From 8dcf70959b64746124243f263f845d95489aca4d Mon Sep 17 00:00:00 2001
From: Ruben van de Ven <git@rubenvandeven.com>
Date: Wed, 12 Nov 2025 23:05:04 +0100
Subject: [PATCH] For dortmund

---
 README.md                                 |   2 +
 SETTINGS/2025-11-dortmund/trajectron.json |  10 +-
 supervisord.conf                          |   3 +-
 tests/stage_lines.ipynb                   | 123 +++++++++++++++-------
 trap/base.py                              |   5 +-
 trap/cv_renderer.py                       |   6 +-
 trap/lidar_tracker.py                     |  35 +++---
 trap/lines.py                             |   1 +
 trap/node.py                              |   5 +-
 trap/prediction_server.py                 |  62 ++++++++---
 trap/process_data.py                      |   5 +-
 trap/stage.py                             |  68 ++++++++----
 trap/stage_renderer.py                    |  73 ++++++++-----
 trap/track_writer.py                      |  32 ++++--
 trap/tracker.py                           |  75 ++++++++++++-
 15 files changed, 368 insertions(+), 137 deletions(-)

diff --git a/README.md b/README.md
index 56d84fd..38e6da7 100644
--- a/README.md
+++ b/README.md
@@ -30,6 +30,8 @@ These are roughly the steps to go from datagathering to training
         * See [[tests/trajectron_maps.ipynb]] for more info how to do so (e.g. the homography map/scale settings, which are also set in process_data)
 
 5. Train Trajectron model `uv run trajectron_train --eval_every 10 --vis_every 1 --train_data_dict NAME_train.pkl --eval_data_dict NAME_val.pkl --offline_scene_graph no --preprocess_workers 8 --log_dir EXPERIMENTS/models --log_tag _NAME --train_epochs 100 --conf EXPERIMENTS/config.json --batch_size 256 --data_dir EXPERIMENTS/trajectron-data `
+    * For faster training disalble edges:
+        ` uv run trajectron_train --eval_every 200           --train_data_dict dortmund-nostep-nosmooth-noise2-offsets1-f2.0-map-2025-11-11_train.pkl           --eval_data_dict dortmund-nostep-nosmooth-noise2-offsets1-f2.0-map-2025-11-11_val.pkl           --offline_scene_graph no --preprocess_workers 8           --log_dir /home/ruben/suspicion/trap/SETTINGS/2025-11-dortmund/models           --log_tag _dortmund-nostep-nosmooth-noise2-offsets1-f2.0-map-2025-11-11           --train_epochs 100           --conf /home/ruben/suspicion/trap/SETTINGS/2025-11-dortmund/trajectron.json            --data_dir SETTINGS/2025-11-dortmund/trajectron           --map_encoding --no_edge_encoding --dynamic_edges yes --no_edge_encoding --edge_influence_combine_method max --batch_size 512`
 6. The run!
     * `uv run supervisord`
     <!-- * On a video file (you can use a wildcard) `DISPLAY=:1 uv run trapserv --remote-log-addr 100.69.123.91 --eval_device cuda:0 --detector ultralytics  --homography ../DATASETS/NAME/homography.json --eval_data_dict EXPERIMENTS/trajectron-data/hof2s-m_test.pkl  --video-src ../DATASETS/NAME/*.mp4 --model_dir EXPERIMENTS/models/models_DATE_NAME/--smooth-predictions  --smooth-tracks --num-samples 3  --render-window --calibration ../DATASETS/NAME/calibration.json` (the DISPLAY environment variable is used here to running over SSH connection and display on local monitor)
diff --git a/SETTINGS/2025-11-dortmund/trajectron.json b/SETTINGS/2025-11-dortmund/trajectron.json
index af9e44d..f233285 100644
--- a/SETTINGS/2025-11-dortmund/trajectron.json
+++ b/SETTINGS/2025-11-dortmund/trajectron.json
@@ -21,7 +21,7 @@
             "hidden_channels": [
                 10,
                 20,
-                10,
+                5,
                 1
             ],
             "output_size": 32,
@@ -41,7 +41,7 @@
         }
     },
     "k": 1,
-    "k_eval": 25,
+    "k_eval": 1,
     "kl_min": 0.07,
     "kl_weight": 100.0,
     "kl_weight_start": 0,
@@ -52,8 +52,8 @@
         "dropout_keep_prob": 0.75
     },
     "MLP_dropout_keep_prob": 0.9,
-    "enc_rnn_dim_edge": 32,
-    "enc_rnn_dim_edge_influence": 32,
+    "enc_rnn_dim_edge": 1,
+    "enc_rnn_dim_edge_influence": 1,
     "enc_rnn_dim_history": 32,
     "enc_rnn_dim_future": 32,
     "dec_rnn_dim": 128,
@@ -105,7 +105,7 @@
     "log_histograms": false,
     "dynamic_edges": "yes",
     "edge_state_combine_method": "sum",
-    "edge_influence_combine_method": "attention",
+    "edge_influence_combine_method": "max",
     "edge_addition_filter": [
         0.25,
         0.5,
diff --git a/supervisord.conf b/supervisord.conf
index 7f32236..3b2bcaa 100644
--- a/supervisord.conf
+++ b/supervisord.conf
@@ -60,7 +60,7 @@ directory=%(here)s
 
