Science Score: 49.0%
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Keywords
Repository
Forbush Decrease Analysis Tool
Basic Info
- Host: GitHub
- Owner: spearhead-he
- License: bsd-3-clause
- Language: Python
- Default Branch: main
- Size: 259 MB
Statistics
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
- Releases: 1
Topics
Metadata Files
README.md
FDAT - Forbush Decrease Analysis Tool
A graphical interface tool for analyzing Forbush decrease and Interplanetary Coronal Mass Ejection (ICME) events, conducting ForbMod best-fit calculations on selected data regions and performing in-situ analysis of ICMEs.
Features
FDAT enables to:
- Plotting and analyzing ICME/Forbush decrease events
- Selecting boundaries for ICME events and related Forbush decreases
- Executing ForbMod best-fit procedures on selected events
- Performing in-situ analysis of ICMEs
- Analyzing sheath regions with support for front region separation
- Conducting Lundquist flux rope fitting for magnetic obstacles
- Exporting mf/sw/gcr data for a chosen range of time
Requirements
Core Dependencies
- Python 3.10.6+
- NumPy 1.24.0
- SciPy 1.10.0
- Matplotlib 3.7.0
- scikit-learn 1.2.0
- Pandas 2.0.0
- PyQt5 5.15.0
- pyqtgraph 0.13.0
- lmfit (only for Lundquist fitting)
Installation
- This tool requires a recent Python (>=3.10) installation. We recommend installing Python via miniforge (this will give you the same
condacommand as if installing Anaconda). - Download this file and extract to a folder of your choice. (Or clone the repository https://github.com/spearhead-he/FDAT with
git). - Open a terminal or miniforge prompt and move to the directory created in step 2.
- Create a new conda environment with all required dependencies by running:
conda env create -f environment.yml
Note:
- If you already have a conda environment with the name fdat, step 4 will fail with an error. In this case, open the file environment.yml with a text editor and replace fdat in the first line with a different name (e.g., fdat2). Afterwards, do step 4 above again. You also need to use this new name in step 2 of Running below!
- If you don't want to use conda, you can in step 4 create a virtual Python environment and install the required packages with pip install -r requirements.txt. Because this could fail in some configurations, it is not recommended.
Running
- Open a terminal or miniforge prompt and move to the directory created in Installation step 2.
- Activate the newly created environment with:
conda activate fdat - Start the tool by running
python FDAT_main.py
Updating
To update your local installation, in principle you need to repeat steps 2 to 4 of Installation, replacing the previous installation:
- Download this file and extract to a folder of your choice, replacing the previous version. (Or update the cloned repository with
git). - Open a terminal or miniforge prompt and move to the directory used in step 1.
- Create a conda environment with all required dependencies (replacing the previously created one) by running:
conda env create -f environment.yml --yes
Data Usage Instructions
Example data
The repository /data includes example IP/GCR CDF files for ICMEs from:
Wind (1995 - 1997)
Helios1 (1974 - 1985)
Solar Orbiter (2020 - 2024)
SOHO/EPHIN (1995-1997)
Full Dataset
The complete dataset with available observations (larger archive) can be downloaded from Google Drive.
- Download the desired year of observations from the full dataset
- Place the downloaded file in the appropriate satellite folder within your FDAT directory
``` FDAT/ data/ IP/ ACE/ WIND/ OMNI/ SolO/ GCR/ EPHIN/ # For ACE, WIND EPHIN_shifted/ # For OMNI nm/ # Neutron monitors SolO/
```
Currently included satellite data ranges: - Solar Orbiter: Apr 2020 - Jul 2024 - OMNI: Jan 1998 - Dec 2024 - ACE: Sep 1997 - Dec 2022 - WIND: Nov 1994 - Sep 2024 - Helios1: Dec 1974 - Jun 1981 - Helios2: Jan 1976 - Mar 1980 - MAVEN: Dec 2014 - Dec 2023 - Ulysses: Nov 1990 - Jul 2009 - Neutron monitors (SoPo): Jan 1998 - Dec 2024
Version
More information about GUI functionality and versions find in readme file.
Contact
For questions and support:
M.Dumbovic (mateja.dumbovic@geof.unizg.hr)
G. Chikunova (chipika3@gmail.com)
Acknowledgement
This tool is developed within the SPEARHEAD (SPEcification, Analysis & Re-calibration of High Energy pArticle Data) project. SPEARHEAD has received funding from the European Unions Horizon Europe programme under grant agreement No 101135044.
The tool reflects only the authors view and the European Commission is not responsible for any use that may be made of the information it contains.
Owner
- Name: SPEARHEAD
- Login: spearhead-he
- Kind: organization
- Website: https://spearhead-he.eu
- Twitter: spearhead_he
- Repositories: 1
- Profile: https://github.com/spearhead-he
SPEcification, Analysis & Re-calibration of High Energy pArticle Data
GitHub Events
Total
- Release event: 1
- Watch event: 1
- Push event: 28
- Public event: 1
- Pull request event: 5
- Fork event: 1
- Create event: 3
Last Year
- Release event: 1
- Watch event: 1
- Push event: 28
- Public event: 1
- Pull request event: 5
- Fork event: 1
- Create event: 3
Dependencies
- PyQt5 *
- matplotlib *
- numpy *
- pandas *
- pyqtgraph *
- scikit-learn *
- scipy *