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import numba
import numpy as np
rescale_functions = {}
def rescale_clip_source(xx, scale, offset, vmin, vmax, yy):
for i in range(xx.size):
val = (xx[i] - offset) * scale
yy[i] = min(max(val, vmin), vmax)
def rescaleData(data, scale, offset, dtype, clip):
data_out = np.empty_like(data, dtype=dtype)
key = (data.dtype.name, data_out.dtype.name)
func = rescale_functions.get(key)
if func is None:
func = numba.guvectorize(
[f'{key[0]}[:],f8,f8,f8,f8,{key[1]}[:]'],
'(n),(),(),(),()->(n)',
nopython=True)(rescale_clip_source)
rescale_functions[key] = func
func(data, scale, offset, clip[0], clip[1], out=data_out)
return data_out
@numba.jit(nopython=True)
def rescale_and_lookup(data, scale, offset, lut):
# data should be floating point and 2d
# lut is 1d
vmin, vmax = 0, lut.shape[0] - 1
out = np.empty_like(data, dtype=lut.dtype)
for (x, y) in np.nditer((data, out)):
val = (x - offset) * scale
val = min(max(val, vmin), vmax)
y[...] = lut[int(val)]
return out
@numba.jit(nopython=True)
def rescale_and_clip(data, scale, offset, vmin, vmax):
# vmin and vmax <= 255
out = np.empty_like(data, dtype=np.uint8)
for (x, y) in np.nditer((data, out)):
val = (x - offset) * scale
val = min(max(val, vmin), vmax)
y[...] = val
return out
@numba.jit(nopython=True)
def numba_take(lut, data):
# numba supports only the 1st two arguments of np.take
return np.take(lut, data)
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