_log):
"""
Success is defined as having converged to a transition state.
"""
with open(gsm_log) as f:
for line in reversed(f.readlines()):
if '-XTS-' in line or '-TS-' in line:
return True
return False
|
9bab6837c8e6b818cceb025c5df9aed78074edcf
| 3,645,980
|
def indicator(function_array_to_be_indicated, its_domain, barrier):
"""the indicator influences the function argument, not value. So here it iterates through x-domain and cuts any
values of function with an argument less than H"""
indicated = []
for index in range(len(its_domain)):
if its_domain[index] > barrier:
indicated.append(function_array_to_be_indicated[index])
else:
indicated.append(0)
return indicated
|
440f423b7b25b0d152bc691acd3d7dea6c785aed
| 3,645,981
|
def _causes_name_clash(candidate, path_list, allowed_occurences=1):
"""Determine if candidate leads to a name clash.
Args:
candidate (tuple): Tuple with parts of a path.
path_list (list): List of pathlib.Paths.
allowed_occurences (int): How often a name can occur before we call it a clash.
Returns:
bool
"""
duplicate_counter = -allowed_occurences
for path in path_list:
parts = tuple(reversed(path.parts))
if len(parts) >= len(candidate) and parts[: len(candidate)] == candidate:
duplicate_counter += 1
return duplicate_counter > 0
|
3b874e4ea6d8780483100e464e3325321c82689e
| 3,645,983
|
def run_eqm(results: Results, options: Options, state: PromisedObject) -> dict:
"""Run the eqm jobs."""
# set user-defined valuess
results['job_opts_eqm'] = edit_calculator_options(
options, ['eqm', 'xtpdft', 'esp2multipole'])
cmd_eqm_write = create_promise_command(
"xtp_parallel -e eqm -o {} -f {} -s 0 -j write", results['job_opts_eqm']['eqm'],
state)
results['job_setup_eqm'] = call_xtp_cmd(
cmd_eqm_write, options.scratch_dir,
expected_output={"eqm_jobs": "eqm.jobs"})
# Select the number of jobs to run based on the input provided by the user
results['job_select_eqm_jobs'] = edit_jobs_file(
results['job_setup_eqm']['eqm_jobs'],
options.eqm_jobs)
jobs_eqm = distribute_eqm_jobs(results, options, state)
# Finally move all the OR_FILES to the same folder in the scratch_dir
names = ('molecule_orb', 'dft_orb', 'mps_file')
return move_results_to_workdir(jobs_eqm, names, options.scratch_dir)
|
941d51a0de22dd4d66f5de68fc315bf318d112cf
| 3,645,984
|
def is_within_boundary(boundary_right_most_x, boundary_top_most_y,
boundary_left_most_x, boundary_bottom_most_y,
cursor):
"""
Checks if cursor is within given boundary
:param boundary_right_most_x:
:param boundary_top_most_y:
:param boundary_left_most_x:
:param boundary_bottom_most_y:
:param cursor:
:return: boolean
"""
if cursor.y < boundary_top_most_y:
return False
elif cursor.y > boundary_bottom_most_y:
return False
elif cursor.x < boundary_left_most_x:
return False
elif cursor.x > boundary_right_most_x:
return False
return True
|
d53106c9d525eb1bb51cfe4c30bc7e143ac6a517
| 3,645,985
|
def save_matchmaking_auth_key(auth_key: str) -> bool:
"""Register a new matchmaking auth key. !This will overwrite the existing matchmaking key for this chain!
Args:
auth_key: auth_key to add for matchmaking
Returns:
Boolean if successful
"""
try:
redis.set_sync(MATCHMAKING_KEY_LOCATION, auth_key)
return True
except Exception:
return False
|
5f18614f2b2950a942e7a98773911b7f58aabd74
| 3,645,987
|
import requests
from bs4 import BeautifulSoup
def get_game_page(url):
"""
Get the HTML for a given URL, where the URL is a game's page in the Xbox
Store
"""
try:
response = requests.get(url)
except (requests.exceptions.MissingSchema, ConnectionError):
return None
game_page = BeautifulSoup(response.content, "html.parser")
return game_page
|
40d578ce8cda0b5139515e03f8308911169e0442
| 3,645,988
|
from typing import List
import logging
import itertools
import asyncio
async def generate_spam_round_tx_xdrs(pool, prioritizers: List[Keypair], prioritized_builders, unprioritized_builders, rnd):
"""Generate transaction XDRs for a single spam round (ledger) according to given builders.
Some of the generated transactions are prioritized using given prioritizer seeds,
and some are unprioritized and not signed by a prioritizer account.
