[240a2d2] | 1 | """ |
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| 2 | Handle Q smearing |
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| 3 | """ |
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| 4 | ##################################################################### |
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| 5 | #This software was developed by the University of Tennessee as part of the |
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| 6 | #Distributed Data Analysis of Neutron Scattering Experiments (DANSE) |
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| 7 | #project funded by the US National Science Foundation. |
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| 8 | #See the license text in license.txt |
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| 9 | #copyright 2008, University of Tennessee |
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| 10 | ###################################################################### |
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| 11 | import numpy |
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| 12 | import math |
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| 13 | import logging |
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| 14 | import sys |
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[a9f579c] | 15 | import numpy as np # type: ignore |
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| 16 | from numpy import pi, exp # type:ignore |
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[f8aa738] | 17 | from sasmodels.resolution import Slit1D, Pinhole1D |
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[157b716] | 18 | from sasmodels.sesans import SesansTransform |
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[f8aa738] | 19 | from sasmodels.resolution2d import Pinhole2D |
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[a9f579c] | 20 | from src.sas.sascalc.data_util.nxsunit import Converter |
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[f8aa738] | 21 | |
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| 22 | def smear_selection(data, model = None): |
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[240a2d2] | 23 | """ |
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[f8aa738] | 24 | Creates the right type of smearer according |
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[240a2d2] | 25 | to the data. |
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| 26 | The canSAS format has a rule that either |
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| 27 | slit smearing data OR resolution smearing data |
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[f8aa738] | 28 | is available. |
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| 29 | |
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[240a2d2] | 30 | For the present purpose, we choose the one that |
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| 31 | has none-zero data. If both slit and resolution |
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[f8aa738] | 32 | smearing arrays are filled with good data |
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[240a2d2] | 33 | (which should not happen), then we choose the |
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[f8aa738] | 34 | resolution smearing data. |
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| 35 | |
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| 36 | :param data: Data1D object |
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[240a2d2] | 37 | :param model: sas.model instance |
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| 38 | """ |
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| 39 | # Sanity check. If we are not dealing with a SAS Data1D |
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| 40 | # object, just return None |
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[a9f579c] | 41 | # This checks for 2D data (does not throw exception because fail is common) |
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[f8aa738] | 42 | if data.__class__.__name__ not in ['Data1D', 'Theory1D']: |
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| 43 | if data == None: |
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[240a2d2] | 44 | return None |
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[f8aa738] | 45 | elif data.dqx_data == None or data.dqy_data == None: |
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[240a2d2] | 46 | return None |
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[2ffe241] | 47 | return PySmear2D(data) |
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[a9f579c] | 48 | # This checks for 1D data with smearing info in the data itself (again, fail is likely; no exceptions) |
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[f8aa738] | 49 | if not hasattr(data, "dx") and not hasattr(data, "dxl")\ |
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| 50 | and not hasattr(data, "dxw"): |
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[240a2d2] | 51 | return None |
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[f8aa738] | 52 | |
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[240a2d2] | 53 | # Look for resolution smearing data |
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[a9f579c] | 54 | # This is the code that checks for SESANS data; it looks for the file loader |
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| 55 | # TODO: change other sanity checks to check for file loader instead of data structure? |
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| 56 | _found_sesans = False |
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| 57 | #if data.dx is not None and data.meta_data['loader']=='SESANS': |
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| 58 | if data.dx is not None and data.isSesans: |
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| 59 | #if data.dx[0] > 0.0: |
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| 60 | if numpy.size(data.dx[data.dx <= 0]) == 0: |
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| 61 | _found_sesans = True |
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| 62 | # if data.dx[0] <= 0.0: |
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| 63 | if numpy.size(data.dx[data.dx <= 0]) > 0: |
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| 64 | raise ValueError('one or more of your dx values are negative, please check the data file!') |
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| 65 | |
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| 66 | if _found_sesans == True: |
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| 67 | #Pre-compute the Hankel matrix (H) |
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| 68 | qmax, qunits = data.sample.zacceptance |
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[157b716] | 69 | SElength = Converter(data._xunit)(data.x, "A") |
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| 70 | zaccept = Converter(qunits)(qmax, "1/A"), |
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| 71 | Rmax = 10000000 |
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| 72 | hankel = SesansTransform(data.x, SElength, zaccept, Rmax) |
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[a9f579c] | 73 | # Then return the actual transform, as if it were a smearing function |
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[157b716] | 74 | return PySmear(hankel, model, offset=0) |
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[a9f579c] | 75 | |
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[240a2d2] | 76 | _found_resolution = False |
