import math import os import unittest import numpy as np import sasdata.dataloader.data_info as data_info from sasdata.dataloader.loader import Loader from sasdata.data_util.manipulations import (Boxavg, Boxsum, CircularAverage, Ring, SectorPhi, SectorQ, SlabX, SlabY, position_and_wavelength_to_q, reader2D_converter) def find(filename): return os.path.join(os.path.dirname(__file__), 'data', filename) class Averaging(unittest.TestCase): """ Test averaging manipulations on a flat distribution """ def setUp(self): """ Create a flat 2D distribution. All averaging results should return the predefined height of the distribution (1.0). """ x_0 = np.ones([100, 100]) dx_0 = np.ones([100, 100]) self.data = data_info.Data2D(data=x_0, err_data=dx_0) detector = data_info.Detector() detector.distance = 1000.0 # mm detector.pixel_size.x = 1.0 # mm detector.pixel_size.y = 1.0 # mm # center in pixel position = (len(x_0)-1)/2 detector.beam_center.x = (len(x_0) - 1) / 2 # pixel number detector.beam_center.y = (len(x_0) - 1) / 2 # pixel number self.data.detector.append(detector) source = data_info.Source() source.wavelength = 10.0 # A self.data.source = source # get_q(dx, dy, det_dist, wavelength) where units are mm,mm,mm,and A # respectively. self.qmin = position_and_wavelength_to_q(1.0, 1.0, detector.distance, source.wavelength) self.qmax = position_and_wavelength_to_q(49.5, 49.5, detector.distance, source.wavelength) self.qstep = len(x_0) x = np.linspace(start=-1 * self.qmax, stop=self.qmax, num=self.qstep, endpoint=True) y = np.linspace(start=-1 * self.qmax, stop=self.qmax, num=self.qstep, endpoint=True) self.data.x_bins = x self.data.y_bins = y self.data = reader2D_converter(self.data) def test_ring_flat_distribution(self): """ Test ring averaging """ r = Ring(r_min=2 * self.qmin, r_max=5 * self.qmin, center_x=self.data.detector[0].beam_center.x, center_y=self.data.detector[0].beam_center.y) r.nbins_phi = 20 o = r(self.data) for i in range(20): self.assertEqual(o.y[i], 1.0) def test_sectorphi_full(self): """ Test sector averaging """ r = SectorPhi(r_min=self.qmin, r_max=3 * self.qmin, phi_min=0, phi_max=math.pi * 2.0) r.nbins_phi = 20 o = r(self.data) for i in range(7): self.assertEqual(o.y[i], 1.0) def test_sectorphi_partial(self): """ """ phi_max = math.pi * 1.5 r = SectorPhi(r_min=self.qmin, r_max=3 * self.qmin, phi_min=0, phi_max=phi_max) self.assertEqual(r.phi_max, phi_max) r.nbins_phi = 20 o = r(self.data) self.assertEqual(r.phi_max, phi_max) for i in range(17): self.assertEqual(o.y[i], 1.0) class DataInfoTests(unittest.TestCase): def setUp(self): filepath = find('MAR07232_rest.h5') self.data_list = Loader().load(filepath) self.data = self.data_list[0] def test_ring(self): """ Test ring averaging """ r = Ring(r_min=.005, r_max=.01, center_x=self.data.detector[0].beam_center.x, center_y=self.data.detector[0].beam_center.y, nbins=20) ##r.nbins_phi = 20 o = r(self.data) filepath = find('ring_testdata.txt') answer_list = Loader().load(filepath) answer = answer_list[0] self.assertEqual(len(answer_list), 1) for i in range(r.nbins_phi - 1): self.assertAlmostEqual(o.x[i + 1], answer.x[i], 4) self.assertAlmostEqual(o.y[i + 1], answer.y[i], 4) self.assertAlmostEqual(o.dy[i + 1], answer.dy[i], 4) def test_circularavg(self): """ Test circular averaging The test data was not generated by IGOR. """ r = CircularAverage(r_min=.00, r_max=.025, bin_width=0.0003) r.nbins_phi = 20 o = r(self.data) filepath = find('avg_testdata.txt') answer = Loader().load(filepath)[0] for i in range(r.nbins_phi): self.assertAlmostEqual(o.x[i], answer.x[i], 4) self.assertAlmostEqual(o.y[i], answer.y[i], 4) self.assertAlmostEqual(o.dy[i], answer.dy[i], 4) def test_box(self): """ Test circular averaging The test data was not generated by IGOR. """ r = Boxsum(x_min=.01, x_max=.015, y_min=0.01, y_max=0.015) s, ds, npoints = r(self.data) self.assertAlmostEqual(s, 34.278990899999997, 4) self.assertAlmostEqual(ds, 8.237259999538685, 4) self.assertAlmostEqual(npoints, 324.0000, 4) r = Boxavg(x_min=.01, x_max=.015, y_min=0.01, y_max=0.015) s, ds = r(self.data) self.assertAlmostEqual(s, 0.10579935462962962, 4) self.assertAlmostEqual(ds, 0.02542364197388483, 4) def test_slabX(self): """ Test slab in X The test data was not generated by IGOR. """ r = SlabX(x_min=-.01, x_max=.01, y_min=-0.0002, y_max=0.0002, bin_width=0.0004) r.fold = False o = r(self.data) filepath = find('slabx_testdata.txt') answer = Loader().load(filepath)[0] for i in range(len(o.x)): self.assertAlmostEqual(o.x[i], answer.x[i], 4) self.assertAlmostEqual(o.y[i], answer.y[i], 4) self.assertAlmostEqual(o.dy[i], answer.dy[i], 4) def test_slabY(self): """ Test slab in Y The test data was not generated by IGOR. """ r = SlabY(x_min=.005, x_max=.01, y_min=- 0.01, y_max=0.01, bin_width=0.0004) r.fold = False o = r(self.data) filepath = find('slaby_testdata.txt') answer = Loader().load(filepath)[0] for i in range(len(o.x)): self.assertAlmostEqual(o.x[i], answer.x[i], 4) self.assertAlmostEqual(o.y[i], answer.y[i], 4) self.assertAlmostEqual(o.dy[i], answer.dy[i], 4) def test_sectorphi_full(self): """ Test sector averaging I(phi) When considering the whole azimuthal range (2pi), the answer should be the same as ring averaging. The test data was not generated by IGOR. """ nbins = 19 phi_min = math.pi / (nbins + 1) phi_max = math.pi * 2 - phi_min r = SectorPhi(r_min=.005, r_max=.01, phi_min=phi_min, phi_max=phi_max, nbins=nbins) o = r(self.data) filepath = find('ring_testdata.txt') answer = Loader().load(filepath)[0] for i in range(len(o.x)): self.assertAlmostEqual(o.x[i], answer.x[i], 4) self.assertAlmostEqual(o.y[i], answer.y[i], 4) self.assertAlmostEqual(o.dy[i], answer.dy[i], 4) def test_sectorphi_quarter(self): """ Test sector averaging I(phi) The test data was not generated by IGOR. """ r = SectorPhi(r_min=.005, r_max=.01, phi_min=0, phi_max=math.pi / 2.0) r.nbins_phi = 20 o = r(self.data) filepath = find('sectorphi_testdata.txt') answer = Loader().load(filepath)[0] for i in range(len(o.x)): self.assertAlmostEqual(o.x[i], answer.x[i], 4) self.assertAlmostEqual(o.y[i], answer.y[i], 4) self.assertAlmostEqual(o.dy[i], answer.dy[i], 4) def test_sectorq_full(self): """ Test sector averaging I(q) The test data was not generated by IGOR. """ r = SectorQ(r_min=.005, r_max=.01, phi_min=0, phi_max=math.pi / 2.0) r.nbins_phi = 20 o = r(self.data) filepath = find('sectorq_testdata.txt') answer = Loader().load(filepath)[0] for i in range(len(o.x)): self.assertAlmostEqual(o.x[i], answer.x[i], 4) self.assertAlmostEqual(o.y[i], answer.y[i], 4) self.assertAlmostEqual(o.dy[i], answer.dy[i], 4) def test_sectorq_log(self): """ Test sector averaging I(q) The test data was not generated by IGOR. """ r = SectorQ(r_min=.005, r_max=.01, phi_min=0, phi_max=math.pi / 2.0, base=10) r.nbins_phi = 20 o = r(self.data) expected_binning = np.logspace(np.log10(0.005), np.log10(0.01), 20, base=10) for i in range(len(o.x)): self.assertAlmostEqual(o.x[i], expected_binning[i], 3) # TODO: Test for Y values (o.y) # print len(self.data.x_bins) # print len(self.data.y_bins) # print self.data.q_data.shape # data_to_bin = np.array(self.data.q_data) # data_to_bin = data_to_bin.reshape(len(self.data.x_bins), len(self.data.y_bins)) # H, xedges, yedges, binnumber = binned_statistic_2d(self.data.x_bins, self.data.y_bins, data_to_bin, # bins=[[0, math.pi / 2.0], expected_binning], statistic='mean') # xedges_width = (xedges[1] - xedges[0]) # xedges_center = xedges[1:] - xedges_width / 2 # yedges_width = (yedges[1] - yedges[0]) # yedges_center = yedges[1:] - yedges_width / 2 # print H.flatten().shape # print o.y.shape if __name__ == '__main__': unittest.main()