| 1 | |
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| 2 | ##################################################################### |
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| 3 | #This software was developed by the University of Tennessee as part of the |
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| 4 | #Distributed Data Analysis of Neutron Scattering Experiments (DANSE) |
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| 5 | #project funded by the US National Science Foundation. |
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| 6 | #See the license text in license.txt |
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| 7 | #copyright 2008, University of Tennessee |
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| 8 | ###################################################################### |
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| 9 | import numpy |
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| 10 | import math |
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| 11 | import logging |
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| 12 | import sys |
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| 13 | import DataLoader.extensions.smearer as smearer |
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| 14 | from DataLoader.smearing_2d import Smearer2D |
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| 15 | |
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| 16 | def smear_selection(data1D, model = None): |
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| 17 | """ |
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| 18 | Creates the right type of smearer according |
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| 19 | to the data. |
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| 20 | |
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| 21 | The canSAS format has a rule that either |
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| 22 | slit smearing data OR resolution smearing data |
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| 23 | is available. |
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| 24 | |
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| 25 | For the present purpose, we choose the one that |
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| 26 | has none-zero data. If both slit and resolution |
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| 27 | smearing arrays are filled with good data |
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| 28 | (which should not happen), then we choose the |
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| 29 | resolution smearing data. |
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| 30 | |
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| 31 | :param data1D: Data1D object |
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| 32 | :param model: sans.model instance |
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| 33 | """ |
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| 34 | # Sanity check. If we are not dealing with a SANS Data1D |
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| 35 | # object, just return None |
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| 36 | if data1D.__class__.__name__ not in ['Data1D', 'Theory1D']: |
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| 37 | if data1D == None: |
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| 38 | return None |
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| 39 | elif data1D.dqx_data == None or data1D.dqy_data == None: |
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| 40 | return None |
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| 41 | return Smearer2D(data1D) |
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| 42 | |
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| 43 | if not hasattr(data1D, "dx") and not hasattr(data1D, "dxl")\ |
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| 44 | and not hasattr(data1D, "dxw"): |
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| 45 | return None |
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| 46 | |
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| 47 | # Look for resolution smearing data |
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| 48 | _found_resolution = False |
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| 49 | if data1D.dx is not None and len(data1D.dx) == len(data1D.x): |
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| 50 | |
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| 51 | # Check that we have non-zero data |
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| 52 | if data1D.dx[0] > 0.0: |
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| 53 | _found_resolution = True |
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| 54 | #print "_found_resolution",_found_resolution |
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| 55 | #print "data1D.dx[0]",data1D.dx[0],data1D.dxl[0] |
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| 56 | # If we found resolution smearing data, return a QSmearer |
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| 57 | if _found_resolution == True: |
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| 58 | return QSmearer(data1D, model) |
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| 59 | |
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| 60 | # Look for slit smearing data |
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| 61 | _found_slit = False |
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| 62 | if data1D.dxl is not None and len(data1D.dxl) == len(data1D.x) \ |
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| 63 | and data1D.dxw is not None and len(data1D.dxw) == len(data1D.x): |
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| 64 | |
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| 65 | # Check that we have non-zero data |
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| 66 | if data1D.dxl[0] > 0.0 or data1D.dxw[0] > 0.0: |
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| 67 | _found_slit = True |
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| 68 | |
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| 69 | # Sanity check: all data should be the same as a function of Q |
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| 70 | for item in data1D.dxl: |
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| 71 | if data1D.dxl[0] != item: |
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| 72 | _found_resolution = False |
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| 73 | break |
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| 74 | |
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| 75 | for item in data1D.dxw: |
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| 76 | if data1D.dxw[0] != item: |
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| 77 | _found_resolution = False |
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| 78 | break |
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| 79 | # If we found slit smearing data, return a slit smearer |
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| 80 | if _found_slit == True: |
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| 81 | return SlitSmearer(data1D) |
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| 82 | return None |
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| 83 | |
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| 84 | |
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| 85 | class _BaseSmearer(object): |
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| 86 | |
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| 87 | def __init__(self): |
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| 88 | self.nbins = 0 |
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| 89 | self.nbins_low = 0 |
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| 90 | self.nbins_high = 0 |
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| 91 | self._weights = None |
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| 92 | ## Internal flag to keep track of C++ smearer initialization |
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| 93 | self._init_complete = False |
