diff --git a/source/tutorials/read_recording.ipynb b/source/tutorials/read_recording.ipynb
index 7456ffa..1b11a07 100644
--- a/source/tutorials/read_recording.ipynb
+++ b/source/tutorials/read_recording.ipynb
@@ -8,7 +8,7 @@
     "In this tutorial, we will show how to load a single Neon recording downloaded from [Pupil Cloud](https://docs.pupil-labs.com/neon/pupil-cloud/) and give an overview of the data structure.\n",
     "\n",
     "## Reading sample data\n",
-    "We will use a sample recording produced by the NCC Lab, called `OfficeWalk`. This project (collection of recordings on Pupil Cloud) contains two recordings and multiple enrichments and can be downloaded with the `get_sample_data()` function. The function returns a `Pathlib.Path` [(reference)](https://docs.python.org/3/library/pathlib.html#pathlib.Path) object pointing to the downloaded and unzipped directory. PyNeon accepts both `Path` and `string` objects but internally always uses `Path`."
+    "We will use a sample recording produced by the NCC Lab, called `boardView`. This project (collection of recordings on Pupil Cloud) contains two recordings downloaded with the `Timeseries Data + Scene Video` option and a marker mapper enrichment. It can be downloaded with the `get_sample_data()` function. The function returns a `Pathlib.Path` [(reference)](https://docs.python.org/3/library/pathlib.html#pathlib.Path) instance pointing to the downloaded and unzipped directory. PyNeon accepts both `Path` and `string` objects but internally always uses `Path`."
    ]
   },
   {
@@ -20,7 +20,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "D:\\GitHub\\pyneon\\data\\OfficeWalk\n"
+      "C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\n"
      ]
     }
    ],
@@ -28,7 +28,7 @@
     "from pyneon import get_sample_data, NeonDataset, NeonRecording\n",
     "\n",
     "# Download sample data (if not existing) and return the path\n",
-    "sample_dir = get_sample_data(\"OfficeWalk\")\n",
+    "sample_dir = get_sample_data(\"boardView\")\n",
     "print(sample_dir)"
    ]
   },
@@ -39,31 +39,29 @@
     "The `OfficeWalk` data has the following structure:\n",
     "\n",
     "```text\n",
-    "OfficeWalk\n",
-    "├── Timeseries Data\n",
-    "│   ├── walk1-e116e606\n",
+    "boardView\n",
+    "├── Timeseries Data + Scene Video\n",
+    "│   ├── boardview1-d4fd9a27\n",
     "│   │   ├── info.json\n",
     "│   │   ├── gaze.csv\n",
     "│   │   └── ....\n",
-    "│   ├── walk2-93b8c234\n",
+    "│   ├── boardview2-713532d5\n",
     "│   │   ├── info.json\n",
     "│   │   ├── gaze.csv\n",
     "│   │   └── ....\n",
     "|   ├── enrichment_info.txt\n",
     "|   └── sections.csv\n",
-    "├── OfficeWalk_FACE-MAPPER_FaceMap\n",
-    "├── OfficeWalk_MARKER-MAPPER_TagMap_csv\n",
-    "└── OfficeWalk_STATIC-IMAGE-MAPPER_ManualMap_csv\n",
+    "└── boardView_MARKER-MAPPER_boardMapping_csv\n",
     "```\n",
     "\n",
-    "The `Timeseries Data` folder contains what PyNeon refers to as a `NeonDataset`. It consists of two recordings, each with its own `info.json` file and data files. These recordings can be loaded either individually as a `NeonRecording` as a collective `NeonDataset`."
+    "The `Timeseries Data + Scene Video` folder contains what PyNeon refers to as a `NeonDataset`. It consists of two recordings, each with its own `info.json` file and data files. These recordings can be loaded either individually as a `NeonRecording` as a collective `NeonDataset`."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To load a `NeonDataset`, specify the path to the `Timeseries Data` folder:"
+    "To load a `NeonDataset`, specify the path to the `Timeseries Data + Scene Video` folder:"
    ]
   },
   {
@@ -80,7 +78,7 @@
     }
    ],
    "source": [
-    "dataset_dir = sample_dir / \"Timeseries Data\"\n",
+    "dataset_dir = sample_dir / \"Timeseries Data + Scene Video\"\n",
     "dataset = NeonDataset(dataset_dir)\n",
     "print(dataset)"
    ]
@@ -102,14 +100,14 @@
      "output_type": "stream",
      "text": [
       "<class 'pyneon.recording.NeonRecording'>\n",
-      "D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk2-93b8c234\n"
+      "C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview2-713532d5\n"
      ]
     }
    ],
    "source": [
-    "first_recording = dataset[0]\n",
-    "print(type(first_recording))\n",
-    "print(first_recording.recording_dir)"
+    "recording = dataset[0]\n",
+    "print(type(recording))\n",
+    "print(recording.recording_dir)"
    ]
   },
   {
@@ -129,12 +127,12 @@
      "output_type": "stream",
      "text": [
       "<class 'pyneon.recording.NeonRecording'>\n",
-      "D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\n"
+      "C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\n"
      ]
     }
    ],
    "source": [
-    "recording_dir = dataset_dir / \"walk1-e116e606\"\n",
+    "recording_dir = dataset_dir / \"boardview1-d4fd9a27\"\n",
     "recording = NeonRecording(recording_dir)\n",
     "print(type(recording))\n",
     "print(recording.recording_dir)"
@@ -158,23 +156,23 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Recording ID: e116e606-5f3f-4d34-8727-040b8762cef8\n",
-      "Wearer ID: bcff2832-cfcb-4f89-abef-7bbfe91ec561\n",
+      "Recording ID: d4fd9a27-3e28-45bf-937f-b9c14c3c1c5e\n",
+      "Wearer ID: af6cd360-443a-4d3d-adda-7dc8510473c2\n",
       "Wearer name: Qian\n",
-      "Recording start time: 2024-08-30 17:37:01.527000\n",
-      "Recording duration: 98.213s\n",
-      "                  exist              filename                                                                                  path\n",
-      "3d_eye_states      True     3d_eye_states.csv     D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\3d_eye_states.csv\n",
-      "blinks             True            blinks.csv            D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\blinks.csv\n",
-      "events             True            events.csv            D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\events.csv\n",
-      "fixations          True         fixations.csv         D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\fixations.csv\n",