 [program:predictor]
 # command=uv run trap_prediction --eval_device cuda:0  --model_dir EXPERIMENTS/models/models_20241229_21_35_13_hof3-m2-ud-split-conv12-f2.0-map-2024-12-29/    --num-samples 1      --map_encoding --eval_data_dict EXPERIMENTS/trajectron-data/hof3-m2-ud-split-nostep-conv12-f2.0-map-2024-12-29_val.pkl --prediction-horizon 120 --gmm-mode True --z-mode
-command=uv run trap_prediction --eval_device cuda:0  --model_dir EXPERIMENTS/models/models_20251107_14_27_55_hof-lidar-m2-ud-nostep-kalsmooth-noise2-offsets1-f2.0-map-2025-11-07/    --num-samples 1      --map_encoding --eval_ata_dict EXPERIMENTS/trajectron-data/hof-lidar-m2-ud-nostep-kalsmooth-noise2-offsets1-f2.0-map-2025-11-07_val.pkl --prediction-horizon 120 --gmm-mode True --z-mode
+command=uv run trap_prediction --eval_device cuda:0  --model_dir SETTINGS/2025-11-dortmund/models/models_20251111_19_06_29_dortmund-nostep-nosmooth-noise2-offsets1-f2.0-map-2025-11-11/    --num-samples 1      --map_encoding --eval_data_dict SETTINGS/2025-11-dortmund/trajectron/dortmund-nostep-nosmooth-noise2-offsets1-f2.0-map-2025-11-12_val.pkl --prediction-horizon 120 --gmm-mode True --z-mode --conf SETTINGS/2025-11-dortmund/trajectron.json
 # command=uv run trap_prediction --eval_device cuda:0  --model_dir EXPERIMENTS/models/models_20251106_11_51_00_hof-lidar-m2-ud-nostep-kalsmooth-noise2-offsets2-f2.0-map-2025-11-06/    --num-samples 1      --map_encoding --eval_data_dict EXPERIMENTS/trajectron-data/hof-lidar-m2-ud-nostep-kalsmooth-noise2-offsets2-f2.0-map-2025-11-06_val.pkl --prediction-horizon 120 --gmm-mode True --z-mode
 #  uv run trajectron_train --continue_training_from EXPERIMENTS/models/models_20241229_21_35_13_hof3-m2-ud-split-conv12-f2.0-map-2024-12-29/ --eval_every 5 --train_data_dict hof3-nostep-conv12-f2.0-map-2024-12-27_train.pkl --eval_data_dict hof3-nostep-conv12-f2.0-map-2024-12-27_val.pkl --offline_scene_graph no --preprocess_workers 8 --log_dir EXPERIMENTS/models --log_tag _hof3-conv12-f2.0-map-2024-12-27 --train_epochs 10 --conf EXPERIMENTS/config.json  --data_dir EXPERIMENTS/trajectron-data --map_encoding
 directory=%(here)s
@@ -94,3 +94,4 @@ stopwaitsecs=60
 [program:superfsmon]
 command=superfsmon trap/stage.py stage
 directory=%(here)s
+autostart=false
\ No newline at end of file
diff --git a/tests/stage_lines.ipynb b/tests/stage_lines.ipynb
index a8af2f8..6afff57 100644
--- a/tests/stage_lines.ipynb
+++ b/tests/stage_lines.ipynb
@@ -66,11 +66,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "DEBUG:history:loaded 846 tracks\n",
-      "DEBUG:history:after filtering left with 350 tracks\n",
+      "INFO:trap.tracker:Read ../EXPERIMENTS/raw/hof-lidar/tracks.pkl\n",
+      "DEBUG:history:loaded 1267 tracks\n",
+      "DEBUG:history:after filtering left with 456 tracks\n",
       "ERROR:trap.base:WARNING, gap between frames 632 -> 322 is negative?\n",
       "ERROR:trap.base:WARNING, gap between frames 1003 -> 895 is negative?\n",
-      "DEBUG:history:interpolated 350 tracks\n",
+      "ERROR:trap.base:WARNING, gap between frames 12264 -> 5779 is negative?\n",
+      "DEBUG:history:interpolated 456 tracks\n",
       "DEBUG:history:smoothed\n",
       "DEBUG:history:projected to world space\n",
       "DEBUG:history:built tree\n"
@@ -80,7 +82,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "loaded 350 tracks in 1.5409131050109863s\n"
+      "loaded 456 tracks in 3.2479774951934814s\n"
      ]
     }
    ],
@@ -102,7 +104,7 @@
     {
      "data": {
       "text/plain": [
-       "<trap.track_history.TrackHistory at 0x7fb2da3c7df0>"
+       "<trap.track_history.TrackHistory at 0x7fb9caa2fe80>"
       ]
      },
      "execution_count": 4,
@@ -124,7 +126,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "DEBUG:matplotlib.pyplot:Loaded backend module://matplotlib_inline.backend_inline version unknown.\n",
+      "DEBUG:matplotlib.pyplot:Loaded backend module://matplotlib_inline.backend_inline version unknown.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "DEBUG:matplotlib.pyplot:Loaded backend module://matplotlib_inline.backend_inline version unknown.\n",
       "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
       "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05\n",
@@ -141,13 +149,7 @@
       "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
       "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
       "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
-      "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
       "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
       "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335\n",
       "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
@@ -390,7 +392,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7fb2d3bd55d0>"
+       "<matplotlib.collections.PathCollection at 0x7fb8b4c4fd30>"
       ]
      },
      "execution_count": 5,
@@ -695,7 +697,7 @@
     {
      "data": {
       "text/plain": [
-       "'16469 track points in the set'"
+       "'21591 track points in the set'"
       ]
      },
      "execution_count": 6,
@@ -726,12 +728,13 @@
      "output_type": "stream",
      "text": [
       "3 14\n",
-      "4 15\n"
+      "4 15\n",
+      "354 3234\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -760,7 +763,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -785,7 +788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 9,
    "id": "6478440c",
    "metadata": {},
    "outputs": [],
@@ -816,7 +819,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 10,
    "id": "3cee2e78",
    "metadata": {},
    "outputs": [
@@ -826,7 +829,7 @@
        "True"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -838,7 +841,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 11,
    "id": "0a6cde44",
    "metadata": {},
    "outputs": [
@@ -862,7 +865,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 45,
    "id": "feee0df1",
    "metadata": {},
    "outputs": [
@@ -872,21 +875,20 @@
      "text": [
       "[[    0.86414       14.03]\n",
       " [     0.9655      13.991]]\n",
-      "---\n",
-      "[     0.9655      13.991]\n",
-      "[     0.9655      13.991]\n",
-      "1 RenderablePoint(position=(1.0603447531038852, 13.959395434695669), color=SrgbaColor(red=0.0, green=1.0, blue=0.0, alpha=1.0))\n"
+      "---\n"
      ]
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "RuntimeError",
+     "evalue": "Not in line stack StartFromClosestPoint",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[45], line 22\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28mprint\u001b[39m(line_history\u001b[38;5;241m.\u001b[39mroot\u001b[38;5;241m.\u001b[39mpoints)\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m---\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 22\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mline_prediction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mStartFromClosestPoint\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mfrom_point)\n\u001b[1;32m     23\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots()\n\u001b[1;32m     24\u001b[0m render(line_history, ax)\n",
+      "File \u001b[0;32m~/suspicion/trap/trap/lines.py:1537\u001b[0m, in \u001b[0;36mLineAnimationStack.get\u001b[0;34m(self, anim)\u001b[0m\n\u001b[1;32m   1534\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m anim \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstack:\n\u001b[1;32m   1535\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstack[anim]\n\u001b[0;32m-> 1537\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNot in line stack \u001b[39m\u001b[38;5;132;01m{\u001b[39;00manim\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: Not in line stack StartFromClosestPoint"
+     ]
     }
    ],
    "source": [
@@ -895,7 +897,7 @@
     "\n",
     "\n",
     "def render(line: LineAnimationStack, ax: Axes):\n",
-    "    rl = line.as_renderable_line(1)\n",
+    "    rl = line.as_renderable_line(.02)\n",
     "    points = np.array([p.position for p in rl.points])\n",
     "\n",
     "    colors = np.array([tuple(p.color.as_array()) for p in rl.points])\n",
@@ -905,7 +907,7 @@
     "    lc = LineCollection(segments, colors=colors[:-1], linewidth=2)\n",
     "    \n",
     "    ax.add_collection(lc)\n",
-    "    ax.scatter(points[:, 0], points[:, 1], c=colors, s=10)\n",
+    "    ax.scatter(points[:, 0], points[:, 1], c=colors, s=4)\n",
     "    \n",
     "    # plt.scatter(closeby[:,0], closeby[:,1], c=colors, alpha=1)\n",
     "\n",
@@ -919,7 +921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 28,
    "id": "3aa9d65f",
    "metadata": {},
    "outputs": [
@@ -929,7 +931,7 @@
        "(1.0, 2.0)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -955,6 +957,49 @@
    "execution_count": null,
    "id": "8c248325",
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "line2 = [[0, -i/10] for i in range(0,1000)]\n",
+    "line1 = [[(i/3)**2, 0] for i in range(0,1000)]\n",
+    "# line3 = [[i/3, -1*(i/10)**2] for i in range(0,1000)]\n",
+    "\n",
+    "# print(line3)\n",
+    "\n",
+    "\n",
+    "line_prediction = LineAnimationStack(StaticLine(line1, prediction_color))\n",
+    "line_prediction.add(RotatingLine(line_prediction.tail, decay_speed=2))\n",
+    "\n",
+    "line_prediction.start()\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "for i in range(40):\n",
+    "    render(line_prediction, ax)\n",
+    "    line_prediction.root.points = line2\n",
+    "\n",
+    "    if (i+1) % 10 == 0:\n",
+    "        print('switch')\n",
+    "        fig, ax = plt.subplots()\n",
+    "        line_prediction.root.color = history_color\n",
+    "        line_prediction.root.points = line3\n",
+    "        line_prediction.start()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1427d291",
+   "metadata": {},
    "outputs": [],
    "source": []
   }
diff --git a/trap/base.py b/trap/base.py
index 0ad07f0..ca6273f 100644
--- a/trap/base.py
+++ b/trap/base.py
@@ -74,6 +74,7 @@ class DetectionState(IntFlag):
     Confirmed = 2 # after tentative
     Lost = 4 # lost when DeepsortTrack.time_since_update > 0 but not Deleted
     Interpolated = 8 # A position estimated through interpolation of adjecent detections
+    # Interpolated = 8 # A position estimated through interpolation of adjecent detections
 
