content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
import subprocess
import dendropy
from shutil import copyfile
if snakemake.params.enabled:
iqtree_cmd = "iqtree --quiet -s " + snakemake.input.alignment + " -t " + snakemake.input.start_tree + \
" -T " + str(snakemake.threads) + " --prefix " + snakemake.params.prefix
if snakemake.params.mode == "full":
iqtree_cmd += " -m " + snakemake.params.model
elif snakemake.params.mode == "fast":
iqtree_cmd += " --fast"
subprocess.run(iqtree_cmd, shell=True, check=True)
else:
copyfile(snakemake.input.start_tree, snakemake.output.unrooted)
tree = dendropy.Tree.get(path=snakemake.output.unrooted, schema="newick")
tree.reroot_at_midpoint(update_bipartitions=True, suppress_unifurcations=False)
tree.reroot_at_midpoint(update_bipartitions=True, suppress_unifurcations=False)
tree.write(path=str(snakemake.output.rooted),
schema="newick",
suppress_rooting=True,
unquoted_underscores=True) | [
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] | 2.42394 | 401 |
import os
import time
import calculate
from github import Github
from django.conf import settings
from calaccess_raw import get_model_list
from calaccess_raw.management.commands import CalAccessCommand
from django.contrib.humanize.templatetags.humanize import intcomma
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L = [11,22,66,22,11,44,55,66,88,77,22,11,44,22,33,77,55,44]
print('The given list is: ')
print(L)
D = {}
for item in L:
if item not in D:
D[item] = L.count(item)
print('Frequency of different items is:')
print(D)
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] | 2.13 | 100 |
""" Data model and data access methods for Candles.
"""
import pytz
from core.models.instruments import Instrument
from django.db import models
class Candle(models.Model):
""" Candle data model.
"""
# pylint: disable=too-many-instance-attributes
instrument = models.ForeignKey(Instrument, on_delete=models.PROTECT)
start_time = models.DateTimeField()
volume = models.PositiveIntegerField()
granularity = models.CharField(max_length=5)
open_bid = models.DecimalField(max_digits=12, decimal_places=6)
high_bid = models.DecimalField(max_digits=12, decimal_places=6)
low_bid = models.DecimalField(max_digits=12, decimal_places=6)
close_bid = models.DecimalField(max_digits=12, decimal_places=6)
open_ask = models.DecimalField(max_digits=12, decimal_places=6)
high_ask = models.DecimalField(max_digits=12, decimal_places=6)
low_ask = models.DecimalField(max_digits=12, decimal_places=6)
close_ask = models.DecimalField(max_digits=12, decimal_places=6)
def create_one(**kwargs):
""" Create a Candle object with the given fields.
Args:
Named arguments.
instrument: Instrument object.
start_time: Datetime object. Candle start time.
volume: Positive integer.
granularity: String. 'D' for Daily.
bid: Dictionary with 'o', 'h', 'l', 'c'
ask: Dictionary with 'o', 'h', 'l', 'c'
Returns:
Candle object with the given fields.
"""
if 'bid' in kwargs:
bid = kwargs.get('bid')
del kwargs['bid']
if bid is not None:
kwargs['open_bid'] = bid.get('o')
kwargs['high_bid'] = bid.get('h')
kwargs['low_bid'] = bid.get('l')
kwargs['close_bid'] = bid.get('c')
if 'ask' in kwargs:
ask = kwargs.get('ask')
del kwargs['ask']
if ask is not None:
kwargs['open_ask'] = ask.get('o')
kwargs['high_ask'] = ask.get('h')
kwargs['low_ask'] = ask.get('l')
kwargs['close_ask'] = ask.get('c')
if 'start_time' in kwargs:
kwargs['start_time'] = add_timezone(kwargs.get('start_time'))
return Candle(**kwargs)
def delete_all():
""" Delete all candles in the database.
Args:
None.
"""
return Candle.objects.all().delete()
def get_all(order_by):
""" Returns all candles in the database.
Args:
order_by: List of strings to order the candles by.
Returns:
List of all Candle objects (QuerySet).
"""
return Candle.objects.all().order_by(*order_by)
def get_candles(**kwargs):
""" Retrieve a list of candles with given conditions.
Args:
kwargs: Named arguments for filtering candles.
instrument: Instrument object. Filter by this instrument.
start: Datetime. Filter candles with later time than 'start'.
end: Datetime. Filter candles with earlier time than 'end'.
granularity: String. Granularity of the querying candle.
order_by: String. Space delimited string of fields to order by.
Returns:
List of Candle objects satisfying the conditions (QuerySet).
"""
candles = Candle.objects.all()
if kwargs.get('instrument') is not None:
candles = candles.filter(instrument=kwargs.get('instrument'))
if kwargs.get('start') is not None:
start_time = add_timezone(kwargs.get('start'))
candles = candles.filter(start_time__gte=start_time)
if kwargs.get('end') is not None:
end_time = add_timezone(kwargs.get('end'))
candles = candles.filter(start_time__lte=end_time)
if kwargs.get('granularity') is not None:
candles = candles.filter(granularity=kwargs.get('granularity'))
if kwargs.get('order_by') is not None:
candles = candles.order_by(kwargs.get('order_by'))
return candles
def get_last(**kwargs):
""" Retrieve the latest candle of given instrument and granularity.
Args:
kwargs: Named arguments for filtering candles.
instrument: Instrument object.
granularity: String. The granularity of the candles.
before: Datetime. Get the last candle before this time.
Returns:
Candle object if exists or None.
"""
candles = get_candles(
instrument=kwargs.get('instrument'),
granularity=kwargs.get('granularity'),
end=kwargs.get('before'),
order_by='-start_time')
if candles:
return candles[0]
def add_timezone(time_record):
""" Add a default America/New_York timezone info to a datetime object.
Args:
time_record: Datetime object.
Returns:
Datetime object with a timezone if time_record did not have tzinfo,
otherwise return time_record itself.
"""
if time_record.tzname() is None:
return time_record.replace(tzinfo=pytz.timezone('America/New_York'))
return time_record
def insert_many(candles):
""" Bulk insert a list of candles.
Args:
candles: List of Candle objects to be inserted.
"""
Candle.objects.bulk_create(candles)
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] | 2.368984 | 2,244 |
#!/usr/bin/env python
# coding: utf-8
import itertools
import random
import numpy as np
import sys, os
import pandas as pd
import torch
from torchsummary import summary
from torchtext import data
import torch.nn as nn
import torch.utils.data
from torch.utils.data import Dataset, TensorDataset,DataLoader, RandomSampler
from torch.utils.tensorboard import SummaryWriter
import torchvision
import torch.nn.functional as F
from sklearn.metrics import roc_auc_score, classification_report, confusion_matrix
from tqdm import tqdm, tqdm_notebook
import warnings
warnings.filterwarnings(action='once')
import pickle
import shutil
import time
import matplotlib.pyplot as plt
import tensorflow as tf
# Import transformers specific packages
from transformers import BertTokenizer, BertModel, BertConfig
from transformers import BertForSequenceClassification, BertForTokenClassification
from transformers import AdamW,get_linear_schedule_with_warmup, pipeline
# Import package for data parallelism to train on multi-GPU machines
from models.Transformers.parallel import DataParallelModel, DataParallelCriterion
# Check if cuda is available
# Set the device and empty cache
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if device =='cuda':
from apex import amp
torch.cuda.empty_cache()
torch.backends.cudnn.deterministic = True
# Class for model training and inference
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] | 3.503759 | 399 |
# -*- coding: utf-8 -*-
#pylint: disable-msg=E0611, E1101, C0103, R0901, R0902, R0903, R0904, W0232
#------------------------------------------------------------------------------
# Copyright (c) 2007-2014, Acoular Development Team.
#------------------------------------------------------------------------------
"""Implements beamformers in the time domain.
.. autosummary::
:toctree: generated/
BeamformerTime
BeamformerTimeTraj
BeamformerTimeSq
BeamformerTimeSqTraj
IntegratorSectorTime
"""
# imports from other packages
from numpy import array, newaxis, empty, sqrt, arange, clip, r_, zeros, \
histogram, unique, cross, dot
from traits.api import Float, CArray, Property, Trait, Bool, Delegate, \
cached_property, List
from traitsui.api import View, Item
from traitsui.menu import OKCancelButtons
# acoular imports
from .internal import digest
from .grids import RectGrid
from .microphones import MicGeom
from .environments import Environment
from .trajectory import Trajectory
from .tprocess import TimeInOut
def const_power_weight( bf ):
"""
Internal helper function for :class:`BeamformerTime`
Provides microphone weighting
to make the power per unit area of the
microphone array geometry constant.
Parameters
----------
bf: :class:`BeamformerTime` object
Returns
-------
array of floats
The weight factors.
"""
r = bf.env.r( bf.c, zeros((3, 1)), bf.mpos.mpos) # distances to center
# round the relative distances to one decimal place
r = (r/r.max()).round(decimals=1)
ru, ind = unique(r, return_inverse=True)
ru = (ru[1:]+ru[:-1])/2
count, bins = histogram(r, r_[0, ru, 1.5*r.max()-0.5*ru[-1]])
bins *= bins
weights = sqrt((bins[1:]-bins[:-1])/count)
weights /= weights.mean()
return weights[ind]
# possible choices for spatial weights
possible_weights = {'none':None,
'power':const_power_weight}
class BeamformerTime( TimeInOut ):
"""
Provides a basic time domain beamformer with time signal output
for a spatially fixed grid.
"""
#: :class:`~acoular.grids.Grid`-derived object that provides the grid locations.
grid = Trait(RectGrid,
desc="beamforming grid")
#: Number of channels in output (=number of grid points).
numchannels = Delegate('grid', 'size')
#: :class:`~acoular.microphones.MicGeom` object that provides the microphone locations.
mpos= Trait(MicGeom,
desc="microphone geometry")
#: :class:`~acoular.environments.Environment` or derived object,
#: which provides information about the sound propagation in the medium.
env = Trait(Environment(), Environment)
#: Spatial weighting function.
weights = Trait('none', possible_weights,
desc="spatial weighting function")
# (from timedomain.possible_weights)
#: The speed of sound, defaults to 343 m/s
c = Float(343.,
desc="speed of sound")
#: Sound travel distances from microphone array center to grid
#: points (readonly).
r0 = Property(
desc="array center to grid distances")
#: Sound travel distances from array microphones to grid
#: points (readonly).
rm = Property(
desc="array center to grid distances")
# internal identifier
digest = Property(
depends_on = ['mpos.digest', 'grid.digest', 'source.digest', 'c', \
'env.digest', 'weights', '__class__'],
)
traits_view = View(
[
[Item('mpos{}', style='custom')],
[Item('grid', style='custom'), '-<>'],
[Item('c', label='speed of sound')],
[Item('env{}', style='custom')],
[Item('weights{}', style='custom')],
'|'
],
title='Beamformer options',
buttons = OKCancelButtons
)
@cached_property
#@cached_property
#@cached_property
def result( self, num=2048 ):
"""
Python generator that yields the beamformer output block-wise.
Parameters
----------
num : integer, defaults to 2048
This parameter defines the size of the blocks to be yielded
(i.e. the number of samples per block)
Returns
-------
Samples in blocks of shape (num, :attr:`numchannels`).
:attr:`numchannels` is usually very large.
The last block may be shorter than num.
"""
if self.weights_:
w = self.weights_(self)[newaxis]
else:
w = 1.0
c = self.c/self.sample_freq
delays = self.rm/c
d_index = array(delays, dtype=int) # integer index
d_interp1 = delays % 1 # 1st coeff for lin interpolation between samples
d_interp2 = 1-d_interp1 # 2nd coeff for lin interpolation
d_index2 = arange(self.mpos.num_mics)
# amp = (self.rm/self.r0[:, newaxis]) # multiplication factor
amp = (w/(self.rm*self.rm)).sum(1) * self.r0
amp = 1.0/(amp[:, newaxis]*self.rm) # multiplication factor
d_interp1 *= amp # premultiplication, to save later ops
d_interp2 *= amp
dmin = d_index.min() # minimum index
dmax = d_index.max()+1 # maximum index
aoff = dmax-dmin # index span
#working copy of data:
zi = empty((aoff+num, self.source.numchannels), dtype=float)
o = empty((num, self.grid.size), dtype=float) # output array
offset = aoff # start offset for working array
ooffset = 0 # offset for output array
for block in self.source.result(num):
ns = block.shape[0] # numbers of samples and channels
maxoffset = ns-dmin # ns - aoff +aoff -dmin
zi[aoff:aoff+ns] = block * w # copy data to working array
# loop over data samples
while offset < maxoffset:
# yield output array if full
if ooffset == num:
yield o
ooffset = 0
# the next line needs to be implemented faster
o[ooffset] = (zi[offset+d_index, d_index2]*d_interp1 + \
zi[offset+d_index+1, d_index2]*d_interp2).sum(-1)
offset += 1
ooffset += 1
# copy remaining samples in front of next block
zi[0:aoff] = zi[-aoff:]
offset -= num
# remaining data chunk
yield o[:ooffset]
class BeamformerTimeSq( BeamformerTime ):
"""
Provides a time domain beamformer with time-dependend
power signal output and possible autopower removal
for a spatially fixed grid.
"""
#: Boolean flag, if 'True' (default), the main diagonal is removed before beamforming.
r_diag = Bool(True,
desc="removal of diagonal")
# internal identifier
digest = Property(
depends_on = ['mpos.digest', 'grid.digest', 'source.digest', 'r_diag', \
'c', 'env.digest', 'weights', '__class__'],
)
traits_view = View(
[
[Item('mpos{}', style='custom')],
[Item('grid', style='custom'), '-<>'],
[Item('r_diag', label='diagonal removed')],
[Item('c', label='speed of sound')],
[Item('env{}', style='custom')],
[Item('weights{}', style='custom')],
'|'
],
title='Beamformer options',
buttons = OKCancelButtons
)
@cached_property
# generator, delivers the beamformer result
def result( self, num=2048 ):
"""
Python generator that yields the *squared* beamformer
output with optional removal of autocorrelation block-wise.
Parameters
----------
num : integer, defaults to 2048
This parameter defines the size of the blocks to be yielded
(i.e. the number of samples per block)
Returns
-------
Samples in blocks of shape \
(num, :attr:`~BeamformerTime.numchannels`).
:attr:`~BeamformerTime.numchannels` is usually very
large (number of grid points).
The last block may be shorter than num.
"""
if self.weights_:
w = self.weights_(self)[newaxis]
else:
w = 1.0
c = self.c/self.source.sample_freq
delays = self.rm/c
d_index = array(delays, dtype=int) # integer index
d_interp1 = delays % 1 # 1st coeff for lin interpolation between samples
d_interp2 = 1-d_interp1 # 2nd coeff for lin interpolation
d_index2 = arange(self.mpos.num_mics)
# amp = (self.rm/self.r0[:, newaxis]) # multiplication factor
amp = (w/(self.rm*self.rm)).sum(1) * self.r0
amp = 1.0/(amp[:, newaxis]*self.rm) # multiplication factor
d_interp1 *= amp # premultiplication, to save later ops
d_interp2 *= amp
dmin = d_index.min() # minimum index
dmax = d_index.max()+1 # maximum index
# print dmin, dmax
aoff = dmax-dmin # index span
#working copy of data:
zi = empty((aoff+num, self.source.numchannels), dtype=float)
o = empty((num, self.grid.size), dtype=float) # output array
temp = empty((self.grid.size, self.source.numchannels), dtype=float)
offset = aoff # start offset for working array
ooffset = 0 # offset for output array
for block in self.source.result(num):
ns = block.shape[0] # numbers of samples and channels
maxoffset = ns-dmin # ns - aoff +aoff -dmin
zi[aoff:aoff+ns] = block * w # copy data to working array
# loop over data samples
while offset < maxoffset:
# yield output array if full
if ooffset == num:
yield o
ooffset = 0
# the next line needs to be implemented faster
temp[:, :] = (zi[offset+d_index, d_index2]*d_interp1 \
+ zi[offset+d_index+1, d_index2]*d_interp2)
if self.r_diag:
# simple sum and remove autopower
o[ooffset] = clip(temp.sum(-1)**2 - \
(temp**2).sum(-1), 1e-100, 1e+100)
else:
# simple sum
o[ooffset] = temp.sum(-1)**2
offset += 1
ooffset += 1
# copy remaining samples in front of next block
zi[0:aoff] = zi[-aoff:]
offset -= num
# remaining data chunk
yield o[:ooffset]
class BeamformerTimeTraj( BeamformerTime ):
"""
Provides a basic time domain beamformer with time signal output
for a grid moving along a trajectory
"""
#: :class:`~acoular.trajectory.Trajectory` or derived object.
#: Start time is assumed to be the same as for the samples.
trajectory = Trait(Trajectory,
desc="trajectory of the grid center")
#: Reference vector, perpendicular to the y-axis of moving grid.
rvec = CArray( dtype=float, shape=(3, ), value=array((0, 0, 0)),
desc="reference vector")
# internal identifier
digest = Property(
depends_on = ['mpos.digest', 'grid.digest', 'source.digest', \
'c', 'weights', 'rvec', 'env.digest', 'trajectory.digest', \
'__class__'],
)
traits_view = View(
[
[Item('mpos{}', style='custom')],
[Item('grid', style='custom'), '-<>'],
[Item('trajectory{}', style='custom')],
[Item('c', label='speed of sound')],
[Item('env{}', style='custom')],
[Item('weights{}', style='custom')],
'|'
],
title='Beamformer options',
buttons = OKCancelButtons
)
@cached_property
def result( self, num=2048 ):
"""
Python generator that yields the beamformer
output block-wise.
Optional removal of autocorrelation.
The "moving" grid can be translated and optionally rotated.
Parameters
----------
num : integer, defaults to 2048
This parameter defines the size of the blocks to be yielded
(i.e. the number of samples per block)
Returns
-------
Samples in blocks of shape \
(num, :attr:`~BeamformerTime.numchannels`).
:attr:`~BeamformerTime.numchannels` is usually very \
large (number of grid points).
The last block may be shorter than num. \
The output starts for signals that were emitted from the grid at t=0.
"""
if self.weights_:
w = self.weights_(self)[newaxis]
else:
w = 1.0
c = self.c/self.source.sample_freq
# temp array for the grid co-ordinates
gpos = self.grid.pos()
# max delay span = sum of
# max diagonal lengths of circumscribing cuboids for grid and micarray
dmax = sqrt(((gpos.max(1)-gpos.min(1))**2).sum())
dmax += sqrt(((self.mpos.mpos.max(1)-self.mpos.mpos.min(1))**2).sum())
dmax = int(dmax/c)+1 # max index span
zi = empty((dmax+num, self.source.numchannels), \
dtype=float) #working copy of data
o = empty((num, self.grid.size), dtype=float) # output array
temp = empty((self.grid.size, self.source.numchannels), dtype=float)
d_index2 = arange(self.mpos.num_mics, dtype=int) # second index (static)
offset = dmax+num # start offset for working array
ooffset = 0 # offset for output array
# generators for trajectory, starting at time zero
start_t = 0.0
g = self.trajectory.traj( start_t, delta_t=1/self.source.sample_freq)
g1 = self.trajectory.traj( start_t, delta_t=1/self.source.sample_freq,
der=1)
rflag = (self.rvec == 0).all() #flag translation vs. rotation
data = self.source.result(num)
flag = True
while flag:
# yield output array if full
if ooffset == num:
yield o
ooffset = 0
if rflag:
# grid is only translated, not rotated
tpos = gpos + array(g.next())[:, newaxis]
else:
# grid is both translated and rotated
loc = array(g.next()) #translation array([0., 0.4, 1.])
dx = array(g1.next()) #direction vector (new x-axis)
dy = cross(self.rvec, dx) # new y-axis
dz = cross(dx, dy) # new z-axis
RM = array((dx, dy, dz)).T # rotation matrix
RM /= sqrt((RM*RM).sum(0)) # column normalized
tpos = dot(RM, gpos)+loc[:, newaxis] # rotation+translation
rm = self.env.r( self.c, tpos, self.mpos.mpos)
r0 = self.env.r( self.c, tpos)
delays = rm/c
d_index = array(delays, dtype=int) # integer index
d_interp1 = delays % 1 # 1st coeff for lin interpolation
d_interp2 = 1-d_interp1 # 2nd coeff for lin interpolation
amp = (w/(rm*rm)).sum(1) * r0
amp = 1.0/(amp[:, newaxis]*rm) # multiplication factor
# now, we have to make sure that the needed data is available
while offset+d_index.max()+2>dmax+num:
# copy remaining samples in front of next block
zi[0:dmax] = zi[-dmax:]
# the offset is adjusted by one block length
offset -= num
# test if data generator is exhausted
try:
# get next data
block = data.next()
except StopIteration:
print loc
flag = False
break
# samples in the block, equals to num except for the last block
ns = block.shape[0]
zi[dmax:dmax+ns] = block * w# copy data to working array
else:
# the next line needs to be implemented faster
# it eats half of the time
temp[:, :] = (zi[offset+d_index, d_index2]*d_interp1 \
+ zi[offset+d_index+1, d_index2]*d_interp2)*amp
o[ooffset] = temp.sum(-1)
offset += 1
ooffset += 1
# remaining data chunk
yield o[:ooffset]
class BeamformerTimeSqTraj( BeamformerTimeSq ):
"""
Provides a time domain beamformer with time-dependent
power signal output and possible autopower removal
for a grid moving along a trajectory.
"""
#: :class:`~acoular.trajectory.Trajectory` or derived object.
#: Start time is assumed to be the same as for the samples.
trajectory = Trait(Trajectory,
desc="trajectory of the grid center")
#: Reference vector, perpendicular to the y-axis of moving grid.
rvec = CArray( dtype=float, shape=(3, ), value=array((0, 0, 0)),
desc="reference vector")
# internal identifier
digest = Property(
depends_on = ['mpos.digest', 'grid.digest', 'source.digest', 'r_diag', \
'c', 'weights', 'rvec', 'env.digest', 'trajectory.digest', \
'__class__'],
)
traits_view = View(
[
[Item('mpos{}', style='custom')],
[Item('grid', style='custom'), '-<>'],
[Item('trajectory{}', style='custom')],
[Item('r_diag', label='diagonal removed')],
[Item('c', label='speed of sound')],
[Item('env{}', style='custom')],
[Item('weights{}', style='custom')],
'|'
],
title='Beamformer options',
buttons = OKCancelButtons
)
@cached_property
def result( self, num=2048 ):
"""
Python generator that yields the *squared* beamformer
output block-wise.
Optional removal of autocorrelation.
The "moving" grid can be translated and optionally rotated.
Parameters
----------
num : integer, defaults to 2048
This parameter defines the size of the blocks to be yielded
(i.e. the number of samples per block)
Returns
-------
Samples in blocks of shape \
(num, :attr:`~BeamformerTime.numchannels`).
:attr:`~BeamformerTime.numchannels` is usually very \
large (number of grid points).
The last block may be shorter than num. \
The output starts for signals that were emitted from the grid at t=0.
"""
if self.weights_:
w = self.weights_(self)[newaxis]
else:
w = 1.0
c = self.c/self.source.sample_freq
# temp array for the grid co-ordinates
gpos = self.grid.pos()
# max delay span = sum of
# max diagonal lengths of circumscribing cuboids for grid and micarray
dmax = sqrt(((gpos.max(1)-gpos.min(1))**2).sum())
dmax += sqrt(((self.mpos.mpos.max(1)-self.mpos.mpos.min(1))**2).sum())
dmax = int(dmax/c)+1 # max index span
zi = empty((dmax+num, self.source.numchannels), \
dtype=float) #working copy of data
o = empty((num, self.grid.size), dtype=float) # output array
temp = empty((self.grid.size, self.source.numchannels), dtype=float)
d_index2 = arange(self.mpos.num_mics, dtype=int) # second index (static)
offset = dmax+num # start offset for working array
ooffset = 0 # offset for output array
# generators for trajectory, starting at time zero
start_t = 0.0
g = self.trajectory.traj( start_t, delta_t=1/self.source.sample_freq)
g1 = self.trajectory.traj( start_t, delta_t=1/self.source.sample_freq,
der=1)
rflag = (self.rvec == 0).all() #flag translation vs. rotation
data = self.source.result(num)
flag = True
while flag:
# yield output array if full
if ooffset == num:
yield o
ooffset = 0
if rflag:
# grid is only translated, not rotated
tpos = gpos + array(g.next())[:, newaxis]
else:
# grid is both translated and rotated
loc = array(g.next()) #translation
dx = array(g1.next()) #direction vector (new x-axis)
dy = cross(self.rvec, dx) # new y-axis
dz = cross(dx, dy) # new z-axis
RM = array((dx, dy, dz)).T # rotation matrix
RM /= sqrt((RM*RM).sum(0)) # column normalized
tpos = dot(RM, gpos)+loc[:, newaxis] # rotation+translation
rm = self.env.r( self.c, tpos, self.mpos.mpos)
r0 = self.env.r( self.c, tpos)
delays = rm/c
d_index = array(delays, dtype=int) # integer index
d_interp1 = delays % 1 # 1st coeff for lin interpolation
d_interp2 = 1-d_interp1 # 2nd coeff for lin interpolation
amp = (w/(rm*rm)).sum(1) * r0
amp = 1.0/(amp[:, newaxis]*rm) # multiplication factor
# now, we have to make sure that the needed data is available
while offset+d_index.max()+2>dmax+num:
# copy remaining samples in front of next block
zi[0:dmax] = zi[-dmax:]
# the offset is adjusted by one block length
offset -= num
# test if data generator is exhausted
try:
# get next data
block = data.next()
except StopIteration:
flag = False
break
# samples in the block, equals to num except for the last block
ns = block.shape[0]
zi[dmax:dmax+ns] = block * w# copy data to working array
else:
# the next line needs to be implemented faster
# it eats half of the time
temp[:, :] = (zi[offset+d_index, d_index2]*d_interp1 \
+ zi[offset+d_index+1, d_index2]*d_interp2)*amp
if self.r_diag:
# simple sum and remove autopower
o[ooffset] = clip(temp.sum(-1)**2 - \
(temp**2).sum(-1), 1e-100, 1e+100)
else:
# simple sum
o[ooffset] = temp.sum(-1)**2
offset += 1
ooffset += 1
# remaining data chunk
yield o[:ooffset]
class IntegratorSectorTime( TimeInOut ):
"""
Provides an Integrator in the time domain.
"""
#: :class:`~acoular.grids.Grid`-derived object that provides the grid locations.
grid = Trait(RectGrid,
desc="beamforming grid")
#: List of sectors in grid
sectors = List()
#: Clipping, in Dezibel relative to maximum (negative values)
clip = Float(-350.0)
#: Number of channels in output (= number of sectors).
numchannels = Property( depends_on = ['sectors', ])
# internal identifier
digest = Property(
depends_on = ['sectors', 'clip', 'grid.digest', 'source.digest', \
'__class__'],
)
traits_view = View(
[
[Item('sectors', style='custom')],
[Item('grid', style='custom'), '-<>'],
'|'
],
title='Integrator',
buttons = OKCancelButtons
)
@cached_property
@cached_property
def result( self, num=1 ):
"""
Python generator that yields the source output integrated over the given
sectors, block-wise.
Parameters
----------
num : integer, defaults to 1
This parameter defines the size of the blocks to be yielded
(i.e. the number of samples per block)
Returns
-------
Samples in blocks of shape (num, :attr:`numchannels`).
:attr:`numchannels` is the number of sectors.
The last block may be shorter than num.
