def call(func_list):
for index, func_obj in enumerate(func_list):
func_name = func_obj.keys()[0]
print yellow("%s\n步骤%d：%s 开始执行" % (dt.strftime(dt.now(), "%Y-%m-%d %H:%M:%S"), index + 1, func_name))
# 单步执行
"""
go_on = raw_input("是否执行步骤%s?[y/n]" %func_name)
if go_on in ['n', 'N', 'no', 'No', 'NO']:
print yellow("程序已被手动终止")
exit(1)
"""
func = eval(func_name)
argv = func_obj[func_name]
argv_str = ""
success = False
if argv != "":
if type(argv) is list:
for argv_s in argv:
argv_str = argv_str + ", %s" % argv_s
func_full_str = func_name + "(" + argv_str[2:] + ")"
success = eval(func_full_str)
else:
argv = eval(argv)
success = func(argv)
else:
success = func()
if not success:
print red("执行步骤：%s 时出错！ Bye！" % func_name)
exit(1)
print yellow("%s\n步骤%d：%s 执行完成\n" % (dt.strftime(dt.now(), "%Y-%m-%d %H:%M:%S"), index + 1, func_name))
print ""
def get_model_data_as_json(self, model_data):
for data in model_data:
if 'write_date' in data and data['write_date']:
data['write_date'] = datetime.strftime(
datetime.strptime(data['write_date'], dtf),
self.wkhtmltopdf_format
)
if 'create_date' in data and data['create_date']:
data['create_date'] = datetime.strftime(
datetime.strptime(data['create_date'], dtf),
self.wkhtmltopdf_format
)
if 'date_started' in data and data['date_started']:
data['date_started'] = datetime.strftime(
datetime.strptime(
data['date_started'],
self.pretty_date_format
),
self.wkhtmltopdf_format
)
if 'date_terminated' in data and data['date_terminated']:
data['date_terminated'] = datetime.strftime(
datetime.strptime(
data['date_terminated'],
self.pretty_date_format
),
self.wkhtmltopdf_format
)
return json.dumps(model_data)
def download_data():
from datetime import timedelta, datetime
# find date range for the split train, val, test (0.8, 0.1, 0.1 of total days)
print('Downloading data for dates {} - {}'.format(
datetime.strftime(c.start, "%Y-%m-%d"),
datetime.strftime(c.end, "%Y-%m-%d")))
split = [0.8, 0.1, 0.1]
cumusplit = [np.sum(split[:i]) for i,s in enumerate(split)]
segment_start_dates = [c.start + timedelta(
days = int((c.end - c.start).days * interv)) for interv in cumusplit][::-1]
stocks_list = map(lambda l: l.strip(), open(c.names_file, 'r').readlines())
by_stock = dict((s, pdr_data.DataReader(s, 'yahoo', c.start, c.end))
for s in stocks_list)
seq = [[],[],[]]
for stock in by_stock:
lastAc = -1
daily_returns = deque(maxlen=c.normalize_std_len)
for rec_date in (c.start + timedelta(days=n) for n in xrange((c.end-c.start).days)):
idx = next(i for i,d in enumerate(segment_start_dates) if rec_date >= d)
try:
d = rec_date.strftime("%Y-%m-%d")
ac = by_stock[stock].ix[d]['Adj Close']
daily_return = (ac - lastAc)/lastAc
if len(daily_returns) == daily_returns.maxlen:
seq[idx].append(daily_return/np.std(daily_returns))
daily_returns.append(daily_return)
lastAc = ac
except KeyError:
pass
return [np.asarray(dat, dtype=np.float32) for dat in seq][::-1]
def action_ship_create(self):
res = super(SaleOrder, self).action_ship_create()
user_tz = self.env['res.users'].browse(self._uid).tz
from_zone = tz.gettz('UTC')
to_zone = tz.gettz(user_tz)
for order in self:
for picking in order.picking_ids:
if order.requested_date:
datetime_requested = \
datetime.strptime(order.requested_date,
'%Y-%m-%d %H:%M:%S').\
replace(tzinfo=from_zone).astimezone(to_zone)
date_requested = datetime.strftime(datetime_requested,
'%Y-%m-%d')
date_effective = date_requested
else:
date_requested = False
datetime_effective = \
datetime.strptime(order.commitment_date,
'%Y-%m-%d %H:%M:%S').\
replace(tzinfo=from_zone).astimezone(to_zone)
date_effective = datetime.strftime(datetime_effective,
'%Y-%m-%d')
vals = {'note': order.note,
'requested_date': date_requested,
'effective_date': date_effective,
}
if order.supplier_id and picking.state != 'cancel' \
and not picking.supplier_id:
vals.update({'supplier_id': order.supplier_id.id})
picking.write(vals)
return res
def __getData(self, post, is_question, unique_key):
""" This will return the page dictionry
"""
page = {'entity':'question' if is_question else 'answer'}
try:
page['uri'] = self.currenturi + '#' + str(post.find('span', 'wintiny').a['href'])
except:
log.info(self.log_msg('uri not found in %s'% self.currenturi))
page['uri'] = self.currenturi
try:
page['data'] = stripHtml(post.find('div', 'fMsg').renderContents())
except:
log.info(self.log_msg('Empty data found in %s'%page['uri']))
return
try:
date_str = [x.strip() for x in stripHtml(post.find('div', 'msgHead').findAll('div','floatl')[-1].renderContents()).splitlines() if x.strip()][-2]
date_object = datetime.strptime(date_str, 'Posted: %b-%d %I:%M %p')
current_year = datetime.utcnow().year
if date_object.month > datetime.utcnow().month:
current_year -= 1
my_object = datetime(year=current_year, month=date_object.month, day=date_object.day, hour=date_object.hour, minute=date_object.minute)
page['posted_date'] = datetime.strftime(my_object, "%Y-%m-%dT%H:%M:%SZ")
except:
page['posted_date'] = datetime.strftime(datetime.utcnow(), "%Y-%m-%dT%H:%M:%SZ")
log.exception(self.log_msg('Posted date not found'))
try:
page['et_author_name'] = stripHtml(post.find('div', id=re.compile('from\-')).renderContents()).replace('From: ','').strip()
except:
log.info(self.log_msg('Author name not found in %s'% self.currenturi))
if len(self.__hierarchy) >= 2:
page['title'] = page['et_thread_topic'] = self.__hierarchy[-1]
page['et_thread_forum'] = self.__hierarchy[-2]
if not is_question:
page['title'] = 'Re: ' + page['title']
return page
def __getData(self, post, is_question):
""" This will return the page dictionry
"""
page = {'entity':'question' if is_question else 'answer'}
try:
post_info = dict([(x.strip().lower(), y.strip()) for x,y in [x.strip().split(':', 1) for x in stripHtml(post.find('b', text='Subject:').findParent('td').renderContents()).splitlines() if x.strip()]])
page['uri'] = post_info['url']
page['title'] = post_info['subject']
except:
log.info(self.log_msg('Cannot find the title, uri details'))
return True
try:
page['data'] = stripHtml(post.findAll('table')[1].find('p').renderContents())
except:
log.info(self.log_msg('Data not found for the url %s'%self.currenturi))
return True
try:
page['posted_date'] = datetime.strftime(datetime.strptime( post_info['date'].split(' ( ')[0].strip(), '%m/%d/%Y %I:%M:%S %p'),"%Y-%m-%dT%H:%M:%SZ")
except:
page['posted_date'] = datetime.strftime(datetime.utcnow(), "%Y-%m-%dT%H:%M:%SZ")
log.info(self.log_msg('Date not be found in %s'% self.currenturi))
try:
page['et_author_name'] = post_info['username'].split(' contact ')[1].strip()
except:
log.info(self.log_msg('Ratings count not found'))
try:
page['ei_data_views_count'] = int(post_info['hits'])
except:
log.exception(self.log_msg('uri not found'))
try:
if is_question:
page['ei_data_replies_count'] = int(post_info['replies'])
except:
log.info(self.log_msg('data replies not found'))
return page
def start_end_date_for_period(period, default_start_date=False, default_end_date=False):
"""Return the start and end date for a goal period based on today
:param str default_start_date: string date in DEFAULT_SERVER_DATE_FORMAT format
:param str default_end_date: string date in DEFAULT_SERVER_DATE_FORMAT format
:return: (start_date, end_date), dates in string format, False if the period is
not defined or unknown"""
today = date.today()
if period == 'daily':
start_date = today
end_date = start_date
elif period == 'weekly':
delta = timedelta(days=today.weekday())
start_date = today - delta
end_date = start_date + timedelta(days=7)
elif period == 'monthly':
month_range = calendar.monthrange(today.year, today.month)
start_date = today.replace(day=1)
end_date = today.replace(day=month_range[1])
elif period == 'yearly':
start_date = today.replace(month=1, day=1)
end_date = today.replace(month=12, day=31)
else: # period == 'once':
start_date = default_start_date # for manual goal, start each time
end_date = default_end_date
return (start_date, end_date)
return (datetime.strftime(start_date, DF), datetime.strftime(end_date, DF))
def main():
argv = docopt(USAGE)
if argv["--start"]:
start = pacific_to_utc(argv["--start"])
elif argv["--last"]:
start = datetime.strftime(
datetime.utcnow() - timedelta(minutes=int(argv["--last"])), "%Y-%m-%dT%H:%M:%SZ")
else:
start = datetime.strftime(datetime.utcnow() - timedelta(days=7), "%Y-%m-%dT%H:%M:%SZ")
if argv["--end"]:
end = pacific_to_utc(argv["--end"])
else:
end = datetime.strftime(datetime.utcnow(),"%Y-%m-%dT%H:%M:%SZ")
pager = PagerDuty(argv['<subdomain>'], argv['<api-token>'], argv['<policy>'])
for command in ['all','wakeups','flakes']:
if argv[command]:
incidents = pager.do_list(command, argv['--no-thurs'], since=start, until=end)
incident_list = list(incidents)
(incident_list_prod, incident_list_staging)=segregation(incident_list)
if incidents:
if argv['--email']:
email_output(incident_list_prod, incident_list_staging, argv['--top'])
elif argv['--top']:
pprint_rankings(top(incident_list_prod, argv['--top']))
pprint_rankings(top(incident_list_staging, argv['--top']))
else:
pprint_incidents(incident_list_prod)
pprint_incidents(incident_list_staging)
if argv['mtr']:
print pager.get_mtr(since=start, until=end)
def plot_basic_stats(start, end, path_list, image_name_list, html_string):
new_path_list = []
new_image_name_list = []
duration = datetime.strftime(start, '%Y%m%d' ) + '_to_' + datetime.strftime(end, '%Y%m%d' ) + '.png'
image_name = 'Vol_euro_from_' + duration
new_path_list.append(FOLDER + image_name)
new_image_name_list.append(image_name)
image_name = 'Occ_euro_from_' + duration
new_path_list.append(FOLDER + image_name)
new_image_name_list.append(image_name)
image_name = 'Place_from_' + duration
new_path_list.append(FOLDER + image_name)
new_image_name_list.append(image_name)
image_name = 'Intraday_algo_vol_from_' + duration
new_path_list.append(FOLDER + image_name)
new_image_name_list.append(image_name)
period = PlotEngine(start_date = start, end_date = end)
period.plot_basic_stats(path = new_path_list)
period.plot_intraday_exec_curve(duration = 'From ' + datetime.strftime(start, '%Y/%m/%d' ) + ' to ' + datetime.strftime(end, '%Y/%m/%d' )).savefig(FOLDER + image_name)
for image in new_image_name_list:
html_string += '<img src="cid:%s">\n' %image
path_list.extend(new_path_list)
image_name_list.extend(new_image_name_list)
return html_string
def savePoint(self, lat=None, lon=None):
if (lat == None or lon == None):
return "Erro - Latitude ou Longitude não foram especificadas"
writer = csv.writer(open('points.csv', 'ab')) #, delimiter=' ', quotechar='|', quoting=csv.QUOTE_MINIMAL
#writer.writerow(['Horario', 'Latitude', 'Longitude'])
writer.writerow([datetime.strftime(datetime.now(), "%d/%m/%Y %H:%M:%S"), str(lat), str(lon)])
return "Point (" + str(lat) + ',' + str(lon) + ") saved at " + datetime.strftime(datetime.now(), "%d/%m/%Y %H:%M:%S")
def lookup_tweets(self, date_time, margin):
"""
Retrieve potentially relevant tweets around a date of a info trafic event
:param date_time: date of info trafic event
:param margin: index date margin
:return: list of tweets
"""
data = None
try:
dt_min, dt_max = self.get_bounds_dates(date_time, 1)
index_name = self.get_tweet_index_name(date_time, margin)
dt_min = datetime.strftime(dt_min, '%Y%m%d%H%M%S')
dt_max = datetime.strftime(dt_max, '%Y%m%d%H%M%S')
q = json.loads(pystache.render(self.query_sel_tweets, {'dt_min': dt_min, 'dt_max': dt_max}))
res = self.es.select_page(index_name, q, 0)
i = 1
data = []
long_data = []
while len(res) != 0:
short_res = map(lambda x: [x['_source']['created_at'], x['_source']['text']], res)
data.extend(short_res)
long_data.extend(res)
res = self.es.select_page(index_name, q, i)
i += 1
except:
traceback.print_exc()
pass
return data, long_data
def fetch(self):
"""Fetch of gamasutra.com
"""
try:
self.__genre = 'review'
self.__task_elements_dict = {
'priority':self.task.priority,
'level': self.task.level,
'last_updated_time':datetime.strftime(datetime.utcnow(),"%Y-%m-%dT%H:%M:%SZ"),
'pickup_date':datetime.strftime(datetime.utcnow(),"%Y-%m-%dT%H:%M:%SZ"),
'connector_instance_log_id': self.task.connector_instance_log_id,
'connector_instance_id':self.task.connector_instance_id,
'workspace_id':self.task.workspace_id,
'client_id':self.task.client_id,
'client_name':self.task.client_name,
'versioned':False,
'category':self.task.instance_data.get('category',''),
'task_log_id':self.task.id }
self.__setSoupForCurrentUri()
self.__setParentPage()
posts = self.__iteratePosts()
for post in posts:
if not self.__addPost(post):
log.debug(self.log_msg('Post not added to self.pages for url\
%s'%self.currenturi))
#return False
break
return True
except:
log.exception(self.log_msg('Exception in fetch for the url %s'%self.currenturi))
return True
def ng_scrape_blammed(soup):
# Scrape web page
#url = ('http://www.newgrounds.com/portal/view/381115')
# PARSE CONTENT
# Page title
title = soup.title.string.split(':')[1].strip()
# Author
author = soup.find_all("em")[5].text.split(' ')[2]
# Author Comments
try:
author_comments = soup.find("h2", text="Author Comments").parent.findNextSibling().text
author_comments = author_comments.replace('\n',' ').replace(',','').strip()
except:
author_comments = ''
# Page Star Rating
stars = soup.find(id='score_number').text
# Date Uploaded
date_uploaded = soup.find(id='eulogy').span.text.split(' ')[0]
date_blammed = soup.find(id='eulogy').span.text.split(' ')[2]
# Standard date formats
date_uploaded = datetime.strftime(datetime.strptime(date_uploaded, '%m-%d-%y').date(), '%m/%d/%Y')
date_blammed = datetime.strftime(datetime.strptime(date_blammed, '%m-%d-%y').date(), '%m/%d/%Y')
return[title, author, date_uploaded, date_blammed, stars, author_comments]
def multi_manpower_add(request):
if request.method=="POST":
print "multi_project_code",request.POST['multi_project_code']
print request.POST['multi_date_from']
print request.POST['multi_date_to']
if request.POST['multi_project_code'] and request.POST['multi_date_from'] and request.POST['multi_date_to']:
project_id=request.POST['multi_project_code']
multi_date_from=request.POST['multi_date_from']
multi_date_to=request.POST['multi_date_to']
try:
project=Project.objects.get(id=project_id)
print "project",project
manpower=ManPower.objects.filter(project=project)
print "manpower",manpower
for m in manpower:
multi_datetime_in=datetime.combine(datetime.strptime(multi_date_to,"%Y-%m-%d"),time(hour=m.time_in.hour,minute=m.time_in.minute,second=m.time_in.second,microsecond=0))
if datetime.strftime(m.time_in,"%Y-%m-%d")==datetime.strftime(m.time_out,"%Y-%m-%d"):
multi_datetime_out=datetime.combine(datetime.strptime(multi_date_to,"%Y-%m-%d"),datetime.strftime(m.time_out,"%H:%M:%S"))
else:
multi_datetime_out=datetime.combine(datetime.strptime(multi_date_to,"%Y-%m-%d")+datetime.timedelta(datetime.strftime(m.time_out,"%Y-%m-%d")-datetime.strftime(m.time_in,"%Y-%m-%d")),datetime.strftime(m.time_out,"%H:%M:%S"))
print "multi_datetime_out",multi_datetime_out
new_manpower=Manpower.objects.create(employee=m.employee,project=m.project,time_in=multi_datetime_in,time_out=multi_datetime_out,lunch=m.lunch,shift=m.shift,working_time=m.working_time,remark=m.remark,create_by=request.user,is_checked=False,checked_by="",is_payed=False,payed_period="")
new_manpower.save()
return render(request,"manpower/manpower_multi_add.html",{"new_manpower":new_manpower})
except Exception as e:
return single_manpower_add(request)
else:
return HttpResponse("Please fill complete information,click <a href='/manpower/add/'>here</a> go back.")
