[Numpy-discussion] [F2PY] Fortran call fails in IDLE / PyScripter
What version of gfortran are you using (i.e. exactly which binary did you download)? GNU Fortran (GCC) 4.4.0 20080603 (experimental) [trunk revision 136333] Is this a write to standard output write (*,*) szText ? yes, it is. I forgot to say that it also works with pydev in Eclipse but I'm looking for a simple interactive python shell that can execute some programs. IPython does the job but is less friendly than IDLE in term of program editing. Anyway, I think I will use it for now. Thanks for your reply. Regards, Christophe On Tue, Dec 2, 2008 at 08:26, Christophe Chappet [EMAIL PROTECTED] http://projects.scipy.org/mailman/listinfo/numpy-discussion wrote: / Hi all, // I compile the followinq code using f2py -c --fcompiler=gnu95 // --compiler=mingw32 -m hello // subroutine AfficheMessage(szText) // character szText*100 // write (*,*) szText // return // end // // Using python console : //import hello //hello.affichemessage( // Hello) // works fine ! // // I do the same in the program window of IDLE and : // - no message is displayed. // - the shell restart (or IDLE crah if launched with -n) // // Same problem with PyScripter IDE. (crash)./ ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] trouble subclassing ndarray
Hello, I'm trying to write a small library of differential geometry, and I have some trouble subclassing ndarray. I'd like an HomogeneousMatrix class that subclasse ndarray and overloads some methods, such as inv(). Here is my first try, the inv() function and the inv_v1() method work as expected, but the inv_v2() and inv_v3() methods do not change the object at all. Can somebody explain me what is happening here ? import numpy as np def inv(H): inverse of an homogeneous matrix R = H[0:3,0:3] p = H[0:3,3:4] return np.vstack( (np.hstack((R.T,-np.dot(R.T,p))), [0,0,0,1])) class HomogeneousMatrix(np.ndarray): def __new__(subtype, data=np.eye(4)): subarr = np.array(data) if htr.ishomogeneousmatrix(subarr): return subarr.view(subtype) else: raise ValueError def inv_v1(self): self[0:4,0:4] = htr.inv(self) def inv_v2(self): data = htr.inv(self) self = HomogeneousMatrix(data) def inv_v3(self): self = htr.inv(self) Thank you ! -- Sébastien ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] trouble subclassing ndarray
Le mercredi 03 décembre 2008, Sébastien Barthélemy a écrit : Hello, Hi Sebastien! I'm trying to write a small library of differential geometry, and I have some trouble subclassing ndarray. I'd like an HomogeneousMatrix class that subclasse ndarray and overloads some methods, such as inv(). Here is my first try, the inv() function and the inv_v1() method work as expected, but the inv_v2() and inv_v3() methods do not change the object at all. Can somebody explain me what is happening here ? import numpy as np def inv(H): inverse of an homogeneous matrix R = H[0:3,0:3] p = H[0:3,3:4] return np.vstack( (np.hstack((R.T,-np.dot(R.T,p))), [0,0,0,1])) class HomogeneousMatrix(np.ndarray): def __new__(subtype, data=np.eye(4)): subarr = np.array(data) if htr.ishomogeneousmatrix(subarr): return subarr.view(subtype) else: raise ValueError def inv_v1(self): self[0:4,0:4] = htr.inv(self) def inv_v2(self): data = htr.inv(self) self = HomogeneousMatrix(data) def inv_v3(self): self = htr.inv(self) There is something I missed: what is htr? I guess htr.inv is the inv function defined before the class. Another point: it seems weird to me that, in the class' methods inv_v2 and inv_v3, you 'unref' the previous instance of HomogeneousMatrix and link the 'self' label to a new instance... In inv_v1, you just modify the coefficient of the Homogeneous Matrix with the coefficient of htr.inv(self) -- Fabrice Silva [EMAIL PROTECTED] LMA UPR CNRS 7051 - équipe S2M ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] trouble subclassing ndarray
2008/12/3 Kevin Jacobs [EMAIL PROTECTED] [EMAIL PROTECTED]: On Wed, Dec 3, 2008 at 9:19 AM, Sébastien Barthélemy [EMAIL PROTECTED] wrote: def inv_v1(self): self[0:4,0:4] = htr.inv(self) def inv_v2(self): data = htr.inv(self) self = HomogeneousMatrix(data) def inv_v3(self): self = htr.inv(self) self is a reference, so you're just overwriting it with references to new values in v2 and v3. The original object is unchanged. Only v1 changes self. You may want to use self[:] = . okay, it seems obvious now. I definitely spent to much time with matlab. Thanks ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] trouble subclassing ndarray
2008/12/3 Fabrice Silva [EMAIL PROTECTED]: Le mercredi 03 décembre 2008, Sébastien Barthélemy a écrit : Hello, Hi Sebastien! Hello Fabrice There is something I missed: what is htr? I guess htr.inv is the inv function defined before the class. yes, I cut-n-pasted the function definition from the htr module and forgot to tell it, sorry Thank you ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] np.loadtxt : yet a new implementation...
