content stringlengths 1 1.04M | input_ids listlengths 1 774k | ratio_char_token float64 0.38 22.9 | token_count int64 1 774k |
|---|---|---|---|
from transformers import EvalPrediction
from utils_qa import postprocess_qa_predictions
| [
6738,
6121,
364,
1330,
26439,
39156,
2867,
198,
6738,
3384,
4487,
62,
20402,
1330,
1281,
14681,
62,
20402,
62,
28764,
9278,
628
] | 4.045455 | 22 |
import numpy as np
def dice(Seg, G):
""" compute dice coefficient """
if (np.sum(G) + np.sum(Seg)) == 0:
dice = 1.0
else:
dice = (2.0 * np.sum(Seg[G == 1])) / (np.sum(Seg) + np.sum(G))
return dice
| [
198,
11748,
299,
32152,
355,
45941,
628,
198,
4299,
17963,
7,
41030,
11,
402,
2599,
198,
220,
220,
220,
37227,
24061,
17963,
35381,
37227,
628,
220,
220,
220,
611,
357,
37659,
13,
16345,
7,
38,
8,
1343,
45941,
13,
16345,
7,
41030,
4... | 2.070796 | 113 |
#Write a Python program to test whether a number is within 100 of 1000 or 2000
print(test(120))
print(test(0))
print(test(80))
print(test(999))
print(test(1000)) | [
2,
16594,
257,
11361,
1430,
284,
1332,
1771,
257,
1271,
318,
1626,
1802,
286,
8576,
393,
4751,
198,
198,
4798,
7,
9288,
7,
10232,
4008,
198,
4798,
7,
9288,
7,
15,
4008,
198,
4798,
7,
9288,
7,
1795,
4008,
198,
4798,
7,
9288,
7,
1... | 3.056604 | 53 |
import time
import vk
| [
11748,
640,
198,
11748,
410,
74,
198
] | 3.142857 | 7 |
import urllib2
import sys
#Simple Class Color
BLACK, RED, GREEN, YELLOW, BLUE, MAGENTA, CYAN, WHITE = range(8)
has_colours = has_colours(sys.stdout)
# Resolver:
printout("*\-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\*\r\n" , RED)
printout("Python ~ Skype Resolver By Phobia\r\n" , CYAN)
printout(" Skype: classified \r\n" , YELLOW)
printout("*\-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\*\r\n" , RED)
SKYPEAPI = "https://www.hackthatapi.com/?command=7&username="
printout("> Skype Username: " , BLUE)
SKYPEUSERNAME = raw_input()
SKYPEAPI = SKYPEAPI + SKYPEUSERNAME
webFile = urllib2.urlopen(SKYPEAPI).read()
#if webFile == ("Invalid Key"):
# printout("Error", RED)
#else:
printout(webFile, GREEN)
print('\r\n') | [
11748,
2956,
297,
571,
17,
198,
11748,
25064,
198,
220,
198,
2,
26437,
5016,
5315,
198,
9148,
8120,
11,
23848,
11,
47606,
11,
575,
23304,
3913,
11,
9878,
8924,
11,
28263,
3525,
32,
11,
30440,
1565,
11,
44925,
796,
2837,
7,
23,
8,
... | 2.39375 | 320 |
# Copyright (c) 2015, 2014 Computational Molecular Biology Group, Free University
# Berlin, 14195 Berlin, Germany.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation and/or
# other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ``AS IS''
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
# ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from __future__ import absolute_import
import numpy as np
from pyemma.util.annotators import deprecated
from six.moves import zip
__author__ = 'Fabian Paul'
__all__ = ['histogram']
@deprecated("Please use pyemma.coordinates.histogram()")
def histogram(transform, dimensions, nbins):
'''Computes the N-dimensional histogram of the transformed data.
Parameters
----------
transform : pyemma.coordinates.transfrom.Transformer object
transform that provides the input data
dimensions : tuple of indices
indices of the dimensions you want to examine
nbins : tuple of ints
number of bins along each dimension
Returns
-------
counts : (bins[0],bins[1],...) ndarray of ints
counts compatible with pyplot.pcolormesh and pyplot.bar
edges : list of (bins[i]) ndarrays
bin edges compatible with pyplot.pcolormesh and pyplot.bar,
see below.
Examples
--------
>>> import matplotlib.pyplot as plt # doctest: +SKIP
Only for ipython notebook
>> %matplotlib inline # doctest: +SKIP
>>> counts, edges=histogram(transform, dimensions=(0,1), nbins=(20, 30)) # doctest: +SKIP
>>> plt.pcolormesh(edges[0], edges[1], counts.T) # doctest: +SKIP
>>> counts, edges=histogram(transform, dimensions=(1,), nbins=(50,)) # doctest: +SKIP
>>> plt.bar(edges[0][:-1], counts, width=edges[0][1:]-edges[0][:-1]) # doctest: +SKIP
'''
maximum = np.ones(len(dimensions)) * (-np.inf)
minimum = np.ones(len(dimensions)) * np.inf
# compute min and max
for _, chunk in transform:
maximum = np.max(
np.vstack((
maximum,
np.max(chunk[:, dimensions], axis=0))),
axis=0)
minimum = np.min(
np.vstack((
minimum,
np.min(chunk[:, dimensions], axis=0))),
axis=0)
# define bins
bins = [np.linspace(m, M, num=n)
for m, M, n in zip(minimum, maximum, nbins)]
res = np.zeros(np.array(nbins) - 1)
# compute actual histogram
for _, chunk in transform:
part, _ = np.histogramdd(chunk[:, dimensions], bins=bins)
res += part
return res, bins
| [
2,
15069,
357,
66,
8,
1853,
11,
1946,
22476,
864,
38275,
24698,
4912,
11,
3232,
2059,
198,
2,
11307,
11,
1478,
22186,
11307,
11,
4486,
13,
198,
2,
1439,
2489,
10395,
13,
198,
2,
198,
2,
2297,
396,
3890,
290,
779,
287,
2723,
290,
... | 2.76753 | 1,312 |
from starlette.authentication import SimpleUser
from starlette_auth_toolkit.base.backends import BaseTokenAuth
from ..utils import get_base_app
TOKEN = "s3kr3t_t0k3n"
| [
6738,
3491,
21348,
13,
41299,
3299,
1330,
17427,
12982,
198,
198,
6738,
3491,
21348,
62,
18439,
62,
25981,
15813,
13,
8692,
13,
1891,
2412,
1330,
7308,
30642,
30515,
198,
198,
6738,
11485,
26791,
1330,
651,
62,
8692,
62,
1324,
198,
198,... | 2.915254 | 59 |
__version__ = "0.1.dev39"
version = "0.1.dev39"
version_tuple = (0, 1)
| [
834,
9641,
834,
796,
366,
15,
13,
16,
13,
7959,
2670,
1,
198,
9641,
796,
366,
15,
13,
16,
13,
7959,
2670,
1,
198,
9641,
62,
83,
29291,
796,
357,
15,
11,
352,
8,
198
] | 2.028571 | 35 |
import os
import fps as fps_mod
if __name__ == "__main__":
main(**fps_mod.collect_args()) | [
11748,
28686,
198,
198,
11748,
32977,
355,
32977,
62,
4666,
628,
198,
198,
361,
11593,
3672,
834,
6624,
366,
834,
12417,
834,
1298,
198,
220,
220,
220,
1388,
7,
1174,
29647,
62,
4666,
13,
33327,
62,
22046,
28955
] | 2.552632 | 38 |
from django.urls import include, path
from django.conf.urls.i18n import i18n_patterns
from django.conf import settings
from django.contrib import admin
from django.views.generic.base import RedirectView
# Uncomment the next two lines to enable the admin:
admin.autodiscover()
admin.site.enable_nav_sidebar = False
admin.site.site_header = 'Software de Historias Clinicas'
admin.site.site_title = 'Historias Clinicas'
admin.site.index_title = "Bienvenido al Software de Historias Clinicas"
urlpatterns = i18n_patterns(
# Examples:
# url(r'^$', 'hist.views.home', name='home'),
# url(r'^hist/', include('hist.foo.urls')),
# Uncomment the admin/doc line below to enable admin documentation:
# url(r'^admin/doc/', include('django.contrib.admindocs.urls')),
# Uncomment the next line to enable the admin:
path('', RedirectView.as_view(url='/admin')),
path('admin/historias/', include('historias.urls')),
path('admin/', admin.site.urls),
)
if settings.DEBUG:
import debug_toolbar
urlpatterns = [
path('__debug__/', include(debug_toolbar.urls)),
] + urlpatterns | [
6738,
42625,
14208,
13,
6371,
82,
1330,
2291,
11,
3108,
198,
6738,
42625,
14208,
13,
10414,
13,
6371,
82,
13,
72,
1507,
77,
1330,
1312,
1507,
77,
62,
33279,
82,
220,
198,
6738,
42625,
14208,
13,
10414,
1330,
6460,
198,
6738,
42625,
... | 2.7543 | 407 |
# encode image of shape (n<=24, 1080, 1920) with Enhanced Run-Length Encoding (ERLE) described in http://www.ti.com/lit/pdf/dlpu018
import numpy as np
import struct
pack32be = struct.Struct('>I').pack # uint32 big endian
def get_header():
'''
generate header defined in section 2.4.2
'''
header = bytearray(0)
# signature
header += bytearray([0x53, 0x70, 0x6c, 0x64])
# width
header += bytearray([1920 % 256, 1920//256])
# height
header += bytearray([1080 % 256, 1080//256])
# number of bytes, will be overwritten later
header += bytearray(4)
# reserved
header += bytearray([0xff]*8)
# background color (BB GG RR 00)
header += bytearray(4)
# reserved
header.append(0)
# compression, 0=Uncompressed, 1=RLE, 2=Enhanced RLE
header.append(2)
# reserved
header.append(1)
header += bytearray(21)
return header
header_template = get_header()
def merge(images):
'''
merge up to 24 binary images into a single 24-bit image, each pixel is an uint32 of format 0x00BBGGRR
'''
image32 = np.zeros((1080, 1920), dtype=np.uint32)
n_img = len(images)
batches = [8]*(n_img//8)
if n_img % 8:
batches.append(n_img % 8)
for i, batch_size in enumerate(batches):
image8 = np.zeros((1080, 1920), dtype=np.uint8)
for j in range(batch_size):
image8 += images[i*8+j]*(1 << j)
image32 += image8*(1 << (i*8))
return image32
def bgr(pixel):
'''
convert an uint32 pixel into [B, G, R] bytes
'''
return pack32be(pixel)[1:4]
def enc128(num):
'''
encode num (up to 32767) into 1 or 2 bytes
'''
return bytearray([(num & 0x7f) | 0x80, num >> 7]) if num >= 128 else bytearray([num])
def run_len(row, idx):
'''
find the length of the longest run starting from idx in row
'''
stride = 128
length = len(row)
j = idx
while j < length and row[j]:
if j % stride == 0 and np.all(row[j:j+stride]):
j += min(stride, length-j)
else:
j += 1
return j-idx
def encode_row(row, same_prev):
'''
encode a row of length 1920 with the format described in section 2.4.3.2
'''
# bool array indicating if same as previous row, shape = (1920, )
# same_prev = np.zeros(1920, dtype=bool) if i==0 else image[i]==image[i-1]
# bool array indicating if same as next element, shape = (1919, )
same = np.logical_not(np.diff(row))
# same as previous row or same as next element, shape = (1919, )
same_either = np.logical_or(same_prev[:1919], same)
j = 0
compressed = bytearray(0)
while j < 1920:
# copy n pixels from previous line
if same_prev[j]:
r = run_len(same_prev, j+1) + 1
j += r
compressed += b'\x00\x01' + enc128(r)
# repeat single pixel n times
elif j < 1919 and same[j]:
r = run_len(same, j+1) + 2
j += r
compressed += enc128(r) + bgr(row[j-1])
# single uncompressed pixel
elif j > 1917 or same_either[j+1]:
compressed += b'\x01' + bgr(row[j])
j += 1
# multiple uncompressed pixels
else:
j_start = j
pixels = bgr(row[j]) + bgr(row[j+1])
j += 2
while j == 1919 or not same_either[j]:
pixels += bgr(row[j])
j += 1
compressed += b'\x00' + enc128(j-j_start) + pixels
return compressed + b'\x00\x00'
def encode(images):
'''
encode image with the format described in section 2.4.3.2.1
'''
# header
encoded = bytearray(header_template)
# uint32 array, shape = (1080, 1920)
image = merge(images)
# image content
for i in range(1080):
# bool array indicating if same as previous row, shape = (1920, )
same_prev = np.zeros(1920, dtype=bool) if i == 0 else image[i] == image[i-1]
encoded += encode_row(image[i], same_prev)
# end of image
encoded += b'\x00\x01\x00'
# pad to 4-byte boundary
encoded += bytearray((-len(encoded)) % 4)
# overwrite number of bytes in header
# uint32 little endian, offset=8
struct.pack_into('<I', encoded, 8, len(encoded))
return encoded
| [
2,
37773,
2939,
286,
5485,
357,
77,
27,
28,
1731,
11,
17729,
11,
14062,
8,
351,
22104,
5660,
12,
24539,
14711,
7656,
357,
1137,
2538,
8,
3417,
287,
2638,
1378,
2503,
13,
20259,
13,
785,
14,
18250,
14,
12315,
14,
25404,
19944,
29159,... | 2.230689 | 1,929 |
import os.path
from setuptools import setup
setup(
name="costBuddy",
license="MIT",
description="Taming AWS cost proactively",
long_description=get_long_description(),
author="A lot of people",
author_email="opensource@intuit.com",
packages=["src"],
python_requires='>=3.6',
classifiers=[
"Development Status :: 6 - Mature",
"Environment :: Console",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.4",
"Programming Language :: Python :: 3.5",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: Implementation :: CPython",
"Programming Language :: Python :: Implementation :: PyPy",
"Topic :: Software Development",
"Topic :: Utilities",
], install_requires=['boto3', 'pytest'])
| [
11748,
28686,
13,
6978,
198,
6738,
900,
37623,
10141,
1330,
9058,
628,
198,
40406,
7,
198,
220,
220,
220,
1438,
2625,
15805,
33,
21584,
1600,
198,
220,
220,
220,
5964,
2625,
36393,
1600,
198,
220,
220,
220,
6764,
2625,
51,
3723,
30865... | 2.751381 | 362 |
author_special_cases = {
"Jeanette Hellgren Kotaleski": ("Jeanette", "Hellgren Kotaleski"),
"Hellgren Kotaleski J": ("Jeanette", "Hellgren Kotaleski"),
"João Pedro Santos": ("João Pedro", "Santos"),
"Yi Ming Lai": ("Yi Ming", "Lai"),
"Luis Georg Romundstad": ("Luis Georg", "Romundstad"),
"Johanna Frost Nylen": ("Johanna", "Frost Nylen"),
"Pål Gunnar Larsson": ("Pål Gunnar", "Larsson"),
"André Sevenius Nilsen": ("André Sevenius", "Nilsen"),
"Gabriel Andrés Fonseca Guerra": ("Gabriel Andrés", "Fonseca Guerra"),
"Pier Stanislao Paolucci": ("Pier Stanislao", "Paolucci"),
"Werner Van Geit": ("Werner", "Van Geit"),
"Sacha van Albada": ("Sacha", "van Albada"),
"Paolo Del Giudice": ("Paolo", "Del Giudice"),
"Ignazio De Blasi": ("Ignazio", "De Blasi"),
"Marc de Kamps": ("Marc", "de Kamps"),
"José Francisco Gómez González": ("José Francisco", "Gómez González"),
"Ivilin Peev Stoianov": ("Ivilin Peev", "Stoianov"),
"BBP-team": ("BBP", "team")
}
| [
198,
198,
9800,
62,
20887,
62,
33964,
796,
1391,
198,
220,
220,
220,
366,
38248,
5857,
5783,
32762,
21702,
2040,
4106,
1298,
5855,
38248,
5857,
1600,
366,
28254,
32762,
21702,
2040,
4106,
12340,
198,
220,
220,
220,
366,
28254,
32762,
21... | 2.343249 | 437 |
from modeltranslation.translator import register, TranslationOptions
from .models import AgeGroup, Compensation, Recruitment, Trait
@register(AgeGroup)
@register(Compensation)
@register(Recruitment)
@register(Trait)
| [
6738,
953,
2120,
26084,
7592,
13,
7645,
41880,
1330,
7881,
11,
33322,
29046,
198,
198,
6738,
764,
27530,
1330,
7129,
13247,
11,
39059,
11,
3311,
4872,
434,
11,
4759,
270,
628,
198,
31,
30238,
7,
23396,
13247,
8,
628,
198,
31,
30238,
... | 3.515625 | 64 |
import torch
from torchvision import transforms, utils
from PIL import Image
import os
import numpy as np
| [
11748,
28034,
198,
6738,
28034,
10178,
1330,
31408,
11,
3384,
4487,
198,
6738,
350,
4146,
1330,
7412,
198,
11748,
28686,
198,
11748,
299,
32152,
355,
45941,
628
] | 3.962963 | 27 |
__author__ = "Lucas Ortega Venzel"
__license__ = "The Unlicense"
__version__ = "0.1"
__maintainer__ = "Lucas Ortega Venzel"
__email__ = "venzellucas@gmail.com"
__status__ = "Testing"
from itertools import product
from tqdm import tqdm
from sklearn.metrics import make_scorer
from sklearn.model_selection import cross_val_score
| [
834,
9800,
834,
796,
366,
22946,
292,
1471,
660,
4908,
9932,
17396,
1,
198,
834,
43085,
834,
796,
366,
464,
791,
43085,
1,
198,
834,
9641,
834,
796,
366,
15,
13,
16,
1,
198,
834,
76,
2913,
10613,
834,
796,
366,
22946,
292,
1471,
... | 2.811966 | 117 |
from django import forms
from django.contrib.auth.models import User
from dal import autocomplete
from .models import BitByteActivity, OffChallenge
| [
6738,
42625,
14208,
1330,
5107,
198,
6738,
42625,
14208,
13,
3642,
822,
13,
18439,
13,
27530,
1330,
11787,
198,
198,
6738,
288,
282,
1330,
1960,
42829,
6677,
198,
198,
6738,
764,
27530,
1330,
4722,
40778,
16516,
11,
3242,
41812,
3540,
6... | 3.642857 | 42 |
# pylint: disable=too-few-public-methods, attribute-defined-outside-init
"""A tiny, async webframework written in Python3."""
import re
import cgi
from io import BytesIO
from urllib.parse import parse_qs
from types import FunctionType
async def default404(_, error=''):
"""Default 404 handler."""
return Response(str(error), status=404)
async def default500(_, error=''):
"""Default 404 handler."""
return Response(str(error), status=500)
class UploadedFile:
"""A file uploaded through a multipart/form POST request."""
class Router:
"""Route different matching string patterns to different urls."""
def add_route(self, route: str, view: FunctionType):
"""Add a route. Compiles a regex string and stores it with a tuple.
Eventually, this should use prefixes so we can more effectively search
through paths. As it stands, finding a path will be O(n)."""
self.routes[route] = [re.compile(route), view]
def route(self, route: str):
"""Decorator for adding a route to the router.
This lets the user add routes pretty easily from Python code
like this:
@router.route(r'^/mypath$')
def mypath(request):
...
return response
"""
return decorator
def dispatch(self, path: str) -> FunctionType:
"""Search for a stored route that matches the given path."""
for route in self.routes:
regex = self.routes[route][0]
match = regex.search(path)
if match:
view = self.routes[route][1]
return view, match.groupdict()
return self.handler404, {"error": "Path {} Not Found".format(path)}
class Request:
"""Represents an incoming HTTP request from the ASGI server.
Handles storing get parameters, the request body,
and giving view function informations on the context they are
being called.
"""
@classmethod
async def create(cls, scope, receive):
"""Async factory method for creating a request object."""
self = Request()
self._scope = scope
self.body = await self.receive_body(receive)
self.path = scope['path']
self.method = scope.get('method', 'GET')
self.headers = {}
for key, value in scope['headers']:
key = key.decode('utf-8')
self.headers[key] = value.decode('utf-8')
self.content_type = self.headers.get(
'content-type',
'application/x-www-form-urlencoded'
)
if self.method == 'GET':
self.GET = self.build_get_params() # pylint: disable=invalid-name
elif self.method == 'POST':
self.POST = self.build_post_params() # pylint: disable=invalid-name
return self
async def receive_body(self, receive):
"""Load all of the body contained in the request and store it.
This is based off a similar example from the Uvicorn documentation.
"""
body = b''
more_body = True
while more_body:
message = await receive()
body += message.get('body', b'')
more_body = message.get('more_body', False)
return body
def build_get_params(self):
"""Construction of more advanced parts of a request."""
get = {}
query_string = parse_qs(self._scope['query_string'].decode('utf-8'))
get.update(query_string)
return get
def build_post_params(self):
"""Construction of POST parameters and content"""
post = {}
# Using the CGI module to parse multipart form data.
# This section is inspired by similar bottle code.
if self.content_type.startswith('multipart/'):
safe_env = {'QUERY_STRING': '', 'REQUEST_METHOD': 'POST'}
if self.content_type:
safe_env.update({'CONTENT_TYPE': self.content_type})
if self.headers.get('content-length'):
safe_env.update({'CONTENT_LENGTH': self.headers['content-length']})
cgi_args = dict(
fp=BytesIO(self.body),
environ=safe_env,
keep_blank_values=True
)
data = cgi.FieldStorage(**cgi_args)
data = data.list or []
for item in data:
if item.filename:
post[item.name] = UploadedFile(item.file, item.name,
item.filename, item.headers)
else:
post[item.name] = item.value
return post
class Response:
"""A response object. Returned by a view."""
async def send_response(self, send):
"""Send an http response."""
await send({
'type': 'http.response.start',
'status': self.status,
'headers': [
[b'content_type', self.content_type],
*self.headers
],
})
await send({
'type': 'http.response.body',
'body': self.body,
})
class Phial:
"""A Phial webserver class.
When called, returns a callback function that the ASGI server can use,
while still having access to the parent Phial class.
Arguments:
router: a Router instance
"""
def __call__(self, scope):
"""The ASGI callback handler."""
return callback
async def handle_http(self, receive, send, scope):
"""HTTP Handler."""
request = await Request.create(scope, receive)
view, url_params = self.router.dispatch(request.path)
try:
response = await view(request, **url_params)
except Exception as error: # pylint: disable=broad-except
response = await self.router.handler500(request, error=error)
await response.send_response(send)
| [
2,
279,
2645,
600,
25,
15560,
28,
18820,
12,
32146,
12,
11377,
12,
24396,
82,
11,
11688,
12,
23211,
12,
43435,
12,
15003,
198,
37811,
32,
7009,
11,
30351,
3992,
30604,
3194,
287,
11361,
18,
526,
15931,
198,
11748,
302,
198,
11748,
2... | 2.331225 | 2,530 |
import os
import time
import numpy as np
import fitsio
catalog_dir = '_catalogs'
box_data_fn = os.path.join(catalog_dir, 'box_data.fits')
data_fn = os.path.join(catalog_dir, 'data.fits')
randoms_fn = os.path.join(catalog_dir, 'randoms.fits')
bias = 2.0
| [
11748,
28686,
198,
11748,
640,
198,
198,
11748,
299,
32152,
355,
45941,
198,
11748,
11414,
952,
628,
198,
9246,
11794,
62,
15908,
796,
705,
62,
9246,
11794,
82,
6,
198,
3524,
62,
7890,
62,
22184,
796,
28686,
13,
6978,
13,
22179,
7,
... | 2.407407 | 108 |
"""MoneroPy - A python toolbox for Monero
Copyright (C) 2016 The MoneroPy Developers.
MoneroPy is released under the BSD 3-Clause license. Use and redistribution of
this software is subject to the license terms in the LICENSE file found in the
top-level directory of this distribution.
Modified by emesik and rooterkyberian:
+ optimized
+ proper exceptions instead of returning errors as results
"""
from typing import List
ALPHABET: List[int] = [
ord(s) for s in ("123456789ABCDEFGHJKLMNPQRSTUVWXYZ"
"abcdefghijkmnopqrstuvwxyz")
]
B58_BASE: int = 58
UINT64_MAX: int = 2 ** 64
ENCODED_BLOCK_SIZES: List[int] = [0, 2, 3, 5, 6, 7, 9, 10, 11]
FULL_BLOCK_SIZE: int = 8
FULL_ENCODED_BLOCK_SIZE: int = 11
def encode(data: bytes) -> str:
"""Encode data bytes as base58 (ex: encoding a Monero address)."""
l_data: int = len(data)
if l_data == 0:
return ""
full_block_count: int = l_data // FULL_BLOCK_SIZE
last_block_size: int = l_data % FULL_BLOCK_SIZE
res_size: int = (full_block_count *
FULL_ENCODED_BLOCK_SIZE +
ENCODED_BLOCK_SIZES[last_block_size])
res: bytearray = bytearray([ALPHABET[0]] * res_size)
for i in range(full_block_count): # type: int
res = encode_block(
data[(i * FULL_BLOCK_SIZE):(i * FULL_BLOCK_SIZE + FULL_BLOCK_SIZE)],
res,
i * FULL_ENCODED_BLOCK_SIZE
)
if last_block_size > 0:
begin: int = full_block_count * FULL_BLOCK_SIZE
end: int = full_block_count * FULL_BLOCK_SIZE + last_block_size
res = encode_block(data[begin:end],
res,
full_block_count * FULL_ENCODED_BLOCK_SIZE)
return bytes(res).decode("ascii")
def decode(encoded_value: str) -> str:
"""Decode a base58 string (ex: a Monero address) into hexidecimal form."""
data_encoded: bytearray = bytearray(encoded_value, encoding="ascii")
l_enc: int = len(data_encoded)
if l_enc == 0:
return ""
full_block_count: int = l_enc // FULL_ENCODED_BLOCK_SIZE
last_block_size: int = l_enc % FULL_ENCODED_BLOCK_SIZE
try:
last_block_decoded_size: int = ENCODED_BLOCK_SIZES.index(last_block_size)
except ValueError:
raise ValueError(f"Invalid encoded length: {l_enc}")
data_size: int = (full_block_count *
FULL_BLOCK_SIZE +
last_block_decoded_size)
data: bytearray = bytearray(data_size)
begin: int
end: int
for i in range(full_block_count): # type: int
begin = i * FULL_ENCODED_BLOCK_SIZE
end = i * FULL_ENCODED_BLOCK_SIZE + FULL_ENCODED_BLOCK_SIZE
data = decode_block(data_encoded[begin:end],
data,
i * FULL_BLOCK_SIZE)
if last_block_size > 0:
begin = full_block_count * FULL_ENCODED_BLOCK_SIZE
end = full_block_count * FULL_ENCODED_BLOCK_SIZE + last_block_size
data = decode_block(data_encoded[begin:end],
data,
full_block_count * FULL_BLOCK_SIZE)
return bin_to_hex(data)
| [
37811,
9069,
3529,
20519,
532,
317,
21015,
2891,
3524,
329,
2892,
3529,
198,
15269,
357,
34,
8,
1584,
383,
2892,
3529,
20519,
34152,
13,
198,
198,
9069,
3529,
20519,
318,
2716,
739,
262,
347,
10305,
513,
12,
2601,
682,
5964,
13,
5765,... | 2.129161 | 1,502 |
import sys
from setuptools import setup, find_packages
sys.path.insert(0, 'AWSIoTPythonSDK')
import AWSIoTPythonSDK
currentVersion = AWSIoTPythonSDK.__version__
setup(
name='AWSIoTPythonSDK',
packages=find_packages(exclude=('tests', )),
version=currentVersion,
description='SDK for connecting to AWS IoT using Python.',
author='Amazon Web Service',
author_email='',
url='https://github.com/aws/aws-iot-device-sdk-python.git',
download_url='https://s3.amazonaws.com/aws-iot-device-sdk-python/aws-iot-device-sdk-python-latest.zip',
keywords=['aws', 'iot', 'mqtt'],
classifiers=[
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Developers",
"Natural Language :: English",
"License :: OSI Approved :: Apache Software License",
"Programming Language :: Python",
"Programming Language :: Python :: 2.7",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.3",
"Programming Language :: Python :: 3.4",
"Programming Language :: Python :: 3.5"
]
)
| [
11748,
25064,
198,
6738,
900,
37623,
10141,
1330,
9058,
11,
1064,
62,
43789,
198,
17597,
13,
6978,
13,
28463,
7,
15,
11,
705,
12298,
11584,
78,
7250,
7535,
10305,
42,
11537,
198,
11748,
14356,
11584,
78,
7250,
7535,
10305,
42,
198,
14... | 2.661871 | 417 |
"""
Module: resources/__init__.py
"""
from mercadopago.resources.advanced_payment import AdvancedPayment
from mercadopago.resources.card_token import CardToken
from mercadopago.resources.card import Card
from mercadopago.resources.customer import Customer
from mercadopago.resources.disbursement_refund import DisbursementRefund
from mercadopago.resources.identification_type import IdentificationType
from mercadopago.resources.merchant_order import MerchantOrder
from mercadopago.resources.payment_methods import PaymentMethods
from mercadopago.resources.payment import Payment
from mercadopago.resources.preference import Preference
from mercadopago.resources.preapproval import PreApproval
from mercadopago.resources.refund import Refund
from mercadopago.resources.user import User
from mercadopago.config.request_options import RequestOptions
from mercadopago.http.http_client import HttpClient
| [
37811,
198,
26796,
25,
4133,
14,
834,
15003,
834,
13,
9078,
198,
37811,
198,
6738,
11991,
324,
404,
3839,
13,
37540,
13,
32225,
2903,
62,
37301,
1330,
13435,
19197,
434,
198,
6738,
11991,
324,
404,
3839,
13,
37540,
13,
9517,
62,
30001... | 3.75 | 240 |
# 数码问题
initial_mat = [] | [
2,
10545,
243,
108,
163,
254,
223,
29785,
106,
165,
95,
246,
198,
198,
36733,
62,
6759,
796,
17635
] | 1.263158 | 19 |
#!/usr/bin/env python
"""
# By: Charles Brandt [code at contextiskey dot com]
# On: 2014.07.09 14:56:09
# License: MIT
# Requires: moments
# based off of:
# /c/templates/scripts/diff_directories.py
# Description:
#
# takes two directory paths as input
# looks at the contents of both directories
# and recursively finds json files with differences between the two of them
be very careful if merging automatically...
this is not a version control system. more like copy contents of directories over one another and keeping the newer version.
potential for data loss there, but shouldn't be a big deal with one primary location.
few different approaches for the diff part:
1. read in json as actual object then compare that way:
http://stackoverflow.com/questions/11141644/how-to-compare-2-json-in-python
2. use dedicated library for the task:
https://pypi.python.org/pypi/json_tools
https://bitbucket.org/vadim_semenov/json_tools/src/75cc15381188c760badbd5b66aef9941a42c93fa?at=default
https://bitbucket.org/vadim_semenov/json_tools/wiki/Home
it employs json-patch:
http://tools.ietf.org/html/draft-ietf-appsawg-json-patch-02
3. diff textually:
http://stackoverflow.com/questions/4599456/textually-diffing-json
python3 sync_jsons.py /path/to/d1 /path/to/d2
it is best if d1 is a subset of d2 (pruned collection derived from bigger one)
If d2 does not yet have any data, use rsync or copy to get the initial set (nothing to synchronize in that case):
rsync -av /d1/*.json /d2/
"""
from __future__ import print_function
from builtins import str
# skelton for command line interaction:
import os, sys
import re
import subprocess, shutil
from datetime import datetime
import json, codecs
#http://docs.python.org/release/2.5.2/lib/module-difflib.html
from difflib import Differ, unified_diff
from pprint import pprint
#from moments.path import Path
from sortable.path import Path
from moments.filters import unaccented_map
#from __future__ import print_function
def diff_json(local, other):
""" Calculates the difference between two JSON documents.
All resulting changes are relative to @a local.
