piel.experimental

piel.experimental#

Submodules#

Attributes#

`configuration_to_measurement_map`
`measurement_composition_method_mapping`

Functions#

`create_experiment_data_collection_from_unique_parameters`(...)	Each individual raw `ExperimentData` can contain multiple operating points or unique parameters which are being
`experiment_data_from_parameter_subset`(...)	Filters the given ExperimentData based on a subset of parameters.
`compose_xarray_dataset_from_experiment_data`(...)	Composes an xarray.Dataset from an ExperimentData instance, using all experiment parameters as coordinates.
`construct_experiment_directories`(→ piel.types.PathTypes)	This function constructs the directories of the experiment configuration. It iterates through the experiment
`construct_experiment_structure`(experiment, ...)	The goal of this function is to construct both the directories and the json file which defines both the experiment
`construct_multimeter_sweep_signal_from_csv`(...)	Construct a multimeter sweep signal from a CSV file.
`construct_sourcemeter_sweep_signal_from_csv`(...)
`construct_multimeter_sweep_signal_from_dataframe`(...)	Construct a multimeter sweep signal from a dataframe.
`construct_sourcemeter_sweep_signal_from_dataframe`(...)
`extract_signal_data_from_csv`(...)	Extract DC sweep data from a CSV file.
`extract_signal_data_from_dataframe`(...)	Extract DC sweep data from a dataframe.
`extract_dc_sweeps_from_operating_point_csv`(...)	Extract DC sweep data from a full operating point CSV file. The operating point CSV file contains the DC sweep data
`extract_dc_sweep_experiment_data_from_csv`(...)	Extract DC sweep data experiment data from a full operating point CSV file. The operating point CSV file contains the DC sweep data
`extract_dc_metrics_from_experiment_data`(experiment_data)
`extract_electro_optic_dc_path_transmission_from_csv`(...)	Converts a CSV file into an ElectroOpticDCPathTransmission instance.
`extract_electro_optic_dc_network_from_measurement_collection`(...)	Converts an ElectroOpticDCMeasurementCollection into an ElectroOpticDCNetworkTransmission instance.
`extract_propagation_delay_data_from_measurement`(...)
`extract_s_parameter_data_from_vna_measurement`(...)
`extract_power_sweep_data_from_vna_measurement`(...)
`extract_power_sweep_s2p_to_network_transmission`(...)	Extracts power sweep data from an S2P file and converts it into a NetworkTransmission object.
`extract_power_sweep_s2p_to_dataframe`(file_path[, ...])	Extracts numerical data from an S2P (Touchstone) file and returns it as a pandas DataFrame.
`convert_row_to_sdict`(row)	Converts a single DataFrame row containing S-parameter data into an SDict.
`extract_data_from_measurement_collection`(...)	The goal of this function is to compose the data from a collection of measurement references.
`extract_data_from_experiment`(...)	This function must be run after data has already been written within the `Experiment` directories
`load_experiment_data_from_directory`(...)	This function will load an Experiment from the metadata stored in the experiment.json directory.
`compose_measurement_from_experiment_instance`(...)	This function is meant to be run after the measurements have been collected and the data exists within the measurement directories.
`compose_measurement_collection_from_experiment`(...)	This function takes a defined experiment and returns a measurement collection from them.
`compose_vna_s_parameter_measurement`(...)	There should only be one .s2p s-parameter file in this directory. If there are more than one, it will read the first one.
`compose_propagation_delay_measurement`(...)	This function will iterate through the instance directory and find the files that correspond to the propagation delay measurement.
`compose_oscilloscope_measurement`(...)	This function composes an OscilloscopeMeasurement from a given directory. The OscilloscopeMeasurement.waveform_file_list
`create_report`()	This functionality is used to create a report of a given directory containing the ExperimentCollection
`create_report_from_experiment_directory`(...[, ...])	First we need to extract the ExperimentData from the directory.
`create_plots_from_experiment_data`(→ list[list[tuple], ...)	This function iterates through all the saved measurement data and generates the corresponding plots
`create_plots_from_experiment_directory`(→ list[tuple])	This function will create the plots from the given experiment directory. It will first extract the ExperimentData
`write_schema_markdown`(schema_json_file, ...)	This function writes the schema markdown file for the experiment configuration. This schema markdown file should
`write_experiment_top_markdown`(experiment, ...[, ...])