     @classmethod
     def from_deepsort_track(cls, track: DeepsortTrack):
@@ -89,11 +90,13 @@ class DetectionState(IntFlag):
     def from_bytetrack_track(cls, track: ByteTrackTrack):
         if track.state == ByteTrackTrackState.New:
             return cls.Tentative
-        if track.state == ByteTrackTrackState.Lost:
+        if track.state == ByteTrackTrackState.Removed:
             return cls.Lost
             # if track.time_since_update > 0:
         if track.state == ByteTrackTrackState.Tracked:
             return cls.Confirmed
+        if track.state == ByteTrackTrackState.Lost:
+            return cls.Tentative
         raise RuntimeError("Should not run into Deleted entries here")
 
 
diff --git a/trap/cv_renderer.py b/trap/cv_renderer.py
index a83e133..6d80a0c 100644
--- a/trap/cv_renderer.py
+++ b/trap/cv_renderer.py
@@ -18,7 +18,7 @@ import pyglet
 import zmq
 from pyglet import shapes
 
-from trap.base import Detection
+from trap.base import Detection, UndistortedCamera
 from trap.counter import CounterListerner
 from trap.frame_emitter import Frame, Track
 from trap.lines import load_lines_from_svg
@@ -151,10 +151,12 @@ class CvRenderer(Node):
             #     logger.debug(f'new video frame  {frame.index}')
 
             
-            if self.frame is None:
+            if self.frame is None and i < 100:
                 # might need to wait a few iterations before first frame comes available
                 time.sleep(.1)
                 continue
+            else:
+                self.frame = Frame(i, np.zeros((1920,1080,3)), camera=UndistortedCamera(12))
             
             try:
                 prediction_frame: Frame = self.prediction_sock.recv_pyobj(zmq.NOBLOCK)
diff --git a/trap/lidar_tracker.py b/trap/lidar_tracker.py
index 70455e3..860e359 100644
--- a/trap/lidar_tracker.py
+++ b/trap/lidar_tracker.py
@@ -208,6 +208,7 @@ class Lidar(Node):
         
         self.track_sock = self.pub(self.config.zmq_trajectory_addr)
         self.detection_sock = self.pub(self.config.zmq_detection_addr)
+        self.lost_track_sock = self.pub(self.config.zmq_lost_addr)
 
         calibration = vd.Calibration.read( "VLP-16.yaml")
         config = vd.Config(model=vd.Model.VLP16, calibration=calibration)
@@ -286,12 +287,12 @@ class Lidar(Node):
                 self.map_outline_volume.bounding_polygon = o3d.utility.Vector3dVector(polygon_points)
                 
                 
-        if self.config.smooth_tracks:
-             # TODO)) make configurable
-            logger.info(f"Smoother enabled, assuming {ASSUMED_FPS} fps")
-            self.smoother = Smoother(window_len=int(ASSUMED_FPS*.6), convolution=True)
-        else:
-            logger.info("Smoother Disabled (enable with --smooth-tracks)")
+        # if self.config.smooth_tracks:
+        #      # TODO)) make configurable
+        #     logger.info(f"Smoother enabled, assuming {ASSUMED_FPS} fps")
+        #     self.smoother = Smoother(window_len=int(ASSUMED_FPS*.6), convolution=True)
+        # else:
+        #     logger.info("Smoother Disabled (enable with --smooth-tracks)")
 
         self.remotes = {}
 
@@ -487,7 +488,7 @@ class Lidar(Node):
                 filtered_pcd = denoised_pcd
 
                 # down sample
-                filtered_pcd = filtered_pcd.voxel_down_sample(voxel_size=0.04)
+                filtered_pcd = filtered_pcd.voxel_down_sample(voxel_size=0.06)
                 stat_downsampled = len(filtered_pcd.points)
                 timers.append(('downsample', time.perf_counter()))
 
@@ -527,7 +528,8 @@ class Lidar(Node):
                 self.logger.debug(f"online tracks: {[t[4] for t in online_tracks]}")
                 removed_tracks = self.tracker.removed_stracks
                 # active_stracks = [track for track in self.tracker.tracked_stracks if track.is_activated]
-                active_stracks = [track for track in self.tracker.tracked_stracks if track.is_activated]
+                # we want both the lost, and currently visible, as otherwise, the predictor goes haywire
+                active_stracks = [track for track in (self.tracker.tracked_stracks + self.tracker.lost_stracks) if track.is_activated]
                 detections = [Detection.from_bytetrack(track, frame_idx) for track in active_stracks]
 
                 counter.set('detections', len(detections))
@@ -549,6 +551,7 @@ class Lidar(Node):
                 for t in removed_tracks:
                     if t.track_id in self.tracks:
                         if t.is_activated:
+                            self.lost_track_sock.send_pyobj(self.tracks[t.track_id])
                             self.logger.info(f"Lost track: {t.track_id}")
                         del self.tracks[t.track_id]
                     # TODO: fix this oddity:
@@ -567,8 +570,8 @@ class Lidar(Node):
                 frame = Frame(frame_idx, None, time.time(), active_tracks,
                                camera.H, camera)
 
