#!/usr/bin/env python
# -*- coding: UTF-8 -*-

import numpy as N
from numpy import array, sin, cos, float64, dot, float_, sqrt,  ceil,  floor, dot, \
                meshgrid, zeros, zeros_like, where,  nan,  pi, isnan, nonzero, rint, \
                linspace, arange, argwhere
from numpy.ma import is_masked, MaskedArray
from numpy.ma import array as ma_array
#from enthought.traits.api import Trait, TraitHandler
from scipy import interpolate
from pylab import griddata, meshgrid

'''Auxiliary functions and classes
'''

#~ class TraitUnitVector(TraitHandler):
    #~ ''' Class to define unit vector trait
#~ 
    #~ Description:
#~ 
    #~ This class defines a unit vector. If the value assigned is not a unit
    #~ vector, it gets automaticaly normalized
    #~ '''
#~ 
    #~ def validate(self, object, name, value):
        #~ try:
            #~ avalue=array(value)          
        #~ except:
            #~ self.error(object, name, value)
        #~ 
        #~ if len(avalue.shape)!=1 or avalue.shape[0]!=3:
            #~ return self.error(object, name, avalue)
#~ 
        #~ avalue=array(avalue/sqrt(dot(avalue,avalue))) 
        #~ return avalue
#~ 
#~ # Trait to define a unit vector based on the unit vector trait
#~ UnitVector = Trait(array([0,0,1], float_),TraitUnitVector())

#~ print "Nota: Hay que revisar las convenciones de las rotaciones para que queden\n\r "\
      #~ "consistentes en x,y,z. Me parece que hay un error en el signo de la \n\r rotacion"\
      #~ "al rededor de alguno de los ejes. Modulo misc \n\r"\
      #~ "si no estoy mal el error esta en la rotacion respecto a los ejez Y y Z"

def rot_x(tx):
    '''Returns the transformation matrix for a rotation around the X axis
    '''
    return array([[1.,0.       ,0.        ],
                    [0.,cos(tx),-sin(tx)],
                    [0.,sin(tx), cos(tx)]]).astype(float64)

def rot_y(ty):
    '''Returns the transformation matrix for a rotation around the Y axis
    '''
    return array([[ cos(ty),0.       ,sin(ty) ],
                    [ 0.       ,1        ,0.        ],
                    [-sin(ty),0.       ,cos(ty) ]]).astype(float64)
def rot_z(tz):
    '''Returns the transformation matrix for a rotation around the Z axis
    '''
    return array([[ cos(tz),-sin(tz),0.       ],
                    [ sin(tz), cos(tz),0.       ],
                    [ 0.       ,0.        ,1.       ]]).astype(float64)



#~ def rot_mat(r):
    #~ '''Returns the transformation matrix for a rotation around the Z,Y,X axes
    #~ 
    #~ The rotation is made first around the Z axis, then around the Y axis, and
    #~ finally around the X axis.
    #~ 
    #~ Parameters
    #~ 
    #~ r= (rx,ry,rz)
    #~ '''
#~ 
    #~ c=cos(r)
    #~ s=sin(r)
#~ 
    #~ rx=array([[1. , 0., 0.],
                #~ [0. , c[0],-s[0]],
                #~ [0. , s[0], c[0]]])
#~ 
    #~ ry=array([[ c[1], 0., s[1]],
                #~ [ 0., 1., 0.],
                #~ [-s[1], 0., c[1]]])
#~ 
#~ 
    #~ rz=array([[ c[2],-s[2], 0.],
                #~ [ s[2], c[2], 0.],
                #~ [ 0., 0., 1.]])
#~ 
#~ 
    #~ tm=dot(rz,dot(ry,rx))
#~ 
    #~ return tm

