tornadopy 0.1.4

A Python library for tornado chart generation and analysis

TornadoPy

A Python library for tornado chart generation and analysis. TornadoPy provides tools for processing Excel-based tornado data and generating professional tornado charts for uncertainty analysis.

Features

• TornadoProcessor: Process Excel files containing tornado analysis data

  • Parse multi-sheet Excel files with complex headers
  • Extract and compute statistics (p90p10, mean, median, minmax, percentiles)
  • Filter data by properties and dynamic fields
  • Case selection with weighted criteria
  • Batch processing for multiple parameters

• tornado_plot: Generate professional tornado charts

  • Customizable colors, fonts, and styling
  • Support for p90/p10 ranges with automatic label placement
  • Reference case lines
  • Custom parameter ordering
  • Export to various image formats

Installation

From source

git clone https://github.com/kkollsga/tornadopy.git
cd tornadopy
pip install -e .

For development

pip install -e ".[dev]"

Quick Start

Processing Tornado Data

from tornadopy import TornadoProcessor

# Load Excel file with tornado data
processor = TornadoProcessor("tornado_data.xlsb")

# Get available parameters
parameters = processor.get_parameters()
print(f"Parameters: {parameters}")

# Get properties for a parameter
properties = processor.get_properties(parameter="Parameter1")
print(f"Properties: {properties}")

# Compute statistics
result = processor.compute(
    stats="p90p10",
    parameter="Parameter1",
    filters={"property": "npv"},
    multiplier=1e-6  # Convert to millions
)
print(f"P90/P10: {result['p90p10']}")

Generating Tornado Charts

from tornadopy import TornadoProcessor, tornado_plot

# Get tornado data
processor = TornadoProcessor("tornado_data.xlsb")
tornado_data = processor.get_tornado_data(
    parameters="all",
    filters={"property": "npv"},
    multiplier=1e-6
)

# Convert to sections format for plotting
sections = []
for param, data in tornado_data.items():
    sections.append({
        "parameter": param,
        "minmax": [data["p10"], data["p90"]],
        "p90p10": [data["p10"], data["p90"]]
    })

# Generate tornado chart
fig, ax, saved = tornado_plot(
    sections=sections,
    title="NPV Tornado Chart",
    subtitle="Base case = 100.0 MM USD",
    base=100.0,
    unit="MM USD",
    outfile="tornado_chart.png"
)

Advanced Usage

Multi-property Analysis

# Compute statistics for multiple properties
result = processor.compute(
    stats=["p90p10", "mean", "median"],
    parameter="Parameter1",
    filters={"property": ["npv", "irr"]}
)
print(f"NPV P90/P10: {result['p90p10'][0]}")
print(f"IRR P90/P10: {result['p90p10'][1]}")

Case Selection with Weighted Criteria

# Find closest cases to p90/p10 with custom weights
result = processor.compute(
    stats="p90p10",
    parameter="Parameter1",
    filters={"property": "npv"},
    case_selection=True,
    selection_criteria={
        "weights": {"npv": 0.6, "irr": 0.4}
    }
)

# Access closest cases
for case in result["closest_cases"]:
    print(f"Case {case['case']}: idx={case['idx']}, value={case['npv']}")

Batch Processing

# Process all parameters at once
results = processor.compute_batch(
    stats="p90p10",
    parameters="all",
    filters={"property": "npv"},
    multiplier=1e-6
)

for result in results:
    print(f"{result['parameter']}: {result['p90p10']}")

Custom Chart Styling

# Create custom styled tornado chart
settings = {
    "figsize": (12, 8),
    "dpi": 200,
    "pos_dark": "#1E88E5",
    "neg_dark": "#D32F2F",
    "show_values": ["min", "max", "p10", "p90"],
    "show_percentage_diff": True,
}

fig, ax, saved = tornado_plot(
    sections=sections,
    title="Custom Styled Tornado Chart",
    base=100.0,
    reference_case=110.0,
    preferred_order=["Param1", "Param2", "Param3"],
    settings=settings,
    outfile="custom_tornado.png"
)

Excel File Format

TornadoPy expects Excel files with the following structure:

[Info rows - optional metadata]
Header Row 1    | Dynamic Field 1 | Dynamic Field 1 | ...
Header Row 2    | Value A         | Value B         | ...
Case            | Property 1      | Property 2      | ...
1               | 123.45          | 67.89           | ...
2               | 234.56          | 78.90           | ...
...

• Multiple header rows are supported and will be combined
• The "Case" row marks the start of data
• Dynamic fields in column A define metadata columns
• Property names are extracted from the last header row

API Reference

TornadoProcessor

Methods

• get_parameters(): Get list of available parameters (sheet names)
• get_properties(parameter): Get available properties for a parameter
• get_unique(field, parameter): Get unique values for a dynamic field
• get_info(parameter): Get metadata for a parameter
• get_case(index, parameter): Get data for a specific case
• compute(stats, parameter, filters, multiplier, options, case_selection, selection_criteria): Compute statistics
• compute_batch(...): Batch compute for multiple parameters
• get_tornado_data(...): Get tornado chart formatted data

tornado_plot

Parameters

• sections: List of section dictionaries with parameter data
• title: Chart title
• subtitle: Chart subtitle
• outfile: Output file path
• base: Base case value
• reference_case: Reference case line value
• unit: Unit label
• preferred_order: List of parameter names for custom ordering
• settings: Dictionary of visual settings

Returns

• fig: Matplotlib figure object
• ax: Matplotlib axes object
• saved: Path to saved file (if outfile specified)

Requirements

• Python >= 3.8
• numpy >= 1.20.0
• polars >= 0.18.0
• pyxlsb >= 1.0.9
• matplotlib >= 3.5.0

License

MIT License - see LICENSE file for details

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Support

For issues and questions, please open an issue on GitHub.