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async def get_token(tkn: Token = Depends(from_authotization_header_nondyn)):
"""
Returns informations about the token currently being used. Requires a
clearance level of 0 or more.
"""
assert_has_clearance(tkn.owner, "sni.read_own_token")
return GetTokenOut.from_record(tkn) | 19ea12ad43a4a61f940e9dce4ca3c4a5d6fbbdf2 | 12,900 |
def import_as_event_history(path):
"""
Import file as event history json format.
Parameters
----------
path : str
Absolute path to file.
Returns
-------
events : list
List of historic events.
"""
# initialise output list
events = []
# import through pandas dataframe
df = pd.read_csv(path)
# verify columns existance
if not 'temperature' in df.columns or not 'unix_time' in df.columns:
print_error('Imported file should have columns \'temperature\' and \'unix_time\'.')
# extract UTC timestamps
tx = pd.to_datetime(df['unix_time'], unit='s')
# iterate events
for i in range(len(df)):
# convert unixtime to DT format
timestamp = dt_timestamp_format(tx[i])
# create event json format
json = api_json_format(timestamp, df['temperature'].iloc[i])
# append output
events.append(json)
return events | 1c4362263d177bf2d2a5561d3ed2048ff23faeb2 | 12,901 |
def reduce_dataset(d: pd.DataFrame, reduction_pars: dict):
"""
Reduces the data contained in a pandas DataFrame
:param d: pandas DataFrame. Each column contains lists of numbers
:param reduction_pars: dict containing 'type' and 'values'. 'type' describes the type of reduction performed on the
lists in d.
:return:
"""
p = pd.DataFrame(index=d.index)
for k in d:
if reduction_pars['type'] == 'bins':
p[k] = list(reduce_matrix(np.vstack(d[k].values), reduction_pars['values']))
if reduction_pars['type'] == 'aggregated_selection':
if np.all(reduction_pars['values'] == np.arange(len(d[k][0]))):
p[k] = d[k]
else:
p[k] = list(aggregated_reduction(np.vstack(d[k].values), reduction_pars['values']))
if reduction_pars['type'] == 'min':
p[k] = np.min(np.vstack(d[k].values), axis=1)
if reduction_pars['type'] == 'max':
p[k] = np.max(np.vstack(d[k].values), axis=1)
if reduction_pars['type'] == 'mean':
p[k] = np.mean(np.vstack(d[k].values), axis=1)
return p | 080bb5486787fab25bbc9347e83ed79d4525abe8 | 12,902 |
def update_office(office_id):
"""Given that i am an admin i should be able to edit a specific political office
When i visit to .../api/v2/offices endpoint using PATCH method"""
if is_admin() is not True:
return is_admin()
if not request.get_json():
return make_response(jsonify({'status': 401, 'message': 'empty body'}, 401))
office_data = request.get_json()
check_missingfields = validate.missing_value_validator(['name', 'type'], office_data)
if check_missingfields is not True:
return check_missingfields
check_emptyfield = validate.empty_string_validator(['name', 'type'], office_data)
if check_emptyfield is not True:
return check_emptyfield
check_if_text_only = validate.text_arrayvalidator(['name', 'type'], office_data)
if check_if_text_only is not True:
return check_if_text_only
office_name = office_data['name']
office_type = office_data['type']
res = office.edit_office(office_id, office_name, office_type)
return res | 897ee73b508caf1e3d463f68d55c030259efb6e5 | 12,903 |
import os
import zipfile
import json
def load_project_resource(file_path: str):
"""
Tries to load a resource:
1. directly
2. from the egg zip file
3. from the egg directory
This is necessary, because the files are bundled with the project.
:return: the file as json
"""
...
if not os.path.isfile(file_path):
try:
egg_path = __file__.split(".egg")[0] + ".egg"
if os.path.isfile(egg_path):
print(f"Try to load instances from ZIP at {egg_path}")
with zipfile.ZipFile(egg_path) as z:
f = z.open(file_path)
data = json.load(f)
else:
print(f"Try to load instances from directory at {egg_path}")
with open(egg_path + '/' + file_path) as f:
data = json.load(f)
except Exception:
raise FileNotFoundError(f"Could not find '{file_path}'. "
"Make sure you run the script from the correct directory.")
else:
with open(file_path) as f:
data = json.load(f)
return data | b9d46e1363fc1ca8b397b1512642b7795a8ea9c9 | 12,904 |
def phraser_on_header(row, phraser):
"""Applies phraser on cleaned header.
To be used with methods such as: `apply(func, axis=1)` or
`apply_by_multiprocessing(func, axis=1, **kwargs)`.
Parameters
----------
row : row of pd.Dataframe
phraser : Phraser instance,
Returns
-------
pd.Series
Examples
--------
>>> import pandas as pd
>>> data = pd.read_pickle('./tutorial/data/emails_anonymized.pickle')
>>> from melusine.nlp_tools.phraser import phraser_on_header
>>> from melusine.nlp_tools.phraser import Phraser
>>> # data contains a 'clean_header' column
>>> phraser = Phraser(columns='clean_header').load(filepath)
>>> data.apply(phraser_on_header, axis=1) # apply to all samples
"""
clean_header = phraser_on_text(row["clean_header"], phraser)
return clean_header | 30b9f11607ce1769b15a1c4fda4a4bc3b0aea94b | 12,905 |
from os.path import exists, isfile, join
def check_c_includes(filename, includes):
""" Check whether file exist in include dirs """
for directory in includes:
path = join(directory, filename)
if exists(path) and isfile(path):
return path | 041feddad25bd41cc0bdd0c4cf05c63996ba73f4 | 12,906 |
def hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):
"""
Args:
box_scores (N, 5): boxes in corner-form and probabilities.
iou_threshold: intersection over union threshold.
top_k: keep top_k results. If k <= 0, keep all the results.
candidate_size: only consider the candidates with the highest scores.
Returns:
picked: a list of indexes of the kept boxes
"""
scores = box_scores[:, -1]
boxes = box_scores[:, :-1]
picked = []
# _, indexes = scores.sort(descending=True)
indexes = np.argsort(scores)
# indexes = indexes[:candidate_size]
indexes = indexes[-candidate_size:]
while len(indexes) > 0:
# current = indexes[0]
current = indexes[-1]
picked.append(current)
if 0 < top_k == len(picked) or len(indexes) == 1:
break
current_box = boxes[current, :]
# indexes = indexes[1:]
indexes = indexes[:-1]
rest_boxes = boxes[indexes, :]
iou = iou_of(
rest_boxes,
np.expand_dims(current_box, axis=0),
)
indexes = indexes[iou <= iou_threshold]
return box_scores[picked, :] | 44a6dbcd0db425196bd91f22907be395d270b3d8 | 12,907 |
def sma(data, span=100):
"""Computes and returns the simple moving average.
Note: the moving average is computed on all columns.
:Input:
:data: pandas.DataFrame with stock prices in columns
:span: int (defaul: 100), number of days/values over which
the average is computed
:Output:
:sma: pandas.DataFrame of simple moving average
"""
return data.rolling(window=span, center=False).mean() | 8f8abf7f851424c20f6cee2ad4a01b934b7b0182 | 12,908 |
def parse_csd(dependencies):
"""Parse C-State Dependency"""
return _CSD_factory(len(csd_data))(csd_data) | 54ab24def420fd8350e1130b98be6b4651464fb8 | 12,909 |
def field_path_get_type(root: HdlType, field_path: TypePath):
"""
Get a data type of element using field path
"""
t = root
for p in field_path:
if isinstance(p, int):
t = t.element_t
else:
assert isinstance(p, str), p
t = t.field_by_name[p].dtype
return t | d6c5f0c750149505e6da78f7b3e3ed602b8f30b0 | 12,910 |
def reverse(rule):
"""
Given a rule X, generate its black/white reversal.
"""
#
# https://www.conwaylife.com/wiki/Black/white_reversal
#
# "The black/white reversal of a pattern is the result of
# toggling the state of each cell in the universe: bringing
# dead cells to life, and killing live cells. The black/white
# reversal of a pattern is sometimes called an anti-pattern;
# for instance, the black/white reversal of a glider (in an
# appropriate rule) is referred to as an anti-glider. The
# black/white reversal of a rule is a transformation of a
# rule in such a way that the black/white reversal of any
# pattern (in the previous sense) will behave the same way
# under the new rule as the unreversed pattern did under the
# original rule."
#
# Note that some rules are their own reversals:
#
# https://www.conwaylife.com/wiki/OCA:Day_%26_Night
#
# See also:
#
# http://golly.sourceforge.net/Help/Algorithms/QuickLife.html#b0emulation
#
# a set of the allowed numbers of neighbours
neighbours = set("012345678")
# split rule at "/"
[born, survive] = rule.split("/")
# drop "B" and "S" and make sets
born = set(born[1:])
survive = set(survive[1:])
# invert neighbour counts using set difference
# - example: B0123478 --> B56, S01234678 --> S5
born_inverse = neighbours - born
survive_inverse = neighbours - survive
# use S(8-x) for the B counts and B(8-x) for the S counts
# - example: B56 --> S23, S5 --> B3
born_complement = map(complement, survive_inverse)
survive_complement = map(complement, born_inverse)
# sort and join
born_final = "B" + "".join(sorted(born_complement))
survive_final = "S" + "".join(sorted(survive_complement))
# new rule
reverse_rule = born_final + "/" + survive_final
return reverse_rule | 0451b2a49257540b8a069f4cdb96d6bff4337cb7 | 12,911 |
import torch
def hsic(k_x: torch.Tensor, k_y: torch.Tensor, centered: bool = False, unbiased: bool = True) -> torch.Tensor:
"""Compute Hilbert-Schmidt Independence Criteron (HSIC)
:param k_x: n by n values of kernel applied to all pairs of x data
:param k_y: n by n values of kernel on y data
:param centered: whether or not at least one kernel is already centered
:param unbiased: if True, use unbiased HSIC estimator of Song et al (2007), else use original estimator of Gretton et al (2005)
:return: scalar score in [0*, inf) measuring dependence of x and y
* note that if unbiased=True, it is possible to get small values below 0.
"""
if k_x.size() != k_y.size():
raise ValueError("RDMs must have the same size!")
n = k_x.size()[0]
if not centered:
h = torch.eye(n, device=k_y.device, dtype=k_y.dtype) - 1/n
k_y = h @ k_y @ h
if unbiased:
# Remove the diagonal
k_x = k_x * (1 - torch.eye(n, device=k_x.device, dtype=k_x.dtype))
k_y = k_y * (1 - torch.eye(n, device=k_y.device, dtype=k_y.dtype))
# Equation (4) from Song et al (2007)
return ((k_x *k_y).sum() - 2*(k_x.sum(dim=0)*k_y.sum(dim=0)).sum()/(n-2) + k_x.sum()*k_y.sum()/((n-1)*(n-2))) / (n*(n-3))
else:
# The original estimator from Gretton et al (2005)
return torch.sum(k_x * k_y) / (n - 1)**2 | 7c91aa5991b90f396abbf835111a456208cbc50a | 12,912 |
def task_group_task_ui_to_app(ui_dict):
"""Converts TaskGroupTask ui dict to App entity."""
return workflow_entity_factory.TaskGroupTaskFactory().create_empty(
obj_id=ui_dict.get("obj_id"),
title=ui_dict["title"],
assignees=emails_to_app_people(ui_dict.get("assignees")),
start_date=str_to_date(ui_dict["start_date"]),
due_date=str_to_date(ui_dict["due_date"])
) | 64ad5bc96b56c2feb41417890c6f04c0f17e4691 | 12,913 |
def int_converter(value):
"""check for *int* value."""
int(value)
return str(value) | ba1b780c7886fccf1203225de249ef129561fd36 | 12,914 |
def wraps(fun, namestr="{fun}", docstr="{doc}", **kwargs):
"""Decorator for a function wrapping another.
Used when wrapping a function to ensure its name and docstring get copied
over.
Args:
fun: function to be wrapped
namestr: Name string to use for wrapped function.
docstr: Docstring to use for wrapped function.
**kwargs: additional string format values.
Return:
Wrapped function.
"""
def _wraps(f):
try:
f.__name__ = namestr.format(fun=get_name(fun), **kwargs)
f.__doc__ = docstr.format(fun=get_name(fun), doc=get_doc(fun), **kwargs)
finally:
return f
return _wraps | af05b43ee3ac2cc8595d35148b0156cd441dce3a | 12,915 |
from re import L
def test_plot_distributed_loads_fixed_left():
"""Test the plotting function for distributed loads and fixed support on the left.
Additionally, test plotting of continuity points.
"""
a = beam(L)
a.add_support(0, "fixed")
a.add_distributed_load(0, L / 2, "-q * x")
a.add_distributed_load(L / 2, L, "q * (L - x)")
a.solve()
fig, ax = a.plot(subs={"q": 1000})
return fig | 2c7c2b37e19e69a66a751bf59c3150f0b7aa3d3f | 12,916 |
import requests
import json
def post_report(coverage):
"""Post coverage report to coveralls.io."""
response = requests.post(URL, files={'json_file': json.dumps(coverage)})
try:
result = response.json()
except ValueError:
result = {'error': 'Failure to submit data. '
'Response [%(status)s]: %(text)s' % {
'status': response.status_code,
'text': response.text}}
print(result)
if 'error' in result:
return result['error']
return 0 | a33affb2791d3dbb7528ce9d4aae6a89f46d03f2 | 12,917 |
import tokenize
def parse_dialogs_per_response(lines,candid_dic,profile_size=None):
"""Parse dialogs provided in the personalized dialog tasks format.
For each dialog, every line is parsed, and the data for the dialog is made by appending
profile, user and bot responses so far, user utterance, bot answer index within candidates dictionary.
If profile is updated during the conversation due to a recognition error,
context_profile is overwritten with the new profile.
