diff --git a/Tests/algorithms_test.ipynb b/Tests/algorithms_test.ipynb
index 05b2635..238190e 100644
--- a/Tests/algorithms_test.ipynb
+++ b/Tests/algorithms_test.ipynb
@@ -1,21 +1,29 @@
 {
  "cells": [
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-06T14:51:27.226527Z",
+     "start_time": "2026-02-06T14:51:25.617943Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2"
    ],
-   "id": "89aa93e9dbedd113"
+   "id": "89aa93e9dbedd113",
+   "outputs": [],
+   "execution_count": 1
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-06T14:51:28.975727Z",
+     "start_time": "2026-02-06T14:51:27.809213Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "%reload_ext autoreload\n",
     "%autoreload 2\n",
@@ -46,13 +54,18 @@
     "from GHA_triaxial.numeric_examples_panou import get_tables as get_tables_panou\n",
     "from GHA_triaxial.numeric_examples_karney import get_random_examples as get_examples_karney"
    ],
-   "id": "2005e5a8854eea1e"
+   "id": "2005e5a8854eea1e",
+   "outputs": [],
+   "execution_count": 2
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-06T14:51:30.011589Z",
+     "start_time": "2026-02-06T14:51:29.626051Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "@contextmanager\n",
     "def suppress_print():\n",
@@ -60,13 +73,18 @@
     "        with redirect_stdout(fnull), redirect_stderr(fnull):\n",
     "            yield"
    ],
-   "id": "90f107a11ff0de7e"
+   "id": "90f107a11ff0de7e",
+   "outputs": [],
+   "execution_count": 3
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-06T14:51:31.030091Z",
+     "start_time": "2026-02-06T14:51:30.833905Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "# dsPart = [60, 125, 600, 1250, 6000, 60000] entspricht bei der Erde ca. 100km, 50km, 10km, 5km, 1km, 100m\n",
     "\n",
@@ -80,13 +98,18 @@
     "dsPart_gha2_ES = [60, 600, 1250]\n",
     "dsPart_gha2_approx = [600, 1250, 6000]"
    ],
-   "id": "fc6c39b8d358e54b"
+   "id": "fc6c39b8d358e54b",
+   "outputs": [],
+   "execution_count": 4
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-06T15:21:03.374105Z",
+     "start_time": "2026-02-06T15:21:03.129308Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "# test = \"Karney\"\n",
     "# test = \"Panou\"\n",
@@ -115,15 +138,12 @@
     "        examples.append([beta0, lamb0, alpha0_ell, s])\n",
     "    pass"
    ],
-   "id": "6770dbd57d475127"
+   "id": "6770dbd57d475127",
+   "outputs": [],
+   "execution_count": 25
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-06T14:13:16.909538Z",
-     "start_time": "2026-02-06T14:13:16.436261Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "if test != \"Random\":\n",
@@ -172,9 +192,10 @@
     "                    example_results[f\"GHA1_ana_{maxM}_{parts}\"] = (nan, nan, nan, nan)\n",
     "\n",
     "        for dsPart in dsPart_gha1_ES:\n",
+    "            ds = ell.ax/dsPart\n",
     "            start = time.perf_counter()\n",
     "            try:\n",
-    "                P1_ES, alpha1_ES = gha1_ES(ell, beta0, lamb0, alpha0_ell, s, maxSegLen=dsPart)\n",
+    "                P1_ES, alpha1_ES = gha1_ES(ell, beta0, lamb0, alpha0_ell, s, maxSegLen=ds)\n",
     "                end = time.perf_counter()\n",
     "                beta1_ES, lamb1_ES = ell.cart2ell(P1_ES)\n",
     "                d_beta1 = abs(beta1_ES - beta1)\n",
