API

MDF

This class acts as a proxy for the MDF2, MDF3 and MDF4 classes. All attribute access is delegated to the underlying _mdf attribute (MDF2, MDF3 or MDF4 object). See MDF3 and MDF4 for available extra methods (MDF2 and MDF3 share the same implementation).

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.

The 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: InputType | None = None, version: str = '4.10', channels: list[str] | None = None, **kwargs)[source]

Unified access to MDF v3 and v4 files. Underlying _mdf’s attributes and methods are linked to the MDF object via setattr. This is done to expose them to the user code and for performance considerations.

Parameters:
namestring | BytesIO | zipfile.ZipFile | bz2.BZ2File | gzip.GzipFile

mdf file name (if provided it must be a real file name), file-like object or compressed file opened as Python object

Changed in version 6.2.0: added support for zipfile.ZipFile, bz2.BZ2File and gzip.GzipFile

versionstring

mdf file version from (‘2.00’, ‘2.10’, ‘2.14’, ‘3.00’, ‘3.10’, ‘3.20’, ‘3.30’, ‘4.00’, ‘4.10’, ‘4.11’, ‘4.20’); default ‘4.10’. This argument is only used for MDF objects created from scratch; for MDF objects created from a file the version is set to file version

channels (None)iterable

channel names that will used for selective loading. This can dramatically improve the file loading time. Default None -> load all channels

New in version 6.1.0.

Changed in version 6.3.0: make the default None

use_display_names (**kwargs)bool

keyword only argument: for MDF4 files parse the XML channel comment to search for the display name; XML parsing is quite expensive so setting this to False can decrease the loading times very much; default False

remove_source_from_channel_names (**kwargs)bool

remove source from channel names (“SpeedXCP3” -> “Speed”)

copy_on_get (**kwargs)bool

copy arrays in the get method; default True

expand_zippedfile (**kwargs)bool

only for bz2.BZ2File and gzip.GzipFile, load the file content into a BytesIO before parsing (avoids the huge performance penalty of doing random reads from the zipped file); default True

raise_on_multiple_occurrences (**kwargs)bool

raise exception when there are multiple channel occurrences in the file and the get call is ambiguous; default True

New in version 7.0.0.

temporary_folder (**kwargs)str | pathlib.Path

folder to use for temporary files

New in version 7.0.0.

Examples

>>> mdf = MDF(version='3.30') # new MDF object with version 3.30
>>> mdf = MDF('path/to/file.mf4') # MDF loaded from file
>>> mdf = MDF(BytesIO(data)) # MDF from file contents
>>> mdf = MDF(zipfile.ZipFile('data.zip')) # MDF creating using the first valid MDF from archive
>>> mdf = MDF(bz2.BZ2File('path/to/data.bz2', 'rb')) # MDF from bz2 object
>>> mdf = MDF(gzip.GzipFile('path/to/data.gzip', 'rb')) # MDF from gzip object
cleanup_timestamps(minimum: float, maximum: float, exp_min: int = -15, exp_max: int = 15, version: str | None = None, progress=None) → MDF[source]

convert MDF to other version

New in version 5.22.0.

Parameters:
minimumfloat

minimum plausible time stamp

maximumfloat

maximum plausible time stamp

exp_min (-15)int

minimum plausible exponent used for the time stamps float values

exp_max (15)int

maximum plausible exponent used for the time stamps float values

versionstr

new mdf file version from (‘2.00’, ‘2.10’, ‘2.14’, ‘3.00’, ‘3.10’, ‘3.20’, ‘3.30’, ‘4.00’, ‘4.10’, ‘4.11’, ‘4.20’); default the same as the input file

Returns:
outMDF

new MDF object

static concatenate(files: Sequence[MDF | InputType], version: str = '4.10', sync: bool = True, add_samples_origin: bool = False, direct_timestamp_continuation: bool = False, progress=None, **kwargs) → MDF[source]

concatenates several files. The files must have the same internal structure (same number of groups, and same channels in each group).

The order of the input files is always preserved, only the samples timestamps are influenced by the sync argument.

Parameters:
fileslist | tuple

list of MDF file names or MDF, zipfile.ZipFile, bz2.BZ2File or gzip.GzipFile instances

..versionchanged:: 6.2.0

added support for zipfile.ZipFile, bz2.BZ2File and gzip.GzipFile

versionstr

merged file version

syncbool

sync the files based on the start of measurement, default True. The order of the input files is preserved, only the samples timestamps are influenced by this argument

add_samples_originbool

option to create a new “__samples_origin” channel that will hold the index of the measurement from where each timestamp originated

direct_timestamp_continuation (False)bool

the time stamps from the next file will be added right after the last time stamp from the previous file; default False

..versionadded:: 6.0.0

kwargs

use_display_names (False) : bool

Returns:
concatenateMDF

new MDF object with concatenated channels

Raises:
MdfExceptionif there are inconsistencies between the files

Examples

>>> conc = MDF.concatenate(
    [
        'path/to/file.mf4',
        MDF(BytesIO(data)),
        MDF(zipfile.ZipFile('data.zip')),
        MDF(bz2.BZ2File('path/to/data.bz2', 'rb')),
        MDF(gzip.GzipFile('path/to/data.gzip', 'rb')),
    ],
    version='4.00',
    sync=False,
)
configure(*, from_other: MDF_v2_v3_v4 | None = None, read_fragment_size: int | None = None, write_fragment_size: int | None = None, use_display_names: bool | None = None, single_bit_uint_as_bool: bool | None = None, integer_interpolation: IntInterpolationModeType | IntegerInterpolation | None = None, copy_on_get: bool | None = None, float_interpolation: FloatInterpolationModeType | FloatInterpolation | None = None, raise_on_multiple_occurrences: bool | None = None, temporary_folder: str | None = None, fill_0_for_missing_computation_channels: bool | None = None) → None[source]

configure MDF parameters

The default values for the options are the following: * read_fragment_size = 0 * write_fragment_size = 4MB * use_display_names = False * single_bit_uint_as_bool = False * integer_interpolation = 0 (fill - use previous sample) * float_interpolation = 1 (linear interpolation) * copy_on_get = False * raise_on_multiple_occurrences = True * temporary_folder = “” * fill_0_for_missing_computation_channels = False

Parameters:
read_fragment_sizeint

size hint of split data blocks, default 8MB; if the initial size is smaller, then no data list is used. The actual split size depends on the data groups’ records size

write_fragment_sizeint

size hint of split data blocks, default 4MB; if the initial size is smaller, then no data list is used. The actual split size depends on the data groups’ records size. Maximum size is 4MB to ensure compatibility with CANape

use_display_namesbool

search for display name in the Channel XML comment

single_bit_uint_as_boolbool

return single bit channels are np.bool arrays

integer_interpolationint

interpolation mode for integer channels:

  • 0 - repeat previous sample

  • 1 - use linear interpolation

  • 2 - hybrid interpolation: channels with integer data type (raw values) that have a conversion that outputs float values will use linear interpolation, otherwise the previous sample is used

Changed in version 6.2.0: added hybrid mode interpolation

copy_on_getbool

copy arrays in the get method

float_interpolationint

interpolation mode for float channels:

  • 0 - repeat previous sample

  • 1 - use linear interpolation

New in version 6.2.0.

raise_on_multiple_occurrencesbool

raise exception when there are multiple channel occurrences in the file and the get call is ambiguous; default True

New in version 6.2.0.

from_otherMDF

copy configuration options from other MDF

New in version 6.2.0.

temporary_folderstr

default folder for temporary files

New in version 7.0.0.

fill_0_for_missing_computation_channelsbool

when a channel required by a computed channel is missing, then fill with 0 values. If false then the computation will fail and the computed channel will be marked as not existing.

New in version 7.1.0.

convert(version: str, progress=None) → MDF[source]

convert MDF to other version

Parameters:
versionstr

new mdf file version from (‘2.00’, ‘2.10’, ‘2.14’, ‘3.00’, ‘3.10’, ‘3.20’, ‘3.30’, ‘4.00’, ‘4.10’, ‘4.11’, ‘4.20’); default ‘4.10’

Returns:
outMDF

new MDF object

cut(start: float | None = None, stop: float | None = None, whence: int = 0, version: str | None = None, include_ends: bool = True, time_from_zero: bool = False, progress=None) → MDF[source]

cut MDF file. start and stop limits are absolute values or values relative to the first timestamp depending on the whence argument.

Parameters:
startfloat

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

stopfloat

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

whenceint

how to search for the start and stop values

  • 0 : absolute

  • 1 : relative to first timestamp

versionstr

new mdf file version from (‘2.00’, ‘2.10’, ‘2.14’, ‘3.00’, ‘3.10’, ‘3.20’, ‘3.30’, ‘4.00’, ‘4.10’, ‘4.11’, 4.20’); default None and in this case the original file version is used

include_endsbool

include the start and stop timestamps after cutting the signal. If start and stop are found in the original timestamps, then the new samples will be computed using interpolation. Default True

time_from_zerobool

start time stamps from 0s in the cut measurement

Returns:
outMDF

new MDF object

export(fmt: Literal['asc', 'csv', 'hdf5', 'mat', 'parquet'], filename: StrPathType | None = None, progress=None, **kwargs) → None[source]

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

The pandas export option was removed. you should use the method to_dataframe instead.