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[f8aa738] | 77 | if data.dx is not None and len(data.dx) == len(data.x): |
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| 78 | |
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[240a2d2] | 79 | # Check that we have non-zero data |
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[f8aa738] | 80 | if data.dx[0] > 0.0: |
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[240a2d2] | 81 | _found_resolution = True |
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| 82 | #print "_found_resolution",_found_resolution |
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| 83 | #print "data1D.dx[0]",data1D.dx[0],data1D.dxl[0] |
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| 84 | # If we found resolution smearing data, return a QSmearer |
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| 85 | if _found_resolution == True: |
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[f8aa738] | 86 | return pinhole_smear(data, model) |
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[240a2d2] | 87 | |
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| 88 | # Look for slit smearing data |
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| 89 | _found_slit = False |
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[f8aa738] | 90 | if data.dxl is not None and len(data.dxl) == len(data.x) \ |
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| 91 | and data.dxw is not None and len(data.dxw) == len(data.x): |
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| 92 | |
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[240a2d2] | 93 | # Check that we have non-zero data |
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[f8aa738] | 94 | if data.dxl[0] > 0.0 or data.dxw[0] > 0.0: |
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[240a2d2] | 95 | _found_slit = True |
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[f8aa738] | 96 | |
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[240a2d2] | 97 | # Sanity check: all data should be the same as a function of Q |
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[f8aa738] | 98 | for item in data.dxl: |
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| 99 | if data.dxl[0] != item: |
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[240a2d2] | 100 | _found_resolution = False |
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| 101 | break |
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[f8aa738] | 102 | |
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| 103 | for item in data.dxw: |
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| 104 | if data.dxw[0] != item: |
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[240a2d2] | 105 | _found_resolution = False |
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| 106 | break |
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| 107 | # If we found slit smearing data, return a slit smearer |
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| 108 | if _found_slit == True: |
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[f8aa738] | 109 | return slit_smear(data, model) |
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[240a2d2] | 110 | return None |
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| 111 | |
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| 112 | |
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| 113 | class PySmear(object): |
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| 114 | """ |
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| 115 | Wrapper for pure python sasmodels resolution functions. |
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| 116 | """ |
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[157b716] | 117 | def __init__(self, resolution, model, offset=None): |
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[240a2d2] | 118 | self.model = model |
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| 119 | self.resolution = resolution |
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[157b716] | 120 | if offset is None: |
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| 121 | offset = numpy.searchsorted(self.resolution.q_calc, self.resolution.q[0]) |
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| 122 | self.offset = offset |
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[240a2d2] | 123 | |
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| 124 | def apply(self, iq_in, first_bin=0, last_bin=None): |
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| 125 | """ |
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| 126 | Apply the resolution function to the data. |
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| 127 | Note that this is called with iq_in matching data.x, but with |
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| 128 | iq_in[first_bin:last_bin] set to theory values for these bins, |
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| 129 | and the remainder left undefined. The first_bin, last_bin values |
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| 130 | should be those returned from get_bin_range. |
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| 131 | The returned value is of the same length as iq_in, with the range |
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| 132 | first_bin:last_bin set to the resolution smeared values. |
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| 133 | """ |
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| 134 | if last_bin is None: last_bin = len(iq_in) |
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| 135 | start, end = first_bin + self.offset, last_bin + self.offset |
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| 136 | q_calc = self.resolution.q_calc |
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| 137 | iq_calc = numpy.empty_like(q_calc) |
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| 138 | if start > 0: |
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| 139 | iq_calc[:start] = self.model.evalDistribution(q_calc[:start]) |
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| 140 | if end+1 < len(q_calc): |
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| 141 | iq_calc[end+1:] = self.model.evalDistribution(q_calc[end+1:]) |
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| 142 | iq_calc[start:end+1] = iq_in[first_bin:last_bin+1] |
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| 143 | smeared = self.resolution.apply(iq_calc) |
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| 144 | return smeared |
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| 145 | __call__ = apply |
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| 146 | |
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| 147 | def get_bin_range(self, q_min=None, q_max=None): |
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| 148 | """ |
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| 149 | For a given q_min, q_max, find the corresponding indices in the data. |
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| 150 | Returns first, last. |
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| 151 | Note that these are indexes into q from the data, not the q_calc |
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| 152 | needed by the resolution function. Note also that these are the |
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| 153 | indices, not the range limits. That is, the complete range will be |
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| 154 | q[first:last+1]. |
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| 155 | """ |
