def __init__(self, axis, source_axis=None, parent=None):
super(AxisBoundsChooser, self).__init__(parent=parent)
if source_axis is not None:
self.axis = source_axis
else:
self.axis = axis
l = QtGui.QVBoxLayout()
l.addWidget(header_label(axis.id))
if source_axis is not None:
minimum, maximum = (float(num) for num in genutil.minmax(source_axis))
bottom, top = (float(num) for num in genutil.minmax(axis))
for i, v in enumerate(source_axis):
if v == bottom:
bot_ind = i
if v == top:
top_ind = i
self.range = RangeWidget(axis_values(source_axis), bottom=bot_ind, top=top_ind)
else:
minimum, maximum = (float(num) for num in genutil.minmax(axis))
self.range = RangeWidget(axis_values(axis))
l.addWidget(self.range)
self.setLayout(l)
emitter = partial(self.boundsEdited.emit, self.axis)
self.range.boundsEdited.connect(emitter)
def __init__(self, axis, source_axis=None, parent=None):
super(AxisBoundsChooser, self).__init__(parent=parent)
if source_axis is not None:
self.axis = source_axis
else:
self.axis = axis
l = QtGui.QVBoxLayout()
l.addWidget(header_label(axis.id))
if source_axis is not None:
minimum, maximum = (float(num)
for num in genutil.minmax(source_axis))
bottom, top = (float(num) for num in genutil.minmax(axis))
for i, v in enumerate(source_axis):
if v == bottom:
bot_ind = i
if v == top:
top_ind = i
self.range = RangeWidget(axis_values(source_axis),
bottom=bot_ind,
top=top_ind)
else:
minimum, maximum = (float(num) for num in genutil.minmax(axis))
self.range = RangeWidget(axis_values(axis))
l.addWidget(self.range)
self.setLayout(l)
emitter = partial(self.boundsEdited.emit, self.axis)
self.range.boundsEdited.connect(emitter)
def extrema(self):
""" Extraction des valeurs minimum et maximum dans les champs modelises et observes """
from genutil import minmax
self.model.extrema = minmax(self.model)
self.obs.extrema = minmax(self.obs)
# Affichage du resultat
m = str(self.model.extrema)
o = str(self.obs.extrema)
def extrema(self):
""" Extraction des valeurs minimum et maximum dans les champs modelises et observes """
from genutil import minmax
self.model.extrema=minmax(self.model)
self.obs.extrema=minmax(self.obs)
# Affichage du resultat
m=str(self.model.extrema)
o=str(self.obs.extrema)
def setupVariableAxes(self):
""" Iterate through the variable's axes and create and initialize an Axis
object for each axis.
"""
if self.var is None:
return
if (self.axisList is None):
if self.cdmsFile is None:
self.axisList = self.var.getAxisList()
self.grid=self.var.getGrid()
else:
try:
self.axisList = self.cdmsFile[self.var].getAxisList()
self.grid=self.cdmsFile[self.var].getGrid()
except:
## Ok this is probably a simple axis
self.axisList=[self.cdmsFile[self.var],]
self.grid=None
self.axisOrder = range(len(self.axisList))
self.clear()
self.setAxesNames()
# Iterate through the variables axes & init each axis widget
axisIndex = 0
didVirtual=False
for axis, axisName in zip(self.axisList, self.axesNames):
virtual = 0
if isinstance(self.grid,(cdms2.hgrid.AbstractHorizontalGrid,cdms2.gengrid.AbstractHorizontalGrid)) and axis in self.grid.getLatitude().getAxisList():
virtual = -1
if didVirtual is False:
didVirtual=True
