ergodic_insurance.visualization_infra package

Submodules

ergodic_insurance.visualization_infra.figure_factory module

Figure factory for creating standardized plots with consistent styling.

This module provides a factory class for creating various types of plots with automatic styling, spacing, and formatting applied consistently.

class FigureFactory(style_manager: StyleManager | None = None, theme: Theme = Theme.DEFAULT, auto_apply: bool = True)[source]

Bases: object

Factory for creating standardized figures with consistent styling.

This class provides methods to create various types of plots with automatic application of themes, consistent formatting, and proper spacing.

Example

>>> factory = FigureFactory(theme=Theme.PRESENTATION)
>>> fig, ax = factory.create_line_plot(
...     x_data=[1, 2, 3, 4],
...     y_data=[10, 20, 15, 25],
...     title="Revenue Growth",
...     x_label="Quarter",
...     y_label="Revenue ($M)"
... )

>>> # Create multiple subplots
>>> fig, axes = factory.create_subplots(
...     rows=2, cols=2,
...     size_type="large",
...     subplot_titles=["Q1", "Q2", "Q3", "Q4"]
... )

create_figure(size_type: str = 'medium', orientation: str = 'landscape', dpi_type: str = 'screen', title: str | None = None) → Tuple[Figure, Axes][source]

Create a basic figure with styling applied.

Parameters:

size_type (str) – Size preset (small, medium, large, blog, technical, presentation)
orientation (str) – Figure orientation (landscape or portrait)
dpi_type (str) – DPI type (screen, web, print)
title (Optional[str]) – Optional figure title

Return type:

Tuple[Figure, Axes]

Returns:

Tuple of (figure, axes)

create_subplots(rows: int = 1, cols: int = 1, size_type: str = 'large', dpi_type: str = 'screen', title: str | None = None, subplot_titles: List[str] | None = None, **kwargs) → Tuple[Figure, Axes | ndarray][source]

Create subplots with consistent styling.

Parameters:

rows (int) – Number of subplot rows
cols (int) – Number of subplot columns
size_type (str) – Size preset
dpi_type (str) – DPI type
title (Optional[str]) – Main figure title
subplot_titles (Optional[List[str]]) – Titles for each subplot
**kwargs – Additional arguments for plt.subplots

Return type:

Tuple[Figure, Union[Axes, ndarray]]

Returns:

Tuple of (figure, axes array)

Create a line plot with automatic formatting.

Parameters:

x_data (Union[List, ndarray, Series]) – X-axis data
y_data (Union[List, ndarray, Series, Dict[str, Union[List, ndarray]]]) – Y-axis data (can be multiple series as dict)
title (Optional[str]) – Plot title
x_label (Optional[str]) – X-axis label
y_label (Optional[str]) – Y-axis label
labels (Optional[List[str]]) – Series labels for legend
size_type (str) – Figure size preset
dpi_type (str) – DPI type
show_legend (bool) – Whether to show legend
show_grid (bool) – Whether to show grid
markers (bool) – Whether to add markers to lines
**kwargs – Additional arguments for plot

Return type:

Tuple[Figure, Axes]

Returns:

Tuple of (figure, axes)

create_bar_plot(categories: List | ndarray, values: List | ndarray | Dict[str, List | ndarray], title: str | None = None, x_label: str | None = None, y_label: str | None = None, labels: List[str] | None = None, size_type: str = 'medium', dpi_type: str = 'screen', orientation: str = 'vertical', show_values: bool = False, value_format: str = '.1f', **kwargs) → Tuple[Figure, Axes][source]

Create a bar plot with automatic formatting.

