def __interpolate_QP(self, wf_originpl_data, wf_interpolated_data):
"""
Name: __interpolate_QP
Parameters:[I] metro_data wf_originpl_data : originpl data. Read-only
[I] metro_data wf_interpolated_data : container of the interpolated
data.
Returns: None
Functions Called: metro_util.interpolate, shift_right
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Add the rain (in mm.) and the snow (in cm.) and store it
in RA. Since 1 cm. is, roughly, 1 mm. of water, the sum
is considerated in mm.
Revision History:
Author Date Reason
Miguel Tremblay July 12th 2004
"""
npRA = wf_originpl_data.get_matrix_col('RA')
npSN = wf_originpl_data.get_matrix_col('SN')
npQP = npSN/10*metro_constant.nSnowWaterRatio \
+ npRA
# Patch because of the -99 at the end of the forecast
fMax = npQP.max()
npQP = numpy.where(npQP < 0, fMax, npQP)
# Compute the amount fell at each time step
npQP = npQP - metro_util.shift_right(npQP, 0)
npSN = npSN - metro_util.shift_right(npSN, 0)
npRA = npRA - metro_util.shift_right(npRA, 0)
# Interpolate these values at each time step
npQP = metro_util.interpolate(self.npTime, npQP)
npSN = metro_util.interpolate(self.npTime, npSN)
npRA = metro_util.interpolate(self.npTime, npRA)
# Shift all the values to have them started at the right time
npQP = metro_util.shift_left(npQP, 0)
npSN = metro_util.shift_left(npSN, 0)
npRA = metro_util.shift_left(npRA, 0)
npQP = npQP * 10e-4 # Set it in meter
npQP = npQP / 3600.0 # Convert by second
npQP = numpy.where(npQP < 0, 0, npQP)
wf_interpolated_data.append_matrix_col('QP', npQP)
wf_interpolated_data.append_matrix_col('SN', npSN)
wf_interpolated_data.append_matrix_col('RA', npRA)
def __interpolate_QP(self, wf_originpl_data, wf_interpolated_data):
"""
Name: __interpolate_QP
Parameters:[I] metro_data wf_originpl_data : originpl data. Read-only
[I] metro_data wf_interpolated_data : container of the interpolated
data.
Returns: None
Functions Called: metro_util.interpolate, shift_right
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Add the rain (in mm.) and the snow (in cm.) and store it
in RA. Since 1 cm. is, roughly, 1 mm. of water, the sum
is considerated in mm.
Revision History:
Author Date Reason
Miguel Tremblay July 12th 2004
"""
npRA = wf_originpl_data.get_matrix_col('RA')
npSN = wf_originpl_data.get_matrix_col('SN')
npQP = npSN/10*metro_constant.nSnowWaterRatio \
+ npRA
# Patch because of the -99 at the end of the forecast
fMax = npQP.max()
npQP = numpy.where(npQP < 0, fMax, npQP)
# Compute the amount fell at each time step
npQP = npQP - metro_util.shift_right(npQP, 0)
npSN = npSN - metro_util.shift_right(npSN, 0)
npRA = npRA - metro_util.shift_right(npRA, 0)
# Interpolate these values at each time step
npQP = metro_util.interpolate(self.npTime, npQP)
npSN = metro_util.interpolate(self.npTime, npSN)
npRA = metro_util.interpolate(self.npTime, npRA)
# Shift all the values to have them started at the right time
npQP = metro_util.shift_left(npQP, 0)
npSN = metro_util.shift_left(npSN, 0)
npRA = metro_util.shift_left(npRA, 0)
npQP = npQP *10e-4 # Set it in meter
npQP = npQP / 3600.0 # Convert by second
npQP = numpy.where(npQP < 0, 0, npQP)
wf_interpolated_data.append_matrix_col('QP', npQP)
wf_interpolated_data.append_matrix_col('SN', npSN)
wf_interpolated_data.append_matrix_col('RA', npRA)
def __set_sf(self, wf_controlled_data, wf_interpolated_data):
"""
Set the theoretical solar flux.