@@ -256,17 +277,10 @@
    ],
    "id": "fc45e0f618a0e4d8",
    "outputs": [],
-   "execution_count": 53
+   "execution_count": null
   },
   {
-   "metadata": {
-    "jupyter": {
-     "is_executing": true
-    },
-    "ExecuteTime": {
-     "start_time": "2026-02-06T14:48:29.343679Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "if test == \"Random\":\n",
@@ -277,8 +291,8 @@
     "\n",
     "        beta0, lamb0, alpha0_ell, s = example\n",
     "        P0 = ell.ell2cart(beta0, lamb0)\n",
+    "        _, _, alpha0_para = alpha_ell2para(ell, beta0, lamb0, alpha0_ell)\n",
     "\n",
-    "        # _, _, alpha0_para = alpha_ell2para(ell, beta0, lamb0, alpha0_ell)\n",
     "        # P1_ana, alpha1_ana_para = gha1_ana(ell, P0, alpha0_para, s, maxM=60, maxPartCircum=4)\n",
     "        # beta1_ana, lamb1_ana = ell.cart2ell(P1_ana)\n",
     "        # _, _, alpha1_ana = alpha_para2ell(ell, beta1_ana, lamb1_ana, alpha1_ana_para)\n",
@@ -320,9 +334,10 @@
     "                example_results[f\"GHA1_approx_{dsPart}\"] = (nan, nan, nan, nan)\n",
     "\n",
     "        for dsPart in dsPart_gha1_ES:\n",
+    "            ds = ell.ax/dsPart\n",
     "            start = time.perf_counter()\n",
     "            try:\n",
-    "                P1_ES, alpha1_ES = gha1_ES(ell, beta0, lamb0, alpha0_ell, s, maxSegLen=dsPart)\n",
+    "                P1_ES, alpha1_ES = gha1_ES(ell, beta0, lamb0, alpha0_ell, s, maxSegLen=ds)\n",
     "                end = time.perf_counter()\n",
     "                beta1_ES, lamb1_ES = ell.cart2ell(P1_ES)\n",
     "                d_beta1 = abs(beta1_ES - beta1)\n",
@@ -386,16 +401,7 @@
     "\n"
    ],
    "id": "6e20a077d522d5",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----- Beispiel 1/30\n",
-      "Fehler in der Umrechnung cart2ell\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "execution_count": null
   },
   {
@@ -410,15 +416,20 @@
    "id": "74fbd4d33c288839"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-06T15:21:05.121195Z",
+     "start_time": "2026-02-06T15:21:04.930958Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
-    "with open(f\"gha_results{test}_all.pkl\", \"rb\") as f:\n",
+    "with open(f\"gha_results{test}.pkl\", \"rb\") as f:\n",
     "    results = pickle.load(f)"
    ],
-   "id": "4c20a0579c0f7038"
+   "id": "4c20a0579c0f7038",
+   "outputs": [],
+   "execution_count": 26
   },
   {
    "metadata": {
@@ -457,12 +468,7 @@
    "execution_count": 61
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-06T14:30:49.469820Z",
-     "start_time": "2026-02-06T14:30:49.058791Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "def format_max(values, is_angle=False):\n",
@@ -548,1838 +554,32 @@
     "    fig.show()"
    ],
    "id": "eeb5a204cc4bbf7d",
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.plotly.v1+json": {
-       "data": [
-        {
-         "cells": {
-          "align": "center",
-          "values": [
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-         "header": {
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-           "sequential": [
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-             0.1111111111111111,
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-             "#26828e"
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-             0.5555555555555556,
