Hi everyone,
Here is a short summary of what I heard today for how we should start
with the pnCCD.
For pnCCD algorithms:
common-mode, pedestals, hot-pixels, quadrant rotations, hit-finders,
support in mikhail's calibManager
For pnCCD online displays: (using matplotlib for now)
shot by shot raw data
shot by shot calibrated data
projections of the above
region-of-interest
strip-charts of interesting quantities
(also display calibration values like noise-map,pedestal-map)
After this we will work on the acqiris as well (acqiris
constant-fraction algos already exist in psana).
Attached below is a 12 line python program that plots a real pnCCD
image and an x-projection (amoc0113 also has pnccd data we can look
at). You can run it on a psana node by saving it to pnccd.py and
doing "sit_setup" and then "ipython pnccd.py". This sort of code
should work online too (although we may have to change matplotlib
settings) as well as with calibrated images.
Display group (dan, mikhail, me) meets Thursday at 10:30. Analysis
group (sebastian, ankush(?), phil, mikhail, me) meets Friday at 1.
See you then...
chris
Large area pnCCD DAQ and Elictronics, Lothar Struder & Robert Hartmann
Use interactive psana framework ~cpo/ipsana/shm.py
:
from psana import * events = DataSource('shmem=1_1_XCS.0').events() src = Source('DetInfo(XcsBeamline.1:Tm6740.5)') import matplotlib.pyplot as plt plt.ion() fig = plt.figure('pulnix') ax = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # x0, y0, h, w for i in range(100): evt = events.next() frame = evt.get(Camera.FrameV1, src) ax.cla() ax.imshow(frame.data16()) fig.canvas.draw() |
On 2014-01-27 Sebastian Carron kindly provide us with data files for pnCCD experiment amoa1214:
Example can be found in PSCalib/test/ex_calib_file_finder.cpp:
// Assume that file is located in /reg/d/psdm/AMO/amotut13/calib/PNCCD::CalibV1/Camp.0:pnCCD.1/pedestals/1-end.data #include "PSCalib/PnccdCalibPars.h" const std::string calib_dir = "/reg/d/psdm/AMO/amotut13/calib"; const std::string group = "PNCCD::CalibV1"; // or std::string() const std::string source = "Camp.0:pnCCD.1"; unsigned long runnum = 10; unsigned print_bits = 255; PSCalib::PnccdCalibPars *calibpars = new PSCalib::PnccdCalibPars(calib_dir, group, source, runnum, print_bits); calibpars->printCalibPars(); ndarray<CalibPars::pedestals_t, 3> peds = calibpars -> pedestals_ndarr(); ndarray<CalibPars::common_mode_t, 1> cmod = calibpars -> common_mode_ndarr(); ndarray<CalibPars::pixel_status_t, 3> stat = calibpars -> pixel_status_ndarr(); ndarray<CalibPars::pixel_gain_t, 3> gain = calibpars -> pixel_gain_ndarr(); ndarray<CalibPars::pixel_rms_t, 3> gain = calibpars -> pixel_gain_ndarr(); // OR: CalibPars::pedestals_t* p_peds = calibpars -> pedestals(); CalibPars::common_mode_t* p_cmod = calibpars -> common_mode(); CalibPars::pixel_status_t* p_stat = calibpars -> pixel_status(); CalibPars::pixel_gain_t* p_gain = calibpars -> pixel_gain(); CalibPars::pixel_rms_t* p_rms = calibpars -> pixel_rms(); const size_t ndim = ndim(); const size_t size = size(); const unsigned* shape = shape(); etc... |
973.941639 881.189675 1050.211 773.263749 899.241302 981.805836 1150.72615 993.084175 1121.15488 1029.76319 1220.14927 903.278339 1097.49944 1066.94949 1263.71044 1053.53872 1194.35915 935.320988 1317 ... |
pdscalibdata::PnccdPedestalsV1::pars_t = float
pdscalibdata::PnccdCommonModeV1::pars_t = uint16_t
pdscalibdata::PnccdPixelStatusV1::pars_t = uint16_t
pdscalibdata::PnccdPixelGainV1::pars_t = float
