content
stringlengths 1
1.05M
| input_ids
sequencelengths 1
883k
| ratio_char_token
float64 1
22.9
| token_count
int64 1
883k
|
|---|---|---|---|
from rest_framework_gis import serializers
from rest_framework import serializers as s
from .models import (
Artificialisee2015to2018,
Artificielle2018,
CommunesSybarval,
CouvertureSol,
EnveloppeUrbaine2018,
Ocsge,
Renaturee2018to2015,
Sybarval,
Voirie2018,
ZonesBaties2018,
UsageSol,
)
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] | 2.422535 | 142 |
from django.contrib import admin
from .models import SearchResult
# Register your models here.
admin.site.register(SearchResult, SearchResultAdmin) | [
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import asyncio
import os
import tempfile
from contextlib import ExitStack
from typing import Text, Optional, List, Union, Dict
from rasa.importers.importer import TrainingDataImporter
from rasa import model
from rasa.model import FingerprintComparisonResult
from rasa.core.domain import Domain
from rasa.utils.common import TempDirectoryPath
from rasa.cli.utils import (
print_success,
print_warning,
print_error,
bcolors,
print_color,
)
from rasa.constants import DEFAULT_MODELS_PATH, DEFAULT_CORE_SUBDIRECTORY_NAME
def train_core(
domain: Union[Domain, Text],
config: Text,
stories: Text,
output: Text,
train_path: Optional[Text] = None,
fixed_model_name: Optional[Text] = None,
additional_arguments: Optional[Dict] = None,
) -> Optional[Text]:
loop = asyncio.get_event_loop()
return loop.run_until_complete(
train_core_async(
domain=domain,
config=config,
stories=stories,
output=output,
train_path=train_path,
fixed_model_name=fixed_model_name,
additional_arguments=additional_arguments,
)
)
def train_nlu(
config: Text,
nlu_data: Text,
output: Text,
train_path: Optional[Text] = None,
fixed_model_name: Optional[Text] = None,
persist_nlu_training_data: bool = False,
) -> Optional[Text]:
"""Trains an NLU model.
Args:
config: Path to the config file for NLU.
nlu_data: Path to the NLU training data.
output: Output path.
train_path: If `None` the model will be trained in a temporary
directory, otherwise in the provided directory.
fixed_model_name: Name of the model to be stored.
persist_nlu_training_data: `True` if the NLU training data should be persisted
with the model.
Returns:
If `train_path` is given it returns the path to the model archive,
otherwise the path to the directory with the trained model files.
"""
loop = asyncio.get_event_loop()
return loop.run_until_complete(
_train_nlu_async(
config,
nlu_data,
output,
train_path,
fixed_model_name,
persist_nlu_training_data,
)
)
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] | 2.385246 | 976 |
# ============================================================================
# FILE: default.py
# AUTHOR: Shougo Matsushita <Shougo.Matsu at gmail.com>
# License: MIT license
# ============================================================================
import re
import typing
from denite.util import echo, error, clearmatch, regex_convert_py_vim
from denite.util import Nvim, UserContext, Candidates, Candidate
from denite.parent import SyncParent
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# -*- coding: utf-8 -*-
"""Context managers implemented for (mostly) internal use"""
import contextlib
import functools
from io import UnsupportedOperation
import os
import sys
__all__ = ["RedirectStdout", "RedirectStderr"]
RedirectStdout = functools.partial(_stdchannel_redirected, sys.stdout)
RedirectStderr = functools.partial(_stdchannel_redirected, sys.stderr)
RedirectNoOp = functools.partial(_stdchannel_redirected, None, "")
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# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from . import __version__ as app_version
app_name = "pos_kiosk"
app_title = "Pos Kiosk"
app_publisher = "9t9it"
app_description = "Kiosk App"
app_icon = "octicon octicon-file-directory"
app_color = "grey"
app_email = "info@9t9it.com"
app_license = "MIT"
# Includes in <head>
# ------------------
# include js, css files in header of desk.html
# app_include_css = "/assets/pos_kiosk/css/pos_kiosk.css"
# app_include_js = "/assets/pos_kiosk/js/pos_kiosk.js"
# include js, css files in header of web template
# web_include_css = "/assets/pos_kiosk/css/pos_kiosk.css"
# web_include_js = "/assets/pos_kiosk/js/pos_kiosk.js"
# include js in page
# page_js = {"page" : "public/js/file.js"}
# page_js = {
# "kiosk": ["public/js/pos_page_js.js", "public/js/includes/number_to_words.js"]
# }
# include js in doctype views
# doctype_js = {"doctype" : "public/js/doctype.js"}
# doctype_list_js = {"doctype" : "public/js/doctype_list.js"}
# doctype_tree_js = {"doctype" : "public/js/doctype_tree.js"}
# doctype_calendar_js = {"doctype" : "public/js/doctype_calendar.js"}
fixtures = [
{
"doctype": "Custom Field",
"filters": [
[
"name",
"in",
[
"Sales Invoice Item-pos_kiosk",
"Mode of Payment-logo"
]
]
]
}
]
# Home Pages
# ----------
# application home page (will override Website Settings)
# home_page = "login"
# website user home page (by Role)
# role_home_page = {
# "Role": "home_page"
# }
# Website user home page (by function)
# get_website_user_home_page = "pos_kiosk.utils.get_home_page"
# Generators
# ----------
# automatically create page for each record of this doctype
# website_generators = ["Web Page"]
# Installation
# ------------
# before_install = "pos_kiosk.install.before_install"
# after_install = "pos_kiosk.install.after_install"
# Desk Notifications
# ------------------
# See frappe.core.notifications.get_notification_config
# notification_config = "pos_kiosk.notifications.get_notification_config"
# Permissions
# -----------
# Permissions evaluated in scripted ways
# permission_query_conditions = {
# "Event": "frappe.desk.doctype.event.event.get_permission_query_conditions",
# }
#
# has_permission = {
# "Event": "frappe.desk.doctype.event.event.has_permission",
# }
# Document Events
# ---------------
# Hook on document methods and events
# doc_events = {
# "*": {
# "on_update": "method",
# "on_cancel": "method",
# "on_trash": "method"
# }
# }
# Scheduled Tasks
# ---------------
# scheduler_events = {
# "all": [
# "pos_kiosk.tasks.all"
# ],
# "daily": [
# "pos_kiosk.tasks.daily"
# ],
# "hourly": [
# "pos_kiosk.tasks.hourly"
# ],
# "weekly": [
# "pos_kiosk.tasks.weekly"
# ]
# "monthly": [
# "pos_kiosk.tasks.monthly"
# ]
# }
# Testing
# -------
# before_tests = "pos_kiosk.install.before_tests"
# Overriding Whitelisted Methods
# ------------------------------
#
# override_whitelisted_methods = {
# "pos_bahrain.api.get_item_details.get_item_details": "pos_kiosk.api.item.get_item_details" # noqa
# }
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] | 2.319625 | 1,386 |
from keras import Model, Input
from keras.layers import Dense, concatenate, LSTM, Reshape, Permute, Embedding, Dropout, Convolution1D, Flatten
from keras.optimizers import Adam
from pypagai.models.base import KerasModel
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from .fully_connected import FullyConnectedLatentVariable
from .convolutional import ConvolutionalLatentVariable
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#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author:
''' PNASNet in PyTorch.
Paper: Progressive Neural Architecture Search
'''
from easyai.base_name.block_name import NormalizationType, ActivationType
from easyai.base_name.backbone_name import BackboneName
from easyai.model.backbone.utility.base_backbone import *
from easyai.model.base_block.utility.utility_block import ConvBNActivationBlock
from easyai.model.base_block.cls.pnasnet_block import CellA, CellB
__all__ = ['pnasnet_A', 'pnasnet_B']
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] | 2.891429 | 175 |
# -*- coding: utf-8 -*-
# coding=utf-8
import json
import os
import math
import logging
import requests
import time
from map_download.cmd.BaseDownloader import DownloadEngine, BaseDownloaderThread, latlng2tile_terrain, BoundBox
if __name__ == '__main__':
if 1:
logger = logging.getLogger('down')
try:
root = r'/Users/cugxy/Documents/data/downloader'
formatter = logging.Formatter('%(levelname)s-%(message)s')
hdlr = logging.StreamHandler()
log_file = os.path.join(root, 'down.log')
file_hdlr = logging.FileHandler(log_file)
file_hdlr.setFormatter(formatter)
logger.addHandler(file_hdlr)
logger.addHandler(hdlr)
logger.setLevel(logging.INFO)
min_lng = -180.0
max_lng = 180.0
min_lat = -90.0
max_lat = 90.0
start_zoom = 0
end_zoom = 5
bbox = BoundBox(max_lat, max_lng, min_lat, min_lng, start_zoom, end_zoom)
d = TerrainDownloadEngine(root, bbox, 8, logger)
d.start()
time.sleep(10000)
logger.error('main thread out')
except Exception as e:
logger.error(e)
if 0:
accessToken = get_access_token()
pass
| [
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62,
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] | 2.001536 | 651 |
# coding: utf-8
"""
Kubernetes
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501
OpenAPI spec version: v1.12.4
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from kubernetes_asyncio.client.api_client import ApiClient
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 10 23:28:58 2017
@author: dhingratul
"""
import urllib.request
import os
from selenium import webdriver
from selenium.webdriver.support.ui import Select
from bs4 import BeautifulSoup
import ssl
import requests
import wget
from PyPDF2 import PdfFileReader
def is_valid_pdf(fn):
"""Check is the PDF valid """
try:
with open(fn, 'rb') as f:
pdf = PdfFileReader(f)
numpages = pdf.numPages
return (numpages > 0)
except Exception as e:
return False
| [
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] | 2.457627 | 236 |
"""
Experiment summary
------------------
Treat each province/state in a country cases over time
as a vector, do a simple K-Nearest Neighbor between
countries. What country has the most similar trajectory
to a given country?
Plots similar countries
"""
import sys
sys.path.insert(0, '..')
from utils import data
import os
import sklearn
import numpy as np
import json
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
# ------------ HYPERPARAMETERS -------------
BASE_PATH = '../COVID-19/csse_covid_19_data/'
# ------------------------------------------
confirmed = os.path.join(
BASE_PATH,
'csse_covid_19_time_series',
'time_series_covid19_confirmed_global.csv')
confirmed = data.load_csv_data(confirmed)
features = []
targets = []
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(111)
cm = plt.get_cmap('jet')
NUM_COLORS = 0
LINE_STYLES = ['solid', 'dashed', 'dotted']
NUM_STYLES = len(LINE_STYLES)
dist_diff = os.path.join('../exp/results/', 'knn_raw.json')
f = open(dist_diff,)
dist_diff = json.load(f)
for region, dist in dist_diff.items():
plt.style.use('fivethirtyeight')
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(111)
cm = plt.get_cmap('jet')
other_region = dist['manhattan'][0]
regions = [region, other_region]
for val in regions:
df = data.filter_by_attribute(
confirmed, "Country/Region", val)
cases, labels = data.get_cases_chronologically(df)
cases = cases.sum(axis=0)
lines = ax.plot(cases, label=val)
ax.set_ylabel('# of confirmed cases')
ax.set_xlabel("Time (days since Jan 22, 2020)")
ax.set_yscale('log')
ax.legend()
plt.tight_layout()
region = region.replace('*', '')
other_region = other_region.replace('*', '')
plt.title(f'Comparing confirmed cases in {region} and {other_region}')
plt.savefig(f'results/raw_manhattan/{region}.png')
plt.close()
print(region) | [
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] | 2.580475 | 758 |
"""
Rational Activation Functions for MXNET
=======================================
This module allows you to create Rational Neural Networks using Learnable
Rational activation functions with MXNET networks.
"""
import mxnet as mx
from mxnet import initializer
from mxnet.gluon import HybridBlock
from rational.utils.get_weights import get_parameters
from rational.mxnet.versions import _version_a, _version_b, _version_c, _version_d
from rational._base.rational_base import Rational_base
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"""Utils for criterion."""
import torch
import torch.nn.functional as F
def normalize(x, axis=-1):
"""Performs L2-Norm."""
num = x
denom = torch.norm(x, 2, axis, keepdim=True).expand_as(x) + 1e-12
return num / denom
# Source : https://github.com/earhian/Humpback-Whale-Identification-1st-/blob/master/models/triplet_loss.py
def euclidean_dist(x, y):
"""Computes Euclidean distance."""
m, n = x.size(0), y.size(0)
xx = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n)
yy = torch.pow(x, 2).sum(1, keepdim=True).expand(m, m).t()
dist = xx + yy - 2 * torch.matmul(x, y.t())
dist = dist.clamp(min=1e-12).sqrt()
return dist
def cosine_dist(x, y):
"""Computes Cosine Distance."""
x = F.normalize(x, dim=1)
y = F.normalize(y, dim=1)
dist = 2 - 2 * torch.mm(x, y.t())
return dist
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] | 2.197917 | 384 |
"""Tests for the sbahn_munich integration"""
line_dict = {
"name": "S3",
"color": "#333333",
"text_color": "#444444",
}
| [
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#! /usr/bin/env python2.7
# -*- coding: latin-1 -*-
from flask import Blueprint
from flask import current_app
from flask import render_template
from flask_login import login_required
homestack = Blueprint("homestack", __name__, url_prefix="/homestack")
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"""Forms for RTD donations"""
import logging
from django import forms
from django.conf import settings
from django.utils.translation import ugettext_lazy as _
from readthedocs.payments.forms import StripeModelForm, StripeResourceMixin
from readthedocs.payments.utils import stripe
from .models import Supporter
log = logging.getLogger(__name__)
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from .base import DataReaderBase
from ..tools import COL, _get_dates, to_float, to_int
import pandas as pd
#from pandas.tseries.frequencies import to_offset
from six.moves import cStringIO as StringIO
import logging
import traceback
import datetime
import json
import token, tokenize
def ymd_to_date(y, m, d):
"""
Returns date
>>> expiration = {u'd': 1, u'm': 12, u'y': 2014}
>>> ymd_to_date(**expiration)
datetime.date(2014, 12, 1)
>>> ymd_to_date(2014, 3, 1)
datetime.date(2014, 3, 1)
"""
return(datetime.date(year=y, month=m, day=d))
def date_to_ymd(date):
"""
Returns dict like {'y': ..., 'm': ..., 'd': ...}
>>> date_to_ymd(datetime.date(year=2010, month=1, day=3))
{'y': 2010, 'm': 1, 'd': 3}
"""
d = {
'y': date.year,
'm': date.month,
'd': date.day
}
return(d)
def fix_lazy_json(in_text):
"""
Handle lazy JSON - to fix expecting property name
this function fixes the json output from google
http://stackoverflow.com/questions/4033633/handling-lazy-json-in-python-expecting-property-name
"""
tokengen = tokenize.generate_tokens(StringIO(in_text).readline)
result = []
for tokid, tokval, _, _, _ in tokengen:
# fix unquoted strings
if (tokid == token.NAME):
if tokval not in ['true', 'false', 'null', '-Infinity', 'Infinity', 'NaN']:
tokid = token.STRING
tokval = u'"%s"' % tokval
# fix single-quoted strings
elif (tokid == token.STRING):
if tokval.startswith ("'"):
tokval = u'"%s"' % tokval[1:-1].replace ('"', '\\"')
# remove invalid commas
elif (tokid == token.OP) and ((tokval == '}') or (tokval == ']')):
if (len(result) > 0) and (result[-1][1] == ','):
result.pop()
# fix single-quoted strings
elif (tokid == token.STRING):
if tokval.startswith ("'"):
tokval = u'"%s"' % tokval[1:-1].replace ('"', '\\"')
result.append((tokid, tokval))
return tokenize.untokenize(result)
if __name__ == "__main__":
import doctest
doctest.testmod()
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] | 2.140267 | 1,048 |
# !/usr/bin/python
# -*- coding: utf-8 -*-
# @time : 2019/11/2 21:08
# @author : Mo
# @function:
from keras_textclassification.data_preprocess.text_preprocess import load_json, save_json
from keras_textclassification.conf.path_config import path_model_dir
path_fast_text_model_vocab2index = path_model_dir + 'vocab2index.json'
path_fast_text_model_l2i_i2l = path_model_dir + 'l2i_i2l.json'
import numpy as np
import os
| [
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355,
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198,
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628,
628,
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] | 2.505814 | 172 |
# Copyright 2014 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
from gpu_tests.gpu_test_expectations import GpuTestExpectations
# See the GpuTestExpectations class for documentation.
| [
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# -*- coding: utf-8 -*-
# Generated by Django 1.11.13 on 2018-06-08 22:54
from __future__ import unicode_literals
from django.conf import settings
import django.contrib.postgres.fields.jsonb
from django.db import migrations, models
import django.db.models.deletion
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] | 2.902174 | 92 |
import setuptools #enables develop
setuptools.setup(
name='pysvm',
version='0.1',
description='PySVM : A NumPy implementation of SVM based on SMO algorithm',
author_email="191300064@smail.nju.edu.cn",
packages=['pysvm'],
license='MIT License',
long_description=open('README.md', encoding='utf-8').read(),
install_requires=[ #
'numpy', 'sklearn'
],
url='https://github.com/Kaslanarian/PySVM',
)
| [
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] | 2.445055 | 182 |
######## Image Object Detection Using Tensorflow-trained Classifier #########
#
# Author: Evan Juras
# Date: 1/15/18
# Description:
# This program uses a TensorFlow-trained classifier to perform object detection.
# It loads the classifier uses it to perform object detection on an image.
# It draws boxes and scores around the objects of interest in the image.
## Some of the code is copied from Google's example at
## https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb
## and some is copied from Dat Tran's example at
## https://github.com/datitran/object_detector_app/blob/master/object_detection_app.py
## but I changed it to make it more understandable to me.
# Import packages
import os
import cv2
import numpy as np
import tensorflow as tf
import sys
# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")
# Import utilites
from utils import label_map_util
from utils import visualization_utils as vis_util
# Name of the directory containing the object detection module we're using
MODEL_NAME = 'inference_graph'
IMAGE_NAME = 'test1.jpg'
# Grab path to current working directory
CWD_PATH = os.getcwd()
# Path to frozen detection graph .pb file, which contains the model that is used
# for object detection.
PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb')
# Path to label map file
PATH_TO_LABELS = os.path.join(CWD_PATH,'training','labelmap.pbtxt')
# Path to image
PATH_TO_IMAGE = os.path.join(CWD_PATH,IMAGE_NAME)
# Number of classes the object detector can identify
NUM_CLASSES = 6
# Load the label map.
# Label maps map indices to category names, so that when our convolution
# network predicts `5`, we know that this corresponds to `king`.
# Here we use internal utility functions, but anything that returns a
# dictionary mapping integers to appropriate string labels would be fine
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
# Load the Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
sess = tf.Session(graph=detection_graph)
# Define input and output tensors (i.e. data) for the object detection classifier
# Input tensor is the image
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Output tensors are the detection boxes, scores, and classes
# Each box represents a part of the image where a particular object was detected
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represents level of confidence for each of the objects.