-             "#1f9e89"
-            ],
-            [
-             0.6666666666666666,
-             "#35b779"
-            ],
-            [
-             0.7777777777777778,
-             "#6ece58"
-            ],
-            [
-             0.8888888888888888,
-             "#b5de2b"
-            ],
-            [
-             1.0,
-             "#fde725"
-            ]
-           ],
-           "sequentialminus": [
-            [
-             0.0,
-             "#440154"
-            ],
-            [
-             0.1111111111111111,
-             "#482878"
-            ],
-            [
-             0.2222222222222222,
-             "#3e4989"
-            ],
-            [
-             0.3333333333333333,
-             "#31688e"
-            ],
-            [
-             0.4444444444444444,
-             "#26828e"
-            ],
-            [
-             0.5555555555555556,
-             "#1f9e89"
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-             0.6666666666666666,
-             "#35b779"
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-             0.7777777777777778,
-             "#6ece58"
-            ],
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-             0.8888888888888888,
-             "#b5de2b"
-            ],
-            [
-             1.0,
-             "#fde725"
-            ]
-           ]
-          },
-          "colorway": [
-           "#1F77B4",
-           "#FF7F0E",
-           "#2CA02C",
-           "#D62728",
-           "#9467BD",
-           "#8C564B",
-           "#E377C2",
-           "#7F7F7F",
-           "#BCBD22",
-           "#17BECF"
-          ],
-          "font": {
-           "color": "rgb(36,36,36)"
-          },
-          "geo": {
-           "bgcolor": "white",
-           "lakecolor": "white",
-           "landcolor": "white",
-           "showlakes": true,
-           "showland": true,
-           "subunitcolor": "white"
-          },
-          "hoverlabel": {
-           "align": "left"
-          },
-          "hovermode": "closest",
-          "mapbox": {
-           "style": "light"
-          },
-          "paper_bgcolor": "white",
-          "plot_bgcolor": "white",
-          "polar": {
-           "angularaxis": {
-            "gridcolor": "rgb(232,232,232)",
-            "linecolor": "rgb(36,36,36)",
-            "showgrid": false,
-            "showline": true,
-            "ticks": "outside"
-           },
-           "bgcolor": "white",
-           "radialaxis": {
-            "gridcolor": "rgb(232,232,232)",
-            "linecolor": "rgb(36,36,36)",
-            "showgrid": false,
-            "showline": true,
-            "ticks": "outside"
-           }
-          },
-          "scene": {
-           "xaxis": {
-            "backgroundcolor": "white",
-            "gridcolor": "rgb(232,232,232)",
-            "gridwidth": 2,
-            "linecolor": "rgb(36,36,36)",
-            "showbackground": true,
-            "showgrid": false,
-            "showline": true,
-            "ticks": "outside",
-            "zeroline": false,
-            "zerolinecolor": "rgb(36,36,36)"
-           },
-           "yaxis": {
-            "backgroundcolor": "white",
-            "gridcolor": "rgb(232,232,232)",
-            "gridwidth": 2,
-            "linecolor": "rgb(36,36,36)",
