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dduan / repository
A sample project to demonstrate how to build and link to Swift frameworks dynamically without using Xcode.
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
Without Xcode, how would one use swiftc to compile and dynamically link to
Swift frameworks?
This project demostrates one way to achieve that.
(It's really just some combinations of commandline options for swiftc in
a Makefile.)
You can read more about it in this blog post
LIBS variable
(separated by a space).APP_NAME to your application's name.SOURCES variable.make, and your app will be built in the build folder.Now you can import the frameworks in your application code the normal way and the app will build and run as expected.
main.swift and Frameworks/Answer are there as an example.
Selected from shared topics, language and repository description—not editorial ratings.
err-him /
Go Sample project to understand Mysql CRUD operation with best practises Includes logging, JWT, Swagger and Transactions
70/100 healthpvarin /
Contains all of the basic structure for a larger scale VHDL project including the Makefile, example code, and a testbench
40/100 healthmohammedari /
A test makefile project of stm32f3discovery. This project contains LED flashing sample source, serial communication sample code, makefile, linker script, gdb script and openocd script. This project is written in C language and tested with "GNU Tools for ARM Embedded Processors" toolchain.
druggles /
http://martinos.org/mne/ 4,861 commits 15 branches 16 releases 33 contributors Python 99.3% Other 0.6% PythonOther mne-python / Merge pull request #1175 from dgwakeman/write_trans … latest commit 1c606eda60 Alexandre Gramfort agramfort authored 8 hours ago bin FIX: Fix examples 3 months ago doc fix markdown VS rst 8 days ago examples cleanup viz 10 days ago mne COM:round 1 a day ago .coveragerc more coveralls 2 months ago .gitignore WIP mne_kit2fiff: from KIT SQD files to fif files a year ago .mailmap update mailmap 3 months ago .travis.yml FIX: Fix coveralls a month ago AUTHORS.rst FIX: Add Teon 9 months ago LICENSE.txt fix copyrights and authors 2 years ago MANIFEST.in FIX: MANIFEST.in 3 months ago Makefile Allow wget to continue download of a partially downloaded file 22 days ago README.rst Update README.rst 2 months ago dictionary.txt ENH: Use codespell 11 months ago setup.cfg FIX: Undo HTML coverage 2 months ago setup.py WIP: Add mapping 3 months ago README.rst Travis mne-python This package is designed for sensor- and source-space analysis of M-EEG data, including frequency-domain and time-frequency analyses and non-parametric statistics. This package is presently evolving quickly and thanks to the adopted open development environment user contributions can be easily incorporated. Get more information This page only contains bare-bones instructions for installing mne-python. If you're familiar with MNE and you're looking for information on using mne-python specifically, jump right to the mne-python homepage. This website includes a tutorial, helpful examples, and a handy function reference, among other things. If you're unfamiliar with MNE, you can visit the MNE homepage for full user documentation. Get the latest code To get the latest code using git, simply type: git clone git://github.com/mne-tools/mne-python.git If you don't have git installed, you can download a zip or tarball of the latest code: http://github.com/mne-tools/mne-python/archives/master Install mne-python As any Python packages, to install MNE-Python, go in the mne-python source code directory and do: python setup.py install or if you don't have admin access to your python setup (permission denied when install) use: python setup.py install --user You can also install the latest release version with easy_install: easy_install -U mne or with pip: pip install mne --upgrade or for the latest development version (the most up to date): pip install -e git+https://github.com/mne-tools/mne-python#egg=mne-dev --user Dependencies The required dependencies to build the software are python >= 2.6, NumPy >= 1.6, SciPy >= 0.7.2 and matplotlib >= 0.98.4. Some isolated functions require pandas >= 0.7.3 and nitime (multitaper analysis). To run the tests you will also need nose >= 0.10. and the MNE sample dataset (will be downloaded automatically when you run an example ... but be patient) To use NVIDIA CUDA for FFT FIR filtering, you will also need to install the NVIDIA CUDA SDK, pycuda, and scikits.cuda. The difficulty of this varies by platform; consider reading the following site for help getting pycuda to work (typically the most difficult to configure): http://wiki.tiker.net/PyCuda/Installation/ Contribute to mne-python Please see the documentation on the mne-python homepage: http://martinos.org/mne/contributing.html Mailing list http://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis Running the test suite To run the test suite, you need nosetests and the coverage modules. Run the test suite using: nosetests from the root of the project. Making a release and uploading it to PyPI This command is only run by project manager, to make a release, and upload in to PyPI: python setup.py sdist bdist_egg register upload Licensing MNE-Python is BSD-licenced (3 clause): This software is OSI Certified Open Source Software. OSI