"""
data = []
context = []
context_profile = []
u = None
r = None
for line in lines:
line=line.strip()
if line:
nid, line = line.split(' ', 1)
nid = int(nid)
if nid == 1 and '\t' not in line:
# Process profile attributes
# format: isCusKnown , cusID , cusName
# format with order info: isCusKnown , cusID , cusName , prefSize , prefDrink , prefExtra (extra can be empty)
# isCusKnown is True or False
# cusID is the ID of the customer: if customer is not known, ID is 0, else starts from 1
# if isCusKnown = False then the profile will only be: False , 0
# after the customer is registered it will be False , cusID , chosenSize , chosenDrink , chosenExtra
# cusName is the name of the customer: if customer is not know, it is empty string, else it is name surname of the customer
if profile_size:
attribs = line.split(' , ')
if len(attribs) < profile_size:
# extend the attributes to the profile size so batch stacking won't be a problem
attribs.extend(['|']*(profile_size-len(attribs))) # append | for empty profile attributes, because it doesn't appear in word_index
else:
attribs = line.split(' ')
for attrib in attribs:
r=tokenize(attrib)
if r[0] != "|": # if it is a profile attribute
# Add temporal encoding, and utterance/response encoding
r.append('$r')
r.append('#'+str(nid))
context_profile.append(r)
else:
# Process conversation turns
if '\t' in line:
# Process turn containing bot response
u, r = line.split('\t')
a = candid_dic[r]
u = tokenize(u)
r = tokenize(r)
data.append((context_profile[:],context[:],u[:],a))
u.append('$u')
u.append('#'+str(nid))
r.append('$r')
r.append('#'+str(nid))
context.append(u)
context.append(r)
elif "True" in line or "False" in line:
# Process updated profile attributes (format: isCusKnown cusID cusName) - same as customer profile attributes.
# These are the true values. If the initial profile attributes are correct, there wouldn't be any updated profile attributes
# Else, it would appear after the name was given by the customer
context_profile = []
if profile_size:
attribs = line.split(' , ')
if len(attribs) < profile_size:
attribs.extend(['|']*(profile_size-len(attribs)))
else:
attribs = line.split(' ')
for attrib in attribs:
r=tokenize(attrib)
# Add temporal encoding, and utterance/response encoding
if r[0] != "|": # if it is a profile attribute
# Add temporal encoding, and utterance/response encoding
r.append('$r')
r.append('#'+str(nid))
context_profile.append(r)
else:
# Process turn without bot response
r=tokenize(line)
r.append('$r')
r.append('#'+str(nid))
context.append(r)
else:
# Clear profile and context when it is a new dialog
context=[]
context_profile=[]
return data | b919a9d970e93da9de6221f29573261f83158e49 | 12,918 |
import time
def get_dist():
"""
Measures the distance of the obstacle from the rover.
Uses a time.sleep call to try to prevent issues with pin writing and
reading. (See official gopigo library)
Returns error strings in the cases of measurements of -1 and 0, as -1
indicates and error, and 0 seems to also indicate a failed reading.
:return: The distance of the obstacle. (cm)
:rtype: either[int, str]
"""
time.sleep(0.01)
dist = gopigo.us_dist(gopigo.USS)
if dist == -1:
return USS_ERROR
elif dist == 0 or dist == 1:
return NOTHING_FOUND
else:
return dist | a615d9938b117821d39b9acdce507f0171583c03 | 12,919 |
from typing import Callable
def map_filter(filter_function: Callable) -> Callable:
"""
returns a version of a function that automatically maps itself across all
elements of a collection
"""
def mapped_filter(arrays, *args, **kwargs):
return [filter_function(array, *args, **kwargs) for array in arrays]
return mapped_filter | a5f9f97d1a0d4acdaa39b9fb72a73b95a81553bb | 12,920 |
from typing import Literal
def compare_models(
champion_model: lightgbm.Booster,
challenger_model: lightgbm.Booster,
valid_df: pd.DataFrame,
comparison_metric: Literal["any", "all", "f1_score", "auc"] = "any"
) -> bool:
"""
A function to compare the performance of the Champion and Challenger models
on the validation dataset comparison metrics
"""
comparison_metrics_directions = {"f1-score": ModelDirection.HIGHER_BETTER,
"auc": ModelDirection.HIGHER_BETTER,
"accuracy": ModelDirection.HIGHER_BETTER}
# Prep datasets
features = valid_df.drop(['target', 'id'], axis=1, errors="ignore")
labels = np.array(valid_df['target'])
valid_dataset = lightgbm.Dataset(data=features, label=labels)
# Calculate Champion and Challenger metrics for each
champion_metrics = get_model_metrics(champion_model, valid_dataset, "Champion")
challenger_metrics = get_model_metrics(challenger_model, valid_dataset, "Challenger")
if comparison_metric not in ['any', 'all']:
logger.info(f"Champion performance for {comparison_metric}: {champion_metrics[comparison_metric]}")
logger.info(f"Challenger performance for {comparison_metric}: {challenger_metrics[comparison_metric]}")
register_model = challenger_metric_better(champ_metrics=champion_metrics,
challenger_metrics=challenger_metrics,
metric_name=comparison_metric,
direction=comparison_metrics_directions[comparison_metric])
else:
comparison_results = {metric: challenger_metric_better(champ_metrics=champion_metrics,
challenger_metrics=challenger_metrics,
metric_name=metric,
direction=comparison_metrics_directions[metric])
for metric in champion_metrics.keys()}
if comparison_metric == "any":
register_model = any(comparison_results.values())
if register_model:
positive_results = [metric for metric, result in comparison_results.items() if result]
for metric in positive_results:
logger.info(f"Challenger Model performed better for '{metric}' on validation data")
else:
logger.info("Champion model performed better for all metrics on validation data")
else:
register_model = all(comparison_results.values())
if register_model:
logger.info("Challenger model performed better on all metrics on validation data")
else:
negative_ressults = [metric for metric, result in comparison_results.items() if not result]
for metric in negative_ressults:
logger.info(f"Champion Model performed better for '{metric}' on validation data")
return register_model | 8f5f522375a4c274c3c80fcbfddad2e8cf450328 | 12,921 |
def check_additional_args(parsedArgs, op, continueWithWarning=False):
"""
Parse additional arguments (rotation, etc.) and validate
:param additionalArgs: user input list of additional parameters e.g. [rotation, 60...]
:param op: operation object (use software_loader.getOperation('operationname')
:return: dictionary containing parsed arguments e.g. {rotation: 60}
"""
# parse additional arguments (rotation, etc.)
# http://stackoverflow.com/questions/6900955/python-convert-list-to-dictionary
if op is None:
print 'Invalid Operation Name {}'.format(op)
return {}
missing = [param for param in op.mandatoryparameters.keys() if
(param not in parsedArgs or len(str(parsedArgs[param])) == 0) and
param != 'inputmaskname' and
('source' not in op.mandatoryparameters[param] or op.mandatoryparameters[param]['source'] == 'image')]
inputmasks = [param for param in op.optionalparameters.keys() if param == 'inputmaskname' and
'purpose' in parsedArgs and parsedArgs['purpose'] == 'clone']
if ('inputmaskname' in op.mandatoryparameters.keys() or 'inputmaskname' in inputmasks) and (
'inputmaskname' not in parsedArgs or parsedArgs['inputmaskname'] is None or len(parsedArgs['inputmaskname']) == 0):
missing.append('inputmaskname')
if missing:
for m in missing:
print 'Mandatory parameter ' + m + ' is missing'
if continueWithWarning is False:
sys.exit(0)
return parsedArgs | ab289271fe4a61ec77ed2b522687dd4df7cbd35c | 12,922 |
import re
def clean_text(text, language):
"""
text: a string
returns: modified initial string (deletes/modifies punctuation and symbols.)
"""
replace_by_blank_symbols = re.compile('\#|\u00bb|\u00a0|\u00d7|\u00a3|\u00eb|\u00fb|\u00fb|\u00f4|\u00c7|\u00ab|\u00a0\ude4c|\udf99|\udfc1|\ude1b|\ude22|\u200b|\u2b07|\uddd0|\ude02|\ud83d|\u2026|\u201c|\udfe2|\u2018|\ude2a|\ud83c|\u2018|\u201d|\u201c|\udc69|\udc97|\ud83e|\udd18|\udffb|\ude2d|\udc80|\ud83e|\udd2a|\ud83e|\udd26|\u200d|\u2642|\ufe0f|\u25b7|\u25c1|\ud83e|\udd26|\udffd|\u200d|\u2642|\ufe0f|\udd21|\ude12|\ud83e|\udd14|\ude03|\ude03|\ude03|\ude1c|\udd81|\ude03|\ude10|\u2728|\udf7f|\ude48|\udc4d|\udffb|\udc47|\ude11|\udd26|\udffe|\u200d|\u2642|\ufe0f|\udd37|\ude44|\udffb|\u200d|\u2640|\udd23|\u2764|\ufe0f|\udc93|\udffc|\u2800|\u275b|\u275c|\udd37|\udffd|\u200d|\u2640|\ufe0f|\u2764|\ude48|\u2728|\ude05|\udc40|\udf8a|\u203c|\u266a|\u203c|\u2744|\u2665|\u23f0|\udea2|\u26a1|\u2022|\u25e1|\uff3f|\u2665|\u270b|\u270a|\udca6|\u203c|\u270c|\u270b|\u270a|\ude14|\u263a|\udf08|\u2753|\udd28|\u20ac|\u266b|\ude35|\ude1a|\u2622|\u263a|\ude09|\udd20|\udd15|\ude08|\udd2c|\ude21|\ude2b|\ude18|\udd25|\udc83|\ude24|\udc3e|\udd95|\udc96|\ude0f|\udc46|\udc4a|\udc7b|\udca8|\udec5|\udca8|\udd94|\ude08|\udca3|\ude2b|\ude24|\ude23|\ude16|\udd8d|\ude06|\ude09|\udd2b|\ude00|\udd95|\ude0d|\udc9e|\udca9|\udf33|\udc0b|\ude21|\udde3|\ude37|\udd2c|\ude21|\ude09|\ude39|\ude42|\ude41|\udc96|\udd24|\udf4f|\ude2b|\ude4a|\udf69|\udd2e|\ude09|\ude01|\udcf7|\ude2f|\ude21|\ude28|\ude43|\udc4a|\uddfa|\uddf2|\udc4a|\ude95|\ude0d|\udf39|\udded|\uddf7|\udded|\udd2c|\udd4a|\udc48|\udc42|\udc41|\udc43|\udc4c|\udd11|\ude0f|\ude29|\ude15|\ude18|\ude01|\udd2d|\ude43|\udd1d|\ude2e|\ude29|\ude00|\ude1f|\udd71|\uddf8|\ude20|\udc4a|\udeab|\udd19|\ude29|\udd42|\udc4a|\udc96|\ude08|\ude0d|\udc43|\udff3|\udc13|\ude0f|\udc4f|\udff9|\udd1d|\udc4a|\udc95|\udcaf|\udd12|\udd95|\udd38|\ude01|\ude2c|\udc49|\ude01|\udf89|\udc36|\ude0f|\udfff|\udd29|\udc4f|\ude0a|\ude1e|\udd2d|\uff46|\uff41|\uff54|\uff45|\uffe3|\u300a|\u300b|\u2708|\u2044|\u25d5|\u273f|\udc8b|\udc8d|\udc51|\udd8b|\udd54|\udc81|\udd80|\uded1|\udd27|\udc4b|\udc8b|\udc51|\udd90|\ude0e')
replace_by_apostrophe_symbol = re.compile('\u2019')
replace_by_dash_symbol = re.compile('\u2014')
replace_by_u_symbols = re.compile('\u00fb|\u00f9')
replace_by_a_symbols = re.compile('\u00e2|\u00e0')
replace_by_c_symbols = re.compile('\u00e7')
replace_by_i_symbols = re.compile('\u00ee|\u00ef')
replace_by_o_symbols = re.compile('\u00f4')
replace_by_oe_symbols = re.compile('\u0153')
replace_by_e_symbols = re.compile('\u00e9|\u00ea|\u0117|\u00e8')
replace_by_blank_symbols_2 = re.compile('\/|\(|\)|\{|\}|\[|\]|\,|\;|\.|\!|\?|\:|&|\n')
text = replace_by_e_symbols.sub('e', text)
text = replace_by_a_symbols.sub('a', text)
text = replace_by_o_symbols.sub('o', text)
text = replace_by_oe_symbols.sub('oe', text)
text = replace_by_u_symbols.sub('e', text)
text = replace_by_i_symbols.sub('e', text)