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| 94 | self._smearer = None |
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| 95 | self.model = None |
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| 96 | |
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| 97 | def __deepcopy__(self, memo={}): |
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| 98 | """ |
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| 99 | Return a valid copy of self. |
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| 100 | Avoid copying the _smearer C object and force a matrix recompute |
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| 101 | when the copy is used. |
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| 102 | """ |
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| 103 | result = _BaseSmearer() |
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| 104 | result.nbins = self.nbins |
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| 105 | return result |
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| 106 | |
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| 107 | def _compute_matrix(self): |
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| 108 | """ |
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| 109 | """ |
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| 110 | return NotImplemented |
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| 111 | |
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| 112 | def get_bin_range(self, q_min=None, q_max=None): |
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| 113 | """ |
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| 114 | |
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| 115 | :param q_min: minimum q-value to smear |
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| 116 | :param q_max: maximum q-value to smear |
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| 117 | |
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| 118 | """ |
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| 119 | # If this is the first time we call for smearing, |
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| 120 | # initialize the C++ smearer object first |
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| 121 | if not self._init_complete: |
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| 122 | self._initialize_smearer() |
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| 123 | if q_min == None: |
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| 124 | q_min = self.min |
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| 125 | if q_max == None: |
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| 126 | q_max = self.max |
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| 127 | |
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| 128 | _qmin_unsmeared, _qmax_unsmeared = self.get_unsmeared_range(q_min, |
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| 129 | q_max) |
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| 130 | _first_bin = None |
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| 131 | _last_bin = None |
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| 132 | |
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| 133 | #step = (self.max - self.min) / (self.nbins - 1.0) |
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| 134 | # Find the first and last bin number in all extrapolated and real data |
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| 135 | try: |
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| 136 | for i in range(self.nbins): |
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| 137 | q_i = smearer.get_q(self._smearer, i) |
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| 138 | if (q_i >= _qmin_unsmeared) and (q_i <= _qmax_unsmeared): |
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| 139 | # Identify first and last bin |
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| 140 | if _first_bin is None: |
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| 141 | _first_bin = i |
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| 142 | else: |
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| 143 | _last_bin = i |
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| 144 | except: |
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| 145 | msg = "_BaseSmearer.get_bin_range: " |
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| 146 | msg += " error getting range\n %s" % sys.exc_value |
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| 147 | raise RuntimeError, msg |
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| 148 | |
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| 149 | # Find the first and last bin number only in the real data |
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| 150 | _first_bin, _last_bin = self._get_unextrapolated_bin( \ |
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| 151 | _first_bin, _last_bin) |
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| 152 | |
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| 153 | return _first_bin, _last_bin |
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| 154 | |
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| 155 | def __call__(self, iq_in, first_bin = 0, last_bin = None): |
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| 156 | """ |
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| 157 | Perform smearing |
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| 158 | """ |
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| 159 | # If this is the first time we call for smearing, |
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| 160 | # initialize the C++ smearer object first |
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| 161 | if not self._init_complete: |
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| 162 | self._initialize_smearer() |
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| 163 | |
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| 164 | if last_bin is None or last_bin >= len(iq_in): |
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| 165 | last_bin = len(iq_in) - 1 |
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| 166 | # Check that the first bin is positive |
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| 167 | if first_bin < 0: |
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| 168 | first_bin = 0 |
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| 169 | |
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| 170 | # With a model given, compute I for the extrapolated points and append |
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| 171 | # to the iq_in |
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| 172 | #iq_in_temp = iq_in |
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| 173 | if self.model != None: |
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| 174 | temp_first, temp_last = self._get_extrapolated_bin( \ |
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| 175 | first_bin, last_bin) |
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| 176 | iq_in_low = self.model.evalDistribution( \ |
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| 177 | numpy.fabs(self.qvalues[0:self.nbins_low])) |
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| 178 | iq_in_high = self.model.evalDistribution( \ |
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| 179 | self.qvalues[(len(self.qvalues) - \ |
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| 180 | self.nbins_high - 1):]) |
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| 181 | # Todo: find out who is sending iq[last_poin] = 0. |
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| 182 | if iq_in[len(iq_in) - 1] == 0: |
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| 183 | iq_in[len(iq_in) - 1] = iq_in_high[0] |
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| 184 | # Append the extrapolated points to the data points |
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| 185 | if self.nbins_low > 0: |
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| 186 | iq_in_temp = numpy.append(iq_in_low, iq_in) |
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| 187 | if self.nbins_high > 0: |
