Newer
Older
~goodIDX, qMask, conv_n_events > 0])
bgEvents = np.mean(n_events[bgIDX])*np.sum(goodIDX*qMask)
intensity, sigma, xStart, xStop = integratePeak(r.readX(0), icProfile, r.readY(0), np.polyval(bgCoefficients, r.readX(
1)), pp_lambda=pp_lambda, fracStop=fracStop, totEvents=totEvents, bgEvents=bgEvents, varFit=chiSq)
# subtract background
icProfile = icProfile - np.polyval(bgCoefficients, r.readX(1))
peak.setIntensity(intensity)
peak.setSigmaIntensity(sigma)
if figsFormat is not None:
plotFit(figsFormat, r, tofWS, fICC, peak.getRunNumber(
), i, energy, chiSq, fitBG, xStart, xStop, bgx0=None)
if keepFitDict:
fitDict[i] = np.array(
[r.readX(0), r.readY(0), r.readY(1), r.readY(2)])
paramList.append([i, energy, np.sum(icProfile), 0.0, chiSq] +
[param.row(i)['Value'] for i in range(param.rowCount())]+[pp_lambda])
mtd.remove('MDbox_'+str(run)+'_'+str(i))
except KeyboardInterrupt:
logger.warning('KeyboardInterrupt: Exiting Program!!!!!!!')
sys.exit()
except: # Error with fitting
# raise
peak.setIntensity(0)
peak.setSigmaIntensity(1)
logger.warning('Error with peak ' + str(i))
paramList.append(
[i, energy, 0.0, 1.0e10, 1.0e10] + [0 for i in range(10)]+[0])
#paramList.append([i, energy, 0.0, 1.0e10,1.0e10] + [0 for i in range(mtd['fit_parameters'].rowCount())]+[0])
continue
mtd.remove('MDbox_'+str(run)+'_'+str(i))
return peaks_ws, paramList, fitDict