Deploy workflow + Coveralls

.github/workflows/release.yml

@@ -0,0 +1,18 @@
+on:
+  release:
+    types: [created]
+jobs:
+  deploy-to-pypi:
+    runs-on: ubuntu-latest
+    env:
+      TWINE_USERNAME: __token__
+      TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }}
+    steps:
+    - uses: actions/checkout@v4
+    - uses: actions/setup-python@v5
+      with:
+        python-version: '3.x'
+    - run: |
+        pip install build wheel twine
+        python -m build
+        twine upload dist/*

.github/workflows/test.yml

@@ -5,16 +5,21 @@ on:
   pull_request:
 
 jobs:
-  Linux:
+  run-tests:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0 # important for Coveralls to read commit history
       - uses: actions/setup-python@v5
         with:
           python-version: '3.x'
-
-      - name: tests
+      - name: tests and coverage
         run: |
-          pip install -e .[advanced,dev]
-          pytest pynumdiff
-
+          pip install -e .[advanced,dev] coveralls
+          coverage run --source=pynumdiff --omit='pynumdiff/_version.py' -m pytest -s
+          coverage xml
+      - uses: coverallsapp/github-action@v2
+        with:
+          format: cobertura
+          file: coverage.xml

README.md

@@ -9,10 +9,13 @@ Python methods for numerical differentiation of noisy data, including multi-obje
 </p>
 
 <p align="center">
+    <img src='https://github.com/florisvb/pynumdiff/actions/workflows/test.yml/badge.svg'/>
     <a href='https://pynumdiff.readthedocs.io/master/'>
         <img src='https://app.readthedocs.org/projects/pynumdiff/badge/?version=master' alt='Documentation Status' /></a>
+    <a href='https://coveralls.io/github/florisvb/PyNumDiff?branch=master'>
+        <img src='https://coveralls.io/repos/github/florisvb/PyNumDiff/badge.svg?branch=master' alt='Coverage Status' /></a>
     <a href="https://badge.fury.io/py/pynumdiff">
-        <img src="https://badge.fury.io/py/pynumdiff.svg" alt="PyPI version" height="18"></a>
+        <img src="https://badge.fury.io/py/pynumdiff.svg" alt="PyPI"></a>
     <a href="https://zenodo.org/badge/latestdoi/159711175">
         <img src="https://zenodo.org/badge/159711175.svg" alt="DOI"></a>
     <a href="https://joss.theoj.org/papers/102257ee4b0142bf49bc18d7c810e9d5">
@@ -24,12 +27,12 @@ Python methods for numerical differentiation of noisy data, including multi-obje
 PyNumDiff is a Python package that implements various methods for computing numerical derivatives of noisy data, which can be a critical step in developing dynamic models or designing control. There are seven different families of methods implemented in this repository:
 
 1. convolutional smoothing followed by finite difference calculation
-2. polynomial-fit-based methods
-3. iterated finite differencing
-4. total variation regularization of a finite difference derivative
-5. Kalman (RTS) smoothing
-6. basis-function-based methods
-7. linear local approximation with linear model
+2. polynomial fit methods
+3. basis function fit methods
+4. iterated finite differencing
+5. total variation regularization of a finite difference derivative
+6. Kalman (RTS) smoothing
+7. local approximation with linear model
 
 Most of these methods have multiple parameters, so we take a principled approach and propose a multi-objective optimization framework for choosing parameters that minimize a loss function to balance the faithfulness and smoothness of the derivative estimate. For more details, refer to [this paper](https://doi.org/10.1109/ACCESS.2020.3034077).