def __call__(self, value, clip=None):
if clip is None:
clip = self.clip
if isinstance(value, (int, float)):
vtype = 'scalar'
val = ma.array([value])
else:
vtype = 'array'
val = ma.asarray(value)
self.autoscale(val)
vmin, vmax = self.vmin, self.vmax
if vmin > vmax:
raise ValueError("minvalue must be less than or equal to maxvalue")
elif vmin<=0:
raise ValueError("values must all be positive")
elif vmin==vmax:
return 0.*value
else:
if clip:
mask = ma.getmask(val)
val = ma.array(nx.clip(val.filled(vmax), vmin, vmax),
mask=mask)
result = (ma.log(val)-nx.log(vmin))/(nx.log(vmax)-nx.log(vmin))
if vtype == 'scalar':
result = result[0]
return result
def __call__(self, value):
if isinstance(value, (int, float)):
vtype = 'scalar'
val = ma.array([value])
else:
vtype = 'array'
val = ma.asarray(value)
self.autoscale(val)
vmin, vmax = self.vmin, self.vmax
if vmin > vmax:
raise ValueError("minvalue must be less than or equal to maxvalue")
elif vmin == vmax:
return 0. * value
else:
if self.clip:
mask = ma.getmaskorNone(val)
val = ma.array(clip(val.filled(vmax), vmin, vmax), mask=mask)
result = (val - vmin) / float(vmax - vmin)
if vtype == 'scalar':
result = result[0]
return result
def __call__(self, value):
if isinstance(value, (int, float)):
vtype = 'scalar'
val = ma.array([value])
else:
vtype = 'array'
val = ma.asarray(value)
self.autoscale(val)
vmin, vmax = self.vmin, self.vmax
if vmin > vmax:
raise ValueError("minvalue must be less than or equal to maxvalue")
elif vmin==vmax:
return 0.*value
else:
if self.clip:
val = clip(val.filled(vmax), vmin, vmax)
result = (1.0/(vmax-vmin))*(val-vmin)
if vtype == 'scalar':
result = result[0]
return result