Package Contents#

create_experiment_data_collection_from_unique_parameters(experiment_data: piel.types.experimental.ExperimentData) → piel.types.experimental.ExperimentDataCollection[source]#

Each individual raw ExperimentData can contain multiple operating points or unique parameters which are being tested. It can be handy to create subsets of ExperimentData -> multiple ExperimentData``s that correspond to relevant operating points stored with the relevant parameters both internally and in an ``ExperimentDataCollection. As such, it is easier to understand the collection of data measurements based on this and perform plotting accordingly in a more relevant implementation. Likewise, the corresponding operating point metadata is encoded in the generated ExperimentData sets.

First, we will need to extract the operating points from the ExperimentData.experiment.parameters. This can be done by identifying the unique elements from the pandas DataFrame. Then, we will need to create a new ExperimentData for each of the operating points. Finally, we will need to create a new ExperimentDataCollection with the new ExperimentData.

experiment_data_from_parameter_subset(experiment_data: piel.types.ExperimentData, subset: pandas.DataFrame) → piel.types.ExperimentData[source]#

Filters the given ExperimentData based on a subset of parameters.

Parameters:

experiment_data (ExperimentData) – The original experiment data.
subset (pd.DataFrame) – The subset of parameters to filter by.

Returns:

A new ExperimentData instance containing only the filtered data.

Return type:

ExperimentData

compose_xarray_dataset_from_experiment_data(experiment_data: piel.types.ExperimentData)[source]#

Composes an xarray.Dataset from an ExperimentData instance, using all experiment parameters as coordinates.

Parameters:

experiment_data (ExperimentData) – The experiment data containing parameters and measurements.

Returns:

An xarray Dataset containing the measurements indexed by all parameters and metric name.

Return type:

xr.Dataset

Raises:

ValueError – If the number of parameters does not match the number of measurement data entries.
AttributeError – If any measurement data entry lacks the measurements attribute.

construct_experiment_directories(experiment: piel.types.experimental.Experiment, parent_directory: piel.types.PathTypes, construct_directory: bool = True, write_schema_markdown: bool = False) → piel.types.PathTypes[source]#

This function constructs the directories of the experiment configuration. It iterates through the experiment instances. It checks that each of these instances is unique. Each experiment.experiment_instance should have a unique name. This directory name is an enumerated integer. This enumerated integer is the index of the experiment instance in the experiment.experiment_instances tuple. This function should be able to create a new experiment configuration from scratch.

A parent directory is defined in which to create the experiment directories. The experiment directory is created in the parent directory. The experiment directory contains the experiment.json file. The experiment.json file contains the experiment configuration. Note that the experiment.json file should be recursive. The experiment directory also contains the experiment instances. The experiment instances are directories. Each experiment instance directory contains the instance.json file. The instance.json file contains the experiment instance configuration. This is as flat as the directory structure gets.

The instance directory will contain the data files alongside all of this metadata information.: The data files are at the top level of the instance directory, and should not have subdirectories.

The data files are manually generated from the corresponding measurements specified in the instance.json file. These will be added after wards from this directory structure creation.

This schema is used to generate a README file for the experiment configuration if write_markdown_schema is true. This README file should contain all the information about the experiment configuration. This includes the experiment instances and their corresponding configurations.

Parameters:

experiment (Experiment) – The experiment configuration to create the directories for.
parent_directory (PathTypes) – The parent directory to create the experiment directory in.
write_markdown_schema (bool) – Writes the json schema into markdown if true. Defaults to false.

Returns:

The path to the experiment directory.