Pierre GM wrote:
I think that treating an explicitly-passed-in ' ' delimiter as
identical to 'no delimiter' is a bad idea. If I say that ' ' is the
delimiter, or '\t' is the delimiter, this should be treated *just*
like ',' being the delimiter, where the expected output is:
['1', '2', '3', '4', '', '5']
Valid point.
Well, all, stay tuned for yet another yet another implementation...
Found a problem. If you read the names from the file and specify
usecols, you end up with the first N names read from the file as the
fields in your output (where N is the number of entries in usecols),
instead of having the names of the columns you asked for.
For instance:
from StringIO import StringIO
from genload_proposal import loadtxt
f = StringIO('stid stnm relh tair\nnrmn 121 45 9.1')
loadtxt(f, usecols=('stid', 'relh', 'tair'), names=True, dtype=None)
array(('nrmn', 45, 9.0996),
dtype=[('stid', '|S4'), ('stnm', 'i8'), ('relh', 'f8')])
What I want to come out is:
array(('nrmn', 45, 9.0996),
dtype=[('stid', '|S4'), ('relh', 'i8'), ('tair', 'f8')])
I've attached a version that fixes this by setting a flag internally if
the names are read from the file. If this flag is true, at the end the
names are filtered down to only the ones that are given in usecols.
I also have one other thought. Is there any way we can make this handle
object arrays, or rather, a field containing objects, specifically
datetime objects? Right now, this does not work because calling view
does not work for object arrays. I'm just looking for a simple way to
store date/time in my record array (currently a string field).
Ryan
--
Ryan May
Graduate Research Assistant
School of Meteorology
University of Oklahoma
Proposal :
Here's an extension to np.loadtxt, designed to take missing values into account.
import itertools
import numpy as np
import numpy.ma as ma
def _is_string_like(obj):
Check whether obj behaves like a string.
try:
obj + ''
except (TypeError, ValueError):
return False
return True
def _to_filehandle(fname, flag='r', return_opened=False):
Returns the filehandle corresponding to a string or a file.
If the string ends in '.gz', the file is automatically unzipped.
Parameters
--
fname : string, filehandle
Name of the file whose filehandle must be returned.
flag : string, optional
Flag indicating the status of the file ('r' for read, 'w' for write).
return_opened : boolean, optional
Whether to return the opening status of the file.
if _is_string_like(fname):
if fname.endswith('.gz'):
import gzip
fhd = gzip.open(fname, flag)
elif fname.endswith('.bz2'):
import bz2
fhd = bz2.BZ2File(fname)
else:
fhd = file(fname, flag)
opened = True
elif hasattr(fname, 'seek'):
fhd = fname
opened = False
else:
raise ValueError('fname must be a string or file handle')
if return_opened:
return fhd, opened
return fhd
def flatten_dtype(ndtype):
Unpack a structured data-type.
names = ndtype.names
if names is None:
return [ndtype]
else:
types = []
for field in names:
(typ, _) = ndtype.fields[field]
flat_dt = flatten_dtype(typ)
types.extend(flat_dt)
return types
def nested_masktype(datatype):
Construct the dtype of a mask for nested elements.
names = datatype.names
if names:
descr = []
for name in names:
(ndtype, _) = datatype.fields[name]
descr.append((name, nested_masktype(ndtype)))
return descr
# Is this some kind of composite a la (np.float,2)
elif datatype.subdtype:
mdescr = list(datatype.subdtype)
mdescr[0] = np.dtype(bool)
return tuple(mdescr)
else:
return np.bool
class LineSplitter:
Defines a function to split a string at a given delimiter or at given places.
