BF: Cast input to np.repeat into int32.

[arokem]

Only an issue on some systems. For example:
http://nipy.bic.berkeley.edu/builders/dipy-py2.7-wheezy-sparc/builds/225/steps/shell_9/logs/stdio

[arokem]

This looks like it could potentially be a bug in numpy. Thoughts from anyone?

[arokem]

Any thoughts about this one? @matthew-brett - does this look like a numpy bug to you? I believe it's the only remaining error on the bots at the moment

[matthew-brett]

Yes, it does look like a bug in numpy, should be so for all 32-bit systems (I can replicate on 32-bit Linux) - see bug referenced above.

What about initializing points_per_line as 32-bit instead?

[matthew-brett]

Sorry - better might be to do:

points_per_line = np.zeros([nb_lines], int)  # use native index type to avoid casting errors later

[seberg]

Yeah. You can use int (or np.int_), but if you want to support 64bits on 64bit platforms (no idea if it matters for this one), you should make it np.intp. np.intp is the correct type for anything related to array sizes.

EDIT: intp maps to ssize_t/Py_SSize_t

[matthew-brett]

Are int and intp different sizes on some platforms? I'd got used to them being the same, but maybe that isn't always true.

[seberg]

No, int (the python one or np.int_) will map to long and on 64 bit windows long is unfortunatly 32bits.

[arokem]

OK - using np.intp now.

[arokem]

Running try_branch here: http://nipy.bic.berkeley.edu/builders/dipy-py2.6-32/builds/653

[matthew-brett]

Could do with a comment here explaining why you're using intp.

[matthew-brett]

I mean why you are using intp instead of np.int64.

[arokem]

Yup. Linking back here in the comment.

On Sun, Nov 29, 2015 at 11:50 AM, Matthew Brett notifications@github.com
wrote:

In dipy/viz/utils.py
#789 (comment):

@@ -159,7 +159,7 @@ def lines_to_vtk_polydata(lines, colors=None):

 # Get lines_array in vtk input format
 lines_array = []

• points_per_line = np.zeros([nb_lines], np.int64)
• points_per_line = np.zeros([nb_lines], np.intp)

I mean why you are using intp instead of np.int64.

—
Reply to this email directly or view it on GitHub
https://github.com/nipy/dipy/pull/789/files#r46096969.

[arokem]

That seems to do the trick: http://nipy.bic.berkeley.edu/builders/dipy-py2.6-32/builds/654