Parameters:

categories (Union[List, ndarray]) – Category labels
values (Union[List, ndarray, Dict[str, Union[List, ndarray]]]) – Values to plot (can be multiple series as dict)
title (Optional[str]) – Plot title
x_label (Optional[str]) – X-axis label
y_label (Optional[str]) – Y-axis label
labels (Optional[List[str]]) – Series labels for legend
size_type (str) – Figure size preset
dpi_type (str) – DPI type
orientation (str) – Bar orientation (vertical or horizontal)
show_values (bool) – Whether to show value labels on bars
value_format (str) – Format string for value labels
**kwargs – Additional arguments for bar plot

Return type:

Tuple[Figure, Axes]

Returns:

Tuple of (figure, axes)

Create a scatter plot with automatic formatting.

Parameters:

x_data (Union[List, ndarray]) – X-axis data
y_data (Union[List, ndarray]) – Y-axis data
title (Optional[str]) – Plot title
x_label (Optional[str]) – X-axis label
y_label (Optional[str]) – Y-axis label
size_type (str) – Figure size preset
dpi_type (str) – DPI type
colors (Union[List, ndarray, None]) – Optional colors for points (for continuous coloring)
sizes (Union[List, ndarray, None]) – Optional sizes for points
labels (Optional[List[str]]) – Optional labels for points
show_colorbar (bool) – Whether to show colorbar when colors provided
**kwargs – Additional arguments for scatter

Return type:

Tuple[Figure, Axes]

Returns:

Tuple of (figure, axes)

create_histogram(data: List | ndarray | Series, title: str | None = None, x_label: str | None = None, y_label: str = 'Frequency', bins: int | str = 'auto', size_type: str = 'medium', dpi_type: str = 'screen', show_statistics: bool = False, show_kde: bool = False, **kwargs) → Tuple[Figure, Axes][source]

Create a histogram with automatic formatting.

Parameters:

data (Union[List, ndarray, Series]) – Data to plot
title (Optional[str]) – Plot title
x_label (Optional[str]) – X-axis label
y_label (str) – Y-axis label
bins (Union[int, str]) – Number of bins or method
size_type (str) – Figure size preset
dpi_type (str) – DPI type
show_statistics (bool) – Whether to show mean/median lines
show_kde (bool) – Whether to overlay KDE
**kwargs – Additional arguments for hist

Return type:

Tuple[Figure, Axes]

Returns:

Tuple of (figure, axes)

create_heatmap(data: ndarray | DataFrame, title: str | None = None, x_labels: List[str] | None = None, y_labels: List[str] | None = None, x_label: str | None = None, y_label: str | None = None, size_type: str = 'medium', dpi_type: str = 'screen', cmap: str = 'RdBu_r', show_values: bool = True, value_format: str = '.2f', **kwargs) → Tuple[Figure, Axes][source]

Create a heatmap with automatic formatting.

Parameters:

data (Union[ndarray, DataFrame]) – 2D data array or DataFrame
title (Optional[str]) – Plot title
x_labels (Optional[List[str]]) – Labels for x-axis
y_labels (Optional[List[str]]) – Labels for y-axis
x_label (Optional[str]) – X-axis title
y_label (Optional[str]) – Y-axis title
size_type (str) – Figure size preset
dpi_type (str) – DPI type
cmap (str) – Colormap name
show_values (bool) – Whether to show values in cells
value_format (str) – Format string for cell values
**kwargs – Additional arguments for imshow

Return type:

Tuple[Figure, Axes]

Returns:

Tuple of (figure, axes)

create_box_plot(data: List[List] | Dict[str, List] | DataFrame, title: str | None = None, x_label: str | None = None, y_label: str | None = None, labels: List[str] | None = None, size_type: str = 'medium', dpi_type: str = 'screen', orientation: str = 'vertical', show_means: bool = True, **kwargs) → Tuple[Figure, Axes][source]

Create a box plot with automatic formatting.

