| 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): |
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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 | """ |
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| 33 | # Sanity check. If we are not dealing with a SANS Data1D |
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| 34 | # object, just return None |
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| 35 | if data1D.__class__.__name__ not in ['Data1D', 'Theory1D']: |
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| 36 | if data1D == None: |
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| 37 | return None |
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| 38 | elif data1D.dqx_data == None or data1D.dqy_data == None: |
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| 39 | return None |
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| 40 | return Smearer2D(data1D) |
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| 41 | |
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| 42 | if not hasattr(data1D, "dx") and not hasattr(data1D, "dxl")\ |
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| 43 | and not hasattr(data1D, "dxw"): |
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| 44 | return None |
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| 45 | |
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| 46 | # Look for resolution smearing data |
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| 47 | _found_resolution = False |
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| 48 | if data1D.dx is not None and len(data1D.dx) == len(data1D.x): |
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| 49 | |
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| 50 | # Check that we have non-zero data |
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| 51 | if data1D.dx[0] > 0.0: |
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| 52 | _found_resolution = True |
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| 53 | #print "_found_resolution",_found_resolution |
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| 54 | #print "data1D.dx[0]",data1D.dx[0],data1D.dxl[0] |
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| 55 | # If we found resolution smearing data, return a QSmearer |
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| 56 | if _found_resolution == True: |
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| 57 | return QSmearer(data1D) |
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| 58 | |
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| 59 | # Look for slit smearing data |
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| 60 | _found_slit = False |
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| 61 | if data1D.dxl is not None and len(data1D.dxl) == len(data1D.x) \ |
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| 62 | and data1D.dxw is not None and len(data1D.dxw) == len(data1D.x): |
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| 63 | |
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| 64 | # Check that we have non-zero data |
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| 65 | if data1D.dxl[0] > 0.0 or data1D.dxw[0] > 0.0: |
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| 66 | _found_slit = True |
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| 67 | |
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| 68 | # Sanity check: all data should be the same as a function of Q |
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| 69 | for item in data1D.dxl: |
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| 70 | if data1D.dxl[0] != item: |
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| 71 | _found_resolution = False |
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| 72 | break |
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| 73 | |
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| 74 | for item in data1D.dxw: |
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| 75 | if data1D.dxw[0] != item: |
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| 76 | _found_resolution = False |
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| 77 | break |
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| 78 | # If we found slit smearing data, return a slit smearer |
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| 79 | if _found_slit == True: |
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| 80 | return SlitSmearer(data1D) |
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| 81 | return None |
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| 82 | |
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| 83 | |
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| 84 | class _BaseSmearer(object): |
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| 85 | |
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| 86 | def __init__(self): |
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| 87 | self.nbins = 0 |
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| 88 | self._weights = None |
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| 89 | ## Internal flag to keep track of C++ smearer initialization |
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| 90 | self._init_complete = False |
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| 91 | self._smearer = None |
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| 92 | |
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| 93 | def __deepcopy__(self, memo={}): |
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| 94 | """ |
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| 95 | Return a valid copy of self. |
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| 96 | Avoid copying the _smearer C object and force a matrix recompute |
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| 97 | when the copy is used. |
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| 98 | """ |
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| 99 | result = _BaseSmearer() |
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| 100 | result.nbins = self.nbins |
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| 101 | return result |
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| 102 | |
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| 103 | def _compute_matrix(self): |
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| 104 | """ |
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| 105 | """ |
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| 106 | return NotImplemented |
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| 107 | |
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| 108 | def get_bin_range(self, q_min=None, q_max=None): |
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| 109 | """ |
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| 110 | |
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| 111 | :param q_min: minimum q-value to smear |
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| 112 | :param q_max: maximum q-value to smear |
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| 113 | |
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| 114 | """ |
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| 115 | # If this is the first time we call for smearing, |
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| 116 | # initialize the C++ smearer object first |
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| 117 | if not self._init_complete: |
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| 118 | self._initialize_smearer() |
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| 119 | if q_min == None: |
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| 120 | q_min = self.min |
