def testUpdate(self): """Update and change the profile to make sure generator is flushed.""" gen = self.gen prof = self.profile # Make sure attributes get updated with a change in the calculation # points. x = arange(0, 9, 0.1) prof.setCalculationPoints(x) self.assertTrue(gen._value is None) val = gen.value self.assertTrue(array_equal(x, prof.ycalc)) self.assertTrue(array_equal(prof.x, prof.ycalc)) self.assertTrue(array_equal(val, prof.ycalc)) self.assertTrue(array_equal(gen._value, prof.ycalc)) # Make sure attributes get updated with a new profile. x = arange(0, 8, 0.1) prof = Profile() prof.setCalculationPoints(x) gen.setProfile(prof) self.assertTrue(gen._value is None) val = gen.value self.assertTrue(array_equal(x, prof.ycalc)) self.assertTrue(array_equal(prof.x, prof.ycalc)) self.assertTrue(array_equal(val, prof.ycalc)) self.assertTrue(array_equal(gen._value, prof.ycalc)) return
def testUpdate(self): """Update and change the profile to make sure generator is flushed.""" gen = self.gen prof = self.profile # Make sure attributes get updated with a change in the calculation # points. x = arange(0, 9, 0.1) prof.setCalculationPoints(x) self.assertTrue(gen._value is None) val = gen.value self.assertTrue(array_equal(x, val)) # Verify generated value listens to changes in profile.x. x3 = x + 3 prof.x = x3 self.assertTrue(array_equal(x3, gen.value)) # Make sure attributes get updated with a new profile. x = arange(0, 8, 0.1) prof = Profile() prof.setCalculationPoints(x) gen.setProfile(prof) self.assertTrue(gen._value is None) self.assertTrue(array_equal(x, gen.value)) return
class TestProfileGenerator(unittest.TestCase): def setUp(self): self.gen = ProfileGenerator("test") self.profile = Profile() x = arange(0, 10, 0.1) self.profile.setCalculationPoints(x) self.gen.setProfile(self.profile) return def testOperation(self): """Test the operation method.""" gen = self.gen prof = self.profile # Try the direct evaluation val = gen.operation() self.assertTrue(array_equal(prof.x, val)) # Try evaluation through __call__ val = gen(2 * prof.x) self.assertTrue(array_equal(2 * prof.x, val)) return def testUpdate(self): """Update and change the profile to make sure generator is flushed.""" gen = self.gen prof = self.profile # Make sure attributes get updated with a change in the calculation # points. x = arange(0, 9, 0.1) prof.setCalculationPoints(x) self.assertTrue(gen._value is None) val = gen.value self.assertTrue(array_equal(x, val)) # Verify generated value listens to changes in profile.x. x3 = x + 3 prof.x = x3 self.assertTrue(array_equal(x3, gen.value)) # Make sure attributes get updated with a new profile. x = arange(0, 8, 0.1) prof = Profile() prof.setCalculationPoints(x) gen.setProfile(prof) self.assertTrue(gen._value is None) self.assertTrue(array_equal(x, gen.value)) return def test_pickling(self): """Test pickling of ProfileGenerator. """ data = pickle.dumps(self.gen) gen2 = pickle.loads(data) self.assertEqual('test', gen2.name) x = self.profile.x self.assertTrue(array_equal(x, gen2.operation())) self.assertTrue(array_equal(3 * x, gen2(3 * x))) return
def testReplacements(self): """Test attribute integrity when objects get replaced.""" fc = self.fitcontribution xobs = arange(0, 10, 0.5) yobs = xobs profile = self.profile profile.setObservedProfile(xobs, yobs) xobs2 = arange(0, 10, 0.8) yobs2 = 0.5 * xobs2 profile2 = Profile() profile2.setObservedProfile(xobs2, yobs2) gen = self.gen # Validate equations fc.setProfile(profile) fc.addProfileGenerator(gen, "I") self.assertTrue(array_equal(gen.value, xobs)) self.assertTrue(array_equal(fc._eq(), xobs)) self.assertAlmostEquals(0, sum(fc._reseq())) eq = fc._eq reseq = fc._reseq # Now set a different profile fc.setProfile(profile2) self.assertTrue(fc.profile is profile2) self.assertTrue(gen.profile is profile2) self.assertTrue(fc._eq is eq) self.assertTrue(fc._reseq is reseq) self.assertTrue(fc._eq._value is None) self.assertTrue(fc._reseq._value is None) # Validate equations self.assertTrue(array_equal(xobs2, gen.value)) self.assertTrue(array_equal(fc._eq(), gen.value)) return
def testReplacements(self): """Test attribute integrity when objects get replaced.""" fc = self.fitcontribution xobs = arange(0, 10, 0.5) yobs = xobs profile = self.profile profile.setObservedProfile(xobs, yobs) xobs2 = arange(0, 10, 0.8) yobs2 = 0.5*xobs2 profile2 = Profile() profile2.setObservedProfile(xobs2, yobs2) gen = self.gen # Validate equations fc.setProfile(profile) fc.addProfileGenerator(gen, "I") self.assertTrue(array_equal(gen.value, xobs)) self.assertTrue(array_equal(fc._eq(), xobs)) self.assertAlmostEquals(0, sum(fc._reseq())) eq = fc._eq reseq = fc._reseq # Now set a different profile fc.setProfile(profile2) self.assertTrue(fc.profile is profile2) self.assertTrue(gen.profile is profile2) self.assertTrue(fc._eq is eq) self.assertTrue(fc._reseq is reseq) self.assertTrue(fc._eq._value is None) self.assertTrue(fc._reseq._value is None) # Validate equations self.assertTrue(array_equal(xobs2, gen.value)) self.assertTrue(array_equal(fc._eq(), gen.value)) return
def setUp(self): self.gen = ProfileGenerator("test") self.profile = Profile() x = arange(0, 10, 0.1) self.profile.setCalculationPoints(x) self.gen.setProfile(self.profile) return
class TestProfileGenerator(unittest.TestCase): def setUp(self): self.gen = ProfileGenerator("test") self.profile = Profile() x = arange(0, 10, 0.1) self.profile.setCalculationPoints(x) self.gen.setProfile(self.profile) return def testOperation(self): """Test the operation method.""" gen = self.gen prof = self.profile # Try the direct evaluation gen.operation() self.assertTrue(array_equal(prof.x, prof.ycalc)) # Try evaluation through __call__ gen(prof.x) self.assertTrue(array_equal(prof.x, prof.ycalc)) return def testUpdate(self): """Update and change the profile to make sure generator is flushed.""" gen = self.gen prof = self.profile # Make sure attributes get updated with a change in the calculation # points. x = arange(0, 9, 0.1) prof.setCalculationPoints(x) self.assertTrue(gen._value is None) val = gen.value self.assertTrue(array_equal(x, prof.ycalc)) self.assertTrue(array_equal(prof.x, prof.ycalc)) self.assertTrue(array_equal(val, prof.ycalc)) self.assertTrue(array_equal(gen._value, prof.ycalc)) # Make sure attributes get updated with a new profile. x = arange(0, 8, 0.1) prof = Profile() prof.setCalculationPoints(x) gen.setProfile(prof) self.assertTrue(gen._value is None) val = gen.value self.assertTrue(array_equal(x, prof.ycalc)) self.assertTrue(array_equal(prof.x, prof.ycalc)) self.assertTrue(array_equal(val, prof.ycalc)) self.assertTrue(array_equal(gen._value, prof.ycalc)) return
