I once wrote a generic n-dimensional binning routine in C that I could find if
anyone is interested in integrating it into numpy... it didn't do size
increases though... and I think I implemented it so that binning by a
non-divisible factor trimmed the extras. It was very-very fast though.
duplicate column in dtype?
I just consolidated some of the columns and the error went away... none had
duplicate field names... hence the question.
On Aug 1, 2011, at 11:18 PM, Pierre GM wrote:
On Aug 2, 2011, at 1:20 AM, Craig Yoshioka wrote:
Is there a limit to the number of fields
have been an invalid
name, or a different error on my part. Out of curiosity, what does recarray
consider an invalid field name?
On Aug 2, 2011, at 12:31 PM, Skipper Seabold wrote:
On Tue, Aug 2, 2011 at 3:19 PM, Craig Yoshioka crai...@me.com wrote:
duplicate column in dtype?
Duplicate
Is there a limit to the number of fields a numpy recarray can have? I was
getting a strange error about a duplicate column name, but it wasn't a
duplicate.
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I want to subclass ndarray to create a class for image and volume data, and
when referencing a file I'd like to have it load the data only when accessed.
That way the class can be used to quickly set and manipulate header values, and
won't load data unless necessary. What is the best way to
Wow, that makes for a great howto example. Thanks.
On Jul 16, 2011, at 7:50 AM, Martin Ling wrote:
Hi all,
I have just pushed a package to GitHub which adds a quaternion dtype to
NumPy: https://github.com/martinling/numpy_quaternion
Some backstory: on Wednesday I gave a talk at SciPy
() structure in your
workaround works...
- Sam
On 7/13/11 12:00 PM, numpy-discussion-requ...@scipy.org
numpy-discussion-requ...@scipy.org wrote:
Date: Tue, 12 Jul 2011 16:39:47 -0700
From: Craig Yoshioka crai...@me.com
Subject: [Numpy-discussion] named ndarray axes
To: NumPy-Discussion
I did take a look at it. It looked way heavier than I needed or wanted, plus
last time I looked it didn't support fancy indexing on axes... It does support
indexing on 'ticks' though.
There is a bit of wheel inventing going on, but I think that's OK, since things
should be well worked out
I brought up a while ago about how it would be nice if numpy arrays could have
their axes 'labeled'.= I got an implementation that works pretty well for
me and in the process learned quite a few things, and was hoping to foster some
more discussion on this topic, as I think I have found a
would anyone object to fixing the numpy mean and stdv functions, so that they
always used a 64-bit value to track sums, or so that they used a running
calculation. That way
np.mean(np.zeros([4000,4000],'f4')+500)
would not equal 511.493408?
`
On May 31, 2011, at 6:08 PM, Charles R Harris
yes, and its probably slower to boot. A quick benchmark on my computer shows
that:
a = np.zeros([4000,4000],'f4')+500
np.mean(a)
takes 0.02 secs
np.mean(a,dtype=np.float64)
takes 0.1 secs
np.mean(a.astype(np.float64))
takes 0.06 secs
so casting the whole array is almost 40% faster than
Thanks, I will. I was just seeing if there was any intention of adding this to
type of support to numpy directly. It would be faster, and I'm sure it would
make projects like dataarray much simpler to implement (dataarray does a lot
more than my suggestion).
On May 26, 2011, at 4:53 AM, Wes
Hi all,
I've read some discussions about adding labeled axes, and even ticks, to numpy
arrays (such as in Luis' dataarray).
I have recently found that the ability to label axes would be very helpful to
me, but I'd like to keep the implementation as lightweight as possible.
The reason I
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