Parameters
--
comment : {'#', string}
Character used to mark the beginning of a comment.
delimiter : var
def __init__(self, delimiter=None, comments='#'):
self.comments = comments
# Delimiter is a character
if delimiter is None:
self._isfixed = False
self.delimiter = None
elif _is_string_like(delimiter):
self._isfixed = False
self.delimiter = delimiter.strip() or None
# Delimiter is a list of field widths
elif hasattr(delimiter, '__iter__'):
self._isfixed = True
idx = np.cumsum([0]+list(delimiter))
self.slices = [slice(i,j) for (i,j) in zip(idx[:-1], idx[1:])]
# Delimiter is a single integer
elif int(delimiter):
self._isfixed = True
Re: [Numpy-discussion] np.loadtxt : yet a new implementation...
If I know my data is already clean and is handled nicely by the old loadtxt, will I be able to turn off and the special handling in order to retain the old load speed? Alan Isaac ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] np.loadtxt : yet a new implementation...
Pierre GM wrote: I can try, but in that case, please write me a unittest, so that I have a clear and unambiguous idea of what you expect. fair enough, though I'm not sure when I'll have time to do it. I do wonder if anyone else thinks it would be useful to have multiple delimiters as an option. I got the idea because with fromfile(), if you specify, say ',' as the delimiter, it won't use '\n', only a comma, so there is no way to quickly read a whole bunch of comma delimited data like: 1,2,3,4 5,6,7,8 so I'd like to be able to say to use either ',' or '\n' as the delimiter. However, if I understand loadtxt() correctly, it's handling the new lines separately anyway (to get a 2-d array), so this use case isn't an issue. So how likely is it that someone would have: 1 2 3, 4, 5 6 7 8, 8, 9 and want to read that into a single 2-d array? I'm not sure I've seen it. -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/ORR(206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception [EMAIL PROTECTED] ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] np.loadtxt : yet a new implementation...
On Dec 3, 2008, at 12:48 PM, Christopher Barker wrote:
Pierre GM wrote:
I can try, but in that case, please write me a unittest, so that I
have a clear and unambiguous idea of what you expect.
fair enough, though I'm not sure when I'll have time to do it.
Oh, don;t worry, nothing too fancy: give me a couple lines of input
data and a line with what you expect. Using Ryan's recent example:
f = StringIO('stid stnm relh tair\nnrmn 121 45 9.1')
test = loadtxt(f, usecols=('stid', 'relh', 'tair'), names=True,
dtype=None)
control=array(('nrmn', 45, 9.0996),
dtype=[('stid', '|S4'), ('relh', 'i8'),
('tair', 'f8')])
That's quite enough for a test.
I do wonder if anyone else thinks it would be useful to have multiple
delimiters as an option. I got the idea because with fromfile(), if
you
specify, say ',' as the delimiter, it won't use '\n', only a comma,
so
there is no way to quickly read a whole bunch of comma delimited
data like:
1,2,3,4
5,6,7,8
so I'd like to be able to say to use either ',' or '\n' as the
delimiter.
I'm not quite sure I follow you.
Do you want to delimiters, one for the field of a record (','), one
for the records (\n) ?
However, if I understand loadtxt() correctly, it's handling the new
lines separately anyway (to get a 2-d array), so this use case isn't
an
issue. So how likely is it that someone would have:
1 2 3, 4, 5
6 7 8, 8, 9
and want to read that into a single 2-d array?
