MAINT: API: mu = MPV

examples/fit.py

@@ -4,10 +4,10 @@
 import pylandau
 
 x = np.arange(0, 100, 0.5)
-mu, eta, sigma, A = 10, 1, 3, 1
-y = pylandau.langau(x, mu, eta, sigma, A) + np.random.normal(0, 0.05, 200)
+mpv, eta, sigma, A = 10, 1, 3, 1
+y = pylandau.langau(x, mpv, eta, sigma, A) + np.random.normal(0, 0.05, 200)
 
-coeff, pcov = curve_fit(pylandau.langau, x, y, p0=(mu, eta, sigma, A))
+coeff, pcov = curve_fit(pylandau.langau, x, y, p0=(mpv, eta, sigma, A))
 plt.plot(x, y, "o")
 plt.plot(x, pylandau.langau(x, *coeff), "-")
 plt.show()

examples/landau_gauss.py

@@ -5,13 +5,13 @@
 import matplotlib.pyplot as plt
 from scipy.ndimage.filters import gaussian_filter1d
 
-mu, eta, sigma, A = 10, 1, 3, 1
+mpv, eta, sigma, A = 10, 1, 3, 1
 x = np.arange(0, 50, 0.01)
-y = pylandau.landau(x, mu=mu, eta=eta, A=A)
+y = pylandau.landau(x, mpv=mpv, eta=eta, A=A)
 y_gconv = gaussian_filter1d(y, sigma=sigma / 0.01)
-y_gconv_2 = pylandau.langau(x, mu, eta, sigma, A)
+y_gconv_2 = pylandau.langau(x, mpv, eta, sigma, A)
 plt.plot(x, y, label='Landau')
 plt.plot(x, y_gconv_2, label='Langau')
-plt.plot(x, y_gconv / np.amax(y_gconv), '--', label='Langau Scipy')
+plt.plot(x, y_gconv, '--', label='Langau Scipy')
 plt.legend(loc=0)
 plt.show()

examples/mpv_fwhm.py

@@ -33,12 +33,11 @@ class NoPeaksFound(Exception):
         return roots[0], roots[1]
 
 x = np.arange(0, 100, 0.01)
-for A, eta, mu in ((1, 1, 10), (1, 2, 30), (0.5, 5, 50)):
-    y = pylandau.landau(x, mu, eta, A)
-    plt.plot(x, y, label='A=%d, eta=%d, mu=%d' % (A, eta, mu))
+for A, eta, mpv in ((1, 1, 10), (1, 2, 30), (0.5, 5, 50)):
+    y = pylandau.landau(x, mpv, eta, A)
+    plt.plot(x, y, label='A=%d, mpv=%d, eta=%d' % (A, mpv, eta))
     x_fwhm_1, x_fwhm_2 = fwhm(x, y)
     plt.plot([x_fwhm_1, x_fwhm_2], [np.max(y) / 2., np.max(y) / 2.], label='FWHM: %1.1f' % np.abs(x_fwhm_1 - x_fwhm_2))
-    x_mpv = x[np.argmax(y)]
-    plt.plot([x_mpv, x_mpv], [0., np.max(y)], label='MPV: %1.1f' % x_mpv)
+    plt.plot([mpv, mpv], [0., np.max(y)], label='MPV: %1.1f' % mpv)
 plt.legend(loc=0)
 plt.show()

examples/plot.py

@@ -11,8 +11,7 @@
     plt.plot(x, pylandau.landau(x, mu, eta, A), '-', label='A=%1.1f, eta=%1.1f, mu=%1.1f' % (A, eta, mu))
 
     # Use the function that calculates the y value given a x value, (e.g. needed for minimizers)
-    y = np.array([pylandau.get_landau_pdf(x_value, mu, eta) for x_value in x])
-    y = y / np.max(y) * A  # Scale maximum to amplitude A
+    y = np.array([pylandau.get_landau(x_value, mu, eta, A) for x_value in x])
     plt.plot(x, y, '--', label='A=%1.1f, eta=%1.1f, mu=%1.1f' % (A, eta, mu))
 plt.legend(loc=0)
 plt.show()