Initial commit from local repository

Author: heptaflar

.github/workflows/python-package.yml

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+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: Python package
+
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.11", "3.12", "3.13"]
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install flake8 pytest
+        python -m pip install .[lint]
+        python -m pip install .[test]
+    - name: Lint with flake8
+      run: |
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings
+        flake8 . --count --exit-zero --ignore E203 --max-complexity=14 --max-line-length=78 --statistics
+    - name: Test with pytest
+      run: |
+        pytest --cov=.

.idea/.gitignore

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+cff-version: 1.2.0
+title: >-
+  Uncertaintyx: tensor-level uncertainty
+  propagation with JAX
+message: >-
+  If you use this software, please cite it using the
+  metadata from this file.
+type: software
+authors:
+  - given-names: Ralf
+    family-names: Quast
+    email: ralf.quast@brockmann-consult.de
+    affiliation: Brockmann Consult GmbH
+    orcid: 'https://orcid.org/0000-0002-6857-6503'
+license: MIT
+repository-code: >-
+  https://github.com/bcdev/uncertaintyx/
+abstract: >-
+  Uncertaintyx is a lightweight framework for
+  tensor‑level uncertainty propagation, fitting
+  of empirical or physics‑informed models, and
+  metrology‑aware workflows. It produces uncertainty
+  tensors by combining tensor‑valued models with
+  algorithmic differentiation backends such as JAX.
+  Conventional NumPy acts as a bidirectional
+  interoperability layer.