Returns diff formatted in form of extended JSON Patch (see IETF draft).
via:
https://bitbucket.org/vadim_semenov/json_tools/src/75cc15381188c760badbd5b66aef9941a42c93fa/lib/diff.py?at=default
"""
result = []
_recursive_diff(local, other, result)
return result
def print_reduced(diff, pretty=True):
""" Prints JSON diff in reduced format (similar to plain diffs).
"""
print(diff)
for action in diff:
if 'add' in action:
print('+', action['add'], action['value'])
elif 'remove' in action:
print('-', action['remove'], action['prev'])
if __name__ == '__main__':
if len (sys.argv) > 1:
if sys.argv[1] in ['--help','help'] or len(sys.argv) < 2:
usage()
d1 = sys.argv[1]
d2 = sys.argv[2]
#go through each directory
#look for matching .json files in both d1 and d2
#if they're exactly the same, skip
#if they're the same filename, but different contents:
# - check which one has newer content (timestamp)
# - if newer is larger, use it
# - if newer is smaller, show visual diff (or an alert at minimum)
#debug mode to show what the differences are
#(diff, added, skipped) = diff_dirs(d1, d2)
#when ready to really sync, uncomment:
(diff, added, skipped) = diff_dirs(d1, d2, sync=True)
print("ADDED:")
print('\n'.join(added))
print("\nSKIPPED:")
print('\n'.join(skipped))
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
37811,
198,
2,
2750,
25,
7516,
13512,
83,
685,
8189,
379,
4732,
271,
2539,
16605,
401,
60,
198,
2,
1550,
25,
1946,
13,
2998,
13,
2931,
1478,
25,
3980,
25,
2931,
198,
2,
13789,
25,
... | 2.695525 | 1,363 |
import numpy as np
def _get_chain_recursive(begin,end,sizes):
#print((begin,end,sizes))
"""
Get chain starting with bead of size sizes[begin] and ending with bead sizes[end-1]
"""
if begin == end:
return np.array([[]])
elif begin == end-1:
return np.array([[0,0,0]])
else:
margin = 0.001 # allow 0.1% intersections
intersecting = True
while intersecting:
midpoint = (begin+end)//2
left_chain = _get_chain_recursive(begin,midpoint,sizes)
right_chain = _get_chain_recursive(midpoint,end,sizes)
chain_offset = (sizes[midpoint] + sizes[midpoint-1])*get_spherical()
right_chain_shifted = right_chain + chain_offset
squared_distances = np.sum((left_chain[:,np.newaxis] - right_chain_shifted[np.newaxis,:])**2, axis = -1)
left_sizes = sizes[begin:midpoint]
right_sizes = sizes[midpoint:end]
shortcuts = (1-margin)*(left_sizes[:,np.newaxis]+right_sizes[np.newaxis,:])**2 - squared_distances
if np.all(shortcuts < 0):
return np.vstack([left_chain,right_chain_shifted])
else:
#print(end-begin)
intersecting = True
if __name__ == "__main__":
print(get_chains(np.ones(17),1))
| [
11748,
299,
32152,
355,
45941,
198,
198,
4299,
4808,
1136,
62,
7983,
62,
8344,
30753,
7,
27471,
11,
437,
11,
82,
4340,
2599,
198,
220,
220,
220,
1303,
4798,
19510,
27471,
11,
437,
11,
82,
4340,
4008,
198,
220,
220,
220,
37227,
198,
... | 1.931544 | 745 |
## -*- coding: utf-8 -*-
##
## Jonathan Salwan - 2014-05-12 - ROPgadget tool
##
## http://twitter.com/JonathanSalwan
## http://shell-storm.org/project/ROPgadget/
##
import ropgadget.args
import ropgadget.binary
import ropgadget.core
import ropgadget.gadgets
import ropgadget.options
import ropgadget.rgutils
import ropgadget.updateAlert
import ropgadget.version
import ropgadget.loaders
import ropgadget.ropchain
| [
2235,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
2235,
198,
2235,
220,
11232,
4849,
8149,
532,
1946,
12,
2713,
12,
1065,
532,
371,
3185,
70,
324,
1136,
2891,
198,
2235,
198,
2235,
220,
2638,
1378,
6956,
13,
785,
1... | 2.656051 | 157 |
# coding=utf-8
# Copyright 2018 The TF-Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A neural network based agent that implements Thompson sampling via dropout.
Implements an agent based on a neural network that predicts arm rewards.
The neural network internally uses dropout to approximate Thompson sampling.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gin
import tensorflow as tf
from tf_agents.bandits.agents import greedy_reward_prediction_agent
from tf_agents.networks import q_network
@gin.configurable
class DropoutThompsonSamplingAgent(
greedy_reward_prediction_agent.GreedyRewardPredictionAgent):
"""A neural network based Thompson sampling agent.
This agent receives parameters for a neural network and trains it to predict
rewards. The action is chosen greedily with respect to the prediction.
The neural network implements dropout for exploration.
"""
def __init__(
self,
time_step_spec,
action_spec,
optimizer,
# Network params.
dropout_rate,
network_layers,
dropout_only_top_layer=True,
observation_and_action_constraint_splitter=None,
# Params for training.
error_loss_fn=tf.compat.v1.losses.mean_squared_error,
gradient_clipping=None,
# Params for debugging.
debug_summaries=False,
summarize_grads_and_vars=False,
enable_summaries=True,
expose_predicted_rewards=False,
train_step_counter=None,
name=None):
"""Creates a Dropout Thompson Sampling Agent.
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A nest of `BoundedTensorSpec` representing the actions.
optimizer: The optimizer to use for training.
dropout_rate: Float in `(0, 1)`, the dropout rate.
network_layers: Tuple of ints determining the sizes of the network layers.
dropout_only_top_layer: Boolean parameter determining if dropout should be
done only in the top layer. True by default.
observation_and_action_constraint_splitter: A function used for masking
valid/invalid actions with each state of the environment. The function
takes in a full observation and returns a tuple consisting of 1) the
part of the observation intended as input to the bandit agent and
policy, and 2) the boolean mask. This function should also work with a
`TensorSpec` as input, and should output `TensorSpec` objects for the
observation and mask.
error_loss_fn: A function for computing the error loss, taking parameters
labels, predictions, and weights (any function from tf.losses would
work). The default is `tf.losses.mean_squared_error`.
gradient_clipping: A float representing the norm length to clip gradients
(or None for no clipping.)
debug_summaries: A Python bool, default False. When True, debug summaries
are gathered.
summarize_grads_and_vars: A Python bool, default False. When True,
gradients and network variable summaries are written during training.
enable_summaries: A Python bool, default True. When False, all summaries
(debug or otherwise) should not be written.
expose_predicted_rewards: (bool) Whether to expose the predicted rewards
in the policy info field under the name 'predicted_rewards'.
train_step_counter: An optional `tf.Variable` to increment every time the
train op is run. Defaults to the `global_step`.
name: Python str name of this agent. All variables in this module will
fall under that name. Defaults to the class name.
Raises:
ValueError: If the action spec contains more than one action or or it is
not a bounded scalar int32 spec with minimum 0.
"""
fc_layer_params = network_layers
dropout_param = {'rate': dropout_rate, 'permanent': True}
if dropout_only_top_layer:
dropout_layer_params = [None] * (len(fc_layer_params) - 1)
dropout_layer_params.append(dropout_param)
else:
dropout_layer_params = [dropout_param] * len(fc_layer_params)
if observation_and_action_constraint_splitter:
input_tensor_spec, _ = observation_and_action_constraint_splitter(
time_step_spec.observation)
else:
input_tensor_spec = time_step_spec.observation
reward_network = q_network.QNetwork(
input_tensor_spec=input_tensor_spec,
action_spec=action_spec,
fc_layer_params=fc_layer_params,
dropout_layer_params=dropout_layer_params)
super(DropoutThompsonSamplingAgent, self).__init__(
time_step_spec=time_step_spec,
action_spec=action_spec,
reward_network=reward_network,
optimizer=optimizer,
observation_and_action_constraint_splitter=(
observation_and_action_constraint_splitter),
error_loss_fn=error_loss_fn,
gradient_clipping=gradient_clipping,
debug_summaries=debug_summaries,
summarize_grads_and_vars=summarize_grads_and_vars,
enable_summaries=enable_summaries,
expose_predicted_rewards=expose_predicted_rewards,
train_step_counter=train_step_counter,
name=name)
| [
2,
19617,
28,
40477,
12,
23,
198,
2,
15069,
2864,
383,
24958,
12,
10262,
658,
46665,
13,
198,
2,
198,
2,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
34156,
15341,
198,
2,
345,
743,
407,
779,
428,
2393,
... | 2.874317 | 2,013 |
from ..entity import TypeEntity
from ..list import List
from ..map import Map
from ..reference import Reference
from ..string import String
from .attribute_definition import AttributeDefinition
from .interface_definition_for_type import InterfaceDefinitionForType
from .property_definition import PropertyDefinition
| [
6738,
11485,
26858,
1330,
5994,
32398,
198,
6738,
11485,
4868,
1330,
7343,
198,
6738,
11485,
8899,
1330,
9347,
198,
6738,
11485,
35790,
1330,
20984,
198,
6738,
11485,
8841,
1330,
10903,
198,
198,
6738,
764,
42348,
62,
46758,
1330,
3460,
4... | 4.818182 | 66 |
# coding: utf-8
"""
Xero Finance API
The Finance API is a collection of endpoints which customers can use in the course of a loan application, which may assist lenders to gain the confidence they need to provide capital. # noqa: E501
Contact: api@xero.com
Generated by: https://openapi-generator.tech
"""
import re # noqa: F401
from xero_python.models import BaseModel
class PnlAccountClass(BaseModel):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
"""
Attributes:
openapi_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.
"""
openapi_types = {"total": "float", "account_types": "list[PnlAccountType]"}
attribute_map = {"total": "total", "account_types": "accountTypes"}
def __init__(self, total=None, account_types=None): # noqa: E501
"""PnlAccountClass - a model defined in OpenAPI""" # noqa: E501
self._total = None
self._account_types = None
self.discriminator = None
if total is not None:
self.total = total
if account_types is not None:
self.account_types = account_types
@property
def total(self):
"""Gets the total of this PnlAccountClass. # noqa: E501
Total revenue/expense value # noqa: E501
:return: The total of this PnlAccountClass. # noqa: E501
:rtype: float
"""
return self._total
@total.setter
def total(self, total):
"""Sets the total of this PnlAccountClass.
Total revenue/expense value # noqa: E501
:param total: The total of this PnlAccountClass. # noqa: E501
:type: float
"""
self._total = total
@property
def account_types(self):
"""Gets the account_types of this PnlAccountClass. # noqa: E501
Contains trading income and other income for revenue section / operating expenses and direct cost for expense section if the data is available for each section. Refer to the account type element below # noqa: E501
:return: The account_types of this PnlAccountClass. # noqa: E501
:rtype: list[PnlAccountType]
"""
return self._account_types
@account_types.setter
def account_types(self, account_types):
"""Sets the account_types of this PnlAccountClass.
Contains trading income and other income for revenue section / operating expenses and direct cost for expense section if the data is available for each section. Refer to the account type element below # noqa: E501
:param account_types: The account_types of this PnlAccountClass. # noqa: E501
:type: list[PnlAccountType]
"""
self._account_types = account_types
| [
2,
19617,
25,
3384,
69,
12,
23,
198,
198,
37811,
198,
220,
220,
220,
1395,
3529,
15007,
7824,
628,
220,
220,
220,
383,
15007,
7824,
318,
257,
4947,
286,
886,
13033,
543,
4297,
460,
779,
287,
262,
1781,
286,
257,
8063,
3586,
11,
54... | 2.701627 | 1,106 |
from flask import Flask, render_template, redirect
from flask_pymongo import PyMongo
import scrape_mars
# Create an instance of Flask
app = Flask(__name__)
# Use flask_pymongo to set up mongo connection
mongo = PyMongo(app, uri="mongodb://localhost:27017/mars_db")
# Route to render index.html template using data from Mongo
@app.route("/")
@app.route("/scrape")
if __name__ == "__main__":
app.run(debug=True)
| [
6738,
42903,
1330,
46947,
11,
8543,
62,
28243,
11,
18941,
198,
6738,
42903,
62,
79,
4948,
25162,
1330,
9485,
44,
25162,
198,
11748,
42778,
62,
76,
945,
198,
198,
2,
13610,
281,
4554,
286,
46947,
198,
1324,
796,
46947,
7,
834,
3672,
... | 2.864865 | 148 |
{
'targets': [
{
'target_name': 'node-minizip',
'sources': [
'src/zip.cc',
'src/zip.h',
'src/zip_api.cc',
'src/zip_async_worker.cc',
'src/zip_async_worker.h',
'src/zip_internal.cc',
'src/zip_internal.h',
'src/zip_reader.cc',
'src/zip_reader.h',
'src/zip_utils.cc',
'src/zip_utils.h',
],
'dependencies': [ 'deps/zlib/zlib.gyp:zlib' ],
'include_dirs': [
'deps/',
'<!(node -e "require(\'nan\')")'
],
'conditions': [
['OS=="win"', {
'defines': [
'OS_WIN',
# _HAS_EXCEPTIONS must match ExceptionHandling in msvs_settings.
'_HAS_EXCEPTIONS=0',
],
}],
['OS=="mac" or OS=="linux"', {
'defines': [
'OS_POSIX',
],
}],
['OS=="linux"', {
'cflags':[
# Don't warn about the "struct foo f = {0};" initialization pattern.
'-Wno-missing-field-initializers',
],
}],
],
},
]
}
| [
90,
198,
220,
705,
83,
853,
1039,
10354,
685,
198,
220,
220,
220,
1391,
198,
220,
220,
220,
220,
220,
705,
16793,
62,
3672,
10354,
705,
17440,
12,
1084,
528,
541,
3256,
198,
220,
220,
220,
220,
220,
705,
82,
2203,
10354,
685,
198,... | 1.674772 | 658 |
import pydash
import json
import maya
import logging
from django.core.exceptions import ObjectDoesNotExist
from django.shortcuts import get_object_or_404
from django.apps import apps
from rest_framework.viewsets import GenericViewSet
from rest_framework.views import APIView
from rest_framework import mixins
from rest_framework.response import Response
from rest_framework import status
from rest_framework.permissions import IsAuthenticated
from talentmap_api.common.mixins import FieldLimitableSerializerMixin
from talentmap_api.user_profile.models import UserProfile
from talentmap_api.messaging.models import Notification
from talentmap_api.bidding.models import BidHandshakeCycle
from talentmap_api.messaging.filters import NotificationFilter
from talentmap_api.messaging.serializers import NotificationSerializer
logger = logging.getLogger(__name__)
class NotificationView(FieldLimitableSerializerMixin,
GenericViewSet,
mixins.ListModelMixin,
mixins.RetrieveModelMixin,
mixins.UpdateModelMixin,
mixins.DestroyModelMixin):
'''
partial_update:
Edits a saved notification
retrieve:
Retrieves a specific notification
list:
Lists all notifications
destroy:
Deletes a specified notification
'''
serializer_class = NotificationSerializer
filter_class = NotificationFilter
permission_classes = (IsAuthenticated,)
| [
11748,
279,
5173,
1077,
198,
11748,
33918,
198,
11748,
743,
64,
198,
11748,
18931,
198,
198,
6738,
42625,
14208,
13,
7295,
13,
1069,
11755,
1330,
9515,
13921,
3673,
3109,
396,
198,
6738,
42625,
14208,
13,
19509,
23779,
1330,
651,
62,
15... | 2.976048 | 501 |
from app import db
from werkzeug.security import generate_password_hash, check_password_hash
from datetime import datetime
from flask_login import UserMixin, AnonymousUserMixin
users_privileges = db.Table('users_privileges', db.Model.metadata,
db.Column('id_privilege', db.Integer, db.ForeignKey('privilege_groups.id')),
db.Column('id_user', db.Integer, db.ForeignKey('users.id'))
)
article_collaborators = db.Table('article_collaborators', db.Model.metadata,
db.Column('id_article', db.Integer, db.ForeignKey('articles.id')),
db.Column('id_collaborator', db.Integer, db.ForeignKey('users.id'))
)
article_tags = db.Table('article_tags', db.Model.metadata,
db.Column('id_article', db.Integer, db.ForeignKey('articles.id')),
db.Column('id_tag', db.Integer, db.ForeignKey('tags.id'))
)
from app import login
@login.user_loader
login.anonymous_user = AnonymousUser
| [
6738,
598,
1330,
20613,
198,
6738,
266,
9587,
2736,
1018,
13,
12961,
1330,
7716,
62,
28712,
62,
17831,
11,
2198,
62,
28712,
62,
17831,
198,
6738,
4818,
8079,
1330,
4818,
8079,
198,
6738,
42903,
62,
38235,
1330,
11787,
35608,
259,
11,
... | 2.915858 | 309 |
'''
==========================================================================
connect_bits2bitstruct.py
==========================================================================
A connect function that connects a bits signal and a bitsrtuct signal that
has the same width.
Author : Yanghui Ou
Date : Feb 24, 2020
'''
from pymtl3 import Bits, connect, get_nbits
from pymtl3.datatypes.bitstructs import _FIELDS, is_bitstruct_class
#-------------------------------------------------------------------------
# _connect_bits2bitstruct_h
#-------------------------------------------------------------------------
# Helper function for connect_bits2bitstruct.
#-------------------------------------------------------------------------
# connect_bits2bitstruct
#-------------------------------------------------------------------------
| [
7061,
6,
198,
23926,
2559,
855,
198,
8443,
62,
9895,
17,
2545,
7249,
13,
9078,
198,
23926,
2559,
855,
198,
32,
2018,
2163,
326,
20417,
257,
10340,
6737,
290,
257,
10340,
17034,
4782,
6737,
326,
198,
10134,
262,
976,
9647,
13,
198,
1... | 5.493421 | 152 |
#!/usr/bin/python
# -*- coding: utf-8 -*-
import os
import sys
sys.path.insert(1, '../')
import tools
BOOT_CONFIG_FILE = "/boot/config.txt"
if __name__ == "__main__":
x= main()
print( x )
| [
2,
48443,
14629,
14,
8800,
14,
29412,
198,
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
198,
11748,
28686,
198,
11748,
25064,
198,
17597,
13,
6978,
13,
28463,
7,
16,
11,
705,
40720,
11537,
198,
11748,
4899,
198,
... | 2.208791 | 91 |
"""Graph export/import utilities."""
from typing import List, Union
import networkx as nx
from rpasdt.algorithm.taxonomies import GraphDataFormatEnum
GRAPH_EXPORTER = {
GraphDataFormatEnum.MULTILINE_ADJLIST: lambda graph: list(
nx.generate_multiline_adjlist(graph)
)
}
GRAPH_IMPORTER = {
GraphDataFormatEnum.MULTILINE_ADJLIST: lambda data: nx.parse_multiline_adjlist(
iter(_fetch_lines(data))
)
}
| [
37811,
37065,
10784,
14,
11748,
20081,
526,
15931,
198,
6738,
19720,
1330,
7343,
11,
4479,
198,
198,
11748,
3127,
87,
355,
299,
87,
198,
198,
6738,
374,
44429,
28664,
13,
282,
42289,
13,
19290,
6326,
444,
1330,
29681,
6601,
26227,
4834,... | 2.471591 | 176 |
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
data = pd.read_csv("kc_house_data.csv")
data.drop('id',axis=1,inplace=True)
data.drop('date',axis=1,inplace=True)
plt.figure(figsize=(15,15))
sns.heatmap(data.corr(),annot=True)
plt.show()
| [
11748,
19798,
292,
355,
279,
67,
198,
11748,
384,
397,
1211,
355,
3013,
82,
198,
11748,
2603,
29487,
8019,
13,
9078,
29487,
355,
458,
83,
198,
198,
7890,
796,
279,
67,
13,
961,
62,
40664,
7203,
74,
66,
62,
4803,
62,
7890,
13,
4066... | 2.318966 | 116 |
import copy,math
import numpy as np
import pandas as pd
# ----------------------------------------------------------------------------
# pre-defined models
# ----------------------------------------------------------------------------
# ----------------------------------------------------------------------------
# pre-defined models
# ----------------------------------------------------------------------------
# class two_state_constant_probability_model(bellman_harris_model_base):
# def __init__(self,f,f0,beta):
# self.beta = beta
# type_names = ['ng','broken','gfp']
#
# p = lambda gt:1-np.exp(-beta*gt)
# Q = lambda gt,t: np.array([[np.exp(-beta*gt),1-np.exp(-beta*gt)],[0,1]])
# bellman_harris_model_base.__init__(self,f,f0,Q,type_names)
| [
11748,
4866,
11,
11018,
198,
11748,
299,
32152,
355,
45941,
198,
11748,
19798,
292,
355,
279,
67,
628,
628,
198,
2,
16529,
10541,
198,
2,
662,
12,
23211,
4981,
198,
2,
16529,
10541,
628,
198,
2,
16529,
10541,
198,
2,
662,
12,
23211,... | 3.265306 | 245 |
import discord
from discord.ext import commands
import pyowm
class WeatherCog:
"""ControlCog"""
global debuglv
debuglv = 0
@commands.command()
@commands.guild_only()
@commands.command()
@commands.guild_only()
| [
11748,
36446,
201,
198,
6738,
36446,
13,
2302,
1330,
9729,
201,
198,
11748,
12972,
322,
76,
201,
198,
201,
198,
201,
198,
201,
198,
201,
198,
4871,
15615,
34,
519,
25,
201,
198,
220,
220,
220,
37227,
15988,
34,
519,
37811,
201,
198,... | 2.15873 | 126 |
import tensorflow as tf
import numpy as np
import cv2
import re
import os
import random
from Data_utils import preprocessing
from functools import partial
def readPFM(file):
"""
Load a pfm file as a numpy array
Args:
file: path to the file to be loaded
Returns:
content of the file as a numpy array
"""
file = open(file, 'rb')
color = None
width = None
height = None
scale = None
endian = None
header = file.readline().rstrip()
if header == b'PF':
color = True
elif header == b'Pf':
color = False
else:
raise Exception('Not a PFM file.')
dims = file.readline()
try:
width, height = list(map(int, dims.split()))
except:
raise Exception('Malformed PFM header.')
scale = float(file.readline().rstrip())
if scale < 0: # little-endian
endian = '<'
scale = -scale
else:
endian = '>' # big-endian
data = np.fromfile(file, endian + 'f')
shape = (height, width, 3) if color else (height, width, 1)
data = np.reshape(data, shape)
data = np.flipud(data)
return data, scale
def read_list_file(path_file):
"""
Read dataset description file encoded as left;right;disp;conf
Args:
path_file: path to the file encoding the database
Returns:
[left,right,gt,conf] 4 list containing the images to be loaded
"""
with open(path_file,'r') as f_in:
lines = f_in.readlines()
lines = [x for x in lines if not x.strip()[0] == '#']
left_file_list = []
right_file_list = []
gt_file_list = []
conf_file_list = []
for l in lines:
to_load = re.split(',|;',l.strip())
left_file_list.append(to_load[0])
right_file_list.append(to_load[1])
if len(to_load)>2:
gt_file_list.append(to_load[2])
if len(to_load)>3:
conf_file_list.append(to_load[3])
return left_file_list,right_file_list,gt_file_list,conf_file_list
def read_image_from_disc(image_path,shape=None,dtype=tf.uint8):
"""
Create a queue to hoold the paths of files to be loaded, then create meta op to read and decode image
Args:
image_path: metaop with path of the image to be loaded
shape: optional shape for the image
Returns:
meta_op with image_data
"""
image_raw = tf.read_file(image_path)
if dtype==tf.uint8:
image = tf.image.decode_image(image_raw)
else:
image = tf.image.decode_png(image_raw,dtype=dtype)
if shape is None:
image.set_shape([None,None,3])
else:
image.set_shape(shape)
return tf.cast(image, dtype=tf.float32)
class dataset():
"""
Class that reads a dataset for deep stereo
"""
################# PUBLIC METHOD #######################
########################################################################################à
class task_library():
"""
Support class to handle definition and generation of adaptation tasks
"""
def _load_sequence(self, filename):
"""
Add a sequence to self._task_dictionary, saving the paths to the different files from filename
"""
assert(os.path.exists(filename))
left_files, right_files, gt_files,_ = read_list_file(filename)
self._task_dictionary[filename] = {
'left': left_files,
'right': right_files,
'gt': gt_files,
'num_frames': len(left_files)
}
def get_task(self):
"""
Generate a task encoded as a 3 X num_frames matrix of path to load to get the respective frames
First row contains paths to left frames,
Second row contains paths to right frames,
Third row contains paths to gt frams
"""
#fetch a random task
picked_task = random.choice(list(self._task_dictionary.keys()))
#fetch all the samples from the current sequence
left_frames = self._task_dictionary[picked_task]['left']
right_frames = self._task_dictionary[picked_task]['right']
gt_frames = self._task_dictionary[picked_task]['gt']
num_frames = self._task_dictionary[picked_task]['num_frames']
max_start_frame = num_frames-self._frame_per_task-1
start_frame_index = random.randint(0,max_start_frame)
task_left = left_frames[start_frame_index:start_frame_index+self._frame_per_task]
task_right = right_frames[start_frame_index:start_frame_index+self._frame_per_task]
gt_frames = gt_frames[start_frame_index:start_frame_index+self._frame_per_task]
result = np.array([task_left,task_right,gt_frames])
return result
def __call__(self):
"""
Generator that returns a number of tasks equal to the number of different seuqences in self._taskLibrary
"""
for i in range(len(self._task_dictionary)):
yield self.get_task()
def __len__(self):
"""
Number of tasks/sequences defined in the library
"""
return len(self._task_dictionary)
class metaDataset():
"""
Class that reads a dataset for deep stereo
"""
def _load_task(self, files):
"""
Load all the image and return them as three lists, [left_files], [right_files], [gt_files]
"""
#from 3xk to kx3
left_files = files[0]
right_files = files[1]
gt_files = files[2]
#read images
left_task_samples = tf.map_fn(read_image_from_disc,left_files,dtype = tf.float32, parallel_iterations=self._sequence_length)
left_task_samples.set_shape([self._sequence_length, None, None, 3])
right_task_samples = tf.map_fn(read_image_from_disc,right_files,dtype = tf.float32, parallel_iterations=self._sequence_length)
right_task_samples.set_shape([self._sequence_length, None, None, 3])
gt_task_samples = tf.map_fn(self._decode_gt, gt_files, dtype=tf.float32, parallel_iterations=self._sequence_length)
gt_task_samples.set_shape([self._sequence_length, None, None, 1])
#alligned image resize
if self._resize_shape[0] is not None:
scale_factor = tf.cast(tf.shape(left_task_samples)[1]//self._resize_shape[1], tf.float32)
left_task_samples = preprocessing.rescale_image(left_task_samples,self._resize_shape)
right_task_samples = preprocessing.rescale_image(right_task_samples,self._resize_shape)
gt_task_samples = tf.image.resize_nearest_neighbor(gt_task_samples,self._resize_shape)/scale_factor
#alligned random crop
if self._crop_shape[0] is not None:
left_task_samples,right_task_samples,gt_task_samples = preprocessing.random_crop(self._crop_shape, [left_task_samples,right_task_samples,gt_task_samples])
#augmentation
if self._augment:
left_task_samples,right_task_samples=preprocessing.augment(left_task_samples,right_task_samples)
return [left_task_samples, right_task_samples, gt_task_samples]
################# PUBLIC METHOD #######################
########################################################àà
| [
11748,
11192,
273,
11125,
355,
48700,
198,
11748,
299,
32152,
355,
45941,
198,
11748,
269,
85,
17,
198,
11748,
302,
198,
11748,
28686,
198,
11748,
4738,
198,
198,
6738,
6060,
62,
26791,
1330,
662,
36948,
198,
6738,
1257,
310,
10141,
133... | 2.390139 | 3,022 |
from rest_framework import serializers
from words.models import Word
class WordSerializer(serializers.ModelSerializer):
"""单词使用这个Serializer"""
class WordCloudSerializer(serializers.ModelSerializer):
"""词云使用这个Serializer"""
class WordTrainSerializer(serializers.ModelSerializer):
"""专项训练->单词测验->组卷用这个Serializer"""
| [
6738,
1334,
62,
30604,
1330,
11389,
11341,
198,
198,
6738,
2456,
13,
27530,
1330,
9678,
628,
198,
4871,
9678,
32634,
7509,
7,
46911,
11341,
13,
17633,
32634,
7509,
2599,
198,
220,
220,
220,
37227,
39355,
243,
46237,
235,
45635,
18796,
1... | 2.381295 | 139 |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from mephisto.abstractions.blueprint import TaskBuilder
class EmptyStaticTaskBuilder(TaskBuilder):
"""
Abstract class for a task builder for static tasks
"""
def build_in_dir(self, build_dir: str):
"""Build the frontend if it doesn't exist, then copy into the server directory"""
raise AssertionError(
"Classes that extend the abstract StaticBlueprint must define a custom "
"TaskBuilder class that pulls the correct frontend together. Examples "
"can be seen in the static_react_task and static_html_task folders. "
"Note that extra static content will be provided in `args.blueprint.extra_source_dir` "
)
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
198,
2,
15069,
357,
66,
8,
3203,
11,
3457,
13,
290,
663,
29116,
13,
198,
2,
770,
2723,
2438,
318,
11971,
739,
262,
17168,
5964,
1043,
287,
262,
198,
2,
38559,
24290,
2393,
287,... | 3.075342 | 292 |
import numpy as np
from torch.autograd import Variable
import torch
import torch.nn as nn
from torch.nn import MSELoss
import torch.optim as optim
from torch.utils.data import DataLoader
import math
from sklearn.metrics import mean_squared_error, mean_absolute_error
import argparse
import logging
import time
import torch.nn.functional as F
import sys
| [
11748,
299,
32152,
355,
45941,
198,
6738,
28034,
13,
2306,
519,
6335,
1330,
35748,
198,
11748,
28034,
198,
11748,
28034,
13,
20471,
355,
299,
77,
198,
6738,
28034,
13,
20471,
1330,
6579,
3698,
793,
198,
11748,
28034,
13,
40085,
355,
643... | 3.639175 | 97 |
from cyaron import *
for i in range(0, 10):
io = IO(file_prefix="a", data_id=i + 1)
n = randint(2, 1001)
m = randint(1, 2001)
io.input_writeln(n, m)
graph = Graph.graph(n, m)
io.input_writeln(graph.to_str(output=Edge.unweighted_edge, shuffle=True))
io.output_gen("../../bin/a")
| [
6738,
3075,
8045,
1330,
1635,
198,
198,
1640,
1312,
287,
2837,
7,
15,
11,
838,
2599,
198,
220,
220,
220,
33245,
796,
24418,
7,
7753,
62,
40290,
2625,
64,
1600,
1366,
62,
312,
28,
72,
1343,
352,
8,
628,
220,
220,
220,
299,
796,
4... | 2.197183 | 142 |
import yaml
import os.path
import shutil
if __name__ == '__main__':
config = yaml.load(open('config.yml', 'r'))
directories = config['directories']
default_analyzed_dirs_path = os.path.join(directories['mri_analysis_scripts'], 'default_dirs.yml')
default_analyzed_dirs = yaml.load(open(default_analyzed_dirs_path, 'r'))
dir_map = {
'localizerDir': 'localizer',
'mprageDir': 'mprage',
't219Dir': 't2_19',
't215Dir': 't2_15',
'run1Dir': 'run1',
'run2Dir': 'run2',
'run3Dir': 'run3',
'run4Dir': 'run4',
'run5Dir': 'run5',
'run6Dir': 'run6',
'fieldmap1Dir': 'fieldmap1',
'fieldmap2Dir': 'fieldmap2',
'segmentedpartialDir': 'ep_seg_partial',
'segmentedwholeDir': 'ep_seg_wholebrain'
}
for d in os.listdir(directories['analyzed_mri']):
dirname = os.path.relpath(d)
if dirname.startswith('s'):
print("executing " + dirname)
analyzed_dirs = default_analyzed_dirs.copy()
