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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

Feel free to reach out for questions or suggestions.

For detailed documentation and examples, visit our GitHub repository.