def __init__(self, datainfo): """Initialize the attributes. datainfo -- The DataInfo object this wraps. """ self._datainfo = datainfo Profile.__init__(self) self._xobs = self._datainfo.x self._yobs = self._datainfo.y if self._datainfo.dy is None or 0 == len(self._datainfo.dy): self._dyobs = ones_like(self.xobs) else: self._dyobs = self._datainfo.dy return
def __init__(self, name="fit", conclass=FitContribution): """Initialization.""" FitRecipe.__init__(self, name) self.fithooks[0].verbose = 3 contribution = conclass("contribution") self.profile = Profile() contribution.setProfile(self.profile) self.addContribution(contribution) self.results = FitResults(self, update=False) # Adopt all the FitContribution methods public = [ aname for aname in dir(contribution) if aname not in dir(self) and not aname.startswith("_") ] for mname in public: method = getattr(contribution, mname) setattr(self, mname, method) return
def setUp(self): self.recipe = FitRecipe("recipe") self.recipe.fithooks[0].verbose = 0 # Set up the Profile self.profile = Profile() x = linspace(0, pi, 10) y = sin(x) self.profile.setObservedProfile(x, y) # Set up the FitContribution self.fitcontribution = FitContribution("cont") self.fitcontribution.setProfile(self.profile) self.fitcontribution.setEquation("A*sin(k*x + c)") self.fitcontribution.A.setValue(1) self.fitcontribution.k.setValue(1) self.fitcontribution.c.setValue(0) self.recipe.addContribution(self.fitcontribution) return
def setObservedProfile(self, xobs, yobs, dyobs = None): """Set the observed profile. This is overloaded to change the value within the datainfo object. Arguments xobs -- Numpy array of the independent variable yobs -- Numpy array of the observed signal. dyobs -- Numpy array of the uncertainty in the observed signal. If dyobs is None (default), it will be set to 1 at each observed xobs. Raises ValueError if len(yobs) != len(xobs) Raises ValueError if dyobs != None and len(dyobs) != len(xobs) """ Profile.setObservedProfile(self, xobs, yobs, dyobs) # Copy the arrays to the _datainfo attribute. self._datainfo.x = self._xobs self._datainfo.y = self._yobs self._datainfo.dy = self._dyobs return
def setObservedProfile(self, xobs, yobs, dyobs=None): """Set the observed profile. This is overloaded to change the value within the datainfo object. Arguments xobs -- Numpy array of the independent variable yobs -- Numpy array of the observed signal. dyobs -- Numpy array of the uncertainty in the observed signal. If dyobs is None (default), it will be set to 1 at each observed xobs. Raises ValueError if len(yobs) != len(xobs) Raises ValueError if dyobs != None and len(dyobs) != len(xobs) """ Profile.setObservedProfile(self, xobs, yobs, dyobs) # Copy the arrays to the _datainfo attribute. self._datainfo.x = self._xobs self._datainfo.y = self._yobs self._datainfo.dy = self._dyobs return
def __init__(self, name = "fit", conclass = FitContribution): """Initialization.""" FitRecipe.__init__(self, name) self.fithooks[0].verbose = 3 contribution = conclass("contribution") self.profile = Profile() contribution.setProfile(self.profile) self.addContribution(contribution) self.results = FitResults(self, update = False) # Adopt all the FitContribution methods public = [aname for aname in dir(contribution) if aname not in dir(self) and not aname.startswith("_")] for mname in public: method = getattr(contribution, mname) setattr(self, mname, method) return
def _create_recipe(self) -> md.MyRecipe: pgs = [] for name, structure in self._structures.items(): pg = md.PDFGenerator(name) pg.setStructure(structure, periodic=True) pgs.append(pg) fc = md.MyContribution(self.__class__.__name__) fc.setProfile(Profile()) for pg in pgs: fc.addProfileGenerator(pg) for name, sf in self._characteristics.items(): argnames = rename_args(sf, "{}_".format(name), fc.xname) fc.registerFunction(sf, name, argnames) fc.setEquation(self._equation) fr = md.MyRecipe() fr.clearFitHooks() fr.addContribution(fc) md.initialize(fr, **self._init_mode) return fr
class TestFitRecipe(unittest.TestCase): def setUp(self): self.recipe = FitRecipe("recipe") self.recipe.fithooks[0].verbose = 0 # Set up the Profile self.profile = Profile() x = linspace(0, pi, 10) y = sin(x) self.profile.setObservedProfile(x, y) # Set up the FitContribution self.fitcontribution = FitContribution("cont") self.fitcontribution.setProfile(self.profile) self.fitcontribution.setEquation("A*sin(k*x + c)") self.fitcontribution.A.setValue(1) self.fitcontribution.k.setValue(1) self.fitcontribution.c.setValue(0) self.recipe.addContribution(self.fitcontribution) return def testFixFree(self): recipe = self.recipe con = self.fitcontribution recipe.addVar(con.A, 2, tag = "tagA") recipe.addVar(con.k, 1, tag = "tagk") recipe.addVar(con.c, 0) recipe.newVar("B", 0) self.assertTrue(recipe.isFree(recipe.A)) recipe.fix("tagA") self.assertFalse(recipe.isFree(recipe.A)) recipe.free("tagA") self.assertTrue(recipe.isFree(recipe.A)) recipe.fix("A") self.assertFalse(recipe.isFree(recipe.A)) recipe.free("A") self.assertTrue(recipe.isFree(recipe.A)) recipe.fix(recipe.A) self.assertFalse(recipe.isFree(recipe.A)) recipe.free(recipe.A) self.assertTrue(recipe.isFree(recipe.A)) recipe.fix(recipe.A) self.assertFalse(recipe.isFree(recipe.A)) recipe.free("all") self.assertTrue(recipe.isFree(recipe.A)) self.assertTrue(recipe.isFree(recipe.k)) self.assertTrue(recipe.isFree(recipe.c)) self.assertTrue(recipe.isFree(recipe.B)) recipe.fix(recipe.A, "tagk", c = 3) self.assertFalse(recipe.isFree(recipe.A)) self.assertFalse(recipe.isFree(recipe.k)) self.assertFalse(recipe.isFree(recipe.c)) self.assertTrue(recipe.isFree(recipe.B)) self.assertEqual(3, recipe.c.value) recipe.fix("all") self.assertFalse(recipe.isFree(recipe.A)) self.assertFalse(recipe.isFree(recipe.k)) self.assertFalse(recipe.isFree(recipe.c)) self.assertFalse(recipe.isFree(recipe.B)) self.assertRaises(ValueError, recipe.free, "junk") self.assertRaises(ValueError, recipe.fix, tagA = 1) self.assertRaises(ValueError, recipe.fix, "junk") return def testVars(self): """Test to see if variables are added and removed properly.""" recipe = self.recipe con = self.fitcontribution recipe.addVar(con.A, 2) recipe.addVar(con.k, 1) recipe.addVar(con.c, 0) recipe.newVar("B", 0) names = recipe.getNames() self.assertEqual(names, ["A", "k", "c", "B"]) values = recipe.getValues() self.assertTrue((values == [2, 1, 0, 0]).all()) # Constrain a parameter to the B-variable to give it a value p = Parameter("Bpar", -1) recipe.constrain(recipe.B, p) values = recipe.getValues() self.assertTrue((values == [2, 1, 0]).all()) recipe.delVar(recipe.B) recipe.fix(recipe.k) names = recipe.getNames() self.assertEqual(names, ["A", "c"]) values = recipe.getValues() self.assertTrue((values == [2, 0]).all()) recipe.fix("all") names = recipe.getNames() self.assertEqual(names, []) values = recipe.getValues() self.assertTrue((values == []).all()) recipe.free("all") names = recipe.getNames() self.assertEqual(3, len(names)) self.assertTrue("A" in names) self.assertTrue("k" in names) self.assertTrue("c" in names) values = recipe.getValues() self.assertEqual(3, len(values)) self.assertTrue(0 in values) self.assertTrue(1 in values) self.assertTrue(2 in values) return def testResidual(self): """Test the residual and everything that can change it.""" # With thing set up as they are, the residual should be 0 res = self.recipe.residual() self.assertAlmostEqual(0, dot(res, res)) # Change the c value to 1 so that the equation evaluates as sin(x+1) x = self.profile.x y = sin(x+1) self.recipe.cont.c.setValue(1) res = self.recipe.residual() self.assertTrue( array_equal(y-self.profile.y, res) ) # Try some constraints # Make c = 2*A, A = Avar var = self.recipe.newVar("Avar") self.recipe.constrain(self.fitcontribution.c, "2*A", {"A" : self.fitcontribution.A}) self.assertEqual(2, self.fitcontribution.c.value) self.recipe.constrain(self.fitcontribution.A, var) self.assertEqual(1, var.getValue()) self.assertEqual(self.recipe.cont.A.getValue(), var.getValue()) # c is constrained to a constrained parameter. self.assertEqual(2, self.fitcontribution.c.value) # The equation should evaluate to sin(x+2) x = self.profile.x y = sin(x+2) res = self.recipe.residual() self.assertTrue( array_equal(y-self.profile.y, res) ) # Now try some restraints. We want c to be exactly zero. It should give # a penalty of (c-0)**2, which is 4 in this case r1 = self.recipe.restrain(self.fitcontribution.c, 0, 0, 1) self.recipe._ready = False res = self.recipe.residual() chi2 = 4 + dot(y - self.profile.y, y - self.profile.y) self.assertAlmostEqual(chi2, dot(res, res) ) # Clear the constraint and restore the value of c to 0. This should # give us chi2 = 0 again. self.recipe.unconstrain(self.fitcontribution.c) self.fitcontribution.c.setValue(0) res = self.recipe.residual([self.recipe.cont.A.getValue()]) chi2 = 0 self.assertAlmostEqual(chi2, dot(res, res) ) # Remove the restraint and variable self.recipe.unrestrain(r1) self.recipe.delVar(self.recipe.Avar) self.recipe._ready = False res = self.recipe.residual() chi2 = 0 self.assertAlmostEqual(chi2, dot(res, res) ) # Add constraints at the fitcontribution level. self.fitcontribution.constrain(self.fitcontribution.c, "2*A") # This should evaluate to sin(x+2) x = self.profile.x y = sin(x+2) res = self.recipe.residual() self.assertTrue( array_equal(y-self.profile.y, res) ) # Add a restraint at the fitcontribution level. r1 = self.fitcontribution.restrain(self.fitcontribution.c, 0, 0, 1) self.recipe._ready = False # The chi2 is the same as above, plus 4 res = self.recipe.residual() x = self.profile.x y = sin(x+2) chi2 = 4 + dot(y - self.profile.y, y - self.profile.y) self.assertAlmostEqual(chi2, dot(res, res) ) # Remove those self.fitcontribution.unrestrain(r1) self.recipe._ready = False self.fitcontribution.unconstrain(self.fitcontribution.c) self.fitcontribution.c.setValue(0) res = self.recipe.residual() chi2 = 0 self.assertAlmostEqual(chi2, dot(res, res) ) # Now try to use the observed profile inside of the equation # Set the equation equal to the data self.fitcontribution.setEquation("y") res = self.recipe.residual() self.assertAlmostEqual(0, dot(res, res)) # Now add the uncertainty. This should give dy/dy = 1 for the residual self.fitcontribution.setEquation("y+dy") res = self.recipe.residual() self.assertAlmostEqual(len(res), dot(res, res)) return def testPrintFitHook(self): "check output from default PrintFitHook." self.recipe.addVar(self.fitcontribution.c) self.recipe.restrain('c', lb=5) pfh, = self.recipe.getFitHooks() out = capturestdout(self.recipe.scalarResidual) self.assertEqual('', out) pfh.verbose = 1 out = capturestdout(self.recipe.scalarResidual) self.assertTrue(out.strip().isdigit()) self.assertFalse('\nRestraints:' in out) pfh.verbose = 2 out = capturestdout(self.recipe.scalarResidual) self.assertTrue('\nResidual:' in out) self.assertTrue('\nRestraints:' in out) self.assertFalse('\nVariables' in out) pfh.verbose = 3 out = capturestdout(self.recipe.scalarResidual) self.assertTrue('\nVariables' in out) self.assertTrue('c = ' in out) return
def setUp(self): self.profile = Profile() return
