in and get
started working on 3.0?
just wondering,
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On 9/14/11 1:01 PM, Christopher Barker wrote:
numpy.ndarray.resize is a different method, and I'm pretty sure it
should be as fast or faster that np.empty + np.append.
My profile:
In [25]: %timeit f1 # numpy.resize()
1000 loops, best of 3: 163 ns per loop
In [26]: %timeit f2
)
return a
a1 = f1()
a2 = f2()
a3 = f3()
if a1.shape == a2.shape == a3.shape:
print they are all returning the same size array
else:
print Something is wrong!
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for a variety of types from a single definition.
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On 8/3/11 3:56 PM, Gökhan Sever wrote:
Back to the reality. After clearing the cache using Warren's suggestion:
In [1]: timeit -n1 -r1 a = np.fromfile('temp.npa', dtype=np.uint16)
1 loops, best of 1: 7.23 s per loop
yup -- that cache sure can be handy!
-Chris
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On 8/4/11 10:02 AM, Christopher Barker wrote:
On 8/4/11 8:53 AM, Jeff Whitaker wrote:
Kiko: I think the difference may be that when you read the data with
netcdf4-python, it tries to unpack the short integers to a float32
array.
Jeff, why is that? is it an netcdf4 convention? I always
indexing?). It
kind of breaks python duck typing (a sequence is a sequence), but it's
useful, too.
So when a list fails to do what you want, try a tuple.
-Chris
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)
(using ipython's timeit)
-Chris
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On 8/3/11 11:09 AM, Ian Stokes-Rees wrote:
On 8/3/11 12:50 PM, Christopher Barker wrote:
As a reference, reading that much data in from a raw file into a numpy
array takes 2.57 on my machine (a rather old Mac, but disks haven't
gotten much faster).
2.57 seconds? or minutes?
sorry -- seconds
?
-Chris
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not seeing cache effects, but maybe you are.
Anyway, we haven't heard from the OP -- I'm not sure what s/he thought
was slow.
-Chris
On Wed, Aug 3, 2011 at 10:50 AM, Christopher Barker
chris.bar...@noaa.gov mailto:chris.bar...@noaa.gov wrote:
On 8/3/11 9:30 AM, Kiko wrote:
I'm
=tfc_dtype)
In [36]: s
Out[36]:
array((32000L, 0.789131, 0.00805999, 3882.22),
dtype=[('nps', 'u8'), ('t', 'f8'), ('e', 'f8'), ('fom', 'f8')])
you were THIS close!
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installer -- if it was
build for the system python2.6, it should not get installed into 2.7.
Unless you did something to force that.
-Chris
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for saving scientific
data?
thank you,
Brian Blais
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Ian
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, of course.
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things to the
left strikes me as a plain old bad idea (and a pain to implement)
-Chris
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about that kind of use case.
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the original value back if you want to stop
ignoring it? Maybe that's not inherent to what an IGNORE means, but it
seems pretty key to me.
-Chris
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if you want to work with the data in C
or Fortran or to tune performance in python.
So as long as there is an API to query and control how things work, I
like that it's hidden from simple python code.
-Chris
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than-
if x:
so as to be unambiguous about what I'm testing for (and because if x ==
0, I don't want the test to fail), so I guess:
if arr[i] is np.NA:
would be perfectly analogous.
-Chris
-Mark
-Chris
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be able to mess with the strides,
etc. do some googling, and check out:
numpy.lib.stride_tricks
-Chris
for N=2, that already takes 0.5 seconds but i intend to use it
for N=3 and N=4 ...
thanks for your input,
q
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1 = masked because of one thing
2 = masked because of another
etc., etc.
This could be pretty powerful
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be that you can mask and unmask a value for
different operations, without losing the value.
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of a new dtype --
wouldn't we lose all sort of opportunities for the compiler and hardware
to optimize? I can only image an if statement with every single
computation. But maybe that isn't any more of a hit that a separate mask.
-Chris
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in
mind.
Isn't that what the PEP 3118 extended buffer protocol is supposed to be for?
Anyway, good stuff!
-Chris
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(but not a standard outside of numpy).
I doubt pickle will ever be your best bet.
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have a way to specify what is in the file.
speaking of hdf5, I see:
pyhdf5io 0.7 - Python module containing high-level hdf5 load and save
functions.
h5py 2.0.0 - Read and write HDF5 files from Python
There is also pytables, which uses HDF5 under the hood.
-Chris
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to take any of this on anyway.
-Chris
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-Discussion@scipy.org
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a_dtype.min_value
etc.
In this case, I have a uint16, so I can hard code it, but it would be
nice to be able to be write the code in a more generic fashion.
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(or placeholders, anyway), which I don't quite get.
oh well, I've found what I need, thanks.