#  To improve speed, this routine was moved to cmisc.pyx
#~ def rot_mat_i(r):
    #~ '''Returns the inverse transformation matrix for a rotation around the Z,Y,X axes
    #~ 
    #~ Parameters
    #~ 
     #~ r= (rx,ry,rz)
     #~ '''
#~ 
    #~ c=cos(r)
    #~ s=sin(r)
#~ 
    #~ rx=array([[ 1., 0., 0.],
                #~ [ 0., c[0], s[0]],
                #~ [ 0.,-s[0], c[0]]])
#~ 
    #~ ry=array([[ c[1], 0.,-s[1]],
                #~ [ 0., 1., 0.],
                #~ [ s[1], 0., c[1]]])
#~ 
#~ 
    #~ rz=array([[ c[2], s[2], 0.],
                #~ [-s[2], c[2], 0.],
                #~ [ 0., 0., 1.]])
#~ 
    #~ # Nota: se hizo una prueba para optimizar escribirndo la expresión del producto
    #~ # escalar, y el resultado fue considerablemente mas lento, toca revisar
#~ 
#~ 
    #~ return dot(rx,dot(ry,rz))


def cross(a,b):
    '''3D Vector product producto vectorial '''
    x1,y1,z1=a
    x2,y2,z2=b
    return array((y1*z2-y2*z1,x2*z1-x1*z2,x1*y2-x2*y1))



def wavelength2RGB(wl):
    '''Function to aproximate and RGB tuple from the wavelength value

    Parameter:
    
    wavelength wavelength in um

    if the wavelength is outside the visible spectrum returns (0,0,0)
    Original code fount at:
    
    http://www.physics.sfasu.edu/astro/color/spectra.html

    '''

    R,G,B=0.,0.,0.

    if (wl>=.380) & (wl<.440):
        R = -1.*(wl-.440)/(.440-.380)
        G = 0.
        B = 1.


    if (wl>=.440) & (wl<.490):
              R = 0.
              G = (wl-.440)/(.490-.440)
              B = 1.

    if (wl>=.490) & (wl<.510):
              R = 0.
              G = 1.
              B = -1.*(wl-.510)/(.510-.490)

    if (wl>=.510) & (wl<.580):
              R = (wl-.510)/(.580-.510)
              G = 1.
              B = 0.
    if (wl>=.580) & (wl<.645):
              R = 1.
              G = -1.*(wl-.645)/(.645-.580)
              B = 0.
    if (wl>=.645) & (wl < .780):
        R = 1.
        G = 0.
        B = 0.
    # LET THE INTENSITY FALL OFF NEAR THE VISION LIMITS

    if (wl>=.700):
        sss =.3+.7* (.780-wl)/(.780-.700)
    elif (wl < .420) :
        sss=.3+.7*(wl-.380)/(.420-.380)
    else :
        sss=1


    R=R*sss
    G=G*sss
    B=B*sss
    return (R,G,B)

def matrix_interpolation(M, i, j, type="bilinear"):
    """Returns the interpolated value of a matrix, when the indices i,j are floating
    point numbers.
    M
        Matrix to interpolate
        
    i,j 
        Indices to interpolate
        
    type
        Interpolation type. supported types: nearest,bilinear
    
    """
    mi, mj=M.shape
    if i<0 or i>mi-2 or j<0 or j>mj-2:
       raise  IndexError("matrix Indexes out of range")
    # Allowed interpolation types
    inter_types=["nearest","bilinear", ]
    if not type in inter_types:
        raise ValueError("Interpolation type not allowed. The allowed types"\
        " are: {0}".format(inter_types))
    if type=="nearest":
        iri=int(round(i))
        irj=int(round(j))
        return M[iri, irj]
    elif type=="bilinear":
        i_s, j_s=floor((i, j))
        #calc 1
        m=M[i_s:i_s+2, j_s:j_s+2]
        iv=array([1-(i-i_s), i-i_s])
        jv=array([[1-(j-j_s),], [j-j_s, ]])
        return dot(iv, dot(m, jv))[0]
        #dx=i-i_s
        #dy=j-j_s
        ##print i, j, i_s, j_s,  dx, dy
        #p1=dx*dy*M[i_s, j_s]
        #p2=(1.-dx)*dy*M[i_s+1, j_s]
        #p3=dx*(1.-dy)*M[i_s, j_s+1]
        #p4=(1.-dx)*(1.-dy)*M[i_s+1, j_s+1]
        #return p1+ p2+ p3+ p4
    print "error"
    return 1.
    
def hitlist2int(x, y, z,  xi, yi):
    """Function that estimates an intensity distribution on a plane from a 
    ray hitlist
    """
    import matplotlib.delaunay as delaunay
    from pylab import griddata, meshgrid
    from scipy import interpolate
    #if xi.ndim != yi.ndim:
    #    raise TypeError("inputs xi and yi must have same number of dimensions (1 or 2)")
    #if xi.ndim != 1 and xi.ndim != 2:
    #    raise TypeError("inputs xi and yi must be 1D or 2D.")
    #if not len(x)==len(y)==len(z):
    #    raise TypeError("inputs x,y,z must all be 1D arrays of the same length")
    # remove masked points.
    #if hasattr(z,'mask'):
    #    x = x.compress(z.mask == False)
    #    y = y.compress(z.mask == False)
    #    z = z.compressed()
    
