diff --git a/docs/pyerrors/fits.html b/docs/pyerrors/fits.html index d5f38b1c..750d4bc3 100644 --- a/docs/pyerrors/fits.html +++ b/docs/pyerrors/fits.html @@ -92,9 +92,6 @@
  • ks_test
    • -
  • - fit_general -
    • @@ -762,10 +759,10 @@ 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') @@ -901,107 +898,6 @@ 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 @@ -1590,10 +1486,10 @@ List of N Obs that are used to constrain the last N fit parameters of func. 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') @@ -1831,143 +1727,6 @@ check if the residuals of the fit are gaussian distributed.

    - -
    -
    #   - - - def - fit_general(x, y, func, silent=False, **kwargs): -
    - -
    - View Source - 
    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
-
    - -
    - -

    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.

    -
    - -