# The score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
# Number of objects detected
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Load image using OpenCV and
# expand image dimensions to have shape: [1, None, None, 3]
# i.e. a single-column array, where each item in the column has the pixel RGB value
image = cv2.imread(PATH_TO_IMAGE)
image_expanded = np.expand_dims(image, axis=0)
# Perform the actual detection by running the model with the image as input
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_expanded})
# Draw the results of the detection (aka 'visulaize the results')
vis_util.visualize_boxes_and_labels_on_image_array(
image,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8,
min_score_thresh=0.60)
# All the results have been drawn on image. Now display the image.
cv2.imshow('Object detector', image)
# Press any key to close the image
cv2.waitKey(0)
# Clean up
cv2.destroyAllWindows()
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5985,
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33967,
17,
13,
41659,
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] | 3.122769 | 1,401 |
from data_collection.management.commands import BaseXpressDemocracyClubCsvImporter
| [
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] | 3.952381 | 21 |
from django.db.models import Q
from django.shortcuts import render
from django.http import Http404
# Create your views here.
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework.decorators import api_view
from .models import Product, Category
from .serializers import ProductSerializer, CategorySerializer | [
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] | 3.923913 | 92 |
from sys import maxsize
| [
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628,
198
] | 3.714286 | 7 |
##########################################################################
#
# Copyright (c) 2010-2012, Image Engine Design Inc. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# * Neither the name of Image Engine Design nor the names of any
# other contributors to this software may be used to endorse or
# promote products derived from this software without specific prior
# written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
# IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER 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 with_statement
import os
import sys
import shutil
import unittest
import IECore
if __name__ == "__main__":
unittest.main()
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1298,
198,
197,
403,
715,
395,
13,
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3419,
198
] | 3.498195 | 554 |
"""Pacman game domain."""
from rlpy.Tools import __rlpy_location__
from .Domain import Domain
from .PacmanPackage import layout, pacman, game, ghostAgents
from .PacmanPackage import graphicsDisplay
import numpy as np
from copy import deepcopy
import os
import time
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = ["Alborz Geramifard", "Robert H. Klein", "Christoph Dann",
"William Dabney", "Jonathan P. How"]
__license__ = "BSD 3-Clause"
__author__ = "Austin Hays"
| [
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9800,
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40245,
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592,
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628,
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] | 2.942529 | 174 |
from zeit.cms.i18n import MessageFactory as _
import zope.interface
import zope.schema
| [
6738,
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72,
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3008,
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] | 3.142857 | 28 |
N, M = map(int, input().split())
for i in range(1, M + 1):
if i % 2 == 1:
j = (i - 1) // 2
print(1 + j, M + 1 - j)
else:
j = (i - 2) // 2
print(M + 2 + j, 2 * M + 1 - j)
| [
45,
11,
337,
796,
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] | 1.743802 | 121 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# 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.
import re
import sys
from setuptools import setup
from setuptools.command.test import test as TestCommand
__version__ = ''
with open('facebookbot/__about__.py', 'r') as fd:
reg = re.compile(r'__version__ = [\'"]([^\'"]*)[\'"]')
for line in fd:
m = reg.match(line)
if m:
__version__ = m.group(1)
break
with open('README.rst', 'r') as fd:
long_description = fd.read()
setup(
name="fbsdk",
version=__version__,
author="Sam Chang",
author_email="t0915290092@gmail.com",
maintainer="Sam Chang",
maintainer_email="t0915290092@gmail.com",
url="https://github.com/boompieman/fbsdk",
description="Facebook Messaging API SDK for Python",
long_description=long_description,
license='Apache License 2.0',
packages=[
"facebookbot", "facebookbot.models"
],
install_requires=_requirements(),
classifiers=[
"Development Status :: 5 - Production/Stable",
"License :: OSI Approved :: Apache Software License",
"Intended Audience :: Developers",
"Programming Language :: Python :: 3",
"Topic :: Software Development"
]
)
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513,
1600,
198,
220,
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366,
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7904,
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7712,
1,
198,
220,
220,
220,
2361,
198,
8,
198
] | 2.744186 | 645 |
# coding=utf-8
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) HuggingFace Inc. team.
#
# 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.
"""PyTorch MMBT model. """
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...modeling_outputs import BaseModelOutputWithPooling, SequenceClassifierOutput
from ...modeling_utils import ModuleUtilsMixin
from ...utils import logging
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "MMBTConfig"
MMBT_START_DOCSTRING = r"""
MMBT model was proposed in [Supervised Multimodal Bitransformers for Classifying Images and Text](https://github.com/facebookresearch/mmbt) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.
It's a supervised multimodal bitransformer model that fuses information from text and other image encoders, and
obtain state-of-the-art performance on various multimodal classification benchmark tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic
methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
pruning heads etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)
subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
general usage and behavior.
Parameters:
config ([`MMBTConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration.
transformer (:class: *~nn.Module*): A text transformer that is used by MMBT.
It should have embeddings, encoder, and pooler attributes.
encoder (:class: *~nn.Module*): Encoder for the second modality.
It should take in a batch of modal inputs and return k, n dimension embeddings.
"""
MMBT_INPUTS_DOCSTRING = r"""
Args:
input_modal (`torch.FloatTensor` of shape `(batch_size, ***)`):
The other modality data. It will be the shape that the encoder for that type expects. e.g. With an Image
Encoder, the shape would be (batch_size, channels, height, width)
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. It does not expect [CLS] token to be added as it's
appended to the end of other modality embeddings. Indices can be obtained using
[`BertTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
modal_start_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Optional start token to be added to Other Modality Embedding. [CLS] Most commonly used for classification
tasks.
modal_end_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Optional end token to be added to Other Modality Embedding. [SEP] Most commonly used.
attention_mask (*optional*) `torch.FloatTensor` of shape `(batch_size, sequence_length)`:
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, sequence_length)`:
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
modal_token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, modal_sequence_length)`:
Segment token indices to indicate different portions of the non-text modality. The embeddings from these
tokens will be summed with the respective token embeddings for the non-text modality.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
modal_position_ids (`torch.LongTensor` of shape `(batch_size, modal_sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings for the non-text modality.
Selected in the range `[0, config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, embedding_dim)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated
vectors than the model's internal embedding lookup matrix.
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
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] | 2.849831 | 2,657 |
from abc import (
ABC,
abstractmethod,
)
import logging
from typing import (
TYPE_CHECKING,
Tuple,
Type,
)
from eth._utils.datatypes import (
Configurable,
)
from eth.db.backends.base import (
BaseAtomicDB,
)
from eth.exceptions import (
BlockNotFound,
)
from eth.validation import (
validate_word,
)
from eth_typing import (
Hash32,
)
from eth_utils import (
ValidationError,
encode_hex,
)
from eth2._utils.ssz import (
validate_imported_block_unchanged,
)
from eth2.beacon.db.chain import (
BaseBeaconChainDB,
BeaconChainDB,
)
from eth2.beacon.exceptions import (
BlockClassError,
StateMachineNotFound,
)
from eth2.beacon.types.blocks import (
BaseBeaconBlock,
)
from eth2.beacon.types.states import (
BeaconState,
)
from eth2.beacon.typing import (
FromBlockParams,
Slot,
)
from eth2.beacon.validation import (
validate_slot,
)
if TYPE_CHECKING:
from eth2.beacon.state_machines.base import ( # noqa: F401
BaseBeaconStateMachine,
)
#
# Block API
#
def get_block_class(self, block_root: Hash32) -> Type[BaseBeaconBlock]:
slot = self.chaindb.get_slot_by_root(block_root)
sm_class = self.get_state_machine_class_for_block_slot(slot)
block_class = sm_class.block_class
return block_class
def create_block_from_parent(self,
parent_block: BaseBeaconBlock,
block_params: FromBlockParams) -> BaseBeaconBlock:
"""
Passthrough helper to the ``StateMachine`` class of the block descending from the
given block.
"""
return self.get_state_machine_class_for_block_slot(
slot=parent_block.slot + 1 if block_params.slot is None else block_params.slot,
).create_block_from_parent(parent_block, block_params)
def get_block_by_root(self, block_root: Hash32) -> BaseBeaconBlock:
"""
Return the requested block as specified by block hash.
Raise ``BlockNotFound`` if there's no block with the given hash in the db.
"""
validate_word(block_root, title="Block Hash")
block_class = self.get_block_class(block_root)
return self.chaindb.get_block_by_root(block_root, block_class)
def get_canonical_head(self) -> BaseBeaconBlock:
"""
Return the block at the canonical chain head.
Raise ``CanonicalHeadNotFound`` if there's no head defined for the canonical chain.
"""
block_root = self.chaindb.get_canonical_head_root()
block_class = self.get_block_class(block_root)
return self.chaindb.get_block_by_root(block_root, block_class)
def get_score(self, block_root: Hash32) -> int:
"""
Return the score of the block with the given hash.
Raise ``BlockNotFound`` if there is no matching black hash.
"""
return self.chaindb.get_score(block_root)
def ensure_block(self, block: BaseBeaconBlock=None) -> BaseBeaconBlock:
"""
Return ``block`` if it is not ``None``, otherwise return the block
of the canonical head.
"""
if block is None:
head = self.get_canonical_head()
return self.create_block_from_parent(head, FromBlockParams())
else:
return block
def get_block(self) -> BaseBeaconBlock:
"""
Return the current TIP block.
"""
return self.get_state_machine().block
def get_canonical_block_by_slot(self, slot: Slot) -> BaseBeaconBlock:
"""
Return the block with the given number in the canonical chain.
Raise ``BlockNotFound`` if there's no block with the given number in the
canonical chain.
"""
validate_slot(slot)
return self.get_block_by_root(self.chaindb.get_canonical_block_root(slot))
def get_canonical_block_root(self, slot: Slot) -> Hash32:
"""
Return the block hash with the given number in the canonical chain.
Raise ``BlockNotFound`` if there's no block with the given number in the
canonical chain.
"""
return self.chaindb.get_canonical_block_root(slot)
def import_block(
self,
block: BaseBeaconBlock,
perform_validation: bool=True
) -> Tuple[BaseBeaconBlock, Tuple[BaseBeaconBlock, ...], Tuple[BaseBeaconBlock, ...]]:
"""
Import a complete block and returns a 3-tuple
- the imported block
- a tuple of blocks which are now part of the canonical chain.
- a tuple of blocks which were canonical and now are no longer canonical.
"""
try:
parent_block = self.get_block_by_root(block.previous_block_root)
except BlockNotFound:
raise ValidationError(
"Attempt to import block #{}. Cannot import block {} before importing "
"its parent block at {}".format(
block.slot,
block.signed_root,
block.previous_block_root,
)
)
base_block_for_import = self.create_block_from_parent(
parent_block,
FromBlockParams(),
)
state, imported_block = self.get_state_machine(base_block_for_import).import_block(block)
# Validate the imported block.
if perform_validation:
validate_imported_block_unchanged(imported_block, block)
# TODO: Now it just persists all state. Should design how to clean up the old state.
self.chaindb.persist_state(state)
(
new_canonical_blocks,
old_canonical_blocks,
) = self.chaindb.persist_block(imported_block, imported_block.__class__)
self.logger.debug(
'IMPORTED_BLOCK: slot %s | signed root %s',
imported_block.slot,
encode_hex(imported_block.signed_root),
)
return imported_block, new_canonical_blocks, old_canonical_blocks
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] | 2.345961 | 2,587 |
#!/usr/local/bin/python3
import paramiko,time
#using as SSH Client
client = paramiko.SSHClient()
# check dir(client) to find available options.
# auto adjust host key verification with yes or no
client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
# time for connecting to remote Cisco IOS
"""
Manually taking input
addr = input('Provide IP address to connect to: ')
user = input('Username: ')
pwd = getpass.getpass('Password: ')"""
# Taking input from files
f1 = open("devices.txt","r")
f2 = open("commands.txt","r")
for line in f1:
client = paramiko.SSHClient()
client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
data = line.split(" ")
# print(data)
addr = data[0]
user = data[1]
pwd = data[2]
f3 = open(addr+".txt","w+")
# print(addr +" "+ user +" " +pwd)
client.connect(addr,username=user,password=pwd,allow_agent=False,look_for_keys=False)
# we have to ask for Shell
device_access = client.invoke_shell()
for line in f2:
device_access.send(line)
time.sleep(1)
output = device_access.recv(55000).decode('ascii')
f3.write(output)
"""
THIS CODE IS FOR SINGLE COMMAND, FOR MULTIPLE COMMANDS CODE BELOW
# send command to the device
device_access.send("ter len 0\nshow run \n")
time.sleep(2)
# receive output from the device, convert it to byte-like format and print it
print(device_access.recv(550000).decode('ascii'))
# We can print the same to a file too
with open("csr1000v.txt","w") as f:
f.write(device_access.recv(550000).decode('ascii'))""" | [
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4480,
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2816,
2388,
737,
12501,
1098,
10786,
292,
979,
72,
6,
4008,
37811
] | 2.658744 | 589 |
# for n in range(400,500):
# i = n // 100
# j = n // 10 % 10
# k = n % 10
# if n == i ** 3 + j ** 3 + k ** 3:
# print(n)
# (16)
# input("():")
# s1 = input("():")
# l1 = s1.split(' ')
# l2 = []
# for i in l1:
# if i.isdigit():
# l2.append(int(i))
# for i in l2:
# if not (i % 6):
# print(i, end=" ")
# (17)
out_l1 = []
while 1:
in_1 = input(":")
nums_l1 = in_1.split(' ')
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2,
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8,
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16,
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17635,
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4514,
352,
25,
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220,
220,
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287,
62,
16,
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7,
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8,
198,
220,
220,
220,
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16,
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62,
16,
13,
35312,
10786,
705,
8,
628,
628,
628,
628,
628,
628,
198
] | 1.74902 | 255 |
"""
A query transformer is a function that accepts a program and returns a program, plus a priority level.
Higher priority transformers are placed closer to the front of the list. Were ensuring is a function,
because were going to evaluate it later 31 .
Well assume there wont be an enormous number of transformer additions,
and walk the list linearly to add a new one.
Well leave a note in case this assumption turns out to be false
a binary search is much more time-optimal for long lists,
but adds a little complexity and doesnt really speed up short lists.
"""
"""
Dagoba.T = [] # transformers (more than meets the eye)
"""
"""
Dagoba.addTransformer = function(fun, priority) {
if(typeof fun != 'function')
return Dagoba.error('Invalid transformer function')
for(var i = 0; i < Dagoba.T.length; i++) # OPT: binary search
if(priority > Dagoba.T[i].priority) break
Dagoba.T.splice(i, 0, {priority: priority, fun: fun})
}
"""
"""
Dagoba.transform = function(program) {
return Dagoba.T.reduce(function(acc, transformer) {
return transformer.fun(acc)
}, program)
}
"""
"""
Dagoba.addAlias = function(newname, oldname, defaults) {
defaults = defaults || [] # default arguments for the alias
Dagoba.addPipetype(newname, function() {}) # because there's no method catchall in js
Dagoba.addTransformer(function(program) {
return program.map(function(step) {
if(step[0] != newname) return step
return [oldname, Dagoba.extend(step[1], defaults)]
})
}, 100) # these need to run early, so they get a high priority
}
"""
"""
Dagoba.extend = function(list, defaults) {
return Object.keys(defaults).reduce(function(acc, key) {
if(typeof list[key] != 'undefined') return acc
acc[key] = defaults[key]
return acc
}, list)
}
"""
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1441,
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198,
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8964,
1351,
8,
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92,
198,
37811,
198
] | 2.632979 | 752 |
#!/usr/bin/python3.6.8+
# -*- coding:utf-8 -*-
"""
@auth: cml
@date: 2020-12-2
@desc: ...
"""
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] | 1.846154 | 52 |
# -*- coding: utf-8 -*-
"""
pyboleto.html
~~~~~~~~~~~~~
Classe Responsvel por fazer o output do boleto em html.
:copyright: 2012 by Artur Felipe de Sousa
:license: BSD, see LICENSE for more details.
"""
import os
import string
import sys
import codecs
import base64
from itertools import chain
if sys.version_info < (3,):
from itertools import izip_longest as zip_longest
zip_longest # chamando para evitar erro de nao uso do zip_longest
else:
from itertools import zip_longest
DIGITS = [
['n', 'n', 'w', 'w', 'n'],
['w', 'n', 'n', 'n', 'w'],
['n', 'w', 'n', 'n', 'w'],
['w', 'w', 'n', 'n', 'n'],
['n', 'n', 'w', 'n', 'w'],
['w', 'n', 'w', 'n', 'n'],
['n', 'w', 'w', 'n', 'n'],
['n', 'n', 'n', 'w', 'w'],
['w', 'n', 'n', 'w', 'n'],
['n', 'w', 'n', 'w', 'n'],
]
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13,
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11,
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220,
220,
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220,
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62,
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395,
355,
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62,
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395,
198,
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220,
220,
19974,
62,
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395,
220,
1303,
442,
321,
25440,
31215,
819,
7940,
1931,
305,
390,
299,
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514,
78,
466,
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62,
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395,
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25,
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220,
422,
340,
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35,
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86,
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6,
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705,
77,
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77,
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705,
86,
6,
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220,
220,
37250,
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220,
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77,
3256,
705,
86,
3256,
705,
77,
6,
4357,
198,
60,
628
] | 2.09204 | 402 |
summary = 0
i = 0
while i < 5:
summary = summary + i
print(summary)
i = i + 1
| [
49736,
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657,
198,
198,
72,
796,
657,
198,
4514,
1312,
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220,
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49736,
8,
198,
220,
220,
220,
1312,
796,
1312,
1343,
352,
198
] | 2.219512 | 41 |
import imtreat
img = imtreat.imageManagerClass.openImageFunction("../images/soleil.png", 0)
img = imtreat.definedModesClass.detailEnhanceFunction(img)
imtreat.imageManagerClass.saveImageFunction("/Tlchargements/", "image_1", ".png", img)
| [
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1600,
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9060,
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1600,
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8,
198
] | 2.939024 | 82 |
# Copyright (c) 2016 TUBITAK BILGEM
# 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.
from oslo_config import cfg
hyperv_opt_group = cfg.OptGroup("hyperv",
title='The Hyper-V feature',
help="""
The hyperv feature allows you to configure the Hyper-V hypervisor
driver to be used within an OpenStack deployment.
""")
hyperv_opts = [
cfg.FloatOpt('dynamic_memory_ratio',
default=1.0,
help="""
Dynamic memory ratio
Enables dynamic memory allocation (ballooning) when set to a value
greater than 1. The value expresses the ratio between the total RAM
assigned to an instance and its startup RAM amount. For example a
ratio of 2.0 for an instance with 1024MB of RAM implies 512MB of
RAM allocated at startup.
Possible values:
* 1.0: Disables dynamic memory allocation (Default).
* Float values greater than 1.0: Enables allocation of total implied
RAM divided by this value for startup.
"""),
cfg.BoolOpt('enable_instance_metrics_collection',
default=False,
help="""
Enable instance metrics collection
Enables metrics collections for an instance by using Hyper-V's
metric APIs. Collected data can be retrieved by other apps and
services, e.g.: Ceilometer.
"""),
cfg.StrOpt('instances_path_share',
default="",
help="""
Instances path share
The name of a Windows share mapped to the "instances_path" dir
and used by the resize feature to copy files to the target host.
If left blank, an administrative share (hidden network share) will
be used, looking for the same "instances_path" used locally.
Possible values:
* "": An administrative share will be used (Default).
* Name of a Windows share.
Related options:
* "instances_path": The directory which will be used if this option
here is left blank.
"""),
cfg.BoolOpt('limit_cpu_features',
default=False,
help="""
Limit CPU features
This flag is needed to support live migration to hosts with
different CPU features and checked during instance creation
in order to limit the CPU features used by the instance.
"""),
cfg.IntOpt('mounted_disk_query_retry_count',
default=10,
min=0,
help="""
Mounted disk query retry count
The number of times to retry checking for a mounted disk.
The query runs until the device can be found or the retry
count is reached.
Possible values:
* Positive integer values. Values greater than 1 is recommended
(Default: 10).
Related options:
* Time interval between disk mount retries is declared with
"mounted_disk_query_retry_interval" option.
"""),
cfg.IntOpt('mounted_disk_query_retry_interval',
default=5,
min=0,
help="""
Mounted disk query retry interval
Interval between checks for a mounted disk, in seconds.
Possible values:
* Time in seconds (Default: 5).
Related options:
* This option is meaningful when the mounted_disk_query_retry_count
is greater than 1.
* The retry loop runs with mounted_disk_query_retry_count and
mounted_disk_query_retry_interval configuration options.
"""),
cfg.IntOpt('power_state_check_timeframe',
default=60,
min=0,
help="""
Power state check timeframe
The timeframe to be checked for instance power state changes.
This option is used to fetch the state of the instance from Hyper-V
through the WMI interface, within the specified timeframe.
Possible values:
* Timeframe in seconds (Default: 60).
"""),
cfg.IntOpt('power_state_event_polling_interval',
default=2,
min=0,
help="""
Power state event polling interval
Instance power state change event polling frequency. Sets the
listener interval for power state events to the given value.
This option enhances the internal lifecycle notifications of
instances that reboot themselves. It is unlikely that an operator
has to change this value.
Possible values:
* Time in seconds (Default: 2).
"""),
cfg.StrOpt('qemu_img_cmd',
default="qemu-img.exe",
help="""
qemu-img command
qemu-img is required for some of the image related operations
like converting between different image types. You can get it
from here: (http://qemu.weilnetz.de/) or you can install the
Cloudbase OpenStack Hyper-V Compute Driver
(https://cloudbase.it/openstack-hyperv-driver/) which automatically
sets the proper path for this config option. You can either give the
full path of qemu-img.exe or set its path in the PATH environment
variable and leave this option to the default value.
Possible values:
* Name of the qemu-img executable, in case it is in the same
directory as the nova-compute service or its path is in the
PATH environment variable (Default).
* Path of qemu-img command (DRIVELETTER:\PATH\TO\QEMU-IMG\COMMAND).
Related options:
* If the config_drive_cdrom option is False, qemu-img will be used to
convert the ISO to a VHD, otherwise the config drive will
remain an ISO. To use config drive with Hyper-V, you must
set the ``mkisofs_cmd`` value to the full path to an ``mkisofs.exe``
installation.
"""),
cfg.StrOpt('vswitch_name',
help="""
External virtual switch name
The Hyper-V Virtual Switch is a software-based layer-2 Ethernet
network switch that is available with the installation of the
Hyper-V server role. The switch includes programmatically managed
and extensible capabilities to connect virtual machines to both
virtual networks and the physical network. In addition, Hyper-V
Virtual Switch provides policy enforcement for security, isolation,
and service levels. The vSwitch represented by this config option
must be an external one (not internal or private).
Possible values:
* If not provided, the first of a list of available vswitches
is used. This list is queried using WQL.
* Virtual switch name.
"""),
cfg.IntOpt('wait_soft_reboot_seconds',
default=60,
min=0,
help="""
Wait soft reboot seconds
Number of seconds to wait for instance to shut down after soft
reboot request is made. We fall back to hard reboot if instance
does not shutdown within this window.
Possible values:
* Time in seconds (Default: 60).
"""),
cfg.BoolOpt('config_drive_cdrom',
default=False,
help="""
Mount config drive as a CD drive.
OpenStack can be configured to write instance metadata to a config drive, which
is then attached to the instance before it boots. The config drive can be
attached as a disk drive (default) or as a CD drive.
Related options:
* This option is meaningful with ``force_config_drive`` option set to ``True``
or when the REST API call to create an instance will have
``--config-drive=True`` flag.
* ``config_drive_format`` option must be set to ``iso9660`` in order to use
CD drive as the config drive image.
* To use config drive with Hyper-V, you must set the
``mkisofs_cmd`` value to the full path to an ``mkisofs.exe`` installation.
Additionally, you must set the ``qemu_img_cmd`` value to the full path
to an ``qemu-img`` command installation.
* You can configure the Compute service to always create a configuration
drive by setting the ``force_config_drive`` option to ``True``.
"""),
cfg.BoolOpt('config_drive_inject_password',
default=False,
help="""
Inject password to config drive.
When enabled, the admin password will be available from the config drive image.
Related options:
* This option is meaningful when used with other options that enable
config drive usage with Hyper-V, such as ``force_config_drive``.
"""),
cfg.IntOpt('volume_attach_retry_count',
default=10,
min=0,
help="""
Volume attach retry count
The number of times to retry attaching a volume. Volume attachment
is retried until success or the given retry count is reached.
Possible values:
* Positive integer values (Default: 10).
Related options:
* Time interval between attachment attempts is declared with
volume_attach_retry_interval option.
"""),
cfg.IntOpt('volume_attach_retry_interval',
default=5,
min=0,
help="""
Volume attach retry interval
Interval between volume attachment attempts, in seconds.
Possible values:
* Time in seconds (Default: 5).
Related options:
* This options is meaningful when volume_attach_retry_count
is greater than 1.
* The retry loop runs with volume_attach_retry_count and
volume_attach_retry_interval configuration options.
"""),
cfg.BoolOpt('enable_remotefx',
default=False,
help="""
Enable RemoteFX feature
This requires at least one DirectX 11 capable graphics adapter for
Windows / Hyper-V Server 2012 R2 or newer and RDS-Virtualization
feature has to be enabled.
Instances with RemoteFX can be requested with the following flavor
extra specs:
**os:resolution**. Guest VM screen resolution size. Acceptable values::
1024x768, 1280x1024, 1600x1200, 1920x1200, 2560x1600, 3840x2160
``3840x2160`` is only available on Windows / Hyper-V Server 2016.
**os:monitors**. Guest VM number of monitors. Acceptable values::
[1, 4] - Windows / Hyper-V Server 2012 R2
[1, 8] - Windows / Hyper-V Server 2016
**os:vram**. Guest VM VRAM amount. Only available on
Windows / Hyper-V Server 2016. Acceptable values::
64, 128, 256, 512, 1024
"""),
cfg.BoolOpt('use_multipath_io',
default=False,
help="""
Use multipath connections when attaching iSCSI or FC disks.
This requires the Multipath IO Windows feature to be enabled. MPIO must be
configured to claim such devices.
"""),
cfg.ListOpt('iscsi_initiator_list',
default=[],
help="""
List of iSCSI initiators that will be used for estabilishing iSCSI sessions.
If none are specified, the Microsoft iSCSI initiator service will choose the
initiator.
""")
]
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1037,
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11,
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262,
198,
259,
8846,
1352,
13,
198,
15931,
4943,
198,
60,
628,
198
] | 3.21266 | 3,207 |
import requests
words_list = requests.get("https://raw.githubusercontent.com/atebits/Words/master/Words/fr.txt").text
words_list = filter(lambda x: len(x) > 4, words_list.split('\n'))
path = input("Chemin d'criture ? (words.txt) ")
if path == "":
path = "./words.txt"
with open(path, "w", encoding="utf-8") as file:
file.write('\n'.join(words_list)) | [
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] | 2.623188 | 138 |
import unittest
from unittest import mock
import os
import subprocess
from testfixtures import TempDirectory
from simplegallery.upload.uploader_factory import get_uploader
if __name__ == '__main__':
unittest.main()
| [
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] | 3.313433 | 67 |
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'add_subject.ui'
#
# Created by: PyQt4 UI code generator 4.11.4
#
# WARNING! All changes made in this file will be lost!
from PyQt4 import QtCore, QtGui
try:
_fromUtf8 = QtCore.QString.fromUtf8
except AttributeError:
try:
_encoding = QtGui.QApplication.UnicodeUTF8
except AttributeError:
if __name__ == "__main__":
import sys
app = QtGui.QApplication(sys.argv)
Dialog_add_subject = QtGui.QDialog()
ui = Ui_Dialog_add_subject()
ui.setupUi(Dialog_add_subject)
Dialog_add_subject.show()
sys.exit(app.exec_())
| [
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] | 2.302817 | 284 |
from django.db.models import signals
from django.test import TestCase
from django.core import management
from django.utils import six
from shared_models import models
PRE_SYNCDB_ARGS = ['app', 'create_models', 'verbosity', 'interactive', 'db']
SYNCDB_DATABASE = 'default'
SYNCDB_VERBOSITY = 1
SYNCDB_INTERACTIVE = False
# We connect receiver here and not in unit test code because we need to
# connect receiver before test runner creates database. That is, sequence of
# actions would be:
#
# 1. Test runner imports this module.
# 2. We connect receiver.
# 3. Test runner calls syncdb for create default database.
# 4. Test runner execute our unit test code.
pre_syncdb_receiver = OneTimeReceiver()
signals.pre_syncdb.connect(pre_syncdb_receiver, sender=models)
| [
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62,
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198,
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874,
13,
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13,
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7,
3866,
62,
27261,
9945,
62,
260,
39729,
11,
29788,
28,
27530,
8,
628
] | 3.273109 | 238 |
# Copyright The PyTorch Lightning team.
#
# 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.
import logging
import os
import re
from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
log = logging.getLogger(__name__)
| [
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7,
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8,
628
] | 3.782828 | 198 |
#!/usr/bin/env python
import time
import os
import math
from trackball import TrackBall
print("""Trackball: Mouse
Use the trackball as a mouse in Raspbian, with right-click
when the switch is pressed.
Press Ctrl+C to exit!
""")
trackball = TrackBall(interrupt_pin=4)
trackball.set_rgbw(0, 0, 0, 0)
# Check for xte (used to control mouse)
use_xte = os.system('which xte') == 0
if use_xte == 0:
raise RuntimeError("xte not found. Did you sudo apt install xautomation?")
while True:
up, down, left, right, switch, state = trackball.read()
# Send movements and clicks to xte
if switch:
cmd = 'xte "mouseclick 1"'
os.system(cmd)
elif right or up or left or down:
x = right - left
x = math.copysign(x**2, x)
y = down - up
y = math.copysign(y**2, y)
cmd = 'xte "mousermove {} {}"'.format(int(x), int(y))
os.system(cmd)
time.sleep(0.0001)
| [
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] | 2.404145 | 386 |
"""GaussianMLPRegressorModel."""
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
from garage.experiment import deterministic
from garage.tf.models import GaussianMLPModel
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import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import os
import argparse
from torch.autograd import Variable
from extensions.utils import progress_bar
from extensions.model_refinery_wrapper import ModelRefineryWrapper
from extensions.refinery_loss import RefineryLoss
from models import ShuffleNetv2_wrapper
from models import DiracDeltaNet_wrapper
parser = argparse.ArgumentParser(description='PyTorch imagenet inference')
parser.add_argument('--datadir', help='path to dataset')
parser.add_argument('--inputdir', help='path to input model')
args = parser.parse_args()
# Data
print('==> Preparing data..')
# Data loading code
valdir = os.path.join(args.datadir, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform_test = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])
#imagenet
testset = datasets.ImageFolder(valdir, transform_test)
num_classes=1000
testloader = torch.utils.data.DataLoader(testset, batch_size=1000, shuffle=False, pin_memory=True, num_workers=30)
use_cuda = torch.cuda.is_available()
print('Using input path: %s' % args.inputdir)
checkpoint = torch.load(args.inputdir)
init_net = checkpoint['net']
net=init_net.to('cpu')
label_refinery=torch.load('./resnet50.t7')
net = ModelRefineryWrapper(net, label_refinery)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
net = nn.DataParallel(net)
net=net.to(device)
criterion = RefineryLoss()
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k)
return res
acc1,acc5,loss=test()
print('top-1 accuracy: {0:.3f}%, top-5 accuracy: {1:.3f}%'.format(acc1,acc5))
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201,
198
] | 2.402585 | 1,083 |
"""Model Parameters Module."""
import torch.optim as optim
from .search import SamplingSearch, GreedySearch, BeamSearch
SEARCH_FACTORY = {
'sampling': SamplingSearch,
'greedy': GreedySearch,
'beam': BeamSearch,
}
OPTIMIZER_FACTORY = {
'adadelta': optim.Adadelta,
'adagrad': optim.Adagrad,
'adam': optim.Adam,
'adamax': optim.Adamax,
'rmsprop': optim.RMSprop,
'sgd': optim.SGD
}
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897,
11,
198,
220,
220,
220,
705,
81,
907,
22930,
10354,
6436,
13,
49,
5653,
22930,
11,
198,
220,
220,
220,
705,
82,
21287,
10354,
6436,
13,
38475,
35,
198,
92,
198
] | 2.404624 | 173 |
#
# Copyright (c) 2021 The GPflux Contributors.
#
# 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.
#
import abc
import numpy as np
import pytest
import tensorflow as tf
import tensorflow_probability as tfp
from gpflow.kullback_leiblers import gauss_kl
from gpflux.encoders import DirectlyParameterizedNormalDiag
from gpflux.layers import LatentVariableLayer, LayerWithObservations, TrackableLayer
tf.keras.backend.set_floatx("float64")
############
# Utilities
############
def _zero_one_normal_prior(w_dim):
""" N(0, I) prior """
return tfp.distributions.MultivariateNormalDiag(loc=np.zeros(w_dim), scale_diag=np.ones(w_dim))
############
# Tests
############
class ArrayMatcher:
def __init__(self, expected):
self.expected = expected
def test_no_tensorflow_metaclass_overwritten():
"""
LayerWithObservations is a subclass of tf.keras.layers.Layer (via TrackableLayer);
this test ensures that TrackableLayer does not have a metaclass, and hence by adding
the ABCMeta to LayerWithObservations we are not accidentally removing some required
TensorFlow magic metaclass.
"""
assert LayerWithObservations.__bases__ == (TrackableLayer,)
assert type(TrackableLayer) is type
assert type(LayerWithObservations) is abc.ABCMeta
| [
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] | 3.170213 | 564 |
#!/usr/bin/env python
#
from actingweb import actor
from actingweb import config
from actingweb import trust
from actingweb import auth
import webapp2
import os
from google.appengine.ext.webapp import template
import json
import logging
import datetime
import time
# /trust handlers
#
# GET /trust with query parameters (relationship, type, and peerid) to retrieve trust relationships (auth: only creator and admins allowed)
# POST /trust with json body to initiate a trust relationship between this
# actor and another (reciprocal relationship) (auth: only creator and admins allowed)
# POST /trust/{relationship} with json body to create new trust
# relationship (see config.py for default relationship and auto-accept, no
# auth required)
# GET /trust/{relationship}}/{actorid} to get details on a specific relationship (auth: creator, admin, or peer secret)
# POST /trust/{relationship}}/{actorid} to send information to a peer about changes in the relationship
# PUT /trust/{relationship}}/{actorid} with a json body to change details on a relationship (baseuri, secret, desc) (auth: creator,
# admin, or peer secret)
# DELETE /trust/{relationship}}/{actorid} to delete a relationship (with
# ?peer=true if the delete is from the peer) (auth: creator, admin, or
# peer secret)
# Handling requests to trust/
# Handling requests to /trust/*, e.g. /trust/friend
# Handling requests to specific relationships, e.g. /trust/friend/12f2ae53bd
application = webapp2.WSGIApplication([
webapp2.Route(r'/<id>/trust<:/?>', rootHandler, name='rootHandler'),
webapp2.Route(r'/<id>/trust/<relationship><:/?>',
relationshipHandler, name='relationshipHandler'),
webapp2.Route(r'/<id>/trust/<relationship>/<peerid><:/?>', trustHandler, name='trustHandler'),
], debug=True)
| [
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] | 3.332721 | 544 |
# Store the version here so:
# 1) we don't load dependencies by storing it in __init__.py
# 2) we can import it in setup.py for the same reason
# 3) we can import it into your module module
# https://stackoverflow.com/questions/458550/standard-way-to-embed-version-into-python-package
__version__ = '1.6.1'
release_notes = {
'1.6.1': """
Fixed GUI crash when loading certain RLBot config files with relative paths for agents.
Fixed agent preset loading to allow multiple agents to saved/loaded correctly if they have the same name. - ima9rd
""",
'1.6.0':"""
Add support for auto starting .NET executables.
""",
'1.5.1': """
Fixed crash with GUI when no default RLBot.cfg file was found.
Updated GUI to launch Rocket League when clicking run if no Rocket League process is found. - ima9rd
""",
'1.5.0': """
Adding a have_internet helper function to help streamline upgrade checks. - ima9rd
""",
'1.4.2': """
Adding support for auto-running java bots during tournaments. To take advantage of this
in your bot, see https://github.com/RLBot/RLBotJavaExample/wiki/Auto-Launching-Java
Plus bug fixes:
- Fixed a bug where auto-run executables would crash when trying to write to stderr.
- Dragging bots to another team in the GUI no longer breaks the config.
""",
'1.3.0': """
Accurate ball prediction for Hoops and Dropshot modes!
- Kipje13, Marvin, NeverCast, et. al.
""",
'1.2.6': """
Fixed a bug where field info was not extracted properly during dropshot mode.
It was reporting 2 goals rather than the expected 140.
""",
'1.2.5': """
***************************************************
* Fix for dodge cancels / half flips! - ccman32 *
***************************************************
Plus:
- Changing the rendering strategy for 3D lines that go past the camera. Formerly it was
"draw it, even though it's crazy sometimes", now it will be "don't draw it".
- Showing the rate that inputs are received for each player index when you press the
[home] key. Toggle back off with the [end] key.
- Fixed a bug where party_member_bot could get influenced by real controller input.
- Creating new presets in the GUI works better now.
- Got rid of the libpng warning seen when using the GUI.
- Giving specific error messages when cfg files are messed up.
""",
'1.2.2': """
- Rearranged the GUI a bit, and made it load and track appearance configs more effectively.
- Fixed bug where RUN button behavior in the GUI would not work after killing bots.
""",
'1.2.0': """
- We now offer a 'RigidBodyTick' thanks to whatisaphone! It's a lower-level representation of
physics data which updates at 120Hz and is not subject to interpolation. You can still make a
great bot without it, but this feature is quite nice for the scientists among us.
See https://github.com/RLBot/RLBotPythonExample/wiki/Rigid-Body-Tick for more details!
- Faster way to access ball prediction data in python. - Skyborg
""",
'1.1.3': """
- Faster way to access ball prediction data in python. - Skyborg
- Java bots will now shut down when the python framework quits. This has been necessary recently
to avoid buggy situations.
- Shutting down the python framework will no longer attempt to kill bots twice in a row.
- Clicking on the "Run" button twice in a row in the GUI will no longer spawn duplicate processes.
""",
'1.1.2': """
Faster way to access ball prediction data in python. - Skyborg
""",
'1.1.1': """
You can now get information about the ball's status in Dropshot mode thanks to hallo_doei!
Read all about it at https://github.com/RLBot/RLBot/wiki/Dropshot
Other changes:
- The loadout config for orange team is now respected again. - ccman32
- Fixed a bug where the GUI would crash with a "KeyError". - hallo_doei
- Avoiding and suppressing some game crashes, and also restoring the
ability to get game tick data during replays and the postgame. - tarehart
- Fixed a bug where bots would dodge when they intended to double jump. -tarehart
""",
'1.0.6': """
The latest Rocket League patch broke dodges for our bots; this update fixes it.
""",
'1.0.5': """
Maximum size for a render message has been decreased again because many people experienced
errors related to memory access. The limit is now only double the original.
""",
'1.0.4': """
- Maximum size for a render message has been increased by a factor of 100. This means you can
draw a lot of lines at once without getting errors.
- Boost amount for cars will now round up to the nearest integer, so 0.3% boost will now appear
as 1 instead of 0.
- Fixed a crash that would commonly happen after a match ends. As a side effect, you can no longer
see up-to-date player data during instant replays.
""",
'1.0.3': """
Time for the big 1.0 release! We actually left "beta" a long time ago so this isn't as big
a milestone as the number implies, but we DO have two great new features!
1. Setting game state. You can manipulate the position, velocity, etc of the ball and the cars!
This can be a great help during bot development, and you can also get creative with it. Visit
the wiki for details and documentation - https://github.com/RLBot/RLBot/wiki/Manipulating-Game-State
Code written by hallo_doei, ccman32, and tarehart
2. Ball prediction. We now provide a list of future ball positions based on chip's excellent
physics modeling. Take advantage of this to do next-level wall reads, catches, and dribbles! You can
read about the math involved here: https://samuelpmish.github.io/notes/RocketLeague/ball_bouncing/
Note: currently the wall bounces are only accurate on the standard arena, not hoops or dropshot.
Documentation and examples can be found here: https://github.com/RLBot/RLBot/wiki/Ball-Path-Prediction
Code written by chip and tarehart
Bonus:
- You can now play on Salty Shores thanks to hallo_doei
- Bug fix for people with spaces in their file path by Zaptive
- Subprocess agent for future Rust support by whatisaphone
""",
'0.0.32': """
More comprehensive fix for Rocket League patch 1.50. Compared to previous version:
- Dropshot tile data is fixed
- Boost pad data is fixed
- Loadout configuration is fixed
Thanks to ccman32 and dtracers for delivering this fix quickly!
""",
'0.0.31': """
Rapid response to Rocket League patch 1.50 with the following known issues:
- Dropshot tile data is missing
- Boost pad data is missing
- Loadout configuration is broken
Thanks to ccman32 and dtracers for delivering this short-term fix quickly.
We will follow this up with a proper fix as soon as possible. You may also choose to stay on
Rocket League 1.49 and RLBot 0.0.30, ask for instructions on discord.