-            "showbackground": true,
-            "showgrid": false,
-            "showline": true,
-            "ticks": "outside",
-            "zeroline": false,
-            "zerolinecolor": "rgb(36,36,36)"
-           },
-           "zaxis": {
-            "backgroundcolor": "white",
-            "gridcolor": "rgb(232,232,232)",
-            "gridwidth": 2,
-            "linecolor": "rgb(36,36,36)",
-            "showbackground": true,
-            "showgrid": false,
-            "showline": true,
-            "ticks": "outside",
-            "zeroline": false,
-            "zerolinecolor": "rgb(36,36,36)"
-           }
-          },
-          "shapedefaults": {
-           "fillcolor": "black",
-           "line": {
-            "width": 0
-           },
-           "opacity": 0.3
-          },
-          "ternary": {
-           "aaxis": {
-            "gridcolor": "rgb(232,232,232)",
-            "linecolor": "rgb(36,36,36)",
-            "showgrid": false,
-            "showline": true,
-            "ticks": "outside"
-           },
-           "baxis": {
-            "gridcolor": "rgb(232,232,232)",
-            "linecolor": "rgb(36,36,36)",
-            "showgrid": false,
-            "showline": true,
-            "ticks": "outside"
-           },
-           "bgcolor": "white",
-           "caxis": {
-            "gridcolor": "rgb(232,232,232)",
-            "linecolor": "rgb(36,36,36)",
-            "showgrid": false,
-            "showline": true,
-            "ticks": "outside"
-           }
-          },
-          "title": {
-           "x": 0.05
-          },
-          "xaxis": {
-           "automargin": true,
-           "gridcolor": "rgb(232,232,232)",
-           "linecolor": "rgb(36,36,36)",
-           "showgrid": false,
-           "showline": true,
-           "ticks": "outside",
-           "title": {
-            "standoff": 15
-           },
-           "zeroline": false,
-           "zerolinecolor": "rgb(36,36,36)"
-          },
-          "yaxis": {
-           "automargin": true,
-           "gridcolor": "rgb(232,232,232)",
-           "linecolor": "rgb(36,36,36)",
-           "showgrid": false,
-           "showline": true,
-           "ticks": "outside",
-           "title": {
-            "standoff": 15
-           },
-           "zeroline": false,
-           "zerolinecolor": "rgb(36,36,36)"
-          }
-         }
-        },
-        "margin": {
-         "l": 20,
-         "r": 20,
-         "t": 60,
-         "b": 20
-        },
-        "title": {
-         "text": "Random - GHA2"
-        },
-        "width": 800,
-        "height": 280
-       },
-       "config": {
-        "plotlyServerURL": "https://plot.ly"
-       }
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
-    }
-   ],
-   "execution_count": 64
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-06T14:37:24.303781Z",
-     "start_time": "2026-02-06T14:37:24.022319Z"
+     "end_time": "2026-02-06T15:21:25.399278Z",
+     "start_time": "2026-02-06T15:21:25.145849Z"
     }
    },
    "cell_type": "code",
    "source": [
     "from collections import defaultdict\n",
     "\n",
-    "def to_latex_sci(x, sig=3):\n",
+    "def to_latex_sci(x, decimals=3, exp_digits=2):\n",
     "    \"\"\"\n",
-    "    Formatiert eine Zahl als LaTeX, z.B. 8.02e-08 -> $8.02\\\\cdot10^{-8}$.\n",