-                if self.config.smooth_tracks:
-                    frame = self.smoother.smooth_frame_tracks(frame)
+                # if self.config.smooth_tracks:
+                #     frame = self.smoother.smooth_frame_tracks(frame)
 
                 timers.append(('smooth', time.perf_counter()))
 
@@ -669,6 +672,10 @@ class Lidar(Node):
                         help='Manually specity communication addr for the trajectory messages',
                         type=str,
                         default="ipc:///tmp/feeds_traj")
+        argparser.add_argument('--zmq-lost-addr',
+                        help='Manually specity communication addr for the trajectory messages',
+                        type=str,
+                        default="ipc:///tmp/feeds_lost")
         argparser.add_argument('--zmq-detection-addr',
                         help='Manually specity communication addr for the detection messages',
                         type=str,
@@ -698,9 +705,9 @@ class Lidar(Node):
                         help='Maximum area (m2) of bounding boxes to consider them for tracking',
                         type=float,
                         default=2)
-        argparser.add_argument("--smooth-tracks",
-                            help="Smooth the tracker tracks before sending them to the predictor",
-                            action='store_true')
+        # argparser.add_argument("--smooth-tracks",
+        #                     help="Smooth the tracker tracks before sending them to the predictor",
+        #                     action='store_true')
         argparser.add_argument("--viz",
                             help="Render pointclouds in open3d",
                             action='store_true')
@@ -902,7 +909,7 @@ def get_cluster_boxes(clusters, min_area = 0, max_area=5):
 
         area = (x_max-x_min) * (y_max - y_min)
         if area < min_area or area > max_area:
-            logger.warning(f"Dropping box {area} ")
+            logger.debug(f"Dropping box {area} ")
             continue
         
 
diff --git a/trap/lines.py b/trap/lines.py
index 5b6e56d..0becab2 100644
--- a/trap/lines.py
+++ b/trap/lines.py
@@ -1198,6 +1198,7 @@ class RotatingLine(LineAnimator):
         # find closest point to start from:
         
         origin =  target_line.points[0]
+        # print(origin, target_line.points[-1])
         # closest_idx = StartFromClosestPoint.find_closest_to_point(origin.position, [p.position for p in self.drawn_points])
         # if closest_idx:
         #     print('cut at', closest_idx)
diff --git a/trap/node.py b/trap/node.py
index 49e5108..f21b99f 100644
--- a/trap/node.py
+++ b/trap/node.py
@@ -161,6 +161,7 @@ class Node():
     def push(self, addr: str):
         "push-pull pair"
         sock = self.zmq_context.socket(zmq.PUSH)
+        # sock.setsockopt(zmq.LINGER, 0)
         sock.connect(addr)
         return sock
     
@@ -210,5 +211,7 @@ def setup_logging(config: Namespace):
 
     logging.basicConfig(
         level=loglevel,
-        handlers=log_handlers # [queue_handler]
+        handlers=log_handlers, # [queue_handler]
+        format="%(asctime)s %(levelname)s:%(name)s:%(message)s",
+        datefmt="%H:%M:%S"
     )
\ No newline at end of file
diff --git a/trap/prediction_server.py b/trap/prediction_server.py
index 2275388..4cbadc1 100644
--- a/trap/prediction_server.py
+++ b/trap/prediction_server.py
@@ -13,6 +13,7 @@ from multiprocessing import Event
 
 import dill
 import numpy as np
+import shapely
 import torch
 import zmq
 from trajectron.environment import Environment, Scene, GeometricMap
@@ -21,6 +22,7 @@ from trajectron.model.online.online_trajectron import OnlineTrajectron
 from trajectron.utils import prediction_output_to_trajectories
 
 from trap.frame_emitter import DataclassJSONEncoder, Frame
+from trap.lines import load_lines_from_svg
 from trap.node import Node
 from trap.tracker import Smoother
 from trap.utils import ImageMap
@@ -52,14 +54,16 @@ def create_online_env(env, hyperparams, scene_idx, init_timestep):
                             init_timestep + 1),
         state=hyperparams['state'])
     online_scene.robot = test_scene.robot
-    online_scene.calculate_scene_graph(attention_radius=env.attention_radius,
+    radius = {k: 0 for k,v in env.attention_radius.items()}
+    
+    online_scene.calculate_scene_graph(attention_radius=radius,
                                        edge_addition_filter=hyperparams['edge_addition_filter'],
                                        edge_removal_filter=hyperparams['edge_removal_filter'])
 
     return Environment(node_type_list=env.node_type_list,
                        standardization=env.standardization,
                        scenes=[online_scene],
-                       attention_radius=env.attention_radius,
+                       attention_radius=radius,
                        robot_type=env.robot_type)
 
 
@@ -161,6 +165,15 @@ class PredictionServer(Node):
         self.trajectory_socket = self.sub(self.config.zmq_trajectory_addr)
         self.prediction_socket = self.pub(self.config.zmq_prediction_addr)
         self.external_predictions = not self.config.zmq_prediction_addr.startswith("ipc://")
+
+        self.cutoff_shape = None
+        if self.config.cutoff_map:
+            
+            self.cutoff_line = load_lines_from_svg(self.config.cutoff_map, 100, '')[0]
+            self.cutoff_shape = shapely.Polygon([p.position for p in self.cutoff_line.points])
+            
+            logger.info(f"{self.cutoff_shape}")
+            
         
 
     def send_frame(self, frame: Frame):
@@ -184,7 +197,8 @@ class PredictionServer(Node):
         # model_dir = 'models/models_04_Oct_2023_21_04_48_eth_vel_ar3'
 
         # Load hyperparameters from json
-        config_file = os.path.join(self.config.model_dir, self.config.conf)
+        # config_file = os.path.join(self.config.model_dir, self.config.conf)
+        config_file = self.config.conf
         if not os.path.exists(config_file):
             raise ValueError('Config json not found!')
         with open(config_file, 'r') as conf_json:
@@ -224,6 +238,9 @@ class PredictionServer(Node):
         logger.info(f"Basing online env on {eval_scene=} -- loaded from {self.config.eval_data_dict}")
         online_env = create_online_env(eval_env, hyperparams, scene_idx, init_timestep)
 
+        print("overriding attention radius")
+        online_env.attention_radius = {(online_env.NodeType.PEDESTRIAN, online_env.NodeType.PEDESTRIAN): 0.1}
+
         # auto-find highest iteration
         model_registrar = ModelRegistrar(self.config.model_dir, self.config.eval_device)
         model_iterations = pathlib.Path(self.config.model_dir).glob('model_registrar-*.pt')
@@ -297,6 +314,7 @@ class PredictionServer(Node):
 
                 input_dict = {}
                 for identifier, track in frame.tracks.items():
+
                     # if len(trajectory['history']) < 7:
                     #     # TODO: these trajectories should still be in the output, but without predictions
                     #     continue
@@ -313,7 +331,16 @@ class PredictionServer(Node):
                     if len(track.history) < 2:
                         continue
                     