All prioritized transactions are expected to be included in the next ledger.
Only one out of all unprioritized transactions is expected to be included in the next ledger.
Return a metadata dictionary with the generated XDRs along with additional information.
"""
logging.info('generating transaction xdrs for round %d', rnd)
# make a cyclic list of builders.
# we will use this list to fetch a destination address for each payment tx,
# making all builders send a tx to the next builder right after them
# in line in a cyclic manner. this is done in order to cycle through
# destination addresses instead of sending call txs to a single destination
# account.
cycl = itertools.cycle(unprioritized_builders)
next(cycl) # make sure the next cycle call will return the next builder after the current one
# generate unprioritized payment transactions
# we generate them first, thus will submit them first,
# because we want to test if prioritized transactions actually get priority over them
loop = asyncio.get_running_loop()
futurs = []
for builder in unprioritized_builders:
dest_address = next(cycl).keypair.address().decode()
f = loop.run_in_executor(
pool, build_and_sign,
builder, dest_address, PAYMENT_AMOUNT, None)
futurs.append(f)
if not futurs:
raise RuntimeError('no futures to gather')
tx_metadata = {}
for tx_hash, tx_xdr in await asyncio.gather(*futurs):
tx_metadata[tx_hash] = {'round': rnd, 'prioritized': False, 'xdr': tx_xdr}
# generate prioritized transactions
futurs = []
cycl = itertools.cycle(prioritized_builders)
for builder, prioritizer in zip(prioritized_builders, prioritizers):
dest_address = next(cycl).keypair.address().decode()
f = loop.run_in_executor(
pool, build_and_sign,
builder, dest_address, PAYMENT_AMOUNT, prioritizer.secret_seed)
futurs.append(f)
if not futurs:
raise RuntimeError('no futures to gather')
for tx_hash, tx_xdr in await asyncio.gather(*futurs):
tx_metadata[tx_hash] = {'round': rnd, 'prioritized': True, 'xdr': tx_xdr}
return tx_metadata
|
7e33455718f6c99ccb9fc1ad6a7c3de47964ec98
| 3,645,989
|
import collections
from datetime import datetime
def json_custom_parser(obj):
"""
A custom json parser to handle json.dumps calls properly for Decimal and Datetime data types.
"""
if isinstance(obj, Decimal):
return float(obj)
elif not isinstance(obj, basestring) and isinstance(obj, collections.Iterable):
return list(obj)
elif isinstance(obj, datetime.datetime) or isinstance(obj, datetime.date):
dot_ix = 19 # 'YYYY-MM-DDTHH:MM:SS.mmmmmm+HH:MM'.find('.')
return obj.isoformat()[:dot_ix]
else:
raise TypeError(obj)
|
fb5d14b4416df4540ed3091dcf229aa7b037003d
| 3,645,990
|
def fs_open(path, flag, mode=default_file_mode):
"""
Open a file, potentially creating it. Return the new fd's id or else -1 if
file can not be opened (or potentially created)
"""
# Check if file should be created if it doesn't exist
O_CREAT = 64
create = flag & 64
# If requested, try to create the file
if create:
try:
filesys.add_file(path, mode, 0)
except AlreadyExistsError:
# File may already exist, which is ok with O_CREAT
pass
except Exception:
return -1
# Call the virtual fs to open the file
try:
inodeid = filesys.open_file(path)
except DoesNotExistError:
return -1
# Add an fd for this file to the open files state
return fstate.create_fd(inodeid)
|
218940a6fc14c47f7a3df6d9a4e1bbc971b6b0b5
| 3,645,992
|
def new_user():
"""
Create Instance of User class to be used by the module
"""
user_details = ['Daudi', 'Jesee', 'dj@mail.com', 'password']
user = Users(user_details)
return user
|
4d5b2c4cad858113fceef150143b9688488000f4
| 3,645,993
|
import math
def normalise_angle(angle: float) -> float:
"""Normalises the angle in the range (-pi, pi].
args:
angle (rad): The angle to normalise.
return:
angle (rad): The normalised angle.
"""
while angle > math.pi:
angle -= 2 * math.pi
while angle <= -math.pi:
angle += 2 * math.pi
return angle
|
0a4cfa6e9da58bfdbb6cd4a04e7a742e8c432002
| 3,645,994
|
def get_hdf_len(*path):
"""
Returns the number of rows in an hdf file as an int.
