Metadata-Version: 1.0
Name: hastl
Version: 0.1
Summary: A fast GPU implementation of STL decomposition with missing values
Home-page: https://github.com/mortvest/hastl
Author: Dmitry Serykh
Author-email: dmitry.serykh@gmail.com
License: MIT
Description: HaSTL
        =================================================================
        
        HaSTL [ˈheɪstiɛl]: A fast GPU implementation of batched Seasonal and Trend
        decomposition using Loess (STL) [1] with missing values and support for both
        CUDA and OpenCL (a rather slow C backend is also available).
        Loosely based on `stlplus <https://github.com/hafen/stlplus>`_, a
        popular library for the R programming language. The GPU code is written in
        `Futhark <https://futhark-lang.org>`_, a small functional language that compiles
        to efficient parallel code.
        
        
        Requirements
        ------------
        
        You would need a working OpenCL or CUDA installation, C compiler and these Python packages:
        
        - futhark-ffi>=0.13.0
        - wheel
        
        
        Installation
        ------------
        
        You may want to run the program in a Python virtual environment. Create it via::
        
          python -m venv env
        
        Then, activate the virtual environment via::
        
          . env/bin/activate
        
        Upgrade pip via::
        
          pip install --upgrade pip
        
        To install the package from the sources, first get the current stable release via::
        
          git clone https://github.com/mortvest/hastl
        
        Install the bfast dependencies via::
        
          pip install -r requirements.txt
        
        Afterwards, you can compile all the backends and install the package (this will take a while)::
        
          python setup.py sdist bdist_wheel
          pip install .
        
        
        Usage
        -----
        Set backend to "cuda", "opencl" or "c" and run::
        
          from hastl import STL
          stl = STL(backend=backend)
          seasonal, trend, remainder = stl.fit(data, n_p=12)
        
        
        References
        ----------
        [1] Cleveland, Robert B., et al. "STL: A seasonal-trend decomposition." J. Off. Stat 6.1 (1990): 3-73.
        
Platform: UNKNOWN
