AeroViz is a Python toolkit for reading, processing, and visualizing aerosol measurement data. It supports 18+ atmospheric instruments with built-in quality control, data processing, and publication-ready visualizations.
Installation
pip install AeroViz
Pre-built for Linux, macOS, and Windows — no compiler needed.
Plotting lives in an extra: pip install "AeroViz[plot]". Details, building from
source and troubleshooting: Installation.
Quick Start
from AeroViz import RawDataReader
df = RawDataReader(
instrument='AE33',
path='/path/to/data',
start='2024-01-01', # optional — omit to read the files' full coverage
end='2024-12-31', # optional
mean_freq='1h', # optional — '1h' resamples to hourly; omit for native resolution
qc=True, # apply quality control
)
print(df[['eBC', 'AAE']].describe())
# Or read everything the files contain, at native resolution:
df_all = RawDataReader('AE33', '/path/to/data')
print(df_all.attrs['coverage_start'], '→', df_all.attrs['coverage_end'])
Supported Instruments
| Category | Instruments |
|---|---|
| Black carbon / absorption | AE33, AE43, BC1054, MA350 |
| Particle sizers | SMPS, APS, GRIMM |
| Mass concentration | TEOM, BAM1020 |
| Optical | NEPH, Aurora |
| Chemical composition | Xact, OCEC, IGAC, Q-ACSM |
See the instrument reference for output columns and per-instrument notes.
Usage
RawDataReader returns a pandas DataFrame. Key options:
start/end— optional date range (omit for the files' full coverage)mean_freq— resample frequency, e.g.'1h'(omit for native resolution)qc— quality control (on by default; flags rows viaQC_Flagand reports rates)fill_missing— pad to the requested range (default) or clamp to coveragesize_range— diameter filter for SMPS / APS
Result metadata — coverage, QC rates, native frequency and more — is attached to
df.attrs. The RawDataReader guide
has the full parameter list, QC flags, and df.attrs reference.
Data Processing & Visualization
from AeroViz import reconstruct_mass, improve, mie, merge_psd, voc_potentials, plot
from AeroViz.plot import timeseries_interactive
- Top-level processing functions — each takes a DataFrame (or a few) and
returns a DataFrame/dict:
reconstruct_mass&volume_ri/kappa(chemistry),improve/mie/retrieve_ri(optical),merge_psd/psd_stats(size distribution),voc_potentials(OFP, SOAP). They are grouped under thechemistry,optical,size,vocnamespaces too. plot— publication-ready matplotlib figures (timeseries,diurnal_pattern,scatter,meteorology.wind_rose, …).timeseries_interactive(df)— quick interactive Plotly viewer; click the legend to toggle columns, orsave='out.html'for a standalone file.
DataProcess(...)is deprecated (it still works but emits aDeprecationWarning); use the top-level functions above.
See the user guide for details.
Documentation
- Full Documentation
- Installation · Getting Started · RawDataReader Usage
- Supported Instruments · Gallery
- Changelog
Contributing
Contributions are welcome! See CONTRIBUTING.md for the development setup and release workflow, and GitHub Issues for bug reports and feature requests.
License
MIT License - see LICENSE for details.
Citation
If you use AeroViz in your research, please cite:
AeroViz: Aerosol Data Processing and Visualization Toolkit
https://github.com/Alex870521/AeroViz
1 maintainer
Metadata
1 maintainer
Release files for AeroViz 0.5.0
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Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| aeroviz-0.5.0.tar.gz | 9.5 MB | Details |
Built distributions (wheels)
Total release size: 70.3 MB
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