MDF

This class acts as a proxy for the MDF3 and MDF4 classes. All attribute access is delegated to the underlying _file attribute (MDF3 or MDF4 object). See MDF3 and MDF4 for available extra methods.

An empty MDF file is created if the name argument is not provided. If the name argument is provided then the file must exist in the filesystem, otherwise an exception is raised.

Best practice is to use the MDF as a context manager. This way all resources are released correctly in case of exceptions.

with MDF(r'test.mdf') as mdf_file:
    # do something
class asammdf.mdf.MDF(name=None, load_measured_data=True, version='3.20')

Unified access to MDF v3 and v4 files.

Parameters:

name : string

mdf file name, if provided it must be a real file name

load_measured_data : bool

load data option; default True

  • if True the data group binary data block will be loaded in RAM
  • if False the channel data is read from disk on request

version : string

mdf file version (‘3.00’, ‘3.10’, ‘3.20’, ‘3.30’, ‘4.00’, ‘4.10’, ‘4.11’); default ‘3.20’

Methods

convert
cut
export
filter
iter_to_pandas
merge
convert(to, load_measured_data=True)

convert MDF to other versions

Parameters:

to : str

new mdf version from (‘3.00’, ‘3.10’, ‘3.20’, ‘3.30’, ‘4.00’, ‘4.10’, ‘4.11’)

load_measured_data : bool

load data option; default True

  • if True the data group binary data block will be loaded in RAM
  • if False the channel data is stored to a temporary file and read from disk on request
Returns:

out : MDF

new MDF object

cut(start=None, stop=None)

convert MDF to other versions

Parameters:

start : float

start time, default None. If None then the start of measurement is used

stop : float

stop time, default . If None then the end of measurement is used

Returns:

out : MDF

new MDF object

export(format, filename=None)

export MDF to other formats. The MDF file name is used is available, else the filename aragument must be provided.

Parameters:

format : string

can be one of the following:

  • csv : CSV export that uses the ”;” delimiter. This option will generate a new csv file for each data group (<MDFNAME>_DataGroup_XX.csv).
  • hdf5 : HDF5 file output; each MDF data group is mapped to a HDF5 group with the name ‘DataGroup_xx’ (where xx is the index)
  • excel : Excel file output (very slow). This option will generate a new excel file for each data group (<MDFNAME>_DataGroup_XX.xlsx)
  • mat : Matlab .mat version 5 export, for Matlab >= 7.6. IN the mat file the channels will be renamed to ‘DataGroup_xx_<channel name>’.

The channel group master will be renamed to ‘DataGroup_xx_<channel name>_master’. ( xx is the data group index starting from 0).

filename : string

export file name

filter(channels)

return new MDF object that contains only the channels listed in channels argument

Parameters:

channels : list

list of channel names to be filtered

Returns:

mdf : MDF

new MDF file

iter_to_pandas()

generator that yields channel groups as pandas DataFrames

static merge(files, load_measured_data=True)

merge several files and return the merged MDF object. The files must have the same internal structure (same number of groups, and same channels in each group)

Parameters:

files : list | tuple

list of MDF file names

load_measured_data : bool

load data option; default True

  • if True the data group binary data block will be loaded in RAM
  • if False the channel data is stored to a temporary file and read from disk on request
Returns:

merged : MDF

new MDF object with merged channels

Raises:

MdfException : if there are inconsistances between the files

merged MDF object

MDF3 and MDF4 classes

Notes about load_measured_data argument

By default when the MDF object is created the raw channel data is loaded into RAM. This will give you the best performance from asammdf.

However if you reach the physical memmory limit asammdf gives you the option use the load_measured_data flag. In this case the raw channel data is not read.

MDF defaults

Advantages

  • best performance

Disadvantages

  • higher RAM usage, there is the chance the file will exceed available RAM

Use case

  • when data fits inside the system RAM

MDF with load_measured_data

Advantages

  • lowest RAM usage
  • can handle files that do not fit in the available physical memory

Disadvantages

  • slow performance for getting channel data
  • must call close method to release the temporary file used in case of appending

Note

it is advised to use the MDF context manager in this case

Use case

  • when default data exceeds available RAM

Note

See benchmarks for the effects of using the flag