"""
inds = [self.grid.indices(*sector) for sector in self.sectors]
gshape = self.grid.shape
o = empty((num, self.numchannels), dtype=float) # output array
for r in self.source.result(num):
ns = r.shape[0]
mapshape = (ns,) + gshape
rmax = r.max()
rmin = rmax * 10**(self.clip/10.0)
r = where(r>rmin, r, 0.0)
i = 0
for ind in inds:
h = r[:].reshape(mapshape)[ (s_[:],) + ind ]
o[:ns, i] = h.reshape(h.shape[0], -1).sum(axis=1)
i += 1
yield o[:ns]
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3419,
1303,
5288,
6376,
198,
220,
220,
220,
220,
220,
220,
220,
288,
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796,
288,
62,
9630,
13,
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16,
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5415,
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1303,
16090,
4866,
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25,
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220,
220,
220,
220,
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1976,
72,
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64,
2364,
10,
22510,
11,
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13,
10459,
13,
22510,
354,
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28,
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267,
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11,
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13,
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28,
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8,
1303,
5072,
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220,
220,
220,
220,
220,
11677,
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257,
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1303,
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329,
1762,
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198,
220,
220,
220,
220,
220,
220,
220,
267,
28968,
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657,
1303,
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329,
5072,
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220,
220,
220,
220,
220,
220,
220,
329,
2512,
287,
2116,
13,
10459,
13,
20274,
7,
22510,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
36545,
796,
2512,
13,
43358,
58,
15,
60,
1303,
3146,
286,
8405,
290,
9619,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
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220,
3509,
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12,
67,
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1303,
36545,
532,
257,
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1343,
64,
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532,
67,
1084,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
72,
58,
64,
2364,
25,
64,
2364,
10,
5907,
60,
796,
2512,
1635,
266,
1303,
4866,
1366,
284,
1762,
7177,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
9052,
625,
1366,
8405,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
981,
11677,
1279,
3509,
28968,
25,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
7800,
5072,
7177,
611,
1336,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
267,
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6624,
997,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7800,
267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
267,
28968,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
262,
1306,
1627,
2476,
284,
307,
9177,
5443,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
267,
58,
2238,
487,
2617,
60,
796,
357,
17027,
58,
28968,
10,
67,
62,
9630,
11,
288,
62,
9630,
17,
60,
9,
67,
62,
3849,
79,
16,
1343,
3467,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
72,
58,
28968,
10,
67,
62,
9630,
10,
16,
11,
288,
62,
9630,
17,
60,
9,
67,
62,
3849,
79,
17,
737,
16345,
32590,
16,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11677,
15853,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
267,
28968,
15853,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4866,
5637,
8405,
287,
2166,
286,
1306,
2512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
72,
58,
15,
25,
64,
2364,
60,
796,
1976,
72,
58,
12,
64,
2364,
47715,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11677,
48185,
997,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
5637,
1366,
16058,
220,
198,
220,
220,
220,
220,
220,
220,
220,
7800,
267,
58,
25,
2238,
487,
2617,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
198,
4871,
25855,
16354,
7575,
50,
80,
7,
25855,
16354,
7575,
15179,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
47081,
257,
640,
7386,
15584,
16354,
351,
640,
12,
45841,
437,
198,
220,
220,
220,
1176,
6737,
5072,
290,
1744,
22320,
789,
9934,
198,
220,
220,
220,
329,
257,
15246,
1927,
5969,
10706,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
198,
220,
220,
220,
1303,
25,
41146,
6056,
11,
611,
705,
17821,
6,
357,
12286,
828,
262,
1388,
40039,
318,
4615,
878,
15584,
15464,
13,
198,
220,
220,
220,
374,
62,
10989,
363,
796,
347,
970,
7,
17821,
11,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1715,
2625,
2787,
8325,
286,
40039,
4943,
628,
220,
220,
220,
1303,
5387,
27421,
198,
220,
220,
220,
16274,
796,
14161,
7,
220,
198,
220,
220,
220,
220,
220,
220,
220,
8338,
62,
261,
796,
37250,
76,
1930,
13,
12894,
395,
3256,
705,
25928,
13,
12894,
395,
3256,
705,
10459,
13,
12894,
395,
3256,
705,
81,
62,
10989,
363,
3256,
3467,
198,
220,
220,
220,
220,
220,
220,
220,
705,
66,
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24330,
13,
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395,
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705,
43775,
3256,
705,
834,
4871,
834,
6,
4357,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
12796,
62,
1177,
796,
3582,
7,
198,
220,
220,
220,
220,
220,
220,
220,
685,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
7449,
10786,
76,
1930,
90,
92,
3256,
3918,
11639,
23144,
11537,
4357,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
7449,
10786,
25928,
3256,
3918,
11639,
23144,
33809,
705,
12,
27,
29,
6,
4357,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
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10786,
81,
62,
10989,
363,
3256,
6167,
11639,
10989,
27923,
4615,
11537,
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220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
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10786,
66,
3256,
6167,
11639,
12287,
286,
2128,
11537,
4357,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
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10786,
24330,
90,
92,
3256,
3918,
11639,
23144,
11537,
4357,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
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10786,
43775,
90,
92,
3256,
3918,
11639,
23144,
11537,
4357,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
91,
6,
198,
220,
220,
220,
220,
220,
220,
220,
16589,
220,
198,
220,
220,
220,
220,
220,
220,
220,
3670,
11639,
3856,
321,
16354,
3689,
3256,
220,
198,
220,
220,
220,
220,
220,
220,
220,
12163,
796,
7477,
34,
21130,
1537,
27288,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
2488,
66,
2317,
62,
26745,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
1303,
17301,
11,
16316,
262,
15584,
16354,
1255,
198,
220,
220,
220,
825,
1255,
7,
2116,
11,
997,
28,
1238,
2780,
15179,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
11361,
17301,
326,
19299,
262,
1635,
16485,
1144,
9,
15584,
16354,
220,
198,
220,
220,
220,
220,
220,
220,
220,
5072,
351,
11902,
9934,
286,
1960,
420,
273,
49501,
2512,
12,
3083,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
220,
220,
220,
220,
997,
1058,
18253,
11,
26235,
284,
36117,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
770,
11507,
15738,
262,
2546,
286,
262,
7021,
284,
307,
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198,
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220,
220,
220,
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220,
220,
220,
220,
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357,
72,
13,
68,
13,
262,
1271,
286,
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583,
2512,
8,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
220,
220,
220,
35656,
198,
220,
220,
220,
220,
220,
220,
220,
3409,
2374,
287,
7021,
286,
5485,
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220,
220,
220,
220,
220,
220,
357,
22510,
11,
1058,
35226,
25,
63,
93,
3856,
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13,
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354,
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63,
737,
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63,
318,
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220,
220,
220,
220,
220,
220,
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1588,
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220,
220,
220,
220,
220,
383,
938,
2512,
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307,
12238,
621,
997,
13,
198,
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220,
220,
220,
220,
220,
220,
37227,
628,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13,
43775,
62,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
266,
796,
2116,
13,
43775,
41052,
944,
38381,
3605,
22704,
60,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
266,
796,
352,
13,
15,
198,
220,
220,
220,
220,
220,
220,
220,
269,
796,
2116,
13,
66,
14,
944,
13,
10459,
13,
39873,
62,
19503,
80,
198,
220,
220,
220,
220,
220,
220,
220,
16119,
796,
2116,
13,
26224,
14,
66,
198,
220,
220,
220,
220,
220,
220,
220,
288,
62,
9630,
796,
7177,
7,
12381,
592,
11,
288,
4906,
28,
600,
8,
1303,
18253,
6376,
198,
220,
220,
220,
220,
220,
220,
220,
288,
62,
3849,
79,
16,
796,
16119,
4064,
352,
1303,
352,
301,
763,
14822,
329,
9493,
39555,
341,
1022,
8405,
198,
220,
220,
220,
220,
220,
220,
220,
288,
62,
3849,
79,
17,
796,
352,
12,
67,
62,
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79,
16,
1303,
362,
358,
763,
14822,
329,
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39555,
341,
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198,
220,
220,
220,
220,
220,
220,
220,
288,
62,
9630,
17,
796,
610,
858,
7,
944,
13,
76,
1930,
13,
22510,
62,
76,
873,
8,
198,
2,
220,
220,
220,
220,
220,
220,
220,
20766,
796,
357,
944,
13,
26224,
14,
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13,
81,
15,
58,
45299,
649,
22704,
12962,
1303,
48473,
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220,
220,
220,
220,
220,
220,
20766,
796,
357,
86,
29006,
944,
13,
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9,
944,
13,
26224,
29720,
16345,
7,
16,
8,
1635,
2116,
13,
81,
15,
198,
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220,
220,
220,
220,
220,
220,
20766,
796,
352,
13,
15,
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696,
58,
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649,
22704,
60,
9,
944,
13,
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8,
1303,
48473,
5766,
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220,
220,
220,
220,
220,
220,
288,
62,
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79,
16,
1635,
28,
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1303,
4199,
586,
24705,
3299,
11,
284,
3613,
1568,
39628,
198,
220,
220,
220,
220,
220,
220,
220,
288,
62,
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17,
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28,
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288,
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62,
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13,
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10,
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5415,
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198,
2,
220,
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220,
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3601,
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288,
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257,
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220,
1303,
16090,
4866,
286,
1366,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1976,
72,
796,
6565,
19510,
64,
2364,
10,
22510,
11,
2116,
13,
10459,
13,
22510,
354,
8961,
828,
288,
4906,
28,
22468,
8,
198,
220,
220,
220,
220,
220,
220,
220,
267,
796,
6565,
19510,
22510,
11,
2116,
13,
25928,
13,
7857,
828,
288,
4906,
28,
22468,
8,
1303,
5072,
7177,
198,
220,
220,
220,
220,
220,
220,
220,
20218,
796,
6565,
19510,
944,
13,
25928,
13,
7857,
11,
2116,
13,
10459,
13,
22510,
354,
8961,
828,
288,
4906,
28,
22468,
8,
198,
220,
220,
220,
220,
220,
220,
220,
11677,
796,
257,
2364,
1303,
923,
11677,
329,
1762,
7177,
198,
220,
220,
220,
220,
220,
220,
220,
267,
28968,
796,
657,
1303,
11677,
329,
5072,
7177,
198,
220,
220,
220,
220,
220,
220,
220,
329,
2512,
287,
2116,
13,
10459,
13,
20274,
7,
22510,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
36545,
796,
2512,
13,
43358,
58,
15,
60,
1303,
3146,
286,
8405,
290,
9619,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3509,
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12,
67,
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1303,
36545,
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257,
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1343,
64,
2364,
532,
67,
1084,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
72,
58,
64,
2364,
25,
64,
2364,
10,
5907,
60,
796,
2512,
1635,
266,
1303,
4866,
1366,
284,
1762,
7177,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
9052,
625,
1366,
8405,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
981,
11677,
1279,
3509,
28968,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
7800,
5072,
7177,
611,
1336,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
267,
28968,
6624,
997,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7800,
267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
267,
28968,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
262,
1306,
1627,
2476,
284,
307,
9177,
5443,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20218,
58,
45299,
1058,
60,
796,
357,
17027,
58,
28968,
10,
67,
62,
9630,
11,
288,
62,
9630,
17,
60,
9,
67,
62,
3849,
79,
16,
3467,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1343,
1976,
72,
58,
28968,
10,
67,
62,
9630,
10,
16,
11,
288,
62,
9630,
17,
60,
9,
67,
62,
3849,
79,
17,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13,
81,
62,
10989,
363,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2829,
2160,
290,
4781,
22320,
789,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
267,
58,
2238,
487,
2617,
60,
796,
10651,
7,
29510,
13,
16345,
32590,
16,
8,
1174,
17,
532,
3467,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
29510,
1174,
17,
737,
16345,
32590,
16,
828,
352,
68,
12,
3064,
11,
352,
68,
10,
3064,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2829,
2160,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
267,
58,
2238,
487,
2617,
60,
796,
20218,
13,
16345,
32590,
16,
8,
1174,
17,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11677,
15853,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
267,
28968,
15853,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4866,
5637,
8405,
287,
2166,
286,
1306,
2512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
72,
58,
15,
25,
64,
2364,
60,
796,
1976,
72,
58,
12,
64,
2364,
47715,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11677,
48185,
997,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
5637,
1366,
16058,
220,
198,
220,
220,
220,
220,
220,
220,
220,
7800,
267,
58,
25,
2238,
487,
2617,
60,
628,
628,
198,
4871,
25855,
16354,
7575,
15721,
73,
7,
25855,
16354,
7575,
15179,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
47081,
257,
4096,
640,
7386,
15584,
16354,
351,
640,
6737,
5072,
198,
220,
220,
220,
329,
257,
10706,
3867,
1863,
257,
22942,
198,
220,
220,
220,
37227,
628,
198,
220,
220,
220,
1303,
25,
1058,
4871,
25,
63,
93,
330,
2852,
283,
13,
9535,
752,
652,
13,
15721,
752,
652,
63,
393,
10944,
2134,
13,
198,
220,
220,
220,
1303,
25,
7253,
640,
318,
9672,
284,
307,
262,
976,
355,
329,
262,
8405,
13,
198,
220,
220,
220,
22942,
796,
4759,
270,
7,
15721,
752,
652,
11,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1715,
2625,
9535,
752,
652,
286,
262,
10706,
3641,
4943,
628,
220,
220,
220,
1303,
25,
20984,
15879,
11,
47190,
284,
262,
331,
12,
22704,
286,
3867,
10706,
13,
198,
220,
220,
220,
374,
35138,
796,
327,
19182,
7,
288,
4906,
28,
22468,
11,
5485,
16193,
18,
11,
10612,
1988,
28,
18747,
19510,
15,
11,
657,
11,
657,
36911,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1715,
2625,
35790,
15879,
4943,
198,
220,
220,
220,
220,
198,
220,
220,
220,
1303,
5387,
27421,
198,
220,
220,
220,
16274,
796,
14161,
7,
220,
198,
220,
220,
220,
220,
220,
220,
220,
8338,
62,
261,
796,
37250,
76,
1930,
13,
12894,
395,
3256,
705,
25928,
13,
12894,
395,
3256,
705,
10459,
13,
12894,
395,
3256,
3467,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
66,
3256,
705,
43775,
3256,
705,
81,
35138,
3256,
705,
24330,
13,
12894,
395,
3256,
705,
9535,
752,
652,
13,
12894,
395,
3256,
3467,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
834,
4871,
834,
6,
4357,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
12796,
62,
1177,
796,
3582,
7,
198,
220,
220,
220,
220,
220,
220,
220,
685,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
7449,
10786,
76,
1930,
90,
92,
3256,
3918,
11639,
23144,
11537,
4357,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
7449,
10786,
25928,
3256,
3918,
11639,
23144,
33809,
705,
12,
27,
29,
6,
4357,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
7449,
10786,
9535,
752,
652,
90,
92,
3256,
3918,
11639,
23144,
11537,
4357,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
7449,
10786,
66,
3256,
6167,
11639,
12287,
286,
2128,
11537,
4357,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
7449,
10786,
24330,
90,
92,
3256,
3918,
11639,
23144,
11537,
4357,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
7449,
10786,
43775,
90,
92,
3256,
3918,
11639,
23144,
11537,
4357,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
91,
6,
198,
220,
220,
220,
220,
220,
220,
220,
16589,
220,
198,
220,
220,
220,
220,
220,
220,
220,
3670,
11639,
3856,
321,
16354,
3689,
3256,
220,
198,
220,
220,
220,
220,
220,
220,
220,
12163,
796,
7477,
34,
21130,
1537,
27288,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
2488,
66,
2317,
62,
26745,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
825,
1255,
7,
2116,
11,
997,
28,
1238,
2780,
15179,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
11361,
17301,
326,
19299,
262,
15584,
16354,
220,
198,
220,
220,
220,
220,
220,
220,
220,
5072,
2512,
12,
3083,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
32233,
9934,
286,
1960,
420,
273,
49501,
13,
198,
220,
220,
220,
220,
220,
220,
220,
383,
366,
31462,
1,
10706,
460,
307,
14251,
290,
42976,
38375,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
220,
220,
220,
220,
997,
1058,
18253,
11,
26235,
284,
36117,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
770,
11507,
15738,
262,
2546,
286,
262,
7021,
284,
307,
26403,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
72,
13,
68,
13,
262,
1271,
286,
8405,
583,
2512,
8,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
220,
220,
220,
35656,
198,
220,
220,
220,
220,
220,
220,
220,
3409,
2374,
287,
7021,
286,
5485,
220,
3467,
198,
220,
220,
220,
220,
220,
220,
220,
357,
22510,
11,
1058,
35226,
25,
63,
93,
3856,
321,
16354,
7575,
13,
22510,
354,
8961,
63,
737,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1058,
35226,
25,
63,
93,
3856,
321,
16354,
7575,
13,
22510,
354,
8961,
63,
318,
3221,
845,
3467,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1588,
357,
17618,
286,
10706,
2173,
737,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
383,
938,
2512,
743,
307,
12238,
621,
997,
13,
3467,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
383,
5072,
4940,
329,
10425,
326,
547,
31234,
422,
262,
10706,
379,
256,
28,
15,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
628,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13,
43775,
62,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
266,
796,
2116,
13,
43775,
41052,
944,
38381,
3605,
22704,
60,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
266,
796,
352,
13,
15,
198,
220,
220,
220,
220,
220,
220,
220,
269,
796,
2116,
13,
66,
14,
944,
13,
10459,
13,
39873,
62,
19503,
80,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
20218,
7177,
329,
262,
10706,
763,
12,
585,
17540,
198,
220,
220,
220,
220,
220,
220,
220,
308,
1930,
796,
2116,
13,
25928,
13,
1930,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
3509,
5711,
11506,
796,
2160,
286,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
3509,
40039,
20428,
286,
4456,
1416,
23098,
13617,
10994,
329,
10706,
290,
12314,
18747,
198,
220,
220,
220,
220,
220,
220,
220,
288,
9806,
796,
19862,
17034,
19510,
7,
70,
1930,
13,
9806,
7,
16,
13219,
70,
1930,
13,
1084,
7,
16,
4008,
1174,
17,
737,
16345,
28955,
198,
220,
220,
220,
220,
220,
220,
220,
288,
9806,
15853,
19862,
17034,
19510,
7,
944,
13,
76,
1930,
13,
76,
1930,
13,
9806,
7,
16,
13219,
944,
13,
76,
1930,
13,
76,
1930,
13,
1084,
7,
16,
4008,
1174,
17,
737,
16345,
28955,
198,
220,
220,
220,
220,
220,
220,
220,
288,
9806,
796,
493,
7,
67,
9806,
14,
66,
47762,
16,
1303,
3509,
6376,
11506,
198,
220,
220,
220,
220,
220,
220,
220,
1976,
72,
796,
6565,
19510,
67,
9806,
10,
22510,
11,
2116,
13,
10459,
13,
22510,
354,
8961,
828,
3467,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
288,
4906,
28,
22468,
8,
1303,
16090,
4866,
286,
1366,
198,
220,
220,
220,
220,
220,
220,
220,
267,
796,
6565,
19510,
22510,
11,
2116,
13,
25928,
13,
7857,
828,
288,
4906,
28,
22468,
8,
1303,
5072,
7177,
198,
220,
220,
220,
220,
220,
220,
220,
20218,
796,
6565,
19510,
944,
13,
25928,
13,
7857,
11,
2116,
13,
10459,
13,
22510,
354,
8961,
828,
288,
4906,
28,
22468,
8,
198,
220,
220,
220,
220,
220,
220,
220,
288,
62,
9630,
17,
796,
610,
858,
7,
944,
13,
76,
1930,
13,
22510,
62,
76,
873,
11,
288,
4906,
28,
600,
8,
1303,
1218,
6376,
357,
12708,
8,
198,
220,
220,
220,
220,
220,
220,
220,
11677,
796,
288,
9806,
10,
22510,
1303,
923,
11677,
329,
1762,
7177,
198,
220,
220,
220,
220,
220,
220,
220,
267,
28968,
796,
657,
1303,
11677,
329,
5072,
7177,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
27298,
329,
22942,
11,
3599,
379,
640,
6632,
198,
220,
220,
220,
220,
220,
220,
220,
923,
62,
83,
796,
657,
13,
15,
198,
220,
220,
220,
220,
220,
220,
220,
308,
796,
2116,
13,
9535,
752,
652,
13,
9535,
73,
7,
923,
62,
83,
11,
25979,
62,
83,
28,
16,
14,
944,
13,
10459,
13,
39873,
62,
19503,
80,
8,
198,
220,
220,
220,
220,
220,
220,
220,
308,
16,
796,
2116,
13,
9535,
752,
652,
13,
9535,
73,
7,
923,
62,
83,
11,
25979,
62,
83,
28,
16,
14,
944,
13,
10459,
13,
39873,
62,
19503,
80,
11,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4587,
28,
16,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
374,
32109,
796,
357,
944,
13,
81,
35138,
6624,
657,
737,
439,
3419,
1303,
32109,
11059,
3691,
13,
13179,
198,
220,
220,
220,
220,
220,
220,
220,
1366,
796,
2116,
13,
10459,
13,
20274,
7,
22510,
8,
198,
220,
220,
220,
220,
220,
220,
220,
6056,
796,
6407,
198,
220,
220,
220,
220,
220,
220,
220,
981,
6056,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
7800,
5072,
7177,
611,
1336,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
267,
28968,
6624,
997,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7800,
267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
267,
28968,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
374,
32109,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
10706,
318,
691,
14251,
11,
407,
38375,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
256,
1930,
796,
308,
1930,
1343,
7177,
7,
70,
13,
19545,
28955,
58,
45299,
649,
22704,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
10706,
318,
1111,
14251,
290,
38375,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1179,
796,
7177,
7,
70,
13,
19545,
28955,
1303,
41519,
7177,
26933,
15,
1539,
657,
13,
19,
11,
352,
8183,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
44332,
796,
7177,
7,
70,
16,
13,
19545,
28955,
1303,
37295,
15879,
357,
3605,
2124,
12,
22704,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20268,
796,
3272,
7,
944,
13,
81,
35138,
11,
44332,
8,
1303,
649,
331,
12,
22704,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
288,
89,
796,
3272,
7,
34350,
11,
20268,
8,
1303,
649,
1976,
12,
22704,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
29820,
796,
7177,
19510,
34350,
11,
20268,
11,
288,
89,
29720,
51,
1303,
13179,
17593,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
29820,
1220,
28,
19862,
17034,
19510,
29138,
9,
29138,
737,
16345,
7,
15,
4008,
1303,
5721,
39279,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
256,
1930,
796,
16605,
7,
29138,
11,
308,
1930,
47762,
17946,
58,
45299,
649,
22704,
60,
1303,
13179,
10,
41519,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
42721,
796,
2116,
13,
24330,
13,
81,
7,
2116,
13,
66,
11,
256,
1930,
11,
2116,
13,
76,
1930,
13,
76,
1930,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
374,
15,
796,
2116,
13,
24330,
13,
81,
7,
2116,
13,
66,
11,
256,
1930,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
16119,
796,
42721,
14,
66,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
288,
62,
9630,
796,
7177,
7,
12381,
592,
11,
288,
4906,
28,
600,
8,
1303,
18253,
6376,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
288,
62,
3849,
79,
16,
796,
16119,
4064,
352,
1303,
352,
301,
763,
14822,
329,
9493,
39555,
341,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
288,
62,
3849,
79,
17,
796,
352,
12,
67,
62,
3849,
79,
16,
1303,
362,
358,
763,
14822,
329,
9493,
39555,
341,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20766,
796,
357,
86,
29006,
26224,
9,
26224,
29720,
16345,
7,
16,
8,
1635,
374,
15,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20766,
796,
352,
13,
15,
29006,
696,
58,
45299,
649,
22704,
60,
9,
26224,
8,
1303,
48473,
5766,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
783,
11,
356,
423,
284,
787,
1654,
326,
262,
2622,
1366,
318,
1695,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
981,
11677,
10,
67,
62,
9630,
13,
9806,
3419,
10,
17,
29,
67,
9806,
10,
22510,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4866,
5637,
8405,
287,
2166,
286,
1306,
2512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
72,
58,
15,
25,
67,
9806,
60,
796,
1976,
72,
58,
12,
67,
9806,
47715,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
262,
11677,
318,
12328,
416,
530,
2512,
4129,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11677,
48185,
997,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1332,
611,
1366,
17301,
318,
19064,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
1306,
1366,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2512,
796,
1366,
13,
19545,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2845,
13707,
29993,
341,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
1179,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6056,
796,
10352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2270,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
8405,
287,
262,
2512,
11,
21767,
284,
997,
2845,
329,
262,
938,
2512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
36545,
796,
2512,
13,
43358,
58,
15,
60,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
72,
58,
67,
9806,
25,
67,
9806,
10,
5907,
60,
796,
2512,
1635,
266,
2,
4866,
1366,
284,
1762,
7177,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
262,
1306,
1627,
2476,
284,
307,
9177,
5443,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
340,
25365,
2063,
286,
262,
640,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20218,
58,
45299,
1058,
60,
796,
357,
17027,
58,
28968,
10,
67,
62,
9630,
11,
288,
62,
9630,
17,
60,
9,
67,
62,
3849,
79,
16,
3467,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1343,
1976,
72,
58,
28968,
10,
67,
62,
9630,
10,
16,
11,
288,
62,
9630,
17,
60,
9,
67,
62,
3849,
79,
17,
27493,
696,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
267,
58,
2238,
487,
2617,
60,
796,
20218,
13,
16345,
32590,
16,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11677,
15853,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
267,
28968,
15853,
352,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
5637,
1366,
16058,
198,
220,
220,
220,
220,
220,
220,
220,
7800,
267,
58,
25,
2238,
487,
2617,
60,
628,
220,
220,
220,
220,
220,
220,
220,
220,
198,
4871,
25855,
16354,
7575,
50,
80,
15721,
73,
7,
25855,
16354,
7575,
50,
80,
15179,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
47081,
257,
640,
7386,
15584,
16354,
351,
640,
12,
21186,
198,
220,
220,
220,
1176,
6737,
5072,
290,
1744,
22320,
789,
9934,
198,
220,
220,
220,
329,
257,
10706,
3867,
1863,
257,
22942,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
198,
220,
220,
220,
1303,
25,
1058,
4871,
25,
63,
93,
330,
2852,
283,
13,
9535,
752,
652,
13,
15721,
752,
652,
63,
393,
10944,
2134,
13,
198,
220,
220,
220,
1303,
25,
7253,
640,
318,
9672,
284,
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19862,
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11,
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25,
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7800,
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220,
220,
220,
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256,
1930,
796,
308,
1930,
1343,
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7,
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13,
19545,
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58,
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649,
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1179,
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7,
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44332,
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3605,
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220,
220,
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20268,
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3272,
7,
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13,
81,
35138,
11,
44332,
8,
1303,
649,
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12,
22704,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
288,
89,
796,
3272,
7,
34350,
11,
20268,
8,
1303,
649,
1976,
12,
22704,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
29820,
796,
7177,
19510,
34350,
11,
20268,
11,
288,
89,
29720,
51,
1303,
13179,
17593,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
29820,
1220,
28,
19862,
17034,
19510,
29138,
9,
29138,
737,
16345,
7,
15,
4008,
1303,
5721,
39279,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
256,
1930,
796,
16605,
7,
29138,
11,
308,
1930,
47762,
17946,
58,
45299,
649,
22704,
60,
1303,
13179,
10,
41519,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
42721,
796,
2116,
13,
24330,
13,
81,
7,
2116,
13,
66,
11,
256,
1930,
11,
2116,
13,
76,
1930,
13,
76,
1930,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
374,
15,
796,
2116,
13,
24330,
13,
81,
7,
2116,
13,
66,
11,
256,
1930,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
16119,
796,
42721,
14,
66,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
288,
62,
9630,
796,
7177,
7,
12381,
592,
11,
288,
4906,
28,
600,
8,
1303,
18253,
6376,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
288,
62,
3849,
79,
16,
796,
16119,
4064,
352,
1303,
352,
301,
763,
14822,
329,
9493,
39555,
341,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
288,
62,
3849,
79,
17,
796,
352,
12,
67,
62,
3849,
79,
16,
1303,
362,
358,
763,
14822,
329,
9493,
39555,
341,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20766,
796,
357,
86,
29006,
26224,
9,
26224,
29720,
16345,
7,
16,
8,
1635,
374,
15,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20766,
796,
352,
13,
15,
29006,
696,
58,
45299,
649,
22704,
60,
9,
26224,
8,
1303,
48473,
5766,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
783,
11,
356,
423,
284,
787,
1654,
326,
262,
2622,
1366,
318,
1695,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
981,
11677,
10,
67,
62,
9630,
13,
9806,
3419,
10,
17,
29,
67,
9806,
10,
22510,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4866,
5637,
8405,
287,
2166,
286,
1306,
2512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
72,
58,
15,
25,
67,
9806,
60,
796,
1976,
72,
58,
12,
67,
9806,
47715,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
262,
11677,
318,
12328,
416,
530,
2512,
4129,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11677,
48185,
997,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1332,
611,
1366,
17301,
318,
19064,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
1306,
1366,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2512,
796,
1366,
13,
19545,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2845,
13707,
29993,
341,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6056,
796,
10352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2270,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
8405,
287,
262,
2512,
11,
21767,
284,
997,
2845,
329,
262,
938,
2512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
36545,
796,
2512,
13,
43358,
58,
15,
60,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
72,
58,
67,
9806,
25,
67,
9806,
10,
5907,
60,
796,
2512,
1635,
266,
2,
4866,
1366,
284,
1762,
7177,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
262,
1306,
1627,
2476,
284,
307,
9177,
5443,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
340,
25365,
2063,
286,
262,
640,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20218,
58,
45299,
1058,
60,
796,
357,
17027,
58,
28968,
10,
67,
62,
9630,
11,
288,
62,
9630,
17,
60,
9,
67,
62,
3849,
79,
16,
3467,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1343,
1976,
72,
58,
28968,
10,
67,
62,
9630,
10,
16,
11,
288,
62,
9630,
17,
60,
9,
67,
62,
3849,
79,
17,
27493,
696,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13,
81,
62,
10989,
363,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2829,
2160,
290,
4781,
22320,
789,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
267,
58,
2238,
487,
2617,
60,
796,
10651,
7,
29510,
13,
16345,
32590,
16,
8,
1174,
17,
532,
3467,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
29510,
1174,
17,
737,
16345,
32590,
16,
828,
352,
68,
12,
3064,
11,
352,
68,
10,
3064,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2829,
2160,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
267,
58,
2238,
487,
2617,
60,
796,
20218,
13,
16345,
32590,
16,
8,
1174,
17,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11677,
15853,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
267,
28968,
15853,
352,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
5637,
1366,
16058,
198,
220,
220,
220,
220,
220,
220,
220,
7800,
267,
58,
25,
2238,
487,
2617,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
198,
4871,
15995,
12392,
50,
9250,
7575,
7,
3862,
818,
7975,
15179,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
47081,
281,
15995,
12392,
287,
262,
640,
7386,
13,
198,
220,
220,
220,
37227,
628,
220,
220,
220,
1303,
25,
1058,
4871,
25,
63,
93,
330,
2852,
283,
13,
2164,
2340,
13,
41339,
63,
12,
34631,
2134,
326,
3769,
262,
10706,
7064,
13,
198,
220,
220,
220,
10706,
796,
4759,
270,
7,
45474,
41339,
11,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1715,
2625,
40045,
15464,
10706,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
1303,
25,
7343,
286,
16020,
287,
10706,
198,
220,
220,
220,
16020,
796,
7343,
3419,
628,
220,
220,
220,
1303,
25,
1012,
4501,
11,
287,
1024,
89,
43837,
3585,
284,
5415,
357,
31591,
3815,
8,
198,
220,
220,
220,
10651,
796,
48436,
32590,
14877,
13,
15,
8,
628,
220,
220,
220,
1303,
25,
7913,
286,
9619,
287,
5072,
46121,
1271,
286,
16020,
737,
198,
220,
220,
220,
997,
354,
8961,
796,
14161,
7,
8338,
62,
261,
796,
37250,
325,
5217,
3256,
33761,
628,
220,
220,
220,
1303,
5387,
27421,
198,
220,
220,
220,
16274,
796,
14161,
7,
220,
198,
220,
220,
220,
220,
220,
220,
220,
8338,
62,
261,
796,
37250,
325,
5217,
3256,
705,
15036,
3256,
705,
25928,
13,
12894,
395,
3256,
705,
10459,
13,
12894,
395,
3256,
3467,
198,
220,
220,
220,
220,
220,
220,
220,
705,
834,
4871,
834,
6,
4357,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
12796,
62,
1177,
796,
3582,
7,
198,
220,
220,
220,
220,
220,
220,
220,
685,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
7449,
10786,
325,
5217,
3256,
3918,
11639,
23144,
11537,
4357,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
7449,
10786,
25928,
3256,
3918,
11639,
23144,
33809,
705,
12,
27,
29,
6,
4357,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
91,
6,
198,
220,
220,
220,
220,
220,
220,
220,
16589,
220,
198,
220,
220,
220,
220,
220,
220,
220,
3670,
11639,
34500,
12392,
3256,
220,
198,
220,
220,
220,
220,
220,
220,
220,
12163,
796,
7477,
34,
21130,
1537,
27288,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
2488,
66,
2317,
62,
26745,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
2488,
66,
2317,
62,
26745,
628,
220,
220,
220,
825,
1255,
7,
2116,
11,
997,
28,
16,
15179,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
11361,
17301,
326,
19299,
262,
2723,
5072,
11521,
625,
262,
1813,
220,
198,
220,
220,
220,
220,
220,
220,
220,
16020,
11,
2512,
12,
3083,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
220,
220,
220,
220,
997,
1058,
18253,
11,
26235,
284,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
770,
11507,
15738,
262,
2546,
286,
262,
7021,
284,
307,
26403,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
72,
13,
68,
13,
262,
1271,
286,
8405,
583,
2512,
8,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
220,
220,
220,
35656,
198,
220,
220,
220,
220,
220,
220,
220,
3409,
2374,
287,
7021,
286,
5485,
357,
22510,
11,
1058,
35226,
25,
63,
22510,
354,
8961,
63,
737,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
35226,
25,
63,
22510,
354,
8961,
63,
318,
262,
1271,
286,
16020,
13,
198,
220,
220,
220,
220,
220,
220,
220,
383,
938,
2512,
743,
307,
12238,
621,
997,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
773,
82,
796,
685,
944,
13,
25928,
13,
521,
1063,
46491,
34914,
8,
329,
6567,
287,
2116,
13,
325,
5217,
60,
198,
220,
220,
220,
220,
220,
220,
220,
308,
43358,
796,
2116,
13,
25928,
13,
43358,
198,
220,
220,
220,
220,
220,
220,
220,
267,
796,
6565,
19510,
22510,
11,
2116,
13,
22510,
354,
8961,
828,
288,
4906,
28,
22468,
8,
1303,
5072,
7177,
198,
220,
220,
220,
220,
220,
220,
220,
329,
374,
287,
2116,
13,
10459,
13,
20274,
7,
22510,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
36545,
796,
374,
13,
43358,
58,
15,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
7800,
267,
58,
25,
5907,
60,
628,
198
] | 2.101289 | 12,025 |
# Dependencies
import sys, json
import classifier
ignore_labels = ['duplicate', 'in-progress', 'pending-publication', 'published', 'waiting-for-user-information', 'high priority']
# simple JSON echo script
for line in sys.stdin:
payload = json.loads(line)
( action, params ) = payload
results = {}
if action == "train_labels":
( user, repo, issues, ignore_labels ) = params
results = classifier.train_issues(user, repo, issues, ignore_labels)
elif action == "predict_labels":
( user, repo, issues ) = params
results = classifier.predict_labels_for_issues(user, repo, issues)
elif action == "similarity":
issues = params[0]
results = classifier.issue_similarity(issues)
print json.dumps(results)
| [
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366,
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414,
1298,
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220,
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220,
220,
220,
220,
2428,
796,
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58,
15,
60,
198,
220,
220,
220,
220,
220,
220,
220,
2482,
796,
1398,
7483,
13,
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62,
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7,
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8,
628,
220,
220,
220,
3601,
33918,
13,
67,
8142,
7,
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8,
198
] | 2.744681 | 282 |
import pytest
from webviz_config.utils._dash_component_utils import calculate_slider_step
@pytest.mark.parametrize(
"min_value,max_value,steps,res",
[
(5, 10, 100, 0.01),
(-10, -5, 100, 0.01),
(-10, 10, 100, 0.1)
]
)
| [
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13,
16,
8,
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220,
220,
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2361,
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8,
198
] | 2.015748 | 127 |
import models
import datetime
from app import db
from data import constants
from logzero import logger
from sqlalchemy import exc
from sqlalchemy.sql import func
from data.subqueries import TestCounts
import re
import os
import pytz
utc = pytz.UTC
| [
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] | 3.577465 | 71 |
TESTRAIL_API_TOKEN = ""
TESTRAIL_URL = ""
TESTRAIL_PWD = "" | [
51,
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] | 1.966667 | 30 |
import sys
import csv
import random
"""
Sets up the data for the Link Prediction experiment.
Given the raw data file, it: 1)removes a specified amount of edges for representation induction, 2) creates a specified amount of negative examples from the remaining edges,
3) splits the positive and negative examples into train, dev and test sets as specified 4) writes the relevant train, dev and test files.
"""
#e.g of split: {'p1': [('date', '=', 1980)]}
#In MATADOR dataset, all Chemical identifiers can be integers but no proteins can be
if __name__ == '__main__':
sys.exit(main(sys.argv))
| [
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7,
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7,
17597,
13,
853,
85,
4008,
198,
220,
220,
220,
220
] | 3.395604 | 182 |
import random
import codecs
| [
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82,
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] | 4 | 7 |
#list ----> we store int,float,string..
