MDF4¶
asammdf tries to emulate the mdf structure using Python builtin data types.
The header attibute is an OrderedDict that holds the file metadata.
The groups attribute is a dictionary list 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_conversions : list of ChannelConversion objects in 1-to-1 relation with the channel list
channel_sources : list of SourceInformation objects in 1-to-1 relation with the channels list
data_block : DataBlock object
texts : dictionay containing TextBlock objects used throughout the mdf
channels : list of dictionaries that contain TextBlock objects ralated to each channel
- name_addr : channel name
- comment_addr : channel comment
channel group : list of dictionaries that contain TextBlock objects ralated to each channel group
- acq_name_addr : channel group acquisition comment
- comment_addr : channel group comment
conversion_tab : list of dictionaries that contain TextBlock objects related to TABX and RTABX channel conversions
- text_{n} : n-th text of the VTABR conversion
- default_addr : default text
conversions : list of dictionaries that containt TextBlock obejcts related to channel conversions
- name_addr : converions name
- unit_addr : channel unit_addr
- comment_addr : converison comment
- formula_addr : formula text; only valid for algebraic conversions
sources : list of dictionaries that containt TextBlock obejcts related to channel sources
- name_addr : source name
- path_addr : source path_addr
- comment_addr : source comment
The file_history attribute is a list of (FileHistory, TextBlock) pairs .
The channel_db attibute is a dictionary that holds the (data group index, channel index) pair for all signals. This is used to speed up the get_signal_by_name method.
The master_db attibute is a dictionary that holds the channel index of the master channel for all data groups. This is used to speed up the get_signal_by_name method.
API¶
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class
asammdf.mdf4.
MDF4
(name=None, load_measured_data=True, version='4.00')¶ If the name exist it will be loaded otherwise an empty file will be created that can be later saved to disk
Parameters: name : string
mdf 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 (‘4.00’, ‘4.10’, ‘4.11’); default ‘4.00’
Attributes
name (string) mdf file name groups (list) list of data groups header (HeaderBlock) mdf file header file_history (list) list of (FileHistory, TextBlock) pairs comment (TextBlock) mdf file comment identification (FileIdentificationBlock) mdf file start block load_measured_data (bool) load measured data option version (str) mdf version channels_db (dict) used for fast channel access by name; for each name key the value is a list of (group index, channel index) tuples masters_db (dict) used for fast master channel access; for each group index key the value is the master channel index Methods
append
attach
close
extract_attachment
get
info
remove
save
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append
(signals, source_info='Python', common_timebase=False)¶ Appends a new data group.
Parameters: signals : list
list on Signal objects
source_info : str
acquisition information; default ‘Python’
common_timebase : bool
flag to hint that the signals have the same timebase
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, timstamps=t, unit='+', name='Positive') >>> s2 = Signal(samples=s2, timstamps=t, unit='-', name='Negative') >>> s3 = Signal(samples=s3, timstamps=t, unit='flts', name='Floats') >>> mdf = MDF4('new.mf4') >>> mdf.append([s1, s2, s3], 'created by asammdf v1.1.0') >>> # 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, 'created by asammdf v1.1.0')
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attach
(data, file_name=None, comment=None, compression=True, mime='application/octet-stream')¶ attach embedded attachment as application/octet-stream
Parameters: data : bytes
data to be attached
file_name : str
string file name
comment : str
attachment comment
compression : bool
use compression for embedded attachment data
mime : str
mime type string
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close
()¶ if the MDF was created with load_measured_data=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
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extract_attachment
(index)¶ extract attachemnt index data. If it is an embedded attachment, then this method creates the new file according to the attachemnt file name information
Parameters: index : int
attachment index
Returns: data : bytes | str
attachment data
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get
(name=None, group=None, index=None, raster=None, samples_only=False)¶ Gets channel samples. Channel can be specified in two ways:
using the first positional argument name
- if there are multiple occurances for this channel then the group and index arguments can be used to select a specific group.
- if there are multiple occurances 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: name : string
name of channel
group : int
0-based group index
index : int
0-based channel index
raster : float
time raster in seconds
samples_only : bool
if True return only the channel samples as numpy array; if False return a Signal object
Returns: res : (numpy.array | Signal)
returns Signal if samples_only*=*False (default option), otherwise returns numpy.array The Signal samples are:
- numpy recarray for channels that have composition/channel array address or for channel of type BYTEARRAY, CANOPENDATE, CANOPENTIME
- numpy array for all the rest
Raises: MdfError :
* if the channel name is not found
* if the group index is out of range
* if the channel index is out of range
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info
()¶ get MDF information as a dict
Examples
>>> mdf = MDF4('test.mdf') >>> mdf.info()
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remove
(group=None, name=None)¶ Remove data group. Use group or name keyword arguments to identify the group’s index. group has priority
Parameters: name : string
name of the channel inside the data group to be removed
group : int
data group index to be removed
Examples
>>> mdf = MDF4('test.mdf') >>> mdf.remove(group=3) >>> mdf.remove(name='VehicleSpeed')
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save
(dst='', overwrite=False, compression=0)¶ Save MDF to dst. If dst is not provided the the destination file name is the MDF name. If overwrite is True then the destination file is overwritten, otherwise the file name is appened with ‘_xx’, were ‘xx’ is the first conter that produces a new file name (that does not already exist in the filesystem)
Parameters: dst : str
destination file name, Default ‘’
overwrite : bool
overwrite flag, default False
compression : int
use compressed data blocks, default 0; only valid since version 4.10
- 0 - no compression
- 1 - deflate (slower, but produces smaller files)
- 2 - transposition + deflate (slowest, but produces the smallest files)