else:
return single_manpower_add(request)
def analize(self, value, field_type, id):
if field_type=='DateTimeField':
try:
value = datetime.strftime(value,self.datetime_format)
except:
value = Grid.UNDEFINED
elif field_type=='DateField':
try:
value = datetime.strftime(value, self.date_format)
except:
value = Grid.UNDEFINED
elif field_type=='TimeField':
try:
value = datetime.strftime(value, self.time_format)
except:
value = Grid.UNDEFINED
elif field_type=='BooleanField':
if value==True:
value = 1
else:
value = 0
elif field_type in ['ForeignKey','OneToOneField']:
try:
value = '<a href="%s" title="%s">%s</a>' % (value.get_absolute_url(), smart_unicode(value) )
except:
value = smart_unicode(value)
elif field_type in ['FileField','ImageField']:
try:
value = value.name
except:
value = Grid.UNDEFINED
return value
def serialize(self, fields = []):
from sys2do.util.common import _info
result = {}
m = self.__mapper__.columns
for cname in fields:
if cname not in m.keys(): continue
colClz = m[cname]
if isinstance(colClz, Column):
v = getattr(self, cname)
if v is None: v = u'' #the value is None
elif m[cname].foreign_keys : #it's a foreign key
tmp_name = cname.replace("_id", '')
try:
v = unicode(getattr(self, tmp_name))
except:
_info(traceback.print_exc())
v = u''
elif isinstance(v, dt) : v = dt.strftime(SYSTEM_DATETIME_FORMAT)
elif isinstance(v, date) : v = dt.strftime(SYSTEM_DATE_FORMAT)
elif isinstance(v, (int, float)) : v = u'%s' % v
else: v = unicode(v)
result[cname] = (v, colClz.doc or cname)
# _info(result)
return result
def fetch(self):
"""Fetch of games.yahoo.com
"""
try:
self.__genre = 'review'
self.__task_elements_dict = {
'priority':self.task.priority,
'level': self.task.level,
'last_updated_time':datetime.strftime(datetime.utcnow(),"%Y-%m-%dT%H:%M:%SZ"),
'pickup_date':datetime.strftime(datetime.utcnow(),"%Y-%m-%dT%H:%M:%SZ"),
'connector_instance_log_id': self.task.connector_instance_log_id,
'connector_instance_id':self.task.connector_instance_id,
'workspace_id':self.task.workspace_id,
'client_id':self.task.client_id,
'client_name':self.task.client_name,
'versioned':False,
'category':self.task.instance_data.get('category',''),
'task_log_id':self.task.id }
self.__setSoupForCurrentUri()
while True:
try:
if not self.__iteratePosts():
log.info(self.log_msg('No more links found'))
break
self.currenturi = self.soup.find('li','next').find('a')['href']
self.__setSoupForCurrentUri()
except:
log.exception(self.log_msg('next page not found'))
break
except:
log.exception(self.log_msg('Exception in fetch for the url %s'%self.currenturi))
return True
def _get_date(self, cr, uid, id, start_date, delay, resource=False, context=None):
"""This method gives the first date after a delay from the start date
considering the working time attached to the company calendar.
Start_date should be a date not an openerp date
"""
if not id:
company_id = self.pool['res.users'].read(cr, uid, uid,
['company_id'],
context=context)['company_id'][0]
company = self.pool['res.company'].read(cr, uid, company_id,
['calendar_id'],
context=context)
if not company['calendar_id']:
raise orm.except_orm(_('Error !'),
_('You need to define a calendar for the company !'))
id = company['calendar_id'][0]
dt_leave = self._get_leaves(cr, uid, id, resource)
calendar_info = self.browse(cr, SUPERUSER_ID, id, context=context)
worked_days = [day['dayofweek'] for day in calendar_info.attendance_ids]
if delay < 0:
delta = -1
else:
delta = 1
while datetime.strftime(start_date, DEFAULT_SERVER_DATE_FORMAT) in dt_leave or str(start_date.weekday()) not in worked_days:
start_date = start_date + timedelta(days=delta)
date = start_date
while delay:
date = date + timedelta(days=delta)
if datetime.strftime(date, DEFAULT_SERVER_DATE_FORMAT) not in dt_leave and str(date.weekday()) in worked_days:
delay = delay - delta
return date
def __getData(self, post):
page = {}
try:
post_tag = BeautifulSoup(post.__str__().replace('/>>','/>'))
table_tag = post_tag.find('table')
if table_tag:
table_tag.extract()
try:
page['data'] = stripHtml(post_tag.renderContents())
page['title']= ''
except:
log.exception(self.log_msg('Data not found for the url %s'%self.currenturi))
return
try:
date_str = stripHtml(table_tag.findAll('strong')[-1].renderContents())
page['posted_date'] = datetime.strftime(datetime.\
strptime(re.sub("(\d+)(st|nd|rd|th)",r"\1",date_str).\
strip(),"%d %B %Y"),"%Y-%m-%dT%H:%M:%SZ")
except:
log.exception(self.log_msg('Posted date not found'))
page['posted_date'] = datetime.strftime(datetime.utcnow(), "%Y-%m-%dT%H:%M:%SZ")
try:
page['et_author_name'] = stripHtml(table_tag.findAll('strong')[0].renderContents())
except:
log.exception(self.log_msg('author name not found'))
except:
log.exception(self.log_msg('post tag not found'))
return page
def load_from_toggl():
"""Load live data from toggl
"""
current_month = datetime.strftime(datetime.today(), '%m')
# find out what month to report. default to the current month
month = raw_input('Enter the month you want to report [%s]: ' % (current_month))
if len(month) == 0:
month = current_month
# make sure we have a two character month
while len(month) < 2:
month = '0' + month
start_date = datetime.today()
monthrange = calendar.monthrange(start_date.year, start_date.month)
data = {'start_date': '%s-01' % (datetime.strftime(start_date, '%Y-%m')),
'end_date': '%s-%s' % (datetime.strftime(start_date, '%Y-%m'), monthrange[1])}
print data
# hit toggl for the time entries
req = urllib2.Request('https://www.toggl.com/api/v6/time_entries.json?' + urllib.urlencode(data))
authn_header = base64.encodestring('%s:api_token' % API_KEY)
req.add_header('Authorization', 'Basic %s' % authn_header)
try:
resp = urllib2.urlopen(req)
except urllib2.HTTPError, msg:
print "Error loading time entries from toggl: %s" % (msg)
exit(1)
def fetch(self):
"""
http://www.mrrebates.com/store_ratings/view_store_ratings.asp?merchant_id=1465
"""
try:
self.__setSoupForCurrentUri()
self.__genre = "Review"
self.__task_elements_dict = {
'priority':self.task.priority,
'level': self.task.level,
'last_updated_time':datetime.strftime(datetime.utcnow(), "%Y-%m-%dT%H:%M:%SZ"),
'pickup_date':datetime.strftime(datetime.utcnow(), "%Y-%m-%dT%H:%M:%SZ"),
'posted_date':datetime.strftime(datetime.utcnow(), "%Y-%m-%dT%H:%M:%SZ"),
'connector_instance_log_id': self.task.connector_instance_log_id,
'connector_instance_id':self.task.connector_instance_id,
'workspace_id':self.task.workspace_id,
'client_id':self.task.client_id,
'client_name':self.task.client_name,
'versioned':False,
'category':self.task.instance_data.get('category',''),
'task_log_id':self.task.id }
while self.__iteratePosts():
try:
self.currenturi = 'http://www.mrrebates.com' + self.soup.find('b', text='Next').parent.parent['href']
self.__setSoupForCurrentUri()
except:
log.info(self.log_msg('No Previous URL found for url \
%s'%self.currenturi))
break
except:
log.exception(self.log_msg('Exception while fetching posts'))
return True
def __init__(self, jid, password, room, nick):
sleekxmpp.ClientXMPP.__init__(self, jid, password)
self.urlhandler = URLHandler(self)
self.room = room
self.nick = nick
# The session_start event will be triggered when
# the bot establishes its connection with the server
# and the XML streams are ready for use. We want to
# listen for this event so that we we can initialize
# our roster.
self.add_event_handler("session_start", self.start)
# The groupchat_message event is triggered whenever a message
# stanza is received from any chat room. If you also also
# register a handler for the 'message' event, MUC messages
# will be processed by both handlers.
self.add_event_handler("groupchat_message", self.muc_message)
# The groupchat_presence event is triggered whenever a
# presence stanza is received from any chat room, including
# any presences you send yourself. To limit event handling
# to a single room, use the events muc::room@server::presence,
# muc::room@server::got_online, or muc::room@server::got_offline.
self.add_event_handler("muc::%s::got_online" % self.room,
self.muc_online)
self.logfile = datetime.strftime(datetime.now(), "logs/%Y%m%d_%H%M.txt")
self.urlfile = datetime.strftime(datetime.now(), "logs/urls.txt")
self.logfile_handle = open(self.logfile, "w")
self.urlfile_handle = open(self.logfile, "a")
self.urls = [] #todo: read file
def copy(self, cr, uid, id, default=None, context=None):
if not default:
default = {}
record = self.browse(cr, uid, id, context=context)
date_start = datetime.strptime(record.date_start,tools.DEFAULT_SERVER_DATETIME_FORMAT)
date_start = date_start + timedelta(days=1)
date_start = datetime.strftime(date_start,tools.DEFAULT_SERVER_DATETIME_FORMAT)
date_end = datetime.strptime(record.date_start,tools.DEFAULT_SERVER_DATETIME_FORMAT)
date_end = date_end + timedelta(days=1)
date_end = datetime.strftime(date_end,tools.DEFAULT_SERVER_DATETIME_FORMAT)
date = datetime.strptime(record.date,tools.DEFAULT_SERVER_DATE_FORMAT)
date = date + timedelta(days=1)
date = datetime.strftime(date,tools.DEFAULT_SERVER_DATE_FORMAT)
#TODO: de introdus pozitii si pentru rute cu data incrementata
default.update({
'log_fuel_ids':[],
'name': self.pool.get('ir.sequence').next_by_code(cr, uid, 'fleet.map.sheet'),
'date': date,
'date_start': date_start,
'date_start': date_end,
})
new_id = super(fleet_map_sheet, self).copy(cr, uid, id, default, context=context)
return new_id
def download():
form = AdvancedSearchForm()
form.business_type.default = 'All Entities'
if form.validate_on_submit():
q_object = {
'query': form.query.data,
'query_limit': form.query_limit.data,
'index_field': form.index_field.data,
'active': form.active.data,
'sort_by': form.sort_by.data,
'sort_order': form.sort_order.data
}
try:
q_object['start_date'] = datetime.strftime(form.start_date.data, '%Y-%m-%d')
q_object['end_date'] = datetime.strftime(form.end_date.data, '%Y-%m-%d')
except TypeError:
q_object['start_date'] = date(year=1990, month=1, day=1)
q_object['end_date'] = datetime.now()
q_object['business_type'] = form.business_type.data
results = query(q_object)
file = StringIO()
writer = csv.DictWriter(file, fieldnames=['name', 'id', 'origin date', 'status', 'type', 'street', 'city', 'state', 'zip'])
writer.writeheader()
for biz in results.all():
row = {'name': biz.nm_name, 'id': biz.id_bus, 'origin date': biz.dt_origin, 'status': biz.status,
'type': biz.type, 'street': biz.street, 'city': biz.city, 'state': biz.state, 'zip': biz.zip}
writer.writerow(row)
file.seek(0)
response = Response(file, content_type='text/csv')
response.headers['Content-Disposition'] = 'attachment; filename=sots_search_results.csv'
return response
def _getBookingsBetweenTimestamps(self, fromDate, toDate,
tz = 'UTC', conferenceId = None, categoryId = None, dateFormat = None):
bookings = []
nBookings = 0
date = None
bookingsForDate = None
for timestamp, s in self._tree.iteritems(fromDate, toDate):
currentDate = unixTimeToDatetime(timestamp, tz).date()
if date != currentDate:
if date is not None and bookingsForDate:
bookings.append((datetime.strftime(date, dateFormat), bookingsForDate))
nBookings += len(bookingsForDate)
date = currentDate
bookingsForDate = []
if conferenceId:
for booking in s:
if booking.getConference().getId() == conferenceId:
bookingsForDate.append(booking)
elif categoryId:
cc = CategoryChecker(categoryId)
for booking in s:
if cc.check(booking.getConference()):
bookingsForDate.append(booking)
else:
bookingsForDate.extend(s)
if date is not None and bookingsForDate:
bookings.append((datetime.strftime(date, dateFormat), bookingsForDate))
nBookings += len(bookingsForDate)
return bookings, nBookings
def gamecheck(self, gameURL):
while True:
try:
response = urllib2.urlopen(gameURL)
break
except:
check = datetime.today()
print datetime.strftime(check, "%d %I:%M %p")
print "gamecheck couldn't find file, trying again..."
