source: sasview/pr_inversion/test/utest_invertor.py @ a17ffdf

"""
    Unit tests for Invertor class
"""
# Disable "missing docstring" complaint
# pylint: disable-msg=C0111
# Disable "too many methods" complaint 
# pylint: disable-msg=R0904 


import unittest, math, numpy, pylab
from sans.pr.invertor import Invertor
        
class TestFiguresOfMerit(unittest.TestCase):
            
    def setUp(self):
        self.invertor = Invertor()
        self.invertor.d_max = 100.0
        
        # Test array
        self.ntest = 5
        self.x_in = numpy.ones(self.ntest)
        for i in range(self.ntest):
            self.x_in[i] = 1.0*(i+1)
       
        x, y, err = load("sphere_80.txt")

        # Choose the right d_max...
        self.invertor.d_max = 160.0
        # Set a small alpha
        self.invertor.alpha = .0007
        # Set data
        self.invertor.x   = x
        self.invertor.y   = y
        self.invertor.err = err
        # Perform inversion
        #out, cov = self.invertor.invert(10)
        
        self.out, self.cov = self.invertor.lstsq(10)

    def test_positive(self):
        """
            Test whether P(r) is positive
        """
        self.assertEqual(self.invertor.get_positive(self.out), 1)
        
    def test_positive_err(self):
        """
            Test whether P(r) is at least 1 sigma greater than zero
            for all r-values
        """
        self.assertTrue(self.invertor.get_pos_err(self.out, self.cov)>0.9)
       
class TestBasicComponent(unittest.TestCase):
    
    def setUp(self):
        self.invertor = Invertor()
        self.invertor.d_max = 100.0
        
        # Test array
        self.ntest = 5
        self.x_in = numpy.ones(self.ntest)
        for i in range(self.ntest):
            self.x_in[i] = 1.0*(i+1)

    def test_has_bck_flag(self):
        """
            Tests the has_bck flag operations
        """
        self.assertEqual(self.invertor.has_bck, False)
        self.invertor.has_bck=True
        self.assertEqual(self.invertor.has_bck, True)
        def doit_float():
            self.invertor.has_bck  = 2.0
        def doit_str():
            self.invertor.has_bck  = 'a'

        self.assertRaises(ValueError, doit_float)
        self.assertRaises(ValueError, doit_str)
        

    def testset_dmax(self):
        """
            Set and read d_max
        """
        value = 15.0
        self.invertor.d_max = value
        self.assertEqual(self.invertor.d_max, value)
        
    def testset_alpha(self):
        """
            Set and read alpha
        """
        value = 15.0
        self.invertor.alpha = value
        self.assertEqual(self.invertor.alpha, value)
        
    def testset_x_1(self):
        """
            Setting and reading the x array the hard way
        """
        # Set x
        self.invertor.x = self.x_in
        
        # Read it back
        npts = self.invertor.get_nx()
        x_out = numpy.ones(npts)
        
        self.invertor.get_x(x_out)

        for i in range(self.ntest):
            self.assertEqual(self.x_in[i], x_out[i])
            
    def testset_x_2(self):
        """
            Setting and reading the x array the easy way
        """
        # Set x
        self.invertor.x = self.x_in
        
        # Read it back
        x_out = self.invertor.x
        
        for i in range(self.ntest):
            self.assertEqual(self.x_in[i], x_out[i])
       
    def testset_y(self):
        """
            Setting and reading the y array the easy way
        """
        # Set y
        self.invertor.y = self.x_in
        
        # Read it back
        y_out = self.invertor.y
        
        for i in range(self.ntest):
            self.assertEqual(self.x_in[i], y_out[i])
       
    def testset_err(self):
        """
            Setting and reading the err array the easy way
        """
        # Set err
        self.invertor.err = self.x_in
        