-      "gaze               True              gaze.csv              D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\gaze.csv\n",
-      "imu                True               imu.csv               D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\imu.csv\n",
-      "labels             True            labels.csv            D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\labels.csv\n",
-      "saccades           True          saccades.csv          D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\saccades.csv\n",
-      "world_timestamps   True  world_timestamps.csv  D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\world_timestamps.csv\n",
-      "scene_video_info  False                  None                                                                                  None\n",
-      "scene_video       False                  None                                                                                  None\n",
+      "Recording start time: 2024-11-26 12:44:48.937000\n",
+      "Recording duration: 32.046s\n",
+      "                 exist                 filename                                                                                                                                path\n",
+      "3d_eye_states     True        3d_eye_states.csv        C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\3d_eye_states.csv\n",
+      "blinks            True               blinks.csv               C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\blinks.csv\n",
+      "events            True               events.csv               C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\events.csv\n",
+      "fixations         True            fixations.csv            C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\fixations.csv\n",
+      "gaze              True                 gaze.csv                 C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\gaze.csv\n",
+      "imu               True                  imu.csv                  C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\imu.csv\n",
+      "labels            True               labels.csv               C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\labels.csv\n",
+      "saccades          True             saccades.csv             C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\saccades.csv\n",
+      "world_timestamps  True     world_timestamps.csv     C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\world_timestamps.csv\n",
+      "scene_video_info  True        scene_camera.json        C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\scene_camera.json\n",
+      "scene_video       True  182240fd_0.0-32.046.mp4  C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\\182240fd_0.0-32.046.mp4\n",
       "\n"
      ]
     }
@@ -187,9 +185,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As seen in the output, this recording includes all data files except the scene video and its metadata because we downloaded only the \"Timeseries Data\" instead of \" \"Timeseries Data + Scene Video\" from Pupil Cloud. For processing video, refer to the [Neon video tutorial](video.ipynb).\n",
+    "As seen in the output, this recording includes all data files. This tutorial will focus on non-video data. For processing video, refer to the [Neon video tutorial](video.ipynb).\n",
     "\n",
-    "Individual data streams can be accessed as properties of the `NeonRecording` object. For example, the gaze data can be accessed as `recording.gaze`, and upon accessing, the tabular data is loaded into memory. On the other hand, if you try to access unavailable data like the video, it will simply return `None` and a warning message."
+    "Individual data streams can be accessed as properties of the `NeonRecording` object. For example, the gaze data can be accessed as `recording.gaze`, and upon accessing, the tabular data is loaded into memory. On the other hand, if you try to access unavailable data, PyNeon will return `None` and a warning message."
    ]
   },
   {
@@ -201,17 +199,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "recording.gaze is <pyneon.stream.NeonGaze object at 0x000001B2167FC830>\n",
-      "recording.fixations is <pyneon.events.NeonFixations object at 0x000001B236FA6DB0>\n",
-      "recording.video is None\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "D:\\GitHub\\pyneon\\pyneon\\recording.py:271: UserWarning: Scene video not loaded because not all video-related files (video, scene_camera.json, world_timestamps.csv) are found.\n",
-      "  warnings.warn(\n"
+      "recording.gaze is <pyneon.stream.NeonGaze object at 0x0000027AE3680C20>\n",
+      "recording.saccades is <pyneon.events.NeonSaccades object at 0x0000027AC22E0B90>\n",
+      "recording.video is < cv2.VideoCapture 0000027AE592DB90>\n"
      ]
     }
    ],
@@ -219,10 +209,8 @@
     "# Gaze and fixation data are available\n",
     "gaze = recording.gaze\n",
     "print(f\"recording.gaze is {gaze}\")\n",
-    "fixations = recording.fixations\n",
-    "print(f\"recording.fixations is {fixations}\")\n",
-    "\n",
-    "# Video is not available\n",
+    "saccades = recording.saccades\n",
+    "print(f\"recording.saccades is {saccades}\")\n",
     "video = recording.video\n",
     "print(f\"recording.video is {video}\")"
    ]
@@ -269,19 +257,19 @@
      "text": [
       "                     gaze x [px]  gaze y [px]  worn  fixation id  blink id  \\\n",
       "timestamp [ns]                                                               \n",
-      "1725032224852161732     1067.486      620.856     1            1      <NA>   \n",
-      "1725032224857165732     1066.920      617.117     1            1      <NA>   \n",
-      "1725032224862161732     1072.699      615.780     1            1      <NA>   \n",
-      "1725032224867161732     1067.447      617.062     1            1      <NA>   \n",
-      "1725032224872161732     1071.564      613.158     1            1      <NA>   \n",
