bluemath_tk.core.plotting package

Submodules

bluemath_tk.core.plotting.base_plotting module

class bluemath_tk.core.plotting.base_plotting.BasePlotting

Bases: ABC

Abstract base class for handling default plotting functionalities across the project.

abstractmethod plot_line(x, y)

Abstract method for plotting a line. Should be implemented by subclasses.

abstractmethod plot_map(markers=None)

Abstract method for plotting a map. Should be implemented by subclasses.

abstractmethod plot_scatter(x, y)

Abstract method for plotting a scatter plot. Should be implemented by subclasses.

class bluemath_tk.core.plotting.base_plotting.DefaultInteractivePlotting

Bases: BasePlotting

Concrete implementation of BasePlotting with interactive plotting behaviors.

plot_line(x, y)

Abstract method for plotting a line. Should be implemented by subclasses.

plot_map(markers=None)

Abstract method for plotting a map. Should be implemented by subclasses.

plot_scatter(x, y)

Abstract method for plotting a scatter plot. Should be implemented by subclasses.

class bluemath_tk.core.plotting.base_plotting.DefaultStaticPlotting(template: str = 'default')

Bases: BasePlotting

Concrete implementation of BasePlotting with static plotting behaviors.

get_subplot(figsize, **kwargs)

get_subplots(**kwargs)

plot_line(ax, **kwargs)

Abstract method for plotting a line. Should be implemented by subclasses.

plot_map(ax, **kwargs)

Abstract method for plotting a map. Should be implemented by subclasses.

plot_pie(ax, **kwargs)

plot_scatter(ax, **kwargs)

Abstract method for plotting a scatter plot. Should be implemented by subclasses.

set_grid(ax, grid=True)

Sets the grid for a given axis.

set_title(ax, title='Plot Title')

Sets the title for a given axis.

set_xlabel(ax, xlabel='X-axis')

Sets the x-axis label for a given axis.

set_xlim(ax, xmin, xmax)

Sets the x-axis limits for a given axis.

set_ylabel(ax, ylabel='Y-axis')

Sets the y-axis label for a given axis.

set_ylim(ax, ymin, ymax)

Sets the y-axis limits for a given axis.

templates = {'default': {'line': {'color': 'blue', 'line_style': '-'}, 'scatter': {'color': 'red', 'marker': 'o', 'size': 10}}}

bluemath_tk.core.plotting.colors module

bluemath_tk.core.plotting.colors.GetFamsColors(num_fams: int) → ndarray

Get colors for wave families.

Parameters:

num_fams (int) – Number of wave families.

Returns:

Array of RGB colors for the specified number of wave families.

Return type:

np.ndarray

bluemath_tk.core.plotting.colors.colormap_bathy(topat: float, topag: float) → ListedColormap

Create custom colormap for bathymetry plot.

Parameters:

• topat (float) – Maximum topography value.

• topag (float) – Minimum bathymetry value.

Returns:

Custom colormap combining YlGnBu_r and turbid colormaps.

Return type:

ListedColormap

bluemath_tk.core.plotting.colors.colormap_spectra() → ListedColormap

Create custom colormap for spectra plots combining RdBu and rainbow colormaps.

Returns:

Custom colormap combining RdBu and rainbow colormaps.

Return type:

ListedColormap

bluemath_tk.core.plotting.colors.colors_awt() → ndarray

Get colors for Annual Weather Types (6 categories).

Returns:

Array of RGB colors for 6 AWT categories

Return type:

np.ndarray

bluemath_tk.core.plotting.colors.colors_dwt(num_clusters: int) → ndarray

Get colors for Daily Weather Types.

Parameters:

num_clusters (int) – Number of clusters to get colors for.

Returns:

Array of RGB colors for the specified number of clusters.

Return type:

np.ndarray

bluemath_tk.core.plotting.colors.colors_fams_3() → ndarray

Get colors for 3 wave families.

Returns:

Array of RGB colors for 3 wave families.

Return type:

np.ndarray

bluemath_tk.core.plotting.colors.colors_interp(num_clusters: int) → ndarray

Generate interpolated colors from Spectral colormap.

Parameters:

num_clusters (int) – Number of clusters to generate colors for.

Returns:

Array of RGB colors interpolated from Spectral colormap.

Return type:

np.ndarray

bluemath_tk.core.plotting.colors.colors_mjo() → ndarray

Get colors for MJO 25 categories.

Returns:

Array of RGB colors for 25 MJO categories.

Return type:

np.ndarray

bluemath_tk.core.plotting.colors.get_cluster_colors(num_clusters: int) → ndarray

Get appropriate colors for clustering based on number of clusters.

Parameters:

num_clusters (int) – Number of clusters to get colors for.

Returns:

Array of RGB colors for the specified number of clusters.

Return type:

np.ndarray

bluemath_tk.core.plotting.colors.get_config_variables() → Dict[str, Dict[str, Any]]

Get configuration variables for different meteorological parameters.

Returns:

Dictionary containing configuration for different variables: - geo500hpa: Geopotential height at 500hPa - mslp: Mean Sea Level Pressure - mslp_grad: MSLP gradient - sst: Sea Surface Temperature - other: Default configuration

Return type:

Dict[str, Dict[str, Any]]

bluemath_tk.core.plotting.scatter module

bluemath_tk.core.plotting.scatter.bias(pred: ndarray, tar: ndarray) → float

Calculate the bias between predicted and target values.

