Re: [Numpy-discussion] Building Windows binaries on OS X
On Tue, Feb 9, 2010 at 9:54 AM, David Cournapeau courn...@gmail.com wrote: On Mon, Feb 8, 2010 at 9:14 PM, Ralf Gommers ralf.gomm...@googlemail.com wrote: Hi David and all, I have a few questions on setting up the build environment on OS X for Windows binaries. I have Wine installed with Python 2.5 and 2.6, MakeNsis and MinGW. The first question is what is meant in the Paver script by cpuid plugin. Wine seems to know what to do with a cpuid instruction, but I can not find a plugin. Searching for cpuid plugin turns up nothing except the NumPy pavement.py file. What is this? That's a small NSIS plugin to detect at install time the exact capabilities of the CPU (SSE2, SSE3, etc...). The sources are found in tools/win32build/cpucaps, and should be built with mingw (Visual Studio is not supported, it uses gcc-specific inline assembly). You then copy the dll into the plugin directory of nsis. Yep got it. There's quite some stuff hidden in tools/ and vendor/ that I never noticed before. Final question is about Atlas and friends. Is 3.8.3 the best version to install? Does it compile out of the box under Wine? Is this page http://www.scipy.org/Installing_SciPy/Windows still up-to-date with regard to the Lapack/Atlas info and does it apply for Wine? Atlas 3.9.x should not be used, it is too unstable IMO (it is a dev version after all, and windows receives little testing compared to unix). I will put the Atlas binaries I am using somewhere That would be *great*. Thanks, Ralf ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Utility function to find array items are in ascending order
Hi, Is there any utility function to find if values in the array are in ascending or descending order. Example: arr = [1, 2, 4, 6] should return true arr2 = [1, 0, 2, -2] should return false Thanks Vishal ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Utility function to find array items are in ascending order
On Tue, Feb 9, 2010 at 7:42 AM, Vishal Rana ranavis...@gmail.com wrote: Hi, Is there any utility function to find if values in the array are in ascending or descending order. Example: arr = [1, 2, 4, 6] should return true arr2 = [1, 0, 2, -2] should return false Thanks Vishal I don't know if it is fast but np.diff should do the trick. You can check if all values are less than or equal to zero. Or if all are greater. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Utility function to find array items are in ascending order
On Tue, Feb 9, 2010 at 7:42 AM, Vishal Rana ranavis...@gmail.com wrote: Hi, Is there any utility function to find if values in the array are in ascending or descending order. Example: arr = [1, 2, 4, 6] should return true arr2 = [1, 0, 2, -2] should return false Thanks Vishal ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion i dont know if there's a utility function, but i'd use: np.all(a[1:] = a[:-1]) ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Utility function to find array items are in ascending order
On Tue, Feb 9, 2010 at 7:51 AM, Brent Pedersen bpede...@gmail.com wrote: On Tue, Feb 9, 2010 at 7:42 AM, Vishal Rana ranavis...@gmail.com wrote: Hi, Is there any utility function to find if values in the array are in ascending or descending order. Example: arr = [1, 2, 4, 6] should return true arr2 = [1, 0, 2, -2] should return false Thanks Vishal ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion i dont know if there's a utility function, but i'd use: np.all(a[1:] = a[:-1]) Yes, that's much better than np.diff. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Utility function to find array items are in ascending order
Thanks On Tue, Feb 9, 2010 at 7:51 AM, Brent Pedersen bpede...@gmail.com wrote: On Tue, Feb 9, 2010 at 7:42 AM, Vishal Rana ranavis...@gmail.com wrote: Hi, Is there any utility function to find if values in the array are in ascending or descending order. Example: arr = [1, 2, 4, 6] should return true arr2 = [1, 0, 2, -2] should return false Thanks Vishal ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion i dont know if there's a utility function, but i'd use: np.all(a[1:] = a[:-1]) ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Emulate left outer join?