# If there is a yaml file with overwrite directories,
# we will merge anything that exists there with the existing defaults
# The overwrite file will only contain entries that have changed,
# so merging is necessary - otherwise we will clobber any defaults
# that haven't changed.
overwrite_analyzed_dirs_path = os.path.join(directories['raw_behavioral'], dirname, dirname + '.yml')
if os.path.exists(overwrite_analyzed_dirs_path):
print("found overwrite file " + overwrite_analyzed_dirs_path)
overwrite_analyzed_dirs = yaml.load(open(overwrite_analyzed_dirs_path, 'r'))
for k in overwrite_analyzed_dirs:
analyzed_dirs[k] = overwrite_analyzed_dirs[k]
# Move all of the files if they exist, whether they were defaults or overrides
for k in dir_map:
move_if_exists(
os.path.join(directories['analyzed_mri'],dirname,analyzed_dirs[k]),
os.path.join(directories['analyzed_mri'],dirname,dir_map[k]))
| [
11748,
331,
43695,
198,
11748,
28686,
13,
6978,
198,
11748,
4423,
346,
628,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
4566,
796,
331,
43695,
13,
2220,
7,
9654,
10786,
11250,
13,
88,
4029,
3256,... | 2.133333 | 1,020 |
import os
import torch
def save_model_w_condition(model, model_dir, model_name, accu, target_accu, rank=0, log=print):
'''
model: this is not the multigpu model
'''
if rank == 0:
if accu > target_accu:
log('\tabove {0:.2f}%'.format(target_accu * 100))
# torch.save(obj=model.state_dict(), f=os.path.join(model_dir, (model_name + '{0:.4f}.pth').format(accu)))
torch.save(obj=model, f=os.path.join(model_dir, (model_name + '{0:.4f}.pth').format(accu)))
| [
11748,
28686,
198,
11748,
28034,
198,
198,
4299,
3613,
62,
19849,
62,
86,
62,
31448,
7,
19849,
11,
2746,
62,
15908,
11,
2746,
62,
3672,
11,
697,
84,
11,
2496,
62,
4134,
84,
11,
4279,
28,
15,
11,
2604,
28,
4798,
2599,
198,
220,
2... | 2.124481 | 241 |
characters116 =['☺','☻','♥','♦','♣','♠','•','◘','○','◙','♂','♀','♪','♫','☼','►','◄','↕','‼','¶','§','▬','↨','↑','↓','→','←','∟','↔','▲','▼','space','!','"','#','$','%','&',"'",'(',')','*','+',',','-','.','/','0','1','2','3','4','5','6','7','8','9',':',';','<','=','>','?','@','A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z','a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']
alpha =['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']
chars91u127 = ['[','\\',']','^','_','`','A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z','{','|','}','~','⌂']
chars128u255 = ['Ç','ü','é','â','ä','à','å','ç','ê','ê','è','ï','î','ì','Ä','Å','É','æ','Æ','ô','ö','ò','û','ù','ÿ','Ö','Ü','¢','£','¥','₧','ƒ','á','í','ó','ú','ñ','Ñ','ª','º','¿','⌐','¬','½','¼','¡','«','»','░','▒','▓','│','┤','╡','╢','╖','╕','╣','║','╗','╝','╜','╛','┐','└','┴','┬','├','─','┼','╞','╟','╚','╔','╩','╦','╠','═','╬','╧','╨','╤','╥','╙','╘','╒','╓','╫','╪','┘','┌','█','▄','▌','▐','▀','α','ß','Γ','π','Σ','σ','µ','τ','Φ','Θ','Ω','δ','∞','φ','ε','∩','≡','±','≥','≤','⌠','⌡','÷','≈','°','∙','·','√','ⁿ','²','■',' ']
##from AltCodesAlpha import alphacodes
chars = chars128u255
altCode = LOGICchars128u255
with open("Output.txt" , 'w', encoding='utf8') as file:
allChars()
| [
10641,
19858,
18298,
796,
17816,
24583,
118,
41707,
24583,
119,
41707,
39908,
41707,
41298,
41707,
17992,
96,
41707,
17992,
254,
41707,
3581,
41707,
15926,
246,
41707,
15926,
233,
41707,
15926,
247,
41707,
17992,
224,
41707,
17992,
222,
41707... | 1.76822 | 837 |
from random import randint
from .circle_class import CircleClass
from .rectangle_class import RectangleClass
from .triangle_class import TriangleClass
| [
6738,
4738,
1330,
43720,
600,
198,
198,
6738,
764,
45597,
62,
4871,
1330,
16291,
9487,
198,
6738,
764,
2554,
9248,
62,
4871,
1330,
48599,
9248,
9487,
198,
6738,
764,
28461,
9248,
62,
4871,
1330,
33233,
9487,
628
] | 4.135135 | 37 |
from typing import Optional, Dict, List, Any
import pendulum
from src.data_models import CalculatedFieldDescription
from src.data_models import Configuration
def add_calculated_fields(*,
current_item: Dict[str, Any],
initial_status,
current_status,
position_list,
lap_list,
total,
charging_process_list,
forecast,
configuration: Configuration,
current_item_index: Optional[int],
now_dt: pendulum.DateTime):
"""
Add hardcoded calculated fields into current_item
Note the prototype is the same for all calculated functions even if all inputs are not used
:param current_item:
:param initial_status:
:param current_status:
:param position_list:
:param lap_list:
:param total:
:param charging_process_list:
:param forecast:
:param configuration:
:param current_item_index:
:param now_dt: time to calculate data for.
:return:
"""
lap_start_time: pendulum.DateTime = current_item['lap_data'][0]['date'] \
if 'lap_data' in current_item and current_item['lap_data'] else None
lap_end_time: pendulum.DateTime = current_item['lap_data'][-1]['date'] \
if 'lap_data' in current_item and current_item['lap_data'] else None
pit_start_time: pendulum.DateTime = current_item['pit_data'][0]['date'] \
if 'pit_data' in current_item and current_item['pit_data'] else None
pit_end_time: pendulum.DateTime = current_item['pit_data'][-1]['date'] \
if 'pit_data' in current_item and current_item['pit_data'] else None
distance: float = current_item['lap_data'][-1]['odometer'] - current_item['lap_data'][0]['odometer'] \
if 'lap_data' in current_item and current_item['lap_data'] else None
lap_duration: pendulum.Period = lap_end_time - lap_start_time if lap_end_time and lap_start_time else None
pit_duration: pendulum.Period = pit_end_time - pit_start_time if pit_end_time and pit_start_time else None
full_duration: pendulum.Period = None
if lap_duration and pit_duration:
full_duration = lap_duration + pit_duration
elif lap_duration:
full_duration = lap_duration
else:
full_duration = pit_duration
lap_avg_speed: float = distance / lap_duration.total_seconds() * 3600 if distance is not None and lap_duration else None
full_avg_speed: float = distance / full_duration.total_seconds() * 3600 if distance is not None and full_duration else None
current_item['lap_start_time'] = lap_start_time
current_item['lap_end_time'] = lap_end_time
current_item['pit_start_time'] = pit_start_time
current_item['pit_end_time'] = pit_end_time
current_item['distance'] = distance
current_item['lap_duration'] = lap_duration
current_item['pit_duration'] = pit_duration
current_item['full_duration'] = full_duration
current_item['lap_avg_speed'] = lap_avg_speed
current_item['full_avg_speed'] = full_avg_speed
# try the magic
d = pendulum.Duration(hours=configuration.hours)
d += current_item['pit_duration']
current_item['total_using_lap'] = full_avg_speed * d.total_seconds() / 3600
| [
6738,
19720,
1330,
32233,
11,
360,
713,
11,
7343,
11,
4377,
198,
11748,
44017,
14452,
198,
198,
6738,
12351,
13,
7890,
62,
27530,
1330,
27131,
515,
15878,
11828,
198,
6738,
12351,
13,
7890,
62,
27530,
1330,
28373,
628,
198,
4299,
751,
... | 2.466521 | 1,374 |
#!/usr/bin/env python
import datetime
import fcntl
import logging
import logging.config
import os
import re
import tempfile
from functools import update_wrapper, partial
import click
import yaml
from ldsbde.core.bde import BDEProcessor
from ldsbde.core.job import Job
L = logging.getLogger("ldsbde")
def with_config(func):
""" Populate ctx.config with the parsed contents of the config file """
f = click.option(
'--config-file',
type=click.Path(),
help="Config file location",
is_eager=True, # do first
expose_value=False, # don't pass parameter through to real command
callback=callback
)
return f(func)
def singleton(wait):
"""
Prevent multiple lds-bde-loader processes fighting each other.
wait should be a boolean whether to block/wait for the other process or not.
"""
return wrap
def with_bde(func):
"""
Populate a ldsbde.core.BDEProcessor instance as the bde argument.
Requires @with_config above.
"""
@click.pass_context
return update_wrapper(wrapper, func)
def save_job(ctx, job):
""" Serialize the job out to a YAML file """
job_path = ctx.config["job_path"]
job_file = os.path.join(job_path, '%s.yml' % job.id)
with open(job_file, 'w') as fd:
yaml.safe_dump(job.serialize(), fd, default_flow_style=False)
def load_job(ctx, job_id):
""" Load a Job from on-disk as a yaml file. """
job_path = ctx.config["job_path"]
job_file = os.path.join(job_path, '%s.yml' % job_id)
if not os.path.exists(job_file):
raise Job.NotFound("Job %s (%s)" % (job_id, job_file))
with open(job_file, 'r') as fd:
data = yaml.safe_load(fd)
if not data:
raise Job.NotFound("Job %s (%s) -- empty" % (job_id, job_file))
return Job.parse(data, job_id=job_id, save_func=partial(save_job, ctx))
def find_jobs(ctx, max_age=None):
"""
Find multiple Jobs from on-disk yaml files (N.yml).
Returns a generator for Job objects in newest-first order.
max_age: restrict the maximum age in days of jobs to return (based on Job.created_at).
"""
# find the existing Job IDs and YAML files
job_ids = []
for fn in os.listdir(ctx.config["job_path"]):
m = re.match(r'([0-9]+)\.yml$', fn)
if m:
job_ids.append(int(m.group(1)))
job_ids.sort(reverse=True)
oldest = None
if max_age:
oldest = datetime.date.today() - datetime.timedelta(days=max_age)
for job_id in job_ids:
job = load_job(ctx, job_id)
if oldest and (job.created_at.date() < oldest):
# stop, we'll only see older jobs from here
break
yield job
def with_job(func):
"""
Populate a ldsbde.job.Job instance as the job argument
Requires @with_config above.
Requires @with_bde above if you want to use with create=True
"""
@click.argument('job_id', type=int, required=True)
@click.pass_context
return update_wrapper(wrapper, func)
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
11748,
4818,
8079,
198,
11748,
277,
66,
429,
75,
198,
11748,
18931,
198,
11748,
18931,
13,
11250,
198,
11748,
28686,
198,
11748,
302,
198,
11748,
20218,
7753,
198,
6738,
1257,
310,
10141... | 2.450363 | 1,239 |
# Time: O(lgn)
# Space: O(n)
# NOT IMPLEMENTED
| [
2,
3862,
25,
220,
440,
7,
75,
4593,
8,
198,
2,
4687,
25,
440,
7,
77,
8,
198,
198,
2,
5626,
30023,
2538,
10979,
1961,
198
] | 1.884615 | 26 |
# Generated by Django 2.2.13 on 2021-05-05 20:58
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
| [
2,
2980,
515,
416,
37770,
362,
13,
17,
13,
1485,
319,
33448,
12,
2713,
12,
2713,
1160,
25,
3365,
198,
198,
6738,
42625,
14208,
13,
10414,
1330,
6460,
198,
6738,
42625,
14208,
13,
9945,
1330,
15720,
602,
11,
4981,
198,
11748,
42625,
... | 3.038462 | 52 |
import argparse
import subprocess
from examc.generator import generate_exam
import sys
if __name__ == "__main__":
examc()
| [
11748,
1822,
29572,
198,
11748,
850,
14681,
198,
6738,
2814,
66,
13,
8612,
1352,
1330,
7716,
62,
1069,
321,
198,
11748,
25064,
628,
198,
198,
361,
11593,
3672,
834,
6624,
366,
834,
12417,
834,
1298,
198,
220,
220,
220,
2814,
66,
3419,... | 3 | 43 |
#!/usr/bin/python -u
# force matplotlib agg backend
import matplotlib
matplotlib.use("agg")
import matplotlib.pyplot as plt
from scipy.ndimage.filters import gaussian_filter1d
from scipy.interpolate import UnivariateSpline
import os
import numpy as np
from argparse import ArgumentParser
# %%%%%%%%%%%%%%%%%%%%%%%%%%%
# PLOTTING-RELATED TWEAKABLES
# %%%%%%%%%%%%%%%%%%%%%%%%%%%
color_map = None
print_title = None
gaussian_enable = None
gaussian_sigma = None
interpolation_enable = None
interpolation_segments = None
interpolation_order = None
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# PLOTTING VALUES OF MULTIPLE MODELS
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if __name__ == "__main__":
main()
| [
2,
48443,
14629,
14,
8800,
14,
29412,
532,
84,
198,
198,
2,
2700,
2603,
29487,
8019,
4194,
30203,
198,
11748,
2603,
29487,
8019,
198,
6759,
29487,
8019,
13,
1904,
7203,
9460,
4943,
198,
11748,
2603,
29487,
8019,
13,
9078,
29487,
355,
... | 2.966102 | 236 |
import csv
from itertools import chain
import click
from joblib import Parallel, delayed
from tqdm import tqdm
from docs.impc_header import header
from utils import (
mousemine_api,
europe_pmc_api,
mongo_access,
config,
nlp,
allele_importer,
)
from utils.solr_access import resolve_allele
@click.command()
@click.option("--use-mousemine", "-m", is_flag=True, help="Use mousemine.")
@click.option("--use-alleles", "-a", is_flag=True, help="Use alleles file.")
@click.option(
"--use-consortium-citations", "-c", is_flag=True, help="Use consortium citations."
)
@click.option("--add-order-id", "-o", is_flag=True, help="Import order ids.")
@click.option("--load-reviewed-pmids", "-p", is_flag=True, help="Load pmids from file.")
@click.option("--import-alleles", "-i", is_flag=True, help="Import load alleles file.")
def harvest(
use_mousemine,
use_alleles,
use_consortium_citations,
add_order_id,
load_reviewed_pmids,
import_alleles,
):
"""Command line application to harvest publications that cite or contain IMPC data resources"""
existing_pmids = mongo_access.get_existing_pmids()
click.secho(header, fg="yellow", bold=True)
update_exisiting_papers = True
update_papers = []
update_pmids = []
harvested_references = {}
keyword_harvest_count = 0
citation_harvest_count = 0
mousemine_harvest_count = 0
if import_alleles:
allele_importer.load_all()
if load_reviewed_pmids:
with open(config.get("DEFAULT", "LOAD_PMIDS_FILE")) as f:
csv_orders = [
{k: v for k, v in row.items()}
for row in csv.DictReader(f, skipinitialspace=True)
]
for line in csv_orders:
pmid = line["PubMed ID"]
if update_exisiting_papers and pmid in existing_pmids:
if pmid not in update_pmids:
paper = mongo_access.get_by_pmid(pmid)
paper["comment"] = line["comment"]
update_papers.append(paper)
update_pmids.append(pmid)
continue
elif pmid in harvested_references:
continue
bibliographic_data = europe_pmc_api.get_paper_by_pmid(pmid)
reviewed_reference = dict(
chain(
{
"alleles": [],
"status": "reviewed",
"datasource": "manual",
"consortiumPaper": False,
"citations": [],
"cites": [],
"alleleCandidates": [],
"citedBy": [],
"comment": line["comment"],
"tags": [],
}.items(),
bibliographic_data.items(),
)
)
harvested_references[pmid] = reviewed_reference
if use_mousemine:
click.secho("Execute Mousemine query", fg="blue")
alleles = mousemine_api.get_mousemine_references_from_webservice()
click.secho("Group results by PMID", fg="blue")
grouped_alleles = mousemine_api.get_pmid2alleles_map(alleles)
for pmid, alleles in grouped_alleles.items():
if update_exisiting_papers and pmid in existing_pmids:
if pmid not in update_pmids:
paper = mongo_access.get_by_pmid(pmid)
update_papers.append(paper)
update_pmids.append(pmid)
continue
elif pmid in harvested_references:
continue
bibliographic_data = europe_pmc_api.get_paper_by_pmid(pmid)
mousemine_reference = dict(
chain(
{
"alleles": alleles,
"status": "reviewed",
"datasource": "mousemine",
"consortiumPaper": False,
"citations": [],
"cites": [],
"alleleCandidates": [],
"citedBy": [],
"comment": "",
"tags": [],
}.items(),
bibliographic_data.items(),
)
)
harvested_references[pmid] = mousemine_reference
mousemine_harvest_count += 1
if use_consortium_citations:
consortium_papers = mongo_access.get_impc_papers()
for paper in consortium_papers:
for citing_paper in europe_pmc_api.get_citing_papers(paper["pmid"]):
if update_exisiting_papers and citing_paper["pmid"] in existing_pmids:
if citing_paper["pmid"] not in update_pmids:
citing_paper = mongo_access.get_by_pmid(citing_paper["pmid"])
update_papers.append(citing_paper)
update_pmids.append(citing_paper["pmid"])
continue
if citing_paper["pmid"] not in harvested_references:
harvested_references[citing_paper["pmid"]] = dict(
chain(
{
"alleles": [],
"status": "pending",
"datasource": "europepmc",
"consortiumPaper": False,
"citations": [],
"citedBy": [],
"alleleCandidates": [],
"comment": "",
"tags": [],
}.items(),
citing_paper.items(),
)
)
else:
harvested_references[citing_paper["pmid"]]["cites"].append(
paper["pmid"]
)
citation_harvest_count += 1
search_results = []
alleles = None
for keyword in config.get("DEFAULT", "TARGET_KEYWORDS").split(","):
search_results.extend(europe_pmc_api.get_papers_by_keyword(keyword))
if use_alleles:
with open(config.get("DEFAULT", "TARGET_ALLELE_FILE")) as f:
alleles = f.read().splitlines()
click.secho(
"Found {} alleles to use".format(len(alleles)),
fg="green",
bold=True,
)
# for keyword in alleles:
# try:
# search_results.extend(europe_pmc_api.get_papers_by_keyword(keyword))
# except Exception as e:
# print('[ERROR] Encountered exception: {}'.format(e.__class__))
for index, paper in enumerate(search_results):
if update_exisiting_papers and paper["pmid"] in existing_pmids:
if paper["pmid"] not in update_pmids:
paper = mongo_access.get_by_pmid(paper["pmid"])
update_papers.append(paper)
update_pmids.append(paper["pmid"])
continue
elif paper["pmid"] in harvested_references:
continue
else:
harvested_references[paper["pmid"]] = dict(
chain(
{
"alleles": [],
"datasource": "europepmc",
"status": "pending",
"citations": [],
"cites": [],
"citedBy": [],
"alleleCandidates": [],
"comment": "",
"tags": [],
}.items(),
paper.items(),
)
)
keyword_harvest_count += 1
click.secho(
"Found {} new references in Mousemine".format(mousemine_harvest_count),
fg="green",
bold=True,
)
click.secho(
"Found {} new references in EuroPMC".format(keyword_harvest_count),
fg="green",
bold=True,
)
click.secho(
"Found {} new references in EuroPMC citing Consortium papers".format(
citation_harvest_count
),
fg="green",
bold=True,
)
all_raw_references = harvested_references.values()
for reference in all_raw_references:
existing_reference = mongo_access.get_by_pmid(reference["pmid"])
if existing_reference:
if (
existing_reference["datasource"] in ["manual", "europepmc"]
and reference["datasource"] == "mousemine"
):
try:
mongo_access.update_by_pmid(
existing_reference["pmid"],
{"alleles": reference["alleles"], "datasource": "mousemine"},
)
except Exception as e:
print('[ERROR] Encountered exception: {}'.format(e.__class__))
if add_order_id:
click.secho("Updating allele info using provided order ids file", fg="blue")
with open(config.get("DEFAULT", "ORDER_ID_FILE"), encoding="utf-8-sig") as f:
csv_orders = [
{k: v for k, v in row.items()}
for row in csv.DictReader(f, skipinitialspace=True)
]
pmid_vs_alleles = dict()
for c in csv_orders:
allele = (
resolve_allele(c["allele"]) if "allele" in c else resolve_allele("")
)
allele["_class"] = "org.impc.publications.models.AlleleRef"
allele["orderId"] = c["order ID"]
if c["PubMed ID"] not in pmid_vs_alleles:
pmid_vs_alleles[c["PubMed ID"]] = []
pmid_vs_alleles[c["PubMed ID"]].append(allele)
for ref in all_raw_references:
ref["alleles"] = (
pmid_vs_alleles[ref["pmid"]] if ref["pmid"] in pmid_vs_alleles else []
)
for ref in update_papers:
ref["alleles"] = (
pmid_vs_alleles[ref["pmid"]]
if ref["pmid"] in pmid_vs_alleles and len(ref["alleles"]) == 0
else ref["alleles"]
)
all_papers = mongo_access.get_all()
for ref in [paper for paper in all_papers if paper["pmid"] not in update_pmids]:
ref["alleles"] = (
pmid_vs_alleles[ref["pmid"]]
if ref["pmid"] in pmid_vs_alleles and len(ref["alleles"]) == 0
else ref["alleles"]
)
update_papers.append(ref)
update_pmids.append(ref["pmid"])
click.secho("NLP Processing", fg="blue")
all_references_processed = Parallel(n_jobs=8)(
delayed(nlp.get_fragments)(reference, alleles)
for reference in tqdm(all_raw_references)
)
if len(all_references_processed) > 0:
mongo_access.insert_all(all_references_processed)
click.secho("Update NLP Processing for existing papers", fg="blue")
if len(update_papers) == 0:
click.secho(" Updating all", fg="blue")
update_papers = mongo_access.get_all()
else:
for paper in mongo_access.get_all():
if paper["pmid"] not in update_pmids:
update_papers.append(paper)
update_pmids.append(paper["pmid"])
update_references_processed = Parallel(n_jobs=8)(
delayed(nlp.get_fragments)(reference, alleles)
for reference in tqdm(update_papers)
)
click.secho(
f"Update existing papers in Mongodb: {len(update_references_processed)}",
fg="blue",
)
for reference in tqdm(update_references_processed):
try:
mongo_access.update_by_pmid(
reference["pmid"],
{
"fragments": reference["fragments"],
"comment": reference["comment"]
if "comment" in reference and reference["comment"] is not None
else "",
"tags": reference["tags"]
if "tags" in reference and reference["tags"] is not None
else [],
"citations": reference["citations"] if "citations" in reference else [],
"alleleCandidates": reference["alleleCandidates"],
"alleles": reference["alleles"] if "alleles" in reference else [],
"correspondence": reference["correspondence"]
if "correspondence" in reference
else [],
},
)
except Exception as e:
print('[ERROR] Encountered exception: {}'.format(e.__class__))
click.secho("Update existing papers in Mongodb", fg="blue")
click.secho("Finished", fg="blue")
if __name__ == "__main__":
harvest()
| [
11748,
269,
21370,
198,
6738,
340,
861,
10141,
1330,
6333,
198,
198,
11748,
3904,
198,
6738,
1693,
8019,
1330,
42945,
11,
11038,
198,
6738,
256,
80,
36020,
1330,
256,
80,
36020,
198,
198,
6738,
34165,
13,
11011,
66,
62,
25677,
1330,
1... | 1.843195 | 7,098 |
# Generated by Django 2.1 on 2018-09-15 13:51
from django.db import migrations, models
import django.db.models.deletion
import uuid
| [
2,
2980,
515,
416,
37770,
362,
13,
16,
319,
2864,
12,
2931,
12,
1314,
1511,
25,
4349,
198,
198,
6738,
42625,
14208,
13,
9945,
1330,
15720,
602,
11,
4981,
198,
11748,
42625,
14208,
13,
9945,
13,
27530,
13,
2934,
1616,
295,
198,
11748... | 2.913043 | 46 |
# -*- encoding: utf-8 -*-
# python 2.7
import argparse
from GmailServiceWrap import GmailServiceWrap
import datetime
import pdfkit
import os
from UberSlip import UberSlipType
# FILL THIS
GMAIL_USER_ID = None
def stapleAndPrintSlips(slips, year, month) :
'''
Staple matching slips toghter and print to pdf
Returns:
void
'''
# dictionary{plate_date:UberSlip}, eg: 30(int):RBS3913
rentalSlips = {}
mainSlips = {}
for us in slips :
if us.slipType == UberSlipType.OldMain or us.slipType == UberSlipType.NewMain:
mainSlips['{}_{}'.format(us.plateNumber, us.date)] = us
else :
rentalSlips['{}_{}'.format(us.plateNumber, us.date)] = us
dirname = '{}-{}'.format(year, month)
try :
os.makedirs(dirname)
except OSError, ose :
pass
if len(mainSlips) != len(rentalSlips) :
# missing matching slip
print ('There are {} main slips, but {} rental slips.'.format(len(mainSlips), len(rentalSlips)))
if len(mainSlips) > len(rentalSlips) :
# try find missing slip
for platenumberAndDate, mainSlip in mainSlips.iteritems() :
try :
rslip = rentalSlips[platenumberAndDate]
except KeyError, ke:
print ('MISSING rental slip at {datetime} with plate number {plate} fare {fare}, check with uber app.'.format(datetime=mainSlip.startDatetime,
plate=mainSlip.plateNumber,
fare=mainSlip.fare))
else :
for platenumberAndDate, rslip in rentalSlips.iteritems() :
try :
mslip = mainSlips[platenumberAndDate]
except KeyError, ke:
print ('MISSING main slip plate {plate}, check with uber app.'.format(plate=rslip.plateNumber))
for platenumberAndDate, mainSlip in mainSlips.iteritems() :
#print platenumber
try :
rentalSlip = rentalSlips[platenumberAndDate]
except KeyError, ke:
continue
filename = './{dir}/{datetime}-{drivername}-{plate}-{fare}'.format(dir=dirname,
datetime=mainSlip.startDatetime,
drivername=mainSlip.driverName,
plate=mainSlip.plateNumber,
fare=mainSlip.fare)
htmlfileMainpath = '{filename}-main.html'.format(filename=filename)
htmlfileRentalpath = '{filename}-rental.html'.format(filename=filename)
# write html to file
with open(htmlfileMainpath, 'w') as h1f :
try :
h1f.write(mainSlip.body)
except UnicodeDecodeError, e:
print ('UnicodeDecodeError at main slip, {} {} '.format( mainSlip.plateNumber, e))
pass
with open(htmlfileRentalpath, 'w') as h2f :
try :
h2f.write(rentalSlip.body.encode('utf-8'))
except UnicodeDecodeError, e:
print ('UnicodeDecodeError at rental slip, {} {} '.format( rentalSlip.plateNumber, e))
pass
# write to pdf
pdffilepath = '{filename}.pdf'.format(filename=filename)
try :
pdfkit.from_file([htmlfileMainpath, htmlfileRentalpath], pdffilepath)
except Exception, e:
pass
print 'main receipt:{}, rental slip:{}, output pdf: {}'.format(mainSlip, rentalSlip, pdffilepath)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--month", type=int, default=0,
help="month to search in number, Jan=1, default: current month")
parser.add_argument("-y", "--year", help="year to search, default: current year", type=int, default=0) #0=current year
args = parser.parse_args()
month = args.month
year = args.year
if month is 0 :
month = datetime.datetime.now().month
if year is 0 :
year = datetime.datetime.now().year
settingfile = './settings.py'
if os.path.isfile(settingfile) :
execfile(settingfile)
print ('loading from settings file.')
print ('Search for uber slips in {}/{}'.format(year, month))
gmailServiceWrap = GmailServiceWrap(GMAIL_USER_ID, args)
'''
for us in gmailServiceWrap.getUberSlips(year, month) :
print '{}'.format(us)
'''
#print gmailServiceWrap.uberSlipsSearch(year, month)
uberSlips = gmailServiceWrap.getUberSlips(year, month)
print ('there are {} slips.'.format(len(uberSlips)) )
stapleAndPrintSlips(uberSlips, year, month)
| [
2,
532,
9,
12,
21004,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
2,
21015,
362,
13,
22,
198,
198,
11748,
1822,
29572,
198,
6738,
33662,
16177,
54,
2416,
1330,
33662,
16177,
54,
2416,
198,
11748,
4818,
8079,
198,
11748,
37124,
15813,
1... | 2.465728 | 1,634 |
# coding: utf-8
from datetime import date, datetime
from typing import List, Dict, Type
from openapi_server.models.base_model_ import Model
from openapi_server.models.extent import Extent
from openapi_server.models.observed_property import ObservedProperty
from openapi_server.models.one_ofintegerarray import OneOfintegerarray
from openapi_server.models.parameter_measurement_approach import ParameterMeasurementApproach
from openapi_server.models.units import Units
from openapi_server import util
class Parameter(Model):
"""NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).
Do not edit the class manually.
"""
def __init__(self, type: object=None, description: str=None, label: str=None, data_type: object=None, unit: Units=None, observed_property: ObservedProperty=None, category_encoding: Dict[str, OneOfintegerarray]=None, extent: Extent=None, id: str=None, measurement_type: ParameterMeasurementApproach=None):
"""Parameter - a model defined in OpenAPI
:param type: The type of this Parameter.
:param description: The description of this Parameter.
:param label: The label of this Parameter.
:param data_type: The data_type of this Parameter.
:param unit: The unit of this Parameter.
:param observed_property: The observed_property of this Parameter.
:param category_encoding: The category_encoding of this Parameter.
:param extent: The extent of this Parameter.
:param id: The id of this Parameter.
:param measurement_type: The measurement_type of this Parameter.
"""
self.openapi_types = {
'type': object,
'description': str,
'label': str,
'data_type': object,
'unit': Units,
'observed_property': ObservedProperty,
'category_encoding': Dict[str, OneOfintegerarray],
'extent': Extent,
'id': str,
'measurement_type': ParameterMeasurementApproach
}
self.attribute_map = {
'type': 'type',
'description': 'description',
'label': 'label',
'data_type': 'data-type',
'unit': 'unit',
'observed_property': 'observedProperty',
'category_encoding': 'categoryEncoding',
'extent': 'extent',
'id': 'id',
'measurement_type': 'measurementType'
}
self._type = type
self._description = description
self._label = label
self._data_type = data_type
self._unit = unit
self._observed_property = observed_property
self._category_encoding = category_encoding
self._extent = extent
self._id = id
self._measurement_type = measurement_type
@classmethod
def from_dict(cls, dikt: dict) -> 'Parameter':
"""Returns the dict as a model
:param dikt: A dict.
:return: The parameter of this Parameter.
"""
return util.deserialize_model(dikt, cls)
@property
def type(self):
"""Gets the type of this Parameter.
type
:return: The type of this Parameter.
:rtype: object
"""
return self._type
@type.setter
def type(self, type):
"""Sets the type of this Parameter.
type
:param type: The type of this Parameter.
:type type: object
"""
allowed_values = [Parameter] # noqa: E501
if type not in allowed_values:
raise ValueError(
"Invalid value for `type` ({0}), must be one of {1}"
.format(type, allowed_values)
)
self._type = type
@property
def description(self):
"""Gets the description of this Parameter.
:return: The description of this Parameter.
:rtype: str
"""
return self._description
@description.setter
def description(self, description):
"""Sets the description of this Parameter.
:param description: The description of this Parameter.
:type description: str
"""
self._description = description
@property
def label(self):
"""Gets the label of this Parameter.
:return: The label of this Parameter.
:rtype: str
"""
return self._label
@label.setter
def label(self, label):
"""Sets the label of this Parameter.
:param label: The label of this Parameter.
:type label: str
"""
self._label = label
@property
def data_type(self):
"""Gets the data_type of this Parameter.
Data type of returned parameter
:return: The data_type of this Parameter.
:rtype: object
"""
return self._data_type
@data_type.setter
def data_type(self, data_type):
"""Sets the data_type of this Parameter.
Data type of returned parameter
:param data_type: The data_type of this Parameter.