Factory is implemented for pnCCD only. CSPAD and CSPAD2x2 will be added soon. |
#include "PSCalib/CalibPars.h" #include "PSCalib/CalibParsStore.h" // Instatiation //Here we assume that code is working inside psana module where evt and env variables are defined through input parameters of call-back methods. //Code below instateates calibpars object using factory static method PSCalib::CalibParsStore::Create: std::string calib_dir = env.calibDir(); // or "/reg/d/psdm/<INS>/<experiment>/calib" std::string group = std::string(); // or something like "PNCCD::CalibV1"; const std::string source = "Camp.0:pnCCD.1"; const std::string key = ""; // key for raw data Pds::Src src; env.get(source, key, &src); PSCalib::CalibPars* calibpars = PSCalib::CalibParsStore::Create(calib_dir, group, src, PSCalib::getRunNumber(evt)); // Access methods calibpars->printCalibPars(); const PSCalib::CalibPars::pedestals_t* peds_data = calibpars->pedestals(); const PSCalib::CalibPars::pixel_gain_t* gain_data = calibpars->pixel_gain(); const PSCalib::CalibPars::pixel_rms_t* rms_data = calibpars->pixel_rms(); const PSCalib::CalibPars::pixel_status_t* stat_data = calibpars->pixel_status(); const PSCalib::CalibPars::common_mode_t* cmod_data = calibpars->common_mode(); |
In order to get rid of detector dependent types of calibration parameters we need to add metadata in the calibration file. All metadata can be listed in the header of the calibration files, for example, using keyward mapping (dictionary):
# RULES: # Lines starting with # in the beginning of the file are considered as comments or pseudo-comments for metadata # Lines without # with space-separated values are used for input of parameters # Empty lines are ignored # Optional fields: # TITLE: This is a file with pedestals # DATE_TIME: 2014-01-30 10:21:23 # AUTHOR: someone # EXPERIMENT: amotut13 # DETECTOR: Camp.0:pnCCD.1 # CALIB_TYPE: pedestals # Mandatory fields to define the ndarray<TYPE,NDIMS> and its shape as unsigned shape[NDIMS] = {DIM1,DIM2,DIM3} # TYPE: float # NDIMS: 3 # DIM1: 4 # DIM2: 255 # DIM3: 255 973.941639 881.189675 1050.211 773.263749 899.241302 981.805836 1150.72615 993.084175 1121.15488 1029.76319 1220.14927 903.278339 1097.49944 1066.94949 1263.71044 1053.53872 1194.35915 935.320988 1317 ... |
Psana::PNCCD::FramesV1
, ndarray<T,3>, where shape=[4][512][512], T=uint16_t, int, float, double, int16_t
Performance: ~13 ms/event
Psana::PNCCD::FullFrameV1
or ndarray<T,3>
for source
and key
parameters ndarray<T,2>, where shape=[1024+gap][1024], T= input type
Performance: ~30 ms/event (copy involves inverse iteration for 180 degree rotation of two bottom frames)
ImgAlgos.PnccdImageProducer - get Psana::PNCCD::FullFrameV1, put ndarray<uint16_t, 2>
ImgAlgos.NDArrAverage - averages ndarray<T, 2>, save in file
For demonstration only! Just in order to confirm that we produce the same image from different objects. In real case image needs to be produced at the final stage. |
ImgAlgos.PnccdNDArrProducer - get Psana::PNCCD::FramesV1
, put ndarray<T, 3>
ImgAlgos.PnccdImageProducer - get ndarray<T,3>, put ndarray<T, 2>
ImgAlgos.NDArrAverage - averages ndarray<T, 2>, save in file
pnCCD image has intensity "strips" in both dimensions;
[4][512][512] array for single event and averaged over 1000 events:
At large number of events common mode should be averaged out. For 1000 events horizontal intensity "stripes" have gone.
This proves that common mode should be evaluated for horizontal stripes.
