-                      r3c67340
+                      r095ab1b
         out=get_q(dx, dy, det_dist, wavelength)
     return out
+def flip_phi(phi):
+    """
+        Correct phi to within the 0 <= to <= 2pi range
+        @return: phi in >=0 and <=2Pi
+    """
+    Pi = math.pi
+    if phi < 0:
+        phi_out = phi  + 2*Pi
+    elif phi > 2*Pi:
+        phi_out = phi  - 2*Pi
+    else:
+        phi_out = phi
+    return phi_out
+def reader2D_converter(data2d=None):
+    """
+        convert old 2d format opened by IhorReader or danse_reader to new Data2D format
+        @param data2d: 2d array of Data2D object
+        @return: 1d arrays of Data2D object
+    """
+    if data2d.data==None or data2d.x_bins==None or data2d.y_bins==None:
+        raise ValueError,"Can't convert this data: data=None..."
+    from DataLoader.data_info import Data2D
+    new_x = numpy.tile(data2d.x_bins, (len(data2d.y_bins),1))
+    new_y = numpy.tile(data2d.y_bins, (len(data2d.x_bins),1))
+    new_y = new_y.swapaxes(0,1)
+    new_data = data2d.data.flatten()
+    new_err_data = data2d.err_data.flatten()
+    qx_data = new_x.flatten()
+    qy_data = new_y.flatten()
+    q_data = numpy.sqrt(qx_data*qx_data+qy_data*qy_data)
+    mask    = numpy.ones(len(new_data), dtype = bool)
+    output = Data2D()
+    output = data2d
+    output.data = new_data
+    output.err_data = new_err_data
+    output.qx_data = qx_data
+    output.qy_data = qy_data
+    output.q_data = q_data
+    output.mask = mask
+    return output
 class _Slab(object):
     """
 …
             raise RuntimeError, "_Slab._avg: invalid number of detectors: %g" % len(data2D.detector)
         pixel_width_x = data2D.detector[0].pixel_size.x
         pixel_width_y = data2D.detector[0].pixel_size.y
         det_dist    = data2D.detector[0].distance
         wavelength  = data2D.source.wavelength
         center_x    = data2D.detector[0].beam_center.x/pixel_width_x
         center_y    = data2D.detector[0].beam_center.y/pixel_width_y
+        # Get data
+        data = data2D.data
+        q_data = data2D.q_data
+        err_data = data2D.err_data
+        qx_data = data2D.qx_data
+        qy_data = data2D.qy_data
         # Build array of Q intervals
         if maj=='x':
+            nbins = int(math.ceil((self.x_max-self.x_min)/self.bin_width))
+            qbins = self.bin_width*numpy.arange(nbins)+self.x_min
+            if self.fold: x_min = 0
+            else:  x_min = self.x_min
+            nbins = int(math.ceil((self.x_max-x_min)/self.bin_width))
+            qbins = self.bin_width*numpy.arange(nbins)+ x_min
         elif maj=='y':
+            nbins = int(math.ceil((self.y_max-self.y_min)/self.bin_width))
+            qbins = self.bin_width*numpy.arange(nbins)+self.y_min
+            if self.fold: y_min = 0
+            else:  y_min = self.y_min
+            nbins = int(math.ceil((self.y_max-y_min)/self.bin_width))
+            qbins = self.bin_width*numpy.arange(nbins)+ y_min
         else:
             raise RuntimeError, "_Slab._avg: unrecognized axis %s" % str(maj)
 …
         err_y = numpy.zeros(nbins)
         y_counts = numpy.zeros(nbins)
+        for i in range(numpy.size(data2D.data,1)):
+            # Min and max x-value for the pixel
+            minx = pixel_width_x*(i - center_x)
+            maxx = pixel_width_x*(i+1.0 - center_x)
+            qxmin = get_q(minx, 0.0, det_dist, wavelength)
+            qxmax = get_q(maxx, 0.0, det_dist, wavelength)
+            # Get the count fraction in x for that pixel
+            frac_min = get_pixel_fraction_square(self.x_min, qxmin, qxmax)
+            frac_max = get_pixel_fraction_square(self.x_max, qxmin, qxmax)
+            frac_x = frac_max - frac_min
+            if frac_x == 0:
+                continue
+            if maj=='x':
+                dx = pixel_width_x*(i+0.5 - center_x)
+                q_value = get_q(dx, 0.0, det_dist, wavelength)
+                if self.fold==False and dx<0:
+                    q_value = -q_value
+                i_q = int(math.ceil((q_value-self.x_min)/self.bin_width)) - 1
+                if i_q<0 or i_q>=nbins:
+                    continue
+            for j in range(numpy.size(data2D.data,0)):
+                # Min and max y-value for the pixel
+                miny = pixel_width_y*(j - center_y)
+                maxy = pixel_width_y*(j+1.0 - center_y)
+                qymin = get_q(0.0, miny, det_dist, wavelength)
+                qymax = get_q(0.0, maxy, det_dist, wavelength)
+                # Get the count fraction in x for that pixel
+                frac_min = get_pixel_fraction_square(self.y_min, qymin, qymax)
+                frac_max = get_pixel_fraction_square(self.y_max, qymin, qymax)
+                frac_y = frac_max - frac_min
+                frac = frac_x * frac_y
+                if frac == 0:
+                    continue
+                if maj=='y':
+                    dy = pixel_width_y*(j+0.5 - center_y)
+                    q_value = get_q(0.0, dy, det_dist, wavelength)
+                    if self.fold==False and dy<0:
+                        q_value = -q_value
+                    i_q = int(math.ceil((q_value-self.y_min)/self.bin_width)) - 1
+                    if i_q<0 or i_q>=nbins:
+                        continue
+                x[i_q]          = q_value
+                y[i_q]         += frac * data2D.data[j][i]