#ordered collection of item --->DAta structure
numbers = [1, 2, 3, 4, 5]
print(numbers)
print(numbers[2])#we can also access by indexing
words = ["Beenash", 'Pervaiz', "Hanan"]
print(words)
print(words[:2]) # by slicing
mixed = [1, 2, 3, 4, "five", "six",2.5, None]
print(mixed)
print(mixed[-1])# negative indexing
#change the data of the list
mixed[1] = 'two'
print(mixed)
#if we change the almost total data of list
numbers[:5] = ['one', 'two','three','four','five']
print(numbers) | [
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8
] | 2.60396 | 202 |
from contacts.contacts_modules import delete_contact
to_delete = delete_contact.DeleteContact()
delete = to_delete.delete_contact("1")
| [
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] | 3.578947 | 38 |
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'MainWindow.ui'
#
# Created by: PyQt5 UI code generator 5.12.2
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
| [
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201,
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201,
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] | 2.57 | 100 |
from django.shortcuts import redirect
from django.http import HttpResponse
from django.template import loader
from http.server import HTTPStatus
from .User import User
import iotweb.views.urls_and_messages as UM
import requests
import json
def devices(request, shdw_id):
"""
GET request: renders the physical devices page
POST request: It's a request to delete one physical device
"""
user = User.get_instance()
if request.POST: # REQUEST TO DELETE DEVICE
url = UM.DB_URL + 'deletePhysicalDevice/{}/'.format(request.POST['device_id'])
headers = {'Authorization': 'Token {}'.format(user.user_token)}
req = requests.get(url=url, headers=headers)
if req.status_code == 200:
return redirect('/viewDevices/{}/'.format(shdw_id))
else:
template = loader.get_template('../templates/error_page.html')
context = {'code_error': req.status_code,
'message': req.text,
'error_name': HTTPStatus(req.status_code).phrase,
'back': '/viewDevices/{}/'.format(shdw_id)
}
if req.status_code == 401:
context['message'] = context['message'] + UM.REFRESH_TOKEN
context['back'] = '/login/'
return HttpResponse(template.render(context, request))
else: # GET - RENDER THE TEMPLATE WITH PHYSICAL DEVICES
template = loader.get_template('../templates/physical_devices.html')
url = UM.DB_URL + 'getShadowDevices/{}/'.format(shdw_id)
headers = {'Authorization': 'Token {}'.format(user.user_token)}
req = requests.get(url=url, headers=headers)
if req.status_code == 200:
devices_list = json.loads(req.text)['devices']
context = {'devices': [], 'shadow_id': shdw_id, 'email': user.user_email}
if devices_list:
for device in devices_list:
json_object = json.loads(device)
# CHECK THIS AGAIN
url_token = UM.DB_URL + 'getTokenById/{}/'.format(json_object['token'])
res_tok = requests.get(url=url_token, headers=headers)
token = json.loads(res_tok.text)['token']
json_object['token'] = token # we replace token id with token value
json_object['id'] = json_object['_id']
url_status = UM.DB_URL + 'getDeviceStatus/{}/'.format(json_object['_id'])
req_status = requests.get(url=url_status, headers=headers)
json_object['STATUS'] = json.loads(req_status.text)['status']
context['devices'].append(json_object)
return HttpResponse(template.render(context, request))
else:
template = loader.get_template('../templates/error_page.html')
context = {'code_error': req.status_code,
'message': req.text,
'error_name': HTTPStatus(req.status_code).phrase,
'back': '/profile/'
}
if req.status_code == 401:
context['message'] = context['message'] + UM.REFRESH_TOKEN
context['back'] = '/login/'
return HttpResponse(template.render(context, request))
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] | 2.140952 | 1,575 |
import logging
from django.conf import settings
from django.contrib.sites.shortcuts import get_current_site
from django.template import loader
from django.utils.safestring import mark_safe
from django.utils.translation import ugettext_lazy as _
from .models import MailTemplate
try:
from django.template.exceptions import TemplateDoesNotExist, TemplateSyntaxError
except ImportError:
from django.template.base import TemplateDoesNotExist, TemplateSyntaxError
logger = logging.getLogger(__name__)
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] | 3.472973 | 148 |
import subprocess
import sys
import getopt
import json
from pyzabbix import ZabbixMetric, ZabbixSender
ipa = ''
ipb = ''
host = ''
try:
opts, args = getopt.getopt(sys.argv[1:], "ha:b:n:")
except getopt.GetoptError:
print 'md32xx.py -a <IPControlerA> -b <IPControlerB> -n <HostNameInZabbix>'
sys.exit(2)
for opt, arg in opts:
if opt == "-h":
print 'md32xx.py -a <IPControlerA> -b <IPControlerB> -n <HostNameInZabbix>'
print 'Parameters:'
print '-a\t\tIP Controler A'
print '-b\t\tIP Controler B'
print '-n\t\tHostName in Zabbix'
print '-h\t\tThis help'
sys.exit()
elif opt == "-a":
ipa = arg
elif opt == "-b":
ipb = arg
elif opt == "-n":
host = arg
# else
# print 'md32xx.py -a <IPControlerA> -b <IPControlerB> -n <HostNameInZabbix>'
# sys.exit(2)
SMCLI = "/opt/IBM_DS/client/SMcli"
CONN = ipa + " " + ipb
cmd = SMCLI + " " + CONN + " -S -c \"set session performanceMonitorInterval=3 performanceMonitorIterations=1;show allLogicalDrives performanceStats;\""
#print cmd
proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True)
(out, err) = proc.communicate()
strings = out.split("\n",7)[7]
#print strings
packet = []
output = []
for str in strings.splitlines():
arr = str.split(',')
output.append({"{#VALUE}": arr[0].replace("\"","")})
packet.append(ZabbixMetric(host, 'total.ios['+arr[0].replace("\"","")+']',arr[1].replace("\"", "")))
packet.append(ZabbixMetric(host, 'read['+arr[0].replace("\"","")+']',arr[2].replace("\"", "")))
packet.append(ZabbixMetric(host, 'read.cache.hit['+arr[0].replace("\"","")+']',arr[3].replace("\"", "")))
packet.append(ZabbixMetric(host, 'write.cache.hit['+arr[0].replace("\"","")+']',arr[4].replace("\"", "")))
packet.append(ZabbixMetric(host, 'ssd.cache.hit['+arr[0].replace("\"","")+']',arr[5].replace("\"", "")))
packet.append(ZabbixMetric(host, 'current.MBs['+arr[0].replace("\"","")+']',arr[6].replace("\"", "")))
packet.append(ZabbixMetric(host, 'max.MBs['+arr[0].replace("\"","")+']',arr[7].replace("\"", "")))
packet.append(ZabbixMetric(host, 'current.ios['+arr[0].replace("\"","")+']',arr[8].replace("\"", "")))
packet.append(ZabbixMetric(host, 'max.ios['+arr[0].replace("\"","")+']',arr[9].replace("\"", "")))
if arr[0].replace("\"","") == "STORAGE SUBSYSTEM TOTALS":
break
#print packet
ZabbixSender(zabbix_server='192.168.10.45', zabbix_port=10051).send(packet)
print '{"data":'
print json.dumps(output)
print '}'
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] | 2.25 | 1,132 |
from heapq import nlargest
# NOTE: this solution assumes the handles are only english letters
handles = ['DogeCoin', 'YangGang', 'HodlForLife',
'fakeDonaldDrumpf', 'GodIsLove', 'BernieOrBust']
new_user = 'iLoveDogs'
obj = SimilarAccounts()
result1 = obj.make_anagram(new_user)
result2 = obj.make_anagram('DogeCoin')
anagram_score = obj.get_score_with_anagram(new_user, 'GodIsLove')
set_score = obj.get_score_with_set(new_user, 'GodIsLove')
print(f"anagram score:", anagram_score)
print(f"set score:", set_score)
k_handles = obj.suggest(new_user, handles, 2)
print(k_handles)
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] | 2.610619 | 226 |
"""Configuration data container with interactive ipywidgets GUI"""
import json
import ipywidgets
import jsonschema
class DictWidget():
"""Container class for configuration data
Constructed from a JSON Schema. Use like a dictionary to store and retrieve configuration data.
Will also create a ipywidget interactive representation via `gui()`. Also supports nested schemata
(i.e. a schema tha contains `object` properties, which are themselves configuration containers."""
def __init__(self, schema):
"""Construct a Configuration object from a JSON schema definition"""
self.schema = schema
self.data = {}
self.callback = None
self.children = {}
# Create GUI
# Widget objects are collected in a dictionary (for update in __setitem__())
# as well as a list (together with their description labels to create a VBox for display).
self.widgets = {}
self.widgetlist = []
for name, props in self.schema['properties'].items():
minimum = props.get('minimum', None)
maximum = props.get('maximum', None)
description = props.get('description', '')
# Containers create new `Configuration` instances - save those children for later
if props['type'] == 'object':
subschema = {"title": name, "type": "object", "properties": props['properties']}
self.children[name] = Configuration(subschema)
else:
# Scalar data elements are displayed as is
if props['type'] == 'integer':
value = self.data.get(name, props.get('default', 0))
widget = ipywidgets.IntSlider(description=name, min=minimum, max=maximum, value=value)
elif props['type'] == 'number':
value = self.data.get(name, props.get('default', 0.0))
widget = ipywidgets.FloatSlider(description=name, min=minimum, max=maximum, value=value)
elif props['type'] == 'string':
# also supports drop down
value = self.data.get(name, props.get('default', ''))
if 'choices' in props:
widget = ipywidgets.Dropdown(options=props['choices'].split(';'), value=value, description=name)
else:
widget = ipywidgets.Text(description=name, value=value)
elif props['type'] == 'boolean':
value = self.data.get(name, props.get('default', False))
widget = ipywidgets.Checkbox(description=name, value=value)
else:
widget = ipywidgets.Label(description=name, value=f"Don't know how to draw {props['type']}")
widget.observe(self.on_value_change, names='value')
# save for self-reference
self.widgets[name] = widget
self.widgetlist.append(ipywidgets.HBox([widget, ipywidgets.Label(value=description)]))
# Add all saved children in a Tab
if self.children:
widget = ipywidgets.Tab([c._gui for c in self.children.values()])
for i, c in enumerate(self.children.keys()):
widget.set_title(i, c)
widget.observe(self.on_value_change, names='value')
# save for self-reference
self.widgets['_children'] = widget
self.widgetlist.append(widget)
# Return all widgets in a VBox
self._gui = ipywidgets.VBox(self.widgetlist)
def from_dict(self, dict_in):
"""Load configuration data from a dictionary.
Will validate input against schema used in object construction."""
jsonschema.validate(dict_in, self.schema)
for key, value in dict_in.items():
if isinstance(value, dict):
self.children[key].from_dict(value)
else:
self[key] = value
def from_json(self, json_in):
"""Load configuration data from JSON."""
temp = json.loads(json_in)
self.from_dict(temp)
def to_json(self):
"""Dump configuration data as JSON."""
if not self.data:
return None
return json.dumps(self.to_dict())
def to_dict(self):
"""Dump configuration data as dictionary."""
ret = dict(self.data)
for name, child in self.children.items():
ret[name] = child.to_dict()
return ret
def __getitem__(self, item):
"""Allow using dict syntax for object retrievel.
Will first try to locate a child configuration object. If that's not found,
it will then look for a data item."""
if item in self.children:
return self.children[item]
return self.data.__getitem__(item)
def __setitem__(self, item, value):
"""Allow using dict syntax for setting values.
Will only allow setting values in accordance with schema used for object
generation."""
if item not in self.schema['properties']:
raise KeyError(f'"{item}" not in schema')
temp = dict(self.data)
temp.__setitem__(item, value)
jsonschema.validate(temp, self.schema)
self.data.__setitem__(item, value)
# update any gui that may exist
if item in self.widgets:
self.widgets[item].value = value
def on_value_change(self, change):
"""Callback for GUI updates."""
key = change['owner'].description
self[key] = change['new']
if self.callback:
self.callback(self.to_dict()) # TODO: expensive!
def interact(self, callback=None):
"""Return an interactive ipywidgets GUI for setting configuration values.
Will call `callback` with a dictionary of data values on change."""
self.callback = callback
# Update children's callbacks, too.
for c in self.children.values():
c.callback = callback
return self._gui
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834,
7,
944,
11,
32815,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
42316,
257,
28373,
2134,
422,
257,
19449,
32815,
6770,
37811,
628,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
15952,
2611,
796,
32815,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
7890,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
47423,
796,
6045,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
17197,
796,
23884,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
13610,
25757,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
370,
17484,
5563,
389,
7723,
287,
257,
22155,
357,
1640,
4296,
287,
11593,
2617,
9186,
834,
28955,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
355,
880,
355,
257,
1351,
357,
45525,
351,
511,
6764,
14722,
284,
2251,
257,
569,
14253,
329,
3359,
737,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
28029,
11407,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
42655,
4868,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1438,
11,
25744,
287,
2116,
13,
15952,
2611,
17816,
48310,
6,
4083,
23814,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5288,
796,
25744,
13,
1136,
10786,
39504,
3256,
6045,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5415,
796,
25744,
13,
1136,
10786,
47033,
3256,
6045,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
796,
25744,
13,
1136,
10786,
11213,
3256,
10148,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2345,
50221,
2251,
649,
4600,
38149,
63,
10245,
532,
3613,
883,
1751,
329,
1568,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
25744,
17816,
4906,
20520,
6624,
705,
15252,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5294,
258,
2611,
796,
19779,
7839,
1298,
1438,
11,
366,
4906,
1298,
366,
15252,
1600,
366,
48310,
1298,
25744,
17816,
48310,
20520,
92,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
17197,
58,
3672,
60,
796,
28373,
7,
7266,
15952,
2611,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
34529,
283,
1366,
4847,
389,
9066,
355,
318,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
25744,
17816,
4906,
20520,
6624,
705,
41433,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13,
7890,
13,
1136,
7,
3672,
11,
25744,
13,
1136,
10786,
12286,
3256,
657,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26295,
796,
20966,
88,
28029,
11407,
13,
5317,
11122,
1304,
7,
11213,
28,
3672,
11,
949,
28,
39504,
11,
3509,
28,
47033,
11,
1988,
28,
8367,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
25744,
17816,
4906,
20520,
6624,
705,
17618,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13,
7890,
13,
1136,
7,
3672,
11,
25744,
13,
1136,
10786,
12286,
3256,
657,
13,
15,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26295,
796,
20966,
88,
28029,
11407,
13,
43879,
11122,
1304,
7,
11213,
28,
3672,
11,
949,
28,
39504,
11,
3509,
28,
47033,
11,
1988,
28,
8367,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
25744,
17816,
4906,
20520,
6624,
705,
8841,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
635,
6971,
4268,
866,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13,
7890,
13,
1136,
7,
3672,
11,
25744,
13,
1136,
10786,
12286,
3256,
10148,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
705,
6679,
1063,
6,
287,
25744,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26295,
796,
20966,
88,
28029,
11407,
13,
26932,
2902,
7,
25811,
28,
1676,
862,
17816,
6679,
1063,
6,
4083,
35312,
10786,
26,
33809,
1988,
28,
8367,
11,
6764,
28,
3672,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26295,
796,
20966,
88,
28029,
11407,
13,
8206,
7,
11213,
28,
3672,
11,
1988,
28,
8367,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
25744,
17816,
4906,
20520,
6624,
705,
2127,
21052,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13,
7890,
13,
1136,
7,
3672,
11,
25744,
13,
1136,
10786,
12286,
3256,
10352,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26295,
796,
20966,
88,
28029,
11407,
13,
9787,
3524,
7,
11213,
28,
3672,
11,
1988,
28,
8367,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26295,
796,
20966,
88,
28029,
11407,
13,
33986,
7,
11213,
28,
3672,
11,
1988,
28,
69,
1,
3987,
470,
760,
703,
284,
3197,
1391,
1676,
862,
17816,
4906,
20520,
92,
4943,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26295,
13,
672,
2655,
303,
7,
944,
13,
261,
62,
8367,
62,
3803,
11,
3891,
11639,
8367,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
3613,
329,
2116,
12,
35790,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
28029,
11407,
58,
3672,
60,
796,
26295,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
42655,
4868,
13,
33295,
7,
541,
88,
28029,
11407,
13,
39,
14253,
26933,
42655,
11,
20966,
88,
28029,
11407,
13,
33986,
7,
8367,
28,
11213,
15437,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
3060,
477,
7448,
1751,
287,
257,
16904,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13,
17197,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26295,
796,
20966,
88,
28029,
11407,
13,
33349,
26933,
66,
13557,
48317,
329,
269,
287,
2116,
13,
17197,
13,
27160,
3419,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1312,
11,
269,
287,
27056,
378,
7,
944,
13,
17197,
13,
13083,
3419,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26295,
13,
2617,
62,
7839,
7,
72,
11,
269,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26295,
13,
672,
2655,
303,
7,
944,
13,
261,
62,
8367,
62,
3803,
11,
3891,
11639,
8367,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
3613,
329,
2116,
12,
35790,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
28029,
11407,
17816,
62,
17197,
20520,
796,
26295,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
42655,
4868,
13,
33295,
7,
42655,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
8229,
477,
40803,
287,
257,
569,
14253,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
48317,
796,
20966,
88,
28029,
11407,
13,
53,
14253,
7,
944,
13,
42655,
4868,
8,
628,
198,
220,
220,
220,
825,
422,
62,
11600,
7,
944,
11,
8633,
62,
259,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
8912,
8398,
1366,
422,
257,
22155,
13,
628,
220,
220,
220,
220,
220,
220,
220,
2561,
26571,
5128,
1028,
32815,
973,
287,
2134,
5103,
526,
15931,
628,
220,
220,
220,
220,
220,
220,
220,
44804,
684,
2395,
2611,
13,
12102,
378,
7,
11600,
62,
259,
11,
2116,
13,
15952,
2611,
8,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
11,
1988,
287,
8633,
62,
259,
13,
23814,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
318,
39098,
7,
8367,
11,
8633,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
17197,
58,
2539,
4083,
6738,
62,
11600,
7,
8367,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
58,
2539,
60,
796,
1988,
628,
198,
220,
220,
220,
825,
422,
62,
17752,
7,
944,
11,
33918,
62,
259,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
8912,
8398,
1366,
422,
19449,
526,
15931,
628,
220,
220,
220,
220,
220,
220,
220,
20218,
796,
33918,
13,
46030,
7,
17752,
62,
259,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
6738,
62,
11600,
7,
29510,
8,
628,
198,
220,
220,
220,
825,
284,
62,
17752,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
35,
931,
8398,
1366,
355,
19449,
526,
15931,
628,
220,
220,
220,
220,
220,
220,
220,
611,
407,
2116,
13,
7890,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
6045,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
33918,
13,
67,
8142,
7,
944,
13,
1462,
62,
11600,
28955,
628,
198,
220,
220,
220,
825,
284,
62,
11600,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
35,
931,
8398,
1366,
355,
22155,
526,
15931,
628,
220,
220,
220,
220,
220,
220,
220,
1005,
796,
8633,
7,
944,
13,
7890,
8,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1438,
11,
1200,
287,
2116,
13,
17197,
13,
23814,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1005,
58,
3672,
60,
796,
1200,
13,
1462,
62,
11600,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
1005,
628,
628,
220,
220,
220,
825,
11593,
1136,
9186,
834,
7,
944,
11,
2378,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
35265,
1262,
8633,
15582,
329,
2134,
13236,
626,
13,
628,
220,
220,
220,
220,
220,
220,
220,
2561,
717,
1949,
284,
17276,
257,
1200,
8398,
2134,
13,
1002,
326,
338,
407,
1043,
11,
198,
220,
220,
220,
220,
220,
220,
220,
340,
481,
788,
804,
329,
257,
1366,
2378,
526,
15931,
628,
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] | 2.356366 | 2,576 |
from nepali_company_registrar.nepal_company_registrar import NepalCompanyRegistrar
| [
6738,
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62,
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396,
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27026,
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198
] | 3.192308 | 26 |
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from datetime import datetime, timedelta
from backtrader.feed import DataBase
from backtrader import TimeFrame, date2num, num2date
from backtrader.utils.py3 import (integer_types, queue, string_types,
with_metaclass)
from backtrader.metabase import MetaParams
from . import igstore
class IGData(with_metaclass(MetaIGData, DataBase)):
'''
params:
'''
#TODO insert params
params = (
('useask', False),
('reconnections', -1),
('qcheck', 5)
)
# States for the Finite State Machine in _load
_ST_FROM, _ST_START, _ST_LIVE, _ST_HISTORBACK, _ST_OVER = range(5)
_store = igstore.IGStore
def islive(self):
'''Returns ``True`` to notify ``Cerebro`` that preloading and runonce
should be deactivated'''
return True
def setenvironment(self, env):
'''Receives an environment (cerebro) and passes it over to the store it
belongs to'''
super(IGData, self).setenvironment(env)
env.addstore(self.o)
def start(self):
'''Starts the IG connecction and gets the real contract and
contractdetails if it exists'''
super(IGData, self).start()
# Create attributes as soon as possible
self._statelivereconn = False # if reconnecting in live state
self._storedmsg = dict() # keep pending live message (under None)
self.qlive = queue.Queue()
self._state = self._ST_OVER
# Kickstart store and get queue to wait on
self.o.start(data=self)
self._start_finish()
self._state = self._ST_START # initial state for _load
self._st_start()
self._reconns = 0
def stop(self):
'''Stops and tells the store to stop'''
super(IGData, self).stop()
self.o.stop()
def _load(self):
'''
steps
1 - check if we status live. If so process message
- Check for error codes in message and change status appropriately
- Process the message as long as the status is not trying to reconnect
- Setup a backfill if data is missing.
2 - If not, is the status set to perform a backfill?
'''
if self._state == self._ST_OVER:
return False
while True:
if self._state == self._ST_LIVE:
try:
msg = (self._storedmsg.pop(None, None) or
self.qlive.get(timeout=self._qcheck))
except queue.Empty:
return None # indicate timeout situation
if msg is None: # Conn broken during historical/backfilling
self.put_notification(self.CONNBROKEN)
self.put_notification(self.DISCONNECTED)
self._state = self._ST_OVER
return False # failed
#TODO handle error messages in feed
#Check for empty data. Sometimes all the fields return None...
if msg['UTM'] is None:
return None
#self._reconns = self.p.reconnections
# Process the message according to expected return type
if not self._statelivereconn:
if self._laststatus != self.LIVE:
if self.qlive.qsize() <= 1: # very short live queue
self.put_notification(self.LIVE)
ret = self._load_tick(msg)
if ret:
return True
# could not load bar ... go and get new one
continue
elif self._state == self._ST_START:
if not self._st_start(instart=False):
self._state = self._ST_OVER
return False
#TODO
# - Check for delays in feed
# - put a self.put_notification(self.DELAYED)
# - Attempt to fill in missing data
# - Setup a backfill of some sort when starting a feed.
# - Set Dissonnected status where appropriate.
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] | 2.117299 | 2,029 |
from photons_products.base import Product, Capability, CapabilityValue
from photons_products.enums import VendorRegistry, Zones, Family
from photons_products import conditions as cond
class Capability(Capability):
"""
.. attribute:: is_light
Is this device a light
.. attribute:: zones
The style of zones. So strips are LINEAR and things like the candle and tile are MATRIX
.. attribute:: has_ir
Do we have infrared capability
.. attribute:: has_hev
Does this device have HEV LEDs
.. attribute:: has_color
Do we have hue control
.. attribute:: has_chain
Do we have a chain of devices
.. attribute:: has_relays
Does this device have relays
.. attribute:: has_buttons
Does this device have physical buttons
.. attribute:: has_unhandled
This product has StateUnhandled
.. attribute:: has_extended_multizone
This product supports extended multizone messages
.. attribute:: has_variable_color_temp
Do we have variable kelvin
.. attribute:: min_kelvin
The min kelvin of this product
.. attribute:: max_kelvin
The max kelvin of this product
.. attribute:: product
The product class associate with this capability
.. attribute:: firmware_major
the firmware_major associated with this product
You can create an instance of this capability with your own firmware_major by calling this instance
.. attribute:: firmware_minor
the firmware_major associated with this product
You can create an instance of this capability with your own firmware_minor by calling this instance
.. autoattribute:: photons_products.lifx.Capability.has_matrix
.. autoattribute:: photons_products.lifx.Capability.has_multizone
"""
is_light = True
zones = CapabilityValue(Zones.SINGLE)
has_ir = CapabilityValue(False)
has_hev = CapabilityValue(False)
has_color = CapabilityValue(False)
has_chain = CapabilityValue(False)
has_relays = CapabilityValue(False)
has_buttons = CapabilityValue(False)
has_unhandled = CapabilityValue(False).until(0, 0, cond.NameHas("SWITCH"), becomes=True)
has_extended_multizone = CapabilityValue(False).until(
2, 77, cond.Family(Family.LCM2), cond.Capability(has_multizone=True), becomes=True
)
has_variable_color_temp = CapabilityValue(True)
min_kelvin = CapabilityValue(2500)
max_kelvin = CapabilityValue(9000)
@property
def has_multizone(self):
"""Return whether we have LINEAR zones"""
return self.zones is Zones.LINEAR
@property
def has_matrix(self):
"""Return whether we have MATRIX zones"""
return self.zones is Zones.MATRIX
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] | 2.916318 | 956 |
# 若中断了直接再次运行即可
# 爬取历史数据建立数据库
if __name__ == '__main__':
from crypto_1min import Bitfinex_api
import threading
initially_urls_queue = Bitfinex_api().create_initially_urls_queue()
# Bitfinex_api().get_all_symbol_detail()
threads = [thread() for i in range(50)]
for thread in threads:
thread.start()
for thread in threads:
thread.join() | [
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] | 1.88835 | 206 |
#! python3
import datetime as dt
import requests
import pandas as pd
import lxml.html as lh
'''' Class containing the code that scrapes the stock ticker/information from various stock & crypto sites'''
class StockScraper:
''' This is a function that scrapes a table from a provided web page.'''
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] | 3.483516 | 91 |
#!/usr/bin/env python
# coding: utf-8
# In[8]:
import rebound
import numpy as np
###############
### IMPORTS ###
###############
params = np.load('sample_params.npy')
###################
### DEFINITIONS ###
###################
radeg = np.pi/180
############################
############################
############################
### SIMULATION ###
############################
############################
############################
t_tot = 2000000
Nout = 100000
times = np.linspace(0,t_tot,Nout)
M0 = 1
num_tr = len(params[0])
sim = rebound.Simulation()
sim.add(m=M0,x=0, y=0, z=0, vx=0, vy=0, vz=0, hash='Sun')
add_tr(sim, params)
sim.add(m=9.543e-4, a=5.2, e=.04839, inc=.022689, Omega=0, omega=0, hash='jupiter')
sim.integrator = 'whfast'
sim.dt = 0.5
sim.move_to_com()
ps = sim.particles
#########################################
## Parameter tracking initialization ##
#########################################
mass = np.zeros(Nout)
x_sol = np.zeros(Nout); y_sol = np.zeros(Nout); z_sol = np.zeros(Nout)
x_sol[0] = ps['Sun'].x
y_sol[0] = ps['Sun'].y
z_sol[0] = ps['Sun'].z
x_jup = np.zeros(Nout); y_jup = np.zeros(Nout); z_jup = np.zeros(Nout)
x_jup[0] = ps['jupiter'].x
y_jup[0] = ps['jupiter'].y
z_jup[0] = ps['jupiter'].z
a_jup = np.zeros(Nout)
e_jup = np.zeros(Nout)
i_jup = np.zeros(Nout)
pmjup = np.zeros(Nout)
lmjup = np.zeros(Nout)
a_jup[0] = ps['jupiter'].a
e_jup[0] = ps['jupiter'].e
i_jup[0] = ps['jupiter'].inc
pmjup[0] = ps['jupiter'].pomega
lmjup[0] = ps['jupiter'].l
a_vals = np.zeros((num_tr, Nout))
e_vals = np.zeros((num_tr, Nout))
i_vals = np.zeros((num_tr, Nout))
omvals = np.zeros((num_tr, Nout))
pmvals = np.zeros((num_tr, Nout))
lmvals = np.zeros((num_tr, Nout))
x_vals = np.zeros((num_tr, Nout))
y_vals = np.zeros((num_tr, Nout))
z_vals = np.zeros((num_tr, Nout))
for moon in range(num_tr):
a_vals[moon,0] = ps['tr_{0}'.format(moon)].a
e_vals[moon,0] = ps['tr_{0}'.format(moon)].e
i_vals[moon,0] = ps['tr_{0}'.format(moon)].inc
lmvals[moon,0] = ps['tr_{0}'.format(moon)].l
omvals[moon,0] = ps['tr_{0}'.format(moon)].Omega
pmvals[moon,0] = ps['tr_{0}'.format(moon)].pomega
x_vals[moon,0] = ps['tr_{0}'.format(moon)].x
y_vals[moon,0] = ps['tr_{0}'.format(moon)].y
z_vals[moon,0] = ps['tr_{0}'.format(moon)].z
###########################
###########################
###########################
#### RUNNING ####
###########################
###########################
###########################
for i, time in enumerate(times):
sim.integrate(time)
sim.move_to_com()
x_sol[i] = ps['Sun'].x
y_sol[i] = ps['Sun'].y
z_sol[i] = ps['Sun'].z
x_jup[i] = ps['jupiter'].x
y_jup[i] = ps['jupiter'].y
z_jup[i] = ps['jupiter'].z
a_jup[i] = ps['jupiter'].a
e_jup[i] = ps['jupiter'].e
i_jup[i] = ps['jupiter'].inc
pmjup[i] = ps['jupiter'].pomega
lmjup[i] = ps['jupiter'].l
for moon in range(num_tr):
a_vals[moon,i] = ps['tr_{0}'.format(moon)].a
e_vals[moon,i] = ps['tr_{0}'.format(moon)].e
i_vals[moon,i] = ps['tr_{0}'.format(moon)].inc
lmvals[moon,i] = ps['tr_{0}'.format(moon)].l
omvals[moon,i] = ps['tr_{0}'.format(moon)].Omega
pmvals[moon,i] = ps['tr_{0}'.format(moon)].pomega
x_vals[moon,i] = ps['tr_{0}'.format(moon)].x
y_vals[moon,i] = ps['tr_{0}'.format(moon)].y
z_vals[moon,i] = ps['tr_{0}'.format(moon)].z
##############
## Saving ##
##############
i_vals/= radeg
i_jup /= radeg
troj_data = np.array((a_vals, e_vals, i_vals, omvals, pmvals, lmvals, x_vals, y_vals, z_vals))
plnt_data = np.array((a_jup, e_jup, i_jup, pmjup, lmjup, x_jup, y_jup, z_jup))
star_data = np.array((mass, lsol, x_sol, y_sol, z_sol))
np.save("Ctrl_Trojandata.npy", troj_data)
np.save("Ctrl_Planetdata.npy", plnt_data)
np.save("Ctrl_Stardata.npy", star_data)
np.save("Ctrl_Timesteps.npy", times)
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] | 2.127568 | 1,850 |
# coding=utf-8
# Copyright 2019 The Google NoisyStudent Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the 'License');
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an 'AS IS' BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import app
from absl import flags
import collections
import json
import copy
import os
import time
import numpy as np
import tensorflow as tf
import utils
FLAGS = flags.FLAGS
flags.DEFINE_string('input_dir', '', '')
flags.DEFINE_string('prediction_dir', '', '')
flags.DEFINE_string('info_dir', '', '')
flags.DEFINE_string('prelim_stats_dir', '', '')
flags.DEFINE_string('output_dir', '', '')
flags.DEFINE_integer(
'num_shards', default=128, help='')
flags.DEFINE_integer(
'only_use_num_shards', default=-1, help='')
flags.DEFINE_integer(
'shard_id', default=0, help='')
flags.DEFINE_integer(
'num_image', default=1300, help='')
flags.DEFINE_integer(
'total_replicas', default=1, help='')
flags.DEFINE_integer(
'total_label_replicas', default=-1, help='')
flags.DEFINE_integer(
'task', default=-1, help='')
flags.DEFINE_integer(
'debug', default=0, help='')
flags.DEFINE_float(
'min_threshold', default=0.0, help='')
flags.DEFINE_float(
'max_prob', default=2, help='sometimes the probability can be greater than 1 due to floating point.')
