converting standard array to record array
Hello all, So i'm using numpy 1.6.0, and trying to convert a (4,4) numpy array of dtype 'f8' into a record array of this dtype: dt = np.dtype([('mat','(4,4)f8')])
Here is the code snippet: In [21]: a = np.random.randn(4,4)
In [22]: a.view(dt)
and the resulting error: ValueError: new type not compatible with array.
Can anyone shed some light for me on why this conversion is not possible? It is certainly technically possible, since the memory layout of the two arrays should be the same. Can anyone recommend a better way to do this conversion? Thanks in advance! -Bryce Ready
On Tue, Aug 30, 2011 at 4:34 PM, Bryce Ready <bryce.ready@gmail.com> wrote:
Hello all,
So i'm using numpy 1.6.0, and trying to convert a (4,4) numpy array of dtype 'f8' into a record array of this dtype:
dt = np.dtype([('mat','(4,4)f8')])
Here is the code snippet:
In [21]: a = np.random.randn(4,4)
In [22]: a.view(dt)
and the resulting error:
ValueError: new type not compatible with array.
Can anyone shed some light for me on why this conversion is not possible? It is certainly technically possible, since the memory layout of the two arrays should be the same.
Can anyone recommend a better way to do this conversion?
I guess it can only convert rows, each row needs the memory size of the dt
np.random.randn(4,4).ravel().view(dt).shape (1,) np.random.randn(2,4,4).reshape(-1,16).view(dt) array([[ ([[1.7107996212005496, 0.64334162481360346, -2.1589367225479004, 1.2302260107072134], [0.90703092017458831, -1.0297890301610224, -0.095086304368665275, 0.35407366904038495], [-1.1083969421298907, 0.83307347286837752, 0.39886399402076494, 0.26313136034262563], [0.81860729029038914, -1.1443047382313905, 0.73326737255810859, 0.34482475392499168]],)], [ ([[0.69027418489768777, 0.25867753263599164, 1.0320990807184023, 0.21836691513066409], [0.45913017094388614, -0.89570247025515981, 0.76452726059163534, -2.2953009964941642], [0.60248580944596275, 1.0863090037733505, -0.10849220482850662, -0.19176089514256078], [-1.0700600508627109, -1.4743316703511105, 0.79193567523155062, 0.82243321942810521]],)]], dtype=[('mat', '<f8', (4, 4))])
Josef
Thanks in advance!
-Bryce Ready
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Ah, I see. So basically I need to prepend dimensions myself to make this work - makes sense. Might be nice if view could do that automatically, but this is workable. Thanks Josef! On Tue, Aug 30, 2011 at 9:49 PM, <josef.pktd@gmail.com> wrote:
Hello all,
So i'm using numpy 1.6.0, and trying to convert a (4,4) numpy array of
On Tue, Aug 30, 2011 at 4:34 PM, Bryce Ready <bryce.ready@gmail.com> wrote: dtype
'f8' into a record array of this dtype:
dt = np.dtype([('mat','(4,4)f8')])
Here is the code snippet:
In [21]: a = np.random.randn(4,4)
In [22]: a.view(dt)
and the resulting error:
ValueError: new type not compatible with array.
Can anyone shed some light for me on why this conversion is not possible? It is certainly technically possible, since the memory layout of the two arrays should be the same.
Can anyone recommend a better way to do this conversion?
I guess it can only convert rows, each row needs the memory size of the dt
np.random.randn(4,4).ravel().view(dt).shape (1,) np.random.randn(2,4,4).reshape(-1,16).view(dt) array([[ ([[1.7107996212005496, 0.64334162481360346, -2.1589367225479004, 1.2302260107072134], [0.90703092017458831, -1.0297890301610224, -0.095086304368665275, 0.35407366904038495], [-1.1083969421298907, 0.83307347286837752, 0.39886399402076494, 0.26313136034262563], [0.81860729029038914, -1.1443047382313905, 0.73326737255810859, 0.34482475392499168]],)], [ ([[0.69027418489768777, 0.25867753263599164, 1.0320990807184023, 0.21836691513066409], [0.45913017094388614, -0.89570247025515981, 0.76452726059163534, -2.2953009964941642], [0.60248580944596275, 1.0863090037733505, -0.10849220482850662, -0.19176089514256078], [-1.0700600508627109, -1.4743316703511105, 0.79193567523155062, 0.82243321942810521]],)]], dtype=[('mat', '<f8', (4, 4))])
Josef
Thanks in advance!
-Bryce Ready
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
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