Parameters:

data (Union[List[List], Dict[str, List], DataFrame]) – Data for box plot (list of lists, dict, or DataFrame)
title (Optional[str]) – Plot title
x_label (Optional[str]) – X-axis label
y_label (Optional[str]) – Y-axis label
labels (Optional[List[str]]) – Labels for each box
size_type (str) – Figure size preset
dpi_type (str) – DPI type
orientation (str) – Plot orientation (vertical or horizontal)
show_means (bool) – Whether to show mean markers
**kwargs – Additional arguments for boxplot

Return type:

Tuple[Figure, Axes]

Returns:

Tuple of (figure, axes)

format_axis_currency(ax: Axes, axis: str = 'y', abbreviate: bool = True, decimals: int = 0) → None[source]

Format axis labels as currency.

Parameters:

ax (Axes) – Matplotlib axes
axis (str) – Which axis to format (x or y)
abbreviate (bool) – Whether to abbreviate large numbers
decimals (int) – Number of decimal places

Return type:

None

format_axis_percentage(ax: Axes, axis: str = 'y', decimals: int = 0) → None[source]

Format axis labels as percentages.

Parameters:

ax (Axes) – Matplotlib axes
axis (str) – Which axis to format (x or y)
decimals (int) – Number of decimal places

Return type:

None

add_annotations(ax: Axes, x: float, y: float, text: str, arrow: bool = True, offset: Tuple[float, float] = (10, 10), **kwargs) → None[source]

Add styled annotation to plot.

Parameters:

ax (Axes) – Matplotlib axes
x (float) – X coordinate
y (float) – Y coordinate
text (str) – Annotation text
arrow (bool) – Whether to show arrow
offset (Tuple[float, float]) – Text offset from point
**kwargs – Additional arguments for annotate

Return type:

None

save_figure(fig: Figure, filename: str, output_type: str = 'web', **kwargs) → None[source]

Save figure with appropriate DPI settings.

Parameters:

fig (Figure) – Figure to save
filename (str) – Output filename
output_type (str) – Output type (screen, web, print)
**kwargs – Additional arguments for savefig

Return type:

None

ergodic_insurance.visualization_infra.style_manager module

Style management for consistent visualization across all reports.

This module provides centralized style configuration for all visualizations, including color palettes, fonts, figure sizes, and DPI settings.

class Theme(*values)[source]

Bases: Enum

Available visualization themes.

DEFAULT = 'default'

COLORBLIND = 'colorblind'

PRESENTATION = 'presentation'

MINIMAL = 'minimal'

PRINT = 'print'

class ColorPalette(primary: str = '#0080C7', secondary: str = '#003F5C', accent: str = '#FF9800', warning: str = '#D32F2F', success: str = '#4CAF50', neutral: str = '#666666', background: str = '#FFFFFF', text: str = '#000000', grid: str = '#E0E0E0', series: List[str] = <factory>) → None[source]

Bases: object

Color palette configuration for a theme.

primary: Main color for primary elements

secondary: Secondary color for supporting elements

accent: Accent color for highlights

warning: Color for warnings or negative values

success: Color for positive values or success states

neutral: Neutral gray tones

background: Background color

text: Text color

grid: Grid line color

series: List of colors for multiple data series

primary: str = '#0080C7'

secondary: str = '#003F5C'

accent: str = '#FF9800'

warning: str = '#D32F2F'

success: str = '#4CAF50'

neutral: str = '#666666'

background: str = '#FFFFFF'

text: str = '#000000'

grid: str = '#E0E0E0'

series: List[str]

class FontConfig(family: str = 'Arial', size_base: int = 11, size_title: int = 14, size_label: int = 12, size_tick: int = 10, size_legend: int = 10, weight_normal: str = 'normal', weight_bold: str = 'bold') → None[source]

Bases: object

Font configuration for a theme.

