Tselect = np.squeeze(InVar[Z1:Z2 + 1, :])
Dselect = np.squeeze(DensVar[Z1:Z2 + 1, :])
VertDim = Zdim
elif (VcoordType == 'p'):
Nvert = Np
Tselect = np.squeeze(InVar[P1:P2 + 1, :])
Dselect = np.squeeze(DensVar[P1:P2 + 1, :])
VertDim = Pdim
# Smooth the input temperature field in the horizontal.
InVarSmooth = np.zeros((Nvert, Nx))
for ivert in range(Nvert):
InVarSmooth[ivert, :] = nu.SmoothLine(np.squeeze(InVar[ivert, :]))
# Take the horizontal gradient of the 2D field.
# Create a 2D delta X array
DeltaX_2D = np.tile(DeltaX.reshape((1, Nx)), (Nvert, 1))
InVarGrad = np.gradient(InVarSmooth, DeltaX_2D, axis=1)
InVarGradSmooth = np.zeros((Nvert, Nx))
for ivert in range(Nvert):
InVarGradSmooth[ivert, :] = nu.SmoothLine(
np.squeeze(InVarGrad[ivert, :]))
# Do vertical average of layer between bottom and top layer
# Use density weighted averaging.
Tbar = np.squeeze(np.average(Tselect, axis=0, weights=Dselect))
# Smooth Tbar in order to mitigate noise in the gradient
print("")
# finish calculations
# entropy deficit
Sdef = (SmSat - Sm) / (SsstSat - Sb)
# potiential intensity
Pi = np.sqrt((Ts - To) * (Ts / To) * CkOverCd * (SsstSat - Sb))
# ventilation index
Vi = WindShear * Sdef / Pi
##### Smoothed versions ####
# wind shear
WindShearSmooth = nu.SmoothLine(WindShear)
# entropy deficit
SmSmooth = nu.SmoothLine(Sm)
SmSatSmooth = nu.SmoothLine(SmSat)
SbSmooth = nu.SmoothLine(Sb)
SsstSatSmooth = nu.SmoothLine(SsstSat)
SdefSmooth = nu.SmoothLine((SmSatSmooth - SmSmooth) / (SsstSatSmooth - Sb))
# potential intensity
TsSmooth = nu.SmoothLine(Ts)
ToSmooth = nu.SmoothLine(To)
PiSmooth = nu.SmoothLine(np.sqrt((TsSmooth - ToSmooth) * (TsSmooth / ToSmooth) * CkOverCd * (SsstSatSmooth - SbSmooth)))