+      "1732621490425631343      697.829      554.242     1            1      <NA>   \n",
+      "1732621490430625343      698.096      556.335     1            1      <NA>   \n",
+      "1732621490435625343      697.810      556.360     1            1      <NA>   \n",
+      "1732621490440625343      695.752      557.903     1            1      <NA>   \n",
+      "1732621490445625343      696.108      558.438     1            1      <NA>   \n",
       "\n",
       "                     azimuth [deg]  elevation [deg]  \n",
       "timestamp [ns]                                       \n",
-      "1725032224852161732      16.213030        -0.748998  \n",
-      "1725032224857165732      16.176285        -0.511733  \n",
-      "1725032224862161732      16.546413        -0.426618  \n",
-      "1725032224867161732      16.210049        -0.508251  \n",
-      "1725032224872161732      16.473521        -0.260388  \n",
+      "1732621490425631343      -7.581023         3.519804  \n",
+      "1732621490430625343      -7.563214         3.385485  \n",
+      "1732621490435625343      -7.581576         3.383787  \n",
+      "1732621490440625343      -7.713686         3.284294  \n",
+      "1732621490445625343      -7.690596         3.250055  \n",
       "gaze x [px]        float64\n",
       "gaze y [px]        float64\n",
       "worn                 Int32\n",
@@ -307,43 +295,43 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                      fixation id   end timestamp [ns]  duration [ms]  \\\n",
-      "start timestamp [ns]                                                    \n",
-      "1725032224852161732             1  1725032225007283732            155   \n",
-      "1725032225027282732             2  1725032225282527732            255   \n",
-      "1725032225347526732             3  1725032225617770732            270   \n",
-      "1725032225667907732             4  1725032225798022732            130   \n",
-      "1725032225833015732             5  1725032225958137732            125   \n",
+      "                      saccade id   end timestamp [ns]  duration [ms]  \\\n",
+      "start timestamp [ns]                                                   \n",
+      "1732621490876132343            1  1732621490891115343             15   \n",
+      "1732621491241357343            2  1732621491291481343             50   \n",
+      "1732621491441602343            3  1732621491516601343             75   \n",
+      "1732621491626723343            4  1732621491696847343             70   \n",
+      "1732621491917092343            5  1732621491977090343             60   \n",
       "\n",
-      "                      fixation x [px]  fixation y [px]  azimuth [deg]  \\\n",
-      "start timestamp [ns]                                                    \n",
-      "1725032224852161732          1069.932          614.843      16.369094   \n",
-      "1725032225027282732           906.439          538.107       5.878844   \n",
-      "1725032225347526732           694.843          533.982      -7.781338   \n",
-      "1725032225667907732           572.983          488.800     -15.679003   \n",
-      "1725032225833015732           601.861          491.125     -13.813521   \n",
+      "                      amplitude [px]  amplitude [deg]  mean velocity [px/s]  \\\n",
+      "start timestamp [ns]                                                          \n",
+      "1732621490876132343        14.938179         0.962102           1025.709879   \n",
+      "1732621491241357343       130.743352         8.378644           2700.713283   \n",
+      "1732621491441602343       241.003342        15.391730           3615.380044   \n",
+      "1732621491626723343       212.619205        13.608618           3757.394092   \n",
+      "1732621491917092343       220.842812        13.914266           4220.180601   \n",
       "\n",
-      "                      elevation [deg]  \n",
-      "start timestamp [ns]                   \n",
-      "1725032224852161732         -0.367312  \n",
-      "1725032225027282732          4.561914  \n",
-      "1725032225347526732          4.819739  \n",
-      "1725032225667907732          7.636408  \n",
-      "1725032225833015732          7.512433  \n",
-      "fixation id             Int32\n",
-      "end timestamp [ns]      Int64\n",
-      "duration [ms]           Int64\n",
-      "fixation x [px]       float64\n",
-      "fixation y [px]       float64\n",
-      "azimuth [deg]         float64\n",
-      "elevation [deg]       float64\n",
+      "                      peak velocity [px/s]  \n",
+      "start timestamp [ns]                        \n",
+      "1732621490876132343            1191.520740  \n",
+      "1732621491241357343            3687.314947  \n",
+      "1732621491441602343            5337.244676  \n",
+      "1732621491626723343            6164.040944  \n",
+      "1732621491917092343            6369.217052  \n",
+      "saccade id                Int32\n",
+      "end timestamp [ns]        Int64\n",
+      "duration [ms]             Int64\n",
+      "amplitude [px]          float64\n",
+      "amplitude [deg]         float64\n",
+      "mean velocity [px/s]    float64\n",
+      "peak velocity [px/s]    float64\n",
       "dtype: object\n"
      ]
     }
    ],
    "source": [
-    "print(fixations.data.head())\n",
-    "print(fixations.data.dtypes)"
+    "print(saccades.data.head())\n",
+    "print(saccades.data.dtypes)"
    ]
   },
   {
@@ -360,7 +348,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Just like any other `pandas.DataFrame`, you can access individual rows, columns, or subsets of the data using the standard indexing and slicing methods. For example, `gaze.data.iloc[0]` returns the first row of the gaze data, and `gaze.data['gaze x [px]']` returns the gaze x-coordinate column."
+    "Just like any other `pandas.DataFrame`, you can access individual rows, columns, or subsets of the data using the standard indexing and slicing methods. For example, `gaze.data.iloc[0]` returns the first row of the gaze data, and `gaze.data['gaze x [px]']` (or `gaze['gaze x [px]']`) returns the gaze x-coordinate column."
