Event display / xtc file browser

Xtc files contain the raw data streamed from the DAQ online system, therefore they are not indexed and the events don't always line up in the "right" order. Therefore it's not straight-forward to browse (back and fourth) through an xtc file. This tool (XtcEventBrowser) is also not a real browser, but allows a simple-to-run interface to the xtc files. The package name is XtcEventBrowser, the executables (xtcbrowser and xtcscanner) are found in the app subdirectory of this package, and all other code is in the src subdirectory.

xtcbrowser

The xtcbrowser is the command to launch the Event Display for xtc files. The package name is XtcEventBrowser. It is written in python, relying on PyQt4 for graphical user interface. The data processing is done via the pyana framework and visualization provided by matplotlib.

Note! This tool is under development... features are being added and new versions available often. This documentation might be slightly outdated, but if you use the tagged version (VXX-XX-XX) mentioned here it should work as advertised.

How to get started

This tool is not in a release yet, so to run, you need to set up an offline release in your directory (See the Account Setup section to set up the analysis environment):

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xtcexplorer (GUI interface to pyana)

The latest tag is V00-01-31.

This is a description and documentation of a GUI tool to explore the XTC data (see Analysis Workbook. Data Formats). The executable is named xtcexplorer, and the software package is named XtcExplorer. The GUI is written in Python/PyQt, while the data is extracted using the LCLS framework pyana (we're currently in process of switching to using psana for this). Visualization of data is obtaned with the plotting package matplotlib, while much of the data algorithms in python relies on NumPy / SciPy libraries.

XTC files contain the raw data (datagrams) streamed from the DAQ online system. The datagrams occur in the order they were flushed from the DAQ system, and thus do not necessarily appear in chronological order. The datagrams are not indexed, so the file must be read sequentially. The pyana and psana frameworks are designed to process these datagrams and present them to you shot by shot (or event by event, in pyana/psana terminology).

For a similar tool to explore the HDF5 data format, look at HDF5 Explorer - Old.

How to get started

1. Set up your release work directory:
   You should always run this code from your own release working directory.
   See Account Setupfor more details on how to set up the analysis environment:

   Code Block
   none none
   [user@pslogin ~] newrel ana-current myrelease [

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1. user@pslogin ~] cd myrelease [

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1. user@pslogin

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1. myrelease] sit_setup

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1. Check out the

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[user@psana0XXX myrelease] addpkg XtcEventBrowser V00-00-18
[user@psana0XXX myrelease] scons

Note! You can omit the "tag" (VXX-XX-XX) to get the latest version in the svn repository, but this may look different from described here.

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1. XtcExplorer package from the SVN software repository:
   This step is optional. But without it, you will not be able to edit/customize any source files.
   You need an afs token to check out the package, which on pslogin is automatic (I think), but if you're on psananeh/psanafeh, issue a kinitcommand first:

   Code Block
   none none
   [user@pslogin myrelease] kinit Password for user@SLAC.STANFORD.EDU:

   Now you're ready to check out the package and "compile":

   Code Block
   none none
   [user@pslogin myrelease] addpkg XtcExplorer [user@pslogin myrelease] scons

   Note! You can also do 'addpkg XtcExplorer VXX-XX-XX' to get a specific version of the package, or 'addpkg XtcExplorer HEAD' to get the very latest version.

2. Run the program:
   Launch the GUI with the command 'xtcexplorer' and optionaly give the input xtc files that you want to read
   as arguments. You can also browse to find files after launching the browser.
   

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1. 
   To have access to the xtc files, you need to login to psananeh/psanafehfor this:

   Code Block
   [user@psanaXXX myrelease] sit_setup [user@psanaXXX myrelease]

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1. xtcexplorer /reg/d/psdm/

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1. CXI/

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1. cxi22010/xtc/

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1. e60-r0465-

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1. s0*

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Description of the GUIs

LCLS Xtc Event Browser

xtcbrowser will launch a GUI, the main browser. It allows you to browse for files, and to run a scan to see what's in the file. (Perhaps "scan" is not a good choice of word... it parses the xtc file and investigate what kind of data is there.)

• File section: Shows a list of currently selected file(s). As you may have guessed, "File Browser" opens a file browser and "Clear File List" clears the current list of files. This section also allows you to add a file name by hand (or paste).
• Scan section: The two buttons to the left allows you to scan the xtc file to get a summary of what datagrams are stored in it. Note, for most purposes, a "Quick Scan" is sufficient. If you need to scan the whole file, e.g. if you want to know the total number of events, number of calibration cycles, etc, you can enable the "Scan File(s)" button. If the files are big, this will take a lot of time...