Parameters:

• pred (np.ndarray) – Array of predicted values.

• tar (np.ndarray) – Array of target/actual values.

Returns:

The bias value (mean difference between predictions and targets).

Return type:

float

bluemath_tk.core.plotting.scatter.density_scatter(x: ndarray, y: ndarray) → Tuple[ndarray, ndarray, ndarray]

Compute a density scatter for two arrays using gaussian KDE.

Parameters:

• x (np.ndarray) – X values for the scatter plot.

• y (np.ndarray) – Y values for the scatter plot.

Returns:

A tuple containing: - Sorted x values - Sorted y values - Density values corresponding to each point

Return type:

Tuple[np.ndarray, np.ndarray, np.ndarray]

bluemath_tk.core.plotting.scatter.plot_scatters_in_triangle(dataframes: List[DataFrame], data_colors: List[str] | None = None, **kwargs) → Tuple[Figure, ndarray]

Plot scatter plots of the dataframes with axes in a triangle arrangement.

Parameters:

• dataframes (List[pd.DataFrame]) – List of dataframes to plot. Each dataframe should contain the same columns.

• data_colors (Optional[List[str]], optional) – List of colors for the dataframes. If None, uses default_colors.

• **kwargs (dict, optional) – Additional keyword arguments for the scatter plot. These will be passed to matplotlib.pyplot.scatter. Common parameters include: - s : float, marker size - alpha : float, transparency - marker : str, marker style

Returns:

A tuple containing: - Figure object - 2D array of Axes objects

Return type:

Tuple[Figure, np.ndarray]

Raises:

ValueError – If the variables in the first dataframe are not present in all other dataframes.

bluemath_tk.core.plotting.scatter.rmse(pred: ndarray, tar: ndarray) → float

Calculate the Root Mean Square Error between predicted and target values.

Parameters:

• pred (np.ndarray) – Array of predicted values.

• tar (np.ndarray) – Array of target/actual values.

Returns:

The Root Mean Square Error value.

Return type:

float

bluemath_tk.core.plotting.scatter.si(pred: ndarray, tar: ndarray) → float

Calculate the Scatter Index between predicted and target values.

Parameters:

• pred (np.ndarray) – Array of predicted values.

• tar (np.ndarray) – Array of target/actual values.

Returns:

The Scatter Index value.

Return type:

float

bluemath_tk.core.plotting.scatter.validation_scatter(axs: Axes, x: ndarray, y: ndarray, xlabel: str, ylabel: str, title: str, cmap: str = 'rainbow') → None

Plot a density scatter and Q-Q plot for validation.

Parameters:

• axs (Axes) – Matplotlib axes to plot on.

• x (np.ndarray) – X values for the scatter plot.

• y (np.ndarray) – Y values for the scatter plot.

• xlabel (str) – Label for the X-axis.

• ylabel (str) – Label for the Y-axis.

• title (str) – Title for the plot.

• cmap (str, optional) – Colormap to use for the scatter plot. Default is “rainbow”.

bluemath_tk.core.plotting.utils module

bluemath_tk.core.plotting.utils.create_cmap_from_colors(color_list: List[str], name: str = 'custom') → LinearSegmentedColormap

Create a colormap from a list of hex colors.

Parameters:

• color_list (List[str]) – List of hex color codes (e.g., [“#ff0000”, “#00ff00”])

• name (str, optional) – Name for the colormap. Default is “custom”.

Returns:

A colormap created from the provided colors.

Return type:

colors.LinearSegmentedColormap

bluemath_tk.core.plotting.utils.get_list_of_colors_for_colormap(cmap: str | Colormap, num_colors: int) → list

Get a list of colors from a colormap.

Parameters:

• cmap (Union[str, Colormap]) – The colormap to use.

• num_colors (int) – The number of colors to generate.

Returns:

A list of colors generated from the colormap.

Return type:

list

bluemath_tk.core.plotting.utils.join_colormaps(cmap1: str | List[str] | Colormap, cmap2: str | List[str] | Colormap, name: str = 'joined_cmap', range1: Tuple[float, float] = (0.0, 1.0), range2: Tuple[float, float] = (0.0, 1.0), value_range1: Tuple[float, float] = None, value_range2: Tuple[float, float] = None) → Tuple[ListedColormap, BoundaryNorm]

Join two colormaps into one, with value ranges specified for each.

Parameters:

• cmap1 (Union[str, List[str], Colormap]) – Input colormaps (name, list of hex codes, or Colormap object).

• cmap2 (Union[str, List[str], Colormap]) – Input colormaps (name, list of hex codes, or Colormap object).

• name (str) – Name of the output colormap.

• range1 (Tuple[float, float]) – Portion of each colormap to use (from 0 to 1).

• range2 (Tuple[float, float]) – Portion of each colormap to use (from 0 to 1).

• value_range1 (Tuple[float, float]) – Value ranges in the data domain corresponding to each colormap.

• value_range2 (Tuple[float, float]) – Value ranges in the data domain corresponding to each colormap.

Returns:

• ListedColormap – Merged colormap object.

• BoundaryNorm – Normalization for mapping data to colors.

Module contents