semvec/pypi-packages huggingface dataset:
snake-charmer 0.13.1
A python package that enables a github repo to release a python project from a pull request
License: MIT

# snake_charmer
A github action for automating python package releases. 

### bUt gItHuB aLrEaDy hAs tHaT . . .

github has an action to publish to pypi. this still requires the developer know about good semantic versioning and keep the project in correct order to do so. This action runs before and actually works with the one provided by github so that releasing a new version is as simple as updating the labels on a pr.

### Project Goals
A github action where simply by tagging a PR with `major-release`, `minor-release`, or `revision-release` will correctly update your setup.py file, create a release via github (using commit history to build a changelog and the previous version defined to properly increment the version) and then publish it to PYPI. This way you only need to open a PR with your last feature for each version to create a release.
lognormcl 2021.4.20
angular power spectra of lognormal random fields
License: MIT
Requires Python: >=3.6

lognormcl
=========

**angular power spectra of lognormal random fields**

This is a minimal Python package for working with the angular power spectra of
lognormal spherical random fields.  It can currently convert between the power
spectra of lognormal random fields and their constituent normal random fields.

The package can be installed using pip:

    pip install lognormcl

Then import the `ln2n` and `n2ln` functions from the package:

    from lognormcl import ln2n, n2ln

For more information, please see the [documentation].

Current functionality covers the absolutely minimal use case.  Please open an
issue on GitHub if you would like to see anything added.

[documentation]: https://cltools.readthedocs.io/lognormcl/
pysharpen 0.2.8
Remote sensing image pansharpening and enhancement.
License: MIT
Requires Python: >=3.4.0


A library for remote sensing image pansharpening and enhancement
================================================================

**The goal is to assemble all the known good methods within one useful python library**

The data processing is based on rasterio bindings to GDAL binaries.
Big files are supported via windowed IO.


Use of library:
---------------


Worker class is initialized with a list of methods that are applied to the pair of images (pan, ms) consequently.
Some of the methods may need the setup stage where they gather the image statistics before processing,
use `worker.setup_methods()` before `worker.process()` to do it. The setup is also carried out with windowed reading.

`worker.process_single` and `worker.process_separate()` applies to the set of files
 and includes methods' setup and application.


Pansharpening methods:
----------------------

By the moment, only the very basic methods are supported:
   1. IHS
   2. Brovey
   3. Generalized IHS for any number of channels


Preprocessing methods:
----------------------


   1. Linear brightness scaling allowes to stretch the brightness to the whole range of the data format or fit to 8bit. The initial values range can be defined in the following variants:

      - min - max,
      - mean +- WIDTH*std


Adding your own method:
-----------------------

You can inherit ImgProc class, implementing process() function to add any preprocessing, pansharpening or postprocessing
function you need.


Command line interface:
-----------------------

CLI contains a subset of the methods that can be used off the shelf,
but for full functionality it is better to use the library

``pysharpen [--method METHOD] [--preprocessing PREPROCESSING] [--resampling RESAMPLING] [--nogeo] [--noclean] pan_file ms_file out_file``



Options:
- method : the main pansharpening method, allowed options are `ihs`, `brovey`, `gihs`, default `ihs`
- preprocessing: the optional step of image preparation before pansharpening allowed options are `minmax` and `meanstd`
(the latter with parameter WIDTH=3), default `none`
- resampling: a method for multispectral image resampling to the resolution of panchrom image.
Allowed options are `bilinear`, `nearest`, `cubic` etc., default `bilinear`
- nogeo: if checked, the images are opened and resampled without regard to the georeference. It is necessary when the
data is not georeferenced at all, or can be used if the image is small and windowed IO is not necessary
- noclean: if checked, the intermediate files are not removed. This can be useful if you need the separated channel-by-channel
to separate files multispectral data

Known issues
------------
The windowed georeferenced IO gives not the same result as whole image IO without georeference,
this is caused by the subpixel misplacement and will be addressed in the next updates.

Contributing
------------
You can describe any problem with the package via issues at github.
Your contributions are always welcome, especially new widely-used pansharpening methods.