Merge pull request #83 from CosmoStat/dummy_main

Pre-release v2.0.0

.github/workflows/cd.yml

@@ -0,0 +1,42 @@
+# This workflow will install Python dependencies, run tests and lint with a single version of Python
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: CD
+
+on:
+  push:
+    branches:
+      - main
+
+
+jobs:
+  docs:
+    name: Deploy API documentation
+    runs-on: [ubuntu-latest]
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      - name: Set up Python 3.10.5
+        uses: actions/setup-python@v3
+        with:
+          python-version: "3.10.5"
+
+      - name: Check Python Version
+        run: python --version
+
+      - name: Install dependencies
+        run: |
+          python -m pip install ".[docs]"
+     
+      - name: Build API documentation
+        run: |
+          sphinx-apidoc -Mfeo docs/source src/wf_psf
+          sphinx-build docs/source docs/build
+       
+      - name: Deploy API documentation
+        uses: peaceiris/[email protected]
+        with:
+           github_token: ${{ secrets.GITHUB_TOKEN }}
+           publish_dir: docs/build

.github/workflows/ci.yml

@@ -0,0 +1,29 @@
+# This workflow will install Python dependencies, run tests and lint with a single version of Python
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: CI
+
+on:
+  pull_request:
+    branches:
+      - main
+
+
+jobs:
+  test-full:
+    runs-on: [ubuntu-latest]
+
+    steps:
+      - name:
+        uses: actions/checkout@v3
+
+      - name: Set up Python 3.10.5
+        uses: actions/setup-python@v3
+        with:
+          python-version: "3.10.5"
+
+      - name: Install dependencies
+        run: python -m pip install ".[test]"
+
+      - name: Test with pytest
+        run: python -m pytest

.gitignore

@@ -5,6 +5,7 @@ __pycache__/
 
 # Remove method comparison data
 method-comparison/compatible-datasets/*
+tf_notebooks
 
 # Log files from slurm
 *.err
@@ -61,11 +62,13 @@ htmlcov/
 .coverage.*
 .cache
 nosetests.xml
+pytest.xml
 coverage.xml
 *.cover
 *.py,cover
 .hypothesis/
 .pytest_cache/
+src/wf_psf/pytest.xml
 
 # Translations
 *.mo
@@ -86,6 +89,11 @@ instance/
 
 # Sphinx documentation
 docs/_build/
+docs/source/wf_psf*.rst
+docs/source/_static/file.png
+docs/source/_static/images/logo_colab.png
+docs/source/_static/minus.png
+docs/source/_static/plus.png
 
 # PyBuilder
 target/
@@ -143,3 +151,7 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+
+
+# WF-PSF Debug
+/debug/

README.md

@@ -3,103 +3,11 @@
 <h1 align='center'>WaveDiff</h1>
 <h2 align='center'>A differentiable data-driven wavefront-based PSF modelling framework.</h2>
 