:type data_type: object
"""
allowed_values = [integer, float, string] # noqa: E501
if data_type not in allowed_values:
raise ValueError(
"Invalid value for `data_type` ({0}), must be one of {1}"
.format(data_type, allowed_values)
)
self._data_type = data_type
@property
def unit(self):
"""Gets the unit of this Parameter.
:return: The unit of this Parameter.
:rtype: Units
"""
return self._unit
@unit.setter
def unit(self, unit):
"""Sets the unit of this Parameter.
:param unit: The unit of this Parameter.
:type unit: Units
"""
self._unit = unit
@property
def observed_property(self):
"""Gets the observed_property of this Parameter.
:return: The observed_property of this Parameter.
:rtype: ObservedProperty
"""
return self._observed_property
@observed_property.setter
def observed_property(self, observed_property):
"""Sets the observed_property of this Parameter.
:param observed_property: The observed_property of this Parameter.
:type observed_property: ObservedProperty
"""
if observed_property is None:
raise ValueError("Invalid value for `observed_property`, must not be `None`")
self._observed_property = observed_property
@property
def category_encoding(self):
"""Gets the category_encoding of this Parameter.
:return: The category_encoding of this Parameter.
:rtype: Dict[str, OneOfintegerarray]
"""
return self._category_encoding
@category_encoding.setter
def category_encoding(self, category_encoding):
"""Sets the category_encoding of this Parameter.
:param category_encoding: The category_encoding of this Parameter.
:type category_encoding: Dict[str, OneOfintegerarray]
"""
self._category_encoding = category_encoding
@property
def extent(self):
"""Gets the extent of this Parameter.
:return: The extent of this Parameter.
:rtype: Extent
"""
return self._extent
@extent.setter
def extent(self, extent):
"""Sets the extent of this Parameter.
:param extent: The extent of this Parameter.
:type extent: Extent
"""
self._extent = extent
@property
def id(self):
"""Gets the id of this Parameter.
Unique ID of the parameter, this is the value used for querying the data
:return: The id of this Parameter.
:rtype: str
"""
return self._id
@id.setter
def id(self, id):
"""Sets the id of this Parameter.
Unique ID of the parameter, this is the value used for querying the data
:param id: The id of this Parameter.
:type id: str
"""
self._id = id
@property
def measurement_type(self):
"""Gets the measurement_type of this Parameter.
:return: The measurement_type of this Parameter.
:rtype: ParameterMeasurementApproach
"""
return self._measurement_type
@measurement_type.setter
def measurement_type(self, measurement_type):
"""Sets the measurement_type of this Parameter.
:param measurement_type: The measurement_type of this Parameter.
:type measurement_type: ParameterMeasurementApproach
"""
self._measurement_type = measurement_type
| [
2,
19617,
25,
3384,
69,
12,
23,
198,
198,
6738,
4818,
8079,
1330,
3128,
11,
4818,
8079,
198,
198,
6738,
19720,
1330,
7343,
11,
360,
713,
11,
5994,
198,
198,
6738,
1280,
15042,
62,
15388,
13,
27530,
13,
8692,
62,
19849,
62,
1330,
9... | 2.421435 | 3,583 |
import datetime
from abc import ABC
from typing import List, Any, Dict
import pyodbc
import sortedcontainers
from .. import constants, sql_queries
from . import metrics
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from .. import clock_sync
| [
11748,
4818,
8079,
198,
6738,
450,
66,
1330,
9738,
198,
6738,
19720,
1330,
7343,
11,
4377,
11,
360,
713,
198,
198,
11748,
12972,
375,
15630,
198,
11748,
23243,
3642,
50221,
198,
198,
6738,
11485,
1330,
38491,
11,
44161,
62,
421,
10640,
... | 3.445946 | 74 |
# inference
import torch, torch.nn as nn, torch.nn.functional as F, random, numpy as np
from torchvision import datasets, transforms
test_batch_size = 100
saved_model_path = '0602-656377418-Garg.pt'
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
checkpoint = torch.load(saved_model_path, map_location=device)
model = Net().to(device)
model.load_state_dict(checkpoint)
model.eval()
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
test_dataset = datasets.ImageFolder('test_original/', transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=test_batch_size)
tot_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
tot_loss += torch.nn.CrossEntropyLoss()(output, target).item() # sum up batch loss
pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
print('Test Loss: {:.6f}, Test Accuracy: {:.2f}%'.format(
tot_loss/(len(test_loader)), 100.0*correct/(len(test_loader)*test_batch_size))) | [
2,
32278,
198,
11748,
28034,
11,
28034,
13,
20471,
355,
299,
77,
11,
28034,
13,
20471,
13,
45124,
355,
376,
11,
4738,
11,
299,
32152,
355,
45941,
198,
6738,
28034,
10178,
1330,
40522,
11,
31408,
198,
198,
9288,
62,
43501,
62,
7857,
... | 2.545635 | 504 |
idade = maior = menor = homens = 0
while True:
sexo = contin = ''
idade = int(input('Digite sua idade: '))
while sexo != 'F' and sexo != 'M':
sexo = str(input('Digite o seu sexo: ')).upper()[0]
if idade >= 18:
maior += 1
if sexo == 'M':
homens += 1
if sexo == 'F':
if idade < 20:
menor += 1
while contin != 'S' and contin != 'N':
contin = str(input('Deseja continuar? [S/N]')).upper()[0]
if contin == 'N':
break
print(f'A quantidade de pessoas maiores de 18 são {maior}')
print(f'A quantidade de homens cadastrados são {homens}')
print(f'A quantidade de meninas abaixo dos 20 anos são {menor}')
| [
312,
671,
796,
17266,
1504,
796,
1450,
273,
796,
3488,
641,
796,
657,
198,
4514,
6407,
25,
198,
220,
220,
220,
1714,
78,
796,
1261,
796,
10148,
198,
220,
220,
220,
4686,
671,
796,
493,
7,
15414,
10786,
19511,
578,
424,
64,
4686,
6... | 2.008357 | 359 |
# -*- coding: utf-8 -*-
# Generated by Django 1.11.7 on 2018-04-25 21:49
from __future__ import unicode_literals
from django.db import migrations, models
import uuid
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
2,
2980,
515,
416,
37770,
352,
13,
1157,
13,
22,
319,
2864,
12,
3023,
12,
1495,
2310,
25,
2920,
198,
6738,
11593,
37443,
834,
1330,
28000,
1098,
62,
17201,
874,
198,
... | 2.754098 | 61 |
import numpy as np
import pytest as pt
from collections import OrderedDict
| [
11748,
299,
32152,
355,
45941,
198,
11748,
12972,
9288,
355,
42975,
198,
6738,
17268,
1330,
14230,
1068,
35,
713,
628
] | 3.8 | 20 |
import torch
from dreamer.carracing import (
DenseModel,
Env,
ObservationDecoder,
ObservationEncoder,
Policy,
Posterior,
Prior,
)
from dreamer import Dreamer
if __name__ == "__main__":
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
weight_dir = "weight"
env = Env(save_mp4="video")
agent = Dreamer(
device=device,
encoder=ObservationEncoder,
prior=Prior,
posterior=Posterior,
decoder=ObservationDecoder,
reward=DenseModel,
policy=Policy,
value=DenseModel,
)
agent.load_weight(weight_dir)
score = 0.0
state = env.reset()
while 1:
action = agent(state, train=False)
state_, reward, done = env.step(action)
score += reward
state = state_
env.render()
if any(done):
break
print("Score:", score)
| [
11748,
28034,
198,
198,
6738,
4320,
263,
13,
66,
3258,
4092,
1330,
357,
198,
220,
220,
220,
360,
1072,
17633,
11,
198,
220,
220,
220,
2039,
85,
11,
198,
220,
220,
220,
11086,
13208,
10707,
12342,
11,
198,
220,
220,
220,
11086,
13208... | 2.184149 | 429 |
"""Build Script for setuptools
This build script must be executed outside of the source code directory.
The version number will be generated using the most recent tag and the number of commits on the master branch.
[TAG].[COMMIT_COUNT]
See Also: https://packaging.python.org/tutorials/packaging-projects/
"""
import setuptools
import os
package_name = "kube_api"
package_description = "A simple Kubernetes Python API"
package_url = "https://github.com/labdave/kube_api"
with open(os.path.join(package_name, "README.md"), "r") as fh:
long_description = fh.read()
with open(os.path.join(package_name, "requirements.txt"), "r") as f:
requirements = f.read().split("\n")
requirements = [r.strip() for r in requirements if r.strip()]
release_version = str(os.popen("cd %s && git tag | tail -1" % package_name).read()).strip()
if not release_version:
raise ValueError("Release version not found.")
commit_version = str(os.popen("cd %s && git rev-list --count master" % package_name).read()).strip()
setuptools.setup(
name=package_name,
version="%s.%s" % (release_version, commit_version),
author="Qiu Qin",
author_email="qiuosier@gmail.com",
description=package_description,
long_description=long_description,
long_description_content_type="text/markdown",
url=package_url,
packages=setuptools.find_packages(),
install_requires=requirements,
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
python_requires='>=3.6',
)
| [
37811,
15580,
12327,
329,
900,
37623,
10141,
198,
198,
1212,
1382,
4226,
1276,
307,
10945,
2354,
286,
262,
2723,
2438,
8619,
13,
198,
464,
2196,
1271,
481,
307,
7560,
1262,
262,
749,
2274,
7621,
290,
262,
1271,
286,
23463,
319,
262,
4... | 2.905455 | 550 |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
# Ravi Krishna 07/23/21
# Various import statements.
import torch
import torch.nn as nn
from nas_searchmanager import SearchManager
from cnn_supernet import ConvSuperNet
from dnas_cnn_data_utils import CNNDataset
import argparse
import pickle
from utils import arch_sampling_str_to_dict, STR_TO_OPTIM
import random
import numpy as np
import hashlib
import time
import os
# Create argument parser.
parser = argparse.ArgumentParser(description="Run DNAS CNN test.")
# Training / search manager parameters.
parser.add_argument("--experiment_id",
type=str,
default=None,
help="Unique experiment ID used as a prefix for all files saved during the experiment, including sampled architectures and logfiles.")
parser.add_argument("--weights_batch_size",
type=int,
default=256,
help="Weights training batch size.")
parser.add_argument("--arch_params_batch_size",
type=int,
default=256,
help="Arch params training batch size.")
parser.add_argument("--initial_temperature",
type=float,
default=1.0,
help="Initial Gumbel Softmax temperature.")
parser.add_argument("--temp_decay_rate",
type=float,
default=0.1,
help="Decay rate of Gumbel Softmax temperature.")
parser.add_argument("--architecture_sampling",
type=str,
default="4:4",
help="Architecture sampling. To sample 4 architecture after 1 epoch of architecture parameters training, 4 after 2, etc. for all 4 epochs, one would write \"1:4,2:4,3:4,4:4\".")
parser.add_argument("--n_warmup_epochs",
type=float,
default=None,
help="Number (possibly float) of warmup epochs i.e. weights-only training before architecture parameters trained.")
parser.add_argument("--n_total_s_net_training_epochs",
type=float,
default=None,
help="Total (possibly float) number of supernet training epochs.")
parser.add_argument("--n_alt_train_epochs",
type=float,
default=1.0,
help="Every n_alt_train_epochs, we switch from training the weights to architecture parameters or vice versa.")
parser.add_argument("--host_gpu_id",
type=int,
default=None,
help="Host GPU ID.")
parser.add_argument("--clip_grad_norm_value",
type=float,
default=100.0,
help="L2 norm at which to clip gradients of supernet.") # Both weights and architecture parameters gradients.
parser.add_argument("--weights_optim_type",
type=str,
choices=["sgd"],
default="sgd",
help="Weights optimizer type.")
parser.add_argument("--arch_params_optim_type",
type=str,
choices=["sgd", "adam", "adagrad"],
default="sgd",
help="Architecture parameters optimizer type.")
parser.add_argument("--weights_lr",
type=float,
default=None,
help="Initial learning rate for architecture weights.")
parser.add_argument("--arch_params_lr",
type=float,
default=None,
help="Initial learning rate for architecture configuration parameters.")
parser.add_argument("--weights_wd",
type=float,
default=0.0,
help="Weight decay for architecture weights.")
parser.add_argument("--arch_params_wd",
type=float,
default=0.0,
help="Weight decay for architecture configuration parameters.")
parser.add_argument("--use_hw_cost",
action="store_true",
help="Whether or not to use HW cost in the DNAS training.")
parser.add_argument("--hw_cost_function",
type=str,
choices=["exponential", "linear"],
default="linear",
help="HW cost function type if --use_hw_cost.")
parser.add_argument("--hw_cost_exp",
type=float,
default=None,
help="HW cost function exponent, provided only if --use_hw_cost and --hw_cost_function=exponential.")
parser.add_argument("--hw_cost_coef",
type=float,
default=0.001,
help="HW cost linear coefficient, provided if --use_hw_cost.")
parser.add_argument("--hw_cost_multiplier",
type=float,
default=1.0,
help="Linear HW cost multiplier to e.g. convert latency numbers measured in seconds to milliseconds.")
parser.add_argument("--weights_lr_base",
type=float,
default=0.9,
help="Weights LR = weights_lr * ((weights_lr_base) ** (num_weights_epochs)). Note that this formula may be applied at every training step or every n_alt_train_epochs.") # Every epoch not currently an option - may be added later as an option.
parser.add_argument("--arch_params_lr_base",
type=float,
default=0.9,
help="Arch params LR = arch_params_lr * ((arch_params_lr_base) ** (num_arch_params_epochs)). Note that this formula may be applied at every training step or every n_alt_train_epochs.") # Every epoch not currenly an option - may be added later.
parser.add_argument("--update_lrs_every_step",
action="store_true",
help="If set, LRs will be updated every step instead of every SearchManager \"epoch\" (usually args.n_alt_train_amt).") # Could update the weights and architecture parameters learning rates at different frequencies.
# Seed.
parser.add_argument("--seed",
type=int,
default=1,
help="Random seed to ensure results can be replicated. This seed is used for random, numpy, and torch.")
# Needed to interface with tuning script.
parser.add_argument("--save_metrics_param",
type=str,
default="",
help="Path at which to save a file to tell tuning.py that this script is done running.")
# Parse arguments.
args = parser.parse_args()
# Set seed.
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# Create the supernet.
cnn_supernet = ConvSuperNet()
# Get dataloaders.
weights_dataloader, arch_params_dataloader = torch.utils.data.DataLoader(CNNDataset("train-weights"), batch_size=args.weights_batch_size), torch.utils.data.DataLoader(CNNDataset("train-archparams"), batch_size=args.arch_params_batch_size)
# Function to deal with OOM errors.
def write_oom_exit(oom_error):
"""
Writes the text of the OOM error to the file
and then exit()s.
"""
# Write OOM error.
oom_error_file = open(f"oom_error_{args.save_metrics_param}", "w")
oom_error_file.write(str(oom_error))
oom_error_file.flush()
# Remove job information file.
os.system(f"rm {job_info_filename}")
# Exit.
exit()
# Move DLRM supernet to GPU.
# Writing OOM error allows for job restarting.
try:
host_device = torch.device(f"cuda:{args.host_gpu_id}" if torch.cuda.is_available() else "dpcpp")
print(f"ATTEMPTING TO MOVE CNN SUPERNET TO GPU {args.host_gpu_id if torch.cuda.is_available() else 'dpcpp'}.")
print(cnn_supernet)
cnn_supernet.to(host_device)
except RuntimeError as oom_error:
write_oom_exit(oom_error)
# Construct various inputs to SearchManager.__init__():
# Optimizer classes.
weights_optim_class = STR_TO_OPTIM[args.weights_optim_type.lower()]
arch_params_optim_class = STR_TO_OPTIM[args.arch_params_optim_type.lower()]
# Optimizer initialization parameters.
weights_optim_init_params = {"lr" : args.weights_lr, "weight_decay" : args.weights_wd}
arch_params_optim_init_params = {"lr" : args.arch_params_lr, "weight_decay" : args.arch_params_wd}
# Functions to fetch parameters that each optimizer should train.
weights_parameters_function = lambda s_net: [param for param_name, param in s_net.named_parameters() if "theta_parameters" not in param_name]
arch_params_parameters_function = lambda s_net : [param for param_name, param in s_net.named_parameters() if "theta_parameters" in param_name]
# Functions which specify how the LR changes during training. Note that
# these functions return the RATIO of the current learning rate to the
# initial learning rate, and not the current learning rate itself.
weights_optim_lr_lambdas = [lambda curr_epoch: (args.weights_lr_base ** curr_epoch)]
arch_params_optim_lr_lambdas = [lambda curr_epoch: (args.arch_params_lr_base ** curr_epoch)]
# Initial learning rates for the different parameters groups in each
# optimizer. Currently there is only one parameter group used per optimizer,
# however, the code supports multiple parameters groups, each with their own
# initial learning rate and learning rate schedule.
weights_initial_lrs = [args.weights_lr]
arch_params_initial_lrs = [args.arch_params_lr]
# CrossEntropyLoss for image classification.
loss_function = nn.CrossEntropyLoss()
# Create search_manager.
search_manager = SearchManager(super_net=cnn_supernet,
init_temp=args.initial_temperature,
temp_decay_rate=args.temp_decay_rate,
n_warmup_epochs=args.n_warmup_epochs,
arch_sampling=arch_sampling_str_to_dict(args.architecture_sampling),
n_total_s_net_train_epochs=args.n_total_s_net_training_epochs,
n_alt_train_amt=args.n_alt_train_epochs,
host_device=host_device,
clip_grad_norm_value=args.clip_grad_norm_value,
w_dataloader=weights_dataloader,
m_dataloader=arch_params_dataloader,
w_optim_class=weights_optim_class,
weights_optim_init_params=weights_optim_init_params,
w_optim_params_func=weights_parameters_function,
m_optim_class=arch_params_optim_class,
mask_optim_init_params=arch_params_optim_init_params,
m_optim_params_func=arch_params_parameters_function,
weights_lr_lambdas=weights_optim_lr_lambdas,
mask_lr_lambdas=arch_params_optim_lr_lambdas,
weights_initial_lrs=weights_initial_lrs,
mask_initial_lrs=arch_params_initial_lrs,
update_lrs_every_step=args.update_lrs_every_step,
loss_function=loss_function,
experiment_id=args.experiment_id,
logfile=args.experiment_id.replace("save_file", "search_manager_logfile"),
use_hw_cost=args.use_hw_cost,
cost_exp=args.hw_cost_exp,
cost_coef=args.hw_cost_coef,
exponential_cost=(True if args.use_hw_cost and args.hw_cost_function == "exponential" else False),
cost_multiplier=args.hw_cost_multiplier)
# Start search process.
try:
search_manager.train_dnas()
except RuntimeError as oom_error:
write_oom_exit(oom_error)
# Once the DNAS process is done, in order to tuning.py to know
# that the script is done running, save a file at the save_metrics_param
# location.
with open(args.save_metrics_param, "wb") as save_metrics_writefile:
pickle.dump({"info" : "SCRIPT COMPLETED"}, save_metrics_writefile)
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
2,
15069,
357,
66,
8,
3203,
11,
3457,
13,
290,
663,
29116,
13,
198,
2,
198,
2,
770,
2723,
2438,
318,
11971,
739,
262,
17168,
5964,
1043,
287,
262,
198,
2,
38559,
24290,
2393,
... | 2.183165 | 5,738 |
import json
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Optional
from flask import request, redirect, render_template, Flask
from flask_socketio import SocketIO
from tool.pid_settings.forms import AllPIDForms, OrderForm
FILE_NAME = 'pid_coef.json'
@dataclass
@dataclass
| [
11748,
33918,
198,
6738,
450,
66,
1330,
9738,
11,
12531,
24396,
198,
6738,
4818,
330,
28958,
1330,
4818,
330,
31172,
198,
6738,
19720,
1330,
32233,
198,
198,
6738,
42903,
1330,
2581,
11,
18941,
11,
8543,
62,
28243,
11,
46947,
198,
6738,... | 3.381443 | 97 |
import os
| [
11748,
28686,
201,
198
] | 2.75 | 4 |
# Copyright 2019 John Reese
# Licensed under the MIT license
import gc
import sys
from sys import stdin, stdout
from time import monotonic
from digitalio import DigitalInOut, Direction, Pull
from supervisor import runtime
from touchio import TouchIn
from .serial import ALL_COMMANDS, VERSION
try:
from typing import Callable, Dict, List, Tuple
except ImportError:
pass
NIB = "NIB"
NIN = "NIN"
NIF = "NIF"
DEBOUNCE = 0.02 # how long to wait on up/down changes
REPEAT = object()
INTERVAL = 0.1
| [
2,
15069,
13130,
1757,
39929,
198,
2,
49962,
739,
262,
17168,
5964,
628,
198,
11748,
308,
66,
198,
11748,
25064,
198,
6738,
25064,
1330,
14367,
259,
11,
14367,
448,
198,
6738,
640,
1330,
937,
313,
9229,
198,
198,
6738,
4875,
952,
1330... | 3.060241 | 166 |
#!/usr/bin/env python
"""Services for asset tracking """
import csv
from xlwt import Workbook
import binascii
import uuid
import re
from ion.util.xlsparser import XLSParser
from pyon.core import bootstrap
from ooi.logging import log
from pyon.core.exception import NotFound, BadRequest, Inconsistent
from pyon.public import IonObject, RT, PRED, LCS, LCE, OT
from interface.objects import EventCategoryEnum, ValueTypeEnum
from interface.services.coi.iorg_management_service import OrgManagementServiceClient
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
198,
37811,
31007,
329,
11171,
9646,
37227,
198,
198,
11748,
269,
21370,
198,
6738,
2124,
75,
46569,
1330,
5521,
2070,
198,
11748,
9874,
292,
979,
72,
198,
11748,
334,
27112,
198,
11748,... | 3.337662 | 154 |
import feedparser
from nose.tools import (
assert_raises,
eq_,
set_trace,
)
from . import DatabaseTest
from ..opds import (
ContentServerAnnotator,
StaticFeedAnnotator,
StaticCOPPANavigationFeed,
)
from ..core.opds import UnfulfillableWork
class MockStaticLane(object):
"""Empty, unobtrusive Lane class that gives any
StaticFeedAnnotator a name to work with."""
| [
11748,
3745,
48610,
198,
198,
6738,
9686,
13,
31391,
1330,
357,
198,
220,
220,
220,
6818,
62,
430,
2696,
11,
198,
220,
220,
220,
37430,
62,
11,
198,
220,
220,
220,
900,
62,
40546,
11,
198,
8,
198,
6738,
764,
1330,
24047,
14402,
19... | 2.836879 | 141 |
import mock
from sms_directions.sms import send_sms
from sms_directions import config
| [
11748,
15290,
198,
198,
6738,
895,
82,
62,
12942,
507,
13,
82,
907,
1330,
3758,
62,
82,
907,
198,
6738,
895,
82,
62,
12942,
507,
1330,
4566,
628
] | 3.142857 | 28 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Copyright (c) 2021, ICGC ARGO
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.
Authors:
Junjun Zhang
"""
import os
import sys
import argparse
import subprocess
from multiprocessing import cpu_count
from glob import glob
import json
import tarfile
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Tool: samtools-stats')
parser.add_argument('-s', '--aligned_seq', type=str,
help='Input aligned seq', required=True)
parser.add_argument('-r', '--reference', type=str,
help='Reference genome', required=True)
parser.add_argument('-t', '--threads', type=int, default=cpu_count(),
help='Number of threads')
args = parser.parse_args()
if not os.path.isfile(args.aligned_seq):
sys.exit('Error: specified aligned seq file %s does not exist or is not accessible!' % args.aligned_seq)
if not os.path.isfile(args.reference):
sys.exit('Error: specified reference file %s does not exist or is not accessible!' % args.reference)
main(args.aligned_seq, args.reference, args.threads)
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
198,
37811,
198,
220,
15069,
357,
66,
8,
33448,
11,
12460,
15916,
5923,
11230,
628,
220,
2448,
3411,
318,
29376,
... | 3.137733 | 697 |
import plotly.figure_factory as ff
import plotly.graph_objects as go
import statistics
import random
import csv
import pandas as pd
df =pd.read_csv("studentMarks.csv")
data = df["Math_score"].tolist()
std_deviation = statistics.stdev(mean_list)
mean = statistics.mean(mean_list)
print("mean of sampling distribution",mean)
fig = ff.create_distplot([data],["student_marks"],show_hist= False)
fig.add_trace(go.Scatter(x=[mean,mean],y = [0,0.20],mode="lines",name= "MEAN"))
fig.show() | [
11748,
7110,
306,
13,
26875,
62,
69,
9548,
355,
31246,
220,
201,
198,
11748,
7110,
306,
13,
34960,
62,
48205,
355,
467,
220,
201,
198,
11748,
7869,
201,
198,
11748,
4738,
201,
198,
11748,
269,
21370,
201,
198,
11748,
19798,
292,
355,
... | 2.626943 | 193 |
# This gets rid of NumPy FutureWarnings that occur at TF import
import warnings
warnings.filterwarnings('ignore',category=FutureWarning)
# This gets rid of TF 2.0 related deprecation warnings
import tensorflow as tf
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
| [
2,
770,
3011,
5755,
286,
31835,
20519,
10898,
54,
1501,
654,
326,
3051,
379,
24958,
1330,
201,
198,
11748,
14601,
201,
198,
40539,
654,
13,
24455,
40539,
654,
10786,
46430,
3256,
22872,
28,
29783,
20361,
8,
201,
198,
201,
198,
2,
770,... | 3.021053 | 95 |
'''
Python version of check_aqc_07_spike_check.f.
Details of the original code are:
c/ DATE: JANUARY 25 2016
c/ AUTHOR: Viktor Gouretski
c/ AUTHOR'S AFFILIATION: Integrated Climate Data Center, University of Hamburg, Hamburg, Germany
c/ PROJECT: International Quality Controlled Ocean DataBase (IQuOD)
c/ TITLE: check_aqc_07_spike_check
c/ PURPOSE:
c to check temperature profile for spikes
'''
from . import ICDC_aqc_01_level_order as ICDC
import numpy as np
def test(p, parameters):
'''Return quality control decisions.
'''
# The test is run on re-ordered data.
nlevels, z, t = ICDC.reordered_data(p, parameters)
qc = np.zeros(nlevels, dtype=bool) # Reordered data may be a subset of available levels.
defaultqc = np.zeros(p.n_levels(), dtype=bool) # Default QC flags for full set of levels.
if nlevels < 3: return defaultqc # Not enough levels to check.
# Ignore any levels outside of limits.
parminover = -2.3
parmaxover = 33.0
use = (t > parminover) & (t < parmaxover)
nuse = np.count_nonzero(use)
if nuse < 3: return defaultqc
zuse = z[use]
tuse = t[use]
origlevels = (np.arange(nlevels))[use]
# Extract sections of the arrays. We are QCing the values
# in the z2 and v3 arrays.
z1 = zuse[0:-2]
z2 = zuse[1:-1]
z3 = zuse[2:]
v1 = tuse[0:-2]
v2 = tuse[1:-1]
v3 = tuse[2:]
ol = origlevels[1:-1]
# Calculate the level of 'spike'.
z13 = z3 - z1
z12 = z2 - z1
z23 = z3 - z2
a = 0.5 * (v1 + v3)
q1 = np.abs(v2 - a)
q2 = np.abs(0.5 * (v3 - v1))
spike = q1 - q2
# Define the threshold at each level.
spikemax = np.ndarray(nuse - 2)
spikemax[:] = 4.0
spikemax[z2 > 1000.0] = 3.0
spikemax[z2 > 2000.0] = 2.0
# Set QC flags.
qc[ol[spike > spikemax]] = True
return ICDC.revert_qc_order(p, qc, parameters)
| [
7061,
6,
198,
37906,
2196,
286,
2198,
62,
30188,
66,
62,
2998,
62,
2777,
522,
62,
9122,
13,
69,
13,
220,
198,
24259,
286,
262,
2656,
2438,
389,
25,
198,
198,
66,
14,
360,
6158,
25,
220,
220,
220,
220,
220,
220,
449,
1565,
52,
... | 2.283848 | 842 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*- ########################################################
# ____ _ __ #
# ___ __ __/ / /__ ___ ______ ______(_) /___ __ #
# / _ \/ // / / (_-</ -_) __/ // / __/ / __/ // / #
# /_//_/\_,_/_/_/___/\__/\__/\_,_/_/ /_/\__/\_, / #
# /___/ team #
# #
# nullscan #
# A modular framework designed to chain and automate security tests #
# #
# FILE #
# http.py #
# #
# AUTHOR #
# noptrix@nullsecurity.net #
# #
################################################################################
# sys imports
import concurrent.futures as cf
from collections import deque
import json
# own imports
from modules.libs.base import Base, tool, timeout
class HTTP(Base):
""" HTTP module (tcp/80,8000,8080,8888) """
def __init__(self, target, opts):
""" init """
Base.__init__(self, target, opts)
return
@tool
def http_headers(self):
"""
DESCR: Dump HTTP headers via a single HTTP HEAD request. (ext)
TOOLS: curl
"""
opts = f"--connect-timeout 3 -m 30 -s -X HEAD -I -A '{self.useragent}'"
opts += f" --url http://{self.target['host']}:{self.target['port']}/"
self._run_tool('curl', opts, nullscan_tool='http_headers', timeout=30)
return
@tool
def http_reqs(self):
"""
DESCR: Send HTTP (head,get,post,options) requests with different HTTP
versions (0.9,1.0,1.1,2). (ext)
TOOLS: curl
"""
threads = 3
with cf.ThreadPoolExecutor(threads) as exe:
for t in self.http_req_types:
for v in self.http_versions:
opts = f"-v --connect-timeout 3 -m 30 -s -A '{self.useragent}'"
opts += f" -X {t.upper()} --http{v}"
opts += f" --url http://{self.target['host']}:{self.target['port']}/"
exe.submit(self._run_tool, 'curl', opts, nullscan_tool=f'http_reqs_{t}')
return
@tool
def http_put(self):
"""
DESCR: Try to send HTTP PUT request with example data to /nullscan.html. (int)
TOOLS: curl
"""
opts = f"-s --connect-timeout 3 -m 30 -X PUT -A '{self.useragent}'"
opts += ' -D /dev/stdout --data pwned'
opts += f" --url http://{self.target['host']}:"
opts += f"{self.target['port']}/nullscan.html"
self._run_tool('curl', opts, nullscan_tool='http_put')
return
@tool
def proxy_check(self):
"""
DESCR: Check for open HTTP proxy. (int)
TOOLS: curl
"""
opts = f"-I -s -x 'http://{self.target['host']}:{self.target['port']}/'"
opts += f" -L https://www.blackarch.org/"
self._run_tool('curl', opts, nullscan_tool='proxy_check')
return
@tool
def davscan(self):
"""
DESCR: Scan webserver and test if WebDAV is enabled. (ext)
TOOLS: davscan
"""
opts = f"-d -m -D 1 -o /tmp/{self.target['host']}"
if self.opts['user'] and self.opts['pass']:
opts += f" -a basic -u {self.opts['user']} -p {self.opts['pass']}"
if self.opts['proxy']:
opts += f" -P {self.opts['proxy']}"
opts += f" http://{self.target['host']}:{self.target['port']}/"
self._run_tool('davscan', opts, escape_codes=True)
return
@tool
def lulzbuster_http(self):
"""
DESCR: Enumerate directories and files on webserver. (ext)
TOOLS: lulzbuster
"""
host = self.target['host']
port = self.target['port']
# better try with hostname
domain = self._read_log('domainname')[0]
hostname = self._read_log('hostname')[0]
if domain:
host = domain
if hostname and hostname in domain:
host = hostname
for f in self.opts['flists']:
self._lulzbuster(host, port, flist=f)
return
@tool
def dirsearch_http(self):
"""
DESCR: Enumerate directories and files on webserver. (ext)
TOOLS: dirsearch
"""
host = self.target['host']
port = self.target['port']
# better try with hostname
domain = self._read_log('domainname')[0]
hostname = self._read_log('hostname')[0]
if domain:
host = domain
if hostname and hostname in domain:
host = hostname
for f in self.opts['flists']:
self._dirsearch(host, port, flist=f)
return
@tool
def gobuster_http(self):
"""
DESCR: Enumerate directories and files on webserver. (ext)
TOOLS: gobuster
"""
host = self.target['host']
port = self.target['port']
# better try with hostname
domain = self._read_log('domainname')[0]
hostname = self._read_log('hostname')[0]
if domain:
host = domain
if hostname and hostname in domain:
host = hostname
for f in self.opts['flists']:
self._gobuster(host, port, flist=f)
return
@tool
def halberd_http(self):
"""
DESCR: Discover http load balancer. (ext)
TOOLS: halberd
"""
self._halberd(self.target['host'], self.target['port'])
return
@tool
def lbmap_http(self):
"""
DESCR: Fingerprint HTTP server. (ext)
TOOLS: lbmap
"""
self._lbmap(self.target['host'], self.target['port'])
return
@tool
def metoscan_http(self):
"""
DESCR: Scan available HTTP methods. (ext)
TOOLS: metoscan
"""
self._metoscan(self.target['host'], self.target['port'])
return
@tool
def httping_http(self):
"""
DESCR: Ping HTTP server. (ext)
TOOLS: httping
"""
self._httping(self.target['host'], self.target['port'])
return
@tool
def httprint_http(self):
"""
DESCR: Fingerprint the web-server. (ext)
TOOLS: httprint
"""
self._httprint(self.target['host'], self.target['port'])
return
@tool
def nikto_http(self):
"""
DESCR: Crawl the web-server for directories, files and vulnerabilities.
