Merge branch 'develop' into documentation

pyerrors/fits.py

@@ -646,10 +646,10 @@ def fit_lin(x, y, **kwargs):
         return y
 
     if all(isinstance(n, Obs) for n in x):
-        out = odr_fit(x, y, f, **kwargs)
+        out = total_least_squares(x, y, f, **kwargs)
         return out.fit_parameters
     elif all(isinstance(n, float) or isinstance(n, int) for n in x) or isinstance(x, np.ndarray):
-        out = standard_fit(x, y, f, **kwargs)
+        out = least_squares(x, y, f, **kwargs)
         return out.fit_parameters
     else:
         raise Exception('Unsupported types for x')
@@ -785,104 +785,3 @@ def ks_test(obs=None):
     plt.draw()
 
     print(scipy.stats.kstest(Qs, 'uniform'))
-
-
-def fit_general(x, y, func, silent=False, **kwargs):
-    """Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
-
-    Plausibility of the results should be checked. To control the numerical differentiation
-    the kwargs of numdifftools.step_generators.MaxStepGenerator can be used.
-
-    func has to be of the form
-
-    def func(a, x):
-        y = a[0] + a[1] * x + a[2] * np.sinh(x)
-        return y
-
-    y has to be a list of Obs, the dvalues of the Obs are used as yerror for the fit.
-    x can either be a list of floats in which case no xerror is assumed, or
-    a list of Obs, where the dvalues of the Obs are used as xerror for the fit.
-
-    Keyword arguments
-    -----------------
-    silent -- If true all output to the console is omitted (default False).
-    initial_guess -- can provide an initial guess for the input parameters. Relevant for non-linear fits
-                     with many parameters.
-    """
-
-    warnings.warn("New fit functions with exact error propagation are now available as alternative.", DeprecationWarning)
-
-    if not callable(func):
-        raise TypeError('func has to be a function.')
-
-    for i in range(10):
-        try:
-            func(np.arange(i), 0)
-        except:
-            pass
-        else:
-            break
-    n_parms = i
-    if not silent:
-        print('Fit with', n_parms, 'parameters')
-
-    global print_output, beta0
-    print_output = 1
-    if 'initial_guess' in kwargs:
-        beta0 = kwargs.get('initial_guess')
-        if len(beta0) != n_parms:
-            raise Exception('Initial guess does not have the correct length.')
-    else:
-        beta0 = np.arange(n_parms)
-
-    if len(x) != len(y):
-        raise Exception('x and y have to have the same length')
-
-    if all(isinstance(n, Obs) for n in x):
-        obs = x + y
-        x_constants = None
-        xerr = [o.dvalue for o in x]
-        yerr = [o.dvalue for o in y]
-    elif all(isinstance(n, float) or isinstance(n, int) for n in x) or isinstance(x, np.ndarray):
-        obs = y
-        x_constants = x
-        xerr = None
-        yerr = [o.dvalue for o in y]
-    else:
-        raise Exception('Unsupported types for x')
-
-    def do_the_fit(obs, **kwargs):
-
-        global print_output, beta0
-
-        func = kwargs.get('function')
-        yerr = kwargs.get('yerr')
-        length = len(yerr)
-
-        xerr = kwargs.get('xerr')
-
-        if length == len(obs):
-            assert 'x_constants' in kwargs
-            data = RealData(kwargs.get('x_constants'), obs, sy=yerr)
-            fit_type = 2
-        elif length == len(obs) // 2:
-            data = RealData(obs[:length], obs[length:], sx=xerr, sy=yerr)
-            fit_type = 0
-        else:
-            raise Exception('x and y do not fit together.')
-
-        model = Model(func)
-
-        odr = ODR(data, model, beta0, partol=np.finfo(np.float64).eps)
-        odr.set_job(fit_type=fit_type, deriv=1)
-        output = odr.run()
-        if print_output and not silent:
-            print(*output.stopreason)
-            print('chisquare/d.o.f.:', output.res_var)
-            print_output = 0
-        beta0 = output.beta
-        return output.beta[kwargs.get('n')]
-    res = []
-    for n in range(n_parms):
-        res.append(derived_observable(do_the_fit, obs, function=func, xerr=xerr, yerr=yerr, x_constants=x_constants, num_grad=True, n=n, **kwargs))
-    return res

