Here are some ideas for additional functions or classes that could enhance this module for creating good-looking reports with data visualizations:
- Color Palette Generator: A function that generates aesthetically pleasing color palettes for charts and graphs. This could include options for different types of palettes (e.g., sequential, diverging, qualitative) and considerations for color blindness.
def generate_color_palette(palette_type: str, num_colors: int, colorblind_safe: bool = True) -> List[str]:
"""Generate a color palette for data visualization."""
...- Figure Layout Manager: A class to help manage the layout of multiple plots in a single figure, making it easier to create complex dashboards or report layouts.
class FigureLayoutManager:
def __init__(self, fig_size: Tuple[float, float], grid_size: Tuple[int, int]):
...
def add_plot(self, row: int, col: int, plot_function: Callable, *args, **kwargs):
...
def adjust_layout(self):
...- Custom Theme Applier: A function to apply a consistent custom theme across all plots in a report, including font styles, background colors, grid styles, etc.
def apply_custom_theme(fig: matplotlib.figure.Figure, theme: dict):
"""Apply a custom theme to a matplotlib figure."""
...- Automated Legend Optimizer: A function to automatically position and format legends for optimal readability and aesthetics.
def optimize_legend(ax: matplotlib.axes.Axes, location: str = 'best'):
"""Optimize legend position and formatting."""
...- Data-Driven Annotation Placer: A smart function that automatically places annotations on a plot based on data values and available space, avoiding overlaps.
def smart_annotate(ax: matplotlib.axes.Axes, x: List[float], y: List[float], labels: List[str]):
"""Intelligently place annotations on a plot."""
...- Interactive Element Generator: Functions to add interactive elements to plots, such as hover tooltips or clickable data points (useful for web-based reports).
def add_hover_tooltip(ax: matplotlib.axes.Axes, x: List[float], y: List[float], tooltip_text: List[str]):
"""Add hover tooltips to data points."""
...- Report Metadata Manager: A class to manage and display metadata about the report, such as data sources, last updated date, and version information.
class ReportMetadata:
def __init__(self, title: str, data_source: str, last_updated: datetime):
...
def add_to_figure(self, fig: matplotlib.figure.Figure):
"""Add metadata to the figure as text."""
...- Axis Formatter: Functions to automatically format axis labels and ticks for better readability, handling different data types (dates, currencies, large numbers, etc.).
def format_axis(ax: matplotlib.axes.Axes, axis: str, data_type: str):
"""Format axis labels and ticks based on data type."""
...- Statistical Annotation Adder: Functions to add statistical information to plots, such as mean lines, confidence intervals, or p-values.
def add_mean_line(ax: matplotlib.axes.Axes, data: List[float], color: str = 'red'):
"""Add a line representing the mean of the data."""
...
def add_confidence_interval(ax: matplotlib.axes.Axes, x: List[float], y: List[float], confidence: float = 0.95):
"""Add confidence interval to a line plot."""
...- Export Utilities: Functions to export plots in various formats suitable for different types of reports (e.g., high-res for print, web-optimized for online reports).
def export_for_print(fig: matplotlib.figure.Figure, filename: str, dpi: int = 300):
"""Export a figure in high resolution for print."""
...
def export_for_web(fig: matplotlib.figure.Figure, filename: str, optimize: bool = True):
"""Export a figure optimized for web display."""
...These additional functions and classes would greatly enhance the capabilities of the module, making it a comprehensive toolkit for creating professional-looking data visualizations and reports.