def testChoose(self):
ct1 = MV2.TransientVariable([1, 1, 2, 0, 1])
ctr = MV2.choose(ct1, [numpy.ma.masked, 10, 20, 30, 40])
self.assertTrue(MV2.allclose(ctr, [10, 10, 20, 100, 10]))
ctx = MV2.TransientVariable([1, 2, 3, 150, 4])
cty = -MV2.TransientVariable([1, 2, 3, 150, 4])
ctr = MV2.choose(MV2.greater(ctx, 100), (ctx, 100))
self.assertTrue(MV2.allclose(ctr, [1, 2, 3, 100, 4]))
ctr = MV2.choose(MV2.greater(ctx, 100), (ctx, cty))
self.assertTrue(MV2.allclose(ctr, [1, 2, 3, -150, 4]))
def surface_maskvariable(var, surf_mask, surf_type=None):
""" Quick script that takes as inputs, a masked variable
and a surface mask to create a masked variable, where the
area not of interest is masked out.
Assumes that the surface mask has values with range 0 to 1.
REMEMBER that the surf_mask denotes area that you want to
have masked out, NOT the area that you want plotted.
Also note: make sure that the mask and the variable have
the same shape
"""
#If no surface mask is given, create mask
if surf_mask is None:
surf_mask = cdutil.generateLandSeaMask(data)
if surf_type is 'land':
surf_mask=surf_mask/100.
if surf_type is 'ocean':
surf_mask=(100.-surf_mask)/100.
#Mask the variable according to the surf_mask - assumes that surf_mask is taken from 'LANDFRAC'
# check to see if the surface mask and variable have the same shape, otherwise regrid surf_mask
if surf_mask.shape!=var.shape:
var_grid=var.getGrid()
surf_mask=surf_mask.regrid(var_grid)
condition=MV2.greater(surf_mask,0.5)
var_masked=MV2.masked_where(condition,var)
return var_masked
def surface_maskvariable(var, surf_mask, surf_type=None):
""" Quick script that takes as inputs, a masked variable
and a surface mask to create a masked variable, where the
area not of interest is masked out.
Assumes that the surface mask has values with range 0 to 1.
REMEMBER that the surf_mask denotes area that you want to
have masked out, NOT the area that you want plotted.
Also note: make sure that the mask and the variable have
the same shape
"""
#If no surface mask is given, create mask
if surf_mask is None:
surf_mask = cdutil.generateLandSeaMask(data)
if surf_type is 'land':
surf_mask = surf_mask / 100.
if surf_type is 'ocean':
surf_mask = (100. - surf_mask) / 100.
#Mask the variable according to the surf_mask - assumes that surf_mask is taken from 'LANDFRAC'
# check to see if the surface mask and variable have the same shape, otherwise regrid surf_mask
if surf_mask.shape != var.shape:
var_grid = var.getGrid()
surf_mask = surf_mask.regrid(var_grid)
condition = MV2.greater(surf_mask, 0.5)
var_masked = MV2.masked_where(condition, var)
return var_masked
def myMakeMask(array, range):
"""Returns the input array masked where the values are not between range[0] and range[1]"""
m1 = MV2.less(array,
range[0]) # mask where it is less than the 1st value
m2 = MV2.greater(
array, range[1]) # mask where it is more than the 2nd value
return MV2.logical_or(m1, m2)
def testMaskingFunctions(self):
xouter = MV2.outerproduct(MV2.arange(5.), [1] * 10)
masked = MV2.masked_greater(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[2:], True))
self.assertTrue(MV2.allequal(masked.mask[:2], False))
masked = MV2.masked_greater_equal(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[1:], True))
self.assertTrue(MV2.allequal(masked.mask[:1], False))
masked = MV2.masked_less(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[:1], True))
self.assertTrue(MV2.allequal(masked.mask[1:], False))
masked = MV2.masked_less_equal(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[:2], True))
self.assertTrue(MV2.allequal(masked.mask[2:], False))
masked = MV2.masked_not_equal(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[1], False))
self.assertTrue(MV2.allequal(masked.mask[0], True))
self.assertTrue(MV2.allequal(masked.mask[2:], True))
masked = MV2.masked_equal(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[1], True))
self.assertTrue(MV2.allequal(masked.mask[0], False))
self.assertTrue(MV2.allequal(masked.mask[2:], False))
masked = MV2.masked_outside(xouter, 1, 3)
self.assertTrue(MV2.allequal(masked.mask[0:1], True))
self.assertTrue(MV2.allequal(masked.mask[1:4], False))
self.assertTrue(MV2.allequal(masked.mask[4:], True))
masked = MV2.masked_where(
MV2.logical_or(MV2.greater(xouter, 3), MV2.less(xouter, 2)),
xouter)
self.assertTrue(MV2.allequal(masked.mask[0:2], True))
self.assertTrue(MV2.allequal(masked.mask[2:4], False))
self.assertTrue(MV2.allequal(masked.mask[4:], True))
def sumregions(potential_reg,potential):
out = potential_reg[0]*1.
wts = potential[0]*1.
