[e5df560] | 1 | |
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| 2 | import math |
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| 3 | import numpy |
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| 4 | import copy |
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| 5 | import time |
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| 6 | import unittest |
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| 7 | from DataLoader.loader import Loader |
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| 8 | from sans.fit.Fitting import Fit |
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| 9 | from sans.models.CylinderModel import CylinderModel |
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| 10 | import sans.models.dispersion_models |
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| 11 | from sans.models.qsmearing import smear_selection |
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| 12 | |
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| 13 | NPTS = 1 |
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| 14 | |
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| 15 | def classMapper(classInstance, classFunc, *args): |
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| 16 | """ |
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| 17 | Take an instance of a class and a function name as a string. |
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| 18 | Execute class.function and return result |
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| 19 | """ |
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| 20 | return getattr(classInstance,classFunc)(*args) |
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| 21 | |
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| 22 | def mapapply(arguments): |
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| 23 | return apply(arguments[0], arguments[1:]) |
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| 24 | |
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| 25 | |
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| 26 | class BatchScipyFit: |
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| 27 | """ |
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| 28 | test fit module using map function |
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| 29 | """ |
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| 30 | def __init__(self, qmin=None, qmax=None): |
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| 31 | """ """ |
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| 32 | self.list_of_fitter = [] |
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| 33 | self.list_of_function = [] |
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| 34 | self.param_to_fit = ['scale', 'length', 'radius'] |
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| 35 | self.list_of_constraints = [] |
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| 36 | self.list_of_mapper = [] |
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| 37 | self.polydisp = sans.models.dispersion_models.models |
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| 38 | self.qmin = qmin |
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| 39 | self.qmax = qmin |
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| 40 | self.reset_value() |
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| 41 | |
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| 42 | def set_range(self, qmin=None, qmax=None): |
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| 43 | self.qmin = qmin |
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| 44 | self.qmax = qmax |
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| 45 | |
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| 46 | def _reset_helper(self, path=None, engine="scipy", npts=NPTS): |
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| 47 | """ |
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| 48 | Set value to fitter engine and prepare inputs for map function |
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| 49 | """ |
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| 50 | for i in range(npts): |
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| 51 | data = Loader().load(path) |
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| 52 | fitter = Fit(engine) |
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| 53 | #create model |
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| 54 | model = CylinderModel() |
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| 55 | model.setParam('scale', 1.0) |
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| 56 | model.setParam('radius', 20.0) |
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| 57 | model.setParam('length', 400.0) |
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| 58 | model.setParam('sldCyl', 4e-006) |
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| 59 | model.setParam('sldSolv', 1e-006) |
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| 60 | model.setParam('background', 0.0) |
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| 61 | for param in model.dispersion.keys(): |
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| 62 | model.set_dispersion(param, self.polydisp['gaussian']()) |
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| 63 | model.setParam('cyl_phi.width', 10) |
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| 64 | model.setParam('cyl_phi.npts', 3) |
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| 65 | model.setParam('cyl_theta.nsigmas', 10) |
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| 66 | """ for 2 data cyl_theta = 60.0 [deg] cyl_phi= 60.0 [deg]""" |
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| 67 | fitter.set_model(model, i, self.param_to_fit, |
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| 68 | self.list_of_constraints) |
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| 69 | #smear data |
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| 70 | current_smearer = smear_selection(data, model) |
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| 71 | fitter.set_data(data=data, id=i, |
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| 72 | smearer=current_smearer, qmin=self.qmin, qmax=self.qmax) |
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| 73 | fitter.select_problem_for_fit(id=i, value=1) |
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| 74 | self.list_of_fitter.append(copy.deepcopy(fitter)) |
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| 75 | self.list_of_function.append('fit') |
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| 76 | self.list_of_mapper.append(classMapper) |
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| 77 | |
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| 78 | def reset_value(self): |
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| 79 | """ |
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| 80 | Initialize inputs for the map function |
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| 81 | """ |
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| 82 | self.list_of_fitter = [] |
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| 83 | self.list_of_function = [] |