Hi, I've been working with numpy for less than a month, having learned about it after finding matplotlib. My foundation in things like set theory is... weak to nonexistent, so I need a little help mapping sql-like thoughts into set-theory thinking :) Some context to help me explain: I'm trying to store, chart, and analyze unix system performance data (sar/sadf output). On a typical system I have about 75 fields/variables, all floats, with identical timestamps... or so we hope. What I want to do in order to save memory/disk space is to stack the timeseries data all into three or four different arrays, and use a single timestamp field for each set. My problem is: I don't know that I can guarantee that the shape of all the individual arrays will be identical along the time axis. I may receive truncated textfiles to parse, or new variables may appear and disappear from the set being reported/recorded. If these were in flat files or database tables, I'd do a left outer join between a master timestamp table and each individual variable's table. But... I don't know the keywords to search for in the numpy docs/web chatter. A thread from just about one year ago left the question hanging: http://article.gmane.org/gmane.comp.python.numeric.general/27942 Examples? Pointers? Shoves toward the correct sections of the docs? Thanks. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Emulate left outer join?
On Tue, Feb 9, 2010 at 15:52, David Carmean d...@halibut.com wrote:
Hi,
I've been working with numpy for less than a month, having learned about
it after finding matplotlib. My foundation in things like set theory is...
weak to nonexistent, so I need a little help mapping sql-like thoughts into
set-theory thinking :)
Some context to help me explain: I'm trying to store, chart, and analyze
unix system performance data (sar/sadf output). On a typical system I have
about 75 fields/variables, all floats, with identical timestamps... or so
we hope. What I want to do in order to save memory/disk space is to stack
the timeseries data all into three or four different arrays, and use a single
timestamp field for each set.
My problem is: I don't know that I can guarantee that the shape of all the
individual arrays will be identical along the time axis. I may receive
truncated textfiles to parse, or new variables may appear and disappear from
the set being reported/recorded.
If these were in flat files or database tables, I'd do a left outer join
between
a master timestamp table and each individual variable's table. But... I
don't
know the keywords to search for in the numpy docs/web chatter. A thread from
just about one year ago left the question hanging:
http://article.gmane.org/gmane.comp.python.numeric.general/27942
Examples? Pointers? Shoves toward the correct sections of the docs?
numpy.lib.recfunctions.join_by(key, r1, r2, jointype='leftouter')
In [23]: numpy.lib.recfunctions.join_by?
Type: function
Base Class: type 'function'
Namespace:Interactive
File: /Users/rkern/svn/numpy/numpy/lib/recfunctions.py
Definition: numpy.lib.recfunctions.join_by(key, r1, r2,
jointype='inner', r1postfix='1', r2postfix='2', defaults=None,
usemask=True, asrecarray=False)
Docstring:
Join arrays `r1` and `r2` on key `key`.
The key should be either a string or a sequence of string corresponding
to the fields used to join the array.
An exception is raised if the `key` field cannot be found in the two input
arrays.
Neither `r1` nor `r2` should have any duplicates along `key`: the presence
of duplicates will make the output quite unreliable. Note that duplicates
are not looked for by the algorithm.
Parameters
--
key : {string, sequence}
A string or a sequence of strings corresponding to the fields used
for comparison.
r1, r2 : arrays
Structured arrays.
jointype : {'inner', 'outer', 'leftouter'}, optional
If 'inner', returns the elements common to both r1 and r2.
If 'outer', returns the common elements as well as the elements of r1
not in r2 and the elements of not in r2.
If 'leftouter', returns the common elements and the elements of r1 not
in r2.
r1postfix : string, optional
String appended to the names of the fields of r1 that are present in r2
but absent of the key.
r2postfix : string, optional
String appended to the names of the fields of r2 that are present in r1
but absent of the key.
defaults : {dictionary}, optional
Dictionary mapping field names to the corresponding default values.
usemask : {True, False}, optional
Whether to return a MaskedArray (or MaskedRecords is `asrecarray==True`)
or a ndarray.
asrecarray : {False, True}, optional
Whether to return a recarray (or MaskedRecords if `usemask==True`) or
just a flexible-type ndarray.
Notes
-
* The output is sorted along the key.
* A temporary array is formed by dropping the fields not in the key for the
two arrays and concatenating the result. This array is then sorted, and
the common entries selected. The output is constructed by
filling the fields
with the selected entries. Matching is not preserved if there are some
duplicates...
For some reason, numpy.lib.recfunctions isn't in the documentation
editor. I'm not sure why.
--
Robert Kern
I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth.