Parameters:
fmtstring

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_<cntr>.csv)

  • hdf5 : HDF5 file output; each MDF data group is mapped to a HDF5 group with the name ‘DataGroup_<cntr>’ (where <cntr> is the index)

  • mat : Matlab .mat version 4, 5 or 7.3 export. If single_time_base==False the channels will be renamed in the mat file to ‘D<cntr>_<channel name>’. The channel group master will be renamed to ‘DM<cntr>_<channel name>’ ( <cntr> is the data group index starting from 0)

  • parquet : export to Apache parquet format

  • asc: Vector ASCII format for bus logging

    New in version 7.3.3.

filenamestring | pathlib.Path

export file name

**kwargs

  • single_time_base: resample all channels to common time base, default False

  • raster: float time raster for resampling. Valid if single_time_base is True

  • time_from_zero: adjust time channel to start from 0

  • use_display_names: use display name instead of standard channel name, if available.

  • empty_channels: behaviour for channels without samples; the options are skip or zeros; default is skip

  • format: only valid for mat export; can be ‘4’, ‘5’ or ‘7.3’, default is ‘5’

  • oned_as: only valid for mat export; can be ‘row’ or ‘column’

  • keep_arrays : keep arrays and structure channels as well as the component channels. If True this can be very slow. If False only the component channels are saved, and their names will be prefixed with the parent channel.

  • reduce_memory_usage : bool reduce memory usage by converting all float columns to float32 and searching for minimum dtype that can reprezent the values found in integer columns; default False

  • compression : str compression to be used

    • for parquet : “GZIP” or “SNAPPY”

    • for hfd5 : “gzip”, “lzf” or “szip”

    • for mat : bool

  • time_as_date (False) : bool export time as local timezone datetimee; only valid for CSV export

    New in version 5.8.0.

  • ignore_value2text_conversions (False) : bool valid only for the channels that have value to text conversions and if raw=False. If this is True then the raw numeric values will be used, and the conversion will not be applied.

    New in version 5.8.0.

  • raw (False) : bool export all channels using the raw values

    New in version 6.0.0.

  • delimiter (‘,’) : str only valid for CSV: see cpython documentation for csv.Dialect.delimiter

    New in version 6.2.0.

  • doublequote (True) : bool only valid for CSV: see cpython documentation for csv.Dialect.doublequote

    New in version 6.2.0.

  • escapechar (None) : str only valid for CSV: see cpython documentation for csv.Dialect.escapechar

    New in version 6.2.0.

  • lineterminator (”\r\n”) : str only valid for CSV: see cpython documentation for csv.Dialect.lineterminator

    New in version 6.2.0.

  • quotechar (‘”’) : str only valid for CSV: see cpython documentation for csv.Dialect.quotechar

    New in version 6.2.0.

  • quoting (“MINIMAL”) : str only valid for CSV: see cpython documentation for csv.Dialect.quoting. Use the last part of the quoting constant name

    New in version 6.2.0.

  • add_units (False) : bool only valid for CSV: add the channel units on the second row of the CSV file

    New in version 7.1.0.

extract_bus_logging(database_files: dict[BusType, Iterable[DbcFileType]], version: str | None = None, ignore_invalid_signals: bool | None = None, consolidated_j1939: bool | None = None, ignore_value2text_conversion: bool = True, prefix: str = '', progress=None) → MDF[source]

extract all possible CAN signal using the provided databases.

Changed in version 6.0.0 from extract_can_logging

Parameters:
database_filesdict

each key will contain an iterable of database files for that bus type. The supported bus types are “CAN”, “LIN”. The iterables will contain the (databases, valid bus) pairs. The database can be a str, pathlib.Path or canamtrix.CanMatrix object. The valid bus is an integer specifying for which bus channel the database can be applied; 0 means any bus channel.

Changed in version 6.0.0: added canmatrix.CanMatrix type

Changed in version 6.3.0: added bus channel fileter

version (None)str

output file version

ignore_invalid_signals (None)bool | None

ignore signals that have all samples equal to their maximum value

New in version 5.7.0.

Deprecated since version 7.0.2: this argument is no longer used and will be removed in the future

consolidated_j1939 (None)bool | None

handle PGNs from all the messages as a single instance

New in version 5.7.0.

Deprecated since version 7.2.0: this argument is no longer used and will be removed in the future

ignore_value2text_conversion (True): bool

ignore value to text conversions

New in version 5.23.0.

prefix (“”)str

prefix that will added to the channel group names and signal names in the output file

New in version 6.3.0.

Returns:
mdfMDF

new MDF file that contains the succesfully extracted signals

Examples

>>> "extrac CAN and LIN bus logging"
>>> mdf = asammdf.MDF(r'bus_logging.mf4')
>>> databases = {
...     "CAN": [("file1.dbc", 0), ("file2.arxml", 2)],
...     "LIN": [("file3.dbc", 0)],
... }
>>> extracted = mdf.extract_bus_logging(database_files=database_files)
>>> ...
>>> "extrac just LIN bus logging"
>>> mdf = asammdf.MDF(r'bus_logging.mf4')
>>> databases = {
...     "LIN": [("file3.dbc", 0)],
... }
>>> extracted = mdf.extract_bus_logging(database_files=database_files)
filter(channels: ChannelsType, version: str | None = None, progress=None) → MDF[source]

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

Parameters:
channelslist

list of items to be filtered; each item can be :

  • a channel name string

  • (channel name, group index, channel index) list or tuple

  • (channel name, group index) list or tuple

  • (None, group index, channel index) list or tuple

versionstr

new mdf file version from (‘2.00’, ‘2.10’, ‘2.14’, ‘3.00’, ‘3.10’, ‘3.20’, ‘3.30’, ‘4.00’, ‘4.10’, ‘4.11’, ‘4.20’); default None and in this case the original file version is used

Returns:
mdfMDF

new MDF file

Examples

>>> from asammdf import MDF, Signal
>>> import numpy as np
>>> t = np.arange(5)
>>> s = np.ones(5)
>>> mdf = MDF()
>>> for i in range(4):
...     sigs = [Signal(s*(i*10+j), t, name='SIG') for j in range(1,4)]
...     mdf.append(sigs)
...
>>> filtered = mdf.filter(['SIG', ('SIG', 3, 1), ['SIG', 2], (None, 1, 2)])
>>> for gp_nr, ch_nr in filtered.channels_db['SIG']:
...     print(filtered.get(group=gp_nr, index=ch_nr))
...
<Signal SIG:
        samples=[ 1.  1.  1.  1.  1.]
        timestamps=[0 1 2 3 4]
        unit=""
        info=None
        comment="">
<Signal SIG:
        samples=[ 31.  31.  31.  31.  31.]
        timestamps=[0 1 2 3 4]
        unit=""
        info=None
        comment="">
<Signal SIG:
        samples=[ 21.  21.  21.  21.  21.]
        timestamps=[0 1 2 3 4]
        unit=""
        info=None
        comment="">
<Signal SIG:
        samples=[ 12.  12.  12.  12.  12.]
        timestamps=[0 1 2 3 4]
        unit=""
        info=None
        comment="">
get_group(index: int, raster: RasterType | None = None, time_from_zero: bool = True, empty_channels: EmptyChannelsType = 'skip', keep_arrays: bool = False, use_display_names: bool = False, time_as_date: bool = False, reduce_memory_usage: bool = False, raw: bool = False, ignore_value2text_conversions: bool = False, only_basenames: bool = False) → pd.DataFrame[source]

get channel group as pandas DataFrames. If there are multiple occurrences for the same channel name, then a counter will be used to make the names unique (<original_name>_<counter>)

Parameters:
indexint

channel group index

use_display_namesbool

use display name instead of standard channel name, if available.

reduce_memory_usagebool

reduce memory usage by converting all float columns to float32 and searching for minimum dtype that can reprezent the values found in integer columns; default False

raw (False)bool

the dataframe will contain the raw channel values

New in version 5.7.0.

ignore_value2text_conversions (False)bool

valid only for the channels that have value to text conversions and if raw=False. If this is True then the raw numeric values will be used, and the conversion will not be applied.

New in version 5.8.0.

keep_arrays (False)bool

keep arrays and structure channels as well as the component channels. If True this can be very slow. If False only the component channels are saved, and their names will be prefixed with the parent channel.

New in version 5.8.0.

empty_channels (“skip”)str

behaviour for channels without samples; the options are skip or zeros; default is skip

New in version 5.8.0.

only_basenames (False)bool

use just the field names, without prefix, for structures and channel arrays

New in version 5.13.0.

rasterfloat | np.array | str

new raster that can be

  • a float step value

  • a channel name who’s timestamps will be used as raster (starting with asammdf 5.5.0)

  • an array (starting with asammdf 5.5.0)

see resample for examples of using this argument

Returns:
dfpandas.DataFrame
iter_channels(skip_master: bool = True, copy_master: bool = True, raw: bool = False) → Iterator[Signal][source]

generator that yields a Signal for each non-master channel

Parameters:
skip_masterbool

do not yield master channels; default True

copy_masterbool

copy master for each yielded channel True

rawbool

return raw channels instead of converted; default False

iter_get(name: str | None = None, group: int | None = None, index: int | None = None, raster: float | None = None, samples_only: typing_extensions.Literal[False] = False, raw: bool = False) → Iterator[Signal][source]
iter_get(name: str | None = None, group: int | None = None, index: int | None = None, raster: float | None = None, samples_only: typing_extensions.Literal[True] = False, raw: bool = False) → Iterator[tuple[NDArray[Any], NDArray[Any] | None]]

iterator over a channel

This is usefull in case of large files with a small number of channels.