-    "    Für normale Zahlen -> $0.216$.\n",
-    "    Für nan -> nan (ohne $...$).\n",
+    "    Immer wissenschaftliche Schreibweise in LaTeX mit:\n",
+    "    - fester Mantisse (z.B. 1.234)\n",
+    "    - Exponent immer mit Vorzeichen (+/-)\n",
+    "    - Exponent immer feste Stellenzahl (z.B. +03, -01)\n",
+    "\n",
+    "    Beispiel:\n",
+    "        0.216 -> $2.160\\\\cdot10^{-01}$\n",
+    "        12.3  -> $1.230\\\\cdot10^{+01}$\n",
     "    \"\"\"\n",
+    "\n",
     "    if x is None:\n",
     "        return \"nan\"\n",
     "    try:\n",
@@ -2390,17 +590,21 @@
     "    if math.isnan(xf):\n",
     "        return \"nan\"\n",
     "\n",
-    "    # nahe 0 -> 0\n",
     "    if xf == 0.0:\n",
-    "        return \"$0$\"\n",
+    "        exp_fmt = f\"+{0:0{exp_digits}d}\"\n",
+    "        mant = f\"{0:.{decimals}f}\"\n",
+    "        return f\"${mant}\\\\cdot10^{{{exp_fmt}}}$\"\n",
     "\n",
-    "    s = f\"{xf:.{sig}g}\"  # z.B. '8.02e-08' oder '0.216'\n",
-    "    if \"e\" in s or \"E\" in s:\n",
-    "        mant, exp = s.lower().split(\"e\")\n",
-    "        exp = int(exp)\n",
-    "        return f\"${mant}\\\\cdot10^{{{exp}}}$\"\n",
-    "    else:\n",
-    "        return f\"${s}$\"\n",
+    "    s = f\"{xf:.{decimals}e}\"  # z.B. 2.160e-01\n",
+    "    mant, exp = s.split(\"e\")\n",
+    "    exp = int(exp)\n",
+    "\n",
+    "    # Exponent formatieren: Vorzeichen + feste Länge\n",
+    "    sign = \"+\" if exp >= 0 else \"-\"\n",
+    "    exp_abs = abs(exp)\n",
+    "    exp_fmt = f\"{sign}{exp_abs:0{exp_digits}d}\"\n",
+    "\n",
+    "    return f\"${mant}\\\\cdot10^{{{exp_fmt}}}$\"\n",
     "\n",
     "\n",
     "def nan_count_for_algorithm(results, example_keys, algorithm):\n",
@@ -2522,7 +726,6 @@
     "    if caption:\n",
     "        lines.append(rf\"\\caption{{{caption}}}\")\n",
     "    lines.append(rf\"\\label{{{label}}}\")\n",
-    "    lines.append(\"\")\n",
     "    lines.append(rf\"\\begin{{tabular}}{{{colspec}}}\")\n",
     "    lines.append(r\"\\hline\")\n",
     "    lines.append(\" & \".join(header_cells) + r\" \\\\\")\n",
@@ -2570,7 +773,7 @@
     "            # Metriken\n",
     "            for mi in range(len(metric_headers)):\n",
     "                m = max_abs_metric(results, example_keys, alg, mi, is_angle=angle_mask[mi], wu=wu)\n",
-    "                row_cells.append(to_latex_sci(m, sig=3))\n",
+    "                row_cells.append(to_latex_sci(m))\n",
     "\n",
     "            # Zeile + Linienlogik wie in deinem Beispiel\n",
     "            latex_row = \" & \".join(row_cells) + r\" \\\\\"\n",
@@ -2581,22 +784,24 @@
     "            lines.append(latex_row)\n",
     "\n",
     "    lines.append(r\"\\end{tabular}\")\n",
-    "    lines.append(\"\")\n",
     "    lines.append(r\"\\end{table}\")\n",
+    "    lines.append(r\"\\noindent\")\n",
     "\n",
     "    return \"\\n\".join(lines)\n",
     "\n",
     "\n",
     "# --- Beispielaufruf ---\n",
     "example_keys = list(key for i, key in enumerate(results.keys()))\n",
-    "example_keys = example_keys[:14]\n",
+    "if test == \"Panou\":\n",
+    "    example_keys = example_keys[:14]\n",
+    "gha = \"GHA2\"\n",
     "# example_keys = list(key for i, key in enumerate(results.keys()) if table_indices[i] == 3)  # gefiltert auf Panou Gruppe etc.\n",