minLat,maxLat=genutil.minmax(self.grid.getLatitude())
minLon,maxLon=genutil.minmax(self.grid.getLongitude())
vLat=cdms2.createAxis(numpy.arange(numpy.floor(minLat),numpy.ceil(maxLat)+.1,.1),id="latitude")
vLon=cdms2.createAxis(numpy.arange(numpy.floor(minLon),numpy.ceil(maxLon)+.1,.1),id="longitude")
virtualLatWidget=QAxis(vLat, "latitude", axisIndex, self,virtual=1)
virtualLonWidget=QAxis(vLon, "longitude", axisIndex, self,virtual=2)
self.axisWidgets.append(virtualLatWidget)
self.axisWidgets.append(virtualLonWidget)
# Create the axis widget
# Virtual: 0: Not virtual
# -1: options to switch to virtual
# 1: is latitude virtual needs option to switch back
# 2: is longitude virtual needs option to switch back
axisWidget = QAxis(axis, axisName, axisIndex, self,virtual=virtual)
self.axisWidgets.append(axisWidget)
axisIndex += 1
self.gridLayout.setRowStretch(self.gridLayout.rowCount(), 1)
def scan_rois(self, variables):
self.rois = {}
for var in variables:
lat, lon = None, None
for axis in var.getAxisList():
if axis.isLatitude():
lat = axis
elif axis.isLongitude():
lon = axis
lat_range = minmax(lat)
lon_range = minmax(lon)
roi = (lat_range, lon_range)
roi_vars = self.rois.get(roi, [])
roi_vars.append(var)
self.rois[roi] = roi_vars
def scan_rois(self, variables):
self.rois = {}
for var in variables:
lat, lon = None, None
for axis in var.getAxisList():
if axis.isLatitude():
lat = axis
elif axis.isLongitude():
lon = axis
lat_range = minmax(lat)
lon_range = minmax(lon)
roi = (lat_range, lon_range)
roi_vars = self.rois.get(roi, [])
roi_vars.append(var)
self.rois[roi] = roi_vars
def vminmax(data, asdict=False, symetric=False):
"""Get min and max of dict, list, tuple, array"""
if isinstance(data, dict):
vmin, vmax = vminmax(data.values())
else:
vmin, vmax = minmax(data)
if symetric:
vmax = min(abs(vmin), abs(vmax))
vmin = -vmax
if asdict:
return dict(vmin=vmin, vmax=vmax)
return vmin, vmax
C=genutil.array_indexing.extract(A,B)
print C,C.dtype.char
C=genutil.arrayindexing.get(A,B)
print C,C.dtype.char
f=cdms2.open(os.path.join(cdms2.__path__[0],'..','..','..','..','sample_data','clt.nc'))
clt=f('clt')
## clt=cdms2.MV2.average(clt,2)
print clt.shape
M=numpy.ma.maximum.reduce(clt,axis=0)
marg=numpy.ma.argmax(clt,axis=0)
M2=genutil.arrayindexing.get(clt,marg)
print M2.shape,M2.mask,genutil.minmax(M2-M)
M=numpy.ma.maximum.reduce(clt,axis=1)
marg=numpy.ma.argmax(clt,axis=1)
marg=cdms2.MV2.array(marg)
marg.setAxis(0,clt.getAxis(0))
marg.setAxis(1,clt.getAxis(2))
print clt.dtype.char,M.shape
M2=genutil.arrayindexing.get(clt,marg,axis=1)
print M2.shape,M2.mask,genutil.minmax(M2-M)
clt=cdms2.MV2.masked_greater(clt,80)
M=numpy.ma.maximum.reduce(clt,axis=1)
print M.mask,'is the mask'
marg=numpy.ma.argmax(clt,axis=1)
from cdms_utils.cdms_compat import *
import cdms_utils.var_utils as vu
import genutil as g
fin = cdms.open(
"/disks/archive/real/prob_land_cc/grid_box_25km/2060-2089/prob_land_cc_grid_box_25km_may_2060-2089_mslp_dmean_tmean_abs.nc"
)
v = fin("cdf_mslp_dmean_tmean_abs")
def p(v):
print v.shape
print v.getAxisIds()
p(v)
x = vu.cleverSqueeze(v)
p(x)
y = vu.cleverSqueeze(v, ["meaning_period"])
p(y)
print "SAME?"
