From 3d46c3bbf15744de7cd634a005c13ff7e9739ce9 Mon Sep 17 00:00:00 2001
From: Emilie Mauduit <emilie.mauduit@obspm.fr>
Date: Wed, 16 Apr 2025 15:42:12 +0200
Subject: [PATCH 1/5] Update README.md

---
 README.md | 88 ++++++++++---------------------------------------------
 1 file changed, 16 insertions(+), 72 deletions(-)

diff --git a/README.md b/README.md
index f21d912..d8c371f 100644
--- a/README.md
+++ b/README.md
@@ -1,74 +1,24 @@
 # A2_S-Bursts
 
-## Getting started
+## How to download
 
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
-
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
+Open your terminal. Go to the folder in which you want to use this algorithm. Copy-paste the https link in the 'Clone' button.
 
 ```
 cd existing_repo
-git remote add origin https://gitlab.bsc.es/extract/extract-use-cases/taska/use-case-a/a2_s-bursts.git
-git branch -M main
-git push -uf origin main
+git clone https://gitlab.bsc.es/extract/extract-use-cases/taska/use-case-a/a2_s-bursts.git
 ```
 
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://gitlab.bsc.es/extract/extract-use-cases/taska/use-case-a/a2_s-bursts/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
----
-
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
-
-## Suggestions for a good README
+## About the TASKA - A2 use case
 
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
-
-## Name
-
-Choose a self-explaining name for your project.
-
-## Description
-
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
-
-## Badges
-
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
-
-## Visuals
-
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
+This project has been been developed in the frame of the [EXTRACT](https://extract-project.eu/) project. It aims at developing a distributed data-mining platform for EXTReme dAta Across the Compute conTinuum. A key use case, Transient Astrophysics with a Square Kilometer Array pathfinder (TASKA), takes advantage of EXTRACT technologies to handle the massive data streams produced by NenuFAR, one of the SKA pathfinders. The use case A2 aims at detecting the presence of fast drifting radio bursts in Jupiter's decametric emissions in real-time. The goals of this project are :
+    - migrating from IDL to Python the algorithm developed by [Mauduit et al, 2023](https://doi.org/10.1038/s41467-023-41617-8)
+    - embedding this algorithm whithin the [MurMuRe](https://gitlab.bsc.es/extract/extract-use-cases/taska/use-case-a/modular_multicast_receiver) pipeline in order to use it for real-time detections during [NenuFAR](https://nenufar.obs-nancay.fr/) observations of Jupiter. 
+    - developing and training a convolutional neural network using a Variational AutoEncoder for anomaly detections, to replace the current algorithm and enhance computing time.
 
 ## Installation
 
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
+For the moment this is not an installable software. When it will be, instructions will be given here.
 
 ## Usage
 
@@ -76,28 +26,22 @@ Use examples liberally, and show the expected output if you can. It's helpful to
 
 ## Support
 
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
-
-## Roadmap
-
-If you have ideas for releases in the future, it is a good idea to list them in the README.
+Any issue encountered while using this software can be raised on the Issue page of this project. The author will gladly look at them and try to find a fix.
 
 ## Contributing
 
-State if you are open to contributions and what your requirements are for accepting them.
-
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
-
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
+Any suggestion on a missing feature, a way to improve the code will be welcomed. Please do not hesitate to reach out.
 
 ## Authors and acknowledgment
 
-Show your appreciation to those who have contributed to the project.
+The author acknowledge the precious help of the other collaborators on this project : C. Viou, F. Nammour, B. Cecconi, J. Girard and L. Bondonneau. 
+
+This project has received funding from the European Union’s Horizon Europe programme under grant agreement number 101093110.
 
 ## License
 
-For open source projects, say how it is licensed.
+[MIT](https://choosealicense.com/licenses/mit/)
 
 ## Project status
 
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
+The project is active. The main branch is maintained up-to-date with the latest working and validated features. Other branches are reserved for developement and are not guaranteed to be working.
-- 
GitLab


From ca607a97ad541c0210fec6ef42ca45e44419b3d2 Mon Sep 17 00:00:00 2001
From: EmilieMauduit <103190830+EmilieMauduit@users.noreply.github.com>
Date: Thu, 17 Apr 2025 12:12:59 +0200
Subject: [PATCH 2/5] Outputreader for NenuFAR or NDA/JunoN

---
 .gitignore                     |  1 +
 pyproject.toml                 | 19 ++++++++-
 sandbox/main_for_dev.py        |  2 +-
 sandbox/notebook_for_dev.ipynb | 73 +++++++++-------------------------
 src/processing_JunoN_data.py   |  2 +-
 src/read_pipeline_output.py    | 48 ++++++++++++++++------
 6 files changed, 75 insertions(+), 70 deletions(-)

diff --git a/.gitignore b/.gitignore
index 58cac68..fbfe0df 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,3 +10,4 @@ runtime_stats.csv
 