        # Read it back
        err_out = self.invertor.err
        
        for i in range(self.ntest):
            self.assertEqual(self.x_in[i], err_out[i])
       
    def test_iq(self):
        """
            Test iq calculation
        """
        q = 0.11
        v1 = 8.0*math.pi**2/q * self.invertor.d_max *math.sin(q*self.invertor.d_max)
        v1 /= ( math.pi**2 - (q*self.invertor.d_max)**2.0 )
        
        pars = numpy.ones(1)
        self.assertAlmostEqual(self.invertor.iq(pars, q), v1, 2)
        
    def test_pr(self):
        """
            Test pr calculation
        """
        r = 10.0
        v1 = 2.0*r*math.sin(math.pi*r/self.invertor.d_max)
        pars = numpy.ones(1)
        self.assertAlmostEqual(self.invertor.pr(pars, r), v1, 2)
        
    def test_getsetters(self):
        self.invertor.new_data = 1.0
        self.assertEqual(self.invertor.new_data, 1.0)
        
        self.assertEqual(self.invertor.test_no_data, None)
        
    def test_inversion(self):
        """
            Test an inversion for which we know the answer
        """
        x, y, err = load("sphere_80.txt")

        # Choose the right d_max...
        self.invertor.d_max = 160.0
        # Set a small alpha
        self.invertor.alpha = 1e-7
        # Set data
        self.invertor.x   = x
        self.invertor.y   = y
        self.invertor.err = err
        # Perform inversion
        out, cov = self.invertor.invert_optimize(10)
        #out, cov = self.invertor.invert(10)
        # This is a very specific case
        # We should make sure it always passes
        self.assertTrue(self.invertor.chi2/len(x)<200.00)
        
        # Check the computed P(r) with the theory
        # for shpere of radius 80
        x = pylab.arange(0.01, self.invertor.d_max, self.invertor.d_max/51.0)
        y = numpy.zeros(len(x))
        dy = numpy.zeros(len(x))
        y_true = numpy.zeros(len(x))

        sum = 0.0
        sum_true = 0.0
        for i in range(len(x)):
            #y[i] = self.invertor.pr(out, x[i])
            (y[i], dy[i]) = self.invertor.pr_err(out, cov, x[i])
            sum += y[i]
            if x[i]<80.0:
                y_true[i] = pr_theory(x[i], 80.0)
            else:
                y_true[i] = 0
            sum_true += y_true[i]
            
        y = y/sum*self.invertor.d_max/len(x)
        dy = dy/sum*self.invertor.d_max/len(x)
        y_true = y_true/sum_true*self.invertor.d_max/len(x)
        
        chi2 = 0.0
        for i in range(len(x)):
            res = (y[i]-y_true[i])/dy[i]
            chi2 += res*res
            
        try:
            self.assertTrue(chi2/51.0<10.0)
        except:
            print "chi2 =", chi2/51.0
            raise
        
    def test_lstsq(self):
        """
            Test an inversion for which we know the answer
        """
        x, y, err = load("sphere_80.txt")

        # Choose the right d_max...
        self.invertor.d_max = 160.0
        # Set a small alpha
        self.invertor.alpha = .005
        # Set data
        self.invertor.x   = x
        self.invertor.y   = y
        self.invertor.err = err
        # Perform inversion
        #out, cov = self.invertor.invert(10)
        
        out, cov = self.invertor.lstsq(10)
         
        
        # This is a very specific case
        # We should make sure it always passes
        try:
            self.assertTrue(self.invertor.chi2/len(x)<200.00)
        except:
            print "Chi2(I(q)) =", self.invertor.chi2/len(x)
            raise
        
        # Check the computed P(r) with the theory
        # for shpere of radius 80
        x = pylab.arange(0.01, self.invertor.d_max, self.invertor.d_max/51.0)
        y = numpy.zeros(len(x))
        dy = numpy.zeros(len(x))
        y_true = numpy.zeros(len(x))

        sum = 0.0
        sum_true = 0.0
        for i in range(len(x)):
            #y[i] = self.invertor.pr(out, x[i])
            (y[i], dy[i]) = self.invertor.pr_err(out, cov, x[i])
            sum += y[i]
            if x[i]<80.0:
                y_true[i] = pr_theory(x[i], 80.0)
            else:
                y_true[i] = 0
            sum_true += y_true[i]
            
        y = y/sum*self.invertor.d_max/len(x)
        dy = dy/sum*self.invertor.d_max/len(x)
        y_true = y_true/sum_true*self.invertor.d_max/len(x)
        
        chi2 = 0.0
        for i in range(len(x)):
            res = (y[i]-y_true[i])/dy[i]
            chi2 += res*res
            
        try:
            self.assertTrue(chi2/51.0<50.0)
        except:
            print "chi2(P(r)) =", chi2/51.0
            raise
        
        # Test the number of peaks
        self.assertEqual(self.invertor.get_peaks(out), 1)
            
    def test_q_zero(self):
        """
            Test error condition where a point has q=0
        """
        x, y, err = load("sphere_80.txt")
        x[0] = 0.0
        
        # Choose the right d_max...
        self.invertor.d_max = 160.0
        # Set a small alpha
        self.invertor.alpha = 1e-7
        # Set data
        def doit():
            self.invertor.x   = x
        self.assertRaises(ValueError, doit)
        
                            
    def test_q_neg(self):
        """
            Test error condition where a point has q<0
        """
        x, y, err = load("sphere_80.txt")
        x[0] = -0.2
        
        # Choose the right d_max...
        self.invertor.d_max = 160.0
        # Set a small alpha
        self.invertor.alpha = 1e-7
        # Set data
        self.invertor.x   = x
        self.invertor.y   = y
        self.invertor.err = err
        # Perform inversion
        out, cov = self.invertor.invert(4)
        
        try:
            self.assertTrue(self.invertor.chi2>0)
        except:
            print "Chi2 =", self.invertor.chi2
            raise
                            
    def test_Iq_zero(self):
        """
            Test error condition where a point has q<0
        """
        x, y, err = load("sphere_80.txt")
        y[0] = 0.0
        
        # Choose the right d_max...
        self.invertor.d_max = 160.0
        # Set a small alpha
        self.invertor.alpha = 1e-7
        # Set data
        self.invertor.x   = x
        self.invertor.y   = y
        self.invertor.err = err
        # Perform inversion
        out, cov = self.invertor.invert(4)
        
        try:
            self.assertTrue(self.invertor.chi2>0)
        except:
            print "Chi2 =", self.invertor.chi2
            raise
        
    def no_test_time(self):
        x, y, err = load("sphere_80.txt")

        # Choose the right d_max...
        self.invertor.d_max = 160.0
        # Set a small alpha
        self.invertor.alpha = 1e-7
        # Set data
        self.invertor.x   = x
        self.invertor.y   = y
        self.invertor.err = err
    
        # time scales like nfunc**2
        # on a Lenovo Intel Core 2 CPU T7400 @ 2.16GHz, 
        # I get time/(nfunc)**2 = 0.022 sec
                            
        out, cov = self.invertor.invert(15)
        t16 = self.invertor.elapsed
        
        out, cov = self.invertor.invert(30)
        t30 = self.invertor.elapsed
        
        t30s = t30/30.0**2
        self.assertTrue( (t30s-t16/16.0**2)/t30s <1.2 )
        
    def test_clone(self):
        self.invertor.x = self.x_in
        clone = self.invertor.clone()
        
        for i in range(len(self.x_in)):
            self.assertEqual(self.x_in[i], clone.x[i])
        
    def test_save(self):
        x, y, err = load("sphere_80.txt")

        # Choose the right d_max...
        self.invertor.d_max = 160.0
        # Set a small alpha
        self.invertor.alpha = .0007
        # Set data
        self.invertor.x   = x
        self.invertor.y   = y
        self.invertor.err = err
        # Perform inversion
        
        out, cov = self.invertor.lstsq(10)
        
        # Save
        self.invertor.to_file("test_output.txt")
    
    def test_load(self):
        self.invertor.from_file("test_output.txt")
        self.assertEqual(self.invertor.d_max, 160.0)
        self.assertEqual(self.invertor.alpha, 0.0007)
        self.assertEqual(self.invertor.chi2, 836.797)
        self.assertAlmostEqual(self.invertor.pr(self.invertor.out, 10.0), 903.31577041, 4)
        
    def test_qmin(self):
        self.invertor.q_min = 1.0
        self.assertEqual(self.invertor.q_min, 1.0)
        
        self.invertor.q_min = None
        self.assertEqual(self.invertor.q_min, None)
        
