From c9acd289624c145982d44c576e21e19c26fd4b1c Mon Sep 17 00:00:00 2001 From: Kevin Schmidt Date: Mon, 30 Jan 2023 12:07:29 +0100 Subject: [PATCH 1/9] update figures --- CHANGES.rst | 34 +++++++++++++++++++++++++++++++++ resources/amp_phase.png | Bin 267923 -> 183788 bytes resources/hist_area_ratios.png | Bin 20023 -> 54628 bytes resources/hist_jet_offsets.png | Bin 19755 -> 57887 bytes resources/hist_mean_diffs.png | Bin 21375 -> 63254 bytes resources/source_plot.png | Bin 83171 -> 108249 bytes 6 files changed, 34 insertions(+) create mode 100644 CHANGES.rst diff --git a/CHANGES.rst b/CHANGES.rst new file mode 100644 index 00000000..93a512c8 --- /dev/null +++ b/CHANGES.rst @@ -0,0 +1,34 @@ +Radionets 0.1.16 (2023-01-30) +============================= + + +API Changes +----------- + + +Bug Fixes +--------- + + +New Features +------------ + +- added creation of uncertainty plots + changed creation and saving/reading of predictions to ``dicts`` + prediction ``dicts`` have 3 or 4 entries depending on uncertainty + added scaled option to ``get_ifft`` + created new dataset class for sampled images + created option for sampling and saving the whole test dataset + updated and wrote new tests [`#129 `__] + + +Maintenance +----------- + +- Add and enable ``towncrier`` in CI. 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CR`LV@ From 8d13e1ff9174c55b97910d47ddd9255c3cfcffce Mon Sep 17 00:00:00 2001 From: Kevin Schmidt Date: Mon, 30 Jan 2023 12:08:58 +0100 Subject: [PATCH 2/9] update README --- README.md | 51 ++++++++++++++++++++++++++------------------------- 1 file changed, 26 insertions(+), 25 deletions(-) diff --git a/README.md b/README.md index ffb48ec9..c67c3db2 100644 --- a/README.md +++ b/README.md @@ -4,17 +4,18 @@ ## Imaging Radio Interferometric Data with Neural Networks -Executables to simulate and analyze radio interferometric data in python. The goal is to reconstruct calibrated observations with +Deep-learning framework for the simulation and analysis of radio interferometric data in Python. The goal is to reconstruct calibrated observations with convolutional Neural Networks to create high-resolution images. For further information, please have a look at our [paper](https://www.aanda.org/component/article?access=doi&doi=10.1051/0004-6361/202142113). -Analysis strategies leading to a reproducible processing and evaluation of data recorded by radio interferometers: -* Simulation of datasets (will be shifted to `vipy` repository) +Analysis strategies leading to reproducible processing and evaluation of data recorded by radio interferometers: +* Simulation of datasets (see also the `radiosim` [repository](https://github.com/radionets-project/radiosim)) +* Simulation of radio interferometer observations (see also the `pyvisgen` [repository](https://github.com/radionets-project/pyvisgen)) * Training of deep learning models * Reconstruction of radio interferometric data -## Installation (must be reworked after shifting simulation part to `vipy` repository) +## Installation -This repository is build up as a python package. It is recommended to create an own conda environment to handle the dependencies of the packages. +This repository is built as a python package. We recommend creating a mamba environment to handle the dependencies of all packages. You can create one by running the following command in this repository: ``` $ conda env create -f environment.yml @@ -28,19 +29,19 @@ after the installation. ## Usage -For each task, executables are installed to your `PATH`. Each of them takes `toml` configuration files as input, to manage data paths and options. -Simulated data is saved in `hdf5`, trained models are saved as `pickle` files. +For each task, executables are installed to your `PATH`. Each takes `toml`` configuration files as input to manage data paths and options. +Simulated data is saved in `hdf5`; trained models are saved as `pickle` files. * `radionets_simulations <...>` - This script is used to simulate radio interferometric datasets for the training of deep learning models. + This script is used to simulate radio interferometric data sets for the training of deep learning models. * `radionets_training <...>` This script is used to train a model on events with known truth - values for the target variable, usually monte carlo simulations. + values for the target variable, usually Monte Carlo simulations. * `radionets_evaluation <...