If the raster keyword argument is not None the output is interpolated accordingly

Parameters:
namestring

name of channel

groupint

0-based group index

indexint

0-based channel index

rasterfloat

time raster in seconds

samples_onlybool
if True return only the channel samples as numpy array; if

False return a Signal object

rawbool

return channel samples without appling the conversion rule; default False

iter_groups(raster: RasterType | None = None, time_from_zero: bool = True, empty_channels: EmptyChannelsType = 'skip', keep_arrays: bool = False, use_display_names: bool = False, time_as_date: bool = False, reduce_memory_usage: bool = False, raw: bool = False, ignore_value2text_conversions: bool = False, only_basenames: bool = False) → Iterator[pd.DataFrame][source]

generator that yields channel groups as pandas DataFrames. If there are multiple occurrences for the same channel name inside a channel group, then a counter will be used to make the names unique (<original_name>_<counter>)

Parameters:
use_display_namesbool

use display name instead of standard channel name, if available.

New in version 5.21.0.

reduce_memory_usagebool

reduce memory usage by converting all float columns to float32 and searching for minimum dtype that can reprezent the values found in integer columns; default False

New in version 5.21.0.

raw (False)bool

the dataframe will contain the raw channel values

New in version 5.21.0.

ignore_value2text_conversions (False)bool

valid only for the channels that have value to text conversions and if raw=False. If this is True then the raw numeric values will be used, and the conversion will not be applied.

New in version 5.21.0.

keep_arrays (False)bool

keep arrays and structure channels as well as the component channels. If True this can be very slow. If False only the component channels are saved, and their names will be prefixed with the parent channel.

New in version 5.21.0.

empty_channels (“skip”)str

behaviour for channels without samples; the options are skip or zeros; default is skip

New in version 5.21.0.

only_basenames (False)bool

use just the field names, without prefix, for structures and channel arrays

New in version 5.21.0.

rasterfloat | np.array | str

new raster that can be

  • a float step value

  • a channel name who’s timestamps will be used as raster (starting with asammdf 5.5.0)

  • an array (starting with asammdf 5.5.0)

see resample for examples of using this argument

New in version 5.21.0.

iter_to_dataframe(channels: ChannelsType | None = None, raster: RasterType | None = None, time_from_zero: bool = True, empty_channels: EmptyChannelsType = 'skip', keep_arrays: bool = False, use_display_names: bool = False, time_as_date: bool = False, reduce_memory_usage: bool = False, raw: bool = False, ignore_value2text_conversions: bool = False, use_interpolation: bool = True, only_basenames: bool = False, chunk_ram_size: int = 209715200, interpolate_outwards_with_nan: bool = False, numeric_1D_only: bool = False, progress=None) → Iterator[pd.DataFrame][source]

generator that yields pandas DataFrame’s that should not exceed 200MB of RAM

New in version 5.15.0.

Parameters:
channelslist

list of items to be filtered (default None); each item can be :

  • a channel name string

  • (channel name, group index, channel index) list or tuple

  • (channel name, group index) list or tuple

  • (None, group index, channel index) list or tuple

rasterfloat | np.array | str

new raster that can be

  • a float step value

  • a channel name who’s timestamps will be used as raster (starting with asammdf 5.5.0)

  • an array (starting with asammdf 5.5.0)

see resample for examples of using this argument

time_from_zerobool

adjust time channel to start from 0; default True

empty_channelsstr

behaviour for channels without samples; the options are skip or zeros; default is skip

use_display_namesbool

use display name instead of standard channel name, if available.

keep_arraysbool

keep arrays and structure channels as well as the component channels. If True this can be very slow. If False only the component channels are saved, and their names will be prefixed with the parent channel.

time_as_datebool

the dataframe index will contain the datetime timestamps according to the measurement start time; default False. If True then the argument time_from_zero will be ignored.

reduce_memory_usagebool

reduce memory usage by converting all float columns to float32 and searching for minimum dtype that can reprezent the values found in integer columns; default False

raw (False)bool

the columns will contain the raw values

ignore_value2text_conversions (False)bool

valid only for the channels that have value to text conversions and if raw=False. If this is True then the raw numeric values will be used, and the conversion will not be applied.

use_interpolation (True)bool

option to perform interpoaltions when multiple timestamp raster are present. If False then dataframe columns will be automatically filled with NaN’s were the dataframe index values are not found in the current column’s timestamps

only_basenames (False)bool

use jsut the field names, without prefix, for structures and channel arrays

interpolate_outwards_with_nanbool

use NaN values for the samples that lie outside of the original signal’s timestamps

chunk_ram_sizeint

desired data frame RAM usage in bytes; default 200 MB

numeric_1D_only (False)bool

only keep the 1D-columns that have numeric values

New in version 7.0.0.

Returns:
dataframepandas.DataFrame

yields pandas DataFrame’s that should not exceed 200MB of RAM

resample(raster: RasterType, version: str | None = None, time_from_zero: bool = False, progress=None) → MDF[source]

resample all channels using the given raster. See configure to select the interpolation method for interger channels

Parameters:
rasterfloat | np.array | str

new raster that can be

  • a float step value

  • a channel name who’s timestamps will be used as raster (starting with asammdf 5.5.0)

  • an array (starting with asammdf 5.5.0)

versionstr

new mdf file version from (‘2.00’, ‘2.10’, ‘2.14’, ‘3.00’, ‘3.10’, ‘3.20’, ‘3.30’, ‘4.00’, ‘4.10’, ‘4.11’, ‘4.20’); default None and in this case the original file version is used

time_from_zerobool

start time stamps from 0s in the cut measurement

Returns:
mdfMDF

new MDF with resampled channels

Examples

>>> from asammdf import MDF, Signal
>>> import numpy as np
>>> mdf = MDF()
>>> sig = Signal(name='S1', samples=[1,2,3,4], timestamps=[1,2,3,4])
>>> mdf.append(sig)
>>> sig = Signal(name='S2', samples=[1,2,3,4], timestamps=[1.1, 3.5, 3.7, 3.9])
>>> mdf.append(sig)
>>> resampled = mdf.resample(raster=0.1)
>>> resampled.select(['S1', 'S2'])
[<Signal S1:
        samples=[1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 4]
        timestamps=[1.  1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.  2.1 2.2 2.3 2.4 2.5 2.6 2.7
 2.8 2.9 3.  3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4. ]
        invalidation_bits=None
        unit=""
        conversion=None
        source=Source(name='Python', path='Python', comment='', source_type=4, bus_type=0)
        comment=""
        mastermeta="('time', 1)"
        raw=True
        display_names={}
        attachment=()>
, <Signal S2:
        samples=[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 3 3 4 4]
        timestamps=[1.  1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.  2.1 2.2 2.3 2.4 2.5 2.6 2.7
 2.8 2.9 3.  3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4. ]
        invalidation_bits=None
        unit=""
        conversion=None
        source=Source(name='Python', path='Python', comment='', source_type=4, bus_type=0)
        comment=""
        mastermeta="('time', 1)"
        raw=True
        display_names={}
        attachment=()>
]
>>> resampled = mdf.resample(raster='S2')
>>> resampled.select(['S1', 'S2'])
[<Signal S1:
        samples=[1 3 3 3]
        timestamps=[1.1 3.5 3.7 3.9]
        invalidation_bits=None
        unit=""
        conversion=None
        source=Source(name='Python', path='Python', comment='', source_type=4, bus_type=0)
        comment=""
        mastermeta="('time', 1)"
        raw=True
        display_names={}
        attachment=()>
, <Signal S2:
        samples=[1 2 3 4]
        timestamps=[1.1 3.5 3.7 3.9]
        invalidation_bits=None
        unit=""
        conversion=None
        source=Source(name='Python', path='Python', comment='', source_type=4, bus_type=0)
        comment=""
        mastermeta="('time', 1)"
        raw=True
        display_names={}
        attachment=()>
]
>>> resampled = mdf.resample(raster=[1.9, 2.0, 2.1])
>>> resampled.select(['S1', 'S2'])
[<Signal S1:
        samples=[1 2 2]
        timestamps=[1.9 2.  2.1]
        invalidation_bits=None
        unit=""
        conversion=None
        source=Source(name='Python', path='Python', comment='', source_type=4, bus_type=0)
        comment=""
        mastermeta="('time', 1)"
        raw=True
        display_names={}
        attachment=()>
, <Signal S2:
        samples=[1 1 1]
        timestamps=[1.9 2.  2.1]
        invalidation_bits=None
        unit=""
        conversion=None
        source=Source(name='Python', path='Python', comment='', source_type=4, bus_type=0)
        comment=""
        mastermeta="('time', 1)"
        raw=True
        display_names={}
        attachment=()>
]
>>> resampled = mdf.resample(raster='S2', time_from_zero=True)
>>> resampled.select(['S1', 'S2'])
[<Signal S1:
        samples=[1 3 3 3]
        timestamps=[0.  2.4 2.6 2.8]
        invalidation_bits=None
        unit=""
        conversion=None
        source=Source(name='Python', path='Python', comment='', source_type=4, bus_type=0)
        comment=""
        mastermeta="('time', 1)"
        raw=True
        display_names={}
        attachment=()>
, <Signal S2:
        samples=[1 2 3 4]
        timestamps=[0.  2.4 2.6 2.8]
        invalidation_bits=None
        unit=""
        conversion=None
        source=Source(name='Python', path='Python', comment='', source_type=4, bus_type=0)
        comment=""
        mastermeta="('time', 1)"
        raw=True
        display_names={}
        attachment=()>
]
static scramble(name: str | PathLike[str], skip_attachments: bool = False, progress=None, **kwargs) → Path[source]

scramble text blocks and keep original file structure

Parameters:
namestr | pathlib.Path

file name

skip_attachmentsbool

skip scrambling of attachments data if True

New in version 5.9.0.

