pyLARDA 3.3

Data cube for handling atmospheric observations of profiling remote sensing instruments.

pyLARDA v3

pyLARDA for accessing and analysing ground based remote sensing data. It tries to simplify following tasks:

• finding netcdf files in a complex folder structure
• loading data from differently formatted netcdfs
• stitching data from consecutive files together
• simplify common plotting tasks

Documentation is available at larda-doc

Requirements

Currently pyLARDA is only targeted on unix operating system.

matplotlib==3.0.2
toml==0.10.0
numpy>=1.19
requests==2.9.1
netCDF4==1.4.2
scipy==1.2.0
msgpack
tqdm
numba
cython
xarray
# and for the documentation
sphinx
recommonmark
sphinx_rtd_theme

Quick Setup

The pyLARDA module can be installed with:

python3 -m venv larda-env
source larda-env/bin/activate
pip3 install -r requirements.txt

mkdir larda3
cd larda3
git clone https://github.com/lacros-tropos/larda.git

python3 setup.py install

Depending on your datasource of choice:

remote

You just need to know the link to the backend backend of choice and may move to Quickstart.

local

For local data it is necessary to include the source in a certain directory structure. For the setup of the config files consult the Guide to config-files.

├── larda        # github managed source code
│   ├── docs
│   ├── examples
│   ├── ListCollector.py
│   ├── pyLARDA  # actual python module
│   ├── README.md
│   ├── requirements.txt
│   └── run_docs.sh
├── larda-cfg  # configuration files
│   ├── campaigns.toml
│   ├── [single campaign].toml
│   └── [single campaign].toml
├── larda-connectordump
│   └── [auto generated subfolder for each campaign]
├── larda-description
│   ├── [...].rst
└── larda-doc           # folder if you want to generate the docs
    └── ... 

Quickstart

Make sure that the module is available at your pythonpath when in doubt use sys.path.append('dir').

import pyLARDA

link_to_backend = 'http://...' 
# or use pyLARDA.LARDA('local')
larda = pyLARDA.LARDA('remote', uri=link_to_backend)
print('available campaigns', larda.campaign_list)
larda.connect('campaign_name')
MIRA_Zg = larda.read("MIRA","Zg", [dt_begin, dt_end], [0, 4000])
fig, ax = pyLARDA.Transformations.plot_timeheight2l
    (MIRA_Zg, range_interval=[500, 3000], z_converter='lin2z')
fig.savefig('MIRA_Z.png', dpi=250)

For more examples refer to the scripts in the examples directory.

Architecture

Documentation

An online version of the documentation is available at https://lacros-tropos.github.io/larda-doc/. For building simply run .\run_docs.sh, when the additinal libraries (sphinx, recommonmark and sphinx_rtd_theme are available; see above).

History

This version of the LACROS research data analyser (LARDA) is based on two prior versions in C and python2 respectively. Major changes are the migration to python3, netcdf4 and the inclusion of radar Doppler spectra.

License

Copyright 2021, pyLARDA-dev-team (Johannes Bühl, Martin Radenz, Willi Schimmel, Teresa Vogl, Moritz Lochmann, Johannes Röttenbacher)

MIT License For details see the LICENSE file.