flags.DEFINE_integer(
'num_label_classes', default=1000, help='')
flags.DEFINE_integer(
'upsample', default=1, help='')
flags.DEFINE_integer(
'only_get_stats', default=0, help='')
flags.DEFINE_string('file_prefix', 'train', '')
flags.DEFINE_string(
'data_type', default='tfrecord', help='')
flags.DEFINE_integer(
'use_top', default=1, help='')
flags.DEFINE_bool(
'eval_imagenet_p', default=False, help='')
flags.DEFINE_bool(
'use_all', default=False, help='')
if __name__ == '__main__':
app.run(main)
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3256,
4277,
28,
16,
11,
1037,
28,
7061,
8,
198,
198,
33152,
13,
7206,
29940,
62,
30388,
7,
198,
220,
220,
220,
705,
18206,
62,
320,
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316,
62,
79,
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28,
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11,
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28,
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8,
198,
198,
33152,
13,
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29940,
62,
30388,
7,
198,
220,
220,
220,
705,
1904,
62,
439,
3256,
4277,
28,
25101,
11,
1037,
28,
7061,
8,
628,
628,
628,
628,
628,
628,
628,
198,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
598,
13,
5143,
7,
12417,
8,
628
] | 2.807059 | 850 |
n = int(input('Digite um número para calcular o fatorial: '))
f = 1
for c in range(n, 0, -1):
print(f'{c}', end='')
print(' x ' if c > 1 else ' = ', end='')
f = f * c
print(f'{f}')
| [
77,
796,
493,
7,
15414,
10786,
19511,
578,
23781,
299,
21356,
647,
78,
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7,
69,
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90,
69,
92,
11537,
198
] | 2.053191 | 94 |
#!python
"""
This module provides pytest tests for the functions from preprocessing.py file
"""
import pytest
import alphaviz.preprocessing as preproc
# def test_preprocess_ckg_output():
# ckg_output_string_correct = "~Q92934;~Q15149"
# ckg_output_string_correct_2 = " ~Q92934; ~Q15149"
# ckg_output_string_wrong = "Q92934; Q15149"
#
# proteins_correct_input = alphaviz.preprocessing.preprocess_ckg_output(ckg_output_string_correct)
# assert len(proteins_correct_input) == 2, \
# "The number of extracted proteins is wrong."
# assert 'Q92934' in proteins_correct_input, \
# "A unique protein is absent in the extracted list of proteins."
#
# assert proteins_correct_input == alphaviz.preprocessing.preprocess_ckg_output(ckg_output_string_correct_2), \
# "Spaces in the ckg string don't influence on the result of the output."
#
# with pytest.raises(ValueError):
# alphaviz.preprocessing.preprocess_ckg_output(ckg_output_string_wrong)
| [
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220,
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62,
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11,
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2,
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220,
220,
220,
220,
220,
220,
220,
366,
464,
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286,
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318,
2642,
526,
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2,
220,
220,
220,
220,
6818,
705,
48,
24,
1959,
2682,
6,
287,
15568,
62,
30283,
62,
15414,
11,
3467,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
366,
32,
3748,
7532,
318,
13717,
287,
262,
21242,
1351,
286,
15568,
526,
198,
2,
198,
2,
220,
220,
220,
220,
6818,
15568,
62,
30283,
62,
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6624,
435,
746,
615,
528,
13,
3866,
36948,
13,
3866,
14681,
62,
694,
70,
62,
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7,
694,
70,
62,
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62,
8841,
62,
30283,
62,
17,
828,
3467,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
366,
4561,
2114,
287,
262,
269,
10025,
4731,
836,
470,
4588,
319,
262,
1255,
286,
262,
5072,
526,
198,
2,
198,
2,
220,
220,
220,
220,
351,
12972,
9288,
13,
430,
2696,
7,
11395,
12331,
2599,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
435,
746,
615,
528,
13,
3866,
36948,
13,
3866,
14681,
62,
694,
70,
62,
22915,
7,
694,
70,
62,
22915,
62,
8841,
62,
36460,
8,
628,
198
] | 2.607792 | 385 |
from . import *
# bind = BindTransmitter(system_id='test_id', password='abc123')
# print bind.get_obj()
# print bind.get_hex()
# print bind.get_bin()
# #print json.dumps(bind.get_obj(), indent=4, sort_keys=True)
# #print json.dumps(decode_pdu(bind.get_hex()), indent=4, sort_keys=True)
# print json.dumps(unpack_pdu(bind.get_bin()), indent=4, sort_keys=True)
# sm = SubmitSM(short_message='testing testing')
# print json.dumps(unpack_pdu(sm.get_bin()), indent=4, sort_keys=True)
# sm.add_message_payload('616263646566676869')
# print json.dumps(unpack_pdu(sm.get_bin()), indent=4, sort_keys=True)
| [
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62,
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44214,
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2996,
2791,
3134,
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3388,
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2,
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67,
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7,
403,
8002,
62,
79,
646,
7,
5796,
13,
1136,
62,
8800,
3419,
828,
33793,
28,
19,
11,
3297,
62,
13083,
28,
17821,
8,
198
] | 2.48996 | 249 |
# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# (C) British Crown Copyright 2017-2020 Met Office.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
# -----------------------------------------------------------------------------
import iris
import iris.analysis
import numpy as np
from six import string_types, integer_types
import iris.coord_categorisation as iccat
import doctest
import os.path
import catnip.config as conf
import iris.exceptions
from dask import array as da
def _get_xy_noborder(mask):
"""
make a function that returns the indices
of where the mask is valid. If the mask is all True (all masked)
raises a ValueError
args
----
mask: mask from numpy array
Returns
-------
x1, x2, y1, y2: int giving space where the data is valid
"""
if np.all(mask):
raise ValueError("All values masked - can't get indices")
ys, xs = np.where(~mask)
x1 = min(xs)
x2 = max(xs) + 1
y1 = min(ys)
y2 = max(ys) + 1
return x1, x2, y1, y2
def add_aux_unrotated_coords(cube):
"""
This function takes a cube that is on a rotated pole
coordinate system and adds to it, two addtional
auxillary coordinates to hold the unrotated coordinate
values.
args
----
cube: iris cube on an rotated pole coordinate system
Returns
-------
cube: input cube with auxilliary coordinates of unrotated
latitude and longitude
Notes
-----
See below for an example that should be run with python3:
>>> file = os.path.join(conf.DATA_DIR, 'mslp.daily.rcm.viet.nc')
>>> cube = iris.load_cube(file)
>>> print([coord.name() for coord in cube.coords()])
['time', 'grid_latitude', 'grid_longitude']
>>> auxcube = add_aux_unrotated_coords(cube)
>>> print([coord.name() for coord in auxcube.coords()])
['time', 'grid_latitude', 'grid_longitude', 'latitude', 'longitude']
>>> print(auxcube.coord('latitude')) # doctest: +NORMALIZE_WHITESPACE
AuxCoord(array([[35.32243855, 35.33914928, 35.355619 , ..., 35.71848081,
35.70883111, 35.69893388],
[35.10317609, 35.11986604, 35.13631525, ..., 35.49871728,
35.48908 , 35.47919551],
[34.88390966, 34.90057895, 34.91700776, ..., 35.27895246,
35.26932754, 35.25945571],
...,
[ 6.13961446, 6.15413611, 6.16844578, ..., 6.48307389,
6.47472284, 6.46615667],
[ 5.92011032, 5.93461779, 5.94891347, ..., 6.26323044,
6.25488773, 6.24633011],
[ 5.70060768, 5.71510098, 5.72938268, ..., 6.04338876,
6.03505439, 6.02650532]]), standard_name=None, \
units=Unit('degrees'), long_name='latitude')
>>> print(auxcube.shape)
(360, 136, 109)
>>> print(auxcube.coord('latitude').shape)
(136, 109)
>>> print(auxcube.coord('longitude').shape)
(136, 109)
"""
if not isinstance(cube, iris.cube.Cube):
raise TypeError("Input is not a cube")
# get cube's coordinate system
cs = cube.coord_system()
if str(cs).find("Rotated") == -1:
raise TypeError(
"The cube is not on a rotated pole, coord system is {}".format(str(cs))
)
auxcube = cube.copy()
# get coord names
# Longitude
xcoord = auxcube.coord(axis="X", dim_coords=True)
# Latitude
ycoord = auxcube.coord(axis="Y", dim_coords=True)
# read in the grid lat/lon points from the cube
glat = auxcube.coord(ycoord).points
glon = auxcube.coord(xcoord).points
# create a rectangular grid out of an array of
# glon and glat values, shape will be len(glat)xlen(glon)
x, y = np.meshgrid(glon, glat)
# get the cube dimensions which corresponds to glon and glat
x_dim = auxcube.coord_dims(xcoord)[0]
y_dim = auxcube.coord_dims(ycoord)[0]
# define two new variables to hold the unrotated coordinates
rlongitude, rlatitude = iris.analysis.cartography.unrotate_pole(
x, y, cs.grid_north_pole_longitude, cs.grid_north_pole_latitude
)
# create two new auxillary coordinates to hold
# the values of the unrotated coordinates
reg_long = iris.coords.AuxCoord(rlongitude, long_name="longitude", units="degrees")
reg_lat = iris.coords.AuxCoord(rlatitude, long_name="latitude", units="degrees")
# add two auxilary coordinates to the cube holding
# regular(unrotated) lat/lon values
auxcube.add_aux_coord(reg_long, [y_dim, x_dim])
auxcube.add_aux_coord(reg_lat, [y_dim, x_dim])
return auxcube
def add_bounds(cube, coord_names, bound_position=0.5):
"""
Simple function to check whether a
coordinate in a cube has bounds, and
add them if it doesn't.
args
----
cube: iris cube
coord_names: string or list of strings containing the name/s
of the coordinates you want to add bounds to.
bound_position: Optional, the desired position of the bounds relative to
the position of the points. Default is 0.5.
Returns
-------
cube: cube with bounds added
Notes
-----
Need to be careful that it is appropriate
to add bounds to the data, e.g. if data
are instantaneous, time bounds are not
appropriate.
An example:
>>> file = os.path.join(conf.DATA_DIR, 'mslp.daily.rcm.viet.nc')
>>> cube = iris.load_cube(file)
>>> bcube = add_bounds(cube, 'time')
time coordinate already has bounds, none will be added
>>> bcube = add_bounds(cube, 'grid_latitude')
grid_latitude bounds added
>>> bcube = add_bounds(cube, ['grid_latitude','grid_longitude'])
grid_latitude bounds added
grid_longitude bounds added
"""
# check if the input is an Iris cube
if not isinstance(cube, iris.cube.Cube):
raise TypeError("Input is not a cube")
# check if the coordinate name input is a string
if not isinstance(coord_names, (string_types, list)):
raise TypeError("Input coordinate must be a string")
bcube = cube.copy()
# find names of dim coords
c_names = [c.name() for c in bcube.coords()]
# if coord_names is a single string, it will be split,
# by the loop this statement checks for that case and
# puts stash into a tuple to prevent splitting.
if isinstance(coord_names, string_types):
coord_names = tuple([coord_names])
for coord in coord_names:
# check if coord is a string
if not isinstance(coord, string_types):
raise TypeError(
"Coordinate {} must be a string, it is currently a {}".format(
str(coord), type(coord)
)
)
# check coord is a coordinate of the cube
if coord not in c_names:
raise AttributeError(
"{} is not a coordinate, available coordinates are: {}".format(
coord, c_names
)
)
# check if the coord already has bounds
if bcube.coord(coord).has_bounds():
print(
("{} coordinate already has bounds, none will be added".format(coord))
)
# add bounds to coord
else:
bcube.coord(coord).guess_bounds(bound_position=bound_position)
print(("{} bounds added".format(coord)))
return bcube
def add_coord_system(cube):
"""
A cube must have a coordinate system in order to be regridded.
This function checks whether a cube has a coordinate system. If
the cube has no coordinate system, the standard the ellipsoid
representation wgs84 (ie. the one used by GPS) is added.
Note: It will not work for rotated pole data without a
coordinate system.
args
----
cube: iris cube
Returns
-------
cube: The copy of the input cube with coordinate system added,
if the cube didn't have one already.
Notes
-----
A simple example:
>>> file = os.path.join(conf.DATA_DIR, 'gtopo30_025deg.nc')
>>> cube = iris.load_cube(file)
>>> print(cube.coord('latitude').coord_system)
None
>>> cscube = add_coord_system(cube)
Coordinate system GeogCS(6371229.0) added to cube
>>> print(cscube.coord('latitude').coord_system)
GeogCS(6371229.0)
"""
# Note: wgs84 is the World Geodetic System, and a standard coord
# system in iris. In GeogCS(6371229.0), 6371229 is the Earth's
# radius in m. See:
# https://scitools.org.uk/iris/docs/v1.9.0/html/iris/iris/coord_systems.html
# check if the input is an Iris cube
if not isinstance(cube, iris.cube.Cube):
raise TypeError("Input is not a cube")
cscube = cube.copy()
cs = cscube.coord_system()
if cs is not None:
if str(cs).find("Rotated") == 0:
# not possible to add a coord system for
# rotated pole cube without knowing the
# rotation. Give error message.
raise TypeError("Error, no coordinate system for rotated pole cube")
else:
coord_names = [coord.name() for coord in cscube.coords(dim_coords=True)]
wgs84_cs = iris.coord_systems.GeogCS(6371229.0)
if "latitude" in coord_names:
cscube.coord("latitude").coord_system = wgs84_cs
if "longitude" in coord_names:
cscube.coord("longitude").coord_system = wgs84_cs
print("Coordinate system GeogCS(6371229.0) added to cube")
return cscube
def add_time_coord_cats(cube):
"""
This function takes in an iris cube, and adds a range of
numeric co-ordinate categorisations to it. Depending
on the data, not all of the coords added will be relevant.
args
----
cube: iris cube that has a coordinate called 'time'
Returns
-------
Cube: cube that has new time categorisation coords added
Notes
-----
test
A simple example:
>>> file = os.path.join(conf.DATA_DIR, 'mslp.daily.rcm.viet.nc')
>>> cube = iris.load_cube(file)
>>> coord_names = [coord.name() for coord in cube.coords()]
>>> print((', '.join(coord_names)))
time, grid_latitude, grid_longitude
>>> ccube = add_time_coord_cats(cube)
>>> coord_names = [coord.name() for coord in ccube.coords()]
>>> print((', '.join(coord_names)))
time, grid_latitude, grid_longitude, day_of_month, day_of_year, month, \
month_number, season, season_number, year
>>> # print every 50th value of the added time cat coords
... for c in coord_names[3:]:
... print(ccube.coord(c).long_name)
... print(ccube.coord(c).points[::50])
...
day_of_month
[ 1 21 11 1 21 11 1 21]
day_of_year
[ 1 51 101 151 201 251 301 351]
month
['Jan' 'Feb' 'Apr' 'Jun' 'Jul' 'Sep' 'Nov' 'Dec']
month_number
[ 1 2 4 6 7 9 11 12]
season
['djf' 'djf' 'mam' 'jja' 'jja' 'son' 'son' 'djf']
season_number
[0 0 1 2 2 3 3 0]
year
[2000 2000 2000 2000 2000 2000 2000 2000]
"""
# most errors pop up when you try to add a coord that has
# previously been added, or the cube doesn't contain the
# necessary attribute.
ccube = cube.copy()
# numeric
try:
iccat.add_day_of_year(ccube, "time")
except AttributeError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
except ValueError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
try:
iccat.add_day_of_month(ccube, "time")
except AttributeError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
except ValueError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
try:
iccat.add_month_number(ccube, "time")
except AttributeError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
except ValueError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
try:
iccat.add_season_number(ccube, "time")
except AttributeError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
except ValueError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
try:
iccat.add_year(ccube, "time")
except AttributeError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
except ValueError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
# strings
try:
iccat.add_month(ccube, "time")
except AttributeError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
except ValueError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
try:
iccat.add_season(ccube, "time")
except AttributeError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
except ValueError as err:
print(("add_time_coord_cats: {}, skipping . . . ".format(err)))
return ccube
def extract_rot_cube(cube, min_lat, min_lon, max_lat, max_lon):
"""
Function etracts the specific region from the cube.
args
----
cube: cube on rotated coord system, used as reference grid for transformation.
Returns
-------
min_lat: The minimum latitude point of the desired extracted cube.
min_lon: The minimum longitude point of the desired extracted cube.
max_lat: The maximum latitude point of the desired extracted cube.
max_lon: The maximum longitude point of the desired extracted cube.
An example:
>>> file = os.path.join(conf.DATA_DIR, 'rcm_monthly.pp')
>>> cube = iris.load_cube(file, 'air_temperature')
>>> min_lat = 50
>>> min_lon = -10
>>> max_lat = 60
>>> max_lon = 0
>>> extracted_cube = extract_rot_cube(cube, min_lat, min_lon, max_lat, max_lon)
>>> max_lat_cube = np.max(extracted_cube.coord('latitude').points)
>>> print(f'{max_lat_cube:.3f}')
61.365
>>> min_lat_cube = np.min(extracted_cube.coord('latitude').points)
>>> print(f'{min_lat_cube:.3f}')
48.213
>>> max_lon_cube = np.max(extracted_cube.coord('longitude').points)
>>> print(f'{max_lon_cube:.3f}')
3.643
>>> min_lon_cube = np.min(extracted_cube.coord('longitude').points)
>>> print(f'{min_lon_cube:.3f}')
-16.292
"""
# adding unrotated coords to the cube
cube = add_aux_unrotated_coords(cube)
# mask the cube using the true lat and lon
lats = cube.coord("latitude").points
lons = cube.coord("longitude").points
select_lons = (lons >= min_lon) & (lons <= max_lon)
select_lats = (lats >= min_lat) & (lats <= max_lat)
selection = select_lats & select_lons
selection = da.broadcast_to(selection, cube.shape)
cube.data = da.ma.masked_where(~selection, cube.core_data())
# grab a single 2D slice of X and Y and take the mask
lon_coord = cube.coord(axis="X", dim_coords=True)
lat_coord = cube.coord(axis="Y", dim_coords=True)
for yx_slice in cube.slices(["grid_latitude", "grid_longitude"]):
cmask = yx_slice.data.mask
break
# now cut the cube down along X and Y coords
x1, x2, y1, y2 = _get_xy_noborder(cmask)
idx = len(cube.shape) * [slice(None)]
idx[cube.coord_dims(cube.coord(axis="x", dim_coords=True))[0]] = slice(x1, x2, 1)
idx[cube.coord_dims(cube.coord(axis="y", dim_coords=True))[0]] = slice(y1, y2, 1)
extracted_cube = cube[tuple(idx)]
return extracted_cube
def remove_forecast_coordinates(iris_cube):
"""A function to remove the forecast_period and
forecast_reference_time coordinates from the UM PP files
args
----
iris_cube: input iris_cube
Returns
-------
iris_cube: iris cube without the forecast_period and forecast_reference_time
coordinates
Notes
-----
See below for examples:
>>> cube_list_fcr = iris.cube.CubeList()
>>> file = os.path.join(conf.DATA_DIR, 'rcm_monthly.pp')
>>> cube_list = iris.load(file)
>>> for cube in cube_list:
... cube_fcr = remove_forecast_coordinates(cube)
... cube_list_fcr.append(cube_fcr)
Removed the forecast_period coordinate from Heavyside function \
on pressure levels cube
Removed the forecast_reference_time coordinate from Heavyside \
function on pressure levels cube
Removed the forecast_period coordinate from air_temperature cube
Removed the forecast_reference_time coordinate from air_temperature cube
Removed the forecast_period coordinate from relative_humidity cube
Removed the forecast_reference_time coordinate from relative_humidity cube
Removed the forecast_period coordinate from specific_humidity cube
Removed the forecast_reference_time coordinate from specific_humidity cube
Removed the forecast_period coordinate from x_wind cube
Removed the forecast_reference_time coordinate from x_wind cube
Removed the forecast_period coordinate from y_wind cube
Removed the forecast_reference_time coordinate from y_wind cube
Now check if the forecast coordinates have been removed
>>> for cube in cube_list_fcr:
... cube_nfc = remove_forecast_coordinates(cube)
'Expected to find exactly 1 forecast_period coordinate, but found none.'
'Expected to find exactly 1 forecast_reference_time coordinate, but found none.'
'Expected to find exactly 1 forecast_period coordinate, but found none.'
'Expected to find exactly 1 forecast_reference_time coordinate, but found none.'
'Expected to find exactly 1 forecast_period coordinate, but found none.'
'Expected to find exactly 1 forecast_reference_time coordinate, but found none.'
'Expected to find exactly 1 forecast_period coordinate, but found none.'
'Expected to find exactly 1 forecast_reference_time coordinate, but found none.'
'Expected to find exactly 1 forecast_period coordinate, but found none.'
'Expected to find exactly 1 forecast_reference_time coordinate, but found none.'
'Expected to find exactly 1 forecast_period coordinate, but found none.'
'Expected to find exactly 1 forecast_reference_time coordinate, but found none.'
"""
try:
iris_cube.remove_coord("forecast_period")
print(
(
"Removed the forecast_period coordinate from {} cube".format(
iris_cube.name()
)
)
)
except iris.exceptions.CoordinateNotFoundError as coord_not_found:
print("{}".format(coord_not_found))
try:
iris_cube.remove_coord("forecast_reference_time")
print(
(
"Removed the forecast_reference_time coordinate from {} cube".format(
iris_cube.name()
)
)
)
except iris.exceptions.CoordinateNotFoundError as coord_not_found:
print("{}".format(coord_not_found))
return iris_cube
def rim_remove(cube, rim_width):
""" Return IRIS cube with rim removed.
args
----
cube: input iris cube
rim_width: integer, number of grid points to remove from edge of lat and long
Returns
-------
rrcube: rim removed cube
Notes
-----
See below for examples:
>>> cube_list_rr = iris.cube.CubeList()
>>> file = os.path.join(conf.DATA_DIR, 'rcm_monthly.pp')
>>> cube_list = iris.load(file)
>>> for cube in cube_list:
... cube_rr = rim_remove(cube, 8)
... cube_list_rr.append(cube_rr)
...
Removed 8 size rim from Heavyside function on pressure levels
Removed 8 size rim from air_temperature
Removed 8 size rim from relative_humidity
Removed 8 size rim from specific_humidity
Removed 8 size rim from x_wind
Removed 8 size rim from y_wind
>>> file = os.path.join(conf.DATA_DIR, 'rcm_mslp_monthly.pp')
>>> mslp_cube = iris.load_cube(file)
>>>
>>> mslp_cube_rr = rim_remove(mslp_cube, 8)
Removed 8 size rim from air_pressure_at_sea_level
>>>
>>> print(len(mslp_cube.coord('grid_latitude').points))
432
>>> print(len(mslp_cube.coord('grid_longitude').points))
444
>>> print(len(mslp_cube.coord('grid_latitude').points))
432
>>> print(len(mslp_cube.coord('grid_longitude').points))
444
>>>
>>> mslp_cube_rrrr = rim_remove(mslp_cube_rr, 8)
WARNING - This cube has already had it's rim removed
Removed 8 size rim from air_pressure_at_sea_level
Now test for failures:
>>> mslp_cube_rr = rim_remove(cube, 8.2) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: Please provide a positive integer for rim_width
>>> mslp_cube_rr = rim_remove(cube, -5) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
IndexError: Please provide a positive integer > 0 for rim_width
>>> mslp_cube_rr = rim_remove(cube, 400) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
IndexError: length of lat or lon coord is < rim_width*2
>>> mslp_cube_rr = rim_remove(cube, 0) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
IndexError: Please provide a positive integer > 0 for rim_width
>>> mslp_cube_rr = rim_remove(cube, 'a') # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: Please provide a positive integer for rim_width
"""
# check if the input is an Iris cube
if not isinstance(cube, iris.cube.Cube):
raise TypeError("Input is not a cube")
# check whether rim_width is an integer
if not isinstance(rim_width, (integer_types)):
raise TypeError("Please provide a positive integer for rim_width")
if rim_width <= 0:
raise IndexError("Please provide a positive integer > 0 for rim_width")
# check whether this cube has already had it's rim removed
if "rim_removed" in cube.attributes:
print("WARNING - This cube has already had it's rim removed")
# Longitude
xcoord = cube.coord(axis="X", dim_coords=True)
# Latitude
ycoord = cube.coord(axis="Y", dim_coords=True)
# make sure specified rim_width is going to work
if len(xcoord.points) <= (rim_width * 2) or len(ycoord.points) <= (rim_width * 2):
raise IndexError("length of lat or lon coord is < rim_width*2")
# Remove rim from Longitude
rrcube = cube.subset(xcoord[rim_width : -1 * rim_width])
# Remove rim from Latitude
rrcube = rrcube.subset(ycoord[rim_width : -1 * rim_width])
# add meta data that rim has been removed
rrcube.attributes["rim_removed"] = "{} point rim removed".format(rim_width)
print(("Removed {} size rim from {}".format(rim_width, cube.name())))
return rrcube
if __name__ == "__main__":
doctest.testmod()
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] | 2.595957 | 9,400 |
"""
Ref: https://dacon.io/competitions/official/235673/talkboard/401911?page=1&dtype=recent
"""
import os
import re
import platform
import itertools
import collections
import pkg_resources # pip install py-rouge
from io import open
if platform.system() == "Windows":
try:
from eunjeon import Mecab
except:
print("please install eunjeon module")
else: # Ubuntu일 경우
from konlpy.tag import Mecab
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] | 2.645963 | 161 |
import functools
import sys
from typing import Set
import nltk
from flair.data import Sentence
from flair.models import SequenceTagger
from langdetect import detect
@functools.lru_cache(maxsize=1)
def get_tagger(language: str) -> SequenceTagger:
"""Return the tagger needed """
if language == "de":
return SequenceTagger.load("de-ner")
if language == "en":
return SequenceTagger.load("ner-fast")
raise Exception("Invalid language")
def filter_text(text: str) -> str:
"""remove unwanted character from the text which can disturb NER"""
filtered = text
for s in "\\\xa0\"'[]()’“”\xad":
filtered = filtered.replace(s, "")
return filtered
def format_entities(entities: Set[str]) -> Set[str]:
"""
Remove
:param entity:
:return:
"""
result = []
for entity in entities:
if entity[-1] in [".", ",", "?", "!", ":"]:
entity = entity[0:-1]
entity = entity.replace("\n", " ")
if entity[-1] == "s" and entity[:-1] in entities:
continue
if not entity:
continue
result.append(entity)
return set(result)
@functools.lru_cache(maxsize=512)
def find_entity(text: str, language: str) -> Set[str]:
"""extract entity using flair"""
global tagger
filtered = filter_text(text)
if not filtered:
return set()
detected_language = detect(filtered)
if language != detected_language:
return set()
sent_tokens = nltk.sent_tokenize(filtered)
sentences = [Sentence(i) for i in sent_tokens]
tagger = get_tagger(language)
tagger.predict(sentences)
flair_entities = []
for sentence in sentences:
flair_entities.extend(
[entity.text for entity in sentence.get_spans("ner")]
)
result = format_entities(set(flair_entities))
return result
if __name__ == "__main__":
text = sys.argv[1]
print(find_entity(text))
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] | 2.475285 | 789 |
from typing import Dict
from abc import abstractmethod, ABC
from pandas import DataFrame
import json
import os
DEFAULT_TRANSLATION_PROVIDER = SchemaTranslationProvider()
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from src.contexts.kms.computed_data.application.find_one.ComputedDataByKeyAndInputFinder import \
ComputedDataByKeyAndInputFinder
from src.contexts.kms.computed_data.application.find_one.ComputedDataByKeyAndInputQuery import \
ComputedDataByKeyAndInputQuery
from src.contexts.kms.computed_data.application.find_one.KmsComputedDataResponse import KmsComputedDataResponse
from src.contexts.kms.computed_data.domain.entities.ComputedDataInput import ComputedDataInput
from src.contexts.kms.computed_data.domain.entities.ComputedDataType import ComputedDataType
from src.contexts.kms.cryptokeys.domain.entities.CryptoKeyId import CryptoKeyId
from src.contexts.shared.domain.QueryHandler import QueryHandler
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] | 3.192825 | 223 |
import apscheduler
from apscheduler.schedulers.blocking import BlockingScheduler
#If you want to cleanup all test resources like vms, volumes, workloads then set
# following cleanup parameter value to True otherwise False
cleanup = True
# pre requisite paramter
pre_req = True
#Test results for reporting
PASS = "PASS"
FAIL = "FAIL"
enabled_tests = ["Attached_Volume_Ceph"]
#Id of workload type "parallel"
parallel="2ddd528d-c9b4-4d7e-8722-cc395140255a"
#Resources to use from file
#Please add your resources one on each line in files: tempest/tempest/vms_file, volumes_file, workloads_file
vms_from_file=False
volumes_from_file=False
workloads_from_file=False
#CLI configuration parameters
workload_type_id="f82ce76f-17fe-438b-aa37-7a023058e50d"
workload_name="clitest"
source_platform="openstack"
snapshot_name = "test-snapshot"
snapshot_type_full = "full"
restore_name = "test-oneclick-restore"
selective_restore_name = "test-selective-restore"
restore_filename = "/opt/restore.json"
vm_license_filename = "test_licenses/tvault_license_10VM.txt"
capacity_license_filename = "test_licenses/tvault_license_100TB.txt"
compute_license_filename = "test_licenses/tvault_license_10compute.txt"
invalid_license_filename = "test_licenses/tvault_license_invalid.txt"
expired_license_filename = "test_licenses/tvault_license_expired.txt"
workload_modify_name = "test2-new"
workload_modify_description = "test2-new-description"
restore_type = "restore"
global_job_scheduler=False
tvault_ip = "192.168.16.254"
tvault_dbusername = "root"
tvault_dbname = "workloadmgr"
tvault_password = "sample-password"
no_of_compute_nodes = 1
compute_node_ip = "192.168.16.75"
compute_node_username = "root"
compute_node_password = "Password1!"
# Scheduler parameter
interval="1 hrs"
interval_update = "7 hrs"
enabled='false'
retention_policy_type="Number of Snapshots to Keep"
retention_policy_type_update = "Number of days to retain Snapshots"
retention_policy_value="3"
retention_policy_value_update = "7"
schedule_report_file="scheduleReport.txt"
sched=BlockingScheduler()
count=0
No_of_Backup=1
# Scheduler policy parameters
policy_name="policy2"
policy_name_update = "policy_update"
fullbackup_interval="8"
fullbackup_interval_update = "7"
# test parameters
key_pair_name = "tempest_test_key_pair"
instance_username = "ubuntu"
snapshot_restore_name = "Tempest Test Restore"
restored_instance_flavor = 2
security_group_id = "baaae013-75d5-4821-806c-2cb259c95fb4"
security_group_name = "test_security"
flavor_name = "test_flavor"
config_yaml = {"compute": ["/etc/nova", "/var/lib/nova", "/var/log/nova"],
"glance": ["/etc/glance", "/var/lib/glance", "/var/log/glance"],
"keystone": ["/etc/keystone", "/var/lib/keystone", "/var/log/keystone"],
"cinder": ["/etc/cinder", "/var/lib/cinder", "/var/log/cinder"],
"neutron": ["/etc/neutron", "/var/lib/neutron"],
"swift": ["/etc/swift", "/var/log/swift/"],
"ceilometer": ["/etc/ceilometer", "/var/log/ceilometer/"],
"orchestration": ["/etc/heat/", "/var/log/heat/"]}
additional_dir = {"tvault-contego": ["/etc/tvault-contego/"]}
bootfromvol_vol_size = 4
volumes_parts = ["/dev/vdb", "/dev/vdc"]
recovery_flavor_ref = 3
recovery_image_ref = "cd056509-666b-41fa-9236-86f202b3e619"
#Email settings data
setting_data = {"smtp_default_recipient": "[email protected]",
"smtp_default_sender": "[email protected]",
"smtp_port": "587",
"smtp_server_name": "smtp.gmail.com",
"smtp_server_password": tvault_password,
"smtp_server_username": "[email protected]",
"smtp_timeout": "10" }
enable_email_notification = {"smtp_email_enable" : 1}
disable_email_notification = {"smtp_email_enable" : 0}
#Parameter for multiple vm workloads etc
vm_count = 8
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14761,
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796,
807,
198
] | 2.414172 | 1,637 |
#!/usr/bin/python
# -*- coding: utf-8 -*-
#
# Copyright 2010 British Broadcasting Corporation and Kamaelia Contributors(1)
#
# (1) Kamaelia Contributors are listed in the AUTHORS file and at
# http://www.kamaelia.org/AUTHORS - please extend this file,
# not this notice.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Builds a basic rooted graph in a simple swarm fashion.
