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def plot_displacement(A, B, save=False, labels=None):
"""
A and B are both num_samples x num_dimensions
for now, num_dimensions must = 2
"""
assert A.shape == B.shape
assert A.shape[1] == 2
if not labels is None:
assert len(labels) == A.shape[0]
delta = B - A
delta_dir = delta/np.linalg.norm(delta, axis=1).reshape(-1, 1)
fig = plt.figure()
# set size
xmin = min(min(A[:, 0]), min(B[:, 0]))
xmax = max(max(A[:, 0]), max(B[:, 0]))
ymin = min(min(A[:, 1]), min(B[:, 1]))
ymax = max(max(A[:, 1]), max(B[:, 1]))
plt.xlim(1.1*xmin, 1.1*xmax)
plt.ylim(1.1*ymin, 1.1*ymax)
# create
# add displacement arrows, possibly labels
offset = 0.05
for i in xrange(A.shape[0]):
plt.arrow(A[i, 0]+offset*delta_dir[i, 0], A[i, 1]+offset*delta_dir[i, 1],
delta[i, 0]-2*offset*delta_dir[i, 0], delta[i, 1]-2*offset*delta_dir[i, 1],
length_includes_head=True, alpha=0.5, color='grey',
head_width=0.08, head_length=0.08, width=0.009)
if not labels is None:
plt.annotate(labels[i], xy=A[i, :], xytext=A[i, :], color='red')
plt.annotate(labels[i], xy=B[i, :], xytext=B[i, :], color='blue')
if labels is None:
# without labels, just plot points
plt.scatter(A[:, 0], A[:, 1], s=35, c='red', linewidths=0)
plt.scatter(B[:, 0], B[:, 1], s=35, c='blue', linewidths=0)
plt.axhline(0, color='grey', linestyle='--')
plt.axvline(0, color='grey', linestyle='--')
# show
if save:
plt.savefig('fig.png')
else:
plt.show()
return True | 8cd05bbe56c590923e5d3a51d2b6eaf933b6a71b | 3,700 |
import math
def t_dp(tdb, rh):
""" Calculates the dew point temperature.
Parameters
----------
tdb: float
dry-bulb air temperature, [°C]
rh: float
relative humidity, [%]
Returns
-------
t_dp: float
dew point temperature, [°C]
"""
c = 257.14
b = 18.678
a = 6.1121
d = 234.5
gamma_m = math.log(rh / 100 * math.exp((b - tdb / d) * (tdb / (c + tdb))))
return round(c * gamma_m / (b - gamma_m), 1) | d3cd7de10ef51f36bc5d5bd978d991d5bfe3ba4c | 3,701 |
import os
import zipfile
def extract_archive(filepath):
"""
Returns the path of the archive
:param str filepath: Path to file to extract or read
:return: path of the archive
:rtype: str
"""
# Checks if file path is a directory
if os.path.isdir(filepath):
path = os.path.abspath(filepath)
print("Archive already extracted. Viewing from {}...".format(path))
return path
# Checks if the filepath is a zipfile and continues to extract if it is
# if not it raises an error
elif not zipfile.is_zipfile(filepath):
# Misuse of TypeError? :P
raise TypeError("{} is not a zipfile".format(filepath))
archive_sha = SHA1_file(
filepath=filepath,
# Add version of slackviewer to hash as well so we can invalidate the cached copy
# if there are new features added
extra=to_bytes(slackviewer.__version__)
)
extracted_path = os.path.join(SLACKVIEWER_TEMP_PATH, archive_sha)
if os.path.exists(extracted_path):
print("{} already exists".format(extracted_path))
else:
# Extract zip
with zipfile.ZipFile(filepath) as zip:
print("{} extracting to {}...".format(filepath, extracted_path))
zip.extractall(path=extracted_path)
print("{} extracted to {}".format(filepath, extracted_path))
# Add additional file with archive info
create_archive_info(filepath, extracted_path, archive_sha)
return extracted_path | 2a066b7bd888833d035d30945d5b24653b0620a6 | 3,702 |
def LinkConfig(reset=0, loopback=0, scrambling=1):
"""Link Configuration of TS1/TS2 Ordered Sets."""
value = ( reset << 0)
value |= ( loopback << 2)
value |= ((not scrambling) << 3)
return value | 901fe6df8bbe8dfa65cd516ac14692594608edfb | 3,703 |
def test_tedlium_release():
"""
Feature: TedliumDataset
Description: test release of tedlium
Expectation: release
set invalid data
get throw error
"""
def test_config(release):
try:
ds.TedliumDataset(DATA_DIR_TEDLIUM_RELEASE12, release)
except (ValueError, TypeError, RuntimeError) as e:
return str(e)
return None
# test the release
assert "release is not within the valid set of ['release1', 'release2', 'release3']" in test_config("invalid")
assert "Argument release with value None is not of type [<class 'str'>]" in test_config(None)
assert "Argument release with value ['list'] is not of type [<class 'str'>]" in test_config(["list"]) | a25f042745d1eea4dca62195e1259b7bcb8beb8d | 3,704 |
def get_data(n, input_dim, y_dim, attention_column=1):
"""
Data generation. x is purely random except that it's first value equals the target y.
In practice, the network should learn that the target = x[attention_column].
Therefore, most of its attention should be focused on the value addressed by attention_column.
:param n: the number of samples to retrieve.
:param input_dim: the number of dimensions of each element in the series.
:param attention_column: the column linked to the target. Everything else is purely random.
:return: x: model inputs, y: model targets
"""
x = np.random.standard_normal(size=(n, input_dim))
y = np.random.randint(low=0, high=2, size=(n, y_dim))
for i in range(y_dim):
x[:, i * 3] = y[:, i]
return x, y | 4c132abec92e8cd0ca6afd06e4032116d3631a50 | 3,705 |
def generate_tautomer_hydrogen_definitions(hydrogens, residue_name, isomer_index):
"""
Creates a hxml file that is used to add hydrogens for a specific tautomer to the heavy-atom skeleton
Parameters
----------
hydrogens: list of tuple
Tuple contains two atom names: (hydrogen-atom-name, heavy-atom-atom-name)
residue_name : str
name of the residue to fill the Residues entry in the xml tree
isomer_index : int
"""
hydrogen_definitions_tree = etree.fromstring("<Residues/>")
hydrogen_file_residue = etree.fromstring("<Residue/>")
hydrogen_file_residue.set("name", residue_name)
for name, parent in hydrogens:
h_xml = etree.fromstring("<H/>")
h_xml.set("name", name)
h_xml.set("parent", parent)
hydrogen_file_residue.append(h_xml)
hydrogen_definitions_tree.append(hydrogen_file_residue)
return hydrogen_definitions_tree | 69ec0c489d93edc7d8fe3ccde8df25c9c2fd01c9 | 3,706 |
def login(client=None, **defaults):
"""
@param host:
@param port:
@param identityName:
@param password:
@param serviceName:
@param perspectiveName:
@returntype: Deferred RemoteReference of Perspective
"""
d = defer.Deferred()
LoginDialog(client, d, defaults)
return d | a6c4593ef5b1fd29f46cba4a6484049bd093b6da | 3,707 |
import time
import json
import traceback
def insertInstrument():
""" Insert a new instrument or edit an existing instrument on a DAQBroker database. Guest users are not allowed to
create instruments. Created instruments are
.. :quickref: Create/Edit instrument; Creates or edits a DAQBroker instrument instrument
:param: Name : (String) unique instrument name
:param: instid : (Integer) unique instrument identifier. Used to edit an existing instrument
:param: description : (String) description of the instrument and its
:param: email : (String) contact information for the instrument operator
:param: Files : (Optional) JSON encoded list of instrument data source objects. Each Contains the following keys:
| ``name`` : (String) name of the data source
| ``metaid`` : (Integer) unique data source identifier. Only used to edit existing data sources
| ``type`` : (Integer) type of instrument data source
| ``node`` : (String) unique network node identifier
| ``remarks`` : (String) JSON encoded object of extra data source information
| ``channels`` : (Optional) JSON encoded list of data channel objects. Each contains the following keys:
| ``Name`` : (String) data channel name
| ``channelid`` : (Integer) unique channel identifier. -1 if the channel is new. Positive integer
if the channel already exists
| ``description`` : (String) data channel description
| ``units`` : (String) data channel physical units
| ``channeltype`` : (Integer) type of data channel
| ``0`` : Number
| ``1`` : Text
| ``2`` : Custom
| ``active`` : (Boolean) channel is shown on interface
| ``fileorder`` : (Integer) Used to order channels in a data source
| ``alias`` : (String) Original data channel name. Kept constant when name changes
| ``remarks`` : (String) JSON encoded object with extra information
| ``oldname`` : (String) Old channel name. Used to detect changes in the channel name
| ``channeltypeOld`` : (Integer) Old channel type. Used to detect changes in the channel type
"""
processRequest = request.get_json()
Session = sessionmaker(bind=current_user.engineObj)
session = Session()
conn = current_user.engineObj.connect()
ctx = MigrationContext.configure(conn)
op = Operations(ctx)
try:
if 'instid' in processRequest:
newInst = False
instid = processRequest['instid']
instrument = session.query(daqbrokerDatabase.instruments).filter_by(instid=instid).first()
else:
newInst = True
maxInst = session.query(func.max(daqbrokerDatabase.instruments.instid)).one_or_none()
# print(maxInst==None)
if maxInst[0]:
maxInstid = maxInst[0]
else:
maxInstid = 0
instid = maxInstid + 1
instrument = daqbrokerDatabase.instruments(
Name=processRequest['Name'],
instid=instid,
active=False,
description=processRequest['description'],
username=current_user.username,
email=processRequest['email'],
insttype=0,
log=None)
# Now I have an object called "instrument" that I can use to add sources
# and metadatas and to those metadatas I should be able to add channels.
for file in processRequest['files']:
if 'metaid' in file:
metadata = session.query(daqbrokerDatabase.instmeta).filter_by(metaid=file["metaid"]).first()
metadata.clock = time.time() * 1000
metadata.name= file['name']
metadata.type=file['type']
metadata.node=file['node']
metadata.remarks=json.dumps(file['remarks'])
else:
maxMeta = session.query(func.max(daqbrokerDatabase.instmeta.metaid)).first()
if maxMeta[0]:
maxMetaid = maxMeta[0]
else:
maxMetaid = 0
metaid = maxMetaid + 1
metadata = daqbrokerDatabase.instmeta(
clock=time.time() * 1000,
name=file['name'],
metaid=metaid,
type=file["type"],
node=file["node"],
remarks=json.dumps(
file['remarks']),
sentRequest=False,
lastAction=0,
lasterrortime=0,
lasterror='',
lockSync=False)
instrument.sources.append(metadata)
channelid = None
if 'channels' in file:
channelsInsert = []
for channel in file['channels']:
if int(channel['channelid']) < 0: # New channel - have to insert
maxChannel = session.query(func.max(daqbrokerDatabase.channels.channelid)).first()
if not channelid:
if maxChannel[0]:
maxChannelid = maxChannel[0]
else:
maxChannelid = 0
channelid = maxChannelid + 1
else:
channelid = channelid + 1
if 'remarks' in channel:
if len(channel["remarks"].keys())>0:
theRemarks = json.dumps(channel["remarks"])
else:
theRemarks = json.dumps({})
else:
theRemarks = json.dumps({})
theChannel = daqbrokerDatabase.channels(
Name=channel["Name"],
channelid=channelid,
channeltype=int(
channel["channeltype"]),
valuetype=0,
units=channel['units'],
description=channel['description'],
active=int(
channel['active']) == 1,
remarks=theRemarks,
lastclock=0,
lastValue=None,
firstClock=0,
fileorder=channel['fileorder'],
alias=channel['alias'])
metadata.channels.append(theChannel)
channelsInsert.append({'name': channel["Name"], 'type': int(channel["channeltype"])})
if not newInst:
extra = ''
if int(channel['channeltype']) == 1:
newType = daqbrokerDatabase.Float
extra = "\"" + channel["Name"] + "\"::double precision"
column = daqbrokerDatabase.Column(channel["Name"], newType)
op.add_column(processRequest['Name'] + "_data", column)
elif int(channel['channeltype']) == 2:
newType = daqbrokerDatabase.Text
column = daqbrokerDatabase.Column(channel["Name"], newType)
op.add_column(processRequest['Name'] + "_data", column)
elif int(channel['channeltype']) == 3:
extra = "\"" + channel["Name"] + "\"::double precision"
theType = daqbrokerDatabase.Float
column = daqbrokerDatabase.Column(channel["Name"], newType)
op.add_column(processRequest['Name'] + "_custom", column)
elif not newInst:
theChannel = session.query(
daqbrokerDatabase.channels).filter_by(
channelid=channel['channelid']).first()
theChannel.Name = channel["Name"]
theChannel.channeltype = int(channel["channeltype"])
theChannel.units = channel['units']
theChannel.description = channel['description']
theChannel.active = int(channel['active']) == 1
theChannel.fileorder = channel['fileorder']
theChannel.alias = channel['alias']
if (not channel['channeltypeOld'] == channel['channeltype']) or (
not channel['oldName'] == str(channel['Name'])):
if not channel['oldName'] == str(channel['Name']):
newName = str(channel['Name'])
oldName = channel['oldName']
else:
oldName = str(channel['Name'])
newName = None
if not channel['channeltypeOld'] == channel['channeltype']:
if channel['channeltype'] == 1 or channel['channeltype'] == 3:
newType = daqbrokerDatabase.Float
extra = "\"" + oldName + "\"::double precision"
else:
newType = daqbrokerDatabase.Text
extra = None
else:
newType = None
if not channel['channeltypeOld'] == channel['channeltype'] and channel['channeltype'] == 3:
if not newName:
theName = oldName
else:
theName = newName
if not newType:
theType = daqbrokerDatabase.Float
else:
theType = newType
column = daqbrokerDatabase.Column(theName, theType)
op.drop_column(processRequest['Name'] + "_data", oldName)
op.add_column(processRequest['Name'] + "_custom", column)
elif not channel['channeltypeOld'] == channel['channeltype'] and channel['channeltypeOld'] != 3:
if not newName:
theName = oldName
else:
theName = newName
if not newType:
if channel['channeltypeOld'] == 1:
theType = daqbrokerDatabase.Float
else:
theType = daqbrokerDatabase.Text
else:
theType = newType
column = daqbrokerDatabase.Column(theName, theType)
op.drop_column(processRequest['Name'] + "_custom", oldName)
op.add_column(processRequest['Name'] + "_data", column)
else:
if channel['channeltype'] == 1 or channel['channeltype'] == 2:
if extra:
op.alter_column(
processRequest['Name'] + "_data",
oldName,
new_column_name=newName,
type_=newType,
postgresql_using=extra)
else:
op.alter_column(
processRequest['Name'] + "_data", oldName, new_column_name=newName, type_=newType)
else:
if extra=='':
op.alter_column(
processRequest['Name'] + "_custom", oldName, new_column_name=newName, type_=newType)
else:
op.alter_column(
processRequest['Name'] + "_data",
oldName,
new_column_name=newName,
type_=newType,
postgresql_using=extra)
elif newInst:
raise InvalidUsage("Cannot issue edit channels on new instrument", status_code=401)
if newInst:
daqbrokerDatabase.createInstrumentTable(processRequest['Name'], channelsInsert, True)
session.add(instrument)
daqbrokerDatabase.daqbroker_database.metadata.create_all(current_user.engineObj)
session.commit()
conn.close()
current_user.updateDB()
return jsonify('done')
except Exception as e:
traceback.print_exc()
session.rollback()
# for statement in deleteStatements:
# connection.execute(statement)
raise InvalidUsage(str(e), status_code=500) | 537b89fa72b161c86c4b49a7a6813cf95df45f09 | 3,708 |
def find_distance_to_major_settlement(country, major_settlements, settlement):
"""
Finds the distance to the nearest major settlement.
