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voodoonet

Machine learning application for detecting liquid droplets in mixed-phase clouds using Doppler cloud radar spectra

https://github.com/actris-cloudnet/voodoonet

Science Score: 77.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ✓
    Committers with academic emails
    1 of 5 committers (20.0%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (11.9%) to scientific vocabulary

Keywords

machine-learning pytorch
Last synced: 6 months ago · JSON representation ·

Repository

Machine learning application for detecting liquid droplets in mixed-phase clouds using Doppler cloud radar spectra

Basic Info
  • Host: GitHub
  • Owner: actris-cloudnet
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 19.5 MB
Statistics
  • Stars: 2
  • Watchers: 4
  • Forks: 1
  • Open Issues: 0
  • Releases: 16
Topics
machine-learning pytorch
Created about 3 years ago · Last pushed about 1 year ago
Metadata Files
Readme Changelog License Citation

README.md

VoodooNet CI PyPI version DOI

VoodooNet

Predicting liquid droplets in mixed-phase clouds beyond lidar attenuation using artificial neural nets and Doppler cloud radar spectra

VOODOO logo

VOODOO is a machine learning approach based convolutional neural networks (CNN) to relate Doppler spectra morphologies to the presence of (supercooled) liquid cloud droplets in mixed-phase clouds.

Installation

Prerequisites

VoodooNet requires Python 3.10.

Before installing VoodooNet, install PyTorch according to your infrastructure. For example on a Linux machine without GPU you might run:

sh pip3 install torch --extra-index-url https://download.pytorch.org/whl/cpu

From PyPI

sh pip3 install voodoonet

Locally for development

sh pip3 install -e .[dev]

Citing

If you wish to acknowledge VoodooNet in your publication, please cite:

Schimmel et al. (2022). Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks. Atmos. Meas. Tech., 15(18), 5343–5366. https://doi.org/10.5194/amt-15-5343-2022

Usage

Make predictions using the default model and settings

```python import glob import voodoonet

rpgfiles = glob.glob('/path/to/rpg/files/*.LV0') probabilityliquid = voodoonet.infer(rpg_files) ```

You can for example plot the resulting liquid probability:

```python import matplotlib.pyplot as plt

plt.pcolor(probability_liquid.T) plt.show() ```

Generate a training data set

Download some RPG-FMCW-94 raw files and corresponding classification files from the Cloudnet data portal API. For example, for Leipzig LIM between 2021-01-10 and 2021-01-15:

sh curl "https://cloudnet.fmi.fi/api/raw-files?dateFrom=2021-01-10&dateTo=2021-01-15&site=leipzig-lim&instrument=rpg-fmcw-94&filenameSuffix=.LV0" | jq '.[]["downloadUrl"]' | xargs -n1 curl -O curl "https://cloudnet.fmi.fi/api/files?dateFrom=2021-01-10&dateTo=2021-01-15&site=leipzig-lim&product=classification" | jq '.[]["downloadUrl"]' | xargs -n1 curl -O

```python import glob import voodoonet

rpgfiles = glob.glob('*.LV0') classificationfiles = glob.glob('*classification.nc') voodoonet.generatetrainingdata(rpgfiles, classificationfiles, 'training-data-set.pt') ```

Alternatively, just use N random days:

python import voodoonet voodoonet.generate_training_data_for_cloudnet('leipzig-lim', 'training-data-set.pt', n_days=5)

Train a VoodooNet model

```python import voodoonet

precomputedtrainingdataset = 'training-data-set.pt' voodoonet.train(precomputedtrainingdataset, 'trained-model.pt') ```

