| 1 | """ |
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| 2 | Data manipulations for 2D data sets. |
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| 3 | Using the meta data information, various types of averaging |
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| 4 | are performed in Q-space |
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| 5 | """ |
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| 6 | |
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| 7 | """ |
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| 8 | This software was developed by the University of Tennessee as part of the |
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| 9 | Distributed Data Analysis of Neutron Scattering Experiments (DANSE) |
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| 10 | project funded by the US National Science Foundation. |
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| 11 | |
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| 12 | See the license text in license.txt |
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| 13 | |
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| 14 | copyright 2008, University of Tennessee |
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| 15 | """ |
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| 16 | #TODO: copy the meta data from the 2D object to the resulting 1D object |
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| 17 | |
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| 18 | from data_info import plottable_2D, Data1D |
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| 19 | import math |
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| 20 | import numpy |
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| 21 | |
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| 22 | def get_q(dx, dy, det_dist, wavelength): |
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| 23 | """ |
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| 24 | @param dx: x-distance from beam center [mm] |
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| 25 | @param dy: y-distance from beam center [mm] |
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| 26 | @return: q-value at the given position |
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| 27 | """ |
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| 28 | # Distance from beam center in the plane of detector |
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| 29 | plane_dist = math.sqrt(dx*dx + dy*dy) |
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| 30 | # Half of the scattering angle |
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| 31 | theta = 0.5*math.atan(plane_dist/det_dist) |
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| 32 | return (4.0*math.pi/wavelength)*math.sin(theta) |
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| 33 | |
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| 34 | def get_q_compo(dx, dy, det_dist, wavelength,compo=None): |
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| 35 | #This reduces tiny error at very large q. |
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| 36 | #Implementation of this func is not started yet.<--ToDo |
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| 37 | if dy==0: |
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| 38 | if dx>=0: |
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| 39 | angle_xy=0 |
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| 40 | else: |
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| 41 | angle_xy=math.pi |
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| 42 | else: |
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| 43 | angle_xy=math.atan(dx/dy) |
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| 44 | |
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| 45 | if compo=="x": |
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| 46 | out=get_q(dx, dy, det_dist, wavelength)*cos(angle_xy) |
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| 47 | elif compo=="y": |
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| 48 | out=get_q(dx, dy, det_dist, wavelength)*sin(angle_xy) |
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| 49 | else: |
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| 50 | out=get_q(dx, dy, det_dist, wavelength) |
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| 51 | return out |
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| 52 | |
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| 53 | class _Slab(object): |
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| 54 | """ |
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| 55 | Compute average I(Q) for a region of interest |
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| 56 | """ |
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| 57 | def __init__(self, x_min=0.0, x_max=0.0, y_min=0.0, y_max=0.0, bin_width=0.001): |
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| 58 | # Minimum Qx value [A-1] |
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| 59 | self.x_min = x_min |
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| 60 | # Maximum Qx value [A-1] |
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| 61 | self.x_max = x_max |
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| 62 | # Minimum Qy value [A-1] |
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| 63 | self.y_min = y_min |
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| 64 | # Maximum Qy value [A-1] |
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| 65 | self.y_max = y_max |
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| 66 | # Bin width (step size) [A-1] |
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| 67 | self.bin_width = bin_width |
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| 68 | # If True, I(|Q|) will be return, otherwise, negative q-values are allowed |
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| 69 | self.fold = False |
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| 70 | |
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| 71 | def __call__(self, data2D): return NotImplemented |
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| 72 | |
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| 73 | def _avg(self, data2D, maj): |
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| 74 | """ |
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| 75 | Compute average I(Q_maj) for a region of interest. |
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| 76 | The major axis is defined as the axis of Q_maj. |
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| 77 | The minor axis is the axis that we average over. |
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| 78 | |
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| 79 | @param data2D: Data2D object |
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| 80 | @param maj_min: min value on the major axis |
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| 81 | @return: Data1D object |
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| 82 | """ |
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| 83 | if len(data2D.detector) != 1: |
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| 84 | raise RuntimeError, "_Slab._avg: invalid number of detectors: %g" % len(data2D.detector) |
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| 85 | |
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| 86 | pixel_width_x = data2D.detector[0].pixel_size.x |
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| 87 | pixel_width_y = data2D.detector[0].pixel_size.y |
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| 88 | det_dist = data2D.detector[0].distance |
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| 89 | wavelength = data2D.source.wavelength |
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| 90 | center_x = data2D.detector[0].beam_center.x/pixel_width_x |
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| 91 | center_y = data2D.detector[0].beam_center.y/pixel_width_y |
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| 92 | |
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| 93 | # Build array of Q intervals |
