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feat: implement step-basic
- Added spec for basic step plot showing discrete changes - Implemented matplotlib version with step function - Implemented seaborn version with enhanced styling - Both implementations support all spec requirements: - Configurable step position (pre/post/mid) - Optional markers at data points - Handles both numeric and categorical x-axis - Full input validation and error handling - Type hints and comprehensive documentation Closes #38 🤖 Generated with Claude Code (https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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‎plots/matplotlib/step/step-basic/default.py‎

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"""
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step-basic: Basic Step Plot
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Implementation for: matplotlib
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Variant: default
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Python: 3.10+
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"""
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import matplotlib.pyplot as plt
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import pandas as pd
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import numpy as np
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from matplotlib.figure import Figure
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def create_plot(
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data: pd.DataFrame,
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x: str,
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y: str,
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where: str = "pre",
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color: str = "steelblue",
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linewidth: float = 2.0,
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alpha: float = 0.9,
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linestyle: str = "-",
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marker: str | None = None,
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markersize: float = 6,
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title: str | None = None,
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xlabel: str | None = None,
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ylabel: str | None = None,
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figsize: tuple[float, float] = (10, 6),
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**kwargs
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) -> "Figure":
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"""
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Create a basic step plot showing discrete changes in values.
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Step plots display data as a series of horizontal and vertical lines,
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showing discrete changes between values. Ideal for visualizing data that
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changes at specific intervals.
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Args:
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data: Input DataFrame with required columns
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x: Column name for x-axis values (sequential or time-based)
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y: Column name for y-axis values (numeric levels)
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where: Position of steps - "pre", "post", or "mid" (default: "pre")
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color: Line color (default: "steelblue")
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linewidth: Line width (default: 2.0)
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alpha: Transparency level 0.0-1.0 (default: 0.9)
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linestyle: Line style - "-", "--", "-.", or ":" (default: "-")
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marker: Optional marker style for data points (default: None)
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markersize: Size of markers if used (default: 6)
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title: Optional plot title (default: None)
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xlabel: Custom x-axis label (default: column name)
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ylabel: Custom y-axis label (default: column name)
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figsize: Figure size in inches (default: (10, 6))
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**kwargs: Additional parameters passed to step function
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Returns:
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Matplotlib Figure object
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Raises:
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ValueError: If data is empty or 'where' parameter is invalid
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KeyError: If required columns not found
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Example:
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>>> import pandas as pd
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>>> data = pd.DataFrame({
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... 'time': [1, 2, 3, 4, 5],
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... 'level': [10, 15, 12, 18, 14]
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... })
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>>> fig = create_plot(data, 'time', 'level')
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"""
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# Input validation
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if data.empty:
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raise ValueError("Data cannot be empty")
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# Check required columns
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required_columns = [x, y]
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missing_columns = []
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for col in required_columns:
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if col not in data.columns:
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missing_columns.append(col)
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if missing_columns:
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available = ", ".join(data.columns)
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missing = ", ".join(missing_columns)
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raise KeyError(f"Column(s) '{missing}' not found. Available columns: {available}")
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# Validate 'where' parameter
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if where not in ["pre", "post", "mid"]:
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raise ValueError(f"'where' parameter must be 'pre', 'post', or 'mid', got '{where}'")
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# Create figure
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fig, ax = plt.subplots(figsize=figsize)
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# Sort data by x values to ensure proper step plot
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plot_data = data[[x, y]].copy()
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plot_data = plot_data.sort_values(by=x)
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# Plot step lines
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ax.step(plot_data[x], plot_data[y],
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where=where,
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color=color,
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linewidth=linewidth,
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alpha=alpha,
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linestyle=linestyle,
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**kwargs)
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# Add markers if specified
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if marker:
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ax.plot(plot_data[x], plot_data[y],
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marker=marker,
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markersize=markersize,
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color=color,
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alpha=alpha,
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linestyle='None') # Only markers, no line
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# Apply styling
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ax.set_xlabel(xlabel or x, fontsize=12)
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ax.set_ylabel(ylabel or y, fontsize=12)
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# Add title if provided
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if title:
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ax.set_title(title, fontsize=14, fontweight='bold')
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# Add subtle grid
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ax.grid(True, alpha=0.3, linestyle='-', linewidth=0.5)
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# Ensure no overlapping labels
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fig.autofmt_xdate() # Automatically format date labels if x is datetime
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# Layout
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plt.tight_layout()
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return fig
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if __name__ == '__main__':
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# Sample data for testing
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np.random.seed(42) # For reproducibility
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# Create sample data representing interest rate changes over time
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data = pd.DataFrame({
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'month': range(1, 13),
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'rate': [2.0, 2.0, 2.25, 2.25, 2.25, 2.5, 2.5, 2.75, 2.75, 2.75, 3.0, 3.0]
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})
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# Create plot
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fig = create_plot(
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data,
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x='month',
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y='rate',
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title='Interest Rate Changes Over Time',
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xlabel='Month',
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ylabel='Interest Rate (%)',
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where='post', # Step occurs after the data point
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marker='o', # Show actual data points
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markersize=4
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)
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# Save for inspection - ALWAYS use 'plot.png' as filename
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plt.savefig('plot.png', dpi=300, bbox_inches='tight')
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print("Plot saved to plot.png")