"""
path = construct_path(*path)
with pd.HDFStore(path) as store:
numrows = store.get_storer('data').nrows
return numrows
|
ad188b2733612e7ed1950a2df0ef5164f9cda021
| 3,645,995
|
def matmul(A, B, transpose_A=False, transpose_B=False, master='/gpu:0'):
"""
distributed matrix multiplication.
A: DistMat,
B: single tensor or a list of tensors.
Note: returns a single tensor or a list of tensors, Not a DistMat.
"""
if isinstance(A, tf.Tensor) or isinstance(A, tf.Variable):
if isinstance(B, tf.Tensor) or isinstance(B, tf.Variable):
return tf.matmul(A, B)
else:
raise NotImplementedError
if transpose_B:
raise NotImplementedError
else:
if transpose_A: # distributed dim is inner axis
if isinstance(B, tf.Tensor) or isinstance(B, tf.Variable):
# broadcast
partial_sums = []
for i, t in enumerate(A.tensors):
with tf.device(t.device):
partial_sums.append(tf.matmul(t, B[A.partition[i]:A.partition[i+1],:], transpose_a=True))
with tf.device(master):
return tf.add_n(partial_sums)
else:
partial_sums = []
for t_A, t_B in zip(A.tensors, B.tensors):
#print(t_A.device)
#print(t_B.device)
#assert t_A.device == t_B.device
with tf.device(t_A.device):
partial_sums.append(tf.matmul(t_A, t_B, transpose_a=True))
with tf.device(master):
return tf.add_n(partial_sums)
# distributed computation necessary
#return tf.add_n([tf.matmul(Apart, Bpart) for Apart, Bpart in zip(A.tensors, B.tensors)])
else: # non-distributed dim is inner axis. merely broacast B.
if isinstance(B, tf.Tensor) or isinstance(B, tf.Variable):
slices = []
for t in A.tensors:
with tf.device(t.device):
slices.append(tf.matmul(t, B))
return distmat.DistMat(slices)
else:
raise NotImplementedError
|
268068cd73b56ef747142ebc2df839d124d406d5
| 3,645,996
|
def celerybeat_started():
"""
Returns true/false depending on whether the celerybeat service is started or not
"""
if is_systemd():
running = 'active' in fabric.api.sudo('systemctl is-active %s' % celerybeat_service_name())
return running
return fabtools.service.is_running(celerybeat_service_name())
|
b3578b6dbe91b9a16342c53c488fe01fc37275cd
| 3,645,997
|
def highest_greedy_score(board, disks):
"""
Compute the highest possible score that can be obtained by dropping each
of the given disks on the given board in a greedy way.
- The disks must be dropped in the order in which they appear in the
given list of disks. Each disk is dropped in the best column as
computed by the function best_drop_for_disk.
- Upon exit from the function, the board reflects the state obtained from
dropping the disks. If not all the given disks can be dropped because
the board gets completely filled, the function only drops the disks it can
drop.
- The function returns a tuple of (1) the highest score followed by (2) a tuple
of columns in which the successive disks have been dropped.
- Upon return, the given list of disks only stores disks that have not been
dropped on the board.
- The function will not take into account possible raises of level while
dropping disks, i.e. the resulting score only reflects scores obtained
from dropping disks as computed by the function drop_disk_at.
- This function must be implemented in a RECURSIVE way.
ASSUMPTIONS
- The given board is a playable board, and each of the given disks is a
proper disk for the given board.
- None of the given disks is cracked.
"""
score = 0
columns = ()
if len(disks) == 0 or Board.is_full(board): # No more disks to drop
return score, columns
else:
disk_to_drop = disks[0]
column_best_drop, score_best_drop = best_drop_for_disk(board, disk_to_drop)
del disks[0]
score, columns = highest_greedy_score(board, disks)
columns = (column_best_drop,) + columns
score += score_best_drop
return score, columns
|
22fc70db81d6051158bdb9bf80a42d81a215dba1
| 3,645,998
|
def normalize_and_discard(df: pd.DataFrame) -> pd.DataFrame:
"""
Normalize numeric values between 0 and 1 and discard records that are out of bounds.
