mcsas3.McData1D.McData1D#

class mcsas3.McData1D.McData1D(df: pandas.DataFrame | None = None, loadFromFile: Path | None = None, resultIndex: int = 1, **kwargs: dict)[source]#

Bases: McData

subclass for managing 1D datasets.

__init__(df: pandas.DataFrame | None = None, loadFromFile: Path | None = None, resultIndex: int = 1, **kwargs: dict) → None[source]#: loadFromFile must be a previous optimization. Else, use any of the other ‘from_*’ functions

Methods

`__init__`([df, loadFromFile, resultIndex])	loadFromFile must be a previous optimization.
`clip`()
`from_csv`(filename[, csvargs])	reads from a three-column csv file, takes pandas from_csv arguments
`from_file`([filename])
`from_nexus`([filename])
`from_pandas`(df)	uses a dataframe as input, should contain 'Q', 'I', and 'ISigma'
`from_pdh`(filename)	reads from a PDH file, re-uses Ingo Bressler's code from the notebook example
`is2D`()
`linkMeasData`([measDataLink])
`load`(filename[, path])
`omit`()	This can skip/omit unwanted ranges of data (for example a data range with an unwanted XRD peak in it).
`prepare`()	runs the clipping and binning (in that order), populates clippedData and binnedData
`processKwargs`(**kwargs)
`reBin`([nbins, IEMin, QEMin])	Unweighted rebinning funcionality with extended uncertainty estimation, adapted from the datamerge methods, as implemented in Paulina's notebook of spring 2020
`store`(filename[, path])	stores the settings in an output file (HDF5)

Attributes

`binnedData`
`binning`
`clippedData`
`csvargs`
`dataRange`
`filename`
`loadKeys`
`loader`
`measData`
`measDataLink`
`nbins`
`omitQRanges`
`pathDict`
`qNudge`
`rawData`
`rawData2D`
`resultIndex`
`storeKeys`

csvargs = None#

from_csv(filename: Path, csvargs: dict = {}) → None[source]#: reads from a three-column csv file, takes pandas from_csv arguments

from_pandas(df: pandas.DataFrame) → None[source]#: uses a dataframe as input, should contain ‘Q’, ‘I’, and ‘ISigma’

from_pdh(filename: Path) → None[source]#: reads from a PDH file, re-uses Ingo Bressler’s code from the notebook example

omit() → None[source]#: This can skip/omit unwanted ranges of data (for example a data range with an unwanted XRD peak in it). Requires an “omitQRanges” list of [[qmin, qmax]]-data ranges to omit.

prepare() → None#: runs the clipping and binning (in that order), populates clippedData and binnedData

reBin(nbins: int | None = None, IEMin: float = 0.01, QEMin: float = 0.01) → None[source]#: Unweighted rebinning funcionality with extended uncertainty estimation, adapted from the datamerge methods, as implemented in Paulina’s notebook of spring 2020

store(filename: Path, path: PurePosixPath | None = None) → None#: stores the settings in an output file (HDF5)