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 | 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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75 | |
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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 | qx_data = new_x.flatten() |
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84 | qy_data = new_y.flatten() |
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85 | q_data = numpy.sqrt(qx_data*qx_data+qy_data*qy_data) |
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86 | if data2d.err_data == None or numpy.any(data2d.err_data<=0): |
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87 | new_err_data = numpy.sqrt(numpy.abs(new_data)) |
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88 | else: |
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89 | new_err_data = data2d.err_data.flatten() |
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90 | mask = numpy.ones(len(new_data), dtype = bool) |
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91 | |
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92 | output = Data2D() |
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93 | output = data2d |
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94 | output.data = new_data |
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95 | output.err_data = new_err_data |
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96 | output.qx_data = qx_data |
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97 | output.qy_data = qy_data |
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98 | output.q_data = q_data |
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99 | output.mask = mask |
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100 | |
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101 | return output |
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102 | |
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103 | class _Slab(object): |
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104 | """ |
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105 | Compute average I(Q) for a region of interest |
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106 | """ |
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107 | 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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108 | # Minimum Qx value [A-1] |
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109 | self.x_min = x_min |
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110 | # Maximum Qx value [A-1] |
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111 | self.x_max = x_max |
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112 | # Minimum Qy value [A-1] |
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113 | self.y_min = y_min |
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114 | # Maximum Qy value [A-1] |
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115 | self.y_max = y_max |
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116 | # Bin width (step size) [A-1] |
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117 | self.bin_width = bin_width |
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118 | # If True, I(|Q|) will be return, otherwise, negative q-values are allowed |
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119 | self.fold = False |
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120 | |
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121 | def __call__(self, data2D): return NotImplemented |
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122 | |
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123 | def _avg(self, data2D, maj): |
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124 | """ |
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125 | Compute average I(Q_maj) for a region of interest. |
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126 | The major axis is defined as the axis of Q_maj. |
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127 | The minor axis is the axis that we average over. |
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128 | |
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129 | @param data2D: Data2D object |
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130 | @param maj_min: min value on the major axis |
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131 | @return: Data1D object |
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132 | """ |
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133 | if len(data2D.detector) != 1: |
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134 | raise RuntimeError, "_Slab._avg: invalid number of detectors: %g" % len(data2D.detector) |
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135 | |
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136 | # Get data |
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137 | data = data2D.data[numpy.isfinite(data2D.data)] |
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138 | q_data = data2D.q_data[numpy.isfinite(data2D.data)] |
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139 | err_data = data2D.err_data[numpy.isfinite(data2D.data)] |
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140 | qx_data = data2D.qx_data[numpy.isfinite(data2D.data)] |
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141 | qy_data = data2D.qy_data[numpy.isfinite(data2D.data)] |
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142 | |
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143 | # Build array of Q intervals |
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144 | if maj=='x': |
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145 | if self.fold: x_min = 0 |
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146 | else: x_min = self.x_min |
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147 | nbins = int(math.ceil((self.x_max-x_min)/self.bin_width)) |
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148 | qbins = self.bin_width*numpy.arange(nbins)+ x_min |
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149 | elif maj=='y': |
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150 | if self.fold: y_min = 0 |
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151 | else: y_min = self.y_min |
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152 | nbins = int(math.ceil((self.y_max-y_min)/self.bin_width)) |
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153 | qbins = self.bin_width*numpy.arange(nbins)+ y_min |
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154 | else: |
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155 | raise RuntimeError, "_Slab._avg: unrecognized axis %s" % str(maj) |