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| 94 | if maj=='x': |
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| 95 | nbins = int(math.ceil((self.x_max-self.x_min)/self.bin_width)) |
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| 96 | qbins = self.bin_width*numpy.arange(nbins)+self.x_min |
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| 97 | elif maj=='y': |
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| 98 | nbins = int(math.ceil((self.y_max-self.y_min)/self.bin_width)) |
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| 99 | qbins = self.bin_width*numpy.arange(nbins)+self.y_min |
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| 100 | else: |
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| 101 | raise RuntimeError, "_Slab._avg: unrecognized axis %s" % str(maj) |
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| 102 | |
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| 103 | x = numpy.zeros(nbins) |
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| 104 | y = numpy.zeros(nbins) |
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| 105 | err_y = numpy.zeros(nbins) |
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| 106 | y_counts = numpy.zeros(nbins) |
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| 107 | |
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| 108 | for i in range(numpy.size(data2D.data,1)): |
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| 109 | # Min and max x-value for the pixel |
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| 110 | minx = pixel_width_x*(i - center_x) |
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| 111 | maxx = pixel_width_x*(i+1.0 - center_x) |
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| 112 | |
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| 113 | qxmin = get_q(minx, 0.0, det_dist, wavelength) |
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| 114 | qxmax = get_q(maxx, 0.0, det_dist, wavelength) |
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| 115 | |
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| 116 | # Get the count fraction in x for that pixel |
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| 117 | frac_min = get_pixel_fraction_square(self.x_min, qxmin, qxmax) |
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| 118 | frac_max = get_pixel_fraction_square(self.x_max, qxmin, qxmax) |
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| 119 | frac_x = frac_max - frac_min |
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| 120 | |
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| 121 | if frac_x == 0: |
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| 122 | continue |
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| 123 | |
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| 124 | if maj=='x': |
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| 125 | dx = pixel_width_x*(i+0.5 - center_x) |
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| 126 | q_value = get_q(dx, 0.0, det_dist, wavelength) |
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| 127 | if self.fold==False and dx<0: |
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| 128 | q_value = -q_value |
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| 129 | i_q = int(math.ceil((q_value-self.x_min)/self.bin_width)) - 1 |
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| 130 | |
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| 131 | if i_q<0 or i_q>=nbins: |
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| 132 | continue |
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| 133 | |
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| 134 | for j in range(numpy.size(data2D.data,0)): |
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| 135 | # Min and max y-value for the pixel |
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| 136 | miny = pixel_width_y*(j - center_y) |
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| 137 | maxy = pixel_width_y*(j+1.0 - center_y) |
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| 138 | |
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| 139 | qymin = get_q(0.0, miny, det_dist, wavelength) |
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| 140 | qymax = get_q(0.0, maxy, det_dist, wavelength) |
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| 141 | |
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| 142 | # Get the count fraction in x for that pixel |
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| 143 | frac_min = get_pixel_fraction_square(self.y_min, qymin, qymax) |
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| 144 | frac_max = get_pixel_fraction_square(self.y_max, qymin, qymax) |
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| 145 | frac_y = frac_max - frac_min |
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| 146 | |
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| 147 | frac = frac_x * frac_y |
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| 148 | |
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| 149 | if frac == 0: |
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| 150 | continue |
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| 151 | |
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| 152 | if maj=='y': |
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| 153 | dy = pixel_width_y*(j+0.5 - center_y) |
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| 154 | q_value = get_q(0.0, dy, det_dist, wavelength) |
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| 155 | if self.fold==False and dy<0: |
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| 156 | q_value = -q_value |
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| 157 | i_q = int(math.ceil((q_value-self.y_min)/self.bin_width)) - 1 |
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| 158 | |
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| 159 | if i_q<0 or i_q>=nbins: |
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| 160 | continue |
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| 161 | |
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| 162 | x[i_q] = q_value |
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| 163 | y[i_q] += frac * data2D.data[j][i] |
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| 164 | if data2D.err_data == None or data2D.err_data[j][i]==0.0: |
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| 165 | err_y[i_q] += frac * frac * math.fabs(data2D.data[j][i]) |
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| 166 | else: |
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| 167 | err_y[i_q] += frac * frac * data2D.err_data[j][i] * data2D.err_data[j][i] |
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| 168 | y_counts[i_q] += frac |
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| 169 | |
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| 170 | # Average the sums |
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| 171 | for i in range(nbins): |
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| 172 | if y_counts[i]>0: |
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| 173 | err_y[i] = math.sqrt(err_y[i])/y_counts[i] |
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| 174 | y[i] = y[i]/y_counts[i] |
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| 175 | |
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| 176 | return Data1D(x=x, y=y, dy=err_y) |
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| 177 | |
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| 178 | class SlabY(_Slab): |
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| 179 | """ |
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| 180 | Compute average I(Qy) for a region of interest |
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| 181 | """ |
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| 182 | def __call__(self, data2D): |
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| 183 | """ |
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| 184 | Compute average I(Qy) for a region of interest |
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| 185 | |
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| 186 | @param data2D: Data2D object |