Returns:
namepathlib.Path

scrambled file name

search(pattern: str, mode: Literal['plain', 'regex', 'wildcard'] | SearchMode = SearchMode.plain, case_insensitive: bool = False) → list[str][source]

search channels

New in version 7.0.0.

Parameters:
patternstr

search pattern

modeLiteral[“plain”, “regex”, “wildcard”] or SearchMode, optional

search mode, by default SearchMode.plain

  • plain : normal name search

  • regex : regular expression based search

  • wildcard : wildcard based search

case_insensitivebool, optional

case sensitivity for the channel name search, by default False

Returns:
list[str]

name of the channels

Raises:
ValueError

unsupported search mode

Examples

>>> mdf = MDF(file_name)
>>> mdf.search('*veh*speed*', case_insensitive=True, mode='wildcard') # case insensitive wildcard based search
['vehicleAverageSpeed', 'vehicleInstantSpeed', 'targetVehicleAverageSpeed', 'targetVehicleInstantSpeed']
>>> mdf.search('^vehicle.*Speed$', case_insensitive=False, mode='regex') # case sensitive regex based search
['vehicleAverageSpeed', 'vehicleInstantSpeed']
select(channels: ChannelsType, record_offset: int = 0, raw: bool = False, copy_master: bool = True, ignore_value2text_conversions: bool = False, record_count: int | None = None, validate: bool = False) → list[Signal][source]

retrieve the channels listed in channels argument as Signal objects

Note

the dataframe argument was removed in version 5.8.0 use the to_dataframe method instead

Parameters:
channelslist

list of items to be filtered; each item can be :

  • a channel name string

  • (channel name, group index, channel index) list or tuple

  • (channel name, group index) list or tuple

  • (None, group index, channel index) list or tuple

record_offsetint

record number offset; optimization to get the last part of signal samples

rawbool

get raw channel samples; default False

copy_masterbool

option to get a new timestamps array for each selected Signal or to use a shared array for channels of the same channel group; default True

ignore_value2text_conversions (False)bool

valid only for the channels that have value to text conversions and if raw=False. If this is True then the raw numeric values will be used, and the conversion will not be applied.

New in version 5.8.0.

validate (False)bool

consider the invalidation bits

New in version 5.16.0.

Returns:
signalslist

list of Signal objects based on the input channel list

Examples

>>> from asammdf import MDF, Signal
>>> import numpy as np
>>> t = np.arange(5)
>>> s = np.ones(5)
>>> mdf = MDF()
>>> for i in range(4):
...     sigs = [Signal(s*(i*10+j), t, name='SIG') for j in range(1,4)]
...     mdf.append(sigs)
...
>>> # select SIG group 0 default index 1 default, SIG group 3 index 1, SIG group 2 index 1 default and channel index 2 from group 1
...
>>> mdf.select(['SIG', ('SIG', 3, 1), ['SIG', 2],  (None, 1, 2)])
[<Signal SIG:
        samples=[ 1.  1.  1.  1.  1.]
        timestamps=[0 1 2 3 4]
        unit=""
        info=None
        comment="">
, <Signal SIG:
        samples=[ 31.  31.  31.  31.  31.]
        timestamps=[0 1 2 3 4]
        unit=""
        info=None
        comment="">
, <Signal SIG:
        samples=[ 21.  21.  21.  21.  21.]
        timestamps=[0 1 2 3 4]
        unit=""
        info=None
        comment="">
, <Signal SIG:
        samples=[ 12.  12.  12.  12.  12.]
        timestamps=[0 1 2 3 4]
        unit=""
        info=None
        comment="">
]
static stack(files: Sequence[MDF | InputType], version: str = '4.10', sync: bool = True, progress=None, **kwargs) → MDF[source]

stack several files and return the stacked MDF object

Parameters:
fileslist | tuple

list of MDF file names or MDF, zipfile.ZipFile, bz2.BZ2File or gzip.GzipFile instances

..versionchanged:: 6.2.0

added support for zipfile.ZipFile, bz2.BZ2File and gzip.GzipFile

versionstr

merged file version

syncbool

sync the files based on the start of measurement, default True

kwargs

use_display_names (False) : bool

Returns:
stackedMDF

new MDF object with stacked channels

Examples

>>> stacked = MDF.stack(
    [
        'path/to/file.mf4',
        MDF(BytesIO(data)),
        MDF(zipfile.ZipFile('data.zip')),
        MDF(bz2.BZ2File('path/to/data.bz2', 'rb')),
        MDF(gzip.GzipFile('path/to/data.gzip', 'rb')),
    ],
    version='4.00',
    sync=False,
)
property start_time: datetime

getter and setter the measurement start timestamp

Returns:
timestampdatetime.datetime

start timestamp

to_dataframe(channels: ChannelsType | None = None, raster: RasterType | None = None, time_from_zero: bool = True, empty_channels: EmptyChannelsType = 'skip', keep_arrays: bool = False, use_display_names: bool = False, time_as_date: bool = False, reduce_memory_usage: bool = False, raw: bool = False, ignore_value2text_conversions: bool = False, use_interpolation: bool = True, only_basenames: bool = False, interpolate_outwards_with_nan: bool = False, numeric_1D_only: bool = False, progress=None) → pd.DataFrame[source]

generate pandas DataFrame

Parameters:
channelslist

list of items to be filtered (default None); each item can be :

  • a channel name string

  • (channel name, group index, channel index) list or tuple

  • (channel name, group index) list or tuple

  • (None, group index, channel index) list or tuple

rasterfloat | np.array | str

new raster that can be

  • a float step value

  • a channel name who’s timestamps will be used as raster (starting with asammdf 5.5.0)

  • an array (starting with asammdf 5.5.0)

see resample for examples of using this argument

time_from_zerobool

adjust time channel to start from 0; default True

empty_channelsstr

behaviour for channels without samples; the options are skip or zeros; default is skip

use_display_namesbool

use display name instead of standard channel name, if available.

keep_arraysbool

keep arrays and structure channels as well as the component channels. If True this can be very slow. If False only the component channels are saved, and their names will be prefixed with the parent channel.

time_as_datebool

the dataframe index will contain the datetime timestamps according to the measurement start time; default False. If True then the argument time_from_zero will be ignored.

reduce_memory_usagebool

reduce memory usage by converting all float columns to float32 and searching for minimum dtype that can reprezent the values found in integer columns; default False

raw (False)bool

the columns will contain the raw values

New in version 5.7.0.

ignore_value2text_conversions (False)bool

valid only for the channels that have value to text conversions and if raw=False. If this is True then the raw numeric values will be used, and the conversion will not be applied.

New in version 5.8.0.

use_interpolation (True)bool

option to perform interpoaltions when multiple timestamp raster are present. If False then dataframe columns will be automatically filled with NaN’s were the dataframe index values are not found in the current column’s timestamps

New in version 5.11.0.

only_basenames (False)bool

use just the field names, without prefix, for structures and channel arrays

New in version 5.13.0.

interpolate_outwards_with_nanbool

use NaN values for the samples that lie outside of the original signal’s timestamps

New in version 5.15.0.

Returns:
dataframepandas.DataFrame
whereis(channel: str, source_name: str | None = None, source_path: str | None = None, acq_name: str | None = None) → tuple[tuple[int, int], ...][source]

get occurrences of channel name in the file

Parameters:
channelstr

channel name string

source_namestr, optional

filter occurrences on source name, by default None

source_pathstr, optional

filter occurrences on source path, by default None

acq_namestr, optional

filter occurrences on channel group acquisition name, by default None

New in version 6.0.0.

Returns:
tuple[tuple[int, int], …]

(gp_idx, cn_idx) pairs

Examples

>>> mdf = MDF(file_name)
>>> mdf.whereis('VehicleSpeed') # "VehicleSpeed" exists in the file
((1, 2), (2, 4))
>>> mdf.whereis('VehicleSPD') # "VehicleSPD" doesn't exist in the file
()

MDF3

class asammdf.blocks.mdf_v3.MDF3(name: BufferedReader | BytesIO | StrPathType | None = None, version: str = '3.30', channels: list[str] | None = None, **kwargs)[source]

The header attibute is a HeaderBlock.