d = y(squeeze=1) - x
p(d)
print g.minmax(d)
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
# Adapted for numpy/ma/cdms2 by convertcdms.py
import sys
import MSU,cdms2
f=cdms2.open('../Data/ta.nc')
ta=f('ta',longitude=(0,360,'co'))
f.close()
f=cdms2.open('../Data/weights.nc')
w=f('weights')
f.close()
critw=0.5 # 50% of data required
msu=MSU.msu(ta,w,critw)
f=cdms2.open('../Data/tam2.nc')
goodtam2=f('tam2',longitude=(0,360,'co'))
f.close()
tam2=msu[...,0]
diff=tam2-goodtam2
import genutil
print genutil.minmax(diff)
if not cdms2.MV2.allclose(tam2,goodtam2):
raise 'Error in MSU module, wrong result outputed for tam2, check if they are very different form 0, could be machine precision error'
else:
print 'MSU seems ok, congratulations!'
from cdms_utils.cdms_compat import *
import cdms_utils.var_utils as vu
import genutil as g
fin = cdms.open("/disks/archive/real/prob_land_cc/grid_box_25km/2060-2089/prob_land_cc_grid_box_25km_may_2060-2089_mslp_dmean_tmean_abs.nc")
v = fin("cdf_mslp_dmean_tmean_abs")
def p(v):
print v.shape
print v.getAxisIds()
p(v)
x = vu.cleverSqueeze(v)
p(x)
y = vu.cleverSqueeze(v, ["meaning_period"])
p(y)
print "SAME?"
d = y(squeeze=1) - x
p(d)
print g.minmax(d)
# Adapted for numpy/ma/cdms2 by convertcdms.py
import sys
import MSU, cdms2
f = cdms2.open('../Data/ta.nc')
ta = f('ta', longitude=(0, 360, 'co'))
f.close()
f = cdms2.open('../Data/weights.nc')
w = f('weights')
f.close()
critw = 0.5 # 50% of data required
msu = MSU.msu(ta, w, critw)
f = cdms2.open('../Data/tam2.nc')
goodtam2 = f('tam2', longitude=(0, 360, 'co'))
f.close()
tam2 = msu[..., 0]
diff = tam2 - goodtam2
import genutil
print genutil.minmax(diff)
if not cdms2.MV2.allclose(tam2, goodtam2):
raise 'Error in MSU module, wrong result outputed for tam2, check if they are very different form 0, could be machine precision error'
else:
print 'MSU seems ok, congratulations!'
select = cdms2.selectors.Selector(lon=slice(0, 3), time=('1950', cdtime.comptime(1960)))
print u(equator)(select).shape
# -> appliquez à la lecture du fichier
# Le module genutil : utilitaires generiques
# - contenu
import genutil
print dir(genutil)
# - statistics standards
print dir(genutil.statistics)
ustd = genutil.statistics.std(u, axis=0) # testez les options
ustd.info()
print N.ma.allclose(ustd, u.std(axis=0))
# - salstat
print dir(genutil.salstat)
print genutil.salstat.kurtosis(u, axis='t') # essayez d'autres fonctions
# - divers
_, uu = genutil.grower(u, u[0]) # testez d'autres slices
print uu.shape
print genutil.minmax(u, u**2)
print u[:, 0, 0](genutil.picker(time=['1960-01-03', '1965-05-03'])) # testez option match
print C, C.dtype.char
C = genutil.arrayindexing.get(A, B)
print C, C.dtype.char
f = cdms2.open(
os.path.join(cdms2.__path__[0], '..', '..', '..', '..', 'sample_data',
'clt.nc'))
clt = f('clt')
## clt=cdms2.MV2.average(clt,2)
print clt.shape
M = numpy.ma.maximum.reduce(clt, axis=0)
marg = numpy.ma.argmax(clt, axis=0)
M2 = genutil.arrayindexing.get(clt, marg)
print M2.shape, M2.mask, genutil.minmax(M2 - M)
M = numpy.ma.maximum.reduce(clt, axis=1)
marg = numpy.ma.argmax(clt, axis=1)