    #if xi.ndim == 1:
    #    xi,yi = meshgrid(xi,yi)
    
    #triangulate data
    
    tri=delaunay.Triangulation(x, y)
    
    #calculate triangles area
    ntriangles=tri.circumcenters.shape[0]
    coord=array(zip(tri.x, tri.y))
   
    #I=zeros((ntriangles, ))
    #xc=zeros((ntriangles, ))
    #yc=zeros((ntriangles, ))
    # for i in range(ntriangles):
    #     i1, i2, i3=tri.triangle_nodes[i]
    #     p1=coord[i1]
    #     p2=coord[i2]
    #     p3=coord[i3]
    #     v1=p1-p2
    #     v2=p3-p2
    #     I[i]=1./(abs(v1[0]*v2[1]-v1[1]*v2[0]))
    #     # the circumcenter data from the triangulation, has some problems so we
    #     # recalculate it
    #     xc[i], yc[i]=(p1+p2+p3)/3.
    # The previous code was replaced by the following code
    ###
    i1=tri.triangle_nodes[:, 0]
    i2=tri.triangle_nodes[:, 1]
    i3=tri.triangle_nodes[:, 2]
    p1=coord[i1]
    p2=coord[i2]
    p3=coord[i3]
    
    v1=p1-p2
    v2=p3-p2
    I=abs(1./(v1[:, 0]*v2[:, 1]-v1[:, 1]*v2[:, 0]))
    
    c=(p1+p2+p3)/3.
    xc=c[:, 0]
    yc=c[:, 1]
    ###
    
    # Because of the triangulation algorithm, there are some really high values 
    # in the intensity data. To filter these values, remove the 5% points of the
    # higher intensity.
    ni=int(0.1*len(I))
    j=I.argsort()[:-ni]
    xc=xc[j]
    yc=yc[j]
    I=I[j]
    I=I/I.max()
    
#    #print tri.circumcenters[:, 0]
#    #print tri.circumcenters.shape
#    print ntriangles,  tri.circumcenters[:, 0].shape,  tri.circumcenters[:, 0].flatten().shape
    
    #itri=delaunay.Triangulation(xc,yc)
    #inti=itri.linear_interpolator(I)
    #xi,yi = meshgrid(xi,yi)
    #d1=itri(xi, yi)
    
    #Interpolacion con Splines
    #di=interpolate.SmoothBivariateSpline(xc, yc, I)
    #d1=di(xi,yi)
    
    #Interpolacion nn, y generación de pupila
    xi,yi = meshgrid(xi,yi)
    d1=griddata(xc, yc, I,xi, yi )
    return d1

def hitlist2int_list(x, y):
    """Function that estimates an intensity distribution on a plane from a 
    ray hitlist. Returns the intensity samples as an x,y,I list
    """
    import matplotlib.delaunay as delaunay
    from pylab import griddata, meshgrid
    from scipy import interpolate
    
    #if xi.ndim != yi.ndim:
    #    raise TypeError("inputs xi and yi must have same number of dimensions (1 or 2)")
    #if xi.ndim != 1 and xi.ndim != 2:
    #    raise TypeError("inputs xi and yi must be 1D or 2D.")
    #if not len(x)==len(y)==len(z):
    #    raise TypeError("inputs x,y,z must all be 1D arrays of the same length")
    # remove masked points.
    #if hasattr(z,'mask'):
    #    x = x.compress(z.mask == False)
    #    y = y.compress(z.mask == False)
    #    z = z.compressed()
    
    #if xi.ndim == 1:
    #    xi,yi = meshgrid(xi,yi)
    
    #triangulate data
    
    tri=delaunay.Triangulation(x, y)
    
    #calculate triangles area
    ntriangles=tri.circumcenters.shape[0]
    coord=array(zip(tri.x, tri.y))
   