-- Umberto Eco
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Re: [Numpy-discussion] Emulate left outer join?
On Tue, Feb 9, 2010 at 5:02 PM, Robert Kern robert.k...@gmail.com wrote: numpy.lib.recfunctions.join_by(key, r1, r2, jointype='leftouter') And if that isn't sufficient, John has in matplotlib.mlab a few other similar utilities that allow for more complex cases: In [2]: mlab.rec_ mlab.rec_append_fields mlab.rec_groupbymlab.rec_keep_fields mlab.rec_drop_fieldsmlab.rec_join mlab.rec_summarize Cheers, f ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Emulate left outer join?
On Tue, Feb 9, 2010 at 4:43 PM, Fernando Perez fperez@gmail.com wrote: On Tue, Feb 9, 2010 at 5:02 PM, Robert Kern robert.k...@gmail.com wrote: numpy.lib.recfunctions.join_by(key, r1, r2, jointype='leftouter') And if that isn't sufficient, John has in matplotlib.mlab a few other similar utilities that allow for more complex cases: The numpy.lib.recfunctions were ported from matplotlib.mlab so most of the functionality is overlapping, but we have added some stuff since the port, eg matplotlib.mlab.recs_join for a multiway join, and some stuff was never ported (rec_summarize, rec_groupby) so it may be worth looking in mlab too. Some of the stuff for mpl is only in svn but most of it is released. Examples are at http://matplotlib.sourceforge.net/examples/misc/rec_join_demo.html http://matplotlib.sourceforge.net/examples/misc/rec_groupby_demo.html JDH ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Emulate left outer join?
On 9-Feb-10, at 5:02 PM, Robert Kern wrote: Examples? Pointers? Shoves toward the correct sections of the docs? numpy.lib.recfunctions.join_by(key, r1, r2, jointype='leftouter') Huh. All these years, how have I missed this? Yet another demonstration of why my never skip over a Kern posting policy exists. David ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Emulate left outer join?
On Tue, Feb 9, 2010 at 17:47, Ralf Gommers ralf.gomm...@googlemail.com wrote: On Wed, Feb 10, 2010 at 6:02 AM, Robert Kern robert.k...@gmail.com wrote: For some reason, numpy.lib.recfunctions isn't in the documentation editor. I'm not sure why. Because it's not in np.lib.__all__ . Then there needs to be a secondary way to add such modules. -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Emulate left outer join?
On Tue, Feb 9, 2010 at 6:52 PM, Robert Kern robert.k...@gmail.com wrote: On Tue, Feb 9, 2010 at 17:47, Ralf Gommers ralf.gomm...@googlemail.com wrote: On Wed, Feb 10, 2010 at 6:02 AM, Robert Kern robert.k...@gmail.com wrote: For some reason, numpy.lib.recfunctions isn't in the documentation editor. I'm not sure why. Because it's not in np.lib.__all__ . Then there needs to be a secondary way to add such modules. Under which namespace should the recfunctions be accessed. I think, it's possible to directly import/reference them in the docs without adding them to lib.__all__ Josef -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Emulate left outer join?
On Tue, Feb 9, 2010 at 18:02, josef.p...@gmail.com wrote: On Tue, Feb 9, 2010 at 6:52 PM, Robert Kern robert.k...@gmail.com wrote: On Tue, Feb 9, 2010 at 17:47, Ralf Gommers ralf.gomm...@googlemail.com wrote: On Wed, Feb 10, 2010 at 6:02 AM, Robert Kern robert.k...@gmail.com wrote: For some reason, numpy.lib.recfunctions isn't in the documentation editor. I'm not sure why. Because it's not in np.lib.__all__ . Then there needs to be a secondary way to add such modules. Under which namespace should the recfunctions be accessed. numpy.lib.recfunctions I think, it's possible to directly import/reference them in the docs without adding them to lib.__all__ Okay. What is that way? What do we need to do to make that happen? -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Emulate left outer join?