#
z = peer()
a = peer()
b = peer()
c = peer()
d = peer()
e = peer()
f = peer()
g = peer()
h = peer()
a.join()
b.join()
c.join()
d.join()
e.join()
f.join()
g.join()
h.join()
for i in z,a,b,c,d,e,f,g,h:
print i
print """
The following should just have been displayed:
peer (ID=0, parentID=None, max=2, children=[1, 2])
peer (ID=1, parentID=0, max=2, children=[3, 4])
peer (ID=2, parentID=0, max=2, children=[5, 6])
peer (ID=3, parentID=1, max=2, children=[7, 8])
peer (ID=4, parentID=1, max=2, children=[])
peer (ID=5, parentID=2, max=2, children=[])
peer (ID=6, parentID=2, max=2, children=[])
peer (ID=7, parentID=3, max=2, children=[])
peer (ID=8, parentID=3, max=2, children=[])"""
| [
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] | 2.766667 | 570 |
# -*- coding: utf-8 -*-
from pandas import DataFrame
from typing import List, Tuple
def dataframe_astype(df: DataFrame, columns: List[Tuple[str, type]]):
""" DataFrame Column Type converter
Parameters
----------
df: DataFrame
Pandas DataFrame
columns: list of tuple of str, type
column name and type for type conversion
Returns
-------
DataFrame
Pandas DataFrame
"""
for column, tp in columns:
if tp == int or tp == float:
df[column] = df[column].str.replace(',|-', '').astype(tp, errors='ignore')
else:
df[column] = df[column].astype(tp, errors='ignore')
return df
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# -*- coding: utf-8 -*-
# Import Python Libs
from __future__ import absolute_import, print_function, unicode_literals
from tempfile import NamedTemporaryFile
import os
# Import Salt Testing Libs
from tests.support.mixins import LoaderModuleMockMixin
from tests.support.unit import TestCase, skipIf
from tests.support.mock import (
MagicMock,
NO_MOCK,
NO_MOCK_REASON,
patch,
mock_open
)
# Import Salt Libs
from salt.exceptions import CommandExecutionError, SaltInvocationError
import salt.modules.timezone as timezone
from salt.ext import six
import salt.utils.platform
import salt.utils.stringutils
GET_ZONE_FILE = 'salt.modules.timezone._get_zone_file'
GET_LOCALTIME_PATH = 'salt.modules.timezone._get_localtime_path'
@skipIf(NO_MOCK, NO_MOCK_REASON)
@skipIf(NO_MOCK, NO_MOCK_REASON)
class TimezoneModuleTestCase(TestCase, LoaderModuleMockMixin):
'''
Timezone test case
'''
TEST_TZ = 'UTC'
@patch('salt.utils.path.which', MagicMock(return_value=False))
def test_get_zone_centos(self):
'''
Test CentOS is recognized
:return:
'''
with patch.dict(timezone.__grains__, {'os': 'centos'}):
with patch('salt.modules.timezone._get_zone_etc_localtime', MagicMock(return_value=self.TEST_TZ)):
assert timezone.get_zone() == self.TEST_TZ
@patch('salt.utils.path.which', MagicMock(return_value=False))
def test_get_zone_os_family_rh_suse(self):
'''
Test RedHat and Suse are recognized
:return:
'''
for osfamily in ['RedHat', 'Suse']:
with patch.dict(timezone.__grains__, {'os_family': [osfamily]}):
with patch('salt.modules.timezone._get_zone_sysconfig', MagicMock(return_value=self.TEST_TZ)):
assert timezone.get_zone() == self.TEST_TZ
@patch('salt.utils.path.which', MagicMock(return_value=False))
def test_get_zone_os_family_debian_gentoo(self):
'''
Test Debian and Gentoo are recognized
:return:
'''
for osfamily in ['Debian', 'Gentoo']:
with patch.dict(timezone.__grains__, {'os_family': [osfamily]}):
with patch('salt.modules.timezone._get_zone_etc_timezone', MagicMock(return_value=self.TEST_TZ)):
assert timezone.get_zone() == self.TEST_TZ
@patch('salt.utils.path.which', MagicMock(return_value=False))
def test_get_zone_os_family_allbsd_nilinuxrt(self):
'''
Test *BSD and NILinuxRT are recognized
:return:
'''
for osfamily in ['FreeBSD', 'OpenBSD', 'NetBSD', 'NILinuxRT']:
with patch.dict(timezone.__grains__, {'os_family': osfamily}):
with patch('salt.modules.timezone._get_zone_etc_localtime', MagicMock(return_value=self.TEST_TZ)):
assert timezone.get_zone() == self.TEST_TZ
@patch('salt.utils.path.which', MagicMock(return_value=False))
def test_get_zone_os_family_slowlaris(self):
'''
Test Slowlaris is recognized
:return:
'''
with patch.dict(timezone.__grains__, {'os_family': ['Solaris']}):
with patch('salt.modules.timezone._get_zone_solaris', MagicMock(return_value=self.TEST_TZ)):
assert timezone.get_zone() == self.TEST_TZ
@patch('salt.utils.path.which', MagicMock(return_value=False))
def test_get_zone_os_family_aix(self):
'''
Test IBM AIX is recognized
:return:
'''
with patch.dict(timezone.__grains__, {'os_family': ['AIX']}):
with patch('salt.modules.timezone._get_zone_aix', MagicMock(return_value=self.TEST_TZ)):
assert timezone.get_zone() == self.TEST_TZ
@skipIf(salt.utils.platform.is_windows(), 'os.symlink not available in Windows')
@patch('salt.utils.path.which', MagicMock(return_value=False))
@patch('os.path.exists', MagicMock(return_value=True))
@patch('os.unlink', MagicMock())
@patch('os.symlink', MagicMock())
def test_set_zone_redhat(self):
'''
Test zone set on RH series
:return:
'''
with patch.dict(timezone.__grains__, {'os_family': ['RedHat']}):
assert timezone.set_zone(self.TEST_TZ)
name, args, kwargs = timezone.__salt__['file.sed'].mock_calls[0]
assert args == ('/etc/sysconfig/clock', '^ZONE=.*', 'ZONE="UTC"')
@skipIf(salt.utils.platform.is_windows(), 'os.symlink not available in Windows')
@patch('salt.utils.path.which', MagicMock(return_value=False))
@patch('os.path.exists', MagicMock(return_value=True))
@patch('os.unlink', MagicMock())
@patch('os.symlink', MagicMock())
def test_set_zone_suse(self):
'''
Test zone set on SUSE series
:return:
'''
with patch.dict(timezone.__grains__, {'os_family': ['Suse']}):
assert timezone.set_zone(self.TEST_TZ)
name, args, kwargs = timezone.__salt__['file.sed'].mock_calls[0]
assert args == ('/etc/sysconfig/clock', '^TIMEZONE=.*', 'TIMEZONE="UTC"')
@skipIf(salt.utils.platform.is_windows(), 'os.symlink not available in Windows')
@patch('salt.utils.path.which', MagicMock(return_value=False))
@patch('os.path.exists', MagicMock(return_value=True))
@patch('os.unlink', MagicMock())
@patch('os.symlink', MagicMock())
def test_set_zone_gentoo(self):
'''
Test zone set on Gentoo series
:return:
'''
with patch.dict(timezone.__grains__, {'os_family': ['Gentoo']}):
with patch('salt.utils.files.fopen', mock_open()) as m_open:
assert timezone.set_zone(self.TEST_TZ)
fh_ = m_open.filehandles['/etc/timezone'][0]
assert fh_.call.args == ('/etc/timezone', 'w'), fh_.call.args
assert fh_.write_calls == ['UTC', '\n'], fh_.write_calls
@skipIf(salt.utils.platform.is_windows(), 'os.symlink not available in Windows')
@patch('salt.utils.path.which', MagicMock(return_value=False))
@patch('os.path.exists', MagicMock(return_value=True))
@patch('os.unlink', MagicMock())
@patch('os.symlink', MagicMock())
def test_set_zone_debian(self):
'''
Test zone set on Debian series
:return:
'''
with patch.dict(timezone.__grains__, {'os_family': ['Debian']}):
with patch('salt.utils.files.fopen', mock_open()) as m_open:
assert timezone.set_zone(self.TEST_TZ)
fh_ = m_open.filehandles['/etc/timezone'][0]
assert fh_.call.args == ('/etc/timezone', 'w'), fh_.call.args
assert fh_.write_calls == ['UTC', '\n'], fh_.write_calls
@skipIf(salt.utils.platform.is_windows(), 'os.symlink not available in Windows')
@patch('salt.utils.path.which', MagicMock(return_value=True))
@patch('os.path.exists', MagicMock(return_value=True))
@patch('os.unlink', MagicMock())
@patch('os.symlink', MagicMock())
def test_get_hwclock_timedate_utc(self):
'''
Test get hwclock UTC/localtime
:return:
'''
with patch('salt.modules.timezone._timedatectl', MagicMock(return_value={'stdout': 'rtc in local tz'})):
assert timezone.get_hwclock() == 'UTC'
with patch('salt.modules.timezone._timedatectl', MagicMock(return_value={'stdout': 'rtc in local tz:yes'})):
assert timezone.get_hwclock() == 'localtime'
@skipIf(salt.utils.platform.is_windows(), 'os.symlink not available in Windows')
@patch('salt.utils.path.which', MagicMock(return_value=False))
@patch('os.path.exists', MagicMock(return_value=True))
@patch('os.unlink', MagicMock())
@patch('os.symlink', MagicMock())
def test_get_hwclock_suse(self):
'''
Test get hwclock on SUSE
:return:
'''
with patch.dict(timezone.__grains__, {'os_family': ['Suse']}):
timezone.get_hwclock()
name, args, kwarg = timezone.__salt__['cmd.run'].mock_calls[0]
assert args == (['tail', '-n', '1', '/etc/adjtime'],)
assert kwarg == {'python_shell': False}
@skipIf(salt.utils.platform.is_windows(), 'os.symlink not available in Windows')
@patch('salt.utils.path.which', MagicMock(return_value=False))
@patch('os.path.exists', MagicMock(return_value=True))
@patch('os.unlink', MagicMock())
@patch('os.symlink', MagicMock())
def test_get_hwclock_redhat(self):
'''
Test get hwclock on RedHat
:return:
'''
with patch.dict(timezone.__grains__, {'os_family': ['RedHat']}):
timezone.get_hwclock()
name, args, kwarg = timezone.__salt__['cmd.run'].mock_calls[0]
assert args == (['tail', '-n', '1', '/etc/adjtime'],)
assert kwarg == {'python_shell': False}
def _test_get_hwclock_debian(self): # TODO: Enable this when testing environment is working properly
'''
Test get hwclock on Debian
:return:
'''
with patch('salt.utils.path.which', MagicMock(return_value=False)):
with patch('os.path.exists', MagicMock(return_value=True)):
with patch('os.unlink', MagicMock()):
with patch('os.symlink', MagicMock()):
with patch.dict(timezone.__grains__, {'os_family': ['Debian']}):
timezone.get_hwclock()
name, args, kwarg = timezone.__salt__['cmd.run'].mock_calls[0]
assert args == (['tail', '-n', '1', '/etc/adjtime'],)
assert kwarg == {'python_shell': False}
@skipIf(salt.utils.platform.is_windows(), 'os.symlink not available in Windows')
@patch('salt.utils.path.which', MagicMock(return_value=False))
@patch('os.path.exists', MagicMock(return_value=True))
@patch('os.unlink', MagicMock())
@patch('os.symlink', MagicMock())
def test_get_hwclock_solaris(self):
'''
Test get hwclock on Solaris
:return:
'''
# Incomplete
with patch.dict(timezone.__grains__, {'os_family': ['Solaris']}):
assert timezone.get_hwclock() == 'UTC'
with patch('salt.utils.files.fopen', mock_open()):
assert timezone.get_hwclock() == 'localtime'
@skipIf(salt.utils.platform.is_windows(), 'os.symlink not available in Windows')
@patch('salt.utils.path.which', MagicMock(return_value=False))
@patch('os.path.exists', MagicMock(return_value=True))
@patch('os.unlink', MagicMock())
@patch('os.symlink', MagicMock())
def test_get_hwclock_aix(self):
'''
Test get hwclock on AIX
:return:
'''
# Incomplete
hwclock = 'localtime'
if not os.path.isfile('/etc/environment'):
hwclock = 'UTC'
with patch.dict(timezone.__grains__, {'os_family': ['AIX']}):
assert timezone.get_hwclock() == hwclock
@skipIf(salt.utils.platform.is_windows(), 'os.symlink not available in Windows')
@patch('salt.utils.path.which', MagicMock(return_value=True))
def test_set_hwclock_timedatectl(self):
'''
Test set hwclock with timedatectl
:return:
'''
timezone.set_hwclock('UTC')
name, args, kwargs = timezone.__salt__['cmd.retcode'].mock_calls[0]
assert args == (['timedatectl', 'set-local-rtc', 'false'],)
timezone.set_hwclock('localtime')
name, args, kwargs = timezone.__salt__['cmd.retcode'].mock_calls[1]
assert args == (['timedatectl', 'set-local-rtc', 'true'],)
@skipIf(salt.utils.platform.is_windows(), 'os.symlink not available in Windows')
@patch('salt.utils.path.which', MagicMock(return_value=False))
@patch('os.path.exists', MagicMock(return_value=True))
@patch('os.unlink', MagicMock())
@patch('os.symlink', MagicMock())
def test_set_hwclock_aix_nilinuxrt(self):
'''
Test set hwclock on AIX and NILinuxRT
:return:
'''
for osfamily in ['AIX', 'NILinuxRT']:
with patch.dict(timezone.__grains__, {'os_family': osfamily}):
with self.assertRaises(SaltInvocationError):
assert timezone.set_hwclock('forty two')
assert timezone.set_hwclock('UTC')
@skipIf(salt.utils.platform.is_windows(), 'os.symlink not available in Windows')
@patch('salt.utils.path.which', MagicMock(return_value=False))
@patch('os.path.exists', MagicMock(return_value=True))
@patch('os.unlink', MagicMock())
@patch('os.symlink', MagicMock())
@patch('salt.modules.timezone.get_zone', MagicMock(return_value='TEST_TIMEZONE'))
def test_set_hwclock_solaris(self):
'''
Test set hwclock on Solaris
:return:
'''
with patch.dict(timezone.__grains__, {'os_family': ['Solaris'],
'cpuarch': 'x86'}):
with self.assertRaises(SaltInvocationError):
assert timezone.set_hwclock('forty two')
assert timezone.set_hwclock('UTC')
name, args, kwargs = timezone.__salt__['cmd.retcode'].mock_calls[0]
assert args == (['rtc', '-z', 'GMT'],)
assert kwargs == {'python_shell': False}
@skipIf(salt.utils.platform.is_windows(), 'os.symlink not available in Windows')
@patch('salt.utils.path.which', MagicMock(return_value=False))
@patch('os.path.exists', MagicMock(return_value=True))
@patch('os.unlink', MagicMock())
@patch('os.symlink', MagicMock())
@patch('salt.modules.timezone.get_zone', MagicMock(return_value='TEST_TIMEZONE'))
def test_set_hwclock_arch(self):
'''
Test set hwclock on arch
:return:
'''
with patch.dict(timezone.__grains__, {'os_family': ['Arch']}):
assert timezone.set_hwclock('UTC')
name, args, kwargs = timezone.__salt__['cmd.retcode'].mock_calls[0]
assert args == (['timezonectl', 'set-local-rtc', 'false'],)
assert kwargs == {'python_shell': False}
@skipIf(salt.utils.platform.is_windows(), 'os.symlink not available in Windows')
@patch('salt.utils.path.which', MagicMock(return_value=False))
@patch('os.path.exists', MagicMock(return_value=True))
@patch('os.unlink', MagicMock())
@patch('os.symlink', MagicMock())
@patch('salt.modules.timezone.get_zone', MagicMock(return_value='TEST_TIMEZONE'))
def test_set_hwclock_redhat(self):
'''
Test set hwclock on RedHat
:return:
'''
with patch.dict(timezone.__grains__, {'os_family': ['RedHat']}):
assert timezone.set_hwclock('UTC')
name, args, kwargs = timezone.__salt__['file.sed'].mock_calls[0]
assert args == ('/etc/sysconfig/clock', '^ZONE=.*', 'ZONE="TEST_TIMEZONE"')
@skipIf(salt.utils.platform.is_windows(), 'os.symlink not available in Windows')
@patch('salt.utils.path.which', MagicMock(return_value=False))
@patch('os.path.exists', MagicMock(return_value=True))
@patch('os.unlink', MagicMock())
@patch('os.symlink', MagicMock())
@patch('salt.modules.timezone.get_zone', MagicMock(return_value='TEST_TIMEZONE'))
def test_set_hwclock_suse(self):
'''
Test set hwclock on SUSE
:return:
'''
with patch.dict(timezone.__grains__, {'os_family': ['Suse']}):
assert timezone.set_hwclock('UTC')
name, args, kwargs = timezone.__salt__['file.sed'].mock_calls[0]
assert args == ('/etc/sysconfig/clock', '^TIMEZONE=.*', 'TIMEZONE="TEST_TIMEZONE"')
@skipIf(salt.utils.platform.is_windows(), 'os.symlink not available in Windows')
@patch('salt.utils.path.which', MagicMock(return_value=False))
@patch('os.path.exists', MagicMock(return_value=True))
@patch('os.unlink', MagicMock())
@patch('os.symlink', MagicMock())
@patch('salt.modules.timezone.get_zone', MagicMock(return_value='TEST_TIMEZONE'))
def test_set_hwclock_debian(self):
'''
Test set hwclock on Debian
:return:
'''
with patch.dict(timezone.__grains__, {'os_family': ['Debian']}):
assert timezone.set_hwclock('UTC')
name, args, kwargs = timezone.__salt__['file.sed'].mock_calls[0]
assert args == ('/etc/default/rcS', '^UTC=.*', 'UTC=yes')
assert timezone.set_hwclock('localtime')
name, args, kwargs = timezone.__salt__['file.sed'].mock_calls[1]
assert args == ('/etc/default/rcS', '^UTC=.*', 'UTC=no')
@skipIf(salt.utils.platform.is_windows(), 'os.symlink not available in Windows')
@patch('salt.utils.path.which', MagicMock(return_value=False))
@patch('os.path.exists', MagicMock(return_value=True))
@patch('os.unlink', MagicMock())
@patch('os.symlink', MagicMock())
@patch('salt.modules.timezone.get_zone', MagicMock(return_value='TEST_TIMEZONE'))
def test_set_hwclock_gentoo(self):
'''
Test set hwclock on Gentoo
:return:
'''
with patch.dict(timezone.__grains__, {'os_family': ['Gentoo']}):
with self.assertRaises(SaltInvocationError):
timezone.set_hwclock('forty two')
timezone.set_hwclock('UTC')
name, args, kwargs = timezone.__salt__['file.sed'].mock_calls[0]
assert args == ('/etc/conf.d/hwclock', '^clock=.*', 'clock="UTC"')
timezone.set_hwclock('localtime')
name, args, kwargs = timezone.__salt__['file.sed'].mock_calls[1]
assert args == ('/etc/conf.d/hwclock', '^clock=.*', 'clock="local"')
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] | 2.149709 | 8,256 |
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Dense, Flatten
import matplotlib.pyplot as plt
import numpy as np
import random
pixel_width = 28
pixel_height = 28
no_of_classes = 10
batch_size = 32
epochs = 10
(features_train, labels_train), (features_test, labels_test) = mnist.load_data()
features_train = features_train.reshape(features_train.shape[0], pixel_width, pixel_height, 1)
features_test = features_test.reshape(features_test.shape[0], pixel_width, pixel_height, 1)
input_shape = (pixel_width, pixel_height, 1)
features_train = features_train.astype('float32')
features_test = features_test.astype('float32')
features_train /= 255
features_test /= 255
labels_train = keras.utils.to_categorical(labels_train, no_of_classes)
labels_test = keras.utils.to_categorical(labels_test, no_of_classes)
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation = 'relu', input_shape = input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(no_of_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
model.fit(features_train, labels_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(features_test, labels_test))
score = model.evaluate(features_test, labels_test, verbose=0)
predictions = model.predict(features_test)
prediction_digits = np.argmax(predictions, axis=1)
plt.figure(figsize=(18, 18))
for i in range(100):
ax = plt.subplot(10, 10, i+1)
plt.xticks([])
plt.xticks([])
plt.yticks([])
plt.grid(False)
image_index = random.randint(0, len(prediction_digits))
plt.imshow(np.squeeze(features_test[image_index]), cmap=plt.cm.gray)
ax.xaxis.label.set_color(get_label_color(prediction_digits[image_index],
np.argmax(labels_test[image_index])))
#print(image_index)
plt.xlabel('Predicted: %d' % prediction_digits[image_index])
plt.show()
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] | 2.473154 | 894 |
test = { 'name': 'q2_1',
'points': 1,
'suites': [ { 'cases': [ {'code': '>>> # Make sure you assigned `binary options` to an array;\n>>> type(binary_options) == np.ndarray\nTrue', 'hidden': False, 'locked': False},
{ 'code': '>>> # Should be a two element array of a binary distribution;\n>>> sorted(set(binary_options)) == sorted(set([0, 1]))\nTrue',
'hidden': False,
'locked': False}],
'scored': True,
'setup': '',
'teardown': '',
'type': 'doctest'}]}
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] | 1.789894 | 376 |
import collections
| [
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] | 5.25 | 4 |
import torch
import torch.nn as nn
from .base_loss import Loss
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] | 3.368421 | 19 |
from .successivehalving import SuccessiveHalving
from .base import WarmStartIteration
from .successiveresampling import SuccessiveResampling
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#!_PYTHONLOC
#
# (C) COPYRIGHT 2009-2019 Ahasuerus
# ALL RIGHTS RESERVED
#
# The copyright notice above does not evidence any actual or
# intended publication of such source code.
#
# Version: $Revision$
# Date: $Date$
import string
import sys
import MySQLdb
from isfdb import *
from common import *
from login import *
from SQLparsing import *
if __name__ == '__main__':
PrintHeader("My Web Sites")
PrintNavbar('mywebsites', 0, 0, 'mywebsites.cgi', 0)
(myID, username, usertoken) = GetUserData()
myID = int(myID)
if not myID:
print 'You must be logged in to modify your list of preferred Web sites'
sys.exit(0)
PrintTrailer('mywebsites', 0, 0)
#Get a list of currently defined Web sites
query = "select site_id, site_name from websites order by site_name"
db.query(query)
result = db.store_result()
row = result.fetch_row()
websites = []
while row:
websites.append(row[0])
row = result.fetch_row()
# Get the currently defined site preferences for the logged-in user
query = "select site_id,user_choice from user_sites where user_id='%d'" % (myID)
db.query(query)
result = db.store_result()
row = result.fetch_row()
user_sites = []
while row:
user_sites.append(row[0])
row = result.fetch_row()
print '<h3>Select Web Sites to link Publications to. At least one Amazon site needs to be selected since ISFDB links to Amazon-hosted images.</h3>'
print '<form id="data" METHOD="POST" ACTION="/cgi-bin/submitmywebsites.cgi">'
print '<ul>'
for website in websites:
checked = 'checked'
for user_site in user_sites:
if user_site[0] == website[0]:
if user_site[1] == 0:
checked = ''
break
print '<li><input type="checkbox" name="site_choice.%s" value="on" %s>%s ' % (website[0], checked, website[1])
print '<input name="site_id.%d" value="%s" type="HIDDEN"></li>' % (website[0], website[1])
print '</ul>'
print '<p>'
print '<input type="SUBMIT" value="Update List of Web Sites">'
print '</form>'
PrintTrailer('mywebsites', 0, 0)
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] | 2.255446 | 1,010 |
from pybars import Compiler
import lxml.etree as etree
import collections
from . import Artifact
import os
class ArtifactGenerator(object):
"""Class used to generate artifacts for WSO2"""
"""Generate the artifact based on the passed template location and data"""
def merge(self, a, b, path=None):
"deep merge dictionary"
if path is None: path = []
for key in b:
if key in a:
if isinstance(a[key], dict) and isinstance(b[key], dict):
self.merge(a[key], b[key], path + [str(key)])
elif a[key] == b[key]:
pass # same leaf value
else:
#raise Exception('Conflict at %s' % '.'.join(path + [str(key)]))
pass
else:
a[key] = b[key]
return a
def get_filepaths(self, directory, function):
"""
This function will generate the file names in a directory
tree by walking the tree either top-down or bottom-up. For each
directory in the tree rooted at directory top (including top itself),
it yields a 3-tuple (dirpath, dirnames, filenames).
"""
file_paths = [] # List which will store all of the full filepaths.
# Walk the tree.
for root, directories, files in os.walk(directory):
for filename in files:
# Join the two strings in order to form the full filepath.
filepath = os.path.join(root, filename)
file_paths = function(filepath, filename, file_paths)
# Add it to the list.
return file_paths # Self-explanatory.
def generateArtifact(self, data, directory):
""" Pass in the generic parameters for the pom file. The method will read the synapse
directory and create the resources dictionary """
synapse_directory = directory
fileList = self.get_filepaths(synapse_directory, synapse_config)
resources = []
for fileObj in fileList:
resources.append({'type': fileObj['fileESBType'], 'fileExtension': fileObj['fileType'], 'resourceName': fileObj['fileName'].split(".")[0]})
#print resources
data['resources'] = resources
artifactArtiObj = self.generate(data, "templates/artifact.hbs")
return artifactArtiObj
#registry_directory = directory + "/gateway-registry/"
#def registry_config(filepath, filename, file_paths):
#typeName = filepath.split("/")
#print typeName
# if typeName[13]=="synapse-config":
# if len(typeName) == 16:
# typeName = typeName[14]
# file_paths.append({'filePath': filepath, 'fileName': filename, 'type': typeName})
#return file_paths
#fileList = self.get_filepaths(registry_directory, registry_config)
def generateCarPom(self, data, directory):
""" Pass in the generic parameters for the pom file. The method will read the synapse
directory and create the resources dictionary """
synapse_directory = directory
fileList = self.get_filepaths(synapse_directory, synapse_config)
resources = []
for fileObj in fileList:
resources.append({'type': fileObj['fileESBType'], 'fileExtension': fileObj['fileType'], 'resourceName': fileObj['fileName'].split(".")[0]})
data['resources'] = resources
print data
artifactCarObj = self.generate(data, "templates/car_pom.hbs")
return artifactCarObj
#registry_directory = directory + "/dev-registry/"
#def registry_config(filepath, filename, file_paths):
#typeName = filepath.split("/")
#print typeName
# if typeName[13]=="synapse-config":
# if len(typeName) == 16:
# typeName = typeName[14]
# file_paths.append({'filePath': filepath, 'fileName': filename, 'type': typeName})
#return file_paths
#fileList = self.get_filepaths(registry_directory, registry_config) | [
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] | 2.637074 | 1,408 |
from math import pi
import os
import time
# sum([n, n, n...]) adds any number of variables
# print (subtract(8, 3))
# print (multiply(5, 3))
# print (double(7))
# print (triple(5))
# print (divide(8, 4))
# print (half(4))
# print (celsius_conv(94))
c_c = celsius_conv
# print (fahrenheit_conv(49))
# print (p_t(3, 4))
# print (p_t2(5, 3))
# print (square(5))
# print (square_root(25))
# print (cube(4))
# print (f_y(1))
f_y = feet_to_yards
# print (i_c(10))
i_c = inches_to_centi
# print (i_f(11))
i_f = inches_to_feet
# print (f_i(30))
f_i = feet_to_inches
# print (blah(8))
# print (convert_mileage(90))
# print (liters_needed(50, 30))
def pie_perc(n):
"""precondition: n > 0
Assuming n people want to eat a pie,
return the percentage each person gets to eat."""
return int(100 / n)
def average_of_best_3(a, b, c, d): # gives the average of the highest 3
"""Use numbers between 0 and 100"""
first = min(a, b, c, d)
second = (a + b + c + d - first)
third = second / 3
return third
circum = circumference
# circum(r) = circumference(r)
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import config
from pymongo import MongoClient
from instagram_api import insta_fetch_feed | [
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] | 3.666667 | 24 |
import os
import pandas as pd
import numpy as np
import math
from random import shuffle
from tensorflow import keras
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from person_counting.data_generators.data_generators import Generator_CSVS
from person_counting.data_generators.data_generators import *
from person_counting.utils.preprocessing import get_filtered_lengths
from person_counting.utils.scaler import FeatureScaler, LabelScaler
from person_counting.utils.preprocessing import apply_file_filters
class Generator_CSVS_CNN(Generator_CSVS):
"""
Generators class to load npy files from
video folder structre like PCDS Dataset and
train CNNs
Arguments (**kwargs)
length_t : Length of the feature's DataFrame in time dimension
length_y : Length of the feature's DataFrame in y direction
file_names : File names to be processed
filter_cols_upper, : Amount of columns to be filtered at end and start of DataFrame
batch_size : Batch size
top_path : Parent path where csv files are contained
label_file : Name of the label file
"""
def create_datagen(
top_path,
sample,
label_file,
augmentation_factor=0,
filter_hour_below=7,
filter_hour_above=24,
filter_category_noisy=False,
supercharge_crowdeds=False,
):
"""
Creates train and test data generators for lstm network.
Arguments:
top_path: Parent directory where shall be searched for training files
sample: sample of hyperparameters used in this run
label_file: Name of the label file containing all the labels
augmentation_factor: Factor how much augmentation shall be done, 1 means
moving every pixel for one position
filter_hour_above: Hour after which videos shall be filtered
filter_category_noisy: Flag if noisy videos shall be filtered
"""
# Load filenames and lengths
length_t, length_y = get_filtered_lengths(top_path, sample)
train_file_names, validation_file_names, test_file_names = get_file_split(
top_path, supercharge_crowdeds=supercharge_crowdeds
)
# Apply filters
train_file_names = apply_file_filters(
df=train_file_names,
filter_hour_above=filter_hour_above,
filter_category_noisy=filter_category_noisy,
filter_hour_below=filter_hour_below,
)
validation_file_names = apply_file_filters(
df=validation_file_names,
filter_hour_above=filter_hour_above,
filter_category_noisy=filter_category_noisy,
filter_hour_below=filter_hour_below,
)
test_file_names = apply_file_filters(
df=test_file_names,
filter_hour_above=filter_hour_above,
filter_category_noisy=filter_category_noisy,
filter_hour_below=filter_hour_below,
)
scale_files = pd.concat([train_file_names, validation_file_names, test_file_names])
print(
"Dataset contains: \n{} training files \n{} validation files \n{} testing files".format(
len(train_file_names), len(validation_file_names), len(test_file_names)
)
)
feature_scaler = FeatureScaler(top_path, scale_files, sample)
label_scaler = LabelScaler(top_path, label_file, scale_files, sample)
gen_train = Generator_CSVS_CNN(
length_t=length_t,
length_y=length_y,
file_names=train_file_names,
feature_scaler=feature_scaler,
label_scaler=label_scaler,
sample=sample,
top_path=top_path,
label_file=label_file,
augmentation_factor=augmentation_factor,
)
# Don't do augmentation here!
gen_validation = Generator_CSVS_CNN(
length_t=length_t,
length_y=length_y,
file_names=validation_file_names,
feature_scaler=feature_scaler,
label_scaler=label_scaler,
sample=sample,
top_path=top_path,
label_file=label_file,
augmentation_factor=0,
)
gen_test = Generator_CSVS_CNN(
length_t=length_t,
length_y=length_y,
file_names=test_file_names,
feature_scaler=feature_scaler,
label_scaler=label_scaler,
sample=sample,
top_path=top_path,
label_file=label_file,
augmentation_factor=0,
)
return gen_train, gen_validation, gen_test
def get_file_split(top_path, supercharge_crowdeds=False):
"""Get filenames previously splitted"""
if top_path[-2:] != "\\\\" and top_path[-1] != "/":
top_path += "/"
if supercharge_crowdeds:
train = top_path + pd.read_csv(
os.path.join(top_path, "supercharged_crowdeds_train_split.csv"), header=None, squeeze=True
)
else:
train = top_path + pd.read_csv(os.path.join(top_path, "train_split.csv"), header=None, squeeze=True)
val = top_path + pd.read_csv(os.path.join(top_path, "validation_split.csv"), header=None, squeeze=True)
test = top_path + pd.read_csv(os.path.join(top_path, "test_split.csv"), header=None, squeeze=True)
train = train.apply(lambda row: row.replace("\\", "/"))
val = val.apply(lambda row: row.replace("\\", "/"))
test = test.apply(lambda row: row.replace("\\", "/"))
return train, val, test
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48774,
198,
220,
220,
220,
1332,
796,
1332,
13,
39014,
7,
50033,
5752,
25,
5752,
13,
33491,
7203,
6852,
1600,
12813,
48774,
628,
220,
220,
220,
1441,
4512,
11,
1188,
11,
1332,
198
] | 2.446412 | 2,202 |
from .supervise import *
| [
6738,
764,
16668,
85,
786,
1330,
1635,
198
] | 3.125 | 8 |
from datetime import datetime
from enum import Enum
from typing import Dict, List, Optional, Tuple
from .account import Account
from .base import BaseModel
class Permission(str, Enum):
"""
Workspace permission levels.
"""
no_permission = "none"
read = "read"
write = "write"
full_control = "all"
| [
6738,
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] | 2.930435 | 115 |
import math
import numpy as np
from sc2.ids.unit_typeid import UnitTypeId
from sc2.helpers.control_group import ControlGroup
from . import constants as C | [
11748,
10688,
198,
198,
11748,
299,
32152,
355,
45941,
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6738,
629,
17,
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198,
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6738,
764,
1330,
38491,
355,
327
] | 3.369565 | 46 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (c) 2019 Adrian Englhardt <[email protected]>
# Licensed under the MIT License - https://opensource.org/licenses/MIT
from .change_detection_helpers import normalize, normalize_2d, normalize_2d_global, prepare_data, smooth_frequency, \
transform_to_cosdist, transform_to_padded_cosdist, relative_frequency, percentual_diff, cut_array, filter_min_freq
from .helpers import expand_path, natural_sort
__all__ = ['normalize', 'normalize_2d', 'normalize_2d_global', 'prepare_data', 'smooth_frequency',
'transform_to_cosdist', 'transform_to_padded_cosdist', 'relative_frequency', 'percentual_diff', 'cut_array',
'filter_min_freq', 'expand_path', 'natural_sort']
| [
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62,
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62,
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17080,
3256,
705,
43762,
62,
35324,
3256,
705,
25067,
723,
62,
26069,
3256,
705,
8968,
62,
18747,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
24455,
62,
1084,
62,
19503,
80,
3256,
705,
11201,
392,
62,
6978,
3256,
705,
11802,
62,
30619,
20520,
198
] | 2.764706 | 272 |
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# random code that helps with debugging/testing the python interfaces and examples
# this is not meant to be run by normal users
from __future__ import with_statement # for python 2.5
__copyright__ = 'Copyright (C) 2009-2010'
__license__ = 'Apache License, Version 2.0'
# random code that helps with debugging/testing the python interfaces and examples
# this is not meant to be run by normal users
from openravepy import *
import openravepy.examples
from openravepy.interfaces import *
from numpy import *
import numpy,time
| [
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6,
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2,
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351,
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290,
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2,
428,
318,
407,
4001,
284,
307,
1057,
416,
3487,
2985,
198,
6738,
1280,
5758,
9078,
1330,
1635,
198,
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1280,
5758,
9078,
13,
1069,
12629,
198,
6738,
1280,
5758,
9078,
13,
3849,
32186,
1330,
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198,
11748,
299,
32152,
11,
2435,
628,
220,
220,
220,
220,
198
] | 3.825623 | 281 |
import package.tuplist as tl
import package.superdict as sd
import pulp as pl
import package.config as conf
import package.params as pm
import numpy as np
import pprint as pp
| [
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] | 3.571429 | 49 |
from django.contrib import admin
from guardian.admin import GuardedModelAdmin
from ephios.core.models import (
Consequence,
Event,
EventType,
LocalParticipation,
Qualification,
QualificationCategory,
QualificationGrant,
Shift,
WorkingHours,
)
admin.site.register(Qualification)
admin.site.register(QualificationGrant)
admin.site.register(QualificationCategory)
admin.site.register(WorkingHours)
admin.site.register(Consequence)
admin.site.register(Shift)
admin.site.register(Event, GuardedModelAdmin)
admin.site.register(EventType)
admin.site.register(LocalParticipation)
| [
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] | 3.117347 | 196 |
"""Strptime-related classes and functions.
CLASSES:
LocaleTime -- Discovers and/or stores locale-specific time information
TimeRE -- Creates regexes for pattern matching a string of text containing
time information as is returned by time.strftime()
FUNCTIONS:
_getlang -- Figure out what language is being used for the locale
strptime -- Calculates the time struct represented by the passed-in string
Requires Python 2.2.1 or higher (mainly because of the use of property()).