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156 | |
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157 | x = numpy.zeros(nbins) |
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158 | y = numpy.zeros(nbins) |
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159 | err_y = numpy.zeros(nbins) |
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160 | y_counts = numpy.zeros(nbins) |
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161 | |
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162 | # Average pixelsize in q space |
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163 | for npts in range(len(data)): |
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164 | # default frac |
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165 | frac_x = 0 |
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166 | frac_y = 0 |
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167 | # get ROI |
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168 | if self.x_min <= qx_data[npts] and self.x_max > qx_data[npts]: |
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169 | frac_x = 1 |
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170 | if self.y_min <= qy_data[npts] and self.y_max > qy_data[npts]: |
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171 | frac_y = 1 |
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172 | |
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173 | frac = frac_x * frac_y |
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174 | |
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175 | if frac == 0: continue |
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176 | |
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177 | # binning: find axis of q |
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178 | if maj=='x': |
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179 | q_value = qx_data[npts] |
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180 | min = x_min |
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181 | if maj=='y': |
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182 | q_value = qy_data[npts] |
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183 | min = y_min |
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184 | if self.fold and q_value<0: q_value = -q_value |
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185 | |
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186 | # bin |
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187 | i_q = int(math.ceil((q_value-min)/self.bin_width)) - 1 |
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188 | |
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189 | # skip outside of max bins |
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190 | if i_q<0 or i_q>=nbins: continue |
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191 | |
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192 | # give it full weight |
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193 | #frac = 1 |
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194 | |
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195 | #TODO: find better definition of x[i_q] based on q_data |
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196 | x[i_q] = min +(i_q+1)*self.bin_width/2.0 |
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197 | y[i_q] += frac * data[npts] |
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198 | |
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199 | if err_data == None or err_data[npts]==0.0: |
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200 | if data[npts] <0: data[npts] = -data[npts] |
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201 | err_y[i_q] += frac * frac * data[npts] |
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202 | else: |
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203 | err_y[i_q] += frac * frac * err_data[npts] * err_data[npts] |
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204 | y_counts[i_q] += frac |
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205 | |
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206 | # Average the sums |
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207 | for n in range(nbins): |
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208 | err_y[n] = math.sqrt(err_y[n]) |
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209 | |
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210 | err_y = err_y/y_counts |
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211 | y = y/y_counts |
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212 | |
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213 | idx = (numpy.isfinite(y)& numpy.isfinite(x)) |
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214 | |
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215 | if not idx.any(): |
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216 | raise ValueError, "Average Error: No points inside ROI to average..." |
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217 | elif len(y[idx])!= nbins: |
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218 | print "resulted",nbins- len(y[idx]),"empty bin(s) due to tight binning..." |
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219 | return Data1D(x=x[idx], y=y[idx], dy=err_y[idx]) |
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220 | |
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221 | class SlabY(_Slab): |
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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 | def __call__(self, data2D): |
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226 | """ |
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227 | Compute average I(Qy) for a region of interest |
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228 | |
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229 | @param data2D: Data2D object |
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230 | @return: Data1D object |
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231 | """ |
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232 | return self._avg(data2D, 'y') |
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233 | |
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234 | class SlabX(_Slab): |
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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 | def __call__(self, data2D): |
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239 | """ |
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240 | Compute average I(Qx) for a region of interest |
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241 | |