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| 187 | @return: Data1D object |
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| 188 | """ |
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| 189 | return self._avg(data2D, 'y') |
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| 190 | |
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| 191 | class SlabX(_Slab): |
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| 192 | """ |
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| 193 | Compute average I(Qx) for a region of interest |
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| 194 | """ |
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| 195 | def __call__(self, data2D): |
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| 196 | """ |
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| 197 | Compute average I(Qx) for a region of interest |
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| 198 | |
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| 199 | @param data2D: Data2D object |
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| 200 | @return: Data1D object |
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| 201 | """ |
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| 202 | return self._avg(data2D, 'x') |
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| 203 | |
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| 204 | class Boxsum(object): |
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| 205 | """ |
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| 206 | Perform the sum of counts in a 2D region of interest. |
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| 207 | """ |
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| 208 | def __init__(self, x_min=0.0, x_max=0.0, y_min=0.0, y_max=0.0): |
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| 209 | # Minimum Qx value [A-1] |
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| 210 | self.x_min = x_min |
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| 211 | # Maximum Qx value [A-1] |
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| 212 | self.x_max = x_max |
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| 213 | # Minimum Qy value [A-1] |
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| 214 | self.y_min = y_min |
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| 215 | # Maximum Qy value [A-1] |
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| 216 | self.y_max = y_max |
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| 217 | |
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| 218 | def __call__(self, data2D): |
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| 219 | """ |
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| 220 | Perform the sum in the region of interest |
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| 221 | |
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| 222 | @param data2D: Data2D object |
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| 223 | @return: number of counts, error on number of counts |
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| 224 | """ |
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| 225 | y, err_y, y_counts = self._sum(data2D) |
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| 226 | |
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| 227 | # Average the sums |
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| 228 | counts = 0 if y_counts==0 else y |
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| 229 | error = 0 if y_counts==0 else math.sqrt(err_y) |
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| 230 | |
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| 231 | return counts, error |
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| 232 | |
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| 233 | def _sum(self, data2D): |
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| 234 | """ |
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| 235 | Perform the sum in the region of interest |
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| 236 | @param data2D: Data2D object |
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| 237 | @return: number of counts, error on number of counts, number of entries summed |
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| 238 | """ |
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| 239 | if len(data2D.detector) != 1: |
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| 240 | raise RuntimeError, "Circular averaging: invalid number of detectors: %g" % len(data2D.detector) |
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| 241 | |
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| 242 | pixel_width_x = data2D.detector[0].pixel_size.x |
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| 243 | pixel_width_y = data2D.detector[0].pixel_size.y |
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| 244 | det_dist = data2D.detector[0].distance |
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| 245 | wavelength = data2D.source.wavelength |
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| 246 | center_x = data2D.detector[0].beam_center.x/pixel_width_x |
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| 247 | center_y = data2D.detector[0].beam_center.y/pixel_width_y |
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| 248 | |
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| 249 | y = 0.0 |
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| 250 | err_y = 0.0 |
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| 251 | y_counts = 0.0 |
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| 252 | sign=1 |
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| 253 | for i in range(numpy.size(data2D.data,1)): |
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| 254 | # Min and max x-value for the pixel |
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| 255 | minx = pixel_width_x*(i - center_x) |
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| 256 | maxx = pixel_width_x*(i+1.0 - center_x) |
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| 257 | if minx>=0: |
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| 258 | sign=1 |
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| 259 | else: |
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| 260 | sign=-1 |
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| 261 | qxmin = sign*get_q(minx, 0.0, det_dist, wavelength) |
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| 262 | if maxx>=0: |
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| 263 | sign=1 |
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| 264 | else: |
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| 265 | sign=-1 |
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| 266 | qxmax = sign*get_q(maxx, 0.0, det_dist, wavelength) |
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| 267 | |
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| 268 | # Get the count fraction in x for that pixel |
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| 269 | frac_min = get_pixel_fraction_square(self.x_min, qxmin, qxmax) |
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| 270 | frac_max = get_pixel_fraction_square(self.x_max, qxmin, qxmax) |
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| 271 | frac_x = frac_max - frac_min |
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| 272 | |
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| 273 | for j in range(numpy.size(data2D.data,0)): |
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| 274 | # Min and max y-value for the pixel |
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| 275 | miny = pixel_width_y*(j - center_y) |
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| 276 | maxy = pixel_width_y*(j+1.0 - center_y) |
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| 277 | if miny>=0: |
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| 278 | sign=1 |
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| 279 | else: |
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| 280 | sign=-1 |
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| 281 | |