text = replace_by_u_symbols.sub('e', text)
text = replace_by_apostrophe_symbol.sub("'", text)
text = replace_by_dash_symbol.sub("_", text)
text = replace_by_blank_symbols.sub('', text)
text = replace_by_blank_symbols_2.sub('', text)
#For English
#text = ''.join([c for c in text if ord(c) < 128])
text = text.replace("\\", "")
STOPWORDS = set(stopwords.words(language))#to be changed
text = text.lower() # lowercase text
text = ' '.join(word for word in text.split() if word not in STOPWORDS) # delete stopwors from text
return text | eaf22844fcd3528c20b34f16c276042810a672f5 | 12,923 |
def parameters():
"""
Dictionary of parameters defining geophysical acquisition systems
"""
return {
"AeroTEM (2007)": {
"type": "time",
"flag": "Zoff",
"channel_start_index": 1,
"channels": {
"[1]": 58.1e-6,
"[2]": 85.9e-6,
"[3]": 113.7e-6,
"[4]": 141.4e-6,
"[5]": 169.2e-6,
"[6]": 197.0e-6,
"[7]": 238.7e-6,
"[8]": 294.2e-6,
"[9]": 349.8e-6,
"[10]": 405.3e-6,
"[11]": 474.8e-6,
"[12]": 558.1e-6,
"[13]": 655.3e-6,
"[14]": 794.2e-6,
"[15]": 988.7e-6,
"[16]": 1280.3e-6,
"[17]": 1738.7e-6,
},
"uncertainty": [
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
],
"waveform": [
[-1.10e-03, 1e-8],
[-8.2500e-04, 5.0e-01],
[-5.50e-04, 1.0e00],
[-2.7500e-04, 5.0e-01],
[0.0e00, 0.0e00],
[2.50e-05, 0.0e00],
[5.0e-05, 0.0e00],
[7.50e-05, 0.0e00],
[1.0e-04, 0.0e00],
[1.2500e-04, 0.0e00],
[1.50e-04, 0.0e00],
[1.7500e-04, 0.0e00],
[2.0e-04, 0.0e00],
[2.2500e-04, 0.0e00],
[2.50e-04, 0.0e00],
[3.0550e-04, 0.0e00],
[3.6100e-04, 0.0e00],
[4.1650e-04, 0.0e00],
[4.7200e-04, 0.0e00],
[5.2750e-04, 0.0e00],
[6.0750e-04, 0.0e00],
[6.8750e-04, 0.0e00],
[7.6750e-04, 0.0e00],
[8.4750e-04, 0.0e00],
[9.2750e-04, 0.0e00],
[1.1275e-03, 0.0e00],
[1.3275e-03, 0.0e00],
[1.5275e-03, 0.0e00],
[1.7275e-03, 0.0e00],
[1.9275e-03, 0.0e00],
[2.1275e-03, 0.0e00],
],
"tx_offsets": [[0, 0, 0]],
"bird_offset": [0, 0, -40],
"comment": "normalization accounts for 2.5m radius loop * 8 turns * 69 A current, nanoTesla",
"normalization": [2.9e-4, 1e-9],
"tx_specs": {"type": "CircularLoop", "a": 1.0, "I": 1.0},
"data_type": "dBzdt",
},
"AeroTEM (2010)": {
"type": "time",
"flag": "Zoff",
"channel_start_index": 1,
"channels": {
"[1]": 67.8e-6,
"[2]": 95.6e-6,
"[3]": 123.4e-6,
"[4]": 151.2e-6,
"[5]": 178.9e-6,
"[6]": 206.7e-6,
"[7]": 262.3e-6,
"[8]": 345.6e-6,
"[9]": 428.9e-6,
"[10]": 512.3e-6,
"[11]": 623.4e-6,
"[12]": 762.3e-6,
"[13]": 928.9e-6,
"[14]": 1165.0e-6,
"[15]": 1526.2e-6,
"[16]": 2081.7e-6,
"[17]": 2942.8e-6,
},
"uncertainty": [
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
[0.05, 5e-0],
],
"waveform": [
[-1.10e-03, 1e-8],
[-8.2500e-04, 5.0e-01],
[-5.50e-04, 1.0e00],
[-2.7500e-04, 5.0e-01],
[0.0e00, 0.0e00],
[2.50e-05, 0.0e00],
[5.0e-05, 0.0e00],
[7.50e-05, 0.0e00],
[1.0e-04, 0.0e00],
[1.2500e-04, 0.0e00],
[1.50e-04, 0.0e00],
[1.7500e-04, 0.0e00],
[2.0e-04, 0.0e00],
[2.2500e-04, 0.0e00],
[2.50e-04, 0.0e00],
[3.0550e-04, 0.0e00],
[3.6100e-04, 0.0e00],
[4.1650e-04, 0.0e00],
[4.7200e-04, 0.0e00],
[5.2750e-04, 0.0e00],
[6.0750e-04, 0.0e00],
[6.8750e-04, 0.0e00],
[7.6750e-04, 0.0e00],
[8.4750e-04, 0.0e00],
[9.2750e-04, 0.0e00],
[1.1275e-03, 0.0e00],
[1.3275e-03, 0.0e00],
[1.5275e-03, 0.0e00],
[1.7275e-03, 0.0e00],
[1.9275e-03, 0.0e00],
[2.1275e-03, 0.0e00],
[2.3275e-03, 0.0e00],
[2.5275e-03, 0.0e00],
[2.7275e-03, 0.0e00],
[2.9275e-03, 0.0e00],
[3.1275e-03, 0.0e00],
],
"tx_offsets": [[0, 0, 0]],
"bird_offset": [0, 0, -40],
"comment": "normalization accounts for 2.5m radius loop, 8 turns * 69 A current, nanoTesla",
"normalization": [2.9e-4, 1e-9],
"tx_specs": {"type": "CircularLoop", "a": 1.0, "I": 1.0},
"data_type": "dBzdt",
},
"DIGHEM": {
"type": "frequency",
"flag": "CPI900",
"channel_start_index": 0,
"channels": {
"CPI900": 900,
"CPI7200": 7200,
"CPI56K": 56000,
"CPQ900": 900,
"CPQ7200": 7200,
"CPQ56K": 56000,
},
"components": {
"CPI900": "real",
"CPQ900": "imag",
"CPI7200": "real",
"CPQ7200": "imag",
"CPI56K": "real",
"CPQ56K": "imag",
},
"tx_offsets": [
[8, 0, 0],
[8, 0, 0],
[6.3, 0, 0],
[8, 0, 0],
[8, 0, 0],
[6.3, 0, 0],
],
"bird_offset": [0, 0, 0],
"uncertainty": [
[0.0, 2],
[0.0, 5],
[0.0, 10],
[0.0, 2],
[0.0, 5],
[0.0, 10],
],
"tx_specs": {"type": "VMD", "a": 1.0, "I": 1.0},
"normalization": "ppm",
},
"GENESIS (2014)": {
"type": "time",
"flag": "emz_step_final",
"channel_start_index": 1,
"channels": {
"0": 9e-6,
"1": 26e-6,
"2": 52.0e-6,
"3": 95e-6,
"4": 156e-6,
"5": 243e-6,
"6": 365e-6,
"7": 547e-6,
"8": 833e-6,
"9": 1259e-6,
"10": 1858e-6,
},
"uncertainty": [
[0.05, 100],
[0.05, 100],
[0.05, 100],
[0.05, 100],
[0.05, 100],
[0.05, 100],
[0.05, 2000],
[0.05, 100],
[0.05, 100],
[0.05, 100],
[0.05, 100],
],
"waveform": "stepoff",
"tx_offsets": [[-90, 0, -43]],
"bird_offset": [-90, 0, -43],
"comment": "normalization accounts for unit dipole moment at the tx_offset, in part-per-million",
"normalization": "ppm",
"tx_specs": {"type": "VMD", "a": 1.0, "I": 1.0},
"data_type": "Bz",
},
"GEOTEM 75 Hz - 2082 Pulse": {
"type": "time",
"flag": "EM_chan",
"channel_start_index": 5,
"channels": {
"1": -1953e-6,
"2": -1562e-6,
"3": -989e-6,
"4": -416e-6,
"5": 163e-6,
"6": 235e-6,
"7": 365e-6,
"8": 521e-6,
"9": 703e-6,
"10": 912e-6,
"11": 1146e-6,
"12": 1407e-6,
"13": 1693e-6,
"14": 2005e-6,
"15": 2344e-6,
"16": 2709e-6,
"17": 3073e-6,
"18": 3464e-6,
"19": 3880e-6,
"20": 4297e-6,
},
"uncertainty": [
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
[0.05, 40.0],
],
"waveform": [
[-2.08000000e-03, 1.22464680e-16],
[-1.83000000e-03, 3.68686212e-01],
[-1.58000000e-03, 6.85427422e-01],
[-1.33000000e-03, 9.05597273e-01],
[-1.08000000e-03, 9.98175554e-01],
[-8.30000000e-04, 9.50118712e-01],
[-5.80000000e-04, 7.68197578e-01],
[-3.30000000e-04, 4.78043417e-01],
[-8.00000000e-05, 1.20536680e-01],
[0.00000000e00, 0.00000000e00],
[1.00000000e-04, 0.00000000e00],
[2.00000000e-04, 0.00000000e00],
[3.00000000e-04, 0.00000000e00],
[4.00000000e-04, 0.00000000e00],
[5.00000000e-04, 0.00000000e00],
[6.00000000e-04, 0.00000000e00],
[7.00000000e-04, 0.00000000e00],
[8.00000000e-04, 0.00000000e00],
[9.00000000e-04, 0.00000000e00],
[1.00000000e-03, 0.00000000e00],
[1.10000000e-03, 0.00000000e00],
[1.20000000e-03, 0.00000000e00],
[1.30000000e-03, 0.00000000e00],
[1.40000000e-03, 0.00000000e00],
[1.50000000e-03, 0.00000000e00],
[1.60000000e-03, 0.00000000e00],
[1.70000000e-03, 0.00000000e00],
[1.80000000e-03, 0.00000000e00],
[1.90000000e-03, 0.00000000e00],
[2.00000000e-03, 0.00000000e00],
[2.10000000e-03, 0.00000000e00],
[2.20000000e-03, 0.00000000e00],
[2.30000000e-03, 0.00000000e00],
[2.40000000e-03, 0.00000000e00],
[2.50000000e-03, 0.00000000e00],
[2.60000000e-03, 0.00000000e00],
[2.70000000e-03, 0.00000000e00],
[2.80000000e-03, 0.00000000e00],
[2.90000000e-03, 0.00000000e00],
[3.00000000e-03, 0.00000000e00],
[3.10000000e-03, 0.00000000e00],
[3.20000000e-03, 0.00000000e00],
[3.30000000e-03, 0.00000000e00],
[3.40000000e-03, 0.00000000e00],
[3.50000000e-03, 0.00000000e00],
[3.60000000e-03, 0.00000000e00],
[3.70000000e-03, 0.00000000e00],
[3.80000000e-03, 0.00000000e00],
[3.90000000e-03, 0.00000000e00],
[4.00000000e-03, 0.00000000e00],
[4.10000000e-03, 0.00000000e00],
[4.20000000e-03, 0.00000000e00],
[4.30000000e-03, 0.00000000e00],
[4.40000000e-03, 0.00000000e00],
],
"tx_offsets": [[-123, 0, -56]],
"bird_offset": [-123, 0, -56],
"comment": "normalization accounts for unit dipole moment at the tx_offset, in part-per-million",
"normalization": "ppm",
"tx_specs": {"type": "VMD", "a": 1.0, "I": 1.0},
"data_type": "Bz",
},
"HELITEM (35C)": {
"type": "time",
"flag": "emz_db",
"channel_start_index": 5,
"channels": {
"[1]": -0.007772,
"[2]": -0.003654,
"[3]": -0.002678,
"[4]": -0.000122,
"[5]": 0.000228,
"[6]": 0.000269,
"[7]": 0.000326,
"[8]": 0.000399,
"[9]": 0.000488,
"[10]": 0.000594,
"[11]": 0.000716,
"[12]": 0.000854,
"[13]": 0.001009,
"[14]": 0.001196,
"[15]": 0.001424,
"[16]": 0.001693,
"[17]": 0.002018,
"[18]": 0.002417,
"[19]": 0.002905,
"[20]": 0.003499,
"[21]": 0.004215,
"[22]": 0.005078,
"[23]": 0.006128,
"[24]": 0.007406,
"[25]": 0.008952,
"[26]": 0.010824,
"[27]": 0.013094,
"[28]": 0.015845,
"[29]": 0.019173,
"[30]": 0.02321,
},
"uncertainty": [
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
[0.05, 2e-1],
],
"waveform": [
[-8.000e-03, 1.000e-03],
[-7.750e-03, 9.802e-02],
[-7.500e-03, 1.950e-01],
[-7.250e-03, 2.902e-01],
[-7.000e-03, 3.826e-01],
[-6.750e-03, 4.713e-01],
[-6.500e-03, 5.555e-01],
[-6.250e-03, 6.344e-01],
[-6.000e-03, 7.071e-01],
[-5.750e-03, 7.730e-01],
[-5.500e-03, 8.315e-01],
[-5.250e-03, 8.820e-01],
[-5.000e-03, 9.239e-01],
[-4.750e-03, 9.569e-01],
[-4.500e-03, 9.808e-01],
[-4.250e-03, 9.951e-01],
[-4.000e-03, 1.000e00],
[-3.750e-03, 9.951e-01],
[-3.500e-03, 9.808e-01],
[-3.250e-03, 9.569e-01],
[-3.000e-03, 9.239e-01],
[-2.750e-03, 8.820e-01],
[-2.500e-03, 8.315e-01],
[-2.250e-03, 7.730e-01],
[-2.000e-03, 7.071e-01],
[-1.750e-03, 6.344e-01],
[-1.500e-03, 5.555e-01],
[-1.250e-03, 4.713e-01],
[-1.000e-03, 3.826e-01],
[-7.500e-04, 2.902e-01],
[-5.000e-04, 1.950e-01],
[-2.500e-04, 9.802e-02],
[0.000e00, 0.000e00],
[5.000e-05, 0.000e00],
[1.000e-04, 0.000e00],
[1.500e-04, 0.000e00],
[2.000e-04, 0.000e00],
[2.500e-04, 0.000e00],
[3.000e-04, 0.000e00],
[3.500e-04, 0.000e00],
[4.000e-04, 0.000e00],
[4.500e-04, 0.000e00],
[5.000e-04, 0.000e00],
[5.500e-04, 0.000e00],
[6.000e-04, 0.000e00],
[6.500e-04, 0.000e00],
[7.000e-04, 0.000e00],
[7.500e-04, 0.000e00],
[8.000e-04, 0.000e00],
[8.500e-04, 0.000e00],
[9.000e-04, 0.000e00],
[9.500e-04, 0.000e00],
[1.000e-03, 0.000e00],
[1.050e-03, 0.000e00],
[1.100e-03, 0.000e00],
[1.150e-03, 0.000e00],
[1.200e-03, 0.000e00],
[1.225e-03, 0.000e00],
[1.475e-03, 0.000e00],
[1.725e-03, 0.000e00],
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"[32]": 1.02080e00,
"[33]": 1.17120e00,
"[34]": 1.34400e00,
"[35]": 1.54560e00,
"[36]": 1.77920e00,
"[37]": 2.04480e00,
"[38]": 2.34560e00,
"[39]": 2.69120e00,
"[40]": 3.08800e00,
"[41]": 3.54240e00,
"[42]": 4.06720e00,
"[43]": 4.67200e00,
"[44]": 5.36640e00,
"[45]": 6.16320e00,
"[46]": 7.07840e00,
"[47]": 8.13120e00,
"[48]": 9.33760e00,
"[49]": 1.07232e01,
"[50]": 1.24896e01,
},
"uncertainty": [
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
[0.05, 1e-3],
],
"waveform": [
[-5.50e00, 1.00000000e-08],
[-5.20e00, 5.00000000e-01],
[-4.90e00, 9.40112151e-01],
[-4.60e00, 9.40178855e-01],
[-4.30e00, 9.40245558e-01],
[-4.00e00, 9.40312262e-01],
[-3.70e00, 9.40378966e-01],
[-3.40e00, 9.40445670e-01],
[-3.10e00, 9.40512373e-01],
[-2.80e00, 9.40579077e-01],
[-2.50e00, 9.40645781e-01],