With the current behaviour, you gonna have
[(1 2 3, 4, 5), (6 7 8, 8, 9)] if you use , as a delimiter,
[(1,2,3,,4,,5),(6,7,8,,8,,9)] if you use as a delimiter.
Mixing delimiter is doable, but I don't think it's that a good idea.
I'm in favor of sticking to one and only field delimiter, and the
default line spearator for record delimiter. In other terms, not
changing anythng.
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Re: [Numpy-discussion] np.loadtxt : yet a new implementation...
On Dec 3, 2008, at 12:32 PM, Alan G Isaac wrote: If I know my data is already clean and is handled nicely by the old loadtxt, will I be able to turn off and the special handling in order to retain the old load speed? Hopefully. I'm looking for the best way to do it. Do you have an example you could send me off-list so that I can play with timers ? Thx in advance. P. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] np.loadtxt : yet a new implementation...
by the way, should this work:
io.loadtxt('junk.dat', delimiter=' ')
for more than one space between numbers, like:
1 2 3 4 5
6 7 8 9 10
I get:
io.loadtxt('junk.dat', delimiter=' ')
Traceback (most recent call last):
File stdin, line 1, in module
File
/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages/numpy/lib/io.py,
line 403, in loadtxt
X.append(tuple([conv(val) for (conv, val) in zip(converters, vals)]))
ValueError: empty string for float()
with the current version.
io.loadtxt('junk.dat', delimiter=None)
array([[ 1., 2., 3., 4., 5.],
[ 6., 7., 8., 9., 10.]])
does work.
--
Christopher Barker, Ph.D.
Oceanographer
Emergency Response Division
NOAA/NOS/ORR(206) 526-6959 voice
7600 Sand Point Way NE (206) 526-6329 fax
Seattle, WA 98115 (206) 526-6317 main reception
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Re: [Numpy-discussion] np.loadtxt : yet a new implementation...
Pierre GM wrote:
Oh, don;t worry, nothing too fancy: give me a couple lines of input
data and a line with what you expect.
I just went and looked at the existing tests, and you're right, it's
very easy -- my first foray into the new nose tests -- very nice!
specify, say ',' as the delimiter, it won't use '\n', only a comma,
so
there is no way to quickly read a whole bunch of comma delimited
data like:
1,2,3,4
5,6,7,8
so I'd like to be able to say to use either ',' or '\n' as the
delimiter.
I'm not quite sure I follow you.
Do you want to delimiters, one for the field of a record (','), one
for the records (\n) ?
well, in the case of fromfile(), it doesn't do records -- it will only
give you a 1-d array, so I want it all as a flat array, and you can
re-size it yourself later. Clearly this is more work (and requires more
knowledge of your data) than using loadtxt, but sometimes I really want
FAST data reading of simple formats.
However, this isn't fromfile() we are talking about now, it's loadtxt()...
So how likely is it that someone would have:
1 2 3, 4, 5
6 7 8, 8, 9
and want to read that into a single 2-d array?
With the current behaviour, you gonna have
[(1 2 3, 4, 5), (6 7 8, 8, 9)] if you use , as a delimiter,
[(1,2,3,,4,,5),(6,7,8,,8,,9)] if you use as a delimiter.
right.
Mixing delimiter is doable, but I don't think it's that a good idea.
I can't come up with a use case at this point, so..
I'm in favor of sticking to one and only field delimiter, and the
default line spearator for record delimiter. In other terms, not
changing anything.
I agree -- sorry for the noise!
-Chris
--
Christopher Barker, Ph.D.
Oceanographer
Emergency Response Division
NOAA/NOS/ORR(206) 526-6959 voice
7600 Sand Point Way NE (206) 526-6329 fax
Seattle, WA 98115 (206) 526-6317 main reception
[EMAIL PROTECTED]
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Re: [Numpy-discussion] np.loadtxt : yet a new implementation...
