[3be3a80] | 1 | """ |
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| 2 | This module is a small tool to allow user to |
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| 3 | control instrumental parameters |
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| 4 | """ |
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| 5 | import numpy |
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| 6 | |
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| 7 | # defaults in cgs unit |
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| 8 | _SAMPLE_A_SIZE = [1.27] |
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| 9 | _SOURCE_A_SIZE = [3.81] |
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| 10 | _SAMPLE_DISTANCE = [1627, 0] |
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| 11 | _SAMPLE_OFFSET = [0, 0] |
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| 12 | _SAMPLE_SIZE = [2.54] |
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| 13 | _SAMPLE_THICKNESS = 0.2 |
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| 14 | _D_DISTANCE = [1000, 0] |
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| 15 | _D_SIZE = [128, 128] |
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| 16 | _D_PIX_SIZE = [0.5, 0.5] |
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| 17 | |
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| 18 | _MIN = 0.0 |
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| 19 | _MAX = 30.0 |
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| 20 | _INTENSITY = 368428 |
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| 21 | _WAVE_LENGTH = 6.0 |
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| 22 | _WAVE_SPREAD = 0.125 |
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| 23 | _MASS = 1.67492729E-24 #[gr] |
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| 24 | X_VAL = numpy.linspace(0, 30, 310) |
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| 25 | _LAMBDA_ARRAY = [[0, 5, 10, 15, 20, 25, 30], [1, 1, 1, 1, 1, 1, 1]] |
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| 26 | |
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| 27 | class Aperture(object): |
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| 28 | """ |
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| 29 | An object class that defines the aperture variables |
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| 30 | """ |
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| 31 | def __init__(self): |
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| 32 | |
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| 33 | # assumes that all aligned at the centers |
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| 34 | # aperture_size [diameter] for pinhole, [dx, dy] for rectangular |
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| 35 | self.sample_size = _SAMPLE_A_SIZE |
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| 36 | self.source_size = _SOURCE_A_SIZE |
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| 37 | self.sample_distance = _SAMPLE_DISTANCE |
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| 38 | |
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| 39 | def set_source_size(self, size =[]): |
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| 40 | """ |
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| 41 | Set the source aperture size |
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| 42 | """ |
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| 43 | if len(size) == 0: |
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| 44 | self.source_size = 0.0 |
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| 45 | else: |
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| 46 | self.source_size = size |
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| 47 | validate(size[0]) |
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| 48 | def set_sample_size(self, size =[]): |
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| 49 | """ |
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| 50 | Set the sample aperture size |
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| 51 | """ |
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| 52 | if len(size) == 0: |
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| 53 | self.sample_size = 0.0 |
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| 54 | else: |
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| 55 | self.sample_size = size |
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| 56 | validate(size[0]) |
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| 57 | |
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| 58 | def set_sample_distance(self, distance = []): |
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| 59 | """ |
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| 60 | Set the sample aperture distance |
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| 61 | """ |
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| 62 | if len(distance) == 0: |
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| 63 | self.sample_distance = 0.0 |
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| 64 | else: |
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| 65 | self.sample_distance = distance |
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| 66 | validate(distance[0]) |
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| 67 | |
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| 68 | |
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| 69 | class Sample(object): |
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| 70 | """ |
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| 71 | An object class that defines the sample variables |
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| 72 | """ |
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| 73 | def __init__(self): |
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| 74 | |
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| 75 | # assumes that all aligned at the centers |
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| 76 | # source2sample or sample2detector distance |
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| 77 | self.distance = _SAMPLE_OFFSET |
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| 78 | self.size = _SAMPLE_SIZE |
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| 79 | self.thickness = _SAMPLE_THICKNESS |
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| 80 | |
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| 81 | |
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| 82 | def set_size(self, size =[]): |
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| 83 | """ |
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| 84 | Set the sample size |
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| 85 | """ |
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| 86 | if len(size) == 0: |
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| 87 | self.sample_size = 0.0 |
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| 88 | else: |
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| 89 | self.sample_size = size |
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| 90 | validate(size[0]) |
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| 91 | |
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| 92 | def set_thickness(self, thickness = 0.0): |
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| 93 | """ |
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| 94 | Set the sample thickness |
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| 95 | """ |
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| 96 | self.thickness = thickness |
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| 97 | validate(thickness) |
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| 98 | |
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| 99 | def set_distance(self, distance = []): |
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| 100 | """ |
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| 101 | Set the sample distance |
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| 102 | """ |
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| 103 | if len(distance) == 0: |
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| 104 | self.distance = 0.0 |
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| 105 | else: |