[-2.20e00, 9.40712484e-01],
[-1.90e00, 9.40779188e-01],
[-1.60e00, 9.40845892e-01],
[-1.30e00, 9.40912595e-01],
[-1.00e00, 9.40979299e-01],
[-7.00e-01, 9.41046003e-01],
[-4.00e-01, 9.41112706e-01],
[-3.50e-01, 9.41123824e-01],
[-3.00e-01, 8.98234294e-01],
[-2.50e-01, 7.89179185e-01],
[-2.00e-01, 6.47911463e-01],
[-1.50e-01, 4.80782576e-01],
[-1.00e-01, 2.95120400e-01],
[-5.00e-02, 9.92697471e-02],
[0.00e-00, 3.68628739e-18],
[5.00e-03, 0.00000000e00],
[1.00e-02, 0.00000000e00],
[1.50e-02, 0.00000000e00],
[2.00e-02, 0.00000000e00],
[2.50e-02, 0.00000000e00],
[3.00e-02, 0.00000000e00],
[3.50e-02, 0.00000000e00],
[4.00e-02, 0.00000000e00],
[4.50e-02, 0.00000000e00],
[5.00e-02, 0.00000000e00],
[5.50e-02, 0.00000000e00],
[6.00e-02, 0.00000000e00],
[6.50e-02, 0.00000000e00],
[7.00e-02, 0.00000000e00],
[7.50e-02, 0.00000000e00],
[8.00e-02, 0.00000000e00],
[8.50e-02, 0.00000000e00],
[9.00e-02, 0.00000000e00],
[9.50e-02, 0.00000000e00],
[1.00e-01, 0.00000000e00],
[1.10e-01, 0.00000000e00],
[1.20e-01, 0.00000000e00],
[1.30e-01, 0.00000000e00],
[1.40e-01, 0.00000000e00],
[1.50e-01, 0.00000000e00],
[1.60e-01, 0.00000000e00],
[1.70e-01, 0.00000000e00],
[1.80e-01, 0.00000000e00],
[1.90e-01, 0.00000000e00],
[2.00e-01, 0.00000000e00],
[2.10e-01, 0.00000000e00],
[2.20e-01, 0.00000000e00],
[2.30e-01, 0.00000000e00],
[2.40e-01, 0.00000000e00],
[2.50e-01, 0.00000000e00],
[2.60e-01, 0.00000000e00],
[2.70e-01, 0.00000000e00],
[2.80e-01, 0.00000000e00],
[2.90e-01, 0.00000000e00],
[3.00e-01, 0.00000000e00],
[3.25e-01, 0.00000000e00],
[3.50e-01, 0.00000000e00],
[3.75e-01, 0.00000000e00],
[4.00e-01, 0.00000000e00],
[4.25e-01, 0.00000000e00],
[4.50e-01, 0.00000000e00],
[4.75e-01, 0.00000000e00],
[5.00e-01, 0.00000000e00],
[5.25e-01, 0.00000000e00],
[5.50e-01, 0.00000000e00],
[5.75e-01, 0.00000000e00],
[6.00e-01, 0.00000000e00],
[7.50e-01, 0.00000000e00],
[9.00e-01, 0.00000000e00],
[1.05e00, 0.00000000e00],
[1.20e00, 0.00000000e00],
[1.35e00, 0.00000000e00],
[1.50e00, 0.00000000e00],
[1.65e00, 0.00000000e00],
[1.80e00, 0.00000000e00],
[1.95e00, 0.00000000e00],
[2.10e00, 0.00000000e00],
[2.25e00, 0.00000000e00],
[2.40e00, 0.00000000e00],
[2.55e00, 0.00000000e00],
[2.70e00, 0.00000000e00],
[2.85e00, 0.00000000e00],
[3.00e00, 0.00000000e00],
[3.15e00, 0.00000000e00],
[3.30e00, 0.00000000e00],
[3.45e00, 0.00000000e00],
[3.60e00, 0.00000000e00],
[3.75e00, 0.00000000e00],
[3.90e00, 0.00000000e00],
[4.00e00, 0.00000000e00],
[4.50e00, 0.00000000e00],
[5.00e00, 0.00000000e00],
[5.50e00, 0.00000000e00],
[6.00e00, 0.00000000e00],
[6.50e00, 0.00000000e00],
[7.00e00, 0.00000000e00],
[7.50e00, 0.00000000e00],
[8.00e00, 0.00000000e00],
[8.50e00, 0.00000000e00],
[9.00e00, 0.00000000e00],
[9.50e00, 0.00000000e00],
[1.00e01, 0.00000000e00],
[1.05e01, 0.00000000e00],
[1.10e01, 0.00000000e00],
[1.15e01, 0.00000000e00],
[1.20e01, 0.00000000e00],
[1.25e01, 0.00000000e00],
],
"tx_offsets": [[0, 0, 0]],
"bird_offset": [0, 0, 0],
"normalization": [3.1416, 1e-12],
"tx_specs": {"type": "CircularLoop", "a": 1.0, "I": 1.0},
"data_type": "dBzdt",
},
} | 5ae2626c2c8a5445457169f3f2866bf8233ee7f3 | 12,924 |
import binascii
def generate_ngrams_and_hashit(tokens, n=3):
"""The function generates and hashes ngrams
which gets from the tokens sequence.
@param tokens - list of tokens
@param n - count of elements in sequences
"""
return [binascii.crc32(bytearray(tokens[i:i + n]))
for i in range(len(tokens) - n + 1)] | eb627add56f51a533c773e0dfea029bfcdb808ee | 12,925 |
from typing import List
def generate_fingerprints(args: Namespace, logger: Logger = None) -> List[List[float]]:
"""
Generate the fingerprints.
:param logger:
:param args: Arguments.
:return: A list of lists of target fingerprints.
"""
# import pdb; pdb.set_trace()
checkpoint_path = args.checkpoint_paths[0]
if logger is None:
logger = create_logger('fingerprints', quiet=False)
print('Loading data')
test_data = get_data(path=args.data_path,
args=args,
use_compound_names=False,
max_data_size=float("inf"),
skip_invalid_smiles=False)
test_data = MoleculeDataset(test_data)
####
test_df = test_data.get_smiles_df()
####
logger.info(f'Total size = {len(test_data):,}')
logger.info(f'Generating...')
# Load model
# import pdb; pdb.set_trace()
model = load_checkpoint(checkpoint_path, cuda=args.cuda, current_args=args, logger=logger)
model_preds = do_generate(
model=model,
data=test_data,
args=args
)
####
test_df["fps"] = model_preds
####
return test_df | aac2b9f342306be7dc79a029c8433dd5f2147d0e | 12,926 |
def transform_xlogx(mat):
"""
Args:
mat(np.array): A two-dimensional array
Returns:
np.array: Let UsV^† be the SVD of mat. Returns
Uf(s)V^†, where f(x) = -2xlogx
"""
U, s, Vd = np.linalg.svd(mat, full_matrices=False)
return (U * (-2.0*s * np.log(s))) @ Vd | f4cf18a8ae9f7a298ffd6b6400a33a9cad5fabbd | 12,927 |
import SimpleITK as sitk
import os
from collections import (
OrderedDict,
) # Need OrderedDict internally to ensure consistent ordering
def get_label_volumes(labelVolume, RefVolume, labelDictionary):
"""
Get label volumes using
1. reference volume and
2. labeldictionary
:param labelVolume:
:param RefVolume:
:param labelDictionary:
:return:
"""
labelImg = sitk.ReadImage(labelVolume, sitk.sitkInt64)
RefImg = sitk.ReadImage(RefVolume, sitk.sitkFloat64)
labelStatFilter = sitk.LabelStatisticsImageFilter()
labelStatFilter.Execute(RefImg, labelImg)
ImageSpacing = RefImg.GetSpacing()
outputLabelVolumes = list()
for value in labelStatFilter.GetLabels():
structVolume = (
ImageSpacing[0]
* ImageSpacing[1]
* ImageSpacing[2]
* labelStatFilter.GetCount(value)
)
labelVolDict = OrderedDict()
labelVolDict["Volume_mm3"] = structVolume
if value in list(labelDictionary.keys()):
print(("{0} --> {1}".format(value, labelDictionary[value])))
labelVolDict["LabelName"] = labelDictionary[value]
else:
print(("** Caution: {0} --> No name exists!".format(value)))
labelVolDict["LabelName"] = "NA"
labelVolDict["LabelCode"] = value
labelVolDict["FileName"] = os.path.abspath(labelVolume)
outputLabelVolumes.append(labelVolDict)
return outputLabelVolumes | b79c5755804192ef9507be98dde06bf8e2750220 | 12,928 |
from datetime import datetime
def get_clip_name_from_unix_time(source_guid, current_clip_start_time):
"""
"""
# convert unix time to
readable_datetime = datetime.fromtimestamp(int(current_clip_start_time)).strftime('%Y_%m_%d_%H_%M_%S')
clipname = source_guid + "_" + readable_datetime
return clipname, readable_datetime | 0a212a76a69507ae3020c1e05ec354a927ad3dae | 12,929 |
def extract_pc_in_box3d(pc, box3d):
"""Extract point cloud in box3d.
Args:
pc (np.ndarray): [N, 3] Point cloud.
box3d (np.ndarray): [8,3] 3d box.
Returns:
np.ndarray: Selected point cloud.
np.ndarray: Indices of selected point cloud.
"""
box3d_roi_inds = in_hull(pc[:, 0:3], box3d)
return pc[box3d_roi_inds, :], box3d_roi_inds | 2d9cb9089631e357ed8ec5ee454203d5eaec1c1d | 12,930 |
import typing
def get_list_as_str(list_to_convert: typing.List[str]) -> str:
"""Convert list into comma separated string, with each element enclosed in single quotes"""
return ", ".join(["'{}'".format(list_item) for list_item in list_to_convert]) | 6b565c3d63d9887f05d5369511b8453c406f7b72 | 12,931 |
def normalize_sides(sides):
"""
Description: Squares the sides of the rectangles and averages the points
so that they fit together
Input:
- sides - Six vertex sets representing the sides of a drawing
Returns:
- norm_sides - Squared and fit sides list
"""
sides_list = []
# Average side vertices and make perfect rectangles
def square_sides(sides):
# Find the min/max x and y values
x = []
y = []
for vert in sides:
x.append(vert[0][0])
y.append(vert[0][1])
minx = 0
miny = 0
maxx = max(x)-min(x)
maxy = max(y)-min(y)
# Construct new squared vertex set with format |1 2|
# |3 4|
squared_side = [[minx,miny],[maxx,miny],[maxx,maxy],[minx,maxy]]
#squared_side = [[minx, maxy], [maxx, maxy], [minx, miny], [maxx, miny]]
return squared_side
squared_right = square_sides(sides[0])
squared_left = square_sides(sides[1])
squared_top = square_sides(sides[2])
squared_back = square_sides(sides[3])
squared_front = square_sides(sides[4])
squared_bottom = square_sides(sides[5])
return squared_front,squared_left,squared_back,squared_right,squared_top,squared_bottom | 855fcc45d14db2eede9fd7ec2fa6bf2f6854950d | 12,932 |
def GetFieldInfo(column: Schema.Column,
force_nested_types: bool = False,
nested_prefix: str = 'Nested_') -> FieldInfo:
"""Returns the corresponding information for provided column.
Args:
column: the column for which to generate the dataclass FieldInfo.
force_nested_types: when True, a nested subclass is generated always,
even for known dataclass types.
nested_prefix: name prefix for nested dataclasses.
Returns:
The corresponding `FieldInfo` class for column.
"""
column.validate()
info = FieldInfo(column.info.name)
nested_name = f'{nested_prefix}{column.info.name}'
sub_nested_name = f'{nested_name}_'
if column.info.column_type in _TYPE_INFO:
info.type_info = _TYPE_INFO[column.info.column_type].copy()
_ApplyLabel(column, info)
elif column.info.column_type == Schema_pb2.ColumnInfo.TYPE_NESTED:
if column.info.message_name and not force_nested_types:
info.type_info = TypeInfo(column.info.message_name)
else:
info.type_info = TypeInfo(nested_name)
nested = NestedType(info.type_info.name)
nested.fields = [
GetFieldInfo(sub_column, force_nested_types, sub_nested_name)
for sub_column in column.fields
]
info.nested.append(nested)
_ApplyLabel(column, info)
elif column.info.column_type in (Schema_pb2.ColumnInfo.TYPE_ARRAY,
Schema_pb2.ColumnInfo.TYPE_SET):
element_info = GetFieldInfo(column.fields[0], force_nested_types,
sub_nested_name)
if column.info.column_type == Schema_pb2.ColumnInfo.TYPE_ARRAY:
name = 'typing.List'
else:
name = 'typing.Set'
info.type_info = TypeInfo(name, None, {'typing'},
[element_info.type_info])
info.nested.extend(element_info.nested)
elif column.info.column_type == Schema_pb2.ColumnInfo.TYPE_MAP:
key_info = GetFieldInfo(column.fields[0], force_nested_types,
sub_nested_name)
value_info = GetFieldInfo(column.fields[1], force_nested_types,
sub_nested_name)
info.type_info = TypeInfo('typing.Dict', None, {'typing'},
[key_info.type_info, value_info.type_info])
info.nested.extend(key_info.nested)
info.nested.extend(value_info.nested)
else:
raise ValueError(f'Unsupported type `{column.info.column_type}` '
f'for field `{column.name()}`')
info.type_info.add_annotations(_GetColumnAnnotations(column))
return info | e788ef090bb8fd8150f2effe42d2db4e8c3fdde6 | 12,933 |
def get_session():
"""
Get the current session.