>` - This script is used to evaluate the performance of the trained deep learning models. + This script is used to evaluate the performance of the trained deep-learning models. -Default configuration files can be found in the examples directory. Additionally, the examples directory contains jupyter notebooks, which show an example -analysis pipeline and the corresponding commands. +Default configuration files can be found in the examples directory. The examples directory contains `jupyter notebooks`, which show an example +analysis pipeline and the corresponding commands. (need a rework) ## Structure of the Repository @@ -51,29 +52,28 @@ An introduction to Neural Networks and an overview of the use of fastai to train ### dl_training -Functions for handling the different training options. Currently, there are the training, the learning rate finder, and the loss plotting mode. +Functions for handling the different training options. Currently, there are the training, the learning rate finder, and the loss plotting mode available. -### simulations (further developed in [vipy](https://github.com/radionets-project/vipy) repository) +### simulations (further developed in [pyvisgen](https://github.com/radionets-project/pyvisgen) repository) Functions to simulate and illustrate radio interferometric observations. At the moment simulations based on the MNIST dataset and simulations of Gaussian sources are possible. We are currently working on simulating visibilities directly in Fourier space. -For more information, visit our corresponding repository [vipy](https://github.com/radionets-project/vipy). In the future, the simulations will be created -using the `vipy` repository, while the `radionets` repository contains the training and evaluation methods. +For more information, visit our corresponding repository [pyvisgen](https://github.com/radionets-project/pyvisgen). In the future, the simulations will be created +using the `pyvisgen` repository, while the `radionets` repository contains the training and evaluation methods. ### evaluation Functions for the evaluation of the training sessions. The available options reach from single, exemplary plots in (u, v) space and image space to methods computing characteristic values on large test datasets. In detail: -* Amplitude and phase for the prediction and the truth. Example image below including the difference between prediction and truth. +* Amplitude and phase for the prediction and the truth. Example image below includes the difference between prediction and truth. ![](resources/amp_phase.png) * Reconstructed source images with additional features, such as MS-SSIM values or the viewing angle. Example image below. ![](resources/source_plot.png) -* Histogram of differences between predicted and true viewing angles. Image includes a comparison with [wsclean](https://gitlab.com/aroffringa/wsclean). -![](resources/hist_jet_offsets.png) -* Histogram of ratio between predicted and true source areas. Image includes a comparison with [wsclean](https://gitlab.com/aroffringa/wsclean). +* Histogram of differences between predicted and true viewing angles. The image includes a comparison with [wsclean](https://gitlab.com/aroffringa/wsclean). +!Histogram of the ratio between predicted and true source areas. The image includes a comparison with [wsclean](https://gitlab.com/aroffringa/wsclean). ![](resources/hist_area_ratios.png) -* Histogram of flux difference in core component. Image includes a comparison with [wsclean](https://gitlab.com/aroffringa/wsclean). +* Histogram of flux difference in the core component. The image includes a comparison with [wsclean](https://gitlab.com/aroffringa/wsclean). ![](resources/hist_mean_diffs.png) * Included, but not yet fully operational * Histogram of differences between predicted and true MS-SSIM values on a dedicated test dataset @@ -85,6 +85,7 @@ All histograms are created on a dedicated test dataset. * Kevin Schmidt [@Kevin2](https://github.com/Kevin2) * Felix Geyer [@FeGeyer](https://github.com/FeGeyer) +* Arne Poggenpohl [@ArnePoggenpohl](https://github.com/ArnePoggenpohl) * Stefan Fröse [@StFroese](https://github.com/StFroese) * Paul-Simon Blomenkamp [@PBlomenkamp](https://github.com/PBlomenkamp) * Kevin Laudamus [@K-Lauda](https://github.com/K-Lauda) @@ -93,7 +94,7 @@ All histograms are created on a dedicated test dataset. ## Versions used and tested -* Python >= 3.6 -* pyTorch >= 1.2.0 -* torchvision >= 0.4.0 -* cudatoolkit >= 9.2 +* Python >= 3.8 +* pyTorch >= 1.11.0 +* torchvision >= 0.12.0 +* cudatoolkit >= 11.3 From 43bbb6401a6c71fd1279614a239c6f6f959ace26 Mon Sep 17 00:00:00 2001 From: Kevin Schmidt Date: Mon, 30 Jan 2023 12:12:09 +0100 Subject: [PATCH 3/9] fix typo --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c67c3db2..5b4b9f45 100644 --- a/README.md +++ b/README.md @@ -29,7 +29,7 @@ after the installation. ## Usage -For each task, executables are installed to your `PATH`. Each takes `toml`` configuration files as input to manage data paths and options. +For each task, executables are installed to your `PATH`. Each takes `toml` configuration files as input to manage data paths and options. Simulated data is saved in `hdf5`; trained models are saved as `pickle` files. * `radionets_simulations <...