time.sleep(20)
jsonResponse = json.load(response)
timeData = jsonResponse["gameData"]["datetime"]
if "timeDate" in timeData:
timestring = timeData["timeDate"] + " " + timeData["ampm"]
date_object = datetime.strptime(timestring, "%Y/%m/%d %I:%M %p")
else:
timestring = timeData["originalDate"] + " " + timeData["time"] + " " + timeData["ampm"]
date_object = datetime.strptime(timestring, "%Y-%m-%d %I:%M %p")
while True:
check = datetime.today()
if date_object >= check:
if (date_object - check).seconds <= self.time_before:
return
else:
print "Last game check: " + datetime.strftime(check, "%d %I:%M %p")
time.sleep(600)
else:
return
def statd(request, q_type = 'today'):
day_format = "%Y-%m-%d"
today = datetime.combine(datetime.today(), time(9, 0));
yesterday = today - timedelta(hours=24);
domain = request.POST.get('domain', '')
domain = '' if domain == "ALL" else domain;
if not q_type:
q_type = 'yestoday'
datas = []
if(q_type == 'today'):
# today = datetime.combine(datetime.today(),time(9, 0)) + timedelta(hours=24)
datas = get_statd(start = datetime.strftime(today, day_format), domain = domain);
elif(q_type == 'yestoday'):
datas = get_statd(start = datetime.strftime(yesterday, day_format), domain = domain);
# today = datetime.combine(datetime.today(), time(9, 0));
# datas = get_statd(start = datetime.strftime(today, day_format), domain = domain);
elif(q_type == 'week'):
week_ago = date.today() - timedelta(days=7);
datas = get_statd(start = datetime.strftime(week_ago, day_format), end = datetime.strftime(yesterday, day_format), domain = domain);
elif(q_type == "custom"):
start = request.POST["start"];
end = request.POST["end"];
message_id = request.POST["msg"];
datas = get_statd(start = start, end = end, message_id = message_id, domain = domain);
domains = set([data["domain_name"] for data in datas]);
return locals();
def compute_attack(full=False):
attackers = []
if full:
for n in graph_fwd.nodes():
attackers.append(n)
else:
append_node_by_ip(entry_node,attackers)
#Append compromised nodes
for ip in compromised:
append_node_by_ip(ip,attackers)
#attackers.append('1884')
# Build attack graph for attackers
print "Generating attack graph..."
print datetime.strftime(datetime.now(), '%Y-%m-%d %H:%M:%S')
attack_graph = generateAttackGraph(graph_fwd, attackers, easiest)
print datetime.strftime(datetime.now(), '%Y-%m-%d %H:%M:%S')
print "Final attack graph generated for the list of attackers: ", attackers
pprint(attack_graph)
t = datetime.strftime(datetime.now(), '%Y-%m-%d-%H-%M-%S')
fName = projectPathName+"\\attack_graph\\attack_graph-"+t+".xml"
# Save attack graph to XML
print "Attack graph exported to XML format and saved in file attack_graph.xml"
saveXML(attack_graph, fName)
def getPlotTitle(self, prof_coll):
modified = prof_coll.isModified() or prof_coll.isInterpolated()
modified_str = "; Modified" if modified else ""
loc = prof_coll.getMeta('loc')
date = prof_coll.getCurrentDate()
run = prof_coll.getMeta('run').strftime("%HZ")
model = prof_coll.getMeta('model')
observed = prof_coll.getMeta('observed')
plot_title = loc + ' ' + datetime.strftime(date, "%Y%m%d/%H%M")
if model == "Archive":
fhour_str = ""
if not prof_coll.getMeta('observed'):
fhour = int(total_seconds(date - prof_coll.getMeta('base_time')) / 3600)
fhour_str = " F%03d" % fhour
plot_title += " (User Selected" + fhour_str + modified_str + ")"
elif model == "Analog":
date = prof_coll.getAnalogDate()
plot_title = loc + ' ' + datetime.strftime(date, "%Y%m%d/%H%M")
plot_title += " (Analog" + modified_str + ")"
elif observed:
plot_title += " (Observed" + modified_str + ")"
else:
fhour = int(total_seconds(date - prof_coll.getMeta('base_time')) / 3600)
plot_title += " (" + run + " " + model + " " + ("F%03d" % fhour) + modified_str + ")"
return plot_title
def meta_ingeststatus(request):
"""
Returns the current information on the ingestion process
"""
ingestions = {}
for ingest in Ingestor.objects.using("default").all():
if ingest.service_name not in ingestions:
ingestions[ingest.service_name] = {
'total': 0,
'remaining': 0,
'current': '',
'completed': 0,
'last_ingest_date': None,
'current_start': None
}
if ingest.completed == 1 and ingest.completed_date:
ingestions[ingest.service_name]['completed'] += 1
if ingestions[ingest.service_name]['last_ingest_date'] is None or ingest.completed_date > ingestions[ingest.service_name]['last_ingest_date'].completed_date:
ingestions[ingest.service_name]['last_ingest_date'] = ingest
else:
ingestions[ingest.service_name]['remaining'] += 1
if ingest.completed == 0 and ingest.started == 1:
ingestions[ingest.service_name]['current'] = ingest.meta
if ingest.started_date is not None:
ingestions[ingest.service_name]['current_start'] = datetime.strftime(ingest.started_date, "%Y-%m-%d %H:%M:%S")
ingestions[ingest.service_name]['total'] += 1
for ingest in ingestions:
if ingestions[ingest]['last_ingest_date'] is not None:
ingestions[ingest]['last_ingest_date'] = datetime.strftime(ingestions[ingest]['last_ingest_date'].completed_date, "%Y-%m-%d %H:%M:%S")
return api.views.api_render(request, ingestions, status.HTTP_200_OK)
def main():
# 导入模型
network = importlib.import_module(Config.model_def) # 相当于导入 .py 文件
# 用时间命名
subdir = datetime.strftime(datetime.now(),'%Y%m%d-%H%M%S')
model_dir = os.path.join(os.path.expanduser(Config.models_base_dir),subdir)
if not os.path.isdir(model_dir):
os.makedirs(model_dir)
# 读取数据
train_set = data_process.get_data_set(Config.data_dir)
# 类别总数
nrof_classes = len(train_set)
pretrained_model = None
if Config.pretrained_model:
pretrained_model = os.path.expanduser(Config.pretrained_model)
print('Pre-trained model: %s'%pretrained_model)
with tf.Graph().as_default():
global_step = tf.Variable(0,trainable=False)
image_list, label_list = data_process.get_image_paths_and_labels(train_set)
assert len(image_list)>0,'The dataset should not empty'
labels = ops.convert_to_tensor(label_list,dtype=tf.int32)
range_size = array_ops.shape(labels)[0]
index_queue = tf.train.range_input_producer(range_size,num_epochs=None,shuffle=True,seed = None,capacity=32)
index_dequeue_op = index_queue.dequeue_many(Config.batch_size*Config.epoch_size,'index_dequeue')
learning_rate_placeholder = tf.placeholder(tf.float32,name='learning_rate')
batch_size_placeholder = tf.placeholder(tf.int32,name='batch_size')
train_flag = tf.placeholder(tf.bool,name='phase_train')
image_paths_placeholder = tf.placeholder(tf.string,shape=(None,1),name='image_paths')
labels_placeholder = tf.placeholder(tf.int64,shape=(None,1),name='labels')
input_queue = data_flow_ops.FIFOQueue(capacity=500000,
dtypes=[tf.string,tf.int64],
shapes=[(1,),(1,)],
shared_name=None,name=None)
enqueue_op = input_queue.enqueue_many([image_paths_placeholder,labels_placeholder],name='enqueue_op')
nrof_preprocess_threads = 4
images_and_labels = []
for _ in range(nrof_preprocess_threads):
filenames, label = input_queue.dequeue()
images = []
for filename in tf.unstack(filenames):
file_contents = tf.read_file(filename)
image = tf.image.decode_image(file_contents, channels=3)
if Config.random_rotate:
image = tf.py_func(data_process.random_rotate_image, [image], tf.uint8)
if Config.random_crop:
image = tf.random_crop(image, [Config.image_size, Config.image_size, 3])
else:
image = tf.image.resize_image_with_crop_or_pad(image, Config.image_size, Config.image_size)
if Config.random_flip:
image = tf.image.random_flip_left_right(image)
# pylint: disable=no-member
image.set_shape((Config.image_size, Config.image_size, 3))
images.append(tf.image.per_image_standardization(image))
images_and_labels.append([images, label])
image_batch,label_batch = tf.train.batch_join(
images_and_labels,batch_size=batch_size_placeholder,
shapes=[(Config.image_size,Config.image_size,3),()],enqueue_many=True,
capacity=4*nrof_preprocess_threads*Config.batch_size,
allow_smaller_final_batch=True)
image_batch = tf.identity(image_batch,'image_batch')
image_batch = tf.identity(image_batch,'input')
label_batch = tf.identity(label_batch,'label_batch')
print('Total number of classes: %d'%nrof_classes)
print('Total number of examples: %d'%len(image_list))
print('Building training graph')
prelogits = network.inference(image_batch,Config.keep_prob,
phase_train = train_flag,bottleneck_layer_size = Config.embedding_size,
weight_decay = Config.weight_decay)
logits = slim.fully_connected(prelogits, len(train_set), activation_fn=None,
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
weights_regularizer=slim.l2_regularizer(Config.weight_decay),
scope='Logits', reuse=False)
embeddings = tf.nn.l2_normalize(prelogits, 1, 1e-10, name='embeddings')
# 添加中心损失
if Config.center_loss_weight >0.0:
prelogits_center_loss,_ = utils.center_loss(prelogits,label_batch,Config.center_loss_alfa,nrof_classes)
tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES,prelogits_center_loss*Config.center_loss_weight)
learning_rate = tf.train.exponential_decay(learning_rate_placeholder,global_step,
Config.learning_rate_decay_epochs*Config.epoch_size,
Config.learning_rate_decay_factor,staircase=True)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=label_batch,logits=logits,
name='cross_entropy_per_example')
cross_entropy_mean = tf.reduce_mean(cross_entropy,name='cross_entropy')
tf.add_to_collection('losses',cross_entropy_mean)
# 把中心损失加到交叉softmax损失上
regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
total_loss = tf.add_n([cross_entropy_mean]+regularization_losses,name='total_loss')
# 一个batch 训练操作并更新模型参数
train_op = train_batch(total_loss,global_step,Config.optimizer,learning_rate,
Config.moving_average_decay,tf.global_variables())
# 创建一个保存器
saver = tf.train.Saver(tf.trainable_variables(),max_to_keep=3)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction = Config.gpu_memory_fraction)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,log_device_placement=False))
sess.run(tf.global_variables_initializer())
# 获得线程坐标，启动填充队列的线程
coord = tf.train.Coordinator()
tf.train.start_queue_runners(coord=coord,sess=sess)
with sess.as_default():
sess.run(tf.local_variables_initializer())
if pretrained_model:
print('Restoring pretrained model: %s'%pretrained_model)
meta_file, ckpt_file = utils.get_model_filenames(Config.pretrained_model)
saver = tf.train.import_meta_graph(os.path.join(Config.pretrained_model, meta_file))
saver.restore(sess, os.path.join(Config.pretrained_model, ckpt_file))
print('Running training')
epoch = 0
while epoch < Config.max_nrof_epochs:
step = sess.run(global_step,feed_dict=None)
utils.save_variables_and_metagraph(sess, saver, model_dir, subdir, step)
print('++++++++++save done++++++++++')
epoch = step // Config.epoch_size
# 训练一个epoch
train(sess,epoch,image_list,label_list,index_dequeue_op,enqueue_op,image_paths_placeholder,labels_placeholder,
learning_rate_placeholder,train_flag,batch_size_placeholder,global_step,
total_loss,train_op,regularization_losses)
utils.save_variables_and_metagraph(sess,saver,model_dir,subdir,step)
return model_dir
def gather_data(
access_token,
access_token_secret,
api_key,
api_key_secret,
chunked_user_list
):
"""
Gather user info based on the chunked list of usernames with the provided
bearer_token.
"""
print("Gathering Data...")
oauth1a = osometweet.OAuth1a(
api_key=api_key,
api_key_secret=api_key_secret,
access_token=access_token,
access_token_secret=access_token_secret
)
ot = osometweet.OsomeTweet(oauth1a)
# Add all user_fields
all_user_fields = osometweet.UserFields(everything=True)
# Get today's date
today = dt.strftime(dt.today(), "%Y-%m-%d_%H-%M")
# Open two files. One for good data, the other for account errors.
with open(f"account_data--{today}.json", 'w') as data_file,\
open(f"account_errors--{today}.json", 'w') as error_file:
# Iterate through the list of lists
for one_hundred_users in chunked_user_list:
response = ot.user_lookup_usernames(
usernames=one_hundred_users,
fields=all_user_fields
)
# Where as the user_ids endpoint always returns both "data" and
# "errors", the username endpoint does the opposite - only
# including these keys if data is present.
if "data" in response:
data = response["data"]
else:
data = None
if "errors" in response:
errors = response["errors"]
else:
errors = None
try:
data_file.writelines(f"{json.dumps(line)}\n" for line in data)
except TypeError:
print(
"No USER data found in this set of users, "
"skipping to the next set."
)
try:
error_file.writelines(
f"{json.dumps(line)}\n" for line in errors
)
except TypeError:
print(
"No problematic users found in this set of user, "
"skipping to the next set."