"""
nearest = nearest_points(settlement['geometry'], major_settlements.unary_union)[1]
geom = LineString([
(
settlement['geometry'].coords[0][0],
settlement['geometry'].coords[0][1]
),
(
nearest.coords[0][0],
nearest.coords[0][1]
),
])
distance_km = round(geom.length / 1e3)
return distance_km | 0bb33d3777ab0f60dfb05e69cb2f5ed711585b17 | 3,709 |
def x_for_half_max_y(xs, ys):
"""Return the x value for which the corresponding y value is half
of the maximum y value. If there is no exact corresponding x value,
one is calculated by linear interpolation from the two
surrounding values.
:param xs: x values
:param ys: y values corresponding to the x values
:return:
"""
if len(xs) != len(ys):
raise ValueError("xs and ys must be of equal length")
half_max_y = max(ys) / 2
for i in range(len(xs)-1):
if ys[i+1] >= half_max_y:
x_dist = xs[i+1] - xs[i]
y_dist = ys[i+1] - ys[i]
y_offset = half_max_y - ys[i]
if y_offset == 0:
return xs[i]
else:
x_offset = y_offset / y_dist * x_dist
return xs[i] + x_offset
return None | b18525664c98dc05d72a29f2904a13372f5696eb | 3,710 |
from typing import Union
from pathlib import Path
from typing import Dict
def get_dict_from_dotenv_file(filename: Union[Path, str]) -> Dict[str, str]:
"""
:param filename: .env file where values are extracted.
:return: a dict with keys and values extracted from the .env file.
"""
result_dict = {}
error_message = 'file {filename}: the line n°{index} is not correct: "{line}"'
with open(filename) as f:
for index, line in enumerate(f):
stripped_line = line.strip()
# we don't take into account comments
if stripped_line.startswith('#'):
continue
# we don't take into account empty lines
if not stripped_line:
continue
parts = stripped_line.split('#') # we remove inline comments if there are any
# we remove set or export command if there are any
new_line = SET_EXPORT_EXPRESSION.sub('', parts[0].strip())
# we get key and value
parts = new_line.split('=')
parts = _sanitize_key_and_value(parts)
if len(parts) != 2 or ITEM_EXPRESSION.match(parts[0]) is None \
or ITEM_EXPRESSION.match(parts[1]) is None:
line_number = index + 1
raise DecodeError(message=error_message.format(filename=filename, index=line_number, line=new_line))
result_dict[parts[0]] = parts[1]
return result_dict | 97bce5a69e29f9606a58a54ed5cc4d05ab45fbca | 3,711 |
def calculate_note_numbers(note_list, key_override = None):
"""
Takes in a list of notes, and replaces the key signature (second
element of each note tuple) with the note's jianpu number.
Parameters
----------
note_list : list of tuples
List of notes to calculate jianpu numbers for.
key_override : str
If this is provided, all notes will be assumed to be in the
given key.
"""
note_list_numbered = []
for note in note_list:
if note[0] != -1:
if(note[1] == 'C' or key_override == 'C'):
offset = 0
elif(note[1] == 'C#' or key_override == 'C#'
or note[1] == 'Db' or key_override == 'Db'):
offset = 1
elif(note[1] == 'D' or key_override == 'D'):
offset = 2
elif(note[1] == 'D#' or key_override == 'D#'
or note[1] == 'Eb' or key_override == 'Eb'):
offset = 3
elif(note[1] == 'E' or key_override == 'E'):
offset = 4
elif(note[1] == 'F' or key_override == 'F'):
offset = 5
elif(note[1] == 'F#' or key_override == 'F#'
or note[1] == 'Gb' or key_override == 'Gb'):
offset = 6
elif(note[1] == 'G' or key_override == 'G'):
offset = 7
elif(note[1] == 'G#' or key_override == 'G#'
or note[1] == 'Ab' or key_override == 'Ab'):
offset = 8
elif(note[1] == 'A' or key_override == 'A'):
offset = 9
elif(note[1] == 'A#' or key_override == 'A#'
or note[1] == 'Bb' or key_override == 'Bb'):
offset = 10
elif(note[1] == 'B' or key_override == 'B'):
offset = 11
num = (note[0]-offset) - ((note[0]-offset)//12)*12
num_to_jianpu = { 0:1,
1:1.5,
2:2,
3:2.5,
4:3,
5:4,
6:4.5,
7:5,
8:5.5,
9:6,
10:6.5,
11:7}
jianpu = num_to_jianpu[num]
note_list_numbered.append((note[0], jianpu, note[2], note[3]))
else:
note_list_numbered.append(note)
return note_list_numbered | a32bbae7f64b381ad3384fdd8ae5c045c6887c87 | 3,712 |
import numpy as np
def _from_Gryzinski(DATA):
"""
This function computes the cross section and energy values from the files
that store information following the Gryzinski Model
"""
a_0 = DATA['a_0']['VALUES']
epsilon_i_H = DATA['epsilon_i_H']['VALUES']
epsilon_i = DATA['epsilon_i']['VALUES']
xi = DATA['xi']['VALUES']
final_E = DATA['Final_E']['VALUES']
Energy_range = np.linspace(epsilon_i, final_E, 200)
u = Energy_range/epsilon_i
gg = (1+2/3*(1-1/(2*u))*np.log(np.e+(u-1)**(1/2)))
g = ((u-1)/u**2)*((u/(u+1))**(3/2))*((1-1/u)**(1/2))*gg
Cross_sections = 4*np.pi*(a_0**2)*((epsilon_i_H/epsilon_i)**2)*xi*g
return(Energy_range, Cross_sections) | 925fb1e76bf23915385cf56e3a663d111615700d | 3,713 |
from haversine import haversine
def stations_within_radius(stations, centre, r):
"""Returns an alphabetically-ordered list of the names of all the stations (in a list of stations objects) within a radius r (in km) of a central point
(which must be a Lat/Long coordinate)"""
# creates empty list
name_list = []
# extracts the necessary data from the list of stations
for i in range(len(stations)):
station_entry = stations[i]
s_coord = station_entry.coord
s_distance = haversine(s_coord, centre)
# Determines if the station is within the radius
if s_distance <= r:
s_name = station_entry.name
name_list.append(s_name)
#sorts the list
name_list.sort()
return name_list | 36bf8312e4295638c1e297f4a31b535c9ee96eaf | 3,714 |
from typing import Union
from typing import Sequence
from typing import Optional
from typing import List
def read_data_with_plugins(
path: Union[str, Sequence[str]],
plugin: Optional[str] = None,
plugin_manager: PluginManager = napari_plugin_manager,
) -> List[LayerData]:
"""Iterate reader hooks and return first non-None LayerData or None.
This function returns as soon as the path has been read successfully,
while catching any plugin exceptions, storing them for later retrievial,
providing useful error messages, and relooping until either layer data is
returned, or no valid readers are found.
Exceptions will be caught and stored as PluginErrors
(in plugins.exceptions.PLUGIN_ERRORS)
Parameters
----------
path : str
The path (file, directory, url) to open
plugin : str, optional
Name of a plugin to use. If provided, will force ``path`` to be read
with the specified ``plugin``. If the requested plugin cannot read
``path``, a PluginCallError will be raised.
plugin_manager : plugins.PluginManager, optional
Instance of a napari PluginManager. by default the main napari
plugin_manager will be used.
Returns
-------
LayerData : list of tuples, or None
LayerData that can be passed to :func:`Viewer._add_layer_from_data()
<napari.components.add_layers_mixin.AddLayersMixin._add_layer_from_data>`.
``LayerData`` is a list tuples, where each tuple is one of
``(data,)``, ``(data, meta)``, or ``(data, meta, layer_type)`` .
If no reader plugins are (or they all error), returns ``None``
Raises
------
PluginCallError
If ``plugin`` is specified but raises an Exception while reading.
"""
hook_caller = plugin_manager.hook.napari_get_reader
if plugin:
if plugin not in plugin_manager.plugins:
names = {i.plugin_name for i in hook_caller.get_hookimpls()}
raise ValueError(
f"There is no registered plugin named '{plugin}'.\n"
f"Names of plugins offering readers are: {names}"
)
reader = hook_caller._call_plugin(plugin, path=path)
if not callable(reader):
raise ValueError(f'Plugin {plugin!r} does not support file {path}')
return reader(path) or []
errors: List[PluginCallError] = []
path = abspath_or_url(path)
skip_impls: List[HookImplementation] = []
layer_data = None
while True:
result = hook_caller.call_with_result_obj(
path=path, _skip_impls=skip_impls
)
reader = result.result # will raise exceptions if any occurred
if not reader:
# we're all out of reader plugins
break
try:
layer_data = reader(path) # try to read data
if layer_data:
break
except Exception as exc:
# collect the error and log it, but don't raise it.
err = PluginCallError(result.implementation, cause=exc)
err.log(logger=logger)
errors.append(err)
# don't try this impl again
skip_impls.append(result.implementation)
if not layer_data:
# if layer_data is empty, it means no plugin could read path
# we just want to provide some useful feedback, which includes
# whether or not paths were passed to plugins as a list.
if isinstance(path, (tuple, list)):
path_repr = f"[{path[0]}, ...] as stack"
else:
path_repr = repr(path)
# TODO: change to a warning notification in a later PR
raise ValueError(f'No plugin found capable of reading {path_repr}.')
if errors:
names = set([repr(e.plugin_name) for e in errors])
err_msg = f"({len(errors)}) error{'s' if len(errors) > 1 else ''} "
err_msg += f"occurred in plugins: {', '.join(names)}. "
err_msg += 'See full error logs in "Plugins → Plugin Errors..."'
logger.error(err_msg)
return layer_data or [] | e6b83c4a55a39b07bfc8eddcd77c82351f91b1d7 | 3,715 |
def ticket_message_url(request, structure_slug, ticket_id): # pragma: no cover
"""
Makes URL redirect to add ticket message by user role
:type structure_slug: String
:type ticket_id: String
:param structure_slug: structure slug
:param ticket_id: ticket code
:return: redirect
"""
structure = get_object_or_404(OrganizationalStructure,
slug=structure_slug)
user_type = get_user_type(request.user, structure)
return redirect('uni_ticket:{}_ticket_message'.format(user_type),
structure_slug, ticket_id) | 29cec06302e943d74236ff82647cd28deb634107 | 3,716 |
from pathlib import Path
from datetime import datetime
from re import T
def load(
fin: Path,
azelfn: Path = None,
treq: list[datetime] = None,
wavelenreq: list[str] = None,
wavelength_altitude_km: dict[str, float] = None,
) -> dict[str, T.Any]:
"""
reads FITS images and spatial az/el calibration for allsky camera
Bdecl is in degrees, from IGRF model
"""
fin = Path(fin).expanduser()
if fin.is_file() and fin.suffix in (".h5", ".hdf5"):
return load_hdf5(fin, treq, wavelenreq)
flist = _slicereq(fin, treq, wavelenreq)
if not flist:
raise FileNotFoundError(f"No files found in {fin}")
# %% load data from good files, discarding bad
imgs = _sift(flist)
# %% camera location
imgs = _camloc(imgs, flist[0].parent)
# %% az / el
imgs = _azel(azelfn, imgs)
# %% projections
imgs = _project(imgs, wavelength_altitude_km)
return imgs | bb656482c1db134c3045ac5a30b9cecb8d9f6716 | 3,717 |
import logging
def loadData(data_source, loc, run, indexes, ntry=0, __text__=None, __prog__=None):
"""
Loads the data from a remote source. Has hooks for progress bars.