Alan G Isaac wrote: If I know my data is already clean and is handled nicely by the old loadtxt, will I be able to turn off and the special handling in order to retain the old load speed? what I'd like to see is a version of loadtxt built on a slightly enhanced fromfile() -- that would be blazingly fast for the easy cases (simple tabular data of one dtype). I don't know if the special-casing should be automatic, or just have it be a separate function. Also, fromfile() needs some work, and it needs to be done in C, which is less fun, so who knows when it will get done. As I think about it, maybe what I really want is a simple version of loadtxt written in C: It would only handle one data type at a time. It would support simple comment lines. It would only support one delimiter (plus newline). It would create a 2-d array from normal, tabular data. You could specify: how many numbers you wanted, or how many rows, or read 'till EOF Actually, this is a lot like matlab's fscanf() someday -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/ORR(206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception [EMAIL PROTECTED] ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] np.loadtxt : yet a new implementation...
On Dec 3, 2008, at 1:00 PM, Christopher Barker wrote:
by the way, should this work:
io.loadtxt('junk.dat', delimiter=' ')
for more than one space between numbers, like:
1 2 3 4 5
6 7 8 9 10
On the version I'm working on, both delimiter='' and delimiter=None
(default) would give you the expected output. delimiter=' ' would
fail, delimiter=' ' would work.
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Re: [Numpy-discussion] np.loadtxt : yet a new implementation...
Alan G Isaac wrote: If I know my data is already clean and is handled nicely by the old loadtxt, will I be able to turn off and the special handling in order to retain the old load speed? Alan Isaac Hi all, that's going in the same direction I was thinking about. When I thought about an improved version of loadtxt, I wished it was fault tolerant without loosing too much performance. So my solution was much simpler than the very nice genloadtxt function -- and it works for me. My ansatz is to leave the existing loadtxt function unchanged. I only replaced the default converter calls by a fault tolerant converter class. I attached a patch against io.py in numpy 1.2.1 The nice thing is that it not only handles missing values, but for example also columns/fields with non-number characters. It just returns nan in these cases. This is of practical importance for many datafiles of astronomical catalogues, for example the Hipparcos catalogue data. Regarding the performance, it is a little bit slower than the original loadtxt, but not much: on my machine, 10x reading in a clean testfile with 3 columns and 2 rows I get the following results: original loadtxt: ~1.3s modified loadtxt: ~1.7s new genloadtxt : ~2.7s So you see, there is some loss of performance, but not as much as with the new converter class. I hope this solution is of interest ... Manuel 237a238,247 class _faultsaveconv(object): def __init__(self,conv): self._conv = conv def __call__(self, x): try: return self._conv(x) except: return np.nan 241c251 return lambda x: bool(int(x)) --- return _faultsaveconv(lambda x: bool(int(x))) 243c253 return lambda x: int(float(x)) --- return _faultsaveconv(lambda x: int(float(x))) 245c255 return float --- return _faultsaveconv(float) 247c257 return complex --- return _faultsaveconv(complex) 249c259 return str --- return _faultsaveconv(str) ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] np.loadtxt : yet a new implementation...
Pierre GM wrote:
On Dec 3, 2008, at 1:00 PM, Christopher Barker wrote:
for more than one space between numbers, like:
1 2 3 4 5
6 7 8 9 10
On the version I'm working on, both delimiter='' and delimiter=None
(default) would give you the expected output.
so empty string and None both mean any white space? also tabs, etc?
delimiter=' ' would fail,
s only exactly that delimiter.
Is that so things like '\t' will work right?
but what about:
4, 5, 34,123,
In that case, ',' is the delimiter, but whitespace is ignored.
or
4\t 5\t 34\t 123.
we're ignoring extra whitespace there, too, so I'm not sure why we
shouldn't ignore it in the ' ' case also.
delimiter=' ' would work.
but in my example, there were sometimes two spaces, sometimes three --
so I think it would fail, no?
1 2 3 4 5.split(' ')
['1', '2', '3', '4', ' 5']
actually, that would work, but four spaces wouldn't.
1 2 3 45.split(' ')
['1', '2', '3', '4', '', '5']
I guess the solution is to use delimiter=None in that case, and is does
make sense that you can't have ' ' mean one or more spaces, but \t
mean only one tab.
-Chris
--
Christopher Barker, Ph.D.
