def _rescale_imshow_rgb(darray, vmin, vmax, robust):
assert robust or vmin is not None or vmax is not None
# There's a cyclic dependency via DataArray, so we can't import from
# xarray.ufuncs in global scope.
from xarray.ufuncs import maximum, minimum
# Calculate vmin and vmax automatically for `robust=True`
if robust:
if vmax is None:
vmax = np.nanpercentile(darray, 100 - ROBUST_PERCENTILE)
if vmin is None:
vmin = np.nanpercentile(darray, ROBUST_PERCENTILE)
# If not robust and one bound is None, calculate the default other bound
# and check that an interval between them exists.
elif vmax is None:
vmax = 255 if np.issubdtype(darray.dtype, np.integer) else 1
if vmax < vmin:
raise ValueError(
'vmin=%r is less than the default vmax (%r) - you must supply '
'a vmax > vmin in this case.' % (vmin, vmax))
elif vmin is None:
vmin = 0
if vmin > vmax:
raise ValueError(
'vmax=%r is less than the default vmin (0) - you must supply '
'a vmin < vmax in this case.' % vmax)
# Scale interval [vmin .. vmax] to [0 .. 1], with darray as 64-bit float
# to avoid precision loss, integer over/underflow, etc with extreme inputs.
# After scaling, downcast to 32-bit float. This substantially reduces
# memory usage after we hand `darray` off to matplotlib.
darray = ((darray.astype('f8') - vmin) / (vmax - vmin)).astype('f4')
return minimum(maximum(darray, 0), 1)
def _rescale_imshow_rgb(darray, vmin, vmax, robust):
assert robust or vmin is not None or vmax is not None
# There's a cyclic dependency via DataArray, so we can't import from
# xarray.ufuncs in global scope.
from xarray.ufuncs import maximum, minimum
# Calculate vmin and vmax automatically for `robust=True`
if robust:
if vmax is None:
vmax = np.nanpercentile(darray, 100 - ROBUST_PERCENTILE)
if vmin is None:
vmin = np.nanpercentile(darray, ROBUST_PERCENTILE)
# If not robust and one bound is None, calculate the default other bound
# and check that an interval between them exists.
elif vmax is None:
vmax = 255 if np.issubdtype(darray.dtype, np.integer) else 1
if vmax < vmin:
raise ValueError(
'vmin=%r is less than the default vmax (%r) - you must supply '
'a vmax > vmin in this case.' % (vmin, vmax))
elif vmin is None:
vmin = 0
if vmin > vmax:
raise ValueError(
'vmax=%r is less than the default vmin (0) - you must supply '
'a vmin < vmax in this case.' % vmax)
# Scale interval [vmin .. vmax] to [0 .. 1], with darray as 64-bit float
# to avoid precision loss, integer over/underflow, etc with extreme inputs.
# After scaling, downcast to 32-bit float. This substantially reduces
# memory usage after we hand `darray` off to matplotlib.
darray = ((darray.astype('f8') - vmin) / (vmax - vmin)).astype('f4')
return minimum(maximum(darray, 0), 1)
