mdfr

ASAM mdf reader in rust

Currently a personal project to learn rust from past experience with python (mdfreader).

mdfr is currently able to be used from python interpreter (using pyO3) to read mdf 3.x and 4.x files. You can install it with 'pip install mdfr' command. It can be used in pure Rust environment as python-polars, numpy, pyo3 are optional features. You can use the Mdfr struct with its methods and eventually its rust-polars methods to manipulate data. Using rayon crate on many parts of the code allows to have fast parsing in a safe and easy way compared to python. To allow efficient data sharing with many other tools, mdfr stores the data using arrow. Polars (pandas equivalent) use is therefore also straight forward. A C/C++ api is also available allowing to get list of channels, units, description and data using the Arrow CDataInterface.

It can be used the following way with python interpreter:

import mdfr
# load file metadata in memory
obj = mdfr.Mdfr('path/to/file.mdf')
# loads all data in memory
obj.load_all_channels_data_in_memory()
# loads a set of channels in memory, for instance in case there is not enough free memory or for performance
obj.load_channels_data_in_memory({'Channel1', 'Channel2'})
# Returns the numpy array
obj.get_channel_data('channel_name')

# .get_channel_* methods to retrieve channel description, unit, related master name, type and data
# .set_channel_* methods to modify channel description, unit, related master name, type and data
# list of channels in file, or lists of channels grouped by master are available
obj.get_master_channel_names_set()

# To manipulate the file data (cut, resample, merge, etc.), it is possible to use polars:
obj.get_polars_series('channel_name') # returns the polars series
# to get complete dataframe including given channel:
obj.get_polars_dataframe('channel_name')
# add and remove channel
obj.add_channel(channel_name, data, master_channel, master_type, master_flag, unit, description)
# to plot a single channel with matplotlib:
obj.plot('channel_name')

# Export to parquet:
obj.export_to_parquet('file_name', compression_option)
# Export to hdf5:
obj.export_to_hdf5('file_name')
# write to mdf4 file, compressed or not
obj.write('file_name', conpression_algo)

It can also be used from Rust directly with the polars feature enabled:

# Cargo.toml
[dependencies]
mdfr = { version = "0.6", features = ["polars"] }

use mdfr::mdfreader::Mdf;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut mdf = Mdf::new("path/to/file.mf4")?;
    mdf.load_all_channels_data_in_memory()?;

    // Single channel as a Polars Series
    let series = mdf.get_channel_polars_series("channel_name")?;
    println!("{}", series);

    // All channels sharing a master as a DataFrame
    // pass None for channels that have no master channel
    let df = mdf.get_channel_polars_dataframe(Some("master_channel_name"))?;
    println!("{}", df);

    // All channel groups as a map of DataFrames keyed by master channel name
    let dataframes = mdf.get_polars_dataframes()?;
    for (master, df) in &dataframes {
        println!("master: {:?}\n{}", master, df);
    }

    Ok(())
}

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Python >=3.8

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  • Download URL: mdfr-0.7.2-cp39-cp39-macosx_11_0_arm64.whl
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  • Size: 3.8 MB
  • Tags: CPython 3.9, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/7.0.0 CPython/3.13.14

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