mwings.parsers.app_pal_mot Module

Parsers for packets sent from PAL App (Motion PAL) or CUE App in Motion PAL Mode (accelerometer measurement).

Parsers for packets sent from PAL App (Motion PAL) or CUE App in Motion PAL Mode (accelerometer measurement), and data structures to store the parsed information.

src/mwings/parsers/app_pal_mot.py

`PacketParser`

A static class parser that interprets packets representing data sent from PAL App (Motion PAL) or CUE App (Motion PAL Mode: accelerometer measurement).

Inherits: common.PacketParserBase

Members inherited from the base class are listed.

`is_valid()`

is_valid(bare_packet)

Checks whether the given bare packet is valid.

Arguments

Name	Type	Description
`bare_packet`	`common.BarePacket`	Raw packet data

Returns

Type	Value	Description
`bool`	`True`	Valid, parsable
	`False`	Invalid, not parsable

`parse()`

parse(bare_packet)

Parses the given bare packet.

Arguments

Name	Type	Description
`bare_packet`	`common.BarePacket`	Raw packet data

Returns

Type	Value	Description
`optional ParsedPacket`	`ParsedPacket` instance	Parsed data
	`None`	Parsing failed

`ParsedPacket`

A data structure to store the result of parsing packets sent from PAL App (Motion PAL) or CUE App (Motion PAL Mode: accelerometer measurement).

Inherits: common.ParsedPacketBase

Members inherited from the base class are also listed.

`ParsedPacket()`

ParsedPacket(*, time_parsed=None, packet_type=PacketType.BARE, sequence_number=None, source_serial_id=0, source_logical_id=0, lqi=None, supply_voltage=None, ai1_voltage=0, sample_count=16, samples_x=<mwings.common.TimeSeries object>, samples_y=<mwings.common.TimeSeries object>, samples_z=<mwings.common.TimeSeries object>, sampling_frequency=25)

Arguments

Including properties inherited from the base class.

Name	Type	Description
`time_parsed`	`optional AwareDatetime`	Parsing timestamp
`packet_type`	`common.PacketType`	Packet type
`sequence_number`	`optional common.UInt16`	Sequence number
`source_serial_id`	`common.UInt32`	Source serial ID
`source_logical_id`	`common.UInt8`	Source logical device ID
`lqi`	`optional common.UInt8`	Radio communication quality
`supply_voltage`	`optional common.UInt16`	Supply voltage (mV)
`router_serial_id`	`common.UInt32`	Serial ID of the first relay device (0x80000000 if none) `v1.0.13+`
`ai1_voltage`	`common.UInt16`	Voltage of `AI1` (mV)
`sample_count`	`common.UInt8`	Number of acceleration samples per axis
`samples_x`	`common.TimeSeries[common.Int16]`	Acceleration samples on X axis
`samples_y`	`common.TimeSeries[common.Int16]`	Acceleration samples on Y axis
`samples_z`	`common.TimeSeries[common.Int16]`	Acceleration samples on Z axis
`sampling_frequency`	`common.UInt16`	Acceleration sampling frequency

You can also pass a dictionary unpacked with **.

In addition to constructor arguments, the following @computed_field fields are available.

Name	Type	Description
`mwings_implementation`	`str`	MWings implementation (only `"python"`)
`mwings_version`	`str`	MWings version (in PEP440 format)
`hostname`	`str`	Host machine name
`system_type`	`str`	System type (e.g., `"Linux"`)

Applied field_validator

source_logical_id: Automatically validates the value range
- check_source_logical_id(lid)
time_parsed: Automatically validates that timezone is attached
- datetime_must_be_clear(dt)
sampling_frequency: Validates the value
- check_sampling_frequency(freq)

Applied field_serializer

packet_type: Serializes enum name as is for JSON etc.
- serialize_packet_type(packet_type)
source_serial_id: Serializes as hexadecimal string for JSON etc.
- serialize_source_serial_id(source_serial_id)
time_parsed: Serializes as ISO 8601 string for JSON etc.
- serialize_time_parsed(dt)

`to_df()`

to_df(include=None, exclude=None, verbose=True)

Converts the parsed data into a pandas DataFrame.

Arguments

Name	Type	Description
`include`	`optional set[str]`	Set of columns (members) to include in the DataFrame
`exclude`	`optional set[str]`	Set of columns (members) to exclude from the DataFrame
`verbose`	`bool`	Whether to include system information (only effective if other arguments are `None`)

Returns

Type	Description
`pd.DataFrame`	Converted DataFrame

Exceptions

EnvironmentError if pandas is not installed

`to_dict()`

to_dict(include=None, exclude=None, verbose=True, spread=False, sort_keys=False)

Converts the parsed data into a dictionary.

Arguments

Name	Type	Description
`include`	`optional set[str]`	Set of keys (members) to include in the dictionary
`exclude`	`optional set[str]`	Set of keys (members) to exclude from the dictionary
`verbose`	`bool`	Whether to include system information (only effective if other arguments are `None`)
`spread`	`bool`	Whether to split non-time-series list-like data
`sort_keys`	`bool`	Whether to sort keys

Even if spread is set, common.TimeSeries[T] time series data is not split.

Returns

Type	Description
`dict[str, Any]`	Converted dictionary

`to_json()`

to_json(include=None, exclude=None, verbose=True, spread=False, indent=2, sort_keys=False)

Converts the parsed data into JSON format.

Arguments

Name	Type	Description
`include`	`optional set[str]`	Set of keys (members) to include in the JSON
`exclude`	`optional set[str]`	Set of keys (members) to exclude from the JSON
`verbose`	`bool`	Whether to include system information (only effective if other arguments are `None`)
`spread`	`bool`	Whether to split non-time-series list-like data
`indent`	`optional int`	Number of spaces for indentation (`None` for no newlines)
`sort_keys`	`bool`	Whether to sort keys

Even if spread is set, common.TimeSeries[T] time series data is not split.

Returns

Type	Description
`str`	Converted JSON string

`model_*()`

In addition, you can use various methods of pydantic.BaseModel.