for i in range(1,potential.shape[0]):
c = MV2.greater(potential[i]-wts,0)
out = MV2.where(c,potential_reg[i],out)
wts = MV2.where(c,potential[i],wts)
return out
def generateLandSeaMask(target,
source=None,
threshold_1=.2,
threshold_2=.3,
regridTool='regrid2'):
"""
Generates a best guess mask on any rectilinear grid, using the method described in `PCMDI's report #58`_
.. _PCMDI's report #58: http://www-pcmdi.llnl.gov/publications/pdf/58.pdf
:param target: either a MV2 object with a grid, or a cdms2 grid (rectilinear grid only)
:type target: MV2 or cdms2
:param source: A fractional (0.0 to 1.0) land sea mask, where 1 means all land
:type source: float
:param threshold_1: criteria 1 for detecting cells with possible increment see report for detail
difference threshold
:type threshold_1: float
:param threshold_2: criteria 2 for detecting cells with possible increment see report for detail
water/land content threshold
:type threshold_2: float
:param regridTool: which cdms2 regridder tool to use, default is regrid2
:type regridTool:
:returns: landsea mask on target grid
"""
if cdms2.isVariable(target):
target = target.getGrid()
if target is None:
raise Exception("Error target data passed do not have a grid")
if not isinstance(target, cdms2.grid.TransientRectGrid):
raise Exception("Error: target grid must be rectilinear")
if source is None:
source = cdms2.open(os.path.join(egg_path, 'navy_land.nc'))('sftlf')
try:
# print("REGRIDDING WITH SOURCE:", source)
navy_frac_t = source.regrid(target, regridTool='regrid2')
except Exception as err:
raise Exception(
"error, cannot regrid source data to target, got error message: %s"
% err)
# First guess, anything greater than 50% is land
mask = MV2.greater(navy_frac_t, .5).astype('i')
UL, UC, UR, ML, MR, LL, LC, LR = create_surrounds(navy_frac_t)
cont = True
i = 0
while cont:
mask2 = improve(mask,
navy_frac_t,
threshold_1,
threshold_2,
UL,
UC,
UR,
ML,
MR,
LL,
LC,
LR,
regridTool=regridTool)
if MV2.allequal(
mask2, mask
) or i > 25: # shouldn't be more than 10 at max, 25 is way safe
cont = False
mask = mask2.astype("i")
i += 1
mask.id = 'sftlf'
return mask
def Es(T, method=None):
"""Computes saturated pressure in Pa given T in K, using the method:
1: Hyland-Wexler formulation, polynomial coeff (absolute norm)
2: Wexler formulation
3: Hyland-Wexler formulation, polynomial coeff (relative error norm)
4: classic Goff Gratch equation
5: 6.112*numpy.ma.exp(17.67*tempc/(tempc+243.5))
Default is method 1
Note: 1 and 2 use method 3 where T is not : 173.15 < T < 473.15
ref for 1, 2 and 3:
Piotr Flatau and al., Journal of Applied Met., Vol 31, Dec 1992
( http://ams.allenpress.com/perlserv/?request=get-document&\
doi=10.1175%2F1520-0450(1992)031%3C1507%3APFTSVP%3E2.0.CO%3B2&ct=1 )
"""
if method is None:
method = 1
if method == 1:
# Put in C
x = T - 273.15
# Water vapor
c0 = 0.611220713e03
c1 = 0.443944344e02
c2 = 0.143195336e01
c3 = 0.263350515e-01
c4 = 0.310636053e-03
c5 = 0.185218710e-05
c6 = 0.103440324e-07
c7 = -0.468258100e-10
c8 = 0.466533033e-13
eswat = c0 + x * (c1 + x * (c2 + x * (c3 + x * (c4 + x * (c5 + x * (c6 + x * (c7 + x * c8)))))))
# ice
c0 = 0.611153246e03
c1 = 0.503261230e02
c2 = 0.188595709e01
c3 = 0.422115970e-01
c4 = 0.620376691e-03
c5 = 0.616082536e-05
c6 = 0.405172828e-07
c7 = 0.161492905e-09
c8 = 0.297886454e-12
esice = c0 + x * (c1 + x * (c2 + x * (c3 + x * (c4 + x * (c5 + x * (c6 + x * (c7 + x * c8)))))))
# Combine
es = MV2.where(MV2.less(T, 273.15), esice, eswat)
# Overwrite values outside valid range with method 2
mn, mx = genutil.minmax(T)
if mn < 173.16 or mx > 473.15:
es2 = Es(T, method=2)
es = MV2.where(MV2.less(T, 173.16), es2, es)
es = MV2.where(MV2.greater(T, 473.15), es2, es)
elif method == 2:
# over water
g0 = -0.29912729e4
g1 = -0.60170128e4
g2 = 0.1887643854e2
g3 = -0.28354721e-1
g4 = 0.17838301e-4
g5 = -0.84150417e-9
g6 = 0.44412543e-12
g7 = 0.2858487e1
# over ice
k0 = -0.58653696e4
k1 = 0.2224103300e2
k2 = 0.13749042e-1
k3 = -0.34031775e-4
k4 = 0.26967687e-7
k5 = 0.6918651
esice = (k0 + (k1 + k5 * MV2.log(T) + (k2 + (k3 + k4 * T) * T) * T) * T) / T # over ice
eswat = (