>` From 97969cded4fb954dec4fa193796f9ed4ddaf3230 Mon Sep 17 00:00:00 2001 From: Kevin Schmidt Date: Mon, 30 Jan 2023 12:16:11 +0100 Subject: [PATCH 4/9] fix figure path --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 5b4b9f45..be085b8f 100644 --- a/README.md +++ b/README.md @@ -71,7 +71,8 @@ methods computing characteristic values on large test datasets. In detail: * Reconstructed source images with additional features, such as MS-SSIM values or the viewing angle. Example image below. ![](resources/source_plot.png) * Histogram of differences between predicted and true viewing angles. The image includes a comparison with [wsclean](https://gitlab.com/aroffringa/wsclean). -!Histogram of the ratio between predicted and true source areas. The image includes a comparison with [wsclean](https://gitlab.com/aroffringa/wsclean). +![](resources/hist_jet_offsets.png) +* Histogram of the ratio between predicted and true source areas. The image includes a comparison with [wsclean](https://gitlab.com/aroffringa/wsclean). ![](resources/hist_area_ratios.png) * Histogram of flux difference in the core component. The image includes a comparison with [wsclean](https://gitlab.com/aroffringa/wsclean). ![](resources/hist_mean_diffs.png) From 51a6c1e895d0ecb91531e408685331dae522fa07 Mon Sep 17 00:00:00 2001 From: Kevin Schmidt Date: Mon, 30 Jan 2023 12:21:00 +0100 Subject: [PATCH 5/9] add changes --- docs/changes/136.maintenance.rst | 1 + 1 file changed, 1 insertion(+) create mode 100644 docs/changes/136.maintenance.rst diff --git a/docs/changes/136.maintenance.rst b/docs/changes/136.maintenance.rst new file mode 100644 index 00000000..4e73f7e3 --- /dev/null +++ b/docs/changes/136.maintenance.rst @@ -0,0 +1 @@ +Update README, use figures from the paper, minor text adjustments \ No newline at end of file From 147f13940bbdb0a43c5a0be690a51cc5068687ae Mon Sep 17 00:00:00 2001 From: Kevin Schmidt Date: Mon, 30 Jan 2023 12:25:59 +0100 Subject: [PATCH 6/9] bump version --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 760c3650..7e088763 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ setup( name="radionets", - version="0.1.17", + version="0.1.18", description="Imaging radio interferometric data with neural networks", url="https://github.com/radionets-project/radionets", author="Kevin Schmidt, Felix Geyer", From 038d0361e4ac8731ccff2c0b3ae25a165cf65145 Mon Sep 17 00:00:00 2001 From: Kevin Schmidt Date: Mon, 30 Jan 2023 12:29:34 +0100 Subject: [PATCH 7/9] changes in v0.1.18 --- CHANGES.rst | 38 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 38 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 93a512c8..73b363ab 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,3 +1,41 @@ +Radionets 0.1.18 (2023-01-30) +============================= + + +API Changes +----------- + + +Bug Fixes +--------- + + +New Features +------------ + +- added creation of uncertainty plots + changed creation and saving/reading of predictions to ``dicts`` + prediction ``dicts`` have 3 or 4 entries depending on uncertainty + added scaled option to ``get_ifft`` + created new dataset class for sampled images + created option for sampling and saving the whole test dataset + updated and wrote new tests [`#129 `__] + + +Maintenance +----------- + +- Add and enable ``towncrier`` in CI. [`#130 `__] + +- publish radionets on pypi [`#134 `__] + +- Update README, use figures from the paper, minor text adjustments [`#136 `__] + + +Refactoring and Optimization +---------------------------- + + Radionets 0.1.16 (2023-01-30) ============================= From 63ddc3bd7f9457bcc80917d72879a9907220c641 Mon Sep 17 00:00:00 2001 From: Kevin Schmidt Date: Mon, 30 Jan 2023 13:49:40 +0100 Subject: [PATCH 8/9] mamba in command --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index be085b8f..31b6f1c0 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,7 @@ Analysis strategies leading to reproducible processing and evaluation of data re This repository is built as a python package. We recommend creating a mamba environment to handle the dependencies of all packages. You can create one by running the following command in this repository: ``` -$ conda env create -f environment.yml +$ mamba env create -f environment.yml ``` Depending on your `cuda` version you have to specify the `cudatoolkit` version used by `pytorch`. If you are working on machines with `cuda` versions < 10.2, please change the version number in the environment.yml file. Since the package `pre-commit` is used, you need to execute From 5f34801c9ef52e01b7bbcbb88bf468fef0df7f87 Mon Sep 17 00:00:00 2001 From: Kevin Schmidt Date: Mon, 30 Jan 2023 13:54:08 +0100 Subject: [PATCH 9/9] add olivia and rune --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 31b6f1c0..af73cdf5 100644 --- a/README.md +++ b/README.md @@ -89,9 +89,11 @@ All histograms are created on a dedicated test dataset. * Arne Poggenpohl [@ArnePoggenpohl](https://github.com/ArnePoggenpohl) * Stefan Fröse [@StFroese](https://github.com/StFroese) * Paul-Simon Blomenkamp [@PBlomenkamp](https://github.com/PBlomenkamp) +* Olivia Locke [@olivialocke](https://github.com/olivialocke) * Kevin Laudamus [@K-Lauda](https://github.com/K-Lauda) * Emiliano Miranda [@emilianozm24](https://github.com/emilianozm24) * Maximilian Büchel [@MaxBue](https://github.com/MaxBue) +* Rune Dominik [@RuneDominik](https://github.com/RuneDominik) ## Versions used and tested

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