Return type:

PathTypes

construct_experiment_structure(experiment: piel.types.experimental.Experiment, parent_directory: piel.types.PathTypes)[source]#: The goal of this function is to construct both the directories and the json file which defines both the experiment and all the experiment instances. It should be able to create a new experiment configuration from scratch.

construct_multimeter_sweep_signal_from_csv(file_path: piel.types.PathTypes, signal_name: str, unit: piel.types.Unit = V, **kwargs) → piel.types.SignalDC[source]#

Construct a multimeter sweep signal from a CSV file.

Parameters:

file_path (PathTypes) – The path to the CSV file.
signal_name (str) – The name of the signal.
unit (Unit) – Determines type of signal.
**kwargs

Returns:

SignalDC – The multimeter sweep signal

construct_sourcemeter_sweep_signal_from_csv(file_path: piel.types.PathTypes, voltage_signal_name: str, current_signal_name: str, **kwargs) → piel.types.SignalDC[source]#

construct_multimeter_sweep_signal_from_dataframe(dataframe: pandas.DataFrame, signal_name: str, signal_kwargs: dict = None, **kwargs) → piel.types.SignalDC[source]#

Construct a multimeter sweep signal from a dataframe.

Parameters:

dataframe (pd.DataFrame) – The dataframe containing the multimeter sweep signal data.
signal_name (str) – The name of the signal.
signal_kwargs (dict) – Additional keyword arguments.
**kwargs

Returns:

SignalDC – The multimeter sweep signal

construct_sourcemeter_sweep_signal_from_dataframe(dataframe: pandas.DataFrame, voltage_signal_name: str, current_signal_name: str, signal_kwargs: dict = None, **kwargs) → piel.types.SignalDC[source]#

extract_signal_data_from_csv(file_path: piel.types.PathTypes, input_signal_name_list: list[piel.types.VoltageCurrentSignalNamePair], output_signal_name_list: list[str], power_signal_name_list: list[piel.types.VoltageCurrentSignalNamePair], **kwargs) → piel.types.SignalDCCollection[source]#

Extract DC sweep data from a CSV file.

Parameters:

file_path (PathTypes) – The path to the CSV file.
input_signal_name_list (list[VoltageCurrentSignalNamePair]) – The pairs of sourcemeter voltage and current signal names.
output_signal_name_list (list[str]) – The multimeter signals.
power_signal_name_list (list[VoltageCurrentSignalNamePair]) – The pairs of sourcemeter voltage and current signal names relating to power lines.
**kwargs – Additional keyword arguments.

Returns:

SignalDCCollection – The DC sweep data.

extract_signal_data_from_dataframe(dataframe: pandas.DataFrame, input_signal_name_list: list[piel.types.VoltageCurrentSignalNamePair], output_signal_name_list: list[str], power_signal_name_list: list[piel.types.VoltageCurrentSignalNamePair], **kwargs) → piel.types.SignalDCCollection[source]#

Extract DC sweep data from a dataframe.

Parameters:

dataframe (pd.DataFrame) – The dataframe containing the DC sweep data.
input_signal_name_list (list[VoltageCurrentSignalNamePair]) – The pairs of sourcemeter voltage and current signal names.
output_signal_name_list (list[str]) – The multimeter signals.
power_signal_name_list (list[VoltageCurrentSignalNamePair]) – The pairs of sourcemeter voltage and current signal names.
**kwargs – Additional keyword arguments.

Returns:

SignalDCCollection – The DC sweep data.

extract_dc_sweeps_from_operating_point_csv(file_path: piel.types.PathTypes, input_signal_name_list: list[piel.types.VoltageCurrentSignalNamePair], output_signal_name_list: list[str], power_signal_name_list: list[piel.types.VoltageCurrentSignalNamePair], unique_operating_point_columns: list[str], **kwargs) → piel.types.DCSweepMeasurementDataCollection[source]#

Extract DC sweep data from a full operating point CSV file. The operating point CSV file contains the DC sweep data for multiple operating points. The unique operating point columns are used to extract the unique operating points from the CSV file. The DC sweep data is then extracted for each unique operating point. The DC sweep data is returned as a DCMeasurementDataCollection. The DCMeasurementDataCollection is a list of DCMeasurementDataTypes.