+                    
+                    
                     node = track.to_trajectron_node(frame.camera, online_env)
+
+                    if self.cutoff_shape:
+                        position = shapely.Point(node.data.data[-1][:2])
+                        if not shapely.contains(self.cutoff_shape, position):
+                            # logger.debug(f"Skip position {position}")
+                            continue
+                    
                     # print(node.data.data[-1])
                     input_dict[node] = np.array(object=node.data.data[-1])
                     # print("history", node.data.data[-10:])
@@ -352,6 +379,7 @@ class PredictionServer(Node):
                     #     )
 
                     # input_dict[node] = np.array(object=[x[-1],y[-1],vx[-1],vy[-1],ax[-1],ay[-1]])
+                    # break # only on
 
                 # print(input_dict)
 
@@ -368,9 +396,11 @@ class PredictionServer(Node):
                     continue
 
             maps = None
+            start_maps = time.time()
             if hyperparams['use_map_encoding']:
                 maps = get_maps_for_input(input_dict, eval_scene, hyperparams, device=self.config.eval_device)
                 
+                
             # print(maps)
 
             # robot_present_and_future = None
@@ -398,7 +428,8 @@ class PredictionServer(Node):
                                                             gmm_mode=self.config.gmm_mode, # "If True: The mode of the Gaussian Mixture Model (GMM) is sampled (see trajectron.model.mgcvae.py)"
                                                             z_mode=self.config.z_mode # "Predictions from the model’s most-likely high-level latent behavior mode" (see trajecton.models.components.discrete_latent:sample_p(most_likely_z=z_mode))
                                                             )
-            
+            print(len(dists), len (preds))
+            intermediate = time.time()
             # unsure what this bit from online_prediction.py does:
             # detailed_preds_dict = dict()
             # for node in eval_scene.nodes:
@@ -418,8 +449,8 @@ class PredictionServer(Node):
          
             
             end = time.time()
-            logger.debug("took %.2f s (= %.2f Hz) w/ %d nodes and %d edges -- init: %.2f s" % (end - start,
-                                                                            1. / (end - start), len(trajectron.nodes),
+            logger.debug("took %.2f s (= %.2f Hz), maps: %.2f, forward: %.2f w/ %d nodes and %d edges -- init: %.2f s" % (end - start,
+                                                                            1. / (end - start), (start-start_maps)/(end - start), (intermediate-start)/(end - start), len(trajectron.nodes),
                                                                             trajectron.scene_graph.get_num_edges(), start-t_init))
 
             # if self.config.center_data:
@@ -441,7 +472,7 @@ class PredictionServer(Node):
             futures_dict = futures_dict[ts_key]
 
             response = {}
-            logger.debug(f"{histories_dict=}")
+            # logger.debug(f"{histories_dict=}")
             for node in histories_dict:
                 history = histories_dict[node]
                 # future = futures_dict[node] # ground truth dict
@@ -509,9 +540,9 @@ class PredictionServer(Node):
                             # default='../Trajectron-plus-plus/experiments/pedestrians/models/models_04_Oct_2023_21_04_48_eth_vel_ar3')
 
         inference_parser.add_argument("--conf",
-                            help="path to json config file for hyperparameters, relative to model_dir",
-                            type=str,
-                            default='config.json')
+                            help="path to json config file for hyperparameters",
+                            type=pathlib.Path,
+                            default='EXPERIMENTS/config.json')
 
         # Model Parameters (hyperparameters)
         inference_parser.add_argument("--offline_scene_graph",
@@ -566,12 +597,12 @@ class PredictionServer(Node):
         inference_parser.add_argument('--batch_size',
                             help='training batch size',
                             type=int,
-                            default=256)
+                            default=512)
 
         inference_parser.add_argument('--k_eval',
                             help='how many samples to take during evaluation',
                             type=int,
-                            default=25)
+                            default=1)
 
         # Data Parameters
         inference_parser.add_argument("--eval_data_dict",
@@ -593,7 +624,7 @@ class PredictionServer(Node):
         inference_parser.add_argument("--eval_device",
                             help="what device to use during inference",
                             type=str,
-                            default="cpu")
+                            default="cuda:0")
 
 
         inference_parser.add_argument('--seed',
@@ -634,6 +665,11 @@ class PredictionServer(Node):
                                     help="Center data around cx and cy. Should also be used when processing data",
                                     action='store_true')
         
+        inference_parser.add_argument('--cutoff-map',
+                            help='specify a map (svg-file) that specifies projection boundaries. In here, degrade chance to be selectede',
+                            type=str,
+                            default="../DATASETS/hof-lidar/map_hof.svg")
+        
 
         return inference_parser
 
diff --git a/trap/process_data.py b/trap/process_data.py
index c7868f9..3ed30a3 100644
--- a/trap/process_data.py
+++ b/trap/process_data.py
@@ -106,6 +106,8 @@ def process_data(src_dir: Path, dst_dir: Path, name: str, smooth_tracks: bool, n
     if map_img_path:
         if not map_img_path.exists():
             raise RuntimeError(f"Map image does not exists {map_img_path}")
+
+        print(f"Using map {map_img_path}")
         
         type_map = {}
         # TODO)) For now, assume the map is a 100x scale of the world coordinates (i.e. 100px per meter)
@@ -358,7 +360,8 @@ def process_data(src_dir: Path, dst_dir: Path, name: str, smooth_tracks: bool, n
           --train_epochs 100 \\
           --conf {target_config}  \\
           --data_dir {dst_dir} \\
-          {"--map_encoding" if map_img_path else ""}
+          {"--map_encoding" if map_img_path else ""} \\
+          --no_edge_encoding
         """)
 
     return names
diff --git a/trap/stage.py b/trap/stage.py
index 2248fa1..d86e278 100644
--- a/trap/stage.py
+++ b/trap/stage.py
@@ -75,9 +75,9 @@ class SceneInfo:
 
