Pass all plot data as a dict

Author: jl-wynen

docs/user-guide/sns-instruments/vanadium_processing.ipynb

@@ -25,6 +25,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import plopp as pp\n",
     "import scipp as sc\n",
     "import scippneutron as scn\n",
     "import scippneutron.peaks\n",
@@ -275,11 +276,26 @@
     "        ), strict=True)\n",
     "\n",
     "    # The actual data\n",
-    "    fig = data.plot(c=\"C0\")\n",
+    "    plot_data = {'data': data, 'removed': removed}\n",
+    "    linestyles = {}\n",
+    "    markers = {}\n",
+    "    colors = {'data': 'C0','removed': 'C2'}\n",
+    "\n",
+    "    # Overlay with fit models evaluated at optimized parameters\n",
+    "    for i, result in enumerate(fit_results):\n",
+    "        if all(not sc.isnan(param).value for param in result.popt.values()):\n",
+    "            best_fit = data[data.dim, result.window[0] : result.window[1]].copy(deep=False)\n",
+    "            best_fit.coords[best_fit.dim] = sc.midpoints(best_fit.coords[best_fit.dim])\n",
+    "            best_fit.data = result.eval_model(best_fit.coords[best_fit.dim])\n",
+    "\n",
+    "            key = f'result_{i}'\n",
+    "            plot_data[key] = best_fit\n",
+    "            linestyles[key] = '-'\n",
+    "            markers[key] = \"none\"\n",
+    "            colors[key] = \"C1\"\n",
+    "\n",
+    "    fig = pp.plot(plot_data, ls=linestyles, marker=markers, c=colors, legend=False)\n",
     "    ax = fig.ax\n",
-    "    xlim = ax.get_xlim()\n",
-    "    ylim = ax.get_ylim()\n",
-    "    removed.plot(ax=ax, c='C2')\n",
     "\n",
     "    # Initial estimates\n",
     "    for estimate, result in zip(peak_estimates, fit_results, strict=True):\n",
@@ -308,17 +324,6 @@
     "        if not result.success:\n",
     "            ax.text(left.value, hi, result.message.split(\":\", 1)[0])\n",
     "\n",
-    "    # Overlay with fit models evaluated at optimized parameters\n",
-    "    for result in fit_results:\n",
-    "        if all(not sc.isnan(param).value for param in result.popt.values()):\n",
-    "            best_fit = data[data.dim, result.window[0] : result.window[1]].copy(deep=False)\n",
-    "            best_fit.coords[best_fit.dim] = sc.midpoints(best_fit.coords[best_fit.dim])\n",
-    "            best_fit.data = result.eval_model(best_fit.coords[best_fit.dim])\n",
-    "            best_fit.plot(ax=ax, c=\"C1\", ls=\"-\", marker=\"none\")\n",
-    "\n",
-    "    ax.set_xlim(xlim)\n",
-    "    # allocate extra space at the top for annotations\n",
-    "    ax.set_ylim((ylim[0], ylim[1] * 1.05))\n",
     "    return fig"
    ]
   },