def plot_figure1(self,S,A,bg=0,x=None,min=None,max=None,delta_isofill=None,delta_isoline=None,days_lines=None):
title = 'Figure 1'
displays=[] # store displays used
if min is None:
min = self.min
if max is None:
max = self.max
if delta_isoline is None:
delta_isoline = self.delta_isoline
if days_lines is None:
days_lines = self.days_lines
if delta_isofill is None:
delta_isofill = self.delta_isofill
if x is None:
x=self.x
m =x.mode
x.mode=1
x.landscape()
tmpl,tmplnoleg,isof,isol1,isol2=graphics.createTemplateandGM(x,min,max,delta_isofill,delta_isoline,days_lines,ntemplate=2)
for gm in isof,isol1,isol2:
gm.datawc_x1=self.datawc_x1
gm.datawc_x2=self.datawc_x2
gm.datawc_y1=self.datawc_y1
gm.datawc_y2=self.datawc_y2
tmpl2=x.createtemplate(source=tmpl.name)
tmpl2.moveto(.54,.2)
tmpl2noleg=x.createtemplate(source=tmpl2.name)
tmpl2noleg.legend.priority=0
for (sym,templ,templnoleg) in [(-1,tmpl,tmplnoleg),(1,tmpl2,tmpl2noleg)]:
if sym==-1:
power=A
else:
power=S
id=power.id
power=MV2.log10(power)
power.id=id
fq=power.getAxis(0)
fq.id='Frequency (CPD)'
w=power.getAxis(1)
w.id='Westward Zonal Wave Number Eastward'
displays.append(x.plot(power,isof,templ,bg=bg))
displays.append(x.plot(power,isol1,templnoleg,bg=bg))
displays.append(x.plot(power,isol2,templnoleg,bg=bg))
tt=x.createtext()
tt.x=[.5]
tt.y=[.97]
tt.height=25
tt.halign='center'
tt.string=[title,]
displays.append(x.plot(tt,bg=bg))
x.update()
x.mode=m
return displays
def plot_figure1(self,
S,
A,
bg=0,
x=None,
min=None,
max=None,
delta_isofill=None,
delta_isoline=None,
days_lines=None):
title = 'Figure 1'
displays = [] # store displays used
if min is None:
min = self.min
if max is None:
max = self.max
if delta_isoline is None:
delta_isoline = self.delta_isoline
if days_lines is None:
days_lines = self.days_lines
if delta_isofill is None:
delta_isofill = self.delta_isofill
if x is None:
x = self.x
x.landscape()
tmpl, tmplnoleg, isof, isol1, isol2 = graphics.createTemplateandGM(
x, min, max, delta_isofill, delta_isoline, days_lines, ntemplate=2)
for gm in isof, isol1, isol2:
gm.datawc_x1 = self.datawc_x1
gm.datawc_x2 = self.datawc_x2
gm.datawc_y1 = self.datawc_y1
gm.datawc_y2 = self.datawc_y2
tmpl2 = x.createtemplate(source=tmpl.name)
tmpl2.moveto(.54, .2)
tmpl2noleg = x.createtemplate(source=tmpl2.name)
tmpl2noleg.legend.priority = 0
for (sym, templ, templnoleg) in [(-1, tmpl, tmplnoleg),
(1, tmpl2, tmpl2noleg)]:
if sym == -1:
power = A
else:
power = S
id = power.id
power = MV2.log10(power)
power.id = id
fq = power.getAxis(0)
fq.id = 'Frequency (CPD)'
w = power.getAxis(1)
w.id = 'Westward Zonal Wave Number Eastward'
displays.append(x.plot(power, isof, templ, bg=bg))
displays.append(x.plot(power, isol1, templnoleg, bg=bg))
displays.append(x.plot(power, isol2, templnoleg, bg=bg))
tt = x.createtext()
tt.x = [.5]
tt.y = [.97]
tt.height = 25
tt.halign = 'center'
tt.string = [
title,
]
displays.append(x.plot(tt, bg=bg))
x.update()
return displays
def calculate_Z(PET, P, WCTOP, WCBOT, year1, year2):
"""
Written by Kate Marvel. Adapted from code by Park Williams (LDEO).