Can be used in Python 2.2 if the following line is added:
True = 1; False = 0
"""
import time
import locale
import calendar
from re import compile as re_compile
from re import IGNORECASE
from datetime import date as datetime_date
__author__ = "Brett Cannon"
__email__ = "[email protected]"
__all__ = ['strptime']
class LocaleTime(object):
"""Stores and handles locale-specific information related to time.
This is not thread-safe! Attributes are lazily calculated and no
precaution is taken to check to see if the locale information has changed
since the creation of the instance in use.
ATTRIBUTES (all read-only after instance creation! Instance variables that
store the values have mangled names):
f_weekday -- full weekday names (7-item list)
a_weekday -- abbreviated weekday names (7-item list)
f_month -- full weekday names (14-item list; dummy value in [0], which
is added by code)
a_month -- abbreviated weekday names (13-item list, dummy value in
[0], which is added by code)
am_pm -- AM/PM representation (2-item list)
LC_date_time -- format string for date/time representation (string)
LC_date -- format string for date representation (string)
LC_time -- format string for time representation (string)
timezone -- daylight- and non-daylight-savings timezone representation
(3-item list; code tacks on blank item at end for
possible lack of timezone such as UTC)
lang -- Language used by instance (string)
"""
def __init__(self, f_weekday=None, a_weekday=None, f_month=None,
a_month=None, am_pm=None, LC_date_time=None, LC_time=None,
LC_date=None, timezone=None, lang=None):
"""Optionally set attributes with passed-in values."""
if f_weekday is None:
self.__f_weekday = None
elif len(f_weekday) == 7:
self.__f_weekday = list(f_weekday)
else:
raise TypeError("full weekday names must be a 7-item sequence")
if a_weekday is None:
self.__a_weekday = None
elif len(a_weekday) == 7:
self.__a_weekday = list(a_weekday)
else:
raise TypeError(
"abbreviated weekday names must be a 7-item sequence")
if f_month is None:
self.__f_month = None
elif len(f_month) == 12:
self.__f_month = self.__pad(f_month, True)
else:
raise TypeError("full month names must be a 12-item sequence")
if a_month is None:
self.__a_month = None
elif len(a_month) == 12:
self.__a_month = self.__pad(a_month, True)
else:
raise TypeError(
"abbreviated month names must be a 12-item sequence")
if am_pm is None:
self.__am_pm = None
elif len(am_pm) == 2:
self.__am_pm = am_pm
else:
raise TypeError("AM/PM representation must be a 2-item sequence")
self.__LC_date_time = LC_date_time
self.__LC_time = LC_time
self.__LC_date = LC_date
self.__timezone = timezone
if timezone:
if len(timezone) != 2:
raise TypeError("timezone names must contain 2 items")
else:
self.__timezone = self.__pad(timezone, False)
if lang:
self.__lang = lang
else:
self.__lang = _getlang()
f_weekday = property(__get_f_weekday, __set_nothing,
doc="Full weekday names")
a_weekday = property(__get_a_weekday, __set_nothing,
doc="Abbreviated weekday names")
f_month = property(__get_f_month, __set_nothing,
doc="Full month names (dummy value at index 0)")
a_month = property(__get_a_month, __set_nothing,
doc="Abbreviated month names (dummy value at index 0)")
am_pm = property(__get_am_pm, __set_nothing, doc="AM/PM representation")
timezone = property(__get_timezone, __set_nothing,
doc="Timezone representation (dummy value at index 2)")
LC_date_time = property(
__get_LC_date_time, __set_nothing,
doc=
"Format string for locale's date/time representation ('%c' format)")
LC_date = property(__get_LC_date, __set_nothing,
doc="Format string for locale's date representation ('%x' format)")
LC_time = property(__get_LC_time, __set_nothing,
doc="Format string for locale's time representation ('%X' format)")
lang = property(lambda self: self.__lang, __set_nothing,
doc="Language used for instance")
class TimeRE(dict):
"""Handle conversion from format directives to regexes."""
def __init__(self, locale_time=None):
"""Init inst with non-locale regexes and store LocaleTime object."""
#XXX: Does 'Y' need to worry about having less or more than 4 digits?
base = super(TimeRE, self)
base.__init__({
# The " \d" option is to make %c from ANSI C work
'd': r"(?P<d>3[0-1]|[1-2]\d|0[1-9]|[1-9]| [1-9])",
'H': r"(?P<H>2[0-3]|[0-1]\d|\d)",
'I': r"(?P<I>1[0-2]|0[1-9]|[1-9])",
'j': r"(?P<j>36[0-6]|3[0-5]\d|[1-2]\d\d|0[1-9]\d|00[1-9]|[1-9]\d|0[1-9]|[1-9])",
'm': r"(?P<m>1[0-2]|0[1-9]|[1-9])",
'M': r"(?P<M>[0-5]\d|\d)",
'S': r"(?P<S>6[0-1]|[0-5]\d|\d)",
'U': r"(?P<U>5[0-3]|[0-4]\d|\d)",
'w': r"(?P<w>[0-6])",
# W is set below by using 'U'
'y': r"(?P<y>\d\d)",
'Y': r"(?P<Y>\d\d\d\d)"})
base.__setitem__('W', base.__getitem__('U'))
if locale_time:
self.locale_time = locale_time
else:
self.locale_time = LocaleTime()
def __getitem__(self, fetch):
"""Try to fetch regex; if it does not exist, construct it."""
try:
return super(TimeRE, self).__getitem__(fetch)
except KeyError:
constructors = {
'A': lambda: self.__seqToRE(self.locale_time.f_weekday, fetch),
'a': lambda: self.__seqToRE(self.locale_time.a_weekday, fetch),
'B': lambda: self.__seqToRE(self.locale_time.f_month[1:],
fetch),
'b': lambda: self.__seqToRE(self.locale_time.a_month[1:],
fetch),
'c': lambda: self.pattern(self.locale_time.LC_date_time),
'p': lambda: self.__seqToRE(self.locale_time.am_pm, fetch),
'x': lambda: self.pattern(self.locale_time.LC_date),
'X': lambda: self.pattern(self.locale_time.LC_time),
'Z': lambda: self.__seqToRE(self.locale_time.timezone, fetch),
'%': lambda: '%',
}
if fetch in constructors:
self[fetch] = constructors[fetch]()
return self[fetch]
else:
raise
def __seqToRE(self, to_convert, directive):
"""Convert a list to a regex string for matching a directive."""
def sorter(a, b):
"""Sort based on length.
Done in case for some strange reason that names in the locale only
differ by a suffix and thus want the name with the suffix to match
first.
"""
try:
a_length = len(a)
except TypeError:
a_length = 0
try:
b_length = len(b)
except TypeError:
b_length = 0
return cmp(b_length, a_length)
to_convert = to_convert[:] # Don't want to change value in-place.
for value in to_convert:
if value != '':
break
else:
return ''
to_convert.sort(sorter)
regex = '|'.join(to_convert)
regex = '(?P<%s>%s' % (directive, regex)
return '%s)' % regex
def pattern(self, format):
"""Return re pattern for the format string.
Need to make sure that any characters that might be interpreted as
regex syntax is escaped.
"""
processed_format = ''
# The sub() call escapes all characters that might be misconstrued
# as regex syntax.
regex_chars = re_compile(r"([\\.^$*+?{}\[\]|])")
format = regex_chars.sub(r"\\\1", format)
whitespace_replacement = re_compile('\s+')
format = whitespace_replacement.sub('\s*', format)
while format.find('%') != -1:
directive_index = format.index('%')+1
processed_format = "%s%s%s" % (processed_format,
format[:directive_index-1],
self[format[directive_index]])
format = format[directive_index+1:]
return "%s%s" % (processed_format, format)
def compile(self, format):
"""Return a compiled re object for the format string."""
return re_compile(self.pattern(format), IGNORECASE)
def strptime(data_string, format="%a %b %d %H:%M:%S %Y"):
"""Return a time struct based on the input data and the format string."""
time_re = TimeRE()
locale_time = time_re.locale_time
format_regex = time_re.compile(format)
found = format_regex.match(data_string)
if not found:
raise ValueError("time data did not match format: data=%s fmt=%s" %
(data_string, format))
if len(data_string) != found.end():
raise ValueError("unconverted data remains: %s" %
data_string[found.end():])
year = 1900
month = day = 1
hour = minute = second = 0
tz = -1
# weekday and julian defaulted to -1 so as to signal need to calculate values
weekday = julian = -1
found_dict = found.groupdict()
for group_key in found_dict.iterkeys():
if group_key == 'y':
year = int(found_dict['y'])
# Open Group specification for strptime() states that a %y
#value in the range of [00, 68] is in the century 2000, while
#[69,99] is in the century 1900
if year <= 68:
year += 2000
else:
year += 1900
elif group_key == 'Y':
year = int(found_dict['Y'])
elif group_key == 'm':
month = int(found_dict['m'])
elif group_key == 'B':
month = _insensitiveindex(locale_time.f_month, found_dict['B'])
elif group_key == 'b':
month = _insensitiveindex(locale_time.a_month, found_dict['b'])
elif group_key == 'd':
day = int(found_dict['d'])
elif group_key == 'H':
hour = int(found_dict['H'])
elif group_key == 'I':
hour = int(found_dict['I'])
ampm = found_dict.get('p', '').lower()
# If there was no AM/PM indicator, we'll treat this like AM
if ampm in ('', locale_time.am_pm[0].lower()):
# We're in AM so the hour is correct unless we're
# looking at 12 midnight.
# 12 midnight == 12 AM == hour 0
if hour == 12:
hour = 0
elif ampm == locale_time.am_pm[1].lower():
# We're in PM so we need to add 12 to the hour unless
# we're looking at 12 noon.
# 12 noon == 12 PM == hour 12
if hour != 12:
hour += 12
elif group_key == 'M':
minute = int(found_dict['M'])
elif group_key == 'S':
second = int(found_dict['S'])
elif group_key == 'A':
weekday = _insensitiveindex(locale_time.f_weekday,
found_dict['A'])
elif group_key == 'a':
weekday = _insensitiveindex(locale_time.a_weekday,
found_dict['a'])
elif group_key == 'w':
weekday = int(found_dict['w'])
if weekday == 0:
weekday = 6
else:
weekday -= 1
elif group_key == 'j':
julian = int(found_dict['j'])
elif group_key == 'Z':
# Since -1 is default value only need to worry about setting tz if
# it can be something other than -1.
found_zone = found_dict['Z'].lower()
if locale_time.timezone[0] == locale_time.timezone[1] and \
time.daylight:
pass #Deals with bad locale setup where timezone info is
# the same; first found on FreeBSD 4.4.
elif found_zone in ("utc", "gmt"):
tz = 0
elif locale_time.timezone[2].lower() == found_zone:
tz = 0
elif time.daylight and \
locale_time.timezone[3].lower() == found_zone:
tz = 1
# Cannot pre-calculate datetime_date() since can change in Julian
#calculation and thus could have different value for the day of the week
#calculation
if julian == -1:
# Need to add 1 to result since first day of the year is 1, not 0.
julian = datetime_date(year, month, day).toordinal() - \
datetime_date(year, 1, 1).toordinal() + 1
else: # Assume that if they bothered to include Julian day it will
#be accurate
datetime_result = datetime_date.fromordinal((julian - 1) + datetime_date(year, 1, 1).toordinal())
year = datetime_result.year
month = datetime_result.month
day = datetime_result.day
if weekday == -1:
weekday = datetime_date(year, month, day).weekday()
return time.struct_time((year, month, day,
hour, minute, second,
weekday, julian, tz))
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] | 2.052365 | 7,104 |
from kivy.adapters.dictadapter import DictAdapter
from kivy.uix.selectableview import SelectableView
from kivy.uix.listview import ListView, ListItemButton
from kivy.uix.gridlayout import GridLayout
from kivy.lang import Builder
from kivy.factory import Factory
from fixtures import integers_dict
# [TODO] Will SelectableView be in the kivy/factory_registers.py,
# as a result of setup.py? ListItemButton? others?
Factory.register('SelectableView', cls=SelectableView)
Factory.register('ListItemButton', cls=ListItemButton)
# [TODO] SelectableView is subclassed here, yet, it is necessary to add the
# index property in the template. Same TODO in list_cascade_images.py.
Builder.load_string('''
[CustomListItem@SelectableView+BoxLayout]:
size_hint_y: ctx.size_hint_y
height: ctx.height
ListItemButton:
text: ctx.text
is_selected: ctx.is_selected
''')
class MainView(GridLayout):
'''Implementation of a list view with a kv template used for the list
item class.
'''
if __name__ == '__main__':
from kivy.base import runTouchApp
runTouchApp(MainView(width=800))
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] | 2.787654 | 405 |
import math
import os
import sys
import numpy as np
from util import load_result
from itertools import combinations
from itertools import product
from ontology import Ontology
def get_max_tree_distance(ontology, tags, debug=False):
"""
Description:
Return max tree distance which can be derived with given tag list.
Parameters:
tags: list of tags used in training. (type: list)
[Example] ['Acoustic guitar', 'Electric Guitar', ..., 'Piano']
"""
# create combination between tags
comb = combinations(tags, 2)
# initiate maximum distance between two tags
max_dist = 0
# loop for every combination
for item in comb:
# calculate distance between two tags
distance = ontology.get_min_distance(item[0], item[1])
if debug:
print("'%s'-'%s': %d" % (item[0], item[1], distance))
# update max_dist if distance > max_dist
max_dist = distance if distance > max_dist else max_dist
return max_dist
def get_min_tag_distance(ontology, tags_x, tags_y):
"""
Description:
Return minimum available tree distance between two videos
Parameters:
tags_x: tags of one video
tags_y: tags of the other video
[Example]
[Example] tags_x = ['Electric Guitar', 'Human Voice']
tags_y = ['Human Voice']
This function with the example above should return 0,
because 'Human Voice' tag exists in both of tag lists.
tags_x = ['Piano', 'Guitar', 'Bass Guitar']
tags_y = ['Accordion']
This function with the example above should return the
distance between 'Accordion' and 'Piano', because its distance
will be the smallest among the followings:
'Piano' - 'Accordion', 'Guitar' - 'Accordion', 'Bass Guitar' - 'Accordion'
"""
products = product(tags_x, tags_y)
min_dist = sys.maxsize
for x, y in products:
distance = ontology.get_min_distance(x, y)
min_dist = min_dist if min_dist < distance else distance
return min_dist
def dist_to_score(ontology, distances, tags=[], max_dist=-1, debug=False):
"""
Description:
Convert distances of K retrieved items into scores
Parameters:
distances: tree distance between query and K retrieved items
[Example] [0, 0, 1, 2, 1, 0, 5, 4, ..., 9] (type: ndarray, len: K)
[Note] score = max_tree_distance - distance
"""
# get maximum tree distance
max_tree_distance = 0
if max_dist >= 0:
max_tree_distance = max_dist
elif len(tags) >= 0:
max_tree_distance = get_max_tree_distance(ontology, tags)
scores = max_tree_distance - distances
return scores
def DCG(scores, k=30, alternate=False):
"""
Description:
Return DCG(Discounted Cumulative Gain) with given score (relevance) list
Parameters:
scores: score list (type: ndarray, len: N)
[Example] [8, 6, 6, 8, 4, 7, ..., 2]
k: length of retrieved items to calculate nDCG
"""
# return zero if scores is None
if scores is None or len(scores) < 1:
return 0.0
# set the number of items in scores
scores = scores[:k]
n_scores = len(scores)
# use alternative formula of DCG
if alternate:
log2i = np.log2(np.asarray(range(1, n_scores + 1)) + 1)
return ((np.power(2, scores) - 1) / log2i).sum()
# use traditional formula of DCG
else:
log2i = np.log2(np.asarray(range(1, n_scores + 1)) + 1)
return (scores / log2i).sum()
def IDCG(scores, k=30, alternate=False):
"""
Description:
Return IDCG(Ideal Discounted Cumulative Gain) with given score (relevance) list
Parameters:
scores: score list (type: ndarray, len: N)
[Example] [8, 6, 6, 8, 4, 7, ..., 2]
k: length of retrieved items to calculate nDCG
"""
if scores is None or len(scores) < 1:
return 0.0
# copy and sort scores in incresing order
s = sorted(scores)
s = s[::-1][:k]
# convert s in decresing order
return DCG(s, k, alternate)
def NDCG(scores, k=30, alternate=False):
"""
Description:
Return nDCG(normalized Discounted Cumulative Gain) with given score (relevance) list
Parameters:
scores: score list (type: ndarray, len: N)
[Example] [8, 6, 6, 8, 4, 7, ..., 2]
"""
# return 0 if scores is empty
if scores is None or len(scores) < 1:
return 0.0
# calculate idcg
idcg = IDCG(scores, k, alternate)
if idcg == 0:
return 0.0
return DCG(scores, k, alternate) / idcg
def do_NDCG(ontology, k, queries, ret_items, tags):
"""
Description:
Return Average nDCG for queries and ret_item
Parameters:
queries: list of N queries (type: list, dimension: 2D, shape: (N, ?))
[Example] [[tag1, tag2, ..., tagK], ..., [tagA, tagB, ..., tagG]]
ret_items: list of N retrieved items (type: list, dimension: 3D, shape: (N, K, ?))
[Example] [[[tagA, tagB, ..., tagG], ..., [tagX, tagY, ..., tagZ]], ... , [ ... ]]
"""
N = len(queries)
ndcgs = 0
# get max_tree_distance
max_tree_distance = get_max_tree_distance(ontology, tags, debug=False)
# for every query, calculate nDCG
for i in range(N):
distances = np.asarray(
[get_min_tag_distance(ontology, queries[i], ret_items[i][j]) for j in range(len(ret_items[i]))]
)
scores = dist_to_score(ontology, distances, max_dist=max_tree_distance)
ndcgs += NDCG(scores, k)
return ndcgs / N
def AP(target, results):
"""
Description:
Return AP(Average Precision) with target and results
Parameters:
target: list of K retrieved items (type: list, len: K)
[Example] [tag1, tag2, ..., tagK]
results: list of N retrieved items (type: list, shape: (N, ?))
[Example] [[tagA, tagB, ..., tagG], ..., [tagX, tagY, ..., tagZ]]
"""
# initiate variables for average precision
n = 1 # the number of result
hit = 0 # the number of hit
ap = 0 # average precision = 1/hit * sum(precision)
len_target = len(target)
for res in results:
(small_set, big_set) = (target, res) if len_target < len(res) else (res, target)
for item in small_set:
if item in big_set: # hit
hit += 1
ap += hit / n
break
n += 1
return ap / hit
def recallAtK(target, results):
"""
Description:
Return 'recall at k' with target and results
Parameters:
target: list of K retrieved items (type: list, len: K)
[Example] [tag1, tag2, ..., tagK]
results: list of N retrieved items (type: list, shape: (N, ?))
[Example] [[tagA, tagB, ..., tagG], ..., [tagX, tagY, ..., tagZ]]
"""
# initiate variables for average precision
recall = 0
K = len(results)
len_target = len(target)
for res in results:
(small_set, big_set) = (target, res) if len_target < len(res) else (res, target)
for item in small_set:
if item in big_set: # hit
recall += 1
break
return recall / K
if __name__ == "__main__":
data_dir = "json"
tags = [
"Acoustic guitar",
"Bass guitar",
"Strum",
"Piano",
"Independent music",
"Wedding music",
"Scary music",
"Firecracker",
"Drip",
]
ontology = Ontology(data_dir)
"""
# Calculate maximum tree distance between tags
print("Calculate maximum tree distance between tags")
max_dist = get_max_tree_distance(ontology, tags, debug=False)
print("Maximum tree distance: ", max_dist, end="\n\n")
# Convert distances to scores with max_dist
print("Convert distances to scores with max_dist")
distances = np.array([0, 0, 1, 2, 1, 0, 5, 4, 8, 9])
print("Distances: ", distances)
scores = dist_to_score(ontology, distances, max_dist=max_dist, debug=True)
print("Scores: ", scores, end="\n\n")
# Convert distances to scores with tags
print("Convert distances to scores with tags")
distances = np.array([0, 0, 1, 2, 1, 0, 5, 4, 8, 9])
print("Distances: ", distances)
scores = dist_to_score(ontology, distances, tags=tags, debug=True)
print("Scores: ", scores, end="\n\n")
# Do DCG, IDCG, NDCG
scores = [3, 2, 3, 0, 1, 2]
print("### Do DCG ###: ", DCG(scores, alternate=False))
print("### Do IDCG ###: ", IDCG(scores))
print("### Do NDCG ###: ", NDCG(scores), end="\n\n")
# Do AP and recall at K
target = ["a", "b", "c"]
results = [["a", "g"], ["d", "e", "f", "b"], ["g", "h", "c"], ["y", "k", "p"]]
print("### AP ###: ", AP(target, results))
print("### Recall at K ###: ", recallAtK(target, results), end="\n\n")
# Do get_min_tag_distance: example1
tags_x = ["Independent music", "Drip"]
tags_y = ["Drip"]
print("@@@ get_min_tag_distance1 @@@: ", get_min_tag_distance(ontology, tags_x, tags_y))
# Do get_min_tag_distance: example2
tags_x = ["Piano", "Guitar", "Bass guitar"]
tags_y = ["Accordion"]
print("@@@ get_min_tag_distance2 @@@: ", get_min_tag_distance(ontology, tags_x, tags_y), end="\n\n")
"""
# Do average nDCG
with open("metadata/all_tags.cls") as fi:
tags = map(lambda x: x[:-1], fi.readlines())
tags = dict((x, i) for i, x in enumerate(tags))
file_names = [
"./results/AVE_aug_ave_i2a.pickle",
"./results/AVE_aug_ave_a2i.pickle",
"./results/AVE_aug_ave_i2i.pickle",
"./results/AVE_aug_ave_a2a.pickle",
]
for f in file_names:
queries, ret_items = load_result(f)
ndcgs = do_NDCG(ontology, 5, queries, ret_items, tags)
print("nDCG: %s" % (f), ndcgs, end="\n\n")
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] | 2.294637 | 4,419 |
#coding:utf-8
import requests
import pprint
import csv
main() | [
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] | 2.481481 | 27 |
#!/usr/bin/env python
#
# pddl_planner.py
# ma-goal-recognition
#
# Created by Felipe Meneguzzi on 2020-03-12.
# Copyright 2020 Felipe Meneguzzi. All rights reserved.
#
from recognizer.pddl.pddl_parser import PDDL_Parser
from recognizer.pddl.state import applicable, apply
import time
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] | 2.745283 | 106 |
from qualang_tools.config.integration_weights_tools import (
convert_integration_weights,
compress_integration_weights,
plot_integration_weights,
)
from qualang_tools.config.waveform_tools import (
drag_gaussian_pulse_waveforms,
drag_cosine_pulse_waveforms,
)
from qualang_tools.config.builder import ConfigBuilder
from qualang_tools.config.components import *
from qualang_tools.config.primitive_components import *
__all__ = [
"drag_gaussian_pulse_waveforms",
"drag_cosine_pulse_waveforms",
"convert_integration_weights",
"compress_integration_weights",
"plot_integration_weights",
"Controller",
"ArbitraryWaveform",
"ConstantWaveform",
"DigitalWaveform",
"MeasurePulse",
"ControlPulse",
"Mixer",
"Element",
"MeasureElement",
"ConstantIntegrationWeights",
"ArbitraryIntegrationWeights",
"ElementCollection",
"ReadoutResonator",
"Transmon",
"FluxTunableTransmon",
"Coupler",
"Oscillator",
"Port",
"AnalogInputPort",
"AnalogOutputPort",
"DigitalInputPort",
"DigitalOutputPort",
"Waveform",
"Pulse",
"Operation",
"IntegrationWeights",
"Weights",
"DigitalSample",
"Matrix2x2",
"AnalogOutputFilter",
"ConfigBuilder",
]
| [
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] | 2.395683 | 556 |
import base64
import json
import os
import re
import zlib
from retrying import retry
from xmlrpc.client import ServerProxy
from api.fixture import load_fixture
from api.subtitle.model import to_model
LANGUAGE = 'en'
NEWLINE_PATTERN = re.compile(r'(\r\n|\r|\n)')
OPENSUBTITLES_URL = 'http://api.opensubtitles.org/xml-rpc'
OPENSUBTITLES_UA = 'subvoc v1.0'
UNICODE_BOM = u'\N{ZERO WIDTH NO-BREAK SPACE}'
class OpenSubtitles:
"""API client to download subtitles from opensubtitles.org"""
def __init__(self, credentials, client=None):
"""Constructor to prepare API connection.
:param credentials: username/password tupel
:param client: optional, custom XMLRPC client
"""
self.token = None
self.credentials = credentials
self.xmlrpc = client or ServerProxy(OPENSUBTITLES_URL, allow_none=True)
def login(self):
"""Request and save authentication token.
:raises RuntimeError: if login fails
"""
username = self.credentials[0]
password = self.credentials[1]
resp = self.xmlrpc.LogIn(username, password, LANGUAGE, OPENSUBTITLES_UA)
self._ensure_success(resp)
self.token = resp.get('token')
def find_by_query(self, query):
"""Find subtitles by query.
Note that it first tries to find and return a local fixture,
and only does an HTTP call if none was found.
:param query: query string describing movie
:returns: list of subtitles that match query
"""
qry = query.lower().strip()
resp = self._fixture('query', qry) \
or self._find({'query': qry, 'sublanguageid': 'eng'})
return self._resp_to_model(resp)
def find_subtitles_for_movie(self, imdb_id):
"""Find subtitle by IMDb ID.
Note that it first tries to find and return a local fixture,
and only does an HTTP call if none was found.
:param imdb_id: IMDb ID of movie (starts with 'tt')
:returns: list of subtitles for movie
"""
search_id = imdb_id.replace('tt', '').lstrip('0')
resp = self._fixture('id', imdb_id) \
or self._find({'imdbid': search_id, 'sublanguageid': 'eng'})
return self._resp_to_model(resp)
def load_text(self, subtitle_id, subtitle_encoding):
"""Load subtitle text for movie.
:param subtitle_id: ID of subtitle
:param subtitle_encoding: encoding of subtitle text
:returns: string with movie subtitle text
"""
resp = self._fixture('subtitle', subtitle_id) \
or self._download(subtitle_id)
text = resp.get('data')[0].get('data')
text = base64.standard_b64decode(text)
text = zlib.decompress(text, 47)
text = str(text, subtitle_encoding)
text = text.lstrip(UNICODE_BOM)
text = re.sub(NEWLINE_PATTERN, '\n', text)
return text
@retry(stop_max_delay=5000, stop_max_attempt_number=3)
@retry(stop_max_delay=5000, stop_max_attempt_number=3)
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] | 2.412184 | 1,264 |
# /usr/bin/env python3.5
# -*- mode: python -*-
# =============================================================================
# @@-COPYRIGHT-START-@@
#
# Copyright (c) 2019, Qualcomm Innovation Center, Inc. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software
# without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
#
# SPDX-License-Identifier: BSD-3-Clause
#
# @@-COPYRIGHT-END-@@
# =============================================================================
""" Prunes layers using Channel Pruning scheme """
from typing import List, Dict, Tuple, Set
import copy
import tensorflow as tf
import numpy as np
# Import aimet specific modules
from aimet_common.defs import CostMetric, LayerCompRatioPair
from aimet_common.utils import AimetLogger
from aimet_common.pruner import Pruner
from aimet_common.channel_pruner import select_channels_to_prune
from aimet_common.cost_calculator import CostCalculator, Cost
from aimet_common.winnow.winnow_utils import update_winnowed_channels
from aimet_tensorflow.utils.graph_saver import save_and_load_graph
from aimet_tensorflow.utils.common import is_op_compressible, get_ordered_ops
from aimet_tensorflow.layer_database import Layer, LayerDatabase
from aimet_tensorflow.utils.op.conv import WeightTensorUtils
from aimet_tensorflow.winnow import winnow
from aimet_tensorflow.channel_pruning.data_subsampler import DataSubSampler
from aimet_tensorflow.channel_pruning.weight_reconstruction import WeightReconstructor
from aimet_tensorflow.common.graph_eval import initialize_uninitialized_vars
logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.ChannelPruning)
class InputChannelPruner(Pruner):
"""
Pruner for Channel Pruning method
"""
def __init__(self, input_op_names: List[str], output_op_names: List[str], data_set: tf.data.Dataset,
batch_size: int, num_reconstruction_samples: int, allow_custom_downsample_ops: bool):
"""
Input Channel Pruner with given dataset, input shape, number of batches and samples per image.
:param input_op_names: list of input op names
:param output_op_names: List of output op names of the model, used to help ConnectedGraph determine valid ops
(to ignore training ops for example).
:param data_set: data set to be used with the model
:param batch_size: batch size
:param num_reconstruction_samples: number of reconstruction samples
:param allow_custom_downsample_ops: allow downsample/upsample ops to be inserted
"""
self._input_op_names = input_op_names
self._output_op_names = output_op_names
self._data_set = data_set
self._batch_size = batch_size
self._num_reconstruction_samples = num_reconstruction_samples
self._allow_custom_downsample_ops = allow_custom_downsample_ops
@staticmethod
def _select_inp_channels(layer: Layer, comp_ratio: float) -> list:
"""
:param layer: layer for which input channels to prune are selected.
:param comp_ratio: the ratio of costs after pruning has taken place
0 < comp_ratio <= 1.
:return: prune_indices: list of input channels indices to prune.
"""
assert layer.module.type == 'Conv2D'
weight_index = WeightTensorUtils.get_tensor_index_in_given_op(layer.module)
weight_tensor = layer.model.run(layer.module.inputs[weight_index])
# Conv2d weight shape in TensorFlow [kh, kw, Nic, Noc]
# re order in the common shape [Noc, Nic, kh, kw]
weight_tensor = np.transpose(weight_tensor, (3, 2, 0, 1))
num_in_channels = weight_tensor.shape[1]
prune_indices = select_channels_to_prune(weight_tensor, comp_ratio, num_in_channels)
return prune_indices
def _data_subsample_and_reconstruction(self, orig_layer: Layer, pruned_layer: Layer, output_mask: List[int],
orig_layer_db: LayerDatabase, comp_layer_db: LayerDatabase):
"""
Collect and sub sampled output data from original layer and input data from pruned layer and set
reconstructed weight and bias to pruned layer in compressed model database
:param orig_layer: layer from original model
:param pruned_layer: layer from potentially compressed model
:param output_mask : output mask that specifies certain output channels to remove
:param orig_layer_db: original Layer database without any compression
:param comp_layer_db: compressed Layer database
:return:
"""
sub_sampled_inp, sub_sampled_out = DataSubSampler.get_sub_sampled_data(orig_layer, pruned_layer,
self._input_op_names, orig_layer_db,
comp_layer_db, self._data_set,
self._batch_size,
self._num_reconstruction_samples)
logger.debug("Input Data size: %s, Output data size: %s", len(sub_sampled_inp), len(sub_sampled_out))
# update the weight and bias (if any) using sub sampled input and output data
WeightReconstructor.reconstruct_params_for_conv2d(pruned_layer, sub_sampled_inp, sub_sampled_out, output_mask)
def _sort_on_occurrence(self, sess: tf.Session, layer_comp_ratio_list: List[LayerCompRatioPair]) -> \
List[LayerCompRatioPair]:
"""
Function takes session and list of conv layer-comp ratio to sort, and sorts them based on
occurrence in the model.
:param sess: tf.Session
:param layer_comp_ratio_list: layer compression ratio list
:return: sorted_layer_comp_ratio_List
"""
sorted_layer_comp_ratio_list = []
ordered_ops = get_ordered_ops(graph=sess.graph, starting_op_names=self._input_op_names)
for op in ordered_ops:
if is_op_compressible(op):
for pair in layer_comp_ratio_list:
if op.name == pair.layer.name:
sorted_layer_comp_ratio_list.append(LayerCompRatioPair(pair.layer, pair.comp_ratio))
return sorted_layer_comp_ratio_list
def calculate_compressed_cost(self, layer_db: LayerDatabase,
layer_comp_ratio_list: List[LayerCompRatioPair]) -> Cost:
"""
Calculate cost of a compressed model given a set of layers and corresponding comp-ratios
:param layer_db: Layer database for original model
:param layer_comp_ratio_list: List of (layer + comp-ratio) pairs
:return: Estimated cost of the compressed model
"""
# sort all the layers in layer_comp_ratio_list based on occurrence
layer_comp_ratio_list = self._sort_on_occurrence(layer_db.model, layer_comp_ratio_list)
detached_op_names = set()
# Copy the db
comp_layer_db = copy.deepcopy(layer_db)
current_sess = comp_layer_db.model
for layer_comp_ratio in layer_comp_ratio_list:
orig_layer = layer_db.find_layer_by_name(layer_comp_ratio.layer.name)
comp_ratio = layer_comp_ratio.comp_ratio
if comp_ratio is not None and comp_ratio < 1.0:
# select input channels of conv2d op to winnow
prune_indices = self._select_inp_channels(orig_layer, comp_ratio)
if not prune_indices:
continue
# Winnow the selected op and modify it's upstream affected ops
current_sess, ordered_modules_list = winnow.winnow_tf_model(current_sess, self._input_op_names,
self._output_op_names,
[(orig_layer.module, prune_indices)],
reshape=self._allow_custom_downsample_ops,
in_place=True, verbose=False)
if not ordered_modules_list:
continue
# Get all the detached op names from updated session graph
for orig_op_name, _, _, _ in ordered_modules_list:
detached_op_names.add(orig_op_name)
# update layer database by excluding the detached ops
comp_layer_db.update_database(current_sess, detached_op_names, update_model=False)
# calculate the cost of this model
compressed_model_cost = CostCalculator.compute_model_cost(comp_layer_db)
# close the session associated with compressed layer database
comp_layer_db.model.close()
return compressed_model_cost
@staticmethod
def _update_pruned_ops_and_masks_info(
ordered_modules_list: List[Tuple[str, tf.Operation, List[List[int]], List[List[int]]]],
orig_layer_name_to_pruned_name_and_mask_dict: Dict[str, Tuple[str, List[int]]],
pruned_name_to_orig_name_dict: Dict[str, str],
detached_op_names: Set[str]):
"""
Update dictionaries with information about newly winnowed ops and masks
:param ordered_modules_list: Output of winnow_tf_model holding information on winnowed ops and masks
:param orig_layer_name_to_pruned_name_and_mask_dict: Dictionary mapping original layer names to most recent
pruned op name and most recent output masks.