family: Font family name

size_base: Base font size

size_title: Title font size

size_label: Label font size

size_tick: Tick label font size

size_legend: Legend font size

weight_normal: Normal font weight

weight_bold: Bold font weight

family: str = 'Arial'

size_base: int = 11

size_title: int = 14

size_label: int = 12

size_tick: int = 10

size_legend: int = 10

weight_normal: str = 'normal'

weight_bold: str = 'bold'

class FigureConfig(size_small: Tuple[float, float] = (6, 4), size_medium: Tuple[float, float] = (8, 6), size_large: Tuple[float, float] = (12, 8), size_blog: Tuple[float, float] = (8, 6), size_technical: Tuple[float, float] = (10, 8), size_presentation: Tuple[float, float] = (10, 7.5), dpi_screen: int = 100, dpi_web: int = 150, dpi_print: int = 300) → None[source]

Bases: object

Figure size and DPI configuration.

size_small: Small figure size (width, height) in inches

size_medium: Medium figure size

size_large: Large figure size

size_blog: Blog-optimized size (8x6)

size_technical: Technical appendix size (10x8)

size_presentation: Presentation slide size

dpi_screen: DPI for screen display

dpi_web: DPI for web publishing (150)

dpi_print: DPI for print quality (300)

size_small: Tuple[float, float] = (6, 4)

size_medium: Tuple[float, float] = (8, 6)

size_large: Tuple[float, float] = (12, 8)

size_blog: Tuple[float, float] = (8, 6)

size_technical: Tuple[float, float] = (10, 8)

size_presentation: Tuple[float, float] = (10, 7.5)

dpi_screen: int = 100

dpi_web: int = 150

dpi_print: int = 300

class GridConfig(show_grid: bool = True, grid_alpha: float = 0.3, grid_linewidth: float = 0.5, spine_top: bool = False, spine_right: bool = False, spine_bottom: bool = True, spine_left: bool = True, spine_linewidth: float = 0.8, tick_major_width: float = 0.8, tick_minor_width: float = 0.4) → None[source]

Bases: object

Grid and axis configuration.

show_grid: Whether to show grid lines

grid_alpha: Grid transparency

grid_linewidth: Grid line width

spine_top: Show top spine

spine_right: Show right spine

spine_bottom: Show bottom spine

spine_left: Show left spine

spine_linewidth: Spine line width

tick_major_width: Major tick width

tick_minor_width: Minor tick width

show_grid: bool = True

grid_alpha: float = 0.3

grid_linewidth: float = 0.5

spine_top: bool = False

spine_right: bool = False

spine_bottom: bool = True

spine_left: bool = True

spine_linewidth: float = 0.8

tick_major_width: float = 0.8

tick_minor_width: float = 0.4

class StyleManager(theme: Theme = Theme.DEFAULT, config_path: str | Path | None = None, custom_colors: Dict[str, str] | None = None, custom_fonts: Dict[str, Any] | None = None)[source]

Bases: object

Manages visualization styles and themes.

This class provides centralized style management for all visualizations, supporting multiple themes, custom configurations, and style inheritance.

Example

>>> style_mgr = StyleManager()
>>> style_mgr.set_theme(Theme.PRESENTATION)
>>> style_mgr.apply_style()
>>> # Create plots with consistent styling

>>> # Or with custom configuration
>>> style_mgr = StyleManager(config_path="custom_style.yaml")
>>> style_mgr.apply_style()

themes: Dict[Theme, Dict[str, Any]]

set_theme(theme: Theme) → None[source]

Set the current theme.

Parameters:: theme (Theme) – Theme to activate
Return type:: None

get_theme_config(theme: Theme | None = None) → Dict[str, Any][source]

Get configuration for a theme.

Parameters:: theme (Optional[Theme]) – Theme to get config for (defaults to current)
Return type:: Dict[str, Any]
Returns:: Theme configuration dictionary

get_colors() → ColorPalette[source]

Get current color palette.

Return type:: ColorPalette
Returns:: Current theme’s color palette

get_fonts() → FontConfig[source]

Get current font configuration.

Return type:: FontConfig
Returns:: Current theme’s font configuration

get_figure_config() → FigureConfig[source]

Get current figure configuration.