Midule description: Module ImgAlgos::NDArrCalib
[ImgAlgos.NDArrCalib] source = DetInfo(Camp.0:pnCCD.0) key_in = pnccd-ndarr key_out = calibrated do_peds = yes do_cmod = no do_stat = no do_mask = no do_bkgd = no do_gain = no do_nrms = no do_thre = yes fname_bkgd = fname_mask = masked_value = 0 threshold_nrms = 0 threshold = 100.0 below_thre_value = 50 bkgd_ind_min = 0 bkgd_ind_max = 1000 bkgd_ind_inc = 2 print_bits = 11 |
Dark run: exp=amoa1214:run=7, all plots are shown for Camp.0:pnCCD.0
event 5
pedestals
and pixel_rms
are generated by the calibman for arrays of shape=[4,512,512] using the same run.
All do_* = no
- that means no correction is applied
do_peds = yes
file with pedestals is loaded automatically from
/reg/d/psdm/AMO/amoa1214/calib/PNCCD::CalibV1/Camp.0:pnCCD.0/pedestals/1-end.data
do_peds = yes do_cmod = yes
files with pedestals and common_mode are loaded automatically from
/reg/d/psdm/AMO/amoa1214/calib/PNCCD::CalibV1/Camp.0:pnCCD.0/
pedestals
/1-end.data
/reg/d/psdm/AMO/amoa1214/calib/PNCCD::CalibV1/Camp.0:pnCCD.0/common_mode/1-end.data
where common mode parameters were set preliminary as:
echo "1 300 50 256 0.2" > /reg/d/psdm/AMO/amoa1214/calib/PNCCD::CalibV1/Camp.0:pnCCD.0/common_mode/1-end.data
Average over each consecutive group of 256 pixels
Common mode subtraction improves the width of intensity distribution.
do_stat = yes
masked_value=0
Calibration type: pixel_status (0-good, 1,2,4,...-bad)
File with pixel status mask was produced in Calibration Manager ROI Mask application
/reg/d/psdm/AMO/amoa1214/calib/PNCCD::CalibV1/Camp.0:pnCCD.0/
pixel_status
/7-7.data
Set bad pixels (1) in the half of frame[1]:
do_mask = yes
fname_mask = pnccd-test-mask.txt
masked_value=0
do_bkgd = yes
fname_bkgd = pnccd-test-mask.txt
For this test the file with pedestals is used:
do_gain = yes
file with gain factors is loaded automatically from
/reg/d/psdm/AMO/amoa1214/calib/PNCCD::CalibV1/Camp.0:pnCCD.0/pixel_gain/7-7.data
Fot this test all gains for [4,512,512] pixels were set to 0.5
do_nrms = yes
below_thre_value = 0
threshold_nrms = 0.5
file with rms values is loaded automatically from
/reg/d/psdm/AMO/amoa1214/calib/PNCCD::CalibV1/Camp.0:pnCCD.0/pixel_rms/1-end.data
do_thre = yes
below_thre_value = 0
threshold = 100
for types
Camp.0.pnCCD.0
Camp.0.pnCCD.1
echo "1 50 10 128 0.2" > /reg/d/psdm/AMO/amob3313/calib/PNCCD::CalibV1/Camp.0:pnCCD.0/common_mode/167-167.data
In dark run processing in Calibration Manager produces pedestals
and pixel_rms
. Then, if thresholds on rms and averaged intensity are set correctly, the pixel_status
can be also produced and deployed under the calib
directory. Calibration manager works with arrays of shape=[4,512,512]. Embedded ROI Mask Editor can be used to generate the ROI mask for pnCCD.
Run Calibration Manager from current release (for release version ≥ ana-0.10.12):
ssh -Y psana cd <your-favorite-NON-RELEASE-directory> sit_setup calibman |
If CalibManager or other packages were recently updated and these updates are wanted to be used:
ssh -Y psana cd <your-favorite-directory> newrel ana-current <release-directory> cd <release-directory> sit_setup addpkg CalibManager HEAD; addpkg <package-name-2> HEAD; addpkg <package-name-3> HEAD; ... scons; calibman |