)
def plot_prophet_forecast(energy_forecast_idf, df_idf_plot, mape, figname):
plt.figure(figsize=(20, 5), linewidth=2)
forecast = energy_forecast_idf[
energy_forecast_idf["ds"] > md.END_TRAIN_DATE]
model_past_fitting = energy_forecast_idf[
energy_forecast_idf["ds"] <= md.END_TRAIN_DATE]
plt.plot(df_idf_plot.index,
df_idf_plot[c.EnergyConso.CONSUMPTION],
color="steelblue",
label="observations",
linewidth=2)
plt.plot(forecast["ds"],
forecast["yhat"],
color="g",
label="forecasting",
linewidth=2)
if len(model_past_fitting) > 0:
plt.plot(model_past_fitting["ds"],
model_past_fitting["yhat"],
color="g",
label="fitting of past values",
linestyle="dashed",
linewidth=2)
plt.fill_between(forecast["ds"],
forecast["yhat_lower"],
forecast["yhat_upper"],
color="green",
alpha=0.2,
label="90% confidence interval")
plt.title("Prophet: Prediction for Ile de France with" +
" MAPE: {}%".format(str(round(100 * mape, 1))),
fontsize=TITLE_FONTSIZE)
plt.grid(which="both")
plt.ylabel("Consumption (MW)", fontsize=LABEL_FONTSIZE)
ax = plt.gca()
ax.set_xlim([
md.END_TRAIN_DATE - timedelta(days=md.NB_HOURS_PRED / 24),
md.END_TRAIN_DATE + timedelta(days=md.NB_HOURS_PRED / 24)
])
ax.set_ylim([12000, 28000])
xticks = [
md.END_TRAIN_DATE + timedelta(days=x)
for x in [-11, -7, -3, 0, 4, 8, 12]
]
ax.set_xticks(xticks, minor=False)
ax.set_xticklabels(
[datetime.strftime(date, "%Y-%m-%d") for date in xticks],
minor=False,
fontsize=LABEL_FONTSIZE)
yticks = np.arange(14000, 28000, step=2000)
ax.set_yticks(yticks)
ax.set_yticklabels([str(x) for x in yticks], fontsize=LABEL_FONTSIZE)
plt.legend()
plt.savefig(
os.path.join(PROPHET_PLOTS,
"{}.png".format(figname.replace(".pkl", ""))))
plt.close()
def main(args):
# 模型，定义在inception_resnet_v1 V2里（）， --model_def models.inception_resnet_v1
network = importlib.import_module(args.model_def)
image_size = (args.image_size, args.image_size)
subdir = datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S')
log_dir = os.path.join(os.path.expanduser(args.logs_base_dir), subdir)
if not os.path.isdir(log_dir): # Create the log directory if it doesn't exist
os.makedirs(log_dir)
model_dir = os.path.join(os.path.expanduser(args.models_base_dir), subdir)
if not os.path.isdir(model_dir): # Create the model directory if it doesn't exist
os.makedirs(model_dir)
stat_file_name = os.path.join(log_dir, 'stat.h5')
# Write arguments to a text file
facenet.write_arguments_to_file(args, os.path.join(log_dir, 'arguments.txt'))
# Store some git revision info in a text file in the log directory
src_path,_ = os.path.split(os.path.realpath(__file__))
facenet.store_revision_info(src_path, log_dir, ' '.join(sys.argv))
np.random.seed(seed=args.seed)
random.seed(args.seed)
dataset = facenet.get_dataset(args.data_dir)
if args.filter_filename:
dataset = filter_dataset(dataset, os.path.expanduser(args.filter_filename),
args.filter_percentile, args.filter_min_nrof_images_per_class)
if args.validation_set_split_ratio>0.0:
train_set, val_set = facenet.split_dataset(dataset, args.validation_set_split_ratio, args.min_nrof_val_images_per_class, 'SPLIT_IMAGES')
else:
train_set, val_set = dataset, []
nrof_classes = len(train_set)
print('Model directory: %s' % model_dir)
print('Log directory: %s' % log_dir)
pretrained_model = None
if args.pretrained_model:
pretrained_model = os.path.expanduser(args.pretrained_model)
print('Pre-trained model: %s' % pretrained_model)
if args.lfw_dir:
print('LFW directory: %s' % args.lfw_dir)
# Read the file containing the pairs used for testing
pairs = lfw.read_pairs(os.path.expanduser(args.lfw_pairs))
# Get the paths for the corresponding images
lfw_paths, actual_issame = lfw.get_paths(os.path.expanduser(args.lfw_dir), pairs)
with tf.Graph().as_default():
tf.set_random_seed(args.seed)
global_step = tf.Variable(0, trainable=False)
# Get a list of image paths and their labels
# 训练数据
image_list, label_list = facenet.get_image_paths_and_labels(train_set)
assert len(image_list)>0, 'The training set should not be empty'
# 测试数据
val_image_list, val_label_list = facenet.get_image_paths_and_labels(val_set)
# Create a queue that produces indices into the image_list and label_list
# tf.convert_to_tensor用于将不同数据变成张量：比如可以让数组变成张量、也可以让列表变成张量。
labels = ops.convert_to_tensor(label_list, dtype=tf.int32)
range_size = array_ops.shape(labels)[0]
# 多线程读取数据，shuffle=True表示不是按顺序存储，可以随机获取，并一直循环。
# https://blog.csdn.net/lyg5623/article/details/69387917
index_queue = tf.train.range_input_producer(range_size, num_epochs=None,
shuffle=True, seed=None, capacity=32)
# epoch 大数据时迭代完一轮时次数，少量数据应该epoch = 全部数据个数/batch
index_dequeue_op = index_queue.dequeue_many(args.batch_size*args.epoch_size, 'index_dequeue')
learning_rate_placeholder = tf.placeholder(tf.float32, name='learning_rate')
batch_size_placeholder = tf.placeholder(tf.int32, name='batch_size')
phase_train_placeholder = tf.placeholder(tf.bool, name='phase_train')
image_paths_placeholder = tf.placeholder(tf.string, shape=(None,1), name='image_paths')
labels_placeholder = tf.placeholder(tf.int32, shape=(None,1), name='labels')
control_placeholder = tf.placeholder(tf.int32, shape=(None,1), name='control')
nrof_preprocess_threads = 4
input_queue = data_flow_ops.FIFOQueue(capacity=2000000,
dtypes=[tf.string, tf.int32, tf.int32],
shapes=[(1,), (1,), (1,)],
shared_name=None, name=None)
enqueue_op = input_queue.enqueue_many([image_paths_placeholder, labels_placeholder, control_placeholder], name='enqueue_op')
image_batch, label_batch = facenet.create_input_pipeline(input_queue, image_size, nrof_preprocess_threads, batch_size_placeholder)
image_batch = tf.identity(image_batch, 'image_batch')
image_batch = tf.identity(image_batch, 'input')
label_batch = tf.identity(label_batch, 'label_batch')
print('Number of classes in training set: %d' % nrof_classes)
print('Number of examples in training set: %d' % len(image_list))
print('Number of classes in validation set: %d' % len(val_set))
print('Number of examples in validation set: %d' % len(val_image_list))
print('Building training graph')
# Build the inference graph
prelogits, _ = network.inference(image_batch, args.keep_probability,
phase_train=phase_train_placeholder, bottleneck_layer_size=args.embedding_size,
weight_decay=args.weight_decay)
# 因为模型输出的(bottleneck_layer_size）没有计算最后一层(映射到图片类型)，这里计算最后一层
logits = slim.fully_connected(prelogits, len(train_set), activation_fn=None,
weights_initializer=slim.initializers.xavier_initializer(),
weights_regularizer=slim.l2_regularizer(args.weight_decay),
scope='Logits', reuse=False)
# 按行进行泛化，行的平方求和再求平方根，得到的值按行除每个行的元素，对深度层面泛化? interface里最后一层输出为128个节点，slim.fully_connected(net, bottleneck_layer_size, activation_fn=None,
#https://blog.csdn.net/abiggg/article/details/79368982
embeddings = tf.nn.l2_normalize(prelogits, 1, 1e-10, name='embeddings')
# 计算loss函数，当然还有其它训练参数也会加到这里来，通过比训练过程中一个weight加到正则化参数里来tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, weight)
# 模型中最后会把这个加到优化的loss中来。
#L= L_softmax + λL_cneter = Softmax(W_i + b_yj) + λ1/2||f(x_i) - c_yj ||_2^2
# Norm for the prelogits
eps = 1e-4
prelogits_norm = tf.reduce_mean(tf.norm(tf.abs(prelogits)+eps, ord=args.prelogits_norm_p, axis=1))
# 模型中最后输出(bottleneck_layer_size每个类型的输出值的个数)的平均值加到正则化loss中，但prelogits_norm_loss_factor貌似为0
tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, prelogits_norm * args.prelogits_norm_loss_factor)
# 计算中心损失及增加的正则化loss中
# Add center loss
prelogits_center_loss, _ = facenet.center_loss(prelogits, label_batch, args.center_loss_alfa, nrof_classes)
tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, prelogits_center_loss * args.center_loss_factor)
learning_rate = tf.train.exponential_decay(learning_rate_placeholder, global_step,
args.learning_rate_decay_epochs*args.epoch_size, args.learning_rate_decay_factor, staircase=True)
tf.summary.scalar('learning_rate', learning_rate)
# Calculate the average cross entropy loss across the batch
# 计算预测损失，和上面框架的Softmax(W_i + b_yj)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=label_batch, logits=logits, name='cross_entropy_per_example')
cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
# 预测损失平均值加到losses变量中
tf.add_to_collection('losses', cross_entropy_mean)
correct_prediction = tf.cast(tf.equal(tf.argmax(logits, 1), tf.cast(label_batch, tf.int64)), tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
#计算总损失，cross_entropy_mean + 前面增加的一些正则化损失(包括模型中增加的)，通过tf.GraphKeys.REGULARIZATION_LOSSES获取出来
# Calculate the total losses
regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
total_loss = tf.add_n([cross_entropy_mean] + regularization_losses, name='total_loss')
# Build a Graph that trains the model with one batch of examples and updates the model parameters
train_op = facenet.train(total_loss, global_step, args.optimizer,
learning_rate, args.moving_average_decay, tf.global_variables(), args.log_histograms)
# Create a saver
saver = tf.train.Saver(tf.trainable_variables(), max_to_keep=3)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.summary.merge_all()
# Start running operations on the Graph.
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
coord = tf.train.Coordinator()
tf.train.start_queue_runners(coord=coord, sess=sess)
with sess.as_default():
if pretrained_model:
print('Restoring pretrained model: %s' % pretrained_model)
saver.restore(sess, pretrained_model)
# Training and validation loop
print('Running training')
nrof_steps = args.max_nrof_epochs*args.epoch_size
nrof_val_samples = int(math.ceil(args.max_nrof_epochs / args.validate_every_n_epochs)) # Validate every validate_every_n_epochs as well as in the last epoch
stat = {
'loss': np.zeros((nrof_steps,), np.float32),
'center_loss': np.zeros((nrof_steps,), np.float32),
'reg_loss': np.zeros((nrof_steps,), np.float32),
'xent_loss': np.zeros((nrof_steps,), np.float32),
'prelogits_norm': np.zeros((nrof_steps,), np.float32),
'accuracy': np.zeros((nrof_steps,), np.float32),
'val_loss': np.zeros((nrof_val_samples,), np.float32),
'val_xent_loss': np.zeros((nrof_val_samples,), np.float32),
'val_accuracy': np.zeros((nrof_val_samples,), np.float32),
'lfw_accuracy': np.zeros((args.max_nrof_epochs,), np.float32),
'lfw_valrate': np.zeros((args.max_nrof_epochs,), np.float32),
'learning_rate': np.zeros((args.max_nrof_epochs,), np.float32),
'time_train': np.zeros((args.max_nrof_epochs,), np.float32),
'time_validate': np.zeros((args.max_nrof_epochs,), np.float32),
'time_evaluate': np.zeros((args.max_nrof_epochs,), np.float32),
'prelogits_hist': np.zeros((args.max_nrof_epochs, 1000), np.float32),
}
for epoch in range(1,args.max_nrof_epochs+1):
step = sess.run(global_step, feed_dict=None)
# Train for one epoch
t = time.time()
# 训练模型
cont = train(args, sess, epoch, image_list, label_list, index_dequeue_op, enqueue_op, image_paths_placeholder, labels_placeholder,
learning_rate_placeholder, phase_train_placeholder, batch_size_placeholder, control_placeholder, global_step,
total_loss, train_op, summary_op, summary_writer, regularization_losses, args.learning_rate_schedule_file,
stat, cross_entropy_mean, accuracy, learning_rate,
prelogits, prelogits_center_loss, args.random_rotate, args.random_crop, args.random_flip, prelogits_norm, args.prelogits_hist_max, args.use_fixed_image_standardization)
stat['time_train'][epoch-1] = time.time() - t
if not cont:
break
# 在测试数据上计算正确率
t = time.time()
if len(val_image_list)>0 and ((epoch-1) % args.validate_every_n_epochs == args.validate_every_n_epochs-1 or epoch==args.max_nrof_epochs):
validate(args, sess, epoch, val_image_list, val_label_list, enqueue_op, image_paths_placeholder, labels_placeholder, control_placeholder,
phase_train_placeholder, batch_size_placeholder,
stat, total_loss, regularization_losses, cross_entropy_mean, accuracy, args.validate_every_n_epochs, args.use_fixed_image_standardization)
stat['time_validate'][epoch-1] = time.time() - t
# Save variables and the metagraph if it doesn't exist already
save_variables_and_metagraph(sess, saver, summary_writer, model_dir, subdir, epoch)
# Evaluate on LFW
t = time.time()
if args.lfw_dir:
evaluate(sess, enqueue_op, image_paths_placeholder, labels_placeholder, phase_train_placeholder, batch_size_placeholder, control_placeholder,
embeddings, label_batch, lfw_paths, actual_issame, args.lfw_batch_size, args.lfw_nrof_folds, log_dir, step, summary_writer, stat, epoch,
args.lfw_distance_metric, args.lfw_subtract_mean, args.lfw_use_flipped_images, args.use_fixed_image_standardization)
stat['time_evaluate'][epoch-1] = time.time() - t
print('Saving statistics')
with h5py.File(stat_file_name, 'w') as f:
for key, value in stat.items():
f.create_dataset(key, data=value)
return model_dir
from matplotlib.finance import quotes_historical_yahoo_ochl # 注matplotlib包里已经没有了quotes_historical_yahoo方法了，改为quotes_historical_yahoo_ochl
from datetime import date
from datetime import datetime
import pandas as pd
import numpy as np
import time
import matplotlib.pyplot as plt # 使用pylab 就直接import pylab as pl
today = date.today()
start = (today.year - 1, today.month, today.day)
quotes = quotes_historical_yahoo_ochl('AXP', start, today) #美国运通公司最近一年股票代码
fields = ['date', 'open', 'close', 'high', 'low', 'volume']
list1 = []
for i in range(0, len(quotes)):
x = date.fromordinal(int(quotes[i][0]))
y = datetime.strftime(x, "%Y-%m-%d")
list1.append(y)
qutoesdf = pd.DataFrame(quotes, index=list1,
columns=fields) # 利用index属性可以将索引改变。 日期为格里高利时间，用函数改变
qutoesdf = qutoesdf.drop(['date'], axis=1)
# print qutoesdf
# 统计近一年每个月的股票开盘天数
listtemp = []
for i in range(0, len(qutoesdf)):
temp = time.strptime(qutoesdf.index[i], "%Y-%m-%d")
listtemp.append(temp.tm_mon)
# print listtemp
tempdf = qutoesdf.copy()
def current_date():
return move_busdays(datetime.strftime(datetime.now(), '%Y-%m-%d'), 0)
def today_date():
return datetime.strftime(datetime.now(), '%Y-%m-%d')
def optimize_diameters_execs(path, inpfile, execs, **kwargs):
""" Optimize diameters
Optimize pipe diameters of a hydraulic network using Genetic Algorithms.