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242 | @param data2D: Data2D object |
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243 | @return: Data1D object |
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244 | """ |
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245 | return self._avg(data2D, 'x') |
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246 | |
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247 | class Boxsum(object): |
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248 | """ |
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249 | Perform the sum of counts in a 2D region of interest. |
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250 | """ |
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251 | def __init__(self, x_min=0.0, x_max=0.0, y_min=0.0, y_max=0.0): |
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252 | # Minimum Qx value [A-1] |
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253 | self.x_min = x_min |
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254 | # Maximum Qx value [A-1] |
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255 | self.x_max = x_max |
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256 | # Minimum Qy value [A-1] |
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257 | self.y_min = y_min |
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258 | # Maximum Qy value [A-1] |
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259 | self.y_max = y_max |
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260 | |
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261 | def __call__(self, data2D): |
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262 | """ |
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263 | Perform the sum in the region of interest |
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264 | |
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265 | @param data2D: Data2D object |
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266 | @return: number of counts, error on number of counts |
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267 | """ |
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268 | y, err_y, y_counts = self._sum(data2D) |
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269 | |
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270 | # Average the sums |
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271 | counts = 0 if y_counts==0 else y |
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272 | error = 0 if y_counts==0 else math.sqrt(err_y) |
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273 | |
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274 | return counts, error |
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275 | |
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276 | def _sum(self, data2D): |
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277 | """ |
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278 | Perform the sum in the region of interest |
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279 | @param data2D: Data2D object |
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280 | @return: number of counts, error on number of counts, number of entries summed |
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281 | """ |
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282 | if len(data2D.detector) != 1: |
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283 | raise RuntimeError, "Circular averaging: invalid number of detectors: %g" % len(data2D.detector) |
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284 | |
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285 | # Get data |
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286 | data = data2D.data[numpy.isfinite(data2D.data)] |
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287 | q_data = data2D.q_data[numpy.isfinite(data2D.data)] |
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288 | err_data = data2D.err_data[numpy.isfinite(data2D.data)] |
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289 | qx_data = data2D.qx_data[numpy.isfinite(data2D.data)] |
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290 | qy_data = data2D.qy_data[numpy.isfinite(data2D.data)] |
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291 | |
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292 | y = 0.0 |
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293 | err_y = 0.0 |
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294 | y_counts = 0.0 |
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295 | |
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296 | # Average pixelsize in q space |
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297 | for npts in range(len(data)): |
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298 | # default frac |
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299 | frac_x = 0 |
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300 | frac_y = 0 |
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301 | |
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302 | # get min and max at each points |
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303 | qx = qx_data[npts] |
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304 | qy = qy_data[npts] |
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305 | |
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306 | # get the ROI |
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307 | if self.x_min <= qx and self.x_max > qx: |
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308 | frac_x = 1 |
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309 | if self.y_min <= qy and self.y_max > qy: |
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310 | frac_y = 1 |
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311 | |
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312 | #Find the fraction along each directions |
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313 | frac = frac_x * frac_y |
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314 | if frac == 0: continue |
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315 | |
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316 | y += frac * data[npts] |
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317 | if err_data == None or err_data[npts]==0.0: |
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318 | if data[npts] <0: data[npts] = -data[npts] |
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319 | err_y += frac * frac * data[npts] |
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320 | else: |