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| 282 | qymin = sign*get_q(0.0, miny, det_dist, wavelength) |
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| 283 | if maxy>=0: |
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| 284 | sign=1 |
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| 285 | else: |
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| 286 | sign=-1 |
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| 287 | |
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| 288 | qymax = sign*get_q(0.0, maxy, det_dist, wavelength) |
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| 289 | |
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| 290 | # Get the count fraction in x for that pixel |
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| 291 | frac_min = get_pixel_fraction_square(self.y_min, qymin, qymax) |
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| 292 | frac_max = get_pixel_fraction_square(self.y_max, qymin, qymax) |
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| 293 | frac_y = frac_max - frac_min |
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| 294 | |
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| 295 | frac = frac_x * frac_y |
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| 296 | |
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| 297 | y += frac * data2D.data[j][i] |
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| 298 | if data2D.err_data == None or data2D.err_data[j][i]==0.0: |
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| 299 | err_y += frac * frac * math.fabs(data2D.data[j][i]) |
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| 300 | else: |
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| 301 | err_y += frac * frac * data2D.err_data[j][i] * data2D.err_data[j][i] |
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| 302 | y_counts += frac |
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| 303 | |
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| 304 | return y, err_y, y_counts |
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| 305 | |
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| 306 | class Boxavg(Boxsum): |
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| 307 | """ |
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| 308 | Perform the average of counts in a 2D region of interest. |
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| 309 | """ |
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| 310 | def __init__(self, x_min=0.0, x_max=0.0, y_min=0.0, y_max=0.0): |
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| 311 | super(Boxavg, self).__init__(x_min=x_min, x_max=x_max, y_min=y_min, y_max=y_max) |
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| 312 | |
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| 313 | def __call__(self, data2D): |
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| 314 | """ |
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| 315 | Perform the sum in the region of interest |
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| 316 | |
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| 317 | @param data2D: Data2D object |
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| 318 | @return: average counts, error on average counts |
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| 319 | """ |
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| 320 | y, err_y, y_counts = self._sum(data2D) |
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| 321 | |
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| 322 | # Average the sums |
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| 323 | counts = 0 if y_counts==0 else y/y_counts |
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| 324 | error = 0 if y_counts==0 else math.sqrt(err_y)/y_counts |
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| 325 | |
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| 326 | return counts, error |
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| 327 | |
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| 328 | def get_pixel_fraction_square(x, xmin, xmax): |
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| 329 | """ |
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| 330 | Return the fraction of the length |
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| 331 | from xmin to x. |
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| 332 | |
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| 333 | A B |
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| 334 | +-----------+---------+ |
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| 335 | xmin x xmax |
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| 336 | |
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| 337 | @param x: x-value |
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| 338 | @param xmin: minimum x for the length considered |
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| 339 | @param xmax: minimum x for the length considered |
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| 340 | @return: (x-xmin)/(xmax-xmin) when xmin < x < xmax |
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| 341 | |
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| 342 | """ |
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| 343 | if x<=xmin: |
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| 344 | return 0.0 |
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| 345 | if x>xmin and x<xmax: |
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| 346 | return (x-xmin)/(xmax-xmin) |
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| 347 | else: |
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| 348 | return 1.0 |
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| 349 | |
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| 350 | |
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| 351 | class CircularAverage(object): |
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| 352 | """ |
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| 353 | Perform circular averaging on 2D data |
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| 354 | |
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| 355 | The data returned is the distribution of counts |
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| 356 | as a function of Q |
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| 357 | """ |
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| 358 | def __init__(self, r_min=0.0, r_max=0.0, bin_width=0.001): |
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| 359 | # Minimum radius included in the average [A-1] |
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| 360 | self.r_min = r_min |
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| 361 | # Maximum radius included in the average [A-1] |
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| 362 | self.r_max = r_max |
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| 363 | # Bin width (step size) [A-1] |
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| 364 | self.bin_width = bin_width |
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| 365 | |
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| 366 | def __call__(self, data2D): |
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| 367 | """ |
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| 368 | Perform circular averaging on the data |
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| 369 | |
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| 370 | @param data2D: Data2D object |
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| 371 | @return: Data1D object |
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| 372 | """ |
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| 373 | if len(data2D.detector) != 1: |
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| 374 | raise RuntimeError, "Circular averaging: invalid number of detectors: %g" % len(data2D.detector) |
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| 375 | |
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| 376 | pixel_width_x = data2D.detector[0].pixel_size.x |
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| 377 | pixel_width_y = data2D.detector[0].pixel_size.y |