 class ScenarioScene(Enum):
     DETECTED = SceneInfo(4, "First detection")
-    SUBSTANTIAL = SceneInfo(6, "Multiple detections")
-    FIRST_PREDICTION = SceneInfo(10, "Prediction is ready")
-    CORRECTED_PREDICTION = SceneInfo(11, "Multiple predictions")
+    TRACKED = SceneInfo(6, "Multiple detections")
+    PREDICTION_AVAILABLE = SceneInfo(10, "Prediction is ready")
+    UPDATED_PREDICTION = SceneInfo(11, "Multiple predictions")
     LOITERING = SceneInfo(7, "Foundto be loitering", takeover_possible=True, takeover_possible_after=10) # TODO: create "possible after"
     PLAY = SceneInfo(7, description="After many predictions; just fooling around", takeover_possible=True, takeover_possible_after=10)
     LOST = SceneInfo(-1, description="Track lost", takeover_possible=True, takeover_possible_after=0) 
@@ -166,6 +166,7 @@ class Scenario(PrioritySlotItem):
     def get_priority(self) -> int:
         # newer higher prio
         distance =  0
+        # todo: check if last point is within bounds
         if self.track and len(self.track.projected_history) > 5:
             distance = np.linalg.norm(self.track.projected_history[-1] - self.track.projected_history[0])
         return (self.scene.value.priority, distance)
@@ -261,17 +262,17 @@ class Scenario(PrioritySlotItem):
     def check_track(self):
         predictions = len(self.prediction_tracks)
         if predictions and self.running_for() < 20:
-            self.set_scene(ScenarioScene.FIRST_PREDICTION)
+            self.set_scene(ScenarioScene.PREDICTION_AVAILABLE)
             return True
-        if predictions and self.running_for() > 120:
+        if predictions and self.running_for() > 60 * 5:
             self.set_scene(ScenarioScene.PLAY)
             return True
         if predictions:
-            self.set_scene(ScenarioScene.CORRECTED_PREDICTION)
+            self.set_scene(ScenarioScene.UPDATED_PREDICTION)
             return True
         if self.track:
-            if len(self.track.projected_history) > TRACK_ASSUMED_FPS * 3:
-                self.set_scene(ScenarioScene.SUBSTANTIAL)
+            if len(self.track.projected_history) > TRACK_ASSUMED_FPS * 2:
+                self.set_scene(ScenarioScene.TRACKED)
             else:
                 self.set_scene(ScenarioScene.DETECTED)
             return True
@@ -360,7 +361,7 @@ class DrawnScenario(Scenario):
         self.line_history = LineAnimationStack(history)
         self.line_history.add(AppendableLineAnimator(self.line_history.tail, draw_decay_speed=120, transition_in_on_init=False))
         self.line_history.add(CropLine(self.line_history.tail, self.MAX_HISTORY))
-        self.line_history.add(SimplifyLine(self.line_history.tail, 0.003)) # Simplify before effects, so they don't distort
+        self.line_history.add(SimplifyLine(self.line_history.tail, 0.002)) # Simplify before effects, so they don't distort
         self.line_history.add(FadedTailLine(self.line_history.tail, TRACK_FADE_AFTER_DURATION * TRACK_ASSUMED_FPS, TRACK_END_FADE))
         self.line_history.add(NoiseLine(self.line_history.tail, amplitude=0, t_factor=.3))
         self.line_history.add(FadeOutJitterLine(self.line_history.tail, frequency=5, t_factor=.5))
@@ -373,9 +374,9 @@ class DrawnScenario(Scenario):
         self.line_prediction.get(StartFromClosestPoint).skip=True
         self.line_prediction.add(RotatingLine(self.line_prediction.tail, decay_speed=16))
         self.line_prediction.get(RotatingLine).skip = False
-        self.line_prediction.add(SegmentLine(self.line_prediction.tail, duration=7, anim_f=SegmentLine.anim_follow_in_front))
+        self.line_prediction.add(SegmentLine(self.line_prediction.tail, duration=7 / 3, anim_f=SegmentLine.anim_follow_in_front))
         self.line_prediction.get(SegmentLine).skip = False
-        self.line_prediction.add(SimplifyLine(self.line_prediction.tail, 0.003)) # Simplify before effects, so they don't distort
+        self.line_prediction.add(SimplifyLine(self.line_prediction.tail, 0.002)) # Simplify before effects, so they don't distort
         GAP_DURATION = 5
         def dash_len(dt, t):
             t=min(1, t/GAP_DURATION)
@@ -573,7 +574,10 @@ class DrawnScenario(Scenario):
         original = self.scene.name
         changed = super().set_scene(scene)
         if changed:
-            self.stage.log_sock.send_string(f"Change {self.track_id}: {original} -> {self.scene.name}")
+            try:
+                self.stage.log_sock.send_string(f"Visitor {self.track_id}: {original} -> {self.scene.name}", zmq.NOBLOCK)
+            except Exception as e:
+                logger.warning("Not sent the scene change message, broken socket?")
         return changed
 
 class NoTracksScenario(PrioritySlotItem):
@@ -622,12 +626,23 @@ class DebugDrawer():
     def __init__(self, stage: Stage):
         self.stage = stage
     
-    def to_renderable_lines(self, dt: DeltaT):
+    def positions_to_renderable_lines(self, dt: DeltaT):
         lines = RenderableLines([], CoordinateSpace.WORLD)
         past_color = SrgbaColor(1,0,1,1)
-        future_color = SrgbaColor(0,1,0,1)
+        current_color = SrgbaColor(1,0,0,.6)
         for scenario in self.stage.scenarios.values():
-            lines.append(StaticLine(scenario.track.projected_history, past_color).as_renderable_line(dt))
+            # lines.append(StaticLine(scenario.track.projected_history, past_color).as_renderable_line(dt).as_simplified(factor=.005))
+            center = scenario.track.projected_history[-1]
+            
+            lines.append(StaticLine([[center[0], center[1]-.2], [center[0], center[1]+.2]], current_color).as_renderable_line(dt))
+            lines.append(StaticLine([[center[0]-.2, center[1]], [center[0]+.2, center[1]]], current_color).as_renderable_line(dt))
+        return lines
+    
+    def predictions_to_renderable_lines(self, dt: DeltaT):
+        lines = RenderableLines([], CoordinateSpace.WORLD)
+        future_color = SrgbaColor(0,1,0,.6)
+        for scenario in self.stage.scenarios.values():
+            # lines.append(StaticLine(scenario.track.projected_history, past_color).as_renderable_line(dt).as_simplified(factor=.005))
             if scenario.active_ptrack:
                 lines.append(StaticLine(scenario.active_ptrack._track.predictions[0], future_color).as_renderable_line(dt))
         return lines
@@ -639,7 +654,8 @@ class DatasetDrawer():
 
         line_color = SrgbaColor(0,1,1,1)
         self.track_line = LineAnimationStack(StaticLine([], line_color))
-        self.track_line.add(SimplifyLine(self.track_line.tail, 0.004)) # Simplify before cropping, to get less noodling
+        # self.track_line.add(SimplifyLine(self.track_line.tail, 0.004)) # Simplify before cropping, to get less noodling
+        self.track_line.add(SimplifyLine(self.track_line.tail, 0.002)) # no laser in dortmund
         self.track_line.add(CropAnimationLine(self.track_line.tail, 50, assume_fps=TRACK_ASSUMED_FPS*20)) # speed up
         
         # self.track_line.add(DashedLine(self.track_line.tail, t_factor=4, loop_offset=True))
@@ -653,11 +669,14 @@ class DatasetDrawer():
     def to_renderable_lines(self, dt: DeltaT):
         lines = RenderableLines([], CoordinateSpace.WORLD)
         if not self.track_line.is_running():
+            # print('update')
             track_id = random.choice(list(self.stage.history.state.tracks.keys()))
             # print('track_id', track_id)
             positions = self.stage.history.state.track_histories[track_id]
             self.track_line.root.points = positions
             self.track_line.start()
+        # else:
+        #     print('-')
         
         lines.lines.append(
             self.track_line.as_renderable_line(dt)
@@ -795,9 +814,11 @@ class Stage(Node):
         # TODO: sometimes very slow!
         t1 = time.perf_counter()
         training_lines = self.auxilary.to_renderable_lines(dt)
-        all_active_tracks = self.debug_drawer.to_renderable_lines(dt)
-
         t2 = time.perf_counter()
+        active_positions = self.debug_drawer.positions_to_renderable_lines(dt)
+        all_predictions = self.debug_drawer.predictions_to_renderable_lines(dt)
+
+        t2b = time.perf_counter()
 
         timings = []
         for scenario in self.active_scenarios:
@@ -807,7 +828,7 @@ class Stage(Node):
         if not len(self.active_scenarios):
             lines = training_lines
 
