8.6.3.1. pymodaq_data.post_treatment.process_to_scalar module

Created the 04/11/2022

@author: Sebastien Weber

class pymodaq_data.post_treatment.process_to_scalar.ArgMaxProcessor[source]

Bases: DataProcessorBase

Methods

operate(sub_data)

Extract info from sub-DataWithAxes

operate(sub_data)[source]

Extract info from sub-DataWithAxes

Retrieve the signal axis values of the maximum position of the data

Notes

For more complex processors, such as the argmin, argmax … , one cannot use directly the numpy function (compared to min, max, mean…). Indeed one has to first flatten the data arrays on the signal axes, then apply the function on the flatten dimension, here get the indexes of the minimum along the flattened dimension (as a function of the eventual navigations dimensions). From this index, on then obtain as many indexes as signal dimensions (1 for 1D Signals, 2 for 2D signals). And we do this for as many data there is in sub_data.

apply_to: DataDim = 3

class pymodaq_data.post_treatment.process_to_scalar.ArgMeanProcessor[source]

Bases: DataProcessorBase

Methods

operate(sub_data)

Extract info from sub-DataWithAxes

operate(sub_data)[source]

Extract info from sub-DataWithAxes

Retrieve the signal mean axis values

Notes

For more complex processors, such as the argmin, argmax … , one cannot use directly the numpy function (compared to min, max, mean…). Indeed one has to first flatten the data arrays on the signal axes, then apply the function on the flatten dimension, here get the indexes of the minimum along the flattened dimension (as a function of the eventual navigations dimensions). From this index, on then obtain as many indexes as signal dimensions (1 for 1D Signals, 2 for 2D signals). And we do this for as many data there is in sub_data.

apply_to: DataDim = 1

class pymodaq_data.post_treatment.process_to_scalar.ArgMinProcessor[source]

Bases: DataProcessorBase

Methods

operate(sub_data)

Extract info from sub-DataWithAxes

operate(sub_data)[source]

Extract info from sub-DataWithAxes

Retrieve the signal axis values of the minimum position of the data

Notes

For more complex processors, such as the argmin, argmax … , one cannot use directly the numpy function (compared to min, max, mean…). Indeed one has to first flatten the data arrays on the signal axes, then apply the function on the flatten dimension, here get the indexes of the minimum along the flattened dimension (as a function of the eventual navigations dimensions). From this index, on then obtain as many indexes as signal dimensions (1 for 1D Signals, 2 for 2D signals). And we do this for as many data there is in sub_data.

apply_to: DataDim = 3

class pymodaq_data.post_treatment.process_to_scalar.ArgStdProcessor[source]

Bases: DataProcessorBase

Methods

operate(sub_data)

Extract info from sub-DataWithAxes

operate(sub_data)[source]

Extract info from sub-DataWithAxes

Retrieve the signal mean axis values

Notes

For more complex processors, such as the argmin, argmax … , one cannot use directly the numpy function (compared to min, max, mean…). Indeed one has to first flatten the data arrays on the signal axes, then apply the function on the flatten dimension, here get the indexes of the minimum along the flattened dimension (as a function of the eventual navigations dimensions). From this index, on then obtain as many indexes as signal dimensions (1 for 1D Signals, 2 for 2D signals). And we do this for as many data there is in sub_data.