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| 188 | iq_in_temp = numpy.append(iq_in_temp, iq_in_high[1:]) |
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| 189 | else: |
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| 190 | temp_first = first_bin |
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| 191 | temp_last = last_bin |
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| 192 | iq_in_temp = iq_in |
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| 193 | |
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| 194 | # Sanity check |
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| 195 | if len(iq_in_temp) != self.nbins: |
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| 196 | msg = "Invalid I(q) vector: inconsistent array " |
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| 197 | msg += " length %d != %s" % (len(iq_in_temp), str(self.nbins)) |
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| 198 | raise RuntimeError, msg |
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| 199 | |
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| 200 | # Storage for smeared I(q) |
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| 201 | iq_out = numpy.zeros(self.nbins) |
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| 202 | |
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| 203 | smear_output = smearer.smear(self._smearer, iq_in_temp, iq_out, |
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| 204 | #0, self.nbins - 1) |
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| 205 | temp_first, temp_last) |
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| 206 | #first_bin, last_bin) |
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| 207 | if smear_output < 0: |
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| 208 | msg = "_BaseSmearer: could not smear, code = %g" % smear_output |
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| 209 | raise RuntimeError, msg |
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| 210 | |
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| 211 | temp_first += self.nbins_low |
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| 212 | temp_last = self.nbins - self.nbins_high |
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| 213 | |
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| 214 | return iq_out[temp_first: temp_last] |
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| 215 | |
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| 216 | def _initialize_smearer(self): |
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| 217 | """ |
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| 218 | """ |
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| 219 | return NotImplemented |
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| 220 | |
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| 221 | def set_model(self, model): |
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| 222 | """ |
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| 223 | Set model |
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| 224 | """ |
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| 225 | if model != None: |
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| 226 | self.model = model |
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| 227 | |
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| 228 | |
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| 229 | def _get_unextrapolated_bin(self, first_bin = 0, last_bin = 0): |
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| 230 | """ |
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| 231 | Get unextrapolated first bin and the last bin |
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| 232 | |
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| 233 | : param first_bin: extrapolated first_bin |
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| 234 | : param last_bin: extrapolated last_bin |
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| 235 | |
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| 236 | : return fist_bin, last_bin: unextrapolated first and last bin |
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| 237 | """ |
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| 238 | # For first bin |
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| 239 | if first_bin <= self.nbins_low: |
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| 240 | first_bin = 0 |
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| 241 | else: |
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| 242 | first_bin = first_bin - self.nbins_low |
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| 243 | # For last bin |
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| 244 | if last_bin >= (self.nbins - self.nbins_high): |
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| 245 | last_bin = self.nbins - (self.nbins_high + self.nbins_low + 1) |
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| 246 | elif last_bin >= self.nbins_low: |
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| 247 | last_bin = last_bin - self.nbins_low |
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| 248 | else: |
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| 249 | last_bin = 0 |
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| 250 | return first_bin, last_bin |
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| 251 | |
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| 252 | def _get_extrapolated_bin(self, first_bin = 0, last_bin = 0): |
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| 253 | """ |
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| 254 | Get extrapolated first bin and the last bin |
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| 255 | |
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| 256 | : param first_bin: unextrapolated first_bin |
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| 257 | : param last_bin: unextrapolated last_bin |
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| 258 | |
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| 259 | : return first_bin, last_bin: extrapolated first and last bin |
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| 260 | """ |
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| 261 | # For the first bin |
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| 262 | # In the case that needs low extrapolation data |
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| 263 | first_bin = 0 |
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| 264 | # For last bin |
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| 265 | if last_bin >= self.nbins - (self.nbins_high + self.nbins_low + 1): |
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| 266 | # In the case that needs higher q extrapolation data |
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| 267 | last_bin = self.nbins - 1 |
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| 268 | else: |
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| 269 | # In the case that doesn't need higher q extrapolation data |
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| 270 | last_bin += self.nbins_low |
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| 271 | |
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| 272 | return first_bin, last_bin |
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| 273 | |
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| 274 | class _SlitSmearer(_BaseSmearer): |
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| 275 | """ |
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| 276 | Slit smearing for I(q) array |
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| 277 | """ |
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| 278 | |
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| 279 | def __init__(self, nbins=None, width=None, height=None, min=None, max=None): |
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| 280 | """ |
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| 281 | Initialization |
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| 282 | |
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| 283 | :param iq: I(q) array [cm-1] |
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| 284 | :param width: slit width [A-1] |