+                if data2D.err_data == None or data2D.err_data[j][i]==0.0:
+                    err_y[i_q] += frac * frac * math.fabs(data2D.data[j][i])
+                else:
+                    err_y[i_q] += frac * frac * data2D.err_data[j][i] * data2D.err_data[j][i]
+                y_counts[i_q]  += frac
+        # Average the sums
+        for i in range(nbins):
+            if y_counts[i]>0:
+                err_y[i] = math.sqrt(err_y[i])/y_counts[i]
+                y[i]     = y[i]/y_counts[i]
+        return Data1D(x=x, y=y, dy=err_y)
+        # Average pixelsize in q space
+        for npts in range(len(data)):
+            # default frac
+            frac_x = 0
+            frac_y = 0
+            # get ROI
+            if self.x_min <= qx_data[npts] and self.x_max > qx_data[npts]:
+                frac_x = 1
+            if self.y_min <= qy_data[npts] and self.y_max > qy_data[npts]:
+                frac_y = 1
+            frac = frac_x * frac_y
+            if frac == 0: continue
+            # binning: find axis of q
+            if maj=='x':
+                q_value = qx_data[npts]
+                min = x_min
+            if maj=='y':
+                q_value = qy_data[npts]
+                min = y_min
+            if self.fold and q_value<0: q_value = -q_value
+            # bin
+            i_q = int(math.ceil((q_value-min)/self.bin_width)) - 1
+            # skip outside of max bins
+            if i_q<0 or i_q>=nbins: continue
+            # give it full weight
+            #frac = 1
+            #TODO: find better definition of x[i_q] based on q_data
+            x[i_q]         = min +(i_q+1)*self.bin_width/2.0
+            y[i_q]         += frac * data[npts]
+            if err_data == None or err_data[npts]==0.0:
+                err_y[i_q] += frac * frac * math.fabs(data[npts])
+            else:
+                err_y[i_q] += frac * frac * err_data[npts] * err_data[npts]
+            y_counts[i_q]  += frac
+        # Average the sums
+        for n in range(nbins):
+            err_y[n] = math.sqrt(err_y[n])
+        err_y = err_y/y_counts
+        y    = y/y_counts
+        idx = (numpy.isfinite(y)& numpy.isfinite(x))
+        if not idx.any():
+            raise ValueError, "Average Error: No points inside ROI to average..."
+        elif len(y[idx])!= nbins:
+            print "resulted",nbins- len(y[idx]),"empty bin(s) due to tight binning..."
+        return Data1D(x=x[idx], y=y[idx], dy=err_y[idx])
 class SlabY(_Slab):
 …
             raise RuntimeError, "Circular averaging: invalid number of detectors: %g" % len(data2D.detector)
         pixel_width_x = data2D.detector[0].pixel_size.x
         pixel_width_y = data2D.detector[0].pixel_size.y
         det_dist    = data2D.detector[0].distance
         wavelength  = data2D.source.wavelength
         center_x    = data2D.detector[0].beam_center.x/pixel_width_x
         center_y    = data2D.detector[0].beam_center.y/pixel_width_y
+        # Get data
+        data = data2D.data
+        q_data = data2D.q_data
+        err_data = data2D.err_data
+        qx_data = data2D.qx_data
+        qy_data = data2D.qy_data
         y  = 0.0
         err_y = 0.0
         y_counts = 0.0
+        sign=1
+        for i in range(numpy.size(data2D.data,1)):
+            # Min and max x-value for the pixel
+            minx = pixel_width_x*(i - center_x)
+            maxx = pixel_width_x*(i+1.0 - center_x)
+            if minx>=0:
+                sign=1
+        # Average pixelsize in q space
+        for npts in range(len(data)):
+            # default frac
+            frac_x = 0
+            frac_y = 0
+            # get min and max at each points
+            qx = qx_data[npts]
+            qy = qy_data[npts]
+            # get the ROI
+            if self.x_min <= qx and self.x_max > qx:
+                frac_x = 1
+            if self.y_min <= qy and self.y_max > qy:
+                frac_y = 1
+            #Find the fraction along each directions
+            frac = frac_x * frac_y
+            if frac == 0: continue
+            y += frac * data[npts]
+            if err_data == None or err_data[npts]==0.0:
+                err_y += frac * frac * math.fabs(data[npts])
             else:
+                sign=-1
+            qxmin = sign*get_q(minx, 0.0, det_dist, wavelength)
+            if maxx>=0:
+                sign=1
+            else:
+                sign=-1
+            qxmax = sign*get_q(maxx, 0.0, det_dist, wavelength)
+            # Get the count fraction in x for that pixel
+            frac_min = get_pixel_fraction_square(self.x_min, qxmin, qxmax)
+            frac_max = get_pixel_fraction_square(self.x_max, qxmin, qxmax)
+            frac_x = frac_max - frac_min
+            for j in range(numpy.size(data2D.data,0)):
+                # Min and max y-value for the pixel
+                miny = pixel_width_y*(j - center_y)
+                maxy = pixel_width_y*(j+1.0 - center_y)
+                if miny>=0:
+                    sign=1
+                else:
+                    sign=-1
+                qymin = sign*get_q(0.0, miny, det_dist, wavelength)
+                if maxy>=0:
+                    sign=1
+                else:
+                    sign=-1
+                qymax = sign*get_q(0.0, maxy, det_dist, wavelength)
+                # Get the count fraction in x for that pixel
+                frac_min = get_pixel_fraction_square(self.y_min, qymin, qymax)