 # Caches
 __pycache__/
+src/__pycache__/
diff --git a/pyproject.toml b/pyproject.toml
index 16e624e..94c7ffd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
 name = "a2-s-bursts"
 version = "0.1.0"
-description = "Add your description here"
+description = "This software allows to detect millisecond bursts in high resolution time-frequency dynamical spectrum, either on stored data or in real-time NenuFAR observations. This software is an updated version of the one published in Mauduit et al, 2023, Nat Coms."
 readme = "README.md"
 requires-python = ">=3.8"
 dependencies = [
@@ -12,6 +12,23 @@ dependencies = [
     "scikit-image>=0.21.0",
     "scipy>=1.10.1",
 ]
+authors = [
+  {name = "Emilie Mauduit", email = "emilie.mauduit@obspm.fr"},
+  {name = "Baptiste Cecconi", email = "baptiste.cecconi@obspm.fr"},
+  {name = "Cedric Viou", email = "cedric.viou@obs-nancay.fr"},
+  {name = "Fadi Nammour", email = "fnammour@ekinox.io"} ,
+  {name = 'Julien Girard", email = "julien.girard@obspm.fr"}
+]
+maintainers = [
+  {name = "Emilie Mauduit", email = "emilie.mauduit@obspm.fr"}
+]
+licence = "MIT"
+
+[project.urls]
+Homepage = "https://extract-project.eu/"
+Documentation = "https://readthedocs.org"
+Repository = "https://gitlab.bsc.es/extract/extract-use-cases/taska/use-case-a/a2_s-bursts"
+"Bug Tracker" = "https://gitlab.bsc.es/extract/extract-use-cases/taska/use-case-a/a2_s-bursts/-/issues"
 
 [tool.pytest.ini_options]
 pythonpath = "src"
diff --git a/sandbox/main_for_dev.py b/sandbox/main_for_dev.py
index 4bca585..eebcf42 100644
--- a/sandbox/main_for_dev.py
+++ b/sandbox/main_for_dev.py
@@ -9,7 +9,7 @@ import h5py
 
 from sburst_detection import process_by_frequency_bands
 from cleaning_tools import rebin_with_averaging
-from read_junon_data import ReadJunonData
+from read_JunoN_data import ReadJunonData
 
 log = logging.getLogger(__name__)
 logging.basicConfig(level=logging.INFO)
diff --git a/sandbox/notebook_for_dev.ipynb b/sandbox/notebook_for_dev.ipynb
index 7c3ef93..e2dd334 100644
--- a/sandbox/notebook_for_dev.ipynb
+++ b/sandbox/notebook_for_dev.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,8 +24,8 @@
     "\n",
     "import sburst_detection as sb_det\n",
     "import cleaning_tools as ct\n",
-    "from read_junon_data import ReadJunonData\n",
-    "from read_pipeline_output import PipelineOutputReader, PolarizationParameters"
+    "from read_JunoN_data import ReadJunonData\n",
+    "from read_pipeline_output import PipelineOutputReader, PolarizationParameters, get_reader"
    ]
   },
   {
@@ -376,89 +376,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path_hdf5 = \"/data/emauduit/EXTRACT/test_nda/\"\n",
-    "list = os.listdir(path_hdf5)\n",
-    "files_list = [f for f in list if os.path.isfile(os.path.join(path_hdf5, f))]\n",
-    "obs_list = [f for f in files_list if \"extract2.h5\" in f]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "filename = '/Users/emauduit/Documents/EXTRACT/codes/archive_code_burst_detection/test_main.h5'"
+    "from read_pipeline_output import PipelineOutputReader, PolarizationParameters, get_reader"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "output_test = PipelineOutputReader(filename)"
+    "path_hdf5 = \"/data/emauduit/EXTRACT/test_nda/\"\n",
+    "list = os.listdir(path_hdf5)\n",
+    "files_list = [f for f in list if os.path.isfile(os.path.join(path_hdf5, f))]\n",
+    "obs_list = [f for f in files_list if \"extract2.h5\" in f]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_LH = output_test.data['LH']"
+    "filename = '/Users/emauduit/Documents/EXTRACT/codes/archive_code_burst_detection/test_main.h5'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_LH = output_test.read_polarization(polarization='LH')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['window_mid_time_jd', 'window_mid_frequency_MHz', 'drift_rate_MHz_s',\n",
-       "       'SNR', 'window_processing_time_s'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_LH.keys"
+    "reader = get_reader(instrument = 'NDA/JunoN', filename=filename)\n",
+    "LH_output = reader.read_polarization(polarization = 'LH')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x700 with 1 Axes>"
       ]
@@ -468,7 +431,7 @@
     }
    ],
    "source": [
-    "df_LH.plot_parameters('SNR','drift_rate_MHz_s')"
+    "LH_output.histogram_parameters(xfield='SNR')"
    ]
   },
   {
diff --git a/src/processing_JunoN_data.py b/src/processing_JunoN_data.py
index a759771..9caf055 100644
--- a/src/processing_JunoN_data.py
+++ b/src/processing_JunoN_data.py
@@ -8,7 +8,7 @@ import h5py
 
