def __call__(self, value, clip=None, midpoint=None):
if clip is None:
clip = self.clip
if cbook.iterable(value):
vtype = 'array'
val = ma.asarray(value).astype(np.float)
else:
vtype = 'scalar'
val = ma.array([value]).astype(np.float)
self.autoscale_None(val)
vmin, vmax = self.vmin, self.vmax
vmid = self.vmid if self.vmid is not None else (vmax + vmin) / 2.0
if midpoint is None:
midpoint = (vmid - vmin) / (vmax - vmin)
if vmin > vmax:
raise ValueError("minvalue must be less than or equal to maxvalue")
elif vmin == vmax:
return 0.0 * val
else:
if clip:
mask = ma.getmask(val)
val = ma.array(np.clip(val.filled(vmax), vmin, vmax),
mask=mask)
result = (val - vmin) * (1.0 / (vmax - vmin))
#result = (ma.arcsinh(val)-np.arcsinh(vmin))/(np.arcsinh(vmax)-np.arcsinh(vmin))
result = ma.arcsinh(result / midpoint) / ma.arcsinh(1. / midpoint)
if vtype == 'scalar':
result = result[0]
return result
def __call__(self, value, clip=None):
method = self.stretch
exponent = self.exponent
midpoint = self.midpoint
if clip is None:
clip = self.clip
if cbook.iterable(value):
vtype = 'array'
val = ma.asarray(value).astype(np.float)
else:
vtype = 'scalar'
val = ma.array([value]).astype(np.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 == vmax:
return 0.0 * val
else:
if clip:
mask = ma.getmask(val)
val = ma.array(np.clip(val.filled(vmax), vmin, vmax),
mask=mask)
result = (val - vmin) * (1.0 / (vmax - vmin))
negative = result < 0.
if self.stretch == 'linear':
pass
elif self.stretch == 'log':
result = ma.log10(result * (self.midpoint - 1.) + 1.) \
/ ma.log10(self.midpoint)
elif self.stretch == 'sqrt':
result = ma.sqrt(result)
elif self.stretch == 'arcsinh':
result = ma.arcsinh(result / self.midpoint) \
/ ma.arcsinh(1. / self.midpoint)
elif self.stretch == 'power':
result = ma.power(result, exponent)
else:
raise Exception("Unknown stretch in APLpyNormalize: %s" %
self.stretch)
result[negative] = -np.inf
if vtype == 'scalar':
result = result[0]
return result
def inverse(self, value):
if not self.scaled():
raise ValueError("Not invertible until scaled")
vmin, vmax = self.vmin, self.vmax
if cbook.iterable(value):
val = ma.asarray(value)
else:
val = value
if self.stretch == 'linear':
pass
elif self.stretch == 'log':
val = (ma.power(10., val * ma.log10(self.midpoint)) -
1.) / (self.midpoint - 1.)
elif self.stretch == 'sqrt':
val = val * val
elif self.stretch == 'arcsinh':
val = self.midpoint * \
ma.sinh(val * ma.arcsinh(1. / self.midpoint))
elif self.stretch == 'power':
val = ma.power(val, (1. / self.exponent))
else:
raise Exception("Unknown stretch in APLpyNormalize: %s" %
self.stretch)
return vmin + val * (vmax - vmin)
def inverse(self, value):
# ORIGINAL MATPLOTLIB CODE
if not self.scaled():
raise ValueError("Not invertible until scaled")
vmin, vmax = self.vmin, self.vmax
# CUSTOM APLPY CODE
if cbook.iterable(value):
val = ma.asarray(value)
else:
val = value
if self.stretch == 'Linear':
pass
elif self.stretch == 'Log':
val = (ma.power(10., val * ma.log10(self.midpoint)) -
1.) / (self.midpoint - 1.)
