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 __call__(self, scaledImageData, alpha=1):
# assumes the data is already normalized (ignoring sentinels)
# clip to be on the safe side
rgbaValues = self.cmap(nx.clip(scaledImageData, 0.,1.))
#replace sentinel data with sentinel colors
for sentinel,rgb in self.sentinels.items():
r,g,b = rgb
rgbaValues[:,:,0] = nx.where(scaledImageData==sentinel, r, rgbaValues[:,:,0])
rgbaValues[:,:,1] = nx.where(scaledImageData==sentinel, g, rgbaValues[:,:,1])
rgbaValues[:,:,2] = nx.where(scaledImageData==sentinel, b, rgbaValues[:,:,2])
rgbaValues[:,:,3] = nx.where(scaledImageData==sentinel, alpha, rgbaValues[:,:,3])
return rgbaValues
def __call__(self, scaledImageData, alpha=1):
# assumes the data is already normalized (ignoring sentinels)
# clip to be on the safe side
rgbaValues = self.cmap(nx.clip(scaledImageData, 0., 1.))
#replace sentinel data with sentinel colors
for sentinel, rgb in self.sentinels.items():
r, g, b = rgb
rgbaValues[:, :, 0] = nx.where(scaledImageData == sentinel, r,
rgbaValues[:, :, 0])
rgbaValues[:, :, 1] = nx.where(scaledImageData == sentinel, g,
rgbaValues[:, :, 1])
rgbaValues[:, :, 2] = nx.where(scaledImageData == sentinel, b,
rgbaValues[:, :, 2])
rgbaValues[:, :, 3] = nx.where(scaledImageData == sentinel, alpha,
rgbaValues[:, :, 3])
return rgbaValues