diff --git a/tests/benchmarks/evaluate_logs.ipynb b/tests/benchmarks/evaluate_logs.ipynb
index 4ad561618..894959d6e 100644
--- a/tests/benchmarks/evaluate_logs.ipynb
+++ b/tests/benchmarks/evaluate_logs.ipynb
@@ -21,7 +21,7 @@
     "#      FOR NOTEBOOK USE     #\n",
     "#     SET DIRECTORY HERE    #\n",
     "#                           #\n",
-    "rootdir = \"varying_sample_size\"\n",
+    "rootdir = \"3d_constant_samples\"\n",
     "#                           #\n",
     "#############################\n",
     "\n",
@@ -77,7 +77,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -96,7 +98,7 @@
     "    output_dataset.append([])\n",
     "\n",
     "    # Acquire node type from the directory\n",
-    "    matchObj = re.match(r'(\\w*)_\\w\\w_', subdir, re.M|re.I)\n",
+    "    matchObj = re.match(r'(\\w*)_[A-Z]', subdir, re.M|re.I)\n",
     "    \n",
     "    # Fill value to array\n",
     "    if matchObj:\n",
@@ -345,24 +347,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "if settings['median_plot']['enabled'] or settings['3d_plot']['enabled']:\n",
-    "    medians = []\n",
-    "    upper_limit = []\n",
-    "    lower_limit = []\n",
+    "medians = []\n",
+    "upper_limit = []\n",
+    "lower_limit = []\n",
     "\n",
-    "    for i, subdir in enumerate(subdirs):\n",
-    "        medians.append([])\n",
-    "        upper_limit.append([])\n",
-    "        lower_limit.append([])\n",
+    "for i, subdir in enumerate(subdirs):\n",
+    "    medians.append([])\n",
+    "    upper_limit.append([])\n",
+    "    lower_limit.append([])\n",
     "\n",
-    "        for (j, csv_vec) in enumerate(output_dataset[i]):  \n",
-    "            medians[i].append(np.median(csv_vec.transpose()[2]) * 1e6)\n",
+    "    for (j, csv_vec) in enumerate(output_dataset[i]):  \n",
+    "        medians[i].append(np.median(csv_vec.transpose()[2]) * 1e6)\n",
     "\n",
-    "            if settings['median_plot']['enabled']:\n",
-    "                # np.sort(recv[i][j] - enq_send[i][j])[int(np.size(recv[i][j]]) / 2)] would be the approximately the median\n",
-    "                # Calculate upper 10% and lower 10%\n",
-    "                upper_limit[i].append(abs(medians[i][j] - 1e6 * np.sort(csv_vec.transpose()[2])[int(9 * np.size(csv_vec.transpose()[2]) / 10)]))\n",
-    "                lower_limit[i].append(abs(medians[i][j] - 1e6 * np.sort(csv_vec.transpose()[2])[int(1 * np.size(csv_vec.transpose()[2]) / 10)]))"
+    "        if settings['median_plot']['enabled']:\n",
+    "            # np.sort(recv[i][j] - enq_send[i][j])[int(np.size(recv[i][j]]) / 2)] would be the approximately the median\n",
+    "            # Calculate upper 10% and lower 10%\n",
+    "            upper_limit[i].append(abs(medians[i][j] - 1e6 * np.sort(csv_vec.transpose()[2])[int(9 * np.size(csv_vec.transpose()[2]) / 10)]))\n",
+    "            lower_limit[i].append(abs(medians[i][j] - 1e6 * np.sort(csv_vec.transpose()[2])[int(1 * np.size(csv_vec.transpose()[2]) / 10)]))"
    ]
   },
   {
@@ -637,14 +638,32 @@
     "                           marker='v', zorder = 2 + i, color=settings['median_plot']['colors'][i],\n",
     "                           label=settings['median_plot']['labels'][i])\n",
     "\n",
+    "        ###################################\n",
+    "        # PRINT MISSED STEPS ##############\n",
+    "        ###################################\n",
+    "        if settings['median_plot']['print_missed_steps']:\n",
+    "            for l, median in enumerate(medians[i]):\n",
+    "                \n",
+    "                p = FancyBboxPatch((x_data[l] + 0.07, median + 0.08), 0.345, 0.26, boxstyle=\"round, pad=0.005\",\n",
+    "                                    ec=\"#dbdbdb\", fc=\"white\", alpha=0.85)\n",
+    "                ax_median.add_patch(p)\n",
+    "                \n",
+    "                ax_median.text(x_data[l] + 0.1, median + 0.15, \"{: >4.2f}%\".format(perc_miss_recv_arr[i][l]))\n",
     "\n",
+    "        \n",
+    "        \n",
     "    ###################################\n",
     "    # SET AXIS OF MEDIAN PLOT #########\n",
     "    ###################################\n",
-    "    ax_median.set_ylim([settings['median_plot']['ticks']['y'][0], settings['median_plot']['ticks']['y'][-1]])\n",
     "    ax_median.set_xticks(np.arange(0, len(settings['median_plot']['ticks']['x']), 1))\n",
     "    ax_median.set_xticklabels(settings['median_plot']['ticks']['x'])\n",
-    "    ax_median.set_yticks(settings['median_plot']['ticks']['y'])\n",
+    "    \n",
