[76e2369] | 1 | """ |
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[f8d0ee7] | 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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[76e2369] | 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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[acb37d9] | 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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[095ab1b] | 52 | |
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| 53 | def flip_phi(phi): |
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| 54 | """ |
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| 55 | Correct phi to within the 0 <= to <= 2pi range |
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| 56 | @return: phi in >=0 and <=2Pi |
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| 57 | """ |
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| 58 | Pi = math.pi |
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| 59 | if phi < 0: |
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| 60 | phi_out = phi + 2*Pi |
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| 61 | elif phi > 2*Pi: |
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| 62 | phi_out = phi - 2*Pi |
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| 63 | else: |
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| 64 | phi_out = phi |
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| 65 | return phi_out |
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| 66 | |
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| 67 | def reader2D_converter(data2d=None): |
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| 68 | """ |
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| 69 | convert old 2d format opened by IhorReader or danse_reader to new Data2D format |
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| 70 | @param data2d: 2d array of Data2D object |
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| 71 | @return: 1d arrays of Data2D object |
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| 72 | """ |
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| 73 | if data2d.data==None or data2d.x_bins==None or data2d.y_bins==None: |
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| 74 | raise ValueError,"Can't convert this data: data=None..." |
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[76e2369] | 75 | |
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[095ab1b] | 76 | from DataLoader.data_info import Data2D |
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| 77 | |
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| 78 | new_x = numpy.tile(data2d.x_bins, (len(data2d.y_bins),1)) |
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| 79 | new_y = numpy.tile(data2d.y_bins, (len(data2d.x_bins),1)) |
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| 80 | new_y = new_y.swapaxes(0,1) |
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| 81 | |
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| 82 | new_data = data2d.data.flatten() |
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| 83 | new_err_data = data2d.err_data.flatten() |
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| 84 | qx_data = new_x.flatten() |
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| 85 | qy_data = new_y.flatten() |
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| 86 | q_data = numpy.sqrt(qx_data*qx_data+qy_data*qy_data) |
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| 87 | mask = numpy.ones(len(new_data), dtype = bool) |
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| 88 | |
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| 89 | output = Data2D() |
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| 90 | output = data2d |
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| 91 | output.data = new_data |
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| 92 | output.err_data = new_err_data |
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| 93 | output.qx_data = qx_data |
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| 94 | output.qy_data = qy_data |
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| 95 | output.q_data = q_data |
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| 96 | output.mask = mask |
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| 97 | |
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| 98 | return output |
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| 99 | |
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[70975f3] | 100 | class _Slab(object): |
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| 101 | """ |
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| 102 | Compute average I(Q) for a region of interest |
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| 103 | """ |
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| 104 | 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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| 105 | # Minimum Qx value [A-1] |
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| 106 | self.x_min = x_min |
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| 107 | # Maximum Qx value [A-1] |
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| 108 | self.x_max = x_max |
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| 109 | # Minimum Qy value [A-1] |
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| 110 | self.y_min = y_min |
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| 111 | # Maximum Qy value [A-1] |
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| 112 | self.y_max = y_max |
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| 113 | # Bin width (step size) [A-1] |
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| 114 | self.bin_width = bin_width |
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| 115 | # If True, I(|Q|) will be return, otherwise, negative q-values are allowed |
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| 116 | self.fold = False |
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| 117 | |
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| 118 | def __call__(self, data2D): return NotImplemented |
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| 119 | |
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| 120 | def _avg(self, data2D, maj): |
