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)

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CASS Heritage

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Online monitor

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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

#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

...

Psana modules for pnCCD

Module ImgAlgos.PnccdNDArrProducer

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

...

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

...

Sequence of modules for raw image averaging

          ImgAlgos.PnccdImageProducer - get Psana::PNCCD::FullFrameV1, put  ndarray<uint16ndarray<const uint16_t, 2>
          ImgAlgos.NDArrAverage            - averages ndarray<Tndarray<const T, 2>, save in file

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New sequence of image averaging

Sequence of modules for calibrated image or ndarray averaging

          ImgAlgos.PnccdNDArrProducer - get Psana::PNCCD::FramesV1,     put  ndarray<Tndarray<const T, 3>

          ImgAlgos.NDArrCalib - getinput (raw) ndarray<const T, Ndim>, put  calibrated ndarray<const T, Ndim>
          ImgAlgos.PnccdImageProducer - get ndarray<Tndarray<const T,3>, put  ndarray<Tndarray<const T, 2>
          ImgAlgos.NDArrAverage            - averages ndarray<Tndarray<const T, 2> or ndarray<const T,3>, save in file

"Natural order" for common mode correction in pnCCD ndarray

...

• psana - Module Examples
• Psana Module Catalog - Old
• Calibration management toolManagement Tool
• CSPAD Calibration
• Large area pnCCD DAQ and Elictronics, Lothar Struder & Robert Hartmann