def _outline(self, X, Y):
"""
Return x, y arrays of colorbar bounding polygon,
taking orientation into account.
"""
N = nx.shape(X)[0]
ii = [0, 1, N - 2, N - 1, 2 * N - 1, 2 * N - 2, N + 1, N, 0]
x = nx.take(nx.ravel(nx.transpose(X)), ii)
y = nx.take(nx.ravel(nx.transpose(Y)), ii)
if self.orientation == "horizontal":
return y, x
return x, y
def _outline(self, X, Y):
'''
Return x, y arrays of colorbar bounding polygon,
taking orientation into account.
'''
N = nx.shape(X)[0]
ii = [0, 1, N - 2, N - 1, 2 * N - 1, 2 * N - 2, N + 1, N, 0]
x = nx.take(nx.ravel(nx.transpose(X)), ii)
y = nx.take(nx.ravel(nx.transpose(Y)), ii)
if self.orientation == 'horizontal':
return y, x
return x, y
def __call__(self, value):
vmin = self.vmin
vmax = self.vmax
if type(value) in [IntType, FloatType]:
vtype = 'scalar'
val = array([value])
else:
vtype = 'array'
val = nx.asarray(value)
# if both vmin is None and vmax is None, we'll automatically
# norm the data to vmin/vmax of the actual data, so the
# clipping step won't be needed.
if vmin is None and vmax is None:
needs_clipping = False
else:
needs_clipping = True
if vmin is None or vmax is None:
rval = nx.ravel(val)
#do this if sentinels (values to ignore in data)
if self.ignore:
sortValues = nx.sort(rval)
if vmin is None:
# find the lowest non-sentinel value
for thisVal in sortValues:
if thisVal not in self.ignore:
vmin = thisVal #vmin is the lowest non-sentinel value
break
else:
vmin = 0.
if vmax is None:
for thisVal in sortValues[::-1]:
if thisVal not in self.ignore:
vmax = thisVal #vmax is the greatest non-sentinel value
break
else:
vmax = 0.
else:
if vmin is None: vmin = min(rval)
if vmax is None: vmax = max(rval)
if vmin > vmax:
raise ValueError("minvalue must be less than or equal to maxvalue")
elif vmin == vmax:
return 0. * value
else:
if needs_clipping:
val = nx.clip(val, vmin, vmax)
result = (1.0 / (vmax - vmin)) * (val - vmin)
# replace sentinels with original (non-normalized) values
for thisIgnore in self.ignore:
result = nx.where(val == thisIgnore, thisIgnore, result)
if vtype == 'scalar':
result = result[0]
return result
def __call__(self, value):
vmin = self.vmin
vmax = self.vmax
if type(value) in [IntType, FloatType]:
vtype = 'scalar'
val = array([value])
else:
vtype = 'array'
val = nx.asarray(value)
# if both vmin is None and vmax is None, we'll automatically
# norm the data to vmin/vmax of the actual data, so the
# clipping step won't be needed.
if vmin is None and vmax is None:
needs_clipping = False
else:
needs_clipping = True
if vmin is None or vmax is None:
rval = nx.ravel(val)
#do this if sentinels (values to ignore in data)
if self.ignore:
sortValues=nx.sort(rval)
if vmin is None:
# find the lowest non-sentinel value
for thisVal in sortValues:
if thisVal not in self.ignore:
vmin=thisVal #vmin is the lowest non-sentinel value
break
else:
vmin=0.
if vmax is None:
for thisVal in sortValues[::-1]:
if thisVal not in self.ignore:
vmax=thisVal #vmax is the greatest non-sentinel value
break
else:
vmax=0.
else:
if vmin is None: vmin = min(rval)
if vmax is None: vmax = max(rval)
if vmin > vmax:
raise ValueError("minvalue must be less than or equal to maxvalue")
elif vmin==vmax:
return 0.*value
else:
if needs_clipping:
val = nx.clip(val,vmin, vmax)
result = (1.0/(vmax-vmin))*(val-vmin)
# replace sentinels with original (non-normalized) values
for thisIgnore in self.ignore:
result = nx.where(val==thisIgnore,thisIgnore,result)
if vtype == 'scalar':
result = result[0]
return result
def _parse_args(self, *args):
X, Y, U, V, C = [None]*5
args = list(args)
if len(args) == 3 or len(args) == 5:
C = nx.ravel(args.pop(-1))
#print 'in parse_args, C:', C
V = nx.ma.asarray(args.pop(-1))
U = nx.ma.asarray(args.pop(-1))
nn = nx.shape(U)
nc = nn[0]
nr = 1
if len(nn) > 1:
nr = nn[1]
if len(args) == 2:
X, Y = [nx.ravel(a) for a in args]
if len(X) == nc and len(Y) == nr:
X, Y = [nx.ravel(a) for a in meshgrid(X, Y)]
else:
X, Y = [nx.ravel(a) for a in meshgrid(nx.arange(nc), nx.arange(nr))]
return X, Y, U, V, C
def __call__(self, lon, lat, inverse=False):
"""
Calling a Proj class instance with the arguments lon, lat will
convert lon/lat (in degrees) to x/y native map projection
coordinates (in meters). If optional keyword 'inverse' is
True (default is False), the inverse transformation from x/y
to lon/lat is performed.
