[Numpy-discussion] Line of best fit!
Hi, I am trying to plot a line of best fit for some data i have, is there a simple way of doing it? Cheers ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Line of best fit!
2008/12/8 James [EMAIL PROTECTED]: I am trying to plot a line of best fit for some data i have, is there a simple way of doing it? Hi James, Take a look at: http://www.scipy.org/Cookbook/FittingData http://www.scipy.org/Cookbook/LinearRegression and the section on least square fitting towards the end of this page in the Scipy docs: http://docs.scipy.org/doc/scipy/reference/tutorial/optimize.html Post again if if these references don't get you going. Cheers, Scott ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] ANN: numpy.i - added managed deallocation to ARGOUTVIEW_ARRAY1 (ARGOUTVIEWM_ARRAY1)
Hello list,
just a quick follow-up on the managed deallocation. This is what I've
done this week-end:
In numpy.i, I have redefined the import_array() function to also take
care of the managed memory initialisation (the _MyDeallocType.tp_new =
PyType_GenericNew; statement). This means that in %init(), the only call
is to import_array(). Basically, the same as with the normal numpy.i.
Only difference in a swig file (.i) between unmanaged and managed
memory allocation is the use of either the ARGOUTVIEW_ARRAY or
ARGOUTVIEWM_ARRAY fragments. Everything else is hidden.
In numpy.i, this is what's now happening (my previous attempts were a
bit clumsy):
%#undef import_array
%#define import_array() {if (_import_array() 0) {PyErr_Print();
PyErr_SetString(PyExc_ImportError, numpy.core.multiarray failed to
import); return; }; _MyDeallocType.tp_new = PyType_GenericNew; if
(PyType_Ready(_MyDeallocType) 0) {PyErr_Print();
PyErr_SetString(PyExc_ImportError, Custom memory management failed to
initialize (numpy.i)); return; } }
%#undef import_array1
%#define import_array1(ret) {if (_import_array() 0) {PyErr_Print();
PyErr_SetString(PyExc_ImportError, numpy.core.multiarray failed to
import); return ret; }; _MyDeallocType.tp_new = PyType_GenericNew; if
(PyType_Ready(_MyDeallocType) 0) {PyErr_Print();
PyErr_SetString(PyExc_ImportError, Custom memory management failed to
initialize (numpy.i)); return ret; } }
%#undef import_array2
%#define import_array2(msg, ret) {if (_import_array() 0)
{PyErr_Print(); PyErr_SetString(PyExc_ImportError, msg); return ret; };
_MyDeallocType.tp_new = PyType_GenericNew; if
(PyType_Ready(_MyDeallocType) 0) {PyErr_Print();
PyErr_SetString(PyExc_ImportError, msg); return ret; } }
My wiki (sorry, haven't moved it to the scipy cookbook yet) has all the
details (the modified numpy.i, explanations, and some test code):
http://code.google.com/p/ezwidgets/wiki/NumpyManagedMemory
Regards,
Egor
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Re: [Numpy-discussion] Python2.4 support
On Sun, Dec 7, 2008 at 23:42, David Cournapeau [EMAIL PROTECTED] wrote: I am strongly against dropping 2.4 support anytime soon. I haven't seen a strong rationale for using = 2.5 features in numpy, supporting 2.4 is not so hard, and 2.4 is still the default python version on many OS (mac os X 10.4 I believe, RHEL for sure, open solaris). Mac OS X 10.4 uses python-2.3, 10.5 uses python-2.5. Cheers Adam ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Python2.4 support
