###################################################################################################
#  Deployment specific global definitions - executed after startup.py
###################################################################################################



###################################################################################################
# Device initialization
###################################################################################################


#scienta.getDataArray().setMonitored(True)

class AcquisitionMode(Readable.ReadableString):
    def read(self):        
        return str(scienta.getAcquisitionMode())
_acquisition_mode=AcquisitionMode()
        
class EnergyMode(Readable.ReadableString):
    def read(self):        
        return str(scienta.getEnergyMode())
_energy_mode=EnergyMode()        
        
class LensMode(Readable.ReadableString):
    def read(self):        
        return str(scienta.getLensMode())
_lens_mode=LensMode()                
        
class DetectorMode(Readable.ReadableString):
    def read(self):        
        return str(scienta.getDetectorMode())
_detector_mode=DetectorMode()   
        
class PassEnergy(Readable):
    def read(self):        
        return scienta.getPassEnergy()
_pass_energy=PassEnergy()
   
class ElementSet(Readable.ReadableString):
    def read(self):        
        return str(scienta.getElementSet())
_element_set=ElementSet()   


###################################################################################################
# Handlig diagnostics 
###################################################################################################

diag_channels = []
 
diag_channels.append(scienta.lowEnergy.readback)    
diag_channels.append(scienta.centerEnergy.readback) 
diag_channels.append(scienta.highEnergy.readback)  
diag_channels.append(scienta.energyStepSize.readback)  
diag_channels.append(scienta.energyWidth)  
diag_channels.append(scienta.energyCount)  
diag_channels.append(scienta.lowThetaY.readback)    
diag_channels.append(scienta.centerThetaY.readback) 
diag_channels.append(scienta.highThetaY.readback)  
diag_channels.append(scienta.thetaYStepSize.readback)  
diag_channels.append(scienta.thetaYWidth)  
diag_channels.append(scienta.thetaYCount)  
diag_channels.append(scienta.lowThetaX)    
diag_channels.append(scienta.centerThetaX.readback) 
diag_channels.append(scienta.highThetaX)  
diag_channels.append(scienta.thetaXStepSize)  
diag_channels.append(scienta.thetaXWidth)  
diag_channels.append(scienta.thetaXCount)  
diag_channels.append(scienta.slicesReadback)  
diag_channels.append(scienta.channelsReadback)  
diag_channels.append(scienta.excitationEnergy)  

diag_channels.append(_acquisition_mode)
diag_channels.append(_energy_mode)
diag_channels.append(_lens_mode)
diag_channels.append(_detector_mode)
diag_channels.append(_pass_energy)
diag_channels.append(_element_set)


diag_channels = sorted(diag_channels, key=lambda channel: channel.name)

def get_diag_name(diag):
    return ch.psi.utils.Str.toTitleCase(diag.getName()).replace(" ", "").replace("Readback", "")
    
def print_diag():
    for f in diag_channels:
        print "%-25s  %s" % (get_diag_name(f) ,  str(f.read()))

def create_diag_datasets(parent = None):
    if parent is None: 
        parent = get_exec_pars().group
    group = parent + "attrs/"
    for f in diag_channels:        
        create_dataset(group+get_diag_name(f) , 's' if (issubclass(type(f), Readable.ReadableString)) else 'd')

def append_diag_datasets(parent = None):
    if parent is None: 
        parent = get_exec_pars().group
    group = parent + "attrs/"
    for f in diag_channels:
        try:
            x = f.read()
            if x is None:
                x = '' if (type(f) is ch.psi.pshell.epics.ChannelString) else float('nan')
            append_dataset(group+get_diag_name(f), x)
        except:
            log("Error sampling " + str(get_diag_name(f)) + ": " + str(sys.exc_info()[1]))


def handle_diagnostics(rec):
    #if beam_ok:     
      if get_exec_pars().save:
          #Saving only once the diag data
          if rec.index == 0:              
              create_diag_datasets()
              append_diag_datasets()



def trigger_scienta():
    """
    Trigger new acquisition
    """
    scienta.start()
    scienta.waitNewImage(-1)


def dummy_trigger_scienta():
    """
    Trigger detector to update the array sizes and calibration
    """
    iterations = scienta.getIterations()
    scienta.setIterations(1)
    try:
        trigger_scienta()
    finally:
        scienta.setIterations(iterations)


###################################################################################################
# Utilities
###################################################################################################

def fit(ydata, xdata = None):
    """
    Gaussian fit
    """
    if xdata is None:
        xdata = frange(0, len(ydata), 1)
    #ydata = to_list(ydata)
    #xdata = to_list(xdata)
    max_y= max(ydata)
    index_max = ydata.index(max_y)
    max_x= xdata[index_max]
    print "Max index:" + str(index_max),
    print " x:" + str(max_x),
    print " y:" + str(max_y)
    gaussians = fit_gaussians(ydata, xdata, [index_max,])
    (norm, mean, sigma) = gaussians[0]
    p = plot([ydata],["data"],[xdata], title="Fit" )[0]
    fitted_gaussian_function = Gaussian(norm, mean, sigma)
    scale_x = [float(min(xdata)), float(max(xdata)) ]
    points = max((len(xdata)+1), 100)
    resolution = (scale_x[1]-scale_x[0]) / points
    fit_y = []
    fit_x = frange(scale_x[0],scale_x[1],resolution, True)
    for x in fit_x:
        fit_y.append(fitted_gaussian_function.value(x))
    p.addSeries(LinePlotSeries("fit"))
    p.getSeries(1).setData(fit_x, fit_y)

    if abs(mean - xdata[index_max]) < ((scale_x[0] + scale_x[1])/2):
        print "Mean -> " +  str(mean)
        p.addMarker(mean, None, "Mean="+str(round(norm,2)), Color.MAGENTA.darker())
        return (norm, mean, sigma)
    else:
        p.addMarker(max_x, None, "Max="+str(round(max_x,2)), Color.GRAY)
        print "Invalid gaussian fit: " +  str(mean)
        return (None, None, None)