removed plot_corrs

pyerrors/pyerrors.py

@@ -1106,124 +1106,6 @@ def load_object(path):
         return pickle.load(file)
 
 
-def plot_corrs(observables, **kwargs):
-    """Plot lists of Obs.
-
-    Parameters
-    ----------
-    observables -- list of lists of Obs, where the nth entry is considered to be the
-                   correlation function
-    at x0=n e.g. [[f_A_0,f_A_1],[f_P_0,f_P_1]] or [f_A,f_P], where f_A and f_P are lists of Obs.
-
-    Keyword arguments
-    -----------------
-    xrange -- list of two values, determining the range of the x-axis e.g. [4, 8]
-    yrange -- list of two values, determining the range of the y-axis e.g. [0.2, 1.1]
-    prange -- list of two values, visualizing the width of the plateau e.g. [10, 15]
-    reference -- float valued variable which is shown as horizontal line for reference
-    plateau -- Obs which is shown as horizontal line with errorbar for reference
-    shift -- shift x by given value
-    label -- list of labels, has to have the same length as observables
-    exp -- plot exponential from fitting routine
-    """
-
-    if 'shift' in kwargs:
-        shift = kwargs.get('shift')
-    else:
-        shift = 0
-
-    if 'label' in kwargs:
-        label = kwargs.get('label')
-        if len(label) != len(observables):
-            raise Exception('label has to be a list with exactly one entry per entry of observables.')
-    else:
-        label = []
-        for j in range(len(observables)):
-            label.append(str(j + 1))
-
-
-    f = plt.figure()
-    for j in range(len(observables)):
-        T = len(observables[j])
-
-        x = np.arange(T) + shift
-        y = np.zeros(T)
-        y_err = np.zeros(T)
-
-        for i in range(T):
-            y[i] = observables[j][i].value
-            y_err[i] = observables[j][i].dvalue
-
-        plt.errorbar(x, y, yerr=y_err, ls='none', fmt='o', capsize=3,
-                        markersize=5, lw=1, label=label[j])
-
-    if kwargs.get('logscale'):
-        plt.yscale('log')
-
-    if 'xrange' in kwargs:
-        xrange = kwargs.get('xrange')
-        plt.xlim(xrange[0], xrange[1])
-        visible_y = y[int(xrange[0] + 0.5):int(xrange[1] + 0.5)]
-        visible_y_err = y_err[int(xrange[0] + 0.5):int(xrange[1] + 0.5)]
-        y_start = np.min(visible_y - visible_y_err)
-        y_stop = np.max(visible_y + visible_y_err)
-        span = y_stop - y_start
-        if np.isfinite(y_start) and np.isfinite(y_stop):
-            plt.ylim(y_start - 0.1 * span, y_stop + 0.1 * span)
-
-    if 'yrange' in kwargs:
-        yrange = kwargs.get('yrange')
-        plt.ylim(yrange[0], yrange[1])
-
-    if 'reference' in kwargs:
-        y_value = kwargs.get('reference')
-        plt.axhline(y=y_value, linewidth=2, color='k', alpha=0.25)
-
-    if 'prange' in kwargs:
-        prange = kwargs.get('prange')
-        plt.axvline(x=prange[0] - 0.5, ls='--', c='k', lw=1, alpha=0.5, marker=',')
-        plt.axvline(x=prange[1] + 0.5, ls='--', c='k', lw=1, alpha=0.5, marker=',')
-
-    if 'plateau' in kwargs:
-        plateau = kwargs.get('plateau')
-        if isinstance(plateau, Obs):
-            plt.axhline(y=plateau.value, linewidth=2, color='k', alpha=0.6, label='Plateau', marker=',', ls='--')
-            plt.axhspan(plateau.value - plateau.dvalue, plateau.value + plateau.dvalue, alpha=0.25, color='k')
-        elif isinstance(plateau, list):
-            for i in range(len(plateau)):
-                plt.axhline(y=plateau[i].value, linewidth=2, color='C' + str(i), alpha=0.6, label='Plateau' + str(i + 1), marker=',', ls='--')
-                plt.axhspan(plateau[i].value - plateau[i].dvalue, plateau[i].value + plateau[i].dvalue,
-                            color='C' + str(i), alpha=0.25)
-        else:
-            raise Exception('Improper input for plateau.')
-
-    if kwargs.get('exp'):
-        fit_result = kwargs.get('exp')
-        y_fit = fit_result[1].value * np.exp(-fit_result[0].value * x)
-        plt.plot(x, y_fit, color='k')
-        if not (fit_result[0].e_names == {} and fit_result[1].e_names == {}):
-            y_fit_err = np.sqrt((y_fit * fit_result[0].dvalue) ** 2 + 2 * covariance(fit_result[0], fit_result[1])* y_fit *
-                                np.exp(-fit_result[0].value * x) + (np.exp(-fit_result[0].value * x) * fit_result[1].dvalue) ** 2)
-            plt.fill_between(x, y_fit + y_fit_err, y_fit - y_fit_err, color='k', alpha=0.1)
-
-    plt.xlabel('$x_0/a$')
-
-    if 'ylabel' in kwargs:
-        plt.ylabel(kwargs.get('ylabel'))
-
-    if 'save' in kwargs:
-        lgd = plt.legend(loc=0)
-    else:
-        lgd = plt.legend(bbox_to_anchor=(1.04, 1), loc='upper left')
-    plt.show()
-
-    if 'save' in kwargs:
-        save = kwargs.get('save')
-        if not isinstance(save, str):
-            raise Exception('save has to be a string.')
-        f.savefig(save + '.pdf', bbox_extra_artists=(lgd,), bbox_inches='tight')
-
-
 def merge_obs(list_of_obs):
     """Combine all observables in list_of_obs into one new observable