    ]
   },
   {
@@ -373,35 +361,35 @@
      "output_type": "stream",
      "text": [
       "First row of gaze data:\n",
-      "gaze x [px]        1067.486\n",
-      "gaze y [px]         620.856\n",
+      "gaze x [px]         697.829\n",
+      "gaze y [px]         554.242\n",
       "worn                    1.0\n",
       "fixation id             1.0\n",
       "blink id               <NA>\n",
-      "azimuth [deg]      16.21303\n",
-      "elevation [deg]   -0.748998\n",
-      "Name: 1725032224852161732, dtype: Float64\n",
+      "azimuth [deg]     -7.581023\n",
+      "elevation [deg]    3.519804\n",
+      "Name: 1732621490425631343, dtype: Float64\n",
       "\n",
       "All gaze x values:\n",
       "timestamp [ns]\n",
-      "1725032224852161732    1067.486\n",
-      "1725032224857165732    1066.920\n",
-      "1725032224862161732    1072.699\n",
-      "1725032224867161732    1067.447\n",
-      "1725032224872161732    1071.564\n",
-      "                         ...   \n",
-      "1725032319717194732     800.364\n",
-      "1725032319722198732     799.722\n",
-      "1725032319727194732     799.901\n",
-      "1725032319732194732     796.982\n",
-      "1725032319737194732     797.285\n",
-      "Name: gaze x [px], Length: 18769, dtype: float64\n"
+      "1732621490425631343    697.829\n",
+      "1732621490430625343    698.096\n",
+      "1732621490435625343    697.810\n",
+      "1732621490440625343    695.752\n",
+      "1732621490445625343    696.108\n",
+      "                        ...   \n",
+      "1732621520958946343    837.027\n",
+      "1732621520964071343    836.595\n",
+      "1732621520969071343    836.974\n",
+      "1732621520974075343    835.169\n",
+      "1732621520979070343    833.797\n",
+      "Name: gaze x [px], Length: 6091, dtype: float64\n"
      ]
     }
    ],
    "source": [
     "print(f\"First row of gaze data:\\n{gaze.data.iloc[0]}\\n\")\n",
-    "print(f\"All gaze x values:\\n{gaze.data['gaze x [px]']}\")"
+    "print(f\"All gaze x values:\\n{gaze['gaze x [px]']}\")"
    ]
   },
   {
@@ -423,10 +411,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1725032224852161732 1725032224857165732 1725032224862161732 ...\n",
-      " 1725032319727194732 1725032319732194732 1725032319737194732]\n",
-      "[0.0000000e+00 5.0040000e-03 1.0000000e-02 ... 9.4875033e+01 9.4880033e+01\n",
-      " 9.4885033e+01]\n"
+      "[1732621490425631343 1732621490430625343 1732621490435625343 ...\n",
+      " 1732621520969071343 1732621520974075343 1732621520979070343]\n",
+      "[0.0000000e+00 4.9940000e-03 9.9940000e-03 ... 3.0543440e+01 3.0548444e+01\n",
+      " 3.0553439e+01]\n"
      ]
     }
    ],
@@ -439,7 +427,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Timestamps (UTC, in ns) and relative time (relative to the stream start, in s) are thus the two units of time that are most commonly used in PyNeon. For example, you can crop the stream by either timestamp or relative time by calling the `crop()` method. The method takes two arguments: `start` and `end`:"
+    "Timestamps (UTC, in ns), relative time (relative to the stream start, in s), and index are the three units of time that are most commonly used in PyNeon. For example, you can crop the stream by either timestamp or relative time by calling the `crop()` method. The method takes `start` and `end` of the crop window in either UTC timestamps or relative time, and uses `by` to specify whether "
    ]
   },
   {
@@ -451,18 +439,49 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "94.885033\n",
-      "9.999289\n"
+      "Gaze data points before cropping: 6091\n",
+      "Gaze data points after cropping: 999\n"
      ]
     }
    ],
    "source": [
-    "# Last data time of the original gaze data\n",
-    "print(gaze.times[-1])\n",
+    "print(f\"Gaze data points before cropping: {len(gaze)}\")\n",
     "\n",
-    "# Crop the gaze data to the first 10 seconds\n",
-    "gaze_cropped = gaze.crop(0, 10, by=\"time\")  # Crop by time\n",
-    "print(gaze_cropped.times[-1])"
+    "# Crop the gaze data to 5-10 seconds\n",
+    "gaze_crop = gaze.crop(5, 10, by=\"time\")  # Crop by time\n",
+    "print(f\"Gaze data points after cropping: {len(gaze_crop)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You may also want to restrict one stream to the temporal range of another stream. This can be done by calling the `restrict()` method. The method takes another `NeonStream` instance as an argument and crops the stream to the intersection of the two streams' temporal ranges."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IMU first timestamp: 1732621495435389343 > Gaze first timestamp: 1732621495430263343\n",
+      "IMU last timestamp: 1732621500421101343 < Gaze last timestamp: 1732621500424901343\n"
+     ]
+    }
+   ],
+   "source": [
+    "imu_crop = recording.imu.restrict(gaze_crop)\n",
+    "saccades_crop = saccades.restrict(gaze_crop)\n",
+    "print(\n",
+    "    f\"IMU first timestamp: {imu_crop.first_ts} > Gaze first timestamp: {gaze_crop.first_ts}\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"IMU last timestamp: {imu_crop.last_ts} < Gaze last timestamp: {gaze_crop.last_ts}\"\n",
+    ")"
    ]
   },
   {
@@ -476,52 +495,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Data streams and events\n",
+    "## An example plot of cropped data\n",
     "\n",
-    "Up to this point, PyNeon simply reads and re-organizes the raw .csv files. Let's plot some samples from the `gaze` and `eye_states` streams and a saccade from the `saccades` events."
