For the transposition template, see Transpose Template

Transpose 2D

class nmrPype.fn.TP.Transpose2D(tp_hyper: bool = True, tp_nohyper: bool = False, tp_auto: bool = True, tp_noauto: bool = False, tp_nohdr: bool = False, tp_noord: bool = False, tp_exch: bool = False, tp_minMax: bool = False, mp_enable: bool = False, mp_proc: int = 0, mp_threads: int = 0)

Data Function object for 2D transposition operations

tp_hyperbool

Transpose in hypercomplex transpose mode

tp_nohyperbool

Suppress hypercomplex mode from occuring

tp_autobool

Automatically determine transposition mode

tp_noautobool

Choose transposition mode in command-line

tp_nohdrbool

Do not update transpose value in header

tp_noordbool

Do not change header FDORDER1 and 2.

tp_exchbool

Exchange header parameters for the two dimensions.

tp_minMaxbool

Update FDMIN and FDMAX.

tp_axisint

Indirect dimension axis to be swapped with direct dimension

mp_enablebool

Enable multiprocessing

mp_procint

Number of processors to utilize for multiprocessing

mp_threadsint

Number of threads to utilize per process

static clArgs(subparser, parent_parser)

Transpose command-line arguments

Adds Transpose parser to the subparser, with its corresponding default args. Called by nmrPype.parse.parser()

Note

Command-line arguments function is only called once for all transpose types

Parameters:

subparser (_SubParsersAction[ArgumentParser]) – Subparser object that will receive function and its arguments

hyperTP(array: ndarray)

Performs a hypercomplex transposition

Parameters:

array (ndarray) – N-dimensional array to swap first two dimensions

Returns:

new_array – Transposed array

Return type:

ndarray

initialize(data: DataFrame)
Initialization follows the following steps:

  • Handle function specific arguments

  • Update any header values before any calculations occur that are independent of the data, such as flags and parameter storage

Parameters:

data (DataFrame) – Target data frame to initialize

process(array: ndarray)

Process is called by function’s run, returns modified array when completed. Likely attached to multiprocessing for speed

Parameters:

array (ndarray) – array to process

Returns:

modified array post-process

Return type:

ndarray

updateHeader(data: DataFrame)

Update the header following the main function’s calculations. Typically this includes header fields that relate to data size.

Parameters:

data (DataFrame) – Target data frame containing header to update

Transpose 3D

class nmrPype.fn.TP.Transpose3D(tp_noord: bool = False, tp_exch: bool = False, tp_minMax: bool = False, mp_enable: bool = False, mp_proc: int = 0, mp_threads: int = 0)

Data Function object for 3D transposition operations

Parameters:

  • tp_noord (bool) – Do not change header FDORDER1 and 2.

  • tp_exch (bool) – Exchange header parameters for the two dimensions.

  • tp_minMax (bool) – Update FDMIN and FDMAX.

  • tp_axis (int) – Indirect dimension axis to be swapped with direct dimension

  • mp_enable (bool) – Enable multiprocessing

  • mp_proc (int) – Number of processors to utilize for multiprocessing

  • mp_threads (int) – Number of threads to utilize per process

TP3D(array: ndarray) → ndarray

Performs a hypercomplex transposition on 3D Data

Parameters:

array (ndarray) – N-dimensional array to swap first and third dimensions

Returns:

new_array – Transposed array

Return type:

ndarray

static clArgs(subparser, parent_parser)

Transpose command-line arguments

Adds Transpose parser to the subparser, with its corresponding default args. Called by nmrPype.parse.parser()

Note

Command-line arguments function is only called once for all transpose types

Parameters:

subparser (_SubParsersAction[ArgumentParser]) – Subparser object that will receive function and its arguments

initialize(data: DataFrame)
Initialization follows the following steps:

  • Handle function specific arguments

  • Update any header values before any calculations occur that are independent of the data, such as flags and parameter storage

Parameters:

data (DataFrame) – Target data frame to initialize

process(array: ndarray)

See nmrPype.fn.function.DataFunction.process() for documentation

updateHeader(data: DataFrame)

Update the header following the main function’s calculations. Typically this includes header fields that relate to data size.

Parameters:

data (DataFrame) – Data frame containing header that will be updated

Transpose 4D

class nmrPype.fn.TP.Transpose4D(tp_noord: bool = False, tp_exch: bool = False, tp_minMax: bool = False, mp_enable: bool = False, mp_proc: int = 0, mp_threads: int = 0)

Data Function object for 4D transposition operations

Parameters:

  • tp_noord (bool) – Do not change header FDORDER1 and 2.

  • tp_exch (bool) – Exchange header parameters for the two dimensions.

  • tp_minMax (bool) – Update FDMIN and FDMAX.

  • tp_axis (int) – Indirect dimension axis to be swapped with direct dimension

  • mp_enable (bool) – Enable multiprocessing

  • mp_proc (int) – Number of processors to utilize for multiprocessing

  • mp_threads (int) – Number of threads to utilize per process

TP4D(array: ndarray) → ndarray

Performs a hypercomplex transposition on 4D Data

Parameters:

array (ndarray) – N-dimensional array to swap first and fourth dimension

Returns:

new_array – Transposed array

Return type:

ndarray

static clArgs(subparser, parent_parser)

Transpose command-line arguments

Adds Transpose parser to the subparser, with its corresponding default args. Called by nmrPype.parse.parser()

Note

Command-line arguments function is only called once for all transpose types

Parameters:

subparser (_SubParsersAction[ArgumentParser]) – Subparser object that will receive function and its arguments

initialize(data: DataFrame)
Initialization follows the following steps:

  • Handle function specific arguments

  • Update any header values before any calculations occur that are independent of the data, such as flags and parameter storage

Parameters:

data (DataFrame) – Target data frame to initialize

process(array: ndarray)

See nmrPype.fn.function.DataFunction.process() for documentation

updateHeader(data: DataFrame)

Update the header following the main function’s calculations. Typically this includes header fields that relate to data size.

Parameters:

data (DataFrame) – Target data frame containing header to update