similar to: mixing effects when joining videos

Hi, in the past two days I wrote Python bindings to libtheora: http://github.com/certik/python-theora Currently it can read any ogv file (that seems to work quite well) and also encode to ogv (this mostly works, but there are some subtle issues to be fixed, I suspect I still have some bug in the way I half the dimensions of the Cb and Cr planes, so when you read ogv and write ogv, the resulting

Hi, I am using oggz-chop in the ubuntu jaunty, am I doing something wrong? $ oggz-chop -o yt1.ogv -s0 -e500 ondrej.ogv Segmentation fault gdb session doesn't reveal much, since it isn't compiled with debugging symbols: (gdb) bt #0 0x00007f9e5d2f0092 in memcpy () from /lib/libc.so.6 #1 0x0000000000403698 in ?? () #2 0x000000000040262e in ?? () #3 0x000000000040285d in ?? () #4

Hi, I managed to get my own FLV videos working over the web: http://certik.github.com/record/ it plays in the opensource flash player (flowplayer), but I have to convert theora to FLV (you can use the script in the package above). My question is, does anyone know if it's possible to get theora itself working? I know firefox3.5 will be able to do it, but I am afraid that will be the only

On 04/06/18 13:51, Ulf Volmer wrote: > On 06.04.2018 18:25, m.roth at 5-cent.us wrote: >> CentOS 7 box. As there's no package in any of the repos, we're trying to >> install scikit-learn in the user's space. It refuses. My late try was, >> after d/l a .whl from last year, hoping that would work with the numpy >> package in the regular repos, I did a pip

Valeri Galtsev wrote: <snip> >>> On Fri, 6 Apr 2018 17:25 , <m.roth at 5-cent.us> wrote: >>> >>>> CentOS 7 box. As there's no package in any of the repos, we're trying >>>> to install scikit-learn in the user's space. It refuses. My late try was, >>>> after d/l a .whl from last year, hoping that would work with the numpy

Per the recent (seeming) consensus regarding incubating new projects under the LLVM organization, I would like to trial the process by requesting to incubate mlir-npcomp <https://github.com/google/mlir-npcomp>. The project is still quite young and has been primarily developed part time by myself and Sean Silva over the last ~2 months. We set it up following discussion of a Numpy/Scipy op set

Hi, I coded some routines for YCbCr <-> RGB conversion, both using floating point numbers and using integer arithmetics, code is attached. Let's say I want to convert from RGB to YCbCr and back. First the integer arithmetics: In [1]: from colors import * In [2]: a = [255, 0, 255] In [3]: RGB2YCbCr(a) Out[3]: (107, 202, 222) In [4]: YCbCr2RGB(RGB2YCbCr(a)) Out[4]: array([255, 1,

Richard Demeny wrote: > Just sudo it > > On Fri, 6 Apr 2018 17:25 , <m.roth at 5-cent.us> wrote: > >> CentOS 7 box. As there's no package in any of the repos, we're trying to >> install scikit-learn in the user's space. It refuses. My late try was, >> after d/l a .whl from last year, hoping that would work with the numpy >> package in the

CentOS 7 box. As there's no package in any of the repos, we're trying to install scikit-learn in the user's space. It refuses. My late try was, after d/l a .whl from last year, hoping that would work with the numpy package in the regular repos, I did a pip install --user scikit-learn..., and it still seems to want to write to system space: OSError: [Errno 13] Permission denied:

How difficult is it to get a good feel for the internals of R, if you want to learn the general code base, but also the CPU intensive stuff ( much of it in C or Fortran?) and the ways in which the general code and the CPU intensive stuff is connected together? R has a very large audience, but my understanding is that only a small group have a good understanding of the internals (and some of those

I have time-series data from a pair of inexpensive self-logging 3-axis accelerometers (http://www.gcdataconcepts.com/xlr8r-1.html). Since I'm not sure of the vibration/shock spectrum I'm measuring, for my initial sensor characterization run the units were mounted together with the sample rate set to the maximum of 640 samples/sec. Unfortunately, at this sample rate there are significant

Hi, here is a webpage for the Python bindings: http://certik.github.com/python-theora/ Changes since the last time: * tests added, e.g. there is a script that downloads some theora tests videos and then I test the python wrappers on them * every single method has a docstring with a doctested example * sphinx documentation added, see the link above * couple minor changes, like better control

Hola erreros Estoy rizando el rizo con esto de las matrices sparse y he encontrado la forma de hacerlo en python, que va rapidísimo. Por tanto tengo una matriz sparse en un objeto python con esta info: <138493x26744 sparse matrix of type '<type 'numpy.int64'> Quiero hacer lo mismo con R pero me he topado con la lentitud de los bucles para rellenar la sparse (también por mi

I've some trouble with installing numpy in python 3.3 on Centos 6.8 as installation request a different python version... [root at sge ~]$ yum install python33 python33-python-tools [root at sge ~]$ scl enable python33 bash ===== *this load Python 3.3.2* === [root at sge ~]$ easy_install pip [root at sge ~]$ pip install numpy Collecting numpy Using cached numpy-1.12.0.zip

Hi, We are looking for some guidance in installing an upgraded Python on our cluster. Our cluster was installed with Rocks 6.0, is running CentOs 6.2, and has python-2.6.6, gcc-4.4.6. We would like to install an upgraded version of Python along with the following modules NumPy Scipy (which will require a compatible version of the Atlas libraries) Mayavi (which will require a compatible

1) What is easy for one person may be very hard for another, so your question is really unanswerable. You do need to know C and Fortran to get through the source code. Get started soon reading the R Internals document if it sounds interesting to you... you are bound to learn something even if you don't stick with it. If you have questions about the internals though, you should read the Posting

Just had a look at scikit-learn. An issue you have with the current version of this package is that is depends on NumPy (>= 1.8.2). The version of NumPy in CentOS 7 base is 1.7.1. You may need to look at building a Python virtual environment. You can google that ;) On Tue, Apr 10, 2018 at 9:43 AM, Richard Grainger <grainger at gmail.com> wrote: > I created the epypel (Extra Extra

I have an irregular time series in a Zoo object, and I've been unable to find any way to do an FFT on it. More precisely, I'd like to do an NFFT (non-equispaced / non-uniform time FFT) on the data. The data is timestamped samples from a cheap self-logging accelerometer. The data is weakly regular, with the following characteristics: - short gaps every ~20ms - large gaps every ~200ms

Hi, I am using reticulate and a virtual environment (not conda) to run Python scripts from RStudio. However, when I try to use my own (existing) virtual environment, reticulate does not use it. If I run my scripts, the installed modules (e.g., py_install("pandas", "mmstat4.hu.data")) are not found. I believe this happens because reticulate is using r-reticulate instead of

Have you tried https://rstudio.github.io/reticulate/ ? Generally speaking, complex nonstandard package specific questions such as yours rarely get a reply here -- there are 20,000+ packages (and counting) after all! As reticulate was created by and integrated with RStudio/Posit, I would think their site and help resources might be a better venue. Of course, if you don't use RStudio, you may