class TestProfile(unittest.TestCase): def setUp(self): self.profile = Profile() return def testInit(self): profile = self.profile self.assertTrue(profile.xobs is None) self.assertTrue(profile.yobs is None) self.assertTrue(profile.dyobs is None) self.assertTrue(profile.x is None) self.assertTrue(profile.y is None) self.assertTrue(profile.dy is None) self.assertTrue(profile.ycalc is None) self.assertEquals(profile.meta, {}) return def testSetObservedProfile(self): """Test the setObservedProfile method.""" # Make a profile with defined dy x = arange(0, 10, 0.1) y = x dy = x prof = self.profile prof.setObservedProfile(x, y, dy) self.assertTrue( array_equal(x, prof.xobs) ) self.assertTrue( array_equal(y, prof.yobs) ) self.assertTrue( array_equal(dy, prof.dyobs) ) # Make a profile with undefined dy x = arange(0, 10, 0.1) y = x dy = None self.profile.setObservedProfile(x, y, dy) self.assertTrue( array_equal(x, prof.xobs) ) self.assertTrue( array_equal(y, prof.yobs) ) self.assertTrue( array_equal(ones_like(prof.xobs), prof.dyobs)) # Get the ranged profile to make sure its the same self.assertTrue( array_equal(x, prof.x) ) self.assertTrue( array_equal(y, prof.y) ) self.assertTrue( array_equal(ones_like(prof.xobs), prof.dy)) return def testSetCalculationRange(self): """Test the setCalculationRange method.""" x = arange(2, 10, 0.5) y = array(x) dy = array(x) prof = self.profile # Check call before data arrays are present self.assertRaises(AttributeError, prof.setCalculationRange) self.assertRaises(AttributeError, prof.setCalculationRange, 0) self.assertRaises(AttributeError, prof.setCalculationRange, 0, 10) self.assertRaises(AttributeError, prof.setCalculationRange, 0, 10, 0.2) prof.setObservedProfile(x, y, dy) # Test normal execution w/o arguments prof.setCalculationRange() self.assertTrue( array_equal(x, prof.x) ) self.assertTrue( array_equal(y, prof.y) ) self.assertTrue( array_equal(dy, prof.dy) ) # Test a lower bound < xmin prof.setCalculationRange(xmin = 0) self.assertTrue( array_equal(x, prof.x) ) self.assertTrue( array_equal(y, prof.y) ) self.assertTrue( array_equal(dy, dprof.y) ) # Test an upper bound > xmax prof.setCalculationRange(xmax = 100) prof.x, prof.y, dprof.y = prof.getRangedProfile() self.assertTrue( array_equal(x, prof.x) ) self.assertTrue( array_equal(y, prof.y) ) self.assertTrue( array_equal(dy, dprof.y) ) # Test xmin > xmax self.assertRaises(ValueError, profile.setCalculationRange, xmin = 10, xmax = 3) # Test xmax - xmin < dx self.assertRaises(ValueError, profile.setCalculationRange, xmin = 3, xmax = 3 + 0.4, dx = 0.5) # Test dx <= 0 self.assertRaises(ValueError, profile.setCalculationRange, dx = 0) self.assertRaises(ValueError, profile.setCalculationRange, dx = -0.000001) # This should be alright profile.setCalculationRange(dx = 0.000001) # Test an internal bound prof.setCalculationRange(4, 7) prof.x, prof.y, dprof.y = prof.getRangedProfile() self.assertTrue( array_equal(prof.x, arange(4, 7.5, 0.5) ) ) self.assertTrue( array_equal(prof.y, arange(4, 7.5, 0.5) ) ) self.assertTrue( array_equal(dprof.y, arange(4, 7.5, 0.5) ) ) # Test a new grid prof.setCalculationRange(4, 7, 0.1) prof.x, prof.y, dprof.y = prof.getRangedProfile() self.assertTrue( array_equal(prof.x, arange(4, 7.1, 0.1) ) ) self.assertAlmostEqual( 0, sum(prof.y- arange(4, 7.1, 0.1))**2 ) self.assertAlmostEqual( 0, sum(dprof.y- arange(4, 7.1, 0.1))**2 ) return def testSetCalculationRange(self): """Test the setCalculationRange method.""" prof = self.profile x = arange(2, 10.5, 0.5) y = array(x) dy = array(x) # Test without data xcalc = arange(3, 12.2, 0.2) prof.setCalculationPoints(xcalc) self.assertTrue( array_equal(xcalc, prof.x) ) # Add the data. This should change the bounds of the calculation array. prof.setObservedProfile(x, y, dy) self.assertTrue( array_equal(arange(3, 10.1, 0.2), prof.x ) ) return def testLoadtxt(self): """Test the loadtxt method""" prof = self.profile data = datafile("testdata.txt") def _test(p): self.assertAlmostEqual(1e-2, p.x[0]) self.assertAlmostEqual(1.105784e-1, p.y[0]) self.assertAlmostEqual(1.802192e-3, p.dy[0]) # Test normal load prof.loadtxt(data, usecols=(0,1,3)) _test(prof) # Test trying to not set unpack prof.loadtxt(data, usecols=(0,1,3), unpack = False) _test(prof) prof.loadtxt(data, float, '#', None, None, 0, (0,1,3), False) _test(prof) # Try not including dy prof.loadtxt(data, usecols=(0,1)) self.assertAlmostEqual(1e-2, prof.x[0]) self.assertAlmostEqual(1.105784e-1, prof.y[0]) self.assertAlmostEqual(1, prof.dy[0]) # Try to include too little self.assertRaises(ValueError, prof.loadtxt, data, usecols=(0,)) return
def setUp(self): self.gen = ProfileGenerator("test") self.profile = Profile() self.fitcontribution = FitContribution("test") return
class TestProfile(unittest.TestCase): def setUp(self): self.profile = Profile() return def testInit(self): profile = self.profile self.assertTrue(profile.xobs is None) self.assertTrue(profile.yobs is None) self.assertTrue(profile.dyobs is None) self.assertTrue(profile.x is None) self.assertTrue(profile.y is None) self.assertTrue(profile.dy is None) self.assertTrue(profile.ycalc is None) self.assertEqual(profile.meta, {}) return def testSetObservedProfile(self): """Test the setObservedProfile method.""" # Make a profile with defined dy x = arange(0, 10, 0.1) y = x dy = x prof = self.profile prof.setObservedProfile(x, y, dy) self.assertTrue( array_equal(x, prof.xobs) ) self.assertTrue( array_equal(y, prof.yobs) ) self.assertTrue( array_equal(dy, prof.dyobs) ) # Make a profile with undefined dy x = arange(0, 10, 0.1) y = x dy = None self.profile.setObservedProfile(x, y, dy) self.assertTrue( array_equal(x, prof.xobs) ) self.assertTrue( array_equal(y, prof.yobs) ) self.assertTrue( array_equal(ones_like(prof.xobs), prof.dyobs)) # Get the ranged profile to make sure its the same self.assertTrue( array_equal(x, prof.x) ) self.assertTrue( array_equal(y, prof.y) ) self.assertTrue( array_equal(ones_like(prof.xobs), prof.dy)) return def testSetCalculationRange(self): """Test the setCalculationRange method.""" x = arange(2, 9.6, 0.5) y = array(x) dy = array(x) prof = self.profile # Check call before data arrays are present self.assertRaises(AttributeError, prof.setCalculationRange) self.assertRaises(AttributeError, prof.setCalculationRange, 0) self.assertRaises(AttributeError, prof.setCalculationRange, 0, 5) self.assertRaises(AttributeError, prof.setCalculationRange, 0, 5, 0.2) # assign data prof.setObservedProfile(x, y, dy) # Test normal execution w/o arguments self.assertTrue(array_equal(x, prof.x)) self.assertTrue(array_equal(y, prof.y)) self.assertTrue(array_equal(dy, prof.dy)) prof.setCalculationRange() self.assertTrue(array_equal(x, prof.x)) self.assertTrue(array_equal(y, prof.y)) self.assertTrue(array_equal(dy, prof.dy)) # Test a lower bound < xmin prof.setCalculationRange(xmin=0) self.assertTrue(array_equal(x, prof.x)) self.assertTrue(array_equal(y, prof.y)) self.assertTrue(array_equal(dy, prof.dy)) # Test an upper bound > xmax prof.setCalculationRange(xmax=100) self.assertTrue(array_equal(x, prof.x)) self.assertTrue(array_equal(y, prof.y)) self.assertTrue(array_equal(dy, prof.dy)) # Test xmin > xmax self.assertRaises(ValueError, prof.setCalculationRange, xmin=10, xmax=3) # Test xmax - xmin < dx self.assertRaises(ValueError, prof.setCalculationRange, xmin=3, xmax=3.9, dx=1.0) # Test dx <= 0 self.assertRaises(ValueError, prof.setCalculationRange, dx=0) self.assertRaises(ValueError, prof.setCalculationRange, dx=-0.000001) # using string other than 'obs' self.assertRaises(ValueError, prof.setCalculationRange, xmin='oobs') self.assertRaises(ValueError, prof.setCalculationRange, xmax='oobs') self.assertRaises(ValueError, prof.setCalculationRange, dx='oobs') # This should be alright prof.setCalculationRange(3, 5) prof.setCalculationRange(xmin='obs', xmax=7, dx=0.001) self.assertEqual(5001, len(prof.x)) self.assertEqual(len(prof.x), len(prof.y)) self.assertEqual(len(prof.x), len(prof.dy)) # Test an internal bound prof.setCalculationRange(4, 7, dx='obs') self.assertTrue(array_equal(prof.x, arange(4, 7.1, 0.5))) self.assertTrue(array_equal(prof.y, arange(4, 7.1, 0.5))) self.assertTrue(array_equal(prof.y, arange(4, 7.1, 0.5))) # test setting only one of the bounds prof.setCalculationRange(xmin=3) self.assertTrue(array_equal(prof.x, arange(3, 7.1, 0.5))) self.assertTrue(array_equal(prof.x, prof.y)) self.assertTrue(array_equal(prof.x, prof.dy)) prof.setCalculationRange(xmax=5.1) self.assertTrue(array_equal(prof.x, arange(3, 5.1, 0.5))) self.assertTrue(array_equal(prof.x, prof.y)) self.assertTrue(array_equal(prof.x, prof.dy)) prof.setCalculationRange(dx=1) self.assertTrue(array_equal(prof.x, arange(3, 5.1))) self.assertTrue(array_equal(prof.x, prof.y)) self.assertTrue(array_equal(prof.x, prof.dy)) # Test a new grid prof.setCalculationRange(4.2, 7, 0.3) self.assertTrue(array_equal(prof.x, arange(4.2, 6.901, 0.3))) self.assertTrue(allclose(prof.x, prof.y)) self.assertTrue(allclose(prof.x, prof.dy)) prof.setCalculationRange(xmin=4.2, xmax=6.001) self.assertTrue(array_equal(prof.x, arange(4.2, 6.001, 0.3))) # resample on a clipped grid prof.setCalculationRange(dx=0.5) self.assertTrue(array_equal(prof.x, arange(4.5, 6.1, 0.5))) return def testSetCalculationPoints(self): """Test the setCalculationPoints method.""" prof = self.profile x = arange(2, 10.5, 0.5) y = array(x) dy = array(x) # Test without data xcalc = arange(3, 12.2, 0.2) prof.setCalculationPoints(xcalc) self.assertTrue( array_equal(xcalc, prof.x) ) # Add the data. This should change the bounds of the calculation array. prof.setObservedProfile(x, y, dy) self.assertTrue( array_equal(arange(3, 10.1, 0.2), prof.x ) ) return def testLoadtxt(self): """Test the loadtxt method""" prof = self.profile data = datafile("testdata.txt") def _test(p): self.assertAlmostEqual(1e-2, p.x[0]) self.assertAlmostEqual(1.105784e-1, p.y[0]) self.assertAlmostEqual(1.802192e-3, p.dy[0]) # Test normal load prof.loadtxt(data, usecols=(0,1,3)) _test(prof) # Test trying to not set unpack prof.loadtxt(data, usecols=(0,1,3), unpack = False) _test(prof) prof.loadtxt(data, float, '#', None, None, 0, (0,1,3), False) _test(prof) # Try not including dy prof.loadtxt(data, usecols=(0,1)) self.assertAlmostEqual(1e-2, prof.x[0]) self.assertAlmostEqual(1.105784e-1, prof.y[0]) self.assertAlmostEqual(1, prof.dy[0]) # Try to include too little self.assertRaises(ValueError, prof.loadtxt, data, usecols=(0,)) return
class TestFitRecipe(unittest.TestCase): def setUp(self): self.recipe = FitRecipe("recipe") self.recipe.fithooks[0].verbose = 0 # Set up the Profile self.profile = Profile() x = linspace(0, pi, 10) y = sin(x) self.profile.setObservedProfile(x, y) # Set up the FitContribution self.fitcontribution = FitContribution("cont") self.fitcontribution.setProfile(self.profile) self.fitcontribution.setEquation("A*sin(k*x + c)") self.fitcontribution.A.setValue(1) self.fitcontribution.k.setValue(1) self.fitcontribution.c.setValue(0) self.recipe.addContribution(self.fitcontribution) return def testFixFree(self): recipe = self.recipe con = self.fitcontribution recipe.addVar(con.A, 2, tag="tagA") recipe.addVar(con.k, 1, tag="tagk") recipe.addVar(con.c, 0) recipe.newVar("B", 0) self.assertTrue(recipe.isFree(recipe.A)) recipe.fix("tagA") self.assertFalse(recipe.isFree(recipe.A)) recipe.free("tagA") self.assertTrue(recipe.isFree(recipe.A)) recipe.fix("A") self.assertFalse(recipe.isFree(recipe.A)) recipe.free("A") self.assertTrue(recipe.isFree(recipe.A)) recipe.fix(recipe.A) self.assertFalse(recipe.isFree(recipe.A)) recipe.free(recipe.A) self.assertTrue(recipe.isFree(recipe.A)) recipe.fix(recipe.A) self.assertFalse(recipe.isFree(recipe.A)) recipe.free("all") self.assertTrue(recipe.isFree(recipe.A)) self.assertTrue(recipe.isFree(recipe.k)) self.assertTrue(recipe.isFree(recipe.c)) self.assertTrue(recipe.isFree(recipe.B)) recipe.fix(recipe.A, "tagk", c=3) self.assertFalse(recipe.isFree(recipe.A)) self.assertFalse(recipe.isFree(recipe.k)) self.assertFalse(recipe.isFree(recipe.c)) self.assertTrue(recipe.isFree(recipe.B)) self.assertEquals(3, recipe.c.value) recipe.fix("all") self.assertFalse(recipe.isFree(recipe.A)) self.assertFalse(recipe.isFree(recipe.k)) self.assertFalse(recipe.isFree(recipe.c)) self.assertFalse(recipe.isFree(recipe.B)) self.assertRaises(ValueError, recipe.free, "junk") self.assertRaises(ValueError, recipe.fix, tagA=1) self.assertRaises(ValueError, recipe.fix, "junk") return