+                frac_max = get_pixel_fraction_square(self.y_max, qymin, qymax)
+                frac_y = frac_max - frac_min
+                frac = frac_x * frac_y
+                y += frac * data2D.data[j][i]
+                if data2D.err_data == None or data2D.err_data[j][i]==0.0:
+                    err_y += frac * frac * math.fabs(data2D.data[j][i])
+                else:
+                    err_y += frac * frac * data2D.err_data[j][i] * data2D.err_data[j][i]
+                y_counts += frac
+                err_y += frac * frac * err_data[npts] * err_data[npts]
+            y_counts += frac
         return y, err_y, y_counts
 class Boxavg(Boxsum):
 …
         as a function of Q
     """
     def __init__(self, r_min=0.0, r_max=0.0, bin_width=0.001):
+    def __init__(self, r_min=0.0, r_max=0.0, bin_width=0.0005):
         # Minimum radius included in the average [A-1]
         self.r_min = r_min
 …
             @return: Data1D object
         """
+        if len(data2D.detector) != 1:
+            raise RuntimeError, "Circular averaging: invalid number of detectors: %g" % len(data2D.detector)
+        pixel_width_x = data2D.detector[0].pixel_size.x
+        pixel_width_y = data2D.detector[0].pixel_size.y
+        det_dist    = data2D.detector[0].distance
+        wavelength  = data2D.source.wavelength
+        center_x    = data2D.detector[0].beam_center.x/pixel_width_x+0.5
+        center_y    = data2D.detector[0].beam_center.y/pixel_width_y+0.5
+        # Find out the maximum Q range
+        xwidth = (numpy.size(data2D.data,1))*pixel_width_x
+        dx_max = xwidth - data2D.detector[0].beam_center.x
+        if xwidth-dx_max>dx_max:
+            dx_max = xwidth-dx_max
+        ywidth = (numpy.size(data2D.data,0))*pixel_width_y
+        dy_max = ywidth - data2D.detector[0].beam_center.y
+        if ywidth-dy_max>dy_max:
+            dy_max = ywidth-dy_max
+        qmax = get_q(dx_max, dy_max, det_dist, wavelength)
+        # Get data
+        data = data2D.data
+        err_data = data2D.err_data
+        q_data = data2D.q_data
+        q_data_max = numpy.max(q_data)
+        if len(data2D.q_data) == None:
+            raise RuntimeError, "Circular averaging: invalid q_data: %g" % data2D.q_data
         # Build array of Q intervals
         nbins = int(math.ceil((qmax-self.r_min)/self.bin_width))
+        nbins = int(math.ceil((self.r_max-self.r_min)/self.bin_width))
         qbins = self.bin_width*numpy.arange(nbins)+self.r_min
         x  = numpy.zeros(nbins)
         y  = numpy.zeros(nbins)
         err_y = numpy.zeros(nbins)
         y_counts = numpy.zeros(nbins)
+        for i in range(numpy.size(data2D.data,1)):
+            dx = pixel_width_x*(i+0.5 - center_x)
+            # Min and max x-value for the pixel
+            minx = pixel_width_x*(i - center_x)
+            maxx = pixel_width_x*(i+1.0 - center_x)
+            for j in range(numpy.size(data2D.data,0)):
+                dy = pixel_width_y*(j+0.5 - center_y)
+                q_value = get_q(dx, dy, det_dist, wavelength)
+                # Min and max y-value for the pixel
+                miny = pixel_width_y*(j - center_y)
+                maxy = pixel_width_y*(j+1.0 - center_y)
+                # Calculate the q-value for each corner
+                # q_[x min or max][y min or max]
+                q_00 = get_q(minx, miny, det_dist, wavelength)
+                q_01 = get_q(minx, maxy, det_dist, wavelength)
+                q_10 = get_q(maxx, miny, det_dist, wavelength)
+                q_11 = get_q(maxx, maxy, det_dist, wavelength)
+                # Look for intercept between each side of the pixel
+                # and the constant-q ring for qmax
+                frac_max = get_pixel_fraction(self.r_max, q_00, q_01, q_10, q_11)
+                # Look for intercept between each side of the pixel
+                # and the constant-q ring for qmin
+                frac_min = get_pixel_fraction(self.r_min, q_00, q_01, q_10, q_11)
+                # We are interested in the region between qmin and qmax
+                # therefore the fraction of the surface of the pixel
+                # that we will use to calculate the number of counts to
+                # include is given by:
+                frac = frac_max - frac_min
+                i_q = int(math.ceil((q_value-self.r_min)/self.bin_width)) - 1
+                if q_value > qmax or q_value < self.r_min:
+                    continue
+                x[i_q]          = q_value
+                y[i_q]         += frac * data2D.data[j][i]
+                if data2D.err_data == None or data2D.err_data[j][i]==0.0:
+                    err_y[i_q] += frac * frac * math.fabs(data2D.data[j][i])
+                else:
+                    err_y[i_q] += frac * frac * data2D.err_data[j][i] * data2D.err_data[j][i]
+                y_counts[i_q]  += frac
+        # Average the sums
+        nzero = 0
+        for i in range(nbins):
+            if y_counts[i]>0:
+                err_y[i] = math.sqrt(err_y[i])/y_counts[i]
+                y[i]     = y[i]/y_counts[i]
+        for npt in range(len(data)):
+            frac = 0
+            # q-value at the pixel (j,i)
+            q_value = q_data[npt]
+            data_n = data[npt]
+            ## No need to calculate the frac when all data are within range
+            if self.r_min >= self.r_max:
+                raise ValueError, "Limit Error: min > max ???"