Calculates the Palmer z-index
Inputs:
PET: Potential evapotranspiration (mm)
PR: Precipitation (mm). NOTE CMIP5 standard is kg/m s-2 so need to run convert_to_mm on CMIP5 output
WCBOT: Soil moisture holding capacity in bottom layer (mm)
WCTOP: Water holding capacity in top layer (mm)
year1: start date of calibration period
year2: stop date of calibration period
Outputs:
PL: Potential loss
ET: Actual evapotranspiration
TL: Total loss
RO: Runoff
R: Recharge
SSS: Updated surface soil moisture
SSU: Updated moisture of underlying soil layers
"""
#INITIALIZE VARS
WCTOT = WCBOT + WCTOP # Total water holding capacity of the soil layers
SS = WCTOP # Surface soil moisture start at full capacity
SU = WCBOT # Underlying layer soil moisture start at full capacity
SP = SS + SU # Combined surface and underlying soil moisture
#Get arrays for output
pldat = MV.zeros(PET.shape)
spdat = MV.zeros(PET.shape)
prdat = MV.zeros(PET.shape)
rdat = MV.zeros(PET.shape)
tldat = MV.zeros(PET.shape)
etdat = MV.zeros(PET.shape)
rodat = MV.zeros(PET.shape)
sssdat = MV.zeros(PET.shape)
ssudat = MV.zeros(PET.shape)
#get rid of badly calibrated negative PET
PET = MV.where(PET < 0, 0, PET)
#fake 10 year spinup
PET_ex = pad_by_10(PET, year1, year2)
P_ex = pad_by_10(P, year1, year2)
#get rid of badly calibrated negative PET
PET = MV.where(PET < 0, 0, PET)
#run 2-layer bucket model
for i in range(PET_ex.shape[0]):
PR = WCTOT - SP
PL, ET, TL, RO, R, SSS, SSU = bucket2d(PET_ex[i], P_ex[i], WCBOT,
WCTOP, SS, SU)
ET = MV.where(ET < 0, 0, ET)
SS = SSS
SU = SSU
SP = SS + SU
if i >= 120:
pldat[i - 120] = PL
spdat[i - 120] = SS + SU
prdat[i - 120] = PR
rdat[i - 120] = R
tldat[i - 120] = TL
etdat[i - 120] = ET
rodat[i - 120] = RO
sssdat[i - 120] = SSS
ssudat[i - 120] = SSU
rdat = MV.where(rdat >= prdat, prdat, rdat)
tldat = MV.where(tldat >= pldat, pldat, tldat)
for X in [spdat, pldat, prdat, rdat, tldat, etdat, rodat]:
X.setAxisList(PET.getAxisList())
#calculate means over calibration period
SPSUM = cdutil.ANNUALCYCLE.climatology(spdat(time=(year1, year2)))
PLSUM = cdutil.ANNUALCYCLE.climatology(pldat(time=(year1, year2)))
PRSUM = cdutil.ANNUALCYCLE.climatology(prdat(time=(year1, year2)))
RSUM = cdutil.ANNUALCYCLE.climatology(rdat(time=(year1, year2)))
TLSUM = cdutil.ANNUALCYCLE.climatology(tldat(time=(year1, year2)))
ETSUM = cdutil.ANNUALCYCLE.climatology(etdat(time=(year1, year2)))
PESUM = cdutil.ANNUALCYCLE.climatology(PET(time=(year1, year2)))
ROSUM = cdutil.ANNUALCYCLE.climatology(rodat(time=(year1, year2)))
PSUM = cdutil.ANNUALCYCLE.climatology(P(time=(year1, year2)))
# CAFEC: Climatology Appropriate for Existing Conditions (Palmer 1965, p 12)
# Calculate the CAFEC coefficients
#Coefficient of evaporation: fraction of mean ET to mean potential ET
alpha = pdsi_coeff(ETSUM, PESUM)
#Coef of Recharge: ratio of mean recharge to mean potential recharge
beta = pdsi_coeff(RSUM, PRSUM)
#Coefficient of Runoff: Ratio of mean runoff to mean potential runoff
gamma = pdsi_coeff(ROSUM, SPSUM)
#Coefficient of Loss: Ratio of mean moisture loss to mean potential moisture loss
delta = pdsi_coeff(TLSUM, PLSUM)
TRAT = (PESUM + RSUM + ROSUM) / (PSUM + TLSUM)
#CAFEC precipitation (needed to maintain "normal" moisture)
nyears = PET.shape[0] / 12
repeat_it = lambda coef: np.ma.repeat(coef, nyears, axis=0)
Phat = PET * repeat_it(alpha) + prdat * repeat_it(
beta) + spdat * repeat_it(gamma) - pldat * repeat_it(delta)
# Moisture departure (difference between precip and precip needed for normal conditions)
DD = P - Phat
DD.setAxisList(P.getAxisList())
SABSD = MV.absolute(DD)
DBAR = cdutil.ANNUALCYCLE.climatology(SABSD(time=(year1, year2)))
# Weird empirical scaling thing to standardize moisture availability departures. THIS IS DUMB.
AKHAT = 1.5 * MV.log10((TRAT + 2.8 * 25.4) / DBAR) + 0.5
AKHAT = MV.where(AKHAT < 0, 0, AKHAT)
annsum = MV.sum(AKHAT * DBAR, axis=0)
AK = 17.67 * 25.4 * AKHAT / annsum
Z = DD / 25.4 * repeat_it(AK)
#Force Z between -16 and 16
Z = MV.where(Z > 16, 16, Z)
Z = MV.where(Z < -16, -16, Z)
Z.setAxisList(P.getAxisList())
Z.id = "z"
Z.units = "mm"
Z.info = "Created by Kate Marvel using calc_Z.py"
Z.creation_date = datetime.datetime.now().isoformat()
Z.long_name = "Palmer Z index"
Z.standard_name = "z_index"
return Z