def testVars(self): """Test to see if variables are added and removed properly.""" recipe = self.recipe con = self.fitcontribution recipe.addVar(con.A, 2) recipe.addVar(con.k, 1) recipe.addVar(con.c, 0) recipe.newVar("B", 0) names = recipe.getNames() self.assertEquals(names, ["A", "k", "c", "B"]) values = recipe.getValues() self.assertTrue((values == [2, 1, 0, 0]).all()) # Constrain a parameter to the B-variable to give it a value p = Parameter("Bpar", -1) recipe.constrain(recipe.B, p) values = recipe.getValues() self.assertTrue((values == [2, 1, 0]).all()) recipe.delVar(recipe.B) recipe.fix(recipe.k) names = recipe.getNames() self.assertEquals(names, ["A", "c"]) values = recipe.getValues() self.assertTrue((values == [2, 0]).all()) recipe.fix("all") names = recipe.getNames() self.assertEquals(names, []) values = recipe.getValues() self.assertTrue((values == []).all()) recipe.free("all") names = recipe.getNames() self.assertEquals(3, len(names)) self.assertTrue("A" in names) self.assertTrue("k" in names) self.assertTrue("c" in names) values = recipe.getValues() self.assertEquals(3, len(values)) self.assertTrue(0 in values) self.assertTrue(1 in values) self.assertTrue(2 in values) return def testResidual(self): """Test the residual and everything that can change it.""" # With thing set up as they are, the residual should be 0 res = self.recipe.residual() self.assertAlmostEquals(0, dot(res, res)) # Change the c value to 1 so that the equation evaluates as sin(x+1) x = self.profile.x y = sin(x + 1) self.recipe.cont.c.setValue(1) res = self.recipe.residual() self.assertTrue(array_equal(y - self.profile.y, res)) # Try some constraints # Make c = 2*A, A = Avar var = self.recipe.newVar("Avar") self.recipe.constrain(self.fitcontribution.c, "2*A", {"A": self.fitcontribution.A}) self.assertEquals(2, self.fitcontribution.c.value) self.recipe.constrain(self.fitcontribution.A, var) self.assertEquals(1, var.getValue()) self.assertEquals(self.recipe.cont.A.getValue(), var.getValue()) # c is constrained to a constrained parameter. self.assertEquals(2, self.fitcontribution.c.value) # The equation should evaluate to sin(x+2) x = self.profile.x y = sin(x + 2) res = self.recipe.residual() self.assertTrue(array_equal(y - self.profile.y, res)) # Now try some restraints. We want c to be exactly zero. It should give # a penalty of (c-0)**2, which is 4 in this case r1 = self.recipe.restrain(self.fitcontribution.c, 0, 0, 1) self.recipe._ready = False res = self.recipe.residual() chi2 = 4 + dot(y - self.profile.y, y - self.profile.y) self.assertAlmostEqual(chi2, dot(res, res)) # Clear the constraint and restore the value of c to 0. This should # give us chi2 = 0 again. self.recipe.unconstrain(self.fitcontribution.c) self.fitcontribution.c.setValue(0) res = self.recipe.residual([self.recipe.cont.A.getValue()]) chi2 = 0 self.assertAlmostEqual(chi2, dot(res, res)) # Remove the restraint and variable self.recipe.unrestrain(r1) self.recipe.delVar(self.recipe.Avar) self.recipe._ready = False res = self.recipe.residual() chi2 = 0 self.assertAlmostEqual(chi2, dot(res, res)) # Add constraints at the fitcontribution level. self.fitcontribution.constrain(self.fitcontribution.c, "2*A") # This should evaluate to sin(x+2) x = self.profile.x y = sin(x + 2) res = self.recipe.residual() self.assertTrue(array_equal(y - self.profile.y, res)) # Add a restraint at the fitcontribution level. r1 = self.fitcontribution.restrain(self.fitcontribution.c, 0, 0, 1) self.recipe._ready = False # The chi2 is the same as above, plus 4 res = self.recipe.residual() x = self.profile.x y = sin(x + 2) chi2 = 4 + dot(y - self.profile.y, y - self.profile.y) self.assertAlmostEqual(chi2, dot(res, res)) # Remove those self.fitcontribution.unrestrain(r1) self.recipe._ready = False self.fitcontribution.unconstrain(self.fitcontribution.c) self.fitcontribution.c.setValue(0) res = self.recipe.residual() chi2 = 0 self.assertAlmostEqual(chi2, dot(res, res)) # Now try to use the observed profile inside of the equation # Set the equation equal to the data self.fitcontribution.setEquation("y") res = self.recipe.residual() self.assertAlmostEquals(0, dot(res, res)) # Now add the uncertainty. This should give dy/dy = 1 for the residual self.fitcontribution.setEquation("y+dy") res = self.recipe.residual() self.assertAlmostEquals(len(res), dot(res, res)) return
class SimpleRecipe(FitRecipe): """SimpleRecipe class. This is a FitRecipe with a built-in Profile (the 'profile' attribute) and FitContribution (the 'contribution' attribute). Unique methods from each of these are exposed through this class to facilitate the creation of a simple fit recipe. Attributes profile -- The built-in Profile object. contribution -- The built-in FitContribution object. results -- The built-in FitResults object. name -- A name for this FitRecipe. fithook -- An object to be called whenever within the residual (default FitHook()) that can pass information out of the system during a refinement. _constraints -- A dictionary of Constraints, indexed by the constrained Parameter. Constraints can be added using the 'constrain' method. _oconstraints -- An ordered list of the constraints from this and all sub-components. _calculators -- A managed dictionary of Calculators. _contributions -- A managed OrderedDict of FitContributions. _parameters -- A managed OrderedDict of parameters (in this case the parameters are varied). _parsets -- A managed dictionary of ParameterSets. _eqfactory -- A diffpy.srfit.equation.builder.EquationFactory instance that is used to create constraints and restraints from string _fixed -- A set of parameters that are not actually varied. _restraintlist -- A list of restraints from this and all sub-components. _restraints -- A set of Restraints. Restraints can be added using the 'restrain' or 'confine' methods. _ready -- A flag indicating if all attributes are ready for the calculation. _tagdict -- A dictionary of tags to variables. _weights -- List of weighing factors for each FitContribution. The weights are multiplied by the residual of the FitContribution when determining the overall residual. Properties names -- Variable names (read only). See getNames. values -- Variable values (read only). See getValues. """ def __init__(self, name = "fit", conclass = FitContribution): """Initialization.""" FitRecipe.