Initial commit

.github/codecov.yml

@@ -0,0 +1,22 @@
+codecov:
+  token: ad953cd4-94de-4240-98cd-fe7037ca4277
+ignore:
+  - "examples"
+  - "tests"
+  - "setup.py"
+comment:
+  layout: "reach, diff, flags, files"
+  behavior: default
+  require_changes: false # if true: only post the comment if coverage changes
+  require_base: no # [yes :: must have a base report to post]
+  require_head: yes # [yes :: must have a head report to post]
+  branches: # branch names that can post comment
+    - "main"
+github_checks:
+  annotations: false
+coverage:
+  status:
+    project:
+      default:
+        target: auto
+        threshold: 80%

.github/workflows/black.yml

@@ -0,0 +1,13 @@
+name: Lint
+
+on: [push, pull_request]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: actions/setup-python@v2
+      - uses: psf/black@stable
+        with:
+          args: ". --check"

.github/workflows/run_tests.yml

@@ -0,0 +1,36 @@
+name: Run Tests
+
+on: [push, pull_request]
+
+jobs:
+  build-images:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest]
+        #os: [ubuntu-latest, macos-latest, windows-latest]
+        python-version: ["3.6", "3.7", "3.8", "3.9", "3.10"]
+    env:
+      os: ${{ matrix.os }}
+      python: ${{ matrix.python-version }}
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          submodules: true
+      - uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install
+        run: |
+          pip install -e .[dev]
+      - name: Execute tests
+        run: |
+          pytest --cov=ndc --cov-report=xml
+      - name: Upload Coverage to Codecov
+        uses: codecov/codecov-action@v1
+        with:
+          file: ./coverage.xml
+          flags: unittests
+          env_vars: OS,PYTHON
+          fail_ci_if_error: true
+          verbose: true

.gitignore

@@ -0,0 +1,133 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# IDEs
+.idea
+.vscode

LICENSE.txt

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 Fabio Muratore
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -0,0 +1,85 @@
+# Numerical Differentiation Leveraging Convolution (ndc)
+
+[![License](https://img.shields.io/badge/license-MIT-brightgreen)](https://opensource.org/licenses/MIT)
+[![codecov](https://codecov.io/gh/famura/ndc/branch/main/graph/badge.svg?token=ESUTNFwtYY)](https://codecov.io/gh/famura/ndc)
+[![isort](https://img.shields.io/badge/imports-isort-green)](https://pycqa.github.io/isort/)
+[![codestyle](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+
+**What for?**
+
+Differentiate signals stored as PyTorch tensors, e.g. measurements obtained from a device or simulation, where automatic differentiation can not be applied.
+
+**Features**
+
+* Theoretically **any order, any stencils, and any step size** (see [this Wiki page](https://en.wikipedia.org/wiki/Finite_difference_coefficient) for information). Be aware that there are numerical limits when computing the filter kernel's coefficients, e.g. small step sized and high orders lead to numerical issues.
+* Works for **multidimensional signals**, assuming that all dimensions share the same step size.
+* Computations can be executed on **CUDA**. However, this has not been tested extensively.
+* Straightforward implementation which you can easily adapt to your needs.
+
+**How?**
+
+The idea of this small repository is to use the duality between convolution, i.e., filtering, and [numerical differentiation](https://en.wikipedia.org/wiki/Numerical_differentiation) to leverage the existing functions for 1-dimensional convolution in order to compute the (time) derivatives.
+
+**Why PyTorch?**
+
+More often then not I received (recorded) simulation data as PyTorch tensors rather than numpy arrays.
+Thus, I think it is nice to have a function to differentiate measurement signals without switching the data type or computation device.
+Moreover, the `torch.conv1d` function fits perfectly for this purpose.
+
+
+## Citing
+
+If you use code or ideas from this repository for your projects or research, please cite it.
+```
+@misc{Muratore_ncd,
+  author = {Fabio Muratore},
+  title = {ndc - Numerical differentiation leveraging convolutions},
+  year = {2022},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/famura/ndc}}
+}
+```
+
+## Installation
+
+To install the core part of the package run
+```
+pip install ndc
+```
+
+For (local) development install the dependencies with
+```
+pip install -e .[dev]
+```
+
+## Usage
+
+Consider a signal `x`, e.g. a measurement you obtained form a device. This package assumes that the signal to differentiate is of shape `(num_steps, dim_data)`
+
+```python
+import torch
+import ndc
+
+# Assuming you got x(t) from somewhere.
+assert isinstance(x, torch.Tensor)
+num_steps, dim_data = x.shape 
+
+# Specify the derivative. Here, the first order central derivative.
+stencils = [-1, 0, 1]
+order = 1
+step_size = dt # should be known from your signal x(t), else use 1
+padding = True # if true, the initial and final values are repeated as often as necessary to match the  length of x 
+
+dx_dt_num = ndc.differentiate_numerically(x, stencils, order, step_size, padding)
+assert dx_dt_num.device == x.device
+if padding:
+    assert dx_dt_num.shape == (num_steps, dim_data)
+else:
+    assert dx_dt_num.shape == (num_steps - sum(s != 0 for s in stencils), dim_data)
+```
+
+
+## Contributions
+
+Maybe you want another padding mode, or you found a way to improve the CUDA support. Please feel free to leave a pull request or issue.