fluxcalc 0.1.1

Flexible flux and turbulence calculations from NetCDF files.

flux_calculations

Flexible flux and turbulence calculations from NetCDF files: sensible heat flux, momentum flux, and turbulent kinetic energy.

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Features

• NetCDF File Aggregation: Combines multiple NetCDF files into a single data frame for each sensor, simplifying data management.
• Outlier Removal: Automatically detects and removes outliers beyond 3 standard deviations within a rolling 100-observation window.
• Advanced Plotting:
  • Visualize 3-dimensional wind data for comprehensive analysis.
  • Generate FFT (Fast Fourier Transform) plots for frequency domain insights.
• Flexible Flux Calculations:
  • Compute Sensible Heat Flux, Momentum Flux, or Turbulent Kinetic Energy (TKE) using a customizable time window (user-defined in minutes).
  • Plot calculated fluxes for easy interpretation and visualization.
• User-Friendly Design: Offers intuitive options for data processing, plotting, and analysis, ensuring flexibility and ease of use.

Installation

pip install fluxcalc

To install the latest version from GitHub:

pip install git+https://github.com/jpiot1/flux_calculations.git

‼️‼️‼️ For development (editable) install with tests: ‼️‼️‼️

git clone https://github.com/jpiot1/flux_calculations.git
cd flux_calculations
env=".venv"
python -m venv "$env"
source "$env/bin/activate"
pip install -e '.[dev]'
pytest -q

‼️‼️‼️ Python API ‼️‼️‼️

import xarray as xr
import fluxcalc as fc

# 1. Open dataset
 ds = xr.open_dataset("input.nc").sortby("time")

# 2. Compute wavelet transforms
 row_wt, col_wt, remnant = cf.get_wavelets(ds.copy(), ds.range.values, ds.time.values)

# 3. Detect clouds & precip
 clouds, precip = cf.get_clouds_and_precip(
     ds.beta_att.T.values, ds.range.values, ds.time.values
 )

# 4. Flags and threshold
 fog, cloud, rain, clear = cf.get_flags(
     ds.beta_att.T.values, ds.range.values, ds.time.values
 )
 thresh = cf.get_thresh(remnant, row_wt, col_wt)

# 5. Denoise & extract layers
 clean = cf.remove_noisy_regions(
     ds.beta_att.T.values, thresh, ds.range.values, ds.time.values
 )
 layers = cf.get_layers(clean.values, clean.range.values)

# 6. Write results
 cf.create_file(
     ds, row_wt, col_wt, remnant,
     clouds, precip, fog, rain, cloud, clear,
     clean, layers, ds.overlap_function,
     output_path="output.nc",
     overwrite=True
 )

Contributing

1. Fork the repo and create a feature branch.
2. Write code, tests, and update docs.
3. Follow style: ruff, mypy, pytest.
4. Submit a pull request.

License

This project is licensed under the GNU Lesser General Public License v2.1 or later. See LICENSE for details.