(ext)
TOOLS: nikto
"""
self._nikto(self.target['host'], self.target['port'])
return
@tool
def crack_http_auth(self):
"""
DESCR: Check HTTP auth type (basic, realm, etc.) and crack login. (int)
TOOLS: python3
"""
with timeout(self.opts['timeout']):
url = f"http://{self.target['host']}:{self.target['port']}/"
self._crack_http_auth(url, 'crack_http_auth')
return
@tool
def crack_tomcat_http(self):
"""
DESCR: Check for tomcat and crack logins using tomcat's default creds. (int)
TOOLS: python3
"""
with timeout(self.opts['timeout']):
# default tomcat creds
users = deque(('tomcat', 'both', 'role1', 'admin', 'manager', 'root'))
pws = deque(('tomcat', 'both', 'role1', 'admin', 'manager', 'root', ''))
threads = len(users)
url = self._is_tomcat(self.target['host'], self.target['port'])
if url:
with cf.ThreadPoolExecutor(threads) as exe:
for us in users:
for pw in pws:
exe.submit(self._crack_tomcat, url, us, pw, 'crack_tomcat_http')
return
@tool
def jexboss_http(self):
"""
DESCR: Check for known java deserialization vulns against JBoss, Jenkins,
and Apache Struts2. (ext)
TOOLS: jexboss
"""
self._jexboss(self.target['host'], self.target['port'], log='jexboss_http')
return
@tool
def snallygaster_http(self):
"""
DESCR: Scan for secret files on web-server. (ext)
TOOLS: snallygaster
"""
target = f"{self.target['host']}:{self.target['port']}"
self._snallygaster(target, 'snallygaster_http')
return
@tool
def tomcatwardeployer_http(self):
"""
DESCR: Apache Tomcat auto WAR deployment & pwning. (ext)
TOOLS: tomcatwardeployer
"""
opts = '-t 5'
if self.opts['user'] and self.opts['pass']:
opts += f" -U {self.opts['user']} -P {self.opts['pass']}"
opts += f" http://{self.target['host']}:{self.target['port']}/"
self._run_tool('tomcatwardeployer', opts, 'tomcatwardeployer_http',
timeout=8)
return
@tool
def findstr_http(self):
"""
DESCR: Find given string in HTTP responses. (int)
TOOLS: curl
"""
url = f"http://{self.target['host']}:{self.target['port']}/"
opts = f"--connect-timeout 2 -m 30 -s -L -A '{self.useragent}' {url}"
cmd = f'curl {opts}'
res = ' '.join(self._run_cmd(cmd))
if self.opts['searchstr'] in res:
idx = res.index(self.opts['searchstr'])
data = f"{url} ==> '{res[idx:idx+int(self.opts['resp_size'])]}'"
self._log('findstr_http', data)
return
@tool
def nmap_http(self):
"""
DESCR: Scan http service with corresponding NSE scripts. (ext)
TOOLS: nmap
"""
nse = 'http-adobe*,http-aff*,http-apache*,http-asp*,http-avaya*,http-awst*,'
nse += 'http-axis*,http-barra*,http-bigip*,http-cakephp*,http-chrono*,'
nse += 'http-cisco-*,http-coldfus*,http-date,http-dlink*,http-drupal*,'
nse += 'http-favicon,http-frontpage*,http-generator,http-git*,http-google*,'
nse += 'http-headers,http-huawei*,http-iis*,http-internal-ip*,http-litesp*,'
nse += 'http-majordomo2*,http-malware*,http-mcmp,http-methods,http-ntlm-*,'
nse += 'http-open-proxy,http-phpmyadm*,http-qnap*,http-robots*,http-robte*,'
nse += 'http-server-head*,http-shellsh*,http-svn*,http-title,http-tplink*,'
nse += 'http-trace*,http-trane*,http-vhosts,http-vlc*,http-vmware*,'
nse += 'http-vuln-*,http-waf*,http-webdav*'
opts = f'-n -sS -Pn --open --nsock-engine epoll --script {nse}'
opts += f" -p {self.target['port']} {self.target['host']}"
self._run_tool('nmap', opts, nullscan_tool='nmap_http')
return
# EOF
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
1303,
29113,
14468,
4242,
21017,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
... | 2.102116 | 4,916 |
import pkgutil
import unittest
| [
11748,
279,
10025,
22602,
198,
11748,
555,
715,
395,
628
] | 3.2 | 10 |
import formatter
import unittest
from test import test_support
htmllib = test_support.import_module('htmllib', deprecated=True)
if __name__ == "__main__":
test_main()
| [
11748,
1296,
1436,
201,
198,
11748,
555,
715,
395,
201,
198,
201,
198,
6738,
1332,
1330,
1332,
62,
11284,
201,
198,
19211,
297,
571,
796,
1332,
62,
11284,
13,
11748,
62,
21412,
10786,
19211,
297,
571,
3256,
39224,
28,
17821,
8,
201,
... | 2.597222 | 72 |
# -*- coding: utf-8 -*-
from django.contrib import admin
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
6738,
42625,
14208,
13,
3642,
822,
1330,
13169,
628
] | 2.521739 | 23 |
# Copyright 2004-2019 Tom Rothamel <pytom@bishoujo.us>
#
# 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.
from __future__ import print_function
import renpy.display
import pygame_sdl2 as pygame
import math
import weakref
import time
import os
from renpy.display.render import blit_lock, IDENTITY, BLIT, DISSOLVE, IMAGEDISSOLVE, PIXELLATE, FLATTEN
# A map from cached surface to rle version of cached surface.
rle_cache = weakref.WeakKeyDictionary()
class Clipper(object):
"""
This is used to calculate the clipping rectangle and update rectangles
used for a particular draw of the screen.
"""
def compute(self, full_redraw):
"""
This returns a clipping rectangle, and a list of update rectangles
that cover the changes between the old and new frames.
"""
# First, get things out of the fields, and update them. This
# allows us to just return without having to do any cleanup
# code.
bl0 = self.old_blits
bl1 = self.blits
old_forced = self.old_forced
forced = self.forced
mutated = self.mutated
self.old_blits = bl1
self.blits = [ ]
self.old_forced = forced
self.forced = set()
self.mutated = set()
sw = renpy.config.screen_width
sh = renpy.config.screen_height
sa = sw * sh
# A tuple representing the size of the fullscreen.
fullscreen = (0, 0, sw, sh)
# Check to see if a full redraw has been forced, and return
# early.
if full_redraw:
return fullscreen, [ fullscreen ]
# Quick checks to see if a dissolve is happening, or something like
# that.
changes = forced | old_forced
if fullscreen in changes:
return fullscreen, [ fullscreen ]
# Compute the differences between the two sets, and add those
# to changes.
i0 = 0
i1 = 0
bl1set = set(bl1)
while True:
if i0 >= len(bl0) or i1 >= len(bl1):
break
b0 = bl0[i0]
b1 = bl1[i1]
if b0 == b1:
if id(b0[5]) in mutated:
changes.add(b0[:5])
i0 += 1
i1 += 1
elif b0 not in bl1set:
changes.add(b0[:5])
i0 += 1
else:
changes.add(b1[:5])
i1 += 1
changes.update(i[:5] for i in bl0[i0:])
changes.update(i[:5] for i in bl1[i1:])
# No changes? Quit.
if not changes:
return None, [ ]
# Compute the sizes of the updated rectangles.
sized = [ ]
for x0, y0, x1, y1, (sx0, sy0, sx1, sy1) in changes:
# Round up by a pixel, to prevent visual artifacts when scaled down.
x1 += 1
y1 += 1
if x0 < sx0:
x0 = sx0
if y0 < sy0:
y0 = sy0
if x1 > sx1:
x1 = sx1
if y1 > sy1:
y1 = sy1
w = x1 - x0
h = y1 - y0
if w <= 0 or h <= 0:
continue
area = w * h
if area >= sa:
return fullscreen, [ fullscreen ]
sized.append((area, x0, y0, x1, y1))
sized.sort()
# The list of non-contiguous updates.
noncont = [ ]
# The total area of noncont.
nca = 0
# Pick the largest area, merge with all overlapping smaller areas, repeat
# until no merge possible.
while sized:
area, x0, y0, x1, y1 = sized.pop()
merged = False
if nca + area >= sa:
return (0, 0, sw, sh), [ (0, 0, sw, sh) ]
i = 0
while i < len(sized):
_iarea, ix0, iy0, ix1, iy1 = sized[i]
if (x0 <= ix0 <= x1 or x0 <= ix1 <= x1) and \
(y0 <= iy0 <= y1 or y0 <= iy1 <= y1):
merged = True
x0 = min(x0, ix0)
x1 = max(x1, ix1)
y0 = min(y0, iy0)
y1 = max(y1, iy1)
area = (x1 - x0) * (y1 - y0)
sized.pop(i)
else:
i += 1
if merged:
sized.append((area, x0, y0, x1, y1))
else:
noncont.append((x0, y0, x1, y1))
nca += area
if not noncont:
return None, [ ]
x0, y0, x1, y1 = noncont.pop()
x0 = int(x0)
y0 = int(y0)
x1 = int(math.ceil(x1))
y1 = int(math.ceil(y1))
# A list of (x, y, w, h) tuples for each update.
updates = [ (x0, y0, x1 - x0, y1 - y0) ]
for ix0, iy0, ix1, iy1 in noncont:
ix0 = int(ix0)
iy0 = int(iy0)
ix1 = int(math.ceil(ix1))
iy1 = int(math.ceil(iy1))
x0 = min(x0, ix0)
y0 = min(y0, iy0)
x1 = max(x1, ix1)
y1 = max(y1, iy1)
updates.append((ix0, iy0, ix1 - ix0, iy1 - iy0))
return (x0, y0, x1 - x0, y1 - y0), updates
clippers = [ Clipper() ]
def surface(w, h, alpha):
"""
Creates a surface that shares a pixel format with the screen. The created
surface will
"""
if alpha:
rv = pygame.Surface((w + 4, h + 4), pygame.SRCALPHA)
else:
rv = pygame.Surface((w + 4, h + 4), 0)
return rv.subsurface((2, 2, w, h))
def draw_special(what, dest, x, y):
"""
This handles the special drawing operations, such as dissolve and
image dissolve. `x` and `y` are the offsets of the thing to be drawn
relative to the destination rectangle, and are always negative.
"""
dw, dh = dest.get_size()
w = min(dw, what.width + x)
h = min(dh, what.height + y)
if w <= 0 or h <= 0:
return
if what.operation == DISSOLVE:
bottom = what.children[0][0].render_to_texture(True)
top = what.children[1][0].render_to_texture(True)
if what.operation_alpha:
target = surface(w, h, True)
else:
target = dest.subsurface((0, 0, w, h))
renpy.display.module.blend(
bottom.subsurface((-x, -y, w, h)),
top.subsurface((-x, -y, w, h)),
target,
int(what.operation_complete * 255))
if what.operation_alpha:
dest.blit(target, (0, 0))
elif what.operation == IMAGEDISSOLVE:
image = what.children[0][0].render_to_texture(True)
bottom = what.children[1][0].render_to_texture(True)
top = what.children[2][0].render_to_texture(True)
if what.operation_alpha:
target = surface(w, h, True)
else:
target = dest.subsurface((0, 0, w, h))
ramplen = what.operation_parameter
ramp = "\x00" * 256
for i in xrange(0, ramplen):
ramp += chr(255 * i / ramplen)
ramp += "\xff" * 256
step = int( what.operation_complete * (256 + ramplen) )
ramp = ramp[step:step+256]
renpy.display.module.imageblend(
bottom.subsurface((-x, -y, w, h)),
top.subsurface((-x, -y, w, h)),
target,
image.subsurface((-x, -y, w, h)),
ramp)
if what.operation_alpha:
dest.blit(target, (0, 0))
elif what.operation == PIXELLATE:
surf = what.children[0][0].render_to_texture(dest.get_masks()[3])
px = what.operation_parameter
renpy.display.module.pixellate(
surf.subsurface((-x, -y, w, h)),
dest.subsurface((0, 0, w, h)),
px, px, px, px)
elif what.operation == FLATTEN:
surf = what.children[0][0].render_to_texture(dest.get_masks()[3])
dest.subsurface((0, 0, w, h)).blit(surf, (0, 0))
else:
raise Exception("Unknown operation: %d" % what.operation)
def draw(dest, clip, what, xo, yo, screen):
"""
This is the simple draw routine, which only works when alpha is 1.0
and the matrices are None. If those aren't the case, draw_complex
is used instead.
`dest` - Either a destination surface, or a clipper.
`clip` - If None, we should draw. Otherwise we should clip, and this is
the rectangle to clip to.
`what` - The Render or Surface we're drawing to.
`xo` - The X offset.
`yo` - The Y offset.
`screen` - True if this is a blit to the screen, False otherwise.
"""
if not isinstance(what, renpy.display.render.Render):
# Pixel-Aligned blit.
if isinstance(xo, int) and isinstance(yo, int):
if screen:
what = rle_cache.get(what, what)
if clip:
w, h = what.get_size()
dest.blits.append((xo, yo, xo + w, yo + h, clip, what, None))
else:
try:
blit_lock.acquire()
dest.blit(what, (xo, yo))
finally:
blit_lock.release()
# Subpixel blit.
else:
if clip:
w, h = what.get_size()
dest.blits.append((xo, yo, xo + w, yo + h, clip, what, None))
else:
renpy.display.module.subpixel(what, dest, xo, yo)
return
if what.text_input:
renpy.display.interface.text_rect = what.screen_rect(xo, yo, None)
# Deal with draw functions.
if what.operation != BLIT:
xo = int(xo)
yo = int(yo)
if clip:
dx0, dy0, dx1, dy1 = clip
dw = dx1 - dx0
dh = dy1 - dy0
else:
dw, dh = dest.get_size()
if xo >= 0:
newx = 0
subx = xo
else:
newx = xo
subx = 0
if yo >= 0:
newy = 0
suby = yo
else:
newy = yo
suby = 0
if subx >= dw or suby >= dh:
return
# newx and newy are the offset of this render relative to the
# subsurface. They can only be negative or 0, as otherwise we
# would make a smaller subsurface.
subw = min(dw - subx, what.width + newx)
subh = min(dh - suby, what.height + newy)
if subw <= 0 or subh <= 0:
return
if clip:
dest.forced.add((subx, suby, subx + subw, suby + subh, clip))
else:
newdest = dest.subsurface((subx, suby, subw, subh))
# what.draw_func(newdest, newx, newy)
draw_special(what, newdest, newx, newy)
return
# Deal with clipping, if necessary.
if what.xclipping or what.yclipping:
if clip:
cx0, cy0, cx1, cy1 = clip
cx0 = max(cx0, xo)
cy0 = max(cy0, yo)
cx1 = min(cx1, xo + what.width)
cy1 = min(cy1, yo + what.height)
if cx0 > cx1 or cy0 > cy1:
return
clip = (cx0, cy0, cx1, cy1)
dest.forced.add(clip + (clip,))
return
else:
# After this code, x and y are the coordinates of the subsurface
# relative to the destination. xo and yo are the offset of the
# upper-left corner relative to the subsurface.
if xo >= 0:
x = xo
xo = 0
else:
x = 0
# xo = xo
if yo >= 0:
y = yo
yo = 0
else:
y = 0
# yo = yo
dw, dh = dest.get_size()
width = min(dw - x, what.width + xo)
height = min(dh - y, what.height + yo)
if width < 0 or height < 0:
return
dest = dest.subsurface((x, y, width, height))
# Deal with alpha and transforms by passing them off to draw_transformed.
if what.alpha != 1 or what.over != 1.0 or (what.forward is not None and what.forward is not IDENTITY):
for child, cxo, cyo, _focus, _main in what.visible_children:
draw_transformed(dest, clip, child, xo + cxo, yo + cyo,
what.alpha * what.over, what.forward, what.reverse)
return
for child, cxo, cyo, _focus, _main in what.visible_children:
draw(dest, clip, child, xo + cxo, yo + cyo, screen)
def do_draw_screen(screen_render, full_redraw, swdraw):
"""
Draws the render produced by render_screen to the screen.
"""
yoffset = xoffset = 0
screen_render.is_opaque()
clip = (xoffset, yoffset, xoffset + screen_render.width, yoffset + screen_render.height)
clipper = clippers[0]
draw(clipper, clip, screen_render, xoffset, yoffset, True)
cliprect, updates = clipper.compute(full_redraw)
if cliprect is None:
return [ ]
x, y, _w, _h = cliprect
dest = swdraw.window.subsurface(cliprect)
draw(dest, None, screen_render, -x, -y, True)
return updates
class SWDraw(object):
"""
This uses the software renderer to draw to the screen.
"""
# private
def show_mouse(self, pos, info):
"""
Actually shows the mouse.
"""
self.mouse_location = pos
self.mouse_info = info
mxo, myo, tex = info
mx, my = pos
mw, mh = tex.get_size()
bx = mx - mxo
by = my - myo
self.mouse_backing_pos = (bx, by)
self.mouse_backing = surface(mw, mh, False)
self.mouse_backing.blit(self.window, (0, 0), (bx, by, mw, mh))
self.screen.blit(tex, (bx, by))
return bx, by, mw, mh
# private
def hide_mouse(self):
"""
Actually hides the mouse.
"""
size = self.mouse_backing.get_size()
self.screen.blit(self.mouse_backing, self.mouse_backing_pos)
rv = self.mouse_backing_pos + size
self.mouse_backing = None
self.mouse_backing_pos = None
self.mouse_location = None
return rv
# private
def draw_mouse(self, show_mouse):
"""
This draws the mouse to the screen, if necessary. It uses the
buffer to minimize the amount of the screen that needs to be
drawn, and only redraws if the mouse has actually been moved.
"""
hardware, x, y, tex = renpy.game.interface.get_mouse_info()
if self.mouse_old_visible != hardware:
pygame.mouse.set_visible(hardware)
self.mouse_old_visible = hardware
# The rest of this is for the software mouse.
if self.suppressed_blit:
return [ ]
if not show_mouse:
tex = None
info = (x, y, tex)
pos = pygame.mouse.get_pos()
if (pos == self.mouse_location and tex and info == self.mouse_info):
return [ ]
updates = [ ]
if self.mouse_location:
updates.append(self.hide_mouse())
if tex and pos and renpy.game.interface.mouse_focused: # @UndefinedVariable
updates.append(self.show_mouse(pos, info))
return updates
def update_mouse(self):
"""
Draws the mouse, and then updates the screen.
"""
updates = self.draw_mouse(True)
if updates:
pygame.display.update(updates)
def screenshot(self, surftree, fullscreen_video):
"""
Returns a pygame surface containing a screenshot.
"""
return self.window
def should_redraw(self, needs_redraw, first_pass, can_block):
"""
Uses the framerate to determine if we can and should redraw.
"""
if not needs_redraw:
return False
framerate = renpy.config.framerate
if framerate is None:
return True
next_frame = self.next_frame
now = pygame.time.get_ticks()
frametime = 1000.0 / framerate
# Handle timer rollover.
if next_frame > now + frametime:
next_frame = now
# It's not yet time for the next frame.
if now < next_frame and not first_pass:
return False
# Otherwise, it is. Schedule the next frame.
# if next_frame + frametime < now:
next_frame = now + frametime
# else:
# next_frame += frametime
self.next_frame = next_frame
return True
def draw_screen(self, surftree, fullscreen_video):
"""
Draws the screen.
"""
if fullscreen_video:
if not self.showing_video:
self.window.fill((0, 0, 0, 255))
w, h = self.window.get_size()
frame = renpy.display.video.render_movie("movie", w, h)
if frame is not None:
surftree = frame
self.full_redraw = True
self.showing_video = True
else:
self.showing_video = False
updates = [ ]
updates.extend(self.draw_mouse(False))
damage = do_draw_screen(surftree, self.full_redraw, self)
if damage:
updates.extend(damage)
self.full_redraw = False
if self.window is self.screen:
updates.extend(self.draw_mouse(True))
pygame.display.update(updates)
else:
if self.scale_fast:
pygame.transform.scale(self.window, self.screen.get_size(), self.screen)
else:
renpy.display.scale.smoothscale(self.window, self.screen.get_size(), self.screen)
self.draw_mouse(True)
pygame.display.flip()
if fullscreen_video:
self.full_redraw = True
def mutated_surface(self, surf):
"""
Called to indicate that the given surface has changed.
"""
for i in clippers:
i.mutated.add(id(surf))
if surf in rle_cache:
del rle_cache[surf]
def load_texture(self, surf, transient=False):
"""
Creates a texture from the surface. In the software implementation,
the only difference between a texture and a surface is that a texture
is in the RLE cache.
"""
if surf in rle_cache:
return rle_cache[surf]
rle_surf = copy_surface(surf)
if not transient:
rle_surf.set_alpha(255, pygame.RLEACCEL)
self.mutated_surface(rle_surf)
rle_cache[surf] = rle_surf
return rle_surf
def solid_texture(self, w, h, color):
"""
Creates a texture filled to the edges with color.
"""
surf = surface(w + 4, h + 4, True)
surf.fill(color)
self.mutated_surface(surf)
surf = surf.subsurface((2, 2, w, h))
self.mutated_surface(surf)
return surf
def kill_textures(self):
"""
Kills all textures and caches of textures.
"""
rle_cache.clear()
def quit(self): # @ReservedAssignment
"""
Shuts down the drawing system.
"""
pygame.display.quit()
return
def event_peek_sleep(self):
"""
Wait a little bit so the CPU doesn't speed up.
"""
time.sleep(.0001)
def get_physical_size(self):
"""
Return the physical width and height of the screen.
"""
return renpy.config.screen_width, renpy.config.screen_height
| [
2,
15069,
5472,
12,
23344,
4186,
16131,
17983,
1279,
9078,
39532,
31,
31795,
280,
7639,
13,
385,
29,
198,
2,
198,
2,
2448,
3411,
318,
29376,
7520,
11,
1479,
286,
3877,
11,
284,
597,
1048,
198,
2,
16727,
257,
4866,
286,
428,
3788,
... | 2.036387 | 10,086 |
import logging
import time
import os
import pytest
from tests.common.utilities import wait_until
from tests.common.config_reload import config_reload
from tests.common.reboot import reboot
DUT_THERMAL_POLICY_FILE = '/usr/share/sonic/device/{}/thermal_policy.json'
DUT_THERMAL_POLICY_BACKUP_FILE = '/usr/share/sonic/device/{}/thermal_policy.json.bak'
BASE_DIR = os.path.dirname(os.path.realpath(__file__))
FILES_DIR = os.path.join(BASE_DIR, 'files')
class BaseMocker:
"""
@summary: Base class for thermal control data mocker
This base class defines the basic interface to be provided by base mocker. Mockers implemented by each
vendor must be a subclass of this base class.
"""
# Mocker type dictionary. Vendor must register their concrete mocker class to this dictionary.
_mocker_type_dict = {}
def __init__(self, dut):
"""
Constructor of a mocker.
:param dut: DUT object representing a SONiC switch under test.
"""
self.dut = dut
def mock_data(self):
"""
Generate mock data.
:return:
"""
pass
def check_result(self, actual_data):
"""
Check actual data with mocked data.
:param actual_data: A dictionary contains actual command line data. Key of the dictionary is the unique id
of a line of command line data. For 'show platform fan', the key is FAN name. Value
of the dictionary is a list of field values for a line.
:return: True if actual data match mocked data else False
"""
pass
def deinit(self):
"""
Destructor. Vendor specific clean up work should do here.
:return:
"""
pass
@classmethod
def register_mocker_type(cls, name, mocker_type):
"""
Register mocker type with its name.
:param name: Name of a mocker type. For example: FanStatusMocker.
:param mocker_type: Class of a mocker.
:return:
"""
cls._mocker_type_dict[name] = mocker_type
@classmethod
def get_mocker_type(cls, name):
"""
Get mocker type by its name.
:param name: Name of a mocker type. For example: FanStatusMocker.
:return: Class of a mocker.
"""
return cls._mocker_type_dict[name] if name in cls._mocker_type_dict else None
def mocker(type_name):
"""
Decorator for register mocker type.
:param type_name: Name of a mocker type.
:return:
"""
return wrapper
@pytest.fixture
def mocker_factory(localhost, duthosts, rand_one_dut_hostname):
"""
Fixture for thermal control data mocker factory.
:return: A function for creating thermal control related data mocker.
"""
mockers = []
duthost = duthosts[rand_one_dut_hostname]
def _create_mocker(dut, mocker_name):
"""
Create vendor specified mocker object by mocker name.
:param dut: DUT object representing a SONiC switch under test.
:param mocker_name: Name of a mocker type.
:return: Created mocker instance.
"""
platform = dut.facts['platform']
mocker_object = None
if 'mlnx' in platform:
from tests.platform_tests.mellanox import mellanox_thermal_control_test_helper
mocker_type = BaseMocker.get_mocker_type(mocker_name)
if mocker_type:
mocker_object = mocker_type(dut)
mockers.append(mocker_object)
else:
pytest.skip("No mocker defined for this platform %s")
return mocker_object
yield _create_mocker
try:
for m in mockers:
m.deinit()
except Exception as e:
reboot(duthost, localhost)
assert 0, "Caught exception while recovering from mock - {}".format(repr(e))
class FanStatusMocker(BaseMocker):
"""
Fan status mocker. Vendor should implement this class to provide a FAN mocker.
This class could mock speed, presence/absence and so on for all FANs and check
the actual data equal to the mocked data.
"""
def check_all_fan_speed(self, expected_speed):
"""
Check all fan speed with a given expect value.
:param expected_speed: Expect FAN speed percentage.
:return: True if match else False.
"""
pass
class SingleFanMocker(BaseMocker):
"""
Single FAN mocker. Vendor should implement this class to provide a FAN mocker.
This class could mock speed, presence/absence for one FAN, check LED color and
other information.
"""
def is_fan_removable(self):
"""
:return: True if FAN is removable else False
"""
pass
def mock_normal(self):
"""
Change the mocked FAN status to 'Present' and normal speed.
:return:
"""
pass
def mock_absence(self):
"""
Change the mocked FAN status to 'Not Present'.
:return:
"""
pass
def mock_presence(self):
"""
Change the mocked FAN status to 'Present'
:return:
"""
pass
def mock_status(self, status):
"""
Change the mocked FAN status to good or bad
:param status: bool value indicate the target status of the FAN.
:return:
"""
pass
def mock_normal_speed(self):
"""
Change the mocked FAN speed to a normal value.
:return:
"""
pass
def mock_under_speed(self):
"""
Change the mocked FAN speed to slower than target speed and exceed speed tolerance.
:return:
"""
pass
def mock_over_speed(self):
"""
Change the mocked FAN speed to faster than target speed and exceed speed tolerance.
:return:
"""
pass
class ThermalStatusMocker(BaseMocker):
"""
Thermal status mocker. Vendor should implement this class to provide a Thermal data mocker.
This class could mock temperature, high threshold, high critical threshold and so on for all
FANs and check the actual data equal to the mocked data.
"""
def check_thermal_algorithm_status(self, expected_status):
"""
Check thermal control algorithm status equal to the given value.
:param expected_status: Expected thermal control status. True means enable, false means disable.
:return: True if match else False.
"""
pass
def check_cli_output_with_mocker(dut, mocker_object, command, max_wait_time, key_index=0):
"""
Check the command line output matches the mocked data.
:param dut: DUT object representing a SONiC switch under test.
:param mocker_object: A mocker instance.
:param command: The command to be executed. E.g, 'show platform fan'
:param max_wait_time: Max wait time.
:return: True if the actual data matches the mocked data.
"""
time.sleep(max_wait_time)
result = dut.show_and_parse(command)
assert len(result) > 0, "Run and parse output of command '{}' failed".format(command)
return mocker_object.check_result(result)
def check_thermal_algorithm_status(dut, mocker_factory, expected_status):
"""
Check thermal control algorithm status.
:param dut: DUT object representing a SONiC switch under test.
:param mocker_factory: Mocker factory.
:param expected_status: Expect thermal control algorithm status.
:return: True if actual thermal control status match expect value.
"""
thermal_mocker = mocker_factory(dut, 'ThermalStatusMocker')
if thermal_mocker is not None:
return thermal_mocker.check_thermal_algorithm_status(expected_status)
return True # if vendor doesn't provide a thermal mocker, ignore this check by return True.
def restart_thermal_control_daemon(dut):
"""
Restart thermal control daemon by killing it and waiting supervisord to restart
it automatically.
:param dut: DUT object representing a SONiC switch under test.
:return:
"""
logging.info('Restarting thermal control daemon...')
find_thermalctld_pid_cmd = 'docker exec -i pmon bash -c \'pgrep -f thermalctld\' | sort'
output = dut.shell(find_thermalctld_pid_cmd)
assert output["rc"] == 0, "Run command '%s' failed" % find_thermalctld_pid_cmd
assert len(output["stdout_lines"]) == 2, "There should be 2 thermalctld process"
pid_0 = int(output["stdout_lines"][0].strip())
pid_1 = int(output["stdout_lines"][1].strip())
# find and kill the parent process
pid_to_kill = pid_0 if pid_0 < pid_1 else pid_1
logging.info('Killing old thermal control daemon with pid: {}'.format(pid_to_kill))
kill_thermalctld_cmd = 'docker exec -i pmon bash -c \'kill {}\''.format(pid_to_kill)
output = dut.command(kill_thermalctld_cmd) # kill thermalctld and wait supervisord auto reboot thermalctld
assert output["rc"] == 0, "Run command '%s' failed" % kill_thermalctld_cmd
# make sure thermalctld has restarted
max_wait_time = 30
while max_wait_time > 0:
max_wait_time -= 1
output = dut.shell(find_thermalctld_pid_cmd)
assert output["rc"] == 0, "Run command '%s' failed" % find_thermalctld_pid_cmd
if len(output["stdout_lines"]) != 2:
time.sleep(1)
continue
new_pid_0 = int(output["stdout_lines"][0].strip())
new_pid_1 = int(output["stdout_lines"][1].strip())
parent_pid = new_pid_0 if new_pid_0 < new_pid_1 else new_pid_1
if parent_pid == pid_to_kill:
logging.info('Old thermal control daemon is still alive, waiting...')
time.sleep(1)
continue
else:
logging.info('New pid of thermal control daemon is {}'.format(parent_pid))
return
# try restore by config reload...
config_reload(dut)
assert 0, 'Wait thermal control daemon restart failed'
class ThermalPolicyFileContext:
"""
Context class to help replace thermal control policy file and restore it automatically.
"""
def __init__(self, dut, src):
"""
Constructor of ThermalPolicyFileContext.
:param dut: DUT object representing a SONiC switch under test.
:param src: Local policy file path.
