diff --git a/pyzebra/fitvol3.py b/pyzebra/fitvol3.py new file mode 100644 index 0000000..e3cf6e4 --- /dev/null +++ b/pyzebra/fitvol3.py @@ -0,0 +1,167 @@ +import numpy as np +from lmfit import Model, Parameters +from scipy.integrate import simps +import matplotlib.pyplot as plt +import uncertainties as u +from lmfit.models import GaussianModel +from lmfit.models import VoigtModel +from lmfit.models import PseudoVoigtModel + + +def bin_data(array, binsize): + if isinstance(binsize, int) and 0 < binsize < len(array): + return [ + np.mean(array[binsize * i : binsize * i + binsize]) + for i in range(int(np.ceil(len(array) / binsize))) + ] + else: + print("Binsize need to be positive integer smaller than lenght of array") + return array + + +def create_uncertanities(y, y_err): + # create array with uncertanities for error propagation + combined = np.array([]) + for i in range(len(y)): + part = u.ufloat(y[i], y_err[i]) + combined = np.append(combined, part) + return combined + + +def find_nearest(array, value): + # find nearest value and return index + array = np.asarray(array) + idx = (np.abs(array - value)).argmin() + return idx + + +# predefined peak positions +# peaks = [6.2, 8.1, 9.9, 11.5] +peaks = [23.5, 24.5] +# peaks = [24] +def fitccl(scan, variable="om", peak_type="gauss", binning=None): + + x = list(scan[variable]) + y = list(scan["Counts"]) + peak_centre = np.mean(x) + if binning is None or binning == 0 or binning == 1: + x = list(scan["om"]) + y = list(scan["Counts"]) + y_err = list(np.sqrt(y)) if scan.get("sigma", None) is None else list(scan["sigma"]) + print(scan["peak_indexes"]) + if not scan["peak_indexes"]: + peak_centre = np.mean(x) + else: + centre = x[int(scan["peak_indexes"])] + else: + x = list(scan["om"]) + if not scan["peak_indexes"]: + peak_centre = np.mean(x) + else: + peak_centre = x[int(scan["peak_indexes"])] + x = bin_data(x, binning) + y = list(scan["Counts"]) + y_err = list(np.sqrt(y)) if scan.get("sigma", None) is None else list(scan["sigma"]) + combined = bin_data(create_uncertanities(y, y_err), binning) + y = [combined[i].n for i in range(len(combined))] + y_err = [combined[i].s for i in range(len(combined))] + + def background(x, slope, intercept): + """background""" + return slope * (x - peak_centre) + intercept + + def gaussian(x, center, g_sigma, amplitude): + """1-d gaussian: gaussian(x, amp, cen, wid)""" + return (amplitude / (np.sqrt(2.0 * np.pi) * g_sigma)) * np.exp( + -((x - center) ** 2) / (2 * g_sigma ** 2) + ) + + def lorentzian(x, center, l_sigma, amplitude): + """1d lorentzian""" + return (amplitude / (1 + ((1 * x - center) / l_sigma) ** 2)) / (np.pi * l_sigma) + + def pseudoVoigt1(x, center, g_sigma, amplitude, l_sigma, fraction): + """PseudoVoight peak with different widths of lorenzian and gaussian""" + return (1 - fraction) * gaussian(x, center, g_sigma, amplitude) + fraction * ( + lorentzian(x, center, l_sigma, amplitude) + ) + + mod = Model(background) + params = Parameters() + params.add_many( + ("slope", 0, True, None, None, None, None), ("intercept", 0, False, None, None, None, None) + ) + for i in range(len(peaks)): + if peak_type == "gauss": + mod = mod + GaussianModel(prefix="p%d_" % (i + 1)) + params.add(str("p%d_" % (i + 1) + "amplitude"), 20, True, 0, None, None) + params.add(str("p%d_" % (i + 1) + "center"), peaks[i], True, None, None, None) + params.add(str("p%d_" % (i + 1) + "sigma"), 0.2, True, 0, 5, None) + elif peak_type == "voigt": + mod = mod + VoigtModel(prefix="p%d_" % (i + 1)) + params.add(str("p%d_" % (i + 1) + "amplitude"), 20, True, 0, None, None) + params.add(str("p%d_" % (i + 1) + "center"), peaks[i], True, None, None, None) + params.add(str("p%d_" % (i + 1) + "sigma"), 0.2, True, 0, 3, None) + params.add(str("p%d_" % (i + 1) + "gamma"), 0.2, True, 0, 5, None) + elif peak_type == "pseudovoigt": + mod = mod + PseudoVoigtModel(prefix="p%d_" % (i + 1)) + params.add(str("p%d_" % (i + 1) + "amplitude"), 20, True, 0, None, None) + params.add(str("p%d_" % (i + 1) + "center"), peaks[i], True, None, None, None) + params.add(str("p%d_" % (i + 1) + "sigma"), 0.2, True, 0, 5, None) + params.add(str("p%d_" % (i + 1) + "fraction"), 0.5, True, -5, 5, None) + elif peak_type == "pseudovoigt1": + mod = mod + Model(pseudoVoigt1, prefix="p%d_" % (i + 1)) + params.add(str("p%d_" % (i + 1) + "amplitude"), 20, True, 0, None, None) + params.add(str("p%d_" % (i + 1) + "center"), peaks[i], True, None, None, None) + params.add(str("p%d_" % (i + 1) + "g_sigma"), 0.2, True, 0, 5, None) + params.add(str("p%d_" % (i + 1) + "l_sigma"), 0.2, True, 0, 5, None) + params.add(str("p%d_" % (i + 1) + "fraction"), 0.5, True, 0, 1, None) + # add parameters + + result = mod.fit( + y, params, weights=[np.abs(1 / y_err[i]) for i in range(len(y_err))], x=x, calc_covar=True + ) + + comps = result.eval_components() + + reportstring = list() + for keys in result.params: + if result.params[keys].value is not None: + str2 = np.around(result.params[keys].value, 3) + else: + str2 = 0 + if result.params[keys].stderr is not None: + str3 = np.around(result.params[keys].stderr, 3) + else: + str3 = 0 + reportstring.append("%s = %2.3f +/- %2.3f" % (keys, str2, str3)) + + reportstring = "\n".join(reportstring) + + plt.figure(figsize=(20, 10)) + plt.plot(x, result.best_fit, "k-", label="Best fit") + + plt.plot(x, y, "b-", label="Original data") + plt.plot(x, comps["background"], "g--", label="Line component") + for i in range(len(peaks)): + plt.plot( + x, + comps[str("p%d_" % (i + 1))], + "r--", + ) + plt.fill_between(x, comps[str("p%d_" % (i + 1))], alpha=0.4, label=str("p%d_" % (i + 1))) + plt.legend() + plt.text( + np.min(x), + np.max(y), + reportstring, + fontsize=9, + verticalalignment="top", + ) + plt.title(str(peak_type)) + + plt.xlabel("Omega [deg]") + plt.ylabel("Counts [a.u.]") + plt.show() + + print(result.fit_report())