Azimuthal Averaging

Run dependent parameters:

First up are functions that will return e.g. the region-of-interest boundaries for each run. During the experiment, this should be kept up-to-date so if the setup changes, the littleData file will get an entry with new boundaries for a just finished range of runs. This way, the littleDataRun script will always use the correct region of interest for each run.

Azimuthal Integration

Another option is to reduce the data by azimuthally averaging the signal. Here, we need to know the center for the integration as well as the detector distance and beam energy. The latter two are mostly important for the q-values of the bins that will also be stored in the littleData. As conditions will change during an experiment, it is convenient to have a function that return the correct integration setup for each (range of) run(s).

run dependent integration parameters

def getAzIntParams(run):
    ret_dict = {'eBeam': 8.015}
    ret_dict['cspad_center'] = [87697.946016760892, 94865.383526655729]
    ret_dict['cspad_dis_to_sam'] = 110.
    return ret_dict

UserData: azimuthal integration

haveCspad = checkDet(ds.env(), 'cspad')
if haveCspad:
    cspad = DetObject('cspad' ,ds.env(), int(run), name='cspad')
    for iROI,ROI in enumerate(ROIs):
        cspad.addROI('ROI_%d'%iROI, ROI)
 
    cspad.azav_eBeam=azIntParams['eBeam']
    if azIntParams.has_key('cspad_center'):
        cspad.azav_center=azIntParams['cspad_center']
        cspad.azav_dis_to_sam=azIntParams['cspad_dis_to_sam']
        try:
            cspad.addAzAv(phiBins=7)
        except:
            pass
    dets.append(cspad)

Use of SmallDataAna_psana

For the use of the interactive features of SmallDataAna_psana, we recommend to start it in an ipython session as interactive grabbing of user input is currently not implemented via the notebook.

Creating an average image

In order to decide on the proper ROI, fit a team center or make a mask, the first step is always to create an image that you would like to use as base. This is achieved using the following function

SDAna In: anaps.AvImage()

This will by default create an average image of 100 events of an area detector.

The full command with all its options and a examples is here:

AvImage

LDana In [1]: anaps.AvImage?
Type:        instancemethod
String form: <bound method LittleDataAna_psana.AvImage of <LittleDataAna_psana.LittleDataAna_psana object at 0x7fd37fe03590>>
File:        /reg/d/psdm/xpp/xpptut15/res/littleData/xppmodules/scripts/LittleDataAna_psana.py
Definition:  anaps.AvImage(self, detname='None', numEvts=100, thresADU=0.0, thresRms=0.0,useLdat=False, nSkip=0, minIpm=-1.0, common_mode=True)
Docstring:   <no docstring>

LDana In [2]: anaps.AvImage()
detectors in event: 
cs140_rob
opal_1
Select detector to select ROI of?:
cs140_rob
try get detector info to make average image for:  cs140_rob
try to make psana Detector with:  cs140_rob
requested  100  used  100

LDana In [3]: anaps.plotAvImage()
plot AvImg_cs140_rob using the 5/99.5 percentiles as plot min/max: (1603.9, 1950.21)

If you would like to take a quick look at your average image before proceeding, use as seen above:

SDAna In: anaps.plotAvImage()

which will result in a figure like this popping up:

Beam center and radius

SDAna In: anaps.FitCircle()

This will by draw the image and let you either select points by clicking or define a threshold above which points are selected and fitted to a single circle. The azimuthal integration code needs a center point which the circle fitting will return.

Below are an example fit: first the image you created first will pop up. You can then either select points by hand or use a threshold (highest x% of pixels). The chosen pixel location will be shown and you are adjust your threshold until you are satisfied. In the last step, the fit is performed and overlaid on the image. The beam center and radius of the circle are printed. You will have to know about your sample to use the radius to extract the detector - sample distance. The latter will not affect she shape of the azimuthally integrated data (a wrong center will!), but the q-bin values will be wrong.

FitCircle has an optional argument (useMask=False/True) that defaults to False. If set to True, the mask stored in the calib directory will be applied.

LDana In [2]: anaps.FitCircle()
plot AvImg_cspad using the 5/99.5 percentiles as plot min/max: (0.199616, 263.383)
Select Circle Points by Mouse?:
n
Select Circle Points with threshold (y/n):
y
min percentile % of selected points:
99.2
thresP 248.786973755
Happy with this threshold (y/n):
y
x,y:  99652.2541599 87977.9162216  R  18603.6250138

Azimuthal integration

SDAna In: anaPS.anaps.addAzInt?
Type:        instancemethod
String form: <bound method LittleDataAna_psana.addAzInt of <LittleDataAna_psana.LittleDataAna_psana object at 0x7f7731f13f10>>
File:        /reg/data/ana13/xpp/xppo6616/results/littleData/xppmodules/scripts/LittleDataAna_psana.py
Definition:  anaps.addAzInt(self, detname=None, phiBins=1, qBin=0.01,eBeam=9.5, center=None, dis_to_sam=None)

This function will set up the azimuthal integration. It will also correct for the LCLS Xray polarization and the geometric acceptance. The center and distance to sample are needed. Then this code can be applied to any average image by:

LDana In [2]: anaps.AzInt?
Type:        instancemethod
String form: <bound method LittleDataAna_psana.AzInt of <LittleDataAna_psana.LittleDataAna_psana object at 0x7f7731f13f10>>
File:        /reg/data/ana13/xpp/xppo6616/results/littleData/xppmodules/scripts/LittleDataAna_psana.py
Definition:  anaps.AzInt(self, detname=None, use_mask=False)

It returns the data arrays. The q-bins are stored as well in anaps.<detname>.azav_q