+    "    if settings['median_plot']['log_scale']:\n",
+    "        ax_median.set_yscale('log')\n",
+    "    else:\n",
+    "        ax_median.set_ylim([settings['median_plot']['ticks']['y'][0], settings['median_plot']['ticks']['y'][-1]])\n",
+    "        ax_median.set_yticks(settings['median_plot']['ticks']['y'])\n",
+    "        \n",
     "    ax_median.set_xlabel(settings['median_plot']['axis_labels']['x'], fontsize=11, family='monospace', labelpad=6)\n",
     "    ax_median.set_ylabel(settings['median_plot']['axis_labels']['y'], fontsize=11, family='monospace', labelpad=6)\n",
     "    ax_median.set_axisbelow(True)\n",
@@ -705,7 +724,29 @@
     "        for k in range(0, len(settings['3d_plot']['ticks']['y'])):\n",
     "            for l in range(0, len(settings['3d_plot']['ticks']['x'])):\n",
     "                Z = np.append(Z, medians[i][k * len(settings['3d_plot']['ticks']['x']) + l])\n",
+    "                \n",
+    "        ###################################\n",
+    "        # PRINT MISSED STEPS ##############\n",
+    "        ###################################\n",
+    "        \n",
+    "        props = dict(boxstyle='round', facecolor='white', alpha=0.8)\n",
+    "        \n",
+    "        # if more than 5% of the samples were missed, print it to figure\n",
+    "        for k in range(0, len(input_dataset[i])):\n",
+    "            if perc_miss_send_arr[i][k] > 5:\n",
+    "                x = k % (len(settings['3d_plot']['ticks']['x']))\n",
+    "                y = np.floor(k / (len(settings['3d_plot']['ticks']['y'])))\n",
+    "                z = Z[k]\n",
+    "                \n",
+    "                x_delta = 0.65\n",
+    "                y_delta = 0.65\n",
+    "                z_delta = 0.5 * (Z[k] - Z[k - len(settings['3d_plot']['ticks']['y'])])\n",
+    "                ax_3d.text(x - x_delta, y - y_delta, z - z_delta, \"{: >4.2f}%\".format(perc_miss_send_arr[i][k]), \n",
+    "                           fontsize=11, family='monospace', color='red', bbox=props)\n",
     "\n",
+    "            \n",
+    "            \n",
+    "            \n",
     "        Z = np.split(Z, len(settings['3d_plot']['ticks']['y']))\n",
     "\n",
     "        # Plot the surface.\n",
@@ -754,22 +795,29 @@
     "\n",
     "        # The markers are too big, so lets create smaller markers\n",
     "        ax_custom = figLegend.add_subplot(111)\n",
-    "        ax_custom.plot(0,0, marker='v', color = 'green', label=\"$\\\\tilde{t}_{min}^{max}$: \"+str(np.min(Z))+ \" µs\", markersize=8, linestyle = 'None')\n",
-    "        ax_custom.plot(0,0, marker='^', color = 'red', label=\"$\\\\tilde{t}_{lat}^{max}$: \"+str(np.max(Z))+ \" µs\", markersize=8, linestyle = 'None')\n",
+    "        ax_custom.plot(0,0, marker='v', color = 'green', label=\"$\\\\max\\\\,\\\\tilde{t}_{lat}$: \"+str(np.min(Z))+ \" µs\", markersize=8, linestyle = 'None')\n",
+    "        ax_custom.plot(0,0, marker='^', color = 'red', label=\"$\\\\max\\\\,\\\\tilde{t}_{lat}$: \"+str(np.max(Z))+ \" µs\", markersize=8, linestyle = 'None')\n",
     "        ax_custom.set_visible(False)\n",
     "\n",
+    "        \n",
+    "        p = FancyBboxPatch((0.1, 0.1), 5, 1, boxstyle=\"round, pad=0.5\", ec=\"#dbdbdb\", fc=\"white\", alpha=0.85)\n",
+    "        \n",
+    "        import matplotlib.legend as mlegend\n",
+    "        handles = []\n",
+    "        handles.append(p)\n",
+    "        text= '% of samples missed by signal generator'\n",
+    "        leg2 = mlegend.Legend(ax_custom, handles, labels=[text], loc = 'upper left', ncol=1,\n",
+    "                              prop={'family':'monospace', 'size':'8'})\n",
     "\n",
-    "        pylab.figlegend(*ax_custom.get_legend_handles_labels(), loc = 'upper left',\n",
-    "                                        prop={'family':'monospace', 'size':'8'}, ncol=settings['3d_plot']['legend_columns'])\n",
+    "        handles, labels = ax_custom.get_legend_handles_labels()\n",
+    "\n",
+    "        leg1= pylab.figlegend(handles, labels, loc = 'upper left', labelspacing=1.2,\n",
+    "                        prop={'family':'monospace', 'size':'8'}, ncol=settings['3d_plot']['legend_columns'])\n",
+    "        leg1._legend_box._children.append(leg2._legend_box._children[1])\n",
+    "        leg1._legend_box.align=\"left\"\n",
+    "        \n",
     "        figLegend.savefig(\"{}/legend_median_3d_plot_{}.pdf\".format(rootdir, settings_array[i][0][2]), format='pdf')"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {