AeroViz

Aerosol Data Processing and Visualization Toolkit for Atmospheric Research

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'])

[!IMPORTANT] Behaviour change: mean_freq no longer defaults to '1h' — the default is now no resampling (native resolution). Pass mean_freq='1h' (or '30min', '1D') for averaging. start / end are 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 via QC_Flag and reports rates)
fill_missing — pad to the requested range (default) or clamp to coverage
size_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 the chemistry, optical, size, voc namespaces 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, or save='out.html' for a standalone file.

DataProcess(...) is deprecated (it still works but emits a DeprecationWarning); 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

Contributors

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

aeroviz-0.4.8.tar.gz (9.5 MB view details)

Uploaded Sep 14, 2026 Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

aeroviz-0.4.8-cp314-cp314-win_amd64.whl (4.6 MB view details)

Uploaded Sep 14, 2026 CPython 3.14Windows x86-64

aeroviz-0.4.8-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (4.0 MB view details)

Uploaded Sep 14, 2026 CPython 3.14manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

aeroviz-0.4.8-cp314-cp314-macosx_14_0_arm64.whl (3.7 MB view details)

Uploaded Sep 14, 2026 CPython 3.14macOS 14.0+ ARM64

aeroviz-0.4.8-cp313-cp313-win_amd64.whl (4.5 MB view details)

Uploaded Sep 14, 2026 CPython 3.13Windows x86-64

aeroviz-0.4.8-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (4.0 MB view details)

Uploaded Sep 14, 2026 CPython 3.13manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

aeroviz-0.4.8-cp313-cp313-macosx_14_0_arm64.whl (3.7 MB view details)

Uploaded Sep 14, 2026 CPython 3.13macOS 14.0+ ARM64

aeroviz-0.4.8-cp312-cp312-win_amd64.whl (4.5 MB view details)

Uploaded Sep 14, 2026 CPython 3.12Windows x86-64

aeroviz-0.4.8-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (4.0 MB view details)

Uploaded Sep 14, 2026 CPython 3.12manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

aeroviz-0.4.8-cp312-cp312-macosx_14_0_arm64.whl (3.7 MB view details)

Uploaded Sep 14, 2026 CPython 3.12macOS 14.0+ ARM64

aeroviz-0.4.8-cp311-cp311-win_amd64.whl (4.5 MB view details)

Uploaded Sep 14, 2026 CPython 3.11Windows x86-64

aeroviz-0.4.8-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (4.0 MB view details)

Uploaded Sep 14, 2026 CPython 3.11manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

aeroviz-0.4.8-cp311-cp311-macosx_14_0_arm64.whl (3.7 MB view details)

Uploaded Sep 14, 2026 CPython 3.11macOS 14.0+ ARM64

aeroviz-0.4.8-cp310-cp310-win_amd64.whl (4.5 MB view details)

Uploaded Sep 14, 2026 CPython 3.10Windows x86-64

aeroviz-0.4.8-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (4.0 MB view details)

Uploaded Sep 14, 2026 CPython 3.10manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

aeroviz-0.4.8-cp310-cp310-macosx_14_0_arm64.whl (3.7 MB view details)

Uploaded Sep 14, 2026 CPython 3.10macOS 14.0+ ARM64

File details

Details for the file aeroviz-0.4.8.tar.gz.

File metadata

Download URL: aeroviz-0.4.8.tar.gz
Upload date: Sep 14, 2026
Size: 9.5 MB
Tags: Source
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for aeroviz-0.4.8.tar.gz
Algorithm	Hash digest
SHA256	`6125b4f9095ef656e69e8f12fd228661720c65e178933bfb7fab9cd7df88df3e`
MD5	`19489bf03821e0999a8640eeace79ac4`
BLAKE2b-256	`b1561344dc7b56746f55bbb272c5d803c34b5bfc470be2454e9200153fb689e2`

Algorithm	Hash digest
SHA256	`75b0fa09e72ae8a847b78ba5c0944e2b9361ed8695846473617359df56348ff0`
MD5	`6312aeef6aa29732dc901cf0c14bb41a`
BLAKE2b-256	`5a04fdbfc039f3b6a3f73d3c6f8266b370c4468d125421114aa5043dfb965856`

Algorithm	Hash digest
SHA256	`db886f232f457687aee0031fa0dd3287cd7da75671f13b7d9808037dd7655d5d`
MD5	`5c81f9e1e0a38e60710015430dce5b7e`
BLAKE2b-256	`635cf2255c870b74f3cc41395501bd83e0cee14254e93e74afa7d03bf62834e2`

Algorithm	Hash digest
SHA256	`27dc9d3fac4986ce3b03ed496badfd6d56ca094510263d98d2931e73bdad4e45`
MD5	`ed941d1a3944c1e74cd05927048e2f99`
BLAKE2b-256	`c4c82fff555efe308accb5be0d6be7aa408e0d8ad02b144fa93101200485bb27`

