History

Marco Cammarata 0ad7587ede fixed bug in removeExt, it did not work with .gz files		2017-03-13 17:42:38 +01:00
..
README.md	never mind ... the instructions were wrong. did not what what I wanted ...	2017-01-06 18:36:56 +01:00
__init__.py	added id9 log file parser	2017-03-09 23:20:57 +01:00
azav.py	added a skip_first and last keyword to limit files being worked on	2017-03-11 11:27:41 +01:00
cell.py	clean up and improved cell submodule, has also an helper for printing reflections on figure	2017-01-20 10:42:44 +01:00
dataReduction.py	code cleanup	2017-02-09 17:17:34 +01:00
example_main_salen.py	updated examples	2017-01-27 15:42:36 +01:00
example_main_tiox.py	updated examples	2017-01-27 15:42:36 +01:00
filters.py	new module with filtering functions	2017-02-09 16:57:37 +01:00
id9.py	added a skip_first and last keyword to limit files being worked on	2017-03-11 11:28:10 +01:00
mask.py	improved masking, now one can add or subtract with GUI; implemented also polygon	2017-03-05 16:40:39 +01:00
peaks.py	added errorbar weighting and filter for nans and inf	2017-01-27 15:40:26 +01:00
utils.py	fixed bug in removeExt, it did not work with .gz files	2017-03-13 17:42:38 +01:00
xray.py	lots of changes, now id9 and pyfai routines have their own wubmodule; worked on storage system (hdf5 or npz based)	2017-01-05 19:22:37 +01:00

README.md

xray

Different utilities to work with time resolved solution/powder scattering data They should be extended to absorption soon

The idea is an hierarchical structure of modules

user → beamline → workhorses

Workhorses

azav.py: uses pyFAI to convert images into 1d curves dataReduction.py: works on the normalization, referencing and averaging.

Both modules uses some common utilities in utils.py including file based storage for persistency and convenience (numpy or hdf5 based); Indeed the data_storage class wraps around dictionaries allowing accessing with .attribute syntax. In utils plotfuncitons are presents

beamline

this step is needed to read the diagnostic information (time delay, monitors, etc). An example for current id9 data collection macros is given (id9.py). Essentially it means creating a dictionary where each key will be added to the main data dictionary (that has keys like q, data, files, etc.)

user

at the user level, a rather simple macro like the one provided as an example (example_main_tiox.py) should be sufficient for most needs.