    #I=zeros((ntriangles, ))
    #xc=zeros((ntriangles, ))
    #yc=zeros((ntriangles, ))
    # for i in range(ntriangles):
    #     i1, i2, i3=tri.triangle_nodes[i]
    #     p1=coord[i1]
    #     p2=coord[i2]
    #     p3=coord[i3]
    #     v1=p1-p2
    #     v2=p3-p2
    #     I[i]=1./(abs(v1[0]*v2[1]-v1[1]*v2[0]))
    #     # the circumcenter data from the triangulation, has some problems so we
    #     # recalculate it
    #     xc[i], yc[i]=(p1+p2+p3)/3.
    # The previous code was replaced by the following code
    ###
    i1=tri.triangle_nodes[:, 0]
    i2=tri.triangle_nodes[:, 1]
    i3=tri.triangle_nodes[:, 2]
    p1=coord[i1]
    p2=coord[i2]
    p3=coord[i3]
    
    v1=p1-p2
    v2=p3-p2
    I=abs(1./(v1[:, 0]*v2[:, 1]-v1[:, 1]*v2[:, 0]))
    
    c=(p1+p2+p3)/3.
    xc=c[:, 0]
    yc=c[:, 1]
    ###
    
    # Because of the triangulation algorithm, there are some really high values 
    # in the intensity data. To filter these values, remove the 5% points of the
    # higher intensity.
    ni=int(0.1*len(I))
    j=I.argsort()[:-ni]
    xc=xc[j]
    yc=yc[j]
    I=I[j]
    I=I/I.max()
    
#    #print tri.circumcenters[:, 0]
#    #print tri.circumcenters.shape
#    print ntriangles,  tri.circumcenters[:, 0].shape,  tri.circumcenters[:, 0].flatten().shape
    
    #itri=delaunay.Triangulation(xc,yc)
    #inti=itri.linear_interpolator(I)
    #xi,yi = meshgrid(xi,yi)
    #d1=itri(xi, yi)
    
    #Interpolacion con Splines
    #di=interpolate.SmoothBivariateSpline(xc, yc, I)
    #d1=di(xi,yi)
    
    return xc,yc,I
    

def unwrapv(inph,in_p=(), uv=2*pi):
    """Return the input matrix unwraped the value given in uv
    
    This is a vectorized routine, but is not as fast as it should
    """
    
    if not is_masked(inph):
        fasei=MaskedArray(inph, isnan(inph))
    else:
        fasei=inph.copy()
        
    
    size=fasei.shape
    nx, ny=size
    # If the initial unwraping point is not given, take the center of the image
    # as initial coordinate
    if in_p==():
        in_p=(int(size[0]/2),int(size[1]/2))

    # Create a temporal space to mark if the points are already unwrapped
    # 0 the point has not been unwrapped
    # 1 the point has not been unwrapped, but it is in the unwrapping list 
    # 2 the point was already unwrapped

    fl=N.zeros(size)

    # List containing the points to unwrap
    l_un=[in_p]
    fl[in_p]=1

    # unwrapped values
    faseo=fasei.copy()
    XI_, YI_= meshgrid(range(-1, 2), range(-1, 2))
    XI_=XI_.flatten()
    YI_=YI_.flatten()
    while len(l_un)>0:
        # remove the first value from the list
        unp=l_un.pop(0)
        #l_un[0:1]=[]
        XI=XI_+unp[0]
        YI=YI_+unp[1]
        #Remove from the list the values where XI is negative
        nxi=XI>-1
        nyi=YI>-1
        nxf=XI<nx
        nyf=YI<ny
        n=nonzero(nxi& nyi & nxf & nyf)
        lco=zip(XI[n], YI[n])
        
        # Put the coordinates of unwrapped the neigbors in the list
        
        
        # And check for wrapping
        nv=0
        wv=0    
        
        
        for co in lco:
            if (fl[co]==0) & (faseo.mask[co]==False):
                fl[co]=1
                l_un.append(co)
            elif fl[co]==2:
                wv=wv+rint((faseo[co]-faseo[unp])/uv)
                nv=nv+1
    
        if nv!=0: 
            wv=wv/nv
            #if wv>=0: wv=int(wv+0.5)
            #else: wv=int(wv-0.5)
        fl[unp]=2
        faseo[unp]=faseo[unp]+wv*uv
    
    return faseo


def unwrap_py(inph,in_p=(), uv=2*pi):
    """Return the input matrix unwraped the valu given in uv
    