Parameters:

file_path (PathTypes) – The path to the operating point CSV file.
input_signal_name_list (list[VoltageCurrentSignalNamePair]) – The pairs of sourcemeter voltage and current signal names.
output_signal_name_list (list[str]) – The multimeter signals.
power_signal_name_list (list[VoltageCurrentSignalNamePair]) – The pairs of sourcemeter voltage and current signal names relating to power lines.
unique_operating_point_columns (list[str]) – The unique operating point columns.
**kwargs – Additional keyword arguments.

Returns:

The DC sweep data collection.

Return type:

DCMeasurementDataCollection

extract_dc_sweep_experiment_data_from_csv(file_path: piel.types.PathTypes, input_signal_name_list: list[piel.types.VoltageCurrentSignalNamePair], output_signal_name_list: list[str], power_signal_name_list: list[piel.types.VoltageCurrentSignalNamePair], unique_operating_point_columns: list[str], **kwargs) → piel.types.ExperimentData[source]#

Extract DC sweep data experiment data from a full operating point CSV file. The operating point CSV file contains the DC sweep data for multiple operating points. The unique operating point columns are used to extract the unique operating points from the CSV file. The DC sweep data is then extracted for each unique operating point. The DC sweep data is returned as a ExperimentData with the unique_operating_point_columns as part of the parameter_list definition, and the sweep data as part of the collection DCSweepMeasurementDataCollection.

Parameters:

file_path (PathTypes) – The path to the operating point CSV file.
input_signal_name_list (list[VoltageCurrentSignalNamePair]) – The pairs of sourcemeter voltage and current signal names.
output_signal_name_list (list[str]) – The multimeter signals.
power_signal_name_list (list[VoltageCurrentSignalNamePair]) – The pairs of sourcemeter voltage and current signal names of the power lines.
unique_operating_point_columns (list[str]) – The unique operating point columns.
**kwargs – Additional keyword arguments.

Returns:

A collection of experiment and metadata to represent a DC sweep analysis.

Return type:

ExperimentData

extract_dc_metrics_from_experiment_data(experiment_data: piel.types.ExperimentData, parameter_column: str = 'driver_b_v_set', label_column_name='ID', **kwargs)[source]#

extract_electro_optic_dc_path_transmission_from_csv(file: str, port_map: piel.types.ConnectionTypes, dc_voltage_column: str = 'bias_v', dc_current_column: str = 'bias_current', optical_power_column: str = 'pm_out') → piel.types.ElectroOpticDCPathTransmission[source]#

Converts a CSV file into an ElectroOpticDCPathTransmission instance.

This function ensures that the output power (pm_out) aligns with the DC voltage (bias_v) and current (bias_current) data by inserting np.nan where pm_out data is missing.

Parameters: - file (str): Path to the CSV file. - connection (ConnectionTypes): The port mapping information. - dc_voltage_column (str): The name of the DC voltage column in the CSV. - dc_current_column (str): The name of the DC current column in the CSV. - optical_power_column (str): The name of the optical power column in the CSV.

Returns: - ElectroOpticDCPathTransmission: The mapped ElectroOpticDCPathTransmission instance.

extract_electro_optic_dc_network_from_measurement_collection(measurement_collection: piel.types.ElectroOpticDCMeasurementCollection, dc_voltage_column: str = 'bias_v', dc_current_column: str = 'bias_current', optical_power_column: str = 'pm_out') → piel.types.ElectroOpticDCNetworkTransmission[source]#

Converts an ElectroOpticDCMeasurementCollection into an ElectroOpticDCNetworkTransmission instance.