Return type:: FigureConfig
Returns:: Current theme’s figure configuration

get_grid_config() → GridConfig[source]

Get current grid configuration.

Return type:: GridConfig
Returns:: Current theme’s grid configuration

update_colors(updates: Dict[str, str]) → None[source]

Update colors in current theme.

Parameters:: updates (Dict[str, str]) – Dictionary of color updates
Return type:: None

update_fonts(updates: Dict[str, Any]) → None[source]

Update fonts in current theme.

Parameters:: updates (Dict[str, Any]) – Dictionary of font updates
Return type:: None

apply_style() → None[source]

Apply current theme to matplotlib.

This updates matplotlib’s rcParams to match the current theme settings.

Return type:: None

get_figure_size(size_type: str = 'medium', orientation: str = 'landscape') → Tuple[float, float][source]

Get figure size for a given type.

Parameters:

size_type (str) – Size type (small, medium, large, blog, technical, presentation)
orientation (str) – Figure orientation (landscape or portrait)

Return type:

Tuple[float, float]

Returns:

Tuple of (width, height) in inches

get_dpi(output_type: str = 'screen') → int[source]

Get DPI for output type.

Parameters:: output_type (str) – Output type (screen, web, print)
Return type:: int
Returns:: DPI value

load_config(config_path: str | Path) → None[source]

Load configuration from YAML file.

Parameters:: config_path (Union[str, Path]) – Path to YAML configuration file
Return type:: None

save_config(config_path: str | Path) → None[source]

Save current configuration to YAML file.

Parameters:: config_path (Union[str, Path]) – Path to save YAML configuration
Return type:: None

create_style_sheet() → Dict[str, Any][source]

Create matplotlib style sheet dictionary.

Return type:: Dict[str, Any]
Returns:: Style sheet dictionary compatible with matplotlib

inherit_from(parent_theme: Theme, modifications: Dict[str, Any]) → Theme[source]

Create a new theme inheriting from a parent with modifications.

Parameters:

parent_theme (Theme) – Theme to inherit from
modifications (Dict[str, Any]) – Dictionary of modifications

Return type:

Theme

Returns:

New theme enum value

Module contents

Visualization infrastructure for professional report generation.

This package provides centralized styling and figure generation capabilities: - StyleManager: Manages themes, colors, fonts, and figure configurations - FigureFactory: Creates standardized plots with consistent styling

class StyleManager(theme: Theme = Theme.DEFAULT, config_path: str | Path | None = None, custom_colors: Dict[str, str] | None = None, custom_fonts: Dict[str, Any] | None = None)[source]

Bases: object

Manages visualization styles and themes.

This class provides centralized style management for all visualizations, supporting multiple themes, custom configurations, and style inheritance.

Example

>>> style_mgr = StyleManager()
>>> style_mgr.set_theme(Theme.PRESENTATION)
>>> style_mgr.apply_style()
>>> # Create plots with consistent styling

>>> # Or with custom configuration
>>> style_mgr = StyleManager(config_path="custom_style.yaml")
>>> style_mgr.apply_style()

themes: Dict[Theme, Dict[str, Any]]

set_theme(theme: Theme) → None[source]

Set the current theme.

Parameters:: theme (Theme) – Theme to activate
Return type:: None

get_theme_config(theme: Theme | None = None) → Dict[str, Any][source]

Get configuration for a theme.

Parameters:: theme (Optional[Theme]) – Theme to get config for (defaults to current)
Return type:: Dict[str, Any]
Returns:: Theme configuration dictionary

get_colors() → ColorPalette[source]

Get current color palette.

Return type:: ColorPalette
Returns:: Current theme’s color palette

get_fonts() → FontConfig[source]

Get current font configuration.

Return type:: FontConfig
Returns:: Current theme’s font configuration

get_figure_config() → FigureConfig[source]

Get current figure configuration.