g0 + (g1 + (g2 + g7 * MV2.log(T) + (g3 + (g4 + (g5 + g6 * T) * T) * T) * T) * T) * T
) / T ** 2 # over water
es = MV2.where(MV2.less(T, 273.15), esice, eswat)
es = MV2.exp(es)
elif method == 3:
# Put in C
x = T - 273.15
# Water vapor
c0 = 0.611213476e03
c1 = 0.444007856e02
c2 = 0.143064234e01
c3 = 0.264461437e-01
c4 = 0.305930558e-03
c5 = 0.196237241e-05
c6 = 0.892344772e-08
c7 = -0.373208410e-10
c8 = 0.209339997e-13
eswat = c0 + x * (c1 + x * (c2 + x * (c3 + x * (c4 + x * (c5 + x * (c6 + x * (c7 + x * c8)))))))
# ice
c0 = 0.611123516e03
c1 = 0.503109514e02
c2 = 0.1888369801e01
c3 = 0.420547422e-01
c4 = 0.614396778e-03
c5 = 0.602780717e-05
c6 = 0.387940929e-07
c7 = 0.149436277e-09
c8 = 0.262655803e-12
esice = c0 + x * (c1 + x * (c2 + x * (c3 + x * (c4 + x * (c5 + x * (c6 + x * (c7 + x * c8)))))))
# Combine
es = MV2.where(MV2.less(T, 273.15), esice, eswat)
# Overwrite values outside valid range with method 2
mn, mx = genutil.minmax(T)
if mn < 173.16 or mx > 473.15:
es2 = Es(T, method=2)
es = MV2.where(MV2.less(T, 173.16), es2, es)
es = MV2.where(MV2.greater(T, 473.15), es2, es)
elif method == 4:
est = 101324.6 # Pa
Ts = 373.16 / T
a = -7.90298
b = 5.02808
c = -1.3816e-7
d = 11.344
f = 8.1328e-3
h = -3.49149
maxexp = int(numpy.log10(numpy.finfo(numpy.float).max))
minexp = 1 - a
es = a * (Ts - 1.0)
es = es + b * numpy.ma.log10(Ts)
A = d * (1.0 - Ts)
A = numpy.ma.masked_greater(A, maxexp)
A = numpy.ma.masked_less(A, minexp)
es = es + c * (numpy.ma.power(10, A) - 1.0)
A = h * (Ts - 1.0)
A = numpy.ma.masked_greater(A, maxexp)
A = numpy.ma.masked_less(A, minexp)
es = es + f * (numpy.ma.power(10, A) - 1.0)
es = est * numpy.ma.power(10, es)
elif method == 5:
tempc = T - 273.15
es = 611.2 * numpy.ma.exp(17.67 * tempc / (tempc + 243.5))
return es
def improve(mask,navy_frac_t,threshold_1,threshold_2,UL,UC,UR,ML,MR,LL,LC,LR,regridTool='regrid2'):
mask_approx = map2four(mask,mask.getGrid(),regridTool=regridTool)
diff = navy_frac_t - mask_approx
## Land point conversion
c1 = MV2.greater(diff,threshold_1)
c2 = MV2.greater(navy_frac_t,threshold_2)
c= MV2.logical_and(c1,c2)
## x.plot(c.astype("i"))
## raw_input()
## x.clear()
## Now figures out local maxima
cUL,cUC,cUR,cML,cMR,cLL,cLC,cLR = create_surrounds(c)
L=c.getAxis(1)
bL=L.getBounds()
if L.isCircular() and bL[-1][1]-bL[0][0] % L.modulo == 0:
c=c[1:-1] # elimnitates north and south poles
tmp = navy_frac_t[1:-1]
else:
c=c[1:-1,1:-1] # elimnitates north and south poles
tmp = navy_frac_t[1:-1,1:-1]
m = MV2.logical_and(c,MV2.greater(tmp,MV2.where(cUL,UL,0.)))
m = MV2.logical_and(m,MV2.greater(tmp,MV2.where(cUC,UC,0.)))
m = MV2.logical_and(m,MV2.greater(tmp,MV2.where(cUR,UR,0.)))
m = MV2.logical_and(m,MV2.greater(tmp,MV2.where(cML,ML,0.)))
m = MV2.logical_and(m,MV2.greater(tmp,MV2.where(cMR,MR,0.)))
m = MV2.logical_and(m,MV2.greater(tmp,MV2.where(cLL,LL,0.)))
m = MV2.logical_and(m,MV2.greater(tmp,MV2.where(cLC,LC,0.)))
m = MV2.logical_and(m,MV2.greater(tmp,MV2.where(cLR,LR,0.)))
# Ok now update the mask by setting these points to land
mask2 = mask*1.
if L.isCircular() and bL[-1][1]-bL[0][0] % L.modulo == 0:
mask2[1:-1] = MV2.where(m,1,mask[1:-1])
else:
mask2[1:-1,1:-1] = MV2.where(m,1,mask[1:-1,1:-1])
## ocean point conversion
c1 = MV2.less(diff,-threshold_1)
c2 = MV2.less(navy_frac_t,1.-threshold_2)
c= MV2.logical_and(c1,c2)
cUL,cUC,cUR,cML,cMR,cLL,cLC,cLR = create_surrounds(c)
L=c.getAxis(1)
bL=L.getBounds()
if L.isCircular() and bL[-1][1]-bL[0][0] % L.modulo == 0:
c=c[1:-1] # elimnitates north and south poles
tmp = navy_frac_t[1:-1]
else:
c=c[1:-1,1:-1] # elimnitates north and south poles
tmp = navy_frac_t[1:-1,1:-1]
## Now figures out local maxima
m = MV2.logical_and(c,MV2.less(tmp,MV2.where(cUL,UL,1.)))
m = MV2.logical_and(m,MV2.less(tmp,MV2.where(cUC,UC,1.)))
m = MV2.logical_and(m,MV2.less(tmp,MV2.where(cUR,UR,1.)))
m = MV2.logical_and(m,MV2.less(tmp,MV2.where(cML,ML,1.)))
m = MV2.logical_and(m,MV2.less(tmp,MV2.where(cMR,MR,1.)))
m = MV2.logical_and(m,MV2.less(tmp,MV2.where(cLL,LL,1.)))
m = MV2.logical_and(m,MV2.less(tmp,MV2.where(cLC,LC,1.)))
m = MV2.logical_and(m,MV2.less(tmp,MV2.where(cLR,LR,1.)))