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| 121 | if q_max == None: |
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| 122 | q_max = self.max |
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| 123 | _qmin_unsmeared, _qmax_unsmeared = self.get_unsmeared_range(q_min, |
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| 124 | q_max) |
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| 125 | _first_bin = None |
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| 126 | _last_bin = None |
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| 127 | |
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| 128 | step = (self.max - self.min) / (self.nbins - 1.0) |
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| 129 | try: |
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| 130 | for i in range(self.nbins): |
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| 131 | q_i = smearer.get_q(self._smearer, i) |
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| 132 | if (q_i >= _qmin_unsmeared) and (q_i <= _qmax_unsmeared): |
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| 133 | # Identify first and last bin |
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| 134 | if _first_bin is None: |
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| 135 | _first_bin = i |
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| 136 | else: |
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| 137 | _last_bin = i |
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| 138 | except: |
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| 139 | msg = "_BaseSmearer.get_bin_range: " |
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| 140 | msg += " error getting range\n %s" % sys.exc_value |
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| 141 | raise RuntimeError, msg |
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| 142 | |
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| 143 | return _first_bin, _last_bin |
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| 144 | |
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| 145 | def __call__(self, iq_in, first_bin=0, last_bin=None): |
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| 146 | """ |
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| 147 | Perform smearing |
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| 148 | """ |
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| 149 | # If this is the first time we call for smearing, |
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| 150 | # initialize the C++ smearer object first |
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| 151 | if not self._init_complete: |
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| 152 | self._initialize_smearer() |
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| 153 | |
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| 154 | # Get the max value for the last bin |
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| 155 | if last_bin is None or last_bin >= len(iq_in): |
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| 156 | last_bin = len(iq_in) - 1 |
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| 157 | # Check that the first bin is positive |
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| 158 | if first_bin < 0: |
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| 159 | first_bin = 0 |
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| 160 | |
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| 161 | # Sanity check |
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| 162 | if len(iq_in) != self.nbins: |
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| 163 | msg = "Invalid I(q) vector: inconsistent array " |
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| 164 | msg += " length %d != %s" % (len(iq_in), str(self.nbins)) |
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| 165 | raise RuntimeError, msg |
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| 166 | |
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| 167 | # Storage for smeared I(q) |
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| 168 | iq_out = numpy.zeros(self.nbins) |
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| 169 | smear_output = smearer.smear(self._smearer, iq_in, iq_out, |
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| 170 | first_bin, last_bin) |
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| 171 | if smear_output < 0: |
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| 172 | msg = "_BaseSmearer: could not smear, code = %g" % smear_output |
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| 173 | raise RuntimeError, msg |
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| 174 | return iq_out |
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| 175 | |
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| 176 | def _initialize_smearer(self): |
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| 177 | """ |
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| 178 | """ |
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| 179 | return NotImplemented |
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| 180 | |
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| 181 | class _SlitSmearer(_BaseSmearer): |
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| 182 | """ |
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| 183 | Slit smearing for I(q) array |
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| 184 | """ |
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| 185 | |
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| 186 | def __init__(self, nbins=None, width=None, height=None, min=None, max=None): |
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| 187 | """ |
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| 188 | Initialization |
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| 189 | |
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| 190 | :param iq: I(q) array [cm-1] |
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| 191 | :param width: slit width [A-1] |
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| 192 | :param height: slit height [A-1] |
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| 193 | :param min: Q_min [A-1] |
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| 194 | :param max: Q_max [A-1] |
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| 195 | |
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| 196 | """ |
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| 197 | _BaseSmearer.__init__(self) |
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| 198 | ## Slit width in Q units |
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| 199 | self.width = width |
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| 200 | ## Slit height in Q units |
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| 201 | self.height = height |
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| 202 | ## Q_min (Min Q-value for I(q)) |
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| 203 | self.min = min |
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| 204 | ## Q_max (Max Q_value for I(q)) |
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| 205 | self.max = max |
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| 206 | ## Number of Q bins |
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| 207 | self.nbins = nbins |
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| 208 | ## Number of points used in the smearing computation |
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| 209 | self.npts = 1000 |
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| 210 | ## Smearing matrix |