Return type:: FigureConfig
Returns:: Current theme’s figure configuration

get_grid_config() → GridConfig[source]

Get current grid configuration.

Return type:: GridConfig
Returns:: Current theme’s grid configuration

update_colors(updates: Dict[str, str]) → None[source]

Update colors in current theme.

Parameters:: updates (Dict[str, str]) – Dictionary of color updates
Return type:: None

update_fonts(updates: Dict[str, Any]) → None[source]

Update fonts in current theme.

Parameters:: updates (Dict[str, Any]) – Dictionary of font updates
Return type:: None

apply_style() → None[source]

Apply current theme to matplotlib.

This updates matplotlib’s rcParams to match the current theme settings.

Return type:: None

get_figure_size(size_type: str = 'medium', orientation: str = 'landscape') → Tuple[float, float][source]

Get figure size for a given type.

Parameters:

size_type (str) – Size type (small, medium, large, blog, technical, presentation)
orientation (str) – Figure orientation (landscape or portrait)

Return type:

Tuple[float, float]

Returns:

Tuple of (width, height) in inches

get_dpi(output_type: str = 'screen') → int[source]

Get DPI for output type.

Parameters:: output_type (str) – Output type (screen, web, print)
Return type:: int
Returns:: DPI value

load_config(config_path: str | Path) → None[source]

Load configuration from YAML file.

Parameters:: config_path (Union[str, Path]) – Path to YAML configuration file
Return type:: None

save_config(config_path: str | Path) → None[source]

Save current configuration to YAML file.

Parameters:: config_path (Union[str, Path]) – Path to save YAML configuration
Return type:: None

create_style_sheet() → Dict[str, Any][source]

Create matplotlib style sheet dictionary.

Return type:: Dict[str, Any]
Returns:: Style sheet dictionary compatible with matplotlib

inherit_from(parent_theme: Theme, modifications: Dict[str, Any]) → Theme[source]

Create a new theme inheriting from a parent with modifications.

Parameters:

parent_theme (Theme) – Theme to inherit from
modifications (Dict[str, Any]) – Dictionary of modifications

Return type:

Theme

Returns:

New theme enum value

class Theme(*values)[source]

Bases: Enum

Available visualization themes.

DEFAULT = 'default'

COLORBLIND = 'colorblind'

PRESENTATION = 'presentation'

MINIMAL = 'minimal'

PRINT = 'print'

class FigureFactory(style_manager: StyleManager | None = None, theme: Theme = Theme.DEFAULT, auto_apply: bool = True)[source]

Bases: object

Factory for creating standardized figures with consistent styling.

This class provides methods to create various types of plots with automatic application of themes, consistent formatting, and proper spacing.

Example

>>> factory = FigureFactory(theme=Theme.PRESENTATION)
>>> fig, ax = factory.create_line_plot(
...     x_data=[1, 2, 3, 4],
...     y_data=[10, 20, 15, 25],
...     title="Revenue Growth",
...     x_label="Quarter",
...     y_label="Revenue ($M)"
... )

>>> # Create multiple subplots
>>> fig, axes = factory.create_subplots(
...     rows=2, cols=2,
...     size_type="large",
...     subplot_titles=["Q1", "Q2", "Q3", "Q4"]
... )

create_figure(size_type: str = 'medium', orientation: str = 'landscape', dpi_type: str = 'screen', title: str | None = None) → Tuple[Figure, Axes][source]

Create a basic figure with styling applied.

Parameters:

size_type (str) – Size preset (small, medium, large, blog, technical, presentation)
orientation (str) – Figure orientation (landscape or portrait)
dpi_type (str) – DPI type (screen, web, print)
title (Optional[str]) – Optional figure title

Return type:

Tuple[Figure, Axes]

Returns:

Tuple of (figure, axes)

create_subplots(rows: int = 1, cols: int = 1, size_type: str = 'large', dpi_type: str = 'screen', title: str | None = None, subplot_titles: List[str] | None = None, **kwargs) → Tuple[Figure, Axes | ndarray][source]

Create subplots with consistent styling.