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321 | err_y += frac * frac * err_data[npts] * err_data[npts] |
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322 | y_counts += frac |
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323 | |
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324 | return y, err_y, y_counts |
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325 | |
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326 | |
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327 | |
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328 | class Boxavg(Boxsum): |
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329 | """ |
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330 | Perform the average of counts in a 2D region of interest. |
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331 | """ |
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332 | def __init__(self, x_min=0.0, x_max=0.0, y_min=0.0, y_max=0.0): |
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333 | super(Boxavg, self).__init__(x_min=x_min, x_max=x_max, y_min=y_min, y_max=y_max) |
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334 | |
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335 | def __call__(self, data2D): |
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336 | """ |
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337 | Perform the sum in the region of interest |
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338 | |
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339 | @param data2D: Data2D object |
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340 | @return: average counts, error on average counts |
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341 | """ |
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342 | y, err_y, y_counts = self._sum(data2D) |
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343 | |
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344 | # Average the sums |
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345 | counts = 0 if y_counts==0 else y/y_counts |
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346 | error = 0 if y_counts==0 else math.sqrt(err_y)/y_counts |
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347 | |
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348 | return counts, error |
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349 | |
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350 | def get_pixel_fraction_square(x, xmin, xmax): |
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351 | """ |
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352 | Return the fraction of the length |
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353 | from xmin to x. |
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354 | |
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355 | A B |
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356 | +-----------+---------+ |
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357 | xmin x xmax |
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358 | |
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359 | @param x: x-value |
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360 | @param xmin: minimum x for the length considered |
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361 | @param xmax: minimum x for the length considered |
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362 | @return: (x-xmin)/(xmax-xmin) when xmin < x < xmax |
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363 | |
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364 | """ |
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365 | if x<=xmin: |
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366 | return 0.0 |
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367 | if x>xmin and x<xmax: |
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368 | return (x-xmin)/(xmax-xmin) |
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369 | else: |
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370 | return 1.0 |
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371 | |
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372 | |
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373 | class CircularAverage(object): |
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374 | """ |
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375 | Perform circular averaging on 2D data |
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376 | |
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377 | The data returned is the distribution of counts |
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378 | as a function of Q |
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379 | """ |
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380 | def __init__(self, r_min=0.0, r_max=0.0, bin_width=0.0005): |
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381 | # Minimum radius included in the average [A-1] |
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382 | self.r_min = r_min |
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383 | # Maximum radius included in the average [A-1] |
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384 | self.r_max = r_max |
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385 | # Bin width (step size) [A-1] |
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386 | self.bin_width = bin_width |
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387 | |
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388 | def __call__(self, data2D): |
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389 | """ |
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390 | Perform circular averaging on the data |
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391 | |
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392 | @param data2D: Data2D object |
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393 | @return: Data1D object |
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394 | """ |
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395 | # Get data |
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396 | data = data2D.data[numpy.isfinite(data2D.data)] |
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397 | q_data = data2D.q_data[numpy.isfinite(data2D.data)] |
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398 | err_data = data2D.err_data[numpy.isfinite(data2D.data)] |
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399 | |
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400 | q_data_max = numpy.max(q_data) |
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401 | |
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402 | if len(data2D.q_data) == None: |