# Ok now update the mask by setting these points to ocean
if L.isCircular() and bL[-1][1]-bL[0][0] % L.modulo == 0:
mask2[1:-1] = MV2.where(m,0,mask2[1:-1])
else:
mask2[1:-1,1:-1] = MV2.where(m,0,mask2[1:-1,1:-1])
mask2.setAxisList(mask.getAxisList())
return mask2
landsea maks on target grid
"""
cdat_info.pingPCMDIdb("cdat","cdutil.generateLandSeaMask")
if cdms2.isVariable(target):
target = target.getGrid()
if target is None:
raise Exception,"Error target data passed do not have a grid"
if not isinstance(target,cdms2.grid.TransientRectGrid):
raise Exception, "Error: target grid must be rectilinear"
if source is None:
source = cdms2.open(os.path.join(sys.prefix,'share','cdutil','navy_land.nc'))('sftlf')
try:
navy_frac_t = source.regrid(target,regridTool='regrid2')
except Exception,err:
raise "error, cannot regrid source data to target, got error message: %s" % err
mask = MV2.greater(navy_frac_t,.5).astype('i') # First guess, anything greater than 50% is land
UL,UC,UR,ML,MR,LL,LC,LR = create_surrounds(navy_frac_t)
cont = True
i=0
while cont:
mask2 = improve(mask,navy_frac_t,threshold_1,threshold_2,UL,UC,UR,ML,MR,LL,LC,LR,regridTool=regridTool)
if MV2.allequal(mask2,mask) or i>25: # shouldn't be more than 10 at max, 25 is way safe
cont=False
mask=mask2
i+=1
mask.id='sftlf'
return mask
def improve(mask,
navy_frac_t,
threshold_1,
threshold_2,
UL,
UC,
UR,
ML,
MR,
LL,
LC,
LR,
regridTool='regrid2'):
mask_approx = map2four(mask, mask.getGrid(), regridTool=regridTool)
diff = navy_frac_t - mask_approx
## Land point conversion
c1 = MV2.greater(diff, threshold_1)
c2 = MV2.greater(navy_frac_t, threshold_2)
c = MV2.logical_and(c1, c2)
## x.plot(c.astype("i"))
## raw_input()
## x.clear()
## Now figures out local maxima
cUL, cUC, cUR, cML, cMR, cLL, cLC, cLR = create_surrounds(c)
L = c.getAxis(1)
bL = L.getBounds()
if L.isCircular() and bL[-1][1] - bL[0][0] % L.modulo == 0:
c = c[1:-1] # elimnitates north and south poles
tmp = navy_frac_t[1:-1]
else:
c = c[1:-1, 1:-1] # elimnitates north and south poles
tmp = navy_frac_t[1:-1, 1:-1]
m = MV2.logical_and(c, MV2.greater(tmp, MV2.where(cUL, UL, 0.)))
m = MV2.logical_and(m, MV2.greater(tmp, MV2.where(cUC, UC, 0.)))
m = MV2.logical_and(m, MV2.greater(tmp, MV2.where(cUR, UR, 0.)))
m = MV2.logical_and(m, MV2.greater(tmp, MV2.where(cML, ML, 0.)))
m = MV2.logical_and(m, MV2.greater(tmp, MV2.where(cMR, MR, 0.)))
m = MV2.logical_and(m, MV2.greater(tmp, MV2.where(cLL, LL, 0.)))
m = MV2.logical_and(m, MV2.greater(tmp, MV2.where(cLC, LC, 0.)))
m = MV2.logical_and(m, MV2.greater(tmp, MV2.where(cLR, LR, 0.)))
# Ok now update the mask by setting these points to land
mask2 = mask * 1.
if L.isCircular() and bL[-1][1] - bL[0][0] % L.modulo == 0:
mask2[1:-1] = MV2.where(m, 1, mask[1:-1])
else:
mask2[1:-1, 1:-1] = MV2.where(m, 1, mask[1:-1, 1:-1])
## ocean point conversion
c1 = MV2.less(diff, -threshold_1)
c2 = MV2.less(navy_frac_t, 1. - threshold_2)
c = MV2.logical_and(c1, c2)
cUL, cUC, cUR, cML, cMR, cLL, cLC, cLR = create_surrounds(c)
L = c.getAxis(1)
bL = L.getBounds()
if L.isCircular() and bL[-1][1] - bL[0][0] % L.modulo == 0:
c = c[1:-1] # elimnitates north and south poles
tmp = navy_frac_t[1:-1]
else:
c = c[1:-1, 1:-1] # elimnitates north and south poles
tmp = navy_frac_t[1:-1, 1:-1]
## Now figures out local maxima
m = MV2.logical_and(c, MV2.less(tmp, MV2.where(cUL, UL, 1.)))
m = MV2.logical_and(m, MV2.less(tmp, MV2.where(cUC, UC, 1.)))
m = MV2.logical_and(m, MV2.less(tmp, MV2.where(cUR, UR, 1.)))
m = MV2.logical_and(m, MV2.less(tmp, MV2.where(cML, ML, 1.)))
m = MV2.logical_and(m, MV2.less(tmp, MV2.where(cMR, MR, 1.)))
m = MV2.logical_and(m, MV2.less(tmp, MV2.where(cLL, LL, 1.)))
m = MV2.logical_and(m, MV2.less(tmp, MV2.where(cLC, LC, 1.)))
m = MV2.logical_and(m, MV2.less(tmp, MV2.where(cLR, LR, 1.)))
# Ok now update the mask by setting these points to ocean
if L.isCircular() and bL[-1][1] - bL[0][0] % L.modulo == 0:
mask2[1:-1] = MV2.where(m, 0, mask2[1:-1])
else:
mask2[1:-1, 1:-1] = MV2.where(m, 0, mask2[1:-1, 1:-1])
mask2.setAxisList(mask.getAxisList())
return mask2
if target is None:
raise Exception, "Error target data passed do not have a grid"
if not isinstance(target, cdms2.grid.TransientRectGrid):
raise Exception, "Error: target grid must be rectilinear"
if source is None:
source = cdms2.open(
os.path.join(sys.prefix, 'share', 'cdutil',
'navy_land.nc'))('sftlf')
try:
navy_frac_t = source.regrid(target, regridTool='regrid2')
except Exception, err:
raise "error, cannot regrid source data to target, got error message: %s" % err
mask = MV2.greater(navy_frac_t, .5).astype(
'i') # First guess, anything greater than 50% is land
UL, UC, UR, ML, MR, LL, LC, LR = create_surrounds(navy_frac_t)
cont = True
i = 0
while cont:
mask2 = improve(mask,
navy_frac_t,
threshold_1,
threshold_2,
UL,
UC,
UR,
ML,
MR,
LL,
LC,
inputFilePathbgn = '/p/user_pub/pmp/pmp_obs_preparation/orig/data/'
inputFilePathend = '/CMAP/'
inputFileName = ['precip.mon.mean.nc']
inputVarName = ['precip']
outputVarName = ['pr']
outputUnits = ['kg m-2 s-1']