The groups attribute is a list of dicts, each one with the following keys

  • data_group - DataGroup object

  • channel_group - ChannelGroup object

  • channels - list of Channel objects with the same order as found in the mdf file

  • channel_dependencies - list of ChannelArrayBlock in case of channel arrays; list of Channel objects in case of structure channel composition

  • data_block - address of data block

  • data_location- integer code for data location (original file, temporary file or memory)

  • data_block_addr - list of raw samples starting addresses

  • data_block_type - list of codes for data block type

  • data_block_size - list of raw samples block size

  • sorted - sorted indicator flag

  • record_size - dict that maps record ID’s to record sizes in bytes

  • size - total size of data block for the current group

  • trigger - Trigger object for current group

Parameters:
namestring | pathlib.Path

mdf file name (if provided it must be a real file name) or file-like object

versionstring

mdf file version (‘2.00’, ‘2.10’, ‘2.14’, ‘3.00’, ‘3.10’, ‘3.20’ or ‘3.30’); default ‘3.30’

callbackfunction

keyword only argument: function to call to update the progress; the function must accept two arguments (the current progress and maximum progress value)

Attributes:
attachmentslist

list of file attachments

channels_dbdict

used for fast channel access by name; for each name key the value is a list of (group index, channel index) tuples

groupslist

list of data group dicts

headerHeaderBlock

mdf file header

identificationFileIdentificationBlock

mdf file start block

last_call_infodict | None

a dict to hold information about the last called method.

New in version 5.12.0.

masters_dbdict
used for fast master channel access; for each group index key the value

is the master channel index

memorystr

memory optimization option

namestring

mdf file name

versionstr

mdf version

add_trigger(group: int, timestamp: float, pre_time: float = 0, post_time: float = 0, comment: str = '') → None[source]

add trigger to data group

Parameters:
groupint

group index

timestampfloat

trigger time

pre_timefloat

trigger pre time; default 0

post_timefloat

trigger post time; default 0

commentstr

trigger comment

append(signals: list[Signal] | Signal | DataFrame, acq_name: str | None = None, acq_source: Source | None = None, comment: str = 'Python', common_timebase: bool = False, units: dict[str, str | bytes] | None = None) → int | None[source]

Appends a new data group.

For channel dependencies type Signals, the samples attribute must be a numpy.recarray

Parameters:
signalslist | Signal | pandas.DataFrame

list of Signal objects, or a single Signal object, or a pandas DataFrame object. All bytes columns in the pandas DataFrame must be latin-1 encoded

acq_namestr

channel group acquisition name

acq_sourceSource

channel group acquisition source

commentstr

channel group comment; default ‘Python’

common_timebasebool

flag to hint that the signals have the same timebase. Only set this if you know for sure that all appended channels share the same time base

unitsdict

will contain the signal units mapped to the signal names when appending a pandas DataFrame

Examples

>>> # case 1 conversion type None
>>> s1 = np.array([1, 2, 3, 4, 5])
>>> s2 = np.array([-1, -2, -3, -4, -5])
>>> s3 = np.array([0.1, 0.04, 0.09, 0.16, 0.25])
>>> t = np.array([0.001, 0.002, 0.003, 0.004, 0.005])
>>> names = ['Positive', 'Negative', 'Float']
>>> units = ['+', '-', '.f']
>>> info = {}
>>> s1 = Signal(samples=s1, timestamps=t, unit='+', name='Positive')
>>> s2 = Signal(samples=s2, timestamps=t, unit='-', name='Negative')
>>> s3 = Signal(samples=s3, timestamps=t, unit='flts', name='Floats')
>>> mdf = MDF3('new.mdf')
>>> mdf.append([s1, s2, s3], comment='created by asammdf v1.1.0')
>>> # case 2: VTAB conversions from channels inside another file
>>> mdf1 = MDF3('in.mdf')
>>> ch1 = mdf1.get("Channel1_VTAB")
>>> ch2 = mdf1.get("Channel2_VTABR")
>>> sigs = [ch1, ch2]
>>> mdf2 = MDF3('out.mdf')
>>> mdf2.append(sigs, comment='created by asammdf v1.1.0')
>>> df = pd.DataFrame.from_dict({'s1': np.array([1, 2, 3, 4, 5]), 's2': np.array([-1, -2, -3, -4, -5])})
>>> units = {'s1': 'V', 's2': 'A'}
>>> mdf2.append(df, units=units)
close() → None[source]

if the MDF was created with memory=’minimum’ and new channels have been appended, then this must be called just before the object is not used anymore to clean-up the temporary file

extend(index: int, signals: list[tuple[NDArray[Any], None]]) → None[source]

Extend a group with new samples. signals contains (values, invalidation_bits) pairs for each extended signal. Since MDF3 does not support invalidation bits, the second item of each pair must be None. The first pair is the master channel’s pair, and the next pairs must respect the same order in which the signals were appended. The samples must have raw or physical values according to the Signals used for the initial append.

Parameters:
indexint

group index

signalslist

list of (numpy.ndarray, None) objects

Examples

>>> # case 1 conversion type None
>>> s1 = np.array([1, 2, 3, 4, 5])
>>> s2 = np.array([-1, -2, -3, -4, -5])
>>> s3 = np.array([0.1, 0.04, 0.09, 0.16, 0.25])
>>> t = np.array([0.001, 0.002, 0.003, 0.004, 0.005])
>>> names = ['Positive', 'Negative', 'Float']
>>> units = ['+', '-', '.f']
>>> s1 = Signal(samples=s1, timestamps=t, unit='+', name='Positive')
>>> s2 = Signal(samples=s2, timestamps=t, unit='-', name='Negative')
>>> s3 = Signal(samples=s3, timestamps=t, unit='flts', name='Floats')
>>> mdf = MDF3('new.mdf')
>>> mdf.append([s1, s2, s3], comment='created by asammdf v1.1.0')
>>> t = np.array([0.006, 0.007, 0.008, 0.009, 0.010])
>>> mdf2.extend(0, [(t, None), (s1.samples, None), (s2.samples, None), (s3.samples, None)])
get(name: str | None = None, group: int | None = None, index: int | None = None, raster: RasterType | None = None, samples_only: typing_extensions.Literal[False] = False, data: bytes | None = None, raw: bool = False, ignore_invalidation_bits: bool = False, record_offset: int = 0, record_count: int | None = None, skip_channel_validation: bool = False) → Signal[source]
get(name: str | None = None, group: int | None = None, index: int | None = None, raster: RasterType | None = None, samples_only: typing_extensions.Literal[True] = False, data: bytes | None = None, raw: bool = False, ignore_invalidation_bits: bool = False, record_offset: int = 0, record_count: int | None = None, skip_channel_validation: bool = False) → tuple[NDArray[Any], None]

Gets channel samples. Channel can be specified in two ways:

  • using the first positional argument name

    • if there are multiple occurrences for this channel then the group and index arguments can be used to select a specific group.

    • if there are multiple occurrences for this channel and either the group or index arguments is None then a warning is issued

  • using the group number (keyword argument group) and the channel number (keyword argument index). Use info method for group and channel numbers

If the raster keyword argument is not None the output is interpolated accordingly.

Parameters:
namestring

name of channel

groupint

0-based group index

indexint

0-based channel index

rasterfloat

time raster in seconds

samples_onlybool

if True return only the channel samples as numpy array; if False return a Signal object

databytes

prevent redundant data read by providing the raw data group samples

rawbool

return channel samples without applying the conversion rule; default False

ignore_invalidation_bitsbool

only defined to have the same API with the MDF v4

record_offsetint

if data=None use this to select the record offset from which the group data should be loaded

skip_channel_validation (False)bool

skip validation of channel name, group index and channel index; defualt False. If True, the caller has to make sure that the group and index arguments are provided and are correct.

..versionadded:: 7.0.0

Returns:
res(numpy.array, None) | Signal

returns Signal if samples_only*=*False (default option), otherwise returns a (numpy.array, None) tuple (for compatibility with MDF v4 class.

The Signal samples are

  • numpy recarray for channels that have CDBLOCK or BYTEARRAY type channels

  • numpy array for all the rest

Raises:
MdfException
  • if the channel name is not found
  • if the group index is out of range
  • if the channel index is out of range

Examples

>>> from asammdf import MDF, Signal
>>> import numpy as np
>>> t = np.arange(5)
>>> s = np.ones(5)
>>> mdf = MDF(version='3.30')
>>> for i in range(4):
...     sigs = [Signal(s*(i*10+j), t, name='Sig') for j in range(1, 4)]
...     mdf.append(sigs)
...
>>> # first group and channel index of the specified channel name
...
>>> mdf.get('Sig')
UserWarning: Multiple occurrences for channel "Sig". Using first occurrence from data group 4. Provide both "group" and "index" arguments to select another data group
<Signal Sig:
        samples=[ 1.  1.  1.  1.  1.]
        timestamps=[0 1 2 3 4]
        unit=""
        info=None
        comment="">
>>> # first channel index in the specified group
...
>>> mdf.get('Sig', 1)
<Signal Sig:
        samples=[ 11.  11.  11.  11.  11.]
        timestamps=[0 1 2 3 4]
        unit=""
        info=None
        comment="">
>>> # channel named Sig from group 1 channel index 2
...
>>> mdf.get('Sig', 1, 2)
<Signal Sig:
        samples=[ 12.  12.  12.  12.  12.]
        timestamps=[0 1 2 3 4]
        unit=""
        info=None
        comment="">
>>> # channel index 1 or group 2
...
>>> mdf.get(None, 2, 1)
<Signal Sig:
        samples=[ 21.  21.  21.  21.  21.]
        timestamps=[0 1 2 3 4]
        unit=""
        info=None
        comment="">
>>> mdf.get(group=2, index=1)
<Signal Sig:
        samples=[ 21.  21.  21.  21.  21.]
        timestamps=[0 1 2 3 4]
        unit=""
        info=None
        comment="">
get_channel_comment(name: str | None = None, group: int | None = None, index: int | None = None) → str[source]

Gets channel comment. Channel can be specified in two ways:

  • using the first positional argument name

    • if there are multiple occurrences for this channel then the group and index arguments can be used to select a specific group.