 from sburst_detection import process_by_frequency_bands
 from cleaning_tools import rebin_with_averaging
-from read_junon_data import ReadJunonData
+from read_JunoN_data import ReadJunonData
 
 import logging
 
diff --git a/src/read_pipeline_output.py b/src/read_pipeline_output.py
index 2137de5..31fbafc 100644
--- a/src/read_pipeline_output.py
+++ b/src/read_pipeline_output.py
@@ -88,23 +88,15 @@ class PolarizationParameters :
 class PipelineOutputReader:
 
     def __init__(self, 
-        filename : str, 
-        instrument : str = 'NDA/JunoN' 
+        filename : str,  
         ) -> None:
 
         self.data = h5py.File(filename,'r')
-        if instrument not in ['NenuFAR', 'NDA/JunoN'] :
-            raise ValueError("Unexpected instrument chosen, available instruments are NenuFAR or NDA/JunoN.")
-        self.instrument = instrument
         self.frequencies = np.asarray(self.data['frequencies_MHz'])
 
     def read_polarization(self, polarization : str) -> PolarizationParameters :
 
-        if (self.instrument == 'NenuFAR') & ((polarization != 'Stokes_I') & (polarization != 'Stokes_V')) :
-            raise ValueError("Wrong polarization chosen for instrument " + self.instrument + ".Possible polarization are Stokes_I or Stokes_V.")
-
-        elif (self.instrument == 'NDA/JunoN') & ((polarization != 'LH') & (polarization != 'RH')) :
-            raise ValueError("Wrong polarization chosen for instrument " + self.instrument + ".Possible polarization are LH or RH.")
+        self._check_polarization(polarization)
 
         data_polar = self.data[polarization]
         parameters_names = ['window_mid_time_jd', 'window_mid_frequency_MHz', 'drift_rate_MHz_s','SNR','window_processing_time_s']
@@ -118,10 +110,42 @@ class PipelineOutputReader:
         dataframe_parameters = pd.DataFrame(parameters_values,columns=parameters_names)
 
         return PolarizationParameters(dataframe_parameters)
+    
+    def _check_polarization(self, polarization : str) -> None :
+        
+        raise NotImplementedError("The selected instrument is not implemented. Available instruments are NenuFAR and NDA/JunoN.")
+
+class ReadPipelineOutputNenuFAR(PipelineOutputReader):
 
+    def __init__(self, filename: str) -> None:
+        super().__init__(filename)
+        self.instrument = "NenuFAR"
+        self.valid_polarizations = ["Stokes_I","Stokes_V"]
 
-class OutputNenuFAR(PipelineOutputReader):
+    def _check_polarization(self, polarization: str) -> None:
+        if polarization not in self.valid_polarizations:
+            raise ValueError(f"Polarization '{polarization}' is not available for NenuFAR. "
+                            f"Polarization should be one the following : {', '.join(self.valid_polarizations)}.")
 
-    pass
+
+class ReadPipelineOutputNDA(PipelineOutputReader):
+
+    def __init__(self, filename: str) -> None:
+        super().__init__(filename)
+        self.instrument = "NDA/JunoN"
+        self.valid_polarizations = ["LH","RH"]
     
+    def _check_polarization(self, polarization: str) -> None:
+        if polarization not in self.valid_polarizations:
+            raise ValueError(f"Polarization '{polarization}' is not available for NDA/JunoN. "
+                            f"Polarization should be one of the following : {', '.join(self.valid_polarizations)}.")
+
+
+def get_reader(instrument : str, filename : str) -> PipelineOutputReader :
 
+    if instrument == "NenuFAR" :
+        return ReadPipelineOutputNenuFAR(filename)
+    elif instrument == "NDA/JunoN":
+        return ReadPipelineOutputNDA(filename)
+    else :
+        raise ValueError(f"Instrument '{instrument}' is not available. Only the following instruments are available : 'NenuFAR', 'NDA/JunoN'.")
-- 
GitLab


