Difference between revisions of "LST1Analysis MVA"
(→Analysis of LST1 data) |
(→Analysis of LST1 data) |
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cd cta-lstchain | cd cta-lstchain | ||
conda env create -f environment.yml | conda env create -f environment.yml | ||
| − | conda activate lst </pre> | + | conda activate lst |
| + | pip install lstchain==0.7.2 | ||
| + | rm environment.yml | ||
| + | </pre> | ||
Files needed to analyze golden Run 442 (20190527): | Files needed to analyze golden Run 442 (20190527): | ||
Revision as of 08:45, 14 April 2021
Contents
Location of LST1 data at PIC
- Directory: /pnfs/pic.es/data/cta/LST/Data/ZFITS/
- Web: LST1 ZFITS files on the web
Same user/password as in CTA portal
Location of LST1 at La Palma
- Directory: /fefs/onsite/data
Use cp01/cp02 to copy out data, never the login machine
Run summary info/data-check
- Run-summary web in GAE
LST1 account
Analysis of LST1 data
Donwload LST analysis software cta-lstchain (wrapper that uses centrally supported ctapipe routines)
git clone https://github.com/cta-observatory/cta-lstchain.git cd cta-lstchain conda env create -f environment.yml conda activate lst pip install lstchain==0.7.2 rm environment.yml
Files needed to analyze golden Run 442 (20190527):
- Pedestal run: pedestal_file_run446_0000.fits
- Low level DRS4 calibration: calibration.hdf5
Run directly jupyter notebook
cd cta-lstchain/notebooks jupyter notebook -> select Analyze_real_muon_data.ipynb
Convert example jupyter notebook to python
cd cta-lstchain/notebooks jupyter nbconvert --to python Analyze_real_muon_data.ipynb
Fix path of data, pedestal and calibration and run!
Lines to fix:
source = event_source(input_url="../data/LST-1.4.Run00442.0001.fits.fz",max_events=None)
pedestal_path="../pedestal/pedestal_file_run446_0000.fits",
with HDF5TableReader('../calibration/calibration.hdf5') as h5_table:
python Analyze_real_muon_data.py
