Pymicra is a Python package designed to make working with micrometeorological datasets more efficient. It is aimed at improving productivity (by allowing us to focus more on micrometeorology) while still being flexible enough to let us program project-specific things.
Please check out the Github page and the documentation.
Here's a quick (incomplete!) list of what Pymicra does:
- Reading, separating and understanding micrometeorological data in virtually any column-separated ASCII format (thanks to pandas).
- Quality control methods (max and min values check, spikes, reverse-arrangement test and etc..
- Rotation of coordinates (2D).
- Detrending of data in the most common ways (block averages, moving averages and polynomial detrending).
- Correction of sensor drift.
- Automatic calculation of most auxiliary variables based on measurements (air density, dry air density, etc.).
- Calculation of spectra and cross-spectra.
- Calculation fluxes and characteristic scales with or without WPL correction.
- Provide common constants generally used in atmospheric sciences.
- Plus all native features of Pandas (interpolation, resampling, grouping, statistical tests, slicing, handling of missing data and etc.).
The package is extensively (almost entirely) based on Pandas, mostly the
pandas.DataFrame class. We use Pint for units control and (generally) Numpy
or Scipy for some numerical functions not contained in Pandas.