""",
'0.0.30': """
- New core dll that is less likely to break when Rocket League is patched - ccman32 and hallo-doei
- Fixed bug resulting in incorrect quickchat - dtracers
- Added more built-in colors to the python rendering manager - Eastvillage
- Fix for items with a ':' not showing up in the GUI - hallo-doei
- Fix for GUI not saving correct path - hallo-doei
- Fix for GUI crash when saving preset then canceling - hallo-doei
- Adding file checking before injection (Resolves #167) - Redox
- Fixed typo in rlbot.cfg - Redox
- Fancy release notes - tarehart and Skyborg
"""
}
release_banner = """
______ _ ______ _
10100 | ___ \ | | ___ \ | | 00101
110011 | |_/ / | | |_/ / ___ | |_ 110011
00110110 | /| | | ___ \/ _ \| __| 01101100
010010 | |\ \| |____| |_/ / (_) | |_ 010010
10010 \_| \_\_____/\____/ \___/ \__| 01001
"""
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# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: enemy_skills.proto
"""Generated protocol buffer code."""
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()
DESCRIPTOR = _descriptor.FileDescriptor(
name='enemy_skills.proto',
package='dadguide_proto',
syntax='proto3',
serialized_options=None,
create_key=_descriptor._internal_create_key,
serialized_pb=b'\n\x12\x65nemy_skills.proto\x12\x0e\x64\x61\x64guide_proto\"\xbf\x02\n\x1cMonsterBehaviorWithOverrides\x12\x12\n\nmonster_id\x18\x01 \x01(\x05\x12-\n\x06levels\x18\x02 \x03(\x0b\x32\x1d.dadguide_proto.LevelBehavior\x12\x36\n\x0flevel_overrides\x18\x03 \x03(\x0b\x32\x1d.dadguide_proto.LevelBehavior\x12\x43\n\x06status\x18\x04 \x01(\x0e\x32\x33.dadguide_proto.MonsterBehaviorWithOverrides.Status\"_\n\x06Status\x12\x10\n\x0cNOT_APPROVED\x10\x00\x12\x12\n\x0e\x41PPROVED_AS_IS\x10\x01\x12\x14\n\x10NEEDS_REAPPROVAL\x10\x02\x12\x19\n\x15\x41PPROVED_WITH_CHANGES\x10\x03\"f\n\x0fMonsterBehavior\x12\x12\n\nmonster_id\x18\x01 \x01(\x05\x12-\n\x06levels\x18\x02 \x03(\x0b\x32\x1d.dadguide_proto.LevelBehavior\x12\x10\n\x08\x61pproved\x18\x03 \x01(\x08\"M\n\rLevelBehavior\x12\r\n\x05level\x18\x01 \x01(\x05\x12-\n\x06groups\x18\x02 \x03(\x0b\x32\x1d.dadguide_proto.BehaviorGroup\"\xd9\x02\n\rBehaviorGroup\x12;\n\ngroup_type\x18\x01 \x01(\x0e\x32\'.dadguide_proto.BehaviorGroup.GroupType\x12,\n\tcondition\x18\x02 \x01(\x0b\x32\x19.dadguide_proto.Condition\x12.\n\x08\x63hildren\x18\x03 \x03(\x0b\x32\x1c.dadguide_proto.BehaviorItem\"\xac\x01\n\tGroupType\x12\x0f\n\x0bUNSPECIFIED\x10\x00\x12\x0b\n\x07PASSIVE\x10\x01\x12\x0b\n\x07PREEMPT\x10\x02\x12\x11\n\rDISPEL_PLAYER\x10\x03\x12\x12\n\x0eMONSTER_STATUS\x10\x04\x12\r\n\tREMAINING\x10\x05\x12\x0c\n\x08STANDARD\x10\x06\x12\t\n\x05\x44\x45\x41TH\x10\x07\x12\x0f\n\x0bUNKNOWN_USE\x10\x08\x12\x14\n\x10HIGHEST_PRIORITY\x10\t\"u\n\x0c\x42\x65haviorItem\x12.\n\x05group\x18\x02 \x01(\x0b\x32\x1d.dadguide_proto.BehaviorGroupH\x00\x12,\n\x08\x62\x65havior\x18\x03 \x01(\x0b\x32\x18.dadguide_proto.BehaviorH\x00\x42\x07\n\x05value\"c\n\x08\x42\x65havior\x12,\n\tcondition\x18\x01 \x01(\x0b\x32\x19.dadguide_proto.Condition\x12\x16\n\x0e\x65nemy_skill_id\x18\x02 \x01(\x05\x12\x11\n\tchild_ids\x18\x03 \x03(\x05\"\x80\x04\n\tCondition\x12\x14\n\x0chp_threshold\x18\x01 \x01(\x05\x12\x12\n\nuse_chance\x18\x02 \x01(\x05\x12\x15\n\rrepeats_every\x18\x03 \x01(\x05\x12\x17\n\x0fglobal_one_time\x18\x04 \x01(\x08\x12\x19\n\x11limited_execution\x18\r \x01(\x05\x12!\n\x19trigger_enemies_remaining\x18\x05 \x01(\x05\x12\x13\n\x0bif_defeated\x18\x06 \x01(\x08\x12\x1f\n\x17if_attributes_available\x18\x07 \x01(\x08\x12\x18\n\x10trigger_monsters\x18\x08 \x03(\x05\x12\x16\n\x0etrigger_combos\x18\t \x01(\x05\x12\x1a\n\x12if_nothing_matched\x18\n \x01(\x08\x12\x14\n\x0ctrigger_turn\x18\x0b \x01(\x05\x12\x18\n\x10trigger_turn_end\x18\x0c \x01(\x05\x12\x1c\n\x14\x61lways_trigger_above\x18\x0e \x01(\x05\x12\x14\n\x0c\x61lways_after\x18\x0f \x01(\x05\x12\x11\n\tskill_set\x18\x10 \x01(\x05\x12\x19\n\x11\x65rased_attributes\x18\x11 \x03(\x05\x12\x13\n\x0b\x64\x61mage_done\x18\x12 \x01(\x05\x12\x1b\n\x13\x61ttributes_attacked\x18\x13 \x03(\x05\x12\x13\n\x0bskills_used\x18\x14 \x01(\x05\x62\x06proto3'
)
_MONSTERBEHAVIORWITHOVERRIDES_STATUS = _descriptor.EnumDescriptor(
name='Status',
full_name='dadguide_proto.MonsterBehaviorWithOverrides.Status',
filename=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
values=[
_descriptor.EnumValueDescriptor(
name='NOT_APPROVED', index=0, number=0,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='APPROVED_AS_IS', index=1, number=1,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='NEEDS_REAPPROVAL', index=2, number=2,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='APPROVED_WITH_CHANGES', index=3, number=3,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
],
containing_type=None,
serialized_options=None,
serialized_start=263,
serialized_end=358,
)
_sym_db.RegisterEnumDescriptor(_MONSTERBEHAVIORWITHOVERRIDES_STATUS)
_BEHAVIORGROUP_GROUPTYPE = _descriptor.EnumDescriptor(
name='GroupType',
full_name='dadguide_proto.BehaviorGroup.GroupType',
filename=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
values=[
_descriptor.EnumValueDescriptor(
name='UNSPECIFIED', index=0, number=0,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='PASSIVE', index=1, number=1,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='PREEMPT', index=2, number=2,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='DISPEL_PLAYER', index=3, number=3,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='MONSTER_STATUS', index=4, number=4,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='REMAINING', index=5, number=5,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='STANDARD', index=6, number=6,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='DEATH', index=7, number=7,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='UNKNOWN_USE', index=8, number=8,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='HIGHEST_PRIORITY', index=9, number=9,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
],
containing_type=None,
serialized_options=None,
serialized_start=717,
serialized_end=889,
)
_sym_db.RegisterEnumDescriptor(_BEHAVIORGROUP_GROUPTYPE)
_MONSTERBEHAVIORWITHOVERRIDES = _descriptor.Descriptor(
name='MonsterBehaviorWithOverrides',
full_name='dadguide_proto.MonsterBehaviorWithOverrides',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='monster_id', full_name='dadguide_proto.MonsterBehaviorWithOverrides.monster_id', index=0,
number=1, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='levels', full_name='dadguide_proto.MonsterBehaviorWithOverrides.levels', index=1,
number=2, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='level_overrides', full_name='dadguide_proto.MonsterBehaviorWithOverrides.level_overrides', index=2,
number=3, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='status', full_name='dadguide_proto.MonsterBehaviorWithOverrides.status', index=3,
number=4, 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, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
_MONSTERBEHAVIORWITHOVERRIDES_STATUS,
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=39,
serialized_end=358,
)
_MONSTERBEHAVIOR = _descriptor.Descriptor(
name='MonsterBehavior',
full_name='dadguide_proto.MonsterBehavior',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='monster_id', full_name='dadguide_proto.MonsterBehavior.monster_id', index=0,
number=1, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='levels', full_name='dadguide_proto.MonsterBehavior.levels', index=1,
number=2, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='approved', full_name='dadguide_proto.MonsterBehavior.approved', 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, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=360,
serialized_end=462,
)
_LEVELBEHAVIOR = _descriptor.Descriptor(
name='LevelBehavior',
full_name='dadguide_proto.LevelBehavior',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='level', full_name='dadguide_proto.LevelBehavior.level', index=0,
number=1, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='groups', full_name='dadguide_proto.LevelBehavior.groups', index=1,
number=2, 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, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=464,
serialized_end=541,
)
_BEHAVIORGROUP = _descriptor.Descriptor(
name='BehaviorGroup',
full_name='dadguide_proto.BehaviorGroup',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='group_type', full_name='dadguide_proto.BehaviorGroup.group_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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='condition', full_name='dadguide_proto.BehaviorGroup.condition', 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='children', full_name='dadguide_proto.BehaviorGroup.children', index=2,
number=3, 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, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
_BEHAVIORGROUP_GROUPTYPE,
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=544,
serialized_end=889,
)
_BEHAVIORITEM = _descriptor.Descriptor(
name='BehaviorItem',
full_name='dadguide_proto.BehaviorItem',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='group', full_name='dadguide_proto.BehaviorItem.group', index=0,
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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='behavior', full_name='dadguide_proto.BehaviorItem.behavior', index=1,
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, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
_descriptor.OneofDescriptor(
name='value', full_name='dadguide_proto.BehaviorItem.value',
index=0, containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[]),
],
serialized_start=891,
serialized_end=1008,
)
_BEHAVIOR = _descriptor.Descriptor(
name='Behavior',
full_name='dadguide_proto.Behavior',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='condition', full_name='dadguide_proto.Behavior.condition', 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='enemy_skill_id', full_name='dadguide_proto.Behavior.enemy_skill_id', 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='child_ids', full_name='dadguide_proto.Behavior.child_ids', index=2,
number=3, type=5, cpp_type=1, 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, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1010,
serialized_end=1109,
)
_CONDITION = _descriptor.Descriptor(
name='Condition',
full_name='dadguide_proto.Condition',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='hp_threshold', full_name='dadguide_proto.Condition.hp_threshold', index=0,
number=1, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='use_chance', full_name='dadguide_proto.Condition.use_chance', 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='repeats_every', full_name='dadguide_proto.Condition.repeats_every', 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='global_one_time', full_name='dadguide_proto.Condition.global_one_time', index=3,
number=4, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='limited_execution', full_name='dadguide_proto.Condition.limited_execution', index=4,
number=13, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='trigger_enemies_remaining', full_name='dadguide_proto.Condition.trigger_enemies_remaining', index=5,
number=5, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='if_defeated', full_name='dadguide_proto.Condition.if_defeated', index=6,
number=6, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='if_attributes_available', full_name='dadguide_proto.Condition.if_attributes_available', index=7,
number=7, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='trigger_monsters', full_name='dadguide_proto.Condition.trigger_monsters', index=8,
number=8, type=5, cpp_type=1, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='trigger_combos', full_name='dadguide_proto.Condition.trigger_combos', index=9,
number=9, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='if_nothing_matched', full_name='dadguide_proto.Condition.if_nothing_matched', index=10,
number=10, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='trigger_turn', full_name='dadguide_proto.Condition.trigger_turn', index=11,
number=11, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='trigger_turn_end', full_name='dadguide_proto.Condition.trigger_turn_end', index=12,
number=12, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='always_trigger_above', full_name='dadguide_proto.Condition.always_trigger_above', index=13,
number=14, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='always_after', full_name='dadguide_proto.Condition.always_after', index=14,
number=15, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='skill_set', full_name='dadguide_proto.Condition.skill_set', index=15,
number=16, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='erased_attributes', full_name='dadguide_proto.Condition.erased_attributes', index=16,
number=17, type=5, cpp_type=1, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='damage_done', full_name='dadguide_proto.Condition.damage_done', index=17,
number=18, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='attributes_attacked', full_name='dadguide_proto.Condition.attributes_attacked', index=18,
number=19, type=5, cpp_type=1, 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, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='skills_used', full_name='dadguide_proto.Condition.skills_used', index=19,
number=20, 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, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1112,
serialized_end=1624,
)
_MONSTERBEHAVIORWITHOVERRIDES.fields_by_name['levels'].message_type = _LEVELBEHAVIOR
_MONSTERBEHAVIORWITHOVERRIDES.fields_by_name['level_overrides'].message_type = _LEVELBEHAVIOR
_MONSTERBEHAVIORWITHOVERRIDES.fields_by_name['status'].enum_type = _MONSTERBEHAVIORWITHOVERRIDES_STATUS
_MONSTERBEHAVIORWITHOVERRIDES_STATUS.containing_type = _MONSTERBEHAVIORWITHOVERRIDES
_MONSTERBEHAVIOR.fields_by_name['levels'].message_type = _LEVELBEHAVIOR
_LEVELBEHAVIOR.fields_by_name['groups'].message_type = _BEHAVIORGROUP
_BEHAVIORGROUP.fields_by_name['group_type'].enum_type = _BEHAVIORGROUP_GROUPTYPE
_BEHAVIORGROUP.fields_by_name['condition'].message_type = _CONDITION
_BEHAVIORGROUP.fields_by_name['children'].message_type = _BEHAVIORITEM
_BEHAVIORGROUP_GROUPTYPE.containing_type = _BEHAVIORGROUP
_BEHAVIORITEM.fields_by_name['group'].message_type = _BEHAVIORGROUP
_BEHAVIORITEM.fields_by_name['behavior'].message_type = _BEHAVIOR
_BEHAVIORITEM.oneofs_by_name['value'].fields.append(
_BEHAVIORITEM.fields_by_name['group'])
_BEHAVIORITEM.fields_by_name['group'].containing_oneof = _BEHAVIORITEM.oneofs_by_name['value']
_BEHAVIORITEM.oneofs_by_name['value'].fields.append(
_BEHAVIORITEM.fields_by_name['behavior'])
_BEHAVIORITEM.fields_by_name['behavior'].containing_oneof = _BEHAVIORITEM.oneofs_by_name['value']
_BEHAVIOR.fields_by_name['condition'].message_type = _CONDITION
DESCRIPTOR.message_types_by_name['MonsterBehaviorWithOverrides'] = _MONSTERBEHAVIORWITHOVERRIDES
DESCRIPTOR.message_types_by_name['MonsterBehavior'] = _MONSTERBEHAVIOR
DESCRIPTOR.message_types_by_name['LevelBehavior'] = _LEVELBEHAVIOR
DESCRIPTOR.message_types_by_name['BehaviorGroup'] = _BEHAVIORGROUP
DESCRIPTOR.message_types_by_name['BehaviorItem'] = _BEHAVIORITEM
DESCRIPTOR.message_types_by_name['Behavior'] = _BEHAVIOR
DESCRIPTOR.message_types_by_name['Condition'] = _CONDITION
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
MonsterBehaviorWithOverrides = _reflection.GeneratedProtocolMessageType('MonsterBehaviorWithOverrides',
(_message.Message,), {
'DESCRIPTOR': _MONSTERBEHAVIORWITHOVERRIDES,
'__module__': 'enemy_skills_pb2'
# @@protoc_insertion_point(class_scope:dadguide_proto.MonsterBehaviorWithOverrides)
})
_sym_db.RegisterMessage(MonsterBehaviorWithOverrides)
MonsterBehavior = _reflection.GeneratedProtocolMessageType('MonsterBehavior', (_message.Message,), {
'DESCRIPTOR': _MONSTERBEHAVIOR,
'__module__': 'enemy_skills_pb2'
# @@protoc_insertion_point(class_scope:dadguide_proto.MonsterBehavior)
})
_sym_db.RegisterMessage(MonsterBehavior)
LevelBehavior = _reflection.GeneratedProtocolMessageType('LevelBehavior', (_message.Message,), {
'DESCRIPTOR': _LEVELBEHAVIOR,
'__module__': 'enemy_skills_pb2'
# @@protoc_insertion_point(class_scope:dadguide_proto.LevelBehavior)
})
_sym_db.RegisterMessage(LevelBehavior)
BehaviorGroup = _reflection.GeneratedProtocolMessageType('BehaviorGroup', (_message.Message,), {
'DESCRIPTOR': _BEHAVIORGROUP,
'__module__': 'enemy_skills_pb2'
# @@protoc_insertion_point(class_scope:dadguide_proto.BehaviorGroup)
})
_sym_db.RegisterMessage(BehaviorGroup)
BehaviorItem = _reflection.GeneratedProtocolMessageType('BehaviorItem', (_message.Message,), {
'DESCRIPTOR': _BEHAVIORITEM,
'__module__': 'enemy_skills_pb2'
# @@protoc_insertion_point(class_scope:dadguide_proto.BehaviorItem)
})
_sym_db.RegisterMessage(BehaviorItem)
Behavior = _reflection.GeneratedProtocolMessageType('Behavior', (_message.Message,), {
'DESCRIPTOR': _BEHAVIOR,
'__module__': 'enemy_skills_pb2'
# @@protoc_insertion_point(class_scope:dadguide_proto.Behavior)
})
_sym_db.RegisterMessage(Behavior)
Condition = _reflection.GeneratedProtocolMessageType('Condition', (_message.Message,), {
'DESCRIPTOR': _CONDITION,
'__module__': 'enemy_skills_pb2'
# @@protoc_insertion_point(class_scope:dadguide_proto.Condition)
})
_sym_db.RegisterMessage(Condition)
# @@protoc_insertion_point(module_scope)
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] | 2.149254 | 15,142 |
#!/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 typing import Any, Dict
import torch.nn as nn
from classy_vision.generic.util import is_pos_int
from classy_vision.heads import ClassyHead, register_head
| [
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62,
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628
] | 3.54902 | 102 |
from floodsystem.stationdata import build_station_list
from floodsystem.flood import stations_highest_rel_level
if __name__ == "__main__":
print("*** Task 2C: CUED Part IA Flood Warning System ***")
run() | [
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] | 3.257576 | 66 |
# This source code is part of the Biotite package and is distributed
# under the 3-Clause BSD License. Please see 'LICENSE.rst' for further
# information.
__name__ = "biotite"
__author__ = "Patrick Kunzmann"
__all__ = ["Copyable"]
import abc
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] | 3.05 | 80 |
import logging
import time
from datetime import timedelta
from typing import List
from homeassistant.components.binary_sensor import (
BinarySensorEntity,
DEVICE_CLASS_MOTION
)
from homeassistant.config_entries import ConfigEntry
from homeassistant.const import ATTR_ATTRIBUTION
from homeassistant.core import HomeAssistant
from wyzeapy.base_client import Device, AccessTokenError
from wyzeapy.client import Client
from wyzeapy.types import PropertyIDs
from .const import DOMAIN
_LOGGER = logging.getLogger(__name__)
ATTRIBUTION = "Data provided by Wyze"
SCAN_INTERVAL = timedelta(seconds=10)
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] | 3.304348 | 184 |
"""
Reads climate modeling grid 0.05 degree MCD43 BRDF files.