Pyana Control Center

Description of the GUIs

The following documentation describes XtcExplorer V00-01-31. For earlier versions, see earlier versions of this confluence page (go to "Tools" -> "Page history").

LCLS XTC Explorer

Typing xtcexplorer will launch the main GUI, which looks something like this:

Image Added

You need to connect an XTC file in order to get any further. You have 4 options:

1. You can give file names as arguments on the command line, and the file will automatically be opened/connected< e.g. xtcexplorer /reg/d/psdm/CXI/cxi22010/xtc/e60-r0465-s0*
2. If you know the Instrument / Experiment number / run number you'd like to investigate, simply select from the pull-down menus.
3. There's also a 'File Browser' that allows you to browse for the files you want. Be aware that many runs are split into several files, in chunks and streams, and to get a complete dataset you do need to use all of the files for a given run. It will probably 'work' for single files too, but we cannot guarantee completely synchronized date, in particular, EPICS data is stored at a different rate than 'event' (shot) data, so if you make use of EPICS PV data, use the complete dataset.
4. Finally, you have the option of adding file names by hand.

Once a file or more has been connected, another GUI will pop up  (possibly after a short delay while the file(s) content is being investigated). This GUI displays information about the file content and help with further processing of the data in pyana.

Image Added

Pyana Control Center

The new GUI (Pyana Control Center) will show After scanning, a new GUI will pop up showing you a list of detectors/devices found in the file. A little more information is written to the terminal window too.

More information on how to run pyana by itself (see 'pyana -h' for more help, or the pyana section of confluence).

The pyana modules

The GUI as described above prepares a configuration file for you to run pyana. You can either run it from the GUI or you can run it from the command line. The configfile sets input parameters for the pyana modules in this package. Feel free to use one or more of these modules as a starting point for your more elaborate pyana analysis!

XtcExplorer
XtcEventBrowser/src/pyana_bld.py          # display of Beam-line data
XtcEventBrowserXtcExplorer/src/pyana_cspadepics.py        # display of CsPadEpics imagePV data
XtcEventBrowserXtcExplorer/src/pyana_imagescan.py         # display of cameramotor imagescan data
XtcEventBrowserXtcExplorer/src/pyana_ipimbencoder.py        # display of diodeencoder data from IPIMB and PIM
XtcEventBrowser
XtcExplorer/src/pyana_waveformipimb.py        # display of waveformdiode data (notfrom fullyIPIMB functionaland yet)PIM
XtcEventBrowserXtcExplorer/src/pyana_epicswaveform.py        # display of Epicswaveform PV data (not fully functional yet)
XtcEventBrowser
XtcExplorer/src/pyana_scanimage.py         # display of motorcamera scanimage data
XtcEventBrowserXtcExplorer/src/pyana_plotter.py      # a plotter module to control the event display

A few things to note about the different detectors / pyana modules:

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Additional helper classes and functions are defined in

XtcExplorer/src/utilities.py          # PyanaOptions                 

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             # BaseData and classes derived thereof      - store numpy arrays for ipython
         

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pyana -m XtcEventBrowser/src/pyana_cspad.py <xtc files>

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Frame                                     - container for a single "plot"
                                      # Plotter                                   - container for a single "canvas"

XtcExplorer/src/cspad.py              # CsPad                                     - for arranging & aligning the CsPad elements

These are imported into the pyana modules wherever needed. You might want to keep this in mind when
using examples from this package. The pyana modules can mostly be run on their own, but they might
need to import this module.

...

A few things to note about the different detectors / pyana modules:

Common configuration parameters

A few parameters are common to all modules, and their default values are set to be the same, but can also be set individually for each module. They are the following:

# parameter name   default value   comment
  plot_every_n      = 100         # If 0, plot only at the end. Else, display every N events
  accumulate_n      = 0           # If 0, accumulate all (no reset). Else, reset stored arrays every N events
  fignum            = 1           # matplotlib figure number

pyana_plotter

This module is added to the end of the job, after other modules. It controls the display mode and ipython.