While my feelings aren't as strong as David's, they are pretty much identical. As a point of reference, Red Hat Enterprise Linux 6 won't come out until at least the first quarter of 2010. Until then we should make a serious effort to support Python 2.4, which ships with RHEL 5. It looks like RHEL 6 will be based on the upcoming Fedora 11 release, which will ship with Python 2.6. That gives us a minimum of one year for 2.4 support. Once RHEL 6 is released, it will take several months before a sizable number of users upgrade. Moin has a detailed list of Python versions for various OSes and hosting services: http://moinmo.in/PollAboutRequiringPython24 At least several months, if not years. RedHat supports each version 7 years, for instance (I don't ask for that long). Currently, I'm still using a RHEL 4, although it is planned to migrate to RHEL 5 next year. So we should still support 2.4 for at least 18 months, in case some big firms use RHEL and Python+Numpy for their tools. -- Information System Engineer, Ph.D. Website: http://matthieu-brucher.developpez.com/ Blogs: http://matt.eifelle.com and http://blog.developpez.com/?blog=92 LinkedIn: http://www.linkedin.com/in/matthieubrucher ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Singular Matrix problem with Matplitlib in Numpy (Windows - AMD64)
Hello. I have been battling with the following error for the past week. The output from the terminal is: Traceback (most recent call last): File C:\development\Python\2_5_2\Lib\site-packages\matplotlib\backends\backend_qt4agg.py, line 86, in paintEvent FigureCanvasAgg.draw(self) File C:\development\Python\2_5_2\Lib\site-packages\matplotlib\backends\backend_agg.py, line 261, in draw self.figure.draw(self.renderer) File C:\development\Python\2_5_2\Lib\site-packages\matplotlib\figure.py, line 765, in draw legend.draw(renderer) File C:\development\Python\2_5_2\Lib\site-packages\matplotlib\legend.py, line 197, in draw self._update_positions(renderer) File C:\development\Python\2_5_2\Lib\site-packages\matplotlib\legend.py, line 513, in _update_positions l,b,w,h = get_tbounds(self.texts[-1]) File C:\development\Python\2_5_2\Lib\site-packages\matplotlib\legend.py, line 499, in get_tbounds bboxa = bbox.inverse_transformed(self.get_transform()) File C:\development\Python\2_5_2\Lib\site-packages\matplotlib\transforms.py, line 478, in inverse_transformed return Bbox(transform.inverted().transform(self.get_points())) File C:\development\Python\2_5_2\Lib\site-packages\matplotlib\transforms.py, line 1338, in inverted self._inverted = Affine2D(inv(mtx)) File C:\development\Python\2_5_2\Lib\site-packages\numpy\linalg\linalg.py, line 350, in inv return wrap(solve(a, identity(a.shape[0], dtype=a.dtype))) File C:\development\Python\2_5_2\Lib\site-packages\numpy\linalg\linalg.py, line 249, in solve raise LinAlgError, 'Singular matrix' numpy.linalg.linalg.LinAlgError: Singular matrix Initially MPL plots a graph but when you try to interact with the widget(for example resize) then the output is displayed and the MPL figure is not updated. Everything works with Windows 32-bit. Linux 32-bit and 64-bit are working correctly. Any ideas would be helpful. Thanks. George. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] how to create a matrix based on a vector?
Hi, I want to create a matrix based on a vector. It is difficult to describe the issue for me in english. Here is an example. Suppose I have an array([3, 6, 8, 12]), I want to create a range based on each element. In this exampe, let us say want to create 4 number with step 2, so I will have [3, 6, 8, 12 5, 8, 10,14 7, 10,12,16 9, 12,14,18] It is a 4 by 4 maxtric in this example. My original array is quite large. but the range I want to create around the number is not big, it is about 30. Does anyone know how to do this efficiently? Thanks Frank _ Send e-mail faster without improving your typing skills. http://windowslive.com/Explore/hotmail?ocid=TXT_TAGLM_WL_hotmail_acq_speed_122008___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] how to create a matrix based on a vector?