__init__(self, name) self.fithooks[0].verbose = 3 contribution = conclass("contribution") self.profile = Profile() contribution.setProfile(self.profile) self.addContribution(contribution) self.results = FitResults(self, update = False) # Adopt all the FitContribution methods public = [aname for aname in dir(contribution) if aname not in dir(self) and not aname.startswith("_")] for mname in public: method = getattr(contribution, mname) setattr(self, mname, method) return # Profile methods def loadParsedData(self, parser): """Load parsed data from a ProfileParser. This sets the xobs, yobs, dyobs arrays as well as the metadata. """ return self.profile.loadParsedData(parser) def setObservedProfile(self, xobs, yobs, dyobs = None): """Set the observed profile. Arguments xobs -- Numpy array of the independent variable yobs -- Numpy array of the observed signal. dyobs -- Numpy array of the uncertainty in the observed signal. If dyobs is None (default), it will be set to 1 at each observed xobs. Raises ValueError if len(yobs) != len(xobs) Raises ValueError if dyobs != None and len(dyobs) != len(xobs) """ return self.profile.setObservedProfile(xobs, yobs, dyobs) def setCalculationRange(self, xmin=None, xmax=None, dx=None): """Set epsilon-inclusive calculation range. Adhere to the observed ``xobs`` points when ``dx`` is the same as in the data. ``xmin`` and ``xmax`` are clipped at the bounds of the observed data. Parameters ---------- xmin : float or "obs", optional The minimum value of the independent variable. Keep the current minimum when not specified. If specified as "obs" reset to the minimum observed value. xmax : float or "obs", optional The maximum value of the independent variable. Keep the current maximum when not specified. If specified as "obs" reset to the maximum observed value. dx : float or "obs", optional The sample spacing in the independent variable. When different from the data, resample the ``x`` as anchored at ``xmin``. Note that xmin is always inclusive (unless clipped). xmax is inclusive if it is within the bounds of the observed data. Raises ------ AttributeError If there is no observed data. ValueError When xmin > xmax or if dx <= 0. Also if dx > xmax - xmin. """ return self.profile.setCalculationRange(xmin, xmax, dx) def setCalculationPoints(self, x): """Set the calculation points. Arguments x -- A non-empty numpy array containing the calculation points. If xobs exists, the bounds of x will be limited to its bounds. This will create y and dy on the specified grid if xobs, yobs and dyobs exist. """ return self.profile.setCalculationPoints(x) def loadtxt(self, *args, **kw): """Use numpy.loadtxt to load data. Arguments are passed to numpy.loadtxt. unpack = True is enforced. The first two arrays returned by numpy.loadtxt are assumed to be x and y. If there is a third array, it is assumed to by dy. Any other arrays are ignored. These are passed to setObservedProfile. Raises ValueError if the call to numpy.loadtxt returns fewer than 2 arrays. Returns the x, y and dy arrays loaded from the file """ return self.profile.loadtxt(*args, **kw) # FitContribution def setEquation(self, eqstr, ns = {}): """Set the profile equation for the FitContribution. This sets the equation that will be used when generating the residual. The equation will be usable within setResidualEquation as "eq", and it takes no arguments. eqstr -- A string representation of the equation. Variables will be extracted from this equation and be given an initial value of 0. ns -- A dictionary of Parameters, indexed by name, that are used in the eqstr, but not registered (default {}). Raises ValueError if ns uses a name that is already used for a variable. """ self.contribution.setEquation(eqstr, ns = {}) # Extract variables for par in self.contribution: # Skip Profile Parameters if par.name in ("x", "y", "dy"): continue if par.value is None: par.value = 0 if par.name not in self._parameters: self.addVar(par) return def __call__(self): """Evaluate the contribution equation.""" return self.contribution.evaluate() # FitResults methods def printResults(self, header = "", footer = ""): """Format and print the results. header -- A header to add to the output (default "") footer -- A footer to add to the output (default "") """ self.results.printResults(header, footer, True) return def saveResults(self, filename, header = "", footer = ""): """Format and save the results. filename - Name of the save file. header -- A header to add to the output (default "") footer -- A footer to add to the output (default "") """ self.results.saveResults(filename, header, footer, True)