"""
self.dut = dut
self.src = src
platform_str = dut.facts['platform']
self.thermal_policy_file_path = DUT_THERMAL_POLICY_FILE.format(platform_str)
self.thermal_policy_file_backup_path = DUT_THERMAL_POLICY_BACKUP_FILE.format(platform_str)
def __enter__(self):
"""
Back up original thermal control policy file and replace it with the given one. Restart
thermal control daemon to make it effect.
:return:
"""
self.dut.command('mv -f {} {}'.format(self.thermal_policy_file_path, self.thermal_policy_file_backup_path))
self.dut.copy(src=os.path.join(FILES_DIR, self.src), dest=self.thermal_policy_file_path)
restart_thermal_control_daemon(self.dut)
def __exit__(self, exc_type, exc_val, exc_tb):
"""
Restore original thermal control policy file. Restart thermal control daemon to make it effect.
:param exc_type: Not used.
:param exc_val: Not used.
:param exc_tb: Not used.
:return:
"""
self.dut.command('mv -f {} {}'.format(self.thermal_policy_file_backup_path, self.thermal_policy_file_path))
restart_thermal_control_daemon(self.dut)
| [
11748,
18931,
198,
11748,
640,
198,
11748,
28686,
198,
198,
11748,
12972,
9288,
198,
198,
6738,
5254,
13,
11321,
13,
315,
2410,
1330,
4043,
62,
28446,
198,
6738,
5254,
13,
11321,
13,
11250,
62,
260,
2220,
1330,
4566,
62,
260,
2220,
19... | 2.515376 | 4,585 |
# coding=utf-8
# Copyright 2019 The Tensor2Tensor Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Diet variables are much more memory-efficient than regular variables.
Using diet variables, we can reduce memory overhead per parameter from
16 bytes to 2 bytes, allowing for up to 4B parameters per GPU.
Functions that build subgraphs with variables can be made to use diet variables
by using the fn_with_diet_vars decorator.
"""
from collections import defaultdict
import copy
import math
from tensor2tensor.layers import common_layers
from tensor2tensor.utils.hparam import HParams
import tensorflow as tf
def diet_adam_optimizer_params():
"""Default hyperparameters for a DietAdamOptimizer.
Returns:
a hyperparameters object.
"""
return HParams(
quantize=True, # use 16-bit fixed-point
quantization_scale=10.0 / tf.int16.max,
optimizer="DietAdam",
learning_rate=1.0,
learning_rate_warmup_steps=2000,
learning_rate_decay_scheme="noam", # "noam" or "none"
epsilon=1e-10,
beta1=0.0, # we can save memory if beta1=0
beta2=0.98,
factored_second_moment_accumulator=True, # this saves memory
)
def diet_expert(x, hidden_size, params):
"""A two-layer feed-forward network with relu activation on hidden layer.
Uses diet variables.
Recomputes hidden layer on backprop to save activation memory.
Args:
x: a Tensor with shape [batch, io_size]
hidden_size: an integer
params: a diet variable HParams object.
Returns:
a Tensor with shape [batch, io_size]
"""
@fn_with_diet_vars(params)
return diet_expert_internal(x)
class DietVariableOptimizer(object):
"""Base class for Diet variable optimizers."""
@property
@property
class DietAdamOptimizer(DietVariableOptimizer):
"""A memory efficient optimizer for memory-efficient variables.
We employ the following techniques:
- 16-bit fixed-point quantization
- inline updates during backprop, instead of through the optimizer. This
keeps the gradients from staying around in memory.
- momentum is optional - saves a slot if it is off (beta1=0.0).
- "factored second-moment accumulator"
(keep row-wise and col-wise averages instead of full accumulator)
- tighter control over operation ordering to make sure that only a small
portion of the decompressed variables and of the variable gradients
are resident in memory at any given time.
All together these techniques reduce the memory footprint per parameter to
a little over 2 bytes, allowing for roughly 4B parameters per GPU. This is
roughly an 8x improvement over the naive version.
Usage:
Diet variables should be created with the
DietAdamOptimizer.get_variable() method. The resulting variables
have extra fields pointing to the optimizer and to the accumulator
slots.
The variable is kept in quantized form, so you need to call
var.optimizer.dequantize(var) to get the value.
The variables are created with trainable=False, so that they will
not be optimized by an ordinary optimizer. Instead, the user is
responsible for making sure that var.optimizer.update(var, grad) is
called during backprop. The reason for this inline update is to
avoid keeping around the gradients for all variables at once. This
is done with the clever use of defuns and control dependencies. See
diet_expert() for an example of how all of this is done.
To facilitate fixed-point quantization and to make it easier to
choose a learning rate, all variables are initialized with unit
normal initialization. If you want smaller values, downscale on the
outside.
"""
def create_slots(self, var):
"""Create the factorized Adam accumulators for diet variables."""
params = self.params
shape = var.get_shape().as_list()
if not hasattr(params, "slots"):
params.slots = defaultdict(dict)
name = var.op.name
slots = params.slots[name]
if params.factored_second_moment_accumulator and len(shape) == 2:
slots["adam_vr"] = tf.get_variable(
name + "_adam_vr", [shape[0], 1],
trainable=False,
initializer=tf.zeros_initializer())
slots["adam_vc"] = tf.get_variable(
name + "_adam_vc", [1, shape[1]],
trainable=False,
initializer=tf.zeros_initializer())
else:
slots["adam_v"] = tf.get_variable(
name + "_adam_v",
shape,
trainable=False,
initializer=tf.zeros_initializer())
if params.beta1 != 0.0:
slots["adam_m"] = tf.get_variable(
name + "_adam_m",
shape,
trainable=False,
initializer=tf.zeros_initializer())
def update_variable(self, var, grad_var):
"""Update the variable and its slots."""
params = self.params
global_step = tf.to_float(self.global_step) + 1
# compute learning rate
lrate = params.learning_rate
if params.learning_rate_decay_scheme == "noam":
lrate *= tf.minimum(global_step * params.learning_rate_warmup_steps**-1.5,
global_step**-0.5)
else:
assert params.learning_rate_decay_scheme == "none"
lrate *= tf.minimum(global_step / params.learning_rate_warmup_steps, 1.0)
# compute adjustment due to second moment
slots = params.slots[var.op.name]
grad_squared = tf.square(grad_var)
beta2_pow = tf.pow(params.beta2, global_step)
if params.factored_second_moment_accumulator and len(var.shape) == 2:
vr_update = tf.assign(slots["adam_vr"], slots["adam_vr"] * params.beta2 +
tf.reduce_mean(grad_squared, 1, keepdims=True) *
(1.0 - params.beta2))
vc_update = tf.assign(slots["adam_vc"], slots["adam_vc"] * params.beta2 +
tf.reduce_mean(grad_squared, 0, keepdims=True) *
(1.0 - params.beta2))
with tf.control_dependencies([vr_update, vc_update]):
vr = tf.sqrt(slots["adam_vr"] / (1.0 - beta2_pow)) + params.epsilon
vc = tf.sqrt(slots["adam_vc"] / (1.0 - beta2_pow)) + params.epsilon
vc /= tf.reduce_mean(vc)
denom = vr * vc
else:
v_update = tf.assign(slots["adam_v"],
slots["adam_v"] * params.beta2 + grad_squared *
(1.0 - params.beta2))
with tf.control_dependencies([v_update]):
denom = tf.sqrt(slots["adam_v"] / (1.0 - beta2_pow)) + params.epsilon
# compute momentum if applicable
if params.beta1 != 0.0:
m_update = tf.assign(slots["adam_m"],
slots["adam_m"] * params.beta1 + grad_var *
(1.0 - params.beta1))
with tf.control_dependencies([m_update]):
grad_var = slots["adam_m"]
# update var
subtrahend = lrate * grad_var / denom
new_val = _quantize(_dequantize(var, params) - subtrahend, params)
return tf.assign(var, new_val)
def _quantize(x, params, randomize=True):
"""Quantize x according to params, optionally randomizing the rounding."""
if not params.quantize:
return x
if not randomize:
return tf.bitcast(
tf.cast(x / params.quantization_scale, tf.int16), tf.float16)
abs_x = tf.abs(x)
sign_x = tf.sign(x)
y = abs_x / params.quantization_scale
y = tf.floor(y + tf.random_uniform(common_layers.shape_list(x)))
y = tf.minimum(y, tf.int16.max) * sign_x
q = tf.bitcast(tf.cast(y, tf.int16), tf.float16)
return q
def _dequantize(q, params):
"""Dequantize q according to params."""
if not params.quantize:
return q
return tf.to_float(tf.bitcast(q, tf.int16)) * params.quantization_scale
def make_diet_var_getter(params):
"""Create a custom variable getter for diet variables according to params."""
def diet_var_initializer(shape, dtype, partition_info=None):
"""Initializer for a diet variable."""
del dtype
del partition_info
with common_layers.fn_device_dependency("diet_init") as out_deps:
float_range = math.sqrt(3)
ret = tf.random_uniform(shape, -float_range, float_range)
if params.quantize:
ret = _quantize(ret, params, randomize=False)
out_deps.append(ret)
return ret
def diet_var_getter(getter, **kwargs):
"""Get diet variable and return it dequantized."""
if params.quantize:
kwargs["dtype"] = tf.float16
kwargs["initializer"] = diet_var_initializer
kwargs["trainable"] = False
base_var = getter(**kwargs)
dequantized = _dequantize(base_var, params)
if not hasattr(params, "dequantized"):
params.dequantized = defaultdict(list)
params.dequantized[base_var.name].append(dequantized)
return dequantized
return diet_var_getter
def _fn_with_diet_vars(fn, args, params):
"""Call function with args; use diet variables according to params."""
vs_ctr = []
def grad_fn(inputs, variables, outputs, output_grads):
"""Custom gradient function."""
del outputs # recomputing below
with common_layers.fn_device_dependency("diet_grad",
output_grads[0].device) as out_dep:
with tf.variable_scope(vs_ctr[0], reuse=True):
outputs = fn(*inputs)
variables = [common_layers.underlying_variable_ref(v) for v in variables]
dequantized_variables = [
params.dequantized[v.name][-1] for v in variables
]
grads = tf.gradients(outputs, inputs + dequantized_variables,
output_grads)
grad_inputs = grads[:len(inputs)]
grad_variables = grads[len(inputs):]
opt = _create_diet_optimizer(params)
# Apply grad_variables here
var_updates = []
for v, dv in zip(variables, grad_variables):
with tf.variable_scope(vs_ctr[0].name):
opt.create_slots(v)
update_op = opt.update_variable(v, dv)
var_updates.append(update_op)
with tf.control_dependencies(var_updates):
grad_inputs = [tf.identity(dx) for dx in grad_inputs]
out_dep.append(grad_inputs)
return grad_inputs, [None] * len(variables)
@common_layers.fn_with_custom_grad(grad_fn, use_global_vars=True)
with common_layers.fn_device_dependency("diet_forward",
args[0].device) as out_dep:
outputs = forward(*args)
out_dep.append(outputs)
return outputs
def fn_with_diet_vars(params):
"""Decorator for graph-building function to use diet variables."""
params = copy.copy(params)
return dec
| [
2,
19617,
28,
40477,
12,
23,
198,
2,
15069,
13130,
383,
309,
22854,
17,
51,
22854,
46665,
13,
198,
2,
198,
2,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
34156,
15341,
198,
2,
345,
743,
407,
779,
428,
... | 2.565328 | 4,309 |
import os
import torch
import argparse
import numpy as np
import nibabel as nib
import utils_segmentation as utils
from matplotlib import pyplot as plt
from dipy.align.reslice import reslice
from nets.localization_network import LocalizationNet
from nets.labelling_network import LabellingNet
from nets.segmentation_network import SegmentationNet
from helper import reorient_nib
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Spine Segmentation Pipeline')
parser.add_argument('--model_dir', default='models')
parser.add_argument('--pat_dir', default='sub-kypho005/post_fracture/ct.nii.gz')
parser.add_argument('--save_dir', default='sub-kypho005/post_fracture/mask_auto.nii.gz')
args = parser.parse_args()
args_dict = vars(args)
segment = SpineSegmentation(args_dict['model_dir'], args_dict['pat_dir'], args_dict['save_dir'])
_ = segment.apply()
| [
11748,
28686,
198,
11748,
28034,
198,
11748,
1822,
29572,
198,
11748,
299,
32152,
355,
45941,
198,
11748,
33272,
9608,
355,
33272,
198,
11748,
3384,
4487,
62,
325,
5154,
341,
355,
3384,
4487,
198,
6738,
2603,
29487,
8019,
1330,
12972,
294... | 2.951299 | 308 |
# Copyright 2021 The FedLearner 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.
# coding: utf-8
import tarfile
| [
2,
15069,
33448,
383,
10169,
14961,
1008,
46665,
13,
1439,
6923,
33876,
13,
198,
2,
198,
2,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
34156,
15341,
198,
2,
345,
743,
407,
779,
428,
2393,
2845,
287,
11846... | 3.792899 | 169 |
# coding=utf-8
import os
import re
import codecs
from utils import create_dico, create_mapping, zero_digits
from utils import iob2, iob_iobes
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
import numpy as np
def load_sentences(path, lower, zeros):
"""
Load sentences. A line must contain at least a word and its tag.
Sentences are separated by empty lines.
"""
sentences = []
sentence = []
max_sentence_length = 0
max_word_length = 0
for line in codecs.open(path, 'r', 'utf8'):
line = zero_digits(line.rstrip()) if zeros else line.rstrip()
if not line:
if len(sentence) > 0:
if 'DOCSTART' not in sentence[0][0]:
# print sentence
# sys.exit()
sentences.append(sentence)
if len(sentence) > max_sentence_length:
max_sentence_length = len(sentence)
sentence = []
else:
tokens = line.split()
assert len(tokens) >= 2
sentence.append(tokens)
if len(tokens[0]) > max_word_length:
max_word_length = len(tokens[0])
if len(sentence) > 0:
if 'DOCSTART' not in sentence[0][0]:
sentences.append(sentence)
if len(sentence) > max_sentence_length:
max_sentence_length = len(sentence)
return sentences, max_sentence_length, max_word_length
def update_tag_scheme(sentences, tag_scheme):
"""
Check and update sentences tagging scheme to IOB2.
Only IOB1 and IOB2 schemes are accepted.
"""
for i, s in enumerate(sentences):
tags = [w[-1] for w in s]
# Check that tags are given in the IOB format
if not iob2(tags):
s_str = '\n'.join(' '.join(w) for w in s)
print s_str.encode("utf8")
raise Exception('Sentences should be given in IOB format! ' +
'Please check sentence %i:\n%s' % (i, s_str))
if tag_scheme == 'iob':
# If format was IOB1, we convert to IOB2
for word, new_tag in zip(s, tags):
word[-1] = new_tag
elif tag_scheme == 'iobes':
new_tags = iob_iobes(tags)
for word, new_tag in zip(s, new_tags):
word[-1] = new_tag
else:
raise Exception('Unknown tagging scheme!')
def word_mapping(sentences, lower):
"""
Create a dictionary and a mapping of words, sorted by frequency.
"""
# words = [[(" ".join(x[0:2])).lower() if lower else " ".join(x[0:2]) for x in s] for s in sentences]
words = [[x[0].lower() if lower else x[0] for x in s] for s in sentences]
# TODO: only roots version, but this effectively damages char embeddings.
# words = [[x[1].split("+")[0].lower() if lower else x[1].split("+")[0] for x in s] for s in sentences]
dico = create_dico(words)
dico['<UNK>'] = 10000000
word_to_id, id_to_word = create_mapping(dico)
print "Found %i unique words (%i in total)" % (
len(dico), sum(len(x) for x in words)
)
return dico, word_to_id, id_to_word
def char_mapping(sentences):
"""
Create a dictionary and mapping of characters, sorted by frequency.
"""
chars = ["".join([w[0] + "".join(w[2:-1]) for w in s]) for s in sentences]
chars.append("+")
chars.append("*")
dico = create_dico(chars)
char_to_id, id_to_char = create_mapping(dico)
print "Found %i unique characters" % len(dico)
return dico, char_to_id, id_to_char
def tag_mapping(sentences):
"""
Create a dictionary and a mapping of tags, sorted by frequency.
"""
tags = [[word[-1] for word in s] for s in sentences]
dico = create_dico(tags)
tag_to_id, id_to_tag = create_mapping(dico)
print "Found %i unique named entity tags" % len(dico)
return dico, tag_to_id, id_to_tag
def morpho_tag_mapping(sentences, morpho_tag_type='wo_root', morpho_tag_column_index=1,
joint_learning=False):
"""
Create a dictionary and a mapping of tags, sorted by frequency.
"""
if morpho_tag_type == 'char':
morpho_tags = ["".join([w[morpho_tag_column_index] for w in s]) for s in sentences]
morpho_tags += [ww for ww in w[2:-1] for w in s for s in sentences]
else:
morpho_tags = extract_morpho_tags_ordered(morpho_tag_type,
sentences, morpho_tag_column_index,
joint_learning=joint_learning)
## TODO: xxx
# print morpho_tags
#morpho_tags = [[word[1].split("+") for word in s] for s in sentences]
# print morpho_tags
morpho_tags.append(["*UNKNOWN*"])
dico = create_dico(morpho_tags)
# print dico
morpho_tag_to_id, id_to_morpho_tag = create_mapping(dico)
print morpho_tag_to_id
print "Found %i unique morpho tags" % len(dico)
return dico, morpho_tag_to_id, id_to_morpho_tag
def cap_feature(s):
"""
Capitalization feature:
0 = low caps
1 = all caps
2 = first letter caps
3 = one capital (not first letter)
"""
if is_number(s):
return 0
elif sum([(str(digit) in s) for digit in range(0, 10)]) > 0:
if "'" in s:
return 1 + cap_characterization(s)
else:
return 1 + 4 + cap_characterization(s)
else:
if "'" in s:
return 1 + 8 + cap_characterization(s)
else:
return 1 + 12 + cap_characterization(s)
def prepare_sentence(str_words, word_to_id, char_to_id, lower=False):
"""
Prepare a sentence for evaluation.
"""
words = [word_to_id[f(w) if f(w) in word_to_id else '<UNK>']
for w in str_words]
chars = [[char_to_id[c] for c in w if c in char_to_id]
for w in str_words]
caps = [cap_feature(w) for w in str_words]
return {
'str_words': str_words,
'words': words,
'chars': chars,
'caps': caps
}
def prepare_dataset(sentences, word_to_id, char_to_id, tag_to_id,
morpho_tag_to_id, lower=False,
morpho_tag_dimension=0,
morpho_tag_type='wo_root',
morpho_tag_column_index=1):
"""
Prepare the dataset. Return a list of lists of dictionaries containing:
- word indexes
- word char indexes
- tag indexes
"""
data = []
for s in sentences:
str_words = [w[0] for w in s]
words = [word_to_id[f(w) if f(w) in word_to_id else '<UNK>']
for w in str_words]
# Skip characters that are not in the training set
chars = [[char_to_id[c] for c in w if c in char_to_id]
for w in str_words]
caps = [cap_feature(w) for w in str_words]
tags = [tag_to_id[w[-1]] for w in s]
if morpho_tag_dimension > 0:
if morpho_tag_type == 'char':
str_morpho_tags = [w[morpho_tag_column_index] for w in s]
morpho_tags = [[morpho_tag_to_id[c] for c in str_morpho_tag if c in morpho_tag_to_id]
for str_morpho_tag in str_morpho_tags]
else:
morpho_tags_in_the_sentence = \
extract_morpho_tags_from_one_sentence_ordered(morpho_tag_type, [],
s, morpho_tag_column_index,
joint_learning=False)
morpho_tags = [[morpho_tag_to_id[morpho_tag] for morpho_tag in ww if morpho_tag in morpho_tag_to_id]
for ww in morpho_tags_in_the_sentence]
# for now we ignore different schemes we did in previous morph. tag parses.
morph_analyzes_tags = [[map(f_morpho_tag_to_id, analysis.split("+")[1:]) if analysis.split("+")[1:] else [morpho_tag_to_id["*UNKNOWN*"]]
for analysis in w[2:-1]] for w in s]
morph_analyzes_roots = [[map(f_char_to_id, list(analysis.split("+")[0])) if list(analysis.split("+")[0]) else [char_to_id["+"]]
for analysis in w[2:-1]] for w in s]
morph_analysis_from_NER_data = [w[morpho_tag_column_index] for w in s]
morph_analyzes_from_FST_unprocessed = [w[2:-1] for w in s]
golden_analysis_indices = []
for w_idx, w in enumerate(s):
found = False
try:
golden_analysis_idx = \
morph_analyzes_from_FST_unprocessed[w_idx]\
.index(morph_analysis_from_NER_data[w_idx])
found = True
except ValueError as e:
# step 1
pass
if not found:
try:
golden_analysis_idx = \
map(remove_Prop_and_lower, morph_analyzes_from_FST_unprocessed[w_idx])\
.index(remove_Prop_and_lower(morph_analysis_from_NER_data[w_idx]))
found = True
except ValueError as e:
pass
if not found:
if len(morph_analyzes_from_FST_unprocessed[w_idx]) == 1:
golden_analysis_idx = 0
else:
# WE expect that this never happens in gungor.ner.14.* files as they have been processed for unfound golden analyses
import random
golden_analysis_idx = random.randint(0, len(morph_analyzes_from_FST_unprocessed[w_idx])-1)
if golden_analysis_idx >= len(morph_analyzes_from_FST_unprocessed[w_idx]) or \
golden_analysis_idx < 0 or \
golden_analysis_idx >= len(morph_analyzes_roots[w_idx]):
logging.error("BEEP at golden analysis idx")
golden_analysis_indices.append(golden_analysis_idx)
data_item = {
'str_words': str_words,
'word_ids': words,
'char_for_ids': chars,
'char_lengths': [len(char) for char in chars],
'cap_ids': caps,
'tag_ids': tags,
'morpho_analyzes_tags': morph_analyzes_tags,
'morpho_analyzes_roots': morph_analyzes_roots,
'golden_morph_analysis_indices': golden_analysis_indices,
'sentence_lengths': len(s),
'max_word_length_in_this_sample': max([len(x) for x in chars])
}
if morpho_tag_dimension > 0:
data_item['morpho_tag_ids'] = morpho_tags
data.append(data_item)
logging.info("Sorting the dataset by sentence length..")
data_sorted_by_sentence_length = sorted(data, key=lambda x: x['sentence_lengths'])
stats = [[x['sentence_lengths'],
x['max_word_length_in_this_sample'],
x['char_lengths']] for x in data]
n_unique_words = set()
for x in data:
for word_id in x['word_ids']:
n_unique_words.add(word_id)
n_unique_words = len(n_unique_words)
n_buckets = min([9, len(sentences)])
print "n_sentences: %d" % len(sentences)
n_samples_to_be_bucketed = len(sentences)/n_buckets
print "n_samples_to_be_binned: %d" % n_samples_to_be_bucketed
buckets = []
for bin_idx in range(n_buckets+1):
logging.info("Forming bin %d.." % bin_idx)
data_to_be_bucketed = data_sorted_by_sentence_length[n_samples_to_be_bucketed*(bin_idx):n_samples_to_be_bucketed*(bin_idx+1)]
if len(data_to_be_bucketed) == 0:
continue
buckets.append(data_to_be_bucketed)
return buckets, stats, n_unique_words, data
def augment_with_pretrained(dictionary, ext_emb_path, words):
"""
Augment the dictionary with words that have a pretrained embedding.
If `words` is None, we add every word that has a pretrained embedding
to the dictionary, otherwise, we only add the words that are given by
`words` (typically the words in the development and test sets.)
"""
print 'Loading pretrained embeddings from %s...' % ext_emb_path
assert os.path.isfile(ext_emb_path)
# Load pretrained embeddings from file
pretrained = set([
line.split()[0].strip()
for line in codecs.open(ext_emb_path, 'r', 'utf-8')
if len(ext_emb_path) > 0
])
# We either add every word in the pretrained file,
# or only words given in the `words` list to which
# we can assign a pretrained embedding
if words is None:
for word in pretrained:
if word not in dictionary:
dictionary[word] = 0
else:
for word in words:
if any(x in pretrained for x in [
word,
word.lower(),
re.sub('\d', '0', word.lower())
]) and word not in dictionary:
dictionary[word] = 0
word_to_id, id_to_word = create_mapping(dictionary)
return dictionary, word_to_id, id_to_word
| [
2,
19617,
28,
40477,
12,
23,
198,
11748,
28686,
198,
11748,
302,
198,
11748,
40481,
82,
198,
6738,
3384,
4487,
1330,
2251,
62,
67,
3713,
11,
2251,
62,
76,
5912,
11,
6632,
62,
12894,
896,
198,
6738,
3384,
4487,
1330,
1312,
672,
17,
... | 2.048934 | 6,376 |
"""
Utilities for fuzzing non-routing configuration. This is the counterpart to interconnect.py
"""
import threading
import tiles
import libpyprjoxide
import fuzzconfig
def fuzz_word_setting(config, name, length, get_sv_substs, desc=""):
"""
Fuzz a multi-bit setting, such as LUT initialisation
:param config: FuzzConfig instance containing target device and tile of interest
:param name: name of the setting to store in the database
:param length: number of bits in the setting
:param get_sv_substs: a callback function, that is called with an array of bits to create a design with that setting
"""
prefix = "thread{}_".format(threading.get_ident())
baseline = config.build_design(config.sv, get_sv_substs([False for _ in range(length)]), prefix)
fz = libpyprjoxide.Fuzzer.word_fuzzer(fuzzconfig.db, baseline, set(config.tiles), name, desc, length, baseline)
for i in range(length):
i_bit = config.build_design(config.sv, get_sv_substs([(_ == i) for _ in range(length)]), prefix)
fz.add_word_sample(fuzzconfig.db, i, i_bit)
fz.solve(fuzzconfig.db)
def fuzz_enum_setting(config, empty_bitfile, name, values, get_sv_substs, include_zeros=True, assume_zero_base=False, min_cover={}, desc=""):
"""
Fuzz a setting with multiple possible values
:param config: FuzzConfig instance containing target device and tile of interest
:param empty_bitfile: a baseline empty bitstream to diff against
:param name: name of the setting to store in the database
:param values: list of values taken by the enum
:param get_sv_substs: a callback function,
:param include_zeros: if set, bits set to zero are not included in db. Needed for settings such as CEMUX which share
bits with routing muxes to prevent conflicts.
