Tutorial

Note

The following material is assembled for the ERAD22 Academy short course ‘Polarimentric microphysical fingerprints in observations and NWP’

Setup

python3 -m venv peakTree-env
source peakTree-env/bin/activate
python3 -m pip install Cython
python3 -m pip install pyLARDA
python3 -m pip install rpgpy
python3 -m pip install jupyter graphviz loess

# either get a fresh clone of peakTree
git clone https://github.com/martin-rdz/peakTree.git
# or pull the latest update
git pull origin master

cd peakTree/tutorials
# start the jupyter notebook (and your default browser should fire up)
jupyter notebook
# better use the following command
# to ensure the correct executable is used
../../peakTree-env/bin/jupyter notebook

Jupyter Notebooks

01_peak_finding_tree_generation.ipynb

As a first step, we are looking at single spectra from a Metek MIRA-35 and how different peak finding parameters affect the generated binary tree.

02_convert_file.ipynb

A full file of spectra (i.e. chunk of 1h) can also be converted conveniently to the peakTree output format.

03_analyze_output.ipynb

As a final step, let’s have a look how to analyze an interpret a time-height slice of multipeak data.

Hints

01_peak_finding_tree_generation.ipynb

pTB = peakTree.peakTreeBuffer(config_file='../instrument_config.toml', system='limrad_punta')
pTB.load('../data/190822_080000_P05_ZEN.LV0', load_to_ram=True)

02_convert_file.ipynb

# it might be necessary to run
!python3 -m pip install rpgpy
# first in it's own cell and restart the kernel

pTB = peakTree.peakTreeBuffer(config_file='../instrument_config.toml', system='limrad_punta')
pTB.load_spec_file('../data/190822_070001_P05_ZEN.LV0', load_to_ram=True)
pTB.assemble_time_height('../output/')