Update variable names in 03_Learn.ipynb

notebooks/assyst/03_Learn.ipynb

@@ -160,14 +160,14 @@
        "\u001b[0;31mDocstring:\u001b[0m\n",
        "Fit a potential to the given structures.\n",
        "\n",
-       "If min_dist, max_dist or level are iterables the function is broadcasted\n",
+       "If rmin, rmax or level are iterables the function is broadcasted\n",
        "over them.\n",
        "\n",
        "Args:\n",
        "    fit_pr (Project): project that contains the fitting jobs\n",
        "    train (TrainingContainer): container that keeps all the structures\n",
-       "    min_dist (float, Iterable[float]): lower cut off of the potential\n",
-       "    max_dist (float, Iterable[float]): upper cut off of the potential\n",
+       "    rmin (float, Iterable[float]): lower cut off of the potential\n",
+       "    rmax (float, Iterable[float]): upper cut off of the potential\n",
        "    level (int, Iterable[int]): level of the potential\n",
        "    energy_weight (float): relative weight of energy error in cost function\n",
        "    force_weight (float): relative weight of force error in cost function\n",
@@ -222,9 +222,9 @@
     "plearn.fit(\n",
     "    pr,\n",
     "    every,\n",
-    "    # min_dist, max_dist and level can be given as scalar values or as lists. If lists one potential is fit for each\n",
-    "    min_dist=2.2,\n",
-    "    max_dist=[4, 5, 6],\n",
+    "    # rmin, rmax and level can be given as scalar values or as lists. If lists one potential is fit for each\n",
+    "    rmin=2.2,\n",
+    "    rmax=[4, 5, 6],\n",
     "    level=[8, 12, 16],\n",
     "    # Whether to re-run a fit but using the results of the first fit as starting values; this sometimes produces a better potential, but not reliably\n",
     "    refit=False,\n",