Re: [Numpy-discussion] f2py output module name
So on Linux too, there is "ABI" suffix too, for generated module... I misunderstood. I was renaming generated module to "fib3.pyd" to be able to do "import fib3", but now I see it's not necessary - it's importable the same regardless the name of generated module :) Thanks ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] f2py output module name
Hi, I'm following this guide: http://docs.scipy.org/doc/numpy-dev/f2py/getting-started.html#the-quick-and-smart-way I'm on Windows with gfortran and VS2015. When I run: f2py -c -m fib3 fib3.f as output I dont get "fib3.pyd", but "fib3.cp35-win_amd64.pyd". Does anyone know how to get correctly named module in this case? Thanks ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Reading data from structured text
Hi, I have data reports in text files, where first 5 lines describe the data following, which is actually continuous time series of 2048 values wrapped in 205 rows and 10 columns, and each file has 12 such sets. If I crop to first dataset and leave the headers (first 5 lines), genfromtxt(skip_header=5) raises ValueError because last row contains 8 records instead 10 like the rest. If I just try to read file as is, with same argument, genfromtxt(skip_header=5), I get many errors as genfromtxt() doesn't seem to be able to detect header present on every dataset. So can I read such file with numpy and learn for future use, or should I just write my reader? ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Modern Fortran vs NumPy syntax
Thanks for providing this. Reference is excellent, especially as I was collecting Fortran and f2py resources, some month ago, and I found nothing similar to answers you expose. Side by side syntax is just great and intuitive And rest is... Thanks On Thu, Feb 7, 2013 at 8:22 PM, Ondřej Čertík ondrej.cer...@gmail.comwrote: Hi, I have recently setup a page about modern Fortran: http://fortran90.org/ and in particular, it has a long section with side by side syntax examples of Python/NumPy vs Fortran: http://fortran90.org/src/rosetta.html I would be very interested if some NumPy gurus would provide me feedback. I personally knew NumPy long before I learned Fortran, and I was amazed that the modern Fortran pretty much allows 1:1 syntax with NumPy, including most of all the fancy indexing etc. Is there some NumPy feature that is not covered there? I would like it to be a nice resource for people who know NumPy to feel like at home with Fortran, and vice versa. I personally use both every day (Fortran a bit more than NumPy). Or of you have any other comments or tips for the site, please let me know. Eventually I'd like to also put there C++ way of doing the same things, but at the moment I want to get Fortran and Python/NumPy done first. Ondrej ___ 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] Simple question about scatter plot graph
Thanks for your reply I suppose, variable length signals are split on equal parts and dominant harmonic is extracted. Then scatter plot shows this pattern, which has some low correlation, but I can't abstract what could be concluded from grid pattern, as I lack statistical knowledge. Maybe it's saying that data is quantized, which can't be easily seen from single sample bar chart, but perhaps scatter plot suggests that? That's only my wild guess On Thu, Nov 1, 2012 at 1:17 AM, josef.p...@gmail.com wrote: I don't have much of an idea what we are supposed to see, except that there might not be much autocorrelation. Is this grided data and some scatter points might actually be many points on top of each other so we don't see all points and not the frequencey distribution? Is y on a continuous, metric scale or are all grid points different categories, observations. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Simple question about scatter plot graph
OK, thanks guys for your suggestions, which I'll try tomorrow I did correlation first, but no significant values Then I did linear regression, one sample to rest and while there I spotted this grid pattern I was using pandas lag_plot, but it's same plot when I do MPL scatter one sample on others ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Fwd: an interesting single-file, cross-platform Python deployment tool.