    • if there are multiple occurrences for this channel and either the group or index arguments is None then a warning is issued

  • using the group number (keyword argument group) and the channel number (keyword argument index). Use info method for group and channel numbers

If the raster keyword argument is not None the output is interpolated accordingly.

Parameters:
namestring

name of channel

groupint

0-based group index

indexint

0-based channel index

Returns:
commentstr

found channel comment

get_channel_name(group: int, index: int) → str[source]

Gets channel name.

Parameters:
groupint

0-based group index

indexint

0-based channel index

Returns:
namestr

found channel name

get_channel_unit(name: str | None = None, group: int | None = None, index: int | None = None) → str[source]

Gets channel unit.

Channel can be specified in two ways:

  • using the first positional argument name

    • if there are multiple occurrences for this channel then the group and index arguments can be used to select a specific group.

    • if there are multiple occurrences for this channel and either the group or index arguments is None then a warning is issued

  • using the group number (keyword argument group) and the channel number (keyword argument index). Use info method for group and channel numbers

If the raster keyword argument is not None the output is interpolated accordingly.

Parameters:
namestring

name of channel

groupint

0-based group index

indexint

0-based channel index

Returns:
unitstr

found channel unit

get_master(index: int, data: bytes | None = None, raster: RasterType | None = None, record_offset: int = 0, record_count: int | None = None, one_piece: bool = False) → NDArray[Any][source]

returns master channel samples for given group

Parameters:
indexint

group index

data(bytes, int)

(data block raw bytes, fragment offset); default None

rasterfloat

raster to be used for interpolation; default None

Deprecated since version 5.13.0.

record_offsetint

if data=None use this to select the record offset from which the group data should be loaded

Returns:
tnumpy.array

master channel samples

info() → dict[str, Any][source]

get MDF information as a dict

Examples

>>> mdf = MDF3('test.mdf')
>>> mdf.info()
iter_get_triggers() → Iterator[TriggerInfoDict][source]

generator that yields triggers

Returns:
trigger_infodict

trigger information with the following keys:

  • comment : trigger comment

  • time : trigger time

  • pre_time : trigger pre time

  • post_time : trigger post time

  • index : trigger index

  • group : data group index of trigger

save(dst: StrPathType, overwrite: bool = False, compression: CompressionType = 0, progress=None, add_history_block: bool = True) → Path | None[source]

Save MDF to dst. If overwrite is True then the destination file is overwritten, otherwise the file name is appended with ‘.<cntr>’, were ‘<cntr>’ is the first counter that produces a new file name (that does not already exist in the filesystem).

Parameters:
dststr | pathlib.Path

destination file name

overwritebool

overwrite flag, default False

compressionint

does nothing for mdf version3; introduced here to share the same API as mdf version 4 files

Returns:
output_filestr

output file name

property start_time: datetime

getter and setter the measurement start timestamp

Returns:
timestampdatetime.datetime

start timestamp

MDF version 2 & 3 blocks

MDF4

class asammdf.blocks.mdf_v4.MDF4(name: BufferedReader | BytesIO | StrPathType | None = None, version: str = '4.10', channels: list[str] | None = None, **kwargs)[source]

The header attibute is a HeaderBlock.

The groups attribute is a list of dicts, each one with the following keys:

  • data_group - DataGroup object

  • channel_group - ChannelGroup object

  • channels - list of Channel objects with the same order as found in the mdf file

  • channel_dependencies - list of ChannelArrayBlock in case of channel arrays; list of Channel objects in case of structure channel composition

  • data_block - address of data block

  • data_location- integer code for data location (original file, temporary file or memory)

  • data_block_addr - list of raw samples starting addresses

  • data_block_type - list of codes for data block type

  • data_block_size - list of raw samples block size

  • sorted - sorted indicator flag

  • record_size - dict that maps record ID’s to record sizes in bytes (including invalidation bytes)

  • param - row size used for transposition, in case of transposed zipped blocks

Parameters:
namestring

mdf file name (if provided it must be a real file name) or file-like object

versionstring

mdf file version (‘4.00’, ‘4.10’, ‘4.11’, ‘4.20’); default ‘4.10’

kwargs
use_display_names (True)bool

keyword only argument: for MDF4 files parse the XML channel comment to search for the display name; XML parsing is quite expensive so setting this to False can decrease the loading times very much; default True

remove_source_from_channel_names (True)bool
copy_on_get (True)bool

copy channel values (np.array) to avoid high memory usage

compact_vlsd (False)bool

use slower method to save the exact sample size for VLSD channels

column_storage (True)bool

use column storage for MDF version >= 4.20

passwordbytes | str

use this password to decode encrypted attachments

Attributes:
attachmentslist

list of file attachments

channels_dbdict

used for fast channel access by name; for each name key the value is a list of (group index, channel index) tuples

eventslist

list event blocks

file_commentTextBlock

file comment TextBlock

file_historylist

list of (FileHistory, TextBlock) pairs

groupslist

list of data group dicts

headerHeaderBlock

mdf file header

identificationFileIdentificationBlock

mdf file start block

last_call_infodict | None

a dict to hold information about the last called method.

New in version 5.12.0.

masters_dbdict
used for fast master channel access; for each group index key the value

is the master channel index

namestring

mdf file name

versionstr

mdf version

append(signals: list[Signal] | Signal | DataFrame, acq_name: str | None = None, acq_source: Source | None = None, comment: str = 'Python', common_timebase: bool = False, units: dict[str, str | bytes] | None = None) → int | None[source]

Appends a new data group.

For channel dependencies type Signals, the samples attribute must be a numpy.recarray

Parameters:
signalslist | Signal | pandas.DataFrame

list of Signal objects, or a single Signal object, or a pandas DataFrame object. All bytes columns in the pandas DataFrame must be utf-8 encoded

acq_namestr

channel group acquisition name

acq_sourceSource

channel group acquisition source

commentstr

channel group comment; default ‘Python’

common_timebasebool

flag to hint that the signals have the same timebase. Only set this if you know for sure that all appended channels share the same time base

unitsdict

will contain the signal units mapped to the signal names when appending a pandas DataFrame

Examples

>>> # case 1 conversion type None
>>> s1 = np.array([1, 2, 3, 4, 5])
>>> s2 = np.array([-1, -2, -3, -4, -5])
>>> s3 = np.array([0.1, 0.04, 0.09, 0.16, 0.25])
>>> t = np.array([0.001, 0.002, 0.003, 0.004, 0.005])
>>> names = ['Positive', 'Negative', 'Float']
>>> units = ['+', '-', '.f']
>>> info = {}
>>> s1 = Signal(samples=s1, timestamps=t, unit='+', name='Positive')
>>> s2 = Signal(samples=s2, timestamps=t, unit='-', name='Negative')
>>> s3 = Signal(samples=s3, timestamps=t, unit='flts', name='Floats')
>>> mdf = MDF4('new.mdf')
>>> mdf.append([s1, s2, s3], comment='created by asammdf v4.0.0')
>>> # case 2: VTAB conversions from channels inside another file
>>> mdf1 = MDF4('in.mf4')
>>> ch1 = mdf1.get("Channel1_VTAB")
>>> ch2 = mdf1.get("Channel2_VTABR")
>>> sigs = [ch1, ch2]
>>> mdf2 = MDF4('out.mf4')
>>> mdf2.append(sigs, comment='created by asammdf v4.0.0')
>>> mdf2.append(ch1, comment='just a single channel')
>>> df = pd.DataFrame.from_dict({'s1': np.array([1, 2, 3, 4, 5]), 's2': np.array([-1, -2, -3, -4, -5])})
>>> units = {'s1': 'V', 's2': 'A'}
>>> mdf2.append(df, units=units)
attach(data: bytes, file_name: str | None = None, hash_sum: bytes | None = None, comment: str = '', compression: bool = True, mime: str = 'application/octet-stream', embedded: bool = True, password: str | bytes | None = None) → int[source]

attach embedded attachment as application/octet-stream.

Parameters:
databytes

data to be attached

file_namestr

string file name

hash_sumbytes

md5 of the data

commentstr

attachment comment

compressionbool

use compression for embedded attachment data

mimestr

mime type string

embeddedbool

attachment is embedded in the file

passwordstr | bytes | None , default None

password used to encrypt the data using AES256 encryption

New in version 7.0.0.

Returns:
indexint

new attachment index

close() → None[source]

if the MDF was created with memory=False and new channels have been appended, then this must be called just before the object is not used anymore to clean-up the temporary file

extend(index: int, signals: list[tuple[NDArray[Any], NDArray[Any] | None]]) → None[source]

Extend a group with new samples. signals contains (values, invalidation_bits) pairs for each extended signal. The first pair is the master channel’s pair, and the next pairs must respect the same order in which the signals were appended. The samples must have raw or physical values according to the Signals used for the initial append.