On Feb 9, 2010, at 6:52 PM, Robert Kern wrote: On Tue, Feb 9, 2010 at 17:47, Ralf Gommers ralf.gomm...@googlemail.com wrote: On Wed, Feb 10, 2010 at 6:02 AM, Robert Kern robert.k...@gmail.com wrote: For some reason, numpy.lib.recfunctions isn't in the documentation editor. I'm not sure why. Because it's not in np.lib.__all__ . Then there needs to be a secondary way to add such modules. All, I started porting JDH's functions from mlab to numpy.lib because I thought it'd be nice to have them directly in the core of numpy, instead of spread out in another package. However, I wanted to get a lot of feedback before advertising them: * Should we put matplotlib.mlab functions directly into numpy ? I do think so, even if I think we should make them a tad more generic and not tie them to recarrays (you can do the same thing with structured arrays without the overhead, albeit without the convenience of access-as-attributes). * If yes to the question above, how should we proceed ? John, you mind committing these functions to numpy.lib.rec_functions yourself ? If you can't, any volunteer (I can do it but it would fall low on my priority list). Once this is settle, then we could think about a way to present them in the reference and/or user manual (like I did for genfromtxt). Let me know what y'all think. P. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy.polynomial.chebyshev (not) in the docs
Similar to the recfunctions, I also don't find the new chebychev polynomials in the docs. Are they linked from any rst file? A search in the online sphinx html docs comes up empty, and http://docs.scipy.org/numpy/docs/numpy-docs/reference/routines.poly.rst/#routines-poly doesn't link to the new functions. The docstrings look nice but maybe nobody sees them. Josef ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Emulate left outer join?
ti, 2010-02-09 kello 18:04 -0600, Robert Kern kirjoitti: On Tue, Feb 9, 2010 at 18:02, josef.p...@gmail.com wrote: [clip] numpy.lib.recfunctions I think, it's possible to directly import/reference them in the docs without adding them to lib.__all__ Okay. What is that way? What do we need to do to make that happen? To get them in the web app, I need to adjust the web app configuration on new.scipy.org. I didn't know about that those functions, so I missed them earlier. Getting them to the docs goes as Josef explained, just add a rst file and refer to it in the others. *** But, should we make these functions available under some less internal-ish namespace? There's numpy.rec at the least -- it could be made a real module to pull in things from core and lib. -- Pauli Virtanen ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Emulate left outer join?
On Feb 9, 2010, at 7:54 PM, Pauli Virtanen wrote: But, should we make these functions available under some less internal-ish namespace? There's numpy.rec at the least -- it could be made a real module to pull in things from core and lib. I still think these functions are more generic than the rec_ prefix let think, and I'd still prefer a decision being made about what should go in the module before thinking too hard about how to advertise it. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Emulate left outer join?
On Tue, Feb 9, 2010 at 7:06 PM, Pierre GM pgmdevl...@gmail.com wrote: On Feb 9, 2010, at 6:52 PM, Robert Kern wrote: On Tue, Feb 9, 2010 at 17:47, Ralf Gommers ralf.gomm...@googlemail.com wrote: On Wed, Feb 10, 2010 at 6:02 AM, Robert Kern robert.k...@gmail.com wrote: For some reason, numpy.lib.recfunctions isn't in the documentation editor. I'm not sure why. Because it's not in np.lib.__all__ . Then there needs to be a secondary way to add such modules. All, I started porting JDH's functions from mlab to numpy.lib because I thought it'd be nice to have them directly in the core of numpy, instead of spread out in another package. However, I wanted to get a lot of feedback before advertising them: chicken and egg problem, without advertising very few users know they exist * Should we put matplotlib.mlab functions directly into numpy ? I do think so, even if I think we should make them a tad more generic and not tie them to recarrays (you can do the same thing with structured arrays without the overhead, albeit without the convenience of access-as-attributes). * If yes to the question above, how should we proceed ? John, you mind committing these functions to numpy.lib.rec_functions yourself ? If you can't, any volunteer (I can do it but it would fall low on my priority list). Once this is settle, then we could think about a way to present them in the reference and/or user manual (like I did for genfromtxt). Let me know what y'all think. P. I think it's very helpful to have more helper functions and documentation to work with structured arrays. I also think that for newcomers the distinction in the documentation between recarrays and arrays with structured dtypes is not very clear, and how to work with structured arrays is not sufficiently documented. Essentially I only learned about them because of an answer Pierre gave once to me on the mailing list and I started to read the matplotlib and numpy source to see how to work with them. It also seems that structured arrays become the more recommended approach than recarrays (e.g. discussion by tabular developers on the mailing list and their switch to structured arrays). So, I'm in favor of advertising them, and advertising them for structured arrays and only secondary for recarrays. I have no idea about a good name that would suggest structured instead of rec. Josef ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Emulate left outer join?
