mcsas3.McData.McData#

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

Bases: object

A simple base class for a data carrier object that can load from a range of sources, and do rebinning for too large datasets. This is inherited by the McData1D and McData2D classes intended for actual use.

__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])
`from_file`([filename])
`from_nexus`([filename])
`from_pandas`([df])
`from_pdh`([filename])
`is2D`()
`linkMeasData`([measDataLink])
`load`(filename[, path])
`omit`()
`prepare`()	runs the clipping and binning (in that order), populates clippedData and binnedData
`processKwargs`(**kwargs)
`reBin`()
`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`

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

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