def receive_message():
try:
if request.method == 'GET':
"""Before allowing people to message your bot, Facebook has implemented a verify token
that confirms all requests that your bot receives came from Facebook."""
token_sent = request.args.get("hub.verify_token")
return verify_fb_token(token_sent)
else:
output = request.get_json()
for event in output['entry']:
messaging = event['messaging']
for message in messaging:
if message.get('message'):
recipient_id = message['sender']['id']
if message['message'].get('text'):
msg = message['message'].get('text')
rollbar.report_message(
"message received[{}]: {}".format(
recipient_id, msg), "info")
opt = Options(MongoCrud(), Chart())
fb_responses = opt.answer_message(
recipient_id, msg)
for response_sent_text in fb_responses:
send_message(opt, recipient_id,
response_sent_text)
return "Message Processed"
except:
rollbar.report_exc_info()
def test_new_value(self):
fb_id = 1234
request = "50.5"
opt = Options(MongoCrud())
response = opt.answer_message(fb_id, request)
expected = "OK, you are {} kg today".format(request)
self.assertEquals(response[0], expected)
def test_stat(self):
fb_id = 1234
request = "Stat"
opt = Options(MongoCrud())
response = opt.answer_message(fb_id, request)
expected = "your planned weigth for today is: 0 kg"
self.assertEquals(response[0], expected)
def test_register(self):
fb_id = 1234
request = "Register 100 2018-06-01 90"
opt = Options(MongoCrud())
response = opt.answer_message(fb_id, request)
expected = "Your plan has been registered"
self.assertEquals(response[0], expected)
def test_random_msg(self):
fb_id = 1234
request = "?"
opt = Options(MongoCrud())
response = opt.answer_message(fb_id, request)
expected = "version:"
first_line = response[0].split("\n")[1].strip()[:len(expected)]
self.assertEquals(first_line, expected)
DISPLAY = 'DISPLAY' in os.environ
if not DISPLAY:
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from tools.options import Options
from network.atloc import AtLoc, AtLocPlus
from torchvision import transforms, models
from tools.utils import quaternion_angular_error, qexp, load_state_dict
from data.dataloaders import SevenScenes, RobotCar, MF, Topo, Topo2, Topo3
from torch.utils.data import DataLoader
from torch.autograd import Variable
import math
# Config
opt = Options().parse()
cuda = torch.cuda.is_available()
device = "cuda:" + ",".join(str(i) for i in opt.gpus) if cuda else "cpu"
# Model
feature_extractor = models.resnet34(pretrained=False)
atloc = AtLoc(feature_extractor,
droprate=opt.test_dropout,
pretrained=False,
lstm=opt.lstm)
if opt.model == 'AtLoc':
model = atloc
elif opt.model == 'AtLocPlus':
model = AtLocPlus(atlocplus=atloc)
else:
raise NotImplementedError
#!/usr/bin/python
# -*- coding: utf-8 -*-
import os
import sys
from file_manager.vhdl_reader import Vhdl_reader
from decorator.pdfdrawer import PdfDrawer
from tools.options import Options
"""
pyVhdl2Sch takes a .vhd file and return a pdf : name_of_the_entity.pdf.
"""
options = Options()
files = []
options.analyse_args(sys.argv)
for i in range(0, len(options.files)):
filename = options.files[i]
try:
os.path.isfile(filename)
except:
print("File do not exist!\n")
options.print_usage()
sys.exit
reader = Vhdl_reader(filename, options)
options.filename = "%s." % reader.entity.name + "%s" % options.format
drawer = PdfDrawer("%s." % reader.entity.name + "%s" %
def init_vars(self):
self.Vhdl_Code = ""
self.options = Options()
DISPLAY = 'DISPLAY' in os.environ
if not DISPLAY:
matplotlib.use('Agg') # 设置后端，Matplotlib绘图并保存图像但不显示图形
import matplotlib.pyplot as plt
from tools.options import Options
from network.atloc import AtLoc
from torchvision import transforms, models
from tools.utils import quaternion_angular_error, qexp, load_state_dict
from torch.utils.data import DataLoader
from data.dataloaders import SevenScenes, RobotCar
from torch.autograd import Variable
# 配置运行环境
opt = Options().parse() # 参数命令解析
cuda = torch.cuda.is_available() # 判断是否有可用gpu设备
device = "cuda" + ",".join(
str(i) for i in opt.gpus) if cuda else "cpu" # 获取gpu或cpu设备信息
# 模型设置
feature_extractor = models.resnet34(pretrained=False) # resnet34模型作为特征提取器
atloc = AtLoc(feature_extractor, droprate=opt.test_dropout,
pretrained=False) # atloc模型实例构建
if opt.model == 'AtLoc':
model = atloc
else:
raise NotImplementedError
# model.eval(),pytorch会自动把BN和DropOut固定住，不会取平均，而是用训练好的值
# 不然的话，一旦test的batch_size过小，很容易就会被BN层导致生成图片颜色失真极大