Changeset e123eb9 in sasview


Ignore:
Timestamp:
May 17, 2017 2:53:56 PM (8 years ago)
Author:
Ricardo M. Ferraz Leal <ricleal@…>
Branches:
master, ESS_GUI, ESS_GUI_Docs, ESS_GUI_batch_fitting, ESS_GUI_bumps_abstraction, ESS_GUI_iss1116, ESS_GUI_iss879, ESS_GUI_iss959, ESS_GUI_opencl, ESS_GUI_ordering, ESS_GUI_sync_sascalc, costrafo411, magnetic_scatt, release-4.2.2, ticket-1009, ticket-1094-headless, ticket-1242-2d-resolution, ticket-1243, ticket-1249, ticket885, unittest-saveload
Children:
46cd1c3
Parents:
5e903e8b
Message:

Test only for the log binning

File:
1 edited

Legend:

Unmodified
Added
Removed
  • test/sasdataloader/test/utest_averaging.py

    rfa4af76 re123eb9  
    33import os 
    44import unittest 
    5  
     5from scipy.stats import binned_statistic_2d 
    66import numpy as np 
    77 
     
    264264            self.assertAlmostEqual(o.dy[i], answer.dy[i], 4) 
    265265 
     266    def test_sectorq_log(self): 
     267        """ 
     268            Test sector averaging I(q) 
     269            The test data was not generated by IGOR. 
     270        """ 
     271 
     272        r = SectorQ(r_min=.005, r_max=.01, phi_min=0, phi_max=math.pi / 2.0, base=10) 
     273        r.nbins_phi = 20 
     274        o = r(self.data) 
     275 
     276        expected_binning = np.logspace(np.log10(0.005), np.log10(0.01), 20, base=10) 
     277        for i in range(len(o.x)): 
     278            self.assertAlmostEqual(o.x[i], expected_binning[i], 3) 
     279         
     280        # TODO: Test for Y values (o.y) 
     281        # print len(self.data.x_bins) 
     282        # print len(self.data.y_bins) 
     283        # print self.data.q_data.shape 
     284        # data_to_bin = np.array(self.data.q_data) 
     285        # data_to_bin = data_to_bin.reshape(len(self.data.x_bins), len(self.data.y_bins)) 
     286        # H, xedges, yedges, binnumber = binned_statistic_2d(self.data.x_bins, self.data.y_bins, data_to_bin, 
     287        #     bins=[[0, math.pi / 2.0], expected_binning], statistic='mean') 
     288        # xedges_width = (xedges[1] - xedges[0]) 
     289        # xedges_center = xedges[1:] - xedges_width / 2 
     290         
     291        # yedges_width = (yedges[1] - yedges[0]) 
     292        # yedges_center = yedges[1:] - yedges_width / 2 
     293         
     294        # print H.flatten().shape 
     295        # print o.y.shape 
     296         
    266297 
    267298if __name__ == '__main__': 
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