• The default display mode is SlideShow mode, where the plot window stays open but updates every n events. In Interactive mode, the figure window must be closed before the next event gets processed. The other modules are set up to work with this one, but should with minor modifications also run fine without it. (The minor modifications would be to issue a "plt.ion()" before the figures are created or replace "plt.draw()" with "plt.show()" to make sure the plot windows stay open.)

• There's also a handle to allow you to use ipython at the end of the job (or after each plot) to investigate your plottet arrays further.

  Code Block
  none none
  display_mode = 2 # Interactive (1) or SlideShow (2) or NoDisplay (0) ipython = False # Drop into ipython after each plot (to allow further investigation of data)

pyana_bld

This module collects data from the BeamLine Detectors (Bld) and plots them in separate windows.

 do_ebeam           = False       #  Plot data from EBeam object
 do_gasdetector     = False       #  Plot data from GasDetector
 do_phasecavity     = False       #  Plot data from PhaseCavity
 do_ipimb           = False       #  Plot data from SharedIpimb  (No longer used, see pyana_ipimb)

Image Added Image Added Image Added Image Added

Example of how to run this pyana module by itself, i.e. no GUI, processing 300 shots and using an xtcexplorer-generated config file:

pyana -n 300 -c xb_pyana_5299.cfg -m XtcExplorer.pyana_bld -m XtcExplorer.pyana_plotter <xtc files>

pyana_waveform

Plots waveform data from Acqiris digitizers. Takes a list of addresses, currently will make one plot for each channel of each device. Plan to add more options.

Configurable options:

  sources           =             # string, space-separated list of addresses (Detector-Id|Device-ID)
  quantities        =             # string containing quantities to plot. "average stack single"

Image Added

Example how to run this pyana module by itself, i.e. no GUI, processing 300 events and using an xtcexplorer-generated config file:

pyana -n 300 -c xb_pyana_5299.cfg -m XtcExplorer.pyana_waveform -m XtcExplorer.pyana_ploter <xtc files>

Or if you add "plt.ion()" to the constructor for SlideShow mode OR "plt.show()" to make_plot functions for interactive plots:

pyana -n 300 -c xb_pyana_5299.cfg -m XtcExplorer.pyana_waveform <xtc files>

pyana_image

This module deals with all camera images, including CsPad images.

• Load dark image from a file for background subtraction
• Filter events: the 'threshold' parameter holds settings for filtering events based on image values. Specify keywords=value separated by whitespaces, e.g.
  • 'lower=400.0 upper=1600.0 type=maximum roi=(200:300,100:400)' which will filter out events that have the maximum pixel in the region of interest below 400 or above 1600.
  • 'lower=500 type=average' will filter out events that have average pixel value in the whole image below 500.
• Apply algorithms to an image (e.g. rotation, shift, etc)
• Display and/or save the image, a background subtacted image, an average image, an image with algorithms applied... etc.
• Save the average image(s) or selected single-shot images (up to a maximum) to file
• In Interactive mode (see pyana_plotter), some interaction with the plot is possible:
  • change intensity range (color range) by left-clicking to set low limit, right-clicking to set high limit, middle-click to reset to full scale.
  • change threshold value and region of interest if a ROI was defined: Click on the rectangle, enter new values when prompted in terminal window
  • switch to SlideShow mode when you are content with the display: Middle-click on the canvas and enter new display mode value (2 for SlideShow).

Options must be specified in a configuration file. Only the sources parameter is required.

  sources      =        # string, Address of Detector-Id|Device-ID
  inputdark    =        # filename of a binary numpy (.npy) with dark image for subtraction
  threshold    =        # Threshold information, specify the keywords that apply: lower=value, upper=value, type=maximum (default) or average, roi=(xmin:xmax,ymin:ymax)'
  algorithms   =        # algorithms to be applied to the image, e.g.: 'rotate:30' to rotate 30 degrees, or shift:(300,200) to shift by (nrows,ncols).
  quantities   =        # quantities to plot or save, e.g. 'image dark average maximum'
  plot_vrange  =        # range (format vmin:vmax) of values for plotting (pixel intensity)
  show_projections =    # bool, include projections onto x,y axes, or not.
  outputfile   =        # base name for output file.
  max_save     = 0      # if larger than zero, will save event images in separate files, up to maximum.
  calib_path   = None   # path to calib directory (Currently only needed for CSPad)