Parameters:
wf_controlled_data (metro_data) : controlled data. Read-only
"""
npTime = wf_controlled_data.get_matrix_col('Time')
# Only interpolate if SF is given
if metro_config.get_value('SF'):
npSF = wf_controlled_data.get_matrix_col('SF')
npSF2 = metro_util.interpolate(npTime, npSF)
if ((self.infdata_exist('HORIZON')) and (self.npAzim != None)):
npFT2 = wf_interpolated_data.get_matrix_col('FORECAST_TIME')
tDate2 = [time.gmtime(x) for x in npFT2]
sunshadw_method = metro_config.get_value('SUNSHADOW_METHOD')
npSF2 = metro_preprocess_sunshadw.\
get_corrected_solar_flux(tDate2, npSF2, \
self.fLat, self.fLon,\
zip(self.npAzim, self.npElev),\
m=sunshadw_method)
wf_interpolated_data.append_matrix_col('SF', npSF2)
return
# Get data
npCloudOctal = wf_controlled_data.get_matrix_col('CC')
npTimeHour = wf_controlled_data.get_matrix_col('Hour')
npForecastedTime = wf_controlled_data.\
get_matrix_col('FORECAST_TIME')
fStartForecastTime = npForecastedTime[0]
# Get solar fluxes for this cloud cover for this specific day
npSF = metro_physics.get_sf(npCloudOctal, npTimeHour, \
npForecastedTime,\
self.fSunrise, self.fSunset,\
self.fLat, self.fLon)
npSF2 = metro_util.interpolate(npTime, npSF)
if ((self.infdata_exist('HORIZON')) and (self.npAzim != None)):
npFT2 = wf_interpolated_data.get_matrix_col('FORECAST_TIME')
tDate2 = [time.gmtime(x) for x in npFT2]
sunshadw_method = metro_config.get_value('SUNSHADOW_METHOD')
npSF2 = metro_preprocess_sunshadw.\
get_corrected_solar_flux(tDate2, npSF2, \
self.fLat, self.fLon,\
zip(self.npAzim, self.npElev),\
m=sunshadw_method)
wf_controlled_data.append_matrix_col('SF', npSF)
wf_interpolated_data.append_matrix_col('SF', npSF2)
def __set_sf(self, wf_controlled_data, wf_interpolated_data):
"""
Set the theoretical solar flux.
Parameters:
wf_controlled_data (metro_data) : controlled data. Read-only
"""
npTime = wf_controlled_data.get_matrix_col('Time')
# Only interpolate if SF is given
if metro_config.get_value('SF'):
npSF = wf_controlled_data.get_matrix_col('SF')
npSF2 = metro_util.interpolate(npTime, npSF)
if ((self.infdata_exist('HORIZON')) and (self.npAzim != None)):
npFT2 = wf_interpolated_data.get_matrix_col('FORECAST_TIME')
tDate2 = [time.gmtime(x) for x in npFT2]
sunshadw_method = metro_config.get_value('SUNSHADOW_METHOD')
npSF2 = metro_preprocess_sunshadw.\
get_corrected_solar_flux(tDate2, npSF2, \
self.fLat, self.fLon,\
zip(self.npAzim, self.npElev),\
m=sunshadw_method)
wf_interpolated_data.append_matrix_col('SF', npSF2)
return
# Get data
npCloudOctal = wf_controlled_data.get_matrix_col('CC')
npTimeHour = wf_controlled_data.get_matrix_col('Hour')
npForecastedTime = wf_controlled_data.\
get_matrix_col('FORECAST_TIME')
fStartForecastTime =npForecastedTime [0]
# Get solar fluxes for this cloud cover for this specific day
npSF = metro_physics.get_sf(npCloudOctal, npTimeHour, \
npForecastedTime,\
self.fSunrise, self.fSunset,\
self.fLat, self.fLon)
npSF2 = metro_util.interpolate(npTime, npSF)
if ((self.infdata_exist('HORIZON')) and (self.npAzim != None)):
npFT2 = wf_interpolated_data.get_matrix_col('FORECAST_TIME')
tDate2 = [time.gmtime(x) for x in npFT2]
sunshadw_method = metro_config.get_value('SUNSHADOW_METHOD')
npSF2 = metro_preprocess_sunshadw.\
get_corrected_solar_flux(tDate2, npSF2, \
self.fLat, self.fLon,\
zip(self.npAzim, self.npElev),\
m=sunshadw_method)
wf_controlled_data.append_matrix_col('SF', npSF)
wf_interpolated_data.append_matrix_col('SF', npSF2)
def __interpolate_TD(self, wf_originpl_data, wf_interpolated_data):
"""
Name: __interpolate_TD
Parameters:[I] metro_data wf_originpl_data : originpl data. Read-only
[I] metro_data wf_interpolated_data : container of the interpolated
data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Interpolate the dew point.
Revision History:
Author Date Reason
Miguel Tremblay July 12th 2004
"""
npTD = wf_originpl_data.get_matrix_col('TD')
npTD = metro_util.interpolate(self.npTime, npTD)
wf_interpolated_data.append_matrix_col('TD', npTD)
def __interpolate_WS(self, wf_originpl_data, wf_interpolated_data):
"""
Name: __interpolate_WS
Parameters:[I] metro_data wf_originpl_data : originpl data. Read-only
[I] metro_data wf_interpolated_data : container of the
interpolated data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Interpolate wind speed. Wind is in km/h and is converted in
m/s by the product with 0.2777777
Revision History:
Author Date Reason
Miguel Tremblay July 12th 2004
"""
npWS = wf_originpl_data.get_matrix_col('WS') * 0.2777777
npWS = metro_util.interpolate(self.npTime, npWS)
wf_interpolated_data.append_matrix_col('WS', npWS)
def __interpolate_WS(self, ro_controlled_data, ro_interpolated_data):
"""
Name: __interpolate_WS
Parameters:[I] metro_data ro_controlled_data : controlled data. Read-only
[I] metro_data ro_interpolated_data : container of the
interpolated data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Does the interpolation of the wind speed.