:param pruned_name_to_orig_name_dict: Dictionary mapping pruned layer names to original layer names (if a layer
was winnowed in multiple rounds of winnow_tf_model, there may be multiple prined layer names mapping to the same
original layer name)
:param detached_op_names: Set holding names of operations which are detached due to winnowing and should not be
used.
"""
for prepruned_op_name, pruned_op, _, output_masks in ordered_modules_list:
detached_op_names.add(prepruned_op_name)
if pruned_op.type == 'Conv2D': # Currently, we only care about tracking information about conv ops
if prepruned_op_name in pruned_name_to_orig_name_dict:
# the op was already pruned once prior to this most recent round of winnowing
original_op_name = pruned_name_to_orig_name_dict[prepruned_op_name]
# Get and update previous pruned op name and output mask
_, running_output_mask = \
orig_layer_name_to_pruned_name_and_mask_dict.get(original_op_name, (None, None))
assert running_output_mask is not None
# Replace previous pruned op name with most recent pruned op name
# Update output mask
update_winnowed_channels(running_output_mask, output_masks[0])
orig_layer_name_to_pruned_name_and_mask_dict[original_op_name] = (pruned_op.name,
running_output_mask)
else:
# This is the first time this op is being pruned
# The name should not show up in either dict
assert prepruned_op_name not in orig_layer_name_to_pruned_name_and_mask_dict
assert prepruned_op_name not in pruned_name_to_orig_name_dict
original_op_name = prepruned_op_name
# Add output channel mask info to layer_to_masks_dict
orig_layer_name_to_pruned_name_and_mask_dict[prepruned_op_name] = (pruned_op.name,
output_masks[0])
# Map pruned op's name to original op name in pruned_to_orig_name_dict
pruned_name_to_orig_name_dict[pruned_op.name] = original_op_name
def _reconstruct_layers(self, layers_to_reconstruct: List[Layer],
orig_layer_name_to_pruned_name_and_mask_dict: Dict[str, Tuple[str, List[int]]],
layer_db: LayerDatabase, comp_layer_db: LayerDatabase):
"""
Reconstruct weights and biases of layers in the layers_to_reconstruct list.
:param layers_to_reconstruct: List of layers to reconstruct weights and biases of
:param orig_layer_name_to_pruned_name_and_mask_dict: Dictionary mapping original layer names to most recent
pruned op name and most recent output masks.
:param layer_db: Original layer database
:param comp_layer_db: Compressed layer database
"""
for layer in layers_to_reconstruct:
# Get output mask of layer, that contains information about all channels winnowed since the start
pruned_layer_name, output_mask = \
orig_layer_name_to_pruned_name_and_mask_dict.get(layer.name, (None, None))
assert pruned_layer_name is not None
pruned_layer = comp_layer_db.find_layer_by_name(pruned_layer_name)
self._data_subsample_and_reconstruction(layer, pruned_layer, output_mask, layer_db, comp_layer_db)
class ChannelPruningCostCalculator(CostCalculator):
""" Cost calculation utilities for Channel Pruning """
def calculate_compressed_cost(self, layer_db: LayerDatabase,
layer_ratio_list: List[LayerCompRatioPair], cost_metric: CostMetric) -> Cost:
"""
Calculate compressed cost of a model given a list of layer-compression-ratio pairs
:param layer_db: Layer database for the original model
:param layer_ratio_list: List of layer, compression-ratio
:param cost_metric: Cost metric to use for compression (mac or memory)
:return: Compressed cost
"""
# Special logic for channel pruning - we first actually prune the model and then determine its cost
# Because it is not easy to estimate it otherwise
compressed_cost = self._pruner.calculate_compressed_cost(layer_db, layer_ratio_list)
return compressed_cost
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220,
220,
220,
220,
220,
220,
220,
1441,
25388,
62,
15805,
198
] | 2.376921 | 6,638 |
import json
from datetime import datetime
from PyQt5.QtWidgets import QWidget, QVBoxLayout, QGridLayout, QHBoxLayout, QLabel, QLineEdit, QPushButton, QMessageBox
from PyQt5.QtGui import QIntValidator
from PyQt5.QtCore import Qt, pyqtSignal
from fitness_tracker.database_wrapper import DatabaseWrapper
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] | 2.980198 | 101 |
from ._plot_metadata_table import plot_data_summary, plot_edit_profiler, get_exported_metadata
from ._demodata import get_lifeexpectancy_data
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# -*- coding: UTF-8 -*-
import re
import json
import multiprocessing
import math
from collections import OrderedDict
import pdb
from django.db.models import Q
from django.db import transaction
from django.conf import settings
from django.views.decorators.csrf import csrf_exempt
from django.shortcuts import render, get_object_or_404
from django.http import HttpResponse, HttpResponseRedirect
from django.contrib.auth.decorators import login_required
from django.contrib.auth.hashers import check_password
from django.core.paginator import Paginator,InvalidPage,EmptyPage,PageNotAnInteger
from .daoora import DaoOra
from .const import Const
from .sendmail import MailSender
from .aes_decryptor import Prpcrypt
from .models import *
from .getnow import getNow
from .tasks import oraAutoReview,mailDba,wechatDba,dingDba
daoora = DaoOra()
prpCryptor = Prpcrypt()
cryColList = ['cert_no','qq','cell','card_no','database_password']
configMap = {
'oracle':ora_primary_config,
'mysql':'my_primary_config'}
daoMap = {
'oracle':daoora,
'mysql':'my_master_config'}
#首页，也是查看所有SQL工单页面，具备翻页功能
#提交oracle sql界面
#将中文名映射为英文名
#判断工单类型，做相应处理
#展示SQL工单详细内容，以及可以人工审核，审核通过即可执行
#人工审核也通过，执行SQL
#终止流程
#工程师确认
#检查登录用户是否为admin
#数据同步
@check_admin
#查询功能
@csrf_exempt
#SQL审核必读
#图表展示
#获取当前请求url
#展示数据库schema列表
#个人中心
#配置用户权限
@check_admin
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] | 1.777339 | 759 |
from NeuralNet.Oli.libs.ProcessingPipeline import ProcessingPipeline
from NeuralNet.Oli.libs.Preprocessor import SimplePreprocessor, IPreprocessor
#__________Configuration__________#
# Path to folder which contains subfolders with the images
IMG_PATH = '../../images/Dataset_2'
# Name for model when saved
MODEL_LOAD_PATH = "SavedModels/LT2"
# Pipeline managing working with keras model
pipeline: ProcessingPipeline = ProcessingPipeline()
# Loads all images and extract features and labels
preprocessor: IPreprocessor = SimplePreprocessor()
x, y = pipeline.load_features_and_preprocess(IMG_PATH, img_preprocessor=preprocessor)
# Load the model from disk
pipeline.load_model(MODEL_LOAD_PATH)
# Make predictions with loaded model
y_pred = pipeline.predict(x)
# Evaluate model on test images and show summary
pipeline.evaluate(y, y_pred)
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796,
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7,
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7,
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8,
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] | 3.335968 | 253 |
from pyrevolve import Checkpoint, Operator
from devito import TimeFunction
class CheckpointOperator(Operator):
"""Devito's concrete implementation of the ABC pyrevolve.Operator. This class wraps
devito.Operator so it conforms to the pyRevolve API. pyRevolve will call apply
with arguments t_start and t_end. Devito calls these arguments t_s and t_e so
the following dict is used to perform the translations between different names.
:param op: The devito.Operator object that this object will wrap
:param args: If devito.Operator.apply() expects any arguments, they can be provided
here to be cached. Any calls to CheckpointOperator.apply() will
automatically include these cached arguments in the call to the
underlying devito.Operator.apply().
"""
t_arg_names = {'t_start': 'time_m', 't_end': 'time_M'}
def apply(self, t_start, t_end):
""" If the devito operator requires some extra arguments in the call to apply
they can be stored in the args property of this object so pyRevolve calls
pyRevolve.Operator.apply() without caring about these extra arguments while
this method passes them on correctly to devito.Operator
"""
# Build the arguments list to invoke the kernel function
args = self.op.arguments(**self._prepare_args(t_start, t_end))
# Invoke kernel function with args
arg_values = [args[p.name] for p in self.op.parameters]
self.op.cfunction(*arg_values)
class DevitoCheckpoint(Checkpoint):
"""Devito's concrete implementation of the Checkpoint abstract base class provided by
pyRevolve. Holds a list of symbol objects that hold data.
"""
def __init__(self, objects):
"""Intialise a checkpoint object. Upon initialisation, a checkpoint
stores only a reference to the objects that are passed into it."""
assert(all(isinstance(o, TimeFunction) for o in objects))
dtypes = set([o.dtype for o in objects])
assert(len(dtypes) == 1)
self._dtype = dtypes.pop()
self.objects = objects
@property
def save(self, ptr):
"""Overwrite live-data in this Checkpoint object with data found at
the ptr location."""
i_ptr_lo = 0
i_ptr_hi = 0
for o in self.objects:
i_ptr_hi = i_ptr_hi + o.size
ptr[i_ptr_lo:i_ptr_hi] = o.data.flatten()[:]
i_ptr_lo = i_ptr_hi
def load(self, ptr):
"""Copy live-data from this Checkpoint object into the memory given by
the ptr."""
i_ptr_lo = 0
i_ptr_hi = 0
for o in self.objects:
i_ptr_hi = i_ptr_hi + o.size
o.data[:] = ptr[i_ptr_lo:i_ptr_hi].reshape(o.shape)
i_ptr_lo = i_ptr_hi
@property
def size(self):
"""The memory consumption of the data contained in a checkpoint."""
return sum([o.size for o in self.objects])
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] | 2.542544 | 1,187 |
#!/usr/bin/python3
# DRAFT
import os
import sys
import json
import numpy as np
import matplotlib.pyplot as plt
if __name__ == "__main__":
if len(sys.argv) == 2:
compare_descriptors(sys.argv[1])
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] | 2.428571 | 84 |
from account.models.instructor_model import InstructorProfile
import json
from school.models.class_model import Class
from school.models.school_model import School
from django.urls.base import reverse
from rest_framework.test import APITestCase
from country.models import Country, City
from django.contrib.auth import get_user_model
User = get_user_model()
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] | 3.495238 | 105 |
import abc
from typing import Any
import torch
AGGREGATION_MODES = ["mean", "max", "min"]
class Metric(metaclass=abc.ABCMeta):
"""abstract class for Metric objects.
Example:
Simple usage of the Metric class::
class MyMetric(Metric):
def _update(self, predictions, truth):
# compute some metric
return metric_value
model = MyModel()
mymetric = MyMetric()
for batch, labels in dataset:
predictions = model(batch)
mymetric.update(predictions, labels)
print(mymetric.get_metric(mode="mean"))
"""
def reset(self) -> None:
"""Clear metrics from class."""
self.metrics = []
def update(self, predictions: torch.Tensor, truth: torch.Tensor) -> None:
"""Compute metric value and append to the metrics array.
Args:
predictions (torch.Tensor): output tensors from model.
truth (torch.Tensor): ground truth tensor.
"""
self.metrics.append(self._update(predictions, truth))
@abc.abstractmethod
def _update(self, predictions: torch.Tensor, truth: torch.Tensor) -> Any:
"""Compute the metric value.
Args:
predictions (torch.Tensor): output tensors from model.
truth (torch.Tensor): ground truth tensor.
"""
def get_metric(self, mode="mean") -> float:
"""Aggregate all values stored in the metric class.
Args:
mode (str, optional): aggregation type. mean, max or min.
Defaults to "mean".
Raises:
ValueError: aggregation mode not supported
Returns:
float: aggregated metric.
"""
if len(self) == 0:
return 0.0
if mode not in AGGREGATION_MODES:
raise ValueError(
f"Mode {mode} not supported. Supported modes: {AGGREGATION_MODES}"
)
if mode == "mean":
return sum(self.metrics) / len(self)
elif mode == "max":
return max(self.metrics)
elif mode == "min":
return min(self.metrics)
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] | 2.181188 | 1,010 |
"""Use this template for creating simple Python3 server"""
from http.server import SimpleHTTPRequestHandler
from socketserver import TCPServer
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import numpy as np
import matplotlib.pyplot as plt
# This import registers the 3D projection, but is otherwise unused.
from mpl_toolkits.mplot3d import Axes3D # noqa: F401 unused import
def lorenz(x, y, z, s=10, r=28, b=2.667):
'''
Given:
x, y, z: a point of interest in three dimensional space
s, r, b: parameters defining the lorenz attractor
Returns:
x_dot, y_dot, z_dot: values of the lorenz attractor's partial
derivatives at the point x, y, z
'''
x_dot = s*(y - x)
y_dot = r*x - y - x*z
z_dot = x*y - b*z
return x_dot, y_dot, z_dot
dt = 0.01
num_steps = 1000
# Need one more for the initial values
xs = np.empty(num_steps + 1)
ys = np.empty(num_steps + 1)
zs = np.empty(num_steps + 1)
# Set initial values
xs[0], ys[0], zs[0] = (0., 1., 1.05)
# Step through "time", calculating the partial derivatives at the current point
# and using them to estimate the next point
for i in range(num_steps):
x_dot, y_dot, z_dot = lorenz(xs[i], ys[i], zs[i])
xs[i + 1] = xs[i] + (x_dot * dt)
ys[i + 1] = ys[i] + (y_dot * dt)
zs[i + 1] = zs[i] + (z_dot * dt)
# Plot
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot(xs, ys, zs, lw=0.5)
ax.set_xlabel("X1 Axis")
ax.set_ylabel("X2 Axis")
ax.set_zlabel("X3 Axis")
ax.set_title("Lorenz 63 noiseless trajectory")
plt.savefig('lorenz-2.pdf') | [
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] | 2.304878 | 574 |
import torch
import torch.nn as nn
import torchvision
import numpy as np
from tqdm import tqdm
from dataset import ImageDataset
if __name__ == '__main__':
device = "cuda:0"
network_size = (4, 512, 256)
learning_rate = 1e-4
iters = 250
mapping_size = 256
B_gauss = torch.randn((mapping_size, 2)).to(device) * 10
ds = ImageDataset("data/fox.jpg", 512)
grid, image = ds[0]
grid = grid.unsqueeze(0).to(device)
image = image.unsqueeze(0).to(device)
test_data = (grid, image)
train_data = (grid[:, ::2, ::2], image[:, ::2, :: 2])
output = train_model(network_size, learning_rate, iters, B_gauss,
train_data=train_data, test_data=(grid, image), device=device)
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] | 2.335423 | 319 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# =============================================================================
# IMPORTS
# =============================================================================
from __future__ import unicode_literals
from unittest import TestCase
from mock import patch
from pandagg.tree.aggs import Aggs
from pandagg.exceptions import InvalidOperationMappingFieldError
from pandagg.aggs import DateHistogram, Terms, Avg, Min, Filter
import tests.testing_samples.data_sample as sample
from tests.testing_samples.mapping_example import MAPPINGS
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] | 3.58046 | 174 |
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""This module contains Google Cloud Tasks links."""
from typing import TYPE_CHECKING, Optional
from airflow.models import BaseOperator
from airflow.providers.google.cloud.links.base import BaseGoogleLink
if TYPE_CHECKING:
from airflow.utils.context import Context
CLOUD_TASKS_BASE_LINK = "https://pantheon.corp.google.com/cloudtasks"
CLOUD_TASKS_QUEUE_LINK = CLOUD_TASKS_BASE_LINK + "/queue/{location}/{queue_id}/tasks?project={project_id}"
CLOUD_TASKS_LINK = CLOUD_TASKS_BASE_LINK + "?project={project_id}"
class CloudTasksQueueLink(BaseGoogleLink):
"""Helper class for constructing Cloud Task Queue Link"""
name = "Cloud Tasks Queue"
key = "cloud_task_queue"
format_str = CLOUD_TASKS_QUEUE_LINK
@staticmethod
def extract_parts(queue_name: Optional[str]):
"""
Extract project_id, location and queue id from queue name:
projects/PROJECT_ID/locations/LOCATION_ID/queues/QUEUE_ID
"""
if not queue_name:
return "", "", ""
parts = queue_name.split("/")
return parts[1], parts[3], parts[5]
@staticmethod
class CloudTasksLink(BaseGoogleLink):
"""Helper class for constructing Cloud Task Link"""
name = "Cloud Tasks"
key = "cloud_task"
format_str = CLOUD_TASKS_LINK
@staticmethod
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51,
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27015,
62,
43,
17248,
628,
220,
220,
220,
2488,
12708,
24396,
198
] | 2.968794 | 705 |
from app.assets import compile_static_assets
from flask import Flask
from flask_assets import Environment
from flask_compress import Compress
from flask_talisman import Talisman
from flask_wtf.csrf import CSRFProtect
from jinja2 import ChoiceLoader
from jinja2 import PackageLoader
from jinja2 import PrefixLoader
app = create_app()
| [
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] | 3.692308 | 91 |
# based on https://github.com/pypa/sampleproject
# MIT License
from io import open
from os import path
# Always prefer setuptools over distutils
from setuptools import find_namespace_packages
from setuptools import setup
import versioneer
here = path.abspath(path.dirname(__file__))
# Get the long description from the README file
with open(path.join(here, 'README.md'), encoding='utf-8') as f:
long_description = f.read()
setup(
name='asreview-insights',
version=versioneer.get_version(),
cmdclass=versioneer.get_cmdclass(),
description='Insight tools for the ASReview project',
long_description=long_description,
long_description_content_type='text/markdown',
url='https://github.com/asreview/asreview-insights',
author='ASReview LAB developers',
author_email='[email protected]',
classifiers=[
'Development Status :: 5 - Production/Stable',
'License :: OSI Approved :: Apache Software License',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9',
],
keywords='asreview plot insights',
packages=find_namespace_packages(include=['asreviewcontrib.*']),
install_requires=[
"numpy",
"matplotlib",
"asreview>=1,<2",
],
extras_require={},
entry_points={
"asreview.entry_points": [
"plot = asreviewcontrib.insights.entrypoint:PlotEntryPoint",
"metrics = asreviewcontrib.insights.entrypoint:MetricsEntryPoint",
]
},
project_urls={
'Bug Reports': "https://github.com/asreview/asreview-insights/issues",
'Source': "https://github.com/asreview/asreview-insights",
},
)
| [
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12567,
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785,
14,
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14,
292,
19023,
12,
1040,
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14,
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1600,
198,
220,
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220,
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220,
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705,
7416,
10354,
366,
5450,
1378,
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14,
292,
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14,
292,
19023,
12,
1040,
2337,
1600,
198,
220,
220,
220,
8964,
198,
8,
198
] | 2.661562 | 653 |
#!/usr/bin/env python
import sys, random
vertices = []
faces = []
with open(sys.argv[1], "r") as file:
lines = file.read().split("\n")
outputobj = ""
for line in lines:
if line.startswith("v"):
parts = line.split(" ")
vertices.append([float(x) for x in parts[1:]])
if line.startswith("f"):
parts = line.split(" ")
faces.append([int(x) for x in parts[1:]])
for face in faces:
for idx in face:
vertex = [x+random.random()/50.0 for x in vertices[idx-1]]
outputobj += "v " + " ".join([str(x) for x in vertex]) + "\n"
for i in xrange(len(faces)):
outputobj += "f {0:d} {1:d} {2:d}".format(3*i+1, 3*i+1+1, 3*i+2+1) + "\n"
with open(sys.argv[2], "w") as outfile:
outfile.write(outputobj)
| [
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] | 1.983051 | 413 |
"""
Scrape ODS data from the HSCIC
"""
import json
import sys
import ffs
sys.path.append(ffs.Path.here().parent)
import scrape
DATA_DIR = ffs.Path.here()/'../../data'
DOWNLOADS = 'http://systems.hscic.gov.uk/data/ods/datadownloads/index'
def check_sanity_of(metadata):
"""
We've just finished scraping, let's make sure we haven't scraped bullshit.
"""
for dataset in metadata:
for resource in dataset['resources']:
if not resource['url']:
print dataset['title']
print dataset['url']
print resource
raise Error('You scraped a resource without noting the URL Larry')
return
def fetch_dataset_metadata(url):
"""
Given a URL, fetch the metadata and resources
from that page, and return it as a dict.
"""
print url
dom = scrape._astree(url)
title = dom.cssselect('h1.documentFirstHeading')[0].text_content().strip()
description_elements = [e.text_content() for e in dom.cssselect('#parent-fieldname-text')[0] if e.tag != 'table']
description = "\n".join(description_elements).strip()
metadata = dict(
url=url,
title=title,
description=description,
)
resources = []
try:
data_tbody = dom.cssselect('table.listing tbody')[1]
except IndexError: # Sometimes the table isn't built that way
data_tbody = dom.cssselect('table.listing tbody')[0]
resource_rows = data_tbody.cssselect('tr')
try:
for row in resource_rows:
if 'haandsa' in url:
try:
description, name, created, _ = row
except ValueError:
description, name, created = row
else:
name, description, created = row
# if 'safehaven' in url:
# import pdb;pdb.set_trace()
resource = {
'url': name.cssselect('a')[0].get('href'),
'name': name.text_content().strip(),
'description': description.text_content().strip()
}
resources.append(resource)
except ValueError: # Sometimes there are more columns
for row in resource_rows:
name, full, excel, created = row
resource = {
'url': full.cssselect('a')[0].get('href'),
'name': 'Full ' + name.text_content().strip(),
'description': name.text_content().strip()
}
resources.append(resource)
if excel.text_content().strip() == 'N/A':
continue
try:
resource = {
'url': excel.cssselect('a')[0].get('href'),
'name': 'Excel ' + name.text_content().strip(),
'description': name.text_content().strip()
}
except IndexError:
import pdb;pdb.set_trace()
print row
resources.append(resource)
metadata['resources'] = resources
return metadata
def fetch_ods_metadata():
"""
* Fetch the list of downloads from the download index
* Iterate through them gathering metadata on each
* Write to a file as one dataset per "Download"
"""
dom = scrape._astree(DOWNLOADS)
downloads = dom.cssselect('table.listing a.internal-link')
categories = list(set(a.get('href') for a in downloads))
metadata = [fetch_dataset_metadata(url) for url in categories]
check_sanity_of(metadata)
metafile = DATA_DIR/'ods.json'
metafile.truncate()
metafile << json.dumps(metadata, indent=2)
return
if __name__ == '__main__':
sys.exit(main())
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] | 2.193341 | 1,712 |
# -*- coding: utf-8 -*-
# Generated by Django 1.10.5 on 2017-07-22 19:45
from __future__ import unicode_literals
from django.db import migrations
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import tkinter as tk
from tkinter import filedialog, Entry, messagebox
from PIL import ImageTk, Image
filenames = []
all_labels = []
base_labels = []
Layer_0 = Image.new(mode = "RGB", size = (1, 1))
left_img_out = Image.new(mode = "RGB", size = (1, 1))
right_img_out = Image.new(mode = "RGB", size = (1, 1))
window = tk.Tk()
window.geometry("1000x600")
window.title("V1.0")
heading = tk.Label(text = "Sprite Layer Compiler", bg="grey", fg="white", width="500",height="3")
heading2 = tk.Label(text = "", bg="grey", fg="white", width="500",height="1")
heading.pack()
heading2.place(x=0,y=350)
Description_Label = tk.Label(window, text = "Sprite Frame Dimensions").place(x=320,y=260)
X_label = tk.Label(window, text = "Pixel X:").place(x=320,y=280)
xval = Entry(window, width="8")
xval.place(x=370,y=280)
xval.focus_set()
Y_label = tk.Label(window, text = "Pixel Y:").place(x=320,y=300)
yval = Entry(window, width="8")
yval.place(x=370,y=300)
yval.focus_set()
tk.Button(window, text='Layer 0', command= lambda: openfile0(60,60),).place(x=10,y=60)
tk.Button(window, text='Layer 1', command= lambda: openfile1(60,90)).place(x=10,y=90)
tk.Button(window, text='Layer 2', command= lambda: openfile1(60,120)).place(x=10,y=120)
tk.Button(window, text='Layer 3', command= lambda: openfile1(60,150)).place(x=10,y=150)
tk.Button(window, text='Layer 4', command= lambda: openfile1(60,180)).place(x=10,y=180)
tk.Button(window, text='Layer 5', command= lambda: openfile1(60,210)).place(x=10,y=210)
tk.Button(window, text='Layer 6', command= lambda: openfile1(60,240)).place(x=10,y=240)
tk.Button(window, text='Preview', command=Preview, bg="grey", fg="white", width="8",height="1").place(x=10,y=280)
tk.Button(window, text='Flip Left', command=FlipLeft, bg="grey", fg="white", width="8",height="1").place(x=80,y=280)
tk.Button(window, text='Clear', command=Clear, bg="#e12120", fg="white", width="8",height="1").place(x=170,y=280)
tk.Button(window, text='Clear All', command=ClearAll, bg="#971414", fg="white", width="8",height="1").place(x=240,y=280)
tk.Button(window, text='Save Right', command=SaveFileRight, bg="green", fg="white", width="8",height="1").place(x=10,y=310)
tk.Button(window, text='Save Left', command=SaveFileLeft, bg="green", fg="white", width="8",height="1").place(x=80,y=310)
window.mainloop()
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198
] | 2.521265 | 917 |
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import plotly.express as px
# This dataframe has 244 lines, but 4 distinct values for `day`
df = px.data.tips()
app = dash.Dash(__name__)
app.layout = html.Div([
html.P("Selector:"),
dcc.Dropdown(
id='names',
value='day',
options=[{'value': x, 'label': x}
for x in ['smoker', 'day', 'time', 'sex']],
clearable=False
),
html.P("Values:"),
dcc.Dropdown(
id='values',
value='total_bill',
options=[{'value': x, 'label': x}
for x in ['total_bill', 'tip', 'size']],
clearable=False
),
dcc.Graph(id="pie-chart"),
])
@app.callback(
Output("pie-chart", "figure"),
[Input("names", "value"),
Input("values", "value")])
#app.run_server(debug=True)
if __name__ == "__main__":
import os
debug = False if os.environ["DASH_DEBUG_MODE"] == "False" else True
app.run_server(host="0.0.0.0", port=8050, debug=debug)
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] | 2.25 | 484 |
# Cron Job -
# Problem Assign -- Contest with isProblem False -- Assign Problem
# Result Assign -- Contest with isResult False
# contest end -- (startTime + duration) <= time.now
#Email
from django.core.mail import send_mail
from codedigger.settings import EMAIL_HOST_USER
## Short Code Contest
# from .utils import login, clean, penalty
# from .models import CodeforcesContest, CodeforcesContestSubmission, CodeforcesContestParticipation
# import requests, random, re
# from codeforces.cron import save_user
# from codeforces.models import user as CodeforcesUser
# from bs4 import BeautifulSoup as bs
# def update_penalty(contest, cookie) :
# contestId = contest.contestId
# groupId = contest.groupId
# page = 0
# prevHandle = None
# while(page < 100):
# page+=1
# url = "https://codeforces.com/group/"+groupId+"/contest/"+str(contestId)+"/standings/page/"+str(page)
# res = requests.get(url , headers = {'Cookie' : cookie})
# soup = bs(res.content,features="html5lib")
# participants = soup.find('table' , {'class' :'standings'}).findAll('tr')
# NProblems = len(participants[0].findAll('th'))-4
# isBreak = False
# isFirst = True
# for participant in participants[1:-1] :
# column = participant.findAll('td')
# user_handle = clean(column[1].find('a').text)
# if isFirst:
# if user_handle == prevHandle:
# isBreak = True
# break
# else :
# prevHandle = user_handle
# isFirst = False
# contest_user,created = CodeforcesUser.objects.get_or_create(handle = user_handle)
# if created :
# url = "https://codeforces.com/api/user.info?handles="+user_handle
# res = requests.get(url)
# if res.status_code == 200:
# data = res.json()
# if data['status'] == 'OK':
# save_user(contest_user , data['result'][0])
# contest_participant,created = CodeforcesContestParticipation.objects.get_or_create(
# contest=contest,
# user=contest_user,
# participantId=participant['participantid'],
# defaults={
# 'isOfficial' : clean(column[0].text) != '',
# 'isVirtual' : column[1].find('sup') != None
# })
# for i in range(NProblems):
# sub = CodeforcesContestSubmission.objects.filter(participant=contest_participant, problemIndex = i)
# newSub = CodeforcesContestSubmission(participant=contest_participant, problemIndex = i)
# if column[4+i].find('span' , {'class' : 'cell-accepted'})!=None and column[4+i]['title'][:3]=='GNU':
# subId = participant.findAll('td')[4+i]['acceptedsubmissionid']
# if sub.exists() and str(sub[0].submissionId) == subId :
# continue
# if sub.exists() :
# sub[0].isSolved = True
# sub[0].submissionId = subId
# sub[0].lang = column[4+i]['title']
# sub[0].penalty = penalty(cookie, contestId, subId, groupId)
# sub[0].save()
# else :
# newSub.isSolved = True
# newSub.submissionId = subId
# newSub.lang = column[4+i]['title']
# newSub.penalty = penalty(cookie, contestId, subId, groupId)
# newSub.save()
# else :
# newSub.isSolved = False
# if not sub.exists() :
# newSub.save()
# if isBreak:
# break
# def update_codeforces_short_code_contests() :
# cookie = login()
# codeforcescontest = CodeforcesContest.objects.filter(Type = "Short Code")
# for contest in codeforcescontest :
# update_penalty(contest, cookie)
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] | 2.342541 | 1,448 |
"""Recognize and extract forms."""
import os
from statistics import fmean
from azure.ai.formrecognizer.aio import FormRecognizerClient, FormTrainingClient
from azure.core.credentials import AzureKeyCredential
class RecognizeCustomFormsSampleAsync:
"""Class to recognize forms in async mode."""
async def recognize_custom_forms(self, custom_model_id, filename):
"""Extract text from custom form.
Args:
custom_model_id: The trained custom model id.
filename: The filename of the document that will be scanned.
Returns:
The header for the table and the extracted text.
"""
endpoint = os.environ["AZURE_FORM_RECOGNIZER_ENDPOINT"]
key = os.environ["AZURE_FORM_RECOGNIZER_KEY"]
model_id = os.getenv("CUSTOM_TRAINED_MODEL_ID", custom_model_id)
async with FormRecognizerClient(
endpoint=endpoint, credential=AzureKeyCredential(key)
) as form_recognizer_client:
# Make sure your form's type is included in the
# list of form types the custom model can recognize
form_url = (
f"https://storage.googleapis.com/"
f"{os.getenv('GS_MEDIA_BUCKET_NAME')}/"
f"{filename}"
)
poller = await form_recognizer_client.begin_recognize_custom_forms_from_url(
model_id=model_id, form_url=form_url, include_field_elements=True
)
forms = await poller.result()
table = []
header = {}
for _, form in enumerate(forms):
row = {}
for idx, (name, field) in enumerate(form.fields.items()):
if idx >= 3:
for value in field.value:
for i, val in value.to_dict()["value"].items():
data = val["value_data"]
# Condition for "No Data"
if data:
words = data["field_elements"]
# Condition for multiple word result
if len(words) > 1:
word_list = [word["text"] for word in words]
confidence_list = [word["confidence"] for word in words]
slug_name = (
val["name"]
.lower()
.replace(" ", "_")
.replace("(", "")
.replace(")", "")
)
row[slug_name] = {
"text": " ".join(word_list),
"confidence": round(fmean(confidence_list), 3),
}
else:
slug_name = (
val["name"]
.lower()
.replace(" ", "_")
.replace("(", "")
.replace(")", "")
)
row[slug_name] = {
"text": words[0]["text"],
"confidence": words[0]["confidence"],
}
else:
slug_name = (
val["name"]
.lower()
.replace(" ", "_")
.replace("(", "")
.replace(")", "")
)
row[slug_name] = {
"text": data,
"confidence": data,
}
if i == "REMARKS":
table.append(row)
row = {}
else:
slug_name = (
name.lower().replace(" ", "_").replace("(", "").replace(")", "")
)
header[slug_name] = {
"text": field.value,
"confidence": field.confidence,
}
return header, table
async def form_recognizer_runner(filename):
"""Runner for the form recognizer.
Args:
filename: The filename of the document to be scanned
Returns:
The form header and the table scanned.