Example of how to run this pyana module by itself, i.e. no GUI, processing 10 events and using an xtcexplorer-generated config file:

pyana -n 10 -c xb_pyana_5299.cfg -m XtcExplorer.pyana_image -m XtcExplorer.pyana_plotter <xtc files>

pyana_ipimb

This module extracts and displays data from IPIMB type detectors, monitoring boards with four diodes each.
The datastream contains two types of information: IpimbData (raw data) and IpmFex (feature extracted data).
What exactly to display is defined in pyana_ipimb.py, but the plotting is done by the DataDisplay object in the displaytools.py library.
This is currently being revised, and has some shortcomings that will be fixed shortly. Currently only one or two diode sources can be displayed at a given time.

sources     # list of IPIMB addresses
quantities  # keywords defining what gets stored. Currently the module stores "everything", so this can be ignored.

Image Added Image Added
Example of how to run this pyana module by itself, i.e. no GUI, processing 300 events and using an xtcexplorer-generated config file:

pyana -n 300 -c xb_pyana_5299.cfg -m XtcExplorer.pyana_ipimb -m XtcExplorer.pyana_plotter <xtc files>

pyana_epics

pv                  # Name(s) of the EPICS PV(s) to dump

Example of how to run this pyana module by itself, i.e. no GUI, processing 200 events and using an xtcexplorer-generated config file:

pyana -n 200 -c xb_pyana_5299.cfg -m XtcExplorer.pyana_epics -m XtcExplorer.pyana_plotter <xtc files>

pyana_scan

This module is different from the others. In runs with several calibration cycles and one (or more) control PV (process variable), this module displays scalar values from other detectors as a function of scan step of the control PV. It currently takes two scalar type inputs to evaluate the scan:

input_epics         # Name(s) of other scalars to correlate in scan
input_scalars       # Name(s) of other scalars to correlate in scan

In addition, the control PV name is added to the parameter list. Currently it is only for display, as the module picks up the active control PV withouth using this parameter.

controlpv = None

Note! The XtcExplorer behaviour is different when you check off the ControlPV checkbox! In this case only the pyana_scan module is activated, and all other selected detectors are added as input to this module.

Image Added Image Added Image Added

Example of how to run this pyana module by itself, i.e. no GUI, processing 200 events and using an xtcexplorer-generated config file:

pyana -n 200 -c xb_pyana_5299.cfg -m XtcExplorer.pyana_scan -m XtcExplorer.pyana_plotter <xtc files>

Helper functions in utilities.py

'utilities.py' is a python module which currently contains several different classes, some related to pyana and others more general, but all developed to help process and plot data with the explorer. This module may soon be split up into several different ones.

Currently it holds the following classes and functions (just a list for now, will add more explanations):

• To facilitate parsing and converting the options of pyana:

  Code Block
  none none
  class PyanaOptions( object ) def __init__( self ): def getOptString(self, options_string) : def getOptStrings(self, options_string) : def getOptStringsDict(self, options_string) : def getOptIntegers(self, options_string): def getOptInteger(self, options_string): def getOptBoolean(self, options_string): def getOptBooleans(self, options_string): def getOptFloats(self, options_string): def getOptFloat(self, options_string):

• Container class to store and plot event data

  Code Block
  none none
  class BaseData( object ): def __init__(self, name,type="BaseData"): def show( self ): def get_plottables_base(self): def get_plottables(self): class BldData( BaseData ): def __init__(self, name, type="BldData"): class IpimbData( BaseData ): def __init__( self, name, type="IpimbData" ): class EncoderData( BaseData ): def __init__( self, name, type="EncoderData" ): class WaveformData( BaseData ): def __init__( self, name, type="WaveformData" ): def get_plottables(self): class EpicsData( BaseData ): def __init__( self, name, type="EpicsData" ): class ScanData( BaseData ) : def __init__(self, name, type="ScanData"): class ImageData( BaseData ): def __init__(self, name, type="ImageData"): def get_plottables(self): class CsPadData( BaseData ): def __init__(self, name, type="CsPadData"):

• Container class to keep track of thresholds. Can be passed between modules and from event to event.