From 993b682e7932bbe89a208c3ee5f727c3152cdec5 Mon Sep 17 00:00:00 2001
From: Emilie Mauduit <emilie.mauduit@obspm.fr>
Date: Thu, 17 Apr 2025 13:56:50 +0200
Subject: [PATCH 3/5] Add licence

---
 LICENSE | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)
 create mode 100644 LICENSE

diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..9fb0e84
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Observatoire de Paris
+
+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.
-- 
GitLab


From 8bc51ed43b051764667cd502ff5fb5e1dade2ac2 Mon Sep 17 00:00:00 2001
From: EmilieMauduit <103190830+EmilieMauduit@users.noreply.github.com>
Date: Thu, 17 Apr 2025 14:09:40 +0200
Subject: [PATCH 4/5] Edit readme

---
 README.md                   | 17 +++++++----------
 src/read_pipeline_output.py |  5 +++++
 2 files changed, 12 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index d8c371f..bbfe85f 100644
--- a/README.md
+++ b/README.md
@@ -1,14 +1,5 @@
 # A2_S-Bursts
 
-## How to download
-
-Open your terminal. Go to the folder in which you want to use this algorithm. Copy-paste the https link in the 'Clone' button.
-
-```
-cd existing_repo
-git clone https://gitlab.bsc.es/extract/extract-use-cases/taska/use-case-a/a2_s-bursts.git
-```
-
 ## About the TASKA - A2 use case
 
 This project has been been developed in the frame of the [EXTRACT](https://extract-project.eu/) project. It aims at developing a distributed data-mining platform for EXTReme dAta Across the Compute conTinuum. A key use case, Transient Astrophysics with a Square Kilometer Array pathfinder (TASKA), takes advantage of EXTRACT technologies to handle the massive data streams produced by NenuFAR, one of the SKA pathfinders. The use case A2 aims at detecting the presence of fast drifting radio bursts in Jupiter's decametric emissions in real-time. The goals of this project are :
@@ -18,7 +9,13 @@ This project has been been developed in the frame of the [EXTRACT](https://extra
 
 ## Installation
 
-For the moment this is not an installable software. When it will be, instructions will be given here.
+Open your terminal. Go to the folder in which you want to use this algorithm. Copy-paste the https link in the 'Clone' button.
+
+```
+cd existing_repo
+git clone https://gitlab.bsc.es/extract/extract-use-cases/taska/use-case-a/a2_s-bursts.git
+```
+
 
 ## Usage
 
diff --git a/src/read_pipeline_output.py b/src/read_pipeline_output.py
index 31fbafc..2d4c957 100644
--- a/src/read_pipeline_output.py
+++ b/src/read_pipeline_output.py
@@ -17,6 +17,11 @@ class PolarizationParameters :
         new_dataframe = pd.concat([self.dataframe_parameters,other.dataframe_parameters])
         return PolarizationParameters(new_dataframe)
 
+    def selection_request(self, condition : str) :
+        pass
+
+    ###### Display methods ######
+
     def plot_parameters(self, 
         xfield : str, 
         yfield : str,
-- 
GitLab


From e9d67d8fc45c64dc1916a55bf2ad40ab23e8b7e9 Mon Sep 17 00:00:00 2001
From: EmilieMauduit <103190830+EmilieMauduit@users.noreply.github.com>
Date: Thu, 17 Apr 2025 18:03:47 +0200
Subject: [PATCH 5/5] Updated the Readme.md, minor modification to
 processing_JunoN_data.py to launch process with uv.

---
 .gitignore                   |   5 +
 README.md                    |  68 ++-
 pyproject.toml               |   4 +-
 src/processing_JunoN_data.py | 245 ++++++-----
 src/read_junon_data.py       |   2 +-
 src/read_pipeline_output.py  |   2 +-
 uv.lock                      | 803 +++++++++++++++++++++++++++++++++++
 7 files changed, 1014 insertions(+), 115 deletions(-)

diff --git a/.gitignore b/.gitignore
index fbfe0df..a1cd28d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,3 +11,8 @@ runtime_stats.csv
 # Caches
 __pycache__/
 src/__pycache__/
+
+#Dossier
+.pytest_cache
+.venv
+.vscode
diff --git a/README.md b/README.md
index bbfe85f..b33f38f 100644
--- a/README.md
+++ b/README.md
@@ -3,7 +3,7 @@
 ## About the TASKA - A2 use case
 
