Sept1 beamtime analysis instructions

Note: This is essentially a copy of the "Feb beamtime instructions" page, but using/testing scripts in beamtime_september_1_2024 git branch.

Anything struck-through has not been tested.

Noise, bad pedestal

To assess noise in keV:

python CalcNoiseAndMean.py -r 91 -e rixx1005922 --special regionCommonMode,slice --label common_testforSept

This calculation needs to be multiplied by 2 to handle the bit shift.

To make a fake pedestal: python CalcNoiseAndMean.py -r 91. If 'CommonMode' is not in special it reverts to raw.

Tested python CalcNoiseAndMean.py -r 91 -e rixx1005922 --label testforSept and it was successful. 

More fakePedestal info at Procedure for cluster-based gain analysis while the calib db is down or pedestals are bad

Standard pedestal

(No evskip 6000 and events 100k unless we're taking 6k + 2k events)

epix10ka_pedestals_calibration -k exp=rixx1003721,run=407 -d epixhr --evskip 6000 --events 100000

 epix10ka_deploy_constants -k exp=rixx1003721,run=407 -d epixhr -D

Check for deployed pedestal:

python nonBasicScript.py rixx1003721 407

This prints out various sorts of information.

Timing scan

sbatch -p milano --nodes 10 --ntasks-per-node 10 --account lcls:rixx1005922 --wrap="mpirun python -u -m mpi4py.run TimeScanParallelSlice.py -r 82"

python MapCompEnOn.py -f $OUTPUT_ROOT/scan/TimeScanParallel__c0_r82_n666_part0.h5 → "OSError: Unable to open file (truncated file: eof = 96, sblock->base_addr = 0, stored_eof = 2048)"

which computes the Onset, the Onset of CompEnOn and the length of a transfer function for each pixel. The first figure displays the map of these 3 parameters  and this map is clickable in order to see each pixel's transfer function in a separate window. Close the figures to stop the program. The picture of the maps is saved here: /sdf/data/lcls/ds/rix/rixx1003721/results/scan/ (.png format)

→ and click on the maps in order to plot the transfer function of pixel selected 

SinglePhotons

To estimate the occupancy, do e.g.

python simplePhotonCounter.py -r 101 -e rixx1005922 --fakePedestal $OUTPUT_ROOT/dark/CalcNoiseAndMean_mean_common_testforSept_r91_step0.npy 

If you have a sensible pedestal, no need to use fakePedestal flag.

To make the h5 files, assuming the normal pedestal works, do
python SimpleClustersParallelSlice.py --special regionCommonMode,FH -r 389

or in parallel : sbatch -p milano --nodes 10 --ntasks-per-node 10 --wrap="mpirun python -u -m mpi4py.run SimpleClustersParallelSlice.py --special regionCommonMode,FH -r 389"

Else do --fakePedestals ... after doing CalcNoiseAndMean
where you'll want FM for FM and AML-M.  Eventually we'll want to have specialized cluster cuts for all modes, but for now check that the seedCut in SimpleClustersParallelSlice.py is about 0.5 photons.
When all the runs are done, you can go to the rix directory and run e.g.
python runAnalyzeH5.py /sdf/data/lcls/ds/rix/rixx1003721/results/lowFlux 384 SimpleClusters 384,385,386,387,388,389
This makes plots and .npy labelled run 384 using the SimpleClusters analysis for the listed runs.
Let me know if this makes sense and works for you.  At the end you can do e.g.
display ../lowFlux/*384*285*62*png
display ../lowFlux/*384*gain*png

LinearityScans

python LinearityPlotsParallelSlice.py -r 84 -e rixx1005922 --label testforSept

python LinearityPlotsParallelSlice.py -r 84 -e rixx1005922 --label testforSept -f $OUTPUT_ROOT/scan/LinearityPlotsParallel_testforSept_c0_r84_n1.h5

Currently configuring some behavior using

self.saturated = [True, False][0]

self.residuals = [True, False][0]

self.profiles = [True, False][0] ## to get unbinned plots, turn this off

self.seabornProfiles = [True, False][1]

To analyze the .npy file and make maps of slopes, fit r**2, ..., and slope ratios:

python analyze_npy.py $OUTPUT_ROOT/scan/LinearityPlotsParallel_r84_sliceFits_testforSept_raw.npy → "TypeError: Invalid shape (3, 192, 13) for image data"

display ../scan/LinearityPlotsParallel_r325_sliceFits_residualTest2_raw_g1slope_g0slope_ratio_map_and_histo.png

Examples of commands that we are running:

Analysis of a TimeScan:

sbatch -p milano --nodes 10 --ntasks-per-node 10 --wrap="mpirun python -u -m mpi4py.run TimeScanParallelSlice.py -r 444 -t 100" OR python TimeScanParallelSlice.py -r 444 --threshold 0 

python MapCompEnOn.py -f ../scan/TimeScanParallel_c0_r444_n1.h5

Pedestals:

epix10ka_pedestals_calibration -k exp=rixx1003721,run=448 -d epixhr --evskip 2000 --events 100000

Deploy the pedestals:

epix10ka_deploy_constants -k exp=rixx1003721,run=448 -d epixhr -D

Check that they are deployed (the mean is):

python nonBasicScript.py rixx1003721 448

(Mean should be around 700 or 800)

For linearity scan:

python LinearityPlotsParallelSlice.py -r 453

python LinearityPlotsParallelSlice.py -r 453 -f ../scan/LinearityPlotsParallel_c0_r453_n1.h5 --label fooBar

python analyze_npy.py ../scan/LinearityPlotsParallel_r454_sliceFits_fooBar_raw.npy

To check the occupancy rate:

python simplePhotonCounter.py -r463 --special slice

To plot stuff vs time:

python EventScanParallel.py -r 457

Single photon:

python runAnalyzeH5.py /sdf/data/lcls/ds/rix/rixx1003721/results/lowFlux 470 SimpleClusters 468,469,470

python AnalyzeH5.py -r 470 -f /sdf/data/lcls/ds/rix/rixx1003721/results/lowFlux/SimpleClusters_c0_r468_n1.h5,/sdf/data/lcls/ds/rix/rixx1003721/results/lowFlux/SimpleClusters_c0_r469_n1.h5,/sdf/data/lcls/ds/rix/rixx1003721/results/lowFlux/SimpleClusters_c0_r470_n1.h5