Warning! This assumes single-objective optimization, so only the first
element of the best solutions tuple is going to be used.
:param str path: path to the input file.
:param str inpfile: EPANET's input file (INP) with network data.
:param int pop: population size or number of individuals.
:param int gen: number of generations.
:param float cxbp: crossover (mating) probability.
:param float mutpb: mutation probability.
:param float indpb: individual mutation probability?
:param str dir: directory to save the results.
:return best: a list of diameters (best solution)
"""
start = datetime.now()
_unit_price = kwargs.get('prices', {})
_popsize = kwargs.get('pop', 200)
_cxpb = kwargs.get('cxpb', 0.9)
_mutpb = kwargs.get('mutpb', 0.02)
_indpb = kwargs.get('indpb', 0.05)
_generations = kwargs.get('gen', 500)
_dir = kwargs.get('dir', 'results/')
f = '%Y_%m_%d-%H_%M_%S'
_stats = _dir + 'ga_dimen_' + datetime.strftime(start, f) + '.csv'
_sol = _stats[:-4] + '.txt'
best = None
stats = pd.DataFrame(
columns=['gen', 'nevals', 'avg', 'std', 'min', 'max', 'bestFit'])
for i in range(execs):
print('Execution {0} of {1} ...'.format(i + 1, execs))
b, p, r = optimize_diameters(path,
inpfile,
prices=_unit_price,
pop=_popsize,
cxpb=_cxpb,
mutpb=_mutpb,
indpb=_indpb,
gen=_generations)
if best is None:
best = b.items[0]
elif b.items[0].fitness.values[0] < best.fitness.values[0]:
best = b.items[0]
print("Best iter: ", best.fitness.values[0])
# print("\nMy Dataframe\n")
df = pd.DataFrame(r)
df['bestFit'] = best.fitness.values[0]
stats = stats.append(df)
# Runtime
runtime = datetime.now() - start
print('Run time: {0} executions in {1}'.format(execs, runtime))
# Save statistics to a text file
# stats.to_csv(_stats)
stats.to_csv(_stats, index=False)
# Plot the convergence chart
plot_convergence(execs, _generations, _stats)
# Save best solution to a text file
with open(_sol, 'a') as f:
f.write('Best solution:\t{0}\n'.format(best))
f.write('Best fitness:\t{0}\n'.format(best.fitness.values[0]))
f.write('Run time:\t{0}'.format(runtime))
return best
import time
from datetime import date, datetime
import click
from sqlalchemy import create_engine
sys.path.append('.')
sys.path.append('../ajna_docs/commons')
from ajna_commons.flask.conf import SQL_URI
from virasana.db import mongodb as db
from virasana.integracao.mercante import mercante_fsfiles
from virasana.integracao.mercante import processa_xml_mercante
from virasana.integracao.mercante import resume_mercante
today = date.today()
str_today = datetime.strftime(today, '%d/%m/%Y')
@click.command()
@click.option('--dias',
default=10,
help='Quantidade de dias a processar para trás - padrão 10')
@click.option('--fim',
default=str_today,
help='Dia de fim (dia/mês/ano) - padrão hoje')
@click.option('--download',
is_flag=True,
help='Baixar arquivos das datas da API do Aniita')
def update(dias, fim, download):
"""Script de linha de comando para integração do arquivo XML."""
end = datetime.strptime(fim, '%d/%m/%Y')
def image_color_histogram(self,
w: int = 12,
h: int = 8,
num_channels=3,
save_path: str = None):
"""
Plot image color channel's histogram
:param w: plot width
:param h: plot height
:param num_channels: image depth (number of channels)
:param save_path: plot image save directory path, if None - do not save
:return:
"""
if save_path and not os.path.isdir(save_path):
raise ValueError("Save path is not a directory or doesn't exists.")
channels = [np.zeros((256, 1)) for i in range(num_channels)]
ch_labels = ["red", "green", "blue"] if num_channels == 3 else ["gray"]
num_images = 0
for ann in tqdm(self._annotations):
if not os.path.exists(ann.img_path):
continue
image = cv2.imread(ann.img_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
num_channels = image.shape[2]
if num_channels != 3:
continue
for j in range(num_channels):
channels[j] += cv2.calcHist(image, [j], None, [256], [0, 256])
num_images += 1
if num_images != 0:
for j in range(num_channels):
channels[j] /= num_images
else:
raise ValueError("Image paths are not correct.")
# create plot
figure = plt.figure(figsize=(w, h))
plt.title("Pixel channel distribution", fontsize=20)
plt.axis("off")
cols, rows = 2, 2
for i in range(1, cols * rows + 1):
figure.add_subplot(rows, cols, i)
if i > num_channels + 1:
break
if i == num_channels + 1:
for j in range(num_channels):
plt.plot(channels[j], color=ch_labels[j], alpha=0.8)
plt.legend(ch_labels)
else:
plt.plot(channels[i - 1], color=ch_labels[i - 1], alpha=0.8)
plt.legend([ch_labels[i - 1]])
plt.ylabel("frequency",
color="gray",
fontsize=14,
fontweight="bold")
plt.xlim([0, 256])
plt.ylim(ymin=0)
if save_path:
plot_save_name = "image_color_histogram_{}.png".format(
datetime.strftime(datetime.today(), "%y%m%d%H%M"))
plt.savefig(os.path.join(save_path, plot_save_name))
else:
plt.show()
def image_size_histogram(self,
w: int = 10,
h: int = 7,
save_path: str = None):
"""
Plot image size frequency distribution
:param w: plot width
:param h: plot height
:param save_path: plot image save directory path, if None - do not save
:return:
"""
if save_path and not os.path.isdir(save_path):
raise ValueError("Save path is not a directory or doesn't exists.")
info_df = self.info_df[["img_width", "img_height"]]
size_images = np.array(info_df)
unique_size_images = np.unique(size_images, axis=0)
if len(unique_size_images.shape) == 1:
unique_size_images = [unique_size_images]
freq_unique_size_images = [
len(self.info_df[size_images == unique_size_image]
["filename"].unique())
for unique_size_image in unique_size_images
]
# гистограмма распределения объектов на изображениях заданной размерности
fig, ax = plt.subplots(figsize=(w, h))
plt.gcf().subplots_adjust(bottom=0.15)
plt.title("Image size distribution", fontsize=20)
plt.ylabel("frequency", color="gray", fontsize=14, fontweight="bold")
rects = plt.bar(np.arange(len(unique_size_images)),
freq_unique_size_images,
color="lightskyblue")
plt.xticks(np.arange(len(unique_size_images)))
ax.set_xticklabels(unique_size_images,
rotation=45,
fontdict={
"horizontalalignment": "center",
"size": 12
})
for rect in rects:
height = rect.get_height()
ax.annotate(
"{}".format(height),
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points",
ha="center",
va="bottom",
)
if save_path:
plot_save_name = "image_size_histogram_{}.png".format(
datetime.strftime(datetime.today(), "%y%m%d%H%M"))
plt.savefig(os.path.join(save_path, plot_save_name))
else:
plt.show()
from datetime import datetime
birthday = datetime(1991, 2, 4, 4)
print(birthday)
print("Year: " + str(birthday.year))
print("Month: " + str(birthday.month))
print("Day: " + str(birthday.day))
print("Hour: " + str(birthday.hour))
print(datetime.now() - birthday)
parsed_date = datetime.strptime('Jan 15, 2018', '%b %d, %Y')
print(parsed_date.month)
date_string = datetime.strftime(datetime.now(), '%b %d, %Y')
print(date_string)
async def test_case_pool_upgrade(docker_setup_and_teardown, pool_handler,
wallet_handler, get_default_trustee,
adder_role, adder_role_num, editor_role,
editor_role_num):
trustee_did, _ = get_default_trustee
# add adder to start pool upgrade
adder_did, adder_vk = await did.create_and_store_my_did(
wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, adder_did,
adder_vk, None, adder_role)
assert res['op'] == 'REPLY'
# add editor to cancel pool upgrade
editor_did, editor_vk = await did.create_and_store_my_did(
wallet_handler, '{}')
res = await send_nym(pool_handler, wallet_handler, trustee_did, editor_did,
editor_vk, None, editor_role)
assert res['op'] == 'REPLY'
# set rule for adding
req = await ledger.build_auth_rule_request(
trustee_did, '109', 'ADD', 'action', '*', 'start',
json.dumps({
'constraint_id': 'ROLE',
'role': adder_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res2 = json.loads(await
ledger.sign_and_submit_request(pool_handler,
wallet_handler,
trustee_did, req))
print(res2)
assert res2['op'] == 'REPLY'
# set rule for editing
req = await ledger.build_auth_rule_request(
trustee_did, '109', 'EDIT', 'action', 'start', 'cancel',
json.dumps({
'constraint_id': 'ROLE',
'role': editor_role_num,
'sig_count': 1,
'need_to_be_owner': False,
'metadata': {}
}))
res3 = json.loads(await
ledger.sign_and_submit_request(pool_handler,
wallet_handler,
trustee_did, req))
print(res3)
assert res3['op'] == 'REPLY'
# start pool upgrade
init_time = 30
version = '1.99.999'
name = 'upgrade' + '_' + version + '_' + datetime.now(
tz=timezone.utc).strftime('%Y-%m-%dT%H:%M:%S%z')
_sha256 = hashlib.sha256().hexdigest()
_timeout = 5
reinstall = False
force = False
package = 'indy-node'
dests = [
'Gw6pDLhcBcoQesN72qfotTgFa7cbuqZpkX3Xo6pLhPhv',
'8ECVSk179mjsjKRLWiQtssMLgp6EPhWXtaYyStWPSGAb',
'DKVxG2fXXTU8yT5N7hGEbXB3dfdAnYv1JczDUHpmDxya',
'4PS3EDQ3dW1tci1Bp6543CfuuebjFrg36kLAUcskGfaA',
'4SWokCJWJc69Tn74VvLS6t2G2ucvXqM9FDMsWJjmsUxe',
'Cv1Ehj43DDM5ttNBmC6VPpEfwXWwfGktHwjDJsTV5Fz8',
'BM8dTooz5uykCbYSAAFwKNkYfT4koomBHsSWHTDtkjhW'
]
docker_7_schedule = json.dumps(
dict({
dest: datetime.strftime(
datetime.now(tz=timezone.utc) +
timedelta(minutes=init_time + i * 5), '%Y-%m-%dT%H:%M:%S%z')
for dest, i in zip(dests, range(len(dests)))
}))
req = await ledger.build_pool_upgrade_request(adder_did, name, version,
'start', _sha256, _timeout,
docker_7_schedule, None,
reinstall, force, package)
res4 = json.loads(await
ledger.sign_and_submit_request(pool_handler,
wallet_handler, adder_did,
req))
print(res4)
assert res4['op'] == 'REPLY'
# cancel pool upgrade
req = await ledger.build_pool_upgrade_request(editor_did, name, version,
'cancel', _sha256, _timeout,
docker_7_schedule, None,
reinstall, force, package)
res5 = json.loads(await
ledger.sign_and_submit_request(pool_handler,
wallet_handler,
editor_did, req))
print(res5)
assert res5['op'] == 'REPLY'
def objects_frequency_scatter(self,
class_name: str = None,
w: int = 12,
h: int = 8,
save_path: str = None):
"""
Plot image objects sizes frequency distribution
:param class_name: monitoring only the specific class name
:param w: plot width
:param h: plot height
:param save_path: plot image save directory path, if None - do not save
:return:
"""
if save_path and not os.path.isdir(save_path):
raise ValueError("Save path is not a directory or doesn't exists.")
