Data Function¶
- class nmrPype.fn.function.DataFunction(params: dict = {})¶
Data Function is a template class for all types of functions to run on the NMR data. New user functions should copy format laid out by this class.
- Parameters:
params (dict) – Dictionary of parameters associated with the designated function
- static clArgs(subparser, parent_parser)¶
Command-line arguments template
clArgs adds function parser to the subparser, with its corresponding default args called by
nmrPype.parse.parser(). The Destinations are formatted typically by {function}_{argument}, e.g. the zf_pad destination stores the pad argument for the zf function.- 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 to manipulate
- static mpPrint(func_name: str, chunk_num: int, chunk_sizes: tuple[int, int], type: str = 'start')¶
Print out multiprocess information for verbose print
- Parameters:
func_name (str) – Name of function to display on verbose print
chunk_num (int) – Number of chunks being processed
chunk_sizes (tuple[int,int]) – Size of majority chunk [0] and outlier chunk [1]
type (str, optional) – Start print or end print type, by default ‘start’
- static nullDeclare(subparser, parent_parser)¶
Null Function declaration
- Parameters:
subparser (_SubParsersAction[ArgumentParser]) – Subparser object that will receive null function
- parallelize(array: ndarray, verb: tuple[int, int, str] = (0, 16, 'H')) ndarray¶
The General Multiprocessing implementation for function, utilizing cores and threads. Parallelize should be overloaded if array_shape changes in processing or process requires more args.
- Parameters:
array (ndarray) – Target data array to process with function
verb (tuple[int,int,str], optional)
print (Tuple containing elements for verbose) –
Verbosity level
Verbosity Increment
Direct Dimension Label
(0 (by default) –
Verbosity level
Verbosity Increment
Direct Dimension Label
16 –
Verbosity level
Verbosity Increment
Direct Dimension Label
'H') –
Verbosity level
Verbosity Increment
Direct Dimension Label
- Returns:
new_array – Updated array after function operation
- Return type:
ndarray
- process(array: ndarray, verb: tuple[int, int, str] = (0, 16, 'H')) ndarray¶
Process is called by function’s run, returns modified array when completed. Likely attached to multiprocessing for speed
- Parameters:
array (ndarray) – Target data array to process with function
verb (tuple[int,int,str], optional)
print (Tuple containing elements for verbose) –
Verbosity level
Verbosity Increment
Direct Dimension Label
(0 (by default) –
Verbosity level
Verbosity Increment
Direct Dimension Label
16 –
Verbosity level
Verbosity Increment
Direct Dimension Label
'H') –
Verbosity level
Verbosity Increment
Direct Dimension Label
- Returns:
Updated array after function operation
- Return type:
ndarray
- run(data: DataFrame) int¶
- Main body of function code.
Initializes Header
Start Process (process data vector by vector in multiprocess)
Update Header
Return information if necessary
Overload run for function specific operations
- Parameters:
data (DataFrame) – Target data to to run function on
- Returns:
Integer exit code (e.g. 0 success 1 fail)
- Return type:
int
- 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
- static verbPrint(func_name: str, index, size, step, verb: tuple[int, str] = (16, 'H'), keepIndex: bool = False)¶
Print out progress through each array using verbosity
- Parameters:
func_name (str) – Name of function to display on verbose print
index (_type_) – Current array out of total arrays to print
size (_type_) – Total array count to print
step (_type_) – How many elements are in each array
verb (tuple[int,int,str], optional) –
- Tuple containing elements for verbose print, by default (16,’H’)
Verbosity Increment
Direct Dimension Label
keepIndex (bool, optional) – Whether or not to divide index by step size, by default False
Transpose Template¶
- class nmrPype.fn.TP.Transpose(tp_noord: bool = False, tp_exch: bool = False, tp_minMax: bool = True, tp_axis: int = 0, params: dict = {})¶
Template Data Function object for 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
- 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
- static headerArgsTP(parser)¶
Helper function to parse commands related to header adjustment.
- 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
- matrixTP(array, dim1, dim2)¶
- parallelize(array: ndarray) ndarray¶
Blanket transpose parralelize implementation for function, utilizing cores and threads. Function Should be overloaded if array_shape changes in processing or process requires more args.
Note
Multiprocessing and mulithreading transpose is likely slower due to stitching.
- Parameters:
array (ndarray) – Target data array to process with function
- Returns:
new_array – Updated array after function operation
- Return type:
ndarray
- process(array: ndarray) 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