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| 106 | self.distance = distance |
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| 107 | if distance[0] != 0.0: |
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| 108 | validate(distance[0]) |
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| 109 | |
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| 110 | |
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| 111 | class Detector(object): |
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| 112 | """ |
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| 113 | An object class that defines the detector variables |
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| 114 | """ |
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| 115 | def __init__(self): |
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| 116 | |
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| 117 | # assumes that all aligned at the centers |
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| 118 | # source2sample or sample2detector distance |
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| 119 | self.distance = _D_DISTANCE |
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| 120 | self.size = _D_SIZE |
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| 121 | self.pix_size = _D_PIX_SIZE |
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| 122 | |
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| 123 | |
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| 124 | |
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| 125 | def set_size(self, size =[]): |
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| 126 | """ |
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| 127 | Set the detector size |
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| 128 | """ |
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| 129 | if len(size) == 0: |
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| 130 | self.size = 0 |
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| 131 | else: |
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| 132 | self.size = size |
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| 133 | validate(size[0]) |
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| 134 | |
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| 135 | def set_pix_size(self, size = []): |
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| 136 | """ |
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| 137 | Set the detector pix_size |
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| 138 | """ |
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| 139 | if len(size) == 0: |
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| 140 | self.pix_size = 0 |
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| 141 | else: |
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| 142 | self.pix_size = size |
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| 143 | validate(size[0]) |
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| 144 | |
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| 145 | def set_distance(self, distance = []): |
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| 146 | """ |
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| 147 | Set the detector distance |
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| 148 | """ |
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| 149 | if len(distance) == 0: |
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| 150 | self.distance = 0 |
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| 151 | else: |
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| 152 | self.distance = distance |
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| 153 | validate(distance[0]) |
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| 154 | |
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| 155 | |
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| 156 | class Neutron(object): |
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| 157 | """ |
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| 158 | An object that defines the wavelength variables |
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| 159 | """ |
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| 160 | def __init__(self): |
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| 161 | |
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| 162 | # intensity in counts/sec |
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| 163 | self.intensity = _INTENSITY |
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| 164 | # neutron mass in cgs unit |
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| 165 | self.mass = _MASS |
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| 166 | # wavelength spectrum |
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| 167 | self.spectrum = [] |
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| 168 | # wavelength |
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| 169 | self.wavelength = _WAVE_LENGTH |
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| 170 | # wavelength spread (FWHM) |
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| 171 | self.wavelength_spread = _WAVE_SPREAD |
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| 172 | # mean wavelength |
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| 173 | self.mean = _WAVE_LENGTH |
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| 174 | # wavelength = distribution after velocity selector |
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| 175 | self.peak = [] |
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| 176 | # std of of the spectrum |
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| 177 | self.std = None |
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| 178 | # min max range of the spectrum |
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| 179 | self.min = _MIN |
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| 180 | self.max = _MAX |
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| 181 | # x-range of the spectrum |
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| 182 | self.x_val = X_VAL |
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| 183 | # default distribution function |
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| 184 | # ex., 'gaussian', or an array |
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| 185 | self.func = _LAMBDA_ARRAY |
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| 186 | # default unit of the thickness |
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| 187 | self.wavelength_unit = 'A' |
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| 188 | |
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| 189 | def set_intensity(self, intensity = 368428): |
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| 190 | """ |
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| 191 | Sets the intensity in counts/sec |
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| 192 | """ |
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| 193 | self.intensity = intensity |
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| 194 | validate(intensity) |
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| 195 | |
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| 196 | def set_wavelength(self, wavelength = _WAVE_LENGTH): |
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| 197 | """ |
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| 198 | Sets the wavelength |
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| 199 | """ |
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| 200 | self.wavelength = wavelength |
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| 201 | validate(wavelength) |
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| 202 | |
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| 203 | def set_mass(self, mass = _MASS): |
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| 204 | """ |
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| 205 | Sets the wavelength |
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| 206 | """ |
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| 207 | self.mass = mass |
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| 208 | validate(mass) |
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| 209 | |
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| 210 | def set_wavelength_spread(self, spread = _WAVE_SPREAD): |