On Mon, Dec 8, 2008 at 11:27, frank wang [EMAIL PROTECTED] wrote: Hi, I want to create a matrix based on a vector. It is difficult to describe the issue for me in english. Here is an example. Suppose I have an array([3, 6, 8, 12]), I want to create a range based on each element. In this exampe, let us say want to create 4 number with step 2, so I will have [3, 6, 8, 12 5, 8, 10,14 7, 10,12,16 9, 12,14,18] It is a 4 by 4 maxtric in this example. My original array is quite large. but the range I want to create around the number is not big, it is about 30. Does anyone know how to do this efficiently? In [1]: from numpy import * In [2]: a = array([3, 6, 8, 12]) In [4]: b = arange(0, 4*2, 2)[:,newaxis] In [5]: a+b Out[5]: array([[ 3, 6, 8, 12], [ 5, 8, 10, 14], [ 7, 10, 12, 16], [ 9, 12, 14, 18]]) -- 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://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Singular Matrix problem with Matplitlib in Numpy (Windows - AMD64)
On Tue, Dec 9, 2008 at 12:50 AM, George Goussard [EMAIL PROTECTED] wrote: Hello. I have been battling with the following error for the past week. The output from the terminal is: What does numpy.test() says ? Did you use an external blas/lapack when you built numpy for AMD64 David ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] checksum on numpy float array
A Sunday 07 December 2008, Brennan Williams escrigué: OK so maybe I should (1) not add some sort of checksum type functionality to my read/write methods these read/write methods simply read/write numpy arrays to a binary file which contains one or more numpy arrays (and nothing else). (2) replace my binary files iwith either HDF5 or PyTables But my app is being used by clients on existing projects - in one case there are over 900 of these numpy binary files in just one project, albeit each file is pretty small (200KB or so) so.. questions. How can I tranparently (or at least with minimum user-pain) replace my existing read/write methods with PyTables or HDF5? My initial thoughts are... (a) have an app version number and a data format version number which i can check against. (b) if data format version 1.0 then read from old binary files (c) if app version number 1.0 then write to new PyTables or HDF5 files (d) get clients to open existing project and then save existing project to semi-transparently convert from old to new formats. Yeah. That would work perfectly. Also, there is a function in PyTables named 'isHDF5File(filename)' that allow you to know whether a file is in HDF5 format or not. You might want to use it and avoid to bother with data format/app version issues. Cheers, Francesc Francesc Alted wrote: A Friday 05 December 2008, Andrew Collette escrigué: Another possibility would be to use HDF5 as a data container. It supports the fletcher32 filter [1] which basically computes a chuksum for evey data chunk written to disk and then always check that the data read satifies the checksum kept on-disk. So, if the HDF5 layer doesn't complain, you are basically safe. There are at least two usable HDF5 interfaces for Python and NumPy: PyTables[2] and h5py [3]. PyTables does have support for that right out-of-the-box. Not sure about h5py though (a quick search in docs doesn't reveal nothing). [1] http://rfc.sunsite.dk/rfc/rfc1071.html [2] http://www.pytables.org [3] http://h5py.alfven.org Hope it helps, Just to confirm that h5py does in fact have fletcher32; it's one of the options you can specify when creating a dataset, although it could use better documentation: http://h5py.alfven.org/docs/guide/hl.html#h5py.highlevel.Group.cre ate _dataset My bad. I've searched for 'fletcher' instead of 'fletcher32'. I naively thought that the search tool in Sphinx allowed for partial name finding. In fact, it is a pity it does not. Cheers, ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion -- Francesc Alted ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] how to create a matrix based on a vector?
I got a lof of help from the experts in this forum. I resitsted to send a thank you reply for fearing spaming the forum. This time I really want to let the people know that I am really appreciate the great help I got. Please let me know if a simple thank you message is not appropriate in this forum. Numpy makes Pyhton a great tools for processing signal. Thank you very much. Frank Date: Mon, 8 Dec 2008 11:30:31 -0600 From: [EMAIL PROTECTED] To: numpy-discussion@scipy.org Subject: Re: [Numpy-discussion] how to create a matrix based on a vector? On Mon, Dec 8, 2008 at 11:27, frank wang [EMAIL PROTECTED] wrote: Hi, I want to create a matrix based on a vector. It is difficult to describe the issue for me in english. Here is an example. Suppose I have an array([3, 6, 8, 12]), I want to create a range based on each element. In this exampe, let us say want to create 4 number with step 2, so I will have [3, 6, 8, 12 5, 8, 10,14 7, 10,12,16 9, 12,14,18] It is a 4 by 4 maxtric in this example. My original array is quite large. but the range I want to create around the number is not big, it is about 30. Does anyone know how to do this efficiently? In [1]: from numpy import * In [2]: a = array([3, 6, 8, 12]) In [4]: b = arange(0, 4*2, 2)[:,newaxis] In [5]: a+b Out[5]: array([[ 3, 6, 8, 12], [ 5, 8, 10, 14], [ 7, 10, 12, 16], [ 9, 12, 14, 18]]) -- 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://projects.scipy.org/mailman/listinfo/numpy-discussion _ Send e-mail faster without improving your typing skills. http://windowslive.com/Explore/hotmail?ocid=TXT_TAGLM_WL_hotmail_acq_speed_122008___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] how to create a matrix based on a vector?