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Robert Kern wrote:
On Fri, Jun 17, 2011 at 13:27, Christopher Barker chris.bar...@noaa.gov
wrote:
Actually, I'm a bit confused about dtypes from an OO design perspective
anyway. I note that the dtypes seem to have all (most?) of the methods
of ndarrays (or placeholders, anyway), which I
here. I wonder if it's just array size though --
won't cPickle time scale with array size? So it may not be size pe-se,
but rather how much computation you need for a given size array.
-Chris
[I've enclosed the OP's slightly altered code]
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Sturla Molden wrote:
On Windows this sucks, because there is no fork system call.
Darn -- I do need to support Windows.
Here we are
stuck with multiprocessing and pickle, even if we use shared memory.
What do you need to pickle if you're using shared memory?
-Chris
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.
Which by the way is what the shared memory arrays I and Gaël made will do,
but we still have the annoying pickle overhead.
Do you have a pointer to that code? It sounds handy.
-Chris
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-CHB
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specify the correct range, but wouldn't
it be preferable if the function provided a
warning when this case occurs ?
---
Re: [Numpy-discussion] np.histogram: upper range bin
Christopher Barker
Thu, 02 Jun 2011 09:19:16 -0700
Peter Butterworth wrote:
in np.histogram the top-most bin edge
need.
Thanks again,
Brandt
Message: 1
Date: Thu, 09 Jun 2011 13:11:40 -0700
From: Christopher Barker chris.bar...@noaa.gov
mailto:chris.bar...@noaa.gov
Subject: Re: [Numpy-discussion] Using multiprocessing (shared
memory
the only one that has brought up science
use, and that only barely (and mostly the simple cases). So do we really
need to have the same dtype useful for finance and particle physics?
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() for that? If so, then the above applies to
that as well.
I know I could look at your code to answer these questions, but I
thought this might help.
-Chris
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to increments of days?
of course, I've lost track of the difference between 'M' and 'M8'
(I've never liked the dtype code anyway -- I far prefer np.float64 to
'd', for instance)
Will there be a linspace for datetimes?
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performance issues that may not make that possible, but I still like it.
maybe two types:
datetime_calendar: for Calendar-type units (months, business days, ...)
datetime_continuous: for linear units (seconds, hours, ...)
or something like that?
-Chris
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that is happening, and the user needs to have a clear and ideally
easy way to define how it should happen.
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calendar, and what calendar you
are using, should be kept clearly distinct.
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to be more explicit:
In [14]: np.histogram(x, bins=np.linspace(0.5, 4.5, 5))
Out[14]: (array([1, 1, 1, 1]), array([ 0.5, 1.5, 2.5, 3.5, 4.5]))
HTH,
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to use, and a well specified way to define new ones.
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, and how at least one
well-respected group has addresses these issues.
http://www.earthsystemmodeling.org/esmf_releases/non_public/ESMF_5_1_0/ESMC_crefdoc/node6.html
(If that long URL breaks, I found that by googling: ESMF calendar date
time)
-Chris
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-- which could be represented in
hours, minutes, seconds, etc?
What I'm getting at is that the difference between calendars is in what
timedeltas mean, not what a unit in time is.
ISO8601 seems quite OK.
All that does is specify a string representation, no?
-Chris
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we come up with needs to
be clearly defined.
-Chris
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.)
I don't see how that is particularly useful, at least not any more
useful that nanprod, nandiv, etc, etc...
What am I missing?
-Chris
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, 2, 3] in A
Out[]: False
In []: [3, 2, 1] in A
Out[]: True
So, obviously the logic behind __contains__ is not so very
straightforward. Perhaps just a bug?
-Chris
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begin to figure out what's wrong.
Good luck,
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never noticed that before.
-Chris
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.
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:
In [20]: np.mgrid[-1:3.25:.25]
Out[20]:
array([-1. , -0.75, -0.5 , -0.25, 0. , 0.25, 0.5 , 0.75, 1. ,
1.25, 1.5 , 1.75, 2. , 2.25, 2.5 , 2.75, 3. ])
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which is klunkier that linspace.
I'd have used two different function names for the different mgrid
functionality, rather that than the complex kludge
-Chris
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Sorry to keep harping on this, but for history's sake, I was one of the
folks that got 'U' introduced in the first place. I was dealing with a
nightmare of unix, mac and dos test files, 'U' was a godsend.
On 4/5/11 4:51 PM, Matthew Brett wrote:
The difference between 'rt' and 'U' is (this is
-- there is a bit more bookeeping code to be written.
DARN -- I think I said my last note was the last on this topic!
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)
Out[110]: '1.235'
HTH, Chris
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to:
- will having an extra \r with Windows files hurt anything? -- probably not.
- Are there many mac-style text files out there anymore? not many.
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be slower than it should be (to be honest,
not profiled).
Is there a faster way?
Maybe numpy should have a ndarray.totuple() method.
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right, but that's better than barfing.
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, but
diverges from the behavior of python's operator.
I think consistency within numpy is more important that consistency with
python -- I expect differences like this from python (long integers,
different data types, etc)
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something to contribute.