+            if self.r_min <= q_value and q_value <= self.r_max: frac = 1
+            if frac == 0: continue
+            i_q = int(math.floor((q_value-self.r_min)/self.bin_width))
+            # Take care of the edge case at phi = 2pi.
+            if i_q == nbins:
+                i_q = nbins -1
+            y[i_q] += frac * data_n
+            if err_data == None or err_data[npt]==0.0:
+                err_y[i_q] += frac * frac * math.fabs(data_n)
             else:
+                nzero += 1
+        ## Get rid of NULL points
+        tx  = numpy.zeros(nbins-nzero)
+        ty  = numpy.zeros(nbins-nzero)
+        terr_y = numpy.zeros(nbins-nzero)
+        j=0
+        for i in range(nbins):
+            if err_y[i] != 0 :
+                tx[j]  = x[i]
+                ty[j]  = y[i]
+                terr_y[j] = err_y[i]
+                j+=1
+        return Data1D(x=tx, y=ty, dy=terr_y)
+                err_y[i_q] += frac * frac * err_data[npt] * err_data[npt]
+            y_counts[i_q]  += frac
+        ## x should be the center value of each bins
+        x = qbins+self.bin_width/2
+        # Average the sums
+        for n in range(nbins):
+            err_y[n] = math.sqrt(err_y[n])
+        err_y = err_y/y_counts
+        y    = y/y_counts
+        idx = (numpy.isfinite(y))&(numpy.isfinite(x))
+        if not idx.any():
+            raise ValueError, "Average Error: No points inside ROI to average..."
+        elif len(y[idx])!= nbins:
+            print "resulted",nbins- len(y[idx]),"empty bin(s) due to tight binning..."
+        return Data1D(x=x[idx], y=y[idx], dy=err_y[idx])
 …
         around the ring as a function of phi.
+    """
+        Phi_min and phi_max should be defined between 0 and 2*pi
+        in anti-clockwise starting from the x- axis on the left-hand side
+    """
+    #Todo: remove center.
     def __init__(self, r_min=0, r_max=0, center_x=0, center_y=0,nbins=20 ):
         # Minimum radius
 …
             raise RuntimeError, "Ring averaging only take plottable_2D objects"
+        data = data2D.data
+        qmin = self.r_min
+        qmax = self.r_max
+        if len(data2D.detector) != 1:
+            raise RuntimeError, "Ring averaging: invalid number of detectors: %g" % len(data2D.detector)
+        pixel_width_x = data2D.detector[0].pixel_size.x
+        pixel_width_y = data2D.detector[0].pixel_size.y
+        det_dist = data2D.detector[0].distance
+        wavelength = data2D.source.wavelength
+        #center_x = self.center_x/pixel_width_x
+        #center_y = self.center_y/pixel_width_y
+        center_x    = data2D.detector[0].beam_center.x/pixel_width_x
+        center_y    = data2D.detector[0].beam_center.y/pixel_width_y
+        Pi = math.pi
+        # Get data
+        data = data2D.data
+        err_data = data2D.err_data
+        q_data = data2D.q_data
+        qx_data = data2D.qx_data
+        qy_data = data2D.qy_data
+        q_data_max = numpy.max(q_data)
+        # Set space for 1d outputs
         phi_bins   = numpy.zeros(self.nbins_phi)
         phi_counts = numpy.zeros(self.nbins_phi)
 …
         phi_err    = numpy.zeros(self.nbins_phi)
+        for i in range(numpy.size(data,1)):
+            dx = pixel_width_x*(i+0.5 - center_x)
+            # Min and max x-value for the pixel
+            minx = pixel_width_x*(i - center_x)
+            maxx = pixel_width_x*(i+1.0 - center_x)
+            for j in range(numpy.size(data,0)):
+                dy = pixel_width_y*(j+0.5 - center_y)
+                q_value = get_q(dx, dy, det_dist, wavelength)
+                # Min and max y-value for the pixel
+                miny = pixel_width_y*(j - center_y)
+                maxy = pixel_width_y*(j+1.0 - center_y)
+                # Calculate the q-value for each corner
+                # q_[x min or max][y min or max]
+                q_00 = get_q(minx, miny, det_dist, wavelength)
+                q_01 = get_q(minx, maxy, det_dist, wavelength)
+                q_10 = get_q(maxx, miny, det_dist, wavelength)
+                q_11 = get_q(maxx, maxy, det_dist, wavelength)
+                # Look for intercept between each side of the pixel
+                # and the constant-q ring for qmax
+                frac_max = get_pixel_fraction(qmax, q_00, q_01, q_10, q_11)
+                # Look for intercept between each side of the pixel
+                # and the constant-q ring for qmin
+                frac_min = get_pixel_fraction(qmin, q_00, q_01, q_10, q_11)
+                # We are interested in the region between qmin and qmax
+                # therefore the fraction of the surface of the pixel
+                # that we will use to calculate the number of counts to
+                # include is given by:
+                frac = frac_max - frac_min
+                i_phi = int(math.ceil(self.nbins_phi*(math.atan2(dy, dx)+math.pi)/(2.0*math.pi))) - 1
+                phi_bins[i_phi] += frac * data[j][i]
+                if data2D.err_data == None or data2D.err_data[j][i]==0.0:
+                    phi_err[i_phi] += frac * frac * math.fabs(data2D.data[j][i])
+                else:
+                    phi_err[i_phi] += frac * frac * data2D.err_data[j][i] * data2D.err_data[j][i]
+                phi_counts[i_phi] += frac
+        for npt in range(len(data)):
+            frac = 0
+            # q-value at the point (npt)
+            q_value = q_data[npt]
+            data_n = data[npt]
+            # phi-value at the point (npt)
+            phi_value=math.atan2(qy_data[npt],qx_data[npt])+Pi
+            if self.r_min <= q_value and q_value <= self.r_max: frac = 1
+            if frac == 0: continue
+            # binning
+            i_phi = int(math.floor((self.nbins_phi)*phi_value/(2*Pi)))
+            # Take care of the edge case at phi = 2pi.
