add smoke test for deJong Fig 5 (#962)

Author: slayoo

tests/smoke_tests/dejong_and_mackay_2022/test_fig_5.py

@@ -0,0 +1,66 @@
+# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
+import matplotlib
+import numpy as np
+from matplotlib import pyplot
+from PySDM_examples.deJong_Mackay_2022 import Settings0D, run_box_breakup
+
+from PySDM.dynamics.collisions.breakup_fragmentations import Straub2010Nf
+from PySDM.dynamics.collisions.coalescence_efficiencies import Straub2010Ec
+from PySDM.physics import si
+
+
+def test_fig_5(plot=False):
+    # arrange
+    settings = Settings0D(seed=44)
+    steps = [0, 30, 60, 180, 540]
+    settings._steps = steps  # pylint: disable=protected-access
+    settings.n_sd = 2**11
+    settings.warn_overflows = False
+    settings.radius_bins_edges = np.logspace(
+        np.log10(10 * si.um), np.log10(2e3 * si.um), num=32, endpoint=True
+    )
+    settings.coal_eff = Straub2010Ec()
+    settings.fragmentation = Straub2010Nf(vmin=settings.X0 * 1e-3, nfmax=10)
+
+    # act
+    (data_x, data_y, _) = run_box_breakup(settings)
+
+    # plot
+    cmap = matplotlib.cm.get_cmap("viridis")
+    for (j, step) in enumerate(steps):
+        if j == 0:
+            kwargs = {"color": "k", "linestyle": "--", "label": "initial"}
+        else:
+            kwargs = {
+                "color": cmap(j / len(steps)),
+                "linestyle": "-",
+                "label": f"t = {step}s",
+            }
+        pyplot.step(data_x, data_y[j] * settings.rho, **kwargs)
+    pyplot.xscale("log")
+    pyplot.xlabel("particle radius (um)")
+    pyplot.ylabel("dm/dlnr (kg/m$^3$ / unit(ln R)")
+    pyplot.legend()
+    if plot:
+        pyplot.show()
+
+    # assert
+    peaks_expected = {
+        0: (33, 0.018),
+        30: (92, 0.011),
+        60: (305, 0.012),
+        180: (717, 0.015),
+        540: (717, 0.015),
+    }
+
+    for (j, step) in enumerate(steps):
+        print(step)
+        peak = np.argmax(data_y[j])
+        np.testing.assert_approx_equal(
+            actual=data_x[peak], desired=peaks_expected[step][0], significant=2
+        )
+        np.testing.assert_approx_equal(
+            actual=data_y[j][peak] * settings.rho,
+            desired=peaks_expected[step][1],
+            significant=2,
+        )