apeck12 / sfx_utils Goto Github PK

First mentioned in PR #27. Provide a histogram of the image values so that the user has more information for choosing the threshold values used during masking.

It might be useful to add an option to set n_images here so that it goes over all images in the run: https://github.com/apeck12/sfx_utils/blob/main/sfx_utils/geom_opt.py#L41

Maybe n_images=-1?

Add information on how to get the ssh key, get started with the jupyter hub and use the right kernel

In addition to information about intensity statistics during a run, it would be helpful to get a visual display of how intensity is distributed in range and in time. This would allow to quickly identify calibration issues, and to identify regime changes during collection that would help for example pinpoint events where the detector could have been at risk of being damaged.

An idea could be to populate an array for each run intensity_distribution(n_events, n_intensity_bins) that could be displayed in the form of a colored table as sketched below.

Considerations on binning:

• The data array could have a finer binning with coarser binning being possible upon plotting (e.g. using plt.hexbin()).
• Several binning strategies could also be explored: linear or symlog.

Random extra thought: a similar plot for the whole experiment (with runs on the X-axis) might also be very useful for experiment summaries.

Add a page in the wiki on what nodes are accessible from outside SLAC (pslogin, psexport), what nodes have access to the data, etc.

(I know you already are working on it Ariana, just thought I'd put this here for my own records)

We'd like to be able to:

• read a given stream file
• do some analysis on the reflections
• select reflections based on this analysis
• write a stream file with only the reflections selected

I believe they are in keV in the assembled image, would be nice to check what they are otherwise - and document it somewhere.

If indeed in keV, it would be nice to document how to convert into "photons" and vice-versa.

Let's discuss here what would be the most convenient way to do this?

To enable this, we need to write a function that performs the reverse of correct_panel here:
https://github.com/cctbx/cctbx_project/blob/master/xfel/util/jungfrau.py
so that the reshaped mask matches the unassembled psana shape.

We'd like to pull out the following statistics per event and plot their trajectory over the course of a given run:

• total intensity
• minimum intensity
• maximum intensity
• median intensity
  By default, we assume that we're examining the calibrated images.