:return: the session
:raises OutsideUnitOfWorkError: if this method is called from outside a UOW
"""
global Session
if Session is None or not Session.registry.has():
raise OutsideUnitOfWorkError
return Session() | 8a1bfb7afff2cc1a843eaa01e59eabea5e083339 | 12,934 |
import functools
import torch
def create_sgd_optimizers_fn(datasets, model, learning_rate, momentum=0.9, weight_decay=0, nesterov=False, scheduler_fn=None, per_step_scheduler_fn=None):
"""
Create a Stochastic gradient descent optimizer for each of the dataset with optional scheduler
Args:
datasets: a dictionary of dataset
model: a model to optimize
learning_rate: the initial learning rate
scheduler_fn: a scheduler, or `None`
momentum: the momentum of the SGD
weight_decay: the weight decay
nesterov: enables Nesterov momentum
per_step_scheduler_fn: the functor to instantiate scheduler to be run per-step (batch)
Returns:
An optimizer
"""
optimizer_fn = functools.partial(
torch.optim.SGD,
lr=learning_rate,
momentum=momentum,
weight_decay=weight_decay,
nesterov=nesterov)
return create_optimizers_fn(datasets, model,
optimizer_fn=optimizer_fn,
scheduler_fn=scheduler_fn,
per_step_scheduler_fn=per_step_scheduler_fn) | 6d76d5dfc0f633a324d7ee9fe347f1bc68bf0492 | 12,935 |
def equalize(img):
"""
Equalize the histogram of input PIL image.
Args:
img (PIL image): Image to be equalized
Returns:
img (PIL image), Equalized image.
"""
if not is_pil(img):
raise TypeError('img should be PIL image. Got {}'.format(type(img)))
return ImageOps.equalize(img) | d31d3590ba7927518475053911bdb4251381815d | 12,936 |
import contextlib
import os
import subprocess
def routemaster_serve_subprocess(unused_tcp_port):
"""
Fixture to spawn a routemaster server as a subprocess.
Yields the process reference, and the port that it can be accessed on.
"""
@contextlib.contextmanager
def _inner(*, wait_for_output=None):
env = os.environ.copy()
env.update({
'DB_HOST': os.environ.get('PG_HOST', 'localhost'),
'DB_PORT': os.environ.get('PG_PORT', '5432'),
'DB_NAME': os.environ.get('PG_DB', 'routemaster_test'),
'DB_USER': os.environ.get('PG_USER', ''),
'DB_PASS': os.environ.get('PG_PASS', ''),
})
try:
proc = subprocess.Popen(
[
'routemaster',
'--config-file=example.yaml',
'serve',
'--bind',
f'127.0.0.1:{unused_tcp_port}',
],
env=env,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
)
if wait_for_output is not None:
all_output = b''
while True:
assert proc.poll() is None, all_output.decode('utf-8')
out_line = proc.stdout.readline()
if wait_for_output in out_line:
break
all_output += out_line
yield proc, unused_tcp_port
finally:
proc.terminate()
return _inner | 4b0d4020a83acc78342d35e7a2e5657b41b12b88 | 12,937 |
def ratingRange(app):
""" Get the rating range of an app. """
rating = 'Unknown'
r = app['rating']
if r >= 0 and r <= 1:
rating = '0-1'
elif r > 1 and r <= 2:
rating = '1-2'
elif r > 2 and r <= 3:
rating = '2-3'
elif r > 3 and r <= 4:
rating = '3-4'
elif r > 4 and r <= 5:
rating = '4-5'
return rating | 69056c367a87e331cd3b606423540250b20f6485 | 12,938 |
def immediate_sister(graph, node1, node2):
"""
is node2 an immediate sister of node1?
"""
return (node2 in sister_nodes(graph, node1) and is_following(graph, node1, node2)) | cb1cdc13e8aceb88e72781a547c9ef1eb2079cb0 | 12,939 |
def get_topic_link(text: str) -> str:
"""
Generate a topic link.
A markdown link, text split with dash.
Args:
text {str} The text value to parse
Returns:
{str} The parsed text
"""
return f"{text.lower().replace(' ', '-')}" | 445afc9358f98323934e0e2788ed52658c7040cb | 12,940 |
def load_data(datapath):
"""Loads data from CSV data file.
Args:
datapath: Location of the training file
Returns:
summary dataframe containing RFM data for btyd models
actuals_df containing additional data columns for calculating error
"""
# Does not used the summary_data_from_transaction_data from the Lifetimes
# library as it wouldn't scale as well. The pre-processing done in BQ instead.
tf.logging.info('Loading data...')
ft_file = '{0}/{1}'.format(datapath, TRAINING_DATA_FILE)
#[START prob_selec]
df_ft = pd.read_csv(ft_file)
# Extracts relevant dataframes for RFM:
# - summary has aggregated values before the threshold date
# - actual_df has values of the overall period.
summary = df_ft[['customer_id', 'frequency_btyd', 'recency', 'T',
'monetary_btyd']]
#[END prob_selec]
summary.columns = ['customer_id', 'frequency', 'recency', 'T',
'monetary_value']
summary = summary.set_index('customer_id')
# additional columns needed for calculating error
actual_df = df_ft[['customer_id', 'frequency_btyd', 'monetary_dnn',
'target_monetary']]
actual_df.columns = ['customer_id', 'train_frequency', 'train_monetary',
'act_target_monetary']
tf.logging.info('Data loaded.')
return summary, actual_df | d7efe25303fa7839a9842d7b04bcdb1f749a4830 | 12,941 |
def rms(a, axis=None):
"""
Calculates the RMS of an array.
Args:
a (ndarray). A sequence of numbers to apply the RMS to.
axis (int). The axis along which to compute. If not given or None,
the RMS for the whole array is computed.
Returns:
ndarray: The RMS of the array along the desired axis or axes.
"""
a = np.array(a)
if axis is None:
div = a.size
else:
div = a.shape[axis]
ms = np.sum(a**2.0, axis=axis) / div
return np.sqrt(ms) | 1b4a2989f8dd06956ae87a2991ef75ca0c6037fc | 12,942 |
import sys
def alpha_114(code, end_date=None, fq="pre"):
"""
公式:
((RANK(DELAY(((HIGH - LOW) / (SUM(CLOSE, 5) / 5)), 2)) * RANK(RANK(VOLUME))) / (((HIGH - LOW) / (SUM(CLOSE, 5) / 5)) / (VWAP - CLOSE)))
Inputs:
code: 股票池
end_date: 查询日期
Outputs:
因子的值
"""
end_date = to_date_str(end_date)
func_name = sys._getframe().f_code.co_name
return JQDataClient.instance().get_alpha_191(**locals()) | abe8f19581458feef8373e243b6bcc7bf6d3001d | 12,943 |
def event_message(iden, event):
"""Return an event message."""
return {
'id': iden,
'type': TYPE_EVENT,
'event': event.as_dict(),
} | 3b30f3f697d0615a55b3b494f73d01c8df5dcb0e | 12,944 |
def logout():
"""Logout."""
logout_user()
flash(lazy_gettext("You are logged out."), "info")
return redirect(url_for("public.home")) | 2e65faed65671e881594a9a3c990bc6151417575 | 12,945 |
import json
def predicate_to_str(predicate: dict) -> str:
"""
谓词转文本
:param predicate: 谓词数据
:return: 文本
"""
result = ""
if "block" in predicate:
result += "检查方块\n\n"
block = predicate["block"]
if "nbt" in block:
result += f"检查nbt：\n``` json\n{try_pretty_json_str(block['nbt'])}\n```\n"
elif "item" in predicate:
result += "检查物品：" + try_translate(minecraft_lang, get_translate_str("item",
predicate["item"].split(':')[0],
predicate["item"].split(':')[-1:][
0])) + "\n\n"
elif "items" in predicate:
result += "检查下列物品：\n"
for item in predicate['items']:
result += " - " + try_translate(minecraft_lang, get_translate_str("item",
item.split(':')[0],
item.split(':')[-1:][
0])) + "\n"
elif "enchantments" in predicate:
result += "检查附魔\n\n"
enchantments = predicate["enchantments"]
for enchantment in enchantments:
result += enchantment_to_str(enchantment) + "\n\n"
elif "nbt" in predicate:
result += f"检查nbt：\n``` json\n{try_pretty_json_str(predicate['nbt'])}\n```\n"
elif "flags" in predicate:
flags = predicate["flags"]
if "is_on_fire" in flags:
if flags["is_on_fire"]:
result += "着火\n"
else:
result += "没有着火\n"
elif "biome" in predicate:
result += "检查生物群系：" + try_translate(minecraft_lang, get_translate_str("biome",
predicate["biome"].split(':')[0],
predicate["biome"].split(':')[-1:][
0])) + "\n"
else:
result += f"（未知的谓词）\n``` json\n{json.dumps(predicate)}\n```"
return result | 799f3fe66fe5ca78635c9d9b3f3a33a11ee63912 | 12,946 |
import os
import importlib
import sys
def module_str_to_class(module_str):
"""Parse module class string to a class
Args:
module_str(str) Dictionary from parsed configuration file
Returns:
type: class
"""
if not validate_module_str(module_str):
raise ValueError("Module string is in wrong format")
module_path, class_name = module_str.split(":")
if os.path.isfile(module_path):
module_name = os.path.basename(module_path).replace(".py", "")
spec = importlib.util.spec_from_file_location(module_name, module_path)
module = importlib.util.module_from_spec(spec)
sys.modules[module_name] = module
spec.loader.exec_module(module)
else:
module = importlib.import_module(module_path)
return getattr(module, class_name) | 94794c2b19a2db0079adae5ec83598c945cffe01 | 12,947 |
from typing import Optional
async def get_postcode(postcode_like: PostCodeLike) -> Optional[Postcode]:
"""
Gets the postcode object for a given postcode string.
Acts as a middleware between us and the API, caching results.
:param postcode_like: The either a string postcode or PostCode object.
:return: The PostCode object else None if the postcode does not exist..
:raises CachingError: When the postcode is not in cache, and the API is unreachable.
"""
if isinstance(postcode_like, Postcode):
return postcode_like
postcode_like = postcode_like.replace(" ", "").upper()
try:
postcode = Postcode.get(Postcode.postcode == postcode_like)
except DoesNotExist:
logger.info(f"Postcode {postcode_like} not cached, fetching from API")
try:
postcode = await fetch_postcode_from_string(postcode_like)
except (ApiError, CircuitBreakerError):
raise CachingError(f"Requested postcode is not cached, and can't be retrieved.")
if postcode is not None:
postcode.save()
return postcode | c0123b52dca8892c2f399892bee8f6ee2d5ada60 | 12,948 |
def oauth2callback():
"""
The 'flow' has this one place to call back to. We'll enter here
more than once as steps in the flow are completed, and need to keep
track of how far we've gotten. The first time we'll do the first
step, the second time we'll skip the first step and do the second,
and so on.
"""
app.logger.debug("Entering oauth2callback")
flow = client.flow_from_clientsecrets(
CLIENT_SECRET_FILE,
scope= SCOPES,
redirect_uri=flask.url_for('oauth2callback', _external=True))
## Note we are *not* redirecting above. We are noting *where*
## we will redirect to, which is this function.
## The *second* time we enter here, it's a callback
## with 'code' set in the URL parameter. If we don't
## see that, it must be the first time through, so we
## need to do step 1.
app.logger.debug("Got flow")
if 'code' not in flask.request.args:
app.logger.debug("Code not in flask.request.args")
auth_uri = flow.step1_get_authorize_url()
return flask.redirect(auth_uri)
## This will redirect back here, but the second time through
## we'll have the 'code' parameter set
else:
## It's the second time through ... we can tell because
## we got the 'code' argument in the URL.
app.logger.debug("Code was in flask.request.args")
auth_code = flask.request.args.get('code')
credentials = flow.step2_exchange(auth_code)
flask.session['credentials'] = credentials.to_json()
## Now I can build the service and execute the query,
## but for the moment I'll just log it and go back to
## the main screen
app.logger.debug("Got credentials")
return flask.redirect(flask.url_for('respond_gcal')) | 6479dfeb27d99f82555eb01abd453d5a96590ba6 | 12,949 |
def get_ebv(path, specs=range(10)):
"""Lookup the EBV value for all targets from the CFRAME fibermap.
Return the median of all non-zero values.
"""
ebvs = []
for (CFRAME,), camera, spec in iterspecs(path, 'cframe', specs=specs, cameras='b'):
ebvs.append(CFRAME['FIBERMAP'].read(columns=['EBV'])['EBV'].astype(np.float32))
ebvs = np.stack(ebvs).reshape(-1)
nonzero = ebvs > 0
ebvs = ebvs[nonzero]
return np.nanmedian(ebvs) | f27fc781109eed9e4e8102c88396f33a735742e9 | 12,950 |
from typing import List
import os
def get_results_df(
job_list: List[str],
output_dir: str,
input_dir: str = None,
) -> pd.DataFrame:
"""Get raw results in DataFrame."""