On Mon, Dec 8, 2008 at 12:40, frank wang [EMAIL PROTECTED] wrote: I got a lof of help from the experts in this forum. I resitsted to send a thank you reply for fearing spaming the forum. This time I really want to let the people know that I am really appreciate the great help I got. Please let me know if a simple thank you message is not appropriate in this forum. Thanks, public or otherwise, are always appreciated. You're quite welcome. -- 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://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] What to use to read and write numpy arrays to a file?
In looking for simple ways to read and write data (in a text readable format) to and from a file and later restoring the actual data when reading back in, I've found that numpy arrays don't seem to play well with repr and eval. E.g. to write some data (mixed types) to a file I can do this (fp is an open file), thedata=[3.0,-4.9+2.0j,'another string'] repvars= repr(thedata)+\n fp.write(repvars) Then to read it back and restore the data each to its original type, strvars= fp.readline() sonofdata= eval(strvars) which gives back the original data list. BUT when I try this with numpy arrays in the data list I find that repr of an array adds extra end-of-lines and that messes up the simple restoration of the data using eval. Am I missing something simple? I know I've seen people recommend ways to save arrays to files, but I'm wondering what is the most straight-forward? I really like the simple, pythonic approach of the repr - eval pairing. Thanks for any advice. (yes, I am googling, too) -- Lou Pecora, my views are my own. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] What to use to read and write numpy arrays to a file?
Hi, The repr - eval pair does not work with numpy. You can simply do a tofile() from file(). Matthieu 2008/12/8 Lou Pecora [EMAIL PROTECTED]: In looking for simple ways to read and write data (in a text readable format) to and from a file and later restoring the actual data when reading back in, I've found that numpy arrays don't seem to play well with repr and eval. E.g. to write some data (mixed types) to a file I can do this (fp is an open file), thedata=[3.0,-4.9+2.0j,'another string'] repvars= repr(thedata)+\n fp.write(repvars) Then to read it back and restore the data each to its original type, strvars= fp.readline() sonofdata= eval(strvars) which gives back the original data list. BUT when I try this with numpy arrays in the data list I find that repr of an array adds extra end-of-lines and that messes up the simple restoration of the data using eval. Am I missing something simple? I know I've seen people recommend ways to save arrays to files, but I'm wondering what is the most straight-forward? I really like the simple, pythonic approach of the repr - eval pairing. Thanks for any advice. (yes, I am googling, too) -- Lou Pecora, my views are my own. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion -- Information System Engineer, Ph.D. Website: http://matthieu-brucher.developpez.com/ Blogs: http://matt.eifelle.com and http://blog.developpez.com/?blog=92 LinkedIn: http://www.linkedin.com/in/matthieubrucher ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] What to use to read and write numpy arrays to a file?
On Mon, Dec 8, 2008 at 14:54, Lou Pecora [EMAIL PROTECTED] wrote: In looking for simple ways to read and write data (in a text readable format) to and from a file and later restoring the actual data when reading back in, I've found that numpy arrays don't seem to play well with repr and eval. E.g. to write some data (mixed types) to a file I can do this (fp is an open file), thedata=[3.0,-4.9+2.0j,'another string'] repvars= repr(thedata)+\n fp.write(repvars) Then to read it back and restore the data each to its original type, strvars= fp.readline() sonofdata= eval(strvars) which gives back the original data list. BUT when I try this with numpy arrays in the data list I find that repr of an array adds extra end-of-lines and that messes up the simple restoration of the data using eval. I don't see any extra end-of-lines. Are you sure you aren't talking about the ... when you are saving large arrays? You will need to use set_printoptions() to disable that (threshold=sys.maxint). You should also adjust use precision=18, suppress=False. That should mostly work, but it's never a certain thing. Am I missing something simple? I know I've seen people recommend ways to save arrays to files, but I'm wondering what is the most straight-forward? I really like the simple, pythonic approach of the repr - eval pairing. Thanks for any advice. (yes, I am googling, too) The most bulletproof way would be to use numpy.save() and numpy.load(), but this is a binary format, not a text one. -- 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://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] What to use to read and write numpy arrays to a file?