"""
# ## 2. Discard values out of range of x and y
df_cleaned = df[(df.x >= 0) & (df.x <= 120) & (df.y >= 0) & (df.y <= (160 / 3))]
print(f'Shape difference {df.shape[0] - df_cleaned.shape[0]}')
# ## 3. Normalize x, y , s, a, dis, o, dir on scale 0-1
# thresholds are determined by examining data from all weeks
df_cleaned.x = df_cleaned.x / df.x.max()
df_cleaned.y = df_cleaned.y / df.y.max()
df_cleaned.s = df_cleaned.s / SPEED_MAX_THRESHOLD
df_cleaned.a = df_cleaned.a / ACCELERATION_MAX_THRESHOLD
df_cleaned.dis = df_cleaned.dis / DISTANCE_MAX_THRESHOLD
df_cleaned.o = df_cleaned.o / 360
df_cleaned.dir = df_cleaned.dir / 360
df_n2 = df_cleaned[[
'time', 'x', 'y', 's', 'a', 'dis', 'o', 'dir', 'event', 'frameId', 'team', 'gameId',
'playId', 'quarter', 'homeHasPossession',
'down', 'playType', 'defendersInTheBox',
'numberOfPassRushers', 'passResult', 'isDefensivePI'
]]
df_n2.quarter /= 5.0 # max quarters
df_n2.down /= 4.0 # max quarters
df_n2.defendersInTheBox /= 11.0
df_n2.numberOfPassRushers /= 11.0
return df_n2
|
0b1a1e6ed76c72797cf7b3f65058592c6ec95b03
| 3,645,999
|
def gravatar_url(email, size=16):
"""Return the gravatar image for the given email address."""
return 'http://www.gravatar.com/avatar/%s?d=identicon&s=%d' % \
(md5(email.strip().lower().encode('utf-8')).hexdigest(), size)
|
d3e24e1898d41df791368e7909461135c8118f90
| 3,646,000
|
def x2bin(v):
"""
convert a value into a binary string
v: int, bytes, bytearray
bytes, bytearray must be in *big* endian.
"""
if isinstance(v, int):
bits = bin(v)
size = 8
elif isinstance(v, (bytes,bytearray)):
bits = bin(int.from_bytes(v, "big"))
size = len(v)*8
return bits[2:].zfill(size)
|
fcb4f1ab05b5a3878939c84074e70fc2d5ee6397
| 3,646,001
|
import re
def normalize_url(url):
"""Function to normalize the url. It will be used as document id value.
Returns:
the normalized url string.
"""
norm_url = re.sub(r'http://', '', url)
norm_url = re.sub(r'https://', '', norm_url)
norm_url = re.sub(r'/', '__', norm_url)
return norm_url
|
79197b9fa1c47da601bdb9c34d626d236b649173
| 3,646,002
|
import fsspec
def is_remote_filesystem(fs: fsspec.AbstractFileSystem) -> bool:
"""
Validates if filesystem has remote protocol.
Args:
fs (``fsspec.spec.AbstractFileSystem``): An abstract super-class for pythonic file-systems, e.g. :code:`fsspec.filesystem(\'file\')` or :class:`datasets.filesystems.S3FileSystem`
"""
if fs is not None and fs.protocol != "file":
return True
else:
return False
|
c40f9bb4845bbd1fc1a4cf9fce2c1b366cd22354
| 3,646,003
|
def get_element_attribute_or_empty(element, attribute_name):
"""
Args:
element (element): The xib's element.
attribute_name (str): The desired attribute's name.
Returns:
The attribute's value, or an empty str if none exists.
"""
return element.attributes[attribute_name].value if element.hasAttribute(attribute_name) else ""
|
dbc7f5c24d321c40b46f1c78950d7cf254719b5c
| 3,646,004
|
def Matcher(y_true, y_pred_logits, y_pred_bbox):
"""
y_true: GT list of len batch with each element is an array of
shape (n_gt_objects, 5) ; n_gt_objects are number of
objects in that image sample and 5 -> (cx,cy,w,h,class_label)
where cordinates are in [0,1] range
y_pred_logits: model output of shape (batch, num_queries, classes)
y_pred_bbox: model output of shape (batch, num_queries, 4) in [0,1] range -> cx,cy,w,h
"""
y_pred_bbox = y_pred_bbox.numpy()
out_loss = 0
batch = len(y_true)
b,num_queries,_ = y_pred_logits.shape
assert b == batch, 'Batch mismatch!!'
batch_query_indices = []
y_pred_logits = tf.math.softmax(y_pred_logits).numpy()
for i in range(batch):
out_cls_loss = -y_pred_logits[i][:,(y_true[i][:,-1]).astype(int)]
out_cdist = distance.cdist(y_pred_bbox[i], y_true[i][:,:4], 'euclidean')
out_iou = []
for j in range(len(y_true[i])):
giou = tfa.losses.giou_loss(cxcywh_to_xyxy(y_pred_bbox[i]), cxcywh_to_xyxy(y_true[i][j,:4][np.newaxis,:]))
out_iou.append(giou)
out_iou = -np.array(out_iou).transpose(1,0)
comb_loss = out_cls_loss + out_cdist + out_iou
row_ind, col_ind = linear_sum_assignment(comb_loss)
batch_query_indices.append((row_ind,col_ind))
return batch_query_indices
|
0918becd40feca73a54ce158a3cb86946cb377ff
| 3,646,005
|
import warnings
def _addBindInput(self, name, type = DEFAULT_TYPE_STRING):
"""(Deprecated) Add a BindInput to this shader reference."""
