def __init__(self):
self.cmds = commands.Commands()
self.fc = forecast.Forecast()
self.timestamps = {
'**:00:00': 'full_hour',
'**:30:00': 'forecast',
'09:30:00': 'standup',
'10:00:00': 'standup'
}
def __init__(self, applet):
self.applet = applet
self.weather_fail = [False, False, False]
self.reset_weather_data()
self.network_handler = self.NetworkHandler()
self.notification = applet.notification.create(
_("Network error in Weather"), network_error_message,
"dialog-warning", 20)
self.setup_context_menu()
self.forecaster = forecast.Forecast(self)
# Set default icons/titles/dialogs so the applet is informative without data
self.set_icon()
self.applet.tooltip.set(
"%s %s..." %
(_("Fetching conditions for"), self.applet.settings['location']))
# Overlays
self.__temp_overlay = OverlayText()
self.__temp_overlay.props.gravity = gtk.gdk.GRAVITY_SOUTH
applet.add_overlay(self.__temp_overlay)
disconnect_overlay = OverlayThemedIcon("gtk-disconnect")
disconnect_overlay.props.alpha = 1.0
throbber_overlay = OverlayThrobber()
for i in (disconnect_overlay, throbber_overlay):
i.props.scale = 0.5
i.props.gravity = gtk.gdk.GRAVITY_SOUTH_EAST
i.props.apply_effects = False
applet.add_overlay(i)
global overlay_fsm
overlay_fsm = OverlayStateMachine(disconnect_overlay, throbber_overlay)
def show_map(widget):
if self.map_dialog is None:
self.fetch_weather_map(show=True)
applet.connect("middle-clicked", show_map)
# Set up the timer which will refresh the conditions, forecast, and weather map
applet.timing.register(self.activate_refresh_cb, update_interval * 60)
applet.timing.delay(self.activate_refresh_cb, 1.0)
def __init__(self, station, run, model, startDate, nDays):
self.startDate = startDate
self.run = run
self.model = model
self.periods = []
self.checkRep()
baseURL = 'https://mesonet.agron.iastate.edu/mos/csv.php'
for i in range(nDays):
forecastDate = startDate + datetime.timedelta(days=i)
modelRun = datetime.datetime.combine(
forecastDate - datetime.timedelta(days=1), self.run)
runText = modelRun.strftime('%Y-%m-%d %H:%M')
url = baseURL + '?station=' + station + '&runtime=' + runText + '&model=' + model
f = util.getData(url)
f.readline()
y = 0
if self.run.hour == 0:
offset = 8
if self.run.hour == 6:
offset = 6
if self.run.hour == 12:
offset = 4
if self.run.hour == 18:
offset = 2
maxTemp = -200
minTemp = 200
maxWind = 0
precip = 0
for line in f:
fields = line.split(',')
fcDate = datetime.datetime.strptime(fields[3].split('+')[0],
'%Y-%m-%d %H:%M:%S')
fcTemp = None
if fields[5] != '':
fcTemp = int(fields[5])
fcWind = 0
if fields[9] != '':
fcWind = float(fields[9])
fcPrecip = 0
if fields[12] != '':
if fields[12] == '1':
fcPrecip = 0.05
elif fields[12] == '2':
fcPrecip = 0.17
elif fields[12] == '3':
fcPrecip = 0.37
elif fields[12] == '4':
fcPrecip = 0.75
elif fields[12] == '5':
fcPrecip = 1
y += 1
if y > offset + 8:
self.periods.append(
forecast.Forecast(maxTemp, minTemp, float(maxWind),
float(precip)))
break
if y > offset:
if fcDate.hour % 6 == 0:
precip += fcPrecip
if fcTemp != None:
if fcTemp > maxTemp:
maxTemp = fcTemp
if fcTemp < minTemp:
minTemp = fcTemp
if fcWind > maxWind:
maxWind = fcWind
f.close()
return
PREDICTION_QUARTER = pd.date_range(start=datetime.now().date(),
end=datetime(2060, 1, 1),
freq='3MS',
closed='right').date[:4]
PREDICTION_QUARTER_BOUNDARIES = [(PREDICTION_QUARTER[i],
PREDICTION_QUARTER[i + 1])
for i in range(len(PREDICTION_QUARTER) - 1)]
df = prep.read_permits('currentPermits.csv')
writer = pd.ExcelWriter(datetime.now().strftime('%d %m %y') + '_forecast.xls',
datetime_format='mmm d yyyy')
for occupancy in OCCUPANCY_GROUPS:
predictor = forecast.Forecast(df,
occupancy_group=occupancy[0],
lower_value_limit=occupancy[1],