:param assume_zero_base: if set, the baseline bitstream is considered the all-zero bitstream
:param min_cover: for each setting in this, run with each value in the array that setting points to, to get a minimal
bit set
"""
prefix = "thread{}_".format(threading.get_ident())
fz = libpyprjoxide.Fuzzer.enum_fuzzer(fuzzconfig.db, empty_bitfile, set(config.tiles), name, desc, include_zeros, assume_zero_base)
for opt in values:
if opt in min_cover:
for c in min_cover[opt]:
opt_bit = config.build_design(config.sv, get_sv_substs((opt, c)), prefix)
fz.add_enum_sample(fuzzconfig.db, opt, opt_bit)
else:
opt_bit = config.build_design(config.sv, get_sv_substs(opt), prefix)
fz.add_enum_sample(fuzzconfig.db, opt, opt_bit)
fz.solve(fuzzconfig.db)
def fuzz_ip_word_setting(config, name, length, get_sv_substs, desc="", default=None):
"""
Fuzz a multi-bit IP setting with an optimum number of bitstreams
:param config: FuzzConfig instance containing target device and tile of interest
:param name: name of the setting to store in the database
:param length: number of bits in the setting
:param get_sv_substs: a callback function, that is called with an array of bits to create a design with that setting
"""
prefix = "thread{}_".format(threading.get_ident())
inverted_mode = False
if default is not None:
for i in range(0, length.bit_length()):
bits = [(j >> i) & 0x1 == 0 for j in range(length)]
if default == bits:
inverted_mode = True
break
baseline = config.build_design(config.sv, get_sv_substs([inverted_mode for _ in range(length)]), prefix)
ipcore, iptype = config.tiles[0].split(":")
fz = libpyprjoxide.IPFuzzer.word_fuzzer(fuzzconfig.db, baseline, ipcore, iptype, name, desc, length, inverted_mode)
for i in range(0, length.bit_length()):
bits = [(j >> i) & 0x1 == (1 if inverted_mode else 0) for j in range(length)]
i_bit = config.build_design(config.sv, get_sv_substs(bits), prefix)
fz.add_word_sample(fuzzconfig.db, bits, i_bit)
fz.solve(fuzzconfig.db)
def fuzz_ip_enum_setting(config, empty_bitfile, name, values, get_sv_substs, desc=""):
"""
Fuzz a multi-bit IP enum with an optimum number of bitstreams
:param config: FuzzConfig instance containing target device and tile of interest
:param empty_bitfile: a baseline empty bitstream to diff against
:param name: name of the setting to store in the database
:param values: list of values taken by the enum
:param get_sv_substs: a callback function,
"""
prefix = "thread{}_".format(threading.get_ident())
ipcore, iptype = config.tiles[0].split(":")
fz = libpyprjoxide.IPFuzzer.enum_fuzzer(fuzzconfig.db, empty_bitfile, ipcore, iptype, name, desc)
for opt in values:
opt_bit = config.build_design(config.sv, get_sv_substs(opt), prefix)
fz.add_enum_sample(fuzzconfig.db, opt, opt_bit)
fz.solve(fuzzconfig.db)
| [
37811,
198,
18274,
2410,
329,
26080,
278,
1729,
12,
81,
13660,
8398,
13,
770,
318,
262,
11283,
284,
987,
8443,
13,
9078,
198,
37811,
198,
198,
11748,
4704,
278,
198,
11748,
19867,
198,
11748,
9195,
9078,
1050,
73,
28885,
198,
11748,
2... | 2.712548 | 1,809 |
import redis
pool = redis.ConnectionPool(host='127.0.0.1', port=6379)
redisCon = redis.Redis(connection_pool=pool, charset='utf-8')
| [
11748,
2266,
271,
201,
198,
201,
198,
7742,
796,
2266,
271,
13,
32048,
27201,
7,
4774,
11639,
16799,
13,
15,
13,
15,
13,
16,
3256,
2493,
28,
21,
29088,
8,
201,
198,
445,
271,
3103,
796,
2266,
271,
13,
7738,
271,
7,
38659,
62,
77... | 2.403509 | 57 |
from pymote import *
from networkx.generators.classic import star_graph,balanced_tree,barbell_graph
from networkx.generators.social import florentine_families_graph
from networkx import Graph, is_connected,draw
from networkx.algorithms.operators import union_all, disjoint_union_all
try:
from matplotlib import pyplot as plt
except ImportError:
raise ImportError("Matplotlib required for show()")
from pymote import energy
en = energy.EnergyModel()
en.TR_RATE = 200
print en.TR_RATE, en, en.energy, en.increase_energy(),\
en.decrease_tx_energy(100), en.decrease_rx_energy(100), en
from pymote import mobility
en = mobility.MobilityModel(mobile_type=1)
#en.HEADING = -en.HEADING
print en.VELOCITY, en.HEADING, en, en.drift(), en.have_moved()
from pymote import propagation
en = propagation.PropagationModel(propagation_type=1)
en2 = propagation.PropagationModel(propagation_type=1)
pr = en.get_power_ratio(d=405, p_tx=1)
print en, pr, propagation.PropagationModel.pw_to_dbm(pr), en.is_rx_ok(), \
en.free_space_distance(p_rx=2.354466826905901e-09)
print en2, en2.cross_over_distance(), en2.get_power_ratio(d=205), \
propagation.PropagationModel.dbm_to_pw(11.6), en2.two_ray_ground_distance()
en2 = propagation.PropagationModel(propagation_type=2)
pr = en2.shadowing(d=405)
print en2, pr, propagation.PropagationModel.pw_to_dbm(pr), en2.shadowing_rssi(d=405)
propagation.PropagationModel.P_TX = 0.0144
en2 = propagation.PropagationModel(propagation_type=2)
pr = en2.shadowing(d=405)
print en2, pr, propagation.PropagationModel.pw_to_dbm(pr), en2.shadowing_rssi(d=405)
| [
6738,
279,
4948,
1258,
1330,
1635,
198,
6738,
3127,
87,
13,
8612,
2024,
13,
49421,
1330,
3491,
62,
34960,
11,
27753,
62,
21048,
11,
5657,
7923,
62,
34960,
198,
6738,
3127,
87,
13,
8612,
2024,
13,
14557,
1330,
781,
382,
429,
500,
62,... | 2.564388 | 629 |
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: google/devtools/cloudtrace_v2/proto/trace.proto
import sys
_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode("latin1"))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
from google.api import annotations_pb2 as google_dot_api_dot_annotations__pb2
from google.protobuf import timestamp_pb2 as google_dot_protobuf_dot_timestamp__pb2
from google.protobuf import wrappers_pb2 as google_dot_protobuf_dot_wrappers__pb2
from google.rpc import status_pb2 as google_dot_rpc_dot_status__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name="google/devtools/cloudtrace_v2/proto/trace.proto",
package="google.devtools.cloudtrace.v2",
syntax="proto3",
serialized_options=_b(
"\n!com.google.devtools.cloudtrace.v2B\nTraceProtoP\001ZGgoogle.golang.org/genproto/googleapis/devtools/cloudtrace/v2;cloudtrace\252\002\025Google.Cloud.Trace.V2\312\002\025Google\\Cloud\\Trace\\V2"
),
serialized_pb=_b(
'\n/google/devtools/cloudtrace_v2/proto/trace.proto\x12\x1dgoogle.devtools.cloudtrace.v2\x1a\x1cgoogle/api/annotations.proto\x1a\x1fgoogle/protobuf/timestamp.proto\x1a\x1egoogle/protobuf/wrappers.proto\x1a\x17google/rpc/status.proto"\xc5\x0f\n\x04Span\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07span_id\x18\x02 \x01(\t\x12\x16\n\x0eparent_span_id\x18\x03 \x01(\t\x12\x46\n\x0c\x64isplay_name\x18\x04 \x01(\x0b\x32\x30.google.devtools.cloudtrace.v2.TruncatableString\x12.\n\nstart_time\x18\x05 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12,\n\x08\x65nd_time\x18\x06 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12\x42\n\nattributes\x18\x07 \x01(\x0b\x32..google.devtools.cloudtrace.v2.Span.Attributes\x12>\n\x0bstack_trace\x18\x08 \x01(\x0b\x32).google.devtools.cloudtrace.v2.StackTrace\x12\x43\n\x0btime_events\x18\t \x01(\x0b\x32..google.devtools.cloudtrace.v2.Span.TimeEvents\x12\x38\n\x05links\x18\n \x01(\x0b\x32).google.devtools.cloudtrace.v2.Span.Links\x12"\n\x06status\x18\x0b \x01(\x0b\x32\x12.google.rpc.Status\x12?\n\x1bsame_process_as_parent_span\x18\x0c \x01(\x0b\x32\x1a.google.protobuf.BoolValue\x12\x35\n\x10\x63hild_span_count\x18\r \x01(\x0b\x32\x1b.google.protobuf.Int32Value\x1a\xeb\x01\n\nAttributes\x12W\n\rattribute_map\x18\x01 \x03(\x0b\x32@.google.devtools.cloudtrace.v2.Span.Attributes.AttributeMapEntry\x12 \n\x18\x64ropped_attributes_count\x18\x02 \x01(\x05\x1a\x62\n\x11\x41ttributeMapEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12<\n\x05value\x18\x02 \x01(\x0b\x32-.google.devtools.cloudtrace.v2.AttributeValue:\x02\x38\x01\x1a\xdf\x04\n\tTimeEvent\x12(\n\x04time\x18\x01 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12N\n\nannotation\x18\x02 \x01(\x0b\x32\x38.google.devtools.cloudtrace.v2.Span.TimeEvent.AnnotationH\x00\x12S\n\rmessage_event\x18\x03 \x01(\x0b\x32:.google.devtools.cloudtrace.v2.Span.TimeEvent.MessageEventH\x00\x1a\x97\x01\n\nAnnotation\x12\x45\n\x0b\x64\x65scription\x18\x01 \x01(\x0b\x32\x30.google.devtools.cloudtrace.v2.TruncatableString\x12\x42\n\nattributes\x18\x02 \x01(\x0b\x32..google.devtools.cloudtrace.v2.Span.Attributes\x1a\xdf\x01\n\x0cMessageEvent\x12M\n\x04type\x18\x01 \x01(\x0e\x32?.google.devtools.cloudtrace.v2.Span.TimeEvent.MessageEvent.Type\x12\n\n\x02id\x18\x02 \x01(\x03\x12\x1f\n\x17uncompressed_size_bytes\x18\x03 \x01(\x03\x12\x1d\n\x15\x63ompressed_size_bytes\x18\x04 \x01(\x03"4\n\x04Type\x12\x14\n\x10TYPE_UNSPECIFIED\x10\x00\x12\x08\n\x04SENT\x10\x01\x12\x0c\n\x08RECEIVED\x10\x02\x42\x07\n\x05value\x1a\x98\x01\n\nTimeEvents\x12\x41\n\ntime_event\x18\x01 \x03(\x0b\x32-.google.devtools.cloudtrace.v2.Span.TimeEvent\x12!\n\x19\x64ropped_annotations_count\x18\x02 \x01(\x05\x12$\n\x1c\x64ropped_message_events_count\x18\x03 \x01(\x05\x1a\xf7\x01\n\x04Link\x12\x10\n\x08trace_id\x18\x01 \x01(\t\x12\x0f\n\x07span_id\x18\x02 \x01(\t\x12;\n\x04type\x18\x03 \x01(\x0e\x32-.google.devtools.cloudtrace.v2.Span.Link.Type\x12\x42\n\nattributes\x18\x04 \x01(\x0b\x32..google.devtools.cloudtrace.v2.Span.Attributes"K\n\x04Type\x12\x14\n\x10TYPE_UNSPECIFIED\x10\x00\x12\x15\n\x11\x43HILD_LINKED_SPAN\x10\x01\x12\x16\n\x12PARENT_LINKED_SPAN\x10\x02\x1a\\\n\x05Links\x12\x36\n\x04link\x18\x01 \x03(\x0b\x32(.google.devtools.cloudtrace.v2.Span.Link\x12\x1b\n\x13\x64ropped_links_count\x18\x02 \x01(\x05"\x8e\x01\n\x0e\x41ttributeValue\x12H\n\x0cstring_value\x18\x01 \x01(\x0b\x32\x30.google.devtools.cloudtrace.v2.TruncatableStringH\x00\x12\x13\n\tint_value\x18\x02 \x01(\x03H\x00\x12\x14\n\nbool_value\x18\x03 \x01(\x08H\x00\x42\x07\n\x05value"\x89\x05\n\nStackTrace\x12K\n\x0cstack_frames\x18\x01 \x01(\x0b\x32\x35.google.devtools.cloudtrace.v2.StackTrace.StackFrames\x12\x1b\n\x13stack_trace_hash_id\x18\x02 \x01(\x03\x1a\x9e\x03\n\nStackFrame\x12G\n\rfunction_name\x18\x01 \x01(\x0b\x32\x30.google.devtools.cloudtrace.v2.TruncatableString\x12P\n\x16original_function_name\x18\x02 \x01(\x0b\x32\x30.google.devtools.cloudtrace.v2.TruncatableString\x12\x43\n\tfile_name\x18\x03 \x01(\x0b\x32\x30.google.devtools.cloudtrace.v2.TruncatableString\x12\x13\n\x0bline_number\x18\x04 \x01(\x03\x12\x15\n\rcolumn_number\x18\x05 \x01(\x03\x12:\n\x0bload_module\x18\x06 \x01(\x0b\x32%.google.devtools.cloudtrace.v2.Module\x12H\n\x0esource_version\x18\x07 \x01(\x0b\x32\x30.google.devtools.cloudtrace.v2.TruncatableString\x1ap\n\x0bStackFrames\x12\x43\n\x05\x66rame\x18\x01 \x03(\x0b\x32\x34.google.devtools.cloudtrace.v2.StackTrace.StackFrame\x12\x1c\n\x14\x64ropped_frames_count\x18\x02 \x01(\x05"\x8e\x01\n\x06Module\x12@\n\x06module\x18\x01 \x01(\x0b\x32\x30.google.devtools.cloudtrace.v2.TruncatableString\x12\x42\n\x08\x62uild_id\x18\x02 \x01(\x0b\x32\x30.google.devtools.cloudtrace.v2.TruncatableString"@\n\x11TruncatableString\x12\r\n\x05value\x18\x01 \x01(\t\x12\x1c\n\x14truncated_byte_count\x18\x02 \x01(\x05\x42\xaa\x01\n!com.google.devtools.cloudtrace.v2B\nTraceProtoP\x01ZGgoogle.golang.org/genproto/googleapis/devtools/cloudtrace/v2;cloudtrace\xaa\x02\x15Google.Cloud.Trace.V2\xca\x02\x15Google\\Cloud\\Trace\\V2b\x06proto3'
),
dependencies=[
google_dot_api_dot_annotations__pb2.DESCRIPTOR,
google_dot_protobuf_dot_timestamp__pb2.DESCRIPTOR,
google_dot_protobuf_dot_wrappers__pb2.DESCRIPTOR,
google_dot_rpc_dot_status__pb2.DESCRIPTOR,
],
)
_SPAN_TIMEEVENT_MESSAGEEVENT_TYPE = _descriptor.EnumDescriptor(
name="Type",
full_name="google.devtools.cloudtrace.v2.Span.TimeEvent.MessageEvent.Type",
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name="TYPE_UNSPECIFIED",
index=0,
number=0,
serialized_options=None,
type=None,
),
_descriptor.EnumValueDescriptor(
name="SENT", index=1, number=1, serialized_options=None, type=None
),
_descriptor.EnumValueDescriptor(
name="RECEIVED", index=2, number=2, serialized_options=None, type=None
),
],
containing_type=None,
serialized_options=None,
serialized_start=1632,
serialized_end=1684,
)
_sym_db.RegisterEnumDescriptor(_SPAN_TIMEEVENT_MESSAGEEVENT_TYPE)
_SPAN_LINK_TYPE = _descriptor.EnumDescriptor(
name="Type",
full_name="google.devtools.cloudtrace.v2.Span.Link.Type",
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name="TYPE_UNSPECIFIED",
index=0,
number=0,
serialized_options=None,
type=None,
),
_descriptor.EnumValueDescriptor(
name="CHILD_LINKED_SPAN",
index=1,
number=1,
serialized_options=None,
type=None,
),
_descriptor.EnumValueDescriptor(
name="PARENT_LINKED_SPAN",
index=2,
number=2,
serialized_options=None,
type=None,
),
],
containing_type=None,
serialized_options=None,
serialized_start=2023,
serialized_end=2098,
)
_sym_db.RegisterEnumDescriptor(_SPAN_LINK_TYPE)
_SPAN_ATTRIBUTES_ATTRIBUTEMAPENTRY = _descriptor.Descriptor(
name="AttributeMapEntry",
full_name="google.devtools.cloudtrace.v2.Span.Attributes.AttributeMapEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="google.devtools.cloudtrace.v2.Span.Attributes.AttributeMapEntry.key",
index=0,
number=1,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=_b("").decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="value",
full_name="google.devtools.cloudtrace.v2.Span.Attributes.AttributeMapEntry.value",
index=1,
number=2,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=_b("8\001"),
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=985,
serialized_end=1083,
)
_SPAN_ATTRIBUTES = _descriptor.Descriptor(
name="Attributes",
full_name="google.devtools.cloudtrace.v2.Span.Attributes",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="attribute_map",
full_name="google.devtools.cloudtrace.v2.Span.Attributes.attribute_map",
index=0,
number=1,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="dropped_attributes_count",
full_name="google.devtools.cloudtrace.v2.Span.Attributes.dropped_attributes_count",
index=1,
number=2,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[_SPAN_ATTRIBUTES_ATTRIBUTEMAPENTRY,],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=848,
serialized_end=1083,
)
_SPAN_TIMEEVENT_ANNOTATION = _descriptor.Descriptor(
name="Annotation",
full_name="google.devtools.cloudtrace.v2.Span.TimeEvent.Annotation",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="description",
full_name="google.devtools.cloudtrace.v2.Span.TimeEvent.Annotation.description",
index=0,
number=1,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="attributes",
full_name="google.devtools.cloudtrace.v2.Span.TimeEvent.Annotation.attributes",
index=1,
number=2,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=1307,
serialized_end=1458,
)
_SPAN_TIMEEVENT_MESSAGEEVENT = _descriptor.Descriptor(
name="MessageEvent",
full_name="google.devtools.cloudtrace.v2.Span.TimeEvent.MessageEvent",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="type",
full_name="google.devtools.cloudtrace.v2.Span.TimeEvent.MessageEvent.type",
index=0,
number=1,
type=14,
cpp_type=8,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="id",
full_name="google.devtools.cloudtrace.v2.Span.TimeEvent.MessageEvent.id",
index=1,
number=2,
type=3,
cpp_type=2,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="uncompressed_size_bytes",
full_name="google.devtools.cloudtrace.v2.Span.TimeEvent.MessageEvent.uncompressed_size_bytes",
index=2,
number=3,
type=3,
cpp_type=2,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="compressed_size_bytes",
full_name="google.devtools.cloudtrace.v2.Span.TimeEvent.MessageEvent.compressed_size_bytes",
index=3,
number=4,
type=3,
cpp_type=2,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[_SPAN_TIMEEVENT_MESSAGEEVENT_TYPE,],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=1461,
serialized_end=1684,
)
_SPAN_TIMEEVENT = _descriptor.Descriptor(
name="TimeEvent",
full_name="google.devtools.cloudtrace.v2.Span.TimeEvent",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="time",
full_name="google.devtools.cloudtrace.v2.Span.TimeEvent.time",
index=0,
number=1,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="annotation",
full_name="google.devtools.cloudtrace.v2.Span.TimeEvent.annotation",
index=1,
number=2,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="message_event",
full_name="google.devtools.cloudtrace.v2.Span.TimeEvent.message_event",
index=2,
number=3,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[_SPAN_TIMEEVENT_ANNOTATION, _SPAN_TIMEEVENT_MESSAGEEVENT,],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[
_descriptor.OneofDescriptor(
name="value",
full_name="google.devtools.cloudtrace.v2.Span.TimeEvent.value",
index=0,
containing_type=None,
fields=[],
),
],
serialized_start=1086,
serialized_end=1693,
)
_SPAN_TIMEEVENTS = _descriptor.Descriptor(
name="TimeEvents",
full_name="google.devtools.cloudtrace.v2.Span.TimeEvents",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="time_event",
full_name="google.devtools.cloudtrace.v2.Span.TimeEvents.time_event",
index=0,
number=1,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="dropped_annotations_count",
full_name="google.devtools.cloudtrace.v2.Span.TimeEvents.dropped_annotations_count",
index=1,
number=2,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="dropped_message_events_count",
full_name="google.devtools.cloudtrace.v2.Span.TimeEvents.dropped_message_events_count",
index=2,
number=3,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=1696,
serialized_end=1848,
)
_SPAN_LINK = _descriptor.Descriptor(
name="Link",
full_name="google.devtools.cloudtrace.v2.Span.Link",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="trace_id",
full_name="google.devtools.cloudtrace.v2.Span.Link.trace_id",
index=0,
number=1,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=_b("").decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="span_id",
full_name="google.devtools.cloudtrace.v2.Span.Link.span_id",
index=1,
number=2,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=_b("").decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="type",
full_name="google.devtools.cloudtrace.v2.Span.Link.type",
index=2,
number=3,
type=14,
cpp_type=8,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="attributes",
full_name="google.devtools.cloudtrace.v2.Span.Link.attributes",
index=3,
number=4,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[_SPAN_LINK_TYPE,],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=1851,
serialized_end=2098,
)
_SPAN_LINKS = _descriptor.Descriptor(
name="Links",
full_name="google.devtools.cloudtrace.v2.Span.Links",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="link",
full_name="google.devtools.cloudtrace.v2.Span.Links.link",
index=0,
number=1,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="dropped_links_count",
full_name="google.devtools.cloudtrace.v2.Span.Links.dropped_links_count",
index=1,
number=2,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=2100,
serialized_end=2192,
)
_SPAN = _descriptor.Descriptor(
name="Span",
full_name="google.devtools.cloudtrace.v2.Span",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="name",
full_name="google.devtools.cloudtrace.v2.Span.name",
index=0,
number=1,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=_b("").decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="span_id",
full_name="google.devtools.cloudtrace.v2.Span.span_id",
index=1,
number=2,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=_b("").decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="parent_span_id",
full_name="google.devtools.cloudtrace.v2.Span.parent_span_id",
index=2,
number=3,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=_b("").decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="display_name",
full_name="google.devtools.cloudtrace.v2.Span.display_name",
index=3,
number=4,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="start_time",
full_name="google.devtools.cloudtrace.v2.Span.start_time",
index=4,
number=5,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="end_time",
full_name="google.devtools.cloudtrace.v2.Span.end_time",
index=5,
number=6,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="attributes",
full_name="google.devtools.cloudtrace.v2.Span.attributes",
index=6,
number=7,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="stack_trace",
full_name="google.devtools.cloudtrace.v2.Span.stack_trace",
index=7,
number=8,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="time_events",
full_name="google.devtools.cloudtrace.v2.Span.time_events",
index=8,
number=9,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="links",
full_name="google.devtools.cloudtrace.v2.Span.links",
index=9,
number=10,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="status",
full_name="google.devtools.cloudtrace.v2.Span.status",
index=10,
number=11,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="same_process_as_parent_span",
full_name="google.devtools.cloudtrace.v2.Span.same_process_as_parent_span",
index=11,
number=12,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="child_span_count",
full_name="google.devtools.cloudtrace.v2.Span.child_span_count",
index=12,
number=13,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[
_SPAN_ATTRIBUTES,
_SPAN_TIMEEVENT,
_SPAN_TIMEEVENTS,
_SPAN_LINK,
_SPAN_LINKS,
],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=203,
serialized_end=2192,
)
_ATTRIBUTEVALUE = _descriptor.Descriptor(
name="AttributeValue",
full_name="google.devtools.cloudtrace.v2.AttributeValue",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="string_value",
full_name="google.devtools.cloudtrace.v2.AttributeValue.string_value",
index=0,
number=1,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="int_value",
full_name="google.devtools.cloudtrace.v2.AttributeValue.int_value",
index=1,
number=2,
type=3,
cpp_type=2,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="bool_value",
full_name="google.devtools.cloudtrace.v2.AttributeValue.bool_value",
index=2,
number=3,
type=8,
cpp_type=7,
label=1,
has_default_value=False,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[
_descriptor.OneofDescriptor(
name="value",
full_name="google.devtools.cloudtrace.v2.AttributeValue.value",
index=0,
containing_type=None,
fields=[],
),
],
serialized_start=2195,
serialized_end=2337,
)
_STACKTRACE_STACKFRAME = _descriptor.Descriptor(
name="StackFrame",
full_name="google.devtools.cloudtrace.v2.StackTrace.StackFrame",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="function_name",
full_name="google.devtools.cloudtrace.v2.StackTrace.StackFrame.function_name",
index=0,
number=1,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="original_function_name",
full_name="google.devtools.cloudtrace.v2.StackTrace.StackFrame.original_function_name",
index=1,
number=2,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="file_name",
full_name="google.devtools.cloudtrace.v2.StackTrace.StackFrame.file_name",
index=2,
number=3,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="line_number",
full_name="google.devtools.cloudtrace.v2.StackTrace.StackFrame.line_number",
index=3,
number=4,
type=3,
cpp_type=2,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="column_number",
full_name="google.devtools.cloudtrace.v2.StackTrace.StackFrame.column_number",
index=4,
number=5,
type=3,
cpp_type=2,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="load_module",
full_name="google.devtools.cloudtrace.v2.StackTrace.StackFrame.load_module",
index=5,
number=6,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="source_version",
full_name="google.devtools.cloudtrace.v2.StackTrace.StackFrame.source_version",
index=6,
number=7,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=2461,
serialized_end=2875,
)
_STACKTRACE_STACKFRAMES = _descriptor.Descriptor(
name="StackFrames",
full_name="google.devtools.cloudtrace.v2.StackTrace.StackFrames",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="frame",
full_name="google.devtools.cloudtrace.v2.StackTrace.StackFrames.frame",
index=0,
number=1,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="dropped_frames_count",
full_name="google.devtools.cloudtrace.v2.StackTrace.StackFrames.dropped_frames_count",
index=1,
number=2,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=2877,
serialized_end=2989,
)
_STACKTRACE = _descriptor.Descriptor(
name="StackTrace",
full_name="google.devtools.cloudtrace.v2.StackTrace",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="stack_frames",
full_name="google.devtools.cloudtrace.v2.StackTrace.stack_frames",
index=0,
number=1,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="stack_trace_hash_id",
full_name="google.devtools.cloudtrace.v2.StackTrace.stack_trace_hash_id",
index=1,
number=2,
type=3,
cpp_type=2,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[_STACKTRACE_STACKFRAME, _STACKTRACE_STACKFRAMES,],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=2340,
serialized_end=2989,
)
_MODULE = _descriptor.Descriptor(
name="Module",
full_name="google.devtools.cloudtrace.v2.Module",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="module",
full_name="google.devtools.cloudtrace.v2.Module.module",
index=0,
number=1,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="build_id",
full_name="google.devtools.cloudtrace.v2.Module.build_id",
index=1,
number=2,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=2992,
serialized_end=3134,
)
_TRUNCATABLESTRING = _descriptor.Descriptor(
name="TruncatableString",
full_name="google.devtools.cloudtrace.v2.TruncatableString",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="value",
full_name="google.devtools.cloudtrace.v2.TruncatableString.value",
index=0,
number=1,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=_b("").decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="truncated_byte_count",
full_name="google.devtools.cloudtrace.v2.TruncatableString.truncated_byte_count",
index=1,
number=2,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=3136,
serialized_end=3200,
)
_SPAN_ATTRIBUTES_ATTRIBUTEMAPENTRY.fields_by_name[
"value"
].message_type = _ATTRIBUTEVALUE
_SPAN_ATTRIBUTES_ATTRIBUTEMAPENTRY.containing_type = _SPAN_ATTRIBUTES
_SPAN_ATTRIBUTES.fields_by_name[
"attribute_map"
].message_type = _SPAN_ATTRIBUTES_ATTRIBUTEMAPENTRY
_SPAN_ATTRIBUTES.containing_type = _SPAN
_SPAN_TIMEEVENT_ANNOTATION.fields_by_name[
"description"
].message_type = _TRUNCATABLESTRING
_SPAN_TIMEEVENT_ANNOTATION.fields_by_name["attributes"].message_type = _SPAN_ATTRIBUTES
_SPAN_TIMEEVENT_ANNOTATION.containing_type = _SPAN_TIMEEVENT
_SPAN_TIMEEVENT_MESSAGEEVENT.fields_by_name[
"type"
].enum_type = _SPAN_TIMEEVENT_MESSAGEEVENT_TYPE
_SPAN_TIMEEVENT_MESSAGEEVENT.containing_type = _SPAN_TIMEEVENT
_SPAN_TIMEEVENT_MESSAGEEVENT_TYPE.containing_type = _SPAN_TIMEEVENT_MESSAGEEVENT
_SPAN_TIMEEVENT.fields_by_name[
"time"
].message_type = google_dot_protobuf_dot_timestamp__pb2._TIMESTAMP
_SPAN_TIMEEVENT.fields_by_name["annotation"].message_type = _SPAN_TIMEEVENT_ANNOTATION
_SPAN_TIMEEVENT.fields_by_name[
"message_event"
].message_type = _SPAN_TIMEEVENT_MESSAGEEVENT
_SPAN_TIMEEVENT.containing_type = _SPAN
_SPAN_TIMEEVENT.oneofs_by_name["value"].fields.append(
_SPAN_TIMEEVENT.fields_by_name["annotation"]
)
_SPAN_TIMEEVENT.fields_by_name[
"annotation"
].containing_oneof = _SPAN_TIMEEVENT.oneofs_by_name["value"]
_SPAN_TIMEEVENT.oneofs_by_name["value"].fields.append(
_SPAN_TIMEEVENT.fields_by_name["message_event"]
)
_SPAN_TIMEEVENT.fields_by_name[
"message_event"
].containing_oneof = _SPAN_TIMEEVENT.oneofs_by_name["value"]
_SPAN_TIMEEVENTS.fields_by_name["time_event"].message_type = _SPAN_TIMEEVENT
_SPAN_TIMEEVENTS.containing_type = _SPAN
_SPAN_LINK.fields_by_name["type"].enum_type = _SPAN_LINK_TYPE
_SPAN_LINK.fields_by_name["attributes"].message_type = _SPAN_ATTRIBUTES
_SPAN_LINK.containing_type = _SPAN
_SPAN_LINK_TYPE.containing_type = _SPAN_LINK
_SPAN_LINKS.fields_by_name["link"].message_type = _SPAN_LINK
_SPAN_LINKS.containing_type = _SPAN
_SPAN.fields_by_name["display_name"].message_type = _TRUNCATABLESTRING
_SPAN.fields_by_name[
"start_time"
].message_type = google_dot_protobuf_dot_timestamp__pb2._TIMESTAMP
_SPAN.fields_by_name[
"end_time"
].message_type = google_dot_protobuf_dot_timestamp__pb2._TIMESTAMP
_SPAN.fields_by_name["attributes"].message_type = _SPAN_ATTRIBUTES
_SPAN.fields_by_name["stack_trace"].message_type = _STACKTRACE
_SPAN.fields_by_name["time_events"].message_type = _SPAN_TIMEEVENTS
_SPAN.fields_by_name["links"].message_type = _SPAN_LINKS
_SPAN.fields_by_name["status"].message_type = google_dot_rpc_dot_status__pb2._STATUS
_SPAN.fields_by_name[
"same_process_as_parent_span"
].message_type = google_dot_protobuf_dot_wrappers__pb2._BOOLVALUE
_SPAN.fields_by_name[
"child_span_count"
].message_type = google_dot_protobuf_dot_wrappers__pb2._INT32VALUE
_ATTRIBUTEVALUE.fields_by_name["string_value"].message_type = _TRUNCATABLESTRING
_ATTRIBUTEVALUE.oneofs_by_name["value"].fields.append(
_ATTRIBUTEVALUE.fields_by_name["string_value"]
)
_ATTRIBUTEVALUE.fields_by_name[
"string_value"
].containing_oneof = _ATTRIBUTEVALUE.oneofs_by_name["value"]
_ATTRIBUTEVALUE.oneofs_by_name["value"].fields.append(
_ATTRIBUTEVALUE.fields_by_name["int_value"]
)
_ATTRIBUTEVALUE.fields_by_name[
"int_value"
].containing_oneof = _ATTRIBUTEVALUE.oneofs_by_name["value"]
_ATTRIBUTEVALUE.oneofs_by_name["value"].fields.append(
_ATTRIBUTEVALUE.fields_by_name["bool_value"]
)
_ATTRIBUTEVALUE.fields_by_name[
"bool_value"
].containing_oneof = _ATTRIBUTEVALUE.oneofs_by_name["value"]
_STACKTRACE_STACKFRAME.fields_by_name["function_name"].message_type = _TRUNCATABLESTRING
_STACKTRACE_STACKFRAME.fields_by_name[
"original_function_name"
].message_type = _TRUNCATABLESTRING
_STACKTRACE_STACKFRAME.fields_by_name["file_name"].message_type = _TRUNCATABLESTRING
_STACKTRACE_STACKFRAME.fields_by_name["load_module"].message_type = _MODULE
_STACKTRACE_STACKFRAME.fields_by_name[
"source_version"
].message_type = _TRUNCATABLESTRING
_STACKTRACE_STACKFRAME.containing_type = _STACKTRACE
_STACKTRACE_STACKFRAMES.fields_by_name["frame"].message_type = _STACKTRACE_STACKFRAME
_STACKTRACE_STACKFRAMES.containing_type = _STACKTRACE
_STACKTRACE.fields_by_name["stack_frames"].message_type = _STACKTRACE_STACKFRAMES
_MODULE.fields_by_name["module"].message_type = _TRUNCATABLESTRING
_MODULE.fields_by_name["build_id"].message_type = _TRUNCATABLESTRING
DESCRIPTOR.message_types_by_name["Span"] = _SPAN
DESCRIPTOR.message_types_by_name["AttributeValue"] = _ATTRIBUTEVALUE
DESCRIPTOR.message_types_by_name["StackTrace"] = _STACKTRACE
DESCRIPTOR.message_types_by_name["Module"] = _MODULE
DESCRIPTOR.message_types_by_name["TruncatableString"] = _TRUNCATABLESTRING
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
Span = _reflection.GeneratedProtocolMessageType(
"Span",
(_message.Message,),
dict(
Attributes=_reflection.GeneratedProtocolMessageType(
"Attributes",
(_message.Message,),
dict(
AttributeMapEntry=_reflection.GeneratedProtocolMessageType(
"AttributeMapEntry",
(_message.Message,),
dict(
DESCRIPTOR=_SPAN_ATTRIBUTES_ATTRIBUTEMAPENTRY,
__module__="google.devtools.cloudtrace_v2.proto.trace_pb2"
# @@protoc_insertion_point(class_scope:google.devtools.cloudtrace.v2.Span.Attributes.AttributeMapEntry)
),
),
DESCRIPTOR=_SPAN_ATTRIBUTES,
__module__="google.devtools.cloudtrace_v2.proto.trace_pb2",
__doc__="""A set of attributes, each in the format ``[KEY]:[VALUE]``.
Attributes:
attribute_map:
The set of attributes. Each attribute's key can be up to 128
bytes long. The value can be a string up to 256 bytes, an
integer, or the Boolean values ``true`` and ``false``. For
example: :: "/instance_id": "my-instance"
"/http/user_agent": "" "/http/request_bytes": 300
"abc.com/myattribute": true
dropped_attributes_count:
The number of attributes that were discarded. Attributes can
be discarded because their keys are too long or because there
are too many attributes. If this value is 0 then all
attributes are valid.
""",
# @@protoc_insertion_point(class_scope:google.devtools.cloudtrace.v2.Span.Attributes)
),
),
TimeEvent=_reflection.GeneratedProtocolMessageType(
"TimeEvent",
(_message.Message,),
dict(
Annotation=_reflection.GeneratedProtocolMessageType(
"Annotation",
(_message.Message,),
dict(
DESCRIPTOR=_SPAN_TIMEEVENT_ANNOTATION,
__module__="google.devtools.cloudtrace_v2.proto.trace_pb2",
__doc__="""Text annotation with a set of attributes.
Attributes:
description:
A user-supplied message describing the event. The maximum
length for the description is 256 bytes.
attributes:
A set of attributes on the annotation. You can have up to 4
attributes per Annotation.
""",
# @@protoc_insertion_point(class_scope:google.devtools.cloudtrace.v2.Span.TimeEvent.Annotation)
),
),
MessageEvent=_reflection.GeneratedProtocolMessageType(
"MessageEvent",
(_message.Message,),
dict(
DESCRIPTOR=_SPAN_TIMEEVENT_MESSAGEEVENT,
__module__="google.devtools.cloudtrace_v2.proto.trace_pb2",
__doc__="""An event describing a message sent/received between Spans.
Attributes:
type:
Type of MessageEvent. Indicates whether the message was sent
or received.
id:
An identifier for the MessageEvent's message that can be used
to match SENT and RECEIVED MessageEvents. It is recommended to
be unique within a Span.
uncompressed_size_bytes:
The number of uncompressed bytes sent or received.
compressed_size_bytes:
The number of compressed bytes sent or received. If missing
assumed to be the same size as uncompressed.
""",
# @@protoc_insertion_point(class_scope:google.devtools.cloudtrace.v2.Span.TimeEvent.MessageEvent)
),
),
DESCRIPTOR=_SPAN_TIMEEVENT,
__module__="google.devtools.cloudtrace_v2.proto.trace_pb2",
__doc__="""A time-stamped annotation or message event in the Span.
Attributes:
time:
The timestamp indicating the time the event occurred.
value:
A ``TimeEvent`` can contain either an ``Annotation`` object or
a ``MessageEvent`` object, but not both.
annotation:
Text annotation with a set of attributes.
message_event:
An event describing a message sent/received between Spans.
