Skip to main content

Install packages from git repo using pip

Packages in git repo (or hg, svn, bzr) can be installed with "pip install -e" directly. Base on the help message of "pip install", we have

  -e VCS+REPOS_URL[@REV]#egg=PACKAGE, --editable=VCS+REPOS_URL[@REV]#egg=PACKAGE
                        Install a package directly from a checkout. Source
                        will be checked out into src/PACKAGE (lower-case) and
                        installed in-place (using setup.py develop). You can
                        run this on an existing directory/checkout (like pip
                        install -e src/mycheckout). This option may be
                        provided multiple times. Possible values for VCS are:
                        svn, git, hg and bzr.


To use it, first make sure you have git installed. Such as "sudo apt-get install git-core" on ubuntu. And then you may follow the below examples(install redis-py):

# Install from master
pip install -e git+https://github.com/andymccurdy/redis-py.git#egg=redis-py
# Install a branch
pip install -e git+https://github.com/andymccurdy/redis-py.git@pubsub#egg=redis-py
# Install a tag
pip install -e git+https://github.com/andymccurdy/redis-py.git@2.8.0#egg=redis-py
# Install a commit
pip install -e git+https://github.com/andymccurdy/redis-py.git@90ba027#egg=redis-py

Base on this post in stackoverflow, you may also specify a line in the requirment file(like reqs.txt):

-e git+https://github.com/andymccurdy/redis-py.git@90ba027#egg=redis-py

And then, install it with "pip install -r reqs.txt".
Labels:

Comments

Post a Comment

Popular posts from this blog

A simple implementation of DTW(Dynamic Time Warping) in C#/python

DTW(Dynamic Time Warping) is a very useful tools for time series analysis. This is a very simple (but not very efficient) c# implementation of DTW, the source code is available at  https://gist.github.com/1966342  . Use the program as below: double[] x = {9,3,1,5,1,2,0,1,0,2,2,8,1,7,0,6,4,4,5}; double[] y = {1,0,5,5,0,1,0,1,0,3,3,2,8,1,0,6,4,4,5}; SimpleDTW dtw = new SimpleDTW(x,y); dtw.calculateDTW(); The python implementation is available at  https://gist.github.com/3265694  . from python-dtw import Dtw import math dtw = Dtw([1, 2, 3, 4, 6], [1, 2, 3, 5],           distance_func=lambda x, y: math.fabs(x - y)) print dtw.calculate() #calculate the distance print dtw.get_path() #calculate the mapping path
14 comments
Read more

Change the default user when start a docker container

When run(start) a docker container from an image, we can specify the default user by passing -u option in command line(In https://docs.docker.com/engine/reference/run/#user ). For example docker run -i -t -u ubuntu ubuntu:latest /bin/bash We can also use the USER instruction in DOCKERFILE to do the same thing(In https://docs.docker.com/engine/reference/builder/#user), note that the option in command line will override the one in the DOCKERFILE. And there is actually another way to start a container with neither DOCKERFILE nor -u option, just by a command like: docker run -i -t ubuntu:latest /bin/bash # with ubuntu as the default user This happens when your start the container from an image committed by a container with ubuntu as the default user. Or in detail: Run a container from some basic images, create ubuntu user inside it, commit the container to CUSTOM_IMAGE:1 . Run a container from CUSTOM_IMAGE:1 with "-u ubuntu" option, and commit the container to CUSTOM...
Post a Comment
Read more

Notes on Sequential Pattern Mining (2) -- Partial Order Pattern Mining and Contrast Mining

1. In , the authors induce TEIRESIAS algorithms to mining combinatorial patterns with gap constraints in biological sequences. The patterns TEIRESIAS mined is similiar with the common sequential patterns, but it could contain "." the wild card which is also in the alphbel of the sequences database standing for any other item available, for example pattern "A..B" is a length-4 pattern, with two arbitrary items between the first A and the last B. Patterns "AC.B", "AADB" are all said to be more specific than pattern "A..B". TEIRESIAS mining all the maximal patterns () with a support over a min threshold K. There some key points of TEIRESIAS algorithms: 1)The growth of the patterns The growth of the patterns is accomplished by convolute current pattern by a short length pattern. Pattern A and pattern B are convolutable if the last L(very small) characters of pattern A is the same as the first L characters of pattern B, then ...
Post a Comment
Read more