tomoscan.esrf.scan.fluoscan.FluoTomoScanBase#

class tomoscan.esrf.scan.fluoscan.FluoTomoScanBase(scan, dataset_basename, detectors=(), skip_angle_inds=None, dtype=<class 'numpy.float32'>, verbose=False, angles=None, el_lines=<factory>, pixel_size=None, energy=None, detected_folders=<factory>)#

Bases: object

Dataset manipulation class.

__init__(scan, dataset_basename, detectors=(), skip_angle_inds=None, dtype=<class 'numpy.float32'>, verbose=False, angles=None, el_lines=<factory>, pixel_size=None, energy=None, detected_folders=<factory>)#

Methods

`__init__`(scan, dataset_basename[, ...])
`detect_detectors`()
`detect_elements`()
`detect_folders`()	List all folders to process.
`detect_rot_angles`()	Build rotation angles list.
`from_identifier`(identifier)	Return the Dataset from a identifier
`get_identifier`()	return the dataset identifier of the scan.
`get_metadata_from_h5_file`()
`load_data`(det, element[, line_ind])	Main function of class to load data.

Attributes

`angles`
`detectors`
`energy`
`pixel_size`
`rot_angles_deg`	rtype: `ndarray`[`Any`, `dtype`[`TypeVar`(`_ScalarType_co`, bound= `generic`, covariant=True)]]
`rot_angles_rad`	rtype: `ndarray`[`Any`, `dtype`[`TypeVar`(`_ScalarType_co`, bound= `generic`, covariant=True)]]
`skip_angle_inds`
`verbose`
`scan`
`dataset_basename`
`el_lines`
`detected_folders`

detect_folders()#

List all folders to process.

Return type:: list[str]

detect_rot_angles()#

Build rotation angles list.

Return type:: None

dtype#: alias of float32

static from_identifier(identifier)#: Return the Dataset from a identifier

get_identifier()#

return the dataset identifier of the scan. The identifier is insure to be unique for each scan and allow the user to store the scan as a string identifier and to retrieve it later from this single identifier.

Return type:: ScanIdentifier

load_data(det, element, line_ind=0)#

Main function of class to load data.

Return type:: ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]