  Code Block
  none none
  class Threshold( object ) :

• Container classes to keep track of matplotlib frames and canvases. This can probably be done with
  matplotlib's own classes, but I couldn't find out to do it easily. For now, I use these ones:

  Code Block
  none none
  class Frame(object): def __init__(self, name="", title=""): def myticks(self, x, pos): def show(self): def update_axes(self): #def set_ticks_new(self, nticks, lotick, hitick, axis="X"): def set_ticks(self, limits = None ): class Plotter(object): def __init__(self): def add_frame(self, name="", title="",contents=None, type=None, aspect='auto'): def plot_all_frames(self, fignum=1, ordered=False): def settings(self , width = 8 # width of a single plot , height = 7 # height of a single plot , nplots=1 # total number of plots in the figure , maxcol=3 # maximum number of columns ): def create_figure(self, fignum, nplots=1): def close_figure(self): def connect(self,plot=None): def onpick(self, event): def onclick(self, event) : def plot_image(self, image, fignum=1, title="", showProj = False, extent=None): def plot_several(self, list_of_arrays, fignum=1, title="" ): def draw_figurelist(self, fignum, event_display_images, title="",showProj=False,extent=None ) : def draw_figure( self, frameimage, title="", fignum=1,position=1, showProj = False,extent=None): def drawframe( self, frameimage, title="", fignum=1,position=1, showProj = False,extent=None):

Interactive plotting with IPython

Since XtcExplorer V00-01-04, you also have the option of launching ipython at the end of the job. The basic support for this has been added to all modules. Each pyana module stores some data (primarily as numpy arrays) which it passes to the pyana_plotter module which launches iPython. In the session you can investigate the data further and make additional plots.

• Do 'whos' to see the workspace/namespace contents.
• Exit the ipython session with Ctrl-D when you are done.
• you can save the workspace using the ipython 'save'

Here are a few screenshot examples:

Full screenshot of all GUIs, plots and ipython session:
Image Added

A couple of close-ups of the ipython terminal:
Image Added Image Added

xtcscanner

This tool also belongs to the XtcExplorer package, and is used by the GUI. But the tool can also be run directly from the command line:

usage: xtcscanner [options] xtc-files ...

options:
  -h, --help            show this help message and exit
  -n NDATAGRAMS, --ndatagrams=NDATAGRAMS
  -v, --verbose
  -l L1_OFFSET, --l1-offset=L1_OFFSET

Example:

Scanning....
Start parsing files:
['/reg/d/psdm/AMO/amo01509/xtc/e8-r0094-s00-c00.xtc', '/reg/d/psdm/AMO/amo01509/xtc/e8-r0094-s01-c00.xtc']
  201 datagrams read in 0.070000 s .   .   .   .   .   .   .
-------------------------------------------------------------
XtcScanner information:
  - 1 calibration cycles.
  - Events per calib cycle:
   [197]

Information from  0  control channels found:
Information from  9  devices found
                      BldInfo:EBeam:             EBeamBld (197)
            BldInfo:FEEGasDetEnergy:             FEEGasDetEnergy (197)
        DetInfo:AmoETof-0|Acqiris-0:  (5 ch)     AcqConfig_V1 (1)   AcqWaveform_V1 (197)
      DetInfo:AmoGasdet-0|Acqiris-0:  (2 ch)     AcqConfig_V1 (1)   AcqWaveform_V1 (197)
        DetInfo:AmoITof-0|Acqiris-0:  (1 ch)     AcqConfig_V1 (1)   AcqWaveform_V1 (197)
        DetInfo:AmoMbes-0|Acqiris-0:  (1 ch)     AcqConfig_V1 (1)   AcqWaveform_V1 (197)
     DetInfo:EpicsArch-0|NoDevice-0:             Epics_V1 (688)
         DetInfo:NoDetector-0|Evr-0:             EvrConfig_V2 (1)
                          ProcInfo::             RunControlConfig_V1 (11)
XtcScanner is done!
-------------------------------------------------------------

Further analysis with pyana

Any serious data analysis will need more customized tools than we can provide in this general GUI interface.

This will require the user / analyst to program his/her own tools. Pyana is a complete framework for programming a user analysis in python.
"Blank" analysis code can be generated with Andy's codegen script (try codegen -h and codegen -p for options). Extensive information about pyana is available on confluence.

Running this package's pyana modules outside of the XtcExplorer

Feel free to use any or all of the XtcExplorer modules as examples or starting point for your analysis. If you have suggestions for features or algorithms that might benefit others too, let me know and perhaps we can add it to the XtcExplorer package.

