def calc_zscale(self,
data,
contrast=0.25,
num_points=1000,
num_per_row=None):
# NOTE: num_per_row is ignored in this implementation
assert len(data.shape) >= 2, \
AutoCutsError("input data should be 2D or greater")
ht, wd = data.shape[:2]
# calculate contrast parameter, if omitted
if contrast == None:
contrast = 0.25
assert (0.0 < contrast <= 1.0), \
AutoCutsError("contrast (%.2f) not in range 0 < c <= 1" % (
contrast))
# remove NaN and Inf from samples
samples = data[numpy.isfinite(data)].flatten()
samples = samples[:num_points]
loval, hival = zscale.zscale_samples(samples, contrast=contrast)
return loval, hival
def calc_zscale(self, data, contrast=0.25, num_points=1000):
# NOTE: num_per_row is ignored in this implementation
assert len(data.shape) >= 2, \
AutoCutsError("input data should be 2D or greater")
ht, wd = data.shape[:2]
# sanity check on contrast parameter
assert (0.0 < contrast <= 1.0), \
AutoCutsError("contrast (%.2f) not in range 0 < c <= 1" % (
contrast))
# remove NaN and Inf from samples
samples = data[numpy.isfinite(data)].flatten()
samples = samples[:num_points]
loval, hival = zscale.zscale_samples(samples, contrast=contrast)
return loval, hival
def calc_zscale(self, data, contrast=0.25, num_points=1000):
# NOTE: num_per_row is ignored in this implementation
assert len(data.shape) >= 2, \
AutoCutsError("input data should be 2D or greater")
ht, wd = data.shape[:2]
# sanity check on contrast parameter
assert (0.0 < contrast <= 1.0), \
AutoCutsError("contrast (%.2f) not in range 0 < c <= 1" % (
contrast))
# remove masked elements, they cause problems
data = data[np.logical_not(np.ma.getmaskarray(data))]
# remove NaN and Inf from samples
samples = data[np.isfinite(data)].flatten()
samples = samples[:num_points]
loval, hival = zscale.zscale_samples(samples, contrast=contrast)
return loval, hival