 This project has been been developed in the frame of the [EXTRACT](https://extract-project.eu/) project. It aims at developing a distributed data-mining platform for EXTReme dAta Across the Compute conTinuum. A key use case, Transient Astrophysics with a Square Kilometer Array pathfinder (TASKA), takes advantage of EXTRACT technologies to handle the massive data streams produced by NenuFAR, one of the SKA pathfinders. The use case A2 aims at detecting the presence of fast drifting radio bursts in Jupiter's decametric emissions in real-time. The goals of this project are :
-    - migrating from IDL to Python the algorithm developed by [Mauduit et al, 2023](https://doi.org/10.1038/s41467-023-41617-8)
+    - migrating from IDL to Python the algorithm developed by EmilieMauduit. (2023). EmilieMauduit/fine_structures_detection: First release (v1.0). Zenodo. https://doi.org/10.5281/zenodo.8098758 and published in [Mauduit et al, 2023](https://doi.org/10.1038/s41467-023-41617-8)
     - embedding this algorithm whithin the [MurMuRe](https://gitlab.bsc.es/extract/extract-use-cases/taska/use-case-a/modular_multicast_receiver) pipeline in order to use it for real-time detections during [NenuFAR](https://nenufar.obs-nancay.fr/) observations of Jupiter. 
     - developing and training a convolutional neural network using a Variational AutoEncoder for anomaly detections, to replace the current algorithm and enhance computing time.
 
@@ -11,15 +11,75 @@ This project has been been developed in the frame of the [EXTRACT](https://extra
 
 Open your terminal. Go to the folder in which you want to use this algorithm. Copy-paste the https link in the 'Clone' button.
 