--- On Mon, 12/8/08, Matthieu Brucher [EMAIL PROTECTED] wrote: From: Matthieu Brucher [EMAIL PROTECTED] Subject: Re: [Numpy-discussion] What to use to read and write numpy arrays to a file? To: Discussion of Numerical Python numpy-discussion@scipy.org Date: Monday, December 8, 2008, 3:56 PM Hi, The repr - eval pair does not work with numpy. You can simply do a tofile() from file(). Matthieu Yes, I found the tofile/fromfile pair, but they don't preserve the shape. Sorry, I should have been clearer on that in my request. I will be saving arrays whose shape I may not know later when I read them in. I'd like that information to be preserved. Thanks. -- Lou Pecora, my views are my own. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] What to use to read and write numpy arrays to a file?
--- On Mon, 12/8/08, Robert Kern [EMAIL PROTECTED] wrote: From: Robert Kern [EMAIL PROTECTED] Subject: Re: [Numpy-discussion] What to use to read and write numpy arrays to a file? The most bulletproof way would be to use numpy.save() and numpy.load(), but this is a binary format, not a text one. -- Robert Kern Thanks, Robert. I may have to go that route, assuming that the save and load pair preserve shape, i.e. I don't have to know the shape when I read back in. -- Lou Pecora, my views are my own. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] What to use to read and write numpy arrays to a file?
On Mon, Dec 8, 2008 at 15:26, Lou Pecora [EMAIL PROTECTED] wrote: --- On Mon, 12/8/08, Robert Kern [EMAIL PROTECTED] wrote: From: Robert Kern [EMAIL PROTECTED] Subject: Re: [Numpy-discussion] What to use to read and write numpy arrays to a file? The most bulletproof way would be to use numpy.save() and numpy.load(), but this is a binary format, not a text one. -- Robert Kern Thanks, Robert. I may have to go that route, assuming that the save and load pair preserve shape, i.e. I don't have to know the shape when I read back in. They do. -- 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://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Line of best fit!
Hi James, 2008/12/8 James [EMAIL PROTECTED]: I have a very simple plot, and the lines join point to point, however i would like to add a line of best fit now onto the chart, i am really new to python etc, and didnt really understand those links! Can anyone help me :) It sounds like the second link, about linear regression, is a good place to start, and I've made a very simple example based on that: --- import numpy as np import matplotlib.pyplot as plt x = np.linspace(0, 10, 11) #1 data_y = np.random.normal(size=x.shape, loc=x, scale=2.5) #2 plt.plot(x, data_y, 'bo') #3 coefs = np.lib.polyfit(x, data_y, 1) #4 fit_y = np.lib.polyval(coefs, x) #5 plt.plot(x, fit_y, 'b--') #6 Line 1 creates an array with the x values I have. Line 2 creates some random data I want to fit, which, in this case happens to be normally distributed around the unity line y=x. The raw data is plotted (assuming you have matplotlib installed as well - I suggest you do) by line 3, with blue circles. Line 4 calculates the coefficients giving the least-squares best fit to a first degree polynomial (i.e. a straight line y = c0 * x + c1). So the values of coefs are c0 and c1 in the previous equation. Line 5 calculates the y values on the fitted polynomial, at given x values, from the coefficients calculated in line 4, and line 6 simply plots these fitted y values, using a dotted blue line. I hope that helps get you started. Keep posting questions on specific issues as they arise, and we'll see what we can do to help. Angus. -- AJC McMorland Post-doctoral research fellow Neurobiology, University of Pittsburgh ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Python2.4 support
Matthieu Brucher wrote: At least several months, if not years. RedHat supports each version 7 years, for instance (I don't ask for that long). Currently, I'm still using a RHEL 4, although it is planned to migrate to RHEL 5 next year. So we should still support 2.4 for at least 18 months, in case some big firms use RHEL and Python+Numpy for their tools. +1 ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] how do I delete unused matrix to save the memory?