Example how to run a pyana module by itself, i.e. no GUI, processing 300 events and using an xtcexplorer-generated config file:

pyana -n 300 -c xb_pyana_5299.cfg -m XtcExplorer.pyana_module -m XtcExplorer.pyana_ploter <xtc files>

Or if you add "plt.ion()" to the constructor for SlideShow mode OR "plt.show()" to make_plot functions for interactive plots, you can also do without the pyana_plotter module:

pyana -n 300 -c xb_pyana_5299.cfg -m XtcExplorer.pyana_module <xtc files>

Submitting to batch

Once you've identified the command line to run your pyana job, you can submit this to the LFS batch queue system as described in Computing (including Analysis).

Be aware that the pyana scripts used by the explorer use matplotlib QtAgg backend to display plots, and this will fail if you submit it to batch processing. While I look for a better solution, you can do the following to work around this problem:

• Configure pyana to plot as little as possible by setting the display mode to NoDisplay
• Add the following two lines to XtcExplorer/src/utilities.py :

  import matplotlib
  matplotlib.use('PDF')

A few words on time and performance

The explorer hasn't been optimized for speed properly, but I strive to keep the pyana examples relatively efficient. But keep in mind
that plotting with matplotlib does take a substansial amount of time. An example:

Running the explorer on a small xtc file from a XCS commissioning run, containing 1267 events, and plotting information
from two IPIMB detectors and a YAG camera image.

• Processing all 1267 events and plotting only at the end took 7.26 s
• Processing all 1267 events, plotting (SlideShow mode) at every 100 events took 25.6 s
• Processing all 1267 events, plotting (SlideShow mode) at every 10 events took 197.0 s

So matplotlib drawing these plots take about 1.5 s each time, quite substantial compared to 5.7 microseconds for pyana to process each event. Other types of plots may take longer to plot, e.g. larger images, waveforms, etc.

Image Added

• pyana_image.py processes generic camera frames, e.g from Pulnix TM6740 device. It allows any number of images, given as a space-separated list of addresses in the
  configuration file.
  • You can set ranges to define good images and dark images. If both are set, you have the option to display good images background subtracted, where background subtraction is based on the average of background images so far collected.
  • Each image can be separately rotated, shifted and scaled (zoomed in/out).
  • Nicknames can be given to the input images. Defaults are Im1, Im2... etc. These names will be used if you plot differences, or other manipulations of the original images.
  • The images are subtracted and differences displayed as well as fourier transform of differences. Examples of what may be displayed. To display other things, at this stage you have to edit pyana_image.py to change this behaviour.

    Code Block

    image_addresses # address string of Detector-Id|Device-ID good_range # threshold values selecting images of interest (Format: low--high) dark_range # threshold values selecting dark images (Format: low--high) image_rotations # rotation angle, in degrees, to be applied to image(s) image_shifts # npixel shifts, format (nx,ny), to be applied to image(s) image_scales # scale factor (float) to be applied to images image_nicknames # nicknames for plot titles image_manipulations # String containing keywords: Diff, FFT output_file # filename. Valid extensions are .hdf5, .txt (ascii) or .npy (numpy binary) n_hdf5 # if output file is hdf5, combine n events in each output file.

    Image Removed

    Displays of three different Pulnix TM6740 images of YAG screens, after rotation/translation. Also shown, differences between images and FFT of differences.

• pyana_ipimb

  Code Block
  ipimb_addresses # list of IPIMB addresses

  Image Removed

• pyana_epics

  Code Block
  pv # Name(s) of the EPICS PV(s) to dump

• pyana_bld

  Code Block
  do_ebeam # Plot data from EBeam object do_gasdetector # Plot data from GasDetector do_phasecavity # Plot data from PhaseCavity

  Image Removed Image Removed Beam energy and position. Gas detector energy measurements

xtcscanner

This is a command-line interface to the XtcScanner class that makes a summary of the xtc file.

usage: xtcscanner [options] xtc-files ...

options:
  -h, --help            show this help message and exit
  -n NDATAGRAMS, --ndatagrams=NDATAGRAMS
  -v, --verbose
  -l L1_OFFSET, --l1-offset=L1_OFFSET

Further analysis with pyana

Any serious data analysis will need more customized tools than we can provide in a GUI interface. This will require the user / analyst to program his/her own tools. Pyana is a complete framework for programming a user analysis in python. The Gui Event Browser can provide simple analysis code that can be expanded by the user. "Blank" analysis code can also be generated with Andy's codegen script (try codegen -h and codegen -p for options).

More information about pyana can be found on confluence.

Data visualization with NumPy (arrays) and MatPlotLib (plots).