"""
if __text__ is not None:
__text__.emit("Decoding File")
if data_source.getName() == "Local WRF-ARW":
url = data_source.getURLList(outlet="Local")[0].replace("file://", "")
decoder = ARWDecoder
dec = decoder((url, loc[0], loc[1]))
else:
decoder, url = data_source.getDecoderAndURL(loc, run, outlet_num=ntry)
logging.info("Using decoder: " + str(decoder))
logging.info("Data URL: " + url)
dec = decoder(url)
if __text__ is not None:
__text__.emit("Creating Profiles")
profs = dec.getProfiles(indexes=indexes)
return profs | d06fd6fb6194dac63911a5b9c3ad267525098cd2 | 3,718 |
def comp_psip_skin(self, u):
"""psip_skin for skin effect computation
Parameters
----------
self : Conductor
An Conductor object
Returns
-------
None
"""
y = (1 / u) * (sinh(u) + sin(u)) / (cosh(u) + cos(u)) # p257 Pyrhonen
# y[u==0]=1
return y | 23fe18a13b56f38c49fd3ca14b557983064c65b3 | 3,719 |
import random
def homepage(var=random.randint(0, 1000)):
"""
The function returns the homepage html template.
"""
return render_template("index.html", var=var) | 17e9033b8abeaa990cd31008861e5c412e35d1d7 | 3,720 |
import numbers
def tier(value):
"""
A special function of ordinals which does not
correspond to any mathematically useful function.
Maps ordinals to small objects, effectively compressing the range.
Used to speed up comparisons when the operands are very different sizes.
In the current version, this is a map from ordinals to 2-tuples of integers,
however, this is subject to change at any time, so please do not retain
long lived records of what tier an ordinal number is.
"""
if isinstance(value, numbers.Real):
value = ordinal(value)
if isinstance(value, ordinal):
return value._tier
raise ValueError('Value is not of a known type representing a mathematical ordinal.') | 851b36cf22c09d8f94168a0aa55292754451e351 | 3,721 |
from .models import Sequence
def get_next_value(
sequence_name="default",
initial_value=1,
reset_value=None,
*,
nowait=False,
using=None,
overrite=None,
):
"""
Return the next value for a given sequence.
"""
# Inner import because models cannot be imported before their application.
if reset_value is not None:
assert initial_value < reset_value
if using is None:
using = router.db_for_write(Sequence)
connection = connections[using]
db_table = connection.ops.quote_name(Sequence._meta.db_table)
if (
connection.vendor == "postgresql"
# Remove when dropping Django 2.2. Django 3.0 requires PostgreSQL 9.5.
and getattr(connection, "pg_version", 0) >= 90500
and reset_value is None
and not nowait
):
# PostgreSQL ≥ 9.5 supports "upsert".
# This is about 3x faster as the naive implementation.
with connection.cursor() as cursor:
cursor.execute(
POSTGRESQL_UPSERT.format(db_table=db_table),
[sequence_name, initial_value],
),
result = cursor.fetchone()
return result[0]
elif connection.vendor == "mysql" and reset_value is None and not nowait:
# MySQL supports "upsert" but not "returning".
# This is about 2x faster as the naive implementation.
with transaction.atomic(using=using, savepoint=False):
with connection.cursor() as cursor:
cursor.execute(
MYSQL_UPSERT.format(db_table=db_table),
[sequence_name, initial_value],
)
cursor.execute(
SELECT.format(db_table=db_table),
[sequence_name],
)
result = cursor.fetchone()
return result[0]
else:
# Default, ORM-based implementation for all other cases.
with transaction.atomic(using=using, savepoint=False):
sequences = Sequence.objects.select_for_update(nowait=nowait)
sequence, created = sequences.get_or_create(
name=sequence_name,
defaults={"last": initial_value},
)
if not created:
sequence.last += 1
if reset_value is not None and sequence.last >= reset_value:
sequence.last = initial_value
if overrite is not None:
sequence.last = overrite
sequence.save()
return sequence.last | 0770c8d4a4bea732bacfe6b7eaa404546bb79699 | 3,722 |
def expected_shd(posterior, ground_truth):
"""Compute the Expected Structural Hamming Distance.
This function computes the Expected SHD between a posterior approximation
given as a collection of samples from the posterior, and the ground-truth
graph used in the original data generation process.
Parameters
----------
posterior : np.ndarray instance
Posterior approximation. The array must have size `(B, N, N)`, where `B`
is the number of sample graphs from the posterior approximation, and `N`
is the number of variables in the graphs.
ground_truth : np.ndarray instance
Adjacency matrix of the ground-truth graph. The array must have size
`(N, N)`, where `N` is the number of variables in the graph.
Returns
-------
e_shd : float
The Expected SHD.
"""
# Compute the pairwise differences
diff = np.abs(posterior - np.expand_dims(ground_truth, axis=0))
diff = diff + diff.transpose((0, 2, 1))
# Ignore double edges
diff = np.minimum(diff, 1)
shds = np.sum(diff, axis=(1, 2)) / 2
return np.mean(shds) | 5e0daf39a13fc0a4cb7a4f5d0a9fe692fdae82db | 3,723 |
import json
def package_list_read(pkgpath):
"""Read package list"""
try:
with open(PACKAGE_LIST_FILE, 'r') as pkglistfile:
return json.loads(pkglistfile.read())
except Exception:
return [] | afb97afd20823563ecfda3b5c908f7ad70322868 | 3,724 |
import pandas
import types
def hpat_pandas_series_le(self, other, level=None, fill_value=None, axis=0):
"""
Pandas Series method :meth:`pandas.Series.le` implementation.
.. only:: developer
Test: python -m hpat.runtests hpat.tests.test_series.TestSeries.test_series_op8
Parameters
----------
self: :class:`pandas.Series`
input arg
other: :obj:`pandas.Series`, :obj:`int` or :obj:`float`
input arg
level: :obj:`int` or name
*unsupported*
fill_value: :obj:`float` or None, default None
*unsupported*
axis: default 0
*unsupported*
Returns
-------
:obj:`pandas.Series`
returns :obj:`pandas.Series` object
"""
_func_name = 'Method le().'
if not isinstance(self, SeriesType):
raise TypingError('{} The object must be a pandas.series. Given: {}'.format(_func_name, self))
if level is not None or fill_value is not None or axis != 0:
raise TypingError('{} Unsupported parameters. Given level: {}, fill_value: {}, axis: {}'.format(_func_name, level, fill_value, axis))
if isinstance(other, SeriesType):
def hpat_pandas_series_le_impl(self, other):
"""
Test: python -m hpat.runtests hpat.tests.test_series.TestSeries.test_series_op8
"""
return pandas.Series(self._data <= other._data)
return hpat_pandas_series_le_impl
if isinstance(other, types.Integer) or isinstance(other, types.Float):
def hpat_pandas_series_le_impl(self, other):
"""
Test: python -m hpat.runtests hpat.tests.test_series.TestSeries.test_series_op8_integer_scalar
Test: python -m hpat.runtests hpat.tests.test_series.TestSeries.test_series_op8_float_scalar
"""
return pandas.Series(self._data <= other)
return hpat_pandas_series_le_impl
raise TypingError('{} The object must be a pandas.series and argument must be a number. Given: {} and other: {}'.format(_func_name, self, other)) | f2969e17dd79b71a033e3c84ffd82e3bf2448554 | 3,725 |
def add_obs_info(telem, obs_stats):
"""
Add observation-specific information to a telemetry table (ok flag, and outlier flag).
This is done as part of get_agasc_id_stats. It is a convenience for writing reports.
:param telem: list of tables
One or more telemetry tables (potentially many observations)
:param obs_stats: table
The result of calc_obs_stats.
:return:
"""
logger.debug(' Adding observation info to telemetry...')
obs_stats['obs_ok'] = (
(obs_stats['n'] > 10)
& (obs_stats['f_track'] > 0.3)
& (obs_stats['lf_variability_100s'] < 1)
)
obs_stats['comments'] = np.zeros(len(obs_stats), dtype='<U80')
telem = vstack(telem)
telem['obs_ok'] = True
telem['obs_outlier'] = False
for s in obs_stats:
obsid = s['obsid']
o = (telem['obsid'] == obsid)
telem['obs_ok'][o] = np.ones(np.sum(o), dtype=bool) * s['obs_ok']
if (np.any(telem['ok'][o]) and s['f_track'] > 0
and np.isfinite(s['q75']) and np.isfinite(s['q25'])):
iqr = s['q75'] - s['q25']
telem['obs_outlier'][o] = (
telem[o]['ok'] & (iqr > 0)
& ((telem[o]['mags'] < s['q25'] - 1.5 * iqr)
| (telem[o]['mags'] > s['q75'] + 1.5 * iqr))
)
logger.debug(f' Adding observation info to telemetry {obsid=}')
return telem | beea2b87337412f34d1a854c14eebf46265f78c9 | 3,726 |
def map_view(request):
"""
Place to show off the new map view
"""
# Define view options
view_options = MVView(
projection='EPSG:4326',
center=[-100, 40],
zoom=3.5,
maxZoom=18,
minZoom=2
)
# Define drawing options
drawing_options = MVDraw(
controls=['Modify', 'Delete', 'Move', 'Point', 'LineString', 'Polygon', 'Box'],
initial='Point',
output_format='GeoJSON'
)
# Define GeoJSON layer
geojson_object = {
'type': 'FeatureCollection',
'crs': {
'type': 'name',
'properties': {
'name': 'EPSG:3857'
}
},
'features': [
{
'type': 'Feature',
'geometry': {
'type': 'Point',
'coordinates': [0, 0]
}
},
{
'type': 'Feature',
'geometry': {
'type': 'LineString',
'coordinates': [[4e6, -2e6], [8e6, 2e6]]
}
},
{
'type': 'Feature',
'geometry': {
'type': 'Polygon',
'coordinates': [[[-5e6, -1e6], [-4e6, 1e6], [-3e6, -1e6]]]
}
}
]
}
# Define layers
map_layers = []
geojson_layer = MVLayer(source='GeoJSON',
options=geojson_object,
editable=False,
legend_title='Test GeoJSON',
legend_extent=[-46.7, -48.5, 74, 59],
legend_classes=[
MVLegendClass('polygon', 'Polygons', fill='rgba(255,255,255,0.8)', stroke='#3d9dcd'),
MVLegendClass('line', 'Lines', stroke='#3d9dcd')
])
map_layers.append(geojson_layer)
if get_geoserver_wms():
# Define GeoServer Layer
geoserver_layer = MVLayer(source='ImageWMS',
options={'url': get_geoserver_wms(),
'params': {'LAYERS': 'topp:states'},
'serverType': 'geoserver'},
legend_title='USA Population',
legend_extent=[-126, 24.5, -66.2, 49],
legend_classes=[
MVLegendClass('polygon', 'Low Density', fill='#00ff00', stroke='#000000'),
MVLegendClass('polygon', 'Medium Density', fill='#ff0000', stroke='#000000'),
MVLegendClass('polygon', 'High Density', fill='#0000ff', stroke='#000000')
])
map_layers.append(geoserver_layer)
# Define KML Layer
kml_layer = MVLayer(source='KML',
options={'url': '/static/tethys_gizmos/data/model.kml'},
legend_title='Park City Watershed',
legend_extent=[-111.60, 40.57, -111.43, 40.70],
legend_classes=[
MVLegendClass('polygon', 'Watershed Boundary', fill='#ff8000'),
MVLegendClass('line', 'Stream Network', stroke='#0000ff'),
])
map_layers.append(kml_layer)
# Tiled ArcGIS REST Layer
arc_gis_layer = MVLayer(source='TileArcGISRest',
options={'url': 'http://sampleserver1.arcgisonline.com/ArcGIS/rest/services/' +
'Specialty/ESRI_StateCityHighway_USA/MapServer'},
legend_title='ESRI USA Highway',
legend_extent=[-173, 17, -65, 72])
map_layers.append(arc_gis_layer)
# Define map view options
map_view_options = MapView(
height='600px',
width='100%',
controls=['ZoomSlider', 'Rotate', 'FullScreen',
{'MousePosition': {'projection': 'EPSG:4326'}},
{'ZoomToExtent': {'projection': 'EPSG:4326', 'extent': [-130, 22, -65, 54]}}],
layers=map_layers,
view=view_options,
basemap='OpenStreetMap',
draw=drawing_options,
legend=True
)
submitted_geometry = request.POST.get('geometry', None)
if submitted_geometry is not None:
messages.info(request, submitted_geometry)
context = {'map_view': map_view_options}
return render(request, 'tethys_gizmos/gizmo_showcase/map_view.html', context) | 5d037262b2c93c538b5a5b6fe076ee04a9d9b5ee | 3,727 |
def decompose_jamo(compound):
"""Return a tuple of jamo character constituents of a compound.
Note: Non-compound characters are echoed back.
WARNING: Archaic jamo compounds will raise NotImplementedError.
"""
if len(compound) != 1:
raise TypeError("decompose_jamo() expects a single character,",
"but received", type(compound), "length",
len(compound))
if compound not in JAMO_COMPOUNDS:
# Strict version:
# raise TypeError("decompose_jamo() expects a compound jamo,",
# "but received", compound)
return compound
return _JAMO_TO_COMPONENTS.get(compound, compound) | 56eb503b47a966d7f88750f7fdc1bcc55ba1aa1b | 3,728 |
from typing import Optional
def cp_in_drive(
source_id: str,
dest_title: Optional[str] = None,
parent_dir_id: Optional[str] = None,
) -> DiyGDriveFile:
"""Copy a specified file in Google Drive and return the created file."""
drive = create_diy_gdrive()
if dest_title is None:
dest_title = build_dest_title(drive, source_id)
return drive.copy_file(source_id, dest_title, parent_dir_id) | 981cfa18da78a160447778cab5f3326f35dbfc59 | 3,729 |
def label_tuning(
text_embeddings,
text_labels,
label_embeddings,
n_steps: int,
reg_coefficient: float,
learning_rate: float,
dropout: float,
) -> np.ndarray:
"""
With N as number of examples, K as number of classes, k as embedding dimension.
Args:
'text_embeddings': float[N,k] of embedded texts
'text_labels': float[N,K] class score for each example.
