不知道这是否是重复项，但我没有找到它。
我想做些简单的话，以至于我无法弄清楚。
我有一维的测试阵列：
A = np.array((1, 2, 3, 4, 5, 6, 7, 8, 9, 10))
我想要的是一个函数，将其称为“ rebin”，它将执行以下操作：
A = rebin(A, 4)
其中4是要输出的一维数组的长度。我想得到的输出如下：
print(A) >>> [val1, val2, val3, val4]
其中valn是新n个bin上的新值，该值由原始数组上的某些插值确定。
我有此代码，改编自IDL：
def rebin(a, newdims, method='linear', centre=False, minusone=False):
'''Arbitrary resampling of source array to new dimension sizes.
Currently only supports maintaining the same number of dimensions.
To use 1-D arrays, first promote them to shape (x,1).
Uses the same parameters and creates the same co-ordinate lookup points
as IDL''s congrid routine, which apparently originally came from a VAX/VMS
routine of the same name.
method:
neighbour - closest value from original data
nearest and linear - uses n x 1-D interpolations using
scipy.interpolate.interp1d
(see Numerical Recipes for validity of use of n 1-D interpolations)
spline - uses ndimage.map_coordinates
centre:
True - interpolation points are at the centres of the bins
False - points are at the front edge of the bin
minusone:
For example- inarray.shape = (i,j) & new dimensions = (x,y)
False - inarray is resampled by factors of (i/x) * (j/y)
True - inarray is resampled by(i-1)/(x-1) * (j-1)/(y-1)
This prevents extrapolation one element beyond bounds of input array.
'''
if not a.dtype in [n.float64, n.float32]:
a = n.cast[float](a)
m1 = n.cast[int](minusone)
ofs = n.cast[int](centre) * 0.5
old = n.array( a.shape )
ndims = len( a.shape )
if len( newdims ) != ndims:
print "[congrid] dimensions error. " \
"This routine currently only support " \
"rebinning to the same number of dimensions."
return None
newdims = n.asarray( newdims, dtype=float )
dimlist = []
if method == 'neighbour':
for i in range( ndims ):
base = n.indices(newdims)[i]
dimlist.append( (old[i] - m1) / (newdims[i] - m1) \
* (base + ofs) - ofs )
cd = n.array( dimlist ).round().astype(int)
newa = a[list( cd )]
return newa
elif method in ['nearest','linear']:
# calculate new dims
for i in range( ndims ):
base = n.arange( newdims[i] )
dimlist.append( (old[i] - m1) / (newdims[i] - m1) \
* (base + ofs) - ofs )
# specify old dims
olddims = [n.arange(i, dtype = n.float) for i in list( a.shape )]
# first interpolation - for ndims = any
mint = scipy.interpolate.interp1d( olddims[-1], a, kind=method )
newa = mint( dimlist[-1] )
trorder = [ndims - 1] + range( ndims - 1 )
for i in range( ndims - 2, -1, -1 ):
newa = newa.transpose( trorder )
mint = scipy.interpolate.interp1d( olddims[i], newa, kind=method )
newa = mint( dimlist[i] )
if ndims > 1:
# need one more transpose to return to original dimensions
newa = newa.transpose( trorder )
return newa
elif method in ['spline']:
oslices = [ slice(0,j) for j in old ]
oldcoords = n.ogrid[oslices]
nslices = [ slice(0,j) for j in list(newdims) ]
newcoords = n.mgrid[nslices]
newcoords_dims = range(n.rank(newcoords))
#make first index last
newcoords_dims.append(newcoords_dims.pop(0))
newcoords_tr = newcoords.transpose(newcoords_dims)
# makes a view that affects newcoords
newcoords_tr += ofs
deltas = (n.asarray(old) - m1) / (newdims - m1)
newcoords_tr *= deltas
newcoords_tr -= ofs
newa = scipy.ndimage.map_coordinates(a, newcoords)
return newa
else:
print "Congrid error: Unrecognized interpolation type.\n", \
"Currently only \'neighbour\', \'nearest\',\'linear\',", \
"and \'spline\' are supported."
return None
根据他们自己的建议，我将所有数组重塑为（n，1），为它们分别指定了两个维度。但是，在上述方法中，olddims[-1]的长度为1（归因于形状中给出的1）。尝试scipy.interpolate.interp1d( olddims[-1], a, kind=method )时，这会从SciPy产生以下错误：
File "/usr/local/lib/python3.7/site-packages/scipy/interpolate/interpolate.py", line 543, in __init__
"least %d entries" % minval)
ValueError: x and y arrays must have at least 2 entries
我真的以为这会比这更琐碎，而且我可能不必要地使它复杂化了。有什么建议吗？