bluemath_tk.predictor package

Submodules

bluemath_tk.predictor.awt module

bluemath_tk.predictor.dwt module

bluemath_tk.predictor.indices module

bluemath_tk.predictor.itca module

bluemath_tk.predictor.iwt module

Module contents

Project: BlueMath_tk Sub-Module: predictor Author: GeoOcean Research Group, Universidad de Cantabria Creation Date: 19 March 2025 Repository: https://github.com/GeoOcean/BlueMath_tk.git Status: Under development (Working)

class bluemath_tk.predictor.XWT(steps: Dict[str, BlueMathModel])[source]

Bases: BlueMathModel, BlueMathPipeline

Xly Weather Types (XWT) class.

This class implements the XWT method to identify and classify weather patterns in a dataset. The XWT method is a combination of Principal Component Analysis (PCA) and K-means clustering (KMA).

steps

The steps of the XWT method.

Type:: Dict[str, BlueMathModel]

num_clusters

The number of clusters.

Type:: int

kma_bmus

The KMA best matching units (BMUs).

Type:: pd.DataFrame

property clusters_annual_probs_df

property clusters_monthly_probs_df

property clusters_perpetual_year_probs_df

property clusters_probs_df

property clusters_seasonal_probs_df

property data: Dataset

fit(data: Dataset, fit_params: Dict[str, Dict[str, Any]] = {}, variable_to_sort_bmus: str = None) → None[source]

Fit the XWT model to the data.

Parameters:

data (xr.Dataset) – The data to fit the model to. Must be PCA formatted.
fit_params (Dict[str, Dict[str, Any]], optional) – The fitting parameters for the PCA and KMA models. Default is {}.
variable_to_sort_bmus (str, optional) – The variable to sort the BMUs. Default is None.

Raises:

XWTError – If the data is not PCA formatted.

property get_conditioned_probabilities

plot_dwts_probs(vmax: float = 0.15, vmax_seasonality: float = 0.15, plot_text: bool = False) → None[source]

Plot Daily Weather Types bmus probabilities.

Parameters:

vmax (float, optional) – The maximum value of the colorbar. Default is 0.15.
vmax_seasonality (float, optional) – The maximum value of the colorbar for seasonality. Default is 0.15.
plot_text (bool, optional) – Whether to plot the text in each cell. Default is False.

Raises:

ValueError – If the kma_bmus time sampling is not daily.

plot_map_features(ax: Axes, land_color: str = array([0.9375, 0.9375, 0.859375])) → None[source]

Plot map features on an axis.

Parameters:

ax (Axes) – The axis to plot the map features on.
land_color (str, optional) – The color of the land. Default is cfeature.COLORS[“land”].

plot_perpetual_year() → Axes[source]

Plot perpetual year bmus probabilities.

Returns:: The plot with the perpetual year bmus probabilities.
Return type:: Axes

plot_xwts(var_to_plot: str, anomaly: bool = False, map_center: tuple = None) → Collection[source]

Plot the XWTs for a variable.

Parameters:

var_to_plot (str) – The variable to plot.
anomaly (bool, optional) – Whether to plot the anomaly of the variable. Default is False.
map_center (tuple, optional) – The center of the map. Default is None.

Returns:

The grid specification with the XWTs plot.

Return type:

GridSpec