def expFit(x,y):
'''
Deprecated. Please update yr code to call this from NumUtils.py
'''
print('Deprecated. Please update yr code to call this from NumUtils.py')
import NumUtils as nu
a,b = nu.expFit(x,y)
return a,b
def linFit(x,y):
'''
Deprecated. Please update yr code to call this from NumUtils.py
'''
print('Deprecated. Please update yr code to call this from NumUtils.py')
import NumUtils as nu
model, R_out = nu.linFit(x,y)
return model, R_out
def upperYieldStress(rheoData, rheoInfo):
import numpy as np
import NumUtils as nu
g, t, gd = (rheoData['gamma'], rheoData['tau'], rheoData['gammadot'])
UI = rheoInfo['upperN']
# upper yield stress
gdU1 = gd[UI[0,0]:UI[0,1]]
tU1 = t[UI[0,0]:UI[0,1]]
gdU2 = gd[UI[1,0]:UI[1,1]]
tU2 = t[UI[1,0]:UI[1,1]]
mU1, rU1 = nu.linFit(gdU1, tU1)
m1, b1 = (mU1[0], mU1[1])
mU2, rU2 = nu.linFit(gdU2, tU2)
m2, b2 = (mU2[0], mU2[1])
tau_y = (b2-b1)/(m1-m2)
uys = {'upperYS' : tau_y,
'gammadot_y' : (tau_y-b1)/m1,
'fit' : np.array([[m1,b1],[m2,b2]])}
def lowerYieldStress(rheoData, rheoInfo):
import numpy as np
import NumUtils as nu
g, t, gd = (rheoData['gamma'], rheoData['tau'], rheoData['gammadot'])
LI = rheoInfo['lowerN']
# lower yield stress
gL1 = g[LI[0,0]:LI[0,1]]
tL1 = t[LI[0,0]:LI[0,1]]
gL2 = g[LI[1,0]:LI[1,1]]
tL1 = g[LI[1,0]:LI[1,1]]
aL1, bL1 = nu.powerFit(tL1, gL1)
aL2, bL2 = nu.powerFit(tL2, gL2)
tau_y = np.exp(np.log(aL2/aL1)/(bL1/bL2))
lys = {'lowerYS' : tau_y,
'gamma_y' : aL1 * tau_y**bL1,
'fit' : np.array([[aL1, bL1],[aL2, bL2]])}
return ys
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)))