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| 378 | det_dist = data2D.detector[0].distance |
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| 379 | wavelength = data2D.source.wavelength |
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| 380 | center_x = data2D.detector[0].beam_center.x/pixel_width_x |
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| 381 | center_y = data2D.detector[0].beam_center.y/pixel_width_y |
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| 382 | |
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| 383 | # Find out the maximum Q range |
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| 384 | xwidth = numpy.size(data2D.data,1)*pixel_width_x |
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| 385 | dx_max = xwidth - data2D.detector[0].beam_center.x |
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| 386 | if xwidth-dx_max>dx_max: |
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| 387 | dx_max = xwidth-dx_max |
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| 388 | |
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| 389 | ywidth = numpy.size(data2D.data,0)*pixel_width_y |
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| 390 | dy_max = ywidth - data2D.detector[0].beam_center.y |
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| 391 | if ywidth-dy_max>dy_max: |
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| 392 | dy_max = ywidth-dy_max |
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| 393 | |
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| 394 | qmax = get_q(dx_max, dy_max, det_dist, wavelength) |
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| 395 | |
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| 396 | # Build array of Q intervals |
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| 397 | nbins = int(math.ceil((qmax-self.r_min)/self.bin_width)) |
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| 398 | qbins = self.bin_width*numpy.arange(nbins)+self.r_min |
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| 399 | |
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| 400 | x = numpy.zeros(nbins) |
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| 401 | y = numpy.zeros(nbins) |
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| 402 | err_y = numpy.zeros(nbins) |
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| 403 | y_counts = numpy.zeros(nbins) |
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| 404 | |
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| 405 | for i in range(numpy.size(data2D.data,1)): |
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| 406 | dx = pixel_width_x*(i+0.5 - center_x) |
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| 407 | |
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| 408 | # Min and max x-value for the pixel |
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| 409 | minx = pixel_width_x*(i - center_x) |
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| 410 | maxx = pixel_width_x*(i+1.0 - center_x) |
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| 411 | |
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| 412 | for j in range(numpy.size(data2D.data,0)): |
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| 413 | dy = pixel_width_y*(j+0.5 - center_y) |
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| 414 | |
|---|
| 415 | q_value = get_q(dx, dy, det_dist, wavelength) |
|---|
| 416 | |
|---|
| 417 | # Min and max y-value for the pixel |
|---|
| 418 | miny = pixel_width_y*(j - center_y) |
|---|
| 419 | maxy = pixel_width_y*(j+1.0 - center_y) |
|---|
| 420 | |
|---|
| 421 | # Calculate the q-value for each corner |
|---|
| 422 | # q_[x min or max][y min or max] |
|---|
| 423 | q_00 = get_q(minx, miny, det_dist, wavelength) |
|---|
| 424 | q_01 = get_q(minx, maxy, det_dist, wavelength) |
|---|
| 425 | q_10 = get_q(maxx, miny, det_dist, wavelength) |
|---|
| 426 | q_11 = get_q(maxx, maxy, det_dist, wavelength) |
|---|
| 427 | |
|---|
| 428 | # Look for intercept between each side of the pixel |
|---|
| 429 | # and the constant-q ring for qmax |
|---|
| 430 | frac_max = get_pixel_fraction(self.r_max, q_00, q_01, q_10, q_11) |
|---|
| 431 | |
|---|
| 432 | # Look for intercept between each side of the pixel |
|---|
| 433 | # and the constant-q ring for qmin |
|---|
| 434 | frac_min = get_pixel_fraction(self.r_min, q_00, q_01, q_10, q_11) |
|---|
| 435 | |
|---|
| 436 | # We are interested in the region between qmin and qmax |
|---|
| 437 | # therefore the fraction of the surface of the pixel |
|---|
| 438 | # that we will use to calculate the number of counts to |
|---|
| 439 | # include is given by: |
|---|
| 440 | frac = frac_max - frac_min |
|---|
| 441 | |
|---|
| 442 | i_q = int(math.ceil((q_value-self.r_min)/self.bin_width)) - 1 |
|---|
| 443 | |
|---|
| 444 | x[i_q] = q_value |
|---|
| 445 | y[i_q] += frac * data2D.data[j][i] |
|---|
| 446 | if data2D.err_data == None or data2D.err_data[j][i]==0.0: |
|---|
| 447 | err_y[i_q] += frac * frac * math.fabs(data2D.data[j][i]) |
|---|
| 448 | else: |
|---|
| 449 | err_y[i_q] += frac * frac * data2D.err_data[j][i] * data2D.err_data[j][i] |
|---|
| 450 | y_counts[i_q] += frac |
|---|
| 451 | |
|---|
| 452 | # Average the sums |
|---|
| 453 | for i in range(nbins): |
|---|
| 454 | if y_counts[i]>0: |
|---|
| 455 | err_y[i] = math.sqrt(err_y[i])/y_counts[i] |
|---|
| 456 | y[i] = y[i]/y_counts[i] |
|---|
| 457 | |
|---|
| 458 | return Data1D(x=x, y=y, dy=err_y) |
|---|
| 459 | |
|---|
| 460 | |
|---|
| 461 | class Ring(object): |
|---|
| 462 | """ |
|---|
| 463 | Defines a ring on a 2D data set. |
|---|
| 464 | The ring is defined by r_min, r_max, and |
|---|
| 465 | the position of the center of the ring. |
|---|
| 466 | |
|---|
| 467 | The data returned is the distribution of counts |
|---|
| 468 | around the ring as a function of phi. |
|---|
| 469 | |
|---|
| 470 | """ |
|---|
| 471 | |
|---|
| 472 | def __init__(self, r_min=0, r_max=0, center_x=0, center_y=0,nbins=20 ): |
|---|
| 473 | # Minimum radius |
|---|
| 474 | self.r_min = r_min |
|---|
| 475 | # Maximum radius |
|---|
| 476 | self.r_max = r_max |
|---|
| 477 | # Center of the ring in x |
|---|
| 478 | self.center_x = center_x |
|---|
| 479 | # Center of the ring in y |
|---|
| 480 | self.center_y = center_y |
|---|
| 481 | # Number of angular bins |
|---|
| 482 | self.nbins_phi = nbins_phi |
|---|
| 483 | |
|---|
| 484 | def __call__(self, data2D): |
|---|
| 485 | """ |
|---|
| 486 | Apply the ring to the data set. |
|---|
| 487 | Returns the angular distribution for a given q range |
|---|
| 488 | |
|---|
| 489 | @param data2D: Data2D object |
|---|
| 490 | @return: Data1D object |
|---|
| 491 | """ |
|---|
| 492 | if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]: |
|---|
| 493 | raise RuntimeError, "Ring averaging only take plottable_2D objects" |
|---|
| 494 | |
|---|
| 495 | data = data2D.data |
|---|
| 496 | qmin = self.r_min |
|---|
| 497 | qmax = self.r_max |
|---|
| 498 | |
|---|
| 499 | if len(data2D.detector) != 1: |
|---|
| 500 | raise RuntimeError, "Ring averaging: invalid number of detectors: %g" % len(data2D.detector) |
|---|
| 501 | pixel_width_x = data2D.detector[0].pixel_size.x |
|---|
| 502 | pixel_width_y = data2D.detector[0].pixel_size.y |
|---|
| 503 | det_dist = data2D.detector[0].distance |
|---|
| 504 | wavelength = data2D.source.wavelength |
|---|
| 505 | #center_x = self.center_x/pixel_width_x |
|---|
| 506 | #center_y = self.center_y/pixel_width_y |
|---|
| 507 | center_x = data2D.detector[0].beam_center.x/pixel_width_x |
|---|
| 508 | center_y = data2D.detector[0].beam_center.y/pixel_width_y |
|---|
| 509 | |
|---|
| 510 | |
|---|
| 511 | phi_bins = numpy.zeros(self.nbins_phi) |
|---|
| 512 | phi_counts = numpy.zeros(self.nbins_phi) |
|---|
| 513 | phi_values = numpy.zeros(self.nbins_phi) |
|---|
| 514 | phi_err = numpy.zeros(self.nbins_phi) |
|---|
| 515 | |
|---|
| 516 | for i in range(numpy.size(data,1)): |
|---|
| 517 | dx = pixel_width_x*(i+0.5 - center_x) |
|---|
| 518 | |
|---|
| 519 | # Min and max x-value for the pixel |
|---|
| 520 | minx = pixel_width_x*(i - center_x) |
|---|
| 521 | maxx = pixel_width_x*(i+1.0 - center_x) |
|---|
| 522 | |
|---|