+    "Below we show how to easily plot the gaze and saccade data we cropped just now. Since PyNeon data are stored in `pandas.DataFrame`, you can use any plotting library that supports `pandas.DataFrame` as input. Here we use `seaaborn` and `matplotlib` to plot the gaze x, y coordinates and the saccade durations (shadowed areas)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "saccade id                                2.0\n",
-      "end timestamp [ns]      1725032225347526656.0\n",
-      "duration [ms]                            65.0\n",
-      "amplitude [px]                      228.36139\n",
-      "amplitude [deg]                     14.676102\n",
-      "mean velocity [px/s]              3685.269894\n",
-      "peak velocity [px/s]              5411.775481\n",
-      "Name: 1725032225282527732, dtype: Float64\n"
-     ]
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "unhashable type: 'numpy.ndarray'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[12], line 19\u001b[0m\n\u001b[0;32m     17\u001b[0m saccade \u001b[38;5;241m=\u001b[39m fixations\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m     18\u001b[0m \u001b[38;5;28mprint\u001b[39m(saccade)\n\u001b[1;32m---> 19\u001b[0m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxvspan\u001b[49m\u001b[43m(\u001b[49m\u001b[43msaccade\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msaccade\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mend timestamp [ns]\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlightgray\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     20\u001b[0m ax\u001b[38;5;241m.\u001b[39mtext(\n\u001b[0;32m     21\u001b[0m     (saccade\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mvalues \u001b[38;5;241m+\u001b[39m saccade[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mend timestamp [ns]\u001b[39m\u001b[38;5;124m\"\u001b[39m]) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m,\n\u001b[0;32m     22\u001b[0m     \u001b[38;5;241m1050\u001b[39m,\n\u001b[0;32m     23\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSaccade\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     24\u001b[0m     horizontalalignment\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     25\u001b[0m )\n\u001b[0;32m     27\u001b[0m \u001b[38;5;66;03m# Visualize gaze x and pupil diameter left\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:1087\u001b[0m, in \u001b[0;36mAxes.axvspan\u001b[1;34m(self, xmin, xmax, ymin, ymax, **kwargs)\u001b[0m\n\u001b[0;32m   1085\u001b[0m \u001b[38;5;66;03m# Strip units away.\u001b[39;00m\n\u001b[0;32m   1086\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_no_units([ymin, ymax], [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mymin\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mymax\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m-> 1087\u001b[0m (xmin, xmax), \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_unit_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mxmin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxmax\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1089\u001b[0m p \u001b[38;5;241m=\u001b[39m mpatches\u001b[38;5;241m.\u001b[39mRectangle((xmin, ymin), xmax \u001b[38;5;241m-\u001b[39m xmin, ymax \u001b[38;5;241m-\u001b[39m ymin, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1090\u001b[0m p\u001b[38;5;241m.\u001b[39mset_transform(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_xaxis_transform(which\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgrid\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n",
-      "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2585\u001b[0m, in \u001b[0;36m_AxesBase._process_unit_info\u001b[1;34m(self, datasets, kwargs, convert)\u001b[0m\n\u001b[0;32m   2583\u001b[0m     \u001b[38;5;66;03m# Update from data if axis is already set but no unit is set yet.\u001b[39;00m\n\u001b[0;32m   2584\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m axis\u001b[38;5;241m.\u001b[39mhave_units():\n\u001b[1;32m-> 2585\u001b[0m         \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2586\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis_name, axis \u001b[38;5;129;01min\u001b[39;00m axis_map\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m   2587\u001b[0m     \u001b[38;5;66;03m# Return if no axis is set.\u001b[39;00m\n\u001b[0;32m   2588\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\axis.py:1756\u001b[0m, in \u001b[0;36mAxis.update_units\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m   1754\u001b[0m neednew \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter \u001b[38;5;241m!=\u001b[39m converter\n\u001b[0;32m   1755\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter \u001b[38;5;241m=\u001b[39m converter\n\u001b[1;32m-> 1756\u001b[0m default \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconverter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdefault_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1757\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m default \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1758\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_units(default)\n",
