You are viewing an old version of this page. View the current version.

Compare with Current View Page History

« Previous Version 32 Next »

Contents

2014-01-22 Meeting minutes

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

 

pnCCD overview

Large area pnCCD DAQ and Elictronics, Lothar Struder & Robert Hartmann

Script form Chris

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()

Walking and talking about unlimited pipeline (processing)

CASS Heritage

Online monitor

Data for tests

On 2014-01-27 Sebastian Carron kindly provide us with data files for pnCCD experiment amoa1214:

  • Dark Run: 169, rear sensors gain 1/64, front 1/1, Imaging mode                                  exp=amoa1214:run=169
  • Run With Hits:  170  Low hit rate though, so you will have to use a hit finder of sorts   exp=amoa1214:run=170

Calibration of pnCCD

New modules for "old-style" calibration:

  • pdscalibdata/include/PnccdBaseV1.h                   - baseclass for pnCCD parameters, defines Segs, Rows, Cols, Size
  • pdscalibdata/include/PnccdPedestalsV1.h            - loads pedestals from file, returns ndarray of pedestals
  • pdscalibdata/include/PnccdCommonModeV1.h     - the same for common mode
  • pdscalibdata/include/PnccdPixelGainV1.h             - the same for pixel gain
  • pdscalibdata/include/PnccdPixelRmsV1.h             - the same for pixel rms
  • pdscalibdata/include/PnccdPixelStatusV1.h          - the same for pixel status
  • PSCalib::PnccdCalibPars                                      - wrapper for all pnCCD types

Detector-dependent interface

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...

Pros

  • Simple format for calibration files - just a text file with pre-defined number of values for each type:
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 ...

Cons

  • Too simple calibration file format, does not allow any metadata or comments.
  • Detector-dependent objects and parameters "knows" about parameters' array type and shape:
    • PSCalib::PnccdCalibPars which depends on PnccdPedestalsV1, PnccdCommonModeV1, ..., PnccdBaseV1
    • pdscalibdata::PnccdPedestalsV1::pars_t      = float
      pdscalibdata::PnccdCommonModeV1::pars_t     = uint16_t 
      pdscalibdata::PnccdPixelStatusV1::pars_t    = uint16_t
      pdscalibdata::PnccdPixelGainV1::pars_t      = float
    • const std::string groupName = "PNCCD::CalibV1";       - do we really need it ?   

Detector-independent interface

  • Interface is declared in the abstract base class PSCalib::CalibPars
  • Access to all detector-dependent classes is hidden in the static factory class PSCalib::CalibParsStore

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();

 

New approach to calibration files with header

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 modules for pnCCD

New module ImgAlgos.PnccdNDArrProducer

  • Get from the event store Psana::PNCCD::FramesV1,
  • Put in the event store   ndarray<T,3>, where shape=[4][512][512], T=uint16_t, int, float, double, int16_t

Performance: ~13 ms/event

Modified module ImgAlgos.PnccdImageProducer

  • Get  from the event store Psana::PNCCD::FullFrameV1 or ndarray<T,3> for source and key parameters
  • Put in the event store   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)

Old sequence of image averaging

          ImgAlgos.PnccdImageProducer - get Psana::PNCCD::FullFrameV1, put  ndarray<uint16_t, 2>
          ImgAlgos.NDArrAverage            - averages ndarray<T, 2>, save in file


 

New sequence of image averaging

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

"Natural order" for common mode correction in pnCCD ndarray

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.

 

Data corrections in module ImgAlgos.NDArrCalib

Midule description: Module ImgAlgos::NDArrCalib 

List of parameters in configuration file

[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

 

 

2014-02-10 Test of the module ImgAlgos.NDArrCalib

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.

Raw data

All do_* = no - that means no correction is applied

Pedestals

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

Common mode

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.


Pixel status

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]:

 

Mask

do_mask = yes

fname_mask = pnccd-test-mask.txt

masked_value=0

Background

do_bkgd = yes

fname_bkgd = pnccd-test-mask.txt

For this test the file with pedestals is used:

 Gain

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


 

N*RMS Threshold

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 

Common Threshold

do_thre = yes

below_thre_value = 0

threshold = 100

Test for amob33314 run 167

Evaluate parameters in Calibration Manager

for types

  • pedestals
  • pixel_status for rms threshold = 15 ADU
  • pixel_rms

Camp.0.pnCCD.0

Camp.0.pnCCD.1

Raw data average and rms

Pedestals subtracted average and rms

Pedestals and common mode subtracted average and rms

echo "1 50 10 128 0.2" > /reg/d/psdm/AMO/amob3313/calib/PNCCD::CalibV1/Camp.0:pnCCD.0/common_mode/167-167.data

 

 Support of pnCCD in Calibration Manager

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.

Get latest version of calibman

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

 

References

  • No labels