     "latex = build_latex_table_from_results(\n",
     "    results,\n",
-    "    gha_prefix=\"GHA2\",\n",
+    "    gha_prefix=gha,\n",
     "    example_keys=example_keys,\n",
-    "    caption=r\"Ergebnisse der Lösungsmethoden der 2. GHA auf einem erdähnlichen Ellipsoid (Gruppe 3)\",\n",
-    "    label=\"tab:results_gha1_g1\",\n",
+    "    caption=f\"Ergebnisse der Lösungsmethoden der {gha[3]}. GHA ({test})\",\n",
+    "    label=f\"tab:results_{test}_{gha}\",\n",
     "    include_nan_col=True,  # True, wenn du NaN-Spalte willst\n",
     "    wu=wu  # wichtig, falls Winkelwerte rad sind\n",
     ")\n",
@@ -2610,26 +815,22 @@
      "text": [
       "\\begin{table}[H]\n",
       "\\centering\n",
-      "\\caption{Ergebnisse der Lösungsmethoden der 2. \\gls{GHA} auf einem erdähnlichen Ellipsoid (Gruppe 3)}\n",
-      "\\label{tab:results_gha1_g1}\n",
-      "\n",
+      "\\caption{Ergebnisse der Lösungsmethoden der 2. GHA (Random)}\n",
+      "\\label{tab:results_Random_GHA2}\n",
       "\\begin{tabular}{|c|c|c|c|c|c|c|}\n",
       "\\hline\n",
       "Methode & Parameterwerte & NaN & $\\max(|\\Delta \\alpha_0|)$ [$''$] & $\\max(|\\Delta \\alpha_1|)$ [$''$] & $\\max(|\\Delta s|)$ [m] & time [s] \\\\\n",
       "\\Xhline{1.5pt}\n",
-      "\\multirow{3}{*}{approximiert} & 600 & 0 & $0.0286$ & $0.0279$ & $0.000769$ & $0.135$ \\\\\n",
-      " & 1250 & 0 & $0.0286$ & $0.0279$ & $0.000192$ & $0.23$ \\\\\n",
-      " & 6000 & 0 & $0.0286$ & $0.0279$ & $2.81\\cdot10^{-6}$ & $1.69$ \\\\\\hline\n",
-      "\\multirow{3}{*}{ES} & 60 & 0 & $0.0286$ & $0.0278$ & $0.0492$ & $4.51$ \\\\\n",
-      " & 600 & 0 & $0.0285$ & $0.0278$ & $0.000769$ & $31$ \\\\\n",
-      " & 1250 & 0 & $0.0284$ & $0.0279$ & $0.000192$ & $59.7$ \\\\\\hline\n",
+      "\\multirow{3}{*}{approximiert} & 600 & 0 & $2.075\\cdot10^{+04}$ & $2.063\\cdot10^{+04}$ & $1.730\\cdot10^{-01}$ & $2.396\\cdot10^{-01}$ \\\\\n",
+      " & 1250 & 0 & $2.075\\cdot10^{+04}$ & $2.063\\cdot10^{+04}$ & $1.744\\cdot10^{-01}$ & $4.663\\cdot10^{-01}$ \\\\\n",
+      " & 6000 & 0 & $2.075\\cdot10^{+04}$ & $2.063\\cdot10^{+04}$ & $1.749\\cdot10^{-01}$ & $3.877\\cdot10^{+00}$ \\\\\\hline\n",
       "\\end{tabular}\n",
-      "\n",
-      "\\end{table}\n"
+      "\\end{table}\n",
+      "\\noindent\n"
      ]
     }
    ],
-   "execution_count": 65
+   "execution_count": 28
   },
   {
    "metadata": {},
diff --git a/Tests/alpha_conversion_test.ipynb b/Tests/alpha_conversion_test.ipynb
index 62c501a..7b9e496 100644
--- a/Tests/alpha_conversion_test.ipynb
+++ b/Tests/alpha_conversion_test.ipynb
@@ -1,30 +1,21 @@
 {
  "cells": [
   {
+   "metadata": {},
    "cell_type": "code",
-   "id": "initial_id",
-   "metadata": {
-    "collapsed": true,
-    "ExecuteTime": {
-     "end_time": "2026-01-20T15:30:31.978159Z",
-     "start_time": "2026-01-20T15:30:31.835157Z"
-    }
-   },
+   "outputs": [],
+   "execution_count": null,
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2"
    ],
-   "outputs": [],
-   "execution_count": 1
+   "id": "a78faf7f4883772f"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-20T15:30:33.910807Z",
-     "start_time": "2026-01-20T15:30:32.803089Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
    "source": [
     "%reload_ext autoreload\n",
     "%autoreload 2\n",
@@ -33,9 +24,56 @@