-        t2b = time.perf_counter()
+        t2c = time.perf_counter()
         # rl_scenario = lines.as_simplified(SimplifyMethod.RDP, .003) # or segmentise (see shapely)
         # rl_training = training_lines.as_simplified(SimplifyMethod.RDP, .003) # or segmentise (see shapely)
         self.counter.set("stage.lines", len(lines.lines))
@@ -820,7 +841,8 @@ class Stage(Node):
             1: lines,
             2: self.debug_lines,
             3: training_lines,
-            4: all_active_tracks,
+            4: active_positions,
+            5: all_predictions,
         }
 
         t4 = time.perf_counter()
@@ -836,7 +858,7 @@ class Stage(Node):
         
         t6 = time.perf_counter()
 
-        t = (t2-t1, t3-t2b, t2b-t2, t4-t3, t5-t4, t6-t5)
+        t = (t2-t1, t2b-t2, t2c-t2b, t3-t2c, t2b-t2, t4-t3, t5-t4, t6-t5)
         if sum(t) > .1:
             print(t)
             print(len(lines.lines))
@@ -883,6 +905,10 @@ class Stage(Node):
                             help='specify a map (svg-file) from which to load lines which will be overlayed',
                             type=str,
                             default="../DATASETS/hof-lidar/map_hof.svg")
+        argparser.add_argument('--cutoff-map',
+                            help='specify a map (svg-file) that specifies projection boundaries. In here, degrade chance to be selectede',
+                            type=str,
+                            default="../DATASETS/hof-lidar/map_hof.svg")
         argparser.add_argument('--max-active-scenarios',
                             help='Maximum number of active scenarios that can be drawn at once (to not overlod the laser)',
                             type=int,
diff --git a/trap/stage_renderer.py b/trap/stage_renderer.py
index ed01d76..4962e4b 100644
--- a/trap/stage_renderer.py
+++ b/trap/stage_renderer.py
@@ -3,6 +3,7 @@
 from argparse import ArgumentParser
 from collections import deque
 import math
+import re
 from typing import List
 import numpy as np
 import pyglet
@@ -11,7 +12,7 @@ import zmq
 from trap.lines import RenderableLayers, message_to_layers
 from trap.node import Node
 
-BG_COLOR = (0,0,0)
+BG_COLOR = (0,0,255)
 class StageRenderer(Node):
     def setup(self):
         # self.prediction_sock = self.sub(self.config.zmq_prediction_addr)
@@ -49,6 +50,9 @@ class StageRenderer(Node):
         # self.window.set_size(1080, 1920)
 
         window_size = self.window.get_size()
+
+        padding = 40
+
         print(window_size)
         self.window.set_handler('on_draw', self.on_draw)
         # self.window.set_handler('on_close', self.on_close)
@@ -68,14 +72,14 @@ class StageRenderer(Node):
         self.text = pyglet.text.document.FormattedDocument("")
         self.text_batch = pyglet.graphics.Batch()
         self.text_layout = pyglet.text.layout.TextLayout(
-            self.text, 20, 350, 
-            width=self.window.get_size()[1],
-            height=self.window.get_size()[0] // 3,
+            self.text, padding, (self.window.get_size()[0]-padding*2) // 2 - 100, 
+            width=self.window.get_size()[1] - 2*padding,
+            height=(self.window.get_size()[0] - padding) // 2,
             multiline=True, wrap_lines=False, batch=self.text_batch)
 
-        max_len = 30
+        max_len = 31
         self.log_msgs = deque([], maxlen=max_len)
-        self.log_msgs.extend(["..."] * max_len)
+        self.log_msgs.extend(["-"] * max_len)
 
 
         translate = (10,-400)
@@ -86,7 +90,6 @@ class StageRenderer(Node):
         max_y = 14.3
         scale = min(smallest_dimension / max_x, smallest_dimension/max_y)
 
-        padding = 40
 
         self.logger.info(f"Use {scale=}")
 
@@ -110,6 +113,8 @@ class StageRenderer(Node):
         clear_area = img_y
         self.clear_transparent = pyglet.shapes.Rectangle(0, window_size[1]-clear_area, window_size[0], clear_area, color=(*BG_COLOR,255//70))
         self.clear_fully= pyglet.shapes.Rectangle(0, 0, window_size[0], window_size[1]-clear_area, color=(*BG_COLOR,255))
+
+        self.window.clear()
         
 
     def check_running(self, dt):
@@ -178,25 +183,23 @@ class StageRenderer(Node):
                     color = (p2.color.as_array()*255).astype(int)
 
                     if i < len(self.lines):
-                        print('reuse')
                         shape = self.lines[i]
                         shape.x = pos1[0]
                         shape.y = pos1[1]
                         shape.x2 = pos2[0]
                         shape.y2 = pos2[1]
                         shape.color = color
+                    else:
+                        self.lines.append(pyglet.shapes.Line(pos1[0], pos1[1],
+                                                pos2[0],
+                                                pos2[1],
+                                                3,
+                                                    color,    
+                                                batch=self.lines_batch))
                     
-                    self.lines.append(pyglet.shapes.Line(pos1[0], pos1[1],
-                                              pos2[0],
-                                               pos2[1],
-                                               3,
-                                                 color,    
-                                            batch=self.lines_batch))
-                    
-        print(len(self.lines), i)
+        
         too_many = len(self.lines) - 1 - i
         if too_many > 0:
-            print('del', too_many)
             for j in reversed(range(i, i+too_many)):
                 self.lines[i].delete()
                 del self.lines[i]
@@ -210,20 +213,38 @@ class StageRenderer(Node):
             font_size=18,
             color=(255, 255, 255, 255),
         ))
+
+
+        colorsmap = {
+            'ANOMALOUS': (255, 0, 0, 255),
+            'LOITERING': (255, 255, 0, 255),
+            'DETECTED': (255, 0, 255, 255),
+            'SUBSTANTIAL': (255, 0, 255, 255),
+            'LOST': (0, 0, 0, 255),
+        }
+
+        matchtext = "".join(self.log_msgs) # find no newlines
+        for state,color in colorsmap.items():
+            for match in re.finditer(state, matchtext):
+                self.text.set_style(match.start(), match.end(), dict(
+                    color=color
+                ))
+
+        
         
     
     def on_draw(self):
         self.receive(.1)
-        self.window.clear()
-        # self.clear_transparent.color = (*BG_COLOR, int(255*self.get_setting('stagerenderer.fade', .27)))
-        # self.clear_transparent.draw()
-        # self.clear_fully.draw()
+        # self.window.clear()
+        self.clear_transparent.color = (*BG_COLOR, int(3))
+        self.clear_transparent.draw()
+        self.clear_fully.draw()
         self.fps_display.draw()
         