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403 | raise RuntimeError, "Circular averaging: invalid q_data: %g" % data2D.q_data |
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404 | |
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405 | # Build array of Q intervals |
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406 | nbins = int(math.ceil((self.r_max-self.r_min)/self.bin_width)) |
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407 | qbins = self.bin_width*numpy.arange(nbins)+self.r_min |
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408 | |
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409 | x = numpy.zeros(nbins) |
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410 | y = numpy.zeros(nbins) |
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411 | err_y = numpy.zeros(nbins) |
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412 | y_counts = numpy.zeros(nbins) |
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413 | |
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414 | for npt in range(len(data)): |
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415 | frac = 0 |
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416 | |
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417 | # q-value at the pixel (j,i) |
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418 | q_value = q_data[npt] |
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419 | |
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420 | data_n = data[npt] |
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421 | |
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422 | ## No need to calculate the frac when all data are within range |
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423 | if self.r_min >= self.r_max: |
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424 | raise ValueError, "Limit Error: min > max ???" |
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425 | |
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426 | if self.r_min <= q_value and q_value <= self.r_max: frac = 1 |
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427 | |
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428 | if frac == 0: continue |
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429 | |
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430 | i_q = int(math.floor((q_value-self.r_min)/self.bin_width)) |
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431 | |
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432 | # Take care of the edge case at phi = 2pi. |
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433 | if i_q == nbins: |
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434 | i_q = nbins -1 |
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435 | |
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436 | y[i_q] += frac * data_n |
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437 | |
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438 | if err_data == None or err_data[npt]==0.0: |
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439 | if data_n <0: data_n = -data_n |
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440 | err_y[i_q] += frac * frac * data_n |
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441 | else: |
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442 | err_y[i_q] += frac * frac * err_data[npt] * err_data[npt] |
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443 | y_counts[i_q] += frac |
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444 | |
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445 | ## x should be the center value of each bins |
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446 | x = qbins+self.bin_width/2 |
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447 | |
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448 | # Average the sums |
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449 | for n in range(nbins): |
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450 | if err_y[n] <0: err_y[n] = -err_y[n] |
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451 | err_y[n] = math.sqrt(err_y[n]) |
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452 | |
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453 | err_y = err_y/y_counts |
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454 | y = y/y_counts |
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455 | idx = (numpy.isfinite(y))&(numpy.isfinite(x)) |
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456 | |
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457 | if not idx.any(): |
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458 | raise ValueError, "Average Error: No points inside ROI to average..." |
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459 | elif len(y[idx])!= nbins: |
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460 | print "resulted",nbins- len(y[idx]),"empty bin(s) due to tight binning..." |
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461 | |
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462 | return Data1D(x=x[idx], y=y[idx], dy=err_y[idx]) |
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463 | |
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464 | |
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465 | class Ring(object): |
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466 | """ |
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467 | Defines a ring on a 2D data set. |
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468 | The ring is defined by r_min, r_max, and |
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469 | the position of the center of the ring. |
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470 | |
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471 | The data returned is the distribution of counts |
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472 | around the ring as a function of phi. |
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473 | |
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474 | Phi_min and phi_max should be defined between 0 and 2*pi |
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475 | in anti-clockwise starting from the x- axis on the left-hand side |
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476 | """ |
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477 | #Todo: remove center. |
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478 | def __init__(self, r_min=0, r_max=0, center_x=0, center_y=0,nbins=20 ): |
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479 | # Minimum radius |
---|
480 | self.r_min = r_min |
---|
481 | # Maximum radius |
---|
