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Image peak finding
Here are a collection of useful algorithms for image analysis: http://docs.scipy.org/doc/scipy/reference/ndimage.html
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CSPad images and tile arangements
CSPad data structure
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The python code for this pyana module example resides in XtcExplorer/src/pyana_image.py.
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Try some plotting of CSPad data using xtcexplorer. Launch the explorer and load xpp48712 run 66 (a dark run):
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elements here is a python list of ElementV1 or ElementV2 (or later versions) objects, each representing one quadrant. The list is not ordered, so to know which quadrant you have, you have to check with element.quad(). To store a local array of the whole CSPad detector, you can do the following.
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CSPad data structure
CSPad data in xtc is a list of elements. In pyana get the list from the evt (event) object (notice the need for the env (environment) object too!):
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elements |
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= evt.getCsPadQuads( |
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.source, env |
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elements here is a python list of ElementV1 or ElementV2 (or later versions) objects, each representing one quadrant. The list is not ordered, so to know which quadrant you have, you have to check with element.quad(). To store a local array of the whole CSPad detector, you can do the following.
- In beginjob, find out from the configuration object what part of the CSPad was in use (sometimes sections are missing):
Code Block none none def beginjob ( self, evt, env ) : : config = env.getConfig(xtc.TypeId.Type.Id_CspadConfig, self.source) if not config: config = env.getConfig(xtc.TypeId.Type.Id_CspadConfig, self.source) if not config: print '*** cspad config object is missing ***' return quads = range(4) # memorize this list of sections for later self.sections = map(config.sections, quads)
- In each event, get the current CSPad data:
Code Block none none def event(self, evt, env): elements = evt.getCsPadQuads(self.source,env) pixel_array = np.zeros((4,8,185,388), dtype="uint16") for element in elements: data = element.data() # the 3-dimensional data array (list of 2d sections) quad = element.quad() # current quadrant number (integer value) # if any sections are missing, insert zeros if len( data ) < 8 : zsec = np.zeros( (185,388), dtype=data.dtype) for i in range (8) : if i not in self.sections[quad] : data = np.insert( data, i, zsec, axis=0 ) pixel_array[quad] = data
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For how to find the correct constants for each experiment, look at the CSPad alignment page.
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Try some plotting of CSPad data using xtcexplorer. Launch the explorer and load xpp48712 run 66 (a dark run):
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xtcexplorer /reg/d/psdm/XPP/xpp48712/xtc/e153-r0066-s00-c00.xtc
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Non-interactive batch analysis
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