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

dtor = math.pi /180.0

def A2E(t,h=1.0,k=1.0,l=1.0, deg =True, ln = False):
    lncorr= 2.e-4 if ln else 0.0    
    d0	=2 * 5.43102 *  (1.0-lncorr) / math.sqrt(h**2+k**2+l**2)
    tt	=	(t * dtor) if (deg or (t>1.0)) else t
    return 12.39842 / (d0*math.sin(tt))

def get_energy():
    t2 = caget("X06DA-OP-MO1:ROX2.RBV",'d')
    e = A2E(abs(t2))
    print 'Energy [keV]:'+ str(e) + '   Wavelength [A]:' + str(12.39842/e)
    return e


#Fitting
from mathutils import estimate_peak_indexes, fit_gaussians, create_fit_point_list, Gaussian
import java.awt.Color as Color

def fit(ydata, xdata = None):
    if xdata is None:
        xdata = frange(0, len(ydata), 1)
    max_y= max(ydata)    
    index_max = ydata.index(max_y)    
    max_x= xdata[index_max]
    print "Max indexs: " + str(index_max),
    print " Max x: " + str(max_x),
    print " Max y: " + str(max_y)
    
    gaussians = fit_gaussians(ydata, xdata, [index_max,])
    (norm, mean, sigma) = gaussians[0]

    p = plot([ydata],["data"],[xdata], context="Fit" )[0] 
    fitted_gaussian_function = Gaussian(norm, mean, sigma)

    scale_x = [float(min(xdata)), float(max(xdata)) ]
    resolution = (scale_x[1]-scale_x[0]) / 100
    print resolution
    fit_y = []
    for x in frange(scale_x[0],scale_x[1],resolution, True):
        fit_y.append(fitted_gaussian_function.value(x))
    fit_x = frange(scale_x[0], scale_x[1]+resolution, resolution)
    #plots = plot([ydata, fit_y] ,["data", "fit"], xdata = [xdata,fit_x], context="Fit")             
    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)

#fit([1,2,3, 3,3,3,4,5,6,12,33,23,15,3,2,1, 1, 1, 1, 1])        


def rock(axis = th1):
    """
    TODO
    """
    run("rock", {"rock_axis":th1})

def fbm():
    """
    TODO
    """
    run("fbn")

def rocknroll():
    """
    TODO
    """
    run("rocknroll")