Make predictions using the new model

```python import glob import voodoonet from voodoonet.utils import VoodooOptions

rpgfiles = glob.glob('/path/to/rpg/files/*.LV0') options = VoodooOptions(trainedmodel='newmodel.pt') probabilityliquid = voodoonet.infer(rpg_files, options=options) ```

Owner

  • Name: ACTRIS Cloudnet
  • Login: actris-cloudnet
  • Kind: organization
  • Email: actris-cloudnet@fmi.fi
  • Location: Helsinki, Finland

ACTRIS Cloud Remote Sensing Unit (CLU)

Citation (CITATION.cff) 

cff-version: 1.2.0
authors:
  - family-names: Schimmel
    given-names: Willi
    orcid: "https://orcid.org/0000-0001-8428-6445"
  - family-names: Tukiainen
    given-names: Simo
    orcid: "https://orcid.org/0000-0002-0651-4622"
title: "VoodooNet"
message: "If you use this software, please cite the article from preferred-citation."
preferred-citation:
  type: article
  authors:
    - family-names: Schimmel
      given-names: Willi
      orcid: "https://orcid.org/0000-0001-8428-6445"
    - family-names: Kalesse-Los
      given-names: Heike
      orcid: "https://orcid.org/0000-0001-6699-7040"
    - family-names: Maahn
      given-names: Maximilian
      orcid: "https://orcid.org/0000-0002-2580-9100"
    - family-names: Vogl
      given-names: Teresa
      orcid: "https://orcid.org/0000-0002-6696-4967"
    - family-names: Foth
      given-names: Andreas
      orcid: "https://orcid.org/0000-0002-1164-3576"
    - family-names: Saavedra Garfias
      given-names: Pablo
      orcid: "https://orcid.org/0000-0002-4596-946X"
    - family-names: Seifert
      given-names: Patric
      orcid: "https://orcid.org/0000-0002-5626-3761"
  title: "Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks"
  journal: "Atmospheric Measurement Techniques"
  volume: 15
  year: 2022
  number: 18
  pages: 5343-5366
  doi: 10.5194/amt-15-5343-2022

GitHub Events

Total
  • Issues event: 1
  • Release event: 1
  • Watch event: 2
  • Delete event: 3
  • Issue comment event: 2
  • Push event: 4
Last Year
  • Issues event: 1
  • Release event: 1
  • Watch event: 2
  • Delete event: 3
  • Issue comment event: 2
  • Push event: 4

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 56
  • Total Committers: 5
  • Avg Commits per committer: 11.2
  • Development Distribution Score (DDS): 0.232
Top Committers
Name Email Commits
Simo Tukiainen s****n@f****i 43
Niko Leskinen n****n@f****i 5
willi w****l@u****e 4
Tuomas Siipola t****a@f****i 3
Willi Schimmel s****l@2****p 1
Committer Domains (Top 20 + Academic)
fmi.fi: 3 22182-mbp.speedport.ip: 1 uni-leipzig.de: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 1
  • Total pull requests: 0
  • Average time to close issues: about 23 hours
  • Average time to close pull requests: N/A
  • Total issue authors: 1
  • Total pull request authors: 0
  • Average comments per issue: 1.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 0
  • Average time to close issues: about 23 hours
  • Average time to close pull requests: N/A
  • Issue authors: 1
  • Pull request authors: 0
  • Average comments per issue: 1.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • donaldcummins (1)
Pull Request Authors
Top Labels
Issue Labels
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 353 last-month
  • Total dependent packages: 1
  • Total dependent repositories: 0
  • Total versions: 16
  • Total maintainers: 3
pypi.org: voodoonet

Machine learning application for detecting liquid droplets in mixed-phase clouds using Doppler cloud radar spectra

  • Homepage: https://github.com/actris-cloudnet/voodoonet
  • Documentation: https://voodoonet.readthedocs.io/
  • License: MIT License Copyright (c) 2022 Willi Schimmel Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
  • Latest release: 0.1.11
    published about 1 year ago
  • Versions: 16
  • Dependent Packages: 1
  • Dependent Repositories: 0
  • Downloads: 353 Last month
Rankings
Dependent packages count: 2.9%
Downloads: 15.3%
Average: 23.7%
Forks count: 30.5%
Dependent repos count: 30.6%
Stargazers count: 39.1%
Maintainers (3)
cloudnet tukiains siiptuo
Last synced: 7 months ago

Dependencies

.github/workflows/publish.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v1 composite
  • softprops/action-gh-release v1 composite
.github/workflows/test.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v1 composite
pyproject.toml pypi
  • numpy *
  • rpgpy >=0.12.1
  • scipy *
  • torchmetrics *
  • tqdm *
  • wandb *