Wind is in km/h and is converted in
m/s by the product with 0.2777777
Revision History:
Author Date Reason
Miguel Tremblay August 11th 2004
"""
npTimeOrig = ro_controlled_data.get_matrix_col('Time')
npWS = ro_controlled_data.get_matrix_col('WS')*0.2777777
npWS = metro_util.interpolate(npTimeOrig, npWS)
ro_interpolated_data.append_matrix_col('WS', npWS)
def __interpolate_AT(self, wf_originpl_data, wf_interpolated_data):
"""
Name: __interpolate_AT
Parameters:[I] metro_data wf_originpl_data : originpl data. Read-only
[I] metro_data wf_processed_data : container of the interpolated
data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Does the interpolation of the air temperature
Revision History:
Author Date Reason
Miguel Tremblay July 12th 2004
"""
npAT = wf_originpl_data.get_matrix_col('AT')
npAT = metro_util.interpolate(self.npTime, npAT)
wf_interpolated_data.append_matrix_col('AT', npAT)
def __interpolate_PI(self, ro_controlled_data, ro_interpolated_data):
"""
Name: __interpolate_PI
Parameters:[I] metro_data ro_controlled_data : controlled data. Read-only
[I] metro_data ro_interpolated_data : container of the
interpolated data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
numpy.where, around
Description: Does the interpolation of presence of precipitation.
"""
npTimeOrig = ro_controlled_data.get_matrix_col('Time')
npPI = ro_controlled_data.get_matrix_col('PI')
npPI = numpy.where(npPI != 1, 0, npPI)
npPI = metro_util.interpolate(npTimeOrig, npPI)
# Round
npPI = numpy.around(npPI)
# Store
ro_interpolated_data.append_matrix_col('PI', npPI)
def __interpolate_WS(self, ro_controlled_data, ro_interpolated_data):
"""
Name: __interpolate_WS
Parameters:[I] metro_data ro_controlled_data : controlled data. Read-only
[I] metro_data ro_interpolated_data : container of the
interpolated data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Does the interpolation of the wind speed.
Wind is in km/h and is converted in
m/s by the product with 0.2777777
Revision History:
Author Date Reason
Miguel Tremblay August 11th 2004
"""
npTimeOrig = ro_controlled_data.get_matrix_col('Time')
npWS = ro_controlled_data.get_matrix_col('WS') * 0.2777777
npWS = metro_util.interpolate(npTimeOrig, npWS)
ro_interpolated_data.append_matrix_col('WS', npWS)
def __interpolate_PI(self, ro_controlled_data, ro_interpolated_data):
"""
Name: __interpolate_PI
Parameters:[I] metro_data ro_controlled_data : controlled data. Read-only
[I] metro_data ro_interpolated_data : container of the
interpolated data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
numpy.where, around
Description: Does the interpolation of presence of precipitation.
"""
npTimeOrig = ro_controlled_data.get_matrix_col('Time')
npPI = ro_controlled_data.get_matrix_col('PI')
npPI = numpy.where(npPI != 1, 0, npPI)
npPI = metro_util.interpolate(npTimeOrig, npPI)
# Round
npPI = numpy.around(npPI)
# Store
ro_interpolated_data.append_matrix_col('PI', npPI)
def __interpolate_CC(self, wf_originpl_data, wf_interpolated_data):
"""
Name: __interpolate_cloud_cover
Parameters:
[I] metro_data wf_controlled_data : controlled data. Read-only
[I] metro_data wf_interpolated_data : container of the interpolated
Returns: None
Description: Interpolate the cloud cover. Added in the roadcast file
to be able to draw the the cloud cover.
Notes: <other information, if any>
Revision History:
Author Date Reason
Miguel Tremblay June 20th 2005
"""
lCC = wf_originpl_data.get_matrix_col('CC')
npCC = numpy.array(lCC)
# Interpolate
npCC = metro_util.interpolate(self.npTime, npCC)
# Round
npCC = numpy.around(npCC)
# Store
wf_interpolated_data.append_matrix_col('CC', npCC)
def __interpolate_WS(self, wf_originpl_data, wf_interpolated_data):
"""
Name: __interpolate_WS
Parameters:[I] metro_data wf_originpl_data : originpl data. Read-only
[I] metro_data wf_interpolated_data : container of the
interpolated data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Interpolate wind speed. Wind is in km/h and is converted in
m/s by the product with 0.2777777
Revision History:
Author Date Reason
Miguel Tremblay July 12th 2004
"""
npWS = wf_originpl_data.get_matrix_col('WS')*0.2777777
npWS = metro_util.interpolate(self.npTime, npWS)
wf_interpolated_data.append_matrix_col('WS', npWS)
def __interpolate_TD(self, wf_originpl_data, wf_interpolated_data):
"""
Name: __interpolate_TD
Parameters:[I] metro_data wf_originpl_data : originpl data. Read-only
[I] metro_data wf_interpolated_data : container of the interpolated
data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Interpolate the dew point.