marg = cdms2.MV2.array(marg)
marg.setAxis(0, clt.getAxis(0))
marg.setAxis(1, clt.getAxis(2))
print clt.dtype.char, M.shape
M2 = genutil.arrayindexing.get(clt, marg, axis=1)
print M2.shape, M2.mask, genutil.minmax(M2 - M)
clt = cdms2.MV2.masked_greater(clt, 80)
M = numpy.ma.maximum.reduce(clt, axis=1)
print M.mask, 'is the mask'
marg = numpy.ma.argmax(clt, axis=1)
def bwr():
cdict = {'red': ((0.,)*3,(.5,1.,1.),(1.,1.,1.)),
'green': ((0.,)*3,(.5,1.,1.),(1.,0.,0.)),
'blue': ((0.,1.,1.),(.5,1.,1.),(1.,0.,0.))}
return matplotlib.colors.LinearSegmentedColormap('bwr',cdict,256)
figure(figsize=(8,8))
subplot(ncol,nrow,nslice)
for i in xrange(nslice):
lon = composites.getLongitude()
lat = composites.getLatitude()
xx,yy = meshgrid(lon,lat)
m = Basemap(resolution='l',lat_0=N.average(lat),lon_0=N.average(lon),
llcrnrlon=min(lon),llcrnrlat=min(lat),
urcrnrlon=max(lon),urcrnrlat=max(lat))
levels = vcs.mkscale(genutil.minmax(composites))
m.contourf(lon,lat,composites[i],levels=levels,cmap=bwr)
clabel(m.contour(lon,lat,composites[i],levels=levels))
title("Phase %i/%i" (i+1,6))
m.drawcoastlines()
m.drawcountries()
m.fillcontinents(color='coral')
m.drawparallels(vcs.mkscale(genutil.minmax(lat)),labels=[1,0,0,0])
m.drawmeridians(vcs.mkscale(genutil.minmax(lon)),labels=[0,0,0,1])
figtext(.5,1.,"\n%s [%s]" % ("El Nino phase composites\nSea surface temperature", composites.units))
savefig(sys.argv[0].replace(".py",".png"))
show()
# Fall back to vcs because we have it!
except:
xe_nea = interp1d(xe, new_time, method='nearest') xe_nea.long_name = 'Nearest' # - linear xe_lin = interp1d(xe, new_time, method='linear') xe_lin.long_name = 'Linear' # - cubic xe_cub = interp1d(xe, new_time, method='cubic') xe_cub.long_name = 'Cubic' # Plots from matplotlib import rcParams ; rcParams['font.size'] = 8 import pylab as P from vacumm.misc.plot import yhide, xscale, savefigs, hov2, curve2 from vacumm.misc.color import cmap_jets from genutil import minmax vmin, vmax = minmax(xe, xe_lin) kwplot = dict(vmin=vmin, vmax=vmax, show=False) kwhov = dict(kwplot) kwhov.update(cmap=cmap_jets(stretch=-.4), colorbar=False, xrotation=25.) kwcurve = dict(kwplot) kwcurve.update(transpose=True, color='r', yhide=True) kwplot.update(order = '-d', title=True) kwhov['date_fmt'] = '%Hh' # - original #print xe.getTime().asComponentTime() hov2(xe, subplot=421, top=.95, hspace=.45, figsize=(5.5, 8), bottom=.06, **kwhov) axlims = P.axis() curve2(xe[:, 15], 'o', subplot=422, **kwcurve) xscale(1.1, keep_min=1) # - nearest hov2(xe_nea, subplot=423, **kwhov)
xe_nea.long_name = 'Nearest'
# - linear
xe_lin = interp1d(xe, new_time, method='linear')
xe_lin.long_name = 'Linear'
# - cubic
xe_cub = interp1d(xe, new_time, method='cubic')
xe_cub.long_name = 'Cubic'
# Plots
from matplotlib import rcParams
rcParams['font.size'] = 8
import pylab as P
from vacumm.misc.plot import yhide, xscale, savefigs, hov2, curve2
from vacumm.misc.color import cmap_jets
from genutil import minmax
vmin, vmax = minmax(xe, xe_lin)
kwplot = dict(vmin=vmin, vmax=vmax, show=False)
kwhov = dict(kwplot)
kwhov.update(cmap=cmap_jets(stretch=-.4), colorbar=False, xrotation=25.)