    The same as unwrapv, but using for-s, written in python
    """
    if not is_masked(inph):
        fasei=MaskedArray(inph, isnan(inph))
    else:
        fasei=inph
        
    nx, ny=(fasei.shape[0],fasei.shape[1]) 
    
    # If the initial unwraping point is not given, take the center of the image
    # as initial coordinate
    if in_p==():
        in_p=(int(nx/2),int(ny/2))

    # Create a temporal space to mark if the points are already unwrapped
    # 0 the point has not been unwrapped
    # 1 the point has not been unwrapped, but it is in the unwrapping list 
    # 2 the point was already unwrapped

    fl=zeros((nx, ny))

    # List containing the points to unwrap
    l_un=[in_p]
    fl[in_p]=1

    # unwrapped values
    faseo=fasei.copy()    
    
    while len(l_un)>0:
        # remove the first value from the list
        cx, cy=l_un.pop(0)
    
        # Put the coordinates of unwrapped the neigbors in the list
        # And check for wrapping
        nv=0
        wv=0    
        
        for i in range(cx-1, cx+2):
            for j in range(cy-1, cy+2):
                if (i>-1) and (i<nx) and (j>-1) and (j<ny):
                    if (fl[i, j]==0)&(faseo.mask[i, j]==False):
                        fl[i, j]=1
                        l_un.append((i, j))
                    elif fl[i, j]==2:
                        wv=wv+rint((faseo[i, j]-faseo[cx, cy])/uv)
                        nv=nv+1        
        if nv!=0: 
            wv=wv/nv

        fl[cx, cy]=2
        faseo[cx, cy]=faseo[cx, cy]+wv*uv
        
    return faseo


    
def interpolate_g(xi,yi,zi,xx,yy,knots=10, error=False,mask=None):
    """Create a grid of zi values interpolating the values from xi,yi,zi
    
    xi,yi,zi  1D Lists  or arrays containing the values to use as base for the interpolation
    xx,yy     1D vectors or lists containing the output coordinates
    samples  tuple containing the shape of the output array.
    knots     number of knots to be used in each direction
    error     if set to true, half of the points (x, y, z) are used to create 
              the interpolation, and half are used to evaluate the interpolation error
    
    
    """
    xi=array(xi)
    yi=array(yi)
    zi=array(zi)
    
    #print xi
    #print yi
    #print zi
    assert xi.ndim==1 ,"xi must ba a 1D array or list" 
    assert yi.ndim==1 ,"yi must ba a 1D array or list" 
    assert zi.ndim==1 ,"zi must ba a 1D array or list" 
    
    assert xx.ndim==1 ,"xx must ba a 1D array or list" 
    assert yy.ndim==1 ,"yy must ba a 1D array or list" 
    
    assert len(xi)==len(yi) and len(xi)==len(zi), "xi, yi, zi must have the same number of items"
    
    
    
    if error==True:
        # Create a list of indexes to be able to select the points that are going 
        # to be used as spline generators, and as control points
        idx=where(arange(len(xi)) %2 ==0, False, True)

   # Use only half of the samples to create the Spline, 
    if error == True:
        isp=argwhere(idx==True)
        ich=argwhere(idx==False)
        
        xsp=xi[isp]
        ysp=yi[isp]
        zsp=zi[isp]
        
        xch=xi[ich]
        ych=yi[ich]
        zch=zi[ich]
    else:
        xsp=xi
        ysp=yi
        zsp=zi
    
    #Distribute homogeneously the knots
    xk=linspace(xsp.min(), xsp.max(),knots)
    yk=linspace(ysp.min(), ysp.max(),knots)
    
    # LSQBivariateSpline using some knots gives smaller error than 
    # SmoothBivariateSpline
    di=interpolate.LSQBivariateSpline(xsp, ysp, zsp, xk[1:-1],  yk[1:-1])
    #print xsp,ysp,zsp
    #di=interpolate.SmoothBivariateSpline(xsp, ysp, zsp)
        
    # Evaluate error
    if error==True:
        zch1=di.ev(xch, ych)
        er=(zch.flatten()-zch1).std()

    if mask==None:
        #d=griddata(xi,  yi,  zi,  xx, yy) #
        d=di(xx,yy).transpose()
    else:
        d=ma_array(di(xx,yy).transpose(), mask=mask)
    
    if error==True: return d, er
    else: return d

####### Fin Funciones auxiliares