Parameters: - measurement_collection (ElectroOpticDCMeasurementCollection): The collection of measurements. - dc_voltage_column (str): Column name for DC voltage in the CSV files. - dc_current_column (str): Column name for DC current in the CSV files. - optical_power_column (str): Column name for optical power in the CSV files.

Returns: - ElectroOpticDCNetworkTransmission: The compiled network transmission instance.

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

extract_s_parameter_data_from_vna_measurement(measurement: piel.types.experimental.VNASParameterMeasurement, **kwargs) → piel.types.experimental.VNASParameterMeasurementData[source]#

extract_power_sweep_data_from_vna_measurement(measurement: piel.types.VNAPowerSweepMeasurement, **kwargs) → piel.types.VNAPowerSweepMeasurementData[source]#

extract_power_sweep_s2p_to_network_transmission(file_path: piel.types.PathTypes, input_frequency_Hz: float = 0, **kwargs) → piel.types.NetworkTransmission[source]#

Extracts power sweep data from an S2P file and converts it into a NetworkTransmission object.

This function combines the functionalities of extracting data from an S2P file into a pandas DataFrame and then converting that DataFrame into a NetworkTransmission instance. It serves as a convenient single-step process for obtaining structured transmission state data from an S2P file.

Parameters:#

file_pathPathTypes: The path to the S2P file to be processed. Can be a string or a Path-like object.
input_frequency_Hzfloat, optional (default=0): The input frequency in Hertz to be added to the DataFrame before conversion.
**kwargs :: Additional keyword arguments to pass to the extract_power_sweep_s2p_to_dataframe function.

Returns:#

: NetworkTransmission

An instance of NetworkTransmission populated with the extracted and converted data.

Example:#

>>> state = extract_power_sweep_s2p_to_network_transmission('path_to_file.s2p',input_frequency_Hz=1e9)
>>> print(state)
NetworkTransmission(p_in_dbm=[-10.0, -9.9977, ...], s_11_db=[-8.311036, -8.307557, ...], ...)

Notes:#

This function internally calls extract_power_sweep_s2p_to_dataframe and convert_power_sweep_s2p_to_frequency_array_state.
Ensure that the NetworkTransmission class is properly defined and accessible in your environment.

extract_power_sweep_s2p_to_dataframe(file_path: piel.types.PathTypes, input_frequency_Hz: float = 0, **kwargs)[source]#

Extracts numerical data from an S2P (Touchstone) file and returns it as a pandas DataFrame.

This function reads an S2P file, parses its numerical data, and organizes it into a structured pandas DataFrame. It skips comment lines and ensures that each data line contains the expected number of columns. If discrepancies are found, warnings are printed, and those lines are skipped.

Parameters:#

file_pathPathTypes: The path to the S2P file to be processed. Can be a string or a Path-like object.
input_frequency_Hzfloat, optional (default=0): The input frequency in Hertz to be added as a column in the resulting DataFrame.
**kwargs :: Additional keyword arguments to pass to the pandas DataFrame constructor.

Returns:#

: pd.DataFrame

A DataFrame containing the extracted data with the following columns: - p_in_dbm : Input power in dBm. - s_11_db : S-parameter S11 in dB. - s_11_deg : S-parameter S11 in degrees. - s_21_db : S-parameter S21 in dB. - s_21_deg : S-parameter S21 in degrees. - s_12_db : S-parameter S12 in dB. - s_12_deg : S-parameter S12 in degrees. - s_22_db : S-parameter S22 in dB. - s_22_deg : S-parameter S22 in degrees. - input_frequency_Hz : The input frequency provided as a parameter.