### BETTER IF THE USER DOES NOT CHANGE ANYTHING BELOW THIS LINE...
for fi in range(len(inputVarName)):
print(fi, inputVarName[fi])
inputFilePath = inputFilePathbgn + inputFilePathend
#%% Process variable (with time axis)
# Open and read input netcdf file
f = cdm.open(inputFilePath + inputFileName[fi])
dtmp = f(inputVarName[fi])
d = MV2.where(MV2.greater(dtmp, -10000.), dtmp, 1.e20)
d.missing = 1.e20
cdutil.times.setTimeBoundsMonthly(d)
d = MV2.divide(d, 86400.) # CONVERT mm/day to kg m-2 s-1
lat = d.getLatitude()
lon = d.getLongitude()
print(d.shape)
#time = d.getTime() ; # Assumes variable is named 'time', for the demo file this is named 'months'
time = d.getAxis(0)
# Rather use a file dimension-based load statement
# Deal with problematic "months since" calendar/time axis
time_bounds = time.getBounds()
# time_bounds[:,0] = time[:]
# time_bounds[:-1,1] = time[1:]
def Es(T, method=None):
"""Computes saturated pressure in Pa given T in K, using the method:
1: Hyland-Wexler formulation, polynomial coeff (absolute norm)
2: Wexler formulation
3: Hyland-Wexler formulation, polynomial coeff (relative error norm)
4: classic Goff Gratch equation
5: 6.112*numpy.ma.exp(17.67*tempc/(tempc+243.5))
Default is method 1
Note: 1 and 2 use method 3 where T is not : 173.15 < T < 473.15
ref for 1, 2 and 3:
Piotr Flatau and al., Journal of Applied Met., Vol 31, Dec 1992 ( http://ams.allenpress.com/perlserv/?request=get-document&doi=10.1175%2F1520-0450(1992)031%3C1507%3APFTSVP%3E2.0.CO%3B2&ct=1 )
"""
if method is None:
method = 1
if method == 1:
## Put in C
x = T - 273.15
## Water vapor
c0 = 0.611220713E03
c1 = 0.443944344E02
c2 = 0.143195336E01
c3 = 0.263350515E-01
c4 = 0.310636053E-03
c5 = 0.185218710E-05
c6 = 0.103440324E-07
c7 = -0.468258100E-10
c8 = 0.466533033E-13
eswat = c0 + x * (c1 + x * (c2 + x * (c3 + x * (c4 + x *
(c5 + x *
(c6 + x *
(c7 + x * c8)))))))
## ice
c0 = .611153246E03
c1 = .503261230E02
c2 = .188595709E01
c3 = .422115970E-01
c4 = .620376691E-03
c5 = .616082536E-05
c6 = .405172828E-07
c7 = .161492905E-09
c8 = .297886454E-12
esice = c0 + x * (c1 + x * (c2 + x * (c3 + x * (c4 + x *
(c5 + x *
(c6 + x *
(c7 + x * c8)))))))
## Combine
es = MV2.where(MV2.less(T, 273.15), esice, eswat)
## Overwrite values outside valid range with method 2
mn, mx = genutil.minmax(T)
if mn < 173.16 or mx > 473.15:
es2 = Es(T, method=2)
es = MV2.where(MV2.less(T, 173.16), es2, es)
es = MV2.where(MV2.greater(T, 473.15), es2, es)
elif method == 2:
# over water
g0 = -0.29912729E4
g1 = -0.60170128E4
g2 = 0.1887643854E2
g3 = -0.28354721E-1
g4 = 0.17838301E-4
g5 = -0.84150417E-9
g6 = 0.44412543E-12
g7 = 0.2858487E1
# over ice
k0 = -0.58653696e4
k1 = 0.2224103300E2
k2 = 0.13749042E-1
k3 = -0.34031775E-4
k4 = 0.26967687E-7
k5 = 0.6918651
esice = (k0 + (k1 + k5 * MV2.log(T) +
(k2 + (k3 + k4 * T) * T) * T) * T) / T # over ice
eswat = (g0 + (g1 + (g2 + g7 * MV2.log(T) +
(g3 + (g4 + (g5 + g6 * T) * T) * T) * T) * T) *
T) / T**2 # over water
es = MV2.where(MV2.less(T, 273.15), esice, eswat)
es = MV2.exp(es)
elif method == 3:
## Put in C
x = T - 273.15
## Water vapor
c0 = 0.611213476E03
c1 = 0.444007856E02
c2 = 0.143064234E01
c3 = 0.264461437E-01
c4 = 0.305930558E-03
c5 = 0.196237241E-05
c6 = 0.892344772E-08
c7 = -0.373208410E-10
c8 = 0.209339997E-13
eswat = c0 + x * (c1 + x * (c2 + x * (c3 + x * (c4 + x *
(c5 + x *
(c6 + x *
(c7 + x * c8)))))))
## ice
c0 = .611123516E03
c1 = .503109514E02
c2 = .1888369801E01
c3 = .420547422E-01
c4 = .614396778E-03
c5 = .602780717E-05
c6 = .387940929E-07
c7 = .149436277E-09
c8 = .262655803E-12
esice = c0 + x * (c1 + x * (c2 + x * (c3 + x * (c4 + x *
(c5 + x *
(c6 + x *
(c7 + x * c8)))))))
## Combine
es = MV2.where(MV2.less(T, 273.15), esice, eswat)
## Overwrite values outside valid range with method 2
mn, mx = genutil.minmax(T)
if mn < 173.16 or mx > 473.15:
es2 = Es(T, method=2)
es = MV2.where(MV2.less(T, 173.16), es2, es)
es = MV2.where(MV2.greater(T, 473.15), es2, es)
elif method == 4:
est = 101324.6 #Pa
Ts = 373.16 / T
a = -7.90298
b = 5.02808
c = -1.3816E-7
d = 11.344
f = 8.1328E-3
h = -3.49149
maxexp = int(numpy.log10(numpy.finfo(numpy.float).max))
minexp = 1 - a
es = a * (Ts - 1.)
es = es + b * numpy.ma.log10(Ts)
A = d * (1. - Ts)
A = numpy.ma.masked_greater(A, maxexp)
A = numpy.ma.masked_less(A, minexp)
es = es + c * (numpy.ma.power(10, A) - 1.)
