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

Quick Setup (pypi)

requires python3.8 or newer

python3 -m venv larda-env
source larda-env/bin/activate

python3 -m pip install pyLARDA

Requirements

The pyLARDA remote backend is only targeted on unix operating system.

Building the documentation requires some more dependencies:

sphinx
recommonmark
sphinx_rtd_theme

Setup (github)

For development, local data sources and the backend, pyLARDA module can be installed with:

python3 -m venv larda-env
source larda-env/bin/activate

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

python3 -m pip install --editable .

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_timeheight2
    (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

overview on the structure

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

MIT License For details see the LICENSE file.