482 | self.r_max = r_max |
---|
483 | # Center of the ring in x |
---|
484 | self.center_x = center_x |
---|
485 | # Center of the ring in y |
---|
486 | self.center_y = center_y |
---|
487 | # Number of angular bins |
---|
488 | self.nbins_phi = nbins |
---|
489 | |
---|
490 | def __call__(self, data2D): |
---|
491 | """ |
---|
492 | Apply the ring to the data set. |
---|
493 | Returns the angular distribution for a given q range |
---|
494 | |
---|
495 | @param data2D: Data2D object |
---|
496 | @return: Data1D object |
---|
497 | """ |
---|
498 | if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]: |
---|
499 | raise RuntimeError, "Ring averaging only take plottable_2D objects" |
---|
500 | |
---|
501 | Pi = math.pi |
---|
502 | |
---|
503 | # Get data |
---|
504 | data = data2D.data[numpy.isfinite(data2D.data)] |
---|
505 | q_data = data2D.q_data[numpy.isfinite(data2D.data)] |
---|
506 | err_data = data2D.err_data[numpy.isfinite(data2D.data)] |
---|
507 | qx_data = data2D.qx_data[numpy.isfinite(data2D.data)] |
---|
508 | qy_data = data2D.qy_data[numpy.isfinite(data2D.data)] |
---|
509 | |
---|
510 | q_data_max = numpy.max(q_data) |
---|
511 | |
---|
512 | # Set space for 1d outputs |
---|
513 | phi_bins = numpy.zeros(self.nbins_phi) |
---|
514 | phi_counts = numpy.zeros(self.nbins_phi) |
---|
515 | phi_values = numpy.zeros(self.nbins_phi) |
---|
516 | phi_err = numpy.zeros(self.nbins_phi) |
---|
517 | |
---|
518 | for npt in range(len(data)): |
---|
519 | frac = 0 |
---|
520 | |
---|
521 | # q-value at the point (npt) |
---|
522 | q_value = q_data[npt] |
---|
523 | |
---|
524 | data_n = data[npt] |
---|
525 | |
---|
526 | # phi-value at the point (npt) |
---|
527 | phi_value=math.atan2(qy_data[npt],qx_data[npt])+Pi |
---|
528 | |
---|
529 | if self.r_min <= q_value and q_value <= self.r_max: frac = 1 |
---|
530 | |
---|
531 | if frac == 0: continue |
---|
532 | |
---|
533 | # binning |
---|
534 | i_phi = int(math.floor((self.nbins_phi)*phi_value/(2*Pi))) |
---|
535 | |
---|
536 | # Take care of the edge case at phi = 2pi. |
---|
537 | if i_phi == self.nbins_phi: |
---|
538 | i_phi = self.nbins_phi -1 |
---|
539 | |
---|
540 | phi_bins[i_phi] += frac * data[npt] |
---|
541 | |
---|
542 | if err_data == None or err_data[npt] ==0.0: |
---|
543 | if data_n <0: data_n = -data_n |
---|
544 | phi_err[i_phi] += frac * frac * math.fabs(data_n) |
---|
545 | else: |
---|
546 | phi_err[i_phi] += frac * frac *err_data[npt]*err_data[npt] |
---|
547 | phi_counts[i_phi] += frac |
---|
548 | |
---|
549 | for i in range(self.nbins_phi): |
---|
550 | phi_bins[i] = phi_bins[i] / phi_counts[i] |
---|
551 | phi_err[i] = math.sqrt(phi_err[i]) / phi_counts[i] |
---|
552 | phi_values[i] = 2.0*math.pi/self.nbins_phi*(1.0*i + 0.5) |
---|
553 | |
---|
554 | idx = (numpy.isfinite(phi_bins)) |
---|
555 | |
---|
556 | if not idx.any(): |
---|
557 | raise ValueError, "Average Error: No points inside ROI to average..." |
---|
558 | elif len(phi_bins[idx])!= self.nbins_phi: |
---|
559 | print "resulted",self.nbins_phi- len(phi_bins[idx]),"empty bin(s) due to tight binning..." |
---|
560 | return Data1D(x=phi_values[idx], y=phi_bins[idx], dy=phi_err[idx]) |
---|
561 | |
---|
562 | def get_pixel_fraction(qmax, q_00, q_01, q_10, q_11): |
---|
563 | """ |
---|
564 | Returns the fraction of the pixel defined by |
---|
565 | the four corners (q_00, q_01, q_10, q_11) that |
---|
566 | has q < qmax. |
---|
567 | |
---|
568 | q_01 q_11 |
---|
569 | y=1 +--------------+ |
---|
570 | | | |
---|
571 | | | |
---|
572 | | | |
---|
573 | y=0 +--------------+ |
---|
574 | q_00 q_10 |
---|
575 | |
---|
576 | x=0 x=1 |
---|
577 | |
---|
578 | """ |
---|
579 | |
---|
580 | # y side for x = minx |
---|
581 | x_0 = get_intercept(qmax, q_00, q_01) |
---|
582 | # y side for x = maxx |
---|
583 | x_1 = get_intercept(qmax, q_10, q_11) |
---|
584 | |
---|
585 | # x side for y = miny |
---|
586 | y_0 = get_intercept(qmax, q_00, q_10) |
---|
587 | # x side for y = maxy |
---|
588 | y_1 = get_intercept(qmax, q_01, q_11) |
---|
589 | |
---|
590 | # surface fraction for a 1x1 pixel |
---|
591 | frac_max = 0 |
---|
592 | |
---|
593 | if x_0 and x_1: |
---|
594 | frac_max = (x_0+x_1)/2.0 |
---|
595 | |
---|
596 | elif y_0 and y_1: |
---|
597 | frac_max = (y_0+y_1)/2.0 |
---|
598 | |
---|
599 | elif x_0 and y_0: |
---|
600 | if q_00 < q_10: |
---|
601 | frac_max = x_0*y_0/2.0 |
---|
602 | else: |
---|
603 | frac_max = 1.0-x_0*y_0/2.0 |
---|
604 | |
---|
605 | elif x_0 and y_1: |
---|
606 | if q_00 < q_10: |
---|
607 | frac_max = x_0*y_1/2.0 |
---|
608 | else: |
---|
609 | frac_max = 1.0-x_0*y_1/2.0 |
---|
610 | |
---|
611 | elif x_1 and y_0: |
---|
612 | if q_00 > q_10: |
---|
613 | frac_max = x_1*y_0/2.0 |
---|
614 | else: |
---|
615 | frac_max = 1.0-x_1*y_0/2.0 |
---|
616 | |
---|
617 | elif x_1 and y_1: |
---|
618 | if q_00 < q_10: |
---|
619 | frac_max = 1.0 - (1.0-x_1)*(1.0-y_1)/2.0 |
---|
620 | else: |
---|
621 | frac_max = (1.0-x_1)*(1.0-y_1)/2.0 |
---|
622 | |
---|
623 | # If we make it here, there is no intercept between |
---|
624 | # this pixel and the constant-q ring. We only need |
---|
625 | # to know if we have to include it or exclude it. |
---|
626 | elif (q_00+q_01+q_10+q_11)/4.0 < qmax: |
---|
627 | frac_max = 1.0 |
---|
628 | |
---|
629 | return frac_max |
---|
630 | |
---|
631 | def get_intercept(q, q_0, q_1): |
---|
632 | """ |
---|
633 | Returns the fraction of the side at which the |
---|
634 | q-value intercept the pixel, None otherwise. |
---|
635 | The values returned is the fraction ON THE SIDE |
---|
636 | OF THE LOWEST Q. |
---|
637 | |
---|
638 | |
---|
639 | |
---|
640 | A B |
---|
641 | +-----------+--------+ |
---|
642 | 0 1 <--- pixel size |
---|
643 | |
---|
644 | Q_0 -------- Q ----- Q_1 <--- equivalent Q range |
---|
645 | |
---|
646 | |
---|
647 | if Q_1 > Q_0, A is returned |
---|
648 | if Q_1 < Q_0, B is returned |
---|
649 | |
---|
650 | if Q is outside the range of [Q_0, Q_1], None is returned |
---|
651 | |
---|
652 | """ |
---|
653 | if q_1 > q_0: |
---|
654 | if (q > q_0 and q <= q_1): |
---|
655 | return (q-q_0)/(q_1 - q_0) |
---|
656 | else: |
---|
657 | if (q > q_1 and q <= q_0): |
---|
658 | return (q-q_1)/(q_0 - q_1) |
---|
659 | return None |
---|
660 | |
---|
661 | class _Sector: |
---|
662 | """ |
---|
663 | Defines a sector region on a 2D data set. |
---|
664 | The sector is defined by r_min, r_max, phi_min, phi_max, |
---|
665 | and the position of the center of the ring |
---|
666 | where phi_min and phi_max are defined by the right and left lines wrt central line |
---|
667 | and phi_max could be less than phi_min. |
---|
668 | |
---|
669 | Phi is defined between 0 and 2*pi in anti-clockwise starting from the x- axis on the left-hand side |
