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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

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!):

elements = evt.getCsPadQuads(self.source, env)

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
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  def beginjob ( self, evt, env ) : 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
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  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

What we have so far gives you a 4d numpy array of all pixels. And if you want to store it in e.g. a numpy array, you can reshape it down to 2 dimensions (this is the format of the official pedestal files made by the translator):

pixels = pixel_array.reshape(1480,388)
np.save("pixel_pedestal_file.npy", pixels )

CSPad tile arrangement

CSPad alignment

Saving data arrays

saving numpy arrays to numpy file

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