For cylindrical equidistant projection ('cyl'), this
does nothing (i.e. x,y == lon,lat).
lon,lat can be either scalar floats or N arrays.
"""
if self.projection == 'cyl': # for cyl x,y == lon,lat
return lon, lat
# if inputs are numarray arrays, get shape and typecode.
try:
shapein = lon.shape
lontypein = lon.typecode()
lattypein = lat.typecode()
except:
shapein = False
# make sure inputs have same shape.
if shapein and lat.shape != shapein:
raise ValueError, 'lon, lat must be scalars or numarrays with the same shape'
if shapein:
x = N.ravel(lon).tolist()
y = N.ravel(lat).tolist()
if inverse:
outx, outy = self._inv(x, y)
else:
outx, outy = self._fwd(x, y)
outx = N.reshape(N.array(outx, lontypein), shapein)
outy = N.reshape(N.array(outy, lattypein), shapein)
else:
if inverse:
outx, outy = self._inv(lon, lat)
else:
outx, outy = self._fwd(lon, lat)
return outx, outy
def __call__(self,lon,lat,inverse=False):
"""
Calling a Proj class instance with the arguments lon, lat will
convert lon/lat (in degrees) to x/y native map projection
coordinates (in meters). If optional keyword 'inverse' is
True (default is False), the inverse transformation from x/y
to lon/lat is performed.
For cylindrical equidistant projection ('cyl'), this
does nothing (i.e. x,y == lon,lat).
lon,lat can be either scalar floats or N arrays.
"""
if self.projection == 'cyl': # for cyl x,y == lon,lat
return lon,lat
# if inputs are numarray arrays, get shape and typecode.
try:
shapein = lon.shape
lontypein = lon.typecode()
lattypein = lat.typecode()
except:
shapein = False
# make sure inputs have same shape.
if shapein and lat.shape != shapein:
raise ValueError, 'lon, lat must be scalars or numarrays with the same shape'
if shapein:
x = N.ravel(lon).tolist()
y = N.ravel(lat).tolist()
if inverse:
outx, outy = self._inv(x,y)
else:
outx, outy = self._fwd(x,y)
outx = N.reshape(N.array(outx,lontypein),shapein)
outy = N.reshape(N.array(outy,lattypein),shapein)
else:
if inverse:
outx,outy = self._inv(lon,lat)
else:
outx,outy = self._fwd(lon,lat)
return outx,outy
def _make_verts(self, U, V):
uv = U+V*1j
uv = nx.ravel(nx.ma.filled(uv, nx.nan))
a = nx.absolute(uv)
if self.scale is None:
sn = max(10, math.sqrt(self.N))
scale = 1.8 * nx.average(a) * sn # crude auto-scaling
scale = scale/self.span
self.scale = scale
length = a/(self.scale*self.width)
X, Y = self._h_arrows(length)
xy = (X+Y*1j) * nx.exp(1j*nx.angle(uv[...,nx.newaxis]))*self.width
xy = xy[:,:,nx.newaxis]
XY = nx.concatenate((xy.real, xy.imag), axis=2)
return XY
def set_UVC(self, U, V, C=None):
self.U = nx.ma.ravel(U)
self.V = nx.ma.ravel(V)
if C is not None:
self.set_array(nx.ravel(C))
self._new_UV = True
# find 4 lon/lat corners of AWIPS grid 221.
llcornerx = 0.; llcornery = 0.
lrcornerx = dx*(nx-1); lrcornery = 0.
ulcornerx = 0.; ulcornery = dy*(ny-1)
urcornerx = dx*(nx-1); urcornery = dy*(ny-1)
llcornerlon, llcornerlat = awips221(llcornerx, llcornery, inverse=True)
lrcornerlon, lrcornerlat = awips221(lrcornerx, lrcornery, inverse=True)
urcornerlon, urcornerlat = awips221(urcornerx, urcornery, inverse=True)
ulcornerlon, ulcornerlat = awips221(ulcornerx, ulcornery, inverse=True)
print '4 corners of AWIPS grid 221:'
print llcornerlon, llcornerlat
print lrcornerlon, lrcornerlat
print urcornerlon, urcornerlat
print ulcornerlon, ulcornerlat
print 'from GRIB docs'
print '(see http://www.nco.ncep.noaa.gov/pmb/docs/on388/tableb.html)'
print ' -145.5 1.0'
print ' -68.318 0.897'
print ' -2.566 46.352'
print ' 148.639 46.635'
# compute lons and lats for the whole AWIPS grid 221 (377x249).
import time; t1 = time.clock()
lons, lats = awips221.makegrid(nx,ny)
t2 = time.clock()
print 'compute lats/lons for all points on AWIPS 221 grid (%sx%s)' %(nx,ny)
print 'max/min lons'
print min(NX.ravel(lons)),max(NX.ravel(lons))
print 'max/min lats'
print min(NX.ravel(lats)),max(NX.ravel(lats))
print 'took',t2-t1,'secs'