On Mon, Jul 2, 2012 at 9:26 PM, Fernando Perez wrote: ANNOUNCING eGenix PyRun - One file Python Runtime Version 1.0.0 An easy-to-use single file relocatable Python run-time - available for Windows, Mac OS X and Unix platforms Quote from http://www.egenix.com/products/python/PyRun/: Windows (x86 - 32/64-bit): eGenix PyRun does not support Windows in the current release. Otherwise seems like interesting for those who need/want to demonstrate some Python approach, in non-Pythonic environment (I don't know of such environment except default Windows). I've heard about Portable Python, has anyone any experience with it, or what's the difference between it and PyRun, in regards of possible NumPy usage? ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy logo in VTK
Damn it, N is inverted and I noticed it now after posting. Sorry about
that, here is correct one:
from numpy import arange, ones
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
o = ones(4)
r = arange(4)
# planes:
for z in arange(3)+1:
ax.bar(r, o*4, zs=z, zdir='x', alpha=.05, width=1)
ax.bar(r, o*4, zs=z, zdir='y', alpha=.05, width=1)
ax.bar(r, o*4, zs=z, zdir='z', alpha=.05, width=1)
# N
for i in [1, 2]:
ax.bar3d([3-i], [0], [i], [.9], [.1], [.9], color='y', linewidth=.1)
ax.bar3d(o+(i*(-1)**i), o-1, r, o-.1, o-.9, o-.1, color='y', linewidth=.1)
# cage
ax.bar3d([0], [0], [0], [4], [4], [4], alpha=.05, color='w', linewidth=0)
plt.show()
# plt.savefig('numpy.png')
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Re: [Numpy-discussion] Numpy logo in VTK
I continued in this mpl trip, with small animation sequence: # animation ax.view_init(90,-90) plt.ion() plt.draw() plt.show() for l in arange(25): ax.set_xlim3d(1.5-.1*l,2.5+.1*l) ax.set_ylim3d(1.5-.1*l,2.5+.1*l) ax.view_init(90-3*l, -90+l) plt.draw() plt.title(NumPy) plt.ioff() plt.show() Try it or check it out on YouTube: www.youtube.com/watch?v=mpYPS_zXAFw Whole script in attachment nl.py Description: Binary data ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy logo in VTK
Yeah, camera is in cliche, I know :D Something more original can be done, perhaps some idea of transforming grid in 2D (in Z plane) for opening sequence and then emerging latices in some analogy with numpy arrays, finishing with complete figure, but I guess not in matplotlib ;) ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy logo in VTK
In the first version this line:
ax.bar3d([i], [0], [i], [.9], [.1], [.9], color='y', linewidth=.1)
is responsible for diagonal in N, and it is inverted.
In the second version you quoted this is corrected with:
ax.bar3d([3-i], [0], [i], [.9], [.1], [.9], color='y', linewidth=.1)
Also snippet for clearing axis decorations, (grid, ticks, lines...) is
posted separately besides first version.
Anyhow attached python script in later mail (with youtube link) has
all this together plus anim sequence
On Wed, Jun 27, 2012 at 6:34 PM, Virgil Stokes v...@it.uu.se wrote:
On 27-Jun-2012 08:04, klo uo wrote:
from numpy import arange, ones
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
o = ones(4)
r = arange(4)
# planes:
for z in arange(3)+1:
ax.bar(r, o*4, zs=z, zdir='x', alpha=.05, width=1)
ax.bar(r, o*4, zs=z, zdir='y', alpha=.05, width=1)
ax.bar(r, o*4, zs=z, zdir='z', alpha=.05, width=1)
# N
for i in [1, 2]:
ax.bar3d([3-i], [0], [i], [.9], [.1], [.9], color='y', linewidth=.1)
ax.bar3d(o+(i*(-1)**i), o-1, r, o-.1, o-.9, o-.1, color='y',
linewidth=.1)
# cage
ax.bar3d([0], [0], [0], [4], [4], [4], alpha=.05, color='w', linewidth=0)
plt.show()
# plt.savefig('numpy.png')
Umh...
The first version that you posted looks ok on my screen (N is not inverted).
And
this version shows no difference in the N; but, it does show tick marks
labeled with numerical values.
--V
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Re: [Numpy-discussion] Numpy logo in VTK
Heh, thanks :) It's free interpretation made from quick idea then immediately shared. Original logo can be made exact I guess with interlaced planes and shallower bars or similar... On Tue, Jun 26, 2012 at 8:19 AM, Anthony Scopatz wrote: This is awesome! ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] [SciPy-Dev] Announce: scikit-learn v0.11
This news did not arrive at scikit-learn-gene...@lists.sourceforge.net Is above list deprecated? BTW thanks for supporting and working on this project ;) On Tue, May 8, 2012 at 1:13 AM, Gael Varoquaux gael.varoqu...@normalesup.org wrote: On behalf of Andy Mueller, our release manager, I am happy to announce the 0.11 release of scikit-learn. This release includes some major new features such as randomized sparse models, gradient boosted regression trees, label propagation and many more. The release also has major improvements in the documentation and in stability. Details can be found on the [1]what's new page. We also have a new page with [2]video tutorials on machine learning with scikit-learn and different aspects of the package. Sources and windows binaries are available on sourceforge, through pypi (http://pypi.python.org/pypi/scikit-learn/0.11) or can be installed directly using pip: pip install -U scikit-learn Thanks again to all the contributors who made this release possible. Cheers, Gaël 1. http://scikit-learn.org/stable/whats_new.html 2. http://scikit-learn.org/stable/presentations.html ___ 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