Parameters:
indexint

group index

signalslist

list on (numpy.ndarray, numpy.ndarray) objects

Examples

>>> # case 1 conversion type None
>>> s1 = np.array([1, 2, 3, 4, 5])
>>> s2 = np.array([-1, -2, -3, -4, -5])
>>> s3 = np.array([0.1, 0.04, 0.09, 0.16, 0.25])
>>> t = np.array([0.001, 0.002, 0.003, 0.004, 0.005])
>>> names = ['Positive', 'Negative', 'Float']
>>> units = ['+', '-', '.f']
>>> s1 = Signal(samples=s1, timestamps=t, unit='+', name='Positive')
>>> s2 = Signal(samples=s2, timestamps=t, unit='-', name='Negative')
>>> s3 = Signal(samples=s3, timestamps=t, unit='flts', name='Floats')
>>> mdf = MDF4('new.mdf')
>>> mdf.append([s1, s2, s3], comment='created by asammdf v1.1.0')
>>> t = np.array([0.006, 0.007, 0.008, 0.009, 0.010])
>>> # extend without invalidation bits
>>> mdf2.extend(0, [(t, None), (s1, None), (s2, None), (s3, None)])
>>> # some invaldiation btis
>>> s1_inv = np.array([0,0,0,1,1], dtype=np.bool)
>>> mdf2.extend(0, [(t, None), (s1.samples, None), (s2.samples, None), (s3.samples, None)])
extract_attachment(index: int | None = None, password: str | bytes | None = None) → tuple[bytes, Path, bytes][source]

extract attachment data by index. If it is an embedded attachment, then this method creates the new file according to the attachment file name information

Parameters:
indexint

attachment index; default None

passwordstr | bytes | None, default None

password used to encrypt the data using AES256 encryption

New in version 7.0.0.

Returns:
data(bytes, pathlib.Path)

tuple of attachment data and path

get(name: str | None = None, group: int | None = None, index: int | None = None, raster: RasterType | None = None, samples_only: typing_extensions.Literal[False] = False, data: bytes | None = None, raw: bool = False, ignore_invalidation_bits: bool = False, record_offset: int = 0, record_count: int | None = None, skip_channel_validation: bool = False) → Signal[source]
get(name: str | None = None, group: int | None = None, index: int | None = None, raster: RasterType | None = None, samples_only: typing_extensions.Literal[True] = False, data: bytes | None = None, raw: bool = False, ignore_invalidation_bits: bool = False, record_offset: int = 0, record_count: int | None = None, skip_channel_validation: bool = False) → tuple[NDArray[Any], NDArray[Any]]

Gets channel samples. The raw data group samples are not loaded to memory so it is advised to use filter or select instead of performing several get calls.

Channel can be specified in two ways:

  • using the first positional argument name

    • if there are multiple occurrences for this channel then the group and index arguments can be used to select a specific group.

    • if there are multiple occurrences for this channel and either the group or index arguments is None then a warning is issued

  • using the group number (keyword argument group) and the channel number (keyword argument index). Use info method for group and channel numbers

If the raster keyword argument is not None the output is interpolated accordingly

Parameters:
namestring

name of channel

groupint

0-based group index

indexint

0-based channel index

rasterfloat

time raster in seconds

samples_onlybool
if True return only the channel samples as numpy array; if

False return a Signal object

databytes

prevent redundant data read by providing the raw data group samples

rawbool

return channel samples without applying the conversion rule; default False

ignore_invalidation_bitsbool

option to ignore invalidation bits

record_offsetint

if data=None use this to select the record offset from which the group data should be loaded

record_countint

number of records to read; default None and in this case all available records are used

skip_channel_validation (False)bool

skip validation of channel name, group index and channel index; defualt False. If True, the caller has to make sure that the group and index arguments are provided and are correct.

..versionadded:: 7.0.0

Returns:
res(numpy.array, numpy.array) | Signal

returns Signal if samples_only*=*False (default option), otherwise returns a (numpy.array, numpy.array) tuple of samples and invalidation bits. If invalidation bits are not used or if ignore_invalidation_bits if False, then the second item will be None.

The Signal samples are:

  • numpy recarray for channels that have composition/channel array address or for channel of type CANOPENDATE, CANOPENTIME

  • numpy array for all the rest

Raises:
MdfException
  • if the channel name is not found
  • if the group index is out of range
  • if the channel index is out of range

Examples

>>> from asammdf import MDF, Signal
>>> import numpy as np
>>> t = np.arange(5)
>>> s = np.ones(5)
>>> mdf = MDF(version='4.10')
>>> for i in range(4):
...     sigs = [Signal(s*(i*10+j), t, name='Sig') for j in range(1, 4)]
...     mdf.append(sigs)
...
>>> # first group and channel index of the specified channel name
...
>>> mdf.get('Sig')
UserWarning: Multiple occurrences for channel "Sig". Using first occurrence from data group 4. Provide both "group" and "index" arguments to select another data group
<Signal Sig:
        samples=[ 1.  1.  1.  1.  1.]
        timestamps=[0 1 2 3 4]
        unit=""
        info=None
        comment="">
>>> # first channel index in the specified group
...
>>> mdf.get('Sig', 1)
<Signal Sig:
        samples=[ 11.  11.  11.  11.  11.]
        timestamps=[0 1 2 3 4]
        unit=""
        info=None
        comment="">
>>> # channel named Sig from group 1 channel index 2
...
>>> mdf.get('Sig', 1, 2)
<Signal Sig:
        samples=[ 12.  12.  12.  12.  12.]
        timestamps=[0 1 2 3 4]
        unit=""
        info=None
        comment="">
>>> # channel index 1 or group 2
...
>>> mdf.get(None, 2, 1)
<Signal Sig:
        samples=[ 21.  21.  21.  21.  21.]
        timestamps=[0 1 2 3 4]
        unit=""
        info=None
        comment="">
>>> mdf.get(group=2, index=1)
<Signal Sig:
        samples=[ 21.  21.  21.  21.  21.]
        timestamps=[0 1 2 3 4]
        unit=""
        info=None
        comment="">
get_bus_signal(bus: BusType, name: str, database: CanMatrix | StrPathType | None = None, ignore_invalidation_bits: bool = False, data: bytes | None = None, raw: bool = False, ignore_value2text_conversion: bool = True) → Signal[source]

get a signal decoded from a raw bus logging. The currently supported buses are CAN and LIN (LDF databases are not supported, they need to be converted to DBC and feed to this function)

New in version 6.0.0.

Parameters:
busstr

“CAN” or “LIN”

namestr

signal name

databasestr

path of external CAN/LIN database file (.dbc or .arxml) or canmatrix.CanMatrix; default None

Changed in version 6.0.0: db and database arguments were merged into this single argument

ignore_invalidation_bitsbool

option to ignore invalidation bits

rawbool

return channel samples without applying the conversion rule; default False

ignore_value2text_conversionbool

return channel samples without values that have a description in .dbc or .arxml file True

Returns:
sigSignal

Signal object with the physical values

get_can_signal(name: str, database: CanMatrix | StrPathType | None = None, ignore_invalidation_bits: bool = False, data: bytes | None = None, raw: bool = False, ignore_value2text_conversion: bool = True) → Signal[source]

get CAN message signal. You can specify an external CAN database ( database argument) or canmatrix database object that has already been loaded from a file (db argument).

The signal name can be specified in the following ways

  • CAN<ID>.<MESSAGE_NAME>.<SIGNAL_NAME> - the ID value starts from 1 and must match the ID found in the measurement (the source CAN bus ID) Example: CAN1.Wheels.FL_WheelSpeed

  • CAN<ID>.CAN_DataFrame_<MESSAGE_ID>.<SIGNAL_NAME> - the ID value starts from 1 and the MESSAGE_ID is the decimal message ID as found in the database. Example: CAN1.CAN_DataFrame_218.FL_WheelSpeed

  • <MESSAGE_NAME>.<SIGNAL_NAME> - in this case the first occurrence of the message name and signal are returned (the same message could be found on multiple CAN buses; for example on CAN1 and CAN3) Example: Wheels.FL_WheelSpeed

  • CAN_DataFrame_<MESSAGE_ID>.<SIGNAL_NAME> - in this case the first occurrence of the message name and signal are returned (the same message could be found on multiple CAN buses; for example on CAN1 and CAN3). Example: CAN_DataFrame_218.FL_WheelSpeed

  • <SIGNAL_NAME> - in this case the first occurrence of the signal name is returned (the same signal name could be found in multiple messages and on multiple CAN buses). Example: FL_WheelSpeed

Parameters:
namestr

signal name

databasestr

path of external CAN database file (.dbc or .arxml) or canmatrix.CanMatrix; default None

Changed in version 6.0.0: db and database arguments were merged into this single argument

ignore_invalidation_bitsbool

option to ignore invalidation bits

rawbool

return channel samples without applying the conversion rule; default False

ignore_value2text_conversionbool

return channel samples without values that have a description in .dbc or .arxml file True

Returns:
sigSignal

Signal object with the physical values

get_channel_comment(name: str | None = None, group: int | None = None, index: int | None = None) → str[source]

Gets channel comment.

Channel can be specified in two ways:

  • using the first positional argument name

    • if there are multiple occurrences for this channel then the group and index arguments can be used to select a specific group.

    • if there are multiple occurrences for this channel and either the group or index arguments is None then a warning is issued

  • using the group number (keyword argument group) and the channel number (keyword argument index). Use info method for group and channel numbers

If the raster keyword argument is not None the output is interpolated accordingly.

Parameters:
namestring

name of channel

groupint

0-based group index

indexint

0-based channel index

Returns:
commentstr

found channel comment

get_channel_name(group: int, index: int) → str[source]

Gets channel name.

