def __call__(self, value, clip=None):
if clip is None:
clip = self.clip
if cbook.iterable(value):
vtype = 'array'
val = ma.asarray(value).astype(npy.float)
else:
vtype = 'scalar'
val = ma.array([value]).astype(npy.float)
self.autoscale_None(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 0.0 * val
else:
if clip:
mask = ma.getmask(val)
val = ma.array(npy.clip(val.filled(vmax), vmin, vmax),
mask=mask)
result = (ma.log(val)-npy.log(vmin))/(npy.log(vmax)-npy.log(vmin))
if vtype == 'scalar':
result = result[0]
return result
def __call__(self, value, clip=None):
if clip is None:
clip = self.clip
if cbook.iterable(value):
vtype = 'array'
val = ma.asarray(value).astype(npy.float)
else:
vtype = 'scalar'
val = ma.array([value]).astype(npy.float)
self.autoscale_None(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 0.0 * val
else:
if clip:
mask = ma.getmask(val)
val = ma.array(npy.clip(val.filled(vmax), vmin, vmax),
mask=mask)
result = (ma.log(val) - npy.log(vmin)) / (npy.log(vmax) -
npy.log(vmin))
if vtype == 'scalar':
result = result[0]
return result
def transform(self, a):
sign = npy.sign(npy.asarray(a))
masked = ma.masked_inside(a, -self.linthresh, self.linthresh, copy=False)
log = sign * ma.log(npy.abs(masked)) / self._log_base
if masked.mask.any():
return npy.asarray(ma.where(masked.mask,
a * self._linadjust,
log))
else:
return npy.asarray(log)
def transform(self, a):
a = _mask_non_positives(a * self.base)
if isinstance(a, MaskedArray):
return ma.log(a) / npy.log(self.base)
return npy.log(a) / npy.log(self.base)