-```
+```bash
 cd existing_repo
 git clone https://gitlab.bsc.es/extract/extract-use-cases/taska/use-case-a/a2_s-bursts.git
 ```
 
-
 ## Usage
 
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
+- ### Open and read data of observations from NDA/JunoN.
+
+```python
+from src.read_JunoN_data import ReadJunoNData
+
+my_observation = ReadJunoNData("myobservation.dat")
+my_observation.header() 
+```
+
+Observation files are too big to be loaded all at once. Thus it has to be read portion by portion. The method load_data_cube() allows to load a given number of spectra at the same time, the index `n` of the first spectra can be given to load `number_of_spectra` from the index `n*number_of_spectra`. The loaded spectra are stored in the `data_sample` attribute, and are replaced everytime the method is called.
+
+```python
+#load only an array of 425 spectra, from the 5*425
+data_cube = my_observation.load_data_cube(number_of_spectra = 425, cube_index = 5) 
+```
+
+According to the processing method presented in [Mauduit et al, 2023](https://doi.org/10.1038/s41467-023-41617-8), the observation will be read slice by slice in a certain way. Due to the various configurations of different observations, a method to compute the best slicing paremeters is available.
+
+```python
+slicing_parameters = observation_data.def_slicing_parameters()
+```
+
+You can display the current `data_sample` in a plot by calling the `plot_dynamical_spectrum()`method. You can choose the canal to plot (0 for LH polarization, 3 for RH polarization). If `figname`is specified, the plot will be saved at the given path.
+
+```python
+my_observation.plot_dynamical_spectrum(slicing_parameters, canal = 0, figname='my_folder/plot_spectra.jpeg')
+```
+
+- ### Process any prepared data_sample.
+
+The detection algorithm is written so that it can take any square-shaped array as an input and process it. How this array is obtained from a global observation, depend on the data used and what time-frequency resolution is necessary. We recommand choosing a resolution that makes a bursts with a drift of $-15\ MHz.s^{-1}$ appear with an angle of $\sim 45\degree$ in the time-frequency plane. This way the accuracy of the method is maximized.
+
+```python
+from src.sburst_detection import process_data_chunk
+
+data_clean, data_fft, data_theta, data_radon,fit_parameters, slope, snr = process_data_chunk(data)
+```
+
+In the case of JunoN data, the file `processing_JunoN_data.py` is ready to use for automatic processsing of a given list of observations. It shows how the data is sliced and processed along the way, writing the results of the processing in a `.h5` file. To run the pipeline go to the terminal in the repository and launch :
+
+```bash
+uv run src/processing_JunoN_data.py --observation_folder /path_to_observations/ --output_folder /path/my_outputs/
+```
+
+- ### Reading the output file
+
+Once the pipeline is successfully applied to an observation, an output file containing the results of the processing is written as an `.h5` (HDF5 format). It can be read and navigated in with the following :
+
+```python
+from read_pipeline_output import output_reader
+
+reader = output_reader(instrument = "NDA/JunoN", filename='my_output.h5')
+LH_results = reader.read_polarization(polarization = 'LH')
+
+#See the different parameters stored
+
+LH_results.keys
+
+#Plot an histogram of SNR values
+
+LH_results.histogram_parameters(xfield='SNR')
+```
 
 ## Support
 
diff --git a/pyproject.toml b/pyproject.toml
index 94c7ffd..b5f60c0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,17 +7,19 @@ requires-python = ">=3.8"
 dependencies = [
     "astropy>=5.2.2",
     "h5py>=3.11.0",
+    "matplotlib>=3.7.5",
     "numpy>=1.24.4",
     "pytest>=8.3.5",
     "scikit-image>=0.21.0",
     "scipy>=1.10.1",
+    "tqdm>=4.67.1",
 ]
 authors = [
   {name = "Emilie Mauduit", email = "emilie.mauduit@obspm.fr"},
   {name = "Baptiste Cecconi", email = "baptiste.cecconi@obspm.fr"},
   {name = "Cedric Viou", email = "cedric.viou@obs-nancay.fr"},
   {name = "Fadi Nammour", email = "fnammour@ekinox.io"} ,
-  {name = 'Julien Girard", email = "julien.girard@obspm.fr"}
+  {name = "Julien Girard", email = "julien.girard@obspm.fr"}
 ]
 maintainers = [
   {name = "Emilie Mauduit", email = "emilie.mauduit@obspm.fr"}
diff --git a/src/processing_JunoN_data.py b/src/processing_JunoN_data.py
index 9caf055..67678ce 100644
--- a/src/processing_JunoN_data.py
+++ b/src/processing_JunoN_data.py
@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
-from pylab import *
 import os
 from tqdm import tqdm
+import argparse
 
 import numpy as np
 import h5py
@@ -15,113 +15,142 @@ import logging
 log = logging.getLogger(__name__)
 logging.basicConfig(level=logging.INFO)
 
-path = "/databf/nda/Juno/2021/04/"  # chemin du répertoire des observations
-path_hdf5 = "/data/emauduit/EXTRACT/test_nda/"  # chemin pour enregistrer les détections
-list = os.listdir(path)
-files_list = [f for f in list if os.path.isfile(os.path.join(path, f))]
-obs_list = [f for f in files_list if "extract2.dat" in f]
 
-for obs_name in obs_list:
-    filename = path + obs_name
-    hdf5_file_path = path_hdf5 + obs_name.split(".")[0] + ".h5"
-
-    observation_data = ReadJunonData(filename)
-    slicing_parameters = observation_data.def_slicing_parameters()
-
-    with h5py.File(hdf5_file_path, "w") as hdf5_file:
-
-        hdf5_file.create_dataset(
-            "frequencies_MHz",
-            data=np.linspace(
-                observation_data.minimum_frequency(
-                    mask=slicing_parameters["frequencies_indexes"]
-                ),
-                observation_data.maximum_frequency(
-                    mask=slicing_parameters["frequencies_indexes"]
-                ),
-                slicing_parameters["final_size_frequencies"],
-            ),
-        )
-
-        group_lh = hdf5_file.create_group("LH")
-        group_rh = hdf5_file.create_group("RH")
-
-        with tqdm(
-            total=slicing_parameters["number_of_cubes"],
-            desc="Reading data cubes",
-            unit="cube",
-        ) as pbar:
-            for cube_index in range(0, slicing_parameters["number_of_cubes"]):
-