"""
import os
import sys
from numpy import loadtxt, array, tile, where, concatenate, flipud
from numpy import ones
from datetime import date, datetime, timedelta
from glob import glob
from pyhdf.SD import SD, HDF4Error
MISSING = 32.767
SDS = dict (
LAND = ('BRDF_Albedo_Parameter1_Band1','BRDF_Albedo_Parameter1_Band2',
'BRDF_Albedo_Parameter1_Band3','BRDF_Albedo_Parameter1_Band4',
'BRDF_Albedo_Parameter1_Band5','BRDF_Albedo_Parameter1_Band6',
'BRDF_Albedo_Parameter1_Band7',
'BRDF_Albedo_Parameter2_Band1','BRDF_Albedo_Parameter2_Band2',
'BRDF_Albedo_Parameter2_Band3','BRDF_Albedo_Parameter2_Band4',
'BRDF_Albedo_Parameter2_Band5','BRDF_Albedo_Parameter2_Band6',
'BRDF_Albedo_Parameter2_Band7',
'BRDF_Albedo_Parameter3_Band1','BRDF_Albedo_Parameter3_Band2',
'BRDF_Albedo_Parameter3_Band3','BRDF_Albedo_Parameter3_Band4',
'BRDF_Albedo_Parameter3_Band5','BRDF_Albedo_Parameter3_Band6',
'BRDF_Albedo_Parameter3_Band7'),
QUAL = ('BRDF_Albedo_Quality',
'Snow_BRDF_Albedo',
'BRDF_Albedo_Ancillary', )
)
ALIAS = dict ( BRDF_Albedo_Parameter1_Band1 = 'KISO_b1_645',
BRDF_Albedo_Parameter1_Band2 = 'KISO_b2_856',
BRDF_Albedo_Parameter1_Band3 = 'KISO_b3_465',
BRDF_Albedo_Parameter1_Band4 = 'KISO_b4_553',
BRDF_Albedo_Parameter1_Band5 = 'KISO_b5_1241',
BRDF_Albedo_Parameter1_Band6 = 'KISO_b6_1629',
BRDF_Albedo_Parameter1_Band7 = 'KISO_b7_2114',
BRDF_Albedo_Parameter2_Band1 = 'KVOL_b1_645',
BRDF_Albedo_Parameter2_Band2 = 'KVOL_b2_856',
BRDF_Albedo_Parameter2_Band3 = 'KVOL_b3_465',
BRDF_Albedo_Parameter2_Band4 = 'KVOL_b4_553',
BRDF_Albedo_Parameter2_Band5 = 'KVOL_b5_1241',
BRDF_Albedo_Parameter2_Band6 = 'KVOL_b6_1629',
BRDF_Albedo_Parameter2_Band7 = 'KVOL_b7_2114',
BRDF_Albedo_Parameter3_Band1 = 'KGEO_b1_645',
BRDF_Albedo_Parameter3_Band2 = 'KGEO_b2_856',
BRDF_Albedo_Parameter3_Band3 = 'KGEO_b3_465',
BRDF_Albedo_Parameter3_Band4 = 'KGEO_b4_553',
BRDF_Albedo_Parameter3_Band5 = 'KGEO_b5_1241',
BRDF_Albedo_Parameter3_Band6 = 'KGEO_b6_1629',
BRDF_Albedo_Parameter3_Band7 = 'KGEO_b7_2114',
)
#...........................................................................
#---
#............................................................................
if __name__ == "__main__":
path = '/nobackup/3/pcastell/MODIS/MCD43C1/MCD43C1.A2005361.005.2008094071946.hdf'
lon = [-2.,-120.,15.2,17.2,170.1]
lat = [88.,40.,-20.,-20.,-55.5]
lon = np.arange(-180,180,1)
lat = np.arange(-90,90,1)
lon,lat = np.meshgrid(lon,lat)
ex = McD43C(path,lon.flatten(),lat.flatte())
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] | 1.8625 | 1,680 |
import pytest
import numpy as np
import copy
from numpy.testing import assert_allclose
from keras.utils import CustomObjectScope
from keras.layers import wrappers, Input, Layer
from keras.layers import RNN
from keras import layers
from keras.models import Sequential, Model, model_from_json
from keras import backend as K
from keras.utils.generic_utils import object_list_uid, to_list
def test_regularizers():
model = Sequential()
model.add(wrappers.TimeDistributed(
layers.Dense(2, kernel_regularizer='l1'), input_shape=(3, 4)))
model.add(layers.Activation('relu'))
model.compile(optimizer='rmsprop', loss='mse')
assert len(model.layers[0].layer.losses) == 1
assert len(model.layers[0].losses) == 1
assert len(model.layers[0].get_losses_for(None)) == 1
assert len(model.losses) == 1
model = Sequential()
model.add(wrappers.TimeDistributed(
layers.Dense(2, activity_regularizer='l1'), input_shape=(3, 4)))
model.add(layers.Activation('relu'))
model.compile(optimizer='rmsprop', loss='mse')
assert len(model.losses) == 1
def test_Bidirectional():
rnn = layers.SimpleRNN
samples = 2
dim = 2
timesteps = 2
output_dim = 2
dropout_rate = 0.2
for mode in ['sum', 'concat']:
x = np.random.random((samples, timesteps, dim))
target_dim = 2 * output_dim if mode == 'concat' else output_dim
y = np.random.random((samples, target_dim))
# test with Sequential model
model = Sequential()
model.add(wrappers.Bidirectional(rnn(output_dim, dropout=dropout_rate,
recurrent_dropout=dropout_rate),
merge_mode=mode,
input_shape=(timesteps, dim)))
model.compile(loss='mse', optimizer='sgd')
model.fit(x, y, epochs=1, batch_size=1)
# test config
model.get_config()
model = model_from_json(model.to_json())
model.summary()
# test stacked bidirectional layers
model = Sequential()
model.add(wrappers.Bidirectional(rnn(output_dim,
return_sequences=True),
merge_mode=mode,
input_shape=(timesteps, dim)))
model.add(wrappers.Bidirectional(rnn(output_dim), merge_mode=mode))
model.compile(loss='mse', optimizer='sgd')
model.fit(x, y, epochs=1, batch_size=1)
# test with functional API
inputs = Input((timesteps, dim))
outputs = wrappers.Bidirectional(rnn(output_dim, dropout=dropout_rate,
recurrent_dropout=dropout_rate),
merge_mode=mode)(inputs)
model = Model(inputs, outputs)
model.compile(loss='mse', optimizer='sgd')
model.fit(x, y, epochs=1, batch_size=1)
# Bidirectional and stateful
inputs = Input(batch_shape=(1, timesteps, dim))
outputs = wrappers.Bidirectional(rnn(output_dim, stateful=True),
merge_mode=mode)(inputs)
model = Model(inputs, outputs)
model.compile(loss='mse', optimizer='sgd')
model.fit(x, y, epochs=1, batch_size=1)
def test_Bidirectional_state_reuse():
rnn = layers.LSTM
samples = 2
dim = 5
timesteps = 3
units = 3
input1 = Input((timesteps, dim))
layer = wrappers.Bidirectional(rnn(units, return_state=True,
return_sequences=True))
state = layer(input1)[1:]
# test passing invalid initial_state: passing a tensor
input2 = Input((timesteps, dim))
with pytest.raises(ValueError):
output = wrappers.Bidirectional(rnn(units))(input2, initial_state=state[0])
# test valid usage: passing a list
output = wrappers.Bidirectional(rnn(units))(input2, initial_state=state)
model = Model([input1, input2], output)
assert len(model.layers) == 4
assert isinstance(model.layers[-1].input, list)
inputs = [np.random.rand(samples, timesteps, dim),
np.random.rand(samples, timesteps, dim)]
outputs = model.predict(inputs)
def test_Bidirectional_trainable():
# test layers that need learning_phase to be set
x = Input(shape=(3, 2))
layer = wrappers.Bidirectional(layers.SimpleRNN(3))
_ = layer(x)
assert len(layer.trainable_weights) == 6
layer.trainable = False
assert len(layer.trainable_weights) == 0
layer.trainable = True
assert len(layer.trainable_weights) == 6
def test_Bidirectional_updates():
x = Input(shape=(3, 2))
layer = wrappers.Bidirectional(layers.SimpleRNN(3))
assert len(layer.updates) == 0
assert len(layer.get_updates_for(None)) == 0
assert len(layer.get_updates_for(x)) == 0
layer.forward_layer.add_update(0, inputs=x)
layer.forward_layer.add_update(1, inputs=None)
layer.backward_layer.add_update(0, inputs=x)
layer.backward_layer.add_update(1, inputs=None)
assert len(layer.updates) == 4
assert len(layer.get_updates_for(None)) == 2
assert len(layer.get_updates_for(x)) == 2
def test_Bidirectional_losses():
x = Input(shape=(3, 2))
layer = wrappers.Bidirectional(
layers.SimpleRNN(3, kernel_regularizer='l1', bias_regularizer='l1'))
_ = layer(x)
assert len(layer.losses) == 4
assert len(layer.get_losses_for(None)) == 4
assert len(layer.get_losses_for(x)) == 0
layer.forward_layer.add_loss(0, inputs=x)
layer.forward_layer.add_loss(1, inputs=None)
layer.backward_layer.add_loss(0, inputs=x)
layer.backward_layer.add_loss(1, inputs=None)
assert len(layer.losses) == 8
assert len(layer.get_losses_for(None)) == 6
assert len(layer.get_losses_for(x)) == 2
if __name__ == '__main__':
pytest.main([__file__])
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] | 2.220352 | 2,673 |
"""Lowest-common-denominator implementations of platform functionality."""
from __future__ import absolute_import, division, print_function, with_statement
import errno
import socket
from tornado.platform import interface
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"""
This script will modulate the blinky lights using the following algorithm:
1) uses user-provided location to obtain row of pixel data from bathy image
2) samples a 'number of LEDs' number of pixels from that row
3) shifts the sampled row data to center it at the location specified by user
4) displays resulting pixels on Blinky Tape
5) shifts next row by a given latitude, also specified by user
6) sleeps for user-specified period of time
Uses the following arguments:
-l/--location: tuple
Location of the user in tuple(lat, lon). This represents the center of the LED strip. Defaults to (0, 0)
-u/--update-interval: int
Update interval of the script, in minutes. Defaults to 10.
-p/--port: str
Serial port of the BlinkyLight (e.g., 'ttyAMA0', 'COM3'). Defaults to 'COM5'.
-d/--delta_latitude: int
Vertical change in latitude every update rate. May be 0, but this will result in a never-changing LEDs.
-i/--image: str
Name of the PNG image that contains the color coded pathymetric data.
The file current named mapserv.png was obtained using the following API:
https://www.gebco.net/data_and_products/gebco_web_services/web_map_service/mapserv?request=getmap&service=wms&BBOX=-90,-180,90,180&format=image/png&height=600&width=1200&crs=EPSG:4326&layers=GEBCO_LATEST_SUB_ICE_TOPO&version=1.3.0
In lieu of providing command line arguments, you may alternatively edit the defaults in bath_config.json.
NOTE: runs via:
runfile('/BlinkyTape_Python/bathymetry_blink/bathymetry_blink.py', wdir='/BlinkyTape_Python/')
(C) 2021 Joseph Post (https://joeycodes.dev)
MIT Licensed
"""
import optparse
import json
from blinkytape import BlinkyTape
from time import sleep
from PIL import Image
import numpy as np
import sys
MAX_ERRORS = 3
num_errors = 0
# Obtain default parameters
with open("./bathymetry_blink/bathy_config.json") as f:
config = json.load(f)
# Default Blinky Tape port on Raspberry Pi is /dev/ttyACM0
parser = optparse.OptionParser()
parser.add_option("-p", "--port", dest="portname",
help="serial port (ex: /dev/ttyACM0)", default=config["port"])
parser.add_option("-l", "--location", dest="location",
help="Location of the center of the LED strip (ex: 70,-110)", default=config["location"])
parser.add_option("-u", "--update-rate", dest="update_rate",
help="How often to update elevation profile (mins) (ex: 5)", default=config["update_rate"])
parser.add_option("-d", "--delta-latitude", dest="delta_latitude",
help="Change in latitude during update (ex: 5)", default=config["delta_latitude"])
parser.add_option("-n", "--num-leds", dest="num_leds",
help="Number of LEDs in strip (ex: 60)", default=config["num_leds"])
parser.add_option("-i", "--image", dest="image_name",
help="Name of the map/bathymetry image (ex: ./mapserv.png)", default=config["image"])
(options, args) = parser.parse_args()
if args:
print("Unknown parameters: " + args)
# grab the values provided by user (or defaults)
port = options.portname
loc = options.location
rate = options.update_rate
delta = options.delta_latitude
n_leds = options.num_leds
i_name = options.image_name
# Some visual indication that it works, for headless setups (green tape)
bt = BlinkyTape(port, n_leds)
bt.displayColor(0, 100, 0)
bt.show()
sleep(2)
while True:
try:
# first, load image
im = Image.open(i_name) # Can be many different formats.
cols, rows = im.size
a = np.asarray(im) # of shape (rows, cols, channels)
# map loc latitude to 0-based index
latitude_index = min(rows - 1, max(0, (int)(((loc[0] - -90) / (90 - -90)) * (rows - 0) + 0)))
longitude_index = min(cols - 1, max(0, (int)(((loc[1] - -180) / (180 - -180)) * (cols - 0) + 0)))
# update the location of the next row of elevation data to take
loc[0] += delta
loc[0] = ((loc[0] + 90) % 180) - 90 # wraps to next pole if overflow
print("Lat index: " + str(latitude_index))
print("Lon index: " + str(longitude_index))
print("Next latitude: " + str(loc[0]))
# grab the applicable pixel indices
indices = [(int)(x*(cols/n_leds)) for x in range(n_leds)]
# sample that row of pixel data
output_pixels = np.take(a[latitude_index], indices, axis=0)
# rotate the row to center around the specified longitude
output_pixels = np.roll(output_pixels, longitude_index, axis=0)
# send all pixel data to bt
for pixel in output_pixels:
print("Sending r: {}, g: {}, b: {}".format(*pixel))
bt.sendPixel(*pixel)
# finally, show the image
bt.show()
# delete variables for memory management
del a
del im
# Tape resets to stored pattern after a few seconds of inactivity
sleep(rate * 60) # Wait specified number of minutes
# sleep(10) # Wait specified number of minutes
except KeyboardInterrupt:
print("Keyboard interrupt, ending program.")
sys.exit()
except RuntimeError as e:
print("Encountered runtime error: " + e.args[0])
# flush any incomplete data
bt.show()
num_errors += 1
if num_errors > MAX_ERRORS:
sys.exit("Error count exceeds that allowed.")
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7464,
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597,
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220,
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611,
997,
62,
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62,
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25,
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220,
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220,
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25064,
13,
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12331,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220
] | 2.548747 | 2,154 |
""" Export Submissions Transform module """
#pylint: disable=too-few-public-methods
import pandas as pd
from .transform import TransformBase
from ..resources.field_configs import FieldConfigs
from ..resources.field_maps import FieldMaps
| [
37811,
36472,
3834,
8481,
26981,
8265,
37227,
198,
2,
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25,
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28,
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62,
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198
] | 3.822581 | 62 |
import pytest
import torch
import ray
from ray.ml.predictors.integrations.torch import TorchPredictor
from ray.ml.train.integrations.torch import TorchTrainer
from ray import train
from ray.ml.examples.pytorch.torch_linear_example import train_func as linear_train_func
def test_torch_e2e(ray_start_4_cpus):
scaling_config = {"num_workers": 2}
trainer = TorchTrainer(
train_loop_per_worker=train_func, scaling_config=scaling_config
)
result = trainer.fit()
predict_dataset = ray.data.range(3)
predictions = predict_dataset.map_batches(
TorchScorer, batch_format="pandas", compute="actors"
)
assert predictions.count() == 3
def test_torch_e2e_state_dict(ray_start_4_cpus):
scaling_config = {"num_workers": 2}
trainer = TorchTrainer(
train_loop_per_worker=train_func, scaling_config=scaling_config
)
result = trainer.fit()
# If loading from a state dict, a model definition must be passed in.
with pytest.raises(ValueError):
TorchPredictor.from_checkpoint(result.checkpoint)
predict_dataset = ray.data.range(3)
predictions = predict_dataset.map_batches(
TorchScorer, batch_format="pandas", compute="actors"
)
assert predictions.count() == 3
if __name__ == "__main__":
import pytest
import sys
sys.exit(pytest.main(["-v", "-x", __file__]))
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1600,
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60,
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198
] | 2.627863 | 524 |
# MIT LICENSE
#
# Copyright 1997 - 2020 by IXIA Keysight
#
# 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 uhd_restpy.base import Base
from uhd_restpy.files import Files
from typing import List, Any, Union
def Apply(self, *args, **kwargs):
# type: (*Any, **Any) -> None
"""Executes the apply operation on the server.
Applies the specified Quick Test.
apply(async_operation=bool)
---------------------------
- async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.
Raises
------
- NotFoundError: The requested resource does not exist on the server
- ServerError: The server has encountered an uncategorized error condition
"""
payload = { "Arg1": self.href }
for i in range(len(args)): payload['Arg%s' % (i + 2)] = args[i]
for item in kwargs.items(): payload[item[0]] = item[1]
return self._execute('apply', payload=payload, response_object=None)
def ApplyAsync(self, *args, **kwargs):
# type: (*Any, **Any) -> None
"""Executes the applyAsync operation on the server.
applyAsync(async_operation=bool)
--------------------------------
- async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.
Raises
------
- NotFoundError: The requested resource does not exist on the server
- ServerError: The server has encountered an uncategorized error condition
"""
payload = { "Arg1": self.href }
for i in range(len(args)): payload['Arg%s' % (i + 2)] = args[i]
for item in kwargs.items(): payload[item[0]] = item[1]
return self._execute('applyAsync', payload=payload, response_object=None)
def ApplyAsyncResult(self, *args, **kwargs):
# type: (*Any, **Any) -> Union[bool, None]
"""Executes the applyAsyncResult operation on the server.
applyAsyncResult(async_operation=bool)bool
------------------------------------------
- async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.
- Returns bool:
Raises
------
- NotFoundError: The requested resource does not exist on the server
- ServerError: The server has encountered an uncategorized error condition
"""
payload = { "Arg1": self.href }
for i in range(len(args)): payload['Arg%s' % (i + 2)] = args[i]
for item in kwargs.items(): payload[item[0]] = item[1]
return self._execute('applyAsyncResult', payload=payload, response_object=None)
def ApplyITWizardConfiguration(self, *args, **kwargs):
# type: (*Any, **Any) -> None
"""Executes the applyITWizardConfiguration operation on the server.
Applies the specified Quick Test.
applyITWizardConfiguration(async_operation=bool)
------------------------------------------------
- async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.
Raises
------
- NotFoundError: The requested resource does not exist on the server
- ServerError: The server has encountered an uncategorized error condition
"""
payload = { "Arg1": self.href }
for i in range(len(args)): payload['Arg%s' % (i + 2)] = args[i]
for item in kwargs.items(): payload[item[0]] = item[1]
return self._execute('applyITWizardConfiguration', payload=payload, response_object=None)
def GenerateReport(self, *args, **kwargs):
# type: (*Any, **Any) -> Union[str, None]
"""Executes the generateReport operation on the server.
Generate a PDF report for the last succesfull test run.
generateReport(async_operation=bool)string
------------------------------------------
- async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.
- Returns str: This method is asynchronous and has no return value.
Raises
------
- NotFoundError: The requested resource does not exist on the server
- ServerError: The server has encountered an uncategorized error condition
"""
payload = { "Arg1": self.href }
for i in range(len(args)): payload['Arg%s' % (i + 2)] = args[i]
for item in kwargs.items(): payload[item[0]] = item[1]
return self._execute('generateReport', payload=payload, response_object=None)
def Run(self, *args, **kwargs):
# type: (*Any, **Any) -> Union[List[str], None]
"""Executes the run operation on the server.
Starts the specified Quick Test and waits for its execution to finish.
The IxNetwork model allows for multiple method Signatures with the same name while python does not.
run(async_operation=bool)list
-----------------------------
- async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.
- Returns list(str): This method is synchronous and returns the result of the test.
run(InputParameters=string, async_operation=bool)list
-----------------------------------------------------
- InputParameters (str): The input arguments of the test.
- async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.
- Returns list(str): This method is synchronous and returns the result of the test.
Raises
------
- NotFoundError: The requested resource does not exist on the server
- ServerError: The server has encountered an uncategorized error condition
"""
payload = { "Arg1": self.href }
for i in range(len(args)): payload['Arg%s' % (i + 2)] = args[i]
for item in kwargs.items(): payload[item[0]] = item[1]
return self._execute('run', payload=payload, response_object=None)
def Start(self, *args, **kwargs):
# type: (*Any, **Any) -> None
"""Executes the start operation on the server.
Starts the specified Quick Test.