| 523 | for j in range(numpy.size(data,0)): |
|---|
| 524 | dy = pixel_width_y*(j+0.5 - center_y) |
|---|
| 525 | |
|---|
| 526 | q_value = get_q(dx, dy, det_dist, wavelength) |
|---|
| 527 | |
|---|
| 528 | # Min and max y-value for the pixel |
|---|
| 529 | miny = pixel_width_y*(j - center_y) |
|---|
| 530 | maxy = pixel_width_y*(j+1.0 - center_y) |
|---|
| 531 | |
|---|
| 532 | # Calculate the q-value for each corner |
|---|
| 533 | # q_[x min or max][y min or max] |
|---|
| 534 | q_00 = get_q(minx, miny, det_dist, wavelength) |
|---|
| 535 | q_01 = get_q(minx, maxy, det_dist, wavelength) |
|---|
| 536 | q_10 = get_q(maxx, miny, det_dist, wavelength) |
|---|
| 537 | q_11 = get_q(maxx, maxy, det_dist, wavelength) |
|---|
| 538 | |
|---|
| 539 | # Look for intercept between each side of the pixel |
|---|
| 540 | # and the constant-q ring for qmax |
|---|
| 541 | frac_max = get_pixel_fraction(qmax, q_00, q_01, q_10, q_11) |
|---|
| 542 | |
|---|
| 543 | # Look for intercept between each side of the pixel |
|---|
| 544 | # and the constant-q ring for qmin |
|---|
| 545 | frac_min = get_pixel_fraction(qmin, q_00, q_01, q_10, q_11) |
|---|
| 546 | |
|---|
| 547 | # We are interested in the region between qmin and qmax |
|---|
| 548 | # therefore the fraction of the surface of the pixel |
|---|
| 549 | # that we will use to calculate the number of counts to |
|---|
| 550 | # include is given by: |
|---|
| 551 | |
|---|
| 552 | frac = frac_max - frac_min |
|---|
| 553 | |
|---|
| 554 | i_phi = int(math.ceil(self.nbins_phi*(math.atan2(dy, dx)+math.pi)/(2.0*math.pi))) - 1 |
|---|
| 555 | |
|---|
| 556 | phi_bins[i_phi] += frac * data[j][i] |
|---|
| 557 | |
|---|
| 558 | if data2D.err_data == None or data2D.err_data[j][i]==0.0: |
|---|
| 559 | phi_err[i_phi] += frac * frac * math.fabs(data2D.data[j][i]) |
|---|
| 560 | else: |
|---|
| 561 | phi_err[i_phi] += frac * frac * data2D.err_data[j][i] * data2D.err_data[j][i] |
|---|
| 562 | phi_counts[i_phi] += frac |
|---|
| 563 | |
|---|
| 564 | for i in range(self.nbins_phi): |
|---|
| 565 | phi_bins[i] = phi_bins[i] / phi_counts[i] |
|---|
| 566 | phi_err[i] = math.sqrt(phi_err[i]) / phi_counts[i] |
|---|
| 567 | phi_values[i] = 2.0*math.pi/self.nbins_phi*(1.0*i + 0.5)-math.pi # move the pi back to -pi <-->+pi |
|---|
| 568 | |
|---|
| 569 | return Data1D(x=phi_values, y=phi_bins, dy=phi_err) |
|---|
| 570 | |
|---|
| 571 | def get_pixel_fraction(qmax, q_00, q_01, q_10, q_11): |
|---|
| 572 | """ |
|---|
| 573 | Returns the fraction of the pixel defined by |
|---|
| 574 | the four corners (q_00, q_01, q_10, q_11) that |
|---|
| 575 | has q < qmax. |
|---|
| 576 | |
|---|
| 577 | q_01 q_11 |
|---|
| 578 | y=1 +--------------+ |
|---|
| 579 | | | |
|---|
| 580 | | | |
|---|
| 581 | | | |
|---|
| 582 | y=0 +--------------+ |
|---|
| 583 | q_00 q_01 |
|---|
| 584 | |
|---|
| 585 | x=0 x=1 |
|---|
| 586 | |
|---|
| 587 | """ |
|---|
| 588 | |
|---|
| 589 | # y side for x = minx |
|---|
| 590 | x_0 = get_intercept(qmax, q_00, q_01) |
|---|
| 591 | # y side for x = maxx |
|---|
| 592 | x_1 = get_intercept(qmax, q_10, q_11) |
|---|
| 593 | |
|---|
| 594 | # x side for y = miny |
|---|
| 595 | y_0 = get_intercept(qmax, q_00, q_10) |
|---|
| 596 | # x side for y = maxy |
|---|
| 597 | y_1 = get_intercept(qmax, q_01, q_11) |
|---|
| 598 | |
|---|
| 599 | # surface fraction for a 1x1 pixel |
|---|
| 600 | frac_max = 0 |
|---|
| 601 | |
|---|
| 602 | if x_0 and x_1: |
|---|
| 603 | frac_max = (x_0+x_1)/2.0 |
|---|
| 604 | |
|---|
| 605 | elif y_0 and y_1: |
|---|
| 606 | frac_max = (y_0+y_1)/2.0 |
|---|
| 607 | |
|---|
| 608 | elif x_0 and y_0: |
|---|
| 609 | if q_00 < q_10: |
|---|
| 610 | frac_max = x_0*y_0/2.0 |
|---|
| 611 | else: |
|---|
| 612 | frac_max = 1.0-x_0*y_0/2.0 |
|---|
| 613 | |
|---|
| 614 | elif x_0 and y_1: |
|---|
| 615 | if q_00 < q_10: |
|---|
| 616 | frac_max = x_0*y_1/2.0 |
|---|
| 617 | else: |
|---|
| 618 | frac_max = 1.0-x_0*y_1/2.0 |
|---|
| 619 | |
|---|
| 620 | elif x_1 and y_0: |
|---|
| 621 | if q_00 > q_10: |
|---|
| 622 | frac_max = x_1*y_0/2.0 |
|---|
| 623 | else: |
|---|
| 624 | frac_max = 1.0-x_1*y_0/2.0 |
|---|
| 625 | |
|---|
| 626 | elif x_1 and y_1: |
|---|
| 627 | if q_00 < q_10: |
|---|
| 628 | frac_max = 1.0 - (1.0-x_1)*(1.0-y_1)/2.0 |
|---|
| 629 | else: |
|---|
| 630 | frac_max = (1.0-x_1)*(1.0-y_1)/2.0 |
|---|
| 631 | |
|---|
| 632 | # If we make it here, there is no intercept between |
|---|
| 633 | # this pixel and the constant-q ring. We only need |
|---|
| 634 | # to know if we have to include it or exclude it. |
|---|
| 635 | elif (q_00+q_01+q_10+q_11)/4.0 < qmax: |
|---|
| 636 | frac_max = 1.0 |
|---|
| 637 | |
|---|
| 638 | return frac_max |
|---|
| 639 | |
|---|
| 640 | def get_intercept(q, q_0, q_1): |
|---|
| 641 | """ |
|---|
| 642 | Returns the fraction of the side at which the |
|---|
| 643 | q-value intercept the pixel, None otherwise. |
|---|
| 644 | The values returned is the fraction ON THE SIDE |
|---|
| 645 | OF THE LOWEST Q. |
|---|
| 646 | |
|---|
| 647 | |
|---|
| 648 | |
|---|
| 649 | A B |
|---|
| 650 | +-----------+--------+ |
|---|
| 651 | 0 1 <--- pixel size |
|---|
| 652 | |
|---|
| 653 | Q_0 -------- Q ----- Q_1 <--- equivalent Q range |
|---|
| 654 | |
|---|
| 655 | |
|---|
| 656 | if Q_1 > Q_0, A is returned |
|---|
| 657 | if Q_1 < Q_0, B is returned |
|---|
| 658 | |
|---|
| 659 | if Q is outside the range of [Q_0, Q_1], None is returned |
|---|
| 660 | |
|---|
| 661 | """ |
|---|
| 662 | if q_1 > q_0: |
|---|
| 663 | if (q > q_0 and q <= q_1): |
|---|
| 664 | return (q-q_0)/(q_1 - q_0) |
|---|
| 665 | else: |
|---|
| 666 | if (q > q_1 and q <= q_0): |
|---|
| 667 | return (q-q_1)/(q_0 - q_1) |
|---|
| 668 | return None |
|---|
| 669 | |
|---|
| 670 | #This class can be removed. |
|---|
| 671 | class _Sectorold: |
|---|
| 672 | """ |
|---|
| 673 | Defines a sector region on a 2D data set. |
|---|
| 674 | The sector is defined by r_min, r_max, phi_min, phi_max, |
|---|
| 675 | and the position of the center of the ring. |
|---|
| 676 | Phi is defined between 0 and 2pi |
|---|
| 677 | """ |
|---|
| 678 | def __init__(self, r_min, r_max, phi_min, phi_max,nbins=20): |
|---|
| 679 | self.r_min = r_min |
|---|
| 680 | self.r_max = r_max |
|---|
| 681 | self.phi_min = phi_min |
|---|
| 682 | self.phi_max = phi_max |
|---|
| 683 | self.nbins = nbins |
|---|
| 684 | |
|---|
| 685 | def _agv(self, data2D, run='phi'): |
|---|
| 686 | """ |
|---|
| 687 | Perform sector averaging. |
|---|
| 688 | |
|---|
| 689 | @param data2D: Data2D object |
|---|
| 690 | @param run: define the varying parameter ('phi' or 'q') |
|---|
| 691 | @return: Data1D object |
|---|
| 692 | """ |
|---|
| 693 | if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]: |
|---|
| 694 | raise RuntimeError, "Ring averaging only take plottable_2D objects" |
|---|
| 695 | |
|---|
| 696 | data = data2D.data |
|---|
| 697 | qmax = self.r_max |
|---|
| 698 | qmin = self.r_min |
|---|
| 699 | |
|---|
| 700 | if len(data2D.detector) != 1: |
|---|
| 701 | raise RuntimeError, "Ring averaging: invalid number of detectors: %g" % len(data2D.detector) |
|---|
| 702 | pixel_width_x = data2D.detector[0].pixel_size.x |
|---|
| 703 | pixel_width_y = data2D.detector[0].pixel_size.y |
|---|
| 704 | det_dist = data2D.detector[0].distance |
|---|
| 705 | wavelength = data2D.source.wavelength |
|---|
| 706 | center_x = data2D.detector[0].beam_center.x/pixel_width_x |
|---|
| 707 | center_y = data2D.detector[0].beam_center.y/pixel_width_y |
|---|
| 708 | |
|---|
| 709 | y = numpy.zeros(self.nbins) |
|---|
| 710 | y_counts = numpy.zeros(self.nbins) |
|---|
| 711 | x = numpy.zeros(self.nbins) |
|---|
| 712 | y_err = numpy.zeros(self.nbins) |
|---|
| 713 | |
|---|
| 714 | for i in range(numpy.size(data,1)): |
|---|
| 715 | dx = pixel_width_x*(i+0.5 - center_x) |
|---|
| 716 | |
|---|
| 717 | # Min and max x-value for the pixel |
|---|
| 718 | minx = pixel_width_x*(i - center_x) |
|---|
| 719 | maxx = pixel_width_x*(i+1.0 - center_x) |
|---|
| 720 | |
|---|
| 721 | for j in range(numpy.size(data,0)): |
|---|
| 722 | dy = pixel_width_y*(j+0.5 - center_y) |
|---|
| 723 | |
|---|
| 724 | q_value = get_q(dx, dy, det_dist, wavelength) |
|---|
| 725 | |
|---|
| 726 | # Min and max y-value for the pixel |
|---|
| 727 | miny = pixel_width_y*(j - center_y) |