class SimpleRecipe(FitRecipe): """SimpleRecipe class. This is a FitRecipe with a built-in Profile (the 'profile' attribute) and FitContribution (the 'contribution' attribute). Unique methods from each of these are exposed through this class to facilitate the creation of a simple fit recipe. Attributes profile -- The built-in Profile object. contribution -- The built-in FitContribution object. results -- The built-in FitResults object. name -- A name for this FitRecipe. fithook -- An object to be called whenever within the residual (default FitHook()) that can pass information out of the system during a refinement. _constraints -- A dictionary of Constraints, indexed by the constrained Parameter. Constraints can be added using the 'constrain' method. _oconstraints -- An ordered list of the constraints from this and all sub-components. _calculators -- A managed dictionary of Calculators. _contributions -- A managed OrderedDict of FitContributions. _parameters -- A managed OrderedDict of parameters (in this case the parameters are varied). _parsets -- A managed dictionary of ParameterSets. _eqfactory -- A diffpy.srfit.equation.builder.EquationFactory instance that is used to create constraints and restraints from string _fixed -- A set of parameters that are not actually varied. _restraintlist -- A list of restraints from this and all sub-components. _restraints -- A set of Restraints. Restraints can be added using the 'restrain' or 'confine' methods. _ready -- A flag indicating if all attributes are ready for the calculation. _tagdict -- A dictionary of tags to variables. _weights -- List of weighing factors for each FitContribution. The weights are multiplied by the residual of the FitContribution when determining the overall residual. Properties names -- Variable names (read only). See getNames. values -- Variable values (read only). See getValues. """ def __init__(self, name="fit", conclass=FitContribution): """Initialization.""" FitRecipe.__init__(self, name) self.fithooks[0].verbose = 3 contribution = conclass("contribution") self.profile = Profile() contribution.setProfile(self.profile) self.addContribution(contribution) self.results = FitResults(self, update=False) # Adopt all the FitContribution methods public = [ aname for aname in dir(contribution) if aname not in dir(self) and not aname.startswith("_") ] for mname in public: method = getattr(contribution, mname) setattr(self, mname, method) return # Profile methods def loadParsedData(self, parser): """Load parsed data from a ProfileParser. This sets the xobs, yobs, dyobs arrays as well as the metadata. """ return self.profile.loadParsedData(parser) def setObservedProfile(self, xobs, yobs, dyobs=None): """Set the observed profile. Arguments xobs -- Numpy array of the independent variable yobs -- Numpy array of the observed signal. dyobs -- Numpy array of the uncertainty in the observed signal. If dyobs is None (default), it will be set to 1 at each observed xobs. Raises ValueError if len(yobs) != len(xobs) Raises ValueError if dyobs != None and len(dyobs) != len(xobs) """ return self.profile.setObservedProfile(xobs, yobs, dyobs) def setCalculationRange(self, xmin=None, xmax=None, dx=None): """Set epsilon-inclusive calculation range. Adhere to the observed ``xobs`` points when ``dx`` is the same as in the data. ``xmin`` and ``xmax`` are clipped at the bounds of the observed data. Parameters ---------- xmin : float or "obs", optional The minimum value of the independent variable. Keep the current minimum when not specified. If specified as "obs" reset to the minimum observed value. xmax : float or "obs", optional The maximum value of the independent variable. Keep the current maximum when not specified. If specified as "obs" reset to the maximum observed value. dx : float or "obs", optional The sample spacing in the independent variable. When different from the data, resample the ``x`` as anchored at ``xmin``. Note that xmin is always inclusive (unless clipped). xmax is inclusive if it is within the bounds of the observed data. Raises ------ AttributeError If there is no observed data. ValueError When xmin > xmax or if dx <= 0. Also if dx > xmax - xmin. """ return self.profile.setCalculationRange(xmin, xmax, dx) def setCalculationPoints(self, x): """Set the calculation points. Arguments x -- A non-empty numpy array containing the calculation points. If xobs exists, the bounds of x will be limited to its bounds. This will create y and dy on the specified grid if xobs, yobs and dyobs exist. """ return self.profile.setCalculationPoints(x) def loadtxt(self, *args, **kw): """Use numpy.loadtxt to load data. Arguments are passed to numpy.loadtxt. unpack = True is enforced. The first two arrays returned by numpy.loadtxt are assumed to be x and y. If there is a third array, it is assumed to by dy. Any other arrays are ignored. These are passed to setObservedProfile. Raises ValueError if the call to numpy.loadtxt returns fewer than 2 arrays. Returns the x, y and dy arrays loaded from the file """ return self.profile.loadtxt(*args, **kw) # FitContribution def setEquation(self, eqstr, ns={}): """Set the profile equation for the FitContribution. This sets the equation that will be used when generating the residual. The equation will be usable within setResidualEquation as "eq", and it takes no arguments. eqstr -- A string representation of the equation. Variables will be extracted from this equation and be given an initial value of 0. ns -- A dictionary of Parameters, indexed by name, that are used in the eqstr, but not registered (default {}). Raises ValueError if ns uses a name that is already used for a variable. """ self.contribution.setEquation(eqstr, ns={}) # Extract variables for par in self.contribution: # Skip Profile Parameters if par.name in ("x", "y", "dy"): continue if par.value is None: par.value = 0 if par.name not in self._parameters: self.addVar(par) return def __call__(self): """Evaluate the contribution equation.""" return self.contribution.evaluate() # FitResults methods def printResults(self, header="", footer=""): """Format and print the results. header -- A header to add to the output (default "") footer -- A footer to add to the output (default "") """ self.results.printResults(header, footer, True) return def saveResults(self, filename, header="", footer=""): """Format and save the results. filename - Name of the save file. header -- A header to add to the output (default "") footer -- A footer to add to the output (default "") """ self.results.saveResults(filename, header, footer, True)