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| 121 | """ |
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| 122 | Compute average I(Q_maj) for a region of interest. |
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| 123 | The major axis is defined as the axis of Q_maj. |
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| 124 | The minor axis is the axis that we average over. |
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| 125 | |
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| 126 | @param data2D: Data2D object |
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| 127 | @param maj_min: min value on the major axis |
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| 128 | @return: Data1D object |
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| 129 | """ |
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| 130 | if len(data2D.detector) != 1: |
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| 131 | raise RuntimeError, "_Slab._avg: invalid number of detectors: %g" % len(data2D.detector) |
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| 132 | |
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[095ab1b] | 133 | # Get data |
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| 134 | data = data2D.data |
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| 135 | q_data = data2D.q_data |
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| 136 | err_data = data2D.err_data |
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| 137 | qx_data = data2D.qx_data |
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| 138 | qy_data = data2D.qy_data |
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| 139 | |
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[70975f3] | 140 | # Build array of Q intervals |
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| 141 | if maj=='x': |
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[095ab1b] | 142 | if self.fold: x_min = 0 |
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| 143 | else: x_min = self.x_min |
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| 144 | nbins = int(math.ceil((self.x_max-x_min)/self.bin_width)) |
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| 145 | qbins = self.bin_width*numpy.arange(nbins)+ x_min |
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[70975f3] | 146 | elif maj=='y': |
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[095ab1b] | 147 | if self.fold: y_min = 0 |
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| 148 | else: y_min = self.y_min |
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| 149 | nbins = int(math.ceil((self.y_max-y_min)/self.bin_width)) |
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| 150 | qbins = self.bin_width*numpy.arange(nbins)+ y_min |
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[70975f3] | 151 | else: |
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| 152 | raise RuntimeError, "_Slab._avg: unrecognized axis %s" % str(maj) |
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| 153 | |
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| 154 | x = numpy.zeros(nbins) |
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| 155 | y = numpy.zeros(nbins) |
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| 156 | err_y = numpy.zeros(nbins) |
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| 157 | y_counts = numpy.zeros(nbins) |
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| 158 | |
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[095ab1b] | 159 | # Average pixelsize in q space |
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| 160 | for npts in range(len(data)): |
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| 161 | # default frac |
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| 162 | frac_x = 0 |
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| 163 | frac_y = 0 |
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| 164 | # get ROI |
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| 165 | if self.x_min <= qx_data[npts] and self.x_max > qx_data[npts]: |
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| 166 | frac_x = 1 |
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| 167 | if self.y_min <= qy_data[npts] and self.y_max > qy_data[npts]: |
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| 168 | frac_y = 1 |
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| 169 | |
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| 170 | frac = frac_x * frac_y |
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| 171 | |
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| 172 | if frac == 0: continue |
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[76e2369] | 173 | |
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[095ab1b] | 174 | # binning: find axis of q |
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| 175 | if maj=='x': |
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| 176 | q_value = qx_data[npts] |
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| 177 | min = x_min |
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| 178 | if maj=='y': |
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| 179 | q_value = qy_data[npts] |
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| 180 | min = y_min |
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| 181 | if self.fold and q_value<0: q_value = -q_value |
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| 182 | |
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| 183 | # bin |
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| 184 | i_q = int(math.ceil((q_value-min)/self.bin_width)) - 1 |
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| 185 | |
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| 186 | # skip outside of max bins |
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| 187 | if i_q<0 or i_q>=nbins: continue |
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| 188 | |
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| 189 | # give it full weight |
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| 190 | #frac = 1 |
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| 191 | |
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| 192 | #TODO: find better definition of x[i_q] based on q_data |