if class_name:
info_df = self.info_df[self.info_df["label"] == class_name]
else:
info_df = self.info_df
box_widths = np.array(info_df["box_width"])
box_heights = np.array(info_df["box_height"])
# relative box shapes
box_widths /= np.array(info_df["img_width"])
box_heights /= np.array(info_df["img_height"])
fig = plt.figure(figsize=(w, h))
grid = plt.GridSpec(4, 4, hspace=0.5, wspace=0.5)
main_ax = fig.add_subplot(grid[:-1, 1:])
y_hist = fig.add_subplot(grid[:-1, 0], xticklabels=[], yticklabels=[])
x_hist = fig.add_subplot(grid[-1, 1:], xticklabels=[], yticklabels=[])
main_ax.set_title("Box size distribution", fontsize=20)
main_ax.set_xlabel("box width/image width",
color="gray",
fontsize=14,
fontweight="bold")
main_ax.set_ylabel("box height/image height",
color="gray",
fontsize=14,
fontweight="bold")
# divide axis by 10 parts
main_ax.xaxis.set_major_locator(plt.MaxNLocator(10))
main_ax.yaxis.set_major_locator(plt.MaxNLocator(10))
y_hist.set_xlabel("height frequency", fontweight="bold")
y_hist.yaxis.set_major_locator(plt.MaxNLocator(10))
x_hist.set_ylabel("width frequency", fontweight="bold")
x_hist.xaxis.set_major_locator(plt.MaxNLocator(10))
main_ax.plot(box_widths,
box_heights,
"bo",
markersize=3,
alpha=0.3,
color="deepskyblue")
# main_ax.legend(['train', 'test'])
x_hist.hist(box_widths,
700,
histtype="stepfilled",
orientation="vertical",
color="deepskyblue")
x_hist.invert_yaxis()
y_hist.hist(box_heights,
700,
histtype="stepfilled",
orientation="horizontal",
color="deepskyblue")
y_hist.invert_xaxis()
if save_path:
plot_save_name = "objects_frequency_histogram_{}.png".format(
datetime.strftime(datetime.today(), "%y%m%d%H%M"))
plt.savefig(os.path.join(save_path, plot_save_name))
else:
plt.show()
network_huawei_ssh_ip = cf.get('network_huawei_ssh', 'network_huawei_ip').split(',')
network_cisco_telnet_ip = cf.get('network_cisco_telnet', 'network_cisco_ip').split(',')
network_dp_ssh_ip = cf.get('network_dp_ssh', 'network_dp_ip').split(',')
network_juniper_ssh_ip = cf.get('network_juniper_ssh', 'network_juniper_ip').split(',')
# 获取用户
network_user = cf.get('network_user','network_user')
# 获取密码
network_password = cf.get('network_user','network_password')
if __name__ == '__main__':
mylogger = logger.Logger()
s = GetNetworkConfig()
# 获取当天日期
now=datetime.now()
today = datetime.strftime(now, '%Y%m%d')
for huawei_ip in network_huawei_telnet_ip:
logging.debug('-------Begin : ' + huawei_ip +'--------')
login_cmd = "telnet "+huawei_ip.strip()
config_file = backupdir+"/"+today+"_"+huawei_ip.strip()+"_config.txt"
s.huawei_telnet_getconfig(login_cmd,network_user,network_password,config_file)
logging.error('Success Backup!'+config_file)
for huawei_ip in network_huawei_ssh_ip:
logging.debug('-------Begin : ' + huawei_ip + '--------')
login_cmd = "ssh ywpt@"+huawei_ip.strip()
config_file = backupdir+"/"+today+"_"+huawei_ip.strip()+"_config.txt"
s.huawei_ssh_getconfig(login_cmd,network_password,config_file)
logging.error('Success Backup!'+config_file)
def eds_file_parsing(self):
self.ensure_one()
record = str(base64.b64decode(self.eds_file), 'iso8859-4',
'strict').encode('iso8859-4', 'strict')
dom = parseString(record)
file_employees = dom.getElementsByTagName('gigv')
if file_employees is None:
return False
emp_obj = self.env['hr.employee']
rel_obj = self.env['hr.employee.relief']
e_ids = [e.id for e in self.employee_ids]
for fe in file_employees:
emp_ids = []
fe_pc = fe.getElementsByTagName('pers_kods')[0].toxml().replace(
'<pers_kods>', '').replace('</pers_kods>', '')
fe_name = fe.getElementsByTagName(
'vards_uzvards')[0].toxml().replace('<vards_uzvards>',
'').replace(
'</vards_uzvards>', '')
if fe_pc:
emp_query_str = """SELECT id FROM hr_employee
WHERE COALESCE(identification_id, '') != ''
AND REPLACE(identification_id, '-', '') = '%s'
AND id %s %s""" % (
fe_pc,
len(e_ids) == 1 and '=' or 'in',
len(e_ids) == 1 and e_ids[0] or tuple(e_ids),
)
self._cr.execute(emp_query_str)
emp_ids = [r['id'] for r in self._cr.dictfetchall()]
if (not e_ids) and fe_name:
emp_query_str = """SELECT emp.id FROM hr_employee AS emp
LEFT JOIN resource_resource AS res ON emp.resource_id = res.id
WHERE UPPER(res.name) = %s
AND emp.id %s %s""" % (
fe_name,
len(e_ids) == 1 and '=' or 'in',
len(e_ids) == 1 and e_ids[0] or tuple(e_ids),
)
self._cr.execute(emp_query_str)
emp_ids = [r['id'] for r in self._cr.dictfetchall()]
if emp_ids:
dep_list = []
dep_main = fe.getElementsByTagName('apgadajamie')
if dep_main:
deps = dep_main[0].getElementsByTagName('apgadajamais')
for dep in deps:
dep_name = dep.getElementsByTagName(
'vards_uzvards')[0].toxml().replace(
'<vards_uzvards>',
'').replace('</vards_uzvards>', '')
dep_df = dep.getElementsByTagName(
'datums_no')[0].toxml().replace(
'<datums_no>', '').replace('</datums_no>', '')
dep_date_from = datetime.strftime(
datetime.strptime(dep_df,
'%Y-%m-%dT%H:%M:%S').date(),
'%Y-%m-%d')
dep_dt = dep.getElementsByTagName(
'datums_lidz')[0].toxml().replace(
'<datums_lidz>',
'').replace('</datums_lidz>', '')
dep_date_to = datetime.strftime(
datetime.strptime(dep_dt,
'%Y-%m-%dT%H:%M:%S').date(),
'%Y-%m-%d')
dep_list.append({
'type': 'dependent',
'name': dep_name,
'date_from': dep_date_from,
'date_to': dep_date_to
})
dis_list = []
add_main = fe.getElementsByTagName('papildu_atvieglojumi')
if add_main:
adds = add_main[0].getElementsByTagName(
'papildu_atvieglojums')
for add in adds:
add_type = add.getElementsByTagName(
'veids')[0].toxml().replace('<veids>', '').replace(
'</veids>', '')
dis_type = False
if add_type == u'1. grupas invalīds':
dis_type = 'disability1'
if add_type == u'2. grupas invalīds':
dis_type = 'disability2'
if add_type == u'3. grupas invalīds':
dis_type = 'disability3'
if dis_type:
dis_df = add.getElementsByTagName(
'datums_no')[0].toxml().replace(
'<datums_no>',
'').replace('</datums_no>', '')
dis_date_from = datetime.strftime(
datetime.strptime(dis_df,
'%Y-%m-%dT%H:%M:%S').date(),
'%Y-%m-%d')
dis_dt = add.getElementsByTagName(
'datums_lidz')[0].toxml().replace(
'<datums_lidz>',
'').replace('</datums_lidz>', '')
dis_date_to = datetime.strftime(
datetime.strptime(dis_dt,
'%Y-%m-%dT%H:%M:%S').date(),
'%Y-%m-%d')
dis_list.append({
'type': dis_type,
'name': add_type,
'date_from': dis_date_from,
'date_to': dis_date_to
})
umm_list = []
umm_main = fe.getElementsByTagName('prognozetie_mnm')
if umm_main:
umms = umm_main[0].getElementsByTagName('prognozetais_mnm')
for umm in umms:
umm_name = umm.getElementsByTagName(
'veids')[0].toxml().replace('<veids>', '').replace(
'</veids>', '')
umm_df = umm.getElementsByTagName(
'datums_no')[0].toxml().replace(
'<datums_no>', '').replace('</datums_no>', '')
umm_date_from = datetime.strftime(
datetime.strptime(umm_df,
'%Y-%m-%dT%H:%M:%S').date(),
'%Y-%m-%d')
umm_dt = umm.getElementsByTagName(
'datums_lidz')[0].toxml().replace(
'<datums_lidz>',
'').replace('</datums_lidz>', '')
umm_date_to = datetime.strftime(
datetime.strptime(umm_dt,
'%Y-%m-%dT%H:%M:%S').date(),
'%Y-%m-%d')
umm_amount = umm.getElementsByTagName(
'summa')[0].toxml().replace('<summa>', '').replace(
'</summa>', '')
umm_list.append({
'type': 'untaxed_month',
'name': umm_name,
'date_from': umm_date_from,
'date_to': umm_date_to,
'amount': float(umm_amount)
})
for emp_id in emp_ids:
for dpl in dep_list:
self._cr.execute(
"""SELECT id FROM hr_employee_relief
WHERE type = 'dependent'
AND employee_id = %s
AND UPPER(name) = %s
AND (date_from is Null OR date_from <= %s)
AND (date_to is Null OR date_to >= %s)""", (
emp_id,
dpl['name'],
dpl['date_to'],
dpl['date_from'],
))
dep_ids = [r['id'] for r in self._cr.dictfetchall()]
if dep_ids:
if len(dep_ids) > 1:
e_dep_ids = []
for dep in rel_obj.browse(dep_ids):
if dep.date_from == dpl[
'date_from'] or dep.date_to == dpl[
'date_to']:
e_dep_ids.append(dep.id)
if e_dep_ids:
dep_ids = e_dep_ids
rel_obj.browse([dep_ids[0]]).write({
'date_from':
dpl['date_from'],
'date_to':
dpl['date_to']
})
if not dep_ids:
dep_data = dpl.copy()
dep_data.update({'employee_id': emp_id})
rel_obj.create(dep_data)
for dsl in dis_list:
diss = rel_obj.search([
('employee_id', '=', emp_id),
('type', '=', dsl['type']),
('date_from', '=', dsl['date_from']),
('date_to', '=', dsl['date_to'])
])
if not diss:
dis_data = dsl.copy()
dis_data.update({'employee_id': emp_id})
rel_obj.create(dis_data)
for uml in umm_list:
umms = rel_obj.search([
('employee_id', '=', emp_id),
('type', '=', 'untaxed_month'),
('date_from', '=', uml['date_from']),
('date_to', '=', uml['date_to'])
])
if umms:
umms.write({
'name': uml['name'],
'amount': uml['amount']
})
else:
umm_data = uml.copy()
umm_data.update({'employee_id': emp_id})
rel_obj.create(umm_data)
# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:
# Set the number of timesteps per sample.
STEP_COUNT = 100 # Typically 100.
# Set the number of samples per condition.
SAMPLE_COUNT = 5 # Typically 5.
# set the number of conditions per iteration.
CONDITIONS = 1 # Typically 2 for Caffe and 1 for LQR.
# Set the number of trajectory iterations to collect.
ITERATIONS = 10 # Typically 10.
x0s = []
ee_tgts = []
reset_conditions = []
common = {
'experiment_name': 'my_experiment' + '_' + \
datetime.strftime(datetime.now(), '%m-%d-%y_%H-%M'),
'experiment_dir': EXP_DIR,
'data_files_dir': EXP_DIR + 'data_files/',
'target_filename': EXP_DIR + 'target.npz',
'log_filename': EXP_DIR + 'log.txt',
'conditions': CONDITIONS,
}
# Set up each condition.
for i in xrange(common['conditions']):
# Use hardcoded default vals init and target locations
ja_x0 = np.zeros(SENSOR_DIMS[JOINT_ANGLES])
ee_pos_x0 = np.zeros((1, 3))
ee_rot_x0 = np.zeros((3, 3))
ee_pos_tgt = EE_POS_TGT
allMergesCommand = "git log --merges --oneline --grep 'to development' | sed -e 's/.*from //g' -e 's/ to developme.*//g' -e 's/.*branch .origin\///g' -e 's/. into development//g'"
allMergesList = str(shell(allMergesCommand,
basedir + "/" + repository)).replace(
"\\n", ",").replace('b"', "").split(',')
FeatureBugfixHotfixMerged = [
x for x in FeatureBugfixHotfix if x.split(' torigin/')[1] in allMergesList
]
# format list allBranches
allBranches = []
format_list(FeatureBugfixHotfix, merged=None)
format_list(FeatureBugfixHotfixMerged, merged='merged')
# define threshold dates
before_day_all = datetime.today() - timedelta(days=int(days_all))
before_day_all_str = datetime.strftime(before_day_all, '%Y-%m-%d')
before_day_merged = datetime.today() - timedelta(days=int(days_merged))
before_day_merged_str = datetime.strftime(before_day_merged, '%Y-%m-%d')
logging.info(
'Defining threshold dates:{} For all branches - "{}"{} For merged branches - "{}"'
.format('\n', before_day_all_str, '\n', before_day_merged_str))
# define a list of branches (both merged and not merged) to be removed
logging.info('Defining all branches that need to be removed')
toRemove = [
x for x in allBranches if x[0] <= before_day_all_str or (
x[2] == 'merged' and x[0] <= before_day_merged_str)
]
logging.info('There are {} branches will be removed'.format(len(toRemove)))
logging.info('Here is the list of branches have to be removed:{} {}'.format(
'\n', toRemove))
def cd(d):
if not d:
return ''
dt = datetime.strptime(d, '%d/%m/%Y')
return datetime.strftime(dt, '%Y/%m/%d')
import os
from datetime import datetime, timedelta
os.environ.update({"DJANGO_SETTINGS_MODULE": "vgsite.settings"})
import django
django.setup()
import requests
from api.views import Api
NOW = datetime.now() + timedelta(hours=8)
print(NOW)
print("===== vg todays card catch =====")
if datetime.strftime(NOW, "%H:%M") == "09:31":
Api("VG").get_vg_today_card()
print("===== ws todays card catch =====")
if datetime.strftime(NOW, "%H:%M") == "09:32":
Api("WS").get_ws_today_card()
for col in numeric_cols:
try:
a[col]=a[col].apply(quantile_replace)
except:
pass
df_list.append(a)
df = pd.concat(df_list,axis=0)
#################
### Find Missing Date-Hour Intervals
print('Finding Missing Data Intervals',end='\n\n')
df['day_hour'] = df['DateTime'].apply(lambda x: datetime.strftime(x, "%Y-%m-%d %H"))
df = df.groupby(['station', 'day_hour']).mean().reset_index() ## Se pierde p01i skyc1 porque son categóricas
#### Crear las horas que no tienen observaciones
df['day_hour'] = pd.to_datetime(df['day_hour'])
minimo = df.day_hour.min()
maximo = df.day_hour.max()
# Acotar la longitud y latitud para tener siempre las mismas coordenadas
#aux = df_clean[df_clean['station'].isin(inter_airport)]
lista = df[['station']].drop_duplicates().values.tolist()
#Crear las fechas
df2 = pd.DataFrame()
def parse1(self, response):
hxs = Selector(response)
if 'listing' in str(response.url):
'''
Assigning default values to items
'''
self.item['management_by_landlord'] = 'None'
self.item['areacode'] = 'None'
self.item['mobile_lister'] = 'None'
self.item['google_place_id'] = 'None'
self.item['Launch_date'] = '0'
self.item['age'] = '0'
self.item['address'] = 'None'
self.item['sublocality'] = 'None'
self.item['lat'] = '0'
self.item['longt'] = '0'
self.item['price_per_sqft'] = '0'
self.item['listing_by'] = 'None'
self.item['name_lister'] = 'None'
self.item['scraped_time'] = dt.now().strftime('%m/%d/%Y %H:%M:%S')
self.item['listing_date'] = dt.now().strftime('%m/%d/%Y %H:%M:%S')