Example:#

>>> df = extract_power_sweep_s2p_to_dataframe('path_to_file.s2p', input_frequency_Hz=1e9)
>>> print(df.head())
   p_in_dbm  s_11_db  s_11_deg  s_21_db  s_21_deg  s_12_db  s_12_deg  s_22_db  s_22_deg  input_frequency_Hz
0   -10.0000 -8.311036  90.38824 -11.35558  137.4781 -55.67513   54.62733  -8.564775 -164.7370         1000000000.0
1    -9.9977 -8.307557  90.38396 -11.34543  137.4497 -55.04230   53.47807  -8.555398 -164.7173         1000000000.0
2    -9.9953 -8.309752  90.35067 -11.35137  137.4250 -55.01111    48.11482  -8.562661 -164.6533         1000000000.0
3    -9.9930 -8.310988  90.35760 -11.34326  137.3693 -56.74514    39.34027  -8.559170 -164.7386         1000000000.0

Notes:#

Lines in the S2P file starting with ‘!’ or ‘#’ are treated as comments or headers and are skipped.
Each valid data line is expected to have exactly 9 numerical values corresponding to the defined columns.
If a line does not have 9 values or contains non-numeric data, a warning or error is printed, and the line is skipped.

convert_row_to_sdict(row)[source]#

Converts a single DataFrame row containing S-parameter data into an SDict.

Parameters:#

rowpd.Series: A pandas Series containing S-parameter data with the following indices: - p_in_dbm - s_11_db - s_11_deg - s_21_db - s_21_deg - s_12_db - s_12_deg - s_22_db - s_22_deg

Returns:#

: SDict

A dictionary mapping PortCombination tuples to complex S-parameter arrays.

Example:#

>>> sdict = convert_row_to_sdict(df.iloc[0])
>>> print(sdict)
{
    ('in0', 'in0'): DeviceArray(-0.03295842+0.0313j, dtype=float32),
    ('in0', 'out0'): DeviceArray(-0.03361994+0.0325j, dtype=float32),
    ('out0', 'in0'): DeviceArray(0.03118884+0.0477j, dtype=float32),
    ('out0', 'out0'): DeviceArray(-0.03206138-0.0143j, dtype=float32)
}

extract_data_from_measurement_collection(measurement_collection: piel.types.experimental.MeasurementCollectionTypes, measurement_to_data_map: dict = measurement_to_data_map, measurement_to_data_method_map: dict = measurement_to_data_method_map, skip_missing: bool = False, **kwargs) → piel.types.experimental.MeasurementDataCollectionTypes[source]#: The goal of this function is to compose the data from a collection of measurement references. Based on each type of measurement, it will apply an extraction function based on the data mapping accordingly. It will return a collection of data measurement which is inherent to the type of the measurement collection provided.

extract_data_from_experiment(experiment: piel.types.experimental.Experiment, experiment_directory: piel.types.PathTypes, composition_kwargs: dict = None, extraction_kwargs: dict = None, **kwargs) → piel.types.experimental.ExperimentData[source]#

This function must be run after data has already been written within the Experiment directories and the metadata has been created accordingly. This will extract all the corresponding measurements collection, and also extract the corresponding data from each setup accordingly. It will create a ExperimentData that collects both the metadata and measurement data.

Parameters:

experiment (Experiment) – The experiment object that contains the metadata of the experiment.
experiment_directory (PathTypes) – The directory where the experiment is located.
**kwargs – Extra keyword arguments passed to the class instantiation.

Returns:

ExperimentData – The data extracted from the experiment.

load_experiment_data_from_directory(experiment_directory: piel.types.PathTypes, **kwargs) → piel.types.experimental.ExperimentData[source]#: This function will load an Experiment from the metadata stored in the experiment.json directory.

compose_measurement_from_experiment_instance(experiment_instance: piel.types.experimental.ExperimentInstance, instance_directory: piel.types.PathTypes, configuration_measurement_map: dict = configuration_to_measurement_map, measurement_composition_method_mapping: dict = measurement_composition_method_mapping, composition_methods_kwargs: dict = None, **kwargs) → piel.types.experimental.MeasurementTypes[source]#: This function is meant to be run after the measurements have been collected and the data exists within the measurement directories. Each experiment instance should correspond to a list of measurement configurations, ie the specific set of measurements

that are required at each directory generated for the measurement instance. Hence, for this function to work properly, it is required to have a mapping between experiment configuration measurement and measurement classes accordingly. The mapping will be between a given MeasurementConfiguration type and a Measurement class which has the references of the data containers accordingly.