-Chris
Do check out the developer zone, maybe search the the open tickets and
see if there are any bugs you want to tackle.
Maybe write some tests for untested code you find.
Good ideas as well, of course.
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to work with data laid out like this.
HTH,
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:
http://www.seapig.org/November2010Notes
-Chris
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being hired by GDIT to work with NOAA, so any
questions about salary, benefits, etc, etc should go to GDIT. However,
feel free to send me questions about our organization, working
conditions, more detail about the nature of the projects etc.
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function written for numpy to handle things like that.
Of course, this is easy to wrap
in a small function but I expect it to be slow when the input size is in
the Mb range.
Never expect -- do a simple solution, then see if it's too slow for
youre needs.
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0.8
0.55.51.50.5 1.5
It's not as robust, but if performance matters, fromfile() should be faster:
f.readline() # to skip the first line
arr = np.fromfile(f, sep=' ', dtype=np.float64).reshape((-1, 5))
(untested)
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On 3/17/11 2:57 PM, Mark Wiebe wrote:
Dtypes being mutable looks like a serious bug to me, it's violating the
definition of 'hashable' given here:
I can imagine other problems is would cause, as well -- is there any
reason that dtypes should be mutable?
-Chris
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, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False], dtype=bool)
that's a boolean array that can be used for indexing, operating with
where, etc.
HTH,
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casting to occur.
I think this is the kind of thing that would be great to have a warning
the first time you do it, but once you understand, the warnings would be
really, really annoying!
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, use Python.
And the fact that using all these in the same program is pretty easy
makes it all possible.
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, and I'm looking forward to seeing what's
next for carray!
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On 3/7/11 5:51 PM, Sturla Molden wrote:
Den 07.03.2011 18:28, skrev Christopher Barker:
1, 2, 3, 4
5, 6
7, 8, 9, 10, 11, 12
13, 14, 15
...
A ragged array, as implemented in C++, Java or C# is just an array of
arrays (or 'a pointer to an array of pointers').
Sure, but as a rule I don't
On 3/10/11 9:51 AM, Francesc Alted wrote:
A Thursday 10 March 2011 18:05:11 Christopher Barker escrigué:
NOTE: this looks like it could use a growable numpy array, much
like one I've written before -- maybe it's time to revive that
project and use it here, fixing some performance issues while
On 3/10/11 11:29 AM, Christopher Barker wrote:
By the way, it would be great to have a growable array that could be
efficiently used in Cython code -- so you could accumulate a bunch of
native C datatype data easily.
I just noticed that carray is written in Cython, so that part should be
easy
, then it shouldn't be changed, but I think it better reflects what
dtypes are all about.
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?
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On 3/7/11 9:33 AM, Francesc Alted wrote:
A Monday 07 March 2011 18:28:11 Christopher Barker escrigué:
I'm setting out to write some code to access and work with ragged
arrays stored in netcdf files. It dawned on me that ragged arrays
are not all that uncommon, so I'm wondering if any of you
(and hence NetCDF4) is that they cannot be compressed (but that
should be addressed in the future).
good to know.
Thanks to both you and Jeff, This has given me some things to ponder.
-Chris
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dependencies -- or at least have something you could
compile and run without a python interpreter running.
-Chris
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-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
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a number of rows as an optional parameter.
not a big deal, now that we have list comprehensions, but still it would
be nice, and it makes sense to put it into loadtxt() for sure.
-Chris
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]])
-Chris
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is meant for
geo-referenced data, but you can ignore the geo information and just get
an image if you want.
-Chris
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by a integer amount (i.e a factor of 2) in
each dimension, I have some Cython code that optimizes that. I'm happy
to send it along.
-Chris
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is whitespace,
which it was in your example. But if it does, it should be pretty fast.
-Chris
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this in 3D.
you'll have to see what visvis is expecting in terms of data types, etc.
HTH,
-Chris
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know 'till you profile. See the enclosed code and figures.
Interestingly both appear to be pretty linear, though the constant is
Much larger for numpy arrays.
-Chris
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I'll subscribe to
both lists anyway, and filter them to the same place in my email. So it
makes little difference to me.
-Chris
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, experimentally, appending numpy arrays (on
that one simple example) appeared to be O(N). Granted, a much larger
constant that for lists, but it sure looks linear to me.
Should it be O(N^2)? Maybe I need to run it for larger N , but I got
impatient as it is.
-Chris
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Christopher Barker, Ph.D
should do for numpy.
HTH,
-Chris
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it would
be too hard, though.
I've enclosed some test code, and my accumulator class, in case you find
it useful.
-Chris
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On 12/6/10 11:00 AM, Benjamin Root wrote:
numpy.lib.recfunctions has a method for easily adding new columns.
cool! There is a lot of other nifty- looking stuff in there too. The OP
should really take a look.
And maybe an appending function is in order, too.
-Chris
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Christopher Barker
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