diff --git a/scripts/deep_analysis_plotter.ipynb b/scripts/deep_analysis_plotter.ipynb
index d9165a9..7010537 100644
--- a/scripts/deep_analysis_plotter.ipynb
+++ b/scripts/deep_analysis_plotter.ipynb
@@ -9,12 +9,13 @@
    "source": [
     "%%cmd\n",
     "python -m pip install flatten_json\n",
-    "python -m pip install pandas"
+    "python -m pip install pandas\n",
+    "python -m pip install matplotlib"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "bbdc923a-44fd-4250-990b-80771446bbf0",
    "metadata": {},
    "outputs": [],
@@ -22,7 +23,9 @@
     "import pandas as pd\n",
     "import json\n",
     "from collections import defaultdict\n",
-    "from flatten_json import flatten"
+    "from flatten_json import flatten\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pathlib"
    ]
   },
   {
@@ -65,16 +68,11 @@
     "                        results_per_frame[frame_index][scope_name] += (\n",
     "                            scope_timing[\"end\"] - scope_timing[\"start\"]\n",
     "                        ) / num_loops / 1_000_000 # in ms\n",
-    "            for metric_name, metric in frame_results[\"metrics\"].items():\n",
-    "                # TODO: Flatten this in rust to fan_speed_rpm\n",
-    "                if metric_name == \"fan_speed\":\n",
-    "                    value = metric[\"Percent\"] if \"Percent\" in metric else metric[\"Rpm\"]\n",
-    "                    results_per_frame[frame_index][\"fan_speed_rpm\"] += (\n",
-    "                        value / num_loops\n",
-    "                    )\n",
-    "                # Filter out unavailable data and the timestamp\n",
-    "                elif metric is not None and metric_name != \"timestamp\":\n",
-    "                    results_per_frame[frame_index][metric_name] += metric / num_loops\n",
+    "            if frame_results[\"metrics\"] is not None:\n",
+    "                for metric_name, metric in frame_results[\"metrics\"].items():\n",
+    "                    # TODO: Flatten this in rust to fan_speed_rpm\n",
+    "                    if metric is not None and metric_name != \"timestamp\":\n",
+    "                        results_per_frame[frame_index][metric_name] += metric / num_loops\n",
     "    # TODO: Aggregate CPU timings\n",
     "    return pd.DataFrame([flatten(x) for x in results_per_frame])\n",
     "\n",
@@ -109,29 +107,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "relevant_metrics = [\n",
-    "    'reflection-hits-shading',\n",
-    "    'water-compositing',\n",
-    "    'blur',\n",
-    "    'diffuse-spatial-filter',\n",
-    "    'spd',\n",
-    "    'sun-direct-lighting',\n",
-    "    'reflection-ray-tracing-inline',\n",
-    "    'trace-diffuse-nee-rays',\n",
-    "    'render pass',\n",
-    "    'shadow-ray-tracing-pipeline',\n",
-    "    'compositing',\n",
-    "    'build-gbuffers_0',\n",
-    "    'scale-raster',\n",
-    "    'Batch refit bottom level',\n",
-    "    'clock_speed_in_mhz',\n",
-    "    'board_power_usage_in_w',\n",
-    "    'vram_usage_in_mb',\n",
-    "    'edge_temperature_in_c'\n",
-    "]\n",
-    "# We want all relevant metrics with the sequence time, to properly plot on the x axis\n",
-    "relevant_metrics_with_time = relevant_metrics + [\"sequence_time_ns\"]\n",
-    "metrics = full_dataset[relevant_metrics_with_time]\n",
+    "metrics = full_dataset\n",
     "\n",
     "# Reshape into sequence time + metric type per row, input file per column\n",
     "metrics = metrics.reset_index().set_index(['sequence_time_ns', 'level_0']).drop('level_1', axis=1)\n",
@@ -150,37 +126,34 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "pathlib.Path('output_analysis').mkdir(parents=True, exist_ok=True) \n",
     "for graph_name in metrics['level_1'].unique():\n",
     "    # Grab the metric we want to plot\n",
     "    selected_metric = metrics[metrics['level_1'] == graph_name]\n",
     "    selected_metric = selected_metric.drop('level_1', axis=1)\n",
     "\n",
-    "    # Filter outliers out of view\n",
-    "    max_mean = selected_metric[files].mean().mean()\n",
-    "    max_mean = 0 if pd.isna(max_mean) else max_mean\n",
-    "    max_std = selected_metric[files].std(axis=1).max()\n",