-        # self.bg_sprite.draw()
+        self.bg_sprite.draw()
 
-        # self.lines_batch.draw()
-        # self.text_batch.draw()
+        self.lines_batch.draw()
+        self.text_batch.draw()
 
 
     @classmethod
@@ -248,8 +269,8 @@ class StageRenderer(Node):
                             type=str,
                             default="SETTINGS/2025-11-dortmund/space/floorplan.png")
         render_parser.add_argument('--monitor',
-                            help='Specify a screen on which to output (eg. HDMI-1)',
+                            help='Specify a screen on which to output (eg. HDMI-0)',
                             type=str,
-                            default="HDMI-1")
+                            default="HDMI-0")
         return render_parser
 
diff --git a/trap/track_writer.py b/trap/track_writer.py
index 6d43c6c..2616ad6 100644
--- a/trap/track_writer.py
+++ b/trap/track_writer.py
@@ -6,18 +6,20 @@ from pathlib import Path
 
 import zmq
 
+from trap.base import Track
 from trap.frame_emitter import Frame
 from trap.node import Node
-from trap.tracker import TrainingDataWriter
+from trap.tracker import TrainingDataWriter, TrainingTrackWriter
 
 
 class TrackWriter(Node):
     def setup(self):
-        self.track_sock = self.sub(self.config.zmq_trajectory_addr)
+        self.track_sock = self.sub(self.config.zmq_lost_addr)
+        self.log_sock = self.push(self.config.zmq_log_addr)
 
 
     def run(self):
-        with TrainingDataWriter(self.config.output_dir) as writer:
+        with TrainingTrackWriter(self.config.output_dir) as writer:
             try:
                 while self.run_loop():
                     zmq_ev = self.track_sock.poll(timeout=1000)
@@ -26,10 +28,20 @@ class TrackWriter(Node):
                         continue
                 
                     try:
-                        frame: Frame = self.track_sock.recv_pyobj()
-                        writer.add(frame, frame.tracks.values())
+                        track: Track = self.track_sock.recv_pyobj()
+
+                        if len(track.history) < 20:
+                            self.logger.debug(f"ignore short track {len(track.history)}")
+                            continue
+
+                        writer.add(track)
                         
-                        self.logger.debug(f"write frame {frame.time:.3f} with {len(frame.tracks)} tracks")
+                        self.logger.info(f"Added track {track.track_id}")
+                        
+                        try:
+                            self.log_sock.send_string(f"Added track {track.track_id} to dataset, {len(track.history)} datapoints", zmq.NOBLOCK)
+                        except Exception as e:
+                            self.logger.warning("Not sent the message, broken socket?")
 
                     except zmq.ZMQError as e:
                         
@@ -44,10 +56,14 @@ class TrackWriter(Node):
     @classmethod
     def arg_parser(cls):
         argparser = ArgumentParser()
-        argparser.add_argument('--zmq-trajectory-addr',
+        argparser.add_argument('--zmq-log-addr',
+                            help='Manually specity communication addr for the log  messages',
+                            type=str,
+                            default="tcp://0.0.0.0:99188")
+        argparser.add_argument('--zmq-lost-addr',
                         help='Manually specity communication addr for the trajectory messages',
                         type=str,
-                        default="ipc:///tmp/feeds_traj")
+                        default="ipc:///tmp/feeds_lost")
         argparser.add_argument("--output-dir",
                     help="Directory to save the video in",
                     required=True,
diff --git a/trap/tracker.py b/trap/tracker.py
index e1cd680..2c1158b 100644
--- a/trap/tracker.py
+++ b/trap/tracker.py
@@ -127,14 +127,37 @@ class TrackReader:
     def __init__(self, path: Path, fps: int, include_blacklisted = False, exclude_whitelisted = False):
         self.blacklist_file = path / "blacklist.jsonl"
         self.whitelist_file = path / "whitelist.jsonl" # for skipping
-        self.tracks_file = path / "tracks.pkl"
+        # self.tracks_file = path / "tracks.pkl"
+        self.tracks_files = path.glob('tracks*.pkl')
 
         # with self.tracks_file.open('r') as fp:
         #     tracks_dict: dict = json.load(fp)
-            
-        with self.tracks_file.open('rb') as fp:
-            tracks: dict = pickle.load(fp)
+        
+        tracks: Dict[str, Track] = {}
+        for tracks_file in self.tracks_files:
+            logger.info(f"Read {tracks_file}")
+            with tracks_file.open('rb') as fp:
+                while True:
+                    # multiple tracks can be pickled separately
+                    try:
+                        trackset: Dict[str, Track] = pickle.load(fp)
+                        for track_id, track in trackset.items():
+                            if len(tracks) < 1:
+                                max_item = 0
+                            else:
+                                max_item = max([int(t) for t in tracks.keys()])
 
+                            if int(track.track_id) < max_item:
+                                track_id = str(max_item+1)
+                            else:
+                                track_id = track.track_id
+
+                            track.track_id = track_id
+                            tracks[track.track_id] = track
+                    except EOFError:
+                        break
+
+        
         
         if self.blacklist_file.exists():
             with jsonlines.open(self.blacklist_file, 'r') as reader:
@@ -255,8 +278,50 @@ class TrainingDataWriter:
         
         self.training_fp.close()
         rewrite_raw_track_files(self.path)
+
+
+class TrainingTrackWriter:
+    """
+    Supersedes TrainingDataWriter, by writing full tracks"""
+    def __init__(self, training_path: Optional[Path]):
+        if training_path is None:
+            self.path = None
+            return
         
-                    
+        if not isinstance(training_path, Path):
+            raise ValueError("save-for-training should be a path")
+        if not training_path.exists():
+            logger.info(f"Making path for training data: {training_path}")
+            training_path.mkdir(parents=True, exist_ok=False)
+        else:
+            logger.warning(f"Path for training-data exists: {training_path}. Continuing assuming that's ok.")
+            
+        self.path = training_path
+
+    def __enter__(self):
+        if self.path:
+            d = datetime.now().isoformat(timespec="minutes")
+            self.training_fp = open(self.path / f'tracks-{d}.pcl', 'wb')
+            logger.debug(f"Writing tracker data to {self.training_fp.name}")
+            # following https://github.com/StanfordASL/Trajectron-plus-plus/blob/master/experiments/pedestrians/process_data.py
+            # self.csv = csv.DictWriter(self.training_fp, fieldnames=FIELDNAMES, delimiter='\t', quoting=csv.QUOTE_NONE)
+            self.count = 0
+        return self
+    
+    def add(self, track: Track):
+        self.count += 1;
+        pickle.dump(track, self.training_fp)
+
+    
+    def __exit__(self, exc_type, exc_value, exc_tb):
+        # ... ignore exception (type, value, traceback)
+        if not self.path:
+            return
+        
+        self.training_fp.close()
+        # rewrite_raw_track_files(self.path)
+
+               
 
 def rewrite_raw_track_files(path: Path):
     source_files = list(sorted(path.glob("*.txt"))) # we loop twice, so need a list instead of generator