---|
670 | """ |
---|
671 | def __init__(self, r_min, r_max, phi_min=0, phi_max=2*math.pi,nbins=20): |
---|
672 | self.r_min = r_min |
---|
673 | self.r_max = r_max |
---|
674 | self.phi_min = phi_min |
---|
675 | self.phi_max = phi_max |
---|
676 | self.nbins = nbins |
---|
677 | |
---|
678 | |
---|
679 | def _agv(self, data2D, run='phi'): |
---|
680 | """ |
---|
681 | Perform sector averaging. |
---|
682 | |
---|
683 | @param data2D: Data2D object |
---|
684 | @param run: define the varying parameter ('phi' , 'q' , or 'q2') |
---|
685 | @return: Data1D object |
---|
686 | """ |
---|
687 | if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]: |
---|
688 | raise RuntimeError, "Ring averaging only take plottable_2D objects" |
---|
689 | Pi = math.pi |
---|
690 | |
---|
691 | # Get the all data & info |
---|
692 | data = data2D.data[numpy.isfinite(data2D.data)] |
---|
693 | q_data = data2D.q_data[numpy.isfinite(data2D.data)] |
---|
694 | err_data = data2D.err_data[numpy.isfinite(data2D.data)] |
---|
695 | qx_data = data2D.qx_data[numpy.isfinite(data2D.data)] |
---|
696 | qy_data = data2D.qy_data[numpy.isfinite(data2D.data)] |
---|
697 | |
---|
698 | #set space for 1d outputs |
---|
699 | x = numpy.zeros(self.nbins) |
---|
700 | y = numpy.zeros(self.nbins) |
---|
701 | y_err = numpy.zeros(self.nbins) |
---|
702 | y_counts = numpy.zeros(self.nbins) |
---|
703 | |
---|
704 | # Get the min and max into the region: 0 <= phi < 2Pi |
---|
705 | phi_min = flip_phi(self.phi_min) |
---|
706 | phi_max = flip_phi(self.phi_max) |
---|
707 | |
---|
708 | q_data_max = numpy.max(q_data) |
---|
709 | |
---|
710 | for n in range(len(data)): |
---|
711 | frac = 0 |
---|
712 | |
---|
713 | # q-value at the pixel (j,i) |
---|
714 | q_value = q_data[n] |
---|
715 | |
---|
716 | |
---|
717 | data_n = data[n] |
---|
718 | |
---|
719 | # Is pixel within range? |
---|
720 | is_in = False |
---|
721 | |
---|
722 | # phi-value of the pixel (j,i) |
---|
723 | phi_value=math.atan2(qy_data[n],qx_data[n])+Pi |
---|
724 | |
---|
725 | ## No need to calculate the frac when all data are within range |
---|
726 | if self.r_min <= q_value and q_value <= self.r_max: frac = 1 |
---|
727 | |
---|
728 | if frac == 0: continue |
---|
729 | |
---|
730 | #In case of two ROIs (symmetric major and minor regions)(for 'q2') |
---|
731 | if run.lower()=='q2': |
---|
732 | ## For minor sector wing |
---|
733 | # Calculate the minor wing phis |
---|
734 | phi_min_minor = flip_phi(phi_min-Pi) |
---|
735 | phi_max_minor = flip_phi(phi_max-Pi) |
---|
736 | # Check if phis of the minor ring is within 0 to 2pi |
---|
737 | if phi_min_minor > phi_max_minor: |
---|
738 | is_in = (phi_value > phi_min_minor or phi_value < phi_max_minor) |
---|
739 | else: |
---|
740 | is_in = (phi_value > phi_min_minor and phi_value < phi_max_minor) |
---|
741 | |
---|
742 | #For all cases(i.e.,for 'q', 'q2', and 'phi') |
---|
743 | #Find pixels within ROI |
---|
744 | if phi_min > phi_max: |
---|
745 | is_in = is_in or (phi_value > phi_min or phi_value < phi_max) |
---|
746 | else: |
---|
747 | is_in = is_in or (phi_value>= phi_min and phi_value <phi_max) |
---|
748 | |
---|
749 | if not is_in: frac = 0 |
---|
750 | if frac == 0: continue |
---|
751 | |
---|
752 | # Check which type of averaging we need |
---|
753 | if run.lower()=='phi': |
---|
754 | i_bin = int(math.floor((self.nbins)*(phi_value-self.phi_min)\ |
---|
755 | /(self.phi_max-self.phi_min))) |
---|
756 | else: |
---|
757 | i_bin = int(math.floor((self.nbins)*(q_value-self.r_min)/(self.r_max-self.r_min))) |
---|
758 | |
---|
759 | # Take care of the edge case at phi = 2pi. |
---|
760 | if i_bin == self.nbins: |
---|
761 | i_bin = self.nbins -1 |
---|
762 | |
---|
763 | ## Get the total y |
---|
764 | y[i_bin] += frac * data_n |
---|
765 | |
---|
766 | if err_data == None or err_data[n] ==0.0: |
---|
767 | if data_n<0: data_n= -data_n |
---|
768 | y_err[i_bin] += frac * frac * data_n |
---|
769 | else: |
---|
770 | y_err[i_bin] += frac * frac * err_data[n]*err_data[n] |
---|
771 | y_counts[i_bin] += frac |
---|
772 | |
---|
773 | # Organize the results |
---|
774 | for i in range(self.nbins): |
---|
775 | y[i] = y[i] / y_counts[i] |
---|
776 | y_err[i] = math.sqrt(y_err[i]) / y_counts[i] |
---|
777 | |
---|
778 | # The type of averaging: phi,q2, or q |
---|
779 | # Calculate x[i]should be at the center of the bin |
---|
780 | if run.lower()=='phi': |
---|
781 | x[i] = (self.phi_max-self.phi_min)/self.nbins*(1.0*i + 0.5)+self.phi_min |
---|
782 | else: |
---|
783 | x[i] = (self.r_max-self.r_min)/self.nbins*(1.0*i + 0.5)+self.r_min |
---|
784 | |
---|
785 | idx = (numpy.isfinite(y)& numpy.isfinite(y_err)) |
---|
786 | |
---|
787 | if not idx.any(): |
---|
788 | raise ValueError, "Average Error: No points inside sector of ROI to average..." |
---|
789 | elif len(y[idx])!= self.nbins: |
---|
790 | print "resulted",self.nbins- len(y[idx]),"empty bin(s) due to tight binning..." |
---|
791 | return Data1D(x=x[idx], y=y[idx], dy=y_err[idx]) |
---|
792 | |
---|
793 | class SectorPhi(_Sector): |
---|
794 | """ |
---|
795 | Sector average as a function of phi. |
---|
796 | I(phi) is return and the data is averaged over Q. |
---|
797 | |
---|
798 | A sector is defined by r_min, r_max, phi_min, phi_max. |
---|
799 | The number of bin in phi also has to be defined. |
---|
800 | """ |
---|
801 | def __call__(self, data2D): |
---|
802 | """ |
---|
803 | Perform sector average and return I(phi). |
---|
804 | |
---|
805 | @param data2D: Data2D object |
---|
806 | @return: Data1D object |
---|
807 | """ |
---|
808 | |
---|
809 | return self._agv(data2D, 'phi') |
---|
810 | |
---|
811 | class SectorQ(_Sector): |
---|
812 | """ |
---|
813 | Sector average as a function of Q for both symatric wings. |
---|
814 | I(Q) is return and the data is averaged over phi. |
---|
815 | |
---|
816 | A sector is defined by r_min, r_max, phi_min, phi_max. |
---|
817 | r_min, r_max, phi_min, phi_max >0. |
---|
818 | The number of bin in Q also has to be defined. |
---|
819 | """ |
---|
820 | def __call__(self, data2D): |
---|
821 | """ |
---|
822 | Perform sector average and return I(Q). |
---|
823 | |
---|
824 | @param data2D: Data2D object |
---|
825 | @return: Data1D object |
---|
826 | """ |
---|
827 | return self._agv(data2D, 'q2') |
---|
828 | |
---|
829 | class Ringcut(object): |
---|
830 | """ |
---|
831 | Defines a ring on a 2D data set. |
---|
832 | The ring is defined by r_min, r_max, and |
---|
833 | the position of the center of the ring. |
---|
834 | |
---|
835 | The data returned is the region inside the ring |
---|
836 | |
---|
837 | Phi_min and phi_max should be defined between 0 and 2*pi |
---|
838 | in anti-clockwise starting from the x- axis on the left-hand side |