Revision History:
Author Date Reason
Miguel Tremblay July 12th 2004
"""
npTD = wf_originpl_data.get_matrix_col('TD')
npTD = metro_util.interpolate(self.npTime, npTD)
wf_interpolated_data.append_matrix_col('TD', npTD)
def __interpolate_AP(self, wf_originpl_data, wf_interpolated_data):
"""
Name: __interpolate_AP
Parameters:[I] metro_data wf_originpl_data : originpl data. Read-only
[I] metro_data wf_interpolated_data : container of the interpolated
data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Interpolate the surface pressure. Pressure in in hectopascal.
Revision History:
Author Date Reason
Miguel Tremblay July 12th 2004
"""
npAP = wf_originpl_data.get_matrix_col('AP')
# Replace invalid date by the normal pressure (1013.25 mb)
npAP = numpy.where(npAP < metro_constant.nLowerPressure,\
metro_constant.fNormalPressure, npAP)
npAP = numpy.where(npAP > metro_constant.nUpperPressure,\
metro_constant.fNormalPressure, npAP)
# Convert it in pascals.
npAP = npAP*100
npAP = metro_util.interpolate(self.npTime, npAP)
wf_interpolated_data.append_matrix_col('AP', npAP)
def __interpolate_CC(self, wf_originpl_data, wf_interpolated_data):
"""
Name: __interpolate_cloud_cover
Parameters:
[I] metro_data wf_controlled_data : controlled data. Read-only
[I] metro_data wf_interpolated_data : container of the interpolated
Returns: None
Description: Interpolate the cloud cover. Added in the roadcast file
to be able to draw the the cloud cover.
Notes: <other information, if any>
Revision History:
Author Date Reason
Miguel Tremblay June 20th 2005
"""
lCC = wf_originpl_data.get_matrix_col('CC')
npCC = numpy.array(lCC)
# Interpolate
npCC = metro_util.interpolate(self.npTime, npCC)
# Round
npCC = numpy.around(npCC)
# Store
wf_interpolated_data.append_matrix_col('CC', npCC)
def __interpolate_AP(self, wf_originpl_data, wf_interpolated_data):
"""
Name: __interpolate_AP
Parameters:[I] metro_data wf_originpl_data : originpl data. Read-only
[I] metro_data wf_interpolated_data : container of the interpolated
data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Interpolate the surface pressure. Pressure in in hectopascal.
Revision History:
Author Date Reason
Miguel Tremblay July 12th 2004
"""
npAP = wf_originpl_data.get_matrix_col('AP')
# Replace invalid date by the normal pressure (1013.25 mb)
npAP = numpy.where(npAP < metro_constant.nLowerPressure,\
metro_constant.fNormalPressure, npAP)
npAP = numpy.where(npAP > metro_constant.nUpperPressure,\
metro_constant.fNormalPressure, npAP)
# Convert it in pascals.
npAP = npAP * 100
npAP = metro_util.interpolate(self.npTime, npAP)
wf_interpolated_data.append_matrix_col('AP', npAP)
def __interpolate_ST(self, ro_controlled_data, ro_interpolated_data):
"""
Name: __interpolate_ST
Parameters:[I] metro_data ro_controlled_data : controlled data.
Read-only
[I] metro_data ro_interpolated_data : container of the
interpolated data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Does the interpolation of road temperature
Revision History:
Author Date Reason
Miguel Tremblay August 11th 2004
"""
npTimeOrig = ro_controlled_data.get_matrix_col('Time')
npST = ro_controlled_data.get_matrix_col('ST')
npST = metro_util.interpolate(npTimeOrig, npST)
ro_interpolated_data.append_matrix_col('ST', npST)
def __interpolate_TD(self, ro_controlled_data, ro_interpolated_data):
"""
Name: __interpolate_TD
Parameters:[I] metro_data ro_controlled_data : controlled data.Read-only
[I] metro_data ro_interpolated_data : container of the
interpolated data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Does the interpolation of the dew point
Revision History:
Author Date Reason
Miguel Tremblay August 5th 2004
"""
npTimeOrig = ro_controlled_data.get_matrix_col('Time')
npTD = ro_controlled_data.get_matrix_col('TD')
npTD = metro_util.interpolate(npTimeOrig, npTD)
ro_interpolated_data.append_matrix_col('TD', npTD)
def __interpolate_ST(self, ro_controlled_data, ro_interpolated_data):
"""
Name: __interpolate_ST
Parameters:[I] metro_data ro_controlled_data : controlled data.