'label_embeddings': float[K,k] class embeddings
Returns:
float[K,k] updated class embeddings
"""
if text_embeddings.shape[0] == 0:
raise ValueError(text_embeddings.shape)
if label_embeddings.shape[0] == 0:
raise ValueError(label_embeddings.shape)
text_embeddings = tf.constant(text_embeddings)
text_labels = tf.constant(text_labels)
label_embeddings = tf.constant(label_embeddings)
init_label_embeddings = label_embeddings
for i in range(n_steps):
with tf.GradientTape() as tape:
tape.watch(label_embeddings)
dot_loss = _get_loss(
text_embeddings,
text_labels,
label_embeddings,
dropout=dropout,
)
drift_loss = tf.reduce_mean(
(label_embeddings - init_label_embeddings) ** 2
)
total_loss = dot_loss + reg_coefficient * drift_loss
gradient = tape.gradient(total_loss + drift_loss, label_embeddings)
label_embeddings = label_embeddings - (learning_rate * gradient)
label_embeddings = label_embeddings.numpy()
return label_embeddings | 83e4181c6600065bfb2cc98b4ca4957ea920ad7c | 3,730 |
def create_nan_filter(tensor):
"""Creates a layer which replace NaN's with zero's."""
return tf.where(tf.is_nan(tensor), tf.zeros_like(tensor), tensor) | 4e03c4c4c275430e5228e2d73b09e24f8c787e71 | 3,731 |
def requestor_is_superuser(requestor):
"""Return True if requestor is superuser."""
return getattr(requestor, "is_superuser", False) | 7b201601cf8a1911aff8271ff71b6d4d51f68f1a | 3,732 |
from typing import Dict
def process(business: Business, # pylint: disable=too-many-branches
filing: Dict,
filing_rec: Filing,
filing_meta: FilingMeta): # pylint: disable=too-many-branches
"""Process the incoming historic conversion filing."""
# Extract the filing information for incorporation
if not (conversion_filing := filing.get('filing', {}).get('conversion')):
raise QueueException(f'CONVL legal_filing:conversion missing from {filing_rec.id}')
if business:
raise QueueException(f'Business Already Exist: CONVL legal_filing:conversion {filing_rec.id}')
if not (corp_num := filing.get('filing', {}).get('business', {}).get('identifier')):
raise QueueException(f'conversion {filing_rec.id} missing the business idnetifier.')
# Initial insert of the business record
business_info_obj = conversion_filing.get('nameRequest')
if not (business := business_info.update_business_info(corp_num, Business(), business_info_obj, filing_rec)):
raise QueueException(f'CONVL conversion {filing_rec.id}, Unable to create business.')
if offices := conversion_filing.get('offices'):
update_offices(business, offices)
if parties := conversion_filing.get('parties'):
update_parties(business, parties)
if share_structure := conversion_filing.get('shareStructure'):
shares.update_share_structure(business, share_structure)
if name_translations := conversion_filing.get('nameTranslations'):
aliases.update_aliases(business, name_translations)
return business, filing_rec | 78f5033251cb90023c2e0c0ad064b92af5212e65 | 3,733 |
def est_const_bsl(bsl,starttime=None,endtime=None,intercept=False,val_tw=None):
"""Performs a linear regression (assuming the intercept at the origin).
The corresponding formula is tt-S*1/v-c = 0 in which tt is the travel
time of the acoustic signal in seconds and 1/v is the reciprocal of the
harmonic mean of the sound speed. The slope S is equal to the constant
baseline length and by default c is assumed to be 0, but can optionally
also be determined (intercept=True).
It needs:
bsl ... pandas.Dataframe with ID of beacon 1 ('ID'), ID of beacon 2
('range_ID'), calculated baseline lengths in metres ('bsl'), one
way traveltime in seconds ('tt'), sound speed at beacon 1 ('ssp1')
in metres per second, sound speed at beacon 2 ('ssp2') in metres per
second, measured traveltime in milliseconds ('range'), turn around
time in milliseconds ('TAT')(eventually harmonic mean of 'ssp1' and
'ssp2' ('hmssp') and reciprocal of harmonic mean of 'ssp1' and
'ssp2' ('1/v'); if they do not exist, they will be calculated) with
corresponding times of measurement for beacon pair.
starttime (optional) ... string with starttime of time window for
estimation of constant baseline length (format: 'YYYY-mm-dd
HH:MM:SS', default: first entry in bsl)
endtime (optional) ... string with endtime of time window for estimation
of constant baseline length (format: 'YYYY-mm-dd HH:MM:SS', default:
last entry in bsl)
intercept (optional) ... specify whether intercept should be set to
0 [False] or should be calculated [True] (default is False)
val_tw (optional) ... specify time window for which estimated constant
baseline length and standard deviation (as well as intercept) will be
stored in returned pandas.Dataframe (format: ['YYYY-mm-dd HH:MM:SS',
'YYYY-mm-dd HH:MM:SS'], default is starttime and endtime)
It returns:
bsl ... pandas.Dataframe with ID of beacon 1 ('ID'), ID of beacon 2
('range_ID'), calculated baseline lengths in metres ('bsl'), one
way traveltime in seconds ('tt'), sound speed at beacon 1 ('ssp1')
in metres per second, sound speed at beacon 2 ('ssp2') in metres per
second, measured traveltime in milliseconds ('range'), turn around
time in milliseconds ('TAT'), harmonic mean of 'ssp1' and 'ssp2'
('hmssp'), reciprocal of harmonic mean of 'ssp1' and 'ssp2' ('1/v'),
constant baseline length ('bsl_const') in given time window and
standard deviation of the measurements compared to the fitted line
in seconds (sigma = sqrt(sum((tt-S*1/v)^2)/(len(1/v)-1)),
'std_dev_tt') in given time window (and intercept ('intercept') )
with corresponding times of measurement for beacon pair.
"""
# check if columns 'hmssp' and '1/v' (harmonic mean of sound speeds and its
# reciprocal already exist in bsl and if not then calculate them
if not set(['hmssp','1/v']).issubset(bsl.columns):
bsl = calc_hmssp_recp_v(bsl)
# end if not set(['hmssp','1/v']).issubset(bsl.columns):
# copy bsl to new pandas.Dataframe to cut it in time
bsl_new = bsl.copy()
# check if time window for estimation of constant baseline length is given
if starttime is not None:
bsl_new = bsl_new.loc[starttime:]
else:
# set startime to first index in bsl
starttime = bsl_new.index[0]
# end if starttime is not None:
if endtime is not None:
bsl_new = bsl_new.loc[:endtime]
else:
# set endtime to last index in bsl
endtime = bsl_new.index[-1]
# end if endtime is not None:
# the numpy function numpy.linalg.lstsq() needs x as (M,N) matrix
if not intercept:
x = bsl_new['1/v'][:,np.newaxis]
else:
x = np.array(([[bsl_new['1/v'][j], 1] for j in range(len(bsl_new))]))
# end if not intercept:
S,residuals,_,_ = np.linalg.lstsq(x,bsl_new['tt'])
sigma = np.sqrt(residuals/(len(x)-1))
# set column 'bsl_const' for values between starttime and endtime to S and
# column 'std_dev_tt' to estimated sigma in bsl
if val_tw is not None:
starttime = val_tw[0]
endtime = val_tw[1]
# end if val_tw is not None:
if not intercept:
bsl.loc[starttime:endtime,'bsl_const'] = S
else:
bsl.loc[starttime:endtime,'bsl_const'] = S[0]
bsl.loc[starttime:endtime,'intercept'] = S[1]
# end if not intercept:
bsl.loc[starttime:endtime,'std_dev_tt'] = sigma
return(bsl) | 906119dcc66f4ab536d4a89c9c9b633bb6835058 | 3,734 |
def SeasonUPdate(temp):
""" Update appliance characteristics given the change in season
Parameters
----------
temp (obj): appliance set object for an individual season
Returns
----------
app_expected_load (float): expected load power in Watts
app_expected_dur (float): expected duration in hours
appliance_set (list of applience objects): applience list for a given season
t_delta_exp_dur (pandas datetime): expected appliance duration
app_index (array): index for each applience
"""
app_expected_load = temp.app_expected_load
app_expected_dur = temp.app_expected_dur
appliance_set = temp.appliance_set
t_delta_exp_dur = temp.t_delta_exp_dur
app_index = np.arange(0,len(temp.appliance_set))
return app_expected_load,app_expected_dur,appliance_set,t_delta_exp_dur,app_index | 7fdfa932bedf2ac17490df6aaeedb547e1774c4d | 3,735 |
def pad_and_reshape(instr_spec, frame_length, F):
"""
:param instr_spec:
:param frame_length:
:param F:
:returns:
"""
spec_shape = tf.shape(instr_spec)
extension_row = tf.zeros((spec_shape[0], spec_shape[1], 1, spec_shape[-1]))
n_extra_row = (frame_length) // 2 + 1 - F
extension = tf.tile(extension_row, [1, 1, n_extra_row, 1])
extended_spec = tf.concat([instr_spec, extension], axis=2)
old_shape = tf.shape(extended_spec)
new_shape = tf.concat([
[old_shape[0] * old_shape[1]],
old_shape[2:]],
axis=0)
processed_instr_spec = tf.reshape(extended_spec, new_shape)
return processed_instr_spec | 097bc2e8f58f1e947b8f69a6163d1c64d2197f9e | 3,736 |
def GetExclusiveStorageForNodes(cfg, node_uuids):
"""Return the exclusive storage flag for all the given nodes.
@type cfg: L{config.ConfigWriter}
@param cfg: cluster configuration
@type node_uuids: list or tuple
@param node_uuids: node UUIDs for which to read the flag
@rtype: dict
@return: mapping from node uuids to exclusive storage flags
@raise errors.OpPrereqError: if any given node name has no corresponding
node
"""
getflag = lambda n: _GetExclusiveStorageFlag(cfg, n)
flags = map(getflag, node_uuids)
return dict(zip(node_uuids, flags)) | b93625bc2b865530bef0c648885f5615905e54c1 | 3,737 |
import csv
def get_read_data(file, dic, keys):
""" Assigns reads to labels"""
r = csv.reader(open(file))
lines = list(r)
vecs_forwards = []
labels_forwards = []
vecs_reverse = []
labels_reverse = []
for key in keys:
for i in dic[key]:
for j in lines:
if i in j[0]:
if '_2.fq' in j[0] or '_R2_' in j[0]:
vecs_reverse.append(j[2:])
labels_reverse.append(key)
else:
vecs_forwards.append(j[2:])
labels_forwards.append(key)
return np.array(vecs_forwards), np.array(labels_forwards), np.array(vecs_reverse), np.array(labels_reverse) | 355c44cbf83ab9506755bda294723bfd1e8a15c1 | 3,738 |
def removeDuplicates(listToRemoveFrom: list[str]):
"""Given list, returns list without duplicates"""
listToReturn: list[str] = []
for item in listToRemoveFrom:
if item not in listToReturn:
listToReturn.append(item)
return listToReturn | 8265e7c560d552bd9e30c0a1140d6668abd1b4d6 | 3,739 |
def check_hms_angle(value):
"""
Validating function for angle sexagesimal representation in hours.
Used in the rich_validator
"""
if isinstance(value, list):
raise validate.ValidateError("expected value angle, found list")
match = hms_angle_re.match(value)
if not match:
raise VdtAngleError("not a valid hour angle: %s" % value)
return hms_to_angle(match.groups()) | bf1b6ec14cc131263913c331cb1d3cb9a06cdc76 | 3,740 |
import logging
def get_logger(module_name):
"""Generates a logger for each module of the project.
By default, the logger logs debug-level information into a
newscrapy.log file and info-level information in console.
Parameters
----------
module_name: str
The name of the module for which the logger should
be generated, in snakecase.
Returns
-------
Logger
A logger for a specific module.
"""
logger = logging.getLogger('%s_logger' % (module_name))
file_handler = logging.FileHandler('newscrapy.log')
console_handler = logging.StreamHandler()
file_formatter = logging.Formatter('%(asctime)s - %(name)s - '
'%(levelname)s - %(message)s')
console_formatter = logging.Formatter('%(message)s')
logger.setLevel(logging.DEBUG)
file_handler.setLevel(logging.DEBUG)
console_handler.setLevel(logging.INFO)
file_handler.setFormatter(file_formatter)
console_handler.setFormatter(console_formatter)
logger.addHandler(file_handler)
logger.addHandler(console_handler)
return logger | df9e07df89c43bc156812834c70b5b007200581e | 3,741 |
def stats():
"""Retrives the count of each object type.
Returns:
JSON object with the number of objects by type."""
return jsonify({
"amenities": storage.count("Amenity"),
"cities": storage.count("City"),
"places": storage.count("Place"),
"reviews": storage.count("Review"),
"states": storage.count("State"),
"users": storage.count("User")
}) | 31ebd630381fe33cdbff507a3d34497423dfd621 | 3,742 |
def addflux2pix(px,py,pixels,fmod):
"""Usage: pixels=addflux2pix(px,py,pixels,fmod)
Drizel Flux onto Pixels using a square PSF of pixel size unity
px,py are the pixel position (integers)
fmod is the flux calculated for (px,py) pixel
and it has the same length as px and py
pixels is the image.
