Visualization Base Classes¶

The visualization system in QWARD provides a structured approach to creating beautiful, informative plots of quantum circuit analysis results. The base classes define the common interface and utilities used by all visualization strategies.

Key Features: - Strategy Pattern: Extensible design for different visualization types - Consistent Styling: Unified plot configuration and appearance - Automatic Integration: Seamless integration with Scanner and metric results - Flexible Output: Support for saving, showing, and customizing plots

Base Visualization Strategy¶

The VisualizationStrategy class is the abstract base class for all visualizers in QWARD. It provides common functionality and defines the interface that all concrete visualizers must implement.

Core Responsibilities: - Output Management: Handles directory creation and file path management - Plot Configuration: Integrates with PlotConfig for consistent styling - Common Utilities: Provides reusable methods for data validation and plot creation - Abstract Interface: Defines create_dashboard() and plot_all() methods

Usage Pattern:

from qward.visualization.base import VisualizationStrategy, PlotConfig
import pandas as pd

class MyCustomVisualizer(VisualizationStrategy):
    def __init__(self, metrics_dict, output_dir="plots", config=None):
        super().__init__(output_dir, config)
        self.metrics_dict = metrics_dict

    def create_dashboard(self, save=True, show=False):
        # Create comprehensive dashboard
        dashboard_plots = {}
        # Implementation here...
        return dashboard_plots

    def plot_all(self, save=True, show=False):
        # Create all individual plots
        all_plots = {}
        # Implementation here...
        return all_plots

API Reference¶

Base classes for QWARD visualization system.

class PlotConfig(figsize=(10, 6), dpi=300, style='default', color_palette=None, save_format='png', grid=True, alpha=0.7, show_titles=True)[source]¶

Bases: object

Configuration for plot appearance and saving.

alpha: float = 0.7¶

color_palette: List[str] = None¶

dpi: int = 300¶

figsize: Tuple[int, int] = (10, 6)¶

grid: bool = True¶

save_format: str = 'png'¶

show_titles: bool = True¶

style: str = 'default'¶

class PlotMetadata(name, method_name, description, plot_type, filename, dependencies=None, category=None)[source]¶

Bases: object

Metadata for individual plots.

category: str = None¶

dependencies: List[str] = None¶

description: str¶

filename: str¶

method_name: str¶

name: str¶

plot_type: PlotType¶

class PlotType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶

Bases: Enum

Enumeration of plot types for metadata.

BAR_CHART = 'bar_chart'¶

GROUPED_BAR = 'grouped_bar'¶

LINE_CHART = 'line_chart'¶

PIE_CHART = 'pie_chart'¶

RADAR_CHART = 'radar_chart'¶

SCATTER_PLOT = 'scatter_plot'¶

STACKED_BAR = 'stacked_bar'¶

class VisualizationStrategy(metrics_dict, output_dir='img', config=None)[source]¶

Bases: ABC

Base class for all QWARD visualization strategies.

Initialize the visualization strategy.

Parameters:

metrics_dict (Dict[str, DataFrame]) – Dictionary containing metrics data for this strategy
output_dir (str) – Directory to save plots
config (Optional[PlotConfig]) – Plot configuration settings

abstract create_dashboard(save=False, show=False)[source]¶

Create a comprehensive dashboard for this strategy’s metrics.

Return type:: Figure

generate_all_plots(save=False, show=False, **kwargs)[source]¶

Generate all available plots.

Parameters:

save (bool) – Whether to save the plots
show (bool) – Whether to display the plots
**kwargs – Additional arguments passed to plot methods

Return type:

Dict[str, Figure]

Returns:

Dictionary mapping plot names to Figure objects

generate_plot(plot_name, save=False, show=False, **kwargs)[source]¶

Generate a single plot by name.

Parameters:

plot_name (str) – Name of the plot to generate
save (bool) – Whether to save the plot
show (bool) – Whether to display the plot
**kwargs – Additional arguments passed to the plot method

Return type:

Figure

Returns:

matplotlib Figure object

Raises:

ValueError – If plot_name is not available

generate_plots(plot_names=None, save=False, show=False, **kwargs)[source]¶

Generate multiple plots and return as dictionary.

Parameters:

plot_names (List[str]) – List of plot names to generate. If None, generates all available plots.
save (bool) – Whether to save the plots
show (bool) – Whether to display the plots
**kwargs – Additional arguments passed to plot methods

Return type:

Dict[str, Figure]

Returns:

Dictionary mapping plot names to Figure objects

abstract classmethod get_available_plots()[source]¶

Return list of available plot names for this strategy.