-    "    max_std = selected_metric[files].max() / 3.0 if pd.isna(max_std) else max_std\n",
     "\n",
     "    # Plot results \n",
     "    selected_metric.infer_objects(copy=False).interpolate(method='linear').plot(\n",
     "        x='sequence_time_s', \n",
     "        ylabel='shader execution time in ms',\n",
     "        xlabel='benchmark timeline in seconds', \n",
-    "        ylim= (max(0, max_mean - max_std * 3), max_mean + max_std * 3),\n",
+    "        #ylim= (max(0, max_mean - max_std * 3), max_mean + max_std * 3),\n",
     "        figsize=(20,10), \n",
     "        colormap='Dark2', \n",
     "        grid=True, \n",
     "        legend=True,\n",
     "        title=graph_name\n",
-    "    )"
+    "    )\n",
+    "    plt.savefig('output_analysis/{}.png'.format(graph_name))"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:base] *",
    "language": "python",
-   "name": "python3"
+   "name": "conda-base-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -192,7 +165,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,
diff --git a/scripts/per_frame_plotter.ipynb b/scripts/per_frame_plotter.ipynb
index 62bc696..65249a5 100644
--- a/scripts/per_frame_plotter.ipynb
+++ b/scripts/per_frame_plotter.ipynb
@@ -18,7 +18,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pathlib\n",
+    "pd.set_option('display.float_format', lambda x: '%.3f' % x)"
    ]
   },
   {
@@ -51,8 +54,24 @@
     "    # Remove empty results\n",
     "    file_scores = file_scores.loc[:, (file_scores != 0).any(axis=0)]\n",
     "    scores[file] = file_scores\n",
+    "scores = pd.concat(scores)\n",
+    "scores['Frame time (ms)'] = (scores['Sequence Time (ns)'].shift(-1) - scores['Sequence Time (ns)']) / 1_000_000\n",
+    "# The final frame always has a negative frame time, probably because it loops\n",
+    "# The first 3 frames are not representative, and are huge outliers\n",
+    "scores['Frame time (ms)'] = scores['Frame time (ms)'].mask(scores['Frame time (ms)'] < 0)\n",
+    "scores['Frame time (ms)'] = scores['Frame time (ms)'].mask(scores['Frame'] < 3)\n",
     "# Reshape into row per input file + sequence time, column per metric\n",
-    "scores = pd.concat(scores).reset_index().set_index(['level_0', 'Sequence Time (ns)']).drop(['level_1', 'Loop Index', 'Frame'], axis=1)\n",
+    "scores = scores.reset_index().set_index(['level_0', 'Sequence Time (ns)']).drop(['level_1', 'Loop Index', 'Frame'], axis=1)\n",
+    "scores['Ray Tracing (ms)'] = scores['Ray Tracing (ns)'] / 1_000_000\n",
+    "scores = scores.drop('Ray Tracing (ns)', axis=1)\n",
+    "scores['Acceleration Structure Build (ms)'] = scores['Acceleration Structure Build (ns)'] / 1_000_000\n",
+    "scores = scores.drop('Acceleration Structure Build (ns)', axis=1)\n",
+    "scores['Rasterization (ms)'] = scores['Rasterization (ns)'] / 1_000_000\n",
+    "scores = scores.drop('Rasterization (ns)', axis=1)\n",
+    "scores['Compute (ms)'] = scores['Compute (ns)'] / 1_000_000\n",
+    "scores = scores.drop('Compute (ns)', axis=1)\n",
+    "scores['Driver (ms)'] = scores['Driver (ns)'] / 1_000_000\n",
+    "scores = scores.drop('Driver (ns)', axis=1)\n",
     "scores"
    ]
   },
@@ -78,6 +97,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "pathlib.Path('output_per_frame').mkdir(parents=True, exist_ok=True) \n",
     "for graph_name in new_scores['level_1'].unique():\n",
     "    graph_data = new_scores[new_scores['level_1'] == graph_name]\n",
     "    graph_data = graph_data.drop('level_1', axis=1)\n",
@@ -88,21 +108,22 @@
     "        ylabel='value',\n",
     "        xlabel='benchmark timeline in seconds', \n",
     "        kind=\"line\", \n",
-    "        logy=True,\n",
+    "        logy=False,\n",
     "        figsize=(20,5), \n",
     "        colormap='Dark2', \n",
     "        grid=True, \n",
     "        rot=0,\n",
     "        title=graph_name\n",
-    "    )"
+    "    )\n",
+    "    plt.savefig('output_per_frame/{}.png'.format(graph_name))"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:base] *",
    "language": "python",
-   "name": "python3"
+   "name": "conda-base-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -114,7 +135,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,