"""
xmax = pixels.shape[0] #Size of pixel array
ymax = pixels.shape[1]
pxmh = px-0.5 #location of reference corner of PSF square
pymh = py-0.5
dx = np.floor(px+0.5)-pxmh
dy = np.floor(py+0.5)-pymh
# Supposing right-left as x axis and up-down as y axis:
# Lower left pixel
npx = int(pxmh) #Numpy arrays start at zero
npy = int(pymh)
#print('n',npx,npy)
if (npx >= 0) & (npx < xmax) & (npy >= 0) & (npy < ymax) :
pixels[npx,npy]=pixels[npx,npy]+fmod*dx*dy
#Same operations are done for the 3 pixels other neighbouring pixels
# Lower right pixel
npx = int(pxmh)+1 #Numpy arrays start at zero
npy = int(pymh)
if (npx >= 0) & (npx < xmax) & (npy >= 0) & (npy < ymax) :
pixels[npx,npy]=pixels[npx,npy]+fmod*(1.0-dx)*dy
# Upper left pixel
npx = int(pxmh) #Numpy arrays start at zero
npy = int(pymh)+1
if (npx >= 0) & (npx < xmax) & (npy >= 0) & (npy < ymax) :
pixels[npx,npy]=pixels[npx,npy]+fmod*dx*(1.0-dy)
# Upper right pixel
npx = int(pxmh)+1 #Numpy arrays start at zero
npy = int(pymh)+1
if (npx >= 0) & (npx < xmax) & (npy >= 0) & (npy < ymax) :
pixels[npx,npy]=pixels[npx,npy]+fmod*(1.0-dx)*(1.0-dy)
return pixels; | 808f99dac20cda962146fee8f2b9878a07804f9b | 3,743 |
def get_dea_landsat_vrt_dict(feat_list):
"""
this func is designed to take all releveant landsat bands
on the dea public database for each scene in stac query.
it results in a list of vrts for each band seperately and maps
them to a dict where band name is the key, list is the value pair.
"""
# notify
print('Getting landsat vrts for each relevant bands.')
# check features type, length
if not isinstance(feat_list, list):
raise TypeError('Features must be a list of xml objects.')
elif not len(feat_list) > 0:
raise ValueError('No features provided.')
# required dea landsat ard band names
bands = [
'nbart_blue',
'nbart_green',
'nbart_red',
'nbart_nir',
'nbart_swir_1',
'nbart_swir_2',
'oa_fmask'
]
# iter each band name and build associated vrt list
band_vrts_dict = {}
for band in bands:
print('Building landsat vrt list for band: {}.'.format(band))
band_vrts_dict[band] = make_vrt_list(feat_list, band=band)
# notify and return
print('Got {} landsat vrt band lists successfully.'.format(len(band_vrts_dict)))
return band_vrts_dict | 79009cc9fbcd085c8e95cf15f4271419d598d1ce | 3,744 |
import logging
import json
def load_json() -> tuple[list["Team"], list["User"]]:
"""Load the Json file."""
logging.debug("Starting to load data file.")
with open(".example.json") as file:
data = json.load(file)
if any(field not in data for field in REQUIRED_DATA_FIELDS):
raise ValueError("Required field is missing.")
team_mapping = {}
users = []
for uid, user_data in data["users"].items():
if any(field not in user_data for field in REQUIRED_USER_FIELDS):
raise ValueError("Required field is missing.")
user = User(uid, **user_data)
users.append(user)
if user_data["team"] not in team_mapping:
team_mapping[user_data["team"]] = []
team_mapping[user_data["team"]].append(user)
teams = []
for tid, team_data in data["teams"].items():
if any(field not in team_data for field in REQUIRED_TEAM_FIELDS):
raise ValueError("Required field is missing.")
team = Team(tid, team_mapping.get(tid, []), None, **team_data)
teams.append(team)
for user in users:
if user.team == tid:
user.team = team
if user.leader:
if team.leader is not None:
raise ValueError(f"Team {tid!r} has more than one leader.")
team.leader = user
for user in users:
if isinstance(user.team, str):
raise ValueError(f"Unknown team {user.team!r}")
logging.debug("Data loaded.")
return teams, users | 602a55faa1f89a6adc7e4246b666b4f098f3b190 | 3,745 |
def is_zh_l_bracket(uni_ch):
"""判断一个 unicode 是否是中文左括号。"""
if uni_ch == u'\uff08':
return True
else:
return False | 3ba18418005824a51de380c898726d050d464ec2 | 3,746 |
def petlink32_to_dynamic_projection_mMR(filename,n_packets,n_radial_bins,n_angles,n_sinograms,time_bins,n_axial,n_azimuthal,angles_axial,angles_azimuthal,size_u,size_v,n_u,n_v,span,n_segments,segments_sizes,michelogram_segments,michelogram_planes, status_callback):
"""Make dynamic compressed projection from list-mode data. """
descriptor = [ {'name':'filename', 'type':'string', 'value':filename ,'size':len(filename)},
{'name':'n_packets', 'type':'long', 'value':n_packets },
{'name':'n_radial_bins', 'type':'uint', 'value':n_radial_bins },
{'name':'n_angles', 'type':'uint', 'value':n_angles },
{'name':'n_sinograms', 'type':'uint', 'value':n_sinograms },
{'name':'n_time_bins', 'type':'uint', 'value':len(time_bins)-1 },
{'name':'time_bins', 'type':'array', 'value':np.int32(time_bins) },
{'name':'n_axial', 'type':'uint', 'value':n_axial },
{'name':'n_azimuthal', 'type':'uint', 'value':n_azimuthal },
{'name':'angles_axial', 'type':'array', 'value':angles_axial },
{'name':'angles_azimuthal', 'type':'array', 'value':angles_azimuthal },
{'name':'size_u', 'type':'float', 'value':size_u },
{'name':'size_v', 'type':'float', 'value':size_v },
{'name':'n_u', 'type':'uint', 'value':n_u },
{'name':'n_v', 'type':'uint', 'value':n_v },
{'name':'span', 'type':'uint', 'value':span },
{'name':'n_segments', 'type':'uint', 'value':n_segments },
{'name':'segments_sizes', 'type':'array', 'value':np.int32(segments_sizes) },
{'name':'michelogram_segments', 'type':'array', 'value':np.int32(michelogram_segments) },
{'name':'michelogram_planes', 'type':'array', 'value':np.int32(michelogram_planes) },
{'name':'status_callback', 'type':'function','value':status_callback, 'arg_types':['uint'] }, ]
r = call_c_function( mMR_c.petlink32_to_dynamic_projection_mMR_michelogram, descriptor )
if not r.status == petlink.status_success():
raise ErrorInCFunction("The execution of 'petlink32_to_dynamic_projection_mMR_michelogram' was unsuccessful.",r.status,'mMR_c.petlink32_to_dynamic_projection_mMR')
return r.dictionary | 9764da2a2fb0c021274133fdd46661a44cf0dc31 | 3,747 |
from typing import Dict
def is_core_recipe(component: Dict) -> bool:
"""
Returns True if a recipe component contains a "Core Recipe"
preparation.
"""
preparations = component.get('recipeItem', {}).get('preparations') or []
return any(prep.get('id') == PreparationEnum.CORE_RECIPE.value for prep in preparations) | 451798c6f31297a80ac43db00243fb2dd85ced46 | 3,748 |
def build_estimator(output_dir, first_layer_size, num_layers, dropout,
learning_rate, save_checkpoints_steps):
"""Builds and returns a DNN Estimator, defined by input parameters.
Args:
output_dir: string, directory to save Estimator.
first_layer_size: int, size of first hidden layer of DNN.
num_layers: int, number of hidden layers.
dropout: float, dropout rate used in training.
learning_rate: float, learning_rate used in training.
save_checkpoints_steps: int, training steps to save Estimator.
Returns:
`Estimator` instance.
"""
# Sets head to default head for DNNClassifier with two classes.
model_params = {
'head':
head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss(),
'feature_columns': [
tf.feature_column.numeric_column(c, shape=[])
for c in constants.FEATURE_COLUMNS
],
'hidden_units': [
max(int(first_layer_size / (pow(2, i))), 2)
for i in range(int(num_layers))
],
'dropout':
dropout,
'optimizer':
tf.train.AdagradOptimizer(learning_rate)
}
def _model_fn(features, labels, mode, params):
"""Build TF graph based on canned DNN classifier."""
key_column = features.pop(constants.KEY_COLUMN, None)
if key_column is None:
raise ValueError('Key is missing from features.')
spec = _dnn_model_fn(features=features, labels=labels, mode=mode, **params)
predictions = spec.predictions
if predictions:
predictions[constants.KEY_COLUMN] = tf.convert_to_tensor_or_sparse_tensor(
key_column)
spec = spec._replace(predictions=predictions)
spec = spec._replace(export_outputs={
'classes': tf.estimator.export.PredictOutput(predictions)
})
return spec
config = tf.estimator.RunConfig(save_checkpoints_steps=save_checkpoints_steps)
return tf.estimator.Estimator(
model_fn=_model_fn,
model_dir=output_dir,
config=config,
params=model_params) | 339e26fd910aa7412b8e2b66845718e440ccada6 | 3,749 |
import json
def importConfig():
"""設定ファイルの読み込み
Returns:
tuple:
str: interface,
str: alexa_remote_control.sh path
list: device list
"""
with open("config.json", "r", encoding="utf-8") as f:
config = json.load(f)
interface = config["interface"]
if not interface:
return False
arc_path = config["arc_path"]
devices = config["device_list"]
return (interface, arc_path, devices) | 84f8fc0deec4aebfe48209b01d1a35f7373d31e6 | 3,750 |
from google.cloud import datalabeling_v1beta1 as datalabeling
import os
def create_dataset(project_id):
"""Creates a dataset for the given Google Cloud project."""
client = datalabeling.DataLabelingServiceClient()
# [END datalabeling_create_dataset_beta]
# If provided, use a provided test endpoint - this will prevent tests on
# this snippet from triggering any action by a real human
if 'DATALABELING_ENDPOINT' in os.environ:
opts = ClientOptions(api_endpoint=os.getenv('DATALABELING_ENDPOINT'))
client = datalabeling.DataLabelingServiceClient(client_options=opts)
# [START datalabeling_create_dataset_beta]
formatted_project_name = client.project_path(project_id)
dataset = datalabeling.types.Dataset(
display_name='YOUR_DATASET_SET_DISPLAY_NAME',
description='YOUR_DESCRIPTION'
)
response = client.create_dataset(formatted_project_name, dataset)
# The format of resource name:
# project_id/{project_id}/datasets/{dataset_id}
print('The dataset resource name: {}'.format(response.name))
print('Display name: {}'.format(response.display_name))
print('Description: {}'.format(response.description))
print('Create time:')
print('\tseconds: {}'.format(response.create_time.seconds))
print('\tnanos: {}\n'.format(response.create_time.nanos))
return response | f808115479503aae52c7ef1b3863164355b5a091 | 3,751 |
from typing import List
from typing import Dict
from typing import Any
def create_local_command(opts: Options, jobs: List[Dict[str, Any]], jobs_metadata: List[Options]) -> str:
"""Create a terminal command to run the jobs locally."""
cmd = ""
for meta, job in zip(jobs_metadata, jobs):
input_file = meta.input.absolute().as_posix()
workdir = meta.workdir.absolute().as_posix()
# Run locally
cmd += f'cd {workdir} && {opts.command} {input_file} & '
return cmd | f5d23c1fb2271b44a323d1d17e9dda35df29fcd7 | 3,752 |
import time
def time_for_log() -> str:
"""Function that print the current time for bot prints"""
return time.strftime("%d/%m %H:%M:%S - ") | 0f964d5c827782ff8cc433e57bb3e78d0a7c7cba | 3,753 |
import math
def _is_int(n) -> bool:
"""
is_int 是判断给定数字 n 是否为整数，
在判断中 n 小于epsilon的小数部分将被忽略，
是则返回 True，否则 False
:param n: 待判断的数字
:return: True if n is A_ub integer, False else
"""
return (n - math.floor(n) < _epsilon) or (math.ceil(n) - n < _epsilon) | 076a82d245333890d6790f65a58e5507905ca68f | 3,754 |
def _cpp_het_stat(A, t_stop, rates, t_start=0. * pq.ms):
"""
Generate a Compound Poisson Process (CPP) with amplitude distribution
A and heterogeneous firing rates r=r[0], r[1], ..., r[-1].
Parameters
----------
A : np.ndarray
CPP's amplitude distribution. A[j] represents the probability of
a synchronous event of size j among the generated spike trains.
The sum over all entries of A must be equal to one.
t_stop : pq.Quantity
The end time of the output spike trains
rates : pq.Quantity
Array of firing rates of each spike train generated with
t_start : pq.Quantity, optional
The start time of the output spike trains
Default: 0 pq.ms
Returns
-------
list of neo.SpikeTrain
List of neo.SpikeTrains with different firing rates, forming
a CPP with amplitude distribution `A`.
"""
# Computation of Parameters of the two CPPs that will be merged
# (uncorrelated with heterog. rates + correlated with homog. rates)
n_spiketrains = len(rates) # number of output spike trains
# amplitude expectation
expected_amplitude = np.dot(A, np.arange(n_spiketrains + 1))
r_sum = np.sum(rates) # sum of all output firing rates
r_min = np.min(rates) # minimum of the firing rates
# rate of the uncorrelated CPP
r_uncorrelated = r_sum - n_spiketrains * r_min
# rate of the correlated CPP
r_correlated = r_sum / expected_amplitude - r_uncorrelated
# rate of the hidden mother process
r_mother = r_uncorrelated + r_correlated
# Check the analytical constraint for the amplitude distribution
if A[1] < (r_uncorrelated / r_mother).rescale(
pq.dimensionless).magnitude:
raise ValueError('A[1] too small / A[i], i>1 too high')
# Compute the amplitude distribution of the correlated CPP, and generate it
A = A * (r_mother / r_correlated).magnitude
A[1] = A[1] - r_uncorrelated / r_correlated
compound_poisson_spiketrains = _cpp_hom_stat(
A, t_stop, r_min, t_start)
# Generate the independent heterogeneous Poisson processes
poisson_spiketrains = \
[homogeneous_poisson_process(rate - r_min, t_start, t_stop)
for rate in rates]
# Pool the correlated CPP and the corresponding Poisson processes
return [_pool_two_spiketrains(compound_poisson_spiketrain,
poisson_spiketrain)
for compound_poisson_spiketrain, poisson_spiketrain
in zip(compound_poisson_spiketrains, poisson_spiketrains)] | adc00577e9a6cb1ff7f9e0313befe98c81332ab1 | 3,755 |
def return_bad_parameter_config() -> CloudSettings:
"""Return a wrongly configured cloud config class."""
CloudSettingsTest = CloudSettings( # noqa: N806
settings_order=[
"init_settings",
"aws_parameter_setting",
"file_secret_settings",
"env_settings",
]
) # noqa: N806
class AWSSettings(CloudSettingsTest): # type: ignore
test: str = "Cool"
prefix_test_store: str = ""
return AWSSettings() | 06f8af87873d571be9c5ae7fd2e563402e57b2d0 | 3,756 |
def update(isamAppliance, instance_id, id, filename=None, contents=None, check_mode=False, force=False):
"""
Update a file in the administration pages root
:param isamAppliance:
:param instance_id:
:param id:
:param name:
:param contents:
:param check_mode:
:param force:
:return:
"""
if force is True or _check_file(isamAppliance, instance_id, id) is True:
if check_mode is True:
return isamAppliance.create_return_object(changed=True)
else:
if filename is not None:
return isamAppliance.invoke_put_files(
"Update a file in the administration page root",
"/wga/reverseproxy/{0}/management_root/{1}".format(instance_id, id),
[
{
'file_formfield': 'file',
'filename': filename,
'mimetype': 'application/octet-stream'
}
],
{
'file': filename,
'type': 'file'
})
elif contents is not None:
return isamAppliance.invoke_put_files(
"Update a file in the administration page root",
"/wga/reverseproxy/{0}/management_root/{1}".format(instance_id, id),
{
'contents': contents,
'type': 'file'
})
else:
return isamAppliance.create_return_object(
warnings=["Either contents or filename parameter need to be provided. Skipping update request."]) | af0b95096638fb34af130623b0929c4394a1a845 | 3,757 |
def view_deflate_encoded_content():
"""Returns Deflate-encoded data.
---
tags:
- Response formats
produces:
- application/json
responses:
200:
description: Defalte-encoded data.
"""
return jsonify(get_dict("origin", "headers", method=request.method, deflated=True)) | ff8d39f75a6cb526b3a61e85234e71efa174a208 | 3,758 |
def predict_from_word_vectors_matrix(tokens, matrix, nlp, POS="NOUN", top_number=constants.DEFAULT_TOP_ASSOCIATIONS):
"""
Make a prediction based on the word vectors
:param tokens:
:param matrix:
:param nlp:
:param POS:
:param top_number:
:return:
"""
vector_results = collect_word_vector_associations(tokens, matrix)
top_results = get_top_results(vector_results, nlp, top_number, POS)
return top_results | 6a491e481238af932994bb8d383baca4da1ebd55 | 3,759 |
import os
def get_luis_keys():
"""Retrieve Keys for LUIS app"""
load_dotenv()
key = os.getenv("LUIS_KEY")
region = os.getenv("LUIS_REGION")
app_id = os.getenv("LUIS_APP_ID")
return key, region, app_id | 94c880c26d08a60ecde6c86952de64d8ea45a46d | 3,760 |
import re
from typing import OrderedDict
def xls_to_dict(path_or_file):
"""
Return a Python dictionary with a key for each worksheet
name. For each sheet there is a list of dictionaries, each
dictionary corresponds to a single row in the worksheet. A
dictionary has keys taken from the column headers and values
equal to the cell value for that row and column.
All the keys and leaf elements are unicode text.
"""
try:
if isinstance(path_or_file, basestring):
workbook = xlrd.open_workbook(filename=path_or_file)
else:
workbook = xlrd.open_workbook(file_contents=path_or_file.read())
except XLRDError as error:
raise PyXFormError("Error reading .xls file: %s" % error)
def xls_to_dict_normal_sheet(sheet):
def iswhitespace(string):
return isinstance(string, basestring) and len(string.strip()) == 0
# Check for duplicate column headers
column_header_list = list()
for column in range(0, sheet.ncols):
column_header = sheet.cell_value(0, column)
if column_header in column_header_list:
raise PyXFormError("Duplicate column header: %s" % column_header)
# xls file with 3 columns mostly have a 3 more columns that are
# blank by default or something, skip during check
if column_header is not None:
if not iswhitespace(column_header):
# strip whitespaces from the header
clean_header = re.sub(r"( )+", " ", column_header.strip())
column_header_list.append(clean_header)
result = []
for row in range(1, sheet.nrows):
row_dict = OrderedDict()
for column in range(0, sheet.ncols):
# Changing to cell_value function
# convert to string, in case it is not string
key = "%s" % sheet.cell_value(0, column)
key = key.strip()
value = sheet.cell_value(row, column)
# remove whitespace at the beginning and end of value
if isinstance(value, basestring):
value = value.strip()
value_type = sheet.cell_type(row, column)
if value is not None:
if not iswhitespace(value):
try:
row_dict[key] = xls_value_to_unicode(
value, value_type, workbook.datemode
)
except XLDateAmbiguous:
raise PyXFormError(
XL_DATE_AMBIGOUS_MSG % (sheet.name, column_header, row)
)
# Taking this condition out so I can get accurate row numbers.
# TODO: Do the same for csvs
# if row_dict != {}:
result.append(row_dict)
return result, _list_to_dict_list(column_header_list)
def xls_value_from_sheet(sheet, row, column):
value = sheet.cell_value(row, column)
value_type = sheet.cell_type(row, column)
if value is not None and value != "":
try:
return xls_value_to_unicode(value, value_type, workbook.datemode)
except XLDateAmbiguous:
raise PyXFormError(XL_DATE_AMBIGOUS_MSG % (sheet.name, column, row))
else:
raise PyXFormError("Empty Value")
result = OrderedDict()
for sheet in workbook.sheets():
# Note that the sheet exists but do no further processing here.
result[sheet.name] = []
# Do not process sheets that have nothing to do with XLSForm.
if sheet.name not in constants.SUPPORTED_SHEET_NAMES:
if len(workbook.sheets()) == 1:
(
result[constants.SURVEY],
result["%s_header" % constants.SURVEY],
) = xls_to_dict_normal_sheet(sheet)
else:
continue
else:
(
result[sheet.name],
result["%s_header" % sheet.name],
) = xls_to_dict_normal_sheet(sheet)
return result | 8ac0ca78dae6bec7025565607ddc174205d0389a | 3,761 |
def blendImg(img_a, img_b, α=0.8, β=1., γ=0.):
"""
The result image is computed as follows:
img_a * α + img_b * β + γ
"""
return cv2.addWeighted(img_a, α, img_b, β, γ) | f60918ba424b0d59e9025c088c0f2c9a3f739fde | 3,762 |
def setup(app):
"""Sets up the extension"""
app.add_autodocumenter(documenters.FunctionDocumenter)
app.add_config_value(
"autoclass_content", "class", True, ENUM("both", "class", "init")
)
app.add_config_value(
"autodoc_member_order",
"alphabetical",
True,
ENUM("alphabetic", "alphabetical", "bysource", "groupwise"),
)
app.add_config_value("autodoc_default_options", {}, True)
app.add_config_value("autodoc_docstring_signature", True, True)
app.add_config_value("autodoc_mock_imports", [], True)
app.add_config_value(
"autodoc_typehints", "signature", True, ENUM("signature", "description", "none")
)
app.add_config_value("autodoc_type_aliases", {}, True)
app.add_config_value("autodoc_warningiserror", True, True)
app.add_config_value("autodoc_inherit_docstrings", True, True)
app.add_event("autodoc-before-process-signature")
app.add_event("autodoc-process-docstring")
app.add_event("autodoc-process-signature")
app.add_event("autodoc-skip-member")
app.connect("config-inited", migrate_autodoc_member_order, priority=800)
app.setup_extension("sphinx.ext.autodoc.type_comment")
app.setup_extension("sphinx.ext.autodoc.typehints")
return {"version": sphinx.__display_version__, "parallel_read_safe": True} | b6cf9cfcc59eb83c10be441faebd24e6000673f3 | 3,763 |
def genoimc_dup4_loc():
"""Create genoimc dup4 sequence location"""
return {
"_id": "ga4gh:VSL.us51izImAQQWr-Hu6Q7HQm-vYvmb-jJo",
"sequence_id": "ga4gh:SQ.-A1QmD_MatoqxvgVxBLZTONHz9-c7nQo",
"interval": {
"type": "SequenceInterval",
"start": {
"value": 30417575,
"comparator": "<=",
"type": "IndefiniteRange"
},
"end": {
"value": 31394018,
"comparator": ">=",
"type": "IndefiniteRange"
}
},
"type": "SequenceLocation"
} | 3ea1b39fed22487bebffc78d45cb493b7d7afa4a | 3,764 |
def compare_versions(a, b):
"""Return 0 if a == b, 1 if a > b, else -1."""
a, b = version_to_ints(a), version_to_ints(b)
for i in range(min(len(a), len(b))):
if a[i] > b[i]:
return 1
elif a[i] < b[i]:
return -1
return 0 | 0b22589164f7d3731edc34af97d306186e677371 | 3,765 |
def get_machine_action_data(machine_action_response):
"""Get machine raw response and returns the machine action info in context and human readable format.
Notes:
Machine action is a collection of actions you can apply on the machine, for more info
https://docs.microsoft.com/en-us/windows/security/threat-protection/microsoft-defender-atp/machineaction
Returns:
dict. Machine action's info
"""
action_data = \
{
"ID": machine_action_response.get('id'),
"Type": machine_action_response.get('type'),
"Scope": machine_action_response.get('scope'),
"Requestor": machine_action_response.get('requestor'),
"RequestorComment": machine_action_response.get('requestorComment'),
"Status": machine_action_response.get('status'),
"MachineID": machine_action_response.get('machineId'),
"ComputerDNSName": machine_action_response.get('computerDnsName'),
"CreationDateTimeUtc": machine_action_response.get('creationDateTimeUtc'),
"LastUpdateTimeUtc": machine_action_response.get('lastUpdateTimeUtc'),
"RelatedFileInfo": {
"FileIdentifier": machine_action_response.get('fileIdentifier'),
"FileIdentifierType": machine_action_response.get('fileIdentifierType')
},
"Commands": machine_action_response.get('commands')
}
return action_data | 1e0ffc37d8d3b5662b64ec28cb850c6277b1bad2 | 3,766 |
import os
def BulkRemove(fname,masterfile=None,edlfile=None):
"""
Given a file with one IP per line, remove the given IPs from the EDL if they are in there
"""
global AutoSave
success = True
removes = list()
if os.path.exists(fname):
with open(fname,"rt") as ip_list:
for ip in ip_list:
removes.append(ip.strip())
Remove(removes,masterfile,edlfile)
else:
success = False
return success | c6ffe54e8b20437f6eb48459e775dd0a38b4c5d2 | 3,767 |
import torch
def convolutionalize(modules, input_size):
"""
Recast `modules` into fully convolutional form.
The conversion transfers weights and infers kernel sizes from the
`input_size` and modules' action on it.
n.b. This only handles the conversion of linear/fully-connected modules,
although other module types could require conversion for correctness.
"""
fully_conv_modules = []
x = torch.zeros((1, ) + input_size)
for m in modules:
if isinstance(m, nn.Linear):
n = nn.Conv2d(x.size(1), m.weight.size(0), kernel_size=(x.size(2), x.size(3)))
n.weight.data.view(-1).copy_(m.weight.data.view(-1))
n.bias.data.view(-1).copy_(m.bias.data.view(-1))
m = n
fully_conv_modules.append(m)
x = m(x)
return fully_conv_modules | 5693a17bac0f39538bfcada3280ce06ef91230a3 | 3,768 |
def is_unique2(s):
"""
Use a list and the int of the character will tell if that character has
already appeared once
"""
d = []
for t in s:
if d[int(t)]:
return False
d[int(t)] = True
return True | b1a1bdea8108690a0e227fd0b75f910bd6b99a07 | 3,769 |
import random
def uncomplete_tree_parallel(x:ATree, mode="full"):
""" Input is tuple (nl, fl, split)
Output is a randomly uncompleted tree,
every node annotated whether it's terminated and what actions are good at that node
"""
fl = x
fl.parent = None
add_descendants_ancestors(fl)
y = ATree("@START@", [])
y.align = fl
y.is_open = True
i = 0
y = assign_gold_actions(y, mode=mode)
choices = [deepcopy(y)] # !! can't cache because different choices !
while not all_terminated(y):
y = mark_for_execution(y, mode=mode)
y = execute_chosen_actions(y, mode=mode)
y = assign_gold_actions(y, mode=mode)
y = adjust_gold(y, mode=mode)
choices.append(deepcopy(y))
i += 1
ret = random.choice(choices[:-1])
return ret | f59e0f0279c9c439034116f769f51d60a924c4af | 3,770 |
def stations_by_river(stations):
"""Give a dictionary to hold the rivers name as keys and their corresponding stations' name as values"""
rivers_name = []
for i in stations:
if i.river not in rivers_name:
rivers_name.append(i.river)
elif i.river in rivers_name:
continue
big_list = []
for n in rivers_name:
lists = []
for y in stations:
if n == y.river:
lists.append(y.name)
elif n != y.river:
continue
lists = sorted(lists)
big_list.append(lists)
dictionary = dict(zip(rivers_name, big_list))
dicti = {}
for key in sorted(dictionary):
dicti.update({key : dictionary[key]})
assert dicti != {}
return dicti | 66fd928415619d175b7069b8c3103a3f7d930aac | 3,771 |
def QA_SU_save_huobi(frequency):
"""
Save huobi kline "smart"
"""
if (frequency not in ["1d", "1day", "day"]):
return QA_SU_save_huobi_min(frequency)
else:
return QA_SU_save_huobi_day(frequency) | cdea45afe6d7e0b61dea517adb8fc484e8eafa38 | 3,772 |
import os
def get_cachefile(filename=None):
"""Resolve cachefile path
"""
if filename is None:
for f in FILENAMES:
if os.path.exists(f):
return f
return IDFILE
else:
return filename | 39046ce95f763720a6ad23584717f4da379cf690 | 3,773 |
def inverse(a):
"""
[description]
calculating the inverse of the number of characters,
we do this to be able to find our departure when we arrive.
this part will be used to decrypt the message received.