self.item['updated_date'] = self.item['listing_date']
self.item['platform'] = 'commonfloor'
self.item['city'] = 'Kolkata'
self.item['data_id'] = response.url.split('/')[-1]
self.item['locality'] = response.xpath(
'//p[@class="proj-value location-name"]/text()').extract_first(
)
self.item['property_type'] = 'Residential'
try:
self.item['Building_name'] = response.xpath(
'//span[@class="subH1"]/text()').extract_first().split(
'in ')[-1].split(self.item['locality'])[0].replace(
'at ', '')
except:
self.item['Building_name'] = 'None'
price = response.xpath(
'//div[@class="project-detail row card"]/div/div[@class="row"]/div[1]/div[1]/p[@class="proj-value"]/span/text()'
).extract_first()
if 'Lakh' in price:
price = str(float(price.split(' Lakh')[0]) * 100000)
self.item['Selling_price'] = price
self.item['Monthly_Rent'] = '0'
elif 'Crore' in price:
price = str(float(price.split(' Crore')[0]) * 10000000)
self.item['Selling_price'] = price
self.item['Monthly_Rent'] = '0'
else:
self.item['Selling_price'] = '0'
self.item['Monthly_Rent'] = '0'
if ((self.item['Selling_price'] == '0')
and (self.item['Monthly_Rent'] == '0')):
self.item['price_on_req'] = 'TRUE'
else:
self.item['price_on_req'] = 'FALSE'
car_area = response.xpath(
'//div[@class="project-detail row card"]/div/div[@class="row"]/div[1]/div[2]/p[@class="proj-value"]/text()'
).extract_first()
if (not '-' in car_area):
self.item['carpet_area'] = car_area.split(' ')[0]
else:
self.item['carpet_area'] = '0'
self.item['config_type'] = response.xpath(
'//div[@class="project-unit row card unit-detail-widget"]/div/div[@class="row"]/div/div[@class="row firstRow"]/div[1]/p/span[1]/text()'
).extract_first() + 'BHK'
self.item['config_type'] = self.item['config_type'].replace(
' ', '')
self.item['txn_type'] = response.xpath(
'//div[@class="project-unit row card unit-detail-widget"]/div/div[@class="row"]/div/div[@class="row otherDetails"]/div[1]/p[2]/text()'
).extract_first()
try:
if 'New' in self.item['txn_type']:
self.item['txn_type'] = 'Sale'
dates = response.xpath(
'//div[@class="project-detail row card"]/div/div[@class="row"]/div[2]/div[1]/p[2]/text()'
).extract_first().replace("'", '20')
self.item['Possession'] = dt.strftime(
dt.strptime(dates, "%b%Y"), "%m/%d/%Y %H:%M:%S")
else:
self.item['Possession'] = '0'
except:
logging.log(logging.ERROR, dates)
self.item['Possession'] = '0'
self.item['Details'] = response.xpath(
'//p[@class="propStatus"]/text()').extract()
titl = response.xpath(
'//div[@class="row otherDetails"]/*/p[@class="title"]/text()'
).extract()
no = titl.index('Listed on')
try:
list1 = self.item['Details'][no]
self.item['listing_date'] = dt.strftime(
dt.strptime(list1, "%d %b"), "%m/%d") + "/2016 00:00:00"
self.item['updated_date'] = self.item['listing_date']
except:
try:
list1 = self.item['Details'][no - 1]
self.item['listing_date'] = dt.strftime(
dt.strptime(list1, "%d %b"),
"%m/%d") + "/2016 00:00:00"
self.item['updated_date'] = self.item['listing_date']
except:
self.item['listing_date'] = dt.now().strftime(
'%m/%d/%Y %H:%M:%S')
self.item['updated_date'] = self.item['listing_date']
self.item['Bua_sqft'] = [
area for area in self.item['Details'] if 'Sq. Ft.' in area
][0].replace(' Sq. Ft.', '')
self.item['Status'] = response.xpath(
'//*[@id="page_container"]/div[2]/div/div/div[1]/div[1]/div[1]/div[2]/div/div/div/div[2]/div[1]/p[2]/@title'
).extract_first()
#self.item['Status'] = [stat for stat in self.item['Details'] if (('Unfurnished' in stat) or ('semi' in stat) or ('fully' in stat))]
#self.item['Status'] = ''.join(self.item['Status'])
self.item['Details'] = ' '.join(self.item['Details'])
try:
listin = response.xpath(
'//*[@id="page_container"]/div[2]/div/div/div[1]/div[1]/div[1]/div[2]/div/div/div/div[2]/div[4]/p[2]/@title'
).extract_first()
if 'Owner' in listin:
self.item['listing_by'] = 'Owner'
elif 'Builder' in listin:
self.item['listing_by'] = 'Builder'
elif 'Broker' in listin:
self.item['listing_by'] = 'Agent'
elif 'Agent' in listin:
self.item['listing_by'] = 'Agent'
else:
self.item['listing_by'] = 'None'
self.item['name_lister'] = listin.split('(')[0]
except:
self.item['listing_by'] = 'None'
self.item['name_lister'] = 'None'
#self.item['scraped_time'] = dt.now().strftime('%m/%d/%Y %H:%M:%S')
if (((not self.item['Monthly_Rent'] == '0') and
(not self.item['Bua_sqft'] == '0') and
(not self.item['Building_name'] == 'None') and
(not self.item['lat'] == '0'))
or ((not self.item['Selling_price'] == '0') and
(not self.item['Bua_sqft'] == '0') and
(not self.item['Building_name'] == 'None') and
(not self.item['lat'] == '0'))
or ((not self.item['price_per_sqft'] == '0') and
(not self.item['Bua_sqft'] == '0') and
(not self.item['Building_name'] == 'None') and
(not self.item['lat'] == '0'))):
self.item['quality4'] = 1
elif (((not self.item['price_per_sqft'] == '0') and
(not self.item['Building_name'] == 'None') and
(not self.item['lat'] == '0'))
or ((not self.item['Selling_price'] == '0') and
(not self.item['Bua_sqft'] == '0') and
(not self.item['lat'] == '0'))
or ((not self.item['Monthly_Rent'] == '0') and
(not self.item['Bua_sqft'] == '0') and
(not self.item['lat'] == '0'))
or ((not self.item['Selling_price'] == '0') and
(not self.item['Bua_sqft'] == '0') and
(not self.item['Building_name'] == 'None'))
or ((not self.item['Monthly_Rent'] == '0') and
(not self.item['Bua_sqft'] == '0') and
(not self.item['Building_name'] == 'None'))):
self.item['quality4'] = 0.5
else:
self.item['quality4'] = 0
if ((not self.item['Building_name'] == 'None')
and (not self.item['listing_date'] == '0')
and (not self.item['txn_type'] == 'None')
and (not self.item['property_type'] == 'None')
and ((not self.item['Selling_price'] == '0') or
(not self.item['Monthly_Rent'] == '0'))):
self.item['quality1'] = 1
else:
self.item['quality1'] = 0
if ((not self.item['Launch_date'] == '0')
or (not self.item['Possession'] == '0')):
self.item['quality2'] = 1
else:
self.item['quality2'] = 0
if ((not self.item['mobile_lister'] == 'None')
or (not self.item['listing_by'] == 'None')
or (not self.item['name_lister'] == 'None')):
self.item['quality3'] = 1
else:
self.item['quality3'] = 0
yield self.item
# Put the folder "data/adaboost" from this repository into metabo/environment/hpo/data
import os
from metabo.eval.evaluate import eval_experiment
from metabo.eval.plot_results import plot_results
from metabo.environment.hpo.prepare_data import prepare_hpo_data
from metabo.policies.taf.generate_taf_data_hpo import generate_taf_data_hpo
from gym.envs.registration import register, registry
from datetime import datetime
# set evaluation parameters
afs_to_evaluate = ["MetaBO", "TAF-ME", "TAF-RANKING", "EI"]
rootdir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "metabo")
logpath = os.path.join(rootdir, "iclr2020", "hpo", "MetaBO-ADABOOST-v0")
savepath = os.path.join(logpath, "eval",
datetime.strftime(datetime.now(), "%Y-%m-%d-%H-%M-%S"))
n_workers = 1
n_episodes = 15 # 15 test sets
prepare_hpo_data(model="adaboost",
datapath=os.path.join(rootdir, "environment", "hpo", "data",
"adaboost"))
# evaluate all afs
for af in afs_to_evaluate:
# set af-specific parameters
if af == "MetaBO":
features = [
"posterior_mean", "posterior_std", "timestep", "budget", "x"
]
pass_X_to_pi = False
def process_pending_domains(self):
'''
process pending domain
- get list domains from kdata cdn
- check is exist in script_data -> next
- insert into script_data in wait_call_cdn_node mode
'''
# read cdn node vhost template
with open(self.vhost_template_file) as f:
template_data = f.read()
# get all kdata cdn domains
domains = self.get_domains_from_api('0')
for item in domains:
'''
{u'status': 1, u'domain': u'cdn.1ly.co', u'user_id': 10, u'origin_server': u'http://kdata.1ly.co', u'created_at': u'2016-10-26 11:45:25', u'messages': u'domain.err_domain', u'updated_at': u'2016-10-27 11:09:01', u'kdata_domain': u'cdn-1ly-co-06601832710.kdatacdn.net', u'id': 7}
'''
domain = item['domain']
kdata_domain = item['kdata_domain']
origin_server = item['origin_server']
kdata_id = item['id']
# domain existed in script db?
check_domain = self._db_domain_fetch_one_by_kdata_id(kdata_id)
if check_domain:
self._log('domain {} existed with data {}'.format(domain, check_domain))
continue
# force check is valid domain
if self.must_check_valid_domain and self.is_valid_domain(domain) == False:
self._log('domain {} is invalid'.format(item['domain']))
self.update_domain(item['id'], 1, 'domain.err_domain')
continue
# force check is valid origin
if self.must_check_valid_origin and self.is_valid_origin(origin_server) == False:
self._log('origin {} is invalid'.format(origin_server))
self.update_domain(item['id'], 1, 'domain.err_origin_server')
continue
origin_server = self.get_cdn_origin(origin_server, domain)
if origin_server is None:
self._log('origin {} is invalid'.format(origin_server))
self.update_domain(item['id'], 1, 'domain.err_origin_server')
continue
replaces = {
'$domain': kdata_domain,
'$origin_ip:port': origin_server,
}
# cdn node vhost data
per_domain_vhost_file = '{}.yml'.format(kdata_domain)
per_domain_template_data = template_data
for find, repl in replaces.items():
per_domain_template_data = per_domain_template_data.replace(find, repl)
''' create yml file'''
with open(per_domain_vhost_file, 'w') as f:
f.write(per_domain_template_data)
''' update db'''
script_data = {
'user_domain': domain
,'kdata_domain': kdata_domain
,'domain_id': str(kdata_id)
,'status': 'wait_call_cdn_node'
,'origin': item['origin_server']
,'comment': 'inserted from script at {}'.format(datetime.strftime(datetime.now(), '%Y-%m-%d %H:%M:%S'))
}
self._db_domain_add(script_data)
cfg = {
'domain': kdata_domain
,'zone_type': 'A'
,'ip': ['123.30.171.238', '127.0.0.1']
,'proxy': False
}
self.kcf.add_config(cfg)
res = self.kcf.add_record()
if not res['status']:
self.push_noti('[ERROR] Domain {} is existed --- {}'.format(kdata_domain, res))
else:
self.push_noti('[OK] Domain {} added'.format(kdata_domain))
#push_ansible here
kansible = Kansible({'playbook_path': '/path/to/playbook.yml'})
kansible.test()
def fetch_incidents_command():
"""
Parent command to wrap events and behavior fetch commands
"""
# Check if it's been run
now = datetime.today()
yesterday = datetime.strftime(now - timedelta(days=1), "%Y-%m-%d")
last_run = demisto.getLastRun()
start_date = yesterday
end_date = yesterday
if "start_time" not in last_run or "complete_for_today" not in last_run:
# first time integration is running
start_date = datetime.strftime(now - timedelta(days=FIRST_RUN),
"%Y-%m-%d")
if last_run.get('complete_for_today') is True and last_run.get(
'start_time') == yesterday:
# wait until tomorrow to try again
demisto.incidents([])
return
# Refresh JWT
token = do_auth()
# Fetch Events
more_events = True
page_token = None
incidents = []
while more_events:
event_incidents, page_token = fetch_events_incidents_command(
start_date, end_date, token, page_token)
for incident in event_incidents:
demisto.debug(
"Adding event incident name={name}, type={type}, severity={severity}"
.format(**incident))
incidents += event_incidents
if page_token is None:
more_events = False
# Fetch Behavior
if BEHAVIOR_ENABLED:
more_behavior = True
next_offset = None
while more_behavior:
behavior_incidents, next_offset = fetch_behavior_incidents_command(
start_date, token, next_offset)
for incident in behavior_incidents:
demisto.debug(
"Adding behavior incident name={name}, type={type}, severity={severity}"
.format(**incident))
incidents += behavior_incidents
if next_offset is None:
more_behavior = False
# Send all Incidents
demisto.incidents(incidents)
# Save last_run
demisto.setLastRun({"complete_for_today": True, "start_time": yesterday})
def update_db(shadow_dir, station_list, dfile='shadows_data.db'):
'''
Update both tables: SHADOWS and STATIONS
'''
conn = sqlite3.connect(dfile)
c = conn.cursor()
today = datetime.strftime(datetime.now(), "%Y/%m/%d")
#header :station_id,station_name,lon,lat
new_stations = pd.read_csv(station_list, header=None)
new_stations.columns = ['station_id', 'station_name', 'lon', 'lat']
new_stations["Date"] = [today] * new_stations.shape[0]
sql_com = "SELECT * FROM STATIONS"
current_stations = pd.read_sql(sql_com, conn)
for station in current_stations.station_id.values:
new_stations.drop(
new_stations.index[new_stations['station_id'] == station],
inplace=True)
if not new_stations.empty:
#if it found something new, update sql data base
print("Updating STATIONS table")
new_stations.to_sql('STATIONS', conn, if_exists='append', index=False)
else:
print("No new stations added to STATIONS table")
return
#read the database again if it was updated
current_stations = pd.read_sql(sql_com, conn)
sql_com = "SELECT * FROM SHADOWS"
shadow_old = pd.read_sql(sql_com, conn)
#extract the info from the ifile
from os.path import normpath, basename
dir_info = basename(normpath(shadow_dir))
#extract data from the name of directory
maxdist, res, horstep, dummy = dir_info.replace("lh_", "").split("_")
stations = []
print("Checking for new data for SHADOWS table")
for ifile in sorted(os.listdir(shadow_dir)):
if ifile.startswith("lh_"): #will probably find shadows.log here