A = h * (Ts - 1.)
A = numpy.ma.masked_greater(A, maxexp)
A = numpy.ma.masked_less(A, minexp)
es = es + f * (numpy.ma.power(10, A) - 1.)
es = est * numpy.ma.power(10, es)
elif method == 5:
tempc = T - 273.15
es = 611.2 * numpy.ma.exp(17.67 * tempc / (tempc + 243.5))
return es
def harmonic(data, k=3):
data = data.reorder('t...')
cdutil.setAxisTimeBoundsDaily(data.getTime())
axislist = data.getAxisList()
dataid = data.id
daily = True
monthly = False
timeAxis = axislist[0]
N = 365. #len(timeAxis)
# P = 10. # 10 year, yearly harmonic oscilation
# P = 10*12 # 10 year, monthly harmonic oscilation
# P = 10*365 # 10 year, daily harmonic oscilation
# if P > N:
# raise ValueError("P('%d') value should not exceed N(%d)" % (P,N))
if k > N/2:
raise ValueError("k value should not exceed (%d) i.e. N/2 value" % (N/2))
if len(timeAxis) > 366:
print 'found more than 1 year data.'
# y_t = dailyClimatology(data, action='sum')
else:
y_t = data
# end of if len(timeAxis) > 366:
Y_0 = cdutil.averager(data, axis='t', action='average', weights='equal')
# make memory free
del data
t = numpy.arange(1, N+1, dtype='float')
otheraxis = list(Y_0.shape)
ax_product = 1
for ax in otheraxis:
ax_product *= ax
otheraxis.insert(0,N)
t = t.repeat(ax_product).reshape(otheraxis)
angle = 2 * math.pi * t/N
Y_k = 0.
for i in range(1,k+1):
kangle = angle*i
A_k = (2./N) * cdutil.averager(y_t * numpy.cos(kangle), axis='t', action='sum')
B_k = (2./N) * cdutil.averager(y_t * numpy.sin(kangle), axis='t', action='sum')
C_k = MV2.sqrt((A_k*A_k) + (B_k*B_k))
# if A_k is positiv, then retain this phase_angle as it is.
# phase_angle should be in degrees
phase_angle = phase_arc_angle = MV2.arctan(B_k/A_k)
# if A_k is zero, then replace phase_angle with pi/2 else retain same
phase_angle = MV2.where(MV2.equal(A_k, 0.), math.pi/2.0, phase_arc_angle)
# if A_k is negative, then add pi with phase_angle (if it is <= pi )
condition1 = MV2.logical_and(MV2.less(A_k, 0.), MV2.less_equal(phase_arc_angle, math.pi))
phase_angle = MV2.where(condition1, phase_arc_angle+math.pi, phase_arc_angle)
# if A_k is negative, then subtract pi from phase_angle (if it is > pi )
condition2 = MV2.logical_and(MV2.less(A_k, 0.), MV2.greater(phase_arc_angle, math.pi))
condition3 = MV2.logical_or(condition1, condition2)
phase_angle = MV2.where(condition3, phase_arc_angle-math.pi, phase_arc_angle)
# make memory free
del phase_arc_angle
if daily and not monthly:
# subtract 15 days lag to adjust phase_angle w.r.t daily
print "Daily Subtraction"
phase_angle -= (15.*2*math.pi)/N
# end of if daily and not monthly:
phase_angle = numpy.array(phase_angle)
# phase_angle = numpy.tile(phase_angle, N).reshape(kangle.shape)
kangle = numpy.array(kangle)
Y_k += C_k * MV2.cos(kangle - phase_angle)
# end of for i in range(1,k+1):
# add mean to the sum of first k-th harmonic of data
Y_k += Y_0
# make memory free
del y_t, Y_0
sumOfMean_and_first_k_harmonic = cdms2.createVariable(Y_k, id=dataid)
sumOfMean_and_first_k_harmonic.setAxisList(axislist)
sumOfMean_and_first_k_harmonic.comments = 'sumOfMean_and_first_%d_harmonic' % k
# make memory free
del Y_k
# return result
return sumOfMean_and_first_k_harmonic
def testWhere(self):
xouter = MV2.outerproduct(MV2.arange(3), MV2.arange(3))
xwhere = MV2.where(MV2.greater(xouter, 3), xouter, -123)
self.assertEqual(xwhere[-1, -1], 4)
xwhere.mask = xwhere == 4
self.assertTrue(MV2.allequal(xwhere, -123))
def harmonic(data, k=3, time_type='daily', phase_shift=15):
"""
Inputs :
data : climatology data
k : Integer no to compute K th harmonic. By default it takes 3.
time_type : daily | monthly | full (time type of input climatology)
'daily' -> it returns 365 days harmonic,
'monthly' -> it returns 12 month harmonic,
'full' -> it retuns harmonic for full length of
input data.
phase_shift : Used to subtract 'phase_shift' days lag to adjust
phase_angle w.r.t daily or monthly. By default it takes
15 days lag to adjust phase_angle w.r.t daily data.
User can pass None disable this option.
Returns :
Returns "sum mean of mean and first K th harmonic" of input
climatology data.
Concept :
Earth science data consists of a strong seasonality component as
indicated by the cycles of repeated patterns in climate variables such
as air pressure, temperature and precipitation. The seasonality forms
the strongest signals in this data and in order to find other patterns,
the seasonality is removed by subtracting the monthly mean values of the
raw data for each month. However since the raw data like air temperature,
pressure, etc. are constantly being generated with the help of satellite
observations, the climate scientists usually use a moving reference base
interval of some years of raw data to calculate the mean in order to
generate the anomaly time series and study the changes with respect to
that.
Fourier series analysis decomposes a signal into an infinite series of
harmonic components. Each of these components is comprised initially of
a sine wave and a cosine wave of equal integer frequency. These two waves
are then combined into a single cosine wave, which has characteristic
amplitude (size of the wave) and phase angle (offset of the wave).
Convergence has been established for bounded piecewise continuous
functions on a closed interval, with special conditions at points of
discontinuity. Its convergence has been established for other conditions
as well, but these are not relevant to the analysis at hand.
