def test2Dfit(fitType = None, verbose = False): testDebug = 0 testOptimizer = 'levmar' roi = [1, 325, 1, 335] conIm = PrincetonSPEFile('testimage.spe')[0].astype(float32) conZ = array(meshgrid(arange(roi[0], roi[0] + roi[1]), arange(roi[2], roi[2] + roi[3]))) if fitType == 'twodgauss': guess = array([ conIm.max(), 162.5, 5, 167.5, 6]) func = [twodgauss] elif fitType == 'twodgausslin': guess = array([ 0.01*conIm.max(), 1e-8, 1e-8, conIm.max(), 162.5, 5, 167.5, 6]) func = [twodlin, twodgauss] times = [] for optimizer in ['leastsq', 'levmar', 'mpfit']: f = fit.fit(x = conZ, y = conIm, funcs = func , guess = guess, debug = testDebug, optimizer = optimizer) t1 = time.time() f.go() t2 = time.time() times.append('Optimizer %s took %0.3f s' % (optimizer, (t2-t1))) for t in times: print t
def test2Dfit(fitType=None, verbose=False): testDebug = 0 testOptimizer = 'levmar' roi = [1, 325, 1, 335] conIm = PrincetonSPEFile('testimage.spe')[0].astype(float32) conZ = array( meshgrid(arange(roi[0], roi[0] + roi[1]), arange(roi[2], roi[2] + roi[3]))) if fitType == 'twodgauss': guess = array([conIm.max(), 162.5, 5, 167.5, 6]) func = [twodgauss] elif fitType == 'twodgausslin': guess = array( [0.01 * conIm.max(), 1e-8, 1e-8, conIm.max(), 162.5, 5, 167.5, 6]) func = [twodlin, twodgauss] times = [] for optimizer in ['leastsq', 'levmar', 'mpfit']: f = fit.fit(x=conZ, y=conIm, funcs=func, guess=guess, debug=testDebug, optimizer=optimizer) t1 = time.time() f.go() t2 = time.time() times.append('Optimizer %s took %0.3f s' % (optimizer, (t2 - t1))) for t in times: print t
def test(): t1 = time.time() conIm = PrincetonSPEFile('testimage2.spe') conIm = conIm.getData().astype(np.float32) t2 = time.time() print "Read took %.3f msec" % ((t2 - t1) * 1000) pylab.figure() pylab.imshow(conIm.sum(0)) t1 = time.time() conIm = princeton.readSPE('testimage2.spe').astype(np.float32) t2 = time.time() print "Read took %.3f msec" % ((t2 - t1) * 1000) pylab.figure() pylab.imshow(conIm.sum(0)) pylab.show()