if input_dir is None:
input_dir = os.path.join(SLURM_DIR, 'inputs')
input_files = [
os.path.join(input_dir, f'{name}.json')
for name in job_list
]
output_dirs = [
os.path.join(output_dir, name)
for name in job_list
]
batches = {}
for i in range(len(input_files)):
batch_sim = read_input_json(input_files[i])
for sim in batch_sim:
sim.load_results(output_dirs[i])
batches[batch_sim.label] = batch_sim
results = []
for batch_label, batch_sim in batches.items():
batch_results = batch_sim.get_results()
# print(
# 'wall_time =',
# str(datetime.timedelta(seconds=batch_sim.wall_time))
# )
# print('n_trials = ', min(sim.n_results for sim in batch_sim))
for sim, batch_result in zip(batch_sim, batch_results):
n_logicals = batch_result['k']
# Small fix for the current situation. TO REMOVE in later versions
if n_logicals == -1:
n_logicals = 1
batch_result['label'] = batch_label
batch_result['noise_direction'] = sim.error_model.direction
eta_x, eta_y, eta_z = get_bias_ratios(sim.error_model.direction)
batch_result['eta_x'] = eta_x
batch_result['eta_y'] = eta_y
batch_result['eta_z'] = eta_z
if len(sim.results['effective_error']) > 0:
codespace = np.array(sim.results['codespace'])
x_errors = np.array(
sim.results['effective_error']
)[:, :n_logicals].any(axis=1)
batch_result['p_x'] = x_errors.mean()
batch_result['p_x_se'] = np.sqrt(
batch_result['p_x']*(1 - batch_result['p_x'])
/ (sim.n_results + 1)
)
z_errors = np.array(
sim.results['effective_error']
)[:, n_logicals:].any(axis=1)
batch_result['p_z'] = z_errors.mean()
batch_result['p_z_se'] = np.sqrt(
batch_result['p_z']*(1 - batch_result['p_z'])
/ (sim.n_results + 1)
)
batch_result['p_undecodable'] = (~codespace).mean()
if n_logicals == 1:
p_pure_x = (
np.array(sim.results['effective_error']) == [1, 0]
).all(axis=1).mean()
p_pure_y = (
np.array(sim.results['effective_error']) == [1, 1]
).all(axis=1).mean()
p_pure_z = (
np.array(sim.results['effective_error']) == [0, 1]
).all(axis=1).mean()
p_pure_x_se = np.sqrt(
p_pure_x * (1-p_pure_x) / (sim.n_results + 1)
)
p_pure_y_se = np.sqrt(
p_pure_y * (1-p_pure_y) / (sim.n_results + 1)
)
p_pure_z_se = np.sqrt(
p_pure_z * (1-p_pure_z) / (sim.n_results + 1)
)
batch_result['p_pure_x'] = p_pure_x
batch_result['p_pure_y'] = p_pure_y
batch_result['p_pure_z'] = p_pure_z
batch_result['p_pure_x_se'] = p_pure_x_se
batch_result['p_pure_y_se'] = p_pure_y_se
batch_result['p_pure_z_se'] = p_pure_z_se
else:
batch_result['p_pure_x'] = np.nan
batch_result['p_pure_y'] = np.nan
batch_result['p_pure_z'] = np.nan
batch_result['p_pure_x_se'] = np.nan
batch_result['p_pure_y_se'] = np.nan
batch_result['p_pure_z_se'] = np.nan
else:
batch_result['p_x'] = np.nan
batch_result['p_x_se'] = np.nan
batch_result['p_z'] = np.nan
batch_result['p_z_se'] = np.nan
batch_result['p_undecodable'] = np.nan
results += batch_results
results_df = pd.DataFrame(results)
return results_df | 38722fd255eee1a484242a00f66d49a7da8e8e30 | 12,951 |
def create_pipeline(pipeline_name: Text, pipeline_root: Text, data_root: Text,
beam_pipeline_args: Text) -> pipeline.Pipeline:
"""Custom component demo pipeline."""
examples = external_input(data_root)
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = CsvExampleGen(input=examples)
hello = component.HelloComponent(
input_data=example_gen.outputs['examples'], name=u'HelloWorld')
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(examples=hello.outputs['output_data'])
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=[example_gen, hello, statistics_gen],
enable_cache=True,
beam_pipeline_args=beam_pipeline_args
) | 7804df79d4e1ac969df5ef5a2399d841b52d4683 | 12,952 |
def toggleAction(*args, **kwargs):
"""A decorator which identifies a class method as a toggle action. """
return ActionFactory(ToggleAction, *args, **kwargs) | c9bc47a1f62ccac95ace344a4def7bd81064793e | 12,953 |
def getHistograph(dataset = {}, variable = ""):
"""
Calculates a histogram-like summary on a variable in a dataset
and returns a dictionary. The keys in the dictionary are unique items
for the selected variable. The values of each dictionary key, is the number
of times the unique item occured in the data set
"""
data = getDatalist(dataGraph = dataset['DATA'], varGraph = dataset['VARIABLES'], variable = variable)
return histograph(data) | e07c0d1b3e9ba8507402189057b186ee0f8dee3c | 12,954 |
def _get_client_by_settings(
client_cls, # type: Type[BaseClient]
bk_app_code=None, # type: Optional[str]
bk_app_secret=None, # type: Optional[str]
accept_language=None, # type: Optional[str]
**kwargs
):
"""Returns a client according to the django settings"""
client = client_cls(**kwargs)
client.update_bkapi_authorization(
bk_app_code=bk_app_code or settings.get(SettingKeys.APP_CODE),
bk_app_secret=bk_app_secret or settings.get(SettingKeys.APP_SECRET),
)
# disable global https verify
if settings.get(SettingKeys.BK_API_CLIENT_ENABLE_SSL_VERIFY):
client.disable_ssl_verify()
if accept_language:
client.session.set_accept_language(accept_language)
return client | efbb42d7795f7e0939d33f2427470e202d6580c9 | 12,955 |
from controllers import sites
import jinja2
def create_and_configure_jinja_environment(
dirs, autoescape=True, handler=None, default_locale='en_US'):
"""Sets up an environment and gets jinja template."""
# Defer to avoid circular import.
locale = None
app_context = sites.get_course_for_current_request()
if app_context:
locale = app_context.get_current_locale()
if not locale:
locale = app_context.default_locale
if not locale:
locale = default_locale
jinja_environment = create_jinja_environment(
jinja2.FileSystemLoader(dirs), locale=locale, autoescape=autoescape)
jinja_environment.filters['gcb_tags'] = get_gcb_tags_filter(handler)
return jinja_environment | 88d285cd436b5af0848c2ca1e45caa3e28a8e074 | 12,956 |
import numpy
def bootstrap_cost(target_values, class_probability_matrix, cost_function,
num_replicates):
"""Bootstraps cost for one set of examples.
E = number of examples
K = number of classes
B = number of bootstrap replicates
:param target_values: length-E numpy array of target values (integers in
range 0...[K - 1]).
:param class_probability_matrix: E-by-K numpy array of predicted
probabilities.
:param cost_function: Cost function, used to evaluate predicted
probabilities. Must be negatively oriented (so that lower values are
better), with the following inputs and outputs.
Input: target_values: Same as input to this method.
Input: class_probability_matrix: Same as input to this method.
Output: cost: Scalar value.
:param num_replicates: Number of bootstrap replicates.
:return: cost_values: length-B numpy array of cost values.
"""
error_checking.assert_is_integer(num_replicates)
error_checking.assert_is_geq(num_replicates, 1)
cost_values = numpy.full(num_replicates, numpy.nan)
if num_replicates == 1:
cost_values[0] = cost_function(target_values,
class_probability_matrix)
else:
for k in range(num_replicates):
_, these_indices = bootstrapping.draw_sample(target_values)
cost_values[k] = cost_function(
target_values[these_indices],
class_probability_matrix[these_indices, ...]
)
print('Average cost = {0:.4f}'.format(numpy.mean(cost_values)))
return cost_values | ba76e9b9614407a4c0d49c16e3b9ab9f0974a820 | 12,957 |
def jdeblend_bob(src_fm, bobbed):
"""
Stronger version of jdeblend() that uses a bobbed clip to deblend.
Parameters:
clip src_fm: Source after field matching, must have field=3 and low cthresh.
clip src: Bobbed source.
Example:
src =
from havsfunc import QTGMC
qtg = QTGMC(src, TFF=True, SourceMatch=3)
vfm = src.vivtc.VFM(order=1, field=3, cthresh=3)
dblend = jdeblend_bob(vfm, qtg)
dblend = jdeblend_kf(dblend, vfm)
"""
bob0 = bobbed.std.SelectEvery(2, 0)
bob1 = bobbed.std.SelectEvery(2, 1)
ab0, bc0, c0 = bob0, bob0[1:] + bob0[-1], bob0[2:] + bob0[-2]
a1, ab1, bc1 = bob1[0] + bob1[:-1], bob1, bob1[1:] + bob1[-1]
dbd = core.std.Expr([a1, ab1, ab0, bc1, bc0, c0], 'y x - z + b c - a + + 2 /')
dbd = core.std.ShufflePlanes([bc0, dbd], [0, 1, 2], vs.YUV)
select_src = [src_fm.std.SelectEvery(5, i) for i in range(5)]
select_dbd = [dbd.std.SelectEvery(5, i) for i in range(5)]
inters = _inter_pattern(select_src, select_dbd)
return core.std.FrameEval(src_fm, partial(_jdeblend_eval, src=src_fm, inters=inters), [src_fm, src_fm[0]+src_fm[:-1]]) | d5e79710d7346a2376656ccfaf9c43a70fa8bc36 | 12,958 |
import optparse
def ParseArgs():
"""Parse the command line options, returning an options object."""
usage = 'Usage: %prog [options] LIST|GET|LATEST'
option_parser = optparse.OptionParser(usage)
AddCommandLineOptions(option_parser)
log_helper.AddCommandLineOptions(option_parser)
options, args = option_parser.parse_args()
if not options.repo_url:
option_parser.error('--repo-url is required')
if len(args) == 1:
action = args[0].lower()
if action in ('list', 'latest', 'get'):
return options, action
option_parser.error(
'A single repository action (LIST, GET, or LATEST) is required') | cfef602975011d4b04e7797a9e1426293f0d0b7b | 12,959 |
import io
def generate_table_definition(schema_and_table, column_info,
primary_key=None, foreign_keys=None,
diststyle=None, distkey=None, sortkey=None):
"""Return a CREATE TABLE statement as a string."""
if not column_info:
raise Exception('No columns specified for {}'.format(schema_and_table))
out = io.StringIO()
out.write('CREATE TABLE {} (\n'.format(schema_and_table))
columns_count = len(column_info)
for i, (column, type_) in enumerate(column_info):
out.write(' "{}" {}'.format(column, type_))
if (i < columns_count - 1) or primary_key or foreign_keys:
out.write(',')
out.write('\n')
if primary_key:
out.write(' PRIMARY KEY({})'.format(primary_key))
if foreign_keys:
out.write(',')
out.write('\n')
foreign_keys = foreign_keys or []
foreign_keys_count = len(foreign_keys)
for i, (key, reftable, refcolumn) in enumerate(foreign_keys):
out.write(' FOREIGN KEY({}) REFERENCES {}({})'.format(
key, reftable, refcolumn
))
if i < foreign_keys_count - 1:
out.write(',')
out.write('\n')
out.write(')\n')
if diststyle:
out.write('DISTSTYLE {}\n'.format(diststyle))
if distkey:
out.write('DISTKEY({})\n'.format(distkey))
if sortkey:
if isinstance(sortkey, str):
out.write('SORTKEY({})\n'.format(sortkey))
elif len(sortkey) == 1:
out.write('SORTKEY({})\n'.format(sortkey[0]))
else:
out.write('COMPOUND SORTKEY({})\n'.format(', '.join(sortkey)))
return out.getvalue() | 383cdc8ed13fbaa45adadec26f31ad0f5ac52fbc | 12,960 |
def gradient_descent_update(x, gradx, learning_rate):
"""
Performs a gradient descent update.
"""
# Return the new value for x
return x - learning_rate * gradx | db5ec512883352f473990eca124c8ad302ec3564 | 12,961 |
def nth_permutation(n, size=0):
"""nth permutation of 0..size-1
where n is from 0 to size! - 1
"""
lehmer = int_to_lehmer(n, size)
return lehmer_to_permutation(lehmer) | b79bb639cbf23296879562fa718218beee86f378 | 12,962 |
def signin(request):
"""
Method for log in of the user
"""
if request.user.is_authenticated:
return_var = render(request, '/')
if request.method == 'POST':
username = request.POST['username']
password = request.POST['password']
user = authenticate(request, username=username, password=password)
if user is not None:
login(request, user)
return_var = redirect('/')
else:
form = AuthenticationForm(request.POST)
return_var = render(request, 'registration/login.html', {'form': form})
else:
form = AuthenticationForm()
return_var = render(request, 'registration/login.html', {'form': form})
return return_var | aadab619cca9d71e3f255f910af7b8d1dd59b8f4 | 12,963 |
def compare_features(f1, f2):
"""Comparison method for feature sorting."""
def get_prefix(feature):
if feature.startswith('e1-'): return 'e1'
if feature.startswith('e2-'): return 'e2'
if feature.startswith('e-'): return 'e'
if feature.startswith('t-'): return 't'
return 'x'
prefixes = {'e': 1, 't': 2, 'e1': 3, 'e2': 4}
p1 = get_prefix(f1)
p2 = get_prefix(f2)
prefix_comparison = cmp(p1, p2)
return cmp(f1, f2) if prefix_comparison == 0 else prefix_comparison | 2b93e2c6b6a9993d13964d726efe855d57f72448 | 12,964 |
def localized_index(lang):
"""
Example view demonstrating rendering a simple HTML page.
"""
context = make_context()
context['lang'] = lang
context['content'] = context['COPY']['content-%s' % lang]
context['form'] = context['COPY']['form-%s' % lang]
context['share'] = context['COPY']['share-%s' % lang]
context['calendar'] = context['COPY']['calendar-%s' % lang]
context['initial_card'] = context['COPY']['config']['initial_card'].__unicode__()
context['cards'] = _make_card_list(lang)
context['us_states'] = us.states.STATES
return make_response(render_template('index.html', **context)) | 7d6878f30270c735a2d97353b813cdf632043b92 | 12,965 |
def build_output_unit_vqa(q_encoding, m_last, num_choices, apply_dropout,
scope='output_unit', reuse=None):
"""
Apply a 2-layer fully-connected network to predict answers. Apply dropout
if specified.
Input:
q_encoding: [N, d], tf.float32
m_last: [N, d], tf.float32
Return:
vqa_scores: [N, num_choices], tf.float32
"""
output_dim = cfg.MODEL.VQA_OUTPUT_DIM
with tf.variable_scope(scope, reuse=reuse):
if cfg.MODEL.VQA_OUTPUT_USE_QUESTION:
fc1 = fc_elu(
'fc1', tf.concat([q_encoding, m_last], axis=1),
output_dim=output_dim)
else:
fc1 = fc_elu('fc1_wo_q', m_last, output_dim=output_dim)
if apply_dropout:
fc1 = tf.nn.dropout(fc1, cfg.TRAIN.DROPOUT_KEEP_PROB)
fc2 = fc('fc2', fc1, output_dim=num_choices,
biases_initializer=tf.constant_initializer(
cfg.TRAIN.VQA_SCORE_INIT))
vqa_scores = fc2
return vqa_scores | 2fc8d0c9246cbb3350af9d20a85964c9f3967618 | 12,966 |
def coding_problem_16(length):
"""
You run a sneaker website and want to record the last N order ids in a log. Implement a data structure to
accomplish this, with the following API:
record(order_id): adds the order_id to the log
get_last(i): gets the ith last element from the log. i is guaranteed to be smaller than or equal to N.