                         
    def test_qmax(self):
        self.invertor.q_max = 1.0
        self.assertEqual(self.invertor.q_max, 1.0)
       
        self.invertor.q_max = None
        self.assertEqual(self.invertor.q_max, None)

class TestErrorConditions(unittest.TestCase):
    
    def setUp(self):
        self.invertor = Invertor()
        self.invertor.d_max = 100.0
        
        # Test array
        self.ntest = 5
        self.x_in = numpy.ones(self.ntest)
        for i in range(self.ntest):
            self.x_in[i] = 1.0*(i+1)

    def test_negative_errs(self):
        """
            Test an inversion for which we know the answer
        """
        x, y, err = load("data_error_1.txt")

        # Choose the right d_max...
        self.invertor.d_max = 160.0
        # Set a small alpha
        self.invertor.alpha = .0007
        # Set data
        self.invertor.x   = x
        self.invertor.y   = y
        self.invertor.err = err
        # Perform inversion
        
        out, cov = self.invertor.lstsq(10)
         
    def test_zero_errs(self):
        """
            Have zero as an error should raise an exception
        """
        x, y, err = load("data_error_2.txt")

        # Set data
        self.invertor.x   = x
        self.invertor.y   = y
        self.invertor.err = err
        # Perform inversion
        self.assertRaises(ValueError, self.invertor.invert, 10)
         
        
    def test_invalid(self):
        """
            Test an inversion for which we know the answer
        """
        x, y, err = load("data_error_1.txt")

        # Set data
        self.invertor.x   = x
        self.invertor.y   = y
        err = numpy.zeros(len(x)-1)
        self.invertor.err = err
        # Perform inversion
        self.assertRaises(RuntimeError, self.invertor.invert, 10)
         
        
    def test_zero_q(self):
        """
            One of the q-values is zero.
            An exception should be raised.
        """
        x, y, err = load("data_error_3.txt")

        # Set data
        self.assertRaises(ValueError, self.invertor.__setattr__, 'x', x)
        
    def test_zero_Iq(self):
        """
            One of the I(q) points has a value of zero
            Should not complain or crash.
        """
        x, y, err = load("data_error_4.txt")

        # Set data
        self.invertor.x   = x
        self.invertor.y   = y
        self.invertor.err = err
        # Perform inversion
        out, cov = self.invertor.lstsq(10)
    
    def test_negative_q(self):
        """
            One q value is negative.
            Makes not sense, but should not complain or crash.
        """
        x, y, err = load("data_error_5.txt")

        # Set data
        self.invertor.x   = x
        self.invertor.y   = y
        self.invertor.err = err
        # Perform inversion
        out, cov = self.invertor.lstsq(10)
    
    def test_negative_Iq(self):
        """
            One I(q) value is negative.
            Makes not sense, but should not complain or crash.
        """
        x, y, err = load("data_error_6.txt")

        # Set data
        self.invertor.x   = x
        self.invertor.y   = y
        self.invertor.err = err
        # Perform inversion
        out, cov = self.invertor.lstsq(10)
        
    def test_nodata(self):
        """
             No data was loaded. Should not complain.
        """
        out, cov = self.invertor.lstsq(10)
    

        
def pr_theory(r, R):
    """
       P(r) for a sphere
    """
    if r<=2*R:
        return 12.0* ((0.5*r/R)**2) * ((1.0-0.5*r/R)**2) * ( 2.0 + 0.5*r/R )
    else:
        return 0.0
    
def load(path = "sphere_60_q0_2.txt"):
    import numpy, math, sys
    # Read the data from the data file
    data_x   = numpy.zeros(0)
    data_y   = numpy.zeros(0)
    data_err = numpy.zeros(0)
    scale    = None
    if not path == None:
        input_f = open(path,'r')
        buff    = input_f.read()
        lines   = buff.split('\n')
        for line in lines:
            try:
                toks = line.split()
                x = float(toks[0])
                y = float(toks[1])
                if len(toks)>2:
                    err = float(toks[2])
                else:
                    if scale==None:
                        scale = 0.15*math.sqrt(y)
                    err = scale*math.sqrt(y)
                data_x = numpy.append(data_x, x)
                data_y = numpy.append(data_y, y)
                data_err = numpy.append(data_err, err)
            except:
                pass
               
    return data_x, data_y, data_err 
       
if __name__ == '__main__':
    unittest.main()