Return type:: List[str]

abstract classmethod get_plot_metadata(plot_name)[source]¶

Get metadata for a specific plot.

Return type:: PlotMetadata

save_plot(fig, filename, **kwargs)[source]¶

Save a plot with consistent settings.

Parameters:

fig (Figure) – Matplotlib figure to save
filename (str) – Name of the file (without extension)
**kwargs (Any) – Additional arguments for savefig

Return type:

str

Returns:

Full path to saved file

show_plot(fig)[source]¶

Display a plot.

Return type:: None

class VisualizationStrategy(metrics_dict, output_dir='img', config=None)[source]¶

Bases: ABC

Base class for all QWARD visualization strategies.

Initialize the visualization strategy.

Parameters:

metrics_dict (Dict[str, DataFrame]) – Dictionary containing metrics data for this strategy
output_dir (str) – Directory to save plots
config (Optional[PlotConfig]) – Plot configuration settings

abstract create_dashboard(save=False, show=False)[source]¶

Create a comprehensive dashboard for this strategy’s metrics.

Return type:: Figure

generate_all_plots(save=False, show=False, **kwargs)[source]¶

Generate all available plots.

Parameters:

save (bool) – Whether to save the plots
show (bool) – Whether to display the plots
**kwargs – Additional arguments passed to plot methods

Return type:

Dict[str, Figure]

Returns:

Dictionary mapping plot names to Figure objects

generate_plot(plot_name, save=False, show=False, **kwargs)[source]¶

Generate a single plot by name.

Parameters:

plot_name (str) – Name of the plot to generate
save (bool) – Whether to save the plot
show (bool) – Whether to display the plot
**kwargs – Additional arguments passed to the plot method

Return type:

Figure

Returns:

matplotlib Figure object

Raises:

ValueError – If plot_name is not available

generate_plots(plot_names=None, save=False, show=False, **kwargs)[source]¶

Generate multiple plots and return as dictionary.

Parameters:

plot_names (List[str]) – List of plot names to generate. If None, generates all available plots.
save (bool) – Whether to save the plots
show (bool) – Whether to display the plots
**kwargs – Additional arguments passed to plot methods

Return type:

Dict[str, Figure]

Returns:

Dictionary mapping plot names to Figure objects

abstract classmethod get_available_plots()[source]¶

Return list of available plot names for this strategy.

Return type:: List[str]

abstract classmethod get_plot_metadata(plot_name)[source]¶

Get metadata for a specific plot.

Return type:: PlotMetadata

save_plot(fig, filename, **kwargs)[source]¶

Save a plot with consistent settings.

Parameters:

fig (Figure) – Matplotlib figure to save
filename (str) – Name of the file (without extension)
**kwargs (Any) – Additional arguments for savefig

Return type:

str

Returns:

Full path to saved file

show_plot(fig)[source]¶

Display a plot.

Return type:: None

Plot Configuration¶

The PlotConfig class provides comprehensive configuration options for plot appearance and behavior.

Configuration Options: - Appearance: Figure size, DPI, color palettes, transparency - Styling: Plot styles (default, quantum, minimal) - Output: Save format (PNG, SVG), grid settings - Extensible: Easy to customize for different visualization needs

Usage Example:

from qward.visualization.base import PlotConfig

# Create custom plot configuration
config = PlotConfig(
    figsize=(12, 8),           # Larger figures
    dpi=150,                   # High quality
    style="quantum",           # Quantum-themed styling
    color_palette=["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728"],
    save_format="svg",         # Vector graphics
    grid=True,                 # Show grid lines
    alpha=0.8                  # Transparency level
)

# Use with any visualizer
from qward.visualization import QiskitVisualizer
visualizer = QiskitVisualizer(metrics_dict, config=config)

class PlotConfig(figsize=(10, 6), dpi=300, style='default', color_palette=None, save_format='png', grid=True, alpha=0.7, show_titles=True)[source]¶

Bases: object

Configuration for plot appearance and saving.

alpha: float = 0.7¶

color_palette: List[str] = None¶

dpi: int = 300¶

figsize: Tuple[int, int] = (10, 6)¶

grid: bool = True¶

save_format: str = 'png'¶

show_titles: bool = True¶

style: str = 'default'¶