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| 285 | :param height: slit height [A-1] |
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| 286 | :param min: Q_min [A-1] |
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| 287 | :param max: Q_max [A-1] |
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| 288 | |
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| 289 | """ |
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| 290 | _BaseSmearer.__init__(self) |
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| 291 | ## Slit width in Q units |
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| 292 | self.width = width |
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| 293 | ## Slit height in Q units |
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| 294 | self.height = height |
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| 295 | ## Q_min (Min Q-value for I(q)) |
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| 296 | self.min = min |
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| 297 | ## Q_max (Max Q_value for I(q)) |
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| 298 | self.max = max |
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| 299 | ## Number of Q bins |
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| 300 | self.nbins = nbins |
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| 301 | ## Number of points used in the smearing computation |
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| 302 | self.npts = 1000 |
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| 303 | ## Smearing matrix |
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| 304 | self._weights = None |
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| 305 | self.qvalues = None |
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| 306 | |
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| 307 | def _initialize_smearer(self): |
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| 308 | """ |
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| 309 | Initialize the C++ smearer object. |
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| 310 | This method HAS to be called before smearing |
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| 311 | """ |
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| 312 | #self._smearer = smearer.new_slit_smearer(self.width, |
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| 313 | # self.height, self.min, self.max, self.nbins) |
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| 314 | self._smearer = smearer.new_slit_smearer_with_q(self.width, |
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| 315 | self.height, self.qvalues) |
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| 316 | self._init_complete = True |
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| 317 | |
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| 318 | def get_unsmeared_range(self, q_min, q_max): |
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| 319 | """ |
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| 320 | Determine the range needed in unsmeared-Q to cover |
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| 321 | the smeared Q range |
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| 322 | """ |
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| 323 | # Range used for input to smearing |
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| 324 | _qmin_unsmeared = q_min |
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| 325 | _qmax_unsmeared = q_max |
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| 326 | try: |
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| 327 | _qmin_unsmeared = self.min |
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| 328 | _qmax_unsmeared = self.max |
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| 329 | except: |
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| 330 | logging.error("_SlitSmearer.get_bin_range: %s" % sys.exc_value) |
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| 331 | return _qmin_unsmeared, _qmax_unsmeared |
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| 332 | |
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| 333 | class SlitSmearer(_SlitSmearer): |
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| 334 | """ |
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| 335 | Adaptor for slit smearing class and SANS data |
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| 336 | """ |
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| 337 | def __init__(self, data1D): |
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| 338 | """ |
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| 339 | Assumption: equally spaced bins of increasing q-values. |
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| 340 | |
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| 341 | :param data1D: data used to set the smearing parameters |
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| 342 | """ |
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| 343 | # Initialization from parent class |
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| 344 | super(SlitSmearer, self).__init__() |
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| 345 | |
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| 346 | ## Slit width |
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| 347 | self.width = 0 |
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| 348 | self.nbins_low = 0 |
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| 349 | self.nbins_high = 0 |
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| 350 | if data1D.dxw is not None and len(data1D.dxw) == len(data1D.x): |
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| 351 | self.width = data1D.dxw[0] |
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| 352 | # Sanity check |
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| 353 | for value in data1D.dxw: |
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| 354 | if value != self.width: |
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| 355 | msg = "Slit smearing parameters must " |
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| 356 | msg += " be the same for all data" |
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| 357 | raise RuntimeError, msg |
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| 358 | ## Slit height |
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| 359 | self.height = 0 |
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| 360 | if data1D.dxl is not None and len(data1D.dxl) == len(data1D.x): |
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| 361 | self.height = data1D.dxl[0] |
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| 362 | # Sanity check |
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| 363 | for value in data1D.dxl: |
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| 364 | if value != self.height: |
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| 365 | msg = "Slit smearing parameters must be" |
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| 366 | msg += " the same for all data" |
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| 367 | raise RuntimeError, msg |
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| 368 | |
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| 369 | ## Number of Q bins |
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| 370 | self.nbins = len(data1D.x) |
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| 371 | ## Minimum Q |
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| 372 | self.min = min(data1D.x) |
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| 373 | ## Maximum |
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| 374 | self.max = max(data1D.x) |
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| 375 | ## Q-values |
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| 376 | self.qvalues = data1D.x |
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| 377 | |
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| 378 | |
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| 379 | class _QSmearer(_BaseSmearer): |
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| 380 | """ |
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| 381 | Perform Gaussian Q smearing |