Oceanographer
Emergency Response Division
NOAA/NOS/ORR(206) 526-6959 voice
7600 Sand Point Way NE (206) 526-6329 fax
Seattle, WA 98115 (206) 526-6317 main reception
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Re: [Numpy-discussion] np.loadtxt : yet a new implementation...
Manuel Metz wrote: Alan G Isaac wrote: If I know my data is already clean and is handled nicely by the old loadtxt, will I be able to turn off and the special handling in order to retain the old load speed? Alan Isaac Hi all, that's going in the same direction I was thinking about. When I thought about an improved version of loadtxt, I wished it was fault tolerant without loosing too much performance. So my solution was much simpler than the very nice genloadtxt function -- and it works for me. My ansatz is to leave the existing loadtxt function unchanged. I only replaced the default converter calls by a fault tolerant converter class. I attached a patch against io.py in numpy 1.2.1 The nice thing is that it not only handles missing values, but for example also columns/fields with non-number characters. It just returns nan in these cases. This is of practical importance for many datafiles of astronomical catalogues, for example the Hipparcos catalogue data. Regarding the performance, it is a little bit slower than the original loadtxt, but not much: on my machine, 10x reading in a clean testfile with 3 columns and 2 rows I get the following results: original loadtxt: ~1.3s modified loadtxt: ~1.7s new genloadtxt : ~2.7s So you see, there is some loss of performance, but not as much as with the new converter class. I hope this solution is of interest ... Manuel Oops, wrong version of the diff file. Wanted to name the class _faulttolerantconv ... 237a238,247 class _faulttolerantconv(object): def __init__(self,conv): self._conv = conv def __call__(self, x): try: return self._conv(x) except: return np.nan 241c251 return lambda x: bool(int(x)) --- return _faulttolerantconv(lambda x: bool(int(x))) 243c253 return lambda x: int(float(x)) --- return _faulttolerantconv(lambda x: int(float(x))) 245c255 return float --- return _faulttolerantconv(float) 247c257 return complex --- return _faulttolerantconv(complex) ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] np.loadtxt : yet a new implementation...
Manuel, Looks nice, I gonna try to see how I can incorporate yours. Note that returning np.nan by default will not work w/ Python 2.6 if you want an int... ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Apply a function to an array elementwise
Hi I want to apply a function (myfunc which takes and returns a scalar) to each element in a multi-dimensioned array (data): I can do this: newdata = numpy.array([myfunc(d) for d in data.flat]).reshape(data.shape) But I'm wondering if there's a faster more numpy way. I've looked at the vectorize function but can't work it out. thanks Eleanor -- View this message in context: http://www.nabble.com/Apply-a-function-to-an-array-elementwise-tp20823768p20823768.html Sent from the Numpy-discussion mailing list archive at Nabble.com. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Apply a function to an array elementwise
Elfnor wrote: Hi I want to apply a function (myfunc which takes and returns a scalar) to each element in a multi-dimensioned array (data): I can do this: newdata = numpy.array([myfunc(d) for d in data.flat]).reshape(data.shape) But I'm wondering if there's a faster more numpy way. I've looked at the vectorize function but can't work it out. from numpy import vectorize new_func = vectorize(myfunc) newdata = new_func(data) Should work. -Travis ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Compiler options for mingw?
On Sun, Nov 30, 2008 at 3:02 PM, David Cournapeau [EMAIL PROTECTED] wrote: No at the moment, but you can easily decompress the .exe content to get the internal .exe (which are straight installers built by python setup.py setup.py bdist_wininst). It should be possible to force an architecture at install time using a command line option, but I don't have the time ATM to support this. I needed it to help me fixing a couple of bugs for old CPU, so it ended up being implemented in the nsis script for scipy now (I will add it to numpy installers too). So from now, any newly releases of both numpy and scipy installers could be overriden: installer-name.exe /arch native - default behavior installer-name.exe /arch nosse - Force installation wo sse, even if SSE-cpu is detected. It does not check that the option is valid, so you can end up requesting SSE3 installer on a SSE2 CPU. But well... David ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