Read-only
[I] metro_data ro_interpolated_data : container of the
interpolated data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Does the interpolation of road temperature
Revision History:
Author Date Reason
Miguel Tremblay August 11th 2004
"""
npTimeOrig = ro_controlled_data.get_matrix_col('Time')
npST = ro_controlled_data.get_matrix_col('ST')
npST = metro_util.interpolate(npTimeOrig, npST)
ro_interpolated_data.append_matrix_col('ST', npST)
def __interpolate_FA(self, wf_originpl_data, wf_interpolated_data):
"""
Name: __interpolate_FA
Parameters:[I] metro_data wf_originpl_data : originpl data. Read-only
[I] metro_data wf_processed_data : container of the interpolated
data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Does the interpolation of the anthropogenic flux
Revision History:
Author Date Reason
Rok Krsmanc October 22th 2010
"""
if metro_config.get_value('FA') == True:
npFA = wf_originpl_data.get_matrix_col('FA')
# If anthropogenic flux is not specified, FA is set to a constant value of 10 W/m^2
else:
lFA = [10]
npFA = numpy.array(lFA)
npFA = metro_util.interpolate(self.npTime, npFA)
wf_interpolated_data.append_matrix_col('FA', npFA)
def __interpolate_AT(self, wf_originpl_data, wf_interpolated_data):
"""
Name: __interpolate_AT
Parameters:[I] metro_data wf_originpl_data : originpl data. Read-only
[I] metro_data wf_processed_data : container of the interpolated
data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Does the interpolation of the air temperature
Revision History:
Author Date Reason
Miguel Tremblay July 12th 2004
"""
npAT = wf_originpl_data.get_matrix_col('AT')
npAT = metro_util.interpolate(self.npTime, npAT)
wf_interpolated_data.append_matrix_col('AT', npAT)
def __interpolate_TD(self, ro_controlled_data, ro_interpolated_data):
"""
Name: __interpolate_TD
Parameters:[I] metro_data ro_controlled_data : controlled data.Read-only
[I] metro_data ro_interpolated_data : container of the
interpolated data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Does the interpolation of the dew point
Revision History:
Author Date Reason
Miguel Tremblay August 5th 2004
"""
npTimeOrig = ro_controlled_data.get_matrix_col('Time')
npTD = ro_controlled_data.get_matrix_col('TD')
npTD = metro_util.interpolate(npTimeOrig, npTD)
ro_interpolated_data.append_matrix_col('TD', npTD)
def __interpolate_FA(self, wf_originpl_data, wf_interpolated_data):
"""
Name: __interpolate_FA
Parameters:[I] metro_data wf_originpl_data : originpl data. Read-only
[I] metro_data wf_processed_data : container of the interpolated
data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Does the interpolation of the anthropogenic flux
Revision History:
Author Date Reason
Rok Krsmanc October 22th 2010
"""
if metro_config.get_value('FA') == True:
npFA = wf_originpl_data.get_matrix_col('FA')
# If anthropogenic flux is not specified, FA is set to a constant value of 10 W/m^2
else:
lFA = [10]
npFA = numpy.array(lFA)
npFA = metro_util.interpolate(self.npTime, npFA)
wf_interpolated_data.append_matrix_col('FA', npFA)
def __interpolate_PI(self, wf_originpl_data, wf_interpolated_data):
"""
Name: __interpolate_PI
Parameters:[I] metro_data wf_originpl_data : originpl data. Read-only
[I] metro_data wf_interpolated_data : container of the interpolated
data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
numpy.where, around
Description: Interpolate the type of precipitation. The nearest neighbor is
is used.
Revision History:
Author Date Reason
Miguel Tremblay July 15th 2004
"""
npRA = wf_originpl_data.get_matrix_col('RA')
npSN = wf_originpl_data.get_matrix_col('SN')
npAT = wf_originpl_data.get_matrix_col('AT')
# Replace the last value if they are not good
if npRA[len(npRA) - 1] < 0:
npRA[len(npRA) - 1] = npRA.max()
if npSN[len(npSN) - 1] < 0:
npSN[len(npSN) - 1] = npSN.max()
npDiffRA = npRA - metro_util.shift_right(npRA, 0)
npDiffSN = npSN - metro_util.shift_right(npSN, 0)
lPI = []
for i in range(0, len(npDiffRA)):
if npDiffRA[i] > 0:
lPI.append(1)
elif npDiffSN[i] > 0:
lPI.append(2)
elif npAT[i] > 0:
lPI.append(1)
else:
lPI.append(2)
npPI = numpy.array(lPI)
# Interpolate
npPI = metro_util.interpolate(self.npTime, npPI)
# Round
npPI = numpy.around(npPI)
# Store
wf_interpolated_data.append_matrix_col('PI', npPI)
def __interpolate_PI(self, wf_originpl_data, wf_interpolated_data):
"""
Name: __interpolate_PI
Parameters:[I] metro_data wf_originpl_data : originpl data. Read-only
[I] metro_data wf_interpolated_data : container of the interpolated
data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
numpy.where, around
Description: Interpolate the type of precipitation. The nearest neighbor is
is used.