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| 211 | self._weights = None |
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| 212 | self.qvalues = None |
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| 213 | |
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| 214 | def _initialize_smearer(self): |
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| 215 | """ |
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| 216 | Initialize the C++ smearer object. |
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| 217 | This method HAS to be called before smearing |
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| 218 | """ |
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| 219 | #self._smearer = smearer.new_slit_smearer(self.width, |
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| 220 | # self.height, self.min, self.max, self.nbins) |
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| 221 | self._smearer = smearer.new_slit_smearer_with_q(self.width, |
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| 222 | self.height, self.qvalues) |
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| 223 | self._init_complete = True |
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| 224 | |
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| 225 | def get_unsmeared_range(self, q_min, q_max): |
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| 226 | """ |
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| 227 | Determine the range needed in unsmeared-Q to cover |
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| 228 | the smeared Q range |
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| 229 | """ |
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| 230 | # Range used for input to smearing |
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| 231 | _qmin_unsmeared = q_min |
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| 232 | _qmax_unsmeared = q_max |
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| 233 | try: |
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| 234 | _qmin_unsmeared = self.min |
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| 235 | _qmax_unsmeared = self.max |
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| 236 | except: |
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| 237 | logging.error("_SlitSmearer.get_bin_range: %s" % sys.exc_value) |
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| 238 | return _qmin_unsmeared, _qmax_unsmeared |
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| 239 | |
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| 240 | class SlitSmearer(_SlitSmearer): |
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| 241 | """ |
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| 242 | Adaptor for slit smearing class and SANS data |
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| 243 | """ |
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| 244 | def __init__(self, data1D): |
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| 245 | """ |
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| 246 | Assumption: equally spaced bins of increasing q-values. |
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| 247 | |
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| 248 | :param data1D: data used to set the smearing parameters |
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| 249 | """ |
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| 250 | # Initialization from parent class |
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| 251 | super(SlitSmearer, self).__init__() |
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| 252 | |
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| 253 | ## Slit width |
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| 254 | self.width = 0 |
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| 255 | if data1D.dxw is not None and len(data1D.dxw) == len(data1D.x): |
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| 256 | self.width = data1D.dxw[0] |
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| 257 | # Sanity check |
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| 258 | for value in data1D.dxw: |
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| 259 | if value != self.width: |
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| 260 | msg = "Slit smearing parameters must " |
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| 261 | msg += " be the same for all data" |
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| 262 | raise RuntimeError, msg |
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| 263 | ## Slit height |
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| 264 | self.height = 0 |
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| 265 | if data1D.dxl is not None and len(data1D.dxl) == len(data1D.x): |
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| 266 | self.height = data1D.dxl[0] |
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| 267 | # Sanity check |
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| 268 | for value in data1D.dxl: |
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| 269 | if value != self.height: |
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| 270 | msg = "Slit smearing parameters must be" |
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| 271 | msg += " the same for all data" |
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| 272 | raise RuntimeError, msg |
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| 273 | |
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| 274 | ## Number of Q bins |
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| 275 | self.nbins = len(data1D.x) |
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| 276 | ## Minimum Q |
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| 277 | self.min = min(data1D.x) |
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| 278 | ## Maximum |
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| 279 | self.max = max(data1D.x) |
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| 280 | ## Q-values |
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| 281 | self.qvalues = data1D.x |
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| 282 | |
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| 283 | |
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| 284 | class _QSmearer(_BaseSmearer): |
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| 285 | """ |
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| 286 | Perform Gaussian Q smearing |
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| 287 | """ |
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| 288 | |
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| 289 | def __init__(self, nbins=None, width=None, min=None, max=None): |
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| 290 | """ |
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| 291 | Initialization |
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| 292 | |
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| 293 | :param nbins: number of Q bins |
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| 294 | :param width: array standard deviation in Q [A-1] |
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| 295 | :param min: Q_min [A-1] |
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| 296 | :param max: Q_max [A-1] |
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| 297 | """ |
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| 298 | _BaseSmearer.__init__(self) |
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| 299 | ## Standard deviation in Q [A-1] |
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| 300 | self.width = width |
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| 301 | ## Q_min (Min Q-value for I(q)) |