elif self.stretch == 'Sqrt':
val = val * val
elif self.stretch == 'Arcsinh':
val = self.midpoint * \
ma.sinh(val * ma.arcsinh(1. / self.midpoint))
elif self.stretch == 'Arccosh':
val = self.midpoint * \
ma.cosh(val * ma.arccosh(1. / self.midpoint))
elif self.stretch == 'Power':
val = ma.power(val, (1. / self.exponent))
elif self.stretch == 'Exp':
val = 1. / np.exp(val)
else:
raise Exception("Unknown stretch in APLpyNormalize: %s" %
self.stretch)
return vmin + val * (vmax - vmin)
def __call__(self,value, clip=None, midpoint=None):
if clip is None:
clip = self.clip
if cbook.iterable(value):
vtype = 'array'
val = ma.asarray(value).astype(np.float)
else:
vtype = 'scalar'
val = ma.array([value]).astype(np.float)
self.autoscale_None(val)
vmin, vmax = self.vmin, self.vmax
vmid = self.vmid if self.vmid is not None else (vmax+vmin)/2.0
if midpoint is None:
midpoint = (vmid - vmin) / (vmax - vmin)
if vmin > vmax:
raise ValueError("minvalue must be less than or equal to maxvalue")
elif vmin==vmax:
return 0.0 * val
else:
if clip:
mask = ma.getmask(val)
val = ma.array(np.clip(val.filled(vmax), vmin, vmax),
mask=mask)
result = (val-vmin) * (1.0/(vmax-vmin))
#result = (ma.arcsinh(val)-np.arcsinh(vmin))/(np.arcsinh(vmax)-np.arcsinh(vmin))
result = ma.arcsinh(result/midpoint) / ma.arcsinh(1./midpoint)
if vtype == 'scalar':
result = result[0]
return result
def inverse(self, value):
# ORIGINAL MATPLOTLIB CODE
if not self.scaled():
raise ValueError("Not invertible until scaled")
vmin, vmax = self.vmin, self.vmax
# CUSTOM APLPY CODE
if cbook.iterable(value):
val = ma.asarray(value)
else:
val = value
if self.stretch == 'linear':
pass
elif self.stretch == 'log':
val = (ma.power(10., val * ma.log10(self.midpoint)) - 1.) / (self.midpoint - 1.)
elif self.stretch == 'sqrt':
val = val * val
elif self.stretch == 'arcsinh':
val = self.midpoint * \
ma.sinh(val * ma.arcsinh(1. / self.midpoint))
elif self.stretch == 'square':
val = ma.power(val, (1. / 2))
elif self.stretch == 'power':
val = ma.power(val, (1. / self.exponent))
else:
raise Exception("Unknown stretch in APLpyNormalize: %s" %
self.stretch)
return vmin + val * (vmax - vmin)
def __call__(self, value, clip=None):
#read in parameters
method = self.stretch
exponent = self.exponent
midpoint = self.midpoint
# ORIGINAL MATPLOTLIB CODE
if clip is None:
clip = self.clip
if cbook.iterable(value):
vtype = 'array'
val = ma.asarray(value).astype(np.float)
else:
vtype = 'scalar'
val = ma.array([value]).astype(np.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 == vmax:
return 0.0 * val
else:
if clip:
mask = ma.getmask(val)
val = ma.array(np.clip(val.filled(vmax), vmin, vmax),
mask=mask)
result = (val - vmin) * (1.0 / (vmax - vmin))
# CUSTOM APLPY CODE
# Keep track of negative values
negative = result < 0.
if self.stretch == 'linear':
pass
elif self.stretch == 'log':
result = ma.log10(result * (self.midpoint - 1.) + 1.) \
/ ma.log10(self.midpoint)
elif self.stretch == 'sqrt':
result = ma.sqrt(result)
elif self.stretch == 'arcsinh':
result = ma.arcsinh(result / self.midpoint) \
/ ma.arcsinh(1. / self.midpoint)
elif self.stretch == 'power':
result = ma.power(result, exponent)
else:
raise Exception("Unknown stretch in APLpyNormalize: %s" %
self.stretch)
# Now set previously negative values to 0, as these are
# different from true NaN values in the FITS image
result[negative] = -np.inf
if vtype == 'scalar':
result = result[0]
return result
def __call__(self, value, clip=None):
#read in parameters
method = self.stretch
exponent = self.exponent
midpoint = self.midpoint
# ORIGINAL MATPLOTLIB CODE
if clip is None:
clip = self.clip
if cbook.iterable(value):
vtype = 'array'
val = ma.asarray(value).astype(np.float)
else:
vtype = 'scalar'
val = ma.array([value]).astype(np.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==vmax:
return 0.0 * val
else:
if clip:
mask = ma.getmask(val)
val = ma.array(np.clip(val.filled(vmax), vmin, vmax),
mask=mask)
result = (val-vmin) * (1.0/(vmax-vmin))
# CUSTOM APLPY CODE
# Keep track of negative values
negative = result < 0.
if self.stretch == 'linear':
pass
elif self.stretch == 'log':
result = ma.log10(result * (self.midpoint - 1.) + 1.) \
/ ma.log10(self.midpoint)
elif self.stretch == 'sqrt':
result = ma.sqrt(result)
elif self.stretch == 'arcsinh':
result = ma.arcsinh(result/self.midpoint) \
/ ma.arcsinh(1./self.midpoint)
elif self.stretch == 'power':
result = ma.power(result, exponent)
else:
raise Exception("Unknown stretch in APLpyNormalize: %s" %
self.stretch)
# Now set previously negative values to 0, as these are
# different from true NaN values in the FITS image
result[negative] = -np.inf
if vtype == 'scalar':
result = result[0]
return result