The IxNetwork model allows for multiple method Signatures with the same name while python does not.
start(async_operation=bool)
---------------------------
- async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.
start(InputParameters=string, async_operation=bool)
---------------------------------------------------
- InputParameters (str): The input arguments of the test.
- async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.
Raises
------
- NotFoundError: The requested resource does not exist on the server
- ServerError: The server has encountered an uncategorized error condition
"""
payload = { "Arg1": self.href }
for i in range(len(args)): payload['Arg%s' % (i + 2)] = args[i]
for item in kwargs.items(): payload[item[0]] = item[1]
return self._execute('start', payload=payload, response_object=None)
def Stop(self, *args, **kwargs):
# type: (*Any, **Any) -> None
"""Executes the stop operation on the server.
Stops the currently running Quick Test.
stop(async_operation=bool)
--------------------------
- async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.
Raises
------
- NotFoundError: The requested resource does not exist on the server
- ServerError: The server has encountered an uncategorized error condition
"""
payload = { "Arg1": self.href }
for i in range(len(args)): payload['Arg%s' % (i + 2)] = args[i]
for item in kwargs.items(): payload[item[0]] = item[1]
return self._execute('stop', payload=payload, response_object=None)
def WaitForTest(self, *args, **kwargs):
# type: (*Any, **Any) -> Union[List[str], None]
"""Executes the waitForTest operation on the server.
Waits for the execution of the specified Quick Test to be completed.
waitForTest(async_operation=bool)list
-------------------------------------
- async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.
- Returns list(str): This method is synchronous and returns the result of the test.
Raises
------
- NotFoundError: The requested resource does not exist on the server
- ServerError: The server has encountered an uncategorized error condition
"""
payload = { "Arg1": self.href }
for i in range(len(args)): payload['Arg%s' % (i + 2)] = args[i]
for item in kwargs.items(): payload[item[0]] = item[1]
return self._execute('waitForTest', payload=payload, response_object=None)
| [
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28,
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2220,
11,
2882,
62,
15252,
28,
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8,
198
] | 3.038642 | 3,623 |
from rest_framework import serializers
from core import models
| [
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1334,
62,
30604,
1330,
11389,
11341,
198,
198,
6738,
4755,
1330,
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] | 4.642857 | 14 |
input = """
c(2).
p(1).
a(2).
d(2,2,1).
okay(X):- c(X), #count{V:a(V),d(V,X,1)} = 1.
ouch(X):- p(X), #count{V:a(V),d(V,X,1)} = 1.
"""
output = """
{a(2), c(2), d(2,2,1), okay(2), p(1)}
"""
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8788,
7,
17,
828,
279,
7,
16,
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198,
37811,
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] | 1.532258 | 124 |
from nonebot import CommandSession, on_command
from langdetect import detect, detect_langs
from aiohttp import ClientSession
from nonebot import get_bot
from nonebot.argparse import ArgumentParser
import time
import hmac
import random, sys
import hashlib
import binascii
import urllib
bot = get_bot()
# API,ttstranslate_api@baidu.com
# coding=utf-8
import hashlib
import urllib
import random
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2956,
297,
571,
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628,
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] | 3.272727 | 121 |
"""
LpSolve wrapper.
Copyright (C) 2018, Guillaume Gonnet
License MIT
"""
from ctypes import *
import sys
import os.path as path
import platform
# Import the DLL
ver = ("x86", "x64")[sys.maxsize > 2**32]
here = path.dirname(__file__)
if sys.platform == "win32":
lib = windll.LoadLibrary(path.abspath(path.join(here, "dll/lpsolve55-%s.dll" % ver)))
elif sys.platform == "linux":
lib = cdll.LoadLibrary(path.abspath(path.join(here, "dll/lpsolve55-%s.so" % ver)))
else:
raise ValueError("Can't load LpSolve library on this platform.")
# Make the bindings
c_double_p = POINTER(c_double)
c_int_p = POINTER(c_int)
lib.make_lp.argtypes = [c_int, c_int]
lib.make_lp.restype = c_void_p
lib.delete_lp.argtypes = [c_void_p]
lib.set_binary.argtypes = [c_void_p, c_int, c_ubyte]
lib.set_binary.restype = c_ubyte
lib.set_int.argtypes = [c_void_p, c_int, c_ubyte]
lib.set_int.restype = c_ubyte
lib.add_constraintex.argtypes = [c_void_p, c_int, c_double_p, c_int_p, c_int, c_double]
lib.add_constraintex.restype = c_ubyte
lib.set_obj_fnex.argtypes = [c_void_p, c_int, c_double_p, c_int_p]
lib.set_obj_fnex.restype = c_ubyte
lib.set_add_rowmode.argtypes = [c_void_p, c_ubyte]
lib.set_add_rowmode.restype = c_ubyte
lib.set_maxim.argtypes = [c_void_p]
lib.write_lp.argtypes = [c_void_p, c_char_p]
lib.write_lp.restype = c_ubyte
lib.set_verbose.argtypes = [c_void_p, c_int]
lib.solve.argtypes = [c_void_p]
lib.solve.restype = c_int
lib.get_variables.argtypes = [c_void_p, c_double_p]
lib.get_variables.restype = c_ubyte
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] | 2.18892 | 704 |
# Copyright 2015 Hewlett-Packard Development Company, L.P.
#
# 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.
#
import random
from cryptography import fernet
import mock
from oslo_db import exception as odb_exceptions
from oslo_utils import uuidutils
from sqlalchemy.orm import exc
from taskflow.types import failure
from octavia.common import constants
from octavia.common import data_models
from octavia.common import utils
from octavia.controller.worker.v2.tasks import database_tasks
from octavia.db import repositories as repo
import octavia.tests.unit.base as base
AMP_ID = uuidutils.generate_uuid()
COMPUTE_ID = uuidutils.generate_uuid()
LB_ID = uuidutils.generate_uuid()
SERVER_GROUP_ID = uuidutils.generate_uuid()
LB_NET_IP = '192.0.2.2'
LISTENER_ID = uuidutils.generate_uuid()
POOL_ID = uuidutils.generate_uuid()
HM_ID = uuidutils.generate_uuid()
MEMBER_ID = uuidutils.generate_uuid()
PORT_ID = uuidutils.generate_uuid()
SUBNET_ID = uuidutils.generate_uuid()
VRRP_PORT_ID = uuidutils.generate_uuid()
HA_PORT_ID = uuidutils.generate_uuid()
L7POLICY_ID = uuidutils.generate_uuid()
L7RULE_ID = uuidutils.generate_uuid()
VIP_IP = '192.0.5.2'
VRRP_IP = '192.0.5.3'
HA_IP = '192.0.5.4'
AMP_ROLE = 'FAKE_ROLE'
VRRP_ID = random.randrange(255)
VRRP_PRIORITY = random.randrange(100)
CACHED_ZONE = 'zone1'
IMAGE_ID = uuidutils.generate_uuid()
COMPUTE_FLAVOR = uuidutils.generate_uuid()
_amphora_mock = mock.MagicMock()
_amphora_mock.id = AMP_ID
_amphora_mock.compute_id = COMPUTE_ID
_amphora_mock.lb_network_ip = LB_NET_IP
_amphora_mock.vrrp_ip = VRRP_IP
_amphora_mock.ha_ip = HA_IP
_amphora_mock.ha_port_id = HA_PORT_ID
_amphora_mock.vrrp_port_id = VRRP_PORT_ID
_amphora_mock.role = AMP_ROLE
_amphora_mock.vrrp_id = VRRP_ID
_amphora_mock.vrrp_priority = VRRP_PRIORITY
_amphorae = [_amphora_mock]
_loadbalancer_mock = mock.MagicMock()
_loadbalancer_mock.id = LB_ID
_loadbalancer_mock.amphorae = [_amphora_mock]
_l7policy_mock = mock.MagicMock()
_l7policy_mock.id = L7POLICY_ID
_l7rule_mock = mock.MagicMock()
_l7rule_mock.id = L7RULE_ID
_listener_mock = mock.MagicMock()
_listener_to_dict_mock = mock.MagicMock(
return_value={'id': LISTENER_ID})
_listener_mock.id = LISTENER_ID
_listener_mock.to_dict = _listener_to_dict_mock
_tf_failure_mock = mock.Mock(spec=failure.Failure)
_vip_mock = mock.MagicMock()
_vip_mock.port_id = PORT_ID
_vip_mock.subnet_id = SUBNET_ID
_vip_mock.ip_address = VIP_IP
_vrrp_group_mock = mock.MagicMock()
_cert_mock = mock.MagicMock()
_compute_mock = mock.MagicMock()
_compute_mock.lb_network_ip = LB_NET_IP
_compute_mock.cached_zone = CACHED_ZONE
_compute_mock.image_id = IMAGE_ID
_compute_mock.compute_flavor = COMPUTE_FLAVOR
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62,
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5570,
796,
24301,
37780,
62,
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10116,
1581,
628
] | 2.481132 | 1,272 |
import time
from bs4 import BeautifulSoup
import requests
import pymysql.cursors
import unittest
if __name__ == '__main__':
unittest.main()
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# pylint: disable=protected-access
import os
import re
import pytest
from dagster import file_relative_path
from dagster.core.errors import DagsterInstanceMigrationRequired
from dagster.core.instance import DagsterInstance, InstanceRef
from dagster.utils.test import restore_directory
# test that we can load runs and events from an old instance
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import matplotlib.pyplot as plt
import pandas as pd
if __name__ == '__main__':
df = pd.read_csv('../data/crimes_dataset_processed_incomplete.csv')
group_by_territory(df)
group_by_year(df)
group_by_month(df)
group_by_time_of_day(df)
group_by_day_of_the_week(df)
group_by_category(df)
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# Unit tests
import unittest
if __name__ == '__main__':
unittest.main()
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import pandas as pd
import numpy as np
import jdatetime
pd.options.mode.chained_assignment = None
# Read Bourseview data for market cap
# Concat all 75 tickers' data
me_list = []
for file_number in range(1, 76):
print(file_number)
me_path = f'E:/Thesis/New Sampling/Daily Data - Bourseview/'\
f'{file_number}.xlsx'
me_df = pd.read_excel(
me_path,
skiprows=7,
usecols=[2, 3, 11],
names=['date', 'open', 'market_cap'],
na_values='-'
)
# Change order from old to new dates
me_df = me_df[::-1].reset_index(drop=True)
me_df['date'] = me_df['date'].str.replace('-', '')
# Delete non-traded days
me_df.dropna(subset=['open'], inplace=True)
me_df.drop(columns='open', inplace=True)
# Create monthly dataframe
me_df = me_df.groupby(me_df['date'].str[:6]).last()
me_df = me_df.drop(columns=['date']).reset_index()
me_df.insert(1, 'ticker_num', file_number)
me_list.append(me_df)
me_df = pd.concat(me_list, ignore_index=True)
me_df = me_df.loc[(me_df['date'] >= '139212') & (me_df['date'] <= '139900')]
me_df.reset_index(drop=True, inplace=True)
# Read rahavard 365 data for calculating returns
close_list = []
for file_number in range(1, 76):
rahavard_path = f'E:/Thesis/New Sampling/Daily Data - Rahavard 365/'\
f'{file_number}.txt'
df = pd.read_csv(
rahavard_path,
usecols=[2, 7],
names=['date', 'close'],
header=0,
dtype={'date': str},
parse_dates=[0]
)
# Solve index reading problem, pandas add 2 index to the df
df.reset_index(drop=True, inplace=True)
# Convert to shamsi dates
df['date'] = df['date'].apply(
lambda x: jdatetime.date.fromgregorian(date=x).strftime('%Y%m%d')
)
# Create monthly dataframe
df = df.groupby(df['date'].str[:6]).last()
df = df.drop(columns=['date']).reset_index()
df.insert(1, 'ticker_num', file_number)
df['monthly_return'] = df['close'].pct_change()
close_list.append(df)
df = pd.concat(close_list, ignore_index=True)
df = df.loc[(df['date'] >= '139212') & (df['date'] <= '139900')]
# Read index df for indicating open market days
index_path = r'E:\Thesis\New Sampling\TEDPIX\ 6.xls'
index_df = pd.read_excel(
index_path,
usecols=[1],
names=['date'],
dtype={'date': str}
)
index_df.dropna(inplace=True)
# The list of all months
months = index_df['date'].str[:6].unique().tolist()
# The list of months that we need for calculating market cap
me_months = [
'139312', '139401', '139402', '139403', '139404', '139405', '139406',
'139407', '139408', '139409', '139410', '139411', '139412', '139501',
'139502', '139503', '139504', '139505', '139506', '139507', '139508',
'139509', '139510', '139511', '139512', '139601', '139602', '139603',
'139604', '139605', '139606', '139607', '139608', '139609', '139610',
'139611', '139612', '139701', '139702', '139703', '139704', '139705',
'139706', '139707', '139708', '139709', '139710', '139711', '139712',
'139801', '139802', '139803', '139804', '139805', '139806', '139807',
'139808', '139809', '139810', '139811', '139812'
]
# The list of months that we need for camculating MOM
mom_months = me_months[1:]
# Merge market cap and price dfs
merged_df = pd.merge(df, me_df, on=['ticker_num', 'date'])
# First, create a NaN column, and then add t-13 prices
merged_df.insert(5, 't-13 price', np.nan)
for month in mom_months:
# Find t-13 prices
for ticker in range(1, 76):
t_13 = months[months.index(month) - 13]
t_13_condtion = (merged_df['date'] == t_13)
ticker_condition = (merged_df['ticker_num'] == ticker)
try:
t_13_price = merged_df.loc[
t_13_condtion
& ticker_condition
]['close'].values[0]
previous_month = me_months[me_months.index(month) - 1]
t_1_condtion = (merged_df['date'] == previous_month)
merged_df.loc[
(t_1_condtion & ticker_condition), 't-13 price'
] = t_13_price
except:
pass
# Calculate last 12 months return for month t (t-1, t-12)
merged_df['past_year_return'] = (
(merged_df['close'] / merged_df['t-13 price'])
- 1
)
mom_list = []
for month in mom_months:
# Check t-13 price condition and t-1 market cap condition
previous_month = months[months.index(month) - 1]
me_condition = (merged_df['date'] == previous_month)
mom_condition = (merged_df['past_year_return'].notna())
portfo_const_df = merged_df.loc[me_condition & mom_condition]
# Split each month ME into two groups
conditions = [
(
portfo_const_df['market_cap']
> portfo_const_df['market_cap'].median()
),
(
portfo_const_df['market_cap']
<= portfo_const_df['market_cap'].median()
)
]
portfolio_size = np.select(conditions, ['B', 'S']).tolist()
portfo_const_df.insert(6, 'size', portfolio_size)
# Split each me portfolio into 3 MOM group
q = [0, .3, .7, 1]
labels = ['L', 'M', 'H']
x_b = portfo_const_df.loc[
portfo_const_df['size'] == 'B'
]['past_year_return']
b_mom = pd.qcut(x=x_b, q=q, labels=labels).to_dict()
x_s = portfo_const_df.loc[
portfo_const_df['size'] == 'S'
]['past_year_return']
s_mom = pd.qcut(x=x_s, q=q, labels=labels).to_dict()
portfo_const_df['mom'] = pd.Series(b_mom)
portfo_const_df['mom'].update(pd.Series(s_mom))
# Extrect portfolio ticker numbers
portfo_const_df['portfolio'] = (
portfo_const_df['size'] + portfo_const_df['mom']
)
bh = portfo_const_df.loc[
portfo_const_df['portfolio'] == 'BH'
]['ticker_num'].tolist()
bl = portfo_const_df.loc[
portfo_const_df['portfolio'] == 'BL'
]['ticker_num'].tolist()
sh = portfo_const_df.loc[
portfo_const_df['portfolio'] == 'SH'
]['ticker_num'].tolist()
sl = portfo_const_df.loc[
portfo_const_df['portfolio'] == 'SL'
]['ticker_num'].tolist()
# Calculating value-weighted return for each portfolio in month t
# Set conditions
month_condition = (merged_df['date'] == month)
bh_condition = merged_df['ticker_num'].isin(bh)
bl_condition = merged_df['ticker_num'].isin(bl)
sh_condition = merged_df['ticker_num'].isin(sh)
sl_condition = merged_df['ticker_num'].isin(sl)
# Construct portfolios
bh_portfolio = merged_df.loc[month_condition & bh_condition]
bl_portfolio = merged_df.loc[month_condition & bl_condition]
sh_portfolio = merged_df.loc[month_condition & sh_condition]
sl_portfolio = merged_df.loc[month_condition & sl_condition]
# Calculate value-weighted returns
bh_return = np.average(
bh_portfolio.monthly_return,
weights=bh_portfolio.market_cap
)
bl_return = np.average(
bl_portfolio.monthly_return,
weights=bl_portfolio.market_cap
)
sh_return = np.average(
sh_portfolio.monthly_return,
weights=sh_portfolio.market_cap
)
sl_return = np.average(
sl_portfolio.monthly_return,
weights=sl_portfolio.market_cap
)
# Calculate MOM, and add it to a list
mom = (
((sh_return + bh_return) / 2)
- ((sl_return + bl_return) / 2)
)
mom_list.append(mom)
mom_df = pd.Series(mom_list).to_excel('mom.xlsx')
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201,
198,
60,
201,
198,
2,
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1351,
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1933,
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329,
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198,
32542,
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41537,
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62,
41537,
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16,
47715,
201,
198,
2,
39407,
1910,
1451,
290,
2756,
288,
9501,
201,
198,
647,
2004,
62,
7568,
796,
279,
67,
13,
647,
469,
7,
7568,
11,
502,
62,
7568,
11,
319,
28,
17816,
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15799,
62,
22510,
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12962,
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2,
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2251,
257,
11013,
45,
5721,
11,
290,
788,
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12,
1485,
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201,
198,
647,
2004,
62,
7568,
13,
28463,
7,
20,
11,
705,
83,
12,
1485,
2756,
3256,
45941,
13,
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201,
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1640,
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1995,
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41537,
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201,
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62,
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1933,
58,
41537,
13,
9630,
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8,
532,
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201,
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220,
220,
220,
220,
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256,
62,
1485,
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17561,
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357,
647,
2004,
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7568,
17816,
4475,
20520,
6624,
256,
62,
1485,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
4378,
263,
62,
31448,
796,
357,
647,
2004,
62,
7568,
17816,
83,
15799,
62,
22510,
20520,
6624,
4378,
263,
8,
201,
198,
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220,
220,
220,
220,
220,
1949,
25,
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220,
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220,
220,
220,
220,
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256,
62,
1485,
62,
20888,
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62,
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13,
17946,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
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256,
62,
1485,
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17561,
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201,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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62,
31448,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2361,
17816,
19836,
6,
4083,
27160,
58,
15,
60,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2180,
62,
8424,
796,
502,
62,
41537,
58,
1326,
62,
41537,
13,
9630,
7,
8424,
8,
532,
352,
60,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
256,
62,
16,
62,
17561,
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796,
357,
647,
2004,
62,
7568,
17816,
4475,
20520,
6624,
2180,
62,
8424,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
23791,
62,
7568,
13,
17946,
58,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
83,
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16,
62,
17561,
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62,
31448,
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12,
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6,
201,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
2361,
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62,
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20888,
201,
198,
220,
220,
220,
220,
220,
220,
220,
2845,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1208,
201,
198,
2,
27131,
378,
938,
1105,
1933,
1441,
329,
1227,
256,
357,
83,
12,
16,
11,
256,
12,
1065,
8,
201,
198,
647,
2004,
62,
7568,
17816,
30119,
62,
1941,
62,
7783,
20520,
796,
357,
201,
198,
220,
220,
220,
357,
647,
2004,
62,
7568,
17816,
19836,
20520,
1220,
23791,
62,
7568,
17816,
83,
12,
1485,
2756,
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201,
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8,
201,
198,
32542,
62,
4868,
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17635,
201,
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1640,
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1995,
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41537,
25,
201,
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220,
220,
220,
1303,
6822,
256,
12,
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2756,
4006,
290,
256,
12,
16,
1910,
1451,
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201,
198,
220,
220,
220,
2180,
62,
8424,
796,
1933,
58,
41537,
13,
9630,
7,
8424,
8,
532,
352,
60,
201,
198,
220,
220,
220,
502,
62,
31448,
796,
357,
647,
2004,
62,
7568,
17816,
4475,
20520,
6624,
2180,
62,
8424,
8,
201,
198,
220,
220,
220,
1995,
62,
31448,
796,
357,
647,
2004,
62,
7568,
17816,
30119,
62,
1941,
62,
7783,
6,
4083,
1662,
2616,
28955,
201,
198,
220,
220,
220,
2493,
6513,
62,
9979,
62,
7568,
796,
23791,
62,
7568,
13,
17946,
58,
1326,
62,
31448,
1222,
1995,
62,
31448,
60,
201,
198,
220,
220,
220,
1303,
27758,
1123,
1227,
11948,
656,
734,
2628,
201,
198,
220,
220,
220,
3403,
796,
685,
201,
198,
220,
220,
220,
220,
220,
220,
220,
357,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2493,
6513,
62,
9979,
62,
7568,
17816,
10728,
62,
11128,
20520,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1875,
2493,
6513,
62,
9979,
62,
7568,
17816,
10728,
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] | 2.119411 | 3,601 |
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: MIT-0
__copyright__ = ('Copyright Amazon.com, Inc. or its affiliates. '
'All Rights Reserved.')
