adjustUncertToExposureTime.py 文件源码

def adjustUncertToExposureTime(facExpTime, uncertMap, evtLenMap):
    '''
    Adjust image uncertainty (measured at exposure time t0)
    to new exposure time

    facExpTime --> new exp.time / reference exp.time =(t/t0)
    uncertMap --> 2d array mapping image uncertainty

    evtLen --> 2d array mapping event duration within image [sec]
                event duration is relative to exposure time
                e.g. duration = 2 means event is 2x longer than 
                exposure time

    More information can be found at ...
    ----
    K.Bedrich: Quantitative Electroluminescence Imaging, PhD Thesis, 2017
    Subsection 5.1.4.3: Exposure Time Dependency
    ----
    '''

    #fit parameters, obtained from ####[simulateUncertDependencyOnExpTime]
    params =  np.array( 
        #a                 facExpTime        f_0             f_inf         
     [[  2.63017121e+00,   3.05873627e-01,   1.00000000e+01, 2.78233309e-01],
      [  2.26467931e+00,   2.86206621e-01,   8.01396977e+00, 2.04089232e-01],
      [  1.27361168e+00,   5.18377189e-01,   3.04180084e+00, 2.61396338e-01],
      [  7.34546040e-01,   7.34549823e-01,   1.86507345e+00, 2.77563156e-01],
      [  3.82715618e-01,   9.32410141e-01,   1.34510254e+00, 2.91228149e-01],
      [  1.71166071e-01,   1.14092885e+00,   1.11243702e+00, 3.07947386e-01],
      [  6.13455410e-02,   1.43802520e+00,   1.02995065e+00, 3.93920802e-01],
      [  1.65383071e-02,   1.75605076e+00,   1.00859395e+00, 5.02132321e-01],
      [  4.55800114e-03,   1.99855711e+00,   9.98819118e-01, 5.99572776e-01]])

    #event duration relative to exposure time:(1/16...16)
    dur = np.array([  0.0625,   0.125 ,   0.25  ,   
                      0.5   ,   1.    ,   2.    ,
                      4.    ,   8.    ,   16.    ])
    #get factors from interpolation:
    a = UnivariateSpline(dur, params[:, 0], k=3, s=0)
    b = UnivariateSpline(dur, params[:, 1], k=3, s=0)
    start = UnivariateSpline(dur, params[:, 2], k=3, s=0)
    end = UnivariateSpline(dur, params[:, 3], k=3, s=0)
    p0 = a(evtLenMap), b(evtLenMap), start(evtLenMap), end(evtLenMap)
    #uncertainty for new exposure time:
    out = uncertMap * _fitfn(facExpTime, *p0)
    # everywhere where there ARE NO EVENTS --> scale uncert. as if would
    # be normal distributed:
    i = evtLenMap == 0
    out[i] = uncertMap[i] * (1 / facExpTime)**0.5
    return out