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| 84 | self.param_to_fit = ['scale', 'length', 'radius'] |
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| 85 | self.list_of_constraints = [] |
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| 86 | self.list_of_mapper = [] |
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| 87 | |
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| 88 | path = "testdata_line3.txt" |
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| 89 | self._reset_helper(path=path, engine="scipy", npts=NPTS) |
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| 90 | path = "testdata_line.txt" |
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| 91 | self._reset_helper(path=path, engine="scipy", npts=NPTS) |
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| 92 | path = "SILIC010_noheader.DAT" |
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| 93 | self._reset_helper(path=path, engine="scipy", npts=NPTS) |
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| 94 | path = "cyl_400_20.txt" |
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| 95 | self._reset_helper(path=path, engine="scipy", npts=NPTS) |
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| 96 | path = "sphere_80.txt" |
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| 97 | self._reset_helper(path=path, engine="scipy", npts=NPTS) |
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| 98 | path = "PolySpheres.txt" |
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| 99 | self._reset_helper(path=path, engine="scipy", npts=NPTS) |
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| 100 | |
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| 101 | def test_map_fit(self): |
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| 102 | """ |
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| 103 | """ |
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| 104 | results = map(classMapper,self.list_of_fitter, self.list_of_function) |
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| 105 | #print len(results) |
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| 106 | #for result in results: |
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| 107 | # print result.fitness, result.stderr, result.pvec |
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| 108 | |
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| 109 | def test_process_map_fit(self, n=1): |
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| 110 | """ |
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| 111 | run fit usong map , n is the number of processes used |
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| 112 | """ |
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| 113 | t0 = time.time() |
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| 114 | print "start fit with %s process(es) at %s" % (str(n), time.strftime(" %H:%M:%S", time.localtime(t0))) |
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| 115 | from multiprocessing import Pool |
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| 116 | temp = zip(self.list_of_mapper, self.list_of_fitter, self.list_of_function) |
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| 117 | results = Pool(n).map(func=mapapply, |
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| 118 | iterable=temp) |
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| 119 | t1 = time.time() |
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| 120 | print "got %s fit results in %ss" % (str(len(results)), str(t1 - t0)) |
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| 121 | |
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| 122 | #for result in results: |
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| 123 | # print result.fitness, result.stderr, result.pvec |
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| 124 | #t2 = time.time() |
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| 125 | #print "print fit results ", time.strftime(" %H:%M:%S", time.localtime(t2)), t2 - t1 |
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| 126 | |
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| 127 | class testBatch(unittest.TestCase): |
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| 128 | """ |
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| 129 | fitting |
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| 130 | """ |
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| 131 | def setUp(self): |
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| 132 | self.test = BatchScipyFit(qmin=None, qmax=None) |
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| 133 | |
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| 134 | def test_fit1(self): |
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| 135 | """test fit with python built in map function---- full range of each data""" |
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| 136 | self.test.test_map_fit() |
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| 137 | |
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| 138 | def test_fit2(self): |
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| 139 | """test fit with python built in map function---- common range for all data""" |
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| 140 | self.test.set_range(qmin=0.013, qmax=0.05) |
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| 141 | self.test.reset_value() |
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| 142 | self.test.test_map_fit() |
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| 143 | |
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| 144 | def test_fit3(self): |
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| 145 | """test fit with data full range using 1 processor and map""" |
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| 146 | self.test.set_range(qmin=None, qmax=None) |
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| 147 | self.test.reset_value() |
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| 148 | self.test.test_process_map_fit(n=1) |
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| 149 | |
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| 150 | def test_fit4(self): |
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| 151 | """test fit with a common fixed range for data using 1 processor and map""" |
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| 152 | self.test.set_range(qmin=0.013, qmax=0.05) |
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| 153 | self.test.reset_value() |
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| 154 | self.test.test_process_map_fit(n=1) |
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| 155 | |
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| 156 | |
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| 157 | if __name__ == '__main__': |
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| 158 | unittest.main() |
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| 159 | |
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| 160 | |
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| 161 | |
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