__version__ = '2.7.1'
__license__ = 'MIT-0'
__author__ = 'Akihiro Nakajima'
__url__ = 'https://github.com/aws-samples/siem-on-amazon-opensearch-service'
import hashlib
import json
import os
import tarfile
import urllib.error
import urllib.parse
import urllib.request
import boto3
# get var from lambda environment
try:
s3bucket_name = os.environ['s3bucket_name']
license_key = os.environ['license_key']
except KeyError:
raise Exception('ERROR: impossible to get lambda environment')
s3key_prefix = os.environ.get('s3key_prefix', 'GeoLite2/')
s3 = boto3.resource('s3')
bucket = s3.Bucket(s3bucket_name)
url = 'https://download.maxmind.com/app/geoip_download?'
put_files = ['GeoLite2-City', 'GeoLite2-ASN', 'GeoLite2-Country']
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] | 2.613699 | 365 |
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Copyright 2010 California Institute of Technology. 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.
#
# United States Government Sponsorship acknowledged. This software is subject to
# U.S. export control laws and regulations and has been classified as 'EAR99 NLR'
# (No [Export] License Required except when exporting to an embargoed country,
# end user, or in support of a prohibited end use). By downloading this software,
# the user agrees to comply with all applicable U.S. export laws and regulations.
# The user has the responsibility to obtain export licenses, or other export
# authority as may be required before exporting this software to any 'EAR99'
# embargoed foreign country or citizen of those countries.
#
# Author: Giangi Sacco
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
import math
import datetime
import logging
from iscesys.Component.Component import Component, Port
from isceobj.Util.mathModule import MathModule as MM
from isceobj.Orbit.Orbit import StateVector
# A class to hold three-dimensional basis vectors
# A class to hold three-dimensional basis vectors for spacecraft baselines
BASELINE_LOCATION = Component.Parameter('baselineLocation',
public_name = 'BASELINE_LOCATION',
default = 'all',
type=str,
mandatory=False,
doc = ('Location at which to compute baselines - "all" implies '+
'top, middle, bottom of master image, '+
'"top" implies near start of master image, '+
'"bottom" implies at bottom of master image, '+
'"middle" implies near middle of master image. '+
'To be used in case there is a large shift between images.')
)
| [
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220,
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220,
220,
220,
220,
220,
220,
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1,
27171,
1,
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2939,
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10,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
2514,
307,
973,
287,
1339,
612,
318,
257,
1588,
6482,
1022,
4263,
2637,
8,
198,
8,
628,
198
] | 3.711039 | 616 |
from random import shuffle as rshuffle
from .card import Card
| [
6738,
4738,
1330,
36273,
355,
374,
1477,
18137,
198,
6738,
764,
9517,
1330,
5172,
628
] | 4.2 | 15 |
# -*- coding: utf-8 -*-
#
# Copyright (C) 2016, 2017 Red Hat, Inc.
# Red Hat Author: Patrick Uiterwijk <puiterwijk@redhat.com>
#
# 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.
"""Client for applications relying on OpenID Connect for authentication."""
from __future__ import print_function
from copy import copy
import json
import logging
from threading import Lock
import time
try:
from StringIO import StringIO
except ImportError:
from io import StringIO
import socket
import os
try:
from urllib import urlencode
except ImportError:
from urllib.parse import urlencode
from uuid import uuid4 as uuidgen
import webbrowser
from wsgiref import simple_server
import requests
import sys
from openidc_client import release
# The ports that we will try to use for our webserver
WEB_PORTS = [12345, 23456]
| [
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198,
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66,
62,
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198,
198,
2,
383,
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326,
356,
481,
1949,
284,
779,
329,
674,
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33,
62,
47,
33002,
796,
685,
10163,
2231,
11,
2242,
29228,
60,
628
] | 3.595285 | 509 |
#-------------------------------------------------------------------------------
#
# WPS 1.0 parameters' XML encoders
#
# Project: EOxServer <http://eoxserver.org>
# Authors: Fabian Schindler <fabian.schindler@eox.at>
# Martin Paces <martin.paces@eox.at>
#
#-------------------------------------------------------------------------------
# Copyright (C) 2013 EOX IT Services GmbH
#
# 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 of this Software or works derived from this 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 eoxserver.services.ows.wps.parameters import (
LiteralData, ComplexData, BoundingBoxData,
AllowedAny, AllowedEnum, AllowedRange, AllowedRangeCollection,
AllowedByReference,
)
from eoxserver.services.ows.wps.v10.util import (
OWS, WPS, NIL, ns_ows,
)
#-------------------------------------------------------------------------------
def encode_input_descr(prm):
""" Encode process description input."""
elem = NIL("Input", *_encode_param_common(prm))
elem.attrib["minOccurs"] = ("1", "0")[bool(prm.is_optional)]
elem.attrib["maxOccurs"] = "1"
if isinstance(prm, LiteralData):
elem.append(_encode_literal(prm, True))
elif isinstance(prm, ComplexData):
elem.append(_encode_complex(prm, True))
elif isinstance(prm, BoundingBoxData):
elem.append(_encode_bbox(prm, True))
return elem
def encode_output_descr(prm):
""" Encode process description output."""
elem = NIL("Output", *_encode_param_common(prm))
if isinstance(prm, LiteralData):
elem.append(_encode_literal(prm, False))
elif isinstance(prm, ComplexData):
elem.append(_encode_complex(prm, False))
elif isinstance(prm, BoundingBoxData):
elem.append(_encode_bbox(prm, False))
return elem
def encode_input_exec(prm):
""" Encode common part of the execure response data input."""
return WPS("Input", *_encode_param_common(prm, False))
def encode_output_exec(prm):
""" Encode common part of the execure response data output."""
return WPS("Output", *_encode_param_common(prm))
def encode_output_def(outdef):
""" Encode the execure response output definition."""
attrib = {}
if outdef.uom is not None:
attrib['uom'] = outdef.uom
if outdef.crs is not None:
attrib['crs'] = outdef.crs
if outdef.mime_type is not None:
attrib['mimeType'] = outdef.mime_type
if outdef.encoding is not None:
attrib['encoding'] = outdef.encoding
if outdef.schema is not None:
attrib['schema'] = outdef.schema
if outdef.as_reference is not None:
attrib['asReference'] = 'true' if outdef.as_reference else 'false'
return WPS("Output", *_encode_param_common(outdef, False), **attrib)
def _encode_param_common(prm, title_required=True):
""" Encode common sub-elements of all XML parameters."""
elist = [OWS("Identifier", prm.identifier)]
if prm.title or title_required:
elist.append(OWS("Title", prm.title or prm.identifier))
if prm.abstract:
elist.append(OWS("Abstract", prm.abstract))
return elist
#-------------------------------------------------------------------------------
#-------------------------------------------------------------------------------
#-------------------------------------------------------------------------------
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] | 3.050907 | 1,434 |
import datetime
import uuid
import simplejson as json
from src.db.s3_client import Client as S3Client
from decimal import Decimal
def get_from_archive(archive_key):
''' Download a VP Save from S3.
:param str archive_key: The vp_save data's location (S3 bucket and file path). This value is required.
'''
if archive_key is None or '/' not in archive_key:
raise ValueError()
bucket, key = archive_key.split('/', 1)
s3_client = S3Client()
try:
archive_object = json.loads(s3_client.get_object(bucket, key)['Body'].read(),parse_float=Decimal)
except Exception as e:
print('ERROR: Error downloading ' + key + ' from ' + bucket + ' bucket. ERROR\n%s' %e)
raise
return archive_object
def build(vp_save={}):
''' Builds and returns a valid vp_save object.
Builds a new vp_save object by creating default values for
required fields and combines any of the given attributes.
'''
vp_save['PK'] = str(uuid.uuid4())
# Set timestamps (for new data)
now = datetime.datetime.now().isoformat()
vp_save['date_created'] = now
vp_save['last_modified'] = now
vp_save['item_type'] = 'vp_save'
return vp_save
def archive(bucket, vp_save_pk, save_data):
''' Archives a vp save data to S3.
Uploads the save data object as a JSON file to S3. The location of the archive
depends on the bucket and the primary key of the save data. If the upload fails,
an exception is raised. If successful, returns the archive location.
:param str bucket: The name of the S3 bucket for the archive. This value is required.
:param str vp_save_pk: The vp_save PK to use as the name of the JSON file. This value is required.
:param obj save_data: The save data object to archive. This value is required.
'''
if bucket is None or len(bucket) <= 0:
raise ValueError()
if vp_save_pk is None or len(vp_save_pk) <= 0:
raise ValueError()
if not save_data:
raise ValueError()
archive_file = __archive_key(save_data) + '/' + vp_save_pk + '.json'
# Upload curation data to S3 archive bucket.
s3_client = S3Client()
try:
s3_client.put_object(
bytes(json.dumps(save_data).encode('UTF-8')),
bucket,
archive_file
)
except Exception as e:
print('ERROR: Error uploading ' + archive_file + ' to ' + bucket + ' bucket. ERROR\n%s' %e)
raise
archive_key_comps = [bucket, archive_file]
return '/'.join(archive_key_comps)
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] | 2.86643 | 846 |
"""
Basic usage
===========
This example presents the basic usage of brokenaxes
"""
import matplotlib.pyplot as plt
from brokenaxes import brokenaxes
import numpy as np
fig = plt.figure(figsize=(5,2))
bax = brokenaxes(xlims=((0, .1), (.4, .7)), ylims=((-1, .7), (.79, 1)), hspace=.05)
x = np.linspace(0, 1, 100)
bax.plot(x, np.sin(10 * x), label='sin')
bax.plot(x, np.cos(10 * x), label='cos')
bax.legend(loc=3)
bax.set_xlabel('time')
bax.set_ylabel('value')
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] | 2.343434 | 198 |
import shutil
import os
import json
import glob
import yaml
import sys
import urllib
import ssl
import csv
import time
import requests
import json
import csv
from rdflib import URIRef, BNode, Literal, Graph
from rdflib.namespace import RDF, RDFS, FOAF, XSD
from rdflib import Namespace
all = Graph()
with open("data/dict.json") as f:
ln_map = json.load(f)
st_path = "../data/index.json"
with open(st_path) as f:
result = json.load(f)
uris = []
for obj in result:
fields = ["spatial", "agential"]
for field in fields:
values = obj[field]
for value in values:
uri = "chname:"+value
if field == "spatial":
uri = "place:"+value
if uri not in uris:
uris.append(uri)
for uri in uris:
print(uri)
tmp = uri.split(":")
prefix = tmp[0]
suffix = tmp[1]
ln = suffix
ln_org = ""
if ln in ln_map:
ln_org = ln
ln = ln_map[ln]
if len(ln) > 20:
continue
# ln = obj["uri"].split(":")[1]
'''
wiki_path = "data/wikidata/"+ln+".json"
wiki = {}
if os.path.exists(wiki_path):
with open(wiki_path) as f:
wiki = json.load(f)
# sameAs
stmt = (subject, URIRef("http://www.w3.org/2002/07/owl#sameAs"), URIRef(wiki_url))
all.add(stmt)
obj = wiki["entities"][wiki_url.split("/")[-1]]
# description
if "descriptions" in obj and "ja" in obj["descriptions"]:
stmt = (subject, URIRef("http://schema.org/description"), Literal(obj["descriptions"]["ja"]["value"], lang="ja"))
all.add(stmt)
# label
if "labels" in obj and "ja" in obj["labels"]:
stmt = (subject, RDFS.label, Literal(obj["labels"]["ja"]["value"]))
all.add(stmt)
ln = wiki_url.split("/")[-1]
'''
db_path = "data/dbpedia_ja/"+ln+".json"
wiki_path = "data/wikidata/"+ln+".json"
db = {}
wiki = {}
if os.path.exists(db_path):
with open(db_path) as f:
db = json.load(f)
if os.path.exists(wiki_path):
with open(wiki_path) as f:
wiki = json.load(f)
db_uri = "http://ja.dbpedia.org/resource/"+ln
if db_uri not in db:
print("not" , db_uri)
continue
# ######
subject = URIRef("https://shibusawa-dlab.github.io/lab1/api/"+prefix+"/"+ln)
if prefix == "chname":
stmt = (subject, RDF.type, URIRef("https://jpsearch.go.jp/term/type/Agent"))
all.add(stmt)
elif prefix == "time":
stmt = (subject, RDF.type, URIRef("https://jpsearch.go.jp/term/type/Time"))
all.add(stmt)
elif prefix == "place":
stmt = (subject, RDF.type, URIRef("https://jpsearch.go.jp/term/type/Place"))
all.add(stmt)
elif prefix == "event":
stmt = (subject, RDF.type, URIRef("https://jpsearch.go.jp/term/type/Event"))
all.add(stmt)
elif prefix == "org":
stmt = (subject, RDF.type, URIRef("https://jpsearch.go.jp/term/type/Organization"))
all.add(stmt)
elif prefix == "keyword":
stmt = (subject, RDF.type, URIRef("https://jpsearch.go.jp/term/type/Keyword"))
all.add(stmt)
elif prefix == "type":
stmt = (subject, RDF.type, URIRef("https://jpsearch.go.jp/term/type/Type"))
all.add(stmt)
# ######
obj = db[db_uri]
stmt = (subject, URIRef("http://www.w3.org/2002/07/owl#sameAs"), URIRef(db_uri))
all.add(stmt)
if "http://dbpedia.org/ontology/thumbnail" in obj:
stmt = (subject, URIRef("http://schema.org/image"), URIRef(obj["http://dbpedia.org/ontology/thumbnail"][0]["value"]))
all.add(stmt)
if "http://www.w3.org/2000/01/rdf-schema#label" in obj:
labels = obj["http://www.w3.org/2000/01/rdf-schema#label"]
for label in labels:
if label["lang"] == "ja":
stmt = (subject, RDFS.label, Literal(label["value"]))
all.add(stmt)
if "http://www.w3.org/2000/01/rdf-schema#comment" in obj:
labels = obj["http://www.w3.org/2000/01/rdf-schema#comment"]
for label in labels:
stmt = (subject, URIRef("http://schema.org/description"), Literal(label["value"], lang=label["lang"]))
all.add(stmt)
if "http://www.w3.org/2002/07/owl#sameAs" in obj:
labels = obj["http://www.w3.org/2002/07/owl#sameAs"]
for label in labels:
value = label["value"]
if "http://dbpedia.org" in value or "http://ja.dbpedia.org" in value or "www.wikidata.org" in value:
stmt = (subject, URIRef("http://www.w3.org/2002/07/owl#sameAs"), URIRef(value))
all.add(stmt)
#
'''
if "point" in obj and prefix == "place":
value = obj["point"]["value"].split(" ")
# addGeo
geoUri = addGeo({
"lat" : float(value[0]),
"long": float(value[1])
})
stmt = (subject, URIRef("http://schema.org/geo"), geoUri)
if suffix not in places:
places[suffix] = {
"lat" : float(value[0]),
"long": float(value[1])
}
all.add(stmt)
'''
#
if ln_org != "" and ln != ln_org:
stmt = (subject, URIRef("http://schema.org/name"), Literal(ln_org))
all.add(stmt)
path = "data/all.json"
all.serialize(destination=path, format='json-ld')
all.serialize(destination=path.replace(".json", ".rdf"), format='pretty-xml') | [
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1600,
27071,
4372,
69,
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5794,
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] | 1.87775 | 3,182 |
import unittest
try:
from unittest.mock import *
except ImportError:
from mock import *
from msgpack import *
import bootloader_read_config
from commands import *
import sys
import json
| [
11748,
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] | 3.15873 | 63 |
import logging
from asyncio import sleep
import discord
from discord.ext import commands
from config import SETTINGS
from crew import crew_embed
from diary import diary_embed
from film import film_embed
from helpers import LetterboxdError
from list_ import list_embed
from review import review_embed
from user import user_embed
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s | %(message)s',
datefmt='%m/%d %H:%M:%S')
bot = commands.Bot(command_prefix='!', case_insensitive=True)
bot.remove_command('help')
# Commands
bot.run(SETTINGS['discord'])
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7,
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] | 3.041026 | 195 |
import asyncio
import logging
import time
from typing import Set, List, Tuple, Optional, TYPE_CHECKING
import uuid
import ray
from ray.experimental.workflow import workflow_context
from ray.experimental.workflow import workflow_storage
from ray.experimental.workflow.common import (Workflow, WorkflowStatus,
WorkflowMetaData, StepType)
from ray.experimental.workflow.step_executor import commit_step
from ray.experimental.workflow.storage import get_global_storage
from ray.experimental.workflow.workflow_access import (
flatten_workflow_output, get_or_create_management_actor,
get_management_actor)
if TYPE_CHECKING:
from ray.experimental.workflow.step_executor import WorkflowExecutionResult
logger = logging.getLogger(__name__)
def run(entry_workflow: Workflow,
workflow_id: Optional[str] = None,
overwrite: bool = True) -> ray.ObjectRef:
"""Run a workflow asynchronously.
# TODO(suquark): The current "run" always overwrite existing workflow.
# We need to fix this later.
"""
store = get_global_storage()
assert ray.is_initialized()
if workflow_id is None:
# Workflow ID format: {Entry workflow UUID}.{Unix time to nanoseconds}
workflow_id = f"{str(uuid.uuid4())}.{time.time():.9f}"
logger.info(f"Workflow job created. [id=\"{workflow_id}\", storage_url="
f"\"{store.storage_url}\"].")
with workflow_context.workflow_step_context(workflow_id,
store.storage_url):
# checkpoint the workflow
ws = workflow_storage.get_workflow_storage(workflow_id)
commit_step(ws, "", entry_workflow)
workflow_manager = get_or_create_management_actor()
ignore_existing = (entry_workflow.data.step_type != StepType.FUNCTION)
# NOTE: It is important to 'ray.get' the returned output. This
# ensures caller of 'run()' holds the reference to the workflow
# result. Otherwise if the actor removes the reference of the
# workflow output, the caller may fail to resolve the result.
result: "WorkflowExecutionResult" = ray.get(
workflow_manager.run_or_resume.remote(workflow_id,
ignore_existing))
if entry_workflow.data.step_type == StepType.FUNCTION:
return flatten_workflow_output(workflow_id,
result.persisted_output)
else:
return flatten_workflow_output(workflow_id, result.volatile_output)
# TODO(suquark): support recovery with ObjectRef inputs.
def resume(workflow_id: str) -> ray.ObjectRef:
"""Resume a workflow asynchronously. See "api.resume()" for details.
"""
storage = get_global_storage()
logger.info(f"Resuming workflow [id=\"{workflow_id}\", storage_url="
f"\"{storage.storage_url}\"].")
workflow_manager = get_or_create_management_actor()
# NOTE: It is important to 'ray.get' the returned output. This
# ensures caller of 'run()' holds the reference to the workflow
# result. Otherwise if the actor removes the reference of the
# workflow output, the caller may fail to resolve the result.
result: "WorkflowExecutionResult" = ray.get(
workflow_manager.run_or_resume.remote(
workflow_id, ignore_existing=False))
logger.info(f"Workflow job {workflow_id} resumed.")
return flatten_workflow_output(workflow_id, result.persisted_output)
def get_output(workflow_id: str, name: Optional[str]) -> ray.ObjectRef:
"""Get the output of a running workflow.
See "api.get_output()" for details.