Reference: Daniel S Wilks, 'Statistical Methods in the Atmospheric
Sciences' second Edition, page no(372-378).
Written By : Arulalan.T
Date : 16.05.2014
"""
data = data.reorder('t...')
cdutil.setAxisTimeBoundsDaily(data.getTime())
axislist = data.getAxisList()
timeAxis = axislist[0]
dataid = data.id
if time_type in ['daily']:
N = 365.0 # must be float
elif time_type[:3] in ['mon']:
N = 12.0 # must be float
elif time_type in ['full']:
N = float(len(timeAxis))
if k > N/2:
raise ValueError("k value should not exceed (%d) i.e. N/2 value" % (N/2))
if len(timeAxis) > 366:
print 'found more than 1 year data.'
raise ValueError("Kindly pass only climatology data")
else:
y_t = data
# end of if len(timeAxis) > 366:
Y_0 = cdutil.averager(data, axis='t', action='average', weights='equal')
# make memory free
del data
t = numpy.arange(1, N+1, dtype='float')
otheraxis = list(Y_0.shape)
ax_product = 1
for ax in otheraxis:
ax_product *= ax
otheraxis.insert(0,N)
t = t.repeat(ax_product).reshape(otheraxis)
angle = 2 * math.pi * t/N
Y_k = 0.
for i in range(1,k+1):
kangle = angle*i
A_k = (2./N) * cdutil.averager(y_t * numpy.cos(kangle), axis='t', action='sum')
B_k = (2./N) * cdutil.averager(y_t * numpy.sin(kangle), axis='t', action='sum')
C_k = MV2.sqrt((A_k*A_k) + (B_k*B_k))
# if A_k is positiv, then retain this phase_angle as it is.
# phase_angle should be in degrees
phase_angle = phase_arc_angle = MV2.arctan(B_k/A_k)
# if A_k is zero, then replace phase_angle with pi/2 else retain same
phase_angle = MV2.where(MV2.equal(A_k, 0.), math.pi/2.0, phase_arc_angle)
# if A_k is negative, then add pi with phase_angle (if it is <= pi )
condition1 = MV2.logical_and(MV2.less(A_k, 0.), MV2.less_equal(phase_arc_angle, math.pi))
phase_angle = MV2.where(condition1, phase_arc_angle+math.pi, phase_arc_angle)
# if A_k is negative, then subtract pi from phase_angle (if it is > pi )
condition2 = MV2.logical_and(MV2.less(A_k, 0.), MV2.greater(phase_arc_angle, math.pi))
condition3 = MV2.logical_or(condition1, condition2)
phase_angle = MV2.where(condition3, phase_arc_angle-math.pi, phase_arc_angle)
# make memory free
del phase_arc_angle
if phase_shift:
# subtract 15 days lag to adjust phase_angle w.r.t daily
phase_angle -= (phase_shift *2 * math.pi) / N
# end of if daily and not monthly:
phase_angle = numpy.array(phase_angle)
kangle = numpy.array(kangle)
Y_k += C_k * MV2.cos(kangle - phase_angle)
# end of for i in range(1,k+1):
# add mean to the sum of first k-th harmonic of data
Y_k += Y_0
# make memory free
del y_t, Y_0
sumOfMean_and_first_k_harmonic = cdms2.createVariable(Y_k, id=dataid)
sumOfMean_and_first_k_harmonic.setAxisList(axislist)
sumOfMean_and_first_k_harmonic.comments = 'sumOfMean_and_first_%d_harmonic' % k
# make memory free
del Y_k
# return result
return sumOfMean_and_first_k_harmonic
## Denominator is multiplied by 1.0 to prevent truncation for integers.
## returned governs return of second quantity, the weights.
xav = MV2.average(xones, axis=1)
xav2 = MV2.average(ud)
xav3 = MV2.average(udat)
xav4, wav4 = MV2.average(udat, weights=MV2.ones(udat.shape, numpy.float), returned=1)
## choose(indices, t)
## Shaped like indices, values t[i] where at indices[i]
## If t[j] is masked, special treatment to preserve type.
ct1 = MV2.TransientVariable([1,1,2,0,1])
ctr = MV2.choose(ct1, [numpy.ma.masked, 10,20,30,40])
if not MV2.allclose(ctr, [10, 10, 20, 100, 10]): markError('choose error 1')
ctx = MV2.TransientVariable([1,2,3,150,4])
cty = -MV2.TransientVariable([1,2,3,150,4])
ctr = MV2.choose(MV2.greater(ctx,100), (ctx, 100))
if not MV2.allclose(ctr, [1,2,3,100,4]): markError('choose error 2')
ctr = MV2.choose(MV2.greater(ctx,100), (ctx, cty))
if not MV2.allclose(ctr, [1,2,3,-150,4]): markError('choose error 3')
## concatenate(arrays, axis=0, axisid=None, axisattributes=None)
## Concatenate the arrays along the given axis. Give the extended axis the id and
## attributes provided - by default, those of the first array.
try:
xcon = MV2.concatenate((ud,vd))
except:
markError('Concatenate error')