+WaveDiff is a differentiable PSF modelling pipeline constructed with [Tensorflow](https://github.com/tensorflow/tensorflow). It was developed at the [CosmoStat lab](https://www.cosmostat.org) at CEA Paris-Saclay.
+
+See the [documentation](https://cosmostat.github.io/wf-psf/) for details on how to install and run WaveDiff.
+
 This repository includes:
 - A differentiable PSF model entirely built in [Tensorflow](https://github.com/tensorflow/tensorflow).
-- A numpy-based PSF simulator [here](https://github.com/tobias-liaudat/wf-psf/blob/main/wf_psf/SimPSFToolkit.py).
+- A [numpy-based PSF simulator](https://github.com/CosmoStat/wf-psf/tree/dummy_main/src/wf_psf/sims).
 - All the scripts, jobs and notebooks required to reproduce the results in [arXiv:2203.04908](http://arxiv.org/abs/2203.04908) and [arXiv:2111.12541](https://arxiv.org/abs/2111.12541).
-
-For more information on how to use the WaveDiff model through configurable scripts see the `long-runs` directory's [README](https://github.com/tobias-liaudat/wf-psf/blob/main/long-runs/README.md). 
-
-## Proposed framework
-
-A schematic of the proposed framework can be seen below. The PSF model is estimated (trained) using star observations in the field-of-view.
-
-<img height=300 src="assets/PSF_model_diagram_v6.png" >
-
-<!-- Visual reconstruction example of the WaveDiff-original PSF model trained on a simplified Euclid-like setting.
-
-<img height=800 src="assets/PSF_reconstruction_example.png" > -->
-
-
-## Install
-
-`wf-psf` is pure python and can be easily installed with `pip`. After cloning the repository, run the following commands:
-
-```bash
-$ cd wf-psf
-$ pip install .
-```
-
-The package can then be imported in Python as `import wf_psf as wf`. We recommend using the release `1.2.0` for stability as the current main branch is under development.
-
-## Requirements
-- [numpy](https://github.com/numpy/numpy) [>=1.19.2]
-- [scipy](https://github.com/scipy/scipy) [>=1.5.2]
-- [TensorFlow](https://www.tensorflow.org/) [==2.4.1]
-- [TensorFlow Addons](https://github.com/tensorflow/addons) [==0.12.1]
-- [Astropy](https://github.com/astropy/astropy) [==4.2]
-- [zernike](https://github.com/jacopoantonello/zernike) [==0.0.31]
-- [opencv-python](https://github.com/opencv/opencv-python) [>=4.5.1.48]
-- [pillow](https://github.com/python-pillow/Pillow) [>=8.1.0]
-- [galsim](https://github.com/GalSim-developers/GalSim) [>=2.3.1]
-
-Optional packages:
-- [matplotlib](https://github.com/matplotlib/matplotlib) [=3.3.2]
-- [seaborn](https://github.com/mwaskom/seaborn) [>=0.11]
-
-
-## Reproducible research
-
-#### [arXiv:2203.04908](http://arxiv.org/abs/2203.04908) Rethinking data-driven point spread function modeling with a differentiable optical model (2022)
-_Submitted._
-
-- Use the release 1.2.0.
-- All the scripts, jobs and notebooks to reproduce the figures from the article can be found [here](https://github.com/tobias-liaudat/wf-psf/tree/main/papers/article_IOP).
-- The trained PSF models are found [here](https://github.com/tobias-liaudat/wf-psf/tree/main/papers/article_IOP/data/models).
-- The input PSF field can be found [here](https://github.com/tobias-liaudat/wf-psf/tree/main/data).
-- The script used to generate the input PSF field is [this one](https://github.com/tobias-liaudat/wf-psf/blob/main/long-runs/LR-PSF-field-gen-coherentFields.py).
-- The code required to run the comparison against pixel-based PSF models is in [this directory](https://github.com/tobias-liaudat/wf-psf/tree/main/method-comparison).
-- The training of the models was done using [this script](https://github.com/tobias-liaudat/wf-psf/blob/main/long-runs/train_eval_plot_script_click.py). In order to match the script's option for the different models with the article you should follow: 
-    - `poly->WaveDiff-original`
-    - `graph->WaveDiff-graph`
-    - `mccd->WaveDiff-Polygraph`
-
-_Note: To run the comparison to other PSF models you need to install them first. See [RCA](https://github.com/CosmoStat/rca), [PSFEx](https://github.com/astromatic/psfex) and [MCCD](https://github.com/CosmoStat/mccd)._
-
-
-#### [arXiv:2111.12541](https://arxiv.org/abs/2111.12541) Rethinking the modeling of the instrumental response of telescopes with a differentiable optical model (2021)
-_NeurIPS 2021 Workshop on Machine Learning and the Physical Sciences._
-
-- Use the release 1.2.0.
-- All the scripts, jobs and notebooks to reproduce the figures from the article can be found [here](https://github.com/tobias-liaudat/wf-psf/tree/main/papers/Neurips2021_ML4Physics_workshop).
-
-
-
-## Citation
-
-If you use `wf-psf` in a scientific publication, we would appreciate citations to the following paper:
-
-*Rethinking data-driven point spread function modeling with a differentiable optical model*, T. Liaudat, J.-L. Starck, M. Kilbinger, P.-A. Frugier, [arXiv:2203.04908](http://arxiv.org/abs/2203.04908), 2022.
-
-
-The BibTeX citation is the following:
-```
-@misc{https://doi.org/10.48550/arxiv.2203.04908,
-  doi = {10.48550/ARXIV.2203.04908},
-  
-  url = {https://arxiv.org/abs/2203.04908},
-  
-  author = {Liaudat, Tobias and Starck, Jean-Luc and Kilbinger, Martin and Frugier, Pierre-Antoine},
-  
-  keywords = {Instrumentation and Methods for Astrophysics (astro-ph.IM), Computer Vision and Pattern Recognition (cs.CV), FOS: Physical sciences, FOS: Physical sciences, FOS: Computer and information sciences, FOS: Computer and information sciences},
-  
-  title = {Rethinking data-driven point spread function modeling with a differentiable optical model},
-  
-  publisher = {arXiv},
-  
-  year = {2022},
-  
-  copyright = {arXiv.org perpetual, non-exclusive license}
-}
-```
-