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| 193 | x[i_q] = min +(i_q+1)*self.bin_width/2.0 |
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| 194 | y[i_q] += frac * data[npts] |
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| 195 | |
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| 196 | if err_data == None or err_data[npts]==0.0: |
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| 197 | err_y[i_q] += frac * frac * math.fabs(data[npts]) |
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| 198 | else: |
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| 199 | err_y[i_q] += frac * frac * err_data[npts] * err_data[npts] |
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| 200 | y_counts[i_q] += frac |
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[8ba103f] | 201 | |
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[095ab1b] | 202 | # Average the sums |
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| 203 | for n in range(nbins): |
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| 204 | err_y[n] = math.sqrt(err_y[n]) |
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| 205 | |
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| 206 | err_y = err_y/y_counts |
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| 207 | y = y/y_counts |
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[70975f3] | 208 | |
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[095ab1b] | 209 | idx = (numpy.isfinite(y)& numpy.isfinite(x)) |
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| 210 | |
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| 211 | if not idx.any(): |
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| 212 | raise ValueError, "Average Error: No points inside ROI to average..." |
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| 213 | elif len(y[idx])!= nbins: |
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| 214 | print "resulted",nbins- len(y[idx]),"empty bin(s) due to tight binning..." |
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| 215 | return Data1D(x=x[idx], y=y[idx], dy=err_y[idx]) |
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[70975f3] | 216 | |
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| 217 | class SlabY(_Slab): |
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| 218 | """ |
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| 219 | Compute average I(Qy) for a region of interest |
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| 220 | """ |
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| 221 | def __call__(self, data2D): |
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| 222 | """ |
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| 223 | Compute average I(Qy) for a region of interest |
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| 224 | |
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| 225 | @param data2D: Data2D object |
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| 226 | @return: Data1D object |
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| 227 | """ |
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| 228 | return self._avg(data2D, 'y') |
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| 229 | |
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| 230 | class SlabX(_Slab): |
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| 231 | """ |
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| 232 | Compute average I(Qx) for a region of interest |
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| 233 | """ |
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| 234 | def __call__(self, data2D): |
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| 235 | """ |
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| 236 | Compute average I(Qx) for a region of interest |
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| 237 | |
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| 238 | @param data2D: Data2D object |
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| 239 | @return: Data1D object |
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| 240 | """ |
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| 241 | return self._avg(data2D, 'x') |
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[f8d0ee7] | 242 | |
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| 243 | class Boxsum(object): |
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| 244 | """ |
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| 245 | Perform the sum of counts in a 2D region of interest. |
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| 246 | """ |
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| 247 | def __init__(self, x_min=0.0, x_max=0.0, y_min=0.0, y_max=0.0): |
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| 248 | # Minimum Qx value [A-1] |
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| 249 | self.x_min = x_min |
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| 250 | # Maximum Qx value [A-1] |
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| 251 | self.x_max = x_max |
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| 252 | # Minimum Qy value [A-1] |
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| 253 | self.y_min = y_min |
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| 254 | # Maximum Qy value [A-1] |
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| 255 | self.y_max = y_max |
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| 256 | |
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| 257 | def __call__(self, data2D): |
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| 258 | """ |
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| 259 | Perform the sum in the region of interest |
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| 260 | |
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| 261 | @param data2D: Data2D object |
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| 262 | @return: number of counts, error on number of counts |
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| 263 | """ |
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| 264 | y, err_y, y_counts = self._sum(data2D) |
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| 265 | |
