On 11/2/07, Eric Firing <[EMAIL PROTECTED]> wrote:
> Now I am not so sure that the use of lists in errorbar is a fossil, but
> I certainly don't understand it. Would you give a summary of when one
> can and cannot use arrays in axes.py, please? The errorbar and bar
> methods seem to be the only victims of this restriction, and it looks
> like some of the instances are accomplishing nothing--the arguments get
> converted to arrays with the next method call anyway. I haven't tried
> to trace things carefully, obviously.
I just wrote some related stuff in the other thread, but will jump in
here. I think I may be being overzealous in my avoidance of arrays. What
we cannot assume is that asarray is creating an array of floats, so we
cannot do scalar array operations, eg 2*x. But we should be able to
assume object arrays, with indexing, and element wise opertations
which are well defined, eg for the canonical date example.
In [3]: import datetime
In [4]: date0 = datetime.date(2004,1,1)
In [5]: days = datetime.timedelta(days=1)
In [6]: d = [date0, date0+days, date0+2*days, date0+3*days]
In [7]: import numpy as n
In [8]: x1 = n.array(d)
In [9]: xerr = n.array([days]*len(x1))
In [10]: x1.dtype
Out[10]: dtype('object')
In [11]: x2.dtype
------------------------------------------------------------
Traceback (most recent call last):
File "<ipython console>", line 1, in ?
NameError: name 'x2' is not defined
In [12]: xerr.dtype
Out[12]: dtype('object')
In [13]: x1 + xerr
Out[13]: array([2004-01-02, 2004-01-03, 2004-01-04, 2004-01-05], dtype=object)
The reason we are bumping into so may problems with errorbar is not
only because it is complex, but because it is doing more arithmetic
than other plotting code.
Ted, can you clarify what kinds of operations are permitted with your
iterable unit objects if they are initialized into numpy object
arrays?
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