Merge branch 'xsuite:main' into dpx_contribution_compensation

contributors_and_credits.txt

@@ -11,5 +11,6 @@ CERN contributors:
  - K. Paraschou
  - M. Schwinzerl
  - F. Soubelet
+ - E. Waagaard
 
 The Particle In Cell implementation is largely based on the PyPIC package (https://github.com/pycomplete/pypic)

examples/longitudinal_kick_in_spacecharge/004_qgaussian_derivative.py

@@ -0,0 +1,63 @@
+# copyright ################################# #
+# This file is part of the Xfields Package.   #
+# Copyright (c) CERN, 2021.                   #
+# ########################################### #
+
+import numpy as np
+from xfields import LongitudinalProfileQGaussian
+import xobjects as xo
+import matplotlib.pyplot as plt
+
+plt.rcParams.update(
+    {
+        "font.family": "serif",
+        "font.size": 20,
+        "axes.titlesize": 20,
+        "axes.labelsize": 18,
+        "xtick.labelsize": 18,
+        "ytick.labelsize": 18,
+        "legend.fontsize": 15,
+        "figure.titlesize": 20,
+    }
+)
+
+# Longitudinal example parameters
+z0 = 0.1
+sigma_z = 0.2
+npart = 1e11
+
+test_context = xo.ContextCpu()
+#test_context = xo.ContextCupy()
+
+for qq in [0.0, 0.5, 0.95, 1.05, 1.3]:
+    lprofile = LongitudinalProfileQGaussian(
+            _context=test_context,
+            number_of_particles=npart,
+            sigma_z=sigma_z,
+            z0=z0,
+            q_parameter=qq)
+
+    # Select range relevant for derivative continuity
+    if qq < 1:
+        z = np.linspace(-0.347, 0.5, 1_000_000)
+    else:
+        z = np.linspace(-3., 3., 1_000_000)
+    z_dev = test_context.nparray_to_context_array(z)
+    lden_dev = lprofile.line_density(z_dev)
+    lden = test_context.nparray_from_context_array(lden_dev)
+
+    lderivative_dev = lprofile.line_derivative(z_dev)
+    lderivative = test_context.nparray_from_context_array(lderivative_dev)
+
+    numerical_derivative = np.gradient(lden, z)
+
+    print('q = {:.2f} max relative difference anal. vs num. : {:.3e}'.format(qq, max((lderivative - numerical_derivative) / numerical_derivative)))
+
+    fig, ax = plt.subplots(1, 1, figsize=(8, 6))
+    ax.plot(z, lderivative, label='Analytical derivative')
+    ax.plot(z, numerical_derivative, ls='--', label='Numerical derivative')
+    ax.text(0.83, 0.92, 'q={:.2f}'.format(qq), color='k', fontsize=15.5, transform=ax.transAxes)
+    ax.set_ylabel('Function value')
+    ax.set_xlabel('Zeta [m]')
+    ax.legend(loc='lower left')
+    plt.show()