kwcurve = dict(kwplot)
kwcurve.update(transpose=True, color='r', yhide=True)
kwplot.update(order='-d', title=True)
kwhov['date_fmt'] = '%Hh'
# - original
#print xe.getTime().asComponentTime()
hov2(xe,
subplot=421,
top=.95,
hspace=.45,
figsize=(5.5, 8),
bottom=.06,
def plot_bathy(bathy, shadow=True, contour=True, shadow_stretch=1., shadow_shapiro=False,
show=True, shadow_alpha=1., shadow_black=.3, white_deep=False, nmax=30,m=None, alpha=1.,
zmin=None, zmax=None, **kwargs):
"""Plot a bathymetry
- *lon*: Longitude range.
- *lat*: Latitude range.
- *show*:Display the figure [default: True]
- *pcolor*: Use pcolor instead of contour [default: False]
- *contour*: Add line contours [default: True]
- *shadow*:Plot south-west shadows instead of filled contours.
- *nmax*: Max number of levels for contours [default: 30]
- *white_deep*: Deep contours are white [default: False]
- All other keyword are passed to :func:`~vacumm.misc.plot.map2`
"""
# Input
bb = bathy
if isinstance(bathy, GriddedBathy):
bathy = bathy.bathy()
if shadow:
xxs = getattr(bb, '_xxs', None)
yys = getattr(bb, '_yys', None)
if xxs is None:
lon2d = bb._lon2d
lat2d = bb._lat2d
elif shadow:
xxs = yys = None
lon2d,lat2d = meshgrid(get_axis(bathy, -1).getValue(),get_axis(bathy, -2).getValue())
# Masking
if 'maxdep' in kwargs:
zmin = -maxdep
if 'maxalt' in kwargs:
zmax = maxalt
if zmin is not None:
bathy[:] = MV2.masked_less(bathy, zmin)
if zmax is not None:
bathy[:] = MV2.masked_greater(bathy, zmax)
# Default arguments for map
if hasattr(bathy, 'long_name'):
kwargs.setdefault('title',bathy.long_name)
if 'cmap' not in kwargs:
vmin, vmax = minmax(bathy)
# print 'cmap topo', vmin, vmax
kwargs['cmap'] = auto_cmap_topo((kwargs.get('vmin', vmin), kwargs.get('vmax', vmax)))
# kwargs.setdefault('ticklabel_size','smaller')
kwargs.setdefault('clabel_fontsize', 8)
kwargs.setdefault('clabel_alpha',.7*alpha)
kwargs.setdefault('clabel_glow_alpha', kwargs['clabel_alpha'])
kwargs.setdefault('fill', 'contourf')
kwargs['nmax'] = nmax
kwargs['show'] = False
kwargs['contour'] = contour
if shadow: kwargs.setdefault('alpha',.5*alpha)
kwargs.setdefault('projection', 'merc')
kwargs.setdefault('fmt', BathyFormatter())
kwargs.setdefault('colorbar_format', BathyFormatter())
kwargs.setdefault('units', False)
kwargs.setdefault('levels_mode','normal')
kwargs.setdefault('bgcolor', '0.8')
kwargs.setdefault('contour_linestyle', '-')
savefig = kwargs.pop('savefig', None)
kwsavefig = kwfilter(kwargs, 'savefig_')
# White contour when dark
if contour and white_deep:
levels = auto_scale(bathy,nmax=nmax)
colors = []
nlevel = len(levels)
for i in range(nlevel):
if i < old_div(nlevel,2):
colors.append('w')
else:
colors.append('k')
kwargs.setdefault('contour_colors',tuple(colors))
# Call to map
m = map2(bathy, m=m, **kwargs)
# Add shadow
if shadow:
# Filter
data = MV.array(bathy,'f',fill_value=0.)
