diff --git a/README.md b/README.md index 1df4abc..a84d743 100644 --- a/README.md +++ b/README.md @@ -32,34 +32,31 @@ If you're looking for a GPU-accelerated version of Ripser, you can find it at [R Ripser.py is available on `pypi` with wheels for all major platforms. To install, type the following command into your environment: ```bash -pip install python +pip install ripser ``` + ### Local build -If the above command fails, you can build `ripser.py` locally. All that is required is that you -install the standard Python numerical computing libraries and Cython. +If the above command fails or if you want to develop and contribute to +`ripser.py`, you can build `ripser.py` locally. To do so, clone this +repository. From within the cloned repository, execute `pip install .` to build +locally, or `pip install -e .` for a local, +[editable](https://setuptools.pypa.io/en/latest/userguide/development_mode.html) +build. Either of the above two commands will install all required dependencies. +Explicitly, the dependencies of `ripser.py` are -Dependencies: +- Cython, +- numpy, +- scipy, +- scikit-learn, +- persim, -- Cython -- numpy -- scipy -- scikit-learn -- persim +and their required dependencies. -**Windows users:** If you are using a Windows machine, you will also need to install [MinGW](http://www.mingw.org) on your system. +**Windows users:** If you are using a Windows machine, you _may_ also need to install [MinGW](http://www.mingw.org) on your system. **Mac users:** Updating your Xcode and Xcode command line tools will probably fix any issues you have with installation. -Cython should be the only library required before installation. To install, type the following commands into your environment: - -``` -pip install cython -``` - -Following this, clone the repository, `cd` into the clone, and execute `pip install -v .` - - #### Optional dependency Ripser.py when compiled from source can have a _steroid_1 shot by replacing the standard `unordered_map` from the STL by one of the fastest implementation available: [robin_hood](https://github.com/martinus/robin-hood-hashing). Benchmarking of Ripser.py using the `robin_hood` implementation showed speed-ups up to **30%**.