+            if i_phi == self.nbins_phi:
+                i_phi =  self.nbins_phi -1
+            phi_bins[i_phi] += frac * data[npt]
+            if err_data == None or err_data[npt] ==0.0:
+                phi_err[i_phi] += frac * frac * math.fabs(data_n)
+            else:
+                phi_err[i_phi] += frac * frac *err_data[npt]*err_data[npt]
+            phi_counts[i_phi] += frac
         for i in range(self.nbins_phi):
             phi_bins[i] = phi_bins[i] / phi_counts[i]
             phi_err[i] = math.sqrt(phi_err[i]) / phi_counts[i]
+            phi_values[i] = 2.0*math.pi/self.nbins_phi*(1.0*i + 0.5)-math.pi # move the pi back to -pi <-->+pi
+        return Data1D(x=phi_values, y=phi_bins, dy=phi_err)
+            phi_values[i] = 2.0*math.pi/self.nbins_phi*(1.0*i + 0.5)
+        idx = (numpy.isfinite(phi_bins))
+        if not idx.any():
+            raise ValueError, "Average Error: No points inside ROI to average..."
+        elif len(phi_bins[idx])!= self.nbins_phi:
+            print "resulted",self.nbins_phi- len(phi_bins[idx]),"empty bin(s) due to tight binning..."
+        return Data1D(x=phi_values[idx], y=phi_bins[idx], dy=phi_err[idx])
 def get_pixel_fraction(qmax, q_00, q_01, q_10, q_11):
 …
     elif (q_00+q_01+q_10+q_11)/4.0 < qmax:
         frac_max = 1.0
     return frac_max
 …
             return (q-q_1)/(q_0 - q_1)
     return None
+#This class can be removed.
+class _Sectorold:
+    """
+        Defines a sector region on a 2D data set.
+        The sector is defined by r_min, r_max, phi_min, phi_max,
+        and the position of the center of the ring.
+        Phi is defined between 0 and 2pi
+    """
+    def __init__(self, r_min, r_max, phi_min, phi_max,nbins=20):
+        self.r_min = r_min
+        self.r_max = r_max
+        self.phi_min = phi_min
+        self.phi_max = phi_max
+        self.nbins = nbins
+    def _agv(self, data2D, run='phi'):
+        """
+            Perform sector averaging.
+            @param data2D: Data2D object
+            @param run:  define the varying parameter ('phi' or 'q')
+            @return: Data1D object
+        """
+        if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]:
+            raise RuntimeError, "Ring averaging only take plottable_2D objects"
+        data = data2D.data
+        qmax = self.r_max
+        qmin = self.r_min
+        if len(data2D.detector) != 1:
+            raise RuntimeError, "Ring averaging: invalid number of detectors: %g" % len(data2D.detector)
+        pixel_width_x = data2D.detector[0].pixel_size.x
+        pixel_width_y = data2D.detector[0].pixel_size.y
+        det_dist      = data2D.detector[0].distance
+        wavelength    = data2D.source.wavelength
+        center_x      = data2D.detector[0].beam_center.x/pixel_width_x
+        center_y      = data2D.detector[0].beam_center.y/pixel_width_y
+        y        = numpy.zeros(self.nbins)
+        y_counts = numpy.zeros(self.nbins)
+        x        = numpy.zeros(self.nbins)
+        y_err    = numpy.zeros(self.nbins)
+        for i in range(numpy.size(data,1)):
+            dx = pixel_width_x*(i+0.5 - center_x)
+            # Min and max x-value for the pixel
+            minx = pixel_width_x*(i - center_x)
+            maxx = pixel_width_x*(i+1.0 - center_x)
+            for j in range(numpy.size(data,0)):
+                dy = pixel_width_y*(j+0.5 - center_y)
+                q_value = get_q(dx, dy, det_dist, wavelength)
+                # Min and max y-value for the pixel
+                miny = pixel_width_y*(j - center_y)
+                maxy = pixel_width_y*(j+1.0 - center_y)
+                # Calculate the q-value for each corner
+                # q_[x min or max][y min or max]
+                q_00 = get_q(minx, miny, det_dist, wavelength)
+                q_01 = get_q(minx, maxy, det_dist, wavelength)
+                q_10 = get_q(maxx, miny, det_dist, wavelength)
+                q_11 = get_q(maxx, maxy, det_dist, wavelength)
+                # Look for intercept between each side of the pixel
+                # and the constant-q ring for qmax
+                frac_max = get_pixel_fraction(qmax, q_00, q_01, q_10, q_11)
+                # Look for intercept between each side of the pixel
+                # and the constant-q ring for qmin
+                frac_min = get_pixel_fraction(qmin, q_00, q_01, q_10, q_11)
+                # We are interested in the region between qmin and qmax
+                # therefore the fraction of the surface of the pixel
+                # that we will use to calculate the number of counts to
+                # include is given by:
+                frac = frac_max - frac_min
+                # Compute phi and check whether it's within the limits
+                phi_value=math.atan2(dy,dx)+math.pi
+ #               if phi_value<self.phi_min or phi_value>self.phi_max:
+                if phi_value<self.phi_min or phi_value>self.phi_max:
+                    continue