"""
sample = RecognizeCustomFormsSampleAsync()
model_id = None
if os.getenv("CONTAINER_SAS_URL"):
endpoint = os.getenv("AZURE_FORM_RECOGNIZER_ENDPOINT")
key = os.getenv("AZURE_FORM_RECOGNIZER_KEY")
if not endpoint or not key:
raise ValueError("Please provide endpoint and API key to run the samples.")
form_training_client = FormTrainingClient(
endpoint=endpoint, credential=AzureKeyCredential(key)
)
async with form_training_client:
model = await (
await form_training_client.begin_training(
os.getenv("CONTAINER_SAS_URL"), use_training_labels=True
)
).result()
model_id = model.model_id
return await sample.recognize_custom_forms(model_id, filename)
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1267,
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13639,
58,
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1018,
62,
3672,
60,
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1391,
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1782,
198,
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1441,
13639,
11,
3084,
628,
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292,
13361,
825,
1296,
62,
26243,
7509,
62,
16737,
7,
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2599,
198,
220,
220,
220,
37227,
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329,
262,
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13,
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220,
943,
14542,
25,
198,
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220,
220,
220,
220,
29472,
25,
383,
29472,
286,
262,
3188,
284,
307,
28660,
628,
220,
220,
220,
16409,
25,
198,
220,
220,
220,
220,
220,
383,
1296,
13639,
290,
262,
3084,
28660,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
6291,
796,
31517,
1096,
15022,
8479,
82,
36674,
42367,
3419,
198,
220,
220,
220,
2746,
62,
312,
796,
6045,
198,
220,
220,
220,
611,
28686,
13,
1136,
24330,
7203,
10943,
30339,
1137,
62,
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1921,
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1,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
36123,
796,
28686,
13,
1136,
24330,
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22778,
11335,
62,
21389,
62,
38827,
7730,
45,
14887,
1137,
62,
1677,
6322,
46,
12394,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
1994,
796,
28686,
13,
1136,
24330,
7203,
22778,
11335,
62,
21389,
62,
38827,
7730,
45,
14887,
1137,
62,
20373,
4943,
628,
220,
220,
220,
220,
220,
220,
220,
611,
407,
36123,
393,
407,
1994,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
11052,
12331,
7203,
5492,
2148,
36123,
290,
7824,
1994,
284,
1057,
262,
8405,
19570,
628,
220,
220,
220,
220,
220,
220,
220,
1296,
62,
34409,
62,
16366,
796,
5178,
44357,
11792,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
36123,
28,
437,
4122,
11,
49920,
28,
26903,
495,
9218,
34,
445,
1843,
7,
2539,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Jan 24 15:39:33 2021
Calculate and plot details of inner Solar System transits as seen from outer
Solar System objects.
Requires package solarsystem (https://pypi.org/project/solarsystem/)
Animation requires imagemagick (imagemagick.org)
@author: keatonb
"""
import solarsystem
import numpy as np
import matplotlib.pyplot as plt
import warnings
from astropy import units as u
from astropy import constants as const
from astropy.time import Time
from scipy.interpolate import interp1d
from datetime import timedelta
from matplotlib import animation
#from NASA Planetary Fact Sheet #km
#https://nssdc.gsfc.nasa.gov/planetary/factsheet/
planetdiameter = {"Mercury":4879,
"Venus":12104,
"Earth":12756,
"Mars":6792,
"Jupiter":142984,
"Saturn":120536,
"Uranus":51118,
"Neptune":49528}
class Geometry:
"""
innerplanet relative to Sun as seen from outerplanet at time
Derived in Bell & Rustamkulov (2021, in prep.)
"""
def __init__(self, innerplanet, outerplanet, time):
"""
Parameters:
innerplanet (str): name of inner planet
outerplanet (str): name of outer planet
time (datetime): timestamp (UTC)
"""
self.innerplanet = innerplanet.capitalize()
self.outerplanet = outerplanet.capitalize()
self.time = time
#Get heliocentric ecliptic planet positions at time:
#(Longitude (l, deg), Latitude (b, deg), Distance (r, AU))
H = solarsystem.Heliocentric(year=time.year, month=time.month,
day=time.day, hour=time.hour,
minute=time.minute + time.second/60 + time.microsecond/1e6)
planets = H.planets()
#Get heliocentric ecliptic planet positions:
#(Longitude (l, deg), Latitude (b, deg), Distance (r, AU))
O = planets[outerplanet]
I = planets[innerplanet]
E = planets["Earth"]
#Convert to spherical position vector:
#[r (AU, b (radians), l (radians)]
rvec = lambda P: np.array([P[2],P[1]*np.pi/180,P[0]*np.pi/180])
rO = rvec(O)
rI = rvec(I)
rE = rvec(E)
#Convert to Cartesian coordinates (x,y,z in AU)
xvec = lambda rP: np.array([rP[0]*np.cos(rP[1])*np.cos(rP[2]),
rP[0]*np.cos(rP[1])*np.sin(rP[2]),
rP[0]*np.sin(rP[1])])
xO = xvec(rO)
xI = xvec(rI)
xE = xvec(rE)
#Get positions relative to outer planet
xO_Sun = - xO
xO_I = xI - xO
xO_E = xE - xO
#Align x-axis with Sun for relative planet positions
#With two rotation matrices: x' = BAx
A = np.array([[-np.cos(rO[2]),-np.sin(rO[2]),0],
[np.sin(rO[2]),-np.cos(rO[2]),0],
[0,0,1]])
B = np.array([[np.cos(rO[1]),0,-np.sin(rO[1])],
[0,1,0],
[np.sin(rO[1]),0,np.cos(rO[1])]])
BA = np.matmul(B, A)
xvecprime = lambda xO_P: np.matmul(BA,xO_P)
xO_Sun_prime = xvecprime(xO_Sun) #Passes a sanity check
xO_I_prime = xvecprime(xO_I)
xO_E_prime = xvecprime(xO_E)
self.xSun = xO_Sun_prime
self.xI = xO_I_prime
self.xE = xO_I_prime
#Convert back to spherical coordinates
#for on-sky positions as seen from O [r (AU,b (radians),l (radians)]
rvecprime = lambda xvecp: np.array([np.sqrt(np.sum(xvecp**2.)),
np.arctan(xvecp[2]/np.sqrt(np.sum(xvecp[:2]**2))),
-np.arctan(xvecp[1]/xvecp[0])])
rO_Sun_prime = rvecprime(xO_Sun_prime) #Passes a sanity check
rO_I_prime = rvecprime(xO_I_prime) #Passes a sanity check
rO_E_prime = rvecprime(xO_E_prime) #Praise Boas!
self.rSun = rO_Sun_prime
self.rI = rO_I_prime
self.rE = rO_I_prime
#Angular separation between inner planet and Sun (radians)
self.theta = np.arccos(np.dot(xO_Sun_prime,xO_I_prime)/(rO_Sun_prime[0]*rO_I_prime[0]))
#Angular diameters of inner planet and Sun (radians)
self.angdiam_Sun = 2*const.R_sun.to(u.AU)/(rO_Sun_prime[0]*u.AU)
self.angdiam_I = planetdiameter[innerplanet]*u.km.to(u.AU)/rO_I_prime[0]
#Are we in transit?
self.intransit = ((self.theta < (self.angdiam_Sun + self.angdiam_I)/2.) &
(rO_I_prime[0] < rO_Sun_prime[0]))
#Fraction of distance toward Solar limb (0 at center)
r = self.theta / (self.angdiam_Sun/2.0)
self.mu = np.sqrt(1-r**2.)
#Light travel time delay to Earth (seconds)
self.timedelay = ((rO_I_prime[0] + rO_E_prime[0])*u.AU/const.c).to(u.s).value
def plot(self, ax=None, fov=(4,4), unit=u.arcsec, show=True,
filename=None, timedelay=True, fontsize=13, **kwargs):
"""
Plot snapshot of Sun, innerplanet from outerplanet
Parameters:
ax (mpl axis): axis to plot to (default: create new fig,ax)
fov (tuple): (width,height) in solar radii
unit (astropy angle unit): unit for axes
show (bool): whether to show plot (default: True)
filename (str): filename to save to (default: None)
timedelay (bool): add light-travel time to text?
fontsize (float): fontsize
**kwargs: args for figure if no axis provided
"""
#Create fig and ax if no ax provided
if ax is None:
fig,ax = plt.subplots(**kwargs)
#Circles must be round
ax.set_aspect(1)
#Angular unit conversion (from radians)
scale = u.radian.to(unit)
#Display sun, planet
sunangrad = scale*self.angdiam_Sun/2.
sun = plt.Circle((0, 0), sunangrad, color='y', zorder = 1)
#Is planet in front of Sun?
infront = self.rI[0] < self.rSun[0]
#The line on this circle makes it look larger than reality,
#but it's almost too small to see without
planet = plt.Circle((scale*self.rI[2], scale*self.rI[1]),
scale*self.angdiam_I/2., color='blue',
zorder=2*infront)
ax.add_patch(sun)
ax.add_patch(planet)
#Add text
time = self.time
if timedelay:
time += timedelta(seconds=self.timedelay)
ax.text(0.03,0.02,(f"{self.innerplanet} from {self.outerplanet} \n" +
time.strftime('%Y-%m-%d %H:%M:%S')),
transform=ax.transAxes, ha='left', va='bottom', fontsize=fontsize)
ax.set_xlabel(fr"$l'$ ({unit.short_names[0]})", fontsize=fontsize)
ax.set_ylabel(fr"$b'$ ({unit.short_names[0]})", fontsize=fontsize)
#Scale axes
ax.set_xlim(-fov[0]*sunangrad/2, fov[0]*sunangrad/2)
ax.set_ylim(-fov[1]*sunangrad/2, fov[1]*sunangrad/2)
#Save plot or show
if filename is not None:
plt.savefig(filename)
if show:
plt.show()
def _limbdarkening(phi, u2=0.88, v2=-0.23):
"""limb darkening law
parameterization from Section 14.7 of Allen's Astrophysical Quantities
(4th ed, Cox, 2000, AIP Press)
default u2,v2 values are for ~V filter @ 600 nm
phi is angle between solar radius vector and line of sight (radians)
normalized so disk integrates to 1
"""
mu = np.cos(phi)
return (1 - u2 - v2 + u2*mu + v2*(mu**2))/(1-u2/3 - v2/2)
class Transit:
"""
Properties and plots of transits in time window.
Calculates:
- MJD (instantaneous and observed) of ingress,egress,midtranist
- Impact parameter (b)
Plots:
- animate (gif)
- traceplot (path)
TODO: lightcurve (simulated)
"""
def __init__(self, innerplanet, outerplanet, starttime, endtime, timestep):
"""
Parameters:
innerplanet (str): name of inner planet
outerplanet (str): name of outer planet
starttime (datetime): timestamp (UTC) before transit
endtime (datetime): timestamp (UTC) before transit
timestep (float): sampling interval (minutes; > 0)
Notes:
Impact parameter, b, is minimum within timestamp
"""
#Check that timestep is positive
if timestep <= 0:
raise Exception("Timestep must be positive.")
if timestep > 10:
warnings.warn("Timesteps longer than 10 minutes may produce poor results")
deltatime = timedelta(minutes=timestep)
self.innerplanet = innerplanet
self.outerplanet = outerplanet
#Compute timestamps
self.times = [starttime]
while self.times[-1] < endtime:
self.times.append(self.times[-1] + deltatime)
self.mjd = np.array([Time(time).mjd for time in self.times])
#Calculate geometry at each timestamp
self.geometry = [Geometry(self.innerplanet, self.outerplanet, time)
for time in self.times]
#Get observed times (corrected for light travel time)
self.mjdobs = self.mjd + np.array([g.timedelay for g in self.geometry])/(24*3600.)
#compute transit start, end, and mid-eclipse times
#in transit when transitsep <= 1
transitsep = [g.theta / ((g.angdiam_Sun+g.angdiam_I)/2.0) for g in self.geometry]
#separate below and after transit
deepest = np.argmin([g.theta / ((g.angdiam_Sun+g.angdiam_I)/2.) for g in self.geometry])
#we'll interpolate precise start and end times
if deepest != 0:
self.startingress_mjd = float(interp1d(transitsep[:deepest],self.mjd[:deepest],
bounds_error=False)(1))
self.startingress_mjdobs = float(interp1d(transitsep[:deepest],self.mjdobs[:deepest],
bounds_error=False)(1))
else:
self.startingress_mjd = np.nan
self.startingress_mjdobs = np.nan
if deepest != len(self.geometry)-1:
self.endegress_mjd = float(interp1d(transitsep[deepest:],self.mjd[deepest:],
bounds_error=False)(1))
self.endegress_mjdobs = float(interp1d(transitsep[deepest:],self.mjdobs[deepest:],
bounds_error=False)(1))
else:
self.endegress_mjd = np.nan
self.endegress_mjdobs = np.nan
self.midtransit_mjd = (self.startingress_mjd + self.endegress_mjd)/2.
self.midtransit_mjdobs = (self.startingress_mjdobs + self.endegress_mjdobs)/2.
self.transitdurationobs = (self.endegress_mjdobs - self.startingress_mjdobs)*24*u.h
#Compute geometry at mid-transit
self.midtransit_geometry = Geometry(self.innerplanet, self.outerplanet,
Time(self.midtransit_mjd,format='mjd').to_datetime())
#Simulate mid-transit (default limb darkening)
phi = np.arcsin(2*self.midtransit_geometry.theta/self.midtransit_geometry.angdiam_Sun)
self.midtransit_depth = ((self.midtransit_geometry.angdiam_I**2/
self.midtransit_geometry.angdiam_Sun**2)*
_limbdarkening(phi))*1e6 # ppm
#Compute impact parameter (good to timestep precision)
self.b = self.midtransit_geometry.theta / ((self.midtransit_geometry.angdiam_Sun)/2.)
def animate(self, filename="Transit.gif", duration=3, figsize=(4,4), dpi=150, **kwargs):
"""Animate the transit
Parameters:
filename (str): file to save animation to
duration (float): loop duration (seconds)
figsize (float,float): width, height in inches
dpi (float): dots per inch
**kwargs: for Geometry plot function
"""
fig,ax = plt.subplots(figsize=figsize)
#No initialization needed
#Animation function to call
#Time between frames
interval = duration/len(self.times)
#Animate it and save!
anim = animation.FuncAnimation(fig, animateframe, init_func=init,
frames=len(self.times), interval=interval,
blit=False)
anim.save(filename, dpi=dpi, fps = 1/interval, writer='imagemagick')
def traceplot(self, ax=None, fov=(4,4), unit=u.arcsec, show=True,
filename=None, plotsun=True, fontsize=13, **kwargs):
"""Plot path of transit across Sun
Parameters:
ax (mpl axis): axis to plot to (default: create new fig,ax)
fov (tuple): (width,height) in solar radii
unit (astropy angle unit or "solarradii"): unit for axes
show (bool): whether to show plot (default: True)
filename (str): filename to save to (default: None)
sun (bool): plot Sun circle? (default: True)
fontsize (float): fontsize
**kwargs: args for figure if no axis provided
"""
#collect relevant details
angdiam_I = np.array([g.angdiam_I for g in self.geometry])
angdiam_Sun = np.array([g.angdiam_Sun for g in self.geometry])
b = np.array([g.rI[1] for g in self.geometry])
l = np.array([g.rI[2] for g in self.geometry])
rI = np.array([g.rI[0] for g in self.geometry])
rSun = np.array([g.rSun[0] for g in self.geometry])
#Are we plotting in solar radii? (useful for overlaying traces)
solarradii = unit == "solarradii"
if solarradii:
unit = u.radian
#Angular unit conversion (from radians)
scale = u.radian.to(unit)
#Get trajectory angle, phi, to plot shadow wide enough
phi = np.arctan(np.diff(b)/np.diff(l))
phi = np.concatenate((phi,[phi[-1]])) # match length
#Create fig and ax if no ax provided
if ax is None:
fig,ax = plt.subplots(**kwargs)
#Circles must be round
ax.set_aspect(1)
#Display sun, using angular size at mid-transit (unless solarradii display units)
midtransit = np.argmin([g.theta / ((g.angdiam_Sun)/2.) for g in self.geometry])
angdiam_Sun = angdiam_Sun[midtransit]
sunangrad = scale*angdiam_Sun/2.
if solarradii: #Handle case for solar radii units
sunangrad = 1
scale = 2./angdiam_Sun
if plotsun: #Only plot sun if requested
sun = plt.Circle((0, 0), sunangrad, color='y', zorder = 1)
ax.add_patch(sun)
#Is planet in front of Sun?
infront = rI[midtransit] < rSun[midtransit]
#Display transit path
linewidth = scale*angdiam_I / np.cos(phi) #Width of shadow path
ax.fill_between(scale*l,scale*b+linewidth/2.,scale*b-linewidth/2,lw=0, fc='0.2',zorder=2*infront)
ax.set_xlabel(fr"$l'$ ({unit.short_names[0]})", fontsize=fontsize)
ax.set_ylabel(fr"$b'$ ({unit.short_names[0]})", fontsize=fontsize)
if solarradii:
ax.set_xlabel("Solar radii", fontsize=fontsize)
ax.set_ylabel("Solar radii", fontsize=fontsize)
#Scale axes
ax.set_xlim(-fov[0]*sunangrad/2, fov[0]*sunangrad/2)
ax.set_ylim(-fov[1]*sunangrad/2, fov[1]*sunangrad/2)
#Save plot or show
if filename is not None:
plt.tight_layout()
plt.savefig(filename)
if show:
plt.tight_layout()
plt.show()
def simlightcurve(self,limbdarkeningfunc = _limbdarkening,
limbdarkening_args = {"u2":0.88, "v2":-0.23}):
"""
Simulate transit light curve with limb darkening
Assumes negligible limb darkening gradient across transiting planet disk
Returns relative model flux at self.mjd_obs
"""
theta = np.array([g.theta for g in self.geometry])
angdiam_Sun = np.array([g.angdiam_Sun for g in self.geometry])
angdiam_I = np.array([g.angdiam_I for g in self.geometry])
#Angle between radial vector and line of sight
phi = np.arcsin(2*theta/angdiam_Sun)
#compute relative flux
lc = 1 - (angdiam_I**2/angdiam_Sun**2)*_limbdarkening(phi,**limbdarkening_args)
lc[np.isnan(lc)] = 1
return lc | [
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] | 1.969306 | 8,601 |
import os.path as osp
import sys
import argparse
import time
import torch
from torchvision import transforms
this_dir = osp.dirname(__file__)
paths = []
paths.append(osp.join(this_dir, '..', 'lib'))
paths.append(osp.join(this_dir, '..', 'lib', 'dataset'))
for path in paths:
if path not in sys.path:
sys.path.insert(0, path)
import models
from core.loss import JointsMSELoss
from utils.utils import get_optimizer
from core.config import config
from core.config import update_config
from core.config import update_dir
from core.config import get_model_name
from core.evaluate import accuracy
from CarJointsDataset import CarJointsDataset
class AverageMeter(object):
"""Computes and stores the average and current value"""
args = parse_args()
model = eval('models.' + 'pose_resnet' + '.get_pose_net')(
config, is_train=False
)
print(model)
# define loss function (criterion) and optimizer
criterion = JointsMSELoss(
use_target_weight=config.LOSS.USE_TARGET_WEIGHT
)
optimizer = get_optimizer(config, model)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, config.TRAIN.LR_STEP, config.TRAIN.LR_FACTOR
)
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = CarJointsDataset(
config,
transforms.Compose([
transforms.ToTensor(),
normalize,
])
)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config.TRAIN.BATCH_SIZE,
shuffle=config.TRAIN.SHUFFLE,
num_workers=config.WORKERS
)
# switch to train mode
model.train()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
acc = AverageMeter()
end = time.time()
for epoch in range(config.TRAIN.BEGIN_EPOCH, config.TRAIN.END_EPOCH):
lr_scheduler.step()
for i, (input, target) in enumerate(train_loader):
# compute output
output = model(input)
loss = criterion(output, target, 0)
# compute gradient and do update step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure accuracy and record loss
losses.update(loss.item(), input.size(0))
_, avg_acc, cnt, pred = accuracy(output.detach().cpu().numpy(),
target.detach().cpu().numpy())
acc.update(avg_acc, cnt)
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % config.PRINT_FREQ == 0:
msg = 'Epoch: [{0}][{1}/{2}]\t' \
'Time {batch_time.val:.3f}s ({batch_time.avg:.3f}s)\t' \
'Speed {speed:.1f} samples/s\t' \
'Data {data_time.val:.3f}s ({data_time.avg:.3f}s)\t' \
'Loss {loss.val:.5f} ({loss.avg:.5f})\t' \
'Accuracy {acc.val:.3f} ({acc.avg:.3f})'.format(
epoch, i, len(train_loader), batch_time=batch_time,
speed=input.size(0)/batch_time.val,
data_time=data_time, loss=losses, acc=acc)
print(msg)
print("End of current epoch")
print("End of training!")
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12915,
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8,
198
] | 2.213545 | 1,447 |
# -*- coding: utf-8 -*-
# DO NOT EDIT THIS FILE!
# This file has been autogenerated by dephell <3
# https://github.com/dephell/dephell
try:
from setuptools import setup
except ImportError:
from distutils.core import setup
import os.path
readme = ''
here = os.path.abspath(os.path.dirname(__file__))
readme_path = os.path.join(here, 'README.rst')
if os.path.exists(readme_path):
with open(readme_path, 'rb') as stream:
readme = stream.read().decode('utf8')
setup(
long_description=readme,
name='qctrl-cirq',
version='0.0.4',
description='Q-CTRL Python Cirq',
python_requires='<3.9,>=3.6.4',
project_urls={"documentation": "", "homepage": "https://q-ctrl.com", "repository": "https://github.com/qctrl/python-cirq"},
author='Q-CTRL',
author_email='[email protected]',
license='Apache-2.0',
keywords='q-ctrl qctrl quantum control',
classifiers=['Development Status :: 5 - Production/Stable', 'Environment :: Console', 'Intended Audience :: Developers', 'Intended Audience :: Education', 'Intended Audience :: Science/Research', 'Natural Language :: English', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Topic :: Internet :: WWW/HTTP', 'Topic :: Scientific/Engineering :: Physics', 'Topic :: Scientific/Engineering :: Visualization', 'Topic :: Software Development :: Embedded Systems', 'Topic :: System :: Distributed Computing'],
packages=['qctrlcirq'],
package_dir={"": "."},
package_data={},
install_requires=['cirq==0.*,>=0.6.0', 'numpy==1.*,>=1.16.0', 'qctrl-open-controls==4.*,>=4.3.0', 'scipy==1.*,>=1.3.0', 'toml==0.*,>=0.10.0'],
extras_require={"dev": ["nbval==0.*,>=0.9.5", "pylama", "pylint", "pylint-runner", "pytest", "qctrl-visualizer==2.*,>=2.1.0", "sphinx==2.*,>=2.2.0"]},
)
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] | 2.614325 | 726 |
import pytest
import urllib.request
import os
import hashlib
from elasticsearch import Elasticsearch, ConnectionError, RequestError, NotFoundError
from time import sleep
from image_match.elasticsearch_driver import SignatureES
from PIL import Image
test_img_url1 = 'https://camo.githubusercontent.com/810bdde0a88bc3f8ce70c5d85d8537c37f707abe/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f7468756d622f652f65632f4d6f6e615f4c6973612c5f62795f4c656f6e6172646f5f64615f56696e63692c5f66726f6d5f4332524d465f7265746f75636865642e6a70672f36383770782d4d6f6e615f4c6973612c5f62795f4c656f6e6172646f5f64615f56696e63692c5f66726f6d5f4332524d465f7265746f75636865642e6a7067'
test_img_url2 = 'https://camo.githubusercontent.com/826e23bc3eca041110a5af467671b012606aa406/68747470733a2f2f63322e737461746963666c69636b722e636f6d2f382f373135382f363831343434343939315f303864383264653537655f7a2e6a7067'
urllib.request.urlretrieve(test_img_url1, 'test1.jpg')
urllib.request.urlretrieve(test_img_url2, 'test2.jpg')
INDEX_NAME = 'test_environment_{}'.format(hashlib.md5(os.urandom(128)).hexdigest()[:12])
DOC_TYPE = 'image'
MAPPINGS = {
"mappings": {
DOC_TYPE: {
"dynamic": True,
"properties": {
"metadata": {
"type": "nested",
"dynamic": True,
"properties": {
"tenant_id": { "type": "keyword" },
"project_id": { "type": "keyword" }
}
}
}
}
}
}
@pytest.fixture(scope='module', autouse=True)
@pytest.fixture(scope='function', autouse=True)
@pytest.fixture(scope='function', autouse=True)
@pytest.fixture
@pytest.fixture
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220,
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198,
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1782,
198,
92,
628,
198,
31,
9078,
9288,
13,
69,
9602,
7,
29982,
11639,
21412,
3256,
1960,
1076,
28,
17821,
8,
198,
198,
31,
9078,
9288,
13,
69,
9602,
7,
29982,
11639,
8818,
3256,
1960,
1076,
28,
17821,
8,
198,
198,
31,
9078,
9288,
13,
69,
9602,
7,
29982,
11639,
8818,
3256,
1960,
1076,
28,
17821,
8,
198,
198,
31,
9078,
9288,
13,
69,
9602,
198,
198,
31,
9078,
9288,
13,
69,
9602,
198
] | 2.059041 | 813 |
BASE_DIRECTORY = None
CACHE = "/tmp/tru"
| [
33,
11159,
62,
17931,
23988,
15513,
796,
6045,
198,
34,
2246,
13909,
796,
12813,
22065,
14,
83,
622,
1,
198
] | 2.05 | 20 |
from src.BandC import *
from src.Exceptions import FileFormatNotFound
__parsers__ = {'.csv': CSV, '.arff': Arff}
__url_parsers__ = {'.csv': URLCSV, '.arff': URLARFF}
def assign_parser(file_path: str, contents: str = None, verbose: bool = False) -> callable:
""" Allocate a specific parser to a file_path
:param file_path: The file path of the dataset to parse
:param contents: The dataset as a string
:param verbose: True for output
:return: A parser object which is able to parse the dataset
"""
# Check file path to see if we need a local or an url parser
parsers = __parsers__
if file_path.startswith('https:'):
print('THIS IS IT')
parsers = __url_parsers__
# Find the correct parser for the file
for parser in parsers:
# Check if we have implemented a parser for this file
if file_path.endswith(parser):
# Check if the dataset has been given as a string
if contents is None:
return parsers[parser](file_path=file_path,
verbose=verbose)
else:
return parsers[parser](file_path=file_path,
contents=contents,
verbose=verbose)
# When the file format is not in our list of parable formats
raise FileFormatNotFound("File format of file: " + file_path + " is unknown")
if __name__ == "__main__":
p = assign_parser("C:/AAA_School/Assignments/BEP/Datasets/Test.csv", verbose=True)
print()
print(p)
print(p.get_dialect)
print()
print(p.parse_file().head(5))
print('Done')
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7,
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220,
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62,
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478,
8,
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7,
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22446,
2256,
7,
20,
4008,
198,
220,
220,
220,
3601,
10786,
45677,
11537,
198
] | 2.352113 | 710 |
from tests.utils import assert_nodes_equal, load_xml, render_node
from zibalzeep import xsd
| [
6738,
5254,
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1330,
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62,
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62,
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2736,
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628,
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] | 3.133333 | 30 |
# -*- coding: utf-8 -*-
#
# Copyright ©2018-2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
docs_mail_merge_test.py -- unit test for docs_mail_merge.py:
1. test credentials file availability
2. test whether project can connect to all 3 APIs
3. test creation (and deletion) of Google Docs file
4. test copying (and deletion) of Google Docs file
5. test getting plain text data
6. test getting data from Google Sheets spreadsheet
"""
import os
import unittest
from googleapiclient import discovery
from docs_mail_merge import (CLIENT_ID_FILE, get_data, get_http_client,
_copy_template)
class TestDocsMailMerge(unittest.TestCase):
'Unit tests for Mail Merge sample'
def project_test():
'Tests whether project credentials file was downloaded from project.'
if os.path.exists(CLIENT_ID_FILE):
return True
raise IOError('''\
ERROR: Must create a Google APIs project, enable both
the Drive and Docs REST APIs, create and download OAuth2
client credentials as %r before unit test can run.''' % CLIENT_ID_FILE)
def gapis_test():
'Tests whether project can connect to all 3 APIs used in the sample.'
HTTP = get_http_client()
discovery.build('drive', 'v3', http=HTTP)
discovery.build('docs', 'v1', http=HTTP)
discovery.build('sheets', 'v4', http=HTTP)
return True
def create_doc_test():
'Tests whether project can create and delete a Google Docs file.'
DRIVE = discovery.build('drive', 'v3', http=get_http_client())
DATA = {
'name': 'Test Doc',
'mimeType': 'application/vnd.google-apps.document',
}
doc_id = DRIVE.files().create(body=DATA, fields='id').execute().get('id')
DRIVE.files().delete(fileId=doc_id, fields='').execute()
return True
def copy_doc_test():
'Tests whether project can copy and delete a Google Docs file.'
DRIVE = discovery.build('drive', 'v3', http=get_http_client())
DOCS_FILE_ID = '1Xycxuuv7OhEQUuzbt_Mw0TPMq02MseSD1vZdBJ3nLjk'
doc_id = _copy_template(DOCS_FILE_ID, 'text', DRIVE)
DRIVE.files().delete(fileId=doc_id, fields='').execute()
return True
def get_text_data_test():
'Tests reading plain text data.'
return get_data('text')
def get_sheets_data_test():
'Tests reading Google Sheets data.'
return get_data('sheets')
if __name__ == '__main__':
unittest.main()
| [
2,
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from os import path
import pytest
import datashader as ds
import rasterio as rio
from pytest import set_trace
BASE_PATH = path.split(__file__)[0]
DATA_PATH = path.abspath(path.join(BASE_PATH, 'data'))
TEST_RASTER_PATH = path.join(DATA_PATH, 'world.rgb.tif')
with rio.open(TEST_RASTER_PATH) as src:
x_range = (src.bounds.left, src.bounds.right)
y_range = (src.bounds.bottom, src.bounds.top)
cvs = ds.Canvas(plot_width=2,
plot_height=2,
x_range=x_range,
y_range=y_range)
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#!/usr/bin/python3
# -*- coding: utf-8 -*-
import os
SETTINGS_PRIORITY = 80
# THESE SETTINGS ARE NEEDED FOR PYSETTINGS
SESSIONLOG_PLUGIN_ICON = os.path.join(os.path.dirname(__file__), 'resources', 'history.png')
SESSIONLOG_PLUGIN_WINDOW_SIZE = 700, 600
SESSIONLOG_PLUGIN_REFRESH_RATE = 1000
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"""Various utility functions."""
import sys
import time
import signal
import numpy as np
def getstop():
"""Returns stop so that stop[0] is True if ctrl+c was hit."""
stop = [False]
_orig = [None]
_orig[0] = signal.signal(signal.SIGINT, handler)
return stop
def saveopt(fname, opt):
"""Save optimizer state to file"""
weights = opt.get_weights()
npz = {('%d' % i): weights[i] for i in range(len(weights))}
np.savez(fname, **npz)
def savemodel(fname, model):
"""Save model weights to file"""
weights = model.get_weights()
npz = {('%d' % i): weights[i] for i in range(len(weights))}
np.savez(fname, **npz)
def loadmodel(fname, model):
"""Restore model weights from file."""
npz = np.load(fname)
weights = [npz['%d' % i] for i in range(len(npz.files))]
model.set_weights(weights)
def loadopt(fname, opt, model):
"""Restore optimizer state from file."""
npz = np.load(fname)
weights = [npz['%d' % i] for i in range(len(npz.files))]
opt._create_all_weights(model.trainable_variables)
opt.set_weights(weights) | [
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import geopandas as gpd
import pandas as pd
from shapely.geometry import Point
from geopandas.tools import sjoin
import sqlite3
#from datetime import datetime, timezone
import datetime
co_county_sf = '/Users/rl/scratch/covid-19/facebook/co_counties/co_counties.shp'
boulder_county_zone_sf = '/Users/rl/scratch/covid-19/facebook/boulder_county_zoning/Zoning__Zoning_Districts.shp'
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from saiqa.Model.UserModel import User
from saiqa.Service.UserService import UserService
from saiqa.Service.QuestionService import QuestionService
from saiqa.Exception.CustomException import FormatError, PasswordMismatchError, EmptyFormError
import re
service = UserService('test')
qser = QuestionService('test')
# List of tests:
# Good Register
# Password Mismatch
# Incorrect Username Format
# Incorrect Password Format 1
# Incorrect Password Format 2
# Bad Register
# Duplicate User | [
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# Coding Practice #0617
#----------------------------------------------------------------------------------
import numpy as np
import cv2
# Go to the directory where the data file is located.
# os.chdir(r'~~') # Please, replace the path with your own.
# 1. Morphological filtering.
# Open an image in B/W.
img = cv2.imread('picture_Texture.jpg',0)
cv2.imshow("Texture", img)
cv2.waitKey(0) # Wait until a key is pressed.
cv2.destroyAllWindows() # Close the open window.
# 1.1. Erosion and dilation:
# Erosion: Turns white pixels into black ones.
# Dilation: Turns black pixels into white ones.
kernel = np.ones((5,5),'uint8')
img_eroded = cv2.erode(img, kernel, iterations=5) # 'iterations' is adjustable.
img_dilated = cv2.dilate(img,kernel,iterations=5) # 'iterations' is adjustable.
cv2.imshow("Eroded", img_eroded)
cv2.waitKey(0) # Wait until a key is pressed.
cv2.destroyAllWindows() # Close the open window.
cv2.imshow("Dilated", img_dilated)
cv2.waitKey(0) # Wait until a key is pressed.
cv2.destroyAllWindows() # Close the open window.
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] | 2.002861 | 699 |
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