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| 266 | # Average the sums |
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| 267 | counts = 0 if y_counts==0 else y |
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| 268 | error = 0 if y_counts==0 else math.sqrt(err_y) |
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| 269 | |
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| 270 | return counts, error |
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| 271 | |
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| 272 | def _sum(self, data2D): |
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| 273 | """ |
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| 274 | Perform the sum in the region of interest |
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| 275 | @param data2D: Data2D object |
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| 276 | @return: number of counts, error on number of counts, number of entries summed |
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| 277 | """ |
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| 278 | if len(data2D.detector) != 1: |
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| 279 | raise RuntimeError, "Circular averaging: invalid number of detectors: %g" % len(data2D.detector) |
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| 280 | |
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[095ab1b] | 281 | # Get data |
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| 282 | data = data2D.data |
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| 283 | q_data = data2D.q_data |
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| 284 | err_data = data2D.err_data |
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| 285 | qx_data = data2D.qx_data |
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| 286 | qy_data = data2D.qy_data |
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| 287 | |
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[f8d0ee7] | 288 | y = 0.0 |
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| 289 | err_y = 0.0 |
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| 290 | y_counts = 0.0 |
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| 291 | |
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[095ab1b] | 292 | # Average pixelsize in q space |
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| 293 | for npts in range(len(data)): |
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| 294 | # default frac |
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| 295 | frac_x = 0 |
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| 296 | frac_y = 0 |
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| 297 | |
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| 298 | # get min and max at each points |
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| 299 | qx = qx_data[npts] |
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| 300 | qy = qy_data[npts] |
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| 301 | |
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| 302 | # get the ROI |
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| 303 | if self.x_min <= qx and self.x_max > qx: |
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| 304 | frac_x = 1 |
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| 305 | if self.y_min <= qy and self.y_max > qy: |
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| 306 | frac_y = 1 |
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| 307 | |
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| 308 | #Find the fraction along each directions |
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| 309 | frac = frac_x * frac_y |
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| 310 | if frac == 0: continue |
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[f8d0ee7] | 311 | |
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[095ab1b] | 312 | y += frac * data[npts] |
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| 313 | if err_data == None or err_data[npts]==0.0: |
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| 314 | err_y += frac * frac * math.fabs(data[npts]) |
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| 315 | else: |
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| 316 | err_y += frac * frac * err_data[npts] * err_data[npts] |
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| 317 | y_counts += frac |
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| 318 | |
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[f8d0ee7] | 319 | return y, err_y, y_counts |
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[095ab1b] | 320 | |
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| 321 | |
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[f8d0ee7] | 322 | |
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| 323 | class Boxavg(Boxsum): |
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| 324 | """ |
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| 325 | Perform the average of counts in a 2D region of interest. |
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| 326 | """ |
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| 327 | def __init__(self, x_min=0.0, x_max=0.0, y_min=0.0, y_max=0.0): |
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| 328 | super(Boxavg, self).__init__(x_min=x_min, x_max=x_max, y_min=y_min, y_max=y_max) |
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| 329 | |
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| 330 | def __call__(self, data2D): |
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| 331 | """ |
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| 332 | Perform the sum in the region of interest |
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| 333 | |
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| 334 | @param data2D: Data2D object |
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| 335 | @return: average counts, error on average counts |
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| 336 | """ |
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| 337 | y, err_y, y_counts = self._sum(data2D) |
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| 338 | |
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| 339 | # Average the sums |