compose_measurement_collection_from_experiment(experiment: piel.types.experimental.Experiment, experiment_directory: piel.types.PathTypes = None, **kwargs) → piel.types.experimental.MeasurementCollectionTypes[source]#: This function takes a defined experiment and returns a measurement collection from them. Note that the complexity of this is verifying that the experiment is composed of the same type of measurements accordingly. TODO this should be validated in the Experiment composition accordingly.

compose_vna_s_parameter_measurement(instance_directory: piel.types.PathTypes, skip_missing: bool = False, **kwargs) → piel.types.experimental.VNASParameterMeasurement[source]#: There should only be one .s2p s-parameter file in this directory. If there are more than one, it will read the first one. This function will iterate through the instance directory and find the .s2p file. It will return a measurement accordingly.

configuration_to_measurement_map#

measurement_composition_method_mapping#

compose_propagation_delay_measurement(instance_directory: piel.types.PathTypes, dut_file_prefix: str = 'Ch1', reference_file_prefix: str = 'Ch2', measurement_file_prefix: str = '', skip_missing: bool = False, **kwargs) → piel.types.experimental.PropagationDelayMeasurement[source]#: This function will iterate through the instance directory and find the files that correspond to the propagation delay measurement. The files are expected to be in the form of: - {dut_file_prefix}_waveform.csv - {reference_file_prefix}_waveform.csv - {measurement_file_prefix}_measurements.csv

compose_oscilloscope_measurement(instance_directory: piel.types.PathTypes, skip_missing: bool = False, **kwargs) → piel.types.experimental.OscilloscopeMeasurement[source]#: This function composes an OscilloscopeMeasurement from a given directory. The OscilloscopeMeasurement.waveform_file_list will be a collection of files that end with a suffix Ch*.csv. This function will compose the list of files in the order of the channel number. The OscilloscopeMeasurement.measurements_file will be a file that ends without a suffix Ch*.csv.

create_report()[source]#

This functionality is used to create a report of a given directory containing the ExperimentCollection and the Experiments themselves.

This uses the metadata of the Experiment`s and the corresponding `ExperimentData to generate the corresponding images.

create_report_from_experiment_directory(experiment_directory: piel.types.PathTypes, plot_output_directory: piel.types.PathTypes = None, report_readme_path: piel.types.PathTypes = None, load_data_kwargs: dict = None, plot_kwargs: dict = None, **kwargs)[source]#: First we need to extract the ExperimentData from the directory. Then we can generate the report from the ExperimentData.

create_plots_from_experiment_data(experiment_data: piel.types.experimental.ExperimentData, plot_output_directory: piel.types.PathTypes = None, experiment_directory: piel.types.PathTypes = None, parametric: bool = False, erase_plot_output_path: bool = False, **kwargs) → list[list[tuple], piel.types.PathTypes][source]#

This function iterates through all the saved measurement data and generates the corresponding plots for the type of data provided using a method as specified.

Returns a list of (Figures,Axes), and a reference list of paths where the image has been saved.

create_plots_from_experiment_directory(experiment_directory: piel.types.PathTypes, plot_output_directory: piel.types.PathTypes = None, **kwargs) → list[tuple][source]#

This function will create the plots from the given experiment directory. It will first extract the ExperimentData: from the directory and then generate the plots based on that.

write_schema_markdown(schema_json_file: piel.types.PathTypes, target_markdown_file: piel.types.PathTypes)[source]#: This function writes the schema markdown file for the experiment configuration. This schema markdown file should contain all the required information to understand the experiment configuration. This should include all the experiment instances and their corresponding configurations.

write_experiment_top_markdown(experiment: piel.types.experimental.Experiment, experiment_directory: piel.types.PathTypes, target_markdown_file: piel.types.PathTypes = None)[source]#