+                # Check which type of averaging we need
+                if run.lower()=='phi':
+                    i_bin = int(math.ceil(self.nbins*(phi_value-self.phi_min)/(self.phi_max-self.phi_min))) - 1
+                else:
+                    # If we don't need this pixel, skip the rest of the work
+                    #TODO: an improvement here would be to compute the average
+                    # Q for the pixel from the part that is covered by
+                    # the ring defined by q_min/q_max rather than the complete
+                    # pixel
+                    if q_value<self.r_min or q_value>self.r_max:
+                        continue
+                    i_bin = int(math.ceil(self.nbins*(q_value-self.r_min)/(self.r_max-self.r_min))) - 1
+                try:
+                    y[i_bin] += frac * data[j][i]
+                except:
+                    import sys
+                    print sys.exc_value
+                    print i_bin, frac
+                if data2D.err_data == None or data2D.err_data[j][i]==0.0:
+                    y_err[i_bin] += frac * frac * math.fabs(data2D.data[j][i])
+                else:
+                    y_err[i_bin] += frac * frac * data2D.err_data[j][i] * data2D.err_data[j][i]
+                y_counts[i_bin] += frac
+        for i in range(self.nbins):
+            y[i] = y[i] / y_counts[i]
+            y_err[i] = math.sqrt(y_err[i]) / y_counts[i]
+            # Check which type of averaging we need
+            if run.lower()=='phi':
+                x[i] = (self.phi_max-self.phi_min)/self.nbins*(1.0*i + 0.5)+self.phi_min
+            else:
+                x[i] = (self.r_max-self.r_min)/self.nbins*(1.0*i + 0.5)+self.r_min
+        return Data1D(x=x, y=y, dy=y_err)
 class _Sector:
     """
 …
         and phi_max could be less than phi_min.
         Phi is defined between 0 and 2pi
     """
     def __init__(self, r_min, r_max, phi_min, phi_max,nbins=20):
+        Phi is defined between 0 and 2*pi in anti-clockwise starting from the x- axis on the left-hand side
+    """
+    def __init__(self, r_min, r_max, phi_min=0, phi_max=2*math.pi,nbins=20):
         self.r_min = r_min
         self.r_max = r_max
 …
         self.nbins = nbins
     def _agv(self, data2D, run='phi'):
         """
 …
             @param data2D: Data2D object
             @param run:  define the varying parameter ('phi' or 'q')
+            @param run:  define the varying parameter ('phi' , 'q' , or 'q2')
             @return: Data1D object
         """
         if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]:
             raise RuntimeError, "Ring averaging only take plottable_2D objects"
+        data = data2D.data
+        qmax = self.r_max
+        qmin = self.r_min
+        if len(data2D.detector) != 1:
+            raise RuntimeError, "Ring averaging: invalid number of detectors: %g" % len(data2D.detector)
+        pixel_width_x = data2D.detector[0].pixel_size.x
+        pixel_width_y = data2D.detector[0].pixel_size.y
+        det_dist      = data2D.detector[0].distance
+        wavelength    = data2D.source.wavelength
+        center_x      = data2D.detector[0].beam_center.x/pixel_width_x
+        center_y      = data2D.detector[0].beam_center.y/pixel_width_y
+        Pi = math.pi
+        # Get the all data & info
+        data = data2D.data
+        err_data = data2D.err_data
+        qx_data = data2D.qx_data
+        qy_data = data2D.qy_data
+        q_data = data2D.q_data
+        #set space for 1d outputs
+        x        = numpy.zeros(self.nbins)
         y        = numpy.zeros(self.nbins)
+        y_err    = numpy.zeros(self.nbins)
         y_counts = numpy.zeros(self.nbins)
+        x        = numpy.zeros(self.nbins)
+        y_err    = numpy.zeros(self.nbins)
+        # This If finds qmax within ROI defined by sector lines
+        temp=0 #to find qmax within ROI or phimax and phimin
+        temp0=1000000
+        temp1=0
+        for i in range(numpy.size(data,1)):
+            dx = pixel_width_x*(i+0.5 - center_x)
+            for j in range(numpy.size(data,0)):
+                dy = pixel_width_y*(j+0.5 - center_y)
+                q_value = get_q(dx, dy, det_dist, wavelength)
+                # Compute phi and check whether it's within the limits
+                phi_value=math.atan2(dy,dx)+math.pi
+                if self.phi_max>2*math.pi:
+                    self.phi_max=self.phi_max-2*math.pi
+                if self.phi_min<0:
+                    self.phi_max=self.phi_max+2*math.pi
+                #In case of two ROI (symmetric major and minor wings)(for 'q2')
+        # Get the min and max into the region: 0 <= phi < 2Pi
+        phi_min = flip_phi(self.phi_min)
+        phi_max = flip_phi(self.phi_max)
+        q_data_max = numpy.max(q_data)
+        for n in range(len(data)):
+                frac = 0
+                # q-value at the pixel (j,i)
+                q_value = q_data[n]
+                data_n = data[n]
+                # Is pixel within range?