Revision History:
Author Date Reason
Miguel Tremblay July 15th 2004
"""
npRA = wf_originpl_data.get_matrix_col('RA')
npSN = wf_originpl_data.get_matrix_col('SN')
npAT = wf_originpl_data.get_matrix_col('AT')
# Replace the last value if they are not good
if npRA[len(npRA)-1] < 0:
npRA[len(npRA)-1] = npRA.max()
if npSN[len(npSN)-1] < 0:
npSN[len(npSN)-1] = npSN.max()
npDiffRA = npRA - metro_util.shift_right(npRA, 0)
npDiffSN = npSN - metro_util.shift_right(npSN, 0)
lPI = []
for i in range(0, len(npDiffRA)):
if npDiffRA[i] > 0:
lPI.append(1)
elif npDiffSN[i] > 0:
lPI.append(2)
elif npAT[i] > 0:
lPI.append(1)
else:
lPI.append(2)
npPI = numpy.array(lPI)
# Interpolate
npPI = metro_util.interpolate(self.npTime, npPI)
# Round
npPI = numpy.around(npPI)
# Store
wf_interpolated_data.append_matrix_col('PI', npPI)
def __interpolate_validatation(self,ro_controlled_data, observation_data):
"""
Name: __interpolate_validatation
Parameters:[I] metro_data ro_controlled_data : controlled data. Read-only
[I] metro_data ro_interpolated_data : container of the
interpolated data.
Returns: None
Functions Called: metro_util.interpolate,
observation_data.get_attribute
observation_data.set_attribute
numpy.around
Description: Does the interpolation of all the attributes that were set in
metro_preprocess_qa_qc_observation
"""
npTimeOrig = ro_controlled_data.get_matrix_col('Time')
npSST = observation_data.get_attribute('SST_VALID')
npAT = observation_data.get_attribute('AT_VALID')
npTD = observation_data.get_attribute('TD_VALID')
npWS = observation_data.get_attribute('WS_VALID')
# Interpolate
npSST = metro_util.interpolate(npTimeOrig, npSST)
npAT = metro_util.interpolate(npTimeOrig, npAT)
npTD = metro_util.interpolate(npTimeOrig, npTD)
npWS = metro_util.interpolate(npTimeOrig, npWS)
# Round
npTD = numpy.floor(npTD)
npAT = numpy.floor(npAT)
npSST = numpy.floor(npSST)
npWS = numpy.floor(npWS)
# Store
observation_data.set_attribute('TD_VALID_INTERPOLATED', npTD)
observation_data.set_attribute('AT_VALID_INTERPOLATED', npAT)
observation_data.set_attribute('SST_VALID_INTERPOLATED', npSST)
observation_data.set_attribute('WS_VALID_INTERPOLATED', npWS)
def __interpolate_validatation(self, ro_controlled_data, observation_data):
"""
Name: __interpolate_validatation
Parameters:[I] metro_data ro_controlled_data : controlled data. Read-only
[I] metro_data ro_interpolated_data : container of the
interpolated data.
Returns: None
Functions Called: metro_util.interpolate,
observation_data.get_attribute
observation_data.set_attribute
numpy.around
Description: Does the interpolation of all the attributes that were set in
metro_preprocess_qa_qc_observation
"""
npTimeOrig = ro_controlled_data.get_matrix_col('Time')
npSST = observation_data.get_attribute('SST_VALID')
npAT = observation_data.get_attribute('AT_VALID')
npTD = observation_data.get_attribute('TD_VALID')
npWS = observation_data.get_attribute('WS_VALID')
# Interpolate
npSST = metro_util.interpolate(npTimeOrig, npSST)
npAT = metro_util.interpolate(npTimeOrig, npAT)
npTD = metro_util.interpolate(npTimeOrig, npTD)
npWS = metro_util.interpolate(npTimeOrig, npWS)
# Round
npTD = numpy.floor(npTD)
npAT = numpy.floor(npAT)
npSST = numpy.floor(npSST)
npWS = numpy.floor(npWS)
# Store
observation_data.set_attribute('TD_VALID_INTERPOLATED', npTD)
observation_data.set_attribute('AT_VALID_INTERPOLATED', npAT)
observation_data.set_attribute('SST_VALID_INTERPOLATED', npSST)
observation_data.set_attribute('WS_VALID_INTERPOLATED', npWS)
def __set_ir(self, wf_controlled_data, wf_interpolated_data):
"""
Set the theoretical infrared flux.