"""
assert ray.is_initialized()
try:
workflow_manager = get_management_actor()
except ValueError as e:
raise ValueError(
"Failed to connect to the workflow management "
"actor. The workflow could have already failed. You can use "
"workflow.resume() to resume the workflow.") from e
output = ray.get(workflow_manager.get_output.remote(workflow_id, name))
return flatten_workflow_output(workflow_id, output)
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62,
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11,
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8,
628,
628,
198
] | 2.580247 | 1,620 |
from .compose import Compose # noqa
from .signature import BinarySignature, Signature, MemorySignature # noqa
from .username import UsernameToken # noqa
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# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
dt=0.1
t = np.arange(0,10,dt)
y0=np.array([10, 0.0, 10, 10])
sol_rk4=RK4_int(orbit,y0,t)
x,y,v_x,v_y = sol_rk4.T
plt.grid()
plt.plot(x,y)
plt.show()
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import os
from django.apps import apps
from django.core.management.base import BaseCommand
from factory_generator.generator import FactoryAppGenerator
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#!/usr/bin/env python
#
# Copyright (c) 2017 10X Genomics, Inc. All rights reserved.
#
import cellranger.analysis.diffexp as cr_diffexp
import cellranger.analysis.io as analysis_io
from cellranger.analysis.singlegenome import SingleGenomeAnalysis
import cellranger.h5_constants as h5_constants
import cellranger.analysis.constants as analysis_constants
import cellranger.matrix as cr_matrix
import cellranger.io as cr_io
import cellranger.library_constants as lib_constants
NUM_THREADS_MIN = 4
#TODO Not clear why this stage takes > 1 thread. Martian thinks it does and kills it on long jobs
__MRO__ = """
stage RUN_DIFFERENTIAL_EXPRESSION(
in h5 matrix_h5,
in h5 clustering_h5,
in bool skip,
in int random_seed,
in int max_clusters,
out h5 diffexp_h5,
out path diffexp_csv,
src py "stages/analyzer/run_differential_expression",
) split using (
in string clustering_key,
)
"""
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] | 2.662921 | 356 |
from sympy.combinatorics import Permutation
from sympy.combinatorics.util import _distribute_gens_by_base
rmul = Permutation.rmul
def _cmp_perm_lists(first, second):
"""
Compare two lists of permutations as sets.
This is used for testing purposes. Since the array form of a
permutation is currently a list, Permutation is not hashable
and cannot be put into a set.
Examples
========
>>> from sympy.combinatorics.permutations import Permutation
>>> from sympy.combinatorics.testutil import _cmp_perm_lists
>>> a = Permutation([0, 2, 3, 4, 1])
>>> b = Permutation([1, 2, 0, 4, 3])
>>> c = Permutation([3, 4, 0, 1, 2])
>>> ls1 = [a, b, c]
>>> ls2 = [b, c, a]
>>> _cmp_perm_lists(ls1, ls2)
True
"""
return {tuple(a) for a in first} == \
{tuple(a) for a in second}
def _verify_bsgs(group, base, gens):
"""
Verify the correctness of a base and strong generating set.
This is a naive implementation using the definition of a base and a strong
generating set relative to it. There are other procedures for
verifying a base and strong generating set, but this one will
serve for more robust testing.
Examples
========
>>> from sympy.combinatorics.named_groups import AlternatingGroup
>>> from sympy.combinatorics.testutil import _verify_bsgs
>>> A = AlternatingGroup(4)
>>> A.schreier_sims()
>>> _verify_bsgs(A, A.base, A.strong_gens)
True
See Also
========
sympy.combinatorics.perm_groups.PermutationGroup.schreier_sims
"""
from sympy.combinatorics.perm_groups import PermutationGroup
strong_gens_distr = _distribute_gens_by_base(base, gens)
current_stabilizer = group
for i in range(len(base)):
candidate = PermutationGroup(strong_gens_distr[i])
if current_stabilizer.order() != candidate.order():
return False
current_stabilizer = current_stabilizer.stabilizer(base[i])
if current_stabilizer.order() != 1:
return False
return True
def _verify_centralizer(group, arg, centr=None):
"""
Verify the centralizer of a group/set/element inside another group.
This is used for testing ``.centralizer()`` from
``sympy.combinatorics.perm_groups``
Examples
========
>>> from sympy.combinatorics.named_groups import (SymmetricGroup,
... AlternatingGroup)
>>> from sympy.combinatorics.perm_groups import PermutationGroup
>>> from sympy.combinatorics.permutations import Permutation
>>> from sympy.combinatorics.testutil import _verify_centralizer
>>> S = SymmetricGroup(5)
>>> A = AlternatingGroup(5)
>>> centr = PermutationGroup([Permutation([0, 1, 2, 3, 4])])
>>> _verify_centralizer(S, A, centr)
True
See Also
========
_naive_list_centralizer,
sympy.combinatorics.perm_groups.PermutationGroup.centralizer,
_cmp_perm_lists
"""
if centr is None:
centr = group.centralizer(arg)
centr_list = list(centr.generate_dimino(af=True))
centr_list_naive = _naive_list_centralizer(group, arg, af=True)
return _cmp_perm_lists(centr_list, centr_list_naive)
def canonicalize_naive(g, dummies, sym, *v):
"""
Canonicalize tensor formed by tensors of the different types
g permutation representing the tensor
dummies list of dummy indices
msym symmetry of the metric
v is a list of (base_i, gens_i, n_i, sym_i) for tensors of type `i`
base_i, gens_i BSGS for tensors of this type
n_i number ot tensors of type `i`
sym_i symmetry under exchange of two component tensors of type `i`
None no symmetry
0 commuting
1 anticommuting
Return 0 if the tensor is zero, else return the array form of
the permutation representing the canonical form of the tensor.
Examples
========
>>> from sympy.combinatorics.testutil import canonicalize_naive
>>> from sympy.combinatorics.tensor_can import get_symmetric_group_sgs
>>> from sympy.combinatorics import Permutation, PermutationGroup
>>> g = Permutation([1, 3, 2, 0, 4, 5])
>>> base2, gens2 = get_symmetric_group_sgs(2)
>>> canonicalize_naive(g, [2, 3], 0, (base2, gens2, 2, 0))
[0, 2, 1, 3, 4, 5]
"""
from sympy.combinatorics.perm_groups import PermutationGroup
from sympy.combinatorics.tensor_can import gens_products, dummy_sgs
from sympy.combinatorics.permutations import Permutation, _af_rmul
v1 = []
for i in range(len(v)):
base_i, gens_i, n_i, sym_i = v[i]
v1.append((base_i, gens_i, [[]]*n_i, sym_i))
size, sbase, sgens = gens_products(*v1)
dgens = dummy_sgs(dummies, sym, size-2)
if isinstance(sym, int):
num_types = 1
dummies = [dummies]
sym = [sym]
else:
num_types = len(sym)
dgens = []
for i in range(num_types):
dgens.extend(dummy_sgs(dummies[i], sym[i], size - 2))
S = PermutationGroup(sgens)
D = PermutationGroup([Permutation(x) for x in dgens])
dlist = list(D.generate(af=True))
g = g.array_form
st = set()
for s in S.generate(af=True):
h = _af_rmul(g, s)
for d in dlist:
q = tuple(_af_rmul(d, h))
st.add(q)
a = list(st)
a.sort()
prev = (0,)*size
for h in a:
if h[:-2] == prev[:-2]:
if h[-1] != prev[-1]:
return 0
prev = h
return list(a[0])
def graph_certificate(gr):
"""
Return a certificate for the graph
gr adjacency list
The graph is assumed to be unoriented and without
external lines.
Associate to each vertex of the graph a symmetric tensor with
number of indices equal to the degree of the vertex; indices
are contracted when they correspond to the same line of the graph.
The canonical form of the tensor gives a certificate for the graph.
This is not an efficient algorithm to get the certificate of a graph.
Examples
========
>>> from sympy.combinatorics.testutil import graph_certificate
>>> gr1 = {0:[1, 2, 3, 5], 1:[0, 2, 4], 2:[0, 1, 3, 4], 3:[0, 2, 4], 4:[1, 2, 3, 5], 5:[0, 4]}
>>> gr2 = {0:[1, 5], 1:[0, 2, 3, 4], 2:[1, 3, 5], 3:[1, 2, 4, 5], 4:[1, 3, 5], 5:[0, 2, 3, 4]}
>>> c1 = graph_certificate(gr1)
>>> c2 = graph_certificate(gr2)
>>> c1
[0, 2, 4, 6, 1, 8, 10, 12, 3, 14, 16, 18, 5, 9, 15, 7, 11, 17, 13, 19, 20, 21]
>>> c1 == c2
True
"""
from sympy.combinatorics.permutations import _af_invert
from sympy.combinatorics.tensor_can import get_symmetric_group_sgs, canonicalize
items = list(gr.items())
items.sort(key=lambda x: len(x[1]), reverse=True)
pvert = [x[0] for x in items]
pvert = _af_invert(pvert)
# the indices of the tensor are twice the number of lines of the graph
num_indices = 0
for v, neigh in items:
num_indices += len(neigh)
# associate to each vertex its indices; for each line
# between two vertices assign the
# even index to the vertex which comes first in items,
# the odd index to the other vertex
vertices = [[] for i in items]
i = 0
for v, neigh in items:
for v2 in neigh:
if pvert[v] < pvert[v2]:
vertices[pvert[v]].append(i)
vertices[pvert[v2]].append(i+1)
i += 2
g = []
for v in vertices:
g.extend(v)
assert len(g) == num_indices
g += [num_indices, num_indices + 1]
size = num_indices + 2
assert sorted(g) == list(range(size))
g = Permutation(g)
vlen = [0]*(len(vertices[0])+1)
for neigh in vertices:
vlen[len(neigh)] += 1
v = []
for i in range(len(vlen)):
n = vlen[i]
if n:
base, gens = get_symmetric_group_sgs(i)
v.append((base, gens, n, 0))
v.reverse()
dummies = list(range(num_indices))
can = canonicalize(g, dummies, 0, *v)
return can
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] | 2.371955 | 3,366 |
localhost = "http://localhost/" # your local host
database = "mysql://root@localhost/vaticChecker" # server://user:pass@localhost/dbname
min_training = 2 # the minimum number of training videos to be considered
recaptcha_secret = "" # recaptcha secret for verification
duplicate_annotations = False # Should the server allow for duplicate annotations?
import os.path
import sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
# TODO: remove on server
import os
os.environ['PYTHON_EGG_CACHE'] = '/tmp/apache'
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"""Timezone helper functions.
This module uses pytz when it's available and fallbacks when it isn't.
"""
from datetime import datetime, timedelta, tzinfo
from threading import local
import time as _time
try:
import pytz
except ImportError:
pytz = None
from django.conf import settings
__all__ = [
'utc', 'get_default_timezone', 'get_current_timezone',
'activate', 'deactivate', 'override',
'is_naive', 'is_aware', 'make_aware', 'make_naive',
]
# UTC and local time zones
ZERO = timedelta(0)
utc = pytz.utc if pytz else UTC()
"""UTC time zone as a tzinfo instance."""
# In order to avoid accessing the settings at compile time,
# wrap the expression in a function and cache the result.
_localtime = None
def get_default_timezone():
"""
Returns the default time zone as a tzinfo instance.
This is the time zone defined by settings.TIME_ZONE.
See also :func:`get_current_timezone`.
"""
global _localtime
if _localtime is None:
if isinstance(settings.TIME_ZONE, basestring) and pytz is not None:
_localtime = pytz.timezone(settings.TIME_ZONE)
else:
_localtime = LocalTimezone()
return _localtime
# This function exists for consistency with get_current_timezone_name
def get_default_timezone_name():
"""
Returns the name of the default time zone.
"""
return _get_timezone_name(get_default_timezone())
_active = local()
def get_current_timezone():
"""
Returns the currently active time zone as a tzinfo instance.
"""
return getattr(_active, "value", get_default_timezone())
def get_current_timezone_name():
"""
Returns the name of the currently active time zone.
"""
return _get_timezone_name(get_current_timezone())
def _get_timezone_name(timezone):
"""
Returns the name of ``timezone``.
"""
try:
# for pytz timezones
return timezone.zone
except AttributeError:
# for regular tzinfo objects
local_now = datetime.now(timezone)
return timezone.tzname(local_now)
# Timezone selection functions.
# These functions don't change os.environ['TZ'] and call time.tzset()
# because it isn't thread safe.
def activate(timezone):
"""
Sets the time zone for the current thread.
The ``timezone`` argument must be an instance of a tzinfo subclass or a
time zone name. If it is a time zone name, pytz is required.
"""
if isinstance(timezone, tzinfo):
_active.value = timezone
elif isinstance(timezone, basestring) and pytz is not None:
_active.value = pytz.timezone(timezone)
else:
raise ValueError("Invalid timezone: %r" % timezone)
def deactivate():
"""
Unsets the time zone for the current thread.
Django will then use the time zone defined by settings.TIME_ZONE.
"""
if hasattr(_active, "value"):
del _active.value
# Templates
def template_localtime(value, use_tz=None):
"""
Checks if value is a datetime and converts it to local time if necessary.
If use_tz is provided and is not None, that will force the value to
be converted (or not), overriding the value of settings.USE_TZ.
This function is designed for use by the template engine.
"""
should_convert = (isinstance(value, datetime)
and (settings.USE_TZ if use_tz is None else use_tz)
and not is_naive(value)
and getattr(value, 'convert_to_local_time', True))
return localtime(value) if should_convert else value
# Utilities
def localtime(value, timezone=None):
"""
Converts an aware datetime.datetime to local time.
Local time is defined by the current time zone, unless another time zone
is specified.
"""
if timezone is None:
timezone = get_current_timezone()
value = value.astimezone(timezone)
if hasattr(timezone, 'normalize'):
# available for pytz time zones
value = timezone.normalize(value)
return value
def now():
"""
Returns an aware or naive datetime.datetime, depending on settings.USE_TZ.
"""
if settings.USE_TZ:
# timeit shows that datetime.now(tz=utc) is 24% slower
return datetime.utcnow().replace(tzinfo=utc)
else:
return datetime.now()
# By design, these four functions don't perform any checks on their arguments.
# The caller should ensure that they don't receive an invalid value like None.
def is_aware(value):
"""
Determines if a given datetime.datetime is aware.
The logic is described in Python's docs:
http://docs.python.org/library/datetime.html#datetime.tzinfo
"""
return value.tzinfo is not None and value.tzinfo.utcoffset(value) is not None
def is_naive(value):
"""
Determines if a given datetime.datetime is naive.
The logic is described in Python's docs:
http://docs.python.org/library/datetime.html#datetime.tzinfo
"""
return value.tzinfo is None or value.tzinfo.utcoffset(value) is None
def make_aware(value, timezone):
"""
Makes a naive datetime.datetime in a given time zone aware.
"""
if hasattr(timezone, 'localize'):
# available for pytz time zones
return timezone.localize(value, is_dst=None)
else:
# may be wrong around DST changes
return value.replace(tzinfo=timezone)
def make_naive(value, timezone):
"""
Makes an aware datetime.datetime naive in a given time zone.
"""
value = value.astimezone(timezone)
if hasattr(timezone, 'normalize'):
# available for pytz time zones
value = timezone.normalize(value)
return value.replace(tzinfo=None)
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1441,
1988,
13,
33491,
7,
22877,
10951,
28,
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8,
198
] | 2.760508 | 2,046 |
import pytest
from pywps import Service
from pywps.tests import assert_response_success
from .common import client_for
from malleefowl.processes import processes
| [
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] | 3.666667 | 45 |
from setuptools import setup
__author__ = "Alex Laird"
__copyright__ = "Copyright 2019, Alex Laird"
__version__ = "1.4.0"
with open("README.md", "r") as f:
long_description = f.read()
setup(
name="pyngrok",
version=__version__,
packages=["pyngrok"],
python_requires=">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*",
install_requires=[
"future",
"pyyaml"
],
entry_points="""
[console_scripts]
ngrok=pyngrok.ngrok:run
""",
description="A Python wrapper for Ngrok.",
long_description=long_description,
long_description_content_type="text/markdown",
author="Alex Laird",
author_email="contact@alexlaird.com",
url="https://github.com/alexdlaird/pyngrok",
download_url="https://github.com/alexdlaird/pyngrok/archive/{}.tar.gz".format(__version__),
keywords=["ngrok", "tunnel", "tunneling", "webhook", "localhost"],
license="MIT",
classifiers=[
"Programming Language :: Python :: 2.7",
"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 :: Libraries :: Python Modules",
"Environment :: Console",
"Environment :: Web Environment",
"Intended Audience :: Developers",
"Intended Audience :: Education",
"Development Status :: 5 - Production/Stable",
"License :: OSI Approved :: MIT License",
"Operating System :: MacOS",
"Operating System :: Microsoft :: Windows",
"Operating System :: POSIX",
"Operating System :: Unix"
]
)
| [
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366,
18843,
803,
4482,
7904,
33501,
1,
198,
220,
220,
220,
2361,
198,
8,
198
] | 2.517147 | 729 |
#!/usr/bin/env python3
import kfp.dsl as dsl
import kfp.gcp as gcp
# Pipeline input variables.
KUBECTL_IMAGE = "gcr.io/mcas-195423/trackml_master_kfp_kubectl"
KUBECTL_IMAGE_VERSION = "1"
TRACKML_IMAGE = "gcr.io/mcas-195423/trackml_master_trackml"
TRACKML_IMAGE_VERSION = "1"
if __name__ == '__main__':
import kfp.compiler as compiler
compiler.Compiler().compile(trackml, __file__ + '.tar.gz')
| [
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11,
11593,
7753,
834,
1343,
45302,
18870,
13,
34586,
11537,
628
] | 2.317919 | 173 |
# -*- coding: utf-8 -*-
import time
import puka
import argparse
import logging
from infcommon import utils
from infrabbitmq import factory as infrabbitmq_factory
from infrabbitmq.rabbitmq import RabbitMQError
from infrabbitmq.events_names import (
TICK_1_SECOND,
TICK_1_MINUTE,
TICK_2_MINUTES,
TICK_5_MINUTES,
TICK_60_MINUTES,
)
if __name__ == '__main__':
try:
parser = argparse.ArgumentParser()
parser.add_argument('-n', '--network', action='store', required=True, help='Network name (ilo, c2k, ...)')
args = parser.parse_args()
network = args.network.split('-')[0]
main(network)
except Exception as exc:
logging.critical("Ticker Fails: {}".format(exc))
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] | 2.391586 | 309 |
# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# 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.
""" Classes to support Encoder-Decoder architectures """
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import os
import torch
from torch import nn
from .modeling_auto import AutoModel, AutoModelWithLMHead
logger = logging.getLogger(__name__)
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] | 3.382671 | 277 |
from airflow import DAG
from airflow.operators.bash_operator import BashOperator
from airflow.operators.python_operator import PythonOperator, BranchPythonOperator
from datetime import datetime, timedelta
import pandas as pd
import random
# Default args definition
default_args = {
'owner': 'Rafael',
'depends_on_past': False,
'start_date': datetime(2020, 11, 29, 18, 20),
'email': ['example1@example.com', 'example2@example.com'],
'email_on_failure': False,
'email_on_retry': False,
'retries': 1,
'Retry_delay': timedelta(minutes=1)
}
# Dag definition
dag = DAG(
'treino-03',
description="Extrai dados do Titanic e calcula idade media para homens ou mulheres",
default_args = default_args,
schedule_interval='*/20 * * * *'
)
get_data = BashOperator(
task_id='get-data',
bash_command='curl https://raw.githubusercontent.com/A3Data/hermione/master/hermione/file_text/train.csv -o /usr/local/airflow/data/train.csv',
dag=dag
)
escolhe_h_m = PythonOperator(
task_id='escolhe-h-m',
python_callable=sorteia_h_m,
dag=dag
)
male_female = BranchPythonOperator(
task_id='condicional',
python_callable=MouF,
provide_context=True,
dag=dag
)
branch_homem = PythonOperator(
task_id='branch_homem',
python_callable=mean_homem,
dag=dag
)
branch_mulher = PythonOperator(
task_id='branch_mulher',
python_callable=mean_mulher,
dag=dag
)
get_data >> escolhe_h_m >> male_female >> [branch_homem, branch_mulher]
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] | 2.460163 | 615 |