+                is_in = False
+                # phi-value of the pixel (j,i)
+                phi_value=math.atan2(qy_data[n],qx_data[n])+Pi
+                ## No need to calculate the frac when all data are within range
+                if self.r_min <= q_value and q_value <= self.r_max: frac = 1
+                if frac == 0: continue
+                #In case of two ROIs (symmetric major and minor regions)(for 'q2')
                 if run.lower()=='q2':
+                    if ((self.phi_max>=0 and self.phi_max<math.pi)and (self.phi_min>=0 and self.phi_min<math.pi)):
+                        temp_max=self.phi_max+math.pi
+                        temp_min=self.phi_min+math.pi
+                    ## For minor sector wing
+                    # Calculate the minor wing phis
+                    phi_min_minor = flip_phi(phi_min-Pi)
+                    phi_max_minor = flip_phi(phi_max-Pi)
+                    # Check if phis of the minor ring is within 0 to 2pi
+                    if phi_min_minor > phi_max_minor:
+                        is_in = (phi_value > phi_min_minor or phi_value < phi_max_minor)
                     else:
+                        temp_max=self.phi_max
+                        temp_min=self.phi_min
+                    if ((temp_max>=math.pi and temp_max<2*math.pi)and (temp_min>=math.pi and temp_min<2*math.pi)):
+                        if (phi_value<temp_min  or phi_value>temp_max):
+                             if (phi_value<temp_min-math.pi  or phi_value>temp_max-math.pi):
+                                 continue
+                    if (self.phi_max<self.phi_min):
+                        tmp_max=self.phi_max+math.pi
+                        tmp_min=self.phi_min-math.pi
+                    else:
+                        tmp_max=self.phi_max
+                        tmp_min=self.phi_min
+                    if (tmp_min<math.pi and tmp_max>math.pi):
+                        if((phi_value>tmp_max and phi_value<tmp_min+math.pi)or (phi_value>tmp_max-math.pi and phi_value<tmp_min)):
+                            continue
+                #In case of one ROI (major only)(i.e.,for 'q')and do nothing for 'phi'.
+                elif run.lower()=='q':
+                    if (self.phi_max>=self.phi_min):
+                        if (phi_value<self.phi_min  or phi_value>self.phi_max):
+                            continue
+                    else:
+                        if (phi_value<self.phi_min and phi_value>self.phi_max):
+                            continue
+                if q_value<qmin or q_value>qmax:
+                        continue
+                if run.lower()=='phi':
+                    if temp1<phi_value:
+                        temp1=phi_value
+                    if temp0>phi_value:
+                        temp0=phi_value
+                elif temp<q_value:
+                    temp=q_value
+        if run.lower()=='phi':
+            self.phi_max=temp1
+            self.phi_min=temp0
+        else:
+            qmax=temp
+        #Beam center is already corrected, but the calculation below assumed it was not.
+        # Thus Beam center shifted back to uncorrected value. ToDo: cleanup the mess.
+        center_x=center_x+0.5
+        center_y=center_y+0.5
+        for i in range(numpy.size(data,1)):
+            dx = pixel_width_x*(i+0.5 - center_x)
+            # Min and max x-value for the pixel
+            minx = pixel_width_x*(i - center_x)
+            maxx = pixel_width_x*(i+1.0 - center_x)
+            for j in range(numpy.size(data,0)):
+                dy = pixel_width_y*(j+0.5 - center_y)
+                q_value = get_q(dx, dy, det_dist, wavelength)
+                # Min and max y-value for the pixel
+                miny = pixel_width_y*(j - center_y)
+                maxy = pixel_width_y*(j+1.0 - center_y)
+                # Calculate the q-value for each corner
+                # q_[x min or max][y min or max]
+                q_00 = get_q(minx, miny, det_dist, wavelength)
+                q_01 = get_q(minx, maxy, det_dist, wavelength)
+                q_10 = get_q(maxx, miny, det_dist, wavelength)
+                q_11 = get_q(maxx, maxy, det_dist, wavelength)
+                # Compute phi and check whether it's within the limits
+                phi_value=math.atan2(dy,dx)+math.pi
+                if self.phi_max>2*math.pi:
+                    self.phi_max=self.phi_max-2*math.pi
+                if self.phi_min<0:
+                    self.phi_max=self.phi_max+2*math.pi
+                # Look for intercept between each side of the pixel
+                # and the constant-q ring for qmax
+                frac_max = get_pixel_fraction(qmax, q_00, q_01, q_10, q_11)
+                # Look for intercept between each side of the pixel
+                # and the constant-q ring for qmin
+                frac_min = get_pixel_fraction(qmin, q_00, q_01, q_10, q_11)
+                # We are interested in the region between qmin and qmax
+                # therefore the fraction of the surface of the pixel
+                # that we will use to calculate the number of counts to
+                # include is given by:
+                frac = frac_max - frac_min
+                #In case of two ROI (symmetric major and minor regions)(for 'q2')
+                if run.lower()=='q2':
+                    if ((self.phi_max>=0 and self.phi_max<math.pi)and (self.phi_min>=0 and self.phi_min<math.pi)):
+                        temp_max=self.phi_max+math.pi
+                        temp_min=self.phi_min+math.pi
+                    else:
+                        temp_max=self.phi_max
+                        temp_min=self.phi_min
+                    if ((temp_max>=math.pi and temp_max<2*math.pi)and (temp_min>=math.pi and temp_min<2*math.pi)):