---|
839 | """ |
---|
840 | def __init__(self, r_min=0, r_max=0, center_x=0, center_y=0 ): |
---|
841 | # Minimum radius |
---|
842 | self.r_min = r_min |
---|
843 | # Maximum radius |
---|
844 | self.r_max = r_max |
---|
845 | # Center of the ring in x |
---|
846 | self.center_x = center_x |
---|
847 | # Center of the ring in y |
---|
848 | self.center_y = center_y |
---|
849 | |
---|
850 | |
---|
851 | def __call__(self, data2D): |
---|
852 | """ |
---|
853 | Apply the ring to the data set. |
---|
854 | Returns the angular distribution for a given q range |
---|
855 | |
---|
856 | @param data2D: Data2D object |
---|
857 | @return: index array in the range |
---|
858 | """ |
---|
859 | if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]: |
---|
860 | raise RuntimeError, "Ring cut only take plottable_2D objects" |
---|
861 | |
---|
862 | # Get data |
---|
863 | qx_data = data2D.qx_data |
---|
864 | qy_data = data2D.qy_data |
---|
865 | mask = data2D.mask |
---|
866 | q_data = numpy.sqrt(qx_data*qx_data+qy_data*qy_data) |
---|
867 | #q_data_max = numpy.max(q_data) |
---|
868 | |
---|
869 | # check whether or not the data point is inside ROI |
---|
870 | out = (self.r_min <= q_data) & (self.r_max >= q_data) |
---|
871 | |
---|
872 | return (out) |
---|
873 | |
---|
874 | |
---|
875 | class Boxcut(object): |
---|
876 | """ |
---|
877 | Find a rectangular 2D region of interest. |
---|
878 | """ |
---|
879 | def __init__(self, x_min=0.0, x_max=0.0, y_min=0.0, y_max=0.0): |
---|
880 | # Minimum Qx value [A-1] |
---|
881 | self.x_min = x_min |
---|
882 | # Maximum Qx value [A-1] |
---|
883 | self.x_max = x_max |
---|
884 | # Minimum Qy value [A-1] |
---|
885 | self.y_min = y_min |
---|
886 | # Maximum Qy value [A-1] |
---|
887 | self.y_max = y_max |
---|
888 | |
---|
889 | def __call__(self, data2D): |
---|
890 | """ |
---|
891 | Find a rectangular 2D region of interest. |
---|
892 | |
---|
893 | @param data2D: Data2D object |
---|
894 | @return: mask, 1d array (len = len(data)) |
---|
895 | with Trues where the data points are inside ROI, otherwise False |
---|
896 | """ |
---|
897 | mask = self._find(data2D) |
---|
898 | |
---|
899 | return mask |
---|
900 | |
---|
901 | def _find(self, data2D): |
---|
902 | """ |
---|
903 | Find a rectangular 2D region of interest. |
---|
904 | @param data2D: Data2D object |
---|
905 | @return: out, 1d array (length = len(data)) |
---|
906 | with Trues where the data points are inside ROI, otherwise Falses |
---|
907 | """ |
---|
908 | if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]: |
---|
909 | raise RuntimeError, "Boxcut take only plottable_2D objects" |
---|
910 | # Get qx_ and qy_data |
---|
911 | qx_data = data2D.qx_data |
---|
912 | qy_data = data2D.qy_data |
---|
913 | mask = data2D.mask |
---|
914 | |
---|
915 | # check whether or not the data point is inside ROI |
---|
916 | outx = (self.x_min <= qx_data) & (self.x_max > qx_data) |
---|
917 | outy = (self.y_min <= qy_data) & (self.y_max > qy_data) |
---|
918 | |
---|
919 | return (outx & outy) |
---|
920 | |
---|
921 | class Sectorcut(object): |
---|
922 | """ |
---|
923 | Defines a sector (major + minor) region on a 2D data set. |
---|
924 | The sector is defined by phi_min, phi_max, |
---|
925 | where phi_min and phi_max are defined by the right and left lines wrt central line. |
---|
926 | |
---|
927 | Phi_min and phi_max are given in units of radian |
---|
928 | and (phi_max-phi_min) should not be larger than pi |
---|
929 | """ |
---|
930 | def __init__(self,phi_min=0, phi_max=math.pi): |
---|
931 | self.phi_min = phi_min |
---|
932 | self.phi_max = phi_max |
---|
933 | |
---|
934 | def __call__(self, data2D): |
---|
935 | """ |
---|
936 | Find a rectangular 2D region of interest. |
---|
937 | |
---|
938 | @param data2D: Data2D object |
---|
939 | @return: mask, 1d array (len = len(data)) |
---|
940 | with Trues where the data points are inside ROI, otherwise False |
---|
941 | """ |
---|
942 | mask = self._find(data2D) |
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943 | |
---|
944 | return mask |
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945 | |
---|
946 | def _find(self, data2D): |
---|
947 | """ |
---|
948 | Find a rectangular 2D region of interest. |
---|
949 | @param data2D: Data2D object |
---|
950 | @return: out, 1d array (length = len(data)) |
---|
951 | with Trues where the data points are inside ROI, otherwise Falses |
---|
952 | """ |
---|
953 | if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]: |
---|
954 | raise RuntimeError, "Sectorcut take only plottable_2D objects" |
---|
955 | Pi = math.pi |
---|
956 | # Get data |
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957 | qx_data = data2D.qx_data |
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958 | qy_data = data2D.qy_data |
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959 | phi_data = numpy.zeros(len(qx_data)) |
---|
960 | |
---|
961 | # get phi from data |
---|
962 | phi_data = numpy.arctan2(qy_data, qx_data) |
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963 | |
---|
964 | # Get the min and max into the region: -pi <= phi < Pi |
---|
965 | phi_min_major = flip_phi(self.phi_min+Pi)-Pi |
---|
966 | phi_max_major = flip_phi(self.phi_max+Pi)-Pi |
---|
967 | # check for major sector |
---|
968 | if phi_min_major > phi_max_major: |
---|
969 | out_major = (phi_min_major <= phi_data) + (phi_max_major > phi_data) |
---|
970 | else: |
---|
971 | out_major = (phi_min_major <= phi_data) & (phi_max_major > phi_data) |
---|
972 | |
---|
973 | # minor sector |
---|
974 | # Get the min and max into the region: -pi <= phi < Pi |
---|
975 | phi_min_minor = flip_phi(self.phi_min)-Pi |
---|
976 | phi_max_minor = flip_phi(self.phi_max)-Pi |
---|
977 | |
---|
978 | # check for minor sector |
---|
979 | if phi_min_minor > phi_max_minor: |
---|
980 | out_minor= (phi_min_minor <= phi_data) + (phi_max_minor>= phi_data) |
---|
981 | else: |
---|
982 | out_minor = (phi_min_minor <= phi_data) & (phi_max_minor >= phi_data) |
---|
983 | out = out_major + out_minor |
---|
984 | |
---|
985 | return out |
---|
986 | |
---|
987 | if __name__ == "__main__": |
---|
988 | |
---|
989 | from loader import Loader |
---|
990 | |
---|
991 | |
---|
992 | d = Loader().load('test/MAR07232_rest.ASC') |
---|
993 | #d = Loader().load('test/MP_New.sans') |
---|
994 | |
---|
995 | |
---|
996 | r = SectorQ(r_min=.000001, r_max=.01, phi_min=0.0, phi_max=2*math.pi) |
---|
997 | o = r(d) |
---|
998 | |
---|
999 | s = Ring(r_min=.000001, r_max=.01) |
---|
1000 | p = s(d) |
---|
1001 | |
---|
1002 | for i in range(len(o.x)): |
---|
1003 | print o.x[i], o.y[i], o.dy[i] |
---|
1004 | |
---|
1005 | |
---|
1006 | |
---|