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| 340 | counts = 0 if y_counts==0 else y/y_counts |
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| 341 | error = 0 if y_counts==0 else math.sqrt(err_y)/y_counts |
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| 342 | |
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| 343 | return counts, error |
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| 344 | |
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| 345 | def get_pixel_fraction_square(x, xmin, xmax): |
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| 346 | """ |
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| 347 | Return the fraction of the length |
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| 348 | from xmin to x. |
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| 349 | |
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| 350 | A B |
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| 351 | +-----------+---------+ |
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| 352 | xmin x xmax |
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| 353 | |
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| 354 | @param x: x-value |
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| 355 | @param xmin: minimum x for the length considered |
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| 356 | @param xmax: minimum x for the length considered |
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| 357 | @return: (x-xmin)/(xmax-xmin) when xmin < x < xmax |
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| 358 | |
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| 359 | """ |
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| 360 | if x<=xmin: |
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| 361 | return 0.0 |
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| 362 | if x>xmin and x<xmax: |
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| 363 | return (x-xmin)/(xmax-xmin) |
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| 364 | else: |
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| 365 | return 1.0 |
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| 366 | |
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[76e2369] | 367 | |
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| 368 | class CircularAverage(object): |
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| 369 | """ |
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| 370 | Perform circular averaging on 2D data |
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| 371 | |
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| 372 | The data returned is the distribution of counts |
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| 373 | as a function of Q |
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| 374 | """ |
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[095ab1b] | 375 | def __init__(self, r_min=0.0, r_max=0.0, bin_width=0.0005): |
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[76e2369] | 376 | # Minimum radius included in the average [A-1] |
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| 377 | self.r_min = r_min |
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| 378 | # Maximum radius included in the average [A-1] |
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| 379 | self.r_max = r_max |
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| 380 | # Bin width (step size) [A-1] |
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| 381 | self.bin_width = bin_width |
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| 382 | |
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| 383 | def __call__(self, data2D): |
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| 384 | """ |
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| 385 | Perform circular averaging on the data |
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| 386 | |
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| 387 | @param data2D: Data2D object |
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| 388 | @return: Data1D object |
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| 389 | """ |
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[095ab1b] | 390 | # Get data |
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| 391 | data = data2D.data |
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| 392 | err_data = data2D.err_data |
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| 393 | q_data = data2D.q_data |
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| 394 | |
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| 395 | q_data_max = numpy.max(q_data) |
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| 396 | |
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| 397 | if len(data2D.q_data) == None: |
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| 398 | raise RuntimeError, "Circular averaging: invalid q_data: %g" % data2D.q_data |
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| 399 | |
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[76e2369] | 400 | # Build array of Q intervals |
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[095ab1b] | 401 | nbins = int(math.ceil((self.r_max-self.r_min)/self.bin_width)) |
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[76e2369] | 402 | qbins = self.bin_width*numpy.arange(nbins)+self.r_min |
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[095ab1b] | 403 | |
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[76e2369] | 404 | x = numpy.zeros(nbins) |
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| 405 | y = numpy.zeros(nbins) |
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| 406 | err_y = numpy.zeros(nbins) |
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| 407 | y_counts = numpy.zeros(nbins) |
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[095ab1b] | 408 | |
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| 409 | for npt in range(len(data)): |
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| 410 | frac = 0 |
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[76e2369] | 411 | |
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[095ab1b] | 412 | # q-value at the pixel (j,i) |
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| 413 | q_value = q_data[npt] |