warnings.warn("This function is deprecated; shader references have been replaced with shader nodes in 1.38.", DeprecationWarning, stacklevel = 2)
return self.addInput(name, type)
|
79404122d895814f64000876e8f926ecc7d54e3e
| 3,646,007
|
import mpmath
def sf(x, c, d, scale):
"""
Survival function of the Burr type XII distribution.
"""
_validate_params(c, d, scale)
with mpmath.extradps(5):
x = mpmath.mpf(x)
c = mpmath.mpf(c)
d = mpmath.mpf(d)
scale = mpmath.mpf(scale)
if x < 0:
return mpmath.mp.one
return (1 + (x/scale)**c)**(-d)
|
2e2649eeb4d32739027eb6ad5a0f3b8c50f7e341
| 3,646,008
|
def substitute_word(text):
"""
word subsitution to make it consistent
"""
words = text.split(" ")
preprocessed = []
for w in words:
substitution = ""
if w == "mister":
substitution = "mr"
elif w == "missus":
substitution = "mrs"
else:
substitution = w
preprocessed.append(substitution)
return " ".join(preprocessed)
|
0709f4223cb06ddfdc5e9704048f418f275429d1
| 3,646,009
|
import re
import requests
from bs4 import BeautifulSoup
def google_wiki(keyword, langid='en', js={}):
"""Google query targets, output if English wikipedia entry is found"""
targets = []
headers = {'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.9; rv:32.0) Gecko/20100101 Firefox/32.0',}
googlerx = re.compile('(http[s]?[^\&]*)') # /url?q=https://fr.wikipedia.org/wiki/La_Banque_postale&sa=U&ei=Zn...
infoboxrx = re.compile('infobox')
domainrx = re.compile('^[a-zA-Z\-]+\.([a-zA-Z\-]+\.)*[a-zA-Z\-]+$')
# query = 'http://www.google.com/search?q=wikipedia%20{}%20{}'.format(langid, keyword)
query = 'http://www.google.com/search?q=wikipedia%20{}'.format(keyword)
r = requests.get(query, headers=headers)
soup = BeautifulSoup(r.content)
keywords = extract_keywords(js)
# phish_tokens = set([word for li in keywords for word in li])
# print(phish_tokens)
for a in soup.find_all('a'):
search = googlerx.search(a.get('href', ''))
if not search:
continue
url = search.groups()[0]
mld, rd = registered_domain(url)
if rd == 'wikipedia.org' and '#' not in url:
# if '.wikipedia.org' in url and '#' not in url:
# if url.startswith('https://{}.wikipedia.org'.format(langid)) and '#' not in url:
wikiurl = url
r = requests.get(url)
html = str(r.content)
wikisoup = BeautifulSoup(r.content)
title = wikisoup.find(id="firstHeading")
title = title.text
if not title or keyword not in title.lower():
continue
print(wikiurl)
infobox = wikisoup.find(class_=infoboxrx)
if infobox:
for anchor in infobox.find_all('a'):
if 'href' in anchor.attrs:
targeturl = anchor['href']
# is the link internal
if targeturl.startswith('/'):
continue
reg_domain = registered_domain(targeturl)[1]
if reg_domain:
t = (title, reg_domain, wikiurl)
print(reg_domain)
targets.append(t)
external_links = wikisoup.find_all('a', class_="external text")
external_domains = set()
for anchor in external_links.find_all('a'):
if 'href' in anchor.attrs:
targeturl = anchor['href']
# is the link internal
if targeturl.startswith('/'):
continue
reg_domain = registered_domain(targeturl)[1]
if reg_domain:
external_domains.add((title, reg_domain, wiki_url))
return targets, sorted(external_domains)
|
23a449b0b8825d043d1ca3722cba4c373fcc5c3c
| 3,646,010
|
def escape(line, chars):
"""Escapes characters 'chars' with '\\' in 'line'."""
def esc_one_char(ch):
if ch in chars:
return "\\" + ch
else:
return ch
return u"".join([esc_one_char(ch) for ch in line])
|
f69409c92eacbbcab4232f7bb0ee244c77a4f219
| 3,646,012
|
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