if shadow_shapiro:
data = shapiro2d(data,fast=True).shape
# Gradient
grd = deriv2d(data,direction=45.,fast=True,fill_value=0.).filled(0.)
grdn = refine(grd, 3)
grdn = norm_atan(grdn,stretch=shadow_stretch).clip(0,1.) ; del grd
# Grid
# im = m.map.imshow(grdn,cmap=P.get_cmap('gist_yarg'),alpha=1) # gist_yarg , YlGnBu
if xxs is None or yys is None:
xx, yy = m(lon2d,lat2d)
xxr = refine(xx, 3)
yyr = refine(yy, 3)
xxs, yys = meshbounds(xxr, yyr)
if isinstance(bb, GriddedBathy):
bb._xxs = xxs
bb._yys = yys
del xx, yy, xxr, yyr
# Cmap
cmap = cmap_custom(( ((1, )*3, 0), ((shadow_black, )*3, 1) ))
# Plot
pp = m.map.pcolormesh(xxs, yys, grdn,cmap=cmap)#P.get_cmap('gist_yarg'))
pp.set_zorder(.9)
pp.set_linewidth(0)
pp.set_alpha(shadow_alpha*N.clip(alpha*2, 0, 1))
del grdn
# Show it?
if savefig:
m.savefig(savefig, **kwsavefig)
if show:
P.show()
return m
lon_id, lon_val = find_nearest(lond, lon1[i])
#print('lat_id = ',lat_id, lat_val, 'lon_id=',lon_id, lon_val)
a = int(lat_id)
b = int(lon_id)
if deltat[a, b] != 0:
print('-------------')
print('i=', i, 'lat1=', lat1[i], 'lon1=', lon1[i])
print('lat_id = ', lat_id, lat_val, 'lon_id=', lon_id, lon_val,
'a=', a, 'b=', b)
print('deltat is', deltat[a, b])
print('/////')
for ilev in range(nlev):
if TSOI_r[i, ilev] != 0:
TSOI_new1[i, ilev] = TSOI_r[i, ilev] + deltat[a, b]
print('ilev = ', ilev, \
'TSOI_r = ',TSOI_r[i,ilev],\
'TSOI_new1 = ', TSOI_new1[i,ilev])
TSOI_new1.id = 'T_SOISNO'
TSOI_new1.setAxisList(TSOI_r.getAxisList())
diff1 = TSOI_new1 - TSOI_r
print(genutil.minmax(diff1))
f3.write(TSOI_new1)
f3.close()
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
return matplotlib.colors.LinearSegmentedColormap('bwr', cdict, 256)
figure(figsize=(8, 8))
subplot(ncol, nrow, nslice)
for i in xrange(nslice):
lon = composites.getLongitude()
lat = composites.getLatitude()
xx, yy = meshgrid(lon, lat)
m = Basemap(resolution='l',
lat_0=N.average(lat),
lon_0=N.average(lon),
llcrnrlon=min(lon),
llcrnrlat=min(lat),
urcrnrlon=max(lon),
urcrnrlat=max(lat))
levels = vcs.mkscale(genutil.minmax(composites))
m.contourf(lon, lat, composites[i], levels=levels, cmap=bwr)
clabel(m.contour(lon, lat, composites[i], levels=levels))
title("Phase %i/%i" (i + 1, 6))
m.drawcoastlines()
m.drawcountries()
m.fillcontinents(color='coral')
m.drawparallels(vcs.mkscale(genutil.minmax(lat)), labels=[1, 0, 0, 0])
m.drawmeridians(vcs.mkscale(genutil.minmax(lon)), labels=[0, 0, 0, 1])
figtext(
.5, 1.,
"\n%s [%s]" % ("El Nino phase composites\nSea surface temperature",
composites.units))
savefig(sys.argv[0].replace(".py", ".png"))
show()