|---|
| 728 | maxy = pixel_width_y*(j+1.0 - center_y) |
|---|
| 729 | |
|---|
| 730 | # Calculate the q-value for each corner |
|---|
| 731 | # q_[x min or max][y min or max] |
|---|
| 732 | q_00 = get_q(minx, miny, det_dist, wavelength) |
|---|
| 733 | q_01 = get_q(minx, maxy, det_dist, wavelength) |
|---|
| 734 | q_10 = get_q(maxx, miny, det_dist, wavelength) |
|---|
| 735 | q_11 = get_q(maxx, maxy, det_dist, wavelength) |
|---|
| 736 | |
|---|
| 737 | # Look for intercept between each side of the pixel |
|---|
| 738 | # and the constant-q ring for qmax |
|---|
| 739 | frac_max = get_pixel_fraction(qmax, q_00, q_01, q_10, q_11) |
|---|
| 740 | |
|---|
| 741 | # Look for intercept between each side of the pixel |
|---|
| 742 | # and the constant-q ring for qmin |
|---|
| 743 | frac_min = get_pixel_fraction(qmin, q_00, q_01, q_10, q_11) |
|---|
| 744 | |
|---|
| 745 | # We are interested in the region between qmin and qmax |
|---|
| 746 | # therefore the fraction of the surface of the pixel |
|---|
| 747 | # that we will use to calculate the number of counts to |
|---|
| 748 | # include is given by: |
|---|
| 749 | |
|---|
| 750 | frac = frac_max - frac_min |
|---|
| 751 | |
|---|
| 752 | # Compute phi and check whether it's within the limits |
|---|
| 753 | phi_value=math.atan2(dy,dx)+math.pi |
|---|
| 754 | # if phi_value<self.phi_min or phi_value>self.phi_max: |
|---|
| 755 | if phi_value<self.phi_min or phi_value>self.phi_max: |
|---|
| 756 | continue |
|---|
| 757 | |
|---|
| 758 | # Check which type of averaging we need |
|---|
| 759 | if run.lower()=='phi': |
|---|
| 760 | i_bin = int(math.ceil(self.nbins*(phi_value-self.phi_min)/(self.phi_max-self.phi_min))) - 1 |
|---|
| 761 | else: |
|---|
| 762 | # If we don't need this pixel, skip the rest of the work |
|---|
| 763 | #TODO: an improvement here would be to compute the average |
|---|
| 764 | # Q for the pixel from the part that is covered by |
|---|
| 765 | # the ring defined by q_min/q_max rather than the complete |
|---|
| 766 | # pixel |
|---|
| 767 | if q_value<self.r_min or q_value>self.r_max: |
|---|
| 768 | continue |
|---|
| 769 | i_bin = int(math.ceil(self.nbins*(q_value-self.r_min)/(self.r_max-self.r_min))) - 1 |
|---|
| 770 | |
|---|
| 771 | try: |
|---|
| 772 | y[i_bin] += frac * data[j][i] |
|---|
| 773 | except: |
|---|
| 774 | import sys |
|---|
| 775 | print sys.exc_value |
|---|
| 776 | print i_bin, frac |
|---|
| 777 | |
|---|
| 778 | if data2D.err_data == None or data2D.err_data[j][i]==0.0: |
|---|
| 779 | y_err[i_bin] += frac * frac * math.fabs(data2D.data[j][i]) |
|---|
| 780 | else: |
|---|
| 781 | y_err[i_bin] += frac * frac * data2D.err_data[j][i] * data2D.err_data[j][i] |
|---|
| 782 | y_counts[i_bin] += frac |
|---|
| 783 | |
|---|
| 784 | for i in range(self.nbins): |
|---|
| 785 | y[i] = y[i] / y_counts[i] |
|---|
| 786 | y_err[i] = math.sqrt(y_err[i]) / y_counts[i] |
|---|
| 787 | # Check which type of averaging we need |
|---|
| 788 | if run.lower()=='phi': |
|---|
| 789 | x[i] = (self.phi_max-self.phi_min)/self.nbins*(1.0*i + 0.5)+self.phi_min |
|---|
| 790 | else: |
|---|
| 791 | x[i] = (self.r_max-self.r_min)/self.nbins*(1.0*i + 0.5)+self.r_min |
|---|
| 792 | |
|---|
| 793 | return Data1D(x=x, y=y, dy=y_err) |
|---|
| 794 | |
|---|
| 795 | class _Sector: |
|---|
| 796 | """ |
|---|
| 797 | Defines a sector region on a 2D data set. |
|---|
| 798 | The sector is defined by r_min, r_max, phi_min, phi_max, |
|---|
| 799 | and the position of the center of the ring |
|---|
| 800 | where phi_min and phi_max are defined by the right and left lines wrt central line |
|---|
| 801 | and phi_max could be less than phi_min. |
|---|
| 802 | |
|---|
| 803 | Phi is defined between 0 and 2pi |
|---|
| 804 | """ |
|---|
| 805 | def __init__(self, r_min, r_max, phi_min, phi_max,nbins=20): |
|---|
| 806 | self.r_min = r_min |
|---|
| 807 | self.r_max = r_max |
|---|
| 808 | self.phi_min = phi_min |
|---|
| 809 | self.phi_max = phi_max |
|---|
| 810 | self.nbins = nbins |
|---|
| 811 | |
|---|
| 812 | def _agv(self, data2D, run='phi'): |
|---|
| 813 | """ |
|---|
| 814 | Perform sector averaging. |
|---|
| 815 | |
|---|
| 816 | @param data2D: Data2D object |
|---|
| 817 | @param run: define the varying parameter ('phi' or 'q') |
|---|
| 818 | @return: Data1D object |
|---|
| 819 | """ |
|---|
| 820 | if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]: |
|---|
| 821 | raise RuntimeError, "Ring averaging only take plottable_2D objects" |
|---|
| 822 | |
|---|
| 823 | data = data2D.data |
|---|
| 824 | qmax = self.r_max |
|---|
| 825 | qmin = self.r_min |
|---|
| 826 | |
|---|
| 827 | if len(data2D.detector) != 1: |
|---|
| 828 | raise RuntimeError, "Ring averaging: invalid number of detectors: %g" % len(data2D.detector) |
|---|
| 829 | pixel_width_x = data2D.detector[0].pixel_size.x |
|---|
| 830 | pixel_width_y = data2D.detector[0].pixel_size.y |
|---|
| 831 | det_dist = data2D.detector[0].distance |
|---|
| 832 | wavelength = data2D.source.wavelength |
|---|
| 833 | center_x = data2D.detector[0].beam_center.x/pixel_width_x |
|---|
| 834 | center_y = data2D.detector[0].beam_center.y/pixel_width_y |
|---|
| 835 | |
|---|
| 836 | y = numpy.zeros(self.nbins) |
|---|
| 837 | y_counts = numpy.zeros(self.nbins) |
|---|
| 838 | x = numpy.zeros(self.nbins) |
|---|
| 839 | y_err = numpy.zeros(self.nbins) |
|---|
| 840 | |
|---|
| 841 | # This If finds qmax within ROI defined by sector lines |
|---|
| 842 | temp=0 #to find qmax within ROI or phimax and phimin |
|---|
| 843 | temp0=1000000 |
|---|
| 844 | temp1=0 |
|---|
| 845 | for i in range(numpy.size(data,1)): |
|---|
| 846 | dx = pixel_width_x*(i+0.5 - center_x) |
|---|
| 847 | for j in range(numpy.size(data,0)): |
|---|
| 848 | |
|---|
| 849 | dy = pixel_width_y*(j+0.5 - center_y) |
|---|
| 850 | q_value = get_q(dx, dy, det_dist, wavelength) |
|---|
| 851 | # Compute phi and check whether it's within the limits |
|---|
| 852 | phi_value=math.atan2(dy,dx)+math.pi |
|---|
| 853 | if self.phi_max>2*math.pi: |
|---|
| 854 | self.phi_max=self.phi_max-2*math.pi |
|---|
| 855 | if self.phi_min<0: |
|---|
| 856 | self.phi_max=self.phi_max+2*math.pi |
|---|
| 857 | |
|---|
| 858 | #In case of two ROI (symmetric major and minor regions)(for 'q2') |
|---|
| 859 | if run.lower()=='q2': |
|---|
| 860 | if ((self.phi_max>=0 and self.phi_max<math.pi)and (self.phi_min>=0 and self.phi_min<math.pi)): |
|---|
| 861 | temp_max=self.phi_max+math.pi |
|---|
| 862 | temp_min=self.phi_min+math.pi |
|---|
| 863 | else: |
|---|
| 864 | temp_max=self.phi_max |
|---|
| 865 | temp_min=self.phi_min |
|---|
| 866 | |
|---|
| 867 | if ((temp_max>=math.pi and temp_max<2*math.pi)and (temp_min>=math.pi and temp_min<2*math.pi)): |
|---|
| 868 | if (phi_value<temp_min or phi_value>temp_max): |
|---|
| 869 | if (phi_value<temp_min-math.pi or phi_value>temp_max-math.pi): |
|---|
| 870 | continue |
|---|
| 871 | if (self.phi_max<self.phi_min): |
|---|
| 872 | tmp_max=self.phi_max+math.pi |
|---|
| 873 | tmp_min=self.phi_min-math.pi |
|---|
| 874 | else: |
|---|
| 875 | tmp_max=self.phi_max |
|---|
| 876 | tmp_min=self.phi_min |
|---|
| 877 | if (tmp_min<math.pi and tmp_max>math.pi): |
|---|
| 878 | if((phi_value>tmp_max and phi_value<tmp_min+math.pi)or (phi_value>tmp_max-math.pi and phi_value<tmp_min)): |
|---|
| 879 | continue |
|---|
| 880 | #In case of one ROI (major only)(i.e.,for 'q' and 'phi') |
|---|
| 881 | else: |
|---|
| 882 | if (self.phi_max>=self.phi_min): |
|---|
| 883 | if (phi_value<self.phi_min or phi_value>self.phi_max): |
|---|
| 884 | continue |
|---|
| 885 | else: |
|---|
| 886 | if (phi_value<self.phi_min and phi_value>self.phi_max): |
|---|
| 887 | continue |
|---|
| 888 | if q_value<qmin or q_value>qmax: |
|---|
| 889 | continue |
|---|
| 890 | |
|---|
| 891 | if run.lower()=='phi': |
|---|
| 892 | if temp1<phi_value: |
|---|
| 893 | temp1=phi_value |
|---|
| 894 | if temp0>phi_value: |
|---|
| 895 | temp0=phi_value |
|---|
| 896 | |
|---|
| 897 | elif temp<q_value: |
|---|
| 898 | temp=q_value |
|---|
| 899 | |
|---|
| 900 | if run.lower()=='phi': |
|---|
| 901 | self.phi_max=temp1 |
|---|
| 902 | self.phi_min=temp0 |
|---|
| 903 | else: |
|---|
| 904 | qmax=temp |
|---|
| 905 | |
|---|
| 906 | for i in range(numpy.size(data,1)): |
|---|