-      "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\category.py:105\u001b[0m, in \u001b[0;36mStrCategoryConverter.default_units\u001b[1;34m(data, axis)\u001b[0m\n\u001b[0;32m    103\u001b[0m \u001b[38;5;66;03m# the conversion call stack is default_units -> axis_info -> convert\u001b[39;00m\n\u001b[0;32m    104\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis\u001b[38;5;241m.\u001b[39munits \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 105\u001b[0m     axis\u001b[38;5;241m.\u001b[39mset_units(\u001b[43mUnitData\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m    106\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    107\u001b[0m     axis\u001b[38;5;241m.\u001b[39munits\u001b[38;5;241m.\u001b[39mupdate(data)\n",
-      "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\category.py:181\u001b[0m, in \u001b[0;36mUnitData.__init__\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    179\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_counter \u001b[38;5;241m=\u001b[39m itertools\u001b[38;5;241m.\u001b[39mcount()\n\u001b[0;32m    180\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 181\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\category.py:214\u001b[0m, in \u001b[0;36mUnitData.update\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    212\u001b[0m \u001b[38;5;66;03m# check if convertible to number:\u001b[39;00m\n\u001b[0;32m    213\u001b[0m convertible \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 214\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m val \u001b[38;5;129;01min\u001b[39;00m \u001b[43mOrderedDict\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfromkeys\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m    215\u001b[0m     \u001b[38;5;66;03m# OrderedDict just iterates over unique values in data.\u001b[39;00m\n\u001b[0;32m    216\u001b[0m     _api\u001b[38;5;241m.\u001b[39mcheck_isinstance((\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbytes\u001b[39m), value\u001b[38;5;241m=\u001b[39mval)\n\u001b[0;32m    217\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m convertible:\n\u001b[0;32m    218\u001b[0m         \u001b[38;5;66;03m# this will only be called so long as convertible is True.\u001b[39;00m\n",
-      "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'numpy.ndarray'"
-     ]
-    },
     {
      "data": {
-      "image/png": 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r1qxobGyMEydOnPO6w4cPxze+8Y248cYbcw8JAAAwFKXql9yh9cQTT8RXv/rVWLZsWdTW1saGDRvikksuiR/+8Idnvaavry++8pWvxCOPPBKXX375kAYFAADIq1T9kiu0ent7Y+fOndHQ0PDBC5SVRUNDQ7S1tZ31um9961sxefLkuP3228/rPj09PdHZ2Vl8dHV15RkTAAAY5bq6ugY0Q09Pzxl7hqtfBpMrtE6dOhV9fX1RXV09YL26ujra29sHveZXv/pVPPPMM7Fx48bzvk9LS0tMmDCh+Kitrc0zJgAAMMrV1tYOaIaWlpYz9gxXvwxm7Ee6+kN0dXXF4sWLY+PGjTFp0qTzvm7lypXR1NRUfH78+HGxBQAAFO3bty9qamqKzysrKz/yaw61XwaTK7QmTZoU5eXl0dHRMWC9o6MjpkyZcsb+3/zmN3H48OGYP39+ca2/v/+PNx47Ng4ePBhXXHHFGddVVlYO+AfV2dmZZ0wAAGCUGzduXIwfP/6ce4arXwaT66ODFRUVMWfOnGhtbR1w49bW1qivrz9j/1VXXRWvvfZa7Nmzp/j40pe+FDfffHPs2bMnCoVCntsDAACct1L2S+6PDjY1NcXSpUtj7ty5MW/evFi3bl10d3fHsmXLIiJiyZIlUVNTEy0tLVFVVRUzZ84ccP2ll14aEXHGOgAAQGql6pfcobVw4cI4efJkrFq1Ktrb22P27Nmxbdu24hfMjhw5EmVlQ/o9yAAAAEmVql/GZFmWJX/VxI4dOxaFQiGOHj0a06dPL/U4AABAiYyUNvDWEwAAQGJCCwAAIDGhBQAAkJjQAgAASExoAQAAJCa0AAAAEhNaAAAAiQktAACAxIQWAABAYkILAAAgMaEFAACQmNACAABITGgBAAAkJrQAAAASE1oAAACJCS0AAIDEhBYAAEBiQgsAACAxoQUAAJCY0AIAAEhMaAEAACQmtAAAABITWgAAAIkJLQAAgMSEFgAAQGJCCwAAIDGhBQAAkJjQAgAASExoAQAAJCa0AAAAEhNaAAAAiQktAACAxIQWAABAYkILAAAgMaEFAACQmNACAABITGgBAAAkJrQAAAASE1oAAACJCS0AAIDEhBYAAEBiQgsAACAxoQUAAJCY0AIAAEhMaAEAACQmtAAAABITWgAAAIkJLQAAgMSEFgAAQGJCCwAAIDGhBQAAkJjQAgAASExoAQAAJCa0AAAAEhNaAAAAiQktAACAxIQWAABAYkILAAAgMaEFAACQmNACAABITGgBAAAkJrQAAAASE1oAAACJCS0AAIDEhBYAAEBiQgsAACCxIYXW+vXrY8aMGVFVVRV1dXWxY8eOs+7duHFj3HjjjTFx4sSYOHFiNDQ0nHM/AABASqXol9yhtXnz5mhqaorm5ubYtWtXzJo1KxobG+PEiROD7t++fXssWrQoXnnllWhra4tCoRC33HJLHD9+PPewAAAAeZSqX8ZkWZbluaCuri6uu+66ePLJJyMior+/PwqFQtx9992xYsWKD72+r68vJk6cGE8++WQsWbLkvO557NixKBQKcfTo0Zg+fXqecQEAgFEkbxuUol8icr6j1dvbGzt37oyGhoYPXqCsLBoaGqKtre28XuPdd9+N9957Ly677LKz7unp6YnOzs7io6urK8+YAADAKNfV1TWgGXp6es7YM1z9MphcoXXq1Kno6+uL6urqAevV1dXR3t5+Xq9x//33x7Rp0wb8Yf9US0tLTJgwofiora3NMyYAADDK1dbWDmiGlpaWM/YMV78MZmyu3R/R2rVrY9OmTbF9+/aoqqo6676VK1