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| 382 | """ |
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| 383 | |
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| 384 | def __init__(self, nbins=None, width=None, min=None, max=None): |
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| 385 | """ |
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| 386 | Initialization |
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| 387 | |
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| 388 | :param nbins: number of Q bins |
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| 389 | :param width: array standard deviation in Q [A-1] |
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| 390 | :param min: Q_min [A-1] |
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| 391 | :param max: Q_max [A-1] |
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| 392 | """ |
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| 393 | _BaseSmearer.__init__(self) |
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| 394 | ## Standard deviation in Q [A-1] |
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| 395 | self.width = width |
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| 396 | ## Q_min (Min Q-value for I(q)) |
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| 397 | self.min = min |
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| 398 | ## Q_max (Max Q_value for I(q)) |
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| 399 | self.max = max |
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| 400 | ## Number of Q bins |
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| 401 | self.nbins = nbins |
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| 402 | ## Smearing matrix |
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| 403 | self._weights = None |
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| 404 | self.qvalues = None |
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| 405 | |
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| 406 | def _initialize_smearer(self): |
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| 407 | """ |
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| 408 | Initialize the C++ smearer object. |
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| 409 | This method HAS to be called before smearing |
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| 410 | """ |
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| 411 | #self._smearer = smearer.new_q_smearer(numpy.asarray(self.width), |
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| 412 | # self.min, self.max, self.nbins) |
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| 413 | self._smearer = smearer.new_q_smearer_with_q(numpy.asarray(self.width), |
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| 414 | self.qvalues) |
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| 415 | self._init_complete = True |
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| 416 | |
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| 417 | def get_unsmeared_range(self, q_min, q_max): |
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| 418 | """ |
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| 419 | Determine the range needed in unsmeared-Q to cover |
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| 420 | the smeared Q range |
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| 421 | Take 3 sigmas as the offset between smeared and unsmeared space |
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| 422 | """ |
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| 423 | # Range used for input to smearing |
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| 424 | _qmin_unsmeared = q_min |
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| 425 | _qmax_unsmeared = q_max |
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| 426 | try: |
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| 427 | offset = 3.0 * max(self.width) |
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| 428 | _qmin_unsmeared = max([self.min, q_min - offset]) |
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| 429 | _qmax_unsmeared = min([self.max, q_max + offset]) |
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| 430 | except: |
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| 431 | logging.error("_QSmearer.get_bin_range: %s" % sys.exc_value) |
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| 432 | return _qmin_unsmeared, _qmax_unsmeared |
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| 433 | |
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| 434 | |
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| 435 | class QSmearer(_QSmearer): |
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| 436 | """ |
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| 437 | Adaptor for Gaussian Q smearing class and SANS data |
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| 438 | """ |
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| 439 | def __init__(self, data1D, model = None): |
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| 440 | """ |
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| 441 | Assumption: equally spaced bins of increasing q-values. |
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| 442 | |
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| 443 | :param data1D: data used to set the smearing parameters |
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| 444 | """ |
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| 445 | # Initialization from parent class |
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| 446 | super(QSmearer, self).__init__() |
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| 447 | data1d_x = [] |
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| 448 | self.nbins_low = 0 |
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| 449 | self.nbins_high = 0 |
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| 450 | self.model = model |
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| 451 | ## Resolution |
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| 452 | #self.width = numpy.zeros(len(data1D.x)) |
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| 453 | if data1D.dx is not None and len(data1D.dx) == len(data1D.x): |
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| 454 | self.width = data1D.dx |
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| 455 | |
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| 456 | if self.model == None: |
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| 457 | data1d_x = data1D.x |
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| 458 | else: |
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| 459 | self.nbins_low, self.nbins_high, self.width, data1d_x = \ |
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| 460 | get_qextrapolate(self.width, data1D.x) |
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| 461 | |
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| 462 | ## Number of Q bins |
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| 463 | self.nbins = len(data1d_x) |
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| 464 | ## Minimum Q |
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| 465 | self.min = min(data1d_x) |
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| 466 | ## Maximum |
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| 467 | self.max = max(data1d_x) |
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| 468 | ## Q-values |
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| 469 | self.qvalues = data1d_x |
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| 470 | |
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| 471 | |
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| 472 | def get_qextrapolate(width, data_x): |
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| 473 | """ |
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| 474 | Make fake data_x points extrapolated outside of the data_x points |
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| 475 | |
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| 476 | : param width: array of std of q resolution |
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| 477 | : param Data1D.x: Data1D.x array |