Parameters:

rows (int) – Number of subplot rows
cols (int) – Number of subplot columns
size_type (str) – Size preset
dpi_type (str) – DPI type
title (Optional[str]) – Main figure title
subplot_titles (Optional[List[str]]) – Titles for each subplot
**kwargs – Additional arguments for plt.subplots

Return type:

Tuple[Figure, Union[Axes, ndarray]]

Returns:

Tuple of (figure, axes array)

Create a line plot with automatic formatting.

Parameters:

x_data (Union[List, ndarray, Series]) – X-axis data
y_data (Union[List, ndarray, Series, Dict[str, Union[List, ndarray]]]) – Y-axis data (can be multiple series as dict)
title (Optional[str]) – Plot title
x_label (Optional[str]) – X-axis label
y_label (Optional[str]) – Y-axis label
labels (Optional[List[str]]) – Series labels for legend
size_type (str) – Figure size preset
dpi_type (str) – DPI type
show_legend (bool) – Whether to show legend
show_grid (bool) – Whether to show grid
markers (bool) – Whether to add markers to lines
**kwargs – Additional arguments for plot

Return type:

Tuple[Figure, Axes]

Returns:

Tuple of (figure, axes)

create_bar_plot(categories: List | ndarray, values: List | ndarray | Dict[str, List | ndarray], title: str | None = None, x_label: str | None = None, y_label: str | None = None, labels: List[str] | None = None, size_type: str = 'medium', dpi_type: str = 'screen', orientation: str = 'vertical', show_values: bool = False, value_format: str = '.1f', **kwargs) → Tuple[Figure, Axes][source]

Create a bar plot with automatic formatting.

Parameters:

categories (Union[List, ndarray]) – Category labels
values (Union[List, ndarray, Dict[str, Union[List, ndarray]]]) – Values to plot (can be multiple series as dict)
title (Optional[str]) – Plot title
x_label (Optional[str]) – X-axis label
y_label (Optional[str]) – Y-axis label
labels (Optional[List[str]]) – Series labels for legend
size_type (str) – Figure size preset
dpi_type (str) – DPI type
orientation (str) – Bar orientation (vertical or horizontal)
show_values (bool) – Whether to show value labels on bars
value_format (str) – Format string for value labels
**kwargs – Additional arguments for bar plot

Return type:

Tuple[Figure, Axes]

Returns:

Tuple of (figure, axes)

Create a scatter plot with automatic formatting.

Parameters:

x_data (Union[List, ndarray]) – X-axis data
y_data (Union[List, ndarray]) – Y-axis data
title (Optional[str]) – Plot title
x_label (Optional[str]) – X-axis label
y_label (Optional[str]) – Y-axis label
size_type (str) – Figure size preset
dpi_type (str) – DPI type
colors (Union[List, ndarray, None]) – Optional colors for points (for continuous coloring)
sizes (Union[List, ndarray, None]) – Optional sizes for points
labels (Optional[List[str]]) – Optional labels for points
show_colorbar (bool) – Whether to show colorbar when colors provided
**kwargs – Additional arguments for scatter

Return type:

Tuple[Figure, Axes]

Returns:

Tuple of (figure, axes)

create_histogram(data: List | ndarray | Series, title: str | None = None, x_label: str | None = None, y_label: str = 'Frequency', bins: int | str = 'auto', size_type: str = 'medium', dpi_type: str = 'screen', show_statistics: bool = False, show_kde: bool = False, **kwargs) → Tuple[Figure, Axes][source]

Create a histogram with automatic formatting.

