What’s new

Version 1.0.0

This release reorganizes the functions with the aim of expanding the package for air quality users and improve the scalability of the package, and adds several new functionalities: one for air quality purposes and three common for climatological and air quality purposes.

Thanks to Juan José Velasco Horcajada for his extremely helpful advice and to María de la Peña García for the logo design!

New Features

  • Added pyclimair.common.compare_probdist() which compares the probability distribution of a variable in a certain period versus the climatological normal.

  • Added pyclimair.common.threevar_windrose() which allows to plot a variable as a function of wind speed and direction and for different temporal aggregations: yearly, seasonal or monthly.

  • Added pyclimair.common.threevar_windrose_trend() that allows to plot the linear trend of a given variable as a function of wind speed and direction and for different temporal aggregations: yearly, seasonal or monthly.

  • Added pyclimair.common.threevar_windrose_probability() which allows to compute the probability of occurrence of a given condition as a function of wind speed and direction and for different temporal aggregations: yearly, seasonal or monthly.

  • Added pyclimair.air.annual_meteogram_with_pollutant() which allows to plot a meteorological meteogram with an air pollutant as a fourth variable.

  • Added pyclimair.common.window_plot() that allows to create rolling window moving-average plots plots of any variable.

Fixes

  • In pyclimair.common.plot_variable_trends(), pyclimair.common.compute_and_plot_exceedances() and pyclimair.common.plot_periodaverages(), when grouping_stat is ‘sum’, if all values are NaN it now plots as NaN instead of zero. This fixes wrong trend and averages computations due to unrealistic zeros.

  • pyclimair.categories_evolution() now shows NaN values, fixing unrealistic frequencies

Enhancements

  • pyclimair.common.categories_evolution() now creates categories’ labels automatically, disappearing the “categories_labels” argument.

  • Former function pyclimair.plot_periodaverages has been renamed to pyclimair.common.plot_periodstats, as it can also plot other statistics than averages.

  • Simplification of the pyclimair.common.plot_data_and_accum_anoms() and pyclimair.common.plot_data_and_annual_cycle() functions: Now they read a single variable instead of a list of variables.

  • In pyclimair.common.plot_data_vs_climate(), bottom y-axis limit set to 0 for “Rainfall” to improve visualization.

  • Improved visuals for pyclimair.common.timeseries_extremevalues() function