""",
# @@protoc_insertion_point(class_scope:google.devtools.cloudtrace.v2.Span.TimeEvent)
),
),
TimeEvents=_reflection.GeneratedProtocolMessageType(
"TimeEvents",
(_message.Message,),
dict(
DESCRIPTOR=_SPAN_TIMEEVENTS,
__module__="google.devtools.cloudtrace_v2.proto.trace_pb2",
__doc__="""A collection of ``TimeEvent``\ s. A ``TimeEvent`` is a time-stamped
annotation on the span, consisting of either user-supplied key:value
pairs, or details of a message sent/received between Spans.
Attributes:
time_event:
A collection of ``TimeEvent``\ s.
dropped_annotations_count:
The number of dropped annotations in all the included time
events. If the value is 0, then no annotations were dropped.
dropped_message_events_count:
The number of dropped message events in all the included time
events. If the value is 0, then no message events were
dropped.
""",
# @@protoc_insertion_point(class_scope:google.devtools.cloudtrace.v2.Span.TimeEvents)
),
),
Link=_reflection.GeneratedProtocolMessageType(
"Link",
(_message.Message,),
dict(
DESCRIPTOR=_SPAN_LINK,
__module__="google.devtools.cloudtrace_v2.proto.trace_pb2",
__doc__="""A pointer from the current span to another span in the same trace or in
a different trace. For example, this can be used in batching operations,
where a single batch handler processes multiple requests from different
traces or when the handler receives a request from a different project.
Attributes:
trace_id:
The [TRACE\_ID] for a trace within a project.
span_id:
The [SPAN\_ID] for a span within a trace.
type:
The relationship of the current span relative to the linked
span.
attributes:
A set of attributes on the link. You have have up to 32
attributes per link.
""",
# @@protoc_insertion_point(class_scope:google.devtools.cloudtrace.v2.Span.Link)
),
),
Links=_reflection.GeneratedProtocolMessageType(
"Links",
(_message.Message,),
dict(
DESCRIPTOR=_SPAN_LINKS,
__module__="google.devtools.cloudtrace_v2.proto.trace_pb2",
__doc__="""A collection of links, which are references from this span to a span in
the same or different trace.
Attributes:
link:
A collection of links.
dropped_links_count:
The number of dropped links after the maximum size was
enforced. If this value is 0, then no links were dropped.
""",
# @@protoc_insertion_point(class_scope:google.devtools.cloudtrace.v2.Span.Links)
),
),
DESCRIPTOR=_SPAN,
__module__="google.devtools.cloudtrace_v2.proto.trace_pb2",
__doc__="""A span represents a single operation within a trace. Spans can be nested
to form a trace tree. Often, a trace contains a root span that describes
the end-to-end latency, and one or more subspans for its sub-operations.
A trace can also contain multiple root spans, or none at all. Spans do
not need to be contiguous—there may be gaps or overlaps between spans in
a trace.
Attributes:
name:
The resource name of the span in the following format: ::
projects/[PROJECT_ID]/traces/[TRACE_ID]/spans/[SPAN_ID]
[TRACE\_ID] is a unique identifier for a trace within a
project; it is a 32-character hexadecimal encoding of a
16-byte array. [SPAN\_ID] is a unique identifier for a span
within a trace; it is a 16-character hexadecimal encoding of
an 8-byte array.
span_id:
The [SPAN\_ID] portion of the span's resource name.
parent_span_id:
The [SPAN\_ID] of this span's parent span. If this is a root
span, then this field must be empty.
display_name:
A description of the span's operation (up to 128 bytes).
Stackdriver Trace displays the description in the {% dynamic
print site\_values.console\_name %}. For example, the display
name can be a qualified method name or a file name and a line
number where the operation is called. A best practice is to
use the same display name within an application and at the
same call point. This makes it easier to correlate spans in
different traces.
start_time:
The start time of the span. On the client side, this is the
time kept by the local machine where the span execution
starts. On the server side, this is the time when the server's
application handler starts running.
end_time:
The end time of the span. On the client side, this is the time
kept by the local machine where the span execution ends. On
the server side, this is the time when the server application
handler stops running.
attributes:
A set of attributes on the span. You can have up to 32
attributes per span.
stack_trace:
Stack trace captured at the start of the span.
time_events:
A set of time events. You can have up to 32 annotations and
128 message events per span.
links:
Links associated with the span. You can have up to 128 links
per Span.
status:
An optional final status for this span.
same_process_as_parent_span:
(Optional) Set this parameter to indicate whether this span is
in the same process as its parent. If you do not set this
parameter, Stackdriver Trace is unable to take advantage of
this helpful information.
child_span_count:
An optional number of child spans that were generated while
this span was active. If set, allows implementation to detect
missing child spans.
""",
# @@protoc_insertion_point(class_scope:google.devtools.cloudtrace.v2.Span)
),
)
_sym_db.RegisterMessage(Span)
_sym_db.RegisterMessage(Span.Attributes)
_sym_db.RegisterMessage(Span.Attributes.AttributeMapEntry)
_sym_db.RegisterMessage(Span.TimeEvent)
_sym_db.RegisterMessage(Span.TimeEvent.Annotation)
_sym_db.RegisterMessage(Span.TimeEvent.MessageEvent)
_sym_db.RegisterMessage(Span.TimeEvents)
_sym_db.RegisterMessage(Span.Link)
_sym_db.RegisterMessage(Span.Links)
AttributeValue = _reflection.GeneratedProtocolMessageType(
"AttributeValue",
(_message.Message,),
dict(
DESCRIPTOR=_ATTRIBUTEVALUE,
__module__="google.devtools.cloudtrace_v2.proto.trace_pb2",
__doc__="""The allowed types for [VALUE] in a ``[KEY]:[VALUE]`` attribute.
Attributes:
value:
The type of the value.
string_value:
A string up to 256 bytes long.
int_value:
A 64-bit signed integer.
bool_value:
A Boolean value represented by ``true`` or ``false``.
""",
# @@protoc_insertion_point(class_scope:google.devtools.cloudtrace.v2.AttributeValue)
),
)
_sym_db.RegisterMessage(AttributeValue)
StackTrace = _reflection.GeneratedProtocolMessageType(
"StackTrace",
(_message.Message,),
dict(
StackFrame=_reflection.GeneratedProtocolMessageType(
"StackFrame",
(_message.Message,),
dict(
DESCRIPTOR=_STACKTRACE_STACKFRAME,
__module__="google.devtools.cloudtrace_v2.proto.trace_pb2",
__doc__="""Represents a single stack frame in a stack trace.
Attributes:
function_name:
The fully-qualified name that uniquely identifies the function
or method that is active in this frame (up to 1024 bytes).
original_function_name:
An un-mangled function name, if ``function_name`` is `mangled
<http://www.avabodh.com/cxxin/namemangling.html>`__. The name
can be fully-qualified (up to 1024 bytes).
file_name:
The name of the source file where the function call appears
(up to 256 bytes).
line_number:
The line number in ``file_name`` where the function call
appears.
column_number:
The column number where the function call appears, if
available. This is important in JavaScript because of its
anonymous functions.
load_module:
The binary module from where the code was loaded.
source_version:
The version of the deployed source code (up to 128 bytes).
""",
# @@protoc_insertion_point(class_scope:google.devtools.cloudtrace.v2.StackTrace.StackFrame)
),
),
StackFrames=_reflection.GeneratedProtocolMessageType(
"StackFrames",
(_message.Message,),
dict(
DESCRIPTOR=_STACKTRACE_STACKFRAMES,
__module__="google.devtools.cloudtrace_v2.proto.trace_pb2",
__doc__="""A collection of stack frames, which can be truncated.
Attributes:
frame:
Stack frames in this call stack.
dropped_frames_count:
The number of stack frames that were dropped because there
were too many stack frames. If this value is 0, then no stack
frames were dropped.
""",
# @@protoc_insertion_point(class_scope:google.devtools.cloudtrace.v2.StackTrace.StackFrames)
),
),
DESCRIPTOR=_STACKTRACE,
__module__="google.devtools.cloudtrace_v2.proto.trace_pb2",
__doc__="""A call stack appearing in a trace.
Attributes:
stack_frames:
Stack frames in this stack trace. A maximum of 128 frames are
allowed.
stack_trace_hash_id:
The hash ID is used to conserve network bandwidth for
duplicate stack traces within a single trace. Often multiple
spans will have identical stack traces. The first occurrence
of a stack trace should contain both the ``stackFrame``
content and a value in ``stackTraceHashId``. Subsequent spans
within the same request can refer to that stack trace by only
setting ``stackTraceHashId``.
""",
# @@protoc_insertion_point(class_scope:google.devtools.cloudtrace.v2.StackTrace)
),
)
_sym_db.RegisterMessage(StackTrace)
_sym_db.RegisterMessage(StackTrace.StackFrame)
_sym_db.RegisterMessage(StackTrace.StackFrames)
Module = _reflection.GeneratedProtocolMessageType(
"Module",
(_message.Message,),
dict(
DESCRIPTOR=_MODULE,
__module__="google.devtools.cloudtrace_v2.proto.trace_pb2",
__doc__="""Binary module.
Attributes:
module:
For example: main binary, kernel modules, and dynamic
libraries such as libc.so, sharedlib.so (up to 256 bytes).
build_id:
A unique identifier for the module, usually a hash of its
contents (up to 128 bytes).
""",
# @@protoc_insertion_point(class_scope:google.devtools.cloudtrace.v2.Module)
),
)
_sym_db.RegisterMessage(Module)
TruncatableString = _reflection.GeneratedProtocolMessageType(
"TruncatableString",
(_message.Message,),
dict(
DESCRIPTOR=_TRUNCATABLESTRING,
__module__="google.devtools.cloudtrace_v2.proto.trace_pb2",
__doc__="""Represents a string that might be shortened to a specified length.
Attributes:
value:
The shortened string. For example, if the original string is
500 bytes long and the limit of the string is 128 bytes, then
``value`` contains the first 128 bytes of the 500-byte string.
Truncation always happens on a UTF8 character boundary. If
there are multi-byte characters in the string, then the length
of the shortened string might be less than the size limit.
truncated_byte_count:
The number of bytes removed from the original string. If this
value is 0, then the string was not shortened.
""",
# @@protoc_insertion_point(class_scope:google.devtools.cloudtrace.v2.TruncatableString)
),
)
_sym_db.RegisterMessage(TruncatableString)
DESCRIPTOR._options = None
_SPAN_ATTRIBUTES_ATTRIBUTEMAPENTRY._options = None
# @@protoc_insertion_point(module_scope)
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
2,
2980,
515,
416,
262,
8435,
11876,
17050,
13,
220,
8410,
5626,
48483,
0,
198,
2,
2723,
25,
23645,
14,
7959,
31391,
14,
17721,
40546,
62,
85,
17,
14,
1676,
1462,
14... | 1.947311 | 34,998 |
"""Models for the Solis PV Inverter integration."""
from __future__ import annotations
from dataclasses import dataclass
from homeassistant.components.sensor import SensorEntityDescription
@dataclass
class SolisSensorEntityDescription(SensorEntityDescription):
"""Sensor entity description for Solis PV Inverter."""
api_key: str | None = None
| [
37811,
5841,
1424,
329,
262,
4294,
271,
31392,
554,
332,
353,
11812,
526,
15931,
198,
6738,
11593,
37443,
834,
1330,
37647,
198,
198,
6738,
4818,
330,
28958,
1330,
4818,
330,
31172,
198,
198,
6738,
1363,
562,
10167,
13,
5589,
3906,
13,
... | 3.708333 | 96 |
# yellowbrick.datasets.path
# Helper functions for looking up dataset paths.
#
# Author: Benjamin Bengfort
# Created: Thu Jul 26 14:10:51 2018 -0400
#
# Copyright (C) 2018 The scikit-yb developers
# For license information, see LICENSE.txt
#
# ID: path.py [7082742] benjamin@bengfort.com $
"""
Helper functions for looking up dataset paths.
"""
##########################################################################
## Imports
##########################################################################
import os
import shutil
from .signature import sha256sum
from yellowbrick.exceptions import DatasetsError
##########################################################################
## Fixtures
##########################################################################
FIXTURES = os.path.join(os.path.dirname(__file__), "fixtures")
##########################################################################
## Dataset path utilities
##########################################################################
def get_data_home(path=None):
"""
Return the path of the Yellowbrick data directory. This folder is used by
dataset loaders to avoid downloading data several times.
By default, this folder is colocated with the code in the install directory
so that data shipped with the package can be easily located. Alternatively
it can be set by the ``$YELLOWBRICK_DATA`` environment variable, or
programmatically by giving a folder path. Note that the ``'~'`` symbol is
expanded to the user home directory, and environment variables are also
expanded when resolving the path.
"""
if path is None:
path = os.environ.get("YELLOWBRICK_DATA", FIXTURES)
path = os.path.expanduser(path)
path = os.path.expandvars(path)
if not os.path.exists(path):
os.makedirs(path)
return path
def find_dataset_path(dataset, data_home=None, fname=None, ext=".csv.gz", raises=True):
"""
Looks up the path to the dataset specified in the data home directory,
which is found using the ``get_data_home`` function. By default data home
is colocated with the code, but can be modified with the YELLOWBRICK_DATA
environment variable, or passing in a different directory.
The file returned will be by default, the name of the dataset in compressed
CSV format. Other files and extensions can be passed in to locate other data
types or auxilliary files.
If the dataset is not found a ``DatasetsError`` is raised by default.
Parameters
----------
dataset : str
The name of the dataset; should either be a folder in data home or
specified in the yellowbrick.datasets.DATASETS variable.
data_home : str, optional
The path on disk where data is stored. If not passed in, it is looked
up from YELLOWBRICK_DATA or the default returned by ``get_data_home``.
fname : str, optional
The filename to look up in the dataset path, by default it will be the
name of the dataset. The fname must include an extension.
ext : str, default: ".csv.gz"
The extension of the data to look up in the dataset path, if the fname
is specified then the ext parameter is ignored. If ext is None then
the directory of the dataset will be returned.
raises : bool, default: True
If the path does not exist, raises a DatasetsError unless this flag is set
to False, at which point None is returned (e.g. for checking if the
path exists or not).
Returns
-------
path : str or None
A path to the requested file, guaranteed to exist if an exception is
not raised during processing of the request (unless None is returned).
raises : DatasetsError
If raise is True and the path does not exist, raises a DatasetsError.
"""
# Figure out the root directory of the datasets
data_home = get_data_home(data_home)
# Figure out the relative path to the dataset
if fname is None:
if ext is None:
path = os.path.join(data_home, dataset)
else:
path = os.path.join(data_home, dataset, "{}{}".format(dataset, ext))
else:
path = os.path.join(data_home, dataset, fname)
# Determine if the path exists
if not os.path.exists(path):
# Suppress exceptions if required
if not raises:
return None
raise DatasetsError(
("could not find dataset at {} - does it need to be downloaded?").format(
path
)
)
return path
def dataset_exists(dataset, data_home=None):
"""
Checks to see if a directory with the name of the specified dataset exists
in the data home directory, found with ``get_data_home``.
Parameters
----------
dataset : str
The name of the dataset; should either be a folder in data home or
specified in the yellowbrick.datasets.DATASETS variable.
data_home : str, optional
The path on disk where data is stored. If not passed in, it is looked
up from YELLOWBRICK_DATA or the default returned by ``get_data_home``.
Returns
-------
exists : bool
If a folder with the dataset name is in the data home directory.
"""
data_home = get_data_home(data_home)
path = os.path.join(data_home, dataset)
return os.path.exists(path) and os.path.isdir(path)
def dataset_archive(dataset, signature, data_home=None, ext=".zip"):
"""
Checks to see if the dataset archive file exists in the data home directory,
found with ``get_data_home``. By specifying the signature, this function
also checks to see if the archive is the latest version by comparing the
sha256sum of the local archive with the specified signature.
Parameters
----------
dataset : str
The name of the dataset; should either be a folder in data home or
specified in the yellowbrick.datasets.DATASETS variable.
signature : str
The SHA 256 signature of the dataset, used to determine if the archive
is the latest version of the dataset or not.
data_home : str, optional
The path on disk where data is stored. If not passed in, it is looked
up from YELLOWBRICK_DATA or the default returned by ``get_data_home``.
ext : str, default: ".zip"
The extension of the archive file.
Returns
-------
exists : bool
True if the dataset archive exists and is the latest version.
"""
data_home = get_data_home(data_home)
path = os.path.join(data_home, dataset + ext)
if os.path.exists(path) and os.path.isfile(path):
return sha256sum(path) == signature
return False
def cleanup_dataset(dataset, data_home=None, ext=".zip"):
"""
Removes the dataset directory and archive file from the data home directory.
Parameters
----------
dataset : str
The name of the dataset; should either be a folder in data home or
specified in the yellowbrick.datasets.DATASETS variable.
data_home : str, optional
The path on disk where data is stored. If not passed in, it is looked
up from YELLOWBRICK_DATA or the default returned by ``get_data_home``.
ext : str, default: ".zip"
The extension of the archive file.
Returns
-------
removed : int
The number of objects removed from data_home.
"""
removed = 0
data_home = get_data_home(data_home)
# Paths to remove
datadir = os.path.join(data_home, dataset)
archive = os.path.join(data_home, dataset + ext)
# Remove directory and contents
if os.path.exists(datadir):
shutil.rmtree(datadir)
removed += 1
# Remove the archive file
if os.path.exists(archive):
os.remove(archive)
removed += 1
return removed
| [
2,
7872,
1671,
624,
13,
19608,
292,
1039,
13,
6978,
198,
2,
5053,
525,
5499,
329,
2045,
510,
27039,
13532,
13,
198,
2,
198,
2,
6434,
25,
220,
14533,
14964,
3319,
198,
2,
15622,
25,
26223,
5979,
2608,
1478,
25,
940,
25,
4349,
2864,... | 3.006098 | 2,624 |
from .api import (Eth, Personal)
from .callablecontract import CallableContract
from .filter import Filter
from .hextools import HexTools
from .keccak import keccak
from .poller import Poller
from .soliditykeccak import solidityKeccak
from .structs import Structs
from .types import Types
from .w3json import w3json
from .wstransport import WSTransport
from .channel import Channel
import asyncio as aio
from attrdict import AttrDict
import logging
from functools import partial
log = logging.getLogger(__name__)
combine = lambda L: { k: v for d in L for k, v in d.items() }
pipe = lambda transport,api: combine([{method:partial(pipeline,transport,getattr(api,method))} for method in dir(api) if method[0] != '_'])
| [
6738,
764,
15042,
1330,
357,
40226,
11,
15644,
8,
198,
6738,
764,
13345,
540,
28484,
1330,
4889,
540,
45845,
198,
6738,
764,
24455,
1330,
25853,
198,
6738,
764,
258,
742,
10141,
1330,
22212,
33637,
198,
6738,
764,
365,
535,
461,
1330,
... | 3.208889 | 225 |
import hashlib
v19 = [0] * 28
B = [0x59, 0x59, 0x59, 0x45, 0x45, 0x45, 0x45, 0x41, 0x59, 0x59, 0x59, 0x45, 0x45, 0x45, 0x45, 0x49, 0x59, 0x59, 0x59, 0x45, 0x45, 0x45, 0x45, 0x53, 0x59, 0x59, 0x59, 0x45, 0x45, 0x45, 0x45, 0x33, 0x59, 0x59, 0x59, 0x59, 0x45, 0x45, 0x45, 0x7B, 0x59, 0x59, 0x59, 0x59, 0x45, 0x45, 0x45, 0x5F, 0x59, 0x59, 0x59, 0x59, 0x45, 0x45, 0x45, 0x5F, 0x59, 0x59, 0x59, 0x59, 0x45, 0x45, 0x45]
C = [0xD4, 0xCF, 0x20, 0x44, 0x81, 0x13, 0xCF, 0x54, 0x6E, 0xD3, 0x50, 0xEF, 0x53, 0xD9, 0xD9, 0x18, 0xD3, 0xD1, 0x11, 0x64, 0xDA, 0xB8, 0x6C, 0x25, 0xFB, 0x08, 0x60, 0x52, 0xE9, 0x59, 0x5C, 0x52, 0x6B, 0xEA, 0x8F, 0x14, 0x44, 0xD9, 0xC8, 0xAE, 0x10, 0xC8, 0x9D, 0x7F, 0xCF, 0xC6, 0x3E, 0x3E, 0x91, 0xAA, 0xA3, 0x21, 0xD6, 0x7B, 0x40, 0xE6, 0x13, 0x4A, 0xBA, 0x0A, 0x10, 0x23, 0x50, 0x28]
E = [0x20, 0x1, 0x2, 0x3, 0x4, 0x5, 0x4, 0x5, 0x6, 0x7, 0x8, 0x9, 0x8, 0x9, 0x0A, 0x0B, 0x0C, 0x0D, 0x0C, 0x0D, 0x0E, 0x0F, 0x10, 0x11, 0x10, 0x11, 0x12, 0x13, 0x14, 0x15, 0x14, 0x15, 0x16, 0x17, 0x18, 0x19, 0x18, 0x19, 0x1A, 0x1B, 0x1C, 0x1D, 0x1C, 0x1D, 0x1E, 0x1F, 0x20, 0x1, 0x10]
P = [0x10, 0x7, 0x14, 0x15, 0x1D, 0x0C, 0x1C, 0x11, 0x1, 0x0F, 0x17, 0x1A, 0x5, 0x12, 0x1F, 0x0A, 0x2, 0x8, 0x18, 0x0E, 0x20, 0x1B, 0x3, 0x9, 0x13, 0x0D, 0x1E, 0x6, 0x16, 0x0B, 0x4, 0x19]
FP = [0x28, 0x8, 0x30, 0x10, 0x38, 0x18, 0x40, 0x20, 0x27, 0x7, 0x2F, 0x0F, 0x37, 0x17, 0x3F, 0x1F, 0x26, 0x6, 0x2E, 0x0E, 0x36, 0x16, 0x3E, 0x1E, 0x25, 0x5, 0x2D, 0x0D, 0x35, 0x15, 0x3D, 0x1D, 0x24, 0x4, 0x2C, 0x0C, 0x34, 0x14, 0x3C, 0x1C, 0x23, 0x3, 0x2B, 0x0B, 0x33, 0x13, 0x3B, 0x1B, 0x22, 0x2, 0x2A, 0x0A, 0x32, 0x12, 0x3A, 0x1A, 0x21, 0x1, 0x29, 0x9, 0x31, 0x11, 0x39, 0x19]
PL = [0x39, 0x31, 0x29, 0x21, 0x19, 0x11, 0x9, 0x1, 0x3A, 0x32, 0x2A, 0x22, 0x1A, 0x12, 0x0A, 0x2, 0x3B, 0x33, 0x2B, 0x23, 0x1B, 0x13, 0x0B, 0x3, 0x3C, 0x34, 0x2C, 0x24, 0x4]
PR = [0x3F, 0x37, 0x2F, 0x27, 0x1F, 0x17, 0x0F, 0x7, 0x3E, 0x36, 0x2E, 0x26, 0x1E, 0x16, 0x0E, 0x6, 0x3D, 0x35, 0x2D, 0x25, 0x1D, 0x15, 0x0D, 0x5, 0x1C, 0x14, 0x0C, 0x4, 0x4]
P2 = [0x0E, 0x11, 0x0B, 0x18, 0x1, 0x5, 0x3, 0x1C, 0x0F, 0x6, 0x15, 0x0A, 0x17, 0x13, 0x0C, 0x4, 0x1A, 0x8, 0x10, 0x7, 0x1B, 0x14, 0x0D, 0x2, 0x29, 0x34, 0x1F, 0x25, 0x2F, 0x37, 0x1E, 0x28, 0x33, 0x2D, 0x21, 0x30, 0x2C, 0x31, 0x27, 0x38, 0x22, 0x35, 0x2E, 0x2A, 0x32, 0x24, 0x1D, 0x20, 0x10]
byte_1900 = [0x3A, 0x32, 0x2A, 0x22, 0x1A, 0x12, 0x0A, 0x2, 0x3C, 0x34, 0x2C, 0x24, 0x1C, 0x14, 0x0C, 0x4, 0x3E, 0x36, 0x2E, 0x26, 0x1E, 0x16, 0x0E, 0x6, 0x40, 0x38, 0x30, 0x28, 0x20, 0x18, 0x10, 0x8, 0x39, 0x31, 0x29, 0x21, 0x19, 0x11, 0x9, 0x1, 0x3B, 0x33, 0x2B, 0x23, 0x1B, 0x13, 0x0B, 0x3, 0x3D, 0x35, 0x2D, 0x25, 0x1D, 0x15, 0x0D, 0x5, 0x3F, 0x37, 0x2F, 0x27, 0x1F, 0x17, 0x0F, 0x7]
A = bytes(input(), 'utf-8')
assert len(A) == 64
v23 = [letter + byte_1900[index] - 1 for index, letter in enumerate(A)]
for j in range(28): # pre process
v19[j] = B[PL[j] - 1]
v19[j + 28] = B[PR[j] - 1]
for k in range(16): # main loop
for l in range(48):
v31 = [letter + E[index] + 31 for index, letter in enumerate(A)]
v4 = v19[0]
v5 = 0
for m in range(28):
v19[m] = v19[m + 1]
v19[m + 28] = v19[m + 29]
v20 = v4
v22 = v5
v31 = [v ^ v19[P2[index] - 1] for index, v in enumerate(v31)]
v17 = hashlib.sha256(''.join(v31))
v18 = [v17[P[ii] - 1] for ii in range(32)]
A = xor(A, v18)
if k != 15:
A = xor(A, v27)
v27 = xor(v27, A)
A = [A[FP[jj] - 1] for jj in range(64)]
assert A == C
| [
11748,
12234,
8019,
198,
198,
85,
1129,
796,
685,
15,
60,
1635,
2579,
198,
33,
796,
685,
15,
87,
3270,
11,
657,
87,
3270,
11,
657,
87,
3270,
11,
657,
87,
2231,
11,
657,
87,
2231,
11,
657,
87,
2231,
11,
657,
87,
2231,
11,
657,
... | 1.456922 | 2,333 |
# -*- coding: utf-8 -*- #
# Copyright 2020 Google LLC. 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.
"""Abstract operation class that command operations will inherit from.
Should typically be executed in a task iterator through
googlecloudsdk.command_lib.storage.tasks.task_executor.
Manual execution example:
>>> class CopyTask(Task):
... def __init__(self, src, dest):
... ...
>>> my_copy_task = new CopyTask('~/Desktop/memes.jpg', '/kernel/')
>>> my_copy_task.Execute()
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
import abc
import collections
import enum
from googlecloudsdk.core.util import debug_output
import six
# Holds information to be passed between tasks.
#
# Attributes:
# topic (Topic): The type of information this message holds.
# payload (Any): The information itself.
Message = collections.namedtuple(
'Message',
['topic', 'payload']
)
# Holds information returned from Task.Execute.
#
# Note that because information here is sent between processes, all data in this
# class must be picklable.
#
# Attributes:
# additional_task_iterators (Optional[Iterable[Iterable[Task]]]): Tasks to be
# executed such that all tasks in each Iterable[Task] are executed before
# any tasks in the next Iterable[Task]. Tasks within each Iterable[Task] are
# unordered. For example, if this value were the following:
#
# [
# [UploadObjectTask(), UploadObjectTask(), UploadObjectTask()],
# [ComposeObjectsTask()]
# ]
#
# All UploadObjectTasks should be completed before the ComposeObjectTask
# could begin, but the UploadObjectTasks could be executed in parallel.
# messages (Optional[Iterable[Message]]): Information to be passed to all
# dependent tasks.
Output = collections.namedtuple(
'Output',
['additional_task_iterators', 'messages']
)
class Task(six.with_metaclass(abc.ABCMeta, object)):
"""Abstract class to represent one command operation.
Attributes:
parallel_processing_key (Optional[Hashable]): Identifies a task during
execution. If this value is not None, the executor will skip this task if
another task being executed is using the same key. If this value is None,
the executor will not skip any tasks based on it.
received_messages (Iterable[Message]): Messages sent to this task
by its dependencies.
report_error (bool): If True, failure of this task should be reported
by updating the exit_code to non-zero. Defaults to True.
"""
@abc.abstractmethod
def execute(self, task_status_queue=None):
"""Performs some work based on class attributes.
Args:
task_status_queue (multiprocessing.Queue): Used by task to report it
progress to a central location.
Returns:
An Output instance, or None.
"""
pass
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
1303,
198,
2,
15069,
12131,
3012,
11419,
13,
1439,
6923,
33876,
13,
198,
2,
198,
2,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
34156,
15341,
198... | 3.295432 | 1,029 |
from django.conf.urls import url
from . import views
from rest_framework import routers
urlpatterns = [
url(r'^$', views.index, name='index'),
url(r'^login/', views.login, name='login'),
url(r'^logout/', views.logout, name='logout'),
url(r'^avatar/(?P<profile_id>[0-9]+)/$', views.avatar, name='avatar'),
url(r'^leagues/$', views.leagues_list, name='leagues_index'),
url(r'^leagues/(?P<league_id>[0-9]+)/$', views.leagues_get, name='leagues_get'),
url(r'^leagues/(?P<league_id>[0-9]+)/users/$', views.leagues_users_list,
name='leagues_users_list'),
url(r'^leagues/(?P<league_id>[0-9]+)/users/(?P<user_id>[0-9]+)/$',
views.leagues_users_get, name='leagues_users_get'),
]
# urlpatterns = [
# url(r'^$', views.index, name='index'),
# ]
| [
6738,
42625,
14208,
13,
10414,
13,
6371,
82,
1330,
19016,
201,
198,
201,
198,
6738,
764,
1330,
5009,
201,
198,
6738,
1334,
62,
30604,
1330,
41144,
201,
198,
201,
198,
6371,
33279,
82,
796,
685,
201,
198,
220,
220,
220,
19016,
7,
81,... | 2.186486 | 370 |
import h5py
import pickle
import numpy as np | [
11748,
289,
20,
9078,
198,
11748,
2298,
293,
198,
11748,
299,
32152,
355,
45941
] | 3.142857 | 14 |
import random as rd | [
11748,
4738,
355,
374,
67
] | 3.8 | 5 |
#!/usr/bin/env python3
# GBA FE Hack Manager by MinN
#
# To use: run this once to create a rom folder for your hack roms and a patch folder for your patches
# Put FE6_clean.gba FE7_clean.gba FE8_clean.gba FE7J_clean.gba FE8J_clean.gba in the parent folder as patching targets
# This tool will not verify your FE roms' checksum so do it yourself
# Put your patches in the patch folder and
# run this script to patch and/or update your hack roms
import sys
import ups
import os
import os.path
import shutil
import re
import platform
from distutils.version import LooseVersion
INHERIT_SAVE = True
# solves various issues
# on macOS, click on a #! script, your cwd is still ~
# and .app bundles doesn't give you a good sys.argv[0]
# This only accepts version number separated by . or - and does not support dates
if __name__ == '__main__':
cd_current()
main()
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
2,
402,
4339,
18630,
18281,
9142,
416,
1855,
45,
198,
2,
198,
2,
1675,
779,
25,
1057,
428,
1752,
284,
2251,
257,
9267,
9483,
329,
534,
8156,
374,
3150,
290,
257,
8529,
9483,
32... | 3.221402 | 271 |
# __all__ = ["trzy_1", "trzy_2", "trzy_3"]
__all__ = ["trzy_1"]
print ("Init modulu ", __name__)
| [
2,
11593,
439,
834,
796,
14631,
2213,
7357,
62,
16,
1600,
366,
2213,
7357,
62,
17,
1600,
366,
2213,
7357,
62,
18,
8973,
198,
834,
439,
834,
796,
14631,
2213,
7357,
62,
16,
8973,
198,
198,
4798,
5855,
31768,
953,
15712,
33172,
11593,... | 2.106383 | 47 |