You should be as efficient with time and space as possible.
Example:
>>> log = coding_problem_16(10)
>>> for id in xrange(20):
... log.record(id)
>>> log.get_last(0)
[]
>>> log.get_last(1)
[19]
>>> log.get_last(5)
[15, 16, 17, 18, 19]
>>> log.record(20)
>>> log.record(21)
>>> log.get_last(1)
[21]
>>> log.get_last(3)
[19, 20, 21]
"""
class OrdersLog(object):
def __init__(self, num):
self.circular_buffer = [None] * num
self.current_index = 0
def record(self, order_id):
self.circular_buffer[self.current_index] = order_id
self.current_index += 1
if self.current_index == len(self.circular_buffer):
self.current_index = 0
def get_last(self, num):
start_index = self.current_index - num
if start_index < 0: # wrap around
return self.circular_buffer[start_index:] + self.circular_buffer[:self.current_index]
else: # no wrapping required
return self.circular_buffer[start_index:self.current_index]
return OrdersLog(length) | 5c1b3e6be920ce826f7c8e8b0fc8fd27cd398079 | 12,967 |
def _check_dimensions(n_grobs, nrow = None, ncol = None):
"""
Internal function to provide non-Null nrow and ncol numbers
given a n_number of images and potentially some information about the
desired nrow/ncols.
Arguments:
----------
n_grobs: int, number of images to be organized
nrow: int, number of rows user wants (Default is None)
ncol: int, number of columns user wants (Default is None)
Returns:
--------
(nrow, ncol) tuple that meets user desires or errors if cannot meet
users expectation
"""
if nrow is None and ncol is None:
nrow = int(np.ceil(np.sqrt(n_grobs)))
ncol = int(np.ceil(n_grobs/nrow))
if nrow is None:
nrow = int(np.ceil(n_grobs/ncol))
if ncol is None:
ncol = int(np.ceil(n_grobs/nrow))
assert n_grobs <= nrow * ncol, \
"nrow * ncol < the number of grobs, please correct"
return nrow, ncol | 20531024ecabc4b4b7b5ce1d8a20774cf2b568ac | 12,968 |
import re
def year_parse(s: str) -> int:
"""Parses a year from a string."""
regex = r"((?:19|20)\d{2})(?:$|[-/]\d{2}[-/]\d{2})"
try:
year = int(re.findall(regex, str(s))[0])
except IndexError:
year = None
return year | f52449aefcba52106fe8c7c03355c237a94775e1 | 12,969 |
import random
def sampleDistribution(d):
"""
Expects d to be a list of tuples
The first element should be the probability
If the tuples are of length 2 then it returns the second element
Otherwise it returns the suffix tuple
"""
# {{{
z = float(sum(t[0] for t in d))
if z == 0.0:
eprint("sampleDistribution: z = 0")
eprint(d)
r = random.random()
u = 0.0
for index, t in enumerate(d):
p = t[0] / z
# This extra condition is needed for floating-point bullshit
if r <= u + p or index == len(d) - 1:
if len(t) <= 2:
return t[1]
else:
return t[1:]
u += p
assert False | 4e47b3dead1e9cc42bf152e6854cbd7cdb255438 | 12,970 |
def logout():
"""
`/register` endpoint
Logs out a user and redirects to the index page.
"""
logout_user()
flash("You are logged out.", "info")
return redirect(url_for("main.index")) | 7054a11253b02522178c528197071c1ae9c3ae81 | 12,971 |
import requests
def players_season_totals(season_end_year, playoffs=False, skip_totals=False,
output_type=None, output_file_path=None, output_write_option=None, json_options=None):
"""
scrape the "Totals" stats of all players from a single year
Args:
season_end_year (int): year in which the season ends, e.g. 2019 for 2018-2019 season
playoffs (bool): whether to grab the playoffs (True) or regular season (False) table
skip_totals (bool): whether (True) or not (False) to skip the rows representing for
the complete year of a player that is traded (no effect for the playoffs)
output_type (str): either csv or json, if you want to save that type of file
output_file_path (str): file you want to save to
output_write_option (str): whether to write (default) or append
json_options (dict): dictionary of options to pass to the json writer
Returns:
a list of rows; each row is a dictionary with items named from COLUMN_RENAMER
"""
try:
values = http_client.players_season_totals(season_end_year,
skip_totals=skip_totals, playoffs=playoffs)
except requests.exceptions.HTTPError as http_error:
if http_error.response.status_code == requests.codes.not_found:
raise InvalidSeason(season_end_year=season_end_year)
else:
raise http_error
return output.output(
values=values,
output_type=output_type,
output_file_path=output_file_path,
output_write_option=output_write_option,
csv_writer=output.players_season_totals_to_csv,
encoder=BasketballReferenceJSONEncoder,
json_options=json_options,
) | ea0fa62e9b7ec644404d7968f2b11b14781b4c37 | 12,972 |
import random
def array_shuffle(x,axis = 0, random_state = 2020):
"""
对多维度数组，在任意轴打乱顺序
:param x: ndarray
:param axis: 打乱的轴
:return:打乱后的数组
"""
new_index = list(range(x.shape[axis]))
random.seed(random_state)
random.shuffle(new_index)
x_new = np.transpose(x, ([axis]+[i for i in list(range(len(x.shape))) if i is not axis]))
x_new = x_new[new_index][:]
new_dim = list(np.array(range(axis))+1)+[0]+list(np.array(range(len(x.shape)-axis-1))+axis+1)
x_new = np.transpose(x_new, tuple(new_dim))
return x_new | df6ff3e9fd1d94ff6a6e633451a88815b16f1e83 | 12,973 |
import tempfile
import os
import subprocess
def cat(out_media_fp, l_in_media_fp):
"""
Args:
out_media_fp(str): Output Media File Path
l_in_media_fp(list): List of Media File Path
Returns:
return_code(int):
"""
ref_vcodec = get_video_codec(l_in_media_fp[0])
ref_acodec = get_audio_codec(l_in_media_fp[0])
ref_vscale = get_video_scale(l_in_media_fp[0])
for f in l_in_media_fp:
if ref_vcodec != get_video_codec(f):
logger.error('Video Codecs are different.')
return -1
if ref_acodec != get_audio_codec(f):
logger.error('Audio Codecs are different.')
return -1
if ref_vscale != get_video_scale(f):
logger.error('Video Scales are different.')
return -1
ffmpeg = FFmpeg.FFmpeg()
ffmpeg.set_output_file(out_media_fp)
ffmpeg.set_input_format('concat')
ffmpeg.set_video_encoder('copy')
ffmpeg.set_audio_encoder('copy')
ffmpeg.set_safe(0)
try:
fpath = tempfile.mkstemp()[1]
with open(fpath, 'w') as fp:
for media_fp in l_in_media_fp:
fp.write('file \'{}\'\n'.format(os.path.abspath(media_fp)))
ffmpeg.add_input_file(fpath)
with ffmpeg.build().run() as proc:
out, err = proc.communicate()
logger.error(err.decode("utf-8"))
os.remove(fpath)
return proc.returncode
except subprocess.CalledProcessError:
logger.error('FFmpeg failed.') | e6fec61dd205ec3fdb2340aac543051ebf97e12e | 12,974 |
import time
def get_recent_activity_rows(chase_driver):
"""Return the 25 most recent CC transactions, plus any pending
transactions.
Returns:
A list of lists containing the columns of the Chase transaction list.
"""
_goto_link(chase_driver, "See activity")
time.sleep(10)
rows = chase_driver.find_elements_by_css_selector("tr.summary")
trans_list = []
for row in rows:
tds = row.find_elements_by_tag_name('td')
tds = tds[1:] # skip the link in first cell
trans_list.append([td.text for td in tds])
return trans_list | 2ac3d6de1cac0e28d2dd9a9d170cef7f1facf007 | 12,975 |
def loglikelihood(x, mean, var, pi):
"""
式(9.28)
"""
lkh = []
for mean_k, var_k, pi_k in zip(mean, var, pi):
lkh.append(pi_k * gaussian_pdf(x, mean_k, var_k))
return np.sum(np.log(np.sum(lkh, 0))) | d2355334b305f1bf2084efea3baee78daf06cc35 | 12,976 |
def calc_E_ST_GJ(E_star_ST):
"""基準一次エネルギー消費量（GJ/年）の計算 (2)
Args:
E_star_ST(float): 基準一次エネルギー消費量（J/年）
Returns:
float: 基準一次エネルギー消費量（GJ/年）
"""
# 小数点以下一位未満の端数があるときはこれを切り上げる
return ceil(E_star_ST / 100) / 10 | 06d7ade15d91c205bd9662dd41d218d8b7867a2f | 12,977 |
def next_line(grd_file):
"""
next_line
Function returns the next line in the file
that is not a blank line, unless the line is
'', which is a typical EOF marker.
"""
done = False
while not done:
line = grd_file.readline()
if line == '':
return line, False
elif line.strip():
return line, True | 337f188930a03142bae59cdb378b09f1ac5e2ecb | 12,978 |
def look_for(room, position, main, sub=None):
"""
:type room: rooms.room_mind.RoomMind
"""
if not room.look_at:
raise ValueError("Invalid room argument")
if position.pos:
position = position.pos
if sub:
return _.find(room.look_at(LOOK_FLAGS, position),
lambda f: f.color == main_to_flag_primary[main] and
f.secondaryColor == sub_to_flag_secondary[sub])
else:
flag_def = flag_definitions[main]
if not flag_def:
# TODO: This is a hack because a common pattern is
# look_for(room, pos, flags.MAIN_DESTRUCT, flags.structure_type_to_flag_sub[structure_type])
# if there is no flag for a given structure, sub will be undefined, and thus this side will be called
# and not the above branch.
return []
return _.find(room.look_at(LOOK_FLAGS, position),
lambda f: f.color == flag_def[0] and f.secondaryColor == flag_def[1]) | 23f521874b7dfbbc2dd00c105ed495a1f9049e00 | 12,979 |
def get_proto(proto):
"""
Returns a protocol number (in the /etc/protocols sense, e.g. 6 for
TCP) for the given input value. For the protocols that have
PROTO_xxx constants defined, this can be provided textually and
case-insensitively, otherwise the provided value gets converted to
an integer and returned.
Returns None if this conversion failed.
"""
protos = {
"ICMP": PROTO_ICMP,
"ICMP6": PROTO_ICMP6,
"SCTP": PROTO_SCTP,
"TCP": PROTO_TCP,
"UDP": PROTO_UDP,
}
try:
return protos[proto.upper()]
except (KeyError, AttributeError):
pass
try:
return int(proto)
except ValueError:
pass
return None | a3632e0731e4227020eb75d5ea27d211e27e188a | 12,980 |
def f5_update_policy_cookie_command(client: Client, policy_md5: str, cookie_id: str,
cookie_name: str,
perform_staging: bool, parameter_type: str,
enforcement_type: str,
attack_signatures_check: bool) -> CommandResults:
"""
Update a given cookie of a specific policy
Args:
client (Client): f5 client.
policy_md5 (str): MD5 hash of the policy.
cookie_id (str): ID of the cookie.
cookie_name (str): The new cookie name to add.
perform_staging (bool): Indicates if the user wishes the new file type to be at staging.
parameter_type (str): Type of the new parameter.
enforcement_type (str): Enforcement type.
attack_signatures_check (bool): Should attack signatures be checked. If enforcement type
is set to 'enforce', this field will not get any value.
"""
result = client.update_policy_cookie(policy_md5, cookie_id, cookie_name, perform_staging,
parameter_type, enforcement_type, attack_signatures_check)
outputs, headers = build_output(OBJECT_FIELDS, result)
readable_output = tableToMarkdown('f5 data for updating cookie:',
outputs, headers, removeNull=True)
command_results = CommandResults(
outputs_prefix='f5.Cookies',
outputs_key_field='id',
readable_output=readable_output,
outputs=remove_empty_elements(outputs),
raw_response=result
)
return command_results | 633015c3dc3c478e17975ea540a6e6ab46b710e1 | 12,981 |
def ping():
"""
Determine if the container is working and healthy. In this sample container, we declare
it healthy if we can load the model successfully.
:return:
"""
health = False
try:
health = model is not None # You can insert a health check here
except:
pass
status = 200 if health else 404
return flask.Response(response='\n', status=status, mimetype='application/json') | 96cc202cf4cf25dd6fb91ae29cac66667ed13d27 | 12,982 |
from .coo import COO
def random(
shape,
density=0.01,
random_state=None,
data_rvs=None,
format='coo'
):
""" Generate a random sparse multidimensional array
Parameters
----------
shape: Tuple[int]
Shape of the array
density: float, optional
Density of the generated array.
random_state : Union[numpy.random.RandomState, int], optional
Random number generator or random seed. If not given, the
singleton numpy.random will be used. This random state will be used
for sampling the sparsity structure, but not necessarily for sampling
the values of the structurally nonzero entries of the matrix.
data_rvs : Callable
Data generation callback. Must accept one single parameter: number of
:code:`nnz` elements, and return one single NumPy array of exactly
that length.
format: str
The format to return the output array in.
Returns
-------
SparseArray
The generated random matrix.
See Also
--------
:obj:`scipy.sparse.rand`
Equivalent Scipy function.
:obj:`numpy.random.rand`
Similar Numpy function.