Algorithm	Hash digest
SHA256	`4d52cbd24fce286cdd3317f271ae907953d17b846d5ab1aa67d55c8571f93232`
MD5	`743914ccd2510eede90096c726b677a8`
BLAKE2b-256	`0efd58c76e4ea87d0b2969120d8eefbd62550c157ac4a664ab3ba667fc69dfba`

Algorithm	Hash digest
SHA256	`1664d49e5d8bdbbf9cec979e3a24db8066332c2164267a0f5fc04844eccebc6f`
MD5	`3928dc3cacc527fe3aa324145e2ee73a`
BLAKE2b-256	`d2a2f32a63138d2d12c156f0a7c22d2f92e9d12b2d8498c947bd61973cdeb8c8`

Algorithm	Hash digest
SHA256	`c245bc77fd0c725a0e8bc040911789925b1d8276d5e7330909feab864f91fb5d`
MD5	`b08d7253edc73f48fe18a1fa37e038a6`
BLAKE2b-256	`6ed5e650c8ea5783f7e515d6bd6bbb72683dd7bea26a74f8774614a8374c6cac`

Algorithm	Hash digest
SHA256	`526261ff51ceeffe6b37c3d906832431bcae4b5761f182db48faec379c235f47`
MD5	`d1f24e55ff2894eb572ed8a73e4d9655`
BLAKE2b-256	`9f11527ef7fa1185f953b624b457a2ca1226719b7bbd46de89c49204f9f1d02d`

Algorithm	Hash digest
SHA256	`95017e2b7540567db3e9d859035f20138c6b257f78840037402aba6739c75d93`
MD5	`1f1d8c8e5a82e09145d79212a537f4e6`
BLAKE2b-256	`0f549ab4a5d03c5254c9daae399d2efae973f7d0e8188c690c26d235b094646f`

Algorithm	Hash digest
SHA256	`329d85438b8485f0c2aaa33a3d81eca54793c26ec1e81958b31a12f6f933c24a`
MD5	`d8a9dfe7de8b67c97c585ac8b1691ee5`
BLAKE2b-256	`21cbb3ed3d3f0973a71050219bafca3c89b1b2e88983ca09d3e71071ccbba4bf`

Algorithm	Hash digest
SHA256	`4e3f481f835a7934c66b6c6e50c644e8a44c1b3187082068fe78faa8753d8fd7`
MD5	`c83d9f49155ed18e66085fd50b458a92`
BLAKE2b-256	`ad3b3fd8965274d994c266e41a10c9d88420538e2a564f64ae7d6f721ceb94f8`

Algorithm	Hash digest
SHA256	`f9346a2c733bd899a223504cdbcea07a809f15146f4175fe9e93d5c5c1ecb9ec`
MD5	`fffb9fb6882af711b39a259b8e81475d`
BLAKE2b-256	`2485b45ae61d8ff173b4c8cdb92d04ed0f8a6765c8ba2171b8620115a44bf59c`

Algorithm	Hash digest
SHA256	`9370159080cad526de5a419ee524a7ea4b18a9d91b832d9f4d43d8c9a76c8de8`
MD5	`b73675f75e483894c3f144fd368d0de6`
BLAKE2b-256	`92374f40c47f510b41fbf6739dc4908b3fb1d11abfa9f31bd30fcdb3330c43fa`

Algorithm	Hash digest
SHA256	`a310f93fc9e155db6114121fd397c6a7c440dd1010a1e36c5c1df94adf0fa5dc`
MD5	`832972e5572f0bac39c14b83db23c203`
BLAKE2b-256	`4b303b93043df1d5a94837a50137a41c9d3a8bfd297a95033bcd713f15abfa4a`

Algorithm	Hash digest
SHA256	`bdc418e2b4a63cda6f9b19a1eaea67e9c8d74b0f1744782c1ef317206889bb6a`
MD5	`4a1735c3f43072987e68fcd9c678b3dd`
BLAKE2b-256	`d4d8d1412c65c8b6a0458066319741713a3c5a257057d362fcc4996f78f8343e`

Algorithm	Hash digest
SHA256	`7ec9da531c5e30eacb6567e7c15d8cf9f25b06e2debef76dd518fa860f392ede`
MD5	`27085793def2f5098fcfbfee4a20485e`
BLAKE2b-256	`6b155048fbb4048155bfedbcb6210b2ca4b4d17cbf7b23e3d8e63ac51f99ff9e`

AeroViz 0.4.8

AeroViz

Installation

Quick Start

Supported Instruments

Usage

Data Processing & Visualization

Documentation

Contributing

License

Citation

Contributors

Project links

Key dates

1 maintainer

Credits

GitHub Statistics

License

Requires

Provides Extra

Tags

Classifiers

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes