python-dcm

python-dcm

A high-performance Python package for handling ETAS DCM(Data Conversion Format) files used in engine calibration tools like INCA, MDA, EHANDBOOK, and CANape.

Quick Start

from dcm import DCM

# Read a DCM file
dcm = DCM.from_file('calibration.dcm')

# Access calibration data
parameter_value = dcm.parameters['ENGINE_SPEED'].value
map_data = dcm.maps['FUEL_MAP'].dataframe
curve_data = dcm.curves['BOOST_CURVE'].series

# Interpolate values
x_points = [1000, 1500, 2000]  # RPM
y_points = [50, 75, 100]       # Load %
interpolated = dcm.maps['FUEL_MAP'].as_function(x_points, y_points)

# Visualize data
dcm.maps['FUEL_MAP'].to_figure()

Key Features

• Easy Data Access: Directly access parameters, curves, and maps with Pandas integration
• Interpolation: Built-in 1D/2D linear interpolation for real-time value calculation
• Visualization: One-line plotting of characteristic curves and maps
• Excel Integration: Import/export calibration data from Excel spreadsheets
• Set Operations: Compare and merge DCM files with |, -, &, and % operators
• Type Support: Handle all DCM data types including fixed/group characteristics

Installation

Requires Python ≥ 3.10

pip install python-dcm

Common Use Cases

Data Access & Manipulation

# Get parameter value
rpm_limit = dcm.parameters['MAX_RPM'].value

# Access map as DataFrame
fuel_map = dcm.maps['FUEL_MAP'].dataframe
fuel_map.iloc[0, 0] = 14.7  # Modify value

# Get curve data
boost_curve = dcm.curves['BOOST_CURVE'].series
max_boost = boost_curve.max()

Excel Integration

# Load calibration data from Excel
dcm.load_from_excel(
    maps_path='maps.xlsx',
    curves_path='curves.xlsx',
    parameters_path='params.xlsx'
)

# Each sheet name becomes the calibration object name

Visualization

import matplotlib.pyplot as plt

# Plot a map with custom settings
fig, ax = dcm.maps['FUEL_MAP'].to_figure(
    cmap='viridis',
    fontsize=12
)
plt.show()

# Plot multiple curves
fig, ax = plt.subplots()
dcm.curves['BOOST_LOW'].to_figure(ax=ax, label='Low')
dcm.curves['BOOST_HIGH'].to_figure(ax=ax, label='High')
plt.legend()

Advanced Features

Parameter Type Conversion

param = dcm.parameters['CONTROL_BITS']
binary = param.as_bin()    # [1, 3, 5] (bits set to 1)
hex_val = param.as_hex()   # [A, F, 1] (hexadecimal digits)

DCM File Comparison

# Find differences between calibrations
modified = old_dcm % new_dcm
print(modified.parameters.keys())  # Changed parameters

# Merge calibrations
combined = dcm1 | dcm2

Supported Data Types

• Parameters (FESTWERT)
• Parameter Blocks (FESTWERTEBLOCK)
• Characteristic Lines (KENNLINIE/FESTKENNLINIE/GRUPPENKENNLINIE)
• Characteristic Maps (KENNFELD/FESTKENNFELD/GRUPPENKENNFELD)
• Distributions (STUETZSTELLENVERTEILUNG)
• Text Strings (TEXTSTRING)

Dependencies

• NumPy ≥ 1.20.0
• Pandas ≥ 1.5.0
• Matplotlib ≥ 3.0.0
• OpenPyXL ≥ 3.1.0

License

MIT License

Contributing

Contributions welcome! Please format code with ruff before submitting PRs.

Contact

• Author: c0sogi
• Email: [e-mail] or [e-mail]

Feel free to reach out for questions or suggestions.

For detailed documentation and examples, visit our GitHub repository.