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| 414 | |
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| 415 | data_n = data[npt] |
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[3c67340] | 416 | |
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[095ab1b] | 417 | ## No need to calculate the frac when all data are within range |
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| 418 | if self.r_min >= self.r_max: |
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| 419 | raise ValueError, "Limit Error: min > max ???" |
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[76e2369] | 420 | |
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[095ab1b] | 421 | if self.r_min <= q_value and q_value <= self.r_max: frac = 1 |
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[2f569b3] | 422 | |
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[095ab1b] | 423 | if frac == 0: continue |
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| 424 | |
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| 425 | i_q = int(math.floor((q_value-self.r_min)/self.bin_width)) |
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| 426 | |
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| 427 | # Take care of the edge case at phi = 2pi. |
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| 428 | if i_q == nbins: |
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| 429 | i_q = nbins -1 |
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| 430 | |
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| 431 | y[i_q] += frac * data_n |
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| 432 | |
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| 433 | if err_data == None or err_data[npt]==0.0: |
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| 434 | err_y[i_q] += frac * frac * math.fabs(data_n) |
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[8ba103f] | 435 | else: |
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[095ab1b] | 436 | err_y[i_q] += frac * frac * err_data[npt] * err_data[npt] |
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| 437 | y_counts[i_q] += frac |
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| 438 | |
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| 439 | ## x should be the center value of each bins |
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| 440 | x = qbins+self.bin_width/2 |
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| 441 | |
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| 442 | # Average the sums |
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| 443 | for n in range(nbins): |
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| 444 | err_y[n] = math.sqrt(err_y[n]) |
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| 445 | |
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| 446 | err_y = err_y/y_counts |
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| 447 | y = y/y_counts |
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| 448 | idx = (numpy.isfinite(y))&(numpy.isfinite(x)) |
---|
| 449 | |
---|
| 450 | if not idx.any(): |
---|
| 451 | raise ValueError, "Average Error: No points inside ROI to average..." |
---|
| 452 | elif len(y[idx])!= nbins: |
---|
| 453 | print "resulted",nbins- len(y[idx]),"empty bin(s) due to tight binning..." |
---|
| 454 | |
---|
| 455 | return Data1D(x=x[idx], y=y[idx], dy=err_y[idx]) |
---|
[76e2369] | 456 | |
---|
| 457 | |
---|
| 458 | class Ring(object): |
---|
| 459 | """ |
---|
| 460 | Defines a ring on a 2D data set. |
---|
| 461 | The ring is defined by r_min, r_max, and |
---|
| 462 | the position of the center of the ring. |
---|
| 463 | |
---|
| 464 | The data returned is the distribution of counts |
---|
| 465 | around the ring as a function of phi. |
---|
| 466 | |
---|
[095ab1b] | 467 | Phi_min and phi_max should be defined between 0 and 2*pi |
---|
| 468 | in anti-clockwise starting from the x- axis on the left-hand side |
---|
[76e2369] | 469 | """ |
---|
[095ab1b] | 470 | #Todo: remove center. |
---|
[bd89dea] | 471 | def __init__(self, r_min=0, r_max=0, center_x=0, center_y=0,nbins=20 ): |
---|
[76e2369] | 472 | # Minimum radius |
---|
| 473 | self.r_min = r_min |
---|
| 474 | # Maximum radius |
---|
| 475 | self.r_max = r_max |
---|
| 476 | # Center of the ring in x |
---|
| 477 | self.center_x = center_x |
---|
| 478 | # Center of the ring in y |
---|
| 479 | self.center_y = center_y |
---|
| 480 | # Number of angular bins |
---|
[8ba103f] | 481 | self.nbins_phi = nbins |
---|
[76e2369] | 482 | |
---|
| 483 | def __call__(self, data2D): |
---|
| 484 | """ |
---|
| 485 | Apply the ring to the data set. |
---|
| 486 | Returns the angular distribution for a given q range |
---|
| 487 | |
---|
| 488 | @param data2D: Data2D object |
---|
| 489 | @return: Data1D object |
---|
| 490 | """ |
---|
| 491 | if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]: |
---|
| 492 | raise RuntimeError, "Ring averaging only take plottable_2D objects" |
---|
| 493 | |
---|
[095ab1b] | 494 | Pi = math.pi |
---|
| 495 | |
---|
| 496 | # Get data |
---|
| 497 | data = data2D.data |
---|
| 498 | err_data = data2D.err_data |
---|
| 499 | q_data = data2D.q_data |
---|
| 500 | qx_data = data2D.qx_data |
---|
| 501 | qy_data = data2D.qy_data |
---|
| 502 | |
---|
| 503 | q_data_max = numpy.max(q_data) |
---|
| 504 | |
---|
| 505 | # Set space for 1d outputs |
---|
[76e2369] | 506 | phi_bins = numpy.zeros(self.nbins_phi) |
---|
| 507 | phi_counts = numpy.zeros(self.nbins_phi) |
---|
| 508 | phi_values = numpy.zeros(self.nbins_phi) |
---|
| 509 | phi_err = numpy.zeros(self.nbins_phi) |
---|
| 510 | |
---|
[095ab1b] | 511 | for npt in range(len(data)): |
---|
| 512 | frac = 0 |
---|
[76e2369] | 513 | |
---|
[095ab1b] | 514 | # q-value at the point (npt) |
---|
| 515 | q_value = q_data[npt] |
---|
| 516 | |
---|
| 517 | data_n = data[npt] |
---|
| 518 | |
---|
| 519 | # phi-value at the point (npt) |
---|
| 520 | phi_value=math.atan2(qy_data[npt],qx_data[npt])+Pi |
---|
[76e2369] | 521 | |
---|
[095ab1b] | 522 | if self.r_min <= q_value and q_value <= self.r_max: frac = 1 |
---|
| 523 | |
---|
| 524 | if frac == 0: continue |
---|
[76e2369] | 525 | |
---|
[095ab1b] | 526 | # binning |
---|
| 527 | i_phi = int(math.floor((self.nbins_phi)*phi_value/(2*Pi))) |
---|
[76e2369] | 528 | |