On Tue, Feb 9, 2010 at 7:02 PM, Pierre GM pgmdevl...@gmail.com wrote:
On Feb 9, 2010, at 7:54 PM, Pauli Virtanen wrote:
But, should we make these functions available under some less
internal-ish namespace? There's numpy.rec at the least -- it could be
made a real module to pull in things from core and lib.
I still think these functions are more generic than the rec_ prefix let
think, and I'd still prefer a decision being made about what should go in the
module before thinking too hard about how to advertise it.
I would love to see many of these as methods of record/structured
arrays, so we could say
r = r1.join('date', r2)
or
rs = r.groupby( ('year', 'month'), stats)
and have totxt, tocsv. etc... from rec2txt, rec2csv, etc... I
think the functionality of mlab.rec_summarize and rec_groupby is very
useful, but the interface is a bit clunky and could be made easier for
the common use cases.
These methods could call the proper functions from np.lib.recfunctions
or wherever, and they would get a lot more visibility to people using
introspection.
JDH
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Re: [Numpy-discussion] Emulate left outer join?
On Feb 9, 2010, at 8:16 PM, John Hunter wrote: I still think these functions are more generic than the rec_ prefix let think, and I'd still prefer a decision being made about what should go in the module before thinking too hard about how to advertise it. I would love to see many of these as methods of record/structured arrays, so we could say Won't work w/ structured arrays, but completely doable for recarrays. Let's define the functions so that they take a structured array as first argument when possible, and add the functions as a methods to np.recarray. That should be fairly transparent, provided we stick to access-as-key instead of access-as-attribute and have totxt, tocsv. etc... from rec2txt, rec2csv, etc... I think the functionality of mlab.rec_summarize and rec_groupby is very useful, but the interface is a bit clunky and could be made easier for the common use cases. Are you going to work on it or should I step in (in a few weeks...). ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy.polynomial.chebyshev (not) in the docs
Are you talking about absence in the Wiki or absence in a NumPy executable.
They're in the former (I've been editing them), and they're in 1.4.0 of the
latter:
import numpy as N
N.version.version
'1.4.0'
from numpy.polynomial import chebyshev as C
help(C.chebfit)
Help on function chebfit in module numpy.polynomial.chebyshev:
chebfit(x, y, deg, rcond=None, full=False)
Least squares fit of Chebyshev series to data.
Fit a Chebyshev series ``p(x) = p[0] * T_{deq}(x) + ... + p[deg] *
T_{0}(x)`` of degree `deg` to points `(x, y)`. Returns a vector of
coefficients `p` that minimises the squared error.
Parameters
--
x : array_like, shape (M,)
x-coordinates of the M sample points ``(x[i], y[i])``.
y : array_like, shape (M,) or (M, K)
y-coordinates of the sample points. Several data sets of sample
points sharing the same x-coordinates can be fitted at once by
passing in a 2D-array that contains one dataset per column.
Etc.
What version of NumPy are you running?
DG
On Tue, Feb 9, 2010 at 4:40 PM, josef.p...@gmail.com wrote:
Similar to the recfunctions, I also don't find the new chebychev
polynomials in the docs.
Are they linked from any rst file?
A search in the online sphinx html docs comes up empty, and
http://docs.scipy.org/numpy/docs/numpy-docs/reference/routines.poly.rst/#routines-poly
doesn't link to the new functions.
The docstrings look nice but maybe nobody sees them.
Josef
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Re: [Numpy-discussion] numpy.polynomial.chebyshev (not) in the docs
On Tue, Feb 9, 2010 at 9:30 PM, David Goldsmith d.l.goldsm...@gmail.com wrote:
Are you talking about absence in the Wiki or absence in a NumPy executable.
They're in the former (I've been editing them), and they're in 1.4.0 of the
latter:
I have them in numpy 1.4, I see them in the doceditor, but not in
http://docs.scipy.org/doc/numpy/search.html?q=chebychevcheck_keywords=yesarea=default
or search for chebfit
I think they are not added to the html docs because they are not
referenced in any rst file.
