Hello! I type in sage notebook 5.8 on my local computer:
import numpy as np
np.mgrid?
....
example:
>>> np.mgrid[-1:1:5j]
array([-1. , -0.5, 0. , 0.5, 1. ])
But when I type in my notebook:
np.mgrid[-1:1:5j]
and press shift-enter, I get mistake: TypeError: Unable to convert -0.200000000000000*I to float; use abs() or real_part() as desired
It seems to me it's the bug...
Sage is interpreting this as complex for some reason. But, np.mgrid[-1:1:0.15] seems to be working for me.
You can bypass the Sage preparser by appending r (for raw) to numerical input.
sage: import numpy as np
sage: np.mgrid[-1:1:5jr]
array([-1. , -0.5, 0. , 0.5, 1. ])
Note that some preparsing is still going on with -1 and 1 which are converted to Sage integers by the preparser before going to numpy's mgrid. If you want to feed numpy's mgrid with raw Python integers:
sage: np.mgrid[-1r:1r:5jr]
array([-1. , -0.5, 0. , 0.5, 1. ])
It's the same result, except less conversion was done on the way.
On a related note, you can get a Python integer by doing
sage: int(1)
but when you do this, the preparsing stage means what is really done is
int(Integer(1))
so the Python integer 1 is converted to a Sage integer and back to a Python integer.
Bypassing the Sage preparser with
sage: 1r
gets you the Python integer straight away.
Asked: 2013-05-03 04:39:59 +0100
Seen: 1,650 times
Last updated: Jun 27 '19
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That's the preparser. Well, in Python typically `1j**2` becomes `(-1+0j)`; `j` is a complex number ala electrical engineering. What surprises me is that numpy would use j for some other purpose.
Indeed, in the documentation for `mgrid`: "However, if the step length is a **complex number** (e.g. 5j)," so basically this is a numpy hack, in my opinion.