---|
[095ab1b] | 529 | # Take care of the edge case at phi = 2pi. |
---|
| 530 | if i_phi == self.nbins_phi: |
---|
| 531 | i_phi = self.nbins_phi -1 |
---|
| 532 | |
---|
| 533 | phi_bins[i_phi] += frac * data[npt] |
---|
[76e2369] | 534 | |
---|
[095ab1b] | 535 | if err_data == None or err_data[npt] ==0.0: |
---|
| 536 | phi_err[i_phi] += frac * frac * math.fabs(data_n) |
---|
| 537 | else: |
---|
| 538 | phi_err[i_phi] += frac * frac *err_data[npt]*err_data[npt] |
---|
| 539 | phi_counts[i_phi] += frac |
---|
| 540 | |
---|
[76e2369] | 541 | for i in range(self.nbins_phi): |
---|
| 542 | phi_bins[i] = phi_bins[i] / phi_counts[i] |
---|
| 543 | phi_err[i] = math.sqrt(phi_err[i]) / phi_counts[i] |
---|
[095ab1b] | 544 | phi_values[i] = 2.0*math.pi/self.nbins_phi*(1.0*i + 0.5) |
---|
[76e2369] | 545 | |
---|
[095ab1b] | 546 | idx = (numpy.isfinite(phi_bins)) |
---|
| 547 | |
---|
| 548 | if not idx.any(): |
---|
| 549 | raise ValueError, "Average Error: No points inside ROI to average..." |
---|
| 550 | elif len(phi_bins[idx])!= self.nbins_phi: |
---|
| 551 | print "resulted",self.nbins_phi- len(phi_bins[idx]),"empty bin(s) due to tight binning..." |
---|
| 552 | return Data1D(x=phi_values[idx], y=phi_bins[idx], dy=phi_err[idx]) |
---|
[76e2369] | 553 | |
---|
| 554 | def get_pixel_fraction(qmax, q_00, q_01, q_10, q_11): |
---|
| 555 | """ |
---|
| 556 | Returns the fraction of the pixel defined by |
---|
| 557 | the four corners (q_00, q_01, q_10, q_11) that |
---|
| 558 | has q < qmax. |
---|
| 559 | |
---|
| 560 | q_01 q_11 |
---|
| 561 | y=1 +--------------+ |
---|
| 562 | | | |
---|
| 563 | | | |
---|
| 564 | | | |
---|
| 565 | y=0 +--------------+ |
---|
[bb0b12c] | 566 | q_00 q_10 |
---|
[76e2369] | 567 | |
---|
| 568 | x=0 x=1 |
---|
| 569 | |
---|
| 570 | """ |
---|
| 571 | |
---|
| 572 | # y side for x = minx |
---|
| 573 | x_0 = get_intercept(qmax, q_00, q_01) |
---|
| 574 | # y side for x = maxx |
---|
| 575 | x_1 = get_intercept(qmax, q_10, q_11) |
---|
| 576 | |
---|
| 577 | # x side for y = miny |
---|
| 578 | y_0 = get_intercept(qmax, q_00, q_10) |
---|
| 579 | # x side for y = maxy |
---|
| 580 | y_1 = get_intercept(qmax, q_01, q_11) |
---|
| 581 | |
---|
| 582 | # surface fraction for a 1x1 pixel |
---|
| 583 | frac_max = 0 |
---|
| 584 | |
---|
| 585 | if x_0 and x_1: |
---|
| 586 | frac_max = (x_0+x_1)/2.0 |
---|
| 587 | |
---|
| 588 | elif y_0 and y_1: |
---|
| 589 | frac_max = (y_0+y_1)/2.0 |
---|
| 590 | |
---|
| 591 | elif x_0 and y_0: |
---|
| 592 | if q_00 < q_10: |
---|
| 593 | frac_max = x_0*y_0/2.0 |
---|
| 594 | else: |
---|
| 595 | frac_max = 1.0-x_0*y_0/2.0 |
---|
| 596 | |
---|
| 597 | elif x_0 and y_1: |
---|
| 598 | if q_00 < q_10: |
---|
| 599 | frac_max = x_0*y_1/2.0 |
---|
| 600 | else: |
---|
| 601 | frac_max = 1.0-x_0*y_1/2.0 |
---|
| 602 | |
---|
| 603 | elif x_1 and y_0: |
---|
| 604 | if q_00 > q_10: |
---|
| 605 | frac_max = x_1*y_0/2.0 |
---|
| 606 | else: |
---|
| 607 | frac_max = 1.0-x_1*y_0/2.0 |
---|
| 608 | |
---|
| 609 | elif x_1 and y_1: |
---|
| 610 | if q_00 < q_10: |
---|
| 611 | frac_max = 1.0 - (1.0-x_1)*(1.0-y_1)/2.0 |
---|
| 612 | else: |
---|
| 613 | frac_max = (1.0-x_1)*(1.0-y_1)/2.0 |
---|
| 614 | |
---|
| 615 | # If we make it here, there is no intercept between |
---|
| 616 | # this pixel and the constant-q ring. We only need |
---|
| 617 | # to know if we have to include it or exclude it. |
---|
| 618 | elif (q_00+q_01+q_10+q_11)/4.0 < qmax: |
---|
| 619 | frac_max = 1.0 |
---|
[095ab1b] | 620 | |
---|
[76e2369] | 621 | return frac_max |
---|
| 622 | |
---|
| 623 | def get_intercept(q, q_0, q_1): |
---|
| 624 | """ |
---|
| 625 | Returns the fraction of the side at which the |
---|
| 626 | q-value intercept the pixel, None otherwise. |
---|
| 627 | The values returned is the fraction ON THE SIDE |
---|
| 628 | OF THE LOWEST Q. |
---|
| 629 | |
---|
| 630 | |
---|
| 631 | |
---|
| 632 | A B |
---|
| 633 | +-----------+--------+ |
---|
| 634 | 0 1 <--- pixel size |
---|
| 635 | |
---|
| 636 | Q_0 -------- Q ----- Q_1 <--- equivalent Q range |
---|
| 637 | |
---|
| 638 | |
---|
| 639 | if Q_1 > Q_0, A is returned |
---|
| 640 | if Q_1 < Q_0, B is returned |
---|
| 641 | |
---|
| 642 | if Q is outside the range of [Q_0, Q_1], None is returned |
---|
| 643 | |
---|
| 644 | """ |
---|
| 645 | if q_1 > q_0: |
---|
| 646 | if (q > q_0 and q <= q_1): |
---|
| 647 | return (q-q_0)/(q_1 - q_0) |
---|
| 648 | else: |
---|
| 649 | if (q > q_1 and q <= q_0): |
---|
| 650 | return (q-q_1)/(q_0 - q_1) |
---|
| 651 | return None |
---|
[095ab1b] | 652 | |
---|
[fb198a9] | 653 | class _Sector: |
---|
| 654 | """ |
---|
| 655 | Defines a sector region on a 2D data set. |
---|
| 656 | The sector is defined by r_min, r_max, phi_min, phi_max, |
---|
| 657 | and the position of the center of the ring |
---|
| 658 | where phi_min and phi_max are defined by the right and left lines wrt central line |
---|
| 659 | and phi_max could be less than phi_min. |
---|
| 660 | |
---|
[095ab1b] | 661 | Phi is defined between 0 and 2*pi in anti-clockwise starting from the x- axis on the left-hand side |
---|
[fb198a9] | 662 | """ |
---|
[095ab1b] | 663 | def __init__(self, r_min, r_max, phi_min=0, phi_max=2*math.pi,nbins=20): |
---|
[fb198a9] | 664 | self.r_min = r_min |
---|
| 665 | self.r_max = r_max |
---|
| 666 | self.phi_min = phi_min |
---|
| 667 | self.phi_max = phi_max |
---|
| 668 | self.nbins = nbins |
---|
| 669 | |
---|
[095ab1b] | 670 | |
---|
[fb198a9] | 671 | def _agv(self, data2D, run='phi'): |
---|
| 672 | """ |
---|
| 673 | Perform sector averaging. |
---|
| 674 | |
---|
| 675 | @param data2D: Data2D object |
---|
[095ab1b] | 676 | @param run: define the varying parameter ('phi' , 'q' , or 'q2') |
---|
[fb198a9] | 677 | @return: Data1D object |
---|
| 678 | """ |
---|
| 679 | if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]: |
---|
| 680 | raise RuntimeError, "Ring averaging only take plottable_2D objects" |
---|
[095ab1b] | 681 | Pi = math.pi |
---|
| 682 | |
---|
| 683 | # Get the all data & info |
---|
| 684 | data = data2D.data |
---|
| 685 | err_data = data2D.err_data |
---|
| 686 | qx_data = data2D.qx_data |
---|
| 687 | qy_data = data2D.qy_data |
---|
| 688 | q_data = data2D.q_data |
---|
| 689 | |
---|
| 690 | #set space for 1d outputs |
---|
| 691 | x = numpy.zeros(self.nbins) |
