On 26 October 2010 21:02, Dewald Pieterse <dewald.pieterse@gmail.com> wrote:
I see my slicing was the problem, np.vstack((test[:1], test)) works perfectly.
Yes and no. np.newaxis (or "None" for short) is a very useful tool; you just stick it in an index expression and it adds an axis of length one there. If what you really wanted to do was pull out one plane of the array, then indexing with a number was the right thing to do. If you want to stack that plane back on the array, just add an axis of length one to it. S = A[1,...] A = np.vstack((S[np.newaxis,...],A)) As a side note, np.newaxis is actually just None, but I find the longer name much clearer, so I try to use it in my own code, and I always use it in examples I'm showing other people. I suppose a third option would be "import numpy.newaxis as na". Anne
On Wed, Oct 27, 2010 at 12:55 AM, <josef.pktd@gmail.com> wrote:
On Tue, Oct 26, 2010 at 8:15 PM, Dewald Pieterse <dewald.pieterse@gmail.com> wrote:
Starting with:
In [93]: test = numpy.array([[[1,1,1],[1,1,1]],[[2,2,2],[2,2,2]],[[3,3,3],[3,3,3]]])
In [94]: test Out[94]: array([[[1, 1, 1], [1, 1, 1]],
[[2, 2, 2], [2, 2, 2]],
[[3, 3, 3], [3, 3, 3]]])
Slicing the complete first row:
In [95]: firstrow = test[0,:,:]
In [96]: firstrow Out[96]: array([[1, 1, 1], [1, 1, 1]])
I want to stack firstrow onto test to end up with:
([[[1, 1, 1], [1, 1, 1]],
[[1, 1, 1], [1, 1, 1]],
[[2, 2, 2], [2, 2, 2]],
[[3, 3, 3], [3, 3, 3]]]
vstack wants the array dimensions to be the same, is this possible without doing 1 dimensional reshape, the actual data I want to do this on is some what larger.
numpy.vstack((firstrow,test))
--------------------------------------------------------------------------- ValueError Traceback (most recent call last)
/mnt/home/home/bmeagle/M/programme/analiseerverwerkteprent.py in <module>() ----> 1 2 3 4 5
/usr/lib64/python2.6/site-packages/numpy/core/shape_base.py in vstack(tup) 212 213 """ --> 214 return _nx.concatenate(map(atleast_2d,tup),0) 215 216 def hstack(tup):
ValueError: arrays must have same number of dimensions
What is the correct python way to do this?
keep the first dimension or add it back in
test = np.array([[[1,1,1],[1,1,1]],[[2,2,2],[2,2,2]],[[3,3,3],[3,3,3]]]) np.vstack((test[:1], test)) array([[[1, 1, 1], [1, 1, 1]],
[[1, 1, 1], [1, 1, 1]],
[[2, 2, 2], [2, 2, 2]],
[[3, 3, 3], [3, 3, 3]]])
np.vstack((test[0][None,...], test)) array([[[1, 1, 1], [1, 1, 1]],
[[1, 1, 1], [1, 1, 1]],
[[2, 2, 2], [2, 2, 2]],
[[3, 3, 3], [3, 3, 3]]])
np.vstack((test[0][None,:,:], test)) array([[[1, 1, 1], [1, 1, 1]],
[[1, 1, 1], [1, 1, 1]],
[[2, 2, 2], [2, 2, 2]],
[[3, 3, 3], [3, 3, 3]]])
I like expand_dims for arbitrary axis, e.g.
ax=1 np.concatenate((np.expand_dims(test[:,0,:],ax), test), ax) array([[[1, 1, 1], [1, 1, 1], [1, 1, 1]],
[[2, 2, 2], [2, 2, 2], [2, 2, 2]],
[[3, 3, 3], [3, 3, 3], [3, 3, 3]]])
Josef
-- Dewald Pieterse
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-- Dewald Pieterse
"A democracy is nothing more than mob rule, where fifty-one percent of the people take away the rights of the other forty-nine." ~ Thomas Jefferson
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