-                observation_data.load_data_cube(
-                    number_spectra=slicing_parameters["number_of_pixel_in_band"],
-                    cube_index=cube_index,
-                )
-
-                LH = rebin_with_averaging(
-                    observation_data.select_canal_and_frequency_range(
-                        frequency_indexes=slicing_parameters["frequencies_indexes"],
-                        number_frequency_channel_remove=slicing_parameters[
-                            "number_of_frequency_channel_to_remove"
-                        ],
-                        canal=0,
-                    ),
-                    (
-                        slicing_parameters["number_of_pixel_in_band"],
-                        slicing_parameters["final_size_frequencies"],
+def parse_arguments():
+    parser = argparse.ArgumentParser(
+        description="Read and process data from a UDP circular buffer shared memory."
+    )
+    parser.add_argument(
+        '-obspath', '--observation_folder',
+        type=str,
+        default="/databf/nda/Juno/2021/04/",
+        help='Path of the folder containing observations to process. (Default :"/databf/nda/Juno/2021/04/")'
+    )
+    parser.add_argument(
+        "-outpath", "--output_folder",
+        type=str,
+        default = "",
+        help='Path of the folder where to store the output files. Default is current folder',
+    )
+
+    return parser.parse_args()
+
+def main():
+    args = parse_arguments()
+
+    path = args.observation_folder  # chemin du répertoire des observations
+    path_hdf5 = args.output_folder  # chemin pour enregistrer les détections
+    
+    list = os.listdir(path)
+    files_list = [f for f in list if os.path.isfile(os.path.join(path, f))]
+    obs_list = [f for f in files_list if "extract2.dat" in f]
+
+    for obs_name in obs_list:
+
+        log.info('Processing : {}'.format(obs_name))
+        filename = path + obs_name
+        hdf5_file_path = path_hdf5 + obs_name.split(".")[0] + ".h5"
+
+        observation_data = ReadJunonData(filename)
+        slicing_parameters = observation_data.def_slicing_parameters()
+
+        with h5py.File(hdf5_file_path, "w") as hdf5_file:
+
+            hdf5_file.create_dataset(
+                "frequencies_MHz",
+                data=np.linspace(
+                    observation_data.minimum_frequency(
+                        mask=slicing_parameters["frequencies_indexes"]
                     ),
-                )
-
-                RH = rebin_with_averaging(
-                    observation_data.select_canal_and_frequency_range(
-                        frequency_indexes=slicing_parameters["frequencies_indexes"],
-                        number_frequency_channel_remove=slicing_parameters[
-                            "number_of_frequency_channel_to_remove"
-                        ],
-                        canal=3,
+                    observation_data.maximum_frequency(
+                        mask=slicing_parameters["frequencies_indexes"]
                     ),
-                    (
-                        slicing_parameters["number_of_pixel_in_band"],
-                        slicing_parameters["final_size_frequencies"],
-                    ),
-                )
-
-                cube_lh = group_lh.create_group(
-                    f"Cube_time_{observation_data.time_sample[0].value}"
-                )
-                cube_lh.create_dataset(
-                    "times_jd", data=observation_data.time_sample.value
-                )
-
-                cube_rh = group_rh.create_group(
-                    f"Cube_time_{observation_data.time_sample[0].value}"
-                )
-                cube_rh.create_dataset(
-                    "times_jd", data=observation_data.time_sample.value
-                )
-
-                process_by_frequency_bands(
-                    data=LH,
-                    cube_polarization=cube_lh,
-                    frequencies=observation_data.frequencies[
-                        slicing_parameters["frequencies_indexes"]
-                    ].value,
-                    times=observation_data.time_sample,
-                    number_of_frequency_bands=slicing_parameters["number_of_frequency_bands"],
-                    number_of_pixel=slicing_parameters["number_of_pixel_in_band"],
-                    number_of_pixel_overlap=slicing_parameters["frequency_channel_overlap"],
-                )
-
-                process_by_frequency_bands(
-                    data=RH,
-                    cube_polarization=cube_rh,
-                    frequencies=observation_data.frequencies[
-                        slicing_parameters["frequencies_indexes"]
-                    ].value,
-                    times=observation_data.time_sample,
-                    number_of_frequency_bands=slicing_parameters["number_of_frequency_bands"],
-                    number_of_pixel=slicing_parameters["number_of_pixel_in_band"],
-                    number_of_pixel_overlap=slicing_parameters["frequency_channel_overlap"],
-                )
-
-                pbar.update(1)
+                    slicing_parameters["final_size_frequencies"],
+                ),
+            )
+
+            group_lh = hdf5_file.create_group("LH")
+            group_rh = hdf5_file.create_group("RH")
+
+            with tqdm(
+                total=slicing_parameters["number_of_cubes"],
+                desc="Reading data cubes",
+                unit="cube",
+            ) as pbar:
+                for cube_index in range(0, slicing_parameters["number_of_cubes"]):
+
+                    observation_data.load_data_cube(
+                        number_spectra=slicing_parameters["number_of_pixel_in_band"],
+                        cube_index=cube_index,
+                    )
+
+                    LH = rebin_with_averaging(
+                        observation_data.select_canal_and_frequency_range(
+                            frequency_indexes=slicing_parameters["frequencies_indexes"],
+                            number_frequency_channel_remove=slicing_parameters[
+                                "number_of_frequency_channel_to_remove"
+                            ],