---|
[fb198a9] | 692 | y = numpy.zeros(self.nbins) |
---|
[095ab1b] | 693 | y_err = numpy.zeros(self.nbins) |
---|
[fb198a9] | 694 | y_counts = numpy.zeros(self.nbins) |
---|
[095ab1b] | 695 | |
---|
| 696 | # Get the min and max into the region: 0 <= phi < 2Pi |
---|
| 697 | phi_min = flip_phi(self.phi_min) |
---|
| 698 | phi_max = flip_phi(self.phi_max) |
---|
[bb0b12c] | 699 | |
---|
[095ab1b] | 700 | q_data_max = numpy.max(q_data) |
---|
| 701 | |
---|
| 702 | for n in range(len(data)): |
---|
| 703 | frac = 0 |
---|
[3c67340] | 704 | |
---|
[095ab1b] | 705 | # q-value at the pixel (j,i) |
---|
| 706 | q_value = q_data[n] |
---|
[fb198a9] | 707 | |
---|
[095ab1b] | 708 | |
---|
| 709 | data_n = data[n] |
---|
[3c67340] | 710 | |
---|
[095ab1b] | 711 | # Is pixel within range? |
---|
| 712 | is_in = False |
---|
[3c67340] | 713 | |
---|
[095ab1b] | 714 | # phi-value of the pixel (j,i) |
---|
| 715 | phi_value=math.atan2(qy_data[n],qx_data[n])+Pi |
---|
[3c67340] | 716 | |
---|
[095ab1b] | 717 | ## No need to calculate the frac when all data are within range |
---|
| 718 | if self.r_min <= q_value and q_value <= self.r_max: frac = 1 |
---|
[3c67340] | 719 | |
---|
[095ab1b] | 720 | if frac == 0: continue |
---|
| 721 | |
---|
| 722 | #In case of two ROIs (symmetric major and minor regions)(for 'q2') |
---|
[3c67340] | 723 | if run.lower()=='q2': |
---|
[095ab1b] | 724 | ## For minor sector wing |
---|
| 725 | # Calculate the minor wing phis |
---|
| 726 | phi_min_minor = flip_phi(phi_min-Pi) |
---|
| 727 | phi_max_minor = flip_phi(phi_max-Pi) |
---|
| 728 | # Check if phis of the minor ring is within 0 to 2pi |
---|
| 729 | if phi_min_minor > phi_max_minor: |
---|
| 730 | is_in = (phi_value > phi_min_minor or phi_value < phi_max_minor) |
---|
[3c67340] | 731 | else: |
---|
[095ab1b] | 732 | is_in = (phi_value > phi_min_minor and phi_value < phi_max_minor) |
---|
[bb0b12c] | 733 | |
---|
[095ab1b] | 734 | #For all cases(i.e.,for 'q', 'q2', and 'phi') |
---|
| 735 | #Find pixels within ROI |
---|
| 736 | if phi_min > phi_max: |
---|
| 737 | is_in = is_in or (phi_value > phi_min or phi_value < phi_max) |
---|
| 738 | else: |
---|
| 739 | is_in = is_in or (phi_value>= phi_min and phi_value <phi_max) |
---|
| 740 | |
---|
| 741 | if not is_in: frac = 0 |
---|
| 742 | if frac == 0: continue |
---|
| 743 | |
---|
[3c67340] | 744 | # Check which type of averaging we need |
---|
| 745 | if run.lower()=='phi': |
---|
[095ab1b] | 746 | i_bin = int(math.floor((self.nbins)*(phi_value-self.phi_min)\ |
---|
| 747 | /(self.phi_max-self.phi_min))) |
---|
[3c67340] | 748 | else: |
---|
[095ab1b] | 749 | i_bin = int(math.floor((self.nbins)*(q_value-self.r_min)/(self.r_max-self.r_min))) |
---|
| 750 | |
---|
| 751 | # Take care of the edge case at phi = 2pi. |
---|
| 752 | if i_bin == self.nbins: |
---|
| 753 | i_bin = self.nbins -1 |
---|
| 754 | |
---|
| 755 | ## Get the total y |
---|
| 756 | y[i_bin] += frac * data_n |
---|
| 757 | |
---|
| 758 | if err_data == None or err_data[n] ==0.0: |
---|
| 759 | y_err[i_bin] += frac * frac * math.fabs(data_n) |
---|
[3c67340] | 760 | else: |
---|
[095ab1b] | 761 | y_err[i_bin] += frac * frac * err_data[n]*err_data[n] |
---|
[3c67340] | 762 | y_counts[i_bin] += frac |
---|
[095ab1b] | 763 | |
---|
| 764 | # Organize the results |
---|
[fb198a9] | 765 | for i in range(self.nbins): |
---|
| 766 | y[i] = y[i] / y_counts[i] |
---|
| 767 | y_err[i] = math.sqrt(y_err[i]) / y_counts[i] |
---|
| 768 | |
---|
[095ab1b] | 769 | # The type of averaging: phi,q2, or q |
---|
| 770 | # Calculate x[i]should be at the center of the bin |
---|
| 771 | if run.lower()=='phi': |
---|
| 772 | x[i] = (self.phi_max-self.phi_min)/self.nbins*(1.0*i + 0.5)+self.phi_min |
---|
| 773 | else: |
---|
| 774 | x[i] = (self.r_max-self.r_min)/self.nbins*(1.0*i + 0.5)+self.r_min |
---|
| 775 | |
---|
| 776 | idx = (numpy.isfinite(y)& numpy.isfinite(y_err)) |
---|
| 777 | |
---|
| 778 | if not idx.any(): |
---|
| 779 | raise ValueError, "Average Error: No points inside sector of ROI to average..." |
---|
| 780 | elif len(y[idx])!= self.nbins: |
---|
| 781 | print "resulted",self.nbins- len(y[idx]),"empty bin(s) due to tight binning..." |
---|
| 782 | return Data1D(x=x[idx], y=y[idx], dy=y_err[idx]) |
---|
[fb198a9] | 783 | |
---|
[2e83ff3] | 784 | class SectorPhi(_Sector): |
---|
| 785 | """ |
---|
| 786 | Sector average as a function of phi. |
---|
| 787 | I(phi) is return and the data is averaged over Q. |
---|
| 788 | |
---|
| 789 | A sector is defined by r_min, r_max, phi_min, phi_max. |
---|
| 790 | The number of bin in phi also has to be defined. |
---|
| 791 | """ |
---|
| 792 | def __call__(self, data2D): |
---|
| 793 | """ |
---|
| 794 | Perform sector average and return I(phi). |
---|
| 795 | |
---|
| 796 | @param data2D: Data2D object |
---|
| 797 | @return: Data1D object |
---|
| 798 | """ |
---|
| 799 | return self._agv(data2D, 'phi') |
---|
[fb198a9] | 800 | |
---|
| 801 | class SectorQ(_Sector): |
---|
| 802 | """ |
---|
| 803 | Sector average as a function of Q for both symatric wings. |
---|
| 804 | I(Q) is return and the data is averaged over phi. |
---|
| 805 | |
---|
| 806 | A sector is defined by r_min, r_max, phi_min, phi_max. |
---|
| 807 | r_min, r_max, phi_min, phi_max >0. |
---|
| 808 | The number of bin in Q also has to be defined. |
---|
| 809 | """ |
---|
| 810 | def __call__(self, data2D): |
---|
| 811 | """ |
---|
| 812 | Perform sector average and return I(Q). |
---|
| 813 | |
---|
| 814 | @param data2D: Data2D object |
---|
| 815 | @return: Data1D object |
---|
| 816 | """ |
---|
| 817 | return self._agv(data2D, 'q2') |
---|
[76e2369] | 818 | if __name__ == "__main__": |
---|
| 819 | |
---|
| 820 | from loader import Loader |
---|
| 821 | |
---|
| 822 | |
---|
[f8d0ee7] | 823 | d = Loader().load('test/MAR07232_rest.ASC') |
---|
| 824 | #d = Loader().load('test/MP_New.sans') |
---|
[76e2369] | 825 | |
---|
| 826 | |
---|
[d9629c53] | 827 | r = SectorQ(r_min=.000001, r_max=.01, phi_min=0.0, phi_max=2*math.pi) |
---|
[f8d0ee7] | 828 | o = r(d) |
---|
| 829 | |
---|
[d9629c53] | 830 | s = Ring(r_min=.000001, r_max=.01) |
---|
[2e83ff3] | 831 | p = s(d) |
---|
[70975f3] | 832 | |
---|
| 833 | for i in range(len(o.x)): |
---|
| 834 | print o.x[i], o.y[i], o.dy[i] |
---|
[76e2369] | 835 | |
---|
| 836 | |
---|
| 837 | |
---|