piel.experimental.devices.DPO73304.extract#

Attributes#

logger

Functions#

`extract_measurement_to_dataframe`(→ pandas.DataFrame)	Extracts the measurement files from a csv file and returns it as a pandas dataframe.
`extract_waveform_to_dataframe`(→ pandas.DataFrame)	Extracts the waveform files from a csv file and returns it as a pandas dataframe.
`extract_to_data_time_signal`(→ piel.types.TimeSignalData)	Extracts the waveform files from a csv file and returns it as a DataTimeSignal that can be used to analyse the signal with other methods.
`extract_propagation_delay_data_from_measurement`(...)
`extract_propagation_delay_measurement_sweep_data`(...)	This function is used to extract the relevant measurement files amd relate them to the sweep parameter. Because
`extract_oscilloscope_data_from_measurement`(...)
`extract_oscilloscope_measurement_data_collection`(...)	This function is used to extract the relevant measurement files amd relate them to the sweep parameter. Because
`extract_to_signal_measurement`(...)	Extracts the measurement files from a csv file and returns it as a SignalMeasurement that can be used to analyse the signal.
`combine_channel_data`(→ piel.types.MultiTimeSignalData)	Extracts the waveform files from a list of csv files and returns it as a MultiTimeSignalData that can be used to analyse the signals together.
`parse_column_name`(...)	Parses a column name to extract the analysis type, unit, and channel information.

Module Contents#

logger#

extract_measurement_to_dataframe(file: piel.types.PathTypes) → pandas.DataFrame[source]#

Extracts the measurement files from a csv file and returns it as a pandas dataframe.

Parameters:: file (PathTypes) – The path to the csv file.
Returns:: The measurement files as a pandas dataframe.
Return type:: pd.DataFrame

extract_waveform_to_dataframe(file: piel.types.PathTypes) → pandas.DataFrame[source]#

Extracts the waveform files from a csv file and returns it as a pandas dataframe.

Parameters:: file (PathTypes) – The path to the csv file.
Returns:: The waveform files as a pandas dataframe.
Return type:: pd.DataFrame

extract_to_data_time_signal(file: piel.types.PathTypes) → piel.types.TimeSignalData[source]#

Extracts the waveform files from a csv file and returns it as a DataTimeSignal that can be used to analyse the signal with other methods.

Parameters:: file (PathTypes) – The path to the csv file.
Returns:: The waveform files as a DataTimeSignal.
Return type:: TimeSignalData

extract_propagation_delay_data_from_measurement(propagation_delay_measurement: piel.types.experimental.PropagationDelayMeasurement) → piel.types.experimental.PropagationDelayMeasurementData[source]#

extract_propagation_delay_measurement_sweep_data(propagation_delay_measurement_sweep: piel.types.experimental.PropagationDelayMeasurementCollection) → piel.types.experimental.PropagationDelayMeasurementDataCollection[source]#: This function is used to extract the relevant measurement files amd relate them to the sweep parameter. Because this function extracts multi-index files then we use xarray to analyze this files more clearly. It aims to extract all the files in the sweep file collection.

extract_oscilloscope_data_from_measurement(oscilloscope_measurement: piel.types.experimental.OscilloscopeMeasurement) → piel.types.experimental.OscilloscopeMeasurementData[source]#

extract_oscilloscope_measurement_data_collection(oscilloscope_measurement_data: piel.types.experimental.OscilloscopeMeasurementData) → piel.types.experimental.OscilloscopeMeasurementDataCollection[source]#: This function is used to extract the relevant measurement files amd relate them to the sweep parameter. Because this function extracts multi-index files then we use xarray to analyze this files more clearly. It aims to extract all the files in the sweep file collection.

extract_to_signal_measurement(file: piel.types.PathTypes, **kwargs) → piel.types.ScalarMetricCollection[source]#

Extracts the measurement files from a csv file and returns it as a SignalMeasurement that can be used to analyse the signal.

Parameters:: file (PathTypes)
Returns:: SignalMetricsMeasurementCollection : dict[str, SignalMetricsData]

combine_channel_data(channel_file: list[piel.types.PathTypes]) → piel.types.MultiTimeSignalData[source]#

Extracts the waveform files from a list of csv files and returns it as a MultiTimeSignalData that can be used to analyse the signals together.

Parameters:: channel_file (list[PathTypes]) – The list of paths to the csv files.
Returns:: The waveform files as a MultiTimeSignalData.
Return type:: MultiTimeSignalData

parse_column_name(name: str) → piel.experimental.devices.DPO73304.types.ParsedColumnInfo[source]#

Parses a column name to extract the analysis type, unit, and channel information.

Expected column name format: <analysis_type>_<channels>__<unit>[_<index>]

Examples

‘delay_ch1_ch2__s_1’ -> analysis_type=’delay’, channels=’ch1_ch2’, unit=’seconds’, index=1 ‘pk-pk_ch2__v’ -> analysis_type=’peak_to_peak’, channels=’ch2’, unit=’V’ ‘neg._duty_cyc_ch2__%’ -> analysis_type=’negative_duty_cycle’, channels=’ch2’, unit=’percent’ ‘amplitude_ch2__v’ -> analysis_type=’amplitude’, channels=’ch2’, unit=’V’

Parameters:: name (str) – The column name to parse.
Returns:: An object containing the extracted information.
Return type:: ParsedColumnInfo
Raises:: ValueError – If the column name does not match the expected pattern or contains an unknown analysis type.