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| 156 | q = self.resolution.q |
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| 157 | first = numpy.searchsorted(q, q_min) |
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| 158 | last = numpy.searchsorted(q, q_max) |
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| 159 | return first, min(last,len(q)-1) |
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| 160 | |
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| 161 | def slit_smear(data, model=None): |
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| 162 | q = data.x |
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| 163 | width = data.dxw if data.dxw is not None else 0 |
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| 164 | height = data.dxl if data.dxl is not None else 0 |
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| 165 | # TODO: width and height seem to be reversed |
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| 166 | return PySmear(Slit1D(q, height, width), model) |
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| 167 | |
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| 168 | def pinhole_smear(data, model=None): |
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| 169 | q = data.x |
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| 170 | width = data.dx if data.dx is not None else 0 |
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[d3911e3] | 171 | return PySmear(Pinhole1D(q, width), model) |
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| 172 | |
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| 173 | |
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| 174 | class PySmear2D(object): |
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| 175 | """ |
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| 176 | Q smearing class for SAS 2d pinhole data |
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| 177 | """ |
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| 178 | |
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| 179 | def __init__(self, data=None, model=None): |
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| 180 | self.data = data |
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| 181 | self.model = model |
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| 182 | self.accuracy = 'Low' |
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| 183 | self.limit = 3.0 |
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| 184 | self.index = None |
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| 185 | self.coords = 'polar' |
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| 186 | self.smearer = True |
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| 187 | |
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| 188 | def set_accuracy(self, accuracy='Low'): |
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| 189 | """ |
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| 190 | Set accuracy. |
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| 191 | |
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| 192 | :param accuracy: string |
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| 193 | """ |
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| 194 | self.accuracy = accuracy |
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| 195 | |
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| 196 | def set_smearer(self, smearer=True): |
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| 197 | """ |
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| 198 | Set whether or not smearer will be used |
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| 199 | |
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| 200 | :param smearer: smear object |
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| 201 | |
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| 202 | """ |
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| 203 | self.smearer = smearer |
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| 204 | |
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| 205 | def set_data(self, data=None): |
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| 206 | """ |
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| 207 | Set data. |
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| 208 | |
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| 209 | :param data: DataLoader.Data_info type |
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| 210 | """ |
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| 211 | self.data = data |
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| 212 | |
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| 213 | def set_model(self, model=None): |
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| 214 | """ |
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| 215 | Set model. |
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| 216 | |
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| 217 | :param model: sas.models instance |
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| 218 | """ |
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| 219 | self.model = model |
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| 220 | |
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| 221 | def set_index(self, index=None): |
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| 222 | """ |
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| 223 | Set index. |
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| 224 | |
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| 225 | :param index: 1d arrays |
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| 226 | """ |
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| 227 | self.index = index |
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| 228 | |
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| 229 | def get_value(self): |
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| 230 | """ |
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| 231 | Over sampling of r_nbins times phi_nbins, calculate Gaussian weights, |
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| 232 | then find smeared intensity |
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| 233 | """ |
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| 234 | if self.smearer: |
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| 235 | res = Pinhole2D(data=self.data, index=self.index, |
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| 236 | nsigma=3.0, accuracy=self.accuracy, |
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| 237 | coords=self.coords) |
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| 238 | val = self.model.evalDistribution(res.q_calc) |
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| 239 | return res.apply(val) |
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| 240 | else: |
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| 241 | index = self.index if self.index is not None else slice(None) |
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| 242 | qx_data = self.data.qx_data[index] |
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| 243 | qy_data = self.data.qy_data[index] |
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| 244 | q_calc = [qx_data, qy_data] |
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| 245 | val = self.model.evalDistribution(q_calc) |
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| 246 | return val |
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| 247 | |
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