     "from GHA_triaxial.utils import alpha_para2ell, alpha_ell2para\n",
     "import numpy as np"
    ],
-   "id": "9ad815aea55574e3",
+   "id": "46aa84a937fea491"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
    "outputs": [],
-   "execution_count": 2
+   "execution_count": null,
+   "source": [
+    "ell = EllipsoidTriaxial.init_name(\"KarneyTest2024\")\n",
+    "diffs = []\n",
+    "for beta_deg in range(-180, 181, 45):\n",
+    "    for lamb_deg in range(-90, 91, 45):\n",
+    "        for alpha_deg in range(0, 360, 45):\n",
+    "            beta = wu.deg2rad(beta_deg)\n",
+    "            lamb = wu.deg2rad(lamb_deg)\n",
+    "            u, v = ell.ell2para(beta, lamb)\n",
+    "            alpha = wu.deg2rad(alpha_deg)\n",
+    "\n",
+    "            alpha_para_1, *_ = alpha_ell2para(ell, beta, lamb, alpha)\n",
+    "            alpha_ell_1, *_ = alpha_para2ell(ell, u, v, alpha_para_1)\n",
+    "            diff_1 = wu.deg2rad(abs(alpha_ell_1 - alpha))/3600\n",
+    "\n",
+    "            alpha_ell_2, *_ = alpha_para2ell(ell, u, v, alpha)\n",
+    "            alpha_para_2, *_ = alpha_ell2para(ell, beta, lamb, alpha_ell_2)\n",
+    "            diff_2 = wu.deg2rad(abs(alpha_para_2 - alpha))/3600\n",
+    "\n",
+    "            diffs.append((beta_deg, lamb_deg, alpha_deg, diff_1, diff_2))\n",
+    "diffs = np.array(diffs)"
+   ],
+   "id": "82fc6cbbe7d5abcb"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "i_max_ell = np.argmax(diffs[:, 3])\n",
+    "max_ell = diffs[i_max_ell, 3]\n",
+    "point_max_ell = diffs[i_max_ell, :3]\n",
+    "\n",
+    "i_max_para = np.argmax(diffs[:, 4])\n",
+    "max_para = diffs[i_max_para, 4]\n",
+    "point_max_para = diffs[i_max_para, :4]\n",
+    "\n",
+    "print(f'Für elliptisches Alpha = {point_max_ell[2]}° und beta = {point_max_ell[0]}°, lamb = {point_max_ell[1]}°: diff = {max_ell}\"')\n",
+    "print(f'Für parametrisches Alpha = {point_max_para[2]}° und beta = {point_max_para[0]}°, lamb = {point_max_para[1]}°: diff = {max_ell}\"')\n",
+    "pass"
+   ],
+   "id": "97b5b8c9ca5377ab"
   },
   {
    "metadata": {
@@ -70,40 +108,6 @@
    "id": "98b9b220118deb3f",
    "outputs": [],
    "execution_count": 6
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-20T15:33:50.497990Z",
-     "start_time": "2026-01-20T15:33:50.261115Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "i_max_ell = np.argmax(diffs[:, 3])\n",
-    "max_ell = diffs[i_max_ell, 3]\n",
-    "point_max_ell = diffs[i_max_ell, :3]\n",
-    "\n",
-    "i_max_para = np.argmax(diffs[:, 4])\n",
-    "max_para = diffs[i_max_para, 4]\n",
-    "point_max_para = diffs[i_max_para, :4]\n",
-    "\n",
-    "print(f'Für elliptisches Alpha = {point_max_ell[2]}° und beta = {point_max_ell[0]}°, lamb = {point_max_ell[1]}°: diff = {max_ell}\"')\n",
-    "print(f'Für parametrisches Alpha = {point_max_para[2]}° und beta = {point_max_para[0]}°, lamb = {point_max_para[1]}°: diff = {max_ell}\"')\n",
-    "pass"
-   ],
-   "id": "3c74b65b0e85e3c2",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Für elliptisches Alpha = 315.0° und beta = -90.0°, lamb = -90.0°: diff = 3.426945967752335e-05\"\n",
-      "Für parametrisches Alpha = 315.0° und beta = -90.0°, lamb = -90.0°: diff = 3.426945967752335e-05\"\n"
-     ]
-    }
-   ],
-   "execution_count": 7
   }
  ],
  "metadata": {