Parameters:
groupint

0-based group index

indexint

0-based channel index

Returns:
namestr

found channel name

get_channel_unit(name: str | None = None, group: int | None = None, index: int | None = None) → str[source]

Gets channel unit.

Channel can be specified in two ways:

  • using the first positional argument name

    • if there are multiple occurrences for this channel then the group and index arguments can be used to select a specific group.

    • if there are multiple occurrences for this channel and either the group or index arguments is None then a warning is issued

  • using the group number (keyword argument group) and the channel number (keyword argument index). Use info method for group and channel numbers

If the raster keyword argument is not None the output is interpolated accordingly.

Parameters:
namestring

name of channel

groupint

0-based group index

indexint

0-based channel index

Returns:
unitstr

found channel unit

get_invalidation_bits(group_index: int, channel: Channel, fragment: tuple[bytes, int, int, ReadableBufferType | None]) → NDArray[bool_][source]

get invalidation indexes for the channel

Parameters:
group_indexint

group index

channelChannel

channel object

fragment(bytes, int)

(fragment bytes, fragment offset)

Returns:
invalidation_bitsiterable

iterable of valid channel indexes; if all are valid None is returned

get_lin_signal(name: str, database: CanMatrix | StrPathType | None = None, ignore_invalidation_bits: bool = False, data: bytes | None = None, raw: bool = False, ignore_value2text_conversion: bool = True) → Signal[source]

get LIN message signal. You can specify an external LIN database ( database argument) or canmatrix database object that has already been loaded from a file (db argument).

The signal name can be specified in the following ways

  • LIN_Frame_<MESSAGE_ID>.<SIGNAL_NAME> - Example: LIN_Frame_218.FL_WheelSpeed

  • <MESSAGE_NAME>.<SIGNAL_NAME> - Example: Wheels.FL_WheelSpeed

  • <SIGNAL_NAME> - Example: FL_WheelSpeed

New in version 6.0.0.

Parameters:
namestr

signal name

databasestr

path of external LIN database file (.dbc, .arxml or .ldf) or canmatrix.CanMatrix; default None

ignore_invalidation_bitsbool

option to ignore invalidation bits

rawbool

return channel samples without applying the conversion rule; default False

ignore_value2text_conversionbool

return channel samples without values that have a description in .dbc, .arxml or .ldf file True

Returns:
sigSignal

Signal object with the physical values

get_master(index: int, data: bytes | None = None, raster: RasterType | None = None, record_offset: int = 0, record_count: int | None = None, one_piece: bool = False) → NDArray[Any][source]

returns master channel samples for given group

Parameters:
indexint

group index

data(bytes, int, int, bytes|None)

(data block raw bytes, fragment offset, count, invalidation bytes); default None

rasterfloat

raster to be used for interpolation; default None

Deprecated since version 5.13.0.

record_offsetint

if data=None use this to select the record offset from which the group data should be loaded

record_countint

number of records to read; default None and in this case all available records are used

Returns:
tnumpy.array

master channel samples

info() → dict[str, Any][source]

get MDF information as a dict

Examples

>>> mdf = MDF4('test.mdf')
>>> mdf.info()
save(dst: WritableBufferType | StrPathType, overwrite: bool = False, compression: CompressionType = 0, progress=None, add_history_block: bool = True) → Path[source]

Save MDF to dst. If overwrite is True then the destination file is overwritten, otherwise the file name is appended with ‘.<cntr>’, were ‘<cntr>’ is the first counter that produces a new file name (that does not already exist in the filesystem)

Parameters:
dststr

destination file name, Default ‘’

overwritebool

overwrite flag, default False

compressionint

use compressed data blocks, default 0; valid since version 4.10

  • 0 - no compression

  • 1 - deflate (slower, but produces smaller files)

  • 2 - transposition + deflate (slowest, but produces the smallest files)

add_history_blockbool

option to add file historyu block

Returns:
output_filepathlib.Path

path to saved file

property start_time: datetime

getter and setter the measurement start timestamp

Returns:
timestampdatetime.datetime

start timestamp

MDF version 4 blocks

Signal

class asammdf.signal.Signal(samples: ArrayLike | None = None, timestamps: ArrayLike | None = None, unit: str = '', name: str = '', conversion: dict[str, Any] | ChannelConversionType | None = None, comment: str = '', raw: bool = True, master_metadata: tuple[str, SyncType] | None = None, display_names: dict[str, str] | None = None, attachment: tuple[bytes, str | None, str | None] | None = None, source: SourceType | None = None, bit_count: int | None = None, invalidation_bits: ArrayLike | None = None, encoding: str | None = None, group_index: int = -1, channel_index: int = -1, flags: Flags = SignalFlags.no_flags)[source]

The Signal represents a channel described by it’s samples and timestamps. It can perform arithmetic operations against other Signal or numeric types. The operations are computed in respect to the timestamps (time correct). The non-float signals are not interpolated, instead the last value relative to the current timestamp is used. samples, timestamps and name are mandatory arguments.

Parameters:
samplesnumpy.array | list | tuple

signal samples

timestampsnumpy.array | list | tuple

signal timestamps

unitstr

signal unit

namestr

signal name

conversiondict | channel conversion block

dict that contains extra conversion information about the signal , default None

commentstr

signal comment, default ‘’

rawbool

signal samples are raw values, with no physical conversion applied

master_metadatalist

master name and sync type

display_namesdict

display names used by mdf version 3

attachmentbytes, name

channel attachment and name from MDF version 4

sourceSource

source information named tuple

bit_countint

bit count; useful for integer channels

invalidation_bitsnumpy.array | None

channel invalidation bits, default None

encodingstr | None

encoding for string signals; default None

flagsSignal.Flags

flags for user defined attributes and stream sync

Flags

alias of SignalFlags

astype(np_type: dtype[Any] | None | Type[Any] | _SupportsDType[dtype[Any]] | str | Tuple[Any, int] | Tuple[Any, SupportsIndex | Sequence[SupportsIndex]] | List[Any] | _DTypeDict | Tuple[Any, Any]) → Signal[source]

returns new Signal with samples of dtype np_type

Parameters:
np_typenp.dtype

new numpy dtye

Returns:
signalSignal

new Signal with the samples of np_type dtype

copy() → Signal[source]

copy all attributes to a new Signal

cut(start: float | None = None, stop: float | None = None, include_ends: bool = True, integer_interpolation_mode: IntInterpolationModeType | IntegerInterpolation = IntegerInterpolation.REPEAT_PREVIOUS_SAMPLE, float_interpolation_mode: FloatInterpolationModeType | FloatInterpolation = FloatInterpolation.LINEAR_INTERPOLATION) → Signal[source]

Cuts the signal according to the start and stop values, by using the insertion indexes in the signal’s time axis.

Parameters:
startfloat

start timestamp for cutting

stopfloat

stop timestamp for cutting

include_endsbool

include the start and stop timestamps after cutting the signal. If start and stop are found in the original timestamps, then the new samples will be computed using interpolation. Default True

integer_interpolation_modeint

interpolation mode for integer signals; default 0

  • 0 - repeat previous samples

  • 1 - linear interpolation

  • 2 - hybrid interpolation: channels with integer data type (raw values) that have a conversion that outputs float values will use linear interpolation, otherwise the previous sample is used

New in version 6.2.0.

float_interpolation_modeint

interpolation mode for float channels; default 1

  • 0 - repeat previous sample

  • 1 - use linear interpolation

New in version 6.2.0.

Returns:
resultSignal

new Signal cut from the original

Examples

>>> new_sig = old_sig.cut(1.0, 10.5)
>>> new_sig.timestamps[0], new_sig.timestamps[-1]
0.98, 10.48
extend(other: Signal) → Signal[source]

extend signal with samples from another signal

Parameters:
otherSignal
Returns:
signalSignal

new extended Signal

interp(new_timestamps: NDArray[Any], integer_interpolation_mode: IntInterpolationModeType | IntegerInterpolation = IntegerInterpolation.REPEAT_PREVIOUS_SAMPLE, float_interpolation_mode: FloatInterpolationModeType | FloatInterpolation = FloatInterpolation.LINEAR_INTERPOLATION) → Signal[source]

returns a new Signal interpolated using the new_timestamps

Parameters:
new_timestampsnp.array

timestamps used for interpolation

integer_interpolation_modeint

interpolation mode for integer signals; default 0

  • 0 - repeat previous samples

  • 1 - linear interpolation

  • 2 - hybrid interpolation: channels with integer data type (raw values) that have a conversion that outputs float values will use linear interpolation, otherwise the previous sample is used

New in version 6.2.0.

float_interpolation_modeint

interpolation mode for float channels; default 1

  • 0 - repeat previous sample

  • 1 - use linear interpolation

New in version 6.2.0.

Returns:
signalSignal

new interpolated Signal

physical() → Signal[source]

get the physical samples values

Returns:
physSignal

new Signal with physical values

plot(validate: bool = True, index_only: bool = False) → None[source]

plot Signal samples. Pyqtgraph is used if it is available; in this case see the GUI plot documentation to see the available commands

Parameters:
validate (True): bool

apply the invalidation bits

index_only (False)bool

use index based X axis. This can be useful if the master (usually time based) is corrupted with NaN, inf or if it is not strictly increasing

validate(copy: bool = True) → Signal[source]

appply invalidation bits if they are available for this signal

Parameters:
copy (True)bool

return a copy of the result

New in version 5.12.0.