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| 302 | self.min = min |
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| 303 | ## Q_max (Max Q_value for I(q)) |
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| 304 | self.max = max |
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| 305 | ## Number of Q bins |
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| 306 | self.nbins = nbins |
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| 307 | ## Smearing matrix |
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| 308 | self._weights = None |
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| 309 | self.qvalues = None |
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| 310 | |
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| 311 | def _initialize_smearer(self): |
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| 312 | """ |
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| 313 | Initialize the C++ smearer object. |
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| 314 | This method HAS to be called before smearing |
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| 315 | """ |
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| 316 | #self._smearer = smearer.new_q_smearer(numpy.asarray(self.width), |
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| 317 | # self.min, self.max, self.nbins) |
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| 318 | self._smearer = smearer.new_q_smearer_with_q(numpy.asarray(self.width), |
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| 319 | self.qvalues) |
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| 320 | self._init_complete = True |
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| 321 | |
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| 322 | def get_unsmeared_range(self, q_min, q_max): |
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| 323 | """ |
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| 324 | Determine the range needed in unsmeared-Q to cover |
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| 325 | the smeared Q range |
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| 326 | Take 3 sigmas as the offset between smeared and unsmeared space |
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| 327 | """ |
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| 328 | # Range used for input to smearing |
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| 329 | _qmin_unsmeared = q_min |
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| 330 | _qmax_unsmeared = q_max |
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| 331 | try: |
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| 332 | offset = 3.0 * max(self.width) |
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| 333 | _qmin_unsmeared = max([self.min, q_min - offset]) |
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| 334 | _qmax_unsmeared = min([self.max, q_max + offset]) |
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| 335 | except: |
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| 336 | logging.error("_QSmearer.get_bin_range: %s" % sys.exc_value) |
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| 337 | return _qmin_unsmeared, _qmax_unsmeared |
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| 338 | |
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| 339 | |
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| 340 | class QSmearer(_QSmearer): |
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| 341 | """ |
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| 342 | Adaptor for Gaussian Q smearing class and SANS data |
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| 343 | """ |
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| 344 | def __init__(self, data1D): |
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| 345 | """ |
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| 346 | Assumption: equally spaced bins of increasing q-values. |
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| 347 | |
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| 348 | :param data1D: data used to set the smearing parameters |
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| 349 | """ |
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| 350 | # Initialization from parent class |
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| 351 | super(QSmearer, self).__init__() |
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| 352 | |
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| 353 | ## Resolution |
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| 354 | self.width = numpy.zeros(len(data1D.x)) |
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| 355 | if data1D.dx is not None and len(data1D.dx) == len(data1D.x): |
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| 356 | self.width = data1D.dx |
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| 357 | |
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| 358 | ## Number of Q bins |
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| 359 | self.nbins = len(data1D.x) |
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| 360 | ## Minimum Q |
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| 361 | self.min = min(data1D.x) |
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| 362 | ## Maximum |
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| 363 | self.max = max(data1D.x) |
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| 364 | ## Q-values |
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| 365 | self.qvalues = data1D.x |
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| 366 | |
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| 367 | |
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| 368 | if __name__ == '__main__': |
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| 369 | x = 0.001 * numpy.arange(1, 11) |
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| 370 | y = 12.0 - numpy.arange(1, 11) |
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| 371 | print x |
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| 372 | #for i in range(10): print i, 0.001 + i*0.008/9.0 |
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| 373 | #for i in range(100): print i, int(math.floor( (i/ (100/9.0)) )) |
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| 374 | s = _SlitSmearer(nbins=10, width=0.0, height=0.005, min=0.001, max=0.010) |
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| 375 | #s = _QSmearer(nbins=10, width=0.001, min=0.001, max=0.010) |
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| 376 | s._compute_matrix() |
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| 377 | |
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| 378 | sy = s(y) |
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| 379 | print sy |
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| 380 | |
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| 381 | if True: |
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| 382 | for i in range(10): |
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| 383 | print x[i], y[i], sy[i] |
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| 384 | #print q, ' : ', s.weight(q), s._compute_iq(q) |
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| 385 | #print q, ' : ', s(q), s._compute_iq(q) |
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| 386 | #s._compute_iq(q) |
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| 387 | |
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| 388 | |
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| 389 | |
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| 390 | |
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