| 907 | dx = pixel_width_x*(i+0.5 - center_x) |
|---|
| 908 | |
|---|
| 909 | # Min and max x-value for the pixel |
|---|
| 910 | minx = pixel_width_x*(i - center_x) |
|---|
| 911 | maxx = pixel_width_x*(i+1.0 - center_x) |
|---|
| 912 | |
|---|
| 913 | for j in range(numpy.size(data,0)): |
|---|
| 914 | dy = pixel_width_y*(j+0.5 - center_y) |
|---|
| 915 | |
|---|
| 916 | q_value = get_q(dx, dy, det_dist, wavelength) |
|---|
| 917 | |
|---|
| 918 | # Min and max y-value for the pixel |
|---|
| 919 | miny = pixel_width_y*(j - center_y) |
|---|
| 920 | maxy = pixel_width_y*(j+1.0 - center_y) |
|---|
| 921 | |
|---|
| 922 | # Calculate the q-value for each corner |
|---|
| 923 | # q_[x min or max][y min or max] |
|---|
| 924 | q_00 = get_q(minx, miny, det_dist, wavelength) |
|---|
| 925 | q_01 = get_q(minx, maxy, det_dist, wavelength) |
|---|
| 926 | q_10 = get_q(maxx, miny, det_dist, wavelength) |
|---|
| 927 | q_11 = get_q(maxx, maxy, det_dist, wavelength) |
|---|
| 928 | |
|---|
| 929 | # Compute phi and check whether it's within the limits |
|---|
| 930 | phi_value=math.atan2(dy,dx)+math.pi |
|---|
| 931 | if self.phi_max>2*math.pi: |
|---|
| 932 | self.phi_max=self.phi_max-2*math.pi |
|---|
| 933 | if self.phi_min<0: |
|---|
| 934 | self.phi_max=self.phi_max+2*math.pi |
|---|
| 935 | |
|---|
| 936 | # Look for intercept between each side of the pixel |
|---|
| 937 | # and the constant-q ring for qmax |
|---|
| 938 | frac_max = get_pixel_fraction(qmax, q_00, q_01, q_10, q_11) |
|---|
| 939 | |
|---|
| 940 | # Look for intercept between each side of the pixel |
|---|
| 941 | # and the constant-q ring for qmin |
|---|
| 942 | frac_min = get_pixel_fraction(qmin, q_00, q_01, q_10, q_11) |
|---|
| 943 | |
|---|
| 944 | # We are interested in the region between qmin and qmax |
|---|
| 945 | # therefore the fraction of the surface of the pixel |
|---|
| 946 | # that we will use to calculate the number of counts to |
|---|
| 947 | # include is given by: |
|---|
| 948 | |
|---|
| 949 | frac = frac_max - frac_min |
|---|
| 950 | |
|---|
| 951 | #In case of two ROI (symmetric major and minor regions)(for 'q2') |
|---|
| 952 | if run.lower()=='q2': |
|---|
| 953 | if ((self.phi_max>=0 and self.phi_max<math.pi)and (self.phi_min>=0 and self.phi_min<math.pi)): |
|---|
| 954 | temp_max=self.phi_max+math.pi |
|---|
| 955 | temp_min=self.phi_min+math.pi |
|---|
| 956 | else: |
|---|
| 957 | temp_max=self.phi_max |
|---|
| 958 | temp_min=self.phi_min |
|---|
| 959 | |
|---|
| 960 | if ((temp_max>=math.pi and temp_max<2*math.pi)and (temp_min>=math.pi and temp_min<2*math.pi)): |
|---|
| 961 | if (phi_value<temp_min or phi_value>temp_max): |
|---|
| 962 | if (phi_value<temp_min-math.pi or phi_value>temp_max-math.pi): |
|---|
| 963 | continue |
|---|
| 964 | if (self.phi_max<self.phi_min): |
|---|
| 965 | tmp_max=self.phi_max+math.pi |
|---|
| 966 | tmp_min=self.phi_min-math.pi |
|---|
| 967 | else: |
|---|
| 968 | tmp_max=self.phi_max |
|---|
| 969 | tmp_min=self.phi_min |
|---|
| 970 | if (tmp_min<math.pi and tmp_max>math.pi): |
|---|
| 971 | if((phi_value>tmp_max and phi_value<tmp_min+math.pi)or (phi_value>tmp_max-math.pi and phi_value<tmp_min)): |
|---|
| 972 | continue |
|---|
| 973 | #In case of one ROI (major only)(i.e.,for 'q' and 'phi') |
|---|
| 974 | else: |
|---|
| 975 | if (self.phi_max>=self.phi_min): |
|---|
| 976 | if (phi_value<self.phi_min or phi_value>self.phi_max): |
|---|
| 977 | continue |
|---|
| 978 | |
|---|
| 979 | else: |
|---|
| 980 | if (phi_value<self.phi_min and phi_value>self.phi_max): |
|---|
| 981 | continue |
|---|
| 982 | #print "qmax=",qmax,qmin |
|---|
| 983 | |
|---|
| 984 | if q_value<qmin or q_value>qmax: |
|---|
| 985 | continue |
|---|
| 986 | |
|---|
| 987 | # Check which type of averaging we need |
|---|
| 988 | if run.lower()=='phi': |
|---|
| 989 | i_bin = int(math.ceil(self.nbins*(phi_value-self.phi_min)/(self.phi_max-self.phi_min))) - 1 |
|---|
| 990 | else: |
|---|
| 991 | # If we don't need this pixel, skip the rest of the work |
|---|
| 992 | #TODO: an improvement here would be to compute the average |
|---|
| 993 | # Q for the pixel from the part that is covered by |
|---|
| 994 | # the ring defined by q_min/q_max rather than the complete |
|---|
| 995 | # pixel |
|---|
| 996 | i_bin = int(math.ceil(self.nbins*(q_value-qmin)/(qmax-qmin))) - 1 |
|---|
| 997 | |
|---|
| 998 | try: |
|---|
| 999 | y[i_bin] += frac * data[j][i] |
|---|
| 1000 | except: |
|---|
| 1001 | import sys |
|---|
| 1002 | print sys.exc_value |
|---|
| 1003 | print i_bin, frac |
|---|
| 1004 | |
|---|
| 1005 | if data2D.err_data == None or data2D.err_data[j][i]==0.0: |
|---|
| 1006 | y_err[i_bin] += frac * frac * math.fabs(data2D.data[j][i]) |
|---|
| 1007 | else: |
|---|
| 1008 | y_err[i_bin] += frac * frac * data2D.err_data[j][i] * data2D.err_data[j][i] |
|---|
| 1009 | y_counts[i_bin] += frac |
|---|
| 1010 | |
|---|
| 1011 | for i in range(self.nbins): |
|---|
| 1012 | y[i] = y[i] / y_counts[i] |
|---|
| 1013 | y_err[i] = math.sqrt(y_err[i]) / y_counts[i] |
|---|
| 1014 | # Check which type of averaging we need |
|---|
| 1015 | if run.lower()=='phi': |
|---|
| 1016 | #Calculate x[i] and put back the origin of angle back to the right hand side (from the left). |
|---|
| 1017 | x[i] = ((self.phi_max-self.phi_min)/self.nbins*(1.0*i + 0.5)+self.phi_min-2*math.pi)*180/math.pi |
|---|
| 1018 | if x[i]<0: |
|---|
| 1019 | x[i]=180+x[i] |
|---|
| 1020 | else: |
|---|
| 1021 | x[i] = (qmax-qmin)/self.nbins*(1.0*i + 0.5)+qmin |
|---|
| 1022 | |
|---|
| 1023 | return Data1D(x=x, y=y, dy=y_err) |
|---|
| 1024 | |
|---|
| 1025 | class SectorPhi(_Sector): |
|---|
| 1026 | """ |
|---|
| 1027 | Sector average as a function of phi. |
|---|
| 1028 | I(phi) is return and the data is averaged over Q. |
|---|
| 1029 | |
|---|
| 1030 | A sector is defined by r_min, r_max, phi_min, phi_max. |
|---|
| 1031 | The number of bin in phi also has to be defined. |
|---|
| 1032 | """ |
|---|
| 1033 | def __call__(self, data2D): |
|---|
| 1034 | """ |
|---|
| 1035 | Perform sector average and return I(phi). |
|---|
| 1036 | |
|---|
| 1037 | @param data2D: Data2D object |
|---|
| 1038 | @return: Data1D object |
|---|
| 1039 | """ |
|---|
| 1040 | return self._agv(data2D, 'phi') |
|---|
| 1041 | |
|---|
| 1042 | class SectorQold(_Sector): |
|---|
| 1043 | """ |
|---|
| 1044 | Sector average as a function of Q. |
|---|
| 1045 | I(Q) is return and the data is averaged over phi. |
|---|
| 1046 | |
|---|
| 1047 | A sector is defined by r_min, r_max, phi_min, phi_max. |
|---|
| 1048 | The number of bin in Q also has to be defined. |
|---|
| 1049 | """ |
|---|
| 1050 | def __call__(self, data2D): |
|---|
| 1051 | """ |
|---|
| 1052 | Perform sector average and return I(Q). |
|---|
| 1053 | |
|---|
| 1054 | @param data2D: Data2D object |
|---|
| 1055 | @return: Data1D object |
|---|
| 1056 | """ |
|---|
| 1057 | return self._agv(data2D, 'q') |
|---|
| 1058 | |
|---|
| 1059 | class SectorQ(_Sector): |
|---|
| 1060 | """ |
|---|
| 1061 | Sector average as a function of Q for both symatric wings. |
|---|
| 1062 | I(Q) is return and the data is averaged over phi. |
|---|
| 1063 | |
|---|
| 1064 | A sector is defined by r_min, r_max, phi_min, phi_max. |
|---|
| 1065 | r_min, r_max, phi_min, phi_max >0. |
|---|
| 1066 | The number of bin in Q also has to be defined. |
|---|
| 1067 | """ |
|---|
| 1068 | def __call__(self, data2D): |
|---|
| 1069 | """ |
|---|
| 1070 | Perform sector average and return I(Q). |
|---|
| 1071 | |
|---|
| 1072 | @param data2D: Data2D object |
|---|
| 1073 | @return: Data1D object |
|---|
| 1074 | """ |
|---|
| 1075 | return self._agv(data2D, 'q2') |
|---|
| 1076 | if __name__ == "__main__": |
|---|
| 1077 | |
|---|
| 1078 | from loader import Loader |
|---|
| 1079 | |
|---|
| 1080 | |
|---|
| 1081 | d = Loader().load('test/MAR07232_rest.ASC') |
|---|
| 1082 | #d = Loader().load('test/MP_New.sans') |
|---|
| 1083 | |
|---|
| 1084 | |
|---|
| 1085 | r = SectorQ(r_min=.000001, r_max=.01, phi_min=0.0, phi_max=2*math.pi) |
|---|
| 1086 | o = r(d) |
|---|
| 1087 | |
|---|
| 1088 | s = Ring(r_min=.000001, r_max=.01) |
|---|
| 1089 | p = s(d) |
|---|
| 1090 | |
|---|
| 1091 | for i in range(len(o.x)): |
|---|
| 1092 | print o.x[i], o.y[i], o.dy[i] |
|---|
| 1093 | |
|---|
| 1094 | |
|---|
| 1095 | |
|---|