That's a different issue from having them in the source and the
doceditor application.
Josef
import numpy as N
N.version.version
'1.4.0'
from numpy.polynomial import chebyshev as C
help(C.chebfit)
Help on function chebfit in module numpy.polynomial.chebyshev:
chebfit(x, y, deg, rcond=None, full=False)
Least squares fit of Chebyshev series to data.
Fit a Chebyshev series ``p(x) = p[0] * T_{deq}(x) + ... + p[deg] *
T_{0}(x)`` of degree `deg` to points `(x, y)`. Returns a vector of
coefficients `p` that minimises the squared error.
Parameters
--
x : array_like, shape (M,)
x-coordinates of the M sample points ``(x[i], y[i])``.
y : array_like, shape (M,) or (M, K)
y-coordinates of the sample points. Several data sets of sample
points sharing the same x-coordinates can be fitted at once by
passing in a 2D-array that contains one dataset per column.
Etc.
What version of NumPy are you running?
DG
On Tue, Feb 9, 2010 at 4:40 PM, josef.p...@gmail.com wrote:
Similar to the recfunctions, I also don't find the new chebychev
polynomials in the docs.
Are they linked from any rst file?
A search in the online sphinx html docs comes up empty, and
http://docs.scipy.org/numpy/docs/numpy-docs/reference/routines.poly.rst/#routines-poly
doesn't link to the new functions.
The docstrings look nice but maybe nobody sees them.
Josef
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Re: [Numpy-discussion] numpy.polynomial.chebyshev (not) in the docs
On Tue, Feb 9, 2010 at 7:52 PM, josef.p...@gmail.com wrote:
On Tue, Feb 9, 2010 at 9:30 PM, David Goldsmith d.l.goldsm...@gmail.com
wrote:
Are you talking about absence in the Wiki or absence in a NumPy
executable.
They're in the former (I've been editing them), and they're in 1.4.0 of
the
latter:
I have them in numpy 1.4, I see them in the doceditor, but not in
http://docs.scipy.org/doc/numpy/search.html?q=chebychevcheck_keywords=yesarea=default
or search for chebfit
I think they are not added to the html docs because they are not
referenced in any rst file.
That's a different issue from having them in the source and the
doceditor application.
Josef
import numpy as N
N.version.version
'1.4.0'
from numpy.polynomial import chebyshev as C
help(C.chebfit)
Help on function chebfit in module numpy.polynomial.chebyshev:
chebfit(x, y, deg, rcond=None, full=False)
Least squares fit of Chebyshev series to data.
Fit a Chebyshev series ``p(x) = p[0] * T_{deq}(x) + ... + p[deg] *
T_{0}(x)`` of degree `deg` to points `(x, y)`. Returns a vector of
coefficients `p` that minimises the squared error.
Parameters
--
x : array_like, shape (M,)
x-coordinates of the M sample points ``(x[i], y[i])``.
y : array_like, shape (M,) or (M, K)
y-coordinates of the sample points. Several data sets of sample
points sharing the same x-coordinates can be fitted at once by
passing in a 2D-array that contains one dataset per column.
Etc.
What version of NumPy are you running?
Hey, the error in the docstring prompted me to make another attempt to guess
my editing password. Success! Thanks.
Chuck
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[Numpy-discussion] long(a) vs a.__long__() for scalar arrays
Hi, I am a bit puzzled by the protocol for long(a) where a is a scalar array. For example, for a = np.float128(1), I was expecting long(a) to call a.__long__, but it does not look like it is the case. int(a) does not call a.__int__ either. Where does the long conversion happen in numpy for scalar arrays ? cheers, David ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] long(a) vs a.__long__() for scalar arrays
On Tue, Feb 9, 2010 at 11:12 PM, David Cournapeau courn...@gmail.comwrote: Hi, I am a bit puzzled by the protocol for long(a) where a is a scalar array. For example, for a = np.float128(1), I was expecting long(a) to call a.__long__, but it does not look like it is the case. int(a) does not call a.__int__ either. Where does the long conversion happen in numpy for scalar arrays ? How did you tell, did you have print statements in the call? I'm curious if np.long the same as long? Chuck ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
