Content

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 ...

pnCCD analysis for users

All other staff looks useful for all users and is migrated to the PSDM page: pnCCD processing pipeline

References

psana - Module Examples
Psana Module Catalog - Old
Calibration Management Tool
Mask Editor
pnCCD processing pipeline - part of this page, which is useful for users
CSPAD Calibration
Large area pnCCD DAQ and Elictronics, Lothar Struder & Robert Hartmann