Hi, I have a program with some variables consume a lot of memory. The first time I run it, it is fine. The second time I run it, I will get MemoryError. If I close the ipython and reopen it again, then I can run the program once. I am looking for a command to delete the intermediate variable once it is not used to save memory like in matlab clear command. Thanks Frank _ Send e-mail faster without improving your typing skills. http://windowslive.com/Explore/hotmail?ocid=TXT_TAGLM_WL_hotmail_acq_speed_122008___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] how do I delete unused matrix to save the memory?
Try: del(myvariable) Travis On Dec 8, 2008, at 7:15 PM, frank wang [EMAIL PROTECTED] wrote: Hi, I have a program with some variables consume a lot of memory. The first time I run it, it is fine. The second time I run it, I will get MemoryError. If I close the ipython and reopen it again, then I can run the program once. I am looking for a command to delete the intermediate variable once it is not used to save memory like in matlab clear command. Thanks Frank Send e-mail faster without improving your typing skills. Get your Hotmail® account. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] when will osx linker option -bundle be reflected in distutils
I was just wondering what plans there were to reflect the different linker options (i.e. -bundle instead of -shared) that are required on OSX in the fcompiler files within distutils. While its a minor thing it always catches the users of my software when they either install fresh or update numpy ... and sometimes on a bad day it even catches me ;-) Prof Garry Willgoose, Australian Professorial Fellow in Environmental Engineering, Director, Centre for Climate Impact Management (C2IM), School of Engineering, The University of Newcastle, Callaghan, 2308 Australia. Centre webpage: www.c2im.org.au Phone: (International) +61 2 4921 6050 (Tues-Fri AM); +61 2 6545 9574 (Fri PM-Mon) FAX: (International) +61 2 4921 6991 (Uni); +61 2 6545 9574 (personal and Telluric) Env. Engg. Secretary: (International) +61 2 4921 6042 email: [EMAIL PROTECTED]; [EMAIL PROTECTED] email-for-life: [EMAIL PROTECTED] personal webpage: www.telluricresearch.com/garry Do not go where the path may lead, go instead where there is no path and leave a trail Ralph Waldo Emerson ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] when will osx linker option -bundle be reflected in distutils
On Mon, Dec 8, 2008 at 18:02, Garry Willgoose [EMAIL PROTECTED] wrote: I was just wondering what plans there were to reflect the different linker options (i.e. -bundle instead of -shared) that are required on OSX in the fcompiler files within distutils. While its a minor thing it always catches the users of my software when they either install fresh or update numpy ... and sometimes on a bad day it even catches me ;-) I'm sorry; I don't follow. What problems are you having? -- 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://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Line of best fit!
2008/12/9 Angus McMorland [EMAIL PROTECTED]: Hi James, 2008/12/8 James [EMAIL PROTECTED]: I have a very simple plot, and the lines join point to point, however i would like to add a line of best fit now onto the chart, i am really new to python etc, and didnt really understand those links! Can anyone help me :) It sounds like the second link, about linear regression, is a good place to start, and I've made a very simple example based on that: --- import numpy as np import matplotlib.pyplot as plt x = np.linspace(0, 10, 11) #1 data_y = np.random.normal(size=x.shape, loc=x, scale=2.5) #2 plt.plot(x, data_y, 'bo') #3 coefs = np.lib.polyfit(x, data_y, 1) #4 fit_y = np.lib.polyval(coefs, x) #5 plt.plot(x, fit_y, 'b--') #6 James, you'll want to add an extra line to the above code snippet so that Matplotlib displays the plot: plt.show() Cheers, Scott ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] genloadtxt : last call
Pierre GM wrote: All, Here's the latest version of genloadtxt, with some recent corrections. With just a couple of tweaking, we end up with some decent speed: it's still slower than np.loadtxt, but only 15% so according to the test at the end of the package. And so, now what ? Should I put the module in numpy.lib.io ? Elsewhere ? Thx for any comment and suggestions. Current version works out of the box for me. Thanks for running point on this. Ryan -- Ryan May Graduate Research Assistant School of Meteorology University of Oklahoma ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