Examples
--------
>>> from sparse import random
>>> from scipy import stats
>>> rvs = lambda x: stats.poisson(25, loc=10).rvs(x, random_state=np.random.RandomState(1))
>>> s = random((2, 3, 4), density=0.25, random_state=np.random.RandomState(1), data_rvs=rvs)
>>> s.todense() # doctest: +NORMALIZE_WHITESPACE
array([[[ 0, 0, 0, 0],
[ 0, 34, 0, 0],
[33, 34, 0, 29]],
<BLANKLINE>
[[30, 0, 0, 34],
[ 0, 0, 0, 0],
[ 0, 0, 0, 0]]])
"""
# Copied, in large part, from scipy.sparse.random
# See https://github.com/scipy/scipy/blob/master/LICENSE.txt
elements = np.prod(shape)
nnz = int(elements * density)
if random_state is None:
random_state = np.random
elif isinstance(random_state, Integral):
random_state = np.random.RandomState(random_state)
if data_rvs is None:
data_rvs = random_state.rand
# Use the algorithm from python's random.sample for k < mn/3.
if elements < 3 * nnz:
ind = random_state.choice(elements, size=nnz, replace=False)
else:
ind = np.empty(nnz, dtype=np.min_scalar_type(elements - 1))
selected = set()
for i in range(nnz):
j = random_state.randint(elements)
while j in selected:
j = random_state.randint(elements)
selected.add(j)
ind[i] = j
data = data_rvs(nnz)
ar = COO(ind[None, :], data, shape=nnz).reshape(shape)
return ar.asformat(format) | f62ba3afc168bf39734897291d094d43bd1ee7f1 | 12,983 |
from pathlib import Path
import hashlib
def file_md5_is_valid(fasta_file: Path, checksum: str) -> bool:
"""
Checks if the FASTA file matches the MD5 checksum argument.
Returns True if it matches and False otherwise.
:param fasta_file: Path object for the FASTA file.
:param checksum: MD5 checksum string.
:return: boolean indicating if the file validates.
"""
md5_hash = hashlib.md5()
with fasta_file.open(mode="rb") as fh:
# Read in small chunks to avoid memory overflow with large files.
while chunk := fh.read(8192):
md5_hash.update(chunk)
return md5_hash.hexdigest() == checksum | ec400afbe29d940d0638a581da7f2ee001b9e985 | 12,984 |
def combine_to_int(values):
"""Combine several byte values to an integer"""
multibyte_value = 0
for byte_id, byte in enumerate(values):
multibyte_value += 2**(4 * byte_id) * byte
return multibyte_value | 58ff7cbee356cdcbe5b26e973de16c5b1cc40afc | 12,985 |
import torch
def loss_fn(x, results, is_valtest=False, **kwargs):
"""
Loss weight (MCAE):
- sni: snippet reconstruction loss
- seg: segment reconstruction loss
- cont: smooth regularization
- reg: sparsity regularization
- con: constrastive loss
- cls: auxilliary classification loss <not used for MCAE-MP>
Loss weight (joint):
- skcon: contrastive loss on the concatenated representation of all joints
- skcls: auxilliary classification loss
"""
default_lsw = dict.fromkeys(
[
'sni', 'seg', 'cont', 'reg', 'con', 'skcon', 'skcls'
], 1.0)
loss_weights = kwargs.get('loss_weights', default_lsw)
losses = {}
mcae_losses = []
sk_pres = results['sk_pres']
sk_lgts = results['sk_lgts']
sk_y = kwargs.get('y', None)
if 'mcae' in results.keys():
mcae_results = results['mcae']
for r in mcae_results:
mcae_losses.append(
mcae_loss(r['x'], r, loss_weights=loss_weights, is_valtest=is_valtest))
for key in loss_weights.keys():
losses[key] = 0
if key in mcae_losses[0][0].keys():
for i in range(len(mcae_results)):
losses[key] += mcae_losses[i][0][key]
else:
losses.pop(key)
elif 'mcae_3d' in results.keys():
r = results['mcae_3d']
mcae_loss_ = mcae_loss(r['x'], r, loss_weights=loss_weights, is_valtest=is_valtest)[0]
for key in loss_weights.keys():
losses[key] = 0
if key in mcae_loss_.keys():
losses[key] += mcae_loss_[key]
else:
losses.pop(key)
if loss_weights.get('skcon', 0) > 0 and not is_valtest:
B = sk_pres.shape[0]
_L = int(B/2)
tau = 0.1
trj_pres = sk_pres.reshape(B, -1)
ori, aug = trj_pres.split(_L, 0)
dist_grid = 1 - cosine_distance(ori, aug)
dist_grid_exp = torch.exp(dist_grid/tau)
losses['skcon'] = -torch.log(
torch.diag(dist_grid_exp) / dist_grid_exp.sum(1)).mean()
if loss_weights.get('skcls', 0) > 0:
losses['skcls'] = F.nll_loss(F.log_softmax(sk_lgts, -1), sk_y)
return losses, default_lsw | ef5953c3350952a1083aabaa8f656834a32ae17c | 12,986 |
def _as_uint32(x: int) -> QVariant:
"""Convert the given int to an uint32 for DBus."""
variant = QVariant(x)
successful = variant.convert(QVariant.UInt)
assert successful
return variant | 08de1fccf4625485fab82c2569932ffd3e006541 | 12,987 |
def svcs_tang_u(Xcp,Ycp,Zcp,gamma_t,R,m,Xcyl,Ycyl,Zcyl,ntheta=180, Ground=False):
"""
Computes the velocity field for nCyl*nr cylinders, extending along z:
nCyl: number of main cylinders
nr : number of concentric cylinders within a main cylinder
INPUTS:
Xcp,Ycp,Zcp: cartesian coordinates of control points where the velocity field is not be computed
gamma_t: array of size (nCyl,nr), distribution of gamma for each cylinder as function of radius
R : array of size (nCyl,nr),
m : array of size (nCyl,nr),
Xcyl,Ycyl,Zcyl: array of size nCyl) giving the center of the rotor
Ground: boolean, True if ground effect is to be accounted for
All inputs (except Ground) should be numpy arrays
"""
Xcp=np.asarray(Xcp)
Ycp=np.asarray(Ycp)
Zcp=np.asarray(Zcp)
ux = np.zeros(Xcp.shape)
uy = np.zeros(Xcp.shape)
uz = np.zeros(Xcp.shape)
nCyl,nr = R.shape
print('Tang. (skewed) ',end='')
for i in np.arange(nCyl):
Xcp0,Ycp0,Zcp0=Xcp-Xcyl[i],Ycp-Ycyl[i],Zcp-Zcyl[i]
if Ground:
YcpMirror = Ycp0+2*Ycyl[i]
Ylist = [Ycp0,YcpMirror]
else:
Ylist = [Ycp0]
for iy,Y in enumerate(Ylist):
for j in np.arange(nr):
if iy==0:
print('.',end='')
else:
print('m',end='')
if np.abs(gamma_t[i,j]) > 0:
ux1,uy1,uz1 = svc_tang_u(Xcp0,Y,Zcp0,gamma_t[i,j],R[i,j],m[i,j],ntheta=ntheta,polar_out=False)
ux = ux + ux1
uy = uy + uy1
uz = uz + uz1
print('')
return ux,uy,uz | f5ee6309a01c493f930086a92db44c93cb33cbba | 12,988 |
def np_to_o3d_images(images):
"""Convert numpy image list to open3d image list
Parameters
----------
images : list[numpy.ndarray]
Returns
o3d_images : list[open3d.open3d.geometry.Image]
-------
"""
o3d_images = []
for image in images:
image = np_to_o3d_image(image)
o3d_images.append(image)
return o3d_images | 419f59c47cba9c22595c8d16ca0d35f105ec4882 | 12,989 |
def compute_mse(y_true, y_pred):
"""ignore zero terms prior to comparing the mse"""
mask = np.nonzero(y_true)
mse = mean_squared_error(y_true[mask], y_pred[mask])
return mse | 3c594c4105f99d8088665b4c0a7345807a667e55 | 12,990 |
def image2d(math_engine, batch_len, batch_width, height, width, channels, dtype="float32"):
"""Creates a blob with two-dimensional multi-channel images.
:param neoml.MathEngine.MathEngine math_engine: the math engine that works with this blob.
:param batch_len: the **BatchLength** dimension of the new blob.
:type batch_len: int, > 0
:param batch_width: the **BatchWidth** dimension of the new blob.
:type batch_width: int, > 0
:param height: the image height.
:type height: int, > 0
:param width: the image width.
:type width: int, > 0
:param channels: the number of channels in the image format.
:type channels: int, > 0
:param dtype: the type of data in the blob.
:type dtype: str, {"float32", "int32"}, default="float32"
"""
if dtype != "float32" and dtype != "int32":
raise ValueError('The `dtype` must be one of {`float32`, `int32`}.')
if batch_len < 1:
raise ValueError('The `batch_len` must be > 0.')
if batch_width < 1:
raise ValueError('The `batch_width` must be > 0.')
if height < 1:
raise ValueError('The `height` must be > 0.')
if width < 1:
raise ValueError('The `width` must be > 0.')
if channels < 1:
raise ValueError('The `channels` must be > 0.')
shape = np.array((batch_len, batch_width, 1, height, width, 1, channels), dtype=np.int32, copy=False)
return Blob(PythonWrapper.tensor(math_engine._internal, shape, dtype)) | d816388f619ac1e09e2bfc705ddab75a490ec023 | 12,991 |
def error_response(error, message):
"""
returns error response
"""
data = {
"status": "error",
"error": error,
"message": message
}
return data | f3e52ea42cb48378f08ecb65f58d2291960e6488 | 12,992 |
import tqdm
def graph_to_text(
graph: MultiDiGraph, quoting: bool = True, verbose: bool = True
) -> str:
"""Turns a graph into
its text representation.
Parameters
----------
graph : MultiDiGraph
Graph to text.
quoting : bool
If true, quotes will be added.
verbose : bool
If true, a progress bar will be displayed.
Examples
--------
>>> import cfpq_data
>>> g = cfpq_data.labeled_cycle_graph(2, edge_label="a", verbose=False)
>>> cfpq_data.graph_to_text(g, verbose=False)
"'0' 'a' '1'\\n'1' 'a' '0'\\n"
>>> cfpq_data.graph_to_text(g, quoting=False, verbose=False)
'0 a 1\\n1 a 0\\n'
Returns
-------
text : str
Graph text representation.
"""
text = ""
for u, v, edge_labels in tqdm(
graph.edges(data=True), disable=not verbose, desc="Generation..."
):
if len(edge_labels.values()) > 0:
for label in edge_labels.values():
if quoting:
text += f"'{u}' '{label}' '{v}'\n"
else:
text += f"{u} {label} {v}\n"
else:
if quoting:
text += f"'{u}' '{v}'\n"
else:
text += f"{u} {v}\n"
return text | a3ccf008f1ebcc62cfe1c8e6620923c665de8768 | 12,993 |
def test_has_valid_dir_structure():
"""Check if the specified dir structure is valid"""
def recurse_contents(contents):
if contents is None:
return None
else:
for key, value in contents.items():
assert(isinstance(key, str))
if value is None:
return None
elif "dir" in value:
recurse_contents(value["dir"])
elif "file" in value:
assert(value["file"] is None or isinstance(value["file"], str) or callable(value["file"]))
if callable(value["file"]):
generator = value["file"]
assert(isinstance(generator("test"), str))
else:
raise Exception("""
Every entry in the directory structure must be
either a directory or a file.
""")
recurse_contents(skeleton.dir_structure) | f2dc8dcb38dc5873dea8500391a1733f9f8a18d1 | 12,994 |
def getFBA(fba):
"""AC factory.
reads a fileobject and creates a dictionary for easy insertation
into a postgresdatabase. Uses Ohlbergs routines to read the files (ACfile)
"""
word = fba.getSpectrumHead()
while word is not None:
stw = fba.stw
mech = fba.Type(word)
datadict = {
'stw': stw,
'mech_type': mech,
}
return datadict
raise EOFError | e5c5f52fe831938400eec5ae15c043ecbf8cf7d1 | 12,995 |
def logtimestamp():
"""
returns a formatted datetime object with the curren year, DOY, and UT
"""
return DT.datetime.utcnow().strftime("%Y-%j-%H:%M:%S") | 71b204c473d8e6a4868d966877dd61909252e891 | 12,996 |
def get_most_common_non_ascii_char(file_path: str) -> str:
"""Return first most common non ascii char"""
with open(file_path, encoding="raw_unicode_escape") as f:
non_ascii = {}
for line in f:
for char in line:
if not char.isascii():
if char in non_ascii:
non_ascii[char] += 1
else:
non_ascii[char] = 1
if non_ascii:
return max(non_ascii, key=non_ascii.get)
else:
return "No non ascii chars in the file" | 5280b637206964d6b386478ddbaeb6ad69f92c8c | 12,997 |
def compute_noise_from_target_epsilon(
target_epsilon,
target_delta,
epochs,
batch_size,
dataset_size,
alphas=None,
approx_ratio=0.01,
):
"""
Takes a target epsilon (eps) and some hyperparameters.
Returns a noise scale that gives an epsilon in [0.99 eps, eps].
The approximation ratio can be tuned.
If alphas is None, we'll explore orders.
"""
steps = compute_steps(epochs, batch_size, dataset_size)
sampling_rate = batch_size / dataset_size
if alphas is None:
alphas = ALPHAS
def get_eps(noise):
rdp = privacy_analysis.compute_rdp(sampling_rate, noise, steps, alphas)
epsilon, order = privacy_analysis.get_privacy_spent(
alphas, rdp, delta=target_delta
)
return epsilon
# Binary search bounds
noise_min = MIN_NOISE
noise_max = MAX_NOISE
# Start with the smallest epsilon possible with reasonable noise
candidate_noise = noise_max
candidate_eps = get_eps(candidate_noise)
if candidate_eps > target_epsilon:
raise ("Cannot reach target eps. Try to increase MAX_NOISE.")
# Search up to approx ratio
while (
candidate_eps < (1 - approx_ratio) * target_epsilon
or candidate_eps > target_epsilon
):
if candidate_eps < (1 - approx_ratio) * target_epsilon:
noise_max = candidate_noise
else:
noise_min = candidate_noise
candidate_noise = (noise_max + noise_min) / 2
candidate_eps = get_eps(candidate_noise)
print("Use noise {} for epsilon {}".format(candidate_noise, candidate_eps))
return candidate_noise | 60bddeaca8e772fa15582fe87516f4e5c5284b75 | 12,998 |
def cart2pol(x, y):
"""
author : Dr. Schaeffer
"""
rho = np.sqrt(x**2 + y**2)
phi = np.arctan2(y, x)
return(rho, phi) | 490e839b9a7c7e369643c27df3bbf4a6cd6779ba | 12,999 |
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