Certified is a certification mark of the Open Source Initiative. Copyright (c) 2011, authors of MNE-Python All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. Neither the names of MNE-Python authors nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission. This software is provided by the copyright holders and contributors "as is" and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. In no event shall the copyright owner or contributors be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software, even if advised of the possibility of such damage. http://martinos.org/mne/ http://martinos.org/mne/ 4,861 commits 15 branches 16 releases 33 contributors Python 99.3% Other 0.6% PythonOther mne-python / Merge pull request #1175 from dgwakeman/write_trans … latest commit 1c606eda60 Alexandre Gramfort agramfort authored 8 hours ago bin FIX: Fix examples 3 months ago doc fix markdown VS rst 8 days ago examples cleanup viz 10 days ago mne COM:round 1 a day ago .coveragerc more coveralls 2 months ago .gitignore WIP mne_kit2fiff: from KIT SQD files to fif files a year ago .mailmap update mailmap 3 months ago .travis.yml FIX: Fix coveralls a month ago AUTHORS.rst FIX: Add Teon 9 months ago LICENSE.txt fix copyrights and authors 2 years ago MANIFEST.in FIX: MANIFEST.in 3 months ago Makefile Allow wget to continue download of a partially downloaded file 22 days ago README.rst Update README.rst 2 months ago dictionary.txt ENH: Use codespell 11 months ago setup.cfg FIX: Undo HTML coverage 2 months ago setup.py WIP: Add mapping 3 months ago README.rst Travis mne-python This package is designed for sensor- and source-space analysis of M-EEG data, including frequency-domain and time-frequency analyses and non-parametric statistics. This package is presently evolving quickly and thanks to the adopted open development environment user contributions can be easily incorporated. Get more information This page only contains bare-bones instructions for installing mne-python. If you're familiar with MNE and you're looking for information on using mne-python specifically, jump right to the mne-python homepage. This website includes a tutorial, helpful examples, and a handy function reference, among other things. If you're unfamiliar with MNE, you can visit the MNE homepage for full user documentation. Get the latest code To get the latest code using git, simply type: git clone git://github.com/mne-tools/mne-python.git If you don't have git installed, you can download a zip or tarball of the latest code: http://github.com/mne-tools/mne-python/archives/master Install mne-python As any Python packages, to install MNE-Python, go in the mne-python source code directory and do: python setup.py install or if you don't have admin access to your python setup (permission denied when install) use: python setup.py install --user You can also install the latest release version with easy_install: easy_install -U mne or with pip: pip install mne --upgrade or for the latest development version (the most up to date): pip install -e git+https://github.com/mne-tools/mne-python#egg=mne-dev --user Dependencies The required dependencies to build the software are python >= 2.6, NumPy >= 1.6, SciPy >= 0.7.2 and matplotlib >= 0.98.4. Some isolated functions require pandas >= 0.7.3 and nitime (multitaper analysis). To run the tests you will also need nose >= 0.10. and the MNE sample dataset (will be downloaded automatically when you run an example ... but be patient) To use NVIDIA CUDA for FFT FIR filtering, you will also need to install the NVIDIA CUDA SDK, pycuda, and scikits.cuda. The difficulty of this varies by platform; consider reading the following site for help getting pycuda to work (typically the most difficult to configure): http://wiki.tiker.net/PyCuda/Installation/ Contribute to mne-python Please see the documentation on the mne-python homepage: http://martinos.org/mne/contributing.html Mailing list http://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis Running the test suite To run the test suite, you need nosetests and the coverage modules. Run the test suite using: nosetests from the root of the project. Making a release and uploading it to PyPI This command is only run by project manager, to make a release, and upload in to PyPI: python setup.py sdist bdist_egg register upload Licensing MNE-Python is BSD-licenced (3 clause): This software is OSI Certified Open Source Software. OSI Certified is a certification mark of the Open Source Initiative. Copyright (c) 2011, authors of MNE-Python All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. Neither the names of MNE-Python authors nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission. This software is provided by the copyright holders and contributors "as is" and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. In no event shall the copyright owner or contributors be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software, even if advised of the possibility of such damage.
43/100 healthZabrimus /
Simple Makefile project which prepares a shared CEF (chromium embedded framework) installation and compiles a sample appication.
38/100 healthmohammedari /
A test makefile project of stm32f3discovery. This project contains LED flashing sample source, makefile, linker script, gdb script and openocd script.
38/100 health