:param a: it is an Int
:return: x -> it is an Int
"""
x = 0
while a * x % 97 != 1:
x = x + 1
return x | 2893d2abda34e4573eb5d9602edc0f8e14246e09 | 3,774 |
from typing import Optional
from typing import Union
def currency_column_to_numeric(
df: pd.DataFrame,
column_name: str,
cleaning_style: Optional[str] = None,
cast_non_numeric: Optional[dict] = None,
fill_all_non_numeric: Optional[Union[float, int]] = None,
remove_non_numeric: bool = False,
) -> pd.DataFrame:
"""Convert currency column to numeric.
This method does not mutate the original DataFrame.
This method allows one to take a column containing currency values,
inadvertently imported as a string, and cast it as a float. This is
usually the case when reading CSV files that were modified in Excel.
Empty strings (i.e. `''`) are retained as `NaN` values.
Example:
>>> import pandas as pd
>>> import janitor
>>> df = pd.DataFrame({
... "a_col": [" 24.56", "-", "(12.12)", "1,000,000"],
... "d_col": ["", "foo", "1.23 dollars", "-1,000 yen"],
... })
>>> df # doctest: +NORMALIZE_WHITESPACE
a_col d_col
0 24.56
1 - foo
2 (12.12) 1.23 dollars
3 1,000,000 -1,000 yen
The default cleaning style.
>>> df.currency_column_to_numeric("d_col")
a_col d_col
0 24.56 NaN
1 - NaN
2 (12.12) 1.23
3 1,000,000 -1000.00
The accounting cleaning style.
>>> df.currency_column_to_numeric("a_col", cleaning_style="accounting") # doctest: +NORMALIZE_WHITESPACE
a_col d_col
0 24.56
1 0.00 foo
2 -12.12 1.23 dollars
3 1000000.00 -1,000 yen
Valid cleaning styles are:
- `None`: Default cleaning is applied. Empty strings are always retained as
`NaN`. Numbers, `-`, `.` are extracted and the resulting string
is cast to a float.
- `'accounting'`: Replaces numbers in parentheses with negatives, removes commas.
:param df: The pandas DataFrame.
:param column_name: The column containing currency values to modify.
:param cleaning_style: What style of cleaning to perform.
:param cast_non_numeric: A dict of how to coerce certain strings to numeric
type. For example, if there are values of 'REORDER' in the DataFrame,
`{'REORDER': 0}` will cast all instances of 'REORDER' to 0.
Only takes effect in the default cleaning style.
:param fill_all_non_numeric: Similar to `cast_non_numeric`, but fills all
strings to the same value. For example, `fill_all_non_numeric=1`, will
make everything that doesn't coerce to a currency `1`.
Only takes effect in the default cleaning style.
:param remove_non_numeric: If set to True, rows of `df` that contain
non-numeric values in the `column_name` column will be removed.
Only takes effect in the default cleaning style.
:raises ValueError: If `cleaning_style` is not one of the accepted styles.
:returns: A pandas DataFrame.
""" # noqa: E501
check("column_name", column_name, [str])
check_column(df, column_name)
column_series = df[column_name]
if cleaning_style == "accounting":
df.loc[:, column_name] = df[column_name].apply(
_clean_accounting_column
)
return df
if cleaning_style is not None:
raise ValueError(
"`cleaning_style` is expected to be one of ('accounting', None). "
f"Got {cleaning_style!r} instead."
)
if cast_non_numeric:
check("cast_non_numeric", cast_non_numeric, [dict])
_make_cc_patrial = partial(
_currency_column_to_numeric,
cast_non_numeric=cast_non_numeric,
)
column_series = column_series.apply(_make_cc_patrial)
if remove_non_numeric:
df = df.loc[column_series != "", :]
# _replace_empty_string_with_none is applied here after the check on
# remove_non_numeric since "" is our indicator that a string was coerced
# in the original column
column_series = _replace_empty_string_with_none(column_series)
if fill_all_non_numeric is not None:
check("fill_all_non_numeric", fill_all_non_numeric, [int, float])
column_series = column_series.fillna(fill_all_non_numeric)
column_series = _replace_original_empty_string_with_none(column_series)
df = df.assign(**{column_name: pd.to_numeric(column_series)})
return df | e382752e5aff389872da69f42a3ec62785df336f | 3,775 |
async def subreddit_type_submissions(sub="wallstreetbets", kind="new"):
"""
"""
comments = []
articles = []
red = await reddit_instance()
subreddit = await red.subreddit(sub)
if kind == "hot":
submissions = subreddit.hot()
elif kind == "top":
submissions = subreddit.top()
elif kind == "new":
submissions = subreddit.new()
elif kind == "random_rising":
submissions = subreddit.random_rising()
else:
submissions = subreddit.random()
async for submission in submissions:
article = clean_submission(submission)
article['subreddit'] = sub
articles.append(article)
top_level_comments = await submission.comments()
print(f"📗 Looking at submission: {article['title'][:40]}...")
for top_level_comment in top_level_comments:
if isinstance(top_level_comment, MoreComments):
continue
comment = clean_comment(top_level_comment)
print(f"🗯️ ... {comment['author']} said {comment['body'][:40]}")
comment['article_id'] = article['id']
comments.append(comment)
return (articles, comments) | 9cc8655575ca8fd3729e220b0ee3fc8e45e4ed56 | 3,776 |
import getopt
import sys
def get_args():
"""Get argument"""
try:
opts, args = getopt.getopt(
sys.argv[1:],
"i:s:t:o:rvh",
["ibam=",
"snp=",
"tag=",
"output=",
"rstat", "verbose", "help"])
except getopt.GetoptError as err:
print(str(err))
usage()
sys.exit(-1)
return opts | 2caf9ac202c788da2587e37e0754a7789fdc8142 | 3,777 |
import typing
import json
def _get_bundle_manifest(
uuid: str,
replica: Replica,
version: typing.Optional[str],
*,
bucket: typing.Optional[str] = None) -> typing.Optional[dict]:
"""
Return the contents of the bundle manifest file from cloud storage, subject to the rules of tombstoning. If version
is None, return the latest version, once again, subject to the rules of tombstoning.
If the bundle cannot be found, return None
"""
uuid = uuid.lower()
handle = Config.get_blobstore_handle(replica)
default_bucket = replica.bucket
# need the ability to use fixture bucket for testing
bucket = default_bucket if bucket is None else bucket
def tombstone_exists(uuid: str, version: typing.Optional[str]):
return test_object_exists(handle, bucket, BundleTombstoneID(uuid=uuid, version=version).to_key())
# handle the following deletion cases
# 1. the whole bundle is deleted
# 2. the specific version of the bundle is deleted
if tombstone_exists(uuid, None) or (version and tombstone_exists(uuid, version)):
return None
# handle the following deletion case
# 3. no version is specified, we want the latest _non-deleted_ version
if version is None:
# list the files and find the one that is the most recent.
prefix = f"bundles/{uuid}."
object_names = handle.list(bucket, prefix)
version = _latest_version_from_object_names(object_names)
if version is None:
# no matches!
return None
bundle_fqid = BundleFQID(uuid=uuid, version=version)
# retrieve the bundle metadata.
try:
bundle_manifest_blob = handle.get(bucket, bundle_fqid.to_key()).decode("utf-8")
return json.loads(bundle_manifest_blob)
except BlobNotFoundError:
return None | 7881e1514a9a645c1f7ee6479baa6e74bae4dabb | 3,778 |
def handler400(request, exception):
"""
This is a Django handler function for 400 Bad Request error
:param request: The Django Request object
:param exception: The exception caught
:return: The 400 error page
"""
context = get_base_context(request)
context.update({
'message': {
'title': '400 Bad Request',
'description': 'Your client has issued a malformed or illegal request.'
}
})
return render(request, 'velarium/base.html', context=context, status=400) | 0dc1b81ec86d675f348728863dfe07efbd936e8e | 3,779 |
import os
import json
def object_trajectory_proposal(vid, fstart, fend, gt=False, verbose=False):
"""
Set gt=True for providing groundtruth bounding box trajectories and
predicting classme feature only
"""
vsig = get_segment_signature(vid, fstart, fend)
name = 'traj_cls_gt' if gt else 'traj_cls'
path = get_feature_path(name, vid)
path = os.path.join(path, '{}-{}.json'.format(vsig, name))
if os.path.exists(path):
if verbose:
print('loading object {} proposal for video segment {}'.format(name, vsig))
with open(path, 'r') as fin:
trajs = json.load(fin)
trajs = [Trajectory(**traj) for traj in trajs]
else:
if verbose:
print('no object {} proposal for video segment {}'.format(name, vsig))
trajs = []
return trajs | c8a0fc1dc8109055a16becc40e94e0e15ec8289e | 3,780 |
def _gather_topk_beams(nested, score_or_log_prob, batch_size, beam_size):
"""Gather top beams from nested structure."""
_, topk_indexes = tf.nn.top_k(score_or_log_prob, k=beam_size)
return _gather_beams(nested, topk_indexes, batch_size, beam_size) | ebdaf391104a3f271a42549708f3e7adfaf8b0b0 | 3,781 |
import scipy
import numpy
def _traceinv_exact(K, B, C, matrix, gram, exponent):
"""
Finds traceinv directly for the purpose of comparison.
"""
# Exact solution of traceinv for band matrix
if B is not None:
if scipy.sparse.isspmatrix(K):
K_ = K.toarray()
B_ = B.toarray()
if C is not None:
C_ = C.toarray()
else:
K_ = K
B_ = B
if C is not None:
C_ = C
if exponent == 0:
if C is not None:
traceinv_exact = numpy.trace(C_ @ B_)
else:
traceinv_exact = numpy.trace(B_)
else:
if gram:
K_ = numpy.matmul(K_.T, K_)
if exponent > 1:
K1 = K_.copy()
for i in range(1, exponent):
K_ = numpy.matmul(K_, K1)
Kinv = numpy.linalg.inv(K_)
Op = numpy.matmul(Kinv, B_)
if C is not None:
Op = Kinv @ C_ @ Op
traceinv_exact = numpy.trace(Op)
elif exponent == 1 and not gram:
# B is identity. Using analytic formula.
traceinv_exact = band_matrix_traceinv(matrix['a'], matrix['b'],
matrix['size'], True)
else:
# B and C are identity. Compute traceinv directly.
if scipy.sparse.isspmatrix(K):
K_ = K.toarray()
else:
K_ = K
if exponent == 0:
traceinv_exact = K_.shape[0]
else:
if gram:
K_ = numpy.matmul(K_.T, K_)
K_temp = K_.copy()
for i in range(1, exponent):
K_ = numpy.matmul(K_, K_temp)
Kinv = numpy.linalg.inv(K_)
traceinv_exact = numpy.trace(Kinv)
return traceinv_exact | 3637a5aa726ef1bf8489783c435c429b59422240 | 3,782 |
def create_feature_vector_of_mean_mfcc_for_song(song_file_path: str) -> ndarray:
"""
Takes in a file path to a song segment and returns a numpy array containing the mean mfcc values
:param song_file_path: str
:return: ndarray
"""
song_segment, sample_rate = librosa.load(song_file_path)
mfccs = librosa.feature.mfcc(y=song_segment, sr=sample_rate, n_mfcc=NUMBER_OF_MFCC)
mfccs_processed = np.mean(mfccs.T, axis=0)
df = pd.DataFrame(mfccs_processed)
z_score_normalized_mfccs = (df.values - df.values.mean()) / df.values.std()
z_score_normalized_mfccs = np.array([i[0] for i in z_score_normalized_mfccs])
return z_score_normalized_mfccs | 8992feafd483bfe7b4af5e715ba1455884e1b710 | 3,783 |
def stations_highest_rel_level(stations, N):
"""Returns a list containing the names of the N stations
with the highest water level relative to the typical range"""
names = [] # create list for names
levels = [] # create list for levels
for i in range(len(stations)): # iterate through stations
if stations[i].relative_water_level() is not None:
# ^checks for valid relative water level
names.append(stations[i].name)
levels.append(stations[i].relative_water_level())
# ^adds names and levels to respective lists
combined = list(zip(names, levels)) # combines names and levels
combined.sort(key=lambda x: x[1], reverse=1) # sorts in reverse
output = [] # create output list
for i in range(N): # iterate up to N
output.append(combined[i][0]) # add station name to output
return output | 780a03a424c9b2f0dedee2e93eb9bd27cc1fce36 | 3,784 |
def read_image(file_name, format=None):
"""
Read an image into the given format.
Will apply rotation and flipping if the image has such exif information.
Args:
file_name (str): image file path
format (str): one of the supported image modes in PIL, or "BGR"
Returns:
image (np.ndarray): an HWC image in the given format.