dGU1NT8fnx48fFFgAAULRv376oqakpPq+srEx+j/Ptl8HkCq1JkyZFeXl5dHR0DFjv6OiIKVOmnPPaxx57LNauXRu/+MUv4pprrjnn3srKygH/oDo7O/OMCQAAjHLjxo2L8ePHn3PPcPXLYHJ9dLCioiLmzJkTra2txbX+/v5obW2N+vr6s1733e9+Nx599NHYtm1bzJ07N/eQAAAAeZWyX3J/dLCpqSmWLl0ac+fOjXnz5sW6deuiu7s7li1bFhERS5YsiZqamuJnJP/lX/4lVq1aFc8991zMmDGj+FnIT3ziE/GJT3xiSEMDAACcj1L1S+7QWrhwYZw8eTJWrVoV7e3tMXv27Ni2bVvxC2ZHjhyJsrIP3ij7/ve/H729vfH3f//3A16nubk5Vq9enff2AAAA561U/ZL792iVgt+jBQAARIycNsj1HS0AAAA+nNACAABITGgBAAAkJrQAAAASE1oAAACJCS0AAIDEhBYAAEBiQgsAACAxoQUAAJCY0AIAAEhMaAEAACQmtAAAABITWgAAAIkJLQAAgMSEFgAAQGJCCwAAIDGhBQAAkJjQAgAASExoAQAAJCa0AAAAEhNaAAAAiQktAACAxIQWAABAYkILAAAgMaEFAACQmNACAABITGgBAAAkJrQAAAASE1oAAACJCS0AAIDEhBYAAEBiQgsAACAxoQUAAJCY0AIAAEhMaAEAACQmtAAAABITWgAAAIkJLQAAgMSEFgAAQGJCCwAAIDGhBQAAkJjQAgAASExoAQAAJCa0AAAAEhNaAAAAiQktAACAxIQWAABAYkILAAAgMaEFAACQmNACAABITGgBAAAkJrQAAAASE1oAAACJCS0AAIDEhBYAAEBiQgsAACAxoQUAAJCY0AIAAEhMaAEAACQmtAAAABITWgAAAIkJLQAAgMSEFgAAQGJCCwAAIDGhBQAAkJjQAgAASGxIobV+/fqYMWNGVFVVRV1dXezYseOc+3/yk5/EVVddFVVVVXH11VfH1q1bhzQsAABAXqXol9yhtXnz5mhqaorm5ubYtWtXzJo1KxobG+PEiROD7n/11Vdj0aJFcfvtt8fu3btjwYIFsWDBgti7d2/uYQEAAPIoVb+MybIsy3NBXV1dXHfddfHkk09GRER/f38UCoW4++67Y8WKFWfsX7hwYXR3d8fPf/7z4trf/M3fxOzZs2PDhg3ndc9jx45FoVCIo0ePxvTp0/OMCwAAjCJ526AU/RIRMfa8d0ZEb29v7Ny5M1auXFlcKysri4aGhmhraxv0mra2tmhqahqw1tjYGC+++OJZ79PT0xM9PT3F5++8805ERLz11lt5xgUAAEaZ95vgnXfeifHjxxfXKysro7KycsDe4eqXweQKrVOnTkVfX19UV1cPWK+uro4DBw4Mek17e/ug+9vb2896n5aWlnjkkUfOWJ83b16ecQEAgFFq5syZA543NzfH6tWrB6wNV78MJldoDZeVK1cOqMjf/e538clPfjL27t0bEyZMKOFkjHZdXV1RW1sb+/bti3HjxpV6HEYxZ43h4qwxXJw1hss777wTM2fOjDfffDMuu+yy4vqfvptVarlCa9KkSVFeXh4dHR0D1js6OmLKlCmDXjNlypRc+yMGf9svIqJQKAx4exBS6+zsjIiImpoaZ40LylljuDhrDBdnjeHy/vm67LLLPvSsDVe/DCbXTx2sqKiIOXPmRGtra3Gtv78/Wltbo76+ftBr6uvrB+yPiHj55ZfPuh8AACCFUvZL7o8ONjU1xdKlS2Pu3Lkxb968WLduXXR3d8eyZcsiImLJkiVRU1MTLS0tERFxzz33xE033RSPP/543HrrrbFp06b49a9/HU8//XTeWwMAAORSqn7JHVoLFy6MkydPxqpVq6K9vT1mz54d27ZtK35h7MiRI1FW9sEbZddff30899xz8dBDD8UDDzwQf/3Xfx0vvvjiGV9eO5fKyspobm7+2H3uktHHWWO4OGsMF2eN4eKsMVzynrVS9EvEEH6PFgAAAOeW6ztaAAAAfDihBQAAkJjQAgAASExoAQAAJPaxCa3169fHjBkzoqqqKurq6mLHjh3n3P+Tn/wkrrrqqqiqqoqrr746tm7dOkyTMtLlOWsbN26MG2+8MSZOnBgTJ06MhoaGDz2b8L68f6+9b9OmTTFmzJhYsGDBhR2QUSPvWXv77bdj+fLlMXXq1KisrIwrr7zSv0c5L3nP2rp16+LTn/50XHzxxVEoFOK+++6LP/zhD8M0LSPRL3/5y5g/f35MmzYtxowZEy+++OKHXrN9+/b43Oc+F5WVlfGpT30qnn322Qs+5/n4WITW5s2bo6mpKZqbm2PXrl0xa9asaGxsjBMnTgy6/9VXX41FixbF7bffHrt3744FCxbEggULYu/evcM8OSNN3rO2ffv2WLRoUbzyyivR1tYWhUIhbrnlljh+/PgwT85Ik/esve/w4cPxjW98I2688cZhmpSRLu9Z6+3tjS984Qtx+PDheP755+PgwYOxcePGqKmpGebJGWnynrXnnnsuVqxYEc3NzbF///545plnYvPmzfHAAw8M8+SMJN3d3TFr1qxYv379ee1/880349Zbb42bb7459uzZE/fee2/ccccd8dJLL13gSc9D9jEwb968bPny5cXnfX192bRp07KWlpZB93/5y1/Obr311gFrdXV12T/+4z9e0DkZ+fKetT91+vTpbNy4cdmPfvSjCzUio8RQztrp06ez66+/PvvBD36QLV26NPu7v/u7YZiUkS7vWfv+97+fXX755Vlvb+9wjcgokfesLV++PPvbv/3bAWtNTU3ZDTfccEHnZPSIiOyFF144555vfvOb2Wc/+9kBawsXLswaGxsv4GTnp+TvaPX29sbOnTujoaGhuFZWVhYNDQ3R1tY26DVtbW0D9kdENDY2nnU/RAztrP2pd999N95777247LLLLtSYjAJDPWvf+ta3YvLkyXH77bcPx5iMAkM5az/72c+ivr4+li9fHtXV1TFz5sxYs2ZN9PX1DdfYjEBDOWvXX3997Ny5s/jxwkOHDsXWrVvji1/84rDMzJ+Hj3MXjC31AKdOnYq+vr7ib2Z+X3V1dRw4cGDQa9rb2wfd397efsHmZOQbyln7U/fff39MmzbtjP9Cw/83lLP2q1/9Kp555pnYs2fPMEzIaDGUs3bo0KH4z//8z/jKV74SW7dujTfeeCO+/vWvx3vvvRfNzc3DMTYj0FDO2m233RanTp2Kz3/+85FlWZw+fTruuusuHx0kqbN1QWdnZ/z+97+Piy++uESTfUy+owUjwdq1a2PTpk3xwgsvRFVVVanHYRTp6uqKxYsXx8aNG2PSpEmlHodRrr+/PyZPnhxPP/10zJkzJxYuXBgPPvhgbNiwodSjMcps37491qxZE0899VTs2rUrfvrTn8aWLVvi0UcfLfVoMCxK/o7WpEmTory8PDo6Ogasd3R0xJQpUwa9ZsqUKbn2Q8TQztr7HnvssVi7dm384he/iGuuueZCjskokPes/eY3v4nDhw/H/Pnzi2v9/f0RETF27Ng4ePBgXHHFFRd2aEakofy9NnXq1LjooouivLy8uPaZz3wm2tvbo7e3NyoqKi7ozIxMQzlrDz/8cCxevDjuuOOOiIi4+uqro7u7O+6888548MEHo6zM/9/PR3e2Lhg/fnxJ382K+Bi8o1VRURFz5syJ1tbW4lp/f3+0trZGfX39oNfU19cP2B8R8fLLL591P0QM7axFRHz3u9+NRx99NLZt2xZz584djlEZ4fKetauuuipee+212LNnT/HxpS99qfgTlAqFwnCOzwgylL/XbrjhhnjjjTeKMR8R8frrr8fUqVNFFmc1lLP27rvvnhFT7wd+lmUXblj+rHysu6DUP40jy7Js06ZNWWVlZfbss89m+/bty+68887s0ksvzdrb27Msy7LFixdnK1asKO7/r//6r2zs2LHZY489lu3fvz9rbm7OLrroouy1114r1R+BESLvWVu7dm1WUVGRPf/889lbb71VfHR1dZXqj8AIkfes/Sk/dZDzlfesHTlyJBs3blz2T//0T9nBgwezn//859nkyZOzb3/726X6IzBC5D1rzc3N2bhx47J///d/zw4dOpT9x3/8R3bFFVdkX/7yl0v1R2AE6Orqynbv3p3t3r07i4jsiSeeyHbv3p399re/zbIsy1asWJEtXry4uP/QoUPZJZdckv3zP/9ztn///mz9+vVZeXl5tm3btlL9EYo+FqGVZVn2r//6r9lf/uVfZhUVFdm8efOy//7v/y7+ZzfddFO2dOnSAft//OMfZ1deeWVWUVGRffazn822bNkyzBMzUuU5a3/1V3+VRcQZj+bm5uEfnBEn799r/5/QIo+8Z+3VV1/N6urqssrKyuzyyy/PvvOd72SnT58e5qkZifKctffeey9bvXp1dsUVV2RVVVVZoVDIvv71r2f/8z//M/yDM2K88sorg/5vr/fP1tKlS7ObbrrpjGtmz56dVVRUZJdffnn2b//2b8M+92DGZJn3bgEAAFIq+Xe0AAAARhuhBQAAkJjQAgAASExoAQAAJCa0AAAAEhNaAAAAiQktAACAxIQWAABAYkILAAAgMaEFAACQmNACAABITGgBAAAk9n+93APIaM1xaAAAAABJRU5ErkJggg==",
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",