Saving (and loading) a numpy array (e.g. image) to (from) a file

If you want to save one array (max 2 dimensions), you can use binary numpy file or ascii file:

import numpy as np

# binary file .npy format
np.save("filename.npy", array)
array = np.load("filename.npy")

# txt file
np.savetxt("filename.dat", array)
array = loadtxt("filename.dat")

If you need to save multiple events/shots in the same file you will need to do some tricks (e.g. flatten the array and stack 1d arrays into 2d arrays where axis2 represent event number). Or you could save as an HDF5 file.

You can save an array or several into an HDF5 file (example from pyana):

import h5py

def beginjob(self,evt,env):
    self.ofile = h5py.File("outputfile.hdf5", 'w') # open for writing (overwrites existing file)
    self.shot_counter = 0

def event(self,evt,env)
    # example: store several arrays from one shot in a group labeled with shot (event) number
    self.shot_counter += 1
    group = self.ofile.create_group("Shot%d" % self.shot_counter)

    image1_source = "CxiSc1-0|TM6740-1"
    image2_source = "CxiSc1-0|TM6740-2"

    frame = evt.getFrameValue(image1_source)
    image1 = frame.data()
    frame = evt.getFrameValue(image2_source)
    image2 = frame.data()

    dataset1 = group.create_dataset("%s"%image1_source,data=image1)
    dataset2 = group.create_dataset("%s"%image2_source,data=image2)

def endjob(self,env)
    self.ofile.close()

Or you can group your datasets any other way you find useful, of course.

A comparison with MatLab.

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MatLab

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MatPlotLib

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Comments

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Loglog plot of one array vs. another

%
%
%
a1 = subplot(121);
loglog(channels(:,1),channels(:,2),'o')
xlabel('CH0')
ylabel('CH1')
a2 = subplot(122);
loglog(channels(:,3),channels(:,4),'o')
xlabel('CH2')
ylabel('CH3')

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Loglog plot of one array vs. another

import matplotlib.pyplot as plt
import numpy as np

a1 = plt.subplot(221)
plt.loglog(channels[:,0],channels[:,1], 'o' )
plt.xlabel('CH0')
plt.ylabel('CH1')
a2 = plt.subplot(222)
plt.loglog(channels[:,2],channels[:,3], 'o' )
plt.xlabel('CH2')
plt.ylabel('CH3')

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]]></ac:plain-text-body></ac:structured-macro>

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test

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test

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Test

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array of limits from graphical input

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array of limits from graphical input

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axes(a1)
hold on
lims(1:2,:) = ginput(2);

axes(a2)
hold on
lims(3:4,:) = ginput(2);

...

lims = np.zeros((4,2),dtype="float")

plt.axes(a1)
plt.hold(True)
lims[0:2,:] = plt.ginput(2)

plt.axes(a2)
plt.hold(True)
lims[2:4,:] = plt.ginput(2)

...

In MatLab, lims is an expandable array that holds limits as set by input from mouse click on the plot (ginput).
NumPy arrays cannot be expanded, so I've declared a 4x2 array of zeros to start with, then fill it with ginput().

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filter

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filter

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fbool1 = (channels(:,1)>min(lims(1:2,1)))&(channels(:,1)<max(lims(1:2,1)))
fbool2 = (channels(:,2)>min(lims(1:2,2)))&(channels(:,2)<max(lims(1:2,2)));
fbool = fbool1&fbool2
loglog(channels(fbool,1),channels(fbool,2),'or')

fbool3 = (channels(:,3)>min(lims(3:4,3)))&(channels(:,3)<max(lims(3:4,3)))
fbool4 = (channels(:,4)>min(lims(3:4,4)))&(channels(:,4)<max(lims(3:4,4)));
fbool = fbool3&fbool4
loglog(channels(fbool,3),channels(fbool,4),'or') 

...

fbools0 = (channels[:,0]>lims[:,0].min())&(channels[:,0]<lims[:,0].max())
fbools1 = (channels[:,1]>lims[:,1].min())&(channels[:,1]<lims[:,1].max())
fbools = fbools0 & fbools1

fbools2 = (channels[:,2]>lims[:,2].min())&(channels[:,2]<lims[:,2].max())
fbools3 = (channels[:,3]>lims[:,3].min())&(channels[:,3]<lims[:,3].max())
fbools = fbools2&fbools3

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Comment

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