‎plots/seaborn/lineplot/step-basic/default.py‎

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"""
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step-basic: Basic Step Plot
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Implementation for: seaborn
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Variant: default
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Python: 3.10+
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"""
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import seaborn as sns
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import matplotlib.pyplot as plt
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import pandas as pd
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import numpy as np
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from matplotlib.figure import Figure
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def create_plot(
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data: pd.DataFrame,
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x: str,
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y: str,
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where: str = "pre",
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color: str = "steelblue",
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linewidth: float = 2.0,
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alpha: float = 0.9,
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linestyle: str = "-",
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marker: str | None = None,
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markersize: float = 6,
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title: str | None = None,
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xlabel: str | None = None,
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ylabel: str | None = None,
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figsize: tuple[float, float] = (10, 6),
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**kwargs
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) -> "Figure":
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"""
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Create a basic step plot showing discrete changes in values using seaborn styling.
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Step plots display data as a series of horizontal and vertical lines,
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showing discrete changes between values. This implementation uses matplotlib's
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step function with seaborn's aesthetic styling for better visual appeal.
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Args:
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data: Input DataFrame with required columns
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x: Column name for x-axis values (sequential or time-based)
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y: Column name for y-axis values (numeric levels)
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where: Position of steps - "pre", "post", or "mid" (default: "pre")
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color: Line color (default: "steelblue")
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linewidth: Line width (default: 2.0)
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alpha: Transparency level 0.0-1.0 (default: 0.9)
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linestyle: Line style - "-", "--", "-.", or ":" (default: "-")
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marker: Optional marker style for data points (default: None)
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markersize: Size of markers if used (default: 6)
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title: Optional plot title (default: None)
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xlabel: Custom x-axis label (default: column name)
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ylabel: Custom y-axis label (default: column name)
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figsize: Figure size in inches (default: (10, 6))
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**kwargs: Additional parameters passed to step function
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Returns:
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Matplotlib Figure object with seaborn styling
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Raises:
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ValueError: If data is empty or 'where' parameter is invalid
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KeyError: If required columns not found
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Example:
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>>> import pandas as pd
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>>> data = pd.DataFrame({
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... 'quarter': ['Q1', 'Q2', 'Q3', 'Q4'],
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... 'revenue': [100, 120, 115, 140]
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... })
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>>> fig = create_plot(data, 'quarter', 'revenue')
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"""
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# Input validation
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if data.empty:
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raise ValueError("Data cannot be empty")
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# Check required columns
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required_columns = [x, y]
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missing_columns = []
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for col in required_columns:
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if col not in data.columns:
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missing_columns.append(col)
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if missing_columns:
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available = ", ".join(data.columns)
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missing = ", ".join(missing_columns)
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raise KeyError(f"Column(s) '{missing}' not found. Available columns: {available}")
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# Validate 'where' parameter
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if where not in ["pre", "post", "mid"]:
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raise ValueError(f"'where' parameter must be 'pre', 'post', or 'mid', got '{where}'")
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# Set seaborn style
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sns.set_style("whitegrid")
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# Create figure with seaborn context
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with sns.plotting_context("notebook", font_scale=1.1):
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fig, ax = plt.subplots(figsize=figsize)
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# Prepare data - ensure it's sorted
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plot_data = data[[x, y]].copy()
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# Handle categorical x-axis by converting to numeric positions
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if plot_data[x].dtype == 'object' or pd.api.types.is_categorical_dtype(plot_data[x]):
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x_labels = plot_data[x].unique()
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x_positions = np.arange(len(plot_data[x]))
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plot_x = x_positions
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use_categorical = True
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else:
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plot_data = plot_data.sort_values(by=x)
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plot_x = plot_data[x]
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use_categorical = False
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# Plot step lines
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ax.step(plot_x, plot_data[y],
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where=where,
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color=color,
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linewidth=linewidth,
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alpha=alpha,
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linestyle=linestyle,
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**kwargs)
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# Add markers if specified
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if marker:
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ax.plot(plot_x, plot_data[y],
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marker=marker,
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markersize=markersize,
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color=color,
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alpha=alpha,
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linestyle='None') # Only markers, no line
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# Handle categorical x-axis labels
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if use_categorical:
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ax.set_xticks(x_positions)
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ax.set_xticklabels(x_labels)
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# Apply labels with seaborn styling
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ax.set_xlabel(xlabel or x, fontsize=12, fontweight='medium')
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ax.set_ylabel(ylabel or y, fontsize=12, fontweight='medium')
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# Add title if provided
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if title:
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ax.set_title(title, fontsize=14, fontweight='bold', pad=20)
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# Customize grid for better visibility
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ax.grid(True, alpha=0.3, linestyle='-', linewidth=0.5)
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ax.set_axisbelow(True) # Grid behind data
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# Remove top and right spines for cleaner look
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sns.despine()
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# Ensure no overlapping labels
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fig.autofmt_xdate() # Automatically format date labels if x is datetime
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# Adjust layout
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plt.tight_layout()
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# Reset seaborn defaults to avoid affecting other plots
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sns.reset_defaults()
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return fig
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if __name__ == '__main__':
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# Sample data for testing
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np.random.seed(42) # For reproducibility
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# Create sample data representing quarterly sales performance
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data = pd.DataFrame({
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'quarter': ['Q1 2023', 'Q2 2023', 'Q3 2023', 'Q4 2023',
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'Q1 2024', 'Q2 2024', 'Q3 2024', 'Q4 2024'],
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'sales': [45, 48, 48, 52, 55, 55, 58, 62]
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})
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# Create plot with seaborn styling
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fig = create_plot(
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data,
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x='quarter',
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y='sales',
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title='Quarterly Sales Performance',
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xlabel='Quarter',
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ylabel='Sales (in millions)',
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where='post', # Step occurs after the data point
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color='#2E86AB', # Nice blue color
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marker='o', # Show actual data points
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markersize=5
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)
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# Save for inspection - ALWAYS use 'plot.png' as filename
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plt.savefig('plot.png', dpi=300, bbox_inches='tight')
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print("Plot saved to plot.png")

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