config/configs.yaml

@@ -0,0 +1,2 @@
+---
+  training_conf: training_config.yaml

config/data_config.yaml

@@ -0,0 +1,44 @@
+# Training and test data sets for training and/or metrics evaluation
+data:
+  training:
+    # Specify directory path to data; Default setting is /path/to/repo/data
+    data_dir: data/coherent_euclid_dataset/
+    file: train_Euclid_res_200_TrainStars_id_001.npy
+    # if training data set file does not exist, generate a new one by setting values below
+    stars: null
+    positions: null
+    SEDS: null
+    zernike_coef: null
+    C_poly: null
+    params: # 
+      d_max: 2
+      max_order: 45
+      x_lims: [0, 1000.0]
+      y_lims: [0, 1000.0]
+      grid_points: [4, 4]
+      n_bins: 20
+      max_wfe_rms: 0.1
+      oversampling_rate: 3.0
+      output_Q: 3.0
+      output_dim: 32
+      LP_filter_length: 2
+      pupil_diameter: 256
+      euclid_obsc: true
+      n_stars: 200
+  test:
+    data_dir: data/coherent_euclid_dataset/
+    file: test_Euclid_res_id_001.npy
+    # If test data set file not provided produce a new one
+    stars: null
+    noisy_stars: null
+    positions: null
+    SEDS: null
+    zernike_coef: null
+    C_poly: null
+    parameters:
+      d_max: 2
+      max_order: 45
+      x_lims: [0, 1000.0]
+      y_lims: [0, 1000.0]
+      grid_points: [4,4]
+      max_wfe_rms: 0.1

config/logging.conf

@@ -0,0 +1,33 @@
+[loggers]
+keys=root, simpleLogger
+
+[handlers]
+keys=consoleHandler, fileHandler
+
+[formatters]
+keys=simpleFormatter
+
+[logger_root]
+level=DEBUG
+handlers=consoleHandler, fileHandler
+
+[logger_simpleLogger]
+level=DEBUG
+handlers=consoleHandler, fileHandler
+qualname=simpleLogger
+propagate=0
+
+[handler_consoleHandler]
+class=StreamHandler
+level=DEBUG
+formatter=simpleFormatter
+args=(sys.stdout,)
+
+[handler_fileHandler]
+class=FileHandler
+level=DEBUG
+formatter=simpleFormatter
+args=('%(filename)s', 'w')
+
+[formatter_simpleFormatter]
+format=%(asctime)s - %(name)s - %(levelname)s - %(message)s