tests/covobs_test.py

@@ -70,3 +70,23 @@ def test_covobs_name_collision():
 def test_covobs_replica_separator():
     with pytest.raises(Exception):
         covobs = pe.cov_Obs(0.5, 0.002, 'test|r2')
+
+
+def test_covobs_init():
+    covobs = pe.cov_Obs(0.5, 0.002, 'test')
+    covobs = pe.cov_Obs([1, 2], [0.1, 0.2], 'test')
+    covobs = pe.cov_Obs([1, 2], np.array([0.1, 0.2]), 'test')
+    covobs = pe.cov_Obs([1, 2], [[0.1, 0.2], [0.1, 0.2]], 'test')
+    covobs = pe.cov_Obs([1, 2], np.array([[0.1, 0.2], [0.1, 0.2]]), 'test')
+
+
+
+def test_covobs_exceptions():
+    with pytest.raises(Exception):
+        covobs = pe.cov_Obs(0.1, [[0.1, 0.2], [0.1, 0.2]], 'test')
+    with pytest.raises(Exception):
+        covobs = pe.cov_Obs(0.1, np.array([[0.1, 0.2], [0.1, 0.2]]), 'test')
+    with pytest.raises(Exception):
+        covobs = pe.cov_Obs([0.5, 0.1], np.array([[2, 1, 3], [1, 2, 3]]), 'test')
+    with pytest.raises(Exception):
+        covobs = pe.cov_Obs([0.5, 0.1], np.random.random((2, 2, 2)), 'test')

tests/fits_test.py

@@ -232,8 +232,7 @@ def test_odr_derivatives():
     out = pe.total_least_squares(x, y, func)
     fit1 = out.fit_parameters
 
-    with pytest.warns(DeprecationWarning):
+    tfit = fit_general(x, y, func, base_step=0.1, step_ratio=1.1, num_steps=20)
-        tfit = pe.fits.fit_general(x, y, func, base_step=0.1, step_ratio=1.1, num_steps=20)
     assert np.abs(np.max(np.array(list(fit1[1].deltas.values()))
                   - np.array(list(tfit[1].deltas.values())))) < 10e-8
 
@@ -274,3 +273,101 @@ def test_r_value_persistence():
     assert np.isclose(fitp[1].value, fitp[1].r_values['a'])
     assert np.isclose(fitp[1].value, fitp[1].r_values['b'])
 
+
+def fit_general(x, y, func, silent=False, **kwargs):
+    """Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
+
+    Plausibility of the results should be checked. To control the numerical differentiation
+    the kwargs of numdifftools.step_generators.MaxStepGenerator can be used.
+
+    func has to be of the form
+
+    def func(a, x):
+        y = a[0] + a[1] * x + a[2] * np.sinh(x)
+        return y
+
+    y has to be a list of Obs, the dvalues of the Obs are used as yerror for the fit.
+    x can either be a list of floats in which case no xerror is assumed, or
+    a list of Obs, where the dvalues of the Obs are used as xerror for the fit.
+
+    Keyword arguments
+    -----------------
+    silent -- If true all output to the console is omitted (default False).
+    initial_guess -- can provide an initial guess for the input parameters. Relevant for non-linear fits
+                     with many parameters.
+    """
+
+    if not callable(func):
+        raise TypeError('func has to be a function.')
+
+    for i in range(10):
+        try:
+            func(np.arange(i), 0)
+        except:
+            pass
+        else:
+            break
+    n_parms = i
+    if not silent:
+        print('Fit with', n_parms, 'parameters')
+
+    global print_output, beta0
+    print_output = 1
+    if 'initial_guess' in kwargs:
+        beta0 = kwargs.get('initial_guess')
+        if len(beta0) != n_parms:
+            raise Exception('Initial guess does not have the correct length.')
+    else:
+        beta0 = np.arange(n_parms)
+
+    if len(x) != len(y):
+        raise Exception('x and y have to have the same length')
+
+    if all(isinstance(n, pe.Obs) for n in x):
+        obs = x + y
+        x_constants = None
+        xerr = [o.dvalue for o in x]
+        yerr = [o.dvalue for o in y]
+    elif all(isinstance(n, float) or isinstance(n, int) for n in x) or isinstance(x, np.ndarray):
+        obs = y
+        x_constants = x
+        xerr = None
+        yerr = [o.dvalue for o in y]
+    else:
+        raise Exception('Unsupported types for x')
+
+    def do_the_fit(obs, **kwargs):
+
+        global print_output, beta0
+
+        func = kwargs.get('function')
+        yerr = kwargs.get('yerr')
+        length = len(yerr)
+
+        xerr = kwargs.get('xerr')
+
+        if length == len(obs):
+            assert 'x_constants' in kwargs
+            data = RealData(kwargs.get('x_constants'), obs, sy=yerr)
+            fit_type = 2
+        elif length == len(obs) // 2:
+            data = RealData(obs[:length], obs[length:], sx=xerr, sy=yerr)
+            fit_type = 0
+        else:
+            raise Exception('x and y do not fit together.')
+
+        model = Model(func)
+
+        odr = ODR(data, model, beta0, partol=np.finfo(np.float64).eps)
+        odr.set_job(fit_type=fit_type, deriv=1)
+        output = odr.run()
+        if print_output and not silent:
+            print(*output.stopreason)
+            print('chisquare/d.o.f.:', output.res_var)
+            print_output = 0
+        beta0 = output.beta
+        return output.beta[kwargs.get('n')]
+    res = []
+    for n in range(n_parms):
+        res.append(pe.derived_observable(do_the_fit, obs, function=func, xerr=xerr, yerr=yerr, x_constants=x_constants, num_grad=True, n=n, **kwargs))
+    return res