nguigs

"""Riemannian k-means on the Grassmann Manifold.

Nicolas Guigui, 29/11/2022
"""

import geomstats.backend as gs
from geomstats.geometry.grassmannian import Grassmannian
from geomstats.learning.online_kmeans import OnlineKMeans

n_samples = 10

robertoostenveld

#!/usr/bin/env python

# Unicorn2lsl streams data from a Unicorn Hybrid Black EEG system to LSL
#
# Copyright (C) 2022 Robert Oostenveld
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

gemeinl

hichamjanati

import pyvista as pv
import numpy as np


make_gif = True

# increase n_points for a higher resolution
n_points = 100
xmin, xmax = -1.2, 1.2
bounds = 1.25 * np.array([xmin, xmax, xmin, xmax, 0., 0.])

jakevdp

Chaser324

Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.

In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.

Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j

dan-blanchard

For ETS's SKLL project, we found out the hard way that Travis-CI's support for numpy and scipy is pretty abysmal. There are pre-installed versions of numpy for some versions of Python, but those are seriously out of date, and scipy is not there are at all. The two most popular approaches for working around this are to (1) build everything from scratch, or (2) use apt-get to install more recent (but still out of date) versions of numpy and scipy. Both of these approaches lead to longer build times, and with the second approach, you still don't have the most recent versions of anything. To circumvent these issues, we've switched to using Miniconda (Anaconda's lightweight cousin) to install everything.

A template for installing a simple Python package that relies on numpy and scipy using Miniconda is provided below. Since it's a common s

leandrosilva

$ python xlog.py sample.log

{'appname': 'test.app', 'timestamp': '2012-09-06 15:19:32', 'hostname': 'codezone.local', 'pid': '68898', 'priority': '132', 'message': 'bla bla bla warn'}
{'appname': 'test.app', 'timestamp': '2012-09-06 15:19:32', 'hostname': 'codezone.local', 'pid': '68902', 'priority': '131', 'message': 'bla bla bla error'}
{'appname': 'Dock', 'timestamp': '2012-09-06 15:19:32', 'hostname': 'codezone.local', 'pid': '154', 'priority': '11', 'message': 'CGSReleaseWindowList: called with 5 invalid window(s)'}
{'appname': 'WindowServer', 'timestamp': '2012-09-06 15:19:32', 'hostname': 'codezone.local', 'pid': '79', 'priority': '11', 'message': 'CGXSetWindowListAlpha: Invalid window 0'}

$ python xlog.py sample.log | grep test.app

{'priority': '132', 'timestamp': '2020-03-04 12:42:40', 'hostname': 'codezone.local', 'appname': 'test.app', 'pid': '68898', 'message': 'bla bla bla warn'}