2010/9/17 Mayank P Jain
> I thought about these options but what I need is excel like interface that
> displays the values for each cell and one can modify and save the files.
>
> This would be convenient way of saving large files in less space and at the
> same time, see them and would remove th
>
>
>
> In [1]: a = np.array([0, 2, 4, 1, 3, 0, 3, 4, 0, 1])
>
> In [2]: lim = 2
>
> In [3]: np.sign(a - lim)
> Out[3]: array([-1, 0, 1, -1, 1, -1, 1, 1, -1, -1])
>
Dooh. Facepalm. I should have thought of that myself! Only one intermediate
array needs to be created then. Thank you. That was
>
>
>
> If there are no NaNs, you only need to make 2 masks by using ones
> instead of empty. Not elegent but a little faster.
>
Good point! Thanks.
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Hi,
Is there an array-like function equivalent with the builtin method for the
Python single-valued comparison cmp(x,y)?
What I would like is a cmp(a, lim), where a is an ndarray and lim is a
single value, and then I need an array back of a's shape giving the
elementwise comparison
array([cmp(a[0
>
> On 9-Sep-09, at 4:48 AM, Francesc Alted wrote:
>
> > Yes, this later is supported in PyTables as long as the underlying
> > filesystem
> > supports files > 2 GB, which is very usual in modern operating
> > systems.
>
> I think the OP said he was on Win32, in which case it should be noted:
> FAT
I have had some resembling challenges in my work, and here appending the
nympy arrays to HDF5 files using PyTables has been the solution for me -
that used in combination with lzo compression/decompression has lead to very
high read/write performance in my application with low memory consumption.
Y
2009/7/27 Sebastian Haase :
> Is PyTables any option for you ?
>
> --
> Sebastian Haase
>
That may indeed be something for me! I had heard the name before but
I never realized exactly what it was. However, i have just seen their
first tutorial video, and it seems like a very, very useful package
a
>
> You could think about using some kind of virtualisation - this is
> exactly the sort of situation where I find it really useful.
>
> You can run a 64 bit host OS, then have 32 bit XP as a 'guest' in
> VMware or Virtualbox or some other virtualisation software. With
> recent CPU's there is very
>
> I think it would be quite complicated. One fundamental "limitation" of
> numpy is that it views a contiguous chunk of memory. You can't have one
> numpy array which is the union of two memory blocks with a hole in
> between, so if you slice every 1000 items, the underlying memory of the
> array
2009/7/24 David Cournapeau :
>
> Well, the questions has popped up a few times already, so I guess this
> is not so obvious :) 32 bits architecture fundamentally means that a
> pointer is 32 bits, so you can only address 2^32 different memory
> locations. The 2Gb instead of 4Gb is a consequence on
>>
>> I tried adding the /3GB switch to boot.ini as you suggested:
>> multi(0)disk(0)rdisk(0)partition(1)\WINDOWS="Microsoft Windows XP
>> Professional" /noexecute=optin /fastdetect /3GB
>> and rebooted the system.
>>
>> Unfortunately that did not change anything for me. I still hit a hard
>> deck
2009/7/24 Citi, Luca :
> Hello!
> I have access to both a 32bit and a 64bit linux machine.
>
> I had to change your code (appended) because I got an error about
> not being able to create a mmap larger than the file.
> Here are the results...
>
> On the 32bit machine:
>
> lc...@xps2:~$ python /tmp/
2009/7/23 Charles R Harris :
>> Maybe I am measuring memory usage wrong?
>
> Hmm, I don't know what you should be looking at in XP. Memmapped files are
> sort of like virtual memory and exist in the address space even if they
> aren't in physical memory. When you address an element that isn't in
>
2009/7/23 Charles R Harris :
>
>
>>
>> Is it due to the 32 bit OS I am using?
>
> It could be. IIRC, 32 bit windows gives user programs 2 GB of addressable
> memory, so your files need to fit in that space even if the data is on disk.
> You aren't using that much memory but you are close and it cou
OS. Win XP SP3, 32 bits
Python: 2.5.4
Numpy: 1.3.0
I have am having some major problems converting a 750 MB recarray into
a 850 MB recarray
To save RAM I would like to use a read-only and a writeable memap for
the two recarrays during the conversion.
So I do something like:
import os
from stat
Concerning the name setmember1d_nu, I personally find it quite verbose
and not the name I would expect as a non-insider coming to numpy and
not knowing all the names of the more special hidden-away functions
and not being a python-wiz either.
I think ain(a,b) would be the name I had expected as an
>2009/3/5 Robert Cimrman :
>
> Great! It's a nice use case for return_inverse=True in unique1d().
>
> I have fixed the formatting, but cannot remove the previous comment.
>
> r.
;-)
Thank you for fixing the formatting,
--Kim
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>2009/3/5 Robert Cimrman :
> I have added your implementation to
> http://projects.scipy.org/numpy/ticket/1036 - is it ok with you to add
> the function eventually into arraysetops.py, under the numpy (BSD) license?
>
> cheers,
> r.
>
Yes, that would be fine with me. In fact that would be an honor!
which was longer and included a loop. I do not know which is the most
efficient one, but I understand this one better.
-- Slaunger
2009/2/25 :
> On Wed, Feb 25, 2009 at 7:28 AM, Kim Hansen wrote:
>> Hi Numpy discussions
>> Quite often I find myself wanting to generate a boolean m
> I just looked under "set routines" in the help file. I really like the
> speed of the windows help file.
Is there a Numpy windows help file?
Cool!
But where is it? I can't find it in my numpy 1.2.1 installation?!?
I like the Python 2.5 Windows help file too and I agree it is a fast
and effic
Yes, this is exactly what I was after, only the function name did not
ring a bell (I still cannot associate it with something meaningful for
my use case). Thanks!
-- Slaunger
2009/2/25 :
> On Wed, Feb 25, 2009 at 7:28 AM, Kim Hansen wrote:
>> Hi Numpy discussions
>> Quite ofte
Hi Numpy discussions
Quite often I find myself wanting to generate a boolean mask for fancy
slicing of some array, where the mask itself is generated by checking
if its value has one of several relevant values (corresponding to
states)
So at the the element level thsi corresponds to checking if
ele
; as your starting point.
>
> If your code is meant only for yourself, I'd suggest to go with the
> inconvenient but working method approach. If other people will want to
> use your class in an array-like manner, you'd have to properly define
> all the functionality that
Hi numpy forum
I need to efficiently handle some large (300 MB) recordlike binary
files, where some data fields are less than a byte and thus cannot be
mapped in a record dtype immediately.
I would like to be able to access these derived arrays in a memory
efficient manner but I cannot figure out
Hi Numpy forum
Let me start out with a generic example:
In [3]: test_byte_str = "".join(["\x12\x03", "\x23\x05", "\x35\x08"])
In [4]: desc = dtype({'names' : ["HIGHlow", "HIGH + low"], 'formats': [uint8, ui
nt8]})
In [5]: r = rec.fromstring(test_byte_str, dtype=desc)
In [6]: r[0]
Out[6]: (18,
Dear numpy-discussion,
I am quite new to Python and numpy.
I am working on a Python application using Scipy, where I need to
unpack and pack quite large amounts of data (GBs) into data structures
and convert them into other data structures. Until now the program has
been running amazingly efficie
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