station = int(ifile.replace("lh_", "").split("_")[1])
get_station = current_stations[current_stations['station_id'] ==
station]
if get_station.empty:
print("Station %d not yet in STATIONS table" % station)
else:
print("Getting SHADOWS for station %d" % station)
print("Reading shadows from %s" %
os.path.join(shadow_dir, ifile))
read_shadows = pd.read_csv(os.path.join(shadow_dir, ifile),
index_col=False)
size = read_shadows.shape[0]
az = read_shadows.azimuth.to_list()
hor = read_shadows.horizon_height.to_list()
lon = get_station.lon.values[0]
lat = get_station.lat.values[0]
station_name = get_station.station_name.values[0]
shadow_new = pd.DataFrame({
"station_id": [station] * size,
"station_name": [station_name] * size,
"resolution": [res] * size,
"maxdistance": [maxdist] * size,
"horizonstep": [horstep] * size,
"azimuth": az,
"horizon_height": hor,
"Date": [today] * size
})
if shadow_old.empty:
shadow_new.to_sql('SHADOWS',
conn,
if_exists='append',
index=False)
else:
#drop from the new data any stations already in old data
for station in shadow_old.station_id.values:
shadow_new.drop(shadow_new.index[
shadow_new['station_id'] == station],
inplace=True)
if not shadow_new.empty:
shadow_new.to_sql('SHADOWS',
conn,
if_exists='append',
index=False)
else:
print("No new data added to the SHADOWS table")
print("database updated")
c.execute('''
INSERT INTO DAILY_STATUS (station_id,station_name,Date)
SELECT DISTINCT clt.station_id, ctr.station_name, clt.Date
FROM STATIONS clt
LEFT JOIN SHADOWS ctr ON clt.station_id = ctr.station_id
''')
c.execute('''
SELECT DISTINCT *
FROM DAILY_STATUS
WHERE Date = (SELECT max(Date) FROM DAILY_STATUS)
''')
df = DataFrame(c.fetchall(),
columns=['station_id', 'station_name', 'Date'])
print("New data")
print(df)
"Miramax", "DreamWorks", "New Line", "MGM", "Lionsgate"
]
STB = 10
ENTRIES = 100
def random_time():
start = datetime.strptime('2017-04-01 12:00', '%Y-%m-%d %H:%M')
end = datetime.strptime('2017-04-21 12:00', '%Y-%m-%d %H:%M')
delta = end - start
int_delta = (delta.days * 24 * 60 * 60) + delta.seconds
random_second = random.randrange(int_delta)
return start + timedelta(seconds=random_second)
count = 0
while count < ENTRIES:
with open(FILENAME, 'a') as file:
box_id = 'stb' + str(random.randint(1, STB))
title = random.choice(TITLES)
provider = random.choice(PROVIDERS)
time = random_time()
revenue = float(random.randint(1, 10))
line = box_id + '|' + title + '|' + provider + '|' + datetime.strftime(time, '%Y-%m-%d') \
+ '|' + '{0:.2f}'.format(revenue) + '|' + datetime.strftime(time, '%H:%M') + '\n'
file.write(line)
count += 1
def convert_date(date_str):
objDate = datetime.strptime(date_str, '%Y-%m-%d')
return datetime.strftime(objDate, '%d %b %Y')
def Community():
user = request.form.get('userid')
page = request.form.get('page')
if not page:
page = 1
else:
page = int(page)
if request.method == 'POST' and request.form.get('sort') == '0':
# 默认是时间顺序排列，和ｐｏｓｔ上来的是０的时候，就热度排序，
# A = Blog.query.order_by(Blog.num_of_view.desc()).paginate(page, per_page=10,
# error_out=False).items # desc()是从大到小，没有desc就是从小到大
A = db.session.query(Blog, User).join(
User, Blog.user_id == User.id).order_by(
Blog.num_of_view.desc()).paginate(page,
per_page=10,
error_out=False).items
payload = []
content = {}
for AA in A:
# name = User.query.get(AA.user_id).nickname
# face = User.query.get(AA.user_id).face
# datetime = AA.create_time
# time = datetime.strftime('%Y-%m-%d %H:%M:%S')
# content = {'id': AA.id, 'title': AA.title, 'authorid': AA.user_id, 'pageviews': AA.num_of_view,
# 'time': time, "author": name, "avatar": face}
name = AA[1].nickname
face = AA[1].face
time = AA[0].create_time.strftime('%Y-%m-%d %H:%M:%S')
content = {
'id': AA[0].id,
'title': AA[0].title,
'authorid': AA[0].user_id,
'pageviews': AA[0].num_of_view,
'time': time,
"author": name,
"avatar": face
}
payload.append(content)
content = {}
data = {"data": payload}
payload = json.dumps(data)
return payload, 200
if request.method == 'POST' and request.form.get('sort') == '2':
if user == None:
return "error"
# 默认是时间顺序排列，和ｐｏｓｔ上来的是2的时候，就只看关注的
# 双表联立查询，将博客的表和关注的表联立起来，返回的是一个列表数组
userblogs = db.session.query(Blog, Follow).join(
Follow, Blog.user_id == Follow.followed_id).filter(
Follow.follower_id == user).paginate(page,
per_page=10,
error_out=False).items
payload = []
content = {}
for blog in userblogs:
AA = blog[0] # 找到对应的博客
name = User.query.get(AA.user_id).nickname
face = User.query.get(AA.user_id).face
datetime = AA.create_time
time = datetime.strftime('%Y-%m-%d %H:%M:%S')
content = {
'id': AA.id,
'title': AA.title,
'authorid': AA.user_id,
'pageviews': AA.num_of_view,
'time': time,
"author": name,
"avatar": face
}
payload.append(content)
content = {}
data = {"data": payload}
payload = json.dumps(data)
return payload, 200
# 新发布的文章时间比较大，就先出现用ｄｅｓｃ从大到小排序
# A = Blog.query.order_by(Blog.create_time.desc()).paginate(page, per_page=10, error_out=False).items
A = db.session.query(Blog, User).join(
User, Blog.user_id == User.id).order_by(
Blog.create_time.desc()).paginate(page,
per_page=10,
error_out=False).items
payload = []
content = {}
for AA in A:
# name = User.query.get(AA.user_id).nickname
# face = User.query.get(AA.user_id).face
# datetime = AA.create_time
name = AA[1].nickname
face = AA[1].face
datetime = AA[0].create_time
time = datetime.strftime('%Y-%m-%d %H:%M:%S')
# content = {'id': AA.id, 'title': AA.title, 'author': name, 'pageviews': AA.num_of_view,
# 'time': time, "authorid": AA.user_id, "avatar": face}
content = {
'id': AA[0].id,
'title': AA[0].title,
'author': name,
'pageviews': AA[0].num_of_view,
'time': time,
"authorid": AA[0].user_id,
"avatar": face
}
payload.append(content)
content = {}
data = {"data": payload}
payload = json.dumps(data)
return payload, 200
def parse(self, response):
record = Selector(response)
data = record.xpath('//div[contains(@id,"resultBlockWrapper")]')
for i in data:
item = MagicbuyhydraItem()
item['name_lister'] = 'None'
item['Details'] = 'None'
item['listing_by'] = 'None'
item['address'] = 'None'
item['sublocality'] = 'None'
item['age'] = '0'
item['google_place_id'] = 'None'
item['lat'] = '0'
item['longt'] = '0'
item['Possession'] = '0'
item['Launch_date'] = '0'
item['mobile_lister'] = 'None'
item['areacode'] = 'None'
item['management_by_landlord'] = 'None'
item['monthly_rent'] = '0'
item['price_per_sqft'] = '0'
item['scraped_time'] = dt.now().strftime('%m/%d/%Y %H:%M:%S')
item['city'] = 'Hyderabad'
item['Building_name'] = i.xpath('div/input[contains(@id,"projectName")]/@value').extract_first()
if item['Building_name'] == '':
item['Building_name'] = 'None'
try:
item['longt'] = i.xpath('div/div[@class="srpColm2"]/div[@class="proColmleft"]/div[@class="proNameWrap proNameWrapBuy"]/div[@class="proNameColm1"]/span[@class="seeOnMapLink seeOnMapLinkBuy"]/span[@class="stopParentLink"]/@onclick').extract_first().split('&')[0].split('?')[-1].split("=")[-1] # .xpath('.//input[@itemprop="latitude"]/@value').extract_first()
if item['longt'] == '':
item['longt'] = '0'
except:
item['longt'] = '0'
try:
item['lat'] = i.xpath('div/div[@class="srpColm2"]/div[@class="proColmleft"]/div[@class="proNameWrap proNameWrapBuy"]/div[@class="proNameColm1"]/span[@class="seeOnMapLink seeOnMapLinkBuy"]/span[@class="stopParentLink"]/@onclick').extract_first().split('&')[1].split("=")[-1]
if item['lat'] == '':
item['lat'] = '0'
except:
item['longt'] = '0'
item['platform'] = 'magicbricks'
item['carpet_area'] = '0'
ids = i.xpath('@id').extract_first()
item['data_id'] = re.findall('[0-9]+', ids)[0]
item['config_type'] = i.xpath('.//input[contains(@id,"bedroomVal")]/@value').extract_first().replace('>', '')
item['config_type'] = item['config_type']+'BHK'
item['property_type'] = i.xpath('.//p[@class="proHeading"]/a/input[2]/@value').extract_first()
if (item['property_type'] == 'Studio Apartment'):
item['config_type'] = '1RK'
####### config_type is assumed as 'None' for default #########
if item['config_type'] == 'BHK':
item['config_type'] = 'None'
try:
sqf = i.xpath('.//input[contains(@id,"propertyArea")]/@value').extract_first()
if 'sqft' in sqf:
item['Bua_sqft'] = re.findall('[0-9]+', sqf)
elif 'kottah' in sqf:
item['Bua_sqft'] = re.findall('[0-9]+', sqf)
item['Bua_sqft'] = str(eval(item['Bua_sqft'][0]) * 720)
else:
item['Bua_sqft'] = '0'
except:
item['Bua_sqft'] = '0'
item['Locality'] = i.xpath('div/div[@class="srpColm2"]/div[@class="proColmleft"]/div[1]/div/p/a/abbr/span[1]/span/text()').extract_first()
stat = i.xpath('.//div[1]/div[2]/div[1]/div[2]/div[1]/text()').extract()
#print("STAT: ", stat)
try:
if 'Under' in stat:
item['Status'] = 'Under Construction'
poss = stat.split('Ready by ')[-1].replace("'", "").replace(')','').replace('\n', '').replace('. Freehold', '')
#print("POSSESSION: ", poss)
yr = str(re.findall('[0-9]+', poss))
yr = yr.replace('[', '').replace(']', '').replace('u', '').replace("'", "")
if 'Jan' in poss:
item['Possession'] = '01/01/' + yr + ' 00:00:00'
if 'Feb' in poss:
item['Possession'] = '01/02/' + yr + ' 00:00:00'
if 'Mar' in poss:
item['Possession'] = '01/03/' + yr + ' 00:00:00'
if 'Apr' in poss:
item['Possession'] = '01/04/' + yr + ' 00:00:00'
if 'May' in poss:
item['Possession'] = '01/05/' + yr + ' 00:00:00'
if 'Jun' in poss:
item['Possession'] = '01/06/' + yr + ' 00:00:00'
if 'Jul' in poss:
item['Possession'] = '01/07/' + yr + ' 00:00:00'
if 'Aug' in poss:
item['Possession'] = '01/08/' + yr + ' 00:00:00'
if 'Sep' in poss:
item['Possession'] = '01/09/' + yr + ' 00:00:00'
if 'Oct' in poss:
item['Possession'] = '01/10/' + yr + ' 00:00:00'
if 'Nov' in poss:
item['Possession'] = '01/11/' + yr + ' 00:00:00'
if 'Dec' in poss:
item['Possession'] = '01/12/' + yr + ' 00:00:00'
else:
item['Status'] = 'Ready to move'
item['Possession'] = '0'
except:
item['Possession'] = '0'
item['Status'] = 'Ready to move'
price = i.xpath('.//div/div[@class="srpColm2"]/div[@class="proColmRight"]/div/div/div/span/text()').extract_first()
#print("PRICE: ", price)
if price == None:
price = i.xpath('.//span[contains(@id,"sqrFtPriceField")]/text()').extract_first()
price = ''.join(re.findall('[0-9]+', price))
#print("PRICE: ", price)
if not price == None:
if 'Lac' in price:
item['Selling_price'] = str(float(price.split()[0])*100000)
elif 'Cr' in price:
item['Selling_price'] = str(float(price.split()[0])*10000000)
else:
item['Selling_price'] = '0'
if item['Selling_price'] == 'None':
item['Selling_price'] = '0'
else:
item['Selling_price'] = '0'
if item['Selling_price'] == '0':
item['price_on_req'] = 'true'
else:
item['price_on_req'] = 'false'
try:
sqft_per = i.xpath('div/div[@class="srpColm2"]/div[@class="proColmRight"]/div[@class="proPriceColm2"]/div[@class="proPriceColm2"]/div[@class="sqrPrice"]/span[@class="sqrPriceField"]/text()').extract_first()
if sqft_per:
item['price_per_sqft'] = ''.join(re.findall('[0-9]+', sqft_per))
else:
item['price_per_sqft'] = '0'
if 'kottah' in sqf:
item['price_per_sqft'] = str(eval(item['price_per_sqft']) / 720)
except:
item['price_per_sqft'] = '0'
try:
item['listing_by'] = i.xpath('.//div[@class="proAgentWrap"]/div[1]/div/div[1]/text()').extract_first()
item['name_lister'] = i.xpath('.//div[@class="proAgentWrap"]/div[@class="comNameElip"]/text()').extract_first().replace("\n", "")
except:
item['listing_by'] = 'None'
item['name_lister'] = 'None'
item['txn_type'] = i.xpath('div/input[contains(@id,"transactionType")]/@value').extract_first()
day = i.xpath('div/input[contains(@id,"createDate")]/@value').extract_first()
try:
item['listing_date'] = dt.strftime(dt.strptime(day, "%b %d, '%y"), '%m/%d/%Y %H:%M:%S')
item['updated_date'] = item['listing_date']
except:
item['listing_date'] = 0
item['updated_date'] = item['listing_date']
if (((not item['Bua_sqft']=='0') and (not item['Building_name']=='None') and (not item['lat']=='0')) or ((not item['Selling_price'] == '0') and (not item['Bua_sqft']=='0') and (not item['Building_name']=='None') and (not item['lat']=='0')) or ((not item['price_per_sqft'] == '0') and (not item['Bua_sqft']=='0') and (not item['Building_name']=='None') and (not item['lat']=='0'))):
item['quality4'] = 1
elif (((not item['price_per_sqft'] == '0') and (not item['Building_name']=='None') and (not item['lat']=='0')) or ((not item['Selling_price'] == '0') and (not item['Bua_sqft']=='0') and (not item['lat']=='0')) or ((not item['Bua_sqft']=='0') and (not item['lat']=='0')) or ((not item['Selling_price'] == '0') and (not item['Bua_sqft']=='0') and (not item['Building_name']=='None')) or ((not item['Bua_sqft']=='0') and (not item['Building_name']=='None'))):
item['quality4'] = 0.5
else:
item['quality4'] = 0
if ((not item['Building_name'] == 'None') and (not item['listing_date'] == '0') and (not item['txn_type'] == 'None') and (not item['property_type'] == 'None') and ((not item['Selling_price'] == '0'))):
item['quality1'] = 1
else:
item['quality1'] = 0
if ((not item['Launch_date'] == '0') and (not item['Possession'] == '0')):
item['quality2'] = 1
else:
item['quality2'] = 0
if ((not item['mobile_lister'] == 'None') or (not item['listing_by'] == 'None') or (not item['name_lister'] == 'None')):
item['quality3'] = 1
else:
item['quality3'] = 0
yield item