Parameters:
wf_controlled_data (metro_data) : controlled data. Read-only
"""
npTime = wf_controlled_data.get_matrix_col('Time')
# Only interpolate if IR is given
if metro_config.get_value('IR'):
npIR = wf_controlled_data.get_matrix_col('IR')
npIR2 = metro_util.interpolate(npTime, npIR)
wf_interpolated_data.append_matrix_col('IR', npIR2)
return
npCloudOctal = wf_controlled_data.get_matrix_col('CC')
(npCoeff1, npCoeff2) = metro_physics.get_cloud_coefficient(npCloudOctal)
npAT = wf_controlled_data.get_matrix_col('AT')
npIR = npCoeff1*npAT+npCoeff2
npIR2 = metro_util.interpolate(npTime, npIR)
wf_controlled_data.append_matrix_col('IR', npIR)
wf_interpolated_data.append_matrix_col('IR', npIR2)
def __interpolate_FT(self, wf_original_data, wf_controlled_data, \
wf_interpolated_data):
"""
Name: __interpolate_FT
Parameters:[I] metro_data wf_original_data : original data. Read-only
[I] metro_data wf_processed_data : container of the
interpolated data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Interpolate forcast time.
Copy self.npTime in wf_processed_data 'FORECAST_TIME'
Revision History:
Author Date Reason
Miguel Tremblay July 13th 2004
"""
npFT = wf_original_data.get_matrix_col('FORECAST_TIME')
npFT = metro_util.interpolate(self.npTime, npFT)
wf_interpolated_data.append_matrix_col('FORECAST_TIME', npFT)
nHourStart = int(metro_date.get_hour(npFT[0]))
# A strange trick of copying the NumPy array locally to set it in the matrix
# must be done (if I remember well), because otherwhise the array are not included
# in the matrix. It might be because NumPy made use of pointers.
npTime = self.npTime
wf_controlled_data.append_matrix_col('Time', npTime)
npTime = metro_util.interpolate(self.npTime, npTime)
npTime = (npTime+30)/3600+nHourStart
wf_interpolated_data.append_matrix_col('Time', npTime)
def __interpolate_FT(self, wf_original_data, wf_controlled_data, \
wf_interpolated_data):
"""
Name: __interpolate_FT
Parameters:[I] metro_data wf_original_data : original data. Read-only
[I] metro_data wf_processed_data : container of the
interpolated data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Interpolate forcast time.
Copy self.npTime in wf_processed_data 'FORECAST_TIME'
Revision History:
Author Date Reason
Miguel Tremblay July 13th 2004
"""
npFT = wf_original_data.get_matrix_col('FORECAST_TIME')
npFT = metro_util.interpolate(self.npTime, npFT)
wf_interpolated_data.append_matrix_col('FORECAST_TIME', npFT)
nHourStart = int(metro_date.get_hour(npFT[0]))
# A strange trick of copying the NumPy array locally to set it in the matrix
# must be done (if I remember well), because otherwhise the array are not included
# in the matrix. It might be because NumPy made use of pointers.
npTime = self.npTime
wf_controlled_data.append_matrix_col('Time', npTime)
npTime = metro_util.interpolate(self.npTime, npTime)
npTime = (npTime + 30) / 3600 + nHourStart
wf_interpolated_data.append_matrix_col('Time', npTime)
def __set_ir(self, wf_controlled_data, wf_interpolated_data):
"""
Set the theoretical infrared flux.
Parameters:
wf_controlled_data (metro_data) : controlled data. Read-only
"""
npTime = wf_controlled_data.get_matrix_col('Time')
# Only interpolate if IR is given
if metro_config.get_value('IR'):
npIR = wf_controlled_data.get_matrix_col('IR')
npIR2 = metro_util.interpolate(npTime, npIR)
wf_interpolated_data.append_matrix_col('IR', npIR2)
return
npCloudOctal = wf_controlled_data.get_matrix_col('CC')
(npCoeff1,
npCoeff2) = metro_physics.get_cloud_coefficient(npCloudOctal)
npAT = wf_controlled_data.get_matrix_col('AT')
npIR = npCoeff1 * npAT + npCoeff2
npIR2 = metro_util.interpolate(npTime, npIR)
wf_controlled_data.append_matrix_col('IR', npIR)
wf_interpolated_data.append_matrix_col('IR', npIR2)
def __set_cc(self, wf_controlled_data, wf_interpolated_data):
"""
In the case that SF and IR are given, put the values of CC to -1.