       "text/plain": [
-       "<Figure size 1000x500 with 2 Axes>"
+       "<Figure size 1000x400 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -532,69 +520,33 @@
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
     "\n",
-    "gaze_color = \"royalblue\"\n",
-    "gyro_color = \"darkorange\"\n",
-    "\n",
-    "imu = recording.imu\n",
-    "fixations = recording.saccades\n",
-    "\n",
     "# Create a figure\n",
-    "fig, ax = plt.subplots(figsize=(10, 5))\n",
-    "ax2 = ax.twinx()\n",
-    "ax.yaxis.label.set_color(gaze_color)\n",
-    "ax2.yaxis.label.set_color(gyro_color)\n",
+    "fig, ax = plt.subplots(figsize=(10, 4))\n",
     "\n",
-    "# Visualize the 2nd saccade\n",
-    "saccade = fixations.data.iloc[1]\n",
-    "print(saccade)\n",
-    "ax.axvspan(saccade.index.values, saccade[\"end timestamp [ns]\"], color=\"lightgray\")\n",
-    "ax.text(\n",
-    "    (saccade.index.values + saccade[\"end timestamp [ns]\"]) / 2,\n",
-    "    1050,\n",
-    "    \"Saccade\",\n",
-    "    horizontalalignment=\"center\",\n",
-    ")\n",
+    "# Visualize the 1st saccade\n",
+    "for _, sac in saccades_crop.data.iterrows():\n",
+    "    ax.axvspan(sac.name, sac[\"end timestamp [ns]\"], color=\"lightgray\")\n",
     "\n",
-    "# Visualize gaze x and pupil diameter left\n",
-    "sns.scatterplot(\n",
+    "# Visualize gaze x and y\n",
+    "sns.lineplot(\n",
     "    ax=ax,\n",
-    "    data=gaze.data.head(100),\n",
-    "    x=gaze.data.index,\n",
+    "    data=gaze_crop.data,\n",
+    "    x=gaze_crop.data.index,\n",
     "    y=\"gaze x [px]\",\n",
-    "    color=gaze_color,\n",
+    "    color=\"b\",\n",
+    "    label=\"Gaze x\",\n",
     ")\n",
-    "sns.scatterplot(\n",
-    "    ax=ax2,\n",
-    "    data=imu.data.head(60),\n",
-    "    x=imu.data.index,\n",
-    "    y=\"gyro x [deg/s]\",\n",
-    "    color=gyro_color,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It's apparent that at the beginning of the recording, there are some missing data points in both the `gaze` and `imu` streams. This is presumably due to the time it takes for the sensors to start up and stabilize. We will show how to handle missing data using resampling in the next tutorial. For now, it's important to be aware of these gaps and that it will require great caution to assume the data is continuously and equally sampled.\n",
-    "\n",
-    "PyNeon also calculates the effective (as opposed to the nominal) sampling frequency of each stream by dividing the number of samples by the duration of the recording."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\n",
-    "    f\"Gaze: nominal sampling frequency = {gaze.sampling_freq_nominal}, \"\n",
-    "    f\"effective sampling frequency = {gaze.sampling_freq_effective}\"\n",
+    "sns.lineplot(\n",
+    "    ax=ax,\n",
+    "    data=gaze_crop.data,\n",
+    "    x=gaze_crop.data.index,\n",
+    "    y=\"gaze y [px]\",\n",
+    "    color=\"g\",\n",
+    "    label=\"Gaze y\",\n",
     ")\n",
-    "print(\n",
-    "    f\"IMU: nominal sampling frequency = {recording.imu.sampling_freq_nominal}, \"\n",
-    "    f\"effective sampling frequency = {recording.imu.sampling_freq_effective}\"\n",
-    ")"
+    "ax.set_ylabel(\"Gaze location (pixels)\")\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
@@ -602,14 +554,36 @@
    "metadata": {},
    "source": [
     "## Visualizing gaze heatmap\n",
-    "Finally, we will show how to plot a heatmap of the gaze/fixation data."
+    "Finally, we will show how to plot a heatmap of the gaze/fixation data. Since it requires gaze, fixation, and video data, the input it takes is an instance of `NeonRecording` that contains all necessary data. The method `plot_heatmap()`, by default, plots a gaze heatmap with fixations overlaid as circles."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 640x480 with 2 Axes>,\n",
+       " <Axes: xlabel='Scene camera x [px]', ylabel='Scene camera y [px]'>)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "recording.plot_distribution()"
    ]
@@ -618,13 +592,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "we can neatly see that the recorded data shows a centre-bias, which is a well-known effect from eye statistics. In y, we can see that fixations tend to occur below the horizon, which is indicative of a walking task where a participant looks at the floor in front of them more often"
+    "We can see a clear centre-bias, as participants tend to look more centrally relative to head position."
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pyneon",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -638,7 +612,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.12.6"
   }
  },
  "nbformat": 4,