+                            canal=0,
+                        ),
+                        (
+                            slicing_parameters["number_of_pixel_in_band"],
+                            slicing_parameters["final_size_frequencies"],
+                        ),
+                    )
+
+                    RH = rebin_with_averaging(
+                        observation_data.select_canal_and_frequency_range(
+                            frequency_indexes=slicing_parameters["frequencies_indexes"],
+                            number_frequency_channel_remove=slicing_parameters[
+                                "number_of_frequency_channel_to_remove"
+                            ],
+                            canal=3,
+                        ),
+                        (
+                            slicing_parameters["number_of_pixel_in_band"],
+                            slicing_parameters["final_size_frequencies"],
+                        ),
+                    )
+
+                    cube_lh = group_lh.create_group(
+                        f"Cube_time_{observation_data.time_sample[0].value}"
+                    )
+                    cube_lh.create_dataset(
+                        "times_jd", data=observation_data.time_sample.value
+                    )
+
+                    cube_rh = group_rh.create_group(
+                        f"Cube_time_{observation_data.time_sample[0].value}"
+                    )
+                    cube_rh.create_dataset(
+                        "times_jd", data=observation_data.time_sample.value
+                    )
+
+                    process_by_frequency_bands(
+                        data=LH,
+                        cube_polarization=cube_lh,
+                        frequencies=observation_data.frequencies[
+                            slicing_parameters["frequencies_indexes"]
+                        ].value,
+                        times=observation_data.time_sample,
+                        number_of_frequency_bands=slicing_parameters["number_of_frequency_bands"],
+                        number_of_pixel=slicing_parameters["number_of_pixel_in_band"],
+                        number_of_pixel_overlap=slicing_parameters["frequency_channel_overlap"],
+                    )
+
+                    process_by_frequency_bands(
+                        data=RH,
+                        cube_polarization=cube_rh,
+                        frequencies=observation_data.frequencies[
+                            slicing_parameters["frequencies_indexes"]
+                        ].value,
+                        times=observation_data.time_sample,
+                        number_of_frequency_bands=slicing_parameters["number_of_frequency_bands"],
+                        number_of_pixel=slicing_parameters["number_of_pixel_in_band"],
+                        number_of_pixel_overlap=slicing_parameters["frequency_channel_overlap"],
+                    )
+
+                    pbar.update(1)
+
+if __name__ == "__main__":
+    main()
diff --git a/src/read_junon_data.py b/src/read_junon_data.py
index ea4f9d3..05b0312 100644
--- a/src/read_junon_data.py
+++ b/src/read_junon_data.py
@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-from pylab import *
+#from pylab import *
 import os
 import logging
 
diff --git a/src/read_pipeline_output.py b/src/read_pipeline_output.py
index 2d4c957..cfa83f9 100644
--- a/src/read_pipeline_output.py
+++ b/src/read_pipeline_output.py
@@ -146,7 +146,7 @@ class ReadPipelineOutputNDA(PipelineOutputReader):
                             f"Polarization should be one of the following : {', '.join(self.valid_polarizations)}.")
 
 
-def get_reader(instrument : str, filename : str) -> PipelineOutputReader :
+def output_reader(instrument : str, filename : str) -> PipelineOutputReader :
 
     if instrument == "NenuFAR" :
         return ReadPipelineOutputNenuFAR(filename)
diff --git a/uv.lock b/uv.lock
index 173a188..54940db 100644
--- a/uv.lock
+++ b/uv.lock
@@ -19,6 +19,9 @@ dependencies = [
     { name = "astropy", version = "7.0.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "h5py", version = "3.11.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
     { name = "h5py", version = "3.13.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.9'" },
+    { name = "matplotlib", version = "3.7.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
+    { name = "matplotlib", version = "3.9.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" },
+    { name = "matplotlib", version = "3.10.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
     { name = "numpy", version = "1.24.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
     { name = "numpy", version = "2.2.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
     { name = "pytest" },
@@ -28,16 +31,19 @@ dependencies = [
     { name = "scipy", version = "1.10.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
     { name = "scipy", version = "1.13.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" },
     { name = "scipy", version = "1.15.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
+    { name = "tqdm" },
 ]
 
 [package.metadata]
 requires-dist = [
     { name = "astropy", specifier = ">=5.2.2" },
     { name = "h5py", specifier = ">=3.11.0" },
+    { name = "matplotlib", specifier = ">=3.7.5" },
     { name = "numpy", specifier = ">=1.24.4" },
     { name = "pytest", specifier = ">=8.3.5" },
     { name = "scikit-image", specifier = ">=0.21.0" },
     { name = "scipy", specifier = ">=1.10.1" },
+    { name = "tqdm", specifier = ">=4.67.1" },
 ]
 
 [[package]]
@@ -236,6 +242,229 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335 },
 ]
 
+[[package]]
+name = "contourpy"
+version = "1.1.1"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version < '3.9'",
+]
+dependencies = [
+    { name = "numpy", version = "1.24.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b1/7d/087ee4295e7580d3f7eb8a8a4e0ec8c7847e60f34135248ccf831cf5bbfc/contourpy-1.1.1.tar.gz", hash = "sha256:96ba37c2e24b7212a77da85004c38e7c4d155d3e72a45eeaf22c1f03f607e8ab", size = 13433167 }
+wheels = [
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-- 
GitLab