Parameters:
wf_controlled_data (metro_data) : controlled data. Read-only
"""
if metro_config.get_value('SF') and metro_config.get_value('IR'):
npTime = wf_controlled_data.get_matrix_col('Time')
npCloudOctal = wf_controlled_data.get_matrix_col('CC')
nLength = len(npCloudOctal)
npCloud = numpy.ones(nLength) * (-1)
npCloud2 = metro_util.interpolate(npTime, npCloud)
wf_controlled_data.set_matrix_col('CC', npCloud)
wf_interpolated_data.set_matrix_col('CC', npCloud2)
def __set_cc(self, wf_controlled_data, wf_interpolated_data):
"""
In the case that SF and IR are given, put the values of CC to -1.
Parameters:
wf_controlled_data (metro_data) : controlled data. Read-only
"""
if metro_config.get_value('SF') and metro_config.get_value('IR'):
npTime = wf_controlled_data.get_matrix_col('Time')
npCloudOctal = wf_controlled_data.get_matrix_col('CC')
nLength = len(npCloudOctal)
npCloud = numpy.ones(nLength) * (-1)
npCloud2 = metro_util.interpolate(npTime, npCloud)
wf_controlled_data.set_matrix_col('CC', npCloud)
wf_interpolated_data.set_matrix_col('CC', npCloud2)
def __interpolate_AT(self, ro_controlled_data, ro_interpolated_data):
"""
Name: __interpolate_AT
Parameters:[I] metro_data ro_controlled_data : controlled data. Read-only
[I] metro_data ro_interpolated_data : container of the interpolated
data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Does the interpolation of the air temperature
"""
npTimeOrig = ro_controlled_data.get_matrix_col('Time')
npAT = ro_controlled_data.get_matrix_col('AT')
npAT = metro_util.interpolate(npTimeOrig, npAT)
ro_interpolated_data.append_matrix_col('AT', npAT)
def __interpolate_AT(self, ro_controlled_data, ro_interpolated_data):
"""
Name: __interpolate_AT
Parameters:[I] metro_data ro_controlled_data : controlled data. Read-only
[I] metro_data ro_interpolated_data : container of the interpolated
data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
Description: Does the interpolation of the air temperature
"""
npTimeOrig = ro_controlled_data.get_matrix_col('Time')
npAT = ro_controlled_data.get_matrix_col('AT')
npAT = metro_util.interpolate(npTimeOrig, npAT)
ro_interpolated_data.append_matrix_col('AT', npAT)
def __interpolate_SC(self, ro_controlled_data, ro_interpolated_data):
"""
Name: __interpolate_SC
Parameters:[I] metro_data ro_controlled_data : controlled data.
Read-only
[I] metro_data ro_interpolated_data : container of the
interpolated data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
numpy.where, around
Description: Does the interpolation of the Road Condition
33 is the SSI code. SEE the documentation for the conversion
between this code and the other standard:
http://documentation.wikia.com/wiki/Road_condition_%28METRo%29
Revision History:
Author Date Reason
Miguel Tremblay August 12th 2004
"""
npTimeOrig = ro_controlled_data.get_matrix_col('Time')
npSC = ro_controlled_data.get_matrix_col('SC')
# Convert
npSC = numpy.where(npSC == 33, 0, 1)
npSC = numpy.where(npSC < 0, 0, npSC)
npSC = numpy.where(npSC > 1, 0, npSC)
npSC = metro_util.interpolate(npTimeOrig, npSC)
# Round
npSC = numpy.around(npSC)
# Store
ro_interpolated_data.append_matrix_col('SC', npSC)
def __interpolate_SC(self, ro_controlled_data, ro_interpolated_data):
"""
Name: __interpolate_SC
Parameters:[I] metro_data ro_controlled_data : controlled data.
Read-only
[I] metro_data ro_interpolated_data : container of the
interpolated data.
Returns: None
Functions Called: metro_util.interpolate,
metro_data.get_matrix_col
metro_data.append_matrix_col
numpy.where, around
Description: Does the interpolation of the Road Condition
33 is the SSI code. SEE the documentation for the conversion
between this code and the other standard:
https://framagit.org/metroprojects/metro/wikis/Road_condition_(METRo)
Revision History:
Author Date Reason
Miguel Tremblay August 12th 2004
"""
npTimeOrig = ro_controlled_data.get_matrix_col('Time')
npSC = ro_controlled_data.get_matrix_col('SC')
# Convert
npSC = numpy.where(npSC == 33, 0, 1)
npSC = numpy.where(npSC < 0, 0, npSC)
npSC = numpy.where(npSC > 1, 0, npSC)
npSC = metro_util.interpolate(npTimeOrig, npSC)
# Round
npSC = numpy.around(npSC)
# Store
ro_interpolated_data.append_matrix_col('SC', npSC)