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| 211 | """ |
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| 212 | Sets the wavelength spread |
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| 213 | """ |
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| 214 | self.wavelength_spread = spread |
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| 215 | if spread != 0.0: |
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| 216 | validate(spread) |
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| 217 | |
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| 218 | def get_intensity(self): |
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| 219 | """ |
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| 220 | To get the value of intensity |
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| 221 | """ |
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| 222 | return self.intensity |
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| 223 | |
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| 224 | def get_wavelength(self): |
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| 225 | """ |
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| 226 | To get the value of wavelength |
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| 227 | """ |
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| 228 | return self.wavelength |
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| 229 | |
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| 230 | def get_mass(self): |
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| 231 | """ |
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| 232 | To get the neutron mass |
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| 233 | """ |
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| 234 | return self.mass |
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| 235 | |
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| 236 | def get_wavelength_spread(self): |
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| 237 | """ |
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| 238 | To get the value of wavelength spread |
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| 239 | """ |
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| 240 | return self.wavelength_spread |
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| 241 | |
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| 242 | def get_ramdom_value(self): |
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| 243 | """ |
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| 244 | To get the value of wave length |
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| 245 | """ |
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| 246 | return self.wavelength |
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| 247 | |
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| 248 | def _set_mean(self): |
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| 249 | """ |
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| 250 | To get mean value of wavelength |
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| 251 | """ |
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| 252 | mean_value = numpy.mean(self.peak[0]* |
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| 253 | self.peak[1]) |
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| 254 | self.mean = mean_value |
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| 255 | |
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| 256 | |
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| 257 | def get_mean_peakvalue(self): |
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| 258 | """ |
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| 259 | To get mean value of wavelength |
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| 260 | """ |
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| 261 | mean_value = numpy.mean(self.peak[1]) |
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| 262 | return mean_value |
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| 263 | |
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| 264 | def get_spectrum(self): |
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| 265 | """ |
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| 266 | To get the wavelength spectrum |
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| 267 | """ |
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| 268 | return self.spectrum |
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| 269 | |
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| 270 | def plot_spectrum(self): |
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| 271 | """ |
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| 272 | To plot the wavelength spactrum |
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| 273 | : requirment: matplotlib.pyplot |
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| 274 | """ |
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| 275 | try: |
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| 276 | import matplotlib.pyplot as plt |
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| 277 | plt.plot(self.x_val, self.spectrum, linewidth = 2, color = 'r') |
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| 278 | plt.legend(['Spectrum'], loc = 'best') |
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| 279 | plt.show() |
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| 280 | except: |
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| 281 | raise RuntimeError, "Can't import matplotlib required to plot..." |
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| 282 | |
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| 283 | def plot_peak(self): |
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| 284 | """ |
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| 285 | To plot the wavelength peak spactrum |
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| 286 | : requirment: matplotlib.pyplot |
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| 287 | """ |
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| 288 | try: |
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| 289 | min = self.mean * (1 - self.wavelength_spread) |
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| 290 | max = self.mean * (1 + self.wavelength_spread) |
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| 291 | x_val = numpy.linspace(min, max, 310) |
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| 292 | import matplotlib.pyplot as plt |
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| 293 | plt.plot(x_val, self.peak, linewidth = 2, color = 'r') |
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| 294 | plt.legend(['peak'], loc = 'best') |
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| 295 | plt.show() |
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| 296 | except: |
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| 297 | raise RuntimeError, "Can't import matplotlib required to plot..." |
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| 298 | |
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| 299 | def validate(value = None): |
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| 300 | """ |
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| 301 | Check if the value is folat > 0.0 |
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| 302 | |
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| 303 | :return value: True / False |
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| 304 | """ |
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| 305 | try: |
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| 306 | val = float(value) |
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| 307 | if val > 0: |
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| 308 | val = True |
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| 309 | else: |
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| 310 | val = False |
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| 311 | except: |
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| 312 | val = False |
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| 313 | if not val: |
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| 314 | raise ValueError, "Got improper value..." |
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