+                        if (phi_value<temp_min  or phi_value>temp_max):
+                            if (phi_value<temp_min-math.pi  or phi_value>temp_max-math.pi):
+                                continue
+                    if (self.phi_max<self.phi_min):
+                        tmp_max=self.phi_max+math.pi
+                        tmp_min=self.phi_min-math.pi
+                    else:
+                        tmp_max=self.phi_max
+                        tmp_min=self.phi_min
+                    if (tmp_min<math.pi and tmp_max>math.pi):
+                        if((phi_value>tmp_max and phi_value<tmp_min+math.pi)or (phi_value>tmp_max-math.pi and phi_value<tmp_min)):
+                            continue
+                #In case of one ROI (major only)(i.e.,for 'q' and 'phi')
+                else:
+                    if (self.phi_max>=self.phi_min):
+                        if (phi_value<self.phi_min  or phi_value>self.phi_max):
+                            continue
+                    else:
+                        if (phi_value<self.phi_min and phi_value>self.phi_max):
+                            continue
+                #print "qmax=",qmax,qmin
+                if q_value<qmin or q_value>qmax:
+                    continue
+                        is_in = (phi_value > phi_min_minor and phi_value < phi_max_minor)
+                #For all cases(i.e.,for 'q', 'q2', and 'phi')
+                #Find pixels within ROI
+                if phi_min > phi_max:
+                    is_in = is_in or (phi_value > phi_min or phi_value < phi_max)
+                else:
+                    is_in = is_in or (phi_value>= phi_min  and  phi_value <phi_max)
+                if not is_in: frac = 0
+                if frac == 0: continue
                 # Check which type of averaging we need
                 if run.lower()=='phi':
+                    i_bin = int(math.ceil(self.nbins*(phi_value-self.phi_min)/(self.phi_max-self.phi_min))) - 1
+                    i_bin    = int(math.floor((self.nbins)*(phi_value-self.phi_min)\
+                                              /(self.phi_max-self.phi_min)))
                 else:
+                    # If we don't need this pixel, skip the rest of the work
+                    #TODO: an improvement here would be to compute the average
+                    # Q for the pixel from the part that is covered by
+                    # the ring defined by q_min/q_max rather than the complete
+                    # pixel
+                    i_bin = int(math.ceil(self.nbins*(q_value-qmin)/(qmax-qmin))) - 1
+                try:
+                    y[i_bin] += frac * data[j][i]
+                except:
+                    import sys
+                    print sys.exc_value
+                    print i_bin, frac
+                if data2D.err_data == None or data2D.err_data[j][i]==0.0:
+                    y_err[i_bin] += frac * frac * math.fabs(data2D.data[j][i])
+                    i_bin = int(math.floor((self.nbins)*(q_value-self.r_min)/(self.r_max-self.r_min)))
+                # Take care of the edge case at phi = 2pi.
+                if i_bin == self.nbins:
+                    i_bin =  self.nbins -1
+                ## Get the total y
+                y[i_bin] += frac * data_n
+                if err_data == None or err_data[n] ==0.0:
+                    y_err[i_bin] += frac * frac * math.fabs(data_n)
                 else:
                     y_err[i_bin] += frac * frac * data2D.err_data[j][i] * data2D.err_data[j][i]
+                    y_err[i_bin] += frac * frac * err_data[n]*err_data[n]
                 y_counts[i_bin] += frac
+        # Organize the results
         for i in range(self.nbins):
             y[i] = y[i] / y_counts[i]
             y_err[i] = math.sqrt(y_err[i]) / y_counts[i]
+            # Check which type of averaging we need
+            if run.lower()=='phi':
+                #Calculate x[i] and put back the origin of angle back to the right hand side (from the left).
+                x[i] = ((self.phi_max-self.phi_min)/self.nbins*(1.0*i + 0.5)+self.phi_min-2*math.pi)*180/math.pi
+                if x[i]<0:
+                    x[i]=180+x[i]
+            # The type of averaging: phi,q2, or q
+            # Calculate x[i]should be at the center of the bin
+            if run.lower()=='phi':
+                x[i] = (self.phi_max-self.phi_min)/self.nbins*(1.0*i + 0.5)+self.phi_min
             else:
+                x[i] = (qmax-qmin)/self.nbins*(1.0*i + 0.5)+qmin
+        return Data1D(x=x, y=y, dy=y_err)
+                x[i] = (self.r_max-self.r_min)/self.nbins*(1.0*i + 0.5)+self.r_min
+        idx = (numpy.isfinite(y)& numpy.isfinite(y_err))
+        if not idx.any():
+            raise ValueError, "Average Error: No points inside sector of ROI to average..."
+        elif len(y[idx])!= self.nbins:
+            print "resulted",self.nbins- len(y[idx]),"empty bin(s) due to tight binning..."
+        return Data1D(x=x[idx], y=y[idx], dy=y_err[idx])
 class SectorPhi(_Sector):
 …
         """
         return self._agv(data2D, 'phi')
-class SectorQold(_Sector):
-    """
-        Sector average as a function of Q.
-        I(Q) is return and the data is averaged over phi.
-        A sector is defined by r_min, r_max, phi_min, phi_max.
-        The number of bin in Q also has to be defined.
-    """
-    def __call__(self, data2D):
-        """
-            Perform sector average and return I(Q).
-            @param data2D: Data2D object
-            @return: Data1D object
-        """
-        return self._agv(data2D, 'q')
 class SectorQ(_Sector):

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SasView

Changeset 095ab1b in sasview

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DataLoader/manipulations.py

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