regression_algorithm=RandomForestRegressor)
current_data = pd.DataFrame(
data={
'numberPending':
predictor.get_quarterly_pending(quarters=QUARTERS),
'valuePending':
predictor.get_quarterly_pending_value(quarters=QUARTERS),
'waitTime':
predictor.get_wait_time(),
'quarter':
QUARTER_BEGINNINGS[:-1]
})
current_data.to_excel(writer,
sheet_name=occupancy[0][:2] + ' timeseries at ' +
datetime.now().strftime('%d %m %y'))
# (c) 2017 kj/P1X
#
# external libs
import os, slackclient, time, re, random
# internal libs
import filesystem, config, logger, display_fast, commands
import whiteboard, forecast, psas
fs = filesystem.Filesystem()
cfg = config.Config()
log = logger.Logger()
cmds = commands.Commands()
wb = whiteboard.Whiteboard()
fc = forecast.Forecast()
sc = slackclient.SlackClient(cfg.get_api_key('slack'))
psa = psas.PublicServiceAnnaucments()
disp = display_fast.Display()
def get_mention(user):
return '<@{user}>'.format(user=user)
bot_slack_mention = get_mention(cfg.get_api_key('bot'))
# The BRAIN
def decode_message(message, user):
if message:
tokens = [re.sub('[^A-Za-z0-9]+', '', word.lower()) for word in message.strip().split()]
# real commands
import os
import argparse
import forecast as fc
# Parse arguments
parser = argparse.ArgumentParser(description='Forecast command line')
parser.add_argument('years', help='Number of years to project', type=int)
parser.add_argument('--auto-open',
help='Whether to open the HTML',
action='store_true')
parser.add_argument('--include-net',
help='Whether to include net worth',
action='store_true')
args = parser.parse_args()
# Build forecasts
directory = os.path.join('.', 'configs')
for filename in os.listdir(directory):
extension = os.path.splitext(filename)[1][1:]
if extension not in ['yml', 'yaml'] or filename == 'sample.yaml': continue
filepath = os.path.join(directory, filename)
forecast = fc.Forecast(filepath, args.years, args.include_net).project()
fc.PlotBuilder(forecast).build(args.auto_open)
fc.CsvBuilder(forecast).build()
def __init__(self, station, startDate, nDays):
self.startDate = startDate
self.periods = []
self.checkRep()
baseURL = 'https://mesonet.agron.iastate.edu/cgi-bin/request/asos.py?data=tmpf&data=sknt&data=p01i&tz=Etc%2FUTC&format=onlycomma&latlon=no&missing=M&trace=T&direct=no&report_type=1&report_type=2'
for i in range(nDays):
forecastDate = startDate + datetime.timedelta(days=i)
nextForecastDate = forecastDate + datetime.timedelta(days=2)
url = baseURL + '&station=' + station + '&year1=' + str(
forecastDate.year) + '&month1=' + str(
forecastDate.month) + '&day1=' + str(
forecastDate.day) + '&year2=' + str(
nextForecastDate.year) + '&month2=' + str(
nextForecastDate.month) + '&day2=' + str(
nextForecastDate.day)
f = util.getData(url)
f.readline()
y = 0
maxTemp = -200
minTemp = 200
maxWind = 0
precip = 0
offset = 81
for line in f:
line = line.strip('\n')
fields = line.split(',')
fcDate = datetime.datetime.strptime(fields[1].split('+')[0],
'%Y-%m-%d %H:%M')
fcTemp = None
if fields[2] != '' and fields[2] != 'M' and fields[2] != 'T':
fcTemp = int(round(float(fields[2])))
fcWind = None
if fields[3] != '' and fields[3] != 'M' and fields[3] != 'T':
fcWind = float(fields[3])
fcPrecip = None
if fields[4] != '' and fields[4] != 'M' and fields[4] != 'T':
fcPrecip = float(fields[4])
y += 1
if y > offset + 314:
self.periods.append(
forecast.Forecast(maxTemp, minTemp, float(maxWind),
float(precip)))
break
if y > offset:
if fcDate.minute % 53 == 0:
if fcPrecip != None:
precip += fcPrecip
if fcTemp != None:
if fcTemp > maxTemp:
maxTemp = fcTemp
if fcTemp < minTemp:
minTemp = fcTemp
if fcWind != None:
if fcWind > maxWind:
maxWind = fcWind
f.close()
return