nxtomomill.converter.hdf5.acquisition.standardacquisition.StandardAcquisition#

class nxtomomill.converter.hdf5.acquisition.standardacquisition.StandardAcquisition(root_url, configuration, detector_sel_callback, start_index, parent=None)[source]#

Bases: BaseAcquisition

Class to collect information from a bliss - hdf scan (see https://bliss.gitlab-pages.esrf.fr/fscan). Once all data is collected a set of NXtomo will be created. Then NXtomo instances will be saved to disk.

Parameters:

root_url (Optional[DataUrl]) – url of the acquisition. Can be None if this is the initialization entry
configuration (TomoHDF5Config) – configuration to use to collect raw data and generate outputs
detector_sel_callback – possible callback to retrieve missing information

__init__(root_url, configuration, detector_sel_callback, start_index, parent=None)[source]#

Methods

`__init__`(root_url, configuration, ...[, parent])
`camera_is_valid`(det_name)
`check_tomo_n`()
`concatenate_pint_quantities`(quantities)	concatenation dedicated to acquisition.
`get_axis_scale_types`()	Return axis display for the detector data to be used by silx view
`get_detector_roi`()
`get_expected_nx_tomo`()	Return the expected number of nxtomo created for this acquisition.
`get_translation_z_frm`(root_node, n_frame, ...)	rtype: `Quantity`
`is_different_sequence`(entry)	Can we have several entries 1.1, 1.2, 1.3.
`is_part_of_same_series`(other)	rtype: `bool`
`parent_root_url`()	rtype: `Optional`[`DataUrl`]
`read_entry`()
`register_step`(url[, entry_type, copy_frames])	type url: `DataUrl`
`to_NXtomos`(request_input, input_callback[, ...])	rtype: `tuple`
`write_as_nxtomo`(shift_entry, ...[, ...])	This function will dump the acquisition to disk as an NXtomo

Attributes

`TITLE_PATHS`
`configuration`
`data_type`
`dim_1`
`dim_2`
`expo_time`
`image_key_control`
`known_machine_current`	Return the dict of all known machine currents.
`lr_flipped`
`n_frames`
`n_frames_actual_bliss_scan`
`raise_error_if_issue`	Should we raise an error if we encounter or an issue or should we just log an error message
`root_url`
`rotation_angle`
`sample_x`	Return the '_sample_x' attribute.
`sample_y`	Return the '_sample_y' attribute.
`start_index`	rtype: `int`
`translation_y`
`translation_z`
`ud_flipped`
`x_flipped`
`y_flipped`

static concatenate_pint_quantities(quantities)[source]#: concatenation dedicated to acquisition. quantities items can be None or a quantity

get_axis_scale_types()#: Return axis display for the detector data to be used by silx view

get_expected_nx_tomo()[source]#: Return the expected number of nxtomo created for this acquisition. This is required to get consistent entry and file name. At lest for automation

is_different_sequence(entry)#: Can we have several entries 1.1, 1.2, 1.3… to consider.

property known_machine_current: dict | None#

Return the dict of all known machine currents. Key is the time stamp, value is the machine current

Return type:: Optional[dict]

property raise_error_if_issue#: Should we raise an error if we encounter or an issue or should we just log an error message

register_step(url, entry_type=None, copy_frames=False)[source]#

Parameters:

url (DataUrl) – entry to be registered and contained in the acquisition
entry_type (Optional[AcquisitionStep]) – type of the entry if know. Overwise will be ‘evaluated’

Return type:

None

property sample_x#: Return the ‘_sample_x’ attribute. In ESRF coordinate

property sample_y#: Return the ‘_sample_y’ attribute. In ESRF coordinate

write_as_nxtomo(shift_entry, input_file_path, request_input, divide_into_sub_files, input_callback=None)#

This function will dump the acquisition to disk as an NXtomo

Parameters:

shift_entry (int) – index of the entry to start saving new nxtomos.
input_file_path (str) – output file path
request_input (bool) – if True the conversion can ask user some missing metadata
divide_into_sub_files (bool) – if True then create one file per NXtomo
input_callback – function to call for users to provide missing metadata

Return type:

tuple