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| 478 | |
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| 479 | : return new_width, data_x_ext: extrapolated width array and x array |
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| 480 | |
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| 481 | : assumption1: data_x is ordered from lower q to higher q |
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| 482 | : assumption2: len(data) = len(width) |
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| 483 | : assumption3: the distance between the data points is more compact |
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| 484 | than the size of width |
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| 485 | : Todo1: Make sure that the assumptions are correct for Data1D |
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| 486 | : Todo2: This fixes the edge problem in Qsmearer but still needs to make |
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| 487 | smearer interface |
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| 488 | """ |
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| 489 | # Length of the width |
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| 490 | length = len(width) |
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| 491 | width_low = math.fabs(width[0]) |
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| 492 | width_high = math.fabs(width[length -1]) |
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| 493 | # Find bin sizes |
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| 494 | bin_size_low = math.fabs(data_x[1] - data_x[0]) |
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| 495 | bin_size_high = math.fabs(data_x[length - 1] - data_x[length - 2]) |
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| 496 | # Compare width(dQ) to the data bin size and take smaller one as the bin |
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| 497 | # size of the extrapolation; this will correct some weird behavior |
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| 498 | # at the edge |
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| 499 | if width_low < (bin_size_low): |
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| 500 | bin_size_low = width_low / 2.0 |
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| 501 | if width_high < (bin_size_high): |
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| 502 | bin_size_high = width_high / 2.0 |
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| 503 | |
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| 504 | # Number of q points required below the 1st data point in order to extend |
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| 505 | # them 3 times of the width (std) |
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| 506 | nbins_low = math.ceil(3 * width_low / bin_size_low) |
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| 507 | # Number of q points required above the last data point |
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| 508 | nbins_high = math.ceil(3 * width_high / (bin_size_high)) |
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| 509 | # Make null q points |
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| 510 | extra_low = numpy.zeros(nbins_low) |
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| 511 | extra_high = numpy.zeros(nbins_high) |
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| 512 | # Give extrapolated values |
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| 513 | ind = 0 |
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| 514 | qvalue = data_x[0] - bin_size_low |
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| 515 | while(ind < nbins_low): |
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| 516 | extra_low[nbins_low - (ind + 1)] = qvalue |
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| 517 | qvalue -= bin_size_low |
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| 518 | ind += 1 |
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| 519 | # Remove the points <= 0 |
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| 520 | #extra_low = extra_low[extra_low > 0] |
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| 521 | #nbins_low = len(extra_low) |
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| 522 | # Reset ind for another extrapolation |
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| 523 | ind = 0 |
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| 524 | qvalue = data_x[length -1] + bin_size_high |
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| 525 | while(ind < nbins_high): |
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| 526 | extra_high[ind] = qvalue |
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| 527 | qvalue += bin_size_high |
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| 528 | ind += 1 |
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| 529 | # Make a new qx array |
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| 530 | data_x_ext = numpy.append(extra_low, data_x) |
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| 531 | data_x_ext = numpy.append(data_x_ext, extra_high) |
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| 532 | |
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| 533 | # Redefine extra_low and high based on corrected nbins |
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| 534 | # And note that it is not necessary for extra_width to be a non-zero |
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| 535 | extra_low = numpy.zeros(nbins_low) |
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| 536 | extra_high = numpy.zeros(nbins_high) |
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| 537 | # Make new width array |
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| 538 | new_width = numpy.append(extra_low, width) |
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| 539 | new_width = numpy.append(new_width, extra_high) |
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| 540 | |
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| 541 | return nbins_low, nbins_high, new_width, data_x_ext |
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| 542 | |
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| 543 | if __name__ == '__main__': |
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| 544 | x = 0.001 * numpy.arange(1, 11) |
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| 545 | y = 12.0 - numpy.arange(1, 11) |
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| 546 | print x |
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| 547 | #for i in range(10): print i, 0.001 + i*0.008/9.0 |
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| 548 | #for i in range(100): print i, int(math.floor( (i/ (100/9.0)) )) |
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| 549 | s = _SlitSmearer(nbins=10, width=0.0, height=0.005, min=0.001, max=0.010) |
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| 550 | #s = _QSmearer(nbins=10, width=0.001, min=0.001, max=0.010) |
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| 551 | s._compute_matrix() |
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| 552 | |
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| 553 | sy = s(y) |
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| 554 | print sy |
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| 555 | |
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| 556 | if True: |
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| 557 | for i in range(10): |
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| 558 | print x[i], y[i], sy[i] |
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| 559 | #print q, ' : ', s.weight(q), s._compute_iq(q) |
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| 560 | #print q, ' : ', s(q), s._compute_iq(q) |
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| 561 | #s._compute_iq(q) |
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| 562 | |
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| 563 | |
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| 564 | |
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| 565 | |
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