Parameters:

data (Union[List, ndarray, Series]) – Data to plot
title (Optional[str]) – Plot title
x_label (Optional[str]) – X-axis label
y_label (str) – Y-axis label
bins (Union[int, str]) – Number of bins or method
size_type (str) – Figure size preset
dpi_type (str) – DPI type
show_statistics (bool) – Whether to show mean/median lines
show_kde (bool) – Whether to overlay KDE
**kwargs – Additional arguments for hist

Return type:

Tuple[Figure, Axes]

Returns:

Tuple of (figure, axes)

create_heatmap(data: ndarray | DataFrame, title: str | None = None, x_labels: List[str] | None = None, y_labels: List[str] | None = None, x_label: str | None = None, y_label: str | None = None, size_type: str = 'medium', dpi_type: str = 'screen', cmap: str = 'RdBu_r', show_values: bool = True, value_format: str = '.2f', **kwargs) → Tuple[Figure, Axes][source]

Create a heatmap with automatic formatting.

Parameters:

data (Union[ndarray, DataFrame]) – 2D data array or DataFrame
title (Optional[str]) – Plot title
x_labels (Optional[List[str]]) – Labels for x-axis
y_labels (Optional[List[str]]) – Labels for y-axis
x_label (Optional[str]) – X-axis title
y_label (Optional[str]) – Y-axis title
size_type (str) – Figure size preset
dpi_type (str) – DPI type
cmap (str) – Colormap name
show_values (bool) – Whether to show values in cells
value_format (str) – Format string for cell values
**kwargs – Additional arguments for imshow

Return type:

Tuple[Figure, Axes]

Returns:

Tuple of (figure, axes)

create_box_plot(data: List[List] | Dict[str, List] | DataFrame, title: str | None = None, x_label: str | None = None, y_label: str | None = None, labels: List[str] | None = None, size_type: str = 'medium', dpi_type: str = 'screen', orientation: str = 'vertical', show_means: bool = True, **kwargs) → Tuple[Figure, Axes][source]

Create a box plot with automatic formatting.

Parameters:

data (Union[List[List], Dict[str, List], DataFrame]) – Data for box plot (list of lists, dict, or DataFrame)
title (Optional[str]) – Plot title
x_label (Optional[str]) – X-axis label
y_label (Optional[str]) – Y-axis label
labels (Optional[List[str]]) – Labels for each box
size_type (str) – Figure size preset
dpi_type (str) – DPI type
orientation (str) – Plot orientation (vertical or horizontal)
show_means (bool) – Whether to show mean markers
**kwargs – Additional arguments for boxplot

Return type:

Tuple[Figure, Axes]

Returns:

Tuple of (figure, axes)

format_axis_currency(ax: Axes, axis: str = 'y', abbreviate: bool = True, decimals: int = 0) → None[source]

Format axis labels as currency.

Parameters:

ax (Axes) – Matplotlib axes
axis (str) – Which axis to format (x or y)
abbreviate (bool) – Whether to abbreviate large numbers
decimals (int) – Number of decimal places

Return type:

None

format_axis_percentage(ax: Axes, axis: str = 'y', decimals: int = 0) → None[source]

Format axis labels as percentages.

Parameters:

ax (Axes) – Matplotlib axes
axis (str) – Which axis to format (x or y)
decimals (int) – Number of decimal places

Return type:

None

add_annotations(ax: Axes, x: float, y: float, text: str, arrow: bool = True, offset: Tuple[float, float] = (10, 10), **kwargs) → None[source]

Add styled annotation to plot.

Parameters:

ax (Axes) – Matplotlib axes
x (float) – X coordinate
y (float) – Y coordinate
text (str) – Annotation text
arrow (bool) – Whether to show arrow
offset (Tuple[float, float]) – Text offset from point
**kwargs – Additional arguments for annotate

Return type:

None

save_figure(fig: Figure, filename: str, output_type: str = 'web', **kwargs) → None[source]

Save figure with appropriate DPI settings.

Parameters:

fig (Figure) – Figure to save
filename (str) – Output filename
output_type (str) – Output type (screen, web, print)
**kwargs – Additional arguments for savefig

Return type:

None