Metadata-Version: 2.1
Name: torch_lfilter
Version: 0.0.3
Summary:  Bring lowpass filtering to PyTorch! 
Home-page: https://github.com/flaport/torch_lfilter
Author: Floris Laporte
Author-email: floris.laporte@gmail.com
License: UNKNOWN
Description: # torch_lfilter
        
        Bring low pass filtering to PyTorch!
        
        This pytorch extension offers a PyTorch alternative for Scipy's
        `lfilter` - with gradient tracking.
        
        ## CPU tensors only (efficiently...)
        
        Although it's certainly the goal to implement an efficient CUDA
        lfilter in C++, for now only the CPU version is implemented in C++.
        That said, the implementation is reasonably fast and doing the
        filtering on the CPU might be a viable option. Moreover, the
        pure-python implementation works on all devices.
        
        ## Installation
        
        The library can be installed with pip:
        
        ```
        pip install torch_lfilter
        ```
        
        Please note that no pre-built wheels exist. This means that `pip` will
        attempt to install the library from source. Make sure you have the
        necessary dependencies installed for your OS.
        
        ## Dependencies
        
        ### Linux
        
        On Linux, having PyTorch installed is often enough to be able install
        the library (along with the typical developer tools for your
        distribution). Run the following inside a conda environment:
        
        ```
        conda install pytorch -c pytorch
        pip install torch_lfilter
        ```
        
        ### Windows
        
        On Windows, the installation process is a bit more involved as
        typically the build dependencies are not installed. To install those,
        download **Visual Studio Community 2017** from
        [here](https://my.visualstudio.com/Downloads?q=visual%20studio%202017&wt.mc_id=o~msft~vscom~older-downloads).
        During installation, go to **Workloads** and select the following
        workloads:
        
        - Desktop development with C++
        - Python development
        
        Then go to **Individual Components** and select the following
        additional items:
        
        - C++/CLI support
        - VC++ 2015.3 v14.00 (v140) toolset for desktop
        
        After installation, run the following commands _inside_ a **x64 Native
        Tools Command Prompt for VS 2017**, after activating your conda
        environment:
        
        ```
        conda install pytorch -c pytorch
        pip install torch_lfilter
        ```
        
        ## License
        
        © Floris Laporte 2020, [GPLv3](license)
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Description-Content-Type: text/markdown