## isMaskedVariable(x)
## Is x a masked variable, that is, an instance of AbstractVariable?
landMask.associated_files = "baseURL: http://rda.ucar.edu/datasets/ds625.0"
landMask.long_name = "Land Area Fraction"
landMask.standard_name = "land_area_fraction"
landMask.units = "%"
landMask.setMissing(1.e20)
# Regrid to current obs data
gridFile = '/clim_obs/obs/atm/mo/tas/JRA25/ac/tas_JRA25_000001-000012_ac.nc'
f_g = cdm.open(gridFile)
grid = f_g('tas').getGrid()
landMask = landMask.regrid(grid, regridTool='ESMF', regridMethod='linear')
f_g.close()
landMask.id = 'sftlf' # Rename
# Deal with interpolated values
landMask[mv.greater(landMask, 75)] = 100 # Fix weird ocean values
landMask[mv.less(landMask, 75)] = 0 # Fix continental halos
landMask[mv.less(landMask, 0)] = 0 # Fix negative values
# Invert land=100, ocean=0
landMask[mv.equal(landMask, 0)] = 50 # Convert ocean
landMask[mv.equal(landMask, 100)] = 0 # Convert ocean
landMask[mv.equal(landMask, 50)] = 100 # Convert ocean
# Create outfile and write
outFile = 'sftlf_pcmdi-metrics_fx_NCAR-JRA25_197901-201401.nc'
# Write variables to file
if os.path.isfile(outFile):
os.remove(outFile)
fOut = cdm.open(outFile, 'w')
# Use function to write standard global atts
def myMakeMask(array, range):
"""Returns the input array masked where the values are not between range[0] and range[1]"""
m1=MV.less (array, range[0]) # mask where it is less than the 1st value
m2=MV.greater(array, range[1]) # mask where it is more than the 2nd value
return MV.logical_or(m1,m2)
climInterp2Max = np.max(climInterp2)
climInterp2Mean = np.mean(climInterp2)
climInterp2Median = np.median(climInterp2)
climInterp2Min = np.min(climInterp2)
climInterp2Str = ''.join(['climInterp2.max():',
'{:{}f}'.format(climInterp2Max,precision),
' mean:','{:{}f}'.format(climInterp2Mean,precision),
' median:','{:{}f}'.format(climInterp2Median,precision),
' min:','{:{}f}'.format(climInterp2Min,precision)])
print(climInterp2Str)
writeToLog(logFile,climInterp2Str)
#print('climInterp2 created')
#pdb.set_trace()
# Mask invalid datapoints
climInterp3 = mv.masked_where(mv.equal(climInterp2,1e+20),climInterp2)
climInterp3 = mv.masked_where(mv.greater(climInterp3,1e+10),climInterp3) ; # Add great to catch fringe values, switched from 1e+20 to 1e+10
print('climInterp3.missing:',climInterp3.missing)
#climInterp3.setMissing(1e+20) ; # Specifically assign missing value
#print('climInterp3 created')
#pdb.set_trace()
'''
import matplotlib.pyplot as plt
climSlice = clim[0,0,:,:] ; plt.figure(1) ; plt.contourf(clim.getLongitude().data,clim.getLatitude().data,climSlice,20) ; #clim
plt.show()
climInterpSlice = climInterp[0,0,:,:] ; plt.figure(2) ; plt.contourf(climInterp.getLongitude().getData(),climInterp.getLatitude().getData(),climInterpSlice,20) ; #climInterp
plt.show()
#climInterp2Slice = climInterp2[0,0,:,:] ; plt.figure(3) ; plt.contourf(climInterp.getLongitude().getData(),climInterp.getLatitude().getData(),climInterp2Slice,20) ; #climInterp2
#plt.show()
climInterp3Slice = climInterp3[0,0,:,:] ; plt.figure(4) ; plt.contourf(climInterp.getLongitude().getData(),climInterp.getLatitude().getData(),climInterp3Slice,20) ; #climInterp3
plt.show()
'''
xav = MV2.average(xones, axis=1)
xav2 = MV2.average(ud)
xav3 = MV2.average(udat)
xav4, wav4 = MV2.average(udat,
weights=MV2.ones(udat.shape, numpy.float),
returned=1)
## choose(indices, t)
## Shaped like indices, values t[i] where at indices[i]
## If t[j] is masked, special treatment to preserve type.
ct1 = MV2.TransientVariable([1, 1, 2, 0, 1])
ctr = MV2.choose(ct1, [numpy.ma.masked, 10, 20, 30, 40])
if not MV2.allclose(ctr, [10, 10, 20, 100, 10]): markError('choose error 1')
ctx = MV2.TransientVariable([1, 2, 3, 150, 4])
cty = -MV2.TransientVariable([1, 2, 3, 150, 4])
ctr = MV2.choose(MV2.greater(ctx, 100), (ctx, 100))
if not MV2.allclose(ctr, [1, 2, 3, 100, 4]): markError('choose error 2')
ctr = MV2.choose(MV2.greater(ctx, 100), (ctx, cty))
if not MV2.allclose(ctr, [1, 2, 3, -150, 4]): markError('choose error 3')
## concatenate(arrays, axis=0, axisid=None, axisattributes=None)
## Concatenate the arrays along the given axis. Give the extended axis the id and
## attributes provided - by default, those of the first array.
try:
xcon = MV2.concatenate((ud, vd))
except:
markError('Concatenate error')
## isMaskedVariable(x)
## Is x a masked variable, that is, an instance of AbstractVariable?
landMask.associated_files = "baseURL: http://rda.ucar.edu/datasets/ds625.0"
landMask.long_name = "Land Area Fraction"
landMask.standard_name = "land_area_fraction"
landMask.units = "%"
landMask.setMissing(1.0e20)
# Regrid to current obs data
gridFile = "/clim_obs/obs/atm/mo/tas/JRA25/ac/tas_JRA25_000001-000012_ac.nc"
f_g = cdm.open(gridFile)
grid = f_g("tas").getGrid()
landMask = landMask.regrid(grid, regridTool="ESMF", regridMethod="linear")
f_g.close()
landMask.id = "sftlf" # Rename
# Deal with interpolated values
landMask[mv.greater(landMask, 75)] = 100 # Fix weird ocean values
landMask[mv.less(landMask, 75)] = 0 # Fix continental halos
landMask[mv.less(landMask, 0)] = 0 # Fix negative values
# Invert land=100, ocean=0
landMask[mv.equal(landMask, 0)] = 50 # Convert ocean
landMask[mv.equal(landMask, 100)] = 0 # Convert ocean
landMask[mv.equal(landMask, 50)] = 100 # Convert ocean
# Create outfile and write
outFile = "sftlf_pcmdi-metrics_fx_NCAR-JRA25_197901-201401.nc"
# Write variables to file
if os.path.isfile(outFile):
os.remove(outFile)
fOut = cdm.open(outFile, "w")
# Use function to write standard global atts
