def tempContingencyTable(forecasts, observations, start_date, end_date, temp="H", stations=None, shift=None, period=None):
"""
tempContingencyTable()
Purpose: Produce a continuous contingency table containing all the temperature forecasts for the period.
Parameters: forecasts [type=dictionary]
Dictionary mapping shift days (e.g. 'Tue_Aft' for Tuesday Afternoon) to their OWLShift objects.
observations [type=dictionary]
Dictionary mapping observation points (e.g. 'OUN' for Norman) to their ASOS objects.
start_date [type=string]
String containing the date of the start of the verification period (format is 'YYYYMMDD_HH:MM').
end_date [type=string]
String containing the date of the end of the verification period (format is 'YYYYMMDD_HH:MM', same as in start_date).
temp [type=string]
String telling whether the high or low temperature is being evaluated. "H" for high and "L" for low.
stations [type=list,tuple,string]
A station or list of stations to include in the contingency table. Optional, defaults to KOUN if not given.
shift [type=string]
The shift to verify (e.g. 'Tue_Aft' for Tuesday Afternoon). Not implemented yet.
period [type=string]
The period to verify (one of '1A', '1B', '2', '3', or '4'). Optional, defaults to '1A' if not given.
Returns: The completed contingency table as a ContinuousContingencyTable object.
"""
temp_ct = ContinuousContingencyTable(np.array([]),np.array([]))
if period is None:
period = OWLShift._forecast_days[0]
if stations is None:
stations = observations.keys()
elif type(stations) not in [ list, tuple ]:
stations = [ stations ]
for shift_name, shift_data in forecasts.iteritems():
for stn in stations:
if temp.upper()=="H":
temp_fcasts = shift_data.getForecasts(period, start_date, end_date, "TMPH", verif_to_fcst[stn])
else:
temp_fcasts = shift_data.getForecasts(period, start_date, end_date, "TMPL", verif_to_fcst[stn])
shift_start_times, shift_end_times = temp_fcasts[:2]
if temp.upper()=="H":
temp_obs = observations[stn].getHighTemps(shift_start_times,shift_end_times)
high_idxs = np.nonzero(temp_obs > 150)
if len(high_idxs[0]) > 0:
print shift_start_times[high_idxs],temp_obs[high_idxs]
else:
temp_obs = observations[stn].getLowTemps(shift_start_times,shift_end_times)
temp_ct.addPairs(temp_fcasts[2],temp_obs)
return temp_ct
def windContingencyTable(forecasts, observations, start_date, end_date, winds="max", stations=None, shift=None, period=None):
"""
windContingencyTable()
Purpose: Produce a continuous contingency table containing all the wind forecasts for the period.
Parameters: forecasts [type=dictionary]
Dictionary mapping shift days (e.g. 'Tue_Aft' for Tuesday Afternoon) to their OWLShift objects.
observations [type=dictionary]
Dictionary mapping observation points (e.g. 'OUN' for Norman) to their ASOS objects.
start_date [type=string]
String containing the date of the start of the verification period (format is 'YYYYMMDD_HH:MM').
end_date [type=string]
String containing the date of the end of the verification period (format is 'YYYYMMDD_HH:MM', same as in start_date).
winds [type=string]
String telling whether the high or low temperature is being evaluated. "H" for high and "L" for low.
stations [type=list,tuple,string]
A station or list of stations to include in the contingency table. Optional, defaults to KOUN if not given.
shift [type=string]
The shift to verify (e.g. 'Tue_Aft' for Tuesday Afternoon). Not implemented yet.
period [type=string]
The period to verify (one of '1A', '1B', '2', '3', or '4'). Optional, defaults to '1A' if not given.
Returns: The completed contingency table as a ContinuousContingencyTable object.
"""
wind_ct = ContinuousContingencyTable(np.array([]),np.array([]))
if period is None:
period = OWLShift._forecast_days[0]
if stations is None:
stations = observations.keys()
elif type(stations) not in [ list, tuple ]:
stations = [ stations ]
for shift_name, shift_data in forecasts.iteritems():
for stn in stations:
if stn == 'KHHW' or stn == 'HHW':
pass
else:
if winds.upper()=="MAX":
wind_fcasts = shift_data.getForecasts(period, start_date, end_date, "WSHI", verif_to_fcst[stn])
else:
wind_fcasts = shift_data.getForecasts(period, start_date, end_date, "WSLO", verif_to_fcst[stn])
shift_start_times, shift_end_times = wind_fcasts[:2]
if winds.upper()=="MAX":
wind_obs = observations[stn].getMaxWinds(shift_start_times,shift_end_times)
else:
wind_obs = observations[stn].getMinWinds(shift_start_times,shift_end_times)
wind_ct.addPairs(wind_fcasts[2],wind_obs)
return wind_ct