"""
with PathManager.open(file_name, "rb") as f:
image = Image.open(f)
# capture and ignore this bug: https://github.com/python-pillow/Pillow/issues/3973
try:
image = ImageOps.exif_transpose(image)
except Exception:
pass
if format is not None:
# PIL only supports RGB, so convert to RGB and flip channels over below
conversion_format = format
if format == "BGR":
conversion_format = "RGB"
image = image.convert(conversion_format)
image = np.asarray(image)
if format == "BGR":
# flip channels if needed
image = image[:, :, ::-1]
# PIL squeezes out the channel dimension for "L", so make it HWC
if format == "L":
image = np.expand_dims(image, -1)
return image | ffa2cd8cbea750a08fa28942e71c48bbcfbefdaf | 3,785 |
def add_global_nodes_edges(g_nx : nx.Graph, feat_data: np.ndarray, adj_list: np.ndarray,
g_feat_data: np.ndarray, g_adj_list: np.ndarray):
"""
:param g_nx:
:param feat_data:
:param adj_list:
:param g_feat_data:
:param g_adj_list:
:return:
"""
feat_data = np.concatenate([feat_data, g_feat_data], 0)
# adj_list.update((k, adj_list[k].union(g_adj_list[k])) for k in range(len(g_adj_list)))
adj_list.update((k, adj_list[k].union(g_adj_list[k])) for k in range(len(feat_data)))
g_edge_list = [[[k, v] for v in vs] for k, vs in g_adj_list.items()]
g_edge_list = [x for sublist in g_edge_list for x in sublist]
g_nx.add_edges_from(g_edge_list)
return g_nx, feat_data, adj_list | 1097becfe88f05008541aaa6c3c074fcd5c3149a | 3,786 |
import os
def _load_readme(file_name: str = "README.md") -> str:
"""
Load readme from a text file.
Args:
file_name (str, optional): File name that contains the readme. Defaults to "README.md".
Returns:
str: Readme text.
"""
with open(os.path.join(_PATH_ROOT, file_name), "r", encoding="utf-8") as file:
readme = file.read()
return readme | 5815ded89c5b952edc8a8b691afe48f99d121be6 | 3,787 |
def get_data_collector_instance(args, config):
"""Get the instance of the data
:param args: arguments of the script
:type args: Namespace
:raises NotImplementedError: no data collector implemented for given data source
:return: instance of the specific data collector
:rtype: subclass of BaseDataCollector
"""
if args.data_source == DATA_SOURCE_RSS:
return RssDataCollector(args.base_url, config[CONFIG_RSS_HEADER])
elif args.data_source == DATA_SOURCE_REDDIT:
return RedditDataCollector(REDDIT_CLIENT_ID, REDDIT_CLIENT_SECRET)
elif args.data_source == DATA_SOURCE_TWITTER:
return TwitterDataCollector(TWITTER_CONSUMER_KEY, TWITTER_CONSUMER_SECRET, TWITTER_BEARER_TOKEN)
else:
raise NotImplementedError | 75fda1231e1489da4b0c10473c9f657b143047c1 | 3,788 |
def timeIntegration(params):
"""Sets up the parameters for time integration
:param params: Parameter dictionary of the model
:type params: dict
:return: Integrated activity variables of the model
:rtype: (numpy.ndarray,)
"""
dt = params["dt"] # Time step for the Euler intergration (ms)
duration = params["duration"] # imulation duration (ms)
RNGseed = params["seed"] # seed for RNG
# ------------------------------------------------------------------------
# local parameters
# See Papadopoulos et al., Relations between large-scale brain connectivity and effects of regional stimulation
# depend on collective dynamical state, arXiv, 2020
tau_exc = params["tau_exc"] #
tau_inh = params["tau_inh"] #
c_excexc = params["c_excexc"] #
c_excinh = params["c_excinh"] #
c_inhexc = params["c_inhexc"] #
c_inhinh = params["c_inhinh"] #
a_exc = params["a_exc"] #
a_inh = params["a_inh"] #
mu_exc = params["mu_exc"] #
mu_inh = params["mu_inh"] #
# external input parameters:
# Parameter of the Ornstein-Uhlenbeck process for the external input(ms)
tau_ou = params["tau_ou"]
# Parameter of the Ornstein-Uhlenbeck (OU) process for the external input ( mV/ms/sqrt(ms) )
sigma_ou = params["sigma_ou"]
# Mean external excitatory input (OU process) (mV/ms)
exc_ou_mean = params["exc_ou_mean"]
# Mean external inhibitory input (OU process) (mV/ms)
inh_ou_mean = params["inh_ou_mean"]
# ------------------------------------------------------------------------
# global coupling parameters
# Connectivity matrix
# Interareal relative coupling strengths (values between 0 and 1), Cmat(i,j) connection from jth to ith
Cmat = params["Cmat"]
N = len(Cmat) # Number of nodes
K_gl = params["K_gl"] # global coupling strength
# Interareal connection delay
lengthMat = params["lengthMat"]
signalV = params["signalV"]
if N == 1:
Dmat = np.zeros((N, N))
else:
# Interareal connection delays, Dmat(i,j) Connnection from jth node to ith (ms)
Dmat = dp.computeDelayMatrix(lengthMat, signalV)
Dmat[np.eye(len(Dmat)) == 1] = np.zeros(len(Dmat))
Dmat_ndt = np.around(Dmat / dt).astype(int) # delay matrix in multiples of dt
params["Dmat_ndt"] = Dmat_ndt
# ------------------------------------------------------------------------
# Initialization
# Floating point issue in np.arange() workaraound: use integers in np.arange()
t = np.arange(1, round(duration, 6) / dt + 1) * dt # Time variable (ms)
sqrt_dt = np.sqrt(dt)
max_global_delay = np.max(Dmat_ndt)
startind = int(max_global_delay + 1) # timestep to start integration at
exc_ou = params["exc_ou"]
inh_ou = params["inh_ou"]
exc_ext = params["exc_ext"]
inh_ext = params["inh_ext"]
# state variable arrays, have length of t + startind
# they store initial conditions AND simulated data
excs = np.zeros((N, startind + len(t)))
inhs = np.zeros((N, startind + len(t)))
# ------------------------------------------------------------------------
# Set initial values
# if initial values are just a Nx1 array
if np.shape(params["exc_init"])[1] == 1:
exc_init = np.dot(params["exc_init"], np.ones((1, startind)))
inh_init = np.dot(params["inh_init"], np.ones((1, startind)))
# if initial values are a Nxt array
else:
exc_init = params["exc_init"][:, -startind:]
inh_init = params["inh_init"][:, -startind:]
# xsd = np.zeros((N,N)) # delayed activity
exc_input_d = np.zeros(N) # delayed input to x
inh_input_d = np.zeros(N) # delayed input to y
np.random.seed(RNGseed)
# Save the noise in the activity array to save memory
excs[:, startind:] = np.random.standard_normal((N, len(t)))
inhs[:, startind:] = np.random.standard_normal((N, len(t)))
excs[:, :startind] = exc_init
inhs[:, :startind] = inh_init
noise_exc = np.zeros((N,))
noise_inh = np.zeros((N,))
# ------------------------------------------------------------------------
return timeIntegration_njit_elementwise(
startind,
t,
dt,
sqrt_dt,
N,
Cmat,
K_gl,
Dmat_ndt,
excs,
inhs,
exc_input_d,
inh_input_d,
exc_ext,
inh_ext,
tau_exc,
tau_inh,
a_exc,
a_inh,
mu_exc,
mu_inh,
c_excexc,
c_excinh,
c_inhexc,
c_inhinh,
noise_exc,
noise_inh,
exc_ou,
inh_ou,
exc_ou_mean,
inh_ou_mean,
tau_ou,
sigma_ou,
) | 24d6702a92f82c6cc7fc1a337cd351b54c567e8b | 3,789 |
def is_role_user(session, user=None, group=None):
# type: (Session, User, Group) -> bool
"""
Takes in a User or a Group and returns a boolean indicating whether
that User/Group is a component of a service account.
Args:
session: the database session
user: a User object to check
group: a Group object to check
Throws:
AssertionError if neither a user nor a group is provided
Returns:
whether the User/Group is a component of a service account
"""
if user is not None:
return user.role_user
assert group is not None
user = User.get(session, name=group.groupname)
if not user:
return False
return user.role_user | 3d6b62b1708882b734031d737fa00f29ba9a9f95 | 3,790 |
def argCOM(y):
"""argCOM(y) returns the location of COM of y."""
idx = np.round(np.sum(y/np.sum(y)*np.arange(len(y))))
return int(idx) | 197ac25043b10575efb7405dba12c0d2e6f9976f | 3,791 |
def fringe(z, z1, z2, rad, a1):
"""
Approximation to the longitudinal profile of a multipole from a permanent magnet assembly.
see Wan et al. 2018 for definition and Enge functions paper (Enge 1964)
"""
zz1 = (z - z1) / (2 * rad / pc.pi)
zz2 = (z - z2) / (2 * rad / pc.pi)
fout = ( (1 / ( 2 * np.tanh((z2 - z1) / (4 * rad / pc.pi)) ) )
* (np.tanh(zz1 + a1 * zz1**2 )
- np.tanh(zz2 - a1 * zz2**2) )
)
return fout | b1d0138937d1c622809d6f559f17430e89259fed | 3,792 |
import random
def random_param_shift(vals, sigmas):
"""Add a random (normal) shift to a parameter set, for testing"""
assert len(vals) == len(sigmas)
shifts = [random.gauss(0, sd) for sd in sigmas]
newvals = [(x + y) for x, y in zip(vals, shifts)]
return newvals | 07430572c5051b7142499bcbdbc90de5abfcbd4d | 3,793 |
def compute_encrypted_request_hash(caller):
"""
This function will compute encrypted request Hash
:return: encrypted request hash
"""
first_string = get_parameter(caller.params_obj, "requesterNonce") or ""
worker_order_id = get_parameter(caller.params_obj, "workOrderId") or ""
worker_id = get_parameter(caller.params_obj, "workerId") or ""
workload_id = get_parameter(caller.params_obj, "workloadId") or ""
requester_id = get_parameter(caller.params_obj, "requesterId") or ""
requester_id = str(requester_id)
first_string += \
worker_order_id + worker_id + workload_id + requester_id
concat_hash = first_string.encode("UTF-8")
hash_1 = crypto_utils.compute_message_hash(concat_hash)
in_data = get_parameter(caller.params_obj, "inData")
out_data = get_parameter(caller.params_obj, "outData")
hash_2 = bytearray()
if in_data is not None:
hash_2 = compute_hash_string(in_data)
hash_3 = bytearray()
if out_data is not None:
hash_3 = compute_hash_string(out_data)
final_string = hash_1 + hash_2 + hash_3
caller.final_hash = crypto_utils.compute_message_hash(final_string)
encrypted_request_hash = crypto_utils.byte_array_to_hex(
crypto_utils.encrypt_data(
caller.final_hash, caller.session_key,
caller.session_iv))
return encrypted_request_hash | cf87c354df550b142030781e8b84ec1cb385489f | 3,794 |
def translate_line_test(string):
"""
Translates raw log line into sequence of integer representations for word tokens with sos and eos tokens.
:param string: Raw log line from auth_h.txt
:return: (list) Sequence of integer representations for word tokens with sos and eos tokens.
"""
data = string.split(",")
time = int(data[0]) # could be used to make categorical variables for day of week and time of day.
src_user, src_domain, dst_user, dst_domain, src_pc, dst_pc = split_line(string)
src_user = lookup(src_user, word_token_inds, None)
src_domain = lookup(src_domain, word_token_inds, domain_counts)
if dst_user.startswith('U'):
dst_user = lookup(dst_user, word_token_inds, None)
else:
dst_user = lookup(dst_user, word_token_inds, pc_counts)
dst_domain = lookup(dst_domain, word_token_inds, domain_counts)
src_pc = lookup(src_pc, word_token_inds, pc_counts)
dst_pc = lookup(dst_pc, word_token_inds, pc_counts)
if data[5].startswith("MICROSOFT_AUTH"): # Deals with file corruption for this value.
data[5] = "MICROSOFT_AUTH"
auth_type = lookup(data[5], word_token_inds, None)
logon_type = lookup(data[6], word_token_inds, None)
auth_orient = lookup(data[7], word_token_inds, None)
success = lookup(data[8].strip(), word_token_inds, None)
return "%s %s %s %s %s %s %s %s %s %s %s %s\n" % (str(sos), src_user, src_domain, dst_user,
dst_domain, src_pc, dst_pc, auth_type,
logon_type, auth_orient, success, str(eos)) | d311eb9c6b398391724e868071d89f2f6c442912 | 3,795 |
def preprocess_signal(signal, sample_rate):
"""
Preprocess a signal for input into a model
Inputs:
signal: Numpy 1D array containing waveform to process
sample_rate: Sampling rate of the input signal
Returns:
spectrogram: STFT of the signal after resampling to 10kHz and adding
preemphasis.
X_in: Scaled STFT input feature for the model
"""
# Compute the spectrogram of the signal
spectrogram = make_stft_features(signal, sample_rate)
# Get the magnitude spectrogram
mag_spec = np.abs(spectrogram)
# Scale the magnitude spectrogram with a square root squashing, and percent
# normalization
X_in = np.sqrt(mag_spec)
m = X_in.min()
M = X_in.max()
X_in = (X_in - m)/(M - m)
return spectrogram, X_in | d2b6c5cb700ae877f7bf8bd4b5a772471e69a75d | 3,796 |
def get_frameheight():
"""return fixed height for extra panel
"""
return 120 | 3bd810eea77af15527d3c1df7ab0b788cfe90000 | 3,797 |
def default_heart_beat_interval() -> int:
"""
:return: in seconds
"""
return 60 | 58171c8fb5632aa2aa46de8138828cce2eaa4d33 | 3,798 |
import re
def email_valid(email):
"""test for valid email address
>>> email_valid('[email protected]')
True
>>> email_valid('test@@testco.com')
False
>>> email_valid('test@testco')
False
"""
if email == '':
return True
email_re = re.compile(
r"(^[-!#$%&'*+/=?^_`{}|~0-9A-Z]+"
r"(\.[-!#$%&'*+/=?^_`{}|~0-9A-Z]+)*" # dot-atom
r'|^"([\001-\010\013\014\016-\037!#-\[\]-\177]|'
r'\\[\001-011\013\014\016-\177])*"' # quoted-string
r')@(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)'
r'+[A-Z]{2,6}\.?$', re.IGNORECASE) # domain
return bool(email_re.match(email)) | c76a621647595c741b1da71734a34372919e800f | 3,799 |
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