Aerosol data processing and visualization toolkit. Read, QC, and analyze data from SMPS, APS, AE33, TEOM, Nephelometer, XRF, and 15+ atmospheric instruments.
Project description
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.
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'])
[!IMPORTANT] Behaviour change:
mean_freqno longer defaults to'1h'— the default is now no resampling (native resolution). Passmean_freq='1h'(or'30min','1D') for averaging.start/endare also optional now.
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 DataProcess, plot
from AeroViz.plot import timeseries_interactive
DataProcess— Chemistry (mass reconstruction, κ), Optical (Mie, IMPROVE, RI retrieval), SizeDistr (SMPS–APS merge, mode fitting), VOC (OFP, SOAP).plot— publication-ready matplotlib figures (time series, diurnal, wind rose, …).timeseries_interactive(df)— quick interactive Plotly viewer; click the legend to toggle columns, orsave='out.html'for a standalone file.
See the user guide for details.
Documentation
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
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